def get_aeff_flavor(self,flavor,aeff_egy_par,aeff_coszen_par):
'''
Creates the 2d aeff file from the parameterized aeff
vs. energy .dat file, an input to the parametric settings file.
'''
aeff_file = aeff_egy_par[flavor]
aeff_arr = np.loadtxt(open_resource(aeff_file)).T
# interpolate
aeff_func = interp1d(aeff_arr[0], aeff_arr[1], kind='linear',
bounds_error=False, fill_value=0)
czcen = get_bin_centers(self.czbins)
ecen = get_bin_centers(self.ebins)
# Get 1D array interpolated values at bin centers, assume no cz dep
aeff1d = aeff_func(ecen)
# Make this into a 2D array:
aeff2d = np.reshape(np.repeat(aeff1d, len(czcen)), (len(ecen), len(czcen)))
# Now add cz-dependence, assuming nu and nu_bar has same dependence:
cz_dep = eval(aeff_coszen_par[flavor.strip('_bar')])(czcen)
# Normalize:
cz_dep *= len(cz_dep)/np.sum(cz_dep)
return (aeff2d*cz_dep)
def __init__(self,ebins,czbins,particle_ID=None,**kwargs):
logging.info('Initializing PIDServicePar...')
#Evaluate the functions at the bin centers
ecen = get_bin_centers(ebins)
czcen = get_bin_centers(czbins)
self.pid_maps = {}
for signature in particle_ID.keys():
#Generate the functions
to_trck_func = eval(particle_ID[signature]['trck'])
to_cscd_func = eval(particle_ID[signature]['cscd'])
#Make maps from the functions evaluated at the bin centers
_,to_trck_map = np.meshgrid(czcen, to_trck_func(ecen))
_,to_cscd_map = np.meshgrid(czcen, to_cscd_func(ecen))
for label,pidmap in [('Track',to_trck_map),('Cascade',to_cscd_map)]:
if (pidmap < 0).any():
raise ValueError('%s PID probabilites can not be negative!'
' Investigate parameterization'%label)
self.pid_maps[signature] = {'trck':to_trck_map,
'cscd':to_cscd_map}
def __init__(self, ebins, czbins, detector_depth=None, earth_model=None,
prop_height=None, oversample_e=None,oversample_cz=None,
**kwargs):
"""
\params:
* ebins: Energy bin edges
* czbins: cos(zenith) bin edges
* earth_model: Earth density model used for matter oscillations.
* detector_depth: Detector depth in km.
* prop_height: Height in the atmosphere to begin in km.
"""
logging.info('Instantiating %s'%self.__class__.__name__)
self.ebins = np.array(ebins)
self.czbins = np.array(czbins)
for ax in [self.ebins, self.czbins]:
if (len(np.shape(ax)) != 1):
raise IndexError('Axes must be 1d! '+str(np.shape(ax)))
report_params(get_params(),['km','','','','km'])
earth_model = find_resource(earth_model)
self.earth_model = earth_model
self.FTYPE = np.float64
self.ebins_fine = oversample_binning(self.ebins, oversample_e)
self.czbins_fine = oversample_binning(self.czbins, oversample_cz)
self.ecen_fine = get_bin_centers(self.ebins_fine)
self.czcen_fine = get_bin_centers(self.czbins_fine)
self.initialize_kernel()
return
def __init__(self, ebins, czbins, detector_depth=None, earth_model=None,
prop_height=None, oversample_e=None,oversample_cz=None,gpu_id=None,
**kwargs):
"""
\params:
* ebins: Energy bin edges
* czbins: cos(zenith) bin edges
* earth_model: Earth density model used for matter oscillations.
* detector_depth: Detector depth in km.
* prop_height: Height in the atmosphere to begin in km.
* gpu_id: If running on a system with multiple GPUs, it will choose
the one with gpu_id. Otherwise, defaults to default context
"""
self.gpu_id = gpu_id
try:
import pycuda.autoinit
self.context = cuda.Device(self.gpu_id).make_context()
print "Initializing PyCUDA using gpu id: %d"%self.gpu_id
except:
import pycuda.autoinit
print "Auto initializing PyCUDA..."
#mfree,mtot = cuda.mem_get_info()
#print "free memory: %s mb",mfree/1.0e6
#print "tot memory: %s mb",mtot/1.0e6
#raw_input("PAUSED...")
logging.info('Instantiating %s'%self.__class__.__name__)
self.ebins = np.array(ebins)
self.czbins = np.array(czbins)
self.prop_height = prop_height
for ax in [self.ebins, self.czbins]:
if (len(np.shape(ax)) != 1):
raise IndexError('Axes must be 1d! '+str(np.shape(ax)))
report_params(get_params(),['km','','','',''])
earth_model = find_resource(earth_model)
self.earth_model = earth_model
self.FTYPE = np.float64
self.ebins_fine = oversample_binning(self.ebins, oversample_e)
self.czbins_fine = oversample_binning(self.czbins, oversample_cz)
self.ecen_fine = get_bin_centers(self.ebins_fine)
self.czcen_fine = get_bin_centers(self.czbins_fine)
self.initialize_kernel(detector_depth,**kwargs)
return
def SaveAeff(aeff,aeff_err,egy_bin_edges,flavor,out_dir):
# Correct for nutau/nutaubar:
for i in range(len(egy_bin_edges)-1):
if(aeff_err[i] < 1.0e-12): aeff_err[i] = 1.0e-12
ecen = get_bin_centers(egy_bin_edges)
splinefit = splrep(ecen,aeff,w=1./np.array(aeff_err), k=3, s=100)
fit_aeff = splev(ecen,splinefit)
outfile = os.path.join(out_dir,"a_eff_"+flavor+".dat")
print "Saving spline fit to file: "+outfile
fh = open(outfile,'w')
for i,energy in enumerate(ecen):
fh.write(str(energy)+' '+str(fit_aeff[i])+'\n')
fh.close()
outfile_data = os.path.join(out_dir,"a_eff_"+flavor+"_data.dat")
print "Saving data to file: "+outfile_data
fh = open(outfile_data,'w')
for i,energy in enumerate(ecen):
fh.write(str(energy)+' '+str(aeff[i])+' '+str(aeff_err[i])+'\n')
fh.close()
return
def SaveAeff(aeff,aeff_err,egy_bin_edges,flavor,out_dir):
# Correct for nutau/nutaubar:
for i in range(len(egy_bin_edges)-1):
if(aeff_err[i] < 1.0e-12): aeff_err[i] = 1.0e-12
ecen = get_bin_centers(egy_bin_edges)
splinefit = splrep(ecen,aeff,w=1./np.array(aeff_err), k=3, s=100)
fit_aeff = splev(ecen,splinefit)
outfile = os.path.join(out_dir,"a_eff_"+flavor+".dat")
print "Saving spline fit to file: "+outfile
fh = open(outfile,'w')
for i,energy in enumerate(ecen):
fh.write(str(energy)+' '+str(fit_aeff[i])+'\n')
fh.close()
outfile_data = os.path.join(out_dir,"a_eff_"+flavor+"_data.dat")
print "Saving data to file: "+outfile_data
fh = open(outfile_data,'w')
for i,energy in enumerate(ecen):
fh.write(str(energy)+' '+str(aeff[i])+' '+str(aeff_err[i])+'\n')
fh.close()
return
def read_param_string(self, param_str):
"""
Parse the dict with the parametrization strings and evaluate for
the bin energies needed.
"""
evals = get_bin_centers(self.ebins)
n_e = len(self.ebins) - 1
n_cz = len(self.czbins) - 1
parametrization = {}
for flavour in param_str:
parametrization[flavour] = {}
for int_type in param_str[flavour]: #['cc', 'nc']
logging.debug('Parsing function strings for %s %s' %
(flavour, int_type))
parametrization[flavour][int_type] = {}
for axis in param_str[flavour][
int_type]: #['energy', 'coszen']
parameters = {}
for par, funcstring in param_str[flavour][int_type][
axis].items():
# this should contain a lambda function:
function = eval(funcstring)
logging.trace(' function: %s' % funcstring)
# evaluate the function at the given energies
vals = function(evals)
# repeat for all cos(zen) bins
parameters[par] = np.repeat(vals, n_cz).reshape(
(n_e, n_cz))
parametrization[flavour][int_type][axis] = copy(parameters)
return parametrization
def plot_error(llr, nbins, **kwargs):
"""Given llr distribution Series, calculates the error bars and plots
them """
hist_vals, xbins = np.histogram(llr, bins=nbins)
bincen = get_bin_centers(xbins)
plt.errorbar(bincen, hist_vals, yerr=np.sqrt(hist_vals), **kwargs)
return hist_vals, bincen
def read_param_string(self, param_str):
"""
Parse the dict with the parametrization strings and evaluate for
the bin energies needed.
"""
evals = get_bin_centers(self.ebins)
n_e = len(self.ebins)-1
n_cz = len(self.czbins)-1
parametrization = {}
for flavour in param_str:
parametrization[flavour] = {}
for int_type in param_str[flavour]: #['cc', 'nc']
logging.debug('Parsing function strings for %s %s'
%(flavour, int_type))
parametrization[flavour][int_type] = {}
for axis in param_str[flavour][int_type]: #['energy', 'coszen']
parameters = {}
for par, funcstring in param_str[flavour][int_type][axis].items():
# this should contain a lambda function:
function = eval(funcstring)
logging.trace(' function: %s'%funcstring)
# evaluate the function at the given energies
vals = function(evals)
# repeat for all cos(zen) bins
parameters[par] = np.repeat(vals,n_cz).reshape((n_e,n_cz))
parametrization[flavour][int_type][axis] = copy(parameters)
return parametrization
def __init__(self, ebins, czbins, detector_depth=None, earth_model=None,
prop_height=None, oversample_e=None,oversample_cz=None,gpu_id=None,
**kwargs):
"""
\params:
* ebins: Energy bin edges
* czbins: cos(zenith) bin edges
* earth_model: Earth density model used for matter oscillations.
* detector_depth: Detector depth in km.
* prop_height: Height in the atmosphere to begin in km.
* gpu_id: If running on a system with multiple GPUs, it will choose
the one with gpu_id. Otherwise, defaults to default context
"""
self.gpu_id = gpu_id
try:
import pycuda.autoinit
self.context = cuda.Device(self.gpu_id).make_context()
print "Initializing PyCUDA using gpu id: %d"%self.gpu_id
except:
import pycuda.autoinit
print "Auto initializing PyCUDA..."
logging.info('Instantiating %s'%self.__class__.__name__)
self.ebins = np.array(ebins)
self.czbins = np.array(czbins)
self.prop_height = prop_height
for ax in [self.ebins, self.czbins]:
if (len(np.shape(ax)) != 1):
raise IndexError('Axes must be 1d! '+str(np.shape(ax)))
report_params(get_params(),['km','','','','km'])
earth_model = find_resource(earth_model)
self.earth_model = earth_model
self.FTYPE = np.float64
self.ebins_fine = oversample_binning(self.ebins, oversample_e)
self.czbins_fine = oversample_binning(self.czbins, oversample_cz)
self.ecen_fine = get_bin_centers(self.ebins_fine)
self.czcen_fine = get_bin_centers(self.czbins_fine)
self.initialize_kernel(detector_depth,**kwargs)
return
def get_osc_probLT_dict(self,
ebins=None,
czbins=None,
oversample_e=None,
oversample_cz=None,
**kwargs):
"""
This will create the oscillation probability map lookup tables
(LT) corresponding to atmospheric neutrinos oscillation
through the earth, and will return a dictionary of maps:
{'nue_maps':[to_nue_map, to_numu_map, to_nutau_map],
'numu_maps: [...],
'nue_bar_maps': [...],
'numu_bar_maps': [...],
'czbins':czbins,
'ebins': ebins}
Will call fill_osc_prob to calculate the individual
probabilities on the fly.
By default, the standard binning is oversampled by a factor 10.
Alternatively, the oversampling factor can be changed or a fine
binning specified explicitly. In the latter case, the oversampling
factor is ignored.
"""
#First initialize the fine binning if not explicitly given
if not check_fine_binning(ebins, self.ebins):
ebins = oversample_binning(self.ebins, oversample_e)
if not check_fine_binning(czbins, self.czbins):
czbins = oversample_binning(self.czbins, oversample_cz)
ecen = get_bin_centers(ebins)
czcen = get_bin_centers(czbins)
osc_prob_dict = {}
for nu in ['nue_maps', 'numu_maps', 'nue_bar_maps', 'numu_bar_maps']:
isbar = '_bar' if 'bar' in nu else ''
osc_prob_dict[nu] = {
'nue' + isbar: [],
'numu' + isbar: [],
'nutau' + isbar: [],
}
evals, czvals = self.fill_osc_prob(osc_prob_dict, ecen, czcen,
**kwargs)
osc_prob_dict['evals'] = evals
osc_prob_dict['czvals'] = czvals
return osc_prob_dict
def __init__(self,pid_data,ebins,czbins):
#Evaluate the functions at the bin centers
ecen = get_bin_centers(ebins)
czcen = get_bin_centers(czbins)
self.pid_maps = {}
for signature in pid_data.keys():
#Generate the functions
to_trck_func = eval(pid_data[signature]['trck'])
to_cscd_func = eval(pid_data[signature]['cscd'])
#Make maps from the functions evaluate at the bin centers
_,to_trck_map = np.meshgrid(czcen, to_trck_func(ecen))
_,to_cscd_map = np.meshgrid(czcen, to_cscd_func(ecen))
self.pid_maps[signature] = {'trck':to_trck_map,
'cscd':to_cscd_map}
def get_median_energy(flux_map):
"""Returns the median energy of the flux_map-expected to be a dict
with keys 'map', 'ebins', 'czbins'
"""
ecen = get_bin_centers(flux_map['ebins'])
energy = ecen[len(ecen) / 2]
return energy
def get_median_energy(flux_map):
"""Returns the median energy of the flux_map-expected to be a dict
with keys 'map', 'ebins', 'czbins'
"""
ecen = get_bin_centers(flux_map['ebins'])
energy = ecen[len(ecen)/2]
return energy
def get_osc_probLT_dict(self,theta12,theta13,theta23,deltam21,deltam31,deltacp,
eminLT = 1.0, emaxLT =80.0, nebinsLT=500,
czminLT=-1.0, czmaxLT= 1.0, nczbinsLT=500):
'''
This will create the oscillation probability map lookup tables
(LT) corresponding to atmospheric neutrinos oscillation
through the earth, and will return a dictionary of maps:
{'nue_maps':[to_nue_map, to_numu_map, to_nutau_map],
'numu_maps: [...],
'nue_bar_maps': [...],
'numu_bar_maps': [...],
'czbins':czbins,
'ebins': ebins}
Uses the BargerPropagator code to calculate the individual
probabilities on the fly.
NOTE: Expects all angles to be in [rad], and all deltam to be in [eV^2]
'''
# First initialize all empty maps to use in osc_prob_dict
ebins = np.logspace(np.log10(eminLT),np.log10(emaxLT),nebinsLT+1)
czbins = np.linspace(czminLT,czmaxLT,nczbinsLT+1)
ecen = get_bin_centers(ebins)
czcen = get_bin_centers(czbins)
osc_prob_dict = {'ebins':ebins, 'czbins':czbins}
shape = (len(ecen),len(czcen))
for nu in ['nue_maps','numu_maps','nue_bar_maps','numu_bar_maps']:
if 'bar' in nu:
osc_prob_dict[nu] = {'nue_bar': np.zeros(shape,dtype=np.float32),
'numu_bar': np.zeros(shape,dtype=np.float32),
'nutau_bar': np.zeros(shape,dtype=np.float32)}
else:
osc_prob_dict[nu] = {'nue': np.zeros(shape,dtype=np.float32),
'numu': np.zeros(shape,dtype=np.float32),
'nutau': np.zeros(shape,dtype=np.float32)}
self.fill_osc_prob(osc_prob_dict, ecen, czcen,
theta12=theta12, theta13=theta13, theta23=theta23,
deltam21=deltam21, deltam31=deltam31, deltacp=deltacp)
return osc_prob_dict
def get_osc_probLT_dict(self, ebins=None, czbins=None,
oversample_e=None,oversample_cz=None, **kwargs):
"""
This will create the oscillation probability map lookup tables
(LT) corresponding to atmospheric neutrinos oscillation
through the earth, and will return a dictionary of maps:
{'nue_maps':[to_nue_map, to_numu_map, to_nutau_map],
'numu_maps: [...],
'nue_bar_maps': [...],
'numu_bar_maps': [...],
'czbins':czbins,
'ebins': ebins}
Will call fill_osc_prob to calculate the individual
probabilities on the fly.
By default, the standard binning is oversampled by a factor 10.
Alternatively, the oversampling factor can be changed or a fine
binning specified explicitly. In the latter case, the oversampling
factor is ignored.
"""
#First initialize the fine binning if not explicitly given
if not check_fine_binning(ebins, self.ebins):
ebins = oversample_binning(self.ebins, oversample_e)
if not check_fine_binning(czbins, self.czbins):
czbins = oversample_binning(self.czbins, oversample_cz)
ecen = get_bin_centers(ebins)
czcen = get_bin_centers(czbins)
osc_prob_dict = {}
for nu in ['nue_maps','numu_maps','nue_bar_maps','numu_bar_maps']:
isbar = '_bar' if 'bar' in nu else ''
osc_prob_dict[nu] = {'nue'+isbar: [],
'numu'+isbar: [],
'nutau'+isbar: [],}
evals,czvals = self.fill_osc_prob(osc_prob_dict, ecen, czcen, **kwargs)
osc_prob_dict['evals'] = evals
osc_prob_dict['czvals'] = czvals
return osc_prob_dict
def get_flux(self, ebins, czbins, prim):
"""Get the flux in units [m^-2 s^-1] for the given
bin edges in energy and cos(zenith) and the primary."""
#Evaluate the flux at the bin centers
evals = get_bin_centers(ebins)
czvals = get_bin_centers(czbins)
# Get the spline interpolation, which is in
# log(flux) as function of log(E), cos(zenith)
return_table = bisplev(np.log10(evals), czvals, self.spline_dict[prim])
return_table = np.power(10., return_table).T
#Flux is given per sr and GeV, so we need to multiply
#by bin width in both dimensions
#Get the bin size in both dimensions
ebin_sizes = get_bin_sizes(ebins)
czbin_sizes = 2. * np.pi * get_bin_sizes(czbins)
bin_sizes = np.meshgrid(ebin_sizes, czbin_sizes)
return_table *= np.abs(bin_sizes[0] * bin_sizes[1])
return return_table.T
def get_flux(self, ebins, czbins, prim):
'''Get the flux in units [m^-2 s^-1] for the given
bin edges in energy and cos(zenith) and the primary.'''
#Evaluate the flux at the bin centers
evals = get_bin_centers(ebins)
czvals = get_bin_centers(czbins)
# Get the spline interpolation, which is in
# log(flux) as function of log(E), cos(zenith)
return_table = bisplev(np.log10(evals), czvals, self.spline_dict[prim])
return_table = np.power(10., return_table).T
#Flux is given per sr and GeV, so we need to multiply
#by bin width in both dimensions
#Get the bin size in both dimensions
ebin_sizes = get_bin_sizes(ebins)
czbin_sizes = 2.*np.pi*get_bin_sizes(czbins)
bin_sizes = np.meshgrid(ebin_sizes, czbin_sizes)
return_table *= np.abs(bin_sizes[0]*bin_sizes[1])
return return_table.T
def apply_delta_index(flux_maps, delta_index, egy_med):
"""
Applies the spectral index systematic to the flux maps by scaling
each bin with (egy_cen/egy_med)^(-delta_index), preserving the total
integral flux Note that only the numu/numu_bar are scaled, because
the nue_numu_ratio will handle the systematic on the nue flux.
"""
for flav in ['numu','numu_bar']:
ecen = get_bin_centers(flux_maps[flav]['ebins'])
scale = np.power((ecen/egy_med),delta_index)
flux_map = flux_maps[flav]['map']
total_flux = flux_map.sum()
logging.trace("flav: %s, total counts before scale: %f"%(flav,total_flux))
scaled_flux = (flux_map.T*scale).T
scaled_flux *= (total_flux/scaled_flux.sum())
flux_maps[flav]['map'] = scaled_flux
logging.trace("flav: %s, total counts after scale: %f"%
(flav,flux_maps[flav]['map'].sum()))
return flux_maps
def apply_delta_index(flux_maps, delta_index, egy_med):
"""
Applies the spectral index systematic to the flux maps by scaling
each bin with (egy_cen/egy_med)^(-delta_index), preserving the total
integral flux Note that only the numu/numu_bar are scaled, because
the nue_numu_ratio will handle the systematic on the nue flux.
"""
for flav in ['numu', 'numu_bar']:
ecen = get_bin_centers(flux_maps[flav]['ebins'])
scale = np.power((ecen / egy_med), delta_index)
flux_map = flux_maps[flav]['map']
total_flux = flux_map.sum()
logging.trace("flav: %s, total counts before scale: %f" %
(flav, total_flux))
scaled_flux = (flux_map.T * scale).T
scaled_flux *= (total_flux / scaled_flux.sum())
flux_maps[flav]['map'] = scaled_flux
logging.trace("flav: %s, total counts after scale: %f" %
(flav, flux_maps[flav]['map'].sum()))
return flux_maps
def _get_reco_kernels(self, flipback=True,
e_reco_scale=None, cz_reco_scale=None,
**kwargs):
"""
Use the parametrization functions to calculate the actual reco
kernels (i.e. 4D histograms). If flipback==True, the zenith angle
part that goes below the zenith will be mirrored back in.
"""
if all([hasattr(self, 'kernels'), e_reco_scale==1., cz_reco_scale==1.]):
logging.info('Using existing kernels for reconstruction')
return self.kernels
logging.info('Creating parametrized reconstruction kernels')
# get binning information
evals, esizes = get_bin_centers(self.ebins), get_bin_sizes(self.ebins)
czvals, czsizes = get_bin_centers(self.czbins), get_bin_sizes(self.czbins)
czbins = self.czbins
n_e, n_cz = len(evals), len(czvals)
# prepare for folding back at lower edge
if not is_linear(self.czbins):
logging.warn("cos(zenith) bins have different "
"sizes! Unable to fold around edge "
"of histogram, will not do that.")
flipback = False
if flipback:
czvals = np.append(czvals-(self.czbins[-1]-self.czbins[0]),
czvals)
czbins = np.append(czbins[:-1]-(self.czbins[-1]-self.czbins[0]),
czbins)
czsizes = np.append(czsizes, czsizes)
# get properly scaled parametrization, initialize kernels
parametrization = self.apply_reco_scales(e_reco_scale, cz_reco_scale)
kernel_dict = {}
for flavour in parametrization:
kernel_dict[flavour] = {}
for int_type in ['cc', 'nc']:
logging.debug('Calculating parametrized reconstruction kernel for %s %s'
%(flavour, int_type))
# create empty kernel
kernel = np.zeros((n_e, n_cz, n_e, n_cz))
# quick handle to parametrization
e_pars = parametrization[flavour][int_type]['energy']
cz_pars = parametrization[flavour][int_type]['coszen']
# loop over every bin in true (energy, coszen)
for (i, j) in itertools.product(range(n_e), range(n_cz)):
e_kern_cdf = double_gauss(self.ebins,
loc1=e_pars['loc1'][i,j]+evals[i],
width1=e_pars['width1'][i,j],
loc2=e_pars['loc2'][i,j]+evals[i],
width2=e_pars['width2'][i,j],
fraction=e_pars['fraction'][i,j])
e_kern_int = np.sum(e_kern_cdf)
offset = n_cz if flipback else 0
cz_kern_cdf = double_gauss(czbins,
loc1=cz_pars['loc1'][i,j]+czvals[j+offset],
width1=cz_pars['width1'][i,j],
loc2=cz_pars['loc2'][i,j]+czvals[j+offset],
width2=cz_pars['width2'][i,j],
fraction=cz_pars['fraction'][i,j])
cz_kern_int = np.sum(cz_kern_cdf)
if flipback:
# fold back
cz_kern_cdf = cz_kern_cdf[:len(czbins)/2][::-1] + cz_kern_cdf[len(czbins)/2:]
kernel[i,j] = np.outer(e_kern_cdf, cz_kern_cdf)
kernel_dict[flavour][int_type] = copy(kernel)
kernel_dict['ebins'] = self.ebins
kernel_dict['czbins'] = self.czbins
return kernel_dict
class PIDServiceParam(PIDServiceBase):
"""
Creates PID kernels from parametrization functions that are stored
in a JSON dict. numpy is accessible as np, and scipy.stats.
Systematic parameters 'PID_offset' and 'PID_scale' are supported.
"""
def __init__(self, ebins, czbins, **kwargs):
"""
Parameters needed to initialize a PID service with parametrizations:
* ebins: Energy bin edges
* czbins: cos(zenith) bin edges
* pid_paramfile: JSON containing the parametrizations
"""
PIDServiceBase.__init__(self, ebins, czbins, **kwargs)
def get_pid_kernels(self,
pid_paramfile=None,
PID_offset=0.,
PID_scale=1.,
**kwargs):
# load parametrization file
logging.info('Opening PID parametrization file %s' % pid_paramfile)
try:
param_str = from_json(find_resource(pid_paramfile))
except IOError, e:
logging.error("Unable to open PID parametrization file %s" %
pid_paramfile)
logging.error(e)
sys.exit(1)
ecen = get_bin_centers(self.ebins)
czcen = get_bin_centers(self.czbins)
self.pid_kernels = {
'binning': {
'ebins': self.ebins,
'czbins': self.czbins
}
}
for signature in param_str.keys():
#Generate the functions
to_trck_func = eval(param_str[signature]['trck'])
to_cscd_func = eval(param_str[signature]['cscd'])
# Make maps from the functions evaluated at the bin centers
#
# NOTE: np.where() is to catch the low energy nutau events
# that are undefined. Often what happens is that the nutau
# parameterization for trck events will drop below 0.0 at
# low energies, but there are no nutau events at these
# energies anyway, so we just set them to zero (and cscd =
# 1.0) if the condition arises.
to_trck = to_trck_func(ecen - PID_offset)
to_cscd = to_cscd_func(ecen - PID_offset)
to_trck = np.where(to_trck < 0.0, 0.0,
np.where(to_trck > 1.0, 1.0, to_trck))
to_cscd = np.where(to_cscd < 0.0, 0.0,
np.where(to_cscd > 1.0, 1.0, to_cscd))
_, to_trck_map = np.meshgrid(czcen, PID_scale * to_trck)
_, to_cscd_map = np.meshgrid(czcen, PID_scale * to_cscd)
self.pid_kernels[signature] = {
'trck': to_trck_map,
'cscd': to_cscd_map
}
return self.pid_kernels
# Set to false, since we are using sin^2(2 theta) variables
kSquared = False
sin2th12Sq = np.sin(2.0*args.theta12)**2
sin2th13Sq = np.sin(2.0*args.theta13)**2
sin2th23Sq = np.sin(2.0*args.theta23)**2
neutrinos = ['nue','numu','nutau']
anti_neutrinos = ['nue_bar','numu_bar','nutau_bar']
nu_barger = {'nue':1,'numu':2,'nutau':3,
'nue_bar':1,'numu_bar':2,'nutau_bar':3}
# Initialize dictionary to hold the osc prob maps
osc_prob_dict = {'ebins':ebins, 'czbins':czbins}
ecen = get_bin_centers(ebins)
czcen = get_bin_centers(czbins)
shape = (len(ebins),len(czbins))
for nu in ['nue_maps','numu_maps','nue_bar_maps','numu_bar_maps']:
isbar = '_bar' if 'bar' in nu else ''
osc_prob_dict[nu] = {'nue'+isbar: np.zeros(shape,dtype=np.float32),
'numu'+isbar: np.zeros(shape,dtype=np.float32),
'nutau'+isbar: np.zeros(shape,dtype=np.float32)}
logging.info("Getting oscillation probability maps...")
total_bins = int(len(ebins)*len(czbins))
mod = total_bins/50
ibin = 0
for icz, coszen in enumerate(czcen):
for ie,energy in enumerate(ecen):
def _get_reco_kernels(self,
flipback=True,
e_reco_scale=None,
cz_reco_scale=None,
**kwargs):
"""
Use the parametrization functions to calculate the actual reco
kernels (i.e. 4D histograms). If flipback==True, the zenith angle
part that goes below the zenith will be mirrored back in.
"""
if all([
hasattr(self, 'kernels'), e_reco_scale == 1.,
cz_reco_scale == 1.
]):
logging.info('Using existing kernels for reconstruction')
return self.kernels
logging.info('Creating parametrized reconstruction kernels')
# get binning information
evals, esizes = get_bin_centers(self.ebins), get_bin_sizes(self.ebins)
czvals, czsizes = get_bin_centers(self.czbins), get_bin_sizes(
self.czbins)
czbins = self.czbins
n_e, n_cz = len(evals), len(czvals)
# prepare for folding back at lower edge
if not is_linear(self.czbins):
logging.warn("cos(zenith) bins have different "
"sizes! Unable to fold around edge "
"of histogram, will not do that.")
flipback = False
if flipback:
czvals = np.append(czvals - (self.czbins[-1] - self.czbins[0]),
czvals)
czbins = np.append(
czbins[:-1] - (self.czbins[-1] - self.czbins[0]), czbins)
czsizes = np.append(czsizes, czsizes)
# get properly scaled parametrization, initialize kernels
parametrization = self.apply_reco_scales(e_reco_scale, cz_reco_scale)
kernel_dict = {}
for flavour in parametrization:
kernel_dict[flavour] = {}
for int_type in ['cc', 'nc']:
logging.debug(
'Calculating parametrized reconstruction kernel for %s %s'
% (flavour, int_type))
# create empty kernel
kernel = np.zeros((n_e, n_cz, n_e, n_cz))
# quick handle to parametrization
e_pars = parametrization[flavour][int_type]['energy']
cz_pars = parametrization[flavour][int_type]['coszen']
# loop over every bin in true (energy, coszen)
for (i, j) in itertools.product(range(n_e), range(n_cz)):
e_kern_cdf = double_gauss(
self.ebins,
loc1=e_pars['loc1'][i, j] + evals[i],
width1=e_pars['width1'][i, j],
loc2=e_pars['loc2'][i, j] + evals[i],
width2=e_pars['width2'][i, j],
fraction=e_pars['fraction'][i, j])
e_kern_int = np.sum(e_kern_cdf)
offset = n_cz if flipback else 0
cz_kern_cdf = double_gauss(
czbins,
loc1=cz_pars['loc1'][i, j] + czvals[j + offset],
width1=cz_pars['width1'][i, j],
loc2=cz_pars['loc2'][i, j] + czvals[j + offset],
width2=cz_pars['width2'][i, j],
fraction=cz_pars['fraction'][i, j])
cz_kern_int = np.sum(cz_kern_cdf)
if flipback:
# fold back
cz_kern_cdf = cz_kern_cdf[:len(czbins) /
2][::-1] + cz_kern_cdf[
len(czbins) / 2:]
kernel[i, j] = np.outer(e_kern_cdf, cz_kern_cdf)
kernel_dict[flavour][int_type] = copy(kernel)
kernel_dict['ebins'] = self.ebins
kernel_dict['czbins'] = self.czbins
return kernel_dict
neutrinos = ['nue', 'numu', 'nutau']
anti_neutrinos = ['nue_bar', 'numu_bar', 'nutau_bar']
nu_barger = {
'nue': 1,
'numu': 2,
'nutau': 3,
'nue_bar': 1,
'numu_bar': 2,
'nutau_bar': 3
}
# Initialize dictionary to hold the osc prob maps
osc_prob_dict = {'ebins': ebins, 'czbins': czbins}
ecen = get_bin_centers(ebins)
czcen = get_bin_centers(czbins)
shape = (len(ebins), len(czbins))
for nu in ['nue_maps', 'numu_maps', 'nue_bar_maps', 'numu_bar_maps']:
isbar = '_bar' if 'bar' in nu else ''
osc_prob_dict[nu] = {
'nue' + isbar: np.zeros(shape, dtype=np.float32),
'numu' + isbar: np.zeros(shape, dtype=np.float32),
'nutau' + isbar: np.zeros(shape, dtype=np.float32)
}
logging.info("Getting oscillation probability maps...")
total_bins = int(len(ebins) * len(czbins))
mod = total_bins / 50
ibin = 0
for icz, coszen in enumerate(czcen):
