__init__.py 75.5 KB
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"""
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# Event source for MAGIC calibrated data files.
# Requires uproot package (https://github.com/scikit-hep/uproot).
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"""

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import logging
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import glob
import re
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import os.path
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from pathlib import Path
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import numpy as np
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import scipy
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import scipy.interpolate

from astropy import units as u
from astropy.time import Time
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from ctapipe.io.eventsource import EventSource
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from ctapipe.io.datalevels import DataLevel

from ctapipe.core import Container
from ctapipe.core import Field

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from ctapipe.containers import ArrayEventContainer
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from ctapipe.containers import PointingContainer, TelescopePointingContainer
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from ctapipe.containers import MonitoringCameraContainer
from ctapipe.containers import PedestalContainer

from ctapipe.instrument import TelescopeDescription
from ctapipe.instrument import SubarrayDescription
from ctapipe.instrument import OpticsDescription
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from ctapipe.instrument import CameraDescription
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__all__ = ['MAGICEventSource']

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LOGGER = logging.getLogger(__name__)
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# MAGIC telescope positions in m wrt. to the center of CTA simulations
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#MAGIC_TEL_POSITIONS = {
#    1: [-27.24, -146.66, 50.00] * u.m,
#    2: [-96.44, -96.77, 51.00] * u.m
#}

# MAGIC telescope positions in m wrt. to the center of MAGIC simulations, from reflector input card
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MAGIC_TEL_POSITIONS = {
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    1: [31.80, -28.10, 0.00] * u.m,
    2: [-31.80, 28.10, 0.00] * u.m
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}
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# MAGIC telescope description
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OPTICS = OpticsDescription.from_name('MAGIC')
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MAGICCAM = CameraDescription.from_name("MAGICCam")
GEOM = MAGICCAM.geometry
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MAGIC_TEL_DESCRIPTION = TelescopeDescription(
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    name='MAGIC', tel_type='MAGIC', optics=OPTICS, camera=MAGICCAM)
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MAGIC_TEL_DESCRIPTIONS = {1: MAGIC_TEL_DESCRIPTION, 2: MAGIC_TEL_DESCRIPTION}
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# trigger patterns:
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MC_TRIGGER_PATTERN = 1
PEDESTAL_TRIGGER_PATTERN = 8
DATA_TRIGGER_PATTERN = 128

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class L3JumpError(Exception):
    """
    Exception raised when L3 trigger number jumps backward.
    """

    def __init__(self, message):
        self.message = message
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class MissingDriveReportError(Exception):
    """
    Exception raised when a subrun does not have drive reports.
    """

    def __init__(self, message):
        self.message = message

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class MAGICEventSource(EventSource):
    """
    EventSource for MAGIC calibrated data.

    This class operates with the MAGIC data run-wise. This means that the files
    corresponding to the same data run are loaded and processed together.
    """
    _count = 0

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    def __init__(self, input_url=None, config=None, parent=None, **kwargs):
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        """
        Constructor

        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.
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        parent : ctapipe.core.Tool
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            Tool executable that is calling this component.
            Passes the correct logger to the component.
            Set to None if no Tool to pass.
        kwargs: dict
            Additional parameters to be passed.
            NOTE: The file mask of the data to read can be passed with
            the 'input_url' parameter.
        """

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        super().__init__(input_url=input_url, config=config, parent=parent, **kwargs)
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        # Retrieving the list of run numbers corresponding to the data files
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        run_info = self.get_run_info_from_name(str(input_url))
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        run_numbers = run_info[0]
        is_mc_runs = run_info[1]
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        self.run_numbers = run_numbers
        self.is_mc = is_mc_runs
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        # Retrieving the data level (so far HARDCODED Sorcerer)
        self.datalevel = DataLevel.DL1_IMAGES

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        # # Setting up the current run with the first run present in the data
        # self.current_run = self._set_active_run(run_number=0)
        self.current_run = None

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        self._subarray_info = SubarrayDescription(
            'MAGIC', MAGIC_TEL_POSITIONS, MAGIC_TEL_DESCRIPTIONS)
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    @staticmethod
    def is_compatible(file_mask):
        """
        This method checks if the specified file mask corresponds
        to MAGIC data files. The result will be True only if all
        the files are of ROOT format and contain an 'Events' tree.

        Parameters
        ----------
        file_mask: str
            A file mask to check

        Returns
        -------
        bool:
            True if the masked files are MAGIC data runs, False otherwise.

        """

        is_magic_root_file = True

        file_list = glob.glob(file_mask)

        for file_path in file_list:
            try:
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                import uproot
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                try:
                    with uproot.open(file_path) as input_data:
                        if 'Events' not in input_data:
                            is_magic_root_file = False
                except ValueError:
                    # uproot raises ValueError if the file is not a ROOT file
                    is_magic_root_file = False

            except ImportError:
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                if re.match(r'.+_m\d_.+root', file_path.lower()) is None:
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                    is_magic_root_file = False

        return is_magic_root_file

    @staticmethod
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    def get_run_info_from_name(file_name):
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        """
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        This internal method extracts the run number and
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        type (data/MC) from the specified file name.
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        Parameters
        ----------
        file_name: str
            A file name to process.

        Returns
        -------
        int:
            A run number of the file.
        """

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        mask_data = r".*\d+_M\d+_(\d+)\.\d+_Y_.*"
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        mask_mc = r".*_M\d_za\d+to\d+_\d_(\d+)_Y_.*"
        mask_mc_alt = r".*_M\d_\d_(\d+)_.*"
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        if re.findall(mask_data, file_name):
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            parsed_info = re.findall(mask_data, file_name)
            is_mc = False
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        elif re.findall(mask_mc, file_name):
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            parsed_info = re.findall(mask_mc, file_name)
            is_mc = True
        else:
            parsed_info = re.findall(mask_mc_alt, file_name)
            is_mc = True
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        try:
            run_number = int(parsed_info[0])
        except IndexError:
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            raise IndexError(
                'Can not identify the run number and type (data/MC) of the file'
                '{:s}'.format(file_name))
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        return run_number, is_mc
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    def _set_active_run(self, run_number):
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        """
        This internal method sets the run that will be used for data loading.

        Parameters
        ----------
        run_number: int
            The run number to use.

        Returns
        -------
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        MarsRun:
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            The run to use
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        """
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        run = dict()
        run['number'] = run_number
        run['read_events'] = 0
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        run['data'] = MarsRun(run_file_mask=str(self.input_url))
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        return run

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    @property
    def subarray(self):
        return self._subarray_info

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    @property
    def is_simulation(self):
        return self.is_mc

    @property
    def datalevels(self):
        return (self.datalevel, )

    @property
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    def obs_ids(self):
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        return self.run_numbers

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    def _generator(self):
        """
        The default event generator. Return the stereo event
        generator instance.

        Returns
        -------

        """

        return self._stereo_event_generator()

    def _stereo_event_generator(self):
        """
        Stereo event generator. Yields DataContainer instances, filled
        with the read event data.

        Returns
        -------

        """

        counter = 0

        # Data container - is initialized once, and data is replaced within it after each yield
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        data = ArrayEventContainer()
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        # Telescopes with data:
        tels_in_file = ["m1", "m2"]
        tels_with_data = {1, 2}

        # Loop over the available data runs
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        for run_number in self.run_numbers:
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            # Removing the previously read data run from memory
            if self.current_run is not None:
                if 'data' in self.current_run:
                    del self.current_run['data']

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            # Setting the new active run (class MarsRun object)
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            self.current_run = self._set_active_run(run_number)
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            # Set monitoring data:
            if not self.is_mc:
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                monitoring_data = self.current_run['data'].monitoring_data
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                for tel_i, tel_id in enumerate(tels_in_file):
                    monitoring_camera = MonitoringCameraContainer()
                    pedestal_info = PedestalContainer()
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                    badpixel_info = PixelStatusContainer()
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                    time_tmp = Time(monitoring_data['M{:d}'.format(
                        tel_i + 1)]['PedestalMJD'], scale='utc', format='mjd')
                    pedestal_info.sample_time = Time(
                        time_tmp, format='unix', scale='utc', precision=9)
                    # hardcoded number of pedestal events averaged over:
                    pedestal_info.n_events = 500
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                    pedestal_info.charge_mean = []
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                    pedestal_info.charge_mean.append(
                        monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFundamental']['Mean'])
                    pedestal_info.charge_mean.append(
                        monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractor']['Mean'])
                    pedestal_info.charge_mean.append(monitoring_data['M{:d}'.format(
                        tel_i + 1)]['PedestalFromExtractorRndm']['Mean'])
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                    pedestal_info.charge_std = []
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                    pedestal_info.charge_std.append(
                        monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFundamental']['Rms'])
                    pedestal_info.charge_std.append(
                        monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractor']['Rms'])
                    pedestal_info.charge_std.append(
                        monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractorRndm']['Rms'])

                    t_range = Time(monitoring_data['M{:d}'.format(
                        tel_i + 1)]['badpixelinfoMJDrange'], scale='utc', format='mjd')

                    badpixel_info.hardware_failing_pixels = monitoring_data['M{:d}'.format(
                        tel_i + 1)]['badpixelinfo']
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                    badpixel_info.sample_time_range = t_range
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                    monitoring_camera.pedestal = pedestal_info
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                    monitoring_camera.pixel_status = badpixel_info
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                    data.mon.tels_with_data = {1, 2}
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                    data.mon.tel[tel_i + 1] = monitoring_camera
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            else:
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                assert self.current_run['data'].mcheader_data['M1'] == self.current_run['data'].mcheader_data['M2'], "Simulation configurations are different for M1 and M2 !!!"
                data.mcheader.num_showers = self.current_run['data'].mcheader_data['M1']['sim_nevents']
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                data.mcheader.shower_reuse = self.current_run['data'].mcheader_data['M1']['sim_reuse']
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                data.mcheader.energy_range_min = (self.current_run['data'].mcheader_data['M1']['sim_emin']).to(u.TeV) # GeV->TeV
                data.mcheader.energy_range_max = (self.current_run['data'].mcheader_data['M1']['sim_emax']).to(u.TeV) # GeV->TeV
                data.mcheader.spectral_index = self.current_run['data'].mcheader_data['M1']['sim_eslope']
                data.mcheader.max_scatter_range = (self.current_run['data'].mcheader_data['M1']['sim_max_impact']).to(u.m) # cm->m
                data.mcheader.max_viewcone_radius = (self.current_run['data'].mcheader_data['M1']['sim_conesemiangle']).to(u.deg)# deg->deg
                if data.mcheader.max_viewcone_radius != 0.:
                    data.mcheader.diffuse = True
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                else:
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                    data.mcheader.diffuse = False
                    
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            # Loop over the events
            for event_i in range(self.current_run['data'].n_stereo_events):
                # Event and run ids
                event_order_number = self.current_run['data'].stereo_ids[event_i][0]
                event_id = self.current_run['data'].event_data['M1']['stereo_event_number'][event_order_number]
                obs_id = self.current_run['number']

                # Reading event data
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                event_data = self.current_run['data'].get_stereo_event_data(
                    event_i)

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                data.meta = event_data['mars_meta']
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                # Event counter
                data.count = counter
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                data.index.obs_id = obs_id
                data.index.event_id = event_id
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                # Setting up the R0 container
                data.r0.tel.clear()

                # Setting up the R1 container
                data.r1.tel.clear()

                # Setting up the DL0 container
                data.dl0.tel.clear()

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                # Setting up the DL1 container
                data.dl1.tel.clear()

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                pointing = PointingContainer()
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                # Filling the DL1 container with the event data
                for tel_i, tel_id in enumerate(tels_in_file):
                    # Creating the telescope pointing container
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                    pointing_tel = TelescopePointingContainer()
                    
                    pointing_tel.azimuth = np.deg2rad(
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                        event_data['{:s}_pointing_az'.format(tel_id)]) * u.rad
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                    pointing_tel.altitude = np.deg2rad(
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                        90 - event_data['{:s}_pointing_zd'.format(tel_id)]) * u.rad
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                    # pointing.ra = np.deg2rad(
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                    #    event_data['{:s}_pointing_ra'.format(tel_id)]) * u.rad
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                    # pointing.dec = np.deg2rad(
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                    #    event_data['{:s}_pointing_dec'.format(tel_id)]) * u.rad
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                    pointing.tel[tel_i + 1] = pointing_tel
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                    # Adding trigger id (MAGIC nomenclature)
                    data.r0.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data['M1']['trigger_pattern'][event_order_number]
                    data.r1.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data['M1']['trigger_pattern'][event_order_number]
                    data.dl0.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data['M1']['trigger_pattern'][event_order_number]

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                    # Adding event charge and peak positions per pixel
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                    data.dl1.tel[tel_i +
                                 1].image = event_data['{:s}_image'.format(tel_id)]
                    data.dl1.tel[tel_i +
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                                 1].peak_time = event_data['{:s}_pulse_time'.format(tel_id)]
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                pointing.array_azimuth = np.deg2rad(event_data['m1_pointing_az']) * u.rad
                pointing.array_altitude = np.deg2rad(90 - event_data['m1_pointing_zd']) * u.rad
                pointing.array_ra = np.deg2rad(event_data['m1_pointing_ra']) * u.rad
                pointing.array_dec = np.deg2rad(90 - event_data['m1_pointing_dec']) * u.rad
                data.pointing = pointing
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                if not self.is_mc:
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                    # Adding the event arrival time
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                    time_tmp = Time(
                        event_data['mjd'], scale='utc', format='mjd')
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                    data.trigger.time = Time(
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                        time_tmp, format='unix', scale='utc', precision=9)
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                else:
                    data.mc.energy = event_data['true_energy'] * u.GeV
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                    data.mc.alt = (np.pi/2 - event_data['true_zd']) * u.rad
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                    # check meaning of 7deg transformation (I.Vovk)
                    data.mc.az = -1 * \
                        (event_data['true_az'] - np.deg2rad(180 - 7)) * u.rad
                    data.mc.shower_primary_id = 1 - \
                        event_data['true_shower_primary_id']
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                    data.mc.h_first_int = event_data['true_h_first_int'] * u.cm
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                    # adding a 7deg rotation between the orientation of corsika (x axis = magnetic north) and MARS (x axis = geographical north) frames
                    # magnetic north is 7 deg westward w.r.t. geographical north
                    rot_corsika = 7 *u.deg
                    data.mc.core_x = (event_data['true_core_x']*np.cos(rot_corsika) - event_data['true_core_y']*np.sin(rot_corsika))* u.cm
                    data.mc.core_y = (event_data['true_core_x']*np.sin(rot_corsika) + event_data['true_core_y']*np.cos(rot_corsika))* u.cm
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                # Setting the telescopes with data
                data.r0.tels_with_data = tels_with_data
                data.r1.tels_with_data = tels_with_data
                data.dl0.tels_with_data = tels_with_data
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                data.trigger.tels_with_trigger = tels_with_data
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                yield data
                counter += 1

        return

    def _mono_event_generator(self, telescope):
        """
        Mono event generator. Yields DataContainer instances, filled
        with the read event data.

        Parameters
        ----------
        telescope: str
            The telescope for which to return events. Can be either "M1" or "M2".

        Returns
        -------

        """

        counter = 0
        telescope = telescope.upper()

        # Data container - is initialized once, and data is replaced within it after each yield
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        data = ArrayEventContainer()
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        # Telescopes with data:
        tels_in_file = ["M1", "M2"]

        if telescope not in tels_in_file:
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            raise ValueError("Specified telescope {:s} is not in the allowed list {}".format(
                telescope, tels_in_file))
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        tel_i = tels_in_file.index(telescope)
        tels_with_data = {tel_i + 1, }

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        # Removing the previously read data run from memory
        if self.current_run is not None:
            if 'data' in self.current_run:
                del self.current_run['data']

        # Setting the new active run
        self.current_run = self._set_active_run(self.run_numbers)

        # Set monitoring data:
        if not self.is_mc:

            monitoring_data = self.current_run['data'].monitoring_data

            monitoring_camera = MonitoringCameraContainer()
            pedestal_info = PedestalContainer()
            badpixel_info = PixelStatusContainer()

            time_tmp = Time(monitoring_data['M{:d}'.format(
                tel_i + 1)]['PedestalMJD'], scale='utc', format='mjd')
            pedestal_info.sample_time = Time(
                time_tmp, format='unix', scale='utc', precision=9)
            pedestal_info.n_events = 500 # hardcoded number of pedestal events averaged over
            pedestal_info.charge_mean = []
            pedestal_info.charge_mean.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFundamental']['Mean'])
            pedestal_info.charge_mean.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractor']['Mean'])
            pedestal_info.charge_mean.append(monitoring_data['M{:d}'.format(
                tel_i + 1)]['PedestalFromExtractorRndm']['Mean'])
            pedestal_info.charge_std = []
            pedestal_info.charge_std.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFundamental']['Rms'])
            pedestal_info.charge_std.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractor']['Rms'])
            pedestal_info.charge_std.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractorRndm']['Rms'])

            t_range = Time(monitoring_data['M{:d}'.format(
                tel_i + 1)]['badpixelinfoMJDrange'], scale='utc', format='mjd')

            badpixel_info.hardware_failing_pixels = monitoring_data['M{:d}'.format(
                tel_i + 1)]['badpixelinfo']
            badpixel_info.sample_time_range = t_range

            monitoring_camera.pedestal = pedestal_info
            monitoring_camera.pixel_status = badpixel_info

            data.mon.tels_with_data = tels_with_data
            data.mon.tel[tel_i + 1] = monitoring_camera

         #fdp (mono version not fully tested)
        else:
            data.mcheader.num_showers = self.current_run['data'].mcheader_data[telescope]['sim_nevents'] # total, including reuse
            data.mcheader.shower_reuse = self.current_run['data'].mcheader_data[telescope]['sim_reuse']
            data.mcheader.energy_range_min = (self.current_run['data'].mcheader_data[telescope]['sim_emin']).to(u.TeV) # GeV->TeV
            data.mcheader.energy_range_max = (self.current_run['data'].mcheader_data[telescope]['sim_emax']).to(u.TeV) # GeV->TeV
            data.mcheader.spectral_index = self.current_run['data'].mcheader_data[telescope]['sim_eslope'] 
            data.mcheader.max_scatter_range = (self.current_run['data'].mcheader_data[telescope]['sim_max_impact']).to(u.m) # cm->m
            data.mcheader.max_viewcone_radius = (self.current_run['data'].mcheader_data[telescope]['sim_conesemiangle']).to(u.deg) # deg->deg
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        if telescope == 'M1':
            n_events = self.current_run['data'].n_mono_events_m1
        else:
            n_events = self.current_run['data'].n_mono_events_m2

        # Loop over the events
        for event_i in range(n_events):
            # Event and run ids
            event_order_number = self.current_run['data'].mono_ids[telescope][event_i]
            event_id = self.current_run['data'].event_data[telescope]['stereo_event_number'][event_order_number]
            obs_id = self.current_run['number']

            # Reading event data
            event_data = self.current_run['data'].get_mono_event_data(
                event_i, telescope=telescope)

            data.meta = event_data['mars_meta']

            # Event counter
            data.count = counter
            data.index.obs_id = obs_id
            data.index.event_id = event_id

            # Setting up the R0 container
            data.r0.tel.clear()
            data.r0.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data[telescope]['trigger_pattern'][event_order_number]

            # Setting up the R1 container
            data.r1.tel.clear()
            data.r1.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data[telescope]['trigger_pattern'][event_order_number]

            # Setting up the DL0 container
            data.dl0.tel.clear()
            data.dl0.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data[telescope]['trigger_pattern'][event_order_number]

            # Setting up the DL1 container
            data.dl1.tel.clear()

            # Creating the telescope pointing container
            pointing = PointingContainer()
            pointing_tel = TelescopePointingContainer()

            pointing_tel.azimuth = np.deg2rad(
                event_data['pointing_az']) * u.rad
            pointing_tel.altitude = np.deg2rad(
                90 - event_data['pointing_zd']) * u.rad
            #pointing.ra = np.deg2rad(event_data['pointing_ra']) * u.rad
            #pointing.dec = np.deg2rad(event_data['pointing_dec']) * u.rad
            
            pointing.tel[tel_i + 1] = pointing_tel
            
            pointing.array_azimuth = np.deg2rad(event_data['pointing_az']) * u.rad
            pointing.array_altitude = np.deg2rad(90 - event_data['pointing_zd']) * u.rad
            pointing.array_ra = np.deg2rad(event_data['pointing_ra']) * u.rad
            pointing.array_dec = np.deg2rad(90 - event_data['pointing_dec']) * u.rad
            
            data.pointing = pointing
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            # Adding event charge and peak positions per pixel
            data.dl1.tel[tel_i + 1].image = event_data['image']
            data.dl1.tel[tel_i + 1].peak_time = event_data['pulse_time']
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            if not self.is_mc:
                # Adding the event arrival time
                time_tmp = Time(
                    event_data['mjd'], scale='utc', format='mjd')
                data.trigger.time = Time(
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                    time_tmp, format='unix', scale='utc', precision=9)
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            else:
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                data.mc.energy = event_data['true_energy'] * u.GeV
                data.mc.alt = (np.pi/2 - event_data['true_zd']) * u.rad
                # check meaning of 7deg transformation (I.Vovk)
                data.mc.az = -1 * \
                    (event_data['true_az'] - np.deg2rad(180 - 7)) * u.rad
                data.mc.shower_primary_id = 1 - \
                    event_data['true_shower_primary_id']
                data.mc.h_first_int = event_data['true_h_first_int'] * u.cm

                # adding a 7deg rotation between the orientation of corsika (x axis = magnetic north) and MARS (x axis = geographical north) frames
                # magnetic north is 7 deg westward w.r.t. geographical north
                rot_corsika = 7 *u.deg
                data.mc.core_x = (event_data['true_core_x']*np.cos(rot_corsika) - event_data['true_core_y']*np.sin(rot_corsika))* u.cm
                data.mc.core_y = (event_data['true_core_x']*np.sin(rot_corsika) + event_data['true_core_y']*np.cos(rot_corsika))* u.cm

            # Setting the telescopes with data
            data.r0.tels_with_data = tels_with_data
            data.r1.tels_with_data = tels_with_data
            data.dl0.tels_with_data = tels_with_data
            data.trigger.tels_with_trigger = tels_with_data

            yield data
            counter += 1
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        return

    def _pedestal_event_generator(self, telescope):
        """
        Pedestal event generator. Yields DataContainer instances, filled
        with the read event data.

        Parameters
        ----------
        telescope: str
            The telescope for which to return events. Can be either "M1" or "M2".

        Returns
        -------

        """

        counter = 0
        telescope = telescope.upper()

        # Data container - is initialized once, and data is replaced within it after each yield
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        data = EventAndMonDataContainer()
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        # Telescopes with data:
        tels_in_file = ["M1", "M2"]

        if telescope not in tels_in_file:
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            raise ValueError("Specified telescope {:s} is not in the allowed list {}".format(
                telescope, tels_in_file))
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        tel_i = tels_in_file.index(telescope)
        tels_with_data = {tel_i + 1, }

        # Loop over the available data runs
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        for run_number in self.run_numbers:
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            # Removing the previously read data run from memory
            if self.current_run is not None:
                if 'data' in self.current_run:
                    del self.current_run['data']

            # Setting the new active run
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            self.current_run = self._set_active_run(run_number)
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            monitoring_data = self.current_run['data'].monitoring_data
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            monitoring_camera = MonitoringCameraContainer()
            pedestal_info = PedestalContainer()
            badpixel_info = PixelStatusContainer()
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            time_tmp = Time(monitoring_data['M{:d}'.format(
                tel_i + 1)]['PedestalMJD'], scale='utc', format='mjd')
            pedestal_info.sample_time = Time(
                time_tmp, format='unix', scale='utc', precision=9)
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            pedestal_info.n_events = 500 # hardcoded number of pedestal events averaged over
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            pedestal_info.charge_mean = []
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            pedestal_info.charge_mean.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFundamental']['Mean'])
            pedestal_info.charge_mean.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractor']['Mean'])
            pedestal_info.charge_mean.append(monitoring_data['M{:d}'.format(
                tel_i + 1)]['PedestalFromExtractorRndm']['Mean'])
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            pedestal_info.charge_std = []
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            pedestal_info.charge_std.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFundamental']['Rms'])
            pedestal_info.charge_std.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractor']['Rms'])
            pedestal_info.charge_std.append(
                monitoring_data['M{:d}'.format(tel_i + 1)]['PedestalFromExtractorRndm']['Rms'])

            t_range = Time(monitoring_data['M{:d}'.format(
                tel_i + 1)]['badpixelinfoMJDrange'], scale='utc', format='mjd')

            badpixel_info.hardware_failing_pixels = monitoring_data['M{:d}'.format(
                tel_i + 1)]['badpixelinfo']
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            badpixel_info.sample_time_range = t_range
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            monitoring_camera.pedestal = pedestal_info
            monitoring_camera.pixel_status = badpixel_info

            data.mon.tels_with_data = tels_with_data
            data.mon.tel[tel_i + 1] = monitoring_camera

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            if telescope == 'M1':
                n_events = self.current_run['data'].n_pedestal_events_m1
            else:
                n_events = self.current_run['data'].n_pedestal_events_m2

            # Loop over the events
            for event_i in range(n_events):
                # Event and run ids
                event_order_number = self.current_run['data'].pedestal_ids[telescope][event_i]
                event_id = self.current_run['data'].event_data[telescope]['stereo_event_number'][event_order_number]
                obs_id = self.current_run['number']

                # Reading event data
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                event_data = self.current_run['data'].get_pedestal_event_data(
                    event_i, telescope=telescope)

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                data.meta = event_data['mars_meta']
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                # Event counter
                data.count = counter
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                data.index.obs_id = obs_id
                data.index.event_id = event_id
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                # Setting up the R0 container
                data.r0.tel.clear()
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                data.r0.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data[telescope]['trigger_pattern'][event_order_number]
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                # Setting up the R1 container
                data.r1.tel.clear()
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                data.r1.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data[telescope]['trigger_pattern'][event_order_number]
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                # Setting up the DL0 container
                data.dl0.tel.clear()
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                data.dl0.tel[tel_i + 1].trigger_type = self.current_run['data'].event_data[telescope]['trigger_pattern'][event_order_number]
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                # Creating the telescope pointing container
                pointing = TelescopePointingContainer()
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                pointing.azimuth = np.deg2rad(
                    event_data['pointing_az']) * u.rad
                pointing.altitude = np.deg2rad(
                    90 - event_data['pointing_zd']) * u.rad
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                #pointing.ra = np.deg2rad(event_data['pointing_ra']) * u.rad
                #pointing.dec = np.deg2rad(event_data['pointing_dec']) * u.rad
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                # Adding the pointing container to the event data
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                data.pointing.tel[tel_i + 1] = pointing
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                # Adding event charge and peak positions per pixel
                data.dl1.tel[tel_i + 1].image = event_data['image']
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                data.dl1.tel[tel_i + 1].peak_time = event_data['pulse_time']
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                # Adding the event arrival time
                time_tmp = Time(event_data['mjd'], scale='utc', format='mjd')
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                data.trigger.time = Time(
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                    time_tmp, format='unix', scale='utc', precision=9)
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                # Setting the telescopes with data
                data.r0.tels_with_data = tels_with_data
                data.r1.tels_with_data = tels_with_data
                data.dl0.tels_with_data = tels_with_data
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                data.trigger.tels_with_trigger = tels_with_data
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                yield data
                counter += 1

        return


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class MarsRun:
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    """
    This class implements reading of the event data from a single MAGIC data run.
    """

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    def __init__(self, run_file_mask, filter_list=None):
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        """
        Constructor of the class. Defines the run to use and the camera pixel arrangement.

        Parameters
        ----------
        run_file_mask: str
            A path mask for files belonging to the run. Must correspond to a single run
            or an exception will be raised. Must correspond to calibrated ("sorcerer"-level)
            data.
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        filter_list: list, optional
            A list of files, to which the run_file_mask should be applied. If None, all the
            files satisfying run_file_mask will be used. Defaults to None.
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        """

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        self.n_camera_pixels = GEOM.n_pixels
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        self.run_file_mask = run_file_mask

        # Preparing the lists of M1/2 data files
        file_list = glob.glob(run_file_mask)
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        # Filtering out extra files if necessary
        if filter_list is not None:
            file_list = list(set(file_list) & set(filter_list))

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        self.m1_file_list = list(
            filter(lambda name: '_M1_' in name, file_list))
        self.m2_file_list = list(
            filter(lambda name: '_M2_' in name, file_list))
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        self.m1_file_list.sort()
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        self.m2_file_list.sort()

        # Retrieving the list of run numbers corresponding to the data files
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        run_info = list(
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            map(MAGICEventSource.get_run_info_from_name, file_list))
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        run_numbers = [i[0] for i in run_info]
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        is_mc_runs = [i[1] for i in run_info]
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        run_numbers = np.unique(run_numbers)
        is_mc_runs = np.unique(is_mc_runs)
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        # Checking if run type (data/MC) is consistent:
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        if len(is_mc_runs) > 1:
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            raise ValueError(
                "Run type is not consistently data or MC: {}".format(is_mc_runs))

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        self.is_mc = is_mc_runs[0]
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        # Checking if a single run is going to be read
        if len(run_numbers) > 1:
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            raise ValueError(
                "Run mask corresponds to more than one run: {}".format(run_numbers))
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        # Reading the data
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        m1_data = self.load_events(
            self.m1_file_list, self.is_mc, self.n_camera_pixels)
        m2_data = self.load_events(
            self.m2_file_list, self.is_mc, self.n_camera_pixels)

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        # Getting the event data
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        self.event_data = dict()
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        self.event_data['M1'] = m1_data[0]
        self.event_data['M2'] = m2_data[0]

        # Getting the monitoring data
        self.monitoring_data = dict()
        self.monitoring_data['M1'] = m1_data[1]
        self.monitoring_data['M2'] = m2_data[1]
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        # Getting the run-wise MC header data
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        if self.is_mc:
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            self.mcheader_data = dict()
            self.mcheader_data['M1'] = m1_data[2]
            self.mcheader_data['M2'] = m2_data[2]
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        # Detecting pedestal events
        self.pedestal_ids = self._find_pedestal_events()
        # Detecting stereo events
        self.stereo_ids = self._find_stereo_events()
        # Detecting mono events
        self.mono_ids = self._find_mono_events()

    @property
    def n_events_m1(self):
        return len(self.event_data['M1']['MJD'])

    @property
    def n_events_m2(self):
        return len(self.event_data['M2']['MJD'])

    @property
    def n_stereo_events(self):
        return len(self.stereo_ids)

    @property
    def n_mono_events_m1(self):
        return len(self.mono_ids['M1'])

    @property
    def n_mono_events_m2(self):
        return len(self.mono_ids['M2'])

    @property
    def n_pedestal_events_m1(self):
        return len(self.pedestal_ids['M1'])

    @property
    def n_pedestal_events_m2(self):
        return len(self.pedestal_ids['M2'])

    @staticmethod
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    def load_events(file_list, is_mc, n_camera_pixels):
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        """
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        This method loads events and monitoring data from the pre-defiled file and returns them as a dictionary.
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        Parameters
        ----------
        file_name: str
            Name of the MAGIC calibrated file to use.
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        is_mc: boolean
            Specify whether Monte Carlo (True) or data (False) events are read
        n_camera_pixels: int
            Number of MAGIC camera pixels (not hardcoded, but specified solely via ctapipe.instrument.CameraGeometry)
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        Returns
        -------
        dict:
            A dictionary with the even properties: charge / arrival time data, trigger, direction etc.
        """

        try:
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            import uproot
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        except ImportError:
            msg = "The `uproot` python module is required to access the MAGIC data"
            raise ImportError(msg)

        event_data = dict()

        event_data['charge'] = []
        event_data['arrival_time'] = []
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        event_data['trigger_pattern'] = np.array([], dtype=np.int32)
        event_data['stereo_event_number'] = np.array([], dtype=np.int32)
        event_data['pointing_zd'] = np.array([])
        event_data['pointing_az'] = np.array([])
        event_data['pointing_ra'] = np.array([])
        event_data['pointing_dec'] = np.array([])
        event_data['MJD'] = np.array([])
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        event_data['mars_meta'] = []
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        # monitoring information (updated from time to time)
        monitoring_data = dict()
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        monitoring_data['badpixelinfo'] = []
        monitoring_data['badpixelinfoMJDrange'] = []
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        monitoring_data['PedestalMJD'] = np.array([])
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        monitoring_data['PedestalFundamental'] = dict()
        monitoring_data['PedestalFundamental']['Mean'] = []
        monitoring_data['PedestalFundamental']['Rms'] = []
        monitoring_data['PedestalFromExtractor'] = dict()
        monitoring_data['PedestalFromExtractor']['Mean'] = []
        monitoring_data['PedestalFromExtractor']['Rms'] = []
        monitoring_data['PedestalFromExtractorRndm'] = dict()
        monitoring_data['PedestalFromExtractorRndm']['Mean'] = []
        monitoring_data['PedestalFromExtractorRndm']['Rms'] = []

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        #MC Header information, dictionary always created, but filled only in case of MC run
        mcheader_data = dict()
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        event_data['file_edges'] = [0]

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        # if no file in the list (e.g. when reading mono information), then simply
        # return empty dicts/array
        if len(file_list) == 0:
            return event_data, monitoring_data, mcheader_data

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        drive_data = dict()
        drive_data['mjd'] = np.array([])
        drive_data['zd']  = np.array([])
        drive_data['az']  = np.array([])
        drive_data['ra']  = np.array([])
        drive_data['dec'] = np.array([])

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        degrees_per_hour = 15.0
        seconds_per_day = 86400.0
        seconds_per_hour = 3600.

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        evt_common_list = [
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            'MCerPhotEvt.fPixels.fPhot',
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            'MArrivalTime.fData',
            'MTriggerPattern.fPrescaled',
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            'MRawEvtHeader.fStereoEvtNumber',
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            'MRawEvtHeader.fDAQEvtNumber',
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        ]

        # Separately, because only used with pre-processed MARS data
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        # to create MPointingPos container
        pointing_array_list = [
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            'MPointingPos.fZd',
            'MPointingPos.fAz',
            'MPointingPos.fRa',
            'MPointingPos.fDec',
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            'MPointingPos.fDevZd',
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            'MPointingPos.fDevAz',
            'MPointingPos.fDevHa',
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            'MPointingPos.fDevDec',
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        ]

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        # Info only applicable for data:
        time_array_list = [
            'MTime.fMjd',
            'MTime.fTime.fMilliSec',
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            'MTime.fNanoSec',
        ]

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        drive_array_list = [
            'MReportDrive.fMjd',
            'MReportDrive.fCurrentZd',
            'MReportDrive.fCurrentAz',
            'MReportDrive.fRa',
            'MReportDrive.fDec'
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        ]
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        pedestal_array_list = [
            'MTimePedestals.fMjd',
            'MTimePedestals.fTime.fMilliSec',
            'MTimePedestals.fNanoSec',
            'MPedPhotFundamental.fArray.fMean',
            'MPedPhotFundamental.fArray.fRms',
            'MPedPhotFromExtractor.fArray.fMean',
            'MPedPhotFromExtractor.fArray.fRms',
            'MPedPhotFromExtractorRndm.fArray.fMean',
            'MPedPhotFromExtractorRndm.fArray.fRms'
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        ]
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        # Info only applicable for MC:
        mc_list = [
            'MMcEvt.fEnergy',
            'MMcEvt.fTheta',
            'MMcEvt.fPhi',
            'MMcEvt.fPartId',
            'MMcEvt.fZFirstInteraction',
            'MMcEvt.fCoreX',
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            'MMcEvt.fCoreY'
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        ]
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        mcheader_list = [
            #'MMcRunHeader.fNumSimulatedShowers',
            'MMcRunHeader.fNumEvents',
            'MMcCorsikaRunHeader.fELowLim', #GeV
            'MMcCorsikaRunHeader.fEUppLim', #GeV
            'MMcCorsikaRunHeader.fSlopeSpec',
            'MMcRunHeader.fImpactMax', #cm
            #'MMcCorsikaRunHeader.fViewconeAngles',
            'MMcRunHeader.fRandomPointingConeSemiAngle' # deg
        ]
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        # Metadata, currently not strictly required
        metainfo_array_list = [
            'MRawRunHeader.fRunNumber',
            'MRawRunHeader.fRunType',
            'MRawRunHeader.fSubRunIndex',
            'MRawRunHeader.fSourceRA',
            'MRawRunHeader.fSourceDEC',
            'MRawRunHeader.fTelescopeNumber']
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        for file_name in file_list:

            input_file = uproot.open(file_name)

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            events = input_file['Events'].arrays(evt_common_list, library="np")
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            # Reading the info common to MC and real data
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            charge = events['MCerPhotEvt.fPixels.fPhot']
            arrival_time = events['MArrivalTime.fData']
            trigger_pattern = events['MTriggerPattern.fPrescaled']
            stereo_event_number = events['MRawEvtHeader.fStereoEvtNumber']
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            # Reading run-wise meta information (same for all events in subrun):
            mars_meta = dict()
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            mars_meta['is_simulation'] = is_mc
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            if is_mc:
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                mc_header_info = input_file['RunHeaders'].arrays(mcheader_list, library="np")
                mcheader_data['sim_nevents']=int(mc_header_info['MMcRunHeader.fNumEvents'][0]) #std: 5000
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                mcheader_data['sim_reuse']= 1 #this value is not written in the magic root file, but since the sim_events already include shower reuse we artificially set it to 1 (actually every shower reused 5 times for std MAGIC MC)
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                mcheader_data['sim_emin']=mc_header_info['MMcCorsikaRunHeader.fELowLim'][0]*u.GeV
                mcheader_data['sim_emax']=mc_header_info['MMcCorsikaRunHeader.fEUppLim'][0]*u.GeV
                mcheader_data['sim_eslope']=mc_header_info['MMcCorsikaRunHeader.fSlopeSpec'][0] #std: -1.6
                mcheader_data['sim_max_impact']=mc_header_info['MMcRunHeader.fImpactMax'][0]*u.cm
                mcheader_data['sim_conesemiangle']=mc_header_info['MMcRunHeader.fRandomPointingConeSemiAngle'][0]*u.deg #std: 2.5 deg, also corsika viewcone is defined by "half of the cone angle".
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            # Reading event timing information:
            if not is_mc:
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                event_times = input_file['Events'].arrays(time_array_list, library="np")
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                # Computing the event arrival time
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                event_mjd = event_times['MTime.fMjd']
                event_millisec = event_times['MTime.fTime.fMilliSec']
                event_nanosec = event_times['MTime.fNanoSec']
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                event_mjd = event_mjd + \
                    (event_millisec / 1e3 + event_nanosec / 1e9) / seconds_per_day
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                event_data['MJD'] = np.concatenate(
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                    (event_data['MJD'], event_mjd))
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            # try to read RunHeaders tree (soft fail if not present, to pass current tests)
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            try:
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                meta_info = input_file['RunHeaders'].arrays(
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                    metainfo_array_list, library="np")
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                mars_meta['origin'] = "MAGIC"
                mars_meta['input_url'] = file_name
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                mars_meta['number'] = int(
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                    meta_info['MRawRunHeader.fRunNumber'][0])
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                mars_meta['number_subrun'] = int(
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                    meta_info['MRawRunHeader.fSubRunIndex'][0])
                mars_meta['source_ra'] = meta_info['MRawRunHeader.fSourceRA'][0] / \
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                    seconds_per_hour * degrees_per_hour * u.deg
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                mars_meta['source_dec'] = meta_info['MRawRunHeader.fSourceDEC'][0] / \
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                    seconds_per_hour * u.deg
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                is_mc_check = int(meta_info['MRawRunHeader.fRunType'][0])
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                if is_mc_check == 0:
                    is_mc_check = False
                elif is_mc_check == 256:
                    is_mc_check = True
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                else:
                    msg = "Run type (Data or MC) of MAGIC data file not recognised."
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                    LOGGER.error(msg)
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                    raise ValueError(msg)
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                if is_mc_check != is_mc:
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                    msg = "Inconsistent run type (data or MC) between file name and runheader content."
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                    LOGGER.error(msg)
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                    raise ValueError(msg)
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                # Reading the info only contained in real data
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                if not is_mc:
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                    badpixelinfo = input_file['RunHeaders']['MBadPixelsCam.fArray.fInfo'].array(
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                        uproot.interpretation.jagged.AsJagged(uproot.interpretation.numerical.AsDtype(np.dtype('>i4'))), library="np")[0].reshape((4, 1183), order='F')
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                    # now we have 4 axes:
                    # 0st axis: empty (?)
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                    # 1st axis: Unsuitable pixels
                    # 2nd axis: Uncalibrated pixels (says why pixel is unsuitable)
                    # 3rd axis: Bad hardware pixels (says why pixel is unsuitable)
                    # Each axis cointains a 32bit integer encoding more information about the specific problem, see MARS software, MBADPixelsPix.h
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                    # take first axis
                    unsuitable_pix_bitinfo = badpixelinfo[1][:n_camera_pixels]
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                    # extract unsuitable bit:
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                    unsuitable_pix = np.zeros(n_camera_pixels, dtype=np.bool)
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                    for i in range(n_camera_pixels):
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                        unsuitable_pix[i] = int('\t{0:08b}'.format(
                            unsuitable_pix_bitinfo[i] & 0xff)[-2])
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                    monitoring_data['badpixelinfo'].append(unsuitable_pix)
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                    # save time interval of badpixel info:
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                    monitoring_data['badpixelinfoMJDrange'].append(
                        [event_mjd[0], event_mjd[-1]])
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            except KeyError:
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                LOGGER.warning(
                    "RunHeaders tree not present in file. Cannot read meta information - will assume it is a real data run.")
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                is_mc = False
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            # try to read Pedestals tree (soft fail if not present)
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            if not is_mc:
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                try:
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                    pedestal_info = input_file['Pedestals'].arrays(
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                        pedestal_array_list, library="np")
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                    pedestal_mjd = pedestal_info['MTimePedestals.fMjd']
                    pedestal_millisec = pedestal_info['MTimePedestals.fTime.fMilliSec']
                    pedestal_nanosec = pedestal_info['MTimePedestals.fNanoSec']
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                    n_pedestals = len(pedestal_mjd)
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                    pedestal_mjd = pedestal_mjd + \
                        (pedestal_millisec / 1e3 +
                         pedestal_nanosec / 1e9) / seconds_per_day
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                    monitoring_data['PedestalMJD'] = np.concatenate(
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                        (monitoring_data['PedestalMJD'], pedestal_mjd))
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                    for quantity in ['Mean', 'Rms']:
                        for i_pedestal in range(n_pedestals):
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                            monitoring_data['PedestalFundamental'][quantity].append(
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                                pedestal_info[f'MPedPhotFundamental.fArray.f{quantity}'][i_pedestal][:n_camera_pixels])