def main():
args = io_parse_arguments()
input_file = args.i
output_path = args.o
filenames = open(input_file, "r")
for index, i_filename in enumerate(filenames.readlines()):
filename = i_filename.strip()
date = filename.split("/")[-2]
charge_file = output_path + "/{}_1400V_charges.txt".format(date)
charge_plot_file = output_path + "/{}_1400V_charge.pdf".format(date)
charge_fit_file = output_path + "/{}_1400V_charge_fit.pdf".format(date)
template_file = output_path + "/{}_1400V_templates.txt".format(date)
print("\n\n>>> Input file: {}".format(filename))
if not test_file(template_file) or not test_file(charge_file):
file = minidom.parse(filename)
print(">>> Parsed file")
traces = file.getElementsByTagName('trace')
store_template(template_file, traces)
template = get_template(template_file)
print(">>> Got template")
store_charges(charge_file, traces, template)
charges = get_charges(charge_file)
print(">>> Got Charges")
plot_charge(charge_plot_file, charges)
pars = plot_fit(charge_fit_file, charges)
print(">>> Finished")
def main():
plt.rcParams["figure.figsize"] = (20, 5)
args = io_parse_arguments()
directory = args.i
output_filename = args.o
try:
filenames_file = open(directory + "filenames.txt", 'r')
except FileNotFoundError as fnf_error:
print(fnf_error)
raise Exception("Error opening data file")
file_names = np.loadtxt(directory + "filenames.txt",
delimiter=',',
dtype={
'names': ['filename'],
'formats': ['S100']
},
unpack=True)
success_rate = [0, 0]
num_average = 7
re_bin = 10
number_total_weeks = 60
last_temp = [[], []]
x_og = []
num_in_week = [[0 for i in range(number_total_weeks + 1)],
[0 for i in range(number_total_weeks + 1)]]
apulse_num_y = [[[] for i in range(10)], [[] for i in range(10)]]
day_nums = [[], []]
color = 'g-'
#for i_file in tqdm.tqdm(range(file_names.size)):
for i_file in range(file_names.size):
#print(directory + "/" + file_names[i_file][0].decode("utf-8"))
file = ROOT.TFile(directory + file_names[i_file][0].decode("utf-8"),
"READ")
date = file_names[i_file][0].decode("utf-8").split("_")[0]
exposure = process_exposure(np.array([int(date)]))[0]
day_num = process_date(np.array([int(date)]))[0]
week_num = int(day_num / num_average)
time = file_names[i_file][0].decode("utf-8").split("_")[1]
if i_file == 0:
name = date + "_" + 'GAO607_apulse_times_1400V'
hist = file.Get(name)
temp = []
the_bin = 0
#print(hist.GetNbinsX())
for i_bin in range(hist.GetNbinsX()):
if the_bin == 0:
temp0 = 0
temp0 += hist.GetBinContent(i_bin)
the_bin += 1
if the_bin == re_bin:
temp.append(temp0)
x_og.append(i_bin * hist.GetBinWidth(i_bin))
the_bin = 0
for i in range(number_total_weeks + 1):
last_temp[0].append(np.zeros_like(np.array(temp)))
last_temp[1].append(np.zeros_like(np.array(temp)))
if day_num < 0:
continue
for i_channel in range(2):
#print(date + "_" + names[i_channel] + "_apulse_times_1400")
hist = file.Get(date + "_" + names[i_channel] +
"_apulse_times_1400V")
hist_0 = file.Get(date + "_" + names[i_channel] +
"_apulse_num_1400V")
try:
hist.GetNbinsX()
except:
del hist
continue
temp = []
the_bin = 0
x = []
for i_bin in range(hist.GetNbinsX()):
if the_bin == 0:
temp0 = 0
temp0 += hist.GetBinContent(i_bin)
the_bin += 1
if the_bin == re_bin:
temp.append(temp0)
x.append(i_bin * hist.GetBinWidth(i_bin))
the_bin = 0
if i_channel == 0:
if 0 < day_num < 98:
color = 'b-'
if day_num >= 98:
color = 'r-'
plt.plot(x, 100 * np.array(temp) / hist_0.GetEntries(), color)
plt.plot(0, 0, 'g-', label="Atmosphere He")
plt.plot(0, 0, 'b-', label="1% He")
plt.plot(0, 0, 'r-', label="10% He")
plt.axvline(1580, ls='--', color='r', label=r"$He^+$")
plt.axvline(2680, ls='--', color='g', label=r"$H_2O^+$")
plt.axvline(1290, ls='--', color='k', label=r"$H_2^+ He^{2+}$")
plt.axvline(2560, ls='--', color='b', label=r"$CH_4^+$")
plt.axvline(3850, ls='--', color='c', label=r"$CO_2^+$")
plt.legend(loc="upper right")
plt.ylim(0, 15)
plt.xlim(800, 7000)
plt.xlabel("afterpulse time in waveform /ns")
plt.ylabel("normalised counts /%")
plt.title("Ch{} Date:{} Exposure:{} atm-day DayNUM:{}".format(
i_channel, date, round(exposure, 2), day_num))
plt.grid()
#plt.yscale('log')
plt.show(block=False)
plt.pause(0.001)
plt.close()
last_temp[i_channel][week_num] += 100 * np.array(
temp) / hist.GetEntries()
num_in_week[i_channel][week_num] += 1
success_rate[i_channel] += 1
del hist
for i_channel in range(2):
hist = file.Get(date + "_" + names[i_channel] +
"_apulse_num_1400V")
try:
hist.GetNbinsX()
except:
del hist
continue
for i_bin in range(len(apulse_num_y[i_channel])):
if hist.GetEntries() > 0:
apulse_num_y[i_channel][i_bin].append(
hist.GetBinContent(i_bin) / hist.GetEntries())
else:
pass
day_nums[i_channel].append(day_num)
del hist
file.Close()
del file
for i_week in range(len(last_temp[0])):
if i_week < 14:
color = 'b-'
elif i_week >= 14:
color = 'r-'
else:
color = 'g-'
plt.plot(x_og, last_temp[0][i_week] / num_in_week[0][i_week], color)
plt.title("Ch:{} Week num:{}".format(0, i_week))
plt.ylim(0, 6)
plt.plot(0, 0, 'g-', label="Atmosphere He")
plt.plot(0, 0, 'b-', label="1% He")
plt.plot(0, 0, 'r-', label="10% He")
plt.axvline(1580, ls='--', color='r', label=r"$He^+$")
plt.axvline(2680, ls='--', color='g', label=r"$H_2O^+$")
plt.axvline(1290, ls='--', color='k', label=r"$H_2^+ He^{2+}$")
plt.axvline(2560, ls='--', color='b', label=r"$CH_4^+$")
plt.axvline(3850, ls='--', color='c', label=r"$CO_2^+$")
plt.xlim(800, 7000)
plt.xlabel("afterpulse time in waveform /ns")
plt.ylabel("normalised counts /%")
plt.legend(loc="upper right")
plt.grid()
plt.show(block=False)
plt.pause(0.01)
plt.close()
#plt.rcParams["figure.figsize"] = (20, 20)'''
'''for i_bin in range(1,len(apulse_num_y[0])):
ylim = 0
if i_bin == 1:
ylim = 100
else:
ylim = 100/ (i_bin*2)
plt.subplot(3,1,1)
plt.plot(day_nums[0],100*np.array(apulse_num_y[0][i_bin]), 'k.')
plt.axvline(98, color='r', ls='--')
plt.axvline(0, color='b', ls='--')
plt.title("Apulse num:{}".format(i_bin-1))
plt.grid()
plt.ylim(0,ylim)
plt.subplot(3,1,2)
plt.plot(day_nums[1],100*np.array(apulse_num_y[1][i_bin]), 'k.')
plt.axvline(98, color='r', ls='--')
plt.axvline(0, color='b', ls='--')
plt.ylabel("Percentage apulses /%")
plt.grid()
plt.ylim(0, ylim)
plt.subplot(3,1,3)
plt.plot(day_nums[0], np.array(apulse_num_y[0][i_bin])/np.array(apulse_num_y[1][i_bin]), 'k.')
plt.axvline(98, color='r', ls='--')
plt.axvline(0, color='b', ls='--')
plt.xlabel("Exposure time /days")
plt.grid()
plt.ylabel("Ratio")
plt.savefig('/Users/willquinn/Desktop/apulse_num_bin_'+str(i_bin))
plt.close()'''
print("Success rate: {} Ch0 {} Ch1".format(
success_rate[0] / file_names.size, success_rate[1] / file_names.size))
return
def main():
args = io_parse_arguments()
input_data_filename = args.i
output_filename = args.o
name = input_data_filename.split("/")[-1]
name = name.split(".root")[0]
gveto_num = 0
xwall_num = 0
mw_8i_num = 0
mw_5i_num = 0
mwall_num = 0
x_array_gveto = []
y_array_gveto = []
z_array_gveto = []
x_array_xwall = []
y_array_xwall = []
z_array_xwall = []
x_array_mw_8i = []
y_array_mw_8i = []
z_array_mw_8i = []
x_array_mw_5i = []
y_array_mw_5i = []
z_array_mw_5i = []
gveto_om_list = [[[0 for i in range(16)], [0 for i in range(16)]],
[[0 for i in range(16)], [0 for i in range(16)]]]
xwall_om_list = [[[[0 for i in range(16)], [0 for i in range(16)]],
[[0 for i in range(16)], [0 for i in range(16)]]],
[[[0 for i in range(16)], [0 for i in range(16)]],
[[0 for i in range(16)], [0 for i in range(16)]]]]
mwall_om_list = [[[0 for i in range(13)] for j in range(20)],
[[0 for i in range(13)] for j in range(20)]]
file = ROOT.TFile(input_data_filename, "READ")
output = ROOT.TFile(output_filename, "RECREATE")
xy_hist = ROOT.TH2I("xy", "xy", 8000, -4000, 4000, 8000, -4000, 4000)
yz_hist = ROOT.TH2I("yz", "yz", 8000, -4000, 4000, 8000, -4000, 4000)
xz_hist = ROOT.TH2I("xz", "xz", 8000, -4000, 4000, 8000, -4000, 4000)
gveto_event_rate = ROOT.TH1I("gveto_rate", "gveto_rate", 1000000, 0,
1000000)
xwall_event_rate = ROOT.TH1I("xwall_rate", "xwall_rate", 1000000, 0,
1000000)
mwall_event_rate_8inch = ROOT.TH1I("mwall_rate_8inch", "mwall_rate_8inch",
1000000, 0, 1000000)
mwall_event_rate_5inch = ROOT.TH1I("mwall_rate_5inch", "mwall_rate_5inch",
1000000, 0, 1000000)
om_2d_hist = ROOT.TH2I(name, name, 24, 0, 24, 36, 0, 36)
for event in file.T:
x = event.x
y = event.y
z = event.z
xy_hist.Fill(x, y)
yz_hist.Fill(y, z)
xz_hist.Fill(x, z)
# Cuts on xy
# Isolate XW and MW OMs
if (-1700 < z < 1700):
# XW
if (-550 < x < 550):
x_array_xwall.append(x)
y_array_xwall.append(y)
z_array_xwall.append(z)
xwall_num += 1
# End 1
if y > 0:
e = 1
# Side 1
if x < 0:
s = 1
# Side 0
else:
s = 0
# End 0
else:
e = 0
# Side 1
if x < 0:
s = 1
# Side 0
else:
s = 0
c = get_xwall_col_num(x)
fill_xwall_om(xwall_om_list, s, e, c, z)
# MW
else:
mwall_num += 1
# Side 1
if x > 0:
s = 1
# Side 0
else:
s = 0
type = fill_mwall_om(mwall_om_list, s, y, z)
if type == 5:
mw_5i_num += 1
x_array_mw_5i.append(x)
y_array_mw_5i.append(y)
z_array_mw_5i.append(z)
else:
mw_8i_num += 1
x_array_mw_8i.append(x)
y_array_mw_8i.append(y)
z_array_mw_8i.append(z)
# The rest are the GV OMs
else:
gveto_num += 1
x_array_gveto.append(x)
y_array_gveto.append(y)
z_array_gveto.append(z)
# Top
if z > 0:
t = 1
# Side 1
if x > 0:
s = 1
# Side 0
else:
s = 0
# Bottom
else:
t = 0
# Side 1
if x > 0:
s = 1
# Side 0
else:
s = 0
fill_gveto_om(gveto_om_list, s, t, y)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(np.array(x_array_mw_5i),
np.array(y_array_mw_5i),
np.array(z_array_mw_5i),
c='g',
marker='.',
label='MW 5\"')
ax.scatter(np.array(x_array_mw_8i),
np.array(y_array_mw_8i),
np.array(z_array_mw_8i),
c='k',
marker='.',
label='MW 8\"')
ax.scatter(np.array(x_array_gveto),
np.array(y_array_gveto),
np.array(z_array_gveto),
c='r',
marker='.',
label='GVeto')
ax.scatter(np.array(x_array_xwall),
np.array(y_array_xwall),
np.array(z_array_xwall),
c='b',
marker='.',
label='XWall')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.legend()
plt.savefig('/Users/willquinn/Desktop/pmt_glass_sim_test/' + name)
plt.close(fig)
gveto_oms = []
xwall_oms = []
mw_5i_oms = []
mw_8i_oms = []
for i_side in range(2):
if i_side == 0:
s = -1
else:
s = 1
for i_end in range(2):
for i_col in range(2):
for i_om in range(len(xwall_om_list[i_side][i_end][i_col])):
if i_end == 1:
x = 23 - i_col
if i_side == 0:
y = i_om + 1
else:
y = i_om + 19
else:
x = 0 + i_col
if i_side == 0:
y = i_om + 1
else:
y = i_om + 19
xwall_event_rate.Fill(
xwall_om_list[i_side][i_end][i_col][i_om])
xwall_oms.append(xwall_om_list[i_side][i_end][i_col][i_om])
for i in range(xwall_om_list[i_side][i_end][i_col][i_om]):
om_2d_hist.Fill(x, y)
for i_om in range(len(gveto_om_list[i_side][i_end])):
if i_side == 0:
if i_end == 1:
y = 17
else:
y = 0
else:
if i_end == 1:
y = 35
else:
y = 18
x = i_om + 4
gveto_event_rate.Fill(gveto_om_list[i_side][i_end][i_om])
gveto_oms.append(gveto_om_list[i_side][i_end][i_om])
for i in range(gveto_om_list[i_side][i_end][i_om]):
om_2d_hist.Fill(x, y)
for i_col in range(len(mwall_om_list[i_side])):
for i_row in range(len(mwall_om_list[i_side][i_col])):
if i_side == 0:
y = 2 + i_row
else:
y = 20 + i_row
x = 2 + i_col
if i_row == 12 or i_row == 0:
mwall_event_rate_5inch.Fill(
mwall_om_list[i_side][i_col][i_row])
mw_5i_oms.append(mwall_om_list[i_side][i_col][i_row])
else:
mwall_event_rate_8inch.Fill(
mwall_om_list[i_side][i_col][i_row])
mw_8i_oms.append(mwall_om_list[i_side][i_col][i_row])
for i in range(mwall_om_list[i_side][i_col][i_row]):
om_2d_hist.Fill(x, y)
gveto_oms_array = np.array(gveto_oms)
g_x = []
for i in range(gveto_oms_array.size):
g_x.append(i)
g_x = np.array(g_x)
xwall_oms_array = np.array(xwall_oms)
x_x = []
for i in range(xwall_oms_array.size):
x_x.append(i)
x_x = np.array(x_x)
mw_5i_oms_array = np.array(mw_5i_oms)
m_x_5 = []
for i in range(mw_5i_oms_array.size):
m_x_5.append(i)
m_x_5 = np.array(m_x_5)
mw_8i_oms_array = np.array(mw_8i_oms)
m_x_8 = []
for i in range(mw_8i_oms_array.size):
m_x_8.append(i)
m_x_8 = np.array(m_x_8)
g_test = 0
x_test = 0
m_test_5 = 0
m_test_8 = 0
if gveto_num > 0:
g_test = 1
if xwall_num > 0:
x_test = 1
if mw_5i_num > 0:
m_test_5 = 1
if mw_8i_num > 0:
m_test_8 = 1
num_gveto_expected = (gveto_num + xwall_num + mw_8i_num + mw_5i_num) / (
g_test * 64 + x_test * 128 + m_test_5 * 80 + m_test_8 *
(189.444 / 101.832) * 440)
g_sig = np.sqrt(num_gveto_expected)
num_xwall_expected = (gveto_num + xwall_num + mw_8i_num + mw_5i_num) / (
g_test * 64 + x_test * 128 + m_test_5 * 80 + m_test_8 *
(189.444 / 101.832) * 440)
x_sig = np.sqrt(num_xwall_expected)
num_mw_5i_expected = (gveto_num + xwall_num + mw_8i_num + mw_5i_num) / (
g_test * 64 + x_test * 128 + m_test_5 * 80 + m_test_8 *
(189.444 / 101.832) * 440)
m_sig_5 = np.sqrt(num_mw_5i_expected)
num_mw_8i_expected = (gveto_num + xwall_num + mw_8i_num + mw_5i_num) / (
g_test * (101.832 / 189.444) * 64 + x_test *
(101.832 / 189.444) * 128 + m_test_5 *
(101.832 / 189.444) * 80 + m_test_8 * 440)
m_sig_8 = np.sqrt(num_mw_8i_expected)
if g_test == 1:
plt.errorbar(g_x,
gveto_oms_array,
yerr=np.sqrt(gveto_oms_array),
fmt='k.',
ecolor='k')
plt.plot([], [], 'k.', label="GVeto Num of Vertexes")
plt.plot(g_x,
np.ones_like(g_x) * num_gveto_expected,
"r-",
label="Expected = {:.3f}".format(num_gveto_expected))
plt.plot(g_x,
np.ones_like(g_x) * (num_gveto_expected + g_sig),
"b-",
label="+/- 1 $\sigma$")
plt.plot(g_x, np.ones_like(g_x) * (num_gveto_expected - g_sig), "b-")
plt.ylabel("Number of vertexes")
plt.legend()
plt.savefig('/Users/willquinn/Desktop/pmt_glass_sim_test/' + name +
"_gvet")
plt.close()
if x_test == 1:
plt.errorbar(x_x,
xwall_oms_array,
yerr=np.sqrt(xwall_oms_array),
fmt='k.',
ecolor='k')
plt.plot([], [], 'k.', label="XWall Num of Vertexes")
plt.plot(x_x,
np.ones_like(x_x) * num_xwall_expected,
"r-",
label="Expected = {:.3f}".format(num_xwall_expected))
plt.plot(x_x,
np.ones_like(x_x) * (num_xwall_expected + x_sig),
"b-",
label="+/- 1 $\sigma$")
plt.plot(x_x, np.ones_like(x_x) * (num_xwall_expected - x_sig), "b-")
plt.ylabel("Number of vertexes")
plt.legend()
plt.savefig('/Users/willquinn/Desktop/pmt_glass_sim_test/' + name +
"_xwal")
plt.close()
if m_test_5 == 1:
plt.errorbar(m_x_5,
mw_5i_oms_array,
yerr=np.sqrt(mw_5i_oms_array),
fmt='k.',
ecolor='k')
plt.plot([], [], 'k.', label="MWall 5\" Num of Vertexes")
plt.plot(m_x_5,
np.ones_like(m_x_5) * num_mw_5i_expected,
"r-",
label="Expected = {:.3f}".format(num_mw_5i_expected))
plt.plot(m_x_5,
np.ones_like(m_x_5) * (num_mw_5i_expected + m_sig_5),
"b-",
label="+/- 1 $\sigma$")
plt.plot(m_x_5,
np.ones_like(m_x_5) * (num_mw_5i_expected - m_sig_5), "b-")
plt.ylabel("Number of vertexes")
plt.legend()
plt.savefig('/Users/willquinn/Desktop/pmt_glass_sim_test/' + name +
"_mw5i")
plt.close()
if m_test_8 == 1:
plt.errorbar(m_x_8,
mw_8i_oms_array,
yerr=np.sqrt(mw_8i_oms_array),
fmt='k.',
ecolor='k')
plt.plot([], [], 'k.', label="MWall 8\" Num of Vertexes")
plt.plot(m_x_8,
np.ones_like(m_x_8) * num_mw_8i_expected,
"r-",
label="Expected = {:.3f}".format(num_mw_8i_expected))
plt.plot(m_x_8,
np.ones_like(m_x_8) * (num_mw_8i_expected + m_sig_8),
"b-",
label="+/- 1 $\sigma$")
plt.plot(m_x_8,
np.ones_like(m_x_8) * (num_mw_8i_expected - m_sig_8), "b-")
plt.ylabel("Number of vertexes")
plt.legend()
plt.savefig('/Users/willquinn/Desktop/pmt_glass_sim_test/' + name +
"_mw8i")
plt.close()
c1 = ROOT.TCanvas(name)
c1.cd()
c1.SetGrid()
om_2d_hist.Draw("colztext")
ROOT.gStyle.SetOptStat(0)
c1.SaveAs("~/Desktop/pmt_glass_sim_test/" + name + ".pdf")
print(">>> Number of simulated vertexes: ",
gveto_num + xwall_num + mw_8i_num + mw_5i_num)
print(">>> Percentage of GVeto vertexes: ",
gveto_num / (gveto_num + xwall_num + mw_8i_num + mw_5i_num) * 100)
print(">>> Mean number of vertexes per GVeto OM: ",
gveto_event_rate.GetMean())
print(">>> StdDev of vertexes over GVeto OMs: ",
gveto_event_rate.GetStdDev())
if gveto_event_rate.GetMean() == 0:
pass
else:
print(">>> Resolution of GVeto vertexes: ",
gveto_event_rate.GetStdDev() / gveto_event_rate.GetMean() * 100)
print(">>> Percentage of XWall vertexes: ",
xwall_num / (gveto_num + xwall_num + mw_8i_num + mw_5i_num) * 100)
print(">>> Mean number of vertexes per XWall OM: ",
xwall_event_rate.GetMean())
print(">>> StdDev of vertexes over XWall OMs: ",
xwall_event_rate.GetStdDev())
if xwall_event_rate.GetMean() == 0:
pass
else:
print(">>> Resolution of XWall vertexes: ",
xwall_event_rate.GetStdDev() / xwall_event_rate.GetMean() * 100)
print(">>> Percentage of MWall 8\" vertexes: ",
mw_8i_num / (gveto_num + xwall_num + mw_8i_num + mw_5i_num) * 100)
print(">>> Mean number of vertexes per MW 8\" OM: ",
mwall_event_rate_8inch.GetMean())
print(">>> StdDev of vertexes over MW 8\" OMs: ",
mwall_event_rate_8inch.GetStdDev())
if mwall_event_rate_8inch.GetMean() == 0:
pass
else:
print(
">>> Resolution of MW \8"
" vertexes: ",
mwall_event_rate_8inch.GetStdDev() /
mwall_event_rate_8inch.GetMean() * 100)
print(">>> Percentage of MWall 5\" vertexes: ",
mw_5i_num / (gveto_num + xwall_num + mw_8i_num + mw_5i_num) * 100)
print(">>> Mean number of vertexes per MW 5\" OM: ",
mwall_event_rate_5inch.GetMean())
print(">>> StdDev of vertexes over MW 5\" OMs: ",
mwall_event_rate_5inch.GetStdDev())
if mwall_event_rate_5inch.GetMean() == 0:
pass
else:
print(
">>> Resolution of MW 5\" vertexes: ",
mwall_event_rate_8inch.GetStdDev() /
mwall_event_rate_5inch.GetMean() * 100)
file.Close()
output.cd()
output.Write()
output.Close()
def main():
args = io_parse_arguments()
input_file = args.i
template_file = args.t
if input_file is None:
input_file = "/Users/willquinn/Desktop/test.root"
if template_file is None:
template_file = "/Users/willquinn/Desktop/templates.root"
#t emplates, comparison_template = get_templates(template_file, comparison=1)
# draw_template_quality(comparison_template, templates)
om_hvs = load_HV(
"/Users/willquinn/Desktop/calorimeter_equalized_04Mar2020.txt")
# draw_HVs(om_hvs)
root_file = ROOT.TFile(input_file, "READ")
tree = root_file.T
charges = [[] for i in range(712)]
apulse_nums = [[] for i in range(712)]
apulse_times = [[] for i in range(712)]
apulse_amplitudes = [[] for i in range(712)]
n_events = [0 for i in range(712)]
i_event = 0
for event in tree:
# baseline = event.baseline
# waveform = np.array(event.waveform)
OM_ID = event.OM_ID
n_events[OM_ID] += 1
# print(event.charge)
# charges[OM_ID].append(event.charge)
main_pulse_time = event.main_pulse_time
for i_pulse in list(event.apulse_times):
apulse_times[OM_ID].append(i_pulse - main_pulse_time)
#print((i_pulse - main_pulse_time)/0.64)
for i_pulse in list(event.apulse_amplitudes):
apulse_amplitudes[OM_ID].append(i_pulse)
apulse_nums[OM_ID].append(event.apulse_num)
'''if event.apulse_num > 4:
sweep_example(om_id_string(OM_ID), templates[OM_ID], waveform-baseline)
break'''
# mf_example(waveform-baseline, [np.array(event.mf_shapes), np.array(event.mf_amplitudes)],
# list(event.apulse_times))
i_event += 1
if not i_event % 100000:
print(f">>> processed {i_event}/{tree.GetEntries()}")
# draw_AAN(apulse_nums)
# draw_PAR(apulse_nums)
draw_ATD(apulse_times)
# draw_AAD(apulse_amplitudes)
# draw_event_map(n_events)
draw_HV_ATD(om_hvs, apulse_times)
# draw_charges(charges)
root_file.Close()
def main():
args = io_parse_arguments()
data_file_Ch0_name = args.i + "resolution_vs_date_Ch0.txt"
data_file_Ch1_name = args.i + "resolution_vs_date_Ch1.txt"
try:
data_file_Ch0 = open(data_file_Ch0_name, 'r')
data_file_Ch1 = open(data_file_Ch1_name, 'r')
except FileNotFoundError as fnf_error:
print(fnf_error)
raise Exception("Error opening data file")
data_Ch0 = np.loadtxt(data_file_Ch0_name, delimiter=',', dtype={
'names': ('unix', 'date', 'timestamp', 'mu', 'mu_err', 'sigma', 'sigma_err', 'res', 'chi_err'),
'formats': ('i4', 'i4', 'i4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4')}, unpack=True)
data_Ch1 = np.loadtxt(data_file_Ch1_name, delimiter=',',
dtype={'names': (
'unix', 'date', 'timestamp', 'mu', 'mu_err', 'sigma', 'sigma_err', 'res', 'chi_err'),
'formats': ('i4', 'i4', 'i4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4')}, unpack=True)
data = [data_Ch0, data_Ch1]
dates = [data_Ch0[1], data_Ch1[1]]
day_nums = [process_date(data_Ch0[1]), process_date(data_Ch1[1])]
exposures = [process_exposure(data_Ch0[1]), process_exposure(data_Ch1[1])]
mus = [data_Ch0[3], data_Ch1[3]]
mu_errs = [data_Ch0[4], data_Ch1[4]]
sigs = [data_Ch0[5], data_Ch1[5]]
sig_errs = [data_Ch0[6], data_Ch1[6]]
res = [data_Ch0[7], data_Ch1[7]]
res_errs = [res[0] * np.sqrt((sig_errs[0] / sigs[0]) ** 2 + (mu_errs[0] / mus[0]) ** 2),
res[1] * np.sqrt((sig_errs[1] / sigs[1]) ** 2 + (mu_errs[1] / mus[1]) ** 2)]
chis = [data_Ch0[8], data_Ch1[8]]
p_guess = [0.02420914, 3]
p_bounds = [[0, 0], [1, 4]]
filter_lists = [[], []]
for i in range(2):
x = day_nums[i]
filter_list = []
for chi_2 in chis[i]:
if chi_2 < 10:
filter_list.append(True)
else:
filter_list.append(False)
for index,element in enumerate(x):
if element < 0:
filter_list[index] = False
else:
pass
filter_lists[i] = filter_list
x_array = np.linspace(np.amin(x[filter_list]), np.amax(x[filter_list]), 10000)
popt, pcov = curve_fit(linear, x[filter_list], res[i][filter_list],
sigma=res_errs[i][filter_list],
p0=p_guess, bounds=p_bounds, maxfev=5000)
chi_2 = chi2(linear(x[filter_list], *popt),
res_errs[i][filter_list],
res[i][filter_list],
2)
print(popt, pcov, chi_2)
fig, ax1 = plt.subplots()
ax1.set_title("1MeV PMT Resolution Ch{}".format(i))
ax1.grid(True)
color = 'tab:red'
plt.errorbar(x[filter_list],res[i][filter_list],
res_errs[i][filter_list],
fmt='.',
color=color)
plt.plot(x_array, linear(x_array, *popt), 'g')
if i ==0:
plt.axvline(98, color='r', ls='--')
plt.axvline(0, color='b', ls='--')
ax1.text(0, 3, 'linear fit: y = ({:.4f}±{:.4f})*x + ({:.3f}±{:.3f}) chi2R: {:.3f}'.format(popt[0],
np.sqrt(
pcov[0][0]),
popt[1],
np.sqrt(
pcov[1][1]),
chi_2))
ax1.set_xlabel("Exposure Time /days")
ax1.set_ylabel("1Mev Resolution /%", color=color)
ax1.set_ylim(2, 4.25)
ax1.tick_params(axis='y', labelcolor=color)
color = 'tab:blue'
ax2 = ax1.twinx()
ax2.plot(x[filter_list], chis[i][filter_list], '.', color=color)
ax2.set_ylabel('Chi2 of bismuth 1MeV fit', color=color) # we already handled the x-label with ax1
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_ylim(0, 10)
fig.tight_layout()
plt.savefig("/Users/willquinn/Desktop/resolution_vs_date_time_" + str(i))
plt.close()
for i in range(2):
x = exposures[i]
filter_list = []
for chi_2 in chis[i]:
if chi_2 < 10:
filter_list.append(True)
else:
filter_list.append(False)
for index,element in enumerate(x):
if element < 0.01:
filter_list[index] = False
else:
pass
filter_lists[i] = filter_list
x_array = np.linspace(np.amin(x[filter_list]), np.amax(x[filter_list]), 10000)
popt, pcov = curve_fit(linear, x[filter_list], res[i][filter_list],
sigma=res_errs[i][filter_list],
p0=p_guess, bounds=p_bounds, maxfev=5000)
chi_2 = chi2(linear(x[filter_list], *popt),
res_errs[i][filter_list],
res[i][filter_list],
2)
print(popt, pcov, chi_2)
fig, ax1 = plt.subplots()
ax1.set_title("1MeV PMT Resolution Ch{}".format(i))
ax1.grid(True)
color = 'tab:red'
plt.errorbar(x[filter_list],res[i][filter_list],
res_errs[i][filter_list],
fmt='.',
color=color)
plt.plot(x_array, linear(x_array, *popt), 'g')
if i == 0:
plt.axvline(1/10 + 97/100, color='r', ls='--')
plt.axvline(29/1000000, color='b', ls='--')
ax1.text(0, 3, 'linear fit: y = ({:.4f}±{:.4f})*x + ({:.3f}±{:.3f}) chi2R: {:.3f}'.format(popt[0],
np.sqrt(
pcov[0][0]),
popt[1],
np.sqrt(
pcov[1][1]),
chi_2))
ax1.set_xlabel("Exposure Time /atm-days")
ax1.set_ylabel("1Mev Resolution /%", color=color)
ax1.set_ylim(2, 4.25)
ax1.tick_params(axis='y', labelcolor=color)
color = 'tab:blue'
ax2 = ax1.twinx()
ax2.plot(x[filter_list], chis[i][filter_list], '.', color=color)
ax2.set_ylabel('Chi2 of bismuth 1MeV fit', color=color) # we already handled the x-label with ax1
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_ylim(0, 10)
fig.tight_layout()
plt.savefig("/Users/willquinn/Desktop/resolution_vs_date_exp_" + str(i))
plt.close()
x_t = []
x_e = []
y = []
y_err = []
for i in range(day_nums[0].size):
for j in range(day_nums[1].size):
if day_nums[0][i] == day_nums[1][j]:
x_t.append(day_nums[0][i])
x_e.append(exposures[0][i])
y.append(res[0][i]/res[1][j])
y_err.append(res[0][i]/res[1][j] * np.sqrt( (res_errs[0][i]/res[0][i])**2 + (res_errs[1][j]/res[1][j])**2 ))
break
plt.errorbar(x_t, y, yerr=y_err, fmt="k.")
plt.axvline(98, color="r", ls="--")
plt.axvline(0, color="b", ls="--")
plt.xlabel("Exposure /days")
plt.ylabel("Ratio res_Ch0/res_Ch1")
plt.title("Ratio of resolution of CH 0 & 1 vs time")
plt.grid()
plt.xlim(np.amin(np.array(x_t)), np.amax(np.array(x_t)))
plt.ylim(0,2)
plt.savefig("/Users/willquinn/Desktop/resolution_ratio_time")
'''x_ch0 = process_date(data_Ch0[1])
x_ch1 = process_date(data_Ch1[1])
x_array = np.linspace(-30, 145, 10000)'''
'''plt.errorbar(x_ch0, data_Ch0[3], data_Ch0[4], fmt='.', color='b')
plt.title("1MeV PMT Charge Ch0")
plt.xlabel("Exposure Time atm-days")
plt.ylabel("1Mev Charge /pC")
plt.ylim(32, 42)
#plt.xscale('log')
plt.grid(True)
plt.show()
plt.errorbar(x_ch1, data_Ch1[3], data_Ch1[4], fmt='.', color='b')
plt.title("1MeV PMT Charge Ch1")
plt.xlabel("Exposure Time /days")
plt.ylabel("1Mev Charge /pC")
plt.ylim(32, 42)
#plt.xscale('log')
plt.grid(True)
plt.show()'''
'''popt_0, pcov_0 = curve_fit(linear, x_ch0, data_Ch0[7],
def main():
args = io_parse_arguments()
input_file = args.i
template_file = args.t
run_num = args.o
if input_file is None:
input_file = "/Users/willquinn/Desktop/test.root"
if template_file is None:
template_file = "/Users/willquinn/Desktop/templates.root"
print(">>> input file: ", input_file)
# templates, comparison_template = get_templates(template_file, comparison=1)
# draw_template(1, om_id_string(1), comparison_template)
# draw_template_quality(comparison_template, templates)
# om_hvs = load_HV("/Users/willquinn/Desktop/SNEMO/calorimeter_equalized_04Mar2020.txt")
# draw_HVs(om_hvs)
gains = get_gain("/Users/williamquinn/Desktop/SNEMO/gains.txt")
# draw_gains(gains)
root_file = ROOT.TFile(input_file, "READ")
tree = root_file.T
raw_charges = [[] for i in range(712)]
amplitudes = [[] for i in range(712)]
raw_amplitudes = [[] for i in range(712)]
charges = [[] for i in range(712)]
baselines = [[] for i in range(712)]
apulse_nums = [[] for i in range(712)]
apulse_times = [[] for i in range(712)]
apulse_amplitudes = [[] for i in range(712)]
n_events = [0 for i in range(712)]
i_event = 0
for event in tree:
baseline = event.baseline
# waveform = np.array(event.waveform)
# plt.plot(waveform)
# plt.show()
OM_ID = event.OM_ID
baselines[OM_ID].append(baseline)
n_events[OM_ID] += 1
#print(-event.charge)
raw_charges[OM_ID].append(-event.raw_charge)
charges[OM_ID].append(-event.charge)
amplitudes[OM_ID].append(-event.amplitude)
raw_amplitudes[OM_ID].append(-event.raw_amplitude)
'''if -event.charge > 1000:
plot_waveform(waveform, baseline, OM_ID)
break'''
'''if event.is_fr and event.is_gveto and OM_ID > 700:
# plot_waveform(waveform, baseline, OM_ID)
#print(baseline, len(np.array(event.mf_shapes)))
mf_example(OM_ID, waveform - baseline, [np.array(event.mf_shapes), np.array(event.mf_amplitudes)],
list(event.apulse_times))'''
main_pulse_time = event.main_pulse_time
for i_pulse in list(event.apulse_times):
apulse_times[OM_ID].append(i_pulse - main_pulse_time)
# print((i_pulse - main_pulse_time)/0.64)
for i_pulse in list(event.apulse_amplitudes):
apulse_amplitudes[OM_ID].append(i_pulse)
apulse_nums[OM_ID].append(event.apulse_num)
'''if event.apulse_num > 4:
print(event.apulse_num)
sweep_example(om_id_string(OM_ID), templates[OM_ID], waveform-baseline)
break'''
# mf_example(waveform-baseline, [np.array(event.mf_shapes), np.array(event.mf_amplitudes)],
# list(event.apulse_times))
i_event += 1
if not i_event % 100000:
print(f">>> processed {i_event}/{tree.GetEntries()}")
# draw_AAN(apulse_nums, run_num)
# draw_PAR(apulse_nums, run_num)
# draw_ATD(apulse_times, run_num)
# draw_AAD(apulse_amplitudes, run_num)
# draw_event_map(n_events, run_num)
# draw_HV_ATD(om_hvs, apulse_times, run_num)
# draw_raw_charges(raw_charges, run_num)
draw_charges(charges, run_num)
# draw_amplitudes(amplitudes, run_num)
# draw_raw_amplitudes(raw_amplitudes, run_num)
# draw_baselines(baselines, run_num)
# draw_baselines_tots(baselines, run_num)
# draw_charge_apulse(charges, apulse_nums, run_num)
# draw_npe_apulse(charges, apulse_nums, gains, run_num)
draw_npe_apulse_tots(charges, apulse_nums, gains, run_num)
root_file.Close()