v1.1.0: headerless mode for audits printed without row/column headings

Sniffs the format on page 1: printed row numbers -> gridded mode
(unchanged); UUID-keyed rows -> new headerless mode, which anchors rows
on per-record UUIDs and takes the column map from the page-1 header
labels. Handles fused cells (ID+Name+Org+networks), mid-value line
wraps, two-timestamp Time Frame ranges, wrapped header labels, and
per-export schema drift. Validated against three monthly JC PD audits:
120/120 cross-engine spot checks, 0 anomalies, field-identical to the
standalone converter it replaces.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012bQ28wRBiQYcSR9FzV9U7c

README.md

@@ -1,5 +1,7 @@
 # pinion-pdf
 
+*Those who would sacrifice essential Balls to preserve a little Ligma deserve Deez Nuts.* — Dave Adams
+
 Someone took a spreadsheet, hit "Print", and handed out the PDF. pinion
 turns that PDF back into a CSV — and then proves the conversion is right.
 
@@ -12,7 +14,7 @@ pip install pymupdf
 python3 -m pinion input.pdf output.csv
 ```
 
-Or install it properly and get a `pinion` command:
+Or install it and get a `pinion` command:
 
 ```
 pip install .
@@ -24,59 +26,79 @@ the first 50 batches first.
 
 ## Why you can't just use a normal PDF tool
 
-The obvious tools (pdftotext, pdfplumber, Camelot, Tabula) fail on printed
-spreadsheets, for three separate reasons.
+The obvious tools (pdftotext, pdfplumber, Camelot, Tabula) have some common
+trappings.
 
-**Trap 1 — text spills over the column next door.** Some columns are too
-narrow for what's in them, so the text is drawn straight over the top of
-the neighbouring cell. You, being a human with a brain, can see the issue;
-a position-based sort cannot. pdfplumber genuinely returns this for one
-cell:
+### Trap 1 — Text Spillover
+Some columns can be too narrow for what's in them, and text spills into neighbors.
+You, being a human with a brain, can see the issue - OCR or position-based sort cannot.
 
-```
-'l,o 0o5k:u5p6 :-0 M0 oAbMil eUTC'
-```
+Something like `pdfplumber` will interleave spillover text, giving you unrecoverable nonsense.
 
-That is `lookup - Mobile` and `05:56:00 AM UTC` interleaved letter by
-letter — unrecoverable once mixed. The fix: place text by where it
-**starts**, never by where it sits. A cell's text always begins at its own
-column's left edge, even when the tail spills past the far edge.
+Our approach measure start points and then uses the PDF's embedded information about write
+order to trace the text.
 
-**Trap 2 — blacked-out cells are invisible to text extraction.** A
-redacted cell has a black rectangle drawn over it. Rectangles are graphics,
-not text, so every text extractor reports the cell as simply empty — and
-you can no longer tell "nobody filled this in" from "somebody removed
-this". pinion finds the rectangles itself and emits a `reason_redacted`
-TRUE/FALSE column plus the bar's width in points (a rough proxy for how
-much text was removed). It also checks whether any text survives *under*
-each bar — if a redaction is cosmetic rather than destructive, it says so
-loudly.
+### Trap 2 - Redactions Get Lost
+Blacked out cells (which are graphics) can be invisible to text extractors.
 
-**Trap 3 — counting lines gives the wrong total.** Every page carries an
-"A B C D" strip labelling the columns, which looks exactly like a row of
-data to a line-counting script. Count lines naively and you invent one
-phantom row per page.
+This might conflate 'redacted' with 'empty', but we generally want to preserve this context.
+
+Our approach searches for redaction boxes, tracks their size, and adds some extra csv fields
+in case this information is relevant later. It also picks up on bad redactions
+(text survival under the boxes).
+
+
+### Trap 3 - Line Counts are Janky
+
+Every page in the typical export format carries an "A B C D ..." strip labeling columns.
+
+To a eegular old parser, that's a row...but it's obviously not.
+
+We avoid phantom rows by stripping those tracking rows from the output.
+
+
+## Two print formats
+
+pinion sniffs page 1 and picks the right strategy on its own:
+
+**Gridded** — Excel printed with row/column headings on. Printed row
+numbers and the letter strip anchor everything; this is the mode described
+below.
+
+**Headerless** — the same audits printed via "Microsoft Print to PDF" with
+headings off. No gridlines, no row numbers, adjacent cells fused into
+single text runs, cells wrapping mid-value, and a schema that drifts
+between exports (one month has a License Plate column, the next doesn't).
+Here pinion anchors rows on the UUID that starts every record and takes
+the column map from the file's own page-1 header labels. Columns absent
+from a given export are reported loudly and left empty — never guessed.
+
+If a file is neither, pinion says so and stops.
 
 ## How the pages are laid out
 
-A wide spreadsheet prints in vertical slices: page 1 holds columns A–D for
-rows 1–32, page 2 holds E–F for those same rows, and so on until the
-letters start over at A and the next 32 rows begin. One set of those pages
-is a "batch"; pinion works out the batch size itself by watching for the
-letter strip to restart.
+A wide spreadsheet prints in vertical slices. In the original data used to build this tool,
+page 1 held columns A-D for 32 rows, page 2 held E-F, etc. 
 
-Rebuilding one spreadsheet row therefore means stitching together pieces
-from several pages — which is why the printed row numbers in the left
-margin matter so much. pinion anchors every row to Excel's own numbering
-and verifies the chain runs 1..N with no gaps or repeats.
+For nomenclature, one set of those pages make a 'batch'. The pattern then repeats.
 
-## Checking its own working
+Rebuilding a row naturally requires stiching together several pages.
+
+We track our calculated rows against the reported row numbers to make sure we haven't
+lost the plot as we process things. Any misses or phantoms -> explosion.
+
+## How We Verify Accuracy
+
+Every run contains internal consistency checks to make sure things haven't gone off the rails.
+
+We track and log any row number deviation, unplaceable text, unplaceable redaction bars,
+and any time we made an assumption regarding a row number, etc because of a redaction bar overlap.
+
+If a layout assumption fails during processing, we stop and yell about it.
 
-pinion never hands you plausible-looking output without evidence. Every run
-ends with:
 
 ```
-CHECKING OUR OWN WORKING
+OUTPUT VALIDATION
 --------------------------------------------------------------
   rows written ............... 1,048,575
   rows expected .............. 1,048,575   ok
@@ -90,7 +112,7 @@ CHECKING OUR OWN WORKING
   blackout bars found ........ 2,714
   rows affected .............. 1,549
   which columns .............. {'G': 2702, 'H': 3, 'I': 1, '?': 8}
-  text readable under bars ... none (deleted, not merely covered)
+  text readable under bars ... none
 ```
 
 If a layout assumption fails, pinion stops with a specific complaint
@@ -98,25 +120,22 @@ instead of guessing. Wrong-but-plausible output is the one failure mode
 this tool must never have. See [VERIFICATION.md](VERIFICATION.md) for how
 the reference conversion was validated.
 
-## A warning about truncated data
+## Data Truncation Warning
 
-Excel tops out at 1,048,576 rows; anything past that was thrown away
-before the PDF existed. pinion warns when a sheet lands exactly on that
-ceiling — data that stops precisely at the maximum was almost certainly
-cut off there, so every total from such a file is a floor, not a full
-count. We can warn about it, but we can't recover data that isn't in the
-PDF.
+Excel tops out at 1,048,576 rows.
 
-## Layout of the code
+If you get a result with 1,048,575 rows + a header row, you must either immediately purchase
+a lottery ticket or consider the possibility of data truncation.
+
+We issue a warning, but the truncated data is an issue in the source PDF.
+
+## General Code Layout
 
 ```
-pinion/page.py     — reading what the PDF drew on a page
-pinion/measure.py  — measuring the layout (one function per fact)
-pinion/convert.py  — rebuilding the rows
-pinion/cli.py      — run it, then check our own working
+pinion/page.py       — Read the PDF page layout
+pinion/measure.py    — Measure the gridded layout
+pinion/convert.py    — Rebuilt rows using our measurements
+pinion/headerless.py — The no-headings variant (UUID + header-label anchors)
+pinion/cli.py        — Format sniffing, execution harness + inline sanity checks
 ```
 
----
-
-*Those who would sacrifice essential Ligma to preserve a little Balls
-deserve Deez Nuts.* — Dave Adams

VERIFICATION.md

@@ -61,3 +61,38 @@ diffing against known-good output or by a check line going red:
   absent: the export was truncated at the ceiling, and roughly 17% of the
   original records are estimated to be missing. All totals from the file
   are floors.
+
+# Headerless mode (v1.1.0)
+
+Validated against three monthly Johnson City TN PD audit PDFs printed via
+"Microsoft Print to PDF" without headings (48/74/69 pages; 2,032 / 2,437 /
+2,607 rows).
+
+Checks passed on all three:
+
+1. **Cross-engine spot check.** 40 random rows per file re-verified
+   against pdftotext: 120/120 after making the probes wrap-tolerant (a
+   cell that wraps mid-value in the PDF never appears as one contiguous
+   string in any text extractor's output — probe fragments, not lines).
+2. **Range sanity.** Every two-timestamp Time Frame parses and runs
+   start <= end (7,074 ranges, 0 disordered).
+3. **Anomaly counters at zero**: no bad UUIDs, no unplaced runs, no
+   strays in the Name/Org windows.
+
+Bugs the checks caught during development:
+
+- **Wrapped header labels** ('Total Networks ' / 'Searched' on stacked
+  runs) made a whole column invisible to name-matching; its values landed
+  in the Org Name window and were silently overwritten. Fixed by merging
+  stacked header runs; guarded by the strays counter.
+- **A false-alarm verification failure**: 43 spot-checks "failed" because
+  the probe assumed cells appear contiguously in pdftotext output. Glyph
+  level inspection proved the CSV was right and the check was naive.
+  Kept here as a reminder: verify the verifier before trusting a red
+  result.
+
+Schema drift is real: of the three sibling exports, one has all 13
+columns, two lack License Plate entirely, and Total Networks Searched
+moves between its own column and being fused into the ID run. Headerless
+mode takes each file's schema from its own header and reports what's
+absent.

pinion/__init__.py

@@ -5,10 +5,11 @@ measures the PDF before it reads it -- page size, column widths, column and
 row counts are all derived from the file -- rebuilds every row, detects
 blacked-out cells, and then checks its own working.
 
-    MAP:  page.py     - reading what the PDF drew on a page
-          measure.py  - measuring the layout
-          convert.py  - rebuilding the rows
-          cli.py      - run it, then check our own working
+    MAP:  page.py       - reading what the PDF drew on a page
+          measure.py    - measuring the gridded layout
+          convert.py    - rebuilding gridded rows
+          headerless.py - the no-headings variant (UUID + header anchors)
+          cli.py        - sniff the format, run it, check our own working
 """
 
-__version__ = "1.0.0"
+__version__ = "1.1.0"

pinion/cli.py

@@ -7,8 +7,10 @@ try:
 except ImportError:
     sys.exit("Missing dependency. Run:  pip install pymupdf")
 
+from .page import all_runs, run_start, run_text
 from .measure import measure
 from .convert import convert
+from .headerless import looks_headerless, convert_headerless
 
 
 def report(label, value, ok=None, bad=""):
@@ -24,9 +26,30 @@ def main():
     ap.add_argument("pdf")
     ap.add_argument("out_csv")
     ap.add_argument("--limit", type=int, default=0,
-                    help="only convert the first N batches, for a trial run")
+                    help="only convert the first N batches (gridded) or "
+                         "pages (headerless), for a trial run")
     args = ap.parse_args()
 
+    # Which of the two known print formats is this? A gridded Excel print
+    # puts its row numbers leftmost on the page; the headerless audit
+    # variant has no headings at all but keys every row with a UUID.
+    doc = pymupdf.open(args.pdf)
+    runs0 = all_runs(doc[0])
+    if not runs0:
+        sys.exit("Page 1 has no text at all. Is this a scan?")
+    leftmost = min(runs0, key=lambda r: run_start(r)[0])
+    if run_text(leftmost).strip().isdigit():
+        doc.close()
+        run_gridded(args)
+    elif looks_headerless(doc[0]):
+        run_headerless(doc, args)
+    else:
+        sys.exit("Unrecognised layout: no printed row numbers (gridded "
+                 "Excel print) and no UUID-keyed rows (headerless audit "
+                 "print). pinion doesn't know this format yet.")
+
+
+def run_gridded(args):
     M = measure(args.pdf)
     n_batches = min(args.limit, M["n_batches"]) if args.limit else M["n_batches"]
 
@@ -76,3 +99,39 @@ def main():
         print("\n  WARNING: exactly 1,048,576 rows. That's an Excel magic number")
         print("  The source was almost certainly truncated.")
         print("  Go tell the source they owe you more data.")
+
+
+def run_headerless(doc, args):
+    n_pages = min(args.limit, doc.page_count) if args.limit else doc.page_count
+    print("HEADERLESS AUDIT PRINT  (no row/column headings; anchoring on")
+    print("the page-1 header labels and per-row UUIDs)")
+    print("-" * 62)
+    with open(args.out_csv, "w", newline="", encoding="utf-8") as fh:
+        r = convert_headerless(doc, csv.writer(fh), n_pages)
+    doc.close()
+    s = r["stats"]
+
+    print(f"\nWritten to {args.out_csv}\n")
+    print("-" * 62)
+    report("rows written", f"{r['n_rows']:,}")
+    report("columns in this export", f"{len(r['names'])}/13",
+           not r["missing"],
+           f"absent from the PDF itself: {r['missing']}")
+    report("org name (derived)", repr(r["org"]))
+    report("time-frame ranges",
+           f"{r['tf_ok']:,} valid pairs, {r['tf_single']} single",
+           r["tf_bad"] == 0, f"{r['tf_bad']} MALFORMED")
+    report("rows with a bad uuid", s["rows_bad_uuid"],
+           s["rows_bad_uuid"] == 0, "ROWS DROPPED")
+    report("unplaced runs", s["runs_no_column"] + s["runs_below_last_anchor"],
+           s["runs_no_column"] + s["runs_below_last_anchor"] == 0,
+           "SOME TEXT LOST")
+    report("strays in name/org windows", s["strays_in_name_org_windows"],
+           s["strays_in_name_org_windows"] == 0, "COLUMN MAPPED WRONG")
+    report("fused SearchTime split", s["searchtime_splits"])
+    report("blackout bars found", len(r["bars"]))
+    if r["missing"]:
+        print(f"\n  NOTE: this export has no {', '.join(r['missing'])} "
+              "column(s).")
+        print("  That data was never in the PDF; the CSV column is left "
+              "empty.")

pinion/headerless.py

@@ -0,0 +1,215 @@
+"""The headerless variant: audit exports printed WITHOUT row/column headings.
+
+Some Flock audit PDFs come out of "Microsoft Print to PDF" with Excel's
+headings switched off. Everything the gridded mode anchors on is missing,
+and this format brings its own traps on top:
+
+  - No gridlines and no printed row numbers, so rows are anchored on the
+    one structural fact left: every row starts with a UUID in the ID
+    column. Column edges come from the page-1 header labels' x-positions.
+  - Adjacent cells FUSE into one text run when columns are tight: ID +
+    Name + Org Name (+ sometimes the networks count) arrive as a single
+    string and must be split apart again. Search Time occasionally fuses
+    with Search Type; the fixed 27-character timestamp gives the split
+    point.
+  - Long header labels wrap onto stacked runs ('Total Networks ' over
+    'Searched'), so header runs sharing an x are merged before matching
+    names. Miss this and a whole column silently lands in the wrong
+    window -- it happened; the strays counter now guards it.
+  - Cells wrap mid-value ('05/05/2026, 04:12:32 ' + 'AM UTC'), and the
+    wrap point moves between exports. Fragments are rejoined by position.
+  - Time Frame is TWO stacked timestamps -- the full start/end range --
+    joined here as 'start - end'.
+  - The schema itself drifts between exports (License Plate present one
+    month, gone the next), so columns are taken from each file's own
+    header, and missing ones are reported, not guessed.
+"""
+
+import re
+import sys
+import collections
+import statistics
+from bisect import bisect_left
+
+from .page import all_runs, run_text, solid_regions
+
+CANON = ["ID", "Name", "Org Name", "Total Networks Searched", "Time Frame",
+         "License Plate", "Reason", "Case #", "Filters", "Search Time",
+         "Search Type", "Text Prompt", "Moderation"]
+UUID = re.compile(r'^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}')
+TS = re.compile(r'^\d{2}/\d{2}/\d{4}, \d{2}:\d{2}:\d{2} [AP]M UTC$')
+TS_PREFIX = re.compile(r'^(\d{2}/\d{2}/\d{4}, \d{2}:\d{2}:\d{2} [AP]M UTC)(.+)$')
+
+
+def looks_headerless(page0):
+    """Does page 1 carry UUID-keyed rows? That's this variant's signature."""
+    return any(UUID.match(run_text(r)) for r in all_runs(page0))
+
+
+def parse_header(page0_runs, header_cut):
+    """Column names and x-positions from the page-1 header band.
+
+    A long label wraps onto stacked runs at the same x, so runs sharing an
+    x are merged (in y-order) before searching for the canonical names.
+    Char positions are kept so names inside a fused label still yield
+    their own x.
+    """
+    hdr = [r for r in page0_runs if r["chars"][0][2][1] < header_cut]
+    by_x = collections.defaultdict(list)
+    for r in hdr:
+        x0 = r["chars"][0][2][0]
+        key = next((k for k in by_x if abs(k - x0) < 1.2), x0)
+        by_x[key].append(r)
+
+    col_x = {}
+    for x0, rs in by_x.items():
+        rs.sort(key=lambda r: r["chars"][0][2][1])
+        text = " ".join(run_text(r).strip() for r in rs)
+        cmap, pos = {}, 0
+        for r in rs:
+            raw = run_text(r)
+            lead = len(raw) - len(raw.lstrip())
+            for i in range(len(raw.strip())):
+                cmap[pos + i] = r["chars"][lead + i]
+            pos += len(raw.strip()) + 1
+        for name in CANON:
+            i = text.find(name)
+            if i >= 0 and name not in col_x and i in cmap:
+                col_x[name] = cmap[i][2][0]
+
+    ordered = sorted((x, n) for n, x in col_x.items())
+    names = [n for _, n in ordered]
+    edges = [x for x, _ in ordered] + [1e9]
+    missing = [n for n in CANON if n not in col_x]
+    return names, edges, missing
+
+
+def convert_headerless(doc, out, n_pages):
+    """Rebuild every row and write it to `out` (a csv.writer). Returns a
+    stats dict for the checks at the end."""
+    p1 = all_runs(doc[0])
+    anchors1 = sorted(r["chars"][0][2][1] for r in p1 if UUID.match(run_text(r)))
+    header_cut = anchors1[0] - 6.0
+    names, edges, missing = parse_header(p1, header_cut)
+
+    def col_at(x):
+        i = bisect_left(edges, x + 2.5) - 1
+        return names[i] if 0 <= i < len(names) else None
+
+    stats = collections.Counter()
+    stats["columns_missing"] = 0    # ensure keys exist even when zero
+    rows, gaps, bars = [], [], []
+    for pno in range(n_pages):
+        runs = all_runs(doc[pno])
+        anchors = sorted((r["chars"][0][2][1], r) for r in runs
+                         if UUID.match(run_text(r)))
+        if not anchors:
+            stats["pages_without_rows"] += 1
+            continue
+        ys = [a[0] for a in anchors]
+        gaps += [b - a for a, b in zip(ys, ys[1:])]
+        page_rows = [collections.defaultdict(list) for _ in anchors]
+
+        # place every text run in its row (nearest anchor at or below it)
+        # and its column (by where the run STARTS)
+        for r in runs:
+            x, y = r["chars"][0][2]
+            if pno == 0 and y < header_cut:
+                continue
+            i = bisect_left(ys, y - 1.0)
+            if i == len(ys):
+                i -= 1
+                if y > ys[-1] + 2.0:
+                    stats["runs_below_last_anchor"] += 1
+            c = col_at(x)
+            if c is None:
+                stats["runs_no_column"] += 1
+                continue
+            page_rows[i][c].append((y, x, run_text(r)))
+
+        # blackout bars, same shape test as the gridded mode
+        pitch = statistics.median(gaps) if gaps else 12.0
+        for box in solid_regions(doc[pno]):
+            if box.width <= 1.2 or box.height <= pitch * 0.5:
+                continue
+            yc = (box.y0 + box.y1) / 2
+            i = min(bisect_left(ys, yc - 1.0), len(ys) - 1)
+            bars.append((pno, i, round(box.width, 1)))
+            page_rows[i]["_bar"] = max(page_rows[i].get("_bar") or 0,
+                                       round(box.width, 1))
+        rows.extend(page_rows)
+
+    # --- assemble cells from fragments ---
+    def join(parts):
+        parts = [p[2].strip() for p in sorted(parts)]
+        parts = [p for p in parts if p]
+        if len(parts) == 2 and all(TS.match(p) for p in parts):
+            return " - ".join(parts)          # Time Frame start/end range
+        return re.sub(r'\s+', ' ', " ".join(parts)).strip()
+
+    out_rows, middles = [], []
+    for row in rows:
+        bar = row.pop("_bar", None)
+        # Name/Org come only from splitting the fused ID run; anything
+        # assigned to them directly means a column landed in the wrong
+        # window and would be silently clobbered below.
+        if row.get("Name") or row.get("Org Name"):
+            stats["strays_in_name_org_windows"] += 1
+        cell = {c: join(v) for c, v in row.items()}
+
+        fused = cell.get("ID", "")
+        m = UUID.match(fused)
+        if not m:
+            stats["rows_bad_uuid"] += 1
+            continue
+        rest = fused[36:]
+        digits = re.search(r'(\d+)$', rest)
+        if digits and not cell.get("Total Networks Searched"):
+            cell["Total Networks Searched"] = digits.group(1)
+            rest = rest[:digits.start()]
+        cell["ID"] = m.group(0)
+        cell["_rest"] = rest
+        middles.append(rest)
+
+        st = cell.get("Search Time", "")
+        pm = TS_PREFIX.match(st)
+        if pm:
+            cell["Search Time"] = pm.group(1)
+            cell["Search Type"] = (pm.group(2) + " "
+                                   + cell.get("Search Type", "")).strip()
+            stats["searchtime_splits"] += 1
+        cell["_bar"] = bar
+        out_rows.append(cell)
+
+    # Name / Org split: the org is the longest common suffix of every
+    # fused middle -- constant per file, while names vary
+    org = middles[0] if middles else ""
+    for s in middles[1:]:
+        while org and not s.endswith(org):
+            org = org[1:]
+    if len(org) < 4:
+        sys.exit(f"Could not derive a constant Org Name (got {org!r}); "
+                 "refusing to split names by guesswork.")
+    for cell in out_rows:
+        cell["Name"] = cell.pop("_rest")[:-len(org)].strip()
+        cell["Org Name"] = org
+
+    out.writerow(CANON + ["reason_redacted", "redaction_bar_pt"])
+    for cell in out_rows:
+        bar = cell.pop("_bar")
+        out.writerow([cell.get(c, "") for c in CANON]
+                     + ["TRUE" if bar else "FALSE", bar or ""])
+
+    tf_ok = tf_single = tf_bad = 0
+    for cell in out_rows:
+        tf = cell.get("Time Frame", "")
+        if " - " in tf:
+            a, b = tf.split(" - ", 1)
+            tf_ok += bool(TS.match(a) and TS.match(b))
+            tf_bad += not (TS.match(a) and TS.match(b))
+        elif tf:
+            tf_single += 1
+    stats["columns_missing"] = len(missing)
+    return {"n_rows": len(out_rows), "org": org, "names": names,
+            "missing": missing, "tf_ok": tf_ok, "tf_single": tf_single,
+            "tf_bad": tf_bad, "bars": bars, "stats": stats}

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pinion-pdf"
-version = "1.0.0"
+version = "1.1.0"
 description = "Turn a spreadsheet that was printed to PDF back into a CSV, and prove the conversion is right."
 readme = "README.md"
 requires-python = ">=3.9"