Python numpy.moveaxis方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False,
                              interpolation='linear'):
    """
    Private function that doesn't support extended axis or keepdims.
    These methods are extended to this function using _ureduce
    See nanpercentile for parameter usage
    """
    if axis is None or a.ndim == 1:
        part = a.ravel()
        result = _nanquantile_1d(part, q, overwrite_input, interpolation)
    else:
        result = np.apply_along_axis(_nanquantile_1d, axis, a, q,
                                     overwrite_input, interpolation)
        # apply_along_axis fills in collapsed axis with results.
        # Move that axis to the beginning to match percentile's
        # convention.
        if q.ndim != 0:
            result = np.moveaxis(result, axis, 0)

    if out is not None:
        out[...] = result
    return result 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def _pre_process(self, image: np.ndarray, shrink: float) -> torch.Tensor:
        """Takes N RGB image and performs and returns a set of bounding boxes as
            detections
        Args:
            image (np.ndarray): shape [N, height, width, 3]
        Returns:
            torch.Tensor: shape [N, 3, height, width]
        """
        assert image.dtype == np.uint8
        height, width = image.shape[1:3]
        image = image.astype(np.float32) - self.mean
        image = np.moveaxis(image, -1, 1)
        image = torch.from_numpy(image)
        if self.max_resolution is not None:
            shrink_factor = self.max_resolution / max((height, width))
            if shrink_factor <= shrink:
                shrink = shrink_factor
        image = torch.nn.functional.interpolate(image, scale_factor=shrink)
        image = image.to(self.device)
        return image 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def test_errors(self):
        x = np.random.randn(1, 2, 3)
        assert_raises_regex(ValueError, 'invalid axis .* `source`',
                            np.moveaxis, x, 3, 0)
        assert_raises_regex(ValueError, 'invalid axis .* `source`',
                            np.moveaxis, x, -4, 0)
        assert_raises_regex(ValueError, 'invalid axis .* `destination`',
                            np.moveaxis, x, 0, 5)
        assert_raises_regex(ValueError, 'repeated axis in `source`',
                            np.moveaxis, x, [0, 0], [0, 1])
        assert_raises_regex(ValueError, 'repeated axis in `destination`',
                            np.moveaxis, x, [0, 1], [1, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, 0, [0, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, [0, 1], [0]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def __init__(self, *dim):
        """
        >>> Id(1)
        Tensor(dom=Dim(1), cod=Dim(1), array=[1])
        >>> list(Id(2).array.flatten())
        [1.0, 0.0, 0.0, 1.0]
        >>> Id(2).array.shape
        (2, 2)
        >>> list(Id(2, 2).array.flatten())[:8]
        [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]
        >>> list(Id(2, 2).array.flatten())[8:]
        [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0]
        """
        dim = dim[0] if isinstance(dim[0], Dim) else Dim(*dim)
        array = functools.reduce(
            lambda a, x: np.tensordot(a, np.identity(x), 0)
            if a.shape else np.identity(x), dim, np.array(1))
        array = np.moveaxis(
            array, [2 * i for i in range(len(dim))], list(range(len(dim))))
        super().__init__(dim, dim, array) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def n_moment(x, counts, c, n):
    x = np.squeeze(x)
    if x.ndim is not 1:
        raise ValueError("scale of x should be 1-dimensional")
    if x.size not in counts.shape:
        raise ValueError("operands could not be broadcast together with shapes %s %s" %(str(x.shape), str(counts.shape)))
    
    if np.sum(counts)==0:
        return 0
    else:
        if x.ndim == 1 and counts.ndim == 1:
            return (np.sum((x-c)**n*counts) / np.sum(counts))**(1./n)
        else:
            
            if x.size in counts.shape:
                dim_ = [i for i, v in enumerate(counts.shape) if v == x.size]
                counts = np.moveaxis(counts, dim_, -1)
                return (np.sum((x-c)**n*counts, axis=-1) / np.sum(counts, axis=-1))**(1./n) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def _nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
                   interpolation='linear'):
    """
    Private function that doesn't support extended axis or keepdims.
    These methods are extended to this function using _ureduce
    See nanpercentile for parameter usage

    """
    if axis is None or a.ndim == 1:
        part = a.ravel()
        result = _nanpercentile1d(part, q, overwrite_input, interpolation)
    else:
        result = np.apply_along_axis(_nanpercentile1d, axis, a, q,
                                     overwrite_input, interpolation)
        # apply_along_axis fills in collapsed axis with results.
        # Move that axis to the beginning to match percentile's
        # convention.
        if q.ndim != 0:
            result = np.moveaxis(result, axis, 0)

    if out is not None:
        out[...] = result
    return result 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def augmentate_and_to_pytorch(item: {}):
    res = augmentate(item)
    return {'data': torch.from_numpy(np.moveaxis(res['data'].astype(np.float32) / 255., -1, 0)),
            'target': torch.from_numpy(np.expand_dims(res['target'].astype(np.float32) / 255, axis=0))} 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def _render_to_rgb(figure, close):
    canvas = plt_backend_agg.FigureCanvasAgg(figure)
    canvas.draw()
    data = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8)
    w, h = figure.canvas.get_width_height()
    image_hwc = data.reshape([h, w, 4])[..., :3]
    image_chw = np.moveaxis(image_hwc, source=2, destination=0)
    if close:
        plt.close(figure)
    return image_chw 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def _process_frame42(frame):
    frame = frame[34:34 + 160, :160]
    # Resize by half, then down to 42x42 (essentially mipmapping). If
    # we resize directly we lose pixels that, when mapped to 42x42,
    # aren't close enough to the pixel boundary.
    frame = cv2.resize(frame, (80, 80))
    frame = cv2.resize(frame, (42, 42))
    frame = frame.mean(2, keepdims=True)
    frame = frame.astype(np.float32)
    frame *= (1.0 / 255.0)
    frame = np.moveaxis(frame, -1, 0)
    return frame 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def test_extended_axis(self):
        o = np.random.normal(size=(71, 23))
        x = np.dstack([o] * 10)
        assert_equal(np.percentile(x, 30, axis=(0, 1)), np.percentile(o, 30))
        x = np.moveaxis(x, -1, 0)
        assert_equal(np.percentile(x, 30, axis=(-2, -1)), np.percentile(o, 30))
        x = x.swapaxes(0, 1).copy()
        assert_equal(np.percentile(x, 30, axis=(0, -1)), np.percentile(o, 30))
        x = x.swapaxes(0, 1).copy()

        assert_equal(np.percentile(x, [25, 60], axis=(0, 1, 2)),
                     np.percentile(x, [25, 60], axis=None))
        assert_equal(np.percentile(x, [25, 60], axis=(0,)),
                     np.percentile(x, [25, 60], axis=0))

        d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11))
        np.random.shuffle(d.ravel())
        assert_equal(np.percentile(d, 25,  axis=(0, 1, 2))[0],
                     np.percentile(d[:,:,:, 0].flatten(), 25))
        assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1],
                     np.percentile(d[:,:, 1,:].flatten(), [10, 90]))
        assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2],
                     np.percentile(d[:,:, 2,:].flatten(), 25))
        assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2],
                     np.percentile(d[2,:,:,:].flatten(), 25))
        assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1],
                     np.percentile(d[2, 1,:,:].flatten(), 25))
        assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1],
                     np.percentile(d[2,:,:, 1].flatten(), 25))
        assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2],
                     np.percentile(d[2,:, 2,:].flatten(), 25)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def test_exceptions(self):
        # test axis must be in bounds
        for ndim in [1, 2, 3]:
            a = np.ones((1,)*ndim)
            np.concatenate((a, a), axis=0)  # OK
            assert_raises(np.AxisError, np.concatenate, (a, a), axis=ndim)
            assert_raises(np.AxisError, np.concatenate, (a, a), axis=-(ndim + 1))

        # Scalars cannot be concatenated
        assert_raises(ValueError, concatenate, (0,))
        assert_raises(ValueError, concatenate, (np.array(0),))

        # test shapes must match except for concatenation axis
        a = np.ones((1, 2, 3))
        b = np.ones((2, 2, 3))
        axis = list(range(3))
        for i in range(3):
            np.concatenate((a, b), axis=axis[0])  # OK
            assert_raises(ValueError, np.concatenate, (a, b), axis=axis[1])
            assert_raises(ValueError, np.concatenate, (a, b), axis=axis[2])
            a = np.moveaxis(a, -1, 0)
            b = np.moveaxis(b, -1, 0)
            axis.append(axis.pop(0))

        # No arrays to concatenate raises ValueError
        assert_raises(ValueError, concatenate, ()) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def test_move_to_end(self):
        x = np.random.randn(5, 6, 7)
        for source, expected in [(0, (6, 7, 5)),
                                 (1, (5, 7, 6)),
                                 (2, (5, 6, 7)),
                                 (-1, (5, 6, 7))]:
            actual = np.moveaxis(x, source, -1).shape
            assert_(actual, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def test_move_new_position(self):
        x = np.random.randn(1, 2, 3, 4)
        for source, destination, expected in [
                (0, 1, (2, 1, 3, 4)),
                (1, 2, (1, 3, 2, 4)),
                (1, -1, (1, 3, 4, 2)),
                ]:
            actual = np.moveaxis(x, source, destination).shape
            assert_(actual, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def test_preserve_order(self):
        x = np.zeros((1, 2, 3, 4))
        for source, destination in [
                (0, 0),
                (3, -1),
                (-1, 3),
                ([0, -1], [0, -1]),
                ([2, 0], [2, 0]),
                (range(4), range(4)),
                ]:
            actual = np.moveaxis(x, source, destination).shape
            assert_(actual, (1, 2, 3, 4)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import moveaxis [as 別名]
def test_move_multiples(self):
        x = np.zeros((0, 1, 2, 3))
        for source, destination, expected in [
                ([0, 1], [2, 3], (2, 3, 0, 1)),
                ([2, 3], [0, 1], (2, 3, 0, 1)),
                ([0, 1, 2], [2, 3, 0], (2, 3, 0, 1)),
                ([3, 0], [1, 0], (0, 3, 1, 2)),
                ([0, 3], [0, 1], (0, 3, 1, 2)),
                ]:
            actual = np.moveaxis(x, source, destination).shape
            assert_(actual, expected)