def main():
description = (
"Generate the pedestals from an R0 file, subtract it from another "
"R0 file, and plot the comparison of residuals from different "
"pedestal methods")
parser = argparse.ArgumentParser(description=description,
formatter_class=Formatter)
parser.add_argument('-f',
'--file',
dest='r0_path',
required=True,
help='R0 file to obtain residuals from')
parser.add_argument('-p',
'--pedestal',
dest='pedestal_r0_path',
required=True,
help='R0 file to generate pedestal from')
parser.add_argument('-o',
'--output',
dest='output_dir',
required=True,
help='directort to store output plots')
args = parser.parse_args()
r0_path = args.r0_path
pedestal_r0_path = args.pedestal_r0_path
output_dir = args.output_dir
create_directory(output_dir)
reader_ped = TIOReader(pedestal_r0_path, max_events=100000)
reader_res = TIOReader(r0_path, max_events=1000)
# Generate Pedestals
pedestal = PedestalTargetCalib(reader_ped.n_pixels, reader_ped.n_samples,
reader_ped.n_cells)
desc = "Generating pedestal"
for wfs in tqdm(reader_ped, total=reader_ped.n_events, desc=desc):
if wfs.missing_packets:
continue
pedestal.add_to_pedestal(wfs, wfs.first_cell_id)
channel_stats = PixelStats(reader_res.n_pixels)
# Subtract Pedestals
desc = "Subtracting pedestal"
for wfs in tqdm(reader_res, total=reader_res.n_events, desc=desc):
if wfs.missing_packets:
continue
subtracted_tc = pedestal.subtract_pedestal(wfs, wfs.first_cell_id)
channel_stats.add_to_stats(subtracted_tc)
# Plot results
p_channel_std = ChannelStd()
p_channel_std.plot(channel_stats.std)
p_channel_std.save(join(output_dir, "channel_std.pdf"))
p_ci_stats = CameraStats(reader_res.mapping)
p_ci_stats.set_image(channel_stats.mean, channel_stats.std)
p_ci_stats.save(join(output_dir, f"ci_stats.pdf"))
def main():
description = (
"Generate the pedestals from an R0 file, subtract it from another "
"R0 file, and plot the comparison of residuals from different "
"pedestal methods"
)
parser = argparse.ArgumentParser(description=description,
formatter_class=Formatter)
parser.add_argument('-f', '--file', dest='r0_path', required=True,
help='R0 file to obtain residuals from')
parser.add_argument('-p', '--pedestal', dest='pedestal_r0_path',
required=True,
help='R0 file to generate pedestal from')
parser.add_argument('-o', '--output', dest='output_dir', required=True,
help='directort to store output plots')
args = parser.parse_args()
r0_path = args.r0_path
pedestal_r0_path = args.pedestal_r0_path
output_dir = args.output_dir
create_directory(output_dir)
reader_ped = TIOReader(pedestal_r0_path, max_events=100000)
reader_res = TIOReader(r0_path, max_events=100000)
# Generate Pedestals
pedestal = PedestalTargetCalib(
reader_ped.n_pixels, reader_ped.n_samples, reader_ped.n_cells
)
desc = "Generating pedestal"
for wfs in tqdm(reader_ped, total=reader_ped.n_events, desc=desc):
if wfs.missing_packets:
continue
pedestal.add_to_pedestal(wfs, wfs.first_cell_id)
online_stats = OnlineStats()
online_hist = OnlineHist(bins=100, range_=(-10, 10))
# Subtract Pedestals
desc = "Subtracting pedestal"
for wfs in tqdm(reader_res, total=reader_res.n_events, desc=desc):
if wfs.missing_packets:
continue
subtracted_tc = pedestal.subtract_pedestal(wfs, wfs.first_cell_id)
online_stats.add_to_stats(subtracted_tc)
online_hist.add(subtracted_tc)
p_hist = HistPlot()
p_hist.plot(
online_hist.hist, online_hist.edges, online_stats.mean, online_stats.std,
)
p_hist.save(join(output_dir, "hist.pdf"))
def process(r0_paths, output_path):
data = []
for ipath, r0_path in enumerate(r0_paths):
print(f"Processing: {ipath+1}/{len(r0_paths)}")
pedestal_path = r0_path.replace(".tio", "_ped.tcal")
regex_r0 = re.search(r".+_tc([\d.]+)_tmc([\d.]+).tio", r0_path)
temperature_r0_chamber = float(regex_r0.group(1))
temperature_r0_primary = float(regex_r0.group(2))
regex_ped = re.search(r".+_tc([\d.]+)_tmc([\d.]+)_ped.tcal",
pedestal_path)
temperature_pedestal_chamber = float(regex_ped.group(1))
temperature_pedestal_primary = float(regex_ped.group(2))
reader = TIOReader(r0_path, max_events=50000)
pedestal = PedestalTargetCalib(reader.n_pixels, reader.n_samples,
reader.n_cells)
pedestal.load_tcal(pedestal_path)
online_stats = OnlineStats()
online_hist = OnlineHist(bins=100, range_=(-10, 10))
# Subtract Pedestals
desc = "Subtracting pedestal"
for wfs in tqdm(reader, total=reader.n_events, desc=desc):
if wfs.missing_packets:
continue
subtracted_tc = pedestal.subtract_pedestal(wfs,
wfs.first_cell_id)[[0]]
online_stats.add_to_stats(subtracted_tc)
online_hist.add(subtracted_tc)
data.append(
dict(
temperature_r0_chamber=temperature_r0_chamber,
temperature_r0_primary=temperature_r0_primary,
temperature_pedestal_chamber=temperature_pedestal_chamber,
temperature_pedestal_primary=temperature_pedestal_primary,
mean=online_stats.mean,
std=online_stats.std,
hist=online_hist.hist,
edges=online_hist.edges,
))
with HDF5Writer(output_path) as writer:
writer.write(data=pd.DataFrame(data))
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 main():
r0_path = "/Users/Jason/Downloads/tempdata/Pedestal_23deg_r0.tio"
r1_int_path = "/Users/Jason/Downloads/tempdata/Pedestal_23deg_r1_int.tio"
r1_rnd_path = "/Users/Jason/Downloads/tempdata/Pedestal_23deg_r1_rnd.tio"
tcal_path = "/Users/Jason/Downloads/tempdata/Pedestal_23deg_ped.tcal"
reader_r0 = TIOReader(r0_path, max_events=10000)
reader_r1_int = TIOReader(r1_int_path, max_events=10000)
reader_r1_rnd = TIOReader(r1_rnd_path, max_events=10000)
# Generate Pedestal
pedestal_tc = PedestalTargetCalib(reader_r0.n_pixels, reader_r0.n_samples,
reader_r0.n_cells)
pedestal_tc.load_tcal(tcal_path)
l_int = []
l_rnd = []
# Subtract Pedestals
desc = "Subtracting pedestal"
z = zip(reader_r0, reader_r1_int, reader_r1_rnd)
it = tqdm(z, total=reader_r0.n_events, desc=desc)
for wfs_r0, wfs_r1_int, wfs_r1_rnd in it:
if wfs_r0.missing_packets:
continue
wfs_r1_flt = pedestal_tc.subtract_pedestal(wfs_r0,
wfs_r0.first_cell_id)
# offset = 700
# scale = 13.6
# wfs_r1_flt = (wfs_r1_flt + offset) * scale
# wfs_r1_int = (wfs_r1_int + offset) * scale
# wfs_r1_rnd = (wfs_r1_rnd + offset) * scale
l_int.append(wfs_r1_flt - wfs_r1_int)
l_rnd.append(wfs_r1_flt - wfs_r1_rnd)
l_int = np.array(l_int).ravel()
l_rnd = np.array(l_rnd).ravel()
plt.hist(l_int, bins=20, histtype='step', label='int')
plt.hist(l_rnd, bins=20, histtype='step', label='rnd')
plt.legend(loc='best')
plt.xlabel("Difference to float ped-sub ADC")
plt.ylabel("N")
plt.show()
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(input_paths):
for path in input_paths:
pedestal_path = path.replace(".tio", "_ped.tcal")
reader = TIOReader(path)
pedestal = PedestalTargetCalib(reader.n_pixels, reader.n_samples,
reader.n_cells)
desc = "Generating pedestal"
for wfs in tqdm(reader, total=reader.n_events, desc=desc):
if wfs.missing_packets:
continue
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 __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 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 get_pedestal(pedestal_path):
return PedestalTargetCalib.from_tcal(pedestal_path)
def main():
description = (
"Generate the pedestals from an R0 file, subtract it from another "
"R0 file, and plot the comparison of residuals from different "
"pedestal methods")
parser = argparse.ArgumentParser(description=description,
formatter_class=Formatter)
parser.add_argument('-f',
'--file',
dest='r0_path',
required=True,
help='R0 file to obtain residuals from')
parser.add_argument('-p',
'--pedestal',
dest='pedestal_r0_path',
required=True,
help='R0 file to generate pedestal from')
parser.add_argument('-o',
'--output',
dest='output_dir',
required=True,
help='directort to store output plots')
args = parser.parse_args()
r0_path = args.r0_path
pedestal_r0_path = args.pedestal_r0_path
output_dir = args.output_dir
create_directory(output_dir)
reader_ped = TIOReader(pedestal_r0_path)
reader_res = TIOReader(r0_path, max_events=1000)
# Generate Pedestals
pedestal_info = (reader_ped.n_pixels, reader_ped.n_samples,
reader_ped.n_cells)
pedestal_tc = PedestalTargetCalib(*pedestal_info)
pedestal_bp = PedestalBlockphase(*pedestal_info)
desc = "Generating pedestal"
for wfs in tqdm(reader_ped, total=reader_ped.n_events, desc=desc):
if wfs.missing_packets:
continue
pedestal_tc.add_to_pedestal(wfs, wfs.first_cell_id)
pedestal_bp.add_to_pedestal(wfs, wfs.first_cell_id)
pstats_tc = PixelStats(reader_res.n_pixels)
pstats_bp = PixelStats(reader_res.n_pixels)
stats_tc = OnlineStats()
stats_bp = OnlineStats()
hist_tc = OnlineHist(100, (-10, 10))
hist_bp = OnlineHist(100, (-10, 10))
wf_list_tc = []
wf_list_bp = []
fci = []
# Subtract Pedestals
desc = "Subtracting pedestal"
for wfs in tqdm(reader_res, total=reader_res.n_events, desc=desc):
if wfs.missing_packets:
continue
subtracted_tc = pedestal_tc.subtract_pedestal(wfs, wfs.first_cell_id)
subtracted_bp = pedestal_bp.subtract_pedestal(wfs, wfs.first_cell_id)
pstats_tc.add_to_stats(subtracted_tc)
stats_tc.add_to_stats(subtracted_tc)
hist_tc.add(subtracted_tc)
pstats_bp.add_to_stats(subtracted_bp)
stats_bp.add_to_stats(subtracted_bp)
hist_bp.add(subtracted_bp)
wf_list_tc.append(subtracted_tc)
wf_list_bp.append(subtracted_bp)
fci.append(wfs.first_cell_id)
# Plot results
label_tc = pedestal_tc.__class__.__name__
label_bp = pedestal_bp.__class__.__name__
p_pix_stats = StatsPlot()
p_pix_stats.plot(pstats_tc.mean, pstats_tc.std, label_tc)
p_pix_stats.plot(pstats_bp.mean, pstats_bp.std, label_bp)
p_pix_stats.save(join(output_dir, "pix_stats.pdf"))
p_ci_stats = Camera2(reader_res.mapping)
p_ci_stats.set_image(pstats_tc.mean, pstats_tc.std)
p_ci_stats.save(join(output_dir, f"ci_stats_{label_tc}.pdf"))
p_ci_stats.set_image(pstats_bp.mean, pstats_bp.std)
p_ci_stats.save(join(output_dir, f"ci_stats_{label_bp}.pdf"))
p_hist = HistPlot()
p_hist.plot(hist_tc.hist, hist_tc.edges, stats_tc.mean, stats_tc.std,
label_tc)
p_hist.plot(hist_bp.hist, hist_bp.edges, stats_bp.mean, stats_bp.std,
label_bp)
p_hist.save(join(output_dir, "hist.pdf"))
p_wf_tc = WaveformPlot()
p_wf_bp = WaveformPlot()
wfs_tc = np.stack(wf_list_tc)
wfs_bp = np.stack(wf_list_bp)
avg_tc = np.average(wfs_tc, axis=0)
avg_bp = np.average(wfs_bp, axis=0)
for iev in range(len(wf_list_tc)):
ev_avg_tc = np.average(wf_list_tc[iev] - avg_tc, axis=0)
ev_avg_bp = np.average(wf_list_bp[iev] - avg_bp, axis=0)
p_wf_tc.plot(ev_avg_tc, fci[iev])
p_wf_bp.plot(ev_avg_bp, fci[iev])
p_wf_tc.save(join(output_dir, f"wfs_{label_tc}.pdf"))
p_wf_bp.save(join(output_dir, f"wfs_{label_bp}.pdf"))