def Coincidences_2D_plot(ce, data_sets, module_order, window):
def plot_2D_bus(fig, sub_title, ce, vmin, vmax, duration):
h, *_ = plt.hist2d(ce.wCh,
ce.gCh,
bins=[80, 40],
range=[[-0.5, 79.5], [79.5, 119.5]],
vmin=vmin,
vmax=vmax,
norm=LogNorm(),
cmap='jet')
xlabel = 'Wire [Channel number]'
ylabel = 'Grid [Channel number]'
fig = stylize(fig, xlabel, ylabel, title=sub_title, colorbar=True)
plt.colorbar()
return fig, h
# Filter clusters
ce = filter_ce_clusters(window, ce)
# Declare parameters (added with condition if empty array)
if ce.shape[0] != 0:
duration = window.measurement_time
vmin = 1
vmax = ce.shape[0] // 4500 + 5
else:
duration = 1
vmin = 1
vmax = 1
title = 'Coincident events (2D)\nData set(s): %s' % data_sets
height = 12
width = 14
# Ensure only coincident events are plotted
ce = ce[(ce.wCh != -1) & (ce.gCh != -1)]
# Retrieve text path
dir_name = os.path.dirname(__file__)
folder_path = os.path.join(
dir_name, '../../text/%s/Coincidences_2D/' % window.data_sets)
mkdir_p(folder_path)
# Plot data and save matrices in text
fig = plt.figure()
for i, bus in enumerate(module_order):
ce_bus = ce[ce.Bus == bus]
number_events = ce_bus.shape[0]
events_per_s = round(number_events / duration, 4)
sub_title = ('Bus %d\n(%d events, %f events/s)' %
(bus, number_events, events_per_s))
plt.subplot(3, 3, i + 1)
fig, h = plot_2D_bus(fig, sub_title, ce_bus, vmin, vmax, duration)
# Save matrix in text
path = folder_path + 'Bus_' + str(bus) + '.txt'
if window.createText.isChecked():
np.savetxt(path, h, fmt="%d", delimiter=",")
fig = set_figure_properties(fig, title, height, width)
return fig
def Coincidences_Front_Top_Side_plot(df, data_sets, module_order,
number_of_detectors, window):
# Ensure we only plot coincident events
df = df[(df.wCh != -1) & (df.gCh != -1)]
df = filter_ce_clusters(window, df)
# Define figure and set figure properties
fig = plt.figure()
title = ('Coincident events (Front, Top, Side)' +
'\nData set(s): %s' % data_sets)
height = 4
width = 14
fig = set_figure_properties(fig, title, height, width)
if df.shape[0] != 0:
vmin = 1
vmax = df.shape[0] // 200 + 5
else:
vmin = 1
vmax = 1
# Retrieve text path
dir_name = os.path.dirname(__file__)
folder_path = os.path.join(
dir_name, '../../text/%s/Projections_2D/' % window.data_sets)
mkdir_p(folder_path)
# Plot front view
plt.subplot(1, 3, 1)
__, h_front = plot_2D_Front(module_order, df, fig, number_of_detectors,
vmin, vmax)
path_front = folder_path + 'Front.txt'
if window.createText.isChecked():
np.savetxt(path_front, h_front, fmt="%d", delimiter=",")
# Plot top view
plt.subplot(1, 3, 2)
__, h_top = plot_2D_Top(module_order, df, fig, number_of_detectors, vmin,
vmax)
path_top = folder_path + 'Top.txt'
if window.createText.isChecked():
np.savetxt(path_top, h_top, fmt="%d", delimiter=",")
# Plot side view
plt.subplot(1, 3, 3)
__, h_side = plot_2D_Side(module_order, df, fig, number_of_detectors, vmin,
vmax)
path_side = folder_path + 'Side.txt'
if window.createText.isChecked():
np.savetxt(path_side, h_side, fmt="%d", delimiter=",")
return fig
def compare_all_shoulders(window):
def get_statistical_uncertainty(value, a, b, c, d):
da = np.sqrt(a)
db = np.sqrt(b)
dc = np.sqrt(c)
dd = np.sqrt(d)
dA = np.sqrt(da**2 + dc**2)
dB = np.sqrt(db**2 + dd**2)
return np.sqrt((dA / (a - c))**2 + (dB / (c - d))**2) * value
# Declare parameters
number_bins = int(window.dE_bins.text())
sigma_interval = [3, 8]
# Declare all input-paths
dir_name = os.path.dirname(__file__)
HR_folder = os.path.join(dir_name, '../../Clusters/MG_new/HR/')
HR_files = np.array(
[file for file in os.listdir(HR_folder) if file[-3:] == '.h5'])
HR_files_sorted = sorted(HR_files, key=lambda element: get_energy(element))
paths = append_folder_and_files(HR_folder, HR_files_sorted)
# Get output path
temp_folder = os.path.join(dir_name, '../../Results/Shoulder/temp/')
mkdir_p(temp_folder)
# Calculate He3-solid angle
__, He3_solid_angle = get_He3_tubes_area_and_solid_angle()
# Declare vectors where results will be stored
detectors = ['ILL', 'ESS_CLB', 'ESS_PA']
FoMs = {'ILL': [], 'ESS_CLB': [], 'ESS_PA': []}
colors = {'ILL': 'blue', 'ESS_CLB': 'red', 'ESS_PA': 'green'}
energies = []
errors = {'ILL': [], 'ESS_CLB': [], 'ESS_PA': []}
count = 0
# Iterate through all data and calculate FoM
for run in range(2):
for detector in detectors:
for i, path in enumerate(
paths[8:20]): # Only look at 2 meV to 30 meV 8->16
# Import parameters
E_i = pd.read_hdf(path, 'E_i')['E_i'].iloc[0]
calibration = (pd.read_hdf(
path, 'calibration')['calibration'].iloc[0])
df_MG_temp = pd.read_hdf(path, 'coincident_events')
df_MG = filter_ce_clusters(window, df_MG_temp)
# Get MG dE-data
df_MG_detector = detector_filter(df_MG, detector)
dE_MG = get_MG_dE(df_MG_detector, calibration, E_i)
# Get He3 dE-data
dE_He3 = get_raw_He3_dE(calibration, E_i)
# Produce histograms
hist_MG, bins = np.histogram(dE_MG,
bins=number_bins,
range=[-E_i / 5, E_i / 5])
hist_He3, __ = np.histogram(dE_He3,
bins=number_bins,
range=[-E_i / 5, E_i / 5])
bin_centers = 0.5 * (bins[1:] + bins[:-1])
# Fit data
p0 = None
area_MG, FWHM_MG, __, __, __, popt_MG, l_p_idx_MG, r_p_idx_MG, back_MG, MG_back_indices = Gaussian_fit(
bin_centers, hist_MG, p0)
area_He3, FWHM_He3, __, __, __, popt_He3, l_p_idx_He3, r_p_idx_He3, back_He3, He3_back_indices = Gaussian_fit(
bin_centers, hist_He3, p0)
# Normalize data
norm_peak_area = area_He3 / area_MG
normalized_hist_MG = hist_MG * norm_peak_area
normalized_MG_back = back_MG * norm_peak_area
# Compare difference at certain interval
x0_MG = popt_MG[1]
sigma_MG = abs(popt_MG[2])
x0_He3 = popt_He3[1]
sigma_He3 = abs(popt_He3[2])
left_idx_MG = find_nearest(
bin_centers, x0_MG + sigma_interval[0] * sigma_MG)
right_idx_MG = find_nearest(
bin_centers, x0_MG + sigma_interval[1] * sigma_MG)
indices_MG = np.arange(left_idx_MG, right_idx_MG + 1, 1)
left_idx_He3 = find_nearest(
bin_centers, x0_He3 + sigma_interval[0] * sigma_He3)
right_idx_He3 = find_nearest(
bin_centers, x0_He3 + sigma_interval[1] * sigma_He3)
indices_He3 = np.arange(left_idx_He3, right_idx_He3 + 1, 1)
area_MG = sum(normalized_hist_MG[indices_MG]
) - len(indices_MG) * normalized_MG_back
area_He3 = sum(
hist_He3[indices_He3]) - len(indices_He3) * back_He3
FoM = area_MG / area_He3
# Find uncertainty
error = get_statistical_uncertainty(
FoM, sum(hist_He3[indices_He3]), sum(hist_MG[indices_MG]),
sum(hist_MG[MG_back_indices]),
sum(hist_He3[He3_back_indices]))
if run == 0:
FoMs[detector].append(FoM)
errors[detector].append(error)
if detector == 'ILL':
energies.append(E_i)
else:
# Plot data
fig = plt.figure()
#main_title = 'Peak shape comparison $^3$He-tubes and Multi-Grid'
#main_title += '\nInterval: ' + str(sigma_interval) + '$\sigma$'
#fig.suptitle(main_title, x=0.5, y=1.02)
fig.set_figheight(6)
fig.set_figwidth(20)
plt.subplot(1, 3, 1)
plt.plot(bin_centers,
normalized_hist_MG,
color='red',
label='Multi-Grid',
zorder=5)
plt.plot(bin_centers[MG_back_indices],
normalized_hist_MG[MG_back_indices],
color='black',
label=None,
zorder=10)
plt.fill_between(bin_centers[indices_MG],
normalized_hist_MG[indices_MG],
np.ones(len(indices_MG)) *
normalized_MG_back,
facecolor='orange',
label='Shoulder area',
alpha=1,
zorder=2)
plt.title(detector + ': ' + calibration)
plt.xlabel('$E_i$ - $E_f$ [meV]')
plt.ylabel('Normalized counts')
plt.yscale('log')
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.legend(loc=1)
plt.subplot(1, 3, 2)
plt.plot(bin_centers[He3_back_indices],
hist_He3[He3_back_indices],
color='black',
label=None,
zorder=6)
plt.plot(bin_centers,
hist_He3,
color='blue',
label='$^3$He-tubes',
zorder=5)
plt.fill_between(bin_centers[indices_He3],
hist_He3[indices_He3],
np.ones(len(indices_He3)) * back_He3,
facecolor='purple',
label='Shoulder area',
alpha=1,
zorder=2)
plt.title('$^3$He-tubes: ' + calibration)
plt.xlabel('$E_i$ - $E_f$ [meV]')
plt.ylabel('Normalized counts')
plt.yscale('log')
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.legend(loc=1)
plt.subplot(1, 3, 3)
for detector_temp in detectors:
plt.errorbar(energies,
FoMs[detector_temp],
errors[detector_temp],
ecolor=colors[detector_temp],
fmt='.',
capsize=5,
color=colors[detector_temp],
label=detector_temp,
zorder=5,
linestyle='-')
# plt.plot(energies, FoMs[detector_temp], '.-',
# color=colors[detector_temp],
# label=detector_temp, zorder=5)
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.xlabel('$E_i$ [meV]')
plt.ylabel('Figure-of-Merit')
plt.yscale('log')
plt.title('Figure-of-Merit')
if ((detector == 'ESS_PA') and (i == 4)):
pass
else:
plt.axvline(x=energies[i],
color=colors[detector],
zorder=5)
plt.legend(loc=2)
# Save fig
count += 1
output_path = temp_folder + str(count) + '.png'
plt.tight_layout()
fig.savefig(output_path, bbox_inches='tight')
output_path_pdf = os.path.join(
dir_name, '../../Results/pdf_files/%s.pdf' % count)
fig.savefig(output_path_pdf, bbox_inches='tight')
plt.close()
images = []
files = os.listdir(temp_folder)
files = [
file[:-4] for file in files
if file[-9:] != '.DS_Store' and file != '.gitignore'
]
output_path = os.path.join(
dir_name, '../../Results/Shoulder/FOM_sweep_shoulder.gif')
for filename in sorted(files, key=int):
images.append(imageio.imread(temp_folder + filename + '.png'))
imageio.mimsave(output_path, images) #format='GIF', duration=0.33)
shutil.rmtree(temp_folder, ignore_errors=True)
def compare_all_shoulders_5x5(window):
# Declare parameters
number_bins = 150
sigma_interval = [3, 8]
detectors = ['ILL', 'ESS_CLB', 'ESS_PA']
FoMs = {'ILL': [], 'ESS_CLB': [], 'ESS_PA': []}
colors = {'ILL': 'blue', 'ESS_CLB': 'red', 'ESS_PA': 'green'}
energies = []
errors = {'ILL': [], 'ESS_CLB': [], 'ESS_PA': []}
count = 0
# Get input-file paths
dir_name = os.path.dirname(__file__)
folder = os.path.join(dir_name, '../../Clusters/MG_V_5x5_HR/')
files = np.array(
[file for file in os.listdir(folder) if file[-3:] == '.h5'])
files_sorted = sorted(
files,
key=lambda element: float(element[element.find('V_5x5_HR_') + len(
'V_5x5_HR_'):element.find('_meV.h5')]))
paths = np.core.defchararray.add(np.array(len(files_sorted) * [folder]),
files_sorted)
# Get output path
temp_folder = os.path.join(dir_name, '../../Results/Shoulder/temp/')
mkdir_p(temp_folder)
for run in range(2):
for detector in detectors:
for i, path in enumerate(paths):
# Import parameters
E_i = pd.read_hdf(path, 'E_i')['E_i'].iloc[0]
calibration = pd.read_hdf(path,
'calibration')['calibration'].iloc[0]
calibration = get_calibration(calibration, E_i)
df_MG_temp = pd.read_hdf(path, 'coincident_events')
df_MG = filter_ce_clusters(window, df_MG_temp)
# Get MG dE-data
df_MG_detector = detector_filter(df_MG, detector)
dE_MG = get_MG_dE(df_MG_detector, calibration, E_i)
# Get He3 dE-data
dE_He3 = get_raw_He3_dE(calibration, E_i)
# Produce histograms
hist_MG, bins = np.histogram(dE_MG,
bins=number_bins,
range=[-E_i / 5, E_i / 5])
hist_He3, __ = np.histogram(dE_He3,
bins=number_bins,
range=[-E_i / 5, E_i / 5])
bin_centers = 0.5 * (bins[1:] + bins[:-1])
# Normalize data
p0 = None
area_MG, FWHM_MG, __, __, __, popt_MG, l_p_idx_MG, r_p_idx_MG, back_MG = Gaussian_fit(
bin_centers, hist_MG, p0)
area_He3, FWHM_He3, __, __, __, popt_He3, l_p_idx_He3, r_p_idx_He3, back_He3 = Gaussian_fit(
bin_centers, hist_He3, p0)
norm_area = area_He3 / area_MG
normalized_hist_MG = hist_MG * norm_area
# THIS IS JUST TEMPORARY REMOVE AFTER USE
fig = plt.figure()
plt.plot(bin_centers,
normalized_hist_MG,
'-',
color='red',
label='Multi-Grid',
zorder=5)
plt.plot(bin_centers,
hist_He3,
'-',
color='blue',
label='$^3$He-tubes',
zorder=5)
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.xlabel('$E_i$ - $E_f$ [meV]')
plt.ylabel('Normalized Counts')
plt.yscale('log')
plt.title(
'Energy transfer - All detectors (5x5 V-sample)\nData set: %s'
% calibration)
plt.legend(loc=1)
output_path_pdf = os.path.join(
dir_name, '../../Results/pdf_files/%s.pdf' % count)
fig.savefig(output_path_pdf, bbox_inches='tight')
plt.close()
# Compare difference at certain interval
x0_MG = popt_MG[1]
sigma_MG = abs(popt_MG[2])
x0_He3 = popt_He3[1]
sigma_He3 = abs(popt_He3[2])
left_idx_MG = find_nearest(
bin_centers, x0_MG + sigma_interval[0] * sigma_MG)
right_idx_MG = find_nearest(
bin_centers, x0_MG + sigma_interval[1] * sigma_MG)
indices_MG = np.arange(left_idx_MG, right_idx_MG + 1, 1)
left_idx_He3 = find_nearest(
bin_centers, x0_He3 + sigma_interval[0] * sigma_He3)
right_idx_He3 = find_nearest(
bin_centers, x0_He3 + sigma_interval[1] * sigma_He3)
indices_He3 = np.arange(left_idx_He3, right_idx_He3 + 1, 1)
area_MG = sum(
normalized_hist_MG[indices_MG]) - len(indices_MG) * back_MG
area_He3 = sum(
hist_He3[indices_He3]) - len(indices_He3) * back_He3
area_diff = sum(normalized_hist_MG[indices_MG] -
hist_He3[indices_MG])
He3_peak_area = sum(
hist_He3[l_p_idx_He3:r_p_idx_He3] -
np.ones(len(bin_centers[l_p_idx_He3:r_p_idx_He3])) *
back_He3)
FoM = area_diff / He3_peak_area
# Find uncertainty
a = sum(hist_MG[indices_MG])
b = sum(hist_He3[indices_MG])
c = sum(hist_He3[l_p_idx_He3:r_p_idx_He3])
d = sum(
np.ones(len(bin_centers[l_p_idx_He3:r_p_idx_He3])) *
back_He3)
da = np.sqrt(a)
db = np.sqrt(b)
dc = np.sqrt(c)
dd = np.sqrt(d)
uncertainty = np.sqrt((np.sqrt(da**2 + db**2) / (a - b))**2 +
(np.sqrt(dc**2 + dd**2) / (c - d))**2)
error = uncertainty * FoM
if run == 0:
FoMs[detector].append(FoM)
errors[detector].append(error)
if detector == 'ILL':
energies.append(E_i)
else:
# Plot data
fig = plt.figure()
#main_title = 'Peak shape comparison $^3$He-tubes and Multi-Grid'
#main_title += '\nInterval: ' + str(sigma_interval) + '$\sigma$'
#fig.suptitle(main_title, x=0.5, y=1.02)
fig.set_figheight(6)
fig.set_figwidth(14)
plt.subplot(1, 2, 1)
plt.plot(bin_centers,
normalized_hist_MG,
color='red',
label='Multi-Grid',
zorder=5)
plt.plot(bin_centers,
hist_He3,
color='blue',
label='$^3$He-tubes',
zorder=5)
plt.fill_between(bin_centers[indices_MG],
normalized_hist_MG[indices_MG],
hist_He3[indices_MG],
facecolor='orange',
label='Shoulder area',
alpha=1,
zorder=2)
plt.fill_between(
bin_centers[l_p_idx_He3:r_p_idx_He3],
hist_He3[l_p_idx_He3:r_p_idx_He3],
np.ones(len(bin_centers[l_p_idx_He3:r_p_idx_He3])) *
back_He3,
facecolor='purple',
label='$^3$He-peak area',
alpha=1,
zorder=2)
plt.title(detector + ': ' + calibration)
plt.xlabel('$E_i$ - $E_f$ [meV]')
plt.ylabel('Normalized counts')
plt.yscale('log')
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.legend(loc=1)
plt.subplot(1, 2, 2)
for detector_temp in detectors:
plt.errorbar(energies,
FoMs[detector_temp],
errors[detector_temp],
ecolor=colors[detector_temp],
fmt='.',
capsize=5,
color=colors[detector_temp],
label=detector_temp,
zorder=5,
linestyle='-')
# plt.plot(energies, FoMs[detector_temp], '.-',
# color=colors[detector_temp],
# label=detector_temp, zorder=5)
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.xlabel('$E_i$ [meV]')
plt.ylabel('Figure-of-Merit')
plt.title('Figure-of-Merit')
if ((detector == 'ESS_PA') and (i == 4)):
pass
else:
plt.axvline(x=energies[i],
color=colors[detector],
zorder=5)
plt.legend(loc=2)
# Save fig
count += 1
output_path = temp_folder + str(count) + '.png'
plt.tight_layout()
fig.savefig(output_path, bbox_inches='tight')
output_path_pdf = os.path.join(
dir_name, '../../Results/pdf_files/%s.pdf' % count)
#fig.savefig(output_path_pdf, bbox_inches='tight')
images = []
files = os.listdir(temp_folder)
files = [
file[:-4] for file in files
if file[-9:] != '.DS_Store' and file != '.gitignore'
]
output_path = os.path.join(
dir_name, '../../Results/Shoulder/FOM_sweep_shoulder.gif')
for filename in sorted(files, key=int):
images.append(imageio.imread(temp_folder + filename + '.png'))
imageio.mimsave(output_path, images) #format='GIF', duration=0.33)
shutil.rmtree(temp_folder, ignore_errors=True)
def background_dependence_on_ADC_threshold(df, data_sets, window):
# Initial filter of clusters
df = filter_ce_clusters(window, df)
df = df[(df.wCh != -1) & (df.gCh != -1)]
if data_sets == "['mvmelst_039.mvmelst']":
df = df[df.Time < 1.5e12]
# Declare parameters
step = 20
thresholds = np.arange(0, 1500 + step, step)
number_thresholds = len(thresholds)
# Declare data folder
data = {'top': [thresholds, np.zeros(number_thresholds)],
'bottom': [thresholds, np.zeros(number_thresholds)],
'middle': [thresholds, np.zeros(number_thresholds)],
'back': [thresholds, np.zeros(number_thresholds)],
'reference': [thresholds, np.zeros(number_thresholds)]
}
# Create output folder
dir_name = os.path.dirname(__file__)
temp_folder = os.path.join(dir_name, '../../Results/temp_folder_ADC/')
mkdir_p(temp_folder)
# Iterate through data
for run in [1, 2]:
for i, threshold in enumerate(thresholds):
df_temp = df[(df.wADC >= threshold) & (df.gADC >= threshold)]
if run == 1:
counts_top = df_temp[(df_temp.Bus >= 6) &
(df_temp.Bus <= 8) &
(df_temp.gCh == 119)
].shape[0]/(20*11)
counts_bottom = df_temp[(df_temp.Bus >= 6) &
(df_temp.Bus <= 8) &
(df_temp.gCh == 80)
].shape[0]/(20*11)
counts_back = df_temp[(df_temp.Bus >= 6) &
(df_temp.Bus <= 8) &
((df_temp.wCh == 19) |
(df_temp.wCh == 39) |
(df_temp.wCh == 59) |
(df_temp.wCh == 79)
)
].shape[0]/(11*40)
counts_middle = df_temp[((df_temp.gCh >= 95)
& (df_temp.gCh <= 105)
) &
(((df_temp.wCh >= 0) &
(df_temp.wCh <= 9)) |
((df_temp.wCh >= 20) &
(df_temp.wCh <= 29)) |
((df_temp.wCh >= 40) &
(df_temp.wCh <= 49)) |
((df_temp.wCh >= 60) &
(df_temp.wCh <= 69))
) &
(df_temp.Bus >= 7)
].shape[0]/(10*10*7)
counts_reference = df_temp[((df_temp.gCh >= 112)
& (df_temp.gCh <= 117)
) &
(((df_temp.wCh >= 0) &
(df_temp.wCh <= 9)) |
((df_temp.wCh >= 20) &
(df_temp.wCh <= 29)) |
((df_temp.wCh >= 40) &
(df_temp.wCh <= 49)) |
((df_temp.wCh >= 60) &
(df_temp.wCh <= 69))
) &
(df_temp.Bus == 6)
].shape[0]/(6*10*4)
data['top'][1][i] = counts_top
data['bottom'][1][i] = counts_bottom
data['middle'][1][i] = counts_middle
data['back'][1][i] = counts_back
data['reference'][1][i] = counts_reference
else:
path = temp_folder + str(threshold) + '.png'
hist_3D_for_ADC_threshold(df_temp, data, threshold, path)
# Save animation
images = []
files = os.listdir(temp_folder)
files = [file[:-4] for file in files
if file[-9:] != '.DS_Store' and file != '.gitignore']
animation_path = os.path.join(dir_name,
'../../Results/Animations/ADC_sweep_3D.gif')
for filename in sorted(files, key=int):
images.append(imageio.imread(temp_folder + filename + '.png'))
imageio.mimsave(animation_path, images)
shutil.rmtree(temp_folder, ignore_errors=True)
def signal_dependence_on_ADC_threshold(df, Ei, calibration, window):
# Declare parameters
bins_full = 1000
bins_part = 500
step = 20
df = filter_ce_clusters(window, df)
thresholds = np.arange(0, 1000 + step, step)
number_thresholds = len(thresholds)
## Plot limits
maximum_full = 1
minimum_full = 0
maximum_part = 1
minimum_part = 0
## Bins with data
gamma_bins = [320, 328]
thermal_bins = [400, 550]
fast_bins = [328, 343]
back_bins = [800, 900]
gamma_bins_part = [150, 225]
fast_bins_part = [225, 350]
# Declare data folder
fast_neutrons = np.zeros(number_thresholds)
thermal_neutrons = np.zeros(number_thresholds)
gammas = np.zeros(number_thresholds)
background = np.zeros(number_thresholds)
# Create output folder
dir_name = os.path.dirname(__file__)
temp_folder = os.path.join(dir_name, '../../Results/temp_folder_ADC/')
mkdir_p(temp_folder)
# Iterate through data
for run in [1, 2]:
for i, threshold in enumerate(thresholds):
df_temp = df[(df.wADC >= threshold) & (df.gADC >= threshold)]
# Define figure and axises
fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(11, 5))
left, bottom, width, height = [0.335, 0.6, 0.13, 0.25]
ax2 = fig.add_axes([left, bottom, width, height])
# Plot data
## First plot, first axis
hist_full, bins_full = np.histogram(df_temp.ToF*62.5e-9*1e6,
bins=bins_full,
range=[0, 16667])
bin_centers_full = 0.5 * (bins_full[1:] + bins_full[:-1])
### Plot full data
ax1.plot(bin_centers_full, hist_full, color='black',
label=None)
ax1.plot(bin_centers_full[thermal_bins[0]:thermal_bins[1]],
hist_full[thermal_bins[0]:thermal_bins[1]],
color='orange', label='Thermal')
ax1.plot(bin_centers_full[fast_bins[0]:fast_bins[1]],
hist_full[fast_bins[0]:fast_bins[1]],
color='blue', label='Fast')
ax1.plot(bin_centers_full[gamma_bins[0]:gamma_bins[1]],
hist_full[gamma_bins[0]:gamma_bins[1]],
color='red', label='Gamma')
ax1.plot(bin_centers_full[back_bins[0]:back_bins[1]],
hist_full[back_bins[0]:back_bins[1]],
color='green', label='Background')
ax1.set_ylim(minimum_full*0.8, maximum_full*1.5)
ax1.set_yscale('log')
ax1.set_xlabel('ToF [µs]')
ax1.set_ylabel('Counts')
ax1.legend(loc=2)
ax1.set_title('ToF histogram: US - 70 meV')
## First plot, second axis
hist_part, bins_part = np.histogram(df_temp.ToF*62.5e-9*1e6,
bins=bins_part,
range=[5250, 5750])
bin_centers_part = 0.5 * (bins_part[1:] + bins_part[:-1])
ax2.plot(bin_centers_part, hist_part, color='black')
ax2.plot(bin_centers_part[gamma_bins_part[0]:gamma_bins_part[1]],
hist_part[gamma_bins_part[0]:gamma_bins_part[1]],
color='red')
ax2.plot(bin_centers_part[fast_bins_part[0]:fast_bins_part[1]],
hist_part[fast_bins_part[0]:fast_bins_part[1]],
color='blue')
ax2.set_yscale('log')
ax2.set_ylim(minimum_part*0.8, maximum_part*1.5)
ax2.set_xlabel('ToF [µs]')
ax2.set_ylabel('Counts')
if run == 1:
if i == 1:
maximum_full = max(hist_full)
maximum_part = max(hist_part)
if i == len(thresholds) - 1:
minimum_full = min(hist_full)
minimum_part = min(hist_part)
# Save data
fast_data = hist_part[fast_bins_part[0]:fast_bins_part[1]]
fast_duration = (bin_centers_part[fast_bins_part[1]]
- bin_centers_part[fast_bins_part[0]])
thermal_data = hist_full[thermal_bins[0]:thermal_bins[1]]
thermal_duration = (bin_centers_full[thermal_bins[1]]
- bin_centers_full[thermal_bins[0]])
gamma_data = hist_part[gamma_bins_part[0]:gamma_bins_part[1]]
gamma_duration = (bin_centers_part[gamma_bins_part[1]]
- bin_centers_part[gamma_bins_part[0]])
background_data = hist_full[back_bins[0]:back_bins[1]]
background_duration = (bin_centers_full[back_bins[1]]
- bin_centers_full[back_bins[0]])
fast_neutrons[i] = sum(fast_data)/fast_duration
thermal_neutrons[i] = sum(thermal_data)/thermal_duration
gammas[i] = sum(gamma_data)/gamma_duration
background[i] = sum(background_data)/background_duration
plt.close()
else:
## Second plot
ax3.plot(thresholds, thermal_neutrons, '.-', color='orange',
label='Thermal')
ax3.plot(thresholds, fast_neutrons, '.-', color='blue',
label='Fast')
ax3.plot(thresholds, gammas, '.-', color='red', label='Gamma')
ax3.plot(thresholds, background, '.-', color='green',
label='Background')
ax3.axvline(x=threshold, color='black')
ax3.axvline(x=500, color='grey', linestyle='--')
ax3.set_xlabel('Threshold [ADC channels]')
ax3.set_ylabel('Counts/µs')
ax3.grid(True, which='major', linestyle='--', zorder=0)
ax3.grid(True, which='minor', linestyle='--', zorder=0)
ax3.legend(loc=2)
ax3.set_yscale('log')
# Save data
output_path = temp_folder + str(threshold) + '.png'
fig.savefig(output_path, bbox_inches='tight')
# Save animation
images = []
files = os.listdir(temp_folder)
files = [file[:-4] for file in files
if file[-9:] != '.DS_Store' and file != '.gitignore']
animation_path = os.path.join(dir_name,
'../../Results/Animations/ADC_sweep.gif')
for filename in sorted(files, key=int):
images.append(imageio.imread(temp_folder + filename + '.png'))
imageio.mimsave(animation_path, images)
shutil.rmtree(temp_folder, ignore_errors=True)