TableLoader#

class ctapipe.io.tableloader.TableLoader(**kwargs: Any)[source]#

Bases: Component

Load telescope-event or subarray-event data from ctapipe HDF5 files

This class provides high-level access to data stored in ctapipe HDF5 files, such as created by the ctapipe-process tool (ProcessorTool).

The following TableLoader methods load data from all relevant tables, depending on the options, and joins them into single tables:

TableLoader.read_subarray_events
TableLoader.read_telescope_events
TableLoader.read_telescope_events_by_type returns a dict with a table per telescope type, which is needed for e.g. DL1 image data that might have different shapes for each of the telescope types as tables do not support variable length columns.

Attributes:

subarraySubarrayDescription: The subarray as read from input_url.

Attributes Summary

`dl1_images`	load extracted images
`dl1_muons`	load muon ring parameters
`dl1_parameters`	load reconstructed image parameters
`dl2`	load available dl2 stereo parameters
`focal_length_choice`	If both nominal and effective focal lengths are available, which one to use for the `CameraFrame` attached to the `CameraGeometry` instances in the `ctapipe.instrument.SubarrayDescription`, which will be used in CameraFrame to TelescopeFrame coordinate transforms.
`input_url`	A path Trait for input/output files.
`instrument`	join subarray instrument information to each event
`observation_info`	join observation information to each event
`pointing`	Load pointing information and interpolate / join to events
`simulated`	load simulated shower information
`true_images`	load simulated shower images
`true_parameters`	load image parameters obtained from true images

Methods Summary

`close`()	Close the underlying hdf5 file
`read_observation_information`()	Read the observation information
`read_shower_distribution`()	Read the simulated shower distribution histograms
`read_simulation_configuration`()	Read the simulation configuration table
`read_subarray_events`([start, stop, dl2, ...])	Read subarray-based event information.
`read_subarray_events_chunked`(chunk_size, ...)	Iterate over chunks of subarray events.
`read_telescope_events`([telescopes, start, ...])	Read telescope-based event information.
`read_telescope_events_by_id`([telescopes, ...])	Read subarray-based event information.
`read_telescope_events_by_id_chunked`(...)	Iterate over chunks of telescope events and return a dict of one table per telescope id.
`read_telescope_events_by_type`([telescopes, ...])	Read subarray-based event information.
`read_telescope_events_by_type_chunked`(...)	Iterate over chunks of telescope events as dicts of telescope type to tables.
`read_telescope_events_chunked`(chunk_size, ...)	Iterate over chunks of telescope events.

Attributes Documentation

dl1_images#: load extracted images

dl1_muons#: load muon ring parameters

dl1_parameters#: load reconstructed image parameters

dl2#: load available dl2 stereo parameters

focal_length_choice#: If both nominal and effective focal lengths are available, which one to use for the CameraFrame attached to the 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

input_url#

A path Trait for input/output files.

Attributes:

exists: boolean or None: If True, path must exist, if False path must not exist
directory_ok: boolean: If False, path must not be a directory
file_ok: boolean: If False, path must not be a file

instrument#: join subarray instrument information to each event

observation_info#: join observation information to each event

pointing#: Load pointing information and interpolate / join to events

simulated#: load simulated shower information

true_images#: load simulated shower images

true_parameters#: load image parameters obtained from true images

Methods Documentation

close()[source]#: Close the underlying hdf5 file

read_observation_information()[source]#: Read the observation information

read_shower_distribution()[source]#: Read the simulated shower distribution histograms

read_simulation_configuration()[source]#: Read the simulation configuration table

read_subarray_events(start=None, stop=None, dl2=None, simulated=None, observation_info=None, keep_order=True)[source]#

Read subarray-based event information.

Parameters:

dl2: bool: load available dl2 stereo parameters
simulated: bool: load simulated shower information
observation_info: bool: join observation information to each event
start: int: First subarray event to read
stop: int: Last subarray event (non-inclusive)

Returns:

table: astropy.io.Table: Table with primary index columns “obs_id” and “event_id”.

read_subarray_events_chunked(chunk_size, *args, **kwargs)[source]#

Iterate over chunks of subarray events.

Parameters:

chunk_size: int: Number of subarray events to load per chunk

read_telescope_events(telescopes=None, start=None, stop=None, dl1_images=None, dl1_parameters=None, dl1_muons=None, dl2=None, simulated=None, true_images=None, true_parameters=None, instrument=None, observation_info=None, pointing=None)[source]#

Read telescope-based event information.

If the corresponding traitlets are True, also subarray event information is joined onto the table.

The start, stop parameters enable to only load parts of the file, note however, to maintain integrity of subarray events, these refer to the subarray indices in the file. E.g. start=0, stop=10 would load the telescope events corresponding to the first 10 subarray events in the input file.

Parameters:

telescopes: Optional[List[Union[int, str, TelescopeDescription]]]: A list containing any combination of telescope IDs and/or telescope descriptions. If None, all available telescopes are read.
start: int: First subarray event to read
stop: int: Last subarray event (non-inclusive)
dl1_images: bool: load extracted images
dl1_parameters: bool: load reconstructed image parameters
dl1_muons: bool: load muon ring parameters
dl2: bool: load available dl2 stereo parameters
simulated: bool: load simulated shower information
true_images: bool: load simulated shower images
true_parameters: bool: load image parameters obtained from true images
instrument: bool: join subarray instrument information to each event
observation_info: bool: join observation information to each event
pointing: bool: join pointing information to each event

Returns:

events: astropy.io.Table: Table with primary index columns “obs_id”, “event_id” and “tel_id”.

read_telescope_events_by_id(telescopes=None, start=None, stop=None, dl1_images=None, dl1_parameters=None, dl1_muons=None, dl2=None, simulated=None, true_images=None, true_parameters=None, instrument=None, observation_info=None, pointing=None) → dict[int, Table][source]#

Read subarray-based event information.

Parameters:

telescopes: List[Union[int, str, TelescopeDescription]]: Any list containing a combination of telescope IDs or telescope_descriptions.
dl1_images: bool: load extracted images
dl1_parameters: bool: load reconstructed image parameters
dl1_muons: bool: load muon ring parameters
dl2: bool: load available dl2 stereo parameters
simulated: bool: load simulated shower information
true_images: bool: load simulated shower images
true_parameters: bool: load image parameters obtained from true images
instrument: bool: join subarray instrument information to each event
observation_info: bool: join observation information to each event
pointing: bool: join pointing information to each event

Returns:

tables: dict(astropy.io.Table): Dictionary of tables organized by telescope ids Table with primary index columns “obs_id”, “event_id” and “tel_id”.

read_telescope_events_by_id_chunked(chunk_size, *args, **kwargs)[source]#

Iterate over chunks of telescope events and return a dict of one table per telescope id.

Parameters:

chunk_size: int: Number of subarray events to load per chunk. The telescope tables might be larger or smaller than chunk_size depending on the selected telescopes.
*args, **kwargs are passed to `read_telescope_events_by_id`

read_telescope_events_by_type(telescopes=None, start=None, stop=None, dl1_images=None, dl1_parameters=None, dl1_muons=None, dl2=None, simulated=None, true_images=None, true_parameters=None, instrument=None, observation_info=None, pointing=None) → dict[str, Table][source]#

Read subarray-based event information.

Parameters:

telescopes: List[Union[int, str, TelescopeDescription]]: Any list containing a combination of telescope IDs or telescope_descriptions.
dl1_images: bool: load extracted images
dl1_parameters: bool: load reconstructed image parameters
dl1_muons: bool: load muon ring parameters
dl2: bool: load available dl2 stereo parameters
simulated: bool: load simulated shower information
true_images: bool: load simulated shower images
true_parameters: bool: load image parameters obtained from true images
instrument: bool: join subarray instrument information to each event
observation_info: bool: join observation information to each event
pointing: bool: join pointing information to each event

Returns:

tables: dict(astropy.io.Table): Dictionary of tables organized by telescope types Table with primary index columns “obs_id”, “event_id” and “tel_id”.

read_telescope_events_by_type_chunked(chunk_size, *args, **kwargs)[source]#

Iterate over chunks of telescope events as dicts of telescope type to tables.

Parameters:

chunk_size: int: Number of subarray events to load per chunk. The telescope tables might be larger or smaller than chunk_size depending on the selected telescopes.
**kwargs are passed to `read_telescope_events`

read_telescope_events_chunked(chunk_size, *args, **kwargs)[source]#

Iterate over chunks of telescope events.

Parameters:

chunk_size: int: Number of subarray events to load per chunk. The telescope tables might be larger or smaller than chunk_size depending on the selected telescopes.
**kwargs are passed to `read_telescope_events`

TableLoader#

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