Mercurial > repos > pointsource_algs / file revision

/*!
This module provides [`RunnableExperiment`] for running chosen algorithms on a chosen experiment.
*/

use crate::fb::{pointsource_fb_reg, pointsource_fista_reg, FBConfig, InsertionConfig};
use crate::forward_model::sensor_grid::{
    //SensorGridBTFN,
    Sensor,
    SensorGrid,
    SensorGridBT,
    Spread,
};
use crate::forward_model::*;
use crate::forward_pdps::{pointsource_fb_pair, pointsource_forward_pdps_pair, ForwardPDPSConfig};
use crate::frank_wolfe::{pointsource_fw_reg, FWConfig, FWVariant, RegTermFW};
use crate::kernels::*;
use crate::measures::merging::{SpikeMerging, SpikeMergingMethod};
use crate::measures::*;
use crate::pdps::{pointsource_pdps_reg, PDPSConfig};
use crate::plot::*;
use crate::prox_penalty::{
    ProxPenalty, ProxTerm, RadonSquared, StepLengthBound, StepLengthBoundPD, StepLengthBoundPair,
};
use crate::regularisation::{NonnegRadonRegTerm, RadonRegTerm, Regularisation, SlidingRegTerm};
use crate::seminorms::*;
use crate::sliding_fb::{pointsource_sliding_fb_reg, SlidingFBConfig, TransportConfig};
use crate::sliding_pdps::{
    pointsource_sliding_fb_pair, pointsource_sliding_pdps_pair, SlidingPDPSConfig,
};
use crate::subproblem::{InnerMethod, InnerSettings};
use crate::tolerance::Tolerance;
use crate::types::*;
use crate::{AlgorithmOverrides, CommandLineArgs};
use alg_tools::bisection_tree::*;
use alg_tools::bounds::{Bounded, MinMaxMapping};
use alg_tools::convex::{Conjugable, Norm222, Prox, Zero};
use alg_tools::direct_product::Pair;
use alg_tools::discrete_gradient::{ForwardNeumann, Grad};
use alg_tools::error::{DynError, DynResult};
use alg_tools::euclidean::{ClosedEuclidean, Euclidean};
use alg_tools::iterate::{
    AlgIteratorFactory, AlgIteratorOptions, BasicAlgIteratorFactory, LoggingIteratorFactory, Timed,
    TimingIteratorFactory, ValueIteratorFactory, Verbose,
};
use alg_tools::lingrid::lingrid;
use alg_tools::linops::{IdOp, RowOp, AXPY};
use alg_tools::logger::Logger;
use alg_tools::mapping::{
    DataTerm, DifferentiableMapping, DifferentiableRealMapping, Instance, RealMapping,
};
use alg_tools::maputil::map3;
use alg_tools::nalgebra_support::ToNalgebraRealField;
use alg_tools::norms::{NormExponent, L1, L2};
use alg_tools::operator_arithmetic::Weighted;
use alg_tools::sets::Cube;
use alg_tools::sets::SetOrd;
use alg_tools::tabledump::TableDump;
use anyhow::anyhow;
use chrono::{DateTime, Utc};
use clap::ValueEnum;
use colored::Colorize;
use cpu_time::ProcessTime;
use nalgebra::base::DVector;
use numeric_literals::replace_float_literals;
use rand::prelude::{SeedableRng, StdRng};
use rand_distr::Distribution;
use serde::{Deserialize, Serialize};
use serde_json;
use std::collections::HashMap;
use std::hash::Hash;
use std::time::Instant;
use thiserror::Error;

//#[cfg(feature = "pyo3")]
//use pyo3::pyclass;

/// Available algorithms and their configurations
#[derive(Copy, Clone, Debug, Serialize, Deserialize)]
pub enum AlgorithmConfig<F: Float> {
    FB(FBConfig<F>, ProxTerm),
    FISTA(FBConfig<F>, ProxTerm),
    FW(FWConfig<F>),
    PDPS(PDPSConfig<F>, ProxTerm),
    SlidingFB(SlidingFBConfig<F>, ProxTerm),
    ForwardPDPS(ForwardPDPSConfig<F>, ProxTerm),
    SlidingPDPS(SlidingPDPSConfig<F>, ProxTerm),
}

fn unpack_tolerance<F: Float>(v: &Vec<F>) -> Tolerance<F> {
    assert!(v.len() == 3);
    Tolerance::Power { initial: v[0], factor: v[1], exponent: v[2] }
}

impl<F: ClapFloat> AlgorithmConfig<F> {
    /// Override supported parameters based on the command line.
    pub fn cli_override(self, cli: &AlgorithmOverrides<F>) -> Self {
        let override_merging = |g: SpikeMergingMethod<F>| SpikeMergingMethod {
            enabled: cli.merge.unwrap_or(g.enabled),
            radius: cli.merge_radius.unwrap_or(g.radius),
            interp: cli.merge_interp.unwrap_or(g.interp),
        };
        let override_inner = |g: InnerSettings<F>| InnerSettings {
            method: cli.inner_method.unwrap_or(g.method),
            fb_τ0: cli.inner_τ0.unwrap_or(g.fb_τ0),
            pdps_τσ0: cli
                .inner_pdps_τσ0
                .as_ref()
                .map_or(g.pdps_τσ0, |τσ0| (τσ0[0], τσ0[1])),
            pp_τ: cli.inner_pp_τ.as_ref().map_or(g.pp_τ, |τ| (τ[0], τ[1])),
            tolerance_mult: cli.inner_tol.unwrap_or(g.tolerance_mult),
            ..g
        };
        let override_fb_generic = |g: InsertionConfig<F>| InsertionConfig {
            bootstrap_insertions: cli
                .bootstrap_insertions
                .as_ref()
                .map_or(g.bootstrap_insertions, |n| Some((n[0], n[1]))),
            merge_every: cli.merge_every.unwrap_or(g.merge_every),
            merging: override_merging(g.merging),
            final_merging: cli.final_merging.unwrap_or(g.final_merging),
            fitness_merging: cli.fitness_merging.unwrap_or(g.fitness_merging),
            inner: override_inner(g.inner),
            tolerance: cli
                .tolerance
                .as_ref()
                .map(unpack_tolerance)
                .unwrap_or(g.tolerance),
            ..g
        };
        let override_transport = |g: TransportConfig<F>| TransportConfig {
            θ0: cli.theta0.unwrap_or(g.θ0),
            tolerance_mult_con: cli.transport_tolerance_pos.unwrap_or(g.tolerance_mult_con),
            adaptation: cli.transport_adaptation.unwrap_or(g.adaptation),
            ..g
        };

        use AlgorithmConfig::*;
        match self {
            FB(fb, prox) => FB(
                FBConfig {
                    τ0: cli.tau0.unwrap_or(fb.τ0),
                    σp0: cli.sigmap0.unwrap_or(fb.σp0),
                    insertion: override_fb_generic(fb.insertion),
                    ..fb
                },
                prox,
            ),
            FISTA(fb, prox) => FISTA(
                FBConfig {
                    τ0: cli.tau0.unwrap_or(fb.τ0),
                    σp0: cli.sigmap0.unwrap_or(fb.σp0),
                    insertion: override_fb_generic(fb.insertion),
                    ..fb
                },
                prox,
            ),
            PDPS(pdps, prox) => PDPS(
                PDPSConfig {
                    τ0: cli.tau0.unwrap_or(pdps.τ0),
                    σ0: cli.sigma0.unwrap_or(pdps.σ0),
                    acceleration: cli.acceleration.unwrap_or(pdps.acceleration),
                    generic: override_fb_generic(pdps.generic),
                    ..pdps
                },
                prox,
            ),
            FW(fw) => FW(FWConfig {
                merging: override_merging(fw.merging),
                tolerance: cli
                    .tolerance
                    .as_ref()
                    .map(unpack_tolerance)
                    .unwrap_or(fw.tolerance),
                ..fw
            }),
            SlidingFB(sfb, prox) => SlidingFB(
                SlidingFBConfig {
                    τ0: cli.tau0.unwrap_or(sfb.τ0),
                    σp0: cli.sigmap0.unwrap_or(sfb.σp0),
                    transport: override_transport(sfb.transport),
                    insertion: override_fb_generic(sfb.insertion),
                    ..sfb
                },
                prox,
            ),
            SlidingPDPS(spdps, prox) => SlidingPDPS(
                SlidingPDPSConfig {
                    τ0: cli.tau0.unwrap_or(spdps.τ0),
                    σp0: cli.sigmap0.unwrap_or(spdps.σp0),
                    σd0: cli.sigma0.unwrap_or(spdps.σd0),
                    //acceleration : cli.acceleration.unwrap_or(pdps.acceleration),
                    transport: override_transport(spdps.transport),
                    insertion: override_fb_generic(spdps.insertion),
                    ..spdps
                },
                prox,
            ),
            ForwardPDPS(fpdps, prox) => ForwardPDPS(
                ForwardPDPSConfig {
                    τ0: cli.tau0.unwrap_or(fpdps.τ0),
                    σp0: cli.sigmap0.unwrap_or(fpdps.σp0),
                    σd0: cli.sigma0.unwrap_or(fpdps.σd0),
                    //acceleration : cli.acceleration.unwrap_or(pdps.acceleration),
                    insertion: override_fb_generic(fpdps.insertion),
                    ..fpdps
                },
                prox,
            ),
        }
    }
}

/// Helper struct for tagging and [`AlgorithmConfig`] or [`ExperimentV2`] with a name.
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct Named<Data> {
    pub name: String,
    #[serde(flatten)]
    pub data: Data,
}

/// Shorthand algorithm configurations, to be used with the command line parser
#[derive(ValueEnum, Debug, Copy, Clone, Eq, PartialEq, Hash, Serialize, Deserialize)]
//#[cfg_attr(feature = "pyo3", pyclass(module = "pointsource_algs"))]
pub enum DefaultAlgorithm {
    /// The μFB forward-backward method
    #[clap(name = "fb")]
    FB,
    /// The μFISTA inertial forward-backward method
    #[clap(name = "fista")]
    FISTA,
    /// The “fully corrective” conditional gradient method
    #[clap(name = "fw")]
    FW,
    /// The “relaxed conditional gradient method
    #[clap(name = "fwrelax")]
    FWRelax,
    /// The μPDPS primal-dual proximal splitting method
    #[clap(name = "pdps")]
    PDPS,
    /// The sliding FB method
    #[clap(name = "sliding_fb", alias = "sfb")]
    SlidingFB,
    /// The sliding PDPS method
    #[clap(name = "sliding_pdps", alias = "spdps")]
    SlidingPDPS,
    /// The PDPS method with a forward step for the smooth function
    #[clap(name = "forward_pdps", alias = "fpdps")]
    ForwardPDPS,

    // Radon variants
    /// The μFB forward-backward method with radon-norm squared proximal term
    #[clap(name = "radon_fb")]
    RadonFB,
    /// The μFISTA inertial forward-backward method with radon-norm squared proximal term
    #[clap(name = "radon_fista")]
    RadonFISTA,
    /// The μPDPS primal-dual proximal splitting method with radon-norm squared proximal term
    #[clap(name = "radon_pdps")]
    RadonPDPS,
    /// The sliding FB method with radon-norm squared proximal term
    #[clap(name = "radon_sliding_fb", alias = "radon_sfb")]
    RadonSlidingFB,
    /// The sliding PDPS method with radon-norm squared proximal term
    #[clap(name = "radon_sliding_pdps", alias = "radon_spdps")]
    RadonSlidingPDPS,
    /// The PDPS method with a forward step for the smooth function with radon-norm squared proximal term
    #[clap(name = "radon_forward_pdps", alias = "radon_fpdps")]
    RadonForwardPDPS,
}

impl DefaultAlgorithm {
    /// Returns the algorithm configuration corresponding to the algorithm shorthand
    pub fn default_config<F: Float>(&self) -> AlgorithmConfig<F> {
        use DefaultAlgorithm::*;
        let radon_insertion = InsertionConfig {
            merging: SpikeMergingMethod { interp: false, ..Default::default() },
            inner: InnerSettings {
                method: InnerMethod::PDPS, // SSN not implemented
                ..Default::default()
            },
            ..Default::default()
        };
        match *self {
            FB => AlgorithmConfig::FB(Default::default(), ProxTerm::Wave),
            FISTA => AlgorithmConfig::FISTA(Default::default(), ProxTerm::Wave),
            FW => AlgorithmConfig::FW(Default::default()),
            FWRelax => {
                AlgorithmConfig::FW(FWConfig { variant: FWVariant::Relaxed, ..Default::default() })
            }
            PDPS => AlgorithmConfig::PDPS(Default::default(), ProxTerm::Wave),
            SlidingFB => AlgorithmConfig::SlidingFB(Default::default(), ProxTerm::Wave),
            SlidingPDPS => AlgorithmConfig::SlidingPDPS(Default::default(), ProxTerm::Wave),
            ForwardPDPS => AlgorithmConfig::ForwardPDPS(Default::default(), ProxTerm::Wave),

            // Radon variants
            RadonFB => AlgorithmConfig::FB(
                FBConfig { insertion: radon_insertion, ..Default::default() },
                ProxTerm::RadonSquared,
            ),
            RadonFISTA => AlgorithmConfig::FISTA(
                FBConfig { insertion: radon_insertion, ..Default::default() },
                ProxTerm::RadonSquared,
            ),
            RadonPDPS => AlgorithmConfig::PDPS(
                PDPSConfig { generic: radon_insertion, ..Default::default() },
                ProxTerm::RadonSquared,
            ),
            RadonSlidingFB => AlgorithmConfig::SlidingFB(
                SlidingFBConfig { insertion: radon_insertion, ..Default::default() },
                ProxTerm::RadonSquared,
            ),
            RadonSlidingPDPS => AlgorithmConfig::SlidingPDPS(
                SlidingPDPSConfig { insertion: radon_insertion, ..Default::default() },
                ProxTerm::RadonSquared,
            ),
            RadonForwardPDPS => AlgorithmConfig::ForwardPDPS(
                ForwardPDPSConfig { insertion: radon_insertion, ..Default::default() },
                ProxTerm::RadonSquared,
            ),
        }
    }

    /// Returns the [`Named`] algorithm corresponding to the algorithm shorthand
    pub fn get_named<F: Float>(&self) -> Named<AlgorithmConfig<F>> {
        self.to_named(self.default_config())
    }

    pub fn to_named<F: Float>(self, alg: AlgorithmConfig<F>) -> Named<AlgorithmConfig<F>> {
        Named { name: self.name(), data: alg }
    }

    pub fn name(self) -> String {
        self.to_possible_value().unwrap().get_name().to_string()
    }
}

// // Floats cannot be hashed directly, so just hash the debug formatting
// // for use as file identifier.
// impl<F : Float> Hash for AlgorithmConfig<F> {
//     fn hash<H: Hasher>(&self, state: &mut H) {
//         format!("{:?}", self).hash(state);
//     }
// }

/// Plotting level configuration
#[derive(Copy, Clone, Eq, PartialEq, Ord, PartialOrd, Serialize, ValueEnum, Debug)]
pub enum PlotLevel {
    /// Plot nothing
    #[clap(name = "none")]
    None,
    /// Plot problem data
    #[clap(name = "data")]
    Data,
    /// Plot iterationwise state
    #[clap(name = "iter")]
    Iter,
}

impl Default for PlotLevel {
    fn default() -> Self {
        Self::Data
    }
}

type DefaultBT<F, const N: usize> = BT<DynamicDepth, F, usize, Bounds<F>, N>;
type DefaultSeminormOp<F, K, const N: usize> = ConvolutionOp<F, K, DefaultBT<F, N>, N>;
type DefaultSG<F, Sensor, Spread, const N: usize> =
    SensorGrid<F, Sensor, Spread, DefaultBT<F, N>, N>;

/// This is a dirty workaround to rust-csv not supporting struct flattening etc.
#[derive(Serialize)]
struct CSVLog<F> {
    iter: usize,
    cpu_time: f64,
    value: F,
    relative_value: F,
    //post_value : F,
    n_spikes: usize,
    inner_iters: usize,
    merged: usize,
    pruned: usize,
    this_iters: usize,
    epsilon: F,
}

/// Collected experiment statistics
#[derive(Clone, Debug, Serialize)]
struct ExperimentStats<F: Float> {
    /// Signal-to-noise ratio in decibels
    ssnr: F,
    /// Proportion of noise in the signal as a number in $[0, 1]$.
    noise_ratio: F,
    /// When the experiment was run (UTC)
    when: DateTime<Utc>,
}

#[replace_float_literals(F::cast_from(literal))]
impl<F: Float> ExperimentStats<F> {
    /// Calculate [`ExperimentStats`] based on a noisy `signal` and the separated `noise` signal.
    fn new<E: Euclidean<F>>(signal: &E, noise: &E) -> Self {
        let s = signal.norm2_squared();
        let n = noise.norm2_squared();
        let noise_ratio = (n / s).sqrt();
        let ssnr = 10.0 * (s / n).log10();
        ExperimentStats { ssnr, noise_ratio, when: Utc::now() }
    }
}
/// Collected algorithm statistics
#[derive(Clone, Debug, Serialize)]
struct AlgorithmStats<F: Float> {
    /// Overall CPU time spent
    cpu_time: F,
    /// Real time spent
    elapsed: F,
}

/// A wrapper for [`serde_json::to_writer_pretty`] that takes a filename as input
/// and outputs a [`DynError`].
fn write_json<T: Serialize>(filename: String, data: &T) -> DynError {
    serde_json::to_writer_pretty(std::fs::File::create(filename)?, data)?;
    Ok(())
}

/// Struct for experiment configurations
#[derive(Debug, Clone, Serialize)]
pub struct ExperimentV2<F, NoiseDistr, S, K, P, const N: usize>
where
    F: Float + ClapFloat,
    [usize; N]: Serialize,
    NoiseDistr: Distribution<F>,
    S: Sensor<N, F>,
    P: Spread<N, F>,
    K: SimpleConvolutionKernel<N, F>,
{
    /// Domain $Ω$.
    pub domain: Cube<N, F>,
    /// Number of sensors along each dimension
    pub sensor_count: [usize; N],
    /// Noise distribution
    pub noise_distr: NoiseDistr,
    /// Seed for random noise generation (for repeatable experiments)
    pub noise_seed: u64,
    /// Sensor $θ$; $θ * ψ$ forms the forward operator $𝒜$.
    pub sensor: S,
    /// Spread $ψ$; $θ * ψ$ forms the forward operator $𝒜$.
    pub spread: P,
    /// Kernel $ρ$ of $𝒟$.
    pub kernel: K,
    /// True point sources
    pub μ_hat: RNDM<N, F>,
    /// Regularisation term and parameter
    pub regularisation: Regularisation<F>,
    /// For plotting : how wide should the kernels be plotted
    pub kernel_plot_width: F,
    /// Data term
    pub dataterm: DataTermType,
    /// A map of default configurations for algorithms
    pub algorithm_overrides: HashMap<DefaultAlgorithm, AlgorithmOverrides<F>>,
    /// Default merge radius
    pub default_merge_radius: F,
}

#[derive(Debug, Clone, Serialize)]
pub struct ExperimentBiased<F, NoiseDistr, S, K, P, B, const N: usize>
where
    F: Float + ClapFloat,
    [usize; N]: Serialize,
    NoiseDistr: Distribution<F>,
    S: Sensor<N, F>,
    P: Spread<N, F>,
    K: SimpleConvolutionKernel<N, F>,
    B: Mapping<Loc<N, F>, Codomain = F> + Serialize + std::fmt::Debug,
{
    /// Basic setup
    pub base: ExperimentV2<F, NoiseDistr, S, K, P, N>,
    /// Weight of TV term
    pub λ: F,
    /// Bias function
    pub bias: B,
}

/// Trait for runnable experiments
pub trait RunnableExperiment<F: ClapFloat> {
    /// Run all algorithms provided, or default algorithms if none provided, on the experiment.
    fn runall(
        &self,
        cli: &CommandLineArgs,
        algs: Option<Vec<Named<AlgorithmConfig<F>>>>,
    ) -> DynError;

    /// Return algorithm default config
    fn algorithm_overrides(&self, alg: DefaultAlgorithm) -> AlgorithmOverrides<F>;

    /// Experiment name
    fn name(&self) -> &str;
}

/// Error codes for running an algorithm on an experiment.
#[derive(Error, Debug)]
pub enum RunError {
    /// Algorithm not implemented for this experiment
    #[error("Algorithm not implemented for this experiment")]
    NotImplemented,
}

use RunError::*;

type DoRunAllIt<'a, F, const N: usize> = LoggingIteratorFactory<
    'a,
    Timed<IterInfo<F>>,
    TimingIteratorFactory<BasicAlgIteratorFactory<IterInfo<F>>>,
>;

pub trait RunnableExperimentExtras<F: ClapFloat>:
    RunnableExperiment<F> + Serialize + Sized
{
    /// Helper function to print experiment start message and save setup.
    /// Returns saving prefix.
    fn start(&self, cli: &CommandLineArgs) -> DynResult<String> {
        let experiment_name = self.name();
        let ser = serde_json::to_string(self);

        println!(
            "{}\n{}",
            format!("Performing experiment {}…", experiment_name).cyan(),
            format!(
                "Experiment settings: {}",
                if let Ok(ref s) = ser {
                    s
                } else {
                    "<serialisation failure>"
                }
            )
            .bright_black(),
        );

        // Set up output directory
        let prefix = format!("{}/{}/", cli.outdir, experiment_name);

        // Save experiment configuration and statistics
        std::fs::create_dir_all(&prefix)?;
        write_json(format!("{prefix}experiment.json"), self)?;
        write_json(format!("{prefix}config.json"), cli)?;

        Ok(prefix)
    }

    /// Helper function to run all algorithms on an experiment.
    fn do_runall<P, Z, Plot, const N: usize>(
        &self,
        prefix: &String,
        cli: &CommandLineArgs,
        algorithms: Vec<Named<AlgorithmConfig<F>>>,
        mut make_plotter: impl FnMut(String) -> Plot,
        mut save_extra: impl FnMut(String, Z) -> DynError,
        init: P,
        mut do_alg: impl FnMut(
            (&AlgorithmConfig<F>, DoRunAllIt<F, N>, Plot, P, String),
        ) -> DynResult<(RNDM<N, F>, Z)>,
    ) -> DynError
    where
        F: for<'b> Deserialize<'b>,
        PlotLookup: Plotting<N>,
        P: Clone,
    {
        let experiment_name = self.name();

        let mut logs = Vec::new();

        let iterator_options = AlgIteratorOptions {
            max_iter: cli.max_iter,
            verbose_iter: cli
                .verbose_iter
                .map_or(Verbose::LogarithmicCap { base: 10, cap: 2 }, |n| {
                    Verbose::Every(n)
                }),
            quiet: cli.quiet,
        };

        // Run the algorithm(s)
        for named @ Named { name: alg_name, data: alg } in algorithms.iter() {
            let this_prefix = format!("{}{}/", prefix, alg_name);

            // Create Logger and IteratorFactory
            let mut logger = Logger::new();
            let iterator = iterator_options.instantiate().timed().into_log(&mut logger);

            let running = if !cli.quiet {
                format!(
                    "{}\n{}\n{}\n",
                    format!("Running {} on experiment {}…", alg_name, experiment_name).cyan(),
                    format!(
                        "Iteration settings: {}",
                        serde_json::to_string(&iterator_options)?
                    )
                    .bright_black(),
                    format!("Algorithm settings: {}", serde_json::to_string(&alg)?).bright_black()
                )
            } else {
                "".to_string()
            };
            //
            // The following is for postprocessing, which has been disabled anyway.
            //
            // let reg : Box<dyn WeightOptim<_, _, _, N>> = match regularisation {
            //     Regularisation::Radon(α) => Box::new(RadonRegTerm(α)),
            //     Regularisation::NonnegRadon(α) => Box::new(NonnegRadonRegTerm(α)),
            // };
            //let findim_data = reg.prepare_optimise_weights(&opA, &b);
            //let inner_config : InnerSettings<F> = Default::default();
            //let inner_it = inner_config.iterator_options;

            // Create plotter and directory if needed.
            let plotter = make_plotter(this_prefix);

            let start = Instant::now();
            let start_cpu = ProcessTime::now();

            let (μ, z) = match do_alg((alg, iterator, plotter, init.clone(), running)) {
                Ok(μ) => μ,
                Err(e) => {
                    let msg = format!(
                        "Skipping algorithm “{alg_name}” for {experiment_name} due to error: {e}"
                    )
                    .red();
                    eprintln!("{}", msg);
                    continue;
                }
            };

            let elapsed = start.elapsed().as_secs_f64();
            let cpu_time = start_cpu.elapsed().as_secs_f64();

            println!(
                "{}",
                format!("Elapsed {elapsed}s (CPU time {cpu_time}s)… ").yellow()
            );

            // Save results
            println!("{}", "Saving results …".green());

            let mkname = |t| format!("{prefix}{alg_name}_{t}");

            write_json(mkname("config.json"), &named)?;
            write_json(mkname("stats.json"), &AlgorithmStats { cpu_time, elapsed })?;
            μ.write_csv(mkname("reco.txt"))?;
            save_extra(mkname(""), z)?;
            //logger.write_csv(mkname("log.txt"))?;
            logs.push((mkname("log.txt"), logger));
        }

        save_logs(
            logs,
            format!("{prefix}valuerange.json"),
            cli.load_valuerange,
        )
    }
}

impl<F, E> RunnableExperimentExtras<F> for E
where
    F: ClapFloat,
    Self: RunnableExperiment<F> + Serialize,
{
}

#[replace_float_literals(F::cast_from(literal))]
impl<F, NoiseDistr, S, K, P, /*PreadjointCodomain, */ const N: usize> RunnableExperiment<F>
    for Named<ExperimentV2<F, NoiseDistr, S, K, P, N>>
where
    F: ClapFloat
        + nalgebra::RealField
        + ToNalgebraRealField<MixedType = F>
        + Default
        + for<'b> Deserialize<'b>,
    [usize; N]: Serialize,
    S: Sensor<N, F> + Copy + Serialize + std::fmt::Debug,
    P: Spread<N, F> + Copy + Serialize + std::fmt::Debug,
    Convolution<S, P>: Spread<N, F>
        + Bounded<F>
        + LocalAnalysis<F, Bounds<F>, N>
        + Copy
        // TODO: shold not have differentiability as a requirement, but
        // decide availability of sliding based on it.
        //+ for<'b> Differentiable<&'b Loc<N, F>, Output = Loc<N, F>>,
        // TODO: very weird that rust only compiles with Differentiable
        // instead of the above one on references, which is required by
        // poitsource_sliding_fb_reg.
        + DifferentiableRealMapping<N, F>
        + Lipschitz<L2, FloatType = F>,
    for<'b> <Convolution<S, P> as DifferentiableMapping<Loc<N, F>>>::Differential<'b>:
        Lipschitz<L2, FloatType = F>, // TODO: should not be required generally, only for sliding_fb.
    AutoConvolution<P>: BoundedBy<F, K>,
    K: SimpleConvolutionKernel<N, F>
        + LocalAnalysis<F, Bounds<F>, N>
        + Copy
        + Serialize
        + std::fmt::Debug,
    Cube<N, F>: P2Minimise<Loc<N, F>, F> + SetOrd,
    PlotLookup: Plotting<N>,
    DefaultBT<F, N>: SensorGridBT<F, S, P, N, Depth = DynamicDepth> + BTSearch<N, F>,
    BTNodeLookup: BTNode<F, usize, Bounds<F>, N>,
    RNDM<N, F>: SpikeMerging<F>,
    NoiseDistr: Distribution<F> + Serialize + std::fmt::Debug,
{
    fn name(&self) -> &str {
        self.name.as_ref()
    }

    fn algorithm_overrides(&self, alg: DefaultAlgorithm) -> AlgorithmOverrides<F> {
        AlgorithmOverrides {
            merge_radius: Some(self.data.default_merge_radius),
            ..self
                .data
                .algorithm_overrides
                .get(&alg)
                .cloned()
                .unwrap_or(Default::default())
        }
    }

    fn runall(
        &self,
        cli: &CommandLineArgs,
        algs: Option<Vec<Named<AlgorithmConfig<F>>>>,
    ) -> DynError {
        // Get experiment configuration
        let &ExperimentV2 {
            domain,
            sensor_count,
            ref noise_distr,
            sensor,
            spread,
            kernel,
            ref μ_hat,
            regularisation,
            kernel_plot_width,
            dataterm,
            noise_seed,
            ..
        } = &self.data;

        // Set up algorithms
        let algorithms = match (algs, dataterm) {
            (Some(algs), _) => algs,
            (None, DataTermType::L222) => vec![DefaultAlgorithm::FB.get_named()],
            (None, DataTermType::L1) => vec![DefaultAlgorithm::PDPS.get_named()],
        };

        // Set up operators
        let depth = DynamicDepth(8);
        let opA = DefaultSG::new(domain, sensor_count, sensor, spread, depth);
        let op𝒟 = DefaultSeminormOp::new(depth, domain, kernel);

        // Set up random number generator.
        let mut rng = StdRng::seed_from_u64(noise_seed);

        // Generate the data and calculate SSNR statistic
        let b_hat = opA.apply(μ_hat);
        let noise = DVector::from_distribution(b_hat.len(), &noise_distr, &mut rng);
        let b = &b_hat + &noise;
        // Need to wrap calc_ssnr into a function to hide ultra-lame nalgebra::RealField
        // overloading log10 and conflicting with standard NumTraits one.
        let stats = ExperimentStats::new(&b, &noise);

        let prefix = self.start(cli)?;
        write_json(format!("{prefix}stats.json"), &stats)?;

        plotall(
            cli,
            &prefix,
            &domain,
            &sensor,
            &kernel,
            &spread,
            &μ_hat,
            &op𝒟,
            &opA,
            &b_hat,
            &b,
            kernel_plot_width,
        )?;

        let plotgrid = lingrid(&domain, &[if N == 1 { 1000 } else { 100 }; N]);
        let make_plotter = |this_prefix| {
            let plot_count = if cli.plot >= PlotLevel::Iter { 2000 } else { 0 };
            SeqPlotter::new(this_prefix, plot_count, plotgrid.clone())
        };

        let save_extra = |_, ()| Ok(());

        let μ0 = None; // Zero init

        match (dataterm, regularisation) {
            (DataTermType::L1, Regularisation::Radon(α)) => {
                let f = DataTerm::new(opA, b, L1.as_mapping());
                let reg = RadonRegTerm(α);
                self.do_runall(
                    &prefix,
                    cli,
                    algorithms,
                    make_plotter,
                    save_extra,
                    μ0,
                    |p| {
                        run_pdps(&f, &reg, &RadonSquared, p, |p| {
                            run_pdps(&f, &reg, &op𝒟, p, |_| Err(NotImplemented.into()))
                        })
                        .map(|μ| (μ, ()))
                    },
                )
            }
            (DataTermType::L1, Regularisation::NonnegRadon(α)) => {
                let f = DataTerm::new(opA, b, L1.as_mapping());
                let reg = NonnegRadonRegTerm(α);
                self.do_runall(
                    &prefix,
                    cli,
                    algorithms,
                    make_plotter,
                    save_extra,
                    μ0,
                    |p| {
                        run_pdps(&f, &reg, &RadonSquared, p, |p| {
                            run_pdps(&f, &reg, &op𝒟, p, |_| Err(NotImplemented.into()))
                        })
                        .map(|μ| (μ, ()))
                    },
                )
            }
            (DataTermType::L222, Regularisation::Radon(α)) => {
                let f = DataTerm::new(opA, b, Norm222::new());
                let reg = RadonRegTerm(α);
                self.do_runall(
                    &prefix,
                    cli,
                    algorithms,
                    make_plotter,
                    save_extra,
                    μ0,
                    |p| {
                        run_fb(&f, &reg, &RadonSquared, p, |p| {
                            run_pdps(&f, &reg, &RadonSquared, p, |p| {
                                run_fb(&f, &reg, &op𝒟, p, |p| {
                                    run_pdps(&f, &reg, &op𝒟, p, |p| {
                                        run_fw(&f, &reg, p, |_| Err(NotImplemented.into()))
                                    })
                                })
                            })
                        })
                        .map(|μ| (μ, ()))
                    },
                )
            }
            (DataTermType::L222, Regularisation::NonnegRadon(α)) => {
                let f = DataTerm::new(opA, b, Norm222::new());
                let reg = NonnegRadonRegTerm(α);
                self.do_runall(
                    &prefix,
                    cli,
                    algorithms,
                    make_plotter,
                    save_extra,
                    μ0,
                    |p| {
                        run_fb(&f, &reg, &RadonSquared, p, |p| {
                            run_pdps(&f, &reg, &RadonSquared, p, |p| {
                                run_fb(&f, &reg, &op𝒟, p, |p| {
                                    run_pdps(&f, &reg, &op𝒟, p, |p| {
                                        run_fw(&f, &reg, p, |_| Err(NotImplemented.into()))
                                    })
                                })
                            })
                        })
                        .map(|μ| (μ, ()))
                    },
                )
            }
        }
    }
}

/// Runs PDPS if `alg` so requests and `prox_penalty` matches.
///
/// Due to the structure of the PDPS, the data term `f` has to have a specific form.
///
/// `cont` gives a continuation attempt to find the algorithm matching the description `alg`.
pub fn run_pdps<'a, F, A, Phi, Reg, P, I, Plot, const N: usize>(
    f: &'a DataTerm<F, RNDM<N, F>, A, Phi>,
    reg: &Reg,
    prox_penalty: &P,
    (alg, iterator, plotter, μ0, running): (
        &AlgorithmConfig<F>,
        I,
        Plot,
        Option<RNDM<N, F>>,
        String,
    ),
    cont: impl FnOnce(
        (&AlgorithmConfig<F>, I, Plot, Option<RNDM<N, F>>, String),
    ) -> DynResult<RNDM<N, F>>,
) -> DynResult<RNDM<N, F>>
where
    F: Float + ToNalgebraRealField,
    A: ForwardModel<RNDM<N, F>, F>,
    Phi: Conjugable<A::Observable, F>,
    for<'b> Phi::Conjugate<'b>: Prox<A::Observable>,
    for<'b> &'b A::Observable: Instance<A::Observable>,
    A::Observable: AXPY,
    Reg: SlidingRegTerm<Loc<N, F>, F>,
    P: ProxPenalty<Loc<N, F>, A::PreadjointCodomain, Reg, F> + StepLengthBoundPD<F, A, RNDM<N, F>>,
    RNDM<N, F>: SpikeMerging<F>,
    I: AlgIteratorFactory<IterInfo<F>>,
    Plot: Plotter<P::ReturnMapping, A::PreadjointCodomain, RNDM<N, F>>,
{
    match alg {
        &AlgorithmConfig::PDPS(ref algconfig, prox_type) if prox_type == P::prox_type() => {
            print!("{running}");
            pointsource_pdps_reg(f, reg, prox_penalty, algconfig, iterator, plotter, μ0)
        }
        _ => cont((alg, iterator, plotter, μ0, running)),
    }
}

/// Runs FB-style algorithms if `alg` so requests and `prox_penalty` matches.
///
/// `cont` gives a continuation attempt to find the algorithm matching the description `alg`.
pub fn run_fb<F, Dat, Reg, P, I, Plot, const N: usize>(
    f: &Dat,
    reg: &Reg,
    prox_penalty: &P,
    (alg, iterator, plotter, μ0, running): (
        &AlgorithmConfig<F>,
        I,
        Plot,
        Option<RNDM<N, F>>,
        String,
    ),
    cont: impl FnOnce(
        (&AlgorithmConfig<F>, I, Plot, Option<RNDM<N, F>>, String),
    ) -> DynResult<RNDM<N, F>>,
) -> DynResult<RNDM<N, F>>
where
    F: Float + ToNalgebraRealField,
    I: AlgIteratorFactory<IterInfo<F>>,
    Dat: DifferentiableMapping<RNDM<N, F>, Codomain = F> + BoundedCurvature<F>,
    Dat::DerivativeDomain: DifferentiableRealMapping<N, F> + ClosedMul<F>,
    RNDM<N, F>: SpikeMerging<F>,
    Reg: SlidingRegTerm<Loc<N, F>, F>,
    P: ProxPenalty<Loc<N, F>, Dat::DerivativeDomain, Reg, F> + StepLengthBound<F, Dat>,
    Plot: Plotter<P::ReturnMapping, Dat::DerivativeDomain, RNDM<N, F>>,
{
    let pt = P::prox_type();

    match alg {
        &AlgorithmConfig::FB(ref algconfig, prox_type) if prox_type == pt => {
            print!("{running}");
            pointsource_fb_reg(f, reg, prox_penalty, algconfig, iterator, plotter, μ0)
        }
        &AlgorithmConfig::FISTA(ref algconfig, prox_type) if prox_type == pt => {
            print!("{running}");
            pointsource_fista_reg(f, reg, prox_penalty, algconfig, iterator, plotter, μ0)
        }
        &AlgorithmConfig::SlidingFB(ref algconfig, prox_type) if prox_type == pt => {
            print!("{running}");
            pointsource_sliding_fb_reg(f, reg, prox_penalty, algconfig, iterator, plotter, μ0)
        }
        _ => cont((alg, iterator, plotter, μ0, running)),
    }
}

/// Runs FB-style algorithms if `alg` so requests and `prox_penalty` matches.
///
/// For the moment, due to restrictions of the Frank–Wolfe implementation, only the
/// $L^2$-squared data term is enabled through the type signatures.
///
/// `cont` gives a continuation attempt to find the algorithm matching the description `alg`.
pub fn run_fw<'a, F, A, Reg, I, Plot, const N: usize>(
    f: &'a DataTerm<F, RNDM<N, F>, A, Norm222<F>>,
    reg: &Reg,
    (alg, iterator, plotter, μ0, running): (
        &AlgorithmConfig<F>,
        I,
        Plot,
        Option<RNDM<N, F>>,
        String,
    ),
    cont: impl FnOnce(
        (&AlgorithmConfig<F>, I, Plot, Option<RNDM<N, F>>, String),
    ) -> DynResult<RNDM<N, F>>,
) -> DynResult<RNDM<N, F>>
where
    F: Float + ToNalgebraRealField,
    I: AlgIteratorFactory<IterInfo<F>>,
    A: ForwardModel<RNDM<N, F>, F>,
    A::PreadjointCodomain: MinMaxMapping<Loc<N, F>, F>,
    for<'b> &'b A::PreadjointCodomain: Instance<A::PreadjointCodomain>,
    Cube<N, F>: P2Minimise<Loc<N, F>, F>,
    RNDM<N, F>: SpikeMerging<F>,
    Reg: RegTermFW<F, A, ValueIteratorFactory<F, AlgIteratorOptions>, N>,
    Plot: Plotter<A::PreadjointCodomain, A::PreadjointCodomain, RNDM<N, F>>,
{
    match alg {
        &AlgorithmConfig::FW(ref algconfig) => {
            print!("{running}");
            pointsource_fw_reg(f, reg, algconfig, iterator, plotter, μ0)
        }
        _ => cont((alg, iterator, plotter, μ0, running)),
    }
}

#[replace_float_literals(F::cast_from(literal))]
impl<F, NoiseDistr, S, K, P, B, const N: usize> RunnableExperiment<F>
    for Named<ExperimentBiased<F, NoiseDistr, S, K, P, B, N>>
where
    F: ClapFloat
        + nalgebra::RealField
        + ToNalgebraRealField<MixedType = F>
        + Default
        + for<'b> Deserialize<'b>,
    [usize; N]: Serialize,
    S: Sensor<N, F> + Copy + Serialize + std::fmt::Debug,
    P: Spread<N, F> + Copy + Serialize + std::fmt::Debug,
    Convolution<S, P>: Spread<N, F>
        + Bounded<F>
        + LocalAnalysis<F, Bounds<F>, N>
        + Copy
        // TODO: shold not have differentiability as a requirement, but
        // decide availability of sliding based on it.
        //+ for<'b> Differentiable<&'b Loc<N, F>, Output = Loc<N, F>>,
        // TODO: very weird that rust only compiles with Differentiable
        // instead of the above one on references, which is required by
        // poitsource_sliding_fb_reg.
        + DifferentiableRealMapping<N, F>
        + Lipschitz<L2, FloatType = F>,
    for<'b> <Convolution<S, P> as DifferentiableMapping<Loc<N, F>>>::Differential<'b>:
        Lipschitz<L2, FloatType = F>, // TODO: should not be required generally, only for sliding_fb.
    AutoConvolution<P>: BoundedBy<F, K>,
    K: SimpleConvolutionKernel<N, F>
        + LocalAnalysis<F, Bounds<F>, N>
        + Copy
        + Serialize
        + std::fmt::Debug,
    Cube<N, F>: P2Minimise<Loc<N, F>, F> + SetOrd,
    PlotLookup: Plotting<N>,
    DefaultBT<F, N>: SensorGridBT<F, S, P, N, Depth = DynamicDepth> + BTSearch<N, F>,
    BTNodeLookup: BTNode<F, usize, Bounds<F>, N>,
    RNDM<N, F>: SpikeMerging<F>,
    NoiseDistr: Distribution<F> + Serialize + std::fmt::Debug,
    B: Mapping<Loc<N, F>, Codomain = F> + Serialize + std::fmt::Debug,
    nalgebra::DVector<F>: ClosedMul<F>,
    // This is mainly required for the final Mul requirement to be defined
    // DefaultSG<F, S, P, N>: ForwardModel<
    //     RNDM<N, F>,
    //     F,
    //     PreadjointCodomain = PreadjointCodomain,
    //     Observable = DVector<F::MixedType>,
    // >,
    // PreadjointCodomain: Bounded<F> + DifferentiableRealMapping<N, F> + std::ops::Mul<F>,
    // Pair<PreadjointCodomain, DVector<F>>: std::ops::Mul<F>,
    // DefaultSeminormOp<F, K, N> :  ProxPenalty<F, PreadjointCodomain, RadonRegTerm<F>, N>,
    // DefaultSeminormOp<F, K, N> :  ProxPenalty<F, PreadjointCodomain, NonnegRadonRegTerm<F>, N>,
    // RadonSquared : ProxPenalty<F, PreadjointCodomain, RadonRegTerm<F>, N>,
    // RadonSquared : ProxPenalty<F, PreadjointCodomain, NonnegRadonRegTerm<F>, N>,
{
    fn name(&self) -> &str {
        self.name.as_ref()
    }

    fn algorithm_overrides(&self, alg: DefaultAlgorithm) -> AlgorithmOverrides<F> {
        AlgorithmOverrides {
            merge_radius: Some(self.data.base.default_merge_radius),
            ..self
                .data
                .base
                .algorithm_overrides
                .get(&alg)
                .cloned()
                .unwrap_or(Default::default())
        }
    }

    fn runall(
        &self,
        cli: &CommandLineArgs,
        algs: Option<Vec<Named<AlgorithmConfig<F>>>>,
    ) -> DynError {
        // Get experiment configuration
        let &ExperimentBiased {
            λ,
            ref bias,
            base:
                ExperimentV2 {
                    domain,
                    sensor_count,
                    ref noise_distr,
                    sensor,
                    spread,
                    kernel,
                    ref μ_hat,
                    regularisation,
                    kernel_plot_width,
                    dataterm,
                    noise_seed,
                    ..
                },
        } = &self.data;

        // Set up algorithms
        let algorithms = match (algs, dataterm) {
            (Some(algs), _) => algs,
            _ => vec![DefaultAlgorithm::SlidingPDPS.get_named()],
        };

        // Set up operators
        let depth = DynamicDepth(8);
        let opA = DefaultSG::new(domain, sensor_count, sensor, spread, depth);
        let op𝒟 = DefaultSeminormOp::new(depth, domain, kernel);
        let opAext = RowOp(opA.clone(), IdOp::new());
        let fnR = Zero::new();
        let h = map3(
            domain.span_start(),
            domain.span_end(),
            sensor_count,
            |a, b, n| (b - a) / F::cast_from(n),
        )
        .into_iter()
        .reduce(NumTraitsFloat::max)
        .unwrap();
        let z = DVector::zeros(sensor_count.iter().product());
        let opKz = Grad::new_for(&z, h, sensor_count, ForwardNeumann).unwrap();
        let y = opKz.apply(&z);
        let fnH = Weighted { base_fn: L1.as_mapping(), weight: λ }; // TODO: L_{2,1}
                                                                    // let zero_y = y.clone();
                                                                    // let zeroBTFN = opA.preadjoint().apply(&zero_y);
                                                                    // let opKμ = ZeroOp::new(&zero_y, zeroBTFN);

        // Set up random number generator.
        let mut rng = StdRng::seed_from_u64(noise_seed);

        // Generate the data and calculate SSNR statistic
        let bias_vec = DVector::from_vec(
            opA.grid()
                .into_iter()
                .map(|v| bias.apply(v))
                .collect::<Vec<F>>(),
        );
        let b_hat: DVector<_> = opA.apply(μ_hat) + &bias_vec;
        let noise = DVector::from_distribution(b_hat.len(), &noise_distr, &mut rng);
        let b = &b_hat + &noise;
        // Need to wrap calc_ssnr into a function to hide ultra-lame nalgebra::RealField
        // overloading log10 and conflicting with standard NumTraits one.
        let stats = ExperimentStats::new(&b, &noise);

        let prefix = self.start(cli)?;
        write_json(format!("{prefix}stats.json"), &stats)?;

        plotall(
            cli,
            &prefix,
            &domain,
            &sensor,
            &kernel,
            &spread,
            &μ_hat,
            &op𝒟,
            &opA,
            &b_hat,
            &b,
            kernel_plot_width,
        )?;

        opA.write_observable(&bias_vec, format!("{prefix}bias"))?;

        let plotgrid = lingrid(&domain, &[if N == 1 { 1000 } else { 100 }; N]);
        let make_plotter = |this_prefix| {
            let plot_count = if cli.plot >= PlotLevel::Iter { 2000 } else { 0 };
            SeqPlotter::new(this_prefix, plot_count, plotgrid.clone())
        };

        let save_extra = |prefix, z| opA.write_observable(&z, format!("{prefix}z"));

        let μ0 = None; // Zero init

        match (dataterm, regularisation) {
            (DataTermType::L222, Regularisation::Radon(α)) => {
                let f = DataTerm::new(opAext, b, Norm222::new());
                let reg = RadonRegTerm(α);
                self.do_runall(
                    &prefix,
                    cli,
                    algorithms,
                    make_plotter,
                    save_extra,
                    (μ0, z, y),
                    |p| {
                        run_pdps_pair(&f, &reg, &RadonSquared, &opKz, &fnR, &fnH, p, |q| {
                            run_pdps_pair(&f, &reg, &op𝒟, &opKz, &fnR, &fnH, q, |_| {
                                Err(NotImplemented.into())
                            })
                        })
                        .map(|Pair(μ, z)| (μ, z))
                    },
                )
            }
            (DataTermType::L222, Regularisation::NonnegRadon(α)) => {
                let f = DataTerm::new(opAext, b, Norm222::new());
                let reg = NonnegRadonRegTerm(α);
                self.do_runall(
                    &prefix,
                    cli,
                    algorithms,
                    make_plotter,
                    save_extra,
                    (μ0, z, y),
                    |p| {
                        run_pdps_pair(&f, &reg, &RadonSquared, &opKz, &fnR, &fnH, p, |q| {
                            run_pdps_pair(&f, &reg, &op𝒟, &opKz, &fnR, &fnH, q, |_| {
                                Err(NotImplemented.into())
                            })
                        })
                        .map(|Pair(μ, z)| (μ, z))
                    },
                )
            }
            _ => Err(NotImplemented.into()),
        }
    }
}

type MeasureZ<F, Z, const N: usize> = Pair<RNDM<N, F>, Z>;

pub fn run_pdps_pair<F, S, Dat, Reg, Z, R, Y, KOpZ, H, P, I, Plot, const N: usize>(
    f: &Dat,
    reg: &Reg,
    prox_penalty: &P,
    opKz: &KOpZ,
    fnR: &R,
    fnH: &H,
    (alg, iterator, plotter, μ0zy, running): (
        &AlgorithmConfig<F>,
        I,
        Plot,
        (Option<RNDM<N, F>>, Z, Y),
        String,
    ),
    cont: impl FnOnce(
        (
            &AlgorithmConfig<F>,
            I,
            Plot,
            (Option<RNDM<N, F>>, Z, Y),
            String,
        ),
    ) -> DynResult<Pair<RNDM<N, F>, Z>>,
) -> DynResult<Pair<RNDM<N, F>, Z>>
where
    F: Float + ToNalgebraRealField,
    I: AlgIteratorFactory<IterInfo<F>>,
    Dat: DifferentiableMapping<MeasureZ<F, Z, N>, Codomain = F, DerivativeDomain = Pair<S, Z>>
        + BoundedCurvature<F>,
    S: DifferentiableRealMapping<N, F> + ClosedMul<F>,
    for<'a> Pair<&'a P, &'a IdOp<Z>>: StepLengthBoundPair<F, Dat>,
    //Pair<S, Z>: ClosedMul<F>,
    RNDM<N, F>: SpikeMerging<F>,
    Reg: SlidingRegTerm<Loc<N, F>, F>,
    P: ProxPenalty<Loc<N, F>, S, Reg, F>,
    KOpZ: BoundedLinear<Z, L2, L2, F, Codomain = Y>
        + GEMV<F, Z>
        + SimplyAdjointable<Z, Y, AdjointCodomain = Z>,
    KOpZ::SimpleAdjoint: GEMV<F, Y>,
    Y: ClosedEuclidean<F> + Clone,
    for<'b> &'b Y: Instance<Y>,
    Z: ClosedEuclidean<F> + Clone + ClosedMul<F>,
    for<'b> &'b Z: Instance<Z>,
    R: Prox<Z, Codomain = F>,
    H: Conjugable<Y, F, Codomain = F>,
    for<'b> H::Conjugate<'b>: Prox<Y>,
    Plot: Plotter<P::ReturnMapping, S, RNDM<N, F>>,
{
    let pt = P::prox_type();

    match alg {
        &AlgorithmConfig::ForwardPDPS(ref algconfig, prox_type) if prox_type == pt => {
            print!("{running}");
            pointsource_forward_pdps_pair(
                f,
                reg,
                prox_penalty,
                algconfig,
                iterator,
                plotter,
                μ0zy,
                opKz,
                fnR,
                fnH,
            )
        }
        &AlgorithmConfig::SlidingPDPS(ref algconfig, prox_type) if prox_type == pt => {
            print!("{running}");
            pointsource_sliding_pdps_pair(
                f,
                reg,
                prox_penalty,
                algconfig,
                iterator,
                plotter,
                μ0zy,
                opKz,
                fnR,
                fnH,
            )
        }
        _ => cont((alg, iterator, plotter, μ0zy, running)),
    }
}

pub fn run_fb_pair<F, S, Dat, Reg, Z, R, P, I, Plot, const N: usize>(
    f: &Dat,
    reg: &Reg,
    prox_penalty: &P,
    fnR: &R,
    (alg, iterator, plotter, μ0z, running): (
        &AlgorithmConfig<F>,
        I,
        Plot,
        (Option<RNDM<N, F>>, Z),
        String,
    ),
    cont: impl FnOnce(
        (
            &AlgorithmConfig<F>,
            I,
            Plot,
            (Option<RNDM<N, F>>, Z),
            String,
        ),
    ) -> DynResult<Pair<RNDM<N, F>, Z>>,
) -> DynResult<Pair<RNDM<N, F>, Z>>
where
    F: Float + ToNalgebraRealField,
    I: AlgIteratorFactory<IterInfo<F>>,
    Dat: DifferentiableMapping<MeasureZ<F, Z, N>, Codomain = F, DerivativeDomain = Pair<S, Z>>
        + BoundedCurvature<F>,
    S: DifferentiableRealMapping<N, F> + ClosedMul<F>,
    RNDM<N, F>: SpikeMerging<F>,
    Reg: SlidingRegTerm<Loc<N, F>, F>,
    P: ProxPenalty<Loc<N, F>, S, Reg, F>,
    for<'a> Pair<&'a P, &'a IdOp<Z>>: StepLengthBoundPair<F, Dat>,
    Z: ClosedEuclidean<F> + AXPY + Clone,
    for<'b> &'b Z: Instance<Z>,
    R: Prox<Z, Codomain = F>,
    Plot: Plotter<P::ReturnMapping, S, RNDM<N, F>>,
    // We should not need to explicitly require this:
    for<'b> &'b Loc<0, F>: Instance<Loc<0, F>>,
{
    let pt = P::prox_type();

    match alg {
        &AlgorithmConfig::FB(ref algconfig, prox_type) if prox_type == pt => {
            print!("{running}");
            pointsource_fb_pair(f, reg, prox_penalty, algconfig, iterator, plotter, μ0z, fnR)
        }
        &AlgorithmConfig::SlidingFB(ref algconfig, prox_type) if prox_type == pt => {
            print!("{running}");
            pointsource_sliding_fb_pair(
                f,
                reg,
                prox_penalty,
                algconfig,
                iterator,
                plotter,
                μ0z,
                fnR,
            )
        }
        _ => cont((alg, iterator, plotter, μ0z, running)),
    }
}

#[derive(Copy, Clone, Debug, Serialize, Deserialize)]
struct ValueRange<F: Float> {
    ini: F,
    min: F,
}

impl<F: Float> ValueRange<F> {
    fn expand_with(self, other: Self) -> Self {
        ValueRange { ini: self.ini.max(other.ini), min: self.min.min(other.min) }
    }
}

/// Calculative minimum and maximum values of all the `logs`, and save them into
/// corresponding file names given as the first elements of the tuples in the vectors.
fn save_logs<F: Float + for<'b> Deserialize<'b>>(
    logs: Vec<(String, Logger<Timed<IterInfo<F>>>)>,
    valuerange_file: String,
    load_valuerange: bool,
) -> DynError {
    // Process logs for relative values
    println!("{}", "Processing logs…");

    // Find minimum value and initial value within a single log
    let proc_single_log = |log: &Logger<Timed<IterInfo<F>>>| {
        let d = log.data();
        let mi = d.iter().map(|i| i.data.value).reduce(NumTraitsFloat::min);
        d.first()
            .map(|i| i.data.value)
            .zip(mi)
            .map(|(ini, min)| ValueRange { ini, min })
    };

    // Find minimum and maximum value over all logs
    let mut v = logs
        .iter()
        .filter_map(|&(_, ref log)| proc_single_log(log))
        .reduce(|v1, v2| v1.expand_with(v2))
        .ok_or(anyhow!("No algorithms found"))?;

    // Load existing range
    if load_valuerange && std::fs::metadata(&valuerange_file).is_ok() {
        let data = std::fs::read_to_string(&valuerange_file)?;
        v = v.expand_with(serde_json::from_str(&data)?);
    }

    let logmap = |Timed { cpu_time, iter, data }| {
        let IterInfo {
            value,
            n_spikes,
            inner_iters,
            merged,
            pruned,
            //postprocessing,
            this_iters,
            ε,
            ..
        } = data;
        // let post_value = match (postprocessing, dataterm) {
        //     (Some(mut μ), DataTermType::L222) => {
        //         // Comparison postprocessing is only implemented for the case handled
        //         // by the FW variants.
        //         reg.optimise_weights(
        //             &mut μ, &opA, &b, &findim_data, &inner_config,
        //             inner_it
        //         );
        //         dataterm.value_at_residual(opA.apply(&μ) - &b)
        //             + regularisation.apply(&μ)
        //     },
        //     _ => value,
        // };
        let relative_value = (value - v.min) / (v.ini - v.min);
        CSVLog {
            iter,
            value,
            relative_value,
            //post_value,
            n_spikes,
            cpu_time: cpu_time.as_secs_f64(),
            inner_iters,
            merged,
            pruned,
            this_iters,
            epsilon: ε,
        }
    };

    println!("{}", "Saving logs …".green());

    serde_json::to_writer_pretty(std::fs::File::create(&valuerange_file)?, &v)?;

    for (name, logger) in logs {
        logger.map(logmap).write_csv(name)?;
    }

    Ok(())
}

/// Plot experiment setup
#[replace_float_literals(F::cast_from(literal))]
fn plotall<F, Sensor, Kernel, Spread, 𝒟, A, const N: usize>(
    cli: &CommandLineArgs,
    prefix: &String,
    domain: &Cube<N, F>,
    sensor: &Sensor,
    kernel: &Kernel,
    spread: &Spread,
    μ_hat: &RNDM<N, F>,
    op𝒟: &𝒟,
    opA: &A,
    b_hat: &A::Observable,
    b: &A::Observable,
    kernel_plot_width: F,
) -> DynError
where
    F: Float + ToNalgebraRealField,
    Sensor: RealMapping<N, F> + Support<N, F> + Clone,
    Spread: RealMapping<N, F> + Support<N, F> + Clone,
    Kernel: RealMapping<N, F> + Support<N, F>,
    Convolution<Sensor, Spread>: DifferentiableRealMapping<N, F> + Support<N, F>,
    𝒟: DiscreteMeasureOp<Loc<N, F>, F>,
    𝒟::Codomain: RealMapping<N, F>,
    A: ForwardModel<RNDM<N, F>, F>,
    for<'a> &'a A::Observable: Instance<A::Observable>,
    A::PreadjointCodomain: DifferentiableRealMapping<N, F> + Bounded<F>,
    PlotLookup: Plotting<N>,
    Cube<N, F>: SetOrd,
{
    if cli.plot < PlotLevel::Data {
        return Ok(());
    }

    let base = Convolution(sensor.clone(), spread.clone());

    let resolution = if N == 1 { 100 } else { 40 };
    let pfx = |n| format!("{prefix}{n}");
    let plotgrid = lingrid(
        &[[-kernel_plot_width, kernel_plot_width]; N].into(),
        &[resolution; N],
    );

    PlotLookup::plot_into_file(sensor, plotgrid, pfx("sensor"));
    PlotLookup::plot_into_file(kernel, plotgrid, pfx("kernel"));
    PlotLookup::plot_into_file(spread, plotgrid, pfx("spread"));
    PlotLookup::plot_into_file(&base, plotgrid, pfx("base_sensor"));

    let plotgrid2 = lingrid(&domain, &[resolution; N]);

    let ω_hat = op𝒟.apply(μ_hat);
    let noise = opA.preadjoint().apply(opA.apply(μ_hat) - b);
    PlotLookup::plot_into_file(&ω_hat, plotgrid2, pfx("omega_hat"));
    PlotLookup::plot_into_file(&noise, plotgrid2, pfx("omega_noise"));

    let preadj_b = opA.preadjoint().apply(b);
    let preadj_b_hat = opA.preadjoint().apply(b_hat);
    //let bounds = preadj_b.bounds().common(&preadj_b_hat.bounds());
    PlotLookup::plot_into_file_spikes(
        Some(&preadj_b),
        Some(&preadj_b_hat),
        plotgrid2,
        &μ_hat,
        pfx("omega_b"),
    );
    PlotLookup::plot_into_file(&preadj_b, plotgrid2, pfx("preadj_b"));
    PlotLookup::plot_into_file(&preadj_b_hat, plotgrid2, pfx("preadj_b_hat"));

    // Save true solution and observables
    μ_hat.write_csv(pfx("orig.txt"))?;
    opA.write_observable(&b_hat, pfx("b_hat"))?;
    opA.write_observable(&b, pfx("b_noisy"))
}