CoreUtilities


NameCoreUtilities JSON
Version 0.0.4 PyPI version JSON
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home_pagehttps://github.com/Ruppert20/CoreUtils-Python
SummaryA comprehensive collection of Python utility functions for data science, file operations, and general-purpose programming
upload_time2025-11-07 20:53:32
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author@Ruppert20
requires_python>=3.13.2
licenseNone
keywords utilities data-science pandas numpy encryption serialization testing helpers
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            # CoreUtils-Python

A comprehensive collection of Python utility functions and modules for data science, file operations, serialization, encryption, and general-purpose programming tasks.

[![Python Version](https://img.shields.io/badge/python-3.13.2+-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Tests](https://img.shields.io/badge/tests-passing-brightgreen.svg)](UNIT_TESTS/)

## Table of Contents

- [Overview](#overview)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Module Documentation](#module-documentation)
  - [Core Utilities](#core-utilities)
  - [Data Processing](#data-processing)
  - [Security & Encryption](#security--encryption)
  - [File Operations](#file-operations)
  - [Testing](#testing)
- [Running Tests](#running-tests)
- [Requirements](#requirements)
- [Contributing](#contributing)
- [License](#license)

## Overview

CoreUtils-Python is a modular collection of well-documented, tested utility functions designed to streamline common programming tasks across data science, system operations, and application development.

**Key Features:**

- 🔧 **Comprehensive Utilities** - Functions, lists, strings, numbers, dictionaries
- 📊 **Data Processing** - pandas, NumPy, Polars, PyArrow integration
- 🔒 **Security** - Encryption, signing, secure serialization, CSV-compatible integrity
- 🧪 **Well Tested** - 418+ unit tests with pytest
- 📝 **Documented** - NumPy-style docstrings throughout
- ⚡ **Performance** - Optimized for large-scale data operations

## Installation

### Basic Installation

```bash
# Clone the repository
git clone https://github.com/Ruppert20/CoreUtils-Python.git
cd CoreUtils-Python

# Install dependencies
pip install -r requirements.txt
```

### Requirements

- Python 3.13.2 or greater
- See [requirements.txt](requirements.txt) for full dependency list

## Quick Start

```python
# Import utilities
from src.generics import notnull, coalesce
from src.lists import chunk_list, flatten_list
from src.strings import convert_identifier_case
from src.numbers import extract_num, isfloat
from src.signature import SignedFile
from datetime import datetime

# Use null checking
if notnull(value):
    process(value)

# Coalesce values
result = coalesce(None, '', default_value)

# Chunk data for batch processing
for chunk in chunk_list(large_list, 100):
    process_batch(chunk)

# Convert naming conventions
camel = convert_identifier_case('user_name', 'camelCase')

# Write signed file with header metadata
header = {"version": "1.0", "created": datetime.now(), "author": "alice"}
SignedFile.write("data.bin", {"key": "value"}, header=header)

# Write CSV with integrity signature (pandas-compatible)
csv_data = b"name,age\nAlice,30\nBob,25\n"
SignedFile.write("data.csv", csv_data, signature_as_comment=True)

# Read back with verification and header
data, meta = SignedFile.read("data.bin", return_header=True)
print(f"Created by {meta['author']} on {meta['created']}")
```

## Module Documentation

### Core Utilities

#### generics.py

Generic utility functions for null handling and object operations.

**Key Functions:**

- `notnull(v)` - Comprehensive null checking (None, empty containers, pd.NA, np.nan)
- `isnull(v)` - Inverse of notnull
- `coalesce(*values)` - Return first non-null value
- `get_name(obj)` - Extract object name

[📝 Code](src/generics.py) | [🧪 Tests](UNIT_TESTS/test_generics.py) | [📖 Documentation](Documentation/generics.md)

---

#### functions.py

Function utilities including dynamic loading, introspection, and debugging.

**Key Functions:**

- `get_func(func_path)` - Dynamically load functions from string paths
- `filter_kwargs(func, kwargs)` - Filter kwargs to match function parameters
- `get_function_signature(func)` - Extract comprehensive function metadata
- `inspect_class(cls)` - Extract class properties and methods
- `is_pickleable(obj)` - Check if object can be pickled

[📝 Code](src/functions.py) | [🧪 Tests](UNIT_TESTS/test_functions.py) | [📖 Documentation](Documentation/functions.md)

---

#### lists.py

List manipulation utilities for chunking, intersection, and flattening.

**Key Functions:**

- `convert_list_to_string(lst, encapsulate=False)` - Convert list to comma-separated string
- `chunk_list(lst, n)` - Split list into equal-sized chunks
- `list_intersection(lst1, lst2)` - Find common elements preserving order
- `flatten_list(nested)` - Recursively flatten nested lists

[📝 Code](src/lists.py) | [🧪 Tests](UNIT_TESTS/test_lists.py) | [📖 Documentation](Documentation/lists.md)

---

#### strings.py

String manipulation including case conversion, cleaning, and parsing.

**Key Functions:**

- `remove_illegal_characters(s, case='snake_case')` - Clean strings for identifiers
- `convert_identifier_case(id, target_format)` - Convert between naming conventions
- `snake_to_camel_case(s)` - Convert snake_case to camelCase
- `camel_to_snake_case(s)` - Convert camelCase to snake_case
- `get_file_name_components(path)` - Parse file paths into components
- `tokenize_id(id_str, token_index)` - Split and extract tokens from IDs

[📝 Code](src/strings.py) | [🧪 Tests](UNIT_TESTS/test_strings.py) | [📖 Documentation](Documentation/strings.md)

---

#### numbers.py

Numerical operations, extraction, and validation.

**Key Functions:**

- `extract_num(input_str, return_pos=0)` - Extract numbers from strings
- `isfloat(value)` - Check if value can be converted to float
- `convert_to_comma_seperated_integer_list(val)` - Convert to comma-separated integers

[📝 Code](src/numbers.py) | [🧪 Tests](UNIT_TESTS/test_numbers.py) | [📖 Documentation](Documentation/numbers.md)

---

#### dictionaries.py

Dictionary utilities for pandas aggregation operations.

**Key Functions:**

- `create_aggregation_dict(col_action_dict, start_col, end_col)` - Create pandas groupby aggregation dictionaries

[📝 Code](src/dictionaries.py) | [🧪 Tests](UNIT_TESTS/test_dictionaries.py) | [📖 Documentation](Documentation/dictionaries.md)

---

#### git.py

Git repository metadata extraction.

**Key Functions:**

- `get_git_metadata()` - Extract comprehensive git repository information

[📝 Code](src/git.py) | [📖 Documentation](Documentation/git.md)

---

### Data Processing

#### core_types.py

Cross-library type classification and detection system.

**Key Features:**

- `CoreDataType` enum - Universal type classification
- Type detection from objects and strings
- Support for pandas, NumPy, Polars, PyArrow
- String representation parsing (JSON, XML, UUID, dates)

[📝 Code](src/core_types.py) | [📖 Documentation](Documentation/core_types.md)

---

#### iterables.py

Memory profiling and object analysis utilities.

**Key Functions:**

- `deep_stats(obj)` - Calculate deep memory size with cycle detection
- `find_large_objects(obj, threshold_kb)` - Identify memory-intensive objects

[📝 Code](src/iterables.py) | [📖 Documentation](Documentation/iterables.md)

---

#### serialization.py

Extended serialization with multi-format support (JSON, YAML, CBOR, Pickle).

**Key Features:**

- XSer class - Destination-aware serialization
- Automatic fallback chain: Structured → CBOR → Pickle
- NumPy array support
- HDF5 and Parquet metadata support

[📝 Code](src/serialization.py) | [📖 Documentation](Documentation/serialization.md)

---

#### enhanced_logging.py

Advanced logging with emoji support, progress bars, and structured output.

**Key Features:**

- Enhanced logger with emoji integration
- Progress bar support
- Structured logging for metrics
- Context managers for scoped logging

[📝 Code](src/enhanced_logging.py) | [📖 Documentation](Documentation/enhanced_logging.md)

---

#### parrallelization.py

Parallel processing utilities with comprehensive error handling.

**Key Features:**

- ParallelProcessor class
- Support for serial, thread-based, and process-based execution
- Metrics collection and reporting
- Integration with enhanced logging

[📝 Code](src/parrallelization.py) | [📖 Documentation](Documentation/parrallelization.md)

---

### Security & Encryption

#### encrypt.py

Encryption utilities using Fernet symmetric encryption.

**Key Features:**

- Encryptor class for data encryption/decryption
- CryptoYAML for encrypted YAML configuration files
- Key generation and management

[📝 Code](src/encrypt.py) | [🧪 Tests](UNIT_TESTS/test_encrypt.py) | [📖 Documentation](Documentation/encrypt.md)

---

#### signature.py

Atomic file writing with cryptographic integrity verification, encryption, and metadata support.

**Key Features:**

- SignedFile class for signed file operations
- SHA-256/HMAC-SHA256 signatures with integrity verification
- Optional Fernet encryption with authenticated HMAC
- **Python object serialization** (via XSer) - auto-serializes dicts, lists, numpy, datetime
- **Optional header metadata** - Store version info, timestamps, and structured metadata
- **CSV-compatible commented signatures** - Write `#` comment signatures for pandas/Excel compatibility
- Atomic writes with platform-independent fsync
- Chunked reading for large files

[📝 Code](src/signature.py) | [🧪 Tests](UNIT_TESTS/test_signature.py) | [📖 Documentation](Documentation/signature.md)

---

### File Operations

#### search.py

Flexible file search utilities with pattern matching and filtering.

**Key Features:**

- FileSearcher class for advanced file searching
- Pattern matching with regex support
- File type filtering and exclusion patterns
- Recursive and non-recursive search modes

[📝 Code](src/search.py) | [🧪 Tests](UNIT_TESTS/test_search.py) | [📖 Documentation](Documentation/search.md)

---

### Testing

#### debugging.py

Testing utilities for random data generation.

**Key Functions:**

- `generate_random_sequence(dtype, n, percent_null, seed)` - Generate deterministic test data
- Random generators for all common data types (TEXT, UUID, INTEGER, FLOAT, DATE, JSON, XML, etc.)
- `debug_print(*args)` - Print debug output with visual separators

[📝 Code](src/debugging.py) | [🧪 Tests](UNIT_TESTS/test_debugging.py) | [📖 Documentation](Documentation/debugging.md)

---

## Running Tests

All tests use pytest and follow the `test_*.py` naming convention.

### Run All Tests

```bash
cd UNIT_TESTS
python run_all_tests.py
```

### Run with Verbose Output

```bash
python run_all_tests.py -v
```

### Run with Coverage

```bash
python run_all_tests.py --coverage
```

### Run Specific Tests

```bash
# Run tests matching a pattern
python run_all_tests.py -k test_generics

# Run a specific test file
pytest test_functions.py -v

# Run a specific test class
pytest test_functions.py::TestGetFunc -v

# Run a specific test method
pytest test_functions.py::TestGetFunc::test_get_builtin_function -v
```

### Test Statistics

- **Total Tests:** 223+
- **Coverage:** Comprehensive coverage of public APIs
- **Frameworks:** pytest (supports both pytest and unittest styles)
- **Status:** ✅ All tests passing

[📖 View Test Documentation](UNIT_TESTS/README.md) | [📊 View Test Summary](UNIT_TESTS/TEST_SUMMARY.md)

---

## Requirements

### Core Dependencies

```
numpy>=2.3.2          # Numerical computing
pandas>=2.2.3         # Data manipulation
```

### Serialization

```
cbor2>=5.7.0          # CBOR encoding
PyYAML>=6.0.2         # YAML support
```

### Security

```
cryptography>=45.0.7  # Encryption and signing
```

### Testing

```
pytest>=8.4.2         # Test framework
pytest-cov>=4.1.0     # Coverage plugin
```

[📖 View Full Requirements](requirements.txt)

---

## Project Structure

```
CoreUtils-Python/
├── src/                          # Source modules
│   ├── core_types.py            # Type classification system
│   ├── debugging.py             # Testing and debugging utilities
│   ├── dictionaries.py          # Dictionary operations
│   ├── encrypt.py               # Encryption utilities
│   ├── encrypted_signature.py  # Combined encryption + signing
│   ├── enhanced_logging.py     # Advanced logging
│   ├── functions.py            # Function utilities
│   ├── generics.py             # Generic utilities
│   ├── git.py                  # Git metadata
│   ├── iterables.py            # Memory profiling
│   ├── lists.py                # List operations
│   ├── numbers.py              # Numerical utilities
│   ├── parrallelization.py     # Parallel processing
│   ├── search.py               # Search utilities
│   ├── serialization.py        # Extended serialization
│   ├── signature.py            # File signing
│   └── strings.py              # String manipulation
│
├── UNIT_TESTS/                  # Test suite
│   ├── test_*.py               # Test modules (223+ tests)
│   ├── run_all_tests.py        # Test runner
│   ├── README.md               # Test documentation
│   └── TEST_SUMMARY.md         # Test results summary
│
├── requirements.txt             # Project dependencies
└── README.md                    # This file
```

---

## Contributing

Contributions are welcome! Please follow these guidelines:

1. **Fork the repository**
2. **Create a feature branch** (`git checkout -b feature/amazing-feature`)
3. **Write tests** for new functionality
4. **Ensure all tests pass** (`python run_all_tests.py`)
5. **Follow existing code style** (NumPy-style docstrings)
6. **Commit changes** (`git commit -m 'Add amazing feature'`)
7. **Push to branch** (`git push origin feature/amazing-feature`)
8. **Open a Pull Request**

### Code Style

- NumPy-style docstrings for all functions and classes
- Type hints where appropriate
- Comprehensive test coverage
- Clear, descriptive variable names

---

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

## Author

**@Ruppert20**

---

## AI Authorship Disclaimer

This package was developed with the assistance of LLM-based coding tools (Claude Code by Anthropic). AI tools were used for the following activities:

- **Code authorship** - Implementation of utilities, functions, and classes
- **Test development** - Creation of comprehensive unit tests
- **Documentation** - Generation of NumPy-style docstrings and README content
- **Code review** - Identification of bugs, edge cases, and improvements

Users should evaluate the code for their specific use cases and report any issues through the GitHub issue tracker.

---

## Acknowledgments

- Built with modern Python 3.13.2+
- Integrates with pandas, NumPy, Polars, and PyArrow
- Inspired by the need for clean, reusable utility functions
- Comprehensive testing ensures reliability
- Developed with assistance from Claude Code (Anthropic)

---

## Quick Links

- [📖 Full Documentation](src/)
- [🧪 Test Suite](UNIT_TESTS/)
- [📊 Test Results](UNIT_TESTS/TEST_SUMMARY.md)
- [📋 Requirements](requirements.txt)
- [🐛 Issue Tracker](https://github.com/Ruppert20/CoreUtils-Python/issues)

---

**Made with ❤️ for the Python community**

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    "description": "# CoreUtils-Python\n\nA comprehensive collection of Python utility functions and modules for data science, file operations, serialization, encryption, and general-purpose programming tasks.\n\n[![Python Version](https://img.shields.io/badge/python-3.13.2+-blue.svg)](https://www.python.org/downloads/)\n[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n[![Tests](https://img.shields.io/badge/tests-passing-brightgreen.svg)](UNIT_TESTS/)\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Module Documentation](#module-documentation)\n  - [Core Utilities](#core-utilities)\n  - [Data Processing](#data-processing)\n  - [Security & Encryption](#security--encryption)\n  - [File Operations](#file-operations)\n  - [Testing](#testing)\n- [Running Tests](#running-tests)\n- [Requirements](#requirements)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Overview\n\nCoreUtils-Python is a modular collection of well-documented, tested utility functions designed to streamline common programming tasks across data science, system operations, and application development.\n\n**Key Features:**\n\n- \ud83d\udd27 **Comprehensive Utilities** - Functions, lists, strings, numbers, dictionaries\n- \ud83d\udcca **Data Processing** - pandas, NumPy, Polars, PyArrow integration\n- \ud83d\udd12 **Security** - Encryption, signing, secure serialization, CSV-compatible integrity\n- \ud83e\uddea **Well Tested** - 418+ unit tests with pytest\n- \ud83d\udcdd **Documented** - NumPy-style docstrings throughout\n- \u26a1 **Performance** - Optimized for large-scale data operations\n\n## Installation\n\n### Basic Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/Ruppert20/CoreUtils-Python.git\ncd CoreUtils-Python\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Requirements\n\n- Python 3.13.2 or greater\n- See [requirements.txt](requirements.txt) for full dependency list\n\n## Quick Start\n\n```python\n# Import utilities\nfrom src.generics import notnull, coalesce\nfrom src.lists import chunk_list, flatten_list\nfrom src.strings import convert_identifier_case\nfrom src.numbers import extract_num, isfloat\nfrom src.signature import SignedFile\nfrom datetime import datetime\n\n# Use null checking\nif notnull(value):\n    process(value)\n\n# Coalesce values\nresult = coalesce(None, '', default_value)\n\n# Chunk data for batch processing\nfor chunk in chunk_list(large_list, 100):\n    process_batch(chunk)\n\n# Convert naming conventions\ncamel = convert_identifier_case('user_name', 'camelCase')\n\n# Write signed file with header metadata\nheader = {\"version\": \"1.0\", \"created\": datetime.now(), \"author\": \"alice\"}\nSignedFile.write(\"data.bin\", {\"key\": \"value\"}, header=header)\n\n# Write CSV with integrity signature (pandas-compatible)\ncsv_data = b\"name,age\\nAlice,30\\nBob,25\\n\"\nSignedFile.write(\"data.csv\", csv_data, signature_as_comment=True)\n\n# Read back with verification and header\ndata, meta = SignedFile.read(\"data.bin\", return_header=True)\nprint(f\"Created by {meta['author']} on {meta['created']}\")\n```\n\n## Module Documentation\n\n### Core Utilities\n\n#### generics.py\n\nGeneric utility functions for null handling and object operations.\n\n**Key Functions:**\n\n- `notnull(v)` - Comprehensive null checking (None, empty containers, pd.NA, np.nan)\n- `isnull(v)` - Inverse of notnull\n- `coalesce(*values)` - Return first non-null value\n- `get_name(obj)` - Extract object name\n\n[\ud83d\udcdd Code](src/generics.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_generics.py) | [\ud83d\udcd6 Documentation](Documentation/generics.md)\n\n---\n\n#### functions.py\n\nFunction utilities including dynamic loading, introspection, and debugging.\n\n**Key Functions:**\n\n- `get_func(func_path)` - Dynamically load functions from string paths\n- `filter_kwargs(func, kwargs)` - Filter kwargs to match function parameters\n- `get_function_signature(func)` - Extract comprehensive function metadata\n- `inspect_class(cls)` - Extract class properties and methods\n- `is_pickleable(obj)` - Check if object can be pickled\n\n[\ud83d\udcdd Code](src/functions.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_functions.py) | [\ud83d\udcd6 Documentation](Documentation/functions.md)\n\n---\n\n#### lists.py\n\nList manipulation utilities for chunking, intersection, and flattening.\n\n**Key Functions:**\n\n- `convert_list_to_string(lst, encapsulate=False)` - Convert list to comma-separated string\n- `chunk_list(lst, n)` - Split list into equal-sized chunks\n- `list_intersection(lst1, lst2)` - Find common elements preserving order\n- `flatten_list(nested)` - Recursively flatten nested lists\n\n[\ud83d\udcdd Code](src/lists.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_lists.py) | [\ud83d\udcd6 Documentation](Documentation/lists.md)\n\n---\n\n#### strings.py\n\nString manipulation including case conversion, cleaning, and parsing.\n\n**Key Functions:**\n\n- `remove_illegal_characters(s, case='snake_case')` - Clean strings for identifiers\n- `convert_identifier_case(id, target_format)` - Convert between naming conventions\n- `snake_to_camel_case(s)` - Convert snake_case to camelCase\n- `camel_to_snake_case(s)` - Convert camelCase to snake_case\n- `get_file_name_components(path)` - Parse file paths into components\n- `tokenize_id(id_str, token_index)` - Split and extract tokens from IDs\n\n[\ud83d\udcdd Code](src/strings.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_strings.py) | [\ud83d\udcd6 Documentation](Documentation/strings.md)\n\n---\n\n#### numbers.py\n\nNumerical operations, extraction, and validation.\n\n**Key Functions:**\n\n- `extract_num(input_str, return_pos=0)` - Extract numbers from strings\n- `isfloat(value)` - Check if value can be converted to float\n- `convert_to_comma_seperated_integer_list(val)` - Convert to comma-separated integers\n\n[\ud83d\udcdd Code](src/numbers.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_numbers.py) | [\ud83d\udcd6 Documentation](Documentation/numbers.md)\n\n---\n\n#### dictionaries.py\n\nDictionary utilities for pandas aggregation operations.\n\n**Key Functions:**\n\n- `create_aggregation_dict(col_action_dict, start_col, end_col)` - Create pandas groupby aggregation dictionaries\n\n[\ud83d\udcdd Code](src/dictionaries.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_dictionaries.py) | [\ud83d\udcd6 Documentation](Documentation/dictionaries.md)\n\n---\n\n#### git.py\n\nGit repository metadata extraction.\n\n**Key Functions:**\n\n- `get_git_metadata()` - Extract comprehensive git repository information\n\n[\ud83d\udcdd Code](src/git.py) | [\ud83d\udcd6 Documentation](Documentation/git.md)\n\n---\n\n### Data Processing\n\n#### core_types.py\n\nCross-library type classification and detection system.\n\n**Key Features:**\n\n- `CoreDataType` enum - Universal type classification\n- Type detection from objects and strings\n- Support for pandas, NumPy, Polars, PyArrow\n- String representation parsing (JSON, XML, UUID, dates)\n\n[\ud83d\udcdd Code](src/core_types.py) | [\ud83d\udcd6 Documentation](Documentation/core_types.md)\n\n---\n\n#### iterables.py\n\nMemory profiling and object analysis utilities.\n\n**Key Functions:**\n\n- `deep_stats(obj)` - Calculate deep memory size with cycle detection\n- `find_large_objects(obj, threshold_kb)` - Identify memory-intensive objects\n\n[\ud83d\udcdd Code](src/iterables.py) | [\ud83d\udcd6 Documentation](Documentation/iterables.md)\n\n---\n\n#### serialization.py\n\nExtended serialization with multi-format support (JSON, YAML, CBOR, Pickle).\n\n**Key Features:**\n\n- XSer class - Destination-aware serialization\n- Automatic fallback chain: Structured \u2192 CBOR \u2192 Pickle\n- NumPy array support\n- HDF5 and Parquet metadata support\n\n[\ud83d\udcdd Code](src/serialization.py) | [\ud83d\udcd6 Documentation](Documentation/serialization.md)\n\n---\n\n#### enhanced_logging.py\n\nAdvanced logging with emoji support, progress bars, and structured output.\n\n**Key Features:**\n\n- Enhanced logger with emoji integration\n- Progress bar support\n- Structured logging for metrics\n- Context managers for scoped logging\n\n[\ud83d\udcdd Code](src/enhanced_logging.py) | [\ud83d\udcd6 Documentation](Documentation/enhanced_logging.md)\n\n---\n\n#### parrallelization.py\n\nParallel processing utilities with comprehensive error handling.\n\n**Key Features:**\n\n- ParallelProcessor class\n- Support for serial, thread-based, and process-based execution\n- Metrics collection and reporting\n- Integration with enhanced logging\n\n[\ud83d\udcdd Code](src/parrallelization.py) | [\ud83d\udcd6 Documentation](Documentation/parrallelization.md)\n\n---\n\n### Security & Encryption\n\n#### encrypt.py\n\nEncryption utilities using Fernet symmetric encryption.\n\n**Key Features:**\n\n- Encryptor class for data encryption/decryption\n- CryptoYAML for encrypted YAML configuration files\n- Key generation and management\n\n[\ud83d\udcdd Code](src/encrypt.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_encrypt.py) | [\ud83d\udcd6 Documentation](Documentation/encrypt.md)\n\n---\n\n#### signature.py\n\nAtomic file writing with cryptographic integrity verification, encryption, and metadata support.\n\n**Key Features:**\n\n- SignedFile class for signed file operations\n- SHA-256/HMAC-SHA256 signatures with integrity verification\n- Optional Fernet encryption with authenticated HMAC\n- **Python object serialization** (via XSer) - auto-serializes dicts, lists, numpy, datetime\n- **Optional header metadata** - Store version info, timestamps, and structured metadata\n- **CSV-compatible commented signatures** - Write `#` comment signatures for pandas/Excel compatibility\n- Atomic writes with platform-independent fsync\n- Chunked reading for large files\n\n[\ud83d\udcdd Code](src/signature.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_signature.py) | [\ud83d\udcd6 Documentation](Documentation/signature.md)\n\n---\n\n### File Operations\n\n#### search.py\n\nFlexible file search utilities with pattern matching and filtering.\n\n**Key Features:**\n\n- FileSearcher class for advanced file searching\n- Pattern matching with regex support\n- File type filtering and exclusion patterns\n- Recursive and non-recursive search modes\n\n[\ud83d\udcdd Code](src/search.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_search.py) | [\ud83d\udcd6 Documentation](Documentation/search.md)\n\n---\n\n### Testing\n\n#### debugging.py\n\nTesting utilities for random data generation.\n\n**Key Functions:**\n\n- `generate_random_sequence(dtype, n, percent_null, seed)` - Generate deterministic test data\n- Random generators for all common data types (TEXT, UUID, INTEGER, FLOAT, DATE, JSON, XML, etc.)\n- `debug_print(*args)` - Print debug output with visual separators\n\n[\ud83d\udcdd Code](src/debugging.py) | [\ud83e\uddea Tests](UNIT_TESTS/test_debugging.py) | [\ud83d\udcd6 Documentation](Documentation/debugging.md)\n\n---\n\n## Running Tests\n\nAll tests use pytest and follow the `test_*.py` naming convention.\n\n### Run All Tests\n\n```bash\ncd UNIT_TESTS\npython run_all_tests.py\n```\n\n### Run with Verbose Output\n\n```bash\npython run_all_tests.py -v\n```\n\n### Run with Coverage\n\n```bash\npython run_all_tests.py --coverage\n```\n\n### Run Specific Tests\n\n```bash\n# Run tests matching a pattern\npython run_all_tests.py -k test_generics\n\n# Run a specific test file\npytest test_functions.py -v\n\n# Run a specific test class\npytest test_functions.py::TestGetFunc -v\n\n# Run a specific test method\npytest test_functions.py::TestGetFunc::test_get_builtin_function -v\n```\n\n### Test Statistics\n\n- **Total Tests:** 223+\n- **Coverage:** Comprehensive coverage of public APIs\n- **Frameworks:** pytest (supports both pytest and unittest styles)\n- **Status:** \u2705 All tests passing\n\n[\ud83d\udcd6 View Test Documentation](UNIT_TESTS/README.md) | [\ud83d\udcca View Test Summary](UNIT_TESTS/TEST_SUMMARY.md)\n\n---\n\n## Requirements\n\n### Core Dependencies\n\n```\nnumpy>=2.3.2          # Numerical computing\npandas>=2.2.3         # Data manipulation\n```\n\n### Serialization\n\n```\ncbor2>=5.7.0          # CBOR encoding\nPyYAML>=6.0.2         # YAML support\n```\n\n### Security\n\n```\ncryptography>=45.0.7  # Encryption and signing\n```\n\n### Testing\n\n```\npytest>=8.4.2         # Test framework\npytest-cov>=4.1.0     # Coverage plugin\n```\n\n[\ud83d\udcd6 View Full Requirements](requirements.txt)\n\n---\n\n## Project Structure\n\n```\nCoreUtils-Python/\n\u251c\u2500\u2500 src/                          # Source modules\n\u2502   \u251c\u2500\u2500 core_types.py            # Type classification system\n\u2502   \u251c\u2500\u2500 debugging.py             # Testing and debugging utilities\n\u2502   \u251c\u2500\u2500 dictionaries.py          # Dictionary operations\n\u2502   \u251c\u2500\u2500 encrypt.py               # Encryption utilities\n\u2502   \u251c\u2500\u2500 encrypted_signature.py  # Combined encryption + signing\n\u2502   \u251c\u2500\u2500 enhanced_logging.py     # Advanced logging\n\u2502   \u251c\u2500\u2500 functions.py            # Function utilities\n\u2502   \u251c\u2500\u2500 generics.py             # Generic utilities\n\u2502   \u251c\u2500\u2500 git.py                  # Git metadata\n\u2502   \u251c\u2500\u2500 iterables.py            # Memory profiling\n\u2502   \u251c\u2500\u2500 lists.py                # List operations\n\u2502   \u251c\u2500\u2500 numbers.py              # Numerical utilities\n\u2502   \u251c\u2500\u2500 parrallelization.py     # Parallel processing\n\u2502   \u251c\u2500\u2500 search.py               # Search utilities\n\u2502   \u251c\u2500\u2500 serialization.py        # Extended serialization\n\u2502   \u251c\u2500\u2500 signature.py            # File signing\n\u2502   \u2514\u2500\u2500 strings.py              # String manipulation\n\u2502\n\u251c\u2500\u2500 UNIT_TESTS/                  # Test suite\n\u2502   \u251c\u2500\u2500 test_*.py               # Test modules (223+ tests)\n\u2502   \u251c\u2500\u2500 run_all_tests.py        # Test runner\n\u2502   \u251c\u2500\u2500 README.md               # Test documentation\n\u2502   \u2514\u2500\u2500 TEST_SUMMARY.md         # Test results summary\n\u2502\n\u251c\u2500\u2500 requirements.txt             # Project dependencies\n\u2514\u2500\u2500 README.md                    # This file\n```\n\n---\n\n## Contributing\n\nContributions are welcome! Please follow these guidelines:\n\n1. **Fork the repository**\n2. **Create a feature branch** (`git checkout -b feature/amazing-feature`)\n3. **Write tests** for new functionality\n4. **Ensure all tests pass** (`python run_all_tests.py`)\n5. **Follow existing code style** (NumPy-style docstrings)\n6. **Commit changes** (`git commit -m 'Add amazing feature'`)\n7. **Push to branch** (`git push origin feature/amazing-feature`)\n8. **Open a Pull Request**\n\n### Code Style\n\n- NumPy-style docstrings for all functions and classes\n- Type hints where appropriate\n- Comprehensive test coverage\n- Clear, descriptive variable names\n\n---\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\n## Author\n\n**@Ruppert20**\n\n---\n\n## AI Authorship Disclaimer\n\nThis package was developed with the assistance of LLM-based coding tools (Claude Code by Anthropic). AI tools were used for the following activities:\n\n- **Code authorship** - Implementation of utilities, functions, and classes\n- **Test development** - Creation of comprehensive unit tests\n- **Documentation** - Generation of NumPy-style docstrings and README content\n- **Code review** - Identification of bugs, edge cases, and improvements\n\nUsers should evaluate the code for their specific use cases and report any issues through the GitHub issue tracker.\n\n---\n\n## Acknowledgments\n\n- Built with modern Python 3.13.2+\n- Integrates with pandas, NumPy, Polars, and PyArrow\n- Inspired by the need for clean, reusable utility functions\n- Comprehensive testing ensures reliability\n- Developed with assistance from Claude Code (Anthropic)\n\n---\n\n## Quick Links\n\n- [\ud83d\udcd6 Full Documentation](src/)\n- [\ud83e\uddea Test Suite](UNIT_TESTS/)\n- [\ud83d\udcca Test Results](UNIT_TESTS/TEST_SUMMARY.md)\n- [\ud83d\udccb Requirements](requirements.txt)\n- [\ud83d\udc1b Issue Tracker](https://github.com/Ruppert20/CoreUtils-Python/issues)\n\n---\n\n**Made with \u2764\ufe0f for the Python community**\n",
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