def PAPER_32_all():
'''
Results from PAPER 32 Jacobs et.al 2014
outputs results[z] = n.array([k, Delta^2, 2-sigma upper, 2-sigma lower])
'''
PAPER_RESULTS_FILES = glob.glob(os.path.dirname(__file__)+'/data/psa32_apj/pspec_*.npz')
PAPER_RESULTS_FILES.sort()
PAPER_RESULTS_FILES = [f for f in PAPER_RESULTS_FILES if '110_149' not in f]
freqs= []
for filename in PAPER_RESULTS_FILES:
try:
freqs.append(n.load(filename)['freq']*1e3)
except(KeyError):
try:
dchan = int(filename.split('/')[-1].split('.')[0].split('_')[2])-int(filename.split('/')[-1].split('.')[0].split('_')[1])
chan = int(filename.split('/')[-1].split('.')[0].split('_')[1]) + dchan/2.
freqs.append(chan/2. + 100) #a pretty good apprximation of chan 2 freq for 500kHz channels
except: continue
freqs = n.array(freqs)
zs = cosmo_units.f212z(freqs*1e6)
results = {}
for files,z in zip(PAPER_RESULTS_FILES,zs):
f=n.load(files)
results[z] = n.array([f['k'],f['k3pk'],f['k3pk']+f['k3err'],f['k3pk']-f['k3err']]).T
return results
def get_k3pk_from_npz(files=None, verbose=False):
"""
Load output from plot_pk_k3pk.npz and returns Delta^2 spectrum.
Return lists of k, Delta^2, Delta^2_err ordered by decreasing redshift
Return format: z, k_magnitude, Delta^2, Delta^2_err
"""
if files is None: # check that files are passed
print 'No Files given for loading'
return [], [], [], []
if len(n.shape(files)) == 0:
files = [files]
freqs = []
if verbose:
print "parsing npz file frequencies"
for filename in files:
if verbose:
print filename,
try:
if verbose:
print "npz..",
freqs.append(n.load(filename)['freq'] * 1e3) # load freq in MHz
if verbose:
print "[Success]"
except (KeyError):
if verbose:
print "[FAIL]"
try:
if verbose:
print "looking for path like RUNNAME/chan_chan/I/pspec.npz"
dchan = (int(filename.split('/')[1].split('_')[1]) -
int(filename.split('/')[1].split('_')[0]))
chan = int(filename.split('/')[1].split('_')[0]) + dchan / 2
freqs.append(chan / 2. + 100)
# a pretty good apprximation of chan 2 freq for 500kHz channels
except (IndexError):
if verbose:
print "[FAIL] no freq found. Skipping..."
if len(freqs) == 0: # check if any files were loaded correctly
print 'No parsable frequencies found'
print 'Exiting'
return [], [], [], []
if verbose:
print "sorting input files by frequency"
files = n.array(files)
files = files[n.argsort(freqs)]
freqs = n.sort(freqs)
if verbose:
print "found freqs"
freqs = n.array(freqs)
if verbose:
print freqs
z = f212z(freqs * 1e6)
if verbose:
print "processing redshifts:", z
k3Pk = []
k3err = []
kmags = []
for i, FILE in enumerate(files):
F = n.load(FILE)
if verbose:
print FILE.split('/')[-1], z[i]
k3Pk.append(F['k3pk'])
k3err.append(F['k3err'])
kmags.append(F['k'])
return z, kmags, k3Pk, k3err
def load_pspecs(files, verbose=False):
"""
Load output from plot_pk_k3pk.npz and returns Delta^2 spectrum.
Return lists of k, Delta^2, Delta^2_err ordered by decreasing redshift
Return standard dict format {z:np.array([k,delta2,ulim,llim])}
files : list of pspec npz files
"""
freqs = []
if verbose:
print "parsing npz file frequencies"
for filename in files:
if verbose:
print filename,
try:
if verbose:
print "npz..",
freqs.append(n.load(filename)['freq'] * 1e3) # load freq in MHz
if verbose:
print "[Success]"
except (KeyError):
if verbose:
print "[FAIL]"
try:
if verbose:
print "looking for path like RUNNAME/chan_chan/I/pspec.npz"
dchan = (int(filename.split('/')[1].split('_')[1]) -
int(filename.split('/')[1].split('_')[0]))
chan = int(filename.split('/')[1].split('_')[0]) + dchan / 2
freqs.append(chan / 2. + 100)
# a pretty good apprximation of chan 2 freq for 500kHz channels
except (IndexError):
if verbose:
print "[FAIL] no freq found."
print " You can't do science without freqs. Exiting"
return None
if verbose:
print "sorting input files by frequency"
files = n.array(files)
files = files[n.argsort(freqs)]
freqs = np.array(n.sort(freqs))
if verbose:
print "found freqs"
print freqs
redshifts = f212z(freqs * 1e6)
if verbose:
print "processing redshifts:", redshifts
data = {}
for i, FILE in enumerate(files):
F = n.load(FILE)
if verbose:
print FILE.split('/')[-1], redshifts[i]
k = F['k']
Delta2 = k**3 / (2 * np.pi**2) * F['pCv_fold']
Delta2_err = k**3 / (2 * np.pi**2) * F['pCv_fold_err']
data[redshifts[i]] = np.array(
[k, Delta2, Delta2 + Delta2_err, Delta2 - Delta2_err]).T
return data
