"""
Diagnose the confluence count in a saved reach graph: real tributary
junctions, or a data artifact (e.g. the same physical reach digitized
twice, or a tile-boundary duplication in BD TOPO).

Works two ways:
  1. Against your EXISTING risle_nodes.csv/risle_edges.csv (no rerun
     needed) -- checks whether each flagged confluence's incoming edges
     have suspiciously similar distance_km/elevation_drop_m, which is
     the signature of two near-identical tronçons rather than two
     genuinely different ones.
  2. Conclusively, once you rerun scripts/build_reach_graphs.py with the
     cleabs fix -- if two incoming edges at the same node share the same
     cleabs (tronçon ID), that's definitive: same tronçon counted twice,
     not two different ones.

Usage:
    python -m scripts.diagnose_confluences --data-root datasets --basin risle
"""
import argparse
from pathlib import Path

import pandas as pd


def diagnose(nodes_path: Path, edges_path: Path, n_samples: int = 10) -> None:
    nodes_df = pd.read_csv(nodes_path)
    edges_df = pd.read_csv(edges_path)

    has_is_confluence = "is_confluence" in nodes_df.columns
    has_toponym = "toponym" in edges_df.columns
    has_cleabs = "cleabs" in edges_df.columns

    if has_is_confluence:
        confluence_codes = set(nodes_df[nodes_df["is_confluence"]]["station_code"])
        confluences = edges_df[edges_df["target"].isin(confluence_codes)]["target"].value_counts()
        raw_in_degree = edges_df["target"].value_counts()
        raw_confluences = raw_in_degree[raw_in_degree >= 2]
        print(f"{len(confluence_codes)} REAL confluence(s) -- different named river, independent "
              f"upstream source, not a split that rejoins downstream -- out of "
              f"{len(raw_confluences)} node(s) with raw in-degree >= 2 "
              f"({len(nodes_df)} total nodes, {len(edges_df)} edges)")
        print(f"  The gap ({len(raw_confluences) - len(confluence_codes)} nodes) is same-river "
              f"segmentation and/or braided channels that split and rejoin -- both excluded "
              f"from the count above, using the authoritative is_confluence column computed "
              f"with full graph topology (this script can't re-derive the split-rejoin check "
              f"from the flat CSV alone, which is exactly why that column exists).")
    elif has_toponym:
        name_counts = edges_df.dropna(subset=["toponym"]).groupby("target")["toponym"].nunique()
        confluences = name_counts[name_counts > 1]
        raw_in_degree = edges_df["target"].value_counts()
        raw_confluences = raw_in_degree[raw_in_degree >= 2]
        print(f"{len(confluences)} REAL confluence(s) (different named river actually joining), "
              f"out of {len(raw_confluences)} node(s) with raw in-degree >= 2 "
              f"({len(nodes_df)} total nodes, {len(edges_df)} edges)")
        print(f"  The gap ({len(raw_confluences) - len(confluences)} nodes) is same-river fine "
              f"tronçon segmentation, not real branching -- this is now excluded from the count.")
        print("  NOTE: this file predates the split-rejoin fix (no is_confluence column) -- a "
              "braided channel with differently-named branches that rejoin downstream would "
              "still be miscounted here as a real confluence. Re-run "
              "scripts/build_reach_graphs.py for the corrected count.")
    else:
        in_degree = edges_df["target"].value_counts()
        confluences = in_degree[in_degree >= 2]
        print(f"{len(confluences)} node(s) with in-degree >= 2, out of {len(nodes_df)} total nodes "
              f"and {len(edges_df)} edges")
        print()
        print("NOTE: this edges.csv doesn't have a 'toponym' column -- it was generated before "
              "the name-based confluence fix. Every number above uses the old, less reliable "
              "in-degree >= 2 rule. Re-run scripts/build_reach_graphs.py for a corrected count "
              "and for this script to give a definitive per-node answer instead of a proxy.")
    print()

    sample = confluences.head(n_samples)
    n_confirmed_duplicate = 0
    n_suspicious = 0

    for node in sample.index:
        incoming = edges_df[edges_df["target"] == node]
        cols = ["source", "target", "distance_km", "elevation_drop_m"]
        cols += [c for c in ("toponym", "cleabs") if c in incoming.columns]
        print(f"Node {node}: {len(incoming)} incoming edge(s)")
        print(incoming[cols].to_string(index=False))

        if has_toponym:
            n_names = incoming["toponym"].nunique()
            if n_names > 1:
                print(f"  -> REAL: {n_names} distinct named river(s) join here.")
            else:
                print(f"  -> Same toponym on every incoming edge -- not counted as a real "
                      f"confluence (this shouldn't appear in the sample above; if it does, "
                      f"something's inconsistent between this script and build_reach_graph.py).")
        elif has_cleabs and incoming["cleabs"].notna().all():
            if incoming["cleabs"].nunique() < len(incoming):
                print("  -> CONFIRMED DUPLICATE: two+ incoming edges share the same cleabs "
                      "(same physical tronçon counted more than once).")
                n_confirmed_duplicate += 1
        else:
            dists = incoming["distance_km"].dropna()
            if len(dists) >= 2 and (dists.max() - dists.min()) < 0.05:
                print("  -> SUSPICIOUS: incoming edges have near-identical distance_km "
                      "(within 50m) -- consistent with the same reach being counted twice, "
                      "not two distinct tributaries joining.")
                n_suspicious += 1
        print()

    print("=" * 60)
    if has_toponym:
        print(f"All {len(sample)} sampled nodes have >1 distinct named river joining -- "
              f"that's the definition being used, so this should always be 100%. If any "
              f"printed 'Same toponym' above, report it as a bug.")
    elif has_cleabs:
        print(f"Of {len(sample)} sampled confluences: {n_confirmed_duplicate} confirmed "
              f"duplicate-tronçon artifacts, {len(sample) - n_confirmed_duplicate} have "
              f"genuinely distinct incoming tronçons (real confluence candidates).")
    else:
        print(f"Of {len(sample)} sampled confluences: {n_suspicious} look suspicious "
              f"(near-identical incoming edge distances). Re-run scripts/build_reach_graphs.py "
              f"for a conclusive, name-based answer instead of this proxy signal.")


def main() -> None:
    parser = argparse.ArgumentParser(description="Diagnose reach-graph confluence counts")
    parser.add_argument("--data-root", type=Path, default=Path("datasets"))
    parser.add_argument("--basin", choices=["eure", "risle"], required=True)
    parser.add_argument("--n-samples", type=int, default=10)
    args = parser.parse_args()

    graph_dir = args.data_root / "reach_graph"
    diagnose(graph_dir / f"{args.basin}_nodes.csv", graph_dir / f"{args.basin}_edges.csv",
              n_samples=args.n_samples)


if __name__ == "__main__":
    main()