### Quick Start: Image Processing Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-ndimage/README.md

A basic example demonstrating image creation, Gaussian filtering, and morphological dilation using scirs2-ndimage.

```rust
use scirs2_ndimage::{filters, morphology};
use scirs2_core::ndarray::Array2;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let image = Array2::<f64>::from_shape_fn((100, 100), |(i, j)| {
        if (i > 30 && i < 70) && (j > 30 && j < 70) { 1.0 } else { 0.0 }
    });

    // Gaussian smoothing
    let smoothed = filters::gaussian_filter(&image, 2.0, None, None)?;

    // Morphological dilation
    let struct_elem = morphology::structuring::generate_disk(3)?;
    let dilated = morphology::binary_dilation(&image, &struct_elem, None, None)?;

    println!("Image processed: {:?}", smoothed.shape());
    Ok(())
}
```

--------------------------------

### Run an Example with Cargo

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-integrate/examples/README.md

Demonstrates the command to execute a specific example using Cargo. Replace `<example_name>` with the desired example's name.

```bash
cargo run --example <example_name>
```

--------------------------------

### Run SciRS2 Neural Showcase Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-neural/examples/README.md

Execute the 'ultrathink_neural_showcase' example, which demonstrates comprehensive ultrathink functionalities.

```bash
cargo run --example ultrathink_neural_showcase
```

--------------------------------

### Run SciRS2 Neural Practical Training Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-neural/examples/README.md

Execute the 'ultrathink_practical_training' example, showcasing a practical training pipeline.

```bash
cargo run --example ultrathink_practical_training
```

--------------------------------

### Run Specific Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-core/examples/README.md

Use this command to run a specific example from the scirs2-core library. Replace `<example_name>` with the name of the example and `<required_features>` with the necessary features listed in Cargo.toml.

```bash
cargo run --example <example_name> --features <required_features>
```

--------------------------------

### Basic Setup for scirs2-autograd

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-autograd/docs/QUICK_REFERENCE.md

Demonstrates the basic setup for using scirs2-autograd, including necessary imports and the run context.

```rust
use scirs2_autograd as ag;
use ag::tensor_ops::*;
use ag::prelude::*;

ag::run(|ctx| {
    // Your code here
});
```

--------------------------------

### Run Adaptive Monte Carlo Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-integrate/docs/examples/adaptive_monte_carlo.md

Execute the adaptive Monte Carlo integration example using Cargo. This command runs the example which automatically analyzes function properties and selects the optimal integration strategy.

```bash
cargo run --example adaptive_monte_carlo
```

--------------------------------

### Install Python Dependencies for Benchmarking

Source: https://github.com/cool-japan/scirs/blob/master/benches/V020_BENCHMARKS.md

Installs necessary Python libraries for running comparison benchmarks.

```bash
pip install numpy scipy pandas matplotlib
```

--------------------------------

### Matrix Decomposition Examples

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-autograd/docs/QUICK_REFERENCE.md

Provides examples for various matrix decomposition techniques including QR, LU, SVD, Cholesky, and Eigendecomposition.

```rust
let (q, r) = qr(&a);                          // QR decomposition
```

```rust
let (l, u, p) = lu(&a);                       // LU with pivoting
```

```rust
let (u, s, v) = svd(&a);                      // Singular value decomposition
```

```rust
let chol = cholesky(&pd_matrix);              // Cholesky (positive definite)
```

```rust
let (vals, vecs) = eigen(&a);                 // Eigendecomposition
```

```rust
let vals_only = eigenvalues(&a);              // Eigenvalues only
```

--------------------------------

### Run SciRS2 Neural XOR Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-neural/examples/README.md

Execute the 'neural_network_xor' example, demonstrating a simple XOR neural network.

```bash
cargo run --example neural_network_xor
```

--------------------------------

### Serve Example Web App for Interactive Testing

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-wasm/WASM_GUIDE.md

Set up and run the example web application locally for interactive testing. Navigate to http://localhost:8080 in your browser.

```bash
cd www
npm install
npm start
```

--------------------------------

### Build and Serve Documentation Locally

Source: https://github.com/cool-japan/scirs/blob/master/docs/en/src/contributing/setup.md

Install mdbook and then build and serve the documentation locally. This allows for previewing changes to the book.

```bash
# Install mdbook
cargo install mdbook

# Build and serve
cd docs/en
mdbook serve --open
```

--------------------------------

### Install SciRS2 Neural Crate with Visualization Support

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-neural/examples/README.md

Install the scirs2-neural crate with optional visualization features. This enables the use of visualization-specific examples and functionalities.

```bash
cargo add scirs2-neural --features="visualization"
```

--------------------------------

### Install evcxr Jupyter Kernel

Source: https://github.com/cool-japan/scirs/blob/master/notebooks/01_getting_started.ipynb

Install the evcxr Jupyter kernel to use Rust in Jupyter notebooks. This is a prerequisite for running the following code examples.

```bash
cargo install --locked evcxr_jupyter
evcxr_jupyter --install
```

--------------------------------

### Install and Run Tests with cargo-nextest

Source: https://github.com/cool-japan/scirs/blob/master/docs/en/src/contributing/setup.md

Install cargo-nextest and run all tests in the workspace, excluding specific crates. This command is useful for a comprehensive test run without resource-intensive examples.

```bash
# Install nextest
cargo install cargo-nextest

# Run all tests (excluding Python bindings and heavy dataset examples)
cargo nextest run --all-features --workspace \
    --exclude scirs2-python \
    --exclude scirs2-datasets
```

--------------------------------

### Run All Tutorials

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-optimize/docs/GETTING_STARTED.md

A main function to execute all tutorial functions sequentially. Ensures all examples are run and provides a final success message.

```rust
fn main() -> Result<(), Box<dyn std::error::Error>> {
    tutorial_1()?;
    tutorial_2()?;
    tutorial_3()?;
    tutorial_4()?;
    tutorial_5()?;
    
    println!("🎉 All tutorials completed successfully!");
    println!("You're ready to tackle real optimization problems!");
    
    Ok(())
}

```

--------------------------------

### Verify scirs2-optimize Installation

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-optimize/docs/GETTING_STARTED.md

A basic Rust program to confirm that scirs2-optimize is installed correctly and can perform a simple minimization. This example minimizes the quadratic function f(x) = x² using the BFGS method.

```rust
// src/main.rs
use scirs2_optimize::prelude::*;
use ndarray::Array1;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("scirs2-optimize is working!");
    
    // Simple quadratic function: f(x) = x²
    let func = |x: &ArrayView1<f64>| x[0].powi(2);
    let x0 = Array1::from_vec(vec![2.0]);
    
    let result = minimize(func, &x0, Method::BFGS, None)?;
    println!("Minimum found at: {:.6}", result.x[0]);
    println!("Function value: {:.6}", result.fun);
    
    Ok(())
}
```

--------------------------------

### Complete Example: Solving Linear System and Gradient Calculation

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-autograd/docs/QUICK_REFERENCE.md

A comprehensive example showing matrix creation, solving a linear system, QR decomposition, loss function definition, and gradient computation.

```rust
use ndarray::array;
use scirs2_autograd as ag;
use ag::tensor_ops::*;
use ag::prelude::*;

fn main() {
    ag::run(|ctx| {
        // Create matrices
        let a = variable(array![[3.0, 1.0], [1.0, 2.0]], ctx);
        let b = convert_to_tensor(array![[5.0], [3.0]], ctx);
        
        // Solve linear system
        let x = solve(&a, &b);
        
        // Compute determinant
        let det = determinant(&a);
        
        // QR decomposition
        let (q, r) = qr(&a);
        
        // Create loss function
        let loss = sum_all(&square(&sub(&matmul(&a, &x), &b))) + square(&det);
        
        // Compute gradients
        let grads = grad(&[&loss], &[&a]);
        
        // Evaluate
        println!("Solution: {:?}", x.eval(ctx).unwrap());
        println!("Determinant: {:?}", det.eval(ctx).unwrap());
        println!("Gradient: {:?}", grads[0].eval(ctx).unwrap());
    });
}
```

--------------------------------

### Node.js Quick Start

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-wasm/README.md

This example demonstrates how to use scirs2-wasm in a Node.js environment. It shows how to create a WasmArray and calculate its determinant and trace.

```javascript
const scirs2 = require('scirs2-wasm');

async function main() {
  const matrix = scirs2.WasmArray.from_shape([2, 2], [1, 2, 3, 4]);

  const det   = scirs2.det(matrix);
  const trace = scirs2.trace(matrix);

  console.log('Determinant:', det);
  console.log('Trace:', trace);
}

main();
```

--------------------------------

### Run Feature Detection Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-vision/examples/README.md

Demonstrates feature detection and descriptor computation. Ensure your input image is placed in the examples/input/ directory.

```bash
cargo run --example feature_detection
```

--------------------------------

### Get Weight and Bias Ranges

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-optimize/docs/EXAMPLES.md

Calculates the start and end indices for weights and biases for each layer in the neural network. This is useful for parameter management.

```rust
fn get_weight_ranges(&self) -> Vec<(usize, usize)> {
    let mut ranges = Vec::new();
    let mut start = 0;
    
    for i in 0..self.architecture.len() - 1 {
        let weight_count = self.architecture[i] * self.architecture[i + 1];
        let bias_count = self.architecture[i + 1];
        ranges.push((start, start + weight_count));
        ranges.push((start + weight_count, start + weight_count + bias_count));
        start += weight_count + bias_count;
    }
    
    ranges
}
```

--------------------------------

### ArrayProtocol Usage Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-core/docs/api_reference.md

Demonstrates how to use the ArrayProtocol trait with ndarray's Array2 to get shape, dimension, and length information. Requires the ndarray crate.

```rust
use scirs2_core::array_protocol::ArrayProtocol;
use ndarray::Array2;

let matrix = Array2::<f64>::zeros((100, 50));
assert_eq!(matrix.shape(), &[100, 50]);
assert_eq!(matrix.ndim(), 2);
assert_eq!(matrix.len(), 5000);
```

--------------------------------

### Environment Setup and Version Check

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-python/notebooks/performance_comparison.ipynb

Imports necessary libraries and prints their versions. This is typically run at the beginning of a notebook to ensure the correct environment is set up.

```python
import numpy as np
import scipy.linalg
import scipy.fft
import scipy.interpolate
import scirs2
import timeit

RNG = np.random.default_rng(42)
print(f'NumPy  : {np.__version__}')
print(f'SciPy  : {scipy.__version__}')
print(f'scirs2 : {scirs2.__version__}')
```

--------------------------------

### Protein Folding Energy Minimization

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-optimize/docs/EXAMPLES.md

Example demonstrating protein folding energy minimization using scirs2-optimize. Includes problem setup, optimization approach, and result analysis.

```python
from scirs2_optimize.optimize import Optimizer
from scirs2_optimize.problems.protein import ProteinFolding

# Define the protein folding problem
problem = ProteinFolding(sequence="AG")

# Initialize the optimizer
optimizer = Optimizer(problem)

# Run the optimization
result = optimizer.optimize()

# Print the result
print(result)
```

--------------------------------

### TypeScript Quick Start

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-wasm/README.md

This example shows how to use scirs2-wasm with TypeScript. It includes initializing the WASM module, creating a WasmMatrix, performing SVD, and checking for SIMD support.

```typescript
import init, * as scirs2 from 'scirs2-wasm';

async function main(): Promise<void> {
  await init();

  // WasmMatrix for 2D linear algebra
  const mat: scirs2.WasmMatrix = scirs2.WasmMatrix.from_rows([
    [1.0, 2.0, 3.0],
    [4.0, 5.0, 6.0],
    [7.0, 8.0, 9.0],
  ]);

  const svd = scirs2.svd(mat);
  console.log('Singular values:', svd.s.to_array());

  // Check SIMD availability
  console.log('SIMD support:', scirs2.has_simd_support());
}

main();
```

--------------------------------

### Run Edge Detection Comparison Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-vision/examples/README.md

Compares different edge detection algorithms including Sobel, Canny, Prewitt, Laplacian, and Laplacian of Gaussian. Input images should be in examples/input/.

```bash
cargo run --example edge_detection_comparison
```

--------------------------------

### Distributed Tracing Setup with Jaeger in Rust

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-core/docs/PRODUCTION_OPERATIONS.md

Configures distributed tracing for the SciRS2 Core application, setting service name, version, environment, and sampling rate. It then installs a Jaeger exporter.

```rust
use scirs2_core::observability::tracing::{TracingConfig, JaegerExporter};

// Configure distributed tracing
let tracing_config = TracingConfig::new()
    .with_service_name("scirs2-core")
    .with_service_version("1.0.0")
    .with_environment("production")
    .with_sampling_rate(0.1); // 10% sampling in production

// Export to Jaeger
let jaeger_exporter = JaegerExporter::new("http://jaeger:14268/api/traces")?;
jaeger_exporter.install_global(tracing_config)?;

// Trace operations
use scirs2_core::observability::tracing::{trace_span, Span};

fn compute_matrix_multiplication(a: &Array2<f64>, b: &Array2<f64>) -> CoreResult<Array2<f64>> {
    let _span = trace_span!("matrix_multiplication")
        .with_attribute("matrix_a_shape", format!("{:?}", a.shape()))
        .with_attribute("matrix_b_shape", format!("{:?}", b.shape()));
    
    // Computation logic here
    Ok(result)
}
```

--------------------------------

### Run SciRS2 Neural Example with Visualization

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-neural/examples/README.md

Execute the 'model_visualization_example' with visualization features enabled using the 'visualization' flag.

```bash
cargo run --example model_visualization_example --features="visualization"
```

--------------------------------

### Run Image Segmentation Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-vision/examples/README.md

Demonstrates thresholding and segmentation techniques for image analysis. Input images are expected in the examples/input/ directory.

```bash
cargo run --example image_segmentation
```

--------------------------------

### Quick Start: Minimize Rosenbrock Function with BFGS

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-optimize/docs/README.md

This example demonstrates how to use the `minimize` function with the BFGS algorithm to find the minimum of the Rosenbrock function. It requires importing necessary types from `scirs2_optimize::prelude` and `ndarray::Array1`.

```rust
use scirs2_optimize::prelude::*;
use ndarray::Array1;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Define the Rosenbrock function
    let rosenbrock = |x: &ArrayView1<f64>| -> f64 {
        let (a, b) = (1.0, 100.0);
        (a - x[0]).powi(2) + b * (x[1] - x[0].powi(2)).powi(2)
    };
    
    // Starting point
    let x0 = Array1::from_vec(vec![-1.2, 1.0]);
    
    // Optimize
    let result = minimize(rosenbrock, &x0, Method::BFGS, None)?;
    
    println!("Solution: [{:.6}, {:.6}]", result.x[0], result.x[1]);
    println!("Function value: {:.2e}", result.fun);
    println!("Converged: {}", result.success);
    
    Ok(())
}
```

--------------------------------

### Constrained Optimization Tutorial

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-optimize/docs/GETTING_STARTED.md

Demonstrates how to minimize a function subject to inequality and equality constraints using the SLSQP method. Requires defining the objective function, constraints, and an initial guess.

```rust
use scirs2_optimize::constrained::*;

fn tutorial_4() -> Result<(), Box<dyn std::error::Error>> {
    println!("=== Tutorial 4: General Constrained Optimization ===");
    
    // Problem: minimize (x-1)² + (y-2)²
    // Subject to: x + y ≤ 3 (inequality)
    //            x² + y² = 5 (equality)
    
    let objective = |x: &ArrayView1<f64>| -> f64 {
        (x[0] - 1.0).powi(2) + (x[1] - 2.0).powi(2)
    };
    
    // Inequality constraint: 3 - x - y ≥ 0
    let ineq_constraint = |x: &ArrayView1<f64>| -> f64 {
        3.0 - x[0] - x[1]
    };
    
    // Equality constraint: x² + y² - 5 = 0
    let eq_constraint = |x: &ArrayView1<f64>| -> f64 {
        x[0].powi(2) + x[1].powi(2) - 5.0
    };
    
    let constraints = vec![
        Constraint {
            fun: Box::new(ineq_constraint),
            constraint_type: ConstraintType::Inequality,
            jac: None, // Auto-computed
        },
        Constraint {
            fun: Box::new(eq_constraint),
            constraint_type: ConstraintType::Equality,
            jac: None,
        },
    ];
    
    // Starting point (on the circle x² + y² = 5)
    let x0 = Array1::from_vec(vec![2.0, 1.0]);
    
    let result = minimize_constrained(
        objective, 
        &x0, 
        &constraints, 
        Method::SLSQP, 
        None
    )?;
    
    println!("Solution: [{:.6}, {:.6}]", result.x[0], result.x[1]);
    println!("Objective value: {:.6}", result.fun);
    
    // Verify constraints
    let ineq_val = ineq_constraint(&result.x.view());
    let eq_val = eq_constraint(&result.x.view());
    
    println!("Inequality constraint (≥0): {:.6}", ineq_val);
    println!("Equality constraint (=0): {:.6}", eq_val);
    
    assert!(ineq_val >= -1e-6, "Inequality constraint violated");
    assert!(eq_val.abs() < 1e-6, "Equality constraint violated");
    
    println!("✓ Tutorial 4 completed successfully!\n");
    Ok(())
}

```

--------------------------------

### Browser Quick Start with ES Modules

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-wasm/README.md

This example demonstrates basic usage of scirs2-wasm in a browser environment using ES Modules. It shows how to initialize the WASM module and perform array operations like addition, mean, and standard deviation.

```javascript
import init, * as scirs2 from 'scirs2-wasm';

async function main() {
  await init();

  const a = new scirs2.WasmArray([1, 2, 3, 4]);
  const b = new scirs2.WasmArray([5, 6, 7, 8]);

  const sum  = scirs2.add(a, b);
  const mean = scirs2.mean(a);
  const std  = scirs2.std(a);

  console.log('Sum:', sum.to_array());
  console.log('Mean:', mean, 'Std:', std);
}

main();
```

--------------------------------

### Install SciRS2 from Source

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-python/docs/guides/quickstart.md

Follow these steps to clone the repository, install Rust and maturin, and then build and install SciRS2 from source for development purposes. Test dependencies are also installed.

```bash
# Clone repository
git clone https://github.com/cool-japan/scirs.git
cd scirs/scirs2-python

# Install Rust if not already installed
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install maturin
pip install maturin

# Build and install
maturin develop --release

# Install test dependencies
pip install pytest numpy scipy scikit-learn
```

--------------------------------

### Run Color Transformations Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-vision/examples/README.md

Shows color space conversions between RGB and HSV, and RGB and LAB. Input images should be in examples/input/.

```bash
cargo run --example color_transformations
```

--------------------------------

### Install SCIRS2 Python

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-python/docs/guides/troubleshooting.md

Install the SCIRS2 Python library using pip. Ensure NumPy is also installed.

```bash
pip install scirs2
pip install numpy
```

--------------------------------

### Install Rust and Build scirs2-python

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-python/docs/guides/troubleshooting.md

Installs Rust using rustup, updates it, installs maturin, and then builds the scirs2-python package in development mode.

```bash
# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Update Rust
rustup update

# Install maturin
pip install maturin

# Build
cd scirs2-python
maturin develop --release
```

--------------------------------

### Build and Run Node.js Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-wasm/WASM_GUIDE.md

Build the WASM module for Node.js and execute a sample Node.js script.

```bash
wasm-pack build --target nodejs
node examples/node_example.js
```

--------------------------------

### Matrix Creation Examples

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-autograd/docs/QUICK_REFERENCE.md

Shows how to create identity, diagonal, and extract diagonals from matrices.

```rust
let identity = eye(3, ctx);                    // 3x3 identity matrix
```

```rust
let diag_mat = diag(&vector);                  // Diagonal matrix from vector
```

```rust
let diag_vec = extract_diag(&matrix);          // Extract diagonal elements
```

--------------------------------

### Run Corner Detection Comparison Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-vision/examples/README.md

Compares different corner detection algorithms such as Harris, Shi-Tomasi, and FAST. Input images are expected in the examples/input/ directory.

```bash
cargo run --example corner_detection_comparison
```

--------------------------------

### Install cargo-scirs2-policy

Source: https://github.com/cool-japan/scirs/blob/master/tools/cargo-scirs2-policy/README.md

Install the tool using cargo.

```bash
cargo install cargo-scirs2-policy
```

--------------------------------

### Install wasm-opt (Binaryen)

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-wasm/WASM_GUIDE.md

Installs `wasm-opt` from the Binaryen toolchain, used for optimizing WebAssembly binaries. Installation methods vary by OS or can be built from source.

```bash
# macOS
brew install binaryen

# Ubuntu/Debian
sudo apt-get install binaryen

# From source
git clone https://github.com/WebAssembly/binaryen
cd binaryen && cmake . && make
```

--------------------------------

### Run Noise Reduction Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-vision/examples/README.md

Demonstrates various noise reduction techniques such as Gaussian blur, bilateral filtering, and median filtering. Input images are expected in examples/input/.

```bash
cargo run --example noise_reduction
```

--------------------------------

### Lorenz System Example in Rust

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-symbolic/TODO.md

This example, located in the examples directory, demonstrates the use of scirs2_symbolic for discovering dynamics related to the Lorenz system, potentially using discover_multi.

```rust
scirs2_symbolic::regression::discover_multi
```

--------------------------------

### Global and Module Configuration

Source: https://github.com/cool-japan/scirs/blob/master/docs/API_REFERENCE.md

Demonstrates how to set global configuration parameters and module-specific configurations using ConfigBuilder and default configurations.

```rust
use scirs2_core::config::{Config, ConfigBuilder};

// Global configuration
let config = ConfigBuilder::new()
    .precision(1e-12)
    .parallel_threshold(1000)
    .gpu_memory_fraction(0.8)
    .build();

scirs2_core::config::set_global_config(config);

// Module-specific configuration
let linalg_config = scirs2_linalg::Config::default()
    .with_backend(scirs2_linalg::Backend::OxiBLAS)
    .with_threads(8);
```

--------------------------------

### Run Non-Rigid Transformations Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-vision/examples/README.md

Demonstrates non-rigid transformation methods including thin-plate splines and elastic deformations. Input images should be placed in examples/input/.

```bash
cargo run --example non_rigid_transformations
```

--------------------------------

### AdvancedBufferPool Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-core/docs/api_reference.md

A practical example demonstrating how to configure and use the AdvancedBufferPool.

```APIDOC
## Example Usage

```rust
use scirs2_core::memory::{AdvancedBufferPool, MemoryConfig, AllocationStrategy};

let config = MemoryConfig {
    strategy: AllocationStrategy::CacheAligned,
    alignment: 64,
    numa_aware: true,
    ..Default::default()
};

let mut pool = AdvancedBufferPool::<f64>::with_config(config);
let buffer = pool.acquire_vec_advanced(1000);

// Use buffer for computation
pool.release_vec_advanced(buffer);

let report = pool.memory_report();
println!("Pool efficiency: {:.2}%", report.pool_efficiency * 100.0);
```
```

--------------------------------

### Physics Pipeline Example in Rust

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-symbolic/TODO.md

An example demonstrating the use of scirs2_symbolic for physics applications, including data generation and regression discovery. This code is intended to be run as a Cargo example.

```rust
scirs2_symbolic::regression::discover
```

--------------------------------

### Testing the array! Macro

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-core/docs/ARRAY_MACRO_FIX.md

Commands to run the example demonstrating the array! macro usage and to execute tests.

```bash
cargo run --example array_macro_usage
```

```bash
cargo test array_macro_test
```

--------------------------------

### Install SciRS2 for Development

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-python/README.md

For development, install maturin and then use 'maturin develop' to build the Rust components from source. This method also avoids the need for system BLAS or FFTW installations.

```bash
pip install maturin
git clone https://github.com/cool-japan/scirs
cd scirs/scirs2-python
maturin develop --release

```

--------------------------------

### Run Canny Edge Detection Example

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-vision/examples/README.md

Demonstrates the Canny edge detector with various parameters. Ensure input images are in the examples/input/ directory.

```bash
cargo run --example canny_edge_detection
```

--------------------------------

### Pendulum Simulation Example in Rust

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-symbolic/TODO.md

A runnable example demonstrating the use of the scirs2_symbolic library for simulating a pendulum. This example can be executed using Cargo and may utilize parallel and SIMD features.

```rust
cargo run --example pendulum --features parallel,simd
```

--------------------------------

### Run Specific Scenarios with Nextest

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-integration-tests/README.md

Execute integration tests for specific module combinations by providing their corresponding keywords. Examples include 'neural' for autograd+neural, 'sparse_linalg' for linalg+sparse, and 'fft_signal' for signal+fft.

```bash
# autograd + neural only
cargo nextest run --all-features -p scirs2-integration-tests neural

# linalg + sparse only
cargo nextest run --all-features -p scirs2-integration-tests sparse_linalg

# signal + fft only
cargo nextest run --all-features -p scirs2-integration-tests fft_signal
```

--------------------------------

### Quick Start: Symbolic Differentiation and Evaluation

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-symbolic/README.md

Demonstrates building an expression, differentiating it symbolically, simplifying the result, and then evaluating it numerically.

```rust
use scirs2_symbolic::{Expr, diff, simplify, eval};
use std::collections::HashMap;

// Build the expression  f(x) = x² + 3x
let x = Expr::var("x");
let f = x.clone().pow(Expr::from(2.0)) + Expr::from(3.0) * x.clone();

// Differentiate symbolically:  f'(x) = 2x + 3
let df = simplify(&diff(&f, "x"));
println!("f'(x) = {}", df);  // 2*x + 3

// Evaluate at x = 2:  2*2 + 3 = 7
let mut vars = HashMap::new();
vars.insert("x", 2.0_f64);
let result = eval(&df, &vars).unwrap();
assert!((result - 7.0).abs() < 1e-10);

```

--------------------------------

### Install wasm-pack

Source: https://github.com/cool-japan/scirs/blob/master/scirs2-wasm/WASM_GUIDE.md

Install the wasm-pack tool if you encounter the 'wasm-pack not found' error.

```bash
cargo install wasm-pack
```