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)