def entry():
args = docopt(__doc__)
files = args['<INPUT>']
ac_levels = convert_list_int(args['--ac_levels'])
dc_levels = convert_list_int(args['--dc_levels'])
if len(dc_levels) == 1:
dc_levels = [
dc_levels[0],
] * len(ac_levels)
assert len(ac_levels) == len(files)
max_events = convert_int(args['--max_events'])
delay_step_ns = convert_float(args['--delay_step_ns'])
time_range_ns = convert_list_float(args['--time_range_ns'])
normalize_range = convert_list_int(args['--normalize_range'])
sampling_ns = 4
parameters = convert_text(args['--parameters'])
template = convert_text(args['--template'])
output = convert_text(args['--output'])
if parameters is None:
parameters = parameters_default
if template is None:
template = template_default
main(files=files,
ac_levels=ac_levels,
dc_levels=dc_levels,
max_events=max_events,
delay_step_ns=delay_step_ns,
time_range_ns=time_range_ns,
sampling_ns=sampling_ns,
normalize_range=normalize_range,
parameters=parameters,
template=template,
adc_noise=1.,
output=output)
def entry():
args = docopt(__doc__)
files = args['<INPUT>']
plot = convert_text(args['--plot'])
event_types = convert_list_int(args['--event_types'])
disable_bar = args['--disable_bar']
trigger_uniformity(files, plot, event_types, disable_bar)
def entry():
args = docopt(__doc__)
files = args['<INPUT>']
aux_basepath = convert_text(args['--aux_basepath'])
max_events = convert_int(args['--max_events'])
dark_filename = args['--dark']
output = convert_text(args['--output'])
output_path = os.path.dirname(output)
if output_path != "" and not os.path.exists(output_path):
raise IOError('Path ' + output_path +
'for output hillas does not exists \n')
bad_pixels = convert_list_int(args['--bad_pixels'])
integral_width = convert_int(args['--integral_width'])
picture_threshold = convert_float(args['--picture_threshold'])
boundary_threshold = convert_float(args['--boundary_threshold'])
debug = args['--debug']
parameters_filename = convert_text(args['--parameters'])
template_filename = convert_text(args['--template'])
nevent_plot = convert_int(args['--nevent_plot'])
event_plot_filename = convert_text(args['--event_plot_filename'])
disable_bar = args['--disable_bar']
saturation_threshold = convert_float(args['--saturation_threshold'])
threshold_pulse = convert_float(args['--threshold_pulse'])
wdw_number = convert_int(args['--wdw_number'])
apply_corr_factor = args['--apply_corr_factor']
if aux_basepath is not None and aux_basepath.lower() == "search":
input_dir = np.unique([os.path.dirname(file) for file in files])
if len(input_dir) > 1:
raise AttributeError(
"Input files must be from the same directory " +
"when searching for auxiliaries files")
input_dir = input_dir[0]
aux_basepath = input_dir.replace('/raw/', '/aux/')
if not os.path.isdir(aux_basepath):
aux_basepath = aux_basepath.replace('/SST1M_01', '/SST1M01')
if not os.path.isdir(aux_basepath):
raise AttributeError("Searching for auxiliaries files failed. " +
"Please use --aux_basepath=PATH")
print('expecting aux files in', aux_basepath)
main_pipeline(
files=files,
aux_basepath=aux_basepath,
max_events=max_events,
dark_filename=dark_filename,
integral_width=integral_width,
debug=debug,
parameters_filename=parameters_filename,
hillas_filename=output,
picture_threshold=picture_threshold,
boundary_threshold=boundary_threshold,
template_filename=template_filename,
bad_pixels=bad_pixels,
disable_bar=disable_bar,
threshold_pulse=threshold_pulse,
saturation_threshold=saturation_threshold,
nevent_plot=nevent_plot,
event_plot_filename=event_plot_filename,
wdw_number=wdw_number,
apply_corr_factor=apply_corr_factor,
)
def entry():
args = docopt(__doc__)
inputs = args['<input_files>']
output_hist = args['--output_hist']
delays_ns = convert_list_float(args['--delays_ns'])
time_range_ns = convert_list_float(args['--time_range_ns'])
amplitude_range = convert_list_float(args['--amplitude_range'])
integration_range = convert_list_int(args['--integration_range'])
charge_range = convert_list_float(args['--charge_range'])
n_bin = convert_int(args['--n_bin'])
disable_bar = args['--disable_bar']
if output_hist is None:
output_hist = os.path.splitext(inputs[0])[0] + '.fits.gz'
print('options selected:')
print('input_files:', inputs)
print('output_hist:', output_hist)
print('time_range_ns:', time_range_ns)
print('amplitude_range:', amplitude_range)
print('integration_range:', integration_range)
print('charge_range:', charge_range)
print('n_bin:', n_bin)
main(input_files=inputs,
output_hist=output_hist,
delays_ns=delays_ns,
time_range_ns=time_range_ns,
amplitude_range=amplitude_range,
integration_range=integration_range,
charge_range=charge_range,
n_bin=n_bin,
disable_bar=disable_bar)
def entry():
args = docopt(__doc__)
files = args['<INPUT>']
max_events = convert_int(args['--max_events'])
output_path = args['--output']
if not os.path.exists(output_path):
raise IOError('Path {} for output does not '
'exists \n'.format(output_path))
pixel_id = convert_pixel_args(args['--pixel'])
dc_levels = convert_list_int(args['--dc_levels'])
n_pixels = len(pixel_id)
n_dc_levels = len(dc_levels)
results_filename = 'baseline_shift_results.npz'
results_filename = os.path.join(output_path, results_filename)
# fmpe_results_filename = args['--gain']
# crosstalk = args['--crosstalk']
# templates = args['--template']
histo = Histogram1D(data_shape=(n_dc_levels, n_pixels),
bin_edges=np.arange(0, 4096))
if args['--compute']:
if n_dc_levels != len(files):
raise ValueError('n_dc levels = {} != '
'n_files = {}'.format(n_dc_levels, len(files)))
baseline_mean = np.zeros((n_dc_levels, n_pixels))
baseline_std = np.zeros((n_dc_levels, n_pixels))
for i, file in tqdm(enumerate(files),
desc='DC level',
total=len(files)):
events = calibration_event_stream(file,
pixel_id=pixel_id,
max_events=max_events)
for count, event in enumerate(events):
baseline_mean[i] += event.data.digicam_baseline
baseline_std[i] += event.data.digicam_baseline**2
histo.fill(event.data.adc_samples, indices=(i, ))
count += 1
baseline_mean[i] = baseline_mean[i] / count
baseline_std[i] = baseline_std[i] / count
baseline_std[i] = baseline_std[i] - baseline_mean[i]**2
baseline_std[i] = np.sqrt(baseline_std[i])
histo.save(os.path.join(output_path, 'raw_histo.pk'))
np.savez(results_filename,
baseline_mean=baseline_mean,
baseline_std=baseline_std,
dc_levels=dc_levels)
if args['--fit']:
data = dict(np.load(results_filename))
baseline_mean = data['baseline_mean']
baseline_std = data['baseline_std']
dc_levels = data['dc_levels']
gain = 5
template_area = 18
crosstalk = 0.08
bias_resistance = 10 * 1E3
cell_capacitance = 50 * 1E-15
baseline_shift = baseline_mean - baseline_mean[0]
nsb_rate = gain * template_area / (baseline_shift * (1 - crosstalk))
nsb_rate = nsb_rate - cell_capacitance * bias_resistance
nsb_rate = 1 / nsb_rate
np.savez(results_filename,
baseline_mean=baseline_mean,
baseline_std=baseline_std,
dc_levels=dc_levels,
nsb_rate=nsb_rate,
baseline_shift=baseline_shift)
if args['--save_figures']:
pass
if args['--display']:
data = dict(np.load(results_filename))
histo = Histogram1D.load(os.path.join(output_path, 'raw_histo.pk'))
baseline_mean = histo.mean()
baseline_std = histo.std()
nsb_rate = data['nsb_rate']
print(baseline_mean.shape)
histo.draw((0, 1))
histo.draw((49, 1))
plt.figure()
plt.plot(dc_levels, baseline_mean)
plt.xlabel('DC DAC level')
plt.ylabel('Baseline mean [LSB]')
plt.figure()
plt.plot(dc_levels, baseline_std)
plt.xlabel('DC DAC level')
plt.ylabel('Baseline std [LSB]')
plt.figure()
plt.plot(nsb_rate, baseline_std)
plt.xlabel('$f_{NSB}$ [GHz]')
plt.ylabel('Baseline std [LSB]')
plt.figure()
plt.plot(baseline_mean, baseline_std)
plt.xlabel('Baseline mean [LSB]')
plt.ylabel('Baseline std [LSB]')
plt.figure()
plt.semilogy(dc_levels, nsb_rate)
plt.xlabel('DC DAC level')
plt.ylabel('$f_{NSB}$ [GHz]')
plt.show()
pass
return
def entry():
args = docopt(__doc__)
files = args['<INPUT>']
max_events = convert_int(args['--max_events'])
pixel_id = convert_pixel_args(args['--pixel'])
base_sub = args['--baseline_subtracted']
raw_histo_filename = args['--output']
event_types = convert_list_int(args['--event_types'])
baseline_filename = args['--baseline_filename']
disable_bar = args['--disable_bar']
if baseline_filename.lower() == 'none':
baseline_filename = None
output_path = os.path.dirname(raw_histo_filename)
if not os.path.exists(output_path) and output_path != "":
raise IOError('Path {} for output '
'does not exists \n'.format(output_path))
if args['--compute']:
compute(files=files,
filename=raw_histo_filename,
max_events=max_events,
pixel_id=pixel_id,
event_types=event_types,
disable_bar=disable_bar,
baseline_subtracted=base_sub)
if baseline_filename:
compute_baseline_histogram(files=files,
filename=baseline_filename,
max_events=max_events,
pixel_id=pixel_id,
disable_bar=disable_bar)
if args['--save_figures']:
raw_histo = Histogram1D.load(raw_histo_filename)
path = os.path.join(output_path, 'figures/', 'raw_histo/')
if not os.path.exists(path):
os.makedirs(path)
figure = plt.figure()
for i, pixel in tqdm(enumerate(pixel_id), total=len(pixel_id)):
axis = figure.add_subplot(111)
figure_path = os.path.join(path, 'pixel_{}.pdf')
try:
raw_histo.draw(index=(i, ), axis=axis, log=True, legend=False)
figure.savefig(figure_path.format(pixel))
except Exception as e:
print('Could not save pixel {} to : {} \n'.format(
pixel, figure_path))
print(e)
axis.remove()
if args['--display']:
raw_histo = Histogram1D.load(raw_histo_filename)
pixel = 0
raw_histo.draw(index=(pixel, ),
log=True,
legend=False,
label='Histogram {}'.format(pixel),
x_label='[LSB]')
mean_value = raw_histo.mean()
plot_histo(mean_value, bins='auto', x_label='Mean value [LSB]')
plot_array_camera(mean_value, label='Mean value [LSB]')
if baseline_filename:
baseline_histo = Histogram1D.load(baseline_filename)
baseline_histo.draw(index=(pixel, ),
log=True,
legend=False,
label='Histogram {}'.format(pixel),
x_label='DigiCam baseline [LSB]')
mean_baseline = baseline_histo.mean()
plot_histo(mean_baseline,
bins='auto',
x_label='Mean DigiCam baseline [LSB]')
plot_array_camera(mean_baseline,
label='Mean DigiCam baseline [LSB]')
plot_array_camera(mean_baseline - mean_value, label='Diff [LSB]')
plot_histo(mean_baseline - mean_value,
bins='auto',
x_label='Diff [LSB]')
plt.show()
def entry():
args = docopt(__doc__)
files = args['<INPUT>']
max_events = convert_int(args['--max_events'])
pixel_id = convert_pixel_args(args['--pixel'])
n_samples = int(args['--n_samples'])
timing_histo_filename = args['--timing_histo_filename']
ac_levels = convert_list_int(args['--ac_levels'])
output_path = os.path.dirname(timing_histo_filename)
results_filename = os.path.join(output_path, 'timing.npz')
if not os.path.exists(output_path):
raise IOError('Path for output does not exists \n')
if args['--compute']:
compute(files,
max_events,
pixel_id,
n_samples,
ac_levels,
timing_histo_filename,
save=True,
time_method=compute_time_from_max)
# or try to use compute_time_from_leading_edge)
if args['--fit']:
timing_histo = Histogram1D.load(timing_histo_filename)
timing = timing_histo.mode()
timing = mode(timing, axis=0)[0][0]
np.savez(results_filename, time=timing)
if args['--save_figures']:
raw_histo = Histogram1D.load(timing_histo_filename)
path = os.path.join(output_path, 'figures/', 'timing_histo/')
if not os.path.exists(path):
os.makedirs(path)
figure = plt.figure()
for i, pixel in tqdm(enumerate(pixel_id), total=len(pixel_id)):
axis = figure.add_subplot(111)
figure_path = path + 'pixel_{}.pdf'
try:
raw_histo.draw(index=(i, ), axis=axis, log=True, legend=False)
figure.savefig(figure_path.format(pixel))
except Exception as e:
print('Could not save pixel {} to : {} \n'.format(
pixel, figure_path))
print(e)
axis.remove()
if args['--display']:
timing_histo = Histogram1D.load(timing_histo_filename)
timing_histo.draw(index=(
len(ac_levels) - 1,
0,
),
log=True,
legend=False)
pulse_time = timing_histo.mode()
plt.figure()
plt.plot(ac_levels, pulse_time)
plt.xlabel('DAC level')
plt.ylabel('Reconstructed pulse time [ns]')
pulse_time = np.load(results_filename)['time']
plot_array_camera(pulse_time,
label='time of pulse [ns]',
allow_pick=True)
plot_parameter(pulse_time, 'time of pulse', '[ns]', bins=20)
plt.show()
return
def entry():
args = docopt(__doc__)
files = args['<INPUT>']
debug = args['--debug']
max_events = convert_int(args['--max_events'])
results_filename = args['--fit_output']
dir_output = os.path.dirname(results_filename)
if not os.path.exists(dir_output):
raise IOError('Path {} for output '
'does not exists \n'.format(dir_output))
pixel_ids = convert_pixel_args(args['--pixel'])
integral_width = int(args['--integral_width'])
shift = int(args['--shift'])
bin_width = int(args['--bin_width'])
ncall = int(args['--ncall'])
ac_levels = convert_list_int(args['--ac_levels'])
n_pixels = len(pixel_ids)
n_ac_levels = len(ac_levels)
adc_min = int(args['--adc_min'])
adc_max = int(args['--adc_max'])
timing_filename = args['--timing']
timing = np.load(timing_filename)['time']
charge_histo_filename = args['--compute_output']
fmpe_results_filename = args['--gain']
if args['--compute']:
if n_ac_levels != len(files):
raise ValueError('n_ac_levels = {} != '
'n_files = {}'.format(n_ac_levels, len(files)))
time = np.zeros((n_ac_levels, n_pixels))
charge_histo = Histogram1D(bin_edges=np.arange(
adc_min * integral_width, adc_max * integral_width, bin_width),
data_shape=(
n_ac_levels,
n_pixels,
))
if os.path.exists(charge_histo_filename):
raise IOError(
'File {} already exists'.format(charge_histo_filename))
for i, (file, ac_level) in tqdm(enumerate(zip(files, ac_levels)),
total=n_ac_levels,
desc='DAC level',
leave=False):
time[i] = timing[pixel_ids]
pulse_indices = time[i] // 4
events = calibration_event_stream(file,
pixel_id=pixel_ids,
max_events=max_events)
# events = compute_baseline_with_min(events)
events = fill_digicam_baseline(events)
events = subtract_baseline(events)
# events = find_pulse_with_max(events)
events = fill_pulse_indices(events, pulse_indices)
events = compute_charge(events, integral_width, shift)
events = compute_amplitude(events)
for event in events:
charge_histo.fill(event.data.reconstructed_charge, indices=i)
charge_histo.save(charge_histo_filename, )
if args['--fit']:
input_parameters = Table.read(fmpe_results_filename, format='fits')
input_parameters = input_parameters.to_pandas()
gain = np.zeros((n_ac_levels, n_pixels)) * np.nan
sigma_e = np.zeros((n_ac_levels, n_pixels)) * np.nan
sigma_s = np.zeros((n_ac_levels, n_pixels)) * np.nan
baseline = np.zeros((n_ac_levels, n_pixels)) * np.nan
mu = np.zeros((n_ac_levels, n_pixels)) * np.nan
mu_xt = np.zeros((n_ac_levels, n_pixels)) * np.nan
amplitude = np.zeros((n_ac_levels, n_pixels)) * np.nan
gain_error = np.zeros((n_ac_levels, n_pixels)) * np.nan
sigma_e_error = np.zeros((n_ac_levels, n_pixels)) * np.nan
sigma_s_error = np.zeros((n_ac_levels, n_pixels)) * np.nan
baseline_error = np.zeros((n_ac_levels, n_pixels)) * np.nan
mu_error = np.zeros((n_ac_levels, n_pixels)) * np.nan
mu_xt_error = np.zeros((n_ac_levels, n_pixels)) * np.nan
amplitude_error = np.zeros((n_ac_levels, n_pixels)) * np.nan
mean = np.zeros((n_ac_levels, n_pixels)) * np.nan
std = np.zeros((n_ac_levels, n_pixels)) * np.nan
chi_2 = np.zeros((n_ac_levels, n_pixels)) * np.nan
ndf = np.zeros((n_ac_levels, n_pixels)) * np.nan
ac_limit = [np.inf] * n_pixels
charge_histo = Histogram1D.load(charge_histo_filename)
for i, ac_level in tqdm(enumerate(ac_levels),
total=n_ac_levels,
desc='DAC level',
leave=False):
for j, pixel_id in tqdm(enumerate(pixel_ids),
total=n_pixels,
desc='Pixel',
leave=False):
histo = charge_histo[i, pixel_id]
mean[i, j] = histo.mean()
std[i, j] = histo.std()
if histo.overflow > 0 or histo.data.sum() == 0:
continue
fit_params_names = describe(mpe_distribution_general)
options = {'fix_n_peaks': True}
fixed_params = {}
for param in fit_params_names:
if param in input_parameters.keys():
name = 'fix_' + param
options[name] = True
fixed_params[param] = input_parameters[param][pixel_id]
if i > 0:
if mu[i - 1, j] > 5:
ac_limit[j] = min(i, ac_limit[j])
ac_limit[j] = int(ac_limit[j])
weights_fit = chi_2[:ac_limit[j], j]
weights_fit = weights_fit / ndf[:ac_limit[j], j]
options['fix_mu_xt'] = True
temp = mu_xt[:ac_limit[j], j] * weights_fit
temp = np.nansum(temp)
temp = temp / np.nansum(weights_fit)
fixed_params['mu_xt'] = temp
try:
fitter = MPEFitter(histogram=histo,
cost='MLE',
pedantic=0,
print_level=0,
throw_nan=True,
fixed_params=fixed_params,
**options)
fitter.fit(ncall=ncall)
if debug:
x_label = '[LSB]'
label = 'Pixel {}'.format(pixel_id)
fitter.draw(legend=False, x_label=x_label, label=label)
fitter.draw_init(legend=False,
x_label=x_label,
label=label)
fitter.draw_fit(legend=False,
x_label=x_label,
label=label)
plt.show()
param = fitter.parameters
param_err = fitter.errors
gain[i, j] = param['gain']
sigma_e[i, j] = param['sigma_e']
sigma_s[i, j] = param['sigma_s']
baseline[i, j] = param['baseline']
mu[i, j] = param['mu']
mu_xt[i, j] = param['mu_xt']
amplitude[i, j] = param['amplitude']
gain_error[i, j] = param_err['gain']
sigma_e_error[i, j] = param_err['sigma_e']
sigma_s_error[i, j] = param_err['sigma_s']
baseline_error[i, j] = param_err['baseline']
mu_error[i, j] = param_err['mu']
mu_xt_error[i, j] = param_err['mu_xt']
amplitude_error[i, j] = param_err['amplitude']
chi_2[i, j] = fitter.fit_test() * fitter.ndf
ndf[i, j] = fitter.ndf
except Exception as e:
print(e)
print('Could not fit pixel {} for DAC level {}'.format(
pixel_id, ac_level))
np.savez(
results_filename,
gain=gain,
sigma_e=sigma_e,
sigma_s=sigma_s,
baseline=baseline,
mu=mu,
mu_xt=mu_xt,
gain_error=gain_error,
sigma_e_error=sigma_e_error,
sigma_s_error=sigma_s_error,
baseline_error=baseline_error,
mu_error=mu_error,
mu_xt_error=mu_xt_error,
chi_2=chi_2,
ndf=ndf,
pixel_ids=pixel_ids,
ac_levels=ac_levels,
amplitude=amplitude,
amplitude_error=amplitude_error,
mean=mean,
std=std,
)
if args['--save_figures']:
pass
if args['--display']:
charge_histo = Histogram1D.load(charge_histo_filename)
charge_histo.draw(index=(0, 0), log=False, legend=False)
pass
return