Metadata-Version: 2.4
Name: fm-index
Version: 2.2.0
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Indexing
Classifier: Typing :: Typed
Requires-Dist: pdoc>=16.0 ; extra == 'dev'
Requires-Dist: ruff>=0.14 ; extra == 'dev'
Requires-Dist: numpy>=2.0.2 ; extra == 'test'
Requires-Dist: pytest>=7.0 ; extra == 'test'
Requires-Dist: pytest-benchmark>=4.0 ; extra == 'test'
Provides-Extra: dev
Provides-Extra: test
License-File: LICENSE
Summary: High-performance FM-index powered by Rust, enabling fast substring search and count/locate queries.
Keywords: fm-index,full-text index,compressed index,substring search,pattern matching,succinct,information retrieval
Author: Koki Watanabe
Requires-Python: >=3.9
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: documentation, https://math-hiyoko.github.io/fm-index
Project-URL: homepage, https://github.com/math-hiyoko/fm-index
Project-URL: repository, https://github.com/math-hiyoko/fm-index

# FM Index

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High-performance FM-index implementation powered by Rust,  
designed for fast substring search on large texts and collections  

- PyPI: https://pypi.org/project/fm-index
- Document: https://math-hiyoko.github.io/fm-index
- Repository: https://github.com/math-hiyoko/fm-index

## Features:
- Fast count / locate substring queries
- Data-parallel optimizations across index construction and queries
- Supports single text and multiple documents
- Pickle serialization support for efficient index persistence
- Safe Rust (no unsafe)

## Installation
```bash
pip install fm-index
```

## FMIndex (Single Document)
### What is FMIndex?
FMIndex builds a compressed index over a single string,  
allowing fast substring search without scanning the original data.  

### Construction Complexity
- Time / Space: `O(|data|)`  

#### Example

```python
from fm_index import FMIndex

genome = "ACGTACGTTGACCTGACTGACTGACTGACGATCGATCGATCGATCGATCG"
fm = FMIndex(data=genome)
```

### Count Substring Occurrences
Counts how many times a pattern appears.  
Time complexity is independent of data size.  

```python
fm.count(pattern="GACTGACT")
# 2
```

### Locate Substring Positions
Returns all starting offsets where the pattern occurs.  

To improve throughput for high-frequency patterns,  
FMIndex applies parallel execution to parts of the locate pipeline.

```python
fm.locate(pattern="GACTGACT")
# [18, 14]
```

### Iterative Locate (Streaming)
For large result sets, iter_locate provides a memory-efficient  
iterator interface that yields positions lazily.

```python
for pos in fm.iter_locate(pattern="GACTGACT"):
    print(pos)
# 18
# 14
```

- Same results as locate
- Does not allocate a result list
- Suitable for streaming and early termination

## MultiFMIndex (Multiple Documents)
MultiFMIndex extends FMIndex to support multiple documents  
while keeping query time independent of corpus size  

Query processing is internally parallelized where possible,  
making multi-document search efficient in practice.  

### Construction Complexity
- Time / Space: `O(|''.join(data)| + len(data) log (len(data)))`   

```python
from fm_index import MultiFMIndex

documents = [
    "政府はAI研究の支援を強化すると発表した。",
    "政府は新たなデータ活用方針を発表した。",
    "政府はサイバーセキュリティ対策を発表した。",
    "専門家はAI検索技術の進化に注目している。",
    "研究者は高速な検索アルゴリズムに注目している。",
    "オープンソース界隈では全文検索ライブラリに注目している。"
]

mfm = MultiFMIndex(data=documents)
```

### Count Across All Documents
```python
mfm.count_all(pattern="検索")
# 3
```

### Count Per Document
```python
mfm.count(pattern="検索")
# {3: 1, 4: 1, 5: 1}

# Count within a specific document
mfm.count(pattern="検索", doc_id=3)
# 1
```

### Locate Per Document
```python
mfm.locate(pattern="検索")
# {5: [13], 4: [7], 3: [6]}

# Locate within a specific document
mfm.locate(pattern="検索", doc_id=3)
# [6]
```

### Iterative Locate (Streaming)
```python
# Iterate across all documents
for doc_id, pos in mfm.iter_locate(pattern="検索"):
    print(doc_id, pos)
# 4 7
# 5 13
# 3 6

# Iterate within a specific document
for doc_id, pos in mfm.iter_locate(pattern="検索", doc_id=3):
    print(doc_id, pos)
# 3 6
```

### Prefix / Suffix Search
```python
mfm.startswith(prefix="政府は")
mfm.endswith(suffix="注目している。")
```

## Serialization (Pickle Support)

Both `FMIndex` and `MultiFMIndex` support Python's pickle protocol,
allowing you to save and load pre-built indices efficiently.

The internal data structures are serialized directly in binary format,
making deserialization much faster than rebuilding the index from scratch.

### Save Index to File
```python
import pickle
from fm_index import FMIndex, MultiFMIndex

# Build and save FMIndex
fm = FMIndex("large genome sequence..." * 10000)
with open("genome.fmindex", "wb") as f:
    pickle.dump(fm, f)

# Build and save MultiFMIndex
mfm = MultiFMIndex(["document1", "document2", ...])
with open("documents.mfmindex", "wb") as f:
    pickle.dump(mfm, f)
```

### Load Index from File
```python
# Load FMIndex
with open("genome.fmindex", "rb") as f:
    fm = pickle.load(f)

# Load MultiFMIndex
with open("documents.mfmindex", "rb") as f:
    mfm = pickle.load(f)

# Use immediately without reconstruction
result = fm.locate("ACGT")
```

This is particularly useful when:
- Working with large datasets where index construction is expensive
- Deploying pre-built indices in production environments
- Sharing indices across different processes or machines

## Safety
- Powered by safe Rust
- Memory-safe by design

## Development & Testing

### Run Tests

```bash
pip install -e ".[test]"
cargo test --all --release
pytest
```

## Formating
```bash
pip install -e ".[dev]"
cargo fmt --all
cargo clippy --all-targets --all-features
ruff format
```

## Generating Docs
```bash
pdoc fm_index \
      --output-directory docs \
      --no-search \
      --no-show-source \
      --docformat markdown \
      --footer-text "© 2026 Koki Watanabe"
```

## References

- P. Ferragina and G. Manzini,  
  Opportunistic data structures with applications,  
  Proceedings 41st Annual Symposium on Foundations of Computer Science,  
  Redondo Beach, CA, USA,  
  2000,  
  pp. 390-398,  
  https://doi.org/10.1109/SFCS.2000.892127.  
- FM Indexを使うとWikipedia全文検索みたいなことができる  
  https://qiita.com/math-hiyoko/items/10d50527504914e00388  

