pydpm_xl 0.1.8

Python library for DPM-XL data processing and analysis

pyDPM

A Python library for DPM-XL (Data Point Model eXtensible Language) data processing, migration, and semantic analysis. pyDPM provides functionality to migrate Access databases to SQLite, validate DPM-XL expressions syntactically and semantically, and perform comprehensive data dictionary validation.

Features

• Database Migration: Convert Access databases (.mdb/.accdb) to SQLite with automatic fallback support
• Expression Validation: Three-tier validation system (syntax → semantics → data dictionary)
• DPM-XL Support: Full language support with ANTLR4-based parsing
• Cross-platform: Works on Linux, Windows, and macOS
• API & CLI: Both programmatic API and command-line interface

Installation

Install dependencies using Poetry:

poetry install

Database Migration Requirements

The migration system supports multiple methods for reading Access databases:

1. mdbtools (Linux/Unix, recommended): Install system package for better performance

   # Ubuntu/Debian
   sudo apt-get install mdbtools
   
   # macOS
   brew install mdbtools
   

2. access_parser (Pure Python fallback): Automatically used if mdbtools unavailable

   • Cross-platform Python library
   • No external dependencies required
   • Included with pyDPM installation

Usage

Database Migration

Migrate Access database to SQLite:

poetry run pydpm migrate-access ./path-to-release.accdb

This creates a SQLite database with migrated tables and views for DPM data access.

Expression Validation

Syntax Validation

Validate DPM-XL expression syntax:

poetry run pydpm syntax "sum({Table1, row A})"

Semantic Validation

Perform comprehensive semantic analysis:

poetry run pydpm semantic "{Table1, row A} + {Table1, row B}"

Semantic validation includes:

• Type checking and inference
• Symbol resolution
• Dependency analysis
• Data dictionary consistency

DPM-XL Expression Examples

# Basic cell reference
{Table1, row A, col 1}

# Arithmetic operations
{Sales, Q1} + {Sales, Q2}

# Aggregations with grouping
sum({Revenue} group by {Country})

# Conditional expressions
if {Status} = "Active" then {Amount} else 0

# Time operations
{Sales} timeshift Q, 1

# Filtering
{Revenue}[{Country} = "US"]

# With clauses for simplification
with {FinancialData}: {Revenue} - {Costs}