pyregtab 0.1.0

pyRegTab: pattern-based extraction of recordsets from tables (RTL / ATP / ITM)

pyRegTab

RegTab: pattern-driven data extraction from document tables with regular structure — the Python port of jRegTab with a native Rust core.

pyRegTab compiles RTL (Regular Table Language) patterns into abstract table patterns (ATP), matches them against a table's syntactic layer (ITM), and interprets the match into a relational recordset:

TableSyntax → RtlCompiler/TablePattern → AtpMatcher → TableInterpreter → Recordset

pyRegTab 0.1.x ≙ jRegTab 0.3.0 (same API, same semantics, same test corpus).

Installation

pip install pyregtab

Binary wheels are published for Windows, Linux and macOS (x86-64 / arm64), CPython ≥ 3.10 (one abi3 wheel per platform). Building from the sdist requires a Rust toolchain.

Example

from pyregtab import TableSyntax, RtlCompiler, AtpMatcher, TableInterpreter

syntax = TableSyntax(3, 3)
syntax.cell(0, 1).set_text("CA");  syntax.cell(0, 2).set_text("HU")
syntax.cell(1, 0).set_text("IKT"); syntax.cell(1, 1).set_text("0 Jan"); syntax.cell(1, 2).set_text("8 Feb")
syntax.cell(2, 0).set_text("SVO"); syntax.cell(2, 1).set_text("31 Jan"); syntax.cell(2, 2).set_text("40 Feb")

pattern = RtlCompiler.compile("""
    [ [] [VAL : 'AIRLINE'->AVP]+ ]
    [ [VAL : 'AIRPORT'->AVP]
      [VAL : (COL, ROW, CL)->REC, 'ND'->AVP " " VAL : 'MON'->AVP]+ ]+
""")

itm = AtpMatcher.match(pattern, syntax)     # InterpretableTable | None
rs = TableInterpreter().interpret(itm)      # Recordset
rs.schema.attributes                        # ['ND', 'AIRLINE', 'AIRPORT', 'MON']
rs[0]["ND"]                                 # '0'
df = rs.to_pandas()                         # extras: pip install pyregtab[pandas]

Patterns can also be built without RTL, via the fluent spec API (TablePattern.of(SubtablePattern.of(...)) — same factories as in Java, snake_case method names), and serialized back to RTL with AtpToRtlSerializer.serialize(pattern).

Named Python predicates are attached to RTL via EXT('name'):

from pyregtab import Bindings

p = RtlCompiler.compile(
    "{ [ [EXT('isTotal') ? VAL : ST*->REC] []+ ] }+",
    Bindings.of().cell("isTotal", lambda cell: cell.text.startswith("Total")),
)

API mapping (Java → Python)

Java	Python
RtlCompiler.compile(String)	RtlCompiler.compile(str) / pyregtab.compile(...)
AtpMatcher.match(p, s) → Optional<InterpretableTable>	AtpMatcher.match(p, s) → InterpretableTable | None
Quantifier.oneOrMore()	Quantifier.one_or_more()
new TableInterpreter().withStrategy(s).interpret(itm)	TableInterpreter().with_strategy(s).interpret(itm)
rs.records().get(0).get("Name")	rs[0]["Name"], rs.records, record.get("Name")
cell.text() / cell.setText(t)	property cell.text (get/set); cell.set_text(t) also works
RtlCompileException	RtlCompileError

Architecture

Everything after the Python call boundary runs in a native core written in Rust (pyregtab._core, built with PyO3 and maturin); the Python layer is a thin re-export.

• grammar/RTL.g4 — the normative specification of the RTL language (a pinned copy from jRegTab; the upstream commit is recorded in grammar/UPSTREAM). The core's parser is a hand-written lexer + recursive descent that structurally follows the grammar rules.
• conformance/ — the shared RTL conformance corpus (also pinned from jRegTab, see conformance/UPSTREAM and conformance/README.md). Both implementations must compile every positive case to the same canonical form and reject every negative case; the corpus runs in CI of both projects. Any RTL language change flows: RTL.g4 in jregtab → corpus extension → both parsers → green corpus in both CIs.
• Regular expressions in RTL constraints are executed by the Rust regex crate (linear-time). The reference fixture corpus uses no lookaround/backreferences (audited), so the dialect is compatible with java.util.regex on this corpus. Documented divergences from Java: \d/\s/\w are Unicode-aware in regex (ASCII in Java), and SUBSTR indices count code points (UTF-16 units in Java) — identical behavior on the entire reference corpus.

Testing

pytest tests runs (1 878 tests):

• the full benchmark suite — tasks 001–150 (Foofah, RegTab, Baikal), every fixture variant, both via RTL patterns and via ATP patterns built with the Python spec API (1 500 task variants in total; fixtures are copied verbatim from jRegTab into tests/fixtures/tasks, ATP builders are mechanically translated from the Java tests by tools/translate_atp.py);
• the RTL conformance corpus (positive canonical forms, fixed points, negative rejections);
• RTL↔ATP round-trip for tasks 001–050;
• API unit tests (syntax layer, extractors, EXT bindings, custom predicates, transformations, interpreter options, GIL-released batch matching from a thread pool).

cargo test additionally runs the conformance corpus and an end-to-end smoke test against the native core alone. Differential testing against the Java reference (tools/differential.py + tools/RecordsetDumpMain.java) compares recordsets cell-by-cell on all 750 task variants — zero mismatches against jRegTab v0.3.0.

Development

python -m venv .venv && . .venv/bin/activate   # or .venv\Scripts\activate
pip install maturin pytest
maturin develop --release
pytest tests -q

License

Apache-2.0