diff --git a/nalgebra-sparse/src/csc.rs b/nalgebra-sparse/src/csc.rs new file mode 100644 index 00000000..1a3f0639 --- /dev/null +++ b/nalgebra-sparse/src/csc.rs @@ -0,0 +1,546 @@ +//! An implementation of the CSC sparse matrix format. + +use crate::{SparseFormatError, SparseFormatErrorKind}; +use crate::pattern::{SparsityPattern, SparsityPatternFormatError, SparsityPatternIter}; + +use std::sync::Arc; +use std::slice::{IterMut, Iter}; +use std::ops::Range; +use num_traits::Zero; +use std::ptr::slice_from_raw_parts_mut; + +/// 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 { + // Cols are major, rows are minor in the sparsity pattern + sparsity_pattern: Arc, + values: Vec, +} + +impl CscMatrix { + /// Create a zero CSC matrix with no explicitly stored entries. + pub fn new(nrows: usize, ncols: usize) -> Self { + Self { + sparsity_pattern: Arc::new(SparsityPattern::new(ncols, nrows)), + values: vec![], + } + } + + /// The number of rows in the matrix. + #[inline] + pub fn nrows(&self) -> usize { + self.sparsity_pattern.minor_dim() + } + + /// The number of columns in the matrix. + #[inline] + pub fn ncols(&self) -> usize { + self.sparsity_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.sparsity_pattern.nnz() + } + + /// The column offsets defining part of the CSC format. + #[inline] + pub fn col_offsets(&self) -> &[usize] { + self.sparsity_pattern.major_offsets() + } + + /// The row indices defining part of the CSC format. + #[inline] + pub fn row_indices(&self) -> &[usize] { + self.sparsity_pattern.minor_indices() + } + + /// The non-zero values defining part of the CSC format. + #[inline] + pub fn values(&self) -> &[T] { + &self.values + } + + /// Mutable access to the non-zero values. + #[inline] + pub fn values_mut(&mut self) -> &mut [T] { + &mut self.values + } + + /// 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, + row_indices: Vec, + values: Vec, + ) -> Result { + 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(Arc::new(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: Arc, values: Vec) + -> Result { + if pattern.nnz() == values.len() { + Ok(Self { + sparsity_pattern: 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 { + CscTripletIter { + pattern_iter: self.sparsity_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 { + CscTripletIterMut { + pattern_iter: self.sparsity_pattern.entries(), + values_mut_iter: self.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 { + 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 { + 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> { + let range = self.get_index_range(index)?; + Some(CscCol { + row_indices: &self.sparsity_pattern.minor_indices()[range.clone()], + values: &self.values[range], + nrows: self.nrows() + }) + } + + /// 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> { + let range = self.get_index_range(index)?; + Some(CscColMut { + nrows: self.nrows(), + row_indices: &self.sparsity_pattern.minor_indices()[range.clone()], + values: &mut self.values[range] + }) + } + + /// Internal method for simplifying access to a column's data. + fn get_index_range(&self, col_index: usize) -> Option> { + let col_begin = *self.sparsity_pattern.major_offsets().get(col_index)?; + let col_end = *self.sparsity_pattern.major_offsets().get(col_index + 1)?; + Some(col_begin .. col_end) + } + + /// An iterator over columns in the matrix. + pub fn col_iter(&self) -> CscColIter { + CscColIter { + current_col_idx: 0, + matrix: self + } + } + + /// A mutable iterator over columns in the matrix. + pub fn col_iter_mut(&mut self) -> CscColIterMut { + CscColIterMut { + current_col_idx: 0, + pattern: &self.sparsity_pattern, + remaining_values: self.values.as_mut_ptr() + } + } + + /// Returns the underlying vector containing the values for the explicitly stored entries. + pub fn take_values(self) -> Vec { + self.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, Vec, Vec) { + // Take an Arc to the pattern, which might be the sole reference to the data after + // taking the values. This is important, because it might let us avoid cloning the data + // further below. + let pattern = self.pattern(); + let values = self.take_values(); + + // Try to take the pattern out of the `Arc` if possible, + // otherwise clone the pattern. + let owned_pattern = Arc::try_unwrap(pattern) + .unwrap_or_else(|arc| SparsityPattern::clone(&*arc)); + let (offsets, indices) = owned_pattern.disassemble(); + + (offsets, indices, values) + } + + /// 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) -> Arc { + Arc::clone(&self.sparsity_pattern) + } +} + +impl CscMatrix { + /// Return the value in the matrix at the given global row/col indices, or `None` if out of + /// bounds. + /// + /// If the indices are in bounds, but no explicitly stored entry is associated with it, + /// `T::zero()` is returned. Note that this method offers no way of distinguishing + /// explicitly stored zero entries from zero values that are only implicitly represented. + /// + /// Each call to this function incurs the cost of a binary search among the explicitly + /// stored column entries for the given row. + #[inline] + pub fn get(&self, row_index: usize, col_index: usize) -> Option { + self.get_col(row_index)?.get(col_index) + } + + /// Same as `get`, but panics if indices are out of bounds. + /// + /// Panics + /// ------ + /// Panics if either index is out of bounds. + #[inline] + pub fn index(&self, row_index: usize, col_index: usize) -> T { + self.get(row_index, col_index).unwrap() + } +} + +/// 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> Iterator for CscTripletIter<'a, T> { + type Item = (usize, usize, &'a T); + + fn next(&mut self) -> Option { + 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 { + 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> { + nrows: usize, + row_indices: &'a [usize], + values: &'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> { + nrows: usize, + row_indices: &'a [usize], + values: &'a mut [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.nrows + } + + /// The number of non-zeros in this column. + #[inline] + pub fn nnz(&self) -> usize { + self.row_indices.len() + } + + /// The row indices corresponding to explicitly stored entries in this column. + #[inline] + pub fn row_indices(&self) -> &[usize] { + self.row_indices + } + + /// The values corresponding to explicitly stored entries in this column. + #[inline] + pub fn values(&self) -> &[T] { + self.values + } + } + + impl<'a, T: Clone + Zero> $name { + /// Return the value in the matrix at the given global row index, or `None` if out of + /// bounds. + /// + /// If the index is in bounds, but no explicitly stored entry is associated with it, + /// `T::zero()` is returned. Note that this method offers no way of distinguishing + /// explicitly stored zero entries from zero values that are only implicitly represented. + /// + /// Each call to this function incurs the cost of a binary search among the explicitly + /// stored row entries for the current column. + pub fn get(&self, global_row_index: usize) -> Option { + let local_index = self.row_indices().binary_search(&global_row_index); + if let Ok(local_index) = local_index { + Some(self.values[local_index].clone()) + } else if global_row_index < self.nrows { + Some(T::zero()) + } else { + None + } + } + } + } +} + +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.values + } + + /// 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.row_indices, self.values) + } +} + +/// Column iterator for [CscMatrix](struct.CscMatrix.html). +pub struct CscColIter<'a, T> { + // The index of the row that will be returned on the next + current_col_idx: usize, + matrix: &'a CscMatrix +} + +impl<'a, T> Iterator for CscColIter<'a, T> { + type Item = CscCol<'a, T>; + + fn next(&mut self) -> Option { + let col = self.matrix.get_col(self.current_col_idx); + self.current_col_idx += 1; + col + } +} + +/// Mutable column iterator for [CscMatrix](struct.CscMatrix.html). +pub struct CscColIterMut<'a, T> { + current_col_idx: usize, + pattern: &'a SparsityPattern, + remaining_values: *mut T, +} + +impl<'a, T> Iterator for CscColIterMut<'a, T> +where + T: 'a +{ + type Item = CscColMut<'a, T>; + + fn next(&mut self) -> Option { + let lane = self.pattern.get_lane(self.current_col_idx); + let nrows = self.pattern.minor_dim(); + + if let Some(row_indices) = lane { + let count = row_indices.len(); + + // Note: I can't think of any way to construct this iterator without unsafe. + let values_in_row; + unsafe { + values_in_row = &mut *slice_from_raw_parts_mut(self.remaining_values, count); + self.remaining_values = self.remaining_values.add(count); + } + self.current_col_idx += 1; + + Some(CscColMut { + nrows, + row_indices, + values: values_in_row + }) + } else { + None + } + } +} \ No newline at end of file diff --git a/nalgebra-sparse/src/csr.rs b/nalgebra-sparse/src/csr.rs index c612e5b4..3adc25bd 100644 --- a/nalgebra-sparse/src/csr.rs +++ b/nalgebra-sparse/src/csr.rs @@ -11,7 +11,7 @@ use std::ptr::slice_from_raw_parts_mut; /// A CSR representation of a sparse matrix. /// -/// The Compressed Row Storage (CSR) format is well-suited as a general-purpose storage format +/// The Compressed Sparse Row (CSR) format is well-suited as a general-purpose storage format /// for many sparse matrix applications. /// /// TODO: Storage explanation and examples diff --git a/nalgebra-sparse/src/lib.rs b/nalgebra-sparse/src/lib.rs index 7bcb78a9..e1820b4a 100644 --- a/nalgebra-sparse/src/lib.rs +++ b/nalgebra-sparse/src/lib.rs @@ -28,7 +28,15 @@ //! - [x] "Disassemble" the CSR matrix into the raw CSR data arrays. //! //! - CSC matrix type. Functionality: -//! - Same as CSR, but with columns instead of rows. +//! - [x] Access to CSC data as slices. +//! - [x] Return number of nnz +//! - [x] Access a given column, which gives convenient access to the data associated +//! with a particular column +//! - [x] Construct from valid CSC data +//! - [ ] Construct from unsorted CSC data +//! - [x] Iterate over entries (i, j, v) in the matrix (+mutable). +//! - [x] Iterate over rows in the matrix (+ mutable). +//! - [x] "Disassemble" the CSC matrix into the raw CSC data arrays. //! - COO matrix type. Functionality: //! - [x] Construct new "empty" COO matrix //! - [x] Construct from triplet arrays. @@ -68,6 +76,7 @@ #![deny(missing_docs)] pub mod coo; +pub mod csc; pub mod csr; pub mod pattern; pub mod ops; diff --git a/nalgebra-sparse/tests/unit_tests/csc.rs b/nalgebra-sparse/tests/unit_tests/csc.rs new file mode 100644 index 00000000..140a5db2 --- /dev/null +++ b/nalgebra-sparse/tests/unit_tests/csc.rs @@ -0,0 +1,254 @@ +use nalgebra_sparse::csc::CscMatrix; +use nalgebra_sparse::SparseFormatErrorKind; + +#[test] +fn csc_matrix_valid_data() { + // Construct matrix from valid data and check that selected methods return results + // that agree with expectations. + + { + // A CSC matrix with zero explicitly stored entries + let offsets = vec![0, 0, 0, 0]; + let indices = vec![]; + let values = Vec::::new(); + let mut matrix = CscMatrix::try_from_csc_data(2, 3, offsets, indices, values).unwrap(); + + assert_eq!(matrix, CscMatrix::new(2, 3)); + + assert_eq!(matrix.nrows(), 2); + assert_eq!(matrix.ncols(), 3); + assert_eq!(matrix.nnz(), 0); + assert_eq!(matrix.col_offsets(), &[0, 0, 0, 0]); + assert_eq!(matrix.row_indices(), &[]); + assert_eq!(matrix.values(), &[]); + + assert!(matrix.triplet_iter().next().is_none()); + assert!(matrix.triplet_iter_mut().next().is_none()); + + assert_eq!(matrix.col(0).nrows(), 2); + assert_eq!(matrix.col(0).nnz(), 0); + assert_eq!(matrix.col(0).row_indices(), &[]); + assert_eq!(matrix.col(0).values(), &[]); + assert_eq!(matrix.col_mut(0).nrows(), 2); + assert_eq!(matrix.col_mut(0).nnz(), 0); + assert_eq!(matrix.col_mut(0).row_indices(), &[]); + assert_eq!(matrix.col_mut(0).values(), &[]); + assert_eq!(matrix.col_mut(0).values_mut(), &[]); + assert_eq!(matrix.col_mut(0).rows_and_values_mut(), ([].as_ref(), [].as_mut())); + + assert_eq!(matrix.col(1).nrows(), 2); + assert_eq!(matrix.col(1).nnz(), 0); + assert_eq!(matrix.col(1).row_indices(), &[]); + assert_eq!(matrix.col(1).values(), &[]); + assert_eq!(matrix.col_mut(1).nrows(), 2); + assert_eq!(matrix.col_mut(1).nnz(), 0); + assert_eq!(matrix.col_mut(1).row_indices(), &[]); + assert_eq!(matrix.col_mut(1).values(), &[]); + assert_eq!(matrix.col_mut(1).values_mut(), &[]); + assert_eq!(matrix.col_mut(1).rows_and_values_mut(), ([].as_ref(), [].as_mut())); + + assert_eq!(matrix.col(2).nrows(), 2); + assert_eq!(matrix.col(2).nnz(), 0); + assert_eq!(matrix.col(2).row_indices(), &[]); + assert_eq!(matrix.col(2).values(), &[]); + assert_eq!(matrix.col_mut(2).nrows(), 2); + assert_eq!(matrix.col_mut(2).nnz(), 0); + assert_eq!(matrix.col_mut(2).row_indices(), &[]); + assert_eq!(matrix.col_mut(2).values(), &[]); + assert_eq!(matrix.col_mut(2).values_mut(), &[]); + assert_eq!(matrix.col_mut(2).rows_and_values_mut(), ([].as_ref(), [].as_mut())); + + assert!(matrix.get_col(3).is_none()); + assert!(matrix.get_col_mut(3).is_none()); + + let (offsets, indices, values) = matrix.disassemble(); + + assert_eq!(offsets, vec![0, 0, 0, 0]); + assert_eq!(indices, vec![]); + assert_eq!(values, vec![]); + } + + { + // An arbitrary CSC matrix + let offsets = vec![0, 2, 2, 5]; + let indices = vec![0, 5, 1, 2, 3]; + let values = vec![0, 1, 2, 3, 4]; + let mut matrix = CscMatrix::try_from_csc_data(6, + 3, + offsets.clone(), + indices.clone(), + values.clone()).unwrap(); + + assert_eq!(matrix.nrows(), 6); + assert_eq!(matrix.ncols(), 3); + assert_eq!(matrix.nnz(), 5); + assert_eq!(matrix.col_offsets(), &[0, 2, 2, 5]); + assert_eq!(matrix.row_indices(), &[0, 5, 1, 2, 3]); + assert_eq!(matrix.values(), &[0, 1, 2, 3, 4]); + + let expected_triplets = vec![(0, 0, 0), (5, 0, 1), (1, 2, 2), (2, 2, 3), (3, 2, 4)]; + assert_eq!(matrix.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect::>(), + expected_triplets); + assert_eq!(matrix.triplet_iter_mut().map(|(i, j, v)| (i, j, *v)).collect::>(), + expected_triplets); + + assert_eq!(matrix.col(0).nrows(), 6); + assert_eq!(matrix.col(0).nnz(), 2); + assert_eq!(matrix.col(0).row_indices(), &[0, 5]); + assert_eq!(matrix.col(0).values(), &[0, 1]); + assert_eq!(matrix.col_mut(0).nrows(), 6); + assert_eq!(matrix.col_mut(0).nnz(), 2); + assert_eq!(matrix.col_mut(0).row_indices(), &[0, 5]); + assert_eq!(matrix.col_mut(0).values(), &[0, 1]); + assert_eq!(matrix.col_mut(0).values_mut(), &[0, 1]); + assert_eq!(matrix.col_mut(0).rows_and_values_mut(), ([0, 5].as_ref(), [0, 1].as_mut())); + + assert_eq!(matrix.col(1).nrows(), 6); + assert_eq!(matrix.col(1).nnz(), 0); + assert_eq!(matrix.col(1).row_indices(), &[]); + assert_eq!(matrix.col(1).values(), &[]); + assert_eq!(matrix.col_mut(1).nrows(), 6); + assert_eq!(matrix.col_mut(1).nnz(), 0); + assert_eq!(matrix.col_mut(1).row_indices(), &[]); + assert_eq!(matrix.col_mut(1).values(), &[]); + assert_eq!(matrix.col_mut(1).values_mut(), &[]); + assert_eq!(matrix.col_mut(1).rows_and_values_mut(), ([].as_ref(), [].as_mut())); + + assert_eq!(matrix.col(2).nrows(), 6); + assert_eq!(matrix.col(2).nnz(), 3); + assert_eq!(matrix.col(2).row_indices(), &[1, 2, 3]); + assert_eq!(matrix.col(2).values(), &[2, 3, 4]); + assert_eq!(matrix.col_mut(2).nrows(), 6); + assert_eq!(matrix.col_mut(2).nnz(), 3); + assert_eq!(matrix.col_mut(2).row_indices(), &[1, 2, 3]); + assert_eq!(matrix.col_mut(2).values(), &[2, 3, 4]); + assert_eq!(matrix.col_mut(2).values_mut(), &[2, 3, 4]); + assert_eq!(matrix.col_mut(2).rows_and_values_mut(), ([1, 2, 3].as_ref(), [2, 3, 4].as_mut())); + + assert!(matrix.get_col(3).is_none()); + assert!(matrix.get_col_mut(3).is_none()); + + let (offsets2, indices2, values2) = matrix.disassemble(); + + assert_eq!(offsets2, offsets); + assert_eq!(indices2, indices); + assert_eq!(values2, values); + } +} + +#[test] +fn csc_matrix_try_from_invalid_csc_data() { + + { + // Empty offset array (invalid length) + let matrix = CscMatrix::try_from_csc_data(0, 0, Vec::new(), Vec::new(), Vec::::new()); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::InvalidStructure); + } + + { + // Offset array invalid length for arbitrary data + let offsets = vec![0, 3, 5]; + let indices = vec![0, 1, 2, 3, 5]; + let values = vec![0, 1, 2, 3, 4]; + + let matrix = CscMatrix::try_from_csc_data(6, 3, offsets, indices, values); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::InvalidStructure); + } + + { + // Invalid first entry in offsets array + let offsets = vec![1, 2, 2, 5]; + let indices = vec![0, 5, 1, 2, 3]; + let values = vec![0, 1, 2, 3, 4]; + let matrix = CscMatrix::try_from_csc_data(6, 3, offsets, indices, values); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::InvalidStructure); + } + + { + // Invalid last entry in offsets array + let offsets = vec![0, 2, 2, 4]; + let indices = vec![0, 5, 1, 2, 3]; + let values = vec![0, 1, 2, 3, 4]; + let matrix = CscMatrix::try_from_csc_data(6, 3, offsets, indices, values); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::InvalidStructure); + } + + { + // Invalid length of offsets array + let offsets = vec![0, 2, 2]; + let indices = vec![0, 5, 1, 2, 3]; + let values = vec![0, 1, 2, 3, 4]; + let matrix = CscMatrix::try_from_csc_data(6, 3, offsets, indices, values); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::InvalidStructure); + } + + { + // Nonmonotonic offsets + let offsets = vec![0, 3, 2, 5]; + let indices = vec![0, 1, 2, 3, 4]; + let values = vec![0, 1, 2, 3, 4]; + let matrix = CscMatrix::try_from_csc_data(6, 3, offsets, indices, values); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::InvalidStructure); + } + + { + // Nonmonotonic minor indices + let offsets = vec![0, 2, 2, 5]; + let indices = vec![0, 2, 3, 1, 4]; + let values = vec![0, 1, 2, 3, 4]; + let matrix = CscMatrix::try_from_csc_data(6, 3, offsets, indices, values); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::InvalidStructure); + } + + { + // Minor index out of bounds + let offsets = vec![0, 2, 2, 5]; + let indices = vec![0, 6, 1, 2, 3]; + let values = vec![0, 1, 2, 3, 4]; + let matrix = CscMatrix::try_from_csc_data(6, 3, offsets, indices, values); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::IndexOutOfBounds); + } + + { + // Duplicate entry + let offsets = vec![0, 2, 2, 5]; + let indices = vec![0, 5, 2, 2, 3]; + let values = vec![0, 1, 2, 3, 4]; + let matrix = CscMatrix::try_from_csc_data(6, 3, offsets, indices, values); + assert_eq!(matrix.unwrap_err().kind(), &SparseFormatErrorKind::DuplicateEntry); + } + +} + +#[test] +fn csc_disassemble_avoids_clone_when_owned() { + // Test that disassemble avoids cloning the sparsity pattern when it holds the sole reference + // to the pattern. We do so by checking that the pointer to the data is unchanged. + + let offsets = vec![0, 2, 2, 5]; + let indices = vec![0, 5, 1, 2, 3]; + let values = vec![0, 1, 2, 3, 4]; + let offsets_ptr = offsets.as_ptr(); + let indices_ptr = indices.as_ptr(); + let values_ptr = values.as_ptr(); + let matrix = CscMatrix::try_from_csc_data(6, + 3, + offsets, + indices, + values).unwrap(); + + let (offsets, indices, values) = matrix.disassemble(); + assert_eq!(offsets.as_ptr(), offsets_ptr); + assert_eq!(indices.as_ptr(), indices_ptr); + assert_eq!(values.as_ptr(), values_ptr); +} + +#[test] +fn csc_matrix_get_index() { + // TODO: Implement tests for ::get() and index() +} + +#[test] +fn csc_matrix_col_iter() { + // TODO +} \ No newline at end of file diff --git a/nalgebra-sparse/tests/unit_tests/mod.rs b/nalgebra-sparse/tests/unit_tests/mod.rs index 2db7ab12..733357c9 100644 --- a/nalgebra-sparse/tests/unit_tests/mod.rs +++ b/nalgebra-sparse/tests/unit_tests/mod.rs @@ -1,4 +1,5 @@ mod coo; mod ops; mod pattern; -mod csr; \ No newline at end of file +mod csr; +mod csc; \ No newline at end of file