def main():
# Handle the input arguments:
##############################
args = pmt_parse_arguments()
input_directory = args.i
config_file_name = args.c
output_directory = args.o
##############################
filenames_txt = input_directory + "/filenames.txt"
try:
print(">>> Reading data from file: {}".format(filenames_txt))
date_file = open(filenames_txt, 'r')
except FileNotFoundError as fnf_error:
print(fnf_error)
raise Exception("Error opening data file {}".format(filenames_txt))
filenames = np.loadtxt(filenames_txt,
delimiter=',',
dtype={
'names': ['filename'],
'formats': ['S100']
},
unpack=True)
topology = [2, 1]
pmt_array = PMT_Array(topology, "summary")
pmt_array.set_pmt_id("GAO607", 0)
pmt_array.set_pmt_id("GAO612", 1)
# Set the cuts you wish to apply
# If you don't do this the defaults are used
if config_file_name is not None:
pmt_array.apply_setting(config_file_name)
print_settings(pmt_array)
for i_file in tqdm.tqdm(range(filenames.size)):
file = filenames[i_file][0].decode("utf-8")
try:
read_tree(input_directory + "/" + file, pmt_array,
output_directory,
file.split(".root")[0] + "_output.root")
except:
print("error reading file:", input_directory + "/" + file)
def main():
# Handle the input arguments:
##############################
args = pmt_parse_arguments()
input_directory = args.i
# config_file_name = args.c
output_directory = args.o
##############################
out_files = [
output_directory + '/res_vs_time_ch0.txt',
output_directory + '/res_vs_time_ch1.txt'
]
create_file(out_files[0])
create_file(out_files[1])
filenames_txt = input_directory + "/filenames.txt"
try:
print(">>> Reading data from file: {}".format(filenames_txt))
date_file = open(filenames_txt, 'r')
except FileNotFoundError as fnf_error:
print(fnf_error)
raise Exception("Error opening data file {}".format(filenames_txt))
filenames = np.loadtxt(filenames_txt,
delimiter=',',
dtype={
'names': ['filename'],
'formats': ['S100']
},
unpack=True)
topology = [2, 1]
pmt_array = PMT_Array(topology, "summary")
pmt_array.set_pmt_id("GAO607", 0)
pmt_array.set_pmt_id("GAO612", 1)
'''# Set the cuts you wish to apply
# If you don't do this the defaults are used
if config_file_name is not None:
pmt_array.apply_setting(config_file_name)
# print_settings(pmt_array)'''
# Set up the containers for the summary
resolutions = [[] for i in range(pmt_array.get_pmt_total_number())]
resolutions_err = [[] for i in range(pmt_array.get_pmt_total_number())]
dates = [[] for i in range(pmt_array.get_pmt_total_number())]
gains = [[] for i in range(pmt_array.get_pmt_total_number())]
gains_err = [[] for i in range(pmt_array.get_pmt_total_number())]
baseline_means = [[] for i in range(pmt_array.get_pmt_total_number())]
baseline_sigs = [[] for i in range(pmt_array.get_pmt_total_number())]
fit_chi2 = [[] for i in range(pmt_array.get_pmt_total_number())]
for i_file in tqdm.tqdm(range(filenames.size)):
file = filenames[i_file][0].decode("utf-8")
date = file.split("_")[0]
voltage = int(file.split("_")[1].split("A")[1])
if voltage == 1000:
pass
else:
continue
fit_parameters = read_file(date, voltage, input_directory + "/" + file,
pmt_array, output_directory)
for i_om in range(pmt_array.get_pmt_total_number()):
if len(fit_parameters[i_om]) > 0:
pass
else:
continue
mu = fit_parameters[i_om][0]["mu"]
mu_err = fit_parameters[i_om][0]["mu_err"]
sig = fit_parameters[i_om][0]["sig"]
sig_err = fit_parameters[i_om][0]["sig_err"]
chi_2 = fit_parameters[i_om][0]["chi2"]
gain = fit_parameters[i_om][0]["gain"]
baseline_mean = fit_parameters[i_om][0]["base_mu"]
baseline_sig = fit_parameters[i_om][0]["base_sig"]
res, res_err = get_resolution(mu, mu_err, sig, sig_err)
resolutions[i_om].append(res)
resolutions_err[i_om].append(res_err)
dates[i_om].append(int(date))
gains[i_om].append(gain)
baseline_means[i_om].append(baseline_mean)
baseline_sigs[i_om].append(baseline_sig)
fit_chi2[i_om].append(chi_2)
write_to_file(
out_files[i_om],
'{},{},{},{},{}'.format(date, res, res_err, chi_2, gain))
# Plot individual summaries
for i_om in range(pmt_array.get_pmt_total_number()):
if len(resolutions[i_om]) > 0:
pass
else:
continue
date = process_date(dates[i_om])
try:
start = np.where(date == 0)[0][0]
except:
start = np.where(date == 1)[0][0]
mid = np.where(date == 98)[0][0]
# print("start:",start)
plt.plot(date[:start + 1],
np.array(gains[i_om][:start + 1]) * 2,
"g.",
label="Atmospheric He")
plt.plot(date[start + 1:mid + 1],
np.array(gains[i_om][start + 1:mid + 1]) * 2,
"b.",
label="1% He")
plt.plot(date[mid + 1:],
np.array(gains[i_om][mid + 1:]) * 2,
"r.",
label="10% He")
plt.axvline(date[start], 0, 100, ls='--', color='k')
plt.axvline(date[mid], 0, 100, ls='--', color='k')
plt.xlabel("exposure days relative to 190611")
plt.ylabel("PMT gain at 1Mev /mV")
plt.title(
pmt_array.get_pmt_object_number(i_om).get_pmt_id() +
" Gain at 1MeV vs exposure time")
plt.grid()
plt.ylim(150, 300)
plt.legend(loc='lower right')
plt.savefig(output_directory + "/summary_plots/" +
pmt_array.get_pmt_object_number(i_om).get_pmt_id() +
"_gains_vs_time.png")
plt.close()
plt.errorbar(date,
baseline_means[i_om],
yerr=baseline_sigs[i_om],
fmt='k.-',
ecolor='r')
plt.grid()
plt.xlabel("exposure days relative to 190611")
plt.ylabel("Baseline mean /mV")
plt.title(
pmt_array.get_pmt_object_number(i_om).get_pmt_id() +
" Baseline mean vs exposure time")
plt.savefig(output_directory + "/summary_plots/" +
pmt_array.get_pmt_object_number(i_om).get_pmt_id() +
"_baseline_mean_vs_time.png")
plt.close()
'''plt.plot(date, baseline_sigs[i_om])
plt.xlabel("exposure days relative to 190611")
plt.ylabel("Baseline std-dev")
plt.title(pmt_array.get_pmt_object_number(i_om).get_pmt_id() + " Baseline std-dev vs exposure time")
plt.savefig(output_directory + "/summary_plots/" +
pmt_array.get_pmt_object_number(i_om).get_pmt_id() + "_baseline_sigma_vs_time")
plt.close()'''
res_filter = []
for i_date in range(len(date)):
if 1 < resolutions[i_om][i_date] < 3.75 and fit_chi2[i_om][
i_date] < 10:
res_filter.append(True)
else:
res_filter.append(False)
popt, pcov = curve_fit(
linear,
np.array(date[start:])[res_filter[start:]],
np.array(resolutions[i_om][start:])[res_filter[start:]],
sigma=np.array(resolutions_err[i_om][start:])[res_filter[start:]],
p0=[0.001, 2],
bounds=[[0, 0], [0.002, 3.5]],
maxfev=500000)
x_array = np.linspace(date[start], np.amax(date), 2)
chi_2 = chi2(
np.array(resolutions[i_om][start:])[res_filter[start:]],
np.array(resolutions_err[i_om][start:])[res_filter[start:]],
linear(date[start:][res_filter[start:]], *popt), len(popt))
plt.errorbar(date[:start + 1],
resolutions[i_om][:start + 1],
yerr=resolutions_err[i_om][:start + 1],
fmt="g.",
label="Atmospheric He")
plt.plot(np.array(date[start:])[res_filter[start:]],
np.array(resolutions[i_om][start:])[res_filter[start:]],
'ko',
label="used values")
plt.errorbar(date[start + 1:mid + 1],
resolutions[i_om][start + 1:mid + 1],
yerr=resolutions_err[i_om][start + 1:mid + 1],
fmt="b.",
label="1% He")
plt.errorbar(date[mid + 1:],
resolutions[i_om][mid + 1:],
yerr=resolutions_err[i_om][mid + 1:],
fmt="r.",
label="10% He")
plt.plot(x_array, linear(x_array, *popt), 'k-')
plt.xlabel(
"exposure days relative to 190611 \n $y = (${:.1e}$ ± ${:.0e})$x + (${:.1e}$ ± ${:.0e}$)$ $\chi^2_R = {:.2}$"
.format(popt[0], np.sqrt(pcov[0, 0]), popt[1], np.sqrt(pcov[1, 1]),
chi_2))
plt.ylabel("Resolution at 1MeV /% $\sigma / \mu$")
plt.title(
pmt_array.get_pmt_object_number(i_om).get_pmt_id() +
" Resolution vs exposure time")
plt.axvline(date[start], 0, 100, ls='--', color='k')
plt.axvline(date[mid], 0, 100, ls='--', color='k')
plt.grid()
plt.ylim(2, 4.5)
plt.xlim(np.amin(date), np.amax(date))
plt.legend(loc='upper right')
plt.tight_layout()
plt.savefig(output_directory + "/summary_plots/" +
pmt_array.get_pmt_object_number(i_om).get_pmt_id() +
"_resolution_vs_time.png")
plt.close()
plt.plot(np.array(date[start:])[res_filter[start:]],
np.array(fit_chi2[i_om][start:])[res_filter[start:]],
'ko',
label="used values")
plt.plot(date[:start + 1],
fit_chi2[i_om][:start + 1],
"g.",
label="Atmospheric He")
plt.plot(date[start + 1:mid + 1],
fit_chi2[i_om][start + 1:mid + 1],
"b.",
label="1% He")
plt.plot(date[mid + 1:],
fit_chi2[i_om][mid + 1:],
"r.",
label="10% He")
plt.xlabel("exposure days relative to 190611")
plt.ylabel("$\chi^2_R$")
plt.title(
pmt_array.get_pmt_object_number(i_om).get_pmt_id() +
" Resolution fit $\chi^2_R$ vs exposure time")
plt.grid()
plt.axvline(date[start], 0, 100, ls='--', color='k')
plt.axvline(date[mid], 0, 100, ls='--', color='k')
plt.legend(loc='upper right')
plt.xlim(np.amin(date), np.amax(date))
plt.ylim(0, 10)
plt.tight_layout()
plt.savefig(output_directory + "/summary_plots/" +
pmt_array.get_pmt_object_number(i_om).get_pmt_id() +
"_resolution_vs_time_chi2.png")
plt.close()
# Plot ratio
x_date = []
ratio = []
ratio_err = []
gain_ratio = []
for i in range(len(dates[0])):
for j in range(len(dates[1])):
if dates[0][i] == dates[1][j]:
x_date.append(dates[0][i])
ratio.append(resolutions[0][i] / resolutions[1][j])
ratio_err.append(
resolutions[0][i] / resolutions[1][j] *
np.sqrt((resolutions_err[0][i] / resolutions[0][i])**2 +
(resolutions_err[1][j] / resolutions[1][j])**2))
gain_ratio.append(gains[0][i] / gains[1][j])
break
popt, pcov = curve_fit(linear,
x_date,
ratio,
sigma=ratio_err,
p0=[1, 1],
bounds=[[0, 0], [10, 10]])
x_date = process_date(x_date)
x_array = np.linspace(np.amin(x_date), np.amax(x_date), 2)
chi_2 = chi2(ratio, ratio_err, linear(x_date, *popt), 2)
plt.errorbar(x_date, ratio, yerr=ratio_err, fmt="k.")
plt.plot(
x_array,
linear(x_array, *popt),
"g-",
label=
"$y = (${:.1e}$ ± ${:.0e})$\\times x + (${:.1e}$ ± ${:.0e}$) \chi^2_R = {:.2}$"
.format(popt[0], np.sqrt(pcov[0, 0]), popt[1], np.sqrt(pcov[1, 1]),
chi_2))
plt.axvline(98, color="r", ls="--")
plt.axvline(0, color="b", ls="--")
plt.xlabel("exposure days relative to 190611")
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_date)), np.amax(np.array(x_date)))
plt.ylim(0, 2)
plt.savefig(output_directory +
"/summary_plots/resolution_ratio_vs_time.png")
plt.close()
plt.plot(x_date, gain_ratio, "k.")
plt.axvline(98, color="r", ls="--")
plt.axvline(0, color="b", ls="--")
plt.xlabel("exposure days relative to 190611")
plt.ylabel("Ratio gain_Ch0/gain_Ch1")
plt.title("Ratio of gain of CH 0 & 1 vs time")
plt.grid()
plt.xlim(np.amin(np.array(x_date)), np.amax(np.array(x_date)))
plt.ylim(0.7, 1)
plt.savefig(output_directory + "/summary_plots/gain_ratio_vs_time.png")
plt.close()
print("<<<< FINISHED >>>")
def main():
# Handle the input arguments:
##############################
args = pmt_parse_arguments()
input_directory = args.i
# config_file_name = args.c
output_directory = args.o
##############################
filenames_txt = input_directory + "/filenames.txt"
try:
print(">>> Reading data from file: {}".format(filenames_txt))
date_file = open(filenames_txt, 'r')
except FileNotFoundError as fnf_error:
print(fnf_error)
raise Exception("Error opening data file {}".format(filenames_txt))
filenames = np.loadtxt(filenames_txt, delimiter=',', dtype={
'names': ['filename'],
'formats': ['S100']}, unpack=True)
topology = [2, 1]
pmt_array = PMT_Array(topology, "summary")
pmt_array.set_pmt_id("GAO607", 0)
pmt_array.set_pmt_id("GAO612", 1)
'''# Set the cuts you wish to apply
# If you don't do this the defaults are used
if config_file_name is not None:
pmt_array.apply_setting(config_file_name)
# print_settings(pmt_array)'''
# Set up the containers for the summary
apulse_rates = [[] for i in range(pmt_array.get_pmt_total_number())]
apulse_rates_err = [[] for i in range(pmt_array.get_pmt_total_number())]
he_apulse_rates = [[] for i in range(pmt_array.get_pmt_total_number())]
he_apulse_rates_err = [[] for i in range(pmt_array.get_pmt_total_number())]
dates = [[] for i in range(pmt_array.get_pmt_total_number())]
out_files = [output_directory+'/apulse_num_ch0.txt', output_directory+'/apulse_num_ch1.txt']
create_file(out_files[0])
create_file(out_files[1])
for i_file in tqdm.tqdm(range(filenames.size)):
file = filenames[i_file][0].decode("utf-8")
date = file.split("_")[0]
voltage = int(file.split("_")[1].split("A")[1])
if voltage == 1400:
pass
else:
continue
apulse_info = read_file(date, voltage, input_directory + "/" + file, pmt_array, output_directory)
for i_om in range(pmt_array.get_pmt_total_number()):
if len(apulse_info[i_om]) > 0:
pass
else:
continue
apulse_rate = apulse_info[i_om][0]["apulse_rate"]
he_apulse_rate = apulse_info[i_om][0]["he_apulse_rate"]
apulse_rate_err = apulse_info[i_om][0]["apulse_rate_err"]
he_apulse_rate_err = apulse_info[i_om][0]["he_apulse_rate_err"]
apulse_rates[i_om].append(apulse_rate)
apulse_rates_err[i_om].append(apulse_rate_err/10)
he_apulse_rates[i_om].append(he_apulse_rate)
he_apulse_rates_err[i_om].append(he_apulse_rate_err/10)
dates[i_om].append(int(date))
write_to_file(out_files[i_om], '{},{},{},{},{}'.format(date, apulse_rate, apulse_rate_err, he_apulse_rate, he_apulse_rate_err))
# Plot individual summaries
for i_om in range(pmt_array.get_pmt_total_number()):
if len(apulse_rates[i_om]) > 0:
pass
else:
continue
date = process_date(dates[i_om])
try:
start = np.where(date == 0)[0][0]
except:
start = np.where(date == 1)[0][0]
mid = np.where(date == 98)[0][0]
print(date)
print("start:", start)
plt.figure(num=None, figsize=(9, 5), dpi=80, facecolor='w', edgecolor='k')
plt.errorbar(date[:start + 1], np.array(apulse_rates[i_om][:start + 1]), yerr=np.array(apulse_rates_err[i_om][:start + 1]),
fmt="g.", label="Atmospheric He")
plt.errorbar(date[start+1:mid + 1], np.array(apulse_rates[i_om][start+1:mid + 1]), yerr=np.array(apulse_rates_err[i_om][start+1:mid + 1]),
fmt="b.", label="1% He")
plt.errorbar(date[mid+1:], np.array(apulse_rates[i_om][mid+1:]), yerr=np.array(apulse_rates_err[i_om][mid+1:]),
fmt="r.", label="10% He")
plt.axvline(date[start], 0, 100, ls='--', color='k')
plt.axvline(date[mid], 0, 100, ls='--', color='k')
plt.xlabel("exposure days relative to 191106")
plt.ylabel("Afterpulse rate /%")
plt.title(pmt_array.get_pmt_object_number(i_om).get_pmt_id() + " afterpulse rate vs exposure time")
plt.grid()
plt.ylim(10,90)
plt.legend(loc='upper left')
plt.savefig(output_directory + "/summary_plots/" +
pmt_array.get_pmt_object_number(i_om).get_pmt_id() + "_apulse_rate_vs_time")
plt.close()
plt.figure(num=None, figsize=(9, 5), dpi=80, facecolor='w', edgecolor='k')
plt.errorbar(date[:start + 1], np.array(he_apulse_rates[i_om][:start + 1]), yerr=np.array(he_apulse_rates_err[i_om][:start + 1]),
fmt="g.", label="Atmospheric He")
plt.errorbar(date[start + 1:mid + 1], np.array(he_apulse_rates[i_om][start + 1:mid + 1]), yerr=np.array(he_apulse_rates_err[i_om][start + 1:mid + 1]),
fmt="b.", label="1% He")
plt.errorbar(date[mid + 1:], np.array(he_apulse_rates[i_om][mid + 1:]), yerr=np.array(he_apulse_rates_err[i_om][mid + 1:]),
fmt="r.", label="10% He")
plt.axvline(date[start], 0, 100, ls='--', color='k')
plt.axvline(date[mid], 0, 100, ls='--', color='k')
plt.xlabel("exposure days relative to 191106")
plt.ylabel("Normalised apulse number")
plt.title(pmt_array.get_pmt_object_number(i_om).get_pmt_id() + " afterpulse rate vs exposure time")
plt.grid()
# plt.ylim(150,300)
plt.legend(loc='lower right')
plt.savefig(output_directory + "/summary_plots/" +
pmt_array.get_pmt_object_number(i_om).get_pmt_id() + "_he_apulse_rate_vs_time")
plt.close()
# Plot ratio
x_date = []
ratio = []
ratio_err = []
gain_ratio = []
he_ratio = []
he_ratio_err = []
for i in range(len(dates[0])):
for j in range(len(dates[1])):
if dates[0][i] == dates[1][j]:
x_date.append(dates[0][i])
if apulse_rates[1][j] == 0:
pass
else:
ratio.append(apulse_rates[0][i] / apulse_rates[1][j])
if he_apulse_rates[1][j] == 0:
pass
else:
he_ratio.append(he_apulse_rates[0][i] / he_apulse_rates[1][j])
break
x_date = process_date(x_date)
plt.plot(x_date, ratio, "k.")
plt.axvline(98, color="r", ls="--")
plt.axvline(0, color="b", ls="--")
plt.xlabel("exposure days relative to 191106")
plt.ylabel("Ratio apulse rate Ch0/Ch1")
plt.title("Ratio of after pulse rates of CH 0 & 1 vs time")
plt.grid()
#plt.xlim(np.amin(np.array(x_date)), np.amax(np.array(x_date)))
#plt.ylim(0, 2)
plt.savefig(output_directory + "/summary_plots/apulse_rate_ratio_vs_time")
plt.close()
plt.plot(x_date, he_ratio, "k.")
plt.axvline(98, color="r", ls="--")
plt.axvline(0, color="b", ls="--")
plt.xlabel("exposure days relative to 191106")
plt.ylabel("Ratio apulse rate Ch0/Ch1")
plt.title("Ratio of after pulse rates of CH 0 & 1 vs time")
plt.grid()
#plt.xlim(np.amin(np.array(x_date)), np.amax(np.array(x_date)))
# plt.ylim(0, 2)
plt.savefig(output_directory + "/summary_plots/he_apulse_rate_ratio_vs_time")
plt.close()
def main():
# Handle the input arguments:
##############################
args = pmt_parse_arguments()
input_directory = args.i
run_id = args.c
# config_file_name = args.c
output_directory = args.o
##############################
filenames_txt = input_directory + "/filenames.txt"
try:
print(">>> Reading data from file: {}".format(filenames_txt))
date_file = open(filenames_txt, 'r')
except FileNotFoundError as fnf_error:
print(fnf_error)
raise Exception("Error opening data file {}".format(filenames_txt))
filenames = np.loadtxt(filenames_txt,
delimiter=',',
dtype={
'names': ['filename'],
'formats': ['S100']
},
unpack=True)
topology = [2, 1]
pmt_array = PMT_Array(topology, run_id + "summary")
pmt_array.set_pmt_id("GAO607", 0)
pmt_array.set_pmt_id("GAO612", 1)
# Set up the containers for the summary
par = [[] for i in range(pmt_array.get_pmt_total_number())]
par_err = [[] for i in range(pmt_array.get_pmt_total_number())]
par_he = [[] for i in range(pmt_array.get_pmt_total_number())]
par_he_err = [[] for i in range(pmt_array.get_pmt_total_number())]
aan = [[] for i in range(pmt_array.get_pmt_total_number())]
aan_err = [[] for i in range(pmt_array.get_pmt_total_number())]
aan_he = [[] for i in range(pmt_array.get_pmt_total_number())]
aan_he_err = [[] for i in range(pmt_array.get_pmt_total_number())]
ap_charge = [[] for i in range(pmt_array.get_pmt_total_number())]
ap_charge_err = [[] for i in range(pmt_array.get_pmt_total_number())]
he_ap_charge = [[] for i in range(pmt_array.get_pmt_total_number())]
he_ap_charge_err = [[] for i in range(pmt_array.get_pmt_total_number())]
ratios = [[] for i in range(pmt_array.get_pmt_total_number())]
he_ratios = [[] for i in range(pmt_array.get_pmt_total_number())]
dates = [[] for i in range(pmt_array.get_pmt_total_number())]
for i_file in tqdm.tqdm(range(filenames.size)):
file = filenames[i_file][0].decode("utf-8")
date = file.split("_")[0]
voltage = int(file.split("_")[1].split("A")[1])
if voltage == 1400:
pass
else:
continue
apulse_info = read_file(date, voltage, input_directory + "/" + file,
pmt_array, output_directory)
for i_om in range(pmt_array.get_pmt_total_number()):
if len(apulse_info[i_om]) > 0:
pass
else:
continue
i_par = apulse_info[i_om][0]["par"]
i_par_he = apulse_info[i_om][0]["par_he"]
i_par_err = apulse_info[i_om][0]["par_err"]
i_par_he_err = apulse_info[i_om][0]["par_he_err"]
i_aan = apulse_info[i_om][0]["aan"]
i_aan_he = apulse_info[i_om][0]["aan_he"]
i_aan_err = apulse_info[i_om][0]["aan_err"]
i_aan_he_err = apulse_info[i_om][0]["aan_he_err"]
i_ap_charge = apulse_info[i_om][0]["ap_charge"]
i_ap_charge_err = apulse_info[i_om][0]["ap_charge_err"]
i_he_ap_charge = apulse_info[i_om][0]["he_ap_charge"]
i_he_ap_charge_err = apulse_info[i_om][0]["he_ap_charge_err"]
i_ratio = apulse_info[i_om][0]["ratio"]
i_he_ratio = apulse_info[i_om][0]["he_ratio"]
par[i_om].append(i_par)
par_err[i_om].append(i_par_err / 10)
par_he[i_om].append(i_par_he)
par_he_err[i_om].append(i_par_he_err / 10)
aan[i_om].append(i_aan)
aan_err[i_om].append(i_aan_err)
aan_he[i_om].append(i_aan_he)
aan_he_err[i_om].append(i_aan_he_err)
ap_charge[i_om].append(i_ap_charge)
ap_charge_err[i_om].append(i_ap_charge_err)
he_ap_charge[i_om].append(i_he_ap_charge)
he_ap_charge_err[i_om].append(i_he_ap_charge_err)
ratios[i_om].append(i_ratio)
he_ratios[i_om].append(i_he_ratio)
dates[i_om].append(int(date))
plot_ap_charge(dates, ap_charge, output_directory, "_" + run_id)
plot_ap_charge(dates, he_ap_charge, output_directory, "_he_" + run_id)
plot_aan(dates, aan, output_directory, "_" + run_id)
plot_aan(dates, aan_he, output_directory, "_he_" + run_id)
plot_aapc_vs_charge(dates, ratios, output_directory, "_" + run_id)
plot_aapc_vs_charge(dates, he_ratios, output_directory, "_he_" + run_id)
'''plot_par_ratio(dates, par, output_directory, "_" + run_id)