diff --git a/benchmarks/geometry_kernels.py b/benchmarks/geometry_kernels.py index 08aea236e..d7e777bd6 100644 --- a/benchmarks/geometry_kernels.py +++ b/benchmarks/geometry_kernels.py @@ -35,6 +35,18 @@ def _unit(v): _X2 = _unit(np.array([0.0, 1.0, 0.3])) _CONST_Z = 0.3 +# Polygon for point-in-polygon (spherical triangle) +_POLY = np.array( + [ + _unit(np.array([1.0, 0.0, 0.1])), + _unit(np.array([0.0, 1.0, 0.1])), + _unit(np.array([-1.0, 0.0, 0.5])), + ], + dtype=np.float64, +) +_Q_INSIDE = _unit(np.array([0.1, 0.3, 0.9])) +_Q_OUTSIDE = _unit(np.array([-0.5, -0.5, -0.7])) + class EFTPrimitives: """Benchmark the low-level EFT building blocks: two_sum, two_prod, @@ -205,3 +217,21 @@ def time_try_gca_const_lat_intersection(self): def time_gca_const_lat_intersection(self): """Layer 3: dispatcher (full public API).""" self.gca_const_lat_intersection(self.gca_cart, _CONST_Z) + + +class PointInPolygonSphere: + """Benchmark the spherical point-in-polygon kernel.""" + + def setup(self): + from uxarray.grid.point_in_face import _point_in_polygon_sphere + + self._point_in_polygon_sphere = _point_in_polygon_sphere + + _point_in_polygon_sphere(_Q_INSIDE, _POLY) + _point_in_polygon_sphere(_Q_OUTSIDE, _POLY) + + def time_point_inside(self): + self._point_in_polygon_sphere(_Q_INSIDE, _POLY) + + def time_point_outside(self): + self._point_in_polygon_sphere(_Q_OUTSIDE, _POLY) diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb new file mode 100644 index 000000000..6c88caba6 --- /dev/null +++ b/docs/user-guide/spherical-geometry-accuracy.ipynb @@ -0,0 +1,601 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "title-cell", + "metadata": {}, + "source": [ + "# Accurate Spherical Geometry\n", + "\n", + "Cross products are at the heart of nearly every geometric test on the sphere — whether a point lies inside a polygon, where two great-circle arcs cross, or which face covers a given latitude. When the two vectors involved are nearly parallel, both products in the subtraction $a_x b_y - a_y b_x$ are nearly equal large numbers and their difference — the physically meaningful result — can lose all significant digits to floating-point cancellation. UXarray reduces this error throughout its geometry stack using **compensated arithmetic** — algorithms built on error-free transformation (EFT) primitives that track key rounding residuals.\n", + "\n", + "This guide covers:\n", + "\n", + "1. The problem: catastrophic cancellation\n", + "2. How UXarray handles it\n", + "3. Seeing it on a real mesh: point-in-polygon\n", + "4. Where it is used in UXarray" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "imports-cell", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T08:26:16.987380Z", + "iopub.status.busy": "2026-07-18T08:26:16.987156Z", + "iopub.status.idle": "2026-07-18T08:26:20.914856Z", + "shell.execute_reply": "2026-07-18T08:26:20.914469Z" + } + }, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import uxarray as ux\n", + "from uxarray.grid.point_in_face import _point_in_polygon_sphere\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "section1-header", + "metadata": {}, + "source": [ + "## 1. The Problem: Catastrophic Cancellation\n", + "\n", + "The cross product measures the **area of the parallelogram** spanned by two vectors. When those vectors are nearly parallel, that area is a tiny difference of two large numbers — and floating-point rounding can reduce it to zero." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "geometric-picture", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T08:26:20.917043Z", + "iopub.status.busy": "2026-07-18T08:26:20.916814Z", + "iopub.status.idle": "2026-07-18T08:26:21.157807Z", + "shell.execute_reply": "2026-07-18T08:26:21.157431Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "fig.subplots_adjust(top=0.82) # leave room for suptitle\n", + "\n", + "# --- Left panel: well-separated vectors ---\n", + "ax = axes[0]\n", + "a1 = np.array([0.6, 0.8])\n", + "b1 = np.array([0.8, 0.2])\n", + "para1 = plt.Polygon(\n", + " [np.array([0, 0]), a1, a1 + b1, b1], alpha=0.25, color=\"steelblue\", zorder=0\n", + ")\n", + "ax.add_patch(para1)\n", + "ax.annotate(\n", + " \"\", xy=a1, xytext=[0, 0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2)\n", + ")\n", + "ax.annotate(\n", + " \"\", xy=b1, xytext=[0, 0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2)\n", + ")\n", + "ax.text(*a1 * 1.08, r\"$\\mathbf{a}$\", fontsize=13, color=\"#1f77b4\")\n", + "ax.text(*b1 * 1.08, r\"$\\mathbf{b}$\", fontsize=13, color=\"#d62728\")\n", + "area1 = abs(a1[0] * b1[1] - a1[1] * b1[0])\n", + "ax.text(\n", + " 0.5,\n", + " 0.96,\n", + " f\"|a × b| = {area1:.3f}\",\n", + " ha=\"center\",\n", + " fontsize=12,\n", + " color=\"steelblue\",\n", + " transform=ax.transAxes,\n", + ")\n", + "ax.set_xlim(-0.1, 1.8)\n", + "ax.set_ylim(-0.1, 1.1)\n", + "ax.set_aspect(\"equal\")\n", + "ax.set_title(\"Well-separated — large, well-conditioned cross product\", fontsize=11)\n", + "ax.axis(\"off\")\n", + "\n", + "# --- Right panel: nearly-parallel vectors ---\n", + "ax = axes[1]\n", + "eps = 0.04\n", + "a2 = np.array([0.8 + eps, 0.6])\n", + "b2 = np.array([0.8, 0.6 + eps])\n", + "a2 /= np.linalg.norm(a2)\n", + "b2 /= np.linalg.norm(b2)\n", + "para2 = plt.Polygon(\n", + " [np.array([0, 0]), a2, a2 + b2, b2], alpha=0.5, color=\"#d62728\", zorder=0\n", + ")\n", + "ax.add_patch(para2)\n", + "ax.annotate(\n", + " \"\", xy=a2, xytext=[0, 0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2)\n", + ")\n", + "ax.annotate(\n", + " \"\", xy=b2, xytext=[0, 0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2)\n", + ")\n", + "ax.text(*(a2 * 1.06 + [0.01, 0.03]), r\"$\\mathbf{a}$\", fontsize=13, color=\"#1f77b4\")\n", + "ax.text(*(b2 * 1.06 - [0.06, 0.0]), r\"$\\mathbf{b}$\", fontsize=13, color=\"#d62728\")\n", + "area2 = abs(a2[0] * b2[1] - a2[1] * b2[0])\n", + "ax.text(\n", + " 0.5,\n", + " 0.96,\n", + " f\"|a × b| = {area2:.4f} ← tiny!\",\n", + " ha=\"center\",\n", + " fontsize=12,\n", + " color=\"#d62728\",\n", + " transform=ax.transAxes,\n", + ")\n", + "ax.set_xlim(-0.1, 1.8)\n", + "ax.set_ylim(-0.1, 1.1)\n", + "ax.set_aspect(\"equal\")\n", + "ax.set_title(\n", + " \"Nearly-parallel — tiny cross product, catastrophic cancellation\", fontsize=11\n", + ")\n", + "ax.axis(\"off\")\n", + "\n", + "fig.suptitle(\n", + " \"Cross product = parallelogram area\\n\"\n", + " \"Small area means two nearly equal numbers are subtracted — digits cancel\",\n", + " fontsize=12,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9a3dc8b0", + "metadata": {}, + "source": [ + "## 2. How UXarray Handles It\n", + "\n", + "UXarray uses **compensated arithmetic** — a family of algorithms that reduce catastrophic cancellation by carrying `(hi, lo)` correction terms through sensitive floating-point operations. There are two distinct layers:\n", + "\n", + "- **Error-free transformations (EFT)** — `two_sum` and `two_prod` are true EFTs: they split a result into a rounded high part and an exact rounding residual so that `hi + lo` equals the true mathematical result with zero information loss.\n", + "- **Compensated algorithms** — `diff_of_products` and `accucross` compose EFT primitives to compute cross-product components accurately. They are *not* error-free in the strict sense (the final result still carries one ulp of error), but they achieve roughly double the effective precision compared to naive floating-point evaluation.\n", + "\n", + "The primitives in UXarray are a Python/Numba port of the EFT tier from the [AccuSphGeom](https://github.com/hongyuchen1030/AccuSphGeom) C++ library by Hongyu Chen ([Chen 2026, EGUsphere](https://egusphere.copernicus.org/preprints/2026/egusphere-2026-636/); [SIAM J. Sci. Comput.](https://doi.org/10.1137/25M1737614)). UXarray does not implement AccuSphGeom's full adaptive-predicate or exact-arithmetic fallback stack. The key building blocks live in `uxarray.utils.computing` and `uxarray.grid.arcs`:\n", + "\n", + "| Function | Module | What it does |\n", + "|---|---|---|\n", + "| `two_sum(a, b)` | `utils.computing` | **EFT**: exact split of `a + b` into `(hi, lo)` |\n", + "| `two_prod(a, b)` | `utils.computing` | **EFT**: exact split of `a * b` into `(hi, lo)` |\n", + "| `diff_of_products(a, b, c, d)` | `utils.computing` | Compensated `a*b - c*d` |\n", + "| `accucross(ax, ay, az, bx, by, bz)` | `utils.computing` | Compensated cross product returning 6 `(hi, lo)` components |\n", + "| `orient3d_on_sphere(a, b, q)` | `grid.arcs` | Sign of `(a×b)·q`: +1, −1, or 0 |\n", + "| `on_minor_arc(q, a, b)` | `grid.arcs` | True if `q` lies on the minor arc from `a` to `b` |\n", + "\n", + "Most users will never call these directly — they are wired into `Grid.get_point_on_face`, intersection, and zonal operations automatically. But if you are writing custom geometry code that operates on unit vectors, `orient3d_on_sphere` is the right tool for any \"which side of a great circle?\" question." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e13b3cd4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T08:26:21.159831Z", + "iopub.status.busy": "2026-07-18T08:26:21.159654Z", + "iopub.status.idle": "2026-07-18T08:26:21.488251Z", + "shell.execute_reply": "2026-07-18T08:26:21.487829Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "North Pole: orient3d = +1 → left of A→B (northern hemisphere)\n", + "South Pole: orient3d = -1 → right of A→B (southern hemisphere)\n", + "On great circle: orient3d = 0 → collinear, not a crossing\n" + ] + } + ], + "source": [ + "from uxarray.grid.arcs import orient3d_on_sphere\n", + "\n", + "# orient3d_on_sphere(A, B, Q) returns the sign of the scalar triple product (A×B)·Q.\n", + "#\n", + "# Geometrically: A and B define a great circle (the equatorial plane here).\n", + "# The sign tells you which hemisphere Q is in relative to that plane:\n", + "#\n", + "# +1 Q is on the LEFT of the directed arc A → B (above the plane by right-hand rule)\n", + "# -1 Q is on the RIGHT of the directed arc A → B (below the plane)\n", + "# 0 Q lies exactly on the great circle through A and B\n", + "#\n", + "# This sign is what every edge-crossing test in point-in-polygon boils down to.\n", + "\n", + "A = np.array([1.0, 0.0, 0.0]) # 0°E on the equator\n", + "B = np.array([0.0, 1.0, 0.0]) # 90°E on the equator\n", + "# A→B defines the equatorial great circle; right-hand normal points to the North Pole.\n", + "\n", + "north_pole = np.array([0.0, 0.0, 1.0])\n", + "south_pole = np.array([0.0, 0.0, -1.0])\n", + "on_equator = np.array([0.0, 1.0, 0.0]) # same as B — on the great circle itself\n", + "\n", + "\n", + "def fmt(v):\n", + " return f\"{v:+d}\" if v != 0 else \" 0\"\n", + "\n", + "\n", + "print(\n", + " f\"North Pole: orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))} → left of A→B (northern hemisphere)\"\n", + ")\n", + "print(\n", + " f\"South Pole: orient3d = {fmt(orient3d_on_sphere(A, B, south_pole))} → right of A→B (southern hemisphere)\"\n", + ")\n", + "print(\n", + " f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))} → collinear, not a crossing\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "section4-header", + "metadata": {}, + "source": [ + "## 3. Seeing It on a Real Mesh: Point-in-Polygon\n", + "\n", + "Point-in-polygon on the sphere works by casting a ray from the query point and counting edge crossings — each crossing test is an `orient3d_on_sphere` sign check. When a query point sits very close to an edge, the cross product of the two edge endpoints is tiny, and its sign is exactly what naive arithmetic gets wrong." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "load-mesh", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T08:26:21.489992Z", + "iopub.status.busy": "2026-07-18T08:26:21.489790Z", + "iopub.status.idle": "2026-07-18T08:26:21.850507Z", + "shell.execute_reply": "2026-07-18T08:26:21.850185Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grid: 5400 faces, 5402 nodes\n" + ] + } + ], + "source": [ + "uxds = ux.tutorial.open_dataset(\"outCSne30-vortex\")\n", + "grid = uxds.uxgrid\n", + "print(f\"Grid: {grid.n_face} faces, {grid.n_node} nodes\")" + ] + }, + { + "cell_type": "markdown", + "id": "pip-setup-text", + "metadata": {}, + "source": [ + "Query points are placed at 50 log-spaced distances from the midpoint of edge V0→V1 on face 0, stepping inward toward the face centroid. The sign of the naive orient3d flips once the distance drops below $\\sim \\varepsilon_\\text{machine} / |V0 \\times V1|$." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "pip-demo", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T08:26:21.852365Z", + "iopub.status.busy": "2026-07-18T08:26:21.852088Z", + "iopub.status.idle": "2026-07-18T08:26:23.415083Z", + "shell.execute_reply": "2026-07-18T08:26:23.414724Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Face 0 edge V0→V1: |V0 × V1| = 0.04851\n", + "Naive sign flips below ε ≈ 4.5e-15 rad (2.89e-05 mm on Earth)\n", + "\n", + "All 50 query points are inside face 0 — correct answer is always 'inside'.\n", + " EFT (orient3d_on_sphere): 50/50 correctly classified as inside\n", + " Naive (raw cross product): 42/50 correctly classified as inside ← 8 misclassified as outside near the edge\n" + ] + } + ], + "source": [ + "def normalize(v):\n", + " v = np.asarray(v, dtype=np.float64)\n", + " return v / np.linalg.norm(v)\n", + "\n", + "\n", + "def lonlat_to_xyz(lon_deg, lat_deg):\n", + " lon, lat = np.radians(lon_deg), np.radians(lat_deg)\n", + " return np.array([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)])\n", + "\n", + "\n", + "def xyz_to_lonlat(v):\n", + " x, y, z = v\n", + " lat = np.degrees(np.arcsin(np.clip(z, -1, 1)))\n", + " lon = np.degrees(np.arctan2(y, x))\n", + " return lon, lat\n", + "\n", + "\n", + "fnc = grid.face_node_connectivity.values\n", + "n_per = grid.n_nodes_per_face.values\n", + "fi = 0\n", + "f0 = fnc[fi, : n_per[fi]]\n", + "lons = grid.node_lon.values[f0]\n", + "lats = grid.node_lat.values[f0]\n", + "vertices = np.array([lonlat_to_xyz(lo, la) for lo, la in zip(lons, lats)])\n", + "\n", + "A, B = vertices[0], vertices[1]\n", + "cx = A[1] * B[2] - A[2] * B[1]\n", + "cy = A[2] * B[0] - A[0] * B[2]\n", + "cz = A[0] * B[1] - A[1] * B[0]\n", + "cross_mag = np.sqrt(cx**2 + cy**2 + cz**2)\n", + "flip_threshold = 2.2e-16 / cross_mag\n", + "flip_mm = flip_threshold * 6.371e6 * 1e3 # radians → mm on Earth\n", + "\n", + "# Place 50 query points stepping from the edge midpoint inward toward the centroid.\n", + "# All 50 are strictly inside the face — the expected answer for every point is \"inside\".\n", + "edge_mid = normalize(vertices[0] + vertices[1])\n", + "centroid_dir = normalize(vertices.sum(axis=0))\n", + "epsilons = np.logspace(-3, -16, 50)\n", + "\n", + "_INSIDE = {1, 2, 3} # _LOC_INSIDE, _LOC_ON_VERTEX, _LOC_ON_EDGE\n", + "results, signed_vals = [], []\n", + "for eps in epsilons:\n", + " q = normalize(edge_mid + eps * centroid_dir)\n", + " results.append(_point_in_polygon_sphere(q, vertices))\n", + " signed_vals.append(cx * q[0] + cy * q[1] + cz * q[2])\n", + "\n", + "n = len(epsilons)\n", + "eft_ok = sum(1 for r in results if r in _INSIDE)\n", + "naive_ok = sum(1 for v in signed_vals if v > 0)\n", + "\n", + "print(f\"Face 0 edge V0→V1: |V0 × V1| = {cross_mag:.5f}\")\n", + "print(\n", + " f\"Naive sign flips below ε ≈ {flip_threshold:.1e} rad ({flip_mm:.2e} mm on Earth)\"\n", + ")\n", + "print()\n", + "print(f\"All {n} query points are inside face 0 — correct answer is always 'inside'.\")\n", + "print(f\" EFT (orient3d_on_sphere): {eft_ok}/{n} correctly classified as inside\")\n", + "print(\n", + " f\" Naive (raw cross product): {naive_ok}/{n} correctly classified as inside\"\n", + " f\" ← {n - naive_ok} misclassified as outside near the edge\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "pip-interp", + "metadata": {}, + "source": [ + "When the query is close enough to the edge, the naive orient3d value rounds to the wrong sign — the crossing test flips and the point is misclassified as outside. A misclassified point on a shared edge is either silently dropped or double-counted in the output. Compensated arithmetic keeps the correct sign down to machine precision." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "geometry-map", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T08:26:23.416927Z", + "iopub.status.busy": "2026-07-18T08:26:23.416772Z", + "iopub.status.idle": "2026-07-18T08:26:25.194151Z", + "shell.execute_reply": "2026-07-18T08:26:25.193810Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "node_lon = grid.node_lon.values\n", + "node_lat = grid.node_lat.values\n", + "\n", + "fig = plt.figure(figsize=(14, 5.5))\n", + "fig.subplots_adjust(wspace=0.08)\n", + "\n", + "# ── Left: zoomed face ──────────────────────────────────────────────────────\n", + "ax = fig.add_subplot(1, 2, 1)\n", + "\n", + "face_lons = np.append(lons, lons[0])\n", + "face_lats = np.append(lats, lats[0])\n", + "ax.fill(face_lons, face_lats, alpha=0.12, color=\"steelblue\", zorder=1)\n", + "ax.plot(face_lons, face_lats, \"-\", color=\"steelblue\", linewidth=1.8, zorder=2)\n", + "ax.plot(\n", + " [lons[0], lons[1]],\n", + " [lats[0], lats[1]],\n", + " \"-\",\n", + " color=\"#d62728\",\n", + " linewidth=3.5,\n", + " zorder=3,\n", + " label=\"Test edge V0 → V1\",\n", + ")\n", + "\n", + "for i, (lo, la) in enumerate(zip(lons, lats)):\n", + " ax.scatter(lo, la, s=90, color=\"steelblue\", zorder=5, clip_on=False)\n", + " ax.annotate(\n", + " f\"V{i}\",\n", + " (lo, la),\n", + " textcoords=\"offset points\",\n", + " xytext=(6, 4),\n", + " fontsize=11,\n", + " fontweight=\"bold\",\n", + " )\n", + "\n", + "cen_lon, cen_lat = xyz_to_lonlat(normalize(vertices.sum(axis=0)))\n", + "ax.scatter(cen_lon, cen_lat, s=70, color=\"#555\", marker=\"+\", linewidths=2.5, zorder=5)\n", + "\n", + "em_lon, em_lat = xyz_to_lonlat(normalize(vertices[0] + vertices[1]))\n", + "ax.scatter(\n", + " em_lon,\n", + " em_lat,\n", + " s=200,\n", + " color=\"#ff7f0e\",\n", + " marker=\"*\",\n", + " zorder=6,\n", + " label=\"Edge midpoint — sweep origin\",\n", + ")\n", + "\n", + "ax.annotate(\n", + " \"\",\n", + " xy=(cen_lon, cen_lat),\n", + " xytext=(em_lon, em_lat),\n", + " arrowprops=dict(arrowstyle=\"-|>\", color=\"#555\", lw=1.5),\n", + ")\n", + "ax.text(\n", + " (em_lon + cen_lon) / 2 + 0.06,\n", + " (em_lat + cen_lat) / 2 + 0.18,\n", + " \"50 query points\\n(ε from 10⁻³ → 10⁻¹⁶)\",\n", + " fontsize=9,\n", + " color=\"#555\",\n", + " style=\"italic\",\n", + ")\n", + "\n", + "ax.scatter(\n", + " em_lon,\n", + " em_lat,\n", + " s=700,\n", + " facecolors=\"none\",\n", + " edgecolors=\"#d62728\",\n", + " linewidths=2,\n", + " zorder=7,\n", + " label=f\"Naive sign wrong below ε ≈ {flip_threshold:.0e} rad (≈ 0.03 mm)\",\n", + ")\n", + "\n", + "q_far = normalize(\n", + " normalize(vertices[0] + vertices[1]) + 1e-3 * normalize(vertices.sum(axis=0))\n", + ")\n", + "qf_lon, qf_lat = xyz_to_lonlat(q_far)\n", + "ax.scatter(qf_lon, qf_lat, s=60, color=\"#1f77b4\", zorder=6)\n", + "ax.annotate(\n", + " \"ε = 10⁻³\\nboth correct\",\n", + " (qf_lon, qf_lat),\n", + " textcoords=\"offset points\",\n", + " xytext=(7, -18),\n", + " fontsize=8.5,\n", + " color=\"#1f77b4\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"Longitude (°)\", fontsize=11)\n", + "ax.set_ylabel(\"Latitude (°)\", fontsize=11)\n", + "ax.set_title(\"Face 0 — query sweep toward centroid\", fontsize=11)\n", + "ax.legend(fontsize=9, loc=\"lower right\")\n", + "ax.grid(True, alpha=0.3)\n", + "pad = 0.55\n", + "ax.set_xlim(lons.min() - pad, lons.max() + pad)\n", + "ax.set_ylim(lats.min() - pad, lats.max() + pad)\n", + "\n", + "# ── Right: global context ──────────────────────────────────────────────────\n", + "ax_global = fig.add_subplot(1, 2, 2, projection=ccrs.Robinson())\n", + "ax_global.set_global()\n", + "ax_global.add_feature(cfeature.OCEAN, color=\"#e8f0f7\", zorder=0)\n", + "ax_global.add_feature(cfeature.COASTLINE, linewidth=0.4, color=\"#999\", zorder=1)\n", + "for fi_g in range(0, grid.n_face, 4):\n", + " verts_g = fnc[fi_g, : n_per[fi_g]]\n", + " lf = node_lon[verts_g]\n", + " la_ = node_lat[verts_g]\n", + " if lf.max() - lf.min() > 180:\n", + " continue\n", + " ax_global.plot(\n", + " np.append(lf, lf[0]),\n", + " np.append(la_, la_[0]),\n", + " \"-\",\n", + " color=\"steelblue\",\n", + " linewidth=0.3,\n", + " alpha=0.5,\n", + " transform=ccrs.PlateCarree(),\n", + " zorder=2,\n", + " )\n", + "ax_global.fill(\n", + " face_lons,\n", + " face_lats,\n", + " alpha=0.8,\n", + " color=\"#d62728\",\n", + " zorder=4,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", + "ax_global.scatter(\n", + " em_lon,\n", + " em_lat,\n", + " s=40,\n", + " color=\"#ff7f0e\",\n", + " marker=\"*\",\n", + " zorder=5,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", + "ax_global.set_title(\"Global context — highlighted face in red\", fontsize=11)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3138ae9a", + "metadata": {}, + "source": [ + "## 4. Where It Is Used in UXarray\n", + "\n", + "Compensated arithmetic is wired into every module that performs geometric predicates on the sphere. The table below maps each user-facing operation to the underlying accurate function that protects it.\n", + "\n", + "| User-facing operation | Module | Accurate function(s) used |\n", + "|---|---|---|\n", + "| `Grid.get_point_on_face()` | `grid/point_in_face.py` | `orient3d_on_sphere`, `on_minor_arc` |\n", + "| Arc–arc intersection (remapping, antimeridian) | `grid/intersections.py` | `accucross`, `accucross_pair`, `on_minor_arc` |\n", + "| Arc–latitude intersection (zonal averages) | `grid/intersections.py` | `accucross`, `acc_sqrt_re`, `on_minor_arc` |\n", + "| Face lat/lon bounds (bounding-box queries) | `grid/bounds.py` | `orient3d_on_sphere` (pole check) |\n", + "| Antimeridian detection & splitting | `grid/geometry.py` | `orient3d_on_sphere`, `on_minor_arc` |\n", + "| Zonal means (`Grid.zonal_mean`) | `core/zonal.py` | via `gca_const_lat_intersection` |\n", + "| Face area integration | `grid/integrate.py` | via `gca_const_lat_intersection` |\n", + "\n", + "If you extend UXarray with custom geometry — for example, a new remapping kernel or a spatial predicate — use `orient3d_on_sphere` from `uxarray.grid.arcs` for any signed orientation test, and `on_minor_arc` for arc-membership tests. Both are Numba-compiled and drop-in replacements for the equivalent naive cross-product code." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/userguide.rst b/docs/userguide.rst index d185a4d59..73e154f13 100644 --- a/docs/userguide.rst +++ b/docs/userguide.rst @@ -97,6 +97,9 @@ Supplementary Guides These user guides provide additional details about specific features in UXarray. +`Accurate Spherical Geometry `_ + How UXarray uses compensated arithmetic to avoid catastrophic cancellation in cross-product and point-in-polygon operations + `Working with HEALPix Grids `_ Use UXarray with HEALPix @@ -131,6 +134,7 @@ These user guides provide additional details about specific features in UXarray. user-guide/dual-mesh.ipynb user-guide/structured.ipynb user-guide/from-points.ipynb + user-guide/spherical-geometry-accuracy.ipynb user-guide/healpix.ipynb user-guide/holoviz.ipynb user-guide/from_file.ipynb diff --git a/test/grid/geometry/test_accusphgeom_baseline.py b/test/grid/geometry/test_accusphgeom_baseline.py index 94565c2c6..e704f2aff 100644 --- a/test/grid/geometry/test_accusphgeom_baseline.py +++ b/test/grid/geometry/test_accusphgeom_baseline.py @@ -6,6 +6,8 @@ Specific C++ tests mirrored here: tests/test_gca_gca_intersection_baseline.cpp — 31 near-tangent GCA pairs tests/test_gca_constlat_intersection_baseline.cpp — 200 arc/latitude cases + tests/test_pip_robust.cpp — simple spherical triangle + tests/test_pip_complicated.cpp — 12-vertex concave polygon The C++ library uses ultra-tight tolerances (3–100 ULP) backed by Shewchuk adaptive precision and a geogram fallback. This Python port implements only @@ -14,6 +16,7 @@ GCA-GCA intersection: 3e-8 (C++ reference: 1e-8) GCA-const-lat intersection: 1e-13 (C++ reference: 3–100 ULP ≈ 7e-16–2e-14) + Point-in-polygon: exact location codes (same as C++) """ import math @@ -23,6 +26,13 @@ import pytest from uxarray.grid.intersections import gca_const_lat_intersection, gca_gca_intersection +from uxarray.grid.point_in_face import ( + _LOC_INSIDE, + _LOC_ON_EDGE, + _LOC_ON_VERTEX, + _LOC_OUTSIDE, + _point_in_polygon_sphere, +) _DATA_DIR = os.path.join(os.path.dirname(__file__), "data", "accusphgeom") _GCA_GCA_CSV = os.path.join( @@ -133,3 +143,66 @@ def test_gca_constlat_intersection_baseline(gca_constlat_rows, idx): dy = result[0, 1] - by err = math.sqrt(dx * dx + dy * dy) assert err < 5e-15, f"case_id={case_id}: err_xy={err:.3e} ≥ 5e-15" + + +# ── Point-in-polygon: simple spherical triangle ─────────────────────────────── +# From test_pip_robust.cpp: triangle A=(1,0,0) B=(0,1,0) C=(0,0,1) + +_SIMPLE_POLY = np.array( + [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], dtype=np.float64 +) + + +def test_pip_simple_on_vertex(): + q = np.array([1.0, 0.0, 0.0]) + assert _point_in_polygon_sphere(q, _SIMPLE_POLY) == _LOC_ON_VERTEX + + +def test_pip_simple_on_edge(): + # Normalize([1,1,0]) — midpoint of edge AB + q = np.array([0.70710678118654752, 0.70710678118654752, 0.0]) + assert _point_in_polygon_sphere(q, _SIMPLE_POLY) == _LOC_ON_EDGE + + +def test_pip_simple_inside(): + q = np.array([1.0, 1.0, 1.0]) + q = q / np.linalg.norm(q) + assert _point_in_polygon_sphere(q, _SIMPLE_POLY) == _LOC_INSIDE + + +# ── Point-in-polygon: complicated 12-vertex polygon ────────────────────────── +# From test_pip_complicated.cpp (Tier 4 / no-global-id overload) + +_COMPLICATED_POLY = np.array( + [ + [0.77114888623389370, -0.15726142646764130, 0.61692644537707060], + [0.45249789144681710, -0.75061357063415830, 0.48148200985709080], + [0.68946150885186746, -0.59933974587969335, 0.40673664307580021], + [0.53398361424012150, -0.82144802877974800, 0.20021147753544170], + [0.72547341102583852, -0.63064441484306173, 0.27563735581699919], + [0.90662646752004000, -0.37288916572560260, 0.19743889808393390], + [0.74736479846796566, -0.64967430761889954, 0.13917310096006544], + [0.75468084319451650, -0.65603404827296060, -0.00872653549837396], + [0.49138625363591330, -0.85368085756667700, -0.17253562867386300], + [0.86555356123625300, -0.23932615843504300, -0.43993183849315200], + [0.73819995144420940, -0.26096774566031860, -0.62205841157622660], + [0.60166139617200880, -0.05234812405382043, -0.79703402578835670], + ], + dtype=np.float64, +) + +_PIP_CASES = [ + ([0.75367527697268680, -0.65515992289232780, -0.05233595624294383], _LOC_INSIDE, "Q1 inside"), + ([0.92054211727315200, -0.38498585550407840, 0.06624274592780397], _LOC_INSIDE, "Q2 inside"), + ([0.53882393432914170, -0.82565565483991800, 0.16721694718218960], _LOC_OUTSIDE, "Q3 outside"), + ([0.63494819288856630, -0.65761549896072850, 0.40544130015845230], _LOC_OUTSIDE, "Q4 outside"), + # Q5 is exactly vertex P8 (0-indexed) + ([0.49138625363591330, -0.85368085756667700, -0.17253562867386300], _LOC_ON_VERTEX, "Q5 on vertex"), +] + + +@pytest.mark.parametrize("q_xyz,expected,name", _PIP_CASES) +def test_pip_complicated(q_xyz, expected, name): + q = np.array(q_xyz, dtype=np.float64) + result = _point_in_polygon_sphere(q, _COMPLICATED_POLY) + assert result == expected, f"{name}: expected {expected}, got {result}" diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py index 626946244..7dedb36c8 100644 --- a/uxarray/grid/bounds.py +++ b/uxarray/grid/bounds.py @@ -1,3 +1,5 @@ +import math + import numpy as np import pandas as pd import xarray as xr @@ -9,6 +11,11 @@ point_within_gca, ) from uxarray.grid.geometry import pole_point_inside_polygon +from uxarray.grid.point_in_face import ( + _LOC_INSIDE, + _LOC_OUTSIDE, + _point_in_polygon_sphere, +) from uxarray.grid.utils import ( _get_cartesian_face_edge_nodes, _get_spherical_face_edge_nodes, @@ -16,6 +23,336 @@ any_close_lat, ) +# Constants for the accurate GCA bounds path. + +# Latitude snap tolerance (degrees): if the GCA arc extreme is within this +# distance of a vertex latitude, snap to the vertex value so that the bounds +# remain tight and vertex-aligned. +_SNAP_TOL_DEG = 1e-4 + +# Face location codes used by _face_location_info. +_FACE_LOC_LOCAL = 0 +_FACE_LOC_NORTH_POLAR = 1 +_FACE_LOC_SOUTH_POLAR = 2 + +_NORTH_POLE = np.array([0.0, 0.0, 1.0]) +_SOUTH_POLE = np.array([0.0, 0.0, -1.0]) + + +# Per-face GCA bounds helpers (accurate path). + + +@njit(cache=True) +def _face_location_info(face_vertices, polar_cap_z): + """Classify a face and return (label, z_min, z_max). + + Iterates over each great-circle edge, finding the interior z-extremum that + the arc can reach beyond its endpoints, and compares the overall z range + against the polar-cap threshold. + + Parameters + ---------- + face_vertices : np.ndarray, shape (n, 3) + Unit-vector vertices of the face. + polar_cap_z : float + sin(polar_cap_latitude); faces whose z-range crosses ±polar_cap_z are + classified as polar candidates. + + Returns + ------- + label : int + _FACE_LOC_LOCAL, _FACE_LOC_NORTH_POLAR, or _FACE_LOC_SOUTH_POLAR. + z_min : float + z_max : float + """ + n = face_vertices.shape[0] + z_max = -np.inf + z_min = np.inf + + for i in range(n): + j = (i + 1) % n + x1 = face_vertices[i] + x2 = face_vertices[j] + z1 = x1[2] + z2 = x2[2] + d = x1[0] * x2[0] + x1[1] * x2[1] + x1[2] * x2[2] + + # Parameter along the arc at which z is extremal (matches C++ get_face_location_info). + denom = (z1 + z2) * (d - 1.0) + a_raw = (z1 * d - z2) / denom if denom != 0.0 else -1.0 + a = min(max(a_raw, 0.0), 1.0) + + one_a = 1.0 - a + y0 = one_a * x1[0] + a * x2[0] + y1 = one_a * x1[1] + a * x2[1] + y2 = one_a * x1[2] + a * x2[2] + norm = math.sqrt(y0 * y0 + y1 * y1 + y2 * y2) + z_ext = y2 / norm + + z_edge_max = z1 if z1 > z2 else z2 + z_edge_min = z1 if z1 < z2 else z2 + + if 0.0 < a_raw < 1.0: + z_max_candidate = z_ext + z_min_candidate = z_ext + else: + z_max_candidate = z_edge_max + z_min_candidate = z_edge_min + + if z_max_candidate > z_max: + z_max = z_max_candidate + if z_min_candidate < z_min: + z_min = z_min_candidate + + north_pole_candidate = z_max >= polar_cap_z + south_pole_candidate = z_min <= -polar_cap_z + local = not (north_pole_candidate or south_pole_candidate) + + label = ( + local * _FACE_LOC_LOCAL + + north_pole_candidate * _FACE_LOC_NORTH_POLAR + + (not north_pole_candidate and south_pole_candidate) * _FACE_LOC_SOUTH_POLAR + ) + return label, z_min, z_max + + +@njit(cache=True) +def _lon_bounds_from_vertices(face_vertices): + """Compute (lon_min, lon_max) in degrees in [0, 360]. + + If the face crosses the antimeridian, returns lon_min > lon_max, which is + the uxarray wrap encoding (lon_min > lon_max signals antimeridian crossing + throughout the bounds and cross-section APIs). AccuSphGeom uses a union-of- + intervals convention instead; this function is needed to translate to the + uxarray encoding and cannot be removed without changing the bounds API. + + The largest-gap algorithm is standard for antimeridian detection on a set + of vertex longitudes: the gap in sorted longitudes opposite the face + interior is the one the face does NOT span. + """ + n = face_vertices.shape[0] + rad_to_deg = 180.0 / math.pi + lons = np.empty(n) + for i in range(n): + x = face_vertices[i] + lon = math.atan2(x[1], x[0]) * rad_to_deg + if lon < 0.0: + lon += 360.0 + lons[i] = lon + + lons_sorted = np.sort(lons) + + # Find the largest gap (including the wrap gap from last to first + 360). + best_gap = 360.0 - (lons_sorted[n - 1] - lons_sorted[0]) + best_idx = -1 # -1 means the best gap is the wrap gap + for i in range(n - 1): + gap = lons_sorted[i + 1] - lons_sorted[i] + if gap > best_gap: + best_gap = gap + best_idx = i + + if best_idx >= 0: + # A non-wrap gap beat the wrap gap — the face crosses the antimeridian. + return lons_sorted[best_idx + 1], lons_sorted[best_idx] + return lons_sorted[0], lons_sorted[n - 1] + + +@njit(cache=True) +def _generate_lat_lon_bounds_local(face_vertices, z_min, z_max, snap_tol_deg): + """Compute (lat_min, lat_max, lon_min, lon_max) in degrees for a non-polar face. + + Uses the z-extrema already computed by ``_face_location_info`` for the + latitude bounds, snapping to vertex latitudes when within ``snap_tol_deg`` + to keep bounds tight. + + Parameters + ---------- + face_vertices : np.ndarray, shape (n, 3) + z_min, z_max : float + Arc z-extrema from ``_face_location_info``. + snap_tol_deg : float + Tolerance in degrees for snapping to vertex latitudes. + + Returns + ------- + lat_min, lat_max, lon_min, lon_max : float + All in degrees; lon in [0, 360] with lon_min > lon_max for + antimeridian-crossing faces. + """ + n = face_vertices.shape[0] + rad_to_deg = 180.0 / math.pi + + ep_lat_max = -np.inf + ep_lat_min = np.inf + for i in range(n): + zc = face_vertices[i, 2] + if zc > 1.0: + zc = 1.0 + elif zc < -1.0: + zc = -1.0 + lat = math.asin(zc) * rad_to_deg + if lat > ep_lat_max: + ep_lat_max = lat + if lat < ep_lat_min: + ep_lat_min = lat + + lon_min, lon_max = _lon_bounds_from_vertices(face_vertices) + + zmx = min(z_max, 1.0) + zmn = max(z_min, -1.0) + lat_max = math.asin(zmx) * rad_to_deg + lat_min = math.asin(zmn) * rad_to_deg + + # Snap arc extrema to vertex values when nearly equal — mask-based (matches C++). + snap_max = 1 if abs(lat_max - ep_lat_max) <= snap_tol_deg else 0 + snap_min = 1 if abs(lat_min - ep_lat_min) <= snap_tol_deg else 0 + lat_max = snap_max * ep_lat_max + (1 - snap_max) * lat_max + lat_min = snap_min * ep_lat_min + (1 - snap_min) * lat_min + + return lat_min, lat_max, lon_min, lon_max + + +@njit(cache=True) +def _generate_lat_lon_bounds_pole(face_vertices, label, z_min, z_max, snap_tol_deg): + """Compute bounds for a polar-candidate face. + + Checks whether the relevant pole (north or south) is inside the polygon + using the SPIP test. If the pole is not enclosed after all, falls back to + the local path. + + Parameters + ---------- + face_vertices : np.ndarray, shape (n, 3) + label : int + _FACE_LOC_NORTH_POLAR or _FACE_LOC_SOUTH_POLAR. + z_min, z_max : float + snap_tol_deg : float + + Returns + ------- + lat_min, lat_max, lon_min, lon_max : float + Degrees; lon in [0, 360], antimeridian-crossing indicated by + lon_min > lon_max. + wraps : bool + True when the face spans the full longitude circle (pole inside face). + """ + n = face_vertices.shape[0] + rad_to_deg = 180.0 / math.pi + + north_loc = ( + _point_in_polygon_sphere(_NORTH_POLE, face_vertices) + if label == _FACE_LOC_NORTH_POLAR + else _LOC_OUTSIDE + ) + south_loc = ( + _point_in_polygon_sphere(_SOUTH_POLE, face_vertices) + if label == _FACE_LOC_SOUTH_POLAR + else _LOC_OUTSIDE + ) + + if north_loc == _LOC_OUTSIDE and south_loc == _LOC_OUTSIDE: + a, b, c, d = _generate_lat_lon_bounds_local( + face_vertices, z_min, z_max, snap_tol_deg + ) + return a, b, c, d, False + + ep_lat_max = -np.inf + ep_lat_min = np.inf + for i in range(n): + zc = face_vertices[i, 2] + if zc > 1.0: + zc = 1.0 + elif zc < -1.0: + zc = -1.0 + lat = math.asin(zc) * rad_to_deg + if lat > ep_lat_max: + ep_lat_max = lat + if lat < ep_lat_min: + ep_lat_min = lat + + lon_min, lon_max = _lon_bounds_from_vertices(face_vertices) + + zmx = min(z_max, 1.0) + zmn = max(z_min, -1.0) + lat_max = math.asin(zmx) * rad_to_deg + lat_min = math.asin(zmn) * rad_to_deg + + snap_max = 1 if abs(lat_max - ep_lat_max) <= snap_tol_deg else 0 + snap_min = 1 if abs(lat_min - ep_lat_min) <= snap_tol_deg else 0 + lat_max = snap_max * ep_lat_max + (1 - snap_max) * lat_max + lat_min = snap_min * ep_lat_min + (1 - snap_min) * lat_min + + if north_loc != _LOC_OUTSIDE: + if north_loc == _LOC_INSIDE: + return lat_min, 90.0, 0.0, 360.0, True + return lat_min, 90.0, lon_min, lon_max, False + + if south_loc == _LOC_INSIDE: + return -90.0, lat_max, 0.0, 360.0, True + return -90.0, lat_max, lon_min, lon_max, False + + +@njit(cache=True, parallel=True) +def _construct_face_bounds_array_gca( + face_node_connectivity, + n_nodes_per_face, + node_x, + node_y, + node_z, + polar_cap_z, + snap_tol_deg, +): + """Parallel GCA bounds computation using the accurate local/polar-cap path. + + Replaces ``_construct_face_bounds_array`` for the common case where all + edges are great-circle arcs (no ``is_latlonface`` or ``is_face_GCA_list`` + overrides). + + Parameters + ---------- + face_node_connectivity : np.ndarray, shape (n_face, max_nodes) + n_nodes_per_face : np.ndarray, shape (n_face,) + node_x, node_y, node_z : np.ndarray, shape (n_node,) + polar_cap_z : float + Precomputed sin(polar_cap_latitude). + snap_tol_deg : float + + Returns + ------- + np.ndarray, shape (n_face, 2, 2) + [[lat_min, lat_max], [lon_min, lon_max]] in radians per face. + """ + n_face = face_node_connectivity.shape[0] + bounds_array = np.empty((n_face, 2, 2), dtype=np.float64) + deg_to_rad = math.pi / 180.0 + + for face_idx in prange(n_face): + k = n_nodes_per_face[face_idx] + verts = np.empty((k, 3)) + for vi in range(k): + node = face_node_connectivity[face_idx, vi] + verts[vi, 0] = node_x[node] + verts[vi, 1] = node_y[node] + verts[vi, 2] = node_z[node] + + label, z_min, z_max = _face_location_info(verts, polar_cap_z) + + if label == _FACE_LOC_LOCAL: + lat_min, lat_max, lon_min, lon_max = _generate_lat_lon_bounds_local( + verts, z_min, z_max, snap_tol_deg + ) + else: + lat_min, lat_max, lon_min, lon_max, _ = _generate_lat_lon_bounds_pole( + verts, label, z_min, z_max, snap_tol_deg + ) + + bounds_array[face_idx, 0, 0] = lat_min * deg_to_rad + bounds_array[face_idx, 0, 1] = lat_max * deg_to_rad + bounds_array[face_idx, 1, 0] = lon_min * deg_to_rad + bounds_array[face_idx, 1, 1] = lon_max * deg_to_rad + + return bounds_array + def _populate_face_bounds( grid, @@ -83,17 +420,30 @@ def _populate_face_bounds( """ grid.normalize_cartesian_coordinates() - bounds_array = _construct_face_bounds_array( - grid.face_node_connectivity.values, - grid.n_nodes_per_face.values, - grid.node_x.values, - grid.node_y.values, - grid.node_z.values, - grid.node_lon.values, - grid.node_lat.values, - is_latlonface, - is_face_GCA_list, - ) + if not is_latlonface and is_face_GCA_list is None: + # Pure GCA grid: use the accurate local/polar-cap path. + bounds_array = _construct_face_bounds_array_gca( + grid.face_node_connectivity.values, + grid.n_nodes_per_face.values, + grid.node_x.values, + grid.node_y.values, + grid.node_z.values, + math.sin(80.0 * math.pi / 180.0), + _SNAP_TOL_DEG, + ) + else: + # Latlon or mixed-edge grids: use the existing path. + bounds_array = _construct_face_bounds_array( + grid.face_node_connectivity.values, + grid.n_nodes_per_face.values, + grid.node_x.values, + grid.node_y.values, + grid.node_z.values, + grid.node_lon.values, + grid.node_lat.values, + is_latlonface, + is_face_GCA_list, + ) bounds_da = xr.DataArray( bounds_array, diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py index a622eb8dc..071a6caa1 100644 --- a/uxarray/grid/point_in_face.py +++ b/uxarray/grid/point_in_face.py @@ -1,79 +1,303 @@ from __future__ import annotations +import math from typing import TYPE_CHECKING import numpy as np from numba import njit, prange -from uxarray.constants import ERROR_TOLERANCE, INT_DTYPE, INT_FILL_VALUE -from uxarray.grid.arcs import point_within_gca -from uxarray.grid.utils import _get_cartesian_face_edge_nodes, _small_angle_of_2_vectors +from uxarray.constants import INT_DTYPE, INT_FILL_VALUE +from uxarray.grid.arcs import ( + _PREDICATE_ZERO_TOL, + _normal_dot_value, + on_minor_arc, +) +from uxarray.grid.utils import _get_cartesian_face_edge_nodes +from uxarray.utils.computing import accucross if TYPE_CHECKING: from numpy.typing import ArrayLike from uxarray.grid.grid import Grid +# Return codes for _point_in_polygon_sphere. +_LOC_OUTSIDE = 0 +_LOC_INSIDE = 1 +_LOC_ON_VERTEX = 2 +_LOC_ON_EDGE = 3 + +# Sign codes for orient3d_on_sphere results. +_SIGN_NEG = -1 +_SIGN_ZERO = 0 +_SIGN_POS = 1 + +_VERTEX_TOL = 1e-12 +_EDGE_TOL = 1e-10 +_RAY_EPS = 1e-8 + @njit(cache=True) -def _face_contains_point(face_edges: np.ndarray, point: np.ndarray) -> bool: +def _ray_endpoint(q): + """Return a unit vector R perpendicular to q for use as the SPIP ray target. + + Constructs R by projecting the coordinate axis least parallel to q onto + the plane perpendicular to q and normalizing. This gives q·R = 0 exactly + (a 90° arc), so q×R has magnitude ≈ 1 — keeping orient3d_on_sphere calls + well-conditioned regardless of q's position. + + A small perturbation is added to reduce the chance that R falls exactly on + a polygon edge's great circle, which would trigger the -1 degenerate path. """ - Determine whether a point lies within a face using the spherical winding-number method. + ax, ay, az = abs(q[0]), abs(q[1]), abs(q[2]) + if ax <= ay and ax <= az: + # Project the x-axis: (1,0,0) - q[0]*q + r0 = 1.0 - q[0] * q[0] + r1 = -q[1] * q[0] + r2 = -q[2] * q[0] + elif ay <= ax and ay <= az: + r0 = -q[0] * q[1] + r1 = 1.0 - q[1] * q[1] + r2 = -q[2] * q[1] + else: + r0 = -q[0] * q[2] + r1 = -q[1] * q[2] + r2 = 1.0 - q[2] * q[2] + r0 += _RAY_EPS + r1 -= _RAY_EPS * 0.7 + r2 += _RAY_EPS * 0.3 + n = math.sqrt(r0 * r0 + r1 * r1 + r2 * r2) + r = np.empty(3) + inv = 1.0 / n + r[0] = r0 * inv + r[1] = r1 * inv + r[2] = r2 * inv + return r + + +@njit(cache=True, inline="always") +def _sign_from_value(v): + """ + Sign of a compensated orient3d value under the standard zero tolerance. + """ + if v > _PREDICATE_ZERO_TOL: + return _SIGN_POS + if v < -_PREDICATE_ZERO_TOL: + return _SIGN_NEG + return _SIGN_ZERO + + +@njit(cache=True, inline="always") +def _counts_as_crossing( + a0, + a1, + a2, + b0, + b1, + b2, + q0, + q1, + q2, + r0, + r1, + r2, + qr_x_hi, + qr_y_hi, + qr_z_hi, + qr_x_lo, + qr_y_lo, + qr_z_lo, +): + """Return 1 if edge AB crosses the minor arc q->R, 0 if not, -1 if degenerate. + + An edge AB crosses ray q->R iff q and R lie on opposite sides of the great + circle plane through AB AND A and B lie on opposite sides of the great + circle plane through q->R. Uses orient3d_on_sphere (compensated) for all + side-of-plane tests. Returns -1 when R lies exactly on plane(AB), which + signals the caller to perturb R and retry. + """ + # A x B: computed once, reused by both the q-side and R-side tests below. + nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo = accucross(a0, a1, a2, b0, b1, b2) + + s_AB_q = _sign_from_value( + _normal_dot_value(nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo, q0, q1, q2) + ) + # q on great circle AB: already caught by edge-membership check; not a crossing. + if s_AB_q == _SIGN_ZERO: + return 0 + + s_AB_R = _sign_from_value( + _normal_dot_value(nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo, r0, r1, r2) + ) + # R on great circle AB: degenerate ray, caller must perturb R. + if s_AB_R == _SIGN_ZERO: + return -1 + # q and R on the same side of plane(AB): no crossing possible. + if s_AB_q == s_AB_R: + return 0 + + # q and R are strictly on opposite sides of plane(AB). + # Now check whether the intersection of the two great circles falls + # inside the minor arc A->B, i.e. A and B are on opposite sides of plane(qR). + s_qR_A = _sign_from_value( + _normal_dot_value( + qr_x_hi, qr_y_hi, qr_z_hi, qr_x_lo, qr_y_lo, qr_z_lo, a0, a1, a2 + ) + ) + s_qR_B = _sign_from_value( + _normal_dot_value( + qr_x_hi, qr_y_hi, qr_z_hi, qr_x_lo, qr_y_lo, qr_z_lo, b0, b1, b2 + ) + ) + + # Common case: neither endpoint lies on the ray plane, so the edge counts + # iff A and B straddle it. + s_qR_prod = s_qR_A * s_qR_B + if s_qR_prod != 0: + return 1 if s_qR_prod < 0 else 0 + + # An endpoint lies exactly on the ray plane. Apply the half-edge rule there: + # count the edge only if the other endpoint is strictly on the negative + # side, so that the two edges meeting at such a vertex are not both counted. + if s_qR_A == _SIGN_ZERO: + if s_qR_B == _SIGN_ZERO: + return 0 # whole edge coplanar with the ray plane: degenerate + return 1 if s_qR_B == _SIGN_NEG else 0 + return 1 if s_qR_A == _SIGN_NEG else 0 + + +@njit(cache=True) +def _point_in_polygon_sphere(q, polygon): + """Spherical point-in-polygon test using the perturbed-antipode ray-casting method. + + Casts a great-circle ray from q toward its perturbed antipode R and counts + how many polygon edges the ray crosses. Uses ``orient3d_on_sphere`` + (compensated) for the crossing test, avoiding the ``arctan2`` calls in the + winding-number approach and the large number of ``np.cross`` allocations. - This function sums the signed central angles between successive vertices of the face - as seen from `point`. If the total absolute winding exceeds π, the point is inside. - Points exactly on a node or edge also count as inside. + Returns one of _LOC_INSIDE, _LOC_OUTSIDE, _LOC_ON_VERTEX, _LOC_ON_EDGE. + + Degenerate-ray handling: when R falls on a polygon edge's great circle, R + is nudged by a fixed perturbation and the loop restarts (up to 4 retries). + AccuSphGeom's Tier-3 approach instead uses Simulation of Simplicity (SoS) + with global vertex IDs to resolve degeneracies without any branching or + retries. SoS requires per-vertex IDs that are not available in the current + UXarray polygon representation, so it is left as future work. Parameters ---------- - face_edges : np.ndarray, shape (n_edges, 2, 3) - Cartesian coordinates (unit-vectors) of each great-circle edge of the face. - Each row is [start_xyz, end_xyz]. - point : np.ndarray, shape (3,) - 3D unit-vector of the query point on the unit sphere. + q : np.ndarray, shape (3,) + Query point (unit vector). + polygon : np.ndarray, shape (n, 3) + Polygon vertices on the unit sphere, ordered. Returns ------- - inside : bool - True if the point is inside the face or lies exactly on a node/edge; False otherwise. + int + Location code: _LOC_OUTSIDE (0), _LOC_INSIDE (1), + _LOC_ON_VERTEX (2), _LOC_ON_EDGE (3). """ - # Check for an exact hit with any of the corner nodes - for e in range(face_edges.shape[0]): - if np.allclose( - face_edges[e, 0], point, rtol=ERROR_TOLERANCE, atol=ERROR_TOLERANCE - ): - return True - if np.allclose( - face_edges[e, 1], point, rtol=ERROR_TOLERANCE, atol=ERROR_TOLERANCE - ): - return True - if point_within_gca(point, face_edges[e, 0], face_edges[e, 1]): - return True + n = polygon.shape[0] - n = face_edges.shape[0] - - total = 0.0 - p = point + # 1. Vertex coincidence check. for i in range(n): - a = face_edges[i, 0] - b = face_edges[i + 1, 0] if i + 1 < n else face_edges[0, 0] + dx = polygon[i, 0] - q[0] + dy = polygon[i, 1] - q[1] + dz = polygon[i, 2] - q[2] + if dx * dx + dy * dy + dz * dz < _VERTEX_TOL * _VERTEX_TOL: + return _LOC_ON_VERTEX - vi = a - p - vj = b - p + # 2. Edge membership check. + for i in range(n): + A = polygon[i] + B = polygon[(i + 1) % n] + if on_minor_arc(q, A, B, _EDGE_TOL): + return _LOC_ON_EDGE + + # 3. Ray-casting crossing count. + # When R hits a degenerate edge, nudge and restart from i=0 so that all + # edges are counted with the same ray — a mid-loop nudge corrupts parity. + R = _ray_endpoint(q) + q0, q1, q2 = q[0], q[1], q[2] + for _retry in range(4): + # The ray plane q x R is the same for every edge of the face, so it is + # computed once per ray pass rather than twice per edge. + qr_x_hi, qr_y_hi, qr_z_hi, qr_x_lo, qr_y_lo, qr_z_lo = accucross( + q0, q1, q2, R[0], R[1], R[2] + ) + inside = False + need_retry = False + for i in range(n): + A = polygon[i] + B = polygon[(i + 1) % n] + c = _counts_as_crossing( + A[0], + A[1], + A[2], + B[0], + B[1], + B[2], + q0, + q1, + q2, + R[0], + R[1], + R[2], + qr_x_hi, + qr_y_hi, + qr_z_hi, + qr_x_lo, + qr_y_lo, + qr_z_lo, + ) + if c < 0: + R[0] += 1e-7 + R[1] -= 1e-7 + R[2] += 5e-8 + n2 = R[0] * R[0] + R[1] * R[1] + R[2] * R[2] + inv = 1.0 / math.sqrt(n2) + R[0] *= inv + R[1] *= inv + R[2] *= inv + need_retry = True + break + if c == 1: + inside = not inside + if not need_retry: + return _LOC_INSIDE if inside else _LOC_OUTSIDE + + return _LOC_OUTSIDE - # check if you’re right on a vertex - if np.linalg.norm(vi) < ERROR_TOLERANCE or np.linalg.norm(vj) < ERROR_TOLERANCE: - return True - ang = _small_angle_of_2_vectors(vi, vj) +@njit(cache=True) +def _face_contains_point(face_edges: np.ndarray, point: np.ndarray) -> bool: + """Determine whether a point lies within a face using spherical ray casting. - # determine sign from cross - c = np.cross(vi, vj) - sign = 1.0 if (c[0] * p[0] + c[1] * p[1] + c[2] * p[2]) >= 0.0 else -1.0 + Delegates to ``_point_in_polygon_sphere`` after extracting the vertex + array from the edge array. Returns True for points strictly inside the + face and for points exactly on an edge or vertex. - total += sign * ang + Parameters + ---------- + face_edges : np.ndarray, shape (n_edges, 2, 3) + Cartesian unit-vector coordinates of each great-circle edge. + Each row is [start_xyz, end_xyz]. + point : np.ndarray, shape (3,) + 3D unit-vector of the query point on the unit sphere. - return np.abs(total) > np.pi + Returns + ------- + bool + True if the point is inside the face or on its boundary. + """ + n = face_edges.shape[0] + # Build the (n, 3) vertex array from the edge start points. + polygon = np.empty((n, 3)) + for i in range(n): + polygon[i, 0] = face_edges[i, 0, 0] + polygon[i, 1] = face_edges[i, 0, 1] + polygon[i, 2] = face_edges[i, 0, 2] + loc = _point_in_polygon_sphere(point, polygon) + return loc != _LOC_OUTSIDE @njit(cache=True)