def get_kappa_vector(self,theta1,theta2,zrange,
n=1):
try:
Nz = len(zrange)
except:
zrange = [zrange]
Nz = 1
N1 = len(theta1)
N2 = len(theta2)
vec_data = numpy.zeros( (Nz,N1,N2), dtype = complex )
theta = theta_comp_to_grid(theta1,theta2)
if n==1:
for i in range(Nz):
vec_data[i] = self.kappa(theta,zrange[i])
else:
dx = theta1[1]-theta1[0]
dy = theta2[1]-theta2[0]
for j in range(N1):
for k in range(N2):
t = theta_comp_to_grid( numpy.linspace(theta1[j]-0.5*dx,
theta1[j]+0.5*dx,
n),
numpy.linspace(theta2[k]-0.5*dy,
theta2[k]+0.5*dy,
n) )
for i in range(Nz):
vec_data[i,j,k] = numpy.mean(self.kappa(t,zrange[i]))
return Lens3D_vector(Nz,N1,N2,vec_data)
def write_to_file(self,filename,
z_range,theta1_range,theta2_range = None):
if(theta2_range == None):
theta2_range = theta1_range
of = open(filename,'w')
of.write('#positions measured in arcmin:\n')
of.write('#redshift theta1 theta2 Sigma kappa gamma1 gamma2\n')
theta = theta_comp_to_grid(theta1_range,theta2_range)
for i in range(len(z_range)):
z = z_range[i]
if i==0:
z0 = 0
else:
z0 = z_range[i-1]
kappa = self.kappa(theta,z)
gamma = self.gamma(theta,z)
Sigma = self.Sigma(theta,z0,z)
for j in range(theta.shape[0]):
for k in range(theta.shape[1]):
of.write('%.3f %.3f %.3f %.6g %.6g %.6g %.6g\n' % \
(z, theta.real[j,k], theta.imag[j,k],
Sigma[j,k],
kappa[j,k],
gamma.real[j,k], gamma.imag[j,k]) )
#end for
#end for
#end for
of.close()
def get_Sigma_vector(self,theta1,theta2,zrange,
n=1):
"""
n tells how many points to evaluate within the pixel
there will be n^2 evaluations per pixel
"""
try:
Nz = len(zrange)
except:
zrange = [zrange]
Nz = 1
N1 = len(theta1)
N2 = len(theta2)
assert n>0
#make zrange[-1] = 0.0
zrange = numpy.concatenate( (zrange,[0.0]) )
vec_data = numpy.zeros( (Nz,N1,N2), dtype = complex )
theta = theta_comp_to_grid(theta1,theta2)
if n==1:
for i in range(Nz):
vec_data[i] = self.Sigma(theta,zrange[i-1],zrange[i])
else:
dx = theta1[1]-theta1[0]
dy = theta2[1]-theta2[0]
for j in range(N1):
for k in range(N2):
t = theta_comp_to_grid( numpy.linspace(theta1[j]-0.5*dx,
theta1[j]+0.5*dx,
n),
numpy.linspace(theta2[k]-0.5*dy,
theta2[k]+0.5*dy,
n) )
for i in range(Nz):
vec_data[i,j,k] = numpy.mean(self.Sigma(t,zrange[i-1],
zrange[i]))
if numpy.any(numpy.isinf(vec_data)):
raise ValueError, "get_Sigma_vector : infinity encountered"
return Lens3D_vector(Nz,N1,N2,vec_data)
def get_gamma_vector(self,theta1,theta2,zrange):
try:
Nz = len(zrange)
except:
zrange = [zrange]
Nz = 1
N1 = len(theta1)
N2 = len(theta2)
vec_data = numpy.zeros( (Nz,N1,N2), dtype = complex )
theta = theta_comp_to_grid(theta1,theta2)
for i in range(Nz):
vec_data[i] = self.gamma(theta,zrange[i])
i = numpy.where(numpy.isnan(vec_data))
vec_data[i] = 0
return Lens3D_vector(Nz,N1,N2,vec_data)
theta_max = 3
sig_min = 200
sig_max = 500
#numpy.random.seed(2)
z = numpy.random.random(N_clusters)*(zmax-zmin)+zmin
theta = numpy.random.random((2,N_clusters))*(theta_max-theta_min)+theta_min
theta = theta[0] + 1j*theta[1]
sig = numpy.random.random(N_clusters)*(sig_max-sig_min)+sig_min
print theta
PS = ProfileSet()
for i in range(N_clusters):
PS.add( SIS(z[i],sig[i],theta[i]) )
z_source = 0.3
theta_1 = numpy.linspace(-3,3,30)
theta_2 = numpy.linspace(-3,3,30)
theta = theta_comp_to_grid(theta_1,theta_2)
pylab.figure()
PS.get_kappa_vector(theta_1,theta_2,(0.1,0.3) ).imshow_lens_plane(1,cmap=pylab.cm.gray,extent = theta_extent(theta))
PS.get_gamma_vector(theta_1,theta_2,(0.1,0.3) ).fieldplot_lens_plane(1,extent=(theta_1[0],theta_1[-1],theta_2[0],theta_2[-1]) )
pylab.figure()
PS.plot_kappa(theta,z_source)
pylab.figure()
PS.plot_gammakappa(theta,0.3,normalize = True)
pylab.show()