def load_od():
od = np.vstack(
(rd.read_h5py_file(path, data_le), rd.read_h5py_file(path, data_he)))
od_psd = rd.slice_selection(od, 21, 60, 90)
od_all = rd.slice_selection(od_psd, 3, 70, 20000)
od_all = rd.slice_selection(od_all, 1, 650, 30000)
od_all = rd.slice_selection(od_all, 17, 4e-9, 7e-9)
c = 2.998e8
m = 511 / c**2
od_all = od_all[abs(20 * np.pi / 180 - abs(
np.arccos(1 - (od_all[:, 3] / od_all[:, 1] * m * c**2 /
(od_all[:, 3] + od_all[:, 1])))) <= 16 * np.pi / 180)]
od_all = od_all[od_all[:, 22] > 0]
od_all = od_all[od_all[:, 23] > 0]
return od, od_psd, od_all
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 SAD_CROSS_GOOD(forward, num_bins=50):
d = rd.read_h5py_file()
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)
# a=np.array([])
# for i in range(len(a_E)):
# if abs(a_E[i]-a_P[i])<0.01:
# a=np.append(a,a_P[i])
margin = 0.01
d = d[abs(
np.arccos(1 - (d[:, 3] / d[:, 1] * m * c**2 / (d[:, 3] + d[:, 1]))) -
np.arccos(-1 * (-d[:, 9] + d[:, 15]) / d[:, 24])) <= margin]
a = rd.calculate_angles(d)
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)
plt.xlabel("Scattering Angle [rad]")
plt.ylabel("Frequency [Counts/Bins]")
def calculate_data_rates():
data = np.array([
rd.cut_selection(rd.read_h5py_file(p_cra0, h5_cra0), 9, 0, True),
rd.cut_selection(rd.read_h5py_file(p_cos, h5_cos), 9, 0, True),
rd.cut_selection(rd.read_h5py_file(p_alb, h5_alb), 9, 0, True),
rd.cut_selection(rd.read_h5py_file(p_act, h5_act), 9, 0, True),
rd.cut_selection(rd.read_h5py_file(p_act, h5_act_f), 9, 0, True)
])
datat = np.array([
rd.read_h5py_file(p_cra0, h5_cra0),
rd.read_h5py_file(p_cos, h5_cos),
rd.read_h5py_file(p_alb, h5_alb),
rd.read_h5py_file(p_act, h5_act),
rd.read_h5py_file(p_act, h5_act_f)
])
for i in range(len(data)):
E = data[i].shape[0]
ET = datat[i].shape[0]
print(E, E / ET, E / sim_times[i], sim_times[i])
def werner_cut_comparison():
fig=plt.figure(figsize=(10,10))
cut1_plot=mpimg.imread(p_werner_cuts+"Cut8.jpg")
plt.imshow(cut1_plot)
d=rd.read_h5py_file(p_data,"Thesis_Simulations\\Real_COMPTEL_data"+"\\h5_vp426_09-43")
dc=rd.read_h5py_file(p_data,"Thesis_Simulations\\Real_COMPTEL_data"+"\\h5_vp426_09-43_c")
E_e=dc[:,3]
E_g=dc[:,1]
c=2.998e8
m=511/c**2
phibar=np.arccos( 1 - (E_e/E_g*m*c**2/(E_e+E_g)) )
phigeo=np.arccos( -( -dc[:,9]+dc[:,15] ) / dc[:,24])
ARM=phibar-phigeo
for i in range(20):
print(i)
print("Phibar:", phibar[i]*180/np.pi,d[i,5])
print("ARM:",ARM[i]*180/np.pi,d[i,14])
def plot_ang_dist(forward, num_bins=100, data_array=None):
d = rd.read_h5py_file()
d = rd.cut_selection(d, 9, 0, forward)
a = rd.calculate_angles(d)
a = data_array if not type(data_array) == type(None) else a
n, bins, patches = plt.hist(a, num_bins)
if forward:
D = NF
x_min = at.fsad_min
x_max = at.fsad_max
else:
D = 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_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 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 events_per_cell(forward=True, D1=True, text=True):
d = rd.read_h5py_file()
if forward:
d = rd.cut_selection(d, 9, 0)
if D1:
N = Nfor1
par1 = 5
par2 = 7
points = rd.D1_points
else:
N = Nfor2
par1 = 11
par2 = 13
points = rd.D2_points
else:
d = rd.cut_selection(d, 9, 0, False)
if D1:
N = Nba1
par1 = 11
par2 = 13
points = rd.D1_points
else:
N = Nba2
par1 = 5
par2 = 7
points = rd.D2_points
rd.plot_histogram_2D(d, par1, par2)
s = 0
for i in N:
s += i
N = N * len(d) / s
if text:
for i in range(len(points)):
plt.text(points[i, 0],
points[i, 1],
"M=" + str(len(rd.select_cell(d, i, forward, D1))) +
"\n E=" + str(int(N[i])),
ha="center",
va="center",
bbox=dict(facecolor='white', edgecolor='none'))
def plot_histogram_and_optimized_distribution(num_bins=100, forward=True):
d = rd.read_h5py_file()
d = rd.cut_selection(d, 9, 0, forward)
data_array = rd.calculate_angles(d)
if forward:
distribution_points, x_min, x_max = fsad, fsad_min, fsad_max
else:
distribution_points, x_min, x_max = bsad, bsad_min, bsad_max
n, bins = plot_histogram_and_distribution(data_array, distribution_points,
x_min, x_max, num_bins)
x_data = bins[:-1] + (bins[1] - bins[0]) / 2
y_data = n
if forward:
popt, pcov = curve_fit(fsad_correction, x_data, y_data)
#plt.plot(x_data,fsad_correction(x_data,*popt),color="C8",lw=3.0)
else:
popt, pcov = curve_fit(bsad_correction, x_data, y_data)
#plt.plot(x_data,bsad_correction(x_data,*popt),color="C8",lw=3.0)
#plt.xlabel("Scattering Angle [rad]")
#plt.ylabel("Frequency [Counts/Bin]")
return popt, pcov
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 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 crab_outlier_distributions():
fig=plt.figure(figsize=(10,13))
grid1=plt.GridSpec(1, 3,hspace=0.2,wspace=0.25,bottom=0.73)
grid2=plt.GridSpec(3,3,hspace=0.05,wspace=0.05,top=0.65)
d=rd.read_h5py_file(p_data+"Thesis_Simulations/Simulation_Batch_3/","Simulation1")
d=rd.cut_selection(d,9,0,True)
c=2.998e8
m=511/c**2
rang=0.08
dc=d[np.abs( np.minimum( np.abs(np.arccos( -( -d[:,9]+d[:,15] ) / d[:,24])*np.sign(d[:,11]-d[:,5]) - np.arccos( 1 - (d[:,3]/d[:,1]*m*c**2/(d[:,3]+d[:,1])) ) ) ,
np.abs(np.arccos( -( -d[:,9]+d[:,15] ) / d[:,24])*np.sign(d[:,11]-d[:,5]) + np.arccos( 1 - (d[:,3]/d[:,1]*m*c**2/(d[:,3]+d[:,1])) ) )
))>rang]
temp=fig.add_subplot(grid1[0,0])
n1,bins1=pc.create_ARM_plot(dc,200,1,True)
n2,bins2=pc.create_ARM_plot(d,200,1,False)
temp.axhline(0,c="black",lw=0.8)
plt.plot(bins1,n2,c="C2")
plt.plot(bins1,n1-n2,c="C3")
temp.set_xlabel("Angle of Incidence [rad]")
temp.set_ylabel("Frequency [Counts/Bin]")
temp.text(0.01,0.99,"(a)",transform=temp.transAxes,ha='left', va='top')
temp=fig.add_subplot(grid1[0,1])
bins1,n1=pc.plot_flight_angle(dc,False,50)
bins2,n2=pc.plot_flight_angle(d,True,50)
n2=n2/np.sum(n2)*np.sum(n1)
temp.axhline(0,c="black",lw=0.8)
plt.plot(bins1,n2,c="C2")
plt.plot(bins1,n1-n2,c="C3")
temp.set_xlabel("Flight Angle [rad]")
secax=temp.secondary_xaxis("top",functions=(lambda d:d*180/np.pi,lambda r:r*np.pi/180))
secax.set_xlabel("Flight Angle [degrees]")
temp.text(0.01,0.99,"(b)",transform=temp.transAxes,ha='left', va='top')
temp=fig.add_subplot(grid1[0,2])
dct=dc[np.logical_and(dc[:,17]>4e-9,dc[:,17]<7.5e-9)]
dt=d[np.logical_and(d[:,17]>4e-9, d[:,17]<7.5e-9)]
bins1,n1=pc.plot_ToF(dct,False,50)
bins2,n2=pc.plot_ToF(dt,True,50)
n2=n2/np.sum(n2)*np.sum(n1)
temp.axhline(0,c="black",lw=0.8)
plt.plot(bins1,n2,c="C2")
plt.plot(bins1,n1-n2,c="C3")
temp.set_xlabel("Time of Flight [ns]")
temp.text(0.01,0.99,"(c)",transform=temp.transAxes,ha='left', va='top')
emp=fig.add_subplot(grid2[2,0])
emp.spines['top'].set_color('none')
emp.spines['bottom'].set_color('none')
emp.spines['left'].set_color('none')
emp.spines['right'].set_color('none')
plt.tick_params(axis='both', which='both', bottom=False, top=False, labelbottom=False, right=False, left=False, labelleft=False)
custom_lines = [Line2D([0], [0], color="C0", lw=10),
Line2D([0], [0], color="C2", lw=1.5),
Line2D([0], [0], color="C3", lw=1.5),]
plt.legend(custom_lines, ['Crab after Cut', 'Crab before Cut' , 'Residual'],loc="lower left")
E1_min=0.04
E2_min=0.4
E_max=20
temp=fig.add_subplot(grid2[0:2,0])
bins1,n1=pc.plot_E2_vert(dc,False,50)
bins2,n2=pc.plot_E2_vert(d,True,50)
n2=n2/np.sum(n2)*np.sum(n1)
temp.axvline(0,c="black",lw=0.8)
plt.plot(n2,bins1,c="C2")
plt.plot(n1-n2,bins1,c="C3")
plt.ylim(E2_min,E_max)
temp.set_ylabel("E2 [MeV]")
temp.set_xlabel("Frequency\n[Counts/Bins]")
temp.text(0.01,0.99,"(d)",transform=temp.transAxes,ha='left', va='top')
temp=fig.add_subplot(grid2[2,1:3])
bins1,n1=pc.plot_E1(dc,False,50)
bins2,n2=pc.plot_E1(d,True,50)
n2=n2/np.sum(n2)*np.sum(n1)
temp.axhline(0,c="black",lw=0.8)
plt.plot(bins1,n2,c="C2")
plt.plot(bins1,n1-n2,c="C3")
temp.set_xlabel("E1 [MeV]")
temp.set_ylabel("Frequency\n[Counts/Bins]")
temp.text(0.01,0.99,"(f)",transform=temp.transAxes,ha='left', va='top')
plt.xlim(E1_min,E_max)
temp=fig.add_subplot(grid2[0:2,1:3])
pcmap=pc.plot_E1_E2(d,temp,label_pos_left=False)
plt.colorbar(pcmap,ax=emp,orientation="horizontal",).set_label("Frequency [Counts/Bin]")
plt.xlim(E1_min,E_max)
plt.ylim(E2_min,E_max)
temp.xaxis.tick_top()
temp.yaxis.tick_right()
temp.xaxis.set_label_position("top")
temp.yaxis.set_label_position("right")
temp.set_xlabel("E1 [MeV]")
temp.set_ylabel("E2 [MeV]")
temp.text(0,1,"(e)",transform=temp.transAxes,ha='left', va='top',bbox=dict(facecolor='white', edgecolor='none'))
plt.savefig(p_plots+'Crab_ARM_Cut.pdf',bbox_inches='tight')
def parameter_cut(num_bins=(1, 1, 1, 1, 1), soft=True, w_fast=False, fit=True):
ranges = ((4e-9, 7e-9), (np.log(70), np.log(20000)),
(np.log(650), np.log(30000)), (np.log(900), np.log(30000)),
(0 * np.pi / 180, 40 * np.pi / 180))
od = load_od()[2]
odc = cut_converter(od)
sim_d, sim_t = (raw_sim_data, sim_times) if w_fast else (raw_sim_data[:-1],
sim_times[:-1])
t_min = np.min(sim_t)
S, bins = np.histogramdd(cut_converter(sim_d[0]),
bins=num_bins,
range=ranges)
S = S / sim_t[0] * t_min
if soft:
B = np.zeros(S.shape)
for i in range(1, len(sim_d)):
B += np.histogramdd(cut_converter(sim_d[i]),
bins=num_bins,
range=ranges)[0] / sim_t[i] * t_min
T = S / (S + B)
T = np.nan_to_num(T)
T = T / np.amax(T)
output = np.empty(od.shape)
output[:, :] = np.NaN
for row in range(len(od)):
if (odc[row, 0] < ranges[0][0] or odc[row, 0] >= ranges[0][1]
or odc[row, 1] < ranges[1][0]
or odc[row, 1] >= ranges[1][1]
or odc[row, 2] < ranges[2][0]
or odc[row, 2] >= ranges[2][1]
or odc[row, 3] < ranges[3][0]
or odc[row, 3] >= ranges[3][1]
or odc[row, 4] < ranges[4][0]
or odc[row, 4] >= ranges[4][1]):
#print(odc[row,:])
continue
for i in range(len(bins[0]) - 1):
if odc[row, 0] >= bins[0][i] and odc[row, 0] < bins[0][i + 1]:
p1 = i
for i in range(len(bins[1]) - 1):
if odc[row, 1] >= bins[1][i] and odc[row, 1] < bins[1][i + 1]:
p2 = i
for i in range(len(bins[2]) - 1):
if odc[row, 2] >= bins[2][i] and odc[row, 2] < bins[2][i + 1]:
p3 = i
for i in range(len(bins[3]) - 1):
if odc[row, 3] >= bins[3][i] and odc[row, 3] < bins[3][i + 1]:
p4 = i
for i in range(len(bins[4]) - 1):
if odc[row, 4] >= bins[4][i] and odc[row, 4] < bins[4][i + 1]:
p5 = i
#print(row)
if np.random.rand() <= T[p1, p2, p3, p4, p5]:
output[row, :] = od[row, :]
else:
odm = np.histogramdd(odc, bins=num_bins, range=ranges)[0]
S = S / np.amax(S)
odm = odm / np.amax(odm)
if fit:
a = minimize(rd.fit_func, 1, (odm, S)).x
print(a)
T = S * a
output = np.empty(od.shape)
output[:, :] = np.NaN
for row in range(len(od)):
if (odc[row, 0] < ranges[0][0] or odc[row, 0] >= ranges[0][1]
or odc[row, 1] < ranges[1][0]
or odc[row, 1] >= ranges[1][1]
or odc[row, 2] < ranges[2][0]
or odc[row, 2] >= ranges[2][1]
or odc[row, 3] < ranges[3][0]
or odc[row, 3] >= ranges[3][1]
or odc[row, 4] < ranges[4][0]
or odc[row, 4] >= ranges[4][1]):
#print(odc[row,:])
continue
for i in range(len(bins[0]) - 1):
if odc[row, 0] >= bins[0][i] and odc[row,
0] < bins[0][i + 1]:
p1 = i
for i in range(len(bins[1]) - 1):
if odc[row, 1] >= bins[1][i] and odc[row,
1] < bins[1][i + 1]:
p2 = i
for i in range(len(bins[2]) - 1):
if odc[row, 2] >= bins[2][i] and odc[row,
2] < bins[2][i + 1]:
p3 = i
for i in range(len(bins[3]) - 1):
if odc[row, 3] >= bins[3][i] and odc[row,
3] < bins[3][i + 1]:
p4 = i
for i in range(len(bins[4]) - 1):
if odc[row, 4] >= bins[4][i] and odc[row,
4] < bins[4][i + 1]:
p5 = i
#print(row)
if T[p1, p2, p3, p4,
p5] > odm[p1, p2, p3, p4, p5] or np.random.rand(
) <= T[p1, p2, p3, p4, p5] / odm[p1, p2, p3, p4, p5]:
output[row, :] = od[row, :]
else:
a = np.min(odm / S)
print(a)
T = S * a
output = np.empty(od.shape)
output[:, :] = np.NaN
for row in range(len(od)):
if (odc[row, 0] < ranges[0][0] or odc[row, 0] >= ranges[0][1]
or odc[row, 1] < ranges[1][0]
or odc[row, 1] >= ranges[1][1]
or odc[row, 2] < ranges[2][0]
or odc[row, 2] >= ranges[2][1]
or odc[row, 3] < ranges[3][0]
or odc[row, 3] >= ranges[3][1]
or odc[row, 4] < ranges[4][0]
or odc[row, 4] >= ranges[4][1]):
#print(odc[row,:])
continue
for i in range(len(bins[0]) - 1):
if odc[row, 0] >= bins[0][i] and odc[row,
0] < bins[0][i + 1]:
p1 = i
for i in range(len(bins[1]) - 1):
if odc[row, 1] >= bins[1][i] and odc[row,
1] < bins[1][i + 1]:
p2 = i
for i in range(len(bins[2]) - 1):
if odc[row, 2] >= bins[2][i] and odc[row,
2] < bins[2][i + 1]:
p3 = i
for i in range(len(bins[3]) - 1):
if odc[row, 3] >= bins[3][i] and odc[row,
3] < bins[3][i + 1]:
p4 = i
for i in range(len(bins[4]) - 1):
if odc[row, 4] >= bins[4][i] and odc[row,
4] < bins[4][i + 1]:
p5 = i
#print(row)
if T[p1, p2, p3, p4,
p5] > odm[p1, p2, p3, p4, p5] or np.random.rand(
) <= T[p1, p2, p3, p4, p5] / odm[p1, p2, p3, p4, p5]:
output[row, :] = od[row, :]
d = rd.read_h5py_file()
d = rd.cut_selection(d, 9, 0, True)
x1, n1 = plot_ARM(output)
x2, n2 = plot_ARM(d, True)
n2 = (n1[48] + n1[49] + n1[50] + n1[51]) / (n2[48] + n2[49] + n2[50] +
n2[51]) * n2
plt.plot(x2, n2, label="Crab Simulation", lw=2.0)
x3, n3 = plot_ARM(od, True)
n3 = (n1[48] + n1[49] + n1[50] + n1[51]) / (n3[48] + n3[49] + n3[50] +
n3[51]) * n3
plt.plot(x3, n3, label="Before Cuts", lw=2.0)
od_psd = rd.slice_selection(od, 21, 60, 90)
od_all = rd.slice_selection(od_psd, 3, 70, 20000)
od_all = rd.slice_selection(od_all, 1, 650, 30000)
od_all = rd.slice_selection(od_all, 17, 4e-9, 7e-9)
c = 2.998e8
m = 511 / c**2
od_all = od_all[abs(20 * np.pi / 180 - abs(
np.arccos(1 - (od_all[:, 3] / od_all[:, 1] * m * c**2 /
(od_all[:, 3] + od_all[:, 1])))) <= 16 * np.pi / 180)]
od_all = od_all[od_all[:, 22] > 0]
od_all = od_all[od_all[:, 23] > 0]
return od, od_psd, od_all
raw_sim_data = [
rd.cut_selection(rd.read_h5py_file(p_pre + p_cra0, h5_cra0), 9, 0, True),
rd.cut_selection(rd.read_h5py_file(p_pre + p_cos, h5_cos), 9, 0, True),
rd.cut_selection(rd.read_h5py_file(p_pre + p_alb, h5_alb), 9, 0, True),
rd.cut_selection(rd.read_h5py_file(p_pre + p_act, h5_act), 9, 0, True),
rd.cut_selection(rd.read_h5py_file(p_pre + p_act, h5_act_f), 9, 0, True)
]
sim_times = np.array([t_cra0, t_cos, t_alb, t_act, t_act_f])
parameters = {
0: "Line",
1: "Time",
2: "E1",
3: "E2",
4: "E Total",
5: "Phibar",
