def check_radius(args):
"""Checks the radius value."""
if not (args.radius or args.suggest or args.aperradius):
print "Must specify an aperture radius."
raise SystemExit
if args.radius and args.aperradius:
print "Must not specify both --aperture and --mcataper."
raise SystemExit
if args.aperradius and not args.radius:
args.radius = aper2deg(args.aperradius)
return args
def datamaker(
band, skypos, outfile, maglimit=20.0, margin=0.005, searchradius=0.1, radius=gt.aper2deg(4), annulus=[0.0083, 0.025]
):
extant_objids = file_setup(outfile)
if extant_objids == False:
print "NOT RUNNING!!*!"
return False
uniques = dt.find_unique_sources(band, skypos[0], skypos[1], searchradius, maglimit=maglimit)
if uniques is None:
print "No sources at this position."
return
for pos in uniques:
mcat = dt.get_mcat_data(pos, margin)
if not mcat:
print "Nothing at {pos}.".format(pos=pos)
continue
extant_objids = file_setup(outfile)
for i, objid in enumerate(mcat["objid"]):
if objid in extant_objids:
print "Already processed."
continue
exp = dt.exp_from_objid(objid)
if exp[band]["t0"] < 0:
print "skip"
continue
data = gAperture(
band,
[mcat["ra"][i], mcat["dec"][i]],
radius,
annulus=annulus,
verbose=0,
coadd=True,
trange=[exp[band]["t0"], exp[band]["t1"]],
)
if data["mag_bgsub_cheese"] and np.isfinite(data["mag_bgsub_cheese"]):
csv_construct = construct_row(i, band, objid, mcat, data)
print csv_construct
with open(outfile, "ab") as csvfile:
spreadsheet = csv.writer(csvfile, delimiter=",", quotechar="|", quoting=csv.QUOTE_MINIMAL)
spreadsheet.writerow(csv_construct)
else:
print "no exp"
return
def datamaker(band,skypos,outfile,maglimit=20.,detsize=0.5,
radius=gt.aper2deg(4),annulus=[0.0083,0.025]):
"""Note: If you wanted to change the default annulus, then a good starting
point would be [0.0083,0.025] (i.e. 30" to 90").
"""
extant_objids = file_setup(outfile)
if extant_objids==False:
print 'NOT RUNNING!!*!'
return False
uniques = dt.find_unique_sources(band,skypos[0],skypos[1],
detsize,maglimit=maglimit)
for pos in uniques:
mcat = dt.get_mcat_data(pos,0.005)
if not mcat:
print 'Nothing at {pos}.'.format(pos=pos)
continue
extant_objids = file_setup(outfile)
for i,objid in enumerate(mcat['objid']):
if objid in extant_objids:
print 'Already processed.'
continue
exp = dt.exp_from_objid(objid)
if exp[band]['t0']<0:
print 'skip'
continue
data = gAperture(band,[mcat['ra'][i],mcat['dec'][i]],radius,
annulus=annulus,verbose=0,coadd=True,
trange=[exp[band]['t0'],exp[band]['t1']])
if (data['mag_bgsub_cheese'] and
np.isfinite(data['mag_bgsub_cheese'])):
csv_construct = construct_row(i,band,objid,mcat,data)
print csv_construct
with open(outfile,'ab') as csvfile:
spreadsheet = csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
spreadsheet.writerow(csv_construct)
else:
print 'no exp'
return
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]]))))
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'$\Delta$'))
plt.axis([13,23,-1.3,1.3])
