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(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 process(input_paths, data_path, poi):
df_list = []
n_files = len(input_paths)
for ifile, f in enumerate(input_paths):
print("Processing File {}/{}".format(ifile, n_files))
reader = TIOReader(f, max_events=1000)
n_events = reader.n_events
n_samples = reader.n_samples
wfs = np.zeros((n_events, n_samples))
desc = "Processing events"
for wf in tqdm(reader, total=n_events, desc=desc):
iev = wf.iev
wfs[iev] = wf[poi]
average_wf = wfs.mean(0)
df_list.append(
pd.DataFrame(
dict(ifile=ifile,
file=f,
wf=average_wf,
isam=np.arange(n_samples))))
df = pd.concat(df_list, ignore_index=True)
with HDF5Writer(data_path) as writer:
writer.write(data=df)
writer.write_metadata(n_files=n_files)
def process(input_path, output_path):
reader = TIOReader(input_path)
dt = np.zeros(reader.n_events - 1)
for iev in trange(1, reader.n_events):
dt[iev - 1] = (reader[iev].t_cpu - reader[iev - 1].t_cpu).value
np.save(output_path, dt)
def main():
# files = glob("/Volumes/gct-jason/data_checs/d181019_dctf/15C/Run*_r0.tio")
files = glob(
"/Volumes/gct-jason/data_checs/d181019_dctf/15C/r1_tf/Run*_r1.tio")
files = sort_file_list(files)
files = files[1:]
n_files = len(files)
mean_p = np.zeros(n_files)
std_p = np.zeros(n_files)
mean_s = np.zeros(n_files)
std_s = np.zeros(n_files)
vped = np.zeros(n_files)
for ifile, f in enumerate(files):
vped_path = f.replace("_r1.tio", "_r0.txt")
vped[ifile] = read_vped(vped_path)
r = TIOReader(f)
n_events = r.n_events
n_pixels = r.n_pixels
n_samples = r.n_samples
samples = np.zeros((n_events, n_pixels, n_samples))
for iev, wf in enumerate(r):
samples[iev] = wf
lookup = samples.mean(0)
mean_p[ifile] = lookup[:, 0].mean()
std_p[ifile] = lookup[:, 0].std()
mean_s[ifile] = lookup[0, :].mean()
std_s[ifile] = lookup[0, :].std()
(_, caps, _) = plt.errorbar(vped,
mean_p,
yerr=std_p,
fmt='o',
mew=1,
markersize=3,
capsize=3,
elinewidth=0.7,
label="")
for cap in caps:
cap.set_markeredgewidth(0.7)
(_, caps, _) = plt.errorbar(vped,
mean_s,
yerr=std_s,
fmt='o',
mew=1,
markersize=3,
capsize=3,
elinewidth=0.7,
label="")
for cap in caps:
cap.set_markeredgewidth(0.7)
# plt.errorbar(vped, mean_p, yerr=std_p)
# plt.errorbar(vped, mean_s, yerr=std_s)
plt.show()
def process(input_path, output_path, poi):
r0_reader = TIOReader(input_path)
n_events = r0_reader.n_events
n_samples = r0_reader.n_samples
isam = np.arange(n_samples, dtype=np.uint16)
df_list = []
desc = "Looping over events"
for r0 in tqdm(r0_reader, total=n_events, desc=desc):
if r0.missing_packets:
continue
iev = r0.iev
fci = r0.first_cell_id
adc = r0[poi]
df_list.append(
pd.DataFrame(dict(
iev=iev,
fci=fci,
isam=isam,
adc=adc,
)))
df = pd.concat(df_list, ignore_index=True)
with HDF5Writer(output_path) as writer:
writer.write(data=df)
writer.add_metadata(poi=poi)
def process(input_path, output_path, hv_path, event_slice):
reader = TIOReader(input_path)
n_events = reader.n_events
n_pixels = reader.n_pixels
n_samples = reader.n_samples
mapping = reader.mapping
mappingtc = reader.tc_mapping
mappingsp = MappingSP(mappingtc)
waveforms = reader[event_slice]
sp_arr = np.vstack(mapping.groupby("superpixel").pixel.apply(np.array))
avg_wfs = waveforms[:, sp_arr].mean((0, 2))
amplitudes = avg_wfs.max(1)
hvs = hv_file_reader(hv_path)
hv255 = np.where(hvs == 255)
df = get_superpixel_mapping(mapping)
camera = CameraImage.from_mapping(df)
image = amplitudes
camera.image = image
camera.add_colorbar("Superpixel-Waveform Max (mV)")
camera.highlight_pixels(hv255, 'red')
camera.save(output_path)
lt75 = np.where(amplitudes < 75)
print(lt75)
def process(input_path, pedestal_path, output_path):
reader = TIOReader(input_path)
wf_calib = WaveformCalibrator(
pedestal_path, reader.n_pixels, reader.n_samples
)
wfs = get_da(reader, wf_calib)
mean, std, mean_pix, std_pix, (hist, edges) = da.compute(
wfs.mean(),
wfs.std(),
wfs.mean(axis=(0, 2)),
wfs.std(axis=(0, 2)),
da.histogram(wfs, bins=1000, range=(-10, 10))
)
np.savez(
output_path,
mean=mean,
std=std,
mean_pix=mean_pix,
std_pix=std_pix,
hist=hist,
edges=edges
)
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 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 process(file):
illumination = 50
r1_path, _, _ = file.get_run_with_illumination(illumination, r1=True)
dl1_path, _, _ = file.get_run_with_illumination(illumination, r1=False)
poi = file.poi
plot_dir = file.waveforms_plot_dir
ff_path = file.ff_path
with HDF5Reader(ff_path) as reader:
df = reader.read("data")
ff_m = df['ff_m'].values
ff_c = df['ff_c'].values
reader = TIOReader(r1_path)
n_events = reader.n_events
n_pixels = reader.n_pixels
n_samples = reader.n_samples
wfs = np.zeros((n_events, n_pixels, n_samples))
desc = "Processing events"
for wf in tqdm(reader, total=n_events, desc=desc):
wfs[wf.iev] = wf
reader = DL1Reader(dl1_path)
iev, pixel, charge_1d = reader.select_columns(['iev', 'pixel', 'charge'])
charge_1d = (charge_1d - ff_c[pixel]) / ff_m[pixel]
charge = np.zeros((n_events, n_pixels))
charge[iev, pixel] = charge_1d
x = np.arange(n_samples)
p_wf = WFPlotter(switch_backend=True)
p_wf.plot(x, wfs[:, poi].T)
p_wf.save(os.path.join(plot_dir, "p{}.pdf".format(poi)))
p_wf = WFPlotter()
p_wf.plot(x, wfs.mean(0).T)
p_wf.save(get_plot(os.path.join(plot_dir, "average.pdf")))
p_hist = HistPlotter(switch_backend=True)
p_hist.plot(charge[:, poi])
p_hist.save(os.path.join(plot_dir, "hist.pdf"))
def get_df(paths, vped_list):
assert (len(paths) == len(vped_list))
readers = [TIOReader(p) for p in paths]
n_files = len(paths)
first_reader = TIOReader(paths[0])
n_pixels = first_reader.n_pixels
n_samples = first_reader.n_samples
# mean = np.zeros((n_files, n_pixels, n_samples))
# std = np.zeros((n_files, n_pixels, n_samples))
# vped = np.zeros((n_files, n_pixels, n_samples))
jpixel, jsample = np.indices((n_pixels, n_samples))
df_list = []
for ifile in trange(n_files):
reader = readers[ifile]
r_n_events = reader.n_events
r_n_pixels = reader.n_pixels
r_n_samples = reader.n_samples
samples = np.zeros((r_n_events, r_n_pixels, r_n_samples))
for iev, wf in enumerate(reader):
samples[iev] = wf
mean = np.mean(samples, 0)
std = np.std(samples, 0)
vped = vped_list[ifile]
df_list.append(pd.DataFrame(dict(
vped_dac=vped,
pixel=jpixel.ravel(),
sample=jsample.ravel(),
mean=mean.ravel(),
std=std.ravel(),
)))
df = pd.concat(df_list, ignore_index=True)
return df
def r0r1cameraplotter(input_path):
reader = TIOReader(input_path) # Load the file
wfs = reader[1]
for m in range (0,13):
camera = plt.figure(figsize=(10, 10))
# Generate a CameraImage object using the classmethod "from_mapping" which accepts the
# mapping object contained in the reader, which converts from pixel ID to pixel
# coordinates (using the Mapping class in TargetCalib)
camera = CameraImage.from_mapping(reader.mapping)
camera.add_colorbar()
camera.image = wfs[:, m*10] # Plot value of the sample at m x 10ns for each pixel
plt.show()
'''
def plot_from_tio():
"""
Use the CHECLabPy mapping dataframe to plot an image
"""
path = "/Users/Jason/Software/CHECLabPy/refdata/Run17473_r1.tio"
r = TIOReader(path, max_events=10)
camera = CameraImage.from_mapping(r.mapping)
camera.add_colorbar("Amplitude (mV)")
camera.annotate_on_telescope_up()
for wf in r:
image = wf[:, 60]
camera.image = image
plt.pause(0.5)
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(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(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(file, tf):
r0_path = file.r0_path
r1_name = os.path.basename(r0_path).replace("_r0", "_r1")
r1_path = os.path.join(tf.r1_dir, r1_name)
get_cell = tf.get_cell
r = TIOReader(r1_path)
n_events = r.n_events
n_pixels = r.n_pixels
n_samples = r.n_samples
samp_arange = np.arange(n_samples, dtype=np.uint16)
for wfs in tqdm(r, total=n_events):
fci = r.first_cell_ids
cells = get_cell(int(fci[0]), samp_arange)
cells_pix = np.tile(cells, (n_pixels, 1))
np.testing.assert_almost_equal(wfs, cells_pix, 1)
def main():
# path = get_astri_2019("d2019-06-10_ledflashers/flasher_comparison_May-June/unit0pattern-low_r1.tio")
# path = get_astri_2019("d2019-06-10_ledflashers/flasher_comparison_May-June/Run13270_r1.tio")
path = get_astri_2019(
"d2019-06-10_ledflashers/flasher_comparison_May-June/Run13401_r1.tio")
output_dir = path.replace("_r1.tio", "")
reader = TIOReader(path, max_events=100)
waveforms = reader[:].mean(0)
mapping = get_superpixel_mapping(reader.mapping)
n_pixels, n_samples = waveforms.shape
n_sp = n_pixels // 4
correction = pd.read_csv(join(DIR, "sp_illumination_profile.dat"),
sep='\t')['correction'].values
sp_waveforms = waveforms.reshape((n_sp, 4, n_samples)).sum(1)
sp_waveforms_corrected = sp_waveforms * correction[:, None]
amplitude = sp_waveforms_corrected.max(1)
median = np.median(amplitude)
threshold = median * 1.10
trigger_off = amplitude > threshold
# trigger_off[[ # TODO: Remove?
# 355, 356, 357, 358, 359, 382, 375, 460, 459, 457, 458, 465, 289, 489,
# 502, 254, 247, 154, 144, 56, 46, 39, 24, 25, 76
# ]] = True
hv_off = np.zeros(n_sp, dtype=bool)
hv_off[[24, 453]] = True
print(f"SP Trigger OFF: {np.where(trigger_off)[0].tolist()}")
print(f"SP HV OFF: {np.where(hv_off)[0].tolist()}")
plot = CameraPlotter(mapping)
plot.highlight_pixels(trigger_off, 'yellow')
plot.highlight_pixels(hv_off, 'red')
plot.plot_waveforms(sp_waveforms)
plot.save(join(output_dir, "led.pdf"))
plot.plot_waveforms(sp_waveforms_corrected)
plot.save(join(output_dir, "uniform.pdf"))
df = pd.DataFrame(
dict(
superpixel=np.arange(n_sp),
hv_on=(~hv_off).astype(int),
trigger_off=trigger_off.astype(int),
))
df.to_csv(join(output_dir, "sp_settings.txt"), sep='\t', index=False)
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('-o', '--output', dest='output_dir', required=True,
help='directort to store output plots')
args = parser.parse_args()
r0_path = args.r0_path
channel = 0
output_dir = args.output_dir
create_directory(output_dir)
reader = TIOReader(r0_path, max_events=100000)
# Generate Pedestals
pedestal = PedestalCellPosition(
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)
# embed()
for cell in range(reader.n_cells):
if (pedestal.hits[channel, cell] == 0).all():
continue
p_cell_wf = CellWaveform()
p_cell_wf.plot(
pedestal.pedestal[channel, cell],
pedestal.std[channel, cell],
pedestal.hits[channel, cell],
cell
)
p_cell_wf.save(
join(output_dir, f"cell_pedestal_vs_position/{cell:04d}.pdf")
)
def process(paths, output_path):
with HDF5Writer(output_path) as writer:
for ipath, path in enumerate(paths):
print(f"File: {ipath+1}/{len(paths)}")
reader = TIOReader(path)
n_events = reader.n_events
for wf in tqdm(reader, total=n_events):
data = dict(
ipath=ipath,
iev=wf.iev,
tack=wf.t_tack,
stale=wf.stale[0],
fci=wf.first_cell_id[0],
)
writer.append(pd.DataFrame(data, index=[0]))
print(
f"Memory Usage = {psutil.Process().memory_info().rss * 1E-9} GB"
)
def main():
path = get_astri_2019("d2019-06-10_ledflashers/flasher_comparison_May-June/unit0pattern-low_r1.tio")
reader = TIOReader(path, max_events=100)
waveforms = reader[:].mean(0)
# Calculate coeff
n_pixels, n_samples = waveforms.shape
n_sp = n_pixels // 4
waveforms_sp = waveforms.reshape(
(n_sp, 4, n_samples)
).sum(1)
correction = (waveforms_sp.max(1).mean() / waveforms_sp.max(1))
df = pd.DataFrame(dict(
superpixel=np.arange(n_sp),
correction=correction,
))
df.to_csv(join(DIR, "sp_illumination_profile.dat"), sep='\t', index=False)
def main():
input_path = "/Users/Jason/Downloads/tempdata/Run06136_r0.tio"
pedestal_path = "/Users/Jason/Downloads/tempdata/Run06136_ped.tcal"
max_events = None
reader = TIOReader(input_path, max_events=max_events)
wf_calib = WaveformCalibrator(
pedestal_path, reader.n_pixels, reader.n_samples
)
dtio = DaskTIO(reader, wf_calib)
ddf = dtio.get_file_df()
# print("here")
df_0, df_2 = dd.compute(
ddf.groupby(['ipix', 'fblock', 'fbpisam'])['r0'].std(),
ddf.groupby(['ipix', 'fci', 'fbpisam'])['r0'].std(),
)
embed()
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(entry, poi):
with stdout_redirector():
input_path, amplitude = entry
df_list = []
desc = "Looping over events"
r0_reader = TIOReader(input_path)
n_events = r0_reader.n_events
n_samples = r0_reader.n_samples
isam = np.arange(n_samples, dtype=np.uint16)
rd = ReducedDataframe()
for r0 in r0_reader:
fci = r0.first_cell_id[poi].item()
rd.add(amplitude, r0[poi].astype(np.int), fci, isam)
df = rd.finish()
return df
def main():
r1_path = "/Volumes/gct-jason/astri_onsky_archive/d2019-05-10_triggerpatterns/Run13357_r1.tio"
trig_path = "/Volumes/gct-jason/astri_onsky_archive/d2019-05-10_triggerpatterns/Run13357_2019-05-10.2100.prt"
threshold = 42
r1_dict = dict()
trigger_dict = dict()
r1_reader = TIOReader(r1_path, skip_events=0, skip_end_events=0)
trigger_reader = TriggerReader(trig_path)
n_pixels = r1_reader.n_pixels
n_superpixels = n_pixels // 4
n_samples = r1_reader.n_samples
for i in range(35):
r1_wfs = r1_reader[i]
trigger_event = trigger_reader.read()
r1_tack = r1_reader.current_tack
trigger_tack = trigger_event.TACK
r1_image = r1_wfs.reshape((n_superpixels, 4, n_samples)).sum(1).max(1)
trigger_image = np.frombuffer(trigger_event.trigg.unpack(), dtype=bool)
r1_dict[r1_tack] = r1_image
trigger_dict[trigger_tack] = trigger_image
p_image = ImagePlotter(r1_reader.mapping)
tack_list = sorted(
list(set().union(r1_dict.keys(), trigger_dict.keys()))
)[:-1]
for tack in tack_list:
r1_image = r1_dict.get(tack, np.zeros(n_superpixels))
trigger_image = trigger_dict.get(tack, np.zeros(n_superpixels))
software_trigger = r1_image > threshold
p_image.set_image(tack, r1_image, software_trigger, trigger_image)
p_image.save(f"T{tack}.png")
def main():
paths = glob(get_astri_2019("*/*_r1.tio"))
pattern = re.compile(r"(?:.+?)/d(.+?)/Run(\d+?)_r1.tio")
d_list = []
for path in paths:
reader = TIOReader(path)
wfs = reader[0]
nudge, temperature = get_nudge_and_temperature_from_reader(reader)
regexp = re.search(pattern, path)
investigation = regexp.group(1)
run_id = regexp.group(2)
d_list.append(
dict(investigation=investigation,
run_id=run_id,
t_cpu=wfs.t_cpu,
nudge=nudge,
temperature=temperature))
df = pd.DataFrame(d_list)
with HDF5Writer(get_data("d190507_check_amplitude_calib/data.h5")) as w:
w.write(data=df)
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)
from CHECLabPy.core.io import TIOReader
from sstcam_sandbox import get_astri_2019
from tqdm import trange
import numpy as np
from matplotlib import pyplot as plt
path = get_astri_2019("d2019-06-14_pedestal/Run13794_r0.tio")
reader = TIOReader(path)
dt = np.zeros(reader.n_events - 1)
for iev in trange(1, reader.n_events):
dt[iev - 1] = (reader[iev].t_cpu - reader[iev - 1].t_cpu).value
plt.hist(dt, bins=100)
plt.yscale("log")
