Michael
e435103113
A complete offline licensing layer (no internet at any step): Core - src/license/ — schema (License, Tier, FeatureFlag), HMAC crypto, JSON storage, LicenseManager singleton with activate/renew/ deactivate/issue_trial. Tier-scaffolded so future SKUs can carve per-tool feature sets without consumer-code edits. - scripts/generate_license.py — creator-only key generator. Mints a DTLIC1: blob the buyer pastes into the activation page. GUI - New activation form component (src/gui/components/activation.py). - hide_streamlit_chrome() now inline-renders the activation form when no valid license is present (every page short-circuits to the form until activated). - Sidebar shows tier + days remaining; renewal warning under 30 days. - New pages/_Activate.py for revisiting the form after activation. CLI - src/license_cli.py — activate / renew / status / trial / deactivate commands. Exempt from the guard. - src/cli_license_guard.py — drop-in guard call added to every tool CLI's main(). Lets --help through; respects DATATOOLS_DEV_MODE. i18n - New activation.* and license.* keys in en.json + es.json (page title, form labels, status badges, renewal warnings, error messages). Pack parity test stays green. Test infrastructure - tests/conftest.py autouse fixture sets DATATOOLS_DEV_MODE=1 so the existing 1916 tests continue to pass. - isolated_license_path / activated_license_manager / unactivated_license_manager fixtures for tests that want to drive the real check. Tests (+79) - tests/test_license.py (40): schema, crypto roundtrip, blob encode/decode, tier→feature mapping, activation flow, name/email mismatch rejection, tamper detection, expiration, renewal, dev-mode bypass. - tests/test_license_cli.py (26): every license_cli command + subprocess tests confirming every tool CLI refuses to run without a license, --help always works, DEV_MODE bypasses. - tests/gui/test_activation.py (13): gate blocks without license, passes with trial, activation form submission unlocks the gate, sidebar status, renewal warning, i18n. Total: 1916 → 1995 tests. All pass under the strict warning filter. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
🌐 Language: English · Español
DataTools
Local CSV / Excel cleaning. CLI + browser GUI, no cloud, no install ceremony. GUI ships with English and Spanish language packs.
Tools
| # | Tool | Status |
|---|---|---|
| 01 | Deduplicator — exact + fuzzy match, 5 normalizers, survivor rules, audit | Ready |
| 02 | Text Cleaner — whitespace, smart chars, BOM, line endings, case ops | Ready |
| 03 | Format Standardizer — dates, phones, emails, addresses, names, currencies, booleans | Ready |
| 04 | Missing Value Handler — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies | Ready |
| 05 | Column Mapper — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder | Ready |
| 06 | Outlier Detector | Coming Soon |
| 07 | Multi-File Merger | Coming Soon |
| 08 | Validator & Reporter | Coming Soon |
| 09 | Pipeline Runner — chain tools with recommended (not forced) order, save/load JSON, automate weekly cleanups | Ready |
Download (non-technical users)
Pre-built installers — no Python required:
| Platform | Download | First-launch note |
|---|---|---|
| macOS | DataTools-X.Y.Z-mac.dmg |
Drag DataTools.app into /Applications, then double-click. |
| Windows | DataTools-X.Y.Z-win-setup.exe |
Run the installer; launches from Start Menu. |
| Linux | DataTools-X.Y.Z-linux-x86_64.AppImage |
chmod +x the file, then double-click. |
Latest release: see GitHub Releases (or the Gumroad listing). The installers are ~150–200 MB; the launcher boots a local server at http://127.0.0.1:8501 and opens your browser. Nothing is sent to the cloud.
Install from source (developers)
pip install -r requirements.txt
Python 3.10+ required.
Run
GUI (recommended):
streamlit run src/gui/app.py
CLI — seven entry points:
python -m src.cli customers.csv [--apply] # dedup
python -m src.cli_text_clean messy.csv [--apply] # text clean
python -m src.cli_format intl.csv [--apply] # format standardize (auto-streams >100 MB)
python -m src.cli_missing holes.csv [--apply] # missing values
python -m src.cli_column_map vendor.csv [--apply] # column mapper
python -m src.cli_pipeline any_file.csv [--apply] # chain tools end-to-end
python -m src.cli_analyze any_file.csv [--json] # scan only
Every CLI runs preview-only by default; add --apply to write output.
Language
The GUI sidebar has a language picker. Packs ship for English and Español (src/i18n/packs/); the choice persists for the session. Adding a language: drop a <code>.json next to en.json mirroring its key tree, then list it in LANGUAGES. See Developer Guide §i18n.
Review & Normalize gate
Every uploaded file passes through a CSV-normalization gate before any tool sees it. The analyzer flags ~15 issue types (whitespace, NBSP / zero-width chars, BOM, encoding, smart punct, dirty headers, null sentinels, mojibake, …) tagged by confidence (high / medium / low) and fix action. The GUI shows each finding with Auto-fix / Skip / Customize, a live before/after preview, and an encoding-override picker. Tool pages refuse to load until the gate passes.
Output
Every run writes:
{input}_<tool>.csv— the cleaned data{input}_changes.csv(text cleaner) or{input}_match_groups.csv(dedup) — audit traillogs/<tool>_YYYYMMDD_HHMMSS.log— debug-level run log
Original input file is never modified.
Docs
- User Guide — install, GUI workflow, gate
- CLI Reference — every flag with recipes
- Requirements — file sizes, encodings, detectors, perf targets
- Technical — architecture, gate internals, fix registry
- Developer Guide — adding fixes / detectors / standardizers
Dependencies
pandas, openpyxl, rapidfuzz, phonenumbers, typer, loguru, charset-normalizer, streamlit. Optional: ftfy for mojibake repair.
License
Proprietary.