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
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
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
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
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]]))))
def test_counts2mag_NUV(self):
self.assertAlmostEqual(gt.counts2mag(10,'NUV'),-2.5*np.log10(10)+20.08)
def test_counts2mag_FUV(self):
self.assertAlmostEqual(gt.counts2mag(10,'FUV'),-2.5*np.log10(10)+18.82)