spine.data.out.TruthParticle

class spine.data.out.TruthParticle(orig_id: int = -1, is_truth: bool = True, index_adapt: ndarray = <factory>, index_g4: ndarray = <factory>, points_adapt: ndarray = <factory>, points_g4: ndarray = <factory>, depositions_q: ndarray = <factory>, depositions_adapt: ndarray = <factory>, depositions_adapt_q: ndarray = <factory>, depositions_g4: ndarray = <factory>, sources_adapt: ndarray = <factory>, units: str = 'cm', id: int = -1, is_contained: bool = False, is_time_contained: bool = False, is_cathode_crosser: bool = False, is_matched: bool = False, cathode_offset: float = nan, index: ndarray = <factory>, orig_index: ndarray = <factory>, points: ndarray = <factory>, depositions: ndarray = <factory>, sources: ndarray = <factory>, match_ids: ndarray = <factory>, match_overlaps: ndarray = <factory>, interaction_id: int = -1, chi2_pid: int = -1, is_primary: bool = False, is_crt_matched: bool = False, is_valid: bool = True, length: float = nan, calo_ke: float = nan, csda_ke: float = nan, mcs_ke: float = nan, shape: int = -1, pid: int = -1, fragment_ids: ndarray = <factory>, start_point: ndarray = <factory>, end_point: ndarray = <factory>, chi2_per_pid: ndarray = <factory>, csda_ke_per_pid: ndarray = <factory>, mcs_ke_per_pid: ndarray = <factory>, crt_ids: ndarray = <factory>, crt_times: ndarray = <factory>, crt_scores: ndarray = <factory>, parent_id: int = -1, group_id: int = -1, nu_id: int = -1, children_id: ndarray = <factory>, mct_index: int = -1, mcst_index: int = -1, group_primary: int = -1, interaction_primary: int = -1, track_id: int = -1, parent_track_id: int = -1, ancestor_track_id: int = -1, pdg_code: int = -1, parent_pdg_code: int = -1, ancestor_pdg_code: int = -1, num_voxels: int = -1, t: float = nan, end_t: float = nan, parent_t: float = nan, ancestor_t: float = nan, energy_init: float = nan, energy_deposit: float = nan, distance_travel: float = nan, creation_process: str = '', parent_creation_process: str = '', ancestor_creation_process: str = '', position: ndarray = <factory>, end_position: ndarray = <factory>, parent_position: ndarray = <factory>, ancestor_position: ndarray = <factory>, first_step: ndarray = <factory>, last_step: ndarray = <factory>, momentum: ndarray = <factory>, end_momentum: ndarray = <factory>, orig_interaction_id: int = -1, orig_parent_id: int = -1, orig_group_id: int = -1, reco_length: float = nan, fragments: list[TruthFragment] = <factory>, orig_children_id: ndarray = <factory>, children_counts: ndarray = <factory>, reco_start_dir: ndarray = <factory>, reco_end_dir: ndarray = <factory>)[source]

Truth particle information.

This inherits all of the attributes of Particle, which contains the G4 truth information for the particle.

fragments

List of fragments that make up this particle

Type:: List[TruthFragment]

orig_interaction_id

Unaltered index of the interaction in the original MC particle list

Type:: int

orig_parent_id

Unaltered index of the particle parent in the original MC particle list

Type:: int

orig_group_id

Unaltered index of the particle group in the original MC particle list

Type:: int

orig_children_id

Unaltered list of the particle children in the original MC particle list

Type:: np.ndarray

children_counts

Number of truth child particle of each shape

Type:: np.ndarray

pdg_code

PDG code corresponding to the PID number

Type:: int

mass

Rest mass of the particle in MeV/c^2

Type:: float

ke

Kinetic energy of the particle in MeV

Type:: float

momentum

3-momentum of the particle at the production point in MeV/c

Type:: np.ndarray

start_dir

Particle direction w.r.t. the start point

Type:: np.ndarray

end_dir

(3) Particle direction w.r.t. the end point (only assigned to track objects)

Type:: np.ndarray

reco_length

Reconstructed length of the particle (only assigned to track objects)

Type:: float

reco_start_dir

Particle direction estimate w.r.t. the start point

Type:: np.ndarray

reco_end_dir

(3) Particle direction estimate w.r.t. the end point (only assigned to track objects)

Type:: np.ndarray

reco_ke

Best-guess reconstructed KE of the particle

Type:: float

reco_momentum

Best-guess reconstructed momentum of the particle

Type:: np.ndarray

Attributes:

depositions_adapt_q_sum: Total deposition value for the entire object in the adapted tensor and in the original units.
depositions_adapt_sum: Total deposition value for the entire object in the adapted tensor.
depositions_g4_sum: Total deposition value for the entire object in the Geant4 tensor.
depositions_q_sum: Total deposition value for the entire object in the original units.
depositions_sum: Total deposition value for the entire object.
end_dir: Converts the final momentum to a direction vector.
end_p: Computes the magnitude of the final momentum.
enum_attrs: Fetches HDF5-friendly enumerator descriptors for enum-backed fields.
enum_dicts: Fetches the dictionary of enumerated attributes and their enumerator descriptors.
enum_values: Fetches reverse enum lookups from stored values to symbolic names.
field_units: Fetches the documented units for each field.
index_attrs: Return the tuple of index-bearing attributes.
ke: Converts the particle initial energy to a kinetic energy.
mass: Computes the rest mass of the particle from its energy/momentum.
module_ids: List of modules that contribute to this object.
num_fragments: Number of fragments that make up this particle.
p: Computes the magnitude of the initial momentum.
reco_ke: Best-guess reconstructed kinetic energy in MeV.
reco_momentum: Best-guess reconstructed momentum in MeV/c.
size: Total number of voxels that make up the object.
size_adapt: Total number of voxels that make up the object in the adapted tensor.
size_g4: Total number of voxels that make up the object in the Geant4 tensor.
start_dir: Converts the initial momentum to a direction vector.

Methods

`as_dict`([lite, include_derived])	Returns the data class as dictionary of (key, value) pairs.
`attr_names`([include_derived, ...])	Return the names of valid attributes on this data class.
`from_dict`(cls_dict)	Builds and returns an object of the class from a dictionary of attributes.
`from_larcv`(particle)	Builds and returns a Particle object from a LArCV Particle object.
`reset_cathode_crosser`()	Resets the cathode crossing information for the object.
`reset_crt_match`()	Reset all the CRT hit matching attributes.
`reset_match`()	Resets the reco/truth matching information for the object.
`scalar_dict`([attrs, lengths, lite])	Returns the data class attributes as a dictionary of scalars.
`set_precision`([precision])	Casts all the vector attributes to a different precision.
`shift_indexes`(shifts)	Apply offsets to index attributes in place.
`to_cm`(meta)	Converts the coordinates of the positional attributes to cm in place.
`to_px`(meta)	Converts the coordinates of the positional attributes to pixel in place.
`value_with_units`(attr)	Fetch an attribute value with its documented units.

__init__(orig_id: int = -1, is_truth: bool = True, index_adapt: ndarray = <factory>, index_g4: ndarray = <factory>, points_adapt: ndarray = <factory>, points_g4: ndarray = <factory>, depositions_q: ndarray = <factory>, depositions_adapt: ndarray = <factory>, depositions_adapt_q: ndarray = <factory>, depositions_g4: ndarray = <factory>, sources_adapt: ndarray = <factory>, units: str = 'cm', id: int = -1, is_contained: bool = False, is_time_contained: bool = False, is_cathode_crosser: bool = False, is_matched: bool = False, cathode_offset: float = nan, index: ndarray = <factory>, orig_index: ndarray = <factory>, points: ndarray = <factory>, depositions: ndarray = <factory>, sources: ndarray = <factory>, match_ids: ndarray = <factory>, match_overlaps: ndarray = <factory>, interaction_id: int = -1, chi2_pid: int = -1, is_primary: bool = False, is_crt_matched: bool = False, is_valid: bool = True, length: float = nan, calo_ke: float = nan, csda_ke: float = nan, mcs_ke: float = nan, shape: int = -1, pid: int = -1, fragment_ids: ndarray = <factory>, start_point: ndarray = <factory>, end_point: ndarray = <factory>, chi2_per_pid: ndarray = <factory>, csda_ke_per_pid: ndarray = <factory>, mcs_ke_per_pid: ndarray = <factory>, crt_ids: ndarray = <factory>, crt_times: ndarray = <factory>, crt_scores: ndarray = <factory>, parent_id: int = -1, group_id: int = -1, nu_id: int = -1, children_id: ndarray = <factory>, mct_index: int = -1, mcst_index: int = -1, group_primary: int = -1, interaction_primary: int = -1, track_id: int = -1, parent_track_id: int = -1, ancestor_track_id: int = -1, pdg_code: int = -1, parent_pdg_code: int = -1, ancestor_pdg_code: int = -1, num_voxels: int = -1, t: float = nan, end_t: float = nan, parent_t: float = nan, ancestor_t: float = nan, energy_init: float = nan, energy_deposit: float = nan, distance_travel: float = nan, creation_process: str = '', parent_creation_process: str = '', ancestor_creation_process: str = '', position: ndarray = <factory>, end_position: ndarray = <factory>, parent_position: ndarray = <factory>, ancestor_position: ndarray = <factory>, first_step: ndarray = <factory>, last_step: ndarray = <factory>, momentum: ndarray = <factory>, end_momentum: ndarray = <factory>, orig_interaction_id: int = -1, orig_parent_id: int = -1, orig_group_id: int = -1, reco_length: float = nan, fragments: list[TruthFragment] = <factory>, orig_children_id: ndarray = <factory>, children_counts: ndarray = <factory>, reco_start_dir: ndarray = <factory>, reco_end_dir: ndarray = <factory>) → None

Methods

`__init__`(orig_id, is_truth, index_adapt, ...)
`as_dict`([lite, include_derived])	Returns the data class as dictionary of (key, value) pairs.
`attr_names`([include_derived, ...])	Return the names of valid attributes on this data class.
`from_dict`(cls_dict)	Builds and returns an object of the class from a dictionary of attributes.
`from_larcv`(particle)	Builds and returns a Particle object from a LArCV Particle object.
`reset_cathode_crosser`()	Resets the cathode crossing information for the object.
`reset_crt_match`()	Reset all the CRT hit matching attributes.
`reset_match`()	Resets the reco/truth matching information for the object.
`scalar_dict`([attrs, lengths, lite])	Returns the data class attributes as a dictionary of scalars.
`set_precision`([precision])	Casts all the vector attributes to a different precision.
`shift_indexes`(shifts)	Apply offsets to index attributes in place.
`to_cm`(meta)	Converts the coordinates of the positional attributes to cm in place.
`to_px`(meta)	Converts the coordinates of the positional attributes to pixel in place.
`value_with_units`(attr)	Fetch an attribute value with its documented units.

Attributes

`ancestor_creation_process`
`ancestor_pdg_code`
`ancestor_t`
`ancestor_track_id`
`calo_ke`
`cathode_offset`
`chi2_pid`
`creation_process`
`csda_ke`
`depositions_adapt_q_sum`	Total deposition value for the entire object in the adapted tensor and in the original units.
`depositions_adapt_sum`	Total deposition value for the entire object in the adapted tensor.
`depositions_g4_sum`	Total deposition value for the entire object in the Geant4 tensor.
`depositions_q_sum`	Total deposition value for the entire object in the original units.
`depositions_sum`	Total deposition value for the entire object.
`distance_travel`
`end_dir`	Converts the final momentum to a direction vector.
`end_p`	Computes the magnitude of the final momentum.
`end_t`
`energy_deposit`
`energy_init`
`enum_attrs`	Fetches HDF5-friendly enumerator descriptors for enum-backed fields.
`enum_dicts`	Fetches the dictionary of enumerated attributes and their enumerator descriptors.
`enum_values`	Fetches reverse enum lookups from stored values to symbolic names.
`field_units`	Fetches the documented units for each field.
`group_id`
`group_primary`
`id`
`index_attrs`	Return the tuple of index-bearing attributes.
`interaction_id`
`interaction_primary`
`is_cathode_crosser`
`is_contained`
`is_crt_matched`
`is_matched`
`is_primary`
`is_time_contained`
`is_truth`
`is_valid`
`ke`	Converts the particle initial energy to a kinetic energy.
`length`
`mass`	Computes the rest mass of the particle from its energy/momentum.
`mcs_ke`
`mcst_index`
`mct_index`
`module_ids`	List of modules that contribute to this object.
`nu_id`
`num_fragments`	Number of fragments that make up this particle.
`num_voxels`
`orig_group_id`
`orig_id`
`orig_interaction_id`
`orig_parent_id`
`p`	Computes the magnitude of the initial momentum.
`parent_creation_process`
`parent_id`
`parent_pdg_code`
`parent_t`
`parent_track_id`
`pdg_code`
`pid`
`reco_ke`	Best-guess reconstructed kinetic energy in MeV.
`reco_length`
`reco_momentum`	Best-guess reconstructed momentum in MeV/c.
`shape`
`size`	Total number of voxels that make up the object.
`size_adapt`	Total number of voxels that make up the object in the adapted tensor.
`size_g4`	Total number of voxels that make up the object in the Geant4 tensor.
`start_dir`	Converts the initial momentum to a direction vector.
`t`
`track_id`
`units`
`fragments`
`orig_children_id`
`children_counts`
`reco_start_dir`
`reco_end_dir`
`children_id`
`position`
`end_position`
`parent_position`
`ancestor_position`
`first_step`
`last_step`
`momentum`
`end_momentum`
`fragment_ids`
`start_point`
`end_point`
`chi2_per_pid`
`csda_ke_per_pid`
`mcs_ke_per_pid`
`crt_ids`
`crt_times`
`crt_scores`
`index`
`orig_index`
`points`
`depositions`
`sources`
`match_ids`
`match_overlaps`
`index_adapt`
`index_g4`
`points_adapt`
`points_g4`
`depositions_q`
`depositions_adapt`
`depositions_adapt_q`
`depositions_g4`
`sources_adapt`

orig_interaction_id: int = -1

orig_parent_id: int = -1

orig_group_id: int = -1

reco_length: float = nan

fragments: list[TruthFragment]

orig_children_id: ndarray

children_counts: ndarray

reco_start_dir: ndarray

reco_end_dir: ndarray

property start_dir: ndarray

Converts the initial momentum to a direction vector.

Returns:

Start direction vector

Return type:

np.ndarray

property end_dir: ndarray

Converts the final momentum to a direction vector.

Note that if a particle stops, this is unreliable as an estimate of the direction of the particle before it stops.

Returns:

End direction vector

Return type:

np.ndarray

property ke: float

Converts the particle initial energy to a kinetic energy.

This only works for particles with a known mass (as defined in spine.constants).

Returns:: Initial kinetic energy of the particle
Return type:: float

property reco_ke: float

Best-guess reconstructed kinetic energy in MeV.

Uses calorimetry for EM activity and this order for track: - CSDA-based estimate if it is available - MCS-based estimate if it is available - Calorimetry if all else fails

Returns:: Best-guess kinetic energy
Return type:: float

property reco_momentum: ndarray

Best-guess reconstructed momentum in MeV/c.

Returns:

Momentum vector

Return type:

np.ndarray