"""PyTorch dataset over gnn_dataset/data/<id>/*.parquet.

One sample = one MATSim scenario as a directed graph (nodes = network
nodes, edges = links). The network is identical in every scenario (same node
and link order), only edge attributes (closed / capacity_factor), population
features and the targets change.

    from torch_dataset import GNNScenarioDataset
    ds = GNNScenarioDataset("/path/to/gnn_dataset")        # data/ + manifest.parquet
    sample = ds[0]                                          # torch_geometric.data.Data if PyG is installed, else dict
    sample.x            [N, 4]   node features: x, y (normalised), n_home, n_activity (persons, scaled to 100 %)
    sample.edge_index   [2, E]   from-node -> to-node (car links only by default)
    sample.edge_attr    [E, 7]   log_length, freespeed, log_capacity, lanes, closed, capacity_factor, capacity_eff
    sample.y            [E, 24]  target per link and hour (default: vc_ratio)
    sample.y_base       [E, 24]  same quantity in base_s0 (for delta losses); absent for baselines
    sample.scenario     dict     scenario.json (class, seeds, perturbation lists, sample_rate ...)
    sample.link_ids     np.array of MATSim link ids (E,)
  with with_cells=True (default) additionally, from the demand tables:
    sample.cell_x       [C, 12]  cx, cy (normalised like x/y) + the 10 weighted cell counts (log1p)
    sample.cell_pos     [C, 2]   raw cell centres (EPSG:2056)
    sample.cell_link_index [2, M]  (cell idx, edge idx) pairs within cell_link_max_dist (default: all, 5 km -> M = 57 M)
    sample.cell_link_dist  [M]     distance in m
    sample.od_index     [2, K]   (orig cell, dest cell) pairs with trips (cells inside the grid only)
    sample.od_trips     [K, 3]   all trips, car trips, AM-peak trips (weighted to 100 %)

Requirements: numpy, pandas, pyarrow, torch. Optional: torch_geometric, scipy
(nearest-node assignment of persons; without scipy the two population node
features are zero).

Targets available: "vc_ratio", "volume", "traveltime_avg", "congestion_index"
(list allowed -> y is [E, 24 * len(targets)], stacked along dim 1).
Missing (link, hour) rows in link_hourly.parquet mean volume 0 / vc 0 /
congestion 1 / free-flow travel time (see README).
"""

import json
import os

import numpy as np
import pandas as pd

DEFAULTS = {"volume": 0.0, "vc_ratio": 0.0, "congestion_index": 1.0}
EDGE_ATTR_NAMES = ["log_length", "freespeed", "log_capacity", "lanes",
                   "closed", "capacity_factor", "capacity_eff"]
NODE_ATTR_NAMES = ["x", "y", "n_home", "n_activity"]


def _load_pyg():
    try:
        from torch_geometric.data import Data  # noqa: F401
        return Data
    except Exception:
        return None


class GNNScenarioDataset:
    """Indexable dataset; works as a torch.utils.data.Dataset (len/getitem)."""

    def __init__(self, root, ids=None, classes=None, targets="vc_ratio",
                 car_only=True, baseline_id=None, with_baseline=True,
                 clip_congestion=50.0, cache=True, as_pyg=True, with_cells=True,
                 cell_link_max_dist=None):
        self.root = root
        self.data_dir = os.path.join(root, "data")
        self.targets = [targets] if isinstance(targets, str) else list(targets)
        self.car_only = car_only
        self.baseline_id = baseline_id
        self.with_baseline = with_baseline
        self.clip_congestion = clip_congestion
        self.cache = cache
        self.cache_dir = os.path.join(root, "cache_torch")
        self._pyg = _load_pyg() if as_pyg else None
        self.with_cells = with_cells and os.path.exists(os.path.join(root, "cell_link_edges.parquet"))
        self.cell_link_max_dist = cell_link_max_dist      # e.g. 2000 -> only pairs within 2 km

        man_path = os.path.join(root, "manifest.parquet")
        if os.path.exists(man_path):
            man = pd.read_parquet(man_path)
            man = man[man.status == "ok"]
        else:  # fall back to directory listing
            ids_ = sorted(d for d in os.listdir(self.data_dir)
                          if os.path.exists(os.path.join(self.data_dir, d, "link_hourly.parquet")))
            man = pd.DataFrame({"scenario_id": ids_,
                                "class": [json.load(open(os.path.join(self.data_dir, d, "scenario.json")))["class"] for d in ids_]})
        if classes is not None:
            man = man[man["class"].isin(list(classes))]
        if ids is not None:
            man = man[man.scenario_id.isin(list(ids))]
        self.ids = list(man.scenario_id)
        self.classes = dict(zip(man.scenario_id, man["class"]))
        if not self.ids:
            raise ValueError("no scenarios selected")
        if baseline_id is None:                     # v1: base_s0, v2: base10_s0 (first class-0 scenario)
            cands = sorted(d for d in os.listdir(self.data_dir) if d.startswith("base"))
            self.baseline_id = "base_s0" if "base_s0" in cands else cands[0]

        self._build_static()
        self._base_y = None
        self._cell_static = None

    # ------------------------------------------------------------------ static
    def _build_static(self):
        """Node table, edge index, node coordinate normalisation (from base)."""
        sdir = os.path.join(self.data_dir, self.baseline_id)
        nodes = pd.read_parquet(os.path.join(sdir, "nodes.parquet"))
        links = pd.read_parquet(os.path.join(sdir, "links.parquet"))
        if self.car_only:
            links = links[links.modes.str.contains(r"(?:^|,)car(?:,|$)")]
        self.link_ids = links.link_id.values.astype(str)
        self._link_mask_index = links.index.values           # row positions in the full links table
        self._link_index = pd.Index(self.link_ids)
        nid = {n: i for i, n in enumerate(nodes.node_id.values)}
        self.edge_index = np.stack([links.from_node.map(nid).values,
                                    links.to_node.map(nid).values]).astype(np.int64)
        self.node_ids = nodes.node_id.values.astype(str)
        xy = nodes[["x", "y"]].values.astype(np.float64)
        self._xy_mean, self._xy_std = xy.mean(0), xy.std(0)
        self.node_xy = ((xy - self._xy_mean) / self._xy_std).astype(np.float32)
        self._raw_xy = xy
        self.link_pos = {l: i for i, l in enumerate(self.link_ids)}
        self._lengths = links.length.values.astype(np.float32)
        self._freespeed = links.freespeed.values.astype(np.float32)
        try:
            from scipy.spatial import cKDTree
            self._tree = cKDTree(xy)
        except Exception:
            self._tree = None

    # --------------------------------------------------------------- per scen
    def _edge_attr(self, links):
        m = self._link_mask_index
        col = lambda c: links[c].values[m]
        fs = np.minimum(col("freespeed"), 60.0)                     # inf on a few pt/ferry links
        cap = col("capacity")
        cf = col("capacity_factor")
        closed = col("closed").astype(np.float32)
        return np.stack([
            np.log1p(col("length")),
            fs,
            np.log1p(cap),
            col("lanes"),
            closed,
            cf,
            np.where(closed > 0, 0.0, cap * cf),                  # effective capacity (0 if closed)
        ], axis=1).astype(np.float32)

    def _node_pop(self, persons, sample_rate):
        out = np.zeros((len(self.node_ids), 2), dtype=np.float32)
        if self._tree is None:
            return out
        w = 1.0 / sample_rate
        home = persons[["home_x", "home_y"]].dropna().values
        act = persons[["main_activity_x", "main_activity_y"]].dropna().values
        if len(home):
            _, i = self._tree.query(home)
            np.add.at(out[:, 0], i, w)
        if len(act):
            _, i = self._tree.query(act)
            np.add.at(out[:, 1], i, w)
        return out

    def _dense_targets(self, link_hourly):
        """sparse link_hourly -> [E, 24 * n_targets] dense array."""
        pos = self._link_index.get_indexer(link_hourly.link_id.values)
        keep = pos >= 0                                             # drop pt-only links etc.
        pos = pos[keep]
        hour = link_hourly.hour.values[keep].astype(np.int64)
        blocks = []
        for t in self.targets:
            if t == "traveltime_avg":
                default = (self._lengths / np.minimum(self._freespeed, 1e6))[:, None].repeat(24, 1)
                y = default.astype(np.float32)
            else:
                y = np.full((len(self.link_ids), 24), DEFAULTS[t], dtype=np.float32)
            vals = link_hourly[t].values[keep].astype(np.float32)
            if t == "congestion_index" and self.clip_congestion:
                vals = np.nan_to_num(np.minimum(vals, self.clip_congestion), nan=1.0)
            y[pos, hour] = vals
            blocks.append(y)
        return np.concatenate(blocks, axis=1)

    def _cache_path(self, sid):
        key = "_".join(self.targets) + ("_car" if self.car_only else "_all")
        return os.path.join(self.cache_dir, "%s__%s.npz" % (sid, key))

    def _load_arrays(self, sid):
        cp = self._cache_path(sid)
        if self.cache and os.path.exists(cp):
            z = np.load(cp)
            if not self.with_cells or "cell_x" in z:
                return z["x"], z["edge_attr"], z["y"]
        sdir = os.path.join(self.data_dir, sid)
        spec = json.load(open(os.path.join(sdir, "scenario.json")))
        links = pd.read_parquet(os.path.join(sdir, "links.parquet"))
        persons = pd.read_parquet(os.path.join(sdir, "persons.parquet"))
        lh = pd.read_parquet(os.path.join(sdir, "link_hourly.parquet"))
        x = np.concatenate([self.node_xy, self._node_pop(persons, spec["sample_rate"])], axis=1)
        ea = self._edge_attr(links)
        y = self._dense_targets(lh)
        extra = {}
        if self.with_cells:
            cell_x, cell_pos, od_index, od_trips = self._cell_arrays(sid)
            extra = dict(cell_x=cell_x, cell_pos=cell_pos, od_index=od_index, od_trips=od_trips)
        if self.cache:
            os.makedirs(self.cache_dir, exist_ok=True)
            np.savez_compressed(cp, x=x, edge_attr=ea, y=y, **extra)
        return x, ea, y

    def base_targets(self):
        if self._base_y is None:
            self._base_y = self._load_arrays(self.baseline_id)[2]
        return self._base_y

    # ---------------------------------------------------------------- cells
    CELL_COLS = ["residents", "residents_employed", "residents_car_available", "residents_age_0_17",
                 "residents_age_18_64", "residents_age_65plus", "jobs", "edu_places", "trips_origin", "trips_dest"]

    def cell_static(self):
        """(cell idx, edge idx) pairs + distance within cell_link_max_dist
        (identical for all scenarios). First call reads the 59 M-row parquet via
        pyarrow and stores compact int32 arrays in cache_torch/; later calls
        take ~1 s."""
        if self._cell_static is not None:
            return self._cell_static
        tag = "car" if self.car_only else "all"
        md = self.cell_link_max_dist
        cp = os.path.join(self.cache_dir, "cell_link_static_%s_%s.npz" % (tag, "all" if md is None else int(md)))
        if self.cache and os.path.exists(cp):
            z = np.load(cp)
            self._cell_static = (z["index"], z["dist"])
            return self._cell_static
        import pyarrow.parquet as pq
        tb = pq.read_table(os.path.join(self.root, "cell_link_edges.parquet"))
        cells, pos, dist = [], [], []
        for i in range(tb.num_columns and len(tb.column("link_id").chunks)):
            lc = tb.column("link_id").chunk(i)
            if hasattr(lc, "dictionary"):
                cat_pos = self._link_index.get_indexer(np.asarray(lc.dictionary).astype(str))
                p_ = cat_pos[np.asarray(lc.indices)]
            else:
                p_ = self._link_index.get_indexer(np.asarray(lc).astype(str))
            c_ = np.asarray(tb.column("cell_id").chunk(i)).astype(np.int32)
            d_ = np.asarray(tb.column("dist_m").chunk(i)).astype(np.float32)
            keep = p_ >= 0
            if md is not None:
                keep &= d_ <= md
            cells.append(c_[keep]); pos.append(p_[keep].astype(np.int32)); dist.append(d_[keep])
        index = np.stack([np.concatenate(cells), np.concatenate(pos)])
        dist = np.concatenate(dist)
        if self.cache:
            os.makedirs(self.cache_dir, exist_ok=True)
            np.savez(cp, index=index, dist=dist)
        self._cell_static = (index, dist)
        return self._cell_static

    def _cell_arrays(self, sid):
        sdir = os.path.join(self.data_dir, sid)
        c = pd.read_parquet(os.path.join(sdir, "cells.parquet"))
        xy = c[["cx", "cy"]].values
        cell_x = np.concatenate([((xy - self._xy_mean) / self._xy_std),
                                 np.log1p(c[self.CELL_COLS].values)], axis=1).astype(np.float32)
        od = pd.read_parquet(os.path.join(sdir, "od_cells.parquet"))
        am = pd.read_parquet(os.path.join(sdir, "od_cells_am.parquet"))
        od = od[(od.orig_cell >= 0) & (od.dest_cell >= 0)]
        am = am[(am.orig_cell >= 0) & (am.dest_cell >= 0)]
        g = od.groupby(["orig_cell", "dest_cell"])
        tot = g.trips.sum()
        car = od[od["mode"].astype(str) == "car"].groupby(["orig_cell", "dest_cell"]).trips.sum()
        amt = am.groupby(["orig_cell", "dest_cell"]).trips.sum()
        tab = pd.DataFrame({"all": tot, "car": car, "am": amt}).fillna(0.0)
        od_index = np.array(list(tab.index), dtype=np.int64).T.reshape(2, -1)
        return cell_x, xy.astype(np.float32), od_index, tab.values.astype(np.float32)

    # ------------------------------------------------------------------ API
    def __len__(self):
        return len(self.ids)

    def scenario(self, sid):
        return json.load(open(os.path.join(self.data_dir, sid, "scenario.json")))

    def __getitem__(self, i):
        import torch
        sid = self.ids[i]
        x, ea, y = self._load_arrays(sid)
        spec = self.scenario(sid)
        d = {
            "x": torch.from_numpy(x),
            "edge_index": torch.from_numpy(self.edge_index),
            "edge_attr": torch.from_numpy(ea),
            "y": torch.from_numpy(y),
            "scenario_id": sid,
            "scenario_class": int(spec["class"]),
        }
        if self.with_baseline and sid != self.baseline_id:
            d["y_base"] = torch.from_numpy(self.base_targets())
        if self.with_cells:
            z = np.load(self._cache_path(sid)) if self.cache else None
            if z is not None and "cell_x" in z:
                cell_x, cell_pos, od_index, od_trips = z["cell_x"], z["cell_pos"], z["od_index"], z["od_trips"]
            else:
                cell_x, cell_pos, od_index, od_trips = self._cell_arrays(sid)
            cli, cld = self.cell_static()
            d.update(cell_x=torch.from_numpy(cell_x), cell_pos=torch.from_numpy(cell_pos),
                     cell_link_index=torch.from_numpy(cli.astype(np.int64)), cell_link_dist=torch.from_numpy(cld),
                     od_index=torch.from_numpy(od_index), od_trips=torch.from_numpy(od_trips))
        if self._pyg is not None:
            data = self._pyg(x=d["x"], edge_index=d["edge_index"], edge_attr=d["edge_attr"], y=d["y"])
            if "y_base" in d:
                data.y_base = d["y_base"]
            for k in ("cell_x", "cell_pos", "cell_link_index", "cell_link_dist", "od_index", "od_trips"):
                if k in d:
                    setattr(data, k, d[k])
            data.scenario_id = sid
            data.scenario_class = d["scenario_class"]
            data.scenario = spec
            data.link_ids = self.link_ids
            return data
        d["scenario"] = spec
        d["link_ids"] = self.link_ids
        return d

    def split(self, val_frac=0.15, test_frac=0.15, seed=0):
        """Scenario-level split, stratified by class; returns three index lists."""
        rng = np.random.default_rng(seed)
        tr, va, te = [], [], []
        by_class = {}
        for i, sid in enumerate(self.ids):
            by_class.setdefault(self.classes[sid], []).append(i)
        for k, idx in by_class.items():
            idx = list(rng.permutation(idx))
            n_te = int(round(len(idx) * test_frac))
            n_va = int(round(len(idx) * val_frac))
            te += idx[:n_te]
            va += idx[n_te:n_te + n_va]
            tr += idx[n_te + n_va:]
        return sorted(tr), sorted(va), sorted(te)

    def build_cache(self, verbose=True):
        """Precompute every scenario's arrays once (numpy only, no torch needed)."""
        for k, sid in enumerate(self.ids):
            self._load_arrays(sid)
            if verbose and (k % 10 == 0 or k == len(self.ids) - 1):
                print("cached %d/%d" % (k + 1, len(self.ids)))


if __name__ == "__main__":
    import sys
    root = sys.argv[1] if len(sys.argv) > 1 else os.path.dirname(os.path.abspath(__file__))
    ds = GNNScenarioDataset(root, as_pyg=False)
    print("scenarios:", len(ds), "| nodes:", len(ds.node_ids), "| edges:", ds.edge_index.shape[1])
    x, ea, y = ds._load_arrays(ds.ids[0])
    print("x", x.shape, "edge_attr", ea.shape, "y", y.shape)
    if "--cache" in sys.argv:
        ds.build_cache()