datatools-dev/docs/REQUIREMENTS.md

# Requirements

Numbered support matrix. Updated with every shipped capability.

## 1. File handling
1.1 Size: ≤ 1.5 GB target (larger works, slower).
1.2 Read: CSV, TSV, XLSX, XLS.
1.3 Write: CSV, TSV.
1.4 Excel: multi-sheet picker.
1.5 Empty file: blocked with `empty_input` error finding.

## 2. Input encodings (auto-detected)
2.1 Unicode: UTF-8, UTF-8-BOM, UTF-16 LE/BE BOM, UTF-16 LE no-BOM.
2.2 Western: cp1252, ISO-8859-1, ISO-8859-15, Mac Roman.
2.3 Eastern European: cp1250, ISO-8859-2.
2.4 Cyrillic: cp1251, KOI8-R.
2.5 CJK: Shift_JIS / cp932, GB18030, Big5, EUC-KR / cp949.
2.6 ASCII → detected as UTF-8.
2.7 User override: any Python codec name.
2.8 BOM: stripped on read, never written.
2.9 Decode failure → `encoding_decode_failed` (error).
2.10 U+FFFD in output → `encoding_uncertain` (error).

## 3. Output encodings
3.1 UTF-8 (default), UTF-8-BOM (Excel-friendly).
3.2 cp1252, ISO-8859-1/15, cp1250, ISO-8859-2, cp1251.
3.3 Shift_JIS, GB18030, Big5, EUC-KR, UTF-16 LE.
3.4 Lossy fallback: `?` + warning when codec can't represent a char.

## 4. Delimiters
4.1 Input auto-detect: `,`, `\t`, `;`, `|`.
4.2 Output: `,` (default), `\t`, `;`, `|`.
4.3 Extension: `.tsv` for tab, `.csv` otherwise.

## 5. Line endings
5.1 Read: LF / CRLF / bare CR — all normalized to LF.
5.2 Embedded in quoted cells: also normalized to LF.
5.3 Write: LF (default), CRLF, CR.
5.4 Mixed → `mixed_line_endings` finding.

## 6. Analyzer detectors

**File-level** (read-time fixes, audit-logged):
- `csv_bom_stripped`, `csv_nul_stripped`, `csv_smart_quotes_folded`, `csv_line_endings_normalized`, `csv_transcoded_to_utf8`, `csv_unquoted_delimiters_repaired`, `csv_unrepairable_rows`.

**Cell-level**:
- `smart_punctuation_in_data`, `nbsp_or_unicode_whitespace`, `zero_width_or_invisible`, `dirty_column_headers`, `whitespace_padding`, `null_like_sentinels`, `suspected_mojibake`, `mixed_case_email_column`, `inconsistent_date_format`, `near_duplicate_rows`, `leading_zero_ids`.

**Encoding integrity**: `encoding_uncertain`, `encoding_decode_failed`, `encoding_lying_bom`, `empty_input`.

Sample size: 1,000 rows (configurable).

## 7. Finding fields
`id`, `severity` (info/warn/error), `confidence` (high/medium/low), `fix_action`, `pre_applied`, `tool`, `count`, `description`, `column`, `samples` (≤5).

## 8. Confidence tiers
- **high** — round-trip safe, one-click auto-fix.
- **medium** — preview before applying.
- **low** — opt-in only, can corrupt if wrong.
- **error** — must resolve or waive before tool pages unlock.

## 9. Decision actions
- `auto` — apply registered fix.
- `skip` — waive (audit-logged).
- `modified` — apply with custom payload.

## 10. Performance (1.5 GB input)
- Initial scan (sample): < 2 s · peak RSS ~110 MB.
- Full-file `repair_bytes`: 30–40 s (UTF-8); non-UTF-8 fold path now
  uses ``str.count`` instead of a Python char-by-char zip walk —
  formerly ~100 s on a 1 GB cp1252 file with smart quotes, now <1 s.
- Full-DataFrame analyze: ~4 min (~25 µs/cell). Near-duplicate detector
  no longer allocates a full-frame copy — peak RSS during the
  near-duplicate pass drops to roughly the size of the string columns
  alone (~50% memory cut on text-heavy 1 GB inputs).
- Full-DataFrame `auto_fix`: ~5 min (~30 µs/cell).
- Output write: ~10 s.
- Recommended RAM: 3–4× input size for the full-Apply path.
- **Format standardizer** (`standardize_dataframe`): ~2.7M rows/sec on
  cache-warm repetition-heavy columns (synthetic 1M-row in-memory
  benchmark, 2 typed columns); the fused single-pass loop replaced a
  3-pass ``.tolist()`` cycle, so per-call overhead is now dominated by
  the underlying parsers (phonenumbers, dateutil) rather than Python
  list materialisation. A 1.5 GB CSV with mixed phone+currency+address
  columns finishes in ~1.5–6 minutes depending on column count.
  `StandardizeOptions.parallel_columns` (default 1, serial) lands the
  thread-pool scaffolding; on CPython 3.12 with the GIL it's
  roughly neutral, but the API is ready for the free-threaded
  (PEP 703) Python 3.13+ build where it will help.
- **Text cleaner** (`clean_dataframe`): ~1M rows/sec on
  repetition-heavy columns (per-call string cache: the pipeline runs
  once per *unique* cell value, not once per row).
- **Missing handler** (`handle_missing`): lazy-copy — when sentinel
  standardization runs but finds nothing, AND no drops AND no fills
  apply, the input frame is returned as-is. On a clean 1 GB file this
  saves the 1 GB allocation that the unconditional upfront copy used
  to take.
- **Column mapper** (`map_columns`): rename + drop both already
  return fresh frames; the explicit upfront `df.copy()` is now
  removed and downstream mutating steps (schema-add, coerce) copy on
  demand via `_ensure_owned()`. Rename-only and identity-mapping
  paths run with zero explicit copies.
- **Deduplicator**:
  - **Exact-only strategies** (every column uses `Algorithm.EXACT` at
    threshold 100 — covers strong-key dedup like email/phone, the
    fallback drop-duplicates path, and explicit "match on this exact
    column" calls) now run in **O(n)** via groupby. Measured: 10k
    rows on an email-exact strategy → 73 ms (was ~30 minutes via the
    old O(n²) pair compare).
  - **Fuzzy strategies** still pair-compare. Opt in to **prefix
    blocking** via `deduplicate(..., blocking_columns=['name'],
    blocking_prefix_len=1)` to partition pairs by a cheap key.
    Measured: 5k rows fuzzy-name dedup → 25.6s with blocking vs.
    179s without (7× faster). Trade-off: cross-block matches are
    missed; lower `blocking_prefix_len` widens blocks.
  - Normalisation pass remains LRU-cached per call so repeat values
    (the common dedup workload) skip re-parsing.

## 11. Tools
1. Deduplicator — Ready
2. Text Cleaner — Ready
3. Format Standardizer — Ready
4. Missing Value Handler — Ready
5. Column Mapper — Ready
6. Outlier Detector — Coming Soon
7. Multi-File Merger — Coming Soon
8. Validator & Reporter — Coming Soon
9. Pipeline Runner — Ready

### 11.a Recommended pipeline order (soft, not enforced)

The Pipeline Runner ships with a `SOFT_DEPENDENCIES` table; the
following ordering is the default and the basis of the warning
surface. Re-ordering is allowed; the runner emits a warning string
and proceeds.

| # | Tool | Why this slot |
|---|------|---------------|
| 1 | column_map (optional, for header alignment) | Multi-vendor unification — rename early so downstream tools see canonical headers |
| 2 | text_clean | NBSP / smart quotes / zero-width pollution silently breaks downstream parsers |
| 3 | format_standardize | Phones / dates / currencies → canonical form before missing detection and dedup |
| 4 | missing | Sentinel detection, imputation, drop strategies — needs canonical types |
| 5 | column_map (optional, for schema enforcement) | Project to target schema, coerce, drop extras AFTER cleaning |
| 6 | dedup | Fuzzy matching is most accurate on canonicalised, sentinel-laundered data |

## 12. Gate (Review & Normalize)
- Gates every tool page.
- Auto-fix button: applies all `confidence=high` findings in one click.
- Per-finding controls: Auto / Skip / Customize.
- Live before/after preview (≤5 sample rows).
- Audit log per fix (id, decision, cells changed).
- Encoding-override picker (16 codepages + custom).
- Advanced output expander: encoding + delimiter + line terminator.
- Result keyed by upload SHA-256; survives reload, invalidated on re-upload.

## 13. Interfaces
- **GUI**: Streamlit, browser-based, local, no internet. Sidebar language picker (English, Español).
- **CLI**: `python -m src.cli` (dedup) · `src.cli_text_clean` · `src.cli_format` · `src.cli_missing` · `src.cli_column_map` · `src.cli_pipeline` · `src.cli_analyze`. (CLI output is English-only.)
- **Python API**: `from src.core import …` (analyze, repair_bytes, clean_dataframe, deduplicate, standardize_dataframe, …).
- **JSON output**: `--json` on `cli_analyze`.
- **Language packs**: `from src.i18n import t, LANGUAGES`. Add `<code>.json` to `src/i18n/packs/` + entry in `LANGUAGES` to add a language.

## 14. Platforms
- Python ≥ 3.10.
- OS: Linux, macOS, Windows.
- Browser: any modern browser.
- Network: not required at runtime.

## 15. Dependencies
- **Core**: pandas, openpyxl, charset-normalizer, typer, loguru.
- **Dedup**: rapidfuzz, phonenumbers.
- **GUI**: streamlit.
- **Optional**: ftfy (mojibake repair).
- **Dev**: pytest, tox.

## 16. Test coverage
- 2,024 tests passing, 0 skipped, 0 xfailed.
  - 1,859 core + CLI tests (run with `pytest -m 'not gui'` for a quick loop).
    Includes 40 license-layer unit tests, 25 license-CLI tests, and
    17 Lite-tier feature-map + guard tests.
  - 165 GUI tests under `tests/gui/` driving Streamlit pages via `AppTest`
    (smoke + EN/ES localization, chrome, gate, workflows, dedup review,
    advanced panels, error paths, findings panel, activation +
    license gate, Lite-tier per-page lock behaviour). Marked `gui`.
  - Includes 15 perf-shape regression tests.
- Fixture corpora: text-cleaner (21), encodings (31), reference UTF-8 (9), format-cleaner (199 buyer cases + 20-row international stress fixture), missing-handler (3 use cases + 16 edge cases), column-mapper (3 use cases + 5 edge cases).
- Run: `python run_tests.py [--tool …] [--fixtures] [--coverage]`.

## 17. Privacy / data handling
- All processing local; no network calls in the data path.
- No telemetry.
- Original input never modified.
- Audit logs: `logs/` next to each run (timestamped).

## 17a. Licensing
- **Storage**: ``~/.datatools/license.json`` (or
  ``$DATATOOLS_LICENSE_PATH`` override). Signed locally with
  HMAC-SHA256.
- **Activation**: buyer pastes a base64-encoded license blob
  (``DTLIC1:...``) on first launch; app verifies the signature
  offline + matches the buyer-entered name/email to the embedded
  values.
- **No free trial**: every license requires a paid blob from the
  seller. The user-facing trial flow (button + ``license_cli trial``
  subcommand) was removed in v1.6 to keep paid-tier economics clean.
- **Lifetime**: every license is 1 year by default. Renewal applies a
  fresh blob without losing the embedded buyer identity. Tier may
  change during renewal (Lite → Core upgrade path).
- **Tiers**:
  - ``lite`` — Deduplicator + Text Cleaner + Format Standardizer.
    Buyer pays once, gets the three universally-useful tools.
  - ``core`` — every Ready tool (all 9 in v1.6).
  - ``pro``, ``enterprise`` — scaffolded for future SKUs; currently
    mirror Core. Add per-SKU restrictions by editing
    ``FEATURES_BY_TIER`` in ``src/license/features.py``.
  - ``trial`` — kept in the enum for backwards compat with any
    field-tested trial licenses but no longer issuable.
- **Feature flags**: every tool has a stable feature id matching its
  ``tool_id`` in :mod:`src.gui.tools_registry`. Adding a future per-
  tool SKU is a one-line change to ``FEATURES_BY_TIER`` — no consumer
  code edits.
- **Per-tool gating**: each tool page (GUI) and tool CLI calls
  ``require_feature(FeatureFlag.<TOOL>)`` at entry. GUI shows an
  upgrade prompt + button to the Activate page; CLI prints a
  message naming the locked feature and exits with code 2.
- **Lock badge**: the home grid shows a red 🔒 Locked pill on tool
  cards the current tier doesn't unlock.
- **Dev bypass**: ``DATATOOLS_DEV_MODE=1`` skips every check (used by
  the test suite and during development).
- **No internet**: signature verification is fully offline. The
  shipped binary embeds the verification secret; see
  ``docs/DECISIONS.md`` for the threat-model discussion.

## 18. Error handling
- Structured hierarchy: `DataToolsError` → `InputValidationError`, `ConfigError`, `FileFormatError`, `FileAccessError`.
- Subclasses extend stdlib `ValueError` / `OSError` so existing handlers still catch them.
- Every error carries: message, file path, column, operation, suggestion, underlying cause.