Python numpy.floor_divide() Examples

def process_image(img_id):
    if 'Pan-Sharpen_' in img_id:
        img_id = img_id.split('Pan-Sharpen_')[1]
    img = io.imread(path.join(test_dir, '_'.join(img_id.split('_')[:4]), 'Pan-Sharpen', 'Pan-Sharpen_' + img_id+'.tif'))
    nir = img[:, :, 3:]
    img = img[:, :, :3]
    np.clip(img, None, threshold, out=img)
    img = np.floor_divide(img, threshold / 255).astype('uint8')
    cv2.imwrite(path.join(test_png, img_id + '.png'), img, [cv2.IMWRITE_PNG_COMPRESSION, 9])

    img2 = io.imread(path.join(test_dir, '_'.join(img_id.split('_')[:4]), 'MS', 'MS_' + img_id+'.tif'))
    img2 = np.rollaxis(img2, 0, 3)
    img2 = cv2.resize(img2, (900, 900), interpolation=cv2.INTER_LANCZOS4)
    
    img_0_3_5 = (np.clip(img2[..., [0, 3, 5]], None, (2000, 3000, 3000)) / (np.array([2000, 3000, 3000]) / 255)).astype('uint8')
    cv2.imwrite(path.join(test_png2, img_id + '.png'), img_0_3_5, [cv2.IMWRITE_PNG_COMPRESSION, 9])
    
    pan = io.imread(path.join(test_dir, '_'.join(img_id.split('_')[:4]), 'PAN', 'PAN_' + img_id+'.tif'))
    pan = pan[..., np.newaxis]
    img_pan_6_7 = np.concatenate([pan, img2[..., 7:], nir], axis=2)
    img_pan_6_7 = (np.clip(img_pan_6_7, None, (3000, 5000, 5000)) / (np.array([3000, 5000, 5000]) / 255)).astype('uint8')
    cv2.imwrite(path.join(test_png3, img_id + '.png'), img_pan_6_7, [cv2.IMWRITE_PNG_COMPRESSION, 9]) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def __init__(self, roi_center=None, roi_size=None, roi_start=None, roi_end=None):
        """
        Args:
            roi_center (list or tuple): voxel coordinates for center of the crop ROI.
            roi_size (list or tuple): size of the crop ROI.
            roi_start (list or tuple): voxel coordinates for start of the crop ROI.
            roi_end (list or tuple): voxel coordinates for end of the crop ROI.
        """
        if roi_center is not None and roi_size is not None:
            roi_center = np.asarray(roi_center, dtype=np.uint16)
            roi_size = np.asarray(roi_size, dtype=np.uint16)
            self.roi_start = np.subtract(roi_center, np.floor_divide(roi_size, 2))
            self.roi_end = np.add(self.roi_start, roi_size)
        else:
            assert roi_start is not None and roi_end is not None, "roi_start and roi_end must be provided."
            self.roi_start = np.asarray(roi_start, dtype=np.uint16)
            self.roi_end = np.asarray(roi_end, dtype=np.uint16)

        assert np.all(self.roi_start >= 0), "all elements of roi_start must be greater than or equal to 0."
        assert np.all(self.roi_end > 0), "all elements of roi_end must be positive."
        assert np.all(self.roi_end >= self.roi_start), "invalid roi range." 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

def test_float_remainder_exact(self):
        # test that float results are exact for small integers. This also
        # holds for the same integers scaled by powers of two.
        nlst = list(range(-127, 0))
        plst = list(range(1, 128))
        dividend = nlst + [0] + plst
        divisor = nlst + plst
        arg = list(itertools.product(dividend, divisor))
        tgt = list(divmod(*t) for t in arg)

        a, b = np.array(arg, dtype=int).T
        # convert exact integer results from Python to float so that
        # signed zero can be used, it is checked.
        tgtdiv, tgtrem = np.array(tgt, dtype=float).T
        tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
        tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)

        for dt in np.typecodes['Float']:
            msg = 'dtype: %s' % (dt,)
            fa = a.astype(dt)
            fb = b.astype(dt)
            div = np.floor_divide(fa, fb)
            rem = np.remainder(fa, fb)
            assert_equal(div, tgtdiv, err_msg=msg)
            assert_equal(rem, tgtrem, err_msg=msg) 

def test_remainder_basic(self):
        dt = np.typecodes['AllInteger'] + np.typecodes['Float']
        for dt1, dt2 in itertools.product(dt, dt):
            for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
                if sg1 == -1 and dt1 in np.typecodes['UnsignedInteger']:
                    continue
                if sg2 == -1 and dt2 in np.typecodes['UnsignedInteger']:
                    continue
                fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
                msg = fmt % (dt1, dt2, sg1, sg2)
                a = np.array(sg1*71, dtype=dt1)
                b = np.array(sg2*19, dtype=dt2)
                div = np.floor_divide(a, b)
                rem = np.remainder(a, b)
                assert_equal(div*b + rem, a, err_msg=msg)
                if sg2 == -1:
                    assert_(b < rem <= 0, msg)
                else:
                    assert_(b > rem >= 0, msg) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def test_csr_from_coo_novals():
    for i in range(50):
        coords = np.random.choice(np.arange(50 * 100, dtype=np.int32), 1000, False)
        rows = np.mod(coords, 100, dtype=np.int32)
        cols = np.floor_divide(coords, 100, dtype=np.int32)

        csr = lm.CSR.from_coo(rows, cols, None, (100, 50))
        assert csr.nrows == 100
        assert csr.ncols == 50
        assert csr.nnz == 1000

        for i in range(100):
            sp = csr.rowptrs[i]
            ep = csr.rowptrs[i+1]
            assert ep - sp == np.sum(rows == i)
            points, = np.nonzero(rows == i)
            po = np.argsort(cols[points])
            points = points[po]
            assert all(np.sort(csr.colinds[sp:ep]) == cols[points])
            assert np.sum(csr.row(i)) == len(points) 

def testInt32Basic(self):
    x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int32)
    y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int32)
    self._compareBoth(x, y, np.add, tf.add)
    self._compareBoth(x, y, np.subtract, tf.sub)
    self._compareBoth(x, y, np.multiply, tf.mul)
    self._compareBoth(x, y, np.true_divide, tf.truediv)
    self._compareBoth(x, y, np.floor_divide, tf.floordiv)
    self._compareBoth(x, y, np.mod, tf.mod)
    self._compareBoth(x, y, np.add, _ADD)
    self._compareBoth(x, y, np.subtract, _SUB)
    self._compareBoth(x, y, np.multiply, _MUL)
    self._compareBoth(x, y, np.true_divide, _TRUEDIV)
    self._compareBoth(x, y, np.floor_divide, _FLOORDIV)
    self._compareBoth(x, y, np.mod, _MOD)
    # _compareBoth tests on GPU only for floating point types, so test
    # _MOD for int32 on GPU by calling _compareGpu
    self._compareGpu(x, y, np.mod, _MOD) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def testDoubleBasic(self):
    x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float64)
    y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float64)
    self._compareBoth(x, y, np.add, tf.add)
    self._compareBoth(x, y, np.subtract, tf.sub)
    self._compareBoth(x, y, np.multiply, tf.mul)
    self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv)
    self._compareBoth(x, y + 0.1, np.floor_divide, tf.floordiv)
    self._compareBoth(x, y, np.add, _ADD)
    self._compareBoth(x, y, np.subtract, _SUB)
    self._compareBoth(x, y, np.multiply, _MUL)
    self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV)
    self._compareBoth(x, y + 0.1, np.floor_divide, _FLOORDIV)
    try:
      from scipy import special  # pylint: disable=g-import-not-at-top
      a_pos_small = np.linspace(0.1, 2, 15).reshape(1, 3, 5).astype(np.float32)
      x_pos_small = np.linspace(0.1, 10, 15).reshape(1, 3, 5).astype(np.float32)
      self._compareBoth(a_pos_small, x_pos_small, special.gammainc, tf.igamma)
      self._compareBoth(a_pos_small, x_pos_small, special.gammaincc, tf.igammac)
    except ImportError as e:
      tf.logging.warn("Cannot test special functions: %s" % str(e)) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def w(w_max, w_min, step):
    linspace_lower = (np.floor_divide(w_min, step)+1)*step
    N = np.floor_divide(w_max-w_min, step)
    linspace_upper = linspace_lower + N*step
    w = np.linspace(linspace_lower, linspace_upper, int(N)+1)
    
    if not np.isclose(w[0], w_min, atol=step/5.):
        w = np.concatenate((np.array([w_min]), w))
        
    if not np.isclose(w[-1], w_max, atol=step/5.):
        w = np.concatenate((w,np.array([w_max])))
    
    return w, len(w)

# Compute dielectric function using Lorentzian model.
# Units of w and ResFreq must match and must be directly proportional to angular frequency. All other parameters are unitless. 

def w(w_max, w_min, step):
    linspace_lower = (np.floor_divide(w_min, step)+1)*step
    N = np.floor_divide(w_max-w_min, step)
    linspace_upper = linspace_lower + N*step
    w = np.linspace(linspace_lower, linspace_upper, int(N)+1)
    
    if not np.isclose(w[0], w_min, atol=step/5.):
        w = np.concatenate((np.array([w_min]), w))
        
    if not np.isclose(w[-1], w_max, atol=step/5.):
        w = np.concatenate((w,np.array([w_max])))
    
    return w, len(w)

# Compute dielectric function using Lorentzian model.
# Units of w and ResFreq must match and must be directly proportional to angular frequency. All other parameters are unitless. 

def w(w_max, w_min, step):
    linspace_lower = (np.floor_divide(w_min, step)+1)*step
    N = np.floor_divide(w_max-w_min, step)
    linspace_upper = linspace_lower + N*step
    w = np.linspace(linspace_lower, linspace_upper, int(N)+1)
    
    if not np.isclose(w[0], w_min, atol=step/5.):
        w = np.concatenate((np.array([w_min]), w))
        
    if not np.isclose(w[-1], w_max, atol=step/5.):
        w = np.concatenate((w,np.array([w_max])))
    
    return w, len(w)

# Compute dielectric function using Lorentzian model.
# Units of w and ResFreq must match and must be directly proportional to angular frequency. All other parameters are unitless. 

def w(w_max, w_min, step):
    linspace_lower = (np.floor_divide(w_min, step)+1)*step
    N = np.floor_divide(w_max-w_min, step)
    linspace_upper = linspace_lower + N*step
    w = np.linspace(linspace_lower, linspace_upper, int(N)+1)
    
    if not np.isclose(w[0], w_min, atol=step/5.):
        w = np.concatenate((np.array([w_min]), w))
        
    if not np.isclose(w[-1], w_max, atol=step/5.):
        w = np.concatenate((w,np.array([w_max])))
    
    return w, len(w)

# Compute dielectric function using Brendel-Bormann (aka Gaussian or Gaussian-convoluted Drude–Lorentz) model.
# Units of w and ResFreq must match and must be directly proportional to angular frequency. All other parameters are unitless. 

def w(w_max, w_min, step):
    linspace_lower = (np.floor_divide(w_min, step)+1)*step
    N = np.floor_divide(w_max-w_min, step)
    linspace_upper = linspace_lower + N*step
    w = np.linspace(linspace_lower, linspace_upper, int(N)+1)
    
    if not np.isclose(w[0], w_min, atol=step/5.):
        w = np.concatenate((np.array([w_min]), w))
        
    if not np.isclose(w[-1], w_max, atol=step/5.):
        w = np.concatenate((w,np.array([w_max])))
    
    return w, len(w)

# Compute dielectric function using Lorentzian model.
# Units of w and ResFreq must match and must be directly proportional to angular frequency. All other parameters are unitless. 

def testFloatBasic(self):
    x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float32)
    y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float32)
    self._compareBoth(x, y, np.add, tf.add, also_compare_variables=True)
    self._compareBoth(x, y, np.subtract, tf.sub)
    self._compareBoth(x, y, np.multiply, tf.mul)
    self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv)
    self._compareBoth(x, y + 0.1, np.floor_divide, tf.floordiv)
    self._compareBoth(x, y, np.add, _ADD)
    self._compareBoth(x, y, np.subtract, _SUB)
    self._compareBoth(x, y, np.multiply, _MUL)
    self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV)
    self._compareBoth(x, y + 0.1, np.floor_divide, _FLOORDIV)
    try:
      from scipy import special  # pylint: disable=g-import-not-at-top
      a_pos_small = np.linspace(0.1, 2, 15).reshape(1, 3, 5).astype(np.float32)
      x_pos_small = np.linspace(0.1, 10, 15).reshape(1, 3, 5).astype(np.float32)
      self._compareBoth(a_pos_small, x_pos_small, special.gammainc, tf.igamma)
      self._compareBoth(a_pos_small, x_pos_small, special.gammaincc, tf.igammac)
      # Need x > 1
      self._compareBoth(x_pos_small + 1, a_pos_small, special.zeta, tf.zeta)
      n_small = np.arange(0, 15).reshape(1, 3, 5).astype(np.float32)
      self._compareBoth(n_small, x_pos_small, special.polygamma, tf.polygamma)
    except ImportError as e:
      tf.logging.warn("Cannot test special functions: %s" % str(e)) 

def pan_to_bgr(src, dst, thresh=3000):
    with rasterio.open(src, 'r') as reader:
        img = np.empty((reader.height,
                        reader.width,
                        reader.count))
        for band in range(reader.count):
            img[:, :, band] = reader.read(band+1)
    img = np.clip(img[:, :, :3], None, thresh)
    img = np.floor_divide(img, thresh/255).astype('uint8')
    cv2.imwrite(dst, img) 

def get_sigma_r2(self, E, onlyOddHarmonics=True, with0eSpread=False):
        """Squared sigma_{r} as by
        Tanaka and Kitamura J. Synchrotron Rad. 16 (2009) 380–386
        that also depends on energy spread."""
        sigma_r02 = self.get_sigma_r02(E)
        if self.eEspread == 0 or with0eSpread:
            return sigma_r02
        harmonic = np.floor_divide(E, self.E1)
#        harmonic[harmonic < 1] = 1
        if onlyOddHarmonics:
            harmonic += harmonic % 2 - 1
        eEspread_norm = PI2 * harmonic * self.Np * self.eEspread
        Qa2 = self.tanaka_kitamura_Qa2(eEspread_norm/4.)  # note 1/4
        return sigma_r02 * Qa2**(2/3.) 

def test_csr_from_coo_rand():
    for i in range(100):
        coords = np.random.choice(np.arange(50 * 100, dtype=np.int32), 1000, False)
        rows = np.mod(coords, 100, dtype=np.int32)
        cols = np.floor_divide(coords, 100, dtype=np.int32)
        vals = np.random.randn(1000)

        csr = lm.CSR.from_coo(rows, cols, vals, (100, 50))
        rowinds = csr.rowinds()
        assert csr.nrows == 100
        assert csr.ncols == 50
        assert csr.nnz == 1000

        for i in range(100):
            sp = csr.rowptrs[i]
            ep = csr.rowptrs[i+1]
            assert ep - sp == np.sum(rows == i)
            points, = np.nonzero(rows == i)
            assert len(points) == ep - sp
            po = np.argsort(cols[points])
            points = points[po]
            assert all(np.sort(csr.colinds[sp:ep]) == cols[points])
            assert all(np.sort(csr.row_cs(i)) == cols[points])
            assert all(csr.values[np.argsort(csr.colinds[sp:ep]) + sp] == vals[points])
            assert all(rowinds[sp:ep] == i)

            row = np.zeros(50)
            row[cols[points]] = vals[points]
            assert np.sum(csr.row(i)) == approx(np.sum(vals[points]))
            assert all(csr.row(i) == row) 

def _is_business_daily(self):
        # quick check: cannot be business daily
        if self.day_deltas != [1, 3]:
            return False

        # probably business daily, but need to confirm
        first_weekday = self.index[0].weekday()
        shifts = np.diff(self.index.asi8)
        shifts = np.floor_divide(shifts, _ONE_DAY)
        weekdays = np.mod(first_weekday + np.cumsum(shifts), 7)
        return np.all(((weekdays == 0) & (shifts == 3)) |
                      ((weekdays > 0) & (weekdays <= 4) & (shifts == 1))) 

def floor_divide_and_remainder(x, y):
    return (np.floor_divide(x, y), np.remainder(x, y)) 

def test_floor_division_complex(self):
        # check that implementation is correct
        msg = "Complex floor division implementation check"
        x = np.array([.9 + 1j, -.1 + 1j, .9 + .5*1j, .9 + 2.*1j], dtype=np.complex128)
        y = np.array([0., -1., 0., 0.], dtype=np.complex128)
        assert_equal(np.floor_divide(x**2, x), y, err_msg=msg)
        # check overflow, underflow
        msg = "Complex floor division overflow/underflow check"
        x = np.array([1.e+110, 1.e-110], dtype=np.complex128)
        y = np.floor_divide(x**2, x)
        assert_equal(y, [1.e+110, 0], err_msg=msg) 

def testOverload(self):
    dtypes = [
        tf.float16,
        tf.float32,
        tf.float64,
        tf.int32,
        tf.int64,
        tf.complex64,
        tf.complex128,
    ]
    funcs = [
        (np.add, _ADD),
        (np.subtract, _SUB),
        (np.multiply, _MUL),
        (np.power, _POW),
        (np.true_divide, _TRUEDIV),
        (np.floor_divide, _FLOORDIV),
    ]
    for dtype in dtypes:
      for np_func, tf_func in funcs:
        if dtype in (tf.complex64, tf.complex128) and tf_func == _FLOORDIV:
          continue  # floordiv makes no sense for complex
        self._compareBinary(10, 5, dtype, np_func, tf_func)
    # Mod only works for int32 and int64.
    for dtype in [tf.int32, tf.int64]:
      self._compareBinary(10, 3, dtype, np.mod, _MOD) 

def testInt64Basic(self):
    x = np.arange(1 << 40, 13 << 40, 2 << 40).reshape(1, 3, 2).astype(np.int64)
    y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int64)
    self._compareBoth(x, y, np.subtract, tf.sub)
    self._compareBoth(x, y, np.multiply, tf.mul)
    self._compareBoth(x, y, np.true_divide, tf.truediv)
    self._compareBoth(x, y, np.floor_divide, tf.floordiv)
    self._compareBoth(x, y, np.mod, tf.mod)
    self._compareBoth(x, y, np.subtract, _SUB)
    self._compareBoth(x, y, np.multiply, _MUL)
    self._compareBoth(x, y, np.true_divide, _TRUEDIV)
    self._compareBoth(x, y, np.floor_divide, _FLOORDIV)
    self._compareBoth(x, y, np.mod, _MOD) 

def testUint16Basic(self):
    x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.uint16)
    y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.uint16)
    self._compareBoth(x, y, np.multiply, tf.mul)
    self._compareBoth(x, y, np.multiply, _MUL)
    self._compareBoth(x, y, np.true_divide, tf.truediv)
    self._compareBoth(x, y, np.floor_divide, tf.floordiv)
    self._compareBoth(x, y, np.true_divide, _TRUEDIV)
    self._compareBoth(x, y, np.floor_divide, _FLOORDIV)