def smooth_the_pixel_by_angles(self,angle_vec,counts_vec): #angle vec of the form [ [theta,phi], [theta,phi], ... ] smoothed_pixel_map = np.zeros(self.npix) #theta_center,phi_center = hp.pix2ang(self.nside,pix_num_center) # theta_center=angle_vec[::,0] # phi_center=angle_vec[::,1] for theta_center,phi_center, count in map(None,angle_vec[0],angle_vec[1],counts_vec): #ta = time.time() #theta_center,phi_center = angle lat = theta_center*360/(2*np.pi) lng = phi_center*360/(2*np.pi) mask_where = masks.mask_ring(0, self.mult_sigma_for_smooth*self.sigma*360/(2*np.pi),lat, lng, self.nside) #mask_where=np.ones(self.npix) #print 'start: ', time.time() - ta mask_pixel_vals = np.where(mask_where == 1)[0] pix_num_center=hp.ang2pix(self.nside,theta_center,phi_center) mask_pixel_vals = np.array(list(set(list(mask_pixel_vals) + [pix_num_center]))) #print 'The mask_pixel_vals are ',mask_pixel_vals #t0 = time.time() vals = np.vectorize(self.gaussian_func_ang)(theta_center,phi_center,mask_pixel_vals) #print 'step vals: ', time.time() - t0 #print 'The vals are ', vals #print 'Sum of vals: ', np.sum(vals) if np.sum(vals)==0: smoothed_pixel_map[pix_num_center] += count else: smoothed_pixel_map[mask_pixel_vals] += count*vals/np.sum(vals) #print 'total: ', time.time() - ta return smoothed_pixel_map #normalize to one
def smooth_the_pixel(self,pix_num_center): smoothed_pixel_map = np.zeros(self.npix) # masks.mask_ring(0, 10*self.sigma, lat, lng, self.nside) theta_center,phi_center = hp.pix2ang(self.nside,pix_num_center) # theta,phi = hp.pix2ang(self.nside,pix_num) lat = theta_center*360/(2*np.pi) lng = phi_center*360/(2*np.pi) mask_where = masks.mask_ring(0, self.mult_sigma_for_smooth*self.sigma*360/(2*np.pi),lat, lng, self.nside) mask_pixel_vals = np.where(mask_where == 1)[0] mask_pixel_vals = np.array(list(set(list(mask_pixel_vals) + [pix_num_center]))) # smoothed_pixel_map = np.vectorize(self.gaussian_func)(pix_num_center,self.pixel_number_array) smoothed_pixel_map[mask_pixel_vals] = np.vectorize(self.gaussian_func)(pix_num_center,mask_pixel_vals) return self.the_map[pix_num_center]*smoothed_pixel_map/np.sum(smoothed_pixel_map) #normalize to one
def smooth_a_quartile(self,pix_num_center,quartile):
# Smooth a pixel for a given quartile
# Load the parameters needed
if quartile==1:
params_index_psf=10
rescale_index_psf=11
if self.eventclass==2:
theta_norm_psf=[0.0000000,9.7381019e-06,0.0024811595,0.022328802,0.080147663,0.17148392,0.30634315,0.41720551]
if self.eventclass==5:
theta_norm_psf=[0.0000000,9.5028121e-07,0.00094418357,0.015514370,0.069725775,0.16437751,0.30868705,0.44075016]
if quartile==2:
params_index_psf=7
rescale_index_psf=8
if self.eventclass==2:
theta_norm_psf=[0.0000000,0.00013001938,0.010239333,0.048691643,0.10790632,0.18585539,0.29140913,0.35576811]
if self.eventclass==5:
theta_norm_psf=[0.0000000,1.6070284e-05,0.0048551576,0.035358049,0.091767466,0.17568974,0.29916159,0.39315185]
if quartile==3:
params_index_psf=4
rescale_index_psf=5
if self.eventclass==2:
theta_norm_psf=[0.0000000,0.00074299273,0.018672204,0.062317201,0.12894928,0.20150553,0.28339386,0.30441893]
if self.eventclass==5:
theta_norm_psf=[0.0000000,0.00015569366,0.010164870,0.048955837,0.11750811,0.19840060,0.29488095,0.32993394]
if quartile==4:
params_index_psf=1
rescale_index_psf=2
if self.eventclass==2:
theta_norm_psf=[4.8923139e-07,0.011167475,0.092594658,0.15382001,0.16862869,0.17309118,0.19837774,0.20231968]
if self.eventclass==5:
theta_norm_psf=[0.0000000,0.0036816313,0.062240006,0.14027030,0.17077023,0.18329804,0.21722594,0.22251374]
if self.eventclass==2:
psf_file_name = 'psf_P8R2_SOURCE_V6_PSF.fits'
elif self.eventclass==5:
psf_file_name = 'psf_P8R2_ULTRACLEANVETO_V6_PSF.fits'
fits_file_name = self.psf_data_dir + psf_file_name
f = fits.open(fits_file_name)
# Finally convert from an energy bin to an energy in GeV
energyvalarray = np.array([0.336605534472,0.423761259986,0.533483821671,0.67161633,0.84551489,1.0644402,1.3400508,1.6870239,2.1238373,2.6737528,3.3660554,4.2376127,5.3348382,6.7161634,8.4551486,10.644402,13.400508,16.870240,21.238374,26.737528,33.660553,42.376126,53.348382,67.161633,84.551489,106.44402,134.00508,168.70240,212.38374,267.37528])
energyval = energyvalarray[self.Ebin]
# Now load up the parameters
kparam=PSFKC.PSF_king(f, theta_norm=theta_norm_psf, rescale_index=rescale_index_psf, params_index=params_index_psf)
fcore=kparam.return_king_params(energyval,'fcore')
score=kparam.return_king_params(energyval,'score')
gcore=kparam.return_king_params(energyval,'gcore')
stail=kparam.return_king_params(energyval,'stail')
gtail=kparam.return_king_params(energyval,'gtail')
SpE=kparam.rescale_factor(energyval)
# Note using the ring mask we only calculate the PSF in a region where it
# is appreciably non-zero so we don't have to calculate it for the full sky
smoothed_pixel_map = np.zeros(self.npix)
theta_center,phi_center = hp.pix2ang(self.nside,pix_num_center)
lat = theta_center
lng = phi_center
# Note we convert the radius to degrees as this is the format mask_ring takes in
mask_where = masks.mask_ring(0, 10*SpE*((score+stail)/2.)*180./np.pi,lat, lng, self.nside)
mask_pixel_vals = np.where(mask_where == 1)[0]
mask_pixel_vals = np.array(list(set(list(mask_pixel_vals) + [pix_num_center])))
smoothed_pixel_map[mask_pixel_vals] = np.vectorize(self.king_func)(pix_num_center,mask_pixel_vals,fcore,score,gcore,stail,gtail,SpE)
return self.the_map[pix_num_center]*smoothed_pixel_map/np.sum(smoothed_pixel_map) #normalize smoothing template to the value of the map at the pixel location