"""
numpy.ma : a package to handle missing or invalid values.

This package was initially written for numarray by Paul F. Dubois
at Lawrence Livermore National Laboratory.
In 2006, the package was completely rewritten by Pierre Gerard-Marchant
(University of Georgia) to make the MaskedArray class a subclass of ndarray,
and to improve support of structured arrays.


Copyright 1999, 2000, 2001 Regents of the University of California.
Released for unlimited redistribution.

* Adapted for numpy_core 2005 by Travis Oliphant and (mainly) Paul Dubois.
* Subclassing of the base `ndarray` 2006 by Pierre Gerard-Marchant
  (pgmdevlist_AT_gmail_DOT_com)
* Improvements suggested by Reggie Dugard (reggie_AT_merfinllc_DOT_com)

.. moduleauthor:: Pierre Gerard-Marchant

"""
# pylint: disable-msg=E1002
import builtins
import operator
import warnings
import textwrap
import re
from functools import reduce

import numpy as np
import numpy.core.umath as umath
import numpy.core.numerictypes as ntypes
from numpy import ndarray, amax, amin, iscomplexobj, bool_, _NoValue
from numpy import array as narray
from numpy.lib.function_base import angle
from numpy.compat import (
    getargspec, formatargspec, long, unicode, bytes
    )
from numpy import expand_dims
from numpy.core.numeric import normalize_axis_tuple
from numpy.core._internal import recursive
from numpy.compat import pickle


__all__ = [
    'MAError', 'MaskError', 'MaskType', 'MaskedArray', 'abs', 'absolute',
    'add', 'all', 'allclose', 'allequal', 'alltrue', 'amax', 'amin',
    'angle', 'anom', 'anomalies', 'any', 'append', 'arange', 'arccos',
    'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctan2', 'arctanh',
    'argmax', 'argmin', 'argsort', 'around', 'array', 'asanyarray',
    'asarray', 'bitwise_and', 'bitwise_or', 'bitwise_xor', 'bool_', 'ceil',
    'choose', 'clip', 'common_fill_value', 'compress', 'compressed',
    'concatenate', 'conjugate', 'convolve', 'copy', 'correlate', 'cos', 'cosh',
    'count', 'cumprod', 'cumsum', 'default_fill_value', 'diag', 'diagonal',
    'diff', 'divide', 'empty', 'empty_like', 'equal', 'exp',
    'expand_dims', 'fabs', 'filled', 'fix_invalid', 'flatten_mask',
    'flatten_structured_array', 'floor', 'floor_divide', 'fmod',
    'frombuffer', 'fromflex', 'fromfunction', 'getdata', 'getmask',
    'getmaskarray', 'greater', 'greater_equal', 'harden_mask', 'hypot',
    'identity', 'ids', 'indices', 'inner', 'innerproduct', 'isMA',
    'isMaskedArray', 'is_mask', 'is_masked', 'isarray', 'left_shift',
    'less', 'less_equal', 'log', 'log10', 'log2',
    'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'make_mask',
    'make_mask_descr', 'make_mask_none', 'mask_or', 'masked',
    'masked_array', 'masked_equal', 'masked_greater',
    'masked_greater_equal', 'masked_inside', 'masked_invalid',
    'masked_less', 'masked_less_equal', 'masked_not_equal',
    'masked_object', 'masked_outside', 'masked_print_option',
    'masked_singleton', 'masked_values', 'masked_where', 'max', 'maximum',
    'maximum_fill_value', 'mean', 'min', 'minimum', 'minimum_fill_value',
    'mod', 'multiply', 'mvoid', 'ndim', 'negative', 'nomask', 'nonzero',
    'not_equal', 'ones', 'outer', 'outerproduct', 'power', 'prod',
    'product', 'ptp', 'put', 'putmask', 'ravel', 'remainder',
    'repeat', 'reshape', 'resize', 'right_shift', 'round', 'round_',
    'set_fill_value', 'shape', 'sin', 'sinh', 'size', 'soften_mask',
    'sometrue', 'sort', 'sqrt', 'squeeze', 'std', 'subtract', 'sum',
    'swapaxes', 'take', 'tan', 'tanh', 'trace', 'transpose', 'true_divide',
    'var', 'where', 'zeros',
    ]

MaskType = np.bool_
nomask = MaskType(0)

class MaskedArrayFutureWarning(FutureWarning):
    pass

def _deprecate_argsort_axis(arr):
    """
    Adjust the axis passed to argsort, warning if necessary

    Parameters
    ----------
    arr
        The array which argsort was called on

    np.ma.argsort has a long-term bug where the default of the axis argument
    is wrong (gh-8701), which now must be kept for backwards compatibility.
    Thankfully, this only makes a difference when arrays are 2- or more-
    dimensional, so we only need a warning then.
    """
    if arr.ndim <= 1:
        # no warning needed - but switch to -1 anyway, to avoid surprising
        # subclasses, which are more likely to implement scalar axes.
        return -1
    else:
        # 2017-04-11, Numpy 1.13.0, gh-8701: warn on axis default
        warnings.warn(
            "In the future the default for argsort will be axis=-1, not the "
            "current None, to match its documentation and np.argsort. "
            "Explicitly pass -1 or None to silence this warning.",
            MaskedArrayFutureWarning, stacklevel=3)
        return None


def doc_note(initialdoc, note):
    """
    Adds a Notes section to an existing docstring.

    """
    if initialdoc is None:
        return
    if note is None:
        return initialdoc

    notesplit = re.split(r'\n\s*?Notes\n\s*?-----', initialdoc)

    notedoc = """\
Notes
    -----
    %s""" % note

    if len(notesplit) > 1:
        notedoc = '\n\n    ' + notedoc + '\n'

    return ''.join(notesplit[:1] + [notedoc] + notesplit[1:])


def get_object_signature(obj):
    """
    Get the signature from obj

    """
    try:
        sig = formatargspec(*getargspec(obj))
    except TypeError:
        sig = ''
    return sig


###############################################################################
#                              Exceptions                                     #
###############################################################################


class MAError(Exception):
    """
    Class for masked array related errors.

    """
    pass


class MaskError(MAError):
    """
    Class for mask related errors.

    """
    pass


###############################################################################
#                           Filling options                                   #
###############################################################################


# b: boolean - c: complex - f: floats - i: integer - O: object - S: string
default_filler = {'b': True,
                  'c': 1.e20 + 0.0j,
                  'f': 1.e20,
                  'i': 999999,
                  'O': '?',
                  'S': b'N/A',
                  'u': 999999,
                  'V': b'???',
                  'U': u'N/A'
                  }

# Add datetime64 and timedelta64 types
for v in ["Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps",
          "fs", "as"]:
    default_filler["M8[" + v + "]"] = np.datetime64("NaT", v)
    default_filler["m8[" + v + "]"] = np.timedelta64("NaT", v)

float_types_list = [np.half, np.single, np.double, np.longdouble,
                    np.csingle, np.cdouble, np.clongdouble]
max_filler = ntypes._minvals
max_filler.update([(k, -np.inf) for k in float_types_list[:4]])
max_filler.update([(k, complex(-np.inf, -np.inf)) for k in float_types_list[-3:]])

min_filler = ntypes._maxvals
min_filler.update([(k,  +np.inf) for k in float_types_list[:4]])
min_filler.update([(k, complex(+np.inf, +np.inf)) for k in float_types_list[-3:]])

del float_types_list

def _recursive_fill_value(dtype, f):
    """
    Recursively produce a fill value for `dtype`, calling f on scalar dtypes
    """
    if dtype.names is not None:
        vals = tuple(_recursive_fill_value(dtype[name], f) for name in dtype.names)
        return np.array(vals, dtype=dtype)[()]  # decay to void scalar from 0d
    elif dtype.subdtype:
        subtype, shape = dtype.subdtype
        subval = _recursive_fill_value(subtype, f)
        return np.full(shape, subval)
    else:
        return f(dtype)


def _get_dtype_of(obj):
    """ Convert the argument for *_fill_value into a dtype """
    if isinstance(obj, np.dtype):
        return obj
    elif hasattr(obj, 'dtype'):
        return obj.dtype
    else:
        return np.asanyarray(obj).dtype


def default_fill_value(obj):
    """
    Return the default fill value for the argument object.

    The default filling value depends on the datatype of the input
    array or the type of the input scalar:

       ========  ========
       datatype  default
       ========  ========
       bool      True
       int       999999
       float     1.e20
       complex   1.e20+0j
       object    '?'
       string    'N/A'
       ========  ========

    For structured types, a structured scalar is returned, with each field the
    default fill value for its type.

    For subarray types, the fill value is an array of the same size containing
    the default scalar fill value.

    Parameters
    ----------
    obj : ndarray, dtype or scalar
        The array data-type or scalar for which the default fill value
        is returned.

    Returns
    -------
    fill_value : scalar
        The default fill value.

    Examples
    --------
    >>> np.ma.default_fill_value(1)
    999999
    >>> np.ma.default_fill_value(np.array([1.1, 2., np.pi]))
    1e+20
    >>> np.ma.default_fill_value(np.dtype(complex))
    (1e+20+0j)

    """
    def _scalar_fill_value(dtype):
        if dtype.kind in 'Mm':
            return default_filler.get(dtype.str[1:], '?')
        else:
            return default_filler.get(dtype.kind, '?')

    dtype = _get_dtype_of(obj)
    return _recursive_fill_value(dtype, _scalar_fill_value)


def _extremum_fill_value(obj, extremum, extremum_name):

    def _scalar_fill_value(dtype):
        try:
            return extremum[dtype]
        except KeyError as e:
            raise TypeError(
                f"Unsuitable type {dtype} for calculating {extremum_name}."
            ) from None

    dtype = _get_dtype_of(obj)
    return _recursive_fill_value(dtype, _scalar_fill_value)


def minimum_fill_value(obj):
    """
    Return the maximum value that can be represented by the dtype of an object.

    This function is useful for calculating a fill value suitable for
    taking the minimum of an array with a given dtype.

    Parameters
    ----------
    obj : ndarray, dtype or scalar
        An object that can be queried for it's numeric type.

    Returns
    -------
    val : scalar
        The maximum representable value.

    Raises
    ------
    TypeError
        If `obj` isn't a suitable numeric type.

    See Also
    --------
    maximum_fill_value : The inverse function.
    set_fill_value : Set the filling value of a masked array.
    MaskedArray.fill_value : Return current fill value.

    Examples
    --------
    >>> import numpy.ma as ma
    >>> a = np.int8()
    >>> ma.minimum_fill_value(a)
    127
    >>> a = np.int32()
    >>> ma.minimum_fill_value(a)
    2147483647

    An array of numeric data can also be passed.

    >>> a = np.array([1, 2, 3], dtype=np.int8)
    >>> ma.minimum_fill_value(a)
    127
    >>> a = np.array([1, 2, 3], dtype=np.float32)
    >>> ma.minimum_fill_value(a)
    inf