def __init__(self,fcore,score,gcore,stail,gtail,SpE,frac=[1],psf_dir='',nside=128,
num_f_bins=10,n_ps=50000,n_pts_per_king=1000,f_trunc=0.01,
king_sampling=10000,save_tag=''):
# Convert floats to arrays
fcore = np.array([fcore]).flatten()
score = np.array([score]).flatten()
gcore = np.array([gcore]).flatten()
stail = np.array([stail]).flatten()
gtail = np.array([gtail]).flatten()
SpE = np.array([SpE]).flatten()
frac = np.array([frac]).flatten()
# Store basic variables to self
self.fcore = fcore
self.score = score
self.gcore = gcore
self.stail = stail
self.gtail = gtail
self.SpE = SpE
self.psf_dir = psf_dir
self.nside = nside
self.num_f_bins = num_f_bins
self.n_ps = n_ps
self.f_trunc = f_trunc
self.save_tag = save_tag
# Setup equal frac if not user defined, and then distribute n_pts_per_king using frac
n_king = len(self.fcore)
self.n_king = n_king
if not len(self.fcore) == len(frac):
frac = np.empty(n_king)
frac.fill(1./n_king)
n_pts_per_king_array = np.empty(n_king)
for i in range(n_king):
n_pts_per_king_array[i]=int(round(n_pts_per_king*frac[i]))
self.n_pts_per_king_array = n_pts_per_king_array
self.total_n_pts_per_king = int(np.sum(n_pts_per_king_array))
# Import king function pdfs
kp = np.array([])
for i in range(n_king):
kp = np.append(kp,kpi.king_pdf(fcore[i],score[i],gcore[i],stail[i],gtail[i],SpE[i],king_sampling=king_sampling))
self.kp = kp
def __init__(self,fcore,score,gcore,stail,gtail,SpE,psf_dir='',nside=128,
num_f_bins=10,n_king=50000,n_pts_per_king=1000,f_trunc=0.01,
king_sampling=10000,save_tag=''):
self.fcore = fcore
self.score = score
self.gcore = gcore
self.stail = stail
self.gtail = gtail
self.SpE = SpE
self.psf_dir = psf_dir
self.nside = nside
self.num_f_bins = num_f_bins
self.n_king = n_king
self.n_pts_per_king = n_pts_per_king
self.f_trunc = f_trunc
self.king_sampling = king_sampling
self.save_tag = save_tag
# Import a king function pdf
self.kp = kpi.king_pdf(self.fcore,self.score,self.gcore,self.stail,self.gtail,self.SpE,king_sampling=self.king_sampling)
]
theta_norm_psf3 = [
0.0000000,
0.00015569366,
0.010164870,
0.048955837,
0.11750811,
0.19840060,
0.29488095,
0.32993394,
]
theta_norm_psf4 = [0.0000000, 0.0036816313, 0.062240006, 0.14027030, 0.17077023, 0.18329804, 0.21722594, 0.22251374]
f = fits.open("/zfs/bsafdi/data/psf_data/" + psf_file_name)
kparam = PSFKC.PSF_kings(
f, theta_norm=theta_norm_psf1, rescale_index=rescale_index_psf1, params_index=params_index_psf1
)
fcore = kparam.return_kings_params(energyval, "fcore")
score = kparam.return_kings_params(energyval, "score")
gcore = kparam.return_kings_params(energyval, "gcore")
stail = kparam.return_kings_params(energyval, "stail")
gtail = kparam.return_kings_params(energyval, "gtail")
SpE = kparam.rescale_factor(energyval)
fcore = np.array([fcore]).flatten()
kpi.king_pdf(fcore[0], score, gcore, stail, gtail, SpE)
theta_norm_psf3=[0.0000000,0.00015569366,0.010164870,0.048955837,0.11750811,0.19840060,0.29488095,0.32993394]
theta_norm_psf4=[0.0000000,0.0036816313,0.062240006,0.14027030,0.17077023,0.18329804,0.21722594,0.22251374]
f = fits.open('/zfs/bsafdi/data/psf_data/' + psf_file_name)
kparam=PSFKC.PSF_kings(f, theta_norm=theta_norm_psf1, rescale_index=rescale_index_psf1, params_index=params_index_psf1)
fcore=kparam.return_kings_params(energyval,'fcore')
score=kparam.return_kings_params(energyval,'score')
gcore=kparam.return_kings_params(energyval,'gcore')
stail=kparam.return_kings_params(energyval,'stail')
gtail=kparam.return_kings_params(energyval,'gtail')
SpE=kparam.rescale_factor(energyval)
kp = kpi.king_pdf(fcore,score,gcore,stail,gtail,SpE)
n_pts_per_king=1000
theta_c = 0.040083808095
phi_c = 2.5
dp = kp.king_pdf(n_pts_per_king)
dangle = np.random.uniform(0,2*np.pi,n_pts_per_king)
dtheta = dp*np.sin(dangle)
dphi = dp*np.cos(dangle)
theta = theta_c + dtheta
phi_temp = phi_c + dphi
print "min theta:",np.min(theta)
print "max theta:",np.max(theta)
