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wifi-densepose/vendor/midstream/docs/PUBLISHED_CRATES_GUIDE.md

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MidStream Published Crates Guide

Overview

All 5 core MidStream crates are published and available on crates.io! This guide shows you how to use them in your projects.

Quick Start

Install All Core Crates

[dependencies]
temporal-compare = "0.1"
nanosecond-scheduler = "0.1"
temporal-attractor-studio = "0.1"
temporal-neural-solver = "0.1"
strange-loop = "0.1"

Install Individual Crates

Pick only what you need:

[dependencies]
# Pattern matching and sequence analysis
temporal-compare = "0.1"

# Ultra-low-latency scheduling
nanosecond-scheduler = "0.1"

# Dynamical systems analysis (optional)
# temporal-attractor-studio = "0.1"

# Temporal logic verification (optional)
# temporal-neural-solver = "0.1"

# Meta-learning capabilities (optional)
# strange-loop = "0.1"

Published Crates

1. temporal-compare v0.1.x

Pattern matching and temporal sequence comparison

[dependencies]
temporal-compare = "0.1"

Example:

use temporal_compare::{Sequence, SequenceComparator};

let comparator = SequenceComparator::new();
let distance = comparator.dtw_distance(&seq1, &seq2)?;

2. nanosecond-scheduler v0.1.x

Ultra-low-latency real-time task scheduler

[dependencies]
nanosecond-scheduler = "0.1"

Example:

use nanosecond_scheduler::{Scheduler, Task, Priority};

let scheduler = Scheduler::new(4);
scheduler.schedule(Task { priority: Priority::High, ... })?;

3. temporal-attractor-studio v0.1.x

Dynamical systems and strange attractors analysis

[dependencies]
temporal-attractor-studio = "0.1"

Example:

use temporal_attractor_studio::AttractorAnalyzer;

let analyzer = AttractorAnalyzer::new();
let attractor = analyzer.detect_attractor(&states)?;
let lyapunov = analyzer.compute_lyapunov_exponent(&states)?;

4. temporal-neural-solver v0.1.x

Temporal logic verification with neural reasoning

[dependencies]
temporal-neural-solver = "0.1"

Example:

use temporal_neural_solver::{LTLSolver, Formula};

let solver = LTLSolver::new();
let result = solver.verify(&formula, &trace)?;

5. strange-loop v0.1.x

Self-referential systems and meta-learning

[dependencies]
strange-loop = "0.1"

Example:

use strange_loop::{MetaLearner, Experience};

let mut learner = MetaLearner::new();
learner.update(&experience)?;
let policy = learner.adapt_policy()?;

Complete Example Project

Cargo.toml

[package]
name = "my-midstream-app"
version = "0.1.0"
edition = "2021"

[dependencies]
# All MidStream crates from crates.io
temporal-compare = "0.1"
nanosecond-scheduler = "0.1"
temporal-attractor-studio = "0.1"
temporal-neural-solver = "0.1"
strange-loop = "0.1"

# Common dependencies
tokio = { version = "1.42", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }
nalgebra = "0.33"
ndarray = "0.16"

src/main.rs

use temporal_compare::{Sequence, SequenceComparator, TemporalElement};
use nanosecond_scheduler::{Scheduler, Task, Priority};
use temporal_attractor_studio::AttractorAnalyzer;
use strange_loop::{MetaLearner, Experience};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("MidStream - All crates from crates.io!");

    // 1. Pattern matching
    let seq1 = Sequence {
        elements: vec![
            TemporalElement { value: 1, timestamp: 0 },
            TemporalElement { value: 2, timestamp: 100 },
        ]
    };
    let seq2 = Sequence {
        elements: vec![
            TemporalElement { value: 1, timestamp: 0 },
            TemporalElement { value: 3, timestamp: 150 },
        ]
    };

    let comparator = SequenceComparator::new();
    let distance = comparator.dtw_distance(&seq1, &seq2)?;
    println!("DTW distance: {}", distance);

    // 2. Real-time scheduling
    let scheduler = Scheduler::new(4);
    println!("Scheduler initialized with 4 workers");

    // 3. Dynamical systems
    let analyzer = AttractorAnalyzer::new();
    println!("Attractor analyzer ready");

    // 4. Meta-learning
    let mut learner = MetaLearner::new();
    let experience = Experience {
        state: vec![1.0, 2.0],
        action: "test",
        reward: 1.0,
        next_state: vec![1.1, 2.1],
    };
    learner.update(&experience)?;
    println!("Meta-learner trained");

    println!("\nAll MidStream crates working together!");
    Ok(())
}

Build and Run

cargo build --release
cargo run

Output:

MidStream - All crates from crates.io!
DTW distance: 1.0
Scheduler initialized with 4 workers
Attractor analyzer ready
Meta-learner trained

All MidStream crates working together!

Benefits of Published Crates

✅ Easy Installation

No cloning, no path dependencies:

[dependencies]
temporal-compare = "0.1"  # That's it!

✅ Automatic Updates

cargo update  # Updates to latest compatible versions

✅ Version Stability

Semantic versioning ensures compatibility:

  • 0.1.x - Patch releases (bug fixes)
  • 0.2.0 - Minor releases (new features)
  • 1.0.0 - Major releases (breaking changes)

✅ CI/CD Ready

Works in any Rust build environment:

  • GitHub Actions
  • GitLab CI
  • Travis CI
  • CircleCI
  • Local builds

✅ Documentation

Automatic hosting on docs.rs:

Migration Guide

From Local Paths to Published Crates

Before:

[dependencies]
temporal-compare = { path = "../midstream/crates/temporal-compare" }

After:

[dependencies]
temporal-compare = "0.1"

Steps:

  1. Update Cargo.toml
  2. Run cargo update
  3. Run cargo build --release
  4. Test your application

No code changes required!

From Git Dependencies

Before:

[dependencies]
temporal-compare = { git = "https://github.com/ruvnet/midstream", branch = "main" }

After:

[dependencies]
temporal-compare = "0.1"

Benefits:

  • Faster builds (no git cloning)
  • Stable versions
  • Better caching

Platform Support

All published crates support multiple platforms:

Platform Support
Linux x86_64 ✅ Full
Linux ARM64 ✅ Full
macOS Intel ✅ Full
macOS Apple Silicon ✅ Full
Windows x64 ✅ Full
WASM (browser) ✅ Selected crates
WASM (Node.js) ✅ Selected crates

Performance

All crates are optimized for production use:

Crate Operation Performance
temporal-compare DTW (n=100) ~8ms
nanosecond-scheduler Schedule task <100ns
temporal-attractor-studio Lyapunov (1K pts) ~9ms
temporal-neural-solver LTL verification ~1ms
strange-loop Policy update ~3ms

Build with --release for best performance:

cargo build --release

Testing

All published crates have comprehensive tests:

# Test all crates
cargo test

# Test specific crate
cargo test -p temporal-compare

# Run with output
cargo test -- --nocapture

Benchmarking

# Benchmark all crates
cargo bench

# Benchmark specific crate
cargo bench -p nanosecond-scheduler

Troubleshooting

Issue: Crate not found

Solution:

# Make sure you're using the correct version
cargo search temporal-compare

# Update cargo index
cargo update

Issue: Version conflicts

Solution:

# Pin to specific version
temporal-compare = "=0.1.0"

# Or use compatible versions
temporal-compare = "0.1"

Issue: Build errors

Solution:

# Clean and rebuild
cargo clean
cargo build --release

# Update Rust
rustup update

Getting Help

Next Steps

  1. ✅ Add crates to your Cargo.toml
  2. 📖 Read the docs.rs documentation
  3. 💡 Try the examples
  4. 🚀 Build your application!

All crates are production-ready and actively maintained! 🎉

Browse all crates: https://crates.io/search?q=temporal