def plot_result(x_p, y_p, y_p_e, smoothed_data, smoothed_data_diff, filename=None):
''' Fit spline to the profile histogramed data, differentiate, determine MPV and plot.
Parameters
----------
x_p, y_p : array like
data points (x,y)
y_p_e : array like
error bars in y
'''
logging.info('Plot results')
plt.close()
p1 = plt.errorbar(x_p * analysis_configuration['vcal_calibration'], y_p, yerr=y_p_e, fmt='o') # plot data with error bars
p2, = plt.plot(x_p * analysis_configuration['vcal_calibration'], smoothed_data, '-r') # plot smoothed data
factor = np.amax(y_p) / np.amin(smoothed_data_diff) * 1.1
p3, = plt.plot(x_p * analysis_configuration['vcal_calibration'], factor * smoothed_data_diff, '-', lw=2) # plot differentiated data
mpv_index = np.argmax(-analysis_utils.smooth_differentiation(x_p, y_p, weigths=1 / y_p_e, order=3, smoothness=analysis_configuration['smoothness'], derivation=1))
p4, = plt.plot([x_p[mpv_index] * analysis_configuration['vcal_calibration'], x_p[mpv_index] * analysis_configuration['vcal_calibration']], [0, factor * smoothed_data_diff[mpv_index]], 'k-', lw=2)
text = 'MPV ' + str(int(x_p[mpv_index] * analysis_configuration['vcal_calibration'])) + ' e'
plt.text(1.01 * x_p[mpv_index] * analysis_configuration['vcal_calibration'], -10. * smoothed_data_diff[mpv_index], text, ha='left')
plt.legend([p1, p2, p3, p4], ['data', 'smoothed spline', 'spline differentiation', text], prop={'size': 12}, loc=0)
plt.title('\'Single hit cluster\'-occupancy for different pixel thresholds')
plt.xlabel('Pixel threshold [e]')
plt.ylabel('Single hit cluster occupancy [a.u.]')
plt.ylim(0, np.amax(y_p) * 1.15)
if filename is None:
plt.show()
else:
filename.savefig(plt.gcf())
return smoothed_data_diff
def plot_result(x_p, y_p, y_p_e, smoothed_data, smoothed_data_diff, filename=None):
''' Fit spline to the profile histogramed data, differentiate, determine MPV and plot.
Parameters
----------
x_p, y_p : array like
data points (x,y)
y_p_e : array like
error bars in y
'''
logging.info('Plot results')
plt.close()
p1 = plt.errorbar(x_p * analysis_configuration['vcal_calibration'], y_p, yerr=y_p_e, fmt='o') # plot data with error bars
p2, = plt.plot(x_p * analysis_configuration['vcal_calibration'], smoothed_data, '-r') # plot smoothed data
factor = np.amax(y_p) / np.amin(smoothed_data_diff) * 1.1
p3, = plt.plot(x_p * analysis_configuration['vcal_calibration'], factor * smoothed_data_diff, '-', lw=2) # plot differentiated data
mpv_index = np.argmax(-analysis_utils.smooth_differentiation(x_p, y_p, weigths=1 / y_p_e, order=3, smoothness=analysis_configuration['smoothness'], derivation=1))
p4, = plt.plot([x_p[mpv_index] * analysis_configuration['vcal_calibration'], x_p[mpv_index] * analysis_configuration['vcal_calibration']], [0, factor * smoothed_data_diff[mpv_index]], 'k-', lw=2)
text = 'MPV ' + str(int(x_p[mpv_index] * analysis_configuration['vcal_calibration'])) + ' e'
plt.text(1.01 * x_p[mpv_index] * analysis_configuration['vcal_calibration'], -10. * smoothed_data_diff[mpv_index], text, ha='left')
plt.legend([p1, p2, p3, p4], ['data', 'smoothed spline', 'spline differentiation', text], prop={'size': 12}, loc=0)
plt.title('\'Single hit cluster\'-occupancy for different pixel thresholds')
plt.xlabel('Pixel threshold [e]')
plt.ylabel('Single hit cluster occupancy [a.u.]')
plt.ylim(0, np.amax(y_p) * 1.15)
if filename is None:
plt.show()
else:
filename.savefig(plt.gcf())
return smoothed_data_diff
def get_calibration_correction(tdc_calibration, tdc_calibration_values, filename_new_calibration): # correct the TDC calibration with the TDC calib in filename_new_calibration by shifting the means
with tb.open_file(filename_new_calibration, 'r') as in_file_2:
charge_calibration_1, charge_calibration_2 = tdc_calibration, in_file_2.root.HitOrCalibration[:, :, :, 1]
plsr_dacs = tdc_calibration_values
if not np.all(plsr_dacs == in_file_2.root.HitOrCalibration._v_attrs.scan_parameter_values):
raise NotImplementedError('The check calibration file has to have the same PlsrDAC values')
# Valid pixel have a calibration in the new and the old calibration
valid_pixel = np.where(~np.all((charge_calibration_1 == 0), axis=2) & ~np.all(np.isnan(charge_calibration_1), axis=2) & ~np.all((charge_calibration_2 == 0), axis=2) & ~np.all(np.isnan(charge_calibration_2), axis=2))
mean_charge_calibration = np.nanmean(charge_calibration_2[valid_pixel], axis=0)
offset_mean = np.nanmean((charge_calibration_2[valid_pixel] - charge_calibration_1[valid_pixel]), axis=0)
dPlsrDAC_dTDC = analysis_utils.smooth_differentiation(plsr_dacs, mean_charge_calibration, order=3, smoothness=0, derivation=1)
plt.clf()
plt.plot(plsr_dacs, offset_mean / dPlsrDAC_dTDC, '.-', label='PlsrDAC')
plt.plot(plsr_dacs, offset_mean, '.-', label='TDC')
plt.grid()
plt.xlabel('PlsrDAC')
plt.ylabel('Mean calibration offset')
plt.legend(loc=0)
plt.title('Mean offset between TDC calibration data, new - old ')
plt.savefig(filename_new_calibration[:-3] + '.pdf')
plt.show()
return offset_mean
def get_calibration_correction(
tdc_calibration, tdc_calibration_values, filename_new_calibration
): # correct the TDC calibration with the TDC calib in filename_new_calibration by shifting the means
with tb.open_file(filename_new_calibration, 'r') as in_file_2:
with PdfPages(
os.path.splitext(filename_new_calibration)[0] +
'.pdf') as output_pdf:
charge_calibration_1, charge_calibration_2 = tdc_calibration, in_file_2.root.HitOrCalibration[:, :, :,
1]
plsr_dacs = tdc_calibration_values
if not np.all(plsr_dacs == in_file_2.root.HitOrCalibration.
_v_attrs.scan_parameter_values):
raise NotImplementedError(
'The check calibration file has to have the same PlsrDAC values'
)
# Valid pixel have a calibration in the new and the old calibration
valid_pixel = np.where(
~np.all((charge_calibration_1 == 0), axis=2)
& ~np.all(np.isnan(charge_calibration_1), axis=2)
& ~np.all((charge_calibration_2 == 0), axis=2)
& ~np.all(np.isnan(charge_calibration_2), axis=2))
mean_charge_calibration = np.nanmean(
charge_calibration_2[valid_pixel], axis=0)
offset_mean = np.nanmean((charge_calibration_2[valid_pixel] -
charge_calibration_1[valid_pixel]),
axis=0)
dPlsrDAC_dTDC = analysis_utils.smooth_differentiation(
plsr_dacs,
mean_charge_calibration,
order=3,
smoothness=0,
derivation=1)
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.plot(plsr_dacs,
offset_mean / dPlsrDAC_dTDC,
'.-',
label='PlsrDAC')
ax.plot(plsr_dacs, offset_mean, '.-', label='TDC')
ax.grid(True)
ax.set_xlabel('PlsrDAC')
ax.set_ylabel('Mean calibration offset')
ax.legend(loc=0)
ax.set_title(
'Mean offset between TDC calibration data, new - old ')
output_pdf.savefig(fig)
return offset_mean
def get_calibration_correction(tdc_calibration, tdc_calibration_values, filename_new_calibration): # correct the TDC calibration with the TDC calib in filename_new_calibration by shifting the means
with tb.open_file(filename_new_calibration, 'r') as in_file_2:
charge_calibration_1, charge_calibration_2 = tdc_calibration, in_file_2.root.HitOrCalibration[:, :, :, 1]
plsr_dacs = tdc_calibration_values
if not np.all(plsr_dacs == in_file_2.root.HitOrCalibration._v_attrs.scan_parameter_values):
raise NotImplementedError('The check calibration file has to have the same PlsrDAC values')
valid_pixel = np.where(np.logical_and(charge_calibration_1.sum(axis=2) > 0, charge_calibration_2.sum(axis=2) > 0)) # valid pixel have a calibration in the new and the old calibration
mean_charge_calibration = charge_calibration_2[valid_pixel].mean(axis=0)
offset_mean = (charge_calibration_1[valid_pixel] - charge_calibration_2[valid_pixel]).mean(axis=0)
dPlsrDAC_dTDC = analysis_utils.smooth_differentiation(plsr_dacs, mean_charge_calibration, order=3, smoothness=0, derivation=1)
plt.clf()
plt.plot(plsr_dacs, offset_mean / dPlsrDAC_dTDC, '.-', label='PlsrDAC')
plt.plot(plsr_dacs, offset_mean, '.-', label='TDC')
plt.grid()
plt.xlabel('PlsrDAC')
plt.ylabel('Mean calibration offset')
plt.legend(loc=0)
plt.title('Mean offset between TDC calibration data, old - new ')
plt.show()
return offset_mean
def analyze_injected_charge(data_analyzed_file):
logging.info('Analyze the injected charge')
with tb.openFile(data_analyzed_file, mode="r") as in_file_h5:
occupancy = in_file_h5.root.HistOcc[:].T
gdacs = analysis_utils.get_scan_parameter(
in_file_h5.root.meta_data[:])['GDAC']
with PdfPages(data_analyzed_file[:-3] + '.pdf') as plot_file:
plotting.plot_scatter(
gdacs,
occupancy.sum(axis=(0, 1)),
title='Single pixel hit rate at different thresholds',
x_label='Threshold setting [GDAC]',
y_label='Single pixel hit rate',
log_x=True,
filename=plot_file)
if analysis_configuration['input_file_calibration']:
with tb.openFile(
analysis_configuration['input_file_calibration'],
mode="r"
) as in_file_calibration_h5: # read calibration file from calibrate_threshold_gdac scan
mean_threshold_calibration = in_file_calibration_h5.root.MeanThresholdCalibration[:]
threshold_calibration_array = in_file_calibration_h5.root.HistThresholdCalibration[:]
gdac_range_calibration = np.array(
in_file_calibration_h5.root.HistThresholdCalibration.
_v_attrs.scan_parameter_values)
gdac_range_source_scan = gdacs
# Select data that is within the given GDAC range, (min_gdac, max_gdac)
sel = np.where(
np.logical_and(
gdac_range_source_scan >=
analysis_configuration['min_gdac'],
gdac_range_source_scan <=
analysis_configuration['max_gdac']))[0]
gdac_range_source_scan = gdac_range_source_scan[sel]
occupancy = occupancy[:, :, sel]
sel = np.where(
np.logical_and(
gdac_range_calibration >=
analysis_configuration['min_gdac'],
gdac_range_calibration <=
analysis_configuration['max_gdac']))[0]
gdac_range_calibration = gdac_range_calibration[sel]
threshold_calibration_array = threshold_calibration_array[:, :,
sel]
logging.info(
'Analyzing source scan data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d',
len(gdac_range_source_scan),
np.min(gdac_range_source_scan),
np.max(gdac_range_source_scan),
np.min(np.gradient(gdac_range_source_scan)),
np.max(np.gradient(gdac_range_source_scan)))
logging.info(
'Use calibration data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d',
len(gdac_range_calibration),
np.min(gdac_range_calibration),
np.max(gdac_range_calibration),
np.min(np.gradient(gdac_range_calibration)),
np.max(np.gradient(gdac_range_calibration)))
# rate_normalization of the total hit number for each GDAC setting
rate_normalization = 1.
if analysis_configuration['normalize_rate']:
rate_normalization = analysis_utils.get_rate_normalization(
hit_file=hit_file,
cluster_file=hit_file,
parameter='GDAC',
reference=analysis_configuration[
'normalization_reference'],
plot=analysis_configuration['plot_normalization'])
# correcting the hit numbers for the different cluster sizes
correction_factors = 1.
if analysis_configuration['use_cluster_rate_correction']:
correction_h5 = tb.openFile(cluster_sizes_file,
mode="r")
cluster_size_histogram = correction_h5.root.AllHistClusterSize[:]
correction_factors = analysis_utils.get_hit_rate_correction(
gdacs=gdac_range_source_scan,
calibration_gdacs=gdac_range_source_scan,
cluster_size_histogram=cluster_size_histogram)
if analysis_configuration['plot_cluster_sizes']:
plot_cluster_sizes(
correction_h5,
in_file_calibration_h5,
gdac_range=gdac_range_source_scan)
pixel_thresholds = analysis_utils.get_pixel_thresholds_from_calibration_array(
gdacs=gdac_range_source_scan,
calibration_gdacs=gdac_range_calibration,
threshold_calibration_array=threshold_calibration_array
) # interpolates the threshold at the source scan GDAC setting from the calibration
pixel_hits = occupancy # create hit array with shape (col, row, ...)
pixel_hits = pixel_hits * correction_factors * rate_normalization
# choose region with pixels that have a sufficient occupancy but are not too hot
good_pixel = analysis_utils.select_good_pixel_region(
pixel_hits,
col_span=analysis_configuration['col_span'],
row_span=analysis_configuration['row_span'],
min_cut_threshold=analysis_configuration[
'min_cut_threshold'],
max_cut_threshold=analysis_configuration[
'max_cut_threshold'])
pixel_mask = ~np.ma.getmaskarray(good_pixel)
selected_pixel_hits = pixel_hits[
pixel_mask, :] # reduce the data to pixels that are in the good pixel region
selected_pixel_thresholds = pixel_thresholds[
pixel_mask, :] # reduce the data to pixels that are in the good pixel region
plotting.plot_occupancy(
good_pixel.T,
title='Selected pixel for analysis (' +
str(len(selected_pixel_hits)) + ')',
filename=plot_file)
# reshape to one dimension
x = selected_pixel_thresholds.flatten()
y = selected_pixel_hits.flatten()
# nothing should be NAN/INF, NAN/INF is not supported yet
if np.isfinite(x).shape != x.shape or np.isfinite(
y).shape != y.shape:
logging.warning(
'There are pixels with NaN or INF threshold or hit values, analysis will fail'
)
# calculated profile histogram
x_p, y_p, y_p_e = analysis_utils.get_profile_histogram(
x, y, n_bins=analysis_configuration['n_bins']
) # profile histogram data
# select only the data point where the calibration worked
selected_data = np.logical_and(
x_p > analysis_configuration['min_thr'] /
analysis_configuration['vcal_calibration'],
x_p < analysis_configuration['max_thr'] /
analysis_configuration['vcal_calibration'])
x_p = x_p[selected_data]
y_p = y_p[selected_data]
y_p_e = y_p_e[selected_data]
if len(y_p_e[y_p_e == 0]) != 0:
logging.warning(
'There are bins without any data, guessing the error bars'
)
y_p_e[y_p_e == 0] = np.amin(y_p_e[y_p_e != 0])
smoothed_data = analysis_utils.smooth_differentiation(
x_p,
y_p,
weigths=1 / y_p_e,
order=3,
smoothness=analysis_configuration['smoothness'],
derivation=0)
smoothed_data_diff = analysis_utils.smooth_differentiation(
x_p,
y_p,
weigths=1 / y_p_e,
order=3,
smoothness=analysis_configuration['smoothness'],
derivation=1)
with tb.openFile(data_analyzed_file[:-3] + '_result.h5',
mode="w") as out_file_h5:
result_1 = np.rec.array(np.column_stack(
(x_p, y_p, y_p_e)),
dtype=[('charge', float),
('count', float),
('count_error', float)])
result_2 = np.rec.array(np.column_stack(
(x_p, smoothed_data)),
dtype=[('charge', float),
('count', float)])
result_3 = np.rec.array(np.column_stack(
(x_p, -smoothed_data_diff)),
dtype=[('charge', float),
('count', float)])
out_1 = out_file_h5.create_table(
out_file_h5.root,
name='ProfileHistogram',
description=result_1.dtype,
title=
'Single pixel count rate combined with a profile histogram',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
out_2 = out_file_h5.create_table(
out_file_h5.root,
name='ProfileHistogramSpline',
description=result_2.dtype,
title=
'Single pixel count rate combined with a profile histogram and spline smoothed',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
out_3 = out_file_h5.create_table(
out_file_h5.root,
name='ChargeHistogram',
description=result_3.dtype,
title=
'Charge histogram with threshold method and per pixel calibration',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
for key, value in analysis_configuration.iteritems():
out_1.attrs[key] = value
out_2.attrs[key] = value
out_3.attrs[key] = value
out_1.append(result_1)
out_2.append(result_2)
out_3.append(result_3)
plot_result(x_p, y_p, y_p_e, smoothed_data,
smoothed_data_diff)
# calculate and plot mean results
x_mean = analysis_utils.get_mean_threshold_from_calibration(
gdac_range_source_scan, mean_threshold_calibration)
y_mean = selected_pixel_hits.mean(axis=(0))
plotting.plot_scatter(
np.array(gdac_range_source_scan),
y_mean,
log_x=True,
plot_range=None,
title=
'Mean single pixel cluster rate at different thresholds',
x_label='threshold setting [GDAC]',
y_label='mean single pixel cluster rate',
filename=plot_file)
plotting.plot_scatter(
x_mean * analysis_configuration['vcal_calibration'],
y_mean,
plot_range=(analysis_configuration['min_thr'],
analysis_configuration['max_thr']),
title=
'Mean single pixel cluster rate at different thresholds',
x_label='mean threshold [e]',
y_label='mean single pixel cluster rate',
filename=plot_file)
if analysis_configuration['use_cluster_rate_correction']:
correction_h5.close()
def analyze_injected_charge(data_analyzed_file):
logging.info('Analyze the injected charge')
with tb.openFile(data_analyzed_file, mode="r") as in_file_h5:
occupancy = in_file_h5.root.HistOcc[:].T
gdacs = analysis_utils.get_scan_parameter(in_file_h5.root.meta_data[:])['GDAC']
with PdfPages(data_analyzed_file[:-3] + '.pdf') as plot_file:
plotting.plot_scatter(gdacs, occupancy.sum(axis=(0, 1)), title='Single pixel hit rate at different thresholds', x_label='Threshold setting [GDAC]', y_label='Single pixel hit rate', log_x=True, filename=plot_file)
if analysis_configuration['input_file_calibration']:
with tb.openFile(analysis_configuration['input_file_calibration'], mode="r") as in_file_calibration_h5: # read calibration file from calibrate_threshold_gdac scan
mean_threshold_calibration = in_file_calibration_h5.root.MeanThresholdCalibration[:]
threshold_calibration_array = in_file_calibration_h5.root.HistThresholdCalibration[:]
gdac_range_calibration = np.array(in_file_calibration_h5.root.HistThresholdCalibration._v_attrs.scan_parameter_values)
gdac_range_source_scan = gdacs
# Select data that is within the given GDAC range, (min_gdac, max_gdac)
sel = np.where(np.logical_and(gdac_range_source_scan >= analysis_configuration['min_gdac'], gdac_range_source_scan <= analysis_configuration['max_gdac']))[0]
gdac_range_source_scan = gdac_range_source_scan[sel]
occupancy = occupancy[:, :, sel]
sel = np.where(np.logical_and(gdac_range_calibration >= analysis_configuration['min_gdac'], gdac_range_calibration <= analysis_configuration['max_gdac']))[0]
gdac_range_calibration = gdac_range_calibration[sel]
threshold_calibration_array = threshold_calibration_array[:, :, sel]
logging.info('Analyzing source scan data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d', len(gdac_range_source_scan), np.min(gdac_range_source_scan), np.max(gdac_range_source_scan), np.min(np.gradient(gdac_range_source_scan)), np.max(np.gradient(gdac_range_source_scan)))
logging.info('Use calibration data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d', len(gdac_range_calibration), np.min(gdac_range_calibration), np.max(gdac_range_calibration), np.min(np.gradient(gdac_range_calibration)), np.max(np.gradient(gdac_range_calibration)))
# rate_normalization of the total hit number for each GDAC setting
rate_normalization = 1.
if analysis_configuration['normalize_rate']:
rate_normalization = analysis_utils.get_rate_normalization(hit_file=hit_file, cluster_file=hit_file, parameter='GDAC', reference=analysis_configuration['normalization_reference'], plot=analysis_configuration['plot_normalization'])
# correcting the hit numbers for the different cluster sizes
correction_factors = 1.
if analysis_configuration['use_cluster_rate_correction']:
correction_h5 = tb.openFile(cluster_sizes_file, mode="r")
cluster_size_histogram = correction_h5.root.AllHistClusterSize[:]
correction_factors = analysis_utils.get_hit_rate_correction(gdacs=gdac_range_source_scan, calibration_gdacs=gdac_range_source_scan, cluster_size_histogram=cluster_size_histogram)
if analysis_configuration['plot_cluster_sizes']:
plot_cluster_sizes(correction_h5, in_file_calibration_h5, gdac_range=gdac_range_source_scan)
pixel_thresholds = analysis_utils.get_pixel_thresholds_from_calibration_array(gdacs=gdac_range_source_scan, calibration_gdacs=gdac_range_calibration, threshold_calibration_array=threshold_calibration_array) # interpolates the threshold at the source scan GDAC setting from the calibration
pixel_hits = occupancy # create hit array with shape (col, row, ...)
pixel_hits = pixel_hits * correction_factors * rate_normalization
# choose region with pixels that have a sufficient occupancy but are not too hot
good_pixel = analysis_utils.select_good_pixel_region(pixel_hits, col_span=analysis_configuration['col_span'], row_span=analysis_configuration['row_span'], min_cut_threshold=analysis_configuration['min_cut_threshold'], max_cut_threshold=analysis_configuration['max_cut_threshold'])
pixel_mask = ~np.ma.getmaskarray(good_pixel)
selected_pixel_hits = pixel_hits[pixel_mask, :] # reduce the data to pixels that are in the good pixel region
selected_pixel_thresholds = pixel_thresholds[pixel_mask, :] # reduce the data to pixels that are in the good pixel region
plotting.plot_occupancy(good_pixel.T, title='Selected pixel for analysis (' + str(len(selected_pixel_hits)) + ')', filename=plot_file)
# reshape to one dimension
x = selected_pixel_thresholds.flatten()
y = selected_pixel_hits.flatten()
# nothing should be NAN/INF, NAN/INF is not supported yet
if np.isfinite(x).shape != x.shape or np.isfinite(y).shape != y.shape:
logging.warning('There are pixels with NaN or INF threshold or hit values, analysis will fail')
# calculated profile histogram
x_p, y_p, y_p_e = analysis_utils.get_profile_histogram(x, y, n_bins=analysis_configuration['n_bins']) # profile histogram data
# select only the data point where the calibration worked
selected_data = np.logical_and(x_p > analysis_configuration['min_thr'] / analysis_configuration['vcal_calibration'], x_p < analysis_configuration['max_thr'] / analysis_configuration['vcal_calibration'])
x_p = x_p[selected_data]
y_p = y_p[selected_data]
y_p_e = y_p_e[selected_data]
if len(y_p_e[y_p_e == 0]) != 0:
logging.warning('There are bins without any data, guessing the error bars')
y_p_e[y_p_e == 0] = np.amin(y_p_e[y_p_e != 0])
smoothed_data = analysis_utils.smooth_differentiation(x_p, y_p, weigths=1 / y_p_e, order=3, smoothness=analysis_configuration['smoothness'], derivation=0)
smoothed_data_diff = analysis_utils.smooth_differentiation(x_p, y_p, weigths=1 / y_p_e, order=3, smoothness=analysis_configuration['smoothness'], derivation=1)
with tb.openFile(data_analyzed_file[:-3] + '_result.h5', mode="w") as out_file_h5:
result_1 = np.rec.array(np.column_stack((x_p, y_p, y_p_e)), dtype=[('charge', float), ('count', float), ('count_error', float)])
result_2 = np.rec.array(np.column_stack((x_p, smoothed_data)), dtype=[('charge', float), ('count', float)])
result_3 = np.rec.array(np.column_stack((x_p, -smoothed_data_diff)), dtype=[('charge', float), ('count', float)])
out_1 = out_file_h5.create_table(out_file_h5.root, name='ProfileHistogram', description=result_1.dtype, title='Single pixel count rate combined with a profile histogram', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_2 = out_file_h5.create_table(out_file_h5.root, name='ProfileHistogramSpline', description=result_2.dtype, title='Single pixel count rate combined with a profile histogram and spline smoothed', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_3 = out_file_h5.create_table(out_file_h5.root, name='ChargeHistogram', description=result_3.dtype, title='Charge histogram with threshold method and per pixel calibration', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
for key, value in analysis_configuration.iteritems():
out_1.attrs[key] = value
out_2.attrs[key] = value
out_3.attrs[key] = value
out_1.append(result_1)
out_2.append(result_2)
out_3.append(result_3)
plot_result(x_p, y_p, y_p_e, smoothed_data, smoothed_data_diff)
# calculate and plot mean results
x_mean = analysis_utils.get_mean_threshold_from_calibration(gdac_range_source_scan, mean_threshold_calibration)
y_mean = selected_pixel_hits.mean(axis=(0))
plotting.plot_scatter(np.array(gdac_range_source_scan), y_mean, log_x=True, plot_range=None, title='Mean single pixel cluster rate at different thresholds', x_label='threshold setting [GDAC]', y_label='mean single pixel cluster rate', filename=plot_file)
plotting.plot_scatter(x_mean * analysis_configuration['vcal_calibration'], y_mean, plot_range=(analysis_configuration['min_thr'], analysis_configuration['max_thr']), title='Mean single pixel cluster rate at different thresholds', x_label='mean threshold [e]', y_label='mean single pixel cluster rate', filename=plot_file)
if analysis_configuration['use_cluster_rate_correction']:
correction_h5.close()
