def read_and_eval(interpfns):
f = fits.open("input/LSST_Ia_HEAD.FITS")
head = f[1].data
f.close()
dat = sncosmo.read_snana_fits("input/LSST_Ia_HEAD.FITS", "input/LSST_Ia_PHOT.FITS")
pdf = PdfPages("LC_plots.pdf")
for i in range(len(head.PEAKMJD)):
if (head.REDSHIFT_HELIO[i] > 0.4) and (head.REDSHIFT_HELIO[i] < 0.6):
plt.figure()
phase = (dat[i]["MJD"] - head.PEAKMJD[i])/(1. + head.REDSHIFT_HELIO[i])
errs = LC_eval(interpfns, phase, magerr = dat[i]["MAGERR"], filt = dat[i]["FLT"], redshift = head.REDSHIFT_HELIO[i])
for filt in 'ugrizY':
inds = np.where((dat[i]["FLT"] == filt)*(dat[i]["MAGERR"] > 0))
plt.plot(phase[inds], dat[i]["MAGERR"][inds], '.', color = {'u': 'm', 'g': 'b', 'r': 'cyan', 'i': 'g', 'z': 'orange', 'Y': 'r'}[filt], label = filt)
plt.legend(loc = 'best')
plt.ylim(0, 0.2)
title = "$\sigma$Mag=%.3f" % errs[0]
for pc in range(1,5):
title += " $\sigma$PC%i=%.3f" % (pc, errs[pc])
plt.title(title)
plt.xlabel("Phase")
plt.ylabel("Mag Err")
pdf.savefig(plt.gcf(), bbox_inches = 'tight')
pdf.close()
def fromSNANAfileroot(cls, snanafileroot, location='./', snids=None,
n=None):
"""
Class constructor from a root file and a location
Parameters
----------
snanafileroot : string, mandatory
root file name for the SNANA which is the prefix to
'_HEAD.FITS', or '_PHOT.FITS'
location : string, optional defaults to current working directory './'
Relative or absolute path to the directory where the head and phot
files are located
snids : integer/string, optional defaults to None
if not None, only SN observations corresponding to SNID snid
are loaded
n : Integer, defaults to None
if not None, only the first n SN light curves are loaded
"""
headfile = cls.snanadatafile(snanafileroot, filetype='head',
location=location)
photfile = cls.snanadatafile(snanafileroot, filetype='phot',
location=location)
print headfile
data = sncosmo.read_snana_fits(head_file=headfile,
phot_file=photfile,
snids=snids, n=n)
return cls(headFile=headfile, photFile=photfile, snids=snids,
n=n)
def get_sndataset(headfile):
photfile = headfile.replace('HEAD', 'PHOT')
sndataset = sncosmo.read_snana_fits(headfile, photfile)
for i in xrange(len(sndataset)):
sndataset[i] = standardize_sn_data(sndataset[i],
headfile=os.path.basename(headfile))
return sndataset
def get_test_sn(headfile='random', snid='random', verbose=True):
if headfile == 'random':
headfilelist = glob('data/*HEAD.FITS')
headfile = choice(headfilelist)
if snid == 'random':
# Get metadata for all the SNe
head_data = pyfits.getdata(headfile, 1, view=np.ndarray)
# Strip trailing whitespace characters from SNID.
if 'SNID' in head_data.dtype.names:
snidlist = np.char.strip(head_data['SNID'])
snid = choice(snidlist)
sndataset = sncosmo.read_snana_fits(headfile,
headfile.replace('HEAD', 'PHOT'),
snids=[str(snid)])
sn = sndataset[0]
if verbose:
print "SNID=%s from %s" % (snid, os.path.basename(headfile))
excludelist = get_sncosmo_excludelist(sn)
if excludelist:
modelname = excludelist[0]
elif sn.meta['SIM_NON1a']:
modelname = 'NON1a.%03i' % sn.meta['SIM_MODEL_INDEX']
else:
modelname = sn.meta['SIM_MODEL_NAME']
print "Type=%s model=%s" % (sn.meta['SIM_TYPE_NAME'].strip(),
modelname)
return sn
def loadSNANAData(snanafileroot, location='/.', snids=None, n=None):
'''
load a SNANA fits file into a list of `~astropy.Table.table` objects.
Parameters
----------
snanafileroot: string, mandatory
root file name for the SNANA which is the prefix to '_HEAD.FITS', or '_PHOT.FITS'
location: string, optional defaults to current working directory './'
directory where the head and phot files are located
snids: integer/string, optional defaults to None
if not None, only SN observations corresponding to SNID snid are loaded
n: Integer, defaults to None
if not None, only the first n SN light curves are loaded
Returns: data
list of `~astropy.Table.Table` each Table containing a light curve of a SN.
..note: The column names of the SNANA data files are not reformated for SNCosmo use
'''
headfile = snanadatafile(snanafileroot, filetype='head', location=location)
photfile = snanadatafile(snanafileroot, filetype='phot', location=location)
data = sncosmo.read_snana_fits(head_file=headfile, phot_file=photfile, snids=snids, n=None)
return data
def __init__(self, headfile, photfile, snids=None, n=None):
"""
Instantiate class from SNANA simulation output files in fits format
Parameters
----------
snids : integer/string, optional defaults to None
if not None, only SN observations corresponding to SNID snid
are loaded
n : Integer, defaults to None
if not None, only the first n SN light curves are loaded
..note: The column names of the SNANA data files are not reformated
for SNCosmo use
"""
self.snList = sncosmo.read_snana_fits(
head_file=headfile, phot_file=photfile, snids=snids, n=n)
def test_read_snana_fits():
fname1 = join(dirname(__file__), "data", "snana_fits_example_head.fits")
fname2 = join(dirname(__file__), "data", "snana_fits_example_phot.fits")
sne = sncosmo.read_snana_fits(fname1, fname2)
assert len(sne) == 2
def test_read_snana_fits():
fname1 = join(dirname(__file__), "data", "snana_fits_example_head.fits")
fname2 = join(dirname(__file__), "data", "snana_fits_example_phot.fits")
sne = sncosmo.read_snana_fits(fname1, fname2)
assert len(sne) == 2