# -*- coding: utf-8 -*-
from functools import reduce
from math import floor as mfloor
from operator import mul
from sys import float_info as sflt

from numpy import all, append, array, corrcoef, dot, exp, fabs
from numpy import log, nan, ndarray, ones, seterr, sign, sqrt, sum, triu
from pandas import DataFrame, Series

from pandas_ta._typing import (
    Array,
    DictLike,
    Float,
    Int,
    IntFloat,
    List,
    Optional
)
from pandas_ta.maps import Imports
from pandas_ta.utils._validate import v_series

__all__ = [
    'fibonacci',
    'erf',
    'combination',
    'geometric_mean',
    'hpoly',
    'linear_regression',
    'log_geometric_mean',
    'pascals_triangle',
    'strided_window',
    'symmetric_triangle',
    'weights',
    'zero',
    'df_error_analysis',
]


def combination(
    n: Int = 1, r: Int = 0,
    repetition: bool = False, multichoose: bool = False
) -> Int:
    """https://stackoverflow.com/questions/4941753/is-there-a-math-ncr-function-in-python"""
    n, r = int(fabs(n)), int(fabs(r))

    if repetition or multichoose:
        n = n + r - 1

    # if r < 0: return None
    r = min(n, n - r)
    if r == 0:
        return 1

    numerator = reduce(mul, range(n, n - r, -1), 1)
    denominator = reduce(mul, range(1, r + 1), 1)
    return numerator // denominator

def erf(x: IntFloat) -> Float:
    """Error Function erf(x)
    The algorithm comes from Handbook of Mathematical Functions, formula 7.1.26.
    Source: https://stackoverflow.com/questions/457408/is-there-an-easily-available-implementation-of-erf-for-python
    """
    x_sign = sign(x)
    x = abs(x)

    # constants
    a1 = 0.254829592
    a2 = -0.284496736
    a3 = 1.421413741
    a4 = -1.453152027
    a5 = 1.061405429
    p = 0.3275911

    # A&S formula 7.1.26
    t = 1.0 / (1.0 + p * x)
    y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2)
               * t + a1) * t * exp(-x * x)
    return x_sign * y  # erf(-x) = -erf(x)

def fibonacci(
    n: Int = 2, weighted: bool = False, zero: bool = False
) -> Array:
    """Fibonacci Sequence as a numpy array"""
    n = int(fabs(n)) if n >= 0 else 2

    if zero:
        a, b = 0, 1
    else:
        n -= 1
        a, b = 1, 1

    result = array([a])
    for _ in range(0, n):
        a, b = b, a + b
        result = append(result, a)

    if weighted:
        fib_sum = sum(result)
        if fib_sum > 0:
            return result / fib_sum
        else:
            return result
    else:
        return result

def geometric_mean(series: Series) -> Float:
    """Returns the Geometric Mean for a Series of positive values."""
    n = series.size
    if n < 1:
        return series.iloc[0]

    has_zeros = 0 in series.values
    if has_zeros:
        series = series.fillna(0) + 1
    if all(series > 0):
        mean = series.prod() ** (1 / n)
        return mean if not has_zeros else mean - 1
    return 0


def hpoly(c: Array, x: IntFloat) -> Float:
    """Horner Calculation for Polynomial Evaluation (hpoly)

    array: np.array of polynomial coefficients
        * Convert list or Series to np.array prior to calling the method for
        best performance
    x: value to evaluate

    Example:
    coeffs_0 = [4, -3, 0, 1] # 4x^3 - 3x^2 + 0x + 1
    coeffs_1 = np.array(coeffs_0) # Faster
    coeffs_2 = pd.Series(coeffs_0).values
    x = -6.5

    hpoly(coeffs_0, x) => -1224.25
    hpoly(coeffs_1, x) or hpoly(coeffs_2, x) => -1224.25 # Faster
    """
    if not isinstance(c, ndarray):
        c_ = array(c)

    m, y = c_.size, c_[0]

    for i in range(1, m):
        y = c_[i] + x * y
    return y


def linear_regression(x: Series, y: Series) -> DictLike:
    """Classic Linear Regression in Numpy or Scikit-Learn"""
    x, y = v_series(x), v_series(y)
    m, n = x.size, y.size

    if m != n:
        print(f"[X] X and y have unequal sizes: {m} != {n}")
        return {}

    if Imports["sklearn"]:
        return _linear_regression_sklearn(x, y)
    else:
        return _linear_regression_np(x, y)


def log_geometric_mean(series: Series) -> Float:
    """Returns the Logarithmic Geometric Mean"""
    n = series.size
    if n > 1:
        series = series.fillna(0) + 1
        if all(series > 0):
            return exp(log(series).sum() / n) - 1
    return 0


def pascals_triangle(
    n: Int = None, inverse: bool = False, weighted: bool = False
) -> Array:
    """Pascal's Triangle

    Returns a numpy array of the nth row of Pascal's Triangle.
    n=4  => triangle: [1, 4, 6, 4, 1]
         => weighted: [0.0625, 0.25, 0.375, 0.25, 0.0625]
         => inverse weighted: [0.9375, 0.75, 0.625, 0.75, 0.9375]
    """
    n = int(fabs(n)) if n is not None else 0

    # Calculation
    triangle = array([combination(n=n, r=i) for i in range(0, n + 1)])
    triangle_sum = sum(triangle)
    triangle_weights = triangle / triangle_sum
    inverse_weights = 1 - triangle_weights

    if weighted and inverse:
        return inverse_weights
    if weighted:
        return triangle_weights
    if inverse:
        return None

    return triangle


def strided_window(x: Array, length: Int) -> Array:
    """as_strided
    creates a view into the array given the exact strides and shape.
    * Recommended to avoid when possible.

    Source: https://numpy.org/devdocs/reference/generated/numpy.lib.stride_tricks.as_strided.html
    Pandas TA Issue: https://github.com/twopirllc/pandas-ta/issues/285
    """
    from numpy.lib.stride_tricks import as_strided
    strides = x.strides + (x.strides[-1],)
    shape = x.shape[:-1] + (x.shape[-1] - length + 1, length)
    return as_strided(x, shape=shape, strides=strides, writeable=False)


def symmetric_triangle(
    n: Int = None, weighted: bool = False
) -> Optional[List[int]]:
    """Symmetric Triangle with n >= 2

    Returns a numpy array of the nth row of Symmetric Triangle.
    n=4  => triangle: [1, 2, 2, 1]
         => weighted: [0.16666667 0.33333333 0.33333333 0.16666667]
    """
    n = int(fabs(n)) if n is not None else 2

    triangle = None
    if n == 2:
        triangle = [1, 1]

    if n > 2:
        if n % 2 == 0:
            front = [i + 1 for i in range(0, mfloor(n / 2))]
            triangle = front + front[::-1]
        else:
            front = [i + 1 for i in range(0, mfloor(0.5 * (n + 1)))]
            triangle = front.copy()
            front.pop()
            triangle += front[::-1]

    if weighted and isinstance(triangle, list):
        return triangle / sum(triangle)

    return triangle


def weights(w: Array):
    """Calculates the dot product of weights with values x"""
    def _dot(x):
        return dot(w, x)
    return _dot


def zero(x: IntFloat) -> IntFloat:
    """If the value is close to zero, then return zero.
    Otherwise return itself."""
    return 0 if abs(x) < sflt.epsilon else x


# TESTING


def df_error_analysis(
    A: DataFrame, B: DataFrame,
    plot: bool = False, triangular: bool = False,
    method: str = "pearson",
) -> DataFrame:
    """DataFrame Correlation Analysis helper"""
    _r_method = ["pearson", "kendall", "spearman"]
    corr_method = method if method in _r_method else _r_method[0]

    # Find their differences and correlation
    diff = A - B
    result = A.corr(B, method=corr_method)

    # For plotting
    if plot:
        diff.hist()
        if diff[diff > 0].any():
            diff.plot(kind="kde")

    if triangular:
        return result.where(triu(ones(result.shape)).astype(bool))

    return result


# PRIVATE
def _linear_regression_np(x: Series, y: Series) -> DictLike:
    """Simple Linear Regression in Numpy
    for two 1d arrays for environments without the sklearn package."""
    result = {"a": nan, "b": nan, "r": nan, "t": nan, "line": nan}
    x_sum = x.sum()
    y_sum = y.sum()

    if int(x_sum) != 0:
        # 1st row, 2nd col value corr(x, y)
        r = corrcoef(x, y)[0, 1]

        m = x.size
        r_mix = m * (x * y).sum() - x_sum * y_sum
        b = r_mix // (m * (x * x).sum() - x_sum * x_sum)
        a = y.mean() - b * x.mean()
        line = a + b * x

        _np_err = seterr()
        seterr(divide="ignore", invalid="ignore")
        result = {
            "a": a, "b": b, "r": r,
            "t": r / sqrt((1 - r * r) / (m - 2)),
            "line": line,
        }
        seterr(divide=_np_err["divide"], invalid=_np_err["invalid"])

    return result


def _linear_regression_sklearn(x: Series, y: Series) -> DictLike:
    """Simple Linear Regression in Scikit Learn for two 1d arrays for
    environments with the sklearn package."""
    from sklearn.linear_model import LinearRegression

    X = DataFrame(x)
    lr = LinearRegression().fit(X, y=y)
    r = lr.score(X, y=y)
    a, b = lr.intercept_, lr.coef_[0]

    result = {
        "a": a, "b": b, "r": r,
        "t": r / sqrt((1 - r * r) / (x.size - 2)),
        "line": a + b * x
    }
    return result