diff -r 92cae2e8f598 -r b3312eee105c src/seminorms.rs
--- a/src/seminorms.rs	Mon Feb 17 14:10:45 2025 -0500
+++ b/src/seminorms.rs	Mon Feb 17 14:10:52 2025 -0500
@@ -4,31 +4,31 @@
 the trait [`DiscreteMeasureOp`].
 */
 
-use std::iter::Zip;
-use std::ops::RangeFrom;
-use alg_tools::types::*;
+use crate::measures::{DeltaMeasure, DiscreteMeasure, Radon, SpikeIter, RNDM};
+use alg_tools::bisection_tree::*;
+use alg_tools::instance::Instance;
+use alg_tools::iter::{FilterMapX, Mappable};
+use alg_tools::linops::{BoundedLinear, Linear, Mapping};
 use alg_tools::loc::Loc;
-use alg_tools::sets::Cube;
-use alg_tools::bisection_tree::*;
 use alg_tools::mapping::RealMapping;
-use alg_tools::iter::{Mappable, FilterMapX};
-use alg_tools::linops::{Mapping, Linear, BoundedLinear};
-use alg_tools::instance::Instance;
 use alg_tools::nalgebra_support::ToNalgebraRealField;
 use alg_tools::norms::Linfinity;
-use crate::measures::{DiscreteMeasure, DeltaMeasure, SpikeIter, Radon, RNDM};
+use alg_tools::sets::Cube;
+use alg_tools::types::*;
+use itertools::Itertools;
 use nalgebra::DMatrix;
+use std::iter::Zip;
 use std::marker::PhantomData;
-use itertools::Itertools;
+use std::ops::RangeFrom;
 
 /// Abstraction for operators $𝒟 ∈ 𝕃(𝒵(Ω); C_c(Ω))$.
 ///
 /// Here $𝒵(Ω) ⊂ ℳ(Ω)$ is the space of sums of delta measures, presented by [`DiscreteMeasure`].
-pub trait DiscreteMeasureOp<Domain, F>
-    : BoundedLinear<DiscreteMeasure<Domain, F>, Radon, Linfinity, F>
+pub trait DiscreteMeasureOp<Domain, F>:
+    BoundedLinear<DiscreteMeasure<Domain, F>, Radon, Linfinity, F>
 where
-    F : Float + ToNalgebraRealField,
-    Domain : 'static + Clone + PartialEq,
+    F: Float + ToNalgebraRealField,
+    Domain: 'static + Clone + PartialEq,
 {
     /// The output type of [`Self::preapply`].
     type PreCodomain;
@@ -40,12 +40,13 @@
     /// for a $x_1, …, x_n$ the coordinates given by the iterator `I`, and $a=(α_1,…,α_n)$.
     /// Here $C_* ∈ 𝕃(C_c(Ω); ℝ^n) $ stands for the preadjoint.
     /// </p>
-    fn findim_matrix<'a, I>(&self, points : I) -> DMatrix<F::MixedType>
-    where I : ExactSizeIterator<Item=&'a Domain> + Clone;
+    fn findim_matrix<'a, I>(&self, points: I) -> DMatrix<F::MixedType>
+    where
+        I: ExactSizeIterator<Item = &'a Domain> + Clone;
 
     /// [`Mapping`] that typically returns an uninitialised [`PreBTFN`]
     /// instead of a full [`BTFN`].
-    fn preapply(&self, μ : DiscreteMeasure<Domain, F>) -> Self::PreCodomain;
+    fn preapply(&self, μ: DiscreteMeasure<Domain, F>) -> Self::PreCodomain;
 }
 
 // Blanket implementation of a measure as a linear functional over a predual
@@ -67,28 +68,37 @@
 //
 
 /// A trait alias for simple convolution kernels.
-pub trait SimpleConvolutionKernel<F : Float, const N : usize>
-: RealMapping<F, N> + Support<F, N> + Bounded<F> + Clone + 'static {}
+pub trait SimpleConvolutionKernel<F: Float, const N: usize>:
+    RealMapping<F, N> + Support<F, N> + Bounded<F> + Clone + 'static
+{
+}
 
-impl<T, F : Float, const N : usize> SimpleConvolutionKernel<F, N> for T
-where T : RealMapping<F, N> + Support<F, N> + Bounded<F> + Clone + 'static {}
+impl<T, F: Float, const N: usize> SimpleConvolutionKernel<F, N> for T where
+    T: RealMapping<F, N> + Support<F, N> + Bounded<F> + Clone + 'static
+{
+}
 
 /// [`SupportGenerator`] for [`ConvolutionOp`].
-#[derive(Clone,Debug)]
-pub struct ConvolutionSupportGenerator<F : Float, K, const N : usize>
-where K : SimpleConvolutionKernel<F, N> {
-    kernel : K,
-    centres : RNDM<F, N>,
+#[derive(Clone, Debug)]
+pub struct ConvolutionSupportGenerator<F: Float, K, const N: usize>
+where
+    K: SimpleConvolutionKernel<F, N>,
+{
+    kernel: K,
+    centres: RNDM<F, N>,
 }
 
-impl<F : Float, K, const N : usize> ConvolutionSupportGenerator<F, K, N>
-where K : SimpleConvolutionKernel<F, N> {
-
+impl<F: Float, K, const N: usize> ConvolutionSupportGenerator<F, K, N>
+where
+    K: SimpleConvolutionKernel<F, N>,
+{
     /// Construct the convolution kernel corresponding to `δ`, i.e., one centered at `δ.x` and
     /// weighted by `δ.α`.
     #[inline]
-    fn construct_kernel<'a>(&'a self, δ : &'a DeltaMeasure<Loc<F, N>, F>)
-    -> Weighted<Shift<K, F, N>, F> {
+    fn construct_kernel<'a>(
+        &'a self,
+        δ: &'a DeltaMeasure<Loc<F, N>, F>,
+    ) -> Weighted<Shift<K, F, N>, F> {
         self.kernel.clone().shift(δ.x).weigh(δ.α)
     }
 
@@ -98,22 +108,27 @@
     #[inline]
     fn construct_kernel_and_id_filtered<'a>(
         &'a self,
-        (id, δ) : (usize, &'a DeltaMeasure<Loc<F, N>, F>)
+        (id, δ): (usize, &'a DeltaMeasure<Loc<F, N>, F>),
     ) -> Option<(usize, Weighted<Shift<K, F, N>, F>)> {
         (δ.α != F::ZERO).then(|| (id.into(), self.construct_kernel(δ)))
     }
 }
 
-impl<F : Float, K, const N : usize> SupportGenerator<F, N>
-for ConvolutionSupportGenerator<F, K, N>
-where K : SimpleConvolutionKernel<F, N> {
+impl<F: Float, K, const N: usize> SupportGenerator<F, N> for ConvolutionSupportGenerator<F, K, N>
+where
+    K: SimpleConvolutionKernel<F, N>,
+{
     type Id = usize;
     type SupportType = Weighted<Shift<K, F, N>, F>;
-    type AllDataIter<'a> = FilterMapX<'a, Zip<RangeFrom<usize>, SpikeIter<'a, Loc<F, N>, F>>,
-                                      Self, (Self::Id, Self::SupportType)>;
+    type AllDataIter<'a> = FilterMapX<
+        'a,
+        Zip<RangeFrom<usize>, SpikeIter<'a, Loc<F, N>, F>>,
+        Self,
+        (Self::Id, Self::SupportType),
+    >;
 
     #[inline]
-    fn support_for(&self, d : Self::Id) -> Self::SupportType {
+    fn support_for(&self, d: Self::Id) -> Self::SupportType {
         self.construct_kernel(&self.centres[d])
     }
 
@@ -124,51 +139,53 @@
 
     #[inline]
     fn all_data(&self) -> Self::AllDataIter<'_> {
-        (0..).zip(self.centres.iter_spikes())
-             .filter_mapX(self, Self::construct_kernel_and_id_filtered)
+        (0..)
+            .zip(self.centres.iter_spikes())
+            .filter_mapX(self, Self::construct_kernel_and_id_filtered)
     }
 }
 
 /// Representation of a convolution operator $𝒟$.
-#[derive(Clone,Debug)]
-pub struct ConvolutionOp<F, K, BT, const N : usize>
-where F : Float + ToNalgebraRealField,
-      BT : BTImpl<F, N, Data=usize>,
-      K : SimpleConvolutionKernel<F, N> {
+#[derive(Clone, Debug)]
+pub struct ConvolutionOp<F, K, BT, const N: usize>
+where
+    F: Float + ToNalgebraRealField,
+    BT: BTImpl<F, N, Data = usize>,
+    K: SimpleConvolutionKernel<F, N>,
+{
     /// Depth of the [`BT`] bisection tree for the outputs [`Mapping::apply`].
-    depth : BT::Depth,
+    depth: BT::Depth,
     /// Domain of the [`BT`] bisection tree for the outputs [`Mapping::apply`].
-    domain : Cube<F, N>,
+    domain: Cube<F, N>,
     /// The convolution kernel
-    kernel : K,
-    _phantoms : PhantomData<(F,BT)>,
+    kernel: K,
+    _phantoms: PhantomData<(F, BT)>,
 }
 
-impl<F, K, BT, const N : usize> ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
-      BT : BTImpl<F, N, Data=usize>,
-      K : SimpleConvolutionKernel<F, N> {
-
+impl<F, K, BT, const N: usize> ConvolutionOp<F, K, BT, N>
+where
+    F: Float + ToNalgebraRealField,
+    BT: BTImpl<F, N, Data = usize>,
+    K: SimpleConvolutionKernel<F, N>,
+{
     /// Creates a new convolution operator $𝒟$ with `kernel` on `domain`.
     ///
     /// The output of [`Mapping::apply`] is a [`BT`] of given `depth`.
-    pub fn new(depth : BT::Depth, domain : Cube<F, N>, kernel : K) -> Self {
+    pub fn new(depth: BT::Depth, domain: Cube<F, N>, kernel: K) -> Self {
         ConvolutionOp {
-            depth : depth,
-            domain : domain,
-            kernel : kernel,
-            _phantoms : PhantomData
+            depth: depth,
+            domain: domain,
+            kernel: kernel,
+            _phantoms: PhantomData,
         }
     }
 
     /// Returns the support generator for this convolution operator.
-    fn support_generator(&self, μ : RNDM<F, N>)
-    -> ConvolutionSupportGenerator<F, K, N> {
-
+    fn support_generator(&self, μ: RNDM<F, N>) -> ConvolutionSupportGenerator<F, K, N> {
         // TODO: can we avoid cloning μ?
         ConvolutionSupportGenerator {
-            kernel : self.kernel.clone(),
-            centres : μ
+            kernel: self.kernel.clone(),
+            centres: μ,
         }
     }
 
@@ -178,43 +195,43 @@
     }
 }
 
-impl<F, K, BT, const N : usize> Mapping<RNDM<F, N>>
-for ConvolutionOp<F, K, BT, N>
+impl<F, K, BT, const N: usize> Mapping<RNDM<F, N>> for ConvolutionOp<F, K, BT, N>
 where
-    F : Float + ToNalgebraRealField,
-    BT : BTImpl<F, N, Data=usize>,
-    K : SimpleConvolutionKernel<F, N>,
-    Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N>
+    F: Float + ToNalgebraRealField,
+    BT: BTImpl<F, N, Data = usize>,
+    K: SimpleConvolutionKernel<F, N>,
+    Weighted<Shift<K, F, N>, F>: LocalAnalysis<F, BT::Agg, N>,
 {
-
     type Codomain = BTFN<F, ConvolutionSupportGenerator<F, K, N>, BT, N>;
 
-    fn apply<I>(&self, μ : I) -> Self::Codomain
-    where I : Instance<RNDM<F, N>> {
+    fn apply<I>(&self, μ: I) -> Self::Codomain
+    where
+        I: Instance<RNDM<F, N>>,
+    {
         let g = self.support_generator(μ.own());
         BTFN::construct(self.domain.clone(), self.depth, g)
     }
 }
 
 /// [`ConvolutionOp`]s as linear operators over [`DiscreteMeasure`]s.
-impl<F, K, BT, const N : usize> Linear<RNDM<F, N>>
-for ConvolutionOp<F, K, BT, N>
+impl<F, K, BT, const N: usize> Linear<RNDM<F, N>> for ConvolutionOp<F, K, BT, N>
 where
-    F : Float + ToNalgebraRealField,
-    BT : BTImpl<F, N, Data=usize>,
-    K : SimpleConvolutionKernel<F, N>,
-    Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N>
-{ }
+    F: Float + ToNalgebraRealField,
+    BT: BTImpl<F, N, Data = usize>,
+    K: SimpleConvolutionKernel<F, N>,
+    Weighted<Shift<K, F, N>, F>: LocalAnalysis<F, BT::Agg, N>,
+{
+}
 
-impl<F, K, BT, const N : usize>
-BoundedLinear<RNDM<F, N>, Radon, Linfinity, F>
-for ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
-      BT : BTImpl<F, N, Data=usize>,
-      K : SimpleConvolutionKernel<F, N>,
-      Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N> {
-
-    fn opnorm_bound(&self, _ : Radon, _ : Linfinity) -> F {
+impl<F, K, BT, const N: usize> BoundedLinear<RNDM<F, N>, Radon, Linfinity, F>
+    for ConvolutionOp<F, K, BT, N>
+where
+    F: Float + ToNalgebraRealField,
+    BT: BTImpl<F, N, Data = usize>,
+    K: SimpleConvolutionKernel<F, N>,
+    Weighted<Shift<K, F, N>, F>: LocalAnalysis<F, BT::Agg, N>,
+{
+    fn opnorm_bound(&self, _: Radon, _: Linfinity) -> F {
         // With μ = ∑_i α_i δ_{x_i}, we have
         // |𝒟μ|_∞
         // = sup_z |∑_i α_i φ(z - x_i)|
@@ -225,31 +242,33 @@
     }
 }
 
-
-impl<F, K, BT, const N : usize> DiscreteMeasureOp<Loc<F, N>, F>
-for ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
-      BT : BTImpl<F, N, Data=usize>,
-      K : SimpleConvolutionKernel<F, N>,
-      Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N> {
+impl<F, K, BT, const N: usize> DiscreteMeasureOp<Loc<F, N>, F> for ConvolutionOp<F, K, BT, N>
+where
+    F: Float + ToNalgebraRealField,
+    BT: BTImpl<F, N, Data = usize>,
+    K: SimpleConvolutionKernel<F, N>,
+    Weighted<Shift<K, F, N>, F>: LocalAnalysis<F, BT::Agg, N>,
+{
     type PreCodomain = PreBTFN<F, ConvolutionSupportGenerator<F, K, N>, N>;
 
-    fn findim_matrix<'a, I>(&self, points : I) -> DMatrix<F::MixedType>
-    where I : ExactSizeIterator<Item=&'a Loc<F,N>> + Clone {
+    fn findim_matrix<'a, I>(&self, points: I) -> DMatrix<F::MixedType>
+    where
+        I: ExactSizeIterator<Item = &'a Loc<F, N>> + Clone,
+    {
         // TODO: Preliminary implementation. It be best to use sparse matrices or
         // possibly explicit operators without matrices
         let n = points.len();
         let points_clone = points.clone();
         let pairs = points.cartesian_product(points_clone);
         let kernel = &self.kernel;
-        let values = pairs.map(|(x, y)| kernel.apply(y-x).to_nalgebra_mixed());
+        let values = pairs.map(|(x, y)| kernel.apply(y - x).to_nalgebra_mixed());
         DMatrix::from_iterator(n, n, values)
     }
 
     /// A version of [`Mapping::apply`] that does not instantiate the [`BTFN`] codomain with
     /// a bisection tree, instead returning a [`PreBTFN`]. This can improve performance when
     /// the output is to be added as the right-hand-side operand to a proper BTFN.
-    fn preapply(&self, μ : RNDM<F, N>) -> Self::PreCodomain {
+    fn preapply(&self, μ: RNDM<F, N>) -> Self::PreCodomain {
         BTFN::new_pre(self.support_generator(μ))
     }
 }
@@ -258,47 +277,46 @@
 /// for [`ConvolutionSupportGenerator`].
 macro_rules! make_convolutionsupportgenerator_scalarop_rhs {
     ($trait:ident, $fn:ident, $trait_assign:ident, $fn_assign:ident) => {
-        impl<F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
-        std::ops::$trait_assign<F>
-        for ConvolutionSupportGenerator<F, K, N> {
-            fn $fn_assign(&mut self, t : F) {
+        impl<F: Float, K: SimpleConvolutionKernel<F, N>, const N: usize> std::ops::$trait_assign<F>
+            for ConvolutionSupportGenerator<F, K, N>
+        {
+            fn $fn_assign(&mut self, t: F) {
                 self.centres.$fn_assign(t);
             }
         }
 
-        impl<F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
-        std::ops::$trait<F>
-        for ConvolutionSupportGenerator<F, K, N> {
+        impl<F: Float, K: SimpleConvolutionKernel<F, N>, const N: usize> std::ops::$trait<F>
+            for ConvolutionSupportGenerator<F, K, N>
+        {
             type Output = ConvolutionSupportGenerator<F, K, N>;
-            fn $fn(mut self, t : F) -> Self::Output {
+            fn $fn(mut self, t: F) -> Self::Output {
                 std::ops::$trait_assign::$fn_assign(&mut self.centres, t);
                 self
             }
         }
-        impl<'a, F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
-        std::ops::$trait<F>
-        for &'a ConvolutionSupportGenerator<F, K, N> {
+        impl<'a, F: Float, K: SimpleConvolutionKernel<F, N>, const N: usize> std::ops::$trait<F>
+            for &'a ConvolutionSupportGenerator<F, K, N>
+        {
             type Output = ConvolutionSupportGenerator<F, K, N>;
-            fn $fn(self, t : F) -> Self::Output {
-                ConvolutionSupportGenerator{
-                    kernel : self.kernel.clone(),
-                    centres : (&self.centres).$fn(t),
+            fn $fn(self, t: F) -> Self::Output {
+                ConvolutionSupportGenerator {
+                    kernel: self.kernel.clone(),
+                    centres: (&self.centres).$fn(t),
                 }
             }
         }
-    }
+    };
 }
 
 make_convolutionsupportgenerator_scalarop_rhs!(Mul, mul, MulAssign, mul_assign);
 make_convolutionsupportgenerator_scalarop_rhs!(Div, div, DivAssign, div_assign);
 
-
 /// Generates an unary operation (e.g. [`std::ops::Neg`]) for [`ConvolutionSupportGenerator`].
 macro_rules! make_convolutionsupportgenerator_unaryop {
     ($trait:ident, $fn:ident) => {
-        impl<F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
-        std::ops::$trait
-        for ConvolutionSupportGenerator<F, K, N> {
+        impl<F: Float, K: SimpleConvolutionKernel<F, N>, const N: usize> std::ops::$trait
+            for ConvolutionSupportGenerator<F, K, N>
+        {
             type Output = ConvolutionSupportGenerator<F, K, N>;
             fn $fn(mut self) -> Self::Output {
                 self.centres = self.centres.$fn();
@@ -306,19 +324,18 @@
             }
         }
 
-        impl<'a, F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
-        std::ops::$trait
-        for &'a ConvolutionSupportGenerator<F, K, N> {
+        impl<'a, F: Float, K: SimpleConvolutionKernel<F, N>, const N: usize> std::ops::$trait
+            for &'a ConvolutionSupportGenerator<F, K, N>
+        {
             type Output = ConvolutionSupportGenerator<F, K, N>;
             fn $fn(self) -> Self::Output {
-                ConvolutionSupportGenerator{
-                    kernel : self.kernel.clone(),
-                    centres : (&self.centres).$fn(),
+                ConvolutionSupportGenerator {
+                    kernel: self.kernel.clone(),
+                    centres: (&self.centres).$fn(),
                 }
             }
         }
-    }
+    };
 }
 
 make_convolutionsupportgenerator_unaryop!(Neg, neg);
-