def test_targetio_calibrator():
from ctapipe_io_targetio import TargetIOEventSource
from ctapipe.calib import CameraR1Calibrator
url_r0 = get_dataset_path("targetmodule_r0.tio")
url_r1 = get_dataset_path("targetmodule_r1.tio")
pedpath = get_dataset_path("targetmodule_ped.tcal")
source_r0 = TargetIOEventSource(input_url=url_r0)
source_r1 = TargetIOEventSource(input_url=url_r1)
r1c = CameraR1Calibrator.from_eventsource(eventsource=source_r0)
event_r0 = source_r0._get_event_by_index(0)
event_r1 = source_r1._get_event_by_index(0)
r1c.calibrate(event_r0)
assert_array_equal(event_r0.r0.tel[0].waveform,
event_r0.r1.tel[0].waveform)
r1c = CameraR1Calibrator.from_eventsource(eventsource=source_r0,
pedestal_path=pedpath)
r1c.calibrate(event_r0)
assert_array_almost_equal(
event_r0.r1.tel[0].waveform,
event_r1.r1.tel[0].waveform,
1,
)
def test_datasets_in_custom_path(tmpdir_factory):
"""
check that a dataset in a user-defined CTAPIPE_SVC_PATH is located
"""
tmpdir1 = tmpdir_factory.mktemp('datasets1')
tmpdir2 = tmpdir_factory.mktemp('datasets2')
os.environ['CTAPIPE_SVC_PATH'] = ":".join([str(tmpdir1), str(tmpdir2)])
# create a dummy dataset to search for:
dataset_name = "test_dataset_1.txt"
dataset_path = str(tmpdir1.join(dataset_name))
with open(dataset_path, "w") as fp:
fp.write("test test test")
# try to find dummy dataset
path = datasets.get_dataset_path(dataset_name)
assert path == dataset_path
with pytest.raises(FileNotFoundError):
datasets.get_dataset_path("does_not_exist")
# try using find_all_matching_datasets:
ds = datasets.find_all_matching_datasets("test.*",
searchpath=os.environ[
'CTAPIPE_SVC_PATH'])
assert dataset_name in ds
def test_find_datasets():
# find all datasets matching pattern
r = datasets.find_all_matching_datasets(r"(.*)\.camgeom\.fits\.gz")
assert len(r) > 3
# get the full filename for a resrouces
assert datasets.get_dataset_path(r[0].name).exists()
# try using a pattern
r = datasets.find_all_matching_datasets(r"(.*)\.camgeom\.fits\.gz", regexp_group=1)
assert not str(r[0]).endswith("gz")
def test_path_url():
class C(Component):
thepath = Path()
c = C()
# test relative
c.thepath = "file://foo.hdf5"
assert c.thepath == (pathlib.Path() / "foo.hdf5").absolute()
# test absolute
c.thepath = "file:///foo.hdf5"
assert c.thepath == pathlib.Path("/foo.hdf5")
# test http downloading
c.thepath = DEFAULT_URL + "optics.ecsv.txt"
assert c.thepath.name == "optics.ecsv.txt"
# test dataset://
c.thepath = "dataset://optics.ecsv.txt"
assert c.thepath == get_dataset_path("optics.ecsv.txt")
def test_optics_from_dump_instrument():
# test with file written by dump-instrument
svc_path_before = os.getenv("CTAPIPE_SVC_PATH")
cwd = os.getcwd()
with tempfile.TemporaryDirectory() as tmp_dir:
os.chdir(tmp_dir)
os.environ["CTAPIPE_SVC_PATH"] = tmp_dir
infile = get_dataset_path("gamma_test_large.simtel.gz")
run_tool(DumpInstrumentTool(), [f"--input={infile}", "--format=ecsv"])
lst = OpticsDescription.from_name("LST_LST_LSTCam",
"MonteCarloArray.optics")
assert lst.num_mirrors == 1
assert lst.equivalent_focal_length.to_value(u.m) == 28
assert lst.num_mirror_tiles == 198
os.chdir(cwd)
if svc_path_before is None:
del os.environ["CTAPIPE_SVC_PATH"]
else:
os.environ["CTAPIPE_SVC_PATH"] = svc_path_before
def find_nice_event():
filename = datasets.get_dataset_path("gamma_test_large.simtel.gz")
source = event_source(filename)
calib = CameraCalibrator()
for i, event in tqdm(enumerate(source)):
# from IPython import embed; embed()
# if i in [0, 1, 2, 3, 4, 39]: # skip ugly events
# print(i, event.mc.energy)
# continue
subarray = event.inst.subarray
calib(event)
for tel_id in event.dl0.tels_with_data:
# Camera Geometry required for hillas parametrization
camgeom = subarray.tel[tel_id].camera
# note the [0] is for channel 0 which is high-gain channel
image = event.dl1.tel[tel_id].image
# Cleaning of the image
cleaned_image = image.copy()
# create a clean mask of pixels above the threshold
cleanmask = tailcuts_clean(
camgeom, image, picture_thresh=10, boundary_thresh=5, min_number_picture_neighbors=3
)
# set all rejected pixels to zero
cleaned_image[~cleanmask] = 0
# Calculate hillas parameters
try:
d = hillas_parameters(camgeom, cleaned_image)
except HillasParameterizationError:
pass # skip failed parameterization (normally no signal)
# from IPython import embed; embed()
tel_name = event.inst.subarray.tel[tel_id].name
if tel_name == 'LST' and d.r < 1 * u.m and d.intensity > 400:
print(i, d.intensity, event.mc.energy)
return tel_id, d, event, cleanmask
import numpy as np
import sys
import matplotlib.pyplot as plt
from ctapipe.visualization import CameraDisplay
import datetime
# unoptimized cleaning levels, copied from
# https://github.com/tudo-astroparticlephysics/cta_preprocessing
cleaning_level = {
'LSTCam': (3.5, 7.5, 2), # ?? (3, 6) for Abelardo...
'FlashCam': (4, 8, 2), # there is some scaling missing?
'ASTRICam': (5, 7, 2),
}
input_url = get_dataset_path('gamma_test_large.simtel.gz')
event_source = event_source(input_url, max_events=2)
calibrator = CameraCalibrator()
for event in event_source:
calibrator(event)
for telescope_id, dl1 in event.dl1.tel.items():
image = dl1.image
peakpos = dl1.pulse_time
camera = event.inst.subarray.tels[telescope_id].camera
# display dl1 images
fig, axs = plt.subplots(1, 1, figsize=(6, 3))
from astropy import units as u
from ctapipe.calib import CameraCalibrator
from ctapipe.coordinates import TiltedGroundFrame, MissingFrameAttributeWarning
from ctapipe.image import hillas_parameters, tailcuts_clean, HillasParameterizationError
from ctapipe.image import timing_parameters
from ctapipe.io import EventSource
from ctapipe.utils import datasets
from ctapipe.visualization import ArrayDisplay
import warnings
if __name__ == "__main__":
warnings.filterwarnings("ignore", category=MissingFrameAttributeWarning)
# importing data from avaiable datasets in ctapipe
filename = datasets.get_dataset_path("gamma_test_large.simtel.gz")
if len(sys.argv) > 1:
filename = sys.argv[1]
# reading the Monte Carlo file for LST
source = EventSource(filename)
# pointing direction of the telescopes
point_azimuth = {}
point_altitude = {}
subarray = source.subarray
calib = CameraCalibrator(subarray=subarray)
off_angles = []
first_event = True
import matplotlib.pylab as plt
import numpy as np
from astropy import units as u
from ctapipe.io import event_source
from ctapipe.utils import datasets
from ctapipe.visualization import ArrayDisplay
if __name__ == '__main__':
plt.figure(figsize=(9.5, 8.5))
# load up a single event, so we can get the subarray info:
source = event_source(
datasets.get_dataset_path("gamma_test_large.simtel.gz"),
max_events=1,
)
event = next(iter(source))
# display the array
subarray = event.inst.subarray
ad = ArrayDisplay(subarray, tel_scale=3.0)
print("Now setting vectors")
plt.pause(1.0)
plt.tight_layout()
for phi in np.linspace(0, 360, 30) * u.deg:
r = np.cos(phi / 2)
from ctapipe.utils.datasets import get_dataset_path
import matplotlib.pyplot as plt
from ctapipe.visualization import ArrayDisplay
# unoptimized cleaning levels, copied from
# https://github.com/tudo-astroparticlephysics/cta_preprocessing
cleaning_level = {
'LSTCam': (3.5, 7.5, 2), # ?? (3, 6) for Abelardo...
'FlashCam': (4, 8, 2), # there is some scaling missing?
'ASTRICam': (5, 7, 2),
}
input_url = get_dataset_path('gamma_test_large.simtel.gz')
event_source = EventSourceFactory.produce(input_url=input_url)
calibrator = CameraCalibrator(
eventsource=event_source,
)
reco = HillasReconstructor()
for event in event_source:
print('Event', event.count)
calibrator.calibrate(event)
# mapping of telescope_id to parameters for stereo reconstruction
hillas_containers = {}
pointing_azimuth = {}
print("============================================")
print("n or [enter] - go to Next event")
print("d - Display the event")
print("p - Print all event data")
print("i - event Info")
print("s - save event image")
print("q - Quit")
return input("Choice: ")
if __name__ == '__main__':
if len(sys.argv) > 1:
filename = sys.argv.pop(1)
else:
filename = get_dataset_path("gamma_test_large.simtel.gz")
plt.style.use("ggplot")
plt.show(block=False)
# loop over events and display menu at each event:
source = event_source(filename)
for event in source:
print(
"EVENT_ID: ", event.r0.event_id, "TELS: ", event.r0.tels_with_data,
"MC Energy:", event.mc.energy
)
while True:
import matplotlib.pylab as plt
import numpy as np
from astropy import units as u
from ctapipe.io import event_source
from ctapipe.utils import datasets
from ctapipe.visualization import ArrayDisplay
if __name__ == "__main__":
plt.figure(figsize=(9.5, 8.5))
# load up a single event, so we can get the subarray info:
source = event_source(
datasets.get_dataset_path("gamma_test_large.simtel.gz"), max_events=1,
)
event = next(iter(source))
# display the array
subarray = source.subarray
ad = ArrayDisplay(subarray, tel_scale=3.0)
print("Now setting vectors")
plt.pause(1.0)
plt.tight_layout()
for phi in np.linspace(0, 360, 30) * u.deg:
r = 200 * np.cos(phi / 2) * u.m
ad.set_vector_rho_phi(r, phi)
#!/usr/bin/env python3
import matplotlib.pylab as plt
import numpy as np
from astropy import units as u
from ctapipe.io import event_source
from ctapipe.utils import datasets
from ctapipe.visualization import ArrayDisplay
if __name__ == '__main__':
plt.figure(figsize=(9.5, 8.5))
# load up a single event, so we can get the subarray info:
source = event_source(datasets.get_dataset_path("gamma_test.simtel.gz"),
max_events=1)
for event in source:
pass
# display the array
subarray = event.inst.subarray
ad = ArrayDisplay(subarray, tel_scale=3.0)
print("Now setting vectors")
plt.pause(1.0)
plt.tight_layout()
for phi in np.linspace(0, 360, 30) * u.deg:
r = np.cos(phi / 2)
ad.set_vector_rho_phi(r, phi)
config_prod3b_CTAS = resource_filename("protopipe", "scripts/tests/test_config_analysis_south.yaml")
config_AdaBoostRegressor = resource_filename("protopipe", "scripts/tests/test_AdaBoostRegressor.yaml")
config_RandomForestRegressor = resource_filename("protopipe", "scripts/tests/test_RandomForestRegressor.yaml")
config_RandomForestClassifier = resource_filename("protopipe", "scripts/tests/test_RandomForestClassifier.yaml")
config_DL3_ED_prod3b = resource_filename("protopipe", "scripts/tests/test_performance_ED_prod3b.yaml")
# TEST FILES
URL_TEST_DATA = "http://cccta-dataserver.in2p3.fr/data/protopipe/testData/"
URL_PROD3B_CTAN = f"{URL_TEST_DATA}/prod3_laPalma_baseline_Az180_Zd20"
URL_PROD3B_CTAS = f"{URL_TEST_DATA}/prod3_Paranal_baseline_Az180_Zd20"
input_data = {
"PROD3B_CTA_NORTH": {"config": config_prod3b_CTAN,
"gamma1": get_dataset_path("gamma1.simtel.gz",
url=f"{URL_PROD3B_CTAN}"),
"gamma2": get_dataset_path("gamma2.simtel.gz",
url=f"{URL_PROD3B_CTAN}"),
"gamma3": get_dataset_path("gamma3.simtel.gz",
url=f"{URL_PROD3B_CTAN}"),
"proton1": get_dataset_path("proton1.simtel.gz",
url=f"{URL_PROD3B_CTAN}"),
"proton2": get_dataset_path("proton2.simtel.gz",
url=f"{URL_PROD3B_CTAN}"),
"electron1": get_dataset_path("electron1.simtel.gz",
url=f"{URL_PROD3B_CTAN}")
},
"PROD3B_CTA_SOUTH": {"config": config_prod3b_CTAS,
"gamma1": get_dataset_path("gamma1.simtel.gz",
url=f"{URL_PROD3B_CTAS}"),