Datasets:

hazemessam
/

SSP

"""Check a built SSP (output of build_ssp.py). Read-only; prints a report and exits non-zero on any failure.
usage: python -I scripts/validate_ssp.py [build/ssp]
Checks
  1. every file has exactly the shared schema (same column names and types)
  2. per row: every per-residue column as long as the sequence; label alphabets (H E C / G H I B E S T C / + P);
     ss3 = TAPE's 8->3 reduction of ss8; ss8 = ss8_pp with P replaced by C, T or S; resolved = 0 -> labelled C;
     rcsb/casp: resolved = 1 -> backbone N, CA, C, O present
  3. rcsb/casp: no duplicate sequences within a file (tape: duplicates reported, kept as TAPE has them)
  4. tape: identical to TAPE's files (same rows in the same order; every field equal; only ints -> letters)
  5. rcsb CASP12: TAPE's 21 chains present (in_tape) and labels agree with TAPE (>= 99.9% on resolved residues)
  6. coverage: every CASP-listed target is in an rcsb row or logged as dropped (with a reason)
  7. summary: rows per file, dropped targets by reason, polyproline handling
Results are saved to {out}/logs/validation.json.
"""
import os, sys, json, glob, collections
sys.path.insert(0, "scripts")
import pyarrow.parquet as pq
import ssp_lib as L

OUT = sys.argv[1] if len(sys.argv) > 1 else "build/ssp"
TAPE_DIR = "data/original/tape/secondary_structure/secondary_structure"
TAPE_FILES = {"train": "secondary_structure_train.json", "validation": "secondary_structure_valid.json",
              "cb513": "secondary_structure_cb513.json", "ts115": "secondary_structure_ts115.json",
              "casp12": "secondary_structure_casp12.json"}
PER_RESIDUE = ["ss3", "ss8", "ss8_pp", "resolved", "valid_mask", "rsa", "asa_max", "phi", "psi", "interface",
               "evolutionary", "n_xyz", "ca_xyz", "c_xyz", "o_xyz", "cb_xyz"]
fail, results = [], {"checks": []}


def check(ok, msg):
    print(("  ok   " if ok else "  FAIL ") + msg, flush=True)
    results["checks"].append({"ok": bool(ok), "check": msg})
    if not ok:
        fail.append(msg)


def rows_of(name, batch=200):
    for b in pq.ParquetFile(f"{OUT}/{name}").iter_batches(batch_size=batch):
        yield from b.to_pylist()


files = sorted(os.path.relpath(f, OUT) for f in glob.glob(f"{OUT}/*/*.parquet"))
meta = {k: pq.ParquetFile(f"{OUT}/{k}") for k in files}
print(f"== {len(files)} files in {OUT}: {files}")

# 1. schema
first = next(iter(meta.values())).schema_arrow
check(all(m.schema_arrow.equals(first) for m in meta.values()), "all files share one schema (names and types)")

# 2-3. per-row consistency and duplicates
S3OF = L.SS8_TO_SS3
for name in files:
    bad, seqs, ids, n = collections.Counter(), collections.Counter(), collections.Counter(), 0
    for r in rows_of(name):
        n += 1
        L_ = len(r["sequence"])
        seqs[r["sequence"]] += 1; ids[r["id"]] += 1
        bad["length"] += any(r[c] is not None and len(r[c]) != L_ for c in PER_RESIDUE)
        bad["no_resolved"] += r["resolved"] is None
        bad["alphabet"] += not (set(r["ss3"]) <= set("HEC") and set(r["ss8"]) <= set("GHIBESTC")
                                and (r["ss8_pp"] is None or set(r["ss8_pp"]) <= set("GHIBESTCP")))
        bad["ss3_from_ss8"] += any(S3OF[a] != b for a, b in zip(r["ss8"], r["ss3"]) if a in S3OF)
        if r["ss8_pp"] is not None:
            bad["ss8_from_ss8_pp"] += any(p != c and not (p == "P" and c in "CTS") for p, c in zip(r["ss8_pp"], r["ss8"]))
        if r["resolved"] is not None:
            bad["unresolved_not_C"] += any(m == 0 and (s != "C" or (r["ss8_pp"] or "C" * L_)[k] != "C")
                                           for k, (m, s) in enumerate(zip(r["resolved"], r["ss8"])))
            if r["ca_xyz"] is not None:  # rcsb / casp: DSSP labels only residues with a complete backbone
                bad["resolved_without_backbone"] += sum(
                    1 for k, m in enumerate(r["resolved"])
                    if m == 1 and any(r[f"{a}_xyz"][k] is None for a in ("n", "ca", "c", "o")))
    dup = sum(v - 1 for v in seqs.values())
    if name.startswith("tape/"):
        check(not any(bad.values()), f"{name}: {n} rows, consistent ({dict(+bad) or 'no problems'}); "
                                     f"kept as in TAPE: {dup} repeated sequences, {sum(v - 1 for v in ids.values())} repeated ids")
    else:
        check(not any(bad.values()) and dup == 0,
              f"{name}: {n} rows, consistent ({dict(+bad) or 'no problems'}), duplicate sequences: {dup}")

# 4. tape == TAPE, field by field, in TAPE's order
for split, fn in TAPE_FILES.items():
    name = f"tape/{split}.parquet"
    if name not in meta:
        continue
    src = json.load(open(f"{TAPE_DIR}/{fn}"))
    diff, k = collections.Counter(), -1
    for k, r in enumerate(rows_of(name)):
        s = src[k]
        diff["id"] += r["id"] != s["id"]
        diff["sequence"] += r["sequence"] != s["primary"]
        diff["ss8"] += r["ss8"] != "".join("GHIBESTC"[c] for c in s["ss8"])
        diff["ss3"] += r["ss3"] != "".join("HEC"[c] for c in s["ss3"])
        diff["resolved=disorder"] += r["resolved"] != s["disorder"]
        for f in ("valid_mask", "rsa", "asa_max", "phi", "psi", "interface", "evolutionary"):
            diff[f] += r[f] != s[f]
        src[k] = None
    diff["row_count"] += k + 1 != len(src)
    del src
    check(not any(diff.values()), f"{name}: identical to TAPE's {fn} ({k + 1} rows; differences: {dict(+diff) or 'none'})")

# 5. rcsb CASP12 vs TAPE
if "rcsb/casp12.parquet" in meta:
    tape_src = {r["id"]: r for r in json.load(open(f"{TAPE_DIR}/{TAPE_FILES['casp12']}"))}
    rr = {r["id"]: r for r in rows_of("rcsb/casp12.parquet")}
    in_tape = {i for i, r in rr.items() if r["in_tape"]}
    check(in_tape == set(tape_src), f"rcsb/casp12: TAPE's 21 chains present and flagged in_tape ({len(in_tape)})")
    from compare_ss_tools_helpers import project
    n = same = 0
    flag = collections.Counter()  # TAPE's disorder flag vs whether the residue has structure in today's PDB entry
    for i in in_tape:
        s = tape_src[i]
        lab, _ = project(s["primary"], rr[i]["sequence"], list(zip(rr[i]["ss8"], rr[i]["resolved"])))
        for k, d in enumerate(s["disorder"]):
            flag[f"tape_disorder_{d}__pdb_" + ("not_aligned" if lab[k] is None else f"resolved_{lab[k][1]}")] += 1
            if d == 1 and lab[k] is not None and lab[k][1] == 1:
                n += 1; same += lab[k][0] == "GHIBESTC"[s["ss8"][k]]
    results["rcsb_casp12_vs_tape"] = {"residues": n, "agree": same, "disorder_flag_vs_pdb": dict(flag)}
    print("       TAPE disorder flag vs structure in today's PDB entry:", dict(flag))
    check(n > 0 and same / n >= 0.999, f"rcsb/casp12: labels agree with TAPE on {100 * same / max(n, 1):.2f}% of {n:,} residues resolved in both")

# 6. coverage of CASP-listed targets by rcsb rows
for f in sorted(glob.glob(f"{OUT}/logs/rcsb_casp*.json")):
    log = json.load(open(f))
    split = log["split"]
    if f"rcsb/{split}.parquet" not in meta:
        continue
    listed = {t for t, c in L.listed_targets(f"data/original/{split}").items() if c}
    covered = {t for r in rows_of(f"rcsb/{split}.parquet") for t in r["casp_targets"]}
    dropped_ids = {d["id"] for d in log["dropped"]}
    deduped_targets = set()  # targets whose whole row was dropped as a duplicate
    for d in log["dropped"]:
        if "-" in d["id"]:
            deduped_targets |= {t for t, v in log["targets"].items() if f"{v['pdb']}-{v['chain']}" == d["id"]}
    missing = listed - covered - dropped_ids - deduped_targets
    check(not missing, f"rcsb/{split}: all {len(listed)} CASP-listed targets covered or logged ({len(covered)} in rows)"
                       + (f"; unaccounted: {sorted(missing)}" if missing else ""))

# 7. summary
print("\n== summary")
for f in sorted(glob.glob(f"{OUT}/logs/*_*.json")):
    log = json.load(open(f))
    if "split" not in log:
        continue
    key = f"{log['source']}/{log['split']}.parquet"
    if log.get("source") is None:
        continue
    reasons = collections.Counter(d["reason"].split(" as ")[0] if d["reason"].startswith("same sequence") else d["reason"] for d in log["dropped"])
    print(f"  {key:26s} rows {meta[key].metadata.num_rows if key in meta else '-':>5} | dropped {len(log['dropped'])}: {dict(reasons)} | polyproline {log.get('polyproline') or '-'}")
print(f"\n{'ALL CHECKS PASSED' if not fail else f'{len(fail)} CHECK(S) FAILED'}")
import datetime
results.update(passed=not fail, date=datetime.date.today().isoformat())
json.dump(results, open(f"{OUT}/logs/validation.json", "w"), indent=1)
sys.exit(1 if fail else 0)

	"""Check a built SSP (output of build_ssp.py). Read-only; prints a report and exits non-zero on any failure.

	usage: python -I scripts/validate_ssp.py [build/ssp]
	Checks
	1. every file has exactly the shared schema (same column names and types)
	2. per row: every per-residue column as long as the sequence; label alphabets (H E C / G H I B E S T C / + P);
	ss3 = TAPE's 8->3 reduction of ss8; ss8 = ss8_pp with P replaced by C, T or S; resolved = 0 -> labelled C;
	rcsb/casp: resolved = 1 -> backbone N, CA, C, O present
	3. rcsb/casp: no duplicate sequences within a file (tape: duplicates reported, kept as TAPE has them)
	4. tape: identical to TAPE's files (same rows in the same order; every field equal; only ints -> letters)
	5. rcsb CASP12: TAPE's 21 chains present (in_tape) and labels agree with TAPE (>= 99.9% on resolved residues)
	6. coverage: every CASP-listed target is in an rcsb row or logged as dropped (with a reason)
	7. summary: rows per file, dropped targets by reason, polyproline handling
	Results are saved to {out}/logs/validation.json.
	"""
	import os, sys, json, glob, collections
	sys.path.insert(0, "scripts")
	import pyarrow.parquet as pq
	import ssp_lib as L

	OUT = sys.argv[1] if len(sys.argv) > 1 else "build/ssp"
	TAPE_DIR = "data/original/tape/secondary_structure/secondary_structure"
	TAPE_FILES = {"train": "secondary_structure_train.json", "validation": "secondary_structure_valid.json",
	"cb513": "secondary_structure_cb513.json", "ts115": "secondary_structure_ts115.json",
	"casp12": "secondary_structure_casp12.json"}
	PER_RESIDUE = ["ss3", "ss8", "ss8_pp", "resolved", "valid_mask", "rsa", "asa_max", "phi", "psi", "interface",
	"evolutionary", "n_xyz", "ca_xyz", "c_xyz", "o_xyz", "cb_xyz"]
	fail, results = [], {"checks": []}


	def check(ok, msg):
	print((" ok " if ok else " FAIL ") + msg, flush=True)
	results["checks"].append({"ok": bool(ok), "check": msg})
	if not ok:
	fail.append(msg)


	def rows_of(name, batch=200):
	for b in pq.ParquetFile(f"{OUT}/{name}").iter_batches(batch_size=batch):
	yield from b.to_pylist()


	files = sorted(os.path.relpath(f, OUT) for f in glob.glob(f"{OUT}//.parquet"))
	meta = {k: pq.ParquetFile(f"{OUT}/{k}") for k in files}
	print(f"== {len(files)} files in {OUT}: {files}")

	# 1. schema
	first = next(iter(meta.values())).schema_arrow
	check(all(m.schema_arrow.equals(first) for m in meta.values()), "all files share one schema (names and types)")

	# 2-3. per-row consistency and duplicates
	S3OF = L.SS8_TO_SS3
	for name in files:
	bad, seqs, ids, n = collections.Counter(), collections.Counter(), collections.Counter(), 0
	for r in rows_of(name):
	n += 1
	L_ = len(r["sequence"])
	seqs[r["sequence"]] += 1; ids[r["id"]] += 1
	bad["length"] += any(r[c] is not None and len(r[c]) != L_ for c in PER_RESIDUE)
	bad["no_resolved"] += r["resolved"] is None
	bad["alphabet"] += not (set(r["ss3"]) <= set("HEC") and set(r["ss8"]) <= set("GHIBESTC")
	and (r["ss8_pp"] is None or set(r["ss8_pp"]) <= set("GHIBESTCP")))
	bad["ss3_from_ss8"] += any(S3OF[a] != b for a, b in zip(r["ss8"], r["ss3"]) if a in S3OF)
	if r["ss8_pp"] is not None:
	bad["ss8_from_ss8_pp"] += any(p != c and not (p == "P" and c in "CTS") for p, c in zip(r["ss8_pp"], r["ss8"]))
	if r["resolved"] is not None:
	bad["unresolved_not_C"] += any(m == 0 and (s != "C" or (r["ss8_pp"] or "C" * L_)[k] != "C")
	for k, (m, s) in enumerate(zip(r["resolved"], r["ss8"])))
	if r["ca_xyz"] is not None: # rcsb / casp: DSSP labels only residues with a complete backbone
	bad["resolved_without_backbone"] += sum(
	1 for k, m in enumerate(r["resolved"])
	if m == 1 and any(r[f"{a}_xyz"][k] is None for a in ("n", "ca", "c", "o")))
	dup = sum(v - 1 for v in seqs.values())
	if name.startswith("tape/"):
	check(not any(bad.values()), f"{name}: {n} rows, consistent ({dict(+bad) or 'no problems'}); "
	f"kept as in TAPE: {dup} repeated sequences, {sum(v - 1 for v in ids.values())} repeated ids")
	else:
	check(not any(bad.values()) and dup == 0,
	f"{name}: {n} rows, consistent ({dict(+bad) or 'no problems'}), duplicate sequences: {dup}")

	# 4. tape == TAPE, field by field, in TAPE's order
	for split, fn in TAPE_FILES.items():
	name = f"tape/{split}.parquet"
	if name not in meta:
	continue
	src = json.load(open(f"{TAPE_DIR}/{fn}"))
	diff, k = collections.Counter(), -1
	for k, r in enumerate(rows_of(name)):
	s = src[k]
	diff["id"] += r["id"] != s["id"]
	diff["sequence"] += r["sequence"] != s["primary"]
	diff["ss8"] += r["ss8"] != "".join("GHIBESTC"[c] for c in s["ss8"])
	diff["ss3"] += r["ss3"] != "".join("HEC"[c] for c in s["ss3"])
	diff["resolved=disorder"] += r["resolved"] != s["disorder"]
	for f in ("valid_mask", "rsa", "asa_max", "phi", "psi", "interface", "evolutionary"):
	diff[f] += r[f] != s[f]
	src[k] = None
	diff["row_count"] += k + 1 != len(src)
	del src
	check(not any(diff.values()), f"{name}: identical to TAPE's {fn} ({k + 1} rows; differences: {dict(+diff) or 'none'})")

	# 5. rcsb CASP12 vs TAPE
	if "rcsb/casp12.parquet" in meta:
	tape_src = {r["id"]: r for r in json.load(open(f"{TAPE_DIR}/{TAPE_FILES['casp12']}"))}
	rr = {r["id"]: r for r in rows_of("rcsb/casp12.parquet")}
	in_tape = {i for i, r in rr.items() if r["in_tape"]}
	check(in_tape == set(tape_src), f"rcsb/casp12: TAPE's 21 chains present and flagged in_tape ({len(in_tape)})")
	from compare_ss_tools_helpers import project
	n = same = 0
	flag = collections.Counter() # TAPE's disorder flag vs whether the residue has structure in today's PDB entry
	for i in in_tape:
	s = tape_src[i]
	lab, _ = project(s["primary"], rr[i]["sequence"], list(zip(rr[i]["ss8"], rr[i]["resolved"])))
	for k, d in enumerate(s["disorder"]):
	flag[f"tape_disorder_{d}__pdb_" + ("not_aligned" if lab[k] is None else f"resolved_{lab[k][1]}")] += 1
	if d == 1 and lab[k] is not None and lab[k][1] == 1:
	n += 1; same += lab[k][0] == "GHIBESTC"[s["ss8"][k]]
	results["rcsb_casp12_vs_tape"] = {"residues": n, "agree": same, "disorder_flag_vs_pdb": dict(flag)}
	print(" TAPE disorder flag vs structure in today's PDB entry:", dict(flag))
	check(n > 0 and same / n >= 0.999, f"rcsb/casp12: labels agree with TAPE on {100 * same / max(n, 1):.2f}% of {n:,} residues resolved in both")

	# 6. coverage of CASP-listed targets by rcsb rows
	for f in sorted(glob.glob(f"{OUT}/logs/rcsb_casp*.json")):
	log = json.load(open(f))
	split = log["split"]
	if f"rcsb/{split}.parquet" not in meta:
	continue
	listed = {t for t, c in L.listed_targets(f"data/original/{split}").items() if c}
	covered = {t for r in rows_of(f"rcsb/{split}.parquet") for t in r["casp_targets"]}
	dropped_ids = {d["id"] for d in log["dropped"]}
	deduped_targets = set() # targets whose whole row was dropped as a duplicate
	for d in log["dropped"]:
	if "-" in d["id"]:
	deduped_targets \|= {t for t, v in log["targets"].items() if f"{v['pdb']}-{v['chain']}" == d["id"]}
	missing = listed - covered - dropped_ids - deduped_targets
	check(not missing, f"rcsb/{split}: all {len(listed)} CASP-listed targets covered or logged ({len(covered)} in rows)"
	+ (f"; unaccounted: {sorted(missing)}" if missing else ""))

	# 7. summary
	print("\n== summary")
	for f in sorted(glob.glob(f"{OUT}/logs/_.json")):
	log = json.load(open(f))
	if "split" not in log:
	continue
	key = f"{log['source']}/{log['split']}.parquet"
	if log.get("source") is None:
	continue
	reasons = collections.Counter(d["reason"].split(" as ")[0] if d["reason"].startswith("same sequence") else d["reason"] for d in log["dropped"])
	print(f" {key:26s} rows {meta[key].metadata.num_rows if key in meta else '-':>5} \| dropped {len(log['dropped'])}: {dict(reasons)} \| polyproline {log.get('polyproline') or '-'}")
	print(f"\n{'ALL CHECKS PASSED' if not fail else f'{len(fail)} CHECK(S) FAILED'}")
	import datetime
	results.update(passed=not fail, date=datetime.date.today().isoformat())
	json.dump(results, open(f"{OUT}/logs/validation.json", "w"), indent=1)
	sys.exit(1 if fail else 0)