import sys
import warnings

import numpy as np
from numpy.testing import *
import numpy.core.umath_tests as umt
from numpy.compat import asbytes
from numpy.testing.utils import WarningManager

class TestUfunc(TestCase):
    def test_pickle(self):
        import pickle
        assert pickle.loads(pickle.dumps(np.sin)) is np.sin

    def test_pickle_withstring(self):
        import pickle
        astring = asbytes("cnumpy.core\n_ufunc_reconstruct\np0\n(S'numpy.core.umath'\np1\nS'cos'\np2\ntp3\nRp4\n.")
        assert pickle.loads(astring) is np.cos

    def test_reduceat_shifting_sum(self) :
        L = 6
        x = np.arange(L)
        idx = np.array(zip(np.arange(L-2), np.arange(L-2)+2)).ravel()
        assert_array_equal(np.add.reduceat(x,idx)[::2], [1,3,5,7])

    def test_generic_loops(self) :
        """Test generic loops.

        The loops to be tested are:

            PyUFunc_ff_f_As_dd_d
            PyUFunc_ff_f
            PyUFunc_dd_d
            PyUFunc_gg_g
            PyUFunc_FF_F_As_DD_D
            PyUFunc_DD_D
            PyUFunc_FF_F
            PyUFunc_GG_G
            PyUFunc_OO_O
            PyUFunc_OO_O_method
            PyUFunc_f_f_As_d_d
            PyUFunc_d_d
            PyUFunc_f_f
            PyUFunc_g_g
            PyUFunc_F_F_As_D_D
            PyUFunc_F_F
            PyUFunc_D_D
            PyUFunc_G_G
            PyUFunc_O_O
            PyUFunc_O_O_method
            PyUFunc_On_Om

        Where:

            f -- float
            d -- double
            g -- long double
            F -- complex float
            D -- complex double
            G -- complex long double
            O -- python object

        It is difficult to assure that each of these loops is entered from the
        Python level as the special cased loops are a moving target and the
        corresponding types are architecture dependent. We probably need to
        define C level testing ufuncs to get at them. For the time being, I've
        just looked at the signatures registered in the build directory to find
        relevant functions.

        Fixme, currently untested:

            PyUFunc_ff_f_As_dd_d
            PyUFunc_FF_F_As_DD_D
            PyUFunc_f_f_As_d_d
            PyUFunc_F_F_As_D_D
            PyUFunc_On_Om

        """
        fone = np.exp
        ftwo = lambda x,y : x**y
        fone_val = 1
        ftwo_val = 1
        # check unary PyUFunc_f_f.
        msg = "PyUFunc_f_f"
        x = np.zeros(10, dtype=np.single)[0::2]
        assert_almost_equal(fone(x), fone_val, err_msg=msg)
        # check unary PyUFunc_d_d.
        msg = "PyUFunc_d_d"
        x = np.zeros(10, dtype=np.double)[0::2]
        assert_almost_equal(fone(x), fone_val, err_msg=msg)
        # check unary PyUFunc_g_g.
        msg = "PyUFunc_g_g"
        x = np.zeros(10, dtype=np.longdouble)[0::2]
        assert_almost_equal(fone(x), fone_val, err_msg=msg)
        # check unary PyUFunc_F_F.
        msg = "PyUFunc_F_F"
        x = np.zeros(10, dtype=np.csingle)[0::2]
        assert_almost_equal(fone(x), fone_val, err_msg=msg)
        # check unary PyUFunc_D_D.
        msg = "PyUFunc_D_D"
        x = np.zeros(10, dtype=np.cdouble)[0::2]
        assert_almost_equal(fone(x), fone_val, err_msg=msg)
        # check unary PyUFunc_G_G.
        msg = "PyUFunc_G_G"
        x = np.zeros(10, dtype=np.clongdouble)[0::2]
        assert_almost_equal(fone(x), fone_val, err_msg=msg)

        # check binary PyUFunc_ff_f.
        msg = "PyUFunc_ff_f"
        x = np.ones(10, dtype=np.single)[0::2]
        assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
        # check binary PyUFunc_dd_d.
        msg = "PyUFunc_dd_d"
        x = np.ones(10, dtype=np.double)[0::2]
        assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
        # check binary PyUFunc_gg_g.
        msg = "PyUFunc_gg_g"
        x = np.ones(10, dtype=np.longdouble)[0::2]
        assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
        # check binary PyUFunc_FF_F.
        msg = "PyUFunc_FF_F"
        x = np.ones(10, dtype=np.csingle)[0::2]
        assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
        # check binary PyUFunc_DD_D.
        msg = "PyUFunc_DD_D"
        x = np.ones(10, dtype=np.cdouble)[0::2]
        assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
        # check binary PyUFunc_GG_G.
        msg = "PyUFunc_GG_G"
        x = np.ones(10, dtype=np.clongdouble)[0::2]
        assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)

        # class to use in testing object method loops
        class foo(object):
            def conjugate(self) :
                return np.bool_(1)
            def logical_xor(self, obj) :
                return np.bool_(1)

        # check unary PyUFunc_O_O
        msg = "PyUFunc_O_O"
        x = np.ones(10, dtype=np.object)[0::2]
        assert_(np.all(np.abs(x) == 1), msg)
        # check unary PyUFunc_O_O_method
        msg = "PyUFunc_O_O_method"
        x = np.zeros(10, dtype=np.object)[0::2]
        for i in range(len(x)) :
            x[i] = foo()
        assert_(np.all(np.conjugate(x) == True), msg)

        # check binary PyUFunc_OO_O
        msg = "PyUFunc_OO_O"
        x = np.ones(10, dtype=np.object)[0::2]
        assert_(np.all(np.add(x,x) == 2), msg)
        # check binary PyUFunc_OO_O_method
        msg = "PyUFunc_OO_O_method"
        x = np.zeros(10, dtype=np.object)[0::2]
        for i in range(len(x)) :
            x[i] = foo()
        assert_(np.all(np.logical_xor(x,x)), msg)

        # check PyUFunc_On_Om
        # fixme -- I don't know how to do this yet

    def test_all_ufunc(self) :
        """Try to check presence and results of all ufuncs.

        The list of ufuncs comes from generate_umath.py and is as follows:

        =====  ====  =============  ===============  ========================
        done   args   function        types                notes
        =====  ====  =============  ===============  ========================
        n      1     conjugate      nums + O
        n      1     absolute       nums + O         complex -> real
        n      1     negative       nums + O
        n      1     sign           nums + O         -> int
        n      1     invert         bool + ints + O  flts raise an error
        n      1     degrees        real + M         cmplx raise an error
        n      1     radians        real + M         cmplx raise an error
        n      1     arccos         flts + M
        n      1     arccosh        flts + M
        n      1     arcsin         flts + M
        n      1     arcsinh        flts + M
        n      1     arctan         flts + M
        n      1     arctanh        flts + M
        n      1     cos            flts + M
        n      1     sin            flts + M
        n      1     tan            flts + M
        n      1     cosh           flts + M
        n      1     sinh           flts + M
        n      1     tanh           flts + M
        n      1     exp            flts + M
        n      1     expm1          flts + M
        n      1     log            flts + M
        n      1     log10          flts + M
        n      1     log1p          flts + M
        n      1     sqrt           flts + M         real x < 0 raises error
        n      1     ceil           real + M
        n      1     trunc          real + M
        n      1     floor          real + M
        n      1     fabs           real + M
        n      1     rint           flts + M
        n      1     isnan          flts             -> bool
        n      1     isinf          flts             -> bool
        n      1     isfinite       flts             -> bool
        n      1     signbit        real             -> bool
        n      1     modf           real             -> (frac, int)
        n      1     logical_not    bool + nums + M  -> bool
        n      2     left_shift     ints + O         flts raise an error
        n      2     right_shift    ints + O         flts raise an error
        n      2     add            bool + nums + O  boolean + is ||
        n      2     subtract       bool + nums + O  boolean - is ^
        n      2     multiply       bool + nums + O  boolean * is &
        n      2     divide         nums + O
        n      2     floor_divide   nums + O
        n      2     true_divide    nums + O         bBhH -> f, iIlLqQ -> d
        n      2     fmod           nums + M
        n      2     power          nums + O
        n      2     greater        bool + nums + O  -> bool
        n      2     greater_equal  bool + nums + O  -> bool
        n      2     less           bool + nums + O  -> bool
        n      2     less_equal     bool + nums + O  -> bool
        n      2     equal          bool + nums + O  -> bool
        n      2     not_equal      bool + nums + O  -> bool
        n      2     logical_and    bool + nums + M  -> bool
        n      2     logical_or     bool + nums + M  -> bool
        n      2     logical_xor    bool + nums + M  -> bool
        n      2     maximum        bool + nums + O
        n      2     minimum        bool + nums + O
        n      2     bitwise_and    bool + ints + O  flts raise an error
        n      2     bitwise_or     bool + ints + O  flts raise an error
        n      2     bitwise_xor    bool + ints + O  flts raise an error
        n      2     arctan2        real + M
        n      2     remainder      ints + real + O
        n      2     hypot          real + M
        =====  ====  =============  ===============  ========================

        Types other than those listed will be accepted, but they are cast to
        the smallest compatible type for which the function is defined. The
        casting rules are:

        bool -> int8 -> float32
        ints -> double

        """
        pass


    def test_signature(self):
        # the arguments to test_signature are: nin, nout, core_signature
        # pass
        assert_equal(umt.test_signature(2,1,"(i),(i)->()"), 1)

        # pass. empty core signature; treat as plain ufunc (with trivial core)
        assert_equal(umt.test_signature(2,1,"(),()->()"), 0)

        # in the following calls, a ValueError should be raised because
        # of error in core signature
        # error: extra parenthesis
        msg = "core_sig: extra parenthesis"
        try:
            ret = umt.test_signature(2,1,"((i)),(i)->()")
            assert_equal(ret, None, err_msg=msg)
        except ValueError: None
        # error: parenthesis matching
        msg = "core_sig: parenthesis matching"
        try:
            ret = umt.test_signature(2,1,"(i),)i(->()")
            assert_equal(ret, None, err_msg=msg)
        except ValueError: None
        # error: incomplete signature. letters outside of parenthesis are ignored
        msg = "core_sig: incomplete signature"
        try:
            ret = umt.test_signature(2,1,"(i),->()")
            assert_equal(ret, None, err_msg=msg)
        except ValueError: None
        # error: incomplete signature. 2 output arguments are specified
        msg = "core_sig: incomplete signature"
        try:
            ret = umt.test_signature(2,2,"(i),(i)->()")
            assert_equal(ret, None, err_msg=msg)
        except ValueError: None

        # more complicated names for variables
        assert_equal(umt.test_signature(2,1,"(i1,i2),(J_1)->(_kAB)"),1)

    def test_get_signature(self):
        assert_equal(umt.inner1d.signature, "(i),(i)->()")

    def test_forced_sig(self):
        a = 0.5*np.arange(3,dtype='f8')
        assert_equal(np.add(a,0.5), [0.5, 1, 1.5])
        assert_equal(np.add(a,0.5,sig='i',casting='unsafe'), [0, 0, 1])
        assert_equal(np.add(a,0.5,sig='ii->i',casting='unsafe'), [0, 0, 1])
        assert_equal(np.add(a,0.5,sig=('i4',),casting='unsafe'), [0, 0, 1])
        assert_equal(np.add(a,0.5,sig=('i4','i4','i4'),
                                            casting='unsafe'), [0, 0, 1])

        b = np.zeros((3,),dtype='f8')
        np.add(a,0.5,out=b)
        assert_equal(b, [0.5, 1, 1.5])
        b[:] = 0
        np.add(a,0.5,sig='i',out=b, casting='unsafe')
        assert_equal(b, [0, 0, 1])
        b[:] = 0
        np.add(a,0.5,sig='ii->i',out=b, casting='unsafe')
        assert_equal(b, [0, 0, 1])
        b[:] = 0
        np.add(a,0.5,sig=('i4',),out=b, casting='unsafe')
        assert_equal(b, [0, 0, 1])
        b[:] = 0
        np.add(a,0.5,sig=('i4','i4','i4'),out=b, casting='unsafe')
        assert_equal(b, [0, 0, 1])


    def test_inner1d(self):
        a = np.arange(6).reshape((2,3))
        assert_array_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1))

    def test_broadcast(self):
        msg = "broadcast"
        a = np.arange(4).reshape((2,1,2))
        b = np.arange(4).reshape((1,2,2))
        assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
        msg = "extend & broadcast loop dimensions"
        b = np.arange(4).reshape((2,2))
        assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
        msg = "broadcast in core dimensions"
        a = np.arange(8).reshape((4,2))
        b = np.arange(4).reshape((4,1))
        assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
        msg = "extend & broadcast core and loop dimensions"
        a = np.arange(8).reshape((4,2))
        b = np.array(7)
        assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
        msg = "broadcast should fail"
        a = np.arange(2).reshape((2,1,1))
        b = np.arange(3).reshape((3,1,1))
        try:
            ret = umt.inner1d(a,b)
            assert_equal(ret, None, err_msg=msg)
        except ValueError: None

    def test_type_cast(self):
        msg = "type cast"
        a = np.arange(6, dtype='short').reshape((2,3))
        assert_array_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1), err_msg=msg)
        msg = "type cast on one argument"
        a = np.arange(6).reshape((2,3))
        b = a+0.1
        assert_array_almost_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1),
            err_msg=msg)

    def test_endian(self):
        msg = "big endian"
        a = np.arange(6, dtype='>i4').reshape((2,3))
        assert_array_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1), err_msg=msg)
        msg = "little endian"
        a = np.arange(6, dtype='<i4').reshape((2,3))
        assert_array_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1), err_msg=msg)

        # Output should always be native-endian
        Ba = np.arange(1, dtype='>f8')
        La = np.arange(1, dtype='<f8')
        assert_equal((Ba+Ba).dtype, np.dtype('f8'))
        assert_equal((Ba+La).dtype, np.dtype('f8'))
        assert_equal((La+Ba).dtype, np.dtype('f8'))
        assert_equal((La+La).dtype, np.dtype('f8'))

        assert_equal(np.absolute(La).dtype, np.dtype('f8'))
        assert_equal(np.absolute(Ba).dtype, np.dtype('f8'))
        assert_equal(np.negative(La).dtype, np.dtype('f8'))
        assert_equal(np.negative(Ba).dtype, np.dtype('f8'))

    def test_incontiguous_array(self):
        msg = "incontiguous memory layout of array"
        x = np.arange(64).reshape((2,2,2,2,2,2))
        a = x[:,0,:,0,:,0]
        b = x[:,1,:,1,:,1]
        a[0,0,0] = -1
        msg2 = "make sure it references to the original array"
        assert_equal(x[0,0,0,0,0,0], -1, err_msg=msg2)
        assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
        x = np.arange(24).reshape(2,3,4)
        a = x.T
        b = x.T
        a[0,0,0] = -1
        assert_equal(x[0,0,0], -1, err_msg=msg2)
        assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)

    def test_output_argument(self):
        msg = "output argument"
        a = np.arange(12).reshape((2,3,2))
        b = np.arange(4).reshape((2,1,2)) + 1
        c = np.zeros((2,3),dtype='int')
        umt.inner1d(a,b,c)
        assert_array_equal(c, np.sum(a*b,axis=-1), err_msg=msg)
        c[:] = -1
        umt.inner1d(a,b,out=c)
        assert_array_equal(c, np.sum(a*b,axis=-1), err_msg=msg)

        msg = "output argument with type cast"
        c = np.zeros((2,3),dtype='int16')
        umt.inner1d(a,b,c)
        assert_array_equal(c, np.sum(a*b,axis=-1), err_msg=msg)
        c[:] = -1
        umt.inner1d(a,b,out=c)
        assert_array_equal(c, np.sum(a*b,axis=-1), err_msg=msg)

        msg = "output argument with incontiguous layout"
        c = np.zeros((2,3,4),dtype='int16')
        umt.inner1d(a,b,c[...,0])
        assert_array_equal(c[...,0], np.sum(a*b,axis=-1), err_msg=msg)
        c[:] = -1
        umt.inner1d(a,b,out=c[...,0])
        assert_array_equal(c[...,0], np.sum(a*b,axis=-1), err_msg=msg)

    def test_innerwt(self):
        a = np.arange(6).reshape((2,3))
        b = np.arange(10,16).reshape((2,3))
        w = np.arange(20,26).reshape((2,3))
        assert_array_equal(umt.innerwt(a,b,w), np.sum(a*b*w,axis=-1))
        a = np.arange(100,124).reshape((2,3,4))
        b = np.arange(200,224).reshape((2,3,4))
        w = np.arange(300,324).reshape((2,3,4))
        assert_array_equal(umt.innerwt(a,b,w), np.sum(a*b*w,axis=-1))

    def test_matrix_multiply(self):
        self.compare_matrix_multiply_results(np.long)
        self.compare_matrix_multiply_results(np.double)

    def compare_matrix_multiply_results(self, tp):
        d1 = np.array(rand(2,3,4), dtype=tp)
        d2 = np.array(rand(2,3,4), dtype=tp)
        msg = "matrix multiply on type %s" % d1.dtype.name

        def permute_n(n):
            if n == 1:
                return ([0],)
            ret = ()
            base = permute_n(n-1)
            for perm in base:
                for i in xrange(n):
                    new = perm + [n-1]
                    new[n-1] = new[i]
                    new[i] = n-1
                    ret += (new,)
            return ret

        def slice_n(n):
            if n == 0:
                return ((),)
            ret = ()
            base = slice_n(n-1)
            for sl in base:
                ret += (sl+(slice(None),),)
                ret += (sl+(slice(0,1),),)
            return ret

        def broadcastable(s1,s2):
            return s1 == s2 or s1 == 1 or s2 == 1

        permute_3 = permute_n(3)
        slice_3 = slice_n(3) + ((slice(None,None,-1),)*3,)

        ref = True
        for p1 in permute_3:
            for p2 in permute_3:
                for s1 in slice_3:
                    for s2 in slice_3:
                        a1 = d1.transpose(p1)[s1]
                        a2 = d2.transpose(p2)[s2]
                        ref = ref and a1.base != None
                        ref = ref and a2.base != None
                        if broadcastable(a1.shape[-1], a2.shape[-2]) and \
                           broadcastable(a1.shape[0], a2.shape[0]):
                            assert_array_almost_equal(
                                umt.matrix_multiply(a1,a2),
                                np.sum(a2[...,np.newaxis].swapaxes(-3,-1) *
                                       a1[...,np.newaxis,:], axis=-1),
                                err_msg = msg+' %s %s' % (str(a1.shape),
                                                          str(a2.shape)))

        assert_equal(ref, True, err_msg="reference check")

    def test_object_logical(self):
        a = np.array([3, None, True, False, "test", ""], dtype=object)
        assert_equal(np.logical_or(a, None),
                        np.array([x or None for x in a], dtype=object))
        assert_equal(np.logical_or(a, True),
                        np.array([x or True for x in a], dtype=object))
        assert_equal(np.logical_or(a, 12),
                        np.array([x or 12 for x in a], dtype=object))
        assert_equal(np.logical_or(a, "blah"),
                        np.array([x or "blah" for x in a], dtype=object))

        assert_equal(np.logical_and(a, None),
                        np.array([x and None for x in a], dtype=object))
        assert_equal(np.logical_and(a, True),
                        np.array([x and True for x in a], dtype=object))
        assert_equal(np.logical_and(a, 12),
                        np.array([x and 12 for x in a], dtype=object))
        assert_equal(np.logical_and(a, "blah"),
                        np.array([x and "blah" for x in a], dtype=object))

        assert_equal(np.logical_not(a),
                        np.array([not x for x in a], dtype=object))

        assert_equal(np.logical_or.reduce(a), 3)
        assert_equal(np.logical_and.reduce(a), None)

    def test_object_array_reduction(self):
        # Reductions on object arrays
        a = np.array(['a', 'b', 'c'], dtype=object)
        assert_equal(np.sum(a), 'abc')
        assert_equal(np.max(a), 'c')
        assert_equal(np.min(a), 'a')
        a = np.array([True, False, True], dtype=object)
        assert_equal(np.sum(a), 2)
        assert_equal(np.prod(a), 0)
        assert_equal(np.any(a), True)
        assert_equal(np.all(a), False)
        assert_equal(np.max(a), True)
        assert_equal(np.min(a), False)

    def test_zerosize_reduction(self):
        # Test with default dtype and object dtype
        for a in [[], np.array([], dtype=object)]:
            assert_equal(np.sum(a), 0)
            assert_equal(np.prod(a), 1)
            assert_equal(np.any(a), False)
            assert_equal(np.all(a), True)
            assert_raises(ValueError, np.max, a)
            assert_raises(ValueError, np.min, a)

    def test_axis_out_of_bounds(self):
        a = np.array([False, False])
        assert_raises(ValueError, a.all, axis=1)
        a = np.array([False, False])
        assert_raises(ValueError, a.all, axis=-2)

        a = np.array([False, False])
        assert_raises(ValueError, a.any, axis=1)
        a = np.array([False, False])
        assert_raises(ValueError, a.any, axis=-2)

    def test_scalar_reduction(self):
        # The functions 'sum', 'prod', etc allow specifying axis=0
        # even for scalars
        assert_equal(np.sum(3, axis=0), 3)
        assert_equal(np.prod(3.5, axis=0), 3.5)
        assert_equal(np.any(True, axis=0), True)
        assert_equal(np.all(False, axis=0), False)
        assert_equal(np.max(3, axis=0), 3)
        assert_equal(np.min(2.5, axis=0), 2.5)

        # Make sure that scalars are coming out from this operation
        assert_(type(np.prod(np.float32(2.5), axis=0)) is np.float32)
        assert_(type(np.sum(np.float32(2.5), axis=0)) is np.float32)
        assert_(type(np.max(np.float32(2.5), axis=0)) is np.float32)
        assert_(type(np.min(np.float32(2.5), axis=0)) is np.float32)

    def test_casting_out_param(self):
        # Test that it's possible to do casts on output
        a = np.ones((200,100), np.int64)
        b = np.ones((200,100), np.int64)
        c = np.ones((200,100), np.float64)
        np.add(a, b, out=c)
        assert_equal(c, 2)

        a = np.zeros(65536)
        b = np.zeros(65536, dtype=np.float32)
        np.subtract(a, 0, out=b)
        assert_equal(b, 0)

    def test_where_param(self):
        # Test that the where= ufunc parameter works with regular arrays
        a = np.arange(7)
        b = np.ones(7)
        c = np.zeros(7)
        np.add(a, b, out=c, where=(a % 2 == 1))
        assert_equal(c, [0,2,0,4,0,6,0])

        a = np.arange(4).reshape(2,2) + 2
        np.power(a, [2,3], out=a, where=[[0,1],[1,0]])
        assert_equal(a, [[2, 27], [16, 5]])
        # Broadcasting the where= parameter
        np.subtract(a, 2, out=a, where=[True,False])
        assert_equal(a, [[0, 27], [14, 5]])

    def test_where_param_buffer_output(self):
        # This test is temporarily skipped because it requires
        # adding masking features to the nditer to work properly

        # With casting on output
        a = np.ones(10, np.int64)
        b = np.ones(10, np.int64)
        c = 1.5 * np.ones(10, np.float64)
        np.add(a, b, out=c, where=[1,0,0,1,0,0,1,1,1,0])
        assert_equal(c, [2,1.5,1.5,2,1.5,1.5,2,2,2,1.5])

    def check_identityless_reduction(self, a):
        # np.minimum.reduce is a identityless reduction

        # Verify that it sees the zero at various positions
        a[...] = 1
        a[1,0,0] = 0
        assert_equal(np.minimum.reduce(a, axis=None), 0)
        assert_equal(np.minimum.reduce(a, axis=(0,1)), [0,1,1,1])
        assert_equal(np.minimum.reduce(a, axis=(0,2)), [0,1,1])
        assert_equal(np.minimum.reduce(a, axis=(1,2)), [1,0])
        assert_equal(np.minimum.reduce(a, axis=0),
                                    [[0,1,1,1], [1,1,1,1], [1,1,1,1]])
        assert_equal(np.minimum.reduce(a, axis=1),
                                    [[1,1,1,1], [0,1,1,1]])
        assert_equal(np.minimum.reduce(a, axis=2),
                                    [[1,1,1], [0,1,1]])
        assert_equal(np.minimum.reduce(a, axis=()), a)

        a[...] = 1
        a[0,1,0] = 0
        assert_equal(np.minimum.reduce(a, axis=None), 0)
        assert_equal(np.minimum.reduce(a, axis=(0,1)), [0,1,1,1])
        assert_equal(np.minimum.reduce(a, axis=(0,2)), [1,0,1])
        assert_equal(np.minimum.reduce(a, axis=(1,2)), [0,1])
        assert_equal(np.minimum.reduce(a, axis=0),
                                    [[1,1,1,1], [0,1,1,1], [1,1,1,1]])
        assert_equal(np.minimum.reduce(a, axis=1),
                                    [[0,1,1,1], [1,1,1,1]])
        assert_equal(np.minimum.reduce(a, axis=2),
                                    [[1,0,1], [1,1,1]])
        assert_equal(np.minimum.reduce(a, axis=()), a)

        a[...] = 1
        a[0,0,1] = 0
        assert_equal(np.minimum.reduce(a, axis=None), 0)
        assert_equal(np.minimum.reduce(a, axis=(0,1)), [1,0,1,1])
        assert_equal(np.minimum.reduce(a, axis=(0,2)), [0,1,1])
        assert_equal(np.minimum.reduce(a, axis=(1,2)), [0,1])
        assert_equal(np.minimum.reduce(a, axis=0),
                                    [[1,0,1,1], [1,1,1,1], [1,1,1,1]])
        assert_equal(np.minimum.reduce(a, axis=1),
                                    [[1,0,1,1], [1,1,1,1]])
        assert_equal(np.minimum.reduce(a, axis=2),
                                    [[0,1,1], [1,1,1]])
        assert_equal(np.minimum.reduce(a, axis=()), a)

    def test_identityless_reduction_corder(self):
        a = np.empty((2,3,4), order='C')
        self.check_identityless_reduction(a)

    def test_identityless_reduction_forder(self):
        a = np.empty((2,3,4), order='F')
        self.check_identityless_reduction(a)

    def test_identityless_reduction_otherorder(self):
        a = np.empty((2,4,3), order='C').swapaxes(1,2)
        self.check_identityless_reduction(a)

    def test_identityless_reduction_noncontig(self):
        a = np.empty((3,5,4), order='C').swapaxes(1,2)
        a = a[1:, 1:, 1:]
        self.check_identityless_reduction(a)

    def test_identityless_reduction_noncontig_unaligned(self):
        a = np.empty((3*4*5*8 + 1,), dtype='i1')
        a = a[1:].view(dtype='f8')
        a.shape = (3,4,5)
        a = a[1:, 1:, 1:]
        self.check_identityless_reduction(a)

    def test_identityless_reduction_nonreorderable(self):
        a = np.array([[8.0, 2.0, 2.0], [1.0, 0.5, 0.25]])

        res = np.divide.reduce(a, axis=0)
        assert_equal(res, [8.0, 4.0, 8.0])

        res = np.divide.reduce(a, axis=1)
        assert_equal(res, [2.0, 8.0])

        res = np.divide.reduce(a, axis=())
        assert_equal(res, a)

        assert_raises(ValueError, np.divide.reduce, a, axis=(0,1))

    def test_reduce_zero_axis(self):
        # If we have a n x m array and do a reduction with axis=1, then we are
        # doing n reductions, and each reduction takes an m-element array. For
        # a reduction operation without an identity, then:
        #   n > 0, m > 0: fine
        #   n = 0, m > 0: fine, doing 0 reductions of m-element arrays
        #   n > 0, m = 0: can't reduce a 0-element array, ValueError
        #   n = 0, m = 0: can't reduce a 0-element array, ValueError (for
        #     consistency with the above case)
        # This test doesn't actually look at return values, it just checks to
        # make sure that error we get an error in exactly those cases where we
        # expect one, and assumes the calculations themselves are done
        # correctly.
        def ok(f, *args, **kwargs):
            f(*args, **kwargs)
        def err(f, *args, **kwargs):
            assert_raises(ValueError, f, *args, **kwargs)
        def t(expect, func, n, m):
            expect(func, np.zeros((n, m)), axis=1)
            expect(func, np.zeros((m, n)), axis=0)
            expect(func, np.zeros((n // 2, n // 2, m)), axis=2)
            expect(func, np.zeros((n // 2, m, n // 2)), axis=1)
            expect(func, np.zeros((n, m // 2, m // 2)), axis=(1, 2))
            expect(func, np.zeros((m // 2, n, m // 2)), axis=(0, 2))
            expect(func, np.zeros((m // 3, m // 3, m // 3,
                                  n // 2, n //2)),
                                 axis=(0, 1, 2))
            # Check what happens if the inner (resp. outer) dimensions are a
            # mix of zero and non-zero:
            expect(func, np.zeros((10, m, n)), axis=(0, 1))
            expect(func, np.zeros((10, n, m)), axis=(0, 2))
            expect(func, np.zeros((m, 10, n)), axis=0)
            expect(func, np.zeros((10, m, n)), axis=1)
            expect(func, np.zeros((10, n, m)), axis=2)
        # np.maximum is just an arbitrary ufunc with no reduction identity
        assert_equal(np.maximum.identity, None)
        t(ok, np.maximum.reduce, 30, 30)
        t(ok, np.maximum.reduce, 0, 30)
        t(err, np.maximum.reduce, 30, 0)
        t(err, np.maximum.reduce, 0, 0)
        err(np.maximum.reduce, [])
        np.maximum.reduce(np.zeros((0, 0)), axis=())

        # all of the combinations are fine for a reduction that has an
        # identity
        t(ok, np.add.reduce, 30, 30)
        t(ok, np.add.reduce, 0, 30)
        t(ok, np.add.reduce, 30, 0)
        t(ok, np.add.reduce, 0, 0)
        np.add.reduce([])
        np.add.reduce(np.zeros((0, 0)), axis=())

        # OTOH, accumulate always makes sense for any combination of n and m,
        # because it maps an m-element array to an m-element array. These
        # tests are simpler because accumulate doesn't accept multiple axes.
        for uf in (np.maximum, np.add):
            uf.accumulate(np.zeros((30, 0)), axis=0)
            uf.accumulate(np.zeros((0, 30)), axis=0)
            uf.accumulate(np.zeros((30, 30)), axis=0)
            uf.accumulate(np.zeros((0, 0)), axis=0)

    def test_safe_casting(self):
        # In old versions of numpy, in-place operations used the 'unsafe'
        # casting rules. In some future version, 'same_kind' will become the
        # default.
        a = np.array([1, 2, 3], dtype=int)
        # Non-in-place addition is fine
        assert_array_equal(assert_no_warnings(np.add, a, 1.1),
                           [2.1, 3.1, 4.1])
        assert_warns(DeprecationWarning, np.add, a, 1.1, out=a)
        assert_array_equal(a, [2, 3, 4])
        def add_inplace(a, b):
            a += b
        assert_warns(DeprecationWarning, add_inplace, a, 1.1)
        assert_array_equal(a, [3, 4, 5])
        # Make sure that explicitly overriding the warning is allowed:
        assert_no_warnings(np.add, a, 1.1, out=a, casting="unsafe")
        assert_array_equal(a, [4, 5, 6])

        # There's no way to propagate exceptions from the place where we issue
        # this deprecation warning, so we must throw the exception away
        # entirely rather than cause it to be raised at some other point, or
        # trigger some other unsuspecting if (PyErr_Occurred()) { ...} at some
        # other location entirely.
        if sys.version_info[0] >= 3:
            from io import StringIO
        else:
            from StringIO import StringIO

        warn_ctx = WarningManager()
        warn_ctx.__enter__()
        try:
            warnings.simplefilter("error")
            old_stderr = sys.stderr
            sys.stderr = StringIO()
            # No error, but dumps to stderr
            a += 1.1
            # No error on the next bit of code executed either
            1 + 1
            assert_("Implicitly casting" in sys.stderr.getvalue())
        finally:
            sys.stderr = old_stderr
            warn_ctx.__exit__()


if __name__ == "__main__":
    run_module_suite()
