Python numpy.fmod() Examples

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
            ]

        # 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_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
            ]

        # 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
            ]

        # 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_vectorized_intrin2(dtype="float32"):
    c2 = tvm.tir.const(2, dtype=dtype)
    test_funcs = [
        (tvm.tir.power, lambda x : np.power(x, 2.0)),
        (tvm.tir.fmod,  lambda x : np.fmod(x, 2.0))
    ]
    def run_test(tvm_intrin, np_func):
        if not tvm.gpu(0).exist or not tvm.runtime.enabled("cuda"):
            print("skip because cuda is not enabled..")
            return

        n = 128
        A = te.placeholder((n,), dtype=dtype, name='A')
        B = te.compute((n,), lambda i: tvm_intrin(A[i], c2), name='B')
        s = sched(B)
        f = tvm.build(s, [A, B], "cuda")
        ctx = tvm.gpu(0)
        a = tvm.nd.array(np.random.uniform(0, 1, size=n).astype(A.dtype), ctx)
        b = tvm.nd.array(np.zeros(shape=(n,)).astype(A.dtype), ctx)
        f(a, b)
        tvm.testing.assert_allclose(b.asnumpy(), np_func(a.asnumpy()), atol=1e-3, rtol=1e-3)

    for func in test_funcs:
        run_test(*func) 

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
            ]

        # 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
            ]

        # 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_mod(self):
    if legacy_opset_pre_ver(10):
      raise unittest.SkipTest("ONNX version {} doesn't support Mod.".format(
          defs.onnx_opset_version()))
    x = self._get_rnd_float32(shape=[5, 5])
    y = self._get_rnd_float32(shape=[5, 5])
    node_def = helper.make_node("Mod", ["X", "Y"], ["Z"], fmod=0)
    output = run_node(node_def, [x, y])
    np.testing.assert_almost_equal(output["Z"], np.mod(x, y))
    node_def = helper.make_node("Mod", ["X", "Y"], ["Z"], fmod=1)
    output = run_node(node_def, [x, y])
    np.testing.assert_almost_equal(output["Z"], np.fmod(x, y)) 

def __setattr__(self, key, value):
        """Set attributes with conversion to ndarray where needed."""
        is_set = hasattr(self, key)  # == False in constructor

        # parameter canonicalization and some validation via reshaping
        if value is None:
            # TODO: maybe forbid for some fields
            pass
        elif key == 'translation':
            value = np.array(value, dtype=np.float32).reshape(3, 1)
        elif key == 'rotation':
            value = np.array(value, dtype=np.float32).reshape(4)
            value[0] = np.fmod(value[0], 2.0 * np.pi)

            if value[0] < 0.0:
                value[0] += 2.0 * np.pi

            value[0] = np.cos(value[0] / 2)

            norm = np.linalg.norm(value[1:4])
            needed_norm = np.sqrt(1 - value[0] * value[0])
            if abs(norm - needed_norm) > _epsilon:
                if norm < _epsilon:
                    raise ValueError('Norm of (x, y, z) part of quaternion too close to zero')
                value[1:4] = value[1:4] / norm * needed_norm
            # assert abs(np.linalg.norm(value) - 1.0) < _epsilon
        elif key == 'scaling':
            value = np.array(value, dtype=np.float32).reshape(3)
        elif key in ['parent_matrix', 'custom_matrix', 'model_matrix']:
            value = np.array(value, dtype=np.float32).reshape((4, 4))

        super(Transform, self).__setattr__(key, value)

        if is_set and key != 'model_matrix':
            self._recompute_matrix()
            self._notify_dependants() 

def __init__(self, input_grid, pitch, apodization, orientation=0, even_grid=False):
		'''An even asphere micro-lens array.

		Parameters
		----------
		input_grid : Grid
			The grid on which the periodic optical element is evaluated.
		pitch : scalar
			The pitch of the periodic optical element.
		apodization : Apodizer
			The apodizer that will be evaluated on the periodic grid.
		orientation : scalar
			The orientation of the periodic optical element.
		even_grid : bool
			This determines whether zero is in between two elements or if it is the center of an element.
		'''
		self.input_grid = input_grid.copy()
		self.input_grid = self.input_grid.rotated(orientation)

		if even_grid:
			xf = (np.fmod(abs(self.input_grid.x), pitch) - pitch / 2) * np.sign(self.input_grid.x)
			yf = (np.fmod(abs(self.input_grid.y), pitch) - pitch / 2) * np.sign(self.input_grid.y)
		else:
			xf = (np.fmod(abs(self.input_grid.x) + pitch / 2, pitch) - pitch / 2) * np.sign(self.input_grid.x)
			yf = (np.fmod(abs(self.input_grid.y) + pitch / 2, pitch) - pitch / 2) * np.sign(self.input_grid.y)

		periodic_grid = CartesianGrid(UnstructuredCoords((xf, yf)))
		self.apodization = apodization(periodic_grid) 

def forward(self, inputs, device):
        x, divisor = inputs
        y = functions.fmod(x, divisor)
        return y, 

def forward_expected(self, inputs):
        x, divisor = inputs
        expected = numpy.fmod(x, divisor)
        expected = numpy.asarray(expected)
        return expected, 

def testFloat(self):
    x = [0.5, 0.7, 0.3]
    for dtype in [np.float32, np.double]:
      # Test scalar and vector versions.
      for denom in [x[0], [x[0]] * 3]:
        x_np = np.array(x, dtype=dtype)
        with self.test_session(use_gpu=True):
          x_tf = constant_op.constant(x_np, shape=x_np.shape)
          y_tf = math_ops.mod(x_tf, denom)
          y_tf_np = y_tf.eval()
          y_np = np.fmod(x_np, denom)
        self.assertAllClose(y_tf_np, y_np, atol=1e-2) 

def testTruncateModInt(self):
    nums, divs = self.intTestData()
    with self.test_session():
      tf_result = math_ops.truncatemod(nums, divs).eval()
      np_result = np.fmod(nums, divs)
      self.assertAllEqual(tf_result, np_result) 

def testTruncateModFloat(self):
    nums, divs = self.floatTestData()
    with self.test_session():
      tf_result = math_ops.truncatemod(nums, divs).eval()
      np_result = np.fmod(nums, divs)
      self.assertAllEqual(tf_result, np_result) 

def wrap_180(self, angle):
		if (angle > 3*np.pi or angle < -3*np.pi):
			angle = np.fmod(angle,2*np.pi)
		if (angle > np.pi):
			angle = angle - 2*np.pi
		if (angle < - np.pi):
			angle = angle + 2*np.pi
		return angle; 

def wrap_180(self, angle):
		if (angle > 3*np.pi or angle < -3*np.pi):
			angle = np.fmod(angle,2*np.pi)
		if (angle > np.pi):
			angle = angle - 2*np.pi
		if (angle < - np.pi):
			angle = angle + 2*np.pi
		return angle; 

def wrap_180(self, angle):
		if (angle > 3*np.pi or angle < -3*np.pi):
			angle = np.fmod(angle,2*np.pi)
		if (angle > np.pi):
			angle = angle - 2*np.pi
		if (angle < - np.pi):
			angle = angle + 2*np.pi
		return angle; 

def wrap_180(self, angle):
		if (angle > 3*np.pi or angle < -3*np.pi):
			angle = np.fmod(angle,2*np.pi)
		if (angle > np.pi):
			angle = angle - 2*np.pi
		if (angle < - np.pi):
			angle = angle + 2*np.pi
		return angle; 

def wrap_180(self, angle):
		if (angle > 3*np.pi or angle < -3*np.pi):
			angle = np.fmod(angle,2*np.pi)
		if (angle > np.pi):
			angle = angle - 2*np.pi
		if (angle < - np.pi):
			angle = angle + 2*np.pi
		return angle; 

def _periodic_boundary(self,x,bound):
        return np.fmod(x,bound)-np.trunc(x/bound)*bound 

def myProj(x):
	angle = torch.norm(x, 2, 1, True)
	axis = F.normalize(x)
	angle = torch.fmod(angle, 2*np.pi)
	return angle*axis


# my model for pose estimation: feature model + 1layer pose model x 12 

def test_fmod(self):
        A, A_rdd = self.make_dense_rdd((8, 3))
        B, B_rdd = self.make_dense_rdd((1, 3))
        np_res = np.fmod(A, B)
        assert_array_equal(
            A_rdd.fmod(B).toarray(), np_res
        )