Python testing.assert_almost_equal方法代碼示例

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_dft_2d(self):
        """Test the discrete Fourier transform on 2D data"""
        N = 16
        da = xr.DataArray(np.random.rand(N,N), dims=['x','y'],
                        coords={'x':range(N),'y':range(N)}
                         )
        ft = xrft.dft(da, shift=False)
        npt.assert_almost_equal(ft.values, np.fft.fftn(da.values))

        ft = xrft.dft(da, shift=False, window=True, detrend='constant')
        dim = da.dims
        window = np.hanning(N) * np.hanning(N)[:, np.newaxis]
        da_prime = (da - da.mean(dim=dim)).values
        npt.assert_almost_equal(ft.values, np.fft.fftn(da_prime*window))

        da = xr.DataArray(np.random.rand(N,N), dims=['x','y'],
                         coords={'x':range(N,0,-1),'y':range(N,0,-1)}
                         )
        assert (xrft.power_spectrum(da, shift=False,
                                   density=True) >= 0.).all() 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_dft_4d(self):
        """Test the discrete Fourier transform on 2D data"""
        N = 16
        da = xr.DataArray(np.random.rand(N,N,N,N),
                         dims=['time','z','y','x'],
                         coords={'time':range(N),'z':range(N),
                                'y':range(N),'x':range(N)}
                         )
        with pytest.raises(ValueError):
            xrft.dft(da.chunk({'time':8}), dim=['y','x'], detrend='linear')
        ft = xrft.dft(da, shift=False)
        npt.assert_almost_equal(ft.values, np.fft.fftn(da.values))

        da_prime = xrft.detrendn(da[:,0].values, [0,1,2]) # cubic detrend over time, y, and x
        npt.assert_almost_equal(xrft.dft(da[:,0].drop('z'),
                                        dim=['time','y','x'],
                                        shift=False, detrend='linear'
                                        ).values,
                                np.fft.fftn(da_prime)) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_dft_real_2d(self):
        """
        Test the real discrete Fourier transform function on one-dimensional
        data. Non-trivial because we need to keep only some of the negative
        frequencies.
        """
        Nx, Ny = 16, 32
        da = xr.DataArray(np.random.rand(Nx, Ny), dims=['x', 'y'],
                          coords={'x': range(Nx), 'y': range(Ny)})
        dx = float(da.x[1] - da.x[0])
        dy = float(da.y[1] - da.y[0])

        daft = xrft.dft(da, real='x')
        npt.assert_almost_equal(daft.values,
                               np.fft.rfftn(da.transpose('y','x')).transpose())
        npt.assert_almost_equal(daft.values,
                               xrft.dft(da, dim=['y'], real='x'))

        actual_freq_x = daft.coords['freq_x'].values
        expected_freq_x = np.fft.rfftfreq(Nx, dx)
        npt.assert_almost_equal(actual_freq_x, expected_freq_x)

        actual_freq_y = daft.coords['freq_y'].values
        expected_freq_y = np.fft.fftfreq(Ny, dy)
        npt.assert_almost_equal(actual_freq_y, expected_freq_y) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_cross_phase_2d(self, dask):
        Ny, Nx = (32, 16)
        x = np.linspace(0, 1, num=Nx, endpoint=False)
        y = np.ones(Ny)
        f = 6
        phase_offset = np.pi/2
        signal1 = np.cos(2*np.pi*f*x)  # frequency = 1/(2*pi)
        signal2 = np.cos(2*np.pi*f*x - phase_offset)
        da1 = xr.DataArray(data=signal1*y[:,np.newaxis], name='a',
                          dims=['y','x'], coords={'y':y, 'x':x})
        da2 = xr.DataArray(data=signal2*y[:,np.newaxis], name='b',
                          dims=['y','x'], coords={'y':y, 'x':x})
        with pytest.raises(ValueError):
            xrft.cross_phase(da1, da2, dim=['y','x'])

        if dask:
            da1 = da1.chunk({'x': 16})
            da2 = da2.chunk({'x': 16})
        cp = xrft.cross_phase(da1, da2, dim=['x'])
        actual_phase_offset = cp.sel(freq_x=f).values
        npt.assert_almost_equal(actual_phase_offset, phase_offset) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_broadcast():
    """Test for broadcasting in onnx operators."""
    input1 = np.random.rand(1, 3, 4, 5).astype("float32")
    input2 = np.random.rand(1, 5).astype("float32")
    inputs = [helper.make_tensor_value_info("input1", TensorProto.FLOAT, shape=(1, 3, 4, 5)),
              helper.make_tensor_value_info("input2", TensorProto.FLOAT, shape=(1, 5))]

    outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=(1, 3, 4, 5))]

    nodes = [helper.make_node("Add", ["input1", "input2"], ["output"])]

    graph = helper.make_graph(nodes,
                              "bcast_test",
                              inputs,
                              outputs)

    bcast_model = helper.make_model(graph)
    
    bkd_rep = mxnet_backend.prepare(bcast_model)
    numpy_op = input1 + input2
    output = bkd_rep.run([input1, input2])
    npt.assert_almost_equal(output[0], numpy_op) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_greater():
    """Test for logical greater in onnx operators."""
    input1 = np.random.rand(1, 3, 4, 5).astype("float32")
    input2 = np.random.rand(1, 5).astype("float32")
    inputs = [helper.make_tensor_value_info("input1", TensorProto.FLOAT, shape=(1, 3, 4, 5)),
              helper.make_tensor_value_info("input2", TensorProto.FLOAT, shape=(1, 5))]

    outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=(1, 3, 4, 5))]

    nodes = [helper.make_node("Greater", ["input1", "input2"], ["output"])]

    graph = helper.make_graph(nodes,
                              "greater_test",
                              inputs,
                              outputs)

    greater_model = helper.make_model(graph)
    
    bkd_rep = mxnet_backend.prepare(greater_model)
    numpy_op = np.greater(input1, input2).astype(np.float32)
    output = bkd_rep.run([input1, input2])
    npt.assert_almost_equal(output[0], numpy_op) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_lesser():
    """Test for logical greater in onnx operators."""
    input1 = np.random.rand(1, 3, 4, 5).astype("float32")
    input2 = np.random.rand(1, 5).astype("float32")
    inputs = [helper.make_tensor_value_info("input1", TensorProto.FLOAT, shape=(1, 3, 4, 5)),
              helper.make_tensor_value_info("input2", TensorProto.FLOAT, shape=(1, 5))]

    outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=(1, 3, 4, 5))]

    nodes = [helper.make_node("Less", ["input1", "input2"], ["output"])]

    graph = helper.make_graph(nodes,
                              "lesser_test",
                              inputs,
                              outputs)

    greater_model = helper.make_model(graph)
    
    bkd_rep = mxnet_backend.prepare(greater_model)
    numpy_op = np.less(input1, input2).astype(np.float32)
    output = bkd_rep.run([input1, input2])
    npt.assert_almost_equal(output[0], numpy_op) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_equal():
    """Test for logical greater in onnx operators."""
    input1 = np.random.rand(1, 3, 4, 5).astype("float32")
    input2 = np.random.rand(1, 5).astype("float32")
    inputs = [helper.make_tensor_value_info("input1", TensorProto.FLOAT, shape=(1, 3, 4, 5)),
              helper.make_tensor_value_info("input2", TensorProto.FLOAT, shape=(1, 5))]

    outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=(1, 3, 4, 5))]

    nodes = [helper.make_node("Equal", ["input1", "input2"], ["output"])]

    graph = helper.make_graph(nodes,
                              "equal_test",
                              inputs,
                              outputs)

    greater_model = helper.make_model(graph)
    
    bkd_rep = mxnet_backend.prepare(greater_model)
    numpy_op = np.equal(input1, input2).astype(np.float32)
    output = bkd_rep.run([input1, input2])
    npt.assert_almost_equal(output[0], numpy_op) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_square():
    input1 = np.random.randint(1, 10, (2, 3)).astype("float32")

    ipsym = mx.sym.Variable("input1")
    square = mx.sym.square(data=ipsym)
    model = mx.mod.Module(symbol=square, data_names=['input1'], label_names=None)
    model.bind(for_training=False, data_shapes=[('input1', np.shape(input1))], label_shapes=None)
    model.init_params()

    args, auxs = model.get_params()
    params = {}
    params.update(args)
    params.update(auxs)

    converted_model = onnx_mxnet.export_model(square, params, [np.shape(input1)], np.float32, "square.onnx")

    sym, arg_params, aux_params = onnx_mxnet.import_model(converted_model)
    result = forward_pass(sym, arg_params, aux_params, ['input1'], input1)

    numpy_op = np.square(input1)

    npt.assert_almost_equal(result, numpy_op) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def testBoundingBoxParser(self):
    bounding_boxes = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6],
                               [1.0, 0.6, 0.8], [1.0, 0.6, 0.7]]).transpose()
    features = {
        'ymin': self._FloatFeature(bounding_boxes[:, 0]),
        'xmin': self._FloatFeature(bounding_boxes[:, 1]),
        'ymax': self._FloatFeature(bounding_boxes[:, 2]),
        'xmax': self._FloatFeature(bounding_boxes[:, 3])
    }

    example = tf.train.Example(features=tf.train.Features(feature=features))

    parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax', 'ymax')
    result = parser.parse(example)
    self.assertIsNotNone(result)
    np_testing.assert_almost_equal(result, bounding_boxes)

    parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax',
                                                 'another_ymax')
    result = parser.parse(example)
    self.assertIsNone(result) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_ImageStimulus_scale():
    # Create a horizontal bar:
    fname = 'test.png'
    shape = (5, 5)
    ndarray = np.zeros(shape, dtype=np.uint8)
    ndarray[2, :] = 255
    imsave(fname, ndarray)
    # Scale phosphene:
    stim = ImageStimulus(fname)
    npt.assert_almost_equal(stim.data, stim.scale(1).data)
    npt.assert_almost_equal(stim.scale(0.1)[12], 1)
    npt.assert_almost_equal(stim.scale(0.1)[:12], 0)
    npt.assert_almost_equal(stim.scale(0.1)[13:], 0)
    with pytest.raises(ValueError):
        stim.scale(0)
    os.remove(fname) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_ImageStimulus_encode():
    stim = ImageStimulus(np.random.rand(4, 5))

    # Amplitude encoding in default range:
    enc = stim.encode()
    npt.assert_almost_equal(enc.time[-1], 500)
    npt.assert_almost_equal(enc.data.max(axis=1).min(), 0)
    npt.assert_almost_equal(enc.data.max(axis=1).max(), 50)

    # Amplitude encoding in custom range:
    enc = stim.encode(amp_range=(2, 43))
    npt.assert_almost_equal(enc.time[-1], 500)
    npt.assert_almost_equal(enc.data.max(axis=1).min(), 2)
    npt.assert_almost_equal(enc.data.max(axis=1).max(), 43)

    with pytest.raises(TypeError):
        stim.encode(pulse={'invalid': 1})
    with pytest.raises(ValueError):
        stim.encode(pulse=LogoUCSB()) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_conv(mode, method):
    reload(convolution)
    # time vector for stimulus (long)
    stim_dur = 0.5  # seconds
    tsample = 0.001 / 1000
    t = np.arange(0, stim_dur, tsample)

    # stimulus (10 Hz anondic and cathodic pulse train)
    stim = np.zeros_like(t)
    stim[::1000] = 1
    stim[100::1000] = -1

    # kernel
    _, gg = gamma(1, 0.005, tsample)

    # make sure conv returns the same result as np.convolve for all modes:
    npconv = np.convolve(stim, gg, mode=mode)
    conv = convolution.conv(stim, gg, mode=mode, method=method)
    npt.assert_equal(conv.shape, npconv.shape)
    npt.assert_almost_equal(conv, npconv)

    with pytest.raises(ValueError):
        convolution.conv(gg, stim, mode="invalid")
    with pytest.raises(ValueError):
        convolution.conv(gg, stim, method="invalid") 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_gamma():
    tsample = 0.005 / 1000

    with pytest.raises(ValueError):
        t, g = gamma(0, 0.1, tsample)
    with pytest.raises(ValueError):
        t, g = gamma(2, -0.1, tsample)
    with pytest.raises(ValueError):
        t, g = gamma(2, 0.1, -tsample)

    for tau in [0.001, 0.01, 0.1]:
        for n in [1, 2, 5]:
            t, g = gamma(n, tau, tsample)
            npt.assert_equal(np.arange(0, t[-1] + tsample / 2.0, tsample), t)
            if n > 1:
                npt.assert_equal(g[0], 0.0)

            # Make sure area under the curve is normalized
            npt.assert_almost_equal(np.trapz(np.abs(g), dx=tsample), 1.0,
                                    decimal=2)

            # Make sure peak sits correctly
            npt.assert_almost_equal(g.argmax() * tsample, tau * (n - 1)) 

# 需要導入模塊: from numpy import testing [as 別名]
# 或者: from numpy.testing import assert_almost_equal [as 別名]
def test_Grid2D(x_range, y_range):
    grid = Grid2D(x_range, y_range, step=1, grid_type='rectangular')
    npt.assert_equal(grid.x_range, x_range)
    npt.assert_equal(grid.y_range, y_range)
    npt.assert_equal(grid.step, 1)
    npt.assert_equal(grid.type, 'rectangular')

    # Grid is created with indexing='xy', so check coordinates:
    npt.assert_equal(grid.x.shape,
                     (np.abs(np.diff(y_range)) + 1,
                      np.abs(np.diff(x_range)) + 1))
    npt.assert_equal(grid.x.shape, grid.y.shape)
    npt.assert_equal(grid.x.shape, grid.shape)
    npt.assert_almost_equal(grid.x[0, 0], x_range[0])
    npt.assert_almost_equal(grid.x[0, -1], x_range[1])
    npt.assert_almost_equal(grid.x[-1, 0], x_range[0])
    npt.assert_almost_equal(grid.x[-1, -1], x_range[1])
    npt.assert_almost_equal(grid.y[0, 0], y_range[0])
    npt.assert_almost_equal(grid.y[0, -1], y_range[0])
    npt.assert_almost_equal(grid.y[-1, 0], y_range[1])
    npt.assert_almost_equal(grid.y[-1, -1], y_range[1])