Struct fibertools_rs::subcommands::predict_m6a::PredictOptions

pub struct PredictOptions<B>
where
    B: Backend<Device = BurnDevice>,
{
    pub keep: bool,
    pub min_ml_score: Option<u8>,
    pub all_calls: bool,
    pub polymerase: PbChem,
    pub batch_size: usize,
    pub model: Vec<u8>,
    pub min_ml: u8,
    pub nuc_opts: NucleosomeParameters,
    pub burn_models: BurnModels<B>,
    pub fake: bool,
    /* private fields */
}

Fields

keep: bool

min_ml_score: Option<u8>

all_calls: bool

polymerase: PbChem

batch_size: usize

model: Vec<u8>

min_ml: u8

nuc_opts: NucleosomeParameters

burn_models: BurnModels<B>

fake: bool

Implementations

impl<B> PredictOptions<B>

where B: Backend<Device = BurnDevice>,

pub fn new( keep: bool, min_ml_score: Option<u8>, all_calls: bool, polymerase: PbChem, batch_size: usize, nuc_opts: NucleosomeParameters, fake: bool, ) -> Self

pub fn progress_style(&self) -> &str

pub fn precision_from_float(&self, value: f32) -> u8

function to find closest value in a btree based on precision

pub fn min_ml_value(&self) -> u8

pub fn float_to_u8(&self, x: f32) -> u8

pub fn predict_m6a_on_records(opts: &Self, records: Vec<&mut Record>) -> usize

group reads together for predictions so we have to move data to the GPU less often

pub fn basemod_from_ml( &self, record: &mut Record, predictions: &[f32], positions: &[usize], base_mod: &str, ) -> BaseMod

Create a basemod object form our predictions

pub fn apply_model(&self, windows: &[f32], count: usize) -> Vec<f32>

Trait Implementations

impl<B> Clone for PredictOptions<B>

where B: Backend<Device = BurnDevice> + Clone,

fn clone(&self) -> PredictOptions<B>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<B> Debug for PredictOptions<B>

where B: Backend<Device = BurnDevice> + Debug,

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

Formats the value using the given formatter.