Struct TimsDatasetDDA 

pub struct TimsDatasetDDA {
    pub loader: TimsDataLoader,
    pub pasef_meta: Vec<PasefMsMsMeta>,
}

Fields

loader: TimsDataLoader

pasef_meta: Vec<PasefMsMsMeta>

Implementations

impl TimsDatasetDDA

pub fn new( bruker_lib_path: &str, data_path: &str, in_memory: bool, use_bruker_sdk: bool, ) -> Self

pub fn new_with_calibration( data_path: &str, in_memory: bool, im_lookup: Vec<f64>, ) -> Self

Create a new DDA dataset with pre-computed ion mobility calibration lookup table.

This enables accurate ion mobility calibration with fast parallel extraction. The im_lookup table should be pre-computed using the Bruker SDK.

Arguments

• data_path - Path to the .d folder
• in_memory - Whether to load all data into memory
• im_lookup - Pre-computed scan→1/K0 lookup table from Bruker SDK

Returns

A new TimsDatasetDDA with LookupIndexConverter (thread-safe, accurate)

pub fn new_with_mz_calibration( data_path: &str, in_memory: bool, tof_intercept: f64, tof_slope: f64, ) -> Self

Create a DDA dataset with regression-derived m/z calibration.

This method uses externally-provided m/z calibration coefficients (e.g., from linear regression on precursor data) instead of the simple boundary model.

Arguments

• data_path - Path to the .d folder
• in_memory - Whether to load all data into memory
• tof_intercept - Intercept for sqrt(mz) = intercept + slope * tof
• tof_slope - Slope for sqrt(mz) = intercept + slope * tof

Returns

A new TimsDatasetDDA with CalibratedIndexConverter (thread-safe, accurate)

pub fn uses_bruker_sdk(&self) -> bool

Check if the Bruker SDK is being used for index conversion. Returns false for both Simple and Lookup converters (which are thread-safe).

pub fn get_selected_precursors(&self) -> Vec<DDAPrecursor>

pub fn get_precursor_frames( &self, min_intensity: f64, max_num_peaks: usize, num_threads: usize, ) -> Vec<TimsFrame>

pub fn extract_precursor_ms1_signals( &self, precursor_coords: Vec<PrecursorCoord>, rt_window_sec: f64, mz_tol_ppm: f64, im_window: f64, n_isotopes: usize, num_threads: usize, ) -> Vec<PrecursorMS1Signal>

Extract MS1 precursor signals for a batch of precursors in parallel.

For each precursor, extracts:

• XIC (chromatographic profile) from MS1 frames in RT window
• Mobilogram (IM profile)
• Isotope envelope
• Statistical moments (mean, variance, skewness, apex, FWHM) for each dimension

Uses batched processing to avoid loading all MS1 frames at once.

Arguments

• precursor_coords - Vector of precursor coordinates (id, mz, rt_sec, mobility, charge)
• rt_window_sec - RT window in seconds (total width)
• mz_tol_ppm - m/z tolerance in ppm
• im_window - IM window in 1/K0 units (total width)
• n_isotopes - Number of isotope peaks to extract
• num_threads - Number of threads for parallel processing

Returns

Vector of PrecursorMS1Signal, one per input precursor

pub fn get_pasef_frame_ms_ms_info(&self) -> Vec<PasefMsMsMeta>

pub fn get_pasef_fragments(&self, num_threads: usize) -> Vec<PASEFDDAFragment>

Get the fragment spectra for all PASEF selected precursors

pub fn get_pasef_fragments_for_precursors( &self, precursor_ids: Option<&[u32]>, num_threads: usize, ) -> Vec<PASEFDDAFragment>

Get fragment spectra for specific precursor IDs only. If precursor_ids is None, returns all fragments (same as get_pasef_fragments). This is more memory-efficient for batched processing.

pub fn get_preprocessed_pasef_fragments( &self, dataset_name: &str, config: SpectrumProcessingConfig, num_threads: usize, ) -> Vec<PreprocessedSpectrum>

Get preprocessed PASEF fragments ready for database search. This method performs parallel processing of all fragment spectra, including:

• Flattening frames across ion mobility dimension
• Deisotoping (optional)
• Filtering to top N peaks
• Computing inverse mobility along scan marginal

Arguments

• dataset_name - Name of the dataset for generating spec_ids
• config - Spectrum processing configuration
• num_threads - Number of threads to use for parallel processing

Returns

Vector of preprocessed spectra ready for Sage search

pub fn sample_pasef_fragment_random( &self, target_scan_apex: i32, experiment_max_scan: i32, ) -> TimsFrame

pub fn sample_pasef_fragments_random( &self, target_scan_apex_values: Vec<i32>, experiment_max_scan: i32, ) -> TimsFrame

pub fn sample_precursor_signal( &self, num_frames: usize, max_intensity: f64, take_probability: f64, ) -> TimsFrame

Trait Implementations

impl IndexConverter for TimsDatasetDDA

fn tof_to_mz(&self, frame_id: u32, tof_values: &Vec<u32>) -> Vec<f64>

fn mz_to_tof(&self, frame_id: u32, mz_values: &Vec<f64>) -> Vec<u32>

fn scan_to_inverse_mobility( &self, frame_id: u32, scan_values: &Vec<u32>, ) -> Vec<f64>

fn inverse_mobility_to_scan( &self, frame_id: u32, inverse_mobility_values: &Vec<f64>, ) -> Vec<u32>

impl TimsData for TimsDatasetDDA

fn get_frame(&self, frame_id: u32) -> TimsFrame

fn get_raw_frame(&self, frame_id: u32) -> RawTimsFrame

fn get_slice(&self, frame_ids: Vec<u32>, num_threads: usize) -> TimsSlice

fn get_acquisition_mode(&self) -> AcquisitionMode

fn get_frame_count(&self) -> i32

fn get_data_path(&self) -> &str