import json
import urllib.request
import numpy as np
import pandas as pd
from Bio.PDB import MMCIFParser, PDBParser, PPBuilder
from common import RES, EXT, abc
from structure_utils import correspondence, tm_score, alphafold_model

PDB = EXT / "pdb"
PDB.mkdir(parents=True, exist_ok=True)
AF = EXT / "alphafold"
A = abc().set_index("gene")


def search(acc):
    q = {"query": {"type": "terminal", "service": "text",
                   "parameters": {"attribute": "rcsb_polymer_entity_container_identifiers.reference_sequence_identifiers.database_accession",
                                  "operator": "exact_match", "value": acc}},
         "return_type": "polymer_entity", "request_options": {"return_all_hits": True}}
    req = urllib.request.Request("https://search.rcsb.org/rcsbsearch/v2/query", data=json.dumps(q).encode(),
                                 headers={"Content-Type": "application/json"})
    body = urllib.request.urlopen(req, timeout=60).read()
    return [h["identifier"] for h in json.loads(body)["result_set"]] if body else []


def ca(structure, chain=None):
    model = structure[0]
    chains = [model[chain]] if chain else list(model)
    res = [r for c in chains for r in c if "CA" in r and r.id[0] == " "]
    seq = "".join(str(pp.get_sequence()) for c in chains for pp in PPBuilder().build_peptides(c))
    return seq, np.array([r["CA"].coord for r in res], dtype=float)


rows = []
for gene, acc in A.uniprot.items():
    for ent in search(acc):
        entry, eid = ent.split("_")
        meta = json.load(urllib.request.urlopen(f"https://data.rcsb.org/rest/v1/core/entry/{entry}", timeout=60))
        pe = json.load(urllib.request.urlopen(f"https://data.rcsb.org/rest/v1/core/polymer_entity/{entry}/{eid}", timeout=60))
        chain = pe["rcsb_polymer_entity_container_identifiers"]["auth_asym_ids"][0]
        f = PDB / f"{entry}.cif"
        if not f.exists():
            urllib.request.urlretrieve(f"https://files.rcsb.org/download/{entry}.cif", f)
        s_exp, x_exp = ca(MMCIFParser(QUIET=True).get_structure(entry, f), chain)
        s_af, x_af = ca(PDBParser(QUIET=True).get_structure(gene, alphafold_model(acc, AF)))
        i_exp, i_af = correspondence(s_exp, s_af)
        tm, rmsd = tm_score(x_exp[i_exp], x_af[i_af], len(s_exp))
        rows.append({"gene": gene, "pdb": entry, "chain": chain, "method": meta["exptl"][0]["method"],
                     "resolution": (meta["rcsb_entry_info"].get("resolution_combined") or [np.nan])[0],
                     "year": meta["rcsb_accession_info"]["initial_release_date"][:4],
                     "doi": (meta.get("citation") or [{}])[0].get("pdbx_database_id_DOI"),
                     "title": meta["struct"]["title"], "modelled_residues": len(s_exp),
                     "uniprot_length": len(s_af), "coverage": len(s_exp) / len(s_af),
                     "tm_score_af_vs_experimental": tm, "rmsd_core": rmsd})
out = pd.DataFrame(rows)
out.to_csv(RES / "experimental_structures.csv", index=False)
pd.set_option("display.width", 220)
pd.set_option("display.max_colwidth", 70)