def main():
plane_mask=True
pmval=1
bcut=False
bmin=-90
bmax=90
lcut=False
lmin=-180
lmax=180
mask_ring=True
inner=0
outer=30
maps_dir = '/zfs/bsafdi/data/'
fermi_data_dir = '/zfs/tslatyer/fermimaps/allsky/'
work_dir = '/zfs/nrodd/NPTFWorking/'
emin=8
emax=9
nside=256
eventclass=5
eventtype=3
newstyle=1
ps_file = '/zfs/nrodd/NPTFWorking/data/ps_data/ps_lists/3FGL_0to15_tiny.txt'
n_ps_run=10
indeg=True
#loadforpsf = fp.fermi_plugin(maps_dir,fermi_data_dir=fermi_data_dir,work_dir=work_dir,CTB_en_min = emin,CTB_en_max=emax,nside=nside,eventclass=eventclass,eventtype=eventtype,newstyle=newstyle)
f = sim.fake_fermi_data(maps_dir,fermi_data_dir=fermi_data_dir,work_dir=work_dir,CTB_en_min = emin,CTB_en_max=emax,nside=nside,eventclass=eventclass,eventtype=eventtype,newstyle=newstyle)
f.load_psf()
sigma_PSF_deg = f.sigma_PSF_deg[0:-1]
print "sigma_PSF_deg:",sigma_PSF_deg
print "Ebin:",emin
print "Eventclass:",eventclass
print "Eventtype:",eventtype
print "Gaussian results:"
start = time.time()
the_map = np.ones(hp.nside2npix(256))
simt.draw_points(sigma_PSF_deg,1000000,0.,0.,the_map)
end = time.time()
#print "Time taken:",end - start
f.load_king_param()
print "King results:"
start = time.time()
the_map = np.ones(hp.nside2npix(256))
simt.draw_points_king(f.fcore_arr[0],f.score_arr[0],f.gcore_arr[0],f.stail_arr[0],f.gtail_arr[0],f.SpE_arr[0],1000000,0.,0.,the_map)
end = time.time()
def main():
# Define basic vars
maps_dir = '/zfs/bsafdi/data/'
fermi_data_dir = '/zfs/tslatyer/fermimaps/allsky/'
work_dir = '/zfs/nrodd/NPTFWorking/'
emin=0
emax=4
nside=256
eventclass=5
eventtype=0
newstyle=1
ps_file = '/zfs/nrodd/NPTFWorking/data/ps_data/ps_lists/3FGL_0to15_tiny.txt'
n_ps_run=10
indeg=True
#loadforpsf = fp.fermi_plugin(maps_dir,fermi_data_dir=fermi_data_dir,work_dir=work_dir,CTB_en_min = emin,CTB_en_max=emax,nside=nside,eventclass=eventclass,eventtype=eventtype,newstyle=newstyle)
f = sim.fake_fermi_data(maps_dir,fermi_data_dir=fermi_data_dir,work_dir=work_dir,CTB_en_min = emin,CTB_en_max=emax,nside=nside,eventclass=eventclass,eventtype=eventtype,newstyle=newstyle,use_king=True)
f.load_psf()
sigma_PSF_deg = f.sigma_PSF_deg[0:-1]
f.load_king_param()
disk_file_name= 'thindiskmodel_ec.fits' #'simplediskmodel_ec.fits'
####################
##########Internal code
###dirs
P_plots_dir = work_dir + 'plots/' + tag + '/' + run_tag_poiss + '/'
NDI_plots_dir = work_dir + 'plots/' + tag + '/' + run_tag_plus_iso + '/'
DI_plots_dir = work_dir + 'plots/' + tag + '/' + run_tag_disk_only + '/'
####fermi fake data
f = sim.fake_fermi_data(maps_dir,CTB_en_min=CTB_start_bin,CTB_en_max=CTB_end_bin,nside=nside,data_name=data_type)
f.add_diffuse(comp = 'p6') #diffuse
f.add_bubbles() #bubbles
f.add_iso() #iso
f.add_nfw() #NFW-DM
f.add_template_from_file('disk',disk_file_name) #we will call template 'disk'
f.use_template_normalization_file(norm_file_path,key_suffix='-0')
if prun:
f_P = copy.deepcopy(f)
if jdrun:
f_DI = copy.deepcopy(f)
if ndirun:
def setup_for_scan():
global f, b, new_template_dict, n_ps, newstyle
# Load Fermi Plugin and its basic functionality
f = sim.fake_fermi_data(maps_dir,fermi_data_dir=fermi_data_dir,work_dir=work_dir,CTB_en_min = emin,CTB_en_max=emax,nside=nside,eventclass=eventclass,eventtype=eventtype,newstyle=newstyle)
if mask_type!='False':
f.make_ps_mask(mask_type=mask_type,force_energy=force_ps_mask,energy_bin=force_ps_mask_bin)
# Add appropriate templates
f.add_diffuse_newstyle(comp = diff, eventclass = eventclass, eventtype = eventtype)
if extraicstemp!='False':
f.add_diffuse_newstyle(comp = extraicstemp, eventclass = eventclass, eventtype = eventtype)
f.add_bubbles(comp = 'bubs')
f.add_iso(comp = 'iso')
if add_ps_model:
f.add_ps_model(comp = 'ps_model')
if nfw_dm:
f.add_nfw(comp = 'nfw')
f.add_template_from_file('disk',disk_file_name) # always add the disk here even if don't use as often needed for NPTF
if norm_file!='False':
if len(add_norm_file_for_comps) > 0 and add_norm_file != 'False':
print 'using first normalization file: ', norm_file
f.use_template_normalization_file(norm_file,key_suffix='-0',dont_use_keys=add_norm_file_for_comps)
print 'using second normalization file: ', add_norm_file
f.use_template_normalization_file(add_norm_file,key_suffix='-0',use_keys=add_norm_file_for_comps)
else:
f.use_template_normalization_file(norm_file,key_suffix='-0')
# This is where things begin to vary from a normal run to make fake data
medians = np.load(plots_dir + 'medians.npy')
norms_dict = {}
# Assumes only template that had a log prior was the NFW
for i in range(len(medians)):
if medians[i,0] == 'nfw-0':
val = 10**float(medians[i,1])
else:
val = float(medians[i,1])
norms_dict[medians[i,0]] = val
#norms_dict = {'p6-0':0.5}
# Now rescale the templates to the norm extracted from the fits
for key in norms_dict.keys():
print 'on key ',key[:-2]
print np.shape(f.template_dict[key[:-2]])
print norms_dict[key]
f.template_dict[key[:-2]] = norms_dict[key]*np.array(f.template_dict[key[:-2]])
# Initiate Poissonian background templates
#f.init_back(['nfw','iso','bubs','p6'])
f.init_back(['p6','bubs','nfw','iso'])
# Make the mask
f.make_mask_total(plane_mask=plane_mask, band_mask_range = [-pmval,pmval], lcut=lcut, lmin=lmin, lmax=lmax, bcut=bcut, bmin=bmin, bmax=bmax, mask_ring=mask_ring, inner=inner, outer=outer)
# Make the fake data
f.make_fake_data()
# Save the fake data and the location of the point sources (called keys)
f.save_fake_data(plots_dir + 'FD_ap.txt.gz')
f.save_fake_data_key(plots_dir + 'FD_ap_key.txt.gz')
def setup_for_scan():
global f, b, new_template_dict, n_ps, newstyle
# Load Fermi Plugin and its basic functionality
f = sim.fake_fermi_data(maps_dir,fermi_data_dir=fermi_data_dir,work_dir=work_dir,CTB_en_min = emin,CTB_en_max=emax,nside=nside,eventclass=eventclass,eventtype=eventtype,newstyle=newstyle)
if mask_type!='False':
f.make_ps_mask(mask_type=mask_type,force_energy=force_ps_mask,energy_bin=force_ps_mask_bin)
# Add appropriate templates
f.add_diffuse_newstyle(comp = diff, eventclass = eventclass, eventtype = eventtype)
if extraicstemp!='False':
f.add_diffuse_newstyle(comp = extraicstemp, eventclass = eventclass, eventtype = eventtype)
f.add_bubbles(comp = 'bubs')
f.add_iso(comp = 'iso')
if add_ps_model:
f.add_ps_model(comp = 'ps_model')
if nfw_dm:
f.add_nfw(comp = 'nfw')
f.add_template_from_file('disk',disk_file_name) # always add the disk here even if don't use as often needed for NPTF
if norm_file!='False':
if len(add_norm_file_for_comps) > 0 and add_norm_file != 'False':
print 'using first normalization file: ', norm_file
f.use_template_normalization_file(norm_file,key_suffix='-0',dont_use_keys=add_norm_file_for_comps)
print 'using second normalization file: ', add_norm_file
f.use_template_normalization_file(add_norm_file,key_suffix='-0',use_keys=add_norm_file_for_comps)
else:
f.use_template_normalization_file(norm_file,key_suffix='-0')
# This is where things begin to vary from a normal run to make fake data
medians = np.load(plots_dir + 'medians.npy')
norms_dict = {}
# Assumes only template that had a log prior was the NFW
for i in range(len(medians)):
if medians[i,0] == 'nfw-0':
val = 10**float(medians[i,1])
else:
val = float(medians[i,1])
norms_dict[medians[i,0]] = val
# Now rescale the templates to the norm extracted from the fits
for key in norms_dict.keys():
print 'on key ',key[:-2]
print np.shape(f.template_dict[key[:-2]])
print norms_dict[key]
f.template_dict[key[:-2]] = norms_dict[key]*np.array(f.template_dict[key[:-2]])
# Initiate Poissonian background templates
f.init_back(['nfw','iso','bubs','p6'])
# Make the mask
f.make_mask_total(plane_mask=plane_mask, band_mask_range = [-pmval,pmval], lcut=lcut, lmin=lmin, lmax=lmax, bcut=bcut, bmin=bmin, bmax=bmax, mask_ring=mask_ring, inner=inner, outer=outer)
def load_ps_template(ps_path):
with open(ps_path, 'rb') as F:
the_list = pickle.load(F)
return the_list
list_disk = load_ps_template(plots_dir + 'disk_ps.txt')
list_iso = load_ps_template(hl_plots_dir + 'iso_ps.txt')
if not just_disk:
list_nfw = load_ps_template(plots_dir + 'nfw_ps.txt')
# Use spectrum norms
bins = emax - emin
if not just_disk:
nfw_norm_reduced=np.array([np.mean(f.template_dict['nfw'][en]) for en in range(bins)])
# Make e-dep breaks - I calculated these in Mathematica
Sb_iso_list = np.array([0.804978, 0.838517, 0.873453, 0.909845, 0.947752, 0.98724, 1.02837, 1.07122, 1.11585, 1.16234])
if not just_disk:
Sb_nfw_list = list_nfw[0][3]*nfw_norm_reduced / np.sum(nfw_norm_reduced)
else:
Sb_disk_list = np.array([1.84547, 1.1267, 0.68787, 0.419958, 0.256393, 0.156533, 0.0955661, 0.058345, 0.0356207, 0.0217471])
# Make theta lists
theta_ps_iso=list_iso[0][0:3]+list(Sb_iso_list)
if not just_disk:
theta_ps_nfw=list_nfw[0][0:3]+list(Sb_nfw_list)
else:
theta_ps_disk=list_disk[0][0:3]+list(Sb_disk_list)
# Load PSF
f.load_king_param()
# Now add the NPTF models
f.init_ps_king(list_iso[1],theta_ps_iso,f.fcore_arr,f.score_arr,f.gcore_arr,f.stail_arr,f.gtail_arr,f.SpE_arr,n_ps=n_ps_max,smin=0.1)
if not just_disk:
f.init_ps_king(list_nfw[1],theta_ps_nfw,f.fcore_arr,f.score_arr,f.gcore_arr,f.stail_arr,f.gtail_arr,f.SpE_arr,n_ps=n_ps_max,smin=0.1)
else:
f.init_ps_king(list_disk[1],theta_ps_disk,f.fcore_arr,f.score_arr,f.gcore_arr,f.stail_arr,f.gtail_arr,f.SpE_arr,n_ps=n_ps_max,smin=0.1)
# Make the fake data
f.make_fake_data()
# Save the fake data and the location of the point sources (called keys)
f.save_fake_data(plots_dir + 'FD.txt.gz')
f.save_fake_data_key(plots_dir + 'FD_key.txt.gz')
disk_file_name= 'thindiskmodel_ec.fits' #'simplediskmodel_ec.fits'
####################
##########Internal code
###dirs
P_plots_dir = work_dir + 'plots/' + tag + '/' + run_tag_poiss + '/'
NDI_plots_dir = work_dir + 'plots/' + tag + '/' + run_tag_plus_iso + '/'
DI_plots_dir = work_dir + 'plots/' + tag + '/' + run_tag_disk_only + '/'
####fermi fake data
f = sim.fake_fermi_data(maps_dir,fermi_data_dir=fermi_data_dir,work_dir=work_dir,CTB_en_min=CTB_start_bin,CTB_en_max=CTB_end_bin+1,nside=nside,eventclass=eventclass,eventtype=eventtype,newstyle=newstyle
f.add_diffuse_newstyle(comp = 'p6', eventclass = eventclass, eventtype = eventtype)
f.add_bubbles() #bubbles
f.add_iso() #iso
f.add_nfw() #NFW-DM
f.add_template_from_file('disk',disk_file_name) #we will call template 'disk'
f.use_template_normalization_file(norm_file_path,key_suffix='-0')
if prun:
f_P = copy.deepcopy(f)
if jdrun:
f_DI = copy.deepcopy(f)
if ndirun:
f_NDI = copy.deepcopy(f)
