#!/usr/bin/env python3
"""Descriptive numbers for a threads CSV (published format).
Usage: python3 -I scripts/stats.py data/threads.csv [--relevant-only]"""
import csv, sys, statistics, collections

rows = list(csv.DictReader(open(sys.argv[1])))
if "--relevant-only" in sys.argv and rows and "relevance" in rows[0]:
    rows = [r for r in rows if r["relevance"] == "yes"]
n = len(rows)
print(f"threads: {n}")


def pct(k, tot):
    return f"{100 * k / tot:.0f}%" if tot else "-"


def med(vals):
    v = [float(x) for x in vals if x not in ("", None)]
    return (f"{statistics.median(v):.1f} h (n={len(v)})") if v else "-"


print(f"resolved: {pct(sum(r['resolved'] == 'yes' for r in rows), n)}")
print(f"maintainer replied: {pct(sum(r['maintainer_replied'] == 'yes' for r in rows), n)}")
print(f"no reply at all (no time_to_first_response): {pct(sum(r['time_to_first_response_h'] == '' for r in rows), n)}")
print(f"median time to first response: {med(r['time_to_first_response_h'] for r in rows)}")
print(f"median time to close (closed only): {med(r['time_to_close_h'] for r in rows)}")
for col in ["thread_type", "repo_group", "target_host", "app_type", "framework", "topic", "error_type",
            "resolution_type", "ai_generated_app", "ai_tool", "coding_confidence"]:
    c = collections.Counter(r[col] or "(empty)" for r in rows)
    print(f"\n{col}: " + ", ".join(f"{k} {v} ({pct(v, n)})" for k, v in c.most_common(20)))
prov = collections.Counter(p for r in rows for p in (r["model_provider"] or "unspecified").split(";"))
print("\nmodel_provider (multi): " + ", ".join(f"{k} {v}" for k, v in prov.most_common(12)))
# topic x resolution / response, for the categories with >= 20 threads
print("\nby topic: n, resolved, median first response")
by = collections.defaultdict(list)
for r in rows:
    by[r["topic"]].append(r)
for k, lst in sorted(by.items(), key=lambda kv: -len(kv[1])):
    if len(lst) >= 20:
        print(f"  {k:22s} {len(lst):5d} {pct(sum(x['resolved'] == 'yes' for x in lst), len(lst)):>5s}  {med(x['time_to_first_response_h'] for x in lst)}")
print("\nby target_host (>= 20): n, resolved, maintainer replied, median first response")
by = collections.defaultdict(list)
for r in rows:
    by[r["target_host"]].append(r)
for k, lst in sorted(by.items(), key=lambda kv: -len(kv[1])):
    if len(lst) >= 20:
        print(f"  {k:22s} {len(lst):5d} {pct(sum(x['resolved'] == 'yes' for x in lst), len(lst)):>5s} "
              f"{pct(sum(x['maintainer_replied'] == 'yes' for x in lst), len(lst)):>5s}  {med(x['time_to_first_response_h'] for x in lst)}")
if rows and "thread_type" in rows[0]:
    types = ["problem", "feature", "docs", "question", "pr_ci", "other"]
    print("\nby thread_type: n, resolved, maintainer replied, no reply, median first response")
    for tt in types:
        lst = [r for r in rows if r["thread_type"] == tt]
        if lst:
            print(f"  {tt:10s} {len(lst):5d} {pct(sum(x['resolved'] == 'yes' for x in lst), len(lst)):>5s} "
                  f"{pct(sum(x['maintainer_replied'] == 'yes' for x in lst), len(lst)):>5s} "
                  f"{pct(sum(x['time_to_first_response_h'] == '' for x in lst), len(lst)):>5s}  {med(x['time_to_first_response_h'] for x in lst)}")
    print("\nthread_type by target_host (hosts >= 20): n, then share of each type")
    by = collections.defaultdict(list)
    for r in rows:
        by[r["target_host"]].append(r)
    print("  " + " " * 22 + "     n " + " ".join(f"{t:>8s}" for t in types))
    for k, lst in sorted(by.items(), key=lambda kv: -len(kv[1])):
        if len(lst) >= 20:
            print(f"  {k:22s} {len(lst):5d} " + " ".join(f"{pct(sum(x['thread_type'] == t for x in lst), len(lst)):>8s}" for t in types))
    for tt in ("feature", "docs"):
        c = collections.Counter(r["target_host"] for r in rows if r["thread_type"] == tt)
        print(f"\nhosts with the most {tt} threads: " + ", ".join(f"{k} {v}" for k, v in c.most_common(8)))
    print("\nthread_type by repo_group: " + "; ".join(
        f"{g}: " + ", ".join(f"{t} {pct(sum(x['thread_type'] == t for x in rows if x['repo_group'] == g), sum(x['repo_group'] == g for x in rows))}" for t in types)
        for g in sorted({r['repo_group'] for r in rows})))
print("\nby year: n")
c = collections.Counter(r["created_at"][:4] for r in rows)
print("  " + ", ".join(f"{k}: {v}" for k, v in sorted(c.items())))