Esempio n. 1
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def dmag_errors(t,band,sigma=3,mode='mag',mags=np.arange(13,24,0.1)):
    """Given an exposure time, give dmag error bars at a range of magnitudes."""
    cnts = gt.mag2counts(mags,band)
    ymin = (cnts*t/t)-sigma*np.sqrt(cnts*t)/t
    ymax = (cnts*t/t)+sigma*np.sqrt(cnts*t)/t
    if mode=='mag':
        ymin=mags-gt.counts2mag(ymin,band)
        ymax=mags-gt.counts2mag(ymax,band)
    return mags,ymin,ymax
Esempio n. 2
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def getcurve(band, ra0, dec0, radius, annulus=None, stepsz=None, lcurve={},
             trange=None, tranges=None, verbose=0, coadd=False, minexp=1.,
             maxgap=1., maskdepth=20, maskradius=1.5,
             photonfile=None, detsize=1.1):
    skyrange = [np.array(annulus).max().tolist() if annulus else radius,
                np.array(annulus).max().tolist() if annulus else radius,]
    if verbose:
        mc.print_inline("Getting exposure ranges.")
    if tranges is None:
        tranges = dbt.fGetTimeRanges(band, [ra0, dec0], trange=trange,
                maxgap=maxgap, minexp=minexp, verbose=verbose, detsize=detsize)
    elif not np.array(tranges).shape:
        print "No exposure time at this location: [{ra},{dec}]".format(
                                                            ra=ra0,dec=dec0)
    # FIXME: Everything goes to hell if no exposure time is available...
    # TODO: Add an ability to specify or exclude specific time ranges
    if verbose:
        mc.print_inline("Moving to photon level operations.")
    # FIXME: This error handling is hideous.
    try:
        lcurve = quickmag(band, ra0, dec0, tranges, radius, annulus=annulus,
                          stepsz=stepsz, verbose=verbose, coadd=coadd,
                          maskdepth=maskdepth,
                          maskradius=maskradius,photonfile=photonfile)
        lcurve['cps'] = lcurve['sources']/lcurve['exptime']
        lcurve['cps_bgsub'] = (lcurve['sources']-
                               lcurve['bg']['simple'])/lcurve['exptime']
        lcurve['cps_bgsub_cheese'] = (lcurve['sources']-
                               lcurve['bg']['cheese'])/lcurve['exptime']
        lcurve['mag'] = gxt.counts2mag(lcurve['cps'],band)
        lcurve['mag_bgsub'] = gxt.counts2mag(lcurve['cps_bgsub'],band)
        lcurve['mag_bgsub_cheese'] = gxt.counts2mag(
                                            lcurve['cps_bgsub_cheese'],band)
        lcurve['flux'] = gxt.counts2flux(lcurve['cps'],band)
        lcurve['flux_bgsub'] = gxt.counts2flux(lcurve['cps_bgsub'],band)
        lcurve['flux_bgsub_cheese'] = gxt.counts2flux(
                                            lcurve['cps_bgsub_cheese'],band)
        lcurve['detrad'] = mc.distance(lcurve['detxs'],lcurve['detys'],400,400)
    except ValueError:
        lcurve['cps']=[]
        lcurve['cps_bgsub']=[]
        lcurve['cps_bgsub_cheese']=[]
        lcurve['mag']=[]
        lcurve['mag_bgsub']=[]
        lcurve['mag_bgsub_cheese']=[]
        lcurve['flux']=[]
        lcurve['flux_bgsub']=[]
        lcurve['flux_bgsub_cheese']=[]
        lcurve['detrad']=[]
    if verbose:
        mc.print_inline("Done.")
        mc.print_inline("")
    return lcurve
Esempio n. 3
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def data_errors(catmag,t,band,sigma=3,mode='mag'):
	if mode!='cps' and mode!='mag':
		print 'mode must be set to "cps" or "mag"'
		exit(0)
	cnt = gt.mag2counts(catmag,band)
	ymin = (cnt*t/t)-sigma*np.sqrt(cnt*t)/t
	ymax = (cnt*t/t)+sigma*np.sqrt(cnt*t)/t
	if mode=='mag':
#		y = gt.counts2mag(cnt*t/t,band)
		ymin = gt.counts2mag(ymin,band)
		ymax = gt.counts2mag(ymax,band)
	return ymin, ymax
Esempio n. 4
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def model_errors(catmag,band,sigma=3,mode='mag',trange=[1,1600]):
	"""Give upper and lower expected bounds as a function of the nominal
	magnitude of a source. Super userful for identifying outliers.
	sigma = how many sigma out to set the bounds
	mode = ['cps','mag'] - units in which to report bounds
	"""
	if mode!='cps' and mode!='mag':
		print 'mode must be set to "cps" or "mag"'
		exit(0)
	x = np.arange(trange[0],trange[1])
	cnt = gt.mag2counts(catmag,band)
	ymin = (cnt*x/x)-sigma*np.sqrt(cnt*x)/x
	ymax = (cnt*x/x)+sigma*np.sqrt(cnt*x)/x
	if mode=='mag':
#		y = gt.counts2mag(cnt*x/x,band)
		ymin = gt.counts2mag(ymin,band)
		ymax = gt.counts2mag(ymax,band)
	return ymin, ymax
Esempio n. 5
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    data[band] = pd.read_csv('{base}{band}.csv'.format(
                                                        base=base,band=band))
    print '{band} sources: {cnt}'.format(
                                band=band,cnt=data[band]['objid'].shape[0])

"""dMag vs. Mag"""
for band in bands:
    dmag = {'gphot_cheese':(lambda band:
                        data[band]['aper4']-data[band]['mag_bgsub_cheese']),
                 'gphot_nomask':(lambda band:
                        data[band]['aper4']-data[band]['mag_bgsub']),
                 'gphot_sigma':(lambda band:
                        data[band]['aper4']-data[band]['mag_bgsub_sigmaclip']),
                 'mcat':lambda band: data[band]['aper4']-
                        gt.counts2mag(gt.mag2counts(data[band]['mag'],band)-
                        data[band]['skybg']*3600**2*mc.area(gt.aper2deg(4)),
                        band)}
bgmodekeys={'gphot_cheese':'mag_bgsub_cheese',
            'gphot_nomask':'mag_bgsub',
            'gphot_sigma':'mag_bgsub_sigmaclip',
            'mcat':'skybg'}
for bgmode in dmag.keys():
    for band in bands:
        fig = plt.figure(figsize=(8*scl,4*scl))
        fig.subplots_adjust(left=0.12,right=0.95,wspace=0.02,
                                                        bottom=0.15,top=0.9)
        dmag_err=gu.dmag_errors(100.,band,sigma=1.41)
        # Make a cut on crazy outliers in the MCAT. Also on det radius and expt.
        ix = ((data[band]['aper4']>0) & (data[band]['aper4']<30) &
              (data[band]['distance']<300) & (data[band]['t_eff']<300) &
              (np.isfinite(np.array(data[band][bgmodekeys[bgmode]]))))
Esempio n. 6
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 def test_counts2mag_NUV(self):
     self.assertAlmostEqual(gt.counts2mag(10,'NUV'),-2.5*np.log10(10)+20.08)
Esempio n. 7
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 def test_counts2mag_FUV(self):
     self.assertAlmostEqual(gt.counts2mag(10,'FUV'),-2.5*np.log10(10)+18.82)