from __future__ import division, absolute_import, print_function

import warnings

import numpy as np
from numpy.testing import (
    assert_, assert_equal, assert_almost_equal, assert_no_warnings,
    assert_raises, assert_array_equal, suppress_warnings
    )


# Test data
_ndat = np.array([[0.6244, np.nan, 0.2692, 0.0116, np.nan, 0.1170],
                  [0.5351, -0.9403, np.nan, 0.2100, 0.4759, 0.2833],
                  [np.nan, np.nan, np.nan, 0.1042, np.nan, -0.5954],
                  [0.1610, np.nan, np.nan, 0.1859, 0.3146, np.nan]])


# Rows of _ndat with nans removed
_rdat = [np.array([0.6244, 0.2692, 0.0116, 0.1170]),
         np.array([0.5351, -0.9403, 0.2100, 0.4759, 0.2833]),
         np.array([0.1042, -0.5954]),
         np.array([0.1610, 0.1859, 0.3146])]

# Rows of _ndat with nans converted to ones
_ndat_ones = np.array([[0.6244, 1.0, 0.2692, 0.0116, 1.0, 0.1170],
                       [0.5351, -0.9403, 1.0, 0.2100, 0.4759, 0.2833],
                       [1.0, 1.0, 1.0, 0.1042, 1.0, -0.5954],
                       [0.1610, 1.0, 1.0, 0.1859, 0.3146, 1.0]])

# Rows of _ndat with nans converted to zeros
_ndat_zeros = np.array([[0.6244, 0.0, 0.2692, 0.0116, 0.0, 0.1170],
                        [0.5351, -0.9403, 0.0, 0.2100, 0.4759, 0.2833],
                        [0.0, 0.0, 0.0, 0.1042, 0.0, -0.5954],
                        [0.1610, 0.0, 0.0, 0.1859, 0.3146, 0.0]])


class TestNanFunctions_MinMax(object):

    nanfuncs = [np.nanmin, np.nanmax]
    stdfuncs = [np.min, np.max]

    def test_mutation(self):
        # Check that passed array is not modified.
        ndat = _ndat.copy()
        for f in self.nanfuncs:
            f(ndat)
            assert_equal(ndat, _ndat)

    def test_keepdims(self):
        mat = np.eye(3)
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            for axis in [None, 0, 1]:
                tgt = rf(mat, axis=axis, keepdims=True)
                res = nf(mat, axis=axis, keepdims=True)
                assert_(res.ndim == tgt.ndim)

    def test_out(self):
        mat = np.eye(3)
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            resout = np.zeros(3)
            tgt = rf(mat, axis=1)
            res = nf(mat, axis=1, out=resout)
            assert_almost_equal(res, resout)
            assert_almost_equal(res, tgt)

    def test_dtype_from_input(self):
        codes = 'efdgFDG'
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            for c in codes:
                mat = np.eye(3, dtype=c)
                tgt = rf(mat, axis=1).dtype.type
                res = nf(mat, axis=1).dtype.type
                assert_(res is tgt)
                # scalar case
                tgt = rf(mat, axis=None).dtype.type
                res = nf(mat, axis=None).dtype.type
                assert_(res is tgt)

    def test_result_values(self):
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            tgt = [rf(d) for d in _rdat]
            res = nf(_ndat, axis=1)
            assert_almost_equal(res, tgt)

    def test_allnans(self):
        mat = np.array([np.nan]*9).reshape(3, 3)
        for f in self.nanfuncs:
            for axis in [None, 0, 1]:
                with warnings.catch_warnings(record=True) as w:
                    warnings.simplefilter('always')
                    assert_(np.isnan(f(mat, axis=axis)).all())
                    assert_(len(w) == 1, 'no warning raised')
                    assert_(issubclass(w[0].category, RuntimeWarning))
            # Check scalars
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                assert_(np.isnan(f(np.nan)))
                assert_(len(w) == 1, 'no warning raised')
                assert_(issubclass(w[0].category, RuntimeWarning))

    def test_masked(self):
        mat = np.ma.fix_invalid(_ndat)
        msk = mat._mask.copy()
        for f in [np.nanmin]:
            res = f(mat, axis=1)
            tgt = f(_ndat, axis=1)
            assert_equal(res, tgt)
            assert_equal(mat._mask, msk)
            assert_(not np.isinf(mat).any())

    def test_scalar(self):
        for f in self.nanfuncs:
            assert_(f(0.) == 0.)

    def test_subclass(self):
        class MyNDArray(np.ndarray):
            pass

        # Check that it works and that type and
        # shape are preserved
        mine = np.eye(3).view(MyNDArray)
        for f in self.nanfuncs:
            res = f(mine, axis=0)
            assert_(isinstance(res, MyNDArray))
            assert_(res.shape == (3,))
            res = f(mine, axis=1)
            assert_(isinstance(res, MyNDArray))
            assert_(res.shape == (3,))
            res = f(mine)
            assert_(res.shape == ())

        # check that rows of nan are dealt with for subclasses (#4628)
        mine[1] = np.nan
        for f in self.nanfuncs:
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                res = f(mine, axis=0)
                assert_(isinstance(res, MyNDArray))
                assert_(not np.any(np.isnan(res)))
                assert_(len(w) == 0)

            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                res = f(mine, axis=1)
                assert_(isinstance(res, MyNDArray))
                assert_(np.isnan(res[1]) and not np.isnan(res[0])
                        and not np.isnan(res[2]))
                assert_(len(w) == 1, 'no warning raised')
                assert_(issubclass(w[0].category, RuntimeWarning))

            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                res = f(mine)
                assert_(res.shape == ())
                assert_(res != np.nan)
                assert_(len(w) == 0)

    def test_object_array(self):
        arr = np.array([[1.0, 2.0], [np.nan, 4.0], [np.nan, np.nan]], dtype=object)
        assert_equal(np.nanmin(arr), 1.0)
        assert_equal(np.nanmin(arr, axis=0), [1.0, 2.0])

        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always')
            # assert_equal does not work on object arrays of nan
            assert_equal(list(np.nanmin(arr, axis=1)), [1.0, 4.0, np.nan])
            assert_(len(w) == 1, 'no warning raised')
            assert_(issubclass(w[0].category, RuntimeWarning))


class TestNanFunctions_ArgminArgmax(object):

    nanfuncs = [np.nanargmin, np.nanargmax]

    def test_mutation(self):
        # Check that passed array is not modified.
        ndat = _ndat.copy()
        for f in self.nanfuncs:
            f(ndat)
            assert_equal(ndat, _ndat)

    def test_result_values(self):
        for f, fcmp in zip(self.nanfuncs, [np.greater, np.less]):
            for row in _ndat:
                with suppress_warnings() as sup:
                    sup.filter(RuntimeWarning, "invalid value encountered in")
                    ind = f(row)
                    val = row[ind]
                    # comparing with NaN is tricky as the result
                    # is always false except for NaN != NaN
                    assert_(not np.isnan(val))
                    assert_(not fcmp(val, row).any())
                    assert_(not np.equal(val, row[:ind]).any())

    def test_allnans(self):
        mat = np.array([np.nan]*9).reshape(3, 3)
        for f in self.nanfuncs:
            for axis in [None, 0, 1]:
                assert_raises(ValueError, f, mat, axis=axis)
            assert_raises(ValueError, f, np.nan)

    def test_empty(self):
        mat = np.zeros((0, 3))
        for f in self.nanfuncs:
            for axis in [0, None]:
                assert_raises(ValueError, f, mat, axis=axis)
            for axis in [1]:
                res = f(mat, axis=axis)
                assert_equal(res, np.zeros(0))

    def test_scalar(self):
        for f in self.nanfuncs:
            assert_(f(0.) == 0.)

    def test_subclass(self):
        class MyNDArray(np.ndarray):
            pass

        # Check that it works and that type and
        # shape are preserved
        mine = np.eye(3).view(MyNDArray)
        for f in self.nanfuncs:
            res = f(mine, axis=0)
            assert_(isinstance(res, MyNDArray))
            assert_(res.shape == (3,))
            res = f(mine, axis=1)
            assert_(isinstance(res, MyNDArray))
            assert_(res.shape == (3,))
            res = f(mine)
            assert_(res.shape == ())


class TestNanFunctions_IntTypes(object):

    int_types = (np.int8, np.int16, np.int32, np.int64, np.uint8,
                 np.uint16, np.uint32, np.uint64)

    mat = np.array([127, 39, 93, 87, 46])

    def integer_arrays(self):
        for dtype in self.int_types:
            yield self.mat.astype(dtype)

    def test_nanmin(self):
        tgt = np.min(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanmin(mat), tgt)

    def test_nanmax(self):
        tgt = np.max(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanmax(mat), tgt)

    def test_nanargmin(self):
        tgt = np.argmin(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanargmin(mat), tgt)

    def test_nanargmax(self):
        tgt = np.argmax(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanargmax(mat), tgt)

    def test_nansum(self):
        tgt = np.sum(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nansum(mat), tgt)

    def test_nanprod(self):
        tgt = np.prod(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanprod(mat), tgt)

    def test_nancumsum(self):
        tgt = np.cumsum(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nancumsum(mat), tgt)

    def test_nancumprod(self):
        tgt = np.cumprod(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nancumprod(mat), tgt)

    def test_nanmean(self):
        tgt = np.mean(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanmean(mat), tgt)

    def test_nanvar(self):
        tgt = np.var(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanvar(mat), tgt)

        tgt = np.var(mat, ddof=1)
        for mat in self.integer_arrays():
            assert_equal(np.nanvar(mat, ddof=1), tgt)

    def test_nanstd(self):
        tgt = np.std(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanstd(mat), tgt)

        tgt = np.std(self.mat, ddof=1)
        for mat in self.integer_arrays():
            assert_equal(np.nanstd(mat, ddof=1), tgt)


class SharedNanFunctionsTestsMixin(object):
    def test_mutation(self):
        # Check that passed array is not modified.
        ndat = _ndat.copy()
        for f in self.nanfuncs:
            f(ndat)
            assert_equal(ndat, _ndat)

    def test_keepdims(self):
        mat = np.eye(3)
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            for axis in [None, 0, 1]:
                tgt = rf(mat, axis=axis, keepdims=True)
                res = nf(mat, axis=axis, keepdims=True)
                assert_(res.ndim == tgt.ndim)

    def test_out(self):
        mat = np.eye(3)
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            resout = np.zeros(3)
            tgt = rf(mat, axis=1)
            res = nf(mat, axis=1, out=resout)
            assert_almost_equal(res, resout)
            assert_almost_equal(res, tgt)

    def test_dtype_from_dtype(self):
        mat = np.eye(3)
        codes = 'efdgFDG'
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            for c in codes:
                with suppress_warnings() as sup:
                    if nf in {np.nanstd, np.nanvar} and c in 'FDG':
                        # Giving the warning is a small bug, see gh-8000
                        sup.filter(np.ComplexWarning)
                    tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
                    res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
                    assert_(res is tgt)