pybiographical 0.2.1

Python library for working with biographical data points for persons and locations

PyBiographical

Python library for biographical data management

Utilities for managing person metadata, location data, and biographical information.

Use Cases:

• Person metadata management (biographical, family research)
• Artifact metadata (media tagging, document organization)
• Location data and resolution
• Fuzzy matching and duplicate detection

Installation

From PyPI (Recommended)

# Install the latest version
pip install pybiographical

# Install with optional fuzzy matching support
pip install pybiographical[matching]

# Install with all optional features
pip install pybiographical[full]

In requirements.txt

# Basic installation
pybiographical>=0.2.0

# With fuzzy matching
pybiographical[matching]>=0.2.0

# With all features
pybiographical[full]>=0.2.0

From Source (for development)

# Clone and install in editable mode
git clone https://github.com/rickhohler/PyBiographical.git
cd PyBiographical
pip install -e .[dev]

Features

RAM Disk Utilities

High-performance I/O using RAM disk for faster file operations (especially useful for cloud-synced directories).

Setup RAM Disk:

Note: The examples below use 12GB as a reference. Adjust the size based on your use case and available RAM.

macOS

# Create RAM disk if it doesn't exist
if [ ! -d "/Volumes/RAMDisk" ]; then
   diskutil erasevolume HFS+ "RAMDisk" `hdiutil attach -nomount ram://25165824`
fi

Linux

# Create RAM disk using tmpfs (12GB)
sudo mkdir -p /mnt/ramdisk
sudo mount -t tmpfs -o size=12G tmpfs /mnt/ramdisk

# To make it permanent, add to /etc/fstab:
# tmpfs /mnt/ramdisk tmpfs nodev,nosuid,size=12G 0 0

Windows (PowerShell as Administrator)

# Using ImDisk Toolkit (free, open-source)
# Download from: https://sourceforge.net/projects/imdisk-toolkit/

# Create 12GB RAM disk at drive R:
imdisk -a -s 12G -m R: -p "/fs:ntfs /q /y"

# Alternative: Use built-in with third-party tools like:
# - AMD Radeon RAMDisk
# - SoftPerfect RAM Disk
# - Dataram RAMDisk

Python Usage:

from pybiographical import RAMDiskContext
from pathlib import Path

# Automatic staging and copy back
with RAMDiskContext(copy_back_to=Path("output")) as ramdisk:
    if ramdisk.available:
        # Write to ramdisk.temp_dir - very fast!
        for i in range(1000):
            output_file = ramdisk.temp_dir / f"result_{i}.json"
            output_file.write_text(f'{{"data": {i}}}')
        # Files automatically copied to "output/" on exit

Performance: 2-4x faster for Google Drive-synced directories.

Modules

PyBiographical.io - File I/O Utilities

YAML Operations:

from pybiographical.io import load_yaml, dump_yaml, get_yaml_handler

# Load/save YAML files
data = load_yaml("person.yaml")
dump_yaml(data, "output.yaml")

# Get YAML handler for custom use
yaml = get_yaml_handler()

Backup & Safety:

from pybiographical.io import create_backup, atomic_write, compute_checksum

# Create timestamped backups
backup_path = create_backup(file_path, backup_dir)

# Atomic file writes (temp + rename)
atomic_write(data, file_path)

# File checksums
checksum = compute_checksum(file_path)

PyBiographical.persons - Person Metadata Management

CRUD Operations:

from pybiographical.persons import PersonCRUD
from pathlib import Path

# Initialize CRUD handler
crud = PersonCRUD(
    persons_dir=Path("data/persons"),
    backup_dir=Path("data/backups"),
    archive_dir=Path("data/archive")
)

# Create person (idempotent with duplicate detection)
person = crud.create(
    given_names="Johann",
    surname="Johnson",
    birth_year=1825,
    birth_place="Harvey, ND",
    gender="Male"
)

# Read by ID
person = crud.read("I382302780374")

# Fuzzy search with confidence scoring
results = crud.search(
    surname="Johnson",
    birth_year=1825,
    fuzzy=True,
    threshold=80
)
for person, confidence in results:
    print(f"{person['name']['full_name']}: {confidence}%")

# Update with nested keys
crud.update(
    "I382302780374",
    {'vital_events.birth.date': '1825-03-15'}
)

# Delete/archive with automatic backup
crud.delete("I382302780374", archive=True)

# Restore from backup
crud.restore("I382302780374")

Validation:

from pybiographical.persons import (
    validate_person_yaml,
    validate_directory,
    fix_schema_version
)

# Validate single file
issues = validate_person_yaml("person.yaml")

# Validate entire directory
results = validate_directory(Path("data/persons"))

# Auto-fix schema versions
fixed = fix_schema_version("person.yaml", make_backup=True)

Models:

from pybiographical.persons import PersonFile, ValidationIssue

# Dataclasses for structured access
person_file = PersonFile(path=Path("person.yaml"), data={...})
issue = ValidationIssue(
    severity="WARNING",
    field="birth_year",
    message="Missing birth year"
)

PyBiographical.names - Name Registry & Confidence Scoring

Name Registry:

from pybiographical.names import NameRegistry, NameEntry

# Initialize registry
registry = NameRegistry("/path/to/metadata/data/names.json")
registry.load()

# Search with confidence scoring
results = registry.search("Schmidt", include_cognates=True)
for result in results:
    print(f"{result.entry.name}: match={result.confidence:.2f}, validity={result.name_confidence:.2f}")
    # Example output: Schmidt: match=1.00, validity=0.85

Confidence Scoring:

The Names Registry supports two types of confidence scores:

1. Match Confidence (result.confidence) - How well the search query matches the name (0.0-1.0)

   • 1.0 = Exact match
   • 0.9 = Spelling variant match
   • 0.8 = Cognate/cross-language match
   • 0.0-0.7 = Fuzzy/phonetic match

2. Name Confidence (result.name_confidence) - How valid the name is as a real person name (0.0-1.0)

   • Factors include etymology, language origin, geographic usage, occurrences
   • Penalties for suspicious patterns (digits, special chars, non-person patterns)
   • Range interpretation:
     • 0.8-1.0 = High quality, well-documented name
     • 0.5-0.79 = Medium quality, valid but limited documentation
     • 0.0-0.49 = Low quality, requires manual review

Confidence Algorithm:

# Base confidence: 0.5
# Positive factors:
#   + 0.1  if has etymology
#   + 0.1  if has language_origin
#   + 0.05 per geographic_usage entry (max +0.2)
#   + 0.1  if 10+ occurrences in data
#   + 0.05 if 5-9 occurrences
# Negative factors:
#   - 0.2  if contains digits
#   - 0.2  if has special characters
#   - 0.1  if all UPPERCASE or lowercase
#   - 0.3  if matches non-person patterns (street, company, etc.)
#   - 0.2  if in stoplist (unknown, unnamed, etc.)
# Final score clamped to [0.0, 1.0]

Search Operations:

# Exact name search
exact_matches = registry.search_exact("Johnson")

# Phonetic search
phonetic_matches = registry.search_phonetic("H460", method="soundex")

# Cognate search (cross-language forms)
cognates = registry.search_cognates("Schmidt")

# Find by etymology
smith_names = registry.find_by_etymology("smith")
# Returns: Smith, Schmidt, Kowalski, Herrera, etc.

# Find by language
german_names = registry.find_by_language("German")

# Find cognate group
group = registry.find_cognate_group("John")
# Returns: {"English": [John], "German": [Johann], "French": [Jean], ...}

CRUD Operations:

# Create name entry
from pybiographical.names import NameEntry, GeographicUsage

entry = NameEntry(
    id="NAME-001",
    name="Johnson",
    name_type="surname",
    confidence=0.95,  # High confidence
    confidence_factors={
        'has_etymology': True,
        'has_language_origin': True,
        'geographic_usage_count': 3,
        'occurrences': 25
    }
)

registry.create(entry)

# Read
entry = registry.read("NAME-001")

# Update
entry.geographic_usage.append(
    GeographicUsage(
        location_id="LOC-12345",
        region="Baden-Württemberg, Germany",
        country="Germany",
        time_period="1800-1900",
        frequency="common"
    )
)
registry.update(entry)

# Delete
registry.delete("NAME-001")

Enrichment Operations:

# Backup before changes
backup_path = registry.backup()
print(f"Backup created: {backup_path}")

# Merge geographic usage from person data
from pybiographical.names import GeographicUsage

usage = [
    GeographicUsage(
        location_id="LOC-12345",
        region="Germany",
        frequency="common",
        time_period="1850-1920"
    )
]

registry.merge_geographic_usage(
    "NAME-001",
    usage,
    merge_strategy="accumulate"  # or "replace"
)

# Update specific fields
registry.update_fields("NAME-001", {
    'confidence': 0.95,
    'notes': 'Verified from historical records'
})

# Save changes
registry.save()

Statistics:

stats = registry.get_statistics()
print(f"Total names: {stats['total_entries']}")
print(f"Surnames: {stats['by_type']['surname']}")
print(f"German names: {stats['by_language']['German']}")
print(f"With etymology: {stats['with_etymology']}")

PyBiographical.matching - Fuzzy Matching & Deduplication

Name Matching:

from pybiographical.matching import fuzzy_match_name, normalize_name

# Fuzzy name matching (0-100 score)
score = fuzzy_match_name(
    "Hans Mueller",
    "John Johnson",
    check_alternates=["Hans"]
)

# Name normalization
clean_name = normalize_name("John Q. Public, Jr.")
# Returns: "john q public"

Location Matching:

from pybiographical.matching import fuzzy_match_location, normalize_location

# Location matching with abbreviations
score = fuzzy_match_location(
    "Harvey, North Dakota",
    "Harvey, ND"
)

# Location normalization
clean_loc = normalize_location("St. Paul, MN, USA")
# Returns: "saint paul mn usa"

Duplicate Detection:

from pybiographical.matching import (
    compute_match_confidence,
    get_detailed_match_breakdown
)

# Overall confidence score
confidence = compute_match_confidence(
    person1={...},
    person2={...}
)

# Detailed breakdown of match factors
breakdown = get_detailed_match_breakdown(person1, person2)
print(f"Name: {breakdown['name_score']}")
print(f"Birth year: {breakdown['birth_year_score']}")
print(f"Overall: {breakdown['overall_confidence']}")

Features:

• Uses rapidfuzz for fast fuzzy matching (optional dependency)
• Fallback to basic string matching if rapidfuzz unavailable
• Configurable thresholds and scoring weights
• Handles alternate spellings and nicknames

PyBiographical.ramdisk - High-Performance I/O

• RAMDisk - RAM disk manager class
• RAMDiskContext - Context manager for automatic staging/cleanup
• get_ramdisk_stats() - Get RAM disk usage statistics
• log_ramdisk_info() - Log RAM disk information

Usage in Projects

Complete Example - Person Metadata Management

from pybiographical.persons import PersonCRUD, validate_directory
from pybiographical.matching import fuzzy_match_name
from pybiographical.io import create_backup
from pathlib import Path
import logging

logging.basicConfig(level=logging.INFO)

# Initialize CRUD handler
crud = PersonCRUD(
    persons_dir=Path("ORGANIZED/DATA/persons"),
    backup_dir=Path("ORGANIZED/BACKUPS"),
    archive_dir=Path("ORGANIZED/ARCHIVE")
)

# Create new person with duplicate detection
try:
    person = crud.create(
        given_names="Johann",
        surname="Johnson",
        birth_year=1825,
        birth_place="Schwenningen, Baden-Württemberg, Germany",
        gender="Male",
        sources=["an online data source biographical dataset Export"],
        check_duplicates=True  # Idempotent - returns existing if 95%+ match
    )
    print(f"Created: {person['person_id']}")
except FileExistsError:
    print("Person already exists")

# Search for potential matches
matches = crud.search(
    surname="Johnson",
    given_names="Johann",
    birth_year=1825,
    fuzzy=True,
    threshold=75
)

for person, confidence in matches:
    print(f"{confidence}% - {person['name']['full_name']}")
    if confidence >= 90:
        print("  → High confidence match!")

# Validate entire directory
results = validate_directory(Path("ORGANIZED/DATA/persons"))
for result in results:
    if result.issues:
        print(f"{result.file.name}: {len(result.issues)} issues")

biographical - Person Import & Matching

from pybiographical.persons import PersonCRUD
from pybiographical import RAMDiskContext
from pathlib import Path

# Use RAM disk for high-performance batch operations
with RAMDiskContext(prefix="biographical", copy_back_to=Path("output")) as ramdisk:
    if ramdisk.available:
        # Initialize CRUD with RAM disk paths
        crud = PersonCRUD(
            persons_dir=ramdisk.temp_dir / "persons",
            backup_dir=ramdisk.temp_dir / "backups",
            archive_dir=ramdisk.temp_dir / "archive"
        )
        
        # Batch import from biographical dataset with duplicate detection
        for gedcom_person in gedcom_data:
            crud.create(
                given_names=gedcom_person.given_names,
                surname=gedcom_person.surname,
                birth_year=gedcom_person.birth_year,
                check_duplicates=True  # Automatic deduplication
            )

metadata - Validation & Cleanup

from pybiographical.persons import validate_directory, fix_schema_version
from pybiographical.io import create_backup
from pathlib import Path

# Validate all person files
persons_dir = Path("ORGANIZED/DATA/persons")
results = validate_directory(persons_dir)

# Auto-fix schema versions
for result in results:
    for issue in result.issues:
        if "schema_version" in issue.message:
            print(f"Fixing schema: {result.file.name}")
            fix_schema_version(result.file.path, make_backup=True)

# Summary
print(f"Validated {len(results)} files")
print(f"Issues found: {sum(len(r.issues) for r in results)}")

Development

Setup

# Clone repo
git clone [e-mail]:username/PyBiographical.git
cd PyBiographical

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

Testing

# Run tests
pytest

# With coverage
pytest --cov=PyBiographical --cov-report=html

Code Quality

# Format code
black src/

# Lint
ruff check src/

Requirements

Core Requirements

• Python ≥3.8
• ruamel.yaml ≥0.17.0 (installed automatically)

Optional Dependencies

• rapidfuzz ≥3.0.0 - For fuzzy matching features

  pip install pybiographical[matching]
  

• Development tools - For contributing

  pip install pybiographical[dev]
  

Platform-Specific Features

RAM Disk Utilities:

• Available on macOS, Linux, and Windows
• Optional - library works without RAM disk
• See RAM Disk Setup for platform-specific instructions

Version History

Version	Date	Changes
0.2.0	2025-11-03	Major Feature Release • Person metadata YAML utilities (load, dump, backup) • Person metadata validation with auto-fix • Fuzzy matching for names and locations • PersonCRUD class with full CRUD operations • Duplicate detection and confidence scoring • Archive/restore functionality • Optional rapidfuzz dependency for performance
0.1.0	2025-11-03	Initial release with RAM disk utilities

Project Tracking

All development, features, and issues are tracked using GitHub Projects:

PyBiographical Project Board

This includes:

• Feature roadmap
• Bug tracking
• Enhancement requests
• Release planning
• Documentation tasks

AI-Assisted Development

When using AI agents for development:

1. Create issues in GitHub Projects with clear descriptions
2. Pull issues into AI context using the sync script in PyBiographicalAI repo
3. Work on issues with full project context
4. Update issues via gh CLI as work progresses
5. Close issues when complete

See the PyBiographicalAI repository for scripts and documentation.

Contributing

See CONTRIBUTING.md for development setup, coding standards, and how to submit pull requests.

License

MIT License - See LICENSE file for details.

Support

For issues or questions:

• GitHub Issues: Create an issue
• GitHub Discussions: Join the discussion
• Project Board: View progress