"""Machine limits for Float32 and Float64 and (long double) if available...

"""
from __future__ import division, absolute_import, print_function

__all__ = ['finfo', 'iinfo']

import warnings

from .machar import MachAr
from .overrides import set_module
from . import numeric
from . import numerictypes as ntypes
from .numeric import array, inf
from .umath import log10, exp2
from . import umath


def _fr0(a):
    """fix rank-0 --> rank-1"""
    if a.ndim == 0:
        a = a.copy()
        a.shape = (1,)
    return a


def _fr1(a):
    """fix rank > 0 --> rank-0"""
    if a.size == 1:
        a = a.copy()
        a.shape = ()
    return a

class MachArLike(object):
    """ Object to simulate MachAr instance """

    def __init__(self,
                 ftype,
                 **kwargs):
        params = _MACHAR_PARAMS[ftype]
        float_conv = lambda v: array([v], ftype)
        float_to_float = lambda v : _fr1(float_conv(v))
        float_to_str = lambda v: (params['fmt'] % array(_fr0(v)[0], ftype))

        self.title = params['title']
        # Parameter types same as for discovered MachAr object.
        self.epsilon = self.eps = float_to_float(kwargs.pop('eps'))
        self.epsneg = float_to_float(kwargs.pop('epsneg'))
        self.xmax = self.huge = float_to_float(kwargs.pop('huge'))
        self.xmin = self.tiny = float_to_float(kwargs.pop('tiny'))
        self.ibeta = params['itype'](kwargs.pop('ibeta'))
        self.__dict__.update(kwargs)
        self.precision = int(-log10(self.eps))
        self.resolution = float_to_float(float_conv(10) ** (-self.precision))
        self._str_eps = float_to_str(self.eps)
        self._str_epsneg = float_to_str(self.epsneg)
        self._str_xmin = float_to_str(self.xmin)
        self._str_xmax = float_to_str(self.xmax)
        self._str_resolution = float_to_str(self.resolution)

_convert_to_float = {
    ntypes.csingle: ntypes.single,
    ntypes.complex_: ntypes.float_,
    ntypes.clongfloat: ntypes.longfloat
    }

# Parameters for creating MachAr / MachAr-like objects
_title_fmt = 'numpy {} precision floating point number'
_MACHAR_PARAMS = {
    ntypes.double: dict(
        itype = ntypes.int64,
        fmt = '%24.16e',
        title = _title_fmt.format('double')),
    ntypes.single: dict(
        itype = ntypes.int32,
        fmt = '%15.7e',
        title = _title_fmt.format('single')),
    ntypes.longdouble: dict(
        itype = ntypes.longlong,
        fmt = '%s',
        title = _title_fmt.format('long double')),
    ntypes.half: dict(
        itype = ntypes.int16,
        fmt = '%12.5e',
        title = _title_fmt.format('half'))}

# Key to identify the floating point type.  Key is result of
# ftype('-0.1').newbyteorder('<').tobytes()
# See:
# https://perl5.git.perl.org/perl.git/blob/3118d7d684b56cbeb702af874f4326683c45f045:/Configure
_KNOWN_TYPES = {}
def _register_type(machar, bytepat):
    _KNOWN_TYPES[bytepat] = machar
_float_ma = {}

def _register_known_types():
    # Known parameters for float16
    # See docstring of MachAr class for description of parameters.
    f16 = ntypes.float16
    float16_ma = MachArLike(f16,
                            machep=-10,
                            negep=-11,
                            minexp=-14,
                            maxexp=16,
                            it=10,
                            iexp=5,
                            ibeta=2,
                            irnd=5,
                            ngrd=0,
                            eps=exp2(f16(-10)),
                            epsneg=exp2(f16(-11)),
                            huge=f16(65504),
                            tiny=f16(2 ** -14))
    _register_type(float16_ma, b'f\xae')
    _float_ma[16] = float16_ma

    # Known parameters for float32
    f32 = ntypes.float32
    float32_ma = MachArLike(f32,
                            machep=-23,
                            negep=-24,
                            minexp=-126,
                            maxexp=128,
                            it=23,
                            iexp=8,
                            ibeta=2,
                            irnd=5,
                            ngrd=0,
                            eps=exp2(f32(-23)),
                            epsneg=exp2(f32(-24)),
                            huge=f32((1 - 2 ** -24) * 2**128),
                            tiny=exp2(f32(-126)))
    _register_type(float32_ma, b'\xcd\xcc\xcc\xbd')
    _float_ma[32] = float32_ma

    # Known parameters for float64
    f64 = ntypes.float64
    epsneg_f64 = 2.0 ** -53.0
    tiny_f64 = 2.0 ** -1022.0
    float64_ma = MachArLike(f64,
                            machep=-52,
                            negep=-53,
                            minexp=-1022,
                            maxexp=1024,
                            it=52,
                            iexp=11,
                            ibeta=2,
                            irnd=5,
                            ngrd=0,
                            eps=2.0 ** -52.0,
                            epsneg=epsneg_f64,
                            huge=(1.0 - epsneg_f64) / tiny_f64 * f64(4),
                            tiny=tiny_f64)
    _register_type(float64_ma, b'\x9a\x99\x99\x99\x99\x99\xb9\xbf')
    _float_ma[64] = float64_ma

    # Known parameters for IEEE 754 128-bit binary float
    ld = ntypes.longdouble
    epsneg_f128 = exp2(ld(-113))
    tiny_f128 = exp2(ld(-16382))
    # Ignore runtime error when this is not f128
    with numeric.errstate(all='ignore'):
        huge_f128 = (ld(1) - epsneg_f128) / tiny_f128 * ld(4)
    float128_ma = MachArLike(ld,
                             machep=-112,
                             negep=-113,
                             minexp=-16382,
                             maxexp=16384,
                             it=112,
                             iexp=15,
                             ibeta=2,
                             irnd=5,
                             ngrd=0,
                             eps=exp2(ld(-112)),
                             epsneg=epsneg_f128,
                             huge=huge_f128,
                             tiny=tiny_f128)
    # IEEE 754 128-bit binary float
    _register_type(float128_ma,
        b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
    _register_type(float128_ma,
        b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
    _float_ma[128] = float128_ma

    # Known parameters for float80 (Intel 80-bit extended precision)
    epsneg_f80 = exp2(ld(-64))
    tiny_f80 = exp2(ld(-16382))
    # Ignore runtime error when this is not f80
    with numeric.errstate(all='ignore'):
        huge_f80 = (ld(1) - epsneg_f80) / tiny_f80 * ld(4)
    float80_ma = MachArLike(ld,
                            machep=-63,
                            negep=-64,
                            minexp=-16382,
                            maxexp=16384,
                            it=63,
                            iexp=15,
                            ibeta=2,
                            irnd=5,
                            ngrd=0,
                            eps=exp2(ld(-63)),
                            epsneg=epsneg_f80,
                            huge=huge_f80,
                            tiny=tiny_f80)
    # float80, first 10 bytes containing actual storage
    _register_type(float80_ma, b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf')
    _float_ma[80] = float80_ma

    # Guessed / known parameters for double double; see:
    # https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic
    # These numbers have the same exponent range as float64, but extended number of
    # digits in the significand.
    huge_dd = (umath.nextafter(ld(inf), ld(0))
                if hasattr(umath, 'nextafter')  # Missing on some platforms?
                else float64_ma.huge)
    float_dd_ma = MachArLike(ld,
                              machep=-105,
                              negep=-106,
                              minexp=-1022,
                              maxexp=1024,
                              it=105,
                              iexp=11,
                              ibeta=2,
                              irnd=5,
                              ngrd=0,
                              eps=exp2(ld(-105)),
                              epsneg= exp2(ld(-106)),
                              huge=huge_dd,
                              tiny=exp2(ld(-1022)))
    # double double; low, high order (e.g. PPC 64)
    _register_type(float_dd_ma,
        b'\x9a\x99\x99\x99\x99\x99Y<\x9a\x99\x99\x99\x99\x99\xb9\xbf')
    # double double; high, low order (e.g. PPC 64 le)
    _register_type(float_dd_ma,
        b'\x9a\x99\x99\x99\x99\x99\xb9\xbf\x9a\x99\x99\x99\x99\x99Y<')
    _float_ma['dd'] = float_dd_ma


def _get_machar(ftype):
    """ Get MachAr instance or MachAr-like instance

    Get parameters for floating point type, by first trying signatures of
    various known floating point types, then, if none match, attempting to
    identify parameters by analysis.

    Parameters
    ----------
    ftype : class
        Numpy floating point type class (e.g. ``np.float64``)

    Returns
    -------
    ma_like : instance of :class:`MachAr` or :class:`MachArLike`
        Object giving floating point parameters for `ftype`.

    Warns
    -----
    UserWarning
        If the binary signature of the float type is not in the dictionary of
        known float types.
    """
    params = _MACHAR_PARAMS.get(ftype)
    if params is None:
        raise ValueError(repr(ftype))
    # Detect known / suspected types
    key = ftype('-0.1').newbyteorder('<').tobytes()
    ma_like = _KNOWN_TYPES.get(key)
    # Could be 80 bit == 10 byte extended precision, where last bytes can be
    # random garbage.  Try comparing first 10 bytes to pattern.
    if ma_like is None and ftype == ntypes.longdouble:
        ma_like = _KNOWN_TYPES.get(key[:10])
    if ma_like is not None:
        return ma_like
    # Fall back to parameter discovery
    warnings.warn(
        'Signature {} for {} does not match any known type: '
        'falling back to type probe function'.format(key, ftype),
        UserWarning, stacklevel=2)
    return _discovered_machar(ftype)


def _discovered_machar(ftype):
    """ Create MachAr instance with found information on float types
    """
    params = _MACHAR_PARAMS[ftype]
    return MachAr(lambda v: array([v], ftype),
                  lambda v:_fr0(v.astype(params['itype']))[0],
                  lambda v:array(_fr0(v)[0], ftype),
                  lambda v: params['fmt'] % array(_fr0(v)[0], ftype),
                  params['title'])


@set_module('numpy')
class finfo(object):
    """
    finfo(dtype)

    Machine limits for floating point types.

    Attributes
    ----------
    bits : int
        The number of bits occupied by the type.
    eps : float
        The smallest representable positive number such that
        ``1.0 + eps != 1.0``.  Type of `eps` is an appropriate floating
        point type.
    epsneg : floating point number of the appropriate type
        The smallest representable positive number such that
        ``1.0 - epsneg != 1.0``.
    iexp : int
        The number of bits in the exponent portion of the floating point
        representation.
    machar : MachAr
        The object which calculated these parameters and holds more
        detailed information.
    machep : int
        The exponent that yields `eps`.
    max : floating point number of the appropriate type
        The largest representable number.
    maxexp : int
        The smallest positive power of the base (2) that causes overflow.
    min : floating point number of the appropriate type
        The smallest representable number, typically ``-max``.
    minexp : int
        The most negative power of the base (2) consistent with there
        being no leading 0's in the mantissa.
    negep : int
        The exponent that yields `epsneg`.
    nexp : int
        The number of bits in the exponent including its sign and bias.
    nmant : int
        The number of bits in the mantissa.
    precision : int
        The approximate number of decimal digits to which this kind of
        float is precise.
    resolution : floating point number of the appropriate type
        The approximate decimal resolution of this type, i.e.,
        ``10**-precision``.
    tiny : float
        The smallest positive usable number.  Type of `tiny` is an
        appropriate floating point type.

    Parameters
    ----------