def plot_aan_ratio(dates: list, aan: list, output_directory: str, name: str):
# Plot ratio
x_date = []
ratio = []
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 aan[1][j] == 0:
pass
else:
ratio.append(aan[0][i] / aan[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 06/11/2019")
plt.ylabel("Ratio AAN Ch0/Ch1")
plt.title("Ratio of AAN 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/aan_ratio_vs_time" + name +
".pdf")
plt.close()
def plot_par(date, par, output_directory: str, pmt_object: PMT_Object,
name: str):
date = process_date(date)
try:
start = np.where(date == 0)[0][0]
except:
start = np.where(date == 1)[0][0]
mid = np.where(date == 98)[0][0]
popt, pcov = curve_fit(f=model_day,
xdata=date[mid + 1:],
ydata=np.array(par[mid + 1:]),
p0=[0.1, 1, 19],
bounds=[[0, 0, 0], [1e5, 1e10, 100]])
plt.figure(num=None, figsize=(9, 5), dpi=80, facecolor='w', edgecolor='k')
plt.plot(date[:start + 1],
np.array(par[:start + 1]),
"g.",
label="Atmospheric He")
plt.plot(date[start + 1:mid + 1],
np.array(par[start + 1:mid + 1]),
"b.",
label="1% He")
plt.plot(date[mid + 1:], np.array(par[mid + 1:]), "r.", label="10% He")
plt.plot(date[mid + 1:],
model_day(date[mid + 1:], *popt),
'k-',
label='model')
plt.axvline(date[start], 0, 100, ls='--', color='k')
plt.axvline(date[mid], 0, 100, ls='--', color='k')
plt.xlabel("exposure days relative to 06/11/2019")
plt.ylabel("Afterpulse rate /%")
plt.title(pmt_object.get_pmt_id() + " PAR vs exposure time")
plt.grid()
plt.ylim(0, 100)
plt.legend(loc='upper left')
plt.savefig(output_directory + "/summary_plots/" +
pmt_object.get_pmt_id() + "_par_vs_time" + name + ".pdf")
print('Param p{}: {:.2e} ± {:.1e}'.format(0, popt[0], np.sqrt(pcov[0, 0])))
print('Param p{}: {:.2e} ± {:.1e}'.format(1, popt[1], np.sqrt(pcov[1, 1])))
print('Param p{}: {:.2e} ± {:.1e}'.format(2, popt[2], np.sqrt(pcov[2, 2])))
# print('Chi2 is:', chi_2)
plt.close()
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():
# 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 plot_base_drift():
data = pd.read_csv(
"/Users/williamquinn/Desktop/PMT_Project/res_vs_time.csv",
header=0,
dtype={
0: int,
1: int,
2: float,
3: float,
4: float,
5: int,
6: float,
7: float
},
engine='python')
data["time"] = process_date(data["date"])
data = data[data["base_mean"] > 900]
fig1 = plt.figure(figsize=figsize, facecolor='white')
#frame1 = fig1.add_axes((.125, .3, .55, .6))
data_ch0 = data[data["pmt"] == 0]
exposed_fit = fit_straight_line(data_ch0['time'].values,
data_ch0['base_mean'].values,
data_ch0['base_sig'].values,
guess=[0, 0])
plt.errorbar(data_ch0['time'].values,
data_ch0['base_mean'].values,
yerr=data_ch0['base_sig'].values,
fmt='.',
label='Exposed',
markersize=3,
capsize=1,
linewidth=1,
capthick=1)
plt.plot([data_ch0['time'].values[0], data_ch0['time'].values[-1]],
linear([data_ch0['time'].values[0], data_ch0['time'].values[-1]],
*exposed_fit["popt"]),
'k-',
zorder=10)
data_ch1 = data[data["pmt"] == 1]
control_fit = fit_straight_line(data_ch1['time'].values,
data_ch1['base_mean'].values,
data_ch1['base_sig'].values,
guess=[0, 0])
plt.errorbar(data_ch1['time'].values,
data_ch1['base_mean'].values,
yerr=data_ch1['base_sig'].values,
fmt='.',
label='Control',
markersize=3,
capsize=1,
linewidth=1,
capthick=1)
plt.plot([data_ch1['time'].values[0], data_ch1['time'].values[-1]],
linear([data_ch1['time'].values[0], data_ch1['time'].values[-1]],
*control_fit["popt"]),
'k-',
zorder=10)
plt.xlabel('Days from 1% He Onset')
plt.ylabel('Voltage /mV')
plt.fill_between([-100, 0], [1000, 1000],
alpha=0.1,
facecolor='green',
label='Atmospheric He')
plt.fill_between([0, 98], [1000, 1000],
alpha=0.1,
facecolor='blue',
label='1% He')
plt.fill_between([98, 500], [1000, 1000],
alpha=0.1,
facecolor='red',
label='10% He')
plt.xlim(-40, 430)
plt.ylim(978, 981)
plt.legend(loc='best', bbox_to_anchor=(1.0, 1))
plt.tight_layout()
plt.savefig("/Users/williamquinn/Desktop/PMT_Project/baseline_drift.pdf")
plt.close(fig1)
plt.figure(figsize=figsize)
plt.xlabel('Days from 1% He Onset')
plt.ylabel('PCT Change /%')
returns_ch0 = data_ch0['base_mean'].pct_change(1) * 100
returns_ch0.plot(label='Exposed')
returns_ch1 = data_ch1['base_mean'].pct_change(1) * 100
returns_ch1.plot(label='Control')
plt.legend(loc='best')
plt.tight_layout()
plt.savefig("/Users/williamquinn/Desktop/PMT_Project/baseline_pct.pdf")
plt.close()
dataframe = pd.concat([
pd.DataFrame(data_ch0['base_mean'].values).shift(1),
pd.DataFrame(data_ch0['base_mean'].values)
],
axis=1)
dataframe.columns = ['t-1', 't+1']
result0 = dataframe.corr()
print(result0)
dataframe = pd.concat([
pd.DataFrame(data_ch1['base_mean'].values).shift(1),
pd.DataFrame(data_ch1['base_mean'].values)
],
axis=1)
dataframe.columns = ['t-1', 't+1']
result1 = dataframe.corr()
print(result1)
plt.figure(figsize=figsize)
lag_plot(data_ch0['base_mean'],
label=r'Exposed $\rho$={:.2f}'.format(result0['t-1'][1]),
alpha=0.5)
plt.legend(loc='best')
plt.tight_layout()
plt.savefig(
"/Users/williamquinn/Desktop/PMT_Project/baseline_cor_exposed.pdf")
plt.figure(figsize=figsize)
lag_plot(data_ch1['base_mean'],
label=r'Control $\rho$={:.2f}'.format(result1['t-1'][1]),
alpha=0.5)
plt.legend(loc='best')
plt.tight_layout()
plt.savefig(
"/Users/williamquinn/Desktop/PMT_Project/baseline_cor_control.pdf")
'''autocorrelation_plot(data_ch0['base_mean'])
plt.grid()
plt.show()'''
'''# create lagged dataset
values = pd.DataFrame(data_ch0['base_mean'].values)
dataframe = pd.concat([values.shift(1), values], axis=1)
dataframe.columns = ['t-1', 't+1']
# split into train and test sets
X = dataframe.values
train, test = X[1:len(X) - 7], X[len(X) - 7:]
train_X, train_y = train[:, 0], train[:, 1]
test_X, test_y = test[:, 0], test[:, 1]
# persistence model
def model_persistence(x):
return x
# walk-forward validation
predictions = []
for x in test_X:
yhat = model_persistence(x)
predictions.append(yhat)
test_score = mean_squared_error(test_y, predictions)
print('Test MSE: %.3f' % test_score)
# plot predictions vs expected
plt.plot(test_y, label='Test')
plt.plot(predictions, label='Prediction')
plt.legend(loc='best')
plt.show()'''
'''X = data_ch0['base_mean'].values
train, test = X[1:len(X) - 7], X[len(X) - 7:]
# train autoregression
model = AutoReg(train, lags=7)
model_fit = model.fit()
print('Coefficients: %s' % model_fit.params)
# make predictions
predictions = model_fit.predict(start=len(train), end=len(train) + len(test) - 1, dynamic=False)
for i in range(len(predictions)):
print('predicted=%f, expected=%f' % (predictions[i], test[i]))
rmse = np.sqrt(mean_squared_error(test, predictions))
print('Test RMSE: %.3f' % rmse)
# plot results
plt.plot(test, label='test')
plt.plot(predictions, label='prediction')
plt.legend()
plt.show()'''
'''data = pd.read_csv("/Users/williamquinn/Desktop/PMT_Project/res_vs_time.csv", header=0,
dtype={0: int, 1: int, 2: float, 3: float, 4: float, 5: int, 6: float, 7: float},
engine='python')
data['date'] = data['date'].astype(str)
data = data[data["pmt"] == 0]
f = []
for date in data['date'].values:
new_date = date[4] + date[5] + '/' + date[2] + date[3] + '/' + '20' + date[0] + date[1]
f.append(new_date)
data['date'] = pd.DatetimeIndex(f, dayfirst=True)
data.set_index('date', inplace=True)
data = data[~data.index.duplicated()]
data = data.asfreq('D')
data["base_mean"] = data["base_mean"].fillna(data["base_mean"].mean())'''
'''decomposed = seasonal_decompose(data['base_mean'], model='additive')
decomposed.plot() # See note below about this
plt.show()'''
# data['stationary'] = data['base_mean'].diff()
'''decomposed = seasonal_decompose(data['stationary'][1:], model='additive')
decomposed.plot() # See note below about this
plt.show()'''
'''# create train/test datasets
def plot_res():
data = pd.read_csv(
"/Users/williamquinn/Desktop/PMT_Project/res_vs_time.csv",
header=0,
dtype={
0: int,
1: int,
2: float,
3: float,
4: float,
5: int,
6: float,
7: float
},
engine='python')
data["time"] = process_date(data["date"])
data = data[(data['date'] == 191023) | (data['date'] == 200212) |
(data['date'] == 200429)
| (data['date'] == 200717) | (data['date'] == 201015) |
(data['date'] == 201216)]
plt.figure(figsize=figsize, facecolor='white')
sel_dates = [191023, 200212, 200429, 200717, 201015, 201216]
chi_cut = 10
data_ch0 = data[data["pmt"] == 0]
x, y, dy = data_ch0[data_ch0['chi_2'] / data_ch0['ndof'] < chi_cut]['time'].values, \
data_ch0[data_ch0['chi_2'] / data_ch0['ndof'] < chi_cut]['res'].values * 100, \
data_ch0[data_ch0['chi_2'] / data_ch0['ndof'] < chi_cut]['res_err'].values * 100
exposed_fit = fit_straight_line(x, y, dy, guess=[0, 0])
plt.errorbar(x,
y,
yerr=dy,
fmt='k.',
label='Exposed',
markersize=3,
capsize=1,
linewidth=1,
capthick=1)
plt.plot([x[0], x[-1]], linear([x[0], x[-1]], *exposed_fit["popt"]), 'r-')
chi_cut = 10
data_ch1 = data[data["pmt"] == 1]
x, y, dy = data_ch1[data_ch1['chi_2'] / data_ch1['ndof'] < chi_cut]['time'].values, \
data_ch1[data_ch1['chi_2'] / data_ch1['ndof'] < chi_cut]['res'].values * 100, \
data_ch1[data_ch1['chi_2'] / data_ch1['ndof'] < chi_cut]['res_err'].values * 100
control_fit = fit_straight_line(x, y, dy, guess=[0, 0])
plt.errorbar(x,
y,
yerr=dy,
fmt='.',
label='Control',
markersize=3,
capsize=1,
linewidth=1,
capthick=1,
color='grey',
ecolor='grey')
plt.plot([x[0], x[-1]], linear([x[0], x[-1]], *control_fit["popt"]), 'r-')
plt.ylim(1.5, 5)
plt.xlabel('Days from 1% He Onset')
plt.ylabel('Resolution at 1MeV /%')
plt.fill_between([-100, 0], [5, 5],
alpha=0.1,
facecolor='green',
label='Atmospheric He')
plt.fill_between([0, 98], [5, 5],
alpha=0.1,
facecolor='blue',
label='1% He')
plt.fill_between([98, 500], [5, 5],
alpha=0.1,
facecolor='red',
label='10% He')
plt.xlim(-30, 430)
plt.legend(loc='best')
plt.tight_layout()
plt.savefig("/Users/williamquinn/Desktop/PMT_Project/res.pdf")
plt.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():
# 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 plot_aapc_vs_charge(dates, ratios, output_directory: str, name: str):
date_0 = process_date(dates[0])
date_1 = process_date(dates[1])
try:
start = np.where(date_0 == 0)[0][0]
except:
start = np.where(date_0 == 1)[0][0]
mid = np.where(date_0 == 98)[0][0]
ratio_0 = np.array(ratios[0])
ratio_1 = np.array(ratios[1])
x = np.array(date_0[mid + 1:]) - date_0[mid + 1:][0]
y = np.array(ratio_0[mid + 1:])
yerr = np.array(ratio_0[mid + 1:]) * 0.01
pars, errs, chi = root_fit(x, y, yerr)
fig1 = plt.figure(num=None,
figsize=(9, 5),
dpi=80,
facecolor='w',
edgecolor='k')
frame1 = fig1.add_axes((.1, .3, .8, .6))
frame1.set_xticklabels([])
plt.errorbar(date_0[:start + 1],
ratio_0[:start + 1],
zorder=0,
yerr=ratio_0[:start + 1] * 0.01,
fmt="g.",
label="Atmospheric He")
plt.errorbar(date_0[start + 1:mid + 1],
ratio_0[start + 1:mid + 1],
zorder=0,
yerr=ratio_0[start + 1:mid + 1] * 0.01,
fmt="b.",
label="1% He")
plt.errorbar(date_0[mid + 1:],
ratio_0[mid + 1:],
zorder=0,
yerr=ratio_0[mid + 1:] * 0.01,
fmt="r.",
label="10% He")
plt.errorbar(date_1,
ratio_1,
zorder=0,
yerr=ratio_1 * 0.01,
fmt="C1.",
label="Control")
plt.plot(date_0[mid + 1:],
Model().func(x, pars),
'k-',
label='model',
zorder=10)
plt.axvline(date_0[start], 0, 100, ls='--', color='k')
plt.axvline(date_0[mid], 0, 100, ls='--', color='k')
handles, labels = plt.gca().get_legend_handles_labels()
patch = matplotlib.patches.Patch(color='white',
label=r'$P_0 =$ {:.4e} ± {:.0e}'.format(
pars[0], errs[0]))
patch_1 = matplotlib.patches.Patch(color='white',
label=r'$P_1 =$ {:.4e} ± {:.0e}'.format(
pars[1], errs[1]))
patch_2 = matplotlib.patches.Patch(color='white',
label=r'$L =$ {:.0f} ± {:.0f}'.format(
pars[2] / (3600 * 24),
errs[2] / (3600 * 24)))
patch_3 = matplotlib.patches.Patch(
color='white', label=r'$\chi^2_R =$ {:.2f}'.format(chi))
handles.extend([patch, patch_1, patch_2, patch_3])
plt.ylabel("charge ratio")
plt.title("Pulse charge apulse charge ratio")
plt.grid()
plt.legend(handles=handles, loc='upper left')
plt.xlim(-30, 420)
frame2 = fig1.add_axes((.1, .1, .8, .2))
plt.xlabel("exposure days relative to 06/11/2019")
plt.axhline(0, ls='--', color='black')
plt.ylabel("(model-data)/model")
plt.grid()
plt.xlim(-30, 420)
plt.errorbar(
date_0[mid + 1:],
(Model().func(x, pars) - ratio_0[mid + 1:]) / Model().func(x, pars),
yerr=ratio_0[mid + 1:] * 0.01 / Model().func(date_0[mid + 1:], pars),
fmt="k.")
plt.savefig(output_directory + "/summary_plots/" +
"ap_charge_charge_vs_time" + name + ".pdf")
plt.close()