def process(r0_path, pedestal, tf, output_path):
reader = TIOReader(r0_path, max_events=1000)
sum = np.zeros(reader.n_samples - 32)
n = 0
for wfs in tqdm(reader, total=reader.n_events):
if wfs.missing_packets:
continue
cells = get_cell_ids_for_waveform(wfs.first_cell_id, reader.n_samples,
reader.n_cells)
wfs = wfs[:, 32:]
wfs.first_cell_id = cells[32]
iev = wfs.iev
first_cell_id = wfs.first_cell_id
wfs = correct_overflow(wfs)
wfs = pedestal.subtract_pedestal(wfs, first_cell_id)
if tf is not None:
wfs = tf.apply_tf(wfs, first_cell_id)
if np.isnan(wfs).any():
continue
sum += wfs.sum(0)
n += wfs.shape[0]
avg = sum / n
avg /= avg.max()
array = np.column_stack([np.arange(avg.size) * 1e-9,
avg]).astype(np.float32)
print(f"Creating file: {output_path}")
np.savetxt(output_path, array)
def _add_to_tf(wfs, fci, amplitude_index, tf, hits, m2):
n_pixels, n_samples = wfs.shape
_, n_cells, _ = tf.shape
cells = get_cell_ids_for_waveform(fci, n_samples, n_cells)
for ipix in prange(n_pixels):
for isam in prange(n_samples):
sample = wfs[ipix, isam]
idx = (ipix, cells[isam], amplitude_index)
tf[idx], hits[idx], m2[idx] = welfords_online_algorithm(
sample, tf[idx], hits[idx], m2[idx])
def _add_to_pedestal(wfs, fci, pedestal, hits, m2):
_, n_samples = wfs.shape
_, n_cells = pedestal.shape
cells = get_cell_ids_for_waveform(fci, n_samples, n_cells)
n_pixels, n_samples = wfs.shape
for ipix in prange(n_pixels):
for isam in prange(n_samples):
sample = wfs[ipix, isam]
idx = (ipix, cells[isam])
pedestal[idx], hits[idx], m2[idx] = welfords_online_algorithm(
sample, pedestal[idx], hits[idx], m2[idx])
def _subtract_pedestal(wfs, fci, pedestal):
subtracted = np.zeros(wfs.shape, dtype=np.float32)
_, n_cells, n_samples = pedestal.shape
cells = get_cell_ids_for_waveform(fci, n_samples, n_cells)
n_pixels, n_samples = wfs.shape
for ipix in prange(n_pixels):
for isam in prange(n_samples):
sample = wfs[ipix, isam]
pedestal_value = pedestal[ipix, cells[isam], isam]
if pedestal_value != 0:
subtracted[ipix, isam] = sample - pedestal_value
return subtracted
def _apply_tf(wfs, fci, tf, amplitudes):
calibrated = np.zeros(wfs.shape, dtype=np.float32)
n_pixels, n_samples = wfs.shape
_, n_cells, _ = tf.shape
cells = get_cell_ids_for_waveform(fci, n_samples, n_cells)
for ipix in prange(n_pixels):
for isam in prange(n_samples):
sample = wfs[ipix, isam]
cell = cells[isam]
tf_i = tf[ipix, cell]
amplitudes_i = amplitudes[ipix, cell]
calibrated[ipix, isam] = np.interp(sample, tf_i, amplitudes_i)
return calibrated
def process(path):
pedestal_path = path.replace("_r0.tio", "_ped.tcal")
reader = TIOReader(path)
pedestal = PedestalTargetCalib(reader.n_pixels, reader.n_samples - 32,
reader.n_cells)
desc = "Generating pedestal"
for wfs in tqdm(reader, total=reader.n_events, desc=desc):
if wfs.missing_packets:
continue
cells = get_cell_ids_for_waveform(wfs.first_cell_id, reader.n_samples,
reader.n_cells)
wfs = wfs[:, 32:]
wfs.first_cell_id = cells[32]
pedestal.add_to_pedestal(wfs, wfs.first_cell_id)
pedestal.save_tcal(pedestal_path)
def process(tf_r0_paths, pedestal_path, tf_path, tf_class):
pedestal = PedestalTargetCalib.from_tcal(pedestal_path)
# Parse amplitudes from filepath
amplitudes = []
readers = []
for path in tf_r0_paths:
regex_ped = re.search(r".+_(-?\d+)_r0.tio", path)
amplitudes.append(int(regex_ped.group(1)))
readers.append(TIOReader(path))
# Instance TF class from first file
tf = tf_class(readers[0].n_pixels, readers[0].n_samples - 32,
readers[0].n_cells, amplitudes)
if tf_class == TFACCrossCorrelation:
# # Estimate range of peak positions
# r = readers[np.abs(np.array(amplitudes) - 500).argmin()]
# peak = r[0].mean(0).argmax()
# tf.set_trange(peak - 5 - 32, peak + 5 - 32)
tf.set_trange(6, 16)
desc0 = "Generating TF"
it = zip(amplitudes, readers)
n_amp = len(amplitudes)
for amplitude, reader in tqdm(it, total=n_amp, desc=desc0):
amplitude_index = tf.get_input_amplitude_index(amplitude)
for iwf, wfs in enumerate(tqdm(reader, total=reader.n_events)):
if wfs.missing_packets:
print("Skipping event")
continue
# Skip to next file when enough hits are reached
if iwf % 1000 == 0:
if (tf.hits[..., amplitude_index] > 100).all():
break
cells = get_cell_ids_for_waveform(wfs.first_cell_id,
reader.n_samples, reader.n_cells)
wfs = wfs[:, 32:]
wfs.first_cell_id = cells[32]
tf.add_to_tf(pedestal.subtract_pedestal(wfs, wfs.first_cell_id),
wfs.first_cell_id, amplitude_index)
tf.save(tf_path)
def process(r0_paths, pedestal, tf, output_path):
readers = [TIOReader(path) for path in r0_paths]
df_list = []
channel = np.arange(readers[0].n_pixels)
desc0 = "Extracting charges"
for reader in tqdm(readers, total=len(readers), desc=desc0):
regex = re.search(r".+Amplitude_([-\d]+)_.+_r0.tio", reader.path)
amplitude = int(regex.group(1))
for wfs in tqdm(reader, total=reader.n_events):
if wfs.missing_packets:
continue
cells = get_cell_ids_for_waveform(wfs.first_cell_id,
reader.n_samples, reader.n_cells)
wfs = wfs[:, 32:]
wfs.first_cell_id = cells[32]
iev = wfs.iev
first_cell_id = wfs.first_cell_id
wfs = correct_overflow(wfs)
wfs = pedestal.subtract_pedestal(wfs, first_cell_id)
if tf is not None:
wfs = tf.apply_tf(wfs, first_cell_id)
charge = wfs[:, 4:21].sum(1)
df_list.append(
pd.DataFrame(
dict(amplitude=amplitude,
iev=iev,
fci=first_cell_id,
channel=channel,
charge=charge)))
df = pd.concat(df_list, ignore_index=True)
with HDF5Writer(output_path) as writer:
writer.write(data=df)
