def run(hist, options, h_type='ADC',prev_fit_result=None):
"""
Fill the adcs histogram out of darkrun/baseline runs
:param h_type: type of histogram to produce: ADC for all samples adcs or SPE for only peaks
:param hist: the histogram to fill
:param options: see analyse_spe.py
:return:
"""
# Reading the file
n_evt, n_batch, batch_num, max_evt = 0, options.n_evt_per_batch, 0, options.evt_max
batch = None
print('--|> Treating the batch #%d of %d events' % (batch_num, n_batch))
for file in options.file_list:
# Open the file
_url = options.directory + options.file_basename % file
inputfile_reader = zfits.zfits_event_source(
url=_url
, data_type='r1', max_events=options.evt_max)
if options.verbose:
print('--|> Moving to file %s' % _url)
# Loop over event in this file
for event in inputfile_reader:
n_evt += 1
if n_evt > max_evt:
break
if (n_evt - n_batch * batch_num) % n_batch/100 == 0:
print("Progress {:2.1%}".format(float(n_evt - batch_num * n_batch) / n_batch), end="\r")
for telid in event.r1.tels_with_data:
if n_evt % n_batch == 0:
print('--|> Treating the batch #%d of %d events' % (batch_num, n_batch))
# Update adc histo
if h_type == 'ADC':
hist.fill_with_batch(batch.reshape(batch.shape[0], batch.shape[1] * batch.shape[2]))
elif h_type == 'SPE':
hist.fill_with_batch(
spe_peaks_in_event_list(batch, prev_fit_result[:, 1, 0], prev_fit_result[:, 2, 0]))
# Reset the batch
batch = None
batch_num += 1
print('--|> Reading the batch #%d of %d events' % (batch_num, n_batch))
# Get the data
data = np.array(list(event.r1.tel[telid].adc_samples.values()))
# Append the data to the batch
if type(batch).__name__ != 'ndarray':
batch = data.reshape(data.shape[0], 1, data.shape[1])
else:
batch = np.append(batch, data.reshape(data.shape[0], 1, data.shape[1]), axis=1)
if options.verbose:
print('--|> Save the data in %s' % (options.output_directory + options.histo_filename))
np.savez_compressed(options.output_directory + options.histo_filename,
adcs=hist.data, adcs_bin_centers=hist.bin_centers)
def run(hist, options, min_evt=5000. * 3, max_evt=5000 * 10):
# Few counters
evt_num, first_evt, first_evt_num = 0, True, 0
for file in options.file_list:
if evt_num > max_evt: break
# read the file
_url = options.directory + options.file_basename % file
if not options.toy_test:
inputfile_reader = zfits.zfits_event_source(url=_url,
data_type='r1',
max_events=100000)
else:
inputfile_reader = ToyReader(filename=_url,
id_list=[0],
max_events=5000,
n_pixel=options.n_pixels)
if options.verbose:
print('--|> Moving to file %s' % _url)
# Loop over event in this file
for event in inputfile_reader:
if evt_num < min_evt:
evt_num += 1
continue
if evt_num > max_evt: break
for telid in event.r1.tels_with_data:
evt_num += 1
if evt_num > max_evt: break
if options.verbose and event.r1.event_id % 1000 == 0:
print("Progress {:2.1%}".format(evt_num / max_evt),
end="\r")
# get the data
data = np.array(list(event.r1.tel[telid].adc_samples.values()))
# subtract the pedestals
data = data
hist.fill(np.argmax(data, axis=1))
# Update the errors
# noinspection PyProtectedMember
hist._compute_errors()
# Save the MPE histos in a file
if options.verbose:
print('--|> Save the data in %s' %
(options.output_directory + options.peak_histo_filename))
np.savez_compressed(options.output_directory + options.peak_histo_filename,
peaks=hist.data,
peaks_bin_centers=hist.bin_centers)
neigh += geom.neighbors[i]
mask[i][np.array(neigh)] = True
return mask
def sum_cluster(data, masks):
data_tmp = np.array(data)
data_tmp = data_tmp.reshape(1, 1296)
data_tmp = np.repeat(data_tmp, 1296, axis=0)
return np.sum(np.ma.masked_array(data_tmp, mask=masks, fill_value=0),
axis=0)
inputfile_reader = zfits.zfits_event_source(
url=
"/data/datasets/CTA/[email protected]_0_000.36.fits.fz",
data_type='r1',
max_events=2130)
if plotting:
plt.figure(0)
displayType = []
plt.subplot(1, 1, 1)
displayType.append(
visualization.CameraDisplay(
geom,
title='Integrated ADC over 200ns, pedestal subtracted',
norm='lin',
cmap='coolwarm'))
neigh_mask = generate_neighbourg_masks()
hollow_neigh_mask = generate_hollow_neighbourg_masks()
def run(hist, options, h_type='ADC', prev_fit_result=None):
"""
Fill the adcs Histogram out of darkrun/baseline runs
:param h_type: type of Histogram to produce: ADC for all samples adcs or SPE for only peaks
:param hist: the Histogram to fill
:param options: see analyse_spe.py
:param prev_fit_result: fit result of a previous step needed for the calculations
:return:
"""
logger = logging.getLogger(sys.modules['__main__'].__name__ + '.' +
__name__)
# Reading the file
n_evt, n_batch, batch_num, max_evt = 0, options.n_evt_per_batch, 0, options.evt_max
batch = None
if not options.mc:
logger.info('Running on DigiCam data')
print(logger)
else:
logger.info('Running on MC data')
logger.debug('Treating the batch #%d of %d events' % (batch_num, n_batch))
for file in options.file_list:
# Open the file
_url = options.directory + options.file_basename % file
if not options.mc:
inputfile_reader = zfits.zfits_event_source(url=_url,
data_type='r1',
max_events=100000)
else:
inputfile_reader = ToyReader(filename=_url,
id_list=[0],
max_events=options.evt_max,
n_pixel=options.n_pixels)
logger.debug('--|> Moving to file %s' % _url)
# Loop over event in this file
for event in inputfile_reader:
n_evt += 1
if n_evt > max_evt:
break
if (n_evt - n_batch * batch_num) % n_batch / 100 == 0:
print(float(n_evt - batch_num * n_batch) / n_batch)
print("Progress {:2.1%}".format(
float(n_evt - batch_num * n_batch) / n_batch),
end="\r")
for telid in event.r1.tels_with_data:
if n_evt % n_batch == 0:
logger.debug('Treating the batch #%d of %d events' %
(batch_num, n_batch))
# Update adc histo
if h_type == 'ADC':
hist.fill_with_batch(
batch.reshape(batch.shape[0],
batch.shape[1] * batch.shape[2]))
elif h_type == 'SPE':
hist.fill_with_batch(
spe_peaks_in_event_list(batch,
prev_fit_result[:, 1, 0],
prev_fit_result[:, 2, 0]))
# Reset the batch
batch = None
batch_num += 1
logger.debug('Reading the batch #%d of %d events' %
(batch_num, n_batch))
# Get the data
data = np.array(list(event.r1.tel[telid].adc_samples.values()))
# Append the data to the batch
if type(batch).__name__ != 'ndarray':
batch = data.reshape(data.shape[0], 1, data.shape[1])
else:
batch = np.append(batch,
data.reshape(data.shape[0], 1,
data.shape[1]),
axis=1)
return
def run(hists, options, peak_positions=None, data_type='r1'):
# Few counters
level, evt_num, first_evt, first_evt_num = 0, 0, True, 0
for file in options.file_list:
if level > len(options.scan_level) - 1:
break
# Get the file
_url = options.directory + options.file_basename % file
if not options.toy_test:
inputfile_reader = zfits.zfits_event_source(url=_url,
data_type='r1',
max_events=100000)
else:
inputfile_reader = ToyReader(filename=_url,
id_list=[0],
max_events=5000,
n_pixel=options.n_pixels)
if options.verbose:
print('--|> Moving to file %s' % _url)
# Loop over event in this file
for event in inputfile_reader:
if level > len(options.scan_level) - 1:
break
for telid in event.r1.tels_with_data:
if first_evt:
first_evt_num = event.r1.tel[telid].eventNumber
first_evt = False
evt_num = event.r1.tel[telid].eventNumber - first_evt_num
if evt_num % options.events_per_level == 0:
level = int(evt_num / options.events_per_level)
if level > len(options.scan_level) - 1:
break
if options.verbose:
print('--|> Moving to DAC Level %d' %
(options.scan_level[level]))
if options.verbose and event.r1.event_id % 100 == 0:
print("Progress {:2.1%}".format(
(evt_num - level * options.events_per_level) /
options.events_per_level),
end="\r")
# get the data
data = np.array(list(event.r1.tel[telid].adc_samples.values()))
# subtract the pedestals
data = data
# put in proper format
data = data.reshape((1, ) + data.shape)
# integration parameter
params = {
"integrator": "nb_peak_integration",
"integration_window": [8, 4],
"integration_sigamp": [2, 4],
"integration_lwt": 0
}
# now integrate
#integration, window, peakpos = integrators.simple_integration(data, params)
# try with the max instead
peak = np.argmax(data[0], axis=1)
if type(peak_positions).__name__ == 'ndarray':
peak = np.argmax(peak_positions, axis=1)
index_max = (
np.arange(0, data[0].shape[0]),
peak,
)
'''
peak_m1 = peak - 1
peak_m1[peak_m1<0]=0
peak_p1 = peak + 1
peak_p1[peak_p1>49]=49
index_max_m1 = (np.arange(0, data[0].shape[0]), peak_m1,)
index_max_p1 = (np.arange(0, data[0].shape[0]), peak_p1,)
h = np.append(data[0][index_max].reshape(data[0][index_max].shape+(1,)),
data[0][index_max_m1].reshape(data[0][index_max_m1].shape+(1,)),axis=1)
h = np.append(h,
data[0][index_max_p1].reshape(data[0][index_max_p1].shape + (1,)),axis=1)
max_value = np.max(h,axis=1)
'''
hists[0].fill(data[0][index_max], indices=(level, ))
# and fill the histos
#if hists[0] : hists[0].fill(integration[0], indices=(level,))
#if hists[1]: hists[1].fill(max_value, indices=(level,))
# Update the errors
for hist in hists:
hist._compute_errors()
# Save the MPE histos in a file
if options.verbose:
print('--|> Save the data in %s' %
(options.output_directory + options.histo_filename))
np.savez_compressed(options.output_directory + options.histo_filename,
mpes=hists[0].data,
mpes_bin_centers=hists[0].bin_centers)