def create_zdist_bright():
zphot_0 = \
read_shear_catalog(BRIGHT_CAT_NPZ,
('zphot',),
None,
remove_z_problem = False)
zphot_1 = \
read_shear_catalog(BRIGHT_CAT_NPZ,
('zphot',),
None,
remove_z_problem = True)
z = np.arange(0, 5, 0.01)
i0 = z.searchsorted(zphot_0[0])
i1 = z.searchsorted(zphot_1[0])
OF_0 = open(BRIGHT_ZDIST, 'w')
OF_1 = open(BRIGHT_ZDIST_ZPROB0, 'w')
for i in range(len(z)):
OF_0.write('%.6f %.6g\n' % (z[i], len(np.where(i0==i)[0])))
OF_1.write('%.6f %.6g\n' % (z[i], len(np.where(i1==i)[0])))
OF_0.close()
OF_1.close()
def create_zdist_bright():
zphot_0 = \
read_shear_catalog(BRIGHT_CAT_NPZ,
('zphot',),
None,
remove_z_problem = False)
zphot_1 = \
read_shear_catalog(BRIGHT_CAT_NPZ,
('zphot',),
None,
remove_z_problem = True)
z = np.arange(0, 5, 0.01)
i0 = z.searchsorted(zphot_0[0])
i1 = z.searchsorted(zphot_1[0])
OF_0 = open(BRIGHT_ZDIST, 'w')
OF_1 = open(BRIGHT_ZDIST_ZPROB0, 'w')
for i in range(len(z)):
OF_0.write('%.6f %.6g\n' % (z[i], len(np.where(i0 == i)[0])))
OF_1.write('%.6f %.6g\n' % (z[i], len(np.where(i1 == i)[0])))
OF_0.close()
OF_1.close()
def fit_zdist(catalog_file, nbins=100, plot=True):
"""
using a COSMOS catalog, return (z0, a, b) such that the distribution
n(z) ~ z^a * exp[-(z/z0)^b]
best describes the observed redshift distribution.
nbins gives the number of bins at which the function should be evaluated.
"""
z = read_shear_catalog(catalog_file, ['zphot'], remove_z_problem=True)[0]
N, bins = np.histogram(z, nbins)
bincenters = 0.5*( bins[1:] + bins[:-1])
N = N * 1./N.sum()
x0 = [0.5, 2.0, 1.5]
ret = optimize.fmin(minfunc, x0, (N, bincenters))
z0, a, b = ret
if plot:
pylab.plot(bincenters, N)
zp = bincenters#np.linspace(z.min(), z.max(), 1000)
nz = zp**a * np.exp(-(zp/z0)**b)
nz /= np.sum(nz)
pylab.plot(zp, nz)
return ret
def plot_distribution(filename):
RA,DEC = read_shear_catalog(filename,('Ra','Dec'),None)
RAmin,RAmax = min(RA), max(RA)
DECmin,DECmax = min(DEC),max(DEC)
xmin = np.floor(RAmin*60.)
xmax = np.ceil(RAmax*60.)
ymin = np.floor(DECmin*60.)
ymax = np.ceil(DECmax*60.)
xrange = np.arange(xmin,xmax+1)/60.
yrange = np.arange(ymin,ymax+1)/60.
H,xedges,yedges = np.histogram2d(RA,DEC,bins=(xrange,yrange))
pylab.imshow(H.T,
origin='lower',
interpolation='nearest',
extent = (xrange[0],xrange[-1],yrange[0],yrange[-1]),
cmap=pylab.cm.binary)
pylab.colorbar().set_label(r'$\mathdefault{n_{gal}\ (arcmin^{-2})}$')
pylab.xlabel('RA (deg)')
pylab.ylabel('DEC (deg)')
return len(RA)
def bootstrap_catalog(catalog_file,
N_bootstraps,
dtheta,
RAmin=None,
DECmin=None,
NRA=None,
NDEC=None):
"""
Estimate shear uncertainty using a bootstrap resampling of shear
data in the catalog.
Parameters
----------
catalog_file : file of COSMOS shear catalog
N_bootstraps : number of resamplings to use
dtheta : pixel size in arcmin
Other Parameters
----------------
If these are unspecified, they will be determined from the data
RAmin : minimum of RA bins (degrees).
NRA : number of RA bins
DECmin : minimum of DEC bins (degrees)
NDEC : number of DEC bins
Returns
-------
(Ngal, dgamma2)
Ngal : array[int], shape = (NRA, NDEC)
number of galaxies in each shear bin
dgamma2 : array[float], shape = (NRA, NDEC)
estimated squared shear error in each shear bin
"""
RA, DEC, gamma1, gamma2 = read_shear_catalog(
catalog_file,
('Ra', 'Dec', 'e1iso_rot4_gr_snCal', 'e2iso_rot4_gr_snCal'), None)
gamma = gamma1 - 1j * gamma2
if RAmin is None:
RAmin = RA.min()
if DECmin is None:
DECmin = DEC.min()
if NRA is None:
NRA = int(np.ceil((RA.max() - RAmin + 1E-8) * 60. / dtheta))
if NDEC is None:
NDEC = int(np.ceil((DEC.max() - DECmin + 1E-8) * 60. / dtheta))
return bootstrap_resample(gamma, RA, DEC, RAmin, dtheta / 60., NRA, DECmin,
dtheta / 60., NDEC, N_bootstraps)
def bootstrap_catalog(catalog_file, N_bootstraps, dtheta,
RAmin = None, DECmin = None,
NRA = None, NDEC = None):
"""
Estimate shear uncertainty using a bootstrap resampling of shear
data in the catalog.
Parameters
----------
catalog_file : file of COSMOS shear catalog
N_bootstraps : number of resamplings to use
dtheta : pixel size in arcmin
Other Parameters
----------------
If these are unspecified, they will be determined from the data
RAmin : minimum of RA bins (degrees).
NRA : number of RA bins
DECmin : minimum of DEC bins (degrees)
NDEC : number of DEC bins
Returns
-------
(Ngal, dgamma2)
Ngal : array[int], shape = (NRA, NDEC)
number of galaxies in each shear bin
dgamma2 : array[float], shape = (NRA, NDEC)
estimated squared shear error in each shear bin
"""
RA, DEC, gamma1, gamma2 = read_shear_catalog(catalog_file,
('Ra', 'Dec',
'e1iso_rot4_gr_snCal',
'e2iso_rot4_gr_snCal'),
None)
gamma = gamma1 - 1j*gamma2
if RAmin is None:
RAmin = RA.min()
if DECmin is None:
DECmin = DEC.min()
if NRA is None:
NRA = int(np.ceil((RA.max() - RAmin + 1E-8) * 60. / dtheta))
if NDEC is None:
NDEC = int(np.ceil((DEC.max() - DECmin + 1E-8) * 60. / dtheta))
return bootstrap_resample(gamma, RA, DEC,
RAmin, dtheta/60., NRA,
DECmin, dtheta/60., NDEC,
N_bootstraps)
def shear_mask_vector(catalog_file, dtheta,
RAmin = None, DECmin = None,
NRA = None, NDEC = None):
"""
return the mask vector for a 2D shear field.
Parameters
----------
catalog_file : file of COSMOS shear catalog
dtheta : pixel size in arcmin
Other Parameters
----------------
If these are unspecified, they will be determined from the data
RAmin : minimum of RA bins (degrees).
NRA : number of RA bins
DECmin : minimum of DEC bins (degrees)
NDEC : number of DEC bins
Returns
-------
(Ngal, RArange, DECrange)
Ngal : array, shape = (NRA, NDEC)
number of galaxies in each bin
RArange : array, shape = (NRA + 1,)
edges of bins in RA
DECrange : array, shape = (NDEC + 1,)
edges of bins in DEC
"""
RA, DEC = read_shear_catalog(catalog_file, ('Ra', 'Dec'), None)
if RAmin is None:
RAmin = RA.min()
if DECmin is None:
DECmin = DEC.min()
if NRA is None:
NRA = int(np.ceil((RA.max() - RAmin + 1E-8) * 60. / dtheta))
if NDEC is None:
NDEC = int(np.ceil((DEC.max() - DECmin + 1E-8) * 60. / dtheta))
RArange = RAmin + (dtheta / 60.) * np.arange(NRA + 1)
DECrange = DECmin + (dtheta / 60.) * np.arange(NDEC + 1)
Ngal, xedges, yedges = np.histogram2d(RA, DEC, bins=(RArange, DECrange))
return Ngal, xedges, yedges
def plot_redshift_distribution(catalog_file, nbins=100, z0=0.5, a=2.0, b=1.5):
z = read_shear_catalog(catalog_file, ['zphot'], remove_z_problem=True)[0]
N, bins = np.histogram(z, nbins)
print N
zmid = 0.5*( bins[1:] + bins[:-1])
N = N * 1./N.sum()
nz = zmid**a * np.exp(-(zmid/z0)**b)
nz /= nz.sum()
pylab.subplot(211)
pylab.plot(zmid, N)
pylab.subplot(212)
pylab.plot(zmid, nz)
def plot_Map(filename, dpix=1.0):
RA, DEC, gamma1, gamma2 = read_shear_catalog(filename,
('Ra','Dec',
'e1iso_rot4_gr_snCal',
'e2iso_rot4_gr_snCal'),
None)
RAmin, RAmax = min(RA), max(RA)
DECmin, DECmax = min(DEC), max(DEC)
dpix_deg = dpix/60.
pos = RA + 1j*DEC
gamma = gamma1 + 1j*gamma2
RA_out = np.arange(RAmin,RAmax+dpix_deg,dpix_deg)
DEC_out = np.arange(DECmin,DECmax+dpix_deg,dpix_deg)
RA_out,DEC_out = np.meshgrid(RA_out,DEC_out)
pos_out = RA_out + 1j*DEC_out
shape = pos_out.shape
Map = Aperture_Mass(gamma, pos, pos_out.reshape(-1))
Map = Map.reshape(shape)
pylab.figure()
pylab.imshow(Map.real.T,
origin='lower')
pylab.colorbar()
pylab.figure()
pylab.imshow(Map.imag.T,
origin='lower')
pylab.colorbar()
def plot_shear(filename, dpix=1.0):
"""
dpix is pixel size in arcmin
"""
RA, DEC, gamma1, gamma2 = read_shear_catalog(filename,
('Ra','Dec',
'e1iso_rot4_gr_snCal',
'e2iso_rot4_gr_snCal'),
None)
Ngal = len(RA)
#bin data into pixels
RAmin, RAmax = min(RA), max(RA)
DECmin, DECmax = min(DEC), max(DEC)
i = np.floor( (RA-RAmin)*60./dpix ).astype(int)
j = np.floor( (DEC-DECmin)*60./dpix ).astype(int)
Nx = np.max(i)+1
Ny = np.max(j)+1
print Nx,Ny
gamma = np.zeros((Nx,Ny), dtype=complex)
ngal = np.zeros((Nx,Ny), dtype=int)
for ind in xrange(Ngal):
gamma[i[ind], j[ind]] += gamma1[ind] - 1j*gamma2[ind]
ngal[i[ind], j[ind]] += 1
izero = np.where(ngal==0)
ngal[izero] = 1
gamma /= ngal
ngal[izero] = 0
kappa = gamma_to_kappa(gamma,dpix)
extent = (-0.5*dpix, (Nx-0.5)*dpix,
-0.5*dpix, (Ny-0.5)*dpix)
pylab.figure()
pylab.imshow(ngal.T, origin='lower', extent=extent, interpolation='nearest')
pylab.colorbar().set_label(r'$\mathdefault{n_{gal} (per pixel)}$')
whiskerplot(gamma, dpix, dpix)
pylab.axis(extent)
pylab.figure()
pylab.imshow(kappa.real.T, origin='lower',
extent=extent)
cb1 = pylab.colorbar()
clim = cb1.get_clim()
pylab.title('kappa: E-mode')
pylab.axis(extent)
pylab.figure()
pylab.imshow(kappa.imag.T, origin='lower',
extent=extent)
cb2 = pylab.colorbar()
cb2.set_clim(clim)
pylab.title('kappa: B-mode')
pylab.axis(extent)
def get_gamma_vector(catalog_file, dtheta,
RAmin = None, DECmin = None,
NRA = None, NDEC = None,
remove_z_problem = True,
N_bootstraps=0):
"""
return the mask vector for a 2D shear field.
Parameters
----------
catalog_file : file of COSMOS shear catalog or list of files
dtheta : pixel size in arcmin
Other Parameters
----------------
If these are unspecified, they will be determined from the data
RAmin : minimum of RA bins (degrees).
NRA : number of RA bins
DECmin : minimum of DEC bins (degrees)
NDEC : number of DEC bins
N_bootstraps : number of bootstraps to perform.
If zero, don't return noise
Returns
-------
(gamma, Ngal, RArange, DECrange)
gamma : array[complex], shape = (NRA, NDEC)
average shear within each bin
Ngal : array[integer], shape = (NRA, NDEC)
number of galaxies in each bin
RArange : array[float], shape = (NRA + 1,)
edges of bins in RA
DECrange : array[float], shape = (NDEC + 1,)
edges of bins in DEC
"""
RA, DEC, gamma1, gamma2 = \
read_shear_catalog(catalog_file,
('Ra', 'Dec',
'e1iso_rot4_gr_snCal',
'e2iso_rot4_gr_snCal'),
None,
remove_z_problem)
if RAmin is None:
RAmin = RA.min()
if DECmin is None:
DECmin = DEC.min()
if NRA is None:
NRA = int(np.ceil((RA.max() - RAmin + 1E-8) * 60. / dtheta))
if NDEC is None:
NDEC = int(np.ceil((DEC.max() - DECmin + 1E-8) * 60. / dtheta))
RArange = RAmin + (dtheta / 60.) * np.arange(NRA + 1)
DECrange = DECmin + (dtheta / 60.) * np.arange(NDEC + 1)
gamma = gamma1 - 1j*gamma2
gamma_mean, Ngal_bins, d2gamma, sigma_estimate = bootstrap_resample(
gamma, RA, DEC,
RAmin, dtheta/60., NRA,
DECmin, dtheta/60., NDEC, N_bootstraps)
if N_bootstraps > 0:
return gamma_mean, Ngal_bins, d2gamma, RArange, DECrange
else:
return gamma_mean, Ngal_bins, RArange, DECrange
