--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/src/forward_model/sensor_grid.rs Mon Feb 17 13:54:53 2025 -0500
@@ -0,0 +1,645 @@
+/*!
+Sensor grid forward model
+*/
+
+use nalgebra::base::{DMatrix, DVector};
+use numeric_literals::replace_float_literals;
+use std::iter::Zip;
+use std::ops::RangeFrom;
+
+use alg_tools::bisection_tree::*;
+use alg_tools::error::DynError;
+use alg_tools::instance::Instance;
+use alg_tools::iter::{MapX, Mappable};
+use alg_tools::lingrid::*;
+pub use alg_tools::linops::*;
+use alg_tools::mapping::{DifferentiableMapping, RealMapping};
+use alg_tools::maputil::map2;
+use alg_tools::nalgebra_support::ToNalgebraRealField;
+use alg_tools::norms::{Linfinity, Norm, L1, L2};
+use alg_tools::tabledump::write_csv;
+
+use super::{AdjointProductBoundedBy, BoundedCurvature, ForwardModel};
+use crate::frank_wolfe::FindimQuadraticModel;
+use crate::kernels::{AutoConvolution, BoundedBy, Convolution};
+use crate::measures::{DiscreteMeasure, Radon};
+use crate::preadjoint_helper::PreadjointHelper;
+use crate::seminorms::{ConvolutionOp, SimpleConvolutionKernel};
+use crate::types::*;
+
+type RNDM<F, const N: usize> = DiscreteMeasure<Loc<F, N>, F>;
+
+pub type ShiftedSensor<F, S, P, const N: usize> = Shift<Convolution<S, P>, F, N>;
+
+/// Trait for physical convolution models. Has blanket implementation for all cases.
+pub trait Spread<F: Float, const N: usize>:
+ 'static + Clone + Support<F, N> + RealMapping<F, N> + Bounded<F>
+{
+}
+
+impl<F, T, const N: usize> Spread<F, N> for T
+where
+ F: Float,
+ T: 'static + Clone + Support<F, N> + Bounded<F> + RealMapping<F, N>,
+{
+}
+
+/// Trait for compactly supported sensors. Has blanket implementation for all cases.
+pub trait Sensor<F: Float, const N: usize>:
+ Spread<F, N> + Norm<F, L1> + Norm<F, Linfinity>
+{
+}
+
+impl<F, T, const N: usize> Sensor<F, N> for T
+where
+ F: Float,
+ T: Spread<F, N> + Norm<F, L1> + Norm<F, Linfinity>,
+{
+}
+
+pub trait SensorGridBT<F, S, P, const N: usize>:
+ Clone + BTImpl<F, N, Data = usize, Agg = Bounds<F>>
+where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+{
+}
+
+impl<F, S, P, T, const N: usize> SensorGridBT<F, S, P, N> for T
+where
+ T: Clone + BTImpl<F, N, Data = usize, Agg = Bounds<F>>,
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+{
+}
+
+// We need type alias bounds to access associated types
+#[allow(type_alias_bounds)]
+pub type SensorGridBTFN<F, S, P, BT: SensorGridBT<F, S, P, N>, const N: usize> =
+ BTFN<F, SensorGridSupportGenerator<F, S, P, N>, BT, N>;
+
+/// Sensor grid forward model
+#[derive(Clone)]
+pub struct SensorGrid<F, S, P, BT, const N: usize>
+where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+ BT: SensorGridBT<F, S, P, N>,
+{
+ domain: Cube<F, N>,
+ sensor_count: [usize; N],
+ sensor: S,
+ spread: P,
+ base_sensor: Convolution<S, P>,
+ bt: BT,
+}
+
+impl<F, S, P, BT, const N: usize> SensorGrid<F, S, P, BT, N>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N> + LocalAnalysis<F, BT::Agg, N>,
+{
+ /// Create a new sensor grid.
+ ///
+ /// The parameter `depth` indicates the search depth of the created [`BT`]s
+ /// for the adjoint values.
+ pub fn new(
+ domain: Cube<F, N>,
+ sensor_count: [usize; N],
+ sensor: S,
+ spread: P,
+ depth: BT::Depth,
+ ) -> Self {
+ let base_sensor = Convolution(sensor.clone(), spread.clone());
+ let bt = BT::new(domain, depth);
+ let mut sensorgrid = SensorGrid {
+ domain,
+ sensor_count,
+ sensor,
+ spread,
+ base_sensor,
+ bt,
+ };
+
+ for (x, id) in sensorgrid.grid().into_iter().zip(0usize..) {
+ let s = sensorgrid.shifted_sensor(x);
+ sensorgrid.bt.insert(id, &s);
+ }
+
+ sensorgrid
+ }
+}
+
+impl<F, S, P, BT, const N: usize> SensorGrid<F, S, P, BT, N>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+{
+ /// Return the grid of sensor locations.
+ pub fn grid(&self) -> LinGrid<F, N> {
+ lingrid_centered(&self.domain, &self.sensor_count)
+ }
+
+ /// Returns the number of sensors (number of grid points)
+ pub fn n_sensors(&self) -> usize {
+ self.sensor_count.iter().product()
+ }
+
+ /// Constructs a sensor shifted by `x`.
+ #[inline]
+ fn shifted_sensor(&self, x: Loc<F, N>) -> ShiftedSensor<F, S, P, N> {
+ self.base_sensor.clone().shift(x)
+ }
+
+ #[inline]
+ fn _zero_observable(&self) -> DVector<F> {
+ DVector::zeros(self.n_sensors())
+ }
+
+ /// Returns the maximum number of overlapping sensors $N_\psi$.
+ pub fn max_overlapping(&self) -> F {
+ let w = self.base_sensor.support_hint().width();
+ let d = map2(self.domain.width(), &self.sensor_count, |wi, &i| {
+ wi / F::cast_from(i)
+ });
+ w.iter()
+ .zip(d.iter())
+ .map(|(&wi, &di)| (wi / di).ceil())
+ .reduce(F::mul)
+ .unwrap()
+ }
+}
+
+impl<F, S, P, BT, const N: usize> Mapping<RNDM<F, N>> for SensorGrid<F, S, P, BT, N>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+{
+ type Codomain = DVector<F>;
+
+ #[inline]
+ fn apply<I: Instance<RNDM<F, N>>>(&self, μ: I) -> DVector<F> {
+ let mut y = self._zero_observable();
+ self.apply_add(&mut y, μ);
+ y
+ }
+}
+
+impl<F, S, P, BT, const N: usize> Linear<RNDM<F, N>> for SensorGrid<F, S, P, BT, N>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+{
+}
+
+#[replace_float_literals(F::cast_from(literal))]
+impl<F, S, P, BT, const N: usize> GEMV<F, RNDM<F, N>, DVector<F>> for SensorGrid<F, S, P, BT, N>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+{
+ fn gemv<I: Instance<RNDM<F, N>>>(&self, y: &mut DVector<F>, α: F, μ: I, β: F) {
+ let grid = self.grid();
+ if β == 0.0 {
+ y.fill(0.0)
+ } else if β != 1.0 {
+ *y *= β; // Need to multiply first, as we have to be able to add to y.
+ }
+ if α == 1.0 {
+ self.apply_add(y, μ)
+ } else {
+ for δ in μ.ref_instance() {
+ for &d in self.bt.iter_at(&δ.x) {
+ let sensor = self.shifted_sensor(grid.entry_linear_unchecked(d));
+ y[d] += sensor.apply(&δ.x) * (α * δ.α);
+ }
+ }
+ }
+ }
+
+ fn apply_add<I: Instance<RNDM<F, N>>>(&self, y: &mut DVector<F>, μ: I) {
+ let grid = self.grid();
+ for δ in μ.ref_instance() {
+ for &d in self.bt.iter_at(&δ.x) {
+ let sensor = self.shifted_sensor(grid.entry_linear_unchecked(d));
+ y[d] += sensor.apply(&δ.x) * δ.α;
+ }
+ }
+ }
+}
+
+impl<F, S, P, BT, const N: usize> BoundedLinear<RNDM<F, N>, Radon, L2, F>
+ for SensorGrid<F, S, P, BT, N>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N, Agg = Bounds<F>>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N> + LocalAnalysis<F, BT::Agg, N>,
+{
+ /// An estimate on the operator norm in $𝕃(ℳ(Ω); ℝ^n)$ with $ℳ(Ω)$ equipped
+ /// with the Radon norm, and $ℝ^n$ with the Euclidean norm.
+ fn opnorm_bound(&self, _: Radon, _: L2) -> F {
+ // With {x_i}_{i=1}^n the grid centres and φ the kernel, we have
+ // |Aμ|_2 = sup_{|z|_2 ≤ 1} ⟨z,Αμ⟩ = sup_{|z|_2 ≤ 1} ⟨A^*z|μ⟩
+ // ≤ sup_{|z|_2 ≤ 1} |A^*z|_∞ |μ|_ℳ
+ // = sup_{|z|_2 ≤ 1} |∑ φ(· - x_i)z_i|_∞ |μ|_ℳ
+ // ≤ sup_{|z|_2 ≤ 1} |φ(y)| ∑_{i:th sensor active at y}|z_i| |μ|_ℳ
+ // where the supremum of |∑ φ(· - x_i)z_i|_∞ is reached at y
+ // ≤ sup_{|z|_2 ≤ 1} |φ|_∞ √N_ψ |z|_2 |μ|_ℳ
+ // where N_ψ is the maximum number of sensors that overlap, and
+ // |z|_2 is restricted to the active sensors.
+ // = |φ|_∞ √N_ψ |μ|_ℳ.
+ // Hence
+ let n = self.max_overlapping();
+ self.base_sensor.bounds().uniform() * n.sqrt()
+ }
+}
+
+type SensorGridPreadjoint<'a, A, F, const N: usize> = PreadjointHelper<'a, A, RNDM<F, N>>;
+
+impl<F, S, P, BT, const N: usize> Preadjointable<RNDM<F, N>, DVector<F>>
+ for SensorGrid<F, S, P, BT, N>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N> + LocalAnalysis<F, BT::Agg, N>,
+{
+ type PreadjointCodomain = BTFN<F, SensorGridSupportGenerator<F, S, P, N>, BT, N>;
+ type Preadjoint<'a>
+ = SensorGridPreadjoint<'a, Self, F, N>
+ where
+ Self: 'a;
+
+ fn preadjoint(&self) -> Self::Preadjoint<'_> {
+ PreadjointHelper::new(self)
+ }
+}
+
+/*
+#[replace_float_literals(F::cast_from(literal))]
+impl<'a, F, S, P, BT, const N : usize> LipschitzValues
+for SensorGridPreadjoint<'a, SensorGrid<F, S, P, BT, N>, F, N>
+where F : Float,
+ BT : SensorGridBT<F, S, P, N>,
+ S : Sensor<F, N>,
+ P : Spread<F, N>,
+ Convolution<S, P> : Spread<F, N> + Lipschitz<L2, FloatType=F> + DifferentiableMapping<Loc<F,N>> + LocalAnalysis<F, BT::Agg, N>,
+ for<'b> <Convolution<S, P> as DifferentiableMapping<Loc<F,N>>>::Differential<'b> : Lipschitz<L2, FloatType=F>,
+{
+
+ type FloatType = F;
+
+ fn value_unit_lipschitz_factor(&self) -> Option<F> {
+ // The Lipschitz factor of the sensors has to be scaled by the square root of twice
+ // the number of overlapping sensors at a single ponit, as Lipschitz estimates involve
+ // two points.
+ let fw = self.forward_op;
+ let n = fw.max_overlapping();
+ fw.base_sensor.lipschitz_factor(L2).map(|l| (2.0 * n).sqrt() * l)
+ }
+
+ fn value_diff_unit_lipschitz_factor(&self) -> Option<F> {
+ // The Lipschitz factor of the sensors has to be scaled by the square root of twice
+ // the number of overlapping sensors at a single ponit, as Lipschitz estimates involve
+ // two points.
+ let fw = self.forward_op;
+ let n = fw.max_overlapping();
+ fw.base_sensor.diff_ref().lipschitz_factor(L2).map(|l| (2.0 * n).sqrt() * l)
+ }
+}
+*/
+
+#[replace_float_literals(F::cast_from(literal))]
+impl<'a, F, S, P, BT, const N: usize> BoundedCurvature for SensorGrid<F, S, P, BT, N>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>
+ + Lipschitz<L2, FloatType = F>
+ + DifferentiableMapping<Loc<F, N>>
+ + LocalAnalysis<F, BT::Agg, N>,
+ for<'b> <Convolution<S, P> as DifferentiableMapping<Loc<F, N>>>::Differential<'b>:
+ Lipschitz<L2, FloatType = F>,
+{
+ type FloatType = F;
+
+ /// Returns factors $ℓ_F$ and $Θ²$ such that
+ /// $B_{F'(μ)} dγ ≤ ℓ_F c_2$ and $⟨F'(μ)+F'(μ+Δ)|Δ⟩ ≤ Θ²|γ|(c_2)‖γ‖$,
+ /// where $Δ=(π_♯^1-π_♯^0)γ$.
+ ///
+ /// See Lemma 3.8, Lemma 5.10, Remark 5.14, and Example 5.15.
+ fn curvature_bound_components(&self) -> (Option<Self::FloatType>, Option<Self::FloatType>) {
+ let n_ψ = self.max_overlapping();
+ let ψ_diff_lip = self.base_sensor.diff_ref().lipschitz_factor(L2);
+ let ψ_lip = self.base_sensor.lipschitz_factor(L2);
+ let ℓ_F = ψ_diff_lip.map(|l| (2.0 * n_ψ).sqrt() * l);
+ let θ2 = ψ_lip.map(|l| 4.0 * n_ψ * l.powi(2));
+
+ (ℓ_F, θ2)
+ }
+}
+
+#[derive(Clone, Debug)]
+pub struct SensorGridSupportGenerator<F, S, P, const N: usize>
+where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+{
+ base_sensor: Convolution<S, P>,
+ grid: LinGrid<F, N>,
+ weights: DVector<F>,
+}
+
+impl<F, S, P, const N: usize> SensorGridSupportGenerator<F, S, P, N>
+where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+{
+ #[inline]
+ fn construct_sensor(&self, id: usize, w: F) -> Weighted<ShiftedSensor<F, S, P, N>, F> {
+ let x = self.grid.entry_linear_unchecked(id);
+ self.base_sensor.clone().shift(x).weigh(w)
+ }
+
+ #[inline]
+ fn construct_sensor_and_id<'a>(
+ &'a self,
+ (id, w): (usize, &'a F),
+ ) -> (usize, Weighted<ShiftedSensor<F, S, P, N>, F>) {
+ (id.into(), self.construct_sensor(id, *w))
+ }
+}
+
+impl<F, S, P, const N: usize> SupportGenerator<F, N> for SensorGridSupportGenerator<F, S, P, N>
+where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+{
+ type Id = usize;
+ type SupportType = Weighted<ShiftedSensor<F, S, P, N>, F>;
+ type AllDataIter<'a>
+ = MapX<
+ 'a,
+ Zip<RangeFrom<usize>, std::slice::Iter<'a, F>>,
+ Self,
+ (Self::Id, Self::SupportType),
+ >
+ where
+ Self: 'a;
+
+ #[inline]
+ fn support_for(&self, d: Self::Id) -> Self::SupportType {
+ self.construct_sensor(d, self.weights[d])
+ }
+
+ #[inline]
+ fn support_count(&self) -> usize {
+ self.weights.len()
+ }
+
+ #[inline]
+ fn all_data(&self) -> Self::AllDataIter<'_> {
+ (0..)
+ .zip(self.weights.as_slice().iter())
+ .mapX(self, Self::construct_sensor_and_id)
+ }
+}
+
+impl<F, S, P, BT, const N: usize> ForwardModel<DiscreteMeasure<Loc<F, N>, F>, F>
+ for SensorGrid<F, S, P, BT, N>
+where
+ F: Float + ToNalgebraRealField<MixedType = F> + nalgebra::RealField,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N> + LocalAnalysis<F, BT::Agg, N>,
+{
+ type Observable = DVector<F>;
+
+ fn write_observable(&self, b: &Self::Observable, prefix: String) -> DynError {
+ let it = self.grid().into_iter().zip(b.iter()).map(|(x, &v)| (x, v));
+ write_csv(it, prefix + ".txt")
+ }
+
+ #[inline]
+ fn zero_observable(&self) -> Self::Observable {
+ self._zero_observable()
+ }
+}
+
+impl<F, S, P, BT, const N: usize> FindimQuadraticModel<Loc<F, N>, F> for SensorGrid<F, S, P, BT, N>
+where
+ F: Float + ToNalgebraRealField<MixedType = F> + nalgebra::RealField,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N> + LocalAnalysis<F, BT::Agg, N>,
+{
+ fn findim_quadratic_model(
+ &self,
+ μ: &DiscreteMeasure<Loc<F, N>, F>,
+ b: &Self::Observable,
+ ) -> (DMatrix<F::MixedType>, DVector<F::MixedType>) {
+ assert_eq!(b.len(), self.n_sensors());
+ let mut mA = DMatrix::zeros(self.n_sensors(), μ.len());
+ let grid = self.grid();
+ for (mut mAcol, δ) in mA.column_iter_mut().zip(μ.iter_spikes()) {
+ for &d in self.bt.iter_at(&δ.x) {
+ let sensor = self.shifted_sensor(grid.entry_linear_unchecked(d));
+ mAcol[d] += sensor.apply(&δ.x);
+ }
+ }
+ let mAt = mA.transpose();
+ (&mAt * mA, &mAt * b)
+ }
+}
+
+/// Implements the calculation a factor $L$ such that $A_*A ≤ L 𝒟$ for $A$ the forward model
+/// and $𝒟$ a seminorm of suitable form.
+///
+/// **This assumes (but does not check) that the sensors are not overlapping.**
+#[replace_float_literals(F::cast_from(literal))]
+impl<F, BT, S, P, K, const N: usize> AdjointProductBoundedBy<RNDM<F, N>, ConvolutionOp<F, K, BT, N>>
+ for SensorGrid<F, S, P, BT, N>
+where
+ F: Float + nalgebra::RealField + ToNalgebraRealField,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+ K: SimpleConvolutionKernel<F, N>,
+ AutoConvolution<P>: BoundedBy<F, K>,
+{
+ type FloatType = F;
+
+ fn adjoint_product_bound(&self, seminorm: &ConvolutionOp<F, K, BT, N>) -> Option<F> {
+ // Sensors should not take on negative values to allow
+ // A_*A to be upper bounded by a simple convolution of `spread`.
+ if self.sensor.bounds().lower() < 0.0 {
+ return None;
+ }
+
+ // Calculate the factor $L_1$ for betwee $ℱ[ψ * ψ] ≤ L_1 ℱ[ρ]$ for $ψ$ the base spread
+ // and $ρ$ the kernel of the seminorm.
+ let l1 = AutoConvolution(self.spread.clone()).bounding_factor(seminorm.kernel())?;
+
+ // Calculate the factor for transitioning from $A_*A$ to `AutoConvolution<P>`, where A
+ // consists of several `Convolution<S, P>` for the physical model `P` and the sensor `S`.
+ let l0 = self.sensor.norm(Linfinity) * self.sensor.norm(L1);
+
+ // The final transition factor is:
+ Some(l0 * l1)
+ }
+}
+
+macro_rules! make_sensorgridsupportgenerator_scalarop_rhs {
+ ($trait:ident, $fn:ident, $trait_assign:ident, $fn_assign:ident) => {
+ impl<F, S, P, const N: usize> std::ops::$trait_assign<F>
+ for SensorGridSupportGenerator<F, S, P, N>
+ where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+ {
+ fn $fn_assign(&mut self, t: F) {
+ self.weights.$fn_assign(t);
+ }
+ }
+
+ impl<F, S, P, const N: usize> std::ops::$trait<F> for SensorGridSupportGenerator<F, S, P, N>
+ where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+ {
+ type Output = SensorGridSupportGenerator<F, S, P, N>;
+ fn $fn(mut self, t: F) -> Self::Output {
+ std::ops::$trait_assign::$fn_assign(&mut self.weights, t);
+ self
+ }
+ }
+
+ impl<'a, F, S, P, const N: usize> std::ops::$trait<F>
+ for &'a SensorGridSupportGenerator<F, S, P, N>
+ where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+ {
+ type Output = SensorGridSupportGenerator<F, S, P, N>;
+ fn $fn(self, t: F) -> Self::Output {
+ SensorGridSupportGenerator {
+ base_sensor: self.base_sensor.clone(),
+ grid: self.grid,
+ weights: (&self.weights).$fn(t),
+ }
+ }
+ }
+ };
+}
+
+make_sensorgridsupportgenerator_scalarop_rhs!(Mul, mul, MulAssign, mul_assign);
+make_sensorgridsupportgenerator_scalarop_rhs!(Div, div, DivAssign, div_assign);
+
+macro_rules! make_sensorgridsupportgenerator_unaryop {
+ ($trait:ident, $fn:ident) => {
+ impl<F, S, P, const N: usize> std::ops::$trait for SensorGridSupportGenerator<F, S, P, N>
+ where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+ {
+ type Output = SensorGridSupportGenerator<F, S, P, N>;
+ fn $fn(mut self) -> Self::Output {
+ self.weights = self.weights.$fn();
+ self
+ }
+ }
+
+ impl<'a, F, S, P, const N: usize> std::ops::$trait
+ for &'a SensorGridSupportGenerator<F, S, P, N>
+ where
+ F: Float,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N>,
+ {
+ type Output = SensorGridSupportGenerator<F, S, P, N>;
+ fn $fn(self) -> Self::Output {
+ SensorGridSupportGenerator {
+ base_sensor: self.base_sensor.clone(),
+ grid: self.grid,
+ weights: (&self.weights).$fn(),
+ }
+ }
+ }
+ };
+}
+
+make_sensorgridsupportgenerator_unaryop!(Neg, neg);
+
+impl<'a, F, S, P, BT, const N: usize> Mapping<DVector<F>>
+ for PreadjointHelper<'a, SensorGrid<F, S, P, BT, N>, RNDM<F, N>>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N> + LocalAnalysis<F, Bounds<F>, N>,
+{
+ type Codomain = SensorGridBTFN<F, S, P, BT, N>;
+
+ fn apply<I: Instance<DVector<F>>>(&self, x: I) -> Self::Codomain {
+ let fwd = &self.forward_op;
+ let generator = SensorGridSupportGenerator {
+ base_sensor: fwd.base_sensor.clone(),
+ grid: fwd.grid(),
+ weights: x.own(),
+ };
+ BTFN::new_refresh(&fwd.bt, generator)
+ }
+}
+
+impl<'a, F, S, P, BT, const N: usize> Linear<DVector<F>>
+ for PreadjointHelper<'a, SensorGrid<F, S, P, BT, N>, RNDM<F, N>>
+where
+ F: Float,
+ BT: SensorGridBT<F, S, P, N>,
+ S: Sensor<F, N>,
+ P: Spread<F, N>,
+ Convolution<S, P>: Spread<F, N> + LocalAnalysis<F, Bounds<F>, N>,
+{
+}