Input/Output (`io`)¶

Introduction¶

ctapipe.io contains functions and classes related to reading, writing, and in-memory storage of event data

Reading Event Data¶

This module provides a set of event sources that are python generators that loop through an input file or stream and fill in Container classes, defined below. They are designed such that ctapipe can be independent of the file format used for event data, and new formats may be supported by simply adding a plug-in.

The underlying mechanism is a set of EventSource sub-classes that read data in various formats, with a common interface and automatic command-line configuration parameters. These are generally constructed in a generic way by using EventSource(file_or_url) which will construct the appropriate EventSource subclass based on the input file’s type.

The resulting EventSource then works like a python collection and can be looped over, providing data for each subsequent event. If looped over multiple times, each will start at the beginning of the file (except in the case of streams that cannot be restarted):

with EventSource(input_url="file.simtel.gz") as source:
    for event in source:
       do_something_with_event(event)

If you need random access to events rather than looping over all events in order, you can use the EventSeeker class to allow random access by event index or event_id. This may not be efficient for some EventSources if the underlying file type does not support random access.

Creating a New EventSource Plugin¶

ctapipe uses entry points to discover possible EventSource implementations. When using EventSource(path), all available implementations are tried and the first where <cls>.is_compatible(path) returns True is returned.

To register an EventSource implementation, a package needs to add an ctapipe_io entry point providing the source implementation, e.g. like this in setup.cfg:

[options.entry_points]
ctapipe_io =
    MyAwesomeEventSource = ctapipe_io_awesome:MyAwesomeEventSource

A basic example can be found in the test_plugin directory.

Container Classes¶

Event data that is intended to be read or written from files is stored in subclasses of Container, the structre of which is defined in the containers module (See reference API below). Each element in the container is a Field, containing the default value, a description, and default unit if necessary. The following rules should be followed when creating a Container for new data:

Containers both provide a way to exchange data (in-memory) between parts of a code, as well as define the schema for any file output files to be written.
All items in a Container should be expected to be updated at the same frequency. Think of a Container as the column definitions of a table, therefore representing a single row in a table. For example, if the container has event-by-event info, it should not have an item in it that does not change between events (that should be in another container), otherwise it will be written out for each event and will waste space.
a Container should be filled in all at once, not at different times during the data processing (to allow for parallelization and to avoid difficulty in reading code).
Containers may contain a dictionary of metadata (in their meta dictionary), that will become headers in any output file (this data must not change per-event, etc)
Algorithms should not update values in a container that have already been filled in by another algorithm. Instead, prefer a new data item, or a second copy of the Container with the updated values.
Fields in a container should be one of the following:

scalar values (int, float, bool)

numpy.NDarray if the data are not scalar (use only simple dtypes that can be written to output files)

a Container class (in the case a hierarchy is needed)

a Map of Container or scalar values, if the hierarchy needs multiple copies of the same Container, organized by some variable-length index (e.g. by tel_id or algorithm name)

Fields that should not be in a container class:

dict

classes that are not a subclass of Container

any other type that cannot be translated automatically into the column of an output table.

Serialization of Containers:¶

The TableWriter and TableReader base classes provide an interface to implement subclasses that write/read Containers to/from table-like data files. Currently the only implementation is for writing HDF5 tables via the HDF5TableWriter. The output that the HDF5TableWriter produces can be read either one-row-at-a-time using the HDF5TableReader, or more generically using the pytables or pandas packages (note however any tables that have array values in a column cannot be read into a pandas.DataFrame, since it only supports scalar values).

Writing Output Files:¶

The DataWriter Component allows one to write a series of events (stored in ctapipe.containers.ArrayEventContainer) to a standardized HDF5 format file following the data model (see Data Models). This includes all related datasets such as the instrument and simulation configuration information, simulated shower and image information, observed images and parameters and reconstruction information. It can be used in an event loop like:

with DataWriter(event_source=source, output_path="events.dl1.h5") as write_data:
    for event in source:
        calibrate(event)
        write_data(event)

Reading Output Tables:¶

In addition to using an EventSource to read R0-DL1 data files, one can also access full tables for files that are in HDF5 format (e.g. DL1 and higher files).

TableLoader: is a a convenient way to load and join together the tables in a ctapipe output file into one or more high-level tables useful for analysis. Which information is read and joined is controlled by the TableLoader’s configuration options.

By default, TableLoader will read the dl1 parameters for each telescope into one big table, joining the simulation information if available:

from ctapipe.io import TableLoader

loader = TableLoader("events.dl1.h5")
events = loader.read_subarray_events()
tel_events = loader.read_telescope_events()

print(loader.subarray, len(events), len(tel_events))

You can also load telescope events for specific selections of telescopes: .. code-block:: python

# by str representation of the type loader.read_telescope_events([“LST_LST_LSTCam”])

# by telescope ids loader.read_telescope_events([1, 2, 3, 15])

# mixture loader.read_telescope_events([1, 2, 3, 4, “MST_MST_NectarCam”])

Loading the DL1 image data for telescopes with different numbers of pixels does not work as astropy tables do not support heterogenous data in columns. In this case, use:

from ctapipe.io import TableLoader

loader = TableLoader("events.dl1.h5", load_dl1_images=True)

# tel_events is now a dict[str] -> Table mapping telescope type names to
# table for that telescope type
tel_events = loader.read_telescope_events_by_type()
print(tel_events["LST_LST_LSTCam"])

For more examples, see TableLoader.

Reading Single HDF5 Tables¶

The read_table function will load any table in an HDF5 table into an astropy.table.QTable in memory, while maintaining units, column descriptions, and other ctapipe metadata. Astropy Tables can also be converted to Pandas tables via their to_pandas() method, as long as the table does not contain any vector columns.

from ctapipe.io import read_table
mctable = read_table("events.dl1.h5", "/simulation/event/subarray/shower")
mctable['logE'] = np.log10(mc_table['energy'])
mctable.write("output.fits")

Standard Metadata Headers¶

The ctapipe.io.metadata package provides functions for generating standard CTA metadata headers and attaching them to output files.

Reference/API¶

ctapipe.io Package¶

ctapipe io module

# order matters to prevent circular imports isort:skip_file

Functions¶

`read_table`(h5file, path[, start, stop, …])	Read a table from an HDF5 file
`write_table`(table, h5file, path[, append, …])	Write a table to an HDF5 file
`get_hdf5_datalevels`(h5file)	Get the data levels present in the hdf5 file

Classes¶

`HDF5TableWriter`(**kwargs)	A very basic table writer that can take a container (or more than one) and write it to an HDF5 file.
`HDF5TableReader`(**kwargs)	Reader that reads a single row of an HDF5 table at once into a Container.
`HDF5Merger`(**kwargs)	Class to copy / append / merge ctapipe hdf5 files
`TableWriter`(**kwargs)	Base class for writing tabular data stored in `Container`
`TableReader`(**kwargs)	Base class for row-wise table readers.
`TableLoader`(**kwargs)	Load telescope-event or subarray-event data from ctapipe HDF5 files
`EventSeeker`(**kwargs)	Provides the functionality to seek through a `EventSource` to find a particular event.
`EventSource`([input_url, config, parent])	Parent class for EventSources.
`SimTelEventSource`([input_url, config, parent])	Read events from a SimTelArray data file (in EventIO format).
`HDF5EventSource`([input_url, config, parent])	Event source for files in the ctapipe DL1 format.
`DataLevel`(value)	Enum of the different Data Levels
`DataWriter`(**kwargs)	Serialize a sequence of events into a HDF5 file, in the correct format

ctapipe.io.tableio Module¶

Base functionality for reading and writing tabular data

Classes¶

`TableReader`(**kwargs)	Base class for row-wise table readers.
`TableWriter`(**kwargs)	Base class for writing tabular data stored in `Container`

ctapipe.io.tableloader Module¶

Class and related functions to read DL1 (a,b) and/or DL2 (a) data from an HDF5 file produced with ctapipe-process.

Classes¶

TableLoader(**kwargs)

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

ctapipe.io.hdf5tableio Module¶

Implementations of TableWriter and -Reader for HDF5 files

Functions¶

split_h5path(path)

Split a path inside an hdf5 file into parent / child

Classes¶

`HDF5TableWriter`(**kwargs)	A very basic table writer that can take a container (or more than one) and write it to an HDF5 file.
`HDF5TableReader`(**kwargs)	Reader that reads a single row of an HDF5 table at once into a Container.

ctapipe.io.metadata Module¶

Management of CTA Reference Metadata, as defined in the CTA Top-Level Data Model document [ctatopleveldatamodel] , version 1A. This information is required to be attached to the header of any files generated.

The class Reference collects all required reference metadata, and can be turned into a flat dictionary. The user should try to fill out all fields, or use a helper to fill them (as in Activity.from_provenance())

ref = Reference(
    contact=Contact(name="Some User", email="user@me.com"),
    product=Product(format='hdf5', ...),
    process=Process(...),
    activity=Activity(...),
    instrument = Instrument(...)
)

some_astropy_table.meta = ref.to_dict()
some_astropy_table.write("output.ecsv")

Functions¶

`write_to_hdf5`(metadata, h5file[, path])	Write metadata fields to a PyTables HDF5 file handle.
`read_metadata`(h5file[, path])	Read metadata from an hdf5 file

Classes¶

`Reference`(**kwargs)	All the reference Metadata required for a CTA output file, plus a way to turn it into a dict() for easy addition to the header of a file
`Contact`(**kwargs)	Contact information
`Process`(**kwargs)	Process (top-level workflow) information
`Product`(**kwargs)	Data product information
`Activity`(**kwargs)	Activity (tool) information
`Instrument`(**kwargs)	Instrumental Context

ctapipe.io.eventsource Module¶

Handles reading of different event/waveform containing files

Classes¶

EventSource([input_url, config, parent])

Parent class for EventSources.

ctapipe.io.simteleventsource Module¶

Functions¶

read_atmosphere_profile_from_simtel(simtelfile)

Read an atmosphere profile from a SimTelArray file as an astropy Table

Classes¶

`SimTelEventSource`([input_url, config, parent])	Read events from a SimTelArray data file (in EventIO format).
`AtmosphereProfileKind`(value)	choice for which kind of atmosphere density profile to load if more than one is present
`MirrorClass`(value)	Enum for the sim_telarray MIRROR_CLASS values

ctapipe.io.hdf5eventsource Module¶

Classes¶

HDF5EventSource([input_url, config, parent])

Event source for files in the ctapipe DL1 format.

ctapipe.io.eventseeker Module¶

Handles seeking to a particular event in a ctapipe.io.EventSource

Classes¶

EventSeeker(**kwargs)

Provides the functionality to seek through a EventSource to find a particular event.

Input/Output (io)¶

Introduction¶

Reading Event Data¶

Creating a New EventSource Plugin¶

Container Classes¶

Serialization of Containers:¶

Writing Output Files:¶

Reading Output Tables:¶

Reading Single HDF5 Tables¶

Standard Metadata Headers¶

Reference/API¶

ctapipe.io Package¶

Functions¶

Classes¶

ctapipe.io.tableio Module¶

Classes¶

ctapipe.io.tableloader Module¶

Classes¶

ctapipe.io.hdf5tableio Module¶

Functions¶

Classes¶

ctapipe.io.metadata Module¶

Functions¶

Classes¶

ctapipe.io.eventsource Module¶

Classes¶

ctapipe.io.simteleventsource Module¶

Functions¶

Classes¶

ctapipe.io.hdf5eventsource Module¶

Classes¶

ctapipe.io.eventseeker Module¶

Classes¶

Input/Output (`io`)¶