def histogram_tdc_hits(input_file_hits, hit_selection_conditions, event_status_select_mask, event_status_condition, calibation_file=None, max_tdc=analysis_configuration['max_tdc'], n_bins=analysis_configuration['n_bins']):
for condition in hit_selection_conditions:
logging.info('Histogram tdc hits with %s', condition)
def get_charge(max_tdc, tdc_calibration_values, tdc_pixel_calibration): # return the charge from calibration
charge_calibration = np.zeros(shape=(80, 336, max_tdc))
for column in range(80):
for row in range(336):
actual_pixel_calibration = tdc_pixel_calibration[column, row, :]
if np.any(actual_pixel_calibration != 0) and np.all(np.isfinite(actual_pixel_calibration)):
interpolation = interp1d(x=actual_pixel_calibration, y=tdc_calibration_values, kind='slinear', bounds_error=False, fill_value=0)
charge_calibration[column, row, :] = interpolation(np.arange(max_tdc))
return charge_calibration
def plot_tdc_tot_correlation(data, condition, output_pdf):
logging.info('Plot correlation histogram for %s', condition)
plt.clf()
data = np.ma.array(data, mask=(data <= 0))
if np.ma.any(data > 0):
cmap = cm.get_cmap('jet', 200)
cmap.set_bad('w')
plt.title('Correlation with %s' % condition)
norm = colors.LogNorm()
z_max = data.max(fill_value=0)
plt.xlabel('TDC')
plt.ylabel('TOT')
im = plt.imshow(data, cmap=cmap, norm=norm, aspect='auto', interpolation='nearest') # , norm=norm)
divider = make_axes_locatable(plt.gca())
plt.gca().invert_yaxis()
cax = divider.append_axes("right", size="5%", pad=0.1)
plt.colorbar(im, cax=cax, ticks=np.linspace(start=0, stop=z_max, num=9, endpoint=True))
output_pdf.savefig()
else:
logging.warning('No data for correlation plotting for %s', condition)
def plot_hits_per_condition(output_pdf):
logging.info('Plot hits selection efficiency histogram for %d conditions', len(hit_selection_conditions) + 2)
labels = ['All Hits', 'Hits of\ngood events']
for condition in hit_selection_conditions:
condition = re.sub('[&]', '\n', condition)
condition = re.sub('[()]', '', condition)
labels.append(condition)
plt.bar(range(len(n_hits_per_condition)), n_hits_per_condition, align='center')
plt.xticks(range(len(n_hits_per_condition)), labels, size=8)
plt.title('Number of hits for different cuts')
plt.yscale('log')
plt.ylabel('#')
plt.grid()
for x, y in zip(np.arange(len(n_hits_per_condition)), n_hits_per_condition):
plt.annotate('%d' % (float(y) / float(n_hits_per_condition[0]) * 100.) + r'%', xy=(x, y / 2.), xycoords='data', color='grey', size=15)
output_pdf.savefig()
def plot_corrected_tdc_hist(x, y, title, output_pdf, point_style='-'):
logging.info('Plot TDC hist with TDC calibration')
plt.clf()
y /= np.amax(y) if y.shape[0] > 0 else y
plt.plot(x, y, point_style)
plt.title(title, size=10)
plt.xlabel('Charge [PlsrDAC]')
plt.ylabel('Count [a.u.]')
plt.grid()
output_pdf.savefig()
# Create data
with tb.openFile(input_file_hits, mode="r") as in_hit_file_h5:
cluster_hit_table = in_hit_file_h5.root.ClusterHits
# Result hists, initialized per condition
pixel_tdc_hists_per_condition = [np.zeros(shape=(80, 336, max_tdc), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
pixel_tdc_timestamp_hists_per_condition = [np.zeros(shape=(80, 336, 256), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
mean_pixel_tdc_hists_per_condition = [np.zeros(shape=(80, 336), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
mean_pixel_tdc_timestamp_hists_per_condition = [np.zeros(shape=(80, 336), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
tdc_hists_per_condition = [np.zeros(shape=(max_tdc), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
tdc_corr_hists_per_condition = [np.zeros(shape=(max_tdc, 16), dtype=np.uint32) for _ in hit_selection_conditions] if hit_selection_conditions else []
n_hits_per_condition = [0 for _ in range(len(hit_selection_conditions) + 2)] # condition 1, 2 are all hits, hits of goode events
logging.info('Select hits and create TDC histograms for %d cut conditions', len(hit_selection_conditions))
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_hit_table.shape[0], term_width=80)
progress_bar.start()
for cluster_hits, _ in analysis_utils.data_aligned_at_events(cluster_hit_table, chunk_size=1e8):
n_hits_per_condition[0] += cluster_hits.shape[0]
selected_events_cluster_hits = cluster_hits[np.logical_and(cluster_hits['TDC'] < max_tdc, (cluster_hits['event_status'] & event_status_select_mask) == event_status_condition)]
n_hits_per_condition[1] += selected_events_cluster_hits.shape[0]
for index, condition in enumerate(hit_selection_conditions):
selected_cluster_hits = analysis_utils.select_hits(selected_events_cluster_hits, condition)
n_hits_per_condition[2 + index] += selected_cluster_hits.shape[0]
column, row, tdc = selected_cluster_hits['column'] - 1, selected_cluster_hits['row'] - 1, selected_cluster_hits['TDC']
pixel_tdc_hists_per_condition[index] += analysis_utils.hist_3d_index(column, row, tdc, shape=(80, 336, max_tdc))
mean_pixel_tdc_hists_per_condition[index] = np.average(pixel_tdc_hists_per_condition[index], axis=2, weights=range(0, max_tdc)) * np.sum(np.arange(0, max_tdc)) / pixel_tdc_hists_per_condition[index].sum(axis=2)
tdc_timestamp = selected_cluster_hits['TDC_time_stamp']
pixel_tdc_timestamp_hists_per_condition[index] += analysis_utils.hist_3d_index(column, row, tdc_timestamp, shape=(80, 336, 256))
mean_pixel_tdc_timestamp_hists_per_condition[index] = np.average(pixel_tdc_timestamp_hists_per_condition[index], axis=2, weights=range(0, 256)) * np.sum(np.arange(0, 256)) / pixel_tdc_timestamp_hists_per_condition[index].sum(axis=2)
tdc_hists_per_condition[index] = pixel_tdc_hists_per_condition[index].sum(axis=(0, 1))
tdc_corr_hists_per_condition[index] += analysis_utils.hist_2d_index(tdc, selected_cluster_hits['tot'], shape=(max_tdc, 16))
progress_bar.update(n_hits_per_condition[0])
progress_bar.finish()
# Take TDC calibration if available and calculate charge for each TDC value and pixel
if calibation_file is not None:
with tb.openFile(calibation_file, mode="r") as in_file_calibration_h5:
tdc_calibration = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 1]
tdc_calibration_values = in_file_calibration_h5.root.HitOrCalibration.attrs.scan_parameter_values[:]
charge_calibration = get_charge(max_tdc, tdc_calibration_values, tdc_calibration)
else:
charge_calibration = None
# Store data of result histograms
with tb.open_file(input_file_hits[:-3] + '_tdc_hists.h5', mode="w") as out_file_h5:
for index, condition in enumerate(hit_selection_conditions):
pixel_tdc_hist_result = np.swapaxes(pixel_tdc_hists_per_condition[index], 0, 1)
pixel_tdc_timestamp_hist_result = np.swapaxes(pixel_tdc_timestamp_hists_per_condition[index], 0, 1)
mean_pixel_tdc_hist_result = np.swapaxes(mean_pixel_tdc_hists_per_condition[index], 0, 1)
mean_pixel_tdc_timestamp_hist_result = np.swapaxes(mean_pixel_tdc_timestamp_hists_per_condition[index], 0, 1)
tdc_hists_per_condition_result = tdc_hists_per_condition[index]
tdc_corr_hist_result = np.swapaxes(tdc_corr_hists_per_condition[index], 0, 1)
# Create result hists
out_1 = out_file_h5.createCArray(out_file_h5.root, name='HistPixelTdcCondition_%d' % index, title='Hist Pixel Tdc with %s' % condition, atom=tb.Atom.from_dtype(pixel_tdc_hist_result.dtype), shape=pixel_tdc_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_2 = out_file_h5.createCArray(out_file_h5.root, name='HistPixelTdcTimestampCondition_%d' % index, title='Hist Pixel Tdc Timestamp with %s' % condition, atom=tb.Atom.from_dtype(pixel_tdc_timestamp_hist_result.dtype), shape=pixel_tdc_timestamp_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_3 = out_file_h5.createCArray(out_file_h5.root, name='HistMeanPixelTdcCondition_%d' % index, title='Hist Mean Pixel Tdc with %s' % condition, atom=tb.Atom.from_dtype(mean_pixel_tdc_hist_result.dtype), shape=mean_pixel_tdc_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_4 = out_file_h5.createCArray(out_file_h5.root, name='HistMeanPixelTdcTimestampCondition_%d' % index, title='Hist Mean Pixel Tdc Timestamp with %s' % condition, atom=tb.Atom.from_dtype(mean_pixel_tdc_timestamp_hist_result.dtype), shape=mean_pixel_tdc_timestamp_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_5 = out_file_h5.createCArray(out_file_h5.root, name='HistTdcCondition_%d' % index, title='Hist Tdc with %s' % condition, atom=tb.Atom.from_dtype(tdc_hists_per_condition_result.dtype), shape=tdc_hists_per_condition_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_6 = out_file_h5.createCArray(out_file_h5.root, name='HistTdcCorrCondition_%d' % index, title='Hist Correlation Tdc/Tot with %s' % condition, atom=tb.Atom.from_dtype(tdc_corr_hist_result.dtype), shape=tdc_corr_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
# Add result hists information
out_1.attrs.dimensions, out_1.attrs.condition, out_1.attrs.tdc_values = 'column, row, TDC value', condition, range(max_tdc)
out_2.attrs.dimensions, out_2.attrs.condition, out_2.attrs.tdc_values = 'column, row, TDC time stamp value', condition, range(256)
out_3.attrs.dimensions, out_3.attrs.condition = 'column, row, mean TDC value', condition
out_4.attrs.dimensions, out_4.attrs.condition = 'column, row, mean TDC time stamp value', condition
out_5.attrs.dimensions, out_5.attrs.condition = 'PlsrDAC', condition
out_6.attrs.dimensions, out_6.attrs.condition = 'TDC, TOT', condition
out_1[:], out_2[:], out_3[:], out_4[:], out_5[:], out_6[:] = pixel_tdc_hist_result, pixel_tdc_timestamp_hist_result, mean_pixel_tdc_hist_result, mean_pixel_tdc_timestamp_hist_result, tdc_hists_per_condition_result, tdc_corr_hist_result
if charge_calibration is not None:
# Select only valid pixel for histograming: they have data and a calibration (that is any charge(TDC) calibration != 0)
valid_pixel = np.where(np.logical_and(charge_calibration[:, :, :max_tdc].sum(axis=2) > 0, pixel_tdc_hist_result[:, :, :max_tdc].swapaxes(0, 1).sum(axis=2) > 0))
mean_charge_calibration = charge_calibration[valid_pixel][:, :max_tdc].mean(axis=0)
mean_tdc_hist = pixel_tdc_hist_result.swapaxes(0, 1)[valid_pixel][:, :max_tdc].mean(axis=0)
result_array = np.rec.array(np.column_stack((mean_charge_calibration, mean_tdc_hist)), dtype=[('charge', float), ('count', float)])
out_6 = out_file_h5.create_table(out_file_h5.root, name='HistMeanTdcCalibratedCondition_%d' % index, description=result_array.dtype, title='Hist Tdc with mean charge calibration and %s' % condition, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_6.attrs.condition = condition
out_6.attrs.n_pixel = valid_pixel[0].shape[0]
out_6.append(result_array)
# Create charge histogram with per pixel TDC(charge) calibration
x, y = charge_calibration[valid_pixel][:, :max_tdc].ravel(), np.ravel(pixel_tdc_hist_result.swapaxes(0, 1)[valid_pixel][:, :max_tdc].ravel())
y, x = y[x > 0], x[x > 0] # remove the hit tdcs without proper calibration plsrDAC(TDC) calibration
x, y, yerr = analysis_utils.get_profile_histogram(x, y, n_bins=n_bins)
result_array = np.rec.array(np.column_stack((x, y, yerr)), dtype=[('charge', float), ('count', float), ('count_error', float)])
out_7 = out_file_h5.create_table(out_file_h5.root, name='HistTdcCalibratedCondition_%d' % index, description=result_array.dtype, title='Hist Tdc with per pixel charge calibration and %s' % condition, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_7.attrs.condition = condition
out_7.attrs.n_pixel = valid_pixel[0].shape[0]
out_7.append(result_array)
# Plot Data
with PdfPages(input_file_hits[:-3] + '_calibrated_tdc_hists.pdf') as output_pdf:
plot_hits_per_condition(output_pdf)
with tb.open_file(input_file_hits[:-3] + '_tdc_hists.h5', mode="r") as in_file_h5:
for node in in_file_h5.root: # go through the data and plot them
if 'MeanPixel' in node.name:
try:
plot_three_way(np.ma.masked_invalid(node[:]) * 1.5625, title='Mean TDC delay, hits with\n%s' % node._v_attrs.condition if 'Timestamp' in node.name else 'Mean TDC, hits with\n%s' % node._v_attrs.condition, filename=output_pdf)
except ValueError:
logging.warning('Cannot plot TDC delay')
elif 'HistTdcCondition' in node.name:
hist_1d = node[:]
entry_index = np.where(hist_1d != 0)
if entry_index[0].shape[0] != 0:
max_index = np.amax(entry_index)
else:
max_index = max_tdc
plot_1d_hist(hist_1d[:max_index + 10], title='TDC histogram, hits with\n%s' % node._v_attrs.condition if 'Timestamp' not in node.name else 'TDC time stamp histogram, hits with\n%s' % node._v_attrs.condition, x_axis_title='TDC' if 'Timestamp' not in node.name else 'TDC time stamp', filename=output_pdf)
elif 'HistPixelTdc' in node.name:
hist_3d = node[:]
entry_index = np.where(hist_3d.sum(axis=(0, 1)) != 0)
if entry_index[0].shape[0] != 0:
max_index = np.amax(entry_index)
else:
max_index = max_tdc
best_pixel_index = np.where(hist_3d.sum(axis=2) == np.amax(node[:].sum(axis=2)))
if best_pixel_index[0].shape[0] == 1: # there could be more than one pixel with most hits
plot_1d_hist(hist_3d[best_pixel_index][0, :max_index], title='TDC histogram of pixel %d, %d\n%s' % (best_pixel_index[1] + 1, best_pixel_index[0] + 1, node._v_attrs.condition) if 'Timestamp' not in node.name else 'TDC time stamp histogram, hits of pixel %d, %d' % (best_pixel_index[1] + 1, best_pixel_index[0] + 1), x_axis_title='TDC' if 'Timestamp' not in node.name else 'TDC time stamp', filename=output_pdf)
elif 'HistTdcCalibratedCondition' in node.name:
plot_corrected_tdc_hist(node[:]['charge'], node[:]['count'], title='TDC histogram, %d pixel, per pixel TDC calib.\n%s' % (node._v_attrs.n_pixel, node._v_attrs.condition), output_pdf=output_pdf)
elif 'HistMeanTdcCalibratedCondition' in node.name:
plot_corrected_tdc_hist(node[:]['charge'], node[:]['count'], title='TDC histogram, %d pixel, mean TDC calib.\n%s' % (node._v_attrs.n_pixel, node._v_attrs.condition), output_pdf=output_pdf)
elif 'HistTdcCorr' in node.name:
plot_tdc_tot_correlation(node[:], node._v_attrs.condition, output_pdf)
def histogram_tdc_hits(input_file_hits, hit_selection_conditions, event_status_select_mask, event_status_condition, calibation_file=None, max_tdc=analysis_configuration['max_tdc'], n_bins=analysis_configuration['n_bins']):
for condition in hit_selection_conditions:
logging.info('Histogram tdc hits with %s', condition)
def get_charge(max_tdc, tdc_calibration_values, tdc_pixel_calibration): # return the charge from calibration
charge_calibration = np.zeros(shape=(80, 336, max_tdc))
for column in range(80):
for row in range(336):
actual_pixel_calibration = tdc_pixel_calibration[column, row, :]
if np.any(actual_pixel_calibration != 0) and np.all(np.isfinite(actual_pixel_calibration)):
interpolation = interp1d(x=actual_pixel_calibration, y=tdc_calibration_values, kind='slinear', bounds_error=False, fill_value=0)
charge_calibration[column, row, :] = interpolation(np.arange(max_tdc))
return charge_calibration
def plot_tdc_tot_correlation(data, condition, output_pdf):
logging.info('Plot correlation histogram for %s', condition)
plt.clf()
data = np.ma.array(data, mask=(data <= 0))
if np.ma.any(data > 0):
cmap = cm.get_cmap('jet', 200)
cmap.set_bad('w')
plt.title('Correlation with %s' % condition)
norm = colors.LogNorm()
z_max = data.max(fill_value=0)
plt.xlabel('TDC')
plt.ylabel('TOT')
im = plt.imshow(data, cmap=cmap, norm=norm, aspect='auto', interpolation='nearest') # , norm=norm)
divider = make_axes_locatable(plt.gca())
plt.gca().invert_yaxis()
cax = divider.append_axes("right", size="5%", pad=0.1)
plt.colorbar(im, cax=cax, ticks=np.linspace(start=0, stop=z_max, num=9, endpoint=True))
output_pdf.savefig()
else:
logging.warning('No data for correlation plotting for %s', condition)
def plot_hits_per_condition(output_pdf):
logging.info('Plot hits selection efficiency histogram for %d conditions', len(hit_selection_conditions) + 2)
labels = ['All Hits', 'Hits of\ngood events']
for condition in hit_selection_conditions:
condition = re.sub('[&]', '\n', condition)
condition = re.sub('[()]', '', condition)
labels.append(condition)
plt.bar(range(len(n_hits_per_condition)), n_hits_per_condition, align='center')
plt.xticks(range(len(n_hits_per_condition)), labels, size=8)
plt.title('Number of hits for different cuts')
plt.yscale('log')
plt.ylabel('#')
plt.grid()
for x, y in zip(np.arange(len(n_hits_per_condition)), n_hits_per_condition):
plt.annotate('%d' % (float(y) / float(n_hits_per_condition[0]) * 100.) + r'%', xy=(x, y / 2.), xycoords='data', color='grey', size=15)
output_pdf.savefig()
def plot_corrected_tdc_hist(x, y, title, output_pdf, point_style='-'):
logging.info('Plot TDC hist with TDC calibration')
plt.clf()
y /= np.amax(y) if y.shape[0] > 0 else y
plt.plot(x, y, point_style)
plt.title(title, size=10)
plt.xlabel('Charge [PlsrDAC]')
plt.ylabel('Count [a.u.]')
plt.grid()
output_pdf.savefig()
# Create data
with tb.openFile(input_file_hits, mode="r") as in_hit_file_h5:
cluster_hit_table = in_hit_file_h5.root.ClusterHits
# Result hists, initialized per condition
pixel_tdc_hists_per_condition = [np.zeros(shape=(80, 336, max_tdc), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
pixel_tdc_timestamp_hists_per_condition = [np.zeros(shape=(80, 336, 256), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
mean_pixel_tdc_hists_per_condition = [np.zeros(shape=(80, 336), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
mean_pixel_tdc_timestamp_hists_per_condition = [np.zeros(shape=(80, 336), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
tdc_hists_per_condition = [np.zeros(shape=(max_tdc), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
tdc_corr_hists_per_condition = [np.zeros(shape=(max_tdc, 16), dtype=np.uint32) for _ in hit_selection_conditions] if hit_selection_conditions else []
n_hits_per_condition = [0 for _ in range(len(hit_selection_conditions) + 2)] # condition 1, 2 are all hits, hits of goode events
logging.info('Select hits and create TDC histograms for %d cut conditions', len(hit_selection_conditions))
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_hit_table.shape[0], term_width=80)
progress_bar.start()
for cluster_hits, _ in analysis_utils.data_aligned_at_events(cluster_hit_table, chunk_size=1e8):
n_hits_per_condition[0] += cluster_hits.shape[0]
selected_events_cluster_hits = cluster_hits[np.logical_and(cluster_hits['TDC'] < max_tdc, (cluster_hits['event_status'] & event_status_select_mask) == event_status_condition)]
n_hits_per_condition[1] += selected_events_cluster_hits.shape[0]
for index, condition in enumerate(hit_selection_conditions):
selected_cluster_hits = analysis_utils.select_hits(selected_events_cluster_hits, condition)
n_hits_per_condition[2 + index] += selected_cluster_hits.shape[0]
column, row, tdc = selected_cluster_hits['column'] - 1, selected_cluster_hits['row'] - 1, selected_cluster_hits['TDC']
pixel_tdc_hists_per_condition[index] += analysis_utils.hist_3d_index(column, row, tdc, shape=(80, 336, max_tdc))
mean_pixel_tdc_hists_per_condition[index] = np.average(pixel_tdc_hists_per_condition[index], axis=2, weights=range(0, max_tdc)) * np.sum(np.arange(0, max_tdc)) / pixel_tdc_hists_per_condition[index].sum(axis=2)
tdc_timestamp = selected_cluster_hits['TDC_time_stamp']
pixel_tdc_timestamp_hists_per_condition[index] += analysis_utils.hist_3d_index(column, row, tdc_timestamp, shape=(80, 336, 256))
mean_pixel_tdc_timestamp_hists_per_condition[index] = np.average(pixel_tdc_timestamp_hists_per_condition[index], axis=2, weights=range(0, 256)) * np.sum(np.arange(0, 256)) / pixel_tdc_timestamp_hists_per_condition[index].sum(axis=2)
tdc_hists_per_condition[index] = pixel_tdc_hists_per_condition[index].sum(axis=(0, 1))
tdc_corr_hists_per_condition[index] += analysis_utils.hist_2d_index(tdc, selected_cluster_hits['tot'], shape=(max_tdc, 16))
progress_bar.update(n_hits_per_condition[0])
progress_bar.finish()
# Take TDC calibration if available and calculate charge for each TDC value and pixel
if calibation_file is not None:
with tb.openFile(calibation_file, mode="r") as in_file_calibration_h5:
tdc_calibration = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 1]
tdc_calibration_values = in_file_calibration_h5.root.HitOrCalibration.attrs.scan_parameter_values[:]
charge_calibration = get_charge(max_tdc, tdc_calibration_values, tdc_calibration)
else:
charge_calibration = None
# Store data of result histograms
with tb.open_file(input_file_hits[:-3] + '_tdc_hists.h5', mode="w") as out_file_h5:
for index, condition in enumerate(hit_selection_conditions):
pixel_tdc_hist_result = np.swapaxes(pixel_tdc_hists_per_condition[index], 0, 1)
pixel_tdc_timestamp_hist_result = np.swapaxes(pixel_tdc_timestamp_hists_per_condition[index], 0, 1)
mean_pixel_tdc_hist_result = np.swapaxes(mean_pixel_tdc_hists_per_condition[index], 0, 1)
mean_pixel_tdc_timestamp_hist_result = np.swapaxes(mean_pixel_tdc_timestamp_hists_per_condition[index], 0, 1)
tdc_hists_per_condition_result = tdc_hists_per_condition[index]
tdc_corr_hist_result = np.swapaxes(tdc_corr_hists_per_condition[index], 0, 1)
# Create result hists
out_1 = out_file_h5.createCArray(out_file_h5.root, name='HistPixelTdcCondition_%d' % index, title='Hist Pixel Tdc with %s' % condition, atom=tb.Atom.from_dtype(pixel_tdc_hist_result.dtype), shape=pixel_tdc_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_2 = out_file_h5.createCArray(out_file_h5.root, name='HistPixelTdcTimestampCondition_%d' % index, title='Hist Pixel Tdc Timestamp with %s' % condition, atom=tb.Atom.from_dtype(pixel_tdc_timestamp_hist_result.dtype), shape=pixel_tdc_timestamp_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_3 = out_file_h5.createCArray(out_file_h5.root, name='HistMeanPixelTdcCondition_%d' % index, title='Hist Mean Pixel Tdc with %s' % condition, atom=tb.Atom.from_dtype(mean_pixel_tdc_hist_result.dtype), shape=mean_pixel_tdc_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_4 = out_file_h5.createCArray(out_file_h5.root, name='HistMeanPixelTdcTimestampCondition_%d' % index, title='Hist Mean Pixel Tdc Timestamp with %s' % condition, atom=tb.Atom.from_dtype(mean_pixel_tdc_timestamp_hist_result.dtype), shape=mean_pixel_tdc_timestamp_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_5 = out_file_h5.createCArray(out_file_h5.root, name='HistTdcCondition_%d' % index, title='Hist Tdc with %s' % condition, atom=tb.Atom.from_dtype(tdc_hists_per_condition_result.dtype), shape=tdc_hists_per_condition_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_6 = out_file_h5.createCArray(out_file_h5.root, name='HistTdcCorrCondition_%d' % index, title='Hist Correlation Tdc/Tot with %s' % condition, atom=tb.Atom.from_dtype(tdc_corr_hist_result.dtype), shape=tdc_corr_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
# Add result hists information
out_1.attrs.dimensions, out_1.attrs.condition, out_1.attrs.tdc_values = 'column, row, TDC value', condition, range(max_tdc)
out_2.attrs.dimensions, out_2.attrs.condition, out_2.attrs.tdc_values = 'column, row, TDC time stamp value', condition, range(256)
out_3.attrs.dimensions, out_3.attrs.condition = 'column, row, mean TDC value', condition
out_4.attrs.dimensions, out_4.attrs.condition = 'column, row, mean TDC time stamp value', condition
out_5.attrs.dimensions, out_5.attrs.condition = 'PlsrDAC', condition
out_6.attrs.dimensions, out_6.attrs.condition = 'TDC, TOT', condition
out_1[:], out_2[:], out_3[:], out_4[:], out_5[:], out_6[:] = pixel_tdc_hist_result, pixel_tdc_timestamp_hist_result, mean_pixel_tdc_hist_result, mean_pixel_tdc_timestamp_hist_result, tdc_hists_per_condition_result, tdc_corr_hist_result
if charge_calibration is not None:
# Select only valid pixel for histograming: they have data and a calibration (that is any charge(TDC) calibration != 0)
valid_pixel = np.where(np.logical_and(charge_calibration[:, :, :max_tdc].sum(axis=2) > 0, pixel_tdc_hist_result[:, :, :max_tdc].swapaxes(0, 1).sum(axis=2) > 0))
mean_charge_calibration = charge_calibration[valid_pixel][:, :max_tdc].mean(axis=0)
mean_tdc_hist = pixel_tdc_hist_result.swapaxes(0, 1)[valid_pixel][:, :max_tdc].mean(axis=0)
result_array = np.rec.array(np.column_stack((mean_charge_calibration, mean_tdc_hist)), dtype=[('charge', float), ('count', float)])
out_6 = out_file_h5.create_table(out_file_h5.root, name='HistMeanTdcCalibratedCondition_%d' % index, description=result_array.dtype, title='Hist Tdc with mean charge calibration and %s' % condition, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_6.attrs.condition = condition
out_6.attrs.n_pixel = valid_pixel[0].shape[0]
out_6.append(result_array)
# Create charge histogram with per pixel TDC(charge) calibration
x, y = charge_calibration[valid_pixel][:, :max_tdc].ravel(), np.ravel(pixel_tdc_hist_result.swapaxes(0, 1)[valid_pixel][:, :max_tdc].ravel())
y, x = y[x > 0], x[x > 0] # remove the hit tdcs without proper calibration plsrDAC(TDC) calibration
x, y, yerr = analysis_utils.get_profile_histogram(x, y, n_bins=n_bins)
result_array = np.rec.array(np.column_stack((x, y, yerr)), dtype=[('charge', float), ('count', float), ('count_error', float)])
out_7 = out_file_h5.create_table(out_file_h5.root, name='HistTdcCalibratedCondition_%d' % index, description=result_array.dtype, title='Hist Tdc with per pixel charge calibration and %s' % condition, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_7.attrs.condition = condition
out_7.attrs.n_pixel = valid_pixel[0].shape[0]
out_7.append(result_array)
# Plot Data
with PdfPages(input_file_hits[:-3] + '_calibrated_tdc_hists.pdf') as output_pdf:
plot_hits_per_condition(output_pdf)
with tb.open_file(input_file_hits[:-3] + '_tdc_hists.h5', mode="r") as in_file_h5:
for node in in_file_h5.root: # go through the data and plot them
if 'MeanPixel' in node.name:
try:
plotThreeWay(np.ma.masked_invalid(node[:]) * 1.5625, title='Mean TDC delay, hits with\n%s' % node._v_attrs.condition if 'Timestamp' in node.name else 'Mean TDC, hits with\n%s' % node._v_attrs.condition, filename=output_pdf)
except ValueError:
logging.warning('Cannot plot TDC delay')
elif 'HistTdcCondition' in node.name:
hist_1d = node[:]
entry_index = np.where(hist_1d != 0)
if entry_index[0].shape[0] != 0:
max_index = np.amax(entry_index)
else:
max_index = max_tdc
plot_1d_hist(hist_1d[:max_index + 10], title='TDC histogram, hits with\n%s' % node._v_attrs.condition if 'Timestamp' not in node.name else 'TDC time stamp histogram, hits with\n%s' % node._v_attrs.condition, x_axis_title='TDC' if 'Timestamp' not in node.name else 'TDC time stamp', filename=output_pdf)
elif 'HistPixelTdc' in node.name:
hist_3d = node[:]
entry_index = np.where(hist_3d.sum(axis=(0, 1)) != 0)
if entry_index[0].shape[0] != 0:
max_index = np.amax(entry_index)
else:
max_index = max_tdc
best_pixel_index = np.where(hist_3d.sum(axis=2) == np.amax(node[:].sum(axis=2)))
if best_pixel_index[0].shape[0] == 1: # there could be more than one pixel with most hits
plot_1d_hist(hist_3d[best_pixel_index][0, :max_index], title='TDC histogram of pixel %d, %d\n%s' % (best_pixel_index[1] + 1, best_pixel_index[0] + 1, node._v_attrs.condition) if 'Timestamp' not in node.name else 'TDC time stamp histogram, hits of pixel %d, %d' % (best_pixel_index[1] + 1, best_pixel_index[0] + 1), x_axis_title='TDC' if 'Timestamp' not in node.name else 'TDC time stamp', filename=output_pdf)
elif 'HistTdcCalibratedCondition' in node.name:
plot_corrected_tdc_hist(node[:]['charge'], node[:]['count'], title='TDC histogram, %d pixel, per pixel TDC calib.\n%s' % (node._v_attrs.n_pixel, node._v_attrs.condition), output_pdf=output_pdf)
elif 'HistMeanTdcCalibratedCondition' in node.name:
plot_corrected_tdc_hist(node[:]['charge'], node[:]['count'], title='TDC histogram, %d pixel, mean TDC calib.\n%s' % (node._v_attrs.n_pixel, node._v_attrs.condition), output_pdf=output_pdf)
elif 'HistTdcCorr' in node.name:
plot_tdc_tot_correlation(node[:], node._v_attrs.condition, output_pdf)
def histogram_tdc_hits(input_file_hits, hit_selection_conditions, event_status_select_mask, event_status_condition, calibration_file=None, correct_calibration=None, max_tdc=1000, ignore_disabled_regions=True, n_bins=200, plot_data=True):
for condition in hit_selection_conditions:
logging.info('Histogram TDC hits with %s', condition)
def get_charge(max_tdc, tdc_calibration_values, tdc_pixel_calibration): # return the charge from calibration
charge_calibration = np.zeros(shape=(80, 336, max_tdc))
for column in range(80):
for row in range(336):
actual_pixel_calibration = tdc_pixel_calibration[column, row, :]
# Only take pixels with at least 3 valid calibration points
if np.count_nonzero(actual_pixel_calibration != 0) > 2 and np.count_nonzero(np.isfinite(actual_pixel_calibration)) > 2:
selected_measurements = np.isfinite(actual_pixel_calibration) # Select valid calibration steps
selected_actual_pixel_calibration = actual_pixel_calibration[selected_measurements]
selected_tdc_calibration_values = tdc_calibration_values[selected_measurements]
interpolation = interp1d(x=selected_actual_pixel_calibration, y=selected_tdc_calibration_values, kind='slinear', bounds_error=False, fill_value=0)
charge_calibration[column, row, :] = interpolation(np.arange(max_tdc))
return charge_calibration
def plot_tdc_tot_correlation(data, condition, output_pdf):
logging.info('Plot correlation histogram for %s', condition)
plt.clf()
data = np.ma.array(data, mask=(data <= 0))
if np.ma.any(data > 0):
cmap = cm.get_cmap('jet', 200)
cmap.set_bad('w')
plt.title('Correlation with %s' % condition)
norm = colors.LogNorm()
z_max = data.max(fill_value=0)
plt.xlabel('TDC')
plt.ylabel('TOT')
im = plt.imshow(data, cmap=cmap, norm=norm, aspect='auto', interpolation='nearest') # , norm=norm)
divider = make_axes_locatable(plt.gca())
plt.gca().invert_yaxis()
cax = divider.append_axes("right", size="5%", pad=0.1)
plt.colorbar(im, cax=cax, ticks=np.linspace(start=0, stop=z_max, num=9, endpoint=True))
output_pdf.savefig()
else:
logging.warning('No data for correlation plotting for %s', condition)
def plot_hits_per_condition(output_pdf):
logging.info('Plot hits selection efficiency histogram for %d conditions', len(hit_selection_conditions) + 2)
labels = ['All Hits', 'Hits of\ngood events']
for condition in hit_selection_conditions:
condition = re.sub('[&]', '\n', condition)
condition = re.sub('[()]', '', condition)
labels.append(condition)
plt.clf()
plt.bar(range(len(n_hits_per_condition)), n_hits_per_condition, align='center')
plt.xticks(range(len(n_hits_per_condition)), labels, size=8)
plt.title('Number of hits for different cuts')
plt.yscale('log')
plt.ylabel('#')
plt.grid()
for x, y in zip(np.arange(len(n_hits_per_condition)), n_hits_per_condition):
plt.annotate('%d' % (float(y) / float(n_hits_per_condition[0]) * 100.) + r'%', xy=(x, y / 2.), xycoords='data', color='grey', size=15)
output_pdf.savefig()
def plot_corrected_tdc_hist(x, y, title, output_pdf, point_style='-'):
logging.info('Plot TDC hist with TDC calibration')
plt.clf()
y /= np.amax(y) if y.shape[0] > 0 else y
plt.plot(x, y, point_style)
plt.title(title, size=10)
plt.xlabel('Charge [PlsrDAC]')
plt.ylabel('Count [a.u.]')
plt.grid()
output_pdf.savefig()
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 delete_disabled_regions(hits, enable_mask):
n_hits = hits.shape[0]
# Tread no hits case
if n_hits == 0:
return hits
# Column, row array with True for disabled pixels
disabled_region = ~enable_mask.astype(np.bool).T.copy()
n_disabled_pixels = np.count_nonzero(disabled_region)
# Extend disabled pixel mask by the neighbouring pixels
neighbour_pixels = [(-1, 0), (1, 0), (0, -1), (0, 1)] # Disable direct neighbouring pixels
for neighbour_pixel in neighbour_pixels:
disabled_region = np.logical_or(disabled_region, shift(disabled_region, shift=neighbour_pixel, cval=0))
logging.info('Masking %d additional pixel neighbouring %d disabled pixels', np.count_nonzero(disabled_region) - n_disabled_pixels, n_disabled_pixels)
# Make 1D selection array with disabled pixels
disabled_pixels = np.where(disabled_region)
disabled_pixels_1d = (disabled_pixels[0] + 1) * disabled_region.shape[1] + (disabled_pixels[1] + 1) # + 1 because pixel index 0,0 has column/row = 1
hits_1d = hits['column'].astype(np.uint32) * disabled_region.shape[1] + hits['row'] # change dtype to fit new number
hits = hits[np.in1d(hits_1d, disabled_pixels_1d, invert=True)]
logging.info('Lost %d hits (%d percent) due to disabling neighbours', n_hits - hits.shape[0], (1. - float(hits.shape[0]) / n_hits) * 100)
return hits
# Create data
with tb.open_file(input_file_hits, mode="r") as in_hit_file_h5:
cluster_hit_table = in_hit_file_h5.root.ClusterHits
try:
enabled_pixels = in_hit_file_h5.root.ClusterHits._v_attrs.enabled_pixels[:]
except AttributeError: # Old and simulate data do not have this info
logging.warning('No enabled pixel mask found in data! Assume all pixels are enabled.')
enabled_pixels = np.ones(shape=(336, 80))
# Result hists, initialized per condition
pixel_tdc_hists_per_condition = [np.zeros(shape=(80, 336, max_tdc), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
pixel_tdc_timestamp_hists_per_condition = [np.zeros(shape=(80, 336, 256), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
mean_pixel_tdc_hists_per_condition = [np.zeros(shape=(80, 336), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
mean_pixel_tdc_timestamp_hists_per_condition = [np.zeros(shape=(80, 336), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
tdc_hists_per_condition = [np.zeros(shape=(max_tdc), dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else []
tdc_corr_hists_per_condition = [np.zeros(shape=(max_tdc, 16), dtype=np.uint32) for _ in hit_selection_conditions] if hit_selection_conditions else []
n_hits_per_condition = [0 for _ in range(len(hit_selection_conditions) + 2)] # condition 1, 2 are all hits, hits of goode events
logging.info('Select hits and create TDC histograms for %d cut conditions', len(hit_selection_conditions))
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_hit_table.shape[0], term_width=80)
progress_bar.start()
for cluster_hits, _ in analysis_utils.data_aligned_at_events(cluster_hit_table, chunk_size=10000000):
n_hits_per_condition[0] += cluster_hits.shape[0]
selected_events_cluster_hits = cluster_hits[np.logical_and(cluster_hits['TDC'] < max_tdc, (cluster_hits['event_status'] & event_status_select_mask) == event_status_condition)]
n_hits_per_condition[1] += selected_events_cluster_hits.shape[0]
for index, condition in enumerate(hit_selection_conditions):
selected_cluster_hits = analysis_utils.select_hits(selected_events_cluster_hits, condition)
if ignore_disabled_regions:
selected_cluster_hits = delete_disabled_regions(hits=selected_cluster_hits, enable_mask=enabled_pixels)
n_hits_per_condition[2 + index] += selected_cluster_hits.shape[0]
column, row, tdc = selected_cluster_hits['column'] - 1, selected_cluster_hits['row'] - 1, selected_cluster_hits['TDC']
pixel_tdc_hists_per_condition[index] += fast_analysis_utils.hist_3d_index(column, row, tdc, shape=(80, 336, max_tdc))
mean_pixel_tdc_hists_per_condition[index] = np.average(pixel_tdc_hists_per_condition[index], axis=2, weights=range(0, max_tdc)) * np.sum(np.arange(0, max_tdc)) / pixel_tdc_hists_per_condition[index].sum(axis=2)
tdc_timestamp = selected_cluster_hits['TDC_time_stamp']
pixel_tdc_timestamp_hists_per_condition[index] += fast_analysis_utils.hist_3d_index(column, row, tdc_timestamp, shape=(80, 336, 256))
mean_pixel_tdc_timestamp_hists_per_condition[index] = np.average(pixel_tdc_timestamp_hists_per_condition[index], axis=2, weights=range(0, 256)) * np.sum(np.arange(0, 256)) / pixel_tdc_timestamp_hists_per_condition[index].sum(axis=2)
tdc_hists_per_condition[index] = pixel_tdc_hists_per_condition[index].sum(axis=(0, 1))
tdc_corr_hists_per_condition[index] += fast_analysis_utils.hist_2d_index(tdc, selected_cluster_hits['tot'], shape=(max_tdc, 16))
progress_bar.update(n_hits_per_condition[0])
progress_bar.finish()
# Take TDC calibration if available and calculate charge for each TDC value and pixel
if calibration_file is not None:
with tb.open_file(calibration_file, mode="r") as in_file_calibration_h5:
tdc_calibration = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 1]
tdc_calibration_values = in_file_calibration_h5.root.HitOrCalibration.attrs.scan_parameter_values[:]
if correct_calibration is not None:
tdc_calibration += get_calibration_correction(tdc_calibration, tdc_calibration_values, correct_calibration)
charge_calibration = get_charge(max_tdc, tdc_calibration_values, tdc_calibration)
else:
charge_calibration = None
# Store data of result histograms
with tb.open_file(input_file_hits[:-3] + '_tdc_hists.h5', mode="w") as out_file_h5:
for index, condition in enumerate(hit_selection_conditions):
pixel_tdc_hist_result = np.swapaxes(pixel_tdc_hists_per_condition[index], 0, 1)
pixel_tdc_timestamp_hist_result = np.swapaxes(pixel_tdc_timestamp_hists_per_condition[index], 0, 1)
mean_pixel_tdc_hist_result = np.swapaxes(mean_pixel_tdc_hists_per_condition[index], 0, 1)
mean_pixel_tdc_timestamp_hist_result = np.swapaxes(mean_pixel_tdc_timestamp_hists_per_condition[index], 0, 1)
tdc_hists_per_condition_result = tdc_hists_per_condition[index]
tdc_corr_hist_result = np.swapaxes(tdc_corr_hists_per_condition[index], 0, 1)
# Create result hists
out_1 = out_file_h5.create_carray(out_file_h5.root, name='HistPixelTdcCondition_%d' % index, title='Hist Pixel Tdc with %s' % condition, atom=tb.Atom.from_dtype(pixel_tdc_hist_result.dtype), shape=pixel_tdc_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_2 = out_file_h5.create_carray(out_file_h5.root, name='HistPixelTdcTimestampCondition_%d' % index, title='Hist Pixel Tdc Timestamp with %s' % condition, atom=tb.Atom.from_dtype(pixel_tdc_timestamp_hist_result.dtype), shape=pixel_tdc_timestamp_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_3 = out_file_h5.create_carray(out_file_h5.root, name='HistMeanPixelTdcCondition_%d' % index, title='Hist Mean Pixel Tdc with %s' % condition, atom=tb.Atom.from_dtype(mean_pixel_tdc_hist_result.dtype), shape=mean_pixel_tdc_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_4 = out_file_h5.create_carray(out_file_h5.root, name='HistMeanPixelTdcTimestampCondition_%d' % index, title='Hist Mean Pixel Tdc Timestamp with %s' % condition, atom=tb.Atom.from_dtype(mean_pixel_tdc_timestamp_hist_result.dtype), shape=mean_pixel_tdc_timestamp_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_5 = out_file_h5.create_carray(out_file_h5.root, name='HistTdcCondition_%d' % index, title='Hist Tdc with %s' % condition, atom=tb.Atom.from_dtype(tdc_hists_per_condition_result.dtype), shape=tdc_hists_per_condition_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_6 = out_file_h5.create_carray(out_file_h5.root, name='HistTdcCorrCondition_%d' % index, title='Hist Correlation Tdc/Tot with %s' % condition, atom=tb.Atom.from_dtype(tdc_corr_hist_result.dtype), shape=tdc_corr_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
# Add result hists information
out_1.attrs.dimensions, out_1.attrs.condition, out_1.attrs.tdc_values = 'column, row, TDC value', condition, range(max_tdc)
out_2.attrs.dimensions, out_2.attrs.condition, out_2.attrs.tdc_values = 'column, row, TDC time stamp value', condition, range(256)
out_3.attrs.dimensions, out_3.attrs.condition = 'column, row, mean TDC value', condition
out_4.attrs.dimensions, out_4.attrs.condition = 'column, row, mean TDC time stamp value', condition
out_5.attrs.dimensions, out_5.attrs.condition = 'PlsrDAC', condition
out_6.attrs.dimensions, out_6.attrs.condition = 'TDC, TOT', condition
out_1[:], out_2[:], out_3[:], out_4[:], out_5[:], out_6[:] = pixel_tdc_hist_result, pixel_tdc_timestamp_hist_result, mean_pixel_tdc_hist_result, mean_pixel_tdc_timestamp_hist_result, tdc_hists_per_condition_result, tdc_corr_hist_result
if charge_calibration is not None:
# Select only valid pixel for histogramming: they have data and a calibration (that is any charge(TDC) calibration != 0)
valid_pixel = np.where(np.logical_and(charge_calibration[:, :, :max_tdc].sum(axis=2) > 0, pixel_tdc_hist_result[:, :, :max_tdc].swapaxes(0, 1).sum(axis=2) > 0))
# Create charge histogram with mean TDC calibration
mean_charge_calibration = charge_calibration[valid_pixel][:, :max_tdc].mean(axis=0)
mean_tdc_hist = pixel_tdc_hist_result.swapaxes(0, 1)[valid_pixel][:, :max_tdc].mean(axis=0)
result_array = np.rec.array(np.column_stack((mean_charge_calibration, mean_tdc_hist)), dtype=[('charge', float), ('count', float)])
out_7 = out_file_h5.create_table(out_file_h5.root, name='HistMeanTdcCalibratedCondition_%d' % index, description=result_array.dtype, title='Hist Tdc with mean charge calibration and %s' % condition, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_7.attrs.condition = condition
out_7.attrs.n_pixel = valid_pixel[0].shape[0]
out_7.attrs.n_hits = pixel_tdc_hist_result.swapaxes(0, 1)[valid_pixel][:, :max_tdc].sum()
out_7.append(result_array)
# Create charge histogram with per pixel TDC calibration
x, y = charge_calibration[valid_pixel][:, :max_tdc].ravel(), np.ravel(pixel_tdc_hist_result.swapaxes(0, 1)[valid_pixel][:, :max_tdc].ravel())
y_hist, x_hist = y[x > 0], x[x > 0] # remove the hit tdcs without proper calibration plsrDAC(TDC) calibration
x, y, yerr = analysis_utils.get_profile_histogram(x_hist, y_hist, n_bins=n_bins)
result_array = np.rec.array(np.column_stack((x, y, yerr)), dtype=[('charge', float), ('count', float), ('count_error', float)])
out_8 = out_file_h5.create_table(out_file_h5.root, name='HistTdcCalibratedCondition_%d' % index, description=result_array.dtype, title='Hist Tdc with per pixel charge calibration and %s' % condition, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_8.attrs.condition = condition
out_8.attrs.n_pixel = valid_pixel[0].shape[0]
out_8.attrs.n_hits = y_hist.sum()
out_8.append(result_array)
# Plot Data
if plot_data:
with PdfPages(input_file_hits[:-3] + '_calibrated_tdc_hists.pdf') as output_pdf:
plot_hits_per_condition(output_pdf)
with tb.open_file(input_file_hits[:-3] + '_tdc_hists.h5', mode="r") as in_file_h5:
for node in in_file_h5.root: # go through the data and plot them
if 'MeanPixel' in node.name:
try:
plot_three_way(np.ma.masked_invalid(node[:]) * 1.5625, title='Mean TDC delay, hits with\n%s' % node._v_attrs.condition[:80] if 'Timestamp' in node.name else 'Mean TDC, hits with\n%s' % node._v_attrs.condition[:80], filename=output_pdf)
except ValueError:
logging.warning('Cannot plot TDC delay')
elif 'HistTdcCondition' in node.name:
hist_1d = node[:]
entry_index = np.where(hist_1d != 0)
if entry_index[0].shape[0] != 0:
max_index = np.amax(entry_index)
else:
max_index = max_tdc
plot_1d_hist(hist_1d[:max_index + 10], title='TDC histogram, hits with\n%s' % node._v_attrs.condition[:80] if 'Timestamp' not in node.name else 'TDC time stamp histogram, hits with\n%s' % node._v_attrs.condition[:80], x_axis_title='TDC' if 'Timestamp' not in node.name else 'TDC time stamp', filename=output_pdf)
elif 'HistPixelTdc' in node.name:
hist_3d = node[:]
entry_index = np.where(hist_3d.sum(axis=(0, 1)) != 0)
if entry_index[0].shape[0] != 0:
max_index = np.amax(entry_index)
else:
max_index = max_tdc
best_pixel_index = np.where(hist_3d.sum(axis=2) == np.amax(node[:].sum(axis=2)))
if best_pixel_index[0].shape[0] == 1: # there could be more than one pixel with most hits
try:
plot_1d_hist(hist_3d[best_pixel_index][0, :max_index], title='TDC histogram of pixel %d, %d\n%s' % (best_pixel_index[1] + 1, best_pixel_index[0] + 1, node._v_attrs.condition[:80]) if 'Timestamp' not in node.name else 'TDC time stamp histogram, hits of pixel %d, %d' % (best_pixel_index[1] + 1, best_pixel_index[0] + 1), x_axis_title='TDC' if 'Timestamp' not in node.name[:80] else 'TDC time stamp', filename=output_pdf)
except IndexError:
logging.warning('Cannot plot pixel TDC histogram')
elif 'HistTdcCalibratedCondition' in node.name:
plot_corrected_tdc_hist(node[:]['charge'], node[:]['count'], title='TDC histogram, %d pixel, per pixel TDC calib.\n%s' % (node._v_attrs.n_pixel, node._v_attrs.condition[:80]), output_pdf=output_pdf)
elif 'HistMeanTdcCalibratedCondition' in node.name:
plot_corrected_tdc_hist(node[:]['charge'], node[:]['count'], title='TDC histogram, %d pixel, mean TDC calib.\n%s' % (node._v_attrs.n_pixel, node._v_attrs.condition[:80]), output_pdf=output_pdf)
elif 'HistTdcCorr' in node.name:
plot_tdc_tot_correlation(node[:], node._v_attrs.condition, output_pdf)
def analyse_n_cluster_per_event(scan_base,
include_no_cluster=False,
time_line_absolute=True,
combine_n_readouts=1000,
chunk_size=10000000,
plot_n_cluster_hists=False,
output_pdf=None,
output_file=None):
''' Determines the number of cluster per event as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts').
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
include_no_cluster: bool
Set to true to also consider all events without any hit.
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
n_cluster = []
start_time_set = False
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5',
mode="r+") as in_cluster_file_h5:
# get data and data pointer
meta_data_array = in_cluster_file_h5.root.meta_data[:]
cluster_table = in_cluster_file_h5.root.Cluster
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack(
(analysis_utils.get_ranges_from_array(
meta_data_array['timestamp_start'][::combine_n_readouts]),
analysis_utils.get_ranges_from_array(
meta_data_array['event_number'][::combine_n_readouts])))
# create a event_numer index (important for speed)
analysis_utils.index_event_number(cluster_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
total_cluster = cluster_table.shape[0]
progress_bar = progressbar.ProgressBar(widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
progressbar.AdaptiveETA()
],
maxval=total_cluster,
term_width=80)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(
parameter_ranges):
logging.debug('Analyze time stamp ' + str(parameter_range[0]) +
' and data from events = [' +
str(parameter_range[2]) + ',' +
str(parameter_range[3]) + '[ ' + str(
int(
float(
float(parameter_index) /
float(len(parameter_ranges)) *
100.0))) + '%')
analyze_data.reset() # resets the data of the last analysis
# loop over the cluster in the actual selected events with optimizations: determine best chunk size, start word index given
readout_cluster_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
hist = None
for clusters, index in analysis_utils.data_aligned_at_events(
cluster_table,
start_event_number=parameter_range[2],
stop_event_number=parameter_range[3],
start_index=index,
chunk_size=best_chunk_size):
n_cluster_per_event = analysis_utils.get_n_cluster_in_events(
clusters['event_number']
)[:,
1] # array with the number of cluster per event, cluster per event are at least 1
if hist is None:
hist = np.histogram(n_cluster_per_event,
bins=10,
range=(0, 10))[0]
else:
hist = np.add(
hist,
np.histogram(n_cluster_per_event,
bins=10,
range=(0, 10))[0])
if include_no_cluster and parameter_range[
3] is not None: # happend for the last readout
hist[0] = (parameter_range[3] -
parameter_range[2]) - len(
n_cluster_per_event
) # add the events without any cluster
readout_cluster_len += clusters.shape[0]
total_cluster -= len(clusters)
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_cluster_len) if int(
1.05 * readout_cluster_len
) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
if plot_n_cluster_hists:
plotting.plot_1d_hist(
hist,
title='Number of cluster per event at ' +
str(parameter_range[0]),
x_axis_title='Number of cluster',
y_axis_title='#',
log_y=True,
filename=output_pdf)
hist = hist.astype('f4') / np.sum(
hist) # calculate fraction from total numbers
if time_line_absolute:
time_stamp.append(parameter_range[0])
else:
if not start_time_set:
start_time = parameter_ranges[0, 0]
start_time_set = True
time_stamp.append((parameter_range[0] - start_time) / 60.0)
n_cluster.append(hist)
progress_bar.finish()
if total_cluster != 0:
logging.warning(
'Not all clusters were selected during analysis. Analysis is therefore not exact'
)
if time_line_absolute:
plotting.plot_scatter_time(
time_stamp,
n_cluster,
title='Number of cluster per event as a function of time',
marker_style='o',
filename=output_pdf,
legend=('0 cluster', '1 cluster', '2 cluster',
'3 cluster') if include_no_cluster else
('0 cluster not plotted', '1 cluster', '2 cluster', '3 cluster'))
else:
plotting.plot_scatter(
time_stamp,
n_cluster,
title='Number of cluster per event as a function of time',
x_label='time [min.]',
marker_style='o',
filename=output_pdf,
legend=('0 cluster', '1 cluster', '2 cluster',
'3 cluster') if include_no_cluster else
('0 cluster not plotted', '1 cluster', '2 cluster', '3 cluster'))
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
cluster_array = np.array(n_cluster)
rec_array = np.array(zip(time_stamp, cluster_array[:, 0],
cluster_array[:, 1], cluster_array[:, 2],
cluster_array[:, 3], cluster_array[:, 4],
cluster_array[:, 5]),
dtype=[('time_stamp', float),
('cluster_0', float),
('cluster_1', float),
('cluster_2', float),
('cluster_3', float),
('cluster_4', float),
('cluster_5', float)
]).view(np.recarray)
try:
n_cluster_table = out_file_h5.create_table(
out_file_h5.root,
name='n_cluster',
description=rec_array,
title='Cluster per event',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
n_cluster_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(
output_file +
' has already a Beamspot note, do not overwrite existing.')
return time_stamp, n_cluster
def analyse_n_cluster_per_event(
scan_base,
include_no_cluster=False,
time_line_absolute=True,
combine_n_readouts=1000,
chunk_size=10000000,
plot_n_cluster_hists=False,
output_pdf=None,
output_file=None,
):
""" Determines the number of cluster per event as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts').
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
include_no_cluster: bool
Set to true to also consider all events without any hit.
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
"""
time_stamp = []
n_cluster = []
start_time_set = False
for data_file in scan_base:
with tb.openFile(data_file + "_interpreted.h5", mode="r+") as in_cluster_file_h5:
# get data and data pointer
meta_data_array = in_cluster_file_h5.root.meta_data[:]
cluster_table = in_cluster_file_h5.root.Cluster
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack(
(
analysis_utils.get_ranges_from_array(meta_data_array["timestamp_start"][::combine_n_readouts]),
analysis_utils.get_ranges_from_array(meta_data_array["event_number"][::combine_n_readouts]),
)
)
# create a event_numer index (important for speed)
analysis_utils.index_event_number(cluster_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
total_cluster = cluster_table.shape[0]
progress_bar = progressbar.ProgressBar(
widgets=[
"",
progressbar.Percentage(),
" ",
progressbar.Bar(marker="*", left="|", right="|"),
" ",
analysis_utils.ETA(),
],
maxval=total_cluster,
term_width=80,
)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(parameter_ranges):
logging.debug(
"Analyze time stamp "
+ str(parameter_range[0])
+ " and data from events = ["
+ str(parameter_range[2])
+ ","
+ str(parameter_range[3])
+ "[ "
+ str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0)))
+ "%"
)
analyze_data.reset() # resets the data of the last analysis
# loop over the cluster in the actual selected events with optimizations: determine best chunk size, start word index given
readout_cluster_len = (
0
) # variable to calculate a optimal chunk size value from the number of hits for speed up
hist = None
for clusters, index in analysis_utils.data_aligned_at_events(
cluster_table,
start_event_number=parameter_range[2],
stop_event_number=parameter_range[3],
start=index,
chunk_size=best_chunk_size,
):
n_cluster_per_event = analysis_utils.get_n_cluster_in_events(clusters["event_number"])[
:, 1
] # array with the number of cluster per event, cluster per event are at least 1
if hist is None:
hist = np.histogram(n_cluster_per_event, bins=10, range=(0, 10))[0]
else:
hist = np.add(hist, np.histogram(n_cluster_per_event, bins=10, range=(0, 10))[0])
if include_no_cluster and parameter_range[3] is not None: # happend for the last readout
hist[0] = (parameter_range[3] - parameter_range[2]) - len(
n_cluster_per_event
) # add the events without any cluster
readout_cluster_len += clusters.shape[0]
total_cluster -= len(clusters)
progress_bar.update(index)
best_chunk_size = (
int(1.5 * readout_cluster_len) if int(1.05 * readout_cluster_len) < chunk_size else chunk_size
) # to increase the readout speed, estimated the number of hits for one read instruction
if plot_n_cluster_hists:
plotting.plot_1d_hist(
hist,
title="Number of cluster per event at " + str(parameter_range[0]),
x_axis_title="Number of cluster",
y_axis_title="#",
log_y=True,
filename=output_pdf,
)
hist = hist.astype("f4") / np.sum(hist) # calculate fraction from total numbers
if time_line_absolute:
time_stamp.append(parameter_range[0])
else:
if not start_time_set:
start_time = parameter_ranges[0, 0]
start_time_set = True
time_stamp.append((parameter_range[0] - start_time) / 60.0)
n_cluster.append(hist)
progress_bar.finish()
if total_cluster != 0:
logging.warning("Not all clusters were selected during analysis. Analysis is therefore not exact")
if time_line_absolute:
plotting.plot_scatter_time(
time_stamp,
n_cluster,
title="Number of cluster per event as a function of time",
marker_style="o",
filename=output_pdf,
legend=("0 cluster", "1 cluster", "2 cluster", "3 cluster")
if include_no_cluster
else ("0 cluster not plotted", "1 cluster", "2 cluster", "3 cluster"),
)
else:
plotting.plot_scatter(
time_stamp,
n_cluster,
title="Number of cluster per event as a function of time",
x_label="time [min.]",
marker_style="o",
filename=output_pdf,
legend=("0 cluster", "1 cluster", "2 cluster", "3 cluster")
if include_no_cluster
else ("0 cluster not plotted", "1 cluster", "2 cluster", "3 cluster"),
)
if output_file:
with tb.openFile(output_file, mode="a") as out_file_h5:
cluster_array = np.array(n_cluster)
rec_array = np.array(
zip(
time_stamp,
cluster_array[:, 0],
cluster_array[:, 1],
cluster_array[:, 2],
cluster_array[:, 3],
cluster_array[:, 4],
cluster_array[:, 5],
),
dtype=[
("time_stamp", float),
("cluster_0", float),
("cluster_1", float),
("cluster_2", float),
("cluster_3", float),
("cluster_4", float),
("cluster_5", float),
],
).view(np.recarray)
try:
n_cluster_table = out_file_h5.createTable(
out_file_h5.root,
name="n_cluster",
description=rec_array,
title="Cluster per event",
filters=tb.Filters(complib="blosc", complevel=5, fletcher32=False),
)
n_cluster_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + " has already a Beamspot note, do not overwrite existing.")
return time_stamp, n_cluster
