def process_mc(simtelfile, dl2_file, mc_type):
"""
Process the MC simulated and reconstructed to extract the relevant
parameters to compute the sensitivity
Paramenters
---------
simtel: simtelarray file
dl2_file: `pandas.DataFrame` dl2 parameters
mc_type: 'string' type of particle
Returns
---------
gammaness: `numpy.ndarray`
angdist2: `numpy.ndarray` angular distance squared
e_reco: `numpy.ndarray` reconstructed energies
n_reco: `int` number of reconstructed events
mc_par: `dict` with simulated parameters
"""
source = SimTelFile(simtelfile)
sim_par = read_sim_par(source)
events = pd.read_hdf(dl2_file)
e_reco = 10**events.mc_energy.to_numpy() * u.GeV
gammaness = events.gammaness
#Get source position in radians
focal_length = source.telescope_descriptions[1]['camera_settings']['focal_length'] * u.m
# If the particle is a gamma ray, it returns the squared angular distance
# from the reconstructed gamma-ray position and the simulated incoming position
if mc_type=='gamma':
events = events[events.mc_type==0]
alt2 = events.mc_alt
az2 = np.arctan(np.tan(events.mc_az))
# If the particle is not a gamma-ray (diffuse protons/electrons), it returns
# the squared angular distance of the reconstructed position w.r.t. the
# center of the camera
else:
events = events[events.mc_type!=0]
alt2 = events.mc_alt_tel
az2 = np.arctan(np.tan(events.mc_az_tel))
src_pos_reco = reco_source_position_sky(events.x.values * u.m,
events.y.values * u.m,
events.disp_dx_rec.values * u.m,
events.disp_dy_rec.values * u.m,
focal_length,
events.mc_alt_tel.values * u.rad,
events.mc_az_tel.values * u.rad)
alt1 = src_pos_reco.alt.rad
az1 = np.arctan(np.tan(src_pos_reco.az.rad))
angdist2 = (angular_separation(az1, alt1, az2, alt2).to_numpy() * u.rad)**2
return gammaness, angdist2.to(u.deg**2), e_reco, sim_par
def __init__(self, config=None, tool=None, **kwargs):
super().__init__(config=config, tool=tool, **kwargs)
self.metadata['is_simulation'] = True
self.file_ = SimTelFile(self.input_url)
self._subarray_info = self.prepare_subarray_info(
self.file_.telescope_descriptions, self.file_.header)
def process_mc(simtelfile, dl2_file):
"""
Process the MC simulated and reconstructed to extract the relevant
parameters to compute the sensitivity
Paramenters
---------
simtel: simtelarray file
dl2_file: `pandas.DataFrame` dl2 parameters
Returns
---------
gammaness: `numpy.ndarray`
theta2: `numpy.ndarray`
e_reco: `numpy.ndarray` reconstructed energies
n_reco: `int` number of reconstructed events
mc_par: `dict` with simulated parameters
"""
source = SimTelFile(simtelfile)
sim_par = read_sim_par(source)
events = pd.read_hdf(dl2_file)
e_reco = 10**events.mc_energy * u.GeV
# n_reco = e_reco.shape[0]
gammaness = events.gammaness
theta2 = (events.src_x - events.src_x_rec)**2 + \
(events.src_y - events.src_y_rec)**2 * u.deg**2
return gammaness, theta2, e_reco, sim_par
def get_spectral_w_pars(filename):
"""
Return parameters required to calculate spectral weighting of an event
Parameters
----------
filename: string, simtelarray file
Returns
-------
array of parameters
"""
particle = utils.guess_type(filename)
source = SimTelFile(filename)
emin, emax = source.mc_run_headers[0]['E_range'] * 1e3 #GeV
spectral_index = source.mc_run_headers[0]['spectral_index']
num_showers = source.mc_run_headers[0]['num_showers']
num_use = source.mc_run_headers[0]['num_use']
Simulated_Events = num_showers * num_use
Max_impact = source.mc_run_headers[0]['core_range'][1] * 1e2 #cm
Area_sim = np.pi * math.pow(Max_impact, 2)
cone = source.mc_run_headers[0]['viewcone'][1]
cone = cone * np.pi / 180
if (cone == 0):
Omega = 1
else:
Omega = 2 * np.pi * (1 - np.cos(cone))
if particle == 'proton':
K_w = 9.6e-11 # GeV^-1 cm^-2 s^-1
index_w = -2.7
E0 = 1000. # GeV
if particle == 'gamma':
K_w = 2.83e-11 # GeV^-1 cm^-2 s^-1
index_w = -2.62
E0 = 1000. # GeV
K = Simulated_Events * (1 + spectral_index) / (emax**(1 + spectral_index) -
emin**(1 + spectral_index))
Int_e1_e2 = K * E0**spectral_index
N_ = Int_e1_e2 * (emax**(index_w + 1) -
emin**(index_w + 1)) / (E0**index_w) / (index_w + 1)
R = K_w * Area_sim * Omega * (emax**(index_w + 1) - emin**(index_w + 1)
) / (E0**index_w) / (index_w + 1)
return E0, spectral_index, index_w, R, N_
def __init__(self,
input_url,
config=None,
parent=None,
gain_selector=None,
**kwargs):
"""
EventSource for simtelarray files using the pyeventio library.
Parameters
----------
config : traitlets.loader.Config
Configuration specified by config file or cmdline arguments.
Used to set traitlet values.
Set to None if no configuration to pass.
tool : ctapipe.core.Tool
Tool executable that is calling this component.
Passes the correct logger to the component.
Set to None if no Tool to pass.
gain_selector : ctapipe.calib.camera.gainselection.GainSelector
The GainSelector to use. If None, then ThresholdGainSelector will be used.
kwargs
"""
super().__init__(input_url=input_url,
config=config,
parent=parent,
**kwargs)
self._camera_cache = {}
self.file_ = SimTelFile(
self.input_url.expanduser(),
allowed_telescopes=self.allowed_tels,
skip_calibration=self.skip_calibration_events,
zcat=not self.back_seekable,
)
if self.back_seekable and self.is_stream:
raise IOError(
"back seekable was required but not possible for inputfile")
self._subarray_info = self.prepare_subarray_info(
self.file_.telescope_descriptions, self.file_.header)
self._mc_header = self._parse_mc_header()
self.start_pos = self.file_.tell()
# Waveforms from simtelarray have both gain channels
# Gain selection is performed by this EventSource to produce R1 waveforms
if gain_selector is None:
gain_selector = ThresholdGainSelector(parent=self)
self.gain_selector = gain_selector
def __init__(self, config=None, parent=None, **kwargs):
super().__init__(config=config, parent=parent, **kwargs)
self.metadata['is_simulation'] = True
# traitlets creates an empty set as default,
# which ctapipe treats as no restriction on the telescopes
# but eventio treats an emty set as "no telescopes allowed"
# so we explicitly pass None in that case
self.file_ = SimTelFile(self.input_url,
allowed_telescopes=self.allowed_tels
if self.allowed_tels else None,
skip_calibration=self.skip_calibration_events)
self._subarray_info = self.prepare_subarray_info(
self.file_.telescope_descriptions, self.file_.header)
self.start_pos = self.file_.tell()
def __init__(self, config=None, parent=None, gain_selector=None, **kwargs):
"""
EventSource for simtelarray files using the pyeventio library.
Parameters
----------
config : traitlets.loader.Config
Configuration specified by config file or cmdline arguments.
Used to set traitlet values.
Set to None if no configuration to pass.
tool : ctapipe.core.Tool
Tool executable that is calling this component.
Passes the correct logger to the component.
Set to None if no Tool to pass.
gain_selector : ctapipe.calib.camera.gainselection.GainSelector
The GainSelector to use. If None, then ThresholdGainSelector will be used.
kwargs
"""
super().__init__(config=config, parent=parent, **kwargs)
self.metadata['is_simulation'] = True
self._camera_cache = {}
# traitlets creates an empty set as default,
# which ctapipe treats as no restriction on the telescopes
# but eventio treats an emty set as "no telescopes allowed"
# so we explicitly pass None in that case
self.file_ = SimTelFile(
Path(self.input_url).expanduser(),
allowed_telescopes=set(self.allowed_tels)
if self.allowed_tels else None,
skip_calibration=self.skip_calibration_events,
zcat=not self.back_seekable,
)
if self.back_seekable and self.is_stream:
raise IOError(
'back seekable was required but not possible for inputfile')
self._subarray_info = self.prepare_subarray_info(
self.file_.telescope_descriptions, self.file_.header)
self.start_pos = self.file_.tell()
# Waveforms from simtelarray have both gain channels
# Gain selection is performed by this EventSource to produce R1 waveforms
if gain_selector is None:
gain_selector = ThresholdGainSelector(parent=self)
self.gain_selector = gain_selector
def __init__(self, config=None, parent=None, **kwargs):
super().__init__(config=config, parent=parent, **kwargs)
self.metadata['is_simulation'] = True
self._camera_cache = {}
# traitlets creates an empty set as default,
# which ctapipe treats as no restriction on the telescopes
# but eventio treats an emty set as "no telescopes allowed"
# so we explicitly pass None in that case
self.file_ = SimTelFile(
self.input_url,
allowed_telescopes=set(self.allowed_tels) if self.allowed_tels else None,
skip_calibration=self.skip_calibration_events,
zcat=not self.back_seekable,
)
if self.back_seekable and self.is_stream:
raise IOError('back seekable was required but not possible for inputfile')
self._subarray_info = self.prepare_subarray_info(
self.file_.telescope_descriptions,
self.file_.header
)
self.start_pos = self.file_.tell()
import time
from pprint import pprint
from eventio.simtel.simtelfile import SimTelFile
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('inputfile')
parser.add_argument('-p', '--print', action='store_true')
args = parser.parse_args()
start_time = time.time()
with SimTelFile(args.inputfile) as f:
for i, event in enumerate(f):
print('Event count: {: 04d}, E = {:8.3f} Tev, #Telescopes={: 3d}'.format(
i, event['mc_shower']['energy'], len(event['telescope_events'])
))
if args.print:
pprint(event)
print(' Duration:', time.time() - start_time)
def simtel_event_source(url,
camera=None,
max_events=None,
allowed_tels=None,
requested_event=None,
use_event_id=False,
event_id=None,
disable_bar=False):
"""A generator that streams data from an EventIO/HESSIO MC data file
(e.g. a standard CTA data file.)
Parameters
----------
url : str
path to file to open
max_events : int, optional
maximum number of events to read
allowed_tels : list[int]
select only a subset of telescope, if None, all are read. This can
be used for example emulate the final CTA data format, where there
would be 1 telescope per file (whereas in current monte-carlo,
they are all interleaved into one file)
requested_event : int
Seek to a paricular event index
use_event_id : bool
If True ,'requested_event' now seeks for a particular event id instead
of index
disable_bar : Unused, for compatibility with other readers
"""
if event_id is not None:
raise ValueError('Event id feature not implemented yet! \n'
'Use event_id=None')
if not SimTelEventSource.is_compatible(url):
raise ValueError(url, 'is not a valid simtel file')
data = DataContainer()
data.meta['origin'] = "hessio"
# some hessio_event_source specific parameters
data.meta['input'] = url
# data.meta['max_events'] = max_events
with SimTelFile(url) as file:
telescope_descriptions = file.telescope_descriptions
subarray_info = SimTelEventSource.prepare_subarray_info(
telescope_descriptions, file.header)
counter = 0
for array_event in tqdm(file, disable=disable_bar):
# Seek to requested event
if requested_event is not None:
current = counter
if use_event_id:
current = event_id
if not current == requested_event:
counter += 1
continue
run_id = file.header['run']
event_id = array_event['event_id']
tels_with_data = set(array_event['telescope_events'].keys())
data.inst.subarray = subarray_info
data.r0.run_id = run_id
data.r0.event_id = event_id
data.r0.tels_with_data = tels_with_data
data.r1.run_id = run_id
data.r1.event_id = event_id
data.r1.tels_with_data = tels_with_data
data.dl0.run_id = run_id
data.dl0.event_id = event_id
data.dl0.tels_with_data = tels_with_data
# handle telescope filtering by taking the intersection of
# tels_with_data and allowed_tels
if allowed_tels is not None:
selected = data.r0.tels_with_data & allowed_tels
if len(selected) == 0:
continue # skip event
data.r0.tels_with_data = selected
data.r1.tels_with_data = selected
data.dl0.tels_with_data = selected
trigger_information = array_event['trigger_information']
mc_event = array_event['mc_event']
mc_shower = array_event['mc_shower']
data.trig.tels_with_trigger = \
trigger_information['triggered_telescopes']
time_s, time_ns = trigger_information['gps_time']
data.trig.gps_time = Time(time_s * u.s,
time_ns * u.ns,
format='unix',
scale='utc')
data.mc.energy = mc_shower['energy'] * u.TeV
data.mc.alt = Angle(mc_shower['altitude'], u.rad)
data.mc.az = Angle(mc_shower['azimuth'], u.rad)
data.mc.core_x = mc_event['xcore'] * u.m
data.mc.core_y = mc_event['ycore'] * u.m
first_int = mc_shower['h_first_int'] * u.m
data.mc.h_first_int = first_int
data.mc.x_max = mc_shower['xmax'] * u.g / (u.cm**2)
data.mc.shower_primary_id = mc_shower['primary_id']
# mc run header data
data.mcheader.run_array_direction = Angle(
file.header['direction'] * u.rad)
mc_run_head = file.mc_run_headers[-1]
data.mcheader.corsika_version = mc_run_head['shower_prog_vers']
data.mcheader.simtel_version = mc_run_head['detector_prog_vers']
data.mcheader.energy_range_min = mc_run_head['E_range'][0] * u.TeV
data.mcheader.energy_range_max = mc_run_head['E_range'][1] * u.TeV
data.mcheader.prod_site_B_total = mc_run_head['B_total'] * u.uT
data.mcheader.prod_site_B_declination = Angle(
mc_run_head['B_declination'] * u.rad)
data.mcheader.prod_site_B_inclination = Angle(
mc_run_head['B_inclination'] * u.rad)
data.mcheader.prod_site_alt = mc_run_head['obsheight'] * u.m
data.mcheader.spectral_index = mc_run_head['spectral_index']
# this should be done in a nicer way to not re-allocate the
# data each time (right now it's just deleted and garbage
# collected)
data.r0.tel.clear()
data.r1.tel.clear()
data.dl0.tel.clear()
data.dl1.tel.clear()
data.mc.tel.clear() # clear the previous telescopes
telescope_events = array_event['telescope_events']
tracking_positions = array_event['tracking_positions']
for tel_id, telescope_event in telescope_events.items():
telescope_description = telescope_descriptions[tel_id]
camera_monitorings = array_event['camera_monitorings'][tel_id]
pedestal = camera_monitorings['pedestal']
laser_calib = array_event['laser_calibrations'][tel_id]
data.mc.tel[tel_id].dc_to_pe = laser_calib['calib']
data.mc.tel[tel_id].pedestal = pedestal
adc_samples = telescope_event.get('adc_samples')
n_pixel = adc_samples.shape[-2]
if adc_samples is None:
adc_samples = telescope_event['adc_sums'][:, :, np.newaxis]
data.r0.tel[tel_id].adc_samples = adc_samples
data.r0.tel[tel_id].num_samples = adc_samples.shape[-1]
# We should not calculate stuff in an event source
# if this is not needed, we calculate it for nothing
data.r0.tel[tel_id].adc_sums = adc_samples.sum(axis=-1)
baseline = pedestal / adc_samples.shape[1]
data.r0.tel[tel_id].digicam_baseline = np.squeeze(baseline)
data.r0.tel[tel_id].camera_event_number = event_id
pixel_settings = telescope_description['pixel_settings']
data.mc.tel[tel_id].reference_pulse_shape = pixel_settings[
'refshape'].astype('float64')
data.mc.tel[tel_id].meta['refstep'] = float(
pixel_settings['ref_step'])
data.mc.tel[tel_id].time_slice = float(
pixel_settings['time_slice'])
data.mc.tel[tel_id].photo_electron_image = array_event.get(
'photoelectrons', {}).get(tel_id)
if data.mc.tel[tel_id].photo_electron_image is None:
data.mc.tel[tel_id].photo_electron_image = np.zeros(
(n_pixel, ), dtype='i2')
tracking_position = tracking_positions[tel_id]
data.mc.tel[tel_id].azimuth_raw = tracking_position[
'azimuth_raw']
data.mc.tel[tel_id].altitude_raw = tracking_position[
'altitude_raw']
data.mc.tel[tel_id].azimuth_cor = tracking_position.get(
'azimuth_cor', 0)
data.mc.tel[tel_id].altitude_cor = tracking_position.get(
'altitude_cor', 0)
yield data
counter += 1
if max_events and counter >= max_events:
return