def fit_all(self,save_output=True,clobber=False,minmatch=20,mymatches=[], \
sigma_reject=5.0,verbose=True):
"""
runs through all the match files and fits the astrometry
clobber -- overwrite output file if it exists
save_output -- save the output file
minmatch -- minimum number of required matches to perform the mapping
mymatches -- list of files to match (instead of what gets populated in prepare)
sigma_reject -- rejection of outliers [not used for now!]
"""
if mymatches != []:
matches = mymatches
else:
matches = self.a.generated_match_files
self.pybast_files = []
for fname, nmatch, t in matches:
if nmatch < minmatch:
print "skipping %s ... too few matches [%i]" % (fname, nmatch)
continue
print "*" * 60
print "Working on %s" % (fname, )
sys.stdout.flush()
outname = "".join(fname.split(".")[0:-1]) + ".pyBAST"
if not clobber and os.path.exists(outname):
print " ... skipping (mapping file exists)"
self.pybast_files.append((outname, fname, t))
continue
data = sdss.csv2rec(fname)
# Parse data array into objects
# the A objects will be the fiducial positions from the deep coadd
# the B objects will be source positions in this epoch
# both are given in arcseconds relative to a fiducial center
objectsA = np.array( [ pyBA.Bivarg(mu=[x["master_dra"],x["master_ddec"]],\
sigma=np.array([x["master_raerr"],x["master_decerr"]])) \
for x in data ] )
objectsB = np.array( [ pyBA.Bivarg(mu=[x["dra"],x["ddec"]], \
sigma=np.array([x["raerr"],x["decerr"]])) \
for x in data ] )
# Suggest starting point for background mapping
S = pyBA.background.suggest_mapping(objectsA, objectsB)
# Get maximum a posteriori background mapping parameters
P = pyBA.background.MAP(objectsA,
objectsB,
mu0=S.mu,
prior=pyBA.Bgmap(),
norm_approx=True)
D = pyBA.Amap(P, objectsA, objectsB)
if verbose:
print " ... conditioning"
sys.stdout.flush()
D.condition()
if verbose:
print D.hyperparams
if save_output:
if clobber and os.path.exists(outname):
os.remove(outname)
output = open(outname, 'wb')
pickle.dump(D, output, protocol=-1)
if verbose:
print " ... wrote %s" % outname
self.pybast_files.append((outname, fname, t))
def fit_all(self,save_output=True,clobber=False,minmatch=20,mymatches=[], \
sigma_reject=5.0,verbose=True):
"""
runs through all the match files and fits the astrometry
clobber -- overwrite output file if it exists
save_output -- save the output file
minmatch -- minimum number of required matches to perform the mapping
mymatches -- list of files to match (instead of what gets populated in prepare)
sigma_reject -- rejection of outliers [not used for now!]
"""
if mymatches != []:
matches = mymatches
else:
matches = self.a.generated_match_files
self.pybast_files = []
for fname,nmatch,t in matches:
if nmatch < minmatch:
print "skipping %s ... too few matches [%i]" % (fname,nmatch)
continue
print "*"*60
print "Working on %s" % (fname,)
sys.stdout.flush()
outname = "".join(fname.split(".")[0:-1]) + ".pyBAST"
if not clobber and os.path.exists(outname):
print " ... skipping (mapping file exists)"
self.pybast_files.append((outname,fname,t))
continue
data = sdss.csv2rec(fname)
# Parse data array into objects
# the A objects will be the fiducial positions from the deep coadd
# the B objects will be source positions in this epoch
# both are given in arcseconds relative to a fiducial center
objectsA = np.array( [ pyBA.Bivarg(mu=[x["master_dra"],x["master_ddec"]],\
sigma=np.array([x["master_raerr"],x["master_decerr"]])) \
for x in data ] )
objectsB = np.array( [ pyBA.Bivarg(mu=[x["dra"],x["ddec"]], \
sigma=np.array([x["raerr"],x["decerr"]])) \
for x in data ] )
# Suggest starting point for background mapping
S = pyBA.background.suggest_mapping(objectsA,objectsB)
# Get maximum a posteriori background mapping parameters
P = pyBA.background.MAP( objectsA, objectsB, mu0=S.mu, prior=pyBA.Bgmap(), norm_approx=True )
D = pyBA.Amap(P,objectsA, objectsB)
if verbose:
print " ... conditioning"
sys.stdout.flush()
D.condition()
if verbose:
print D.hyperparams
if save_output:
if clobber and os.path.exists(outname):
os.remove(outname)
output = open(outname, 'wb')
pickle.dump(D, output, protocol=-1)
if verbose:
print " ... wrote %s" % outname
self.pybast_files.append((outname,fname,t))
# Demo script to analyse data from Stripe 82
#
# This is minimal version of the procedure carried out in the
# corresponding notebook (sdss_demo.ipynb).
import pyBA
import sdss
import numpy as np
np.set_printoptions(linewidth=125, suppress=True, precision=3)
# Load data
data = sdss.csv2rec("match_astrom_342.1914_-0.90194_run4198.dat")
nties = len(data)
print nties
# Parse data array into objects
# the A objects will be the fiducial positions from the deep coadd
# the B objects will be source positions in this epoch
# both are given in arcseconds relative to a fiducial center
objectsA = np.array([
pyBA.Bivarg(mu=[x["master_dra"], x["master_ddec"]],
sigma=np.array([x["master_raerr"], x["master_decerr"]]))
for x in data
])
objectsB = np.array([
pyBA.Bivarg(mu=[x["dra"], x["ddec"]],
sigma=np.array([x["raerr"], x["decerr"]])) for x in data
])
# Suggest starting point for background mapping
# Demo script to analyse data from Stripe 82
#
# This is minimal version of the procedure carried out in the
# corresponding notebook (sdss_demo.ipynb).
import pyBA
import sdss
import numpy as np
np.set_printoptions(linewidth=125,suppress=True,precision=3)
# Load data
data = sdss.csv2rec("match_astrom_342.1914_-0.90194_run4198.dat")
nties = len(data)
print nties
# Parse data array into objects
# the A objects will be the fiducial positions from the deep coadd
# the B objects will be source positions in this epoch
# both are given in arcseconds relative to a fiducial center
objectsA = np.array( [ pyBA.Bivarg(mu=[x["master_dra"],x["master_ddec"]],sigma=np.array([x["master_raerr"],x["master_decerr"]])) for x in data ] )
objectsB = np.array( [ pyBA.Bivarg(mu=[x["dra"],x["ddec"]], sigma=np.array([x["raerr"],x["decerr"]])) for x in data ] )
# Suggest starting point for background mapping
S = pyBA.background.suggest_mapping(objectsA,objectsB)
# Get maximum a posteriori background mapping parameters
P = pyBA.background.MAP( objectsA, objectsB, mu0=S.mu, prior=pyBA.Bgmap(), norm_approx=True )
print P.mu
pass
