def plot_haloness(skynum, kernel=exppow(), N=20.):
gal_x,gal_y,gal_e1,gal_e2 = read_sky(skynum).T
nhalo, halo_coords = read_halos(skynum)
margin = 100
xplot = np.linspace(min(gal_x)-margin, max(gal_x)+margin, N)
yplot = np.linspace(min(gal_y)-margin, max(gal_y)+margin, N)
X,Y = np.meshgrid(xplot,yplot)
Z = np.zeros(X.shape)
f = optsky.fwrapper(gal_x=gal_x, gal_y=gal_y, gal_e1=gal_e1, gal_e2=gal_e2,
nhalo=nhalo, kernel=kernel, has_width=False)
for idx, x in enumerate(xplot):
for idy, y in enumerate(yplot):
coords = np.array([x,y])
Z[idy, idx] = f(coords)
#plt.contourf(X, Y, Z)
plt.pcolor(X, Y, Z)
plt.colorbar()
plt.hold(True)
for ihalo in range(nhalo):
plt.scatter(halo_coords[ihalo*2], halo_coords[ihalo*2 + 1],\
color='white', s=50)
plt.axis((min(gal_x)-margin, max(gal_x)+margin, min(gal_y)-margin, max(gal_y)+margin))
plt.title("Objective function value for Sky " + str(skynum))
plt.show()
def diagnostic(Nrange, kernel=exppow()):
plt.figure()
plt.hold(True)
for skynum in range(201, 231):
nhalo, halo_coords = read_halos(skynum)
gal_x,gal_y,gal_e1,gal_e2 = read_sky(skynum).T
print skynum
f = optsky.fwrapper(gal_x=gal_x, gal_y=gal_y,
gal_e1=gal_e1, gal_e2=gal_e2,
nhalo=nhalo, kernel=kernel)
val_true = f(halo_coords)
val_array = np.zeros(len(Nrange))
for ii in range(len(Nrange)):
dm_x, dm_y, val = optsky.predict(skynum, Ngrid=Nrange[ii])
val_array[ii] = val
val_array = (val_array - val_true)/val_true
plt.plot(Nrange, val_array, linewidth=0.5, color='black', linestyle='-')
#plt.title('Training Sky ' + str(skynum) + ': '\
# + str(nhalo) + ' halos')
plt.xlabel(r'$\mathrm{number \ of \ random \ starts}$')
plt.ylabel(r'$\mathrm{normalized \ objective \ value}$')
plt.plot(Nrange, np.zeros(len(Nrange)), linewidth=2.0, color='blue', linestyle='--')
plt.axis('tight')
plt.show()
def plot_likelihood(skynum, pdf=None, kernel=exppow_lim(), N=20):
if (pdf == None):
pdf = optsky.build_pdf(kernel)
nhalo, halo_coords = read_halos(skynum)
sky = read_sky(skynum)
gal_x, gal_y, gal_e1, gal_e2 = sky.T
f = optsky.fwrapper(gal_x=gal_x, gal_y=gal_y,
gal_e1=gal_e1, gal_e2=gal_e2,
nhalo=nhalo, kernel=kernel, pdf=pdf)
margin = 0
xplot = np.linspace(0.0, 4200.0, N)
yplot = np.linspace(0.0, 4200.0, N)
X,Y = np.meshgrid(xplot,yplot)
Z = np.zeros(X.shape)
for idx, x in enumerate(xplot):
for idy, y in enumerate(yplot):
Z[idy, idx] = f(np.array([x,y]))
#plt.contourf(X, Y, Z)
plt.pcolor(X, Y, Z)
plt.colorbar()
plt.hold(True)
nhalo, halo_coords = read_halos(skynum)
for ihalo in range(nhalo):
plt.scatter(halo_coords[ihalo], halo_coords[ihalo + nhalo],\
color='white', s=50)
plt.axis((min(gal_x)-margin, max(gal_x)+margin, min(gal_y)-margin, max(gal_y)+margin))
plt.show()