nalgebra/src/geometry/reflection.rs

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use std::mem::MaybeUninit;
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use crate::base::constraint::{AreMultipliable, DimEq, SameNumberOfRows, ShapeConstraint};
use crate::base::{Const, Matrix, Unit, Vector};
use crate::dimension::{Dim, 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;
/// A reflection wrt. a plane.
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pub struct Reflection<T, D:Dim, S> {
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axis: Vector<T, D, S>,
bias: T,
}
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impl<T: ComplexField, S: Storage<T, Const<D>>, const D: usize> Reflection<T, Const<D>, S> {
/// Creates a new reflection wrt. the plane orthogonal to the given axis and that contains the
/// point `pt`.
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pub fn new_containing_point(axis: Unit<Vector<T, Const<D>, S>>, pt: &Point<T, D>) -> Self {
let bias = axis.dotc(&pt.coords);
Self::new(axis, bias)
}
}
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impl<T: ComplexField, D: Dim, S: Storage<T, D>> Reflection<T, D, S> {
/// Creates a new reflection wrt the plane orthogonal to the given axis and bias.
///
/// The bias is the position of the plane on the axis. In particular, a bias equal to zero
/// represents a plane that passes through the origin.
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pub fn new(axis: Unit<Vector<T, D, S>>, bias: T) -> Self {
Self {
axis: axis.into_inner(),
bias,
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}
}
/// The reflexion axis.
#[must_use]
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pub fn axis(&self) -> &Vector<T, D, S> {
&self.axis
}
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// TODO: naming convention: reflect_to, reflect_assign ?
/// Applies the reflection to the columns of `rhs`.
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pub fn reflect<R2: Dim, C2: Dim, S2>(&self, rhs: &mut Matrix<T, R2, C2, S2>)
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where
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S2: StorageMut<T, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R2, D>,
{
for i in 0..rhs.ncols() {
// NOTE: we borrow the column twice here. First it is borrowed immutably for the
// dot product, and then mutably. Somehow, this allows significantly
// better optimizations of the dot product from the compiler.
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let m_two: T = crate::convert(-2.0f64);
let factor = (self.axis.dotc(&rhs.column(i)) - self.bias) * m_two;
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rhs.column_mut(i).axpy(factor, &self.axis, T::one());
}
}
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// TODO: 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<T, R2, C2, S2>, sign: T)
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where
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S2: StorageMut<T, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R2, D>,
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{
for i in 0..rhs.ncols() {
// NOTE: we borrow the column twice here. First it is borrowed immutably for the
// dot product, and then mutably. Somehow, this allows significantly
// better optimizations of the dot product from the compiler.
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let m_two = sign.scale(crate::convert(-2.0f64));
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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/// Applies the reflection to the rows of `lhs`.
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pub fn reflect_rows<R2: Dim, C2: Dim, S2, S3>(
&self,
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lhs: &mut Matrix<T, R2, C2, S2>,
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work: &mut Vector<MaybeUninit<T>, R2, S3>,
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) where
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S2: StorageMut<T, R2, C2>,
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S3: StorageMut<MaybeUninit<T>, R2>,
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ShapeConstraint: DimEq<C2, D> + AreMultipliable<R2, C2, D, U1>,
{
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lhs.mul_to(&self.axis, work);
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let mut work = unsafe { work.assume_init_mut() };
if !self.bias.is_zero() {
work.add_scalar_mut(-self.bias);
}
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let m_two: T = crate::convert(-2.0f64);
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lhs.gerc(m_two, &work, &self.axis, T::one());
}
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/// Applies the reflection to the rows of `lhs`.
pub fn reflect_rows_with_sign<R2: Dim, C2: Dim, S2, S3>(
&self,
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lhs: &mut Matrix<T, R2, C2, S2>,
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work: &mut Vector<MaybeUninit<T>, R2, S3>,
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sign: T,
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) where
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S2: StorageMut<T, R2, C2>,
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S3: StorageMut<MaybeUninit<T>, R2>,
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ShapeConstraint: DimEq<C2, D> + AreMultipliable<R2, C2, D, U1>,
{
lhs.mul_to(&self.axis, work);
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let mut work = unsafe { work.assume_init_mut() };
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if !self.bias.is_zero() {
work.add_scalar_mut(-self.bias);
}
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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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}
}