pm-data-tools 0.2.0

Universal parser and validator for project management data. Supports NISTA compliance.

PM Data Tools

Universal parser and validator for project management data. Supports NISTA compliance.

Part of the PDA Platform.

Overview

PM Data Tools provides a canonical data model and conversion utilities for project management data across multiple platforms and standards. Built for UK Government NISTA (National Infrastructure and Service Transformation Authority) standard compliance.

Features

• Canonical data model for project management data
• Schema mappings for major PM tools:
  • Microsoft Project (MSPDI XML)
  • Primavera P6 (XER, PMXML)
  • Jira
  • Microsoft Planner
  • Monday.com
  • Asana
  • Smartsheet
• Validation framework for structural and semantic rules
• CLI tools for conversion, validation, and inspection
• GMPP-aligned for UK government project data
• NISTA-ready (placeholder for official schema)
• 100% test coverage with comprehensive test suite

Installation

pip install pm-data-tools

For development:

pip install pm-data-tools[dev]

Quick Start

Parse from supported formats

from pm_data_tools.schemas.monday import MondayParser
from pm_data_tools.schemas.asana import AsanaParser
from pm_data_tools.schemas.smartsheet import SmartsheetParser
from pm_data_tools.schemas.gmpp import GMPPParser

# Parse Monday.com data
monday_parser = MondayParser()
project = monday_parser.parse_file("monday_board.json")

# Parse Asana data
asana_parser = AsanaParser()
project = asana_parser.parse_file("asana_project.json")

# Parse Smartsheet data
smartsheet_parser = SmartsheetParser()
project = smartsheet_parser.parse_file("smartsheet.json")

# Parse GMPP CSV data
gmpp_parser = GMPPParser()
projects = gmpp_parser.parse_file("gmpp_projects.csv")

Validate project data

from pm_data_tools import validate

result = validate("project.xml")

if result.valid:
    print("✓ Validation passed")
else:
    for error in result.errors:
        print(f"✗ {error.code}: {error.message}")

CLI Usage

# Convert formats
pm-data-tools convert project.xml project.json --to canonical

# Validate project file
pm-data-tools validate project.xml

# Inspect project structure
pm-data-tools inspect project.xml

Canonical Data Model

The canonical model is a superset of all supported formats, enabling lossless conversion between tools.

Core entities:

• Project - Container with metadata, schedule, and financials
• Task - Work items with WBS, schedule, progress, and costs
• Resource - People, equipment, materials with rates
• Assignment - Task-resource allocation
• Dependency - Task relationships (FS, FF, SS, SF)
• Risk - Risk register entries with probability/impact
• Milestone - Key project dates
• Calendar - Working time definitions

Supported Formats

Format	Read	Write	Status	Coverage
Monday.com (JSON API)	✅	🚧	v0.1.0	97% (32 tests)
Asana (JSON API)	✅	🚧	v0.1.0	99% (20 tests)
Smartsheet (JSON API)	✅	🚧	v0.1.0	94% (21 tests)
GMPP (CSV)	✅	🚧	v0.1.0	99% (21 tests)
Microsoft Project (MSPDI)	🚧	🚧	Planned	-
Primavera P6 (XER)	🚧	🚧	Planned	-
Primavera P6 (PMXML)	🚧	🚧	Planned	-
Jira (JSON API)	🚧	🚧	Planned	-
NISTA	🚧	🚧	Awaiting schema	-

NISTA Alignment

This library is designed to support the UK Government's Programme and Project Data Standard launched in December 2024. The GMPP schema module provides current alignment; full NISTA support will be added when the official schema is published.

Development

Setup

# Clone repository
git clone https://github.com/PDA-Task-Force/pda-platform.git
cd pm-data-tools

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests with coverage
pytest

# Run specific test file
pytest tests/test_models/test_task.py

# Run with coverage report
pytest --cov-report=html

Code Quality

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy src/pm_data_tools

Architecture

PM Data Tools follows a three-layer architecture:

1. Models Layer (src/pm_data_tools/models/) - Canonical data structures
2. Schemas Layer (src/pm_data_tools/schemas/) - Format-specific parsers and writers
3. Validation Layer (src/pm_data_tools/validators/) - Structural and semantic validation

All conversions pass through the canonical model:

Source Format → Parser → Canonical Model → Writer → Target Format

This ensures:

• Lossless roundtrip conversion (Source → Canonical → Source preserves data)
• Consistent validation (all formats validated against same rules)
• Extensibility (new formats only need parser/writer, not N² converters)

Contributing

See CONTRIBUTING.md for development guidelines, coding standards, and how to submit contributions.

Acknowledgements

Developed by members of the PDA Task Force.

This work was made possible by:

• The PDA Task Force White Paper identifying AI implementation barriers in UK project delivery
• The NISTA Programme and Project Data Standard and its 12-month trial period

Sponsored by the UK Government's Infrastructure and Projects Authority (IPA) research initiative.

License

MIT License - see LICENSE for details.

Links

• Documentation
• Issues
• Changelog
• NISTA Programme and Project Data Standard
• UK Government Major Projects Portfolio (GMPP)