"""Automatically adapted for numpy Sep 19, 2005 by convertcode.py

"""
import functools
import warnings

__all__ = ['iscomplexobj', 'isrealobj', 'imag', 'iscomplex',
           'isreal', 'nan_to_num', 'real', 'real_if_close',
           'typename', 'asfarray', 'mintypecode', 'asscalar',
           'common_type']

import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf


array_function_dispatch = functools.partial(
    overrides.array_function_dispatch, module='numpy')


_typecodes_by_elsize = 'GDFgdfQqLlIiHhBb?'


@set_module('numpy')
def mintypecode(typechars, typeset='GDFgdf', default='d'):
    """
    Return the character for the minimum-size type to which given types can
    be safely cast.

    The returned type character must represent the smallest size dtype such
    that an array of the returned type can handle the data from an array of
    all types in `typechars` (or if `typechars` is an array, then its
    dtype.char).

    Parameters
    ----------
    typechars : list of str or array_like
        If a list of strings, each string should represent a dtype.
        If array_like, the character representation of the array dtype is used.
    typeset : str or list of str, optional
        The set of characters that the returned character is chosen from.
        The default set is 'GDFgdf'.
    default : str, optional
        The default character, this is returned if none of the characters in
        `typechars` matches a character in `typeset`.

    Returns
    -------
    typechar : str
        The character representing the minimum-size type that was found.

    See Also
    --------
    dtype, sctype2char, maximum_sctype

    Examples
    --------
    >>> np.mintypecode(['d', 'f', 'S'])
    'd'
    >>> x = np.array([1.1, 2-3.j])
    >>> np.mintypecode(x)
    'D'

    >>> np.mintypecode('abceh', default='G')
    'G'

    """
    typecodes = ((isinstance(t, str) and t) or asarray(t).dtype.char
                 for t in typechars)
    intersection = set(t for t in typecodes if t in typeset)
    if not intersection:
        return default
    if 'F' in intersection and 'd' in intersection:
        return 'D'
    return min(intersection, key=_typecodes_by_elsize.index)


def _asfarray_dispatcher(a, dtype=None):
    return (a,)


@array_function_dispatch(_asfarray_dispatcher)
def asfarray(a, dtype=_nx.float_):
    """
    Return an array converted to a float type.

    Parameters
    ----------
    a : array_like
        The input array.
    dtype : str or dtype object, optional
        Float type code to coerce input array `a`.  If `dtype` is one of the
        'int' dtypes, it is replaced with float64.

    Returns
    -------
    out : ndarray
        The input `a` as a float ndarray.

    Examples
    --------
    >>> np.asfarray([2, 3])
    array([2.,  3.])
    >>> np.asfarray([2, 3], dtype='float')
    array([2.,  3.])
    >>> np.asfarray([2, 3], dtype='int8')
    array([2.,  3.])

    """
    if not _nx.issubdtype(dtype, _nx.inexact):
        dtype = _nx.float_
    return asarray(a, dtype=dtype)


def _real_dispatcher(val):
    return (val,)


@array_function_dispatch(_real_dispatcher)
def real(val):
    """
    Return the real part of the complex argument.

    Parameters
    ----------
    val : array_like
        Input array.

    Returns
    -------
    out : ndarray or scalar
        The real component of the complex argument. If `val` is real, the type
        of `val` is used for the output.  If `val` has complex elements, the
        returned type is float.

    See Also
    --------
    real_if_close, imag, angle

    Examples
    --------
    >>> a = np.array([1+2j, 3+4j, 5+6j])
    >>> a.real
    array([1.,  3.,  5.])
    >>> a.real = 9
    >>> a
    array([9.+2.j,  9.+4.j,  9.+6.j])
    >>> a.real = np.array([9, 8, 7])
    >>> a
    array([9.+2.j,  8.+4.j,  7.+6.j])
    >>> np.real(1 + 1j)
    1.0

    """
    try:
        return val.real
    except AttributeError:
        return asanyarray(val).real


def _imag_dispatcher(val):
    return (val,)


@array_function_dispatch(_imag_dispatcher)
def imag(val):
    """
    Return the imaginary part of the complex argument.

    Parameters
    ----------
    val : array_like
        Input array.

    Returns
    -------
    out : ndarray or scalar
        The imaginary component of the complex argument. If `val` is real,
        the type of `val` is used for the output.  If `val` has complex
        elements, the returned type is float.

    See Also
    --------
    real, angle, real_if_close

    Examples
    --------
    >>> a = np.array([1+2j, 3+4j, 5+6j])
    >>> a.imag
    array([2.,  4.,  6.])
    >>> a.imag = np.array([8, 10, 12])
    >>> a
    array([1. +8.j,  3.+10.j,  5.+12.j])
    >>> np.imag(1 + 1j)
    1.0

    """
    try:
        return val.imag
    except AttributeError:
        return asanyarray(val).imag


def _is_type_dispatcher(x):
    return (x,)


@array_function_dispatch(_is_type_dispatcher)
def iscomplex(x):
    """
    Returns a bool array, where True if input element is complex.

    What is tested is whether the input has a non-zero imaginary part, not if
    the input type is complex.

    Parameters
    ----------
    x : array_like
        Input array.

    Returns
    -------
    out : ndarray of bools
        Output array.

    See Also
    --------
    isreal
    iscomplexobj : Return True if x is a complex type or an array of complex
                   numbers.

    Examples
    --------
    >>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
    array([ True, False, False, False, False,  True])

    """
    ax = asanyarray(x)
    if issubclass(ax.dtype.type, _nx.complexfloating):
        return ax.imag != 0
    res = zeros(ax.shape, bool)
    return res[()]   # convert to scalar if needed


@array_function_dispatch(_is_type_dispatcher)
def isreal(x):
    """
    Returns a bool array, where True if input element is real.

    If element has complex type with zero complex part, the return value
    for that element is True.

    Parameters
    ----------
    x : array_like
        Input array.

    Returns
    -------
    out : ndarray, bool
        Boolean array of same shape as `x`.

    See Also
    --------
    iscomplex
    isrealobj : Return True if x is not a complex type.

    Examples
    --------
    >>> np.isreal([1+1j, 1+0j, 4.5, 3, 2, 2j])
    array([False,  True,  True,  True,  True, False])

    """
    return imag(x) == 0


@array_function_dispatch(_is_type_dispatcher)
def iscomplexobj(x):
    """
    Check for a complex type or an array of complex numbers.

    The type of the input is checked, not the value. Even if the input
    has an imaginary part equal to zero, `iscomplexobj` evaluates to True.

    Parameters
    ----------
    x : any
        The input can be of any type and shape.

    Returns
    -------
    iscomplexobj : bool
        The return value, True if `x` is of a complex type or has at least
        one complex element.

    See Also
    --------
    isrealobj, iscomplex

    Examples
    --------
    >>> np.iscomplexobj(1)
    False
    >>> np.iscomplexobj(1+0j)
    True
    >>> np.iscomplexobj([3, 1+0j, True])
    True

    """
    try:
        dtype = x.dtype
        type_ = dtype.type
    except AttributeError:
        type_ = asarray(x).dtype.type
    return issubclass(type_, _nx.complexfloating)


@array_function_dispatch(_is_type_dispatcher)
def isrealobj(x):
    """
    Return True if x is a not complex type or an array of complex numbers.

    The type of the input is checked, not the value. So even if the input
    has an imaginary part equal to zero, `isrealobj` evaluates to False
    if the data type is complex.

    Parameters
    ----------
    x : any
        The input can be of any type and shape.

    Returns
    -------
    y : bool
        The return value, False if `x` is of a complex type.

    See Also
    --------
    iscomplexobj, isreal

    Examples
    --------
    >>> np.isrealobj(1)