r"""
assemble.py -> 4-GROUP blueprint tensors (F_total=54) for AtmosIQ V1.
Groups (MODEL_BLUEPRINT v1.1 §1): A satellite 18 | B meteorology 12 | C static 13 | D calendar 11.
Group C is YEAR-VERSIONED (Data_Schema §2): 5 static (grid_nodes) + 4 yearly + land_use (node_features_yearly).
Also builds completeness scalar + train-fold norm.

Outputs to data/tensors/: groupA.npy groupB.npy groupC_static.npy groupC_yearly.npy
  groupC_landuse.npy year_of_day.npy years.npy calendar_num.npy grap.npy physics.npy
  completeness.npy targets.npy target_mask.npy edges.npz node_split.npy dates.npy norm.npz meta.json
Run: python data\models\assemble.py
"""
from __future__ import annotations
import os, json, pathlib, numpy as np, pandas as pd

OUT=pathlib.Path(os.environ.get("ATMOS_OUT","data/ingest"))
GRID=pathlib.Path(os.environ.get("ATMOS_GRID","data/grid/grid_nodes.parquet"))
GRAPH=pathlib.Path(os.environ.get("ATMOS_GRAPH","data/graph"))
SPLIT=pathlib.Path(os.environ.get("ATMOS_SPLITS","data/splits"))/"spatial_split.parquet"
DEST=pathlib.Path(os.environ.get("ATMOS_TENSORS","data/tensors")); DEST.mkdir(parents=True,exist_ok=True)
EDGE=os.environ.get("ATMOS_EDGES","graph_edges_static_r40_k8.parquet")
TARGETS=["pm2_5","pm10","no2","so2","co","o3","nh3"]
NFY="node_features_yearly.parquet"

# ---- GROUP MEMBERSHIP (verify vs Data_Schema §1/§2) ----
A_SAT=["s5p_no2_column","s5p_so2_column","s5p_co_column","s5p_o3_column","s5p_hcho_column",
       "modis_aod","viirs_aod","fire_count","frp"]                              # 5 S5P + 2 AOD + 2 fire = 9
A_FLAGS=["s5p_no2_column_present","s5p_co_column_present","s5p_so2_column_present","s5p_o3_column_present",
         "s5p_hcho_column_present","s5p_aer_ai_present","s5p_ch4_column_present","modis_aod_present","viirs_aod_present"]  # 9
B_MET=["era5_temperature","era5_boundary_layer_height","era5_relative_humidity","era5_surface_pressure",
       "era5_cloud_cover","era5_wind_u","era5_wind_v","era5_rainfall","s5p_cloud_fraction","s5p_aer_ai",
       "s5p_ch4_column","u_1330ist"]                                            # 12
C_STATIC=["elevation_m","projection_x","projection_y","distance_to_highway_km","distance_to_industry_km"]  # 5
C_YEARLY=["road_density","industrial_density","population_density","ndvi_annual_mean"]                     # 4

import sys as _sys, pathlib as _pl
_sys.path.insert(0, str(_pl.Path(__file__).resolve().parent))
from build_group_d import build as _build_group_d, COLS as GROUP_D_COLS   # event calendar (festivals, stubble, monsoon, COVID, GRAP...)
def calendar(dates):
    return _build_group_d(dates)   # calendar_num [D,K], grap_stage [D]

def main():
    gn=pd.read_parquet(GRID); pos={n:i for i,n in enumerate(gn.node_id.astype(str))}; N=len(pos)
    missS=[c for c in C_STATIC if c not in gn.columns]
    if missS: raise SystemExit(f"grid_nodes missing static Group C: {missS}")
    nfy_path=OUT/NFY
    if not nfy_path.exists(): raise SystemExit(f"MISSING {nfy_path} — run GEE Group C pipeline first.")
    nfy=pd.read_parquet(nfy_path)
    print(f"nodes {N} | node_features_yearly {len(nfy):,} rows, years {sorted(nfy.year.unique())}")

    sp=pd.read_parquet(SPLIT); fmap={"train":0,"val":1,"test":2,"buffer":3}
    nsplit=np.full(N,3,np.uint8)
    for n,s in zip(sp.node_id.astype(str),sp.split): nsplit[pos[n]]=fmap.get(s,3)
    ed=pd.read_parquet(GRAPH/EDGE)
    ei=np.stack([ed.src_node.astype(str).map(pos).values,ed.dst_node.astype(str).map(pos).values]).astype(np.int64)

    parts=sorted((OUT/"train_ready").glob("*.parquet"))
    dates=sorted(set().union(*[set(pd.read_parquet(p,columns=["date"]).date.astype(str)) for p in parts]))
    didx={d:i for i,d in enumerate(dates)}; D=len(dates)
    print(f"days {D}")

    A=np.full((D,N,len(A_SAT)+len(A_FLAGS)),0.,np.float32)
    B=np.full((D,N,len(B_MET)),np.nan,np.float32)
    PH=np.full((D,N,3),0.,np.float32)          # raw u_1330ist,v_1330ist,blh for physics
    Y=np.full((D,N,7),np.nan,np.float32)
    for p in parts:
        df=pd.read_parquet(p); di=df.date.astype(str).map(didx).values; ni=df.node_id.astype(str).map(pos).values
        ok=~pd.isna(di)&~pd.isna(ni); di=di[ok].astype(int); ni=ni[ok].astype(int); df=df[ok]
        for j,c in enumerate(A_SAT): A[di,ni,j]=(pd.to_numeric(df[c],errors="coerce").fillna(0) if c in df else 0)
        for j,c in enumerate(A_FLAGS): A[di,ni,len(A_SAT)+j]=(pd.to_numeric(df[c],errors="coerce").fillna(0) if c in df else 0)
        for j,c in enumerate(B_MET): B[di,ni,j]=(pd.to_numeric(df[c],errors="coerce") if c in df else np.nan)
        for j,c in enumerate(TARGETS): Y[di,ni,j]=pd.to_numeric(df[c],errors="coerce")
        for j,c in enumerate(["u_1330ist","v_1330ist","era5_boundary_layer_height"]):
            if c in df: PH[di,ni,j]=pd.to_numeric(df[c],errors="coerce").fillna(0)
        print(f"  {p.stem}",end="\r")
    print()

    # ---- Group C: static (grid_nodes) + yearly (node_features_yearly) ----
    Cstat=gn.set_index("node_id").loc[gn.node_id][C_STATIC].to_numpy(np.float32)          # [N,5]
    years=sorted(int(y) for y in nfy.year.unique()); yidx={y:i for i,y in enumerate(years)}; Ky=len(years)
    Cyear=np.zeros((Ky,N,len(C_YEARLY)),np.float32); LUyear=np.zeros((Ky,N),np.int64)
    nfy=nfy.copy(); nfy["p"]=nfy.node_id.astype(str).map(pos)
    nfy=nfy.dropna(subset=["p"])
    pi=nfy.p.astype(int).values; yi=nfy.year.astype(int).map(yidx).values
    for j,c in enumerate(C_YEARLY): Cyear[yi,pi,j]=pd.to_numeric(nfy[c],errors="coerce").fillna(0).values
    LUyear[yi,pi]=pd.to_numeric(nfy["land_use_class"],errors="coerce").fillna(0).astype(int).values
    day_years=pd.to_datetime(dates).year.values
    year_of_day=np.array([yidx.get(int(y), yidx[min(max(int(y),years[0]),years[-1])]) for y in day_years],np.int64)

    Dnum,Dgrap=calendar(dates); print(f"Group D: calendar_num{Dnum.shape} grap_stage 0..{int(Dgrap.max())}")
    completeness=A[:,:,len(A_SAT):].mean(-1).astype(np.float32)                            # [D,N]

    # ---- normalization on TRAIN-fold ----
    tr=np.where(nsplit==0)[0]
    def nf(X): s=X[:,tr,:].reshape(-1,X.shape[-1]); m=np.nanmean(s,0); d=np.nanstd(s,0); d[(d==0)|~np.isfinite(d)]=1; return m,d
    Am,Asd=nf(A[:,:,:len(A_SAT)]); Bm,Bsd=nf(B)
    A[:,:,:len(A_SAT)]=np.nan_to_num((A[:,:,:len(A_SAT)]-Am)/Asd); B=np.nan_to_num((B-Bm)/Bsd)
    Csm=np.nanmean(Cstat[tr],0); Cssd=np.nanstd(Cstat[tr],0); Cssd[(Cssd==0)|~np.isfinite(Cssd)]=1
    Cstat=np.nan_to_num((Cstat-Csm)/Cssd)
    yt=Cyear[:,tr,:].reshape(-1,len(C_YEARLY)); Cym=np.nanmean(yt,0); Cysd=np.nanstd(yt,0); Cysd[(Cysd==0)|~np.isfinite(Cysd)]=1
    Cyear=np.nan_to_num((Cyear-Cym)/Cysd)

    np.save(DEST/"groupA.npy",A.astype(np.float16)); np.save(DEST/"groupB.npy",B.astype(np.float16))
    np.save(DEST/"groupC_static.npy",Cstat.astype(np.float16))
    np.save(DEST/"groupC_yearly.npy",Cyear.astype(np.float16)); np.save(DEST/"groupC_landuse.npy",LUyear)
    np.save(DEST/"year_of_day.npy",year_of_day); np.save(DEST/"years.npy",np.array(years))
    np.save(DEST/"calendar_num.npy",Dnum); np.save(DEST/"grap.npy",Dgrap); np.save(DEST/"physics.npy",PH.astype(np.float16))
    np.save(DEST/"completeness.npy",completeness)
    # per-pollutant log-target standardization stats (TRAIN fold) -> level scale across pollutants
    yeps=1e-3; ylog=np.log(np.clip(Y,0,None)+yeps)
    trmask=(nsplit==0)
    ylm=np.zeros(7,np.float32); yls=np.ones(7,np.float32)
    for j in range(7):
        v=ylog[:,trmask,j]; v=v[~np.isnan(v)]
        if len(v): ylm[j]=v.mean(); yls[j]=v.std()+1e-6
    print("  per-pollutant log-target mean:", np.round(ylm,2), "std:", np.round(yls,2))
    mask=(~np.isnan(Y)).astype(np.uint8)
    np.save(DEST/"targets.npy",np.nan_to_num(Y).astype(np.float16)); np.save(DEST/"target_mask.npy",mask)
    np.savez(DEST/"edges.npz",ei=ei,e_hat=ed.e_hat.values.astype(np.float32),n_hat=ed.n_hat.values.astype(np.float32))
    np.save(DEST/"node_split.npy",nsplit); np.save(DEST/"dates.npy",np.array(dates))
    np.savez(DEST/"norm.npz",Am=Am,Asd=Asd,Bm=Bm,Bsd=Bsd,Csm=Csm,Cssd=Cssd,Cym=Cym,Cysd=Cysd,y_logmean=ylm,y_logstd=yls)
    json.dump(dict(N=int(N),D=int(D),years=years,dimsA=int(A.shape[-1]),dimsB=int(B.shape[-1]),
                   dimsC_static=len(C_STATIC),dimsC_yearly=len(C_YEARLY),n_landuse=int(LUyear.max())+1,
                   dimsD_num=int(Dnum.shape[1]),n_grap=int(Dgrap.max())+1,group_d_cols=GROUP_D_COLS,
                   A_SAT=A_SAT,A_FLAGS=A_FLAGS,B_MET=B_MET,C_STATIC=C_STATIC,C_YEARLY=C_YEARLY),
              open(DEST/"meta.json","w"),indent=1)
    print(f"assembled -> {DEST}")
    print(f"  A{A.shape} B{B.shape} Cstatic{Cstat.shape} Cyearly{Cyear.shape} landuse<= {int(LUyear.max())} physics{PH.shape}")
    print(f"  labeled node-days: {int((mask.sum(-1)>0).sum()):,} | set model cfg n_landuse={int(LUyear.max())+1}")

if __name__=="__main__": main()