Python numpy.fmod() Examples
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Example #1
Source File: test_ufunc.py From auto-alt-text-lambda-api with MIT License | 6 votes |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
def forward(self, inputs, device): x, divisor = inputs y = functions.fmod(x, divisor) return y,