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 error(data,band,radius,annulus):
N_a = 1
N_b0 = (mc.area(annulus[1])-mc.area(annulus[0]))/mc.area(radius)
N_b = data[band]['bg_eff_area']/mc.area(radius)
B0 = data[band]['bg']
B = data[band]['bg_cheese']
S = gt.mag2counts(data[band]['mag'],band)*data[band]['t_eff']
s2 = {'bg_cheese_err':(S-B)+(N_a+(N_a**2.)/N_b),
'bg_err':(S-B0)+(N_a+(N_a**2.)/N_b0)}
return s2
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
for band in bands:
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) &
def test_mag2counts_NUV(self):
self.assertAlmostEqual(gt.mag2counts(16,'NUV'),10.**(-(16-20.08)/2.5))
def test_mag2counts_FUV(self):
self.assertAlmostEqual(gt.mag2counts(16,'FUV'),10.**(-(16-18.82)/2.5))
bands = ['NUV','FUV']
base = 'calrun_'
data = {}
for band in bands:
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"""
dmag = {'gphot_cheese':(lambda band:
data[band]['aper4']-data[band]['mag_bgsub_cheese']),
'gphot_nomask':(lambda band:
data[band]['aper4']-data[band]['mag_bgsub']),
'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)}
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))
plt.subplot(1,2,1)
plt.title('{band} {d}Mag vs. AB Mag (n={n},{bg}_bg)'.format(
d=r'$\Delta$',band=band,n=ix.shape[0],bg=bgmode))
plt.xlabel('AB Magnitude (MCAT)')
plt.ylabel(r'{d}Magnitude (MCAT-gPhoton)'.format(d=r'$\Delta$'))
