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 plot_flight_angle(d, return_values=False, num_bins=100):
a = rd.calculate_angles(d)
if return_values:
n, bins = np.histogram(a, num_bins)
x_dist = bins[:-1] + (bins[1] - bins[0]) / 2
return x_dist, n
n, bins, patches = plt.hist(a, num_bins)
x_dist = bins[:-1] + (bins[1] - bins[0]) / 2
return x_dist, n
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_flight_angle_sum(remove_fast=True,
return_values=False,
num_bins=50,
lims=(0, 0.7)):
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.calculate_angles(sim_d[i]), 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 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")