Struct MatsubaraSampling 

pub struct MatsubaraSampling<S: StatisticsType> { /* private fields */ }

Matsubara sampling for full frequency range (positive and negative)

General complex problem without symmetry → complex coefficients

Implementations

impl<S: StatisticsType> MatsubaraSampling<S>

pub fn new(basis: &impl Basis<S>) -> Self

where S: 'static,

Create Matsubara sampling with default sampling points

Uses extrema-based sampling point selection (symmetric: positive and negative frequencies).

pub fn with_sampling_points( basis: &impl Basis<S>, sampling_points: Vec<MatsubaraFreq<S>>, ) -> Self

where S: 'static,

Create Matsubara sampling with custom sampling points

pub fn from_matrix( sampling_points: Vec<MatsubaraFreq<S>>, matrix: DTensor<Complex<f64>, 2>, ) -> Self

Create Matsubara sampling with custom sampling points and pre-computed matrix

This constructor is useful when the sampling matrix is already computed (e.g., from external sources or for testing).

Arguments

• sampling_points - Matsubara frequency sampling points
• matrix - Pre-computed sampling matrix (n_points × basis_size)

Returns

A new MatsubaraSampling object

Panics

Panics if sampling_points is empty or if matrix dimensions don’t match

pub fn sampling_points(&self) -> &[MatsubaraFreq<S>]

Get sampling points

pub fn n_sampling_points(&self) -> usize

Number of sampling points

pub fn basis_size(&self) -> usize

Basis size

pub fn matrix(&self) -> &DTensor<Complex<f64>, 2>

Get the sampling matrix

pub fn evaluate(&self, coeffs: &[Complex<f64>]) -> Vec<Complex<f64>>

Evaluate complex basis coefficients at sampling points

Arguments

• coeffs - Complex basis coefficients (length = basis_size)

Returns

Complex values at Matsubara frequencies (length = n_sampling_points)

pub fn fit(&self, values: &[Complex<f64>]) -> Vec<Complex<f64>>

Fit complex basis coefficients from values at sampling points

Arguments

• values - Complex values at Matsubara frequencies (length = n_sampling_points)

Returns

Fitted complex basis coefficients (length = basis_size)

pub fn evaluate_nd<T: MatsubaraCoeffs>( &self, backend: Option<&GemmBackendHandle>, coeffs: &Slice<T, DynRank>, dim: usize, ) -> Tensor<Complex<f64>, DynRank>

Evaluate N-dimensional coefficients at Matsubara sampling points

This method dispatches to the appropriate implementation based on the coefficient type at compile time using the MatsubaraCoeffs trait.

Type Parameter

• T - Must implement MatsubaraCoeffs (currently f64 or Complex<f64>)

Arguments

• backend - Optional GEMM backend handle
• coeffs - N-dimensional tensor of basis coefficients
• dim - Dimension along which to evaluate

Returns

N-dimensional tensor of complex values at Matsubara frequencies

pub fn evaluate_nd_real( &self, backend: Option<&GemmBackendHandle>, coeffs: &Tensor<f64, DynRank>, dim: usize, ) -> Tensor<Complex<f64>, DynRank>

Evaluate real basis coefficients at Matsubara sampling points (N-dimensional)

This method takes real coefficients and produces complex values, useful when working with symmetry-exploiting representations or real-valued IR coefficients.

Arguments

• backend - Optional GEMM backend handle (None uses default)
• coeffs - N-dimensional tensor of real basis coefficients
• dim - Dimension along which to evaluate (must have size = basis_size)

Returns

N-dimensional tensor of complex values at Matsubara frequencies

pub fn fit_nd( &self, backend: Option<&GemmBackendHandle>, values: &Tensor<Complex<f64>, DynRank>, dim: usize, ) -> Tensor<Complex<f64>, DynRank>

Fit N-dimensional array of complex values to complex basis coefficients

Arguments

• backend - Optional GEMM backend handle (None uses default)
• values - N-dimensional tensor of complex values at Matsubara frequencies
• dim - Dimension along which to fit (must have size = n_sampling_points)

Returns

N-dimensional tensor of complex basis coefficients

pub fn fit_nd_real( &self, backend: Option<&GemmBackendHandle>, values: &Tensor<Complex<f64>, DynRank>, dim: usize, ) -> Tensor<f64, DynRank>

Fit N-dimensional array of complex values to real basis coefficients

This method fits complex Matsubara values to real IR coefficients. Takes the real part of the least-squares solution.

Arguments

• backend - Optional GEMM backend handle (None uses default)
• values - N-dimensional tensor of complex values at Matsubara frequencies
• dim - Dimension along which to fit (must have size = n_sampling_points)

Returns

N-dimensional tensor of real basis coefficients

pub fn evaluate_nd_to<T: MatsubaraCoeffs>( &self, backend: Option<&GemmBackendHandle>, coeffs: &Slice<T, DynRank>, dim: usize, out: &mut Tensor<Complex<f64>, DynRank>, )

Evaluate basis coefficients at Matsubara sampling points (N-dimensional) with in-place output

Type Parameters

• T - Coefficient type (f64 or Complex)

Arguments

• coeffs - N-dimensional tensor with coeffs.shape().dim(dim) == basis_size
• dim - Dimension along which to evaluate (0-indexed)
• out - Output tensor with out.shape().dim(dim) == n_sampling_points (Complex)

pub fn fit_nd_to( &self, backend: Option<&GemmBackendHandle>, values: &Tensor<Complex<f64>, DynRank>, dim: usize, out: &mut Tensor<Complex<f64>, DynRank>, )

Fit N-dimensional complex values to complex coefficients with in-place output

Arguments

• values - N-dimensional tensor with values.shape().dim(dim) == n_sampling_points
• dim - Dimension along which to fit (0-indexed)
• out - Output tensor with out.shape().dim(dim) == basis_size (Complex)

Trait Implementations

impl<S: StatisticsType> InplaceFitter for MatsubaraSampling<S>

InplaceFitter implementation for MatsubaraSampling

Delegates to ComplexMatrixFitter which supports:

• zz: Complex input → Complex output (full support)
• dz: Real input → Complex output (evaluate only)
• zd: Complex input → Real output (fit only, takes real part)

fn n_points(&self) -> usize

Number of sampling points

fn basis_size(&self) -> usize

Number of basis functions

fn evaluate_nd_dz_to( &self, backend: Option<&GemmBackendHandle>, coeffs: &Slice<f64, DynRank>, dim: usize, out: &mut ViewMut<'_, Complex<f64>, DynRank>, ) -> bool

Evaluate ND: f64 coeffs → Complex values

fn evaluate_nd_zz_to( &self, backend: Option<&GemmBackendHandle>, coeffs: &Slice<Complex<f64>, DynRank>, dim: usize, out: &mut ViewMut<'_, Complex<f64>, DynRank>, ) -> bool

Evaluate ND: Complex coeffs → Complex values

fn fit_nd_zd_to( &self, backend: Option<&GemmBackendHandle>, values: &Slice<Complex<f64>, DynRank>, dim: usize, out: &mut ViewMut<'_, f64, DynRank>, ) -> bool

Fit ND: Complex values → f64 coeffs

fn fit_nd_zz_to( &self, backend: Option<&GemmBackendHandle>, values: &Slice<Complex<f64>, DynRank>, dim: usize, out: &mut ViewMut<'_, Complex<f64>, DynRank>, ) -> bool

Fit ND: Complex values → Complex coeffs

fn evaluate_nd_dd_to( &self, backend: Option<&GemmBackendHandle>, coeffs: &Slice<f64, DynRank>, dim: usize, out: &mut ViewMut<'_, f64, DynRank>, ) -> bool

Evaluate ND: f64 coeffs → f64 values

fn evaluate_nd_zd_to( &self, backend: Option<&GemmBackendHandle>, coeffs: &Slice<Complex<f64>, DynRank>, dim: usize, out: &mut ViewMut<'_, f64, DynRank>, ) -> bool

Evaluate ND: Complex coeffs → f64 values

fn fit_nd_dd_to( &self, backend: Option<&GemmBackendHandle>, values: &Slice<f64, DynRank>, dim: usize, out: &mut ViewMut<'_, f64, DynRank>, ) -> bool

Fit ND: f64 values → f64 coeffs

fn fit_nd_dz_to( &self, backend: Option<&GemmBackendHandle>, values: &Slice<f64, DynRank>, dim: usize, out: &mut ViewMut<'_, Complex<f64>, DynRank>, ) -> bool

Fit ND: f64 values → Complex coeffs