Python numpy.meshgrid方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)):
  """ A wrapper function to generate anchors given different scales
    Also return the number of anchors in variable 'length'
  """
  anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
  A = anchors.shape[0]
  shift_x = np.arange(0, width) * feat_stride
  shift_y = np.arange(0, height) * feat_stride
  shift_x, shift_y = np.meshgrid(shift_x, shift_y)
  shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
  K = shifts.shape[0]
  # width changes faster, so here it is H, W, C
  anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
  anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
  length = np.int32(anchors.shape[0])

  return anchors, length 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def image_reslice(image, spec, method=None, fill=0, dtype=None, weights=None, image_type=None):
    '''
    image_reslice(image, spec) yields a duplicate of the given image resliced to have the voxels
      indicated by the given image spec. Note that spec may be an image itself.

    Optional arguments that can be passed to image_interpolate() (asside from affine) are allowed
    here and are passed through.
    '''
    if image_type is None and is_image(image): image_type = to_image_type(image)
    spec = to_image_spec(spec)
    image = to_image(image)
    # we make a big mesh and interpolate at these points...
    imsh = spec['image_shape']
    (args, kw) = ([np.arange(n) for n in imsh[:3]], {'indexing': 'ij'})
    ijk = np.asarray([u.flatten() for u in np.meshgrid(*args, **kw)])
    ijk = np.dot(spec['affine'], np.vstack([ijk, np.ones([1,ijk.shape[1]])]))[:3]
    # interpolate here...
    u = image_interpolate(image, ijk, method=method, fill=fill, dtype=dtype, weights=weights)
    return to_image((np.reshape(u, imsh), spec), image_type=image_type) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def get_point_cloud_from_z(Y, camera_matrix):
  """Projects the depth image Y into a 3D point cloud.
  Inputs:
    Y is ...xHxW
    camera_matrix
  Outputs:
    X is positive going right
    Y is positive into the image
    Z is positive up in the image
    XYZ is ...xHxWx3
  """
  x, z = np.meshgrid(np.arange(Y.shape[-1]),
                     np.arange(Y.shape[-2]-1, -1, -1))
  for i in range(Y.ndim-2):
    x = np.expand_dims(x, axis=0)
    z = np.expand_dims(z, axis=0)
  X = (x-camera_matrix.xc) * Y / camera_matrix.f
  Z = (z-camera_matrix.zc) * Y / camera_matrix.f
  XYZ = np.concatenate((X[...,np.newaxis], Y[...,np.newaxis],
                        Z[...,np.newaxis]), axis=X.ndim)
  return XYZ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def test_2x3(self):
        # Loading the water depth map
        dat = loadtxt('data/WaterDepth1.dat')
        X, Y = meshgrid(linspace(0., 1000., 50), linspace(0., 1000., 50))
        depth = array(zip(X.flatten(), Y.flatten(), dat.flatten()))
        borders = array([[200, 200], [150, 500], [200, 800], [600, 900], [700, 700], [900, 500], [800, 200], [500, 100], [200, 200]])
        baseline = array([[587.5, 223.07692308], [525., 346.15384615], [837.5, 530.76923077], [525., 530.76923077], [525., 838.46153846], [837.5, 469.23076923]])

        wt_desc = WTDescFromWTG('data/V80-2MW-offshore.wtg').wt_desc
        wt_layout = GenericWindFarmTurbineLayout([WTPC(wt_desc=wt_desc, position=pos) for pos in baseline])

        t = Topfarm(
            baseline_layout = wt_layout,
            borders = borders,
            depth_map = depth,
            dist_WT_D = 5.0,
            distribution='spiral',
            wind_speeds=[4., 8., 20.],
            wind_directions=linspace(0., 360., 36)[:-1]
        )

        t.run()

        self.fail('make save function')
        t.save() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def generate_anchors(self, image_width: int, image_height: int, num_x_anchors: int, num_y_anchors: int) -> Tensor:
        center_ys = np.linspace(start=0, stop=image_height, num=num_y_anchors + 2)[1:-1]
        center_xs = np.linspace(start=0, stop=image_width, num=num_x_anchors + 2)[1:-1]
        ratios = np.array(self._anchor_ratios)
        ratios = ratios[:, 0] / ratios[:, 1]
        sizes = np.array(self._anchor_sizes)

        # NOTE: it's important to let `center_ys` be the major index (i.e., move horizontally and then vertically) for consistency with 2D convolution
        # giving the string 'ij' returns a meshgrid with matrix indexing, i.e., with shape (#center_ys, #center_xs, #ratios)
        center_ys, center_xs, ratios, sizes = np.meshgrid(center_ys, center_xs, ratios, sizes, indexing='ij')

        center_ys = center_ys.reshape(-1)
        center_xs = center_xs.reshape(-1)
        ratios = ratios.reshape(-1)
        sizes = sizes.reshape(-1)

        widths = sizes * np.sqrt(1 / ratios)
        heights = sizes * np.sqrt(ratios)

        center_based_anchor_bboxes = np.stack((center_xs, center_ys, widths, heights), axis=1)
        center_based_anchor_bboxes = torch.from_numpy(center_based_anchor_bboxes).float()
        anchor_bboxes = BBox.from_center_base(center_based_anchor_bboxes)

        return anchor_bboxes 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def test_dist_sma_radius(self):
        """
        Test that sma and radius values outside of the range have zero probability
        """
        
        for mod in self.allmods:
            if 'dist_sma_radius' in mod.__dict__:
                with RedirectStreams(stdout=self.dev_null):
                    pp = mod(**self.spec)
                
                a = np.logspace(np.log10(pp.arange[0].value/10.),np.log10(pp.arange[1].value*100),100)
                Rp = np.logspace(np.log10(pp.Rprange[0].value/10.),np.log10(pp.Rprange[1].value*100),100)
                
                aa, RR = np.meshgrid(a,Rp)
                
                fr = pp.dist_sma_radius(aa,RR)
                self.assertTrue(np.all(fr[aa < pp.arange[0].value] == 0),'dist_sma_radius low bound failed on sma for %s'%mod.__name__)
                self.assertTrue(np.all(fr[aa > pp.arange[1].value] == 0),'dist_sma_radius high bound failed on sma for %s'%mod.__name__)
                self.assertTrue(np.all(fr[RR < pp.Rprange[0].value] == 0),'dist_sma_radius low bound failed on radius for %s'%mod.__name__)
                self.assertTrue(np.all(fr[RR > pp.Rprange[1].value] == 0),'dist_sma_radius high bound failed on radius for %s'%mod.__name__)
                self.assertTrue(np.all(fr[(aa > pp.arange[0].value) & (aa < pp.arange[1].value) & (RR > pp.Rprange[0].value) & (RR < pp.Rprange[1].value)] > 0),'dist_sma_radius is improper pdf for %s'%mod.__name__) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def calcfbetaInput(self):
        # table 17 in Leinert et al. (1998)
        # Zodiacal Light brightness function of solar LON (rows) and LAT (columns)
        # values given in W m−2 sr−1 μm−1 for a wavelength of 500 nm
        path = os.path.split(inspect.getfile(self.__class__))[0]
        Izod = np.loadtxt(os.path.join(path, 'Leinert98_table17.txt'))*1e-8 # W/m2/sr/um
        # create data point coordinates
        lon_pts = np.array([0., 5, 10, 15, 20, 25, 30, 35, 40, 45, 60, 75, 90,
                105, 120, 135, 150, 165, 180]) # deg
        lat_pts = np.array([0., 5, 10, 15, 20, 25, 30, 45, 60, 75, 90]) # deg
        y_pts, x_pts = np.meshgrid(lat_pts, lon_pts)
        points = np.array(list(zip(np.concatenate(x_pts), np.concatenate(y_pts))))
        # create data values, normalized by (90,0) value
        z = Izod/Izod[12,0]
        values = z.reshape(z.size)
        return  points, values 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def get_L_cluster_cut(L, node_label):
  adj = L - np.diag(np.diag(L))
  adj[adj != 0] = 1.0
  num_nodes = adj.shape[0]
  idx_row, idx_col = np.meshgrid(range(num_nodes), range(num_nodes))
  idx_row, idx_col = idx_row.flatten().astype(
      np.int64), idx_col.flatten().astype(np.int64)
  mask = (node_label[idx_row] == node_label[idx_col]).reshape(
      num_nodes, num_nodes).astype(np.float)

  adj_cluster = adj * mask
  adj_cut = adj - adj_cluster
  L_cut = get_laplacian(adj_cut, graph_laplacian_type='L4')
  L_cluster = get_laplacian(adj_cluster, graph_laplacian_type='L4')

  return L_cluster, L_cut 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def ov_order(model, slc=None):
    nocc = k_nocc(model)
    if slc is None:
        slc = numpy.ones((len(model.mo_coeff), model.mo_coeff[0].shape[1]), dtype=bool)
    e_occ = tuple(e[:o][s[:o]] for e, o, s in zip(model.mo_energy, nocc, slc))
    e_virt = tuple(e[o:][s[o:]] for e, o, s in zip(model.mo_energy, nocc, slc))
    sort_o = []
    sort_v = []
    for o in e_occ:
        for v in e_virt:
            _v, _o = numpy.meshgrid(v, o)
            sort_o.append(_o.reshape(-1))
            sort_v.append(_v.reshape(-1))
    sort_o, sort_v = numpy.concatenate(sort_o), numpy.concatenate(sort_v)
    vals = numpy.array(
        list(zip(sort_o, sort_v)),
        dtype=[('o', sort_o[0].dtype), ('v', sort_v[0].dtype)]
    )
    result = numpy.argsort(vals, order=('o', 'v'))
    # Double for other blocks
    return numpy.concatenate([result, result + len(result)]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def test_draw_polygon_2D(self):
        X, Y = np.meshgrid(np.linspace(-8, 8, 25, dtype=float), np.linspace(-8, 8, 25, dtype=float))
        nodes = np.vstack([X.reshape(-1), Y.reshape(-1)]).T
        tri = scipy.spatial.Delaunay(nodes)
        poly = np.array([[5.5, 5.5], [5.5, -5.5], [-5.5, -5.5], [-5.5, 5.5]])
        #angles = np.linspace(0, 2*np.pi, endpoint=False, num=20)
        #poly = np.vstack([5*np.sin(angles), 5*np.cos(angles)]).T
        trs = tri.simplices[:, [1,0,2]]
        new_points, _ = electrode_placement._draw_polygon_2D(
            poly, tri.points, trs, ends=True)
        bar = np.mean(new_points[trs], axis=1)
        m = new_points[trs[:, 1:]] -\
            new_points[trs[:, 0]][:, None, :]
        area = .5 * -np.linalg.det(m)
        inside = electrode_placement._point_inside_polygon(poly, bar, tol=1e-3)
        #plt.triplot(new_points[:, 0], new_points[:, 1], tri.simplices.copy())
        #plt.show()
        assert np.isclose(np.sum(area[inside]), 11**2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def test_inside_complex_polygon(self):
        X, Y = np.meshgrid(np.linspace(-8, 8, 17, dtype=float), np.linspace(-8, 8, 17, dtype=float))
        nodes = np.vstack([X.reshape(-1), Y.reshape(-1)]).T
        tri = scipy.spatial.Delaunay(nodes)
        poly = np.array([[5, 5], [5, -5], [-5, -5], [-5, 5]])
        hole1 = np.array([[2, 2], [2, -2], [-2, -2], [-2, 2]])
        hole2 = np.array([[4, 4], [4, 3], [3, 3], [3, 4]])
        trs = tri.simplices[:, [1,0,2]]
        inside = electrode_placement._inside_complex_polygon(poly, tri.points,
                                                             trs,
                                                             holes=[hole1, hole2],
                                                             tol=1e-3)
        m = tri.points[trs[inside, 1:]] -\
            tri.points[trs[inside, 0]][:, None, :]
        area = .5 * -np.linalg.det(m)
        assert np.isclose(np.sum(area), 100-16-1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def test_find_tetrahedron_with_points_non_convex(self, sphere3_msh):
        X, Y, Z = np.meshgrid(np.linspace(-100, 100, 100),
                              np.linspace(-40, 40, 100), [0])
        points = np.vstack([X.reshape(-1), Y.reshape(-1), Z.reshape(-1)]).T
        dist = np.linalg.norm(points, axis=1)

        msh = sphere3_msh.crop_mesh(5)
        points_outside = points[(dist > 95) + (dist < 89)]
        th_with_points, bar = msh.find_tetrahedron_with_points(points_outside)
        assert np.all(th_with_points == -1)
        assert np.allclose(bar, 0)

        points_inside = points[(dist <= 94) * (dist >= 91)]
        th_with_points, bar = msh.find_tetrahedron_with_points(points_inside)
        eps = 1e-3
        assert np.all(th_with_points != -1)
        assert np.all(bar >= 0 - eps)
        assert np.all(bar <= 1. + eps)
        th_coords = \
            msh.nodes[msh.elm[th_with_points]]
        assert np.allclose(np.einsum('ikj, ik -> ij', th_coords, bar), points_inside) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def test_inside_volume(self, sphere3_msh):
        X, Y, Z = np.meshgrid(np.linspace(-100, 100, 100),
                              np.linspace(-40, 40, 10), [0])
        np.random.seed(0)
        points = np.vstack([X.reshape(-1), Y.reshape(-1), Z.reshape(-1)]).T
        points += np.random.random_sample(points.shape) - .5
        dist = np.linalg.norm(points, axis=1)

        msh = sphere3_msh.crop_mesh([4, 5])
        points_outside = points[(dist > 95) + (dist < 84)]
        inside = msh.test_inside_volume(points_outside)
        assert np.all(~inside)

        points_inside = points[(dist <= 94) * (dist >= 86)]
        inside = msh.test_inside_volume(points_inside)
        assert np.all(inside) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def test_to_nonlinear_grid(self, sphere3_msh):
        import nibabel
        data = sphere3_msh.elm.tag1
        f = mesh_io.ElementData(data, mesh=sphere3_msh)
        affine = np.array([[1, 0, 0, -100.5],
                           [0, 1, 0, -5],
                           [0, 0, 1, 0],
                           [0, 0, 0, 1]], dtype=float)
        x, y, z = np.meshgrid(np.arange(-100, 100),
                              np.arange(-5, 5),
                              np.arange(0, 1),
                              indexing='ij')
        nonl_transform = np.concatenate(
            (x[..., None], y[..., None], z[..., None]), axis=3).astype(float)
        img = nibabel.Nifti1Pair(nonl_transform, affine)
        tempf = tempfile.NamedTemporaryFile(suffix='.nii', delete=False)
        fn = tempf.name
        tempf.close()
        nibabel.save(img, fn)
        interp = f.to_deformed_grid(fn, fn, method='assign')
        interp = interp.get_data()
        assert np.isclose(interp[100, 5, 0], 3)
        assert np.isclose(interp[187, 5, 0], 4)
        assert np.isclose(interp[193, 5, 0], 5)
        assert np.isclose(interp[198, 5, 0], 0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import meshgrid [as 別名]
def test_to_nonlinear_grid_crop(self, sphere3_msh):
        import nibabel
        data = sphere3_msh.elm.tag1
        f = mesh_io.ElementData(data, mesh=sphere3_msh)
        affine = np.array([[1, 0, 0, -100.5],
                           [0, 1, 0, -5],
                           [0, 0, 1, 0],
                           [0, 0, 0, 1]], dtype=float)
        x, y, z = np.meshgrid(np.arange(-100, 100),
                              np.arange(-5, 5),
                              np.arange(0, 1),
                              indexing='ij')
        nonl_transform = np.concatenate(
            (x[..., None], y[..., None], z[..., None]), axis=3).astype(float)
        img = nibabel.Nifti1Pair(nonl_transform, affine)
        tempf = tempfile.NamedTemporaryFile(suffix='.nii', delete=False)
        fn = tempf.name
        tempf.close()
        nibabel.save(img, fn)
        interp = f.to_deformed_grid(fn, fn, tags=3, method='assign')
        interp = interp.get_data()
        assert np.isclose(interp[100, 5, 0], 3)
        assert np.isclose(interp[187, 5, 0], 0)
        assert np.isclose(interp[193, 5, 0], 0)
        assert np.isclose(interp[198, 5, 0], 0)