dpm-xl 1.1.0

Parse and evaluate EBA DPM-XL validation rules in Python.

A Python parser and evaluator for DPM-XL, the expression language of EBA validation rules and similar checks. Give it a rule and your report data; it tells you which records fail and explains each failure in plain numbers.

dpm-xl is an independent project, not affiliated with or endorsed by the European Banking Authority.

Installation

pip install dpm-xl               # Python 3.11+, no dependencies
pip install "dpm-xl[columnar]"   # adds the polars evaluator

Quickstart

from dpmxl import MemoryProvider, Number, TableLayout, evaluate_rule, explain

layout = TableLayout("C_01.00", rows=("0010", "0020", "0030"), columns=("0010", "0020"))
values = {  # (table, row, column, sheet, open keys) -> value with XBRL decimals
    ("C_01.00", "0010", "0010", None, ()): Number.of(100, 0),
    ("C_01.00", "0020", "0010", None, ()): Number.of(60, 0),
    ("C_01.00", "0030", "0010", None, ()): Number.of(40, 0),
    ("C_01.00", "0010", "0020", None, ()): Number.of(1204, 0),
    ("C_01.00", "0020", "0020", None, ()): Number.of(1180, 0),
    ("C_01.00", "0030", "0020", None, ()): Number.of(31, 0),
}
provider = MemoryProvider([layout], values)

expression = (
    "with {tC_01.00, c*, default: 0, interval: true}: {r0010} = {r0020} + {r0030}"
)
result = evaluate_rule(expression, provider, precondition="{v_C_01.00}")

print(result.status, result.counts)
for outcome in result.failures:
    print(outcome.key, explain(result.evaluation, expression, outcome.key).summary)

failed {'passed': 1, 'failed': 1, 'null': 0}
{'c': '0020'} 1,204 should equal 1,211 (1,180 + 31): difference 7, at least 5.5 given the reported precision

On the command line, against a JSON data file:

dpm-xl check rules.json            # report every problem in every expression
dpm-xl eval rules.json data.json   # evaluate and explain every rule

Features

• Parses every construct of the DPM-XL grammar, including every EBA validation rule and precondition of DPM releases 3.4–4.4 (14,000+ distinct expressions, about 0.1 ms each).
• Evaluates them with EBA's interval arithmetic, null logic and key joins, and points each failure at the cells involved.
• Finds problems before evaluation, for editors and CI: every syntax error, unknown functions and tables, coordinates a table lacks, keys and operand types, each with its position and, where one fits, a suggested fix.
• Has no dependencies. The optional columnar extra adds a polars evaluator for reports with millions of open-table rows.
• Reads no files and knows no DPM release: data comes through a three-method provider, so the same evaluator runs against a report upload, a database or test fixtures. dpm-xbrl reads xBRL-CSV and XBRL-XML reports.

Toolkit

Part of the dpm toolkit, three Python packages that work together:

Package	What it does
	Parse and evaluate DPM-XL validation rules (this package)
	Read, write and check xBRL-CSV and XBRL-XML reports
	Generate test reports that satisfy DPM-XL rules

The toolkit tour takes one report through all three: generated with dpm-synth, read with dpm-xbrl, then evaluated and explained with dpm-xl.

Documentation

Documentation: https://luukhopman.github.io/dpm-xl/

• Getting started: a first rule, end to end.
• How-to guides: connect your data, evaluate rule files, explain failures in a UI, evaluate large reports, run rules in threads, check rules in CI, build editor support, and read an XBRL report and evaluate it with dpm-xbrl.
• Concepts: records and keys, null logic, interval arithmetic and the rest of the semantics.
• Reference: the language, the CLI, the data formats and the Python API.
• Using dpm-xl with AI agents, with llms.txt and llms-full.txt.
• Changelog, stability policy, benchmarks and security policy.

Development

uv sync
uv run pytest
uv run ruff check
uv run ruff format --check
uv run ty check
uv run python scripts/build_docs.py

See contributing.

Licence

MIT. See LICENSE.