Python spatial.Voronoi方法代碼示例

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def test_ridges(self, name):
        # Check that the ridges computed by Voronoi indeed separate
        # the regions of nearest neighborhood, by comparing the result
        # to KDTree.

        points = DATASETS[name]

        tree = KDTree(points)
        vor = qhull.Voronoi(points)

        for p, v in vor.ridge_dict.items():
            # consider only finite ridges
            if not np.all(np.asarray(v) >= 0):
                continue

            ridge_midpoint = vor.vertices[v].mean(axis=0)
            d = 1e-6 * (points[p[0]] - ridge_midpoint)

            dist, k = tree.query(ridge_midpoint + d, k=1)
            assert_equal(k, p[0])

            dist, k = tree.query(ridge_midpoint - d, k=1)
            assert_equal(k, p[1]) 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def plot_cluster_voronoi(data, algo):
    # passing input space to set up voronoi regions.
    points = np.hstack((np.reshape(data[:,0], (len(data[:,0]), 1)), np.reshape(data[:,1], (len(data[:,1]), 1))))
    vor = Voronoi(points)
    # use helper Voronoi
    regions, vertices = voronoi_finite_polygons_2d(vor)
    fig, ax = plt.subplots()
    plot = ax.scatter([], [])
    indice = 0
    for region in regions:
        ax.plot(data[:,0][indice], data[:,1][indice], 'ko')
        polygon = vertices[region]
        # if it isn't gradient based we just color red or blue depending on whether that point uses the machine in question
        color = algo.labels_[indice]
        # we assume only two 
        if color == 0:
            color = 'r'
        else:
            color = 'b'
        ax.fill(*zip(*polygon), alpha=0.4, color=color, label="")
        indice += 1
    ax.axis('equal')
    plt.xlim(vor.min_bound[0] - 0.1, vor.max_bound[0] + 0.1)
    plt.ylim(vor.min_bound[1] - 0.1, vor.max_bound[1] + 0.1) 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def get_wigner_seitz(recLat):
    kpoints = []
    for i in range(-1, 2):
        for j in range(-1, 2):
            for k in range(-1, 2):
                vec = i * recLat[0] + j * recLat[1] + k * recLat[2]
                kpoints.append(vec)
    brill = Voronoi(np.array(kpoints))
    faces = []
    for idict in brill.ridge_dict:
        if idict[0] == 13 or idict[1] == 13:
            faces.append(brill.ridge_dict[idict])
    verts = brill.vertices
    poly = []
    for ix in range(len(faces)):
        temp = []
        for iy in range(len(faces[ix])):
            temp.append(verts[faces[ix][iy]])
        poly.append(np.array(temp))
    return np.array(poly) 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def wigner_seitz(self):
        """
        
        Returns
        -------
        TYPE
            Using the Wigner-Seitz Method, this function finds the 1st 
            Brillouin Zone in terms of vertices and faces 
        """
        
        kpoints = []
        for i in range(-1, 2):
            for j in range(-1, 2):
                for k in range(-1, 2):
                    vec = i * self.reciprocal[0] + j * \
                        self.reciprocal[1] + k * self.reciprocal[2]
                    kpoints.append(vec)
        brill = Voronoi(np.array(kpoints))
        faces = []
        for idict in brill.ridge_dict:
            if idict[0] == 13 or idict[1] == 13:
                faces.append(brill.ridge_dict[idict])
                
        verts = brill.vertices
        return np.array(verts), np.array(faces) 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def test_voronoi_name_mapping(xy_of_hex):
    """Test scipy Voronoi names are mapped to landlab-style names."""
    voronoi = Voronoi(xy_of_hex)
    delaunay = Delaunay(xy_of_hex)
    graph = VoronoiDelaunay(xy_of_hex)

    assert np.all(graph.x_of_node == approx(voronoi.points[:, 0]))
    assert np.all(graph.y_of_node == approx(voronoi.points[:, 1]))

    assert np.all(graph.x_of_corner == approx(voronoi.vertices[:, 0]))
    assert np.all(graph.y_of_corner == approx(voronoi.vertices[:, 1]))

    assert np.all(graph.nodes_at_link == voronoi.ridge_points)

    assert tuple(graph.n_corners_at_cell) == tuple(
        len(region) for region in voronoi.regions
    )
    for cell, corners in enumerate(graph.corners_at_cell):
        assert np.all(corners[: graph.n_corners_at_cell[cell]] == voronoi.regions[cell])
        assert np.all(corners[graph.n_corners_at_cell[cell] :] == -1)
    assert np.all(graph.corners_at_face == voronoi.ridge_vertices)
    assert np.all(graph.nodes_at_face == voronoi.ridge_points)
    assert np.all(graph.cell_at_node == voronoi.point_region)

    assert np.all(graph.nodes_at_patch == delaunay.simplices) 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def voronoi_from_points_numpy(points):
    """Generate a voronoi diagram from a set of points.

    Parameters
    ----------
    points : list of list of float
        XYZ coordinates of the voronoi sites.

    Returns
    -------

    Examples
    --------
    >>>

    """
    points = asarray(points)
    voronoi = Voronoi(points)
    return voronoi


# ==============================================================================
# Main
# ============================================================================== 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def relaxLloyd(pts,strength):
    for i in range(strength):
        vor = Voronoi(pts)
        newpts = []
        for idx in range(len(vor.points)):
            pt = vor.points[idx,:]
            region = vor.regions[vor.point_region[idx]]
            if -1 in region:
                newpts.append(pt)
            else:
                vxs = np.asarray([vor.vertices[i,:] for i in region])
                newpt = centroidnp(vxs)
                newpts.append(newpt)
        pts = np.array(newpts) 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def _voronoi(structure, limit=5, tol=1e-2):
    lps = structure.lat_params
    limits = np.array([ int(np.ceil(limit/lps[i])) for i in range(3)])

    # Create a new cell large enough to find neighbors at least `limit` away.
    new_struct = structure.transform(limits+1, in_place=False)
    nns = defaultdict(list)

    # look for neighbors for each atom in the structure
    for i, atom in enumerate(structure.atoms):
        atom.neighbors = []

        # center on the atom so that its voronoi cell is fully described
        new_struct.recenter(atom, middle=True)
        tess = Voronoi(new_struct.cartesian_coords)
        for j, r in enumerate(tess.ridge_points):
            if not i in r: # only ridges involving the specified atom matter
                continue
            inds = tess.ridge_vertices[j]
            verts = tess.vertices[inds]

            # check that the area of the facet is large enough
            if _get_facet_area(verts) < tol:
                continue

            # Get the indices of all points which this atom shares a ridge with
            ind = [k for k in tess.ridge_points[j] if k != i ][0]
            # map the atom index back into the original cell. 
            atom.neighbors.append(atom)
        ## nns[atom] = atom.neighbors
        ## `qmpy.Atom` objects that are not saved are not hashable, and hence
        ## cannot be used as dictionary keys. Unit tests will fail.
        nns[i] = atom.neighbors
    return nns 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def get_equivalent_voronoi_vertices(
        self, return_box=False, minimum_dist=0.1, symprec=1e-5, angle_tolerance=-1.0
    ):
        """
            This function gives the positions of spatially equivalent Voronoi vertices in lists, which
            most likely represent interstitial points or vacancies (along with other high symmetry points)
            Each list item contains an array of positions which are spacially equivalent.
            This function does not work if there are Hs atoms in the box

            Args:
                return_box: True, if the box containing atoms on the positions of Voronoi vertices
                            should be returned (which are represented by Hs atoms)
                minimum_dist: Minimum distance between two Voronoi vertices to be considered as one

            Returns: List of numpy array positions of spacially equivalent Voronoi vertices

        """

        _, box_copy = self._get_voronoi_vertices(minimum_dist=minimum_dist)
        list_positions = []
        sym = box_copy.get_symmetry(symprec=symprec, angle_tolerance=angle_tolerance)
        for ind in set(sym["equivalent_atoms"][box_copy.select_index("Hs")]):
            list_positions.append(box_copy.positions[sym["equivalent_atoms"] == ind])
        if return_box:
            return list_positions, box_copy
        else:
            return list_positions 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def _get_centre(self, gdf):
        """
        Returns centre coords of gdf.
        """
        bounds = gdf["geometry"].bounds
        centre_x = (bounds["maxx"].max() + bounds["minx"].min()) / 2
        centre_y = (bounds["maxy"].max() + bounds["miny"].min()) / 2
        return centre_x, centre_y

    # densify geometry before Voronoi tesselation 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def _regions(self, voronoi_diagram, unique_id, ids, crs):
        """
        Generate GeoDataFrame of Voronoi regions from scipy.spatial.Voronoi.
        """
        # generate DataFrame of results
        regions = pd.DataFrame()
        regions[unique_id] = ids  # add unique id
        regions["region"] = voronoi_diagram.point_region  # add region id for each point

        # add vertices of each polygon
        vertices = []
        for region in regions.region:
            vertices.append(voronoi_diagram.regions[region])
        regions["vertices"] = vertices

        # convert vertices to Polygons
        polygons = []
        for region in tqdm(regions.vertices, desc="Vertices to Polygons"):
            if -1 not in region:
                polygons.append(Polygon(voronoi_diagram.vertices[region]))
            else:
                polygons.append(None)
        # save polygons as geometry column
        regions["geometry"] = polygons

        # generate GeoDataFrame
        regions_gdf = gpd.GeoDataFrame(regions.dropna(), geometry="geometry")
        regions_gdf = regions_gdf.loc[
            regions_gdf["geometry"].length < 1000000
        ]  # delete errors
        regions_gdf = regions_gdf.loc[
            regions_gdf[unique_id] != -1
        ]  # delete hull-based cells
        regions_gdf.crs = crs
        return regions_gdf 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def _get_voronoi_vertices_and_ridges(self):
        borders = self._get_densified_borders()

        voronoi_diagram = Voronoi(borders)
        vertices = voronoi_diagram.vertices
        ridges = voronoi_diagram.ridge_vertices

        return vertices, ridges 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def test_issue_8051(self):
        points = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2],[2, 0], [2, 1], [2, 2]])
        Voronoi(points) 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def test_masked_array_fails(self):
        masked_array = np.ma.masked_all(1)
        assert_raises(ValueError, qhull.Voronoi, masked_array) 

# 需要導入模塊: from scipy import spatial [as 別名]
# 或者: from scipy.spatial import Voronoi [as 別名]
def test_simple(self):
        # Simple case with known Voronoi diagram
        points = [(0, 0), (0, 1), (0, 2),
                  (1, 0), (1, 1), (1, 2),
                  (2, 0), (2, 1), (2, 2)]

        # qhull v o Fv Qbb Qc Qz < dat
        output = """
        2
        5 10 1
        -10.101 -10.101
           0.5    0.5
           1.5    0.5
           0.5    1.5
           1.5    1.5
        2 0 1
        3 3 0 1
        2 0 3
        3 2 0 1
        4 4 3 1 2
        3 4 0 3
        2 0 2
        3 4 0 2
        2 0 4
        0
        12
        4 0 3 0 1
        4 0 1 0 1
        4 1 4 1 3
        4 1 2 0 3
        4 2 5 0 3
        4 3 4 1 2
        4 3 6 0 2
        4 4 5 3 4
        4 4 7 2 4
        4 5 8 0 4
        4 6 7 0 2
        4 7 8 0 4
        """
        self._compare_qvoronoi(points, output)