from __future__ import annotations

from dataclasses import dataclass, field
from itertools import product
from math import prod
from typing import Any

import numpy as np
from numpy._typing import ArrayLike

from functions import pulse_attn
from .distributions import BaseDistribution
from .motions import BaseMotion

__all__ = [
    'SimParameter',
    'MoleculeParameter',
    'StimEchoParameter',
    'SpectrumParameter',
    'DistParameter',
    'MotionParameter',
    'Parameter',
]


@dataclass
class SimParameter:
    seed: int | None
    num_walker: int
    t_max: float

    def totext(self) -> str:
        return f'num_traj={self.num_walker}\nseed={self.seed}'


@dataclass
class MoleculeParameter:
    delta: float
    eta: float


@dataclass
class StimEchoParameter:
    t_evo: ArrayLike
    t_mix: ArrayLike
    t_echo: float
    t_max: float = field(init=False)

    def __post_init__(self):
        self.t_max = np.max(self.t_mix) + 2 * np.max(self.t_evo) + 2*self.t_echo


@dataclass
class SpectrumParameter:
    dwell_time: float
    num_points: int
    t_echo: ArrayLike
    lb: float
    t_pulse: float
    t_acq: ArrayLike = field(init=False)
    freq: ArrayLike = field(init=False)
    t_max: float = field(init=False)
    dampening: ArrayLike = field(init=False)
    pulse_attn: ArrayLike = field(init=False)

    def __post_init__(self):
        self.t_acq = np.arange(self.num_points) * self.dwell_time
        self.dampening = np.exp(-self.lb * self.t_acq)
        self.t_max = np.max(self.t_acq) + 2 * np.max(self.t_echo)
        self.freq = np.fft.fftshift(np.fft.fftfreq(self.num_points, self.dwell_time))
        self.pulse_attn = pulse_attn(self.freq, self.t_pulse)

    def totext(self) -> str:
        return (f'dwell_time{self.dwell_time}\n'
                f'num_points={self.num_points}\n'
                f't_echo={self.t_echo}\n'
                f'lb={self.lb}\n'
                f't_pulse={self.t_pulse}')


@dataclass
class DistParameter:
    dist_type: BaseDistribution
    variables: field(default_factory=dict)
    num_variables: int = 0
    iter: field(init=False) = None

    def __post_init__(self):
        self.num_variables = prod(map(len, self.variables.values()))

    def __iter__(self):
        return self

    def __next__(self) -> dict[str, Any]:
        if self.iter is None:
            self.iter = product(*self.variables.values())
        try:
            return dict(zip(self.variables.keys(), next(self.iter)))
        except StopIteration:
            self.iter = None
            raise StopIteration


@dataclass
class MotionParameter:
    model: BaseMotion
    variables: field(default_factory=dict)
    num_variables: int = 0
    iter: field(init=False) = None

    def __post_init__(self):
        self.num_variables = prod(map(len, self.variables.values()))

    def __iter__(self):
        return self

    def __next__(self) -> dict[str, Any]:
        if self.iter is None:
            self.iter = product(*self.variables.values())
        try:
            return dict(zip(self.variables.keys(), next(self.iter)))
        except StopIteration:
            self.iter = None
            raise StopIteration


@dataclass
class Parameter:
    ste: StimEchoParameter | None
    spec: SpectrumParameter | None
    sim: SimParameter
    dist: DistParameter
    motion: MotionParameter
    molecule: MoleculeParameter

    def totext(self, sim: bool = True, spec: bool = True) -> str:
        text = []
        if sim:
            text.append(self.sim.totext())

        if spec:
            text.append(self.spec.totext())

        return '\n'.join(text)