def fitter_timing(datadate,samplerate_name,shaping):
if samplerate_name == 'INVALID':
return("Failed")
t_fitter,v_fitter,_ = rw('G:/data/watchman/'+datadate+'_watchman_spe/d2/d2_average.txt',1)
uspl = us(t_fitter,v_fitter)
filedir = 'G:/data/watchman/'+str(datadate)+'_watchman_spe/studies/phase/'+samplerate_name+'/'
Nloops = len(os.listdir(filedir + 'phase=0/phase_'+shaping))
for i in range(Nloops):
print(i)
filename_exact = 'G:/data/watchman/'+str(datadate)+'_watchman_spe/d3/d3_raw_gained/D3--waveforms--%05d.txt' % i
t,v,_ = rw(filename_exact,5)
v = -1*v
ET = t[0:400]
EV = v[0:400]
chi2_min = -1
x_min = -1
y_min = -1
shift_min = -1
shifts = np.arange(0,80/20000000000,1e-11)
for shift in shifts:
pre_OV = uspl(ET + shift)
A = np.array([[np.sum(np.square(pre_OV)),np.sum(pre_OV),np.sum(np.multiply(pre_OV,EV))],[np.sum(pre_OV),len(EV),np.sum(EV)]])
A[0] = A[0]/A[0][0]
A[1] = np.subtract(A[1],A[1][0]*A[0])
A[1] = A[1]/A[1][1]
A[0] = np.subtract(A[0],A[0][1]*A[1])
x = A[0][2]
y = A[1][2]
OV = x*pre_OV + y
chi2 = chi_squared(OV,EV)
if chi2_min < 0:
shift_min = shift
chi2_min = chi2
x_min = x
y_min = y
elif chi2 < chi2_min:
shift_min = shift
chi2_min = chi2
x_min = x
y_min = y
v_fit = x_min*uspl(t_fitter + shift_min) + y_min
t_cross = timing_extraction(t_fitter,v_fit)
if t_cross <= -0.5e-10 or chi2_min >= 7.95e-5 or t_cross >= 0.5e-10:
fig,ax = plt.subplots()
ax.plot(t,v)
ax.plot(t_fitter,v_fit)
ax.axvline(0,color = 'Black')
ax.axvline(t_cross,color = 'Red')
ax.set_title(str(t_cross) + ', ' + str(chi2_min) + ', ' + str(i))
plt.get_current_fig_manager().window.showMaximized()
plt.show(block = False)
plt.pause(0.1)
fig.savefig('G:/data/watchman/20190724_watchman_spe/studies/phase/Histograms/'+samplerate_name+'/Doubles/%05d.png' % i,dpi = 500)
print("Was double!")
plt.close()
return("Passed")
def p3(new_fsps, datadate, numhead, scale, phase_array, n_box, n_delay, n_att,
num_phases):
#establishing directory names
if new_fsps == 1000000000:
sample_rate = '1 Gsps'
elif new_fsps == 500000000:
sample_rate = '500 Msps'
elif new_fsps == 250000000:
sample_rate = '250 Msps'
else:
sample_rate = 'trash'
filename = 'G:/data/watchman/' + datadate + '_watchman_spe/d2/d2_average.txt'
t, v, _ = rw(filename, numhead)
uspl = us(t, v)
t_cross_array = []
for i in range(len(phase_array)):
print('ndel=' + str(n_delay) + ', natt=' + str(n_att) + ', ' + str(i))
start_value = t[0] + i / (new_fsps * num_phases)
end_value = t[-1]
t_array = np.arange(start_value, end_value, 1 / new_fsps)
v_array = -1 * uspl(t_array)
t_array = t_array - i / (new_fsps * num_phases)
scale_height = scale / np.max(v_array)
v_scaled = v_array * scale_height
v_digit = digitize(v_scaled, 0)
t_avg, v_avg = boxcar_wf(t_array, v_digit, n_box)
v_delay = delay_wf(v_avg, n_delay)
v_att = attenuate_wf(v_avg, n_att)
v_sum = sum_wf(v_att, v_delay)
t_cross, _, _ = zc_locator(t_avg, v_sum)
t_cross_array.append(t_cross)
t_cross_array = t_cross_array - t_cross_array[0]
t_cross_array = np.flip(np.asarray(t_cross_array))
true_timing_array = np.flip(np.zeros(num_phases) - phase_array)
fig, ax = plt.subplots()
ax.plot(true_timing_array, t_cross_array)
ax.set_title('Recovered Timing vs. True Timing')
ax.set_xlabel('True Timing (Seconds)')
ax.set_ylabel('Recovered Timing (Seconds)')
plt.get_current_fig_manager().window.showMaximized()
plt.show(block=False)
savedir = 'G:/data/watchman/' + str(
datadate
) + '_watchman_spe/studies/phase/' + sample_rate + '/Histograms/averages_splined/'
if not os.path.exists(savedir):
os.makedirs(savedir)
filename = 'ndelay=' + str(n_delay) + '_natt=' + str(
n_att) + '_Phases=%d.png' % num_phases
savename = savedir + filename
fig.savefig(savename, dpi=500)
plt.close()
def establish_templates(new_fsps, datadate, numhead, scale_array, phase_array,
noise, fsps, steps, scale_mean):
#establishing directory names
if new_fsps == 1000000000:
sample_rate = '1 Gsps'
elif new_fsps == 500000000:
sample_rate = '500 Msps'
elif new_fsps == 250000000:
sample_rate = '250 Msps'
else:
sample_rate = 'trash'
filename = 'G:/data/watchman/' + datadate + '_watchman_spe/d2/d2_average.txt'
t, v, header = rw(filename, numhead)
uspl = us(t, v)
for i in range(len(phase_array)):
print(i)
writedir_noise = 'G:/data/watchman/' + datadate + '_watchman_spe/studies/phase/' + sample_rate + '/file_template/phase=' + str(
i) + '/phase_raw_gained_analyzed_noised/'
writedir_peaked = 'G:/data/watchman/' + datadate + '_watchman_spe/studies/phase/' + sample_rate + '/file_template/phase=' + str(
i) + '/phase_raw_gained_analyzed_peaked/'
#creating directories if they don't exist
if not os.path.exists(writedir_noise):
os.makedirs(writedir_noise)
if not os.path.exists(writedir_peaked):
os.makedirs(writedir_peaked)
start_value = t[i]
end_value = t[-1]
t_array = np.arange(start_value, end_value, 1 / new_fsps)
v_array = uspl(t_array)
Nloops = len(
scale_array) #establishing how many files to cycle through
for j in range(Nloops):
scale_height_noise = scale_mean / np.max(v_array)
scale_height_peaked = scale_array[j] / np.max(v_array)
v_scaled_noise = v_array * scale_height_noise
v_scaled_peaked = v_array * scale_height_peaked
print('%s/%s, File: %05d' %
(i + 1, steps,
j)) #printing number of file currently being processed
#establishing read and write names
writename_noise = writedir_noise + 'Phase--waveforms--%05d.txt' % j
writename_peaked = writedir_peaked + 'Phase--waveforms--%05d.txt' % j
#digitizing waveform values
v_digit_noise = digitize(v_scaled_noise, noise)
v_digit_peaked = digitize(v_scaled_peaked, noise)
#saving downsampled and digitized waveforms
ww(t_array - i / fsps, v_digit_noise, writename_noise, header)
ww(t_array - i / fsps, v_digit_peaked, writename_peaked, header)
return ()
def fitter_timing(datadate,samplerate_name,shaping):
if samplerate_name == 'INVALID':
return("Failed")
t_fitter,v_fitter,_ = rw('G:/data/watchman/'+datadate+'_watchman_spe/d2/d2_average.txt',1)
uspl = us(t_fitter,v_fitter)
filedir = 'G:/data/watchman/'+str(datadate)+'_watchman_spe/studies/phase/'+samplerate_name+'/'
Nloops = len(os.listdir(filedir + 'phase=0/phase_'+shaping))
for i in range(Nloops):
filename_exact = 'G:/data/watchman/'+str(datadate)+'_watchman_spe/d3/d3_raw_gained/D3--waveforms--%05d.txt' % i
t_exact,v_exact,_ = rw(filename_exact,5)
j = 0
check_value = 0
while j < 80 and check_value == 0:
print(str(i) + ', Phase=' + str(j))
filename = filedir + 'phase='+str(j)+'/phase_'+shaping+'/Phase--waveforms--%05d.txt' % i
t,v,_ = rw(filename,5)
v = -1*v
ET = t[0:10]
EV = v[0:10]
chi2_min = -1
x_min = -1
y_min = -1
shift_min = -1
shifts = np.arange(0,80/20000000000,1e-11)
for shift in shifts:
pre_OV = uspl(ET + shift)
A = np.array([[np.sum(np.square(pre_OV)),np.sum(pre_OV),np.sum(np.multiply(pre_OV,EV))],[np.sum(pre_OV),len(EV),np.sum(EV)]])
A[0] = A[0]/A[0][0]
A[1] = np.subtract(A[1],A[1][0]*A[0])
A[1] = A[1]/A[1][1]
A[0] = np.subtract(A[0],A[0][1]*A[1])
x = A[0][2]
y = A[1][2]
OV = x*pre_OV + y
chi2 = chi_squared(OV,EV)
if chi2_min < 0:
shift_min = shift
chi2_min = chi2
x_min = x
y_min = y
elif chi2 < chi2_min:
shift_min = shift
chi2_min = chi2
x_min = x
y_min = y
v_fit = x_min*uspl(t_fitter + shift_min) + y_min
t_cross = timing_extraction(t_fitter,v_fit)
timing_check = -1*j/20000000000 - t_cross
if timing_check <= -1e-9 or chi2_min >= 5000 or timing_check >= 1e-9:
t_exact = t_exact - j/20000000000
v_peak_ind = np.where(v == np.amax(v))[0][0]
t_peak = t[v_peak_ind]
t_exact_check = np.abs(t_exact - t_peak)
t_exact_loc = np.where(t_exact_check == np.amin(t_exact_check))[0][0]
v_exact = v[v_peak_ind]/v_exact[t_exact_loc]*v_exact
fig,ax = plt.subplots()
ax.plot(t,v)
ax.plot(t_fitter,v_fit)
ax.plot(t_exact,v_exact)
ax.axvline(-1*j/20000000000,color = 'Black')
ax.axvline(t_cross,color = 'Red')
ax.set_title(str(t_cross) + ', ' + str(chi2_min) + ', ' + str(i))
plt.get_current_fig_manager().window.showMaximized()
plt.show(block = False)
plt.pause(0.1)
fig.savefig('G:/data/watchman/20190724_watchman_spe/studies/phase/Histograms/'+samplerate_name+'/Doubles Copy/%05d.png' % i,dpi = 500)
print("Was double!")
plt.close()
check_value += 1
j += 1
return("Passed")
def fitter_timing(datadate, numhead, samplerate, samplerate_name, shaping):
if samplerate_name == 'INVALID':
return ("Failed")
phase_time = 1 / 20000000000
maxphase = int(20000000000 / samplerate + 0.5)
t_fitter, v_fitter, _ = rw(
'G:/data/watchman/' + datadate + '_watchman_spe/d2/d2_average.txt', 1)
uspl = us(t_fitter, v_fitter)
filedir = 'G:/data/watchman/' + str(
datadate
) + '_watchman_spe/studies/phase/' + samplerate_name + '/file_template/'
Nloops = len(os.listdir(filedir + 'phase=0/phase_' + shaping))
difference_list = []
chi_list = []
for i in range(Nloops):
print(i)
j = random.randint(0, maxphase - 1)
filename = filedir + 'phase=' + str(
j) + '/phase_' + shaping + '/Phase--waveforms--%05d.txt' % i
t, v, _ = rw(filename, numhead)
v = -1 * v
ET = t[0:10]
EV = v[0:10]
chi2_min = -1
x_min = -1
y_min = -1
shift_min = -1
shifts = np.arange(0, 80 / 20000000000, 1e-11)
for shift in shifts:
pre_OV = uspl(ET + shift)
A = np.array([[
np.sum(np.square(pre_OV)),
np.sum(pre_OV),
np.sum(np.multiply(pre_OV, EV))
], [np.sum(pre_OV), len(EV), np.sum(EV)]])
A[0] = A[0] / A[0][0]
A[1] = np.subtract(A[1], A[1][0] * A[0])
A[1] = A[1] / A[1][1]
A[0] = np.subtract(A[0], A[0][1] * A[1])
x = A[0][2]
y = A[1][2]
OV = x * pre_OV + y
chi2 = chi_squared(OV, EV)
if chi2_min < 0:
shift_min = shift
chi2_min = chi2
x_min = x
y_min = y
elif chi2 < chi2_min:
shift_min = shift
chi2_min = chi2
x_min = x
y_min = y
v_fit = x_min * uspl(t_fitter + shift_min) + y_min
t_cross = timing_extraction(t_fitter, v_fit)
#if (-1*j*phase_time - t_cross) < -1e-7:
# _,ax = plt.subplots()
# ax.plot(t,v)
# ax.plot(t_fitter,v_fit)
# ax.scatter(ET,EV)
# ax.axvline(-1*j*phase_time,color = 'Black')
# ax.axvline(t_cross,color = 'Red')
# ax.set_title(str(-1*j*phase_time - t_cross) + ', ' + str(chi2_min) + ', ' + str(i) + ', ' + str(j))
# plt.get_current_fig_manager().window.showMaximized()
# plt.show()
difference_list.append((-1 * j * phase_time - t_cross)[0])
chi_list.append(chi2_min)
difference_list = np.asarray(difference_list)
chi_list = np.asarray(chi_list)
true_mean = '%5g' % (np.mean(difference_list) * 1e12)
true_std = '%5g' % (np.std(difference_list) * 1e12)
bins = 30 #np.linspace(-2.2e-9,2.2e-9,num = 100)
histo_data, bins_data = np.histogram(difference_list, bins=bins)
binwidth = (bins_data[1] - bins_data[0])
binscenters = np.array([
0.5 * (bins_data[i] + bins_data[i + 1])
for i in range(len(bins_data) - 1)
])
FontSize = 32
plt.rcParams.update({'font.size': FontSize})
_, ax = plt.subplots()
ax.bar(binscenters, histo_data, width=binwidth)
ax.set_xlabel('True Timing - Recovered Timing')
ax.set_ylabel('Count')
ax.set_title(samplerate_name + ' - Fitter Timings')
ax.text(0.05,
0.95,
'Distribution Parameters:\nMean: ' + true_mean +
' ps\nStandard Deviation: ' + true_std + ' ps',
transform=ax.transAxes,
fontsize=FontSize,
verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='White', alpha=0.5))
plt.get_current_fig_manager().window.showMaximized()
plt.show()
plt.close()
bins = 30 #np.linspace(0,30200,num = 100)
histo_data, bins_data = np.histogram(chi_list, bins=bins)
binwidth = (bins_data[1] - bins_data[0])
binscenters = np.array([
0.5 * (bins_data[i] + bins_data[i + 1])
for i in range(len(bins_data) - 1)
])
plt.rcParams.update({'font.size': FontSize})
plt.bar(binscenters, histo_data, width=binwidth, log=True)
plt.xlabel('Chi Squared')
plt.ylabel('Count')
plt.title(samplerate_name + ' - Chi Squared Counts - Fitter Timings')
plt.get_current_fig_manager().window.showMaximized()
plt.show()
plt.close()
_, ax = plt.subplots()
h = ax.hist2d(difference_list, chi_list, bins=30, norm=LogNorm())
plt.colorbar(h[3], ax=ax)
ax.set_title(samplerate_name +
' - Chi Squared vs. Timing Corrections - Fitter Timings')
ax.set_xlabel('Timing Corrections')
ax.set_ylabel('Chi Squared')
plt.get_current_fig_manager().window.showMaximized()
plt.show()
plt.close()
return ("Passed")
def fitter_timing(datadate, samplerate, samplerate_name, shaping):
if samplerate_name == 'INVALID':
return ("Failed")
phase_time = 1 / 20000000000
maxphase = int(20000000000 / samplerate + 0.5)
t_fitter, v_fitter, _ = rw(
'G:/data/watchman/' + datadate + '_watchman_spe/d2/d2_average.txt', 1)
uspl = us(t_fitter, v_fitter)
filedir = 'G:/data/watchman/' + str(
datadate) + '_watchman_spe/studies/phase/' + samplerate_name + '/'
doubles_dir = 'G:/data/watchman/20190724_watchman_spe/studies/phase/Histograms/250 Msps/Doubles Combined/'
doubles_array = os.listdir(doubles_dir)
print(doubles_array)
Nloops = len(os.listdir(filedir + 'phase=0/phase_' + shaping))
difference_list = []
chi_list = []
for i in range(Nloops):
print(i)
if '%05d.png' % i in doubles_array:
print('Skipped!')
else:
j = random.randint(0, maxphase - 1)
filename = filedir + 'phase=' + str(
j) + '/phase_' + shaping + '/Phase--waveforms--%05d.txt' % i
##filename_exact = 'G:/data/watchman/'+str(datadate)+'_watchman_spe/d3/d3_raw_gained/D3--waveforms--%05d.txt' % i
t, v, _ = rw(filename, 5)
v = -1 * v
##t_exact,v_exact,_ = rw(filename_exact,5)
##t_exact -= 1*j*phase_time
##time_locator = t[4]
##t_min_array = abs(t_exact - time_locator)
##time_index = np.where(t_min_array == np.amin(t_min_array))[0][0]
##v_exact = v_exact * v[4]/v_exact[time_index]
ET = t[0:10]
EV = v[0:10]
chi2_min = -1
x_min = -1
y_min = -1
shift_min = -1
shifts = np.arange(0, 80 / 20000000000, 1e-11)
for shift in shifts:
pre_OV = uspl(ET + shift)
A = np.array([[
np.sum(np.square(pre_OV)),
np.sum(pre_OV),
np.sum(np.multiply(pre_OV, EV))
], [np.sum(pre_OV), len(EV),
np.sum(EV)]])
A[0] = A[0] / A[0][0]
A[1] = np.subtract(A[1], A[1][0] * A[0])
A[1] = A[1] / A[1][1]
A[0] = np.subtract(A[0], A[0][1] * A[1])
x = A[0][2]
y = A[1][2]
OV = x * pre_OV + y
chi2 = chi_squared(OV, EV)
if chi2_min < 0:
shift_min = shift
chi2_min = chi2
x_min = x
y_min = y
elif chi2 < chi2_min:
shift_min = shift
chi2_min = chi2
x_min = x
y_min = y
v_fit = x_min * uspl(t_fitter + shift_min) + y_min
t_cross = timing_extraction(t_fitter, v_fit)
##if -1*j*phase_time - t_cross <= -1e-9 or chi2_min >= 2750 or -1*j*phase_time - t_cross >= 1e-9:
## fig,ax = plt.subplots()
## ax.plot(t,v)
## ax.plot(t_fitter,v_fit)
## ax.plot(t_exact,v_exact)
## ax.scatter(ET,EV)
## ax.axvline(-1*j*phase_time,color = 'Black')
## ax.axvline(t_cross,color = 'Red')
## ax.set_title(str(-1*j*phase_time - t_cross) + ', ' + str(chi2_min) + ', ' + str(i) + ', ' + str(j))
## plt.get_current_fig_manager().window.showMaximized()
## plt.show(block = False)
## plt.pause(0.1)
## fig.savefig('G:/data/watchman/20190724_watchman_spe/studies/phase/Histograms/250 Msps/Doubles/%05d.png' % i,dpi = 500)
## print("Was double!")
## plt.close()
##else:
## difference_list.append((-1*j*phase_time - t_cross)[0])
## chi_list.append(chi2_min)
difference_list.append((-1 * j * phase_time - t_cross)[0])
chi_list.append(chi2_min)
difference_list = np.asarray(difference_list)
chi_list = np.asarray(chi_list)
true_mean = '%5g' % (np.mean(difference_list) * 1e12)
true_std = '%5g' % (np.std(difference_list) * 1e12)
bins = np.linspace(-2.2e-9, 2.2e-9, num=100)
histo_data, bins_data = np.histogram(difference_list, bins=bins)
binwidth = (bins_data[1] - bins_data[0])
binscenters = np.array([
0.5 * (bins_data[i] + bins_data[i + 1])
for i in range(len(bins_data) - 1)
])
FontSize = 32
plt.rcParams.update({'font.size': FontSize})
_, ax = plt.subplots()
ax.bar(binscenters, histo_data, width=binwidth)
ax.set_xlabel('True Timing - Recovered Timing')
ax.set_ylabel('Count')
ax.set_title(samplerate_name + ' - Fitter Timings, Doubles Removed')
ax.text(0.05,
0.95,
'Distribution Parameters:\nMean: ' + true_mean +
' ps\nStandard Deviation: ' + true_std + ' ps',
transform=ax.transAxes,
fontsize=FontSize,
verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='White', alpha=0.5))
plt.get_current_fig_manager().window.showMaximized()
plt.show()
plt.close()
bins = np.linspace(0, 30200, num=100)
histo_data, bins_data = np.histogram(chi_list, bins=bins)
binwidth = (bins_data[1] - bins_data[0])
binscenters = np.array([
0.5 * (bins_data[i] + bins_data[i + 1])
for i in range(len(bins_data) - 1)
])
plt.rcParams.update({'font.size': FontSize})
plt.bar(binscenters, histo_data, width=binwidth, log=True)
plt.xlabel('Chi Squared')
plt.ylabel('Count')
plt.title(samplerate_name +
' - Chi Squared Counts - Fitter Timings, Doubles Removed')
plt.get_current_fig_manager().window.showMaximized()
plt.show()
plt.close()
_, ax = plt.subplots()
h = ax.hist2d(difference_list, chi_list, bins=65, norm=LogNorm())
plt.colorbar(h[3], ax=ax)
ax.set_title(
samplerate_name +
' - Chi Squared vs. Timing Corrections - Fitter Timings, Doubles Removed'
)
ax.set_xlabel('Timing Corrections')
ax.set_ylabel('Chi Squared')
plt.get_current_fig_manager().window.showMaximized()
plt.show()
plt.close()
return ("Passed")