pytauargus

Statistical disclosure control (SDC) for tabular data, as a Python package and a headless tauargus command. It runs legacy Tau-Argus 4.1 .arb batch files without the GUI and solves with the open-source HiGHS solver. Part of tauargus-engine.

Install

pip install pytauargus
tauargus --version

Wheels bundle HiGHS and the native engine; no other installation is needed. Available for Python 3.10-3.13 on macOS arm64, Linux x86_64 and aarch64 (manylinux_2_28) and Windows x64. Other platforms: build from source (see the repository).

Cloud native

Built to run unattended in containers, CI and batch services:

• Headless: no GUI, no display, no interactive prompts.
• Self-contained: one pip install; the solver (HiGHS) and native libraries are bundled in the wheel. No licensed solver, license server, system packages or Windows registry.
• Stateless: input and output are plain files in the working directory; scratch files go to the system temp directory.
• Scriptable: success is exit code 0; errors go to stderr with a non-zero exit code.
• Portable: wheels for Linux (manylinux_2_28, so any glibc 2.28+ image such as python:3.x-slim), macOS arm64 and Windows x64.

A ready-made image is published to GitHub Container Registry with each release (linux/amd64 and linux/arm64):

docker run --rm -v "$PWD":/work ghcr.io/trygu/tauargus run batch.arb

Pin a version with ghcr.io/trygu/tauargus:0.2.0. To build your own image:

FROM python:3.12-slim
RUN pip install --no-cache-dir pytauargus
WORKDIR /work
ENTRYPOINT ["tauargus"]

What problem it solves

Publishing a table can disclose information about individual respondents: a cell with few contributors, or one dominated by a single contributor. Such primary cells are suppressed, but totals would still reveal them, so secondary cells are suppressed as well, chosen to minimise information loss. Alternatively the table can be controlled-rounded. tauargus does this from a batch file.

Quick start

Put the data (.asc), its metadata (.rda) and a batch file in one folder. Paths in a batch file are relative to the working directory.

<OPENMICRODATA> "tau_testW.asc"
<OPENMETADATA>  "Tau_TestW.rda"
<SPECIFYTABLE>  "Size""Region"|"Var2"||
<SAFETYRULE>    NK(2,75)|NK(0,0)
<READMICRODATA>
<SUPPRESS>      MOD(1)
<WRITETABLE>    (1,3,AS-,"safe-table.txt")

tauargus run demo.arb

This reads the microdata, builds the Size x Region table of Var2, flags cells failing the dominance rule NK(2,75) (two largest contributors may not make up more than 75 %), applies secondary suppression with the modular method, and writes the protected table as code/value pairs, with suppressed values masked. The sample inputs are in the repository's data/ folder.

Batch file commands

The grammar and parameters follow the legacy Tau-Argus 4.1 manual.

Command line

tauargus run      batch.arb   # execute the whole batch
tauargus explore  batch.arb   # load data + metadata, list variables
tauargus compute  batch.arb   # table computation only
tauargus suppress batch.arb   # compute, then apply suppression
tauargus round    batch.arb   # compute, then round
tauargus audit    batch.arb   # feasibility intervals of suppressed cells
tauargus save     batch.arb   # compute, then write tables
tauargus tables   batch.arb   # print a table summary
tauargus version

Exit code is 0 on success and non-zero on an error, with a message on stderr, so it fits into scripts and pipelines.

Audit (Intervalle)

A suppressed cell is only protected if the range of values it could take and stay consistent with the published totals — its realized lower/upper bounds, the feasibility interval — is small enough that the original value can no longer be reconstructed.

In legacy Tau-Argus this ran in a separate intervalle.exe (standalone Delphi/Pascal executable); Tau-Argus wrote a .JJ file, launched it externally and read the intervals back. The rewrite ports that logic into the engine (TauAuditJj in the csp module), so it runs in-process over a temporary .JJ file with nothing external to install:

from pytauargus.engine import run_batch

engine = run_batch("demo.arb")
rows = engine.audit(0)   # one dict per suppressed cell:
# {"cell", "min", "max", "value", "status", "unsafe"}

The realized bounds are stored on the table (readable via get_table_cell). tauargus save demo.arb --audit --format intermediate writes the legacy INTERMEDIATE (type-5) audit file with the realized-interval columns.

Python API

from pytauargus.engine import run_batch

engine = run_batch("demo.arb")   # parse and execute; returns the Engine

Lower-level pieces: pytauargus.batch.parse_batch(path) returns the parsed command list, and pytauargus.engine.Engine executes commands one at a time.

Generating batch inputs

pytauargus also writes the batch inputs, as a port of rtauargus, so it emits the exact formats the engine reads:

from pytauargus.arb import micro_arb

micro_arb(
    asc_filename="donnees.asc",
    explanatory_vars=[["REGION"], ["SEXE"]],
    response_var="CA",
    safety_rules=["NK(1,85)", "FREQ(3,10)"],
    suppress="GH(.,100)",
    output_names=["tab1.csv", "tab2.csv"],
)

micro_arb writes a .arb batch; pytauargus.hrc.write_hrc writes .hrc hierarchy files and pytauargus.rda.write_rda writes .rda metadata text. The generator does not produce the fixed-width .asc microdata file itself; it references that file by name.

License

EUPL-1.2. Tau-Argus is (c) Statistics Netherlands.