fastpy-rs 0.0.6

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fastpy-rs

FastPy-RS is a high-performance Python library that provides optimized implementations of common functions using Rust. It's designed to be a collection of frequently used functions where performance matters, offering significant speed improvements over pure Python implementations.

Features

• Blazing Fast: Leverages Rust's performance to provide significant speedups
• Easy to Use: Simple Python interface
• Secure: Written in Rust, ensuring high security

Currently Available Functions

1. Token Frequency Counter

   • Counts word frequencies in a text (case-insensitive)
   • Example: ai.token_frequency("Hello hello world! This is a test. Test passed!") returns {'hello': 2, 'world': 1, 'this': 1, 'is': 1, 'a': 1, 'test': 2, 'passed': 1}

2. Base64 Encoding

   • Encodes binary data to base64 string
   • Example: datatools.base64_encode(b"hello") returns b'aGVsbG8='

3. SHA-256 Hashing

   • Computes SHA-256 hash of binary or string data
   • Example: crypto.sha256(b"hello") returns "2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824"
   • Also available as sha256_str("hello") for string inputs

Installation

pip install fastpy-rs

Or from source:

pip install maturin
maturin develop

Usage

from fastpy_rs import ai

# Count word frequencies in a text
text = "Hello hello world! This is a test. Test passed!"
frequencies = ai.token_frequency(text)
print(frequencies)
# Output: {'hello': 2, 'world': 1, 'this': 1, 'is': 1, 'a': 1, 'test': 2, 'passed': 1}

Performance

Token Frequency Counter

Performance comparison between the Rust implementation and a Python implementation using spaCy, a popular industrial-strength NLP library:

Performance Test Results (average time per call):
Rust implementation: 0.000022 seconds
Python implementation: 0.014737 seconds
Speedup: 681.94x

The test compares the tokenization and frequency counting of a text sample. The Rust implementation shows a significant performance improvement over the Python/spaCy implementation, being approximately 682x faster in our tests. Note that spaCy provides additional NLP features beyond simple tokenization, while our Rust implementation is optimized specifically for the token frequency counting task.

Base64 Encoding

Performance comparison between the Rust implementation and Python's built-in base64 module:

Base64 Performance Test Results (average time per call):
Rust implementation: 0.00000487 seconds
Python implementation: 0.00003055 seconds
Speedup: 6.27x

Our Rust implementation now outperforms Python's built-in base64 module, being approximately 6.27x faster.

SHA-256 Hashing

Performance comparison between the Rust implementation and Python's hashlib module:

SHA-256 Performance Test Results (average time per call):
Rust implementation: 0.005949 seconds
Python implementation: 0.006331 seconds
Speedup: 1.06x

Our Rust implementation is slightly faster than Python's built-in hashlib, showing consistent performance improvements.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Setup

1. Install Rust: https://www.rust-lang.org/tools/install
2. Install maturin: pip install maturin
3. Clone the repository
4. Build in development mode: maturin develop
5. Run tests: pytest tests/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Roadmap

📦 JSON / Data

1. parse_json(string) -> dict
2. serialize_json(obj, pretty=False) -> str
3. parse_large_json_file(filepath) -> dict
4. extract_json_field(json_str, path: str) -> Any (JSONPath-like)
5. compare_json(json1, json2) -> bool
6. validate_json(schema: dict, data: dict) -> bool
7. minify_json(json_str: str) -> str
8. pretty_print_json(json_str: str) -> str
9. merge_json_objects(json1: dict, json2: dict) -> dict
10. flatten_json(nested_dict: dict) -> dict

---

🌐 HTTP / Networking

11. http_get(url, headers=None, timeout=10) -> str
12. http_post(url, data, headers=None) -> str
13. http_download(url, dest_path)
14. http_request(method, url, headers, body) -> (code, body)
15. fetch_json(url) -> dict
16. http_head(url) -> headers
17. http_retry_request(...)
18. http_stream_lines(url) -> Iterator[str]
19. http_check_redirect_chain(url) -> List[str]
20. http_measure_latency(url) -> float

---

🔐 Hashing / Crypto

21. sha256(data: bytes | str) -> str
22. md5(data: bytes | str) -> str
23. hmac_sha256(key, message) -> str
24. blake3_hash(data) -> str
25. is_valid_sha256(hexstr: str) -> bool
26. secure_compare(a: str, b: str) -> bool

---

🧮 Data Processing / Encoding

27. base64_encode(data: bytes) -> str
28. base64_decode(data: str) -> bytes
29. gzip_compress(data: bytes) -> bytes
30. gzip_decompress(data: bytes) -> bytes
31. url_encode(str) -> str
32. url_decode(str) -> str
33. csv_parse(csv_string) -> List[Dict]
34. csv_serialize(data: List[Dict]) -> str
35. bloom_filter_create(size: int, hash_funcs: int)
36. bloom_filter_check(item: str) -> bool

---

⏱️ Performance / Utils

37. benchmark_fn(callable, *args, **kwargs) -> float
38. parallel_map(func, list, threads=4) -> list
39. fast_deduplication(list) -> list
40. sort_large_list(list) -> list
41. fuzzy_string_match(a, b) -> score
42. levenshtein_distance(a, b) -> int
43. tokenize_text(text: str) -> List[str]
44. fast_word_count(text: str) -> Dict[str, int]
45. regex_search(pattern, text) -> List[str]
46. regex_replace(pattern, repl, text) -> str

---

🧠 AI/ML Preprocessing

47. normalize_vector(vec: List[float]) -> List[float]
48. cosine_similarity(vec1, vec2) -> float
49. token_frequency(text: str) -> Dict[str, int]
50. encode_text_fast(text: str) -> List[int]