def plot_kappa_simple(catalog_file,
remove_z_problem=False,
Npix=128,
kappa_filter=4,
kappa_cutoff=0):
"""
kappa_filter is in pixels
"""
gamma_plot, Ngal, RArange_g, DECrange_g = get_gamma_vector(
catalog_file, dtheta=2.0, remove_z_problem=remove_z_problem)
# get a square grid for the kappa measurement
NRA = NDEC = Npix
RAmin = RArange_g[0]
RAmax = RArange_g[-1]
DECmin = DECrange_g[0]
DECmax = DECrange_g[-1]
dtheta = (RAmax - RAmin) * 1. / NRA * 60
# DEC range is smaller than RA range: make half the
# zeros on the bottom
DECmin -= 0.5 * (DECmin + NDEC * dtheta / 60. - DECmax)
DECmax = DECmin + NDEC * dtheta / 60.
# get the shear realization
gamma, Ngal, RArange, DECrange = get_gamma_vector(
catalog_file,
dtheta,
RAmin,
DECmin,
NRA,
NDEC + 1,
remove_z_problem=remove_z_problem)
kappa = gamma_to_kappa(gamma.T, dtheta)
if kappa_filter:
kappa = filters.gaussian_filter(kappa.real, kappa_filter) \
+ 1j * filters.gaussian_filter(kappa.imag, kappa_filter)
if kappa_cutoff is not None:
kappa[np.where(kappa < kappa_cutoff)] = kappa_cutoff
pylab.figure(figsize=(10, 8))
pylab.imshow(kappa.real.T,
origin='lower',
cmap=BkBuW(1.0),
extent=(RAmin, RAmax, DECmin, DECmax))
pylab.xlim(pylab.xlim()[::-1])
pylab.title('COSMOS convergence map: Kaiser-Squires')
pylab.colorbar().set_label(r'$\kappa\ \mathrm{(E-mode)}$')
#conjugate gamma: we're flipping the x-axis
whiskerplot(gamma_plot.conj(), RArange_g, DECrange_g, color='w')
pylab.ylim(DECmin, 2.9)
def plot_shear(catalog_file, dtheta):
gamma, Ngal, RArange, DECrange = get_gamma_vector(
catalog_file, dtheta, remove_z_problem=False)
pylab.imshow(Ngal.T, origin='lower',
cmap=BkBuW(0.8),
extent=(RArange[0], RArange[-1], DECrange[0], DECrange[-1]))
pylab.xlim(pylab.xlim()[::-1])
whiskerplot(gamma, RArange, DECrange, color='w')
def plot_shear(catalog_file, dtheta):
gamma, Ngal, RArange, DECrange = get_gamma_vector(catalog_file,
dtheta,
remove_z_problem=False)
pylab.imshow(Ngal.T,
origin='lower',
cmap=BkBuW(0.8),
extent=(RArange[0], RArange[-1], DECrange[0], DECrange[-1]))
pylab.xlim(pylab.xlim()[::-1])
whiskerplot(gamma, RArange, DECrange, color='w')
def plot_kappa_simple(catalog_file, remove_z_problem=False,
Npix=128, kappa_filter=4,
kappa_cutoff=0):
"""
kappa_filter is in pixels
"""
gamma_plot, Ngal, RArange_g, DECrange_g = get_gamma_vector(
catalog_file, dtheta=2.0, remove_z_problem=remove_z_problem)
# get a square grid for the kappa measurement
NRA = NDEC = Npix
RAmin = RArange_g[0]
RAmax = RArange_g[-1]
DECmin = DECrange_g[0]
DECmax = DECrange_g[-1]
dtheta = (RAmax-RAmin) * 1./NRA * 60
# DEC range is smaller than RA range: make half the
# zeros on the bottom
DECmin -= 0.5 * (DECmin + NDEC * dtheta/60. - DECmax)
DECmax = DECmin + NDEC * dtheta/60.
# get the shear realization
gamma, Ngal, RArange, DECrange = get_gamma_vector(
catalog_file, dtheta, RAmin, DECmin, NRA, NDEC+1,
remove_z_problem=remove_z_problem)
kappa = gamma_to_kappa(gamma.T, dtheta)
if kappa_filter:
kappa = filters.gaussian_filter(kappa.real, kappa_filter) \
+ 1j * filters.gaussian_filter(kappa.imag, kappa_filter)
if kappa_cutoff is not None:
kappa[np.where(kappa < kappa_cutoff)] = kappa_cutoff
pylab.figure(figsize=(10,8))
pylab.imshow(kappa.real.T,
origin='lower',
cmap=BkBuW(1.0),
extent=(RAmin, RAmax, DECmin, DECmax))
pylab.xlim(pylab.xlim()[::-1])
pylab.title('COSMOS convergence map: Kaiser-Squires')
pylab.colorbar().set_label(r'$\kappa\ \mathrm{(E-mode)}$')
#conjugate gamma: we're flipping the x-axis
whiskerplot(gamma_plot.conj(), RArange_g, DECrange_g, color='w')
pylab.ylim(DECmin, 2.9)
def plot_reconstruction_2():
nmodes=900
alpha=0.15
RAmin=11.5
DECmin=36
xlim=(15,45)
ylim=(20,50)
RAlim = (RAmin,RAmin+1)
DEClim = (DECmin,DECmin+1)
RAside = (RAlim[1]-RAlim[0])
DECside = (DEClim[1]-DEClim[0])
params.load('../run/base_params.dat')
params.load('../run/params_sig0.1.dat')
gamma_out_masked_1, noise = get_reconstruction(nmodes,
alpha,
True,
RAmin,
DECmin)
gamma_out_unmasked_1, noise = get_reconstruction(nmodes,
alpha,
False,
RAmin,
DECmin)
mask_1 = get_mask(False,RAlim,DEClim)
params.load('../run/params_sig0.1_f0.35.dat')
gamma_out_masked_2, noise = get_reconstruction(nmodes,
alpha,
True,
RAmin,
DECmin)
mask_2 = get_mask(False,RAlim,DEClim)
params.load('../run/params_sig0.1_f0.5.dat')
gamma_out_masked_3, noise = get_reconstruction(nmodes,
alpha,
True,
RAmin,
DECmin)
mask_3 = get_mask(False,RAlim,DEClim)
offset = (RAmin,DECmin)
dtheta = params.dtheta/60.
xlim = (offset[0] + xlim[0]/60.,
offset[0] + xlim[1]/60.)
ylim = (offset[1] + ylim[0]/60.,
offset[1] + ylim[1]/60.)
extent=(offset[0],offset[0]+RAside,
offset[1],offset[1]+DECside)
#--------------------------------------------------
pylab.figure(figsize=(8,8))
ax = pylab.subplot(221)
pylab.imshow(mask_1.T*0,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=pylab.cm.binary)
whiskerplot(gamma_out_unmasked_1,dtheta,dtheta,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
#pylab.title(r'$\mathdefault{unmasked\ KL}$')
#pylab.xlabel('')
pylab.text(0.97,0.97,'no mask',
transform = ax.transAxes,
va = 'top',
ha = 'right',
bbox=dict(facecolor='w',edgecolor='w') )
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#--------------------------------------------------
ax = pylab.subplot(222)
pylab.imshow(mask_1.T,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=GreyWhite)
pylab.clim(0,1)
whiskerplot(gamma_out_masked_1,dtheta,dtheta,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
#pylab.title(r'$\mathdefault{masked\ KL}$')
#pylab.xlabel('')
pylab.ylabel('')
pylab.text(0.97,0.97,'20% mask',
transform = ax.transAxes,
va = 'top',
ha = 'right',
bbox=dict(facecolor='w',edgecolor='w') )
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#--------------------------------------------------
ax = pylab.subplot(223)
pylab.imshow(mask_2.T,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=GreyWhite)
pylab.clim(0,1)
whiskerplot(gamma_out_masked_2,dtheta,dtheta,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
#pylab.title(r'$\mathdefault{masked\ KL}$')
pylab.text(0.97,0.97,'35% mask',
transform = ax.transAxes,
va = 'top',
ha = 'right',
bbox=dict(facecolor='w',edgecolor='w') )
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#--------------------------------------------------
ax = pylab.subplot(224)
pylab.imshow(mask_3.T,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=GreyWhite)
pylab.clim(0,1)
whiskerplot(gamma_out_masked_3,dtheta,dtheta,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
#pylab.title(r'$\mathdefault{masked\ KL}$')
pylab.ylabel('')
pylab.text(0.97,0.97,'50% mask',
transform = ax.transAxes,
va = 'top',
ha = 'right',
bbox=dict(facecolor='w',edgecolor='w') )
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#--------------------------------------------------
pylab.figtext(0.5,0.97,r'$\mathdefault{KL\ Reconstructions}$',
ha='center',va='top',
fontsize=16)
pylab.figtext(0.5,0.94,r'$\mathdefault{n=900\ modes,\ \ n_{gal}=100/arcmin^2}$',
ha='center',va='top',
fontsize=14)
def plot_with_mask_4(nmodes=900,
alpha=0.15,
RAmin = 10,
DECmin = 35,
xlim=(0,60),
ylim=(0,60),
plot_SN=False):
RAlim = (RAmin,RAmin+1)
DEClim = (DECmin,DECmin+1)
gamma_out_masked, noise = get_reconstruction(nmodes,
alpha,
True,
RAmin,
DECmin)
gamma_out_unmasked, noise = get_reconstruction(nmodes,
alpha,
False,
RAmin,
DECmin)
if plot_SN:
gamma_out_masked /= numpy.sqrt(noise_masked)
gamma_out_unmasked /= numpy.sqrt(noise_unmasked)
diff1 = gamma_out_masked-gamma_out_unmasked
rms1 = numpy.sqrt(numpy.mean(abs(diff1[10:50,10:50])**2))
mask = get_mask(False,RAlim,DEClim)
gamma_true = get_from_parsed('gamma',RAlim=RAlim,DEClim=DEClim)
diff2 = gamma_out_unmasked-gamma_true
rms2 = numpy.sqrt(numpy.mean(abs(diff2[10:50,10:50])**2))
diff3 = gamma_out_masked-gamma_true
rms3 = numpy.sqrt(numpy.mean(abs(diff3[10:50,10:50])**2))
assert gamma_out_unmasked.shape == gamma_true.shape
dtheta = params.dtheta
RAside = (RAlim[1]-RAlim[0])*60
DECside = (DEClim[1]-DEClim[0])*60
pylab.figure( figsize=(10,10) )
#----------------------------------------
pylab.subplot(221)
pylab.imshow(mask.T*0,
origin='lower',
extent=(0,RAside,0,DECside),
interpolation='nearest',
cmap=pylab.cm.binary)
pylab.clim(0,1)
whiskerplot(gamma_true,params.dtheta,params.dtheta)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title('shear field (noiseless)')
#----------------------------------------
pylab.subplot(222)
pylab.imshow(mask.T*0,
origin='lower',
extent=(0,RAside,0,DECside),
interpolation='nearest',
cmap=pylab.cm.binary)
pylab.clim(0,1)
whiskerplot(gamma_true+noise,params.dtheta,params.dtheta)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title('shear field (with noise)')
#----------------------------------------
pylab.subplot(223)
pylab.imshow(mask.T*0,
origin='lower',
extent=(0,RAside,0,DECside),
interpolation='nearest',
cmap=pylab.cm.binary)
pylab.clim(0,1)
whiskerplot(gamma_out_unmasked,params.dtheta,params.dtheta)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title('unmasked reconstruction')
#----------------------------------------
pylab.subplot(224)
pylab.imshow(mask.T,
origin='lower',
extent=(0,RAside,0,DECside),
interpolation='nearest',
cmap=GreyWhite)
pylab.clim(0,1)
whiskerplot(gamma_out_masked,params.dtheta,params.dtheta)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title('masked reconstruction')
#----------------------------------------
print "%i %.2f %.4f %.4f %.4f" % (nmodes,alpha,rms1,rms2,rms3)
def plot_with_mask_6_horiz(nmodes=900,
alpha=0.15,
RAmin = 10,
DECmin = 35,
xlim=(0,60),
ylim=(0,60),
plot_SN=False,
additional_n = [500,1500],
additional_a = [0.0,0.0] ):
from six_squares import six_squares
RAlim = (RAmin,RAmin+1)
DEClim = (DECmin,DECmin+1)
combine = 1
if combine==1:
scale = 5
elif combine==2:
scale = 4
assert len(additional_n) == 2
assert len(additional_a) == 2
gamma_out_masked, noise = get_reconstruction(nmodes,
alpha,
True,
RAmin,
DECmin)
gamma_out_unmasked, noise = get_reconstruction(nmodes,
alpha,
False,
RAmin,
DECmin)
g1,n1 = get_reconstruction(additional_n[0],
additional_a[0],
True,
RAmin,
DECmin)
g2,n2 = get_reconstruction(additional_n[1],
additional_a[1],
True,
RAmin,
DECmin)
if plot_SN:
gamma_out_masked /= numpy.sqrt(noise_masked)
gamma_out_unmasked /= numpy.sqrt(noise_unmasked)
g1 /= numpy.sqrt(n1)
g2 /= numpy.sqrt(n2)
diff1 = gamma_out_masked-gamma_out_unmasked
rms1 = numpy.sqrt(numpy.mean(abs(diff1[10:50,10:50])**2))
mask = get_mask(False,RAlim,DEClim)
gamma_true = get_from_parsed('gamma',RAlim=RAlim,DEClim=DEClim)
diff2 = gamma_out_unmasked-gamma_true
rms2 = numpy.sqrt(numpy.mean(abs(diff2[10:50,10:50])**2))
diff3 = gamma_out_masked-gamma_true
rms3 = numpy.sqrt(numpy.mean(abs(diff3[10:50,10:50])**2))
assert gamma_out_unmasked.shape == gamma_true.shape
dtheta = params.dtheta/60.
RAside = (RAlim[1]-RAlim[0])
DECside = (DEClim[1]-DEClim[0])
#pylab.figure( figsize=(10,8) )
ax_list = six_squares(11,8)
offset = (RAmin,DECmin)
dtheta = params.dtheta/60.
xlim = (offset[0] + xlim[0]/60.,
offset[0] + xlim[1]/60.)
ylim = (offset[1] + ylim[0]/60.,
offset[1] + ylim[1]/60.)
extent=(offset[0],offset[0]+RAside,
offset[1],offset[1]+DECside)
#----------------------------------------
ax = ax_list[0]
pylab.sca(ax)
#ax = pylab.subplot(231)
pylab.imshow(mask.T*0,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=pylab.cm.binary)
pylab.clim(0,1)
whiskerplot(gamma_true,dtheta,dtheta,
combine=combine,scale=scale,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title('shear field (noiseless)')
pylab.xlabel('')
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#----------------------------------------
ax = ax_list[1]
pylab.sca(ax)
#ax = pylab.subplot(232)
pylab.imshow(mask.T*0,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=pylab.cm.binary)
pylab.clim(0,1)
whiskerplot(gamma_true+noise,dtheta,dtheta,
combine=combine,scale=scale,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title('shear field (with noise)')
pylab.xlabel('')
pylab.ylabel('')
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#----------------------------------------
ax = ax_list[2]
pylab.sca(ax)
#ax = pylab.subplot(233)
pylab.imshow(mask.T*0,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=pylab.cm.binary)
pylab.clim(0,1)
whiskerplot(gamma_out_unmasked,dtheta,dtheta,
combine=combine,scale=scale,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title('unmasked KL (n=%i)' % nmodes)
pylab.xlabel('')
pylab.ylabel('')
#pylab.text(0.97,0.97,'n=%i' % nmodes,
# transform = pylab.gca().transAxes,
# va = 'top',
# ha = 'right',
# bbox=dict(facecolor='w',edgecolor='w') )
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#----------------------------------------
ax = ax_list[3]
pylab.sca(ax)
#ax = pylab.subplot(234)
pylab.imshow(mask.T,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=GreyWhite)
pylab.clim(0,1)
whiskerplot(g1,dtheta,dtheta,
combine=combine,scale=scale,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title(r'$\mathdefault{masked\ KL\ (n=%i)}$' % additional_n[0])
#pylab.text(0.97,0.97,'n=%i' % additional_n[0],
# transform = pylab.gca().transAxes,
# va = 'top',
# ha = 'right',
# bbox=dict(facecolor='w',edgecolor='w') )
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#----------------------------------------
ax = ax_list[4]
pylab.sca(ax)
#ax = pylab.subplot(235)
pylab.imshow(mask.T,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=GreyWhite)
pylab.clim(0,1)
whiskerplot(gamma_out_masked,dtheta,dtheta,
combine=combine,scale=scale,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title(r'$\mathdefault{masked\ KL\ (n=%i)}$' % nmodes)
pylab.ylabel('')
#pylab.text(0.97,0.97,'n=%i' % nmodes,
# transform = pylab.gca().transAxes,
# va = 'top',
# ha = 'right',
# bbox=dict(facecolor='w',edgecolor='w') )
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#----------------------------------------
ax = ax_list[5]
pylab.sca(ax)
#ax = pylab.subplot(236)
pylab.imshow(mask.T,
origin='lower',
extent=extent,
interpolation='nearest',
cmap=GreyWhite)
pylab.clim(0,1)
whiskerplot(g2,dtheta,dtheta,
combine=combine,scale=scale,offset=offset)
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.title(r'$\mathdefault{masked\ KL\ (n=%i)}$' % additional_n[1])
pylab.ylabel('')
#pylab.text(0.97,0.97,'n=%i' % additional_n[1],
# transform = pylab.gca().transAxes,
# va = 'top',
# ha = 'right',
# bbox=dict(facecolor='w',edgecolor='w') )
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
#----------------------------------------
pylab.suptitle(r'$\mathdefault{Reconstructions\ with\ n_{gal}=20/arcmin^2}$',
fontsize=16)
