base_network: use GeoSeries.voronoi_polygons instead of custom solution (#1172)
* base_network: use GeoSeries.voronoi_polygons instead of custom solution * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * base_network: move voronoi calculations into separate function * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * base_network: fix typo in function voronoi() * base_network: refine voronoi function * add release note [no ci] --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Fabian Neumann
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@ -55,6 +55,9 @@ Upcoming Release
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excess power) at every AC bus. This can speed up the solving process as the
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curtailment decision is aggregated into a single generator per region.
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* In :mod:`base_network`, replace own voronoi polygon calculation function with
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Geopandas `gdf.voronoi_polygons` method.
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PyPSA-Eur 0.11.0 (25th May 2024)
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=====================================
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@ -72,6 +72,7 @@ Creates the network topology from an ENTSO-E map extract, and create Voronoi sha
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"""
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import logging
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import warnings
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from itertools import product
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import geopandas as gpd
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@ -85,9 +86,9 @@ import shapely.wkt
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import yaml
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from _helpers import REGION_COLS, configure_logging, get_snapshots, set_scenario_config
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from packaging.version import Version, parse
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from scipy import spatial
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from scipy.sparse import csgraph
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from shapely.geometry import LineString, Point, Polygon
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from scipy.spatial import KDTree
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from shapely.geometry import LineString, Point
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PD_GE_2_2 = parse(pd.__version__) >= Version("2.2")
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@ -118,7 +119,7 @@ def _find_closest_links(links, new_links, distance_upper_bound=1.5):
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querycoords = np.vstack(
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[new_links[["x1", "y1", "x2", "y2"]], new_links[["x2", "y2", "x1", "y1"]]]
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)
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tree = spatial.KDTree(treecoords)
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tree = KDTree(treecoords)
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dist, ind = tree.query(querycoords, distance_upper_bound=distance_upper_bound)
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found_b = ind < len(links)
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found_i = np.arange(len(new_links) * 2)[found_b] % len(new_links)
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@ -273,7 +274,7 @@ def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
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return buses, links
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tree_buses = buses.query("carrier=='AC'")
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tree = spatial.KDTree(tree_buses[["x", "y"]])
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tree = KDTree(tree_buses[["x", "y"]])
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_, ind0 = tree.query(links_tyndp[["x1", "y1"]])
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ind0_b = ind0 < len(tree_buses)
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links_tyndp.loc[ind0_b, "bus0"] = tree_buses.index[ind0[ind0_b]]
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@ -788,59 +789,26 @@ def base_network(
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return n
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def voronoi_partition_pts(points, outline):
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def voronoi(points, outline, crs=4326):
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"""
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Compute the polygons of a voronoi partition of `points` within the polygon
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`outline`. Taken from
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https://github.com/FRESNA/vresutils/blob/master/vresutils/graph.py.
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Attributes
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----------
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points : Nx2 - ndarray[dtype=float]
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outline : Polygon
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Returns
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-------
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polygons : N - ndarray[dtype=Polygon|MultiPolygon]
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Create Voronoi polygons from a set of points within an outline.
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"""
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points = np.asarray(points)
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if len(points) == 1:
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polygons = [outline]
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else:
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xmin, ymin = np.amin(points, axis=0)
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xmax, ymax = np.amax(points, axis=0)
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xspan = xmax - xmin
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yspan = ymax - ymin
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# to avoid any network positions outside all Voronoi cells, append
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# the corners of a rectangle framing these points
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vor = spatial.Voronoi(
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np.vstack(
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(
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points,
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[
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[xmin - 3.0 * xspan, ymin - 3.0 * yspan],
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[xmin - 3.0 * xspan, ymax + 3.0 * yspan],
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[xmax + 3.0 * xspan, ymin - 3.0 * yspan],
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[xmax + 3.0 * xspan, ymax + 3.0 * yspan],
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],
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)
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)
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pts = gpd.GeoSeries(
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gpd.points_from_xy(points.x, points.y),
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index=points.index,
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crs=crs,
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)
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voronoi = pts.voronoi_polygons(extend_to=outline).clip(outline)
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polygons = []
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for i in range(len(points)):
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poly = Polygon(vor.vertices[vor.regions[vor.point_region[i]]])
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# can be removed with shapely 2.1 where order is preserved
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# https://github.com/shapely/shapely/issues/2020
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=UserWarning)
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pts = gpd.GeoDataFrame(geometry=pts)
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voronoi = gpd.GeoDataFrame(geometry=voronoi)
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joined = gpd.sjoin_nearest(pts, voronoi, how="right")
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if not poly.is_valid:
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poly = poly.buffer(0)
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with np.errstate(invalid="ignore"):
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poly = poly.intersection(outline)
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polygons.append(poly)
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return polygons
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return joined.dissolve(by="Bus").squeeze()
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def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
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@ -870,9 +838,7 @@ def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
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"name": onshore_locs.index,
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"x": onshore_locs["x"],
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"y": onshore_locs["y"],
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"geometry": voronoi_partition_pts(
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onshore_locs.values, onshore_shape
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),
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"geometry": voronoi(onshore_locs, onshore_shape),
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"country": country,
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},
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crs=n.crs,
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@ -888,7 +854,7 @@ def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
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"name": offshore_locs.index,
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"x": offshore_locs["x"],
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"y": offshore_locs["y"],
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"geometry": voronoi_partition_pts(offshore_locs.values, offshore_shape),
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"geometry": voronoi(offshore_locs, offshore_shape),
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"country": country,
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},
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crs=n.crs,
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