Python numpy.fmod() Examples

The following are 30 code examples of numpy.fmod(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: test_ufunc.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up
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) 
Example #2
Source File: test_ufunc.py    From keras-lambda with MIT License 6 votes vote down vote up
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) 
Example #3
Source File: test_ufunc.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up
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) 
Example #4
Source File: test_ufunc.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up
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) 
Example #5
Source File: test_target_codegen_cuda.py    From incubator-tvm with Apache License 2.0 6 votes vote down vote up
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) 
Example #6
Source File: test_ufunc.py    From coffeegrindsize with MIT License 6 votes vote down vote up
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) 
Example #7
Source File: test_ufunc.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up
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) 
Example #8
Source File: test_ufunc.py    From mxnet-lambda with Apache License 2.0 6 votes vote down vote up
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) 
Example #9
Source File: test_ufunc.py    From pySINDy with MIT License 6 votes vote down vote up
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) 
Example #10
Source File: test_ufunc.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up
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) 
Example #11
Source File: test_ufunc.py    From vnpy_crypto with MIT License 6 votes vote down vote up
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) 
Example #12
Source File: test_ufunc.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
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) 
Example #13
Source File: test_ufunc.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up
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) 
Example #14
Source File: math_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
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) 
Example #15
Source File: test_ufunc.py    From recruit with Apache License 2.0 5 votes vote down vote up
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) 
Example #16
Source File: test_node.py    From onnx-tensorflow with Apache License 2.0 5 votes vote down vote up
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)) 
Example #17
Source File: test_forward.py    From incubator-tvm with Apache License 2.0 5 votes vote down vote up
def test_mod():
    # Mod
    verify_mod(x_shape=[1, 32, 32], y_shape=[1, 32, 32], fmod=0)

    verify_mod(x_shape=[1, 32, 32], y_shape=[1, 1, 32], fmod=0, dtype="int32")

    # fmod
    verify_mod(x_shape=[1, 1, 32], y_shape=[1, 32, 32], fmod=1)

    verify_mod(x_shape=[1, 32, 32], y_shape=[1, 32, 32], fmod=1, dtype="int32") 
Example #18
Source File: test_forward.py    From incubator-tvm with Apache License 2.0 5 votes vote down vote up
def verify_mod(x_shape, y_shape, fmod, dtype='float32'):
    x_np = np.random.uniform(size=x_shape).astype(dtype)
    y_np = np.random.uniform(size=y_shape).astype(dtype)
    y_np = np.where(y_np==0, 1, y_np) #remove 0's to avoid division by zero error

    if fmod:
        np_out = np.fmod(x_np, y_np)
    else:
        np_out = np.mod(x_np, y_np)

    out_shape = np_out.shape
    mod_node = helper.make_node("Mod",
                                inputs=["x", "y"],
                                outputs=["z"],
                                fmod=fmod)

    onnx_dtype = TensorProto.FLOAT if dtype == "float32" else TensorProto.INT32
    graph = helper.make_graph([mod_node],
                              "mod_test",
                              inputs=[helper.make_tensor_value_info("x",
                                                                    onnx_dtype, list(x_shape)),
                                      helper.make_tensor_value_info("y",
                                                                    onnx_dtype, list(y_shape))],
                              outputs=[helper.make_tensor_value_info("z",
                                                                    onnx_dtype, list(out_shape))])
    model = helper.make_model(graph, producer_name='mod_test')

    for target, ctx in ctx_list():
        tvm_out = get_tvm_output(
            model, [x_np, y_np], target, ctx, out_shape)
        tvm.testing.assert_allclose(np_out, tvm_out, rtol=1e-5, atol=1e-5) 
Example #19
Source File: transform.py    From K3D-jupyter with MIT License 5 votes vote down vote up
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() 
Example #20
Source File: circlefit.py    From resonator_tools with GNU General Public License v2.0 5 votes vote down vote up
def _periodic_boundary(self,x,bound):
        return np.fmod(x,bound)-np.trunc(x/bound)*bound 
Example #21
Source File: synthetic.py    From kombine with MIT License 5 votes vote down vote up
def filter_times(self, ts, tsundown):
        tsmod=np.fmod(ts, 24.0)

        tsmod = tsmod - np.fmod(tsundown, 24.0)

        tsmod[tsmod < 0] += 24.0

        return ts[tsmod < 12.0] 
Example #22
Source File: block.py    From bifrost with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def calculate_bin_indices(
            self, tstart, tsamp, data_size):
        """Calculate the bin that each time sample should be
            added to
        @param[in] tstart Time of the first element (s)
        @param[in] tsamp Difference between the times of
            consecutive elements (s)
        @param[in] data_size Number of elements
        @return Which bin each sample is folded into
        """
        arrival_time = tstart + tsamp * np.arange(data_size)
        phase = np.fmod(arrival_time, self.period)
        return np.floor(phase / self.period * self.bins).astype(int) 
Example #23
Source File: image2.py    From nnabla-examples with Apache License 2.0 5 votes vote down vote up
def distort_h(im, hue):
    tim = im[..., 0] + np.float32(255 * hue + 256)
    np.fmod(tim, 256, out=tim)
    im[..., 0] = tim 
Example #24
Source File: test_rdd.py    From sparkit-learn with Apache License 2.0 5 votes vote down vote up
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
        ) 
Example #25
Source File: periodic_optical_element.py    From hcipy with MIT License 5 votes vote down vote up
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) 
Example #26
Source File: math_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
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) 
Example #27
Source File: math_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
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) 
Example #28
Source File: evaluateGeodesicRegressionModel.py    From multi-modal-regression with MIT License 5 votes vote down vote up
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 
Example #29
Source File: circlefit.py    From qkit with GNU General Public License v2.0 5 votes vote down vote up
def _periodic_boundary(self,x,bound):
        return np.fmod(x,bound)-np.trunc(x/bound)*bound 
Example #30
Source File: test_fmod.py    From chainer with MIT License 5 votes vote down vote up
def forward(self, inputs, device):
        x, divisor = inputs
        y = functions.fmod(x, divisor)
        return y,