Python Sed.flambdaTofnu方法代码示例

# 需要导入模块: from lsst.sims.photUtils.Sed import Sed [as 别名]
# 或者: from lsst.sims.photUtils.Sed.Sed import flambdaTofnu [as 别名]
    def testAlternateBandpassesStars(self):
        """
        This will test our ability to do photometry using non-LSST bandpasses.

        It will first calculate the magnitudes using the getters in cartoonPhotometryStars.

        It will then load the alternate bandpass files 'by hand' and re-calculate the magnitudes
        and make sure that the magnitude values agree.  This is guarding against the possibility
        that some default value did not change and the code actually ended up loading the
        LSST bandpasses.
        """

        obs_metadata_pointed = ObservationMetaData(
            mjd=2013.23, boundType="circle", unrefractedRA=200.0, unrefractedDec=-30.0, boundLength=1.0
        )

        bandpassDir = os.path.join(lsst.utils.getPackageDir("sims_photUtils"), "tests", "cartoonSedTestData")

        cartoon_dict = BandpassDict.loadTotalBandpassesFromFiles(
            ["u", "g", "r", "i", "z"], bandpassDir=bandpassDir, bandpassRoot="test_bandpass_"
        )

        testBandPasses = {}
        keys = ["u", "g", "r", "i", "z"]

        bplist = []

        for kk in keys:
            testBandPasses[kk] = Bandpass()
            testBandPasses[kk].readThroughput(os.path.join(bandpassDir, "test_bandpass_%s.dat" % kk))
            bplist.append(testBandPasses[kk])

        sedObj = Sed()
        phiArray, waveLenStep = sedObj.setupPhiArray(bplist)

        sedFileName = os.path.join(lsst.utils.getPackageDir("sims_sed_library"), "starSED", "kurucz")
        sedFileName = os.path.join(sedFileName, "km20_5750.fits_g40_5790.gz")
        ss = Sed()
        ss.readSED_flambda(sedFileName)

        controlBandpass = Bandpass()
        controlBandpass.imsimBandpass()
        ff = ss.calcFluxNorm(22.0, controlBandpass)
        ss.multiplyFluxNorm(ff)

        testMags = cartoon_dict.magListForSed(ss)

        ss.resampleSED(wavelen_match=bplist[0].wavelen)
        ss.flambdaTofnu()
        mags = -2.5 * numpy.log10(numpy.sum(phiArray * ss.fnu, axis=1) * waveLenStep) - ss.zp
        self.assertTrue(len(mags) == len(testMags))
        self.assertTrue(len(mags) > 0)
        for j in range(len(mags)):
            self.assertAlmostEqual(mags[j], testMags[j], 10)

# 需要导入模块: from lsst.sims.photUtils.Sed import Sed [as 别名]
# 或者: from lsst.sims.photUtils.Sed.Sed import flambdaTofnu [as 别名]
    def calcMagNorm(self, objectMags, sedObj, bandpassDict, mag_error = None,
                    redshift = None, filtRange = None):

        """
        This will find the magNorm value that gives the closest match to the magnitudes of the object
        using the matched SED. Uses scipy.optimize.leastsq to find the values of fluxNorm that minimizes
        the function: ((flux_obs - (fluxNorm*flux_model))/flux_error)**2.

        @param [in] objectMags are the magnitude values for the object with extinction matching that of
        the SED object. In the normal case using the selectSED routines above it will be dereddened mags.

        @param [in] sedObj is an Sed class instance that is set with the wavelength and flux of the
        matched SED

        @param [in] bandpassDict is a BandpassDict class instance with the Bandpasses set to those
        for the magnitudes given for the catalog object

        @param [in] mag_error are provided error values for magnitudes in objectMags. If none provided
        then this defaults to 1.0. This should be an array of the same length as objectMags.

        @param [in] redshift is the redshift of the object if the magnitude is observed

        @param [in] filtRange is a selected range of filters specified by their indices in the bandpassList
        to match up against. Used when missing data in some magnitude bands.

        @param [out] bestMagNorm is the magnitude normalization for the given magnitudes and SED
        """

        import scipy.optimize as opt

        sedTest = Sed()
        sedTest.setSED(sedObj.wavelen, flambda = sedObj.flambda)
        if redshift is not None:
            sedTest.redshiftSED(redshift)
        imSimBand = Bandpass()
        imSimBand.imsimBandpass()
        zp = -2.5*np.log10(3631)  #Note using default AB zeropoint
        flux_obs = np.power(10,(objectMags + zp)/(-2.5))
        sedTest.resampleSED(wavelen_match=bandpassDict.values()[0].wavelen)
        sedTest.flambdaTofnu()
        flux_model = sedTest.manyFluxCalc(bandpassDict.phiArray, bandpassDict.wavelenStep)
        if filtRange is not None:
            flux_obs = flux_obs[filtRange]
            flux_model = flux_model[filtRange]
        if mag_error is None:
            flux_error = np.ones(len(flux_obs))
        else:
            flux_error = np.abs(flux_obs*(np.log(10)/(-2.5))*mag_error)
        bestFluxNorm = opt.leastsq(lambda x: ((flux_obs - (x*flux_model))/flux_error), 1.0)[0][0]
        sedTest.multiplyFluxNorm(bestFluxNorm)
        bestMagNorm = sedTest.calcMag(imSimBand)
        return bestMagNorm