def Calc_m5(self, filtre): filtre_trans = self.system[filtre] wavelen_min, wavelen_max, wavelen_step = filtre_trans.getWavelenLimits( None, None, None) bandpass = Bandpass(wavelen=filtre_trans.wavelen, sb=filtre_trans.sb) flatSedb = Sed() flatSedb.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0b = np.power(10., -0.4 * self.mag_sky[filtre]) flatSedb.multiplyFluxNorm(flux0b) photParams = PhotometricParameters(bandpass=filtre) norm = photParams.platescale**2 / 2. * photParams.exptime / photParams.gain if self.atmos: self.data['m5'][filtre] = SignalToNoise.calcM5( flatSedb, self.atmosphere[filtre], self.system[filtre], photParams=photParams, FWHMeff=self.FWHMeff[filtre]) adu_int = flatSedb.calcADU(bandpass=self.atmosphere[filtre], photParams=photParams) self.data['flux_sky'][filtre] = adu_int * norm else: self.data['m5'][filtre] = SignalToNoise.calcM5( flatSedb, self.system[filtre], self.system[filtre], photParams=photParams, FWHMeff=self.FWHMeff[filtre]) adu_int = flatSedb.calcADU(bandpass=self.system[filtre], photParams=photParams) self.data['flux_sky'][filtre] = adu_int * norm
def testAstrometricError(self): fwhmGeom = 0.7 m5 = 24.5 # For bright objects, error should be systematic floor mag = 10 astrometricErr = snr.calcAstrometricError(mag, m5, fwhmGeom=fwhmGeom, nvisit=1, systematicFloor=10) self.assertAlmostEqual(astrometricErr, 10, 3) # Even if you increase the number of visits, the systemic floor doesn't change astrometricErr = snr.calcAstrometricError(mag, m5, fwhmGeom=fwhmGeom, nvisit=100) self.assertAlmostEqual(astrometricErr, 10, 3) # For a single visit, fainter source, larger error and nvisits matters mag = 24.5 astrometricErr1 = snr.calcAstrometricError(mag, m5, fwhmGeom=fwhmGeom, nvisit=1, systematicFloor=10) astrometricErr100 = snr.calcAstrometricError(mag, m5, fwhmGeom=fwhmGeom, nvisit=100, systematicFloor=10) self.assertGreater(astrometricErr1, astrometricErr100) self.assertAlmostEqual(astrometricErr1, 140.357, 3)
def testNoSystematicUncertainty(self): """ Test that systematic uncertainty is handled correctly when set to None. """ m5 = [23.5, 24.3, 22.1, 20.0, 19.5, 21.7] photParams = PhotometricParameters(sigmaSys=0.0) obs_metadata = ObservationMetaData( unrefractedRA=23.0, unrefractedDec=45.0, m5=m5, bandpassName=self.filterNameList ) magnitudes = [] for bp in self.bpList: mag = self.starSED.calcMag(bp) magnitudes.append(mag) skySedList = [] for bp, hardware, filterName in zip(self.bpList, self.hardwareList, self.filterNameList): skyDummy = Sed() skyDummy.readSED_flambda(os.path.join(lsst.utils.getPackageDir("throughputs"), "baseline", "darksky.dat")) normalizedSkyDummy = setM5( obs_metadata.m5[filterName], skyDummy, bp, hardware, seeing=LSSTdefaults().seeing(filterName), photParams=photParams, ) skySedList.append(normalizedSkyDummy) sigmaList = snr.calcMagError_m5(numpy.array(magnitudes), numpy.array(self.bpList), numpy.array(m5), photParams) for i in range(len(self.bpList)): snrat = snr.calcSNR_sed( self.starSED, self.bpList[i], skySedList[i], self.hardwareList[i], seeing=LSSTdefaults().seeing(self.filterNameList[i]), photParams=PhotometricParameters(), ) testSNR, gamma = snr.calcSNR_m5( numpy.array([magnitudes[i]]), [self.bpList[i]], numpy.array([m5[i]]), photParams=PhotometricParameters(sigmaSys=0.0), ) self.assertAlmostEqual( snrat, testSNR[0], 10, msg="failed on calcSNR_m5 test %e != %e " % (snrat, testSNR[0]) ) control = snr.magErrorFromSNR(testSNR) msg = "%e is not %e; failed" % (sigmaList[i], control) self.assertAlmostEqual(sigmaList[i], control, 10, msg=msg)
def testFWHMconversions(self): FWHMeff = 0.8 FWHMgeom = snr.FWHMeff2FWHMgeom(FWHMeff) self.assertEqual(FWHMgeom, (0.822 * FWHMeff + 0.052)) FWHMgeom = 0.8 FWHMeff = snr.FWHMgeom2FWHMeff(FWHMgeom) self.assertEqual(FWHMeff, (FWHMgeom - 0.052) / 0.822)
def testSystematicUncertainty(self): """ Test that systematic uncertainty is added correctly. """ sigmaSys = 0.002 m5_list = [23.5, 24.3, 22.1, 20.0, 19.5, 21.7] photParams = PhotometricParameters(sigmaSys=sigmaSys) obs_metadata = ObservationMetaData(pointingRA=23.0, pointingDec=45.0, m5=m5_list, bandpassName=self.filterNameList) magnitude_list = [] for bp in self.bpList: mag = self.starSED.calcMag(bp) magnitude_list.append(mag) for bp, hardware, filterName, mm, m5 in \ zip(self.bpList, self.hardwareList, self.filterNameList, magnitude_list, m5_list): skyDummy = Sed() skyDummy.readSED_flambda( os.path.join(lsst.utils.getPackageDir('throughputs'), 'baseline', 'darksky.dat')) normalizedSkyDummy = setM5( obs_metadata.m5[filterName], skyDummy, bp, hardware, FWHMeff=LSSTdefaults().FWHMeff(filterName), photParams=photParams) sigma, gamma = snr.calcMagError_m5(mm, bp, m5, photParams) snrat = snr.calcSNR_sed(self.starSED, bp, normalizedSkyDummy, hardware, FWHMeff=LSSTdefaults().FWHMeff(filterName), photParams=PhotometricParameters()) testSNR, gamma = snr.calcSNR_m5( mm, bp, m5, photParams=PhotometricParameters(sigmaSys=0.0)) self.assertAlmostEqual(snrat, testSNR, 10, msg='failed on calcSNR_m5 test %e != %e ' % (snrat, testSNR)) control = np.sqrt( np.power(snr.magErrorFromSNR(testSNR), 2) + np.power(sigmaSys, 2)) msg = '%e is not %e; failed' % (sigma, control) self.assertAlmostEqual(sigma, control, 10, msg=msg)
def testSignalToNoise(self): """ Test that calcSNR_m5 and calcSNR_sed give similar results """ defaults = LSSTdefaults() photParams = PhotometricParameters() m5 = [] for i in range(len(self.hardwareList)): m5.append( snr.calcM5( self.skySed, self.bpList[i], self.hardwareList[i], photParams, seeing=defaults.seeing(self.filterNameList[i]), ) ) sedDir = lsst.utils.getPackageDir("sims_sed_library") sedDir = os.path.join(sedDir, "starSED", "kurucz") fileNameList = os.listdir(sedDir) numpy.random.seed(42) offset = numpy.random.random_sample(len(fileNameList)) * 2.0 for ix, name in enumerate(fileNameList): if ix > 100: break spectrum = Sed() spectrum.readSED_flambda(os.path.join(sedDir, name)) ff = spectrum.calcFluxNorm(m5[2] - offset[ix], self.bpList[2]) spectrum.multiplyFluxNorm(ff) magList = [] controlList = [] magList = [] for i in range(len(self.bpList)): controlList.append( snr.calcSNR_sed( spectrum, self.bpList[i], self.skySed, self.hardwareList[i], photParams, defaults.seeing(self.filterNameList[i]), ) ) magList.append(spectrum.calcMag(self.bpList[i])) testList, gammaList = snr.calcSNR_m5( numpy.array(magList), numpy.array(self.bpList), numpy.array(m5), photParams ) for tt, cc in zip(controlList, testList): msg = "%e != %e " % (tt, cc) self.assertTrue(numpy.abs(tt / cc - 1.0) < 0.001, msg=msg)
def Get_m5(self,filtre,mag_SN,msky,photParams,FWHMeff): wavelen_min, wavelen_max, wavelen_step=self.transmission.lsst_system[filtre].getWavelenLimits(None,None,None) flatSed = Sed() flatSed.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0=np.power(10.,-0.4*msky) flatSed.multiplyFluxNorm(flux0) m5_calc=SignalToNoise.calcM5(flatSed,self.transmission.lsst_atmos_aerosol[filtre],self.transmission.lsst_system[filtre],photParams=photParams,FWHMeff=FWHMeff) snr_m5_through,gamma_through=SignalToNoise.calcSNR_m5(mag_SN,self.transmission.lsst_atmos_aerosol[filtre],m5_calc,photParams) return m5_calc,snr_m5_through
def testVerboseSNR(self): """ Make sure that calcSNR_sed has everything it needs to run in verbose mode """ photParams = PhotometricParameters() # create a cartoon spectrum to test on spectrum = Sed() spectrum.setFlatSED() spectrum.multiplyFluxNorm(1.0e-9) snr.calcSNR_sed(spectrum, self.bpList[0], self.skySed, self.hardwareList[0], photParams, FWHMeff=0.7, verbose=True)
def Calc_Sky(self, paper, infos, transmission): Diameter = 6.5 #m Deltat = 30 #s platescale = 0.2 #arsec gain = 2.3 for filtre in self.filters: filtre_trans = transmission.lsst_system[filtre] wavelen_min, wavelen_max, wavelen_step = filtre_trans.getWavelenLimits( None, None, None) photParams = PhotometricParameters() #photParams._exptime=30. bandpass = Bandpass(wavelen=filtre_trans.wavelen, sb=filtre_trans.sb) infos['Skyb'][filtre] = 5455 * np.power( Diameter / 6.5, 2.) * np.power(Deltat / 30., 2.) * np.power( platescale, 2.) * np.power( 10., 0.4 * (25. - infos['mbsky'][filtre])) * infos['Sigmab'][filtre] Zb = 181.8 * np.power(Diameter / 6.5, 2.) * infos['Tb'][filtre] mbZ = 25. + 2.5 * np.log10(Zb) flatSed = Sed() flatSed.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0 = np.power(10., -0.4 * mbZ) flatSed.multiplyFluxNorm(flux0) counts = flatSed.calcADU( bandpass, photParams=photParams) #number of counts for exptime infos['mb_Z'][filtre] = mbZ infos['counts_mb_Z'][filtre] = counts / photParams.exptime flatSedb = Sed() flatSedb.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0b = np.power(10., -0.4 * infos['mbsky'][filtre]) flatSedb.multiplyFluxNorm(flux0b) FWHMeff = SignalToNoise.FWHMgeom2FWHMeff(paper['Seeing'][filtre]) #FWHMeff = paper['Seeing'][filtre] #m5_calc=SignalToNoise.calcM5(flatSedb,transmission.lsst_atmos[filtre],transmission.lsst_system[filtre],photParams=photParams,FWHMeff=FWHMeff) m5_calc = SignalToNoise.calcM5( flatSedb, transmission.lsst_atmos[filtre], transmission.lsst_system[filtre], photParams=photParams, FWHMeff=self.paper['FWHMeff'][filtre]) infos['fiveSigmaDepth'][filtre] = m5_calc
def testVerboseSNR(self): """ Make sure that calcSNR_sed has everything it needs to run in verbose mode """ defaults = LSSTdefaults() photParams = PhotometricParameters() #create a cartoon spectrum to test on spectrum = Sed() spectrum.setFlatSED() spectrum.multiplyFluxNorm(1.0e-9) snr.calcSNR_sed(spectrum, self.bpList[0], self.skySed, self.hardwareList[0], photParams, seeing=0.7, verbose=True)
def testSignalToNoise(self): """ Test that calcSNR_m5 and calcSNR_sed give similar results """ defaults = LSSTdefaults() photParams = PhotometricParameters() m5 = [] for i in range(len(self.hardwareList)): m5.append(snr.calcM5(self.skySed, self.bpList[i], self.hardwareList[i], photParams, seeing=defaults.seeing(self.filterNameList[i]))) sedDir = lsst.utils.getPackageDir('sims_sed_library') sedDir = os.path.join(sedDir, 'starSED', 'kurucz') fileNameList = os.listdir(sedDir) numpy.random.seed(42) offset = numpy.random.random_sample(len(fileNameList))*2.0 for ix, name in enumerate(fileNameList): if ix>100: break spectrum = Sed() spectrum.readSED_flambda(os.path.join(sedDir, name)) ff = spectrum.calcFluxNorm(m5[2]-offset[ix], self.bpList[2]) spectrum.multiplyFluxNorm(ff) magList = [] controlList = [] magList = [] for i in range(len(self.bpList)): controlList.append(snr.calcSNR_sed(spectrum, self.bpList[i], self.skySed, self.hardwareList[i], photParams, defaults.seeing(self.filterNameList[i]))) magList.append(spectrum.calcMag(self.bpList[i])) testList, gammaList = snr.calcSNR_m5(numpy.array(magList), numpy.array(self.bpList), numpy.array(m5), photParams) for tt, cc in zip(controlList, testList): msg = '%e != %e ' % (tt, cc) self.assertTrue(numpy.abs(tt/cc - 1.0) < 0.001, msg=msg)
def testNoSystematicUncertainty(self): """ Test that systematic uncertainty is handled correctly when set to None. """ m5_list = [23.5, 24.3, 22.1, 20.0, 19.5, 21.7] photParams= PhotometricParameters(sigmaSys=0.0) obs_metadata = ObservationMetaData(pointingRA=23.0, pointingDec=45.0, m5=m5_list, bandpassName=self.filterNameList) magnitude_list = [] for bp in self.bpList: mag = self.starSED.calcMag(bp) magnitude_list.append(mag) skySedList = [] for bp, hardware, filterName, mm, m5 in \ zip(self.bpList, self.hardwareList, self.filterNameList, magnitude_list, m5_list): skyDummy = Sed() skyDummy.readSED_flambda(os.path.join(lsst.utils.getPackageDir('throughputs'), 'baseline', 'darksky.dat')) normalizedSkyDummy = setM5(obs_metadata.m5[filterName], skyDummy, bp, hardware, FWHMeff=LSSTdefaults().FWHMeff(filterName), photParams=photParams) sigma, gamma = snr.calcMagError_m5(mm, bp, m5, photParams) snrat = snr.calcSNR_sed(self.starSED, bp, normalizedSkyDummy, hardware, FWHMeff=LSSTdefaults().FWHMeff(filterName), photParams=PhotometricParameters()) testSNR, gamma = snr.calcSNR_m5(mm, bp, m5, photParams=PhotometricParameters(sigmaSys=0.0)) self.assertAlmostEqual(snrat, testSNR, 10, msg = 'failed on calcSNR_m5 test %e != %e ' \ % (snrat, testSNR)) control = snr.magErrorFromSNR(testSNR) msg = '%e is not %e; failed' % (sigma, control) self.assertAlmostEqual(sigma, control, 10, msg=msg)
def testNoSystematicUncertainty(self): """ Test that systematic uncertainty is handled correctly when set to None. """ m5 = [23.5, 24.3, 22.1, 20.0, 19.5, 21.7] photParams= PhotometricParameters(sigmaSys=0.0) obs_metadata = ObservationMetaData(unrefractedRA=23.0, unrefractedDec=45.0, m5=m5, bandpassName=self.filterNameList) magnitudes = [] for bp in self.bpList: mag = self.starSED.calcMag(bp) magnitudes.append(mag) skySedList = [] for bp, hardware, filterName in zip(self.bpList, self.hardwareList, self.filterNameList): skyDummy = Sed() skyDummy.readSED_flambda(os.path.join(lsst.utils.getPackageDir('throughputs'), 'baseline', 'darksky.dat')) normalizedSkyDummy = setM5(obs_metadata.m5[filterName], skyDummy, bp, hardware, seeing=LSSTdefaults().seeing(filterName), photParams=photParams) skySedList.append(normalizedSkyDummy) sigmaList = snr.calcMagError_m5(numpy.array(magnitudes), numpy.array(self.bpList), \ numpy.array(m5), photParams) for i in range(len(self.bpList)): snrat = snr.calcSNR_sed(self.starSED, self.bpList[i], skySedList[i], self.hardwareList[i], seeing=LSSTdefaults().seeing(self.filterNameList[i]), photParams=PhotometricParameters()) testSNR, gamma = snr.calcSNR_m5(numpy.array([magnitudes[i]]), [self.bpList[i]], numpy.array([m5[i]]), photParams=PhotometricParameters(sigmaSys=0.0)) self.assertAlmostEqual(snrat, testSNR[0], 10, msg = 'failed on calcSNR_m5 test %e != %e ' \ % (snrat, testSNR[0])) control = snr.magErrorFromSNR(testSNR) msg = '%e is not %e; failed' % (sigmaList[i], control) self.assertAlmostEqual(sigmaList[i], control, 10, msg=msg)
def Calc_m5(self, filtre): """ Calc_m5(filtre): Compute m5 or SNR at five sigma Tool function implemented by Phillie Gris (IN2P3) """ # get telescope passband (no atmosphere) filtre_trans = self.system[filtre] wavelen_min, wavelen_max, wavelen_step = filtre_trans.getWavelenLimits( None, None, None) bandpass = Bandpass(wavelen=filtre_trans.wavelen, sb=filtre_trans.sb) # create a Flat sed S_nu from the sky brightness magnitude flatSedb = Sed() flatSedb.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0b = np.power(10., -0.4 * self.mag_sky[filtre]) flatSedb.multiplyFluxNorm(flux0b) # Get LSST photometric parameters photParams = PhotometricParameters(bandpass=filtre) norm = photParams.platescale**2 / 2. * photParams.exptime / photParams.gain # Use LSST sims (SignalToNoise) to calculate M5 with atmosphere or without atmosphere if self.atmos: self.data['m5'][filtre] = SignalToNoise.calcM5( flatSedb, self.atmosphere[filtre], self.system[filtre], photParams=photParams, FWHMeff=self.FWHMeff[filtre]) adu_int = flatSedb.calcADU(bandpass=self.atmosphere[filtre], photParams=photParams) self.data['flux_sky'][filtre] = adu_int * norm else: self.data['m5'][filtre] = SignalToNoise.calcM5( flatSedb, self.system[filtre], self.system[filtre], photParams=photParams, FWHMeff=self.FWHMeff[filtre]) adu_int = flatSedb.calcADU(bandpass=self.system[filtre], photParams=photParams) self.data['flux_sky'][filtre] = adu_int * norm
def testMagError(self): """ Make sure that calcMagError_sed and calcMagError_m5 agree to within 0.001 """ defaults = LSSTdefaults() photParams = PhotometricParameters() # create a cartoon spectrum to test on spectrum = Sed() spectrum.setFlatSED() spectrum.multiplyFluxNorm(1.0e-9) # find the magnitudes of that spectrum in our bandpasses magList = [] for total in self.bpList: magList.append(spectrum.calcMag(total)) magList = np.array(magList) # try for different normalizations of the skySED for fNorm in np.arange(1.0, 5.0, 1.0): self.skySed.multiplyFluxNorm(fNorm) for total, hardware, filterName, mm in \ zip(self.bpList, self.hardwareList, self.filterNameList, magList): FWHMeff = defaults.FWHMeff(filterName) m5 = snr.calcM5(self.skySed, total, hardware, photParams, FWHMeff=FWHMeff) sigma_sed = snr.calcMagError_sed(spectrum, total, self.skySed, hardware, photParams, FWHMeff=FWHMeff) sigma_m5, gamma = snr.calcMagError_m5(mm, total, m5, photParams) self.assertAlmostEqual(sigma_m5, sigma_sed, 3)
def CalcMyABMagnitudesError_filter(self, band, SkyBrightnessMag, FWHMGeom): """ CalcMyABMagnitudesError_filter(self,band,SkyBrightnessMag,FWHMGeom) - author : Sylvie Dagoret-Campagne - affiliation : LAL/IN2P3/CNRS/FRANCE - date : July 5th 2018 Calculate magnitude errors for one band. Input args: - band : filter band - SkyBrighnessMag : Sky Brighness Magnitude in the band - FWHMGeom : Geometrical PSF in the band """ filtre_atm = self.lsst_atmos[band] filtre_syst = self.lsst_system[band] wavelen_min, wavelen_max, wavelen_step = filtre_syst.getWavelenLimits( None, None, None) #calculation of effective PSF FWHMeff = SignalToNoise.FWHMgeom2FWHMeff(FWHMGeom) photParams = PhotometricParameters(bandpass=band) # create a Flat sed S_nu from the sky brightness magnitude skysed = Sed() skysed.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0b = np.power(10., -0.4 * SkyBrightnessMag) skysed.multiplyFluxNorm(flux0b) #calcMagError filled according doc mag_err = SignalToNoise.calcMagError_sed(self.sed, filtre_atm, skysed, filtre_syst, photParams, FWHMeff, verbose=False) return mag_err
def testError_arr(self): """ Test that calcMagError_m5 works on numpy arrays of magnitudes """ rng = np.random.RandomState(17) mag_list = rng.random_sample(100)*5.0 + 15.0 photParams = PhotometricParameters() bp = self.bpList[0] m5 = 24.0 control_list = [] for mm in mag_list: sig, gamma = snr.calcMagError_m5(mm, bp, m5, photParams) control_list.append(sig) control_list = np.array(control_list) test_list, gamma = snr.calcMagError_m5(mag_list, bp, m5, photParams) np.testing.assert_array_equal(control_list, test_list)
def testSNR_arr(self): """ Test that calcSNR_m5 works on numpy arrays of magnitudes """ numpy.random.seed(17) mag_list = numpy.random.random_sample(100)*5.0 + 15.0 photParams = PhotometricParameters() bp = self.bpList[0] m5 = 24.0 control_list = [] for mm in mag_list: ratio, gamma = snr.calcSNR_m5(mm, bp, m5, photParams) control_list.append(ratio) control_list = numpy.array(control_list) test_list, gamma = snr.calcSNR_m5(mag_list, bp, m5, photParams) numpy.testing.assert_array_equal(control_list, test_list)
def testError_arr(self): """ Test that calcMagError_m5 works on numpy arrays of magnitudes """ rng = np.random.RandomState(17) mag_list = rng.random_sample(100) * 5.0 + 15.0 photParams = PhotometricParameters() bp = self.bpList[0] m5 = 24.0 control_list = [] for mm in mag_list: sig, gamma = snr.calcMagError_m5(mm, bp, m5, photParams) control_list.append(sig) control_list = np.array(control_list) test_list, gamma = snr.calcMagError_m5(mag_list, bp, m5, photParams) np.testing.assert_array_equal(control_list, test_list)
def testSignalToNoise(self): """ Test that calcSNR_m5 and calcSNR_sed give similar results """ defaults = LSSTdefaults() photParams = PhotometricParameters() m5 = [] for i in range(len(self.hardwareList)): m5.append( snr.calcM5(self.skySed, self.bpList[i], self.hardwareList[i], photParams, FWHMeff=defaults.FWHMeff(self.filterNameList[i]))) sedDir = os.path.join(lsst.utils.getPackageDir('sims_photUtils'), 'tests/cartoonSedTestData/starSed/') sedDir = os.path.join(sedDir, 'kurucz') fileNameList = os.listdir(sedDir) rng = np.random.RandomState(42) offset = rng.random_sample(len(fileNameList)) * 2.0 for ix, name in enumerate(fileNameList): if ix > 100: break spectrum = Sed() spectrum.readSED_flambda(os.path.join(sedDir, name)) ff = spectrum.calcFluxNorm(m5[2] - offset[ix], self.bpList[2]) spectrum.multiplyFluxNorm(ff) for i in range(len(self.bpList)): control_snr = snr.calcSNR_sed( spectrum, self.bpList[i], self.skySed, self.hardwareList[i], photParams, defaults.FWHMeff(self.filterNameList[i])) mag = spectrum.calcMag(self.bpList[i]) test_snr, gamma = snr.calcSNR_m5(mag, self.bpList[i], m5[i], photParams) self.assertLess((test_snr - control_snr) / control_snr, 0.001)
def testMagError(self): """ Make sure that calcMagError_sed and calcMagError_m5 agree to within 0.001 """ defaults = LSSTdefaults() photParams = PhotometricParameters() #create a cartoon spectrum to test on spectrum = Sed() spectrum.setFlatSED() spectrum.multiplyFluxNorm(1.0e-9) #find the magnitudes of that spectrum in our bandpasses magList = [] for total in self.bpList: magList.append(spectrum.calcMag(total)) magList = numpy.array(magList) #try for different normalizations of the skySED for fNorm in numpy.arange(1.0, 5.0, 1.0): self.skySed.multiplyFluxNorm(fNorm) m5List = [] magSed = [] for total, hardware, filterName in \ zip(self.bpList, self.hardwareList, self.filterNameList): seeing = defaults.seeing(filterName) m5List.append(snr.calcM5(self.skySed, total, hardware, photParams,seeing=seeing)) magSed.append(snr.calcMagError_sed(spectrum, total, self.skySed, hardware, photParams, seeing=seeing)) magSed = numpy.array(magSed) magM5 = snr.calcMagError_m5(magList, self.bpList, numpy.array(m5List), photParams) numpy.testing.assert_array_almost_equal(magM5, magSed, decimal=3)
def calc_m5_photUtils(hardware, system, darksky, visitFilter, filtsky, FWHMeff, expTime, airmass, tauCloud=0): m5 = np.zeros(len(expTime)) for i in range(len(m5)): photParams = PhotometricParameters(exptime=expTime[i] / 2.0, nexp=2) skysed = copy.deepcopy(darksky) fluxnorm = skysed.calcFluxNorm(filtsky[i], system[visitFilter[i]]) skysed.multiplyFluxNorm(fluxnorm) # Calculate the m5 value (this is for x=1.0 because we used x=1.0 in the atmosphere for system) m5[i] = SignalToNoise.calcM5( skysed, system[visitFilter[i]], hardware[visitFilter[i]], photParams, FWHMeff=FWHMeff[i] ) return m5
def testSNRexceptions(self): """ test that calcSNR_m5 raises an exception when arguments are not of the right shape. """ photParams = PhotometricParameters() shortGamma = numpy.array([1.0, 1.0]) shortMagnitudes = numpy.array([22.0, 23.0]) magnitudes = 22.0*numpy.ones(6) self.assertRaises(RuntimeError, snr.calcSNR_m5, magnitudes, self.bpList, shortMagnitudes, photParams) self.assertRaises(RuntimeError, snr.calcSNR_m5, shortMagnitudes, self.bpList, magnitudes, photParams) self.assertRaises(RuntimeError, snr.calcSNR_m5, magnitudes, self.bpList, magnitudes, photParams, gamma=shortGamma) signalToNoise, gg = snr.calcSNR_m5(magnitudes, self.bpList, magnitudes, photParams)
def testSignalToNoise(self): """ Test that calcSNR_m5 and calcSNR_sed give similar results """ defaults = LSSTdefaults() photParams = PhotometricParameters() m5 = [] for i in range(len(self.hardwareList)): m5.append(snr.calcM5(self.skySed, self.bpList[i], self.hardwareList[i], photParams, FWHMeff=defaults.FWHMeff(self.filterNameList[i]))) sedDir = os.path.join(lsst.utils.getPackageDir('sims_photUtils'), 'tests/cartoonSedTestData/starSed/') sedDir = os.path.join(sedDir, 'kurucz') fileNameList = os.listdir(sedDir) rng = np.random.RandomState(42) offset = rng.random_sample(len(fileNameList))*2.0 for ix, name in enumerate(fileNameList): if ix > 100: break spectrum = Sed() spectrum.readSED_flambda(os.path.join(sedDir, name)) ff = spectrum.calcFluxNorm(m5[2]-offset[ix], self.bpList[2]) spectrum.multiplyFluxNorm(ff) for i in range(len(self.bpList)): control_snr = snr.calcSNR_sed(spectrum, self.bpList[i], self.skySed, self.hardwareList[i], photParams, defaults.FWHMeff(self.filterNameList[i])) mag = spectrum.calcMag(self.bpList[i]) test_snr, gamma = snr.calcSNR_m5(mag, self.bpList[i], m5[i], photParams) self.assertLess((test_snr-control_snr)/control_snr, 0.001)
def Calc_m5(self, filtre): filtre_trans = self.throughputs.system[filtre] wavelen_min, wavelen_max, wavelen_step = filtre_trans.getWavelenLimits( None, None, None) bandpass = Bandpass(wavelen=filtre_trans.wavelen, sb=filtre_trans.sb) flatSedb = Sed() flatSedb.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0b = np.power(10., -0.4 * self.mag_sky[filtre]) flatSedb.multiplyFluxNorm(flux0b) if self.atmos: self.data['m5'][filtre] = SignalToNoise.calcM5( flatSedb, self.throughputs.atmosphere[filtre], self.throughputs.system[filtre], photParams=self.photParams, FWHMeff=self.FWHMeff[filtre]) adu_int = flatSedb.calcADU( bandpass=self.throughputs.atmosphere[filtre], photParams=self.photParams) self.data['flux_sky'][ filtre] = adu_int * self.pixel_area / self.expTime[ filtre] / self.gain else: self.data['m5'][filtre] = SignalToNoise.calcM5( flatSedb, self.throughputs.system[filtre], self.throughputs.system[filtre], photParams=self.photParams, FWHMeff=self.FWHMeff[filtre]) adu_int = flatSedb.calcADU( bandpass=self.throughputs.system[filtre], photParams=self.photParams) self.data['flux_sky'][ filtre] = adu_int * self.pixel_area / self.expTime[ filtre] / self.gain
def testMagError(self): """ Make sure that calcMagError_sed and calcMagError_m5 agree to within 0.001 """ defaults = LSSTdefaults() photParams = PhotometricParameters() # create a cartoon spectrum to test on spectrum = Sed() spectrum.setFlatSED() spectrum.multiplyFluxNorm(1.0e-9) # find the magnitudes of that spectrum in our bandpasses magList = [] for total in self.bpList: magList.append(spectrum.calcMag(total)) magList = numpy.array(magList) # try for different normalizations of the skySED for fNorm in numpy.arange(1.0, 5.0, 1.0): self.skySed.multiplyFluxNorm(fNorm) m5List = [] magSed = [] for total, hardware, filterName in zip(self.bpList, self.hardwareList, self.filterNameList): seeing = defaults.seeing(filterName) m5List.append(snr.calcM5(self.skySed, total, hardware, photParams, seeing=seeing)) magSed.append(snr.calcMagError_sed(spectrum, total, self.skySed, hardware, photParams, seeing=seeing)) magSed = numpy.array(magSed) magM5 = snr.calcMagError_m5(magList, self.bpList, numpy.array(m5List), photParams) numpy.testing.assert_array_almost_equal(magM5, magSed, decimal=3)
def testSNRexceptions(self): """ test that calcSNR_m5 raises an exception when arguments are not of the right shape. """ photParams = PhotometricParameters() shortGamma = numpy.array([1.0, 1.0]) shortMagnitudes = numpy.array([22.0, 23.0]) magnitudes = 22.0 * numpy.ones(6) self.assertRaises(RuntimeError, snr.calcSNR_m5, magnitudes, self.bpList, shortMagnitudes, photParams) self.assertRaises(RuntimeError, snr.calcSNR_m5, shortMagnitudes, self.bpList, magnitudes, photParams) self.assertRaises( RuntimeError, snr.calcSNR_m5, magnitudes, self.bpList, magnitudes, photParams, gamma=shortGamma ) signalToNoise, gg = snr.calcSNR_m5(magnitudes, self.bpList, magnitudes, photParams)
def calcSNR_Flux(self, df, transm): """ Method to estimate SNRs and fluxes (in e.sec) using lsst sims estimators Parameters --------------- df: pandas df data to process transm : array throughputs Returns ---------- original df plus the following cols: gamma: gamma values snr_m5: snr values flux_e_sec: flux in pe/sec """ # estimate SNR # Get photometric parameters to estimate SNR photParams = [ PhotometricParameters( exptime=vv[self.exptimeCol] / vv[self.nexpCol], nexp=vv[self.nexpCol]) for index, vv in df.iterrows() ] nvals = range(len(df)) calc = [ SignalToNoise.calcSNR_m5(df.iloc[i]['mag'], transm[i], df.iloc[i][self.m5Col], photParams[i]) for i in nvals ] df['snr_m5'] = [calc[i][0] for i in nvals] df['gamma'] = [calc[i][1] for i in nvals] # estimate the flux in elec.sec-1 df['flux_e_sec'] = self.telescope.mag_to_flux_e_sec( df['mag'].values, df[self.filterCol].values, df[self.exptimeCol] / df[self.nexpCol], df[self.nexpCol])[:, 1] return df
def calc_m5_photUtils(hardware, system, darksky, visitFilter, filtsky, FWHMeff, expTime, airmass, tauCloud=0): m5 = np.zeros(len(expTime)) for i in range(len(m5)): photParams = PhotometricParameters(exptime=expTime[i] / 2.0, nexp=2) skysed = copy.deepcopy(darksky) fluxnorm = skysed.calcFluxNorm(filtsky[i], system[visitFilter[i]]) skysed.multiplyFluxNorm(fluxnorm) # Calculate the m5 value (this is for x=1.0 because we used x=1.0 in the atmosphere for system) m5[i] = SignalToNoise.calcM5(skysed, system[visitFilter[i]], hardware[visitFilter[i]], photParams, FWHMeff=FWHMeff[i]) return m5
def CalcMyABMagnitudesErrors(self): """ CalcMyABMagnitudesErrors(self) - author : Sylvie Dagoret-Campagne - affiliation : LAL/IN2P3/CNRS/FRANCE - date : July 4th 2018 Calculate magnitude errors for all bands """ all_magABErr = [] for i, band in enumerate(self.filterlist): filtre_atm = self.lsst_atmos[band] filtre_syst = self.lsst_system[band] wavelen_min, wavelen_max, wavelen_step = filtre_syst.getWavelenLimits( None, None, None) photParams = PhotometricParameters(bandpass=band) FWHMeff = self.data['FWHMeff'][band] # create a Flat sed S_nu from the sky brightness magnitude skysed = Sed() skysed.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0b = np.power(10., -0.4 * self.mag_sky[band]) skysed.multiplyFluxNorm(flux0b) #calcMagError filled according doc magerr=SignalToNoise.calcMagError_sed( \ self.sed,filtre_atm,skysed,filtre_syst,photParams,FWHMeff,verbose=False) all_magABErr.append(magerr) return np.array(all_magABErr)
def get(self, what, band): """ Decorator to access quantities Parameters --------------- what: str parameter to estimate band: str filter """ filter_trans = self.system[band] wavelen_min, wavelen_max, wavelen_step = filter_trans.getWavelenLimits( None, None, None) bandpass = Bandpass(wavelen=filter_trans.wavelen, sb=filter_trans.sb) flatSedb = Sed() flatSedb.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0b = np.power(10., -0.4 * self.mag_sky(band)) flatSedb.multiplyFluxNorm(flux0b) photParams = PhotometricParameters(bandpass=band) norm = photParams.platescale**2 / 2. * photParams.exptime / photParams.gain trans = filter_trans if self.atmos: trans = self.atmosphere[band] self.data['m5'][band] = SignalToNoise.calcM5( flatSedb, trans, filter_trans, photParams=photParams, FWHMeff=self.FWHMeff(band)) adu_int = flatSedb.calcADU(bandpass=trans, photParams=photParams) self.data['flux_sky'][band] = adu_int * norm
def calcM5(hardware, system, atmos, title='m5'): """ Calculate m5 values for all filters in hardware and system. Prints all values that go into "table 2" of the overview paper. Returns dictionary of m5 values. """ # photParams stores default values for the exposure time, nexp, size of the primary, # readnoise, gain, platescale, etc. # See https://github.com/lsst/sims_photUtils/blob/master/python/lsst/sims/photUtils/PhotometricParameters.py effarea = np.pi * (6.423 / 2. * 100.)**2 photParams_zp = PhotometricParameters(exptime=1, nexp=1, gain=1, effarea=effarea, readnoise=8.8, othernoise=0, darkcurrent=0.2) photParams = PhotometricParameters(gain=1.0, effarea=effarea, readnoise=8.8, othernoise=0, darkcurrent=0.2) photParams_infinity = PhotometricParameters(gain=1.0, readnoise=0, darkcurrent=0, othernoise=0, effarea=effarea) # lsstDefaults stores default values for the FWHMeff. # See https://github.com/lsst/sims_photUtils/blob/master/python/lsst/sims/photUtils/LSSTdefaults.py lsstDefaults = LSSTdefaults() darksky = Sed() darksky.readSED_flambda(os.path.join('../siteProperties', 'darksky.dat')) flatSed = Sed() flatSed.setFlatSED() m5 = {} Tb = {} Sb = {} kAtm = {} Cm = {} dCm_infinity = {} sourceCounts = {} skyCounts = {} skyMag = {} gamma = {} for f in system: m5[f] = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams, FWHMeff=lsstDefaults.FWHMeff(f)) fNorm = flatSed.calcFluxNorm(m5[f], system[f]) flatSed.multiplyFluxNorm(fNorm) sourceCounts[f] = flatSed.calcADU(system[f], photParams=photParams) # Calculate the Skycounts expected in this bandpass. skyCounts[f] = (darksky.calcADU(hardware[f], photParams=photParams) * photParams.platescale**2) # Calculate the sky surface brightness. skyMag[f] = darksky.calcMag(hardware[f]) # Calculate the gamma value. gamma[f] = SignalToNoise.calcGamma(system[f], m5[f], photParams) # Calculate the "Throughput Integral" (this is the hardware + atmosphere) dwavelen = np.mean(np.diff(system[f].wavelen)) Tb[f] = np.sum(system[f].sb / system[f].wavelen) * dwavelen # Calculate the "Sigma" 'system integral' (this is the hardware only) Sb[f] = np.sum(hardware[f].sb / hardware[f].wavelen) * dwavelen # Calculate km - atmospheric extinction in a particular bandpass kAtm[f] = -2.5 * np.log10(Tb[f] / Sb[f]) # Calculate the Cm and Cm_Infinity values. # m5 = Cm + 0.5*(msky - 21) + 2.5log10(0.7/FWHMeff) + 1.25log10(t/30) - km(X-1.0) # Exptime should be 30 seconds and X=1.0 exptime = photParams.exptime * photParams.nexp if exptime != 30.0: print "Whoa, exposure time was not as expected - got %s not 30 seconds. Please edit Cm calculation." % ( exptime) # Assumes atmosphere used in system throughput is X=1.0 X = 1.0 Cm[f] = (m5[f] - 0.5 * (skyMag[f] - 21) - 2.5 * np.log10(0.7 / lsstDefaults.FWHMeff(f))) # Calculate Cm_Infinity by setting readout noise to zero. m5inf = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams_infinity, FWHMeff=lsstDefaults.FWHMeff(f)) Cm_infinity = (m5inf - 0.5 * (skyMag[f] - 21) - 2.5 * np.log10(0.7 / lsstDefaults.FWHMeff(f))) dCm_infinity[f] = Cm_infinity - Cm[f] print title print 'Filter FWHMeff FWHMgeom SkyMag SkyCounts Tb Sb kAtm Gamma Cm dCm_infinity m5 SourceCounts' for f in ('u', 'g', 'r', 'i', 'z', 'y'): print '%s %.2f %.2f %.2f %.1f %.3f %.3f %.4f %.6f %.2f %.2f %.2f %.2f'\ %(f, lsstDefaults.FWHMeff(f), SignalToNoise.FWHMeff2FWHMgeom(lsstDefaults.FWHMeff(f)), skyMag[f], skyCounts[f], Tb[f], Sb[f], kAtm[f], gamma[f], Cm[f], dCm_infinity[f], m5[f], sourceCounts[f]) # Show what these look like individually (add sky & m5 limits on throughput curves) plt.figure() for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2, label=f) plt.plot(atmosphere.wavelen, atmosphere.sb, 'k:', label='X=1.0') plt.legend(loc='center right', fontsize='smaller') plt.xlim(300, 1100) plt.ylim(0, 1) plt.xlabel('Wavelength (nm)') plt.ylabel('Throughput') plt.title('System Throughputs') plt.grid(True) plt.figure() ax = plt.gca() # Add dark sky ax2 = ax.twinx() plt.sca(ax2) skyab = -2.5 * np.log10(darksky.fnu) - darksky.zp ax2.plot(darksky.wavelen, skyab, 'k-', linewidth=0.8, label='Dark sky mags') ax2.set_ylabel('AB mags') ax2.set_ylim(24, 14) plt.sca(ax) # end of dark sky handles = [] for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2) myline = mlines.Line2D([], [], color=filtercolors[f], linestyle='-', linewidth=2, label='%s: m5 %.1f (sky %.1f)' % (f, m5[f], skyMag[f])) handles.append(myline) plt.plot(atmos.wavelen, atmos.sb, 'k:', label='Atmosphere, X=1.0') # Add legend for dark sky. myline = mlines.Line2D([], [], color='k', linestyle='-', label='Dark sky AB mags/arcsec^2') handles.append(myline) # end of dark sky legend line plt.legend(loc=(0.01, 0.69), handles=handles, fancybox=True, numpoints=1, fontsize='small') plt.ylim(0, 1) plt.xlim(300, 1100) plt.xlabel('Wavelength (nm)') plt.ylabel('Fractional Throughput Response') plt.title('System total response curves %s' % (title)) return m5
def calcM5(hardware, system, atmos, title='m5'): """ Calculate m5 values for all filters in hardware and system. Prints all values that go into "table 2" of the overview paper. Returns dictionary of m5 values. """ # photParams stores default values for the exposure time, nexp, size of the primary, # readnoise, gain, platescale, etc. # See https://github.com/lsst/sims_photUtils/blob/master/python/lsst/sims/photUtils/PhotometricParameters.py photParams = PhotometricParameters(gain=1) photParams_infinity = PhotometricParameters(readnoise=0, darkcurrent=0, othernoise=0, gain=1) # lsstDefaults stores default values for the FWHMeff. # See https://github.com/lsst/sims_photUtils/blob/master/python/lsst/sims/photUtils/LSSTdefaults.py lsstDefaults = LSSTdefaults() darksky = Sed() darksky.readSED_flambda(os.path.join('../siteProperties', 'darksky.dat')) flatSed = Sed() flatSed.setFlatSED() m5 = {} Tb = {} Sb = {} kAtm = {} Cm = {} dCm_infinity = {} sourceCounts = {} skyCounts = {} skyMag = {} gamma = {} for f in system: m5[f] = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams, FWHMeff=lsstDefaults.FWHMeff(f)) fNorm = flatSed.calcFluxNorm(m5[f], system[f]) flatSed.multiplyFluxNorm(fNorm) sourceCounts[f] = flatSed.calcADU(system[f], photParams=photParams) # Calculate the Skycounts expected in this bandpass. skyCounts[f] = (darksky.calcADU(hardware[f], photParams=photParams) * photParams.platescale**2) # Calculate the sky surface brightness. skyMag[f] = darksky.calcMag(hardware[f]) # Calculate the gamma value. gamma[f] = SignalToNoise.calcGamma(system[f], m5[f], photParams) # Calculate the "Throughput Integral" (this is the hardware + atmosphere) dwavelen = np.mean(np.diff(system[f].wavelen)) Tb[f] = np.sum(system[f].sb / system[f].wavelen) * dwavelen # Calculate the "Sigma" 'system integral' (this is the hardware only) Sb[f] = np.sum(hardware[f].sb / hardware[f].wavelen) * dwavelen # Calculate km - atmospheric extinction in a particular bandpass kAtm[f] = -2.5*np.log10(Tb[f] / Sb[f]) # Calculate the Cm and Cm_Infinity values. # m5 = Cm + 0.5*(msky - 21) + 2.5log10(0.7/FWHMeff) + 1.25log10(t/30) - km(X-1.0) # Exptime should be 30 seconds and X=1.0 exptime = photParams.exptime * photParams.nexp if exptime != 30.0: print "Whoa, exposure time was not as expected - got %s not 30 seconds. Please edit Cm calculation." %(exptime) # Assumes atmosphere used in system throughput is X=1.0 X = 1.0 Cm[f] = (m5[f] - 0.5*(skyMag[f] - 21) - 2.5*np.log10(0.7/lsstDefaults.FWHMeff(f))) # Calculate Cm_Infinity by setting readout noise to zero. m5inf = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams_infinity, FWHMeff=lsstDefaults.FWHMeff(f)) Cm_infinity = (m5inf - 0.5*(skyMag[f] - 21) - 2.5*np.log10(0.7/lsstDefaults.FWHMeff(f))) dCm_infinity[f] = Cm_infinity - Cm[f] print title print 'Filter FWHMeff FWHMgeom SkyMag SkyCounts Tb Sb kAtm Gamma Cm dCm_infinity m5 SourceCounts' for f in ('u', 'g' ,'r', 'i', 'z', 'y'): print '%s %.2f %.2f %.2f %.1f %.3f %.3f %.4f %.6f %.2f %.2f %.2f %.2f'\ %(f, lsstDefaults.FWHMeff(f), SignalToNoise.FWHMeff2FWHMgeom(lsstDefaults.FWHMeff(f)), skyMag[f], skyCounts[f], Tb[f], Sb[f], kAtm[f], gamma[f], Cm[f], dCm_infinity[f], m5[f], sourceCounts[f]) # Show what these look like individually (add sky & m5 limits on throughput curves) plt.figure() for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2, label=f) plt.plot(atmosphere.wavelen, atmosphere.sb, 'k:', label='X=1.0') plt.legend(loc='center right', fontsize='smaller') plt.xlim(300, 1100) plt.ylim(0, 1) plt.xlabel('Wavelength (nm)') plt.ylabel('Throughput') plt.title('System Throughputs') plt.grid(True) plt.figure() ax = plt.gca() # Add dark sky ax2 = ax.twinx() plt.sca(ax2) skyab = -2.5*np.log10(darksky.fnu) - darksky.zp ax2.plot(darksky.wavelen, skyab, 'k-', linewidth=0.8, label='Dark sky mags') ax2.set_ylabel('AB mags') ax2.set_ylim(24, 14) plt.sca(ax) # end of dark sky handles = [] for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2) myline = mlines.Line2D([], [], color=filtercolors[f], linestyle='-', linewidth=2, label = '%s: m5 %.1f (sky %.1f)' %(f, m5[f], skyMag[f])) handles.append(myline) plt.plot(atmos.wavelen, atmos.sb, 'k:', label='Atmosphere, X=1.0') # Add legend for dark sky. myline = mlines.Line2D([], [], color='k', linestyle='-', label='Dark sky AB mags/arcsec^2') handles.append(myline) # end of dark sky legend line plt.legend(loc=(0.01, 0.69), handles=handles, fancybox=True, numpoints=1, fontsize='small') plt.ylim(0, 1) plt.xlim(300, 1100) plt.xlabel('Wavelength (nm)') plt.ylabel('Fractional Throughput Response') plt.title('System total response curves %s' %(title)) return m5
def Simulate_and_Fit_LC(self, observations, transmission, zmin, zmax): #print 'time',observations['expMJD'],observations['filter'] #print 'simulate and fit' ra = observations[self.fieldRA][0] dec = observations[self.fieldDec][0] if self.SN.sn_type == 'Ia': mbsim = self.SN.SN._source.peakmag('bessellb', 'vega') else: mbsim = -1 #This will be the data for sncosmo fitting table_for_fit = {} table_for_fit['error_calc'] = Table(names=('time', 'flux', 'fluxerr', 'band', 'zp', 'zpsys'), dtype=('f8', 'f8', 'f8', 'S7', 'f4', 'S4')) table_for_fit['error_coadd_calc'] = Table( names=('time', 'flux', 'fluxerr', 'band', 'zp', 'zpsys'), dtype=('f8', 'f8', 'f8', 'S7', 'f4', 'S4')) table_for_fit['error_opsim'] = Table(names=('time', 'flux', 'fluxerr', 'band', 'zp', 'zpsys'), dtype=('f8', 'f8', 'f8', 'S7', 'f4', 'S4')) table_for_fit['error_coadd_opsim'] = Table( names=('time', 'flux', 'fluxerr', 'band', 'zp', 'zpsys'), dtype=('f8', 'f8', 'f8', 'S7', 'f4', 'S4')) table_for_fit['error_through'] = Table( names=('time', 'flux', 'fluxerr', 'band', 'zp', 'zpsys'), dtype=('f8', 'f8', 'f8', 'S7', 'f4', 'S4')) table_for_fit['error_coadd_through'] = Table( names=('time', 'flux', 'fluxerr', 'band', 'zp', 'zpsys'), dtype=('f8', 'f8', 'f8', 'S7', 'f4', 'S4')) mytype = [('obsHistID', np.int), ('filtSkyBrightness', np.float), ('airmass', np.float), ('moonPhase', np.float), ('fieldRA', np.float), ('fieldDec', np.float), ('visitExpTime', np.float), ('expDate', np.int), ('filter', np.dtype('a15')), ('fieldID', np.int), ('fiveSigmaDepth', np.float), ('ditheredDec', np.float), ('expMJD', np.float), ('ditheredRA', np.float), ('rawSeeing', np.float), ('flux', np.float), ('err_flux', np.float), ('err_flux_opsim', np.float), ('err_flux_through', np.float), ('finSeeing', np.float), ('katm_opsim', np.float), ('katm_calc', np.float), ('m5_calc', np.float), ('Tb', np.float), ('Sigmab', np.float), ('Cm', np.float), ('dCm', np.float), ('mag_SN', np.float), ('snr_m5_through', np.float), ('snr_m5_opsim', np.float), ('gamma_through', np.float), ('gamma_opsim', np.float), ('snr_SED', np.float)] myobservations = np.zeros((60, 1), dtype=mytype) #print 'Nobservations',len(observations) nobs = -1 for filtre in self.filterNames: obs_filtre = observations[np.where( observations['filter'] == filtre)] #print 'ehehe',obs_filtre for obs in obs_filtre: nobs += 1 if len(myobservations) <= nobs: myobservations = np.resize(myobservations, (len(myobservations) + 100, 1)) for name in observations.dtype.names: myobservations[name][nobs] = obs[name] #print 'time uu',obs['expMJD'] seeing = obs['rawSeeing'] #seeing=obs['finSeeing'] time_obs = obs['expMJD'] m5_opsim = obs['fiveSigmaDepth'] #print 'getting SED' sed_SN = self.SN.get_SED(time_obs) #print 'got SED',sed_SN.wavelen,sed_SN.flambda,obs['expMJD'] """ outf = open('SN_'+str(time)+'.dat', 'wb') for i,wave in enumerate(sn.SEDfromSNcosmo.wavelen): print >> outf,wave,sn.SEDfromSNcosmo.flambda[i] outf.close() """ #print 'loading transmission airmass' transmission.Load_Atmosphere(obs['airmass']) flux_SN = sed_SN.calcFlux( bandpass=transmission.lsst_atmos_aerosol[filtre]) #print 'this is my flux',flux_SN #flux_SN=sed_SN.calcFlux(bandpass=transmission.lsst_system[filtre]) / 3631.0 myup = transmission.darksky.calcInteg( transmission.lsst_system[filtre]) """ wavelen, sb = transmission.lsst_system[filtre].multiplyThroughputs(transmission.lsst_atmos[filtre].wavelen, transmission.lsst_atmos[filtre].sb) lsst_total= Bandpass(wavelen=wavelen, sb=sb) """ Tb = self.Calc_Integ(transmission.lsst_atmos[filtre]) Sigmab = self.Calc_Integ(transmission.lsst_system[filtre]) katm = -2.5 * np.log10(Tb / Sigmab) mbsky_through = -2.5 * np.log10(myup / (3631. * Sigmab)) #print 'there mbsky',filtre,mbsky_through,obs['filtSkyBrightness'],katm,self.kAtm[filtre],Tb,Sigmab,obs['airmass'] Filter_Wavelength_Correction = np.power( 500.0 / self.params.filterWave[filtre], 0.3) Airmass_Correction = math.pow(obs['airmass'], 0.6) FWHM_Sys = self.params.FWHM_Sys_Zenith * Airmass_Correction FWHM_Atm = seeing * Filter_Wavelength_Correction * Airmass_Correction finSeeing = self.params.scaleToNeff * math.sqrt( np.power(FWHM_Sys, 2) + self.params.atmNeffFactor * np.power(FWHM_Atm, 2)) #print 'hello pal',filtre,finSeeing,obs['visitExpTime'] Tscale = obs['visitExpTime'] / 30.0 * np.power( 10.0, -0.4 * (obs['filtSkyBrightness'] - self.params.msky[filtre])) dCm = self.params.dCm_infinity[filtre] - 1.25 * np.log10( 1 + np.power(10., 0.8 * self.params.dCm_infinity[filtre] - 1.) / Tscale) m5_recalc = dCm + self.params.Cm[filtre] + 0.5 * ( obs['filtSkyBrightness'] - 21.) + 2.5 * np.log10( 0.7 / finSeeing) - self.params.kAtm[filtre] * ( obs['airmass'] - 1.) + 1.25 * np.log10( obs['visitExpTime'] / 30.) myobservations['Cm'][nobs] = self.params.Cm[filtre] myobservations['dCm'][nobs] = dCm myobservations['finSeeing'][nobs] = finSeeing myobservations['Tb'][nobs] = Tb myobservations['Sigmab'][nobs] = Sigmab myobservations['katm_calc'][nobs] = katm myobservations['katm_opsim'][nobs] = self.params.kAtm[filtre] wavelen_min, wavelen_max, wavelen_step = transmission.lsst_system[ filtre].getWavelenLimits(None, None, None) flatSed = Sed() flatSed.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0 = np.power(10., -0.4 * obs['filtSkyBrightness']) flatSed.multiplyFluxNorm(flux0) if flux_SN > 0: #print 'positive flux',flux_SN mag_SN = -2.5 * np.log10(flux_SN / 3631.0) FWHMeff = SignalToNoise.FWHMgeom2FWHMeff(finSeeing) #FWHMeff = SignalToNoise.FWHMgeom2FWHMeff(seeing) photParams = PhotometricParameters() snr_SN = SignalToNoise.calcSNR_sed( sed_SN, transmission.lsst_atmos_aerosol[filtre], transmission.darksky, transmission.lsst_system[filtre], photParams, FWHMeff=FWHMeff, verbose=False) #m5_calc=SignalToNoise.calcM5(transmission.darksky,transmission.lsst_atmos_aerosol[filtre],transmission.lsst_system[filtre],photParams=photParams,FWHMeff=FWHMeff) m5_calc = SignalToNoise.calcM5( flatSed, transmission.lsst_atmos_aerosol[filtre], transmission.lsst_system[filtre], photParams=photParams, FWHMeff=FWHMeff) snr_m5_through, gamma_through = SignalToNoise.calcSNR_m5( mag_SN, transmission.lsst_atmos_aerosol[filtre], m5_calc, photParams) snr_m5_opsim, gamma_opsim = SignalToNoise.calcSNR_m5( mag_SN, transmission.lsst_atmos_aerosol[filtre], m5_opsim, photParams) #print 'm5 diff',filtre,m5_calc,m5_opsim,m5_calc/m5_opsim,m5_recalc,(m5_opsim/m5_recalc) err_flux_SN = flux_SN / snr_SN err_flux_SN_opsim = flux_SN / snr_m5_opsim err_flux_SN_through = flux_SN / snr_m5_through #print 'test errors',flux_SN,err_flux_SN,err_flux_SN_opsim,err_flux_SN_through,err_flux_SN_through/err_flux_SN_opsim,m5_opsim-m5_calc myobservations['mag_SN'][nobs] = mag_SN myobservations['flux'][nobs] = flux_SN myobservations['err_flux'][nobs] = err_flux_SN myobservations['err_flux_opsim'][nobs] = err_flux_SN_opsim myobservations['err_flux_through'][ nobs] = err_flux_SN_through myobservations['m5_calc'][nobs] = m5_calc myobservations['snr_m5_through'][nobs] = snr_m5_through myobservations['snr_m5_opsim'][nobs] = snr_m5_opsim myobservations['gamma_through'][nobs] = gamma_through myobservations['gamma_opsim'][nobs] = gamma_opsim myobservations['snr_SED'][nobs] = snr_SN #print 'SNR',flux_SN,flux_SN/err_flux_SN,flux_SN/err_flux_SN_opsim #if flux_SN/err_flux_SN >=5: #table_for_fit['error_calc'].add_row((time_obs,flux_SN,err_flux_SN,'LSST::'+filtre,25,'ab')) #if flux_SN/err_flux_SN_opsim >=5.: table_for_fit['error_opsim'].add_row( (time_obs, flux_SN, err_flux_SN_opsim, 'LSST::' + filtre, 25, 'ab')) table_for_fit['error_through'].add_row( (time_obs, flux_SN, err_flux_SN_through, 'LSST::' + filtre, 25, 'ab')) #print 'Getting fluxes and errors',time.time()-self.thetime,filtre,nobs else: err_flux_SN = -999. err_flux_SN_opsim = -999. myobservations['mag_SN'][nobs] = -999 myobservations['flux'][nobs] = flux_SN myobservations['err_flux'][nobs] = -999. myobservations['err_flux_opsim'][nobs] = -999. myobservations['err_flux_through'][nobs] = -999. myobservations['m5_calc'][nobs] = -999. myobservations['snr_m5_through'][nobs] = -999 myobservations['snr_m5_opsim'][nobs] = -999 myobservations['gamma_through'][nobs] = -999 myobservations['gamma_opsim'][nobs] = -999 myobservations['snr_SED'][nobs] = -999 #print 'flux SN',flux_SN,err_flux_SN,mag_SN,snr_SN,snr_m5_through,snr_m5_opsim #t.add_row((time,flux_SN,err_flux_SN,'LSST::'+filtre,0.,'vega')) #break #t = Table([list(data['time']), list(data['band']),data['flux'],data['fluxerr'],data['flux_aero'],data['fluxerr_aero'],data['fluxerr_new'],data['zp'],data['zpsys']], names=('time','band','flux','fluxerr','flux_aero','fluxerr_aero','fluxerr_new','zp','zpsys'), meta={'name': 'first table'}) #model.set(z=0.5) #print SN.SN myobservations = np.resize(myobservations, (nobs + 1, 1)) #print 'there obs',myobservations #print 'Getting coadds',time.time()-self.thetime for band in ['u', 'g', 'r', 'i', 'z', 'y']: #sela=table_for_fit['error_calc'][np.where(table_for_fit['error_calc']['band']=='LSST::'+band)] #sela=sela[np.where(np.logical_and(sela['flux']/sela['fluxerr']>5.,sela['flux']>0.))] selb = table_for_fit['error_opsim'][np.where( table_for_fit['error_opsim']['band'] == 'LSST::' + band)] #selb=selb[np.where(np.logical_and(selb['flux']/selb['fluxerr']>5.,selb['flux']>0.))] selc = table_for_fit['error_through'][np.where( table_for_fit['error_through']['band'] == 'LSST::' + band)] #table_for_fit['error_coadd_calc']=vstack([table_for_fit['error_coadd_calc'],self.Get_coadd(sela)]) table_for_fit['error_coadd_opsim'] = vstack( [table_for_fit['error_coadd_opsim'], self.Get_coadd(selb)]) table_for_fit['error_coadd_through'] = vstack( [table_for_fit['error_coadd_through'], self.Get_coadd(selc)]) #print 'There we go fitting',time.time()-self.thetime dict_fit = {} #for val in ['error_calc','error_coadd_calc','error_opsim','error_coadd_opsim']: for val in ['error_coadd_opsim', 'error_coadd_through']: dict_fit[val] = {} dict_fit[val]['sncosmo_fitted'] = {} dict_fit[val]['table_for_fit'] = table_for_fit[val] #print 'fit',val,time.time()-self.thetime res, fitted_model, mbfit, fit_status = self.Fit_SN( table_for_fit[val], zmin, zmax) if res is not None: dict_fit[val]['sncosmo_res'] = res #self.dict_fit[val]['fitted_model']=fitted_model for i, par in enumerate(fitted_model.param_names): dict_fit[val]['sncosmo_fitted'][ par] = fitted_model.parameters[i] dict_fit[val]['mbfit'] = mbfit dict_fit[val]['fit_status'] = fit_status return dict_fit, mbsim, myobservations
def __call__(self, obs, out_q=None): #print 'hello here',len(mjds),filtre #time_begin=time.time() """ mjds=obs['mjd'] airmass=obs['airmass'] m5=obs['m5sigmadepth'] filtre=obs['band'] expTime=obs['exptime'] """ fluxes = 10. * self.SN.flux(obs['mjd'], self.wave) wavelength = self.wave / 10. wavelength = np.repeat(wavelength[np.newaxis, :], len(fluxes), 0) SED_time = Sed(wavelen=wavelength, flambda=fluxes) #print 'total elapse time seds',time.time()-time_begin """ time_begin=time.time() for i in range(len(SED_time.wavelen)): photParams=PhotometricParameters(nexp=expTime[i]/15.) sed=Sed(wavelen=SED_time.wavelen[i],flambda=SED_time.flambda[i]) self.transmission.Load_Atmosphere(airmass[i]) trans=self.transmission.atmosphere[filtre] flux_SN=sed.calcFlux(bandpass=trans) if flux_SN >0: mag_SN=-2.5 * np.log10(flux_SN / 3631.0) snr_m5_opsim,gamma_opsim=SignalToNoise.calcSNR_m5(mag_SN,trans,m5[i],photParams) err_flux_SN=flux_SN/snr_m5_opsim e_per_sec = sed.calcADU(bandpass=trans, photParams=photParams) #number of ADU counts for expTime #e_per_sec = sed.calcADU(bandpass=self.transmission.lsst_atmos[filtre], photParams=photParams) #print 'alors',e_per_sec,expTime[i],photParams.gain e_per_sec/=expTime[i]/photParams.gain #print 'alors b',e_per_sec #print 'ref',filtre,i,mjds[i],e_per_sec #self.lc[filtre].append(e_per_sec) r.append((e_per_sec,mjds[i],flux_SN)) #self.table_for_fit.add_row((mjds[i],flux_SN,err_flux_SN,'LSST::'+filtre,25,'ab')) #self.table_for_fit.add_row((mjds[i],mag_SN,mag_SN/snr_m5_opsim,'LSST::'+filtre,25,'ab')) self.table_obs.add_row((mjds[i],flux_SN,err_flux_SN,'LSST::'+filtre,2.5*np.log10(3631),'ab',airmass[i],m5[i],expTime[i],e_per_sec,self.telescope.mag_to_flux(m5[i],filtre))) #print 'there we go',filtre,e_per_sec,self.telescope.mag_to_flux(m5[i],filtre) #print 'total elapse time GEN LC',time.time()-time_begin """ #time_begin=time.time() fluxes = [] transes = [] seds = [ Sed(wavelen=SED_time.wavelen[i], flambda=SED_time.flambda[i]) for i in range(len(SED_time.wavelen)) ] #photParams=[] #time_begin=time.time() """ for i in range(len(SED_time.wavelen)): #photParams=PhotometricParameters(nexp=expTime[i]/15.) #sed=Sed(wavelen=SED_time.wavelen[i],flambda=SED_time.flambda[i]) self.transmission.Load_Atmosphere(airmass[i]) trans=self.transmission.atmosphere[filtre] #flux_SN=sed.calcFlux(bandpass=trans) #fluxes.append(flux_SN) transes.append(trans) #seds.append(sed) """ transes = [ self.transmission.atmosphere[obs['band'][i][-1]] for i in range(len(SED_time.wavelen)) ] #print 'total elapse time sed',time.time()-time_begin,len(transes) fluxes = [ seds[i].calcFlux(bandpass=transes[i]) for i in range(len(SED_time.wavelen)) ] #print 'total elapse time sed',time.time()-time_begin #print 'before',len(fluxes),len(seds),len(transes),len(m5),len(expTime),len(mjds),self.param['X1'],self.param['Color'] #print fluxes,mjds #time_begin=time.time() table_obs = Table(obs) snr_m5 = [] e_per_sec_list = [] for i in range(len(SED_time.wavelen)): photParams = PhotometricParameters(nexp=table_obs['exptime'][i] / 15.) flux_SN = fluxes[i] if flux_SN > 0: trans = self.transmission.atmosphere[table_obs['band'][i][-1]] mag_SN = -2.5 * np.log10(flux_SN / 3631.0) snr_m5_opsim, gamma_opsim = SignalToNoise.calcSNR_m5( mag_SN, trans, table_obs['m5sigmadepth'][i], photParams) err_flux_SN = flux_SN / snr_m5_opsim e_per_sec = seds[i].calcADU( bandpass=trans, photParams=photParams) #number of ADU counts for expTime #e_per_sec = sed.calcADU(bandpass=self.transmission.lsst_atmos[filtre], photParams=photParams) #print 'alors',e_per_sec,expTime[i],photParams.gain e_per_sec /= table_obs['exptime'][i] / photParams.gain #print('hello',mag_SN,flux_SN,e_per_sec,snr_m5_opsim) snr_m5.append(snr_m5_opsim) e_per_sec_list.append(e_per_sec) #print fluxes,mags else: snr_m5.append(1) e_per_sec_list.append(1) #print('passed') table_obs.add_column(Column(fluxes, name='flux')) table_obs.add_column(Column(snr_m5, name='snr_m5')) table_obs.add_column(Column(e_per_sec_list, name='flux_e')) idx = table_obs['flux'] >= 0. table_obs = table_obs[idx] """ mags=-2.5 * np.log10(table_obs['flux'] / 3631.0) def snr(bands,mags,sky,seeing,expTime,ron): pixel_scale=0.2 mag_sky=dict(zip('ugrizy',[22.95,22.24,21.20,20.47,19.60,18.63])) FWHMeff = {'u':0.92, 'g':0.87, 'r':0.83, 'i':0.80, 'z':0.78, 'y':0.76} neff=np.array(2.266*(seeing/pixel_scale)**2) flux_e=self.telescope.mag_to_flux(mags,[band[-1] for band in bands])*expTime #flux_sky=self.telescope.mag_to_flux([mag_sky[band[-1]] for band in bands],[band[-1] for band in bands])*expTime flux_sky=self.telescope.mag_to_flux(sky,[band[-1] for band in bands])*expTime #res=flux_e/np.sqrt((flux_sky*pixel_scale**2+ron**2)*neff) res=flux_e/np.sqrt(flux_e+(flux_sky*pixel_scale**2+ron**2)*neff) #for i in range(len(mags)): #print mags[i],flux_e[i],res[i] return res,flux_e/expTime snrs,flux_e=snr(table_obs['band'],mags,table_obs['sky'],table_obs['seeing'],table_obs['exptime'],5.) print('ici',flux_e,snrs) """ table_obs.add_column( Column(table_obs['flux'] / table_obs['snr_m5'], name='fluxerr')) #table_obs.add_column(Column(flux_e, name='flux_e')) #table_obs.add_column(Column(flux_e/snrs, name='flux_e_err')) table_obs.add_column( Column(table_obs['flux_e'] / table_obs['snr_m5'], name='flux_e_err')) table_obs.add_column( Column([2.5 * np.log10(3631)] * len(table_obs), name='zp')) table_obs.add_column(Column(['ab'] * len(table_obs), name='zpsys')) #table_obs.add_column(Column(self.telescope.mag_to_flux(mags,[band[-1] for band in table_obs['band']]),name='flux_e_sec')) #table_obs.add_column(Column(self.telescope.mag_to_flux(obs['m5sigmadepth'],[band[-1] for band in table_obs['band']]),'flux_5sigma_e_sec')) table_obs['band'][:] = np.array( [b.replace('LSSTPG', 'LSST') for b in table_obs['band']]) table_obs.rename_column('mjd', 'time') table_obs.rename_column('m5sigmadepth', 'm5') for colname in [ 'exptime', 'rawSeeing', 'seeing', 'moon_frac', 'sky', 'kAtm', 'airmass', 'm5', 'Nexp', 'Ra', 'Dec' ]: if colname in table_obs.colnames: table_obs.remove_column(colname) #print(table_obs[['flux_e','snr_m5']]) """ table_obs.remove_column('exptime') table_obs.remove_column('rawSeeing') table_obs.remove_column('seeing') table_obs.remove_column('moon_frac') table_obs.remove_column('sky') table_obs.remove_column('kAtm') table_obs.remove_column('airmass') table_obs.remove_column('m5') table_obs.remove_column('Nexp') table_obs.remove_column('Ra') table_obs.remove_column('Dec') """ #table_obs.remove_column('flux_e_sec') #table_obs.remove_column('flux_5sigma_e_sec') #print len(table_obs) #print table_obs #print len(table_obs),table_obs.dtype #print np.array(np.sort(table_obs,order='band')) """ idx=fluxes > 0. fluxes=fluxes[idx] seds=np.array(seds)[idx] transes=np.array(transes)[idx] m5=m5[idx] expTime=expTime[idx] photParams=[PhotometricParameters(nexp=expTime[i]/15.) for i in range(len(expTime))] airmass=airmass[idx] mjds=mjds[idx] filtre=filtre[idx] #print 'allors',len(fluxes),len(seds),len(transes),len(m5),len(expTime),len(photParams),len(mjds) #print fluxes,mjds mags=-2.5 * np.log10(fluxes / 3631.0) gamma = np.asarray([SignalToNoise.calcGamma(transes[i],m5[i],photParams[i]) for i in range(len(mags))]) x=np.power(10.,0.4*(mags-m5)) snr_m5_gamma=1./np.asarray(np.sqrt((0.04-gamma)*x+gamma*(x**2))) #snr_m5_gamma_orig=[SignalToNoise.calcSNR_m5(mags[i],transes[i],m5[i],photParams[i]) for i in range(len(mags))] #print 'hhh',snr_m5_gamma,snr_m5_gamma_orig #snr_m5_opsim=[SignalToNoise.calcSNR_m5(mags[i],transes[i],m5[i],photParams[i]) for i in range(len(mags))] err_fluxes=fluxes/snr_m5_gamma #err_fluxes_orig=[fluxes[i]/snr_m5_gamma_orig[i][0] for i in range(len(fluxes))] e_per_sec = [seds[i].calcADU(bandpass=transes[i], photParams=photParams[i]) for i in range(len(transes))] #number of ADU counts for expTime e_per_sec=[e_per_sec[i]/(expTime[i]/photParams[i].gain) for i in range(len(e_per_sec))] #mags_new=[self.telescope.flux_to_mag(e_per_sec[i],filtre[i][-1])[0] for i in range(len(e_per_sec))] #print 10**(-0.4*(mags-mags_new)) print filtre[:] print 'test',e_per_sec,self.telescope.mag_to_flux(mags,[band[-1] for band in filtre]),e_per_sec/self.telescope.mag_to_flux(mags,[band[-1] for band in filtre]) #snrs=snr(filtre,mags,obs['sky'],np.array([0.8]*len(mags)),obs['exptime'],5.) print snr_m5_gamma,snrs,np.median(snr_m5_gamma/snrs),np.mean(snr_m5_gamma/snrs),np.min(snr_m5_gamma/snrs),np.max(snr_m5_gamma/snrs) table_obs=Table() table_obs.add_column(Column(mjds, name='time')) table_obs.add_column(Column(fluxes, name='flux')) table_obs.add_column(Column(err_fluxes, name='fluxerr')) table_obs.add_column(Column(['LSST::'+filtre[i][-1] for i in range(len(filtre))], name='band')) table_obs.add_column(Column([2.5*np.log10(3631)]*len(mjds),name='zp')) table_obs.add_column(Column(['ab']*len(mjds),name='zpsys')) table_obs.add_column(Column(airmass,name='airmass')) table_obs.add_column(Column(m5,name='m5')) table_obs.add_column(Column(expTime,name='expTime')) table_obs.add_column(Column(e_per_sec,name='flux_e_sec')) table_obs.add_column(Column(self.telescope.mag_to_flux(m5,[filtre[i][-1] for i in range(len(filtre))]),'flux_5sigma_e_sec')) #print 'total elapse time GEN LC b',time.time()-time_begin #print table_obs """ if out_q is not None: out_q.put({filtre[0][-1]: table_obs}) return table_obs
def calcM5s(hardware, system, atmos, title='m5'): photParams = PhotometricParameters() lsstDefaults = LSSTdefaults() darksky = Sed() darksky.readSED_flambda(os.path.join(os.getenv('SYSENG_THROUGHPUTS_DIR'), 'siteProperties', 'darksky.dat')) flatSed = Sed() flatSed.setFlatSED() m5 = {} sourceCounts = {} skyCounts = {} skyMag = {} gamma = {} for f in system: m5[f] = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams, seeing=lsstDefaults.seeing(f)) fNorm = flatSed.calcFluxNorm(m5[f], system[f]) flatSed.multiplyFluxNorm(fNorm) sourceCounts[f] = flatSed.calcADU(system[f], photParams=photParams) # Calculate the Skycounts expected in this bandpass. skyCounts[f] = darksky.calcADU(hardware[f], photParams=photParams) * photParams.platescale**2 # Calculate the sky surface brightness. skyMag[f] = darksky.calcMag(hardware[f]) # Calculate the gamma value. gamma[f] = SignalToNoise.calcGamma(system[f], m5[f], photParams) print title print 'Filter m5 SourceCounts SkyCounts SkyMag Gamma' for f in ('u', 'g' ,'r', 'i', 'z', 'y'): print '%s %.2f %.1f %.2f %.2f %.6f' %(f, m5[f], sourceCounts[f], skyCounts[f], skyMag[f], gamma[f]) # Show what these look like individually (add sky & m5 limits on throughput curves) plt.figure() ax = plt.gca() # Add dark sky ax2 = ax.twinx() plt.sca(ax2) skyab = -2.5*np.log10(darksky.fnu) - darksky.zp ax2.plot(darksky.wavelen, skyab, 'k-', linewidth=0.8, label='Dark sky mags') ax2.set_ylabel('AB mags') ax2.set_ylim(24, 10) plt.sca(ax) # end of dark sky handles = [] for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2) myline = mlines.Line2D([], [], color=filtercolors[f], linestyle='-', linewidth=2, label = '%s: m5 %.1f (sky %.1f)' %(f, m5[f], skyMag[f])) handles.append(myline) plt.plot(atmos.wavelen, atmos.sb, 'k:', label='Atmosphere, X=1.2') # Add legend for dark sky. myline = mlines.Line2D([], [], color='k', linestyle='-', label='Dark sky AB mags') handles.append(myline) # end of dark sky legend line plt.legend(loc=(0.01, 0.69), handles=handles, fancybox=True, numpoints=1, fontsize='small') plt.ylim(0, 1) plt.xlim(300, 1100) plt.xlabel('Wavelength (nm)') plt.ylabel('Fractional Throughput Response') if title == 'Vendor combo': title = '' plt.title('System total response curves %s' %(title)) if title == '': plt.savefig('throughputs.pdf', format='pdf', dpi=600) return m5
def calcM5(hardware, system, atmos, title="m5"): effarea = np.pi * (6.423 / 2.0 * 100.0) ** 2 photParams = PhotometricParameters(effarea=effarea) lsstDefaults = LSSTdefaults() darksky = Sed() darksky.readSED_flambda(os.path.join("../siteProperties", "darksky.dat")) flatSed = Sed() flatSed.setFlatSED() m5 = {} sourceCounts = {} skyCounts = {} skyMag = {} gamma = {} for f in system: m5[f] = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams, FWHMeff=lsstDefaults.FWHMeff(f)) fNorm = flatSed.calcFluxNorm(m5[f], system[f]) flatSed.multiplyFluxNorm(fNorm) sourceCounts[f] = flatSed.calcADU(system[f], photParams=photParams) # Calculate the Skycounts expected in this bandpass. skyCounts[f] = darksky.calcADU(hardware[f], photParams=photParams) * photParams.platescale ** 2 # Calculate the sky surface brightness. skyMag[f] = darksky.calcMag(hardware[f]) # Calculate the gamma value. gamma[f] = SignalToNoise.calcGamma(system[f], m5[f], photParams) print title print "Filter m5 SourceCounts SkyCounts SkyMag Gamma" for f in ("u", "g", "r", "i", "z", "y"): print "%s %.2f %.1f %.2f %.2f %.6f" % (f, m5[f], sourceCounts[f], skyCounts[f], skyMag[f], gamma[f]) # Show what these look like individually (add sky & m5 limits on throughput curves) plt.figure() ax = plt.gca() # Add dark sky ax2 = ax.twinx() plt.sca(ax2) skyab = -2.5 * np.log10(darksky.fnu) - darksky.zp ax2.plot(darksky.wavelen, skyab, "k-", linewidth=0.8, label="Dark sky mags") ax2.set_ylabel("AB mags") ax2.set_ylim(24, 14) plt.sca(ax) # end of dark sky handles = [] for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2) myline = mlines.Line2D( [], [], color=filtercolors[f], linestyle="-", linewidth=2, label="%s: m5 %.1f (sky %.1f)" % (f, m5[f], skyMag[f]), ) handles.append(myline) plt.plot(atmos.wavelen, atmos.sb, "k:", label="Atmosphere, X=1.0 with aerosols") # Add legend for dark sky. myline = mlines.Line2D([], [], color="k", linestyle="-", label="Dark sky AB mags") handles.append(myline) # end of dark sky legend line plt.legend(loc=(0.01, 0.69), handles=handles, fancybox=True, numpoints=1, fontsize="small") plt.ylim(0, 1) plt.xlim(300, 1100) plt.xlabel("Wavelength (nm)") plt.ylabel("Fractional Throughput Response") if title == "Vendor combo": title = "" plt.title("System total response curves %s" % (title)) plt.savefig("../plots/system+sky" + title + ".png", format="png", dpi=600) return m5
def Simulate_and_Fit_LC(self, observations, transmission, zmin, zmax): #print 'start Simulation',time.time()-self.start_time #print 'time',observations['expMJD'],observations['filter'] #print 'simulate and fit' ra = observations[self.fieldRA][0] dec = observations[self.fieldDec][0] if self.SN.sn_type == 'Ia': mbsim = self.SN.SN._source.peakmag('bessellb', 'vega') else: mbsim = -1 #This will be the data for sncosmo fitting table_for_fit = {} table_for_fit['error_coadd_opsim'] = Table( names=('time', 'flux', 'fluxerr', 'band', 'zp', 'zpsys'), dtype=('f8', 'f8', 'f8', 'S7', 'f4', 'S4')) table_for_fit['error_coadd_through'] = Table( names=('time', 'flux', 'fluxerr', 'band', 'zp', 'zpsys'), dtype=('f8', 'f8', 'f8', 'S7', 'f4', 'S4')) """ table_for_fit['error_opsim'] = Table(names=('time','flux','fluxerr','band','zp','zpsys'), dtype=('f8', 'f8','f8','S7','f4','S4')) table_for_fit['error_through'] = Table(names=('time','flux','fluxerr','band','zp','zpsys'), dtype=('f8', 'f8','f8','S7','f4','S4')) """ mytype = [('obsHistID', np.int), ('filtSkyBrightness', np.float), ('airmass', np.float), ('moonPhase', np.float), ('fieldRA', np.float), ('fieldDec', np.float), ('visitExpTime', np.float), ('expDate', np.int), ('filter', np.dtype('a15')), ('fieldID', np.int), ('fiveSigmaDepth', np.float), ('ditheredDec', np.float), ('expMJD', np.float), ('ditheredRA', np.float), ('rawSeeing', np.float), ('flux', np.float), ('err_flux', np.float), ('err_flux_opsim', np.float), ('err_flux_through', np.float), ('finSeeing', np.float), ('katm_opsim', np.float), ('katm_calc', np.float), ('m5_calc', np.float), ('Tb', np.float), ('Sigmab', np.float), ('Cm', np.float), ('dCm', np.float), ('mag_SN', np.float), ('snr_m5_through', np.float), ('snr_m5_opsim', np.float), ('gamma_through', np.float), ('gamma_opsim', np.float), ('snr_SED', np.float)] myobservations = np.zeros((60, 1), dtype=mytype) #print 'Nobservations',len(observations) nobs = -1 for obs in observations: nobs += 1 filtre = obs['filter'] if len(myobservations) <= nobs: myobservations = np.resize(myobservations, (len(myobservations) + 100, 1)) for name in self.cols_restricted: myobservations[name][nobs] = obs[name] seeing = obs['rawSeeing'] time_obs = obs['expMJD'] m5_opsim = obs['fiveSigmaDepth'] sed_SN = self.SN.get_SED(time_obs) transmission.Load_Atmosphere(obs['airmass']) flux_SN = sed_SN.calcFlux( bandpass=transmission.lsst_atmos_aerosol[filtre]) myup = 0 Tb = 0 Sigmab = 0 katm = 0 mbsky_through = 0 """ Filter_Wavelength_Correction = np.power(500.0 / self.params.filterWave[filtre], 0.3) Airmass_Correction = math.pow(obs['airmass'],0.6) FWHM_Sys = self.params.FWHM_Sys_Zenith * Airmass_Correction FWHM_Atm = seeing * Filter_Wavelength_Correction * Airmass_Correction finSeeing = self.params.scaleToNeff * math.sqrt(np.power(FWHM_Sys,2) + self.params.atmNeffFactor * np.power(FWHM_Atm,2)) Tscale = obs['visitExpTime']/ 30.0 * np.power(10.0, -0.4*(obs['filtSkyBrightness'] - self.params.msky[filtre])) dCm = self.params.dCm_infinity[filtre] - 1.25*np.log10(1 + np.power(10.,0.8*self.params.dCm_infinity[filtre]- 1.)/Tscale) m5_recalc=dCm+self.params.Cm[filtre]+0.5*(obs['filtSkyBrightness']-21.)+2.5*np.log10(0.7/finSeeing)-self.params.kAtm[filtre]*(obs['airmass']-1.)+1.25*np.log10(obs['visitExpTime']/30.) """ """ myobservations['Cm'][nobs]=self.params.Cm[filtre] myobservations['dCm'][nobs]=dCm myobservations['finSeeing'][nobs]=finSeeing myobservations['Tb'][nobs]=Tb myobservations['Sigmab'][nobs]=Sigmab myobservations['katm_calc'][nobs]=katm myobservations['katm_opsim'][nobs]=self.params.kAtm[filtre] """ #print 'Flux',time.time()-self.start_time if flux_SN > 0: wavelen_min, wavelen_max, wavelen_step = transmission.lsst_system[ filtre].getWavelenLimits(None, None, None) flatSed = Sed() flatSed.setFlatSED(wavelen_min, wavelen_max, wavelen_step) flux0 = np.power(10., -0.4 * obs['filtSkyBrightness']) flatSed.multiplyFluxNorm(flux0) mag_SN = -2.5 * np.log10(flux_SN / 3631.0) #FWHMeff = SignalToNoise.FWHMgeom2FWHMeff(finSeeing) FWHMeff = obs['FWHMeff'] photParams = PhotometricParameters(nexp=obs['visitExpTime'] / 15.) m5_calc = SignalToNoise.calcM5( flatSed, transmission.lsst_atmos_aerosol[filtre], transmission.lsst_system[filtre], photParams=photParams, FWHMeff=FWHMeff) snr_m5_through, gamma_through = SignalToNoise.calcSNR_m5( mag_SN, transmission.lsst_atmos_aerosol[filtre], m5_calc, photParams) m5_opsim += 1.25 * np.log10(obs['visitExpTime'] / 30.) snr_m5_opsim, gamma_opsim = SignalToNoise.calcSNR_m5( mag_SN, transmission.lsst_atmos_aerosol[filtre], m5_opsim, photParams) err_flux_SN = 0 err_flux_SN_opsim = flux_SN / snr_m5_opsim err_flux_SN_through = flux_SN / snr_m5_through myobservations['mag_SN'][nobs] = mag_SN myobservations['flux'][nobs] = flux_SN myobservations['err_flux'][nobs] = err_flux_SN myobservations['err_flux_opsim'][nobs] = err_flux_SN_opsim myobservations['err_flux_through'][nobs] = err_flux_SN_through myobservations['m5_calc'][nobs] = m5_calc myobservations['snr_m5_through'][nobs] = snr_m5_through myobservations['snr_m5_opsim'][nobs] = snr_m5_opsim myobservations['gamma_through'][nobs] = gamma_through myobservations['gamma_opsim'][nobs] = gamma_opsim #myobservations['snr_SED'][nobs]=snr_SN #print 'SNR',flux_SN,flux_SN/err_flux_SN,flux_SN/err_flux_SN_opsim #if flux_SN/err_flux_SN >=5: #table_for_fit['error_calc'].add_row((time_obs,flux_SN,err_flux_SN,'LSST::'+filtre,25,'ab')) #if flux_SN/err_flux_SN_opsim >=5.: table_for_fit['error_coadd_opsim'].add_row( (time_obs, flux_SN, err_flux_SN_opsim, 'LSST::' + filtre, 25, 'ab')) table_for_fit['error_coadd_through'].add_row( (time_obs, flux_SN, err_flux_SN_through, 'LSST::' + filtre, 25, 'ab')) #print 'Getting fluxes and errors',time.time()-self.thetime,filtre,nobs else: err_flux_SN = -999. err_flux_SN_opsim = -999. myobservations['mag_SN'][nobs] = -999 myobservations['flux'][nobs] = flux_SN myobservations['err_flux'][nobs] = -999. myobservations['err_flux_opsim'][nobs] = -999. myobservations['err_flux_through'][nobs] = -999. myobservations['m5_calc'][nobs] = -999. myobservations['snr_m5_through'][nobs] = -999 myobservations['snr_m5_opsim'][nobs] = -999 myobservations['gamma_through'][nobs] = -999 myobservations['gamma_opsim'][nobs] = -999 myobservations['snr_SED'][nobs] = -999 myobservations = np.resize(myobservations, (nobs + 1, 1)) """ print 'Preparing table_for_fit',time.time()-self.start_time for band in ['u','g','r','i','z','y']: selb=table_for_fit['error_opsim'][np.where(table_for_fit['error_opsim']['band']=='LSST::'+band)] selc=table_for_fit['error_through'][np.where(table_for_fit['error_through']['band']=='LSST::'+band)] table_for_fit['error_coadd_opsim']=vstack([table_for_fit['error_coadd_opsim'],self.Get_coadd(selb)]) table_for_fit['error_coadd_through']=vstack([table_for_fit['error_coadd_through'],self.Get_coadd(selc)]) """ #print 'There we go fitting',time.time()-self.thetime dict_fit = {} #for val in ['error_calc','error_coadd_calc','error_opsim','error_coadd_opsim']: for val in ['error_coadd_opsim', 'error_coadd_through']: #print 'Go for fit',time.time()-self.start_time dict_fit[val] = {} dict_fit[val]['sncosmo_fitted'] = {} dict_fit[val]['table_for_fit'] = table_for_fit[val] #print 'fit',val,time.time()-self.thetime res, fitted_model, mbfit, fit_status = self.Fit_SN( table_for_fit[val], zmin, zmax) if res is not None: dict_fit[val]['sncosmo_res'] = res #self.dict_fit[val]['fitted_model']=fitted_model for i, par in enumerate(fitted_model.param_names): dict_fit[val]['sncosmo_fitted'][ par] = fitted_model.parameters[i] dict_fit[val]['mbfit'] = mbfit dict_fit[val]['fit_status'] = fit_status #print 'end of Fit',time.time()-self.start_time return dict_fit, mbsim, myobservations
def process_stellar_chunk(chunk, filter_obs, mjd_obs, m5_obs, coadd_m5, obs_md_list, proper_chip, variability_cache, out_data): t_start_chunk = time.time() #print('processing %d' % len(chunk)) ct_first = 0 ct_at_all = 0 ct_tot = 0 n_t = len(filter_obs) n_obj = len(chunk) coadd_visits = {} coadd_visits['u'] = 6 coadd_visits['g'] = 8 coadd_visits['r'] = 18 coadd_visits['i'] = 18 coadd_visits['z'] = 16 coadd_visits['y'] = 16 # from the overview paper # table 2; take m5 row and add Delta m5 row # to get down to airmass 1.2 m5_single = {} m5_single['u'] = 23.57 m5_single['g'] = 24.65 m5_single['r'] = 24.21 m5_single['i'] = 23.79 m5_single['z'] = 23.21 m5_single['y'] = 22.31 gamma_coadd = {} for bp in 'ugrizy': gamma_coadd[bp] = None gamma_single = {} for bp in 'ugrizy': gamma_single[bp] = [None] * n_t dflux = np.zeros((n_obj, n_t), dtype=float) dflux_for_mlt(chunk, filter_obs, mjd_obs, variability_cache, dflux) dflux_for_kepler(chunk, filter_obs, mjd_obs, variability_cache, dflux) dflux_for_rrly(chunk, filter_obs, mjd_obs, variability_cache, dflux) dummy_sed = Sed() lsst_bp = BandpassDict.loadTotalBandpassesFromFiles() flux_q = np.zeros((6, n_obj), dtype=float) flux_coadd = np.zeros((6, n_obj), dtype=float) mag_coadd = np.zeros((6, n_obj), dtype=float) snr_coadd = np.zeros((6, n_obj), dtype=float) snr_single = {} snr_single_mag_grid = np.arange(14.0, 30.0, 0.05) phot_params_single = PhotometricParameters(nexp=1, exptime=30.0) t_start_snr = time.time() photometry_mask = np.zeros((n_obj, n_t), dtype=bool) photometry_mask_1d = np.zeros(n_obj, dtype=bool) for i_bp, bp in enumerate('ugrizy'): valid_obs = np.where(filter_obs == i_bp) phot_params_coadd = PhotometricParameters(nexp=1, exptime=30.0 * coadd_visits[bp]) flux_q[i_bp] = dummy_sed.fluxFromMag(chunk['%smag' % bp]) dflux_sub = dflux[:, valid_obs[0]] assert dflux_sub.shape == (n_obj, len(valid_obs[0])) dflux_mean = np.mean(dflux_sub, axis=1) assert dflux_mean.shape == (n_obj, ) flux_coadd[i_bp] = flux_q[i_bp] + dflux_mean mag_coadd[i_bp] = dummy_sed.magFromFlux(flux_coadd[i_bp]) (snr_coadd[i_bp], gamma) = SNR.calcSNR_m5(mag_coadd[i_bp], lsst_bp[bp], coadd_m5[bp], phot_params_coadd) (snr_single[bp], gamma) = SNR.calcSNR_m5(snr_single_mag_grid, lsst_bp[bp], m5_single[bp], phot_params_single) #print('got all snr in %e' % (time.time()-t_start_snr)) t_start_obj = time.time() snr_arr = np.zeros((n_obj, n_t), dtype=float) for i_bp, bp in enumerate('ugrizy'): valid_obs = np.where(filter_obs == i_bp) if len(valid_obs[0]) == 0: continue n_bp = len(valid_obs[0]) dflux_bp = dflux[:, valid_obs[0]] flux0_arr = flux_q[i_bp] flux_tot = flux0_arr[:, None] + dflux_bp mag_tot = dummy_sed.magFromFlux(flux_tot) snr_single_val = np.interp(mag_tot, snr_single_mag_grid, snr_single[bp]) noise_coadd = flux_coadd[i_bp] / snr_coadd[i_bp] noise_single = flux_tot / snr_single_val noise = np.sqrt(noise_coadd[:, None]**2 + noise_single**2) dflux_thresh = 5.0 * noise dflux_bp = np.abs(dflux_bp) detected = (dflux_bp >= dflux_thresh) snr_arr[:, valid_obs[0]] = dflux_bp / noise for i_obj in range(n_obj): if detected[i_obj].any(): photometry_mask_1d[i_obj] = True photometry_mask[i_obj, valid_obs[0]] = detected[i_obj] t_before_chip = time.time() chip_mask = apply_focal_plane(chunk['ra'], chunk['decl'], photometry_mask_1d, obs_md_list, filter_obs, proper_chip) duration = (time.time() - t_before_chip) / 3600.0 for i_obj in range(n_obj): if photometry_mask_1d[i_obj]: detected = photometry_mask[i_obj, :] & chip_mask[i_obj, :] if detected.any(): unq = chunk['simobjid'][i_obj] first_dex = np.where(detected)[0].min() out_data[unq] = (mjd_obs[first_dex], snr_arr[i_obj, first_dex], chunk['var_type'][i_obj]) if detected[0]: ct_first += 1 else: ct_at_all += 1
def Simulate_LC(self): for obs in self.obs: filtre = obs['band'].split('::')[1] #seeing=obs['rawSeeing'] time_obs = obs['mjd'] #m5_opsim=obs['fiveSigmaDepth'] m5_opsim = obs['m5sigmadepth'] sed_SN = self.SN.get_SED(time_obs) self.transmission.Load_Atmosphere(obs['airmass']) flux_SN = sed_SN.calcFlux( bandpass=self.transmission.lsst_atmos_aerosol[filtre]) #visittime=obs['visitExpTime'] visittime = obs['exptime'] photParams = PhotometricParameters(nexp=visittime / 15.) e_per_sec = sed_SN.calcADU( bandpass=self.transmission.lsst_atmos_aerosol[filtre], photParams=photParams) #number of ADU counts for expTime e_per_sec /= visittime / photParams.gain """ Filter_Wavelength_Correction = np.power(500.0 / self.params.filterWave[filtre], 0.3) Airmass_Correction = math.pow(obs['airmass'],0.6) FWHM_Sys = self.params.FWHM_Sys_Zenith * Airmass_Correction FWHM_Atm = seeing * Filter_Wavelength_Correction * Airmass_Correction finSeeing = self.params.scaleToNeff * math.sqrt(np.power(FWHM_Sys,2) + self.params.atmNeffFactor * np.power(FWHM_Atm,2)) FWHMeff = SignalToNoise.FWHMgeom2FWHMeff(finSeeing) """ #print 'alors pal',finSeeing,FWHMeff,obs['FWHMgeom'],obs['FWHMeff'],SignalToNoise.FWHMgeom2FWHMeff(obs['FWHMgeom']) FWHMeff = obs['FWHMeff'] if flux_SN > 0: mag_SN = -2.5 * np.log10(flux_SN / 3631.0) #print 'hello',finSeeing,FWHMeff m5_calc, snr_m5_through = self.Get_m5(filtre, mag_SN, obs['sky'], photParams, FWHMeff) m5_opsim += 1.25 * np.log10(visittime / 30.) snr_m5_opsim, gamma_opsim = SignalToNoise.calcSNR_m5( mag_SN, self.transmission.lsst_atmos_aerosol[filtre], m5_opsim, photParams) err_flux_SN_opsim = flux_SN / snr_m5_opsim err_flux_SN_through = flux_SN / snr_m5_through self.table_for_fit['error_coadd_opsim'].add_row( (time_obs, flux_SN, err_flux_SN_opsim, 'LSST::' + filtre, 25, 'ab')) self.table_for_fit['error_coadd_through'].add_row( (time_obs, flux_SN, err_flux_SN_through, 'LSST::' + filtre, 25, 'ab')) """ flatSed_err = Sed() flatSed_err.setFlatSED(wavelen_min, wavelen_max, wavelen_step) #flatSed_err.multiplyFluxNorm(err_flux_SN_through) err_mag_SN=-2.5 * np.log10( err_flux_SN_through/ 3631.0) flux0_err=np.power(10.,-0.4*err_mag_SN) flatSed_err.multiplyFluxNorm(flux0_err) e_per_sec_err = flatSed_err.calcADU(bandpass=self.transmission.lsst_atmos_aerosol[filtre], photParams=photParams) #number of ADU counts for expTime e_per_sec_err/=obs['visitExpTime']/2.3 """ #print 'test',e_per_sec_err,e_per_sec/snr_m5_through #print 'hello',filtre,obs['expMJD'],obs['visitExpTime'],obs['rawSeeing'],obs['moon_frac'],obs['filtSkyBrightness'],obs['kAtm'],obs['airmass'],obs['fiveSigmaDepth'],obs['Nexp'],e_per_sec,e_per_sec_err,flux_SN,err_flux_SN_through self.table_LC.add_row( ('LSST::' + filtre, obs['mjd'], visittime, FWHMeff, obs['moon_frac'], obs['sky'], obs['kAtm'], obs['airmass'], obs['m5sigmadepth'], obs['Nexp'], e_per_sec, e_per_sec / snr_m5_through)) if self.syste: resu = [ 'LSST::' + filtre, obs['mjd'], visittime, FWHMeff, obs['moon_frac'], obs['sky'], obs['kAtm'], obs['airmass'], m5_opsim, obs['Nexp'], e_per_sec, e_per_sec / snr_m5_through, mag_SN, mag_SN / snr_m5_through, m5_calc ] for i in range(1, 6, 1): m5_calc_plus, snr_m5_plus = self.Get_m5( filtre, mag_SN, obs['sky'] + float(i) / 10., photParams, FWHMeff) m5_calc_minus, snr_m5_minus = self.Get_m5( filtre, mag_SN, obs['sky'] - float(i) / 10., photParams, FWHMeff) resu.append(mag_SN / snr_m5_plus) resu.append(mag_SN / snr_m5_minus) resu.append(m5_calc_plus) resu.append(m5_calc_minus) resu.append(self.z) self.table_LC_syste.add_row(tuple(resu)) else: self.table_LC.add_row( ('LSST::' + filtre, obs['mjd'], visittime, FWHMeff, obs['moon_frac'], obs['sky'], obs['kAtm'], obs['airmass'], obs['m5sigmadepth'], obs['Nexp'], -1., -1.)) self.table_LC.sort(['filter', 'expMJD'])
def calcM5(hardware, system, atmos, title='m5', return_t2_values=False): """ Calculate m5 values for all filters in hardware and system. Prints all values that go into "table 2" of the overview paper. Returns dictionary of m5 values. """ # photParams stores default values for the exposure time, nexp, size of the primary, # readnoise, gain, platescale, etc. # See https://github.com/lsst/sims_photUtils/blob/master/python/lsst/sims/photUtils/PhotometricParameters.py effarea = np.pi * (6.423/2.*100.)**2 photParams_zp = PhotometricParameters(exptime=1, nexp=1, gain=1, effarea=effarea, readnoise=8.8, othernoise=0, darkcurrent=0.2) photParams = PhotometricParameters(gain=1.0, effarea=effarea, readnoise=8.8, othernoise=0, darkcurrent=0.2) photParams_infinity = PhotometricParameters(gain=1.0, readnoise=0, darkcurrent=0, othernoise=0, effarea=effarea) # lsstDefaults stores default values for the FWHMeff. # See https://github.com/lsst/sims_photUtils/blob/master/python/lsst/sims/photUtils/LSSTdefaults.py lsstDefaults = LSSTdefaults() darksky = Sed() darksky.readSED_flambda(os.path.join(getPackageDir('syseng_throughputs'), 'siteProperties', 'darksky.dat')) flatSed = Sed() flatSed.setFlatSED() m5 = {} Tb = {} Sb = {} kAtm = {} Cm = {} dCm_infinity = {} sourceCounts = {} skyCounts = {} skyMag = {} gamma = {} zpT = {} FWHMgeom = {} FWHMeff = {} for f in system: zpT[f] = system[f].calcZP_t(photParams_zp) m5[f] = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams, FWHMeff=lsstDefaults.FWHMeff(f)) fNorm = flatSed.calcFluxNorm(m5[f], system[f]) flatSed.multiplyFluxNorm(fNorm) sourceCounts[f] = flatSed.calcADU(system[f], photParams=photParams) # Calculate the Skycounts expected in this bandpass. skyCounts[f] = (darksky.calcADU(hardware[f], photParams=photParams) * photParams.platescale**2) # Calculate the sky surface brightness. skyMag[f] = darksky.calcMag(hardware[f]) # Calculate the gamma value. gamma[f] = SignalToNoise.calcGamma(system[f], m5[f], photParams) # Calculate the "Throughput Integral" (this is the hardware + atmosphere) dwavelen = np.mean(np.diff(system[f].wavelen)) Tb[f] = np.sum(system[f].sb / system[f].wavelen) * dwavelen # Calculate the "Sigma" 'system integral' (this is the hardware only) Sb[f] = np.sum(hardware[f].sb / hardware[f].wavelen) * dwavelen # Calculate km - atmospheric extinction in a particular bandpass kAtm[f] = -2.5*np.log10(Tb[f] / Sb[f]) # Calculate the Cm and Cm_Infinity values. # m5 = Cm + 0.5*(msky - 21) + 2.5log10(0.7/FWHMeff) + 1.25log10(t/30) - km(X-1.0) # Assumes atmosphere used in system throughput is X=1.0 X = 1.0 Cm[f] = (m5[f] - 0.5*(skyMag[f] - 21) - 2.5*np.log10(0.7/lsstDefaults.FWHMeff(f)) - 1.25*np.log10((photParams.exptime*photParams.nexp)/30.0) + kAtm[f]*(X-1.0)) # Calculate Cm_Infinity by setting readout noise to zero. m5inf = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams_infinity, FWHMeff=lsstDefaults.FWHMeff(f)) Cm_infinity = (m5inf - 0.5*(skyMag[f] - 21) - 2.5*np.log10(0.7/lsstDefaults.FWHMeff(f)) - 1.25*np.log10((photParams.exptime*photParams.nexp)/30.0) + kAtm[f]*(X-1.0)) dCm_infinity[f] = Cm_infinity - Cm[f] print 'Filter FWHMeff FWHMgeom SkyMag SkyCounts Zp_t Tb Sb kAtm Gamma Cm dCm_infinity m5 SourceCounts' for f in ('u', 'g' ,'r', 'i', 'z', 'y'): FWHMeff[f] = lsstDefaults.FWHMeff(f) FWHMgeom[f] = SignalToNoise.FWHMeff2FWHMgeom(lsstDefaults.FWHMeff(f)) print '%s %.2f %.2f %.2f %.1f %.2f %.3f %.3f %.4f %.6f %.2f %.2f %.2f %.2f'\ % (f, FWHMeff[f], FWHMgeom[f], skyMag[f], skyCounts[f], zpT[f], Tb[f], Sb[f], kAtm[f], gamma[f], Cm[f], dCm_infinity[f], m5[f], sourceCounts[f]) if return_t2_values: return {'FHWMeff': FWHMeff, 'FWHMgeom': FWHMgeom, 'skyMag': skyMag, 'skycounts': skyCounts, 'zpT': zpT, 'Tb': Tb, 'Sb': Sb, 'kAtm': kAtm, 'gamma': gamma, 'Cm': Cm, 'dCm_infinity': dCm_infinity, 'm5': m5, 'sourceCounts': sourceCounts} for f in filterlist: m5_cm = Cm[f] + 0.5*(skyMag[f] - 21.0) + 2.5*np.log10(0.7/lsstDefaults.FWHMeff(f)) if m5_cm - m5[f] > 0.001: raise ValueError('Cm calculation for %s band is incorrect! m5_cm != m5_snr' %f) # Show what these look like individually (add sky & m5 limits on throughput curves) plt.figure() for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2, label=f) plt.plot(atmosphere.wavelen, atmosphere.sb, 'k:', label='X=1.0') plt.legend(loc='center right', fontsize='smaller') plt.xlim(300, 1100) plt.ylim(0, 1) plt.xlabel('Wavelength (nm)') plt.ylabel('Throughput') plt.title('System Throughputs') plt.grid(True) plt.savefig('../plots/throughputs.png', format='png') plt.figure() ax = plt.gca() # Add dark sky ax2 = ax.twinx() plt.sca(ax2) skyab = np.zeros(len(darksky.fnu)) condition = np.where(darksky.fnu > 0) skyab[condition] = -2.5*np.log10(darksky.fnu[condition]) - darksky.zp ax2.plot(darksky.wavelen, skyab, 'k-', linewidth=0.8, label='Dark sky mags') ax2.set_ylabel('AB mags') ax2.set_ylim(24, 14) plt.sca(ax) # end of dark sky handles = [] for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2) myline = mlines.Line2D([], [], color=filtercolors[f], linestyle='-', linewidth=2, label = '%s: m5 %.1f (sky %.1f)' %(f, m5[f], skyMag[f])) handles.append(myline) plt.plot(atmos.wavelen, atmos.sb, 'k:', label='Atmosphere, X=1.0') # Add legend for dark sky. myline = mlines.Line2D([], [], color='k', linestyle='-', label='Dark sky AB mags/arcsec^2') handles.append(myline) # end of dark sky legend line plt.legend(loc=(0.01, 0.69), handles=handles, fancybox=True, numpoints=1, fontsize='small') plt.ylim(0, 1) plt.xlim(300, 1100) plt.xlabel('Wavelength (nm)') plt.ylabel('Fractional Throughput Response') plt.title('System total response curves %s' %(title)) plt.savefig('../plots/system+sky' + title + '.png', format='png', dpi=600) return m5
#plt.plot(sed_SNb.wavelen*(1.+redshift)/(1.+redshiftb),ratio_cosmo*sed_SNb.flambda,linestyle='-',color='g') Filter_Wavelength_Correction = np.power(500.0 / filterWave[band], 0.3) Airmass_Correction = math.pow(obs['airmass'], 0.6) FWHM_Sys = FWHM_Sys_Zenith * Airmass_Correction FWHM_Atm = seeing * Filter_Wavelength_Correction * Airmass_Correction finSeeing = scaleToNeff * math.sqrt( np.power(FWHM_Sys, 2) + atmNeffFactor * np.power(FWHM_Atm, 2)) #print 'flux_SN',band,flux_SN if flux_SN > 0: mag_SN = -2.5 * np.log10(flux_SN) plt.show() FWHMeff = SignalToNoise.FWHMgeom2FWHMeff(finSeeing) photParams = PhotometricParameters() snr_SN = SignalToNoise.calcSNR_sed( sed_SN, transmission.lsst_atmos_aerosol[band], transmission.darksky, transmission.lsst_system[band], photParams, FWHMeff=FWHMeff, verbose=False) m5_calc = SignalToNoise.calcM5( transmission.darksky, transmission.lsst_atmos_aerosol[band], transmission.lsst_system[band], photParams=photParams, FWHMeff=FWHMeff)
def process_stellar_chunk(chunk, filter_obs, mjd_obs, m5_obs, coadd_m5, m5_single, obs_md_list, proper_chip, variability_cache, out_data, lock): t_start_chunk = time.time() #print('processing %d' % len(chunk)) ct_first = 0 ct_at_all = 0 ct_tot = 0 n_t = len(filter_obs) n_obj = len(chunk) coadd_visits = {} coadd_visits['u'] = 6 coadd_visits['g'] = 8 coadd_visits['r'] = 18 coadd_visits['i'] = 18 coadd_visits['z'] = 16 coadd_visits['y'] = 16 gamma_coadd = {} for bp in 'ugrizy': gamma_coadd[bp] = None gamma_single = {} for bp in 'ugrizy': gamma_single[bp] = [None]*n_t dflux = np.zeros((n_obj,n_t), dtype=float) dflux_for_mlt(chunk, filter_obs, mjd_obs, variability_cache, dflux) dflux_for_kepler(chunk, filter_obs, mjd_obs, variability_cache, dflux) dflux_for_rrly(chunk, filter_obs, mjd_obs, variability_cache, dflux) dummy_sed = Sed() lsst_bp = BandpassDict.loadTotalBandpassesFromFiles() flux_q = np.zeros((6,n_obj), dtype=float) flux_coadd = np.zeros((6,n_obj), dtype=float) mag_coadd = np.zeros((6,n_obj), dtype=float) snr_coadd = np.zeros((6,n_obj), dtype=float) snr_single = {} snr_single_mag_grid = np.arange(14.0, 30.0, 0.05) phot_params_single = PhotometricParameters(nexp=1, exptime=30.0) t_start_snr = time.time() photometry_mask = np.zeros((n_obj, n_t), dtype=bool) photometry_mask_1d = np.zeros(n_obj, dtype=bool) for i_bp, bp in enumerate('ugrizy'): valid_obs = np.where(filter_obs == i_bp) phot_params_coadd = PhotometricParameters(nexp=1, exptime=30.0*coadd_visits[bp]) flux_q[i_bp] = dummy_sed.fluxFromMag(chunk['%smag' % bp]) dflux_sub = dflux[:,valid_obs[0]] assert dflux_sub.shape == (n_obj, len(valid_obs[0])) dflux_mean = np.mean(dflux_sub, axis=1) assert dflux_mean.shape==(n_obj,) flux_coadd[i_bp] = flux_q[i_bp]+dflux_mean mag_coadd[i_bp] = dummy_sed.magFromFlux(flux_coadd[i_bp]) (snr_coadd[i_bp], gamma) = SNR.calcSNR_m5(mag_coadd[i_bp], lsst_bp[bp], coadd_m5[bp], phot_params_coadd) (snr_single[bp], gamma) = SNR.calcSNR_m5(snr_single_mag_grid, lsst_bp[bp], m5_single[bp], phot_params_single) #print('got all snr in %e' % (time.time()-t_start_snr)) t_start_obj = time.time() snr_arr = np.zeros((n_obj, n_t), dtype=float) for i_bp, bp in enumerate('ugrizy'): valid_obs = np.where(filter_obs==i_bp) if len(valid_obs[0])==0: continue n_bp = len(valid_obs[0]) dflux_bp = dflux[:,valid_obs[0]] flux0_arr = flux_q[i_bp] flux_tot = flux0_arr[:,None] + dflux_bp mag_tot = dummy_sed.magFromFlux(flux_tot) snr_single_val = np.interp(mag_tot, snr_single_mag_grid, snr_single[bp]) noise_coadd = flux_coadd[i_bp]/snr_coadd[i_bp] noise_single = flux_tot/snr_single_val noise = np.sqrt(noise_coadd[:,None]**2+noise_single**2) dflux_thresh = 5.0*noise dflux_bp = np.abs(dflux_bp) detected = (dflux_bp>=dflux_thresh) snr_arr[:,valid_obs[0]] = dflux_bp/noise for i_obj in range(n_obj): if detected[i_obj].any(): photometry_mask_1d[i_obj] = True photometry_mask[i_obj,valid_obs[0]] = detected[i_obj] t_before_chip = time.time() chip_mask = apply_focal_plane(chunk['ra'], chunk['decl'], photometry_mask_1d, obs_md_list, filter_obs, proper_chip) duration = (time.time()-t_before_chip)/3600.0 unq_out = -1*np.ones(n_obj, dtype=int) mjd_out = -1.0*np.ones(n_obj, dtype=float) snr_out = -1.0*np.ones(n_obj, dtype=float) var_type_out = -1*np.ones(n_obj, dtype=int) for i_obj in range(n_obj): if photometry_mask_1d[i_obj]: detected = photometry_mask[i_obj,:] & chip_mask[i_obj,:] if detected.any(): unq_out[i_obj] = chunk['simobjid'][i_obj] first_dex = np.where(detected)[0].min() mjd_out[i_obj] = mjd_obs[first_dex] snr_out[i_obj] = snr_arr[i_obj, first_dex] var_type_out[i_obj] = chunk['var_type'][i_obj] valid = np.where(unq_out>=0) unq_out = unq_out[valid] mjd_out = mjd_out[valid] var_type_out = var_type_out[valid] snr_out = snr_out[valid] with lock: existing_keys = list(out_data.keys()) key_val = 0 if len(existing_keys)>0: key_val = max(existing_keys) while key_val in existing_keys: key_val += 1 out_data[key_val] = (None, None, None, None) out_data[key_val] = (unq_out, mjd_out, snr_out, var_type_out)
def calcM5(hardware, system, atmos, title='m5', X=1.0, return_t2_values=False): """ Calculate m5 values for all filters in hardware and system. Prints all values that go into "table 2" of the overview paper. Returns dictionary of m5 values. """ # photParams stores default values for the exposure time, nexp, size of the primary, # readnoise, gain, platescale, etc. # See https://github.com/lsst/sims_photUtils/blob/master/python/lsst/sims/photUtils/PhotometricParameters.py effarea = np.pi * (6.423/2.*100.)**2 photParams_zp = PhotometricParameters(exptime=1, nexp=1, gain=1, effarea=effarea, readnoise=8.8, othernoise=0, darkcurrent=0.2) photParams = PhotometricParameters(gain=1.0, effarea=effarea, readnoise=8.8, othernoise=0, darkcurrent=0.2) photParams_infinity = PhotometricParameters(gain=1.0, readnoise=0, darkcurrent=0, othernoise=0, effarea=effarea) # lsstDefaults stores default values for the FWHMeff. # See https://github.com/lsst/sims_photUtils/blob/master/python/lsst/sims/photUtils/LSSTdefaults.py lsstDefaults = LSSTdefaults() darksky = Sed() darksky.readSED_flambda(os.path.join(getPackageDir('syseng_throughputs'), 'siteProperties', 'darksky.dat')) flatSed = Sed() flatSed.setFlatSED() m5 = {} Tb = {} Sb = {} kAtm = {} Cm = {} dCm_infinity = {} sourceCounts = {} skyCounts = {} skyMag = {} gamma = {} zpT = {} FWHMgeom = {} FWHMeff = {} for f in system: zpT[f] = system[f].calcZP_t(photParams_zp) eff_wavelen = system[f].calcEffWavelen()[1] FWHMeff[f] = scale_seeing(0.62, eff_wavelen, X)[0] m5[f] = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams, FWHMeff=FWHMeff[f]) fNorm = flatSed.calcFluxNorm(m5[f], system[f]) flatSed.multiplyFluxNorm(fNorm) sourceCounts[f] = flatSed.calcADU(system[f], photParams=photParams) # Calculate the Skycounts expected in this bandpass. skyCounts[f] = (darksky.calcADU(hardware[f], photParams=photParams) * photParams.platescale**2) # Calculate the sky surface brightness. skyMag[f] = darksky.calcMag(hardware[f]) # Calculate the gamma value. gamma[f] = SignalToNoise.calcGamma(system[f], m5[f], photParams) # Calculate the "Throughput Integral" (this is the hardware + atmosphere) dwavelen = np.mean(np.diff(system[f].wavelen)) Tb[f] = np.sum(system[f].sb / system[f].wavelen) * dwavelen # Calculate the "Sigma" 'system integral' (this is the hardware only) Sb[f] = np.sum(hardware[f].sb / hardware[f].wavelen) * dwavelen # Calculate km - atmospheric extinction in a particular bandpass kAtm[f] = -2.5*np.log10(Tb[f] / Sb[f]) # Calculate the Cm and Cm_Infinity values. # m5 = Cm + 0.5*(msky - 21) + 2.5log10(0.7/FWHMeff) + 1.25log10(t/30) - km(X-1.0) # Assumes atmosphere used in system throughput is X=1.0 Cm[f] = (m5[f] - 0.5*(skyMag[f] - 21) - 2.5*np.log10(0.7/FWHMeff[f]) - 1.25*np.log10((photParams.exptime*photParams.nexp)/30.0) + kAtm[f]*(X-1.0)) # Calculate Cm_Infinity by setting readout noise to zero. m5inf = SignalToNoise.calcM5(darksky, system[f], hardware[f], photParams_infinity, FWHMeff=FWHMeff[f]) Cm_infinity = (m5inf - 0.5*(skyMag[f] - 21) - 2.5*np.log10(0.7/FWHMeff[f]) - 1.25*np.log10((photParams.exptime*photParams.nexp)/30.0) + kAtm[f]*(X-1.0)) dCm_infinity[f] = Cm_infinity - Cm[f] print('Filter FWHMeff FWHMgeom SkyMag SkyCounts Zp_t Tb Sb kAtm Gamma Cm dCm_infinity m5 SourceCounts') for f in ('u', 'g' ,'r', 'i', 'z', 'y'): FWHMgeom[f] = SignalToNoise.FWHMeff2FWHMgeom(FWHMeff[f]) print('%s %.2f %.2f %.2f %.1f %.2f %.3f %.3f %.4f %.6f %.2f %.2f %.2f %.2f'\ % (f, FWHMeff[f], FWHMgeom[f], skyMag[f], skyCounts[f], zpT[f], Tb[f], Sb[f], kAtm[f], gamma[f], Cm[f], dCm_infinity[f], m5[f], sourceCounts[f])) for f in filterlist: m5_cm = Cm[f] + 0.5*(skyMag[f] - 21.0) + 2.5*np.log10(0.7/FWHMeff[f]) - kAtm[f]*(X-1.0) if m5_cm - m5[f] > 0.001: raise ValueError('Cm calculation for %s band is incorrect! m5_cm != m5_snr' %f) if return_t2_values: return {'FWHMeff': FWHMeff, 'FWHMgeom': FWHMgeom, 'skyMag': skyMag, 'skycounts': skyCounts, 'zpT': zpT, 'Tb': Tb, 'Sb': Sb, 'kAtm': kAtm, 'gamma': gamma, 'Cm': Cm, 'dCm_infinity': dCm_infinity, 'm5': m5, 'sourceCounts': sourceCounts} # Show what these look like individually (add sky & m5 limits on throughput curves) plt.figure() for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2, label=f) plt.plot(atmos.wavelen, atmos.sb, 'k:', label='X=1.0') plt.legend(loc='center right', fontsize='smaller') plt.xlim(300, 1100) plt.ylim(0, 1) plt.xlabel('Wavelength (nm)') plt.ylabel('Throughput') plt.title('System Throughputs') plt.grid(True) plt.savefig('../plots/throughputs.png', format='png') plt.figure() ax = plt.gca() # Add dark sky ax2 = ax.twinx() plt.sca(ax2) skyab = np.zeros(len(darksky.fnu)) condition = np.where(darksky.fnu > 0) skyab[condition] = -2.5*np.log10(darksky.fnu[condition]) - darksky.zp ax2.plot(darksky.wavelen, skyab, 'k-', linewidth=0.8, label='Dark sky mags') ax2.set_ylabel('AB mags') ax2.set_ylim(24, 14) plt.sca(ax) # end of dark sky handles = [] for f in filterlist: plt.plot(system[f].wavelen, system[f].sb, color=filtercolors[f], linewidth=2) myline = mlines.Line2D([], [], color=filtercolors[f], linestyle='-', linewidth=2, label = '%s: m5 %.1f (sky %.1f)' %(f, m5[f], skyMag[f])) handles.append(myline) plt.plot(atmos.wavelen, atmos.sb, 'k:', label='Atmosphere, X=1.0') # Add legend for dark sky. myline = mlines.Line2D([], [], color='k', linestyle='-', label='Dark sky AB mags/arcsec^2') handles.append(myline) # end of dark sky legend line plt.legend(loc=(0.01, 0.69), handles=handles, fancybox=True, numpoints=1, fontsize='small') plt.ylim(0, 1) plt.xlim(300, 1100) plt.xlabel('Wavelength (nm)') plt.ylabel('Fractional Throughput Response') plt.title('System total response curves %s' %(title)) plt.savefig('../plots/system+sky' + title + '.png', format='png', dpi=600) return m5