def _info_resources():
""" display all known resources """
print('\n*** ctapipe resources ***\n')
print("ctapipe_resources version: {}".format(
ctapipe_resources.__version__))
print("CTAPIPE_SVC_PATH: (directories where resources are searched)")
if os.getenv('CTAPIPE_SVC_PATH') is not None:
for dir in datasets.get_searchpath_dirs():
print("\t * {}".format(dir))
else:
print("\t no path is set")
print("")
all_resources = sorted(datasets.find_all_matching_datasets("\w.*"))
locations = [
os.path.dirname(datasets.get_dataset(name)) for name in all_resources
]
home = os.path.expanduser("~")
resource_dir = os.path.dirname(
datasets.get_dataset("HESS-I.camgeom.fits.gz"))
fmt = "{name:<30.30s} : {loc:<30.30s}"
print(fmt.format(name="RESOURCE NAME", loc="LOCATION"))
print("-" * 70)
for name, loc in zip(all_resources, locations):
if name.endswith(".py") or name.startswith("_"):
continue
loc = loc.replace(resource_dir, "[ctapipe_resources]")
loc = loc.replace(home, "~")
print(fmt.format(name=name, loc=loc))
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(dataset_name)
assert path == dataset_path
with pytest.raises(FileNotFoundError):
badpath = datasets.get_dataset("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 _info_resources():
""" display all known resources """
print('\n*** ctapipe resources ***\n')
print("ctapipe_resources version: {}".format(ctapipe_resources.__version__))
print("CTAPIPE_SVC_PATH: (directories where resources are searched)")
if os.getenv('CTAPIPE_SVC_PATH') is not None:
for dir in datasets.get_searchpath_dirs():
print("\t * {}".format(dir))
else:
print("\t no path is set")
print("")
all_resources = sorted(datasets.find_all_matching_datasets("\w.*"))
locations = [os.path.dirname(datasets.get_dataset(name))
for name in all_resources]
home = os.path.expanduser("~")
resource_dir = os.path.dirname(datasets.get_dataset(
"HESS-I.camgeom.fits.gz"))
fmt = "{name:<30.30s} : {loc:<30.30s}"
print(fmt.format(name="RESOURCE NAME", loc="LOCATION"))
print("-"*70)
for name, loc in zip(all_resources, locations):
if name.endswith(".py") or name.startswith("_"):
continue
loc = loc.replace(resource_dir, "[ctapipe_resources]")
loc = loc.replace(home, "~")
print(fmt.format(name=name, loc=loc))
def test_find_datasets():
# find all datasets matching pattern
r = datasets.find_all_matching_datasets("(.*)\.camgeom\.fits\.gz")
assert len(r) > 3
# get the full filename for a resrouces
assert os.path.exists(datasets.get_dataset(r[0]))
# try using a pattern
r = datasets.find_all_matching_datasets("(.*)\.camgeom\.fits\.gz",
regexp_group=1)
assert not r[0].endswith("gz")
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("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:
response = get_input()
if response.startswith("d"):
from pywicta.denoising import inverse_transform_sampling
from pywicta.denoising.inverse_transform_sampling import EmpiricalDistribution
import ctapipe
from ctapipe.utils.datasets import get_dataset
###############################################################################
# Ignore warnings.
import warnings
warnings.filterwarnings('ignore')
###############################################################################
# Get images from ctapipe embedded datasets.
SIMTEL_FILE = get_dataset('gamma_test_large.simtel.gz')
###############################################################################
# Choose the instrument to use.
#cam_id = None
#cam_id = "ASTRICam"
#cam_id = "CHEC"
#cam_id = "DigiCam"
#cam_id = "FlashCam"
#cam_id = "NectarCam"
cam_id = "LSTCam"
###############################################################################
# Configure the trace integration as in the CTA Mars analysis.
#!/usr/bin/env python3
import numpy as np
from astropy.table import Table
from matplotlib import pyplot as plt
from ctapipe.utils import datasets
from ctapipe.visualization import ArrayDisplay
if __name__ == '__main__':
plt.style.use("ggplot")
plt.figure(figsize=(10, 8))
arrayfile = datasets.get_dataset("PROD2_telconfig.fits.gz")
tels = Table.read(arrayfile, hdu="TELESCOPE_LEVEL0")
adisp = ArrayDisplay(tels['TelX'],
tels['TelY'],
tels['MirrorArea'] * 2,
title='PROD2 telescopes',
autoupdate=True)
plt.tight_layout()
values = np.zeros(len(tels))
# do a small animation to show various trigger patterns:
for ii in range(20):
# generate a random trigger pattern and integrated intensity:
#!/usr/bin/env python3
import numpy as np
from astropy.table import Table
from ctapipe.utils import datasets
from ctapipe.visualization import ArrayDisplay
from matplotlib import pyplot as plt
if __name__ == '__main__':
plt.style.use("ggplot")
plt.figure(figsize=(10, 8))
arrayfile = datasets.get_dataset("PROD2_telconfig.fits.gz")
tels = Table.read(arrayfile, hdu="TELESCOPE_LEVEL0")
adisp = ArrayDisplay(tels['TelX'], tels['TelY'], tels['MirrorArea'] * 2,
title='PROD2 telescopes', autoupdate=True)
plt.tight_layout()
values = np.zeros(len(tels))
# do a small animation to show various trigger patterns:
for ii in range(20):
# generate a random trigger pattern and integrated intensity:
ntrig = np.random.poisson(10)
trigmask = np.random.random_integers(len(tels) - 1, size=ntrig)
values[:] = 0