import datetime
import json

import numpy as np
from ase.utils import reader, writer


class MyEncoder(json.JSONEncoder):
    def default(self, obj):
        if hasattr(obj, 'todict'):
            d = obj.todict()

            if not isinstance(d, dict):
                raise RuntimeError('todict() of {} returned object of type {} '
                                   'but should have returned dict'
                                   .format(obj, type(d)))
            if hasattr(obj, 'ase_objtype'):
                d['__ase_objtype__'] = obj.ase_objtype

            return d
        if isinstance(obj, np.ndarray):
            flatobj = obj.ravel()
            if np.iscomplexobj(obj):
                flatobj.dtype = obj.real.dtype
            return {'__ndarray__': (obj.shape,
                                    obj.dtype.name,
                                    flatobj.tolist())}
        if isinstance(obj, np.integer):
            return int(obj)
        if isinstance(obj, np.bool_):
            return bool(obj)
        if isinstance(obj, datetime.datetime):
            return {'__datetime__': obj.isoformat()}
        if isinstance(obj, complex):
            return {'__complex__': (obj.real, obj.imag)}
        return json.JSONEncoder.default(self, obj)


encode = MyEncoder().encode


def object_hook(dct):
    if '__datetime__' in dct:
        return datetime.datetime.strptime(dct['__datetime__'],
                                          '%Y-%m-%dT%H:%M:%S.%f')

    if '__complex__' in dct:
        return complex(*dct['__complex__'])

    if '__ndarray__' in dct:
        return create_ndarray(*dct['__ndarray__'])

    # No longer used (only here for backwards compatibility):
    if '__complex_ndarray__' in dct:
        r, i = (np.array(x) for x in dct['__complex_ndarray__'])
        return r + i * 1j

    if '__ase_objtype__' in dct:
        objtype = dct.pop('__ase_objtype__')
        dct = numpyfy(dct)
        return create_ase_object(objtype, dct)

    return dct


def create_ndarray(shape, dtype, data):
    """Create ndarray from shape, dtype and flattened data."""
    array = np.empty(shape, dtype=dtype)
    flatbuf = array.ravel()
    if np.iscomplexobj(array):
        flatbuf.dtype = array.real.dtype
    flatbuf[:] = data
    return array


def create_ase_object(objtype, dct):
    # We just try each object type one after another and instantiate
    # them manually, depending on which kind it is.
    # We can formalize this later if it ever becomes necessary.
    if objtype == 'cell':
        from ase.cell import Cell
        dct.pop('pbc', None)  # compatibility; we once had pbc
        obj = Cell(**dct)
    elif objtype == 'bandstructure':
        from ase.spectrum.band_structure import BandStructure
        obj = BandStructure(**dct)
    elif objtype == 'bandpath':
        from ase.dft.kpoints import BandPath
        obj = BandPath(path=dct.pop('labelseq'), **dct)
    elif objtype == 'atoms':
        from ase import Atoms
        obj = Atoms.fromdict(dct)
    elif objtype == 'vibrationsdata':
        from ase.vibrations import VibrationsData
        obj = VibrationsData.fromdict(dct)
    else:
        raise ValueError('Do not know how to decode object type {} '
                         'into an actual object'.format(objtype))
    assert obj.ase_objtype == objtype
    return obj


mydecode = json.JSONDecoder(object_hook=object_hook).decode


def intkey(key):
    """Convert str to int if possible."""
    try:
        return int(key)
    except ValueError:
        return key


def fix_int_keys_in_dicts(obj):
    """Convert "int" keys: "1" -> 1.

    The json.dump() function will convert int keys in dicts to str keys.
    This function goes the other way.
    """
    if isinstance(obj, dict):
        return {intkey(key): fix_int_keys_in_dicts(value)
                for key, value in obj.items()}
    return obj


def numpyfy(obj):
    if isinstance(obj, dict):
        if '__complex_ndarray__' in obj:
            r, i = (np.array(x) for x in obj['__complex_ndarray__'])
            return r + i * 1j
    if isinstance(obj, list) and len(obj) > 0:
        try:
            a = np.array(obj)
        except ValueError:
            pass
        else:
            if a.dtype in [bool, int, float]:
                return a
        obj = [numpyfy(value) for value in obj]
    return obj


def decode(txt, always_array=True):
    obj = mydecode(txt)
    obj = fix_int_keys_in_dicts(obj)
    if always_array:
        obj = numpyfy(obj)
    return obj


@reader
def read_json(fd, always_array=True):
    dct = decode(fd.read(), always_array=always_array)
    return dct


@writer
def write_json(fd, obj):
    fd.write(encode(obj))
