def plot(self):
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting plotting...')
with tb.open_file(h5_filename, 'r') as h5_file:
# Q: Maybe Plotting should not know about the file?
with plotting.Plotting(h5_filename) as p:
Vthreshold_start = p.run_config['Vthreshold_start']
Vthreshold_stop = p.run_config['Vthreshold_stop']
p.plot_parameter_page()
mask = h5_file.root.configuration.mask_matrix[:]
thr_matrix = h5_file.root.configuration.thr_matrix[:],
p.plot_distribution(thr_matrix, plot_range=np.arange(-0.5, 16.5, 1), title='TDAC distribution', x_axis_title='TDAC', y_axis_title='# of hits', suffix='tdac_distribution')
vth_hist = h5_file.root.interpreted.HitDistribution[:].T
p.plot_scurves(vth_hist, range(Vthreshold_start, Vthreshold_stop), scan_parameter_name="Vthreshold")
vths = h5_file.root.interpreted.PixelThresholdMap[:]
p.plot_occupancy(vths, z_label='Threshold', title='Threshold', show_sum=False, suffix='threshold_map', z_min=Vthreshold_start, z_max=Vthreshold_stop)
p.plot_distribution(vths, plot_range=np.arange(Vthreshold_start-0.5, Vthreshold_stop-0.5, 1), x_axis_title='Vthreshold', title='Threshold distribution', suffix='threshold_distribution')
def plot(self, status = None, plot_queue = None, **kwargs):
'''
Plot data and histograms of the scan
If there is a status queue information about the status of the scan are put into it
'''
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting plotting...')
if status != None:
status.put("Create Plots")
with tb.open_file(h5_filename, 'r+') as h5_file:
with plotting.Plotting(h5_filename) as p:
# Read needed configuration parameters
Vthreshold_start = int(p.run_config[b'Vthreshold_start'])
Vthreshold_stop = int(p.run_config[b'Vthreshold_stop'])
n_injections = int(p.run_config[b'n_injections'])
# Plot a page with all parameters
p.plot_parameter_page()
mask = h5_file.root.configuration.mask_matrix[:]
# Plot the occupancy matrix
occ_masked = np.ma.masked_array(h5_file.root.interpreted.HistOcc[:], mask)
p.plot_occupancy(occ_masked, title='Integrated Occupancy', z_max='median', suffix='occupancy', plot_queue=plot_queue)
# Plot the equalisation bits histograms
thr_matrix = h5_file.root.configuration.thr_matrix[:],
p.plot_distribution(thr_matrix, plot_range=np.arange(-0.5, 16.5, 1), title='TDAC distribution', x_axis_title='TDAC', y_axis_title='# of hits', suffix='tdac_distribution', plot_queue=plot_queue)
# Plot the S-Curve histogram
scurve_hist = h5_file.root.interpreted.HistSCurve[:].T
max_occ = n_injections * 5
p.plot_scurves(scurve_hist, list(range(Vthreshold_start, Vthreshold_stop)), scan_parameter_name="Vthreshold", max_occ=max_occ, plot_queue=plot_queue)
# Do not plot pixels with converged S-Curve fits
chi2_sel = h5_file.root.interpreted.Chi2Map[:] > 0. # Mask not converged fits (chi2 = 0)
mask[~chi2_sel] = True
# Plot the threshold distribution based on the S-Curve fits
hist = np.ma.masked_array(h5_file.root.interpreted.ThresholdMap[:], mask)
p.plot_distribution(hist, plot_range=np.arange(Vthreshold_start-0.5, Vthreshold_stop-0.5, 1), x_axis_title='Vthreshold', title='Threshold distribution', suffix='threshold_distribution', plot_queue=plot_queue)
# Plot the occupancy
p.plot_occupancy(hist, z_label='Threshold', title='Threshold', show_sum=False, suffix='threshold_map', z_min=Vthreshold_start, z_max=Vthreshold_stop, plot_queue=plot_queue)
# Plot the noise map
hist = np.ma.masked_array(h5_file.root.interpreted.NoiseMap[:], mask)
p.plot_distribution(hist, plot_range=np.arange(0.1, 20, 0.1), title='Noise distribution', suffix='noise_distribution', plot_queue=plot_queue)
p.plot_occupancy(hist, z_label='Noise', title='Noise', show_sum=False, suffix='noise_map', z_min=0.1, z_max=20.0, plot_queue=plot_queue)
def plot(self):
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting plotting...')
with tb.open_file(h5_filename, 'r') as h5_file:
# Q: Maybe Plotting should not know about the file?
with plotting.Plotting(h5_filename) as p:
VTP_fine_start = p.run_config['VTP_fine_start']
VTP_fine_stop = p.run_config['VTP_fine_stop']
n_injections = p.run_config['n_injections']
p.plot_parameter_page()
mask = h5_file.root.configuration.mask_matrix[:]
occ_masked = np.ma.masked_array(h5_file.root.interpreted.HistOcc[:], mask)
p.plot_occupancy(occ_masked, title='Integrated Occupancy', z_max='median', suffix='occupancy')
thr_matrix = h5_file.root.configuration.thr_matrix[:],
p.plot_distribution(thr_matrix, plot_range=np.arange(-0.5, 16.5, 1), title='TDAC distribution', x_axis_title='TDAC', y_axis_title='# of hits', suffix='tdac_distribution')
scurve_hist = h5_file.root.interpreted.HistSCurve[:].T
max_occ = n_injections + 10
p.plot_scurves(scurve_hist, range(VTP_fine_start, VTP_fine_stop), scan_parameter_name="VTP_fine", max_occ=max_occ)
chi2_sel = h5_file.root.interpreted.Chi2Map[:] > 0. # Mask not converged fits (chi2 = 0)
mask[~chi2_sel] = True
hist = np.ma.masked_array(h5_file.root.interpreted.ThresholdMap[:], mask)
p.plot_distribution(hist, plot_range=np.arange((VTP_fine_start-0.5, VTP_fine_stop-0.5, 1), x_axis_title='VTP_fine', title='Threshold distribution', suffix='threshold_distribution')
p.plot_occupancy(hist, z_label='Threshold', title='Threshold', show_sum=False, suffix='threshold_map', z_min=VTP_fine_start, z_max=VTP_fine_stop)
hist = np.ma.masked_array(h5_file.root.interpreted.NoiseMap[:], mask)
p.plot_distribution(hist, plot_range=np.arange(0.1, 4, 0.1), title='Noise distribution', suffix='noise_distribution')
p.plot_occupancy(hist, z_label='Noise', title='Noise', show_sum=False, suffix='noise_map', z_min=0.1, z_max=4.0)
if __name__ == "__main__":
scan = TestpulseScan()
scan.start(**local_configuration)
scan.analyze()
scan.plot()
def plot(self, status=None, plot_queue=None, **kwargs):
'''
Plot data and histograms of the scan
If there is a status queue information about the status of the scan are put into it
'''
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting plotting...')
if status != None:
status.put("Create Plots")
with tb.open_file(h5_filename, 'r+') as h5_file:
with plotting.Plotting(h5_filename) as p:
# Read needed configuration parameters
Vthreshold_start = int(p.run_config[b'Vthreshold_start'])
Vthreshold_stop = int(p.run_config[b'Vthreshold_stop'])
# Plot a page with all parameters
p.plot_parameter_page()
mask = h5_file.root.configuration.mask_matrix[:]
# Plot the equalisation bits histograms
thr_matrix = h5_file.root.configuration.thr_matrix[:],
p.plot_distribution(thr_matrix,
plot_range=np.arange(-0.5, 16.5, 1),
title='TDAC distribution',
x_axis_title='TDAC',
y_axis_title='# of hits',
suffix='tdac_distribution',
plot_queue=plot_queue)
# Plot the noise pixels histogram
noise_curve = h5_file.root.interpreted.NoiseCurve[:]
p._plot_1d_hist(hist=noise_curve,
plot_range=list(
range(Vthreshold_start, Vthreshold_stop)),
x_axis_title='Threshold',
y_axis_title='Number of active pixels',
plot_queue=plot_queue)
def plot(self, status=None, plot_queue=None, **kwargs):
'''
Plot data and histograms of the scan
If there is a status queue information about the status of the scan are put into it
'''
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting plotting...')
if status != None:
status.put("Create Plots")
with tb.open_file(h5_filename, 'r+') as h5_file:
with plotting.Plotting(h5_filename) as p:
# Read needed configuration parameters
VTP_fine_start = int(p.run_config[b'VTP_fine_start'])
VTP_fine_stop = int(p.run_config[b'VTP_fine_stop'])
VTP_coarse = int(p.dacs[b'VTP_coarse'])
# Plot a page with all parameters
p.plot_parameter_page()
mask = h5_file.root.configuration.mask_matrix[:]
# Plot the equalisation bits histograms
thr_matrix = h5_file.root.configuration.thr_matrix[:],
p.plot_distribution(thr_matrix,
plot_range=np.arange(-0.5, 16.5, 1),
title='TDAC distribution',
x_axis_title='TDAC',
y_axis_title='# of hits',
suffix='tdac_distribution',
plot_queue=plot_queue)
# Plot the ToT-Curve histogram
ToT_hist = h5_file.root.interpreted.HistToTCurve[:].T
p.plot_scurves(ToT_hist.astype(int),
list(range(VTP_fine_start, VTP_fine_stop)),
electron_axis=False,
scan_parameter_name="VTP_fine",
max_occ=250,
ylabel='ToT Clock Cycles',
title='ToT curves',
plot_queue=plot_queue)
# Plot the mean ToT-Curve with fit
mean = h5_file.root.interpreted.mean_curve[:]
fit_params = h5_file.root.interpreted.fit_params[:]
a = [
float(item["value"]) for item in fit_params
if item[0] == b'a'
][0]
ac = [
float(item["stddev"]) for item in fit_params
if item[0] == b'a'
][0]
b = [
float(item["value"]) for item in fit_params
if item[0] == b'b'
][0]
bc = [
float(item["stddev"]) for item in fit_params
if item[0] == b'b'
][0]
c = [
float(item["value"]) for item in fit_params
if item[0] == b'c'
][0]
cc = [
float(item["stddev"]) for item in fit_params
if item[0] == b'c'
][0]
t = [
float(item["value"]) for item in fit_params
if item[0] == b't'
][0]
tc = [
float(item["stddev"]) for item in fit_params
if item[0] == b't'
][0]
mean['tot']
mean['tot_error']
points = np.linspace(t * 1.001, len(mean['tot']), 500)
fit = analysis.totcurve(points, a, b, c, t)
p.plot_two_functions(
range(len(mean['tot'])),
mean['tot'],
range(len(mean['tot'])),
mean['tot_error'],
points,
fit,
y_plot_range=[0, np.amax(fit[1])],
label_1='mean ToT',
label_2=
'fit with \na=(%.2f+/-%.2f), \nb=(%.2f+/-%.2f), \nc=(%.2f+/-%.2f), \nt=(%.2f+/-%.2f)'
% (a, ac, b, bc, c, cc, t, tc),
x_axis_title='VTP [2.5 mV]',
y_axis_title='ToT Clock Cycles [25 ns]',
title='ToT fit',
suffix='ToT fit',
plot_queue=plot_queue)
def plot(self, file_name, args):
self.logger.info('Starting plotting...')
new_file = args_dict["new_file"]
if new_file:
file_name = file_name.replace("data_take", "analysis")
with tb.open_file(file_name, 'r+') as h5_file:
with plotting.Plotting(file_name) as p:
p.plot_parameter_page()
hit_data_x = np.empty(0, dtype=np.uint32)
hit_data_y = np.empty(0, dtype=np.uint32)
tot = np.empty(0, dtype=np.uint32)
toa = np.empty(0, dtype=np.uint32)
toa_comb = np.empty(0, dtype=np.uint64)
# iterate over all hit_data groups in the hdf5 file and build arrays for the histograms
for group in h5_file.root.interpreted:
hit_data = group[:]
hit_data_x = np.concatenate((hit_data_x, hit_data['x']),
axis=None)
hit_data_y = np.concatenate((hit_data_y, hit_data['y']),
axis=None)
tot = np.concatenate((tot, hit_data['TOT']), axis=None)
toa = np.concatenate((toa, hit_data['TOA']), axis=None)
toa_comb = np.concatenate(
(toa_comb, hit_data['TOA_Combined']), axis=None)
# Plot general hit properties
# Plot the occupancy matrix
pix_occ = np.bincount(hit_data_x * 256 + hit_data_y,
minlength=256 * 256).astype(np.uint32)
hist_occ = np.reshape(pix_occ, (256, 256)).T
p.plot_occupancy(hist_occ,
title='Integrated Occupancy',
z_max='maximum',
suffix='occupancy')
# Plot the ToT-Curve histogram
p.plot_distribution(tot,
plot_range=np.arange(
np.amin(tot) - 0.5,
np.median(tot) * 7, 5),
x_axis_title='ToT',
title='ToT distribution',
suffix='ToT_distribution',
fit=False)
# Plot the ToA-Curve histogram
p.plot_distribution(toa,
plot_range=np.arange(
np.amin(toa) - 0.5, np.amax(toa), 100),
x_axis_title='ToA',
title='ToA distribution',
suffix='ToA_distribution',
fit=False)
# Plot the ToA_Combined-Curve histogram
p.plot_distribution(
toa_comb,
plot_range=np.arange(
np.amin(toa_comb), np.amax(toa_comb),
(np.amax(toa_comb) - np.amin(toa_comb)) // 100),
x_axis_title='ToA_Combined',
title='ToA_Combined distribution',
suffix='ToA_Combined_distribution',
fit=False)
hist_size = np.empty(0, dtype=np.uint32)
hist_sum = np.empty(0, dtype=np.uint32)
first = True
# iterate over all run_* groups in the hdf5 file and build arrays for the histograms
for group in h5_file.root.reconstruction:
hist_size = np.concatenate((hist_size, group.hits[:]),
axis=None)
hist_sum = np.concatenate((hist_sum, group.sumTOT[:]),
axis=None)
if first == False:
histcha = np.concatenate((histcha, group.TOT[:]),
axis=0)
histtoa = np.concatenate((histtoa, group.TOA[:]),
axis=0)
histindex = np.concatenate(
(histindex, group.hit_index[:]), axis=0)
else:
histcha = np.array(group.TOT[:], dtype=object)
histtoa = np.array(group.TOA[:], dtype=object)
histindex = np.array(group.hit_index[:], dtype=object)
first = False
"""histindex = histindex.flatten()
u, c = np.unique(histindex, return_counts=True)
dup = u[c > 1]
print(dup)"""
# Plot cluster properties
num_cluster = len(hist_size)
# Plot the Cluster Size
p.plot_distribution(hist_size,
plot_range=np.arange(
-0.5,
np.median(hist_size) * 7 + 0.5, 1),
x_axis_title='Cluster Size',
y_axis_title='# of clusters',
title='Size distribution' +
r' ($\Sigma$ = {0})'.format(num_cluster),
suffix='Size_distribution',
fit=False)
# Plot the Cluster ToT
p.plot_distribution(hist_sum,
plot_range=np.arange(
np.amin(hist_sum) - 0.5,
np.median(hist_sum) * 7, 5),
x_axis_title='Cluster ToT',
y_axis_title='# of clusters',
title='Cluster ToT distribution',
suffix='Cluster_toT_distribution',
fit=False)
# Plot the Cluster ToT for all clusters with more than one pixel
histm1 = hist_sum[hist_size != 1]
p.plot_distribution(
histm1,
plot_range=np.arange(
np.amin(hist_sum) - 0.5,
np.median(hist_sum) * 7, 5),
x_axis_title=
'Cluster ToT for clusters with more than one pixel',
y_axis_title='# of clusters',
title=
'Cluster ToT distribution for clusters with more than one pixel',
suffix='Cluster_toT_distribution',
fit=False)
# Plot the Cluster ToT for all clusters with one pixel
histe1 = hist_sum[hist_size == 1]
p.plot_distribution(
histe1,
plot_range=np.arange(
np.amin(hist_sum) - 0.5,
np.median(hist_sum) * 7, 5),
x_axis_title='Cluster ToT for clusters with one pixel',
y_axis_title='# of clusters',
title=
'Cluster ToT distribution for clusters with one pixel',
suffix=
'Cluster_toT_distribution for clusters with one pixel',
fit=False)
# K7
#a = 10.17
#b = -4307.6
#c = -52649.2
#t = 268.85
# I7 THR=800
#a = 8.8
#b = -3910.2
#c = -66090.6
#t = 258.61
# I7 THR=1000
a = 8.0
b = -2964.3
c = -46339.0
t = 206.31
# I7 THR=1100
#a = 7.4
#b = -2288.9
#c = -7204.0
#t = 236.44
histch = np.zeros(len(histcha))
for i, el in enumerate(histcha):
for value in el:
histch[i] += 3 * np.abs(
100 * 0.005 -
(-(b - value * 25 - t * a) / (2 * a) + np.sqrt(
((b - value * 25 - t * a) / (2 * a))**2 -
(value * 25 * t - b * t - c) / a)) * 2 / 2.5 *
0.0025) / 1.602 * 10**4
#p.plot_distribution(histch, plot_range = np.arange(np.amin(histch)-0.5, np.median(histch) *7, 500), x_axis_title='Number of electrons per cluster', y_axis_title='# of clusters', title='Number of electrons per cluster', suffix='Number of electrons per cluster', fit=False)
p.plot_distribution(
histch,
plot_range=np.arange(0, 48000, 500),
x_axis_title='Number of electrons per cluster',
y_axis_title='# of clusters',
title='Number of electrons per cluster',
suffix='Number of electrons per cluster',
fit=False)
histchm1 = histch[hist_size != 1]
p.plot_distribution(
histchm1,
plot_range=np.arange(
np.amin(histch) - 0.5,
np.median(histch) * 7, 500),
x_axis_title=
'Number of electrons per cluster for clusters with more than one pixel',
y_axis_title='# of clusters',
title=
'Number of electrons per cluster for clusters with more than one pixel',
suffix=
'Number of electrons per cluster for clusters with more than one pixel',
fit=False)
histche1 = histch[hist_size == 1]
p.plot_distribution(
histche1,
plot_range=np.arange(
np.amin(histch) - 0.5,
np.median(histch) * 7, 500),
x_axis_title=
'Number of electrons per cluster for clusters with only one pixel',
y_axis_title='# of clusters',
title=
'Number of electrons per cluster for clusters with only one pixel',
suffix=
'Number of electrons per cluster for clusters with only one pixel',
fit=False)
histche2 = histch[hist_size == 2]
p.plot_distribution(
histche2,
plot_range=np.arange(
np.amin(histch) - 0.5,
np.median(histch) * 7, 500),
x_axis_title=
'Number of electrons per cluster for clusters with two pixel',
y_axis_title='# of clusters',
title=
'Number of electrons per cluster for clusters with two pixel',
suffix=
'Number of electrons per cluster for clusters with only one pixel',
fit=False)
histche3 = histch[hist_size == 3]
p.plot_distribution(
histche3,
plot_range=np.arange(
np.amin(histch) - 0.5,
np.median(histch) * 7, 500),
x_axis_title=
'Number of electrons per cluster for clusters with three pixel',
y_axis_title='# of clusters',
title=
'Number of electrons per cluster for clusters with three pixel',
suffix=
'Number of electrons per cluster for clusters with only one pixel',
fit=False)
histche4 = histch[hist_size == 4]
p.plot_distribution(
histche4,
plot_range=np.arange(
np.amin(histch) - 0.5,
np.median(histch) * 7, 500),
x_axis_title=
'Number of electrons per cluster for clusters with four pixel',
y_axis_title='# of clusters',
title=
'Number of electrons per cluster for clusters with four pixel',
suffix=
'Number of electrons per cluster for clusters with only one pixel',
fit=False)
# Plot the charge for all pixels
hist = np.empty(len(tot))
for i, value in enumerate(tot):
hist[i] = 3 * np.abs(
100 * 0.005 -
(-(b - value * 25 - t * a) /
(2 * a) + np.sqrt(((b - value * 25 - t * a) /
(2 * a))**2 -
(value * 25 * t - b * t - c) / a)) *
2 / 2.5 * 0.0025) / 1.602 * 10**4
p.plot_distribution(
hist,
plot_range=np.arange(0 - 0.5,
np.median(hist) * 7, 500),
x_axis_title='Number of electrons per pixel',
y_axis_title='# of pixels',
title='Number of electrons per pixel',
suffix='Number of electrons per pixel',
fit=False)
# plot the ToA spread in the clusters
hist_spread = np.empty(len(tot))
ind = 0
for i, el in enumerate(histtoa):
if not len(el) == 1:
m = np.mean(el)
for value in el:
hist_spread[ind] = value - m
ind += 1
p.plot_distribution(
hist_spread,
plot_range=np.arange(-10.125, 10.125, 0.25),
x_axis_title='Deviation from mean ToA of cluster',
y_axis_title='# of pixels',
title='Deviation from mean ToA of cluster',
suffix='Deviation from mean ToA of cluster',
fit=True)
def plot(self, status = None, plot_queue = None, **kwargs):
'''
Plot data and histograms of the scan
If there is a status queue information about the status of the scan are put into it
'''
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting plotting...')
if status != None:
status.put("Create Plots")
with tb.open_file(h5_filename, 'r+') as h5_file:
with plotting.Plotting(h5_filename, iteration = 0) as p:
# Read needed configuration parameters
iterations = int(p.run_config[b'n_pulse_heights'])
Vthreshold_start = int(p.run_config[b'Vthreshold_start'])
Vthreshold_stop = int(p.run_config[b'Vthreshold_stop'])
n_injections = int(p.run_config[b'n_injections'])
# Plot a page with all parameters
p.plot_parameter_page()
# Plot the equalisation bits histograms
thr_matrix = h5_file.root.configuration.thr_matrix_0[:],
p.plot_distribution(thr_matrix, plot_range=np.arange(-0.5, 16.5, 1), title='TDAC distribution', x_axis_title='TDAC', y_axis_title='# of hits', suffix='tdac_distribution', plot_queue=plot_queue)
mask = h5_file.root.configuration.mask_matrix_0[:]
# remove the calibration node if it already exists
try:
h5_file.remove_node(h5_file.root.calibration, recursive=True)
except:
pass
# create a group for the calibration results
h5_file.create_group(h5_file.root, 'calibration', 'Threshold calibration results')
# create a table for the calibration results
data_type = {'names': ['pulse_height', 'threshold', 'threshold_error'],
'formats': ['double', 'double', 'double']}
calib_results = np.recarray(iterations, dtype=data_type)
# create arrays for the calibration data points
pulse_heights = np.zeros(iterations, dtype=float)
thresholds = np.zeros(iterations, dtype=float)
errors = np.zeros(iterations, dtype=float)
# iterate though the iteration to plot the iteration specific results
for iteration in range(iterations):
HistSCurve_call = ('h5_file.root.interpreted_' + str(iteration) + '.HistSCurve' + '[:]')
Chi2Map_call = ('h5_file.root.interpreted_' + str(iteration) + '.Chi2Map' + '[:]')
ThresholdMap_call = ('h5_file.root.interpreted_' + str(iteration) + '.ThresholdMap' + '[:]')
# Plot the S-Curve histogram
scurve_hist = eval(HistSCurve_call).T
max_occ = n_injections * 5
p.plot_scurves(scurve_hist, list(range(Vthreshold_start, Vthreshold_stop)), scan_parameter_name="Vthreshold", max_occ=max_occ, plot_queue=plot_queue)
# Do not plot pixels with converged S-Curve fits
chi2_sel = eval(Chi2Map_call) > 0. # Mask not converged fits (chi2 = 0)
mask[~chi2_sel] = True
# Plot the threshold distribution based on the S-Curve fits
hist = np.ma.masked_array(eval(ThresholdMap_call), mask)
it_parameters, it_errors = p.plot_distribution(hist, plot_range=np.arange(Vthreshold_start-0.5, Vthreshold_stop-0.5, 1), x_axis_title='Vthreshold', title='Threshold distribution', suffix='threshold_distribution', plot_queue=plot_queue)
# Fill the iteration results in the calibration parameter arrays
pulse_heights[iteration] = ((211 + (100 // iterations) * iteration) - 200) * 46.75
thresholds[iteration] = it_parameters[1]
errors[iteration] = it_parameters[2]
# Fill the table with the calibration results
calib_results['pulse_height'] = pulse_heights
calib_results['threshold'] = thresholds
calib_results['threshold_error'] = errors
# Save the table to the HDF5 file
h5_file.create_table(h5_file.root.calibration, 'calibration_results', calib_results)
# Create the calibration plot
p.plot_datapoints(pulse_heights, thresholds, x_plot_range=np.arange(0, 5500, 1), y_plot_range=np.arange(0, 3000, 1), y_err = errors, x_axis_title = 'Charge in electrons', y_axis_title = 'Threshold', title='Threshold calibration', suffix='threshold_calibration', plot_queue=plot_queue)