def generate_singly_diff_fluxes(n_bins,datafile=config["nu_flux"]):
"""
This is the newest, most streamlined function for generating the singly-differential flux arrays.
It has the same functionality as the old ones, but now it skips the step of filling in the regular flux array before swapping to the deposited energy flux array.
It goes over the different kind of fluxes and builds up a 2D or 3D array, convolving the entries with the relevant differential cross section
the dimensionality depends on whether or we are integrating over the zenith angles
"""
print("Depositing Energy")
data = Data(datafile)
e_min = 10*const.GeV
e_max = 5*const.PeV
extra = 0
all_angles = data.angles
event_edges = np.logspace(np.log10(e_min), np.log10(e_max)+extra,n_bins+1)
e_deposited_edges = np.logspace(np.log10(e_min) , np.log10(e_max),n_bins+1)
angle_edges = np.linspace(min(all_angles), max(all_angles), n_bins+1) # angle is in cos(zenith), so like -1->0
nuflux = {}
errs = {}
for key in data.get_keys(): #flavor, current, interaction
if "Mu_nu" in key:
continue
fluxy, erry = do_for_key(event_edges,e_deposited_edges,key,data=data, angles=angle_edges)
nuflux[key] = fluxy
errs[key] = erry
# if global variable "angle" isn't none, then we can separate out just a single angle
return(event_edges,e_deposited_edges, nuflux, angle_edges, errs)
def downsize(filename, destination):
if os.path.exists(destination):
print("Already done! Skipping {}".format(destination))
return
dataobj = Data(filename)
flux = {}
for key in dataobj.get_keys():
flux[key] = np.zeros(shape=(Ebin, zbin))
for i_e in range(Ebin):
for i_z in range(zbin):
flux[key][i_e][i_z] = dataobj.get_flux( energies[i_e], key=key, angle=zeniths[i_z])
# okay now we pickle
all_data = {}
all_data["e_true"] = energies
all_data["a_true"] = zeniths
all_data["flux"] = flux
f = open(destination, 'wb')
pickle.dump(all_data, f, -1)
f.close()
print("Saved {}".format(destination))
0.0, 1.3)
null_dat = Data(null_pt, 4)
sterile_dat = Data(sterile_pt, 4)
n_bin = 100
_ang_range = (min(null_dat.angles), max(null_dat.angles))
_eng_range = (min(null_dat.energies), max(null_dat.energies))
angles = np.linspace(_ang_range[0], _ang_range[1], n_bin + 1)
energies = np.logspace(log10(_eng_range[0]), log10(_eng_range[1]), n_bin)
#angles = null_dat._angles
#energies = np.array(null_dat._energies)
key_list = null_dat.get_keys()
#null_flux = np.zeros(shape=np.shape(null_dat._fluxes[key_list[0]]))
#sterile_flux = np.zeros(shape=np.shape(sterile_dat._fluxes[key_list[0]]))
null_flux = np.zeros(shape=(n_bin, n_bin + 1))
sterile_flux = np.zeros(shape=(n_bin, n_bin + 1))
for key in key_list:
for i in range(len(energies)):
for j in range(len(angles)):
energy = energies[i]
angle = angles[j]
# null_flux[i][j] += null_dat._fluxes[key][i][j]