Michael
d9e32e578b
Reported earlier: synchronous file writes in ``log_event`` blocked the GUI render thread on hostile filesystems (Windows antivirus on ``~/.datatools/logs/`` is the prime suspect). A blocking ``open`` call doesn't raise — try/except can't recover from it — so the only safe re-enable is to take file I/O off the render path. Refactor: - ``log_event`` and friends push events onto a ``deque(maxlen=5000)`` via ``put_nowait`` and return in microseconds. - A single daemon thread (``datatools-audit-writer``) drains the queue and writes batches. Holds the queue lock only long enough to snapshot + clear, then does I/O outside the lock so producers can keep enqueueing. - ``audit_log_path()`` is now pure path arithmetic — no ``mkdir`` no ``open``. The writer thread does the directory creation off the request path, so any hang there only affects the writer. - Bounded queue means an unwritable disk doesn't unbounded-grow memory; the queue caps at 5000 and overflow drops OLDEST events so the most-recent (most-diagnostic) ones survive. - First write failure prints once to stderr; subsequent failures are silent so logs don't drown the launcher terminal. - ``flush_audit_log(timeout_s=0.5)`` drains the queue and signals the writer to exit; bounded so a stuck disk can't delay shutdown. Other changes in this commit: - ``shutdown_app`` now emits a "Session ending" event and calls ``flush_audit_log`` before kicking the os._exit timer, so the closing session's events make it to disk. - The Diagnostics sidebar in ``hide_streamlit_chrome`` is re-enabled (the ``if False:`` gate is removed). Wrapped in try/except defensively — render errors print to stderr, never blank the page. - ``_DISABLED`` kill-switch is gone. The async design IS the safety mechanism now. Tests in ``tests/test_audit.py``: - log_event burst of 1000 events completes in well under 1s (proves non-blocking). - Events queued before flush land on disk with the expected JSON shape; session_start renders; idempotent. - Pointing the audit dir at a file (so mkdir fails) doesn't hang or crash the producer. - Non-JSON extras are str()-coerced rather than dropped. 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 | Find Duplicates — exact + fuzzy match, 5 normalizers, survivor rules, audit | Ready |
| 02 | Clean Text — whitespace, smart chars, BOM, line endings, case ops | Ready |
| 03 | Standardize Formats — dates, phones, emails, addresses, names, currencies, booleans | Ready |
| 04 | Fix Missing Values — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies | Ready |
| 05 | Map Columns — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder | Ready |
| 06 | Find Unusual Values | Coming Soon |
| 07 | Combine Files | Coming Soon |
| 08 | Quality Check | Coming Soon |
| 09 | Automated Workflows — 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.