def ToT_vs_ToA(shot):
# Go to the @shot folder
try:
path = path_to_shot(shot)
except:
raise SystemExit
# Get the data
Data_tpx3_cent = np.genfromtxt('%s.csv' % shot, delimiter=',')
# Read the first row in the .csv file and get index of the required signal
with open('%s.csv' % shot, newline='') as f:
reader = csv.reader(f)
row1 = next(reader)
try:
index1 = row1.index('#ToA')
index2 = row1.index('#ToTtotal[arb]')
time_new = np.array([row[index1]
for row in Data_tpx3_cent]) * 25 / 4096 / 1e6
tot = np.array([row[index2] for row in Data_tpx3_cent])
except:
print("No 'ToA' or 'ToT' in the list")
raise SystemExit
bins1 = np.arange(time_new[0], time_new[-1], 1.5625)
bins2 = np.arange(0, 25000, 25)
H, xedges, yedges = np.histogram2d(time_new, tot, [bins1, bins2])
a = np.histogram(time_new, bins=bins1)
# Time and data from the histogram
timee = a[1]
data = a[0]
# Get rid off the noisy "ones" for an appropriate plot
ones = np.where(data == 1)[0]
for i in range(0, len(ones)):
data[ones[i]] = 0
# Define the range limits for the appropriate plot
try:
l_lim, r_lim = int(np.nonzero(data)[0][0]), int(
np.nonzero(data)[0][-1])
left, right = timee[l_lim] - 10, timee[r_lim] + 10
except:
print("Could not compute 'left' and 'right'")
y, x = np.meshgrid(yedges, xedges)
data_masked = np.ma.masked_where(H == 0, H)
fig = plt.figure()
fig1 = plt.gca()
plt.rcParams.update({'font.size': 16})
try:
plt.xlim(left, right)
except:
pass
yticks = fig1.yaxis.get_major_ticks()
yticks[0].label1.set_visible(False)
# plt.pcolormesh(x, y, H)
plt.pcolormesh(x, y, data_masked)
plt.colorbar(label="Hits")
plt.title("Shot %s: ToT vs ToA" % shot)
plt.xlabel("ToA, [ms]")
plt.ylabel("ToT, [a.u.]")
# Go to the Figures folder
try:
os.chdir("%s/Figures" % path)
except:
print("No folder, creating one")
os.mkdir("%s/Figures" % path)
os.chdir("%s/Figures" % path)
fig.set_size_inches(20., 12., forward=True)
fig.savefig('%s:ToTvsToA.png' % shot)
plt.close(fig)
def hitmap_double(shot):
# Go to the @shot folder
try:
path = path_to_shot(shot)
except:
raise SystemExit
# Get the data
Data_tpx3_cent = np.genfromtxt('%s.csv' % shot, delimiter=',')
# Read the first row in the .csv file and get index of the required signal
with open('%s.csv' % shot, newline='') as f:
reader = csv.reader(f)
row1 = next(reader)
try:
index1 = row1.index('#Col')
index2 = row1.index('#Row')
index3 = row1.index('#Centroid')
col_total = np.array([row[index1] for row in Data_tpx3_cent])
row_total = np.array([row[index2] for row in Data_tpx3_cent])
cent = np.array([row[index3] for row in Data_tpx3_cent])
except:
print("No 'ToA' in the list")
raise SystemExit
# Prepare arrays for the cluster separation
col_ones = np.zeros(len(col_total))
row_ones = np.zeros(len(row_total))
col_noones = np.zeros(len(col_total))
row_noones = np.zeros(len(row_total))
# Fill the arrays with appropriate data defined by the cluster size
for i in range(0, len(col_total)):
if cent[i] == 1:
col_ones[i] = col_total[i]
row_ones[i] = row_total[i]
col_noones[i] = 0
row_noones[i] = 0
else:
col_ones[i] = 0
row_ones[i] = 0
col_noones[i] = col_total[i]
row_noones[i] = row_total[i]
# Compute histograms: H - all data, H1 - with particular cluster size
H, xedges, yedges = np.histogram2d(row_total, col_total, 255,
[[0, 255], [0, 255]])
H1, xedges1, yedges1 = np.histogram2d(row_ones, col_ones, 255,
[[0, 255], [0, 255]])
H2, xedges2, xedges2 = np.histogram2d(row_noones, col_noones, 255,
[[0, 255], [0, 255]])
row_left = min(row_total)
row_right = max(row_total)
col_left = min(col_total)
col_right = max(col_total)
row_l = int(row_right - row_left)
col_l = int(col_right - col_left)
H_, xedges_, yedges_ = np.histogram2d(
row_total, col_total, (row_l, col_l),
[[row_left, row_right], [col_left, col_right]])
H_1, xedges_1, yedges_1 = np.histogram2d(
row_ones, col_ones, (row_l, col_l),
[[row_left, row_right], [col_left, col_right]])
H_2, xedges_2, ydeges_2 = np.histogram2d(
row_noones, col_noones, (row_l, col_l),
[[row_left, row_right], [col_left, col_right]])
# Mask zeros to get a white color of the background
H_masked = np.ma.masked_where(H == 0, H)
H1_masked = np.ma.masked_where(H1 == 0, H1)
H2_masked = np.ma.masked_where(H2 == 0, H2)
H__masked = np.ma.masked_where(H_ == 0, H_)
H_1_masked = np.ma.masked_where(H_1 == 0, H_1)
H_2_masked = np.ma.masked_where(H_2 == 0, H_2)
# Limits for the same colorbars
vmin = 0
vmax = np.max(H)
# =============================================================================
# Plot
# =============================================================================
fig, axes = plt.subplots(2, 3)
fig.subplots_adjust(hspace=0.6, wspace=0.6)
plt.rcParams.update({'font.size': 16})
fig1 = axes[0, 0].imshow(H_masked,
interpolation='none',
origin='low',
vmin=vmin,
vmax=vmax)
fig.colorbar(fig1, ax=axes[0, 0], label="Hits", fraction=0.046, pad=0.04)
axes[0, 0].set_title("Shot %s: Hitmap" % shot)
axes[0, 0].set_xlabel("Pixels, x axis")
axes[0, 0].set_ylabel("Pixels, y axis")
fig2 = axes[0, 1].imshow(H1_masked,
interpolation='none',
origin='low',
vmin=vmin,
vmax=vmax)
fig.colorbar(fig2, ax=axes[0, 1], label="Hits", fraction=0.046, pad=0.04)
axes[0, 1].set_title("Hitmap, cluster=1")
axes[0, 1].set_xlabel("Pixels, x axis")
axes[0, 1].set_ylabel("Pixels, y axis")
fig3 = axes[0, 2].imshow(H2_masked,
interpolation='none',
origin='low',
vmin=vmin,
vmax=vmax)
fig.colorbar(fig3, ax=axes[0, 2], label="Hits", fraction=0.046, pad=0.04)
axes[0, 2].set_title("Hitmap, cluster>1")
axes[0, 2].set_xlabel("Pixels, x axis")
axes[0, 2].set_ylabel("Pixels, y axis")
fig4 = axes[1, 0].imshow(H__masked,
interpolation='none',
origin='low',
vmin=vmin,
vmax=vmax)
fig.colorbar(fig4, ax=axes[1, 0], label="Hits", fraction=0.046, pad=0.04)
axes[1, 0].set_title("Hitmap")
axes[1, 0].set_xlabel("Pixels, x axis")
axes[1, 0].set_ylabel("Pixels, y axis")
fig5 = axes[1, 1].imshow(H_1_masked,
interpolation='none',
origin='low',
vmin=vmin,
vmax=vmax)
fig.colorbar(fig5, ax=axes[1, 1], label="Hits", fraction=0.046, pad=0.04)
axes[1, 1].set_title("Hitmap, cluster=1")
axes[1, 1].set_xlabel("Pixels, x axis")
axes[1, 1].set_ylabel("Pixels, y axis")
fig6 = axes[1, 2].imshow(H_2_masked,
interpolation='none',
origin='low',
vmin=vmin,
vmax=vmax)
fig.colorbar(fig6, ax=axes[1, 2], label="Hits", fraction=0.046, pad=0.04)
axes[1, 2].set_title("Hitmap, cluster>1")
axes[1, 2].set_xlabel("Pixels, x axis")
axes[1, 2].set_ylabel("Pixels, y axis")
# Go to the Figures folder
try:
os.chdir("%s/Figures" % path)
except:
print("No folder, creating one")
os.mkdir("%s/Figures" % path)
os.chdir("%s/Figures" % path)
fig.set_size_inches(20., 12., forward=True)
plt.savefig('%s:Hitmap_double.pdf' % shot)
plt.close(fig)
def ToA(shot):
# Go to the @shot folder
try:
path = path_to_shot(shot)
except:
raise SystemExit
# Get the data
Data_tpx3_cent = np.genfromtxt('%s.csv' % shot, delimiter=',')
# Read the first row in the .csv file and get index of the required signal
with open('%s.csv' % shot, newline='') as f:
reader = csv.reader(f)
row1 = next(reader)
try:
index = row1.index('#ToA')
time_new = np.array([row[index]
for row in Data_tpx3_cent]) * 25 / 4096 / 1e6
except:
print("No 'ToA' in the list")
# Define the bins with the step of 1.5625
bins1 = int((time_new[-1] - time_new[0]) / 1.5625)
# Prepare figure and compute the histogram
fig = plt.figure()
plt.rcParams.update({'font.size': 16})
plt.title("Shot %s: ToA" % shot)
plt.xlabel("Time, [ms]")
plt.ylabel("Hits, [a.u.]")
a = plt.hist(time_new,
bins1, (time_new[0], time_new[-1]),
histtype='step',
fill=False)
# Get rid off the noisy "ones" for an appropriate plot
data = a[0]
timee = a[1]
ones = np.where(data < 10)[0]
for i in range(0, len(ones)):
data[ones[i]] = 0
# Define limits of x axis for an appropriate plot
try:
left, right = int(timee[np.nonzero(data)[0][0]] -
10), int(timee[np.nonzero(data)[0][-1]] + 10)
plt.xlim(left, right)
except:
print("Probably no data at all or just a weak signal")
pass
# Show the result within the limits
plt.show()
# Go to the Figures folder
try:
os.chdir("%s/Figures" % path)
except:
print("No folder, creating one")
os.mkdir("%s/Figures" % path)
os.chdir("%s/Figures" % path)
fig.set_size_inches(20., 12., forward=True)
fig.savefig('%s:ToA_plt.pdf' % shot)
plt.close(fig)
def ToT_cluster(shot):
# Go to the @shot folder
try:
path = path_to_shot(shot)
except:
raise SystemExit
# Get the data
Data_tpx3_cent = np.genfromtxt('%s.csv' % shot, delimiter=',')
# Read the first row in the .csv file and get index of the required signal
with open('%s.csv' % shot, newline='') as f:
reader = csv.reader(f)
row1 = next(reader)
try:
index1 = row1.index('#ToTtotal[arb]')
index2 = row1.index('#Centroid')
tot_total = np.array([row[index1]
for row in Data_tpx3_cent]) # Total ToT
cent = np.array([row[index2] for row in Data_tpx3_cent])
except:
print("No 'ToA' in the list")
raise SystemExit
# Prepare empty arrays for the indices of particular clusters
ones_full = np.zeros(len(cent))
twos_full = np.zeros(len(cent))
threes_full = np.zeros(len(cent))
fours_full = np.zeros(len(cent))
fives_full = np.zeros(len(cent))
# Fill the arrays with indices
for i in range(0, len(cent)):
if cent[i] == 1:
ones_full[i] = i
elif cent[i] == 2:
twos_full[i] = i
elif cent[i] == 3:
threes_full[i] = i
elif cent[i] == 4:
fours_full[i] = i
else:
fives_full[i] = i
# Cut off the empty part, leaving an array of indices of clusters
ones = ones_full[ones_full != 0]
twos = twos_full[twos_full != 0]
threes = threes_full[threes_full != 0]
fours = fours_full[fours_full != 0]
fives = fives_full[fives_full != 0]
# Transform float to integer for further manipulation
ones = ones.astype(int)
twos = twos.astype(int)
threes = threes.astype(int)
fours = fours.astype(int)
fives = fives.astype(int)
# Prepair array for the data from ToT_total
ones_tot = np.zeros(len(ones))
twos_tot = np.zeros(len(twos))
threes_tot = np.zeros(len(threes))
fours_tot = np.zeros(len(fours))
fives_tot = np.zeros(len(fives))
# Fill the ToT arrays with the corresponding data
for j in range(0, len(ones)):
a = ones[j]
ones_tot[j] = tot_total[a]
a, j = 0, 0
for j in range(0, len(twos)):
a = twos[j]
twos_tot[j] = tot_total[a]
a, j = 0, 0
for j in range(0, len(threes)):
a = threes[j]
threes_tot[j] = tot_total[a]
a, j = 0, 0
for j in range(0, len(fours)):
a = fours[j]
fours_tot[j] = tot_total[a]
a, j = 0, 0
for j in range(0, len(fives)):
a = fives[j]
fives_tot[j] = tot_total[a]
# Prepair a tuple of arrays filled with ToT clusters for stacked histogram
tot_multi = [ones_tot, twos_tot, threes_tot, fours_tot, fives_tot]
labels = ['1', '2', '3', '4', '5']
# To add some text to the legend
empty = np.zeros(len(tot_multi))
# Bin value==25
# bins1 = np.arange(0,25000,1000)
# Comptute a histogram
# hist = np.histogram(tot_multi, bins=bins1)
# timee = hist[0]
# data = hist[1]
# =============================================================================
# Plot
# =============================================================================
fig = plt.figure()
plt.rcParams.update({'font.size': 16})
plt.title("Shot %s: ToT by clusters" % shot)
plt.xlabel("ToT, [a.u.]")
plt.ylabel("Hits, [a.u.]")
plt.plot(empty, color='white', label="Cluster size:")
plt.hist(tot_multi,
1000, (0, 25000),
histtype='step',
stacked=True,
fill=False,
label=labels)
# plt.plot(timee, data)
plt.legend()
# Go to the Figures folder
try:
os.chdir("%s/Figures" % path)
except:
print("No folder, creating one")
os.mkdir("%s/Figures" % path)
os.chdir("%s/Figures" % path)
fig.set_size_inches(20., 12., forward=True)
plt.savefig('%s:ToT_cluster.pdf' % shot)
plt.close(fig)
def ToA_cluster(shot):
# Go to the @shot folder
try:
path = path_to_shot(shot)
except:
raise SystemExit
# Get the data
Data_tpx3_cent = np.genfromtxt('%s.csv' % shot, delimiter=',')
# Read the first row in the .csv file and get index of the required signal
with open('%s.csv' % shot, newline='') as f:
reader = csv.reader(f)
row1 = next(reader)
try:
index1 = row1.index('#ToA')
index2 = row1.index('#Centroid')
time_new = np.array([row[index1]
for row in Data_tpx3_cent]) * 25 / 4096 / 1e6
cent = np.array([row[index2] for row in Data_tpx3_cent])
except:
print("No 'ToA' in the list")
raise SystemExit
# Define the bins with the step of 1.5625
bins1 = np.arange(time_new[0], time_new[-1], 1.5625)
# Compute a histogram
a = np.histogram(time_new, bins=bins1)
# Prepare empty arrays for the indices of particular clusters
ones_full = np.zeros(len(cent))
twos_full = np.zeros(len(cent))
threes_full = np.zeros(len(cent))
fours_full = np.zeros(len(cent))
fives_full = np.zeros(len(cent))
# Fill the arrays with indices
for i in range(0, len(cent)):
if cent[i] == 1:
ones_full[i] = i
elif cent[i] == 2:
twos_full[i] = i
elif cent[i] == 3:
threes_full[i] = i
elif cent[i] == 4:
fours_full[i] = i
else:
fives_full[i] = i
# Cut off the empty part, leaving an array of indices of clusters
ones = ones_full[ones_full != 0]
twos = twos_full[twos_full != 0]
threes = threes_full[threes_full != 0]
fours = fours_full[fours_full != 0]
fives = fives_full[fives_full != 0]
# Transform float to integer for further manipulation
ones = ones.astype(int)
twos = twos.astype(int)
threes = threes.astype(int)
fours = fours.astype(int)
fives = fives.astype(int)
# Prepair array for the data from time_new
ones_toa = np.zeros(len(ones))
twos_toa = np.zeros(len(twos))
threes_toa = np.zeros(len(threes))
fours_toa = np.zeros(len(fours))
fives_toa = np.zeros(len(fives))
# Fill the toa arrays with the corresponding data
for j in range(0, len(ones)):
a = ones[j]
ones_toa[j] = time_new[a]
a, j = 0, 0
for j in range(0, len(twos)):
a = twos[j]
twos_toa[j] = time_new[a]
a, j = 0, 0
for j in range(0, len(threes)):
a = threes[j]
threes_toa[j] = time_new[a]
a, j = 0, 0
for j in range(0, len(fours)):
a = fours[j]
fours_toa[j] = time_new[a]
a, j = 0, 0
for j in range(0, len(fives)):
a = fives[j]
fives_toa[j] = time_new[a]
# Prepair a tuple of arrays filled with toa clusters for stacked histogram
toa_multi = [ones_toa, twos_toa, threes_toa, fours_toa, fives_toa]
labels = ['1', '2', '3', '4', '5']
bins = int((time_new[-1] - time_new[0]) / 1.5625)
empty = np.zeros(len(time_new))
# Define the range limits for the appropriate plot
# left, right = int(np.nonzero(data)[0][0]), int(np.nonzero(data)[0][-1])
fig = plt.figure()
plt.rcParams.update({'font.size': 16})
plt.title("Shot %s: ToA by clusters" % shot)
plt.xlabel("Time, [ms]")
plt.ylabel("Hits, [a.u.]")
plt.plot(empty, color='white', label="Cluster size:")
a = plt.hist(toa_multi,
bins, (time_new[0], time_new[-1]),
histtype='step',
stacked=True,
fill=False,
label=labels)
# Get rid off the noisy "ones" for an appropriate plot
data = None
for i in range(0, 4):
if max(a[0][i + 1]) > max(a[0][i]):
data = a[0][i + 1]
timee = a[1]
ones = np.where(data < 10)[0]
for i in range(0, len(ones)):
data[ones[i]] = 0
# Define limits of x axis for an appropriate plot
try:
left, right = int(timee[np.nonzero(data)[0][0]] -
10), int(timee[np.nonzero(data)[0][-1]] + 10)
plt.xlim(left, right)
except:
print("Probably no data at all or just a weak signal")
pass
plt.legend(loc="upper left")
# Go to the Figures folder
try:
os.chdir("%s/Figures" % path)
except:
print("No folder, creating one")
os.mkdir("%s/Figures" % path)
os.chdir("%s/Figures" % path)
fig.set_size_inches(20., 12., forward=True)
plt.savefig('%s:ToA_cluster.pdf' % shot)
plt.close(fig)