def __init__(self, n_pixels, n_samples, mapping=None, **kwargs):
super().__init__(n_pixels, n_samples, **kwargs)
if mapping is None:
raise ValueError("A mapping must be passed "
"to CtapipeNeighbourPeakIntegrator")
try:
from ctapipe.image.extractor import NeighborPeakWindowSum
except ImportError:
msg = ("ctapipe not found. Please either install ctapipe or "
"disable the columns from WaveformReducer {} ({})".format(
self.__class__.__name__, self.columns))
raise ImportError(msg)
camera = get_ctapipe_camera_geometry(mapping)
self.window_size = self.kwargs.get("window_size", 6)
self.window_shift = self.kwargs.get("window_shift", 3)
self.integrator = NeighborPeakWindowSum(
window_shift=self.window_shift,
window_width=self.window_size,
lwt=0,
)
self.integrator.neighbors = camera.neighbor_matrix_where
def main():
paths = [
"/Volumes/gct-jason/astri_onsky_archive/d2019-05-15_simulations/proton/run1_dl1.h5",
]
df_list = []
for ipath, path in enumerate(paths):
with DL1Reader(path) as reader:
n_events = reader.get_metadata()['n_events']
mapping = reader.get_mapping()
geom = get_ctapipe_camera_geometry(mapping, plate_scale=37.56e-3)
desc = "Looping over events"
it = reader.iterate_over_events()
for df in tqdm(it, total=n_events, desc=desc):
iev = df['iev'].values[0]
image = df['photons'].values
time = df['pulse_time'].values
mask = obtain_cleaning_mask(geom, image, time)
if not mask.any():
continue
image_m = image[mask]
time_m = time[mask]
geom_m = geom[mask]
try:
hillas = hillas_parameters(geom_m, image_m)
except HillasParameterizationError:
continue
# timing_parameters(geom_m, image_m, time_m, hillas)
gt0 = image_m > 0
pix_x = geom_m.pix_x[gt0]
pix_y = geom_m.pix_y[gt0]
peakpos = time_m[gt0]
intensity = image_m[gt0]
longi, trans = camera_to_shower_coordinates(
pix_x, pix_y, hillas.x, hillas.y, hillas.psi)
longi = longi.value
trans = trans.value
# df_list.append(pd.DataFrame(dict(
# ipath=ipath,
# iev=iev,
# longi=longi,
# peakpos=peakpos,
# )))
p_relation = RelationPlotter()
p_relation.plot(longi, peakpos, intensity)
p_relation.save(
get_plot(
f"d190524_time_gradient/relation/i{ipath}_e{iev}.pdf"))
def __init__(self, n_pixels, n_samples, mapping=None, **kwargs):
super().__init__(n_pixels, n_samples, **kwargs)
if mapping is None:
raise ValueError("A mapping must be passed "
"to SlidingWindowNeighbour")
try:
from ctapipe.image.extractor import neighbor_average_waveform
except ImportError:
msg = ("ctapipe not found. Please either install ctapipe or "
"disable the columns from WaveformReducer {} ({})".format(
self.__class__.__name__, self.columns))
raise ImportError(msg)
camera = get_ctapipe_camera_geometry(mapping)
self.neighbor_func = neighbor_average_waveform
self.neighbors = camera.neighbor_matrix_where
def main():
description = ('Plot the images of the events in a hillas '
'list from generate_list_from_hillas.py')
parser = argparse.ArgumentParser(description=description,
formatter_class=Formatter)
parser.add_argument('-f',
'--file',
dest='input_path',
required=True,
help='path to a hillas list file created by '
'generate_list_from_hillas')
parser.add_argument('-n',
'--max_events',
dest='max_events',
type=int,
help='number of events to plot')
args = parser.parse_args()
input_path = args.input_path
output = splitext(input_path)[0]
max_events = args.max_events
df = pd.read_csv(input_path, sep='\t')
# Open all files
hillas_paths = set()
for _, row in df.iterrows():
hillas_paths.add(row['path'])
readers = {
path: DL1Reader(path.replace("_hillas.h5", "_dl1.h5"))
for path in hillas_paths
}
mapping = readers[list(hillas_paths)[0]].mapping
# mapping.metadata['size'] *= 1.01
geom = get_ctapipe_camera_geometry(mapping)
p_image = ImagePlotter(mapping)
n_events = df.index.size
if max_events is not None and n_events > max_events:
n_events = max_events
desc = "Looping over events"
for i, row in tqdm(df.iterrows(), total=n_events, desc=desc):
if i >= n_events:
break
hillas_path = row['path']
iev = row['iev']
iobs = row['iobs']
reader = readers[hillas_path]
df = reader[iev]
image_c = df['photons'].values
image_t = df['pulse_time'].values
mask = obtain_cleaning_mask(geom, image_c, image_t)
if not mask.any():
continue
try:
hillas = hillas_parameters(geom[mask], image_c[mask])
except HillasParameterizationError:
continue
p_image.set_image(iev, image_c, image_t, mask=mask, hillas=hillas)
p_image.save(join(output, f"camera_images/i{i}_r{iobs}_e{iev}.pdf"))
def main():
input_path = "event_list.txt"
output_path = "cherenkov.npz"
df = pd.read_csv(input_path, sep='\t')
first_path = df.iloc[0]['path'].replace("_hillas.h5", "_r1.tio")
first_reader = TIOReader(first_path)
n_pixels = first_reader.n_pixels
n_samples = first_reader.n_samples
mapping = first_reader.mapping
mapping.metadata['size'] *= 1.01 # TODO: WHY?!
reference_pulse_path = first_reader.reference_pulse_path
geom = get_ctapipe_camera_geometry(mapping)
charge_extractor = OnskyExtractor(
n_pixels,
n_samples,
mapping=mapping,
reference_pulse_path=reference_pulse_path,
)
time_calibrator = TimeCalibrator()
# Open all files
hillas_paths = set()
for _, row in df.iterrows():
hillas_paths.add(row['path'])
readers = dict()
amplitude_calibrators = dict()
for path in hillas_paths:
r1_path = path.replace("_hillas.h5", "_r1.tio")
reader = TIOReader(r1_path)
nudge, temperature = get_nudge_and_temperature_from_reader(reader)
amplitude_calibrator = OnskyAmplitudeCalibrator(nudge, temperature)
readers[path] = reader
amplitude_calibrators[path] = amplitude_calibrator
n_events = df.index.size
frames_array = []
min_array = []
max_array = []
desc = "Looping over events"
for i, row in tqdm(df.iterrows(), total=n_events, desc=desc):
if i >= n_events:
break
hillas_path = row['path']
iev = row['iev']
iobs = row['iobs']
reader = readers[hillas_path]
amplitude_calibrator = amplitude_calibrators[hillas_path]
waveforms = reader[iev]
shifted = time_calibrator(waveforms)
extracted = charge_extractor.process(shifted)
charge = extracted['charge_onsky']
time = extracted['t_onsky']
photons = amplitude_calibrator(charge, np.arange(n_pixels))
pe = photons * 0.25
mask = obtain_cleaning_mask(geom, photons, time)
if not mask.any():
msg = f"No pixels survived cleaning for: RUN {iobs} IEV {iev}"
raise ValueError(msg)
photons_ma = np.ma.masked_array(photons, mask=~mask)
min_pixel = photons_ma.argmin()
max_pixel = photons_ma.argmax()
min_image = -4
max_image = 0.7 * pe.max()
min_gf = shifted[max_pixel, :20].min()
max_gf = shifted[max_pixel].max() * 0.8
st = int(np.min(time[mask]) - 3)
et = int(np.max(time[mask]) + 6)
st = st if st > 0 else 0
et = et if et < n_samples else n_samples
frames = shifted[:, st:et:4]
min_ = np.full(frames.shape[-1], min_gf)
max_ = np.full(frames.shape[-1], max_gf)
frames_array.append(frames)
min_array.append(min_)
max_array.append(max_)
np.savez(
output_path,
frames=np.column_stack(frames_array),
min=np.concatenate(min_array),
max=np.concatenate(max_array),
)
def main():
description = 'Plot DL1 images'
parser = argparse.ArgumentParser(description=description,
formatter_class=Formatter)
parser.add_argument('-f',
'--file',
dest='input_path',
required=True,
help='path to the HDF5 dl1 file')
parser.add_argument('-o',
'--output',
dest='output_dir',
help='directory to save the plots')
parser.add_argument('-s', dest='start_event', help='Start event', type=int)
parser.add_argument('-m', dest='max_events', help='Max events', type=int)
args = parser.parse_args()
input_path = args.input_path
output = args.output_dir
start_event = args.start_event
max_events = args.max_events
plot_mask = True
plot_hillas = True
if output is None:
output = input_path.replace("_dl1.h5", "_dl1images")
if start_event is None:
start_event = 0
with DL1Reader(input_path) as reader:
n_events = reader.get_metadata()['n_events']
mapping = reader.get_mapping()
geom = get_ctapipe_camera_geometry(mapping)
if max_events and max_events < n_events:
n_events = max_events
p_event = EventPlotter(mapping, geom)
desc = "Looping over events"
events = range(start_event, n_events)
it = enumerate(reader.iterate_over_selected_events(events))
for ientry, df in tqdm(it, total=n_events - start_event, desc=desc):
iev = df['iev'].values[0]
if iev < start_event:
continue
if iev >= n_events:
break
image_c = df['photons'].values
image_t = df['pulse_time'].values
mask = obtain_cleaning_mask(geom, image_c, image_t)
if not mask.any():
continue
try:
hillas = hillas_parameters(geom[mask], image_c[mask])
except HillasParameterizationError:
continue
p_event.set_image(iev, image_c, image_t, mask=mask, hillas=hillas)
p_event.save(join(output, f"{iev}.png"))
def main():
path = get_data("d190717_alpha/wobble.h5")
with pd.HDFStore(path, mode='r') as store:
df = store['data'].loc[::4]
mapping = store['mapping']
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
mapping.metadata = store.get_storer('mapping').attrs.metadata
tc = TimeCalibrator()
geom = get_ctapipe_camera_geometry(mapping)
n_row = df.index.size
p_camera = CameraMovie(mapping, get_plot(
"d190717_alpha/wobble_animation_goldfish/frames/{:04d}.png"
))
for _, row in tqdm(df.iterrows(), total=n_row):
timestamp = row['timestamp']
iobs = row['iobs']
iev = row['iev']
x_src = row['x_src']
y_src = row['y_src']
dl1 = row['dl1'].values
time = row['dl1_pulse_time'].values
r1 = row['r1']
x_cog = row['x_cog']
y_cog = row['y_cog']
psi = row['psi']
p_camera.set_source_position(x_src, y_src)
n_pixels, n_samples = r1.shape
shifted = tc(r1)
mask = obtain_cleaning_mask(geom, dl1, time)
if not mask.any():
msg = f"No pixels survived cleaning for: RUN {iobs} IEV {iev}"
print(msg)
continue
# raise ValueError(msg)
dl1_ma = np.ma.masked_array(dl1, mask=~mask)
min_pixel = dl1_ma.argmin()
max_pixel = dl1_ma.argmax()
min_image = -4
max_image = 0.7 * dl1.max()
min_gf = shifted[max_pixel, :20].min()
max_gf = shifted[max_pixel].max() * 0.8
st = int(np.min(time[mask]) - 3)
et = int(np.max(time[mask]) + 6)
st = st if st > 0 else 0
et = et if et < n_samples else n_samples
# embed()
p_camera.set_image(dl1, min_image, max_image)
for t in range(st, et, 3):
slice_ = shifted[:, t]
p_camera.set_timestamp(timestamp + pd.Timedelta(f"{t}ns"))
p_camera.set_goldfish(slice_, min_gf, max_gf)
p_camera.save_frame()
def process(dataset):
name = dataset.__class__.__name__
directory = dataset.directory
event_dict = dataset.events
output_dir = get_plot(f"d190506_astri_publicity/{name}")
goldfish_path = join(output_dir, 'goldfish.gif')
image_path = join(output_dir, 'image.gif')
first_run = list(event_dict.keys())[0]
first_reader = TIOReader(join(directory, first_run + "_r1.tio"))
mapping = first_reader.mapping
p_image = CameraAnimation.from_mapping(mapping)
p_image.add_colorbar("Amplitude (Photoelectons)")
p_goldfish = CameraAnimation.from_mapping(mapping)
p_goldfish.add_colorbar("Amplitude (mV)")
for irun, run in enumerate(event_dict.keys()):
print(f"Processing run: {run} ({irun+1}/{len(event_dict)})")
path = join(directory, run + "_r1.tio")
reader = TIOReader(path)
n_events = reader.n_events
n_pixels = reader.n_pixels
n_samples = reader.n_samples
reference_pulse_path = reader.reference_pulse_path
geom = get_ctapipe_camera_geometry(mapping, plate_scale=37.56e-3)
charge_extractor = CrossCorrelationNeighbour(
n_pixels,
n_samples,
mapping=mapping,
reference_pulse_path=reference_pulse_path,
)
time_calibrator = TimeCalibrator()
nudge, temperature = get_nudge_and_temperature_from_reader(reader)
amplitude_calibrator = AstriAmplitudeCalibrator(nudge, temperature)
for event in tqdm(event_dict[run]):
wfs = reader[event]
shifted = time_calibrator(wfs)
extracted = charge_extractor.process(shifted)
charge = extracted['charge_cc_nn']
time = extracted['t_cc_nn']
photons = amplitude_calibrator(charge, np.arange(n_pixels))
pe = photons * 0.25
mask = tailcut(geom, photons, time)
if not mask.any():
msg = f"No pixels survived cleaning for: {run} {event}"
# raise ValueError(msg)
print(msg)
three_largest = np.argsort(photons)[:-3]
mask = np.zeros(n_pixels, dtype=np.bool)
mask[three_largest] = True
photons_nan = photons
photons_nan[~mask] = np.nan
min_pixel = np.nanargmin(photons_nan)
max_pixel = np.nanargmax(photons_nan)
min_image = 0
max_image = 0.7 * pe.max()
min_goldfish = shifted[max_pixel, :20].min()
max_goldfish = shifted[max_pixel].max() * 0.8
p_image.add_image(run, event, 0, pe, min_image, max_image)
start_time = int(np.min(time[mask]) - 2)
end_time = int(np.max(time[mask]) + 2)
for t in range(start_time, end_time):
if 0 <= t < n_samples:
p_goldfish.add_image(run, event, t, shifted[:, t],
min_goldfish, max_goldfish)
p_image.animate(300, image_path)
p_goldfish.animate(20, goldfish_path)
def main():
description = 'Loop over R0 or R1 file and plot camera images'
parser = argparse.ArgumentParser(description=description,
formatter_class=Formatter)
parser.add_argument('-f',
'--file',
dest='input_path',
required=True,
help='path to a hillas list file created by '
'generate_list_from_hillas')
parser.add_argument('-n',
'--max_events',
dest='max_events',
type=int,
help='number of events to plot')
args = parser.parse_args()
input_path = args.input_path
output_dir = splitext(input_path)[0]
max_events = args.max_events
df = pd.read_csv(input_path, sep='\t')
first_path = df.iloc[0]['path'].replace("_hillas.h5", "_r1.tio")
first_reader = TIOReader(first_path)
n_pixels = first_reader.n_pixels
n_samples = first_reader.n_samples
mapping = first_reader.mapping
mapping.metadata['size'] *= 1.01 # TODO: WHY?!
reference_pulse_path = first_reader.reference_pulse_path
geom = get_ctapipe_camera_geometry(mapping, plate_scale=37.56e-3)
charge_extractor = OnskyExtractor(
n_pixels,
n_samples,
mapping=mapping,
reference_pulse_path=reference_pulse_path,
)
time_calibrator = TimeCalibrator()
# Open all files
hillas_paths = set()
for _, row in df.iterrows():
hillas_paths.add(row['path'])
readers = dict()
amplitude_calibrators = dict()
for path in hillas_paths:
r1_path = path.replace("_hillas.h5", "_r1.tio")
reader = TIOReader(r1_path)
nudge, temperature = get_nudge_and_temperature_from_reader(reader)
amplitude_calibrator = OnskyAmplitudeCalibrator(nudge, temperature)
readers[path] = reader
amplitude_calibrators[path] = amplitude_calibrator
p_animation = CameraAnimation(mapping)
n_events = df.index.size
if max_events is not None and n_events > max_events:
n_events = max_events
desc = "Looping over events"
for i, row in tqdm(df.iterrows(), total=n_events, desc=desc):
if i >= n_events:
break
hillas_path = row['path']
iev = row['iev']
iobs = row['iobs']
tduration = row['tduration']
reader = readers[hillas_path]
amplitude_calibrator = amplitude_calibrators[hillas_path]
waveforms = reader[iev]
shifted = time_calibrator(waveforms)
extracted = charge_extractor.process(shifted)
charge = extracted['charge_onsky']
time = extracted['t_onsky']
photons = amplitude_calibrator(charge, np.arange(n_pixels))
pe = photons * 0.25
mask = obtain_cleaning_mask(geom, photons, time)
if not mask.any():
msg = f"No pixels survived cleaning for: RUN {iobs} IEV {iev}"
raise ValueError(msg)
photons_ma = np.ma.masked_array(photons, mask=~mask)
min_pixel = photons_ma.argmin()
max_pixel = photons_ma.argmax()
min_image = -4
max_image = 0.7 * pe.max()
min_gf = shifted[max_pixel, :20].min()
max_gf = shifted[max_pixel].max() * 0.8
st = int(np.min(time[mask]) - 3)
et = int(np.max(time[mask]) + 6)
st = st if st > 0 else 0
et = et if et < n_samples else n_samples
p_animation.set_meta(i, iobs, iev, tduration)
p_animation.set_image(pe, min_image, max_image)
p_animation.set_waveforms(shifted[:, st:et:4], min_gf, max_gf)
p_animation.animate(output_dir, interval=50)
