def optimize_ToF_parameters(forward=True, num_bins=100, data=None):
d = rd.read_h5py_file()
d = data if not type(data) == type(None) else d
d = rd.slice_selection(d, 17, 3.5e-9, 9e-9)
if forward:
df = rd.cut_selection(d, 9, 0)
tf = rd.extract_parameter(df, 17)
n, bins, patches = plt.hist(tf, num_bins)
x = bins
x_data = bins[:-1] + (bins[1] - bins[0]) / 2
y_data = n
popt, pcov = curve_fit(forward_ToF, x_data, y_data,
(0.4e-9, 150, 1e-13))
y = np.array([forward_ToF(i, popt[0], popt[1], popt[2]) for i in x])
plt.plot(x, y, lw=2.0)
else:
db = rd.cut_selection(d, 9, 0, False)
tb = rd.extract_parameter(db, 17)
n, bins, patches = plt.hist(tb, num_bins)
x = bins
x_data = bins[:-1] + (bins[1] - bins[0]) / 2
y_data = n
popt, pcov = curve_fit(backward_ToF, x_data, y_data,
(0.4e-9, 150, 1e-13))
y = np.array([backward_ToF(i, popt[0], popt[1], popt[2]) for i in x])
plt.plot(x, y, lw=2.0)
# plt.xlabel("Time of Flight [s]")
# plt.ylabel("Frequency [Counts/Bin]")
return popt, pcov
def plot_AoI_sum(remove_fast=True,
return_values=False,
num_bins=50,
lims=(-0.5, 0.5)):
sim_d, sim_t = (raw_sim_data,
sim_times) if not remove_fast else (raw_sim_data[:-1],
sim_times[:-1])
x = np.linspace(lims[0], lims[1], num_bins)
x_dist = x[:-1] + (x[1] - x[0]) / 2
n = [np.zeros(len(x) - 1) for i in range(len(sim_d))]
for i in range(len(n)):
d = sim_d[i]
E_e = rd.extract_parameter(d, 3)
E_g = rd.extract_parameter(d, 1)
c = 2.998e8
m = 511 / c**2
a = np.arccos(1 - (E_e / E_g * m * c**2 / (E_e + E_g)))
tra_a = np.arccos(
-(-d[:, 9] + d[:, 15]) / d[:, 24]) * np.sign(d[:, 11] - d[:, 5])
or_a = np.zeros(len(tra_a))
for j in range(len(or_a)):
if abs(tra_a[j] - a[j]) < abs(tra_a[j] + a[j]):
or_a[j] = tra_a[j] - a[j]
else:
or_a[j] = tra_a[j] + a[j]
n[i] = (np.histogram(or_a, bins=x) / sim_times[i] * np.min(sim_t))[0]
ns = np.zeros(len(n[0]))
for i in n:
ns += i
if return_values:
return x, n
n2, bins, patches = plt.hist(x_dist, bins=x, weights=ns)
return x, n2
def plot_ET(d, return_values=False, num_bins=100):
E = rd.extract_parameter(d, 1) / 1000 + rd.extract_parameter(d, 3) / 1000
logbins = np.geomspace(0.4, 40, num_bins)
if return_values:
n, bins = np.histogram(E, bins=logbins)
x_dist = np.zeros(len(logbins) - 1)
for i in range(len(x_dist)):
x_dist[i] = np.exp((np.log(bins[i + 1]) + np.log(bins[i])) / 2)
return x_dist, n
n, bins, patches = plt.hist(E, bins=logbins)
plt.xscale("log")
x_dist = np.zeros(len(logbins) - 1)
for i in range(len(x_dist)):
x_dist[i] = np.exp((np.log(bins[i + 1]) + np.log(bins[i])) / 2)
return x_dist, n
def plot_ToF(d, return_values=False, num_bins=100):
t = rd.extract_parameter(d, 17) * 1e9
if return_values:
n, bins = np.histogram(t, num_bins)
x_dist = bins[:-1] + (bins[1] - bins[0]) / 2
return x_dist, n
n, bins, patches = plt.hist(t, num_bins)
x_dist = bins[:-1] + (bins[1] - bins[0]) / 2
return x_dist, n
def SAD_E(forward, num_bins=100, data_array=None):
d = rd.read_h5py_file() if type(data_array) == type(None) else data_array
d = rd.cut_selection(d, 9, 0, forward)
E_e = rd.extract_parameter(d, 3)
E_g = rd.extract_parameter(d, 1)
c = 2.998e8
m = 511 / c**2
a = np.arccos(1 - (E_e / E_g * m * c**2 / (E_e + E_g)))
n, bins, patches = plt.hist(a, num_bins)
if forward:
D = pd.NF
x_min = at.fsad_min
x_max = at.fsad_max
else:
D = pd.NB
x_min = at.bsad_min
x_max = at.bsad_max
D = D * np.amax(n) / np.amax(D)
rd.plot_points(D, x_min, x_max, 2.0)
def plot_ET_sum(remove_fast=True,
return_values=False,
num_bins=50,
lims=(0.4, 40)):
sim_d, sim_t = (raw_sim_data,
sim_times) if not remove_fast else (raw_sim_data[:-1],
sim_times[:-1])
x = np.geomspace(lims[0], lims[1], num_bins)
n = [np.zeros(len(x) - 1) for i in range(len(sim_d))]
for i in range(len(n)):
d = sim_d[i]
d = rd.extract_parameter(d, 1) / 1000 + rd.extract_parameter(d,
3) / 1000
n[i] = (np.histogram(d, bins=x)[0] / sim_times[i] * np.min(sim_t))
ns = np.zeros(len(n[0]))
for i in n:
ns += i
if return_values:
return x, n
n2, bins, patches = plt.hist(x[:-1], bins=x, weights=ns)
plt.xscale("log")
return x, n2
def create_AoI_plot(d, num_bins=200, rang=0.5, ploto=True):
E_e = rd.extract_parameter(d, 3)
E_g = rd.extract_parameter(d, 1)
c = 2.998e8
m = 511 / c**2
a = np.arccos(1 - (E_e / E_g * m * c**2 / (E_e + E_g)))
tra_a = np.arccos(
-(-d[:, 9] + d[:, 15]) / d[:, 24]) * np.sign(d[:, 11] - d[:, 5])
or_a = np.zeros(len(tra_a))
for i in range(len(or_a)):
if abs(tra_a[i] - a[i]) < abs(tra_a[i] + a[i]):
or_a[i] = tra_a[i] - a[i]
else:
or_a[i] = tra_a[i] + a[i]
or_a = or_a[or_a[:] > -rang]
or_a = or_a[or_a[:] < rang]
if not ploto:
n, bins = np.histogram(or_a, num_bins)
else:
n, bins, patches = plt.hist(or_a, num_bins)
bins = bins[:-1] + (bins[1] - bins[0]) / 2
return n, bins
def plot_AoI(d, return_values=False, num_bins=100):
E_e = rd.extract_parameter(d, 3)
E_g = rd.extract_parameter(d, 1)
c = 2.998e8
m = 511 / c**2
a = np.arccos(1 - (E_e / E_g * m * c**2 / (E_e + E_g)))
tra_a = np.arccos(
-(-d[:, 9] + d[:, 15]) / d[:, 24]) * np.sign(d[:, 11] - d[:, 5])
or_a = np.zeros(len(tra_a))
for i in range(len(or_a)):
if abs(tra_a[i] - a[i]) < abs(tra_a[i] + a[i]):
or_a[i] = tra_a[i] - a[i]
else:
or_a[i] = tra_a[i] + a[i]
or_a = or_a[or_a[:] > -0.5]
or_a = or_a[or_a[:] < 0.5]
if return_values:
n, bins = np.histogram(or_a, num_bins)
x_dist = bins[:-1] + (bins[1] - bins[0]) / 2
return x_dist, n
n, bins, patches = plt.hist(or_a, num_bins)
x_dist = bins[:-1] + (bins[1] - bins[0]) / 2
return x_dist, n
def SAD_E_vs_Pos(forward, size=100, data_array=None):
d = rd.read_h5py_file() if type(data_array) == None else data_array
d = rd.cut_selection(d, 9, 0, forward)
E_e = rd.extract_parameter(d, 3)
E_g = rd.extract_parameter(d, 1)
c = 2.998e8
m = 511 / c**2
a_E = np.arccos(1 - (E_e / E_g * m * c**2 / (E_e + E_g)))
a_P = rd.calculate_angles(d)
c = 0
while c < len(a_E):
if np.isnan(a_E[c]):
a_E = np.delete(a_E, c)
a_P = np.delete(a_P, c)
c += -1
c += 1
plt.hist2d(a_E, a_P, bins=(size, size))
if forward:
x = np.linspace(at.fsad_min, at.fsad_max, size)
plt.plot(x, x, color="C1")
else:
x = np.linspace(at.bsad_min, at.bsad_max, size)
plt.plot(x, x, color="C1")
def plot_E_dist(
forward,
num_bins=300,
):
x_min = 0000
x_max = 5000
y_min = 0
#y_max=140000
scale1 = 1.8
scale2 = 1
d = rd.read_h5py_file()
d = rd.cut_selection(d, 9, 0, forward)
E = rd.extract_parameter(d, 1) + rd.extract_parameter(d, 3)
n, bins, patches = plt.hist(E, num_bins)
x = np.linspace(x_min, x_max, 100)
y = np.array([at.energy_distribution(i) for i in x])
y = y * np.amax(n) / np.amax(y) * scale1
plt.plot(x, y, label="A")
if forward:
t_min, t_max, sad = at.fsad_min, at.fsad_max, at.fsad
else:
t_min, t_max, sad = at.bsad_min, at.bsad_max, at.bsad
y2 = np.array([
quad(integrand1, t_min, t_max, (i, forward, t_min, t_max, sad), 0,
10e-5, 10e-5)[0] for i in x
])
y2 = y2 * np.amax(n) / np.amax(y2) * scale2
plt.plot(x, y2, label="B")
plt.xlabel("Total Detected Energy [keV]")
plt.ylabel("Frequency [Counts/Bin]")
plt.legend()
plt.xlim(x_min, x_max)
plt.ylim(0, 50000)
def plot_E_all():
scale1 = 1.7
x_min = 0
x_max = 6000
d = rd.read_h5py_file()
d = rd.cut_selection(d, 9, 0, True)
E = rd.extract_parameter(d, 1) # + rd.extract_parameter(d, 3)
n, bins, patches = plt.hist(E, 500)
x = np.linspace(x_min, x_max, 200)
y = np.array([at.energy_distribution(i) for i in x])
y = y * np.amax(n) / np.amax(y) * scale1
#plt.plot(x,y,label="A")
plt.xlim(x_min, x_max)
plt.xlabel("Detected Energy [keV]")
plt.ylabel("Frequency [Counts/Bin]")
def plot_ToF_sum(remove_fast=True,
return_values=False,
num_bins=50,
lims=(0, 8)):
sim_d, sim_t = (raw_sim_data,
sim_times) if not remove_fast else (raw_sim_data[:-1],
sim_times[:-1])
x = np.linspace(lims[0], lims[1], num_bins)
x_dist = x[:-1] + (x[1] - x[0]) / 2
n = [np.zeros(len(x) - 1) for i in range(len(sim_d))]
for i in range(len(n)):
n[i] = (
np.histogram(rd.extract_parameter(sim_d[i], 17) * 1e9, bins=x)[0] /
sim_times[i] * np.min(sim_t))
ns = np.zeros(len(n[0]))
for i in n:
ns += i
if return_values:
return x, n
n2, bins, patches = plt.hist(x_dist, bins=x, weights=ns)
return x, n2
def E1_E2_2D_hist():
fig=plt.figure(figsize=(8,12))
grid=plt.GridSpec(4, 2,hspace=0.15,wspace=0.30)
pa=fig.add_subplot(grid[:,:])
pa.spines['top'].set_color('none')
pa.spines['bottom'].set_color('none')
pa.spines['left'].set_color('none')
pa.spines['right'].set_color('none')
plt.tick_params(axis='both', which='both', bottom=False, top=False, labelbottom=False, right=False, left=False, labelleft=False)
pa.set_xlabel("E1 [MeV]",labelpad=30)
pa.set_ylabel("E2 [MeV]",labelpad=40)
rows={0:"1500",1:"5000",2:"15000",3:"50000"}
energies={0:"1.5",1:"5",2:"15",3:"50"}
columns={0:True,1:False}
letters=np.array([["a","b"],
["c","d"],
["e","f"],
["g","h"]])
num_bins=100
for row in range(len(rows)):
for column in range(len(columns)):
temp=fig.add_subplot(grid[row,column])
d=rd.read_h5py_file(p_data+"Thesis_Simulations/"+"Mono_"+rows[row]+"/","Mono_"+rows[row]+"_h5")
d=rd.cut_selection(d,9,0,columns[column])
if columns[column]:
E1=rd.extract_parameter(d, 3)/1000
E2=rd.extract_parameter(d, 1)/1000
else:
E1=rd.extract_parameter(d, 1)/1000
E2=rd.extract_parameter(d, 3)/1000
logbins1 = np.geomspace(E1.min(), E1.max(), num_bins)
logbins2 = np.geomspace(E2.min(), E2.max(), num_bins)
counts, _, _ = np.histogram2d(E1, E2, bins=(logbins1, logbins2))
temp.pcolormesh(logbins1, logbins2, counts.T)
temp.plot()
temp.set_xscale('log')
temp.set_yscale('log')
temp.plot(logbins1,float(energies[row])-logbins1,color="C1",lw=1.0)
plt.ylim(logbins2[0],logbins2[-1])
if column==1:
if row==1:
plt.ylim(1.5,logbins2[-1])
elif row==2:
plt.ylim(5,logbins2[-1])
elif row==3:
plt.ylim(10,logbins2[-1])
if row==0:
if column==0:
temp.set_xlabel("Forward Photons")
temp.xaxis.set_label_position("top")
elif column==1:
temp.set_xlabel("Backward Photons")
temp.xaxis.set_label_position("top")
if column==1:
temp.set_ylabel(energies[row]+"MeV")
temp.yaxis.set_label_position("right")
if column==0:
angs=(1,10,20,30,40,50)
ang_lines=np.zeros((len(angs),num_bins))
for ang in range(len(angs)):
for b in range(num_bins):
e_sca=logbins2[b]
wl_sca=6.625e-34*3e8/(1.602e-19*float(e_sca)*1e6)
wl_ini=wl_sca-2.426e-12*(1-np.cos(angs[ang]*np.pi/180))
e_ini=6.625e-34*3e8/(1.602e-19*wl_ini)*1e-6
ang_lines[ang,b]=e_ini-e_sca if e_ini-e_sca>0 else float("NaN")
for ang in range(len(angs)):
temp.plot(ang_lines[ang],logbins2,color="C6",lw=1)
for b in range(num_bins):
if ang_lines[ang,b]>E1.min():
plt.text(ang_lines[ang,b],logbins2[b],str(angs[ang])+"$^\circ$",color="bisque")
break
else:
mult=10
logbinse=np.geomspace(E1.min(), E1.max(), num_bins*mult)
angs=(180,140)
ang_lines=np.zeros((len(angs),num_bins*mult))
for ang in range(len(angs)):
for b in range(num_bins*mult):
e_sca=logbinse[b]
wl_sca=6.625e-34*3e8/(1.602e-19*float(e_sca)*1e6)
wl_ini=wl_sca-2.426e-12*(1-np.cos(angs[ang]*np.pi/180))
e_ini=6.625e-34*3e8/(1.602e-19*wl_ini)*1e-6
ang_lines[ang,b]=e_ini-e_sca if e_ini-e_sca>0 else float("NaN")
left_shift=((3/4,1),
(2/3,1),
(3/5,1),
(1/2,1))
for ang in range(len(angs)):
temp.plot(logbinse,ang_lines[ang],color="C6",lw=1)
for b in range(num_bins*mult):
if ang_lines[ang,b]>E2.min():
plt.text(logbinse[b]*left_shift[row][ang],ang_lines[ang,b],str(angs[ang])+"$^\circ$",color="bisque")
break
plt.xlim(E1.min(),np.amax(E1))
plt.ylim(E2.min(),np.amax(E2))
temp.text(-0.10,0.97,"("+letters[row,column]+")",transform=temp.transAxes,ha='left', va='center')
plt.savefig(p_plots+'E1_E2_2D_hist.pdf',bbox_inches='tight')
def plot_E1_E2(d,
plot_space,
num_bins=100,
label_pos_left=True,
E1_min=0.04,
E2_min=0.4,
E_max=20):
E1 = rd.extract_parameter(d, 3) / 1000
E2 = rd.extract_parameter(d, 1) / 1000
logbins1 = np.geomspace(E1_min, E_max, num_bins)
logbins2 = np.geomspace(E2_min, E_max, num_bins)
counts, _, _ = np.histogram2d(E1, E2, bins=(logbins1, logbins2))
pcmap = plot_space.pcolormesh(logbins1, logbins2, counts.T)
plot_space.plot()
plot_space.set_xscale('log')
plot_space.set_yscale('log')
mult = 10
angs = (1, 10, 20, 30, 40, 50)
ang_lines = np.zeros((len(angs), num_bins * mult))
logbins1a = np.geomspace(E1_min, E_max, num_bins * mult)
logbins2a = np.geomspace(E2_min, E_max, num_bins * mult)
for ang in range(len(angs)):
for b in range(num_bins * mult):
e_sca = logbins2a[b]
wl_sca = 6.625e-34 * 3e8 / (1.602e-19 * float(e_sca) * 1e6)
wl_ini = wl_sca - 2.426e-12 * (1 - np.cos(angs[ang] * np.pi / 180))
e_ini = 6.625e-34 * 3e8 / (1.602e-19 * wl_ini) * 1e-6
ang_lines[ang,
b] = e_ini - e_sca if e_ini - e_sca > 0 else float("NaN")
if label_pos_left:
for ang in range(len(angs)):
plot_space.plot(ang_lines[ang], logbins2a, color="C6", lw=1)
for b in range(num_bins * mult):
if ang_lines[ang, b] > E1.min():
plt.text(ang_lines[ang, b],
logbins2a[b],
str(angs[ang]) + "$^\circ$",
color="bisque")
break
else:
for ang in range(len(angs)):
plot_space.plot(ang_lines[ang], logbins2a, color="C6", lw=1)
for b in range(num_bins * mult):
if ang_lines[ang, b] > 14 or logbins2a[b] > E_max:
plt.text(ang_lines[ang, b - 1],
logbins2a[b - 1] / 1.15,
str(angs[ang]) + "$^\circ$",
color="bisque")
break
equi_ener = [0.75, 1.5, 3, 6, 12]
for ene in equi_ener:
plot_space.plot(ene - logbins2a, logbins2a, color="C1", lw=1.0)
plt.text(ene - E2_min,
E2_min * 1.05,
str(ene) + "MeV",
color="lightyellow")
plt.xlim(E1.min(), np.amax(E1))
plt.ylim(E2.min(), np.amax(E2))
return pcmap
