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)
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)
h[::2] = hist
h[1::2] = hist
b = numpy.zeros(2*len(histE))
b[::2] = binsE[:-1]
b[1::2] = binsE[1:]
if fmt is None:
return pylab.plot(b,h,label=label)
else:
return pylab.plot(b,h,fmt,label=label)
if __name__ == '__main__':
gamma,kappa,Ngal = get_from_parsed( 'gamma','kappa','Ngal',
RAlim=(10,15),
DEClim=(35,40) )
r = 5.6 #arcmin
dtheta = params.dtheta
N1,N2 = gamma.shape
slow = False
if slow:
pos_x = dtheta * numpy.arange(N1)
pos_y = dtheta * numpy.arange(N2)
pos = pos_x[:,None] + 1j*pos_y[None,:]
Map_E, Map_B = Map_map(gamma,pos,pos,r)
else:
Map_E, Map_B = tiled_Map(gamma,dtheta,r)
