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 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"))