def CPConvergenceTest():
value = []
error = []
eventFiles = dm.GetFileNames('\Samples') # get filenames
eventFiles_CP = dm.GetFileNames('\Samples_CP')
for i in range(len(eventFiles)):
p = dm.AmpGendf(eventFiles[i], False) # generate particle data
pbar = dm.AmpGendf(eventFiles_CP[i], True) # generate CP particle data
C_T = kin.Scalar_TP(kin.Vector_3(p['p_3']), kin.Vector_3(p['p_4']),
kin.Vector_3(
p['p_1'])) # calcualtes scalar triple product
C_Tbar = -kin.Scalar_TP(
kin.Vector_3(pbar['p_3']), kin.Vector_3(pbar['p_4']),
kin.Vector_3(pbar['p_1'])) # -sign for parity flip
A_T = kin.TP_Amplitude(C_T) # calculate parity asymmetries
A_Tbar = kin.TP_Amplitude(C_Tbar)
A_CP = kin.A_CP(A_T, A_Tbar) # calculate A_CP
value.append(A_CP[0])
error.append(A_CP[1])
pt.ErrorPlot([np.linspace(1, 10, len(eventFiles)), value],
axis=True,
y_error=error,
x_axis="Number of Events ($10^{5}$)",
y_axis="$\mathcal{A}_{CP}$") # plots data
def Seed_test():
fileNames = dm.GetFileNames('\seed_test') # get filenames
events = fileNames[0:5] # split the dataset in half
events_CP = fileNames[5:10] # make this half CP data
value = []
error = []
for i in range(5):
p = dm.AmpGendf(events[i], False) # generate particle data
pbar = dm.AmpGendf(events_CP[i], True)
C_T = kin.Scalar_TP(kin.Vector_3(p['p_3']), kin.Vector_3(p['p_4']),
kin.Vector_3(
p['p_1'])) # calcualtes scalar triple product
C_Tbar = -kin.Scalar_TP(kin.Vector_3(
pbar['p_3']), kin.Vector_3(pbar['p_4']), kin.Vector_3(pbar['p_1']))
A_T = kin.TP_Amplitude(C_T) # calculate parity asymmetries
A_Tbar = kin.TP_Amplitude(C_Tbar)
A_CP = kin.A_CP(A_T, A_Tbar) # calculate A_CP
value.append(A_CP[0])
error.append(A_CP[1])
pt.ErrorPlot([np.linspace(1, 5, 5), value],
axis=True,
y_error=error,
x_axis="Iteration",
y_axis="$\mathcal{A}_{CP}$") # plots data
def Convergence_test2():
direc = "\Samples" # direectory to look for
direc_CP = direc + "_CP" # directory for conjugate events
samples = ["1K", "10K", "100K", "1000K", "10000K"] # folder names
A_Ts = []
A_Tbars = []
A_CPs = []
"""Will go through each folder and will calculate the asymmetry for a given event size.
Does this for each seed and merges the data into one list"""
for i in range(len(samples)):
filenames = dm.GetFileNames(
direc + "\\" +
samples[i]) # get ROOT file for the particular sample
filenames_CP = dm.GetFileNames(direc_CP + "\\" + samples[i] +
"_CP") # .. conjugate sample
C_T = []
C_Tbar = []
"""Opens the jth files for the regular and conjugate sample and computed C_T"""
for j in range(len(filenames)):
p = dm.AmpGendf(filenames[j], False) # gets data from the file
pbar = dm.AmpGendf(filenames_CP[j], True) # gets conjugate data
tmp = kin.Scalar_TP(kin.Vector_3(p["p_3"]), kin.Vector_3(p["p_4"]),
kin.Vector_3(p["p_1"])) # C_T
C_T.append(tmp) # add to the list
tmp = -kin.Scalar_TP(kin.Vector_3(pbar["p_3"]),
kin.Vector_3(pbar["p_4"]),
kin.Vector_3(pbar["p_1"])) # -C_Tbar
C_Tbar.append(tmp)
C_T = np.hstack(C_T) # merges the data from each individual seed
C_Tbar = np.hstack(C_Tbar)
A_T = kin.TP_Amplitude(C_T)
A_Tbar = kin.TP_Amplitude(C_Tbar)
A_CPs.append(kin.A_CP(A_T,
A_Tbar)) # add asymmetries to a list to save
A_Ts.append(A_T)
A_Tbars.append(A_Tbar)
print(i)
np.save("Convergence_test/A_T",
A_Ts) # save calculated data into a file for later use
np.save("Convergence_test/A_Tbar", A_Tbars)
np.save("Convergence_test/A_CP", A_CPs)
def RetrieveData(generator, filename, CP=False):
if generator == 'AmpGen':
particles = dm.AmpGendf(filename, CP)
if generator == 'MINT': # MINT data no longer used
particles = dm.MINTdf()
return particles
