Source code for xugrid.ugrid.ugrid2d

from __future__ import annotations

import warnings
from itertools import chain
from typing import Any, Dict, Optional, Sequence, Tuple, Union

import numpy as np
import pandas as pd
import xarray as xr
from numba_celltree import CellTree2d
from numpy.typing import ArrayLike
from scipy.sparse import coo_matrix, csr_matrix
from scipy.sparse.csgraph import reverse_cuthill_mckee

import xugrid
from xugrid import conversion
from xugrid import meshkernel_utils as mku
from xugrid.constants import (
    FILL_VALUE,
    BoolArray,
    FloatArray,
    FloatDType,
    IntArray,
    IntDType,
    PolygonArray,
    SparseMatrix,
)
from xugrid.core.utils import either_dict_or_kwargs
from xugrid.ugrid import connectivity, conventions
from xugrid.ugrid.ugridbase import AbstractUgrid, as_pandas_index
from xugrid.ugrid.voronoi import voronoi_topology


def section_coordinates(
    edges: FloatArray, xy: FloatArray, dim: str, index: IntArray, name: str
) -> Tuple[IntArray, dict]:
    # TODO: add boundaries xy[:, 0] and xy[:, 1]
    xy_mid = 0.5 * (xy[:, 0, :] + xy[:, 1, :])
    s = np.linalg.norm(xy_mid - edges[0, 0], axis=1)
    order = np.argsort(s)
    coords = {
        f"{name}_x": (dim, xy_mid[order, 0]),
        f"{name}_y": (dim, xy_mid[order, 1]),
        f"{name}_s": (dim, s[order]),
    }
    return coords, index[order]


def numeric_bound(v: Union[float, None], other: float):
    if v is None:
        return other
    else:
        return v


[docs] class Ugrid2d(AbstractUgrid): """ This class stores the topological data of a 2-D unstructured grid. Parameters ---------- node_x: ndarray of floats node_y: ndarray of floats fill_value: int face_node_connectivity: ndarray of integers name: string, optional Mesh name. Defaults to "mesh2d". edge_node_connectivity: ndarray of integers, optional dataset: xr.Dataset, optional indexes: Dict[str, str], optional When a dataset is provided, a mapping from the UGRID role to the dataset variable name. E.g. {"face_x": "mesh2d_face_lon"}. projected: bool, optional Whether node_x and node_y are longitude and latitude or projected x and y coordinates. Used to write the appropriate standard_name in the coordinate attributes. crs: Any, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`, such as an authority string (eg "EPSG:4326") or a WKT string. attrs: Dict[str, str], optional UGRID topology attributes. Should not be provided together with dataset: if other names are required, update the dataset instead. A name entry is ignored, as name is given explicitly. start_index: int, 0 or 1, default is 0. Start index of the connectivity arrays. Must match the start index of the provided face_node_connectivity and edge_node_connectivity. """
[docs] def __init__( self, node_x: FloatArray, node_y: FloatArray, fill_value: int, face_node_connectivity: Union[IntArray, SparseMatrix], name: str = "mesh2d", edge_node_connectivity: IntArray = None, dataset: xr.Dataset = None, indexes: Dict[str, str] = None, projected: bool = True, crs: Any = None, attrs: Dict[str, str] = None, start_index: int = 0, ): self.node_x = np.ascontiguousarray(node_x) self.node_y = np.ascontiguousarray(node_y) self.fill_value = fill_value self.start_index = start_index self.name = name self.projected = projected if isinstance(face_node_connectivity, np.ndarray): self.face_node_connectivity = face_node_connectivity.copy() elif isinstance(face_node_connectivity, (coo_matrix, csr_matrix)): self.face_node_connectivity = connectivity.to_dense(face_node_connectivity) else: raise TypeError( "face_node_connectivity should be an array of integers or a sparse matrix" ) # Ensure the fill value is FILL_VALUE (-1) and the array is 0-based. if self.fill_value != -1 or self.start_index != 0: is_fill = self.face_node_connectivity == self.fill_value if self.start_index != 0: self.face_node_connectivity[~is_fill] -= self.start_index if self.fill_value != FILL_VALUE: self.face_node_connectivity[is_fill] = FILL_VALUE # TODO: do this in validation instead. While UGRID conventions demand it, # where does it go wrong? # self.face_node_connectivity = connectivity.counterclockwise( # face_node_connectivity, self.fill_value, self.node_coordinates # ) self._initialize_indexes_attrs(name, dataset, indexes, attrs) self._dataset = dataset # Optional attributes, deferred initialization # Meshkernel self._mesh = None self._meshkernel = None # Celltree self._celltree = None # Perimeter self._perimeter = None # Area self._area = None # Centroids self._centroids = None self._circumcenters = None # Bounds self._xmin = None self._xmax = None self._ymin = None self._ymax = None # Edges self._edge_x = None self._edge_y = None # Connectivity self._edge_node_connectivity = edge_node_connectivity if self._edge_node_connectivity is not None: self._edge_node_connectivity -= self.start_index self._edge_face_connectivity = None self._node_node_connectivity = None self._node_edge_connectivity = None self._node_face_connectivity = None self._face_edge_connectivity = None self._face_face_connectivity = None self._boundary_node_connectivity = None # Derived topology self._triangulation = None self._voronoi_topology = None self._centroid_triangulation = None # crs if crs is None: self.crs = None else: import pyproj self.crs = pyproj.CRS.from_user_input(crs)
def _clear_geometry_properties(self): """Clear all properties that may have been invalidated""" # Meshkernel self._mesh = None self._meshkernel = None # Celltree self._celltree = None # Perimeter self._perimeter = None # Area self._area = None # Centroids self._centroids = None self._circumcenters = None # Bounds self._xmin = None self._xmax = None self._ymin = None self._ymax = None # Edges self._edge_x = None self._edge_y = None # Derived topology self._triangulation = None self._voronoi_topology = None self._centroid_triangulation = None
[docs] @classmethod def from_meshkernel( cls, mesh, name: str = "mesh2d", projected: bool = True, crs: Any = None, ): """ Create a 2D UGRID topology from a MeshKernel Mesh2d object. Parameters ---------- mesh: MeshKernel.Mesh2d name: str Mesh name. Defaults to "mesh2d". projected: bool Whether node_x and node_y are longitude and latitude or projected x and y coordinates. Used to write the appropriate standard_name in the coordinate attributes. crs: Any, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`, such as an authority string (eg "EPSG:4326") or a WKT string. Returns ------- grid: Ugrid2d """ n_face = len(mesh.nodes_per_face) n_max_node = mesh.nodes_per_face.max() face_node_connectivity = np.full((n_face, n_max_node), FILL_VALUE) isnode = connectivity.ragged_index(n_face, n_max_node, mesh.nodes_per_face) face_node_connectivity[isnode] = mesh.face_nodes edge_node_connectivity = np.reshape(mesh.edge_nodes, (-1, 2)) return cls( node_x=mesh.node_x, node_y=mesh.node_y, fill_value=FILL_VALUE, face_node_connectivity=face_node_connectivity, edge_node_connectivity=edge_node_connectivity, name=name, projected=projected, crs=crs, )
[docs] @classmethod def from_dataset(cls, dataset: xr.Dataset, topology: str = None): """ Extract the 2D UGRID topology information from an xarray Dataset. Parameters ---------- dataset: xr.Dataset Dataset containing topology information stored according to UGRID conventions. Returns ------- grid: Ugrid1dAdapter """ ds = dataset if not isinstance(ds, xr.Dataset): raise TypeError( "Ugrid should be initialized with xarray.Dataset. " f"Received instead: {type(ds)}" ) if topology is None: topology = cls._single_topology(ds) indexes = {} # Collect names connectivity = ds.ugrid_roles.connectivity[topology] coordinates = ds.ugrid_roles.coordinates[topology] ugrid_vars = ( [topology] + list(connectivity.values()) + list(chain.from_iterable(chain.from_iterable(coordinates.values()))) ) x_index = coordinates["node_coordinates"][0][0] y_index = coordinates["node_coordinates"][1][0] node_x_coordinates = ds[x_index].astype(FloatDType).to_numpy() node_y_coordinates = ds[y_index].astype(FloatDType).to_numpy() face_nodes = connectivity["face_node_connectivity"] fill_value = ds[face_nodes].encoding.get("_FillValue", -1) start_index = ds[face_nodes].attrs.get("start_index", 0) face_node_connectivity = cls._prepare_connectivity( ds[face_nodes], fill_value, dtype=IntDType ).to_numpy() edge_nodes = connectivity.get("edge_node_connectivity") if edge_nodes: edge_node_connectivity = cls._prepare_connectivity( ds[edge_nodes], fill_value, dtype=IntDType ).to_numpy() # Make sure the single passed start index is valid for both # connectivity arrays. edge_start_index = ds[edge_nodes].attrs.get("start_index", 0) if edge_start_index != start_index: # start_index = 1, edge_start_index = 0, then add one # start_index = 0, edge_start_index = 1, then subtract one edge_node_connectivity += start_index - edge_start_index else: edge_node_connectivity = None indexes["node_x"] = x_index indexes["node_y"] = y_index projected = False # TODO return cls( node_x_coordinates, node_y_coordinates, fill_value, face_node_connectivity, name=topology, edge_node_connectivity=edge_node_connectivity, dataset=ds[ugrid_vars], indexes=indexes, projected=projected, crs=None, start_index=start_index, )
def _get_name_and_attrs(self, name: str): key = f"{name}_connectivity" attrs = conventions.DEFAULT_ATTRS[key] if "start_index" in attrs: attrs["start_index"] = self.start_index if "_FillValue" in attrs: attrs["_FillValue"] = self.fill_value return self._attrs[key], attrs
[docs] def to_dataset( self, other: xr.Dataset = None, optional_attributes: bool = False ) -> xr.Dataset: node_x = self._indexes["node_x"] node_y = self._indexes["node_y"] face_nodes, face_nodes_attrs = self._get_name_and_attrs("face_node") nmax_node_dim = self._attrs["max_face_nodes_dimension"] edge_nodes, edge_nodes_attrs = self._get_name_and_attrs("edge_node") data_vars = { self.name: 0, face_nodes: xr.DataArray( data=self._adjust_connectivity(self.face_node_connectivity), attrs=face_nodes_attrs, dims=(self.face_dimension, nmax_node_dim), ), } if self.edge_node_connectivity is not None or optional_attributes: data_vars[edge_nodes] = xr.DataArray( data=self._adjust_connectivity(self.edge_node_connectivity), attrs=edge_nodes_attrs, dims=(self.edge_dimension, "two"), ) if optional_attributes: face_edges, face_edges_attrs = self._get_name_and_attrs("face_edge") face_faces, face_faces_attrs = self._get_name_and_attrs("face_face") edge_faces, edge_faces_attrs = self._get_name_and_attrs("edge_face") bound_nodes, bound_nodes_attrs = self._get_name_and_attrs("boundary_node") boundary_edge_dim = self._attrs["boundary_edge_dimension"] data_vars[face_edges] = xr.DataArray( data=self._adjust_connectivity(self.face_edge_connectivity), attrs=face_edges_attrs, dims=(self.face_dimension, nmax_node_dim), ) data_vars[face_faces] = xr.DataArray( data=self._adjust_connectivity( connectivity.to_dense( self.face_face_connectivity, self.n_max_node_per_face ) ), attrs=face_faces_attrs, dims=(self.face_dimension, nmax_node_dim), ) data_vars[edge_faces] = xr.DataArray( data=self._adjust_connectivity(self.edge_face_connectivity), attrs=edge_faces_attrs, dims=(self.edge_dimension, "two"), ) data_vars[bound_nodes] = xr.DataArray( data=self._adjust_connectivity(self.boundary_node_connectivity), attrs=bound_nodes_attrs, dims=(boundary_edge_dim, "two"), ) attrs = {"Conventions": "CF-1.9 UGRID-1.0"} if other is not None: attrs.update(other.attrs) dataset = xr.Dataset(data_vars, attrs=attrs) if self._dataset: dataset = dataset.merge(self._dataset, compat="override") if other is not None: dataset = dataset.merge(other) if node_x not in dataset or node_y not in dataset: dataset = self.assign_node_coords(dataset) if optional_attributes: dataset = self.assign_face_coords(dataset) dataset = self.assign_edge_coords(dataset) dataset[self.name].attrs = self._filtered_attrs(dataset) return dataset
# These are all optional/derived UGRID attributes. They are not computed by # default, only when called upon. @property def n_face(self) -> int: """Return the number of faces in the UGRID2D topology.""" return self.face_node_connectivity.shape[0] @property def n_max_node_per_face(self) -> int: """ Return the maximum number of nodes that a face can contain in the UGRID2D topology. """ return self.face_node_connectivity.shape[1] @property def n_node_per_face(self) -> IntArray: return (self.face_node_connectivity != FILL_VALUE).sum(axis=1) @property def core_dimension(self): return self.face_dimension @property def dims(self): """Set of UGRID dimension names: node dimension, edge dimension, face_dimension.""" return { self.node_dimension, self.edge_dimension, self.face_dimension, } @property def sizes(self): return { self.node_dimension: self.n_node, self.edge_dimension: self.n_edge, self.face_dimension: self.n_face, } @property def max_face_node_dimension(self) -> str: return self._attrs["max_face_nodes_dimension"] @property def max_connectivity_sizes(self) -> dict[str, int]: return { self.max_face_node_dimension: self.n_max_node_per_face, } @property def max_connectivity_dimensions(self) -> tuple[str]: return (self.max_face_node_dimension,) @property def topology_dimension(self): """Highest dimensionality of the geometric elements: 2""" return 2 @property def face_dimension(self): """Return the name of the face dimension.""" return self._attrs["face_dimension"] def _edge_connectivity(self): ( self._edge_node_connectivity, self._face_edge_connectivity, ) = connectivity.edge_connectivity( self.face_node_connectivity, self._edge_node_connectivity, ) @property def edge_node_connectivity(self) -> IntArray: """ Edge to node connectivity. Every edge consists of a connection between two nodes. Returns ------- connectivity: ndarray of integers with shape ``(n_edge, 2)``. """ if self._edge_node_connectivity is None: self._edge_connectivity() return self._edge_node_connectivity @edge_node_connectivity.setter def edge_node_connectivity(self, value): self._edge_node_connectivity = value @property def face_edge_connectivity(self) -> csr_matrix: """ Face to edge connectivity. Returns ------- connectivity: csr_matrix """ if self._face_edge_connectivity is None: self._edge_connectivity() return self._face_edge_connectivity @property def boundary_node_connectivity(self) -> IntArray: """ Boundary node connectivity Returns ------- connectivity: ndarray of integers with shape ``(n_boundary_edge, 2)`` """ if self._boundary_node_connectivity is None: self._boundary_node_connectivity = connectivity.boundary_node_connectivity( self.edge_face_connectivity, self.edge_node_connectivity, ) return self._boundary_node_connectivity @property def centroids(self) -> FloatArray: """ Centroid (x, y) of every face. Returns ------- centroids: ndarray of floats with shape ``(n_face, 2)`` """ if self._centroids is None: self._centroids = connectivity.centroids( self.face_node_connectivity, self.node_x, self.node_y, ) return self._centroids @property def circumcenters(self): """ Circumenter (x, y) of every face; only works for fully triangular grids. """ if self._circumcenters is None: self._circumcenters = connectivity.circumcenters( self.face_node_connectivity, self.node_x, self.node_y, ) return self._circumcenters @property def area(self) -> FloatArray: """Area of every face.""" if self._area is None: self._area = connectivity.area( self.face_node_connectivity, self.node_x, self.node_y, ) return self._area @property def perimeter(self) -> FloatArray: """Perimeter length of every face.""" if self._perimeter is None: self._perimeter = connectivity.perimeter( self.face_node_connectivity, self.node_x, self.node_y, ) return self._perimeter @property def face_bounds(self): """ Returns a numpy array with columns ``minx, miny, maxx, maxy``, describing the bounds of every face in the grid. Returns ------- face_bounds: np.ndarray of shape (n_face, 4) """ x = self.node_x[self.face_node_connectivity] y = self.node_y[self.face_node_connectivity] isfill = self.face_node_connectivity == FILL_VALUE x[isfill] = np.nan y[isfill] = np.nan return np.column_stack( [ np.nanmin(x, axis=1), np.nanmin(y, axis=1), np.nanmax(x, axis=1), np.nanmax(y, axis=1), ] ) @property def face_x(self): """x-coordinate of centroid of every face""" return self.centroids[:, 0] @property def face_y(self): """y-coordinate of centroid of every face""" return self.centroids[:, 1] @property def face_coordinates(self) -> FloatArray: """ Centroid (x, y) of every face. Returns ------- centroids: ndarray of floats with shape ``(n_face, 2)`` """ return self.centroids @property def face_node_coordinates(self) -> FloatArray: """ Node coordinates of every face. "Fill node" coordinates are set as NaN. Returns ------- face_node_coordinates: ndarray of floats with shape ``(n_face, n_max_node_per_face, 2)`` """ coords = np.full( (self.n_face, self.n_max_node_per_face, 2), np.nan, dtype=FloatDType ) is_node = self.face_node_connectivity != FILL_VALUE index = self.face_node_connectivity[is_node] coords[is_node, :] = self.node_coordinates[index] return coords @property def edge_face_connectivity(self) -> IntArray: """ Edge to face connectivity. An edge may belong to a single face (exterior edge), or it may be shared by two faces (interior edge). An exterior edge will contain a FILL_VALUE of -1 for the second column. Returns ------- connectivity: ndarray of integers with shape ``(n_edge, 2)``. """ if self._edge_face_connectivity is None: self._edge_face_connectivity = connectivity.invert_dense( self.face_edge_connectivity ) return self._edge_face_connectivity @property def face_face_connectivity(self) -> csr_matrix: """ Face to face connectivity. Derived from shared edges. The connectivity is represented as an adjacency matrix in CSR format, with the row and column indices as a (0-based) face index. The data of the matrix contains the edge index as every connection is formed by a shared edge. Returns ------- connectivity: csr_matrix """ if self._face_face_connectivity is None: self._face_face_connectivity = connectivity.face_face_connectivity( self.edge_face_connectivity ) return self._face_face_connectivity @property def node_face_connectivity(self): """ Node to face connectivity. Inverted from face node connectivity. Returns ------- connectivity: csr_matrix """ if self._node_face_connectivity is None: self._node_face_connectivity = connectivity.invert_dense_to_sparse( self.face_node_connectivity ) return self._node_face_connectivity @property def coords(self): """Dictionary for grid coordinates.""" return { self.node_dimension: self.node_coordinates, self.edge_dimension: self.edge_coordinates, self.face_dimension: self.face_coordinates, }
[docs] def get_coordinates(self, dim: str) -> FloatArray: """Return the coordinates for the specified UGRID dimension.""" if dim == self.node_dimension: return self.node_coordinates elif dim == self.edge_dimension: return self.edge_coordinates elif dim == self.face_dimension: return self.face_coordinates else: raise ValueError( f"Expected {self.node_dimension}, {self.edge_dimension}, or " f"{self.face_dimension}; got: {dim}", )
[docs] def get_connectivity_matrix(self, dim: str, xy_weights: bool): """Return the connectivity matrix for the specified UGRID dimension.""" if dim == self.node_dimension: connectivity = self.node_node_connectivity.copy() coordinates = self.node_coordinates elif dim == self.face_dimension: connectivity = self.face_face_connectivity.copy() coordinates = self.centroids else: raise ValueError( f"Expected {self.node_dimension} or {self.face_dimension}; got: {dim}" ) if xy_weights: connectivity.data = self._connectivity_weights(connectivity, coordinates) return connectivity
@property def mesh(self) -> "mk.Mesh2d": # type: ignore # noqa """ Create if needed, and return meshkernel Mesh2d object. Returns ------- mesh: meshkernel.Mesh2d """ import meshkernel as mk edge_nodes = self.edge_node_connectivity.ravel().astype(np.int32) is_node = self.face_node_connectivity != FILL_VALUE nodes_per_face = is_node.sum(axis=1).astype(np.int32) face_nodes = self.face_node_connectivity[is_node].ravel().astype(np.int32) if self._mesh is None: self._mesh = mk.Mesh2d( node_x=self.node_x, node_y=self.node_y, edge_nodes=edge_nodes, face_nodes=face_nodes, nodes_per_face=nodes_per_face, ) return self._mesh @property def meshkernel(self) -> "mk.MeshKernel": # type: ignore # noqa """ Create if needed, and return meshkernel MeshKernel instance. Returns ------- meshkernel: meshkernel.MeshKernel """ import meshkernel as mk if self._meshkernel is None: if self.is_geographic: mk_projection = mk.ProjectionType.SPHERICAL else: mk_projection = mk.ProjectionType.CARTESIAN self._meshkernel = mk.MeshKernel(mk_projection) self._meshkernel.mesh2d_set(self.mesh) return self._meshkernel @property def voronoi_topology(self): """ Centroidal Voronoi tesselation of this UGRID2D topology. Returns ------- vertices: ndarray of floats with shape ``(n_centroids, 2)`` face_node_connectivity: csr_matrix Describes face node connectivity of voronoi topology. face_index: 1d array of integers """ if self._voronoi_topology is None: vertices, faces, face_index, _ = voronoi_topology( self.node_face_connectivity, self.node_coordinates, self.centroids, self.edge_face_connectivity, self.edge_node_connectivity, add_exterior=True, add_vertices=False, ) self._voronoi_topology = vertices, faces, face_index return self._voronoi_topology @property def centroid_triangulation(self): """ Triangulation of centroidal voronoi tesselation. Required for e.g. contouring face data, which takes triangles and associated values at the triangle vertices. Returns ------- vertices: ndarray of floats with shape ``(n_centroids, 2)`` face_node_connectivity: ndarray of integers with shape ``(n_triangle, 3)`` Describes face node connectivity of triangle topology. face_index: 1d array of integers """ if self._centroid_triangulation is None: nodes, faces, face_index = self.voronoi_topology triangles, _ = connectivity.triangulate(faces) triangulation = (nodes[:, 0].copy(), nodes[:, 1].copy(), triangles) self._centroid_triangulation = (triangulation, face_index) return self._centroid_triangulation @property def triangulation(self): """ Triangulation of the UGRID2D topology. Returns ------- triangulation: tuple Contains node_x, node_y, triangle face_node_connectivity. triangle_face_connectivity: 1d array of integers Identifies the original face for every triangle. """ if self._triangulation is None: triangles, triangle_face_connectivity = connectivity.triangulate( self.face_node_connectivity ) triangulation = (self.node_x, self.node_y, triangles) self._triangulation = (triangulation, triangle_face_connectivity) return self._triangulation @property def exterior_edges(self) -> IntArray: """ Get all exterior edges, i.e. edges with no other face. Returns ------- edge_index: 1d array of integers """ # Numpy argwhere doesn't return a 1D array return np.nonzero(self.edge_face_connectivity[:, 1] == FILL_VALUE)[0] @property def exterior_faces(self) -> IntArray: """ Get all exterior faces, i.e. faces with an unshared edge. Returns ------- face_index: 1d array of integers """ exterior_edges = self.exterior_edges exterior_faces = self.edge_face_connectivity[exterior_edges].ravel() return np.unique(exterior_faces[exterior_faces != FILL_VALUE]) @property def celltree(self): """ Initializes the celltree if needed, and returns celltree. A celltree is a search structure for spatial lookups in unstructured grids. """ if self._celltree is None: self._celltree = CellTree2d( self.node_coordinates, self.face_node_connectivity, FILL_VALUE ) return self._celltree
[docs] def validate_edge_node_connectivity(self): """ Mark valid edges, by comparing face_node_connectivity and edge_node_connectivity. Edges that are not part of a face, as well as duplicate edges are marked ``False``. An error is raised if the face_node_connectivity defines more unique edges than the edge_node_connectivity. Returns ------- valid: np.ndarray of bool Marks for every edge whether it is valid. Examples -------- To purge invalid edges and associated data from a dataset that contains un-associated or duplicate edges: >>> uds = xugrid.open_dataset("example.nc") >>> valid = uds.ugrid.grid.validate_edge_node_connectivity() >>> purged = uds.isel({grid.edge_dimension: valid}) """ return connectivity.validate_edge_node_connectivity( self.face_node_connectivity, self.edge_node_connectivity, )
[docs] def assign_face_coords( self, obj: Union[xr.DataArray, xr.Dataset], ) -> Union[xr.DataArray, xr.Dataset]: """ Assign face coordinates from the grid to the object. Returns a new object with all the original data in addition to the new node coordinates of the grid. Parameters ---------- obj: xr.DataArray or xr.Dataset Returns ------- assigned (same type as obj) """ xname = self._indexes.get("face_x", f"{self.name}_face_x") yname = self._indexes.get("face_y", f"{self.name}_face_y") x_attrs = conventions.DEFAULT_ATTRS["face_x"][self.projected] y_attrs = conventions.DEFAULT_ATTRS["face_y"][self.projected] coords = { xname: xr.DataArray( data=self.face_x, dims=(self.face_dimension,), attrs=x_attrs, ), yname: xr.DataArray( data=self.face_y, dims=(self.face_dimension,), attrs=y_attrs, ), } return obj.assign_coords(coords)
[docs] def locate_points(self, points: FloatArray): """ Find in which face points are located. Parameters ---------- points: ndarray of floats with shape ``(n_point, 2)`` Returns ------- face_index: ndarray of integers with shape ``(n_points,)`` """ return self.celltree.locate_points(points)
[docs] def intersect_edges(self, edges: FloatArray): """ Find in which face edges are located and compute the intersection with the face edges. Parameters ---------- edges: ndarray of floats with shape ``(n_edge, 2, 2)`` The first dimensions represents the different edges. The second dimensions represents the start and end of every edge. The third dimensions reresent the x and y coordinate of every vertex. Returns ------- edge_index: ndarray of integers with shape ``(n_intersection,)`` face_index: ndarray of integers with shape ``(n_intersection,)`` intersections: ndarray of float with shape ``(n_intersection, 2, 2)`` """ return self.celltree.intersect_edges(edges)
[docs] def locate_bounding_box( self, xmin: float, ymin: float, xmax: float, ymax: float ) -> IntArray: """ Find which faces are located in the bounding box. The centroids of the faces are used. Parameters ---------- xmin: float, ymin: float, xmax: float, ymax: float Returns ------- face_index: ndarray of bools with shape ``(n_face,)`` """ return np.nonzero( (self.face_x >= xmin) & (self.face_x < xmax) & (self.face_y >= ymin) & (self.face_y < ymax) )[0]
[docs] def compute_barycentric_weights( self, points: FloatArray ) -> Tuple[IntArray, FloatArray]: """ Find in which face the points are located, and compute the barycentric weight for every vertex of the face. Parameters ---------- points: ndarray of floats with shape ``(n_point, 2)`` Returns ------- face_index: ndarray of integers with shape ``(n_points,)`` weights: ndarray of floats with shape ```(n_points, n_max_node)`` """ return self.celltree.compute_barycentric_weights(points)
[docs] def rasterize_like( self, x: FloatArray, y: FloatArray ) -> Tuple[FloatArray, FloatArray, IntArray]: """ Rasterize unstructured grid by sampling on the x and y coordinates. Parameters ---------- x: 1d array of floats with shape ``(ncol,)`` y: 1d array of floats with shape ``(nrow,)`` Returns ------- x: 1d array of floats with shape ``(ncol,)`` y: 1d array of floats with shape ``(nrow,)`` face_index: 1d array of integers with shape ``(nrow * ncol,)`` """ yy, xx = np.meshgrid(y, x, indexing="ij") nodes = np.column_stack([xx.ravel(), yy.ravel()]) index = self.celltree.locate_points(nodes).reshape((y.size, x.size)) return x, y, index
[docs] def rasterize( self, resolution: float, bounds: Optional[Tuple[float, float, float, float]] = None, ) -> Tuple[FloatArray, FloatArray, IntArray]: """ Rasterize unstructured grid by sampling. x and y coordinates are generated from the bounds of the UGRID2D topology and the provided resolution. Parameters ---------- resolution: float Spacing in x and y. bounds: tuple of four floats, optional xmin, ymin, xmax, ymax Returns ------- x: 1d array of floats with shape ``(ncol,)`` y: 1d array of floats with shape ``(nrow,)`` face_index: 1d array of integers with shape ``(nrow * ncol,)`` """ if bounds is None: bounds = self.bounds xmin, ymin, xmax, ymax = bounds d = abs(resolution) xmin = np.floor(xmin / d) * d xmax = np.ceil(xmax / d) * d ymin = np.floor(ymin / d) * d ymax = np.ceil(ymax / d) * d x = np.arange(xmin + 0.5 * d, xmax, d) y = np.arange(ymax - 0.5 * d, ymin, -d) return self.rasterize_like(x, y)
[docs] def topology_subset( self, face_index: Union[BoolArray, IntArray], return_index: bool = False ): """ Create a new UGRID1D topology for a subset of this topology. Parameters ---------- face_index: 1d array of integers or bool Edges of the subset. return_index: bool, optional Whether to return node_index, edge_index, face_index. Returns ------- subset: Ugrid2d indexes: dict Dictionary with keys node dimension, edge dimension, face dimension and values their respective index. Only returned if return_index is True. """ if not isinstance(face_index, pd.Index): face_index = as_pandas_index(face_index, self.n_face) # The pandas index may only contain uniques. So if size matches, it may # be the identity. range_index = pd.RangeIndex(0, self.n_face) if face_index.size == self.n_face and face_index.equals(range_index): # TODO: return self.copy instead? if return_index: indexes = { self.node_dimension: pd.RangeIndex(0, self.n_node), self.edge_dimension: pd.RangeIndex(0, self.n_edge), self.face_dimension: range_index, } return self, indexes else: return self index = face_index.to_numpy() face_subset = self.face_node_connectivity[index] node_index = np.unique(face_subset.ravel()) node_index = node_index[node_index != FILL_VALUE] new_faces = connectivity.renumber(face_subset) node_x = self.node_x[node_index] node_y = self.node_y[node_index] edge_index = None new_edges = None if self.edge_node_connectivity is not None: edge_index = np.unique(self.face_edge_connectivity[index].ravel()) edge_index = edge_index[edge_index != FILL_VALUE] edge_subset = self.edge_node_connectivity[edge_index] new_edges = connectivity.renumber(edge_subset) grid = Ugrid2d( node_x, node_y, FILL_VALUE, new_faces, name=self.name, edge_node_connectivity=new_edges, indexes=self._indexes, projected=self.projected, crs=self.crs, attrs=self._attrs, ) self._propagate_properties(grid) if return_index: indexes = { self.node_dimension: pd.Index(node_index), self.face_dimension: face_index, } if edge_index is not None: indexes[self.edge_dimension] = pd.Index(edge_index) return grid, indexes else: return grid
def clip_box( self, xmin: float, ymin: float, xmax: float, ymax: float, ): xmin, ymin, xmax, ymax = self.bounds bounds = [xmin, ymin, xmax, ymax] face_index = self.locate_bounding_box(*bounds) return self.topology_subset(face_index)
[docs] def isel(self, indexers=None, return_index=False, **indexers_kwargs): """ Select based on node, edge, or face. Face selection always results in a valid UGRID topology. Node or edge selection may result in invalid topologies (incomplete faces), and will error in such a case. Parameters ---------- indexers: dict of str to np.ndarray of integers or bools return_index: bool, optional Whether to return node_index, edge_index, face_index. Returns ------- obj: xr.Dataset or xr.DataArray grid: Ugrid2d indexes: dict Dictionary with keys node dimension, edge dimension, face dimension and values their respective index. Only returned if return_index is True. """ indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel") alldims = set(self.dims) invalid = indexers.keys() - alldims if invalid: raise ValueError( f"Dimensions {invalid} do not exist. Expected one of {alldims}" ) indexers = {k: as_pandas_index(v, self.sizes[k]) for k, v in indexers.items()} nodedim = self.node_dimension edgedim = self.edge_dimension facedim = self.face_dimension face_index = {} if nodedim in indexers: node_index = indexers[nodedim] face_index[nodedim] = np.unique( self.node_face_connectivity[node_index].data ) if edgedim in indexers: edge_index = indexers[edgedim] index = np.unique(self.edge_face_connectivity[edge_index]) face_index[edgedim] = index[index != FILL_VALUE] if facedim in indexers: face_index[facedim] = indexers[facedim] # Convert all to pandas index. face_index = {k: as_pandas_index(v, self.n_face) for k, v in face_index.items()} # Check the indexes against each other. index = self._precheck(face_index) grid, finalized_indexers = self.topology_subset(index, return_index=True) self._postcheck(indexers, finalized_indexers) if return_index: return grid, finalized_indexers else: return grid
def _validate_indexer(self, indexer) -> Union[slice, np.ndarray]: if isinstance(indexer, slice): s = indexer if s.start is not None and s.stop is not None: if s.start >= s.stop: raise ValueError( "slice stop should be larger than slice start, received: " f"start: {s.start}, stop: {s.stop}" ) if s.step is not None: indexer = np.arange(s.start, s.stop, s.step) elif s.start is None or s.stop is None: if s.step is not None: raise ValueError( "step should be None if slice start or stop is None" ) else: # Convert it into a 1d numpy array if isinstance(indexer, xr.DataArray): indexer = indexer.to_numpy() if isinstance(indexer, (list, np.ndarray, int, float)): indexer = np.atleast_1d(indexer) else: raise TypeError( f"Invalid indexer type: {type(indexer).__name__}, " "allowed types: integer, float, list, numpy array, xarray DataArray" ) if indexer.ndim > 1: raise ValueError("index should be 0d or 1d") return indexer def _sel_box( self, obj, x: slice, y: slice, ): xmin, ymin, xmax, ymax = self.bounds bounds = [ numeric_bound(x.start, xmin), numeric_bound(y.start, ymin), numeric_bound(x.stop, xmax), numeric_bound(y.stop, ymax), ] face_index = self.locate_bounding_box(*bounds) grid, indexes = self.topology_subset(face_index, return_index=True) indexes = {k: v for k, v in indexes.items() if k in obj.dims} new_obj = obj.isel(indexes) return new_obj, grid def _sel_line( self, obj, start, end, ): edges = np.array([[start, end]]) _, index, xy = self.intersect_edges(edges) coords, index = section_coordinates( edges, xy, self.face_dimension, index, self.name ) return obj.isel({self.face_dimension: index}).assign_coords(coords) def _sel_yline( self, obj, x: float, y: slice, ): xmin, _, xmax, _ = self.bounds if y.size != 1: raise ValueError( "If x is a slice without steps, y should be a single value" ) y = y[0] xstart = numeric_bound(x.start, xmin) xstop = numeric_bound(x.stop, xmax) return self._sel_line(obj, start=(xstart, y), end=(xstop, y)) def _sel_xline( self, obj, x: float, y: slice, ): _, ymin, _, ymax = self.bounds if x.size != 1: raise ValueError( "If y is a slice without steps, x should be a single value" ) x = x[0] ystart = numeric_bound(y.start, ymin) ystop = numeric_bound(y.stop, ymax) return self._sel_line(obj, start=(x, ystart), end=(x, ystop))
[docs] def sel_points( self, obj, x: FloatArray, y: FloatArray, out_of_bounds="warn", fill_value=np.nan ): """ Select points in the unstructured grid. Parameters ---------- x: 1d array of floats with shape ``(n_points,)`` y: 1d array of floats with shape ``(n_points,)`` obj: xr.DataArray or xr.Dataset out_of_bounds: str, default ``"warn"`` What to do when points are located outside of any feature: * raise: raise a ValueError. * ignore: return ``fill_value`` for the out of bounds points. * warn: give a warning and return NaN for the out of bounds points. * drop: drop the out of bounds points. They may be identified via the ``index`` coordinate of the returned selection. fill_value: scalar, DataArray, Dataset, or callable, optional, default: np.nan Value to assign to out-of-bounds points if out_of_bounds is warn or ignore. Forwarded to xarray's ``.where()`` method. Returns ------- selection: xr.DataArray or xr.Dataset The name of the topology is prefixed in the x, y coordinates. """ options = ("warn", "raise", "ignore", "drop") if out_of_bounds not in options: str_options = ", ".join(options) raise ValueError( f"out_of_bounds must be one of {str_options}, " f"received: {out_of_bounds}" ) x = np.atleast_1d(x) y = np.atleast_1d(y) if x.shape != y.shape: raise ValueError("shape of x does not match shape of y") if x.ndim != 1: raise ValueError("x and y must be 1d") dim = self.face_dimension xy = np.column_stack([x, y]) index = self.locate_points(xy) keep = slice(None, None) # keep all by default condition = None valid = index != -1 if not valid.all(): msg = "Not all points are located inside of the grid." if out_of_bounds == "raise": raise ValueError(msg) elif out_of_bounds in ("warn", "ignore"): if out_of_bounds == "warn": warnings.warn(msg) condition = xr.DataArray(valid, dims=(dim,)) elif out_of_bounds == "drop": index = index[valid] keep = valid # Create the selection DataArray or Dataset coords = { f"{self.name}_index": (dim, np.arange(len(xy))[keep]), f"{self.name}_x": (dim, xy[keep, 0]), f"{self.name}_y": (dim, xy[keep, 1]), } selection = obj.isel({dim: index}).assign_coords(coords) # Set values to fill_value for out-of-bounds if condition is not None: selection = selection.where(condition, other=fill_value) return selection
[docs] def intersect_line(self, obj, start: Sequence[float], end: Sequence[float]): """ Intersect a line with this grid, and fetch the values of the intersected faces. Parameters ---------- obj: xr.DataArray or xr.Dataset start: sequence of two floats coordinate pair (x, y), designating the start point of the line. end: sequence of two floats coordinate pair (x, y), designating the end point of the line. Returns ------- selection: xr.DataArray or xr.Dataset The name of the topology is prefixed in the x, y and s (spatium=distance) coordinates. """ if (len(start) != 2) or (len(end) != 2): raise ValueError("Start and end coordinate pairs must have length two") return self._sel_line(obj, start, end)
[docs] def intersect_linestring( self, obj: Union[xr.DataArray, xr.Dataset], linestring: "shapely.geometry.LineString", # type: ignore # noqa ) -> Union[xr.DataArray, xr.Dataset]: """ Intersect linestrings with this grid, and fetch the values of the intersected faces. Parameters ---------- obj: xr.DataArray or xr.Dataset linestring: shapely.geometry.lineString Returns ------- selection: xr.DataArray or xr.Dataset The name of the topology is prefixed in the x, y and s (spatium=distance) coordinates. """ import shapely xy = shapely.get_coordinates([linestring]) edges = np.stack((xy[:-1], xy[1:]), axis=1) edge_index, face_index, intersections = self.intersect_edges(edges) # Compute the cumulative length along the edges edge_length = np.linalg.norm(edges[:, 1] - edges[:, 0], axis=1) cumulative_length = np.empty_like(edge_length) cumulative_length[0] = 0 np.cumsum(edge_length[:-1], out=cumulative_length[1:]) # Compute the distance for every intersection to the start of the linestring. intersection_centroid = intersections.mean(axis=1) distance_node_to_intersection = np.linalg.norm( intersection_centroid - edges[edge_index, 0], axis=1 ) s = distance_node_to_intersection + cumulative_length[edge_index] # Now sort everything according to s. sorter = np.argsort(s) face_index = face_index[sorter] intersection_centroid = intersection_centroid[sorter] intersections = intersections[sorter] facedim = self.face_dimension coords = { f"{self.name}_s": (facedim, s[sorter]), f"{self.name}_x": (facedim, intersection_centroid[:, 0]), f"{self.name}_y": (facedim, intersection_centroid[:, 1]), } return obj.isel({facedim: face_index}).assign_coords(coords)
[docs] def sel(self, obj, x=None, y=None): """ Find selection in the UGRID x and y coordinates. The indexing for x and y always occurs orthogonally, i.e.: ``.sel(x=[0.0, 5.0], y=[10.0, 15.0])`` results in a four points. For vectorized indexing (equal to ``zip``ing through x and y), see ``.sel_points``. Parameters ---------- obj: xr.DataArray or xr.Dataset x: float, 1d array, slice y: float, 1d array, slice Returns ------- dimension: str as_ugrid: bool index: 1d array of integers coords: dict """ if x is None: x = slice(None, None) if y is None: y = slice(None, None) x = self._validate_indexer(x) y = self._validate_indexer(y) if isinstance(x, slice) and isinstance(y, slice): f = self._sel_box elif isinstance(x, slice) and isinstance(y, np.ndarray): f = self._sel_yline elif isinstance(x, np.ndarray) and isinstance(y, slice): f = self._sel_xline elif isinstance(x, np.ndarray) and isinstance(y, np.ndarray): # Orthogonal points y, x = [a.ravel() for a in np.meshgrid(y, x, indexing="ij")] f = self.sel_points else: raise TypeError( f"Invalid indexer types: {type(x).__name__}, and {type(y).__name__}" ) return f(obj, x, y)
[docs] def label_partitions(self, n_part: int) -> "xugrid.UgridDataArray": """ Generate partition labesl for this grid topology using METIS: https://github.com/KarypisLab/METIS This method utilizes the pymetis Python bindings: https://github.com/inducer/pymetis Parameters ---------- n_part: integer The number of parts to partition the mesh. Returns ------- partition_labels: UgridDataArray of integers """ import pymetis adjacency_matrix = self.face_face_connectivity _, partition_index = pymetis.part_graph( nparts=n_part, xadj=adjacency_matrix.indptr, adjncy=adjacency_matrix.indices, ) return xugrid.UgridDataArray( obj=xr.DataArray( data=np.array(partition_index), dims=(self.core_dimension,), name="labels", ), grid=self, )
[docs] def partition(self, n_part: int): """ Partition this grid topology using METIS: https://github.com/KarypisLab/METIS This method utilizes the pymetis Python bindings: https://github.com/inducer/pymetis Parameters ---------- n_part: integer The number of parts to partition the mesh. Returns ------- partitions """ from xugrid.ugrid.partitioning import labels_to_indices labels = self.label_partitions(n_part) indices = labels_to_indices(labels.values) return [self.topology_subset(index) for index in indices]
[docs] @staticmethod def merge_partitions( grids: Sequence["Ugrid2d"], ) -> tuple["Ugrid2d", dict[str, np.array]]: """ Merge grid partitions into a single whole. Duplicate faces are included only once, and removed from subsequent partitions before merging. Parameters ---------- grids: sequence of Ugrid2d Returns ------- merged: Ugrid2d """ from xugrid.ugrid import partitioning # Grab a sample grid grid = next(iter(grids)) node_coordinates, node_indexes, node_inverse = partitioning.merge_nodes(grids) new_faces, face_indexes = partitioning.merge_faces(grids, node_inverse) indexes = { grid.node_dimension: node_indexes, grid.face_dimension: face_indexes, } if grid._edge_node_connectivity is not None: new_edges, edge_indexes = partitioning.merge_edges(grids, node_inverse) indexes[grid.edge_dimension] = edge_indexes else: new_edges = None merged_grid = Ugrid2d( *node_coordinates.T, FILL_VALUE, new_faces, name=grid.name, edge_node_connectivity=new_edges, indexes=grid._indexes, projected=grid.projected, crs=grid.crs, attrs=grid._attrs, ) # Maintain fill_value, start_index grid._propagate_properties(merged_grid) return merged_grid, indexes
[docs] def to_periodic(self, obj=None): """ Convert this grid to a periodic grid, where the rightmost nodes are equal to the leftmost nodes. Note: for this to work, the y-coordinates on the left boundary must match those on the right boundary exactly. Returns ------- periodic_grid: Ugrid2d aligned: xr.DataArray or xr.Dataset """ xmin, _, xmax, _ = self.bounds coordinates = self.node_coordinates is_right = np.isclose(coordinates[:, 0], xmax) is_left = np.isclose(coordinates[:, 0], xmin) node_y = coordinates[:, 1] if not np.allclose(np.sort(node_y[is_left]), np.sort(node_y[is_right])): raise ValueError( "y-coordinates of the left and right boundaries do not match" ) # Discard the rightmost nodes. Preserve the order in the faces, and the # order of the nodes. coordinates[is_right, 0] = xmin _, node_index, inverse = np.unique( coordinates, return_index=True, return_inverse=True, axis=0 ) inverse = inverse.ravel() # Create a mapping of the inverse index to the new node index. new_index = connectivity.renumber(node_index) new_faces = new_index[inverse[self.face_node_connectivity]] # Get the selection of nodes, and keep the order. node_index.sort() new_xy = self.node_coordinates[node_index] # Preserve the order of the edge_node_connectivity if it is present. new_edges = None edge_index = None if self._edge_node_connectivity is not None: new_edges = inverse[self.edge_node_connectivity] new_edges.sort(axis=1) _, edge_index = np.unique(new_edges, axis=0, return_index=True) edge_index.sort() new_edges = new_index[new_edges][edge_index] new = Ugrid2d( node_x=new_xy[:, 0], node_y=new_xy[:, 1], face_node_connectivity=new_faces, fill_value=FILL_VALUE, name=self.name, edge_node_connectivity=new_edges, indexes=self._indexes, projected=self.projected, crs=self.crs, attrs=self.attrs, ) self._propagate_properties(new) if obj is not None: indexes = { self.face_dimension: pd.RangeIndex(0, self.n_face), self.node_dimension: pd.Index(node_index), } if edge_index is not None: indexes[self.edge_dimension] = pd.Index(edge_index) indexes = {k: v for k, v in indexes.items() if k in obj.dims} return new, obj.isel(**indexes) else: return new
[docs] def to_nonperiodic(self, xmax: float, obj=None): """ Convert this grid from a periodic grid (where the rightmost boundary shares its nodes with the leftmost boundary) to an aperiodic grid, where the leftmost nodes are separate from the rightmost nodes. Parameters ---------- xmax: float The x-value of the newly created rightmost boundary nodes. obj: xr.DataArray or xr.Dataset Returns ------- nonperiodic_grid: Ugrid2d aligned: xr.DataArray or xr.Dataset """ xleft, _, xright, _ = self.bounds half_domain = 0.5 * (xright - xleft) # Extract all x coordinates for every face. Then identify the nodes # which have a value of e.g. -180, while the max x value for the face # is 180.0. These nodes should be duplicated. x = self.face_node_coordinates[..., 0] is_periodic = (np.nanmax(x, axis=1)[:, np.newaxis] - x) > half_domain periodic_nodes = self.face_node_connectivity[is_periodic] uniques, new_nodes = np.unique(periodic_nodes, return_inverse=True) new_x = np.full(uniques.size, xmax) new_y = self.node_y[uniques] new_faces = self.face_node_connectivity.copy() new_faces[is_periodic] = new_nodes + self.n_node # edge_node_connectivity must be rederived, since we've added a number # of new edges and new nodes. new = Ugrid2d( node_x=np.concatenate((self.node_x, new_x)), node_y=np.concatenate((self.node_y, new_y)), face_node_connectivity=new_faces, fill_value=FILL_VALUE, name=self.name, edge_node_connectivity=None, indexes=self._indexes, projected=self.projected, crs=self.crs, attrs=self.attrs, ) self._propagate_properties(new) edge_index = None if self._edge_node_connectivity is not None: # If there is edge associated data, we need to duplicate the data # of the edges. It is impossible(?) to do this on the edges # directly, due to the possible presence of "symmetric" edges: # 2 # /|\ # / | \ # 0__1__0 # # (0, 1) and (1, 0) are topologically distinct, but only in the # face definition. In the new grid, the 0 on the right will have # become node 3, creating distinct edges. # # Note that any data with the edge is only stored once, which is # incorrect(!), but a given for these grids and would be a problem # for the simulation code producing these results. # # We use a casting trick to collapse two integers into one so we # can use searchsorted easily. edges = ( np.sort(self.edge_node_connectivity, axis=1) .astype(np.int32) .view(np.int64) .ravel() ) # Create a mapping of the new nodes created above, to the original nodes. # Then, find the new edges in the old using searchsorted. mapping = np.concatenate((np.arange(self.n_node), uniques)) new_edges = ( np.sort(mapping[new.edge_node_connectivity], axis=1) .astype(np.int32) .view(np.int64) .ravel() ) edge_index = np.searchsorted(edges, new_edges, sorter=np.argsort(edges)) # Reshuffle to keep the original order as intact as possible; how # much benefit does this actually give? sorter = np.argsort(edge_index) new._edge_node_connectivity = new._edge_node_connectivity[sorter] edge_index = edge_index[sorter] if obj is not None: indexes = { self.face_dimension: pd.RangeIndex(0, self.n_face), self.node_dimension: pd.Index( np.concatenate((np.arange(self.n_node), uniques)) ), } if edge_index is not None: indexes[self.edge_dimension] = pd.Index(edge_index) indexes = {k: v for k, v in indexes.items() if k in obj.dims} return new, obj.isel(**indexes) else: return new
[docs] def reindex_like( self, other: "Ugrid2d", obj: Union[xr.DataArray, xr.Dataset], tolerance: float = 0.0, ): """ Conform a DataArray or Dataset to match the topology of another Ugrid2D topology. The topologies must be exactly equivalent: only the order of the nodes, edges, and faces may differ. Parameters ---------- other: Ugrid2d obj: DataArray or Dataset tolerance: float, default value 0.0. Maximum distance between inexact coordinate matches. Returns ------- reindexed: DataArray or Dataset """ if not isinstance(other, Ugrid2d): raise TypeError(f"Expected Ugrid2d, received: {type(other).__name__}") indexers = { self.node_dimension: connectivity.index_like( xy_a=self.node_coordinates, xy_b=other.node_coordinates, tolerance=tolerance, ), self.face_dimension: connectivity.index_like( xy_a=self.centroids, xy_b=other.centroids, tolerance=tolerance, ), } if other._edge_node_connectivity is not None: indexers[self.edge_dimension] = connectivity.index_like( xy_a=self.edge_coordinates, xy_b=other.edge_coordinates, tolerance=tolerance, ) return obj.isel(indexers, missing_dims="ignore")
[docs] def triangulate(self): """ Triangulate this UGRID2D topology, breaks more complex polygons down into triangles. Returns ------- triangles: Ugrid2d """ triangles, _ = connectivity.triangulate(self.face_node_connectivity) grid = Ugrid2d(self.node_x, self.node_y, FILL_VALUE, triangles) self._propagate_properties(grid) return grid
def _tesselate_voronoi(self, centroids, add_exterior, add_vertices, skip_concave): if add_exterior: edge_face_connectivity = self.edge_face_connectivity edge_node_connectivity = self.edge_node_connectivity else: edge_face_connectivity = None edge_node_connectivity = None vertices, faces, _, _ = voronoi_topology( self.node_face_connectivity, self.node_coordinates, centroids, edge_face_connectivity, edge_node_connectivity, add_exterior, add_vertices, skip_concave, ) grid = Ugrid2d(vertices[:, 0], vertices[:, 1], FILL_VALUE, faces) self._propagate_properties(grid) return grid
[docs] def tesselate_centroidal_voronoi( self, add_exterior=True, add_vertices=True, skip_concave=False ): """ Create a centroidal Voronoi tesselation of this UGRID2D topology. Such a tesselation is not guaranteed to produce convex cells. To ensure convexity, set ``add_vertices=False`` -- this will result in a different exterior, however. Parameters ---------- add_exterior: bool, default: True add_vertices: bool, default: True skip_concave: bool, default: False Returns ------- tesselation: Ugrid2d """ return self._tesselate_voronoi( self.centroids, add_exterior, add_vertices, skip_concave )
[docs] def tesselate_circumcenter_voronoi( self, add_exterior=True, add_vertices=True, skip_concave=False ): """ Create a circumcenter Voronoi tesselation of this UGRID2D topology. Such a tesselation is not guaranteed to produce convex cells. To ensure convexity, set ``add_vertices=False`` -- this will result in a different exterior, however. Parameters ---------- add_exterior: bool, default: True add_vertices: bool, default: True skip_concave: bool, default: False Returns ------- tesselation: Ugrid2d """ return self._tesselate_voronoi( self.circumcenters, add_exterior, add_vertices, skip_concave )
[docs] def reverse_cuthill_mckee(self, dimension=None): """ Reduces bandwith of the connectivity matrix. Wraps :py:func:`scipy.sparse.csgraph.reverse_cuthill_mckee`. Returns ------- reordered: Ugrid2d """ # TODO: dispatch on dimension? reordering = reverse_cuthill_mckee( graph=self.face_face_connectivity, symmetric_mode=True, ) reordered_grid = Ugrid2d( self.node_x, self.node_y, FILL_VALUE, self.face_node_connectivity[reordering], ) self._propagate_properties(reordered_grid) return reordered_grid, reordering
def refine_polygon( self, polygon: "shapely.geometry.Polygon", # type: ignore # noqa min_face_size: float, refine_intersected: bool = True, use_mass_center_when_refining: bool = True, refinement_type: str = "refinement_levels", connect_hanging_nodes: bool = True, account_for_samples_outside_face: bool = True, max_refinement_iterations: int = 10, ): import meshkernel as mk geometry_list = mku.to_geometry_list(polygon) refinement_type = mku.either_string_or_enum(refinement_type, mk.RefinementType) self._initialize_mesh_kernel() mesh_refinement_params = mk.MeshRefinementParameters( refine_intersected, use_mass_center_when_refining, min_face_size, refinement_type, connect_hanging_nodes, account_for_samples_outside_face, max_refinement_iterations, ) self._meshkernel.mesh2d_refine_based_on_polygon( geometry_list, mesh_refinement_params, ) def delete_polygon( self, polygon: "shapely.geometry.Polygon", # type: ignore # noqa delete_option: str = "all_face_circumenters", invert_deletion: bool = False, ): import meshkernel as mk geometry_list = mku.to_geometry_list(polygon) delete_option = mku.either_string_or_enum(delete_option, mk.DeleteMeshOption) self._initialize_mesh_kernel() self._meshkernel.mesh2d_delete(geometry_list, delete_option, invert_deletion) @staticmethod def from_polygon( polygon: "shapely.geometry.Polygon", # type: ignore # noqa ): import meshkernel as mk geometry_list = mku.to_geometry_list(polygon) _mesh_kernel = mk.MeshKernel() _mesh_kernel.mesh2d_make_mesh_from_polygon(geometry_list) mesh = _mesh_kernel.mesh2d_get() n_max_node = mesh.nodes_per_face.max() ds = Ugrid2d.topology_dataset( mesh.node_x, mesh.node_y, mesh.face_nodes.reshape((-1, n_max_node)), ) ugrid = Ugrid2d(ds) ugrid.mesh = mesh ugrid._meshkernel = _mesh_kernel return ugrid
[docs] @staticmethod def earcut_triangulate_polygons(polygons, return_index: bool = False): """ Break down polygons using mapbox_earcut, and create a mesh from the resulting triangles. Parameters ---------- polygons: ndarray of shapely polygons return_index: bool, default is False. Returns ------- grid: xugrid.Ugrid2d index: ndarray of integer, optional The polygon index for each triangle. Only provided if ``return_index`` is True. """ return xugrid.ugrid.burn.grid_from_earcut_polygons( polygons, return_index=return_index )
[docs] @classmethod def from_geodataframe(cls, geodataframe: "geopandas.GeoDataFrame") -> "Ugrid2d": # type: ignore # noqa """ Convert a geodataframe of polygons to UGRID2D topology. Parameters ---------- geodataframe: geopandas GeoDataFrame Returns ------- topology: Ugrid2d """ import geopandas as gpd if not isinstance(geodataframe, gpd.GeoDataFrame): raise TypeError( f"Expected GeoDataFrame, received: {type(geodataframe).__name__}" ) return cls.from_shapely(geodataframe.geometry.to_numpy(), crs=geodataframe.crs)
[docs] @staticmethod def from_shapely(geometry: PolygonArray, crs=None) -> "Ugrid2d": """ Convert an array of shapely polygons to UGRID2D topology. Parameters ---------- geometry: np.ndarray of shapely polygons crs: Any, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`, such as an authority string (eg "EPSG:4326") or a WKT string. Returns ------- topology: Ugrid2d """ import shapely if not (shapely.get_type_id(geometry) == shapely.GeometryType.POLYGON).all(): raise TypeError( "Can only create Ugrid2d from shapely Polygon geometries, " "geometry contains other types of geometries." ) x, y, face_node_connectivity = conversion.polygons_to_faces(geometry) return Ugrid2d(x, y, FILL_VALUE, face_node_connectivity, crs=crs)
@staticmethod def _from_intervals_helper( node_x: np.ndarray, node_y: np.ndarray, nx: int, ny: int, name: str ) -> "Ugrid2d": linear_index = np.arange(node_x.size, dtype=IntDType).reshape((ny + 1, nx + 1)) # Allocate face_node_connectivity face_nodes = np.empty((ny * nx, 4), dtype=IntDType) # Set connectivity in counterclockwise manner left, right = slice(None, -1), slice(1, None) lower, upper = slice(None, -1), slice(1, None) if node_x[1] < node_x[0]: # x_decreasing left, right = right, left if node_y[ny + 1] < node_y[0]: # y_decreasing lower, upper = upper, lower face_nodes[:, 0] = linear_index[lower, left].ravel() face_nodes[:, 1] = linear_index[lower, right].ravel() face_nodes[:, 2] = linear_index[upper, right].ravel() face_nodes[:, 3] = linear_index[upper, left].ravel() return Ugrid2d(node_x, node_y, -1, face_nodes, name=name)
[docs] @staticmethod def from_structured_intervals1d( x_intervals: np.ndarray, y_intervals: np.ndarray, name: str = "mesh2d", ) -> "Ugrid2d": """ Create a Ugrid2d topology from a structured topology based on 1D intervals. Parameters ---------- x_intervals: np.ndarray of shape (M + 1,) x-coordinate interval values for N rows and M columns. y_intervals: np.ndarray of shape (N + 1,) y-coordinate interval values for N rows and M columns. name: str """ x_intervals = np.asarray(x_intervals) y_intervals = np.asarray(y_intervals) nx = x_intervals.shape[0] - 1 ny = y_intervals.shape[0] - 1 node_y, node_x = ( a.ravel() for a in np.meshgrid(y_intervals, x_intervals, indexing="ij") ) return Ugrid2d._from_intervals_helper(node_x, node_y, nx, ny, name=name)
[docs] @staticmethod def from_structured_intervals2d( x_intervals: np.ndarray, y_intervals: np.ndarray, name: str = "mesh2d", ) -> "Ugrid2d": """ Create a Ugrid2d topology from a structured topology based on 2D intervals. Parameters ---------- x_intervals: np.ndarray of shape shape (N + 1, M + 1) x-coordinate interval values for N rows and M columns. y_intervals: np.ndarray of shape shape (N + 1, M + 1) y-coordinate interval values for N rows and M columns. name: str """ x_intervals = np.asarray(x_intervals) y_intervals = np.asarray(y_intervals) shape = x_intervals.shape if (x_intervals.ndim != 2) or (y_intervals.ndim != 2): raise ValueError("Dimensions of intervals must be 2D.") if shape != y_intervals.shape: raise ValueError( "Interval shapes must match. Found: " f"x_intervals: {shape}, versus y_intervals: {y_intervals.shape}" ) nx = shape[1] - 1 ny = shape[0] - 1 node_x = x_intervals.ravel() node_y = y_intervals.ravel() return Ugrid2d._from_intervals_helper(node_x, node_y, nx, ny, name=name)
[docs] @staticmethod def from_structured_bounds( x_bounds: np.ndarray, y_bounds: np.ndarray, name: str = "mesh2d", return_index: bool = False, ) -> Union["Ugrid2d", Tuple["Ugrid2d", Union[BoolArray, slice]]]: """ Create a Ugrid2d topology from a structured topology based on 2D or 3D bounds. The bounds contain the lower and upper cell boundary for each cell for 2D, and the four corner vertices in case of 3D bounds. The order of the corners in bounds_x and bounds_y must be consistent with each other, but may be arbitrary: this method ensures counterclockwise orientation for UGRID. Inactive cells are assumed to be marked with one or more NaN values for their corner coordinates. These coordinates are discarded and the cells are marked in the optionally returned index. Parameters ---------- x_bounds: np.ndarray of shape (M, 2) or (N, M, 4). x-coordinate bounds for N rows and M columns. y_bounds: np.ndarray of shape (N, 2) or (N, M, 4). y-coordinate bounds for N rows and M columns. name: str return_index: bool, default is False. Returns ------- grid: Ugrid2d index: np.ndarray of bool | slice Indicates which cells are part of the Ugrid2d topology. Provided if ``return_index`` is True. """ x_shape = x_bounds.shape y_shape = y_bounds.shape ndim = x_bounds.ndim if ndim == 2: nx, _ = x_shape ny, _ = y_shape x = conversion.bounds1d_to_vertices(x_bounds) y = conversion.bounds1d_to_vertices(y_bounds) node_y, node_x = (a.ravel() for a in np.meshgrid(y, x, indexing="ij")) grid = Ugrid2d._from_intervals_helper(node_x, node_y, nx, ny, name) index = slice(None, None) elif ndim == 3: if x_shape != y_shape: raise ValueError( f"Bounds shapes do not match: {x_shape} versus {y_shape}" ) x, y, face_node_connectivity, index = conversion.bounds2d_to_topology2d( x_bounds, y_bounds ) grid = Ugrid2d(x, y, -1, face_node_connectivity, name=name) else: raise ValueError(f"Expected 2 or 3 dimensions on bounds, received: {ndim}") if return_index: return grid, index else: return grid
@staticmethod def _from_structured_singlecoord( data: Union[xr.DataArray, xr.Dataset], x: str | None = None, y: str | None = None, name: str = "mesh2d", ) -> "Ugrid2d": # This method assumes the coordinates are 1D. if x is None or y is None: x, y = conversion.infer_xy_coords(data) if x is None or y is None: raise ValueError( "Could not infer bounds. Please provide x and y explicitly." ) x_intervals = conversion.infer_interval_breaks1d(data, x) y_intervals = conversion.infer_interval_breaks1d(data, y) return Ugrid2d.from_structured_intervals1d(x_intervals, y_intervals, name) @staticmethod def _from_structured_multicoord( data: Union[xr.DataArray, xr.Dataset], x: str, y: str, name: str = "mesh2d", ) -> "Ugrid2d": # This method assumes the coordinates are 2D and thereby supports rotated # or (approximated) curvilinear topologies. xv = conversion.infer_interval_breaks(data[x], axis=1, check_monotonic=True) xv = conversion.infer_interval_breaks(xv, axis=0) yv = conversion.infer_interval_breaks(data[y], axis=1) yv = conversion.infer_interval_breaks(yv, axis=0, check_monotonic=True) return Ugrid2d.from_structured_intervals2d(xv, yv, name) @staticmethod def from_structured_multicoord( data: Union[xr.DataArray, xr.Dataset], x: str | None = None, y: str | None = None, name: str = "mesh2d", ) -> "Ugrid2d": warnings.warn( "Ugrid2d.from_structured_multicoord has been deprecated. " "Use Ugrid2d.from_structured instead.", FutureWarning, ) return Ugrid2d.from_structured(data, x, y, name)
[docs] @staticmethod def from_structured( data: Union[xr.DataArray, xr.Dataset], x: str | None = None, y: str | None = None, name: str = "mesh2d", return_dims: bool = False, ): """ Create a Ugrid2d topology from a structured topology axis-aligned rectilinear, rotated or (approximated) curvilinear topologies. By default, this method looks for: 1. ``"x"`` and ``"y"`` dimensions. 2. ``"longitude"`` and ``"latitude"`` dimensions. 3. ``"axis"`` attributes of "X" or "Y" on coordinates. 4. ``"standard_name"`` attributes of "longitude", "latitude", "projection_x_coordinate", or "project_y_coordinate" on coordinate variables. Specify the x and y coordinate names explicitly otherwise. Parameters ---------- data: xr.DataArray or xr.Dataset x: str, optional Name of the 1D or 2D coordinate to use as the UGRID x-coordinate. y: str, optional Name of the 1D or 2D coordinate to use as the UGRID y-coordinate. return_dims: bool If True, returns a tuple containing the name of the y and x dimensions. Returns ------- grid: Ugrid2d dims: tuple of str, optional Provided if ``return_dims`` is True. """ if (x is None) ^ (y is None): raise ValueError("Provide both x and y, or neither.") if x is None: x, y = conversion.infer_xy_coords(data) else: coords = set(data.coords) missing_coords = {x, y} - coords if missing_coords: raise ValueError( f"Coordinates {x} and {y} are not present, " f"expected one of: {coords}" ) # Find out if it's multi-dimensional ndim = data[x].ndim if ndim == 1: grid = Ugrid2d._from_structured_singlecoord(data, x=x, y=y, name=name) dims = (data[y].dims[0], data[x].dims[0]) elif ndim == 2: grid = Ugrid2d._from_structured_multicoord(data, x=x, y=y, name=name) dims = tuple(data[x].dims) else: raise ValueError(f"x and y must be 1D or 2D. Found: {ndim}") if return_dims: return grid, dims else: return grid
[docs] def to_shapely(self, dim): """ Convert UGRID topology to shapely objects. * nodes: points * edges: linestrings * faces: polygons Parameters ---------- dim: str Node, edge, or face dimension. Returns ------- geometry: ndarray of shapely.Geometry """ if dim == self.face_dimension: return conversion.faces_to_polygons( self.node_x, self.node_y, self.face_node_connectivity, ) elif dim == self.node_dimension: return conversion.nodes_to_points( self.node_x, self.node_y, ) elif dim == self.edge_dimension: return conversion.edges_to_linestrings( self.node_x, self.node_y, self.edge_node_connectivity, ) else: raise ValueError( f"Dimension {dim} is not a face, node, or edge dimension of the" " Ugrid2d topology." )
[docs] def bounding_polygon(self) -> "shapely.Polygon": # type: ignore # noqa """ Construct the bounding polygon of the grid. This polygon may include holes if the grid also contains holes. """ import shapely def _bbox_area(bounds): return (bounds[2] - bounds[0]) * (bounds[3] - bounds[1]) edges = self.node_coordinates[self.boundary_node_connectivity] collection = shapely.polygonize(shapely.linestrings(edges)) polygon = max(collection.geoms, key=lambda x: _bbox_area(x.bounds)) return polygon
[docs] def create_data_array(self, data: ArrayLike, facet: str) -> "xugrid.UgridDataArray": """ Create a UgridDataArray from this grid and a 1D array of values. Parameters ---------- data: array like Values for this array. Must be a ``numpy.ndarray`` or castable to it. grid: Ugrid1d, Ugrid2d facet: str With which facet to associate the data. Options for Ugrid1d are, ``"node"`` or ``"edge"``. Options for Ugrid2d are ``"node"``, ``"edge"``, or ``"face"``. Returns ------- uda: UgridDataArray """ if facet == "node": dimension = self.node_dimension elif facet == "edge": dimension = self.edge_dimension elif facet == "face": dimension = self.face_dimension else: raise ValueError(f"Invalid facet: {facet}. Must be one of: node, edge.") return self._create_data_array(data, dimension)