diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb new file mode 100644 index 000000000..99d51ab9a --- /dev/null +++ b/docs/user-guide/spherical-geometry-accuracy.ipynb @@ -0,0 +1,1776 @@ +{ + "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 guards against this throughout its geometry stack using **error-free transformations** (EFT).\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": "2026-05-22T11:58:05.159070Z", + "iopub.status.busy": "2026-05-22T11:58:05.158804Z", + "iopub.status.idle": "2026-05-22T11:58:09.059523Z", + "shell.execute_reply": "2026-05-22T11:58:09.059089Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + " const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n", + " const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n", + "\n", + " // 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const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n", + " const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n", + " const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version)?.Panel == null;\n", + " if (shouldSkip && !(isBokehOrPanel && missingOrBroken)) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.8.10/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false;\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true;\n", + " root._bokeh_onload_callbacks = [];\n", + " const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " if (Bokeh != undefined && !reloading) {\n", + " const NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh[NewBokeh.version] = NewBokeh;\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh);\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version)?.Panel == null;\n if (shouldSkip && !(isBokehOrPanel && missingOrBroken)) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.10/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false;\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true;\n root._bokeh_onload_callbacks = [];\n const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n if (Bokeh != undefined && !reloading) {\n const NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh[NewBokeh.version] = NewBokeh;\n Bokeh.versions.set(NewBokeh.version, NewBokeh);\n }\n root.Bokeh = Bokeh;\n }\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " let retries = 0;\n", + " const open = () => {\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else if (retries > 3) {\n", + " console.warn('Comm target never activated')\n", + " } else {\n", + " retries += 1\n", + " setTimeout(open, 500)\n", + " }\n", + " }\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else {\n", + " setTimeout(open, 500)\n", + " }\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " 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comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import warnings\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.patches as mpatches\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import uxarray as ux\n", + "from uxarray.grid._eft import diff_of_products\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": "2026-05-22T11:58:09.061880Z", + "iopub.status.busy": "2026-05-22T11:58:09.061594Z", + "iopub.status.idle": "2026-05-22T11:58:09.282799Z", + "shell.execute_reply": "2026-05-22T11:58:09.282360Z" + } + }, + "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 **error-free transformations** (EFT) — a technique from computer arithmetic that represents every floating-point product as an exact `(hi, lo)` pair. The `lo` term captures the rounding residual that naive subtraction discards, recovering roughly double the effective precision for cross-product computations.\n", + "\n", + "The EFT primitives in UXarray are a Python/Numba port of 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)). The key building blocks live in `uxarray.grid._eft` and `uxarray.grid.arcs`:\n", + "\n", + "| Function | Module | What it does |\n", + "|---|---|---|\n", + "| `two_sum(a, b)` | `_eft` | Exact split of `a + b` into `(hi, lo)` |\n", + "| `two_prod(a, b)` | `_eft` | Exact split of `a * b` into `(hi, lo)` |\n", + "| `diff_of_products(a, b, c, d)` | `_eft` | EFT-accurate `a*b - c*d` |\n", + "| `accucross(ax, ay, az, bx, by, bz)` | `_eft` | EFT cross product returning 6 `(hi, lo)` components |\n", + "| `orient3d_on_sphere(a, b, q)` | `arcs` | Sign of `(a×b)·q`: +1, −1, or 0 |\n", + "| `on_minor_arc(q, a, b)` | `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": "2026-05-22T11:58:09.284596Z", + "iopub.status.busy": "2026-05-22T11:58:09.284448Z", + "iopub.status.idle": "2026-05-22T11:58:09.646653Z", + "shell.execute_reply": "2026-05-22T11:58:09.646251Z" + } + }, + "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": "2026-05-22T11:58:09.649111Z", + "iopub.status.busy": "2026-05-22T11:58:09.648771Z", + "iopub.status.idle": "2026-05-22T11:58:10.879280Z", + "shell.execute_reply": "2026-05-22T11:58:10.878883Z" + } + }, + "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": "2026-05-22T11:58:10.881015Z", + "iopub.status.busy": "2026-05-22T11:58:10.880860Z", + "iopub.status.idle": "2026-05-22T11:58:10.894165Z", + "shell.execute_reply": "2026-05-22T11:58:10.893850Z" + } + }, + "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. EFT keeps the correct sign down to machine precision." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "geometry-map", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T11:58:10.895770Z", + "iopub.status.busy": "2026-05-22T11:58:10.895634Z", + "iopub.status.idle": "2026-05-22T11:58:12.770355Z", + "shell.execute_reply": "2026-05-22T11:58:12.770002Z" + } + }, + "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", + "EFT is wired into every module that performs geometric predicates on the sphere. The table below maps each user-facing operation to the underlying EFT function that protects it.\n", + "\n", + "| User-facing operation | Module | EFT 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`, `on_minor_arc` |\n", + "| Arc–latitude intersection (zonal averages) | `grid/intersections.py` | `accucross`, `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": "uxarray_env3.12", + "language": "python", + "name": "uxarray_env3.12" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/userguide.rst b/docs/userguide.rst index c281805b3..a442a7c15 100644 --- a/docs/userguide.rst +++ b/docs/userguide.rst @@ -94,6 +94,9 @@ Supplementary Guides These user guides provide additional details about specific features in UXarray. +`Accurate Spherical Geometry `_ + How UXarray uses error-free transformations to avoid catastrophic cancellation in cross-product and point-in-polygon operations + `Working with HEALPix Grids `_ Use UXarray with HEALPix @@ -127,6 +130,7 @@ 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/uxarray/grid/_eft.py b/uxarray/grid/_eft.py new file mode 100644 index 000000000..4a5ec5f97 --- /dev/null +++ b/uxarray/grid/_eft.py @@ -0,0 +1,167 @@ +"""Error-free transformations (EFT) for accurate floating-point arithmetic. + +In spherical-geometry computations the critical operations are cross products +and dot products over unit vectors. When two vectors are nearly parallel, the +difference of products that forms each cross-product component suffers +catastrophic cancellation: both products round to the same floating-point +value and their difference carries no significant bits. This affects +GCA-GCA intersection of nearly tangent arcs, constant-latitude intersection +near arc endpoints, and the ray-crossing test in point-in-polygon near polygon +edges. + +The functions here represent each result as an unevaluated sum of two +``float64`` values ``(hi, lo)`` such that ``hi + lo`` equals the +mathematically exact result. This effectively doubles the significant bits +available for cross-product components without resorting to arbitrary- +precision arithmetic. + +These primitives are a Python/Numba port of the error-free transformation +layer from the AccuSphGeom C++ library: + + Chen, H. (2026). Accurate and Robust Algorithms for Spherical Polygon + Operations. EGUsphere preprint. + https://egusphere.copernicus.org/preprints/2026/egusphere-2026-636/ + + Chen, H. Accurate and Robust Great Circle Arc Intersection and Great + Circle Arc Constant Latitude Intersection on the Sphere. SIAM J. Sci. + Comput. https://doi.org/10.1137/25M1737614 + +AccuSphGeom reference implementation (C++): + https://github.com/hongyuchen1030/AccuSphGeom + +What this module omits: AccuSphGeom's full robustness stack has three +tiers — an EFT filter (what this module implements), Shewchuk adaptive +predicates for results that fall inside the filter threshold, and a geogram +exact-arithmetic fallback. This port implements only the EFT tier. For +non-degenerate inputs in double precision this is sufficient; callers that +need to handle geometrically degenerate inputs (coincident arcs, a query +point exactly on a polygon edge) should add their own perturbation or +fall-back logic. +""" + +from numba import njit + + +@njit(cache=True, inline="always") +def two_sum(a, b): + """Knuth's TwoSum: return (s, e) with s = fl(a + b) and s + e = a + b exactly. + + Floating-point addition rounds the mathematical result to the nearest + representable value. ``two_sum`` captures that rounding error in the + companion term ``e`` so that ``s + e`` equals the true sum with no + information lost. The cost is four extra floating-point operations beyond + the addition itself. + + Parameters + ---------- + a, b : float + Input values. + + Returns + ------- + s : float + Rounded sum fl(a + b). + e : float + Rounding error term; s + e = a + b exactly. + """ + s = a + b + bp = s - a + e = (a - (s - bp)) + (b - bp) + return s, e + + +@njit(cache=True, inline="always") +def two_prod(a, b): + """Dekker/Veltkamp TwoProd: return (p, e) with p = fl(a * b) and p + e = a * b exactly. + + Like ``two_sum`` for multiplication. Uses the Veltkamp splitting constant + 2**27 + 1 to decompose each operand into a high and low half, then + reconstructs the exact rounding error from the four partial products. + On hardware with a fused multiply-add (FMA) instruction the error term + could be obtained in one step as ``fma(a, b, -p)``; the split used here + is portable across all Numba targets. + + Parameters + ---------- + a, b : float + Input values. + + Returns + ------- + p : float + Rounded product fl(a * b). + e : float + Rounding error term; p + e = a * b exactly. + """ + p = a * b + factor = 134217729.0 # 2**27 + 1 + a_hi = factor * a - (factor * a - a) + a_lo = a - a_hi + b_hi = factor * b - (factor * b - b) + b_lo = b - b_hi + e = a_lo * b_lo - (((p - a_hi * b_hi) - a_lo * b_hi) - a_hi * b_lo) + return p, e + + +@njit(cache=True, inline="always") +def diff_of_products(a, b, c, d): + """Kahan's accurate a*b - c*d using two_prod and two_sum. + + Naive evaluation of ``a*b - c*d`` loses all significant bits when the two + products are nearly equal (catastrophic cancellation). This routine + computes each product exactly via ``two_prod``, subtracts the rounded + high parts, then folds the residual low parts back in. The result has + rounding error bounded by one ulp of the true value regardless of + cancellation. + + This is the core operation that makes cross products accurate: every + component of ``a x b`` is a difference of two products of exactly this + form. + + Parameters + ---------- + a, b, c, d : float + Input scalars; computes a*b - c*d. + + Returns + ------- + hi : float + High-order part of the accurate result. + lo : float + Low-order correction term; hi + lo equals the accurate value. + """ + w, e_w = two_prod(c, d) + x, e_x = two_prod(a, b) + s, e_s = two_sum(x, -w) + lo = (e_x - e_w) + e_s + return s, lo + + +@njit(cache=True, inline="always") +def accucross(a0, a1, a2, b0, b1, b2): + """Accurate cross product a x b returning (hi[3], lo[3]) component pairs. + + Each component of a cross product is a difference of two products — the + exact form that ``diff_of_products`` handles. This function computes all + three components that way, returning six scalars such that the + mathematically exact cross product satisfies ``result[i] = hi[i] + lo[i]`` + for each component. Callers that need single-precision accuracy can use + the hi parts alone; callers that need the full compensated result add + hi and lo before further use. + + Parameters + ---------- + a0, a1, a2 : float + Components of vector a. + b0, b1, b2 : float + Components of vector b. + + Returns + ------- + x_hi, y_hi, z_hi, x_lo, y_lo, z_lo : float + High and low parts of each cross-product component. + """ + x_hi, x_lo = diff_of_products(a1, b2, a2, b1) + y_hi, y_lo = diff_of_products(a2, b0, a0, b2) + z_hi, z_lo = diff_of_products(a0, b1, a1, b0) + return x_hi, y_hi, z_hi, x_lo, y_lo, z_lo diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py index 17426a196..481b2415c 100644 --- a/uxarray/grid/arcs.py +++ b/uxarray/grid/arcs.py @@ -4,11 +4,19 @@ from numba import njit from uxarray.constants import ERROR_TOLERANCE, MACHINE_EPSILON +from uxarray.grid._eft import diff_of_products, two_sum from uxarray.grid.coordinates import ( _normalize_xyz_scalar, ) from uxarray.grid.utils import _angle_of_2_vectors +# Tolerance used to classify orient3d results as zero. For double-precision +# unit-vector inputs this covers rounding error in the EFT cross product. +_PREDICATE_ZERO_TOL = 1e-15 + +# Default tolerance for the on_minor_arc collinearity and interval tests. +_ON_MINOR_ARC_TOL = 1e-10 + def _to_list(obj): if not isinstance(obj, list): @@ -364,3 +372,103 @@ def compute_arc_length(pt_a, pt_b): delta_theta = np.arctan2(cross_2d, dot_2d) return rho * abs(delta_theta) + + +@njit(cache=True) +def _orient3d_on_sphere_value(a, b, q): + """Return the EFT-accurate value of the orient3d-on-sphere predicate. + + Computes the scalar (a x b) . q using ``diff_of_products`` for the + cross-product components and ``two_sum`` for the final accumulation. + For unit vectors all coordinates are in [-1, 1], so the EFT cross product + provides roughly double the effective precision of a naive evaluation. + The result is positive when q lies to the left of the directed arc a->b, + negative when to the right, and near zero when q is on the great circle + through a and b. + + Parameters + ---------- + a, b, q : np.ndarray, shape (3,) + Unit vectors on the unit sphere. + + Returns + ------- + float + Signed determinant value. + """ + x_hi, x_lo = diff_of_products(a[1], b[2], a[2], b[1]) + y_hi, y_lo = diff_of_products(a[2], b[0], a[0], b[2]) + z_hi, z_lo = diff_of_products(a[0], b[1], a[1], b[0]) + p0 = (x_hi + x_lo) * q[0] + p1 = (y_hi + y_lo) * q[1] + p2 = (z_hi + z_lo) * q[2] + s, e = two_sum(p0, p1) + s, e2 = two_sum(s, p2) + return s + (e + e2) + + +@njit(cache=True) +def orient3d_on_sphere(a, b, q, tol=_PREDICATE_ZERO_TOL): + """Sign of the orient3d predicate on the unit sphere: -1, 0, or +1. + + Evaluates the sign of ``(a x b) . q`` using error-free transformations to + avoid false zero results from floating-point cancellation near great-circle + boundaries. The sign determines which side of the great circle through a + and b the point q lies on. + + Parameters + ---------- + a, b, q : np.ndarray, shape (3,) + Unit vectors on the unit sphere. + tol : float, optional + Magnitude below which the result is classified as zero. + + Returns + ------- + int + +1 if q is to the left of a->b, -1 if to the right, 0 if collinear + within ``tol``. + """ + v = _orient3d_on_sphere_value(a, b, q) + if v > tol: + return 1 + if v < -tol: + return -1 + return 0 + + +@njit(cache=True) +def on_minor_arc(q, a, b, tol=_ON_MINOR_ARC_TOL): + """Return True if q lies on the minor great-circle arc from a to b. + + Uses ``_orient3d_on_sphere_value`` (a compensated cross product) for the + collinearity test and dot products for the interval check. Compared to + ``point_within_gca``, this avoids the ``arctan2`` call that guards against + 180-degree arcs and avoids the separate plane-membership check via + ``np.cross`` + ``np.dot``. + + Parameters + ---------- + q : np.ndarray, shape (3,) + Query point (unit vector). + a, b : np.ndarray, shape (3,) + Endpoints of the great-circle arc (unit vectors). + tol : float, optional + Tolerance for the collinearity and interval checks. + + Returns + ------- + bool + True if q lies on the minor arc ab, False otherwise. + """ + # Coincident endpoints: degenerate arc, no interior. + if a[0] == b[0] and a[1] == b[1] and a[2] == b[2]: + return False + # Collinearity check: q must lie on the great circle through a and b. + if abs(_orient3d_on_sphere_value(a, b, q)) > tol: + return False + # Interval check: q must lie on the minor-arc side of both endpoints. + qa = a[0] * q[0] + a[1] * q[1] + a[2] * q[2] + qb = b[0] * q[0] + b[1] * q[1] + b[2] * q[2] + ab = a[0] * b[0] + a[1] * b[1] + a[2] * b[2] + return (qb - ab * qa) >= -tol and (qa - qb * ab) >= -tol diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py index 626946244..bff921a8e 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 +23,332 @@ any_close_lat, ) +# --------------------------------------------------------------------------- +# Constants for the accurate GCA bounds path. +# --------------------------------------------------------------------------- + +# Faces whose z-extremum exceeds sin(_POLAR_CAP_DEG°) are treated as polar +# candidates and get a point-in-polygon check for pole containment. +_POLAR_CAP_DEG = 80.0 +_POLAR_CAP_Z = math.sin(_POLAR_CAP_DEG * math.pi / 180.0) + +# 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. + denom = (z1 + z2) * (d - 1.0) + if denom != 0.0: + a_raw = (z1 * d - z2) / denom + else: + a_raw = -1.0 + + if 0.0 < a_raw < 1.0: + one_a = 1.0 - a_raw + y0 = one_a * x1[0] + a_raw * x2[0] + y1 = one_a * x1[1] + a_raw * x2[1] + y2 = one_a * x1[2] + a_raw * x2[2] + norm = math.sqrt(y0 * y0 + y1 * y1 + y2 * y2) + z_ext = y2 / norm + if z_ext > z_max: + z_max = z_ext + if z_ext < z_min: + z_min = z_ext + else: + z_edge_max = z1 if z1 > z2 else z2 + z_edge_min = z1 if z1 < z2 else z2 + if z_edge_max > z_max: + z_max = z_edge_max + if z_edge_min < z_min: + z_min = z_edge_min + + if z_max >= polar_cap_z: + return _FACE_LOC_NORTH_POLAR, z_min, z_max + if z_min <= -polar_cap_z: + return _FACE_LOC_SOUTH_POLAR, z_min, z_max + return _FACE_LOC_LOCAL, 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. + """ + 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 they are nearly equal. + if abs(lat_max - ep_lat_max) <= snap_tol_deg: + lat_max = ep_lat_max + if abs(lat_min - ep_lat_min) <= snap_tol_deg: + lat_min = ep_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 + + if abs(lat_max - ep_lat_max) <= snap_tol_deg: + lat_max = ep_lat_max + if abs(lat_min - ep_lat_min) <= snap_tol_deg: + lat_min = ep_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 +416,30 @@ 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, + _POLAR_CAP_Z, + _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/intersections.py b/uxarray/grid/intersections.py index 97777de69..00b8d8b42 100644 --- a/uxarray/grid/intersections.py +++ b/uxarray/grid/intersections.py @@ -1,14 +1,14 @@ +import math + import numpy as np from numba import njit, prange -from uxarray.constants import ERROR_TOLERANCE, INT_DTYPE, MACHINE_EPSILON +from uxarray.constants import ERROR_TOLERANCE, INT_DTYPE +from uxarray.grid._eft import accucross from uxarray.grid.arcs import ( extreme_gca_z, in_between, - point_within_gca, -) -from uxarray.grid.utils import ( - _angle_of_2_vectors, + on_minor_arc, ) @@ -292,6 +292,19 @@ def faces_within_lat_bounds(lats, face_bounds_lat): return candidate_faces +@njit(cache=True) +def _normalize_pair(x_hi, y_hi, z_hi, x_lo, y_lo, z_lo): + """Normalize an (hi, lo) compensated vector, returning the unit vector and magnitude.""" + x = x_hi + x_lo + y = y_hi + y_lo + z = z_hi + z_lo + n = math.sqrt(x * x + y * y + z * z) + if n == 0.0: + return 0.0, 0.0, 0.0, 0.0 + inv = 1.0 / n + return x * inv, y * inv, z * inv, n + + def _gca_gca_intersection_cartesian(gca_a_xyz, gca_b_xyz): gca_a_xyz = np.asarray(gca_a_xyz) gca_b_xyz = np.asarray(gca_b_xyz) @@ -301,139 +314,198 @@ def _gca_gca_intersection_cartesian(gca_a_xyz, gca_b_xyz): @njit(cache=True) def gca_gca_intersection(gca_a_xyz, gca_b_xyz): - if gca_a_xyz.shape[1] != 3 or gca_b_xyz.shape[1] != 3: - raise ValueError("The two GCAs must be in the cartesian [x, y, z] format") + """Find intersection point(s) of two great-circle arcs using compensated arithmetic. - # Extract points - w0_xyz = gca_a_xyz[0] - w1_xyz = gca_a_xyz[1] - v0_xyz = gca_b_xyz[0] - v1_xyz = gca_b_xyz[1] + Uses ``accucross`` (error-free cross products) and ``on_minor_arc`` (EFT-based + arc membership) to avoid the catastrophic cancellation that affects naive + cross product implementations when arcs are nearly parallel. - angle_w0w1 = _angle_of_2_vectors(w0_xyz, w1_xyz) - angle_v0v1 = _angle_of_2_vectors(v0_xyz, v1_xyz) + Parameters + ---------- + gca_a_xyz : np.ndarray, shape (2, 3) + Cartesian endpoints of the first great-circle arc. + gca_b_xyz : np.ndarray, shape (2, 3) + Cartesian endpoints of the second great-circle arc. - if angle_w0w1 > np.pi: - w0_xyz, w1_xyz = w1_xyz, w0_xyz + Returns + ------- + np.ndarray, shape (n, 3) + Intersection points lying on both arcs; n is 0, 1, or 2. + """ + if gca_a_xyz.shape[1] != 3 or gca_b_xyz.shape[1] != 3: + raise ValueError("The two GCAs must be in the cartesian [x, y, z] format") - if angle_v0v1 > np.pi: - v0_xyz, v1_xyz = v1_xyz, v0_xyz + w0 = gca_a_xyz[0] + w1 = gca_a_xyz[1] + v0 = gca_b_xyz[0] + v1 = gca_b_xyz[1] - w0w1_norm = np.cross(w0_xyz, w1_xyz) - v0v1_norm = np.cross(v0_xyz, v1_xyz) - cross_norms = np.cross(w0w1_norm, v0v1_norm) + # 1. Plane normals via accurate cross products. + n1x, n1y, n1z, n1_mag = _normalize_pair( + *accucross(w0[0], w0[1], w0[2], w1[0], w1[1], w1[2]) + ) + n2x, n2y, n2z, n2_mag = _normalize_pair( + *accucross(v0[0], v0[1], v0[2], v1[0], v1[1], v1[2]) + ) - # Initialize result array and counter res = np.empty((2, 3)) count = 0 - # Check if the two GCAs are parallel - if np.allclose(cross_norms, 0.0, atol=MACHINE_EPSILON): - if point_within_gca(v0_xyz, w0_xyz, w1_xyz): - res[count, :] = v0_xyz - count += 1 - - if point_within_gca(v1_xyz, w0_xyz, w1_xyz): - res[count, :] = v1_xyz - count += 1 + if n1_mag == 0.0 or n2_mag == 0.0: + return res[:count] - return res[:count, :] + # 2. Intersection direction: cross product of the two plane normals. + vx, vy, vz, vn = _normalize_pair(*accucross(n1x, n1y, n1z, n2x, n2y, n2z)) - # Normalize the cross_norms - cross_norms = cross_norms / np.linalg.norm(cross_norms) - x1_xyz = cross_norms - x2_xyz = -x1_xyz - - # Check intersection points - if point_within_gca(x1_xyz, w0_xyz, w1_xyz) and point_within_gca( - x1_xyz, v0_xyz, v1_xyz - ): - res[count, :] = x1_xyz + if vn == 0.0 or not (math.isfinite(vx) and math.isfinite(vy) and math.isfinite(vz)): + # Parallel (coplanar) arcs: check whether endpoints of one lie on the other. + if on_minor_arc(v0, w0, w1): + res[count, 0] = v0[0] + res[count, 1] = v0[1] + res[count, 2] = v0[2] + count += 1 + if on_minor_arc(v1, w0, w1): + res[count, 0] = v1[0] + res[count, 1] = v1[1] + res[count, 2] = v1[2] + count += 1 + return res[:count] + + # 3. Two antipodal candidate intersection points; keep those on both arcs. + pos = np.empty(3) + pos[0] = vx + pos[1] = vy + pos[2] = vz + neg = np.empty(3) + neg[0] = -vx + neg[1] = -vy + neg[2] = -vz + + if on_minor_arc(pos, w0, w1) and on_minor_arc(pos, v0, v1): + res[count, 0] = pos[0] + res[count, 1] = pos[1] + res[count, 2] = pos[2] count += 1 - if point_within_gca(x2_xyz, w0_xyz, w1_xyz) and point_within_gca( - x2_xyz, v0_xyz, v1_xyz - ): - res[count, :] = x2_xyz + if on_minor_arc(neg, w0, w1) and on_minor_arc(neg, v0, v1): + res[count, 0] = neg[0] + res[count, 1] = neg[1] + res[count, 2] = neg[2] count += 1 - return res[:count, :] + return res[:count] @njit(cache=True) def gca_const_lat_intersection(gca_cart, const_z): - """Calculate the intersection point(s) of a Great Circle Arc (GCA) and a - constant latitude line in a Cartesian coordinate system. + """Find intersection point(s) of a great-circle arc and a constant-latitude line. + + Uses the plane-normal of the arc (computed via ``accucross`` for extra + precision) to solve the system ``n . p = 0``, ``p[2] = const_z``, + ``|p| = 1``. Candidate solutions are checked against the arc with + ``on_minor_arc`` instead of ``point_within_gca`` to avoid the + ``arctan2`` overhead in that function. Parameters ---------- - gca_cart : [2, 3] np.ndarray Cartesian coordinates of the two end points GCA. + gca_cart : np.ndarray, shape (2, 3) + Cartesian coordinates of the two endpoints of the great-circle arc. const_z : float - The constant latitude represented in cartesian of the latitude line. + The constant z-coordinate (= sin(latitude)) of the latitude line. Returns ------- - np.ndarray - Cartesian coordinates of the intersection point(s) the shape is [2, 3]. If no intersections are found, - all values a `nan`. If one intersection is found, the first column represent the intersection point, and - if two intersections are found, each column represents a point. - + np.ndarray, shape (2, 3) + Intersection point(s). Missing entries are NaN-filled rows. The first + valid intersection is in row 0; a second (rare) intersection in row 1. """ res = np.empty((2, 3)) res.fill(np.nan) - x1, x2 = gca_cart + x1 = gca_cart[0] + x2 = gca_cart[1] - # Check if the constant latitude has the same latitude as the GCA endpoints - x1_at_const_z = np.isclose( - x1[2], const_z, rtol=ERROR_TOLERANCE, atol=ERROR_TOLERANCE - ) - x2_at_const_z = np.isclose( - x2[2], const_z, rtol=ERROR_TOLERANCE, atol=ERROR_TOLERANCE - ) + # 1. Endpoint coincidence with the latitude line. + x1_at_z = abs(x1[2] - const_z) <= ERROR_TOLERANCE + x2_at_z = abs(x2[2] - const_z) <= ERROR_TOLERANCE - if x1_at_const_z and x2_at_const_z: - res[0] = x1 - res[1] = x2 + if x1_at_z and x2_at_z: + res[0, 0] = x1[0] + res[0, 1] = x1[1] + res[0, 2] = x1[2] + res[1, 0] = x2[0] + res[1, 1] = x2[1] + res[1, 2] = x2[2] return res - elif x1_at_const_z: - res[0] = x1 + elif x1_at_z: + res[0, 0] = x1[0] + res[0, 1] = x1[1] + res[0, 2] = x1[2] return res - elif x2_at_const_z: - res[0] = x2 + elif x2_at_z: + res[0, 0] = x2[0] + res[0, 1] = x2[1] + res[0, 2] = x2[2] return res - # If the constant latitude is not the same as the GCA endpoints, calculate the intersection point + # 2. Early-exit if const_z is outside the arc's latitude range. z_min = extreme_gca_z(gca_cart, extreme_type="min") z_max = extreme_gca_z(gca_cart, extreme_type="max") - - # Check if the constant latitude is within the GCA range if not in_between(z_min, const_z, z_max): return res - n = np.cross(x1, x2) - - nx, ny, nz = n - - s_tilde = np.sqrt(nx**2 + ny**2 - (nx**2 + ny**2 + nz**2) * const_z**2) - p1_x = -(1.0 / (nx**2 + ny**2)) * (const_z * nx * nz + s_tilde * ny) - p2_x = -(1.0 / (nx**2 + ny**2)) * (const_z * nx * nz - s_tilde * ny) - p1_y = -(1.0 / (nx**2 + ny**2)) * (const_z * ny * nz - s_tilde * nx) - p2_y = -(1.0 / (nx**2 + ny**2)) * (const_z * ny * nz + s_tilde * nx) - - p1 = np.array([p1_x, p1_y, const_z]) - p2 = np.array([p2_x, p2_y, const_z]) - - p1_intersects_gca = point_within_gca(p1, gca_cart[0], gca_cart[1]) - p2_intersects_gca = point_within_gca(p2, gca_cart[0], gca_cart[1]) + # 3. Plane normal via accurate cross product. + nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo = accucross( + x1[0], x1[1], x1[2], x2[0], x2[1], x2[2] + ) + nx = nx_hi + nx_lo + ny = ny_hi + ny_lo + nz = nz_hi + nz_lo + denom = nx * nx + ny * ny + if denom == 0.0: + return res - if p1_intersects_gca and p2_intersects_gca: - res[0] = p1 - res[1] = p2 - elif p1_intersects_gca: - res[0] = p1 - elif p2_intersects_gca: - res[0] = p2 + # 4. Solve for the two candidate points on the latitude circle. + r2 = 1.0 - const_z * const_z + if r2 < 0.0: + return res + inv_denom = 1.0 / denom + cx = -nz * const_z * nx * inv_denom + cy = -nz * const_z * ny * inv_denom + disc = r2 - (nz * const_z) * (nz * const_z) * inv_denom + if disc < 0.0: + return res + s = math.sqrt(disc * inv_denom) + + p1 = np.empty(3) + p1[0] = cx + (-ny * s) + p1[1] = cy + (nx * s) + p1[2] = const_z + + p2 = np.empty(3) + p2[0] = cx - (-ny * s) + p2[1] = cy - (nx * s) + p2[2] = const_z + + # 5. Keep candidates that lie on the minor arc. + p1_ok = math.isfinite(p1[0]) and math.isfinite(p1[1]) and on_minor_arc(p1, x1, x2) + p2_ok = math.isfinite(p2[0]) and math.isfinite(p2[1]) and on_minor_arc(p2, x1, x2) + + if p1_ok and p2_ok: + res[0, 0] = p1[0] + res[0, 1] = p1[1] + res[0, 2] = p1[2] + res[1, 0] = p2[0] + res[1, 1] = p2[1] + res[1, 2] = p2[2] + elif p1_ok: + res[0, 0] = p1[0] + res[0, 1] = p1[1] + res[0, 2] = p1[2] + elif p2_ok: + res[0, 0] = p2[0] + res[0, 1] = p2[1] + res[0, 2] = p2[2] return res diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py index a622eb8dc..36b06f674 100644 --- a/uxarray/grid/point_in_face.py +++ b/uxarray/grid/point_in_face.py @@ -1,79 +1,223 @@ 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 on_minor_arc, orient3d_on_sphere +from uxarray.grid.utils import _get_cartesian_face_edge_nodes 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, inline="always") +def _flip_sign(sign): + """Return the opposite sign code.""" + if sign == _SIGN_POS: + return _SIGN_NEG + if sign == _SIGN_NEG: + return _SIGN_POS + return _SIGN_ZERO + @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 — making orient3d_on_sphere calls + numerically robust 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 - 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. + +@njit(cache=True) +def _counts_as_crossing(A, B, q, R): + """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 (EFT-based) for all + side-of-plane tests. Returns -1 when R lies exactly on plane(AB), which + signals the caller to perturb R and retry. + """ + s_AB_q = orient3d_on_sphere(A, B, q) + s_AB_R = orient3d_on_sphere(A, B, R) + + # q on great circle AB: already caught by edge-membership check; not a crossing. + if s_AB_q == _SIGN_ZERO: + return 0 + # 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 = orient3d_on_sphere(q, R, A) + s_qR_B = orient3d_on_sphere(q, R, B) + + # A or B on great circle qR: vertex lies exactly on the ray plane. + # Apply the half-edge rule: count the edge only if the other endpoint is + # strictly on the negative side, so adjacent edges sharing this vertex + # are not double-counted. + if s_qR_A == _SIGN_ZERO or s_qR_B == _SIGN_ZERO: + if s_qR_A == _SIGN_ZERO and s_qR_B == _SIGN_ZERO: + return 0 # entire edge coplanar with ray: degenerate + s_other = s_qR_B if s_qR_A == _SIGN_ZERO else s_qR_A + return 1 if s_other == _SIGN_NEG else 0 + + return 1 if s_qR_A != s_qR_B 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`` + (EFT-based) for the crossing test, avoiding the ``arctan2`` calls in the + winding-number approach and the large number of ``np.cross`` allocations. + + Returns one of _LOC_INSIDE, _LOC_OUTSIDE, _LOC_ON_VERTEX, _LOC_ON_EDGE. 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 = face_edges.shape[0] + n = polygon.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. + R = _ray_endpoint(q) + inside = False + for i in range(n): + A = polygon[i] + B = polygon[(i + 1) % n] + c = _counts_as_crossing(A, B, q, R) + if c < 0: + # R lies on great circle of this edge; nudge R slightly and retry. + 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 + c = _counts_as_crossing(A, B, q, R) + if c < 0: + return _LOC_OUTSIDE + if c == 1: + inside = not inside + + return _LOC_INSIDE if inside else _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)