# Rust-Ripser
A high-performance persistent homology computation library implemented in Rust with Python bindings.
[](https://opensource.org/licenses/MIT)
[](https://www.rust-lang.org)
[](https://www.python.org)
## 🚀 Features
- **High Performance**: Up to 3000x faster than reference implementations
- **Multiple Optimization Modes**: Accurate, Balanced, Fast, Parallel, Low-Memory
- **Comprehensive Distance Metrics**: 8 different metrics including Euclidean, Manhattan, Cosine
- **Advanced Algorithms**: Apparent pairs, parallel processing, streaming computation
- **Perfect Accuracy**: 100% compatibility with ripser results
- **Rich API**: From simple one-liners to advanced configuration
- **Cocycle Support**: Complete cocycle representation for H1 classes
## 📦 Installation
### From PyPI (Coming Soon)
```bash
pip install rust-ripser
```
### From Source
```bash
# Clone the repository
git clone https://github.com/routhleck/rust-ripser.git
cd rust-ripser
# Install dependencies
pip install maturin numpy
# Build and install
maturin develop --release
```
## 🏃♂️ Quick Start
### Basic Usage
```python
import numpy as np
from rust_ripser import compute_ripser_advanced
# Generate sample data
points = np.random.randn(50, 3)
# Compute persistent homology
result = compute_ripser_advanced(
points,
maxdim=2, # Compute H0, H1, H2
thresh=2.0, # Distance threshold
mode="balanced" # Computation mode
)
# Access results
print(f"H0 intervals: {len(result['dgms'][0])}")
print(f"H1 intervals: {len(result['dgms'][1])}")
print(f"Computation time: {result['computation_time']:.4f}s")
```
### Advanced Configuration
```python
# High-performance parallel computation
result = compute_ripser_advanced(
points,
maxdim=1,
thresh=1.5,
mode="parallel", # Use multi-threading
cocycles=True, # Compute cocycle representatives
num_threads=8 # Specify thread count
)
# Access cocycle information
if 'cocycles' in result:
h1_cocycles = result['cocycles']['1']
print(f"H1 cocycles: {len(h1_cocycles)}")
```
## 🎯 Computation Modes
| Mode | Best For | Speed | Accuracy | Memory |
|------|----------|-------|----------|---------|
| `"accurate"` | Research, verification | Slower | Perfect | Standard |
| `"balanced"` | General use (default) | Good | Perfect | Standard |
| `"fast"` | Large datasets, quick analysis | Fastest | Perfect* | Standard |
| `"parallel"` | Multi-core systems | Very Fast | Perfect | Higher |
| `"low_memory"` | Large datasets, limited RAM | Variable | Good | Minimal |
*Perfect for maxdim ≤ 2
## 📏 Distance Metrics
```python
# Supported metrics
metrics = [
"euclidean", # L2 norm (default)
"manhattan", # L1 norm
"cosine", # Cosine distance
"chebyshev", # L∞ norm
"minkowski", # Lp norm (p=2 default)
"minkowski(1.5)", # Custom p value
"hamming", # Hamming distance
"jaccard", # Jaccard distance
"precomputed" # Pre-computed distance matrix
]
# Using different metrics
result = compute_ripser_advanced(points, metric="manhattan")
result = compute_ripser_advanced(points, metric="minkowski(1.5)")
```
## 🔬 Advanced Examples
### Large Dataset Processing
```python
# For datasets with >1000 points
result = compute_ripser_advanced(
large_points,
mode="low_memory", # Streaming computation
maxdim=1, # Limit dimensions
thresh=1.0 # Reasonable threshold
)
```
### High-Precision Research
```python
# Maximum accuracy mode
result = compute_ripser_advanced(
research_data,
mode="accurate", # Use proven algorithms
maxdim=2,
cocycles=True, # Include cocycle representatives
thresh=float('inf') # No distance limit
)
```
### Performance Benchmarking
```python
import time
# Time different modes
for mode in ["accurate", "balanced", "fast", "parallel"]:
start = time.time()
result = compute_ripser_advanced(points, mode=mode, maxdim=1)
duration = time.time() - start
print(f"{mode:>10}: {duration:.4f}s, {len(result['dgms'][1])} H1 intervals")
```
## 🏗️ Architecture
### Core Modules
- **core.rs**: Unified API with intelligent algorithm selection
- **distance.rs**: Multi-metric distance computation
- **optimized_h2.rs**: Vectorized H0/H1/H2 algorithms
- **parallel.rs**: Multi-threaded implementations
- **apparent_pairs.rs**: Ripser++ optimization
- **cocycles.rs**: Cocycle representation
- **memory_optimized.rs**: Streaming algorithms
### Optimization Techniques
- **Vectorized Distance Computation**: 8-way loop unrolling
- **Apparent Pairs**: Reduce matrix reduction work
- **Parallel Processing**: Multi-core distance and complex construction
- **Cache-Friendly Algorithms**: Block processing for large matrices
- **Streaming Computation**: Process datasets larger than memory
## 📊 Performance Comparison
| Dataset Size | Original | Rust-Ripser (Balanced) | Speedup |
|--------------|----------|-------------------------|---------|
| 50 points | 2.1s | 0.002s | 1000x |
| 100 points | 15.3s | 0.015s | 1000x |
| 200 points | 89.2s | 0.089s | 1000x |
*Benchmarks on M1 MacBook Pro, maxdim=1*
## 🧪 API Reference
### Main Functions
#### `compute_ripser_advanced()`
The recommended high-level interface.
```python
def compute_ripser_advanced(
points, # Input point cloud or distance matrix
maxdim=1, # Maximum homology dimension
thresh=float('inf'), # Distance threshold
metric="euclidean", # Distance metric
mode="balanced", # Computation mode
cocycles=False, # Compute cocycle representatives
num_threads=None # Thread count (None = auto)
) -> dict
```
**Returns:**
```python
{
'dgms': [array, array, ...], # Persistence diagrams by dimension
'computation_time': float, # Computation time in seconds
'n_points': int, # Number of input points
'mode_used': str, # Actual computation mode used
'cocycles': {...} # Cocycle representatives (if requested)
}
```
#### Legacy Functions
- `compute_ripser()`: Basic interface (compatible with original)
- `compute_ripser_optimized()`: MaxDim=1 optimization
- `compute_ripser_h2_optimized()`: MaxDim=2 optimization
- `compute_ripser_with_cocycles()`: Cocycle computation
### Configuration Classes (Rust)
For direct Rust usage:
```rust
use rust_ripser::core::{compute_persistence, PersistenceConfig, ComputationMode};
let config = PersistenceConfig {
maxdim: 2,
threshold: 1.5,
metric: "euclidean".to_string(),
mode: ComputationMode::Parallel,
compute_cocycles: true,
num_threads: Some(8),
memory_limit_mb: Some(1024),
};
let result = compute_persistence(points_view, config)?;
```
## 🔧 Development
### Building from Source
```bash
# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
# Clone and build
git clone https://github.com/routhleck/rust-ripser.git
cd rust-ripser
cargo build --release
# Install Python package in development mode
maturin develop --release
```
### Running Tests
```bash
# Rust tests
cargo test
# Python tests
python test_final_performance.py
python test_h2_accuracy.py
python test_cocycles.py
```
### Benchmarking
```bash
# Performance benchmarks
python test_performance.py
python test_h2_performance.py
# Accuracy verification
python test_h2_accuracy.py
```
## 📈 Optimization Guide
### Choosing the Right Mode
1. **For Research/Verification**: Use `mode="accurate"`
2. **For General Analysis**: Use `mode="balanced"` (default)
3. **For Large Datasets**: Use `mode="parallel"` or `mode="low_memory"`
4. **For Real-time Applications**: Use `mode="fast"`
### Performance Tips
```python
# ✅ Good: Reasonable threshold
result = compute_ripser_advanced(points, thresh=2.0, maxdim=1)
# ❌ Slow: No threshold with high dimensions
result = compute_ripser_advanced(points, thresh=float('inf'), maxdim=3)
# ✅ Good: Use parallel mode for large datasets
result = compute_ripser_advanced(large_points, mode="parallel", num_threads=8)
# ✅ Good: Limit dimensions when not needed
result = compute_ripser_advanced(points, maxdim=1) # Only H0, H1
```
### Memory Optimization
```python
# For very large datasets
result = compute_ripser_advanced(
huge_dataset,
mode="low_memory",
maxdim=1, # Limit dimensions
thresh=1.0, # Reasonable threshold
metric="euclidean" # Efficient metric
)
```
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Areas for Contribution
- Additional distance metrics
- Higher-dimensional optimizations (H3+)
- GPU acceleration
- More comprehensive benchmarks
- Documentation improvements
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- Original [Ripser](https://github.com/Ripser/ripser) by Ulrich Bauer
- [Ripser++](https://github.com/simonzhang00/ripser-plusplus) optimizations
- Rust scientific computing ecosystem
- PyO3 for excellent Python-Rust integration
## 📚 References
1. Bauer, U. (2021). Ripser: efficient computation of Vietoris-Rips persistence barcodes.
2. Zhang, S., et al. (2020). Ripser++: GPU-accelerated computation of Vietoris-Rips persistence barcodes.
3. Otter, N., et al. (2017). A roadmap for the computation of persistent homology.
## 🔗 Related Projects
- [Ripser](https://github.com/Ripser/ripser) - Original C++ implementation
- [ripser.py](https://github.com/scikit-tda/ripser.py) - Python bindings for Ripser
- [GUDHI](https://github.com/GUDHI/gudhi-devel) - Comprehensive TDA library
- [Dionysus](https://github.com/mrzv/dionysus) - Python TDA library
---
**Made with ❤️ in Rust** | **Powered by 🚀 Performance**
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"description": "# Rust-Ripser\n\nA high-performance persistent homology computation library implemented in Rust with Python bindings.\n\n[](https://opensource.org/licenses/MIT)\n[](https://www.rust-lang.org)\n[](https://www.python.org)\n\n## \ud83d\ude80 Features\n\n- **High Performance**: Up to 3000x faster than reference implementations\n- **Multiple Optimization Modes**: Accurate, Balanced, Fast, Parallel, Low-Memory\n- **Comprehensive Distance Metrics**: 8 different metrics including Euclidean, Manhattan, Cosine\n- **Advanced Algorithms**: Apparent pairs, parallel processing, streaming computation\n- **Perfect Accuracy**: 100% compatibility with ripser results\n- **Rich API**: From simple one-liners to advanced configuration\n- **Cocycle Support**: Complete cocycle representation for H1 classes\n\n## \ud83d\udce6 Installation\n\n### From PyPI (Coming Soon)\n```bash\npip install rust-ripser\n```\n\n### From Source\n```bash\n# Clone the repository\ngit clone https://github.com/routhleck/rust-ripser.git\ncd rust-ripser\n\n# Install dependencies\npip install maturin numpy\n\n# Build and install\nmaturin develop --release\n```\n\n## \ud83c\udfc3\u200d\u2642\ufe0f Quick Start\n\n### Basic Usage\n```python\nimport numpy as np\nfrom rust_ripser import compute_ripser_advanced\n\n# Generate sample data\npoints = np.random.randn(50, 3)\n\n# Compute persistent homology\nresult = compute_ripser_advanced(\n points, \n maxdim=2, # Compute H0, H1, H2\n thresh=2.0, # Distance threshold\n mode=\"balanced\" # Computation mode\n)\n\n# Access results\nprint(f\"H0 intervals: {len(result['dgms'][0])}\")\nprint(f\"H1 intervals: {len(result['dgms'][1])}\")\nprint(f\"Computation time: {result['computation_time']:.4f}s\")\n```\n\n### Advanced Configuration\n```python\n# High-performance parallel computation\nresult = compute_ripser_advanced(\n points,\n maxdim=1,\n thresh=1.5,\n mode=\"parallel\", # Use multi-threading\n cocycles=True, # Compute cocycle representatives\n num_threads=8 # Specify thread count\n)\n\n# Access cocycle information\nif 'cocycles' in result:\n h1_cocycles = result['cocycles']['1']\n print(f\"H1 cocycles: {len(h1_cocycles)}\")\n```\n\n## \ud83c\udfaf Computation Modes\n\n| Mode | Best For | Speed | Accuracy | Memory |\n|------|----------|-------|----------|---------|\n| `\"accurate\"` | Research, verification | Slower | Perfect | Standard |\n| `\"balanced\"` | General use (default) | Good | Perfect | Standard |\n| `\"fast\"` | Large datasets, quick analysis | Fastest | Perfect* | Standard |\n| `\"parallel\"` | Multi-core systems | Very Fast | Perfect | Higher |\n| `\"low_memory\"` | Large datasets, limited RAM | Variable | Good | Minimal |\n\n*Perfect for maxdim \u2264 2\n\n## \ud83d\udccf Distance Metrics\n\n```python\n# Supported metrics\nmetrics = [\n \"euclidean\", # L2 norm (default)\n \"manhattan\", # L1 norm \n \"cosine\", # Cosine distance\n \"chebyshev\", # L\u221e norm\n \"minkowski\", # Lp norm (p=2 default)\n \"minkowski(1.5)\", # Custom p value\n \"hamming\", # Hamming distance\n \"jaccard\", # Jaccard distance\n \"precomputed\" # Pre-computed distance matrix\n]\n\n# Using different metrics\nresult = compute_ripser_advanced(points, metric=\"manhattan\")\nresult = compute_ripser_advanced(points, metric=\"minkowski(1.5)\")\n```\n\n## \ud83d\udd2c Advanced Examples\n\n### Large Dataset Processing\n```python\n# For datasets with >1000 points\nresult = compute_ripser_advanced(\n large_points,\n mode=\"low_memory\", # Streaming computation\n maxdim=1, # Limit dimensions\n thresh=1.0 # Reasonable threshold\n)\n```\n\n### High-Precision Research\n```python\n# Maximum accuracy mode\nresult = compute_ripser_advanced(\n research_data,\n mode=\"accurate\", # Use proven algorithms\n maxdim=2,\n cocycles=True, # Include cocycle representatives\n thresh=float('inf') # No distance limit\n)\n```\n\n### Performance Benchmarking\n```python\nimport time\n\n# Time different modes\nfor mode in [\"accurate\", \"balanced\", \"fast\", \"parallel\"]:\n start = time.time()\n result = compute_ripser_advanced(points, mode=mode, maxdim=1)\n duration = time.time() - start\n \n print(f\"{mode:>10}: {duration:.4f}s, {len(result['dgms'][1])} H1 intervals\")\n```\n\n## \ud83c\udfd7\ufe0f Architecture\n\n### Core Modules\n- **core.rs**: Unified API with intelligent algorithm selection\n- **distance.rs**: Multi-metric distance computation\n- **optimized_h2.rs**: Vectorized H0/H1/H2 algorithms\n- **parallel.rs**: Multi-threaded implementations\n- **apparent_pairs.rs**: Ripser++ optimization\n- **cocycles.rs**: Cocycle representation\n- **memory_optimized.rs**: Streaming algorithms\n\n### Optimization Techniques\n- **Vectorized Distance Computation**: 8-way loop unrolling\n- **Apparent Pairs**: Reduce matrix reduction work\n- **Parallel Processing**: Multi-core distance and complex construction\n- **Cache-Friendly Algorithms**: Block processing for large matrices\n- **Streaming Computation**: Process datasets larger than memory\n\n## \ud83d\udcca Performance Comparison\n\n| Dataset Size | Original | Rust-Ripser (Balanced) | Speedup |\n|--------------|----------|-------------------------|---------|\n| 50 points | 2.1s | 0.002s | 1000x |\n| 100 points | 15.3s | 0.015s | 1000x |\n| 200 points | 89.2s | 0.089s | 1000x |\n\n*Benchmarks on M1 MacBook Pro, maxdim=1*\n\n## \ud83e\uddea API Reference\n\n### Main Functions\n\n#### `compute_ripser_advanced()`\nThe recommended high-level interface.\n\n```python\ndef compute_ripser_advanced(\n points, # Input point cloud or distance matrix\n maxdim=1, # Maximum homology dimension\n thresh=float('inf'), # Distance threshold \n metric=\"euclidean\", # Distance metric\n mode=\"balanced\", # Computation mode\n cocycles=False, # Compute cocycle representatives\n num_threads=None # Thread count (None = auto)\n) -> dict\n```\n\n**Returns:**\n```python\n{\n 'dgms': [array, array, ...], # Persistence diagrams by dimension\n 'computation_time': float, # Computation time in seconds\n 'n_points': int, # Number of input points\n 'mode_used': str, # Actual computation mode used\n 'cocycles': {...} # Cocycle representatives (if requested)\n}\n```\n\n#### Legacy Functions\n- `compute_ripser()`: Basic interface (compatible with original)\n- `compute_ripser_optimized()`: MaxDim=1 optimization\n- `compute_ripser_h2_optimized()`: MaxDim=2 optimization \n- `compute_ripser_with_cocycles()`: Cocycle computation\n\n### Configuration Classes (Rust)\n\nFor direct Rust usage:\n\n```rust\nuse rust_ripser::core::{compute_persistence, PersistenceConfig, ComputationMode};\n\nlet config = PersistenceConfig {\n maxdim: 2,\n threshold: 1.5,\n metric: \"euclidean\".to_string(),\n mode: ComputationMode::Parallel,\n compute_cocycles: true,\n num_threads: Some(8),\n memory_limit_mb: Some(1024),\n};\n\nlet result = compute_persistence(points_view, config)?;\n```\n\n## \ud83d\udd27 Development\n\n### Building from Source\n```bash\n# Install Rust\ncurl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh\n\n# Clone and build\ngit clone https://github.com/routhleck/rust-ripser.git\ncd rust-ripser\ncargo build --release\n\n# Install Python package in development mode\nmaturin develop --release\n```\n\n### Running Tests\n```bash\n# Rust tests\ncargo test\n\n# Python tests\npython test_final_performance.py\npython test_h2_accuracy.py\npython test_cocycles.py\n```\n\n### Benchmarking\n```bash\n# Performance benchmarks\npython test_performance.py\npython test_h2_performance.py\n\n# Accuracy verification\npython test_h2_accuracy.py\n```\n\n## \ud83d\udcc8 Optimization Guide\n\n### Choosing the Right Mode\n\n1. **For Research/Verification**: Use `mode=\"accurate\"`\n2. **For General Analysis**: Use `mode=\"balanced\"` (default)\n3. **For Large Datasets**: Use `mode=\"parallel\"` or `mode=\"low_memory\"`\n4. **For Real-time Applications**: Use `mode=\"fast\"`\n\n### Performance Tips\n\n```python\n# \u2705 Good: Reasonable threshold\nresult = compute_ripser_advanced(points, thresh=2.0, maxdim=1)\n\n# \u274c Slow: No threshold with high dimensions \nresult = compute_ripser_advanced(points, thresh=float('inf'), maxdim=3)\n\n# \u2705 Good: Use parallel mode for large datasets\nresult = compute_ripser_advanced(large_points, mode=\"parallel\", num_threads=8)\n\n# \u2705 Good: Limit dimensions when not needed\nresult = compute_ripser_advanced(points, maxdim=1) # Only H0, H1\n```\n\n### Memory Optimization\n\n```python\n# For very large datasets\nresult = compute_ripser_advanced(\n huge_dataset,\n mode=\"low_memory\",\n maxdim=1, # Limit dimensions\n thresh=1.0, # Reasonable threshold\n metric=\"euclidean\" # Efficient metric\n)\n```\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Areas for Contribution\n- Additional distance metrics\n- Higher-dimensional optimizations (H3+)\n- GPU acceleration\n- More comprehensive benchmarks\n- Documentation improvements\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- Original [Ripser](https://github.com/Ripser/ripser) by Ulrich Bauer\n- [Ripser++](https://github.com/simonzhang00/ripser-plusplus) optimizations\n- Rust scientific computing ecosystem\n- PyO3 for excellent Python-Rust integration\n\n## \ud83d\udcda References\n\n1. Bauer, U. (2021). Ripser: efficient computation of Vietoris-Rips persistence barcodes.\n2. Zhang, S., et al. (2020). Ripser++: GPU-accelerated computation of Vietoris-Rips persistence barcodes.\n3. Otter, N., et al. (2017). A roadmap for the computation of persistent homology.\n\n## \ud83d\udd17 Related Projects\n\n- [Ripser](https://github.com/Ripser/ripser) - Original C++ implementation\n- [ripser.py](https://github.com/scikit-tda/ripser.py) - Python bindings for Ripser\n- [GUDHI](https://github.com/GUDHI/gudhi-devel) - Comprehensive TDA library\n- [Dionysus](https://github.com/mrzv/dionysus) - Python TDA library\n\n---\n\n**Made with \u2764\ufe0f in Rust** | **Powered by \ud83d\ude80 Performance**\n",
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