import pickle
from dataclasses import dataclass
from pathlib import Path
from typing import Tuple

import h5py
import numpy as np
import pandas as pd
from pandas import DataFrame


@dataclass
class ParticlesMeta:
    particle_mass: float


def read_file(file: Path) -> Tuple[pd.DataFrame, ParticlesMeta]:
    cache_file = file.with_suffix(".cache.pickle")
    meta_cache_file = file.with_suffix(".cache_meta.pickle")
    if not (cache_file.exists() and meta_cache_file.exists()):
        reference_file = h5py.File(file)
        has_fof = "FOFGroupIDs" in reference_file["PartType1"]

        masses = reference_file["PartType1"]["Masses"]
        if not np.all(masses == masses[0]):
            raise ValueError("only equal mass particles are supported for now")
        df = pd.DataFrame(
            reference_file["PartType1"]["Coordinates"], columns=["X", "Y", "Z"]
        )
        if has_fof:
            df2 = pd.DataFrame(
                reference_file["PartType1"]["FOFGroupIDs"], columns=["FOFGroupIDs"]
            ).astype("category")
            df = df.merge(df2, "outer", left_index=True, right_index=True)
            del df2
        df3 = pd.DataFrame(
            reference_file["PartType1"]["ParticleIDs"], columns=["ParticleIDs"]
        )

        df = df.merge(df3, "outer", left_index=True, right_index=True)
        del df3
        df.set_index("ParticleIDs", inplace=True)
        if has_fof:
            print("sorting")
            df.sort_values("FOFGroupIDs", inplace=True)
        meta = ParticlesMeta(particle_mass=masses[0])
        print("saving cache")
        with meta_cache_file.open("wb") as f:
            pickle.dump(meta, f)
        df.to_pickle(str(cache_file))
        reference_file.close()
        return df, meta
    print("from cache")
    df = pd.read_pickle(str(cache_file))
    with meta_cache_file.open("rb") as f:
        meta = pickle.load(f)
    return df, meta


def read_halo_file(file: Path) -> DataFrame:
    # file = path / "fof_output_0004.hdf5"
    reference_file = h5py.File(file)
    df1 = pd.DataFrame(reference_file["Groups"]["Centres"], columns=["X", "Y", "Z"])
    df2 = pd.DataFrame(reference_file["Groups"]["GroupIDs"], columns=["GroupIDs"])
    df3 = pd.DataFrame(reference_file["Groups"]["Masses"], columns=["Masses"])
    df4 = pd.DataFrame(reference_file["Groups"]["Sizes"], columns=["Sizes"])
    df = df1.merge(df2, "outer", left_index=True, right_index=True)
    df = df.merge(df3, "outer", left_index=True, right_index=True)
    df = df.merge(df4, "outer", left_index=True, right_index=True)
    df.set_index("GroupIDs", inplace=True)
    return df


def read_fof_file(path: Path):
    file = path / ""

def read_g4_file(file: Path, zoom_type: str) -> Tuple[np.ndarray, np.ndarray]:
    reference_file = h5py.File(file)

    hubble_param = reference_file["Parameters"].attrs["HubbleParam"]
    if zoom_type == 'pbh':
        highres_parttype = 'PartType0'
        lowres_parttype = 'PartType1'
    elif zoom_type == 'cdm':
        highres_parttype = 'PartType1'
        lowres_parttype = 'PartType2'
        highres_mass = reference_file['Header'].attrs['MassTable'][1] / hubble_param
    else:
        raise ValueError('Please select pbh or cdm as zoom_type!')

    highres_coordinates = reference_file[highres_parttype]["Coordinates"][:] #all coordinates in Mpc/h without adaption!
    lowres_coordinates = reference_file[lowres_parttype]["Coordinates"][:]

    reference_file.close()

    return highres_coordinates, lowres_coordinates