Python numpy.mgrid方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def __init__(self, anchors, shape, sigma=0.5, randrange=None):
        """
        Args:
            anchors (list): list of center coordinates in range [0,1].
            shape(list or tuple): image shape in [h, w].
            sigma (float): sigma for Gaussian weight
            randrange (int): offset range. Defaults to shape[0] / 8
        """
        logger.warn("GaussianDeform is slow. Consider using it with 4 or more prefetching processes.")
        super(GaussianDeform, self).__init__()
        self.anchors = anchors
        self.K = len(self.anchors)
        self.shape = shape
        self.grid = np.mgrid[0:self.shape[0], 0:self.shape[1]].transpose(1, 2, 0)
        self.grid = self.grid.astype('float32')  # HxWx2

        gm = GaussianMap(self.shape, sigma=sigma)
        self.gws = np.array([gm.get_gaussian_weight(ank)
                             for ank in self.anchors], dtype='float32')  # KxHxW
        self.gws = self.gws.transpose(1, 2, 0)  # HxWxK
        if randrange is None:
            self.randrange = self.shape[0] / 8
        else:
            self.randrange = randrange
        self.sigma = sigma 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def same_transform(taff, ornt, shape):
    # Applying transformations implied by `ornt` to a made-up array
    # ``arr`` of shape `shape`, results in ``t_arr``. When the point
    # indices from ``arr`` are transformed by (the inverse of) `taff`,
    # and we index into ``t_arr`` with these transformed points, then we
    # should get the same values as we would from indexing into arr with
    # the untransformed points. 
    shape = np.array(shape)
    size = np.prod(shape)
    arr = np.arange(size).reshape(shape)
    # apply ornt transformations
    t_arr = apply_orientation(arr, ornt)
    # get all point indices in arr
    i,j,k = shape
    arr_pts = np.mgrid[:i,:j,:k].reshape((3,-1))
    # inverse of taff takes us from point index in arr to point index in
    # t_arr
    itaff = np.linalg.inv(taff)
    # apply itaff so that points indexed in t_arr should correspond 
    o2t_pts = np.dot(itaff[:3,:3], arr_pts) + itaff[:3,3][:,None]
    assert np.allclose(np.round(o2t_pts), o2t_pts)
    # fancy index out the t_arr values
    vals = t_arr[list(o2t_pts.astype('i'))]
    return np.all(vals == arr.ravel()) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def _compute_q_vectors(self, box):
        """ Compute the q-vectors compatible with the current box dimensions.

            Calculated quantities:
            q_vectors : two 2D arrays forming the grid of q-values, similar
                        to a meshgrid
            Qxy       : array of the different q-vectors
            Q         : squared module of Qxy with the first element missing
                        (no Q = 0.0)
        """
        self.box = np.roll(box, 2 - self.normal)
        nmax = list(map(int, np.ceil(self.box[0:2] / self.alpha)))
        self.q_vectors = np.mgrid[0:nmax[0], 0:nmax[1]] * 1.0
        self.q_vectors[0] *= 2. * np.pi / box[0]
        self.q_vectors[1] *= 2. * np.pi / box[1]
        self.modes_shape = self.q_vectors[0].shape
        qx = self.q_vectors[0][:, 0]
        qy = self.q_vectors[1][0]
        Qx = np.repeat(qx, len(qy))
        Qy = np.tile(qy, len(qx))
        self.Qxy = np.vstack((Qx, Qy)).T
        self.Q = np.sqrt(np.sum(self.Qxy * self.Qxy, axis=1)[1:]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def _():
        """ additional tests

        here we generate a paraboloid (x^2+y^2) and a hyperbolic paraboloid
        (x^2-y^2) to check that the curvature code gives the right answers for
        the Gaussian (4, -4) and mean (2, 0) curvatures

        >>> import pytim
        >>> x,y=np.mgrid[-5:5,-5:5.]/2.
        >>> p = np.asarray(list(zip(x.flatten(),y.flatten())))
        >>> z1 = p[:,0]**2+p[:,1]**2
        >>> z2 = p[:,0]**2-p[:,1]**2
        >>>
        >>> for z in [z1, z2]:
        ...     pp = np.asarray(list(zip(x.flatten()+5,y.flatten()+5,z)))
        ...     curv = pytim.observables.Curvature(cutoff=1.,warning=False).compute(pp)
        ...     val =  (curv[np.logical_and(p[:,0]==0,p[:,1]==0)])
        ...     # add and subtract 1e3 to be sure to have -0 -> 0
        ...     print(np.array_str((val+1e3)-1e3, precision=2, suppress_small=True))
        [[4. 2.]]
        [[-4.  0.]]


        """
# 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def hyperball(ndim, radius):
    """Return a binary morphological filter containing pixels within `radius`.

    Parameters
    ----------
    ndim : int
        The number of dimensions of the filter.
    radius : int
        The radius of the filter.

    Returns
    -------
    ball : array of bool, shape [2 * radius + 1,] * ndim
        The required structural element
    """
    size = 2 * radius + 1
    center = [(radius,) * ndim]

    coords = np.mgrid[[slice(None, size),] * ndim].reshape(ndim, -1).T
    distances = np.ravel(spatial.distance_matrix(coords, center))
    selector = distances <= radius

    ball = np.zeros((size,) * ndim, dtype=bool)
    ball.ravel()[selector] = True
    return ball 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def __init__(self, pos, size):
        """
        pos is (...,3) array of the bar positions (the corner of each bar)
        size is (...,3) array of the sizes of each bar
        """
        nCubes = reduce(lambda a,b: a*b, pos.shape[:-1])
        cubeVerts = np.mgrid[0:2,0:2,0:2].reshape(3,8).transpose().reshape(1,8,3)
        cubeFaces = np.array([
            [0,1,2], [3,2,1],
            [4,5,6], [7,6,5],
            [0,1,4], [5,4,1],
            [2,3,6], [7,6,3],
            [0,2,4], [6,4,2],
            [1,3,5], [7,5,3]]).reshape(1,12,3)
        size = size.reshape((nCubes, 1, 3))
        pos = pos.reshape((nCubes, 1, 3))
        verts = cubeVerts * size + pos
        faces = cubeFaces + (np.arange(nCubes) * 8).reshape(nCubes,1,1)
        md = MeshData(verts.reshape(nCubes*8,3), faces.reshape(nCubes*12,3))
        
        GLMeshItem.__init__(self, meshdata=md, shader='shaded', smooth=False) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def _generate_anchors_one_layer(h_I, w_I, h_l, w_l):
    """
    generate anchors on on layer
    return a ndarray with shape (h_l, w_l, 4), and the last dimmension in the order:[cx, cy, w, h]
    """
    y, x = np.mgrid[0: h_l, 0:w_l]
    cy = (y + config.anchor_offset) / h_l * h_I
    cx = (x + config.anchor_offset) / w_l * w_I
    
    anchor_scale = _get_scale(w_I, w_l)
    anchor_w = np.ones_like(cx) * anchor_scale
    anchor_h = np.ones_like(cx) * anchor_scale # cx.shape == cy.shape
    
    anchors = np.asarray([cx, cy, anchor_w, anchor_h])
    anchors = np.transpose(anchors, (1, 2, 0))
    
    return anchors 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def _tf_fspecial_gauss(size, sigma):
    """Function to mimic the 'fspecial' gaussian MATLAB function
    """
    x_data, y_data = np.mgrid[-size//2 + 1:size//2 + 1, -size//2 + 1:size//2 + 1]

    x_data = np.expand_dims(x_data, axis=-1)
    x_data = np.expand_dims(x_data, axis=-1)

    y_data = np.expand_dims(y_data, axis=-1)
    y_data = np.expand_dims(y_data, axis=-1)

    x = tf.constant(x_data, dtype=tf.float32)
    y = tf.constant(y_data, dtype=tf.float32)

    g = tf.exp(-((x**2 + y**2)/(2.0*sigma**2)))
    return g / tf.reduce_sum(g) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def quality(func, mesh, interpolator='nn', n=33):
    """Compute a quality factor (the quantity r**2 from TOMS792).

    interpolator must be in ('linear', 'nn').
    """
    fz = func(mesh.x, mesh.y)
    tri = Triangulation(mesh.x, mesh.y)
    intp = getattr(tri,
                   interpolator + '_extrapolator')(fz, bbox=(0., 1., 0., 1.))
    Y, X = np.mgrid[0:1:complex(0, n), 0:1:complex(0, n)]
    Z = func(X, Y)
    iz = intp[0:1:complex(0, n), 0:1:complex(0, n)]
    #nans = np.isnan(iz)
    #numgood = n*n - np.sum(np.array(nans.flat, np.int32))
    numgood = n * n

    SE = (Z - iz) ** 2
    SSE = np.sum(SE.flat)
    meanZ = np.sum(Z.flat) / numgood
    SM = (Z - meanZ) ** 2
    SSM = np.sum(SM.flat)

    r2 = 1.0 - SSE / SSM
    print(func.func_name, r2, SSE, SSM, numgood)
    return r2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def __init__(self, anchors, shape, sigma=0.5, randrange=None):
        """
        :param anchors: in [0,1] coordinate
        :param shape: image shape in [h, w]
        :param sigma: sigma for Gaussian weight
        :param randrange: default to shape[0] / 8
        """
        logger.warn("GaussianDeform is slow. Consider using it with 4 or more prefetching processes.")
        super(GaussianDeform, self).__init__()
        self.anchors = anchors
        self.K = len(self.anchors)
        self.shape = shape
        self.grid = np.mgrid[0:self.shape[0], 0:self.shape[1]].transpose(1,2,0)
        self.grid = self.grid.astype('float32') # HxWx2

        gm = GaussianMap(self.shape, sigma=sigma)
        self.gws = np.array([gm.get_gaussian_weight(ank)
                             for ank in self.anchors], dtype='float32') # KxHxW
        self.gws = self.gws.transpose(1, 2, 0)  #HxWxK
        if randrange is None:
            self.randrange = self.shape[0] / 8
        else:
            self.randrange = randrange 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def random_warp( image ):
    assert image.shape == (256,256,3)
    range_ = numpy.linspace( 128-80, 128+80, 5 )
    mapx = numpy.broadcast_to( range_, (5,5) )
    mapy = mapx.T

    mapx = mapx + numpy.random.normal( size=(5,5), scale=5 )
    mapy = mapy + numpy.random.normal( size=(5,5), scale=5 )

    interp_mapx = cv2.resize( mapx, (80,80) )[8:72,8:72].astype('float32')
    interp_mapy = cv2.resize( mapy, (80,80) )[8:72,8:72].astype('float32')

    warped_image = cv2.remap( image, interp_mapx, interp_mapy, cv2.INTER_LINEAR )

    src_points = numpy.stack( [ mapx.ravel(), mapy.ravel() ], axis=-1 )
    dst_points = numpy.mgrid[0:65:16,0:65:16].T.reshape(-1,2)
    mat = umeyama( src_points, dst_points, True )[0:2]

    target_image = cv2.warpAffine( image, mat, (64,64) )

    return warped_image, target_image 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def plot(self, smearing=0.1):
        renderer = Renderer()
        N = len(self.structure)
        V = self.structure.get_volume()
        xs = np.mgrid[0.5:self.limit:1000j]
        dr = xs[1] - xs[0]
        norms = [ ( (x+dr/2)**3 - (x-dr/2)**3) for x in xs ]
        for pair in self.pairs:
            e1, e2 = pair
            vals = np.zeros(xs.shape)
            nanb = self.structure.comp[e1]*self.structure.comp[e2]
            prefactor = 1.0/(smearing*np.sqrt(2*np.pi))
            #prefactor = V**2 * (N-1)/N
            for w, d in zip(self.weights[pair], self.distances[pair]):
                if not d:
                    continue
                vals += np.exp(-(d-xs)**2/(2*smearing**2))*w
            vals = prefactor*vals/norms
            vals = [ v if v > 1e-4 else 0.0 for v in vals ]
            line = Line(zip(xs, vals), label='%s-%s' % (e1, e2))
            renderer.add(line)

        renderer.xaxis.label = 'interatomic distance [Å]'
        return renderer 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def test_order_zero_2d_fails_on_extrapolation():
    a = np.mgrid[0:5, 0:5][0].reshape(25)
    b = np.mgrid[0:5, 0:5][1].reshape(25)
    df = pd.DataFrame({'a': a + 0.5, 'b': b + 0.5, 'c': b*3, 'garbage': ['test']*len(a)})
    df = make_bin_edges(df, 'a')
    df = make_bin_edges(df, 'b')
    df = df.sample(frac=1)  # Shuffle table to assure interpolation works given unsorted input

    i = Interpolation(df, ('garbage',), [('a', 'a_left', 'a_right'), ('b', 'b_left', 'b_right')],
                      order=0, extrapolate=False)

    column = np.arange(4, step=0.011)
    query = pd.DataFrame({'a': column, 'b': column, 'garbage': ['test']*(len(column))})

    with pytest.raises(ValueError) as error:
        i(query)

    message = error.value.args[0]

    assert 'Extrapolation' in message and 'a' in message 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def get_passive_elements(self):
        X, Y = np.mgrid[self.passive_min_x:self.passive_max_x + 1,
            self.passive_min_y:self.passive_max_y]
        pairs = np.vstack([X.ravel(), Y.ravel()]).T
        passive_to_ids = np.vectorize(lambda pair: xy_to_id(*pair,
            nelx=self.nelx - 1, nely=self.nely - 1), signature="(m)->()")
        return passive_to_ids(pairs) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import mgrid [as 別名]
def _FSpecialGauss(size, sigma):
  """Function to mimic the 'fspecial' gaussian MATLAB function."""
  radius = size // 2
  offset = 0.0
  start, stop = -radius, radius + 1
  if size % 2 == 0:
    offset = 0.5
    stop -= 1
  x, y = np.mgrid[offset + start:stop, offset + start:stop]
  assert len(x) == size
  g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2)))
  return g / g.sum()