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()
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 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[:] gdacs = analysis_utils.get_scan_parameter( in_file_h5.root.meta_data[:])['GDAC'] 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 = mean_threshold_calibration['gdac'] gdac_range_source_scan = gdacs 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) print correction_factors 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 = np.swapaxes( occupancy, 0, 1) # 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='Select ' + str(len(selected_pixel_hits)) + ' pixels for analysis') # reshape to one dimension x = selected_pixel_thresholds.flatten() y = selected_pixel_hits.flatten() #nothing should be NAN, NAN 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] plot_result(x_p, y_p, y_p_e) # 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') 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') if analysis_configuration['use_cluster_rate_correction']: correction_h5.close()
def histogram_tdc_hits(input_file_hits, hit_selection_conditions, event_status_select_mask, event_status_condition, calibation_file=None, max_tdc=2000): 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): 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 with tb.openFile(input_file_hits, mode="r") as in_hit_file_h5: cluster_hit_table = in_hit_file_h5.root.ClusterHits shape_tdc_hist, shape_mean_tdc_hist = (80, 336, max_tdc), (80, 336) shape_tdc_timestamp_hist, shape_mean_tdc_timestamp_hist = (80, 336, 256), (80, 336) tdc_hists_per_condition = [np.zeros(shape=shape_tdc_hist, dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else [] tdc_timestamp_hists_per_condition = [np.zeros(shape=shape_tdc_timestamp_hist, dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else [] mean_tdc_hists_per_condition = [np.zeros(shape=shape_mean_tdc_hist, dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else [] mean_tdc_timestamp_hists_per_condition = [np.zeros(shape=shape_mean_tdc_timestamp_hist, dtype=np.uint16) for _ in hit_selection_conditions] if hit_selection_conditions else [] n_hits_per_condition = [0 for _ in range(len(hit_selection_conditions) + 2)] # 1/2 condition are all hits / hits of goode events for cluster_hits, _ in analysis_utils.data_aligned_at_events(cluster_hit_table, chunk_size=2e7): n_hits_per_condition[0] += cluster_hits.shape[0] selected_events_cluster_hits = cluster_hits[(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'] tdc_hists_per_condition[index] += analysis_utils.hist_3d_index(column, row, tdc, shape=shape_tdc_hist) mean_tdc_hists_per_condition[index] = np.average(tdc_hists_per_condition[index], axis=2, weights=range(0, max_tdc)) * np.sum(np.arange(0, max_tdc)) / tdc_hists_per_condition[index].sum(axis=2) tdc_timestamp = selected_cluster_hits['TDC_time_stamp'] tdc_timestamp_hists_per_condition[index] += analysis_utils.hist_3d_index(column, row, tdc_timestamp, shape=shape_tdc_timestamp_hist) mean_tdc_timestamp_hists_per_condition[index] = np.average(tdc_timestamp_hists_per_condition[index], axis=2, weights=range(0, shape_tdc_timestamp_hist[2])) * np.sum(np.arange(0, shape_tdc_timestamp_hist[2])) / tdc_timestamp_hists_per_condition[index].sum(axis=2) plotThreeWay(mean_tdc_hists_per_condition[0].T * 1.5625, title='Mean TDC, condition 1', filename='test_tdc.pdf') # , minimum=50, maximum=250) plotThreeWay(mean_tdc_timestamp_hists_per_condition[0].T * 1.5625, title='Mean TDC delay, condition 1', filename='test_tdc_ts.pdf', minimum=20, maximum=60) 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): tdc_hist_result = np.swapaxes(tdc_hists_per_condition[index], 0, 1) tdc_timestamp_hist_result = np.swapaxes(tdc_timestamp_hists_per_condition[index], 0, 1) out = out_file_h5.createCArray(out_file_h5.root, name='HistPixelTdcCondition_%d' % index, title='Hist PixelTdc with %s' % condition, atom=tb.Atom.from_dtype(tdc_hist_result.dtype), shape=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 PixelTdcTimestamp with %s' % condition, atom=tb.Atom.from_dtype(tdc_timestamp_hist_result.dtype), shape=tdc_timestamp_hist_result.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False)) out.attrs.dimensions = 'column, row, TDC value' out.attrs.condition = condition out.attrs.tdc_values = range(max_tdc) out_2.attrs.dimensions = 'column, row, TDC time stamp value' out_2.attrs.condition = condition out_2.attrs.tdc_values = range(shape_tdc_timestamp_hist[2]) out[:] = tdc_hist_result out_2[:] = tdc_timestamp_hist_result with PdfPages(input_file_hits[:-3] + '_calibrated_tdc_hists.pdf') as output_pdf: logging.info('Create 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.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() if calibation_file is not None: with tb.openFile(calibation_file, mode="r") as in_file_h5: tdc_calibration = in_file_h5.root.HitOrCalibration[:, :, 1:, 1] tdc_calibration_values = in_file_h5.root.HitOrCalibration.attrs.scan_parameter_values[1:] charge = get_charge(max_tdc, tdc_calibration_values, tdc_calibration) plt.clf() with tb.openFile(input_file_hits[:-3] + '_calibrated_tdc_hists.h5', mode="w") as out_file_h5: logging.info('Create corrected TDC histogram for %d conditions' % len(hit_selection_conditions)) for index, condition in enumerate(hit_selection_conditions): c_str = re.sub('[&]', '\n', condition) x, y = [], [] for column in range(0, 80, 1): for row in range(0, 336, 1): if tdc_hists_per_condition[0][column, row, :].sum() < analysis_configuration['min_pixel_hits']: continue x.extend(charge[column, row, :].ravel()) y.extend(tdc_hists_per_condition[index][column, row, :].ravel()) x, y, _ = analysis_utils.get_profile_histogram(np.array(x) * 55., np.array(y), n_bins=120) result = np.zeros(shape=(x.shape[0], ), dtype=[("x", np.float), ("y", np.float)]) result['x'], result['y'] = x, y actual_tdc_hist_table = out_file_h5.create_table(out_file_h5.root, name='TdcHistTableCondition%d' % index, description=result.dtype, title='TDC histogram', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False)) actual_tdc_hist_table.append(result) actual_tdc_hist_table.attrs.condition = condition if index == 0: normalization = 100. / np.amax(y) plt.plot(x, y * normalization, '.', label=c_str) # Plot hists into one plot plt.plot([27.82 * 55., 27.82 * 55.], [0, 100], label='Threshold %d e' % (28.82 * 55.), linewidth=2) plt.ylim((0, 100)) plt.legend(loc=0, prop={'size': 12}) plt.xlabel('Charge [e]') plt.ylabel('#') plt.grid() output_pdf.savefig()
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)