implement Storage.inspect and add back the arrow storage

Cargo.toml

@@ -24,16 +24,20 @@ itertools = "0.14.0"
 thiserror = "2.0.3"
 rand_chacha = "0.9.0"
 anyhow = "1.0.72"
-faer = { version = "0.22.6", default-features = false, features = ["linalg"] }
+faer = { version = "0.23.2", default-features = false, features = ["linalg"] }
 pulp = "0.21.4"
 rayon = "1.10.0"
-zarrs = { version = "0.21.0", features = [
+zarrs = { version = "0.22.0", features = [
   "filesystem",
   "gzip",
   "sharding",
   "async",
 ], optional = true }
 ndarray = { version = "0.16.1", optional = true }
+arrow = { version = "56.2.0", optional = true }
+arrow-schema = { version = "56.2.0", features = [
+  "canonical_extension_types",
+], optional = true }
 nuts-derive = { path = "./nuts-derive" }
 nuts-storable = { path = "./nuts-storable" }
 serde = { version = "1.0.219", features = ["derive"] }
@@ -50,13 +54,14 @@ equator = "0.4.2"
 serde_json = "1.0"
 ndarray = "0.16.1"
 tempfile = "3.0"
-zarrs_object_store = "0.4.3"
+zarrs_object_store = "0.5.0"
 object_store = "0.12.0"
 tokio = { version = "1.0", features = ["rt", "rt-multi-thread"] }
 
 [features]
 zarr = ["dep:zarrs", "dep:tokio"]
 ndarray = ["dep:ndarray"]
+arrow = ["dep:arrow", "dep:arrow-schema"]
 
 [[bench]]
 name = "sample"

examples/arrow_trace.rs

@@ -0,0 +1,343 @@
+//! Arrow backend example for MCMC trace storage
+//!
+//! This example demonstrates how to use the nuts-rs library with Arrow storage
+//! for running MCMC sampling on a multivariate normal distribution. It shows:
+//!
+//! - Setting up a custom probability model
+//! - Configuring Arrow storage for results
+//! - Running multiple parallel chains
+//! - Monitoring progress during sampling
+//! - Accessing results in Arrow/Parquet format
+
+use std::{
+    collections::HashMap,
+    f64,
+    time::{Duration, Instant},
+};
+
+use anyhow::Result;
+use nuts_rs::{
+    ArrowConfig, CpuLogpFunc, CpuMath, CpuMathError, DiagGradNutsSettings, LogpError, Model,
+    Sampler, SamplerWaitResult, Storable,
+};
+use nuts_storable::{HasDims, Value};
+use rand::Rng;
+use thiserror::Error;
+
+/// A multivariate normal distribution model
+///
+/// This represents a probability distribution with mean μ and precision matrix P,
+/// where the log probability is: logp(x) = -0.5 * (x - μ)^T * P * (x - μ)
+#[derive(Clone, Debug)]
+struct MultivariateNormal {
+    mean: Vec<f64>,
+    precision: Vec<Vec<f64>>, // Inverse of covariance matrix
+}
+
+impl MultivariateNormal {
+    fn new(mean: Vec<f64>, precision: Vec<Vec<f64>>) -> Self {
+        Self { mean, precision }
+    }
+}
+
+/// Custom error type for log probability calculations
+///
+/// MCMC samplers need to distinguish between recoverable errors (like numerical
+/// issues that can be handled by rejecting the proposal) and non-recoverable
+/// errors (like programming bugs that should stop sampling).
+#[allow(dead_code)]
+#[derive(Debug, Error)]
+enum MyLogpError {
+    #[error("Recoverable error in logp calculation: {0}")]
+    Recoverable(String),
+    #[error("Non-recoverable error in logp calculation: {0}")]
+    NonRecoverable(String),
+}
+
+impl LogpError for MyLogpError {
+    fn is_recoverable(&self) -> bool {
+        matches!(self, MyLogpError::Recoverable(_))
+    }
+}
+
+/// Implementation of the log probability function for multivariate normal
+///
+/// This struct contains the model parameters and implements the mathematical
+/// operations needed for MCMC sampling: computing log probability and gradients.
+#[derive(Clone)]
+struct MvnLogp {
+    model: MultivariateNormal,
+    buffer: Vec<f64>, // Temporary buffer for computations
+}
+
+impl HasDims for MvnLogp {
+    /// Define dimension names and sizes for storage
+    ///
+    /// This tells the storage system what array dimensions to expect.
+    /// These dimensions will be used to structure the output data using
+    /// Arrow's FixedShapeTensor extension type.
+    fn dim_sizes(&self) -> HashMap<String, u64> {
+        HashMap::from([
+            // Dimension for the actual parameter vector x
+            ("x".to_string(), self.model.mean.len() as u64),
+        ])
+    }
+
+    fn coords(&self) -> HashMap<String, nuts_storable::Value> {
+        HashMap::from([(
+            "x".to_string(),
+            Value::Strings(vec!["x1".to_string(), "x2".to_string()]),
+        )])
+    }
+}
+
+/// Additional quantities computed from each sample
+///
+/// The `Storable` derive macro automatically generates code to store this
+/// struct in the trace. The `dims` attribute specifies which dimension
+/// each field should use. Multi-dimensional fields will be stored as
+/// FixedShapeTensor extension types in Arrow format.
+#[derive(Storable)]
+struct ExpandedDraw {
+    /// Store the parameter values with dimension "x"
+    #[storable(dims("x"))]
+    prec: Vec<f64>,
+    /// A scalar derived quantity (difference between first two parameters)
+    diff: f64,
+}
+
+impl CpuLogpFunc for MvnLogp {
+    type LogpError = MyLogpError;
+    type FlowParameters = (); // No parameter transformations needed
+    type ExpandedVector = ExpandedDraw;
+
+    /// Return the dimensionality of the parameter space
+    fn dim(&self) -> usize {
+        self.model.mean.len()
+    }
+
+    /// Compute log probability and gradient
+    ///
+    /// This is the core mathematical function that MCMC uses to explore
+    /// the parameter space. It computes both the log probability density
+    /// and its gradient for efficient sampling with Hamiltonian Monte Carlo.
+    fn logp(&mut self, x: &[f64], grad: &mut [f64]) -> Result<f64, Self::LogpError> {
+        let n = x.len();
+
+        // Compute (x - mean)
+        let diff = &mut self.buffer;
+        for i in 0..n {
+            diff[i] = x[i] - self.model.mean[i];
+        }
+
+        let mut quad = 0.0;
+
+        // Compute quadratic form and gradient: logp = -0.5 * diff^T * P * diff
+        for i in 0..n {
+            // Compute i-th component of P * diff
+            let mut pdot = 0.0;
+            for j in 0..n {
+                let pij = self.model.precision[i][j];
+                pdot += pij * diff[j];
+                quad += diff[i] * pij * diff[j];
+            }
+            // Gradient of logp w.r.t. x_i: derivative of -0.5 * diff^T P diff is -(P * diff)_i
+            grad[i] = -pdot;
+        }
+
+        Ok(-0.5 * quad)
+    }
+
+    /// Compute additional quantities from each sample
+    ///
+    /// This function is called for each accepted sample to compute derived
+    /// quantities that should be stored in the trace. These might be
+    /// transformed parameters, predictions, or other quantities of interest.
+    fn expand_vector<R: Rng + ?Sized>(
+        &mut self,
+        _rng: &mut R,
+        array: &[f64],
+    ) -> Result<Self::ExpandedVector, CpuMathError> {
+        // Store the raw parameter values and compute a simple derived quantity
+        Ok(ExpandedDraw {
+            prec: array.to_vec(),
+            diff: array[1] - array[0], // Example: difference between first two parameters
+        })
+    }
+
+    fn vector_coord(&self) -> Option<Value> {
+        Some(Value::Strings(vec!["x1".to_string(), "x2".to_string()]))
+    }
+}
+
+/// The complete MCMC model
+///
+/// This struct implements the Model trait, which is the main interface
+/// that samplers use. It provides access to the mathematical operations
+/// and handles initialization of the sampling chains.
+struct MvnModel {
+    math: CpuMath<MvnLogp>,
+}
+
+impl Model for MvnModel {
+    type Math<'model>
+        = CpuMath<MvnLogp>
+    where
+        Self: 'model;
+
+    fn math<R: Rng + ?Sized>(&self, _rng: &mut R) -> Result<Self::Math<'_>> {
+        Ok(self.math.clone())
+    }
+
+    /// Generate random initial positions for the chain
+    ///
+    /// Good initialization is important for MCMC efficiency. The starting
+    /// points should be in a reasonable region of the parameter space
+    /// where the log probability is finite.
+    fn init_position<R: Rng + ?Sized>(&self, rng: &mut R, position: &mut [f64]) -> Result<()> {
+        // Initialize each parameter randomly in the range [-2, 2]
+        // For this simple example, this should put us in a reasonable
+        // region around the mode of the distribution
+        for p in position.iter_mut() {
+            *p = rng.random_range(-2.0..2.0);
+        }
+        Ok(())
+    }
+}
+
+fn main() -> Result<()> {
+    println!("=== Multivariate Normal MCMC with Arrow Storage ===\n");
+
+    // Create a 2D multivariate normal distribution
+    // This creates a distribution with mean [0, 0] and precision matrix
+    // [[1.0, 0.5], [0.5, 1.0]], which corresponds to some correlation
+    // between the two parameters
+    let mean = vec![0.0, 0.0];
+    let precision = vec![vec![1.0, 0.5], vec![0.5, 1.0]];
+    let mvn = MultivariateNormal::new(mean, precision);
+
+    println!("Model: 2D Multivariate Normal");
+    println!("Mean: {:?}", mvn.mean);
+    println!("Precision matrix: {:?}\n", mvn.precision);
+
+    // Configure output location
+    let output_path = "mcmc_output";
+    println!("Output will be saved to: {}/\n", output_path);
+
+    // Sampling configuration
+    let num_chains = 4; // Run 4 parallel chains for better convergence assessment
+    let num_tune = 500; // Warmup samples to tune the sampler
+    let num_draws = 500; // Post-warmup samples to keep
+
+    println!("Sampling configuration:");
+    println!("  Chains: {}", num_chains);
+    println!("  Warmup samples: {}", num_tune);
+    println!("  Sampling draws: {}", num_draws);
+
+    // Configure MCMC settings
+    // DiagGradNutsSettings provides sensible defaults for the NUTS sampler
+    let mut settings = DiagGradNutsSettings::default();
+    settings.num_chains = num_chains as _;
+    settings.num_tune = num_tune;
+    settings.num_draws = num_draws as _;
+    settings.seed = 54; // For reproducible results
+
+    // Create the model instance
+    let model = MvnModel {
+        math: CpuMath::new(MvnLogp {
+            model: mvn,
+            buffer: vec![0.0; 2],
+        }),
+    };
+
+    // Start sampling
+    println!("\nStarting MCMC sampling...\n");
+    let start = Instant::now();
+
+    // Configure Arrow storage - it automatically determines capacity from settings
+    let arrow_config = ArrowConfig::new();
+
+    // Create sampler with 4 worker threads
+    // The sampler runs asynchronously, so we can monitor progress
+    let mut sampler = Some(Sampler::new(model, settings, arrow_config, 4, None)?);
+
+    let mut num_progress_updates = 0;
+
+    // Main sampling loop with progress monitoring
+    // This demonstrates how to monitor long-running sampling jobs
+    while let Some(sampler_) = sampler.take() {
+        match sampler_.wait_timeout(Duration::from_millis(50)) {
+            // Sampling completed successfully
+            SamplerWaitResult::Trace(traces) => {
+                println!("✓ Sampling completed in {:?}", start.elapsed());
+
+                // Display information about the resulting Arrow data
+                println!("✓ MCMC traces stored in Arrow format");
+                println!("\nTrace summary:");
+                println!("  Number of chains: {}", traces.len());
+
+                if let Some(first_trace) = traces.first() {
+                    println!(
+                        "  Posterior samples: {} rows, {} columns",
+                        first_trace.posterior.num_rows(),
+                        first_trace.posterior.num_columns()
+                    );
+                    println!(
+                        "  Sample stats: {} rows, {} columns",
+                        first_trace.sample_stats.num_rows(),
+                        first_trace.sample_stats.num_columns()
+                    );
+
+                    // Show column names
+                    println!("\n  Posterior columns:");
+                    for field in first_trace.posterior.schema().fields() {
+                        println!(
+                            "    {} ({} {:?})",
+                            field.name(),
+                            field.data_type(),
+                            field.metadata(),
+                        );
+                    }
+
+                    println!("\n  Sample stats columns:");
+                    for field in first_trace.sample_stats.schema().fields() {
+                        println!(
+                            "    {} ({} {:?})",
+                            field.name(),
+                            field.data_type(),
+                            field.metadata(),
+                        );
+                    }
+                }
+                break;
+            }
+
+            // Timeout - sampler is still running, show progress
+            SamplerWaitResult::Timeout(mut sampler_) => {
+                num_progress_updates += 1;
+                println!("Progress update {}:", num_progress_updates);
+
+                // Get current progress from all chains
+                let progress = sampler_.progress()?;
+                for (i, chain) in progress.iter().enumerate() {
+                    let phase = if chain.tuning { "warmup" } else { "sampling" };
+                    println!(
+                        "  Chain {}: {} samples ({} divergences), step size: {:.6} [{}]",
+                        i, chain.finished_draws, chain.divergences, chain.step_size, phase
+                    );
+                }
+                println!(); // Add blank line for readability
+
+                sampler = Some(sampler_);
+            }
+
+            // An error occurred during sampling
+            SamplerWaitResult::Err(err, _) => {
+                eprintln!("✗ Sampling failed: {}", err);
+                return Err(err);
+            }
+        }
+    }
+
+    Ok(())
+}