Struct Dataset 

pub struct Dataset {
    pub events: Vec<Event>,
    /* private fields */
}

A collection of EventData with optional metadata for name-based lookups.

Fields

events: Vec<Event>

The EventData contained in the Dataset

Implementations

impl Dataset

pub fn iter(&self) -> DatasetIter<'_>

Iterate over all events in the dataset. When MPI is enabled, this will visit every event across all ranks, fetching remote events on demand.

pub fn metadata(&self) -> &DatasetMetadata

Borrow the dataset metadata used for name lookups.

pub fn metadata_arc(&self) -> Arc<DatasetMetadata>

Clone the internal metadata handle for external consumers (e.g., language bindings).

pub fn p4_names(&self) -> &[String]

Names corresponding to stored four-momenta.

pub fn aux_names(&self) -> &[String]

Names corresponding to stored auxiliary scalars.

pub fn p4_index(&self, name: &str) -> Option<usize>

Resolve the index of a four-momentum by name.

pub fn aux_index(&self, name: &str) -> Option<usize>

Resolve the index of an auxiliary scalar by name.

pub fn named_event(&self, index: usize) -> Event

Borrow event data together with metadata-based helpers as an Event view.

pub fn p4_by_name(&self, event_index: usize, name: &str) -> Option<Vec4>

Retrieve a four-momentum by name for the event at event_index.

pub fn aux_by_name(&self, event_index: usize, name: &str) -> Option<f64>

Retrieve an auxiliary scalar by name for the event at event_index.

pub fn index_local(&self, index: usize) -> &Event

Get a reference to the EventData at the given index in the Dataset (non-MPI version).

Notes

This method is not intended to be called in analyses but rather in writing methods that have mpi-feature-gated versions. Most users should just index into a Dataset as if it were any other Vec:

let ds: Dataset = Dataset::new(events);
let event_0 = ds[0];

impl Dataset

pub fn new_local( events: Vec<Arc<EventData>>, metadata: Arc<DatasetMetadata>, ) -> Self

Create a new Dataset from a list of EventData (non-MPI version).

Notes

This method is not intended to be called in analyses but rather in writing methods that have mpi-feature-gated versions. Most users should just call Dataset::new instead.

pub fn new(events: Vec<Arc<EventData>>) -> Self

Create a new Dataset from a list of EventData.

This method is prefered for external use because it contains proper MPI construction methods. Constructing a Dataset manually is possible, but may cause issues when interfacing with MPI and should be avoided unless you know what you are doing.

pub fn new_with_metadata( events: Vec<Arc<EventData>>, metadata: Arc<DatasetMetadata>, ) -> Self

Create a dataset with explicit metadata for name-based lookups. Create a dataset with explicit metadata for name-based lookups.

pub fn n_events_local(&self) -> usize

The number of EventDatas in the Dataset (non-MPI version).

Notes

This method is not intended to be called in analyses but rather in writing methods that have mpi-feature-gated versions. Most users should just call Dataset::n_events instead.

pub fn n_events(&self) -> usize

The number of EventDatas in the Dataset.

impl Dataset

pub fn weights_local(&self) -> Vec<f64>

Extract a list of weights over each EventData in the Dataset (non-MPI version).

Notes

This method is not intended to be called in analyses but rather in writing methods that have mpi-feature-gated versions. Most users should just call Dataset::weights instead.

pub fn weights(&self) -> Vec<f64>

Extract a list of weights over each EventData in the Dataset.

pub fn n_events_weighted_local(&self) -> f64

Returns the sum of the weights for each EventData in the Dataset (non-MPI version).

Notes

This method is not intended to be called in analyses but rather in writing methods that have mpi-feature-gated versions. Most users should just call Dataset::n_events_weighted instead.

pub fn n_events_weighted(&self) -> f64

Returns the sum of the weights for each EventData in the Dataset.

pub fn bootstrap_local(&self, seed: usize) -> Arc<Dataset>

Generate a new dataset with the same length by resampling the events in the original datset with replacement. This can be used to perform error analysis via the bootstrap method. (non-MPI version).

Notes

This method is not intended to be called in analyses but rather in writing methods that have mpi-feature-gated versions. Most users should just call Dataset::bootstrap instead.

pub fn bootstrap(&self, seed: usize) -> Arc<Dataset>

Generate a new dataset with the same length by resampling the events in the original datset with replacement. This can be used to perform error analysis via the bootstrap method.

pub fn filter( &self, expression: &VariableExpression, ) -> LadduResult<Arc<Dataset>>

Filter the Dataset by a given VariableExpression, selecting events for which the expression returns true.

pub fn bin_by<V>( &self, variable: V, bins: usize, range: (f64, f64), ) -> LadduResult<BinnedDataset>

where V: Variable,

Bin a Dataset by the value of the given Variable into a number of bins within the given range.

pub fn boost_to_rest_frame_of<S>(&self, names: &[S]) -> Arc<Dataset>

where S: AsRef<str>,

Boost all the four-momenta in all EventDatas to the rest frame of the given set of four-momenta identified by name.

pub fn evaluate<V: Variable>(&self, variable: &V) -> LadduResult<Vec<f64>>

Evaluate a Variable on every event in the Dataset.

pub fn from_parquet( file_path: &str, options: &DatasetReadOptions, ) -> LadduResult<Arc<Dataset>>

Open a file and construct a Dataset using named columns.

Each entry in p4_names identifies a particle; the loader expects columns named {identifier}_px, {identifier}_py, {identifier}_pz, and {identifier}_e for each identifier (component suffixes are matched case-insensitively). Auxiliary scalars are listed explicitly via aux_names and stored in the same order. Columns may be encoded as Float32 or Float64 and are promoted to f64 on load.

Load a dataset from a Parquet file.

pub fn from_root( file_path: &str, options: &DatasetReadOptions, ) -> LadduResult<Arc<Dataset>>

Load a dataset from a ROOT TTree using the oxyroot backend.

When reading ROOT files containing multiple trees, set DatasetReadOptions::tree to select one.

pub fn to_parquet( &self, file_path: &str, options: &DatasetWriteOptions, ) -> LadduResult<()>

Write the dataset to a Parquet file.

pub fn to_root( &self, file_path: &str, options: &DatasetWriteOptions, ) -> LadduResult<()>

Write the dataset to a ROOT TTree using the oxyroot backend.

Trait Implementations

impl Add<&Dataset> for &Dataset

type Output = Dataset

The resulting type after applying the + operator.

fn add(self, rhs: &Dataset) -> Self::Output

Performs the + operation.

impl Add<&Dataset> for Dataset

type Output = Dataset

The resulting type after applying the + operator.

fn add(self, rhs: &Dataset) -> Self::Output

Performs the + operation.

impl Add<Dataset> for &Dataset

type Output = Dataset

The resulting type after applying the + operator.

fn add(self, rhs: Dataset) -> Self::Output

Performs the + operation.

impl Add for Dataset

type Output = Dataset

The resulting type after applying the + operator.

fn add(self, rhs: Dataset) -> Self::Output

Performs the + operation.

impl Clone for Dataset

fn clone(&self) -> Dataset

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Dataset

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Index<usize> for Dataset

type Output = Event

The returned type after indexing.

fn index(&self, index: usize) -> &Self::Output

Performs the indexing (container[index]) operation.

impl IntoIterator for Dataset

type Item = Event

The type of the elements being iterated over.

type IntoIter = DatasetIntoIter

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.