from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np
from collections import namedtuple
from typing import Literal

import scipy.stats


@flojoy
def DESCRIBE(
    default: OrderedPair | Matrix,
    axis: int = 0,
    ddof: int = 1,
    bias: bool = True,
    nan_policy: str = "propagate",
    select_return: Literal["nobs", "mean", "variance", "skewness", "kurtosis"] = "nobs",
) -> OrderedPair | Matrix | Scalar:
    """The DESCRIBE node is based on a numpy or scipy function.

    The description of that function is as follows:

        Compute several descriptive statistics of the passed array.

    Parameters
    ----------
    select_return : This function has returns multiple objects ['nobs', 'mean', 'variance', 'skewness', 'kurtosis'].
        Select the desired one to return.
        See the respective function docs for descriptors.
    a : array_like
        Input data.
    axis : int or None, optional
        Axis along which statistics are calculated. Default is 0.
        If None, compute over the whole array 'a'.
    ddof : int, optional
        Delta degrees of freedom (only for variance). Default is 1.
    bias : bool, optional
        If False, then the skewness and kurtosis calculations are corrected for statistical bias.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan.
        The following options are available (default is 'propagate'):
        'propagate': returns nan
        'raise': throws an error
        'omit': performs the calculations ignoring nan values

    Returns
    -------
    DataContainer
        type 'ordered pair', 'scalar', or 'matrix'
    """

    result = scipy.stats.describe(
        a=default.y,
        axis=axis,
        ddof=ddof,
        bias=bias,
        nan_policy=nan_policy,
    )

    return_list = ["nobs", "mean", "variance", "skewness", "kurtosis"]
    if isinstance(result, tuple):
        res_dict = {}
        num = min(len(result), len(return_list))
        for i in range(num):
            res_dict[return_list[i]] = result[i]
        result = res_dict[select_return]
    else:
        result = result._asdict()
        result = result[select_return]

    if isinstance(result, np.ndarray):
        result = OrderedPair(x=default.x, y=result)
    else:
        assert isinstance(
            result, np.number | float | int
        ), f"Expected np.number, float or int for result, got {type(result)}"
        result = Scalar(c=float(result))

    return result

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