"""Base class for sparse matrice with a .data attribute

    subclasses must provide a _with_data() method that
    creates a new matrix with the same sparsity pattern
    as self but with a different data array

"""

from __future__ import division, print_function, absolute_import

import numpy as np

from .base import spmatrix, _ufuncs_with_fixed_point_at_zero
from .sputils import isscalarlike, validateaxis

__all__ = []


# TODO implement all relevant operations
# use .data.__methods__() instead of /=, *=, etc.
class _data_matrix(spmatrix):
    def __init__(self):
        spmatrix.__init__(self)

    def _get_dtype(self):
        return self.data.dtype

    def _set_dtype(self, newtype):
        self.data.dtype = newtype
    dtype = property(fget=_get_dtype, fset=_set_dtype)

    def _deduped_data(self):
        if hasattr(self, 'sum_duplicates'):
            self.sum_duplicates()
        return self.data

    def __abs__(self):
        return self._with_data(abs(self._deduped_data()))

    def _real(self):
        return self._with_data(self.data.real)

    def _imag(self):
        return self._with_data(self.data.imag)

    def __neg__(self):
        if self.dtype.kind == 'b':
            raise NotImplementedError('negating a sparse boolean '
                                      'matrix is not supported')
        return self._with_data(-self.data)

    def __imul__(self, other):  # self *= other
        if isscalarlike(other):
            self.data *= other
            return self
        else:
            return NotImplemented

    def __itruediv__(self, other):  # self /= other
        if isscalarlike(other):
            recip = 1.0 / other
            self.data *= recip
            return self
        else:
            return NotImplemented

    def astype(self, t):
        return self._with_data(self._deduped_data().astype(t))

    def conj(self):
        return self._with_data(self.data.conj())

    def copy(self):
        return self._with_data(self.data.copy(), copy=True)

    def count_nonzero(self):
        return np.count_nonzero(self._deduped_data())

    count_nonzero.__doc__ = spmatrix.count_nonzero.__doc__

    def power(self, n, dtype=None):
        """
        This function performs element-wise power.

        Parameters
        ----------
        n : n is a scalar

        dtype : If dtype is not specified, the current dtype will be preserved.
        """
        if not isscalarlike(n):
            raise NotImplementedError("input is not scalar")

        data = self._deduped_data()
        if dtype is not None:
            data = data.astype(dtype)
        return self._with_data(data ** n)

    ###########################
    # Multiplication handlers #
    ###########################

    def _mul_scalar(self, other):
        return self._with_data(self.data * other)


# Add the numpy unary ufuncs for which func(0) = 0 to _data_matrix.
for npfunc in _ufuncs_with_fixed_point_at_zero:
    name = npfunc.__name__

    def _create_method(op):
        def method(self):
            result = op(self.data)
            x = self._with_data(result, copy=True)
            return x

        method.__doc__ = ("Element-wise %s.\n\n"
                          "See numpy.%s for more information." % (name, name))
        method.__name__ = name

        return method

    setattr(_data_matrix, name, _create_method(npfunc))


class _minmax_mixin(object):
    """Mixin for min and max methods.

    These are not implemented for dia_matrix, hence the separate class.
    """

    def _min_or_max_axis(self, axis, min_or_max):
        N = self.shape[axis]
        if N == 0:
            raise ValueError("zero-size array to reduction operation")
        M = self.shape[1 - axis]

        mat = self.tocsc() if axis == 0 else self.tocsr()
        mat.sum_duplicates()

        major_index, value = mat._minor_reduce(min_or_max)
        not_full = np.diff(mat.indptr)[major_index] < N
        value[not_full] = min_or_max(value[not_full], 0)

        mask = value != 0
        major_index = np.compress(mask, major_index)
        value = np.compress(mask, value)

        from . import coo_matrix
        if axis == 0:
            return coo_matrix((value, (np.zeros(len(value)), major_index)),
                              dtype=self.dtype, shape=(1, M))
        else:
            return coo_matrix((value, (major_index, np.zeros(len(value)))),
                              dtype=self.dtype, shape=(M, 1))

    def _min_or_max(self, axis, out, min_or_max):
        if out is not None:
            raise ValueError(("Sparse matrices do not support "
                              "an 'out' parameter."))

        validateaxis(axis)

        if axis is None:
            if 0 in self.shape:
                raise ValueError("zero-size array to reduction operation")

            zero = self.dtype.type(0)
            if self.nnz == 0:
                return zero
            m = min_or_max.reduce(self._deduped_data().ravel())
            if self.nnz != np.product(self.shape):
                m = min_or_max(zero, m)
            return m

        if axis < 0:
            axis += 2

        if (axis == 0) or (axis == 1):
            return self._min_or_max_axis(axis, min_or_max)
        else:
            raise ValueError("axis out of range")

    def max(self, axis=None, out=None):
        """
        Return the maximum of the matrix or maximum along an axis.
        This takes all elements into account, not just the non-zero ones.

        Parameters
        ----------
        axis : {-2, -1, 0, 1, None} optional
            Axis along which the sum is computed. The default is to
            compute the maximum over all the matrix elements, returning
            a scalar (i.e. `axis` = `None`).

        out : None, optional
            This argument is in the signature *solely* for NumPy
            compatibility reasons. Do not pass in anything except
            for the default value, as this argument is not used.

        Returns
        -------
        amax : coo_matrix or scalar
            Maximum of `a`. If `axis` is None, the result is a scalar value.
            If `axis` is given, the result is a sparse.coo_matrix of dimension
            ``a.ndim - 1``.

        See Also
        --------
        min : The minimum value of a sparse matrix along a given axis.
        np.matrix.max : NumPy's implementation of 'max' for matrices

        """
        return self._min_or_max(axis, out, np.maximum)

    def min(self, axis=None, out=None):
        """
        Return the minimum of the matrix or maximum along an axis.
        This takes all elements into account, not just the non-zero ones.

        Parameters
        ----------
        axis : {-2, -1, 0, 1, None} optional
            Axis along which the sum is computed. The default is to
            compute the minimum over all the matrix elements, returning
            a scalar (i.e. `axis` = `None`).

        out : None, optional
            This argument is in the signature *solely* for NumPy
            compatibility reasons. Do not pass in anything except for
            the default value, as this argument is not used.

        Returns
        -------
        amin : coo_matrix or scalar
            Minimum of `a`. If `axis` is None, the result is a scalar value.
            If `axis` is given, the result is a sparse.coo_matrix of dimension
            ``a.ndim - 1``.

        See Also
        --------
        max : The maximum value of a sparse matrix along a given axis.
        np.matrix.min : NumPy's implementation of 'min' for matrices

        """
        return self._min_or_max(axis, out, np.minimum)
