refactor: dedup, consolidate, harden public APIs across core modules

Closes 16 high-value findings from a parallel cross-module review.

Refactors:
- New src/core/_constants.py centralizes USPS street-suffix
  abbreviations, US state names, and 2-letter postal codes — one source
  of truth for both normalize_address (matching keys) and
  standardize_address (display formatting). Eliminates ~80 lines of
  duplicated dicts across normalizers.py and format_standardize.py.
- format_standardize.py: collapse 4 identical nested _err() helpers
  into one shared _err_or_passthrough() module function; drop a dead
  duplicate `return _err("not a phone number")` branch in
  standardize_phone.
- format_standardize.py: precompile per-locale month-name regexes
  (_MONTH_LOCALE_PATTERNS) and per-state-name regexes
  (_STATE_NAME_PATTERNS) at import time — they were rebuilt on every
  cell, a measurable hot path on million-row inputs.
- dedup.py: extract _is_missing(value) helper; one definition of
  "this cell is None / NaN / pd.NA" instead of two.
- fixes.py: extract _is_string_column(ser) helper; one dtype check
  instead of three duplicates across _apply_to_strings,
  _vectorized_translate, _vectorized_regex_sub.

Production-readiness:
- format_standardize.standardize_dataframe now logs a warning when
  more than 10% of typed cells are unparseable — surfaces the
  silently-broken-pipeline failure mode.
- StandardizeOptions.from_dict validates date_order / phone_format /
  currency_decimal / name_case / boolean_style / *_error_policy
  enum values up front, with a clear error message instead of a deep
  crash inside the per-cell function.
- StandardizeOptions.from_file and DeduplicationConfig.from_file wrap
  read + json.loads with descriptive OSError / ValueError messages
  including the file path.
- standardize_date(month_locales=...) validates locale codes against
  the available set instead of silently passing through unknown ones.
- io.read_file rejects chunk_size <= 0 (was silently failing inside
  pandas) and logs the resolved suffix + chunk_size at info level so
  data-pipeline runs are debuggable.
- io.read_file's FileNotFoundError gains explanatory context.
- io.write_file, text_clean.clean_dataframe, and dedup.deduplicate
  now reject non-DataFrame inputs with clear TypeError instead of
  cryptic pandas tracebacks downstream.
- dedup.deduplicate validates that survivor_rule=KEEP_MOST_RECENT has
  a usable date_column up front; the helper _select_survivor now
  raises (instead of silently falling back to keep_first) when called
  directly with bad arguments.
- dedup.deduplicate gains a structured no-op return when strategies
  is empty after auto-detection — preserves schema instead of crashing.
- analyze._detect_inconsistent_date_format narrows its bare except to
  (TypeError, ValueError) and logs a debug line so genuine bugs don't
  hide behind silent skip.

Tests:
- tests/test_audit_fixes.py grows by 11 cases covering the new
  validation paths (chunk_size, DataFrame guards, KEEP_MOST_RECENT
  date_column, enum validation, locale validation, JSON error wrapping).

Full project suite: 1208 passed, 4 skipped, 17 xfailed.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/core/io.py

@@ -184,9 +184,18 @@ def read_file(
     """
     filepath = Path(path)
     if not filepath.exists():
-        raise FileNotFoundError(f"File not found: {filepath}")
+        raise FileNotFoundError(
+            f"Input file not found: {filepath} "
+            f"(required for encoding/delimiter detection and reading)"
+        )
+    if chunk_size is not None and chunk_size <= 0:
+        raise ValueError(f"chunk_size must be positive; got {chunk_size}")
 
     suffix = filepath.suffix.lower()
+    logger.info(
+        "read_file: {} (suffix={}, chunk_size={})",
+        filepath, suffix, chunk_size,
+    )
     if suffix in (".xlsx", ".xls"):
         return _read_excel(filepath, header_row=header_row, sheet_name=sheet_name)
     else:
@@ -362,6 +371,10 @@ def write_file(
 
     Returns the resolved output Path.
     """
+    if not isinstance(df, pd.DataFrame):
+        raise TypeError(
+            f"write_file() requires a pandas DataFrame; got {type(df).__name__}"
+        )
     out = Path(path)
     fmt = file_format or out.suffix.lstrip(".").lower()
     if fmt in ("xlsx", "xls"):