1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134

//! utilities for convert array

use ndarray::*;

use super::error::*;
use super::lapack_traits::UPLO;
use super::layout::*;
use super::types::Conjugate;

pub fn into_col<S>(a: ArrayBase<S, Ix1>) -> ArrayBase<S, Ix2>
where
    S: Data,
{
    let n = a.len();
    a.into_shape((n, 1)).unwrap()
}

pub fn into_row<S>(a: ArrayBase<S, Ix1>) -> ArrayBase<S, Ix2>
where
    S: Data,
{
    let n = a.len();
    a.into_shape((1, n)).unwrap()
}

pub fn flatten<S>(a: ArrayBase<S, Ix2>) -> ArrayBase<S, Ix1>
where
    S: Data,
{
    let n = a.len();
    a.into_shape(n).unwrap()
}

pub fn into_matrix<A, S>(l: MatrixLayout, a: Vec<A>) -> Result<ArrayBase<S, Ix2>>
where
    S: DataOwned<Elem = A>,
{
    Ok(ArrayBase::from_shape_vec(l.as_shape(), a)?)
}

fn uninitialized<A, S>(l: MatrixLayout) -> ArrayBase<S, Ix2>
where
    A: Copy,
    S: DataOwned<Elem = A>,
{
    unsafe { ArrayBase::uninitialized(l.as_shape()) }
}

pub fn replicate<A, Sv, So, D>(a: &ArrayBase<Sv, D>) -> ArrayBase<So, D>
where
    A: Copy,
    Sv: Data<Elem = A>,
    So: DataOwned<Elem = A> + DataMut,
    D: Dimension,
{
    let mut b = unsafe { ArrayBase::uninitialized(a.dim()) };
    b.assign(a);
    b
}

fn clone_with_layout<A, Si, So>(l: MatrixLayout, a: &ArrayBase<Si, Ix2>) -> ArrayBase<So, Ix2>
where
    A: Copy,
    Si: Data<Elem = A>,
    So: DataOwned<Elem = A> + DataMut,
{
    let mut b = uninitialized(l);
    b.assign(a);
    b
}

pub fn transpose_data<A, S>(a: &mut ArrayBase<S, Ix2>) -> Result<&mut ArrayBase<S, Ix2>>
where
    A: Copy,
    S: DataOwned<Elem = A> + DataMut,
{
    let l = a.layout()?.toggle_order();
    let new = clone_with_layout(l, a);
    ::std::mem::replace(a, new);
    Ok(a)
}

pub fn generalize<A, S, D>(a: Array<A, D>) -> ArrayBase<S, D>
where
    S: DataOwned<Elem = A>,
    D: Dimension,
{
    // FIXME
    // https://github.com/bluss/rust-ndarray/issues/325
    let strides: Vec<isize> = a.strides().to_vec();
    let new = if a.is_standard_layout() {
        ArrayBase::from_shape_vec(a.dim(), a.into_raw_vec()).unwrap()
    } else {
        ArrayBase::from_shape_vec(a.dim().f(), a.into_raw_vec()).unwrap()
    };
    assert_eq!(
        new.strides(),
        strides.as_slice(),
        "Custom stride is not supported"
    );
    new
}

/// Fills in the remainder of a Hermitian matrix that's represented by only one
/// triangle.
///
/// LAPACK methods on Hermitian matrices usually read/write only one triangular
/// portion of the matrix. This function fills in the other half based on the
/// data in the triangular portion corresponding to `uplo`.
///
/// ***Panics*** if `a` is not square.
pub(crate) fn triangular_fill_hermitian<A, S>(a: &mut ArrayBase<S, Ix2>, uplo: UPLO)
where
    A: Conjugate,
    S: DataMut<Elem = A>,
{
    assert!(a.is_square());
    match uplo {
        UPLO::Upper => {
            for row in 0..a.rows() {
                for col in 0..row {
                    a[(row, col)] = a[(col, row)].conj();
                }
            }
        }
        UPLO::Lower => {
            for col in 0..a.cols() {
                for row in 0..col {
                    a[(row, col)] = a[(col, row)].conj();
                }
            }
        }
    }
}