def write_membership(self,filename):
"""
Write a catalog file of the likelihood region including
membership properties.
Parameters:
-----------
filename : output filename
Returns:
--------
None
"""
# Column names
name_objid = self.config['catalog']['objid_field']
name_mag_1 = self.config['catalog']['mag_1_field']
name_mag_2 = self.config['catalog']['mag_2_field']
name_mag_err_1 = self.config['catalog']['mag_err_1_field']
name_mag_err_2 = self.config['catalog']['mag_err_2_field']
# Coordinate conversion
#ra,dec = gal2cel(self.catalog.lon,self.catalog.lat)
glon,glat = self.catalog.glon_glat
ra,dec = self.catalog.ra_dec
# Angular and isochrone separations
sep = angsep(self.source.lon,self.source.lat,
self.catalog.lon,self.catalog.lat)
isosep = self.isochrone.separation(self.catalog.mag_1,self.catalog.mag_2)
# If size becomes an issue we can make everything float32
data = odict()
data[name_objid] = self.catalog.objid
data['GLON'] = glon
data['GLAT'] = glat
data['RA'] = ra
data['DEC'] = dec
data[name_mag_1] = self.catalog.mag_1
data[name_mag_err_1] = self.catalog.mag_err_1
data[name_mag_2] = self.catalog.mag_2
data[name_mag_err_2] = self.catalog.mag_err_2
data['COLOR'] = self.catalog.color
data['ANGSEP'] = sep.astype(np.float32)
data['ISOSEP'] = isosep.astype(np.float32)
data['PROB'] = self.p.astype(np.float32)
# HIERARCH allows header keywords longer than 8 characters
header = []
for param,value in self.source.params.items():
card = dict(name='HIERARCH %s'%param.upper(),
value=value.value,
comment=param)
header.append(card)
card = dict(name='HIERARCH %s'%'TS',value=self.ts(),
comment='test statistic')
header.append(card)
card = dict(name='HIERARCH %s'%'TIMESTAMP',value=time.asctime(),
comment='creation time')
header.append(card)
fitsio.write(filename,data,header=header,clobber=True)
def match_query(lon1, lat1, lon2, lat2, eps=0.01, n_jobs=1):
"""
Perform a KDTree match after transforming to Cartesian coordinates.
Parameters
----------
lon1 : longitude from first array (deg)
lat1 : latitude from first array (deg)
lon2 : longitude from second array (deg)
lat2 : latitude from second array (deg)
eps : precision (see `cKDTree.query`)
n_jobs : Number of workers to use for processing (see `cKDTree.query`)
Returns:
--------
idx1 : index into the first array
idx2 : index into the second array
ds : angular seperation (deg)
"""
coords1 = projector(lon1, lat1)
coords2 = projector(lon2, lat2)
tree = cKDTree(coords2)
idx1 = np.arange(lon1.size)
idx2 = tree.query(coords1, eps=eps, n_jobs=n_jobs)[1]
ds = angsep(lon1, lat1, lon2[idx2], lat2[idx2])
return np.atleast_1d(idx1), np.atleast_1d(idx2), np.atleast_1d(ds)
def write_membership2(self, filename):
ra, dec = gal2cel(self.catalog.lon, self.catalog.lat)
name_objid = self.config['catalog']['objid_field']
name_mag_1 = self.config['catalog']['mag_1_field']
name_mag_2 = self.config['catalog']['mag_2_field']
name_mag_err_1 = self.config['catalog']['mag_err_1_field']
name_mag_err_2 = self.config['catalog']['mag_err_2_field']
# Angular and isochrone separations
sep = angsep(self.source.lon, self.source.lat, self.catalog.lon,
self.catalog.lat)
isosep = self.isochrone.separation(self.catalog.mag_1,
self.catalog.mag_2)
columns = [
pyfits.Column(name=name_objid,
format='K',
array=self.catalog.objid),
pyfits.Column(name='GLON', format='D', array=self.catalog.lon),
pyfits.Column(name='GLAT', format='D', array=self.catalog.lat),
pyfits.Column(name='RA', format='D', array=ra),
pyfits.Column(name='DEC', format='D', array=dec),
pyfits.Column(name=name_mag_1,
format='E',
array=self.catalog.mag_1),
pyfits.Column(name=name_mag_err_1,
format='E',
array=self.catalog.mag_err_1),
pyfits.Column(name=name_mag_2,
format='E',
array=self.catalog.mag_2),
pyfits.Column(name=name_mag_err_2,
format='E',
array=self.catalog.mag_err_2),
pyfits.Column(name='COLOR', format='E', array=self.catalog.color),
pyfits.Column(name='ANGSEP', format='E', array=sep),
pyfits.Column(name='ISOSEP', format='E', array=isosep),
pyfits.Column(name='PROB', format='E', array=self.p),
]
hdu = pyfits.new_table(columns)
for param, value in self.source.params.items():
# HIERARCH allows header keywords longer than 8 characters
name = 'HIERARCH %s' % param.upper()
hdu.header.set(name, value.value, param)
name = 'HIERARCH %s' % 'TS'
hdu.header.set(name, self.ts())
name = 'HIERARCH %s' % 'TIMESTAMP'
hdu.header.set(name, time.asctime())
hdu.writeto(filename, clobber=True)
def write_membership(self,filename):
ra,dec = gal2cel(self.catalog.lon,self.catalog.lat)
name_objid = self.config['catalog']['objid_field']
name_mag_1 = self.config['catalog']['mag_1_field']
name_mag_2 = self.config['catalog']['mag_2_field']
name_mag_err_1 = self.config['catalog']['mag_err_1_field']
name_mag_err_2 = self.config['catalog']['mag_err_2_field']
# Angular and isochrone separations
sep = angsep(self.source.lon,self.source.lat,self.catalog.lon,self.catalog.lat)
isosep = self.isochrone.separation(self.catalog.mag_1,self.catalog.mag_2)
columns = [
pyfits.Column(name=name_objid,format='K',array=self.catalog.objid),
pyfits.Column(name='GLON',format='D',array=self.catalog.lon),
pyfits.Column(name='GLAT',format='D',array=self.catalog.lat),
pyfits.Column(name='RA',format='D',array=ra),
pyfits.Column(name='DEC',format='D',array=dec),
pyfits.Column(name=name_mag_1,format='E',array=self.catalog.mag_1),
pyfits.Column(name=name_mag_err_1,format='E',array=self.catalog.mag_err_1),
pyfits.Column(name=name_mag_2,format='E',array=self.catalog.mag_2),
pyfits.Column(name=name_mag_err_2,format='E',array=self.catalog.mag_err_2),
pyfits.Column(name='COLOR',format='E',array=self.catalog.color),
pyfits.Column(name='ANGSEP',format='E',array=sep),
pyfits.Column(name='ISOSEP',format='E',array=isosep),
pyfits.Column(name='PROB',format='E',array=self.p),
]
hdu = pyfits.new_table(columns)
for param,value in self.source.params.items():
# HIERARCH allows header keywords longer than 8 characters
name = 'HIERARCH %s'%param.upper()
hdu.header.set(name,value.value,param)
name = 'HIERARCH %s'%'TS'
hdu.header.set(name,self.ts())
name = 'HIERARCH %s'%'TIMESTAMP'
hdu.header.set(name,time.asctime())
hdu.writeto(filename,clobber=True)
def pdf(self, lon, lat):
if self.projector is None:
radius = angsep(self.lon, self.lat, lon, lat)
return self.norm * self._pdf(radius)
else:
return super(RadialKernel, self).pdf(lon, lat)
def angsep(self, lon, lat):
return angsep(self.lon, self.lat, lon, lat)
if (gcm['EXPNUM']!=qslr['EXPNUM']).any() \
or (gcm['CCDNUM']!=qslr['CCDNUM']).any():
msg = "GCM and qSLR do not match"
raise Exception(msg)
gcm = gcm[np.lexsort((gcm['CCDNUM'], gcm['EXPNUM']))]
qslr = qslr[np.lexsort((qslr['CCDNUM'], qslr['EXPNUM']))]
if (gcm['EXPNUM']!=qslr['EXPNUM']).any() \
or (gcm['CCDNUM']!=qslr['CCDNUM']).any():
msg = "GCM and qSLR do not match"
raise Exception(msg)
dzero = gcm['MAG_ZERO'] - qslr['MAG_ZERO']
sep = angsep(gcm['RA'], gcm['DEC'], qslr['RA_MEAN'], qslr['DEC_MEAN'])
plt.figure()
plotting.draw_hist(dzero, normed=False)
plt.legend(loc='upper right')
plt.xlabel(r'${\rm ZP_{GCM} - ZP_{qSLR}}')
plt.ylabel("Number of CCDs")
gcm_bands = dict()
qslr_bands = dict()
diff_bands = dict()
for b in BANDS:
zp_g = gcm['MAG_ZERO'][gcm['BAND'] == b]
zp_q = qslr['MAG_ZERO'][qslr['BAND'] == b]
delta = zp_g - zp_q
labels = qslr[HPX][qslr['BAND'] == b]
plt.title('HPX %05d (g < 30)'%opts.pix)
plt.xlabel('RA (deg)')
plt.ylabel('DEC (deg)')
plt.savefig(pltdir+'spatial_se_%05d.png'%opts.pix)
plt.figure()
sel = coadd['MAG_PSF_G'] < maglim
plt.hist2d(coadd['RA'][sel],coadd['DEC'][sel],bins=250,norm=colors.LogNorm())
plt.colorbar(label='log(Counts)')
plt.title('HPX %05d (g < %s)'%(opts.pix,maglim))
plt.xlabel('RA (deg)')
plt.ylabel('DEC (deg)')
plt.savefig(pltdir+'spatial_coadd_%05d.png'%opts.pix)
# Matching and separation
sep = angsep(coadd['RA'][inv],coadd['DEC'][inv],se['RA'],se['DEC'])
plt.figure()
n,b,p = plt.hist(sep*3600.,**kwargs)
c = (b[:-1]+b[1:])/2.
peak = c[np.argmax(n)]; text = '%.1f (mas)'%(peak*1e3)
plot_peak(peak,text,lw=2,ls='--',c='r')
plt.xlabel("Separation (arcsec)")
plt.ylabel("Counts")
plt.savefig(pltdir+'angsep_%05d.png'%opts.pix,bbox_inches='tight')
plt.figure()
plt.hist(sep*3600.,log=True,**kwargs)
c = (b[:-1]+b[1:])/2.
peak = c[np.argmax(n)]; text = '%.1f (mas)'%(peak*1e3)
plot_peak(peak,text,lw=2,ls='--',c='r')
def write_membership(self, filename):
"""
Write a catalog file of the likelihood region including
membership properties.
Parameters:
-----------
filename : output filename
Returns:
--------
None
"""
# Column names
name_objid = self.config['catalog']['objid_field']
name_mag_1 = self.config['catalog']['mag_1_field']
name_mag_2 = self.config['catalog']['mag_2_field']
name_mag_err_1 = self.config['catalog']['mag_err_1_field']
name_mag_err_2 = self.config['catalog']['mag_err_2_field']
# Coordinate conversion
#ra,dec = gal2cel(self.catalog.lon,self.catalog.lat)
glon, glat = self.catalog.glon_glat
ra, dec = self.catalog.ra_dec
# Angular and isochrone separations
sep = angsep(self.source.lon, self.source.lat, self.catalog.lon,
self.catalog.lat)
isosep = self.isochrone.separation(self.catalog.mag_1,
self.catalog.mag_2)
# If size becomes an issue we can make everything float32
data = odict()
data[name_objid] = self.catalog.objid
data['GLON'] = glon
data['GLAT'] = glat
data['RA'] = ra
data['DEC'] = dec
data[name_mag_1] = self.catalog.mag_1
data[name_mag_err_1] = self.catalog.mag_err_1
data[name_mag_2] = self.catalog.mag_2
data[name_mag_err_2] = self.catalog.mag_err_2
data['COLOR'] = self.catalog.color
data['ANGSEP'] = sep.astype(np.float32)
data['ISOSEP'] = isosep.astype(np.float32)
data['PROB'] = self.p.astype(np.float32)
# HIERARCH allows header keywords longer than 8 characters
header = []
for param, value in self.source.params.items():
card = dict(name='HIERARCH %s' % param.upper(),
value=value.value,
comment=param)
header.append(card)
card = dict(name='HIERARCH %s' % 'TS',
value=self.ts(),
comment='test statistic')
header.append(card)
card = dict(name='HIERARCH %s' % 'TIMESTAMP',
value=time.asctime(),
comment='creation time')
header.append(card)
fitsio.write(filename, data, header=header, clobber=True)
def internal_astrometry(catfile,hpxfile,nside=128,band='r',plot=False):
""" Calculate internal relative astrometry.
Parameters
----------
catfile : merged catalog file
hpxfile : single epoch catalog file(s)
nside : nside for calculation
band : band to use
plot : plot output
Returns
-------
stats : output statistics
"""
nice = os.nice(0)
os.nice(10-nice)
if band=='all': band = 'r'
#print catfile,hpxfile,nside
#catfile = glob.glob('cat/*_%05d.fits'%pix)[0]
if not os.path.exists(catfile):
msg = "Couldn't find %s"%catfile
raise Exception(msg)
columns = [OBJECT_ID,'RA','DEC']
spread,mag,nepochs = bfields(['WAVG_SPREAD_MODEL','MAG_PSF','NEPOCHS'],band)
columns += [spread,mag,nepochs]
cat = load_infiles([catfile],columns)
# Select stars with 17 < mag < 21
sel = (np.fabs(cat[spread])<0.002) & \
(cat[mag]>17) & \
(cat[mag]<21) & \
(cat[nepochs] > 1)
cat = cat[sel]
if len(cat) == 0:
print("WARNING: No objects passing selection in: %s"%catfile)
return np.array([],dtype=int), np.array([])
#hpxfiles = glob.glob('hpx/%s/*_%05d.fits'%(band,pix))
hpx = load_infiles(hpxfile, [OBJECT_ID, 'RA', 'DEC'])
hpx = hpx[np.in1d(hpx[OBJECT_ID],cat[OBJECT_ID])]
if len(hpx) == 0:
print("WARNING: No matched objects in: %s"%hpxfile)
return np.array([],dtype=int), np.array([])
#keyfile = 'key/key_hpx_%05d.fits'%pix
#key = load_infiles([keyfile],[OBJECT_ID,'FILENAME','OBJECT_NUMBER'])
#key = key[np.in1d(key[OBJECT_ID],cat[OBJECT_ID])]
#
#key_id = np.char.add(key['FILENAME'],key['OBJECT_NUMBER'].astype(str))
#hpx_id = np.char.add(hpx['FILENAME'],hpx['OBJECT_NUMBER'].astype(str))
#
#hpx = hpx[np.in1d(hpx_id,key_id)]
uid,inv,cts = np.unique(hpx[OBJECT_ID],False,True,True)
# Make sure that the order matches between coadd and single epoch.
if not np.all(uid == cat[OBJECT_ID]):
cat = cat[np.in1d(cat[OBJECT_ID],hpx[OBJECT_ID])]
if not np.all(uid == cat[OBJECT_ID]):
cat = cat[np.argsort(cat[OBJECT_ID])]
assert np.all(uid == cat[OBJECT_ID])
ra,dec = cat['RA'][inv],cat['DEC'][inv]
sepdeg = angsep(ra,dec,hpx['RA'],hpx['DEC'])
sepsec = sepdeg * 3600.
sepmas = sepsec * 1000.
sel = [sepsec > 1e-5]
sep = sepmas[sel]
pix = ang2pix(nside,ra[sel],dec[sel])
upix = np.unique(pix)
peak = nd.median(sep,labels=pix,index=upix)
if plot:
plt.figure()
draw_angsep(sep)
if isinstance(plot,basestring):
outfile = plot
plt.savefig(outfile,bbox_inches='tight')
return upix,peak
def pdf(self, lon, lat):
if self.projector is None:
radius = angsep(self.lon,self.lat,lon,lat)
return self.norm*self._pdf(radius)
else:
return super(RadialKernel,self).pdf(lon,lat)
def angsep(self,lon,lat):
return angsep(self.lon,self.lat,lon,lat)