src/run.rs

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

use numeric_literals::replace_float_literals;
use colored::Colorize;
use serde::{Serialize, Deserialize};
use serde_json;
use nalgebra::base::DVector;
use std::hash::Hash;
use chrono::{DateTime, Utc};
use cpu_time::ProcessTime;
use clap::ValueEnum;
use std::collections::HashMap;
use std::time::Instant;

use rand::prelude::{
    StdRng,
    SeedableRng
};
use rand_distr::Distribution;

use alg_tools::bisection_tree::*;
use alg_tools::iterate::{
    Timed,
    AlgIteratorOptions,
    Verbose,
    AlgIteratorFactory,
    LoggingIteratorFactory,
    TimingIteratorFactory,
    BasicAlgIteratorFactory,
};
use alg_tools::logger::Logger;
use alg_tools::error::{
    DynError,
    DynResult,
};
use alg_tools::tabledump::TableDump;
use alg_tools::sets::Cube;
use alg_tools::mapping::{
    RealMapping,
    DifferentiableMapping,
    DifferentiableRealMapping,
    Instance
};
use alg_tools::nalgebra_support::ToNalgebraRealField;
use alg_tools::euclidean::Euclidean;
use alg_tools::lingrid::{lingrid, LinSpace};
use alg_tools::sets::SetOrd;
use alg_tools::linops::{RowOp, IdOp /*, ZeroOp*/};
use alg_tools::discrete_gradient::{Grad, ForwardNeumann};
use alg_tools::convex::Zero;
use alg_tools::maputil::map3;
use alg_tools::direct_product::Pair;

use crate::kernels::*;
use crate::types::*;
use crate::measures::*;
use crate::measures::merging::SpikeMerging;
use crate::forward_model::*;
use crate::forward_model::sensor_grid::{
    SensorGrid,
    SensorGridBT,
    //SensorGridBTFN,
    Sensor,
    Spread,
};

use crate::fb::{
    FBConfig,
    FBGenericConfig,
    pointsource_fb_reg,
    pointsource_fista_reg,
};
use crate::radon_fb::{
    RadonFBConfig,
    pointsource_radon_fb_reg,
    pointsource_radon_fista_reg,
};
use crate::sliding_fb::{
    SlidingFBConfig,
    TransportConfig,
    pointsource_sliding_fb_reg
};
use crate::sliding_pdps::{
    SlidingPDPSConfig,
    pointsource_sliding_pdps_pair
};
use crate::forward_pdps::{
    ForwardPDPSConfig,
    pointsource_forward_pdps_pair
};
use crate::pdps::{
    PDPSConfig,
    pointsource_pdps_reg,
};
use crate::frank_wolfe::{
    FWConfig,
    FWVariant,
    pointsource_fw_reg,
    //WeightOptim,
};
//use crate::subproblem::InnerSettings;
use crate::seminorms::*;
use crate::plot::*;
use crate::{AlgorithmOverrides, CommandLineArgs};
use crate::tolerance::Tolerance;
use crate::regularisation::{
    Regularisation,
    RadonRegTerm,
    NonnegRadonRegTerm
};
use crate::dataterm::{
    L1,
    L2Squared,
};
use alg_tools::norms::{L2, NormExponent};
use alg_tools::operator_arithmetic::Weighted;
use anyhow::anyhow;

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

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_fb_generic = |g : FBGenericConfig<F>| {
            FBGenericConfig {
                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 : cli.merging.clone().unwrap_or(g.merging),
                final_merging : cli.final_merging.clone().unwrap_or(g.final_merging),
                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_ω: cli.transport_tolerance_omega.unwrap_or(g.tolerance_ω),
                tolerance_dv: cli.transport_tolerance_dv.unwrap_or(g.tolerance_dv),
                adaptation: cli.transport_adaptation.unwrap_or(g.adaptation),
                .. g
            }
        };

        use AlgorithmConfig::*;
        match self {
            FB(fb) => FB(FBConfig {
                τ0 : cli.tau0.unwrap_or(fb.τ0),
                generic : override_fb_generic(fb.generic),
                .. fb
            }),
            FISTA(fb) => FISTA(FBConfig {
                τ0 : cli.tau0.unwrap_or(fb.τ0),
                generic : override_fb_generic(fb.generic),
                .. fb
            }),
            PDPS(pdps) => 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
            }),
            FW(fw) => FW(FWConfig {
                merging : cli.merging.clone().unwrap_or(fw.merging),
                tolerance : cli.tolerance.as_ref().map(unpack_tolerance).unwrap_or(fw.tolerance),
                .. fw
            }),
            RadonFB(fb) => RadonFB(RadonFBConfig {
                τ0 : cli.tau0.unwrap_or(fb.τ0),
                insertion : override_fb_generic(fb.insertion),
                .. fb
            }),
            RadonFISTA(fb) => RadonFISTA(RadonFBConfig {
                τ0 : cli.tau0.unwrap_or(fb.τ0),
                insertion : override_fb_generic(fb.insertion),
                .. fb
            }),
            SlidingFB(sfb) => SlidingFB(SlidingFBConfig {
                τ0 : cli.tau0.unwrap_or(sfb.τ0),
                transport : override_transport(sfb.transport),
                insertion : override_fb_generic(sfb.insertion),
                .. sfb
            }),
            SlidingPDPS(spdps) => 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
            }),
            ForwardPDPS(fpdps) => 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
            }),
        }
    }
}

/// 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)]
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 RadonFB forward-backward method
    #[clap(name = "radon_fb")]
    RadonFB,
    /// The RadonFISTA inertial forward-backward method
    #[clap(name = "radon_fista")]
    RadonFISTA,
    /// 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,
}

impl DefaultAlgorithm {
    /// Returns the algorithm configuration corresponding to the algorithm shorthand
    pub fn default_config<F : Float>(&self) -> AlgorithmConfig<F> {
        use DefaultAlgorithm::*;
        match *self {
            FB => AlgorithmConfig::FB(Default::default()),
            FISTA => AlgorithmConfig::FISTA(Default::default()),
            FW => AlgorithmConfig::FW(Default::default()),
            FWRelax => AlgorithmConfig::FW(FWConfig{
                variant : FWVariant::Relaxed,
                .. Default::default()
            }),
            PDPS => AlgorithmConfig::PDPS(Default::default()),
            RadonFB => AlgorithmConfig::RadonFB(Default::default()),
            RadonFISTA => AlgorithmConfig::RadonFISTA(Default::default()),
            SlidingFB => AlgorithmConfig::SlidingFB(Default::default()),
            SlidingPDPS => AlgorithmConfig::SlidingPDPS(Default::default()),
            ForwardPDPS => AlgorithmConfig::ForwardPDPS(Default::default()),
        }
    }

    /// 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>> {
        let name = self.to_possible_value().unwrap().get_name().to_string();
        Named{ name , data : alg }
    }
}


// // 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,
}

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,
}

/// 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,
      [usize; N] : Serialize,
      NoiseDistr : Distribution<F>,
      S : Sensor<F, N>,
      P : Spread<F, N>,
      K : SimpleConvolutionKernel<F, N>,
{
    /// Domain $Ω$.
    pub domain : Cube<F, N>,
    /// 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<F, N>,
    /// 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 : DataTerm,
    /// A map of default configurations for algorithms
    #[serde(skip)]
    pub algorithm_defaults : HashMap<DefaultAlgorithm, AlgorithmConfig<F>>,
}

#[derive(Debug, Clone, Serialize)]
pub struct ExperimentBiased<F, NoiseDistr, S, K, P, B, const N : usize>
where F : Float,
      [usize; N] : Serialize,
      NoiseDistr : Distribution<F>,
      S : Sensor<F, N>,
      P : Spread<F, N>,
      K : SimpleConvolutionKernel<F, N>,
      B : Mapping<Loc<F, N>, 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_defaults(&self, alg : DefaultAlgorithm) -> Option<AlgorithmConfig<F>>;
}

/// Helper function to print experiment start message and save setup.
/// Returns saving prefix.
fn start_experiment<E, S>(
    experiment : &Named<E>,
    cli : &CommandLineArgs,
    stats : S,
) -> DynResult<String>
where
    E : Serialize + std::fmt::Debug,
    S : Serialize,
{
    let Named { name : experiment_name, data } = experiment;

    println!("{}\n{}",
                format!("Performing experiment {}…", experiment_name).cyan(),
                format!("{:?}", data).bright_black());

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

    // Save experiment configuration and statistics
    let mkname_e = |t| format!("{prefix}{t}.json", prefix = prefix, t = t);
    std::fs::create_dir_all(&prefix)?;
    write_json(mkname_e("experiment"), experiment)?;
    write_json(mkname_e("config"), cli)?;
    write_json(mkname_e("stats"), &stats)?;

    Ok(prefix)
}

/// Error codes for running an algorithm on an experiment.
enum RunError {
    /// Algorithm not implemented for this experiment
    NotImplemented,
}

use RunError::*;

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

/// Helper function to run all algorithms on an experiment.
fn do_runall<F : Float, Z, const N : usize>(
    experiment_name : &String,
    prefix : &String,
    cli : &CommandLineArgs,
    algorithms : Vec<Named<AlgorithmConfig<F>>>,
    plotgrid : LinSpace<Loc<F, N>, [usize; N]>,
    mut save_extra : impl FnMut(String, Z) -> DynError,
    mut do_alg : impl FnMut(
        &AlgorithmConfig<F>,
        DoRunAllIt<F, N>,
        SeqPlotter<F, N>,
        String,
    ) -> Result<(RNDM<F, N>, Z), RunError>,
) ->  DynError
where
    PlotLookup : Plotting<N>,
{
    let mut logs = Vec::new();

    let iterator_options = AlgIteratorOptions{
            max_iter : cli.max_iter,
            verbose_iter : cli.verbose_iter
                                .map_or(Verbose::Logarithmic(10),
                                        |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!("{:?}", iterator_options).bright_black(),
                    format!("{:?}", 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 plot_count = if cli.plot >= PlotLevel::Iter { 2000 } else { 0 };
        let plotter = SeqPlotter::new(this_prefix, plot_count, plotgrid.clone());

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

        let (μ, z) = match do_alg(alg, iterator, plotter, running) {
            Ok(μ) => μ,
            Err(RunError::NotImplemented) => {
                let msg = format!("Algorithm “{alg_name}” not implemented for {experiment_name}. \
                                   Skipping.").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)
}

#[replace_float_literals(F::cast_from(literal))]
impl<F, NoiseDistr, S, K, P, const N : usize> RunnableExperiment<F> for
Named<ExperimentV2<F, NoiseDistr, S, K, P, N>>
where
    F : ClapFloat + nalgebra::RealField + ToNalgebraRealField<MixedType=F>,
    [usize; N] : Serialize,
    S : Sensor<F, N> + Copy + Serialize + std::fmt::Debug,
    P : Spread<F, N> + Copy + Serialize + std::fmt::Debug,
    Convolution<S, P>: Spread<F, N> + 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<F, N>, Output = Loc<F, N>>,
                        // 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<F, N>
                        + Lipschitz<L2, FloatType=F>,
    for<'b> <Convolution<S, P> as DifferentiableMapping<Loc<F,N>>>::Differential<'b> : Lipschitz<L2, FloatType=F>, // TODO: should not be required generally, only for sliding_fb.
    AutoConvolution<P> : BoundedBy<F, K>,
    K : SimpleConvolutionKernel<F, N>
        + LocalAnalysis<F, Bounds<F>, N>
        + Copy + Serialize + std::fmt::Debug,
    Cube<F, N>: P2Minimise<Loc<F, N>, F> + SetOrd,
    PlotLookup : Plotting<N>,
    DefaultBT<F, N> : SensorGridBT<F, S, P, N, Depth=DynamicDepth> + BTSearch<F, N>,
    BTNodeLookup: BTNode<F, usize, Bounds<F>, N>,
    RNDM<F, N> : SpikeMerging<F>,
    NoiseDistr : Distribution<F> + Serialize + std::fmt::Debug
{

    fn algorithm_defaults(&self, alg : DefaultAlgorithm) -> Option<AlgorithmConfig<F>> {
        self.data.algorithm_defaults.get(&alg).cloned()
    }

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

        // Set up algorithms
        let algorithms = match (algs, dataterm) {
            (Some(algs), _) => algs,
            (None, DataTerm::L2Squared) => vec![DefaultAlgorithm::FB.get_named()],
            (None, DataTerm::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 : DVector<_> = 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 = start_experiment(&self, cli, 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 save_extra = |_, ()| Ok(());

        do_runall(experiment_name, &prefix, cli, algorithms, plotgrid, save_extra,
            |alg, iterator, plotter, running|
        {
            let μ = match alg {
                AlgorithmConfig::FB(ref algconfig) => {
                    match (regularisation, dataterm) {
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_fb_reg(
                                &opA, &b, NonnegRadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter
                            )
                        }),
                        (Regularisation::Radon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_fb_reg(
                                &opA, &b, RadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter
                            )
                        }),
                        _ => Err(NotImplemented)
                    }
                },
                AlgorithmConfig::FISTA(ref algconfig) => {
                    match (regularisation, dataterm) {
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_fista_reg(
                                &opA, &b, NonnegRadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter
                            )
                        }),
                        (Regularisation::Radon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_fista_reg(
                                &opA, &b, RadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter
                            )
                        }),
                        _ => Err(NotImplemented),
                    }
                },
                AlgorithmConfig::RadonFB(ref algconfig) => {
                    match (regularisation, dataterm) {
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_radon_fb_reg(
                                &opA, &b, NonnegRadonRegTerm(α), algconfig,
                                iterator, plotter
                            )
                        }),
                        (Regularisation::Radon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_radon_fb_reg(
                                &opA, &b, RadonRegTerm(α), algconfig,
                                iterator, plotter
                            )
                        }),
                        _ => Err(NotImplemented),
                    }
                },
                AlgorithmConfig::RadonFISTA(ref algconfig) => {
                    match (regularisation, dataterm) {
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_radon_fista_reg(
                                &opA, &b, NonnegRadonRegTerm(α), algconfig,
                                iterator, plotter
                            )
                        }),
                        (Regularisation::Radon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_radon_fista_reg(
                                &opA, &b, RadonRegTerm(α), algconfig,
                                iterator, plotter
                            )
                        }),
                        _ => Err(NotImplemented),
                    }
                },
                AlgorithmConfig::SlidingFB(ref algconfig) => {
                    match (regularisation, dataterm) {
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_sliding_fb_reg(
                                &opA, &b, NonnegRadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter
                            )
                        }),
                        (Regularisation::Radon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_sliding_fb_reg(
                                &opA, &b, RadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter
                            )
                        }),
                        _ => Err(NotImplemented),
                    }
                },
                AlgorithmConfig::PDPS(ref algconfig) => {
                    print!("{running}");
                    match (regularisation, dataterm) {
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            pointsource_pdps_reg(
                                &opA, &b, NonnegRadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter, L2Squared
                            )
                        }),
                        (Regularisation::Radon(α),DataTerm::L2Squared) => Ok({
                            pointsource_pdps_reg(
                                &opA, &b, RadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter, L2Squared
                            )
                        }),
                        (Regularisation::NonnegRadon(α), DataTerm::L1) => Ok({
                            pointsource_pdps_reg(
                                &opA, &b, NonnegRadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter, L1
                            )
                        }),
                        (Regularisation::Radon(α), DataTerm::L1) => Ok({
                            pointsource_pdps_reg(
                                &opA, &b, RadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter, L1
                            )
                        }),
                    }
                },
                AlgorithmConfig::FW(ref algconfig) => {
                    match (regularisation, dataterm) {
                        (Regularisation::Radon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_fw_reg(&opA, &b, RadonRegTerm(α),
                                               algconfig, iterator, plotter)
                        }),
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_fw_reg(&opA, &b, NonnegRadonRegTerm(α),
                                               algconfig, iterator, plotter)
                        }),
                        _ => Err(NotImplemented),
                    }
                },
                _ => Err(NotImplemented),
            }?;
            Ok((μ, ()))
        })
    }
}


#[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>,
    [usize; N] : Serialize,
    S : Sensor<F, N> + Copy + Serialize + std::fmt::Debug,
    P : Spread<F, N> + Copy + Serialize + std::fmt::Debug,
    Convolution<S, P>: Spread<F, N> + 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<F, N>, Output = Loc<F, N>>,
                        // 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<F, N>
                        + Lipschitz<L2, FloatType=F>,
    for<'b> <Convolution<S, P> as DifferentiableMapping<Loc<F,N>>>::Differential<'b> : Lipschitz<L2, FloatType=F>, // TODO: should not be required generally, only for sliding_fb.
    AutoConvolution<P> : BoundedBy<F, K>,
    K : SimpleConvolutionKernel<F, N>
        + LocalAnalysis<F, Bounds<F>, N>
        + Copy + Serialize + std::fmt::Debug,
    Cube<F, N>: P2Minimise<Loc<F, N>, F> + SetOrd,
    PlotLookup : Plotting<N>,
    DefaultBT<F, N> : SensorGridBT<F, S, P, N, Depth=DynamicDepth> + BTSearch<F, N>,
    BTNodeLookup: BTNode<F, usize, Bounds<F>, N>,
    RNDM<F, N> : SpikeMerging<F>,
    NoiseDistr : Distribution<F> + Serialize + std::fmt::Debug,
    B : Mapping<Loc<F, N>, Codomain = F> + Serialize + std::fmt::Debug,
{

    fn algorithm_defaults(&self, alg : DefaultAlgorithm) -> Option<AlgorithmConfig<F>> {
        self.data.base.algorithm_defaults.get(&alg).cloned()
    }

    fn runall(&self, cli : &CommandLineArgs,
              algs : Option<Vec<Named<AlgorithmConfig<F>>>>) -> DynError {
        // Get experiment configuration
        let &Named {
            name : ref experiment_name,
            data : 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;

        // 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 = start_experiment(&self, cli, 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 save_extra = |prefix, z| opA.write_observable(&z, format!("{prefix}z"));

        // Run the algorithms
        do_runall(experiment_name, &prefix, cli, algorithms, plotgrid, save_extra,
            |alg, iterator, plotter, running|
        {
            let Pair(μ, z) = match alg {
                AlgorithmConfig::ForwardPDPS(ref algconfig) => {
                    match (regularisation, dataterm) {
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_forward_pdps_pair(
                                &opAext, &b, NonnegRadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter,
                                /* opKμ, */ &opKz, &fnR, &fnH, z.clone(), y.clone(),
                            )
                        }),
                        (Regularisation::Radon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_forward_pdps_pair(
                                &opAext, &b, RadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter,
                                /* opKμ, */ &opKz, &fnR, &fnH, z.clone(), y.clone(),
                            )
                        }),
                        _ => Err(NotImplemented)
                    }
                },
                AlgorithmConfig::SlidingPDPS(ref algconfig) => {
                    match (regularisation, dataterm) {
                        (Regularisation::NonnegRadon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_sliding_pdps_pair(
                                &opAext, &b, NonnegRadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter,
                                /* opKμ, */ &opKz, &fnR, &fnH, z.clone(), y.clone(),
                            )
                        }),
                        (Regularisation::Radon(α), DataTerm::L2Squared) => Ok({
                            print!("{running}");
                            pointsource_sliding_pdps_pair(
                                &opAext, &b, RadonRegTerm(α), &op𝒟, algconfig,
                                iterator, plotter,
                                /* opKμ, */ &opKz, &fnR, &fnH, z.clone(), y.clone(),
                            )
                        }),
                        _ => Err(NotImplemented)
                    }
                },
                _ => Err(NotImplemented)
            }?;
            Ok((μ, z))
        })
    }
}


/// 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, const N : usize>(
    logs : Vec<(String, Logger<Timed<IterInfo<F, N>>>)>
) -> 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, N>>>| {
        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)
    };

    // Find minimum and maximum value over all logs
    let (v_ini, v_min) = logs.iter()
                             .filter_map(|&(_, ref log)| proc_single_log(log))
                             .reduce(|(i1, m1), (i2, m2)| (i1.max(i2), m1.min(m2)))
                             .ok_or(anyhow!("No algorithms found"))?;

    let logmap = |Timed { cpu_time, iter, data }| {
        let IterInfo {
            value,
            n_spikes,
            inner_iters,
            merged,
            pruned,
            //postprocessing,
            this_iters,
            ..
        } = data;
        // let post_value = match (postprocessing, dataterm) {
        //     (Some(mut μ), DataTerm::L2Squared) => {
        //         // 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
        }
    };

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

    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<F, N>,
    sensor : &Sensor,
    kernel : &Kernel,
    spread : &Spread,
    μ_hat : &RNDM<F, N>,
    op𝒟 : &𝒟,
    opA : &A,
    b_hat : &A::Observable,
    b : &A::Observable,
    kernel_plot_width : F,
) -> DynError
where F : Float + ToNalgebraRealField,
      Sensor : RealMapping<F, N> + Support<F, N> + Clone,
      Spread : RealMapping<F, N> + Support<F, N> + Clone,
      Kernel : RealMapping<F, N> + Support<F, N>,
      Convolution<Sensor, Spread> : DifferentiableRealMapping<F, N> + Support<F, N>,
      𝒟 : DiscreteMeasureOp<Loc<F, N>, F>,
      𝒟::Codomain : RealMapping<F, N>,
      A : ForwardModel<RNDM<F, N>, F>,
      for<'a> &'a A::Observable : Instance<A::Observable>,
      A::PreadjointCodomain : DifferentiableRealMapping<F, N> + Bounded<F>,
      PlotLookup : Plotting<N>,
      Cube<F, N> : 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"))
}