def process(tf_r0_paths, pedestal_path, tf_path):
pedestal = PedestalTargetCalib.from_tcal(pedestal_path)
# Parse amplitudes from filepath
amplitudes = []
readers = []
for path in tf_r0_paths:
regex_ped = re.search(r".+VPED_(\d+).tio", path)
amplitudes.append(int(regex_ped.group(1)))
readers.append(TIOReader(path))
# Instance TF class from first file
tf = TFDC(readers[0].n_pixels, readers[0].n_samples - 32,
readers[0].n_cells, amplitudes)
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(reader):
if wfs.missing_packets:
continue
# Skip to next file when enough hits are reached
if iwf % 1000 == 0:
if (tf.hits[..., amplitude_index] > 100).all():
break
tf.add_to_tf(pedestal.subtract_pedestal(wfs, wfs.first_cell_id),
wfs.first_cell_id, amplitude_index)
tf.save(tf_path)
def __init__(self, paths):
lookup = []
for path in paths:
regex_r0 = re.search(r".+_tc([\d.]+)_tmc([\d.]+)_ped.tcal", path)
temperature_chamber = float(regex_r0.group(1))
temperature_primary = float(regex_r0.group(2))
pedestal = PedestalTargetCalib.from_tcal(path)
lookup.append(
dict(
temperature_chamber=temperature_chamber,
temperature_primary=temperature_primary,
pedestal=pedestal,
))
self.lookup = pd.DataFrame(lookup)
self.lookup = self.lookup.set_index("temperature_primary")
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 __init__(self, paths):
self.pedestal_instance = None
temperature_list = []
pedestal_list = []
for path in paths:
regex_r0 = re.search(r".+_tc([\d.]+)_tmc([\d.]+)_ped.tcal", path)
temperature_primary = float(regex_r0.group(2))
pedestal = PedestalTargetCalib.from_tcal(path)
temperature_list.append(temperature_primary)
pedestal_list.append(pedestal._pedestal)
if self.pedestal_instance is None:
self.pedestal_instance = pedestal
self.interp_f = PchipInterpolator(np.array(temperature_list),
np.array(pedestal_list),
axis=0,
extrapolate=True)
def __init__(self, paths):
self.pedestal_instance = None
temperature_list = []
pedestal_list = []
for path in paths:
regex_r0 = re.search(r".+_tc([\d.]+)_tmc([\d.]+)_ped.tcal", path)
temperature_primary = float(regex_r0.group(2))
pedestal = PedestalTargetCalib.from_tcal(path)
temperature_list.append(temperature_primary)
pedestal_list.append(pedestal._pedestal)
if self.pedestal_instance is None:
self.pedestal_instance = pedestal
self.interp_f = interp1d(temperature_list,
pedestal_list,
axis=0,
fill_value="extrapolate")
def process(pedestal_paths, output_path):
pedestal_paths = sort_file_list(pedestal_paths)
data = []
reference_values = None
reference_spread = None
for path in pedestal_paths:
regex_ped = re.search(r".+_tc([\d.]+)_tmc([\d.]+)_ped.tcal", path)
temperature_pedestal_primary = float(regex_ped.group(2))
pedestal = PedestalTargetCalib.from_tcal(path)
values = pedestal.pedestal
spread = pedestal.std
if reference_values is None:
reference_values = values
reference_spread = spread
delta = values - reference_values
delta_mean = delta.mean()
delta_std = delta.std()
delta_channel_mean = delta[0].mean()
delta_channel_std = delta[0].std()
# delta_spread = spread - reference_spread
spread_mean = spread.mean()
spread_std = spread.std()
data.append(
dict(
temperature=temperature_pedestal_primary,
delta_mean=delta_mean,
delta_std=delta_std,
delta_channel_mean=delta_channel_mean,
delta_channel_std=delta_channel_std,
spread_mean=spread_mean,
spread_std=spread_std,
))
with HDF5Writer(output_path) as writer:
writer.write(data=pd.DataFrame(data))
def process(pedestal_path, output_dir):
pedestal = PedestalTargetCalib.from_tcal(pedestal_path)
std_mean = np.mean(pedestal.std)
std_std = np.std(pedestal.std)
std_hist, std_edges = np.histogram(pedestal.std, bins=100)
p_hist = HistPlot()
p_hist.plot(std_hist, std_edges, std_mean, std_std)
p_hist.save(join(output_dir, "hist.pdf"))
p_hist2d_pedestal = Hist2D()
p_hist2d_pedestal.plot(pedestal.pedestal[0], pedestal.hits[0],
"Pedestal Mean (ADC)")
p_hist2d_pedestal.save(join(output_dir, "hist2d_mean.pdf"))
p_hist2d_std = Hist2D()
p_hist2d_std.plot(pedestal.std[0], pedestal.hits[0],
"Pedestal Stddev (ADC)")
p_hist2d_std.save(join(output_dir, "hist2d_std.pdf"))
def get_pedestal(pedestal_path):
return PedestalTargetCalib.from_tcal(pedestal_path)