forked from M-Labs/nalgebra
73 lines
2.8 KiB
Rust
73 lines
2.8 KiB
Rust
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use alga::general::Real;
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use core::{DefaultAllocator, Scalar, Unit, Matrix, Vector};
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use core::constraint::{ShapeConstraint, SameNumberOfRows, DimEq, AreMultipliable};
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use core::allocator::Allocator;
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use dimension::{Dim, DimName, U1};
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use storage::{Storage, StorageMut};
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use geometry::Point;
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/// A reflection wrt. a plane.
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pub struct Reflection<N: Scalar, D: Dim, S: Storage<N, D>> {
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axis: Vector<N, D, S>,
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bias: N
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}
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impl<N: Real, D: Dim, S: Storage<N, D>> Reflection<N, D, S> {
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/// Creates a new reflection wrt the plane orthogonal to the given axis and bias.
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///
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/// The bias is the position of the plane on the axis. In particular, a bias equal to zero
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/// represents a plane that passes through the origin.
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pub fn new(axis: Unit<Vector<N, D, S>>, bias: N) -> Reflection<N, D, S> {
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Reflection { axis: axis.unwrap(), bias: bias }
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}
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/// Creates a new reflection wrt. the plane orthogonal to the given axis and that contains the
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/// point `pt`.
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pub fn new_containing_point(axis: Unit<Vector<N, D, S>>, pt: &Point<N, D>) -> Reflection<N, D, S>
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where D: DimName,
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DefaultAllocator: Allocator<N, D> {
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let bias = pt.coords.dot(axis.as_ref());
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Self::new(axis, bias)
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}
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/// The reflexion axis.
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pub fn axis(&self) -> &Vector<N, D, S> {
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&self.axis
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}
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// FIXME: naming convension: reflect_to, reflect_assign ?
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/// Applies the reflection to the columns of `rhs`.
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pub fn reflect<R2: Dim, C2: Dim, S2>(&self, rhs: &mut Matrix<N, R2, C2, S2>)
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where S2: StorageMut<N, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R2, D> {
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for i in 0 .. rhs.ncols() {
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// NOTE: we borrow the column twice here. First it is borrowed immutably for the
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// dot product, and then mutably. Somehow, this allows significantly
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// better optimizations of the dot product from the compiler.
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let m_two: N = ::convert(-2.0f64);
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let factor = (rhs.column(i).dot(&self.axis) - self.bias) * m_two;
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rhs.column_mut(i).axpy(factor, &self.axis, N::one());
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}
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}
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/// Applies the reflection to the rows of `rhs`.
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pub fn reflect_rows<R2: Dim, C2: Dim, S2, S3>(&self,
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rhs: &mut Matrix<N, R2, C2, S2>,
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work: &mut Vector<N, R2, S3>)
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where S2: StorageMut<N, R2, C2>,
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S3: StorageMut<N, R2>,
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ShapeConstraint: DimEq<C2, D> + AreMultipliable<R2, C2, D, U1> {
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rhs.mul_to(&self.axis, work);
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if !self.bias.is_zero() {
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work.add_scalar_mut(-self.bias);
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}
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let m_two: N = ::convert(-2.0f64);
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rhs.ger(m_two, &work, &self.axis, N::one());
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}
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}
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