forked from M-Labs/nalgebra
594 lines
20 KiB
Rust
594 lines
20 KiB
Rust
//! An implementation of the CSC sparse matrix format.
|
|
|
|
use crate::{SparseFormatError, SparseFormatErrorKind, SparseEntry, SparseEntryMut};
|
|
use crate::pattern::{SparsityPattern, SparsityPatternFormatError, SparsityPatternIter};
|
|
use crate::csr::CsrMatrix;
|
|
use crate::cs::{CsMatrix, CsLane, CsLaneMut, CsLaneIter, CsLaneIterMut};
|
|
|
|
use std::slice::{IterMut, Iter};
|
|
use num_traits::{One};
|
|
use nalgebra::Scalar;
|
|
|
|
/// A CSC representation of a sparse matrix.
|
|
///
|
|
/// The Compressed Sparse Column (CSC) format is well-suited as a general-purpose storage format
|
|
/// for many sparse matrix applications.
|
|
///
|
|
/// TODO: Storage explanation and examples
|
|
///
|
|
#[derive(Debug, Clone, PartialEq, Eq)]
|
|
pub struct CscMatrix<T> {
|
|
// Cols are major, rows are minor in the sparsity pattern
|
|
pub(crate) cs: CsMatrix<T>,
|
|
}
|
|
|
|
impl<T> CscMatrix<T> {
|
|
/// Create a zero CSC matrix with no explicitly stored entries.
|
|
pub fn new(nrows: usize, ncols: usize) -> Self {
|
|
Self {
|
|
cs: CsMatrix::new(ncols, nrows)
|
|
}
|
|
}
|
|
|
|
/// The number of rows in the matrix.
|
|
#[inline]
|
|
pub fn nrows(&self) -> usize {
|
|
self.cs.pattern().minor_dim()
|
|
}
|
|
|
|
/// The number of columns in the matrix.
|
|
#[inline]
|
|
pub fn ncols(&self) -> usize {
|
|
self.cs.pattern().major_dim()
|
|
}
|
|
|
|
/// The number of non-zeros in the matrix.
|
|
///
|
|
/// Note that this corresponds to the number of explicitly stored entries, *not* the actual
|
|
/// number of algebraically zero entries in the matrix. Explicitly stored entries can still
|
|
/// be zero. Corresponds to the number of entries in the sparsity pattern.
|
|
#[inline]
|
|
pub fn nnz(&self) -> usize {
|
|
self.pattern().nnz()
|
|
}
|
|
|
|
/// The column offsets defining part of the CSC format.
|
|
#[inline]
|
|
pub fn col_offsets(&self) -> &[usize] {
|
|
self.pattern().major_offsets()
|
|
}
|
|
|
|
/// The row indices defining part of the CSC format.
|
|
#[inline]
|
|
pub fn row_indices(&self) -> &[usize] {
|
|
self.pattern().minor_indices()
|
|
}
|
|
|
|
/// The non-zero values defining part of the CSC format.
|
|
#[inline]
|
|
pub fn values(&self) -> &[T] {
|
|
self.cs.values()
|
|
}
|
|
|
|
/// Mutable access to the non-zero values.
|
|
#[inline]
|
|
pub fn values_mut(&mut self) -> &mut [T] {
|
|
self.cs.values_mut()
|
|
}
|
|
|
|
/// Try to construct a CSC matrix from raw CSC data.
|
|
///
|
|
/// It is assumed that each column contains unique and sorted row indices that are in
|
|
/// bounds with respect to the number of rows in the matrix. If this is not the case,
|
|
/// an error is returned to indicate the failure.
|
|
///
|
|
/// An error is returned if the data given does not conform to the CSC storage format.
|
|
/// See the documentation for [CscMatrix](struct.CscMatrix.html) for more information.
|
|
pub fn try_from_csc_data(
|
|
num_rows: usize,
|
|
num_cols: usize,
|
|
col_offsets: Vec<usize>,
|
|
row_indices: Vec<usize>,
|
|
values: Vec<T>,
|
|
) -> Result<Self, SparseFormatError> {
|
|
let pattern = SparsityPattern::try_from_offsets_and_indices(
|
|
num_cols, num_rows, col_offsets, row_indices)
|
|
.map_err(pattern_format_error_to_csc_error)?;
|
|
Self::try_from_pattern_and_values(pattern, values)
|
|
}
|
|
|
|
/// Try to construct a CSC matrix from a sparsity pattern and associated non-zero values.
|
|
///
|
|
/// Returns an error if the number of values does not match the number of minor indices
|
|
/// in the pattern.
|
|
pub fn try_from_pattern_and_values(pattern: SparsityPattern, values: Vec<T>)
|
|
-> Result<Self, SparseFormatError> {
|
|
if pattern.nnz() == values.len() {
|
|
Ok(Self {
|
|
cs: CsMatrix::from_pattern_and_values(pattern, values)
|
|
})
|
|
} else {
|
|
Err(SparseFormatError::from_kind_and_msg(
|
|
SparseFormatErrorKind::InvalidStructure,
|
|
"Number of values and row indices must be the same"))
|
|
}
|
|
}
|
|
|
|
/// An iterator over non-zero triplets (i, j, v).
|
|
///
|
|
/// The iteration happens in column-major fashion, meaning that j increases monotonically,
|
|
/// and i increases monotonically within each row.
|
|
///
|
|
/// Examples
|
|
/// --------
|
|
/// ```
|
|
/// # use nalgebra_sparse::csc::CscMatrix;
|
|
/// let col_offsets = vec![0, 2, 3, 4];
|
|
/// let row_indices = vec![0, 2, 1, 0];
|
|
/// let values = vec![1, 3, 2, 4];
|
|
/// let mut csc = CscMatrix::try_from_csc_data(4, 3, col_offsets, row_indices, values)
|
|
/// .unwrap();
|
|
///
|
|
/// let triplets: Vec<_> = csc.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect();
|
|
/// assert_eq!(triplets, vec![(0, 0, 1), (2, 0, 3), (1, 1, 2), (0, 2, 4)]);
|
|
/// ```
|
|
pub fn triplet_iter(&self) -> CscTripletIter<T> {
|
|
CscTripletIter {
|
|
pattern_iter: self.pattern().entries(),
|
|
values_iter: self.values().iter()
|
|
}
|
|
}
|
|
|
|
/// A mutable iterator over non-zero triplets (i, j, v).
|
|
///
|
|
/// Iteration happens in the same order as for [triplet_iter](#method.triplet_iter).
|
|
///
|
|
/// Examples
|
|
/// --------
|
|
/// ```
|
|
/// # use nalgebra_sparse::csc::CscMatrix;
|
|
/// let col_offsets = vec![0, 2, 3, 4];
|
|
/// let row_indices = vec![0, 2, 1, 0];
|
|
/// let values = vec![1, 3, 2, 4];
|
|
/// // Using the same data as in the `triplet_iter` example
|
|
/// let mut csc = CscMatrix::try_from_csc_data(4, 3, col_offsets, row_indices, values)
|
|
/// .unwrap();
|
|
///
|
|
/// // Zero out lower-triangular terms
|
|
/// csc.triplet_iter_mut()
|
|
/// .filter(|(i, j, _)| j < i)
|
|
/// .for_each(|(_, _, v)| *v = 0);
|
|
///
|
|
/// let triplets: Vec<_> = csc.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect();
|
|
/// assert_eq!(triplets, vec![(0, 0, 1), (2, 0, 0), (1, 1, 2), (0, 2, 4)]);
|
|
/// ```
|
|
pub fn triplet_iter_mut(&mut self) -> CscTripletIterMut<T> {
|
|
let (pattern, values) = self.cs.pattern_and_values_mut();
|
|
CscTripletIterMut {
|
|
pattern_iter: pattern.entries(),
|
|
values_mut_iter: values.iter_mut()
|
|
}
|
|
}
|
|
|
|
/// Return the column at the given column index.
|
|
///
|
|
/// Panics
|
|
/// ------
|
|
/// Panics if column index is out of bounds.
|
|
#[inline]
|
|
pub fn col(&self, index: usize) -> CscCol<T> {
|
|
self.get_col(index)
|
|
.expect("Row index must be in bounds")
|
|
}
|
|
|
|
/// Mutable column access for the given column index.
|
|
///
|
|
/// Panics
|
|
/// ------
|
|
/// Panics if column index is out of bounds.
|
|
#[inline]
|
|
pub fn col_mut(&mut self, index: usize) -> CscColMut<T> {
|
|
self.get_col_mut(index)
|
|
.expect("Row index must be in bounds")
|
|
}
|
|
|
|
/// Return the column at the given column index, or `None` if out of bounds.
|
|
#[inline]
|
|
pub fn get_col(&self, index: usize) -> Option<CscCol<T>> {
|
|
self.cs
|
|
.get_lane(index)
|
|
.map(|lane| CscCol { lane })
|
|
}
|
|
|
|
/// Mutable column access for the given column index, or `None` if out of bounds.
|
|
#[inline]
|
|
pub fn get_col_mut(&mut self, index: usize) -> Option<CscColMut<T>> {
|
|
self.cs
|
|
.get_lane_mut(index)
|
|
.map(|lane| CscColMut { lane })
|
|
}
|
|
|
|
/// An iterator over columns in the matrix.
|
|
pub fn col_iter(&self) -> CscColIter<T> {
|
|
CscColIter {
|
|
lane_iter: CsLaneIter::new(self.pattern(), self.values())
|
|
}
|
|
}
|
|
|
|
/// A mutable iterator over columns in the matrix.
|
|
pub fn col_iter_mut(&mut self) -> CscColIterMut<T> {
|
|
let (pattern, values) = self.cs.pattern_and_values_mut();
|
|
CscColIterMut {
|
|
lane_iter: CsLaneIterMut::new(pattern, values)
|
|
}
|
|
}
|
|
|
|
/// Disassembles the CSC matrix into its underlying offset, index and value arrays.
|
|
///
|
|
/// If the matrix contains the sole reference to the sparsity pattern,
|
|
/// then the data is returned as-is. Otherwise, the sparsity pattern is cloned.
|
|
///
|
|
/// Examples
|
|
/// --------
|
|
///
|
|
/// ```
|
|
/// # use nalgebra_sparse::csc::CscMatrix;
|
|
/// let col_offsets = vec![0, 2, 3, 4];
|
|
/// let row_indices = vec![0, 2, 1, 0];
|
|
/// let values = vec![1, 3, 2, 4];
|
|
/// let mut csc = CscMatrix::try_from_csc_data(
|
|
/// 4,
|
|
/// 3,
|
|
/// col_offsets.clone(),
|
|
/// row_indices.clone(),
|
|
/// values.clone())
|
|
/// .unwrap();
|
|
/// let (col_offsets2, row_indices2, values2) = csc.disassemble();
|
|
/// assert_eq!(col_offsets2, col_offsets);
|
|
/// assert_eq!(row_indices2, row_indices);
|
|
/// assert_eq!(values2, values);
|
|
/// ```
|
|
pub fn disassemble(self) -> (Vec<usize>, Vec<usize>, Vec<T>) {
|
|
self.cs.disassemble()
|
|
}
|
|
|
|
/// Returns the sparsity pattern and values associated with this matrix.
|
|
pub fn into_pattern_and_values(self) -> (SparsityPattern, Vec<T>) {
|
|
self.cs.into_pattern_and_values()
|
|
}
|
|
|
|
/// Returns a reference to the sparsity pattern and a mutable reference to the values.
|
|
#[inline]
|
|
pub fn pattern_and_values_mut(&mut self) -> (&SparsityPattern, &mut [T]) {
|
|
self.cs.pattern_and_values_mut()
|
|
}
|
|
|
|
/// Returns the underlying sparsity pattern.
|
|
///
|
|
/// The sparsity pattern is stored internally inside an `Arc`. This allows users to re-use
|
|
/// the same sparsity pattern for multiple matrices without storing the same pattern multiple
|
|
/// times in memory.
|
|
pub fn pattern(&self) -> &SparsityPattern {
|
|
self.cs.pattern()
|
|
}
|
|
|
|
/// Reinterprets the CSC matrix as its transpose represented by a CSR matrix.
|
|
///
|
|
/// This operation does not touch the CSC data, and is effectively a no-op.
|
|
pub fn transpose_as_csr(self) -> CsrMatrix<T> {
|
|
let (pattern, values) = self.cs.take_pattern_and_values();
|
|
CsrMatrix::try_from_pattern_and_values(pattern, values).unwrap()
|
|
}
|
|
|
|
/// Returns an entry for the given row/col indices, or `None` if the indices are out of bounds.
|
|
///
|
|
/// Each call to this function incurs the cost of a binary search among the explicitly
|
|
/// stored row entries for the given column.
|
|
pub fn get_entry(&self, row_index: usize, col_index: usize) -> Option<SparseEntry<T>> {
|
|
self.cs.get_entry(col_index, row_index)
|
|
}
|
|
|
|
/// Returns a mutable entry for the given row/col indices, or `None` if the indices are out
|
|
/// of bounds.
|
|
///
|
|
/// Each call to this function incurs the cost of a binary search among the explicitly
|
|
/// stored row entries for the given column.
|
|
pub fn get_entry_mut(&mut self, row_index: usize, col_index: usize)
|
|
-> Option<SparseEntryMut<T>> {
|
|
self.cs.get_entry_mut(col_index, row_index)
|
|
}
|
|
|
|
/// Returns an entry for the given row/col indices.
|
|
///
|
|
/// Same as `get_entry`, except that it directly panics upon encountering row/col indices
|
|
/// out of bounds.
|
|
///
|
|
/// Panics
|
|
/// ------
|
|
/// Panics if `row_index` or `col_index` is out of bounds.
|
|
pub fn index_entry(&self, row_index: usize, col_index: usize) -> SparseEntry<T> {
|
|
self.get_entry(row_index, col_index)
|
|
.expect("Out of bounds matrix indices encountered")
|
|
}
|
|
|
|
/// Returns a mutable entry for the given row/col indices.
|
|
///
|
|
/// Same as `get_entry_mut`, except that it directly panics upon encountering row/col indices
|
|
/// out of bounds.
|
|
///
|
|
/// Panics
|
|
/// ------
|
|
/// Panics if `row_index` or `col_index` is out of bounds.
|
|
pub fn index_entry_mut(&mut self, row_index: usize, col_index: usize) -> SparseEntryMut<T> {
|
|
self.get_entry_mut(row_index, col_index)
|
|
.expect("Out of bounds matrix indices encountered")
|
|
}
|
|
|
|
/// Returns a triplet of slices `(row_offsets, col_indices, values)` that make up the CSC data.
|
|
pub fn csc_data(&self) -> (&[usize], &[usize], &[T]) {
|
|
self.cs.cs_data()
|
|
}
|
|
|
|
/// Returns a triplet of slices `(row_offsets, col_indices, values)` that make up the CSC data,
|
|
/// where the `values` array is mutable.
|
|
pub fn csc_data_mut(&mut self) -> (&[usize], &[usize], &mut [T]) {
|
|
self.cs.cs_data_mut()
|
|
}
|
|
|
|
/// Creates a sparse matrix that contains only the explicit entries decided by the
|
|
/// given predicate.
|
|
pub fn filter<P>(&self, predicate: P) -> Self
|
|
where
|
|
T: Clone,
|
|
P: Fn(usize, usize, &T) -> bool
|
|
{
|
|
// Note: Predicate uses (row, col, value), so we have to switch around since
|
|
// cs uses (major, minor, value)
|
|
Self { cs: self.cs.filter(|col_idx, row_idx, v| predicate(row_idx, col_idx, v)) }
|
|
}
|
|
|
|
/// Returns a new matrix representing the upper triangular part of this matrix.
|
|
///
|
|
/// The result includes the diagonal of the matrix.
|
|
pub fn upper_triangle(&self) -> Self
|
|
where
|
|
T: Clone
|
|
{
|
|
self.filter(|i, j, _| i <= j)
|
|
}
|
|
|
|
/// Returns a new matrix representing the lower triangular part of this matrix.
|
|
///
|
|
/// The result includes the diagonal of the matrix.
|
|
pub fn lower_triangle(&self) -> Self
|
|
where
|
|
T: Clone
|
|
{
|
|
self.filter(|i, j, _| i >= j)
|
|
}
|
|
|
|
/// Returns the diagonal of the matrix as a sparse matrix.
|
|
pub fn diagonal_as_matrix(&self) -> Self
|
|
where
|
|
T: Clone
|
|
{
|
|
self.filter(|i, j, _| i == j)
|
|
}
|
|
}
|
|
|
|
impl<T> CscMatrix<T>
|
|
where
|
|
T: Scalar
|
|
{
|
|
/// Compute the transpose of the matrix.
|
|
pub fn transpose(&self) -> CscMatrix<T> {
|
|
CsrMatrix::from(self).transpose_as_csc()
|
|
}
|
|
}
|
|
|
|
impl<T: Scalar + One> CscMatrix<T> {
|
|
/// TODO
|
|
#[inline]
|
|
pub fn identity(n: usize) -> Self {
|
|
Self {
|
|
cs: CsMatrix::identity(n)
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Convert pattern format errors into more meaningful CSC-specific errors.
|
|
///
|
|
/// This ensures that the terminology is consistent: we are talking about rows and columns,
|
|
/// not lanes, major and minor dimensions.
|
|
fn pattern_format_error_to_csc_error(err: SparsityPatternFormatError) -> SparseFormatError {
|
|
use SparsityPatternFormatError::*;
|
|
use SparsityPatternFormatError::DuplicateEntry as PatternDuplicateEntry;
|
|
use SparseFormatError as E;
|
|
use SparseFormatErrorKind as K;
|
|
|
|
match err {
|
|
InvalidOffsetArrayLength => E::from_kind_and_msg(
|
|
K::InvalidStructure,
|
|
"Length of col offset array is not equal to ncols + 1."),
|
|
InvalidOffsetFirstLast => E::from_kind_and_msg(
|
|
K::InvalidStructure,
|
|
"First or last col offset is inconsistent with format specification."),
|
|
NonmonotonicOffsets => E::from_kind_and_msg(
|
|
K::InvalidStructure,
|
|
"Col offsets are not monotonically increasing."),
|
|
NonmonotonicMinorIndices => E::from_kind_and_msg(
|
|
K::InvalidStructure,
|
|
"Row indices are not monotonically increasing (sorted) within each column."),
|
|
MinorIndexOutOfBounds => E::from_kind_and_msg(
|
|
K::IndexOutOfBounds,
|
|
"Row indices are out of bounds."),
|
|
PatternDuplicateEntry => E::from_kind_and_msg(
|
|
K::DuplicateEntry,
|
|
"Matrix data contains duplicate entries."),
|
|
}
|
|
}
|
|
|
|
/// Iterator type for iterating over triplets in a CSC matrix.
|
|
#[derive(Debug)]
|
|
pub struct CscTripletIter<'a, T> {
|
|
pattern_iter: SparsityPatternIter<'a>,
|
|
values_iter: Iter<'a, T>
|
|
}
|
|
|
|
impl<'a, T: Clone> CscTripletIter<'a, T> {
|
|
/// Adapts the triplet iterator to return owned values.
|
|
///
|
|
/// The triplet iterator returns references to the values. This method adapts the iterator
|
|
/// so that the values are cloned.
|
|
#[inline]
|
|
pub fn cloned_values(self) -> impl 'a + Iterator<Item=(usize, usize, T)> {
|
|
self.map(|(i, j, v)| (i, j, v.clone()))
|
|
}
|
|
}
|
|
|
|
impl<'a, T> Iterator for CscTripletIter<'a, T> {
|
|
type Item = (usize, usize, &'a T);
|
|
|
|
fn next(&mut self) -> Option<Self::Item> {
|
|
let next_entry = self.pattern_iter.next();
|
|
let next_value = self.values_iter.next();
|
|
|
|
match (next_entry, next_value) {
|
|
(Some((i, j)), Some(v)) => Some((j, i, v)),
|
|
_ => None
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Iterator type for mutably iterating over triplets in a CSC matrix.
|
|
#[derive(Debug)]
|
|
pub struct CscTripletIterMut<'a, T> {
|
|
pattern_iter: SparsityPatternIter<'a>,
|
|
values_mut_iter: IterMut<'a, T>
|
|
}
|
|
|
|
impl<'a, T> Iterator for CscTripletIterMut<'a, T> {
|
|
type Item = (usize, usize, &'a mut T);
|
|
|
|
#[inline]
|
|
fn next(&mut self) -> Option<Self::Item> {
|
|
let next_entry = self.pattern_iter.next();
|
|
let next_value = self.values_mut_iter.next();
|
|
|
|
match (next_entry, next_value) {
|
|
(Some((i, j)), Some(v)) => Some((j, i, v)),
|
|
_ => None
|
|
}
|
|
}
|
|
}
|
|
|
|
/// An immutable representation of a column in a CSC matrix.
|
|
#[derive(Debug, Clone, PartialEq, Eq)]
|
|
pub struct CscCol<'a, T> {
|
|
lane: CsLane<'a, T>
|
|
}
|
|
|
|
/// A mutable representation of a column in a CSC matrix.
|
|
///
|
|
/// Note that only explicitly stored entries can be mutated. The sparsity pattern belonging
|
|
/// to the column cannot be modified.
|
|
#[derive(Debug, PartialEq, Eq)]
|
|
pub struct CscColMut<'a, T> {
|
|
lane: CsLaneMut<'a, T>
|
|
}
|
|
|
|
/// Implement the methods common to both CscCol and CscColMut
|
|
macro_rules! impl_csc_col_common_methods {
|
|
($name:ty) => {
|
|
impl<'a, T> $name {
|
|
/// The number of global rows in the column.
|
|
#[inline]
|
|
pub fn nrows(&self) -> usize {
|
|
self.lane.minor_dim()
|
|
}
|
|
|
|
/// The number of non-zeros in this column.
|
|
#[inline]
|
|
pub fn nnz(&self) -> usize {
|
|
self.lane.nnz()
|
|
}
|
|
|
|
/// The row indices corresponding to explicitly stored entries in this column.
|
|
#[inline]
|
|
pub fn row_indices(&self) -> &[usize] {
|
|
self.lane.minor_indices()
|
|
}
|
|
|
|
/// The values corresponding to explicitly stored entries in this column.
|
|
#[inline]
|
|
pub fn values(&self) -> &[T] {
|
|
self.lane.values()
|
|
}
|
|
|
|
/// Returns an entry for the given global row index.
|
|
///
|
|
/// Each call to this function incurs the cost of a binary search among the explicitly
|
|
/// stored row entries.
|
|
pub fn get_entry(&self, global_row_index: usize) -> Option<SparseEntry<T>> {
|
|
self.lane.get_entry(global_row_index)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
impl_csc_col_common_methods!(CscCol<'a, T>);
|
|
impl_csc_col_common_methods!(CscColMut<'a, T>);
|
|
|
|
impl<'a, T> CscColMut<'a, T> {
|
|
/// Mutable access to the values corresponding to explicitly stored entries in this column.
|
|
pub fn values_mut(&mut self) -> &mut [T] {
|
|
self.lane.values_mut()
|
|
}
|
|
|
|
/// Provides simultaneous access to row indices and mutable values corresponding to the
|
|
/// explicitly stored entries in this column.
|
|
///
|
|
/// This method primarily facilitates low-level access for methods that process data stored
|
|
/// in CSC format directly.
|
|
pub fn rows_and_values_mut(&mut self) -> (&[usize], &mut [T]) {
|
|
self.lane.indices_and_values_mut()
|
|
}
|
|
|
|
/// Returns a mutable entry for the given global row index.
|
|
pub fn get_entry_mut(&mut self, global_row_index: usize) -> Option<SparseEntryMut<T>> {
|
|
self.lane.get_entry_mut(global_row_index)
|
|
}
|
|
}
|
|
|
|
/// Column iterator for [CscMatrix](struct.CscMatrix.html).
|
|
pub struct CscColIter<'a, T> {
|
|
lane_iter: CsLaneIter<'a, T>
|
|
}
|
|
|
|
impl<'a, T> Iterator for CscColIter<'a, T> {
|
|
type Item = CscCol<'a, T>;
|
|
|
|
fn next(&mut self) -> Option<Self::Item> {
|
|
self.lane_iter
|
|
.next()
|
|
.map(|lane| CscCol { lane })
|
|
}
|
|
}
|
|
|
|
/// Mutable column iterator for [CscMatrix](struct.CscMatrix.html).
|
|
pub struct CscColIterMut<'a, T> {
|
|
lane_iter: CsLaneIterMut<'a, T>
|
|
}
|
|
|
|
impl<'a, T> Iterator for CscColIterMut<'a, T>
|
|
where
|
|
T: 'a
|
|
{
|
|
type Item = CscColMut<'a, T>;
|
|
|
|
fn next(&mut self) -> Option<Self::Item> {
|
|
self.lane_iter
|
|
.next()
|
|
.map(|lane| CscColMut { lane })
|
|
}
|
|
} |