# Process data from lower-data levels up to DL1 and DL2, including image extraction and optionally image parameterization as well as muon
# analysis and shower reconstruction.
ProcessorTool:


  #config_files: traitlets.Undefined

  # Enforce dl1 recomputation even if already present in the input file
  force_recompute_dl1: False

  # Enforce dl2 recomputation even if already present in the input file
  force_recompute_dl2: False


  #log_config: traitlets.Undefined

  # The date format used by logging formatters for %(asctime)s
  log_datefmt: '%Y-%m-%d %H:%M:%S'

  # Filename for the log
  log_file: None

  # Logging Level for File Logging
  log_file_level: INFO

  # The Logging format template
  log_format: '[%(name)s]%(highlevel)s %(message)s'

  # Set the log level by value or name.
  log_level: 30

  # Configure additional log handlers.  The default stderr logs handler is configured by the log_level, log_datefmt and log_format settings.
  # This configuration can be used to configure additional handlers (e.g. to output the log to a file) or for finer control over the default
  # handlers.  If provided this should be a logging configuration dictionary, for more information see:
  # https://docs.python.org/3/library/logging.config.html#logging-config-dictschema  This dictionary is merged with the base logging
  # configuration which defines the following:  * A logging formatter intended for interactive use called ``console``. * A logging handler that
  # writes to stderr called ``console`` which uses the formatter ``console``. * A logger with the name of this application set to ``DEBUG``
  # level.  This example adds a new handler that writes to a file:  .. code-block:: python  c.Application.logging_config = { 'handlers': {
  # 'file': { 'class': 'logging.FileHandler', 'level': 'DEBUG', 'filename': '<path/to/file>', } }, 'loggers': { '<application-name>': { 'level':
  # 'DEBUG', # NOTE: if you don't list the default "console" # handler here then it will be disabled 'handlers': ['console', 'file'], }, } }
  #logging_config: traitlets.Undefined


  overwrite: False

  # show progress bar during processing
  progress_bar: False


  #provenance_log: traitlets.Undefined


  quiet: False

  # Instead of starting the Application, dump configuration to stdout
  show_config: False

  # Instead of starting the Application, dump configuration to stdout (as JSON)
  show_config_json: False

  # Calibrator to handle the full camera calibration chain, in order to fill the DL1 data level in the event container.
  CameraCalibrator:

    # Apply peak time shift corrections. Apply the remaining absolute and fractional time shift corrections to the peak time after pulse
    # extraction. If `apply_waveform_time_shift` is False, this will apply the full time shift.
    #apply_peak_time_shift: traitlets.Undefined

    # Apply waveform time shift corrections. The minimal integer shift to synchronize waveforms is applied before peak extraction if this option
    # is True.
    #apply_waveform_time_shift: traitlets.Undefined

    # The name of a DataVolumeReducer subclass. Possible values: ['NullDataVolumeReducer', 'TailCutsDataVolumeReducer']
    data_volume_reducer_type: 'NullDataVolumeReducer'

    # Name of the ImageExtractor subclass to be used. The name of a ImageExtractor subclass. Possible values: ['FullWaveformSum',
    # 'FixedWindowSum', 'GlobalPeakWindowSum', 'LocalPeakWindowSum', 'SlidingWindowMaxSum', 'NeighborPeakWindowSum', 'TwoPassWindowSum',
    # 'FlashCamExtractor', 'BaselineSubtractedNeighborPeakWindowSum']
    #image_extractor_type: traitlets.Undefined

    # Name of the InvalidPixelHandler to use. Possible values: ['NeighborAverage']
    invalid_pixel_handler_type: 'NeighborAverage'

  # Serialize a sequence of events into a HDF5 file, in the correct format
  DataWriter:

    # compression level, 0=None, 9=maximum
    compression_level: 5

    # compressor algorithm to use.
    compression_type: blosc:zstd


    #contact_info: traitlets.Undefined

    # Additional metadata keywords and values that describe this data. This should be a dictionary where the keys will be appended to the
    # CONTEXT section of the output file's attributes. Keys can be hierarchical by using a space between each level, e.g. ``SIMULATION
    # PRODUCTION`` would make a key PRODUCTION grouped under the key SIMULATION
    #context_metadata: traitlets.Undefined


    image_dtype: 'int32'


    image_offset: 0


    image_scale: 10.0


    #instrument_info: traitlets.Undefined

    # output filename
    output_path: events.dl1.h5

    # overwrite output file if it exists
    overwrite: False


    peak_time_dtype: 'int16'


    peak_time_offset: 0


    peak_time_scale: 100.0


    transform_image: False


    transform_peak_time: False


    transform_waveform: False


    waveform_dtype: 'int32'


    waveform_offset: 0


    waveform_scale: 1000.0

    # Store DL1 Images if available
    write_images: False

    # Generate PyTables index datasets for all tables that contain an event_id or tel_id. These speed up in-kernel pytables operations,but add
    # some overhead to the file. They can also be generated and attached after the file is written
    write_index_tables: False

    # Store muon parameters if available
    write_muon_parameters: False

    # Store DL1 image parameters if available
    write_parameters: True

    # Store R0 waveforms if available
    write_raw_waveforms: False

    # Store DL2 stereo shower parameters if available
    write_showers: False

    # Store R1 waveforms if available
    write_waveforms: False

  # Takes DL1/Image data and cleans and parametrizes the images into DL1/parameters. Should be run after CameraCalibrator to produce all DL1
  # information.
  ImageProcessor:

    # If true, apply ImageModifier to dl1 images.
    #apply_image_modifier: traitlets.Undefined

    # The name of a ImageCleaner subclass. Possible values: ['TailcutsImageCleaner', 'MARSImageCleaner', 'FACTImageCleaner',
    # 'TimeConstrainedImageCleaner']
    image_cleaner_type: 'TailcutsImageCleaner'

    # Whether to calculate parameters in the telescope or camera frame
    use_telescope_frame: True

  # Takes cleaned images and extracts muon rings. Should be run after ImageProcessor.
  MuonProcessor:

    # Threshold for calculating the ``ring_completeness``.
    #completeness_threshold: traitlets.Undefined

    # Pedestal noise rms.
    #pedestal: traitlets.Undefined

    # Ring width for intensity ratio computation in units of pixel diameter.
    #ratio_width: traitlets.Undefined

  # Run the stereo event reconstruction on the input events.
  ShowerProcessor:

    # The stereo reconstructors to be used. The reconstructors are applied in the order given, which is important if e.g. the
    # `~ctapipe.reco.ParticleClassifier` uses the output of the `~ctapipe.reco.EnergyRegressor` as input.. Possible values:
    # ['DispReconstructor', 'HillasIntersection', 'HillasReconstructor', 'EnergyRegressor', 'ParticleClassifier']
    #reconstructor_types: traitlets.Undefined

  # Instrumental Context
  Instrument:


    class_: Other


    id_: 'unspecified'

    # Which site of CTA (or external telescope) this instrument is associated with
    site: Other


    subtype: 'unspecified'


    type_: 'unspecified'


    version: 'unspecified'

  # Contact information
  Contact:


    email: 'unknown'


    name: 'unknown'


    organization: 'unknown'

  # A stereo trigger that can remove telescope events from subarray events.
  SoftwareTrigger:

    # Minimum number of telescopes required globally. Events with fewer telescopes will be filtered out completely.
    min_telescopes: 1

    # Minimum number of telescopes required for a specific type. In events with fewer telescopes of that type , those telescopes will be removed
    # from the array event. This might result in the event not fullfilling ``min_telescopes`` anymore and thus being filtered completely.
    #min_telescopes_of_type: traitlets.Undefined

  # Parent class for EventSources.
  EventSource:

    # list of allowed tel_ids, others will be ignored. If None, all telescopes in the input stream will be included
    allowed_tels: None

    # Path to the input file containing events.
    #input_url: traitlets.Undefined

    # Maximum number of events that will be read from the file
    max_events: None

  # Event source for files in the ctapipe DL1 format. For general information about the concept of event sources, take a look at the parent
  # class ctapipe.io.EventSource.
  HDF5EventSource:

    # list of allowed tel_ids, others will be ignored. If None, all telescopes in the input stream will be included
    allowed_tels: None

    # If both nominal and effective focal lengths are available,  which one to use for the `~ctapipe.coordinates.CameraFrame` attached to the
    # `~ctapipe.instrument.CameraGeometry` instances in the `~ctapipe.instrument.SubarrayDescription` which will be used in CameraFrame to
    # TelescopeFrame coordinate transforms. The 'nominal' focal length is the one used during  the simulation, the 'effective' focal length is
    # computed using specialized  ray-tracing from a point light source
    focal_length_choice: FocalLengthKind.EFFECTIVE

    # Path to the input file containing events.
    #input_url: traitlets.Undefined

    # Maximum number of events that will be read from the file
    max_events: None

  # Read events from a SimTelArray data file (in EventIO format).
  SimTelEventSource:

    # list of allowed tel_ids, others will be ignored. If None, all telescopes in the input stream will be included
    allowed_tels: None

    # Which type of atmosphere density profile to load from the file, in case more than one exists.  If set to AUTO, TABLE will be attempted
    # first and if missing, FIVELAYER will be loaded.
    atmosphere_profile_choice: AtmosphereProfileKind.AUTO

    # Require the event source to be backwards seekable. This will reduce in slower read speed for gzipped files and is not possible for zstd
    # compressed files
    back_seekable: False

    # Factor to transform ADC counts into number of photoelectrons. Corrects the DC_to_PHE factor.
    calib_scale: 1.0

    # Factor to shift the R1 photoelectron samples. Can be used to simulate mis-calibration.
    calib_shift: 0.0

    # If both nominal and effective focal lengths are available in the SimTelArray file, which one to use for the
    # `~ctapipe.coordinates.CameraFrame` attached to the `~ctapipe.instrument.CameraGeometry` instances in the
    # `~ctapipe.instrument.SubarrayDescription`, which will be used in CameraFrame to TelescopeFrame coordinate transforms.  The 'nominal' focal
    # length is the one used during  the simulation, the 'effective' focal length is computed using specialized  ray-tracing from a point light
    # source
    focal_length_choice: FocalLengthKind.EFFECTIVE

    # The name of a GainSelector subclass. Possible values: ['ManualGainSelector', 'ThresholdGainSelector']
    gain_selector_type: 'ThresholdGainSelector'

    # Path to the input file containing events.
    #input_url: traitlets.Undefined

    # Maximum number of events that will be read from the file
    max_events: None

    # Skip calibration events
    skip_calibration_events: True

  # Clean images using the standard picture/boundary technique. See `ctapipe.image.tailcuts_clean`
  TailcutsImageCleaner:

    # second-level threshold in photoelectrons.
    #boundary_threshold_pe: traitlets.Undefined

    # If False, pixels with less neighbors than ``min_picture_neighbors`` areremoved.
    #keep_isolated_pixels: traitlets.Undefined

    # Minimum number of neighbors above threshold to consider.
    #min_picture_neighbors: traitlets.Undefined

    # top-level threshold in photoelectrons.
    #picture_threshold_pe: traitlets.Undefined

  # 1st-pass MARS-like Image cleaner (See `ctapipe.image.mars_cleaning_1st_pass`)
  MARSImageCleaner:

    # second-level threshold in photoelectrons.
    #boundary_threshold_pe: traitlets.Undefined

    # If False, pixels with less neighbors than ``min_picture_neighbors`` areremoved.
    #keep_isolated_pixels: traitlets.Undefined

    # Minimum number of neighbors above threshold to consider.
    #min_picture_neighbors: traitlets.Undefined

    # top-level threshold in photoelectrons.
    #picture_threshold_pe: traitlets.Undefined

  # Clean images using the FACT technique. See `ctapipe.image.fact_image_cleaning` for algorithm details
  FACTImageCleaner:

    # second-level threshold in photoelectrons.
    #boundary_threshold_pe: traitlets.Undefined

    # If False, pixels with less neighbors than ``min_picture_neighbors`` areremoved.
    #keep_isolated_pixels: traitlets.Undefined

    # Minimum number of neighbors above threshold to consider.
    #min_picture_neighbors: traitlets.Undefined

    # top-level threshold in photoelectrons.
    #picture_threshold_pe: traitlets.Undefined

    # arrival time limit for neighboring pixels, in ns.
    #time_limit_ns: traitlets.Undefined

  # MAGIC-like Image cleaner with timing information (See `ctapipe.image.time_constrained_clean`)
  TimeConstrainedImageCleaner:

    # second-level threshold in photoelectrons.
    #boundary_threshold_pe: traitlets.Undefined

    # If False, pixels with less neighbors than ``min_picture_neighbors`` areremoved.
    #keep_isolated_pixels: traitlets.Undefined

    # Minimum number of neighbors above threshold to consider.
    #min_picture_neighbors: traitlets.Undefined

    # top-level threshold in photoelectrons.
    #picture_threshold_pe: traitlets.Undefined

    # arrival time limit for neighboring boundary pixels, in ns.
    #time_limit_boundary_ns: traitlets.Undefined

    # arrival time limit for neighboring core pixels, in ns.
    #time_limit_core_ns: traitlets.Undefined

  # Extractor that sums within a fixed window defined by the user.
  FixedWindowSum:

    # Apply the integration window correction.
    #apply_integration_correction: traitlets.Undefined

    # Manually select index where the peak is located.
    #peak_index: traitlets.Undefined

    # Define the shift of the integration window from the peak_index (peak_index - shift).
    #window_shift: traitlets.Undefined

    # Define the width of the integration window.
    #window_width: traitlets.Undefined

  # Extractor which sums in a window about the peak from the global average waveform.
  GlobalPeakWindowSum:

    # Apply the integration window correction.
    #apply_integration_correction: traitlets.Undefined

    # Fraction of pixels to use for finding the integration window. By default, the full camera is used. If fraction is smaller 1, only the
    # brightest pixels will be averaged to find the peak position.
    #pixel_fraction: traitlets.Undefined

    # Define the shift of the integration window from the peak_index (peak_index - shift).
    #window_shift: traitlets.Undefined

    # Define the width of the integration window.
    #window_width: traitlets.Undefined

  # Extractor which sums in a window about the peak in each pixel's waveform.
  LocalPeakWindowSum:

    # Apply the integration window correction.
    #apply_integration_correction: traitlets.Undefined

    # Define the shift of the integration windowfrom the peak_index (peak_index - shift).
    #window_shift: traitlets.Undefined

    # Define the width of the integration window.
    #window_width: traitlets.Undefined

  # Sliding window extractor that maximizes the signal in window_width consecutive slices.
  SlidingWindowMaxSum:

    # Apply the integration window correction.
    #apply_integration_correction: traitlets.Undefined

    # Define the width of the integration window.
    #window_width: traitlets.Undefined

  # Extractor which sums in a window about the peak defined by the wavefroms in neighboring pixels.
  NeighborPeakWindowSum:

    # Apply the integration window correction.
    #apply_integration_correction: traitlets.Undefined

    # Weight of the local pixel (0: peak from neighbors only, 1: local pixel counts as much as any neighbor).
    #local_weight: traitlets.Undefined

    # Define the shift of the integration window from the peak_index (peak_index - shift).
    #window_shift: traitlets.Undefined

    # Define the width of the integration window.
    #window_width: traitlets.Undefined

  # Extractor based on [1]_ which integrates the waveform a second time using a time-gradient linear fit. This is in particular the version
  # implemented in the CTA-MARS analysis pipeline [2]_.
  TwoPassWindowSum:

    # Apply the integration window correction.
    #apply_integration_correction: traitlets.Undefined

    # Picture threshold for internal tail-cuts pass.
    #core_threshold: traitlets.Undefined

    # only run the first pass of the extractor, for debugging purposes
    disable_second_pass: False

    # Name of the InvalidPixelHandler to apply in the first pass.. Possible values: ['NeighborAverage']
    invalid_pixel_handler_type: 'NeighborAverage'

  # Image extractor applying signal preprocessing for FlashCam
  FlashCamExtractor:

    # Apply the integration window correction.
    #apply_integration_correction: traitlets.Undefined

    # Effective Cherenkov time profile std. dev. (in nanoseconds) to assume for calculating the gain correction.
    #effective_time_profile_std: traitlets.Undefined

    # Fraction of the peak value down to which samples are accumulated in the leading edge centroid calculation.
    #leading_edge_rel_descend_limit: traitlets.Undefined

    # Calculate leading edge time instead of peak time.
    #leading_edge_timing: traitlets.Undefined

    # Weight of the local pixel (0: peak from neighbors only, 1: local pixel counts as much as any neighbor).
    #local_weight: traitlets.Undefined

    # (Soft) clipping level of a pixel's contribution to a neighbour sum (set to 0 or inf to disable clipping).
    #neighbour_sum_clipping: traitlets.Undefined

    # Define the upsampling factor for waveforms.
    #upsampling: traitlets.Undefined

    # Define the shift of the integration window from the peak_index (peak_index - shift).
    #window_shift: traitlets.Undefined

    # Define the width of the integration window.
    #window_width: traitlets.Undefined

  # Extractor that first subtracts the baseline before summing in a window about the peak defined by the wavefroms in neighboring pixels.
  BaselineSubtractedNeighborPeakWindowSum:

    # Apply the integration window correction.
    #apply_integration_correction: traitlets.Undefined

    # End sample for baseline estimation
    baseline_end: 10

    # Start sample for baseline estimation
    baseline_start: 0

    # Weight of the local pixel (0: peak from neighbors only, 1: local pixel counts as much as any neighbor).
    #local_weight: traitlets.Undefined

    # Define the shift of the integration window from the peak_index (peak_index - shift).
    #window_shift: traitlets.Undefined

    # Define the width of the integration window.
    #window_width: traitlets.Undefined

  # Manually choose a gain channel.
  ManualGainSelector:

    # Which gain channel to retain
    channel: HIGH

  # Select gain channel according to a maximum threshold value.
  ThresholdGainSelector:

    # Threshold value in waveform sample units. If a waveform contains a sample above this threshold, use the low gain channel for that pixel.
    threshold: 4000

  # Manages a set of user-configurable (at runtime or in a config file) selection criteria that operate on the same type of input. Each time it
  # is called, it returns a boolean array of whether or not each criterion passed. It  also keeps track of the total number of times each
  # criterium is passed, as well as a cumulative product of criterium (i.e. the criteria applied in-order)
  QualityQuery:

    # list of tuples of ('<description', 'expression string') to accept (select) a given data value.  E.g. ``[('mycut', 'x > 3'),]``. You may
    # use ``numpy`` as ``np`` and ``astropy.units`` as ``u``, but no other modules.
    #quality_criteria: traitlets.Undefined

  # for configuring image-wise data checks
  ImageQualityQuery:

    # list of tuples of ('<description', 'expression string') to accept (select) a given data value.  E.g. ``[('mycut', 'x > 3'),]``. You may
    # use ``numpy`` as ``np`` and ``astropy.units`` as ``u``, but no other modules.
    #quality_criteria: traitlets.Undefined

  # For configuring quality checks performed on image parameters before rings are fitted.
  ImageParameterQuery:

    # Quality cuts as list of tuples of ('description', 'expression string'), e.g. ``[('min_pixels', 'dl1_params.morphology.n_pixels >
    # 100'),]``, to select muon images for analysis. You may use ``numpy`` as ``np`` and ``astropy.units`` as ``u``, but no other modules. DL1
    # image parameters can be accessed by prefixing the wanted parameter with ``dl1_params``.
    #quality_criteria: traitlets.Undefined

  # For configuring quality checks performed on the extracted rings before computing efficiency parameters.
  RingQuery:

    # Quality cuts as list of tuples of ('description', 'expression string'), e.g. ``[('radius_not_nan', 'np.isfinite(ring.radius.value)'),]``,
    # to select fitted muons for further intensity fitting. You may use ``numpy`` as ``np`` and ``astropy.units`` as ``u``, but no other
    # modules. Ring parameters and geometry can be accessed by prefixing the wanted parameter with ``parameters`` or ``ring``, the ring mask can
    # be accessed as ``mask``.
    #quality_criteria: traitlets.Undefined

  # Quality criteria for dl1 parameters checked for telescope events to enter into stereo reconstruction
  StereoQualityQuery:

    # list of tuples of ('<description', 'expression string') to accept (select) a given data value.  E.g. ``[('mycut', 'x > 3'),]``. You may
    # use ``numpy`` as ``np`` and ``astropy.units`` as ``u``, but no other modules.
    #quality_criteria: traitlets.Undefined

  # Quality criteria for machine learning models with different defaults
  MLQualityQuery:

    # list of tuples of ('<description', 'expression string') to accept (select) a given data value.  E.g. ``[('mycut', 'x > 3'),]``. You may
    # use ``numpy`` as ``np`` and ``astropy.units`` as ``u``, but no other modules.
    #quality_criteria: traitlets.Undefined

  # Component to tune simulated background to overserved NSB values. A differentiation between bright and dim pixels is taking place because
  # this happens at DL1a level and in general the integration window would change for peak-searching extraction algorithms with different
  # background levels introducing a bias to the charge in dim pixels.
  ImageModifier:

    # extra bias to add in dim pixels.
    #noise_bias_dim_pixels: traitlets.Undefined

    # If True subtract the expected noise from the image.
    #noise_correct_bias: traitlets.Undefined

    # expected extra noise in bright pixels.
    #noise_level_bright_pixels: traitlets.Undefined

    # expected extra noise in dim pixels.
    #noise_level_dim_pixels: traitlets.Undefined

    # separation between dim and bright pixels.
    #noise_transition_charge: traitlets.Undefined

    # Fraction of light to move to each neighbor.
    #psf_smear_factor: traitlets.Undefined

    # Seed for the random number generator
    rng_seed: 1337

  # Check that an event has one of the allowed types
  EventTypeFilter:

    # The allowed types. Set to None to allow all types.Possible values: FLATFIELD or 0, SINGLE_PE or 1, SKY_PEDESTAL or 2, DARK_PEDESTAL or 3,
    # ELECTRONIC_PEDESTAL or 4, OTHER_CALIBRATION or 15, MUON or 16, HARDWARE_STEREO or 17, DAQ or 24, SUBARRAY or 32, UNKNOWN or 255.
    allowed_types: None

  # Predict absolute value and sign for disp origin reconstruction for each telescope.
  DispReconstructor:

    # Features to use for both models
    #features: traitlets.Undefined

    # If given, load serialized model from this path
    load_path: None

    # If True, the model is trained to predict the natural logarithm of the absolute value.
    log_target: False

    # Which scikit-learn regression model to use.
    norm_cls: None

    # kwargs for the sklearn regressor
    #norm_config: traitlets.Undefined


    prefix: 'disp'

    # Which scikit-learn classification model to use.
    sign_cls: None

    # kwargs for the sklearn classifier
    #sign_config: traitlets.Undefined

    # Which stereo combination method to use. Possible values: ['StereoMeanCombiner']
    stereo_combiner_cls: 'StereoMeanCombiner'

  # This class is a simple re-implementation of Hillas parameter based event reconstruction. e.g. https://arxiv.org/abs/astro-ph/0607333
  HillasIntersection:

    # name of atmosphere profile to use
    atmosphere_profile_name: paranal

    # Weighting Method name
    weighting: Konrad

  # Use a scikit-learn regression model per telescope type to predict primary energy
  EnergyRegressor:

    # Features to use for this model
    #features: traitlets.Undefined

    # If given, load serialized model from this path
    load_path: None

    # If True, the model is trained to predict the natural logarithm.
    log_target: False

    # Which scikit-learn regression model to use.
    model_cls: None

    # kwargs for the sklearn model
    #model_config: traitlets.Undefined

    # Prefix for the output of this model. If None, ``model_cls`` is used.
    prefix: 'None'

    # Which stereo combination method to use. Possible values: ['StereoMeanCombiner']
    stereo_combiner_cls: 'StereoMeanCombiner'

  # Predict dl2 particle classification
  ParticleClassifier:

    # Features to use for this model
    #features: traitlets.Undefined

    # The label to fill in case no prediction could be made
    invalid_class: -1

    # If given, load serialized model from this path
    load_path: None

    # Which scikit-learn classification model to use.
    model_cls: None

    # kwargs for the sklearn model
    #model_config: traitlets.Undefined

    # Particle id (in simtel system) of the positive class. Default is 0 for gammas.
    positive_class: 0

    # Prefix for the output of this model. If None, ``model_cls`` is used.
    prefix: 'None'

    # Which stereo combination method to use. Possible values: ['StereoMeanCombiner']
    stereo_combiner_cls: 'StereoMeanCombiner'