def wrap_process_catalog(run_param, trans_table, print_only=False):
r"""Based on the ini file, load the catalog and general parameters
"""
(root, extension) = os.path.splitext(run_param['catalog_filename'])
if extension == ".dat":
print "opening a catalog in the self-describing text format"
catalog = sd.load_selfdescribing_numpy(run_param['catalog_filename'],
swaps=trans_table,
verbose=run_param['verbose'])
if extension == ".pickle":
import pickle
import catalog
print "opening a catalog as a pickled catalog object"
catalog = pickle.load(open(run_param['catalog_filename'], "r"))
if not print_only:
augmented_catalog = process_catalog.process_ptsrc_catalog_alpha(
catalog, run_param)
outputshelve = shelve.open(run_param['augmented_catalog'],
flag="n",
protocol=-1)
outputshelve.update(augmented_catalog)
outputshelve.close()
else:
print catalog
def wrap_process_catalog(run_param, trans_table, print_only=False):
r"""Based on the ini file, load the catalog and general parameters
"""
(root, extension) = os.path.splitext(run_param['catalog_filename'])
if extension == ".dat":
print "opening a catalog in the self-describing text format"
catalog = sd.load_selfdescribing_numpy(run_param['catalog_filename'],
swaps=trans_table,
verbose=run_param['verbose'])
if extension == ".pickle":
import pickle
import catalog
print "opening a catalog as a pickled catalog object"
catalog = pickle.load(open(run_param['catalog_filename'], "r"))
if not print_only:
augmented_catalog = process_catalog.process_ptsrc_catalog_alpha(
catalog, run_param)
outputshelve = shelve.open(run_param['augmented_catalog'], flag="n",
protocol=-1)
outputshelve.update(augmented_catalog)
outputshelve.close()
else:
print catalog
def dnds_tucci(input_s, freqtag, replacenan=0.):
"""
alternate dN/dS from Tucci
freqtag = 148GHz or 220GHz
return radio counts as dN/dS per Jy per deg^2
input_s is flux vector in Jy
"""
table_filename = "counts_data/ns_%s_modC2Ex.dat" % freqtag
# read dN/dS against logs
logdnds_model = sd.load_selfdescribing_numpy(table_filename)
interpolant = sp.interpolate.interp1d(np.log10(logdnds_model['flux']),
np.log10(logdnds_model['dnds_total']),
bounds_error=False, fill_value=np.nan)
dnds = 10. ** interpolant(np.log10(input_s)) * (math.pi / 180.) ** 2.
dnds[np.isnan(dnds)] = replacenan
return dnds
def dnds_ir(input_s, freqtag, replacenan=0.):
"""
return IR counts as dN/dS per Jy per deg^2
http://www.ias.u-psud.fr/irgalaxies/model.php
input_s is flux vector in Jy
freqtag is the suffix of the counts file to load, e.g. '143GHz'
"""
table_filename = "counts_data/Bethermin_model_counts_" + \
freqtag + ".txt"
# read dN/dS(per Jy per sr) against S(Jy)
logdnds_model = sd.load_selfdescribing_numpy(table_filename)
interpolant = sp.interpolate.interp1d(np.log10(logdnds_model['flux']),
np.log10(logdnds_model['dnds_median']),
bounds_error=False, fill_value=np.nan)
dnds = 10. ** interpolant(np.log10(input_s)) * (math.pi / 180.) ** 2.
dnds[np.isnan(dnds)] = replacenan
return dnds
def dnds_radio(input_s, freqtag, replacenan=0.):
"""
return radio counts as dN/dS per Jy per deg^2
model: http://web.oapd.inaf.it/rstools/srccnt/srccnt_tables.html
input_s is flux vector in Jy
freqtag is the suffix of the counts file to load, e.g. '143GHz'
"""
table_filename = "counts_data/Radio_model_counts_" + freqtag + ".txt"
# read log10(S^2.5*dN/dS) against logs
logdnds_model = sd.load_selfdescribing_numpy(table_filename)
interpolant = sp.interpolate.interp1d(logdnds_model['logs'],
logdnds_model['dnds_total'],
bounds_error=False,
fill_value=np.nan)
dnds = 10.**interpolant(np.log10(input_s)) / input_s**2.5
dnds *= (math.pi / 180.)**2.
dnds[np.isnan(dnds)] = replacenan
return dnds
def dnds_tucci(input_s, freqtag, replacenan=0.):
"""
alternate dN/dS from Tucci
freqtag = 148GHz or 220GHz
return radio counts as dN/dS per Jy per deg^2
input_s is flux vector in Jy
"""
table_filename = "counts_data/ns_%s_modC2Ex.dat" % freqtag
# read dN/dS against logs
logdnds_model = sd.load_selfdescribing_numpy(table_filename)
interpolant = sp.interpolate.interp1d(np.log10(logdnds_model['flux']),
np.log10(
logdnds_model['dnds_total']),
bounds_error=False,
fill_value=np.nan)
dnds = 10.**interpolant(np.log10(input_s)) * (math.pi / 180.)**2.
dnds[np.isnan(dnds)] = replacenan
return dnds
def dnds_radio(input_s, freqtag, replacenan=0.):
"""
return radio counts as dN/dS per Jy per deg^2
model: http://web.oapd.inaf.it/rstools/srccnt/srccnt_tables.html
input_s is flux vector in Jy
freqtag is the suffix of the counts file to load, e.g. '143GHz'
"""
table_filename = "counts_data/Radio_model_counts_" + freqtag + ".txt"
# read log10(S^2.5*dN/dS) against logs
logdnds_model = sd.load_selfdescribing_numpy(table_filename)
interpolant = sp.interpolate.interp1d(logdnds_model['logs'],
logdnds_model['dnds_total'],
bounds_error=False,
fill_value=np.nan)
dnds = 10. ** interpolant(np.log10(input_s)) / input_s ** 2.5
dnds *= (math.pi / 180.) ** 2.
dnds[np.isnan(dnds)] = replacenan
return dnds
def dnds_ir(input_s, freqtag, replacenan=0.):
"""
return IR counts as dN/dS per Jy per deg^2
http://www.ias.u-psud.fr/irgalaxies/model.php
input_s is flux vector in Jy
freqtag is the suffix of the counts file to load, e.g. '143GHz'
"""
table_filename = "counts_data/Bethermin_model_counts_" + \
freqtag + ".txt"
# read dN/dS(per Jy per sr) against S(Jy)
logdnds_model = sd.load_selfdescribing_numpy(table_filename)
interpolant = sp.interpolate.interp1d(np.log10(logdnds_model['flux']),
np.log10(
logdnds_model['dnds_median']),
bounds_error=False,
fill_value=np.nan)
dnds = 10.**interpolant(np.log10(input_s)) * (math.pi / 180.)**2.
dnds[np.isnan(dnds)] = replacenan
return dnds
def compare_catalogs(params, translate, septol=1e-2):
"""compare a deboosted catalog with one in literature
plot "comparison.dat" 2:8:1:3:7:9 ..., 5:11:4:6:10:12 with xyerrorbars
"""
augmented_catalog = shelve.open(params['augmented_catalog'], flag="r")
comp_catalog = sd.load_selfdescribing_numpy(params['comparison_catalog'],
swaps=translate,
verbose=params['verbose'])
(cra, cdec, csnr150, csnr220) = (comp_catalog[:]["ra"],
comp_catalog[:]["dec"],
comp_catalog[:]["SNR150"],
comp_catalog[:]["SNR220"])
outfile = open("comparison/comparison.dat", "w")
raw_outfile = open("comparison/raw_comparison.dat", "w")
for srcname in augmented_catalog:
entry = augmented_catalog[srcname]
(ra, dec) = (entry["ra"], entry["dec"])
dra = cra - ra
ddec = cdec - dec
delta = np.sqrt(dra * dra + ddec * ddec)
minsep = np.min(delta)
if (np.min(delta) > septol):
print "no associated source in comparison catalog for index:" + \
repr(srcname)
continue
comp_index = np.where(delta == minsep)[0][0]
orig = comp_catalog[comp_index]
# find the range of the original catalog's non-deboosted fluxes
orig_noise_flux1 = orig["S_150r"] / orig["SNR150"]
orig_noise_flux2 = orig["S_220r"] / orig["SNR220"]
orig_rawflux1 = np.array([orig["S_150r"] - orig_noise_flux1,
orig["S_150r"],
orig["S_150r"] + orig_noise_flux1])
orig_rawflux2 = np.array([orig["S_220r"] - orig_noise_flux2,
orig["S_220r"],
orig["S_220r"] + orig_noise_flux2])
# find the range of the new catalog's non-deboosted fluxes
rawflux1 = np.array([entry[params['flux1name']] -
entry[params['sigma1name']],
entry[params['flux1name']],
entry[params['flux1name']] +
entry[params['sigma1name']]])
rawflux2 = np.array([entry[params['flux2name']] -
entry[params['sigma2name']],
entry[params['flux2name']],
entry[params['flux2name']] +
entry[params['sigma2name']]])
fluxarray = []
fluxarray.extend(rawflux1 * 1000.)
fluxarray.extend(orig_rawflux1)
fluxarray.extend(rawflux2 * 1000.)
fluxarray.extend(orig_rawflux2)
raw_outfile.write(("%5.3g " * 12 + "\n") % tuple(fluxarray))
# now find the deboosted flux in the original catalog
orig_flux1 = np.array([orig["S_150d"],
orig["S_150d_up"],
orig["S_150d_down"]])
orig_flux2 = np.array([orig["S_220d"],
orig["S_220d_up"],
orig["S_220d_down"]])
orig_alpha = np.array([orig["d_alpha"],
orig["d_alpha_up"],
orig["d_alpha_down"]])
reband_factor = (152./149.) ** orig_alpha[0]
print "scaling for index ", orig_alpha, reband_factor
frange1 = [orig_flux1[0] - orig_flux1[2], orig_flux1[0],
orig_flux1[0] + orig_flux1[1]]
frange1 /= reband_factor
frange2 = [orig_flux2[0] - orig_flux2[2], orig_flux2[0],
orig_flux2[0] + orig_flux2[1]]
alrange = [orig_alpha[0] - orig_alpha[2], orig_alpha[0],
orig_alpha[0] + orig_alpha[1]]
fluxarray = []
fluxarray.extend(entry["posterior"]["flux1"] * 1000.)
fluxarray.extend(frange1)
fluxarray.extend(entry["posterior"]["flux2"] * 1000.)
fluxarray.extend(frange2)
fluxarray.extend(entry["posterior"]["alpha"])
fluxarray.extend(alrange)
outfile.write(("%5.3g " * 18 + "\n") % tuple(fluxarray))
print "=" * 80
# can optionally print "_posterior" and "_posterior_swap" for checking
print srcname, comp_index, cra[comp_index], ra, \
cdec[comp_index], dec, minsep, \
csnr150[comp_index], csnr220[comp_index]
print "S1" + "-" * 78
print utils.pm_error(entry["posterior"]["flux1"] * 1000., "%5.3g")
print orig_flux1
if (np.all(orig_flux1 > 0)):
print (np.array(utils.pm_vector(entry["posterior"]["flux1"] * \
1000.)) - orig_flux1) / orig_flux1 * 100.
print "S2" + "-" * 60
print utils.pm_error(entry["posterior"]["flux2"] * 1000., "%5.3g")
print orig_flux2
if (np.all(orig_flux2 > 0)):
print (np.array(utils.pm_vector(entry["posterior"]["flux2"] * \
1000.)) - orig_flux2) / orig_flux2 * 100.
print "ind" + "-" * 69
print utils.pm_error(entry["posterior"]["alpha"], "%5.3g")
print orig_alpha
if (np.all(orig_alpha > 0)):
print (np.array(utils.pm_vector(entry["posterior"]["alpha"])) -
orig_alpha) / orig_alpha * 100.
print "P(a>t) new: " + repr(entry["posterior"]["prob_exceed"]) + \
" old: " + repr(orig["palphagt1"])
augmented_catalog.close()
def compare_catalogs(params, translate, septol=1e-2):
"""compare a deboosted catalog with one in literature
plot "comparison.dat" 2:8:1:3:7:9 ..., 5:11:4:6:10:12 with xyerrorbars
"""
augmented_catalog = shelve.open(params['augmented_catalog'], flag="r")
comp_catalog = sd.load_selfdescribing_numpy(params['comparison_catalog'],
swaps=translate,
verbose=params['verbose'])
(cra, cdec, csnr150,
csnr220) = (comp_catalog[:]["ra"], comp_catalog[:]["dec"],
comp_catalog[:]["SNR150"], comp_catalog[:]["SNR220"])
outfile = open("comparison/comparison.dat", "w")
raw_outfile = open("comparison/raw_comparison.dat", "w")
for srcname in augmented_catalog:
entry = augmented_catalog[srcname]
(ra, dec) = (entry["ra"], entry["dec"])
dra = cra - ra
ddec = cdec - dec
delta = np.sqrt(dra * dra + ddec * ddec)
minsep = np.min(delta)
if (np.min(delta) > septol):
print "no associated source in comparison catalog for index:" + \
repr(srcname)
continue
comp_index = np.where(delta == minsep)[0][0]
orig = comp_catalog[comp_index]
# find the range of the original catalog's non-deboosted fluxes
orig_noise_flux1 = orig["S_150r"] / orig["SNR150"]
orig_noise_flux2 = orig["S_220r"] / orig["SNR220"]
orig_rawflux1 = np.array([
orig["S_150r"] - orig_noise_flux1, orig["S_150r"],
orig["S_150r"] + orig_noise_flux1
])
orig_rawflux2 = np.array([
orig["S_220r"] - orig_noise_flux2, orig["S_220r"],
orig["S_220r"] + orig_noise_flux2
])
# find the range of the new catalog's non-deboosted fluxes
rawflux1 = np.array([
entry[params['flux1name']] - entry[params['sigma1name']],
entry[params['flux1name']],
entry[params['flux1name']] + entry[params['sigma1name']]
])
rawflux2 = np.array([
entry[params['flux2name']] - entry[params['sigma2name']],
entry[params['flux2name']],
entry[params['flux2name']] + entry[params['sigma2name']]
])
fluxarray = []
fluxarray.extend(rawflux1 * 1000.)
fluxarray.extend(orig_rawflux1)
fluxarray.extend(rawflux2 * 1000.)
fluxarray.extend(orig_rawflux2)
raw_outfile.write(("%5.3g " * 12 + "\n") % tuple(fluxarray))
# now find the deboosted flux in the original catalog
orig_flux1 = np.array(
[orig["S_150d"], orig["S_150d_up"], orig["S_150d_down"]])
orig_flux2 = np.array(
[orig["S_220d"], orig["S_220d_up"], orig["S_220d_down"]])
orig_alpha = np.array(
[orig["d_alpha"], orig["d_alpha_up"], orig["d_alpha_down"]])
reband_factor = (152. / 149.)**orig_alpha[0]
print "scaling for index ", orig_alpha, reband_factor
frange1 = [
orig_flux1[0] - orig_flux1[2], orig_flux1[0],
orig_flux1[0] + orig_flux1[1]
]
frange1 /= reband_factor
frange2 = [
orig_flux2[0] - orig_flux2[2], orig_flux2[0],
orig_flux2[0] + orig_flux2[1]
]
alrange = [
orig_alpha[0] - orig_alpha[2], orig_alpha[0],
orig_alpha[0] + orig_alpha[1]
]
fluxarray = []
fluxarray.extend(entry["posterior"]["flux1"] * 1000.)
fluxarray.extend(frange1)
fluxarray.extend(entry["posterior"]["flux2"] * 1000.)
fluxarray.extend(frange2)
fluxarray.extend(entry["posterior"]["alpha"])
fluxarray.extend(alrange)
outfile.write(("%5.3g " * 18 + "\n") % tuple(fluxarray))
print "=" * 80
# can optionally print "_posterior" and "_posterior_swap" for checking
print srcname, comp_index, cra[comp_index], ra, \
cdec[comp_index], dec, minsep, \
csnr150[comp_index], csnr220[comp_index]
print "S1" + "-" * 78
print utils.pm_error(entry["posterior"]["flux1"] * 1000., "%5.3g")
print orig_flux1
if (np.all(orig_flux1 > 0)):
print (np.array(utils.pm_vector(entry["posterior"]["flux1"] * \
1000.)) - orig_flux1) / orig_flux1 * 100.
print "S2" + "-" * 60
print utils.pm_error(entry["posterior"]["flux2"] * 1000., "%5.3g")
print orig_flux2
if (np.all(orig_flux2 > 0)):
print (np.array(utils.pm_vector(entry["posterior"]["flux2"] * \
1000.)) - orig_flux2) / orig_flux2 * 100.
print "ind" + "-" * 69
print utils.pm_error(entry["posterior"]["alpha"], "%5.3g")
print orig_alpha
if (np.all(orig_alpha > 0)):
print(
np.array(utils.pm_vector(entry["posterior"]["alpha"])) -
orig_alpha) / orig_alpha * 100.
print "P(a>t) new: " + repr(entry["posterior"]["prob_exceed"]) + \
" old: " + repr(orig["palphagt1"])
augmented_catalog.close()