forked from M-Labs/nalgebra
119 lines
4.1 KiB
Rust
119 lines
4.1 KiB
Rust
use crate::base::allocator::Allocator;
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use crate::base::constraint::{AreMultipliable, DimEq, SameNumberOfRows, ShapeConstraint};
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use crate::base::{DefaultAllocator, Matrix, Scalar, Unit, Vector};
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use crate::dimension::{Dim, DimName, U1};
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use crate::storage::{Storage, StorageMut};
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use simba::scalar::ComplexField;
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use crate::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: ComplexField, 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) -> Self {
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Self {
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axis: axis.into_inner(),
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bias,
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}
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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>) -> Self
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where
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D: DimName,
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DefaultAllocator: Allocator<N, D>,
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{
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let bias = axis.dotc(&pt.coords);
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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 convention: 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
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S2: StorageMut<N, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R2, D>,
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{
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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 = crate::convert(-2.0f64);
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let factor = (self.axis.dotc(&rhs.column(i)) - 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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// FIXME: naming convention: reflect_to, reflect_assign ?
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/// Applies the reflection to the columns of `rhs`.
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pub fn reflect_with_sign<R2: Dim, C2: Dim, S2>(&self, rhs: &mut Matrix<N, R2, C2, S2>, sign: N)
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where
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S2: StorageMut<N, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R2, D>,
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{
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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 = sign.scale(crate::convert(-2.0f64));
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let factor = (self.axis.dotc(&rhs.column(i)) - self.bias) * m_two;
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rhs.column_mut(i).axpy(factor, &self.axis, sign);
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}
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}
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/// Applies the reflection to the rows of `lhs`.
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pub fn reflect_rows<R2: Dim, C2: Dim, S2, S3>(
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&self,
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lhs: &mut Matrix<N, R2, C2, S2>,
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work: &mut Vector<N, R2, S3>,
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) where
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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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{
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lhs.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 = crate::convert(-2.0f64);
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lhs.gerc(m_two, &work, &self.axis, N::one());
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}
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/// Applies the reflection to the rows of `lhs`.
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pub fn reflect_rows_with_sign<R2: Dim, C2: Dim, S2, S3>(
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&self,
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lhs: &mut Matrix<N, R2, C2, S2>,
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work: &mut Vector<N, R2, S3>,
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sign: N,
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) where
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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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{
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lhs.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 = sign.scale(crate::convert(-2.0f64));
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lhs.gerc(m_two, &work, &self.axis, sign);
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}
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}
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