def from_tree_transform(cls, node, ctx):
return functional_models.Sersic2D(amplitude=node['amplitude'],
r_eff=node['r_eff'],
n=node['n'],
x_0=node['x_0'],
y_0=node['y_0'],
ellip=node['ellip'],
theta=node['theta'])
def prob3b():
#image is already cropped, so no need for a true cutout (just use the whole data payload)
data = fits.getdata("../res/ngc5055.fits")
pa = 100.0 #position angle
max_count_core = 23755
#ellipticity = (425.8-202.6)/425.8 #roughly 61 deg
ellipticity = (425.44 - 279.89) / 425.44 #roughly 49 deg
h_init = int(
dist(450., 450., 295., 410.)
) #using contours on ds9 image, approx location of center counts to 1/e
#this is the center (450,450) to one edge(295,410)
best_h = 259.63 #308.92 #309. #half-light radius ... r_1/2 for r_e = r_1/2 / ln(2) ~~ 445.68 pixels
x, y = np.meshgrid(np.arange(data.shape[1]), np.arange(
data.shape[0])) #in ds9, y is 1st index, x is second
#minimize the residual to fit h?
#
# d = data[288:594,168:640]
# max_count_core = np.max(d)
# best_h = h_init
# best_chi2 = 9e99
# for h in range(h_init+90,h_init+120):
# model = models.Sersic2D(amplitude=1, r_eff=h,n=1.0,
# x_0=len(x)/2.,y_0=len(y)/2.,
# ellip=ellipticity,theta=(pa-90.)*np.pi/180.)
# img = model(x, y)[288:594,168:640]
# img *= max_count_core/np.max(img)
#
# chi2, scale = chi_sqr2D(d, img, None)
# print (chi2,h)
#
# if chi2 < best_chi2:
# best_chi2 = chi2
# best_h = h
#
# print("Best",best_chi2,best_h)
#amplitude = surface brightness at r_eff not at center, but still okay, since scaling up afterward so the core
# counts match
#refine from 309
# h_init = 309.
# h_init = 260.
# d = data[288:594,168:640]
# max_count_core = np.max(d)
# best_h = h_init
# best_chi2 = 9e99
# for deci in range(-100,101):
# h = float(h_init)+float(deci)/100.
# model = models.Sersic2D(amplitude=1, r_eff=h,n=1.0,
# x_0=len(x)/2.,y_0=len(y)/2.,
# ellip=ellipticity,theta=(pa-90.)*np.pi/180.)
# img = model(x, y)[288:594,168:640]
# img *= max_count_core/np.max(img)
#
# chi2, scale = chi_sqr2D(d, img, None)
# print (chi2,h)
#
# if chi2 < best_chi2:
# best_chi2 = chi2
# best_h = h
#
# print("Best",best_chi2,best_h)
#best_h = 259.63
# # fit scale
#d = data[288:594,168:640]
# max_count_core = np.max(d)
# best_h = h_init
# best_chi2 = 9e99
# best_scale = 4459
# for sc in range(best_scale-100,best_scale+100):
# model = models.Sersic2D(amplitude=sc, r_eff=best_h,n=1.0,
# x_0=len(x)/2.,y_0=len(y)/2.,
# ellip=ellipticity,theta=(pa-90.)*np.pi/180.)
# img = model(x, y)[288:594,168:640]
#
# chi2, scale = chi_sqr2D(d, img, None)
# print ("Scale", chi2,sc)
#
# if chi2 < best_chi2:
# best_chi2 = chi2
# best_scale = sc
#
# print("Best Scale",best_chi2,best_scale)
#try scaling
# best_h = h_init
# best_chi2 = 9e99
# best_scale = 4459
# model = models.Sersic2D(amplitude=1, r_eff=best_h, n=1.0,
# x_0=len(x)/2.,y_0=len(y)/2.,
# ellip=ellipticity,theta=(pa-90.)*np.pi/180.)
# for sc in range(best_scale-100,best_scale+100):
# img = model(x, y) # .swapaxes(0,1)
# img *= sc
# chi2, scale = chi_sqr2D(d, img, None)
# print ("Scale", chi2,sc)
model = models.Sersic2D(amplitude=1,
r_eff=best_h,
n=1.0,
x_0=len(x) / 2.,
y_0=len(y) / 2.,
ellip=ellipticity,
theta=(pa - 90.) * np.pi / 180.)
img = model(x, y) # .swapaxes(0,1)
img *= max_count_core / np.max(img)
plt.figure(figsize=(13, 4)) # 2 cols, 1row
plt.subplot(121) #image
plt.title("NGC 5055")
plt.imshow(data, origin="lower", cmap="gray")
cbar = plt.colorbar()
cbar.set_label('Counts')
plt.xlabel("pix")
plt.ylabel("pix")
plt.subplot(122) #model
plt.title(r"$\Sigma (r)$ model")
plt.imshow(img, origin="lower", cmap="gray")
plt.xlabel("pix")
plt.ylabel("pix")
cbar = plt.colorbar()
cbar.set_label('Counts')
plt.savefig(op.join(OUTDIR, "hw2_prob3b.png"))
plt.show()
return data, model, img