From 878e07582669ac3d34e8e0d0f7226c5b9a2c746b Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 22 May 2026 06:58:34 -0500
Subject: [PATCH 01/51] Port AccuSphGeom EFT algorithms and add spherical
 geometry user guide

Closes #1509
---
 .../spherical-geometry-accuracy.ipynb         | 1666 +++++++++++++++++
 docs/userguide.rst                            |    4 +
 uxarray/grid/_eft.py                          |  167 ++
 uxarray/grid/arcs.py                          |  108 ++
 uxarray/grid/bounds.py                        |  365 +++-
 uxarray/grid/intersections.py                 |  263 ++-
 uxarray/grid/point_in_face.py                 |  232 ++-
 7 files changed, 2656 insertions(+), 149 deletions(-)
 create mode 100644 docs/user-guide/spherical-geometry-accuracy.ipynb
 create mode 100644 uxarray/grid/_eft.py

diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
new file mode 100644
index 000000000..c8f6e6aff
--- /dev/null
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -0,0 +1,1666 @@
+{
+ "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": {
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+      "application/vnd.holoviews_load.v0+json": "(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  // 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; 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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\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.8.10/dist/panel.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",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        })\n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "  if (id !== undefined) {\n",
+       "    var nchildren = toinsert.length;\n",
+       "    var html_node = toinsert[nchildren-1].children[0];\n",
+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
+       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
+       "    for (var i in nodelist) {\n",
+       "      if (nodelist.hasOwnProperty(i)) {\n",
+       "        scripts.push(nodelist[i])\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
+       "      var newScript = document.createElement(\"script\");\n",
+       "      var attrs = [];\n",
+       "      var nodemap = oldScript.attributes;\n",
+       "      for (var j in nodemap) {\n",
+       "        if (nodemap.hasOwnProperty(j)) {\n",
+       "          attrs.push(nodemap[j])\n",
+       "        }\n",
+       "      }\n",
+       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
+       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var 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",
+       " */\n",
+       "function 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",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function 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",
+       "\n",
+       "if (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"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((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        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        })\n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var 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"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='511425db-17a9-43d0-9390-9143fd70fc44'>\n",
+       "  <div id=\"a5581328-170e-4d70-a959-f524dc20052d\" data-root-id=\"511425db-17a9-43d0-9390-9143fd70fc44\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"57699642-6e25-4f82-8f94-98775ebd7625\":{\"version\":\"3.7.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"511425db-17a9-43d0-9390-9143fd70fc44\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"f62f5983-225d-46af-8a2f-f3902bfcedb1\",\"attributes\":{\"plot_id\":\"511425db-17a9-43d0-9390-9143fd70fc44\",\"comm_id\":\"376348b687e7487291ac0ae5732287a4\",\"client_comm_id\":\"722736ecfe324560808c4506f3e031ad\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"start\",\"kind\":\"Any\",\"default\":0},{\"name\":\"end\",\"kind\":\"Any\",\"default\":100},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"holoviews.plotting.bokeh.raster.HoverModel\",\"properties\":[{\"name\":\"xy\",\"kind\":\"Any\",\"default\":null},{\"name\":\"data\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"57699642-6e25-4f82-8f94-98775ebd7625\",\"roots\":{\"511425db-17a9-43d0-9390-9143fd70fc44\":\"a5581328-170e-4d70-a959-f524dc20052d\"},\"root_ids\":[\"511425db-17a9-43d0-9390-9143fd70fc44\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  async function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t  for (const child of root_el.children) {\n",
+       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
+       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
+       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
+       "\t  }\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(version);\n",
+       "    } else if (root.Bokeh.version === version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "511425db-17a9-43d0-9390-9143fd70fc44"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(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",
+       "    },\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",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        })\n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "  if (id !== undefined) {\n",
+       "    var nchildren = toinsert.length;\n",
+       "    var html_node = toinsert[nchildren-1].children[0];\n",
+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
+       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
+       "    for (var i in nodelist) {\n",
+       "      if (nodelist.hasOwnProperty(i)) {\n",
+       "        scripts.push(nodelist[i])\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
+       "      var newScript = document.createElement(\"script\");\n",
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+       "        if (nodemap.hasOwnProperty(j)) {\n",
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+       "        }\n",
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+       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
+       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var 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",
+       " */\n",
+       "function 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",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function 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",
+       "\n",
+       "if (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"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((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        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        })\n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var 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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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "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([np.array([0,0]), a1, a1+b1, b1], alpha=0.25, color=\"steelblue\", zorder=0)\n",
+    "ax.add_patch(para1)\n",
+    "ax.annotate(\"\", xy=a1, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2))\n",
+    "ax.annotate(\"\", xy=b1, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2))\n",
+    "ax.text(*a1*1.08, r\"$\\mathbf{a}$\", fontsize=13, color=\"#1f77b4\")\n",
+    "ax.text(*b1*1.08, r\"$\\mathbf{b}$\", fontsize=13, color=\"#d62728\")\n",
+    "area1 = abs(a1[0]*b1[1] - a1[1]*b1[0])\n",
+    "ax.text(0.5, 0.96, f\"|a × b| = {area1:.3f}\", ha=\"center\", fontsize=12, color=\"steelblue\",\n",
+    "        transform=ax.transAxes)\n",
+    "ax.set_xlim(-0.1, 1.8); ax.set_ylim(-0.1, 1.1)\n",
+    "ax.set_aspect(\"equal\")\n",
+    "ax.set_title(\"Well-separated  —  large, well-conditioned cross product\", fontsize=11)\n",
+    "ax.axis(\"off\")\n",
+    "\n",
+    "# --- Right panel: nearly-parallel vectors ---\n",
+    "ax = axes[1]\n",
+    "eps = 0.04\n",
+    "a2 = np.array([0.8 + eps, 0.6]); b2 = np.array([0.8, 0.6 + eps])\n",
+    "a2 /= np.linalg.norm(a2); b2 /= np.linalg.norm(b2)\n",
+    "para2 = plt.Polygon([np.array([0,0]), a2, a2+b2, b2], alpha=0.5, color=\"#d62728\", zorder=0)\n",
+    "ax.add_patch(para2)\n",
+    "ax.annotate(\"\", xy=a2, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2))\n",
+    "ax.annotate(\"\", xy=b2, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2))\n",
+    "ax.text(*(a2*1.06 + [0.01, 0.03]), r\"$\\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(0.5, 0.96, f\"|a × b| = {area2:.4f}  ←  tiny!\", ha=\"center\", fontsize=12,\n",
+    "        color=\"#d62728\", transform=ax.transAxes)\n",
+    "ax.set_xlim(-0.1, 1.8); ax.set_ylim(-0.1, 1.1)\n",
+    "ax.set_aspect(\"equal\")\n",
+    "ax.set_title(\"Nearly-parallel  —  tiny cross product, catastrophic cancellation\", fontsize=11)\n",
+    "ax.axis(\"off\")\n",
+    "\n",
+    "fig.suptitle(\"Cross product = parallelogram area\\n\"\n",
+    "             \"Small area means two nearly equal numbers are subtracted — digits cancel\",\n",
+    "             fontsize=12)\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",
+    "def fmt(v):\n",
+    "    return f\"{v:+d}\" if v != 0 else \" 0\"\n",
+    "\n",
+    "print(f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  → left  of A→B (northern hemisphere)\")\n",
+    "print(f\"South Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, south_pole))}  → right of A→B (southern hemisphere)\")\n",
+    "print(f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  → collinear, not a crossing\")"
+   ]
+  },
+  {
+   "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),\n",
+    "                     np.cos(lat) * np.sin(lon),\n",
+    "                     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(f\"Naive sign flips below ε ≈ {flip_threshold:.1e} rad  ({flip_mm:.2e} mm on Earth)\")\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(f\"  Naive (raw cross product):  {naive_ok}/{n} correctly classified as inside\"\n",
+    "      f\"  ← {n - naive_ok} misclassified as outside near the edge\")"
+   ]
+  },
+  {
+   "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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RwzqZl4jCDQVA9sFkocBLgY0V3hj+yHxQZvebp5BHrnfxxRfL+KdLiMfKPjByorlDgY4V2tg/FI6YZ4v9yONTFEVRFEVRlBOGY511W1E+CLxVQjIqOn3xi190xMXFSYWwq666yrFhwwaPFalYCauwsNBhsVikMtTFF1/sqKiocP2dlc5uueUWR1JSkqyTmpoq25tMpbPf/e53jtzcXHkfP+OFF15wHAvefvttx/z582U/Zs2a5XjxxRelkpi5Shr54Q9/6EhLS5N2ZSWq119/fUwFLFax+uUvf+mYM2eObC8mJsaxYsUKxyOPPDKpvjE499xzHQUFBZPaf+7X4sWLpWIdt7tw4UKPFbfY39wfc6Wt888/f0wVMHP1LlbY4ja5zJ49W8aNuaIcK7vdc889UkUrICBAxsGFF14obeheYY5jKTMz0xEYGCjVsvbt2zfhsbG6GfuE7zFX12pubnZ84hOfkOPh39jGa9euHfP+Rx99VN73s5/9zPWaMdbdq8BN9rxglbS77rprzGexXR9++GFHfHy8VLu75JJLpJLbRFXSSF9fn+O+++5ztSPPo09+8pNjqgiybdnPrEA2Y8YMxx133OGoqqoat6rc5z73Odlnc/W3O++805GQkCBV1tg/E41Lti3bmG3NNmfbsxKagafP7u/vd3zmM59xREVFOSIiIuR8YpVGrZKmKIqiKIqinCj48J9jLVopinJ8wVw2XLwly/4g6ezsFPcNXVhf/OIXcaJD9wsTKTOBt6IoiqIoiqIoyomChqQpinJc0NXVhX379uHxxx+XsDAj15CiKIqiKIqiKIry4aOCkaIoxwXML8R8TqxW9sQTT3itQKUoiqIoiqIoiqJ88GhImqIoiqIoiqIoiqIoijIK39H/VRRFURRFURRFURRFUaY7KhgpiqIoiqIoiqIoiqIoo1DBSFEURVEURVEURVEURRmFJr32wPDwMGpraxEeHi7VmhRFURRFUYjD4ZCqjsnJyfD11eduiqIoiqKcvKhg5AGKRazUpCiKoiiK4omqqiqkpqZq4yiKoiiKctJyQglGl1xyCXbs2IHGxkZER0fj7LPPxg9+8AN5ykfWrl2Ln/70p9i0aRM6OzsxY8YM3HvvvfjYxz42pc+hs8i4GYyIiBjlPGpqakJcXNy0fKo4nY9/Oh/7dD/+6XzsRI9/+va/9r3nvuf9BR8qGfcKiqIoiqIoJysnlGB0xhln4P7770dSUhJqamrwpS99CVdddRXWr18vf+fPuXPn4itf+QoSEhLw4osv4qabbkJkZCQuvvjiSX+OEYZGschdMLLZbPLadJs4TPfjn87HPt2PfzofO9Hjn779r30/ft9ryLqiKIqiKCc7J5RgdM8997h+z8jIwH333YfLLrsMg4ODCAgIEDHJzOc+9zm8+uqreO6556YkGCmKoiiKoiiKoiiKokxnTtjHpa2trfjLX/6ClStXiljkjY6ODsTExHyo+6YoiqIoiqIoiqIoinIic0I5jAjDzR599FH09vZi+fLlEnbmjWeeeQabN2/Gr3/963G32d/fL4s5P4Fhx+diwN9ZHcX82nRiOh//dD726X780/nYiR7/9O1/7XvPfT8dx4KiKIqiKNOTYy4YMayMiavHY//+/cjPz5ffmcT6lltuQUVFBR5++GHJUUTRyD2XwJtvvombb74Zv/3tbzF79uxxt/+9731PtuUOk10yf4H5JpGOJd5ATrdcFtP9+KfzsU/345/Ox070+Kdv/2vfe+77rq6uY9oviqIoiqIoHxY+Dt4JHUMoyrS0tIy7TnZ2NiwWy5jXq6urpVIJk12vWLHC9fpbb72Fiy66CI888ghuv/32CffBk8OI221ra9MqaSamc8Wc6Xzs0/34p/OxEz3+6dv/2vfeq6SxUivFJHNhDEVRFEVRlJONY+4w4o0YlyPBsIWbxZ61a9dKgmu6liYjFpHAwEBZ3OENovsEgU4mT69PF6bz8U/nY5/uxz+dj53o8U/f/te+H9v303EcKIqiKIoyPTnmgtFk2bhxo+QjOuWUU+TJXmlpKR544AHk5OS43EUMQ6NYxOpoV155Jerr6+V1upM08bWiKIqiKIqiKIqiKMrkOGEek4WEhOC5557DWWedhZkzZ0oeo7lz50r4meEOeuKJJyQZNnMSJSUluZYrrrjiWO++oiiKoiiKoiiKoijKCcMJ4zAqLCzEmjVrxl3nT3/6kyyKoiiKoiiKoiiKoijKNHAYKYqiKIqiKIqiKIqiKB8OKhgpiqIoiqIoiqIoiqIoo1DBSFEURVEURVEURVEURRmFCkaKoiiKoiiKoiiKoijKKFQwUhRFURRFURRFURRFUUahgpGiKIqiKIqiKIqiKIoyChWMFEVRFEVRFEVRFEVRlFGoYKQoiqIoiqIoiqIoiqKMQgUjRVEURVEURVEURVEUZRQqGCmKoiiKoiiKoiiKoiijUMFIURRFURRFURRFURRFGYUKRoqiKIqiKIqiKIqiKMooVDBSFEVRFEVRFEVRFEVRRqGCkaIoiqIoiqIoiqIoijIKFYwURVEURVEURVEURVGUUahgpBxVTj/9dPj4+MhSUVEx5u/PPfec6++f//zn8aMf/QjLli1DXFwcAgICEBkZiRUrVuC3v/2t9oyiKIqiKIqiKIqiHCNUMFKOKtdee63r93/84x9j/v7ss8+6fr/uuuvw73//G5s2bUJzczPsdjs6OzuxYcMG3H777fjBD36gvaMoiqIoiqIoiqIoxwAVjJSjylVXXQV/f3/5/Zlnnhn1N5vNhhdffFF+z8zMxPLly3H99dfjnXfeQWtrKzo6OvDggw+61v/rX/+qvaMoiqIoiqIoiqIoxwAVjJSjCkPLzjzzTPmdziFzWNr//vc/dHd3j3Ii3XXXXTjllFMQHR2NiIgIfPGLX3StzxA1RVEURVEURVEURVE+fFQwUj60sDT3cDR32tra8OMf/9j1/zvuuEN7R1EURVEURVEURVGOASoYKUedK664AhaLZVRYmjkcLT8/H/Pnz3et//TTT0sS7JiYGHzzm9+Er68vHnnkEdx6663aO4qiKIqiKIqiKIpyDFDBSDnqREVF4bzzzhsVlvbyyy+jq6trjAPJE8PDwxKappXSFEVRFEVRFEVRFOXYoIKR8qGEpY0Xjsb/OxwOSXz9+9//XnIX8f/33nuviEeKoiiKoiiKoiiKony4qGCkfCBceumlCA4Olt+feuopvPDCC/L7vHnzJCTNE0x8/clPfhKzZ8+W/7NqWmNjo/aQoiiKoiiKoiiKonzIqGCkfCCEhYXhoosukt937NjhCkczu4v4+te//nVs27ZN/t7Z2Sni0t69e+XvrJoWGxurPaQoiqIoiqIoiqIoHzIqGCkfGJ5yFZkFo/b2dnznO9/BokWLRByKjIzEjTfeiMHBQfn7gw8+CH9/f+0hRVEURVEURVEURfmQ0dm4MiHtPf14ZUcVtpc3o6O7D5Fhh7AgKw7nzU9FVGig1/fRYRQeHu5yFy1btgyZmZmuv2dnZ+PjH/84NmzYgNraWvT19cFqtWLx4sX41Kc+hY985CPaO4qiKIqiKIqiKIpyDFDBSPHKgH0I//fKPhGLhh0OOBwjf2jqxY5DLXhibRHOX5CGO84tgMXfb8z7mcOIYWbeSE9Px5/+9CftAUVRFEVRFEVRFEU5zlDBSPEqFt3/l03YU9V6WCgywdeGHA68tK0Slc3d+O5Hl3oUjRRFURRFURRFURRFOfHQHEaKR3716j6vYpEZ/n1PZausryiKoiiKoiiKoijKyYEKRorHnEUvb6+aUCwy4Hpcv6N3QFtTURRFURRFURRFUU4CVDBSxvDKjmrJWTQVuD5FI0VRFEVRFEVRFEVRTnxUMFLGsONQ86TdRQZcn+9TFEVRFEVRFEVRFOXERwUjZQw9tsEjapXa1h60dNm0RRVFURRFURRFURTlBEerpCljCA0KOKJWqW/vw0d/9gYy4sKwOCcOi3LiUJgeo9XTFEVRFEVRFEVRFOUEQwUjZQzzM2OxvXzqYWkGFU3dsvxzQzks/r4iGlFAWpgdJ2KSj4+PtrqiKIqiKIqiKIqiHMeoYKSM4bz5qXhibRGGpqAY+fn64J6LC1Fa34ldFa0oa+wUwWnAPoytZc2yAPthDQ/CouxYEZAWZMUiIsSiPaAoiqIoiqIoiqIoxxkqGCljiAoNxPkL0vDStspJuYzoF1pdkIS5GVZZLl+WJXmQ9lW3iXi0u7IVrd39si5zHL26s1oWvm9GcqSIR1zyU6Lg56tptRRFURRFURRFURTlWKOCkeKRO84tQGVTN/ZUtY4rGlH0mZkciY+dNmNMHqQlufGyOBwOyW9E4WhXRQsO1LSL84ibPVjbIctf3ylBiMUf87OsrvxHiVEh2juKoiiKoiiKoiiKcgxQwUjxiMXfD9/92FL86tV9eHl7FYaGHWOEIl9fH3EWUSwK8PPuDGLOoqToEFnOnZeKwaFhHKxtFwFpd0Urqlp6ZL3eATvWFzXIQpKjQ1zi0bxMK4ItOlwVRVEURVEURVEU5cNAZ+DKuKLRZy8sxE2r8/C5P6wTl1Cgvy9mJEWiIC0apxUkISJ46jmIKC7NTouR5bpVQHtPP/ZWMXytBXsq29BlG5T1att68Z8tFbL4+/pgVmo0luTGYVF2HLITI+CrybMVRVEURVEURVEU5QNBBSNlUjmN4iKCRTBKiQ7CvZfOhY+P71Hd/qr8RFmGHQ4JhXO6j1pQXN8p7ib7sMP5WmUr/rCmCJEhFiwcSZ5NASk6LFB7UlEURVEURVEURVGOEioYKccVdA1lxofL8pHFGegbsEvOI8N91NDRJ+t19A7gzT21spDshHARjigg0f1Ed5SiKIqiKIqiKIqiKEeGCkbKcQ3zFi3IipWFNHU4k2dz2VvdBtvAkLxe1tAly7PvlSEwwA9zM2Jc7qNUa6jkUVIURVEURVEURVEUZXKoYKScUMRFBuPMwhRZ7EPDKG3odCXPPtTYJZXX+geHsLmkSRYSHxEkibO5UHgKCwo41oehKIqiKIqiKIqiKMc1KhgpJyz+fr6YmRwly1XLs9HVN4i9Va0uAam9d0DWa+y04X/bq2RhyNvMlEgszonHouxY5CVHwc9X3UeKoiiKoiiKoiiKYkYFI+WkITw4AMvzEmRxOByoae3F7soW7KpoxcHaDgwODUtS7f3V7bL8+a2DCAvyx/ysWCzJicPC7DjERwYf68NQFEVRFEVRFEVRlGPO0St1pSjHEcxZxNxFFyxIx1cum4//u/0U3HvpPJw/PxXJ0SGu9bptdry7vx4/fXE3bvzFGtz6+Fr86pW92FzSCNugMz+SoiiKoijKh8H//vc/XHjhhYiLi0NAQAASEhJw0UUX4W9/+xuGh4dd651++um4+OKLp7TtQ4cOyf3RP/7xj6Oyr9zWj3/8Yxwv7NixAw899BB6e3s/kO23t7fL9vft24cPm8n2HfcvLCxsytuf7PsyMzNx9913u/7/iU98AnPmzJny5032fdyv9evX42gSFRUl2x2PgYEB3HzzzXIest1/9rOf4Vizdu1a2ZctW7bgeIRtpDljT07UYaRMC1g1rTA9RpaPngq0dvdjT2WrVF/bW9WGnn67rFfV0iPL85sOIcDPF7PTorE4Nw4Ls2IR4mCGJEVRFEVRlKPP/fffj+9973u4/PLL8eijjyIpKQkNDQ3417/+hRtuuAExMTE477zztOnHEYwefvhhETRCQg4/HDyaghG3T6GjoKDguOyHW2+9VQTGD4sHHngAPT09H9j22d4UslauXIkPkyeffBJ//vOf8cQTTyAnJ0eEsmPNwoUL8d5772HWrFnHeleUaYYKRsq0JCYsEKcVJMkyPOzAoaYuyX1EAam0vhPDDkgI245DLbKQyGB/LM6Nx5LceEmeHRUaeKwPQ1EURVGUk4D//ve/IhY9+OCDY9wPV199NT73uc+J40hRxiM1NVWWDwuKKScjBw4cQHJyMj72sY/heCEiIgLLly//wLbf19eH4GBNzaGMRUPSlGmPr68PshMicOmSTDxw1SI8ftup+NyFc3DGnGTEhge52qejz443dtfi+8/vwHWPvI67fvsO/rjmAHZXtEjFNkVRFEVRlCPhkUceEUfR17/+dY9/X7p0KRYsWDDuNt5++21xYnDSFxsbi09+8pNobW0dsx4dIbfccgsiIyPFtfSFL3wBdrvTaU3q6urkvdnZ2bKtGTNmiPupv7//iN0a3PegoCDZL4bcVVRUuP6+e/ducU6FhobKPl111VWorKwctQ2Guvzwhz8UMY1hetwOQ4YMd8uf/vQn+T8xwojMrpDq6mpxafF9PKbTTjsNW7duHRVOY7FYsH37dtdrpaWl4m756le/KiFhWVlZLgGP2+fC1z9MbDabOKiio6NlvHzpS18a1XeeQsv27t0rx8v2Z1/+5S9/wWWXXSZhje6wL0455RRxaNFJ9corr0w5tOzdd9919ffcuXPx2muvYf78+bKupzArrsu+5xg394kR3nTvvfe62pvrE+YqZThkXl4eAgMDZaz+9Kc/HbP9f//738jPz5d94fY3b96MieC4+clPfoKqqqpR/UwR6brrrkNaWpq0D11mXM8cKkp4nvA85j5x3yjguR87nUJnnnmma8x/9KMfRWNj45RD0iY6L7xhjJNNmzZhxYoV0j6PPfaY/G3//v249NJLZb+4f3Ss8Vww09nZiZtuugnh4eFyvn35y18eNQ6VkwsVjBTFjZBAfyzKicPNZ8zETz6+HD+8cRluPC0Xs5PDEOjvPGUYnFZS34mn15XiS09uwJU/fhUPPr0ZL2w5hNrWD86aqyiKoijKyQUnWuvWrZMJpL//kZn/OdE+55xzZAL37LPP4gc/+AFeeOEFXHDBBRgaGp2TkeIPJ7nPPPOMTMZ/+ctfjhKqmpubRUiiiPXyyy/LZJChOXfccceU9+tHP/oRPv7xj2PRokV47rnn8Pvf/15Ei6amJvk7J+UUM1paWvDUU0/hV7/6FbZt24bVq1ejq6tr1LYYpldcXCz78o1vfAN//etf8a1vfUv+xkmtcQzcZ07In3/+efl/W1ubiCAMWeOx/vOf/5SJMNvbmKTTwbVq1SoRlSjKsM04Ic7NzZWwKIoz3H/y3e9+V7bPha9/mHzta1+Dr6+v9B37g4LF7373u3FdI+eee66rfeli+/73vz9KmDEYHBwURw3FDbZdfHw8rrzySnnvZKHYeP7558s4NMbXpz/9adTU1IxZt76+Hp/97GdlHa7Ldmc4JveDsH3JZz7zGVd7MyzL6C+OAY4tuvO4z1/5yldk/Biwv7n/HG/sO657zTXXTCh88tivvfZaJCYmjupnHsPMmTPx+OOP46WXXsLtt9+Ob37zm64xaMDP5LlD0ZX7xnPALOBwexTrKMj8/e9/x29+8xsRsijSHAnjnRcT5WmiUMUxz9xpHCdlZWUiOlNopgjLbfFcPeuss0a1G4+N7cSxxM9lXq/jIc+T8sGgIWmKMg5U7hOjQpAQGYTFKUEIi4pGSX2XM/9RZSsqmrplPdvAEDYUN8pC+J7FObEiPM3LtCI0UG3kiqIoiqKMhRNyTsboXDBDF4VZ7KFQwMUT3/nOd2SC++KLL7pC17g9Onc4uf3IRz4yKozoj3/8o/zOv1NUoPDACTedK4WFhaOSWVNIocDCCTddCJPND9TR0SFOBk6sf/3rX7teN0+M6QqhQPDqq6+KSEXoOKF7gxNWigUGnLTTHUMoSlBYYhJoTlrpcjDCoyhO0WlhwIks8w/RTUERhHACTHcKj5MODd7v8fPoiKGgxu1RVOFEns4jY78IBYgPMjRoPJYtW4Zf/OIX8jsFwjfffFPawJuYx35mHiwKkobjavHixSKEuYeTUUBgW9IBRiiO0FVFMYGiwmRgf1L0pFBC0YhwG6eeeuqYdSlKvPXWW5g9e7b8n2PsjDPOwMaNG0XgM9o4PT19VHvT7UKRhOIQxxY5++yzJdk5xT2+xvOEx8L3MgeYn5+frEd3Gd1148F+5rlEd5D5czlmuBjnJveRn8l9YSgpoZuKx06h5frrr3e91/z7fffdJ31AEctwUfGco1OL56rR/pNlvPNiPHje8bpBccyA5zjPQx4HXUeEAhLdUhR777zzThGHuO8UKikcGdcRnhfKyYk6jBRlCvj7+WJWajSuXpmDb123BI/esgp3nDsLq2YmIDLEeUNB6tt78eLWSjz8zFZc/ePX8IU/rcdf3ynGwdp2DGvybEVRFEVR3HCvMEQnDMUfY6EbwxvvvPOOCDHmPEd0DLAiFEOEzNDFYYYhYJz4MhzJmAxTZKFowwk2t0nnCZ1QdCBMFjopuN3xJujcbzp9DLGIMIRo3rx5Y/abAokZ7h9DzSaCYhSFCH4Gj4ELBQS6mMwhShkZGXLcXCgA0D3CifyRQKHP+KzJLu5OME+wT6fSBjw+HoM5PI+/s33dochC4cW8Hvt/Mm1s/jy2tSEWEQor5v41YI4gQywyjoVM9Hmvv/66y8ljbj/uO11LdK0RCk8USg2xyBjrRwodUBwXFNsoJvG8oOOLrqrubucD5DfeeEMEVYaueYLnA8U7hjWaxwjFSwq8kwmZc+dIzwviniCd58oll1wiop+xbxSRKaIZ+8afvEaYryNsY4Y5Kicn6jBSlPdBRIgFK2cmysKLZ1VzD3ZXtoj7qLi2A/ZhB4aGHVKJjcsTaw8iIjgAC7JjsTgnDouy42A15UlSFEVRFGV6YbVaZQLqPsmjm8GYpHESNx4Mu2IOE3f4mnseI8NlY16HcOJLKJgwNw5D0Tj554SR+3HXXXfJpHmyGKFMFAbG22/mt5nMflP8MkPnz2TyKjHEbsOGDR6Thru7bCi6MUcQJ/O33XYbjhRu15ynaTJQsJooJ5KnNhivT9indEu5wzFAZ5kZikOGm2qy2/f0eZ6cJu5jztuxkIk+j/3Je26zi8wMBSO2JffF/XOZONpwzkwVOvB++9vfimhEFxv3nzmSvv3tb8s+MycQxzwdP97Ky3O8c2zdc889snja96lypOcFhS33fFdsW0M0dcfoH7YrzyVeF8x4uv4oJwcqGCnKUYJfDulxYbJctCgD/YNDOFDTLpXXWIGtvt35xdzZN4i39tbJQjLjwiR0jUthegws/oefhCiKoiiKcnLDp/kM+6I7gZNJwxHBCRlDV4j7RN4dOjg8Jc1lOJK7u8N9Pa5DjHw8zIFEgYr5bgwYhnIkQhipra31WrlrvP2m6+JowM9gqI6nvC4U6sww5IbtznCdz3/+85Kf5Uhg/qipJgl335ejAfuUuXzcYZubXUBH8/OM/FTun3e0YH/ynpsONE/nBUPpjH1x/1wma56KAGaG58WnPvUpEY4MGH7mPuYpqFDQ8iQaUdzh6wx79OTI8SaCfRB42j+2LV1HPA/cMcYL25XnB8Uvs2hkXEeUkw8VjBTlAyIwwE/yF3EhzZ02EY7oQKLbqG/AaT0+1NQtyz83lMPi7yuikbiPcuKQHhvm9SmFoiiKoignB6xUdvHFF0tC5QceeGDK72fYD3O1MBeRkTibeUiYu4d/M8NktWZ3A/Od0G1ghF/ReeI+ETdypEwFVl/idplHhxWqvO03k/6aJ59FRUXYtWuXKz/KZPHmUGGoEhM+z5o1S/LkeOPpp5+WJMRMms1tcELPsBtjYj9ZBww50lC2o82SJUukSl15ebmryhtdTDt37hwzLo7W5zFfFROWGwIDww49VeubDHSyuLe3kUeIbh5zbi53OOYo3DEBtSHCcqwfKe7nBcVdjhn3scaE80zibc4NZMDxx/OClcjoTDre4P7v2bNHQtDMoXzufWxcR4xzlG3B649ycqKCkaJ8SMRGBOGMOcmyDA0Po6yhyykgVbSirLETTG00YB/G1rJmWfDafglXY/LsxTnxmJ9lRUTw+E8YFUVRFEU58eBTfSbDZZUjOkI42eSTfCaO5oSbuVnGc4QwlwqT01J0YqJoPu3n9jhpdk+iy6TBLL3NPCtMkEsnEQUkQ7BhTpSf//znksyXLh+KLSUlJVM+JlaBYvgOHRmsysZwL/5komYmAaZ7ip9LQYm5eXgMFAdY7YzJij2VYR8PCkKEibkp8hgiGMU4Cl7MWcTqWtw2XTDMccNwOe4DXVAMuWPyaCbwNRIAM4Ey25WhTUyETIfI3/72NxFf6AhikuyJ3F/HEvYzExtzXDAhNGEich6LtwTq7we2JauIcTyz+hkFS34unTNH8nnsU4Z9MWk2xRa6hzgm2Vc33nijfAYTgdPxcvDgQRlbhnDB8U9xg2OBjhnm32KS8yMNSeN5wZA05gji8fA43V1kFFx4vlFI4XnGfaNYRqGKYiRh1TTm7eI5znOQ5x3DUSnwsr9YQe1Ywb5im/Ec4NhnmBmvPUxOzj7gecvjp5BKBx7PV+a6YlswabpycqJJrxXlGODn64sZSZG4YlkWHrxmER6/9RTcfcFsnFaQhJiww5bkli4bXtlRje/8cxuu/clr+Ozv38WTaw9ib1WriE6KoiiKopwcULhhlTM6GTjB5aSSCaOZjPoPf/iDTPy9wZwqTFjLkBsmA+ZEmpN2VrhydwpwOwyZYeJdVgjj5Nu8bYpWLLfNn5zQcoJtVOaaKsyDxH1nAmxOMikCcWJv5JZhol9ORjlpZmJtTlKZkHnt2rVTDpmiK4JiCAUuijyG+4RhQsxhxFxJFK8oTlHYoNOGE3rCduY+mKvD8ZiZ14dhSISCB8UtunXocuHEmkLT8Qz336hAx/Zlf3zxi1+UPEMU9I42FDk55ugwYoJpjmmKj8yVcySfR/GPIuMFF1wg7c3KdUbf0KFDhw/HOau4UZChKGgeDwwj43jj2GPfcf0jDf375S9/KdunIMvxQjGSoWXuMFk9E9TTacX9pmBpzhXEsclwOibKpkBEgYkJ1ilwMqH2sYSfz2qCPGd4DaJwROGtp6dHxFEDntMMW+V4uummm0TIo4CknJz4OPiNoYyCX7a8qPGpDpOjGfCCxVhYfsl9EKr88cyXnnhP3DDZcSF48Nol8PGZXsfvcAyjvaUFUVbrB37sPCXr2nqlvXdVtOJAbTsG7WPFoZBAf8zPtGJJbjwWZsciMWpyZW6PhOk89qfzsRM9/unb/9r3nvve2z2CoijKiQAdLyyTTtHMKAf/QVJcXCyV7ygy0LWlKMqJhYakKcpxBnMWJceEynLe/DQM2IdQXNchoWusvlbd0iPr9fbbsb6oQRaSEhMieY+Y/2hehhVBFj29FUVRFEVRpjPMqcPQIoYOMSEzXVTMOTPVHFGT5atf/aq4URjuxzAw5uWi84jON0VRTjx0RqkoxzmsmjY7LUaW6wC09/RjT2UbdlW2yM9u26CsV9Pai5rWCvxncwX8fX1QkBYtuY+YAykrIQK+mjxbURRFURRlWkGHJMO3ampqJCE6w/DWrFkj4YAfBMxlw9A/5tFiSBxz8jBvj3sJd0VRTgxUMFKUE4yo0ECcMitRlmGHA5VN3SPhay0oru/E8LAD9mGHhLNx+cMavseChVlMnh2HhdlxiDblSVIURVEURVFOTpjPisuHBSv1cVEU5eRABSNFOYGhaygzPlyWjyzOQN+AHQdq2kU8oojU2OEsRdreM4A1e2plIdkJ4eI+WpQTK86lAL/plZtFURRFURRFURRFGR8VjBTlJCLY4o8FWbGykMaOPuwZcR/tq26HbXBIXi9r6JLlmfWlCArwQ2FGDJbkxEkOpJSYUMmjpCiKoiiKoiiKokxfVDBSlJOY+MhgnFmYIot9aBil9Z3YXdUqCbQPNXaBJRIpIm0uaZLFeM+ibGf4GoWn0KCAY30YiqIoiqIoiqIoyoeMCkaKMk3w9/PFzJQoWa5ano2uvgHsrWqT0DUKSO29Ay5X0v+2V8nCkLf8lCgszLYiO9of1lgHplllcUVRFEVRFEVRlGmJCkaKMk0JD7ZgeV6CLA6HAzWtPSIc7apsRVFtO+xDDkmqva+6TRYS9kqpuI5YeY3ha3ERwcf6MBRFURRlWtHf34/169fj1VdfxWuvvS7VqJTjH4f4uo/6Rs0/xoXZBnygKQeUiQkICMDKlStw7rnn4pxzzkFSUpI22zRGBSNFUSRnUao1TJYLFqZjwD4kybMpINGBVNvWK63UbRvEO/vrZCFpsWFO8Sg7DnMzrAgM8NPWVBRFUZSjTFNTE1588UU8//zzeOONNxAWHoHVZ5yFm2+/E+kZGWPWdzhGRAT56ZQT+BDI+Bv/kR/G/+WnY+R9h9fj74e35fzF9b6R1+Tlke05P9J4bew+eGIiEeP9CC3cNt8/bOzjsHNrbAvHMDAsf+P+GuuMtNOwY+Rvh1/jOq499XHutZHz0Uj96Cs/fSBmbAo0Pj7i1ubfR/90Llx/9GvGe0Ze4zZ9R9Yd+YDDf/MZ+Rvkbz7yN+frzu0ccbMp05ze3l5sWP8ufvbzX+Dmm2/G7NlzcOmll+Dyyy/HggULNNfpNEMFI0VRxmDx9xMBiAtp6erDpn1VKGkZELdRT79dXq9q7pbl+Y2HpNLanPRoZ/W17Fip3KbJsxVFURTlyKitrcUzzzyD5557ThxFOflzMH/lWXjg0duRmp3n+o7t9LaBEeHB+eth8cHPtfiO+t2fgoP5/36Hf+fr8vcRUYNqiFMkMotDo8Ul43dDTBqmCCPCjAPDwyYR5giVDb738KH6jLst7rcILYb4Yvq/83ceu7Ge8f/DAo/8bhKIJrt/5uPm7g4ZbWBuC7bTqLZxvmasaz4u8zEbx+3tdffXevvtKBy5rzuecLi3zagxMuwaK6PabaSN+Dvf536sRwq3xftX3gcfT7yf8+RIOf3Ms3Hf1x9Ca2sL3lrzOl5+6UX87OenIyoqCpddeimuueYarFq1Cr6aq+Kk54QSjC655BLs2LEDjY2NiI6Oxtlnn40f/OAHSE5Olr8XFRXhjjvuwL59+9DR0SGvf/SjH8WDDz4o1jpFUY6MmLBALM+JxvlLrXA4fFDe2CXOI1ZfK2volC/twaFhbC9vkeW3I+9xJs+Ox4LsWESGWLT5FUVRFGUcmpub8c9//hN/+9vfsG7dOixbsQofuewq/OLXf0RScsr7bjvzpNujgDHy0/m6c3374JBrwm6el48RWUyCEn9SbJpIhOHPkxGKM7w/6rYNoG9gSI4zNMgfEcEWhAcHwN/Xd8QRBKcQ5+cU5Yx2ez9ClTe4P8cbrORb39YzSsgL8PdFWFCALKGB/vJAkm0S4OMr68i4chP7DGfW+4UFYsobOxEZEihFYI6FMMRiNJ29A+LqN+D/IzzcR9PZz2rHrJLM35mv9GgTE2PF5VddKwvDYde98xZeeuHfuOyyyxEcEoxrr7kG119/PRYtWqQPik9STijB6IwzzsD9998vcZQ1NTX40pe+hKuuukqeuhCKQjfddBMWLlwo6ufOnTtx2223YXh4GN/97neP9e4rykkBv6BzEiNkuWxpptwU0XW0u6JFRKTmrn5Zr7W7H6/tqpGFX+Fcf0luvOQ+mpUS9YF8qSmKoijKiYbNZsMLL7yAP/7xj3jttddQULgAZ15wKb7wrV8gPjERfj4+GPL1QR0n1i4h5rC4IK6gEXHBOZl2Tr49IRNsv5NDpGGRDhbwMOOpLcyveWu/oy1chQT6Y056jOv/vX29aGvvQndfJ+raB0WME9eXfLYvQoL8ER4YgGCKJKGhCPC3uLlvhkeJe+5C36i/uTm4DHpsTnf48QTbyNxOhmjDe8u+ATt6B+xyTGbYbsEWP2ljCiXu95PO8MKRNhnVHkYbOl1fzr8Pj3JzETrqDjVS7BtEdkLEB3bs3Icu2wA6egcwaB92vU4BKGJEsBpPBONxDtiHYRu0o8s2iKbOPjkOd7gNbjPIclhYOtLxHhgYiDPPPleW7/34Z3h77Rr865/P4owzz0RCfAJuuulGfPzjH0eGhzBZ5cTFx3G0PHzHgP/85z+47LLLRO305iD6whe+gM2bN+Odd96Z9HY7OzsRGRkpLqWIiMMXCl5U6G6Kj4+fdva7Lz3xnogB2XEhePDaJfDxmV7H73AMo72lBVFW67Q79qkcPy8nDR19I8mzW3Cguh39pi9BA37Rz8uk+yhOlqToEByvTOfznujxT9/+17733Pfe7hEUZSrw+3Ljxo3405/+hL///e+IjrHimutvwJXXXO/KSWRMfI3JPyeDzontyMTXHLpEQYFuoCEH2nv6ZeFE2hoehFkp0SKKHE24P/2DQ6MWOo3d4es5iZFHNcchP7usvhORoRZEh/ijo60V/TYbQsPC0dLSjI72dnBPRlIRjeQrYniYj7zucle58hM527Dd5hQPkqMsmJ2diriE8RP9DtntGBqywxIYJH3VWFeD1pYm2AfpDHG6XozwvMDAIISHh8PP3w9+fmwLHxGB7ENDGBoaFnGk2zaE7v5BdHb3IsBhdwkYoWFhSM/Mlv40HDZ+I2KTUwijEEiHEsYIYx90GFOPbVDyXHoaXnQGBVn8XWIF/3809ofjvm/Q7hSV+u1jxp0rB9QkhMIPu70MmCu0qIb3yEMICwwQV5Wx74ZryPj5fh6w8nO6+wbR0m0TYco2MCTbZc7So3nvTdH71Zf/i2f+9hcJX1u16hR84hMfx5VXXomwsLCj9jnKseGEchiZaW1txV/+8hesXLnSq1hUUlKCl19+GVdcccW426LgxMV8M0hEdea39Aj8Xb68Ta9NHxyjxIPpBo/Z+ZRn+h37VI8/ITIICXOTcfbcZHlKVFzXKWIjl8rmHlmH9uwNBxtkIYlRwZI4mwm0mTeJT42OF6b3ea/HP537fzof+3jHP13bQzk6tLS04Mknn8RvfvNb1NTW4NLLr8KTT/8TS5atGDNZFXFAJrKet8Xx2dU3KOKQMWnme9LjwuW79Egnmgx/obODk0uKGp6Qia2/LwIt/ggJDEBMWJDkPHI/Br7/YG0HchMjXZPiI6XqUBnqG5vQ2DOMxHA/9Pn5oMnPD1arFeFhIWhubkB8XDzmzSmAn5+RAHr0nazkcfLxETGmucuGDTuK0NBQi8CAAOTEBMHPMYj2tjqUHuxCfW21rG/cAfN3w7XjdGr5wd/fHwP9/bDZ+lBdWYGwyBhcftllCLJIGmrXZzuTW49um6b2Ljz/zN88HuvQyM+IqGikW+MRONiO/i4b+m19GBhwuqo4/wkJDUdaZvaHHgrU2mWT9mPIWE5ChEdBcnDE/cJx1N7bP8pFY4b5gigoRYUETmqM8LNCAxmuduKmG+Exe8onRTGMIivD0uhwau60TXwOUoz18UGvbXCMeCbhfYEBSI9lXqajI9h5IigoCJdcdqUsjQ31eO7Zv+MHP/ox7rrrLlx33XX41Kc+hcWLF2vI2gnKCecw+spXvoJHH31UsrcvX75cKkbwi8IMRaRt27aJCHT77bfj//7v/8Z9MvzQQw/h4YcfHvP6wYMH5WmA+SaRTxT5ZHG6PWn+7otFKKrrRmaMBZ8/L9eo/zBtYK2Mnq4uhIaHT7tjP5rH39lnx8GGbuyv60JRfTe6bMYt0WF4z5GbEIa5qRGYkxqBjNiQY5rjYDqf90SPf/r2v/a9577v6upCXl6eOoyUScNbbTrdf/3rX0t+olmF8/GRq2/A6edehOCQUFdCW3PSaXE8MHeLyQ3BO/aO3n6ZUBowz0t0WOBRTdJb3dItE1U6ECYKi5kMfHjE0PX8lKhJ7efgwADqaqrQ0tSAvt4eRERGwW4fRHBoBLaWNSN/RpbTpcOHWA4KCL4ICQ5CfEy45Aby85LYmxPvlu5+VDe0oKW5FUO2TrRWl8h9R1JqOoKDQ8UVlD+7ENaocFdibCNhOOGkyZiU013T0mXD4JBTWObrvT09iI6KkPdGhwYiLjJYPnu8kCJXdbSRpMuD9iFxZ//3X/9AbHwi4hOTEBQcCt8AC+Djh7b2NrS3tqGnpwf24SHMmFUIv4DAUaFqnuDfc5Mi30c/DqG8rg1d3T2wRoYgLiIE3V0dMoa5UDybCt0jYVR0CQX4+yHaYkddRZn0dWR0jLRpV0c7YmLjkJE9A+8HtjX75/2cJxsPNiA2Igi2vl7UVleMqjYYHBQEa2wcIiIjRah1D4U0zuFA/6OXZ8js8iMU0N6vKDuRW4nHNdn9Z5vv2rkdf3nij/jns08jNycXt912K2644Qb5XlVOHI65YHTfffdJ4urx2L9/P/Lz813JAOkuqqioEJGHA46ikfnLrKqqSm7omMPo3nvvxWc/+1l8+ctfnpLDKC0tDW1tbWNC0ljWNC4ubtpNHL785w3YXdkmIWnfuIZJzXynYUhWK6KsMdPu2D+o4+cXXVVzD/ZUMXl2K0rqOmH3EHsdERyABVmxWJQTi0VZsYgJDzoqnz/p/ZzG5z3R45++/a9977nveY/AwhsakqZMBMfKE088gccee1zCG6+5/mP46E03Y2b+rLHnG0POKDyYQs8ocBg5WIzcJCwgwTwkHzScHlBcaeu2yeclR4eOcpEYE3BjwkpHBMWP8aYVnGwzmfB41FVXoqmxHmkZ2UhNSoBFQnJ80NzWgbq6BlRWlKOtpVnCweS89KHAZEdiairOPvMMcfVwX7iQ/gE7mlvb0d3Ti4bWDgQP98IaEwX74ABKi/YjNSML8xcsRERYyMhEf6wTSJxcFIc6bRJCRLhOVIhF7kkYauWp/dp7BtDY2Sd9yHUSo0NE4Jts+7MpuSubt+/Bji3voWnAgrT0TKSnJCAqOkZEiqnksGLS64ly8vBzmxvr0dnRLsJIYnKaCHf9/TZU19SirnMAkeERiLbGyvph4eEyh7L19qCrvUVy1+TlzZTUA+7CAoVDupIYIkVCgwIQFxHkWq+9tQXVFWUoPbgf1rgEDA70IyE5DVExViSnpo+73+a8R1w85fKheEjRIzzYIs72qQqhtgG7FH3pbyzF8GC/CJmcn1osFpl3VlRWo7WtFb6+fkhISUd4ZLTzPDblmmJY2MzkKByPGAm3WQnZk1vJyJHFdrQE+CIhMmTSoaY93d3413PP4qkn/oCi/fukKNVnPvMZzJ079wM6GuWkEox4M0Z77nhkZ2fLyehOdXW1CDtMer1ixQqP733qqafEZcQT2RkzPDGaw2gsmsNIcxh90DmceMO5n8mzR8LX6tv7PK7HcqdG9bU56dEfeOlTzeMyfXP4TPf+n87HPt7xaw4jZSL4oJNueIae5eXPws233oGPXHaFhG18kHDS3NbTLyFlvLuPCguENSzwfTuEevoHUdfaK+KFGboZAkfCifiT4s5gf7+4TiIioxFguncfHBzEri0bYAkKkv2JjIoWMYL35hSILJZAEQa6uzrx7ppXMNBvwxVXXw9rVJhMups7ulFSXIz6mkp0dXZg/pKVKMzPlZxD1BvMIskba99F6cF9yJ6RL6+FRURKjh8pcT9kR29Pt1OIG7KLMLGgcJZXF9DOQy2SbDwrgY7nMMnJM1Uk/K3TJvc4PJbleQmTDr3nNK20sg7FRfuRP2ce6rqGEBcR7LFi1kRMRjDidW/D229gxqw5iIu1or6uFhERkUhMiEN7RzfKykrRTOdXT7eEw4WEholox9YLCotAbFKGuKMoPHA8mqGgFRsejLAg/6PiWluzp0ZcXKzKS9GJwqZzGd/Fw/OjoaNXxmyKNVQEkMlCEbe4rgMpMWFwDPSgqrxEXqO7KjQ0TJxWfv7+OLhvN1LSM9FUXyc5rnjsEVExaB8OQmG2s7r3VOGY3VrWJMdLoYbV9ihATmX/PXGguk2cXhQ/KYJSyOP4HO/+mvfsbMOBQTrkILnSKGZPpl/37NqJP/7u13ju2aexePESfOYzd0tOYq1ofvxyzBOF8MkdlyPByCNgdgd5WodfUvw5WcFIUZQPH375zc+KlYXw5sopHrVgb1Wb5D0ihxq7ZPnnhnKJxy5Mj8Hi3Hgszo5FWmyYxkcriqIoHzq8z3zppZfwyE9/ivXr1uGiS6/AX/75IhYuWuJygRxtOGlmSJRRfpufwcmkIQpQPCqt7xShhK9zOZKJOkNdxgtl4rHT9bP74H4J1QmwBCIyKgqrzjjPtc7mdWvR1FCHrNyZiI6NQ0d7mzgxmCTaz89fRIjY+GRkzyxAXEqmuDLWbdmOEH9gYKAfrc2NcAwPY8HSVYiNiUG8NdIl8lBUYVhTTV29JL3m+qeceb6EszXU1VDlRXBoKCIjIiQyITIiHJYAf1eo2nhtMi/TipkpUVKR7VBTl7zGsDeGmtEB7f5eujLauvvFSWLknhGBLNgiQtFUk38ziXlpcRHmL1khwnVOqFP4oQgVFerZrcX2MELTXJXChh3ikJkIfsaSlatRUrQXjfW1yM7OEeGIyZcTYyMBn2zps/TsGSgvPoCgkBCEh0UgLjEZ4REfXpgRBaFz5qZKRV62N/MjRQT7TkqMotjGhU4k3k9y/VRr6KQeQFKcoUOIY8HiH4C0vLnOcNFhOwZsfejv60Nfdxcyc/JETGtpapSQwp6ebtRUlqOkth2ZcecgPHzqBRPKGzslNxnvfSnY0PVW2ewUPw24LxSRwoMCZKxN5nzPT41G1mCEuL84Rij2dfY6E+ZTOPK0DW6beZEIP59JtXmtIaFB/iJqehPt5sydh5/84nF8/eFv4+mnnsSX7v0yPvf5z+PTd9yBO++8c0yqGeXYc8wdRpOFlSRY7eyUU04RK3hpaSkeeOABNDQ0YO/evVLmj0mwqU4WFhbK/7ds2YJ77rkHZ5xxhjiNJos6jMaiDiN1GB3LKnG86Spr6JLqaxSQyhq7nLHjHuzui7PjsCgnTsLY+PTl/aIuC3WZTFeXjY59dRgpk6sORCfRI4/8FG3t7bj+47fi8utuQmS01RVSxnBr8+02J2AUFDhh5QRwsmFKDKdp6eqX3Dmcw1HsYLJpftdNVH6bE+vKpi609w7g9NnJUxKOmNSZgk4bRRuGog0OoK+3V4QFuogoHBQf2Ct5DunkSc/KQUp61qjPP1jXITma+HtHe6tsk+6epvpa1FRVoLe7E/QAnXrWeQgKCpY8PjXNHciK9EFddYWE/zBUasGiJUhLcj5oZtvuPVgm+Y4YOu8bFgtHQKg4PXh4PV2daKw5BFt3B+y2bsA+IK9f+9EbERkWPOnj99QPTZ02dPU5k08z9Cwq1OISk5hTiv9/P84Po0Jet82Od956E/MXrxjl2NpT2eI1NNFZJcxUCWwkZI2OMG8ikydsfX147e0N8A/wx/wFC9Dba8OBfXswe04hwoID8d6alyQEjaFkfgH+WHX6uUd8vObjpgjK8EaKDpPNycO2YiJu9gX7mPeDEcFjHS+eQtd4ftJ1ND8zdko5gPgehiiac0cZ4aTG+U6hk/fNdBn19/ehob4BKZH+aG9pFBfe3IVL4R8w/vlLmNieoWIpMaHjrsfjoyOQyfDNuc4o3mTEHc7LO7lrjU3ainAsW8MD5VrlrU25f85qf4Po7R9EQWrMpJxwQ0NDWPP6q/jN47/E1s0b8YlPfEKqnOfk5Ex6f5WT3GE0WUJCQvDcc8/hwQcflCRvSUlJOP/88/H1r39dxCFCOyDzITFZNU9UXsTuvvtuEY0URTlx4RfVjKRIWa5YniVfRvuq2rCrskVEpLaeAZcr6eUdVbLwweOM5KgRASlWkm2+X9uuoiiKohDm03z88cfxy0cfRXR0DO74zD244uprXfeknuAErratRyZYydEh4lKZTFGH2tYeVDR1ISHKmYR6MvlXOOGj88JwH5EUaxjmpE/sMpKcPZ0dqK+pQl9fr0yIB+EHOwLQ3tmNYPRL4ncjRi0zd6ZT3Oh0JkA2i0WkqqEVAUM2+Nn6JImxz0A/stIy0FhXDN/uLsxMtcLXL17Cmtoq9yM5Ox/bt+9BqL0DtdZYZGTlSthZSGiopLGw2+0IDw9DY1Mz3nv7DQwO+yB3yVlIjo9GbMRhIcgeF4aG0l1ISEpBhDUBDl9/wMcXJQ3d6DzUitmp0QgPYTiTM+H4ZEU0OlGcE3fn5N1IWj5VKCxQdKATjK4OVhszHEgd3X14+6218A+wSJuyP5j82WBO+gfvwugb8sXCRYvg6GlBU9leDA8PYeXiebCEhEnls4WrL5Tws9AUpzhSWt8h4suR5OjhOVHT2iPjVsKbQi2oae0WhxVFL+Y68iRWGPA8Yt9zoVBbVd+CkooeDNkHJQSR1eV8fH0xbB9EemqKpDTh57yfBNSTEUNyEiPl8+k0OlRRgkvOPAfR4cHYtf+gnC+lRfvkHGMUDPdPcDikz6NiYhEVEwN//wA5/xny1dzZJ+3gzT3E42GeMCNXGNuV/UIBbSpwjCdFHxan6OBq7baJ084d3luzWl5ESAASoiZ3TRv1fj8/nHPeBbLs3rkDv3rs5ygoKMDFF18suYhZ5Eo5tpwwDqMPE3UYjUUdRuowOpYOo/HgJay2rdcZvlbRigO17R5Lt/LLjOFui3PiJAcSb7wng7os1GGkDqPp564imsNI8QSLr/z4xz/GY489hnkLFuHTn/k8zjz7XK/nCCevzMnHJ/Asgc1J2JFUMnKW2O5zTaBjww87i0Tg6RsUF5ERBsUky9FhQZPOFzM8EtYllata27F5/VtSoSswMEgeyAb5OeAY7ENUdBQQFIXoQIjriKE1oeER6GhrEVfVvp3bYAkMxOKVp0leIpamX7txJ+bPSJEHuZbgMOzduU2qknG/7YOD6O3tkUnmgnlz8cqb76DL7ou85GhxKzG8jGIJxQrOWHjsDGULCg5GWGQUehEKf4sFGbHhHsP+mBy7tHg/Bmw2Z0Ln3mH02x2IDvVHqL8PWONqeGgI0TFWFOTnITR48g6cycLP7R2wo717QMKInLXWnCIHRQfm4aFoxfD7nMQICQGkmFRe0yDtuezUM4/6Pk1mn4tq20X8mcz44foMlaMIMxUXE8dbfVuv9B1FOE8he+7JsjnuKYCYHwLy8xnyyDA69md4RIS4engdp1OKSbslPYmvLzJy8hB2BCFh49HZ3iZhmfyc2upKWOPiRfjheRUUGIS09DTJt5WaGI9AkzOM+90zMiYCAvxlaFCg7ejsQmNTI5qbmuScnjVnPnz8/KW9uJjdQ4TtRiGJiyGC8ZpQ09ItopWnaw7Pq11bN0huJefOuP6RtmMbxSUkSa4vijoMNX2/uacmS3VVJX77q8ckSfbSJUvx8MMP4dRTT/1QPlsZiwpGHlDBaCwqGKlgdLwKRu7w6dTB2g5X8uzqlh6P6/HGRMSjnFjMy7B6TWSpgpEKRioYqWCkSa8VFmn50Y9+hMcefxzzFi3DLXd/CfMWOvMTjQr9GSnlzgkdBRxqGIlRIZJI9mjBCSQdtZwQGvM3Vn6i8DBVMYoJfI3KYAxx4oSTD1gM8YUTWopHdElYYxPQTKdEUydiAgZx6tkXoKq8FO1trYixxkpoDSfnSSlpSExJk/cfqq7H5i1bkJpghb8lED09TJQ7gABLEAKDQtDd3SXhZ2lZuZLzJdDihyj/QQl5Y45Sui44cQ0JDoHFEoA+Wz/2bN+CxOwC9Az5Iz0ufMIE0myvmpYeCR+iYOceAijlv7dtQlxCIuYV5ImQQ+cPHS+cfDO/zZFUpuP9yKbiRhE3Qiz+koScuWW8TbopLjAxdqo1TPIjbdi6E23NTVi04tRxj405Ho2wKKMilzN30bCrwp5R3YqFQyabL4fjaTLiD/fbmQh6bNuOx4aD9VLVjrkoJ3t+sK86+waknRiyRjGEYs2+3duRmZWDrMwM+Pv7obikDPV1NQgJC5dxy4TqR5LLlmIVw6woVrq3L9v+UFkJent7ERgUjLSUZOQkRSI0JATBgQHimKPA6+fnDBE0QwGZ56qnCnsG/IyG5jbs3rUT8UnJSE7N8NomDOGjGElRzRCNOXbTJ8jtyYp0DN1kOGlyUooIqK1tbeJqrGnuQqC9C1Gh/miqrZb1wyOiEBgUiJjYBMwqnI/3C9uXx+ktt1dbWyt+8/ij+N2vH8PiRYvx0EMP4rTTTnvfn6tMDRWMPKCC0VhUMFLB6EQRjDzFfRvuoz1VbaPs+QZ8sjc7lcmzne4jJgw1vmBVMFLBSAUjFYxUMJq+tLW14fvf/744ipauWIUvfeV+LFy81JWr5HDZe+fEx5hMOh0+3p/Ic6I0UcLlD4Ptu/ejr6cTEYHOKmKcfNLZ4+vnJ86izNw8cQqRmqpD2LF5AwZDEpCbGC5CDhMd01Hk72+RPKKsEGU+pu6+AXT29sM+0C/hegH+fqipKkdbYz18WWo+MQV+fr4Y6O9Ff59N3su3u8wO3JQrFsIBP/8ACTEbDgwXwcxToATDaVgpixNycV0NOaQa1niiD7/rt21aj+zZi0QAYf4bvkdCpVp60Dc4JImfJ5sc2YC5qhhOxPuKyYgpPB6KIdUV5bD3tIm7Y+mq08e9x+HknuKJWbiUnz6m//v4SEn4iaqkGVQ1d8M+koPLHfYREy+zPTjRZ/tQiDqSCnJGPii62wj7jWFl7gKCkYfLEGGNSnF8vbykSKrqnbJiGXpt/Xj33XVISk2TKnzjnV/s24lC0pgPiKIfwwUpaom46uA49JV8Vdveexs5efkoyMtEj82Oho6+SYXk7apoETHJLHYau8ohzd8PFBWh9lCpJInOSEuVCn9BwSFH/ZohLqfuLjQ31KO1dxB2vxDER1gQFuiPrr5+OAIjndc4nsMOG/wcgwgLjxwVIjleGztzGjmTabufrzwWCnJSsS4mFMFexN/29jYRjn77q0exaOEifPe73/FaIV2ZxjmMFEVRjgQ+HTt1VpIsvAmsaOrG7ooW7KpsRUl9pzMh6ZADOytaZPn9G3yPBYuy48SBND9zxKqrKIqiTBvobqFI9J3vfAezC+fh2X+/JEKRgeS9maJhQUKiOm2SeJqTeGPyNNnk1ZMNfaKbZaLtMA9QdUUZ/Aa7kJpTgMKMGNkPfidS4GloaUdxSSn2vLIGBfMXIyDAAgREY9aqC0UsoAvGyA1TevCA5IrhZHb2vEWyfSbC7mS+Ih8fSWpbdagU85euRG1jPWLjEpCzeBH6B+woK94vbqKcvAK0DjZKOFFDXS0io2MkKTC3T9EkOiZW3E0WHx+pxEa3yIz8OaMSQZvdFpyEsq1ZPXUigYfCU0N7L/otMRjq68KcGemuv/G9WSMiC7fJewhWQqMDiG0wEcw7QwcNw7XqHUB2YoTLbcJ95SS6o2dAXDOsUFVeWQM4htHf1Yb4qFDMKlww4T0OBRfmwzqasN28YW5jCgE8pqmIaJ7zQTmhcMS+4PaNilv8DOpW1rBA5CYefqBHmHB797ZNuPqjN4nIsnPXbuTOLEBEVPSoz6Hjj+Ka+aEht0NBg8mgvTlcGB64enYyyhs6UVdbg57GCoRHRkkbsGqfNdYqYhWVTfaFVLWr75TQwvHgmOBDTObX9IRUwQuejfqUJNQ3t2Pzjt2IDQ1AZ0eb7HdQUIhTTCUUOKOiJczT388fw45hrF/7GiKjYkT45Wp06llj42GNTxxTvp7bG/YLgi0oDhkJwRJWSNa8/B/Ztq+fv5xvMwsXwO4fK/3RZgeGOvpk3fEqQNL9RqGS4iLFSm/tLC7A1h7YWoYkT5u7sy0qKhpfvv8B3H7n3fj1Y7/EOeecg/POO09yF+fm5o7b1sr7RwUjRVGmDbxJy4oPl+WSJZlys7O/ul0qr+2qaJWbLsIqG2/srpGFpFuDsXxmGxbnxKMgLXpcC7GiKIpy4sKJ4DPPPIP77vuqlGJ/9Dd/xBlnnfO+hBwKAcy1R+cRk/eygIMZTlrpnGjq7JOfnIRN1gkiDo2OPpmYkSCLH6pt3VL0wVPyWQpFh0qKxFGQkzsDC+bOQf+gHVtKGtDUUI/G+jrJ9xNk8YU1KgI+PU2o2P4WEpNTERIWJtXN2m19GO5MklAWCjbtrc2S64SlxA/s2Qn74ICEqV1w4UUirL366usifLQ0NmDRkuUoKT6I0uKD4lhOTsuU5NpDQ4NYuHAhWts7ZT8Y+jIzPw8RYWEy2X9n7ZvSBxSpbH29kgiYOWuS00aH6XAdTkq5TBRORXGC3//JMaEieqRFW3Bw/x4U5KSNOwk2PmeycF2GTjFXzcaDjSIMGs4WTqQjQwIQOeSDnrZW+Pc2ICrairT8RZJHZjLwnoTj4EhFm6libuPJnlN0LNE9EhMWOG4BEq7DMEPjfRQnmHPSWyLlqooyrD7nIkSGBqKmvknGtyEW0cVj8fMVpxnbhuOBYoS57+xDQ/jfG28jNDgYsaG+IpDk5s9Ga3MTqivLsWjZKTLG2X/1Fj9UVtdiTlY2MpMTPI4Rhh/SmUVX2XhVybgPFMAoanpKSM3jTYgOk6VwRpqE/OUlRaKnb0DEJFapo9zM3ynyllVUovzgAfQODKKqfRDRQZGIiU9EYf4MWa9/YBC1dXUo2rMTfv5+yJ05e5TYyrA5S4CfJMqnK4450havOA2HSg/KtWJ4aBhVZcUS2hdEp6AlELYefxR1dIEaXF5KtMewQo51Vi3m+Kxt7cUg84WFBso1ztwPHBPpseHS50ysfbC2XcRW9/6icPSVr30Dn7jlNvzoe9+Wyui33nqrFMWKjY312t7K+0MFI0VRpi20py/MjpWF0Eq8p7JVbjIoJPHJH6ls6UPl+jI8s75Mbu7mZjB8LV4qsCXHHH17sKIoivLhs2XLFtx19904dKgCt372Kzj/smvkyTonf5zQGOE9DJ9guNl4DI9MfFh6nRNr5hLxFv7C7xquRxcFhSJOqr1ud5ihOTaZ1NGgRLcPw3PMoRwUQg7WtCOPopFpUktHEd07s2bPQVoSQ0190NTaid27d6GjtRX+lgCsXDALmelpCAl0igHt3SsRGmSRdSuqaxEdFYV3316L1uYGnL56NeobmyXvCd0+DOui+2bYNxCLVp0hYlFPr01yG3V2tkuFtC2bNiAnbxay82aho6MD67bsRmHhHEk2Xd/RD/uQP2YsOAUxUWGwDflisMuGiooKSXodGhaBmLh4JCYkoqKiHA11NbDGJUhI3JF8D7PNCtJipJ/2VzSiprwYc2Zmj1qHx1Pd3O0KScuIm9ix5A1OqJfnxXssS77lwAGUFO2TNkzPypWcOJOF9yFMmM3Ke97KuxOGTE0EnR6dvYOucLOJhLOJ4Hg9WNeONGuYtCUdWsY+Me8Wx7q384LtNF5uIwoZrDQWa412Vthra5cQSYOC1GhUNjs/j3nEPOX3ojCTbg3B0mXLUN7YjcggPxzcuwP5hfNxYO9ObN3wjlQso0OHoZrWED/87V8vIz46Qlwu7EpXeCpdRyPNTRGXgsd4omVSdIikTKB+xvtRaW8P45ivOcPUfBA2Uv3MQPxDfsCs3CxZ6NJZEuAn/ciHn8xFRSsSr1vxCUnIzcxAXWMLdmx5D3PmL5Zjk7Zsa0JSRKT8f2BgEE0dPeiz+6DZxlDVYCxcOAeJ0WHos9nQ19eHnt4+1De1oqu3D8G+QyjtOCSOKzoDWSXR3cXEc4Zhi+wLVgUsqe+Qa6p7onMeIwVCLpJSoqJVKua5i28JiUn48c8fw6133IVvP/R15OTkiGj0mc98ZsxnK+8fzWHkAc1hNBbNYaQ5jE7UHEZHirMUaSd2VbZgR2kjqttsh9MomODTj8U5rL4Wj/mZ1qOa2PR4QHM4Td8cTtr3nvve2z2CcuLCUu33338/nvzzn3HB+RfgqmuuxVkXXCJ/4wTHmAxyAsYJGSc5vPZ7g0/H6WBNjQ5GeJA/Otpb0dxYj8GBAcTExiM2PgFDDl80dQ9gcMghlcwSIkPGnZxXNnXJhJsTKk6yORkdTyShm2VLaRNOK0hyrdfd1YmD+3ZL7hGGfnHCze+1yKhoSSRttw9K4ukrr75GBANP2+dkb8PuUiQlxkuOnC1bt4kjw0gozEl5e1O9VPjKirGgjxXQfP2QnTNDkl7TsTBv8XJp1+f/twZ+vQ24+prrEB3urFzqEjuGhqVE+76qNmQnhMvE25zzie/fsfcg6mur0DUAKUGen5eLQFaaOgJamxtRUlyM1r4hhIRHSdJuuiDo3mF/T5RY2whto4OC4YVTEVTeeW8jYuMSkZOVhj1790sIEJ1TLF3PRMYUBSdyS1FE5KR8VN6ikdxFU4FVtZhTivvFJOH8aR4HhtiTHO053wzdXx1trRIK1dvbh6LaNszKSEByUtKopNOHK/vZJIzLMSJ8MQRssnAMb9iyDcF+w+IQKpw7D7v37BKHHEU3w2lEdwsdTjwOd+F297bNyJ0xA5mpiTL2tpU2Y+PmzUgKtotrju8pKz6A0848B5kpCbIOE0sXlVUhJzNFhB5X4nvJvzW19naGJPbLTzrpPKSMEhhKxzQJE3Ggps1jZTteO+geogjD86qtowsNtVUI8x2Uc6emslzEHlYVZB4y5i8b6B/A4OAA/AKD0dZjR/6sAnEhEW6ejiDjvGB/crzU1jWiouKQVD+kg4nXu6joGHF+8frH7VFYoiDqFxiElp4h6R9P49RwsjEJ/0Tj/+21a/Dle+5GSEgIHnv0UZx+uve8X8rUUYeRoiiKp4ujny9mpkQhLzkCZ8+IgF9IBPZVt0voGp8I8eku4ZPJl7ZVycIvPMajO6uvxUnYAW8iFEVRlOMP5tb5zW9+g69//esomD0HTzz1N8yaXYhoa+yoSQtDtqpbe6TCFR8MeJoUcvLKMvOs2NTV1YOu/mE0VTtdCRGREcjNmIHoiFC88e5GHKxtQ3XpAcybnQdfhwM+ISHoGE5AYBArhwXLdwknWOaQEU6cmNeFIdUey8cPD6Ol04bm9i5YLBZ0dbYhFv30MbiOlUmq8+fMQ3tbi+Q3oTjEYyk+sEdcOqwoxQpTL/33v1IefMWq05CV5ny/AfPLnL9slrxv0D6M9ppIDLVWIDNvliuEiuEr52Rnob2xRgSjJQvni5DE/a8sL5V1mNMnJsgBa2IumppaZAYaZLGIK4mih4+fr7RTXnIkfDw8ruHnz87PhX94LKJCLDhYXIJ33tsEh48f0rNnwN/PT5IwU1jjhHOiiTzzL/lhGGnWUPR0t2KgeQh5ecsmTIrMamp17b0irBgPjJo62+X7fyKxhsLYjn0HUVVTJ4mr39myS9qQJdltlk7Zf465+vbeUeXSPTEVkWo8GNqYmzR+bqZSukP8Dgt3FCIZqkjBsbaqAplZ2ejrH0Bzvy/OWFKA9pZm7Nm+WcbC7PmLJNyQ/cGk1VwoGjElAJ17k32gx/aiyJJfUIiOPoZAtmPdexsRFOAr+/HmKy/gjPM+IqIRhTSGlFGQOdTYJWOM1fLo6qvvHgLqO9ELpyCZEhOC6y8+Ezu2bXGVnr/osqtcjjv2Kfd5yZwcHA0ozAZFOcXS8fCU3N0bnsY6hU+K3IbQPZAYgdLIcBHQGpuaJIw0KSkJ6SkJLuHLSOjvP3K94WuSu2mkHdw/MyjAH9npyXLNoBBOFxLD4CrKiiWElNez4KBAeQDT1dGGnu5u2Gx9ImDTlTSZh3JGO7gf42mnn4nnX3gZ//foz3HxxR/BxRdfhJ/85CdISUmZdLsp3lHBSFEUZRIwDnt5XoIs/MKqbukZCV9rRVFduzwh45OnfdVtsjz51kHn5CIrFkty4yTsjWEDiqIoyrFn9+7duO2221FbV4+fPf5bnHv+hWPWYWhXdWu3uAi8CQB0VLA0NUWSxKRE5ObmIDzMWUnLcMTwu4GJjfl0/yNnrRQXRb99meQVoUDT2dGF+oYGtLU0Soluzsjo0ODT+KCgIHEAxMYnylN2upcSIgMx2NuBprZu9A75oLO9HUODA4gI8kMABmHzgSS47R20oaRoL2qrKhEWHi6iCIWtaFY38g9CXVuvOKHopkV4CiIjQpAcEybuQgpIMVGHHXSG04qTdbNIFZ+QgOaWFuzcshHZefmorqxAbZcdHeF+GLbbkTNzlohFrJb25ptr5PNZrpvHkrn6DAkpYiLf7Vs3uqpDBViC0TYcjOQIfzSUQ1wPbMcZeTNlMsrfmSSak3+G3XHSnRa3QPavoqYeDQ0NyMqdKQ92WOSiMMM6YYWy4gN7MX/REvj6OCQkkTmYuD1PGga3y7Zjm/DegImYzXl5WFmLoTR8fTzXsYTn9A4hIDoV6G5A6HA38nNykJ41WoygWMjQqsnmtTpSJiNKMB+XuGkwjAN79kjC5/CICHF2kNWnnSol5UvqOmCJGEL3ABAclYD04DDs274FNZUVGOgvEgcV3ScVzd0iFE2mshgFn+qWbvmdri+jKltMeBCGY8NR0xrjdPZFBmP+kpWj3ksHDfMSMfeOIzAEJZ1BSEqIxZkrl2DXtg0ISY5GYmy0a5srVizHW2+9JeGUdHgFW46te3yyzqUAPz9xGRFWcWPorOHOM8NzPjcpUoSkrDSKPMljtiUONVdm7cP7MdGeOKvo+cASEYroiFy57vF8kTDaoAA5Z/JnhMl2KCwdKC7Frq0bEZeYhJCQMHFk8pyngBQUEoqgoGBJfM/hGRwSgnfe+J9USgwICDyc+Js4HPjYjR/HlVdfi0d/+XPMmjUL3/3ud/HpT396lLtNmToakuYBDUkbi4akaUjadAtJM3BMou95E1NU2y43iHQfMbmpJ/gkZ9FI+BorZEw2YeSxRMOSNCRtOobjjTf2NSTtxIb5N775zW/iZz/7Ga6+6TbcctcXERoa6grjEXeLr4+EbwSOVAMzHD0MqWDeHE5mbP196O/rk6fms2fPhjXSs/NnItyTFZsn7XTkdPX0iphUVXFIXEFdXZ1o6/dBeEQ0Eq3hCPQdQmJCAiLCQhHg5zMSPseJow9a2jrRwfc3tqBrYBgx1jgEBgSgqqYW3e0tCAv0FVGoraVJwlR6hi04f/UKmSD39w+Iy6irpw/NzU2Sf4hOq66hAGRYQxEVHSVVzph4Oi0zRyZ2G997Fz2+4ZiVFou+rnap2sTy8OeefYZM9g7VNmHtmtfRY4lDRlIsklLSpEx5efE+ZGRkIs4ajd7+QezYV4zchHBkZR8WTvg5pUX7cNY556KlyybuJlaiMibSfQODKCuvRH1dnSQrD7SmSfsx55C3JMtse06YuQ2GUTGBssUSKE4Yhg0umJM/ZqLOB0XsI+Y+Gs9BTJGQoglDdszV1Phe9g8/u6a2HpUVh1DVPiCJttNSU70KA3Rkued78RSGSMHEnMfIcInwJ9uBeWS8wXbl5J4V0ox2cae4rh2RPjZUV5SicO5cJMZZJ3Rh8R6JCd2bO/vQ1d2DsNAQtDbW41BdMxLC/BHk7yPnEUWkiEjPwhH3iyGWTKBsVPFyh+cnXX4DviHod/hJcmUmlG5pakR/fx8aamtESKQo1NraitaWFrnHo5BKB15vdxdm5M8S8YR9u/dgGQ6VlYiAy3CtZUsWyblhhm1MEY19SiHEGzsPtYzkKHJWRXTlRPM5HD7I4zqarnRzGBrHA+HnUmCl8GtU/5toG+sP1CMhKlgeejIcdjLiFfucIiffT5cf8zUZgjvF3poWhqr6ICM2TNqlq8eGhqZmdHV1IS4uFgH+AbAPM2y2R0T0AH9Wf3OgsaFeXF/9NhtiExIxb+ESuQ7FRwVLH3T39KGtrQ1t7W1Yu+YN/OKnj4jL6He/+60kyFaODBWMPKCC0VhUMFLBSAWjyYtlvOniTSXFI5ZNZZUPd3jTMSc9Bouy4ySEjTe1x2PybBWMVDBSwUgFo5OFtWvX4rbbbpMcVFdceTWuvemTCAoOHT25Hplsu4cA1ddWo+TAPsRl5otLISE6FClxUSL2TPXazc9grhhnfho/mXCyMhQnVp6gyEC3U0dnFyIiwiV3zFRy0+ytahWhwJwfhvvASV1tQzNqaypRWV6O8OQchDr65HhYSSkwMAjVlYcQERkpCad77L6oaepEXFyM0wrkcIhLRML2+npRX1kqjpzIIH9ERlslMfa8+QuQFBcj6+zcXyJVzhjyV1FWghkFc7Fv1w6kpGcgJyNNqkvt3F+KvrYGzF2w2BWS19fbi4qyg7DZbKhsH0RMjBVnL5sjpe2Ntm9u68SGDe/BLzgcvuGJyE62IsItQbABxabK5i55L9uBk/SEcAvKD+6R77yUlFTMzM3yOnmny4VuCbrO3BNgs6+MinfiiAj0h5/dhg4Kbv39MscYsg+JUMF2SExJl5wxRZKknGHso+8zOLFn/hp+HsfJzJTR5eLNFNW0IVEm5r6uvDpsHiO/Dsubj+dSojNK8jDZncnL3R1HA4ODaKouQ1psOOYVzhEn0ZHANqeIwfEulb6GHejo7kZZaRk6OzokVDIlPVN+krqaShzcuxvR1jh0DQIR8WmIjgwXEYI91Fhfi8a6GmlHCpt2S4QcR3CAL6KDfWCNjUN7awt6e3qw6tRTERPhTPQ8PHJeVdXUoKaqWkLZmMvqnPMvRGxUuDjoyqtqUbR/HwYRAJ/IZOSlOZPFG7BdKcBQAGRIVorVuW1P94XsR4pxRpJsbn942OnUo7hpjClv0C1HxxqFKTrYKbRM/drjTIY9GeGH7cMHobNSo+RayBQMFKDYZ3w/w9u8XYd4rEaxgLTYUI+5qTjGKpu6YRu0y7ZYmc3bPtW39co5xe3ZBgZQV98gbqOc1ARpO4YnZsaFjxLtuP+t7V343ne/hf977DF84QtfkPBjujaVqaGCkQdUMBqLCkYqGKlgdGTuKt4IsUrF7pHwNT4l9JTU0BoeiIUUj7Kd4WuMkT8eUMFIBSMVjFQwOtHp7u7Gl7/8Zfz5z3/Gt7/9bdx9991o6ejGnt27MXv+4gnfz4lzfWsPtmzfhovPWoWokEC0dPfLpFTsBxKqM4R5XvIbGXBCyNwrXCUlJsw1ueH2WT2qd8COnISIo+4+5USLgSSeynfTRVF8YD+G/IPQOeCL9Ch/2R8mxKbo02/rwxmnnybCCL/PisoOiZOD7juuJwEq1I6Gh6XS1MYNGyRnybJVq5GeloLIsMOVRI0S4K+9sQYRUTHi6KCgFBcdLutw4vfGmjexcNkq1/5xP5iom+6TqkOlWLT8VGzauAEz58wHP91of05cKUIMDdgw3N0seZoYlsTkvQb8fFY840SVCXuNqlk9Pb14d+MWZObORE5y7KSqn1LIYaUnigScENe39Ylzgm9jPiF+p3d09mLLtq0IDQ2TcuQBARaEhjNP1NjJMwUHVmjle81CDSfITIrOimJ80DTefjFEL9Ua6tXxw/uPIw1rY66ZvTu2Yv6CBUiMi/EoFFAk4K5zf4/kARiPmyFKza3tKCkpFtfPrMIFsq0dm9dj/uLlKC7ah7rqSiw+9Ry8t2WHJFfOz0qRyn4tXf1o7+5FgMOG7JQEhIeGuAQzcXWx0p3Fs8jCsdHc0Yfm1jbkZSZJOJexT1t27kFYeBTWbdmJVStXYFaa1WtierZ9coxn0YhhYBwX3hI4H6hplwTv3qrwFdd1iEjGaw3b2qjka5wDHCv8+0Thl+9HaHYXRdmu/DxW6PM07nieMIUDxUiOYbrk3IVYbo9iFPNYcVueXHAUusoauuQnx7B7G7H/OP55HjFfFR1yZnbu3IlbbrlFHKZ//OMfsXTp0vfZOtMLzWGkKIryAcInUfzy4nLZ0iz50txX1Y7dlS0iIvEGh/DnazurZeFXKWPLl+TEYWFOHGalOJ/uKIqiKFNjzZo1MlHIyMiQSUN2drY83a+sqnaWMecMdyTkxD13jFEeu761E/GRQciODcFAXy+CokKREuO8heZEh4u3SSDFCVZoYqgQHRUspuApYSzDUXqau/DewQacPntsPhEznBQxJwjdq5yQMUfPeHDbB2s7RDDie+kwoHjFyZdvSDTyFq6QSSGTR9PBwP1r6+zGMF0wEeGu7x9+n7F0NxnKz5PPdlbikqOQ767E5BSsXLkSYcFjH3pIMl1fICsnF8MOX8yfWygTv+6+fpQfqkR9fZ3kajLjrLLmK8nECxcuRdnB/QgPCUK2aVLorNA0jMSo4BFXUQaq65uwfdM6BAeHIie/AJ39QHvvgJR3d6941lhXhXkzszArLxXby5ulXej4Gg+2SX5KtIhUm0uaMCs1Wtwlsi+DQyg9VI3SkmIUzFsoOVgmgu0wO9UZqu4xqfmQIQ54JyrUIvsTe5TzJfKz9+3ciuXLliMmynNCbJ5Tksg8LBA1rc48QxwTHPMUSTw5sarqmtDc0or4uFjERNOt5yuhlAMIQHRKLsoOVaCurg7JyclITs3Azq2bEBIWhpS0TDRWlyMu2IHTzlktScF7B4aQl0KHlmcHFnN0u1d14znZ0N4nDhfCc8ASHCbhUkYZdwpYzH2UkJSG5KgghARZJOk37+nc4ZihaMH9SfSQyJpiEN3nPMfcBQ3CEEuGMXIseYJiIPeXgoon8dcQMTku3y8cw3SasS14LOaHmMb1yggN5Jh790A9VpsqMprPE0MAoqDKHGyEwhG3yXFD9xATlzMZOcewJyiG0X3F6ynHGaFwZLQjry38P69vbEPmnTM79+fNm4cNGzbgRz/6kVRQ++xnP4uHHnpI3UaTRB1GHlCH0VjUYaQOI3UYHf38TfLUur1PhCNajffXtMsTVndCLP7y5HpxbpyEsPEJ0oeFOozUYaQOI3UYnaiuonvvvRdPPvlnfOozn8fXv/Y1WAKcJbApCLzx+htYsGgpEmKjJPfLpg3rsWDpSknK3NbajPKqOjR29CIyyBedDZWS3DcjOxc5OTOQk5kqgg1LzDOvh1F5yFMYCnOXLMuL9xiS4Sz33SPlqMMCneEs3vLt0M3EiSgnheJiiA6RiSef8g8NOSQUaTw4UTNyG1Hc4sTM22cdCWxTI9TGCO0zwvsM143L5VPfjD07d0gIka8vHR9AQnKqiEXe8qUxlO3Anh2YMfNwjhlje9t37UNbmzO30GmnrERggL+0U3FFDSrLShCaki8Tf4p1no55y4Z1iM+ahfDQYGTGR0iI0WTh57PoRa+tHy0tLeK86ursRFS0FWlZOXI83C8KdHRlcILLsCQzDbXVkhuL64VFRIqbqrK8RNwzwSGhIpYxPI8VyE47+wKppOdtX8rqO5EeFyZOH6MPjITlNa09EgrvDXGMuU34+RornM3Mz0dqYpxX5xDHF8ekfdCOxuZmxFqtCA8LlnNLciP1DiA7PkKSMBvb3b57P/bv2YnwiEgJP2OupcZeH6TFR8HR343evn502v1h6x9AqMUHfgPdOPu889DRTtHBB9aY6HGTinuD58y2smZJtM3cPBQXzHB/mYuSVW/bO7ux7t13pB/nL1yMtMRYEVxr23pE2PHktCpv6JT98nRd4DlPJxHv44xQQY5JI48RXUoUU7wd1/7qNq+CEuG2ud/eoEuJzqToUItcuyZyM/I8okhFsYcwL1RcZLDr/OO2GE7JsDVvzih3OBbptGRILo+fQq83wX08QYvufbYdhTj385rnG3MocUy657zas2cPbr75ZvmOePLJJ7FkyZIpffZ0RB1GiqIoxwjeePGmgcu581LlyQm/zBkzvquyVZ5KE4Yp8KkzF8L1mfeIy9wM65inpYqiKNMZPkm+4YYbJBHzF+77GmbOnoedReXIyUyTnCF0CPQHxaK4aD8OHhiWgA4maKZQtH/vbnQHxCEvMw1nrkqQCX5rezfsw3Yp1d4z6COVMPnUfXaa98k34USF12m6DigYcSJP6lqdT9QN8cCbQGFUFyJGqIa7M4GTPj504MR3vDAgVhObKpzY0RXbY2OVMnh0TZDmTpuUlmf5+sOTYOcEmNugS4uCUV//ILZu34m66gr4B1gwu2AJ6rocEhrWRVGsscv1oIKJuAN8fRAb6oeBgX7U11Zh9Rlnwxoxeh/6B4dRU12JpatOR0tTAzZs2oJZswqwe9cuyXEyd9EymexTuGA/UDSjaGENC5R9q2rpRrvdghmBQJ4XZwbXo5vLPT9KRXU9SktLRPxg6BtLuMclpiA7r8DlAmOZehIZapEcKy3dtlEOFFbCO7B3lyTZTkpOE5fFoZIi5M2ciZDgYHR2dmHhvDnSnm9vDMbe4grMnJHjUVBgu3OCT8eGkcSdrxnJlem48EZrl3O/jCphrlDJshKp0sccVt7GlzM0E9i2bTuGhofErVddXSXC1vLFC+QepbemfZR7hNtaUDgL+TNnYMOGjUjKnIGG7mGsiAqEvb8XYRFZ2LbxXSyZM1cEwuauPpTXd6C2tRc5ibGTFic8wXNm2Yx4ERTiPDh1eN6yrw9Ut4l4c8H554sIaoSzcfxQaGEIIUMSPRFg9+wI437zXOR4pNDozGNkdwmuTkHbe7U6nv/MDcTrAfdxqu3AcTsnLVrEGoaKDY4411ipjgKX+7iiOE0hOwVO9xzFMrqY2B78GwUjipBTSdbNdd1F06nC9qeTii6xotoOcTe6j0+2Fa+h7oLRnDlz8N577+GHP/whVq9eLeHKX/va1xAQcGwr4R3P6CxDURTlOIFfbgWp0bJcuypHnpDQvkzxiD+NmzLehL6wpUIWf4YIpEbLpGRRTpxYmqeSDFVRFOVkYXBwEA8//DB+8pNHcPun78S9X/2GhFXRoVFRWoygGZnYX9mM9qpiFMzIRmxcAnxMT6YZehIRHo75MzJR39qFHaUNaGhsQnVlGRISU1CQP0MqXmVNIcccwy5mh8RIzhE+/ee8imKBt+S4ZpirhCE93nKiSN6VThu6+wbFEcHJ7ZHCZLYNHX2jXuO+UugKDvSTsDtPghEnkRSLWPnT09/43TUrLQpVdY2SM4rJngsXLJUy2Rt3HURihAXZ+bNgCQx0haDt370dlsgkhAYFIjDIV0qbUyyKDnc6NtiWXJgkNyjAF7MK5mDH5veQV1CIuIQkEYsYhhYcfHh/ORFmGJGUsu/pF9GPLhyGucTNm4mG2irkZYwOBeRDHIpM/ElRj6FWM5LoUvLBvqIyNDc3Yva8RWNcUTsPNYtjgsJEitsDHb7GbXb2DsjYYF6jqOgY1FQeQlh4BJbMn4PsjBSU1Heic4Ah6xaUN3SJqGkJDUdbZSlq2xJlPzyRP477ZDzoIKGQYQ5/Ly8+gPw0K9LTMz26nw3WF9Uj22qBfWgIs+ctlHxWTFRuH3SGPXLc877GfULP/4cEBmBGXj6efWcfTl84Azs2voPIqCgRmwIDA8U5lpOejIRIJrj2cebjmsQ9jozLERegpzw87B+2P/MKOcOZnHm0DCjEUAhh7kmeg+55dSiCzcsYP2eZNziWpuJiM8N7PIqQHP90J3Jsmmntto37fo557nNEsEUWAyZU5zlOEY3HZoTkmTHEMsMlRpfPRHm1XNsfHMLW0iYRLXk9O5L7VGfIXSfSrIcdWHSHcWx5cseNBwXem2/9lOQte+D++/DSSy/hqaeeQl5e3pT3azqggpGiKMpxCm9SVuUnysKJAR1HRvJsOpH4FIqlkCWkrbIVf3yzSN7DsrNOASnWFQagKIpyMlNUVISPfvRjUkHnpdffQsGcwyWUrfGJEiby2tsbkRRhwdIli10ChRk6RChYMPSHIUDdrZ2wNzciPzUOi5fMQ9T7uJ7SEcCJzVRgmFlRbbu4fIwJL3+ng8Q5UfQRh8TyPGelsvdDQ0evhOh42g4FDub+8QTDnFJiRgtJkjTbNoC3t+6HY6AHtcUDElrF8uUxVityMlLw3p5DyEiyIjosGJvXr8WCpaskLInuouY+H4QNN8M/yBf01bKvdjQ1YP78BRiCr4gADI05UNPmDEUJCEP6zHk4WLRXwgytcQlej5PHx+9F83djU20L4uMTRk2eDzU5q6hx4mxM7vn6vqpWxIYHoqqyXPbZU3tRXGBeQm/uX06aGcJFxwqFibkLl2LbxnUy7lpzciRPjlRES46SHD+VVVXo6e6WcLVVp64eKZ7hOKoPh3g/YYhFzFnE8L/IyGjMLZiJ4rr2cR1qS3PjsX7rLvTZA7C3vF6SnudmZ2LBnAxJVMzcMuPlYUyMj8GilEDJT1Xd3g9rdjpS48PR09mGjLRUOdaDNe1yfzORq09yIzU7KxBS4OQDNYbpcRsUW8yhS2w/CoYU73hflZUQLqIE+45CF2GuIE8uJHIsKts6RTZ/WTyFvElOtiOAzjLmYOKDSopRk2Gyyfl5vWrt6ZfrVLfNLgIiH3am013pFg7oDveFwh/7RaoZRgY7/++W/PpI+kJysGEYDz70ENasWYuFCxfiBz/4Ae68887jsmrxsUQFI0VRlBMA35EbVy4XL8qQm0l+6RoCkvFkmF/2a/fWymLcuBrha7PTot+XjVtRFOV4gxPE3/72t/jiF7+IG2++Ffd9/SFxJox2zvSicGYO9m1vQknRXnR3dyIkJAyFCw/nruAkmU4kTspZGayDOXGYaDYzCwvnOZMzG/D6W9bYJZ/NazMnPlPNpULhx1lRavz3cULrDH3xcz1Rp8tposkaBQC6DXj8Ul66u1++B8b7DvA2SWJYtKfjGx4JUeH+ECNXTkt7F959byPC49NRkJ8DPz8/bN+0XkL+QkJC8Y//rUVkRBjCLEBtVSP8/PydfwsNQ2OvA4sXLxrzsKOupgovv7UBq5YvESGlrqkN+ze/hfzZ8zAjOx0Vjd0obbFjAaZOZHQ06murkZqSiJK6TgRb/JCbGDlG5GDbWUN88MY7G5CQmuG1vSJDAqVv3auSDY/0Cb+n6UpimBu3SZdbbv5sdLS3YdfOnRiy29HSa8ehIh/Ex0QiMSUdoWGHHR8UMVo6bZJL5mhCZxDbgWXsZ8+Zi9SkOAn1YYSUJ3GK/d/Q3I6dO7aLC4kOqaK9O3HZ2SsRYAlAaxdzfAVNOMbprj5t5VJxrNTWNyI6Ogq9Aw7YwvxQ227DYEsf8pIiJxzzzBfGkD6OR3PSchYRMe6Z6Cxyd+LRaTQ3I0YqFVLEy4o/nOj9RGMiEVFC7Wra5He6u+IjgkaJNnQZ8fjHg3mWEqJCJiUY0WHIkDBDLKfox4WCN7dD5xGFL7oF3c8nhnVuL2uW+9jUxMMhkXQ4Gcmv+cpEgqQ30pLicO3VV8l5OLtwPmbNyse3vv1tvPLqq/jD73+P2NjYKW/zZEUFI0VRlBMQfsHPz4qVhdCmvaeyTSYWtNobeRP4ZJjLP94rE+swcx4Z4Wu09epTFEVRTlRaW1tx22234Z131+FbP/89lq5ajeo2hmQ4wzI4aafQYVQNKlywWCqhVVdXI8oa5xKKGA7E/DcJiUno7++XqkwJ8XNl8sUk0cZ1kvkyyhu7YPHzlQksJymSB6e5WxwpdAF5KgltnmCz6hDDf1jMgEIMnTvmCkTucB8K060SHjbe9VqSSbf0oLd/0PU+ii4Mw+KTebpSveUZ4cQswM/Pa8jbeDmWePzM9cJtlFdWoaPNORm1pszA3NxE1z4vWn4KGutrYbcPodvui866RgzZh7Bg6QrE+vkg0OKP3eWNCPMbQER0gkuAstvtkhy7C6GICA1CedEeWDBTqmqlZmSjqqIce0sqpNLa+SsWjT2GYZZqHx53chseEYXqinLUN7YgMCBIBBL3tqYb4d2NW1HXYUNqVh5S4sbPCWUk8eWEmW4QI2SGVcQ4wXXfPvP+cDHg+uyzbA/OHo6z+rYOj4IR95OuNE991tNvl3sAT0LR3qISlOy3IS4hASl58zHoa5FcW3TasHqeJxiW19BQhznzF4sjb9e2TVi+fCWiRkIHw4K8j2sKBTy3DMcP24P3NdnpzrBAnrHMm2Pk9ZkMrCLG0EyjIqEZbpvuJI5nFhnJSogYJWTx88c7dycDj4m76kmUXbO7xmNoKfuLgoknt9DG4gYRHw24bY5jLuFBAUeU8NspljnzEXE81JuqxLEvKKyNJ75wPFB85jnFyoSB/sxx5D0PG3Nu8oEmr3kU1o0E4xQJjRDRpk6b3LfybxSrjHOD7XhqQZLc3+6tahPhiQmy+Xe+n8Ix25znCe9veWwUHP39/ZCaFO8Sz4zKkBwXXN8wYTFvFx1oFKAWzi1AYlIK8vJn4cc/+C7mzp0rIWpnnnnmlNv4ZEQFI0VRlJMA5kU4Yw6XZPlCpw3cmTy7RSY4/IJkDoItpU2yON8TJFXXKCBReDIn9FQURTmeeffdd3Hd9dejYPZcvLl+M2JjnQLQePj6W9DpCEF0Wj4Ghgbx5nvbpPpZQlIS4rILUZhp9ThZ4hNoCkKcHM1MjnQLa4EkbQbTzXiB+eeY/4bXYU4ajYSvnCxxopTuEz7u9XcyE2Ze3znZ81ZSmxNvb+4DhuB02QbEgWGGHztegloKUpzQlh6qQm1pMbLTsxA3N9+rsBWfmIzqijLEB/QiNjkJGdkzUFZahN1792HJqWejuWIfYnMycXD/bhGTmO+I32dtiMSywhnIS1kk7q/SshLs7euT9ixv7sHpq1cjJtQyJpcQQ41YjYmCDSvRRYcFSbJrT/uXVzAX+3Ztw1mrT5EHLuwXui/opKAwRvcJxb7TVi6ZtFPXEI3Yt1OtSCcCSoCfuEHcE51LaI6XBOT8GyfeZmeTgVGO3J29u7ZhdWEGstNTZPxzXFIQ4OR6YHAYMeGBHsdTcXklFi1ZgoCRxM+BgUHo6u5yCUbe6B+prEU3D7dD4YbVrDyde1NJpmyUeqdLhu5rhkqanWo8P5h03M+PeXfwvnC6dIyNOFyJozv7BqVKmbtAuTA7VsQqup3coahIIdH9+GelRIs4YwhZbCuKH2y/4voOEf88ndPVLd1yXoYF+Xs9F/k6290smrHPh9xyIrlzqLFbwhONcEvuC8NS+ZPjlLnZzFXnjD7htimsU7SJCglEUkyI7Dv3g445nqvekn3z/pYL24/CEduKSf6lOmGfTUI8KW7vKG/Cnq0bkZ4YI0JxRkqCbJ+fzVxVbEdey4w2k6TuTV2yDHc3obmxAalp6fj2936I//zreVxyySW4++678e1vf1tyHk1npvfRK4qinITwxpRhDFyuWJ4lMeD8kt1d2SIiUluPszwqn+q8vKNKFt6TzUiOwpIR95H7pEhRFOV4gDlumGeCN/H3f+ObuOVTk8s30dDeK6FARqWxTevWYuWCRYiLjZZJkM9INSn3sDE6NFkJiU+zjckrJyANLR3YV16LpMREEYHMf5f9HMmlwusv3QBGsmR3OBF6a18tLlqYPu5xGOFe3p7+U8wap7jSSHl7L0JOJCd1gVMOWe7uG8DWrdvEXTJ/ycpJ9QNdQVwMgoNCsOKUU9HZVo9VSxdJmIhxrNwcJ36vvLURew+WIzQ8AoFBQQhNyIY1wA8NbT04J8+CSLcqSFLBrKnL6UJIceZl4jY5+aYbgUJFakzYqP5m2BzL2POYggIDMCslSpwN7+6vkzETaO9CpilniieniKe+mUy5cG/5iDj5ZhU4hshN1g1sHOtUcAw7kDUiFhnb4HHKsXrRfujO6LANiXtkcCQhtiMiCZv3FCPWGjOmVL35WClMzkmPdt1jUFClmMWcjKy0x5CyiYQiJmemAMS+ce8TunX4QIxCRm2rU4xh3isKcBTS3AW48fCeK8pHhED38DaOW0n07lZmnuOA1QaZz4e5yczkJUeKK8w9PxOdh6ziZ7isDDFQBEEfHxEwPYm5jR02EVNr23pcbhoeAsUh5v7ylluL7TJe23A/6EYyv5/CmCFOUsziPvE6a7jfzds21qMAaxQA4D7xnpRJx90Tk7tDYYlLc2cfnn19M3ztfeJMbOnsQ1JyMjJSk5GSFI/s3Fzs37cHERHhiIkIle0yfxKvxxQqKdpxfzjOmedq26b1QHgS5hQscDm6CuYuxBXXfgyfuvkGvPPOu3j66b8hLS0N0xUVjBRFUU5y+IXMErJceCNJyzaFIwpIB2o75GaPEw1+kXJ56u1iuWlj8mw+FcuM9EF8/LE+CkVRpjtNTU248cYbcaDoIJ5/6TXMm79wwvdwUknxIDYiGDkJ4eJCKd6/B2kZWRj0DRTnCJPhehICOnudeYY4UeO1k5P3vUWleO2tdRh2+GB+QS7SrNnYvn2bVGiLibFizux8cRpQQGA5+dnpYyfPLKld1dIjk1FObL2JRfw73RLMQ2S4iOZljg0pIub38318Ys8n8kwOK+8d9Fzmm3DiPpUUIGyL5rYubNmyCbkzZ0uy8COFIYKkrroSifFW17EwF41z34BTls5DSWk5DpXtwMJlq+AbECiT4VlpMWParatvQCatzPcXbJrYcj2jvDbblO1i8R3d5zkzC7B+/XpxEzAUbnZhoRSdaO3owZ7yMsxfsmLU+pwgs8w7S5PT7cCQqHBT5SlvcGywTxmmQ+jO4MQ1xk34Yr+EBH4ID258nGLAVAp3NXb2YUZKNHx7mpAzIgAODgygsawT6/ZV46x5GfIat9vT14/Ori4M8O+9QGFWwqgHUhRewoOjZFxtK2sW95SfW9+4h9yxahrDk+im5hBgKJP5HGZ/U3hKjmGerX4URI+t0uYNumDYr+xf43xi6JJZOKKLiAIEhSuGWhnwWsF8kXuqWjEnLWaUiEgBkMnOGUpmdhRyv+mEqm/vHVOJkCILE/a7O5MMMckTdGtRyDGLSTwGVlOkc46u8iOBh8993V/dJj/p4DLnP+JrFJMmSmRN8YwL3YMUC40caJOFTsE4ywDSChfI9TkiOABVh0pRXbQTAb5+CI+IlJxffX19QMRhQY+fw3N1c1EtYO/HgvwMlJZXIDMjE8lpGSIwUlTmtYN9nTczH/99/W08cN+XsGDBAjz55JO48MILMR1RwUhRFGUawRsmPhHjcv6CNLkhOljb4UqezS9Mwtj2dw/Uy0JSY8qwODdOQtiYHHKiGwJFUZSjybp163DV1ddg9vzF+MNzryEiIhI1Ld0iAEWFWsaIMka1JLoW6O55d83LGLIPItoah44+OwIGLYjtPYTliwq9PlVn/g2GPjR19OLl93bjUHkZero6cf45ZyIiJEiSFK9ftw6zCudLwuba6gqsXfu2VPUaHhpGaHg4dtjtyE2KRlRYIKoZkjE4JIL8jKTRFZvME2KK+hSVeL2m+4eTU/5OdwzdTd7211nC3Bkqw7wz6XFhR6XQgXuIUk9fH/bu2Co5oSyBh0UOTngZvsfJFifOdBDQxcNJpDkXizusTMcqat6OKyo8FHkzcqXEupH8mflPKNIV7dkJ/wB/5M+ZL84DCnHzM8eWO+d3Hd1izG3Dyb8nIiKjXKIQE0/v3r4JB/wDJAxuzoIlo5xKXJiDh+4ytjFfL6nvcIX6eILtwwkyxTBrWBCyIw7nMiqqaZNcKkcjr6BRWYpuPD4MoljApaPHc9zkoMMXtc2diI0KFbHTKJVuOJXc94njjGN36cL52L3/IA7u2428gkJxmiVnz0ZjZTG2+nSjt6cH/Tab5KBiv9V3D6O+sRk+vW1YtnDOqO3ys+iEzk+N8poPh84VJkqma8wY1zw3JH9WY5f83104MnJ5TQaKKRTveO5Q/DPCyigg8SGbu2uIggzbmYIOcyIZUCRikmcez5z0mFFuKTq/eb9F1xPdc+wftid/8npFd5RZmOJ1jeImxw7bhfvEfeA26ZzhdcI9l1FcZJCEYHE8mduBIhMdRmwvT9cFnh/8G8e0pzFsVJUjvBYw3JPtxX1JimZ7+aK5yzZhJTsDjqGJHHgcFxTK2UZst5yECBGMODLN1xSGt1L0McYUz9nEuBh5YFBeWQ2LJRClxUWIscaiq7kR/qFW/O2/b6GvoQwXXnK5vIdiHcXFraVNcgxSjS4kBD/5xeNYecqpuPa663Dnpz+N73znO9MuRG16Ha2iKIoyCt408IaGy/WnOCsK8ckYxaO9Va1SApVUt/agelMP/rXpkNzs8stUkmdnx4mVWJNnK4ryQcAJw89//nN87Wtfw/0Pfgs333aHPFXmBJiTb+br4OTLHWei1UEJ5+GT+PT8BRgc6IU9IBIZfsMY6m6WJMytnTMQGxkkkyGKS4bYQSiSbNi0FTZbH2JCw9DhZ0PezAw0Vh9CX2QUIqNipCQ6J8okOTUDSSnpMlFniBMrrdVUVeC9+hoRPNJjw72GgxBOwBg6w/XMrgWzgMUJqqe/kclO1CYTcsNwPAnfCmAeFGdICcUEhqSs37Qdpy5ZOEosoqOJYhdDhJwTYYf85ASSoTjughHFHYaidHW0oq6mGvPmzJYQsIhgi8cE1VXVNUhIdCZEJn19vdi3cxsK5y3AgX175DU6iDhZL6nvFLEqOTpUws+MHCveqim1dNnks7m+kYCcIse8xSvGfLexsATXyzVVbSL8nZXVDtZ1SCJzT/3MY7MNDHlMrkxXDcesu8NE+ogunf5BEeOYKJ3/lzYYsKMgLWZM/3E/6Bo6LNw5l0xT5TAzNp8QtLa2ITDQguZBmzMf1qAdO3bvx9lL8pGSEDvqWOnKY9EM5gKyWALgE+4US6Tf/SxYvmKFM8F5R4fYUoaHhtDV0Y5QHx8kh/vBNyxGQpI4pihO8BylWENnh7ewJB7rO/vrcFpB0hixg/+XBMgDdqw7UC/rHElIPbdB9xL7zjwGKdowhG5fVRsy4sOlH83nJB1rPFco9tDhxgduvEZxDHJsmRNasx3pQOrtdwotFFSducJ8x4wpAwrePL84diiaOQUUhytxtbtgxG3xWmYkWjdDsdqozucOz1+6oJjTjNdVwnOeY9N9PBvhfWaRm/tGAW88uO76oga5hqXFhsq+9vQNoK2jA52dXejs7ITdPigCD8ejr68/tu4vhY+/BTGBDliQh+rWIFjCYqTyYlpmDmLjnUnyAwKc/cLxxvPXmSMJOLBvN6yxCcjIzUd/X4/rvC6YlYfSmpmoaR+AXzdFUB/pd54/FE3NXHnN9Zi3YBFuufE6bNq0WULUEhKcnzsdUMFIURRFccEnUqfOSpKFN/yHGjqxuagaxU02lDR0yQ0hrfcsX8vl928ckCdWDF2jgMSfk8nZoCiKMhHd3d249dZb8dbbb+Pvz7+IJcsOhwTRXTQ45JDEr54mWZx4G5Nvht122yzot0fI5K23uwtvvLMZqRlZ+PezTyFrRr681lBXg9POOg8zs9NlUrW/6CAGBwdEFOrp7kJsfKKEO8DHV0SINvsQIvxG30pzXygWmStf7dm+BZEWp+hCEco5gR/r3JDkr32DHhNgc8JIlwEnZd4Eo/Ggc4Dv5XbYbgYUSni9d6equWdMSA15b381oi12lBXvx+x5zqpkEurc2uPKF+Q7Ek5GkYYJpA1XggEn1TzOKN9e9Pe14PqLThenGNuUOV4KPVTyokZC8S3KGouG2hqpardi5QpEhYWgvDQItr5eBAWHuMJdODFle1Fg4yTWU6gYRRi6vuhYoFjAz2d7ZMVHyETe07iiO6mkrgORoZYxk262KsUEOo08VSPjpJuTUh6/+/5wnylWuDtMjD6iMCDlxyOCXGIIxTj2p/t37mBfpwg0/L6OtsaKc2o8YmJj0dZQhoLcNDmm1o5ubN27E4kJqdi1cyeiTjsVYcGHP4PbpaNn9779sA8MYOas2eLMoNhJsYx9yQm/f0AAFixaLG1iiIg8fmNMGSF9XbZBZMSGjZv4naLNhQvTxYXDhSFpbAsDijZcVsxMnFAsorDEJPbcJs8lo5+5b3QGUSyhAGQWcLhNPljj63399lFJx9kvFBp4zWBfUAQbr7w9txUePHlBi/tldgtNhoTIYBTVdoz8zykc0TXk5+MjY8nTfdqQwyHVG0NjA0a1FSupsX95HnuqzMb+nMw1if1P1xXzCfGazLak8HWoaA+s0c4wsrjEZAQEBEj44kB/P7aWt2DhgoVIiAmXMNG9O7bAx9cXbX3DsA35IbynD6OzJTnPQyPZdXNXPyJS8tHc2YXBTlaxC0bYgF2ESXFMpbq/2zu5M/Lw0utv4wuf/TQWLlqEfzz7LFasGB2merKigpGiKIriEX6ZZiWEI9o/HtdYrbANDstTQGf+o1ZJmk14k/jG7hpZCPOBGO6jgrToKSWYVBRFISUlJbj0sssQFW3Fa2+9h7iRp8icaJQ1dMiEbbLiNKuYmSdcnJicesGVaOjoR0ZMJlpamoAhP5x90eVwDA/hrXUb0N7agujYOEl+aryHYREMeeITfob10m1Z2tApeYjGC3vJK5iD/fv3YmhoSCYzQ4ODWHXKKglrM2MIEJxIc1LJHEi8vhJOur05ZCYzUaPATyGEYTRmocNTgmQjhMn9s5hzJzEmAoVZZ+C9995zvc48Ltyue4gRQ3VYUckMRSS6EfKSIjE8FCLl7CmwOcUlu0zKzXDCSjdKYFgMguKADZu3YV5+LuYyV9SICyQzKxM11ZXInpHveh/3nTlLPOVHoZhkJMVmsQdjcs91uR8H65zCjac+FTdLShSKazuQzjxJFj8R4vh9SBGQScyTPIhFBgx1Yq7AvJQAmdm29/ZLyW+2FyfzQ0MO+PqPFhu4ze7GzjFhQgydonvOfB4wvO9QyUFk5s7Avl3bEZ84Vgx0h86MmORUvPPOO/D1o6jli1lzF6G+awCR1hDs3XcASxfOdfUv8+kwJKm+bBiFK1bIeKVwFRYY4MobFRUdg472Vry95nVcfNGFzkqCHtpyKqXs2U90IbGtGtpZar1VzguOSz64ch877jB8q6K5GxY/unki5X1MUM0xanYUcRzQvUinNcVGuk0ocFKQosDBvyW4aXAMdeRyvGBUJ3PPYcSx6u26yUuBu0jK9APso5K6YVimkuRqzLadYhETfBsJ1VnRsbO3H7tsQ4gMTYAjyILQsGDZh8AgpyvrbGucCLnM/UQhbt7i5aO2SUGYQiuvxzw/GAJZX1slf6fTkGNz8aw0vPv228iMTkVwaBiqW3rQ5OiT83c8Yc8ToWFh+NXvn8TvfvUYzj77bPzwhz/EnXdOrvDCiYwKRoqiKMqk4NM4ikBcCO3zFI9o0d9f0y432oSx81z+vq5UwgHmZVhdAlJyTMhJ/8WqKMr749VXX8V1112Ha66/AQ9887uufBEMmWWyXeavORLhxBCc6C4IsfghLnAAdU11iA/xQ6dvKJ57awfi/PsQFxki7gi6WHi9osBA14GR2Nicw43OFE5cG9v7kO8lNw78AhCalCcJVymADDUcRHCg58klQ3w5seL1li4BI3+RNzhp4mSW4hLzIxV6mDRzosgJldmRYThsPIVO0S3ExLnuVDZ1y0OA2sYWERVqqyrgFxgKBoq4Ow/YxlLC2pS/hYIbq2GxzcSJ5e8v7dzea0NjO3OfjE5MzAk+16fjiC4LVu9sz02RMcDEu0YlprhYK4oOHMBkoChFkYDJwz2F5Yhw4+sjzihv8NgpGvG7j0ID8xF5Cydyx2dE9Nhc3Ii4yGARfRgq5l6hzwz/RoeVe4gRzwEjPM2gvKQIixcvRkdnFwItQSg+sFfeN2/RsnH3Kz4pRRZCYfNQbQOqy0uRaI0UEaqrx4aIsGCPQiPbkUnljffu3rZJnHkUW1vbO6S9KdgySfVkc2pR4GB+RYqxZuHDaENWGuNC50vCJLbLUDVux1zNkIIgRR5+DkUUhpcZ0O3EMDT+jTBUkfmRjvS6c6wxchgZIZeeoPjJHGhsi9iR8E4Dhrh5e/jH6wjvBXltMIfrmeFoofBGZ1pSVIhLwG9oaEReRiJSk6KkLylsU8Azrksc+7yOcDzUSdW7HnGO8jg4Digi8RzYWtYk51Ggw4amhjqsXn06wkbExEMNXciZNR9N9TVob2tDcmo6IqyJKBZH3+F9mSw+Pj647dN3o3DefNx600exc+dOPProo7CMhCafjKhgpCiKohwRnMwkzE3B2XNTZELFpznO5NktMrHgDQJj7jcWN8pivIfiEZd5WdYJy6gqijJ94CT0kUcewTcefBBf/Mb3cf6lV6OiuceVgJciCid85okKn/bzOsLcHONNuvl+Pqlu7+yCX28z6jrbpKoTky139AORCek4I82BiqLdCAoKQlJqBsKiYkSw4HvpOjC7EPiakcSYbidOsNw/jwJXS7dNJl7Me8P3d/fZ8F4lq5J53ldOfFmhcrzjoCOF4Ui87lI/YHgTE1yzYpQn6CrwlOeJ4V+eynKznZhziKEoZrGH+8+JJ8WusPAIhIYEo7S6HvXN7WhLSUNElFMIMkrMu4tIFA442WOoiMXHOfns6x9AVVOPhDObxRDuM0U6ukaM1xnqFEIXQUvPqMTVTH7NxXsJ9MNwDNFdQvcTE/UaE2g6ERjqRDGITq6Jwpq4T/MyJw5n8bRPdOEsy5ta/hMKG5z8sl+cFfuGJASKCa7N0GERFhqCQy02ZM1ZjLKKatiHh2Qcc133UDlx63Qc7mdScmAvfALDUJCZBltvD3LzZ2Pr9u04bdUKcVFRo2rr6ETQiAvEDKvdZWVlIykpAVu2boPNL1yEQApwzH3EscHzlPcC3p0uTjdKdny4iKEUZHmOc6y6nzeTdRkyDKq4rlNCWc1uOHFTpUbLuczP4fXFEEqMvx0t6Mqi0OmeoJ+7Yr6ukd0VLeJkDHML06MLzb1iGq9BdW09IojxOsjz9EgezBnuLV5fKOzwnCe8vnAce6OutVcEWFZlpHvH39cp5JgdcTwHeIyGK6i2qkfyIFVUViK/oFDW4fFSvPEkYvP9KdYw13W3rr1XjpfONrq+GErovN4Ei2POOOcoYM3NtMo1bcg3AwUFBdixYyeGhocwMzVDtsXQOIpN7i44ivBGJUMDJrunKM+HBstXnoKX33wXn7zhOpxxxpl47rl/nrR5jVQwUhRFUd7/l4mfrzwJ53L1imz5kuUNH8WjPZVtcvNPeGP6322VsvALnesvYfW1nDi5ifc2iVIU5eSmv78ft912G157/XX884WXsWDh4nHXd+YwGhZnA50ozPNC9w5FmlWmnDxMQH2oqhbVzR2wDHYhLNAPwyM5VuLSctGLIKRGHhZ8UpISXZMtZ6JZu0ykDLGIkygjiTLzqFB0MCNha83OfWNuHF7XzJM3SSI97BQnuN0pt5OdYXHdMkGarNuBOUvojKLAwGNizhhOslq7bOLWcoeCvjNhrOfrsTUqAtaFc+X3GVlp4tp5d916RMYEwhqX4PV9DEFx9VX/IMpKihAUEYf5WaPzUDFRMAUxHqO7iMTwN/fKU4eqatBs8xPHAEUlilLj5cPhpJSV9ehW4ESbM3a6J9gfntrUqNTkKRm3Jyg+MWSKY6TfPiR5fcZzdkwGusM20ZU04hLjZJvCE9vUTERUNA5V1yPEEoLoIKC3vgSLV5wqVfwoGrnD9l05M3HUaz01/lh92iIcqqrHwX27RBxkiGZxZT1a+4Zx8MB+YKgfixYu9CCIOeDn74fwkCDMmbtAzgO2KXP2hAdHus6RzSVNWO5BNGNbM/SdE3gKAFzo/KHYxbAk7i9deOM5irgNijNmYYbiD+83eH0wqm0ZgiZFLCZmpybCPjtSFxH7ndvnOGW7UDAxh0RK9bmRsH2zW4fnA8VR87qz02Owt9IpYJkpre+Q89gsqtAZxHOa4hlFXo45Hgv3h6GfU6lqy/bl+WG4btiWFKS8Vf6T4x4alrY2xGdJgt3aI+cqxSOGtBn9ZbiCuDDxfV17P4Ja+uDna5PjmOiayPfzmpsY5UBLd79c19zHQmpGNrZu34GVyxa7rhN0WNLhRvd7QHQS9mx9B8GhEfD3D5Zj5L2qu2htCfDFYPewCMjGZ2Q5HCgbqUBHwSo1LR3/+t/r+NLn7sSixYvxwn/+gwULFuBkQwUjRVEU5ajDpzor8hJk4ZcxnzoZ7iNWkWFox/DIjSGXJ9YelBt8Plk3wtfcwycURTk5aW5uxmWXXS7l2l95cx0Sxsm7wskIJ03mHEaczPCpNa81THrrGB5Gexurb1WhpqYGVd1ATLA/kq3h8PMPgjUhBR1DAfC3+CM/JnRMtSsDTvLzg51JcLl9CQ0adsjkw8jV4g4napzQcSLv7niSkIu9BxAREy9OgKlMeA0C/X1l8jKVSS0nl0yAywkZnSosuz2eg2YqFabYXiGBAVi5YjlKSsqwfdM6JCSnylN+inJhEZyUOSebzpLgvuivbUJXTSVOXTAHGSnxYwQmhrPRUULRhd8DPFa6oTgpnuMWtkaxajgwCgsLAlBXXYz8wvlo6WaCcKdLylyhyn2/6VjgpJqihie3K7+jOPHlZ7DsWHSoxRV65c3BRlct95eTU07U+TpzFnGMjueAo1BB90Jv/6Dsj7liG6Eo40lgMbAPDsI+ZBfH3HtvvY6sBaciPS8F8xYuxoE9O7FgyeST8zocw7I/JQf3Y0b+bLS2NCE8MgrvbNyC+HALVi2ah4CgMGe4T2OXnJNGrioKhlWHSpGVloz6jj6PIZKHGp2Cpye2lTeLyOou+LH96KyhC2gcs4sIvLzH4LnDvuW2zEIfJ/pMWE7hQNpNXHoMEwzzeL5NBoqvL245JEImt08RlkIF82Q1tPe6EmSzDykE0WXEdjHGMXNUMU8X990Qd7guQ+4kPDTmsChN8YXnsrn9ON6YrJrnilm84zWkpqVHBCp3p5/zfT4y7mPCA+X9noRevjae2EmXJyupjUmCPSL80MnHc8KTwNfS1IjZOclIToyQ8TYZYYvXUF73uV/urk4Da1w8OtpasLeoBLNn5rpEI17XOE5bwgKxb18cmnqGEB/trG7o6dzk9YPtU9bQKZ8VE+5sX45DCs0c+xw3ISEheOw3f8SjP/sxVq9ejb/85S/4yEc+gpMJFYwURVGUDxR+sfPJExdWOeGNAZMUMqEkRSROxAifYr29r04WwqobRvgab8Qm+3RXUZQThwMHDuCiiy9GwZy5+PP//U5uvr3hKYcRJ4WclDAfTODQADavexPtbS0YHhpGXEIS8mcV4LSkZMA3QJLW8vrT7aBLIWxSwggnCCxzz9xHDmbrmSBXCveL+1dc1y6TEzpTatt6RCSnWLNqQQF2bduE6LA5YyadjSNJrjl3YdltPvF2x31SR1GDkyiWwmYIhaey1kdSVW2qsIrW/MJZyM3JRl1Dg4SIHSotQrQ1HhnZuVJl7lDpQal0lJCQiDNOX43AAM/TED4wMIfGtHbbxM3lXm2N7UUnEnMc+fpYRbgq3rcbA/02BCfkYnASE1AjAa8ZioLVrd0yVjhZTw0Mc7na+H1lznVjxigrbh5X7K+shAiZdLqHEhGKChQtuA8MG6TQRD2EgsZkXUk1leVobmxAgMUiFaSS0zPR2NiEtvhwzMhMxfDwEEoP7gcinTmKJoLnTWVVtVSrYtWqvt5edHd1Iid/LvKSo+VzhKEBNFRWoKOnD3GB2ZLrqKmxHmlpGSL6sZoY+4hODGPSbriNvR0bHzZRoG3rpjuHieWdbUkxhULJ7LQYr7l0+D46kbgOP88IladIyfxDrtBGVsdi4u4BOwL8/SbtbDaqulEk4fuNccP3n1GYIq5BJt82PoduGYY7BQY4RQ7Cfad7ivtlHs8zRsQwdyGJ4g5zNBljij+ZtJsiplngShoRl8wuJbrQuM/ucHs+8IE1PBDdNjtaOm2oGrCLwMTPn4pg7BSLHCKCsZ15vhj7xWtT74AdWR76mud3xaFyqSLJXHEUGseDfWmEB9OpPl5II2Hly9Kifdi0bQcS03JEzGUIKYVIa0QwLly9BOvefhMJi5bDd5ztcPzlJUeJQMTPp2DnO+JyYrVD3stScOJ6n7nnXmRm5+D666/HN7/5Tdxzzz0nTc5OFYwURVGUDxUKP8yjYORSYPgBb4ooIO2tbpOn84R2Zi7PbSyXGxHeyDB0jZMJPtU5Wb6IFWW6snbtWlx++RW4+oZP4J6vPAA7/EZyEo3ORySTC1a1YoUqU4gGJ6OcgBoC0uZdGxETG4+cmQWIiol1Jcs28JSvhzf9dC9MdD0x57egoMDVvb2H17jkmDBxDQT5+6CvuVLEkaCQZDQ3tUjlKh4eRSJO8jjJlUS+UcFIHMkDxMmJ2XFghpNFJqd14nS+cELsLqgcDThR4zWZkz/uL3/S4WEWAYwwI4pjwUEWzMhKl32sOHQI6Vk5zr/b7eI4Yh6cybijRofGjD0ulrZnO5udFtnpyUiIj8P6vRUY7m1FSsboSSj7bTyXD+GxFtW0iUPM3UVGRxLHXGVzl4iI7vvL9qdAYCT1NuB+0t1FccvdFUGBhJN6c5l2vpO7aeSC8gZFuQO7t8MaG4czTz9NnElvrnkT85euxM6yOmzduQddA7NgjY1HE3O1HDiA3KTDVaYIP4P9SiHDaJuYuHiUFx/ArILZ2Lt7h4T5+fj6o6q8GJG+KdKPzFUUFByMwIh4nDEvAfv2Mrn2ME479RQRKkhmvDOPV0l9h5zDnGgbooQ3jDFMUYaiCscbE4PzPsE9FNHMO/vrkBEbNipEzwiV5zVlT1WbnF8UYQwm42jh/rJPGVrFNqIgEuAfin1VbZL43DhW9iNDD3nOmxPVMxk6XdQUl4wwMopldMsxfw7DJwkPi78zD5nZEeR8f7sIJDx2tiEdQRz/FEoNKJ5QFEl1K9DH/XAPG+S6FY1d4kiigGK4uXiNlXPYMnrMUZA2kmZ7E8gJxzEdTWwzvs6xNV6I2YzZ87DvUCN8enpQdWg9MnNmSmW9rs4OCaHMzMlDZ3sb3n57LXIWrEZeaoy0I8cSz0OKNexTOoHckSIskalobKpHy64dOP3U5eJYpHjPNm1qbERsfBIiIscK7J5IinaG9NItSNGIohhzO+Uk+ItYxtf4/49cegVSUtPw8euvRnFxMX75y1+O+R46ETnxj0BRFEU5oeHEY/XsZFl4Q1/W2OkSkFiy2hmLP4ytZc2yAPvlydjCbKf7aGFW7PvOD6EoyofL008/jVtuuQX3f/P7uOK6G+U8p3BCkZjOGovvYdcHJ9p8Eh5scUg4GidpfHLNp/lmkWTJytWT/nzm0qEjgZV06BrhZI6TwfGSJhvV0jgx57VqRlKUVwGCkzAmzD24bzfCw8JQW12B9OwZMsku2rNL1mE+IU6yPCXWpQuAT9I9TbiYRJiT1/cjmnOyyupprH5kMGgfGjUJJZyUsZ15nGwbLuWNnWMqV+2vbpdj5rWaAkRdYzOah03Cvo8PLIEW6WNO1I2J9pHCSS/dZsxrQ1cF+8/pMLFj4YxEbN5Ygcb6WsQnJruOt6OnX9w7nNi7J+Q24Hbio0LQ1NWHNMvoBxMUMShQBvh5T1ieEhMm7pqs+AgR/JhTxin8+CDWw2dSEGMbM3TO/FkcixyX7iInxy1D1yRfTksThvzCERsWJxP0ju5ehISFwW4fxEB7IwYdPmhpaYM1KhL5uVnoaGvGwEC/CD4GDKthmGN9fR3a25kIvh+dPTactXQ2UhKsiIlciY6uHrTYHJgTZEFbUy0G+204ZdUqyfFSUt8Fa0QITl2xZMyxcayYy7tTVPRUut0TDAWi8EJxgO45c/JzT6zKT0R5Q5dM3im4mF1j7FOON55vZsFoMlAoYv+lWcNHOVq4P3QE0V1iFoIozrA/DeGK+0wBcUtpkzzwMtxRDGtl21Ngcq7ndB8xt5YZnitM/k0RhtvmvvCUdc+dRlitz9iesU0KJ0xGbQ7N5OfYRxJam2GbOQWj0a/z/TxvKLaw/yjoM1zO3ZnH/9NNR7ifPFe85T7i9jptwzh7Ua4IVY7gGNQ1NsJ3yIZly5ejuakZe7ZvRmRUDCwhkdi/eyusIUuQFO/Md0aBlcfE86u+vV0qr40K4fT1cZ7rqakIRSzefecdLF22HMGBwc5rlTUBtTXV4kYM9JC83Rwa3G0blHHDfqZIWNnEhODO6yb3xd3ptHDRErz0+lu44ZrLceWVV8p3XXDw1Mbd8YYKRoqiKMpxA7/keQPG5bKlWXKDzid5DF3jwhtHwp+v7ayWhbeQM5IjsTjbmTx7VurokABFUY4vfvGLX+BrX/safv3Hp3DOeRfIa7wpZ/JXJsF3P395s25M9IwKUQl+vi73BUNf6jt6ZcJE0WK8iSUnMk63ki9mJEe5BCI6jdbsqsaq/KQxzhJOzpmHzVwtzQitpWA1nguEIVjVleUiZvFJM8Mk0jIyXGIBJ2HuiNDQ1ift4Ukwmkxpcu4fw9sY1pWXHDlKCDOSX3NyZ0xgeYysYmWG6zFJtzmHCh0jDH8xOz0obDAZs9klU11VhdyUwyF1rU2N8A+LErHDB/2SvJtQfGNi3qmGHNMdwYXvr27uRnVrj0zM81OcAsvceQtQVlaGqNgEZ8hiRDBmpkRLHzJpOAUkb0muOZnv6O0XMYphZJz4ctLMCSOTJY/nUuL467YNYOehZhFKkmPGTqzdoXuE4qDhNmFf0BXDtnYXjLjf7A8KIMNxYdi7Y4tMyvsH7Whv74AlMAiNdbXITo2HNSYa1ugoV9suKCzA9i0bEJ+QjPTs3JHzxIGGku3Iys5B6oI8cfExHK5gRKAJCQ6UJXQkWXpwVLzM9itbeuT7Od8tKfN4ePpe5vlIkYOTf44h93NXctVMIp8hxzeFIo4H7j/HJ5NjN3XapO+YuNhT9S338c5rA/vBSPrM8TE/0ypuJ+bioVDiPBYfEY14f8IKhUY5eUM8YL5Gc8iaEcpkhq9NBiP590Rw34z9IxSYuM/u1b8IQ2wpfESFBSJ8xGHJa4GnEDZKI87KgT6uazXD75hMn697EoXYbu6ishm6tnjt5NhkyJfDEYn69khJxM8QufSUBKQmJ6C8ohohPsWYM3cWDhQVoaY5CTnpydI/zgTYIeIyYugqr5eG24ltz+3zOlfbNoCknDnY8N57mDtvPmanWfHWlr2o6hhEUkcXEt0EIzoMjesCzzPb4JAkRaeznX2YGT86J5Qn0tIzJBn2Jz56Nc4++xy88MJ/EBPj3Vl3vKOCkaIoinLcwqeMS3LjZXGWU+1zikcVLdhf0y5Pw3gzc7C2Q5a/vlsiyRtZdYfiEUUk8w2UoijHDp7DX/3qV/Hb3/4Oz/zrv1i4eKnLQcQbe/dQHk/w7+ZQEuaWYMluukaY64aTBm6Lk2qzgGEkJea6vPE3JnNGmWZO0FldzZwXhKILXSuSh80aNmrixTBZigPbypqwdIbn/BucfDX1OmAbCnSFJcTGJ+LAnh3ISk+Fxd9HrleceHMCR9GAziVOolKsoWOS1U4EJ2FGKWweH51BnDS6tyiPl0lpzblgOAF0Fyc4eabYZIbCDBNPmyf8bHfmjTHg8fQPOZAU5Gzjgf5+1NQ3IK8wRSaY5gk6Q+/YlgbcX074ue90kk4YKujnK8KX4Www+q20tAQ+oXFyXBR9DLGC22M4GccIv0cWZMd53C7DXDgWtpU1S84ZTj4n6+ii+8OTA8QbFBk2lzSKe0iOyd8XUSGBmJMxdoJJ8YTOMO5bZVkxUtIyJNxty95SdDZWY97i5aiuKEdtdRVm5+WMEreS460IX326uC0kVNDHB63NjcjKzJJ1XW3q7zdG3KB7w+ngGP+4GCrEcESKYJMJ+aJYxDFGYZPf6dxdnruGAOMJnrMcN3R5mEVP2feRMDRn2Ga7hABS2BkPhlzx2sA25b5QOKWAbCRuNsINeS2g+GiMNbYRxzOr163MT3BdU9if4+XX+bDg/nkTydgmvBZSVHYWDHCeN57yc7GdzQIxBaKwxEjpA2/5pMaD1yiKoewbY/8M8YcLz4P3ihqwYmYC8rLTJTzt7Tdfw7JTzhDBvb0jFI2dFhGkYkIDxdHH/UtzCxMlHLO8TvPeMTRlFnbu2o2enGzMmZGB3rYGVHcBfkF9kgPPgL8zH1x2QqTrmt9tG5RwtGw399p4REfH4OnnXsSdt34cq045Ba++8grS0tJwIqKCkaIoinJCYL6hOHdeqjxJLK7twK7KFuyubJOJAWGoyvqiBllIcnSIs/JaThzmZVqPuBKKoihHDsvb33XXXXjxv//Ff15Zg9wZefK6IciYhYTJQIGF4at0JRiTc97o86k3f5pDMDj5MwSRVOvhiagITHR2yHUldIzYs728Wa4dZhcKJzucXIrLKTIEqTNGiywSQtPdj7ZuGwL8/LBsYaHkjzGq+zQ21GLW7EJXWBPdCcxRYg0LlNwaE7kj+fmcXHt6es+JnXuZdYpQ7kKHVCRyc/TQ8WN+jcdPscG8P6xURiHAvD3DKWAWfErqOrGiMAcbN7wnjpcD+3YjLiN/lKjE7fO9hRmHQ43Ydnur2iQEh/1IBxcnspxUjpcLhe9jaJ2tfwBlFdUoO7gf7b2DSJmVhEI6gkz7y/2js4LMyxqbVNwMJ4Z8WOEN9ikFSoobZnFyPCgI0rHE0CNzO473OWb4WWWdziTvzPXS3dUlyYOHgmOwiJXQeHzlJUhMTsX+kkMomJHp+hz+7OntHclJ5AubrQ9NDXU4e+lZh/ePAuMRRjqyH9gedM5QhGB5d8Jk5J4Sr1eOuHnYzjxfuXAMM1SLuXA8hWryM5gTSCoVWvxdbiLm8DKfp/ybebx5g6Lh4JADC7KcoU6E1yIeBz+HIrYhltBdyFxKfGjFUDXjc5bPPCwWnSg4qxv6T+i6Mq45FHfd1+U57MkZyHUpomUlhEs/ubv4eE1ZNiNerk1sY4ZE8tpstCGvn85KcyPhe/HRSExOQ9GeneKMqzpUjLyCQtiYHL6hQwTg8RyKxr1jfGQQKgIKsO1gCQIsDbANWDA3JnCMgy0mLMh5njU4Q2+t4UEiktHJztf49/HcU2YYiva7J/+G+774OZxy6ql4c80aZGdn40RD75oVRVGUExI+2SpIi5blulXOm3dONmgF31PZJvZ5UtvWi/9sqZCFN3m8CWXYC3Mg8Qn+eDlLFEV5/wwNDeGTt9yCN9e+jZ8/8Tx8whMkFxFF3/iIYClXbIhAnJzw5n88NwfDFuhKYniQMRmR0IPWHmSMJCQ1I0mHA/3FWcMJsb+vrzhlmEyXYUqe4OSFYgaTy0o4ia+PhKVxwi+TVbfJE/edlbXouGFoHMNqjWNIjYvAgeo2+A/3o6ujA3NmzxFnJP/KSdF4SYDZHgypoROA4gknr519g1JRy9xGnEhP5lLmvh73l2E7dDqZocuCwpI5J0pzZx9OmZXk+j9dWdw3tjefxNMpRDFtxkhel1kFhaiurkR02iwsnZnk2l+KApwozkmLcV1/XWJRQrg4SxmCIzlK2nrlIYD39nGgvKoGZSUlIoJ0d3YgKDgE2bGhSE+Olv5jzhW6VhgOx2PgBNO9/4wJ8EQTf/YHxRA6WOhCYj8zxM0QBD3BEEM6b+iI5eFKSKN9yBWCNhXMfT530TJpN55HdINRCCLZefkoPViEloEA9Ps3A45htHd0YXjIjnBf5nFyYOfWjbBYLAhLyBRxxhLgJ9W3BoaGx3X3jAcriPHcYP9lJQS4+od5hdzh2KFImR43euLNw3NWOov22PYcIzzvjRAtuokYMsTE2jyXzKFTk4E5uzhmWYHNHLrJEEaKBMynyNeNawqFArpWeC9xtApv8JrE8UTB1izQmsPaDCjYGuFyhNdRiqWsVsZ7Ip6HHO+TCWGbLBzjHPO2Qed5yDBV5ljzBtuTjisR9Rs6Zf/YlikxIaOcfhz/XNh/vN4Y5x/PD7PTietmZGSgvr4eba3NiI6xitMoNSMLM5LGr65mRsZHYiTS4xagqb5GqgsmxEROUB2tV9qYrjK+n3nr6GplHjc6D81jgOc5hW6ejzx2CsMUSjlefvjTX+LB+78sotGaN95Afn4+TiRUMFIURVFOCnizzsSXXOQpcnM3dlc4w9eK6532dd64GPmQ/rCmSMpcM8GuJM/OjpUnR4qiHD1YyemGG27Ajp278MLLryORJe49wAlkbWu3Mx8LQ01HQsgWZLEUsnMCy/OaISF0LPBm3nhNchL5+UpCUk+TOEnGGh/hqq4zOOQsiz2RWMyJb15KlDgeCMt8exMUOEmwDzkTYbvDCdGhQ+VosfggYKAfr/zvRWRk5WLponnj7gOvWZtLmmRfebzGusYk0Zx8mZ9PIWz0+50ChRm2LUNRDCGIk2ufEaeTGY8V11JGHxtDPVbNShQhhJM9TpISI0Nck9WMlHiEhIejuqVXcoxwIksxgiEkdEIZYp/hGqEwxzY3KK5tQ3FZOcL9hlBdSqHLB8EhIWi2+cHPzw8Wn0HYu1tFIJo9fzF2b9v0/+z9B5hd2VXmja8Kt3LOOUpVJVUph5bUaqmjccABjG2CxzD2h7GZ/zB4ABuMw0MyQzDGQ/oPMDAzH4wDJhucQ2flnFWqnHPO4Xt+69S+te+pG9Xddts6bz/3UVfVvSfsdM9697veJaUVVTI2MiyNzXv0/bQbCjNSvvAeCUYQQlD0js4o4ULQHqyanvHKojodx7DVFhBQqKXsSlgGpN4oiebyacIfCUIs0hikbYA9rvHb5XyGF+G8qN1Ax707WsEsr7BY+kaHZGRlQhITEiQzO1f6Z1elrDRHJifG1bSaVDbAWDL9F7/ilFu34ZC7DtFIHzIf6Xv3tU/MLWoqpQ0Ig2CplRA8EIwQgkU5Tjtwr5BLEDTBvKWeuzmghJCbDKHPMBs3ZdcdKnYruEe8iEiFshWIkFyoYnheQOVnjs+hkn3xqk406w24nzQsd6UyUh25f+6TfuQeZhZXAqr+kZbJemgTRBBY9s8QWKydKM5MHzI2UFPbY4YqcpBgpg85Ln0Yjd8j49b2W4LcouIY1xcMrEWMSV6m3aa0BP2kZKX6VKllg3tnftB3mNabqm02airLpK+vVz3JTt3olh3VZTIyNKgqO6qphQKkN/2HmpTvB0he2ry0vFJfkQBxNb/kVEfj/1mTF5fXtM0hiu1qiWxSsmkBwQZxx7i6OzCpmw2096994nd1/Tpx8qR8/Wtfk927d8v3CjzCyIMHDx48fN9BjQkLM/X1xoPVuvODP4ISSF1jKoUG7AB961qfvgCydsgjXiiXjOzcgwcP96cs+g//4T/I1Ws35O+/+BUpLCwKqRiamMOc2SF8jI8NPhuGLAIEQwRxc4vLTrW0pESZnlvSdKVgihE3THUdAwIUdoRDVfKxfUoiAaIAAhpCykmtWVPfDAJRAvOW6gIZ7OmUoycfl86uThno7Za2zl6pry7fUBisbrkOyByIcHf6AwEPbWEHrmZH21YEcQ3uqlAEZ5BwLxdoH44ZrOoZ7V2UTSpImt4jxrGk9KAqsN+PagTCivs36WUX7/RK652bsre5STIys+TqxbMyMzcnU2tpsrPGSQFajk+S1dwqSUhNkZvXLknNtgZ54Vtfk+SUFC3HnejzSXqGU+46mM+UMfqmf8zYQylEKlyw9EDGWH5Wiiqe3MQQ7+c+ULzYZAfB/dDknFZJs4EnFmbdxifH9n9CtYXyyyaIbAIvKJm3gZSUVCmvqpGi0nJJGpmThop89czKKyiU9qE26e4YleraOr1nQyhAPOpYCmG7AwEAwWf6UCvsLSxrQGwAKWaTfaZ9CbCDpYEzBulrlHOQZ9zjKtXICjJCpo1TSZX5RXUy5hrn5x4I0rme3VZ6ow3OQz+jkKHtOIaTbraZ9kR/GkWhXsvamqruCP7DmdoHA5+FZKbdDOYWV6WlKtdPOPP/+C4yXgy4D7N+GIIagshOX4TsIl3OzGvWB9qedjNzin5CgWnPfVJ3ORd/Y03CB4vPudP2OD/rTTiVFv0TyrCbYwcj1CCKkxLm9fpDgXsMRhYBrqe5eZd84avPyutOHpVTZ85IcfV2mZ+flmsXz8mO3fuURDZg/tAHzDuuFdIStfnQ5II+FwYzAnePGRSJKDlTkxK17Wgb+o9+SUzYev+0N/fOeVgPlLAuzVaSEmUYSqNf+eiv6Rx94okn5Omnn5adO3fK9wI8wsiDBw8ePHzfg8CSB1QTKOFbcrUb9dGYXO8Z10oigF1wXn/3Ypvuqu2qypXGolQ5uTtNH+xfLgm6Bw8PgmfRe97zHrl46XJYsgjJPwGWIWUgPlAf1AQhgZiTGBibB3qCEzzKmJemelo0RrtG+TE4OackBYFTNQFXhM+SjsZOdUGI0twEeqgcqLAFkYI3kj+oppR7WppcvnRRmluapbuzQ168dEO6xhekvLhA1St7oyRyUDJxrzZpwbUTDGekJL4q16n4jWAwWECIhwjBcvfajPQPDEtXd4/41hZUNdHX0ymrKytS07RLzl9rlYL1SRnobpfjj/+AfnZ0ak4uXbspOyoLpKG6XOre9ZNy51679Pd2yezMtKoP8gu3kkV4Z9GGbu8bAnDa9XrXuDRV5GxRlKEo6BqZlpGN4F2JwY20OQL7YIohlE0Er6aCE2BsozxT4s8KXpUwyEoNUNFAYjiphJH7tWRDNYHROPe/MJ8mM1NT0tfdKSUlRXK0uSYmhQxEDIo+uw/7xte1MpUNVCtjy4uqwAIQMcxPNl5CgftB1cvLpNZFSgk0XkcQe5A+zAV+thVANiDfUNxwHdlpTpDPnKTv8A6jHLvxsOH+OE40qYk21O8rLs4/jrgvFDgHtxX6jwOBAaFoSC1+j2IFHydDGpI2CUGEwov24338jvXJqHJIBeM4NhkEUYdRtBkz3BvEsf0eUtbYOGOdUNPqFJ+S9G7QVrQtxBJAtQjJHorIcYM1mvGFUgxlDfcIWcT907+h1liul/UrFFHF/bWPzMjxPdulv6dTDh/YL/fu3JCZ5Xjp6huV7oGvylNPPSmJiZtjNWGDpOX/aUv6nfmGwpHxyrUFKvfWnXTTRSf1juuHnKsrdoyuo/EpY/3gvSiS8DyiHyGPeK5cWVvTsf6BX/plWVpaksefeEKefeYZ2b59u7za4RFGHjx48ODhgQOByOPZ5fJ4S7k+yBDkoTy60jmmD/Y8KhFMnLs3Iufuifzti926Q2fMsyGewqkSPHh4kEHwh8H10888K//0718LSRaRSsbubWluhj84RXURqVQ94EHfBEgEbMxhFAGkXBF0EAgGUx0RNHWOzKhCwRhEQwTxeQL4YBWVWCPwL+JfAifeG0p1xO9DBfeoPLr7B+UL//5tDaq211RISVaiGuuSNkHaxlb/GMckGe8do1ggCHHfG/ccbVD3agOKI1QN5y9dlf47NyQzu0jqqmqlYVudrKyJPPPCGbnbOSCvOX4goF0hfTDC3d1UJzu21WiAlxQfJyWl5VJeViGnXnxOBvt7gxJGpKaQhkYAaY81TU8bm1USIRSxAmnE90Tn0LSSREoMJgdPYzNBNCQUqg9UNJyDeDxFVT6B763Iy9BUlzrr+4VzYHRupyKFAyTb8EC/LCflSHdHqySnpspDhw6o70qs6VQQWJAWNpgvbvWVuzIYY5V5Ge35DIliAzIOXyTO7yYSzPkiEWm5GUn63c192Gl0zoZQnhI2t3rnA8ziI5FFnJP0I4hCFFTc48wivkt5+v+c46GGIn2m2FWVr9fOGEPlcttSFTHmUbGQ3mTmLoT43f6pgPegqCrNdSqVOSRbsir1jPKwsiBdiR5zD0qg+/3KnPuFVKMN3Io2NzimTb4Zc3q3EilUu3ONRn3EegkBg0cWYz6UVxtrX1pSgtztn/Cr79xph6ruy0iRybl46e1vk+q6bZKamiaJiUtStLNGkrKL5VbvlKaLQsCyFpCGTDoZaWGkz9EG/I3vF6MkhIizn+W4TiqvmappjME2zK8hNqM0umauU00N0oj+ZDzxvcD3HXOHDYcPfvijajpvSKOamhp5NcMjjDx48ODBwwMNY27I661H6vThjZ1LDCd54ONBHWAM+6WL3fri2ZX3H9ogkPj/WIw2v5/w6KOPqrQadHR0qDmljX/4h3+Qt771rfr//+W//Bd9MPrqV78q169fl6GhIcnNzZWWlhb5yEc+IidOnPiu3IOHlxe/8iu/Iv/8r1+UP/4//yhz8Rn6YE4KjO33QBDCwzyEEYbV/D/pYaF8iEIBtSA75c7utBOcQq6cujsojzaX+Y/FgzrKEAIpTJlNuXF2lTGrBu4S8pAJBO8ERwQWxoOGYIPgm4AkGMJdf3JSklRVlMnyRL/MT41K1d5m/T273exCuwkjyBR21+0qQvcLgh+z2+72p9m851U1AjYgyCQ4NZ+BCCC9wqTN0DakXJC+S19CghDsxaRimVuUZ597Vgb6euShh0/Kttpqvz/T08+fkoyCMklOy1TCBfUEZAsqNN/anKSuz0mSzyd9g8OysLgoE0uJkp2RIh3dfZJT3SLj3beDnpMA3fhTUaWJnX/GAfcQjb8VQTFpdNEWTaAK343uMe1H2i7UGCGghYSwA3IC1baBqQDCSBV2SyuyuLSqBtqQnRAJEGEpKWmaDjq7vC5VlbVSkJiwUYZ+M0XKLie/Sf6gHEoLIH5QCdE+BozFaCprqQGzawzgPza/vKoOQxATkHKh2s+YoJP+Q8oabVFT6KgAbV+nSGuFKjxKszXV73rXmJJ7KG9Mmhq/DzWPQ4FnhBduD8jju8r9c5J5AbGDkTvPFJwXAoc+h4TmOiEmIP8gUIzXE+oTNqogk1gDSU1F1cJ9GyIDsoHj+Kz5n7y86ieMOBcKIjsdFd8upxqf05fMSYhye1wxZ7nucHOV+wvmc/TszQEl4jgWiiiuxd2XtEMoPzC3UbQhyBij3SOzqgJiTqJk4xxqM1CUqYTrrRsJ+p1SVFYrPXevymBfn5Q05UpORtqW1E/mM4Q8ac+3e8elfIMgor1QTw1OzEnGhvKPc/Be1jy+nzDgt42uO4amNP053Jjj+hmetBvfZagDbUKaMcccZZ5/7Nc/IYsLC/LEk0/KC88/L8XF0Rt4f6fhEUYePHjw4MGDK5Cg5CuvtbVVud3eJx1Tjln27V6nshMeBzyc8fp/n7mr8mZUR0jQD9QVBhhqfr/jHe94h58w+sIXviC/8Au/EPD3v/u7v/P//4/+6I8qwbS4uOj/XX9/v76+9rWvyec+9zl5+9vf/h28eg8vNz71qU/JX/zFX8q/fOWbsm17g+4yE/CV5QYGZQRKzCXSQQl2eag2qh2CUnxSnAArOWjJZGfnd1ornRlPF0ghiByChMdaygPei+nx7uqCgGCXYAGyieDM7b9CgB3K6JqAAKKKXetQlbEIxrU6mysFY3l2QorKG6VtsF0qqut055zrI0hBMeP2p3ErN2KBmwzimlE+mPuBDCAgqi9xiDw1HbaCTv3M1HyAuoBUE4gJA4oLEDhynwS8pADhEUfgZRNKBKUEy/gJGXKdv93rGZTnv/VVSU/PlB98y1ulIDtjk+TDz2hlRRorCsSXlKzkPd5WI5Nz0lKVI76EFElYrpOJiXEZWF6V3olFGWu7IuXlZRqIVRcfk67VhJCKMONPRZph29CU7CjP9ROJAX2GP9T80hb/lVBkx8yG+oRg2R67O6Mo8w4g5BgPECp4BqE8g9jYahI/q4bRBKBFvnglv/h/lGzZuXkyNNAn/XevyLFjxyQrfas6AkIBssTMCeYdJc3t8UYAjirGgPfsrtkkFKMFcxz1H6bO9PvotONdBIwyht/z3UoXsGljqqExz/j8hTs90jMwLIlzQ/o9kpcVqHwyahV8kUg/sr2/mN8QN6hYzt3okPy8PKkqztQ+CrfZYwgMu3IXXjSP7CxVBQl9yue1amtFrlzrRlXE7xwDddIReU4w1dxYoyA7C7NTdB4y1iEWmJvMIcgXrt2+Jn5vk7jBQBWvSGDeuvuS9CtfWuxV8XjGMZ5IY9qXk6rKxgcqlsqzpAXi52RAuxmFEuvhnf5J/1oCoDzz0xLUG4g5klhQJyODM5LS3yHJBbkieduC33tmihKuEKTMG9q0doMQCnZvrFWQRvR5dlqyEr60F31OW7sN7E1/4b9GmzLW+E4LVYUT0Pe/+TuflP/03v8or3vd6+Xpp78tmZnhFWDfLcStG5rWgx9TU1OSnZ0tk5OTkpWVFZCPz25oUVGRv3Tlg4Jf/N8varBUV5gmH3/HIYmLe7Duf52SpKOjkpOf/8Dd+4N+/w/yvQPv/gP7H3k2D1xUXmNN7B2bC9puBJnGPJsHvWCGsK92RPudNzw8LGVlZRrYHT58WE6fPu3/28LCghQWFsrMzIwqi9rb26W2tlZ+7ud+Tt72trfpd+wnPvEJ+Z3f+R19f3Nzs1y7dk1ezfcf6hnBg8jf/u3fyvve9z75/D//u+w/cEiDLQIJUzUmGqBmgWzhoVwD2OkFDbhes7fSH4jgEUSqg/GWAHwGAogALRjBBDnSOzajJaEhhXkvZsyh0nwI4Eg7wePGeCRBhKCGIqA2u+tuYqFvfFbXCYKMca3MsxkwLC4uSOut67J95x6ZW1iSBMyrV9eVpIAkIE0uWnUVZBv3DIlCO0/NLcuhbYUBnzeB6ubPmDLnBhBKtnIKkoN+MoE2x4f8MP4ptCE77caLh2tQf5iNyk60kZOGs2k+zDlQdFARimulDfFZ4hiQiV/+6tekqKRM9rTskGRfILmG0oa/93Z1yP6HHpaEhERNLzv34tPSsHO3FBaX6vsIHFFU5CWtSHd7q2zfsUuuXzona3julO+Q4ryM+6qASQBM0KrV2ZISZXl1NUAlZwOyDJJzdXVd0lMSpSAzVe89VsWcwcX2EQ1aIU3VMDjR8bQxMBUDbV8kzmf/DPAxogT5yUeOS5JrXjgqis0y9MwplICxEJTMI5RfmiaZlKBjhw0U9z2jvmEs2gE6gTtjX1V084ty6vRZSUhMlOyyelWCMMcmZ+fl8uWrsry0KCtxCXL5doc8vKdRDuzbI6kWyctYQXViKqBBDOg6skGIjU3OyK3bt2VqclLyCopkenJcFubnpaFph2yv3VotC5IOJRC3QYoU5BrqH7viG+oYngcgPMx9MWaYdxAGpl1ZwxijjCHWm2AV5l4tIEUP5RrQCm3pydI9OhuwjgRbWwyZvMOq8mYQLm0QIhRCjvFMGrFZj1lbbnSPbyGgWDO++fSzsnv/Yf+6PDzYL0P9vdLd2SZ123dIY/PuiFXqooGp1LmyuqaEEf3I9UJq2iQW4wwFOmODc5hNEuZisO8iN/Az+g/v+GHhK/Lf//3fJSkpdgLvlYanMPLgwYMHDx6iBMEpwZDJxcd/AJNKgiQeiCkJC/A74fVPZzp057G5Mtfvf2QC0O8XQAg9/vjjmmZ25swZ6ezs9KelfelLX1KyyCiRwJUrVwJ20X77t39b/uzP/kyJmLt3736X7sLDS8U3v/lNee973yt/+X8+o2QRO+mQRTxER0sW8XCOfMcEvTx8o0B6YldFwMM+KhbSCfgXFYZ6YCQnht3N5e+kFvDAT4UgUxUrFPCwIP2FIJTPEggSiJbkbDXXJd1hdGZBd5TLctP9QQIpH3bVo/a7t2THjp1Skp8uXSPraqJ6P2QyAcnFthEN9o1pM9dp349j3Bu+3ZdW1gICmrHZRdlhBYH0h13mG/WWm3BCKbb592k1KzfXQYDFtaHIAIWJ8UoYGTUEfzt45BFVMfFZ1krIA+NfQrwNQTQ/Oyu3r1+R1LR06evpkrKKahkdHlTCSKsZTS9oH2Xl5MruAw/pZyt3HpSpeYfwc/th0X7hPLIgf1i/SYsjBcW8F2IEHxJ+54YJbm01Gt47HCdYWg4qBMYM40rvdcNzxSBSJbtog1+qxBUUl8jNO62yp7kx4G+QLLaSBXKwKDs0scb7RyYmxZeYJDmZqXoNKJKMAs0hZWd13NuKF+YpJJRNFqG2Qd1nFEc3bt1Rw+7llRV58eI1SVpblJNH9srIYL8UFpdISmaeHnvf6rLUb6sPIIsI2En1oeqZMVVGpcNYujc4KeNTczLWdUu2NzVLfaOTAgrWVlfl+pULsq2mYgsZ99zNAXlkR6nfW4gxyXhG+cQc4f2ca0dFjlzrGldikHEIsUSb0hbms5Bo7kqHr1YYjyNT4p7+dY8J+s5OUwwH2oz0fvqCtRpCz25rlF8QT5wPwrKve1ZJfcYiRuXucc48iY/DUB2Tb4dgh3Cem52R7U0tsry8FEBQMb9Ib4PoR7nkrhoZCuYYkOWzC8taIIE+dPt5AVXBGqPrDf891sk7/RM6993qVTcgiP7n//mMvPWNr5Wf/Mmf1I2XV5swxSOMPHjw4MGDh/sEEu8TO0v1RYDMgwkEEp4ErYNT+jt2yi51jOrrL79xS3fQDtQVaOra/rqC+043eTUBMgjCyJ2W5k5HA27JNbtr+G2A8vLNNCIP3zvAj+qHf/iH5RO/94fy+JOv0YCLHVZbARTp4Zy0oez0ZH/aD0QLQUMwhYYhlHiQR6Vgq2QigYd38wDPdaIKCvVZAj7uAdVIMANtA0iQYCoq0iAIllgnFudnVbVWlO/cTzDSwQ7MCbQgpPGV4Wc7LYzmYB0x1Z2CwTbSBW4DYlRJNllg/m7aWisq+TZLr2u1rKRE/2cIwAjsTP/yd9Y7Qw5xPAJAYyKu6W49E9qfJgikUh0qLpNuwzm/drFNcuMXJW5tSdurvKJCqiorZXhkVH+enBiT6ekp2dbolKPm+poqcpXkaR90PKoghCD83Ol9JnjkFnwJCVJduJn+ZsNUzXT7lTA2aUG8TFCy2SC4hbSwz0lbkB5F29jGuhALqJEYW4bggHyLRGQFg1u9EUzNQbofippIcAik4OfnuOcvX9PA/N6dm3Lw6AmprCgPGFOQdtxDXXFgu6O6432QKFT5ykzx6frARgpzcHhsWibGx6SqzqkWdWhpUe7cui7jc6uynJwn56/flbn5RanISZLMrGwpygskbfHAoU8hoEqsFFDG6tryoly+clWO7GnUz5p7Qem3MD+nqj9dB6wKi4zP1+2rVPVQbnqSvzoWhAFrBRtEXDttxfin70kHRBHlrrz1vQpT4j6YkT53hwoJxSJrAH1KG7rTb806fhIvuQ0fSJRJzFFbTWTOBxnDi3WARS6YtxLr9dDMsqwNOKnHvJ+5RTXEK+dPS2GJ41tHH5Muy5iG1Kav8LbDxwiVYKj1nHVzaHJO5y3kUhWET4pviwm3GxyPuY9qz1T4RIVEOi6EG4rTcMjIzJS/+fw/yBueelQ++tGPym/91m/JqwkeYeTBgwcPHjy8DGDniwCA15sO1WgAw67zFRRInWO6O22CpK9f6dUXD1EoMFAeoUBCzh1rFZtXAyAL3v/+9yv58/nPf14JI9LRvvjFL+rfm5qaZO/evUE/+3u/93syOzur/08Zdg/fWxgYGFD/hbe+8/+RQ0+8SYkfiAN2c0N5D5H6xAM1xA3DnYdqUxmHB32CesiJUJXIDOwHeQIW1B+QO6V5aRF3dQlkCApQQ3CdKBKCBXpKiER4WiZQwJeEAIFjEDRDCJDCQnoO94xCBi+ZSOb4BBzcC+QEJtwEr7aZLeB3BELhQOCDsglSjYCafrHJDNQDXJ85NuastJv5mbYkWDI/s27hf2TQPzGnfWX+jinvw00lej6Mr+1qUAC/KVJ1jaJKyb5xR8HEcSiPfv52l0z23JWS+mrp7xmU/KIS6R5bkJ6eVklNWJGinEyprKmXjMyt44LgEXNjSAma2C7fzfFR+kDem+ARpRmKAFUAuIJBAlnUWXZpbI7BmEFBQupgsPPjP0MKm0nhc9JX1nW8c2328VFV2fOD8YeCJhyR6AbkEhsUxoOI/qQdfYnOGONaIMnGx8YlXtbl9N1BeWj7prEu7WGPLXv4M4a5nqSEeElPhWRNlJnpKWls3qOUwdTEuHyzZ0TecOJgYHn5+LgtpNPuDUKQ85HChiKQsaVB/MS0PP2tr8nu/Q9ttkVVjSQkJMjEUJdkZGRIdnK81JWW6u/GR4elvaNbaqor/OQabceL/oHMKct1yAj67+Ll6/LaEw9JcopDrkI6UkVuempCHjlxUobGZ/R7mr5g7jI+jJk245drxTcLA2R+Z8y8WbNMWpJWPHSRk68UIKYgWY16kHmOGi3aohuMO0gXxgyv+zHSp61sVdnghJMSaldXA6xRjClDELMe8WKc0Kah0oHtuesGPlY5WRmyp6ZA51b/hicRRB2E4OjQoKanbWtq3vTFUiJUtDoZJH7X8Iysy7pUF2QGeJZBgNGXKP3q4+OU6GUM1RRlhr0mA8ezKEfvDfIQIotUY9SfjI9Im4OFRcXyt3/3T/LG1zymKfw//dM/La8WeISRBw8ePHjw8AqAAAIiiBfgQfnqhvoIY1ke5An5ePDk9bnn72mgtbc6X82z99cV6kPw9wJycnLkB37gB+Rf//Vf/WlpFy9elOnp6YB0NDf+4i/+Qj72sY/p/z/55JPyoQ996Dt63R5eGubm5uQHf/CN8tCx4/Jbv/kbEXfWdWe/f1L9SQgaCF4gSNglhmABBDMVBYGljiNhesNvhGCbYAYiArUJD+1GHWCAMgYfHnaPTcDH70g1Yec5VGl6VCFDU/NSX7xVSWRS0VBNkEpA8EYKhgnk7968JtsbmwKMh7l/FES8z91GhtQIBbudCcq4/7t9k2rGbQDhQ4AOQcN1ELTjMWMA6bFnw7yYc0JqmSDQVOkK5kdiYHt44NXkS8jXduGeODeBKIGTSRmCLDIBE2vfvUFMpnOUMOjo6JTWnmHJysqUxx49qUqBvQePyLNnr0p2xqI8fKBZZpbiVEEyMh8nGWE4FXcFL4K+npEZHXN230IoQgadax2WfXUFWwJnSCRKY6OKcvyDnIC3OEhKogE+SXOLM3KhbVh9hyBFIULdqiH6j3GJMs30PwEpY8L2WjGV/Qw5SLvxflPem2NLvrNZESrlENK1e35ETZ6XEwMD1mBlzul3yLzTZ85I19i8lOdnS2VNrST6kmQuIVM6792RppY9eg/PfP7fZVfjuDRUFujPKKxQV4QCwThkWoXl38zGQmXtNllOTNf71ZSj+HhNT6PseGp6pjx01FEegdz8Arl25aK8ePqUPPXEk1JcmBtQOp45j7KNCms1RRmyVF8h3R33/ASCXue9u5KaV6okKutAbYlTiYuxwvczRK1Ry0AoMn75PeMbAo02jVbRGAwcb2BiPoCcM0q8SIAYWRmZ8ROQjtfSjD+VLBJY6xjbqOgwHuda3KmUsYD5Zkyq3WBNT0t2NgHsNYv0U9sLyoD2Z+0MplQypPXdzj450lKrPzNPmNcmnW0+pVBmRsbl6EMH5fbNmzI/PycLS8ty+7pIy+69+r1iqq05XlPj+t1j1iXWNMYw1RKZW3wHQYK1D0055I9rrXaDucr3F9MRIhf/J5SOrKPRVBYE2xsa5a/+5nPyzre/RUmjp556Sl4N8AgjDx48ePDg4TsAHjZ4Pbm7Qh8QUSSgPEKBxEMK4CHu1N0hfQFKyqr3UV2h7KnNj6ia+G4CUgjCyKSlXbhwYUs6mo0//uM/VvNrHvZOnjwp//iP/yiJid5jyfcK2Kl/17vepdWrPvnf/zSqNAx26zEPdcggn0zMTsnh7UX+cc1YgPQIFQBvuQbSDoanlVwxqWumlgsP6LZvCAECD/QEMLZ/EUEqSiOCWTdHw7EI7CBk2A0n7QxSiHO5CR3Sjwgi3ebEVKrix9rKMlX6ENgw/znf0uqaVnmzd685D6SL7bXB592mrSgYUYc4apoEKc9PUw8Xzk1KB8opW9USDtyjTVzZKploP29SzfIyRBUQtveR46eUEGCOS/D9tWdPSZovXuIzi2THrj06NpaXlvT1rdOXZN+unVKc7xA0qanOPccaqGuTxTlBebB+hTQLFSzze1PdKloCk3YjqA9H+AGCVC0Nnp2qQSZjkDFqTNVlI+2HsWH6hmpuNlZXluXurWuyvLysvi7cq84fVDC+JOkeX5DlkWQ1vm7Z0SBdo46S04Dzjk5MSl9fvyQnJUleXp5MTE1JR9s9qW/YIaklCVKQvCqDvR1y/MhBWVqpEJkfl2sXz8qu/Yflh3/ghJy7eEHudpdIZVmpesrE0j+M6e7uHplYTZGs+DgNzOHGSANj7tZtb9rymeycPClsOCSZszPymS9+Qw7v3y2HW7b7SVw7tQnsatouZy9ekZGhASkoKtG/H3v0KXnu+RdkanZe6ixPQfoY4oY5TrBfslEVjf6IxZA+ElCpQaCaMQWJTt9H03a8Z3Fl1U/CQEyyrkYLyBK85QwZzNxEteRWB70cYNyi+jIqNtY22hUEm3NcF+s25DXrH+QfpOfE1IwU5mZJanKC5MTPKWk8Mr9p2K8+RtmpMjAy5pBE83Ny/OghnbdTM7MyOjYpnd13ZX6pVknd7PQkXeMgdO15zXFoH1RbKIsg4bie+pJsVYhTYQ/1Uai00cXlNVXOcU2QimYcRevhZ3Ds+CPyO3/wR1ox9tSpU9LYGOg99t2A92TmwYMHDx48fKe/fDdMEXm97Vi97hJinEn1tWvd4/pACQhqvni+S1/svBG8GPNsJM6vpkorb37zmyU1NVXm5+flb/7mb+TevXv6+z179mhKmo3f//3fl1/6pV/S/0eZBFnEZz187+DXfu3X5Oy58/Klbz4rycmRfbjcBshUISPNxSZBnVSQ4J8nKCIAIHAjXQvyxeysm91boyYhWDTnMbjaNapzxnhJEKySygAJQeBh72pDIrHLDIFL0GPKaQNIGAIbVAhuUsAd8E2Oj8nwQJ8cP3ZUCWKMUQlITNqpqcJlV7YioMHPhkBDK8bNLWlbseNt71LbpsiOH8xmZTJS+iC3ogVEhO2RRDtGmxrlTjtRHx4X4ecODlE7ffv5M5KWXSjzcakyMb0oqelragSdkuSTnPp90rzhTeJXpm2kedhpHSgOMKPlffR5sPXQMdXNVbWDBnAQctMLSt6X5m4Nkt1qCMZsRpAAkaAW8o+2Iri1yclIZJEBqVOX2kfUiyo3PUWKKx2lSwDRZp2bdDNbJXb+9HOy98BhqSjO1zG7hmZ1nY2HZSWRUkfmpK4wQ5KTfQEm0eY+z5y7oJ8pLCpVs+D2zk5JiE+QfYeP6Xsun7ksmdVFSjhNzTqV0Bq318niwqIM9HarCuiJE8dlcKBP+u5ekYbGJlle3Rzf3BuEFx5etI87bWp+cVl6hiekqWWvzmleamI+s6ibKbs3FHD2NVNmHeVtRmm27KgtkYtXrsv/+y/flH0tTbJ3e/kWUgfyGQ+sGzdvqIG6STejytqdG1flXzvaZFdzszRvVPbjGpnvpKkxVsycfjl9ibh+FHZGpYfyxb0OhAPjDT8gY6bPnEApFI0fIn1ImxgSlxdz2PE0i27cQnQy3lgzC7OpihecTDVtyYu+gxTlvm2jfAMUopAtzAWtfDa14BQTuX5dcpLWtcLf0uKibNu2TfILnQqLtBkqytyMFBmZnJek+RHZt3uX+iQOjIxLQV6OzM3OSce9O2qMXVmeq2uqSf0MRfygRGwoz/FvRlTkp0uqL0HG1iWg39yoL87StuE5jfFCf0IG3k+lxLf96I/Lnds3VcF7+vQpJXO/m/AIIw8ePHjw4OG7DHYJjzUW64uHJcrYOulro5pqsqIPdOsqs+f1v799Rx9q9tcWaPoaCqTvdhUWvCbe8IY3qLro0qVLIdVFv/mbv6mmjuAtb3mLfO5zn3tVlpH1EBqf/exn5VN/+Ifyr1/5luTnF/hVPaEeiiFlCO6N4gXfC0jSaAMkgiFiTQINPnehfUTJBR7MCaCNf1FCXFzIh3PmCIHC0KRTUYtAn8AtmJIGMopKauzC28dCkQHJRJDiVEEKPm5N8NXT1S4HDh7UYIeUNYIV26OMYJQddOCk6C2owoRzcF4CbUiuUAEKIHDi/g0pw3H4bLTmyZwv1Z8O5fxsB56R+pb0PpRBBngFRUql5Z4X5qalsbZWBmfXZW9NgaysrSkxBtkFqUdbO6lyThqjTQw653FUFZBBpFCR/kbbVhakBw18If4gnFBzBquQB3kHKUl7okCw78kGqiSIfMYjChTGDwQfqX+hjHG1bydRNC3rZ0z7Mn5IPQ4Fx2tm85iMu8yEzZ/zC4uUhCQwt8kYh+RIkZHZFcnMSNV2hGy1VVYmpXTX/kObKV0lZf6/TYyNysrqqipzSEF7/vnnJa9qh0hZtrTsaJRTZ87J0tKiFJeWS0lZhVas626/J/da78pDR45IdnqKkkUQq8x9CFOIRHvOzywsydzSmp/4MMCcmHkMQTA7Oy3ra+viS06V7oklVXgYw2I8jQ7u2y2z84vylWdOS0N1cVDyIj0jQybXM+Sbpy7J/l07lQhKS8+QvYeOyvDggAwP9Mi/9w9Jc125zn2+j482OuRSJKDmY12hIh9qtWg2cZibtgcZY2dxYx2IBoxjngO4D/o2PyNZ7g5MBcxb1gQV1wW5HioH2gbtkNiojKJdjyH2mC+cj7TijgXSfx1/olBw1rMUfQWbH5PzS7IzP9f/Xu6xq6NdkhNE8mt2Sll2ssTFx/tVyLQhxvMQV1Qb1Cp1yWUyNjYqBfl58sy3vqnpi7QPhtgZOfkxVayjH4uz06R1cFLnN8dHNRRuXYWIg4gi5c68l5Q5TdUMkbYXDr/y0V+Tu7dvydve9nb5yle+/F1VYHuEkQcPHjx48PAqAg9L7LTxesP+qo1c+wlVSOCjYLwPCD6evtGvL0DFH6M+wpvifj0JCM6/cqlbLraPyOTMvGRndMi+2kL5gb0VEXcwSUuDMLLhJowMWQT+6Z/+aYs6pb29XXP3Pbw6ce3aNTXj/NO//N/StGOn3/iYINWQC5jv2p4cL94e0AdogmaCPQJzHsCjAUEu/l+NG+XcCQghS6k0iBIPomB1fV3nSyjvC2BKJHM8CKxwpAbH58Gf3eKG0hxNDUJNQlDO52qKts4tUxWMf7n/7cUZsrLslH42xyQlzihYCOgoN369e8xvQkvAtbq6Jge3FQU1Vg4GAj3jG8OxCVYICA0IZlkrbMLj0LZN82oUTASvED0ESfAOdhl4vJ0IfIw/Em0XqG6a1PdDsKWnJKrRc03K1uCIdoe0gKToHxiUns42/X129S4NtJLiSavL0PdQkh2VCX2LKgFCyaR1ECBCPtBWZj2CqKnMT1cVEm2ZkBQ8qINIchup8376hWsgqONeISzwt6Ed3QE3KTZupRPHJIUFzyN73YWUpP0hBIqyUpU4jEUVCkFkzI3NtdqEI+O/c3ROJpbigvYtFaxQSXAPnJdrNiTc8PikehSlDzo+U4Dvmp0bSrO09HQZGJ2QRF+B5CyIVDbskjMXrkh+yppUVZTJ0SOH5Nade3L+1HMakJdVVkvNtgZVI734wgvy5OOP6nE4rwnS3ebtV7smJDc7U1Pgqmpq1buI8ZuTvC63Lp+VpORkyc7K1hS7ru4e6Rmfl+WFCmmqKfcrf/i+grSoy0+SxYWlAMLIqb7ozLk3PrJbrt64LXfv3Jbc0iqd3/QVZNIL3/6qNOzcJW33FuXE4d1Rka2MZzZ1mKesK/Q18zkcaWKDSnGQ35BTQE3FrWp6kKCQp4b8ZN7h+QUJR39ClNt9W+giQUj7BObzENx7avL8ld1QFd7qXfd7nLEOuNNeQ4G+xKMH5ZBRIvJztPfuBtxZ3MY1Q+xChC2vrsvg4KAUVDaFVTsyb036LgoyCM5r16/p+N2554AMDg5J/5zI6uy4EtF8T0ST8sy6TN/uKM+VqbwlnRvRjAvakr5kTHJdrPs6NqYX/BVA2VDAu8/MZTYEMcd2P18xH/7kz/9afvA1j8ov//IvqzL7uwWPMPLgwYMHDx5exeDBmJ1LY1DLgwcPZ5BHyOYJZgG7WLz+/lS7BhmQRoZA4iEp0m4pge6ffeWGkkU8OPoNdIfn5FLHqPzvb9+W1+6rlPe9ZmdIMgqFEXJwY3b90EMPeeTP9xEmJyflh37oh+W9P/tz8prXvl5/R1BjSg8DAm13UP74Lse3i6CFh2cMT01gglKEIAbfkmCVaJQAsY6Hz0ldkVPJiAfsaNIwbPDQb5RBPLgTPAcbz9wPaoY7/RP6fvuabZjKWb4E0i/Sdb5yr0+fviRHWxoDggwCKuat8TkqyExW9ZApK29KQUdrYKul6nvHNeAwyElP8t8P94ZiyPiV8DNpWTbou1AwZd6NwgkCo90iGMDju8q1HecWV5Ussok4gizGQ39fr9y5+IKSURXVdVJVWy9PvP4tkpCUKqMzi/73G48i2tmkKLnXKK5/e2lOQPoffQBZgm+SHQxyfoimUEEwf0fFaRNSgGpKkI92lTQDVBgoDjimSXukL3nf9a5xNRA2ay1kBmSeuVbW7miJQABpRlqkuXz6wlYSrSdlSmbcgjSVl+vPbkLGrWrQ8dLaIb093XpP23e0BKhKuC+D8dER2ZaXIIV5qZoeOb+UIA8dPCAjw12qIqqorJbsghKpS86U3s42GRkelO1NzZKekSmJPl9AaqnxJhubnJELo32SmJggBQWF8tC2QnlutEvOXLklz5+/KnmFxZKZHC85KfH63YFJvGnLXevrMj27INdu3pJnXjgta3EJWvmN5shLXpPi0jLJzgwkLL51rU9ViMbPaE9zk8i1m7IwOyxdEqcEZ2luhqqk8DcaGxmWsxcuy5GDeyN+X9IPkC1mrdOqehueVNGgLC9NvnG1d2P+Oam4dt9CLjKPjQqIsU91L6PSjKQGou+HJub9ayfrbvfIrH9MUC2PfmEOoKqCsFDT8QTnGvgblQ4hPPBZgwix26Q4J1WroxmfJ+6beRju/jnmxMy89Pb2SUN9rd4joB1Zo0xFwb7uWR3rY8tJUp8SXHmF6pBVD1LXXmMbdu6Wvq4OmZ2ZkW8996LUbG9WEpR1AVXq3YFJvZ9IBtbMf9Yfjp23kSYcrc8Ux77cMaLKOtOnOZbhvKYF6sZgpn/dud07rmSn2+8IQvMv/89n5HVPPKJz4m1ve5t8N+ARRh48ePDgwcP3EHiIebS5TF8EagTUap7dOSZtQ1P6YE7Kx/m2EX3J127qjiCKDAgkKgKZXU0DHkY//Ldn5Fr3WEClJQN+h4rj3y90aUD7iR8/HDTIxodoaiowoNx6rPDlwD28uk2uqZj0Cx/6sP6OFCtUDIYsIiDmQTtYRRjjX2R7C2lp8URnh5vPEnDgpWMeyvnZJkB4f05a7CaiW+5lfV09M1CzYPpCQAxJ4AbXQcpTOIy7SAEwPTEm6cnxkpkd+Fl2nDmXCXDc5tKofPDLsOcKJBukytzisr+Etvk8AdwbD4ZW4/VuOX5sZtbu66HNyq2f7QAoM5WXb8vnh3o75OqF81JY2SgJadmyZ3e9EgqANELbHwoFmglAgyExPl4JQhRBrGH4nVANDxLGDqA1rZf0srV1WV5Z1fUvVP/i9dQ2OCl1VvU70oxQi5mA1g1IAtRPxM/0AQo4ODv7XgABpq0QYp21FUJ2uib/upV5phx9KCwvLfvLxUeDsalZ6WxvU48irtsmiNwYHxuRHbv2yb3bN6S6pl5Sk53zFOY0OcRmW6tcuH1W3vDYUUmMr5ezF6/K1S9/S546tl/WVlf9qZagp39Ievr65fLtTnloT6MkJqXI+XNnZG5mRlJSU6WpqlCys3NlV8sOHUs0kTutkAA7OyNVjh3cK4fX1mVmbkFW1lHuOYF4sstsHjzWUqabKChXIHxZR3a3NMnTz5+V69eel2MPH/eTDJ1td7Utt23fFlVbus/lGO5vfR9jaWhqQecvqhlTmY77e82eypDHZ5zYKYRcOyb50QLyYW7J2bgBKJeYb+5rhqQPRtTzN86PEor1hzR5u5obaxkbVzZhFEllNDo5I2dOn5LC4jJ54fQZeeTYESX8eLYgnZg+htxjHo1PL0hu4oKkZaQHXdeUyEpP9t+TbYCdnJomRcUlUpKRJxcvX5eyvEOSkZai6lbIb9RVEDQQ/G6vOwPWdDYLzPpfu7F54P4+YE6jLGP+sgaQNs01MHfDkY7OGkKqW24AEW1+tlG/bbt8+k/+XN7znvfIrl27tnhCfifgEUYePHjw4MHD9yh46CTg4fVDD9WqjJ0UEsgj5PLsUJvA/quXe/TFIwy74ZBHvNiB/f9/9UZIssgGf+ehkPf/3Ot3fWdu0sOrAp/+9Kfl4qVL8pVvv6DeIQTDPHij9jAPzuPWz5FAeg0KDPN+AiJ3FSz8cQh8CD7x87HfHwkENygu2Ak3KQumOhapIBV56X6iC0NkUgTqijNDGr9CMgG3WoUAAZ8x2zuJwDwxOU3VUJAaNgzZo7vts0t+AoefuS6TDsbPKPs0nScjWVOuTHpYtKBama1WisXMGqBetFN83D+HgkN0LcvM/JIszkzKD731RyQ5OUVVAYYsMsE0qomEBEdRRD+HS6VFJWDaa3JuUb58sUsVGKSw+K9xkWNOa3qbIbBIMSNAxFzYff18lvdhZpuRnKj3yDWY9LRgcAxts5Sop395b6hxE4xYMDDeR5j2EiSjxooFkxNjUlqyO6r3Mn4vX74sO/fsj8qbJ4NUsPWVDbXQ1nuort8uYytJcu7Mi5KRmS17G6vlbk+GTIyPSkV1rTz33PNS0bBbS5f3DExJ+617kpKRK0lJKZo6pEbmt2/I4088Kb6EBElM2Oq7BenHnEC1ZpSEnBs1X27mVmIRBR0EhLleU/UKZZzjceWQOqTwHWre5lfD4We0tLggO5tbpKwwenNh1ipIZ/oQQJazhpnUWLzIUB+iImL+Ql4xPqMtsc77+E4361SGK20tEiBVOb9RtfGv/XMkMDdoU6NChETlWcJ4AEG28DxA37FxQJqcScWyQV9D3l68cEFa9h3SKojzc7MyND4tXz11TZLWFqSyKFvqa2ulpDBXv1teeO4ZqW/aqeuGmyxinBiFEESLY4DteMvRpwVFxXLlQqe0VJRLwbG98tyFy1Jd3yRVhRk6t1EMsQ5gyr63tiCoaoi1Bg8j7rk0N01/Jl2OFFMzbli/WJNZ+yG6WJP6xuf075HmmLkH7oe1yvwcitR/3Q++SX7y3HvlR37kbXL27JnveJEQjzDy4MGDBw8evk/AgyUeFrx4SONhh6prpF0QPBOQrW/4jvD6v8+2avUPHnqj1f3wwP3li93yk482xpRe4eF7F5iYf+QjH5HP/sO/Sl5evgafBAo7K3NV5QaJ0jYwpURktNDUMivVrGN4SmoKA1PZ9tQ4XjkEWag42OWNFijhIBMgTSALTDDnro5FGsbSMr5DK1oWOS05fgux1TtGNR5HrcIOs532BFEAyUPgQODILvPY+Ljsa6qXytLQhsYEFnjhECAYjyEqoRlggE3gYXbs2cWOxdgecs2en6Sm2SbdBFmkorBOmBRUAkRDiBGo0zbGu4NgDDVYJEzPLcrdtja5fvmSTK0my8kj+zTo41juGMrspnMtBKKoyaIFfiT0BcGVURlwePqGCkc2sUdgC3k+txCc8DJpd6ZSVbAUNtrJ8RTarJrlruIVDI7CZF6WltckybeVVCJYvB+QPpWZlSUpVtU0NyBxmTv07cLyikzMLEhKymagyf2QpgNhh3rLRlJSsla6nJoYU7PhYMjOyRVfyioJVdLV3ipVDQe0P2hPUtoSlmdke02FzvOJ/g7Jq6hVsgisrq5KTm6ezstQxBz9wJxgfjheUIlSU5QRlJxjfKO2hbxAhUjKlwnaUeuwMULKJG3RnyZSWl4u8QkbKVGpqVJdt11a796RjMwMyQtCRgUDBAIpVJDZkM605+ra5hzNy0zR71ZD+EA8xFINjfkPiWhK3pdv+HTZJvj0L/3MfRnze0OU8v5L7aN677QLihrS5qL93qaKH+uaIWRLclL1Z7MO2emsjHPWLOa5G+09/dLW2iqpaem6Fvh8STIy1C8z05NSUlwr2yqLpb13SL749Bl57PgRJXbXNd1waz+vb6xNkDyGRHQMsDOVKIa0gWjb0bJXbt64Ko8+ckzKspOkODNBSSWjcGadIF0/HAHPfbORsLSyqkTd2PSizhUDlFm0pVENsiaxTkebumaq2xlSkJ8h9BjvbrUm+NCvflyJtA9+8IPyR3/0R/KdhEcYefDgwYMHD9+H4GGZXS9er9lToQ+z+H+gPGJnHIk5mNe0nNjAgymk0Tsern8FrtzDqwmYFP/Yj/+4vP8/f0AOH3HKbRMU5Gc6Zd8J9tmF5iHfBHKQOzxgAx6c2em2pf+kmhFkmYd1jsN7QqWaEQBQEcdAK4mNz0p+BqlGmz4nBpA/BJucmxcmpDe6xyQrLdlPGHAPPWMzShKhNnL7BnH9EK4EWgRuBGIEAswhQ0gQSPSNOUbXvB+DVAKRG8NrkpkevlIY6gkIo8Ls1KA70uzU48ljQPl4+2cD9SFZWdP7sZULEMJZab4AXxsqVhkQeBLscFruBYJNCZuN6yCuQslAGziB8FoAwUeAjEIKQJQR4HC8i5cuy8TYiCTllMjhmiqtnmX62K22MrAVAwYETQTXBFH4q7iNao0ajRcBOMQHhQFQ60AAEszRNhAN9FukCkcg2M4+45uxxjEY55FSFG0wTrk+lArRBJDhAHHJtZgKYVQoW1yL13QrM+/c6W70cV56snYpnx+cWpKVlRV/tSUC/4m5RVlZRQGyFjAG6bfx2WUpKW1QJRTjn3OjdjPvI90uPilV2m9dlaLSMvElJuh5aOf6xp1y+dyLkp2VKUV52VJbWSZz8yMSJxBGcVqRra29Qy62j0pxdoqU5qVvuX7HRH1FiQ9eBNfMN9uUHTBOSOfbveEfRYofVcS4P0gOc73MffpxempKUlLTAr7PVlNyZSZxUZ69cFvefHJfVH3CfOPcrBHBxhfzgrUhWFXEYHAM+Vf9ZIH6+MwsBsxhtzqJNKmSbMgx5zsfgsm0g5phZ6Wowg6Fj44f1zhkLSYdmPfSn2lJiVKYnaJjimPQ70ZFpSlsbDAtrWxJY+Nnd7+AydlFeeZSm1SUlkhVTfmm8mtboxSVlMvE/IrO11315ZI4NyRTU9MympQojz76qFy5ek0GB3qlYccufx8yR5nXXPfd/gntU1SkKMhsI3xfUpKsrVJpEXIoQZJUAbUmY9MLDkEcJWmHR9KFtmGpK8lWVaF7nUaxaZN6EFcoKYOllgHWaUep6XhC8flz94b1+9CA9g1GGPl8PvnTv/hf8uSJI/La175WPSO/U/AIIw8ePHjw4OEBAA/j+MPw+tGHHQXCta5x+dwLrTI5t1lFKRrwEHapY8QjjB4AfOhDH5KMzCz5wC/9sv93pqRxMBCQOcbW2fpgP7OwIn1jM37CiN9B3vD5xIRFfeB3ymhHF4jzeQJ4dtnZjSUo5uGa3f4AdZEVEBDUUemPwJCqYlpyOo4d/Iyg6SHMDVKlWqrzAtQmBHwQS9/CrLYwU+cUqgHIEJO2AIpLK6S1rU0O7m3xl93GfNY+1pEGR2kRDLrjnLyZCmTSXMzPSlxZVXYoV46/iU0oHd5eFLYNaQPbc4eg0VZuQP6EUyIQlGNUawirWz3jspaTJLeuXZLklFRZzKiU5cQMJZZMKgwBV7QYnZ5XhRj3jhqN67XTEQmkIQwNUB1wPU4Q76QCYTiLQiUrPyloZTTWsVCpZ+oPN0gVKgyKIQzjdVyFak/GAMexTdj31YVWmEUDxirXAehr5pWpYgbh0tfdKQXp8VKcFzxNk/GGxwxE143brVJRXh5QmtudOmRDyYGsPCUzaUdIQ+ZEf9ttkbUllXoMz65K79Cw5Gakyc6KbUqGLK2uSqo4Y3Xn7v1y48YNKTp+VAqLiuTGtavS09UulTX16om2urIsyfFrSuxwr5wDgseUXoewRLViwBoyOOlUCTV44daAjquK0owAZQgvNkcgSWwig3ZEUbS4MK+kEcQD850+zqsrk9NXbvsrGbrBXOb8dlrn8R0OIRoO9vFYI+gPY4bMuGGOON5NcTr3GceGYAy3TgCq76Eys8fd6PSin5wNRuK4Sbm5xUStskr7Q9TSF0YFxe+fuzmgXmb0B2OmY3hGVUvRgD7b09wow1135blTfVJQWiG52dmSnpKmaYAFmRm6lqHGIrVxYrBLbt7rlNcf3ydHDx+Qu21dcu3iOWnee0CrhgHaEoKZF9cLYUOf2KTs0uKiqpRo+/nFJaeaY1GONFfl6fsh8oKlvxoyKiPVp2swc/rw9tB9YOa8SdWjH/FLY9PEkOAcEwKTlD2HhIvX9ZO+4We7smEk1NTWySd+91PyU//xP8qN69elsPClrTHRwiOMPHjw4MGDhwcQPOQc31EiX7/SEzNhBHiw9fD9jWeffVb+6q/+Sr7x3JmAQDMcqJqGrw/gYZhgyE7B4HePtpRrIEcp5+7RmQDySSv1hCl7zMM8ig1TJY3g336/pjalJfl/R0AB2cLP8RKnVYYinYPjstOPB4lRnUBc8DPBHsGoqV7kkGLL/kpMeg8JSRqUpG+kqU3NL8dUTv1W34SWycb/RY+3LtKwcT4AccHPJkAiwLPPHwkYdaM8scmWaH1R/F4xG+2nJrOJ8Rq05mVnSmZ2jsxMTcqb3vSQ/h2FCLv6BKZ2EB6pDziHqWjHy23QTCqOXZGNPkBhYHbtHaVLXOA5jRn26ppWKCKgxLsIFYYhKQwgnNj1J5AFVGFDfWFAv9OOpKmA7HSHFIulah9tA5kJAQjcfcD1hqpiRvCclZ0jC4srMjIxLclJSZKemrRlnHGvp89dkIX5BcktDq8IhcSZnpzQNqMflxbm5Ma1VinMz9cqYgTRCwmJShhV1dbJ7pxcuXT+jDTtOSjTCyt6P7aS0JeULIuLi+pl1d3VJSvLy5K1YQSPD9qBQw/J0y+el8bKx3Q+mcpcpi8SLeNnJV02NitskAbLmsB8xaOG+zdq2qKslC3KNcbc7l275cLFC3oNiXnVarrtnCNRvXVIP01KjPP3Qf/YnCqxSIlbXFn1Vw+MBqQpOcSfo+LiX0jAhPgEP4FFCp0xk2dco+pzV7gLBe6PtCkz7hjLKJJCqfncgBTpWJz2q4nyMxMCSDl+51RCdNY5yDXj2RRIQAdWeTPguIyJhiOHZGV1VZ5/7lnpHs+Q1LgVyUxPkfn5eVnxZclEdqpU1tTJ5MS4xA2Py9efeUGOHtovDXVVqha6evGs7N5/eAuRx2ZBZuomeTU00Kfz4vb1K7J37z7tp507d8gz567LrtpifyodfWIqzgH6xBRCYB3pGZmJehOjKDtVCXNNl5tb0vawySijzGJdMuMmlqprbrztR39cvvTFf5af+7mfk8985jPynYBHGHnw4MGDBw8PMKI14HTD+DJ4+P5NRfuP7363fPDDH5PauuhSD0mjIsg2ShWCC4LgYMEVv3OXTucBGo8NAnPiAgIN/m6CBEgGgnIj/+fBHONt+8GevxMwpiUTSPhUjWSTLSAcUWGA+gaiA9LGKaHNLnymBlN4gWze80KAUoPrmZhblpLMRFUKQMYEC6QMjAeUHQhlpyZpalWli2QxYHfcDjRIcYhlHhNgmzYEBKiG5IsGuntuVUNC7UOQT3CdlpYuJ558nf+66X83EULAja+aUTDxdwLcUMbRwbGuKSkFmal+jxW7rdzpjajYIBMhikxaF0Cpxv2TRmIHkLML+F1t3iNkiH0fKARod5OmAqEUrmiAmqZreszmNep9Z6YErVIVDfDguX3zumNCv7AgjU07pLK8RG7duSfT01PSO7ksva2rkpmVLduaWpTEhBQaHuiTyclx/YxGuXEOWUR1s4LCIllZWZaOe3dkdj1Jdjc1yI3L5+T86efkoSffIpV12yTNFye9XR3SeueWJCUlSbIvUV/2XAaQDOMJBfKPX3eM8mtrmiQtc3PcYdybW1Akzzz3vKSlpcn27Q0yt7AkL5w5r+9vbGjQNYGxQqn0DN+aDM9Tyn6PPyBn7vGCGDapW6wLjG93iptBQW6mPPHoCRmdmJavPn9eFobbVaXH59JTk5W0SUpM8lfuo+9Q5gLGEOM9VDUwo94zY5nU3e6RZb/hshusUWqkLBsG3MmJ6jcVLVhbjCG/PQdCqaSCgfXWNuOmMhrEh03+cZ5QqkNIljN3h/zzBYKe9jHm36gxGXvMtccfe0z6BodlejVJSjMT5Oq1a7I+OyJdw4W6lmakJEtTfa2cmluWyxfPS8lTT0ptRYkaZbffvSV1DTvC3kt/b7cM9vdKaXmFFOY5a1pZYa6sa+U+R1XEdw8qH/ue8R4yClJgijq4iWQbDvHn9DOEPZsKVFcL9v3Cd4dNUrlT2WIB/frfPvlpOXnkgPzzP/+zvPnNb5ZXGh5h5MGDBw8ePDzAwLgYuXQsxe55Dt27YUjs4fsTH/vYx9SU9qff///Tn9WnZ35ZH7Yx/iQYswMgHp4JpIwhK0EM6qJYPF9QzpBWxTk0dQ3DVS1DH+f/u20M7VSwCVTW4CXipK3NqXoDAivawMkN1CUoR0xQwHFR1hllCya6kEpNVioYAWVtSa7cHu3S95MeZadvQGYRnGDOCqjadqyxOKAtbeLCDYgKW8XCNdjKl0iVo7RfxudV+eCYXWPmvRn4AAIlgBEyfZHsiw/4O6Scnc5GoIc/EmdIz8iSBFQoYcA9UNmRgNT01fjskp9wIJCzSRQIJjfpRnoanyWVDuIokoILkmh9eiO9zyKM6C9IPbx5bNgpKxCVvI/0HwPIIoiKzbTBzWpZwKRjklqHRxDHg9iyx6vtCRUMxqA3FOq2b5bXXl5ekkvnTkt7W5vkFRRKRU29jHeNSU5uupoM941OS3dvv0x0jEhldbU0NTZIos9R0kGuqQoiMUFVREYxMjA+q6TWzt17JTe/QBITEvRefGnJUlPfINW121Q1Egr0WXVZkZRklEhX+z1J88WrQbk9vqloVZ5TKasrK3Ll8iXpmViSfU01Sjy++OKLeu1ra6uSlJEnC0kZ0nH3uiwtt2whXyBeWDvsID4cuLaivCx56pGH1N8nO9WnHkzMZ+aUMWCnz7pHHL8/wNxDHWITRsxpzK+NbxtzzpSf5zrDmVyHWpvCET7uOc38R2VEXzGnmS/MC5vIZs2hWZjPzGt7TuM/B5lhVJOQH8ypUCXn3YAYgvA1az3td7t3QvbUOKXl+a5wlFXrOq/rKku1aMLFS1ekcWeLpKZtriUQf6SgrvvSZHk9QZZXMd33SX1NpXR2dqhZOmRiKHBPR48eVcWjPw0wLk4JuxttvbK3ocq6x0kl9QH9SWqcUaTxL8US+P6Js/oBIo2NEaYm61RzVa6ek7YK117MMa4JYhnFJP0A+cZ4MyrGYGPA8WDaOg6KS0rl13/79+R973+/nDx5UnJyYieeYoFHGHnw4MGDBw8PME7sLJW/P9WuD3PRIiEuTl67r/IVvS4P3z1cuXJF/uRP/kS+/K3n/Q/n7aTuZKc6KRmLpPZMy8FtRf5AnYBjfW3dnzbDDm0sJdwJtvB4MGok/FAwBrXVSTMLjnooNz1Jd37dqWAGPOBD6tgpS/cLO/gkCOMhn2o8+PawO21XhsPjhEpipO+RfkOFNgJT00YEc1c6RlWVUpKTo9fJvAtXSt4NSDjKOENAoIIhADfBKYAkg6iwyR/+jqm4wY6KHA3c1CgXG2IrbYfj9o45CqKFpWXtB9rZPgfkCalDgFiG6nHc4wwGyivLEQk6CJq6rM20RT5vK5ZoR8xy+Zegl2sKlurFZ43CJJrUGwJa1FGo2FBR0D8QinbVKRsYaaNQU68jl3KA8Y1Kwb5mgktbxcVcMVXDUNvFssbaQSN9QBBuK0kAAa8dS3aNr0hR0oikZ2aqiqgoPU3JhLa71zWvsbqwQPY2P+pXp9EO3P+qml6v6hxuqczVsQFHS9pb6+CUxEu83O4ZlfqMsoDgluppvjDkjN9sOiNTduzaKwN9PXLn5i1JnCvWdQVjbcjKnvElDai3teyXtLFZKdkgWPYdPiZTE+MyOjIkVy+elje97Z2SODsk8wt4zAQPzmNTqTmkcOfQtJJ9nSMzOi4gUAyYm5AWoRQ94Hr3mJJ/xi/IpJJGCwgXyAfWPwg8hgmG8kY5x/inUIWt6iUNFF80QDobJCz9SpOzRnEsG50j00q2zy8tO+nAs0tK6ADfxvi0zbUhgRvLolcpQYIwr1lnmGusyxzDEGvM76udo3qNrJuD3e2ylpYXQBY5x0EZisF0tgwPJsqNm3fkwJ5mbfeKykpVD5VVOKQPwBNrfGRY/59emZ2ZdtKPrevmHvbs3iVf/OYpWawt1/WLNc1O++M9tBvtYNaaomzH46p4Y32kH1hvUZtxDtYANjGird7J9xFzrrHMp9cKwcfP9LNpZ8fA3SnaABgXrPfBviNITfuHv/us/Oqv/qp+X7+S8AgjDx48ePDg4QEGD3gnd5bKt673hU2pMOA55gf2VUZdmtfD9xZITXnf+94v/8/7/pM07dipv2NHFUNPI89nl5Vy4PZDub2LTpCrSoSNgBqFBp4i/Gx2Vhl3dgBOWobbp6euOJBwaqnK12MT7LADTnpRtODBe2hyTlMOYvETstE2MKVpF6hEgnlPcA+QZAQWU2tJcu5Ovxzd4VQGAnggoa4wc0eNUsOmPAQqCQBBWFwchtdxGkCS0mCTauzQm11zgErLJou4d7eviw2UElQGMulXBEz4D9mwjX65RsiSzt5BuXnjulbHwtSc9nE8iJzqae5A3r4n41dkB/G8GDeMPVWuRWmya/oaYg3DXvd5uXfUEKjTQhFFpp25B4LKUCQE90Dwzbkgn6psAml5VQNEc18El6RJhoIhIczYZJxz74BgEfK0pjAwMGVsmHk3MjQgZaVF8oZH9klXT79TLWx8XAY6+2VPyw4pK8rfMu75iXm9SWg6fWGAiowXAexQm0/Hrj1nHTPfMSU8VGWR6lOVRSh/n/QMx+T37s1rsrq6omXWTz7lVHpizjDWUPsY9HS2S29XuxJTB44c189CNNnfUyj+qPgJCRsqBS0c+AxrDSQGCqVwBInxuYHkskG6qp2uyDFDmSr7lSNWX1OV7FL7iI75tKQEKc1JU2WjfTzmoK1UpDqe/fdQKXIGjB/GvTnn0spkwDXaxstcH2tv4BxdkwttIzqf6WvmNCos8/lyo0oqT/J7KbH2mOtinPEyCsXe4XEpraoNe834Zl05f1rn0t3WNrnX0SnpRbVSsHHd+E1Njo/JsWPHdFwyFxeXHCJXPcam52RhYUEWFhelrbVVdtRXyLnWIdlVnR90o6F4Q2UEYcR8JDWQ9UPi2N4AAQAASURBVLR4Y73knKiEDFgfWKMYD6EqfNoqIdqT9R51FWs384b1O6A6YVaqfo8Y9SH3hOLKNvw34HOf+L1PyRPHD8u73/1uOXDggLxS8AgjDx48ePDg4QHHT5zYrg8pPCyFI414rkFp8L7XOESCh+8//PVf/7V0dXfJ33zhX/RnHpyRzRufILtKWShoGsKGaSzA2Lq6KFMfkHm4bh2Y1KDDlA7uHZ3Z8C3a3FV1p0HZxzZVkAy4JoIQp0x88IAPsobgV81wszcrMcWCh8JULNIH+8EpnT8ED+X5OTI71iVXr09KU0ODZKWnqPm1XbodP6QdVhCISmBoI90BEEBT7cxOdUIRGAoEgHbQTN9FYdcUALx6qgp9AQqlUD4bmPiOT87K+XNnJCevQHbs3qel3guz1zTwoU0gXiBnzH3zOwhH59rCXxxBlqmGZMMxMt9Uo7kxrt4jydI2OK1KDM5tEx2MK7sfgiFSdSr6hn4iPRMTdvfx3MbIqI3sMUtf0S7G1BlCiYDa3Cvj+EhDiUQD0nRI+app2KP9X1+9QVJWlYX9HOfQsRa3qRx1i6CYW2fPXdDKZu725vPMaUgr7sOkE9nBLb8zxERmVo40Nu8OmorH391tWFZZLbMLC5KRlSsVZaX+e3XoFnMRjuqHsupcK6lHKFxiAabO4YBiD2KMcQMxYnxuDFh3GNNGmUKVLFLAbFIComl6YWnzokX8BBDXe6wpdF8HW9P4VSw+RcxH/JjM+OI++saCm2sbA2wb3DukGCo9VJ7cL4SQSU2nD+YWV/X3rOsmFS0YcQax+MYnjsn169fl1rVhaWrZG7De0I7G1y0nL1+ee+4FyczOloeOPKyKHK57ZnZWRrpb5cTRQ3L3Xrt0trfK3OyMJPp88uRTPyDzCwty4dxZySsoUsVnQ/NuSU5OkfIwXW1URufvDeu/tJU75bkiL0NVQGYMsGlBxUV7w4S0M9Y9k1LK3DbVDVHp8goFoxKDhEfpx5zju9HtKWVQV79N3v+fPyA/8zPvk9OnT4VN13sp8AgjDx48ePDg4QEHD3a/9OY98n+fbZWnb/TrrpkdN/AAE7+RhgZZFEsKjYfvHUxNTcmvfPjD8t9+/9OSnu48KFNVylby4LlDCftoAxUCSUgiY4CKHxHBhSGLAAECFadMOpvjDbGZAhUJBGczi8saxHNc9/URtBDYp28EPATqkFSopNxQX5wpx8fErt4WCdzTieayzbmhAUWd9PV0yTNPPy1Hjj/iJ34gS0h9sM2uOS+kLZ5itsrEXZEoHAhKTbUlQHtEk64VzjNHd8eD9PX07IKcOXvWKZ++54AGY6B/aMrvM+UQDE5VucBS7+lKrjltQUpObOtJ39iM+s2ghkEV4A6kUCQQrDOuGEvXusdkV1Ve0DHL+CTNBBKRtJposb+uMOoKRw5JtiyV+ZuECwElnk0mzQiyMJJnUTAMD/ZLd0ebNLfskrm12Ndl55ym4t2m0slGVna2dHfcUx8jU9rcDfqafjb+VwYo7mxiDGI3WhD8FpXX6Tgm/c4XLzI7vyAFOZvBOesK1wz5ggqmc3iGZMKQ/lAQWBDYqImiBWMH1UqoNc8xtp71E0Z8n7Ke2YQRVdZsBVOsaWukuhoSAZD+iPLOqHYiAQKVlDNDGKGoQjkWC+hjyCLuj/NCLps0NNBYnq1VA9lQAAtLq/r94fZwUtI/M00efuigXL5xR8cvFdIA72cusC5ALFXWbAsge+mHstxUud1zS1ZXluTzX/ymVOSnSn5urvpqtbfelhdfeEHH9e4DD8noLCR6nH99igTWE9oq1DNOarJTKc9UeeT7DELIJsbYGGBuZ1lKUtSC0a7FVHGDADVEPeNI02g3iDg3/vMHflG+8Nm/lf/1v/6XvOc975FXAh5h5MGDBw8ePHjQh52feqxRfvhIrXz9Sq/805kObZWKvDR5zd4q+YG9FTGVjPbwvYff/u3flu3bG+X1b3SqruDzAAlBUEZwAenC7mlpbvTpQaR7sOsfqPQJ3LUlaLb9OfRhfOPB2E84hakCRnBmHq7NzrepagQwnq6wiBTS7txjmfNA2PDgz056qDQkfG36J+YcQ19LaRBsVx7gt5GSmipfev6K7Gtu2AgU49SHyZjMmnsgyDD3DekRKzHLZ0wAvbS8qqQEnjQGkFSk8xlTY8gcAmsThGA+TRBIgG+Mxm2fHv3E+rpWlzp37qxW3qIClw36zlaiQNhA4BkQZNmG2VyrHQJpf69uEozBwPG5bj4LmUAwFiwoNYEx6TjuQIsxBXGwWQI7OBnA9TMuCF5tpVMksoh2NgSoM1acdKzN8wdWuuOeYyEHue62u7dkR2WBnDjxiI7XexsknAHkDUSVY27ukLX2mOXemBucl7ltV40y4Jp37WjQdLLL505pmhB9jvdMcsqm6XcoZKUmSuLagqSlZygBBKljg/nGmAlVKY7fE3gTNA8MDMpacpYqPMx9MF8Mx0UbMC5sLx77PAThtHl8GHWOUUkRtJs1IlJfM35Y1zgHYOxi6m6DMRB4PscLKdr02JLcNCXrUQTxOczJu4ZnAggj5gG+ZsbMnusyvkqcG3KHtjNm9vglRWsSDsry0vT7wKiSyvPSpHVgyk8YoZSy1V2mYpx7jaLDHA+1OGlp2i7PvnBKxkaGVA3EGpiTkazHYS1nTEPgmPssy02Xc1duSGFaqrz56BG5euOmXLrTJUWFCdLf0yX5BUVSu71RJqempX14TtU8EDaovoKB7zO+o1iLaa9Q67gNlHAX20f8qcWcw1Z2luWmKcFpCCOuHcLHrvgZDkbNZVRF6xsEIf1vr512tcFf/bXflI98+IPyjne8QzIywqsn7wceYeTBgwcPHjx48IOHP8ghQxi9/kCVvPVIdGXVPXzvorOzUz796U/LP3/pG/6HWpQfGKM6lXcc8107hYFgHcWA2QBWc+CiTMu42gmg/NWmqDK2uBJU2WPD3lEmqCfYWhxjZztOyQc7uHRL9QnuIAgMCIjcxtJUuLKvARNrvFNI0+DzlDsmYDTgPvD2weiaNsEYN1jlGgOjFPGXls/IlMJUpyJhqABxcGI+wKcHBZQ7RYf7IKjHtwUyj2DQBLNK5EwvaLDMpXG/BHR2gAIhpATRhpqEoCbQPyNF579pM0gMO/1mdGpevnXupszNTEpVzXYZmhNJTN6sOhZKIRMuSHK3B+oEOAWII/7Errzdv47ZdMKmH1N2qrRtVKfabH8Ji/7xWQ2WIRE5FgRBWvLm+HAqyUGekfqWqOdg3IaDm4AgAA2lCAjm20RbM67cxyTA5txzi8vqf2KI1d7uDolPzZL0/DI1KXf6O33LmMIY2fhgtfY7BJYBqhzOS1szvyEhbOUf4/5K55jjz5OQJqXbd8vK7JTMTE3IYH+PzM/NyXxittQW7Qh6nxgU93V3Sm5ennTNQM6JpqQlJG32J2oSZw2BUHHUhiiVzLiAzFKSgbk+PiSHWxpkfD6wMlskZRbrFOQZHkwE9ph7G3LVfg+EC/cK+QwxEm6DxN3fu6rzwqbEQtLYKhT8fyYtY2t7fDO3Ia7wLTKqFK4Vryyue3N+B/Y39wBxavobgsxOjdxTna/rnOlvCCD3PZ2+OxRw3xClRikFidO5hIJrk9gIZ+SunlOu9qDSJvPNeD6x3hw6dFBefOFFiYuLl5LcPO0f7sNUHmOdMoQRa01+SaUsjbTJpcuXZffu3XKre0R8SclS31ilZCbtNzIfp/3NNUIOU5WSypD+61hYVpKP+cRchTgNV7Vw3epv+oLPhKqKxvv4DuIc5j5ZW20TcAP6gjWd9/I5Qwjx76WOUW1/h8TCNyr0eHzTW94qf/Fnfyy/+7u/K7/+678uLzc8wsiDBw8ePHjw4OEBx0c+8hH5wTf/sOzeu8//Ox6201NCm3miirG9jDA9tcufE+AQkBqlBQ/uZsc7GqA8ILAxVWj4/J3+iQD1EOewlTqoQUxKFIAYINjGv4OdYAIH2wRaFQ7rmyXOIQvcwTyBF3835AwBzLaSwOCCa8N/CAUSwemuqvxNVVRcnKQlOZV7+obGZG5uTkqLiyQtxQkGbBNc/p8Aguu2lQ0ok+gP/CwIJMdmFwPamuPYZtRuQAIlxscFqFjcZA1BaKjddT7/rdNXpLIgXWoOHNXfUXEMH6PNtiQw21TWEJxHm7pot6NRC3FOzH13VjhViQxBaBvuk/biVqdACEL6GULIDdrXViQxzmyVhaPcSPYHyiNT81vS5ugngnpIBhMz2+Mw2pLk5j5n5pekyFJBoAYjoM1OR22RKLnpKTrfDGE0OjQobzl+fIsiyEa8u79dJCfjK5x6Bh4mPyPZX40L3Op1zJe5f8iNf3z6gpy93qrKDvqatgXLy0taFe3xR09Kkg+j6nVp6+qTOzeuqMIvOzdfCoqKMSVS5QWGz0p6zixK9/C0ph3hPcM1G0IIgiojPU3G5zfL3AOGGB5DTsW8lC0BPupI0mqNCiQrzbfhg5Xob3/GMv585jOsW24Q3EMsML4gbW2j6EjqWwgP0rVQCnFtzGM8zGzC6GzrkLYFxF1JTqp0DE8HpDFF8tUipZJrNCQFx4CkMCREKBWX28PHba5tp9axZmwquJgDS37/uGjAvBqe8qkxv5lD9wam5fjDx+SFU6e1yuLautNGpi9QxtnG0llpyTJVWC+93W1SOj4peWkJkpJXLplZznUy5klZNXMakgbfPJtwIfUNgt6sgaz5wRRfrDdDk/NK1LGemH6ONL9L89KVoDXK15y0ZB2jNmHE3xnfkLQcV8+zkXbIve+rdfyhogHv//VP/K78yJteJz/7sz8rJSXR+Z9FC48w8uDBgwcPHjx4eIBx48YN+cIXviDPnL4U9Wd4uLUDXAIo27ga2N5HJk3KDlpIjyBoQ24fTLGD2sTeBWcX1j4nwRFBFwENQZIhe+yKNfx+T02BUwq+d1wDQZtwIii3AyICR7vEOyoTdnlNYE1w6a5cxnH5O4SUUy3I2Qk38CX6ZHlpWT97/dpVyczOkTu3b8rRY8ckNzNdg1B8k4yvCe1I1Sd3pR2bkHDSxqKvCjU0Gbi7rf0Vhmxwo62zVwiha+q2BfaHdUzaAG8fm4yzAzATcJNiEk59Y0DAx/iwxwZkHMGgITscD5VAZc7u6vyAlLNIcL+FfrL9jCDvirKTAitzjcxosA+ZybjjvmIB/jOyQaxxfxAD9riFjIFcNdcBEWMIwqXFBVVUxOr9FCu4rlAKEvqOufxDJ/fLM88+J4tDM1JUUi5lpc7cio93Ana8xabG57RCV11VmdRWlsnS6prcvtMqA73dMj23IGPT81JZmK0m13MjA3K9rV/mijJkZnpKsrJzpWchRRZnJ2V1dU29fNzAl8ypKreg6aikAy6uZPrJMtaAGWu88zOkoBm7XCdjKtx4RPXC2sF6hNKGFCPaxiZtw4GqZ1+91C01c868phqamzxnTtjkHIjF2Jp1i3GYUeLMB0icgYmJoGlMocD92GlqXJOt4rTJJCUNVxwi2gbrulYEy0zZkmbJ741XFmsDx1VVW1ycPHz0iI6lsoY9cqVzVFWgrBWkoaEKNGpHU70vO6FcXjh3VShalzQ+5091Zl2kb0zb8eJ3gQqvZJ3HZn5BrrH22+nSVHvkHupKHFUmpFO0afncG20JSUQbcA1uRRh+c7a3EWR8++C0KqPuB/sPHpYTjz4un/jEJ+S///f/Li8nPMLIgwcPHjx48ODhAcbHP/5xecdPvEuqqqujer9T2p5S57kB5I7bR8b9AG2TRTysm9+zkw6ZQABnHsiN0sZOyxqZng/wBiEIeLipRMkTiB+OUWmRVDZ48Cf4YKfYNpo2Jq5Oet2ikjX27jGpSXYaHgomO6gw1ZGMvw3X4A4iKQu+traqJMHw3Jrk1ZRLyuyMxMc5wQuBYzjlFQGwHahwzbGQRYB7K8nJCVBmkRbTPzwmSb4kSUhMkLnZWcnPy5FkX2JAWh3BXVdnh5SWb5JF/nsLE8y61T2MGdQTkCEESgR1BH4maIuG3GHMmQpj/EuKC4SgG5AvqMLoj2CkB+cyKZWRjIMdP6nN9p7eSAc0ah9UZcGUOjqGl1ZkdAqSKz5g3NjqlGBQlZuleuJnm3zDDyhW9VasYBwHM8G2wZx+7NETMjO7IG0dHdLTeU9ycvMlIytLK1Yx5iF5+RcigfvAf2hPc6Pfy+ne4LRkJq5IW1u75OXnyaGD+6WlKl8GhsdlcmZWcuZnZXJuQUrrm7WSJ3PODdqC9YMX64btKQNBxLxFfURaJmlwzGmUR6FUcO77Zqyxvpm5jUoRdU0wAoG1Db8ZCD1DRjtFI6rCtqWZa6afHaJrJSBNMBwYg8urq1ur4MUA2o9URnPdpACj1gumqFHSMMi4RzXEvO4fm1WDaPqcuWgUXZxjiOIJG0QWyh0IvAzfmiwtOl5uvFDUYY7P9KWt3dXpSoqLJTM9TZKSU2QpMEtRlXGo1TgORBAEsj1/inNSpW1gyr/28C9rSqnlhQ6JyCZA/MbnfAkJunYFm+uMOdY0SElDANn+cMFAe3A8Q2xB0uElFqyyXLT44Ic/Jq9/8oR88IMflIqKCnm54BFGHjx48ODBgwcPDyiuXr0q//Zv/yYvnL/q/x0PzgRyBEcJCag8EgLk/Khh7FQJAi92/qM1cDXn4IEaFQNKDXyE7OCDgIuHcPOAzr88sLsfwOOtnVuuI5xZsikDboAiATKGnWPuxy4HDow59OLymgxMOMEuahZbmUOQYFLmAMGJbfJM8AiZ1j+zJuVTC5KZRECwpuk2pF+IRK7eg4rKVhdBYtnnAASaBEUEaPxLkAmpRMrW1NS03Oga0/PSXvx+aGJOrp55VopKyvQ6JuZWZEUSZW52WhoaG6Svq1Oa6iqlsb6azBNZWFmXXEt1cz8VvSBa8IYiMCIQI03QPgxKHoJVAkqC5VDBFsGUMT2mz8P5SXGvduzFOIIooi85j0kZcYM2RNVAe2akBHrTcAwCUfvnZEp4WSluTnomKZ2JSiZCWMQCVa9sGI87P28SkaiLFhfmNbULTxrT745/1ya5AIlqty+koQ3Uc/1jjlKNY/MvSiBDtjBWwljU+MH7c7PS5MDunbK82iQdXb0yOjoqDx06IENTi3ofxhMMRRgknUmbIiinSh3pNw8fORiQ8lRSmCvxCXGSVV4sKUk+/ziHoAsHt3IOsg9PItYTiAzSoS5dvSn9vd1KaiWnpEhhVaMemzbj+uy0VVulZFKvIFsZqzZh5Fz3uqQl+5QEwaunTAJVJeHAsW1fI4yaIV3chBFjgTXJeMTZ1wAxZ5t+j0wvBPgY8f/tQ9OqCmJt51/UT2bccA2szYYwMmqdWMBaylpj1FJcJ9XrzBqGovRb1/o0nZU5yHxGdZZbkCXJqanqz1VRVSt5GZD8zjwzqY5upGc464DPxanRhqiUSCctzk7bMs+NZxZtCcHFmmDGpK3YYo0q3lhr+Z5BtWh7HUGCssbyHcnYYiwbc+toCF2uDfLekMkVeRlbzmGv77QrpCB9Huz7dmfLLnnN694gv/VbvyV/9md/Jg8kYfSmN71JLl26JENDQ5KbmytPPvmk/M7v/I6UlZVteW9ra6vs27dPGfiJia1u+R48ePDgwYMHDw86MMl824+9U0rLyvVnPBQIgHk45mF6eXVdDZhtwogS0TyssgOsn1leDUhViFT9xxga24E+Aa+tQOLhG78LHqaXltc0uMXLJhxsMohrII2EQDWUGofTY+odajcXYmxsekGDgVDH0bLzGz4rqAEINm1Dba6/vjhbfHP5MjfaJ0vriZKesCIJhcXS09cn+TmO0iIcCJZQRdAGkCr0ka1IUnPbtTVJSojXe4GYmV9clva7t2Rhfk7SMjIlc2FGBu/1KMmwe+9emVoak6ycXKlv3On300B1MTo6IgN9fZJaWCkd7W1SWlIsWekpQrw2Nbug5wYEkfjO2IjU726vIK7TVvcQ6B/eXqTpgwTjHM82MA8GN0FIe0Neus2EbeBdYgLRYCBg1mpuLoN1A3cAzjghvcSA+YNiwU51jJVgI8gmEKXfIcVstd3t65elvKpGTt0d0qDW8Z6K1+u20y1PNpdtGJwH7xOUTwTDqJ9I92LsEjjbqTOowoJVHXNXuTPngDzaXlupL5CUuKyBrmx0B9e6vBJIXD21p0L9fe70TfpVWQYXL1yQhASfPPHocfV5OnvunDz6yLGAz9PeVLpi/NrKIgOujQDc9BHXPDE+Ki37DkpKapoac99q65a4eId8dpvNGyIFxYohjLgn+zrt9Lj7hVbT6p3QuUy1M0cNGfieSx0jSu4kJzpznfG+p2aT0MTnyO4Pe10GjGvGbFVBlp5ndXVdyZydFc518znmtt3njAt3aly4dDzWbT5vUv64Ts5pwHEeaylTogZlEdfBWKSPTh5/WL5NWlpFtcRba0Ww9FnIGkieYCovzgGpzjWEGv8onE7dGVTVKmSW+32oqgYn5/yEEZeDotEGHntUAbVT3fheDLe+uM/BhgL3wRxnTDHnbLAJweYGJCXjm/HrTp+z8fO/8CF5w1Mn5dd+7dekqCi8kvH7kjB67LHH5MMf/rCUlpZKb2+v/OIv/qL8yI/8iLzwwgsB71teXpYf+7Efk0ceeWTL3zx48ODBgwcPHjyIdHd3y+c//3n59ovn/c2BEgL1BgGxT+JlfHx2S1UztxLHjRvd4wH+Kuy42z4a7OK7g9JgFWB4CDc+SFsDlvDloNkZRg3Eji+fC0b4hCq1bIAKhlcoEMxDepjqU6RbuXeyDaFUU98gt29ckdTsfOlvv63Vorrah/z3BUFAYIbCwK40B4x6heDH+GHYYMfZBKoEZq3XBqV9sE1qtzdJbl5guha+MG332rTMeXXd9o1r5BocNUFxUZG+MK7Oq2+Qu/fuycE9LVJVmicZaeuSmx887ZA0IdraDuy4XtsLyA3O61YH6TXkpElxzlaixZTpDtfvKLoSUuNU4YMyCG8Su70iGT2DcObhgEAeMpK2VmIpNz1A2bO0GpjCFoosMsbRU/PL/vQ8e85AFNGeEAmq9FhdlSsXzkhJaZnUVZdL35ij0jMYnw1M1Yqo+NvwWVFlny9B1UTudK9IRsvM9cB5ta5zYDONB++YTbKAdjHKGPs6UTbxMu1isHf/IfnHv/uMHDx8SDq7erR0epwEEkbrG+sB6UvqM5SbvoUwtAk9zldRUSkjQwNSWVOv3klDAy9KRW6jmowHA/1wtWtMDfS5PFQ/jWXhvWacKmJb1yk1e15YlomZRZ0f5lppK8YT/zFuk4PM9RRfohrum99Dtthpa5H63ChrdB7IRrXB8UD1G+bf4cAcfP7WQIB/GcSuIbGDETSMM5to5T2mChpoLNtc3zMzM5XoTkpJC5t6C6FKm1GEAGNs+t2e24boCQXm7MNN4ec630Gdw9M6hrkH21sO4GnF91nNhsoUdRGK1WCEEYQ5CjvmGGuG8SxDcUUlRK6HOWArVgHfQZBrNpHJ+hYKqIyOHDsuf/zHf/yyVUz7niKMPvCBD/j/v7q6Wn75l39Z3vKWtyhB5LO0aFT6aGpqkieeeMIjjDx48ODBgwcPHoLg05/+tDz5mtdKXb3jTUNwQzBkP6SThhFqJzMYeBjmodekTBEksPvvTrHSMuKpSbpryu6pu+qYG3YQwu7+ta5RJWiUjMpJ20I8EISzawwpRICK+WgwrxtDMrBbTWAeiURyg93pUB4jjhfFZlvi7TI6MClJK8tqCox58bPXOiU/J1tSfE7ZZC2b7boXWzXihh1cT0zPydmzZ2V8JVmO7T8sPt9WsiYjM0u2NTUH/A7Vkq3UMcdcXV2RhDjn+gsKCh2Ff37wNiQQgigwARueI+yY2/5EqIboR60KFKZEdKh+h1DDbBrVD8fAGydYOpoh+hiLWmUtRPtxjapMKcz0+xFFA4JCgvxQyjS7opNRIbiVLxilM89oL9KfUPbYsMkjfF06O7uV4Ni9Z4+UFxeo2XAsKaDBwKed+nwOaMpInkVucA+2igUizTa3J8jGIBpSGPUF7RYpw8nu88KcDEnKq5DPffFbkpaWJovryTI1tyDZ6ZbZeqLjQ8T4Y5zhUzTYO79FXWMTvaOrKTIxeE9Tn/AYg8Bl7jxy/GGds27Sg5/LctOkoiAjZLvbZs4Awpoxa8gDru1Cu6MQykzxSX5WinSPzAaQW0bpEwqMe8gmkzLHmsW8itbnyMAm4PFtYw2Mdg5wf0XZKQHti2F/YGWzeCXYICNRZfGdwHXaxJ0N87nJmXkZHhqU0opqudM3sancyUhWDzq77Tkm3yOs85pCOjGrZFsoPzgM80nnJSUxWv8vvsem5pekujD494Iq5lY3B7Qx2LbXAEhhCB6+qxgT9BmqIpNWG4nYMiSfTbihSGL9CkXI/+zPfUDe9+7/oFwJ8+aBIoxsjI2Nyd/+7d/KsWPHAsiib37zm/J3f/d3mrr2D//wD1Eda3FxUV8GU1NT+i+yXV4G/L/ubli/e3Bg7/A8ePfPPTtVSh68e3/Q7/9Bvnfg3f+D2f8Bu/prD+b3Xqjv/O+Xtpifn5e/+qu/kr/6m8/7f4diwU6jIeBGpRELCHypKmOg/gyuB2IMf01JZdJoCChiMfAlTaCpIldJDgitm73jAUE/O7k2j8BDva0AMe/pn5hTxQAP8hzL7WFh4HhczOnf7Yf7cGlPpi005WBsVq5dvaIh+pPHD0p/b6pMToxJQ/Nu+erTL8obnzgmxXnZfmPvWIgAszNNUHLq1CkNfPunV4OSRaEwPrMQkF5GWgSKipHeAWludsglVBKsBaHgNoOFsMuz1Br077GmEg2oCXRQsrl9YqK5Vwg6KtBxjDv9ExrQhapcRNsnTyWENLt2iKukkO2NVw5jhKDXnheRAryljWCRsU8gTrPZlfcAgZ9tEo8XVjCsLC/L1YtnpWnHTmlq2CaZacmOObJrjN8P9LatLnVMkmP3prKB0gNfJQP65lhjiSqoII06h6a1zaMFhODRIw/JC9/8ihQXpklm9S75p2+ckqeOH/b3iW3ubKpRBUujI3WQPkHRgeIwba1Wrl06L8179qvirrisQp5+5lnJyM6V3JIqXRNQ+9EmXD9Ky3Bzk/UBhSBzgbRdPmOrQrg25mooIisaQJpAvJi5wzUuLIf3dHIDkoE1zah7IFc5ZiykKWuZTY5lpgQe0/gVQXLgEzc6jZoq8vHb2jtkW2OzpKalS/5SnK5DtCvHQEHGNZrNCL43SMc1FQZRoxq1pw3WCkhE+h2yxr5OG1wrRA5zwhhsa/VLX/hNBEggSEjag3UFEgvi3xBGEJAUErDVgFxPLGCs811pyDDWIO49FGF0/MSjUlRSqgrin/qpn5IHjjD60Ic+pBKrubk5OXLkiHzxi1/0/w2DNRrlb/7mbyQrK/xOlY3f/u3f1jw/N4aHh2VhYSHgIXFy0smvtfMqHwQsLTs5m2urKzIxNipx8mDd/7qsyezMtEjc+gN37w/6/T/I9w68+38w+3/OKjkyMzOjyoIHDaG+86enp+X7AX//938vhUXFcuTYw/7fkYaEKsap2BKnD9q2OsMpKR/azJMgVr2JrL+reiWI/4Q7JcEAnx5KCxOk8zf8MNwpHbYihmCC99oKmZ6xmQBTbgI4d4rQjZ4xTSMwqXEEGu4qbgSXxL6cgwAt1spkxqgVhcHRfTvl7s2rup5s31Yvzz37jCSm50p9bY309fZJYU6Wxu6xVr4iiCCIvNs1IOsp2TI0uxZAfBHEEGgBv6IgOTHANJvghQAH0O+QcJAZiYXF0tfXL/nZGZKVlSl37tyW5aUlNQseHR5UcopAOxjwOkoJUs6bsYGSKlgKIqox+pEgDRWP2z+IfkzZSHUkUCrIDPR4YVc/kkKmVQPMFH/qIBWgbKJLg0at7rSmaZWV+Y4JbSzgGjAfLshMVaLopVQzu3fnhqqKKkoKA35PKhLXCAmjRrgrDsFtAzUWJJt9+kYrZVJNxicnlMQwFdHcVa8izXk3qOxm+9UYcHyC9WAV6VD/qcF+fLz2O6pD+3z5malSua1RqkqLVeGWuL4kF260SunDu/3vi0SaoCRLiIuT5kpHXYLapLikRDJSk+TapXOyc/d+iUvLlbTyNJkYG5HJW1cku7RO065u37krI8NDEp+QIA8fO6JpYcHAvGM8QV7Tzq9EFTvmh6Y1zS/5TZDpYxuQF6g4zelZl1GzmHRRxv+3r/cpwQLpSLtPL5CKuFmFbDXIWm4jNz1Jx55RTxVmo5aa2bKeGyN1k25og0IHk/PL4ktw1gQI+JGRYdm116nWyVhh7pm0PV58RxnQvpAzSp5usKes8zZ5zf/3jM4qSWeIVszI7etknYbA5hAQMxiku1OgwwHC7XL7iFQXZSrB6G4zjmPUe+ZvpPCxzobapAjW73w3cq2kINP3fD/bqiP3Od/5k++W//E//sf3B2GEVArj6nC4efOmppiBX/qlX5L3vOc90tnZqSTPu971LiWNaJif/umflh//8R+XEydOxHQNv/IrvyL/9b/+1wCFUWVlpRQWFgYQTzw8ch5+/6ARRkm+Nv03PiFRcvLyJW5DovygQNUF63GSk5f3wN37g37/D/K9A+/+H8z+91kPoBkZGS+bceL3EkJ956ekRGdm+WoHD5I8UNoPxY+2OMbXPNjyYC2SHvDwiyErQbwJTHlYt01eO4an9QHYPBjjwRKs7HI4oP7g4Z4AkqDoeve4bN9QlQDnATkuqE+QAQEwKiSCdqoVcUxbFcKDOqln5mFdjXtdsQEBBGoWc1wCFVP1Z/O86xqsoVyAIHECo02iKsDPKDlbmvcelKsXzshMZbWUlFVIR0+/7G6okTvXL8md9i7JLyxS8seA64IQIkgwlYoIiOydan6mnYdHxiQ/L1cKs9MC/KMwz7WVUVwzQa0NU+LdKCk4L6qs5MJiuXbxrOQUlUnP0IQMzYmMX7mthFFBVpo88/UvyWvf/Db9LAoAdznoWAJmzonSi3ujLanuRLBnq3DcQRxKqBJXugVjgfQPgk0UBW7QnbYiye2jxHmpGmUILe4pmJEyRAqqCVR4kDf2PAhmmhx4jZGDUQjLy9euy8panBQsJ6rZ8a6qPD95CjFDX6mPS2K8pKYlaalwG7SPrWYhNcyGUYGYaoDckzv4PH9vxEUCrAfcK31FG6D8cKoqOqbJsYB74jqNgfi5e8MBZvSsOTU1tdJx64oSRlU19XLj2y/I2NSc5Gc7hC8pjjd6xtX4PVhKEmQR97FJmqIGmlUvKNR49+7clNXMMtlRmS9xpTkyN1sip6/cken+Vikpr9RqguNjI9LW3iXV1ZVaXY65X12Y4R9PKF6o9hYLHI+nzXmzvjHnObZpxzRM962S8qypqGSY+5BALa6US9YLvHWMCgkymGprZv3iXK/ZU+mfd04RgsA2wxfIkZ9tpkNBrpt7Za3Tfqp2CF4nNSs29S1+UYw30i9R5pCCNjSzqimCwPHtWg87f/gsBBBNBaGk1Ren5v1zkDaijc37nQIFgcdjYwCSzyiCSH9jPXMrSNfW19WQHxUZhzNrE991++oCCV032MCwK65B2HPdGdb6hqJxdMZZ9wCkkPqBbaxPrGeQdHpPicyZ3JDG4+BH3v5j8psf/1WthLpr1y75niaMfuEXfiEi81VXV+f//4KCAn01NDTIjh07lNhBgnv06FFNR/uXf/kX+f3f/319r5GSU7r0z//8z+Xd73530OMnJyfryw0eEN3EkLKEQX7//Y/NAUnQ9CAFTgZOicQH894f9Pt/kO8dePf/4PW/HcxQOebB+84L/Z3//dAW9+7dkzNnzsif/5/PBv07D8CUs7bBQyyeQbYJNZJ4G6gDCHTwMTGGvqHKlocCAYIJWgna05MX/KoSu4wxJAm7rkpKWX8HRslgqifxrx1EQgqgHAEEQpia2soHEzjbRIKSUta8MNdBoEwgRcqaO+3NjeTkFDl49IRcOvuiVNbUyfzIqFMBbUEkfmZd5uJmAnyWCJ6J2Wo3TLC5LneboxxQ35akOMlMSdxSTYhS6nYJeFQlwSp/bfpvxAWM9eSUVLnb2Se5Sauyq75CCotLZKRxu0yPj8ra/CaRjDcJaS3G0DicMXUwECwZlRjXR8BnqwkABGDK2Kyqdvw+IRbBQRBlfGwI7DCfdVdRCzjnklOJygb+O/mWYS3ly211mvFrIUCmXQtLsrYQcJEAEcA1onKh6hKBtpuoudXeK4WZyXLkwB79Gwo4O9ClnyOlNkWbXaaKQlWabCV3Ic9sVZI71QsSYtpS4y2vrAaQG7GA+6RyFQQcyhlDotD+PWNzEpeaIwN9PUq2Mn/Onzsrj554RJJ8CarsAMx1lC5UN7SJBdYI+tukRUHI0e7M6bzcbLl9e146ui9L0lyJenyhnKuqrVdSAANmyNGanfvk2Yu35V7/qNSVFUpJQYH6r0FEcRxDvKkz1DoEg7O+sIyYOcf/oxSB8IFoY/4vLjuV7pxPOusTXkZmTLjbPNKaikKLim6GMIJgogplMHDdwby4IK6NKgyw9rQNTvsJI+Y33wUDE/P+OWSbm0cDUrkgx8ry4vS4oyMjkpoRSMqznjC/IFwyUhK1rdlIMIQun+NF21OdDHUQ1xr4/Lp5PP1eWnEUOoYg4tgQNUZ1yb2jyrQJo+6RGb0W2gPja64pUlXIYBXXIO+5flUKudoLoot7yczz6XUzPvidIZkipcK6kZ2TI2940w9p6vmnPvUp+a4SRjj2k7aVnh69IaINdu543Q+Mj4DxH3rxxRf1egz++Z//WdVLVEorL3d2zTx48ODBgwcPHh5kfPazn5VHH39K8vLyo/6M82C/+cDKQ687/SBYipkNdzoBO/I2UeNOHQMEXjaBSRoZD9o8fEN+UGq7OURVH3a+bTWOTUpx/ZAKBD6kIdjEAikdttKHB3d3QM+1bttQaACCpmCpd849OEoQrgcSKiEhUTc/x6/e08/taKiX9ltXpa5oT8COMbvJBCeGfGHXO5hfD4EwKWIEuG44aVyb1+UEW9F7yDTsaJF/+eq3pDI3WfYddtIXr1y5Igd3NUlW1eaztZ3iFgyYT5u+pQ3YLbfvhQCKNA0DAm337jlKKIJt0lToQ5RiwZQ6/A6lVygDc3b68ZihP2wyBDjkTZy/36bmFgO8XfgdAXawcRUMvN9JodyseEWgqUH2ulPaO1jAPjM5LgcO7fCPOw1OX6pp0csAd3MzliKNJ0qXQ2Kw+ZwY71REC+U7ZatuRJx2Z/w+1lIu/eM58pVvvyjb1tNEEpKkoLhE7rZ1SHPj5rinT5mX9tx0jpGgBCyBP3OAVCqjsEpPSVIyd3VlQubnZv39BgGytLwkN65ckL2HjsrQ+JQcO3JY0hJWZWpiXK5duSR9k8vSmZkgvqQkLcKkbbRB/DjHEIlbW1LP3ZzMNCUMUCcZ4sFNrBoCJ9b0VxuQid2jgZXPYgX9Rcl7sz6bim+hjNmDAfUZJt8mbY1+JXXNEFnOfNgw2CdVrL1NCovrVd3a1X5XVldWZGfjTkd5Nr2gpeQNSeNOaeVYnCdYdTLjdce6TuoiRJdtSg8RxbFlYyngqozKx2B1bT3gc7QLpGSoNcas+fZ3Am2ItxvfkayX7rULQojrMN+hkJuOOlbuGz/8tnfIL/6Xn5VPfvKTL2mzK2bCCJ+g//t//6987Wtfk9OnT8vIyIj+PikpSVU/lLJ/29veJidPnpSXE5wL9/rjx49Lbm6u7o599KMflfr6elUXARRHNs6dO6eN09LS8rJeiwcPHjx48ODBw/cqPvvZz8n7f26z8mw04KHdDmwJvtzlfyMB8scEcjz4k+JmE0bs2trEAyRCMBKGh3aTcmD7V0R3DeuqhiAgIuUsGOFAegSEhjFQdZRVGWHJDDdQjxCgGBAo7CjPccibONHn54rMOMlP90lCYqpkHnhIrl86K0UFJ/3+G+4S8hwPn4xgRrGUBo8mBQxlhduAORxID2nec0AJEo4/Pzen1g2jQ/0x+YVCFpkUDgJPUl5s0oCddFRKRhXirtxmgIqrtmjT3DwWaN+PO8H69tLQURj9D5nIeHUTIlQ8g/QIB4g+Pm+AmmRnZa6lmEkIW/luYWFeZmamJTtzc8xxqy+1Ktr94CV6YCvoZ6OGgpBDMWP3PYQnbWbM72kfo1QLqFKWlyGPH9kt91pbpWnXPlldSZfb1y5LfV11SF8hA1Q2zOeD24q2pKyxfuzbt1f6ps/JxOystGrJ8jj1+rl5+YLs3rtP8rKzpK3t25JVUCZFOVlabZA5lzowqXNjeXlJU9voa9vAfB0/sMIUjWMfe/RkgHl6LIjFU+fl8E6iD1B6uY4c0zFYxyBwTN/Tp3f6JqXFIvghTkem5mV2ekozgvKz0qRveFRVXfil+ZVnfC/kmPTJ2AYlBOWFtmHZVZ0v6ckZIdvLFDdgrrn7qSArRUkrQ5IZw22bMDKft7+/qMhmyL9IJDPtZau0jDoqFiWTG4+cfEwW5hfk+eefV47mFSeMurq65GMf+5juSuXl5anh9M/+7M/qDgnpXBMTE9LR0aEkDXnxtbW18vGPf1x+4id+Ql4OUBKOqmccc3Z2VkpLS+W1r32tfOQjHwmaTubBgwcPHjx48OAhELdu3ZLW1rvyA6/7Qf/v+sdn/QbNBoYgAKg6eNg3fi5qHuryDYoEd+qYs5MeuOOJjwRpBWMDC36Sxe3P4Yb7GvDB4NgoN4IZinLtx3eUhj0mQQwhCQqAYMEX12gCXlWhzC9tIZDwt9lmfR51lVH6tOw9KDevXlRVwvXL52X3gYe04m9FdZ2cOnNWHj5yOKjCgADOTsFiF5xrudnWKzUNO9UToyw3PcDEGWVY4DECA0+IDYJ1GwTUtqfK9AJGq+vii1uX1DRnZ3xlxVFTRAO3GTMkmNsXiNQmFFQjG5WKSW1kPMTS9xCOKAHyNwxyg5VGfzhC30MGQmpwPcGCNP5mqxNIpXPzVpCp9tjhuvAcigZkSty4fF6aW1okOYjyyP++NSelzRhec10QHDYRgzrBPQdtoLogZdO+TwJimyDjPcx/CJdY0wyDgX539z2EAumGELkmDRCz+2CoLC2S3t4+GR8ZkoKiEvUW+ta3npbSup3iS05W9QrBvns+cu1P7K4IeV1Zacmyp3mn3L19S2Z6bkpCYqLMr69LVU2NlBXiYRgn+/bslm+cuixx9WVSWFwq8b5kbXcluZKc64UsstdOFCPr8T7JKyiUM+fOS0vzTsnO2KouCQfUaRCodropfU87kQLIGIDUtsl25rSdygaZwZgx7QJpe7NnImCdwcjaTnuaW1zV/mc9gSTlo3wu2nHgvkejnLLJLxR+V+50y63WNqlr2Kn31d3eKY8eapbrV68GPSZ+VDb4DBU+s9KcNnAr9vj5SENJ2GvlHlETsgkS7HstPTlRyWYDSBx3mh/XYa+dmFKzvsaSSsa5bSNrCDfHQD8lgPR3zK/X/JsXVPALBr5X3vCmt8jnPve57wxhtHPnTlUOoSxC5RNuoFNdjDJuv/7rvy7d3d1qbP1SgVkTHkWxAG+kl8MZ3IMHDx48ePDg4fsB//qv/yrHTz4mGZmZAcbIdgoHO/H2c168OB4TeNTwkMoDv/0Ay8+kCRHgkr6TlpS45aHbNvx0zkk5+8DHUNREtqLIHZxAkBBshiKquA4ennnoJ/iECEMpFC5tAJKCQMM+Tzj1hyFhIKOMioQqPsbbaVMZFLqyFL/fsWufXDzzgqSkpsrc7Ix6phAAT01OSHtXr1RXlmlFL3tX362igIggdS7FFyfFOel+AsEmjCKRY07a22bf02bLy6uyvDCn10L6XNL6igxNEhw73jso9ysKNz/DzjveP/QnL3fA5r6mYEBNZCuK3H1Pmy5HOA7XBklHgEXbkVoWTgXHeGI828Rio2XoHCrNhnnAnNHrTk5U42MbdLvd925j7XDo7rgn2xuapGNqTW73baZwulUVjD3mEGopY4J7t38qgDA6sTN83+OfVJab5v8M52Du24RRaU6akp+0FYSD+zYgocx8SE9x+jBWNQSfKc+j7x1VRzDTbEgrXVfi4iQtNUXiN/yWissqJCe/QJ47c1He9NRxTVXE9JxDVBdkhCwVr5XsphcDqnehAmssP6L/z306ls+bfQlZVVHfJJMrC3L99GUpLi6WprqqgOPSzzr/Nz6jZe+XVqSqdpvOJ3x3tzfukNqK0pBjAqKBFE5zXuaCXfURtA9Nq0LSl5ak/c94twmjIw3FW45rTO05Le0IKWH7YEEw2Ws0BQNYH5x5wmchKFjbrOsYnFIPKe3/5MQgRHdgX5JmBXnFv1zL3dY2mR0bltecPKpZQVzjTM+ypKZu3i8bFeZ6g8GpYumToqxUJY4gUTh+OKKGNqZvzDGDGaXbiIuL03Q2SHnGGH1nfPACVEhTC351EtfFWhFLShmk75m7QwEG9u6UT0hefPM4PtdPVdBQ1dLAa9/wRvnwL/68/NEf/dF9q8+iJoyuX78u1dVOmbtIwJPoP/2n/6QKpL6+vvu6MA8ePHjw4MGDBw8vL6gs+/o3vzUkceNWgwQjctyYWVjR4J7jcDx22UkXsokIzGEpUW12l/nZ9kQKBvdOdsfQlPgSE/TYqJU4nk1OmDLFPMzz0G4qgrkJo1nLpBcPpN010Xs5AdLTzMN60PYgIHL53rhBetlKWqH0jI1J0tSKTA0Oa4WlmvoGuXzulFSWl8gb9leFfcDX3fb1FVVHQKSgQijOeWn2pK2t9yR1ZVKGB/skISFBWvYekuz4FRm6d0Wq67YpgZRdXBRwXRMzixrAzC6taLtCMNjVokzpd0MCBWuPSH0/sVEq3FG4ORWRIAODqYiM6bnbNBsQzKP6YMwmJ8Y7hJE1TiMBAgJyNdpy2MEA8dU/MRtgess4JmidHB+T3S3NUl+9WdkpGGhDiJ5wnmGxYtNRZhOkCYYDFaOcuRCn/T6/NKuVyuyKgvS/IXyiCViDpXve6plQQoqhs56cI3duX5PdqalKtOI/lJGeKt19w7KjtkwDbGPSbHyK7LkPuc1xmC/ucu/2ud1XwbXvrS1Qsnxpd508/exzMjeVLqmFxQGkLgrF9GRn/EJKoAIEWdk5sufgUTl34aLc6R2X2soyZRdpmwNWla2D9YUR24nUzFi8yMDdti5pb78nBYXFsm/XzojvZ32NBCrxMddQBLIpMDm/FHAvzGOMqCFAVF20vCTXB0ZlYbRXlpcWJTs3T1NeDbjv4pJSaW/v8PtloZSj3ziWzn38zzKS/cSkqdCm1fQ2UnaZ+8VB5/68kkUo5ibjlyJWNLRRCQGZHHrd52+2CilYH0Ls9I3N+ivK8R0EuWXIPubS0cbisP3P9yzff+a7TyuwTW2my7lx7PgJGRoalBs3bkhzc7PcD6L+VomWLLLBzXpm0x48ePDgwYMHD999YB9AIZA/+JO/CPSOydt8aDa7v7FgdNo5BioH1DY8kJsUN3wonv3Gl2XfI69RVYSpqMMDczCz33ABMg/qxgeCXXu3HwbKElKyDLgGymfbYOeeIJvAggCEwMJNTkBIodZBfUTaAQGivVsdzrAXYKhMAMA14oejRqeuNkWVc7Bluz/o4Tocz4o4DSxHJ6akvCgykTU5NSUZmZvl0e/X64LPQlQNDUwq0ffIE6+TqxfPyujwkGRkZsjBh45K653bqgJABdO896DfRJVT+knFzJQt1X9oL+6LnXHalmA9lPIjFOgHKsIRLBH4EZz2js3GFPBB0jHeCaxKc9M1eOsJYg6MooEgFdNa0g33WwEwxFwktRRQpcTCsva/O92TsUXqlF/Zs7YuHcPTMjY6LL6kZFWMRCIMHDP4wPPF3PWu93POWD2LIGch8SDwzDLiruoFaTOK4fSSM/dpl1jBeDFqGFRNU4s75Pb1K9LYvFtJo8KCQhkeHVPCaNOkOfBmbvdNqAqS6+Hv7uu0Te77IZQ30jdtFY5R+jAGDh0+LGdOvSj5FmFE+pga3CeS4rSkBEqc1dA6Z1KyJTNuXhrLc/T4XJedpnU/KpD4IF43XD/r7ODImAo4UDKWVdbI+OiIdPUNisQFkmVOytlm2lo0oB3tYgekBEPu0D4cB3Idc+i7A5MyNDIm91rvSWJCgpx4aJ+mtwZDaUWVXDj9nBQWO32JwbXxMeIeIY/bBqa2GJvb8FdR3EgfZa7f6BnXNFdDrIQilBmrzFuq/zVb3mPZLqPtcDDpwqwftlrSrI32Zoq7GEQkKEE0iedbhp9ksokqN1BrPXLycVUXv+KEkQcPHjx48ODBg4fvXXzjG9+Q+m3bpaJyM42CFAPbkwUiwzZ4jgYEGfYxIIZMIJ/o88n+hx6WpLgVOfP8l+XEU6+TlJStHh6ohzgOO795mSlbDI/dJsjsJhOc2UDJwFFN5SkIJHuX3JTVRg0TCl0j0/pQb6oY9Y3P+av6RAsURt0rMxo0oQIozE4NCOQcA/H4kOROXiE+Lb1hCSOTWlKYn68eohXVtRGvS1PwxkdlZHBAFhfmlRyYTcjQ4IW/pWdkSs22Etm7wznW0RNPOJ8ZG5VLF87J2uqqbG9olKKiIrly/rQ0teyRlNTI/hy0g9MPqRGrqBH8QD64yUQCsGTfRpW1uDglEAhMbUAGQShxrmA+KxCZpKiZsWoHlAYE74xBjsF4CxZURgKBIso27oXgzk1qEZTa92dSGHs62rSQTzQBu5scMOlTMSuKXqKpNdcQyYTYKL5CgX6EUKC9UY+QZulWcdhThfeV5GVIdul+uXP7tjQ275H0tHQ5094vjRNz2nfBAm8u0yhQAL48dioPROG1rnEpyk7R1MZwpBL33NHeISXlgSlpEEXXe8aUZIZEIW3JfS/ZObky3XtL+obH1B+J67jV3iPVZcWSlnJ/ijHGPcSkUb7NzC/Kiy+ecvzm1taktLxSC1SR9ppXUCS3rl6Usm2BRaH4LGRerMolQ3axJg0MDMi1S72SmZKoRF59/Ta5feuGLC0uqlH4a04cUe82G6Q0MgZoJ4oRsN6ixDLjmypmxkya3/H9YFRbBhBjzFXmG2s8x4G4NwQfc4qUuXCKPL77ILcYP6z9qBAhmYtjLGePoT/fTRBMKC3tsQipbhtb329f940F+idFwmNPPiVf+eI/3bdNUEym1+9973vVZZtqaH/yJ3+ixtcePHjw4MGDBw8eXv346le/Kicfe2JLxTBUAiZNyO2FQJDNjnmSL16SEhI0wLb/Hiy9SCtJbbyHh+WcvHxZX1tTc+dvf+XfJCUlRU48+Xo1ljUg2IC4wESYh3Z2eHdaXkKoMmwfD4dACCQV9tQU6O8dyf+6zC0uB3hToBgiIDUgwHITC5AI9u4vKqEUl9SfHWOOxTVy3WlJCQGmo/Z1B4Oj4koK2YaLCwtacSlclSTagxQgzt83tSJpg1NbPFEoIW6qTRE0Xr92VfZtK5Vt2+olMyND+sem5MvPXZaikilHnZRbKbmuQJtrXfGlS3XTHpHVFblx7aL6mu5sbpHWu7dkYXFJ0osgmKJP63IDJQR9WVuUqQEru+UoiuxKZo7yYbOv8Exyk0oY23K9HUPTmurhLo2N4TepUwak0LmN1wneUTKFA3NE1QNzSxqokvJoe3odrC8K+3m3isMh/wjsV2VxaUkyghCU7v5n7I7OLCq55ShpIFsTtwSutsoGg2QqRdnt0T04JZlTDsnAMHQbUjPOjHcU7Q1pZ/cD/8t8fylYXV/XuY2CBNIXfx7gTiezwXUmJyepagYU5WdLecqizM4vyq2ZRSXQ6KdwoD0ZI/ZalZ3mi6haY07d6h6RK/f6pLK+STIXlzUdyRACkfqfamC7DxyWqxfOSkdWlvSPOKQk6Vo7G+uDfsbd/5DfxuSdF+sURJohjKhu2De9JtubnNQzWnTd5yic1CsIYnNqVm72bKpauC/GpU0Y4cnDeheq/xl/qEaZR2cvXJH15VWpbNgtVYWZcunsC9LT2yPFpeVKUoUCUwFSBpKF/kdtx/FMP4RS3dht0lSeq+sIpDHqwPWN5ErznRHsGLSbTbwy9yErjfLPEFXFrs8xH5gXrFXMQ85tiGf7uyMU3JdCuwb7Pgt1r4A0RtZ3k9rG9zT9F0q1++jjT8rHP/xBLRyWnp7+yhFG73znO1XG9Pd///fyP//n/5Q3vvGN0tPT41Uo8+DBgwcPHjx4+B7AV7/6NfnN3/2DmCqGQSgVZ6fpwyhGv33js5qiYx6yTQlywAMvyp9gz/cEMOxyl5SWy61rl6W3q10qa7c5JdQXVzTwMAFXeXKGLCwHKjvYgdZUjw31Bw/sKFHc4O81G0bH7oARAomdZ9RMEEoEKBi7GoRSStgP6jykU60sJy1Z0xsIGtzERCTwcI8Kyl+FbN6pQmXQ1d4qJdv3qrqBU3NZ7HTjbWFAULO7Jk8Dq8GOZClKE93Bt0GfmQBmeLBfKspK5NDeFn+g3Hr9svzgI3skNS10ADE4udn/FNlayqqSy5cuyo5de2TP3n1yu71Xzl6/JTOzs6o8cqu+ogHkEP3GdUGk8XIre4yCgMCMIJDPBEsRQV2Qmercszs1jrZmdx9FA8eD6CM1LRaY/od4xKcHNZm7WlIkENia+cM10ReQfZSKv3jhojx28riSPTYgpw5v3wy6qwozNOCl/0NVreLvdjoV6ikbKO3Cqe0AbUWaJ6QA19A7NhcwDhmbrQOTqubjWkzKYCyggtzqRrUno0ZyK3sgCG/1josvIUH9cCCM4MISEx1yQ1PHWvbIzcvnZP/Bg1KUl62EmBu0dc/ojCxSWQyDfsubDcKAdSEcICO7+oelv/22PHxgv6wnpsrcwoqfMIrqfvHykQTZe+iolo/Pr4iX6bklGehvlR0NdVaaWuCacWjbZv9Trh3QDm5FGmP77t17kpmZFTL1qWFHi1y/fEF2HX9YMlK3eoEZkJpXkbfZ/yguGYeb6h+fXGwfkdmpSZmZWpTt2xv86p+8/CK5d+eW7DnwUNj2QFE0NrsomanpDvmalbpl/jNvMWSn7RgfkGP4J9ntzhyCeMJkmnkVjMhU/7DxWZ2zrDP2XaM+on+N512wNukZhYR3KhLyPUTKGSQXKtJowT3Y5A4EHeomey5CBtobCXzPsCFiQHU57pe5E42hfk1tnZSUlsozzzwjr3vd66K+Vv81R/vGCxcuyG/8xm8oK/Wud71Lfv7nf14roG3bti3mk3rw4MGDBw8ePHj4zqGtrU16e3vk6LHjUX/GSZVxPCrsnW07FQbi4vD2Yn8wzt/dO/Q86CckxElGsk/LPy8uLkh8QoIM9PUoiQT5Q5qBAQ/TbqWDqV7FTjq7yBBIdlWgYHCngvDQDbll/JbcIPgnAIJo4n2mpHQoEsYQZtGYw9ogoDI7w4Bz2PeSlpauiix3BaNQ5cn37d0rZ8+dk137DoU8J4RRQWG1Pwjq7u2X3LyCsGQRoK/t/p+cnpX5mRTp7erQF/2+vy5fyiuL5Jlvfk3ScwolMW5N5vpSVNVUXbddplaTNHgnODdBuj2GCOJtwiwY2MU37x2Zwrh2OWK7u/uYoBfDa8zWg+3Eo6QwVa7wSaGf3GSMu/8hj2LxfAGoEjqHp/3EKtcCCeJck1OJjr8RFIbq/1j8v14KuH+bmJ1bWg1QOxC8H2ss8c9/7o3qZO4gm/QeCDrGEqSe3a6ovoKROzaMMor+x/ycIJ1Ae319zX89pEDtPnhELl44I4cOHZKC3EAClXkN4VdRkBF0vJl74njMa1WquMgvVeItTcqBA/slMytbrwfSIBYwXiC/DFnFOC3OzZDJXu7FUaDwr3v+2+0ear4MjkzI5cuXVNVJBblQvj7JKanSsHOXPPvss9K0s0XqKoNvHBh/qs1rj9eNA+Nzh2l4dnqRfOObN2T/3kOytLKuBQdYG0h/bdjZIucuXZHanft1M8GppuYLmJsYOC+MB/p8uWEIMuYf5CT+QuGM52knX2LgvOScVDkj5ZprcYM1yfQ3mxiMMzfpnJpEJUinMibg3/apqZgIIzYtLneMqppJryshfsvnuQ57nekdndFrMsUngl1/ONAeJx59Qqvdv6KE0Zvf/Gb56Ec/qrlvn/nMZ6SxsVHq6upiPqEHDx48ePDgwYOH77x/0YFDhyU9I3p/Iiog2ekJ4apb8UAazBBYH3KTElRRQ6DBw37b8JzUFa5LeVWNvofUqqLsTcIENU1uRnDPEx6Ug6WMQCIQvLHDHKqKjb1DG0pNQ4CglX8ynB3kSCakBDD2PRvDZKM6oskSXOXdI11HcfV2uXLlqhRnJkpVaeSKSRlpKY5SZWFeA0ETHBPcYTre1XFPK0llpm+qmqYwy84KXXI+FJKSk2XHQw8H/duuPfs0gG/Z0ahBEEH9i88/IyOLifLkIw/JwvKaknCoB+w2IOCmgpYJiI3ZcDDQ1nb6n7u8O8RLKLVHc4RUwaXlNekbndXzGwVRJBNxSmy7xzz9z32a+QKRYgJe0GT9vxuYXr9UX6FXEpAdcDtWZl/Y+Q8gixrLsnVeTc8tawn4nRW5fsKEY0LQkgoYiXzj+AT8vGhfDOKnJyckKydXxxX91tiyV27cvCmPHD0cMI5sg/xQIJ2V47Lu4WOzNWV1WUbHJiUzI1MJI1RVbk8a1EDcj0kHRJlDdTVzLaxfwdYwVcWgGgvSBhBESqyGIQpQUF64cF6JY3yC3CodPHzmlpYlO3Ej5SozS5pa9kpn212pLi8OqlSjP+wUysw0n85XuzACf+feUpJ8YiyY7g0NyIkTj2jK6p2OfinLy9BjMC9QqdIUVQVO6ief5bvBnVLlTsMC/B1yPdhmAfOfz6BADLYhYM4XDhDRbHBAYgUjlnPSk9S8P2/j+ynY+uD+DuA7pSB7M20VZaStFgsGrt9OU4OYpRJaZXJs/oI2jp98VP70Dz95X5+NmjAiDe0P/uAP5E//9E+ltrZWHzxMdQQPHjx48ODBgwcPr16ws3j8xGMxfQaFBWlXBgQKsRpAj84s6MOuUSrxIL57zx6pL8v3BwNl+ekaRJgUMqqK7Y2x1D2KANKLKJm94k83C/1wzUM9Kh+bXLA9c4IB5Ym5Zq6Vc7qDRapuleak6X3yXgKkWKtCrcQlqULi5vULsr7eLMWFeWErYBGI7dmzW27cuCkry8tqbNvV2+8oPjKTpbq6RqorSuRu/5S+/4UzF+TenZvymje+Nex1uJVeszPT0nrrusz3+6SkvFLPAy6dfVHPu7KyrPe8fds2uX7ztgaSGGkPzU1Lf3enkk3D/X0yOL0q88VpkrphmA05Z4yi9f7X1rdUlYsE1GFqdDu7JP1jc9pepFKZHXk3lDSbhxDdVE/w3ocjpGjan9dS4lPzAWQgZBP3gv+OXQEvWiwvL8Wc0vWdhGOUHbvFNuOCucaLT9sKO9qJIJ/UNkOy0bbRHLOurkbOnT0rLXsP+n8/ML0ibUMzUtgzJjsqQ68jKM7oLzst7fFd5SHfr6bYly9LSUW1FJWU+r2sUKa503gZE4a4drzEHB+gcEhJSZXxySkpzt+6DnGNtEkowohrOHfugtQ37PCbSrMu2J50tD3Xa4955icqw1ut7bJzu5MO5yaZbCPsYBW5IM1y8/Olr6dLyiocE3DM8C9dvirTU5NSV1OlJBpkPm3A94F7TvAdwHHNuGAeDwwNyVB3u6RlZKh6rLzS2WAIR5hBJFE9kftGLUiaV6h2N5UMbT85iL1wSIiP1zGz+fmVLes7318QTeb7h/ncPjgV4HMWCbQRpCNqLUC/x5r67MbxE4/K+9/zkzI0NKTpw68IYYRB4Yc//OH7uT4PHjx48ODBgwcP3yXwYPutb39b/ue7f2aLabLZzCZOQyJvp4ax207QoelPifFabjhcKeNg4CHXrm6mVWfysgICEx6kwz1M8+BMqgsP0XZw5wZ/M8E7u8TBdo8J8HngR/XDv9GYlNqKK8oZ054EDhAUdtoQ4G82ScE5YyU/UMsU52RK894D0t7aKmcv35CsnBypL97jD/64PzuNh346+fAR6ewdlPm5Odm+Y5fMLK5qtaKluDgli7heVSItLsihh0/6CRoDCJ4G6364X4JfJ70jXlYWl2RlZUUSElPl7AvPyJOvf4sGp+VVtTIy1C9JyU4ffvVL/yYlFZWSmZUjhaU7ZDFpQCqqq6W7o01TrlZXVzTlzoCKZ7zCgWvN3KigFkxBRjCfn5ri3/lnVx8S0jawNqa4BHSMPgK9vbXRl8oGKORkI9hNS/ZJXXFWQBBPG6b6EiIqk8LBpos4Hvfm9uOC7Oob2wzc6VcUcXY1MuaancrmJjY4RufQtPg2VBRcMm1sglRA/+PtAynA+yAtXiqhZYgMA8aXXb0sGLgX1gFNgUpL8q8fBTlZcuDgYTl35rRkV7fo+sWrJGO3PHvustQUPSKpFinM3ILUhZigohn/2qlfodDe3S+trXclMzNDBucTRIamN9L1krasIfS9PUaNOigSYVRSViGdnZ1KGDnKnjU9lpnntH2+1Uxdw9OaIsj77txtkzVJlMbcTRUV56PMvfEHU0WWq0oYKVqr6SVy7sY1WU/J1bGH+ssoilhTz98b1v5hDECE2GSfud+9u5rl2edeUMUXyiXS3SbHx5RYTk5JUyPrcND13/UdcPfmNdnZskemZ2bU1201JV+SfAmqIgvWlqwhXL8ZS4wx1EDMUQPagDnMeDLjAeVhND5ABmwUGMILAs1WD4KctCRNwzRVIe9nLYDcM8ScSfc0BQzcflw22NAJlR5XUFAoO5pb5Nvf/ra8/e1vj+l6YkuA8+DBgwcPHjx48PA9hZs3b8rszIzsO7DpccMDM6WjTeClxtWu59qHm0o0YCX4JkggrcwOhDAPhcixwY5uuLQJgvRYPX9IbWK3FmNQzsm1QNTY1X3CleE2gRHEVXUhqRFOulSoKko8iNMe/J1qPwaQFQ81uGvmSBS73oH3S8CNgTfBCi+CDlOZR+9neVXNV0FqUZU0liXI3GifPP3cC7Jn927JzkzXwMjtcaKBd4Wj+gkF2o40tbz8QhkfmAxpigsIPPKzUrQddAz4EuTx40dE1lZkx659+h7MmePjUiWp0LGpINBsyS9UDxWjsCopLtL/r6qtF6mtl7SBSVUMRAsCJl5U6iNAcsyeEwIq4C2trgWkj3Cfbh+sGz3jmspkUg1DKX+MeogxQN/YJEykVJLVKD2NaNP5pVX15GIMoI6DlFvf+LxDTDoVpAiOSY+xMTy5IHXFmf57Ri1DgGlfq21OHWqcc15ImFDG2Cd3lm6QVs4awPy225X7uNQxGuAnA6FTalUWdM9Fk+YUC4xXmFbRm5hXWq0gE4PkFCnKy5LtjU1ytb1XtpXl+pUzRSVlcuHSZTl2+ID2tyHIGstz/O3m9oay5ygEeffItKTGLci9WzflwNFHos6usdOp6L9g6hDWLWcNWFXT77K8HOm4d0d9sfjMjZ6JDa8yJ92v2LVuQhaxhr945pxU5KXJSlpRQNUv+jaSqTskKpsAK6MZUpLlk8U1PIqWdI1kHacvH20p0342pIU7tQtShn7JrWyUZ85ckm1NzVKen6VrgN0esQJiOS01SUoKyqWns03mFxYlLzPbnzoMkVZfvKnkyt4wzzZjUc3FXee9NzCl6zjphvQPBPL0QqDS0MB42S0srwYQzxR9CAfOG2x8u1PsjOE9qZpO6nZiwIYN44I1is9A1hUFScNzr4O8P5yf0rGHH3llCaMvfelLMZskDQ8PqzH2/v37Y/qcBw8ePHjw4MGDh5cHTz/9tBw49JAkbaQqGNgPrwRkdvqZ+TuGtDwAB3sEhSyyCQfUCG7zWns3FvCAHMmTJ6R3zYbywTxEG+AXgQm22cUPFpwQGNm+F/iKmEplgACkY3hKgy0ewgm8IafuB2ZHmCCKYMO+Vnw2+D2pHgQIBC+QAzZhFNTjJmebzM/NypWrVzUNIiuvSHfS/cH7xjk5FYqQUG1M2XEtqx3EHyQYTHsEenlsjiMIDTfplFtUEBB0UZqb/rlfkGLIfRLUmcDOTfaQwpLim1MSAeJledUp9W2DNEW3wsIGajDGMPeEesikt9gkTCSo38tGoErf09fu4JFrhzSh/3lBEChxMDun6gz6L1L1wi39dx8BeVwUaWX2GhAKKCps5SEkjE0YMYZsYgaSzP57NKD/uRbUT2Ub93+nf9JPdlVXlsuXTt/SuZSXmewoAHNyZaq7X8an5iQvO10S4uJ0noUzDMcUnbSt0fFpGenvksXFRSksLJRtO5qjJouc8eekgtGvpGO5e+da15hkpfm0/1F1UX2M++Mc8wtLSmxGAm0wPDGtisKGnbvVGNmt6IwEozxaW1sVn88niRInrb3D0nblrOw7eFjqq8t1lGyuAVuVgIxXKqkxt4ozDmgVxZ61Jmms3FwHWJvsOWs8q8KNq0RfkszMzEluZprOi/kVx+jaEEIcgzlrTLlJA267N6nznH4Opuzh+4HPm7kDIQbRahNGzHnGqFO1zyEpo10vg33vzC463wMQQEYhy/EutI3oZoIhinpHZwMIo0ieW9yLW3UUCUcffkQ++d9+M+b7iHpE/czP/Izk5eXJu9/9bnnrW98q5eXBczxXV1eVucIY+3Of+5z84R/+oUcYefDgwYMHDx48fBcJIx4UDYKZCmv1nxirPbkBKeDeBXfvxrrTWVDSoMpwSqr7VFJvpxs45s3hrwv10UImVW3m1LiYz9iVfdwgGKDKTpUVOE7MYbyd6g8cotkRdwcRkE4YkwLugbSCBpcvEoojW9kFcRVtSW68Rpr3HJCBiVmZGu6Tb37r25JfUCRVVZVy43ardN2ZU+KgtKxcdjTWa8llGwRt3/z6V6W0olo6OjtlYS1Rbl/vkISEBEn0+WQ1Jbx/x/2AwM0mw4K1qykhzfgjeCbwtIN6NcONkM6zr7ZAx5/xL4HMDJfmxBhwqz7GZwliM/znpj8JhmMZA5A//WOzei0oNLgf1Aw2nBSsrMDKg/Fxsri0pBYg0QSmtKJ9aeoqdB/Td30LlREblHCI8B78nALOGWQMXO8eU5KHuU4Qz8teB0ghdaex2UCFc7AuXxZmp6RzIdXvJVVaUi4Dg4OSl12nRI7tOUYgv+wKuMdnl2RbSbZ03b4i+/bskcXVeC3BnrtRaj2aMcC8c4yTHUIwKy1Z0lMCQ25IDVupAynHOphfVCydXT3S3FgfdhyQenr+0i0py0yQxuY9zjGTE3UsxUIY0caQGcTvXV3d2sbX23rlsZNPyI3LF6SqonSLUs8NKl9q6m1akpQV5Ynv4CH5t6fPSHnOHiV6gDttK5ih/I3uMb0exgHfBW2td6WgsFCGx6ZkfGxUCurKA4yxeQ9kj1nraa+Hm0pVPYpnEMMMUomUNJsgYs01KhzI6HnXOsDaYJPgs4s+VRyGq8q2VT3kqC8Zz2wOsBbR5wa8lxRh28DfqY53f8SUAe0Xbgwcefi43Lx5Q0ZGRqSgIPr1PuoRdffuXTW8hgD6wAc+IJWVlbJ7925lXZOTk2ViYkLa29vlypUrmt/8xje+UZ577jnZs8cZxB48ePDgwYMHDx6+8zh16rS89cfeFUCOoAp4SekCGyXI3Yj0sOsOEjgOD+f8y04u5abtCloQMAQQkR6kCc5MAOa+H4IgjmN+zUM1qQw2OLe9q0/KmDu1DvWCXUKbgPRg/WaKEgHpkShS1uz7IGgNp3oJBnaq6+vrJT5um4yPjsilqzckL69QdtTt0nun6tHFKzfkwJ7mgPYmeMEUd2lxSZZpi6U5qd9Wr0qT+fl5efrcNSnL2usP8iLBNgGPFsHeb0pIMwYIPG/1TMhuy/ScvoHcsdNtgsH2QnKPFwiEEWsMoEZwe88QPNokG+QPaiEbBKCo5swYQ9l0oH6TFIVw3BchZcUNc1+k3Rw6fDiqz7iNp7Uce0xn3SzfHnjc2I7BEhCrp1GwMYDShDRTYyZ/pXN0S/ofKanZ6UlBxwDH3N3SLM+/8LzU1DdqOhRt1N1xT9Y2CAaID3zZjCqDoJr0NAPG3+zcopw6d1GrCvJa2yCXbeD/Rr+HGgOqgrJ8oKKB43O0JiWlFXLx7ItSVVkuWenBSSrG8p07t6WmfpvUleA3FO+/nzFSi2MopEV1QshViOjhwX5ZX1+VuoZm8fmSlESeI10rPSVqvx36oSg/W034b1+/InsPHpGExOAl7N3QdbkkW8cA68B8coGUl5RIV3e3VNbUSUIq3kCzqoTluyeon1J8nN/HCpjUMwNIH4gc1nG6D5KtziJvg5mFZ6clabvahBGkFF5IZgwwZiGtzbli9foz/WerkKKB+1opIMC1BqskqX/PL5Dauno5c+aMvP71r4/6PFFfEaQQRBEvFERUSTt79qycO3dOFhYWVH3U2NioCqQ3v/nNMbtve/DgwYMHDx48eHh5MTg4KF1dnbJ33wH/7whwCDom5hzyg4detyLHqD42KxwlapUbeyferph2P6STTTgQ9BBsZ6Q4Ch0Dp/LVZgUtPkO6UDhsVU85D/D5GckhCQ41wt4gpgig8MqhFLR9f6b6Vdx9VL8KBQKE4pzELb4WxjOGfzNSEgMq+WBG3bnumJ2urydJSlGtpoSYe6+pb5AvPXNWJpfvSEVpkTIDHHNPTb7s2dngr9zE/cysOMH0emKaHN6/T1pvXZO9h47q72gDo8IxPhp25bnFpcAqai8VjpF4suOnZYHfEZyhGjCIpIbb0s9xjhmu8S8KBltlh8qA9CS3+otr23afFdBCgbF379Y1yS8slpwMhyRA9YGXFeoWrbCVmBDgNYTigcDX3Appb25139XOUTWqNu+hkpxNbICu4ZmNwNf52R2skiYDYWKOAaFWU+T4gAHaMlY/omAw5yfwNSXEbWDIT2DMPZu1BoLPRkZaspx45BE5c+68jI+N6DzIyMyWkaEBWV93KoDVlWRJWW5aIJm4vi73Onqkvb1NxhfjtFpYUWGBEkuslW6vM8YAxIYZA2atfEnKkESUPiuSlpyihtHnL1yUow8dluVVx/TcpPKSyre2oelK8iXJ7d4JP7HNNdjrBEDpZMa0enMtrMjOylw/wQBxerNnXCZmk0TiMjTb1KRy5hUUSl9/vywWljhm73YKWv6mVx2kBWuV3QZpKclSv227DPT3RKxu5gbXxvdRdUWpfPuZZ6W0tFSWlhalqrBEz4OBthkDqGnCwd0n8RvXHm4doA0hqVl36PvZxRX1GbKB95E9Bpw0ttWQVRmDgWtxPM+c9sbMGw8mew5yftYA9ZBT36Y1TZU1156RmqTkqjGCp13CeQiC/QcOyalTp14ZwsjGo48+qi8PHjx48ODBgwcPr16cPn1atjc0Slb25o6nXTUmGJwUMd6XvRnQDk5JsZVdxYMpwRRkjkG2lXpkduIJMNk5RX3jBgEsKTvhYHxebNjkFP/fPjStxFIo3wrOHamk8aAa6TqAkMLs2j4vARuk2UsJCoGpOuYPetcCVTOoYNj9JmghcKPCGp+JpfQz/dfY0CAzvbdkKSNeg9+R1cDrxijVNksFPQMjMm6ZUXPPRoWjQfVAYFU17oDUK5s0cVcvQinA9dCW3E+w9ovUphzT7XnjJigJ2AhkqZIWbKzRxjbhGQykqph74ZyUencTKATHL6UCmjGc74ib8hMttHNBYZHMTo37g8QrHaMa2KKiIPUGZYVNGKFmiATS+Owqfm5Da4iZSP5MkEXGXJ025zogHWxVHNcfyjzakF9O6mWiBtTRtp9NQNhlyu2/28DLDAJh//590tbeKa23rsv2HS2qMoJ4SUp0yqwb0P7dfQMyMjIqMzPTsnvfIekenZWB2SWZXp1UMpQ1xW1az/Xb90A6GURGOG+kYEQxVc6MQmbFIvxQ+HFvp+8OyLaSXDV7R92CuTTzAC8mUheLMxMlxeU754azjm+u96wl9JchjJiXoUycC4vL5NbVi+JLz/MbrBtvrt6xuYBUS/rpdt/mOgDhur28TF544QUljPgcRFuyj/U8Iax3kUFleZlcG+yR+flZWZiflxuXzqpaqXZbo9803z0GMI8mNZl1gHkTbG2JtA7wuRdvD+r8g9TJTU+W+o3vQgP63z4y6zTrdiyEUUayT250jwesMW6yp3VgUskgiF9NN5t1yCtDZEeqMBoM+w4ekqe//uWYPuNVSfPgwYMHDx48ePg+xYULF2T3HqeiVbSYdhE5PDS7BR0E07xCgQd50hR4D0oWgmBejeW5AcGL2cUlgIjGrwjYQQDBC4EdcQN+SJyX4CpYUMDfuDdSW1AZ2A/nx5rCVxdjlzlS6XfIM+4Vgs18hmDbDiR3Vxdo4BxKIUPAC5ET4KMTo4qHNiFwqd1/WAb7eqS19Z609k/IylSF1NfXyfjEpBTl5wbcP22D/cS2xp1Bj+kEyYG/I9AJatBtAb8lgjd25AlU3QoH+hxikf4w5b+jgft9BKiMNYgjUz7enXJmwHWMTM1rG9sVhWJNJQsG1CCMa/rNgHa2iQrGBPfq7tfLfV2yuEIpdYfMsdOa4uOclJ9YcB+iv4htzlxzp6BFIq9QanGvqFTmx2YdlSAppBbZwdxRUinFp2sAL6ZRQlz0Y4Djk1bKWPNlFsrgwJSs37mj3l/jk1Narh6SqG9oRCYmpqS1o0dW4pNkR02JVNQ4nkFcU1WMw8CX4KRG2fOce2b+G0KD8zZY5B1kC/dozLzdKCwqke6xcSlvrtg8D1X0Nkg0vH0mxkalpGzz79GmvrE+RFrLQOJGKhmZRHOLKdI3MCLT05NSV1u9JRVsZ5B1gH6G4FlZXpb4hEQZnJxXcgPiiDnqbhPHIH/e72HGOp5VWCqDfW2SmZWtKXL05bVL52Tf4YfVpNvddihwaFPmOOPAbAAEM1mnLSkgMDo9L8XZaX6yh3OfbMZaPTTSkn2qRDXtyL9uVRxm5869GjXUqjSUZfvHCSqqtBRnQyQUicp7+S4zf2fMTM0tR+17Fwx79+2XP/z9/xbTZzzCyIMHDx48ePDg4fsUeEu27DsY02eCVRCLlbTg4RgyyA7I3ek75fnpuiOMaoEAgl32UhfREynVA4ULO8Kcy6glOI/tJERlMsymAWl0KF1WY4ymSVHgs5ABBAqGmLJBwMBOvEkxIFXFbTzNtYaDuxw8aSqxmNiaVLEUn6OGKimv1Nd61pgkJCyo4qztzk3Z/9BxObhnkxwiplldWZGk5NBm4fcDAh1bxdI+5FSis/+OJ4kpXw1M+xlEm+5DoGmUL+6xBqGEesSUqcfro39iLmwJ6mBgLnCtjAcCXDd5gs8VigRTiYm/Q2TaCBWsUyHL+Aq9RP/5Vw73cV1q8JuWFOAvZatRDImGmon5RZuxfmxYecc0DlCvmBLoFfm75BvPnxff8rQMj4zpsV64dEt9eXJyc5UcnV5ak0KXeiQSmCuQAYwB1gJSBlGB2MDTJlzqYqQ5nVdQJJdbLwSqrBKc6mtKpqSnS1/fQMRrNWPQXAdj304viwTS4y5dviK52fvlzu2b+rus7BxZWo7sK8c5MzOzZG5uVj8DWWUT+e42od9oU5twxUsJNZFWyKuolq72Vl2n8PtCaeQGJPbIhoePmdvu89jVEBmTEM18pjI5evOnzFSfrgMYZqOU5fvL/R2JapU0WPN7lIWcOz/TIYz4faTvVTYXnO+4JP/9ce0vBU07mmV4aEjT1YuLI3vu6bW+pDN68ODBgwcPHjx4eNXi6tVr8vZ3/seYPuPeLUdqb/sVRQMCKpscCeZxxC4pr4IQGXI8hF9qH9V0MwN2ZVGsGLCLX5abHqDwcPvqQBbZahOInFABG+ogghbiIDsNjJ1djo3yCv+LYDvCBCA22XG/lecCFFRK/kSf6qKfWV5V0sJ/T+vrqhYoKi6TopIyqa7dJm13b8n84nZJ3diphvzAJ8TANlKNFnz+5pWLUlW7LaRxNu1rtwn/XxBGqQZIhbObm74gPSmW64OMIOUnWoNxFAqMYcxz7b42wTdjLljqG2SCrZTSMRDlEOAzC4vLmrbzUlMfnePJyw7uPVaTa1KobJujYPfmrqbnBud8/taAFFppeYxxm4BwVEnrej5AO7a0NEt/x225e/u6jI0WS1Jqhhw7WO//zIzli6Xn2VCnQd5xLIJ1btceN8m+eCWK8Fyz/WRsMDbtcRNrf6KmWVtdlcmZecnJTNusaLbkVAnrHxiQ3PzIaYnJiY7axqx3HMNdHTAckpJThOJe3G98fIKUVVZJe1ubpgEnrzVIQ21V2Htj3VldDe8zZCvxtvjvlGTLnRtTsjA/p/c7vJgoK5NjcqW1Xa7d65OS8grZ01Tn/75i/TUKz1CAPLbXAlXDhiFhllZWZWp+WUlPs+agkOtedFIzSV0LthaxMWHPFUjkvvHZiGmgNjg+BJ/5Lno51oX0jAypqa3TzaSnnnoqqs94hJEHDx48ePDgwcP3IUgluHevVZp2bCpJqBLEw7957uSBlodRAh9bYWRMpgGBc3Nlnv9nHsjZXSVg1leC8wqs/uWkCPmvZTl24mNucVUfxk1QyIM95IFNGBnDXkglCCruLS1CuoUTxFvXurAsfePOjjP3ACmEQsAmjNz+Ka8UuJ/k0Rkl7Qx5tbMiL4DIudM3EeAVRHxkly4nLYPULNvsFpLLICsnV+ZTiuSLT5+T5h1N+r6e4UkNJAwIKvGEsQ1/3TwBFd5QgqB8wH+np/2OFBWXyOTEmFZq8yUlSWJebIa3wUBX2YQf10U/20E8KjWCTcYBwZwbpIVESnd0jGud4JYAFKKRMWC39U5rHgSDu5Ib/EO0nj1VddvlzOnT0rJ3vxqOc830Je1u0uz81zo6KzOLDpkBKA9fmpse4HOEmsH4FvE+9/0zz+8OTAakt/AeeyyRImkfg3a3vWsAqT84uuC1Q7sRKAeoyDaInJcCjsZ6Yvsp4ZtkE0YE9jd7JrQNMMnGWwjj8LGRYWnZe0hVLm61CU2ySrrs8pIMDfTJQH+vjC75ZHvjDr/ZP2uBPdZMNcZwuJ8iAG7kllTK11+4ILt3tegcN+XSOfbUxKRU1W7XtcDuV+atPUaTkxJV4Wb7h9kEvDHGtocoazWVw0yK1vrqitzqGZfx6RVJW02R5KI6qclZkc72O1JSVCTZGana9qxdnAcDb74X8BTjWtdc6WsGkCwoiYIRrzbwoVpaXJBzLz4jq+lFsqehRpoqC7UK2/L0sAxNlgaszyg9OSaqr8yUTVLI3D/3pYrRtNAkDIqjyQ1/Ph/kX3ycjK+v+7/T+HlHhHRc1EvMF0NmcU1uA/VI4DuTAgV8FxhjcUd9FziP+8dnA0gr1qxw31k8E1y7ds0jjDx48ODBgwcPHh5k3Lt3T9LS0qSkdNOPgaDDLvnLw6Z7xzmSl8vE3KJWbuGheX5uWZZXnSo+20s3/SiQ6ZN+ZB7ICWjtNLdoQMDuNh12P9vjn8JDOBXfMIUlHavWMnk1ldgghfBJMWlPNvAdwvvEEAME5wTjsUADH/VScqqccR53cADRAfnm/8wqqSzJAf4aTeW5GnQR7LpTswDtnpuREvAZdxAcTUCLEfba+Lyszo3JzsZ6udfRJQU5eQHBjrusuRtXb7fJ8uSwSEKiJPiSJb14u1y++qJUV5Rpalt2Tp48c/W6zM1WKUnF/RBABYNWp5ud8aefhIOSPy5zmxM7S1V9wdjEO2uLt8kq3kBxOgYwa6eP3SmFtsk3wJQ4VoNrQxgR4DEG6G+3ysyuQGjG38H6IiU0Siuq5fyZU7JtR4tU5KdpsBmsPRiftqE189hd/v2gqyJasHakup5NurjNq6Mx1+4bm1OPJgiq5ZVFJZDssUNbQEqYlDRUL7ESSLSB3QzOz4HtAqnNPdEW3SOzui5BGjTuaNFUppa9B/1zdWx6Tjq6e2VoZFTGM+MkLS1dysrKpLK6Sr7+3PmAcQC5cL9g3BHgO0qlTdLCVHyzyUhIF+a//35qS6T99pgkrs1LbXlJwP06yp1VJYvsa3WvBdGYIrsJWTzFlJzaWHuzc/MkJzdRBhdTAt43ldUkl69ckYePHNI5xfxj7M8uOOtsR8+ArE8Nya79DmmMf5m5Pu6Fc5By60sMrvq7e/OaFBSXSG5egSSnpEpiok8W5mblzp07sjgzLsOD/eLzJUvG1HQAOXKkoVjbEqKPimrMa8YFa6pJW2SMkg7N3OM63GOJ/mIdNWTW8sqakjKxgPWF8zAGSVvkXm1vM8DfGa8GpGWj5jXrO9eFPxTV7EhvDqZY5dL5TrA3BSJVb6zbtl3bMVq8ZIURg7+/v1+Kior85lgePHjw4MGDBw8evrvggbC2blt4D6DFzZ3WaEHQTxBi0gcICHkwt7G7Jt////wdosNWGBEwUc7ZJoRsHyLAg7z9s/r7BNmNZmc1VGDEAzreEezMk14ULBWNgMKUkg6nhjIVgrh/rt8OtBc3dth54OdYtE1NUaASA/IgIOCCrFtZjcnjaGmZdMGXp5Q9JM7k+LjeS0lxsYx035bSiqqoPtvf0yUrS4vymscf0XulDyh7f+f0jDTuaJaK4nwNZpsa6uUb3/iGzM0NO75RcfGyVpEjY6PD0t3RJrOrCXK7tV0yMzMlISFRtu1olm0VxQE+P+7xq34hLjKN9xDghqpSBEHRMzYruelJSm4EG0duVYiSP0GamvE8v7yi44DrIE1y8xgbPk3xcTqG8JKyVT/mPKGC/EIC5Px8uXbxrNSWF4Wcu8H4lu+W7RGkh32PblIWrzJe5t4Jnt2BL+SJMQcG/Bni151eGgmMOfojoE/y05WMvH7pnHSML0t7Al5miVJbWSpPHt6pqWtGpTMyMa1jNHoyyBkHkAP2mIUkol+ZF4wD/HncfclahMdNqCAf5VdDY5PcvXVNaiCMNn6PCo70z+WlTaLBwFRgfCmpSyiESMEymJ2ekvT0DMnLDyQgIThHhwbkm08/J5n5xVJRXqGkBevpmbPnZXR6RU4ePqzpfVu+EzaIdfc6e6N7TMcT93CltUdKZlbk4TyHtMS7aG5mRrrbBqW6rFhSUlKlsrZe4lK2prSqoX2IwgwQ12PTi6qMNOS7u71QKdK3hszyJTr+UcEAOWXGQUFmqn9tYZ3GP4/75J50HFj9bb6fSG8161Ywz7NI3898h6L4FYnej61+23b5t3/6QtTvv2+G5ytf+Yp8/OMfl4sXLyrDeebMGdm/f7+8973vlZMnT8pP/MRP3O+hPXjw4MGDBw8ePLxEtLa2Sl39pl9HMBDYFOfEaKq88QC8eYzwxswEh+6/8xkIF7MrSoCA8sImiAgsOy0iiniSgD8WcJ0HIigtgAkYePjn4dukMhhc6xrTB3PUEVTIQdFkE0YEjJHK3Udqx2jA9XH+lwPJySkyPTUp//LFL8q2vQ9LZlaW9Pd2SWl5VYCnC32TkBB4nSNDg1Jf1yRZaZuB+d17HVJTW6/Vm6pLnbag3/HJMEbO/UMjcuPKBUlKSpaHH35Yy3OnJ8XJSH+PZOemS0N5gQxOLQQE3yjNbGUCxJudQhkNolFdGRhSg7RKW2FE26PAoa+5L9KVqPxkkxN2CfP7BUqKgqJSOXvuojx0aH9UpInaQ98HSbD+HfA9Cjx+XNAxT6Bs0s1MlTH1kLLST0enFuTWuqOAguCJVvnDOffu2injUzNyKClJ0lKS/OmnbnR390h+QVEAQcxYsDE2vSBDU/NKADAXeeGBY5OALVXhUxe5fneaYTCg1IMoQQ3DnFAi5doNyc0vlJlp5kTgPCANzO1BFwkQKLZnmZIjFpGNumegr0d27N5abbN2e5NjYn7rhly51SqlR3fLuQsXJbegUBLy0/xkkRvMq2DfGYwN2vHOjauSFTcnhaWVfgJs7+Fjcvb0KVlNa5bF+HVJz62SyZV4SY3Bk8kg0ncCJDjrDmTNmqaBrehmgA0UiKZIAd8NRsFozLa55kgqTdpgzlJzcYxY0xnpP5tsjQZsJN292/rKEkaf+cxn5J3vfKe8/e1vl5/+6Z/Wl0F9fb389V//tUcYefDgwYMHDx48fBfR3t4uFVXVMfn5RAs72OKBl+A5FkBU2bvLwYI3u+SyCd7sYJPf4eFBoEPQhjrHPLjHApQ+hpDg4Z/AzPb54DykMdhkAGkbLxWoi2JVdxG85mbEhzSRNjBBhwkj7DYhOCTIyMnLl+q67TIyNCCXLl2WNz1xVK5euybJKcOaBkLKByqgxYV5Sc/Mkqraev19b3eHntPnCkrjEhIxPJGExMB+tfu5prxYqsuK/H+Ln5iXR48/rGks58+ekfmFeUlMCCTEWqq2KhNsvw526e8NTmqQzDjMYBwkxW4crdWjNsYB4wwlkn2MlVU8TFAkbJqsm9LdLzcqqmulr7tTrly/Jft3OX4694Nw4+CVJodeChhf7rj5WFOJ//8JkG1Sw6QRqRmzb6Mke9rmOICgKcjJjNhW4+NjMusr8a8HEAeVBYFeMJwbVYg9d4fCZwBtQSz9WVhcKl3d3bJje52SGIMDfdK4c5dcvXhO1tKLpb5kr/+9zMlghFG4cYASxq6EyRpoq2nwD6KsfdudW1pZDkNu973k5+fLRN+4XLhyTRISfXrNUxHSokIB0vrm1Yvy2GvfJBPLm/cDac06nLg0LXsOHtXMpotnT8nURJ8sFT+kBt0ARSkeRL6EOF3L8aSzjeijAWvW1c7RjQIIcUrskLZsg7UIhZhJHYZQjrWCGd9Xr8Qa4q6O50ZlVZX09fXKyspKVBli90UY/cZv/Ib8/M//vHzyk59UdZFNGDU3N8unPvWp+zmsBw8ePHjw4MGDh5cJXV1dcuzkE/6f1Sx1flOtAdwpAXYJeuczIjkZyQHpXpiKAhOITc4ubTEADUVkiJVWYVfyMiRGKJiHdnfgZgI6duDxcGHX1zZ95feDk3P+HVh2iSEVbM+LSJ5NoVLhYgXnJm0Bb5Xl1XVNiyjLSw9oAwJeu9kI4kyZcHMtmKDa6SdF2YEqg2tdo9pWJkBCMYWfjfmZQGLT2DxFfAU1UiwD0tHVLfXbGuXyhbNyYeG2pGXlycwiJrtpshafKbfvtklq4j3JyMyU4w8fk+du9ktC/IQSDygb0rJyJT8vReprqsOaTduBG92CP0dKUprU1W+TG7duSlF1A5qtqJUJS+pVk6yVj1AFcW89IzPSaPnBkHpJyXszDiDeGNNGDQAiqQHCBWD3PQ5QMi0E+joReHYOz0i8L0eVFkk5JRr02ulLqFMC/YbWtxAbZ1uH1LSbS47bIMTwdzHgXpjveL0YcsZtiIzJvE0eMEYJnG2yZHM8Mg7iAzxZYjEdt8G1hVNNcKwE1ziAUC7OSVXDa/yUUCR1Lq6oB4y7pLqj7onTfqgpylAlE0TkyvKyHN9THoU66KWbWtOWjEv+ZRygYgm4n6UV6R6dFYnPlAtXrspsXIa03r4uTTV1WjWscWeLXG4bkMt3uyQjM9shCdbWJT8zsNrcxbZh7S8z71Bu7akpCPgO6BzBGHvzuyAwzXJdfLnlMj0+Ls9fuK5kJmOiuiDTr4zJycmT689elrXaSq1cxjrMXLTBcVRpGGYcLC0uyrVT35Zjjz6lKW/zE6R0bRJgzXsPqh8VaXI9ne2SlpaqmyLDgwNSXlXjnz+kmXFftC/r3+LKWgDhzziAqDFEGnMQM3ejbGS9twnKYCCVkTGXnmwRbS41WiRA1kF0Bo6DQCKUMUqxCnvp4TptlSVj1/5e19tS2WHw8+JryL339fVJVVXVK0MYtbW1yetf//qgf0tPT5fJyftjFD148ODBgwcPHjy8POjq6pa3V1T6fyZgiGRiS9DCg7UJLniYtf0sgAk6eeAkcOLB1SZU+AyBZnrK5mMmZJAd8BrSiZ1fXgTREAf3k9JFcMkDNC/ObYMyxhAuZvecHWP3eyIBcgFfD4NgKQPsatuqI/UFyk1TIsOAct+Tc4t6LQQXmI/bBASBQlaqLyDVze1rEk3KE4SKnR7j9jQh+LL9RMBybopcPn9K2u7clMzsXKksr5fZ3lvSsKNeCopK5Mv//m/y0COPS2luqmSkJml/Pr6r3H98Asjy/IwAAhLigApbdrU5dv0DDHanF3Ss0PcZnLeuQc3afSvTUlFVK9GAIJkgnkAUlZmplmcDI2x7HBA0uwOzaM5jt2OwcUBKilO5b4OgXF1TRZJNsMRvjAPGANdjVyUDmAdTwhsya2k4Xcqyk2R4JvB+7KqFoUC722leptqZAdcUaT2gX+1jcH8oCu37eWpPhZ9Q436NX5EBhBQeRbbJOPduV1uD3LkX74wDxhBzOxyxEAy0q189l56sL7eJd+/YjLa3IaeZtxAMoG9gSA2eI54n3jlPpHHg+MpIyHHAWGKOmHFgzwsAGQFxA7FZlrVPrl8+L/G+bPUZu3fnptRUVcmbHq2QF194Ueq3hw76Oad9bLsCJsDbjVcodLa3Su/kkvzIa0/K888/r8cy48AQRlRE/Kk3PyEJiYk6DlZX17cUOYCkwevOVqNy71UWIT46NSc1e0/IWnK29g28nNM/zti5e/OqprNShbG4tExamrbL3MKinD133k8Ysb4xfhhvrL+83OsoKY+spWaMcS4lfyJU2LSh43QJwij0ZyKNA11PdBws69rI9+g2VxVCzLIhQe0+ct9PpOqNbvh8PikpLZXOzs5XjjAqKSmRW7duyRNPbO5aGVy5ckWqq8PLnz148ODBgwcPHjy8sujt65WSss0KadHATS6EMoAGvI/KM267DD5TVZihAVuoB9z9dVQ3c8ywebin/LXbHNikm3FNJs3EPqYSRhG8Opxr3CSzOE8ofw8CHYJ1SDDbfBoSDSWG8UzRFAxXgAVZ5K405FYh2NXpgoFgIlaT35cLBHwHj57Q/x8fHZF/+8bT0lJbKnEbBsA5+QWyurIk2elbS0nTHsHaVFUIhZkB1XvcAfzRxmJ93//H3nmA2XVV139P7713TdGMeu+Si3DBBmPAkFCSAAlJqIGAKSYQakIN/zRCQkgInQChg8HgKnfJ6r1L03vv9f/9zp3z5twzr45Gtozv4husmffeveee9u5ed+21R6ksNz4lOdmZUlexQw4cPCRdHe2Sk5fvKvut0s4SY13eNvRzOOSCOQ/UZwJ8RKW9jU+pkvYm2cXvlLs3K93NnwdjLlIPYkgTEho2QeQXs20rr6yRA/v3S17VSrlmMBOYsPE3D1i/VGMzCQSbvLLnAQRygdW35txBmcF+YM4t5kBo4Y+7+iCfYS4o4/6LF6R2xZp56Y+oPmzCDwUYRJg6YlSUaz4CSAJzP4As0Z44/lJu/YF2jk04e05Kapps2LJTLnU6KU9JyckyODQkRfnZyotsbHRUEhKDV0NbKLZff5Nc7BiU/QcOSFJyigwO9EtsHObWbuIdskj3T3Ts/MUFqYsJtVnh0Savdq6qcH0vMNk00TjQ36tSqOq7RmR4ZEgkfUZS2/qlf3BQ+TxpxMf5V7mZmOVpfOD7hbXqDzOz80ClECvFXpRvDp5o7FGV1pxjOobr9jwwH8BAZEMimfPArHboDxjnB2rblaCoqFgVLgsHCyKMXv/618vHP/5xWbZsmdx4443qb3TEsWPH5POf/7y87W1vW8hhPXjw4MGDBw8ePCwCxsfHpburSwoKgsvqQ4EALtsgacIBN9cmsePvCTz3jXGxzhNVu7y5/gwBBjfb2oT4Unu/67i0zVF0OE9wAwWr+r+8n5t72zAZQ2u8MbTfDsc0n9gGqrYTDASZZgW4cABhFKkJ9tVAVk6urNu0TQpTYxRhAyqql8npE8eltjQv7PQ8/+lo/skmfswuXrFipex55CHZlHGdr+y3VjIRZNqEUXPXkPTNBm6QboHKtuu51D8yLqmWMuBUY4+6Nm3MjOrANC/mnDYx4O/47t8jNwuakRlfJglEAaTREwcOSvJ0nSytLLuiCljB2urvuP7GK9JkLAiEQOMRah7YQO2kxxDSySSMaCsVpjSRg2LD9jnS16OPMTgyofq7f3hUtRHi9GxLr2qzmgex0YoEXF0xp8gj2A/HSN9E7ALS2CDjTaNlvIN0+0lB6+loE6ksU55hXEMkiKSSWk5egfRNJ8lIa6+ULamW08ePSHpOniRmRfYwgrVlKrN0O8x/U8mO86Gisr8XpiCRRkekonqFZEQPSwykz+SIPLTncaktmVun8TExKk1Sqwwd4m3Kr1E0fQyhS/ofqW8mTKP9+FiHhEouilPfWwA1Uqg0VvYiCDD9mYS46HkEcijwGbvy4GIgv6Dw6hJGkEXHjx9XVQ8wuQK33367dHR0yB133CH33HPPQg7rwYMHDx48ePDgYRHQ1tbmKGFy5wIbfDu4edVxAkERAX2wwGGhlby4ydVQN8wRKmccssH5jA7gTYUIwP9HewBpMsH2mEEV5DOwjYtRAb9J5KAsQLFSmT+nCrG9RBaChaiFxqemXUFSsGo52g+EQNH2fiIQZtz05+1gnd8htHRXRaG6iIly9R1Kgtz8OYVEdGycJCUlyNDImGSmJYU9hgslwDJSk2Tz1u1y5OBeSS5epqq1URod41u7X1H5uKvrTalrMkHApeeBUqslxClvLjugNVUhBJFXCuXZsoDPmEwN5uQoXxobzylaYOmSMp+iijF2OAXHX8lcy5AlXIMefcacn/buHmlqbJT+vn6Z4MMzIksql0hVebHExsS4SL4ICzb5R9R8UdKVHFfvBzZBjPoDpZI5D+zzcP1aqQQZRNCfnRovT+8/pAzgAevJVIkxdyItVW+Xt3fUT5FWvwpcSQ0T6nOnjsuxU+ekr7dHquKcal76nC6T++kZ934wPeMjSzRMEoO1w5Zi7iu8t7JulZw6/Izs3HWd1Dc0yqHjR6V419Z5FRQDgf07UGl6gCdRfmGxtLU0qypriYnufYZ1gAqSdZyZnqOuv3ZFruRkpklSarprHW9ZWuDai22yjushTRIiSFc5w+TehrkftEQPK7+0SPzs4uNiVN/qzyykohmfYczCgfpeMAjBYJ5reVebMIqPj5ef/exn8tBDD8nvfvc76ezslOzsbLn55pvVjwcPHjx48ODBg4fnDu3t7ZKVla28CjQutg0ojxBFNMw4xru5GUkuQ2szsNZBj5nug4k0PhTcaJueOaZ5c9eA4z3BU/WYmGiZosqNRRyECsCUb5BBOoVCoLQo09zVH3RZ5CuFL31lClJuWnqHx10pONzwX2x3p1/QXtOomAC/sXvQF6jRR3bK08nGHh8JQ/f1KhVMji8gcareOdW7dO/aqpiW7iFFTmFQTHjBfCCIMYNkfjfTRVSAGYVHzRyJcuBCp4t8I8A1061QepDKl5Ecp4JFdT4r7tFBrD0XOFZxXpbEbNgoD+49JsPNpyQzK0empiaV59FUN1XbaiQxaT555W8ehOP5sxjgcpR5siJnp1UqiV0Jr75zwGeOq9eYGZgyl1BMmWk1HCNv9Xo5e+q4Ug6mFVSoPqXbCApZy0UYPqclqrVH/1Exq7V3RL0H4qitpUmazhyW1LR0yS8qkczCCmnqHlYk5cGTx6RnZEbS0tNktaGq4jhm+hjz2DbXPt7Q7SIXIDnM/uYzp5t61d6h5gHKnwBKk8hImeCv+5sH/srd9w+OyPDgoDJZDlaxLRJhF2vQIZocwoJ0Johz20fHTHNl/ZO2qvuSvm/pHnZ9bm7tR0ntitVysblTUkpXyqWOwVmCwCHnTAI8KS7GtR/gO2SncKKyREXJfjDQ16fUeutrCiUxKXlWaRUjjd0j0jUWo/aFxNRsWb0sXo4dekbWbNii3oO6SxNc6nqmpqUyP803Dk4qn2PqzzUyD7SPWHdnu/T39cjO7VslOytDjh09pNR1S6prlerLBoQ2KqTjhw9KenqaVNQsCzgWzpxz/80sehAu4meryUlChKbWY5OOv9b0tOoTmzAy/e/oN6cKX7LLTwllrPm9HBXgwYhOoaWPMfoOlu6Wl5cvbW1NV48w0ti9e7f68eDBgwcPHjx48HDtQD3Mm1WBa3CPad4od/aPqLSNSAJr1DekdJleDbY/Eeka2viUm2RugClvbeLwpS6XTxBBtpkGRtWYpu4hFVBA6ECIRPqEPhxowoiglifBEFX2DT0+FKSpmU/sKa9uEzEEZU5wEi1lOamuIJqAEYUTxtCB+m2JEeQFC0BMYueS9Cs1gAaBhFkdzB9of2l2qo9kIsjTla6C+ascbZ2RxARn3BkLlFlmQGL7E+n0Ic5H0KzK0me4++xofbcrsIf4WFmW5SuDXZibpYgNDNOVGiA2WpLyRqUoPUaOHDoo1bXLJT0z+PVGAq2+Gp/9MQHhwZy0vU/MNcXYMw8Ye9YWKZQ2YUQ/mASRPQ+Cpb5BElB2/Ojx4/LyF21TZC39e/xcozy+94SkxTnMBv2emJgoJSWlMjw8LA31l6S0okry6mp8wSaElEq3zEiSosyNcvrYYcnMWOVTrw2OjElpdrIkBTH1BVxrMHNt+mD3qmK1rvRcMElqgNrDXHbMSd5jGsC39Aw5fRw9lyIUqfLHBm06dOSILKmp8/3NuX5HschcwFNJVcibpVx47Vxrv8sDi/4yPcoYl7beEUV6Q5QyL2wFC0G+OQ/wx2JuaP6aft1qVLWzAfGXkxejqk1q82l/88k0lQ6EjOR4HxF4+NJxkZh0aW64pNRLpKEVFBSpsYjqT5Ws+ClZUponMzOZcnRqVJobLivDacbMvB7WAX1nru/dq0rUXusQqjOSmYyB9TkZGRpUZNHAyKS0Ds1IZlmdHD1yWLomYqW4uETKc+eugXHnGqOjkyW/Zq06R6TVycKBJnOn1HeDo9q0Va42+cv3o/kdyv7Q0T/k+55h7ppVL/3537EHQrabhNGm6uCpb5CLuelJrjlmzwMbWdnZcubEEVlUwojSrJEgHMdtDx48ePDgwYMHD4uPrq6ueYSRDW6Cg1V4CSdtKhC08WlcgIQcyKJgQbP2DdKVl2grgZEJrYDR6gRu7LkZN9OguBkn0Odpqw4uTcKF4IXAhoo/8THR81Q/gJt3s60ELLYnxtKi4MalTgrI4htaE2RHWupdpfsZRGG4ptHR0TxhR2EU71QmC+O8yquK6j8Brp2xMkknVAp2Kt5LNpT7AnjmQU5qogqmMq7bJXv27JH8ypUyNOE2tSYAJoDScKr9OePG9fLjkFDuqm6cX6eplObMD/DxzDEJoPmkX+jgPFiqYTgoKimX1oEZde3JKakyMjwsUYlpUl27TAqynPOTwjcxPiatzY2SkJgk6zbvUOMXCHFx8Uq1cersOelvi5OB/j4ZHOyX/IJi2b5lQ8RzLKhBvp80ReaxSTqhiEIdZQKyQQfwELAZKQnuFLypaTlwocNVmZC0s0pjvUMKU6HRIRmmpLm9S0bHZ1zqIuYYewtzAGVkQWaSO01vNkA39xF7HphEV2QpaAtIW1tkIr1m2UrZs/ew3Lb9BjVn9jz6mFIakQZXtXS5nDx6UCpKClTfL62qlMefeMJXocxuG8SQDfaa+GhSCkVamxpkZnpSdu3Yogiy4bEpKc5Kka6ONqnMS5WVq+vkYvuA6/Prq5w0a03yM+729xiqN7NYA/1qkuDMo87+UTWPGG9FsMa709KYWsyDmOhoRfwxF6heaAKyyK5AZ5KYtCuUqbVKVzT2YEeBF5lnEXPV9usKBWyFOjo7F5cwWrJkSUQM7lSEF+rBgwcPHjx48OBhcdDd3S2ZmcHVQtwoR+LHoG+QzXS06QU84Y8kYNaVlwIZWps366R4OKlsc+/lKb9Oi+CG3A7sCP5J6YqkvQRBJuESDpxy34tjVmxiIQoLJ20tyipLH8Y8mJmR/oFByUpL9mtovRhQSg4/xzVNkTVSEuNl7foNcv+je+X6XTtdShjG2SSMUAahBGLuQiyhViHQNI+nA9FgbVssw+krRXpmplTVlcvY2KiqkgWpaRokE+hDFGlfHn+wL6Wqdrn0nb4kWbnpUrnUSfE5ceSAHD99XlbVVV/1a3fN5Vl1R2Diyf98RZVk7gm26q2xa0iRiSgEKcd+/thFyVqyyvWe5aXBFWusnSsl/QIdN1LCSJMNiwlSwSAlH3viSdm5Y7vs2LZNHn38camuW6GItcmJCaU+S0tOlKggewDKKptYN0FlNwjN3TdcP5eGFxMlTY1d0tfaKCvWbvSpfAJdOySPPy0eZJH9QMKcX6z93uExpVziO5C+t78bzLS+cIGCyDS5DpvsoeBBdIzLKDsSsB4inTtZ2TnS3dUd3vHDPehPfvIT378HBweVsXV1dbW86lWvkoKCAmltbZUf/ehHcuHCBfnc5z4XUYM9ePDg4VoDHgFPPb5H5XOPDo/IjbfcKjk5kVXF8ODBg4fnCv39/ZKe4S7fPTQ66fNF4b4Z+bzb4NYpK0/mRZQRnJlkAje2ZrC2EHNn09D6SmAHsJOWkSuAICCoWEyQahepmXM412waWfNfVbVJ+dTMXRO+KKTrIXMgPLBTMfgMlZ9M0FazvapKmDEPGHOb/CHQIxB1yk9Hycz0lIyNjUleTlZEqqRIYZNZoVCcly3Zefky0NcrSfnOd7S/AJPrM/2WHDPqyNv/bPBFpnGtngcE32Z/s+5QxRHK4ZHCWsazyATzhLmqwedNdSBjiOrK9MRKy8iWzKy5faN25To5uH+f5OblK6KQN7Gmgo3RQrgU7b0TZf0e6TFCxcy0G3Uac/qZfc/IslVrpW0o8gZfDfLM8Uqyq+zNzQHto2Tuv7RiYHTCIbBn9wS7D5wqYG7ihnlgzifmkLknJKSkSXFBmjz22ONy4/XXya5dO+Xhx56S4tIyKa1eJk/tOyA37NzqkGdWe3Xf0KbJsfl9C+HU29Ml9RfPyeYtW13+dhAu586fl52b1wVVxEUKTajo7wfl8RUTvehVKWMV2RPZgxgfYWT4UwWaB8G+G8ZCfDfYSE9Pl4HBgfCuK9yLefnLX+7791/8xV+oCmlf+9rXXO9517veJX/6p38q999/v7z+9a8P99AePHjwcM2hp7tLfv3Ln/l+X7txo0cYefDg4XmDvr4+ZWBrIjc90amWM/s73iXmDefg6KTy0VApN7NBK6k8picON/QX2/pdN7V2mfpTTT2um1k8GUyzWW5kW3uHXRWA7KejpP+QTkbaE4ED581IiXcpQrTBtK70xvFM4iMcRYgyqx6fUmkdfJ52kEZgBhKkLpjXS3Blmjtr3xbaaB7XTFODfGnrH/aZm2pvF9vHRZV1nw0a6HvMxM33pSTESv/whEPkzI6hCYI+PJfMtCn62kzH4DP6yb/uddK83G3plSz+NjMjQ0NDcvj4KXnJjtWSPKvi4fydA6M+01pgelIB0rsguPQQ0LcoQMxS6B19Iy6CAa8oM4WIfuQ4uqIfPwS75vXl5jrmrbl5ub4ANtS4O4Gt+2/KrHrWlFYHl9pEVo876UxJ8XNzwyYBm7oGXSWwIedIU8PTZu4aHQ8WDZtwxeAWs3DGmlF2DGynXPOJdCxzHnDddroM/Wb2NQo8U01HmyBTzT3BXsuYFI9LnLT1DcvYVJS6trTEOCkx0vUgJJj/gYgiXj9yuds1P2izOSdJGcUYW48b426nvtJ+9hI1D2b9YLi+ubGOmk05dKol+psD/GlsfFKOHD4seQVFygeobcitLNEpsHo/YO1SUc8kyegXM6i3y56zfzV3D7n2I9vDjPOY84DDlee502EPXeqSzJR4p3JZlDM3TCN/vIuGx0ZlYGTCR7ZhmGzicsegOoYGKbq015wbrEtzT2CPyKD6YF6BnDp7XiqXLJH8Jcvl9KljUl5VI11DE3L/M2fk1s11kpCQID1dnRIfm+gyyveXmvfMqUvS0dyoDJdzylfI5a5Ryc5I9fUthEt0ar48+sxRZXgNbIN02t8xMOL7bqCPUY2Ze7b2ytLfDSo9znjYEY6aSxcymFL7wZwXn/kAontwTKJm09D0HsH3lL2Xsk/o99AO13dqTLRaq+a+wvo2gWcW18Ac0F5J+emOyb1GcojvBhvM/YF+93gFwoIeufzwhz9UP/7wute9Tl7zmtfMI5M8ePDgwYMHDx48PHsKo9RUN2FkVjLzh+mZaZWqYaby2AFAOAaqBMDB/IkIcrYFMXPVwSFeQroE9MjElLT3jbgMhlE6kGKiy0lzP26rnxxCYi4wsNVGTV1D6qktVYB44k4QQvqS6U+CibcOPAKB4DWoJ1OGYy4cDJzDTIPAlNw+ZShjbC4VryczkCCYNGEbDvtDkuHncbzxtKxfvUIqy4pcr28MkcJFUGr60hDsc00mdi2fO6Y/QC6qqkE5ThocP/Wdgy4Csig3Qx46eUJiGzokPj7BR476qyKkQaxYaKkATjT2KMKEsWQuUEFsWclc8Ldkdu4HU1ZBfJjzgDnLPDTBfAoG2lZiEYX2fLKJHX9gjPABCzQPCLD9lRK3kRYzIcvK8hThAYkJaWFiTUXwlE7WMHPO9HeyU8XMymyBwHlZH455tmPUTh9posBRGFHdj2pdzpo3fWzA4PC4PPjEM5KflycxMemqX00Df3CutU8REIqkjIlWFcNQwZgE1nXLi4LuCbo4QDC/q1Dm9CA9Kc61J9jkCeR2Sl5wTzn6xZ4HwyhRwvhuwJvozMmjcvDoCSmvrJa63dvl6aeflpzEKLnccFn2Ro/ImrXrZO9TT8raTdv8HoP998KZk5Kdmy/dba1y23UblZm97hPl3zOr/OGBwF27lskDDz3iWkcmugZHlSm8Sv+axn/IMQzn/aePH5HM7BxJiktX+zjzgPOrqp2mMisqSpG/JmxVEMQ7BHxKYpyPrOaYZrvWV+aq/SCY4o7XgvldUQnTroY5/xjiOoY/H71wiiaYSE1NVVlj2AjFxMQsPmHEQQ8ePKhURjYOHDiwqBIyDx48ePDgwYMHD5GB9KGUjOCBnI2rlWa0UJhqIm66x60b5FAeE3w+WJUhfc2YG+tggifJpOmYuBpm1eFApaBEmDblz9PoSvxWRkdHVPpIVVndFafhKM1apH5LswSkSfTZgV15bqrcvHOTnDpxTJZv2OL3OKYqI1hwXmSoM8zKeGAha+NqeN08m9DzCUP4ub89N21R5d0NAsiuvMjrgcgrrqOpvUvqTx+S6zaskrT0wEbEzLeirDkCZWrKISUi2ROYKY5G89pDJOl+quriijVy+OAB6e4bkCX5xfKiG69Xisxf7Tkgw0ND8szep2Vo0FFc+dsjSD1LSUmR/r5uyS8okJzMuX2b99vVL1lnefmFcvbUMampW6ne093ZoV7Lysn1KYaiZybl8JN7pKZuhUi0Q0YODfartpQtqVIKsmAI9dAiapZ4zjBM1FEHRvrdYPfJYqQzKu+qK1yI8bMVLycmJq4OYfQnf/In8tGPflRGRkbkFa94heTn50t7e7vyOfrsZz8rb33rWxfWcg8ePHh4Dj2LSEPTOH/ujOv1C+fOIgh2mcWFqkDkwYMHD88VxsfHJT7eLY0PBZ70LgY5cjX8Pa6W4TBBnfl02PEJuXKS5rm6ZvQHEfitBkXDpQvS19Mt19+wWzoH8cu5QigCLNLPhBcUDQ4OSLKlqHsuxtA/QXTtkLALwRUXMrpKxk8qTTTMMTl/uUkunD8nazZuk9jYyMJfZ15EGJyrFNbIPhL43IuLYEbSgVBRVS3Hz1yQ9TVF6juCHqxbWi2t509IcVmFqqIWqK39fb2yc8cOlXJF6qLptcXea2eGoSrs7++Vnu5Oyc0vVJ5HHW0tyoy7qf6idA1NSWpijMRFTUt0TIzk5BfKYOeQ+mxsXLzU1C6X1pamkIRRKCgyK7gN0IKwGCTyQgzSbcTHxfsIo8TExMUnjP7hH/5BLbbPf/7z8slPftL3d072jne8Q5FGHjx48PB8AgbXpmeRjV/85Eeu32+/4+Xykjtf+Sy0zIMHDx4iBzeBsXGx8yT25lNJUrrMp6ekEI2PTLjSHuwKPJhqklpkIjs1weXlwnlMvxLSM0yg4sH7wQT+C2ZpZM5BuXvzGKSNmegbHlNeIxqkk5mpWFwrbTGRGBfrahvnUe+ZjXWoTmPHPUr+b5Qs5mVSCEy/FfuazXY5v0+pSlYm8IIxlTOkTZnX3Ds8Ps/HpXtg1FVGGzWEmbpEmkLf+KR7DK24IpwxJP1nuKVVNmzYKJMSI0Pj7n4kWDHbChg/sw84D/5JGhjvzvPrGRxz9S2BkOmtwrWS1pVo+HvYSrO+4QnZe/CoLF+9Xtp6BlUQmZ7k9jQZGBl3+czYYwhIl1vsMeT92vdJAx8rM9izx5CAuXfUWodWkMn4KYNbA6QimoQvfduRNNcW0slMsLZJ7zGBaocUHI3egSGR2GTlYURf+TNIR4ll9q09hgTIzAPXGFolwPm8reiiT8yUMl435xNVrgotvx7mimuPi4mW3p5uuXTpoqzZsEUYUnvessaSQoxhYlxSRGOIKqlncDzoGOLFMxjpGFrr1t8ex7WY+wY+Y+b1qPVjbXLz9rioKMlJTfCtj+SUVGnpGpSLrb2SnORcJ9e/adMGeeqpp1U1vtiEpHnrg3VoCgvtMeT9BZnuvj1y+pKMRyfJ0vW7ZGJmRh5/+FdSVV6miCdFdkVFS8/IlCTGiJRUrZSe4Yk5cm5mRg4f2CtF5TXzxnneHjc4plLaNDCWN9MT6Vt82iaN99jppVdrDO3vKdaHawwnUb3NRPQ9ZSM2Ls73cCkUFkQYQRZBGn34wx+Wo0ePSktLixQVFcnq1aslKyt0PqYHDx48ePDgwYOHqwv7hhJfINMDqLmHEtNzN8j4jHBjaipubBNWjFwLMpJdN/x4YpjBUnVBuuuG2TQx1gEZN9rmZ5q7h11G0vidmM2HLLLJE4yBi43UEXwsTMII8oSbaNO/g3LqaUlz6SgVeWkuUgyf4eQE99PWlt5hKTI8YzgGbdcVd7gh5xrNtB3TrwXgpQSBYabZ2X4YfMZMQeOcZmUv0OpnDM1+ZAwh18wxrJg3hsNSYAT0pOAxjviCaJSkx0tzX6JkpTlmv7WG4bImYCAgTO+N+s4BWVk250WztDDDFSQT/NvG2IwZXlWBjJr5TGVBmotoqrEMxyEzVq1aKV0dzapUd1RMrMQkZciq6lKJm1XZNfe4x5B+xF/GHI+qwgz3GFp+XZif25WV7DG00yTxsUlNtMawJ/gYEtiixnCPobstnNccw4HZMTTHg34jCPZdn7UOIZwYQ3PcaYtprp0w3itFZSU+QsXfGDZ0DUmZYYJtjyF9zLkhqDVs0/jGzkEVrGsyCjKCv5ljzRw0Y1/6OsFKT2R/MtfesUvtMth8RtZu2i5R0dHS2YcRdbSLiGoOMYb0s33Nbb0jLv8njmGOIdfCdZvkmj2GjfYYjk4oQsX0kAs1hhCH7KemaX2LNYb0h3uPi5m3r/AZMw2PdQlhpEEbtqxdJocPHZTt27cqQqOmMEPNU6pxxsTGqnmdmhTnay9rn/nEa8Mjo5IYlzJvDNmbbJ+pxq4BKclJU+3s6+2R3tEoWbV+s0qb4jutu7dPhkfGJSMzc973FOfi8BMJGb4HDKhIm9q65Nzxy5KXnSEFRSWSlp6h9gB7DE3CCB8rriUmOvg6NOc6awqCyPSrY8zMY9hjyH7Nd6jpLRf6eyr0GNI2taYCEEb6/iAcFdsV1RmFHLr++uuv5BAePHjw4MGDBw8eFhmko01OuJ9kEuiaJqz2E1iUEqFK0BPEmscgKLCl8SZp4w+QIgSg5nF4kmvCrE4VCATv5jES+mPmtTUxxDXbJJQ/YK5qHoPA3CRCOE8oI2luylHgYPgdqC0meRcIocaQ10OViiZ2CTWG3c3nZcXSGheZYF8PyhkzSG7tdbfFPEcgkJ5iHiMx3n0MgnuT8PN/PVGytrZCYpZVqgD6hz/8gWSV1ikPlInxcSksKZOYqNR5Y2hfTzhm4EqNEmQMw7lmewztub9YYxho7EwwhsHm0/TogNSUrwvaHoig4GPoVhz5w4wO0GcJO5QcBN4mbHNqf2D/0m3hGNgJ3Lh9o8tflzFMvsIx5JqD9RtkUXzq1R9Dgn72lWDzKaw9zs9eahIJeOaUF+VJgoxLa2ODrF3peJpx/qSkFDl74qhklK+Q9Ow5Pzj6H8+fsiWVcuLkSdm+eWPIMezpH5K+7k5ZW1MiKcnx0nyhVVavXS8nDu9XZNDU5KRcvnBWypZUS1nxfJPt2hWr5eTRQ77vqfbWZrl49pTEpOXJuvXrJS5qSk6dOKnS6OJj4698DKPnfx9Soc1EqGtGwGRXflzQ95Q1hhwjmBk3qX4gLi6MY8sC8M1vfjPke97whjcs5NAePHjw8Jxg287rpW75SpeH0S9+8n++31/2yldJdU2dy8PIgwcPHq5VcBNIWtrVBjekEzPTV2zYuVjmwKYXDf+5QpsHv+C4kTZX+WFcIya4801Y5/f/9NS0FOTnBvT2Ua4uV8n9OFI/Iactc4H8zbfeJsNT0UoBAYF0/sJFOX3ykJRmbpHEpKQrGMOInWye98bYSckp0t7ZJWVF+Vf9XOaQz1xhP5IKdvzUWUlKSpYEQzHoGMlfm1hI2/x95mrOp/zCYjm070lZVlejVEbKaHxlrSSnpsqJS21SVTSnMKQZkGIYjLe3NMuxk2cU0RQIkGX79u2TJdVLlV8RGB8fk/KqGkmcTpeRsXFpa2mSTduvVySwP8TFxavUQ12JDA+kzvZWae8bUMR/YU62yPLlUt/QIJJW5Osv/NouNXVJ3EiOlJYvUWmtC8H0DJ54kX5mOuLPhItg++jE5FUmjN70pjeFbJRHGHnw4OH5BAysg5lYV9Usleqltc9qmzx48OBhocBXkgpXVxuOMWhkAYpjdBr8MzydRqpPEMHx8bKxfWlIgTBBagZHjbJk+qZfyjw/n9EJaesb8ZVNRtGSlRLvUlrhF0FbVGWeKCdpLNIKNaoy0TXCC9gBJVdkt21sbESOHj8h5WWlvhLYrs8skqnv/OPO/xvpGvhe4S/Ff5kDpqKFVB7MdOf8SVBzOcEsfjxrVtRK53iCPPzUfsnKyZf4+ASJSUpxpVaCS+396rN6LqBCcfnSTM/45oGaCwuM8q6GkfHVQuXSZXLs4D7Jz70hpOppsWF3E+myo7N7gtoXZmZUSpfuT6bjmeZeGZ+YlJOnz0h8QpIUlFa4jsFnLncOuFIc7etCmcFeosaZilwx0ZKfnuQab3yBSHnl1Lan07NpvE661bM5nWhfflGxnD1/SVbVVavf2RNLi/LlsWeOSVN9gpRWVM0aks9dT0V1rZw9cSTosdWeGh0tSbOk7vjYmFrTKGcqSorlxOlzkpmVI3lFJb49QX832F5ipkInIb9aUuL7Ze/TT8rE8IDkFhSpdg3JqEphvXT+jBSVlEhlTa1kxI3L4f1Pq2psKZm50j8Ro86v1zwqUFOdxneL+d1Ae/j36MiIj5wOBRRGwZRAVwtcOwinOMaCCKOenh6/f7vvvvvkS1/6knz3u99dyGE9ePDg4ZrBhQvnnusmePDgwcOCkZGRIS0d3a6/QYwQHHFfzs18/8jEPJKmoWvQeX2WDUDibqYEEUjpp7fqM9MzLt8UcKGt3+WPgMpjZVmWL3ggviJ9ib/rtAY7BQ0PjYS4WFVCXQVt6jPDLg+JncuDV8EhpWDL0uClk/EMwWeGANJJJ5ie50tDH2DSPW0EqqYfCzhyqUtSDK8a3ldbnOn6HULCNEC2DaAJcvBcMoG/j+mz09iFBwv9GKUCRQInewxNU1mA/4qZ7sZn6F9N+BDwYNRqIrt8mRw+dlTaBqdlaZUTLJm+MwRrZ1p6FVkzF+u4GSQIPYy7Ae+hD0j7sj0/IHt0W+zYG6UIJEBJToryE6Ev6AOTyFlfmSuhsKE6T6SvQYqL0mV4eFi6Os/Lyc7zyteGZheXlcvUdKLyVNEkZX3XoOs89CN9p4lMdT3p7rlvrg0AqYAnjpnGhpeY/R4TkCJcoxlDMj5mqiem5Jrg4n20CVLDNoA2yVICX+a62bfMA31+9oR5pr4TMzKVnK9KqC+vrZGo6Cgpy01xGdQ3dQ35xs+Zk+45zHlONvaosdPXxH/M9cHvZ1v65sgfxiTbTegxDzRxw3xs7xlRfk96fRB037l5iRw9eVbKs5Okqna52DC9owKBNciYsRcwxozXyMSk65ohDTBJh6xgrtg+PJhit/eOqP1Lg73XPD/mx+yV5riX5bo9chhjl7l2bLSUG+/BhJy5MjjKXur0o53Wpv2tTLAGzX2l3f5usN5vfjdMx2bJ4RNHJDEtW5aWOIoiUoyrl62U5u4OqT9wXCmA2Cf1voP/cW9vj/zy0UMSHRMrZaXF0j84KDGxCbKlJk+lDE5OTitlY9fAmExN9ynVT2FhsQwNDsjPf/6wbNq6U06fPCaTidmSlBiv9k9FKEWJXGzpkvKcZElITFLpaynR0TIyJJJXWKw8i7LjJuR485jUrlgjSSkpcuHMKVlemShnTx6TV968UzLS5ojHJWUl0tjaJicuNMnkcL8UFBRIfkm5MtvmAYT53cD12d8N6THjct8D90plTZ2sWrdJjjf2ukgm+rKuJMv1O15Peq8Epjk3YK7ZY8hcsv3Ugn032BgYGJC0tDRXuuaiEkbchPj721ve8hYZHR2VD3zgA/LrX/96IYf24MGDh2sC+5587LluggcPHjwsGOnp6XL2wiXX3wislUJmNmgzDaP1jSs34cXZKSrw4AaaANgkjNYuCR2cQwCZN9WXOwbUzbQ2ieZJcKggn+CWYFDfEHNM/DAWG5yHm3wd2CXIfBWFaYwaCJBF5jXbxAF9yk8wEKSZx4DMgtTLTZtr085lha50KrttmoQz/XgaOgddhNHyUidYMQkJm7wanZiRkowE2b1lhSQlxClSxwREyu5VJcGvZ2RcaoszfE/PIS8Ibk3CaGNVXlg+LybRAfEZKTLTkmX39Tt9QSHzcUx5fKEKmJZjx4/LhdY+Kc/ZIAmz5aZJYTFBH4bjM2WOISQghIlJ5WyvKwz6ecYQEtb0PoFYMAkjvX70POC/en2ZVQRNcoF1aPYjnkHMBXNPsNUytH/Dymo5e/KoTI90S1lpmfIWMsmTF60OPg/YVziXSfaeanLPJ9vI3B/YC8yqaKwPW5HDuVqam2TF2g1yJYCUio921h3Eiq0AsY2LbVDprjQ31eU9Y+8JaypCWxswT4PtK5grm4Q988AeQ8ggk6hin7H3lXUhvhsgJKmgp9ducfp62bPvkFQU3DBLBEbJDatKpXcwRw4ePOhqs8bGrTvlxOV2SZ7qk+Yzh5UC52z7oEz3ZsvatavlxMnTkpmVJZWQu5CksaVy8dxpSSsuldjYOElMjJeMrGzpHxiUJYWlvjEZHh2VYweelraUGKlbsVqlsSUkJMnU9JQcPbBXUm+4WTKzcyQ7N0+lWI4ODcqWbTvkyOGDUrtsuYssAvFxMVJVViwSnyK5aUnS2toqZ44fVKTXZFK+zBSk+95fZFXp03jxy/9Annrkfnlyz/1SsGxryDE0TbL9AbLIta/0jSii1ySMdtQF/26wMTjQL2npwefxophe+8PKlStV9TQPHjx4eL7i0sUL0tLc7Ps9OSVFMjO9CpAePHh4fhFG/X1zT6+BWTraHyBNCLgWWx4PKUMAZVZOC+cztEXfEGslUqTgST/XQ1WZ+DjnibQJgitbVbEYWIy0I9o6ZvlQherDmBgnNcIMJOymhEotmpqakovnTsldt16nyCJ1DJ8/R/jXpVM0omeJDP17xCmPdgrdArqWIJnUQkq7c/2QQabaa8vGdTJ1vEEFmFRkIm1tMcZQ+StdBecjex77gz0P7LnDMZLigx9HpxzVLl8txw49IzHxyZKWFl6QebV9g5hKtiKtq3dAEpOSlZeNPxBko4pjP0iIi1ZEaahxjnTeL/QzCwHn0OsrGMx5oBVakXw3OIT/XF8lp6RKRmaWShNbvbzWd7z29g6VNuYP8QmJkp6RKVUF5b6/xbX2SfRgqzy651EZGR6SNRu3+l6D4MF/aGJiXErKl8jB/c8ohdD5w6ckunbOwygqKlbWbtgs6XFTUlhcKkWlc8fvaGtRRtm0u7i0Qo4f3q9e37/vaSmvXS2p6cmKDOMhhj1efC2Q0lpbVS5VS0qls2dInj56Wo4caFbKIaq2BQJpvvlFJdJYf1laGi5JTdE6WUyMT03PM8KO5PsV9Pf3S3rac0AYIfH86le/KiUlwZlmDx48eLiW8etf/NT1+/DQkNRfviy5ecFTGzx48ODhWkFOTo709HRF9BkCyKthEk2pcltlEQrc/I5NTknMeJQK9Pm8TRyQeoUHkelxUpbjroZFWlR+ZrIMDY0phcvA8ISsWeIOaC629avPEFQQQNpVbp4rQGrY6SWhPxOt0sWuBARpBYVFSpWjQUBokj8LMTf3Z3Ye8hizJBNjO87PrJ+UCZQmzT3DroCPvjNVK6TToI7gvaTxkNK1ojRLBYT6PMNTUZJZtkx+t+dpqayukemY0GqikFiI15My1746pvALBWtjxer18vTTT6ngPBKoa1kE8oQuYA2PTUyr/zppU6av0KT88pG9UlVd61NxsG8sM9J/WnqGVWrh8Bgl7KcVgQSJaCpwSDGS2c+zJ6KUC1XlzYb2srkWQdtsRWEosCfaijsMsIf7muSJp/fJ5o3rJTE+ziEhAhBGgUDqViAw71UluNQ06enqlFPHDklrc6f09tZKfFKqTEwxDyYkMzPTNUYX2/vVdUZFpUj/6Iycv9ipVHk7d9+qXu/s7Zf9R45LQtSUJKZmSFl5uRrr1Ybqi5Qu0iSBHsv1mHZPjChfL8gtm2yEnCLFq7+vV6XXJaakKxJ/oR5VgcADGDPdcSHo6e6SHEzArxZhtHr16nkXPD4+Lo2NjTIyMhJWFTUPHjx4uFbVRSeOzTfm+80vfybrNmwKK9fXgwcPHq4Jwqjb7WEUDhYjMIUUIHjTJsX41NjBU33ngLrp1bADO0gmfB0wM6X0Op5BZkoLgCwyZfp4dWBGm249WTfTs0zPEJCbnqR+dLtpk/2k2fRk0u8jDcQsed02W1IeoovAirbYAYJSb82aowZT0+jXCTQiJYzoZ/szkQYpKASOnGt0CKLZcaNNjsog/OPwyaHRSRmNmZKpqZlZ0s/9nubuIZeX1ujEpCwvyXIpIgjcIP4IkFCG1BbNed/I7DkgCiECAqU8EQyaqZUQBzYxeMvaMjVmzMNjJ0/J0OCQHO08rxRH9GH3wKi09A4rvxqteOPfJQbZQNokZIVj1hul1oF53rDmgTIhvzZISxOUNS8uLZeL9fVSVbg6MqJnYkr5n3HtE7M/JroGRqWjf64cPPsGpuSmR9TEbEojewFpUHhqmftKU1uH5GRmyIolBQHTfyCv8IPS85r1ji+Nic01TkU4xoA5PzHl9vuhbYcudklmyhwxzXww09S4Vn5Ib9JeO/PIhelpNdbKe8jPXLhac4B5nxwB8QumUHvGzq+mVV23Qro72+XxJ56WXTu3q3SwYCbKEH2kXDIP8Iiy/btYL3hi6e4439or2VlZkpkVIyvXbpRjh/ZJfn6BIogzp+PUXCDd0VbbsHcFSgObmZ6WkeERWb9urfpuoP3154/KdEqBrCrP9o3Fhqpc11iNT07PpiemSn1DqvT39sjl/hllsN/V2S5xsfFy4sRRWVtVKKPDwxIbFydVy9ZI48CUSnd97IF7ZfctL5m379BW/uekBIY3LrTnSglJ7g9yc0OnmC+YMNq4ceO8C6IaR2lpqdx1112yfPl8kzEPHjx4eD6qizTIiT9ycL+s2xjZkzUPHjx4eC7AjWBXZ+c8T5NOIyjTXhVuQ163PwwEjAlu8Ln5ttOazJtzvC54Sg9xQoUh/G6ocGaCQM2+oTcJFhQ/plLIH3TQrj9DYNY/Ob7gdBE7hWfuPKLIKp0GRNADgWESRjuWFfoCTIKhzFR3ShN/O3ypS/nS6EDQNj/V/WC2TStgTILFVtgsyZ9rG4HwuZY+V5qd/X4CNjtIhgDTBs/xCRi5jsrxhm5F1gFIP4ynTRy82OlKZWFurDXUW7SdeQCpAnkCmVZV4Cb9SA9aVpLpMm5FWWaOw5alwUu6h6Ncsoeb3/2lxzFmWekpsmvLBqXxeebgURno71OpNDOzviCmEbZNQG6szvPNA4dsmHYZ3gL6FZN3PQ8YD1PZoMp8dw0qFVSgdBN/Jri0zawUBbliwjYyZg1C3JqwTe4hPs1geyImVfq7m9W16YCV6zFT5PiMJl0A7yOFBnUX18E6rSl0p8JAFpnzADK4C6WPAbOP/OHy5XqlegkGR3Vi7QUBAm/mgp63Jrj2vIxElz+UTUzpdT09OwcgR/Isg/TTTb1q73XaFKXGB8JCn4/pgf+Qq/3WlOUzZkUwwPiZe6ft+cVnslOzXfPtYvuAi6BiXZueaxh0Q+qZ61IbfWfn5ivhyA/ufViGhoflTFOP8vtJysiTzSuWuNNjZ7+HUEJCiFfku/cD5jQ+VZoAyoypk+OX2iQ9M0ulgJGSVjI4IHkFwQseBEP9xfNy8vwl2bHrBl/7+fn1g4/J6ESd3/S8GCt9c/WKFXLk2HFpbuuX7euWS0v7eYlLTZNly1ZI7LSzJyyprlXeSaVFzjxrKs6Tjvozsmqd21/rfFu/xMWwh83t16U5KS6/NFP1BpwKfVGu3+s7B10pmhkpCa6HJTa6ulAY5Vw9wujrX//6Qj7mwYMHD9c8Gi67TWJNXL580SOMPHjw8LxAfn6+qkrDjbx+4kv1mcqCdB8hwU0mwUZpQqqL+AgGPpqaFBc0WDIVF+GCIMk0xg7rM8p/CMNqx4eItCVbuWBWdeMGe3DEUTmYioFQUGlqUwTIxu8ByAZ/AaYOmlEkmCoEu99CGenqYMIk2iB+SNFJTpgljKKi5KY1pUFVCqTYYH5sklG0RRMhXMfaskwZaL8sm9avDdhXBFZmkG8bY5MiEiqVx26eTn2LBARJ+jOMLcGtTSDRb1p1xBxGLVAcwLDWeQ/aD5Hi4mJpaGxUhJEimcJQfczNA/9+UZBFweYB5tamwXU4JrgE4ZBvJmF0w8rioPOAPoHINQ13wzG5P9g8LYMjY5KRkugLps15YKu7mCek/12pP1EoxMTEyWBfr0hJnlJgQHbPU3PMzLhIPuabJkXDbhvKuzCVIJBRCdExfs30mRtUY9OgXeZx+Wwog3SIXZMw1oopkzDCDFnDrF6pwb7LeZcYc9ImQkPtTXgHDUiK9Ncfl81bdygi7IGnDsvhZ56UZStWKU8pUl2n+vskPr5YsnKcymg2dOEFjazcfCmdTvL5BeGdxE8wmA8RGCsq3JnfC63NDZKUmuFU4uyP8Y1FWkaGdHR0SXlJaPuJjLRk2bxxnXQ+cUxVWttx463S0lQvxy+0Sn5FviK2zOtT/VtZJU0Nl6ThwhnJzMqWnFmbC+b5EmMeUJXP9igzCVh/UERbZpKr2ihrORhh1NnRLqVFBVePMHrRi14kX/7yl2XZsmXzXjtz5oy89a1vlQcffHAhh/bgwYOH5xTvfM/7VVpaR3ub3H/fvZKaliY33fxiScvMlDVXWHXDgwcPHp4tUAqYm2b2spLSMt9N8dj4lE9RhJyfwDkScDNvppItFiAlUJbYZAs3/9zs4ztDsGeqO5SaprVPXQefQ9lkVyIzA14dPNj+HScaun2BPcfkptu80eb99NOVGHAvlndFIHPwSM6riLYQPkfLVq2V1uZGeXrfAdmxddOz5sfiL6XOnAeocXjdrAyln9Kn9sWpz5MiUmUpF9aZ84CUybFJl7IOHK3v9s0NVZ0tKV5ysjPl5MkTs3+b37ZrMXXsSuZfOO+rrl0uJ06clG2b1l21eU2KTqA0LeYBZBD7kJmytmrlcvnevXskJStPKRshqsot8nqpkc7IsdhXMMM3gW+Nnu60gflUYFSXYwpcjfUwE0TtFAia7NFgXXPtgeCvTx2SJuqK5/Xk5ISqQnbgmadk69ZtUlNdLYlRY9LV0a6MqyGNVq1aLU1NjXLg6cdk47br5rWHsTX3Z9oW6CEC46+8zSanVEVNswIiyizdn4lxsSplVaOiulZy8wtU5TV9rUqxk58hff19IrOEEQS47oaoKFHzAKJdp5Oyh/R1tctN121V5FBJ2RIZjcuSMj8V4kBpRaVMToxLakamPPbgfbJpx/WSmpouMzNuMpG2mOrVcEAqr5kiCWEaai61tbYqs/+rRhg9/PDDytTKH/j7nj17FnJYDx48eHjOUVxapn7OnT2tCKOkpGTZuHmrZObkSFSU51/kwYOH5wdICcjNy5N2gzBKiotRhIkOlJ9L4oOberxpIIm4QXZKdbtvS4/Vd6mgD++apLhY6RwYdRFGplFtOOAG2iwHrgERpNUa3Gifb+13EUaQMpQkT4qfu7G3OQ2tcAl2k341gmtF4EVY5U1VrZsKPe6oBvD3oPJUfnZklbHCBf1NigyB3+j4pHQPjUlpdqrf1DeIQf7b1jfsIowI5s2APhQI9uxUP92XWinD/DzZ2KNSCPWoMXykzJFapWErjsKZB4sBey45eqhnB6QHXTp/JmJFYCTzAMWErU47dKlTBf+ME2l+KDFMwgiity4vQWosj6tgfWiThgAySCtqGM+zrX0WYTSjUnchL+fa/9zMA+BOfZ1vTh0K7AWRVtjyByoLVq9Zr/aMAwcPSmPPmBSlxaiFk5WdrQiklKQEycmsk8SkRFWRMCo6WjIys6VsSZW6Dkgg0hH5XoCkRyGKR5GJk43d6gEDbSYtjnRGcz/g79tqC4LuayY478hgr3S2NcvqtevV9xHfOSxtvR+MjE/K7/aekISxbomLi5PqmhrJysqSqbhkOXDqki9FzibvzXnAeSqXOmKbW+64S8ZGR+XgviekYyRKim++waecYj+PdF0pE2xjDFlHOmUwEDraW6WwMLiC7YqrpAX60nviiSeUDNqDBw8efh/wLFRF9eDBg4erguKiYmlpapL1Gzap30lXIXVlMcCNsfZo4ebUTAMg+LvU7vZGQWJP6oQGHhVUKoKsSUlJkKLZJ7cmIAfMtBtSqSKtNMPNP0/dUR+F8znaaasHCEZsRYsNUjjw5NABgvI5SU9ShIPpP0QTuLHnxww4FwpVTW5i4qp9pqp2uRw88IzceMMNftKrZv1ZMDGmepl1PbzmpFvN9SfXXW341xDkExRy7Jz0ROWTZKdj2KlvtndPOICEmJqZiWAeOKmFwyNjyrwW4CmypiIhonlA3+DJYvqRQDoxxbSfj+0xFQ78Er0LETst8B4HRcXI+ISkJTnXZc4D1A4mUIA4KU5zJ4P8M9OcmAcqpS7IPIAsMucB+8G8NLYQhUmYB3STNi4P1bfsSTYBw961rjI3onlAv3BNZsoQHlN6njEP2EevFOy9kZI/C/lMIDAHktKzpTwlS6I6B2RFWbbqz57uTnl6335pG5jw9Tv/X1CQJ9NT4/LoA7+R6170YpkaGZB9Z49KrMxIZla6lJVVSMP5o9IeFS2p6enS1twok8n5smuTY7re19sjXS2X5UjHWYmLj5flq9eH1U7WHOdn36E9Z04cVQ+I9+97ShISEiUrO0eSc5yq7zMzM7J33zMiM8mydsMWmZickBOHD8iatevkjS/dKY88+oSUZMRJYtJ80hrlY6wxDyDBynNTJWU2te76m26X7/32KfnRz++V2rrlkp6RLjPxaQt6uGB+BuUc6yUYWlpaVMrtohJGn/nMZ9SPbtDu3bvnLcqxsTGZnJyUt7/97eEe1oMHDx48ePDgwcNVQEVFuTQ1Nvh+J0ja29TrIipsjw+UPp0DI640B9sAleCQYIcbYQKdoqzkeTe4BFqmP4ft02KnjoWffuV4FtkBKd5EBJwEd6aHEtWwaGfrxLBfg2d/IFB1UiHCD6IIDgkEXMbYvcMuwmjX8iIVnHEN9GGFZa5KUHnk8lz1Ja0AM0mzORJGfMQUVZ9MNHUNzgs+Cc59gVqUqFQ+iBoNm3/o6BvxBeTDMZny4IEzsqGuwqW04ByX2wfU03j6qizXrQyiDyBoyo3qdra/DX22GNBpJZBJKNeYs6ZJN6a+qIoaxoZ87+d124zdOqi0tLZKRlZ4paf9zXvaAjGaYcSS1xnzwAkg3fMAhQXjY1Z+glgwfU64VtNnhr4mXcZEfceAT33G2NteKbwGsWuaYdvrmPVD+/VrnAejfLzRMC4G7CecSxMfZvoPYJ6RYmMqhq7GPGCdE4cOjY7L8NiUDI5NqD40PZkauoaUOgniSKuFaK/p/bQYoBvNeYA60lYh4THFPGDdo/Ipt9RO+BPR/6ZShLVm+sfh12VWcWRemOsNmK8zhmqMjP5mvqHSMZVzdtoufj9mRTPWflV+ukuhRVsgQ3WlSL3+OGd2Tp5MxqZKdXysr5rh9NSUPH30jCQlTsnmHTeoTms5e1BVaYSkxWN6cnxUEXykcjU3Xpas7FyJzipU6hxUbqMjwzI+JjIVNS0VpRW+tnC9w+OT6nuBdmUmx7t8wRo6B5WiVe+TmZVrpaogw+dndfbUcbl48pRMD+VKZmaW9PV0S05NpVJEoaQiZffE8eNyw3U7ZMWqlXLwmWeketkK1XYT0dY84KGBy6cqJkb+6Pad0tvbK431l6Xl0hnZvONG1zGYA+zFJnFpV2dknzH3A9asmX5pg/Xb1toiFRVzfRYMYa+OHTt2yN13360G4JOf/KS87nWvU1XRTGCqSIW0l73sZeEe1oMHDx48ePDgwcNVQHl5uTQahBHB7I2rnKemgUAARYBlEio22UPZ8WCANInUuDgUCKoIuLjBJ2jSOHypU5EyaYnxKpC1qz4lxsdIWmKcLwWJ+1hurM3r05XfNAiAxyeTIiKMKPc+PjEtSbPm04G8nhwFk38zZEgnriWYIXKovgf0kUky0SeQA9q8mrS8ncuCVxki2KIyHH1QmZ8mB/c9JT1DhS7CyPQFCpwut3jzQJFCKJlQiRhKM0gMypxDSKQnxykl27lWt3UG7y3LSfF9hjGm0phJGFHdyzTGbu4ZlLGWi7Ju8/YFt5kYz18PBJsHLB3IH7Ov7XmAciMUGHNzHthGxuEo52yTdUgFKp5hQhw7a0Rslh/3B7iKxVRrM/Z4GKGiMAE5MByfLecvN8mSshLJTktQhJhJGEG8UU1OA9N/0p9Mwqh3aK4yHPNlMZSAlKVPnFWqzZsH8dHibxQgE5fkpc0zpzexJkTlOPrKJp39FSkIVaiAPdI8BgSSrQgN1RaOYfrsSFS0FBaXuAiV6266Xc0riJkjB/YqUgiSqLpuhRoLCLaLLb1y7NAzkpmTK2ODw5JTUCLr65y0a7OSJMQghCF7anP3sIswUvPAuGbmiUmWkjrWfPSyXO4ZlxP1Z6WgfKUr3Qw1UUZWlhw8elzWrV6p2ndw7xOyYcvOoAQze3OCkdoM+vt65cCTe1Sq50Bni8RHMd/mxpxrWVqU4SOaUHKSumsilOLNX+Vn1vCip6TdcMMN6gfQoX/+538uJSXBbzo8ePDg4XmL54ePpQcPHjwExJIlS+TBhyPzleQmu9vyClosBEsn42YcssqsyMXNtQ5uIB+o/JOS6L51JSAwn7jb4Eksx9FBl7/zm8G3NsKFaDJxprlXBRnK9yTeCUT0k3IQj6H45Jyh+EJSCggIroaJMn0KeRVpZTg+A2FERkFmVpY0t3WFRVi5FGEhzLVNcO0EyTpNx1S9aSKH8USNYvdvTlqCK+XRhqq+hllxVGBzbbMS0dDYuJw5ekBWrpqrdsQcxRCZdCoCfdZKelJcUHXKQsbUH8l0tcylFwJakpyaKm2d3VJaGDpQVaXrI2i/WoOzHizmdVP9SyM5PmbeeENIbF5RJUf2PyXJ1WXzUtr0sV1tm56Z976N1Xm+f2sFkAnSejFV5pogr5MT4pSCJRjBrBSLEXoL2SpHVZ1tAabY/vrhSsE6NYn7cGAbWrNf2nsS/kAa1bUr5OTxo6qyWElFpUoZoyLkibMXJXpiUnon+2X5ilWSOVutzyRYTEUlCjfTfy6ctDxIqxvWVc1LwTYRm1EkZ8+dk5O/fFjSUpMlLjVHTh47JCvWbFBVFf1hxs9aSEtLl7pVa2RsZERVarMfttifGRqfnOf3FykaLl+W0tIyX/W5UFjQ2T72sY8t5GMePHjw8DzEtXOT9vuAQ/v3yaOPPCR/9d4PPNdN8eDh9x5Lly6V//6fr0f0GU2wLBa4+SWQI1gwVS7cBGMqrBUWBDXc6C83ym+vDENFYYMgwQysCGrO1HcryT5Pt4OmIIUwwuUJu6q0Nj6pUpxWlc+1j2AQs2zOwTESYoNXL3s2ET9b5S2SJED6jfHS2owlVUvlt3uekom1VfPSVYKTJc6/daU71EAmIIOae/BycY4JGcN764rnAq7tdQVhEFPBSRnGgvkGyUd6FISgv2ps+r379u2XmpqlkpKa5iI+IApJ5+E9KE8wQ68zSLRxg+RkztAHEVfUgjCymnatVGNTaiHSXauWyqljR6Qof1fI61OfiZ4jBLXnmQn6EdUQIDZGRQEhZBLXu0OoIxnPjJREqaislq6ONikomv/+/pEJNT5aecK8iQlCZmkFkAn2Ekhq9i6VDjs6IfWdgy5lIKmRJtntmHiHb8zuI2WMtWYTLuHAIYoXnzDyR3yE8xm9P0MWdQ+OqX3SxNmWXt9xeW9s/lLZtSnLR2xA0t6+Y7VMz0wrr6Hw/HyojOluaxuEX3SUMtS2TbVNtDTVy/jYmJrvNt9O2tf1m9fI9PS09A0MybFz9RI96RDsGqQl6/YwDmZan6+tsxXWwOFnnpJHH7hXbrnjVQFJ4uHRCeWLdiW4cOGcLF1aE/b7wyaM7rzzTvniF7+obj74dzBwgT/72c/CboQHDx48PJ/xr//vc3Lx/Dm16Wu8/28+JoVFjplcV2eH/N//flvOnTkjqWlp8opXv0bWrt8oL0Ss27hZ/YSLe97zTnnHe94vZeXh5Vl78OBhDrW1tWpv4oY2lBlsJEoGAueugVEV0ExMTqlADSImz/Ao0V473JRDEpEWYgY7ilQxqpP5S5UIF0NjEyqNhMCANilFwyzZT/rVlqUFKnBCGXCpY+CKSmKrSmuJcUpZoirTzAY8BLZamcD5CQxyjVQYnSoByWB2Mf2CckqD4BNPGeUDMhvkRGr0bQNfDu1DE/ZnIJkMwismNlY9/b54uUFqq5z9mGvpGRpT/TA+Ow9IXzMVN6jVUAdBqKHMIsXGBP1EipBJDFDOOhLocvfMSQgoVE12MKuJSNrM+4a6J+Z5P2k0tLQpn5Ls3Dy/5wGMD+NGSpOJZQbhiWpBpaBYkSbXp9UmOoCuKTJKcc9ApI0qUoNAd7EMiRez3D39o+d678DYbBqnQwrbxubM3/qOIeWTA3nJ/GCemIAsMg2tm5QhdOTmzYxP9xB+ZlMyGNXnMpkG6ytzVZtJX2vFn2l8UgqsamyhYJZ6Z2+jAvrQqDttbUNVnu/a8aoiHcqsfMW8hxxBnaSBasRMQ4SYHhh19gK1HienIiaMxqfcKbxXC/QJBBDfB8wBlontG6S/E/Tez0+2sQY1CWV/J9gqGMytQ4H0Ukg6+rmhoV6Gm09LVXWN1K1cq86/o65QtZn3XO4YDKhGa5tN3QoGvluzMtJUNVJITnPv0am/mizNYbIE8YZKLlku163e5Nrv+T7gh7F3KlxGphb1h/Pnzqr7g0UnjAYGBmRqyrmg/v7+a0oa6cGDBw/PJRrqL8t77/mIlJbNJzWGBgfln//hs3L97pvkT//yHXL6xHH5xn9/RWo+W6eqJDyX0Lnt4QaSzza6uzplZGTER7x58OAhMlRVVcnExIQyvtakKzfSPPXkplabKhNUmYa02khVw06DUMHBwKgyV9U3sdzYm4RRJKlLkaSt8WOnAHEzTeCR6Md8W4OgCQLHJnEAJENT96AiNAA347lpiX6VRhq8l4AuLnZ+8ELf6mOZoGmYffOUXF8rSiWTMMJXg6fvkGAEBnbfE+weu9ztS83za2Y8Od8Y204xtA2RCVoI2HX/MaYX2/ukz/DKqKuplgunD0pFWYkkxMUqVQhkD+l4PD1HeQFxV2hc+45lwT0yGBcCKRPBxDTKzHZsUo2NK01jdEKm0hKlIt+Zk4FA3ycn+P/uPVbfrcZo/4GTUrV0mUo7yktP9PUJY0H/hwuC0JRZTysTkEW2t5A571XFuLRERfKpymNTM6454i/QZIzxPTLfB5ml1ziwq7FBsKKM0QSqDuRNj5+5CmcOOBYKKycmJN0pShng1xSmq7Gk7083u4lfSBCTCAkHVLQLdmvi9Mu0a53RfsjA+OgZKc9NlvwCg4QzwLyhn3RfKULr4jkpW1KtjnmiwVGiAa6Hqm0Qz762oWA05p5TBt1/Y5XJdKyTumaCsSvISHbtmaxZU0tXmpOixp5qdPp68WkzcaKh2+WDBVGD340G5t/tfcNqbep5YK8PjKq1CThgPUL4maQE/Wpek12yne8UxiI9LdF3rRAxJmEU6jtBkY0RkFs6fVib7pvtZ49gDx/q65KsygI5P94t+YUlcmT/0+pzGRhZ9/Yoc+1V65wqoiYutvdLR1ubTCdkS3dHu+R2D0pBVkpQVVUw1ZVeW6G8odp6h+epJdV3woSjZGMe2MdhnbPv6q8KhjkjJd7l3eWPMHrZS26TRSeMHnroId+/H3744bBP4MGDBw/PR4Qr/+5ob5eJ8XEp8iN9Br/+5U+lpLRUbn7xS9Tvq9etV08rGhvqpW7ZCr+feeyRB+W+X/1CRkdHZPO2ndLa0ixbtu+QbTuukx9895uqwMArXv1a9V5u2t73V2+Rv/n4pyUvP19VCLn/vntl75OPy+BAv1RWL5U/euOf+fKpP/PJv5U16zbImZMnFNF1512vlicff1Q+9NFP+c5/7Mgh+fEPvicf/vjfS7R1Y6Ffq6mtk9Mnj8voyKhc/6Kb5aV3vlK9jpLhd7/+pTz+6CMyMjwsldU18vo3/Jnyv9Dn57204eD+feq9q9aul6ce26PIoVtuf6ncevsdShXxL1/8rLohvee975QoiZJPfvaLqqrDT3/0A2ltblLnWlJVLe/46/eFNVYePLzQwF6xdGmtnDp5wkcYqZvJ5AQXQUSgYv4eyryUQIIbdH+kyJUAIogAwCRqTOLDX9qaebMdCKEUOqQnZaUm+gJlbs4xijarzBBwQZCg+uCHzxCU2IF8MGiSSRNGZjCvQZATDAQkkEXBlFmmyiVcQ2TbGJtrRI1hY7psiRw9cUo2rlmp5gBqK004kF7SMxhZOiPB3uhIcPUTAVHXwJhLSUIwZM5Z03/IH8wqUYGgfI2GuqWuLFfKi7MUqTY8HusjC+h75oA209XjeKWgSaYxNP0aimCxA00IM6oEmvNxW21wsg71EwG9OefCNVk/efSg1CytU33CWjCNmRcDylvIGCuIutNNvbPj5/jyQKqaxuu68tfMWL8MDs1VRQy1F/R0dUh7S7NER8dIVmGp6nsqP2rCDEPkWiNFElUKVbZSk5y9gCDeTnkKhfEwyBH26WDgWkKpNLkOfS2BAFlkHoPrtYkPVJrBAElTkp3g61vGJpRv0Px2TIX8TmEvMKv6QY7nZyapNFMNvh/om2MH9ylj6tjYWNm0ZYsU5GRKTUWJIuA6Ojtl2fLl8uTjj8nM9LQrQ0DPv/jRTtm1a5ccPx4l7Z3dyitJfzfxOmSr+j5IilPVIBcDPKwxSURg7nP+QH9A2Oq+5/dQaeXcv//NB98fdrsWdHVUScP0urh4/lPflpYW+epXvyof/ehHF3JoDx48eLimEEpN2VB/SX0x/e0971VKnVWr18ldf/g6iU9IUE/2n37icfmzt7x9nqQbkskfHrr/Pnni0Ufkr+7+oOTk5Mr/fvsbcv7saXnlHzgEUWN9vVIraUAmxcbGSW6eI3/+5tf+U0ZHRuTd77tHklNS5Hvf+rr89P++L29481tUe1qbmyUhIUHe8Gd/KVnZOdLe1io/+eH3VYlNgksIqJ/88H/l5a/6Q5WCMDPjfvKLUqGzo11ueNHN8to/fpNcOHdWpeSt37hZiktK5Yff+7a0t7XI3fd8RFJT0+T73/2mfP8735C3vPOvfefXgSslRCGAXnTLbfKxT39Bzp05LV/+53+Q3Te/WBFNt730TmltbZE3vvkt6v18/j++9I/ypj9/myxbsVL1IaSXBw8eAmP16lVy8vgxueXFt6vfubmlxHOom9BIgfqCQD6SNBJuak2lBE9Ws40y9BpXmrZmeiVBiKDeMIN9npabag3eawdMVKEhOOEpL8oTJ+XMTe4QJJEKRxUcf0+aOQ99fy2CtCmuKVSqA74wVC/q6BlQqiICah3kkfpmexSFAmoHSBgID/1g3Q40IYvMdCVKTEPgRQLSZVDKoBxhbJgDeNiY49TS3ilTfS2yev1m9d0PUTQ1Nb8UOv46A7PzwFaWQB5ACtIn4aaS8UDETKNcEPz4Hl0ttLc2S3JyipSXBCfpIgHV1/AA06o3UpvKclNc/crcNFPdtBG6XwToi6Oz6jz6ir2QFKGO9lZZvnqtnDtzStLyil2pY5zTTmEl3Ux5F41MqJLnrBuzTL0O2tlHWCP+7iPZS5KS467Io4pUt6uRrmgbQIcDm4gjxTDSBwq9w2OqL001lJ1SSL/a1dpM9ZdGc+Nlyc7JlfWrl7vahdqrvnNU+ibj5PH7H5e8vAIZpiiCpQTU52cMi0uLpf7YRUmML3N937EXkIpIm0hvtK+XfYKjQKiG6/e0EG8o4EphGxkPqi7q7+uThvp6Wb169dUljD7xiU/Ibbfd5pcwam5uVq97hJEHDx5eCCBn+WN//3nJzMqW5qZG+Y9//UflU3THK14ljQ2XFRGDGkdjeGhIETp8kdlAYXPvz38q77r7g5Jf4DwZ3LRtu+x96gmVloWipqmxXkoNPx8Iq5KyMvVlcfrUCZXy9onP/oMkJjpfFjuuu0G++42v+cpochf1p3/xNkUWAY6bkBCvjltZVaPUTWnp6QE9liCMNm3dLje86Bb1O9dGWyF+UE7te+oJ+finv6D6AGzZtlP++ytf8p0/KSnJd25UVjuv362Op9ti3nTwOgoiM70PRdPkhHMzASlXvTT8HGwPHl6IWLNmjew/dCRk1aYr9clJTYhTN8+mwoEgkJt/k0Qwb/apdkVgYldDCgYIKbt6EEEGKRM65YbXTSNi7ZmiU6+otoQiQt9U+1P6+AOKAEgGfvyhsXNQBTijvY6pL0EhnhY6hY7gAx8XyAp/pNRCoXxwFhhoaBDU0DdpYfCIkEatra2SX1SqPqMDJV2JzAQEG+mLI8ro2FFsQP5oYor/kkKDQiCQt1SMRUbig2NXrmLMIeNIzeB7rmdw3GWWzXwpzUlVaguOBQkFkWgaq7e1tcuODauDpqA5Cq+4gIoaxpdrnJkZ9fkdoUIwFUOoCCDI6HOuicuOJNUtMJ4dxqilsV6uu25X2PON/ujoG1V+QU6fOCmSpNBpMAcg8wIRlnzOJHKAfXrlpzQ0Jpc78Aoblx7pVsS4GTxrdR7rc3B0Uu596AlZVZEj5UV5cuHsGZUCFo5PEO3MTuNnviKQY+OVBvmtiWjmlGmKzbpg34uPcfyQIH8i9ieanPKb5nQtANWS2e/0SWe/s0drUpD1VWmk0rIvAtQ64X4fDA6PymhnvUxPTUnNspXqcx1dPXLifJOsWLVa+YUxpquNAgX8nh0/LdVFmVJeXakUVXY6Jwr93Lx8tW8MDw1JfFz8vO9I5j9z1qyWaaK+a1B53bXOfh9Aoq0oy/KtG+YGKlWHqPdPLC4EHC/YvDh54pgUFRdLbm7oCodXRBgFu6lAYZSZ6b+UnAcPHjz8vqGg0DG00+QRRItWvXR3dUlGRobExc19CZ05fVIyMjL9+vKcPX1SkTVlFU61BE2ScA5KjaIm4p5Sk0lacaS9k44dPihjY6Py0Q++1/c67y8sKvIpelDuaMIGsJeXL6mS+kuX1Hl+88ufy1vf9Z6A19vUUC93/eHr5z2tQE106sRxReBosggMDg6oJ5H6/CbZBSF086zqAUC45eUXKPmwfv+uG17ke520tte/8c/kvnt/Id/91tdk3YbNcuddf6BIKA8ePPjHxo0b5WshKqWhtiGYs5/UasNWXe3LTBVDHWRWtCFYw5PDBE9dKwvSfTfDqINMYsN+ehwOCPgItkxPnlNNPSp9DH8gfyokVfltNtWNG2kqHF3uHHAFNNqQ1Ux9s4mpUOC9EBJmagCEmSaM+DuBCWoXbur93U8TBJLuQkAKQQU5AjkRLADgNZO4WYgiDIURigkbtJFg3TGynVFzJCcvX44dfEay8ovl7GW3d1WaRaSgHoI0o22MO14nBEmaGIAMCvY0HDhKJvxqnLZq41fbf6giL01qizPUeQgUzblGUI5qxTlntCISGAcT9PeFtgFJSBif9YaKUiqUSMD1QBSYc9v2pmGeUq2L9Br6lnlmkmXa2wqvI1RbtCslPjaopxYqpYUgnE/pOaDUU2NjyvfF9LA529LnUjfZxB8G1niqFGYlqb5n7Ej3M6HJgkBgH7L7yCZ5SVmj75lvPT09kpaVEpAIVj48MTOCmG3jujVqTkVFR0nUzIy09JAC6SgBQ1VV9AeuD8NnCEoNe0/CaJs9pqVnXL1f3dtZqk/2Afqd+c8Pa8gkKhUxcIXmx6yRcIgK+nHa+D7QZKcGSisTXJPphQU5B1HK33Qqnt0n/pRCwdBUf1GOHT4tt9+wVbXv+KH9kle1Uo4dPyUvvn6r5GWlqv6zzzM6Oi4Xzh2TdZu2SUwsKrAYl+JUEY+DIzKZEK2+Wyajk+Typf0SPdgqG7dfF7bvJ+u3yPCcUkUgLMIJlSr7APsb12AT0XgaMU+mJ8YlMTFeUpMSXcQWe7jtcxcKhw7slw3r10f0mbC/Wb73ve+pH8DEuvvuu+cRQ6Ojo/LMM8/Izp07I2qEBw8ePFx7WNiTuubGBilfUqn+DcljY89DD8h1u2/y+4UzODDgIlvAwWf2Kg8kdeymRikqKXF99uTxo3LLbS/1kUukcd12x8v9tg3Fk26biSWVVVJ/+aJ0dXXIspWrpGJJld/Po5bqaG+T9Iw5hQCpLrQHJRCEV3KyO2A8fOAZ9ZRHn7+0rFz9e6C/Xwb6+6S0fIlLLaUJJdRWXV2dUlruvF9j6/ad6gcy7t/+6QvyzNNPynU3zpFKHjx4cGPLli1y8cJ56e7ukuxZspgbftsUOTXRHRgdudyllBHc9MbERCuVCGXkdXARTsl7AgOT6OB4KCsiIYq4eTfbyvkxgzXhz0/GJGMItDAxxY+Fm2ulHLGkMKZXi1NRzW2qrAkgjhXIe0SntmmCiDZ1D45Knq84veMpEgzayJdAwqk+Nj3PT4ng0uwTxjPHUj2dae6dF7ib6i78Yc4296lr0QqrDMsYW6fycRwCaoLolWV4gsRJSfkSOXVkv2zdtlXSkua8S/wbP8e5ybsQ/hr+AAE0R1LMnwP0uan8Qq2h05hAbHS0NHQNSUlOSsC0SSq4ZWTESXpmhgrE8KphbtlzgGA5kBLGIeKC3z+E8qnSHAfBJlWumAcQnOYc1UbzGpwyP8N9XDtQpl2mFw+/Q0qgygGMoVm1S5NBTCP6DJ8eKsjlp2e5xvuOjcGrmOoqirrf+T3SCmisK8y1IR70/ZlNsNAmlFw9Xe1K8WwbBDPHUYBBLI4OD8qZE0ekrm6Zeg9tSkvPkKiJQVleOlftkCIBxYnuuQZh7ZA4MYFL2YdQ/pim24EAeUxbnepzU4p8XWHsuxiDd/WNqD3Gd1xrb0U9w1yeO+a0y9x+fAJT7BEXsWZfF3un8pSa3Qcg3zNT4l0FBHYtn3t46g8YuEMK6r0zXKLKVi219TlVCR0fy0l56a0vUn0ERoZH5OCRI5KbOC3pqUm+sbLX8MXWHklNSFS2C/5S8Nj3q7JiJCc7WyqLM9Waf9Udt0hPZ6dcvnBWKmvq1PcYnwlE5DskcPA5Tl+YhJI/0H6uD//QttExSSmuVWop3XfspQPDE661HurBwcEDz8jWrVvlqhBGBApUSgNMqqGhoXll5vC/eMMb3iAf+MAHImqEBw8ePFyzCPJ9hjEzZYbxGoKs+e1vfint7a3yp3/peBZVLKmUgYF+RaRAwvzm3l9IT1en7Lpht9/joTpCMUTaV15evjzy0P1y9PBBuePlr1KvY8yHbw+paXxZ/PoXP1UeRJpUQSn05GOPyKZtOyQ3N0+Gh4fk7OlTymCa93Ps63bPJ1cqKqvkqScelfGxMbnno38X8HpbmhvVcfY9/YRSU6E2wp8I42zIsYrKanl8z8OqullKappKb8OX6H1/87E5/6UX3ez8u+Gykvua6qCGy5dUWzR5ptIsjKfIeDutXL1WMjIzZXRkWD2k0ASUBw8e/CMnJ0eqq2vkwDP75OZbnaooZrpWIFDi2UyhIFjgCbF98x0MSglilJ7PSI5XaUM2YaSq3cx6V+ADROCub4h5Ss8NfCSpF9wwo2LRT2tRN+AdR5oIgQ9BfTDSQldUs4HJKekHWnFlkzCcb3Bs0kcYcf2hyAN/bW/rHVFBGz+EE2ZACFwl2AOA8Qrm/USFu1AgKDbnAONAsJiSGC15BUWSlJwijz/2uFTV1EjNkjK/AZKdTsY4QsT4rX42Pil9Q+Nq7MxrRI2BCikSk2n6nuB4LvUtWgXK+OQ46U3RLt8q1baEeBXvOO2OdhnpajR0DSoVlVlhzrVO+N6KcMz9m2DPOFXHWDsJ7ipVQAfJoWBXYzNBAB9K2QNZ5JpHZ05IxbLI1AlK6WWQl2oNRgWugNeLae84pt5z/ZqeHKcI61D7jyLXLl6UNRu2SEvf6DzFxtKCVNl7+IT0dPfIxg3rfA/gzlyol0P7npSdu19sXHuUX+Np1FEoz3RaJHPcrFJIlUOUgVcC+oi5xTx1yIxYldJlgjURSp3H2rP3AZOoYa/aVlsQsi3mMZq7hyJWAykzZ6OtjsF1TMA5gEcYZJa5T9EPq8tzZLS/U9gYt2zc5CJ6VtZVSUxqtkpv1GQ5ZKhN0F6/pkKeOjImv3n4CWXFIDHzlbVdHe2ycnmd6quMlAT109vZIcmz1Y35LkDtqucA7TDXJCQTc9Y8ZqTQykOOTVcNj+HX5E7XYxzWLAlepMIGBWfe/pa/iOgzYe+8b3zjG9UP2L17t/z7v/+7LFu2LKKTefDgwcPvE06dPC6P73lIVQNDGVS3fKXcfc/fqrQyQNlOKoR962tfVYqZ2uUr5F3v/5AvRctGVc1S2XXjbvmnz39aESk33nyrIl7wKAJUE3visT3ydx/9kGRmZsnKNWsVcV84W6ENpQ1kzT9+7u9VhbWUlFRFsJAmp/yPmiitPafo0UAd1NfbK7fcfodk5wT+4mlqaJBVa9YpcuwD7367UhrdfsfLZeuOXer1lavXyI7rb5R/+MwnZWJ8Qvkbvet9H1IEjz6/JngarPQ0UH/5kvI0Ajm5ubJu42b5xIc/oFLUPvHZL6ovuZ/9+AcyNTmlPKBe+erXqBQ7Dx48BMe2bVvlvnt/7iOMwgEKDZ5w64A7KyVB2vqGIyKMIIj6hsd85AvHwsfBxKV2J4BHPQKRRNqQmR4XTpoVN9A9g2PSMzTmPAmPcRNMBH4E/5oAIIjCbylSEOCYQYE27Nbnoq0osyhF76g1YuelToVCII+pKwVBR6Qpdv6uHy8iHYSnpqXL+s3b5eK50/JIQ4OsXbtOsjNS3AHpbDogJBMgALKJGpRMqr/iY1SqIelapkJME3DBwPtRSkCu0X1OFb+5OcCxTIKEvuDpvYmxsXHJSA5OxHA9ZhBrkzA0mXLizEXOyXqJlD9aLC+TqwEe4qQkBa/eZYPLN9V6jL9NLh693KXS7wiI2TdQ9pjQBGowsF898NQhGY3Pkosdg44/2uwcYH5QUj06JkbWVldLwfY1Eh/nEMuzVyZLqmtVumUo6LTWQGQsRCUEH3OA+c4ciHRMlVLxWvUnUirKyNqmzJxnSRz+zfeCSTqplLKGbrWHonKDmG+1UmSH+nult7tL6i+dkxffdvu8vYw+Zr9t7iHt1fksKXs20cwe8JIdq6Wnf1ieeOopqVjqrljMg92cvLx53z0x6gHIhPr+4PqDzYG+kXFFJvG9xHcpcztiE/ChMV/qdU3dSrn3J9+X7KrIzP5N4Mt05MA+ZXi9efPmiD67IA+jhx56aCEf8+DBg4ffK0CW8BMMm7ftUD/h4q4/eJ36ARjtUbEMNQ+gutlfvdet4Lzp1jkPIMgjKrTxY4OnaF/816/4PWdPV5ekpaX7UtsCAWNsKqG99OV3yZ/8qf+nEy+985XqJ9T5b739jnnv+cRn/sH1fsy5Tbzjr98XtH0ePHjwDx70feWr/x1R92DmiseLNqYlSLKNhm1wI629X3R6xOOnW6V4VlFCwIBaxI0oqchzzK8BigrS3yJ5gk1wgc8Dxwkn1YX32OkgXNuhS53KA4VrIOUiMzlhnj+ESWRwXgLbhLgkXx/p8u6oKghG7afokBT4feCpQwDB61xrMCJnMUytMZKlPaHSYIIZjBPA2koXylFX1S6XkeEhOXDwgCQkJcuGNasUqaDmQnSUHGvo9ik1SHE0vU309ZlV0JTH0+R0RAEzZAL9Q8pVOP3ENZk+WKCxs196ZtIkZbRPvU5gaT/R9wdzTnDMdZUJLmIyxUpDhNhi3hBIqjmQEBvWea4FKDIuiGcK85T+MNch3lhmRTPUXnZKIQG1OQcI+sMFc5QKd4cOH5fSvExZt8pRhpiAaKA4yaZ1q11rGnJKedb09EjZkmqlQNMf1amhoYgqG+yZet/UxKQ950kv0ylzypsoIdZFjI5MTF4xYRROWtRCEKpAgvoeEGfta9j7Btdt+oPxIAFCX/vQAfMUfT3dcuTA0+oB7UtedpekBiAt+Qh7OAUVgoH2Q27fcN1OefLJp6S/M11q6laoa9tz8JRsXrdGpcHyoCQ3HUP+aOXV2dp6VhIy8l2pf/4IftPMW1XUG51wfYaxIdWP4/IdwBxQKeDG9wDEOWpbQJGXlVuuk5SUyD21NB578Ddy9OgxWb16TcR+0wsijABPix988EE5c+aMSguwB+E97wlsmurBgwcP1zqerfK0wUBVMYic9IyrV0hgbHRUfvDdb4VlHk2q3HU33nTV2uLBg4ergxtuuEHe/va3K6VjuCbxBDBU/eImFjKFJ7ekFZjgKXFr74gvgFQpFNFRPgUGAcN1IbwtIGZ4Eq/TFbhxttPFuCEnXQ31CGokgvClxRm+J7ah0jJ0wMJNO4Gcv/Q2iCoCDVIvUCnxdPdEY49Kg9HgfAQ2+ok1fUNqgr/0NWWY7EeNpbyCkuMlJy1BKW9QU7X34VE0l/JBP5tPrGk37zWflBNwcI5Ahqc2yQQpoYKWCAgjnqIzNjylZ/xRTthjY3qkpBTXSUt7tzzyyB7Z/aLdSilAoGwHyyHPq1RJ7oCZoIuUHEpGg7GJaVlrpGKY6UuBgB8P6WT5mUnzSCVl7Dw5IeUFmYr04XwQV5A7ZsqZDY7jr4JXIGISQBYxz1hXEJ2YwxOUmgQWaYnmWOoKgBqMrfaVuZpE06RabxNq/PkZmCC503h9alqONzheV05bnXQjc2w2VjueQFcCtQeMTqg5wNjQ5xCtKXEzcujgQZUe+aLtGyUu3v/8brh0XtZu2ChnWnpVmqu5NhkH0uCraldIT2ufL/0KghUjbUhIfX1OemX4/e2PmNT9xrxiTXGec619sqp8rs/4G6mbrD/AOe21HmoPYM5EksbpD8wx2sic1d8D9oMDjJlNtR7vx0/MrIK3ZWnwtDeOb6Yq2x5Hl86fka27dsuJM+elpW9EOgZH1VzYsazQtZZXVwRPz8KAHe+nkuxktT7TkhNl943Xy8HDx+Tk0YMyNTUlJYUFsr7aIf7p/8OXumVDVa7kZmfKyYkJNS4m4cl3QjD1kKqol+r+ztHfeewPrBdSsTG6N78HUKpeNIKR4eg0qchImWdGH2wPwMbixNGDMj46Ktuuv0kefOQxufHGGyRSLGgWUUqTm4+zZ8+6SpGajfUIIw8ePPw+YKGVRxaLMCqeVRddDfz4B99T3kVbt++SbTuvC8vQu8hPdTcPHjxc26iurlZpnvv3Pi27brjRd4OO4a1JnkCaaJk993QYtxIsOkqIGKkzDHPV+2NiVOBlEgK2ND8UuGGGdAhG+pDiQzsJ9AjAuLmG0Al2kz6CusciWGgrBADpHkrtlJnkV8nEayis8FIx/Xf4PE+FNZFBalYo1ZUNzofxLoa9pGlxDPwuTKwxgh5dqc4mJI7Wo9pxSj0DiBzTiBwVB8bXulQ3b4OcM2GPFUEf59b387S1c2BEeegwNygRbRsr2x4pakzGkmTPnj2yavUaKS3Mi1i1EB8331/o8KUuFUzpKnSmYsUfIBVIBTNJJ1VxKo5+oaoXRu9xUjarRFCEzNSkIkic/otRAa/dR6YJuzIAnnBKxS+kWJXjURSvPm/7HhEIm+e0X8dHxuwj3oNxvWmqzryyzdFNQMI2dQ26TNxtL5akuBildFAG9rExUsRSn5l2HZN5SGUyjVBjEx7c16urxqFAYUwhVM42dcmJs0dl9frNSoHhzxNLr++oqGgZm5xRqjEUjBC9BNooUaiaReU3G5AtqGDMtcdY9Rn9upCUQzNuZl/jx/YpY68rzZlPDpmA0EblqOcGfWISJvxOBS4zvc8mse35zVivWzK3B0zOGkJDirCXQALb3l7sG+YeQCVEu1piWB5HhvoUVRoPLQAWC1gTtPWPy6a1KxUBx9idkp6gakL2RLg0UyEG6cj31blZLzMURKyZjetWS1dvv/JQS0mZq3TJtbbPGm1PTjmVLVlrKMTmUrfdFeHCAWuTCqK0n3nGfpVl7c/rq/LmkUOmAkntAbPfP873xLSkJzvXo9HV0eZ8f0WJJCYmyZOP7ZHPf+6z8qwQRu9973slNzdXKYzKysrk6aefloKCAvn2t78t3/zmN+VXv/rVQg7rwYMHDx4MYI4dyCB7MRAofS0QPv/P/37V2uLBg4erB25+X7R7tzy65yEfYZQ8mwJhqifs4CGYskIfo6Vn6Iraxg2z4zszF3XZT8UJSHkqrG+WSa9ClWGCz/cOjSsl0cwsYbSsNNMXLBBkEDxpgoAgC/+ZYAbSECQE1VohALm1Mjl7rgAMBteWWokgHvKBQM5fOghkVCQeRYxdXGyU37bZY2cqisyS3sFgBnqQB2aVN66Bn0igvE3iMmX1hq1y4sgBKcrPDanIINCBZHCCNIc4sk2dUR/4U2qYgEhs7h72pdERYJlpTsD0siLg1n02MDQk6Rlp80gVG2a1OsjC3mHHo8QEviUY4oabysQ0mZgMPCeUisSPgokx1oQvASeBeqCA0x/4DFXFzOpyoczRa2vrZP+Bg7Jr+1bH0Fz1n1wRZowAXMMea4LyyuIMn1DhQkOrHD9+UnZtmyOLGDuCe+1LxH9NQ2CV9pSW6COnuX7I6v72Rikq9V9Aw1FxzY0vY7p2Sa6v3ZAldmU59gw+wj7lT/nBtURi4g/slFVVgj0h1pX6ZI8dY8tPKAQzxeZ6TdVLOKDf7Qp2oWB/B7BPluWkKhX88UP7ZeOmLTI44RB+gcBaZv9X3wEzoghDjqHboo/PnlaX5KxjfJMgWJjL+dkZMjDep9R+GuzliXHOfsjHbZN75pCunKjBHgqRnJYYH7TkvUl2oTiirYHgGKC7j8V8Nr+/+C7me8FEQXGpJKemSVJysnR2dMj5c2eV6OdZIYx4avAv//IvUlRU5BuA8vJy+Zu/+Rv173e+853y61//eiGH9uDBgwcPHjx48LDIuOWWW+Rfv/RvIn/7CfU7QZBNXKg0ptkqYgsFSgTTCNqsgsZT48ExSlLHuJ7ILi/JUiRRIFk9hBEVznS1G3/v43XUBFWFzlNbbuIhkHSwQHtMRQbXaB+G95xt6VWfV2Wvk+JVuoU/okIbGtvgCTbn1gbcgOOYT8/9l2K/Mr8R+s9OW4tU2UPgS7AXjsl0IJ8jFEakchGY64BKpxnSL/wQ2Jnl3Ukn4/M28RJOJTHddo57uXNQ6oozfERNKKUL6hlIH8Z9fHxCEuPjlb+UUjckxkpuWlLIue5PGYcZOpX5tPqM9miiUsNsu05riwSKWjPWr1JDLyCVPtJstoysbGmsvyi9/UOSk5mqguhw9gvmAUQK40Qgz/ib4729rmBeSfj5bXXIooaWDjl95rSsWb9REhLngvsTDT1qLmkfHJNAod3NbR1K1aGhlHvR0apYSOmSah9BwOcYs1CkB+2xKz6C5u5BtX9oUpuhNUkXyIRICaPFIJ0i9SK6kuPayh/2CF39DDKfvjXTNfkOQBFqfm5yYkKOH94vmzZtFomNk8z4udecue8+B6mReORpHzNSUEkz1oDg16olDZvMnphCQdiv9iMUhihZc2b3sqSEOElPz5Djh56RulXrlOqJeWKnJLP+mcuXBgZ8yh+q6Onx8vfAgO+KKylIoK5vdNKVBggYX21r8dieR2TDhg2SlRW6SqqNBbWsr69P8vLylClpenq6tLe3+17bvn27PPbYY3I1cOeddypiKjExUZFVf/InfyLNzc2+1y9duqQ6xv556qmnrkp7PHjw8PsLd5Z+aFDe/jOf+IiqIBYpLl04L5/55N/K+9/1NrnvVz+P+PMenjt887+/Is/s9b5jPFz7uOmmm+TQwQPS3zc/xcb2E4oEEAbK22Jy2iEDpmfmHYPKYZAITnWhVOVPY4K0rGCBi1bl8MM5UAbZShBu0AlAdcBBKoldaQk1ACllBCz6uNp/Rys2UJDwBJmUrqbuIVe6TjjgCS+pMzz558k9P9p3R4MAkqBU/5AGEWnVNu1RpAFZRvpgJCDYMau4QTYRzEdcOW18So0HY0O6jibmkpKSpX9gUJmJt3QPqeCsIj9NXb8ZNEEohUMWOSqyMRUIEnya39IEWxlJcUFVPZABGNlqvxXeS+CtUkwuXZLCggJZX5mrntozn9T4LyCIox9J7dTjD1kiVluZw3r88VWJlOtxyBPzd2deRYKFWjUWlZTLpcuX1b8VwTxbnYqxh3Az15RWcpH+CilWlJ2i5uqUMe9UUBuCLFLthSzq6JcHnjwoWaV1KgXPfA2ST1fw8wfGmv7W61GpQwb6VTVavf/gI8SYQWigVloIocL4FmWl+Mbf5tMgJTr6Rlx7QKQkPXM50qpb849x5ZXYdOl3/st+S5/ZlSGphKkqSlLhNiVBKdpIdTPBuDGPhocGFSFDRTs8hdat3yB52elqbvHggLGjv5hT+gGCRlxMlBo3/R3APDRJGFIRSQfmWHr8Sas2gSJxeWmWSrHke4y05NREp5+5zk3rV0vtsmWqffgDBQIPCPgeYfwxdDfVQcxDUv/M8ddrd2pyUlqa6pVXdDDYD2U0gs3XRx95SN0HLAQLmmmVlZXS0tKi/r1y5Ur51re+JXfc4VS8+clPfiLZ2XM51Itd5QMVE2RRU1OTvO9975NXv/rV8sQTT7jed//996t2aeQEKRPtwYMHD8EQzs3C8PCQfOVL/ySv+aM3SkpqeGkAJr77zf+RW257SUTV1BYTExPj8u3/+S9pbKiXjvY2dR07r3fSVjR4Dc+jhsuXVUn71/7xG6WqZmnQ4zY3Ncpvf/1LGR9zgsc/f9tfqQcNi4n//dbX5cL5c9LW2iw3vfglcucrX+16vauzQ/7vf78t586ckdS0NHnFq18ja9dvDHncX/zk/+To4UOycfMW2bJ1u/rbww/8Trq7O2V6akqu332z5BcUyqat2+W+X/1CNm3ZtqjX5cHDYqO0tFSqq2vk8UcfkdvvuFP9Df+GXkMNgpLmYlu/S/bPjSlEADfX41PTKkAkXUjfiEMynW7GSyNepWDwtJX/mrB9jgjUA93wBgIEAUE25/FXDY1goqFr0Jdew85tqzZIJSGwQTU00u0YjRI82J4c7PtU9dKVvUx09o9IzxAGrXh5JMwjKGwSxh/sFA/61P6qOVbfrYgHTawQUJrpJxBaXAdP6XW1LdI68iT8VBCOD1EVP2vIyjFt76CRWVWAGv/JadUe7b0BSMUgIKQdTrWnOMmffeKemp4ufb19kpiY4VJ2cB76KJLqU5A8kD0QCwRzNpGTgO+R4ScFCWQTF5BBjD/eH3jYQLDR/v72PpmYmJT0zCxXAOuPfNAVzpjTzJtICUVgp9spg1/rPScaupXSS4+/7S1EwE3pbl4lGHX8FmcW/Z6HPkKZYY5/VUGuMpFmDDkjxJpjsByn5lS+VaiDuWKmPSk1XYREHH2OYmxocEBqSvOltsJJC5s7pjt1jDWlCVWC7872VrnxhuslPi5WjX9j15AMj05IW2O9rFq+fN752JtsVZg5/sxD9pqFqEIgOkyyQ5Hh1ntIlUoM4C8HtIcb+6ImIyJtC/ufqZK0jaYBqjBK1GvTe2CmZKEcdAiYIV+1rxpjrPXYmOPf1jfiM42emZ6W1pZGVRV4oK9XKcbWbdggSQkJkhAb7SPSuN5zrY4PnZmKqWGTP6xze/8nvbI0N3V2/AdV26sDpF2zv/irtNbSPSyDEzGSX1EnJ48dkjUbtrhe96cesqsG0nb2IvN1/anJyUm5dO6M7D1RLytXrfKNh/bw06D9fBeyPzPujB3+bIGgqr89/KD85be+Kc8aYfTSl75Ufvvb38of/uEfykc+8hF5+ctfLvn5+RIXF6cMsT/3uc/J1YBppF1RUSH33HOPvOIVr5CJiQl1bpMgKiycM4vz4MGDh6sJyIXK6hqpXTb/piMUKOXa1toi6zZu9vs6FRtirCcgi43xsXFZvW6D3Pzil8gXPv0JKatY4nq9q6tTvvofX5JXv+aPZP1fb5HH9zwkX/+v/5CPf/oLQQmg4pJSedOfv1URUn/30b9Re3WCZUwZCHwGk0okv4HAkxj6/brdN8mX/vHzUl5e4Xodtdc//8Nn5frdN8mf/uU75PSJ4/KN//6K1Hy2TlJSghN7L3vlq6W/v8/XF088+ohcvnheSXtz8woUWQRKy8rl4oVzijRMTo7M9NCDh2cbt956izzy8IM+wigzNUH2n+9QT1E17Cf9BIMQSxAkBPgE7nhEaG8MSKZQ3jKOMeiUjyCAkOocGJ1nFEqQR/CBLwzpIiaxEqr6jbppnp7xPeUmVvCXUsL7SmZvvEP51fhFVJTkpSeq4+AZg7KKQNk0Gg1VscyGP3UBwZcZmKGMMlO/6ENS7iDz6CsIBMbJhO1pgqKg1kjZIrjDGNtUhNnHgITj2iDkGMe2vmFX6huk2uoK/+OfkpouvV0dkpboDiCTZwNdmzAiKEUFwHwkVcisfLYpRKUtAjICRMaeuRUVwMeJ/tO+PMxJVAN7Dp2RpctWSTig3RA3ECTNPY7xNPMs1BoIBn/zggDRHH/IUhM5qYlqLVKpkH5jDjAvTZxu6lHqhkDG6Hp9o/7zHTfNfR0EpqR46fFnHjKfk1NTVYpXRXGBbK5xKkqFC4zF/fla0b6u/lEZGB1Xa8oM2PU5RifS5OGHz837LH3AMfFxgQSlS4uznP3lwpmTUlJaplKKgCaC6LuuS2OSGGblSMD+wrhApNA3zAfIEE1g+CNdQkFlxBi/M16QRba3kAnISvoLIoW1z/gXWelIx+q7XCok+t0sXMA6xm8LbzcNO6WJa2QdQfJCWvFAwQQEjvZ0Chda+TM40C9nThxR91klxcWSXFunyMmWXooCREmSkeoVao4xh1GYsU5Yk8wF04jdXGvaUNwhmSIkLkfHpbYkU1p74+XoiTGJru9yVbdjTZokXDh+deb4Q5jFp2ZIeWKGjHdckBVrNgQcf+Y8ezffC4ytWbnNxpnTp6Svt0d27twpzxph9JnPfMb379tvv10pfFAWUa6VHHn+drXR3d0t3/nOd2THjh0uskinro2Ojkptba184AMfUL8Hw9jYmPrR6O/v9zHSpiSMfyt39BAysd9PzE34GaM6wgsFXLMjh3/hXfsL9vpnN3l4/2DXjorm6Scel4988tO+90BUUKr+zKmTMjE5IZmZWfKu931Q5T6bOLBvr3z76/+lPnfPe98pK1etkQ2bt8ivf/EzWbVmnapgtqSyWv78be+UQweeUUqWzo52ycnLkz943R9LdU2tOs6Pvv9dRVhMU9726BFJS0+XP3vL2+XypYvywH33qj3tltteKrfe/lK/15CckqyUNBfPn5PomBgpKi72XQv//c2vfi4bN2+VTVsdFQ1KqP/73+9IV2e7ehry6MMPuo5XsaRStmx3vpR+/uMfysH9++SOV9wl8fFxYc+h++79hbQ0Ncqf/uXbAxJm0THRsmX7Dunr61UlcUvLK1zHv/cXP1Gk1U233qZ+X7V2rTpWY/1lKSwuVv1pIjcvX3bffKvv94b6y/KyV94lk2MT8tQTj6k+LyuvkP/3ub+XrTt2qIoTqJboM5RZ5RbR9nyGeZMzoyr4vIDWfojv/OdzX9x6663y7r9+j+vmOdSNOEEoQZFW7kAcEWAEL5LsBuQGJBNpGoHMsglEmXYoVTD/1MF4JCokAigCDpQ44aR4+Hsij1qG4IuUNlQA9nsyZlOVCEZ0YGDfzNNubR5LihrBFkFspAbSpscIVYcIRjQxQSAYTkoKwa0+Bgok/Hmy02J847DOeNLtD1w/16mP4agaMPoNfW7MiMfGRiUjSitA5lRJkFeZRnxz6GKnUvRAWEAUUsksUjDHGLPlJZlhBe1aDUZqiS7HjrKHOYfxLeSpfRzSXgjQeLrPD0FnW++IizCCyGP8CUbxMorA4zwg7MAzkALKHrtgpJOtdAl0DH8+PZU1dXL0wF4pzMuRhFlT4HChfI+MfoV8Od/Wp9ZWTnqiFGcny+kA40+lNlLISF1KNh78sGaZ35Asep8B42Ojag5u3bR+3rHa27ifmtvJtH8R1wtxEmjvidIeZ8nxUt854FIHjkN6KdPlGbVGMv2oFBfDn0idP8jrENmOGnOONLH3KX8KKhPMc4jrSPet+epJ9xpqbW6S0bZz6qHgrl27pGtwQkYmJiV6YlryM5LVPhes+pk/8KDBIYNSwk7VswlrrTqkbxl/m9Sc0WrPqChFRi7JS52Xbtg1MCZjE5Pqe8hRf1IFLbL+yypZKp0Xj0pGbp5cPHda8suq5l2TaVQfDh558H657rrrlK3PQnBlyY+z2LRpk/oBDQ0N8t3vflde//rXy9XABz/4QfnSl74kw8PDsm3bNvnlL3/pey01NVW++MUvKvaMp94/+tGPlALppz/9aVDSCALsE59wTCBNdHR0KOLJvEnEv0mZeS1yWsW1jvEJJ997empSeru7JGph9lfPW8zItJLBStTMC+7aX6jXPzDr8zE9OSVDg/0Br33Pg/dLzdJaicJboatL/e2H//sdRUy85/0fUoQ2/kaTY+O+1zWqqqpl9023Sktzk/zh6/5Y/e2B392nFEebNm+Vv777HvW3Rx96QH77m1/Ja1//BkWAHDq4X/7ry/8qd3/wI0qBgwfSQH+/OsYdd75S/vc735Sv/Os/KYLnbe98j1w4f1a++62vy4YNm4Iqds6cPCH5+QUyaHic9PX3qicTkE26/ZQ4Bd0dHUppc8ut8x8S6Pdef8OLpLS0XJ54bI/UVAdPYTOxefM2+eaxr8r/fOXLctcfvDbonnv61AnllRETFeU7L0QWRB59YvY7+3hPV5fk5+UHbTefHxkaksnxcTX3x0ZHJC0lVbra25UR4/DAoIwODSvVFClq/Nse3+czhsfn0lIGBwddXoUvFAT6zh8YcFfzeT6B1P6mxga5eOG8VFY5Jq+hoEvRFwTwTgkHBE54TkAS6Jtu+waY9VuYnexSIRF4aQNbExyDIBMlkpmmYasn/IFUL64J1Yy/oExV58lKUUQAAQQBT15Goi89jafsdqqTjRWz7eCzeG5MjU3OCz4IiiAUIJP8kRvapHuuYlm8GodIlCx8FhJN9zXXjSpKpyCGdYy4GKVEwJBWj6VdoS4gZgkv1D+kkzld4PhRlVhPw2lruNWYVEWwHicdz+yPnUYpen+A6KSKWmFmkiJCUaE9/uReF2lAv0M4MvfO9/Wr+WCSLhAoeNDoHkQJZfuxbJxVQ9FOAlnG2ATzBxKW9L0rMZgPhathaKwRGxsneYXFcqm+Seqq3epeGxA5+E7pFCG7WRA1kMQ6FTaUKmM6LkkOnDgvuzav9f0NUsOf0mVwcEBZpTBupHmxv6BcYh3X11+WZSvXutrBfsO4oKQbn5hWaafB1gvEjKlSYR5DwnMN+IF1D43NC+4hJWKDrH3bnyiSqoq+c4xh3B6Z0sXGQtQyNlBAoYLUZFV/b680nTsqK2++WZYuKVbkS1PPiCwrmUsHhZCvKpivDgKaoIV0Ng3nTeVUoGthDrLvY4ztr98hwvEv0j5prFPzO2VgdEJVPtNIS0uXfUcOSNJklZRXOt+nWtWk9/6ewXGXkhYSjvENtvanZ6Jk/UanyiT3IdEZxUEVrOHgwft/Ky976cIFPYtCGJnYu3evMqMOlzAirSxUCtvJkydl2bJl6t/vf//75c1vfrNcvnxZkTxveMMbFGnEwOfm5sp73/te3+c2b96sTLG/8IUvBCWMPvShD7k+x9P4srIyZeyNqbcGg8Z5tOH3CwnxcRfUf6NjYiUzO0exwi8kKNXCTJRkZme/4K79hXr9qe1t6r8xsbFKVh/o2ilRecvtL5VMwystMTlJESl5hYVqz8jKDfwEt7OzQyprlvo+z+9bd+ySm2fVQKSk3f/b38hr//hNsnL1GvW3G266RX724x/KxNSk5OTnS1trq7zhzX8hK9Y4Nz5lS5ZIVHS03HnXH6jfY+N5wjkjGdnZ8xSZrrZ0dcqSqmrXtZw/f1Z9KdbULfNdP35GXFdZZZUkBZByt7Y0yy9/+iOJjo5R6tM7XnmX67ga//T5TysPokCov3xJNm7dLus3Og8l/KG7p0fKlyxxHZ+AGDJn3abNvmsmR54njfSPv7ZooOZ68rE9MjY+Jr+591dy2+13yLad18mvfvFT1a+v/MPXSXZenk9hBiqqq8NOt3s+IG7WFFg/jCHt/IWGQN/5C31Cdy2Asbz++uvl/vt+LX/xtneG9Rl/N9ba68YkXPBSoAIagbDzZDrWR/ZwDHwt0pP9l5oHKAs6++cIIm6obWKCwAPSBEAWEICaqW42nAo1My6fGZ6Yo2DAnJcn+cnxMcqIVz/RzkpJVDJ/njBD0qgS3m39Lj8jyCqMuzWZo01fbTUSAUGglD1MgAlytWcQn8dsWfcPr9GOotlzqEpelnlyKGhVkg4+laLG8PkJ2+dobMJHGPnzaOKYnIeAC4KK9IjaogxfGWqCHVJ1qFAUCYlhKqzoH8aBMXdMhZOVD00wAs2eGxwvPTFGzl28JPc1tqs0kMKMRCmtqPS9h/MxbpjW8mOrcmZmCRCMcPV7dTU8G5zbXxU1PLD6hsYkOnp0rpJecrzrvfThlVaxWkiAHwlSU9NloL9XkWLO+E+qIBvixw7m8ToLtPYZ20Tj3oS5YhOs+M5os+rYuASJTQi+FrSHEUrg7o42n48M/mf1HYOqzX3DExJrnHdsYlrtUY45f5pqF+SFSRihHrPXur8x4m/sYfzYuNwxoMbb9Asz08/oSzM1lPUWqVcWpJTpRbeQqmq0wxxHf4pPyA/UNMrnaNL5zjQJVvYP9jW99s/3Ncltt94q1WWFhorI3X92xUjaQWocgJyDxNKFC/yBsbY90iCZVpRmqb2bhxeJcbGK3PGXGqn37JONPa6/X2wbUPNHo7qmRqaSsqS/+Ty/hbX3M5/4/sR8W1dQw1dJzxP1t+lppXhcu8lR9euqfQsFWQ88tP3P//jytUMYRYq7775b3vSmNwV9T1VVle/fkEL8kG62fPlyRexQBY3qbP6wdetW+d3vfhf0+Nzk+7vR5wbRJoacL4f5f//9x9yCImh8oZAGJpyqey/Ma38hXr95ncGunXKsBYXFvtfY7IuKS1Va2W/v/ZW85GWvkJtve0nA80C+bN91g+/zTQ31svuWF/t+b29rVgqXlavX+m5KRkeHVTCbmpomXZ2dMjk5IctXrvF9Brnv6rXr5n5vaZa8/AKJjw9OaJCqteuG3a7rJP03a5Ys038/e/qUVFRWBfXsoQ/+4u3vllB461+9V5FiNqamJuVrX/myJCUny9p1G4LOO9pNDrz5np7ubsnIyHBd89kzpyUjI1O1Ldjx1m/con40WYpyaPfNc2Ni4sihg7K0brm6Kf19gnkDHBXtfO+9EOHvO//53hcve9nL5Ec/+Zlfwoj9i4CcONMMUOgHXW4ecDNOcGgGDzyRrchPlczkFPXZcy19EUnouWHGUNRsC0Gz7VNRnoefhnPewdFYlQJgP30lcCGlDcKAIHBVebbrRp7r0Kka+N2gJtGBEURAe99w0GAQJQyf0wGfLhkfiXKHoIEnz/r4HI9gRgcYuj/o50BtgcghxUyXpOeHJ+/6fXh6dHfOXVswqPTL2fHnOPoYBHzPnOtQVX00AWEH/hfa+1VqE9ePIqm5Z1j1ibYzCCd9zQZxnBnkKrUVgbxlGG2D9hOU4TVDcL91ab4a86HRcTl/4ZJcbmyRlbWVcvPOzYJu+FK7WzHot+S14T/FdTJfNJFEwBlpdTKq6DHOZkBPUGiOE/PjYrtTlltfvwkCTeaMM/ZR6r9JcbELUiw5afdzaYPmMQhsTVJMGx+rz8mM9Aw7KVj0C8ot3lbfMeC6FvM6/cEmYIbH5nvLQBZpFcoTTRclKTHV73XQFgg5jonvUnF2qnpYpNVN7B0QGGcv1MtMglvRpgiR2Lm2MoftKmKQIfh6TU05RvX2uIQD28DYThUj7Q5j6JleZx9iHzT9ibhOiHPGRa97jmn2IW0392dIF3/klZ7f2njfXNucwzSPZl+F3DCJHdrOtUCw0w6bYNWkunr/1JT09XbLhnVrfHPM8RGam2+cz15PkEW1xZm+NQjh5m9PQxmEgpK5zPWbxtITU05/6HRTvp8m8DCbbR97qV2swUZNYbpaDzxsAOzNZdkpcmKuYHtI0IdmmiCp2Iy3ORa035wTNlnEHGe8GG9+MPwPRihhdl1RsUSWLg1f5X/NEUY8ueNnIdA+Aqb/kI1Dhw6pqmoePHjwsNhgDyJ1yVQcPL7nYTmw72n58Cf+XnJyg+9tpHbhSVRaXq5+HxjoV3485cbTTsgiSBMzUEABg4EjBswHn9krRcUlrlSz+suX5dbbX+by4sHfJxhQ47Q0N0tpuduHx1YkYTTNNd7+slfIYoBr84df/fwn6gnLm9/6DqXyCgauj2plJvwpqfY89IAyyF6sgN9Je3tMXvaKVy3K8Tx4eLYII6rM9vb2KH81TfawwzgBQ5RKHYPM4Ck4IIWDoCFQCoUmBUzPF8gKnnKH8lkxMToxqQJ4nfJFOogJUsPcKqT56VF4itBWPCYIDhxPormAVFWGGxpT1dxATmqCCnJ0AEL7IaYSu4dU6oL+nEka2P4xBI225wZP3gmgAlUu4ik5QZz2h8lMjncpZvjcstK5NA1/wLCWgJZz0wZS9Ejt06beBH8oKcwgzlYIaONext1RSs0odZM2NScQWVuZo1RKgXxFuE4IQV//KFXSpCRHh6+QQbFFkKQJCeaTSVrq9LpgQLVF4MetylgdAAEAAElEQVT8YM5ybaSRnDl1Svr7eiUrr1Cqlq+RfCNgs5unzYwJgHXaEPNHB2T8jb7RSWzKcNZSXHEtOpjzB1WGPkRao1kRzx9UquM05shRKgiFZGkdG3YpVbSX1ty1Rs0LPCEFuEYCeF1pbYlx7rUVeBTF+CWi4uLiZXTMUeNpUnBCGQ5HaiSMh81cuip9Pd/A10llZK6SZhZnqYQpU49ZOh5YBPb0j1bT8Z1vchD0G+loeQVV7naMjEtLT5RDfsRGzyNdAPNck9/sCWNG+jbQhsuBxp42hapmptVtgcA5WF9cK+mRoxPOnK0z0rogXSAczWuuNEyg2ZshK1jXTtpolLpeM7WL9Kxgexh7C/us3rNCGX63NjXIkqoaV5+yf6P0OtXkqHmUCsyPt5K59zgV+WJd8xiiiHYsLcpUDzRaut0eeTZ4j0lmNXUNzVaCc1SJg36+u1KMfV/PRfrvcmqatLU0SWZuYdCxd/rH/TeIR/P9XD/9HgjsNSgcCzKS1b9Rdl1sH5E1QYpC/PpXv5CXvcypZv+8JYzCxdNPPy379u1T5lhZWVly/vx5+du//Vuprq72qYu+8Y1vSHx8vKxf7xib/fjHP5avfe1r8l//9V/PcetfOHjlHbfJk48/qv697/BJKZsNhDV+9YufyZvf4KQr/sVb3y6f+swXZP++vfL5z3xK9j+zT7HPq1atkXff/X65edao1oOH5wLhSLm5CcnJyZWe7i4pLCpWf2tuapDMrGxJSZ2twtHbo9KYbMNr0FhfrxQvpHzp3zFeNtO8UMOQ0nXy+FGpXbZCTp04plK93vjmt6jXGxrqXWQQN8VUnoBQmjvPZSlfMkdC2aQXah5IF24U8wsKXJUnq2qWqipw+CpBUGGwnZycLOs3BE4RWwxgVB0dFa1uSP1BqQ8mJ2RocEh6e7oVaUZlNf1+jLch4M6ePikVS6rkN/f+Qnq6OpWCarHw6MMPSHpGhmzYvHXRjunBw9VGZWWlrF27Vu79xc/l9X/yRvU3iBGCBV1VjKeXBBOaMHKC3+DHJeUERYcOpiBgeBJfGYAwIughUDRNd3lSzVPwQOkXtgpJ+SlZBamJw0uzHY8SXU0GAkhfG9e092y7On9hFsam88+FBwnEg1YS8G8URIHSn/ylelxuH5CE+BifMkN5I+Wk+oIDjkWwrgkj/h7KG8kGaXK0LTfdaZdOnzMRqoIRT9UJvugnHdTYqptAygTfMRJiVYqIDqa0z1Fysv/gkUCLH570a/DUHZIx0JNyW8mjFVEmCKIJeHXQSp8+9tQzUlqYJ2tq6tRnnjrTpsaL+eCPAMMEnGth7CHPCMggGQO1yx8xhAKCc2hlBnMMZZw+XwzG2WNXli7GsSALzTlpK1W0l1YgqHSYnFSfqTG/Q1qa0KmI/oDp9NDQsCsInpqtVuYP9D9jxNozVUeQ0wTvtprH/NzI2LjsPXBEfd9XVC2VznH3nIQgMj2w8B/yVS6bnpbe/iFp7h5UKagjA859me3pSCVGyL6m7kFFxDD2wbxxGAO7fy4x9srnbMavmsgmOxaSNgipyb6p92fGjXabCOUHBmkOuajTVfk8xIuJUObR7InmtaF2JA3WH1irIxMzMhVFBbu5+cH8hbwPpELk2sw9mn61FUjso6Sb6eM2dY+4KmSyr+Jdxt/0PmcDBRN9cLmDsZ9WhFowEiaKlMPZa12/drU89PAj0j2ZqCrx6bHnmkwDdr6HzPOrazPUVeEA5Zn22dLzyVT92cCL+de/+rk8cP/98oIgjAhQIIA+9rGPydDQkFIN3XbbbfKRj3zElU72qU99SvkbsQnge/T9739fXv3qVz+nbX8h4eWvfJWPMPrlz38ib3unOyXlFz/9se/fr7jrD+TJxx+T19z1Mhkfnyvpu2/vU/Inr321fPk/vyavfPUfPout9+BhPkI9HV2+arVcvnhBlq9crX4ndel/v/11+dsPOL5ouXl5qtKXX8Ko4bKUlJW7freVQFThesOf/aX88HvfVuqjwsJiRRYtW7HKRwatXrve5fmDfxLVaeaOWy87rrvRb/sfe+RBdWyND/71OxT59eFPfFr9TuUvjLn/5YufU4Qu53rbu+8Oqfq5UoRK8Tp+9LB85Uv/5Pv90x//sCQkJMoX/uXf1ZhlZGbJ69/wZ/Ktr31VEW61y1fIu97/oaBpdJGgq7NDHvrdfaovrqaxqAcPVwOvfe1rlQ+aJowIPNpa+nw32SoFZcbtIRMKpCNRclkTRooAmZqZp4ghKOGQEDqQKfEx0b7AJxxDZz4L2cATZW7M7QpOqJAwN9YqJF3VzXdtUVGyrbZABQQQI1wngSFtMVUU3JBX5scFVBCFgjZk1oGQbrMOsAgkIRZ0G01z2XARTvpcKNB/pO/pQIbrjDSIpa/MJ/JzPkdzBABpStpMHx8p5gIKCd3m8Kr+zCiFmS4Dblf4gnAYm8QPxykl39jYKNHxSVJc6nzPcq7tdYWzJrz9itjw5+kECaAVPgRktml1KDD3TZUOKXIoUTS5EC1RcqHNMdRn7Om3yEuxL75HkeM5Ff77eWhGMG6rd2zC6FRTr+/fqM8cJdDc2AVb91zj2UtNcvDgSamurpTU0kLpnhDJzwie5jY2OeXbV5YuXyWHDh1UD5SevnxKopOzpLSkWvzp1SCu9NjZabfhphuaY2+n5qpKhaOTqq8Ze9ZbpP40eMVlpyUENKcOB3a6HWqhjAgM9YH2E9NA4Yg6S4N9E8Un18j8noqOkzP1rbJpZbVvvusxCgRIFkht1ivqHz5nq884lLl+2M9RGGpAOO1eVaxSZTE+h9TyR1CZxvsQ8eGOfQKpskuq5Fxrhyxf43guA9K4TbAHopp11n28dA6MhJUubAIy2zTRbusdlpLswNXuHrr/t5Kbk+srTrZQhH3Hn5aWFtZmhkT/amD16tXy4IPu0s023vjGN6ofD88d7nj5K+Qj97xPzYOf//THLsIIlvN3v/2N+jelqSnjfd22jYosIpj+wU9/KZmZmfKqO1+iqrh8+IPvk9te+rKAproePFwLuO7Gm+S//v1f5bY7Xq5+xyvor977wbA+e+NNt6ofjVtu81/2fu36jerHH97+7rtdv69as079mPjo3wUuLHD97pvVTzDsuv5GueOVr7qm/Ku4xn/9z68Hfc/mbTvUz2ID4uybX/uqvOaP36iUTR48PN/wmte8RhUdQTlYUOik7RPkmWajpEgRRIRblYuggMDFTCmz1T8cmgAhkJIpHEA0caNNQOBP+m+qkGgPfhMQK/PelxinnrDz5Jt0FpVyZpmvavgLHFDy0H7ag1LGVuEQEPPkOznBuZknQGnpcYeou5YXqaDKMXUdmvfknONjfM2TfpV6ZgXii0FW0347pSpSQAIcq+9S16HB9dJe7VEHWbSsZE6tQcBsVoELB6RdTafOSF3JnJ+JCYgoAirSaEhFPHq2Xq7b5vjRmdAl0Z30jsC2Fs4x57fv8KVONd4cA9IrVAoWpCgEmr6bhTjaUVeoUj9Jg8IXhT40QcAK4YonlekppUH62GLwReZhWa+RTqmxqSg529QtCfHOGmbtmx4t+hymUme42R1IBwNr4syZ05JfXiMlhbkhS7xDBEPM8jmtMkxKTpE1G7bI8NCgLEt2fMPo31Dz3iYV2CeoeoaCiJSxUMo7YPcnZBLrmrXSPTCq1gUm/BqQCZCpqBMDeVLxeXPOoTgyFTXhwpxTKL8iPQb7m1M1c24iuoym+4bVXOBv42Oj8tMHH5aYxFQ1NuEKa/gO4ruJqor+FGiOB5Kj6MIY3alkNn9caCdECz9Otcng3zn22B6r71ZjkZEUL4VZSfPWfO/olGQlzbVPFUWIc8+fDVV5as6hhoOk59+mAknPSfYcf2ue1+z5gBIumCH6j//v+/Ka1/zhFX9XxEZiTu09RfUQCrm5ebLr+hvk4QcfkIP7n5GG+npfWhol/XBq10qkI4cPKvNc8Iq7Xi3r1m9Q/37jn75ZPv2pj0t3d5c89MDv5CV3BK5w58HDcw0Ig7/91Gef62Z4eBYRExMj7/nA33h97uF5i9LSUtm9e7d8/7vflne99/2+tDTIFYIG5Y0SH6tuak3CCGUH6gwNAn6Cd33zTCU023zVTqE660fJ5A8Enhha9w076WS6ShnVi0KBIPxMc68KwqsL04Oqg3hN+/XYx+BaAj3954k+AQwBPaoXSCqTDAu3qpk2YvVnDEwATxAEIdU67ni82FWIIJQgR5T5LYHZ5PS8akbKOyMIwUS8h9KGPp/Cl2XWT0a/n991lSLz+nW/MY6Un06dVUz4zjs15VOQ24QkgT9G2jZhxHlJbWSuMY/Ma91QFTy9jms+cKFTBeA8uS/NjJe0IGQkx7eDZG2UHoyY4NppF4E6KSwE8Bgpa+ggOspMQbPSDXkNQiJQKg7jztpDteF4FE0qbxO9tlij59v6lVKEgNE2iQY6kA807qw92s74cjw8xBwfI7cvlDmPeR/pOxrVxVmSEj0lNQYZ6D6HM/fCAddIME+6FOlhyoA7SiQjI12KCpODjokmiZJn1WH+iBZS6DT8kT0QNRAJgfqLQJ/qf/QB7STttLIg3UcqMr/DMR3nPZq0tAGRhAIraTJG2sZH1BiyB5UZihn2aWe9O+POXmW+rkzMZ8sV+bsWXdyAceW//EBGmO+lL1BDaUBuVhakufrN35o3wbzV+yf7wMhUjLxi9w1qTF3vw8C9f1SNO31iKmhCpdada+1XJOHEVJ8UZyW7+iEQ7DXPuCYG2evHJqYUGc0DBggszhkbHa2+W4BSM7a0y8YVc5XSWntRuc7/XmH9oub0lxrX2DmolLp9w8Oq7yDxzLQ4HlLQV01dg2qv57y0KxA6Otrlvnt/KV/8/DG5UoRNGH384x+/4pN5eGHgzle+ShFGdlqaKx3tVa+WwwcP+n6vqa01/l3n+/fRw4c8wsjDc4SrW47WgwcPHp5LvOUtb5H3vu998s6/vlullqC4IUXKKWkdrVIrzMo8ADKClAOdesQTUYIXnWYVjsE1wYJJHKRDHAyP+4InboIpZwyRQMpRXmGGukE3y9qHAt4twQy69c03KipSofy9j4AQEISiKMDvyAwoUJZAFkAc6PQ30k/MYARCAQUV1ZoykxNUcGeXeg8G+sj2weAcJhmxsSrPF/ARTDhG3e7r2X++QxFSmkAgGDENtQlgW3qG1H85px1QqfLkcTE+M23tl6ONn4G/4Dc6Bg+naXVeSBazChzBf0PnkOtcTurSjOorgrGzLW4/plCgDbuWF6r+Vf5GquKQO60S4otx4Lz+xl2lDvYMS4zyJnIMkP2pjPisDvpt7yDG4lxrn2SlJqo5xvkIuCMBQbiZrkKQ6DJvT02QLUvznXGfdMZez0MNvY70uEPQmZWjIByY55CeDjkDeeoee45rGmnb11qUnyfdXR0yU17otz9tDxp/QJUIWYUig/OTTqZJBSqEjQwNSeqs12MgbKrOD5pCBAFJ4E8f+SM46CN8q7oGMIEW1z5ngmtkXnAc59oMlc7opLT3jihShdSjQF5OweAYSSf7iFTtn2Zi57LCWcKHcZ9y+Qjpvc1U/vB5KsyZfksQjZBsvr3eqjQJWWSPu70OIlGGMn4FJeVSWVrgmyd8d0ASMafz0hMlJjoxYoVbdUF60L2ePYfrrCpICzgPld9cXIyaa/S9rapt6h7ykVjs5cqLa3TStUYgVmsnYyV+dn83jfLDAe0kTU2veYhc248O8ox9Ta93/hsspe0H3/u27Ny564qqoz3vPIw8PH/w0pe9XO65+68Vm6zT0sx0tKW1dbJq9Vp58He/dfm0mOmPGp0dHc9y6z14sODZ03jw4OH3EHfeeae8/R3vkEceekB233RLWCa5EEjcxGo1BYERSohIQCDG01eIDZ5ic6PMza8OPrjx5+mpqSwh7rI9hsz0DcqLE0Drm+dwvCc4HjffBPZxMTHqabB5gw9BhNKFQIFAk+CGs+uy7gQf2tBag5jFrKaGgTLkGMfBDHd8YkoREpF6lpikB8bQw+OTvif9OoAl6Ark6kLwavYnwZ95TPM1f0gKo0KZ33b7UhMJLuOUYoxj4WMFeQERZ0OXTg963JkZZf6KKgHix/Sn0n2r1BXR8wNJxhLyinHnvYyvqYhj7qBK4u8EfYw759PjHg5Q4DAPCNQhIQjsbb+lULDb7U+Np6830HziWsyxtQNQ2hRpu2xkZGXLhbOnZHBkTNKS5x8L43DKpjumymMyMDo+r73MLXPcHS8l5/qHR8clNs7xeGK9QfxAMuSkJ7naHmrNMxYo4pj7yog6O9W1n9AGSFXGnfO09A6rVMElRmUxG+xb5rWwh0Hi6Up7pAtBGEYCWx1okuka5poPFMrb6x1S0Px8MEPnQN5Ykfi44f3FdZAiimIKX6FY+so4NONoVgSjUpqpXtNgD23tGZGuwVH1/WGSzKHGHQNzVEeQ0eyZFXmp7lS82UqVjDPXjTk2JKrZjlErdRYCyt3OKFlTlKAefrBfsG+gvIoEeNGZlRGZf3wHmVDV7PiJD7zXa0DUf+cb/yOf/vu/k8XAtWNI4eH3BlR9uvFFjieKTksjtWxwYMCnQAoGc6Py0iA9ePDgwYOHxQeVEN/yl38p//nlfw37M6r09qxCSIObcAKkYDCrWfHUHqNOPHtUQFuYPu8Jufai0YAIap81OtbgiS7Ko+nZJ6+QMoHAU/oTDd2u1BrSvGgVZZghiyC+TPKL69JtoJ14cdjVaFBFEMDq61MpRENuTxyIJQJRAjhUPfbTa4I5/YPKhwDGvA8imDH7l2N19burGYUDewxId7iaUKllfb2Sken4DWFciyk6487564ozVDpPMKAccKdDTSvVDMEfqp0VZVkqJc8fePreMzwppy2/HFQfqJsYd0hL+p2UHo2E2BgVtAMCcoJNx8B7DvgeMU729ZrgmhlrgkD8e+yUQ66Dc0PiMIdCpS9yPF19KVwsxj10OMbamErv3btPBcp25Tr+1tQ9rAJp+p2+CEVQmueub2iQhLRslcqqy8D7SyE1z8feoA3lTUAUMu65aUnKk8a8Nohc5qje55gbttIcIsGfSbo/5RleRexLeB1p8FlMlxl32gfZbZPO+hj+2nUl4xXJXMCU21RX2WPqD/Z1cG2QXSiVIFIY8/yUaBkccfYuR2VqPwBwk1Lsx8466Vdzh34I5D3EHs97UQe62jXj7OWkz2EUfuRy93z10KyJtiJkYqJca5Xj9jBOs33gVGd0t3N0zNk/6DO+J1BmmebfPNBg/jLuKAVJOzTHiWOrazf6A4JtIYUQNO6/79cyPDwkr3zlK2Ux4CmMPFwV4FH029/c60tLI7VMA78ikJOb5/vbQP/cU4/BWZ8j5z3B89U9ePDgwYMHDwvDO9/5TvnCF74gJ44dlRWrnEqPoVCcleJKQ+PGmKA3LSnDFViZQTjy/VXl2b4bYsw/g4Fj1nc66V6+Km59I1KY6SZr8EzSgZC/dC9u9gk6CDI5Fu3WhrykFhCIQEbxGQI8buqDKYYICJzUAedGnqfjfUPjigjhhj92tg2BSjf7A+0mAIYcQTmEsW5qUrxPPYE66PFTrVJb7JTAJkXNJjBCgf5jPHRaH8fkKXmkAYmpSoJEU8aus8GsNjA30d3ZJdX5c8HYDSuLgx6fANJMu9JpbDlps+XRMTKOiXb5BdkBMqQKlZn2HT4peXl5Kt3GbDftJT0sNTFa/RvlEASQrrbHHA1FkqxdkuP4Vs0Gp6gpbLVJKGj1D/2G6oZUQ1PhR7BNVSXGm1QipbCZuTJz8quBzv4R6RuekY6xGHnyRL1ExSbI9rqCuZTT5Hj1e2SYUf1y4swFaWttlaTCWqkpTPeNIYSznS7GPOHv0bPpVcGUj/QpSj2bmDGVK/xu+xytXZKrFCuopliv4RApJvBjQ8EEkQh5BJmCAs00CYdQ1d5WyfFzKsJwoQgmo90LqaRHu8wUtX6rghpkD2mEJlgDq8rnVEuk7NkpklSpHRsdE0mbVZkapAqfN02taTfpeaYCCXLHXGPsFfQX+yZkEmOSljx37ZD37PO+81M8wPL8IQ3N9P9iL1hu+HHxmbriTDk9W+mPPYr0OQ3mwPETJ6W0ojJgf/YMjiqiErBnQnivrshWaiHA9wdpmBBLKfGxykcuEpLQH778L/8o737XuyR+1oz+SuERRh6uCm57yR2quhnlrH/0g/+VS5cuqr+vXLVapaSB1WvX+t5//uxZ37/PnTnt+/fqte5qTx48PFtY5Gq1Hjx48HDNIT8/X97whjfIl//1n+RLX/nveca/ygR5tky2VsYQvLY2DvtUGCA+zi1Y53MQMTqVAjUG6hnTAycYIAw4L0GgUvBM44HjvmWFlOHmXj8NJsAxCSFwobVfVkJUzd6YmwohgjCe9EIq6cAwOT5mNuByzkWwcaqxR10z6XiU8sa3SBNGBMWYlPKj+200Qq8aglQINpQstCM5zykhrQkj+uK65UUyMjGpFE8QPXYaBgGdU7HIeT8BEP2i35eVkuCkpcxW7eF9jK8Jgi/GFHNmqnMRCHEMHazyXwIdXWLeTBty3uD8x/Q9mZyYkLa2BllWUxlW0IsSgeuA0NEltM2UIK7LNo+2gaIoKylWsmPHZUXtWpUaxLjrvuAYzEUdwPF3cy4D5oX2o/Ln00LQqoNhbdKNCW4k0IG8qryUnKDStUyQzkgfMB8ZX/6baZg2Mz6MKXObY1CJjaDeJAGd+Tip+tFRBrpvbFhXzCvGW437tOMfZVZdIiA3YZOMBPWMeWnmCjl/5oQUVi6PKHUJQLYxTlpJRxrXmYuXVfGbVes2+Tx5NBgvk5Slf9gnUCtybn/pq6RBQtAFmofsWRCN7HOsPfYFe+zZFyCWdRsirS5IBS9d7p0+Zl81CQ1w3YoiRZ4w3n1DQz4iU4N+4txcB2uRNpl9QT+Qnuu8xyHrzWMw71hfem+HMEHRaZJjrEHz2piqGF5r6FRQfVzaCikSCqQWjo6N+sgc1qL2LWPcTMUh6YCoQDW4Hr336OtALVRXkuFr+4nGHtd18Pum6jzXXC41SCyun0p1kF/svxyTMTHnCP/OTU90FKkzM+o7B8Jdo6m1U0ZHRyQ7x/9DEOaR6f3EuLOGTD8l5iVrQPXj0JgafzM9DSKpb2jMSUVkrcfFKAVcoLl84Jm9cuzoYfn1vb+UxYJHGHm4KkhJTZWbbr1Nfvmzn8ixo0fmqYvAmrXrZWndMlUp7ac//j95/RveJBkZGfKN/3FuWrOzc3y+Ch48PFewK4Z48ODBw+8T3v/+98uqVavk7g/+jVRWOVVeCJYJOvAS4gaVYIsgWRMpGMyGUgih1tEBF2oAng7bBsShyAOeyOM54c+slJt2UtI0YaQUOlaaFe1VgfnsOfkd8kqrE1A9QcCgRkhPildPvE1lUy4+KelJSg3A+whaCJBdbs8G/PnJQPIQuJBKAZkDeQPhZCpeTMLCX/8Q/BIIBSrjzXEhtTBs1Wa4BJaavOF1fggmlRe0MpZ2B6KkyBDY0I/0N/0UHzsXNIdKI/JHEhAgDk9MOEbqYYw7vh+QfEuLM/2W0LbB2NppgszXhvrLUlK+RP3Ok3rmhSaMmDcEw1TRI1gn8LVVGChJaDOBM6XB1ZgHAOPFWjEBYXHgYocaaycl0SFygs19f0oQPZ/sNEYNjs24EPwzRwmITfUV84VgU7UJ1YJVkQ3iDA5Jm15zDAJ5M40qlCpMIyExUaanppWXUaSpcMx/5plWLQ4Oj8rhCxdk3ebt847FdZBOaRq78zt+L3oOQoLZJc2p5siepMfchiaCUNfgbwUJuDFI6puubugmMLp8ZAD7EiS32X7mhbmX4eO1xPLG4hoCrXXOQV+xrrX5MRUSTcKI/WRwZEKRb47xu/hSrkDXbKqY006HXCPFNSVv7nyba4Lv7xzb7EPWiVnZLODn+npl9coV6t8QJJwf5aS/vYPvnGWzSh9Fjrb1S5VBHjOvS3NSfP2kCCWrUiAG74w564f2Ke83g/Dk/LuWFymCGKIZFSbfCYEQFeVOV4OMvfexQ7J8xUo1DnxXUJnTHHOqZ5p7Jx5Jdl855J//ta5IqoFR5ZnEv1G3nWnpnU2Z9I//94XPqqIWmZn+KxcuBB5h5OGqpqVBGLn+9qo/cP3+uS/+k7z2rjulv79PbnvRda7F8/ef+welUvLgwYMHDx48XB3U1NTI6173Ovni5z7tUxlxg0vKgK7OBZlyvq3Pp8oIBb7DCfh5UqqfrqLCMRVBWgGBsgK/Bk2M6CerwXxK9Dkw04WQIV2NakGmbwQostLnuMm+2D7gK9UMiaJfg5ziNQJmmxzhqbAu12z6N4UDiByCYYI8nYYCmWMbjJupb3zGVD6FAkQApJdphmvzD6byxx8YF4gUgh4AqUU7I6mEZIN2TExTVn0ugCI4I+2OFAzaSIClDaW5XsqpB4ejDIMcoc/s4AvCq6G5VXZWVvvaQCCoSR36iLnFD2NNQOnPP4ixYD7xE+mYM48hi8wxZx76M/RdKGifJpkgITDCtePuUCXG+TSBvyY+WAOhPHqCoaJ6qew/cV421hT45jJjRD93D4wpFRPkTmV+movkMFUgEAl7n9kvNctWuAJv+pBjaDWh+RrpqQVxc/ECZA/zygQqLn5oy5kWlFn+r0GXPGfMIzN5nlKqSpRn9CEGzRgZm+vOXpOMn62ECgbmvVZ4Rc+udUh9E/YeaAOliqm0Yo37I9ACQc85s//ZN0xVGr/TzxBiqjDB0JisrchVxL2+3nVLAluO8BmIbx4yQE45VeuSXMbTKJBIHXM8hZziBfYex3cP3kUQ5YcvdfmUoTbo0xWlWT41bbj98MyJi7K0LE/qSrPVNfOwgb1YjwF/o+36mvkMc9WsVhcKzT3Dvu9h+nx4fFzNz0Ck7DN7n5InH9sj3/3WN2Qx4RFGHkKCRa/Lyzb1jMoXfnZEGQ1ev6JI3UQGws233qaqn2mz6w2bNkt5eYXrPTt2Xic/+eV98vnPfEr2P7NPpqamZNWqNfLuu9+vPu/BgwcPHjx4uLr46Ec/KitWrJB33/0BlTbOjTk3u9pPhhtentib6VqhwFPti20DPsUDiiGCc5MwwheCNASCAW6AST0h2DKDj2DgxhyjU8gXf9VyaGtT96S6UUchRPARKIjWAWUoXxJ/aifaAFEBKQFRYN/ME4QQgHJdEBWkgZhKJz53trnX592D6oMn5lUFC/cY4ly2p1MwoLyg/zVhpNMCI4FJEqJcIOVkaGxakUP8jePRV6vLs33BH5WRIgHjQ0rM0qL0eWOhytmfOiHZReWqshGkEm1AieAPPM3nJ9IxZ+wgF+kzysAH62M95gT7JlhTZol6SI5IFHiLAUN8Z/y+sPPT9vikVBkaGZPegWHJTk/xqRUhHSvy09RYQAKYVelMMF+f3rdfsnPzJS09c56HTrXhYzTPcDnKMUlWqWRJ8fNSva50zFGQoO4gNYlj2+1AhabPqdOObEAW6DFn/CNdXxDueMhp9AxBHvhXnwWDi2wbd1cACwXTw409jXltkl7MA9LMWHOop3iNfZ8+iY6Olgml/ooJeQ7GkfQ9syACx6byXFdvv5w+e0EunR6WrqFJOlN2blwTkOyByAunGqD5eebi8YYeNaZluakunyPVxqFROX3+krzkRTtd+6Xtg8U+rsec1800u1DgetlrTFKcBzm1horQxuc//Sn5q7/6K+XftpjwCCMPAcFi+ff7Tsh9hxp8m9rY5LQca+hRi+hHT12UG1YUyR9dv3TeQgKog87Vt4bs4Y2bt8j3f/wLbyQ8ePDgwYOH5wCVlZXyxje+UT77qY/Jf3/rf9XfuEnGkFUrfiCAuPENpjIicCMWUdVmoqMVUUDAyN+USsfyMMJvCPWHDq4IMjhnoPQn7ZWhvZAIdNaHMNCOi4mRQxc7lb+JflIbDP5UBaQ/UALe9hPRwAOEYxM8Qbpwy8RNvibXUDa19Y74fG/4XaW+zV43JJpJpPmrNoWhL8enX1FPaGNcHfwRlPcbxtYESTyFDydVBHAcfzF0MBKDNqry9b42JMjRy10+7yPGtawoX5qaWqSmssxXoSzcABXSp6V7SMqNtESUAIHa8syhI5KRniZnB6IltW9EqgvS/So4pjs7FCsXnZ/vd8zpZ8cnZz75p4NDUgwJQlEOQXJC+pljGIp4MT1KHOWB40ujAfFGtS/WDWl1yqg5IS4iRUookJrob/0GfD/VnKz+grxzGXkXZaviNZowmrFUL+OUjfcTvPP3x5/eJ5NxaVKUXeh6LdQadyoVOuexjZYDwbwG1hvHgNgLpCoiDmIPIVWKVFjWvElmoJKE+DYfttskhWkKDdliCtfoW47LGoFQwZTb9rqxlTyohUJVGrTjOpOsc9Z2iPG2qnexP0OOa6IoNSHO58uk2jQ8rvY3nVrlkNbOOacmJyUmJsbVp5im24pS9rKtSwvU+enXcxcuyfDQoIyPTyi/oK6xWIlPzZKC4nJZn50q42OjcvbkMTl7fFKWr1wppYV588bRXo8oq5gvgchLfI1Wljmqo4tt/Sr1ld85zsjYhNy/50lZuWKZIsEA+wWxsHlermNFcrYrfdZ8sKH9qPgba5vxNseXvcVcO9rEO9De8ugjD8mhA8/IT370Q1lseISRB79ggf/Nd/bKsQbKTs5/nT+xiB463qwY/fe/Yp1f0siDh+cvPNdrDx48vHDw8Y9/XGpra+XJxx+T7Tt3qZtYFUihMJ41zOXJLylgWlLPvcKZ5j4VPGmgttAqHtIBgoEb6pZeboodUoLPErQ6JZejDTNapzIRptS8jxvnQL4uNvBAMo2T/QHVC+kdhZkpftMWxsYxoJ1ST3d5HwbYJumhU984D6QB1wBxpNVVKIl4ys7TZa6RIMVfOW0Nf0EMgSSpCLRPm6PSD5o8wyfn6bPtPm8LFdiMT7oII4ITbYbL66rcvaEYgw4ggKNl9DfpQ6Ta6Pdo7x8d9Ohj6P6lbVuWug2ehmLypb2lURFG/uGuYkZKEsEa56KvCJ5RVkCABQOkzUBfn6zZuFXKgpBcM9PT0vGed0v04KDE33yLpL7yLokpLnG9hznOtXNe0h4hIUwVEQEwwR9zQJusM94mYQQRalZ8C0Yg8ZppjAucansJaszxXmHt0R7T1N0xOp/rP9vPCY8V/jZH4rrPMz5B6tC4Iv1QvZGSZlbHok8hiXUQq89lpv5srXWP9/6zHZK2Yulcf1vTfFyNa7SrjScbu+XoseOSl58vxYVFiiQIlD7kD5ALwfqXsWEsIDb8kZX0rXOt/Wq8IYJsYgUw15mH/JjqMH0M06uLdRaMGFVphMYyZ01DGEA6ofqB/IWMM/saUkuD6+UcJkHBMXQalDZINtuA4g7imz2d8aafzZRT5hzkiHntzB+zUlmoPZ02Q6zZpMfI8LDExcervmQP4fsEwikpLkaRfSZ4T0tHj5w/f16GhwaksKRc8otKJSYmVhKTHM8xc7z52+oNm1WWylN7HpDeddslOjpG+SOpY7V3SXlxvq+vegbHlOm/Uj7FRCtDb5OYo2386DW/tChDCSVAfXO7nDh+TKZSCqSiMNdF/phr0wbtNckilENMKx6O0McQbaR9LyuZ62tM2B3i31nnjC+klT9w7R//yD3y4Q9/WLKzg4/RQuARRh784j9+eyIgWWSC108398l39pyVN+12qp958PB7hWdRHu7BgwcPzxUKCwvlnnvukY9/+IPy6wcfVU9OUQThQ5EY65jiEiyYwR431ARYpicGN+FmoBwKSn3TN+J7kgrB0dA55CMhuM+AvNC+QwQA2sjUBq+hACCQ5sabgCScFJvi7GQZHptSQX5rr1OZyKxCAyFE0EkbtAE2wZUO5ggE8FDS0E/kTV8irofgSVcf4gk95wukWrIBGUFAi5KFII8f+kGDgGdHXeE8dYAJ0oEg93ivLlsNQaDbwHVCMiVgcB4PQZXgGm/mANdqBrF24GxDBXdGMEY1MTNVDjKKOabTeRQhqdQbGT7C8EKbEzjZgSLkUn3HoKPWGO6V9Iys0ORMdLREJSRKTEuTTPz8J9Jz/68l//pkmXjF30tMZZ2vTZS2XpKZptqKUS39oNUrmjixr9MEpClBpFbo2ylpoUA/jUxPOSqOREyQY1UanIntIcb7cuegGkPlfTwzo1Rua5bMqVzyMvCtclIHk2Pmj/f41JRKBdMkEsew22AjKdntL+QOwqcVAUmXTExOSW9fvzx55KzETQzKjvUrJS09QwXHZhtM0Ico7SBgTeVFqDXukFzpSrHFuSGOzP2JdYhpNnMSEgZCAwWYJsZV2XaDWHWUWO5zluamqgqKytg6JiriCmrsW7RBkYcJsTI4NuEiIMG22uDjTdochuV6vOlLU9WEKk7PLX/jzb4G6aFJJj4fSfU/W4GmKy/OTI3LmRNHZNXqNXKpY0CZWWuChiqUEDasZQguxqenf1AOHTwg+ZUrZDx1RjJyM1xEZ6DxRr2Um1+oUtZ2rVoiRy51KuVRdnKMIow0mnuGlFJRk+oHLnS6jL7rOwaUqlGjrW9EVci82Ngq58+ekdUbtkjPEGmp/aqv2RMd/7jw+6qxm0qNGT4iiTloK2v9jXega//+d78tQwMD8u53v1uuBjzCyMM88MTqNwcbwi4rztseOdEir9pWGfaNjwcPHjx48ODh2sJ73vMe+Y+vfEV+8L3vyGv/6E9U4BAqfYggGqWxVroQjJmVxkIB4ufwpU719FQH3aYPhFMm3nmarm/MCaDxNTGfjkPGcNNNkEXFI8gRf35FnIMn7QQouo2kcbX39frS7Tg2qiOerCsVjSKe5lJCOC5BBeoI3T/c/0BU6bbjbUOAroNt7Z2iQaBie27g20GA5s+PSQcKJmnC5wORc7qUugmUUZjxFmU5bUTxRJCr793oV3/eK3YbIkpbU+bBc+2D8Dp4sXPO7yVmrvoeQFmC0asGgSVkA4H43oNHpHLJEomNT1DBLdfNfMMLq/XieVm3ece886OaQbHEWGqSKqasXKTxssTm50t60kXJk/3S+I0PS8zH/0+9TnBKkAj4DGSfTYypQDjIdWvzZN0/g6PuCn4Qj5BRZlqhCa7NJB38kVShxpsA3FRmoZoyodMXA8Gp6jV3UKUKCUHOjI6MSGNzq6QvXaLiiKauQblY3yQd7W3qAVxyXJS0n59tM+mV6UWypma1rw9UxTQjlqAPWMuoMCDNIIoCVa2jP1nbpurNnEfsC5oQMk3n2Qv03GDtQdjEx83NWaqImZXwUP/gyWaC9DOdgkabOaatQKL9gVIKzTaocwzPJxCCjTfHp7CAHm/mF+ScCdNo3B9Q3Zj7PeqzGkMtFAqsycGRcZdPE6nMJw/vk+UrV0l2VroMdgyqsVBkb2OLPH3ojDSmx/jWUu+Ys1aWVNdJelqKjEyN+PU8goxnf+WcaytyfP0anZonE0Pdyl+o7dIF6RsYlhXLN/nmLd8zrG8931hnpppNVSWzUv06+kYkPXZSVfVGwcjDlLyMWF81QfZyM8sG0hvSMJC3mfruSorztYnvEfokmBeav/WtMdDfL5/9u4/Lv33pXyUhIXJPq3DgEUYe5uG+Q40hzeD8falAGt2x0W1q7cGDBw8ePHh4fgDvwX/8f/9P3vrWt8ltL71DMjP9y99NQDZw066JC012aIJnnonn2KTPIFaTE1UFPEF2UrX8gRQRgn6d4gUJg7rGJIzw81hmpE40dk26FD7qPcNjqswx5BZVl0zCJzUx3kdC8VM8k6IIIO2tgdkspJQmoSDKTGIsLz1RmXxzDE4JcTU4SmDrv2qRnYIEaBuBj0kMcO26HwlaSacgMPSRUmGWtFbnTIiV0e65IJIgJVhqnD9AmJljy/gTrNrV1Bz/kUmpb+uR1IS51yDN6oozA6YUOoGc3SbH1Bgfk9/95lcymVIgKysLJT01VXr6puXYsaOytq7GRbxAHkI8QT4wZrQxJ81pc3xZqYw9FaXSh9KKHMVM2vRJ0W489hj4A8obPGc4o60E8XdNdopV7zApheO+al2cjjmlybPYBahUFhs6bcj83Sxnb481SrGSpauk+cJJVUJ9dHRUBofHpbS4QNYt2+bzezEBuWCOmyqPbqlenBL1cT4/Mdt4H+URa5O2sRZQkAQC89YfITufKJtrA3ObvYCU1JzUBBkYnXCZT9tgH3OndU1JQ+eA2mu0UoS1G+oheyQG5J0DIy6POCq0BSu9bkMVGzDGVhE4ftqgxnp8UgZH8FOL8pnkA66HsbKJj4ysbGmor5fh6TgpzkqWc5ca5cL5czIyEy9bNq6T7LQk3/6cOTyhvOw4r9qfZx8SmKR6a8+w6kPI+dRENwnX1tkjGVHD0t01JYlJyTI1MyIZyXNr73LHgEpV0+B7BSJRg+8Mznm8oVuRbyq9b3pCTp44IWtnyaJgezkqOlRUkIOaNKQQgt6zAWnYpnE1vlDVBfONrHt7uuTi2VOyfotjrB0IX/jMp2TF8uXyqle9Sq4WPMLIwzwcutQZtrrI3ECOPn5IXjzTJrFr10tUXGSVPTx4uNYQ6mbRgwcPHn4fcdddd8l//ud/ymc+9XH53Bf/OazPlOem+cgT9k4IHbwwTAJHVxIjnSQ7JUEudw4o4sBfMB0oANPGopAko5bCIzfD8XrQT9iVr5ChfCIIJCWFtDo+k5wQo4IFnV5VkJEoP9t3UV68rkIFpBAaKBE0YQTZ0tM6poI/AhWCI46pSSkCTE1o8XdSTAjCIql8BbFByk15nhPA6FLNmpQihWTfuXbJGXLKixP4Qr6FSxgBmkL7dJAVaTW1vDSnnyGyuHYCI8bDJIxONHSrtiUnxEnszLgMT88FsgSZ4fhPORWRppVihLGmj3dt3yaH0tKlvr1PouMSZXh0VNra2mR5Xa3Ep86dg0ATVYqeX5OzxyGYjR5ql7zYxyXj1qPqvdPTTj+kZXRJxj86fjxDm94p4/l/KBeiHALCH8Gg+5w+YKwjJXdYC6QI6fnK3KZfkxOc6+B6IUEgaRyPo/leL1cbztyeC4TxXTEVR7zOWBMwM9aoJlp7J2Ttpm3S0dYq+UXJkpoWnkKFY7f3j8x68LgJJOaYqd6AbNHoHhhVih9SzugzCFsMhM25AFlottkGa4j0QUggfwogrQ5UKa+Dzh4QbiVHwPwg1UtXRVQeOpPTojVQHA+DeuDsTYGVSIHAmjZJKtRFkZRvb1MEU7K6RtIUIZxMIpTxgdhQ6YsosGJJo5qQXGN4A6kTq5Yuk5bGetm7/5AUp0XLdFyKpJYsU1S6JosApBz7sR5/1rDpC4RajdFDPcZ+y/qgzRr8XlpWKomT/dLR1iKNzS0yMxmljPBNjyWId5RqzGnmm6lg4rh8R+iUvvMtvdJ18bhDFhmG3YHAd0xl/lxan10VkD7j4QYpzpwLkg5Te3/j3dfTLfEJiXLu1HGpWbbS7/mOHTks3/r6f8v+/fuvaoVFjzDyMA9sChEjKkqGOntk4J2fFElOlrhNmyVuxy6J275DYgrc1Q48eHg+wXMw8uDBwwsJ3HT+27/9m6xZs0Ze8/o/lg0bN/uCmq6BMd9NNoEsBAeBAwE1VY50KgLBFH4cJkh/Yj/VT6QJzlCAhEt2lOWmyJOn2xQZBWFTlJXjPn5aopxu6lH/1SRUS8+Qj7DhhjwhLtZnTMvNumMUPKWCLbxZdtQVyf4LHer68LhQlceM1Lc1FTnqvW2zagM+a5JSGpyLdAWdsmCCwBSPIIIWW1FFP1JZTkOXatbXwI82leZvBNN2+XCUU+29IxI/GwTxPvpaB48EgSebelQfcjxFwPWOuNL3CHAhaaamppU5Ln2piQ2O09IwPGueHaVMdEl1M8Gc0ERcf/OYJKVHVuKZ4x682KUICNQGmjDjmjatXSXtD+yR6MQMKabfSx11BKoSTUTRj1yXmXbCvFVjM9Qu6T33z51rVs2k/wuSD/6nrH7tK2UkO1UFfPhTDVkpZRqmEbIJFDcEnPnpSX4Dd/rITFNziDt3Cpr2KGLOkvZiE226AqEGxI1ZJQzlDek02gSZY5iATKE0u7mm8e7RwevUFHN9WBEc/C0xLlZyjQCfz5DWpueOSY7mhXnvz1qCw6EqGAqZQOmVGoqsMd6DqgnyUisJUaCY5CWBO3vG6eZeVeadNmviRmNDVZ76HKbDpEgGAu0wU89MIoPzsKb9pewxF0zPJQzraYs5F7bNmoczFrw/x0gfU2o5i3hgnLWyhbWIIXrGrDmyowyaOz/jAlGC4bhe06wtUx3E5zkOoH85tvY80kqv2qJM39zAv6c0e37KbyAkZOZLWmGcpGQmq3ls95Wu4Kb/xj7LvNDXrNWptcWZrn4xTdohEnkIIMnZsnJtsUymNMlkxwW5dLle1q6oVX1z3YoiRUxRzZEVX2P4sZlQe2O0SPelE7Jq/SZl2M0cUV5TmY7flQ31XTLjzDnAXMNY3PyOMwsD8Lqar4bSDCJJP7DuGpyRpKRsGRnsCJjue8/73q18i5YvXy5XEx5h5GEe7I00LMzMSNL4rJh3eFgm9jyifkBMdbXEbdspcTt2SuzatRIV66mPPHjw4MGDh2sVNTU1ygD7vX/1Nvntw09IvKpu45AR3GCT+sHv3NzqG3hTTeQPBNQQOkjzCQoI6ghGzYpoJriZ5uacwIKbc96za3lR8HNkJKnAKDstQRlncx5uvXVYUpaTopQm+qk1AeqR+i7JSIpXahSCIRRFk9PTcvRyt1IvEMhfv6LYdw7eo4MUAgR8PyKBrq7F03qdDqaJN50aZ5phkwKDqsEOUglolMeUdXyCD1KdNMkC2XChda5iGwSTViMo8iw+1lXlDkAyVOSnKT8lvDj4vEmImAax/mCqliYnxiU/NUmRF/4UCIw/6oo0jJ1nX2csAoF4ckXdUtmz/5AU3rhTEUUQIabSwN+Tdh0MT+avkq4/+p1k/vLNEtNzXqKiZwmjaBLfomQqu0Z6X/pfMpW7TOJm1QwEcPRpJIBkIvBWqY89qOFEirKTfeoXFEYYP2uouRpA2cxrpmrG7ItgBuSO4s+pfsd/zZQlABkGscs4s6Yx92bux0c78yEQ6Tl3jW7/LJQp+Cb5A3MOj1Tmk+mdZBq1BwLzlCAfdQZz1Rxf5o/ZBpucAaxpCGvUeoyjmQ6kQf/iYQaJFonAnDHTah5UlXyWc9nkntlmU+Fng33OTlXj2mmb/gykiKn04t/sy/yXvlJjbRDAKLAggGmXMp2fmlIkoAnI8EDQx9Tnd1RIjp+bv/dCskM+mgol/JN2Lgu8fysfvOwUVaUREpNTmdcAQW+Oq/pusPqYdcKcJ20RRWtGRoZU1d4gR/Y/LdPLa0Vn3EGWk0KMssxObzT3i71PPy0r1myQxERnDTR2OpXQUDJBUkP+aAUqwA8OdZEG8w31bSAwb821oLySDCIvKXpSjh49qgh/5QVnfU9+7av/IZ0d7fKRj3xErja8Ouge5mHdktyIC0NxS7a07Zzf16bOn5fR73xTBt7xFum59SYZuOf9Mvqzn8h0e7vX+x48ePDgwcM1iA996EMSHxsr//zFz/tUG9ygO2bNTooYv3ODHy7KSF3rGPT9zs00N9n203qUExAqifFO2li4IChBJYCXEOWpIUnMp9gQLNzoa0XFpY5+RbAQ5KAG0QE7QdX6ylxZVZ7teoJtA0LETqfjGJRD1gSFDRQzGMOi5CDYJwCBxDKfkh+51KXIKtqofYvChVYlaWjSTF8bgSukHYSdftJvG5sn4wvCE/ZY54k/QVUk6nNVCprS3ZMTMjDQr0gJVEwmIDdONfXMljKPVuMdDmj/krIi6R+PkvOtvar9BNO2h5JpWHz/kUY1byEsVTCbu0y6X/srvxri7tf8Ur1un9M0PQYQYhAugVLRUOMwr1CfMc4EsgT+GqwhAkp8WyL1DQ0XEA/0C0EpY+2vjyBKWNvKz8ry/AoFRRoYvjcTFokAMcI484OvC4DQjQT0MYQlaTtUlUJN5A+sEVJITUNjE1wfaw7lYCCSALAWTEUY8565HIjMg8RiD2DvYZzV+jbWiqqy1zM8W0bdMW+PNMYyyRrd76bCRe3Fs9UT9Zo2X6fPMZ+mbxgf1qJNqgUDn6eingZEi0mIQbL5xrl9QF2rmQYY+vpmlB8Y3w0qtbfQ2RvNVFBI9FSV8jgsZ1t61XqGALPB33sHx3wV71q7+yQuHjWT+33MEfMa2P8hNAHr4MixE1JcWq6q/gHmAEQO84M9m3ObKaLMv57BUd/eSztYS6H8sjT4TsJsW5NFtKGhvVfyUuPk0vnTKj3NxOVLl+Szn/qYfO2//1tSUsJPSV4oPIWRh3l48bpS+cbDp1VJ0XARPT0t284/HfqNw0My8fCD6ocaGDE1S1XaGulrsatXe+ojD9cMfNP/KuYEe/DgwcO1iri4OPmf//ma7Nq1S15yx52ycvUalQZBkA/JotOuSPXgxjucksIEatwIcyOPkoGn/TwpJj3D9/R6ljTRT9l5es/NuB2wmzBTYbYuLQjq5cBT60MXu1SQRwCpA1yCBYy0UblAEjV1DanXdOU0EwRDmK36Ow9/6x8mWBqW8QmeCjspS5pYIpAhsNbBuVL6zFYB4738bWttgVIFQCRp75hIvJB4Ss316IAokEopEAgmeYqulVg8kVdeSmEq0Bnn5u4ppTiZiE5Ux/KntKJymYZJpviDef1DI2OyLC9WlhYHVrWRvvLAkUalarpxVbEiATkGbSHorOt/XKJk2qdAc/47I/FNT8lY9W1qPAj+AnnV8DrjUk/q3mzaHulneh6jIDJJQ/5uG4yThkTwTZAdyfgGwpV+PlLCaGLKMYcmmIeIY12YqagEzXgbadKV/sAwORjsfrhueVHQ6yLQp/+Z67pMeTCEG8BrMFcggyA0UPBwLabJMalcZsoR6hZTWcN631ydp9LQOBZrPxKyxi4eAOHtz3g8EBRJBeHkUmW5/Y5CgTlKNUL6GjIMFRfEmwb7FISonvsQR6b3kL82mePEv29aXRJw7FAfsZ9BLmPk7a//mHutvUMq3beywPE4AqcvNknneJwi7khJ1ufnGsbGxyUrLXm2Et+wjA0PSGtnj8jEqKTHz0h51VLfuugeHJW6kizf59UeYighSd/D+wjVLGPEuC2bfX84YA8w1UmYcydM9En18pWKeI+JiZGGS+elt6dbVq7dKHe/623yR3/0R7J79255NuARRh7mgU33tvVlcu+B+rBkmazvmyvTpeLP3iBDj+yR0RMnwu7VqXNn1c/ot74hUSkpErt1m8Rv3ylx23ZIdF5kOe8ePHjw4MGDh8XDhg0b5L3vfa+8861vll8/8KgkJiYqcge1gE554iaXm1szNUaDm3SCRogDHURV5DmETXF2sjK/pmqOGSgQfFEmva4kTv2d4ACSihtyV1qHL01lXEbHp2RlebYKisIJGGmzWbXGNLYl8EDhBLlivwdwPoJHiAjiI1IS7FQrHTSX5yaqYISAZ3Ri0pfWRfoUT/l1IE2qEqlVWj1BMGeWZbdBwHLgQqfPY0QpYGaVBQAij+BKE0b8nTaHSxgpUsvw09Gklj845bsn1Vibnihcd1s/KUh4HCVIZqo7yKPNBK46tUaXkTcDWdIw2vtG1ZP3/uExWVWWrSpvHTx0SMoqKoNeQ26aY1KMwgeySJ8T0hMSbuyxnzjtL94qg9d/VFL3fELim/dKwtlfSV/ZzSrdEjUB72W8bHWOo8pxUmD0vMDXRa+DcMgb+tmf/5EGZKyZ4kU6odnHi12sg4/7a7YeY4LgnHQnpRQwJ6jOxfxirdC3LsNqFaRHu8kPy/uFNkOmQApDIkJ2mp5gofqxpijT77zmuPgjQWRBXkRKFJng+vhhfqJiWlGW7XuN+WGSilyjXQFR+Sj5qYqoTZJZOxomwQYwwTfTnrQ5dbhQVRUNsh1FG31sg2tjjNlLbX81PtPR76gpIY7MFGI+p4hu7Xs1Pe3yHtJjAQHueOA5flx2ClywcUZpis9UIJILXzgeLLD2TGJMKeASU6Sv77LseXKfVJaXSUlRoZy7cFH2731Cyiqq5IbrdsojB8/KaHeT5ObkSG1JrtT3Tkh1RYHr+KVGehyqt5Ls1HmkFw8b/Nm68FAEIlGvZeYL6aH688xRjLe1KozvoIHebpWeXFVSIDmZ18mZM2dloH9AEpOT5Gv/+e9y+dIF+dUvfibPFjzCyINfvPXWFUoaeKyhOyhpxFxHsv3O12+R+NjdIu9+t0x2dMjgo4/J4KN7ZOjxJ2S6vz+sXp4ZGpKJBx9QPyCmtk7iII+275DYVaiPvOnqwYMHDx48PJv42Mc+Jvfdd5/8/cc/Ip/67D+o4Icn3JiG6tQOAj6zRD0BBoQFsn1SSVQFqL4RRWQQbGysDvxAiJto0ie4KdcECv/lKbZprnv4YqcKECCUCEDqO9zlkYPBHxGkQVCUluTfQ4en0ihU1i3JUe0kYOZJM+bapPZow2ECWAJL/kuQU56XpoJ/TQxAPpxr7VOGqCqFJCVBmscDlwL310cco5Kn+rSDlI6WPslIiVe/84O5relpY6bJAPqMVBNtiEwsg5JIB4MEfqR+6AAMlYFJ6JCKCGEHWUYKD8dn/DV5pj2tMmKXysmTp2TbpvWuAAsPEQIpzql/pz2abCOIfujQJRnquCxZqVREm5RfnZ2W/PR4WbpspSQl/3/2vgM8jvLq+qy2qfcuy713G3cbCJ2QQAgdkhBKIJAGBEISSOVPgzTSCCUQIF8CSQgkgdCrDca4YBv33mSr95W02vo/545eaTSaXa2MbWzrPTyDpd2dmbfNaO+Zc89N69UfrjGScsoTpDQ3XTa2URkekzQgsUP12MThs9A6YjraT7gRSHKi8eJ/I3XVH9EWcspaY/uVr5Bhct4u60aRE9YAl4o4q0qKCjqSSDQw7q8SoB0YYCoCSqXtmAmjoGWO6VNjBgNRzpuaY48zqZcnEQ2j2T5FBnIOzf1ioMxx5ZyTBGGapJgVdylcOHdq/uxAfy8zOWFVy7B9JFNZ4ZBrubktEDc9j3On2qJgJYuM6lZ+uSdRjVJYnCUEN9MeVbn2RGEl4EgQWoknrmG2ieeypnYmAo6J8hfj+ahyNBNGvOdwHszknTktj6ofMaA3VVgzp1ptrWyWOVLpVrzuzYQXlS3KB4qkBY9nVXPOGhXbs8wgT3quRfpgmX8n4UUChGQnHxYY6XCx14wdYhHnCjwfixZwLniPVdUlOZ7lBRkYWbIAnZ1+VO3fh21bN6OgqBSLTj0bWzeuw0uvL4E/KQUL58yHIylJ/o5lRdp7zfO+OiNVjmQn1ydJtSF5iV3PbAvvlUqdxLHeeqC315ikX7udci2HQyFU7K9AgTuAWYsWyPlCwRA8Hi/cnk5Rrf7kru/hxRdfRGZmYn/vDgV0BK5hC7KcP/nMHNz/8ka8uHpft+GZAu+3/ENKJRLJpV65tAUFyL7g07JFQyF0rF0L3+Il8C1ejM5NmxIe8fDWLbL5H3sEjowMuOfME+NsUR/lxTZn09DQ0NDQ0Dh0qWlPPPEEZsyYgVNOPxOnnn6mPB0tzo70qkRlBoN2EgfmajLbugiNRMq3q+CbgRBTe0ge8HdFGPGcWWn06TDICxVAsuT2QEpJm0FCi1FXLLUHv+jTSHXGyPzuPvOpOkkiEmRWzw4GSOa0MD7lZ0CjlFZ8Ys7vVgySGdAyMGTFHDtzYzsw6GIwVsJS4F3KGZJZKljrz4ScOVgkHPh5toOBKIM/ZdJqlLc2vuvZzbGosfxBIcMIkl4M0qxqq+ycPOzZsU1UZm5Xz3E4HiRyGMwa1YcMLx9lgcy1VZCbCUdzEsZOmAKnyyWqH6uSjSQH+00vHa6t2mZ/r5QUHj8vwyteWazkRwXAuLJsRIbcINYI3UhyYlnh5dKP0SY1AfvOVDOWwjaX304EJEaVeTsDcY73wUKRemb0l/LCc/Ka4fd3ji1JVTNhRMUWSZBYBsy8/szjTcLGbLbcH4w0IENlx9StqsZ2jB/SQ8ZSdUKiSAXnJFCtZK4o9BrbhHzi2vL5A7aKL2P/EHbXGJ5Z5nQhqsB4fa7b24DxZdkJ3YMIq1E7q9pZFSSseMV+kgghYWen3okFdX9T4Pqwti3edcyx4f1jaIExx8ZYtfcijE4YWSBG43aphiQKuY8irMSwuiOYcLoaCRnD3D5JyD/OMfug7sFiFt8e6J4L/s57hrmS30BAsouppnYFADjHvA9srmg0FDymQgKE15uMYSPHyKYwbdZcbKloxMyhBXB0res9dT6MsdxjFo4vkXsU729sQzxTfitIZHGNd/9ebahzzfdUprIprFy6GNPHTcDoYWUyZ+8uX4VwOITMrBwMGzUWZ5+yUFS/J554Io4kNGGkERO80L52zhRcefJYvLSmAmt21aLJ14Hs9BRMH1EgXkfWKgBWUBWUesIJshXecjOC1TVoe5vk0RK0vfMOIr7ETA6jra0IvPaKbIRz3HiDPJq/EK5Jk+GIUwZTQ+PgoErtamhoaAxusGrab37zG9x043V4+a2lKCkt6zfo4pdiPjFVT1aZBmb+vT+wOtXybTUS8JMcIlHT6/2CDGytbOoOmklcbDvAVLYcCUxIMPELPr024lV/5WepKOG/LNtOZRDJDBI75i/1JCCMPhleJiRaGOgy4GIgbC73rBQzT76zHaOKssSrh0QUj232N2FAoNJd7EBlgSpdnpnq7pUKQlKNAVJJ13AysKXSKVEwqGN6iArWaRlFJY1Cf0GjkUIWTShtjU/uGbT2es3hQENrp5BwVEVQVWYmZEQR40qSfV1u+/lbu7tOyEKzuTkDWKqtSIwoZYKau/7AQJCkGdcpVRxqfkl2cn+ruoTBK9MxqQiJdT2I+iQ7FT0JLj0g4UdPH5KL3OyqBX5YmNvltrQxlj9TPAxEoUMSgePH64VzMWVYbu+UNQth0skKgqYxpqKJ80FvIqXeiUZThGBliXgrccB9OUfmdEp1Hl4b/DyJyERBpRKJYmUiTeLPrJ5R4Hvmil4K/LxhEO1Bdqq3DzEn6WWmdDOq2AaivuHn2R41xyQIre2LN8eVQjj3nN+4nySe7kZyin5tJHLpa8dzm8/HymYlvdLpBq4uMqce00ie6larqkmBbR9I+zuCUbQGot2VATl+9HuzI1DTvG6MLOp9H+IDAVXJk/GwlZRjGhoJRDU/VBpRaRgvPXLsxCnYu3sHRpaXYtXatXB7PBgzarK8942bv4LsrCxR/R5paMJIo1/wIrh04ShcPH8EampqUFhY2Ke0X6JwFxUi+8ILZYsGg+hYs0aURySQOrdsSfg44S2bZfP/+WE4MrPgntulPpo7H0m5PUythoaGhoaGxofH1VdfjSVLluD6qz+Lfz37Ejwe+6pU1kCZVc/ER0b9bvmyL0/Ju1JnSDhQOaBSo+bHKbnNL/VUGqggh1/W8zJTsGFfgwSFPAYVHiRdGFhROWMNnng++iORrFCpTFRFMVAlScAv9/xdBbmK3GFAaqgewuK7QtWCNZCmiep5s0d0n4PqByoWGDwWJuhBwn6QEBF1CL1OgmFRFCl1BUkjBuWxfu8PNO5mIKYMxo3fjapPiSDV45R+qdREeo1YVV58It/YEcHGfY3Sj7Gm4H/B+L7zSxJr687dyM1mSlgSIkhCa0szMjKzJLCjCoU+LwR9UEjYmJVVDM5IGvI1ziHJhkTBQI7EENck1w1Nnfkzj2FHoPH8JKh4Lq4DVkmyqgfigYQTPWE4Z1RAcSxp83CsgGu71U/D66CMA9UlZtXTlDil2hXUWKlS7eaxy+kyaw+ZiEi+T7N6pglVhnoTHLwnUBHFwJzplMMLMlHRYDyYHlWc2YeQ47iLF1kM1RTnUqqcNbRJ36hyGgjJRv8gqt2UipDkk9lEn8czkwf8fSBpbfTt6X2theNWgLOC15L5Xmz9nX0XM3N/sKu6W49nF8HP2hE3Zt8iNT/qPl+cnZg6h+czxizax+iaRKv6uxILRrGAUK97YSgYxNZN61FUUoqsnFx0NNWi2NWJfbVeMQYnYZno9ReiR1tTu8ynYcjdDLfTKQ86CP6NIJmk1Ej8nQos64OF2upKbN34AabNmg9vcgr27dmJnLx8vPnWYpSWD0Nhcal87sm//gXPP/tvvP/++3B9BBYtmjDS+MjgcLuROnu2bIW33opgVRV8S5agjeqjpUsRaUvsSVm0pRmBV16Sjfpp5/gJQh7RPNs5YaJWH2loaGhoaHzYv9kOB+677z4sWLAQP/zOt/Dje36V0AMnfpkmCcGYjF+aK+p8or5RT135dJpBG/0tGNDxizcJC6txrB0YUJIUYLUqPr1mcEGVAdMz+ESYJBKJIp6Xpe4Z7FE5op4E8x8qO5hOwtcUccK0GG4MCHh865d8HkdVAWJgYg1EGWhxU+ksTFshGOQMBCS91PmpTFIeJyoI4tP6D/bU90qbIdk1MTWxoIcEHkuWq36TgKOnTUZKVsL7k8iiioBKGQbVVE0oLymOzeb9TUhxOTCyMEMMsEnE2BmkK3QGQ9i2ZTOyc3Lha/PD70rD1rpOtOzdLwQV55hjz/nlmuS5GJhv2d8oxyWxwyCXwWp2HOKMAR/TRagm5powrzceg8cVxVRXWl4scB2rteAPpA3Yo4hrVa03+smYfaJIJpg9iujLcyiqqQ0E9HQhEaHSgcygDxevQa5Ta1sTBVUdJFQIa5l09pMl1nme0pw0UQqyPSSdOW5mtZ4Z9HniNcJrgwSAVdFBopdkREayW9YrlSskje3mmfPLtWCkXyWeUmgQLIa6jXPLdFySgtZ2mMdsIOonzodZjUWyMcWTeLYFrxGOC9Np6clDMpsqIQXetzk3VEYyBZlkz0BM1akKYnt4f+d+VJlRFZooOHYkyKketBpo8z7CNUHVmjUF2Uz2kjzj/JJg5DF27diCocOGo76uBlUHKpCbV4Cqil0YNrlQUud4f1Hec/F87gj2a2RRlpCN/GytRX3G8WNb1fxyvsxkKt/bvX2LkOgebzKWLX4NLrcH+QVFqKk8gAlTpiM5xSDJ1q1dgztvvwX//ve/MXToUHwU0ISRxlEDd3Exci6+WLZoIID21VQfvSUEUue2bYkdJBpFeNNG2fwPPwRHFtVH87u8j+YjKTvxEocaGhoaGhoaPUhJScHTT/8LJ5xwAqbPnIWLL7uiO/ggYUNSiEEev7TzSTS/pDOYXr+3QVKEaIBN1Y85AGBAwKpoyuyYfg9GlbTshEp8M9WNqqKJQ3K7n/6TIKLyhMEEq7DxdZIJ9KGg0oXkFEkSw2Cb/h9RSa+gUoRPshls8gs//Tz6U6jYpRHtqm7pZUyrwIB7bF5v8mnjvgYZDwZUJK8YgKtjsn0kz5RqSCrxdBlMkyTi79OG907VswtsSF6pYJSkzuguYoXjyzExB618Km4mdEjGsKS0AgkbVb6cx+CxSWQwOGNQb04xYj/YzmhGKiqqapCfk93HmNkKMc7OyERp+XB0+jvwwsZmnDo8DSeMNIIxzhWJnozkULcfD8eM51H94NxSyRSLWKFfFdvMQJLjTtNZrhmmFZnJBavyRKU6kqiwUxxZ1SEkKWlSy2ZwH86vuU08F8dDrVtWFCMpoY5tt4bM4Bpnu9UhuS/THxVI1JrnlkG/NWWG3kw9/TOuR+tnSKbR20ldowocu1ieX4mAQTTHheqfWHNlkEZZco2zfbwOeE/pL2WSRI/VXJ/zx7lmKiWJKHWdkXDmPcfwu0qO2Y6BVFpTZIsC143VIDyemsVaPY3kktmsmkQs7wVqfnlPMB+PZLfZ6J7EhFnd1NTeKc4LXDMk4qxrmu1V17laBwPx7lEG1Lm5PfczO7Bd9S3+Pv52PC/vQ0wvFc+xaFSIQqnUl56MBeOKe60ZRRRxbs3XMa8Ppr3y+P72dpQU5WNkebGo1hYvW4WkvBHwelyYkJveTXKv2lGLYpM/mxVUrHK81HVLBRofgpjVZ7w3mf3CzNi+vxZbNm9GcXExSktLsLM+gGEjc8VnKT0jC7n5Bd19a2iox7VXXo477rgDp59+Oj4qaMJI46iEw+NB2tw5suEb30CwstIwzl6yGO1L30WkvZdVYUxEm5sRePlF2UR9NHGSKI9YeU3UR4chX1zj+IB6knIkn+RpaGhoHO0YMWIEnnzySXz605/GyFGjcMLsuUIW8Au7Ss3il3Q+AWaAwd/jpaaQtCDpQ5JFlUVmQEi1RSK+MwxyrKQJv7gz8CXRQmNlEhkkFBgEpSW7+qRtCHGUmyb3fZJG9Dvxul0xySIGQwwyWWrZjtQi6WVUT2uXtpCEYqBjl+rFIIltFcPZrtQkHpJkBsF9zaqiktw0CaKsqqdYoDqAQbIaS7aDnk0qdYJpVea/c9Yx51scO+W1wr4ztUIF1v0ZL1NlEEzJwLbNG/BBezuGTZrdp1qWmOF2GCoxvjdmwiTs3r5NqhqdPW0mGvzBXqQZ28iAmGooqhb4Gsc5EX+sd7dUyTozjx/Xm6qsRI7Irtoe54ceWQyC+TkGlvkZKZJiaPc9gUSbk2QH5zYSFdKNihf+rozNmcJHRZVaF1wLJAkSTSviWWl4bFbrsZ1qTU4f0fu6sCpEOKZmcpD7mxFPCZYo2B8amjPdyZrSNHloYgVsuJ8iS+yuN1XJjuMYyzSe80vTa17TVmN2knYkBw+mkl0scD2bCSIaZjOdNFGwTer+Y1TI663emmEzt2odcm2SLFLzx/sx7wFmKHN7O6hUPXU8ruV4aj07mMmtWCQRVT283qgU4v3eTsnJe+yGfY1yH6FZtp3ylIQNiW/2yXxf4doj8cZ5LUz3oKmLpOXY/O+t5UhPT8esSSO61xRJMd7f+PckFjnYGQzLvV+NLc/bGQonZOTNOdq8fScOHKjE+afNR2dnAIvfex+lxWUYO3ZEn88HAgF84XOXY9YJM/Gtb30LHyU0YaRxTMBdUoKcSy+RTdRH778P31uLhUAKbN+RuPpow3p0cPvTA3Dk5PSoj+bOQ1JW4sy5hoaGhobGYMWZZ56Jn/zkJ7jqM5fihdcWY0j5UAm6VRUrlfrFtA9FfMQDlRlMhyAJQGKCATMDhPqWDnn6y3Lr9IdhABbPt8IKEgwMFhhQGD5G6XE9QhggGV/8Y3/5l3L0rX5JB6MBNgMeElLm1AgG8CogY9BCU2ymjPBJuRlMr1GBCYMWpvAxUDMHd2xTXWsHdq9tlWCJBBLPmWhFNc4FCQuVOiemwOFwN7FgJTusv7NN9INRhBENwRm8xlJiWMH2bm3xwtfSIt4cHnSKUkARB+zH4o2VmDo0T0gspmltqmhCa2cIo8ZNkBSN/C41DQMzpdrg03uqK0jeqJSwRMBgkOPL4M1sOExCgfNhSxbRV+lAE4YVZsgcMAWF+3MdrNlVJ8SMedykqh9LaXcFwJK2kp4swah5zrhOWGKbSjaa43KumB6UKJiaQ8WXAueTbVVl763o7wHYQFKOEgGVhbz+uFZ4vUi1uOaOmMoLgu0n+dPSTv+fzG4yzLbKF5WBDW1ybRVJ1cAOpHqDvfxuzMTykNw0SQulcoTzyGuCVavYRqt6RqlZqFAciK+QAglCR7WR8kp/tAEUlxNCg+ohhaa2QP9Fhkzjw7abCQz6OA3JS5ys4r3STJZzLVOxdyjAceE80duIKi8zyWW3Nng/7M9XiOtLPPC61i+vZeMaN4gxXleN9XWSgrZ15148/cZKLJgyGhMnjO++XklYcYz7U1Ft3k+TfrcoyKQyZK1PHpT0d13Rq2jXrl1AWj7OPf0krFm7Fq3tfpSOmoCxZXm2+9zxjVvQ3ubD44+/cNDewYcKmjDSODbVR/PmyVb0zdsR3L9fvI9IILUtW4ZoR0+Vj3iINjYi8OLzsoFVOCZNFuWRe8EiOMeO0+ojDQ0NDQ2NGPja176GDRs24POXX4z/vvga0tLT5Qu0MiPlF30GcooE6g+KAAjU0X8iYng8iGrAI1/iSSbQn4eKoVFd6W7xwOCBHhgM9hIhmdST43jeFSqwUOlyDAQZnFBJ5G8ISf/pn2KGkEcxUxs6UJrbe2z41J3HMYPeQvPHGmbMTJ070GAYJE81KauoCiDRZjcu9GdhEKlILAbUnJdEFQ/0eCFRxiBbpaJRtZCIATA/2x52wlM2GRW+DuzcXI3RIzwSwHJsSKBQLUGiRKXNjSzOxKr32zFs9ITugJJEDskBjj/nlsRCcXZav6oQmnoz5UYRDhwjrieuJTE8z00TNQd9dKiWsqahcc7X7q7HtOF5fVJmuGY4t+ZgneuW5JJVeUXy1EpGcTxnjy6UvtBzSZRbXZXd1LHYNnrV2JE9JNd6EUZJxrpPFB+WIGKQzvlgsE9YzdZJlpBgVFUKeW1wbJhCaF2nSrnG4ScxXJiVLGliVJ3YrWmmH3FNkqxVqXu8HkniUZHHe4R1LnmPIbnAVEG2neNqJqUIXmM7qlvEaJkbze25NnlfsFZliwf6linVIMmLgaiLeG2aiXb2ieRKonPKdamUOGwD07T6q2qpoDznmPap1gfTrQ5GbW/241LggwFWR7QSYFwbq3fWyhoaSOofwc/znkw/NraYLbWml3qTk7F313YEsrJx1QVno6KhQ5SaVPlxflUKdSKEc7SLLF6y8YDhA1fdImspM9Xb637ElFp6JTXW1yK/sBjZQ8cj29GO995bhvTsXKRlDsGYGATVQ3/8PV556XmsXLECaWkD80Y7HNCEkcYxD3dZGXIuu0y2SCCAjpUru9RHSxDYuTOxg0QiCK37QLaOB++HIyfXII+YvjZ3HhwZiT/R1NDQ0NDQON7BAOIPf/gDzjrrbHzx2ivx6F//IUEVAzk+RaaigoHf7pqW7spbyjiWQYxdagGDNz59ZhCvKh6RjOETcqYe8fgkkBjQFWam2JajZ4BPcoTnIWHEz/B48QIelYrgZelsn1+CO2vainryTo8eBkIqAGOQweCE+5s9RxIBg2k+oSYYuFA9wwBTpYtZx5tl0amm4WY1GObv7KsiEOjRoXx8ONZMK2Gwlp7iljE0qssl1k6qAajwUWkYDNIZSJuVPQw0GajS/JoECOdfzQ9Jmso9B1CSGkJaXgYWTioVLykqB+iDQwKL7eE5xJ8myYGSTLdUXKsO9xCOVE1QfcHgrL/gjkQRybV0r8vwGMlKkTZ1j32G0T6+x2NZvXsUXEms7pfSrUBQwTPTFklwWZVWXNvch583KgOmCLnGdRMrEOZa57GtQTRTKOl7pQJ3zimJNqVSYjtIcnJ8SYgxBU6Mug8ROP4cb84p1zyVFeaqZLxOpFR9DOKQRCXJG7Nqg2ubY2Mdb44nSTbzvNJzR+1vJX/4G9ujyCjzNUVfJt6H7IhaHofEnSJAzaABNNWANDNWfSLJyvsDFY4DIYzMqsH+1EFmcP7YNgWOO6+RRAkbtpWpkgqVVBsNoNQ8IWl7KT0ka6Lg2ud9xjB8ZtWxsKxXVYGS4Py6nAbZyuvBqELXLg8JeI3yPVuFDlWmbZ1Cplu9oAi2lWvKjqQiUtPSMW3WvF7rkERneyfbOLBUxOb2gBDOJ04slXPxnFTT8Wf+nfN3dGDHlg1IcjoxdOgwTJk0AS+t2Ao0b0LWiOEYPWEydtf7e3lEmfHi/57F3T/+IV577TUMGTIERwM0YaRxXCGJ6qMFC2Qr+va3EKiogG/xYrRRffTee4j6e4wb4yHa2IDA88/Jxkc2zslTEJ02HaHTzoBr3HjtazOYoD2MNDQ0NGzhdrvFBHvBwoX4zrduw09//mt52v/B7noJHBQ+2FMnQQPTzBhkk1wZW5bdJ6WKX/rNASnB3xkU0+SVT4HlaXJptgT6jdWd8sVfBTYMPlZurxWzWxWckzigKomkhF3gxnQrEikqqGXQwyCLx+e5VQUxFTyay2xT+cMn4oZPi9/WV4jBE9tnV+qer6nXGSSyoo8ah3iwq5TF45gVLAyYGZSpdBqO++qddXC5khCNRNARDEtVKkWikAxj6p+qtMRxYFlyBs4MhKj2oB+IitvJS5AoU21VBBCPydQ3jiv9okis0c+krT4bxUX5mDSyVNpCRQANv7kP+8KxJtG3aV8dqnZvRavPh9wkh7xmNuS1I/LMIMFBPywSQmbja/aPbSIhpcg+ZXoeDyQYlDKLc0kCI9rVDutaJdj38V3G0ySKOKdUMNDYeaCwmq6TfPU4e0gnji/TeEjASFn6SBSFWUl9TK3NpevNvkRWU2xrlS56RPF65nXDdUBFhhl2/e9DmKR65F5Aco4EFNObmIZorS44rjSn2+BYtZfn5DW/scJYW2Zw3tg/qjvogabmmH0iQWwlkqywkkVUHTngkGvYPF5c81x7ZsNoXqskN7mOSF70tyYHAhK+JML21LV2pZJGJZU2UXAtkDRTRvUc4/7GwoyBkFtm8HzK2JvEv6RHRqOi+jETRgTXFE3M2U8SqVRZ2imgDCNr3muNVFTeo3k/4DzbpR0S6j7Ba4/qvHgKRI5NRorxeRJAXE/8u8F1bdeeaJdpOvum/NKo9ORamFieI0T31k3rEOjsxJTJU5Cfa1RaXLN+E9qbGzBi9BT4IlFs2dkk9zTuR/C+O3esUUlz9fsr8eXrr8ajjz6KuXPn4miBJow0jmt4hgxB7hVXyBbp7ET78hXie0QCKbBnT2IHiUQQ/mAt8MFatP7lMTjy8kR55Jm/AK4585CUkXjuvIaGhoaGxvGE7OxsPP+//2HuvHkYNmw4bvzqzZg6vLcnAwMIBtqsskSSiETD9qoWSTuxBhN2YMAztjRLgkMSHPyyzS/1JG7W7qrDjJFGNSR+yZ85Ml8Ca6Y48HNGSptbggz6TpgJA4JkEYkABpAkXZJMldOoelF+O2ofc5lteq2QECAxY65OZQbbyNQcfpZKASpO7LyHeP5YRrSGUbVPgm5WmjP8h+KPGYNYX2eomzBiIEwliiKVGMgwTUoRRlRPMPhR3kIkwajsUqkxp0wpE/WXUj/xswymlcqIJB3HKjPV+DyPs+1ADwEwurwQm/fWYPyIEnmfKiG2hUSgGjvO6bvLVyGrcAhKikeLobS5VHUi4HE5lqpinwIDTPaRayg7hkrBHPxy/KxBo1KLWMmOWFBeXlYwMCWRycCe689OZWCubKfA1CKzmofrKK0wdkBMsiieqbXVFNsKBs8MuFX7uH5I3g6EgOA4r9heI9ci54ZzbOc5xX7xvsBUKK4drnmq2NjnWAqZ/K5j8vhZaV6UZKfapn6RoKLiJR6xk53qlXuE8v8xjKZbxHvJfI+iApCf4/XEa4j3lL21rdJPazrqwUCRZAcLpjgeLpDAocqH6Zsluca9vNda9IZ6qfgMjzTjXmNWiJHUMdSF9tcQry+uVb7P69+szOPvvBdurmjsJmatbSSpw3NyP84Vq2LGSsnjdUbihtch55TrjmuA97YTJ5R0r/1Q2CjkQHWlIvk591wP9DDqaG/D+vVrMX7CRAwtK5b7T2t7J1auXIms3DyctnC2rCn+HZw5sqBbcctjKjJ07549uPLSC3HXXXfhoosuwtEETRhpDBokeb1IP3GRbLjjDgT27jUqry1+C+3vLUe0s+dpaDxE6+sReO6/ssHphGvKVMM4e/5COEeP0eojDQ0NDY1BheHDh+N/zz2HU089FSWlpTj/wkt6vc/AgWoPfllmQMcvy1ROkNgJhqMJpXoweOBTfqau0AeF5AvTE6xBLwkSPv2lDwg/R4KDwQLPyy/9fLLNgFU9SWe6E4M9pi8xuGAaDYMflcrFlCC7FAe+TxKCm53ih+ATdx5fpS3x/CSOSCIp4isRUKFDQ2Z6vDBlhoRRenL8qkXsAwNuNbbsQ2+/m97pS+p91ReOGYN1ZY7NsWD7VcoHyQN+XqWWqI3kG8dWgj1WaKszUoPqa6sxdXSZkEgqFY3H5vgyQFPEUJYninFlueI50h/ES6TFj1Ak0k0UqPQykj5b9jf28qih0oU9JoFnB7aH6iQqH5ra2mR8rF4ohDXQFXKtvk0IC7P5ecyg29cpqhj+u7OlRZRB3NesQmOajtnfSlKTYphZHy4wiKZiR7WL1wmD8RE2xIvyYaJBM9eGIuS4lujnkwhIRKV3hrB8W7VcN0Ny0/v1ySIJxADc7hqlyoTKIR6Da5j3BEl5tPmsUvuREKPqinPDe4uZaCBBwPdVKhHNxbnWSaCYiRISVLxOeTzeoxLxxjmaQeUXb29c9znpHrkmOJZpBT3rQKWXmcHfuW5JTFtJsHiEK+fKUFb1Jouo7Npf39aVVpjRZ/1xbTIVmYostZ9Ux9tTL/dac4qieiBA4pukuJoj3s86A2HMHlXYfU+PRKN4b1s1xpZkS6oe92U1S55DEbI7t24SY2qP14uG5lZs3rQZHf4OjBg9DplZ2d1VFkkgc53zGCyaQB82/l5fX4fLLzwXl156CW655RYcbdCEkcaghWfoUOR+9jOyRfx+tC9f3kUgLUZw797EDhIOI7RmtWwd9/0ejoICeOYZ3keuOXOQlK7VRxoaGhoaxz9mzZqFf/7zn7jggguQk5uHk085rdf7/PLNQItfkqPRlO4UKppkG346qX2CPXqKMC2HwRsDBENZlJaQ944iaqhYYMUhpu/QZJoEj5UwkNSk3DT5Us+AhIQO/YwYmJhTgmLBjixiAMOn8Wb/FgYlPA+Pn6gJrZkIK811xQ2cGeiyLewvAxsGS/HAgMdc4t5IZensJrKo7GB6nkrbYqAohEGX6ohP7jmfKhWPn2f6H4kGVaaccyieJohix7at8CanYb3DgWQ300E8mDost1clrJkzZmLlqpXIKyhCYUkpvN6+xJE5LZB+LZIyZzFDZwDPdnD+mWbHQD+vK63FDhwHqhrolaVIn56UmIhU2LIjg6iG4BgwICah0NFgKIO4nu08iwyfpnRpJz1blNk7SRYzaprb5dhK5SNl2g+BgmUgIBliqMY8PaRiONpn3EgEs/2s9EbPGpUWdTBEiWF4Hb+KGg2MzGmb1vMwGOe8cZ2oKlkEf2eaHcnWWOl0DN7N6WcKVLXwnOa0TxJDFXU+Sa9V7WHaHc89pjhLCDTuRxKNKrqBmjkfCXBMmMbp6wjKOifMqjSCXbO+pj5rBa8zph1ynXBeOP7xSFQ7zyH1t4JpsPS1Yvuo5OT4jSjK6EU2ca65RkkmkWA0n4uvkbCmstWcwkaSiEQiX1PV5Hjv57plipw5LZJIcjgwf2yx3M/5GbbZmq48ecZsBIMBbN20Xoij8uGjkNYVA/LYUmXRNBa8L3Gd89pua2vDZy/5NGZMn4Z77733qBQeaMJIQ4M3g+RkpJ90kmzAnQjs3i3EEQkkEknRQCChcYrW1qLz2f/IJuqjqdO71EcL4Bw1+qi8CWjYQ5lM6hnT0NDQSAxnnXUW/vjHP+LaKy/HU/99AdNnnNDrff4NpJcLA3N+6WYg53E75Us9v1QrlQnvv6t21ooKgwE8g3Eqixjs8fdE/5Ya3iNZQlh0BMK9PG3swMCApIgyQ43nf8FAkEG88lWyA4kDBtP0caG3jzo3AyBz2WuFZVurRRFE49e8DHqjGFXJEgEJsqKugJqqHaZKWIkxtpNjT7WQ150kJBOVAurJO4N8puEowojBM4MxpTri/jy+qpDGwI1kgjI552c4PxsrGnp5xHAcxoyfjJrK/ZK6QZIolk9TXk4GPnbySdi9rwJrVryLuYtO6fOZd7dUS9Uys98KCUFryoiaf6Z/cbxjnZNrcfHGAzh5UmkvIo/7k6gkycPg3xr4qhQTrmnup9LPVGW1WaMKes0f28ExNJ+DhAvJNWvQrFQ5fJ/n5lcScyqYeF41MYg25pKV7GicPlCjYjNUhTV1DM4x17mqgqbGxAzDzLt3GhWvIe5nrQpnHm+aGJNcoDEyCUgrYWYFFXbGWjTICjXmdlizq04IDqtvGNehqvg3ELD/NDpmG9kOkre89qua2np5HvE9eg/xNWV4rabDfI/gNWiuUEcOzJw2xff4ttXoeyBQ1dI4vp3BiPzMuTJXrDQq0/mlMh9JFZ7faqZPJHoPos8Qx6k8zyBEY4GEDdWchkIx1MfsnOC9hXNIIpjjOLrY/t7NceJckPwxE728ZnkM7mcdR65N9lWtOd4rSUAPK8ywLcZA8Bi8L5pVoUppRI8ldc2EMofJ+k/rMvzn/ZJ94N8ylR7Mv2OSjprqQTAYxBc+fwWyMjLwf//3f3CaPMqOJmjCSEPDBp7hw5HL7corEenogG/ZMtS99DIiK1ciWFGRuPpo9SrZOv7wWyQVFnVVXlsA9+y5cBwFZRI1NDQ0NDQOJa688krU1tbi8gs/hd89/rRI8gkGeQwMlYyfwS5f4xd0piiZ/VokjWV0oVRDYyChlEV8om82v44FBoR8osvP8SlwokauDPQZSJjNUO1Q19IhgQqDEaMiW28DaRVg8PwMKhhEUYVCsN127SEJw6CHBAUDHvrcGCqTqK2ZdiwoZY8d2UVCg8dm2oY/GBJTWHNQzXHnfuagMdj1eTXeVAww4GFKmdon2d3THwZFk4f29rBSqKk6IF5X/ZEaPFd5WSm2btks64TBLoMwFTCeNLFEntBzzKjQIThvVA9RASVBq8ko2KwEsAPXGMkiMfVOcnQHvCpthOvPWpGP73GNcTysxAW9nuwqIJHIoO+K2QOJwaudp495LArcfRU3kj6ZmybzwxSautYOMW0295vEpHkurYozBso8v5pb1V4z+bNwfHFcwkCq9zl7K9X4L9MZreoRXiccT75PdQVVGiSpqCSxq4RGUF3IvlHxp9LJOH4k6jj2dkQTUzi5Rnlsda0JKdDI1Mf0PteGSruMBc7VpPJcWW+GsXZASJ5J5TndY8P+U31iVRsxLZX3CQWqKanEUn5Hqv/m+x/XMM+lQLKb3mckZMxrzzy2JGGmDOu57piq2eoPyrmosCN5zPVdZLqVsB/mdKxEwHaRZGI/rKojs4m/FZx77sN7SarHKWljPC+Px7myU3VxncTyhlPgmuFneB9uqumUSoG8JngdxCIUeT/lebkPCTUqMq2qov4Q6vI04jWo7i9UGPIY9F5TYFvYD55LgfcS8UILh/HVG65FQ20N3nrrTXi9B2c4fiSgCSMNjX6QlJKC9JNPRvuECSgoKEBoD72P3kIb1UcrViAaDCY0hpGaanT+5xnZ4HLBNU2pjxbCOWKkVh9paGhoaBwXuPXWW9Hc3IyvX3sp/v38Kxg5arQ8aRVZftdTXPOTbjuodDA++aWprDy19bolsOQT+qw0T3cApcDgl8EVA6SJQ3IljYEBWyzfEgUGD/TZ8HUEkJ7ikbSaWE+aSeTwHCowJ8GjvHTYP6o8mBqhgl8jgDACBFV2mk+XrSA5pFJx2FaVQmH3xJ+BHtuQl2F4BQ0ERllrw4PIDrGqDylQdTIQAqvXvh4v2trakZ4cP81v7e46rFm7DiWlI2UeqKzh2DKo5LhyTElqMHhnkE51Bl9jcEaigoRCLF8pBSuZweCSATADf5JRVGOQeCR5ZGfybKgjjKCTqjAGiTwf1Ww8rR0puGhCiZBZVCXwejB8YfqqixIB92ebuXWbD1u6O214fFNrgteQIt3s1lsiQbQYEZsM0jn2JFCp8jCTCiRFeDRzZcOkrrncUd1sSxrsb/AJ4aLUGQTXPO8DJJ94HutYM0CnaoVzQyUTwXsC9zH3R1VBY8VAgil/VmLQzpjbbi2t3LIPntYKNKeOR3Z2LoJdpvJmIoKkFYmTkpwegpCeWVSfOEwkE9eWedzoxWVWtnDdk0BX5AzPv7fO16tNXGdUYanj2pmV8zyJrD3l66TGnvcvEokDAT2dSFpZ54pjSsNxu7RkM8TDq+vaj7UGOXbUbfWnVlPntfogcW7a/KHuazkWWjuC3etSjZ94ZTn7GpZzLdqZmEciEXz9qzdi88b1eOvNN5GV1X/q80cJTRhpaAwAIskeOUK2vKuuQqStDW3vLe+uvBY8cCCxA4VCCK1aKVvH736DpOJiIY5kmzUbjtT4X9g0jiB0GqGGhobGgPHDH/4QHR0duPhTH8cz/3sVQ4cNk6fvZuPPRMBUIgZaJAwYRClTYwaDfM1MXvDLOZ+0k3hRFaoYBAjBlOqxJamY6ubzhySVivuQbDD8TyIoz0vrFagKolFR51D5wvPwewE35UfD6j3yVDmp79NtBsexjK6pBilJsCIYP8tz7K0LdhtXk0hh0DRQb6QjifLhI7F58yYULJgrY8GAncGXUjYpSHqV24FpY8u7X2N6k/gUmVKRSG6RPOPrrqQkMYUmwRPPKFlVRaIah75KVKmY1Wr8mUQlySIGqLHGk+uQ1QDZB6b2UG3AYJrzEssnyUweEoo0MoPjQa6RQW+soNWcztS9H9McbSqExQP7ynS3DwuON69RM9lEos9aIY79IXnL8VfkkmoH1XkkfK37UC20dX+TmJereRKCttUvBKzZyN0KXg8k8MzpRwqqHDrPN7TLyJjkEu8puRnJCRnxK4iBe/MBeIuG49W3l2P8pOkIwC1pk4oYMxQ2RgqbAhUpTAtV9xhJcepKaVNgPzk26jj8DH3dzNXgSKqbK8lxXPhx8/rhGmXaWSyIAT29jPx9H4KzZHyixDTHlSmK1s9znEkKmgmjnkp4htI01vHYP3aFxD7v/bFILnPKKMebl0kivlEcUxY9YEohq+2paoK8lkk8mkFii+1W5CPHjeuexGuiSlae79vfuAUr3luKJYsXo7Dw8FW2O1TQhJGGxodAUloaMk49RTbeAAI7d8L31mIhkNpXrgISVR9VVaHzmX/JBrcbrukzJXXNM38hkoYP1+qjjwKxv4NoaGhoaPQDfpm+5557hDS68Nyz8a9nXzRIo9JsCa6LsnpXhYoHfo4BBfdTqRsMSKxqGH5pZ1Uiqk7oacOgisEiz9nQ6sfm/Y190lh4PD7tJUHgdnnkuAxqSRztqzOCGXOlLKaScGMgyvbw+AxMGcQwsCGJdDDmtmwr220oRwxD5FhBFD9DNYSZzGKwR5WT2RuJfVLBDAm6VJab/xC+KB8WqWnp8LUH8PoH+zAkn0/3HT2GzllGSh8DsEBrHZK93l4EAsd0bGmWVFmj55KaQ6aDjC4muRK/8pIyx/UHDM8cldqklBlMkTHvH2vs7da5WhMMaFWqXiKwzoVhuu5DdrpR4t1ot2EEbb5WGNRbS8QrT7CBgJ8nMWqFnTpLvuOGIgap0BkUAtZcbp7XmDKKjwcG9ST3SPRRGSWlybtIMjtCVyoslmYLkUPSiMQH+09CJxGlmzWIV4bYnHuqfxQRw/5SsUeT9oFeIWoNoKMWJ82ehoKiEiEszMTGhn2NMo8kQDgGXCs0wzab6h/oUtmYyaH6lg6p+qhgGPf3VlYyrcp8L+AYWYlpkh9W8tOcsqjM8q1pZkS864r3IpJaJAwJj9v4LO+zZnBd8Brm+uF8+/yB7nunHSnL+zWPy+ucCh3uzzFVDw5i3WPNaiuuKSoO7SodKlCVRJKOf0uUh15Omge7qlksofc1tqOqWZSdVISZUyzN/kT9gX9rvvut2/DGqy8JWVRaWopjAZow0tA4lOqjUaNky7vmaoR9bWh/b1kXgbQEocrKxA4UDCK04j3ZOn77aySVlBq+R0xfO2E2HClHtkqGhoaGhobGwf5d/O1vfys/f/oTp+Of/3kBI0aOwqiiLCFbqA4wp4Dwy3tlU3u3goIeO64kI2BkcGt92msHpUAioUOCiOQPg3+qBhh0r9xRK94jyneCgR6DKz455rmZjsZAgUESUxb4NHn1rjrMGVPYy+dEKUXE6La2VfrCoMTOzJpg8LO7pkWIMgYn1uDe7P/BQISpdCQ37LyIWIbd+pSd57ZWLmIAT7UOy8QzUGNwzoCKpJryUFHBWXuAygCnEGiy0ZDcEsipeYl2BYr8lQbdZnJhW2VTr3GyloyfMHECFq/4AFOHz0NhVs9YUSXE/dxOB/bv24u5s+ej3tfZVVXMCNA4JxzfD/ZwPoq6z5NIWg19YghzihCPR/KIc8zAj/MZq0IX+85UNRINsdQEdoQNlWgc2/wETMxZkW5IfnovMojrgEooM5jWyDQqsxqEfbCCpARPJ2l8NullHL9A13H4Wa4Nkoy87syEmTJp5vrgtcH1y7Q6M2GUKMFG8BrjdcjUJo5lIumpvAfwOiRpGOsa4xrnZ+O1hWuK9wb63pjXKQksVkgcaTHTlrLwNa1CXNBHKBZ5MmHKjF6/WwkEqo1IFlEJVt3cIXM4fUSP5xDHn4Qz7zlsH69BVhtTFQp71EUBFGdnyfVHgpFVDa1rsba5Q+aKxFG89ZFIymI8KB8lEm3m9LftlX3TaAmm6tKXrqjLv8oObDurkXFtWH3ASDSy+iNTVpUpvALHQ3k/mQkizh8N6Pl3xpwaxrEkscR/lUk5wXlhejAVjVaSaWRRpswhCS9F6HI9qjXBe7wQt07eOw2VldmInp5Ft9/yVbyz5E28+cYbGDp0KI4VaMJIQ+MwwZmehozTTpNNqhVs24a2JUuMymurVklaWiKIVB5A59NPyQaPB64ZM0V5RAIpqXyoVh9paGhoaBy1YInh3/3ud0hJScF5Z5+Oe//8D4waO16+jIctqTWG4iEsqhIVNCdqdG0FgyiSFEb6kfGkmd/bpw3L61PtikEJg1BD4dEmgRufkDP1jETSCSMLYpriMjDkE29JA4nRFsPjqEOOw8CN/hfBcFR8VRh896081VNxyw4MSlRFKwUqW6ymxobfD3qZYCsjZzNhJMGlr1PK0JN08geDEmyyTL058H1j/f7uyleSUoaoBHFmXxcGSQWZyd2EnDLoZeAkfjvpGZg9aRTeXLERnzp5ZvecsoIdSSO2i2smEOgUFQlNbGlwTLUAs49o4jtndA9ZtG/3Tqm81t7WiozMbIwcMx4Om6CeZtNUQfAc5kpqBMk+blRY2IFjzTZQgcDPMHWJxBiJCQa+sUgEKqdIvjBNTpmYc9o4F1aShOPE6nZmgkB50ZhVKISqamfe17oeeG76qpDgUe/xWDNG9CYJnM4kIYwY5IohfVEWki3XGYNfzoV5LfA1ZRIfD2wbVRlmVR+vt9mjB5aGw7V8wqgC2/d4j+B1y3Mog3alFrGC/chIdgv552Xaan66XO8kHKzGxxyXijqf4W0VMkyt1Rrsj+Tq036HQ+5p3WRgb75DiNfJNNYOR7qvQU6bmTwUz6Ekh7RdUs7gEIJvfFlvpZVSTh2KysxcOySu7XyBeM+x8+fh2jCboFt96eKB90LeK8z3J97XqDbkPbM0N7WXekkpB0nk8Doz39t5DRxo8GFoQXovvzu+zuPxdaUiIhGsiEGzossMB1VYye4+6dRGWlqLVMPk2uIa5LXG9GT6lvG6CYVCuOlL1+GDNe+LsmjIkCE4lqAJIw2NIwCpIjJ2rGx5116LsM+HtnffRdvixUIghaqrEztQIIDQe8tkw72/RFJZmeF7RPXRzBPgSNbqo0M+dwMWJ2toaGho2KWnpaWl4WtXXoC/PvVvzJg5yzZYoDcESR6qBRiUKaNrPplmqoA5kIgFKUvdFaRRQUIVkCeBdBl+nuSHkY7m625PIoFXvCpLDGgYwMp3AY+rm3ShoorB30ADUJJJNJFViiIGbon6ZxieS71fUwGQmURhYG01LZ4+PF8UQ2YVDtUnqtQ4wfQMqnnGlLqESOH5qPjaeqC5O5DNzs1H2s5teH9HNWaPKYLL6ZTPcaw37W/CsLGTsH71SkyePgs56ckSxJHQsM5fZcVeVFdWyHwzDejAvj0oHzEKbQGIOoMBnHlc2T+SNSQIqNyhmoNKAQak5vQkM/geySJz2gn7z43z9/7OOhkXq7qIa4jpZYqEUOmTDC4ZSFrnm4SS1TdHGf0mMqck1swg2Uklj1kFpLxZzOgvgCfYLpJkZmKQRBnJVGuFN/aPxByVL2avJaawJXLtkoQ60NAuBA0Jy3jqMRJFnEsSd1xjav6o0KFRNFPY7Agt5VNFkmHVjlpZd9b0WKUMU/PH+U0vzhIFDM97qMFzkOyIN0IcC7uUMSsGaoRvBtd0Q1unXNMKNIKmX1d/EMVTq1+IT3oTmX2qEgWVQFz3nFeqjHjv5NhwLdvN5cYKwxDcnJ5IsopkKdcFX++jrEsySK3KhnZRLHm61FyqiudA0CnqxGZZa0rZFukIyt+vU6aUyVz4/X586Qufx97du4QsKiqysIXHADRhpKHxEcCZno7MM86QTdRHW7fCt9gwzm5fvZq6xYSOE9m/H51P/UM2eL1wzzihK31tEZzlPWaRGgOHVQKuoaGhoXHw4Jf2H/zgB8jMzMRF530cDz36V5x6+pm2nyOhwsCMAS4DdfVkmsE9XzNXWTIHOrWtHUZqTcQwcGUAwAAkUX8JBSMdLTOhYIHpEfQf4pPvWIGaSh1iQGEOgBkgMnBJlOxRYFBjVpkwwGpqD4gHkB2snjTcn8oEc+pOsuU1tpP7mKuJMa2PgTgVROp4NAun2kY9lefrDJ7oAcIgvns8CzKws6rFMK11uTBu4lRsWv8BntqzDyfOmgh/MCJP5mvrm7Bryzp4wu1Yu+o9zFl4cswgrrh0CDo7/cgvLEZLcyOGTpqL3XUdMp5ULFE1RUUR15AKNtk+qnhITJCso3eSmVSxguksDEjtglWmztD7yC4VbWd1q4yDNVhlxT+7QJqqB84Tjykm4MluCbz7q1oXb23yGIcCvH6sKU0cDzv/IxItrB7HynXm64HzIObNNgQYybXqJhIxISEGeS0RJOrMKT9miPKwogkzRub3uRdw/vnShr0NmDHSXpVEkIi0Kp04B7zHUG1nnSdFIpvvDaI+M5W459yRfCJZeTDV744U7HyqeJ8iyUkFT4pJoWVXqZFQnkQk6Kgi4+E4x1Q82ZHsan3Xt3aKUi9WaiH923ivr06gwuXEITniScU2ktBU5eupfIp1P+ZDCEW8MRWRxKE53bm/CosKTF9jmi/3Veuav3McFVHV3NSEq664GJFwCG+++Qby8von3o5GaMJIQ+MjhjxxHDdOtvzrrkO4pQVtS981Kq9RfVRbm9iBOjsRXLZUNvz6F0gaUm4oj6hAmjETjuTEKz5oaGhoaGgcDnz961+XJ6xfuPJy3P2r3+Hiy66IGTSkJrtEraJ8RRg8U72wdlcdJg3N7RXEb6000pmGFvcQKVRAMB2KBFN/qTMNPj/21vqEwBlWmNHH8NQuYGUAOWtUoQTTTGegoKIk11BFmcGAgmQMPTaYGkdFEfvHEIPERSxC5O1NldJnEkskaeJV7IpV1Yn7W5UlTKshIWcmnagYoaeHOcVEyqXX+3oFyCRbGJSplDkGSkz3IFGgVDPsDxUlVPEoNRJJFW4MSHmMjMwsjBg1Bm+9/gr+U7kbUyaOw7TJE7F74/vIz/DixJPOwsoVK1BfW428Avsn8kw9Gz5qrPy8uzEETzDSS2kg5e5TPTI3VBKYyReOZSLqGqZBSVnx6hZJG1ImvQxo+f3NThlGdUajzy/qIPq1KAUXSU3CmhJJmMedJISvIygpm9ZUIK67sKQzuvqpBheB5yDM12OB65dkl9kni35TVnCMSfRYg/Vh+elSZt4uhemD3fVCJFoJBPriiDKNHjMWoonXGIlApgJRiaTe93ddZxyf6Zb0u0TAVCISCKyWpYgDlcZJ4tDsuUaiiybsvL8oUppzt6u6Be2dwV6pUySyOXdsF+8xhyJdLBGwPaKW6lp78VRDvBeJ2b5lzXFs2VfrnPL6olG3GEbHKWXP64fXC+87HL/RxZlyr7Ezb1dIlCjlOJIoYttJHPH6HIhKiKmzKn1WkchU66npUWbgRZb28L5ImO83BqFoqFqJA/srcMXF52PUiOH4xz/+gdRjuAK2Jow0NI4yODMzkXn2WbKJ+mjzZklbowKpY82axNVHFfvQ+Y8nZRP10QmzutPXnGXHVu6shoaGhsbxg8985jMoLi7GBRdcgG07d+OqG24STad4sUSiEpiJV4TXLYEkA371BZ5BcnlBX4URDVxJUJgJIpIgJFq4P30xVFqYGXw6zifZNIKmMS3bwXQIbmW56bbeHQy+dlS2YMqw3O5UEvExikSldDb3pUKBZIk6HwNFFUiQ9GJQSaIpVioagywGzFQv8Xwkvxh0EvmZyQmrkjiO1jQWBkZ2pBNjbquagJXX+JRf9YMBHvvIIJTBKINAVXmKxJEK3Bl4kmBQxyN5wjHguCgUFBXj3Asuxgcr38PurRtQsXMrwuEQCotLRaWxaOF8LHlnKTra21FaPky8jQiqnpiyZCbmJg/NFd8lzj9JCaVKUCboTJVZv7cek4cO/Ak/x5DkkgpK+S+DzFg+OSQPaMrLdnJNcu44fJxvein1B65tswm6GfTUoYqD61aRN/S2sVYMo/qBwSuPJVuSw7ZkOhUWTDczp49JO02qCYJjameEbgXXCdcI17i5/RwTw6cp2IdQpQE1SR4VuCvwXFx7G/Y1YMqwvvNGxRuvT6qXqPQiKaF8dazkBs/N65xpTvH6wOuKZCeVMNurSLRG0dEZljVkvhdwbrnW+Lr5eJwXrjkzWSTEUmUTCrNTRYGi1Fr8d2J5bq/zk8il75WYlXfNG73OrCo4VZnMPG8k9SaVG/ckBaYG8jgcE/Prdqoh3l9okG8dN7aTbUrz9h5TzqkdAajAewQNokm48J5snhPOFx8GjI2RCmpuE/tKw/F4n+McmNvCsaC/WqKVA3lPI4lmToMkwbuzpkXWQ58Kbi1+SW9V48hrkQUNFKG4edNGfObiT+GsM8/EAw88IKrKYxnHdus1NAaD+mjCBNnyv3g9ws3NaFu61CCQlixBuK4ucfXR0ndkwy+BpKHDhDjyzF8A1/SZcHgHJocfVNAWRhoaGhqHHKeddhreeustnHPOJ1C5bzd+9PN7kexlqpPh+ZPa5WGkAv5EQIKGASGfEjNoJWGk9o9leCxBiCgJuvx9ujyClAH2/oaQVPcxP3VnAFeYnSJKCga5LHPt6ArulGkx/X/49N2OVIhFBpjBIElVEmKgqrxiGMAxkB1oGpsZsQJmuzQ8klxWko2BGVNQDDLCGDcGS1YViNWc2Q5ebzJmLTgJVQcqxJMoOSUFY8ZNNN5zO3HSwgXYvnsPVi1fhraIG8npmSgsLBaz5noYvjqqfSQaVRDHtcMx4niRtGHKXjwjccJQirWwQzKn1iDdGpT2B649pZrheiLJEC/o7Q8kqhikk4wzwy74J/nJc5JPkIpaoLLNY7vOrOljRqn7QK81ZjX6jQeu262VzbJueXyqpdhtzqfH2XftSZ8yU0ShoXySSLyQyCIBFO/6575UIK3f1yCpUHZrm8cimUTVCgk/ggbJscgErieSctyMFNdIr/RNVU6dJJ05XYokHe8z5uuI64/3CSuxRIWKNYWU+/PYJEfUvPk6g0L6mNci11FzW2cfEoiklPVatfscz8HUPytIenAZ0KCfSisFpjay4uJAwdRgXkdWIpAEmvJgI0FmR5ob/lQGAch7Osklkk79VeMz/It8YmhOhR4Vndn0vsvoSaM1g8QS79VcGyTnzUbabJ+1mp4iKrnZeeYRb7z2Cq6/+rO45eabJQ37SKnJDic0YaShcQzBmZWFzI9/XLZoJAL/pk1G5bW3FqNj7Vr+dUnoOJG9e9DJ7cm/AcnJcJ8wu8f7qLT0sPfjWID5qY2GhoaGxqHH9OnTsWLFcnzyk+fimssvwMN/eQLZ2TlCUjBoVWaiAwmyGcBRDcIUs837G8U7R6UoMGDjMalGUuSGIm8Y2NKfRxENygCbQVtFgw/++nC3ckWZGKsUue1VzXA7WdksTd5nsMNA02oIPBDw6bZdv4WYijEcNPBlcERQKcB0LCqrPkzAYuc5Q3IhPfnQ+bOwfSVl5eJJVFN1ABvXrcFmpxO5uXkYPXok3Om52NawHeMLXWit3o7hk8eI2ohjT5UHFVQqcOcc00+JqW9GSXqgMDNFSIX+FAZUco0pofcIUNXUgaqmJtnXnIYUCwziuT8JK7vx5lxayQx+zyCJyeCd+3C+stI8Mb1XzCl+CtbqaAqJ+ufQu8qqHslO9Ypp8cGSkuwLCQYG7mwv0/L6W4M8F4P31TtrJX2QBAlTlxIynE9yYKqNAonkAckAXt/KvJ7nIWFCRRMP3Z8xs6RSovdYknyieotKwrzMZFFMkZSiKslKKJLc5X3EqkLitJnXlaFGa+9ToYvmzWMsa5fjaiZK1THtvLh4HusYUvVjR1iTZKeXFAmaRO67XL8kFrmf3Zot7SIOx6V45LMkoUgC8pYiaWwWApefoe8YFTwkG82phmwvPdDYJDvjb95vOVb8PFMV1frnMTdVNMo6YPqw+VwqRXhsSU/KI9fMrpqWPg8W4kE9aCAeffhB3PXdb+P+++/H5z73ORwv0ISRhsYxCubup0yaJFv+DTcg3NQE3zvviO+RqI8aGhI7kN+P4DtLZAPuRtKw4fB0eR+5ps+Aw5PYDVNDQ0NDQ2OgKCsrw5Ili3HFFVfgE6efjL/8/WmMHDVaFAfpXrfhYVQ08Oo1fHrPwJe+KSR9GMiLkiKGQoRPoPPSvZK2wiCeyiAGQnx2zKAiVpU1RTipJ9s8l6q6FQsMVhhsUn0SKyCmOocqBJ6b55Vy4CmGyiPWPgxAOVY8fruqdtQYElKBRMjRCioOaPrMgDLkSEfW0IkYmpeGxoY6rFixEuFwGLMnDEMnvJg7eUp3ahrHnYqD7Sx5T1+krvEh+LvZsDsW6HnDymwkFpXCQKlkqI5ggMv1UJKdGjOA5JyTuKLqgKQOp4frt7+Akwo0FZjyGE3tnbKGWtuDkmJnXW8sb25VWFC9Y1VwDATJbhc6A73T+0iScOxige9xvpjORhUGSQmrCohqkIGC48/U0Hgm9bzOOFZ2PlBmUK3EtjGt00pmkAjhMezSTROBSjcj8cTUJCphSHZZSSuSFEyJNY8t9+E9xkpgkqQgSWMG11Ie0zxN1zvHnte3lXzkOiXBZoZRvdApZArTEbnx/Dyu1eybIJnWH0mo9udaZLO4HnndmlPwFHifYjoXrx+pjpjqiUkCMm2T64kqQRL+DkufSe4o8tcMo6Jlm/xrJeao2CIJV2apFkhQhcrzkbjiOvEmObtJYzOBlChCoZAQRf/6xxN46aWXsGjRIhxP0ISRhsZxAmd2NrI+8QnZRH20YSN8i98SAqnjgw+MxwwJILJnN/zcnvgrkJIC96w5oj6Kjp8AHKPu/hoaGhoaRy/S09PxzDPP4Jvf/CY+ftqJuP/hx3HKaWdIUMjAakd1swQS1hQOBn18qk+/H+Xn0tTG1KM0UQrxS/9AUoiUcoiKCKYkcGPcwGCoP8KK7/NcbBMfN8dTRjD4YfTDtBYiN8MrgbJ5HwazKrWFwRArCzFQY5oFTbP764d4QHWlEdH0mqSRVTFgViQRVLpwzJm6NFCCLh44Jgw0OR/WQPetDQdkvhiokjwhWUEyYnt1K8aUFPYyvKbqalctX/d2E0H8PMm59XsbhIDjOhmIyob756RRYdYhqYZmskIFvPRGskvhkb5FDbKIqgcZs2yjv1TIkRhgaoxdap6hSAp2pyxybLi+uQ6YwmQXsHKtq/Qz8c7yuEThQUXFwYIKIxKLCiQjeC2Zza3NfWXVOI6XpGLmpMp8Mq2IwXkiSixF+nB8eK1Z5ygWWUTFIMkZjjE9m+yMiM3gOm5vMYiNNJMajudlipe5cp4CSR++x+s/kbXD/WNV+uL8UgHHf2X9uJKQ6nEJMTKxvLeKiNc117+5PereZiWWeO9QqZWcK44F280bivWeQ58vvke1GMlvqr04fnYKnf5A8ovEEw27xRjbNE9cr7HAsSRpFks1p8DlzmOar1+uE5LmUtnQxsyaqtDKhjYhmMzrhqQPx4kElVWxpaCIdZ8/ZBBgwTDyMry9SGOOHVPi3F0KwFhoamrEDddciarK/XjvvfcwatQoHG/QhJGGxvGqPpoyWbaCL38ZocZGtL39jlF5bcnbCDc2Jnagjg4El7wlG9E8ciQ8NM6m99G0GXC4D03J1qMZx0PusYaGhsbRDqfTiV/84heYOnUqrr3yclzz5dtw2dU3dKVgOaSCGYkMcyDHYEnSM/LSpEKaAgM0lY4WT60QCzxnf343sdDfk2mmqzF7XCkwVBoGU1e4JwMmkjjmvz0MthjsWdOREgXJJ5ItVh+agqxk8R5iIKaIAgbYJJjo3WEGg7eV22tQYgmQQzYqDgZaK3fUSkoKwb4w0KQaxlxRipg/rkiIMxIlauyoFHIlpYsywfy0nyQAg0MqikjCcP6pICBHR/IgUcLCDLabxJydUoEkCIkkuypdBMdMmaybg1l+1iAsO6SNsQJwu5RFrt1YFe/MqhCSNxxnr7uvETXf27ivUQgw6+tWpRnbTTUO1ShqzTFANl9P3f3qMrS2el3RhJ2kQazx53mbfEbKFp9detxJQo5xHkn2xfqeRdKEZAcJAK4Ps/KEQT7JM6s/T3ebhGD2ig8ZU8hIBJBEyE3z2qYnUvFH0oDXGO8pnDeuQypaBqo2IXifMht1kzAlEco5t5InHBdXkkMIJrNiaLqlihlB0kZV6FLnYfdJtlpBVY1dmtrBgNd5LC8prkHeH+xIZs4Nq471B14vJAU5p4VZqahqaus2PY9F3pG45H2N88U1mJHskbQ0rl+ulf5S6tg2zrFZmaj6SgUSyV5+Jtil6uL88d8ZIwu6P0tz66uuuBhTJk/Cf5YtQ2Zm4g8ojiU4otqoow9aWlqQlZWF5ubmXhMfiURQU1ODwsLCbjnsYMJg7v/x1PdoOAz/hg3ie8TUNf+6dQmrj3ohNRXu2VQfLZQUtqRC+7K3xxIivlaEt2xGaMsWVG/ZjPWrliMjJxdTZs9F2oQJcE2YhKSyIYOKRIpGI2iqr0d2Xh4cjmN77R8MBmv/GdTe+ODb8vP1Z4zHhfOOvydmB3vfj/UdQePQYcWKFTj/05/G/IUn4uf3/iFuOWKV3tUZighBpII7BptMR2MgZvX7sIIBAtUgTFGgmTPTJhJN81FeGPT+YNCiyq7HwppddZJuZBcEMbDmE3NyDMr8dyBgVSJVCYvtJ/HEtlCB1eoP9ilVTZKAgan1XAzGSfBY01NoqjuqKKtXAK1UAFbVAgM3pgSa07LYP6YYMpgz95/HIElF8sAc5KnXrZ/nmJNwYYDKAL8/9YLah30V/5iuKk12BrpcCwxAA0FWQnP38VmxjreqvGVVy3DM+Z6dwo1teW9bNfIzUoTMI0mogm0SUNaKZwMFCRb206p+4VgORHFnB1a+Y3BvJQfoGcX1YiUl2ddVO2vFK4iEknl+uVap+InlI0TygF5cyvzdrp8kFUmsxLu+SbJs2t+IiUNy+lx3Ukq9ukVICivhRS8d+pOx4t3xCF6PvOdRlcl1TwIyVhVBzoX1GieBwvnjtULvLbM6xwzlX0S1Tn/3Va4JfpZznqh3HY+vqsZRDZpoZTQ7kLDlscwKtGjXPV6Zpau19uL/nsVXbrgWN990E374wx8e8/FhPGjCyAaaMDr+SZOB4njue6ihAW1vvy0EEv9lJbaDgXPUaCGPWH3NNXUqHK5jQ30U3rsH/qefQvDtJYhU7Ov38470dLimToP3vPPhXnQSHMd4qcz+MFgJk8Hef00YacLoo0ZVVRUuvPAiNLe0ihn28BEj436e5MfumlYJks0kBQMQliK3vk4wSGLJbgYxTJ1gsCsEVGO7BPwkf+JVh+LTbRIXyj+HgalRXScsHjZ2fiDcp7KpXRQMTC1jeoc5MKLqgoqVRE2LYwWCVPPQE4dt4jMhHs/OV4bnsyoHVCU2EjXWtjNAtKZYkTCi+ss8vsrQebTJkJpgexiEW8khmb/aViFL1OtGSe0OqWxl57mSCNgOKkwY3HJOSAqQTDRUJIYx+YcZa+uckqQjUcYAO556RrWtnWlQrUZ1LM4bxzFeqlWiQS+Dc+v6OxSEEdvJtCqljmKbea2QdODfjfE2KUD8PFPf7BRIYkwejcbsM6/ptGSjippCpIskJmFEwvBgzbm5lkmskbCyrgG+TsLPXGZdgdc420zVEwm/Y+khItc81wGvMbab9yCqkEiy2JFCVqKQpIyqoEZzciq5Yl0/XBe89kiA87pobgugJDe1l6fT4QLJL6479o/rrj9SmeOyq7pV+mQm1rned1b3NsGmp9rdP/4h/vzQ/fjTn/6ESy65BMc7ju9IR0NDo1+4cnORdd55slF9RL8jVXmNSqREEd6xXTb//z0GR1oaXHPmwcPKa/MXIqmgR755NECk90vfhv/vTyC0/L2B7evzIbj0HdkcBYVI/vQF8F58GZIyDt5DQENDQ0OjN4qLi/Hmm2/gtttuw1mnLMTv7n8YZ559Tvzy2mXZEszxCTHJEQZFDOiG5KeLskWZQisw0KG6hh4syrNF+WWooJTHY3qanUEu095IRKUnG+8x6OJ55Yl3i18UOTQVLs1N7Q5YmFal2sDgi+oGBlQ03CY55XQ64hIY726pkiCZqUFUpth9lv1WZtz9gbEug0H2j21LcbtEQUTCgYSN+Wk936eSy2omzfHi+JorhPFfqpEM4qSHRJAxKsiQNDQzIcX5G5afgc0Vjd3qFZ6DgeYJow7+O8SK7TUyXmbVDvvHeRJ1SU2rpG6xDwcb+Ks55byTOKGBMUm4/o4nXlNed78BdHN7pwTpVNvQu4fkS7xjM1g2EyyHElRdMBBXpthGH0joJGOI215hQrUIKxBmpvaupkWCiRuJn1iEEedJXYckdUlOBsNRSXc82LRRczn1kYVMp+wZS84hCUoOrzV1jfcEEqxcT5yDxraAECIEyUKrWfORBNsdCBkqIY4n22o3Pu2d4ZikJNtu53VGMEXRqHqXLCmosfpJVSDNozlHXBecPzXnJJdIIpOg688jTbyXGtoQikTjGs7HqpbGc1JFyAcGqhgBCcKZIwv6KECZCse/ATQdN79X19Ih6YJjSrK6+1BXV4svfeEqVFcdEL+iCRMmYDBAK4xsoBVGg09l0x8Ga99DdXXwdamP+G+0tfWgjuMcM1Z8j9wLFsE1ecpHqsqJ1Nej7Z6fIvjWG73fcLvhHDsOrvETZNvl8+G/z/0bQ0rLcPZJpyC5qhLhzZsR2rQR0Yb6Xrs6CgqQ9u3vwLPg+KqKMJgVNoO9/1phpBVGRxP+9re/4Ytf/CKu+eKX8LVb72DELwEAgxupmOQxyA5zsMHAUj1FJxmSkeyWz9gFQwy2qHRoag9giEVVxHPwWAyEGIBZ05iUzw0DIJIO1ifZDN4+2FMnCplYT7mVjxEDFKsixwymj7QxuM5KkUCIShLlkcOAiqlRiaRnWUHixAg0jX/ZHhICJB1Y+csMjsPumhZRRjHQ5nhy6wiG4esI9PG3oVKD7SQhJCqarvay/QwcEw0EPwyYOkbChcGrXbogCZ5wOBrTo6VXYNnil7VEFcLBeNscDEg8UtHDtcT5pxqLIMlpp+hZv7deFBFJan5IADocklZjl+7G45H84PyYq6Px+KocvRlcHwMlRnjsPbU+IQ04jlS98XpM1GCaxAyvUbYnFtlApVdFQ5usS5IF/R1XXXe8hl1OhxBBVCPSzNvq/cP2k+RkqpLVF43ri2NlJWiU+TLTYhW4/sQ42sYbidcGjaqN+5rRPnWPY8qX9drmXJHAUqQux4X3J5prk2CzW88cQ5Jddtcd1Vw0oD9YxZ26T1qrlZnHkCQQr6FYikHOBYkdVnfj/ZTrl3Pf0hEUkjBWSptZNWhNSxOVab3PuJ+l9y4uQNKexL8y22bfOY5UR5GUNxNr769cjuuu+gzmz5uHRx55BBmD6EGxVhhpaGjEvkHk5yP7/PORed55qD5QiYyqSrSTQFq8GJ0bNyU8cuFtW2XzP/6opHS5584z0tfmzUdS/pFTH3W+8jLaf/EzRE1pd0lDhsD76Yvg/eS5SMrq+SLVuXoV9r2RBnd+PhzzFyClizBgBbrg8mXo/NdTCL6zhH+lEK2the/rN8FzzieR+vXbkJQ+eP6IaGhoaBxuXHHFFZg2bRouuPBCvPvOUvzktw+guKgEDgmIIakjjAEYJKrqUQMpI899GBiQBGFAw5Q0HouBg6h1UjyoCXTIe9aUDaqWuJFoYcBFMNBhUGn44bSKL0g8IofnZyAdy+xYgYHWsAKjjwycVPDEwJLVsqjymViei4HCCGKdyEogE4pjwsCYgS2f5pP/UUEtAywrVPUnhmg0Oz4YQise2Iaalg4JzjkOKvXOTAxybhkQKt8jpSaStKOmdlFPmKszWaE8k9gPEhZMbyPJRJA4ipe2qMC1QHUFg3WCc8fgtb/gnIErCQA1R+b0K5IFdmDATiKR6gx+Zwl3zU8sco7rXSpouahYM0hWgqoL9tW638GoaNh2mhGHIhFRciTqT6PAcbZ6cPUminzwupySQsZz8FokaUDPmVjEnvm6U/NDk3WrB44iHMaW9ihNFIxKin1N8knC8do3E25KBbSpotFWAUZiMD3F00XCGko4/sxzZ6X1va5pCG41kScMBaB9n3mf4rpT1w6vAd47CM411ZAHCyrJuCZ7VXujd1hzh5yD80+fIyupLN5tXWnAVnNzgmuT47Zmdz0Wji/utXaUhx2JVK57M6HP/qlr3o74JLi2zR5VHDuSXhwjs4fRA3/4Le75yV2466678PWvf/2YSkM8FNCEkYaGRkJwuJxInTkT6bNmofDmmxGsqTEqry1ejLZ33kEkQfURU7oCr70qG0FVD5VHUnlt0uTDoj7izd7/yEPoeOiBnv5kZyP11tvhOe0MqSpnt48d+FnPvAWyhQ/sR/s9P0Nw2VJ5L/D8cwhv3YKM3/weSXn5h7wfGhoaGoMVkyZNwqqVK3HjjTfiik+cgt8/+AhOPuU0eY8VlxhskDApL0g/aI8MFfjxKTWfVjPgYGDA8sz9pZswUCGZxIBDVVxi0E7F0IcxYTWDZdhD4SisHINR/t0raXCxwACaygYGSEwV+TABDwmSNCQ+xnbpfB8WTF1jQK3KfKvy9AyIt+5v6uNZRWKG80OVFtcJyUbOKxU38cZCGW+TQFTEkFFZKUtIGKofSBqQcIpFOhmeUM3dZISquET1iVJcxfKO4fHtTKG5NmORAgOtjCU+PF3pgmbkZnhFFZSoCoxrn0ouzoFdip+dh5YZKqWPc5oICUeScD+JIpaKN5mxe5Kcoq4hkUQTcc4L10g8cH1MsiFbuV5I8Ewbnt+HLCIhxJRTa/VClepo9bDiz7yvjKd/l4XE4j3Dznib9zXe3+yIVq5NuyqQVPTFWoucI5Ioqs8kaEZk9k/gcZ02tHXKvcPOLL6nmmOq9IXXCAsBdFdjzLYn8JkOS/K5ODut+xo2jyPTA3luKp+sVQU5/+v21Ms1bDan5zriNcmKj9xnIPdfjomZxG1sbMBNN16PTRvX4bXXXsO8efMwGKEJIw0NjYOCu7AQ2Rd8WrZoKISONWvgW7zEUB9t3pzwcUiwcPM/+jAcmZlwz6H6aAHc8xYgKc++WsNA0XH/ffA/9kj3757TTkfqbd9CUk5fKXcfxPkj6iwtQ/qvf4vAc/9F+72/RLStDeHt29Byw3XIvP9Ph6z9GhoaGhpAeno6Hn/8cUkHuOazl+K6G7+C2771HbhcLgkwGaAxwK1P8veqZtMfJBjy+UW9wWCBgU+syk39gfvTFFo9dOivDXy6/v7O2q4S2B6ptBYreCP5xJQSBooEA/m8DCMNrb+ixyTBstNS5Em/MhpWoDKIQe/R9tRcSsd3hiTgs6a4UKVC/ycSAWaCMMXkZVXvoyKrt9qIyp5xZcbnE+kv54aqCKbIWMGgn+Nm+AvFDqlo5k0iUgWuDpPHFIkfvm8HvseUGfGVEUqnB0yDpDLqUIAkAFU0VpKG60oRWnZQlanUWmIqGNcx15Py/uoP3JdpSA2+Tvk9O90risHMYDiu4o7EB1M9Z4wosE1RE2VLa4dcj0znOliwLySSeM2QwFD9pKKFiicrMaPSxKym7gSVQrxerSQPr2eSbNaqdrJPQ1sf83nla0Xlo3UseSySjFOH2X//jEVMWsFjmdNehThP82BfnX1aowIJr/aGEHz+kPh69Ze2yXsulVLm+xHHYn99m/i58T4ei/Ch9xgVlfWtHXL/JlQ6GQmk9LwPZ669cvky3HDtlZgxfTrWrF6N3NyBqzePF2jCSEND40ODqqDUWbNkK/z6LQhWVxvG2YuXoG3pUkR8RjnK/hBtaUHg1ZdlI5zjJ0jVNaavuSZOgsMZ3yTPDv4n/tqLLEr52i1IueKzOFTgH1HvuZ+Ca9oMtH7tRkSqqhDZtxetN39FSCMagGtoaGhoHLp77rXXXitPei++5BK8vfhN/OHBRzFs+HB5jyoGqjeoIiFZYPY2svrqmI9JVQOVJCQcyvIM74yBQD3VVt4xiZARDGqpgJhcniuBDskcZYItFdQsPicMNJl2oc7HfpIgY1DP/2I9+Vd9ZICV6k21TQfhue2UKRwrn0qLchv+K0wtOdTkEvuzYV+jtNHcZhIxUmbbYnwraSZl2UK2JLuDvUqvcz+SFVQgbNjbIESbeR0MpO3zxxV3pxsypmVQr8ghklk8PwP6WKllXFNUAtml6xFMbYtVuYzNZDodVRYk/MzgnMQqYz5QsD8kf7qLgkhaTkQIklZ/MKYRck1Tu3go2ZECJIBIXsTyG2JQz3WnSAazkTKJIl5L++t9vZQjZnC8Z40qFLWMSj3j/uaqeLHM6jmWLe1BIUASMianos40fySF6PNnvV547tW76uS+0xEIg6dWKX4kxQiqhaz7cA1QfWZ37ZEUshv7fXVtcq34qnqnJXK8eQ862OuTY8e5IbhmOYbmtS0+WjbkohlWpVA88JqmGolpkdXN7d19GFHUY5YdC0ZqLtV+7l5VB61EJck1qo14XKaE8j4W7/7OKmi/+eU9+P29v8CPfvQj3HzzzUcdmX6koQkjDQ2NQw53URGyL7pItmgwiPbVq7srr3Vu3ZrwccKbN8nmf+RPcGRmwT1PeR8tSEgdFNqyGe2//03376m3fRPJFx2e8pfOoUORcf+f0PrFaxGprhbPJp477Zt3HJbzaWhoaAxmqBS1W2+9FaedOBe3ff9nOOOTF3S/L2Wiq5vFP8gKBg58ak5Fj7VaEANBKgQYLJGE6C9QYFDN4JapYtyHBAF3YdBmR1YpRLvIIgaXSnFgqIy8PRXUalqEpGBAZG2nWaVCiBIkTlMZILHP1jQmvl6YmSJP+e0IIyp7fB3touJhoNjQ5V1EkFhgcGhnRPvBnvru1Der+onHVMSXuT8M7kmUWZUzNEYmMcY0P3P6D/ehEoxkB/1fVFoSg1oGn5wTkhHsc2NDHVJS0pCcEluVowyfreSPSjfkGHOc9jeEZA2RVLFTkljbTvKE1eLUXKrqdjT3ZUpQLLKJr9upTpQZsh1IbpF0jKW6sfP2YmBOAlUdkuNFPyAeY3xp7P6RWKUayEzWKbASHufEXCGv73qMxEwJ5Lpim+KVeldphiTP6D9keCQZVbVikWnKAJ3rgibWBBVInJNESWJRLdlcK+wHyV+mWMm1EvR3V1/kmpxio/ohUUt/HuWJZb5e2M4ZI+ztDZjWRqXToSYySLTZKZoUuB7X722IqWCyrjdeU3YG2GaoypRcD6pQwcFAVR00XwtURBlpuG50BkOo9wel0iPvryRDT5xQ0usYFfv24ivXX4OGhjq8/fbbmDFjxkG15XiDJow0NDQOKxxuN9LmzJGt8NZbEayqMnyPlixB2ztLEWk3nij0h2hLMwIvvyQbv407J0wU8sgzf4H8bFUfkahq+38/4KMC+T35yqsGRhb1I++3g7O4BBm/vQ/NV30W6OhA5zP/gufU0+GePWfAx9LQ0NDQiI+UlBTcd999OPvss3HNNddg/Yq38ZN7foXMrPhpFyQAGKjyqT8r5JDcUcQLAwu+x4Bia2UzctO8Mf1PtlU2S0BEdYQ5KGLgc6ChHf6GkKRcUDFhFwSRIGHqBc2gmd5iPoYig3isTRVNMcuOK/SX+sGUKqpkGBxTtWAmFEiumatjWdvopNeJxVvFTHqNK+tLGI0pzkJlU7uteobpPVQyWE2M+Vmm7fB9KzFE5RiDfXqeMOXPDI4vK5eRoGAl21SPUxQnDKhbmpuwcc16pGVkYv/e3Tjp9I/3mQsqYXhec7BOlYKVdOEYK/UCicVEvLLM6hSV5sNzqep2iaYImcF2suqZHUgykIS0U4CQzIwFqnUGCpImVU1NKM7unc5IYo3tIMHHa8xu7ZBMLc/PkPVjJY1IwlY1tktgTxVNf1XZSNTS1JgVz2Klsalx57pSKVVU8PHYbCuJKWIgZvl2YL/iEcV2Yzh12MA8p4iBVDLjfLT6A9JPBxx9yFoz3E5Hl2l2z/H5O8nNNr9RnY/KrEQqz6lbElU9sZRiZpgVRaJ8bGhDMByVORuID5pRbMCokkl1obo3kqzl/ZT3Qab+Thvem/T6zzNP4fZbvopLLr4Yv/71r5GmMwS6oQkjDQ2NIwp3cTFyLrlEtmgggPb3V3cRSIvRuW17YgeJRhHeuEE2/8MPioG1e+58I31t7jwkZeeg49GHxU+IcI4Zi5TrbhhwW0s8bbjT/TBqWs4EEvQjcg4bjtQvfw3tv7hbfm/78V3I+ts/4Eg9+MoTGhoaGhqxcd555+GDDz7A5z//eZx24hz85r4/YcGiE/sdMgayDH5UOgQDbUXaKMKmodWPLfsbbQ1pRxZliLqIAQiDGlXBioGPCsoYONHw2O00qnOZfWxY/lkRFiSYqEZggEwiRQVsDPLjPfFPFDxmJNMgLMx+LATbxBSa/jxurGlV7ENuRrIofKyBugTOHqeMHz9jhpQvr/fZvkdChmNqRyiRNGN6CedjjEX5YqSoGWqWYCCA6gN7semD1WjztaJ0yDBqOTBj9vxexIMqw84gmXOvxpwBJ9vH+SCZaEe+HIyxurW6XSyoalpM/xuo2oJKFqqajhR4ru1VLdJWRToaJuwe8f6JR2ykKtKoshmjizPFJJneQJwPKoRipbPFq3gWy+RZVbizjid/JzlBAi+WbxmvlcORhnk4wLbynsPr1Zq6VZTF9Mb4FhEk0Xh/4LXO65pkExVnfD2WakuRbsp0n2bpnE81Xkyt4zVL37H+1EZmxSaVa1xXrLJ2oMG4P/FaLMpOsVWDScW0xnYpDsB7q3X98Dg07h5R2HOfJ5qbmnDH7bfgjVdfxsN/+hMuvPDCuG0cjNCEkYaGxkcGh8eDtHlzZcPt30DwwAHDOJvqo3ffRTRR9VFTEwIvvSAb1UdJ4ycgsq0r9c3pRNp3fyBKp4FiUVYlMhwd8O9+AeERJyS8n/eCixB4/VWE3l+FSFUlOv/7byRfdsWAz6+hoaGhkRhKS0vx0ksv4Q9/+AM+d+mnceU11+Gbd34fycnJCaVDmJ9KZ6a6xeNE+cYw6LV6yChiiIEJAxWWvKdagmSAWXFBkokb1TE0UJ4zprBP4MnARlWQYsDPdjCAZSDLILi/wJnHZqDEdC4GevRJsTOK5ZN2cxqbAgmrjGRDPWAHEjdM76MCyOorwkCSwWBeurdPvxhgbukaE2t7SAxR1UE/ESsRxfmgvw2DW6ZGMXXEnNZGhZj8bhM0hkJBfPD+e8jOzUdBcSlOmzHbthIqq1+t2F6D2aML+xA4nG+W/ub4s+ITA1gGr/0RPQTb7fMHZB1xHhLxybGC6XeEdc0xyE1LdgkZE8vfpT81zsEgVqoeQZKU6VckVA+mGiBJoyG5afhgt1Htyi4Fz0oocOoTJZOUqoqKEuu4sF8ke0lC0IvJCsNfqFWWGc9pXoNUzRj+OwdvqD1QJEIkcu3weqUqcqA+bATHgmQt53pYAVV6see0oakZtb4QkpwuWecji+2rrXGuqPxbs6uuVwl7M7iGeA+jKpAV0cyEDtVJvEcxvayykdXkkvtc+ySlqErj+rF6KHFfeo3xHkXS0Iwlb72Bm750HaZOmYL169ejpKR3ipqGAU0YaWhoHDVwl5Yi57JLZYsEAuhYtUp8j0ggBXbsSOwgzO3ftLHnd5dLjK9FfTRnHpL6SVUwY0FmlfybufslNOI7Ce/HL6f0S2q5wkiB8z/9FLyXXn5MPJ3S0NDQOFbBoOSrX/0qzjjjDHz2s5/DmacsxA/u+R3GTZyKMP82RAwlgQIDC+UfxACJhAh9L6g8YdCSaNoH7+1UzXBjKhsVRQx4eHz+XtvqlxLudmSRXQCt0rgYHPf3RJ5o6vI6UUbYJDgU2cDAlv4sduW3zQFdvACcRFNeerIEbGr8zOlb9PKhGoHkkRWjijKFSMtJT0YkEumeBzUNLIs9b2yRDdlkqImYQkPFQn8GuEQkHMbmdWswasw45OXmYOWK5WJg67LZl4H+xyaXCSFEkodzZVUSsUVcA9EEUv44FiQnSCIwrUlV3uNrHCamBHJ99DefXH80MLdTu6igmWNil+ZFMOhWYJuZusZ/Gcj7OnpUJ1aoam123eQY+AOhmH5EiaQbxQPHfcbIgpjvS8Wz5g7x+6FSj+D6porPPJ6cAyrKGupq4Wttlrn3epORW1AsfkjK+JiECtcWU6esahMF9pdk0dCC9D5qMp6H651zO224vccQyU5VzVDNA5c41zGJNbtrRfWLJJYVvtYWVO3fh+aOEE6aPhrFJaW2+zu4WoNtot5ramxAWfkwpKSm9VFUxSOT+kuTZP8rK/Zi9fsrkT18CuZOHt3nXkkyjtcV0xKpNOQ4W5WSPA79vTg3vO/xXmG9zswqQJLqse7J4pXkdkrqHLeecxjjT8WT+R7S3t6On971PTzxf4/h7rvvxo033qi/o8eBJow0NDSOSiRRfTR/vmxF3/omAhX7JW2NBFLbe+8h2tHzByEuOjsReOF/siEpCa5JU+Cev0AIJOfYcbZPHonU9gqUedvkZ2/LTjgbdyCSOybh9rtGjoJr5gmGymjvHoRWrtBeRhoaGhpHAOPHj8e77y6VCjfXXvJJXHfj1/C1276JlGRvt6kqA/q9da1CBgzN73mSTvVKvGpj/UEpeBgsUinEVJCxJVkHFYwkQhYxkC7oSsWxGmGrgIvBLcmC8UP6LxYRC0wds6aPJQIGeKNLsiS9S5EXVBGoeYgXnNoZC8cCg8/1a1Zi7LjxKC0uwNIVq1HdGkR0536kZ2SLKoeEjXke2AYqnZSSiIbWVCiQIOCY1bd0SBpccZwy4orYo4Ey09qU1wr7SfNuZeBNImhTRaMQU1ZTbzPo0RTLR4dkB8mkWGQRoUx8OR4MlpVSjeudBtuxwGExgnL7tCxeK+zDQK4NklBUfpDoMdpEMqInVak/MK2K6jnux7kz+3gpRSAPRb+quuoDqD5QgYzMTBQXF2PsmFFwOZ1oa2/HsqXv4IT5J8HFB4iBkHhd8Rqxqk0UFDnF9+2IQhIcJGZjGT/TwDo33StrSwzKwxFs2bIBDfV1mHzCQiFSYhFG7JPyz5IUq317UFdTJf2ae9p87KptQ/XO9cjJyYE3OUVIsfY2nxBl9bXV2Nfgh3tovigrhwwpx9rVK5GaXQhPZj5cbg8a2zoNMtwBVFVWoKG2RiY/IzNLjtXR3gaXyy2/BwKd8LWQ8OxtqM8fc/MLUVJcjMkTRmDj3loMzUtFUjSEupZOSU9zOh0ozU1HSX46XDEU/kxhI0lJIshqOs655zVJotWOSLLCfK31h2VL38bNX/6itP/999/HmDGJf7cfrHBEraULNNDS0oKsrCw0NzcjM7PnxsknIzU1NSgsLJSnWIMNg7n/g7nvR2P/I52daF+x0iCQFi9BYNeugzqOIzfPII+4UX1kut7XPHkXNu6rRitSkenowIkzp2DIqV8c0PEDr70C353fkp89Z56N9Lt+jGMN0WgETfX1yM7Lg8Px0c/9kcZg7T8NXW988G35+fozxuPCeaMw2BDrvhfrO4LG0Yk1a9bg85+/CsFwGPf+4QFMmz6z1/sMIA1ip3cq2UDAgFz8eNKT+w1s4oEk04Z9Dd1qpUQIo437GpDOymIZsY1hSRYwMItVvl31gQEcA92BGOoeLWhqqMeGnfsxfuxobNu9D85oFJPHj+42IKavS01zu5AOymvKCkUc0UeH4xArqLfu88rafZg+PD9uGhBVDyRclG+VHei1I5XRYlTn4/48TrxjfBjEq0imvIDiEXxsH32IlLqNJCzHkeuT48K/KxV1vj7+U1ao8vS8BughZb2mSGzwMxV7dqKqtgG7G0OYNSofs2dMFdLLPHb83N4DNdiwfh1y8vJRUlberbgJhUJorK9Fbl4BnC4jzZF+SlQDxjKZV+bo1rQnHqu6sgKb91Qj3etEqos5bAa7wr8lw4aPRHV1JbZV+VCe5TL6z2ZKFB4V8qfd34kaXwgl6UkG0RSJYMjQoRg1fJgQSFyXJKvSPcDaNWsNpVBSEtLT05GZmYXiwkJsPNCCvMzkbsKwxdcGN8KoO7AXDoehLOJ+PHVZ2RCUlw+RzzY20TDeidS0NFQ1tGJ/TSMKc9IwqqxQlDtqTLk/p4ME8Oq169DWRoLJhcrWsBBDeWkeZHid8jC2s7MTu3ZsxSlnnRszdlD+R+wXVYdUI7Gf6V4XSnLTDiqtLhbYVqqK/vaXR/H//t//w0033QSnpWCOhj20wkhDQ+OYQ5LXi/RFC2Ur+va3Edi3T4yzxTz7naX8y53QcaIN9Qj871nZ6HXkmjwF1bNPxs8iI7G+4UQ4EQbF6BE4cO9yJ6buX4XvfXICym1KyNrBffLHAK9XVE6hDes+ZK81NDQ0NAaK6dOnY+XKFfjxj3+M8z9+Oq678Su45RvflgprBFO16J0ixqwHmnpVTLMDgy2qdfh5VTKbwRSNYZuqW0TxQJXKQJQYZrUTK/dQmUEVAv8lgUBj4ViVlyayjHcwLIoY+nsQNOjlE3sSSGwbU6MYuPfXBqY70UPJ6p3DFJp0r1HtayBGxIcDsTx60jMyUd+4AW3tnUgJNmPshMm9xkwZnFc1daCmuUnSmaxzpBRHAwH3OXNauQS9LGcf7Qp6zZ4+VMlwrfRH9LB6HUkVazUzphWSdKlp9ktqz0cBSc2LGuskFinK9crrIxapROXWUFNltFhkAOeXhtkkOUkk0H+ICpqqAxVoa20RcsfXGUFKThFGT5yK01gVrmut2x1rWFkRCvJzxXNny6ZNXWvIIHIKi4qxecNaSWfk9dfsj6I9twg+f48aL9XrFl8zeuSQbOS1pUii+poq1NfViNG6K6sYC2ZNl7Xmdjm7iFeDYJHx86Rj4uQU8eziayotk61ubG5BRVMQ80uzu9RF/FrqkGtZgammVNvwWKeevMh27KawMnFyFyElMPoRGTsUgXAEYSr9utLiqPTjuqSiqj3qAcJAhy+A0vwsTBhaIOQZ22hHXPO1uSdMRzzsq6xBbXUlAp2dSO6639rND8dUkbi8/5Tl9hBUh8qbi15F37j5KygtKZGHCFpVNDBohZENtMLo2FCZHEkM5r4fa/2vuPkWtL74ovzsKilBqLLS9nOOpCiSc4K8C/bsm5yPG6fdhjZXMiKOvn8gnY4o0twOPH5OCoZmmsfBgWDhFMDVV67fct3VCK37QH7Ofun1AXkoHQ0YrAqbwd5/rTDSCqPjEaykdu2116K+oRE/+cVvMH/Ryb28jbgpBcHk8lzbwJipEkwtoscOVQbWIIZkCyueUW1BkobBZbxAh8okBoL097DzGWIQzgpu9F9hewoyk/s12mWKFANtBl9ES0cQ04fnxW2HQVK19SlZr47HftPYuyQ7NS6hxvFjkCtpZ93pZ0nMCO9OSYunnCIRx7QZzkkspQv9kKg+IcKhEFqam5Cdmycqo8bGRiAtD1W7tyI3IwVlQ4dLeo0ZPDbThjjfqsT6oQTHnWomVnri+iJBYFWkJAoGyvSB4fhz9uKl3XDsaIpseBahe+z5r9riqcdI5BjEUF+fKvU707/imVtzDXH+47WTvl7rdh5Aur9KVCkTpszo9d1SeRHRJ6e60YfWzgiGF2Vh5PCh6IQbgSDXgFeuwVhrmsdgtS+ShiTiFEKRCPxd1wXfI3nCz3IthCJR1De1YPfOnRg9flL3cTifJGRJmNKEPhqJYPuWjfB3tKO0tAylZaWoaelEeyAkhLMaa7X+eY56VhBzGL5psf7mrt5ZJyq1WLk/JHemDM3FhwXT6VS1NM437ylM2bQjadbvazTuTQMgijmWJKFIXLW3tWPT5s0IBgMyx9k5eXC73UKypaVnYNjI+OlgvI/ur28zfMRspppt5uuxUikbGurxgzu/hRee+4+oir7yla9oVdFBQCuMNDQ0jit0bt7cXYFt9Msv9VReW7wY7cuXI9rZKe9nj2pD8Qm9n+J9vfOraI8mIwL7P4zhqANtgTB+9J81eMr7w17vtXzsR+iYfm2ffZzjJ3QTRuEtm5E0Z+4h66uGhoaGRuKYOnUqli1bht/97nf4wpWX4dSzzsXX77wL+Xl53cEdzYb5byxuhcE/K2sxON9W2SwqHiqK1BN9BodUr0iah69TDLBZ6YufsQvWSf5kp4Wwr94wdqUhr5k4YnCuVC8ko6gy2bivUdoQS+lBQsZcAjuSwNN5euPE+gyPl5vulMB7X70vLmHEakf0OmK7GTiSiApHwqJU4MMnpcqKFeBR9bO31odxZYbSwg4kApg+xbnauG41MrOysWv7FuQWFCI7Mx0TxpRib14G9tY04cD+Cvi3bsK4SdMQiDp7lQrnWCcKzierR/n8IXjdSbJvLOKLa0L1rz+D4f7AOUnEV4vto5KJ60z5FvWMvUGIMOVw+gh7k2aCXjIkK+JV4eoPXHecG6pyOA5sF8eMKrdgMAS/34+2lka4O1sw/8T5qKtvxLrVK8TgiMReXkERdm7bjKzsbIwcMQJzZmWLyfem/Y1o6IxiTHFfc3Iz2E/6QJG8YRl5klMkHAiuX5ZkT7cQrkI6JAHhYBi7dmwXfx7ze16XA4XpTnS0tWJP7T7U19ZgzLjxGFFeKvcKItnrQSAY7iaeO00/cyPxMSGOh1iy24VZowvh/RBjnyjYV3eXt1gsKENqV5IDy7fV4KSJ8auHsY+8Pqi043pnJbP6VpJoUZSPnoDinDRRcdXU1iIQCGDKlCnYunWbzD2Jo6KSMvlXgfPG49HDaERRRh/Te0UIso1UoNm1/+l/Ponvfft2zJ8/Dxs2bEB5eflBjZeGJow0NDSOI0jJ0b175WfPqFFwuN3wDBuG3M9x+ywiHR1CGpFAalvyJhq2bkDuWMNQcUu0HO9Hx/Z7jjCcWBkdh82RcoxL2idP/NqnX4uOyZ+x/bxrzFgYFBUQ3rsHbk0YaWhoaHxkoGfFzTffjAsuuABf+tKXcNEZ83HH9+/C5Z/9/IAUtCqNggQJDY8ZgJkrqzHoU0bRVIes2lErgY01XYfECgkms5qoIxC2JY+oMiJRRbPdgXglJUpYUJVEIoWpQ1QHWIM09i2W8keBhs07qptjGjcTJDa42fnEkCSaPDQXWyuNtDFrhSqC6qLMFLekEKZm5sLjcWHunLl4d9m7qKoIoKy0RJRS9EDZdiANzb4OvPrOCkyZNBHDinITqrhmVjRV1LeJBxbng4QIyQCqyEh+kVzh67HIrf7GnoocqoIykt1CHh5MaXqCa4zrMZ76jOexA4k8qrP27treTVZQYcMAXpku83WPNxl+f4eolgQmD57CkjIJ+kOhIEqzPFi1vRK5aW50sEBJsB2OzlapcJaekY7hwwtRXjpNvHEyhhRjaGmhEIq79lSgubEeJ8yahfzsjF7ESSyD6Z6+haW6GIkLmthbSSVVQZCVz0hGKKNlguvj/dVrEAoGUFQ6REgrgsq1HVs2wpucDA8LsaSloaiwEJMnjhcCz9w+KnAGosKxwlAiDXx/UUAFQtI3bsrcPR54TVH5xnWs1q3yEqKK0EhJdSAnzYOxpVkxiS4rSUQPOLOCip5uBBVaVPRx7M+eXt59j8yZNQPt/gB279sv1Q1nzlvUXTWNxQKobLIjz8SQvtWPwswU27Zt3bIZd97+dWzdsgn33/9HXHjhhboC2oeEVhhpaGgcN4gGAvzmIz870/v6ECSlpCD95JNli0bvRGD3brS+8AekVT2KF8OzxbOIhFB/4OdeCs/CmFAFDizPg39rDdwV/xDzbOfIUb3+MDnSev54KnWThoaGhsZHi6FDh+LZZ5/Ff/7zH9x08834v8cewU9/cS+mzzhhQMdhYMrAhsEXTYFVoMQgSnkBMUBiYMMgKB7MaiJzKhpJHBJEJGvGl2UnHPwwzYVBbCIECdUy9E8iWUUSjIoQBoRmMEhm8BdPNcN2pnpcErzG6i+Jot01LRK426mVeAxWqKI6iyoRu8+QSBtXmoWNuzuwY+tWTBg7GqedegoWv/0OXnnlJcxfsAglBbni8URMHJqLlStWoTLYiiFDR/Q7HmwbFQzspqpapcCfSTgQnHeSX52hiJCGpbmxlUdmMEBnAE2DZRqWt/oDQh4pBZYaByraSALEOyaPxUCfRMmBij2Spsc+0niY6VMkeeRzkYhsLS3NaG1uRGtLi1TCIlGRk5uLefPmwe12IRgI4b3l78k6y8srwKJFC+XB2oHKKpSWFMmYuJKY6pYkxAPX6poPPkB15X5JN3I6XSh0ROHsdCIvMwM5OeXIzcmRFDC7dSPrMxLB8KFl8LiGYiCgcodEEQkXzkmscTJURA4hlpiCZfb0qqislvS3uSeeIl5YbW3t2LNzKwcMixYuRFpK33StIw2a8ze1BdDqD/a5LlM9TiEK8wqTRd3VH2FE0AtpY0WjeD+pNUcFFsfQ7JtkhVRua4xNEpnB+x/VXb7OkKgsT586pJfKknOWkerFpDEjRDn5wpvvIivZiQyvAwGHA3VdBH9J2VAxLCdZzmuNRJ9dhTtfayt+ec9P8OeH7sf111+PZ//zjBSo0BhkhNF5550nRlX0UmE5wdNPPx133303SktLey3kX/7yl3jwwQexZ88e5OfnyxOkO++88yNtu4aGxhFAONzzcz+VD0RmPGIEvF/6BdDydbT84REkNUfp+dcvaIRd156JnW8WItThBCpWIfT+KnT8/jdIKiqCe/5Co/LarDmA+YmRxUhUQ0NDQ+OjA/8OnH/++TjzzDPxs5/9DJ8+5wxcdOkVuP3O7yE3L99I7WFKicXniOFabpq3l8qHJALVQ1SkNLUHJIg6WLWIHXnEQDdeKo4dWbT1QLMYPStVEAM0lqqnga8dicTxYBoRtwKk9PXSofIpTp+aGxtElZGckootextRlp8Jr9sj6W4eEgndnkYOUQ9tr2yWlBO7IJ9tGV2cJcbHDBTZBbMZN1UxWzasRWZ2jpAaVM+kJ7twyskn4o03F2PtmjXwjRmLMcOHyLGy0lJw8okLsHTZCjQ11ouXSjzQN4jz7oQD/iDT0Oy/U/B19oXkElMQDdVR/O8fHMvtVS1iZq7IMJKMSpGhwP7uqjZISLuKbRyDde8vl+pWG6raUZPP1EggLz8Py5e/J6QO12haWqqIgTbtrUFbfhqyc3KRm5uDEcOHIy3VC4+zJw2K52QqUfm4abLWmZK0p9bwu4E7HXvr2kRVRUUT/XTcNFB2JmH+rBkyRypFqz/wOmGKXFO78SCNa4KrlMTUyKLYxI8Viuyg/1Btsx/FOSl91javBZJKVNaQuDWTVnWNLdi/dzfGT56KlLR0LFu9QSpgjhk7Blnpaaho7AC4dYG7pridKM5JjBhU6YDd9xLZmCpo/EwkQvAw/ZXrjCrF/qoYJmIQzWOwaiLnMtGxViChzHNwDKwVByU1sitVjOPM++DQgvj3LTZ15PAhKCopFsWa3GG7iswFAkFs2L4DnVv2irKLnmN2XkvPPPV3/PC7d2DsmDFYvny5pLxpDFLC6JRTTsEdd9yBkpIS7N+/H7fddhsuuugiLF26tPszLJH38ssv4xe/+IUsloaGBtk0NDSOfzhYkawLUb8/8R0zS5E562JEXt/WJa+Oj0jUgejOqEEWWd+rrkbnv5+WDS4XkspNT8s88Y1K4+EnL2zGyj2N2N/kx32XT8cJw3J6fSH57evb8dwHVfIk+oo55bhy3rC4x1u+uwH/XVuJ+rYA5o/M7ffzGhoaGscrUlNTcdddd+Hzn/88brrpZsyfORlXXv81XHb19UhLSZVUNbNxMIPaHdUt8jPT0MwBFwMxGuN+GFgrUZGkGQj5RHVPVVNbH6NrBulUBuyt85lIpCQhlaguiJfmJl46XlfMam0M2pi6xLQmEhiBcBR71vqEdOF5i0qHYuK0mb1IOI4VFUtBf7uk/bhc7j7npE9RR2dI+m8OlKVseHMeOtrbkRR24OUXn0f58FFob2uV6lVjJ07Btk3rMGxIaTfZw/1nzZqJxYuXSMoV1SSxAmsG8dz495UGvjXNzTLfTD8joaZQ19JhpMdkpWJ8HI8aBZIFKvi3S7czg+bObJ6VLJL0ocYG8W0aN34Chg8pxoR6n5RlZ+oU1yNLphuEBMfbMHteUDpS2hiP1OHaJkE3sZyfi73mWjoCQnpRXUdwHJ1xOAoSnlRBtXYE5Hcem0qfcdm9CQB+bme1oRhiRbj+yBGu28whxncrrm22if0mqcN0UBrKU90yvsy+3/RV4nju3V+DNdsrMW1sOT5x4oyY5+XYsH1Md4wHElRMFXOZjK8NI/LehvD8XCKEEauuWckZO5AQphKJpuD9geq1gYJzRdJGKY04vlQ3MaWS6WkE+zNhAEpIdZ0r8Nii2Ny3H5X7diPV5cQJU8YjJbVvCuu777yNH373W6iuqsSvfvkLXH755R+5Gux4xDFFGN1yyy3dPw8bNgzf+ta35MlQMBgUCeSmTZvwxz/+EevXr8e4cePkcyNG9C871dDQOD7gcDrhzM5GuKkJgX37BlSG8+wpJbj3te0JfTbscGLhgfX9fzAUQmTXzu5f/Y/8CZHdu+BesAjuWbPhiFFm1A5jizJw9qQi3PWcYeptxtOrD2DF7kb8/fq5aOsM4Ut/W41RBelYOCr2E9Q5w3Nla2gL4Pan12nCSENDY9Bj1KhReO65Z/HGG2/gttu+gX8/+Si+eecPcPFlV/TxN2JAxuCW/jYMUJmK1B8BEAskROjLweMpwofBHEkS+nTEImnsYPi0tIj6w/r3j0FwhgfwNzUgN68AKalpQuZs3FmB1ORkjCrLlzQmmtMm9aPStQMrRxFFJaVCPOTn5SISCSMlJQ1FZeXIy0lDOBxGS1OjEB6OSBhhZy62bt2E5OQUaa/b45GUquKycmRm5cDl6iGptm5ch9LyYZKmQnJp7IQeFYHP1yolvFl1iyW8d23bLGksKvDn9wFWi2NVuqKRk/H2yrUYOmI0PMlUiiRJdSs7Uo4qCabQFWUbhBuD4v0NYaMkeShspMeU9U8UKTS2BWRsWKKdYJ8ZbFNpZFYxkVSjuorm3wrtbT7s3LpZxjQrJwfz589HZppRha8snylyfSvcDRQcL6eT5IajX6KGxERtS4et+skKpiax79NH5MU9Nskupl1S4UWPKpI98cyZzWDqHjeVqkYFW3/EV052JkpGTxXVXXl+hpA6sb43MuWUle9mjIhfcXBfnU/W1PDC+OuCxO2oYnvzdzMM0/rer/Fe0dIeFOLO7E1FQjLPolTrDyRnSLZRvZSo35msudw02Zi+ye+eXKsHa/DO9U6VWEurD1u3bUdrSzPyi4oxZfos26pm27ZuwY++/x0sffst3H777fj6178uxL/G4cExRRiZQdXQX//6VyxYsEDIIoK56CNHjsRzzz2Hs88+W/44MG3tnnvuQW5u7DKEnZ2dsim0tLR0Sz25KfBnHtP82mDCYO7/YO77sdZ/78SJaF+6FOH6egQqK+EuLk5ov7GF6ZiZG8DaBjoUxf6izHenZfgx+4ufRduSJWhftUqIoSWlU/HnieegMTkDmYF2fHXNU5hVs6XXvtHmJnQ+8y/Z4HbDNX1Gd/pa0rBhcb+AXDjDqFDB776U67Lcu8IL6yvxmbnlyE11yfapaSV4cX0VFozMwSubarC2ornXsW46dZRIyP+5aj/++f5+XDCdX+7t55avy5PcGO8f7xis/TeXUo6KImBw9T/efW8wjsVgAxXtK1Ysx5NPPolv33EHHvzj7/DNO76HM84+p9d9msEt0zoY7BxobJOgmEGXNbXIDkxf4tP5FI9T/Hj4VN6a9sSgkKlO/oawBIxZaV7xXokXlPF4THWil4lqK19jm3geVijasv4DtLY0yXtOlxuO3GEIVm/HZpdLCJeCohJMmDJdjJBTunz49u7cLoqcoSPHYHu1TwJztpceSfyXRMNZ510kHjo1lfsxpHyoGBxnZ+cImbNt61Yc2LfbKK2dnYPS0hI4khyor6s3Kk4NLRfiLdAZgMOZhJ07dmJndZU8FOb9l9ddcmoqaior5LoMBoLizcP0N/rwsK/FxSXIzi5FRkY6kj1umZM1u+rEwJsmvjTKpjJkWGEG8tNmYvfunRgzYooEzKz+xmCbfkKx1BnsozlNkIregaoZqD4zK9C4dpjeReKF7VAg4cGUPPNcr125DKPGTcT40cPj+sx8WNCXprqpQ1KOrBD1RygiJCfVMxv2NuLEid5+fbJGFGWiwB/E+r0N4rVkp5ZRlblUJTv20a4CViKg39aUfoyyDzS0YVNFIyYNzRUCjENtN59cF5v3N4m6TBllxwKJQPajNLevj6aVBCLRwrHoDxxr3i/YBgUSUmwzCZtY6ZJ2YF84xjwe20DQjJx+YxwLHpOqyYGsa5Kddj5jvH9VNrZLOiTTabmpKnBshxBVza3Yf+AAmhqZDeSAy+3CkGEjZZ3boWLfXvzml/fgn0/+FVdffTUef3Q7Cgt7qtppHB4cc4TRN7/5Tfz+979He3u7mLORHFLYuXOn+Bb985//xOOPPy5PMahKYtra66+/HvOYP/3pT/HDH/YukU3U1tZK+UcF/rFiXitvBAOppHG8YDD3fzD3/Vjrf2T4cKArTbV66VJ4Fi1KeN+fpT+JixsugA8ptqQRyaJ0dOBnOc8g9Ml74f3kJ+Bpb0dw1fv43UoXvrXxaczctgJNnjREVBURC34/9QK8WT6j54Ud3DYDji2YlNSGH85IBqZMgyPZ/ksJ/8j6mpvRVN/zxXJnbRuK3CE01dfL76UpUbxW3SK/zy50YnZhb8K8ralR/j1jeDJOLh+Bq/++FWePtH9CGEUEbb5WwBGFA0f33B8ODNb+twd61pfP5xPvwMGGWPe91lbDU0Tj+Abn/IorrpBqavfddx9uu/nLQkh87bZv47QzP97lqWOUL+eTcZJHVJ2wJDzL3rPSV7wUEhIuDKioCKKhLVOzrOXMeUxFUHAdUtGxq7rFKH3uThJDaAaLBqlNEiUg/jzVByrEGITHIuHbEAa2dIYR6OI6HamFyM0uQrrHgWZ/BMmsjDZ2PILBgHx3JmlEgqiktBS+Vp4vihNmzUZbm0+qGVU0B5CbTOdjL5LTMuBwJSM5LV3UQOGkTDiyk7CpogHRSB06OzZgZHE25s+dDY/bJcGjuY9DivJ7/X1zJnllXEePGSO/U9UTDEe71VfsO993uxwoyEhGe0cnUlK88LpZ1a13kDs0P0NKstPDpizX8FvhvstWrBYFA9PWCI4hiT9Jq2vxY1tlk4x9LNUR8WH8qcxgapJdwG1VSFN1lVtQiIa6GjjH9mRPUGHClDlJeTKlTRqm1I7em9PwG+oPJBdfX7dfvLisYJOo+qH3FFVwC8YXJVx1jp+n8o2eSPS5ISHGuSE5xXnmsXPSvPJ6ol5IHwYkxLLSPOjoDEtqYUcwLOvLDK47enfREL4/YoakX2NbJ4YWpMu8mH3PrN5FVCvNHp0Y0UGiZe6YwgGTk4qUYsqk8nriXHGMqWyyzhtJa6bcrd/XiMLMZNsqhv2B97L9De0ybiRYqbzkNejrCKCizoemNj+qqqrRUF8HhyMJYacHo8qLMHnG7Piqrb178Ltf/wJ//9tfJLuInsYqm0jj8MMRNT9G/AjAtDIaV8cDU83Gjx8vP9fV1Ym6iMQQSR66n5M04iKjI/pDDz2ELVu2YOxYozz2+++/jxNOOAGbN2+OubDsFEbl5eVobGxEZmZmry+PJJEKCgqO+qD5cGAw938w9/1Y63/ryy/jwM1G+mr2ZZei6HvfS2zHjkY4fjEGe8L5uC14A1ZGx8GJSFeVEaN62izHFvzCfT+GOesQvW07kNIjFf/sw8vxxZNGYE60Eb4lS0R91LHq/d5G3InC44FrxkzDOHvBQjhNPkgXPfAevv3xcThhaM+5F9zzFp764lyUZhkk06o9jfjJi1vxry/OjXmK59ZV4d2dDSIjnl6ehavm23sY8cluU30DsvOY3nB0z/3hwGDtv6Q2PvSO/HzdaeNwwbyRGGyIdd/jdwQW3iCZZP6OoHF8gw8qaXtw9z33IK+gGNd/7Rs4/axzJAjvNnPuCtSpCnGyslmCAR4DO6ZKkRAiGMRSyRBr/5qaWry7ej06oh6MyvfCHQ1KKhcV91ybw4cNRWqyQUDwWz5jb56DJJeoKOj3wnSiVr+cY3hBhryoIgK+H6v9DOzX7a3HhCG56Ojwo6GxEa2tPjQ01Ms1w33SMzO7qnP50eTrRFOrD6ctmoMhxQVxx4FqquXba4RU6CY8nD0eMAYRYowzq7ntrW0VL6B4xBzDHLZ5Z1Uz9lbVobOlTggROwWDKlFO9QUDbQbZLOc+0ECdx6kXFYcxvt3EEP12UjwJk02hYBC1NVWor6mS+Rs9egzKS4t6pdlRsUNfIjMhoYgK9ttMWLDq3twxRsn4/sDPH07Shgqq1bvqRGFDoiJR42W2S4gZnxG3cXg5xlSFUX2XKHmVKJQKJ5FrmRW8uC7NxF3P1pvAk/TLw6gSI2Hz1oZKScmjV1Qi48t9Glo7hSjktci5UdUA+zPBpp8R1xtJNbPqifPV1NKGbduNNDOny4WsvCIEXBkIR5na6JaUxlj+aSSKfvurn+MfT/wfPv3pT+O73/0uJk60Vx9pHMcKo1tvvRVXXXVV3M8wzUyBVc+4kRCaMGGCEDvLli2TPF6aYfPphiKLCH6G2Lt3b0zCyOv1ymYFvyBag2OjLGPf1wcLBnP/B3Pfj6X+py9cKN5A0Y4OtPz3WRTe9g040+ObEwq2vghEwxieVIOnvD/E5km34qWsi9HUGYEr2I4LI89j/PpfGV+l+eRv24vA9Cu6d//E1BJ88f/elycpv738PJxx3XU48J3vovmpp+T9pPR0RHxdlUb6QyCA0HvLZOu491dIGjIE7nkLhTxClGfgl42eeWC6QXsg0v1aWyAiho/xCI5zp5bKlujc81iDiTAZ7P03B0hMGznar/sjed8brGMx2EF/DH5nvfHGG/HAAw/g7u9/Aw/e+1N88cs34YKLL4XXe/BFDRiM5memyCaGxu2GkogED1NFmMLE1/j0X9RFTi/GDilAS0MNRg0fi9HDy2IHn12XspMklullGgJzO5gAOjPFK6loye405GQaf18NlVMXIWW6f/DzH+ypR4lF6WoHEgenTCrFzuoWTCzPjUtYMACmiot+MUwtKstLl9dIWoXC9BgyxoPGuQcq9oonU3ZWDhxDhqEj7Owuz01FCMkhBakQl5nSrewaCBgwsy2d9DZKTxaljBoLvsfUM6bJ8WcFpyOCpKAf0WAb/O3tCIWCiIQjknrOmKa4qBhz5sxGarK3z3jQA2dogVEG3YjP45MCbFeiONwKH6aMLRhXnDC5dKBLtcLxLMxKFp8jc2W3Rl8AO6paugmeZLdL0tlYNv7DmCAPxJPnYBQ5hwu8FhaOLxaDdRJpVvB6pbqRRuTBrgebJGk5ZmOzs2R844H3I/p5kZzkNUNiyXwPItlacaAaO7ZvE5KodMhwePPK5WFUhBUg+6kyt27tGtz/h9/g2X8/LSrP1atXd8f0GoOQMOKTO24HA+UjoNRBCxculMoIO3bsEONCYuvWrd0m2RoaGsc/nBkZyDr3XDT94x+ItLej+b//Qe4VPcROTGz8t/FvchZwwYMYP/YsjO+6zzAdp7Dwu8CUucAzX6RjKLDh392E0Y5aH/7fcxvx1A0LMLnMMGcM+9rQ+vzz8jMJrFGvvYpQZSXu+NcHeL7ZwwP3acLk+l34f+/+qc/rkYoKdD71d9nCZ3wb7X94Ff5ZY8X/yFlejhF5adKGMYXGF9xtNT6MLEiAJNPQ0NDQGDBxRLsDEke0P/jVr36Nn/3oB/jCF2/ElVdfh6zs+IFWf2BwW717s1TAmjRtFjo6nVi+0wevI4QR5aVSEp2pYU3+NuQVFCHZ6xbVTaJg0M3A7mCDaAbQPIbZT0WBx+UDDJIBTFmiyoCfH5Kb3lXyPDWh9BtW3qJyZlJ5TtwqWewDCRMqHO7/y1NwhP0ozk5DfkGhBKkEjb2nzJzTp78k3yrqfaJMob/OwZr1EiScmGLFaaC6gl5RVrAfVEIpNRRT/fbs3CZ+QM2RVGRlpGPOxIlwe1xwu4w5jeeRxICcwfcwqsOOEKgWkfTJQEh8dexSuIQ4BDCqKHNA3jrWY9S30suoQ47P9VSWlyYkpR2cXUSH8jvi+iR5tHJHrajV+vMd+qggaaThiKS80eSbxBj/tcv9od/P1GF9jezjgUTOlGG52LK/SVJguW9ze09GTUaXX9FA54lphCSieHxeN2w3/dvoFZbudaHV1441a1YjPTMLxSMnoakjhPoAvbE8KI1TXY7j8ebrr+K+3/4aq1a8J4KSDRs2YPTo0QNqn8ZxSBglivfeew8rVqzAokWLRG5LUoiyNBJDVBcRNLieOXMmrrnmGtx7770S6H35y1/GGWec0Ut1pKGhcXwj5/LLhDAiGh75M7LPPx9J/VVPqNkEDD8RuPBPQEaMp17jzga+tAz41xeAGqMaDMEvMVT9BLqeGlKSXHH/n4SwIrI++Um4srJk+9Wd40GdUrilBW1L34Vv8WL4lixGuLYubvOCDiei9KNAFP5Nm9D09v/g/tXPJV3t1Dmfwv+90YrZpWlohxP/XluJb5+tc7s1NDQ0DheSk5PFCuELX/gCnn/+edxzz89x7y/vwXkXXYELPnM1yoeN7CprHh8MDkmuqLQc+giNHjcJObn5WL9mlVT8GjF6HHLz8qR0dGXFHqSmpWHq9GkoyOltihwLVGDQV4QKF5IrDBR79cXtlICSlaL6S+kh8THBpny8OfhlAEkPFxX8qrShxPQkHA8XJgzJxvp9DeK3Yk6rUt3tTp/rSqXjd/3dlQ2IdDSg09+EaEdEfFHS0u0JFTN5czBorK9DfV0NAp2dqPK7UVxcBK/HI0bmJL04nvLdoKvBnZ1+8ZZihTjOR3p6BkqGj0Eg4sS8/PSEyqCbsfVAk6g77Ig7M3h6o7R7Ui8lVaJgf+gxZJADxtykelzIy0wWIidW2uKO6hZ5b2RRxoDTxOgJVtnUbqR5Ohxy7v7St7j2SFqQzOJnSaTZrVM70D9sbx296WJfS6qbkq7ZNZ5U/w2UjGI6HVU9atzcToeMKYlWKtt4L7AbU87drppW8doaCHgsjgNTzOiRVpR98JXMFFgNctaogj5G6K3tASz9YDt27d6DMRMmIeryiK3D6OLMuERXR0cHnnnqH3jo/t+jtqYaX/3KV/D0U/+QjCKNowOuY+mJztNPP43vf//7aGtrk/QzVkL7zne+051ORpk4K6V99atfxUknnYS0tDR8/OMfxy9/+cuPuvkaGhpHEMkTJiB13jy0L1uGYEUFan71axR/5874O5EI8qT1fCuIhcwS4KrngEBb90tl2Sn44XmTcNs/1qK6xY8xbdX48Yt/MiySaTD4uc/2OYwzMxOZZ58lG70eOjdvhm/xEiGQOtas6aNAunPh9ViXbygnv7Pwevn30Zd/jKJ9e3HWvt9j7+RzcXFju3yZv7QgiHkpPYb9GhoaGhqHB/zu+clPflK25cuX49f33ovPfOJkLDzxZFxz3Q049YyzbMtCm0GChdWEWHlrw9qV6GysRGFuDvILi+RvEsvFc2PQRQKpuLgYLqcb7f6gHFuVAuefLxVkk5Q40EUS0R+EBs52FZkY7JHYYYrJzmp/L5KLx2JQbCU/7MD3SHhxs6uYNFDwOFOG5olapNsjqsvI2w7sR0vVbrRH/cguLpMUNKqLDgca6muxf+9uTJo0CSnJKahvqMe+ffvQGQXGTZoKfzAinlQcV4Xd2zZizIjhOGH2GCFEmEqXmuLFyGym5gw8zXVSef8pfoR4V3WVLE9E4WVFaU6q+AVROcW1kAj4PWRcabasaxJaGcksWZ+esDqGhA+VQar9PP/2KqMAAZGd6hU1EQ+niFCSYmwjybpEQZWZUanQJaq2RIgtMbkPR7BhX2PC42EGlVMTTOl0iYLXFFVsJHwH0kcFEtKHCuILxoqGwTDCkTA6OwPi7edra0Pz/q3wdvowoiAD3hjFWxT27N6Nxx55EE/85TGUlZXhpq9+BVdeeSVSUg5dWzWOE9ProxFc9DTTthpa9qSmFA5KD4PB3P/B3Pdjtf+B3bux81PnI9qVsjr08ceQNmfOYe97NBjE7ksvg3+joUDKu/56FH7dMOFOFOGmJrQtXWoQSEuWINxV/WygSBo2DO75i+CZv0BMtB0ez0GaPtcjO4/Gn8fG3B9KDNb+U3J+44Nvy8/XnzEeF84zyMrBhFjXfqzvCBoaClVVVXjwwQdx/wMPwOPx4qprr8eln/kccnPjl/kmhOipa8beikr4aBIbCSDcVg/qS+cs/JgEYTVVB+Dv6EAoGOj2bOlGNAqPNxm1AS8yszJRkp+FrBSPqGnSkwfm58LAmD4nVDY0+PxYNKEkZlrQ0YCOziCWr1wlVhUFRSUoLh3S799thkEBlolnmlUghNq6BgRDIWRlM/2nNwlCtVOyC1i/egUWnbgIGSk9qiDOw/bd+1F5oALjJ0/vc56qmmps3LQVIYcbLgeQn2aYo3d0tGPGCbNRUpDzoZUfhwsco40VjUL60LTb7n0qfKjUocKa/6qqXOrvSXmeYW59KNpCcpPkCVU348tyBkTccH8qpup9fiFCSTIN5JoggbmpokkIJiqfBgoSaHb+QN0KvaBRlY1rkWloZpGioeQJY/rwg1fesP001+cY2ikgScyyXyT5SEyZvax4P6hvbsWO7TukeizVQySsXS43UtPTkZKSJvcnErWxSHL+XX3rjdfw54cewJuvv4LzzjtPhB4nnnjih/Ka0ji80ISRDTRhdPyQBocKg7nvx3L/6x99FDU/M6owOgvyMfyvf4VnaE/FsUPddyqFKml0/fTT8rtn9CiMePppJB0EUWM+pn/jJvgWv4W2xUvQ8cEHtv5H/SIlBe4TZhuV1+h9VJqY4fVgJUwGe/81YaQJI40Pj2AwiH//+9/43e9+j+XL38Mnzjsfn/38tZi/cJFR8j7ak2olla261SCR7kpXrFhU2dAqipsxJTko6CfolqDS70drS5OkTbEy0dwFiyQA9flDvT5LQ+BYRBIJkIr6NrR2BMTAuSg75UMFdAzud9a0ICPZ06taVJJNJSmpMmepKJUopP/BMLZu34ma6mrjRabEMSUr5EVnkhclhXlwdQW0q5a9jUioE0X5efCSyQkFUFo+VFLNxk6c0n1cqr9eefVlRF2pKB8+EgW52aIM8jhCCLS3SrW4jrY2CZQnTZ8Vv31+PwKdfvj9HTI/2zatx8KTT8XoYWUx9yORsL/eKJzBcUqkEld/qqyBgO1eu7teCAw7ooRjwVS1FK+RXvVhfLIOF5gO9v5OVsnzoigrxRgvZ7zxI6nXY+LONMs9Na2S4hXPWyse3ttWjaxU+/RDlZ5GUpaqJ3oLHaz5eCxiiPNEooybXR/4Wf79J1FM4o8kUUOLD/v2V6Gqvhlji1IxbsxopGcM7GFJVeUBPPnXv+Bvf/mzVE+87gtfwA033IAhQ4YcVP80jiw0YWQDTRgdX6TBocBg7vux3P9oOIy9n78K7StXyu+u0hIM+/Of4RmACX6ifSexU3XXXWh68u/GC243hv/tr0iZ0vOF81Ag1NiItneWom0JvY/eRrih4aCOkzR8BDwLFgp55Jo+Aw63/RO6wUqYDPb+a8JIE0YahxabNm3CQw89hMceexwZWdk49+LP4uzzL0Z+fqGYG5tJEkUGJDmSusvIxyJPwqEQnvvX37p/z8nLR3pGFtxut/z9yssvwPQpE/sEniQAaJrMtByDSFJBpZHaxsBxSG7aIUkvI4FDZQVLfPO8igzrJstMxJm1NDxJNLOIiu8z3SmRgF2lYvH4wWAIDU3N2Lm/GvurG6QSGcVSWclJQDgo3lFutwcer1f+1jMgHjZyTK/jNTc1oqZyPzr9HfI7Sbj2oAMRlxcOdwoyM9IwrDAbuVnpaG/zYd/unaICM8Zb/i/jm5ySjOTkFKSmcktDdnYW0lO8tn1iwE9zbq/LiaEFRlqX7ThZxs88tmr8SP7NHl140POoDK0PlRLKqkxi2+mPQ/Pkw0E4mQnaWONlN34Kbf4QZozI+1DtYhsOR7+2V7VIap4ymuf4ZafFJob6u17rW/1o6TA8z9549SXMnjsf5SWFQhYm2v5wOIw3XnsFf33sEbz68os49dRTxfft3HPPlfuTxrEDTRjZQBNGxxdpcCgwmPt+rPefBMveK69E57bt8rszPx+lP/sZ0hctPGR95zmqvvc9tL7yqvFCUhLKfvVLZJ59Ng4nRH20YQN8bxnG2f4P1vU4gQ4Eqalwz5oN94JFokByFvVYkw5WwmSw918TRpow0jg8YLrUM888gwceeBDvvPM2Tj39LFxy+WdwxtnnwPMh1KhER3sb6murpQJXIBCQwG7y5Ckoyusp794fhNCJRgdsVBwLJAQ27GsQT5pDcUxVoY1lvO3SowZCHLSyzH1DG9o6AmJK7UYIjlA72lpbJdjliKWmZ6CobCj8oSiqqmtRU1ePUNBQarncLqSnMf0mCR6XA85oRFIIgxz7JAemTJmK9DTDN4h8XaKKKUmbau6QoF1VsjoUJM2WA01Cth0JcN5ZSU1VVKMpsh1IbJCEUBW32juDQmQyTdAKqmOoimOqFBU4B6u+OR5B9VNrF8FjzTTj2GamuJGe7O5DHnGe6LdFJaP6+shxppk396mqbcTatWsw7YS54guWCLZs3oR/PPF/eOrvTwjZfc3VV0txgOHDhx+y/mocWWjCyAaaMDr+SIMPi8Hc9+Oh/8GaGuy79gvo3Lat+7Xsiy9G4TdvhzO9rwnoQPre8vLLqPrhXT0+Q04nSn/2U2Sdey6ONEINDWh75x0hkNreflu8kA4GzpGjRHnkXrAAzilT0dzSMugIEwVNGGkPI+1hpHG4sGvXLjz22GOiOuJ3z/MvvBgXXXo5ps2YJUGfWXkTCoXg7/QjHI7C7U023qcawqTYsYLEBku3JyfTzygLaSnJyMlMQ2FupniUuF2GYTZxuNKH1u1tkHPYqRwYoBpKqq40K/WvKZ1KKa56/wzsrmkV0oC+OocCHGsSNDQlVqQZx7ShqQnV+/fDkxTFkLJiDC8vQ2Z6ClyWdKVDpcShuuN/q/agKDvVtgKXGJubxqJ3Ch/HTo1lX8UaS6EfLsKIVduYwsi+U5XGVEchgrrS1LyuD6cYUl5TfJjBOapt7hDCT6rv5aRicnnOYVnDyjRcrQd1rZmvzVjvmdVdMXqFZLdLKuTRh+lweFhx3EjakUzi1nOfMP7lWsnL8EqanB0Bxz7trqjExvXr5DvwtNnzxLPIirq6Wvz3mX/hH3/7CzZv2ohPfepTuPrqq6WCeX+m/xpHPzRhZANNGB2fpMGHwWDu+/HSf5InFbfcgvZ3l3W/5iosRM5nP4vsiy6EKzc34b4z1Y1m1I3/91chZhSi6Rko//ndyDjllA/V1l+/shVVzX7cfdHUgz4G2+hfv75LfbQE/nXrDu5ArDIzbRpSP3YKPPMXIanw4OXsxyI0YaQJI00YaRxu8O/M22+/jUf+/Gc8/a+nkZyaivknnorZCxZh7NgJCIeDYnpNs3UGdfS+SYJBnDCdzevxwpvMLUVSnVhJze3xwuUy0kfok8Ny7s0trfB1+EVRE4YTZg0HQ0UJbqMRzJg2CUW5mRLwM6A9XEoOg5gx0swMAswccNPHqec94/ee943XohhbmnXYzaIlLSlK8fBHr2hRBEZ/ZIV1vPgv53FUcdZhbRvTtpj6xvQoM4fJoaPChYopppzFmjO2kylqJIaolgmGezMu3I0eP0xdo/cWU7AOB1G0t84nvltuVw/5ZiYtrV5bdu8pojMemNbIKnAkv1QdKu5Dwo0pZfQuOxpQUV2HnTt2oGp/BXLzC2UeWltb8c6SxVj67lIsX/YuZs+ejauuugqXXHIJsrOPjJJN48hAE0Y20ITR8UsaHCwGc9+Pp/4zhavp739H9c9/gWh7e/fr9O/JOOsspM2fh+RJk+AdNarb00f1nXRS58ZN6Fj3AVr++yyC+/f3Ovbq8qkYe/ePMGtmb8+D/vDujnp86+kP8NY3TjmkhJEVofp6IbeEQHrnHUSamw/qOM7RYwzj7AWL4JoyBQ6bJ013PbcJQ3JScM3CY19+rAkjTRhpwkjjSIKGsC+++CKeeOIJPPvssxg5ciTO//QFOP/883HCzBm9zLLpzxMKhdHh98Pf2YmO9g60t7fLxrQoqpJ4DyMxlJTkFBIpEAzJ66GwUYGJMSqJmYgwRjR3DmPIsJFCWjHoZ3oQiQcG5qpyEkkaqnuOFyjiyk5NYlWSqNes5FWfanUxIERCNwFhkFBmcsFKNphVVYfSxPpIQ8yU/UFJneKaUsNFDy2SIwrsJ4lKkkv890gTJjQXpxKrNMde4TXQdWQ2szd+N5F+ls+ptcQ0sea2ABrbOrurzY0tyRKz7Y8a1TW1eO655/Df//wbL730EiZNmoRLL71UtmED8AfVOLZw/NztNTQ0NPoBS4DmXH450k48CdU/+Ql8b7wh35ajwSBanntONvmcxwNXcTGSvB5EQ2EEm5rQ2Nhoe0waaRfe8nV8YX0K7k2gZPJHBVdeHrI+9SnZqD5itTXf4sVSea1tw0YkISpPmBXCjiTxY7AivH2bbP6/PAZHWhpcc+YZ5tnzFiCpoOCI9klDQ0PjeENycrKQQ9x8Pp+QRvQ8OuVjJyM7JxennnkOTjnjbMycPVdKWPfATekFkr2ZSM4xlBiGYigqfjoqjUkRELzf8wEQlUtMN3YlQdQU9OPhZ81KiWOFoHjtgwohI/qDw0TY8GdlEky1FlPnjJ+TZDzk56Su11kVzeWUz/FnSUtTBE+M9LSECAVLGhMrU3Urrqwklg0xpV4yn97uNR53Urm9mvpwg+PNVLVDYaJ+uEBV1optNcjLTEZti1+2eFBja56SbiLQRPrJtWT6XUzuqVyyvD6QtXSkwDW7bds2vPDCC1LxkUrI6dOnS8rZPffcg3Hjxn3UTdQ4AtCEkYaGxqCDZ0gZyu/7AwJ796Lx739H81P/QtikuIkGAgju3Rv7AA4H0k5cJORT+kknwcH87A2vY9WeRnzzqQ/Q0B7ABTOG4M5PTJAvD/yy95vXtuEfK/eJvPqcKcW445wJ8kX0qj8vRyAcwcTvvSiHXv29M+RffyiML//tfby5uQajCtPxu8tnYFhemm1znl9Xid+8ug0Vje0oyU7BvZdOx+SyLGytbsWdz6zD5qpWDM1NxffPnYQ5I3KlvVcv92PuhLPxlmsWNo1vxqvTAzhplRtf2fI/PDVkLtKDfvzuzXuxtGQS/jL+LNSlZGNMUwVuWv1PFHUY5NlOZybuD4zBztUueFe+jasa1sI1ehRe9A+RLz6PL9uLU8cV4HufnHBoJ1BDQ0NjECA9PR2XX365bFQevf766xK0fefWL6OhoQEnnXQSzjjjDFEhaSQOw1vG8JeJWP5V/kU9v3cRPL0+p34+/KNO3oCbpCB2EX20E+TvDhO5IJ9xmF4j+cDPdH8WONDYjq259t8jNHpQpwcDbW1teOutt/Dyyy+juroaJ554Ii666CI8/vjjKC8v1yM0yKAJIw0NjUELz9ChKPrGN1Dwta+hY81aqThG3x//xo3ieRTp7ITD5ULU60XK6NFImTxJUtZSpk+Hu6Skz/H+u+YAnrx+nvz8uYeX48kVe/GZucPw95X7hNT5140LkOpx4rrHV+L3r2/HbWeNw6NXz+mTkka8tKEKj1w1G7+5dDq+/fQ6/OqVrfjNZTP6nJMk1R3PrMMDnz0Bs4fnYl9ju5BUNIe89rEVuHrBCPztunl4ZWO1/L7k9lOQ3fWE75k1+/HY1XNQlpMCN1MNVz2PjeddhX+PdyDw7lJsaD4R9xV+DD9Y9jBGtFThX6NPxj2zrsAvl/wBba5k3LHgenx288v40dKH4Hd5UJOag1ErnsWqmZeiNNiKzxcG4MZCROoLkJSXf9jmUUNDQ2MwKI/OOecc2UhYbNq0Ca+88opsTF/TGFzoxVXZEldR27cOt9+TxvEBlr1fuHAhHnzwQSGLUlONan8agxOaMNLQ0Bj0SPJ6kTZ3jmwfxr/p6oXDUZhppAh84cQRePr9/UIYPbv2AK47aSRKs1PkvZtPHyskEAmjWFg4Kh8LRhkky7nTSvGj/220/dxTq/bhijlDMXekkQ6nVEjLdzVIrvw1i0bI7+dMKcGf39mF1zfX4IKZQ+S1y2YPxciC3hVmvnzqaBQPywXmz8YDz6zDla4wpk7/LJJWr8El776LJ8eehrrkTKzLH4WStnp8Yve7sp872IGM5o6eA3V2IvDaawi89qr86hw3vqvy2kK4Jk02VFkaGhoaGgMGVSQTJ06U7aabbtIjqKGhoaFx2KAJIw0NDY1DBEUIqZ9rWo389+oWP8pM7/FnvhYP+ek9JpApHifaOsO2nzvQ5Me0IX2rUfD4pdm9DRuN83Z2/16S1dfQsSSrp537mzrw9M4GPOQogSO3FPjEOYgEQghe9nk0bq1GcXs9EkV4y2bZ/I8+DEdmJtxz5xvm2fQ+ilGhTkNDQ0NDQ0NDQ0Pjo4MmjDQ0NDQOEQ40dfT6ubCrwkZRZrKQL+b3+BrxYdXhJIWYhmYFj08yqXf7/DhpbI8xtd25za+RULrl9DE4b1xaH4VV65r9WPXWNhSfP8Ewzl66FJG2NuMY/fg6RFtaEHjlJdkI54SJQh55FiySn7X6SENDQ0NDQ0NDQ+Ojx7FbH1tDQ0PjKMNj7+4WVRG3h9/ehU9MNXyOPjm1FH9ashOVzR1obg/i3te24dxpxnv56R7U+4wysweDC2cOwd/e24sVuxvE12JPfZuQU9PLDdXRY0t3S2WUF9dXYlNVC04ZV5jwsS+eVY7Hl+3B5pp2OXaLPyheTMQp4wuxpyWIF4fNRdG996LwtbfQ9puHkfeFa5GX5kZlWuIV48KbNsL/yJ/Q8oWr0HTOGfB97050vvA8IjEq02loaGhoaGhoaGhoHH5ohZGGhobGIcInppTi0geWoaEtgE/PKMNls41KEpfOLhey6NN/WIpQJIqzJhXhq6eOkfdGF2bgzElFWHj361Iyd+V3Th/QOWcNz8UPPzUZdzy9Toii0q4qaUw/+9PnZ+E7/16PX7y0BeW5qXjwc7OQk5Z4SduZQ3Pw3U9MwE9e2oQD/9qGdK8Li8bkix9SZrIbj10zB3c9uxE/+d8mpHld4sk067YFuK7Why89+h4uHjkbJ/sr8LU3HkKkva8Kyg7R5mYEXn5RNsqdqDii8ogKJFEf9eMjpaGhoaGhoaGhoaFxaOCI8rGxRi+0tLQgKysLzc3NyMzMPCjz2+MRg7n/g7nvg73/g7nvh6r/0UAA7e+/D99bi+FbshiB7TsO6jiOnBzD+2jBQrjnzkNSVl/vpkONaDSCpvp6ZOflwcH6xIMEbZ1B3Pjg2/Lz9WeMx4XzRmGwIdbaj/UdQUNDQ0NDQ0PjeINWGGloaGhoHFY4PB6kzZsnW9E3b0dw/374liyBj95H776LaIepulocRBsbEXjxedmQlATnxEnwkDyav1CqsGn1kYaGhoaGhoaGhsahgyaMNDQ0NDSOKNxlZci57DLZIoEAOlau7FIfLUFg587EDhKJILx+HTq4PXg/HDm5cM9n5bVFhvpIKz80NDQ0NDQ0NDQ0PhQ0YaShoaGh8ZEhieqjBQtkK/r2txCoqIBv8WKj8tqyZYj6e1d6i4VoYwMCz/9PNqqPXJOniPJIvI/GjtPqIw0NDQ0NDQ0NDY0BQhNGGhoaGhpHDTxDhiD3iitki3R2on3FSvgWvyUEUmD37sQOEokg9MFa2ToeuA+OvDy45y2Q9DXXnHlIysg43N3Q0NDQ0NDQ0NDQOOahCSMNjcMM+sqHQiGEw+Fj2vw1GAzC7/cPOuPnwdz3o6H/rlknIJvb17+OwIEDaF+5Cu0rV8C/9gMx004U4fdXwv/+SsD5e1EcuadPh2v6TCQNGw6HwxHX9DoSDiEY6BxUptfhYAgFaU752Y2wzP9ggtPpHJTXu4aGhoaGhoaGGZow0tA4jAgEAqisrER7giXFj2bSi8RBa2tr3OD6eMRg7vtR2f8pk42NBT47OxHt7ESEZMYACFl+Uqim1kZgYzPg9cLBzeMFkvr2MRKOoLlucBEmHN4vzC+UnzOS/di1axcGG1JSUuB2uz/qZmhoaGhoaGhofGTQhJGGxmECg2wGWXxSXVpaCo/Hc3QE3B9CJeVyuY7ZPhwsBnPfj6X+0zw70taGSHu7bMJ4DBTRqJBGjpRUIDVVqrsRVAfyOh5s8+5qNIju3DQvstK8GEx9J9lfW1sLn8+HoqIirTbS0NDQ0NDQGJTQhJGGxmECAw6SRuXl5UhNTT2mx/lYIQ0OBwZz34+p/icnA12V0aKRiEEetbYi3OpDNJh46hpVS7I1NcLhcgPpaXAlp8CZkjKojLMj0SicrpD87PF6kczxHWTqIq55kv5MyeTPGhoaGhoaGhqDDfobkIbGYYb2wdDQOLIgsePMyJDNFY2K15GQRz6fEEmJqo+ioSDQ1ASgCeHqKjhIGqWnw5GWLilsGoPj3k3SVENDQ0NDQ0NjMEITRhoaGhoaxy2oiiK5k+T1wpWfj2g4LKSRkEetrYgGg4kdiMRTe7tsQI2oj4Q84kYF4SBSH2loaGhoaGhoaAwO6G+4GhoaRz2mT5+ORx999KNuhsZxAIfTCWdmJjylpfCOHQvv6NFwFxcjKS2N7FLCx6H6KNLUiHDFPoS2bUV4715EGhoGVLlNQ0NDQ0NDQ0ND42iGJow0NAYx0tPTuzea+nq93u7fP/7xjx+0omPNmjU4FnHPPfdg8uTJtu8tWrQI3/ve9+TnAwcO4JxzzkFaWhqGDh2Khx566Ii2ZeHChd1tuf766zFu3DhJn7n33nsPeTuOZ3CtJiUni/LIO2IEksePh2foUDhzcuEYSHUsUR+1IVJTjfDOHQjv2I5IVRWiPh/NgHC8Y8uWLVi5cqVsnfR/sqCxsbH7/b1796KlpQU7duzABx980P36pk2bPpK2a2hoaGhoaGhoxIYmjDQ0BjFYAUhtJ554Iu6+++7u31944QUMNlx55ZUS/C5fvrzX63xt2bJluOaaa+T3yy+/HMXFxaipqcE///lPfOMb38Bbb7014PM1Nzd/6LZMmzYN9913H+bMmTPg82vEUB+V9aiPXEVFRsrZQNRHQbP6aAvC+45v9VFOTk4vcsgK82u5ubloamqS11gYQENDQ0NDQ0ND4+iFJow0NDRs8f777+OUU06RAG/MmDF4+OGHe703b948ZGZmIj8/H+eee668rkiLBQsWiErpJz/5ie2xqS7gPgUFBRg2bBh+9KMfSUU5hd///vdSXS4vLw933nlnn/1/97vfdb//ne98p0/K2quvviptyc7OxqRJk/Df//43oVkmCfSJT3wCf/7zn3u9/thjj+HUU0/F8OHDpe1vv/02fvrTn4rCaO7cufjMZz6DRx55ZEAr6fXXX8fs2bNRVVU1oLbwd9UW4stf/jJOO+20QVfF6kiqj5zl5fB2q49yjOppA1EftVnUR9Vd6qPjxEyZhJGqoGcljHhdkyAiPB6P3Bd43ZSVlYkyTkNDQ0NDQ0ND4+iFNr3W0DhC2H76GQi3th7R8WaVqNGvvjLg/UhinHHGGfjjH/+ICy+8EBs3bsRZZ52F0aNH4/TTT8dXvvIVIXyWLl0qJaffe+892Y9qGAaOfJ0kjh3a29uF4Lj55pvxr3/9S87F9K6SkhJce+21QqSQJHrxxRdxwgkn4Ic//CHWr1/fvf9rr70m6VgvvfSSnINk04YNG7rfZ5rLxRdfLMf+2Mc+Jm0h8cK2JRKgsg1U9/z6178WEiYcDuOvf/0rfvnLX3Yfn20tKirq3oftoMpnICDpw3ZxPKlOIvmVSFsef/xx/OpXvxrQuTQOTeW1pMxMUSCxala0s1Ou50irDxExwk6w8lowiChJFW405E5NgyM9zai85vEck1PldruRkZEhqWZtbW2Slsb0VqWiU2QwyWfCbq1raGhoaGhoaGgcfdAKIw2NIwQJLhk8HcHtYAmqv/zlLzjppJNwySWXiLcRvXRIXPztb3/rDhD37NkjXj4MDPnZRPG///1PFAkkjKg4oAfQTTfd1H1skjNU7MyfP1/e/8EPfiCKBAV+ju9TQcT3v/vd7/Z6/4EHHsBVV10lhAx9feg99MlPfhL/+Mc/EmofyauUlBQhnAim5jEA/vSnPy2/M12PyiUz+HtrjLGmAkoqddls9Bwi2fX973//oNqi8dGqj9wFBfCOHIHkCePhKS/vUh+5Bqg+8iFSrdRHO+RnKpKONfWRIoOsKiNrOpqGhoaGhoaGhsaxA00YaWho9MHu3bvx/PPPCxHCjQTPH/7wh+70KaZf+f1+UQCNHz9eUsgGcmwqhtSxud16663dxyYJxTQ1BZJTVPQo8H2mo8V6n8e///77ex3/P//5j+yXCEiQff7zn+9OBWOqGz2LlGKCKTVW7yH+ToWFHe644w7U1tbabiTNRowYgW9/+9sJtYX/kixTbdE4iryPsrLgKSuDd9w4eEeNEu+jJHofYSDeRwFEGhvE8yi0dYt4IEUaG0WVdLSD15k1Lc2cjkaFXKqMh4aGhoaGhoaGxrECnZKmoaHRByRkqGJ58skn5Xem4IRCIbi61BOjRo2S1Ci+/s4770haFRVBJJBU0Bjv2PwcjZvtUFpaKuolBaa8VVZW9np/37593b+zXeb3eXwqln72s58d9MzSUJpVyli96dlnn5W0NoWpU6cK+UTD68LCQnmNVeGmTJlieywGyXaB8htvvCHKocWLF4ufS6JtWbFixUH3S+PwQ9RjKSlISkkBCgoQDYUQaWszFIY+n/yesPqIn6fXUTUkXU3S1tLTB2zCfSTAewM9zUieqrQ0pp9a09E0NDQ0NDQ0NDSOHWiFkYbGEfQTSsrKOqIbz3kw+NznPideQiQ0SNhwIymiyAqSRdXV1RIcU1nA1C+qYQh6+9AYOhaYHsZ96flDlRJ9eVj5680335T3qeZhWhp9kVhF6a677pIAVIHvMy2NBArbRQ8j8/tf/OIXRYlDQobHZuD67rvvDqhsN02+mcpGLyQSRKxEpkCyjGXtqRxiQExvJLaXfkMDAQ3FOZ7KvPpg2kJwjDiODMxJnvFn/qtxdIApaqI+GjKkR31UWNilPkocrLDWoz7aelSqj6xpaeZ0NHMlNQ0NDQ0NDQ0NjWMDWmGkoXGEcDDm0x8VqHihqfQ3v/lNIWBIRjD1jOSNqkJ2++23i58PCaKf//zn3SbX/+///T987Wtfwxe+8AXZ/1vf+lavYzOlS+3P45HgIAnD0vQE1Uo8Bs22Ozo6cMMNN4iHkgLfp+fP+eefL+/feOONGMsS6F1pWjNmzMATTzwh3kEkiUhmsW2/+MUvBjQGJICYDqbaZQaPz/6xyhuDZCqATj755AGPs9k4O5G2cMysOPPMM8U0m1iyZIm0l+ND7yeNo1h9VFgoaqOwzyfKI5pnR8OJqo8ifdVHVB6lfbTqI0Ue837R0NAg1zZBHy5uGhoaGhoaGhoaxxYcUeaUaPQCK71kZWWJtJ4SewV+CVZpKPxSPNgwmPt/MH1nsLRr1y7xqDnWS56bU9L6Szk70qDChlWXWFWNyp/B1PcjAd3/IzP/Unmtw4+wj5XXWhHp6Di4AyUl9a685nYf1GEi0Sj21Prk54LMZOSkJ3YPo7rQrCxSBLTZZ4zKP5WqtnbtWvmXaZtU0xFUKx4Nf2NISLM/I0eO7JVWGus7goaGhoaGhobG8QatMNLQ0Djm8PTTT+PjH/+4BJ1UEpEwmj179kfdLA2ND6c+Sk1BUqqd+qgV0XA4sQNFqD5qlU2O6/Ea6iNuVPkkQHqFI1G0dPSkujW3B8AnS5kpHric8Ykcpp5ZCSOrfxFTUq0m9EzvVOTRuHHjYprIa2hoaGhoaGhoHDlowkhDQ+OYw1/+8hcxg6Yqg+lm//3vf+HxeD7qZmloHFLvI1d2NvO8utRHHQaBNED1UTTQiWhDJ9BQb6iP0rqURySQukzsuz8bBep9nfD5g3JOhUAogroWP+pbO5GZ6kZBZgqSYhBP5rQ0Ii0tTVf109DQ0NDQ0NA4RqEJIw0NjWMOzzzzzEfdBA2NI6w+SjWMss3qI1V5bSDqI6qVWrvUR15vN3kUTUlBdVMH/MHYxyKJ1NwWQCAYQVlemi1pRLJo5syZcZvBSofcNDQ0NDQ0NDQ0jm5owkhDQ0NDQ+NYVx91kUcDUh91dspG9VFjWi783sQqt3UEQqht6UBR1sAqvWloaGhoaGhoaBxb0ISRhoaGhobG8aA+KipCNBjs8T5KUH0UcSTBlyBZpNDSHkReeqRfTyMNDQ0NDQ0NDY1jF/qbnobG0Y667cDPRxv/amhoaMQBq6K5cnLgKS+Hd/x4eEaMhKugAElxKjX6vGkDHlMqm1o6AnouNDQ0NDQ0NDSOY2jCSEPjaMe6fwJttcD6pz7qlmhoaBxj6iNnWircRUXwjh6N5HHj4C4rgzMzC44kZ/fnOt3egzp+e2foELZWQ0NDQ0NDQ0PjaIMmjDQ0jnZseNr4d33XvxoaGhofRn00tBzeCVQfjYArvwAR58Flp0ciPZXUNDQ0NDQ0NDQ0jj9owkhD42hG3TagbmvXz1uOirS0e++9Fx/72MdwNGDv3r1IT09Hc3Oz7ftNTU2isti9e/dhP5fG4cFPfvITXH755Ql/nnO0bt06PR0JqY/S4C4ugisldrpaPCQl9a2SpqGhoaGhoaGhcfxAE0YaGkczNv4HcHRdpvx3038O+SlI/ni9Xgm01Zafn49jAUOHDoXP50NWVtZRdy6OK8k1jQ+HO+64A0888UTCn+ccTZkyRQ/7AJDqdR3R/TQ0NDQ0NDQ0NI4NaMJIQ+NoxoZn6C5r/ByNHLa0tLvvvlsCbbXV1dUdlvNoaAwEwWBQD9gRQGaKRxRHAwE/z/00NDQ0NDQ0NDSOX2jCSEPjo0TQD+x9D9i7rO+25QWgej2Zop7P83e+bvd5HofHO8TYsGED5s+fj9zcXJx66qk4cOBAn/fnzZuHjIwMnHLKKbj99tt7pazV1NTgM5/5DEpKSlBaWoqb+X897gAAMkZJREFUb74ZnZ2dtud68803kZ2djfvuuw9lZWXIyckRlc7mzZsxd+5cZGZm4vzzz0dbW5t8nqlmDFyZekbwuDfeeKO0dcSIEXjqqd5G4VdddRWuueYaOQaVVFOnTsXbb7/d/X5rayuuv/56aSu3G264Iea5eKzrrrsOl112mfR93Lhx0n7i1ltvxZIlS/DNb35TzvPxj38chwO7du3C6aefLqon9nnhwoVob2/H3//+d5kThQsvvFD6o8D2ffWrX+2udvXb3/4W48ePl7Hn3G3atKn7syQQb7rpJgwbNgyFhYW48soru9Py1Jg89NBDGD58OPLy8vClL30JgUDs6lkvv/wyZsyYIW2eOXMmXn311e73OKbXXnstLrnkEpnr+++/Hz/4wQ9kvhJdb2zPmjVr5Gfue+655+IrX/mK9I0qMY6NRm+4nEnITHUPaFj4ee6noaGhoaGhoaFx/EJ/29PQ+Cjx/mPAI2cCj5zVd3visp50NAX+ztftPs/j8HiHEKFQCOedd54QRVVVVfjxj3+MP/3pT70UIHyfhEh9fT1+9rOf4ZFHHul+n2QE3y8uLsaOHTvEW2bt2rX40Y9+FPOcJG1IRJAM+ec//4nbbrtNNpI/+/btw/bt2/HAAw/Y7sv2vfvuu1i/fj1Wr16Np5/uq8j629/+JqQEiR+SG2yfIoFIjPD43J9tJVF1yy23xGwryQeSStz/c5/7nBAexC9/+UuceOKJ3cqtF154AYcDd955J0aPHi2KsOrqavz85z+Hy+USAmXVqlUylpwDkmLJycndRNDrr78uc0r88Y9/xMMPP4xnn31WjnPBBRcIyaJIH45VQ0ODzBvnhHNOAsaMZ555RkgajtnSpUvx05/+1La9HNtPfepT+O53vyvrhelmHH8eV4HpZ2p++K8Z/a03O7z00v9v7z7Ao6jWN4B/6T2h11ClNwER7qWFIkWKVEV6UbwiV/8XURHpiAiiIl6QIlIEvICCiCCodBXhghS9NJFeQwi9pM//eQ/MMrvZTSObze68v+dZ2c3Ozs6ZMxsz737nzPfSuHFjtTyOu+eff17tF7JWMDxIgvwzNsQMy2F5IiIiIvJsDIyIXKl2X5G6L9x/YGdICIahpfXY+Lq6/7i3viwYPny4qsDQby1atFA/R/iCEAGVGv7+/qrSqFu3bpbX7dixQ52II7jA86gCMj6/e/duOXr0qAoygoODVQUKQgKENmkZN26cWh+qZ1A5gwCjRIkSqiqlTZs2smfPHruvW7JkiVo/KpnQjjFjxqRaBkEJ1odgBWFP4cKFZc2aNZKSkqJej7AD24l5nDDh8qJFi9Rz9mBbEM74+PhI//795dSpU2p/5BQ/Pz+5cOGCCthwv379+mq/oU0VKlRQVU4IclAd1K5dO9m8ebMKfxCI6VU5M2bMkPHjx0v58uXVPnnllVfk7t27snPnTomJiZEVK1aoCiTsz5CQELUsgrLk5GTLduD4wPPY7ziWsM/swevwvgil8F5du3aVhg0bWs1R1LJlS2nVqpV4e3urY8YovePNHlQxoWIJfYRQD0HYn3/en0ieLLy9vKR4/hCJCHE8PA0/x/NYDssTERERkWfjjJVEruQXKNJmikjZpiKrXhRJuC1Jd1Ik7oqfxF31k+QEb9GSvUS8NfH21SQgPEkC8yWIf1iyqPM1Lx+RgFCRTrNFKmZ92BNCEgwVs4XhZwgBEEag2ggQPuiVKngeQ51w8q/DsB8MGwIEGagUQeijQ8WLMWywhaFGQUEPqhcQGiAAMT5G1Y492B5sn85439HP8PjcuXMqHEGYgKFVurJly6phbo7mdELllA5hCqB6BYFTehYuXCiDBw+WjMDwvCNHjqT6OYI4hDUI1nAyjwqn0aNHq7AFw7UQEGEbcR9hHwIx7EsMxcNwP72PevXqpQIVHfbD2bNnVVUSwjKET0ZYPyrOdLb7HPvTHqzTuH/1fYyfG48fR9I73tLrI+wjHFusMLIPIVDhiGDJH5oiN+4myJ34JElOThEfH281wTXmLOIwNCIiIiLzYGBElAvEpZSWq9eekVsb10vSTcdhis7bN0WCCiZInnolJezlxeKVr4RTtgthEU7SMRRIrzrA5eWNzyM4QJikn8Qbn0dVEOa9QRVMTsD2oMoHlSe226LD80ZYBoFMwYIFVdUKAhQ9oMJ9XEEO1Ua2czelB6FKWvr27atuDwP7FvM9AYaDoTIMVwjDnEUIiRAEoi2oGsI+QUUV2onnjH2EeaJat26dav3oW7QD+wxzCtlWnmD/AJ7X95m+P+2JjIy0mjNKXweGjGVkv6V3vFH2QCiULzRQ8oZoln2d2UmxiYiIiMj9cUgakYug0ub62rVystuzcqJzF7m2am2GwiJISfKW2xcC5dyqS/JXx14SM32GJDthXhZMLozqoLfffltVnWCYknHSYDyPoUgIJhAq7dq1S5YvX255/vHHH1eBxMiRIy3z6SBccNacPt27d1fz2iDcQWUThk/Zwvw9a9euVSfCmKwZYVbbtm1VUNGjRw813AnDtvQ5dlB9k174Yw8CFMzb5EzY1whMsF/RD6gS0oOUqKgoNe8QhhVi2BeeR2CDKiN9/iJAlROqkvQKphs3bsg333yj+gvVOZhwGnM76VVWCGwwZ5ER9jP2N/Y7jgVMcm4Pho9hYnCsH/sfc0xt27ZNTRyeEekdb0RERERElH0YGBG5QOK5c3Lmuefk/NDX5O7+/ZafewUHS/BjtSRfxVtS7O9XpVTzy1K6ZYyUfiJGIhvHSsHqNyQs8q74Bj0IlpIuXZLL06fL8Xbt5da2bVnaHv1qXsYbAhMMRVu9erW6shUCEMxPg6uM6fA8Tv4xBxCGOOGKVQhYUJUDCDDwHIYoVa5cWc1BhHAGkx87A4KpOnXqSLVq1aRmzZpWV9fSIRRCUITgAXPzYPv14VnTpk1TQ6aqVKkiVatWVRNKf/jhh1naFgzxwxXA8D6YP8gZMLE15i1Cf2HIGSaJxqTQgKootAM3fbhc8+bN1VXUjBU9mMAaQ9kwrxCqiNBPxjmm5s+fr9pQt25d9Twm88b7GmEia+xv7HdUMiFoswf7EyER5pZCEImgCeEThqVlRHrHGxERERERZR8vDV9NkxV8w44TW1w6GidIOszlgUuEYxhIVioO3J2Z25+VtsfFxamrP+Hy7pgLBvBxu7b8S7k0ebKk3LljWTagQgXJ26O7RLRvL96HlouswZW5HH808am9XeZfcnVXrNzavBkbaHkuonNnKfzWcPEJDX2oNlu/X8aHpvzjH/9Q+wuhTG6DYAThB4ZgOaPtniit9mM4GY7vq1evqv3qCs4+3szc/2ZuO2Dyd1QJItA0TsDu6G8EIiIiIk9jrrN+IhfSUlIkeuK7cnHMGEtY5FukiETOmillvlkleZ99VrxRCXJwFWbnffBCbx/rf5H0evtIqPd+KTFjujzyw/cSUr++5bnrK1fKqT59JCmHrtaFK3Hhcvc4ad+4caMa8vT000/nyHuT+fB4IyIiIiLKGQyMiHLom/qL48bLVcPlxiO6dpGy366WsCZNHnx7f+eKyImfkC7de4yfF6go0mO5SP4KD4IkLVnkxDaRu1fFPzJSSnw2V4qMH3cvcBKR+IOH5FTvPpJ05YrT23b8+HE1twyGRQ0aNEjNIYRLoxPxeCMiIiIicl8ckmYHh6TZxyFpWR+SdmP2bImdOeveE97eUvTttyVPl86pX7R3icg3L+GjeW9IWr1BIi3GifgGiCTGiWwYK7Jz5oPnO84UqdnD8vL448fl9IDnJOn+Jc8Dq1aVUosXibfhMvVZYeahKWZuO7D95u1/s/c9h6QRERGR2bHCiMjJ7u7e/SAs8vKSYpMn2w+LAMPRIDDiXlXRk5PuhUXgF3jvcfdl956HA/eXvy+gbFkVEPkWKqQexx04IDGZmK+HiIiIiIiICBgYETlTXJxcHjvO8rDQ0Fclon0aV8y6dEikdCORwTtFKrSyv0zF1iIv7RAp1VDk0sFUT2OIWsnP5orX/StHXfl8kdzZvTsbGkNERERERERmwcCIyJkWL5Gks2fV3aDatSVf//5pL48gqO+3ImFF0l4uvKhIvzX3lrcjoHx5Kfivf917oGlyfsQISYmLy1obiIiIiIiIyHQYGBE5SVJMjMj69eq+V2CgFJv4jnj5PLjSmV0BodZXSEsLlsPyDuTr01uCatVS9xNPnZbrq1dnYuuJiIiIiIjIzBgYETnJza+/FklOVvfz9e4t/qVL5+i+RjhVeMQIy+Or/1mqJrElIiIiIiIiSg8DIyIn0JKS5OaKlfc/Zd6S99luLtnPQdWqSuCjNdT9+EOHJG7/fpdsBxEREREREbkXBkZETnD71x2SHB2t7gc3biR+xYu7bD/n7d7dcv+aHmJlsyeffFI++eQTyUkvvviiDBs2TMyuX79+8i99vqoswOXS9+3bl63bRM6TJ08e2bJli8Pnb9y4IY888ojEYEgsOZScnCzVq1eXQ4cOcS8REREROcDAiMgJ7hpOwENat3bpPg5v1Urk/txJd+1UGDVp0kR8fHzk999/t/zs2rVrKkg4efJkht5j3bp18tJLL0lOmjVrlkyePDlH35NyL4QoOGZDQ0Mtt3/+859ZXl9CQoJ07dpVypQpI/7+/rJq1Sqr5/HZsH2/9u3bi6t98MEH0rFjRylYsOBDr+v8+fPy1FNPqbaVLFlSPv3003SXb9OmjYSEhKRaPj4+Xv2uKVSokISHh0ulSpVkzpw54ir4nffaa6/JW2+95bJtICIiIsrtfF29AUSeKO7AAcv9gKpVXbot3kFBElCunMQfOSLxx46pq6V5BwZaLZM3b14ZPny4rF271mXb6QmSkpLUiSiCBMp5ERERKuzMLg0bNpRXXnlFevbs6XCZs2fPqqqfzEpMTBQ/Pz/J7uMPIcyPP/6YLevr0aOHCsyio6PlwIED0qpVK6lQoYJERUXZXb579+6quunSpUvyv//9z2p5X19f+fe//y2VK1dW9w8ePChNmzZVjxs1aiSugEDw5ZdfltOnT6uAi4iIiIisscKIyJmBUUiI+EZGunwfB+qhVXKyxB8+nOp5VAf98ssvsm3bNruv37t3r6oOyJ8/v6pcwIlhbGys5Xk899FHH6n7jz76qHz++eephqy9++676v6tW7dU5QdO0FBt0KdPH7l+/brd90VVwoABA6RAgQIqDKhWrZrs2rXL7lAsbDuGmISFhUnnzp3lueeeU8sYq0EWLVok5cqVUyf4eA4n7fZge1HBBNi2oKAgefPNN9VjTByOffDbb7+px1jv9OnT1bahsgLt2717tzRo0EC9T5UqVeQ///mPZd1jx45VlSjYB3ge+2HZsmVWbcZwu3z58qmT9c8++yzdai8MQ0JVCSpBatSoIT///LPlObRx9OjR6kQe/YeKEVSC2IO2oUIFy+L9W7duLcePH1fPYRv/9re/WZbt0qWLFC1a1PJ46NCh6uTbnp9++klq166t+gbb8Oyzz4orLF26VO0f7PfHH39ctm/f7nBZVBXh+EKYgRDwYS1YsEBq1qwpY8aMkSJFiqh9gGOlQ4cO6nOA47tx48ay31AFmJKSIqNGjZLChQtLsWLFZMaMGWm+x3//+1811ArH4sM6duyYOo4mTJigjut69eqp4GzevHlpLo/Pub3lsQ/x+URYBDimcfvrr78cVoyhnzDUtXjx4irUxu+Yw4cPq3WjSgnH/O3bt60+43i/smXLqs/CG2+8IRcuXJAWLVqo5RFcXbx40fIe2E4cBwzKiYiIiOxjYESUzVLu3JEkff6QUqVyRbVJYKWKlvvxdoIHhAOYD0gPRWx5e3vLO++8o062UDlw7tw5h8v27t1bBTM6vGbjxo3Sq1cv9RgB0JUrV9QQuBMnTqhAw9HQoYULF6oTaJxUonJk5cqV6mTb1tWrV1UQMmTIEHX/+eeflyVLltgdOofwC9UN2CZ7ywAqHzZv3mw5cS1durRl3hhsN07Ka9WqZVn+iy++kB9++EEFN2gPghYEAphHZubMmTJw4EAVyOm+//57FQ4gdMMJObb35s2b6jk8RuCEig7MLfQ1rraXDrw/AjLsI4R/2Bd6pc2IESPUe+NkHifPqPhwFNig3z788EM1/AqhUtWqVdW6ULmCUBAhGbYTwRLWFxgYaJkDZtOmTdKsWTO760W/YJ9gm3A8IARxBP2DwA+hFSpU5s+fr7Zlz549qiIkLi7O4WsRwCBYiYyMVGEFjlPdd999p4YgIbjB8YeKOgR3xuAzKxDO4JjEfkKYkRZ8dhCYoKIF+xqBEKp48DlAFQ+OqWeeecZyNUNsK25bt25VnwEcF/pxYg+OFwz1st0nCE4Q1iCwwr7HMYxtmDp1qtVn1QjLIBBEWKXD641DV7OyfLt27dRxgyAVy3bq1Mlhe9BWBEHYP19++aXqP9y++uorOXPmjNons2fPtnoNPrd//PGHCs+mTZum9ieCJnwWEQJOnDjRanlsB+fwIiIiIrKPgRFRNsOQL4vg4Fyxf71DQi33tbh4u8ugmuLUqVOp5mrRq4ZQMYMhNDjJe/XVVx1OvIsTdZzg6ifrqK5BlUaJEiXUSduKFStUpQSqB/AN//jx41X1CkIYW3g/nDQilMBJNMIOrMfWmjVrVEiAMAon5JhHpXnz5qmWQ6UNqlwQKiDA0KuE7AVGevsQhCDQQgUFAiE8RqUCQjQdTsixzoCAABVKoQIJ1TbYfiyLUADhlw7VNjiRRdUFAjbMl/Pnn39awh+EcTj5RtUJKlLSg6AG4Qfajuok9BH2CfYZKjQQAmF9OGFGIIUACSfcthAeYAgWwgWc1OPkGsuhqgvrxP5HtRBOsEuVKqVO/nGCjgAGYQhCJXtQEYJtwQ37BEGUI6hwQoiwfPly9S+2qXz58mofYv3YLnsQlGC7sL0IVvBe2CcIZQDH3Ouvv672PfoOoRRegyApK1D1tnPnThVmICjCNqKSBceII+hPBHjoh+DgYFX10q1bN/U5QLvGjRunjgO9AgyBJo4jbCeWnzRpkqU99iAsxTptq6qwbgxVQ9/jc4ntfOyxx9T+QihnD4Im26F2eOwosMro8jguURWEzxeq1FC9lxbsE+yvJ554QgXb6FP8DsC+xOccQaLRyJEj1f5EEITfWxhWiOMNn00cT7bLY39hvxERERGRmwdG+AYXwzfwxy9OfnCiZRxagaEeepm78YY/HolyjDH4yAXVRYoh3NBSUgczgBM3hBOYBNY2vME3+TjBxtAQnGChWujy5ct214PPJgIMvXoHw9Mw7AxQLYATXgy1wsmkPjQIJ/DGoSI6fMYxdAwhCE7Qcd/e++L3gG2QZG9OEmN1En4vODr5RaiAShZU+SAQQXtw4omwxF4ljfG9MKcNKpKMMEQGP7e3HfgdhX2vb4ttWzIytwrCG9vHCAawr3ByjmomfX/jvXECbi8wst12nGQjCNO3Xa+8wj7AfYRyeIwbhnph2JA9OHZQKYPQA8OI0oLACCfweiUQAkUEjRs2bFBtcTSMEO1CtQ9CONxHQILqND2Iw7GHY1vfD7ghMMF+Qr8aJ6/OCCxXt25dFYBhXe+//77atrSGueHzYwwa7969qyrCsM/xudL3vX6M41gw9i1CO/SJI9j/toFV37591fGMoBZBLkIW7BcMNXz66adVtZ2j9tkOFcVjBK4Puzz6CEEqqqqmTJnisD14rTFQwvFjrGDCYwRVRrbPp7c89pej45aIiIjI7NwqMMIJCr51PnLkiPrjF9/4Y4iCDqXqGHJhvOFbRvxRTJRTvIwVEAkJuWLHa/EPqoq8A+xXaACGNSHQMVbDwKBBg9TJLgIUnGAtXrzYMmwmrWFpqDrBCTsqCQBBCE6YcSKM4Un6DeEM1m8LFTM4yccJLqqMMIwGFQe2EGrYBiBYNqtwQouQBZVPCC0wMS9+/2AyYcyVhPtGxhAAlU628w3hMX6eEbZtyUg7UBlmhNdgf2K+IJwkoxLGuL8RVNSvXz/Vemy3HZVP6Ct9242BEUIzVPwgbMHwPtt9YoQ5r1Ddhfe1V8FmhHAQXwqgAgvBFoazIYzA/Ek45vQ5cNJjOxQUxx7CKON+QACFai5UwCFI0G9ZoX9BkRbjcQLYHlS5YXgfPlf6vtc/WzgWjH2LyaQxx5UjGAKG/z8aYU4f9Bcqc1BVhP+HYh4vBLsY7ofKKHsQAKLv8Z46BGyoPsuO5QEB29GjR8WVEJhhvxERERGRmwdGOHHASQO+ccXJDv7Q37Fjh+UbZ5xU4Jtl/YZvL/HHIE6CiXKKd3CweOnfitupmnGFBMNJp2+B/GkGJZiryHaeD5zM4vOFKgiEGWlVBQCGfuBEFyEu7utVG/hcosIEQ7z0KgpUFjmapwcnujjpxBw6+rAde4FB27Zt1XahKgXLrl+/Xr32YSAAwRwo+hWhEJDgBBvbkNakwqjgwEkzhoJhWxCooNpKr7JKD8KV9957T+0XVGi8/fbb6b4GbcXEvXg/XMocYTn2CQIKBDCYkFoPoTBnj3GSbSNUjmECb/zeRDCB4T0InlAFBtgXCO9+/fVXVXGFyhqESWifo/mLAMOHEM4gjEQYgvU7qhTCtr3wwgsqxMHxg+FwWBZVTvid7yiUQZCF4WFYP9qIkBNDkfRAZPDgweq4RUCDZe7cuaOqloyVX7awDxBmYnlsA+7r1XcI4RBi4jFCJswBhm37+9//LhmFzxWOJ1S4YB22l3jHsYChdAiBELZh3iXb0MkIFU+AYFfXv39/+fjjj1XfYe4qBEaoZsN744sXe0EtYA4pDEPFnEfYV5gTCP3s6P+n+vJog73l8TlG4Ip24DjF8YrnHQ2JywnYThxf+MwSERERkZsHRkb41h9/bCI4cnRp4rlz56o5N9K7ZC9OCvDHs/EGOLmxveHEwd7PzXIzc/sz2nbNy0sC9Emmo6Ml6do1y/wtrrpZrtqGYUZVqlg9B8bHGD6ECgTjz1EJgblecOKPqzphGePztuvAMBJUFWFyZ1QbGZ9D6IL1IIRAAIXPpz7njO0NoQlOmhFMYBgbXodKFdv3xAk3QicMC8KyGI6EykJUqDjaRkc/02+onsHvAgRHeIyQCO3SHztaB94f+wpVWKjwQfiB8Agn045eY3yMOW5QrYHqSFQ+4IptYGyL7WuxjxAU4b0RDqCKB/fxPMI/BO0IdDDEB1Um6Bd7742+QpiHuYkQ7iEcWr16teUqYWgPtgs3VC7hNVgvTrzRj472JX5XYz2Y2wl9qM/bZG9Z/D63/RlCkvSOccxNg6owhJPoK4QS3377reW1aBOu4IUJyHG84HhCIIjAx9E6K1asqNqJii3MNYT+xxBLPIcKV6wTxzDWhZAG+xWPHa3Ptt/xJQj2LYZNYZv1q9DpzyPswZxg2LcY1ojjAX3oaP1YF443VBWltT8B4VZ6+xTzaWHIHq7ihs/z5MmT1T7Wn0cgp1cbGpdHP9suj8ANYRLaiuMI9/F7BcduRvdXep/hzC6PybPxecawT0fv7+hvASIiIiIz8NKMfxW5AXyLi2/AcYKCP64xgSb++LSFb4JRzo9vpDEhbVow95G9YS4YSmOcfwF/JOIbf5zwpPUtr6cyc/sz2/Y7H38s8SvvVc0UmD5d8kQ1FlfRUlLkRIOGknLrlvgUKiRlNm7I/Do0TZ1Y44Q0N1z1LSNQYYMqGFRlPAxXtx1VlJjwF1Uhrnh/V7ff1dyt/Qg5UWmEyjYEN2Zqe2Z/p9epU0cFXghA7UE1FIYJInxFYKvDZxFfRuH/CbaTjBMRERF5EpcHRgh08C1kWlD2r18qGMNYUF2E4S4IeXACj9DI9o9ZXJkJQ0Aw3MA46aWjCiPjvBD4gxvzXdhecQZ/YGLyVfwRbrbAxOztz2zbr3/zjVwcfm94SXivXlJshPVQk5x0Z+9eOd2jp7of2qypRM6YkaX1oELAUTVfboDL2mNyX5zcoXIAn39UyGD+oYeVk23HcDZUq6AyA8NqUWGC8FufRNwVcnvfO5uZ22/mtuOLJ1SSYTJyVJrZTpTNwIiIiIg8XcZmD3UizK2BKx+lBaX4OlwpCTd8u4cTQQQ7+Abedt4IDEfDcIH0wiLAVWfsXXkGwYBtOIBgyt7PzcLM7c9M28OaNJFoDCFKSJBbq1eL9tpQ8XZwOXBnu7Z06YPtatEyS5UCyJX11+XWSgMMScIcPKg+xBAhhMaOKgdyc9sRTr766qvqynQ4ScUl0DHMzFX73R363pnM3H4zt93I9ve+Gf//R0RERObk8sAIFRtZLZvX5xGwvWoMJj7FBKiYM4PIFXzz5pWQli3k1pq1koJ5sb5bJ3k6d8rx7Ui6ckVurluv7vtEREh4m3vz4XgiDD172OFnuQGuXoUJgomIiIiIiFzJbb4mwxVpMHcRTqQwHA1XBcJkmbgyi211ESb8xEmXPlkskSuEPfOM5X7snDmSEheX49sQO+dT0e5fjSqiaxfxtlNJR0REREREROS2gRGGZqxcuVKaN2+urlyDS/XiSkJbt261Gk6GqiNcWhvD3PQr+xC5QkD16iL3r5aWcPKkxPz73zn6/nf27JUrCxeq+17+/pK3e/ccfX8iInemVzGbeTgeERERmZvLh6RlVPXq1VVVUXowt8CZM2dyZJuI0oIg02vwP0UbMkQkKUmuzF8gAVFRElijhtN3HKqZzmN41v057fMMGiQpBQqoSVyzOpcJLlPu6+trupMnM7cd2H7z9r9Z+x7tTkhIUBPQg1kn/SYiIiJym8CIyN0gvCwb1VhO9+ktifPm4+tquYDwaOJEkUKFnPfGycki738gcurUvccVysvVRg3l6okTD3UChW/b0SYznTiave3A9pu3/83e90FBQaq6mZNcExERkVkxMCJyIn9/fyk7ZIic3P2bxP/+u0jsFfEd/7YUmTNb/IoXz/b3w3xFMaNGye0dO9Rjr8BAKTZpkvgbrjSYFThpjI2Nlfz585vu5MnMbQe237z9b+a+x5B2tDkmJsbVm0JERETkMgyMiJzM289PSs6aKad69ZaE48cl6dw5udivvxSbPElC6tfPtvdJjI6WC8OHy+3tv6rHXn5+EjljuoRmw6XlceKIYRmBgYGmO3E0c9uB7Tdv/7Pv781hRERERGRW5vrrl8hFfPPlk5Lz54n/I4+ox0kxMXJ6wHNyYcxYSb51+6GHjVxbsVKOt2v/ICwKCJDiH0+T0AYNsmX7iYiIiIiIyFwYGBHlEL/ChaXU4kUSXK+e5WfXli2T423byuXZcyQpNjZT69MSEuTGd9/JqZ695MKIEZJy86b6uW/BglJy7qcS1rRptreBiIiIiIiIzIFD0ohy8gOXN6+qNLq6dKlcev8D0e7ckaToaImZOlVipk+X8BZPSHC9v0lg1aoSWKG8ePn7W1USJZ49K3H/+5/c3f+7XF+zRpIvX7Zaf0SHDlL4reHiExHBfiUiIiIiIqIsY2BkB07M4caNG6nmc7h586Yp57Iwe/uzu+2+7dpJoVq15PrUqRL308846EQSE+XGd+vUTfHzE5/8+dW/kpSkKoi0W7fsr690aQl/+Z8S1KiRqAFuNsfuw2Lfm/O4N3vfm739Zm57Wu3X/zbQ/1YgIiIi8lReGv/iSeXs2bNSokQJV/QHmVCkn588E5FHukRESF7fjGe4SZomG2/dlP9cvSb/vXvHqdtIRETWzpw5I5GRkdwtRERE5LEYGDn4VvH8+fMSFhYmXl5eVt8qIkjCH4nh4eFiNmZuf060XYuPl4SDByXx8GFJOHxYEo/8KSk3bqi5irx8fcQrMEh8y5QR/8qVxK9yZfGvWlV88uWTnMC+N+dxb/a+N3v7zdz2tNqP79lQeVSsWDFTVl4RERGReXBImh34AzCtbw3xh6MZ/3jWmbn9Tm97VNS9Wy7FvjfncW/2vjd7+83cdkftj+A8cURERGQC/GqMiIiIiIiIiIisMDAiIiIiIiIiIiIrDIwyISAgQMaMGaP+NSMzt9/MbTd7+83cdmD7zdv/7Hvz9j0RERERcNJrIiIiIiIiIiKywgojIiIiIiIiIiKywsCIiIiIiIiIiIisMDAiIiIiIiIiIiIrDIyIiIiIiIiIiMgKAyM74uPjpWbNmuLl5SX79u2z/PzkyZPqZ7a3HTt2SFrsvWbp0qVilvafPn1a2rZtK8HBwVKoUCF5/fXXJSkpSdyp7UZ//fWXhIWFSZ48edJdn6f0fVbb7059n1b7jxw5Ik2bNpXChQtLYGCglC1bVkaOHCmJiYke0//Z3XZP6fstW7ZIhw4dpGjRohISEqKWWbJkSbrrc6e+d0b73an/HbU9Li5O+vXrJ9WrVxdfX1/p2LFjhtZXunTpVH0/adIkJ7aAiIiIyDl8nbRet/bGG29IsWLFZP/+/Xaf37Bhg1StWtXyOH/+/Omuc/78+dK6dWvL44ycbHtC+5OTk9VJQ5EiRWT79u1y4cIF6dOnj/j5+cnEiRPF3dqOk+Tu3btLo0aNVHsywpP6PjPtd7e+T6v92GZse+3atVX/4fmBAwdKSkpKum1xl/7PzrZ7Ut9j+2vUqCHDhg1TodmaNWtUWyIiIqRdu3Ye0ffZ3X53639HbUc7goKC5JVXXpEVK1Zkap3jx49XnxMdQnYiIiIit6ORle+++06rVKmSduDAAQ27Z+/evZbnTpw4kepnGYHXfP3116ZsP9bn7e2tXbx40fKzmTNnauHh4Vp8fLzmLm3XvfHGG1qvXr20+fPnaxEREabp+6y03536PqPtNxoyZIjWsGFDj+j/7G67p/d9mzZttP79+3tE3zuj/e7U/xlte9++fbUOHTpkaJ2lSpXSpk6dms1bSkRERJTzOCTNIDo6Wn0juGjRIlVG78hTTz2lSuwbNmwoq1evzlAwN3jwYClQoIDUrVtX5s2bh6BOzND+X3/9VZXz45tpXatWreTGjRty4MABcae2b9q0Sb788kuZMWNGptbtKX2f2fa7S99n5tg3Dstbv369REVFuX3/O6Ptntz3cP36dcmXL5/b972z2u8u/Z+VtmcUhqCh+rZWrVoyZcqUXDscj4iIiCgtHJJ2H/6Qx1wFL774otSpU0fN12MrNDRUPvjgA2nQoIF4e3urEnXMabBq1SoVoqRVmt6sWTP1B+kPP/wgL730kty6dUuVuXt6+y9evGh10gD6YzznLm2PjY1VyyxevFjCw8MzvG5P6fustN8d+j6j7dfVr19f9uzZo+Y8eeGFF1T/unP/O6vtntj3uuXLl8uuXbtk9uzZbt33zmy/O/R/VtqeUehjDOFEqIYhecOHD1fD8j788MNsew8iIiKiHKF5uGHDhqky87Ruhw4d0qZNm6Y1aNBAS0pKytTwq969e6c7LMXWqFGjtMjISM0M7R84cKDWsmVLq5/dvn1brRtDAdyl7Z06dVLr02V0SJqn9H1W2u/KvnfWsX/69Gk1dOWLL77Qihcvrk2ePDlX9r+r2+6JfQ+bNm3SgoODtYULF5rms5+V9nvK7/2sDkmz9dlnn2m+vr5aXFzcQ7WNiIiIKKd54T/iwWJiYlR1RFpw1Z9nnnlGvv32W3U1E+OElz4+PtKzZ09ZuHCh3ddieM6ECRPUt4cZtXbtWjVZKK7AEhAQIJ7c/tGjR6tha8Yrz5w4cUK9J6oVUK7vDm3HZLWoDtDhY4NJf7HMnDlzZMCAAR7d91lpvyv7PieOfVRbodLm5s2batnc1P+ubrsn9v3WrVvVRM6oEkHbM8tdP/tZab+n/N43QjXStWvXVEVtZmEYXrVq1eTw4cNSsWLFTL+eiIiIyGVyPKLKpU6dOqX98ccfltv333+vvm386quvtDNnzjh83fPPP6/VqlUrU+81YcIELW/evJoZ2q9PfhodHW352ezZs9Xkp7nl29aMtP3gwYNWy6APw8LC1P0rV654fN9npf3u0PcPc+yjygJVAwkJCW7b/85qu6f1/ebNm7WQkBBt+vTpWX6v3Nb3zmy/O/R/Zo/9h6kwWrx4sdofmfl/BREREVFuwMDIAXvl6QsWLFDDMVDOjts777yj/gicN2+eZZmVK1dqFStWtDxevXq19umnn6o/SI8ePap98sknqqR/9OjRmhnaj3L/atWqqeEJ+/bt09avX68VLFhQGz58uJZbZWQ4nr0hWZ7c91lpvzv2vaP244Rv2bJlKjg7duyYul+sWDGtZ8+eHtX/2dV2T+p7fRgWtv3ChQuWW2xsrEf1fXa23x3739HvPQzDxM/at2+vNWnSRN03LrNz507V9rNnz6rH27dvV1dIQ7vxecHnB23v06dPjreJiIiI6GExMMpkYFK5cmX1xzO+Ka1bt6725ZdfpjqRNhZurVu3TqtZs6YWGhqqvqF99NFHtVmzZmnJycmaGdoPJ0+e1J588kktKChIK1CggDZ06FAtMTFR87TAxJP7Pivtd8e+d9T+pUuXarVr17b0ZZUqVbSJEydqd+/e9aj+z662e1Lfo7LE3jw4UVFRHtX32dl+d+x/R7/3SpUqZbf9xuorPMbr4bffftPq1aunfj8GBgaq/2fi85JbKquIiIiIMsPj5zAiIiIiIiIiIqLM8c7k8kRERERERERE5OEYGBERERERERERkRUGRkREREREREREZIWBERERERERERERWWFgREREREREREREVhgYERERERERERGRFQZGRERERERERERkhYERETnV2LFjJTQ0NFft5X79+km1atUsj/ft26e2886dO9n2Hlinl5eXbNmyJdvWWbduXZkxY4blsaZp8n//938SHh4ujz76qHpP3c2bNyVfvnzyyy+/ZNv7ExERERGReTAwIiLTGTVqlHzxxReWxwhaxo0bl62BUXb7+uuv5eTJkzJgwADLz9CGH374Qb766ivp0KGDdOvWzfJcWFiYvPzyy/LWW2+5aIuJiIiIiMidMTAiItN55JFHpEaNGuJOPvroI+nevbsEBQVZfrZ9+3YZPHiwtGzZUsaPHy+xsbFy+fJly/MIl7Zt2yb79+930VYTEREREZG7YmBERC73xx9/SKtWrSQkJEQiIiKka9eucvr0aatlMLzrvffeU0PHChcuLAUKFJD+/fvL7du3rZb7+eefpVatWhIYGKhCoR9//FFq1qyphqHZG5K2YMECtR4oWLCgep/SpUunOZwuT5486jmjCRMmSJEiRdTynTt3lkuXLqV6HYaQvf/++1KhQgUJCAiQsmXLytSpU9PdPydOnJCffvpJ7RejMmXKqMojvBf+BQxD05UqVUoNY0MbiYiIiIiIMsM3U0sTEWWzM2fOSOPGjVXVz+LFiyUuLk5GjBghUVFR8vvvv6uhVbrp06dLo0aNZOHChfLnn3/K66+/rsKjSZMmqecvXLggrVu3ltq1a8vy5cvl+vXrMmjQIPUvQiN72rZtKyNHjlSBz/r161VghTAnM7BdGOb22muvyRNPPKFCqueeey7VcphvaO7cuap99erVUxVCw4YNU1VDL774osP1b9y4UXx9fVX4Y4TXrFixQu0DrAP7z9vb+nuA+vXrq+0hIiIiIiLKDAZGRORSqLBJTExUc/Ho1TGoEKpSpYqqjME8PLqiRYvKkiVL1H0EQ3v27FHz9+iBEdaFYGXt2rWWoAlVOAiZHEFVEcIqeOyxx1TlUmYkJyfLu+++K71795YpU6aon6FaClU/ixYtsix37NgxFSzNmjVLXnjhBfUzhEuYNwnzJ+FntmGPbteuXZaqJCNUM2FS6+PHj0uhQoXU5Ne2MBn2tGnT1CTYxvCNiIiIiIgoLRySRkQuhaFWzZo1sxpKValSJRV0YHiZUYsWLaweI1Q6e/asVbDStGlTq2CkYcOGVuvObnj/8+fPS6dOnax+bjt8bMOGDerfLl26SFJSkuWG0OjixYuq0soRVE4h2LIHIVO5cuXshkWAAAxD4aKjo7PQOiIiIiIiMitWGBGRS129etXucDEMs7py5UqquYOM/P39JT4+3ipYKV++fKp1ofrGWfCe9t4D22+EyagR3DiqYEJghDmH7MEwvcwOk9Ppr7t7926WXk9ERERERObEwIiIXArVP/YmiEZFDIZhZQaGrMXExKT6ub31ZwQmzsZwOSM8vnXrltV72nsP24oetBMTaqNqCkGXrYoVKzrcDrz25MmTWWrDtWvX1L/58+fP0uuJiIiIiMicOCSNiFwKQ8YwqTMqjXRHjhxRE17jucx4/PHHZdOmTWq+HuOQN9tKJVt6gINKHqPIyEhJSEhQ8w/psH7MW2RcBqGRfpUyHeZWMmrevLn6NzY2VurUqZPqltb8QgiTcKW0rEDQhIm8cQU3IiIiIiKijGKFERE5HQIW2wAFcNWvIUOGyPz586Vly5bq6mEIbXDVspIlS0q/fv0y9T5Y1yeffKKufIYrqKG6BhNKYxiYowmloXLlyurfGTNmSMeOHSU4OFiqV68uTz75pISEhMjAgQPV1cwwXxEmkEblkc7Hx0fefPNNdQU0DEPDPEuYwHvz5s1W74FqqcGDB6vJsbFtuEoaqpVwtTcsu2rVKofb16BBAxk/frx6fwRUmbF79251pbS02k9ERERERGSLZxBE5HQIgZ5++ulUt23btkmJEiVk69atkjdvXunZs6e6WhgmvN6yZUumr+qFSp9169apCiNMOo2rlyHgwdXEUGXjCK7KNnbsWHVZeoQr7du3twzjwmXrMdwMQdLcuXPl888/TzWfEK7khmAKz2Hy66NHj6plbX388ccyYcIEWbp0qQq1evXqJcuWLZOoqKg029WkSRO1LWhbZiCQwmTbthNwExERERERpcdLwyysREQeCuENrro2b9486du3r7iroUOHyt69e9WQuIxau3at9OjRQ86dO6dCMyIiIiIiooxiYEREHmX48OFSo0YNKVasmBw/flwmTpyorhB2+PBhtw5NcDW2cuXKyfbt21UFVkY0a9ZMVSeNHj3a6dtHRERERESehXMYEZFHwSTVmG8IVykLCgpSgcmUKVPcOizSh9stWLDA7lXg7MGV3DDUDfM6ERERERERZRYrjIiIiIiIiIiIyAonvSYiIiIiIiIiIisMjIiIiIiIiIiIyAoDIyIiIiIiIiIissLAiIiIiIiIiIiIrDAwIiIiIiIiIiIiKwyMiIiIiIiIiIjICgMjIiIiIiIiIiKywsCIiIiIiIiIiIisMDAiIiIiIiIiIiIx+n/nEk1ujcy4IQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1400x550 with 2 Axes>"
+      ]
+     },
+     "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([lons[0], lons[1]], [lats[0], lats[1]], \"-\", color=\"#d62728\",\n",
+    "        linewidth=3.5, zorder=3, label=\"Test edge  V0 → V1\")\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(f\"V{i}\", (lo, la), textcoords=\"offset points\",\n",
+    "                xytext=(6, 4), fontsize=11, fontweight=\"bold\")\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(em_lon, em_lat, s=200, color=\"#ff7f0e\", marker=\"*\", zorder=6,\n",
+    "           label=\"Edge midpoint — sweep origin\")\n",
+    "\n",
+    "ax.annotate(\"\", xy=(cen_lon, cen_lat), xytext=(em_lon, em_lat),\n",
+    "            arrowprops=dict(arrowstyle=\"-|>\", color=\"#555\", lw=1.5))\n",
+    "ax.text((em_lon + cen_lon) / 2 + 0.06, (em_lat + cen_lat) / 2 + 0.18,\n",
+    "        \"50 query points\\n(ε from 10⁻³ → 10⁻¹⁶)\", fontsize=9, color=\"#555\", style=\"italic\")\n",
+    "\n",
+    "ax.scatter(em_lon, em_lat, s=700, facecolors=\"none\", edgecolors=\"#d62728\",\n",
+    "           linewidths=2, zorder=7,\n",
+    "           label=f\"Naive sign wrong below ε ≈ {flip_threshold:.0e} rad (≈ 0.03 mm)\")\n",
+    "\n",
+    "q_far = normalize(normalize(vertices[0] + vertices[1]) + 1e-3 * normalize(vertices.sum(axis=0)))\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(\"ε = 10⁻³\\nboth correct\", (qf_lon, qf_lat),\n",
+    "            textcoords=\"offset points\", xytext=(7, -18), fontsize=8.5, color=\"#1f77b4\")\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]; la_ = node_lat[verts_g]\n",
+    "    if lf.max() - lf.min() > 180:\n",
+    "        continue\n",
+    "    ax_global.plot(np.append(lf, lf[0]), np.append(la_, la_[0]), \"-\",\n",
+    "                   color=\"steelblue\", linewidth=0.3, alpha=0.5,\n",
+    "                   transform=ccrs.PlateCarree(), zorder=2)\n",
+    "ax_global.fill(face_lons, face_lats, alpha=0.8, color=\"#d62728\", zorder=4,\n",
+    "               transform=ccrs.PlateCarree())\n",
+    "ax_global.scatter(em_lon, em_lat, s=40, color=\"#ff7f0e\", marker=\"*\", zorder=5,\n",
+    "                  transform=ccrs.PlateCarree())\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 <user-guide/spherical-geometry-accuracy.ipynb>`_
+ How UXarray uses error-free transformations to avoid catastrophic cancellation in cross-product and point-in-polygon operations
+
 `Working with HEALPix Grids <user-guide/healpix.ipynb>`_
  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..4b609664e 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 +11,7 @@
     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 +19,333 @@
     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]
+    max_nodes = face_node_connectivity.shape[1]
+    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 +413,30 @@ 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..0b7c7d242 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -1,15 +1,16 @@
+import math
+
 import numpy as np
 from numba import njit, prange
 
 from uxarray.constants import ERROR_TOLERANCE, INT_DTYPE, MACHINE_EPSILON
+from uxarray.grid._eft import accucross
 from uxarray.grid.arcs import (
     extreme_gca_z,
     in_between,
+    on_minor_arc,
     point_within_gca,
 )
-from uxarray.grid.utils import (
-    _angle_of_2_vectors,
-)
 
 
 @njit(parallel=True, nogil=True, cache=True)
@@ -292,6 +293,19 @@ 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 +315,200 @@ 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
-
-        return res[:count, :]
+    if n1_mag == 0.0 or n2_mag == 0.0:
+        return res[:count]
 
-    # Normalize the cross_norms
-    cross_norms = cross_norms / np.linalg.norm(cross_norms)
-    x1_xyz = cross_norms
-    x2_xyz = -x1_xyz
+    # 2. Intersection direction: cross product of the two plane normals.
+    vx, vy, vz, vn = _normalize_pair(
+        *accucross(n1x, n1y, n1z, n2x, n2y, n2z)
+    )
 
-    # 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..b969cb7b5 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 _orient3d_on_sphere_value, 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)

From 1bbf3499a846d19fc02921e0af0543ac8221f193 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 22 May 2026 07:54:45 -0500
Subject: [PATCH 02/51] Fix pre-commit: remove unused imports and variable

---
 uxarray/grid/bounds.py        | 7 +++++--
 uxarray/grid/intersections.py | 7 ++-----
 uxarray/grid/point_in_face.py | 2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
index 4b609664e..bff921a8e 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -11,7 +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.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,
@@ -315,7 +319,6 @@ def _construct_face_bounds_array_gca(
         [[lat_min, lat_max], [lon_min, lon_max]] in radians per face.
     """
     n_face = face_node_connectivity.shape[0]
-    max_nodes = face_node_connectivity.shape[1]
     bounds_array = np.empty((n_face, 2, 2), dtype=np.float64)
     deg_to_rad = math.pi / 180.0
 
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 0b7c7d242..00b8d8b42 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -3,13 +3,12 @@
 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,
     on_minor_arc,
-    point_within_gca,
 )
 
 
@@ -356,9 +355,7 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
         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)
-    )
+    vx, vy, vz, vn = _normalize_pair(*accucross(n1x, n1y, n1z, n2x, n2y, n2z))
 
     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.
diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py
index b969cb7b5..36b06f674 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -7,7 +7,7 @@
 from numba import njit, prange
 
 from uxarray.constants import INT_DTYPE, INT_FILL_VALUE
-from uxarray.grid.arcs import _orient3d_on_sphere_value, on_minor_arc, orient3d_on_sphere
+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 1034a18fce108810f8de7179f6da60796f9fda5e Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 22 May 2026 08:02:26 -0500
Subject: [PATCH 03/51] Fix pre-commit: split semicolons in notebook cells

---
 .../spherical-geometry-accuracy.ipynb         | 240 +++++++++++++-----
 1 file changed, 175 insertions(+), 65 deletions(-)

diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index c8f6e6aff..99d51ab9a 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -1264,16 +1264,30 @@
     "ax = axes[0]\n",
     "a1 = np.array([0.6, 0.8])\n",
     "b1 = np.array([0.8, 0.2])\n",
-    "para1 = plt.Polygon([np.array([0,0]), a1, a1+b1, b1], alpha=0.25, color=\"steelblue\", zorder=0)\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(\"\", xy=a1, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2))\n",
-    "ax.annotate(\"\", xy=b1, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2))\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(0.5, 0.96, f\"|a × b| = {area1:.3f}\", ha=\"center\", fontsize=12, color=\"steelblue\",\n",
-    "        transform=ax.transAxes)\n",
-    "ax.set_xlim(-0.1, 1.8); ax.set_ylim(-0.1, 1.1)\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",
@@ -1281,25 +1295,45 @@
     "# --- Right panel: nearly-parallel vectors ---\n",
     "ax = axes[1]\n",
     "eps = 0.04\n",
-    "a2 = np.array([0.8 + eps, 0.6]); b2 = np.array([0.8, 0.6 + eps])\n",
-    "a2 /= np.linalg.norm(a2); b2 /= np.linalg.norm(b2)\n",
-    "para2 = plt.Polygon([np.array([0,0]), a2, a2+b2, b2], alpha=0.5, color=\"#d62728\", zorder=0)\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(\"\", xy=a2, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2))\n",
-    "ax.annotate(\"\", xy=b2, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2))\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(0.5, 0.96, f\"|a × b| = {area2:.4f}  ←  tiny!\", ha=\"center\", fontsize=12,\n",
-    "        color=\"#d62728\", transform=ax.transAxes)\n",
-    "ax.set_xlim(-0.1, 1.8); ax.set_ylim(-0.1, 1.1)\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(\"Nearly-parallel  —  tiny cross product, catastrophic cancellation\", fontsize=11)\n",
+    "ax.set_title(\n",
+    "    \"Nearly-parallel  —  tiny cross product, catastrophic cancellation\", fontsize=11\n",
+    ")\n",
     "ax.axis(\"off\")\n",
     "\n",
-    "fig.suptitle(\"Cross product = parallelogram area\\n\"\n",
-    "             \"Small area means two nearly equal numbers are subtracted — digits cancel\",\n",
-    "             fontsize=12)\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()"
    ]
   },
@@ -1363,20 +1397,28 @@
     "#\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 = 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",
+    "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",
+    "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",
-    "print(f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  → left  of A→B (northern hemisphere)\")\n",
-    "print(f\"South Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, south_pole))}  → right of A→B (southern hemisphere)\")\n",
-    "print(f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  → collinear, not a crossing\")"
+    "\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",
+    ")"
    ]
   },
   {
@@ -1458,9 +1500,7 @@
     "\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),\n",
-    "                     np.cos(lat) * np.sin(lon),\n",
-    "                     np.sin(lat)])\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",
@@ -1473,7 +1513,7 @@
     "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",
+    "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",
@@ -1492,7 +1532,7 @@
     "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",
+    "_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",
@@ -1500,16 +1540,20 @@
     "    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",
+    "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(f\"Naive sign flips below ε ≈ {flip_threshold:.1e} rad  ({flip_mm:.2e} mm on Earth)\")\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(f\"  Naive (raw cross product):  {naive_ok}/{n} correctly classified as inside\"\n",
-    "      f\"  ← {n - naive_ok} misclassified as outside near the edge\")"
+    "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",
+    ")"
    ]
   },
   {
@@ -1558,35 +1602,80 @@
     "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([lons[0], lons[1]], [lats[0], lats[1]], \"-\", color=\"#d62728\",\n",
-    "        linewidth=3.5, zorder=3, label=\"Test edge  V0 → V1\")\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(f\"V{i}\", (lo, la), textcoords=\"offset points\",\n",
-    "                xytext=(6, 4), fontsize=11, fontweight=\"bold\")\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(em_lon, em_lat, s=200, color=\"#ff7f0e\", marker=\"*\", zorder=6,\n",
-    "           label=\"Edge midpoint — sweep origin\")\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(\"\", xy=(cen_lon, cen_lat), xytext=(em_lon, em_lat),\n",
-    "            arrowprops=dict(arrowstyle=\"-|>\", color=\"#555\", lw=1.5))\n",
-    "ax.text((em_lon + cen_lon) / 2 + 0.06, (em_lat + cen_lat) / 2 + 0.18,\n",
-    "        \"50 query points\\n(ε from 10⁻³ → 10⁻¹⁶)\", fontsize=9, color=\"#555\", style=\"italic\")\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(em_lon, em_lat, s=700, facecolors=\"none\", edgecolors=\"#d62728\",\n",
-    "           linewidths=2, zorder=7,\n",
-    "           label=f\"Naive sign wrong below ε ≈ {flip_threshold:.0e} rad (≈ 0.03 mm)\")\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(normalize(vertices[0] + vertices[1]) + 1e-3 * normalize(vertices.sum(axis=0)))\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(\"ε = 10⁻³\\nboth correct\", (qf_lon, qf_lat),\n",
-    "            textcoords=\"offset points\", xytext=(7, -18), fontsize=8.5, color=\"#1f77b4\")\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",
@@ -1603,17 +1692,38 @@
     "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]; la_ = node_lat[verts_g]\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(np.append(lf, lf[0]), np.append(la_, la_[0]), \"-\",\n",
-    "                   color=\"steelblue\", linewidth=0.3, alpha=0.5,\n",
-    "                   transform=ccrs.PlateCarree(), zorder=2)\n",
-    "ax_global.fill(face_lons, face_lats, alpha=0.8, color=\"#d62728\", zorder=4,\n",
-    "               transform=ccrs.PlateCarree())\n",
-    "ax_global.scatter(em_lon, em_lat, s=40, color=\"#ff7f0e\", marker=\"*\", zorder=5,\n",
-    "                  transform=ccrs.PlateCarree())\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()"

From fb74e75752149a535295dae828c06e102a39380c Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 28 May 2026 10:19:35 -0500
Subject: [PATCH 04/51] Address Hongyu's review: port AccuSphGeom compensated
 arithmetic, rewrite intersections, add 241 baseline testsgit status! - most
 came from accusphere

---
 docs/api.rst                                  |   19 +-
 .../spherical-geometry-accuracy.ipynb         | 1332 +----------------
 docs/userguide.rst                            |    2 +-
 .../gca_constlat_cases_with_baseline.csv      |  201 +++
 ..._pairs_seed20251104_N100_with_baseline.csv |   32 +
 .../geometry/test_accusphgeom_baseline.py     |  208 +++
 uxarray/grid/_eft.py                          |  167 ---
 uxarray/grid/arcs.py                          |   10 +-
 uxarray/grid/bounds.py                        |   65 +-
 uxarray/grid/intersections.py                 |  247 +--
 uxarray/grid/point_in_face.py                 |    4 +-
 uxarray/utils/computing.py                    |  936 +++++-------
 12 files changed, 1071 insertions(+), 2152 deletions(-)
 create mode 100644 test/grid/geometry/data/accusphgeom/gca_constlat_cases_with_baseline.csv
 create mode 100644 test/grid/geometry/data/accusphgeom/gca_gca_pairs_seed20251104_N100_with_baseline.csv
 create mode 100644 test/grid/geometry/test_accusphgeom_baseline.py
 delete mode 100644 uxarray/grid/_eft.py

diff --git a/docs/api.rst b/docs/api.rst
index 9cdaf9992..11a4c17a5 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -584,13 +584,24 @@ Arcs
    grid.arcs.in_between
    grid.arcs.point_within_gca
    grid.arcs.extreme_gca_latitude
+   grid.arcs.orient3d_on_sphere
+   grid.arcs.on_minor_arc
 
 
-Accurate Computing
-------------------
+Compensated Arithmetic
+----------------------
+
+Numba-compiled primitives used throughout the geometry stack to avoid
+catastrophic cancellation in cross-product and dot-product operations.
+``two_sum`` and ``two_prod`` are true error-free transformations (EFT);
+the higher-level functions are compensated algorithms built on top of them.
 
 .. autosummary::
    :toctree: generated/
 
-   utils.computing.cross_fma
-   utils.computing.dot_fma
+   utils.computing.two_sum
+   utils.computing.two_prod
+   utils.computing.diff_of_products
+   utils.computing.accucross
+   utils.computing.accucross_pair
+   utils.computing.acc_sqrt_re
diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index 99d51ab9a..321bd1824 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -4,22 +4,11 @@
    "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"
-   ]
+   "source": "# Accurate Spherical Geometry\n\nCross products are at the heart of nearly every geometric test on the sphere \u2014 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 \u2014 the physically meaningful result \u2014 can lose all significant digits to floating-point cancellation. UXarray guards against this throughout its geometry stack using **compensated arithmetic** \u2014 algorithms built on error-free transformation (EFT) primitives that track every rounding residual exactly.\n\nThis guide covers:\n\n1. The problem: catastrophic cancellation\n2. How UXarray handles it\n3. Seeing it on a real mesh: point-in-polygon\n4. Where it is used in UXarray"
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "imports-cell",
    "metadata": {
     "execution": {
@@ -29,1198 +18,8 @@
      "shell.execute_reply": "2026-05-22T11:58:09.059089Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "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",
-       "  // 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",
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-       "      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",
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-       "      root._bokeh_is_loading = css_urls.length + 0;\n",
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-       "      }\n",
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-       "      element.onload = on_load;\n",
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-       "      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\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.8.10/dist/panel.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 = 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  // 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\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.8.10/dist/panel.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",
-       "        comm_promise.then((comm) => {\n",
-       "          window.PyViz.comms[comm_id] = comm;\n",
-       "          if (msg_handler) {\n",
-       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
-       "            function processIteratorResult(result) {\n",
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-       "              var content = {data: message.data};\n",
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-       "            return messages.next().then(processIteratorResult);\n",
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-       "        })\n",
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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"holoviews.plotting.bokeh.raster.HoverModel\",\"properties\":[{\"name\":\"xy\",\"kind\":\"Any\",\"default\":null},{\"name\":\"data\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
-       "  var render_items = [{\"docid\":\"57699642-6e25-4f82-8f94-98775ebd7625\",\"roots\":{\"511425db-17a9-43d0-9390-9143fd70fc44\":\"a5581328-170e-4d70-a959-f524dc20052d\"},\"root_ids\":[\"511425db-17a9-43d0-9390-9143fd70fc44\"]}];\n",
-       "  var docs = Object.values(docs_json)\n",
-       "  if (!docs) {\n",
-       "    return\n",
-       "  }\n",
-       "  const version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
-       "  async function embed_document(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
-       "\tconst id_el = document.getElementById(root_id)\n",
-       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t  for (const child of root_el.children) {\n",
-       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
-       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
-       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
-       "\t  }\n",
-       "\t}\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  function get_bokeh(root) {\n",
-       "    if (root.Bokeh === undefined) {\n",
-       "      return null\n",
-       "    } else if (root.Bokeh.version !== version) {\n",
-       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(version)) {\n",
-       "\treturn null\n",
-       "      }\n",
-       "      return root.Bokeh.versions.get(version);\n",
-       "    } else if (root.Bokeh.version === version) {\n",
-       "      return root.Bokeh\n",
-       "    }\n",
-       "    return null\n",
-       "  }\n",
-       "  function is_loaded(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
-       "  }\n",
-       "  if (is_loaded(root)) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (is_loaded(root)) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 200) {\n",
-       "          clearInterval(timer);\n",
-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ]
-     },
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "511425db-17a9-43d0-9390-9143fd70fc44"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(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",
-       "    },\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));"
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-       "  if (id !== undefined) {\n",
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-       "    var scripts = [];\n",
-       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
-       "    for (var i in nodelist) {\n",
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-       "    output_area._hv_plot_id = id;\n",
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-       "    var bk_div = document.createElement(\"div\");\n",
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-       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
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-       "  delete PyViz.plot_index[id];\n",
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-       "    );\n",
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-       "\n",
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-       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
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-       "if (window.Jupyter !== undefined) {\n",
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-     },
-     "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\")"
-   ]
+   "outputs": [],
+   "source": "import warnings\n\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport uxarray as ux\nfrom uxarray.grid.point_in_face import _point_in_polygon_sphere\n\nwarnings.filterwarnings(\"ignore\")"
   },
   {
    "cell_type": "markdown",
@@ -1229,7 +28,7 @@
    "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."
+    "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 \u2014 and floating-point rounding can reduce it to zero."
    ]
   },
   {
@@ -1280,7 +79,7 @@
     "ax.text(\n",
     "    0.5,\n",
     "    0.96,\n",
-    "    f\"|a × b| = {area1:.3f}\",\n",
+    "    f\"|a \u00d7 b| = {area1:.3f}\",\n",
     "    ha=\"center\",\n",
     "    fontsize=12,\n",
     "    color=\"steelblue\",\n",
@@ -1289,7 +88,7 @@
     "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.set_title(\"Well-separated  \u2014  large, well-conditioned cross product\", fontsize=11)\n",
     "ax.axis(\"off\")\n",
     "\n",
     "# --- Right panel: nearly-parallel vectors ---\n",
@@ -1315,7 +114,7 @@
     "ax.text(\n",
     "    0.5,\n",
     "    0.96,\n",
-    "    f\"|a × b| = {area2:.4f}  ←  tiny!\",\n",
+    "    f\"|a \u00d7 b| = {area2:.4f}  \u2190  tiny!\",\n",
     "    ha=\"center\",\n",
     "    fontsize=12,\n",
     "    color=\"#d62728\",\n",
@@ -1325,13 +124,13 @@
     "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",
+    "    \"Nearly-parallel  \u2014  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",
+    "    \"Small area means two nearly equal numbers are subtracted \u2014 digits cancel\",\n",
     "    fontsize=12,\n",
     ")\n",
     "plt.show()"
@@ -1341,24 +140,7 @@
    "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."
-   ]
+   "source": "## 2. How UXarray Handles It\n\nUXarray uses **compensated arithmetic** \u2014 a family of algorithms that prevent catastrophic cancellation by representing floating-point operations as exact `(hi, lo)` pairs. There are two distinct layers:\n\n- **Error-free transformations (EFT)** \u2014 `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** \u2014 `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\nThe 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.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\u00d7b)\u00b7q`: +1, \u22121, or 0 |\n| `on_minor_arc(q, a, b)` | `grid.arcs` | True if `q` lies on the minor arc from `a` to `b` |\n\nMost users will never call these directly \u2014 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",
@@ -1377,33 +159,33 @@
      "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"
+      "North Pole:      orient3d = +1  \u2192 left  of A\u2192B (northern hemisphere)\n",
+      "South Pole:      orient3d = -1  \u2192 right of A\u2192B (southern hemisphere)\n",
+      "On great circle: orient3d =  0  \u2192 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",
+    "# orient3d_on_sphere(A, B, Q) returns the sign of the scalar triple product (A\u00d7B)\u00b7Q.\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",
+    "#   +1  Q is on the LEFT  of the directed arc A \u2192 B (above the plane by right-hand rule)\n",
+    "#   -1  Q is on the RIGHT of the directed arc A \u2192 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",
+    "A = np.array([1.0, 0.0, 0.0])  # 0\u00b0E on the equator\n",
+    "B = np.array([0.0, 1.0, 0.0])  # 90\u00b0E on the equator\n",
+    "# A\u2192B 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",
+    "on_equator = np.array([0.0, 1.0, 0.0])  # same as B \u2014 on the great circle itself\n",
     "\n",
     "\n",
     "def fmt(v):\n",
@@ -1411,13 +193,13 @@
     "\n",
     "\n",
     "print(\n",
-    "    f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  → left  of A→B (northern hemisphere)\"\n",
+    "    f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  \u2192 left  of A\u2192B (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",
+    "    f\"South Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, south_pole))}  \u2192 right of A\u2192B (southern hemisphere)\"\n",
     ")\n",
     "print(\n",
-    "    f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  → collinear, not a crossing\"\n",
+    "    f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  \u2192 collinear, not a crossing\"\n",
     ")"
    ]
   },
@@ -1428,7 +210,7 @@
    "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."
+    "Point-in-polygon on the sphere works by casting a ray from the query point and counting edge crossings \u2014 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."
    ]
   },
   {
@@ -1463,7 +245,7 @@
    "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 $\\sim \\varepsilon_\\text{machine} / |V0 \\times V1|$."
+    "Query points are placed at 50 log-spaced distances from the midpoint of edge V0\u2192V1 on face 0, stepping inward toward the face centroid. The sign of the naive orient3d flips once the distance drops below $\\sim \\varepsilon_\\text{machine} / |V0 \\times V1|$."
    ]
   },
   {
@@ -1483,12 +265,12 @@
      "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",
+      "Face 0 edge V0\u2192V1:  |V0 \u00d7 V1| = 0.04851\n",
+      "Naive sign flips below \u03b5 \u2248 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",
+      "All 50 query points are inside face 0 \u2014 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"
+      "  Naive (raw cross product):  42/50 correctly classified as inside  \u2190 8 misclassified as outside near the edge\n"
      ]
     }
    ],
@@ -1524,10 +306,10 @@
     "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",
+    "flip_mm = flip_threshold * 6.371e6 * 1e3  # radians \u2192 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",
+    "# All 50 are strictly inside the face \u2014 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",
@@ -1543,16 +325,16 @@
     "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(f\"Face 0 edge V0\u2192V1:  |V0 \u00d7 V1| = {cross_mag:.5f}\")\n",
     "print(\n",
-    "    f\"Naive sign flips below ε ≈ {flip_threshold:.1e} rad  ({flip_mm:.2e} mm on Earth)\"\n",
+    "    f\"Naive sign flips below \u03b5 \u2248 {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\"All {n} query points are inside face 0 \u2014 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",
+    "    f\"  \u2190 {n - naive_ok} misclassified as outside near the edge\"\n",
     ")"
    ]
   },
@@ -1561,7 +343,7 @@
    "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."
+    "When the query is close enough to the edge, the naive orient3d value rounds to the wrong sign \u2014 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."
    ]
   },
   {
@@ -1595,7 +377,7 @@
     "fig = plt.figure(figsize=(14, 5.5))\n",
     "fig.subplots_adjust(wspace=0.08)\n",
     "\n",
-    "# ── Left: zoomed face ──────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Left: zoomed face \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "ax = fig.add_subplot(1, 2, 1)\n",
     "\n",
     "face_lons = np.append(lons, lons[0])\n",
@@ -1609,7 +391,7 @@
     "    color=\"#d62728\",\n",
     "    linewidth=3.5,\n",
     "    zorder=3,\n",
-    "    label=\"Test edge  V0 → V1\",\n",
+    "    label=\"Test edge  V0 \u2192 V1\",\n",
     ")\n",
     "\n",
     "for i, (lo, la) in enumerate(zip(lons, lats)):\n",
@@ -1634,7 +416,7 @@
     "    color=\"#ff7f0e\",\n",
     "    marker=\"*\",\n",
     "    zorder=6,\n",
-    "    label=\"Edge midpoint — sweep origin\",\n",
+    "    label=\"Edge midpoint \u2014 sweep origin\",\n",
     ")\n",
     "\n",
     "ax.annotate(\n",
@@ -1646,7 +428,7 @@
     "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",
+    "    \"50 query points\\n(\u03b5 from 10\u207b\u00b3 \u2192 10\u207b\u00b9\u2076)\",\n",
     "    fontsize=9,\n",
     "    color=\"#555\",\n",
     "    style=\"italic\",\n",
@@ -1660,7 +442,7 @@
     "    edgecolors=\"#d62728\",\n",
     "    linewidths=2,\n",
     "    zorder=7,\n",
-    "    label=f\"Naive sign wrong below ε ≈ {flip_threshold:.0e} rad (≈ 0.03 mm)\",\n",
+    "    label=f\"Naive sign wrong below \u03b5 \u2248 {flip_threshold:.0e} rad (\u2248 0.03 mm)\",\n",
     ")\n",
     "\n",
     "q_far = normalize(\n",
@@ -1669,7 +451,7 @@
     "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",
+    "    \"\u03b5 = 10\u207b\u00b3\\nboth correct\",\n",
     "    (qf_lon, qf_lat),\n",
     "    textcoords=\"offset points\",\n",
     "    xytext=(7, -18),\n",
@@ -1677,16 +459,16 @@
     "    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.set_xlabel(\"Longitude (\u00b0)\", fontsize=11)\n",
+    "ax.set_ylabel(\"Latitude (\u00b0)\", fontsize=11)\n",
+    "ax.set_title(\"Face 0 \u2014 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",
+    "# \u2500\u2500 Right: global context \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\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",
@@ -1724,7 +506,7 @@
     "    zorder=5,\n",
     "    transform=ccrs.PlateCarree(),\n",
     ")\n",
-    "ax_global.set_title(\"Global context — highlighted face in red\", fontsize=11)\n",
+    "ax_global.set_title(\"Global context \u2014 highlighted face in red\", fontsize=11)\n",
     "\n",
     "plt.show()"
    ]
@@ -1733,23 +515,7 @@
    "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."
-   ]
+   "source": "## 4. Where It Is Used in UXarray\n\nCompensated 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\u2013arc intersection (remapping, antimeridian) | `grid/intersections.py` | `accucross`, `accucross_pair`, `on_minor_arc` |\n| Arc\u2013latitude 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\nIf you extend UXarray with custom geometry \u2014 for example, a new remapping kernel or a spatial predicate \u2014 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": {
diff --git a/docs/userguide.rst b/docs/userguide.rst
index a442a7c15..e96d4c9a1 100644
--- a/docs/userguide.rst
+++ b/docs/userguide.rst
@@ -95,7 +95,7 @@ Supplementary Guides
 These user guides provide additional details about specific features in UXarray.
 
 `Accurate Spherical Geometry <user-guide/spherical-geometry-accuracy.ipynb>`_
- How UXarray uses error-free transformations to avoid catastrophic cancellation in cross-product and point-in-polygon operations
+ How UXarray uses compensated arithmetic to avoid catastrophic cancellation in cross-product and point-in-polygon operations
 
 `Working with HEALPix Grids <user-guide/healpix.ipynb>`_
  Use UXarray with HEALPix
diff --git a/test/grid/geometry/data/accusphgeom/gca_constlat_cases_with_baseline.csv b/test/grid/geometry/data/accusphgeom/gca_constlat_cases_with_baseline.csv
new file mode 100644
index 000000000..ce456ea88
--- /dev/null
+++ b/test/grid/geometry/data/accusphgeom/gca_constlat_cases_with_baseline.csv
@@ -0,0 +1,201 @@
+case_id,a0_x_sig,a0_x_exp,a0_y_sig,a0_y_exp,a0_z_sig,a0_z_exp,a1_x_sig,a1_x_exp,a1_y_sig,a1_y_exp,a1_z_sig,a1_z_exp,z0_sig,z0_exp,baseline_x_sig,baseline_x_exp,baseline_y_sig,baseline_y_exp
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diff --git a/test/grid/geometry/data/accusphgeom/gca_gca_pairs_seed20251104_N100_with_baseline.csv b/test/grid/geometry/data/accusphgeom/gca_gca_pairs_seed20251104_N100_with_baseline.csv
new file mode 100644
index 000000000..8941026a4
--- /dev/null
+++ b/test/grid/geometry/data/accusphgeom/gca_gca_pairs_seed20251104_N100_with_baseline.csv
@@ -0,0 +1,32 @@
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diff --git a/test/grid/geometry/test_accusphgeom_baseline.py b/test/grid/geometry/test_accusphgeom_baseline.py
new file mode 100644
index 000000000..e704f2aff
--- /dev/null
+++ b/test/grid/geometry/test_accusphgeom_baseline.py
@@ -0,0 +1,208 @@
+"""Baseline regression tests ported from the AccuSphGeom C++ test suite.
+
+Test data and expected results are taken directly from:
+    https://github.com/hongyuchen1030/AccuSphGeom
+
+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
+the EFT tier, so the tolerances here reflect what double-precision EFT can
+achieve:
+
+    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
+import os
+
+import numpy as np
+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(
+    _DATA_DIR, "gca_gca_pairs_seed20251104_N100_with_baseline.csv"
+)
+_GCA_CONSTLAT_CSV = os.path.join(
+    _DATA_DIR, "gca_constlat_cases_with_baseline.csv"
+)
+
+
+def _sigexp(sig, exp):
+    return math.ldexp(int(sig), int(exp))
+
+
+def _parse_vec3(fields, start):
+    return np.array(
+        [
+            _sigexp(fields[start], fields[start + 1]),
+            _sigexp(fields[start + 2], fields[start + 3]),
+            _sigexp(fields[start + 4], fields[start + 5]),
+        ]
+    )
+
+
+def _parse_scalar(fields, start):
+    return _sigexp(fields[start], fields[start + 1])
+
+
+# ── GCA-GCA intersection ──────────────────────────────────────────────────────
+
+
+def _load_gca_gca():
+    rows = []
+    with open(_GCA_GCA_CSV) as f:
+        next(f)
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            fields = line.split(",")
+            assert len(fields) == 32
+            pair_id = int(fields[0])
+            a0 = _parse_vec3(fields, 2)
+            a1 = _parse_vec3(fields, 8)
+            b0 = _parse_vec3(fields, 14)
+            b1 = _parse_vec3(fields, 20)
+            baseline = _parse_vec3(fields, 26)
+            rows.append((pair_id, a0, a1, b0, b1, baseline))
+    return rows
+
+
+@pytest.fixture(scope="module")
+def gca_gca_rows():
+    return _load_gca_gca()
+
+
+def test_gca_gca_row_count(gca_gca_rows):
+    assert len(gca_gca_rows) == 31
+
+
+@pytest.mark.parametrize("idx", range(31))
+def test_gca_gca_intersection_baseline(gca_gca_rows, idx):
+    pair_id, a0, a1, b0, b1, baseline = gca_gca_rows[idx]
+    result = gca_gca_intersection(np.stack([a0, a1]), np.stack([b0, b1]))
+    assert result.shape[0] >= 1, f"pair_id={pair_id}: expected intersection, got none"
+    err = float(np.linalg.norm(result[0] - baseline))
+    assert err < 1e-15, f"pair_id={pair_id}: err={err:.3e} ≥ 1e-15"
+
+
+# ── GCA-const-lat intersection ────────────────────────────────────────────────
+
+
+def _load_gca_constlat():
+    rows = []
+    with open(_GCA_CONSTLAT_CSV) as f:
+        next(f)
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            fields = line.split(",")
+            assert len(fields) == 19
+            case_id = int(fields[0])
+            a0 = _parse_vec3(fields, 1)
+            a1 = _parse_vec3(fields, 7)
+            z0 = _parse_scalar(fields, 13)
+            bx = _parse_scalar(fields, 15)
+            by = _parse_scalar(fields, 17)
+            rows.append((case_id, a0, a1, z0, bx, by))
+    return rows
+
+
+@pytest.fixture(scope="module")
+def gca_constlat_rows():
+    return _load_gca_constlat()
+
+
+def test_gca_constlat_row_count(gca_constlat_rows):
+    assert len(gca_constlat_rows) == 200
+
+
+@pytest.mark.parametrize("idx", range(200))
+def test_gca_constlat_intersection_baseline(gca_constlat_rows, idx):
+    case_id, a0, a1, z0, bx, by = gca_constlat_rows[idx]
+    result = gca_const_lat_intersection(np.stack([a0, a1]), z0)
+    assert not np.all(np.isnan(result[0])), f"case_id={case_id}: no intersection returned"
+    dx = result[0, 0] - bx
+    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/_eft.py b/uxarray/grid/_eft.py
deleted file mode 100644
index 4a5ec5f97..000000000
--- a/uxarray/grid/_eft.py
+++ /dev/null
@@ -1,167 +0,0 @@
-"""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 481b2415c..7363f44a9 100644
--- a/uxarray/grid/arcs.py
+++ b/uxarray/grid/arcs.py
@@ -4,14 +4,14 @@
 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
+from uxarray.utils.computing import diff_of_products, two_sum
 
 # Tolerance used to classify orient3d results as zero.  For double-precision
-# unit-vector inputs this covers rounding error in the EFT cross product.
+# unit-vector inputs this covers rounding error in the compensated cross product.
 _PREDICATE_ZERO_TOL = 1e-15
 
 # Default tolerance for the on_minor_arc collinearity and interval tests.
@@ -376,11 +376,11 @@ def compute_arc_length(pt_a, pt_b):
 
 @njit(cache=True)
 def _orient3d_on_sphere_value(a, b, q):
-    """Return the EFT-accurate value of the orient3d-on-sphere predicate.
+    """Return the accurately computed 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
+    For unit vectors all coordinates are in [-1, 1], so the compensated 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
@@ -411,7 +411,7 @@ def _orient3d_on_sphere_value(a, b, q):
 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
+    Evaluates the sign of ``(a x b) . q`` using compensated arithmetic 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.
diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
index bff921a8e..2d07a601a 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -86,31 +86,30 @@ def _face_location_info(face_vertices, polar_cap_z):
         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.
+        # Parameter along the arc at which z is extremal (matches C++ get_face_location_info).
         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
+        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
+
+        # Mask-based selection: use z_ext only when the extremum is interior (a_raw in (0,1)).
+        use_ext = 1 if (0.0 < a_raw < 1.0) else 0
+        z_max_candidate = use_ext * z_ext + (1 - use_ext) * z_edge_max
+        z_min_candidate = use_ext * z_ext + (1 - use_ext) * 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
 
     if z_max >= polar_cap_z:
         return _FACE_LOC_NORTH_POLAR, z_min, z_max
@@ -199,11 +198,11 @@ def _generate_lat_lon_bounds_local(face_vertices, z_min, z_max, snap_tol_deg):
     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
+    # 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
 
@@ -273,10 +272,10 @@ def _generate_lat_lon_bounds_pole(face_vertices, label, z_min, z_max, snap_tol_d
     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
+    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:
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 00b8d8b42..f71bf3d4f 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -4,11 +4,17 @@
 from numba import njit, prange
 
 from uxarray.constants import ERROR_TOLERANCE, INT_DTYPE
-from uxarray.grid._eft import accucross
-from uxarray.grid.arcs import (
-    extreme_gca_z,
-    in_between,
-    on_minor_arc,
+from uxarray.grid.arcs import on_minor_arc
+from uxarray.utils.computing import (
+    _cdp2,
+    _cdp4,
+    _sum_sq_c2,
+    _sum_sq_c3,
+    acc_sqrt_re,
+    accucross,
+    accucross_pair,
+    two_prod,
+    two_sum,
 )
 
 
@@ -292,19 +298,6 @@ 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)
@@ -316,7 +309,7 @@ def _gca_gca_intersection_cartesian(gca_a_xyz, gca_b_xyz):
 def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     """Find intersection point(s) of two great-circle arcs using compensated arithmetic.
 
-    Uses ``accucross`` (error-free cross products) and ``on_minor_arc`` (EFT-based
+    Uses ``accucross`` (compensated cross products) and ``on_minor_arc`` (compensated
     arc membership) to avoid the catastrophic cancellation that affects naive
     cross product implementations when arcs are nearly parallel.
 
@@ -340,24 +333,56 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     v0 = gca_b_xyz[0]
     v1 = gca_b_xyz[1]
 
-    # 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])
+    # 1. Plane normals via accurate cross products — keep compensated (hi, lo).
+    n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = 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])
+    n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo = accucross(
+        v0[0], v0[1], v0[2], v1[0], v1[1], v1[2]
     )
 
     res = np.empty((2, 3))
     count = 0
 
-    if n1_mag == 0.0 or n2_mag == 0.0:
+    # Degenerate check: collapsed (zero-length) input arc.
+    n1x = n1x_hi + n1x_lo
+    n1y = n1y_hi + n1y_lo
+    n1z = n1z_hi + n1z_lo
+    n2x = n2x_hi + n2x_lo
+    n2y = n2y_hi + n2y_lo
+    n2z = n2z_hi + n2z_lo
+    if (
+        n1x * n1x + n1y * n1y + n1z * n1z == 0.0
+        or n2x * n2x + n2y * n2y + n2z * n2z == 0.0
+    ):
         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))
-
-    if vn == 0.0 or not (math.isfinite(vx) and math.isfinite(vy) and math.isfinite(vz)):
+    # 2. Intersection direction: compensated cross of the two plane normals.
+    vx_hi, vy_hi, vz_hi, vx_lo, vy_lo, vz_lo = accucross_pair(
+        n1x_hi,
+        n1y_hi,
+        n1z_hi,
+        n1x_lo,
+        n1y_lo,
+        n1z_lo,
+        n2x_hi,
+        n2y_hi,
+        n2z_hi,
+        n2x_lo,
+        n2y_lo,
+        n2z_lo,
+    )
+    vx = vx_hi + vx_lo
+    vy = vy_hi + vy_lo
+    vz = vz_hi + vz_lo
+    vn = math.sqrt(vx * vx + vy * vy + vz * vz)
+
+    if vn == 0.0 or not (
+        math.isfinite(vx)
+        and math.isfinite(vy)
+        and math.isfinite(vz)
+        and math.isfinite(vn)
+    ):
         # Parallel (coplanar) arcs: check whether endpoints of one lie on the other.
         if on_minor_arc(v0, w0, w1):
             res[count, 0] = v0[0]
@@ -372,14 +397,15 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
         return res[:count]
 
     # 3. Two antipodal candidate intersection points; keep those on both arcs.
+    inv = 1.0 / vn
     pos = np.empty(3)
-    pos[0] = vx
-    pos[1] = vy
-    pos[2] = vz
+    pos[0] = vx * inv
+    pos[1] = vy * inv
+    pos[2] = vz * inv
     neg = np.empty(3)
-    neg[0] = -vx
-    neg[1] = -vy
-    neg[2] = -vz
+    neg[0] = -pos[0]
+    neg[1] = -pos[1]
+    neg[2] = -pos[2]
 
     if on_minor_arc(pos, w0, w1) and on_minor_arc(pos, v0, v1):
         res[count, 0] = pos[0]
@@ -400,11 +426,20 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
 def gca_const_lat_intersection(gca_cart, const_z):
     """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.
+    Implements the ``accux_constlat`` algorithm from AccuSphGeom
+    (gca_constlat_intersection.hpp) using compensated arithmetic throughout
+    to achieve near-machine-precision accuracy even for arcs nearly tangent
+    to the latitude circle.
+
+    The algorithm:
+    1. Compute the arc's plane normal n = a × b via ``accucross`` (compensated).
+    2. Compute s2 = nx² + ny² and s3 = |n|² using compensated sum-of-squares
+       on the (hi, lo) pairs from ``accucross``.
+    3. Compute the discriminant planar_sq = s2 − s3·z₀² using compensated
+       arithmetic; take its accurate square root via ``acc_sqrt_re``.
+    4. Compute the two candidate intersection points using compensated 2-term
+       dot products for the x and y numerators, divided by s2.
+    5. Retain each candidate that is finite and lies on the minor arc.
 
     Parameters
     ----------
@@ -425,72 +460,108 @@ def gca_const_lat_intersection(gca_cart, const_z):
     x1 = gca_cart[0]
     x2 = gca_cart[1]
 
-    # 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_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_z:
-        res[0, 0] = x1[0]
-        res[0, 1] = x1[1]
-        res[0, 2] = x1[2]
-        return res
-    elif x2_at_z:
-        res[0, 0] = x2[0]
-        res[0, 1] = x2[1]
-        res[0, 2] = x2[2]
-        return res
+    # 1. Plane normal via compensated cross product (keeps hi, lo residuals).
+    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]
+    )
 
-    # 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")
-    if not in_between(z_min, const_z, z_max):
+    # 2. s2 = nx²+ny²  (compensated, on hi/lo pairs — matches sum_of_squares_c<2>).
+    s2_hi, s2_lo = _sum_sq_c2(nx_hi, nx_lo, ny_hi, ny_lo)
+    denom = s2_hi + s2_lo
+    if denom == 0.0:
         return res
 
-    # 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]
+    # 3. s3 = |n|² = nx²+ny²+nz²  (compensated — matches sum_of_squares_c<3>).
+    s3_hi, s3_lo = _sum_sq_c3(nx_hi, nx_lo, ny_hi, ny_lo, nz_hi, nz_lo)
+
+    # 4. zsq = z₀² exactly (two_prod replaces two_prod_fma; same exact result).
+    zsq_hi, zsq_lo = two_prod(const_z, const_z)
+
+    # 5. d = s3 · zsq  via 4-term compensated dot product matching C++:
+    #    compensated_dot_product({s3_hi, s3_hi, s3_lo, s3_lo},
+    #                            {zsq_hi, zsq_lo, zsq_hi, zsq_lo})
+    d_hi, d_lo = _cdp4(
+        s3_hi,
+        zsq_hi,
+        s3_hi,
+        zsq_lo,
+        s3_lo,
+        zsq_hi,
+        s3_lo,
+        zsq_lo,
     )
+    # Note: Numba doesn't allow negative sign in function args, so negate d_hi explicitly.
+    neg_d_hi = -d_hi
+
+    # 6. planar_sq = s2 − d  (compensated two_sum on the high parts + low correction).
+    e_hi, e_lo = two_sum(s2_hi, neg_d_hi)
+    planar_sq = e_hi + (e_lo + s2_lo - d_lo)
+
+    if planar_sq < 0.0:
+        return res
+
+    # 7. Accurate square root of discriminant.
+    s_root, s_corr = acc_sqrt_re(planar_sq)
+
+    # Collapse compensated values to scalars for the final formula.
     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
+    planar = s_root + s_corr
+
+    # 8. Numerators via 2-term compensated dot products (matches C++ accux_constlat).
+    #    x_pos = -(nx*nz*z₀  + (−ny)*planar) / denom
+    #    y_pos = -(ny*nz*z₀  +   nx *planar) / denom
+    #    x_neg = -(nx*nz*z₀  +   ny *planar) / denom
+    #    y_neg = -(ny*nz*z₀  + (−nx)*planar) / denom
+    xp_hi, xp_lo = _cdp2(nx * nz, const_z, -ny, planar)
+    yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
+    xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
+    yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
 
-    # 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[0] = -(xp_hi + xp_lo) * inv_denom
+    p1[1] = -(yp_hi + yp_lo) * inv_denom
     p1[2] = const_z
 
     p2 = np.empty(3)
-    p2[0] = cx - (-ny * s)
-    p2[1] = cy - (nx * s)
+    p2[0] = -(xn_hi + xn_lo) * inv_denom
+    p2[1] = -(yn_hi + yn_lo) * inv_denom
     p2[2] = const_z
 
-    # 5. Keep candidates that lie on the minor arc.
+    # 9a. Snap computed (x, y) to any arc endpoint that lies exactly on the latitude.
+    #     Adjacent edges sharing such an endpoint would otherwise return slightly
+    #     different coordinates; snapping gives them the same exact value so that
+    #     deduplication in the caller works correctly.  Matches Hongyu's suggestion
+    #     of mask-selection to snap after computing rather than branching out early.
+    _snap_sq = 1e-14  # distance² ≈ (1e-7)² — well above algorithm error (~1e-15)
+    for xe in (x1, x2):
+        if abs(xe[2] - const_z) <= ERROR_TOLERANCE:
+            dx = p1[0] - xe[0]
+            dy = p1[1] - xe[1]
+            if dx * dx + dy * dy < _snap_sq:
+                p1[0] = xe[0]
+                p1[1] = xe[1]
+            dx = p2[0] - xe[0]
+            dy = p2[1] - xe[1]
+            if dx * dx + dy * dy < _snap_sq:
+                p2[0] = xe[0]
+                p2[1] = xe[1]
+
+    # 9b. Retain each candidate that is finite and lies 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)
 
+    # When both candidates are valid but nearly identical (tangent/endpoint case),
+    # treat as a single intersection — same as the C++ scalar gca_constlat_intersection
+    # which returns only one point when status==0 (exactly one candidate lies on the arc).
+    if p1_ok and p2_ok:
+        dx = p1[0] - p2[0]
+        dy = p1[1] - p2[1]
+        if dx * dx + dy * dy < _snap_sq:
+            p2_ok = False
+
     if p1_ok and p2_ok:
         res[0, 0] = p1[0]
         res[0, 1] = p1[1]
diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py
index 36b06f674..7a10934ae 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -85,7 +85,7 @@ def _counts_as_crossing(A, B, q, R):
 
     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
+    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.
     """
@@ -127,7 +127,7 @@ def _point_in_polygon_sphere(q, polygon):
 
     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
+    (compensated) 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.
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index ca5ca5183..5f99ac010 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -1,628 +1,426 @@
-import sys
-
-import numpy as np
-
-
-def _fmms(a, b, c, d):
-    """
-    Calculate the difference of products using the FMA (fused multiply-add) operation: (a * b) - (c * d).
-
-    This operation leverages the fused multiply-add operation when available on the system and rounds the result only once.
-    The relative error of this operation is bounded by 1.5 ulps when no overflow and underflow occur.
-
-    Parameters
-    ----------
-    a (float): The first value of the first product.
-    b (float): The second value of the first product.
-    c (float): The first value of the second product.
-    d (float): The second value of the second product.
-
-    Returns
-    -------
-    float: The difference of the two products.
-
-    Example
-    -------
-    >>> _fmms(3.0, 2.0, 1.0, 1.0)
-    5.0
-
-    Reference
-    ---------
-    Claude-Pierre Jeannerod, Nicolas Louvet, and Jean-Michel Muller, Further
-    analysis of Kahan’s algorithm for the accurate computation of 2 x 2 determinants,
-    Mathematics of Computation, vol. 82, no. 284, pp. 2245-2264, 2013.
-    [Read more](https://ens-lyon.hal.science/ensl-00649347) (DOI: 10.1090/S0025-5718-2013-02679-8)
-    """
-    import pyfma
-
-    cd = c * d
-    err = pyfma.fma(-c, d, cd)
-    dop = pyfma.fma(a, b, -cd)
-    return dop + err
-
-
-def cross_fma(v1, v2):
-    """Calculate the cross product of two 3D vectors utilizing the fused
-    multiply-add operation.
-
-    Parameters
-    ----------
-    v1 (np.array): The first vector of size 3.
-    v2 (np.array): The second vector of size 3.
-
-    Returns
-    -------
-    np.array: The cross product vector of size 3.
-
-    Example
-    -------
-    >>> v1 = np.array([1.0, 2.0, 3.0])
-    >>> v2 = np.array([4.0, 5.0, 6.0])
-    >>> cross_fma(v1, v2)
-    array([-3.0, 6.0, -3.0])
-    """
-    x = _fmms(v1[1], v2[2], v1[2], v2[1])
-    y = _fmms(v1[2], v2[0], v1[0], v2[2])
-    z = _fmms(v1[0], v2[1], v1[1], v2[0])
-    return np.array([x, y, z])
-
-
-def dot_fma(v1, v2):
-    """Calculate the dot product of two vectors using the FMA (fused multiply-
-    add) operation.
-
-    This implementation leverages the FMA operation to provide a more accurate result. Currently the ComptDot product
-    algorithm is used, which provides a relative error of approvimately u + n^2u^2cond(v1 dot v2), where u is 0.5 ulps,
-    n is the length of the vectors, and cond(v1 dot v2) is the condition number of the naive dot product of v1 and v2.
-    This operatin takes approvimately 3 + 10 * n flops, where n is the length of the vectors.
-
-    Parameters
-    ----------
-    v1 : list of float
-        The first vector.
-    v2 : list of float
-        The second vector. Must be the same length as v1.
-
-    Returns
-    -------
-    float
-        The dot product of the two vectors.
-
-    Raises
-    ------
-    ValueError
-        If the input vectors `v1` and `v2` are not of the same length.
-
-    Examples
-    --------
-    >>> dot_fma([1.0, 2.0, 3.0], [4.0, 5.0, 6.0])
-    32.0
-
-    References
-    ----------
-    S. Graillat, Ph. Langlois, and N. Louvet. "Accurate dot products with FMA." Presented at RNC 7, 2007, Nancy, France.
-    DALI-LP2A Laboratory, University of Perpignan, France.
-    """
-    if len(v1) != len(v2):
-        raise ValueError("Input vectors must be of the same length")
-
-    s, c = _two_prod_fma(v1[0], v2[0])
-    for i in range(1, len(v1)):
-        p, pi = _two_prod_fma(v1[i], v2[i])
-        s, signma = _two_sum(s, p)
-        c = c + pi + signma
-
-    return s + c
-
-
-def _two_prod_fma(a, b):
-    """Error-free transformation of the product of two floating-point numbers
-    using FMA, such that a * b = x + y exactly.
+"""Compensated floating-point primitives for accurate spherical geometry.
+
+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.
+
+Naming note
+-----------
+The term "error-free transformation" (EFT) strictly applies to ``two_sum``
+and ``two_prod``, which capture their rounding errors exactly so that
+``hi + lo`` equals the mathematical result with zero information loss.
+``diff_of_products``, ``accucross``, and ``accucross_pair`` use those EFT
+building blocks to achieve near-double precision for cross products, but they
+are compensated algorithms, not zero-error transformations.
+
+All functions are ``@njit``-compiled and use the portable Veltkamp-splitting
+form of ``two_prod`` (no FMA dependency), making them suitable for use inside
+Numba-compiled geometry kernels.
+
+These primitives are a Python/Numba port of 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.
+"""
+
+import math
+
+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
-        The floating-point numbers to be multiplied.
+        Input values.
 
     Returns
     -------
-    tuple of float
-        The product and the error term.
-
-    Examples
-    --------
-    >>> _two_prod_fma(1.0, 2.0)
-    (2.0, 0.0)
-
-    Reference
-    ---------
-    Stef Graillat. Accurate Floating Point Product and Exponentiation.
-    IEEE Transactions on Computers, 58(7), 994–1000, 2009.10.1109/TC.2008.215.
+    s : float
+        Rounded sum fl(a + b).
+    e : float
+        Rounding error term; s + e = a + b exactly.
     """
-    import pyfma
+    s = a + b
+    bp = s - a
+    e = (a - (s - bp)) + (b - bp)
+    return s, e
 
-    x = a * b
-    y = pyfma.fma(a, b, -x)
-    return x, y
 
+@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.
 
-def _err_fmac(a, b, c):
-    """Error-free transformation for the FMA operation. such that x =
-    FMA(a,b,c) and a * b + c = x + y + z exactly. Thhis function is only
-    available in round to the nearest mode and takes approximately 17 flops.
-
-    Parameters
-    ----------
-    a, b, c : float
-        The operands for the FMA operation.
-
-    Returns
-    -------
-    tuple of float
-        The result of the FMA operation and two error terms.
-
-    References
-    ----------
-    Graillat, Stef & Langlois, Philippe & Louvet, Nicolas. (2006). Improving the compensated Horner scheme with
-    a Fused Multiply and Add. 2. 1323-1327. 10.1145/1141277.1141585.
-
-    Ogita, Takeshi & Rump, Siegfried & Oishi, Shin’ichi. (2005). Accurate Sum and Dot Product.
-    SIAM J. Scientific Computing. 26. 1955-1988. 10.1137/030601818.
-    """
-    if sys.float_info.rounds == 1:
-        import pyfma
-
-        x = pyfma.fma(a, b, c)
-        u1, u2 = _fast_two_mult(a, b)
-        alpha1, alpha2 = _two_sum(c, u2)
-        beta1, beta2 = _two_sum(u1, alpha1)
-        gamma = (beta1 - x) + beta2
-        y, z = _fast_two_sum(gamma, alpha2)
-        return x, y, z
-    else:
-        raise ValueError(
-            "3FMA operation is only available in round to the nearest mode. and the current mode is "
-            + str(sys.float_info.rounds)
-        )
-
-
-def _two_sum(a, b):
-    """Error-free transformation of the sum of two floating-point numbers such
-    that a + b = x + y 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
-        The floating-point numbers to be added.
+        Input values.
 
     Returns
     -------
-    tuple of float
-        The sum and the error term.
-
-    Examples
-    --------
-    >>> _two_sum(1.0, 2.0)
-    (3.0, 0.0)
-
-    Reference
-    ---------
-    D. Knuth. 1998. The Art of Computer Programming (3rd ed.). Vol. 2. Addison-Wesley, Reading, MA.
+    p : float
+        Rounded product fl(a * b).
+    e : float
+        Rounding error term; p + e = a * b exactly.
     """
-    x = a + b
-    z = x - a
-    y = (a - (x - z)) + (b - z)
-    return x, y
-
-
-def _fast_two_mult(a, b):
-    """Error-free transformation of the product of two floating-point numbers
-    such that a * b = x + y exactly.
-
-    This function is faster than the _two_prod_fma function.
+    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 : float
-        The floating-point numbers to be multiplied.
+    a, b, c, d : float
+        Input scalars; computes a*b - c*d.
 
     Returns
     -------
-    tuple of float
-        The product and the error term.
-
-    References
-    ----------
-    Vincent Lefèvre, Nicolas Louvet, Jean-Michel Muller, Joris Picot, and Laurence Rideau. 2023.
-    Accurate Calculation of Euclidean Norms Using Double-word Arithmetic.
-    ACM Trans. Math. Softw. 49, 1, Article 1 (March 2023), 34 pages. https://doi.org/10.1145/3568672
+    hi : float
+        High-order part of the accurate result.
+    lo : float
+        Low-order correction term; hi + lo equals the accurate value.
     """
-    import pyfma
+    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
 
-    x = a * b
-    y = pyfma.fma(a, b, -x)
-    return x, y
 
+@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.
 
-def _fast_two_sum(a, b):
-    """Compute a fast error-free transformation of the sum of two floating-
-    point numbers.
-
-    This function is a faster alternative to `_two_sum` for computing the sum
-    of two floating-point numbers `a` and `b`, such that a + b = x + y exactly.
-    Note: |a| must be no less than |b|.
+    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
     ----------
-    a, b : float
-        The floating-point numbers to be added. It is required that |a| >= |b|.
+    a0, a1, a2 : float
+        Components of vector a.
+    b0, b1, b2 : float
+        Components of vector b.
 
     Returns
     -------
-    tuple of float
-        The rounded sum of `a` and `b`, and the error term. The error term represents the difference between the exact sum and the rounded sum.
-
-    Raises
-    ------
-    ValueError
-        If |a| < |b|.
-
-    Examples
-    --------
-    >>> _fast_two_sum(2.0, 1.0)
-    (3.0, 0.0)
-
-    >>> _fast_two_sum(1.0, 2.0)
-    Traceback (most recent call last):
-        ...
-    ValueError: |a| must be greater than or equal to |b|.
-
-    Reference
-    ---------
-    T. J. Dekker. A Floating-Point Technique for Extending the Available Precision.
-    Numerische Mathematik, 18(3), 224–242,1971. 10.1007/BF01397083.
-    Available at: https://doi.org/10.1007/BF01397083.
+    x_hi, y_hi, z_hi, x_lo, y_lo, z_lo : float
+        High and low parts of each cross-product component.
     """
-    if abs(a) >= abs(b):
-        x = a + b
-        b_tile = x - a
-        y = b - b_tile
-        return x, y
-
-    else:
-        raise ValueError("|a| must be greater than or equal to |b|.")
-
-
-def _comp_prod_fma(vec):
-    """Compute the compensated product using Fused Multiply-Add (FMA).
-
-    This function computes the product of elements in a vector using a
-    compensated algorithm with Fused Multiply-Add to reduce numerical errors.
-
-    Parameters
-    ----------
-    vec : list of float
-        The vector whose elements are to be multiplied.
-
-    Returns
-    -------
-    float
-        The compensated product of the elements in the vector.
-
-    Examples
-    --------
-    >>> _comp_prod_fma([1.1, 2.2, 3.3])
-    7.986000000000001
-
-    Reference
-    ---------
-    Takeshi Ogita, Siegfried M. Rump, and Shin'ichi Oishi. 2005. Accurate Sum and Dot Product.
-    SIAM J. Sci. Comput. 26, 6 (2005), 1955–1988. https://doi.org/10.1137/030601818
+    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
+
+
+@njit(cache=True, inline="always")
+def _cdp8(
+    a0,
+    a1,
+    a2,
+    a3,
+    a4,
+    a5,
+    a6,
+    a7,
+    b0,
+    b1,
+    b2,
+    b3,
+    b4,
+    b5,
+    b6,
+    b7,
+):
+    """Compensated sum of 8 exact products: Σ ai*bi, i=0..7.
+
+    Uses ``two_prod`` + ``two_sum`` accumulation (Ogita-Rump-Oishi style)
+    so the result has error bounded by one ulp of the true value regardless
+    of cancellation in intermediate sums.
     """
-    import pyfma
-
-    p1 = vec[0]
-    e1 = 0.0
-    for i in range(1, len(vec)):
-        p_i, pi = _two_prod_fma(p1, vec[i])
-        ei = pyfma.fma(e1, vec[i], pi)
-        p1 = p_i
-        e1 = ei
-    res = p1 + e1
-    return res
-
-
-def _sum_of_squares_re(vec):
-    """Compute the sum of squares of a vector using a compensated algorithm.
-
-    This function calculates the sum of squares of the elements in a vector,
-    employing a compensation technique to reduce numerical errors.
+    s, lo = two_prod(a0, b0)
+    p, e = two_prod(a1, b1)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a2, b2)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a3, b3)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a4, b4)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a5, b5)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a6, b6)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a7, b7)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    return s, lo
+
+
+@njit(cache=True, inline="always")
+def accucross_pair(
+    ax_hi,
+    ay_hi,
+    az_hi,
+    ax_lo,
+    ay_lo,
+    az_lo,
+    bx_hi,
+    by_hi,
+    bz_hi,
+    bx_lo,
+    by_lo,
+    bz_lo,
+):
+    """Compensated cross product of two compensated vectors.
+
+    Computes (a_hi + a_lo) × (b_hi + b_lo) using a compensated 8-term dot
+    product for each component, matching the two-argument ``accucross`` overload
+    in the AccuSphGeom C++ library.  This is more accurate than collapsing
+    (hi, lo) to a single float before the cross product.
 
     Parameters
     ----------
-    vec : list of float
-        The vector whose elements' squares are to be summed.
+    ax_hi, ay_hi, az_hi : float
+        High parts of vector a.
+    ax_lo, ay_lo, az_lo : float
+        Low  parts of vector a (rounding residuals from a prior compensated operation).
+    bx_hi, by_hi, bz_hi : float
+        High parts of vector b.
+    bx_lo, by_lo, bz_lo : float
+        Low  parts of vector b.
 
     Returns
     -------
-    float
-        The compensated sum of the squares of the elements in the vector.
-
-    Examples
-    --------
-    >>> _sum_of_squares_re([1.0, 2.0, 3.0])
-    14.0
-
-    Reference
-    ---------
-    Stef Graillat, Christoph Lauter, PING Tak Peter Tang,
-    Naoya Yamanaka, and Shin’ichi Oishi. Efficient Calculations of Faith-
-    fully Rounded L2-Norms of n-Vectors. ACM Transactions on Mathemat-
-    ical Software, 41(4), Article 24, 2015. 10.1145/2699469. Available at:
-    https://doi.org/10.1145/2699469.
+    x_hi, y_hi, z_hi, x_lo, y_lo, z_lo : float
+        Compensated cross-product components.
     """
-    P, p = _two_square(vec)
-    S, s = _two_sum(P[0], P[1])
-    for i in range(2, len(vec)):
-        H, h = _two_sum(S, P[i])
-        S, s = _two_sum(H, s + h)
-    sump = sum(p)
-    H, h = _two_sum(S, sump)
-    S, s = _fast_two_sum(H, s + h)
-    return S + s
-
-
-def _vec_sum(p):
-    """Compute the sum of a vector using a compensated summation algorithm.
-
-    This function calculates the sum of the elements in a vector using a
-    compensated summation algorithm to reduce numerical errors.
-
-    Parameters
-    ----------
-    p : list of float
-        The vector whose elements are to be summed.
-
-    Returns
-    -------
-    float
-        The compensated sum of the elements in the vector.
-
-    Examples
-    --------
-    >>> _vec_sum([1.0, 2.0, 3.0])
-    6.0
-
-    Reference
-    ---------
-    Takeshi Ogita, Siegfried M. Rump, and Shin'ichi Oishi. 2005. Accurate Sum and Dot Product.
-    SIAM J. Sci. Comput. 26, 6 (2005), 1955–1988. https://doi.org/10.1137/030601818
+    # x = (ay*bz) - (az*by), expanded over all four hi/lo cross-terms
+    x_hi, x_lo = _cdp8(
+        ay_hi,
+        ay_hi,
+        ay_lo,
+        ay_lo,
+        -az_hi,
+        -az_hi,
+        -az_lo,
+        -az_lo,
+        bz_hi,
+        bz_lo,
+        bz_hi,
+        bz_lo,
+        by_hi,
+        by_lo,
+        by_hi,
+        by_lo,
+    )
+    # y = (az*bx) - (ax*bz)
+    y_hi, y_lo = _cdp8(
+        az_hi,
+        az_hi,
+        az_lo,
+        az_lo,
+        -ax_hi,
+        -ax_hi,
+        -ax_lo,
+        -ax_lo,
+        bx_hi,
+        bx_lo,
+        bx_hi,
+        bx_lo,
+        bz_hi,
+        bz_lo,
+        bz_hi,
+        bz_lo,
+    )
+    # z = (ax*by) - (ay*bx)
+    z_hi, z_lo = _cdp8(
+        ax_hi,
+        ax_hi,
+        ax_lo,
+        ax_lo,
+        -ay_hi,
+        -ay_hi,
+        -ay_lo,
+        -ay_lo,
+        by_hi,
+        by_lo,
+        by_hi,
+        by_lo,
+        bx_hi,
+        bx_lo,
+        bx_hi,
+        bx_lo,
+    )
+    return x_hi, y_hi, z_hi, x_lo, y_lo, z_lo
+
+
+# ---------------------------------------------------------------------------
+# Compensated dot products and sum-of-squares (fixed small sizes)
+#
+# These port accusphgeom::numeric::compensated_dot_product and
+# accusphgeom::numeric::sum_of_squares_c from eft.hpp, using our Veltkamp-
+# splitting two_prod instead of FMA.  Fixed-size variants are used because
+# Numba does not support generic runtime-length accumulations inside @njit.
+# ---------------------------------------------------------------------------
+
+
+@njit(cache=True, inline="always")
+def _cdp2(a0, b0, a1, b1):
+    """Compensated dot product of 2 pairs: a0*b0 + a1*b1."""
+    s, lo = two_prod(a0, b0)
+    p, e = two_prod(a1, b1)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    return s2, lo
+
+
+@njit(cache=True, inline="always")
+def _cdp4(a0, b0, a1, b1, a2, b2, a3, b3):
+    """Compensated dot product of 4 pairs: Σ ai*bi, i=0..3."""
+    s, lo = two_prod(a0, b0)
+    p, e = two_prod(a1, b1)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a2, b2)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a3, b3)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    return s2, lo
+
+
+@njit(cache=True, inline="always")
+def _sum_sq_c2(h0, l0, h1, l1):
+    """Compensated sum of squares for 2 (hi, lo) pairs: h0²+l0²+h1²+l1².
+
+    Mirrors sum_of_squares_c<T, 2> from accusphgeom/numeric/eft.hpp, which
+    constructs lhs = rhs = [h0, l0, h1, l1] and calls compensated_dot_product.
+    Used to compute nx²+ny² accurately from the compensated normal (hi, lo).
     """
-    pi_1 = p[0]
-    sigma_i1 = 0
-
-    for i in range(1, len(p)):
-        pi, qi = _two_sum(pi_1, p[i])
-        sigma_i = sigma_i1 + qi
-        pi_1 = pi
-        sigma_i1 = sigma_i
+    return _cdp4(h0, h0, l0, l0, h1, h1, l1, l1)
 
-    res = pi_1 + sigma_i1
-    return res
 
+@njit(cache=True, inline="always")
+def _sum_sq_c3(h0, l0, h1, l1, h2, l2):
+    """Compensated sum of squares for 3 (hi, lo) pairs: h0²+l0²+h1²+l1²+h2²+l2².
 
-def _norm_faithful(x):
-    """Compute the faithful norm of a vector.
-
-    This function calculates the faithful norm (L2 norm) of a vector,
-    which is a more numerically stable version of the Euclidean norm.
-
-    Parameters
-    ----------
-    x : list of float
-        The vector whose norm is to be computed.
-
-    Returns
-    -------
-    float
-        The faithful norm of the vector.
-
-    Examples
-    --------
-    >>> _norm_faithful([1.0, 2.0, 3.0])
-    3.7416573867739413
+    Mirrors sum_of_squares_c<T, 3> from accusphgeom/numeric/eft.hpp, which
+    constructs lhs = rhs = [h0, l0, h1, l1, h2, l2] and calls a 6-term CDP.
+    We use _cdp8 with two zero-padding pairs (adding zero products).
+    Used to compute |n|² = nx²+ny²+nz² accurately from the compensated normal.
     """
-    return _norm_l(x)
-
+    # lhs = rhs = [h0, l0, h1, l1, h2, l2, 0, 0]
+    return _cdp8(h0, l0, h1, l1, h2, l2, 0.0, 0.0, h0, l0, h1, l1, h2, l2, 0.0, 0.0)
 
-def _norm_l(x):
-    """Compute the L2 norm (Euclidean norm) of a vector using a compensated
-    algorithm.
-
-    This function calculates the L2 norm of a vector, employing a compensation
-    technique to reduce numerical errors during the computation. It involves
-    computing the sum of squares of the vector elements in a numerically stable way.
-
-    Parameters
-    ----------
-    x : list of float
-        The vector whose L2 norm is to be computed.
-
-    Returns
-    -------
-    float
-        The compensated L2 norm of the vector.
-
-    Examples
-    --------
-    >>> _norm_l([1.0, 2.0, 3.0])
-    3.7416573867739413
-
-    Reference
-    ---------
-    Vincent Lef`evre, Nicolas Louvet, Jean-Michel Muller,
-    Joris Picot, and Laurence Rideau. Accurate Calculation of Euclidean
-    Norms Using Double-Word Arithmetic. ACM Transactions on Mathemat-
-    ical Software, 49(1), 1–34, March 2023. 10.1145/3568672
-    """
-    P, p = _two_square(x)
-    S, s = _two_sum(P[0], P[1])
-    for i in range(2, len(x)):
-        H, h = _two_sum(S, P[i])
-        S, s = _two_sum(H, s + h)
-    sump = sum(p)
-    H, h = _two_sum(S, sump)
-    S, s = _fast_two_sum(H, s + h)
-    res = _acc_sqrt(S, s)
-    return res
-
-
-def _norm_g(x):
-    """Compute the compensated Euclidean norm of a vector.
-
-    This function calculates the Euclidean norm (L2 norm) of a vector,
-    using a compensated algorithm to reduce numerical errors.
-
-    Parameters
-    ----------
-    x : list of float
-        The vector whose norm is to be computed.
-
-    Returns
-    -------
-    float
-        The compensated Euclidean norm of the vector.
-
-    Examples
-    --------
-    >>> _norm_g([1.0, 2.0, 3.0])
-    3.7416573867739413
-
-    Reference
-    ---------
-    Stef Graillat, Christoph Lauter, PING Tak Peter Tang,
-    Naoya Yamanaka, and Shin’ichi Oishi. Efficient Calculations of Faith-
-    fully Rounded L2-Norms of n-Vectors. ACM Transactions on Mathemat-
-    ical Software, 41(4), Article 24, 2015. 10.1145/2699469. Available at:
-    https://doi.org/10.1145/2699469.
-    """
-    S = 0
-    s = 0
-    for x_i in x:
-        P, p = _two_prod_fma(x_i, x_i)
-        H, h = _two_sum(S, P)
-        c = s + p
-        d = h + c
-        S, s = _fast_two_sum(H, d)
-    res = _acc_sqrt(S, s)
-    return res
-
-
-def _two_square(Aa):
-    """Compute the square of a number with a compensation for the round-off
-    error.
-
-    This function calculates the square of a given number and compensates
-    for the round-off error that occurs during the squaring.
-
-    Parameters
-    ----------
-    Aa : float
-        The number to be squared.
-
-    Returns
-    -------
-    tuple of float
-        The square of the number and the compensated round-off error.
-
-    Examples
-    --------
-    >>> _two_square(2.0)
-    (4.0, 0.0)
-
-    Reference
-    ---------
-    Siegfried Rump. Fast and accurate computation of the Euclidean norm of a vector. J
-    apan Journal of Industrial and Applied Mathematics, 40, 2023. 10.1007/s13160-023-00593-8
-    """
-    P = Aa * Aa
-    A, a = _split(Aa)
-    p = a * a - ((P - A * A) - 2 * a * A)
-    return P, p
-
-
-def _acc_sqrt(T, t):
-    """Compute the accurate square root of a number with a compensation for
-    round-off error.
-
-    This function calculates the square root of a number, taking into account
-    a compensation term for the round-off error.
-
-    Parameters
-    ----------
-    T : float
-        The number whose square root is to be computed.
-    t : float
-        The compensation term for round-off error.
-
-    Returns
-    -------
-    float
-        The accurate square root of the number.
-
-    Examples
-    --------
-    >>> _acc_sqrt(9.0, 0.0)
-    3.0
-
-    References
-    ----------
-    Vincent Lef`evre, Nicolas Louvet, Jean-Michel Muller,
-    Joris Picot, and Laurence Rideau. Accurate Calculation of Euclidean
-    Norms Using Double-Word Arithmetic. ACM Transactions on Mathematical Software, 49(1), 1–34, March 2023. 10.1145/3568672
-
-    Marko Lange and Siegfried Rump. Faithfully Rounded
-    Floating-point Computations. ACM Transactions on Mathematical Soft-
-    ware, 46, 1-20, 2020. 10.1145/3290955
-    """
-    P = np.sqrt(T)
-    H, h = _two_square(P)
-    r = (T - H) - h
-    r = t + r
-    p = r / (2 * P)
-    res = P + p
-    return res
 
+@njit(cache=True, inline="always")
+def acc_sqrt_re(value, error=0.0):
+    """Accurate square root: return (root, correction) s.t. root+correction ≈ sqrt(value+error).
 
-def _split(a):
-    """Split a floating-point number into two parts: The rounded floating point
-    presentation and its error. This can be utlized to substitute the FMA
-    operation on the software level.
+    Mirrors accusphgeom::numeric::acc_sqrt_re from eft.hpp.  Computes
+    root = fl(sqrt(value)), measures the rounding error of root*root via
+    two_prod, then recovers a correction term from the residual.  When
+    ``error`` is provided (e.g. the ``lo`` half of a compensated sum),
+    it is folded into the residual so the correction accounts for the
+    full compensated input.
 
     Parameters
     ----------
-    a : float
-        The number to be split.
+    value : float
+        Non-negative scalar (the ``hi`` part of a compensated value).
+    error : float, optional
+        Low-order correction to ``value`` (default 0.0).
 
     Returns
     -------
-    tuple of float
-        The high and low precision parts of the number.
-
-    Examples
-    --------
-    >>> _split(12345.6789)
-    (12345.67578125, 0.00311875)
-
-    Reference
-    ---------
-     T. J. Dekker. A Floating-Point Technique for Extending the Available Precision.
-     Numerische Mathematik, 18(3), 224–242,
-    1971. 10.1007/BF01397083. Available at: https://doi.org/10.1007/
-    BF01397083.
-    27
+    root : float
+        Rounded sqrt, fl(sqrt(value)).
+    correction : float
+        Additive correction; root + correction ≈ sqrt(value + error) to ~1 ulp.
     """
-    y = (2**27 + 1) * a
-    x = y - (y - a)
-    y = a - x
-    return x, y
+    root = math.sqrt(value)
+    if root == 0.0:
+        return 0.0, 0.0
+    sq_hi, sq_lo = two_prod(root, root)
+    residual = (value - sq_hi) + (error - sq_lo)
+    correction = residual / (2.0 * root)
+    return root, correction

From 256b347f923680958d6e4b236983671aa04694bb Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 29 May 2026 18:10:59 -0500
Subject: [PATCH 05/51] Fix RTD: remove RST-invalid numbered list from
 gca_const_lat_intersection docstring

---
 uxarray/grid/intersections.py | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index f71bf3d4f..7ccaace12 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -431,15 +431,10 @@ def gca_const_lat_intersection(gca_cart, const_z):
     to achieve near-machine-precision accuracy even for arcs nearly tangent
     to the latitude circle.
 
-    The algorithm:
-    1. Compute the arc's plane normal n = a × b via ``accucross`` (compensated).
-    2. Compute s2 = nx² + ny² and s3 = |n|² using compensated sum-of-squares
-       on the (hi, lo) pairs from ``accucross``.
-    3. Compute the discriminant planar_sq = s2 − s3·z₀² using compensated
-       arithmetic; take its accurate square root via ``acc_sqrt_re``.
-    4. Compute the two candidate intersection points using compensated 2-term
-       dot products for the x and y numerators, divided by s2.
-    5. Retain each candidate that is finite and lies on the minor arc.
+    Computes the plane normal via ``accucross``, forms the discriminant using
+    compensated sum-of-squares and ``acc_sqrt_re``, solves for the two candidate
+    intersection points with compensated dot products, and retains only those
+    that are finite and lie on the minor arc.
 
     Parameters
     ----------

From e8999e640f2211c4e6f5195e38109fd529f96387 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sun, 31 May 2026 21:33:20 -0500
Subject: [PATCH 06/51] Fix RTD notebook kernel metadata

---
 docs/user-guide/spherical-geometry-accuracy.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index 321bd1824..b57a7a5f9 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -520,9 +520,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "uxarray_env3.12",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "uxarray_env3.12"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

From 0220276fb22846250637f03e241d5de58df87348 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 4 Jun 2026 13:20:45 -0500
Subject: [PATCH 07/51] Address AccuSphGeom review feedback

---
 uxarray/grid/bounds.py        |  22 ++--
 uxarray/grid/intersections.py | 229 +++++++++++++---------------------
 uxarray/utils/computing.py    |  10 +-
 3 files changed, 105 insertions(+), 156 deletions(-)

diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
index 2d07a601a..84a941ebc 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -27,11 +27,6 @@
 # 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.
@@ -111,11 +106,16 @@ def _face_location_info(face_vertices, polar_cap_z):
         if z_min_candidate < z_min:
             z_min = z_min_candidate
 
-    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
+    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)
@@ -423,7 +423,7 @@ def _populate_face_bounds(
             grid.node_x.values,
             grid.node_y.values,
             grid.node_z.values,
-            _POLAR_CAP_Z,
+            math.sin(80.0 * math.pi / 180.0),
             _SNAP_TOL_DEG,
         )
     else:
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 7ccaace12..e87816056 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -312,18 +312,6 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     Uses ``accucross`` (compensated cross products) and ``on_minor_arc`` (compensated
     arc membership) to avoid the catastrophic cancellation that affects naive
     cross product implementations when arcs are nearly parallel.
-
-    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.
-
-    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")
@@ -333,7 +321,6 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     v0 = gca_b_xyz[0]
     v1 = gca_b_xyz[1]
 
-    # 1. Plane normals via accurate cross products — keep compensated (hi, lo).
     n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = accucross(
         w0[0], w0[1], w0[2], w1[0], w1[1], w1[2]
     )
@@ -344,20 +331,6 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     res = np.empty((2, 3))
     count = 0
 
-    # Degenerate check: collapsed (zero-length) input arc.
-    n1x = n1x_hi + n1x_lo
-    n1y = n1y_hi + n1y_lo
-    n1z = n1z_hi + n1z_lo
-    n2x = n2x_hi + n2x_lo
-    n2y = n2y_hi + n2y_lo
-    n2z = n2z_hi + n2z_lo
-    if (
-        n1x * n1x + n1y * n1y + n1z * n1z == 0.0
-        or n2x * n2x + n2y * n2y + n2z * n2z == 0.0
-    ):
-        return res[:count]
-
-    # 2. Intersection direction: compensated cross of the two plane normals.
     vx_hi, vy_hi, vz_hi, vx_lo, vy_lo, vz_lo = accucross_pair(
         n1x_hi,
         n1y_hi,
@@ -383,7 +356,8 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
         and math.isfinite(vz)
         and math.isfinite(vn)
     ):
-        # Parallel (coplanar) arcs: check whether endpoints of one lie on the other.
+        # Coplanar overlap is outside the AccuXGCA intersection kernel. Preserve
+        # the historical UXarray behavior by detecting shared endpoints here.
         if on_minor_arc(v0, w0, w1):
             res[count, 0] = v0[0]
             res[count, 1] = v0[1]
@@ -396,7 +370,6 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
             count += 1
         return res[:count]
 
-    # 3. Two antipodal candidate intersection points; keep those on both arcs.
     inv = 1.0 / vn
     pos = np.empty(3)
     pos[0] = vx * inv
@@ -423,58 +396,19 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
 
 
 @njit(cache=True)
-def gca_const_lat_intersection(gca_cart, const_z):
-    """Find intersection point(s) of a great-circle arc and a constant-latitude line.
-
-    Implements the ``accux_constlat`` algorithm from AccuSphGeom
-    (gca_constlat_intersection.hpp) using compensated arithmetic throughout
-    to achieve near-machine-precision accuracy even for arcs nearly tangent
-    to the latitude circle.
-
-    Computes the plane normal via ``accucross``, forms the discriminant using
-    compensated sum-of-squares and ``acc_sqrt_re``, solves for the two candidate
-    intersection points with compensated dot products, and retains only those
-    that are finite and lie on the minor arc.
-
-    Parameters
-    ----------
-    gca_cart : np.ndarray, shape (2, 3)
-        Cartesian coordinates of the two endpoints of the great-circle arc.
-    const_z : float
-        The constant z-coordinate (= sin(latitude)) of the latitude line.
-
-    Returns
-    -------
-    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)
-
+def _try_gca_const_lat_intersection(gca_cart, const_z):
+    """AccuSphGeom-style GCA/constant-latitude kernel."""
     x1 = gca_cart[0]
     x2 = gca_cart[1]
 
-    # 1. Plane normal via compensated cross product (keeps hi, lo residuals).
     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]
     )
 
-    # 2. s2 = nx²+ny²  (compensated, on hi/lo pairs — matches sum_of_squares_c<2>).
     s2_hi, s2_lo = _sum_sq_c2(nx_hi, nx_lo, ny_hi, ny_lo)
     denom = s2_hi + s2_lo
-    if denom == 0.0:
-        return res
-
-    # 3. s3 = |n|² = nx²+ny²+nz²  (compensated — matches sum_of_squares_c<3>).
     s3_hi, s3_lo = _sum_sq_c3(nx_hi, nx_lo, ny_hi, ny_lo, nz_hi, nz_lo)
-
-    # 4. zsq = z₀² exactly (two_prod replaces two_prod_fma; same exact result).
     zsq_hi, zsq_lo = two_prod(const_z, const_z)
-
-    # 5. d = s3 · zsq  via 4-term compensated dot product matching C++:
-    #    compensated_dot_product({s3_hi, s3_hi, s3_lo, s3_lo},
-    #                            {zsq_hi, zsq_lo, zsq_hi, zsq_lo})
     d_hi, d_lo = _cdp4(
         s3_hi,
         zsq_hi,
@@ -485,93 +419,108 @@ def gca_const_lat_intersection(gca_cart, const_z):
         s3_lo,
         zsq_lo,
     )
-    # Note: Numba doesn't allow negative sign in function args, so negate d_hi explicitly.
-    neg_d_hi = -d_hi
 
-    # 6. planar_sq = s2 − d  (compensated two_sum on the high parts + low correction).
-    e_hi, e_lo = two_sum(s2_hi, neg_d_hi)
+    e_hi, e_lo = two_sum(s2_hi, -d_hi)
     planar_sq = e_hi + (e_lo + s2_lo - d_lo)
-
     if planar_sq < 0.0:
-        return res
-
-    # 7. Accurate square root of discriminant.
-    s_root, s_corr = acc_sqrt_re(planar_sq)
+        root_arg = np.nan
+    else:
+        root_arg = planar_sq
+    s_root, s_corr = acc_sqrt_re(root_arg)
 
-    # Collapse compensated values to scalars for the final formula.
     nx = nx_hi + nx_lo
     ny = ny_hi + ny_lo
     nz = nz_hi + nz_lo
     planar = s_root + s_corr
 
-    # 8. Numerators via 2-term compensated dot products (matches C++ accux_constlat).
-    #    x_pos = -(nx*nz*z₀  + (−ny)*planar) / denom
-    #    y_pos = -(ny*nz*z₀  +   nx *planar) / denom
-    #    x_neg = -(nx*nz*z₀  +   ny *planar) / denom
-    #    y_neg = -(ny*nz*z₀  + (−nx)*planar) / denom
     xp_hi, xp_lo = _cdp2(nx * nz, const_z, -ny, planar)
     yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
     xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
     yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
 
     inv_denom = 1.0 / denom
-    p1 = np.empty(3)
-    p1[0] = -(xp_hi + xp_lo) * inv_denom
-    p1[1] = -(yp_hi + yp_lo) * inv_denom
-    p1[2] = const_z
-
-    p2 = np.empty(3)
-    p2[0] = -(xn_hi + xn_lo) * inv_denom
-    p2[1] = -(yn_hi + yn_lo) * inv_denom
-    p2[2] = const_z
-
-    # 9a. Snap computed (x, y) to any arc endpoint that lies exactly on the latitude.
-    #     Adjacent edges sharing such an endpoint would otherwise return slightly
-    #     different coordinates; snapping gives them the same exact value so that
-    #     deduplication in the caller works correctly.  Matches Hongyu's suggestion
-    #     of mask-selection to snap after computing rather than branching out early.
-    _snap_sq = 1e-14  # distance² ≈ (1e-7)² — well above algorithm error (~1e-15)
+    pos = np.empty(3)
+    pos[0] = -(xp_hi + xp_lo) * inv_denom
+    pos[1] = -(yp_hi + yp_lo) * inv_denom
+    pos[2] = const_z
+
+    neg = np.empty(3)
+    neg[0] = -(xn_hi + xn_lo) * inv_denom
+    neg[1] = -(yn_hi + yn_lo) * inv_denom
+    neg[2] = const_z
+
+    pos_valid = (
+        math.isfinite(pos[0]) and math.isfinite(pos[1]) and on_minor_arc(pos, x1, x2)
+    )
+    neg_valid = (
+        math.isfinite(neg[0]) and math.isfinite(neg[1]) and on_minor_arc(neg, x1, x2)
+    )
+
+    pos_mask = 1.0 if pos_valid and not neg_valid else 0.0
+    neg_mask = 1.0 if neg_valid and not pos_valid else 0.0
+    point = np.empty(3)
+    point[0] = pos_mask * pos[0] + neg_mask * neg[0]
+    point[1] = pos_mask * pos[1] + neg_mask * neg[1]
+    point[2] = pos_mask * pos[2] + neg_mask * neg[2]
+
+    both = 1 if pos_valid and neg_valid else 0
+    none = 1 if (not pos_valid and not neg_valid) else 0
+    status = both + none * 2
+    return point, status, pos, neg
+
+
+@njit(cache=True)
+def _snap_const_lat_endpoint(point, x1, x2, const_z):
+    snap_sq = 1e-14
+    out = np.empty(3)
+    out[0] = point[0]
+    out[1] = point[1]
+    out[2] = point[2]
     for xe in (x1, x2):
         if abs(xe[2] - const_z) <= ERROR_TOLERANCE:
-            dx = p1[0] - xe[0]
-            dy = p1[1] - xe[1]
-            if dx * dx + dy * dy < _snap_sq:
-                p1[0] = xe[0]
-                p1[1] = xe[1]
-            dx = p2[0] - xe[0]
-            dy = p2[1] - xe[1]
-            if dx * dx + dy * dy < _snap_sq:
-                p2[0] = xe[0]
-                p2[1] = xe[1]
-
-    # 9b. Retain each candidate that is finite and lies 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)
-
-    # When both candidates are valid but nearly identical (tangent/endpoint case),
-    # treat as a single intersection — same as the C++ scalar gca_constlat_intersection
-    # which returns only one point when status==0 (exactly one candidate lies on the arc).
-    if p1_ok and p2_ok:
-        dx = p1[0] - p2[0]
-        dy = p1[1] - p2[1]
-        if dx * dx + dy * dy < _snap_sq:
-            p2_ok = False
-
-    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]
+            dx = out[0] - xe[0]
+            dy = out[1] - xe[1]
+            if dx * dx + dy * dy < snap_sq:
+                out[0] = xe[0]
+                out[1] = xe[1]
+    return out
+
+
+@njit(cache=True)
+def gca_const_lat_intersection(gca_cart, const_z):
+    """Find intersection point(s) of a great-circle arc and a constant-latitude line.
+
+    The core computation follows AccuSphGeom's status-code kernel; endpoint
+    snapping and UXarray's NaN-filled result packaging are isolated in this wrapper.
+    """
+    res = np.empty((2, 3))
+    res.fill(np.nan)
+
+    point, status, pos, neg = _try_gca_const_lat_intersection(gca_cart, const_z)
+    x1 = gca_cart[0]
+    x2 = gca_cart[1]
+
+    if status == 0:
+        point = _snap_const_lat_endpoint(point, x1, x2, const_z)
+        res[0, 0] = point[0]
+        res[0, 1] = point[1]
+        res[0, 2] = point[2]
+    elif status == 1:
+        pos = _snap_const_lat_endpoint(pos, x1, x2, const_z)
+        neg = _snap_const_lat_endpoint(neg, x1, x2, const_z)
+        dx = pos[0] - neg[0]
+        dy = pos[1] - neg[1]
+        if dx * dx + dy * dy < 1e-14:
+            res[0, 0] = pos[0]
+            res[0, 1] = pos[1]
+            res[0, 2] = pos[2]
+        else:
+            res[0, 0] = pos[0]
+            res[0, 1] = pos[1]
+            res[0, 2] = pos[2]
+            res[1, 0] = neg[0]
+            res[1, 1] = neg[1]
+            res[1, 2] = neg[2]
 
     return res
 
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index 5f99ac010..bd0a8f78b 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -38,11 +38,11 @@
 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.
+exact-arithmetic fallback. This port implements only the EFT tier. The
+compensated cross-product routines are roughly twice as accurate as direct
+floating-point cross products while retaining the same vectorizable operation
+structure; callers that need the full robustness stack should add an adaptive
+predicate or exact-arithmetic fallback.
 """
 
 import math

From 0fe327c96012ce5941c4d497e4d40c76a10074a8 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 4 Jun 2026 14:28:34 -0500
Subject: [PATCH 08/51] Separate intersection kernels into three layers:
 numerical core, status/mask, dispatcher

---
 uxarray/grid/intersections.py | 249 +++++++++++++++++++++-------------
 1 file changed, 157 insertions(+), 92 deletions(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index e87816056..1d139b535 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -305,32 +305,24 @@ def _gca_gca_intersection_cartesian(gca_a_xyz, gca_b_xyz):
     return gca_gca_intersection(gca_a_xyz, gca_b_xyz)
 
 
-@njit(cache=True)
-def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
-    """Find intersection point(s) of two great-circle arcs using compensated arithmetic.
+@njit(cache=True, inline="always")
+def _accux_gca(w0, w1, v0, v1):
+    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_gca``).
 
-    Uses ``accucross`` (compensated cross products) and ``on_minor_arc`` (compensated
-    arc membership) to avoid the catastrophic cancellation that affects naive
-    cross product implementations when arcs are nearly parallel.
-    """
-    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")
-
-    w0 = gca_a_xyz[0]
-    w1 = gca_a_xyz[1]
-    v0 = gca_b_xyz[0]
-    v1 = gca_b_xyz[1]
+    Computes the two antipodal candidate intersection points of the great-circle
+    arcs w0-w1 and v0-v1.  No branching, no validity filtering.
 
+    Returns
+    -------
+    pos, neg : np.ndarray, shape (3,)
+        Two antipodal candidate unit vectors.
+    """
     n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = accucross(
         w0[0], w0[1], w0[2], w1[0], w1[1], w1[2]
     )
     n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo = accucross(
         v0[0], v0[1], v0[2], v1[0], v1[1], v1[2]
     )
-
-    res = np.empty((2, 3))
-    count = 0
-
     vx_hi, vy_hi, vz_hi, vx_lo, vy_lo, vz_lo = accucross_pair(
         n1x_hi,
         n1y_hi,
@@ -349,28 +341,9 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     vy = vy_hi + vy_lo
     vz = vz_hi + vz_lo
     vn = math.sqrt(vx * vx + vy * vy + vz * vz)
-
-    if vn == 0.0 or not (
-        math.isfinite(vx)
-        and math.isfinite(vy)
-        and math.isfinite(vz)
-        and math.isfinite(vn)
-    ):
-        # Coplanar overlap is outside the AccuXGCA intersection kernel. Preserve
-        # the historical UXarray behavior by detecting shared endpoints here.
-        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]
-
-    inv = 1.0 / vn
+    # Use np.inf safely when vn==0 (coplanar arcs): the resulting pos/neg
+    # will be non-finite, so the status layer marks them invalid without branching.
+    inv = 1.0 / vn if vn != 0.0 else np.inf
     pos = np.empty(3)
     pos[0] = vx * inv
     pos[1] = vy * inv
@@ -379,98 +352,192 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     neg[0] = -pos[0]
     neg[1] = -pos[1]
     neg[2] = -pos[2]
+    return pos, neg
 
-    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 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
+@njit(cache=True)
+def _try_gca_gca_intersection(w0, w1, v0, v1):
+    """Layer 2 — batch/status layer (mirrors AccuSphGeom ``try_gca_gca_intersection``).
 
-    return res[:count]
+    Calls the pure numerical kernel, applies integer mask arithmetic to determine
+    validity, selects the output point without if/else branching in the hot path.
+
+    Status codes mirror AccuSphGeom:
+        0  exactly one candidate is valid
+        1  both candidates are valid
+        2  neither candidate is valid  (includes coplanar/parallel case)
+    """
+    pos, neg = _accux_gca(w0, w1, v0, v1)
+
+    pos_fin = (
+        1
+        if math.isfinite(pos[0]) and math.isfinite(pos[1]) and math.isfinite(pos[2])
+        else 0
+    )
+    neg_fin = (
+        1
+        if math.isfinite(neg[0]) and math.isfinite(neg[1]) and math.isfinite(neg[2])
+        else 0
+    )
+    pos_on_a = 1 if (pos_fin and on_minor_arc(pos, w0, w1)) else 0
+    pos_on_b = 1 if (pos_fin and on_minor_arc(pos, v0, v1)) else 0
+    neg_on_a = 1 if (neg_fin and on_minor_arc(neg, w0, w1)) else 0
+    neg_on_b = 1 if (neg_fin and on_minor_arc(neg, v0, v1)) else 0
+
+    pos_valid = pos_fin * pos_on_a * pos_on_b
+    neg_valid = neg_fin * neg_on_a * neg_on_b
+
+    pos_mask = pos_valid * (1 - neg_valid)
+    neg_mask = neg_valid * (1 - pos_valid)
+
+    point = np.empty(3)
+    point[0] = pos_mask * pos[0] + neg_mask * neg[0]
+    point[1] = pos_mask * pos[1] + neg_mask * neg[1]
+    point[2] = pos_mask * pos[2] + neg_mask * neg[2]
+
+    both = pos_valid * neg_valid
+    none = (1 - pos_valid) * (1 - neg_valid)
+    status = both + none * 2
+    return point, status, pos, neg
 
 
 @njit(cache=True)
-def _try_gca_const_lat_intersection(gca_cart, const_z):
-    """AccuSphGeom-style GCA/constant-latitude kernel."""
-    x1 = gca_cart[0]
-    x2 = gca_cart[1]
+def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
+    """Layer 3 — dispatcher / convenience API.
 
+    Calls the batch/status layer and packages results into UXarray's existing
+    array-returning API (0, 1, or 2 rows).  Coplanar/shared-endpoint handling
+    lives here, outside the numerical core.
+    """
+    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")
+
+    w0 = gca_a_xyz[0]
+    w1 = gca_a_xyz[1]
+    v0 = gca_b_xyz[0]
+    v1 = gca_b_xyz[1]
+
+    point, status, pos, neg = _try_gca_gca_intersection(w0, w1, v0, v1)
+
+    res = np.empty((2, 3))
+    count = 0
+    if status == 0:
+        res[0, 0] = point[0]
+        res[0, 1] = point[1]
+        res[0, 2] = point[2]
+        count = 1
+    elif status == 1:
+        res[0, 0] = pos[0]
+        res[0, 1] = pos[1]
+        res[0, 2] = pos[2]
+        res[1, 0] = neg[0]
+        res[1, 1] = neg[1]
+        res[1, 2] = neg[2]
+        count = 2
+    else:
+        # status == 2: no candidate on both arcs.
+        # Check for coplanar overlap (shared endpoints) outside the kernel.
+        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]
+
+
+@njit(cache=True, inline="always")
+def _accux_constlat(x1, x2, const_z):
+    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_constlat``).
+
+    Computes the two candidate intersection points between the great-circle arc
+    defined by unit vectors *x1*, *x2* and the constant-latitude plane z = const_z.
+    No branching, no validity filtering — all operations follow the exact compensated
+    sequence from AccuSphGeom so that the error bound holds.
+
+    Returns
+    -------
+    pos, neg : np.ndarray, shape (3,)
+        Two antipodal candidate points.  Invalid inputs propagate as non-finite
+        coordinates; the caller uses masks/status to identify validity.
+    """
     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]
     )
-
     s2_hi, s2_lo = _sum_sq_c2(nx_hi, nx_lo, ny_hi, ny_lo)
     denom = s2_hi + s2_lo
     s3_hi, s3_lo = _sum_sq_c3(nx_hi, nx_lo, ny_hi, ny_lo, nz_hi, nz_lo)
     zsq_hi, zsq_lo = two_prod(const_z, const_z)
-    d_hi, d_lo = _cdp4(
-        s3_hi,
-        zsq_hi,
-        s3_hi,
-        zsq_lo,
-        s3_lo,
-        zsq_hi,
-        s3_lo,
-        zsq_lo,
-    )
-
+    d_hi, d_lo = _cdp4(s3_hi, zsq_hi, s3_hi, zsq_lo, s3_lo, zsq_hi, s3_lo, zsq_lo)
     e_hi, e_lo = two_sum(s2_hi, -d_hi)
     planar_sq = e_hi + (e_lo + s2_lo - d_lo)
-    if planar_sq < 0.0:
-        root_arg = np.nan
-    else:
-        root_arg = planar_sq
-    s_root, s_corr = acc_sqrt_re(root_arg)
-
+    s_root, s_corr = acc_sqrt_re(planar_sq)
     nx = nx_hi + nx_lo
     ny = ny_hi + ny_lo
     nz = nz_hi + nz_lo
     planar = s_root + s_corr
-
     xp_hi, xp_lo = _cdp2(nx * nz, const_z, -ny, planar)
     yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
     xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
     yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
-
     inv_denom = 1.0 / denom
     pos = np.empty(3)
     pos[0] = -(xp_hi + xp_lo) * inv_denom
     pos[1] = -(yp_hi + yp_lo) * inv_denom
     pos[2] = const_z
-
     neg = np.empty(3)
     neg[0] = -(xn_hi + xn_lo) * inv_denom
     neg[1] = -(yn_hi + yn_lo) * inv_denom
     neg[2] = const_z
+    return pos, neg
 
-    pos_valid = (
-        math.isfinite(pos[0]) and math.isfinite(pos[1]) and on_minor_arc(pos, x1, x2)
-    )
-    neg_valid = (
-        math.isfinite(neg[0]) and math.isfinite(neg[1]) and on_minor_arc(neg, x1, x2)
-    )
 
-    pos_mask = 1.0 if pos_valid and not neg_valid else 0.0
-    neg_mask = 1.0 if neg_valid and not pos_valid else 0.0
+@njit(cache=True)
+def _try_gca_const_lat_intersection(gca_cart, const_z):
+    """Layer 2 — batch/status layer (mirrors AccuSphGeom ``try_gca_constlat_intersection``).
+
+    Calls the pure numerical kernel, computes integer validity masks (0 or 1)
+    for each candidate using finiteness and arc-membership tests, then selects
+    the output point via integer arithmetic — no if/else branching in the hot path.
+
+    Status codes mirror AccuSphGeom:
+        0  exactly one candidate is valid  (normal case)
+        1  both candidates are valid
+        2  neither candidate is valid
+    """
+    x1 = gca_cart[0]
+    x2 = gca_cart[1]
+    pos, neg = _accux_constlat(x1, x2, const_z)
+
+    pos_fin = 1 if math.isfinite(pos[0]) and math.isfinite(pos[1]) else 0
+    neg_fin = 1 if math.isfinite(neg[0]) and math.isfinite(neg[1]) else 0
+    pos_on = 1 if (pos_fin and on_minor_arc(pos, x1, x2)) else 0
+    neg_on = 1 if (neg_fin and on_minor_arc(neg, x1, x2)) else 0
+
+    pos_valid = pos_fin * pos_on
+    neg_valid = neg_fin * neg_on
+
+    pos_mask = pos_valid * (1 - neg_valid)
+    neg_mask = neg_valid * (1 - pos_valid)
+
     point = np.empty(3)
     point[0] = pos_mask * pos[0] + neg_mask * neg[0]
     point[1] = pos_mask * pos[1] + neg_mask * neg[1]
     point[2] = pos_mask * pos[2] + neg_mask * neg[2]
 
-    both = 1 if pos_valid and neg_valid else 0
-    none = 1 if (not pos_valid and not neg_valid) else 0
+    both = pos_valid * neg_valid
+    none = (1 - pos_valid) * (1 - neg_valid)
     status = both + none * 2
     return point, status, pos, neg
 
 
 @njit(cache=True)
 def _snap_const_lat_endpoint(point, x1, x2, const_z):
+    """Snap a candidate point to an arc endpoint when the endpoint lies on the latitude."""
     snap_sq = 1e-14
     out = np.empty(3)
     out[0] = point[0]
@@ -488,18 +555,17 @@ def _snap_const_lat_endpoint(point, x1, x2, const_z):
 
 @njit(cache=True)
 def gca_const_lat_intersection(gca_cart, const_z):
-    """Find intersection point(s) of a great-circle arc and a constant-latitude line.
+    """Layer 3 — dispatcher / convenience API.
 
-    The core computation follows AccuSphGeom's status-code kernel; endpoint
-    snapping and UXarray's NaN-filled result packaging are isolated in this wrapper.
+    Calls the batch/status kernel, applies endpoint snapping, and packages the
+    result in UXarray's NaN-filled (2, 3) format.  All UXarray-specific branching
+    lives here so the numerical core and status layers stay uniform.
     """
     res = np.empty((2, 3))
     res.fill(np.nan)
-
     point, status, pos, neg = _try_gca_const_lat_intersection(gca_cart, const_z)
     x1 = gca_cart[0]
     x2 = gca_cart[1]
-
     if status == 0:
         point = _snap_const_lat_endpoint(point, x1, x2, const_z)
         res[0, 0] = point[0]
@@ -521,7 +587,6 @@ def gca_const_lat_intersection(gca_cart, const_z):
             res[1, 0] = neg[0]
             res[1, 1] = neg[1]
             res[1, 2] = neg[2]
-
     return res
 
 

From 94c06fe766adeba06423dc64a6124b30a1682fdb Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 4 Jun 2026 14:44:15 -0500
Subject: [PATCH 09/51] Fix NaN/Inf propagation in intersection kernels for
 denom=0 and planar_sq<0

---
 uxarray/grid/intersections.py | 4 +++-
 uxarray/utils/computing.py    | 4 ++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 1d139b535..a3530e136 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -484,7 +484,9 @@ def _accux_constlat(x1, x2, const_z):
     yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
     xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
     yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
-    inv_denom = 1.0 / denom
+    # denom == 0 means the arc is vertical (normal has no x/y component).
+    # Produce inf so the isfinite mask in the status layer rejects candidates.
+    inv_denom = 1.0 / denom if denom != 0.0 else np.inf
     pos = np.empty(3)
     pos[0] = -(xp_hi + xp_lo) * inv_denom
     pos[1] = -(yp_hi + yp_lo) * inv_denom
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index bd0a8f78b..6ce5e0db2 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -417,6 +417,10 @@ def acc_sqrt_re(value, error=0.0):
     correction : float
         Additive correction; root + correction ≈ sqrt(value + error) to ~1 ulp.
     """
+    # Negative value means no real intersection; return NaN so that the
+    # isfinite mask in the status layer rejects this candidate without a branch.
+    if value < 0.0:
+        return math.nan, 0.0
     root = math.sqrt(value)
     if root == 0.0:
         return 0.0, 0.0

From 2f4147b2ecc503de83e26c56248aacc191e50e38 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Wed, 10 Jun 2026 13:22:12 -0500
Subject: [PATCH 10/51] Add geometry kernel benchmarks and to_raster
 auto-extent test

- benchmarks/geometry_kernels.py: ASV micro-benchmarks for all three
  layers of the EFT intersection stack (_accux_gca, _try_gca_gca_intersection,
  gca_gca_intersection, _accux_constlat, _try_gca_const_lat_intersection,
  gca_const_lat_intersection) plus EFT primitives and point-in-polygon;
  all functions warmed before timing so results reflect steady-state cost
- test/test_plot.py: add test_to_raster_auto_extent verifying that the
  axis limits change and the raster contains finite data
---
 benchmarks/geometry_kernels.py | 195 +++++++++++++++++++++++++++++++++
 test/test_plot.py              |  19 ++++
 2 files changed, 214 insertions(+)
 create mode 100644 benchmarks/geometry_kernels.py

diff --git a/benchmarks/geometry_kernels.py b/benchmarks/geometry_kernels.py
new file mode 100644
index 000000000..f5a8bd2de
--- /dev/null
+++ b/benchmarks/geometry_kernels.py
@@ -0,0 +1,195 @@
+"""Micro-benchmarks for the EFT-based spherical geometry kernels.
+
+These benchmarks target the individual functions introduced in the AccuSphGeom
+port (PR #1513) so that the per-kernel overhead of compensated arithmetic can
+be measured independently of higher-level UXarray operations.
+
+All Numba functions are warmed (compiled) during ``setup`` so that benchmark
+timings reflect steady-state throughput, not JIT compilation.
+"""
+
+import numpy as np
+
+from uxarray.grid.arcs import on_minor_arc, orient3d_on_sphere
+from uxarray.grid.intersections import (
+    _accux_constlat,
+    _accux_gca,
+    _try_gca_const_lat_intersection,
+    _try_gca_gca_intersection,
+    gca_const_lat_intersection,
+    gca_gca_intersection,
+)
+from uxarray.grid.point_in_face import _point_in_polygon_sphere
+from uxarray.utils.computing import (
+    acc_sqrt_re,
+    accucross,
+    accucross_pair,
+    diff_of_products,
+    two_prod,
+    two_sum,
+)
+
+
+def _unit(v):
+    return v / np.linalg.norm(v)
+
+
+# ---------------------------------------------------------------------------
+# Representative inputs — chosen to exercise the near-tangent regime
+# ---------------------------------------------------------------------------
+
+# Two arcs that intersect at a small angle (near-tangent, stress-tests EFT)
+_W0 = _unit(np.array([1.0, 0.0, 0.1]))
+_W1 = _unit(np.array([0.0, 1.0, 0.1]))
+_V0 = _unit(np.array([0.5, -0.1, 0.8]))
+_V1 = _unit(np.array([0.5, 0.9, 0.05]))
+
+# Arc for const-lat test
+_X1 = _unit(np.array([1.0, 0.0, 0.3]))
+_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,
+    diff_of_products, and acc_sqrt_re."""
+
+    def setup(self):
+        # Warm Numba
+        two_sum(1.0, 1e-16)
+        two_prod(1.23456789, 9.87654321)
+        diff_of_products(1.0, 2.0, 3.0, 4.0)
+        acc_sqrt_re(1.0 - 1e-15)
+
+    def time_two_sum(self):
+        two_sum(1.23456789012345678, 9.87654321098765432e-16)
+
+    def time_two_prod(self):
+        two_prod(1.23456789012345678, 9.87654321098765432)
+
+    def time_diff_of_products(self):
+        diff_of_products(1.23456789, 9.87654321, 1.23456788, 9.87654322)
+
+    def time_acc_sqrt_re(self):
+        acc_sqrt_re(1.0 - 1e-15)
+
+
+class AccucrossKernels:
+    """Benchmark the compensated cross-product kernels."""
+
+    def setup(self):
+        accucross(
+            _W0[0], _W0[1], _W0[2],
+            _W1[0], _W1[1], _W1[2],
+        )
+        accucross_pair(
+            1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+            0.0, 1.0, 0.0, 0.0, 0.0, 0.0,
+        )
+
+    def time_accucross(self):
+        accucross(
+            _W0[0], _W0[1], _W0[2],
+            _W1[0], _W1[1], _W1[2],
+        )
+
+    def time_accucross_pair(self):
+        n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = accucross(
+            _W0[0], _W0[1], _W0[2], _W1[0], _W1[1], _W1[2]
+        )
+        n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo = accucross(
+            _V0[0], _V0[1], _V0[2], _V1[0], _V1[1], _V1[2]
+        )
+        accucross_pair(
+            n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo,
+            n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo,
+        )
+
+
+class OrientPredicates:
+    """Benchmark the orient3d and on_minor_arc predicates."""
+
+    def setup(self):
+        orient3d_on_sphere(_W0, _W1, _V0)
+        on_minor_arc(_V0, _W0, _W1)
+
+    def time_orient3d_on_sphere(self):
+        orient3d_on_sphere(_W0, _W1, _V0)
+
+    def time_on_minor_arc(self):
+        on_minor_arc(_V0, _W0, _W1)
+
+
+class GCAGCAIntersection:
+    """Benchmark all three layers of the GCA-GCA intersection stack."""
+
+    def setup(self):
+        gca_a = np.stack([_W0, _W1])
+        gca_b = np.stack([_V0, _V1])
+        _accux_gca(_W0, _W1, _V0, _V1)
+        _try_gca_gca_intersection(_W0, _W1, _V0, _V1)
+        gca_gca_intersection(gca_a, gca_b)
+        self.gca_a = gca_a
+        self.gca_b = gca_b
+
+    def time_accux_gca_kernel(self):
+        """Layer 1: pure numerical kernel."""
+        _accux_gca(_W0, _W1, _V0, _V1)
+
+    def time_try_gca_gca_intersection(self):
+        """Layer 2: batch/status layer."""
+        _try_gca_gca_intersection(_W0, _W1, _V0, _V1)
+
+    def time_gca_gca_intersection(self):
+        """Layer 3: dispatcher (full public API)."""
+        gca_gca_intersection(self.gca_a, self.gca_b)
+
+
+class GCAConstLatIntersection:
+    """Benchmark all three layers of the GCA / constant-latitude intersection stack."""
+
+    def setup(self):
+        gca_cart = np.stack([_X1, _X2])
+        _accux_constlat(_X1, _X2, _CONST_Z)
+        _try_gca_const_lat_intersection(gca_cart, _CONST_Z)
+        gca_const_lat_intersection(gca_cart, _CONST_Z)
+        self.gca_cart = gca_cart
+
+    def time_accux_constlat_kernel(self):
+        """Layer 1: pure numerical kernel."""
+        _accux_constlat(_X1, _X2, _CONST_Z)
+
+    def time_try_gca_const_lat_intersection(self):
+        """Layer 2: batch/status layer."""
+        _try_gca_const_lat_intersection(self.gca_cart, _CONST_Z)
+
+    def time_gca_const_lat_intersection(self):
+        """Layer 3: dispatcher (full public API)."""
+        gca_const_lat_intersection(self.gca_cart, _CONST_Z)
+
+
+class PointInPolygonSphere:
+    """Benchmark the spherical point-in-polygon kernel."""
+
+    def setup(self):
+        # Warm Numba
+        _point_in_polygon_sphere(_Q_INSIDE, _POLY)
+        _point_in_polygon_sphere(_Q_OUTSIDE, _POLY)
+
+    def time_point_inside(self):
+        _point_in_polygon_sphere(_Q_INSIDE, _POLY)
+
+    def time_point_outside(self):
+        _point_in_polygon_sphere(_Q_OUTSIDE, _POLY)
diff --git a/test/test_plot.py b/test/test_plot.py
index eb7cdcb8f..6e8b93311 100644
--- a/test/test_plot.py
+++ b/test/test_plot.py
@@ -127,6 +127,25 @@ def test_to_raster_with_extra_dims(gridpath):
 
     assert isinstance(raster, np.ndarray)
 
+def test_to_raster_auto_extent(gridpath):
+    fig, ax = plt.subplots(
+        subplot_kw={'projection': ccrs.Robinson()},
+        constrained_layout=True,
+    )
+
+    xlim0, ylim0 = ax.get_xlim(), ax.get_ylim()
+
+    mesh_path = gridpath("mpas", "QU", "oQU480.231010.nc")
+    uxds = ux.open_dataset(mesh_path, mesh_path)
+
+    raster = uxds['bottomDepth'].to_raster(ax=ax, pixel_ratio=0.5)
+
+    xlim1, ylim1 = ax.get_xlim(), ax.get_ylim()
+    assert not (np.allclose(xlim0, xlim1) and np.allclose(ylim0, ylim1))
+
+    finite = raster[np.isfinite(raster)]
+    assert finite.size > 0
+    assert finite.std() > 0
 
 
 def test_to_raster_reuse_mapping(gridpath, tmpdir):

From 3b3f30cdc4e0c399e55c3f0a40a859ecc48d0115 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Wed, 10 Jun 2026 13:34:04 -0500
Subject: [PATCH 11/51] Address AccuSphGeom review: precision wording, dead
 code removal, correctness fixes

Review comments addressed:
- Remove "near-double precision" / "sufficient" overclaims; say "roughly twice
  as accurate" and note the robustness tier boundary clearly
- Explain _lon_bounds_from_vertices is required for UXarray antimeridian
  encoding and cannot be removed
- Add block comment before _no_extreme functions clarifying they are
  pre-existing edge screeners unrelated to the EFT stack
- Document SoS as explicit future work in _point_in_polygon_sphere docstring
- L2 pos_fin/neg_fin: replace ternary with int(); exploit neg=-pos symmetry
- Label computation: drop dead local*0 term, use integer mask arithmetic
- Remove vertex-lat snap from bounds: _face_location_info already captures
  interior arc extrema accurately via the compensated kernel
- _ON_MINOR_ARC_TOL: document intentional 1e-10 vs C++ 1e-8 divergence

Bug fixes:
- on_minor_arc: add antipodal-endpoint guard; a x b = 0 for antipodal inputs
  so every point on the great circle passes the collinearity test (false pos)
- bounds.py: replace mask arithmetic use_ext*z_ext + (1-use_ext)*z_edge with
  plain if/else; 0*NaN = NaN propagates when norm=0, if/else does not
- _point_in_polygon_sphere: ray-nudge now restarts the loop from i=0 so all
  edges are counted with the same ray (mid-loop nudge corrupted crossing parity)

Cleanup:
- Remove _flip_sign, _SIGN_NEG, _SIGN_POS, _SIGN_ZERO dead code from
  point_in_face.py; inline literals in _counts_as_crossing
- Remove _SNAP_TOL_DEG constant and snap_tol_deg parameter throughout bounds.py
- Notebook: fix Grid.get_point_on_face -> get_faces_containing_point; remove
  incorrect geometry.py row from Section 4 table; add accucross_pair and
  acc_sqrt_re to Section 2 building-blocks table
---
 uxarray/grid/arcs.py          |  10 +++
 uxarray/grid/bounds.py        | 112 +++++++++-------------------------
 uxarray/grid/intersections.py |  41 +++++++------
 uxarray/grid/point_in_face.py |  78 +++++++++++------------
 uxarray/utils/computing.py    |  19 +++---
 5 files changed, 110 insertions(+), 150 deletions(-)

diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py
index 7363f44a9..2ee53edb8 100644
--- a/uxarray/grid/arcs.py
+++ b/uxarray/grid/arcs.py
@@ -15,6 +15,11 @@
 _PREDICATE_ZERO_TOL = 1e-15
 
 # Default tolerance for the on_minor_arc collinearity and interval tests.
+# Intentionally tighter than AccuSphGeom's 1e-8 default: using 1e-10 keeps
+# borderline near-endpoint candidates out of the valid set, which produces
+# better accuracy on the AccuSphGeom baseline suite (err < 1e-15 vs ~1e-10
+# with the looser C++ default). The C++ tolerance was tuned for SIMD batch
+# throughput; the scalar Python path is more sensitive to spurious candidates.
 _ON_MINOR_ARC_TOL = 1e-10
 
 
@@ -464,6 +469,11 @@ def on_minor_arc(q, a, b, tol=_ON_MINOR_ARC_TOL):
     # Coincident endpoints: degenerate arc, no interior.
     if a[0] == b[0] and a[1] == b[1] and a[2] == b[2]:
         return False
+    # Antipodal endpoints: a×b = 0, so every point on the great circle passes
+    # the collinearity test and the interval conditions degenerate to 0 >= -tol,
+    # causing false positives for all points on the great circle.
+    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
diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
index 84a941ebc..0a45bb86d 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -27,11 +27,6 @@
 # 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
@@ -96,10 +91,12 @@ def _face_location_info(face_vertices, polar_cap_z):
         z_edge_max = z1 if z1 > z2 else z2
         z_edge_min = z1 if z1 < z2 else z2
 
-        # Mask-based selection: use z_ext only when the extremum is interior (a_raw in (0,1)).
-        use_ext = 1 if (0.0 < a_raw < 1.0) else 0
-        z_max_candidate = use_ext * z_ext + (1 - use_ext) * z_edge_max
-        z_min_candidate = use_ext * z_ext + (1 - use_ext) * z_edge_min
+        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
@@ -108,12 +105,10 @@ def _face_location_info(face_vertices, polar_cap_z):
 
     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
+        north_pole_candidate * _FACE_LOC_NORTH_POLAR
+        + (1 - north_pole_candidate) * south_pole_candidate * _FACE_LOC_SOUTH_POLAR
     )
     return label, z_min, z_max
 
@@ -123,7 +118,14 @@ 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.
+    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
@@ -153,20 +155,19 @@ def _lon_bounds_from_vertices(face_vertices):
 
 
 @njit(cache=True)
-def _generate_lat_lon_bounds_local(face_vertices, z_min, z_max, snap_tol_deg):
+def _generate_lat_lon_bounds_local(face_vertices, z_min, z_max):
     """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.
+    This is a UXarray-specific post-kernel formatting step, not part of the EFT
+    computation. It converts the arc z-extrema from ``_face_location_info``
+    (which already accounts for interior arc extrema via the compensated kernel)
+    into degree lat/lon bounds in uxarray's encoding.
 
     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
     -------
@@ -174,41 +175,15 @@ def _generate_lat_lon_bounds_local(face_vertices, z_min, z_max, snap_tol_deg):
         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
-
+    lat_max = math.asin(min(z_max, 1.0)) * rad_to_deg
+    lat_min = math.asin(max(z_min, -1.0)) * rad_to_deg
     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):
+def _generate_lat_lon_bounds_pole(face_vertices, label, z_min, z_max):
     """Compute bounds for a polar-candidate face.
 
     Checks whether the relevant pole (north or south) is inside the polygon
@@ -221,7 +196,6 @@ def _generate_lat_lon_bounds_pole(face_vertices, label, z_min, z_max, snap_tol_d
     label : int
         _FACE_LOC_NORTH_POLAR or _FACE_LOC_SOUTH_POLAR.
     z_min, z_max : float
-    snap_tol_deg : float
 
     Returns
     -------
@@ -231,7 +205,6 @@ def _generate_lat_lon_bounds_pole(face_vertices, label, z_min, z_max, snap_tol_d
     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 = (
@@ -246,36 +219,12 @@ def _generate_lat_lon_bounds_pole(face_vertices, label, z_min, z_max, snap_tol_d
     )
 
     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
-        )
+        a, b, c, d = _generate_lat_lon_bounds_local(face_vertices, z_min, z_max)
         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
+    lat_max = math.asin(min(z_max, 1.0)) * rad_to_deg
+    lat_min = math.asin(max(z_min, -1.0)) * rad_to_deg
 
     if north_loc != _LOC_OUTSIDE:
         if north_loc == _LOC_INSIDE:
@@ -295,7 +244,6 @@ def _construct_face_bounds_array_gca(
     node_y,
     node_z,
     polar_cap_z,
-    snap_tol_deg,
 ):
     """Parallel GCA bounds computation using the accurate local/polar-cap path.
 
@@ -310,7 +258,6 @@ def _construct_face_bounds_array_gca(
     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
     -------
@@ -334,11 +281,11 @@ def _construct_face_bounds_array_gca(
 
         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
+                verts, z_min, z_max
             )
         else:
             lat_min, lat_max, lon_min, lon_max, _ = _generate_lat_lon_bounds_pole(
-                verts, label, z_min, z_max, snap_tol_deg
+                verts, label, z_min, z_max
             )
 
         bounds_array[face_idx, 0, 0] = lat_min * deg_to_rad
@@ -424,7 +371,6 @@ def _populate_face_bounds(
             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.
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index a3530e136..f4558911a 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -17,6 +17,16 @@
     two_sum,
 )
 
+# ---------------------------------------------------------------------------
+# Edge screeners (pre-existing, unrelated to the EFT intersection kernels below).
+#
+# These two functions are fast O(n) passes used by Grid.get_edges_at_constant_*
+# to identify candidate edges before the expensive GCA intersection is computed.
+# "no_extreme" means arc z-extrema along the great circle are not considered —
+# only the endpoint z/lon values are checked.  They are not part of the
+# AccuSphGeom-derived EFT stack.
+# ---------------------------------------------------------------------------
+
 
 @njit(parallel=True, nogil=True, cache=True)
 def constant_lat_intersections_no_extreme(lat, edge_node_z, n_edge):
@@ -369,20 +379,17 @@ def _try_gca_gca_intersection(w0, w1, v0, v1):
     """
     pos, neg = _accux_gca(w0, w1, v0, v1)
 
-    pos_fin = (
-        1
-        if math.isfinite(pos[0]) and math.isfinite(pos[1]) and math.isfinite(pos[2])
-        else 0
-    )
-    neg_fin = (
-        1
-        if math.isfinite(neg[0]) and math.isfinite(neg[1]) and math.isfinite(neg[2])
-        else 0
+    pos_fin = int(
+        math.isfinite(pos[0]) and math.isfinite(pos[1]) and math.isfinite(pos[2])
     )
-    pos_on_a = 1 if (pos_fin and on_minor_arc(pos, w0, w1)) else 0
-    pos_on_b = 1 if (pos_fin and on_minor_arc(pos, v0, v1)) else 0
-    neg_on_a = 1 if (neg_fin and on_minor_arc(neg, w0, w1)) else 0
-    neg_on_b = 1 if (neg_fin and on_minor_arc(neg, v0, v1)) else 0
+    # neg = -pos exactly, so neg is finite iff pos is finite.
+    neg_fin = pos_fin
+    # on_minor_arc must be guarded by the finiteness mask — calling it with inf
+    # inputs is undefined. The guard is the only unavoidable branch in L2.
+    pos_on_a = pos_fin * int(on_minor_arc(pos, w0, w1)) if pos_fin else 0
+    pos_on_b = pos_fin * int(on_minor_arc(pos, v0, v1)) if pos_fin else 0
+    neg_on_a = neg_fin * int(on_minor_arc(neg, w0, w1)) if neg_fin else 0
+    neg_on_b = neg_fin * int(on_minor_arc(neg, v0, v1)) if neg_fin else 0
 
     pos_valid = pos_fin * pos_on_a * pos_on_b
     neg_valid = neg_fin * neg_on_a * neg_on_b
@@ -515,10 +522,10 @@ def _try_gca_const_lat_intersection(gca_cart, const_z):
     x2 = gca_cart[1]
     pos, neg = _accux_constlat(x1, x2, const_z)
 
-    pos_fin = 1 if math.isfinite(pos[0]) and math.isfinite(pos[1]) else 0
-    neg_fin = 1 if math.isfinite(neg[0]) and math.isfinite(neg[1]) else 0
-    pos_on = 1 if (pos_fin and on_minor_arc(pos, x1, x2)) else 0
-    neg_on = 1 if (neg_fin and on_minor_arc(neg, x1, x2)) else 0
+    pos_fin = int(math.isfinite(pos[0]) and math.isfinite(pos[1]))
+    neg_fin = int(math.isfinite(neg[0]) and math.isfinite(neg[1]))
+    pos_on = pos_fin * int(on_minor_arc(pos, x1, x2)) if pos_fin else 0
+    neg_on = neg_fin * int(on_minor_arc(neg, x1, x2)) if neg_fin else 0
 
     pos_valid = pos_fin * pos_on
     neg_valid = neg_fin * neg_on
diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py
index 7a10934ae..c6d13f41a 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -21,26 +21,11 @@
 _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 _ray_endpoint(q):
     """Return a unit vector R perpendicular to q for use as the SPIP ray target.
@@ -93,10 +78,10 @@ def _counts_as_crossing(A, B, q, R):
     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:
+    if s_AB_q == 0:
         return 0
     # R on great circle AB: degenerate ray, caller must perturb R.
-    if s_AB_R == _SIGN_ZERO:
+    if s_AB_R == 0:
         return -1
     # q and R on the same side of plane(AB): no crossing possible.
     if s_AB_q == s_AB_R:
@@ -112,11 +97,11 @@ def _counts_as_crossing(A, B, q, R):
     # 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:
+    if s_qR_A == 0 or s_qR_B == 0:
+        if s_qR_A == 0 and s_qR_B == 0:
             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
+        s_other = s_qR_B if s_qR_A == 0 else s_qR_A
+        return 1 if s_other == -1 else 0
 
     return 1 if s_qR_A != s_qR_B else 0
 
@@ -132,6 +117,13 @@ def _point_in_polygon_sphere(q, polygon):
 
     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
     ----------
     q : np.ndarray, shape (3,)
@@ -163,29 +155,33 @@ def _point_in_polygon_sphere(q, polygon):
             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)
-    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
+    for _retry in range(4):
+        inside = False
+        need_retry = 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:
-                return _LOC_OUTSIDE
-        if c == 1:
-            inside = not inside
-
-    return _LOC_INSIDE if inside else _LOC_OUTSIDE
+                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
 
 
 @njit(cache=True)
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index 6ce5e0db2..3222e1bf5 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -15,8 +15,9 @@
 and ``two_prod``, which capture their rounding errors exactly so that
 ``hi + lo`` equals the mathematical result with zero information loss.
 ``diff_of_products``, ``accucross``, and ``accucross_pair`` use those EFT
-building blocks to achieve near-double precision for cross products, but they
-are compensated algorithms, not zero-error transformations.
+building blocks as compensated algorithms that are roughly twice as accurate
+as direct floating-point cross products, but they are not zero-error
+transformations.
 
 All functions are ``@njit``-compiled and use the portable Veltkamp-splitting
 form of ``two_prod`` (no FMA dependency), making them suitable for use inside
@@ -36,13 +37,13 @@
     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. The
-compensated cross-product routines are roughly twice as accurate as direct
-floating-point cross products while retaining the same vectorizable operation
-structure; callers that need the full robustness stack should add an adaptive
-predicate or exact-arithmetic fallback.
+tiers — a compensated-arithmetic 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 first tier. The compensated routines are roughly twice as accurate as
+direct floating-point equivalents while retaining the same vectorizable
+operation structure; robustness against all degenerate inputs would require
+adding an adaptive predicate or exact-arithmetic fallback tier.
 """
 
 import math

From e33a26cfef8b9b5f85943ad20dda6b3e52c24e2d Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Wed, 10 Jun 2026 14:22:45 -0500
Subject: [PATCH 12/51] Pin tornado<6.5.7 to fix Windows CI; remove dead shim;
 fix notebook API name

- ci/environment.yml: pin tornado<6.5.7 to avoid ssl.SSLError in panel 1.9.3
  on Python 3.11 Windows (conda-forge regression, 2026-06-10)
- intersections.py: remove _gca_gca_intersection_cartesian shim (dead code);
  add comment explaining _snap_const_lat_endpoint snap_sq constant
- test_intersections.py: update 4 call sites to use gca_gca_intersection directly
- spherical-geometry-accuracy.ipynb: fix stale Grid.get_point_on_face ->
  Grid.get_faces_containing_point (2 occurrences)
---
 ci/environment.yml                            |  1 +
 .../spherical-geometry-accuracy.ipynb         | 92 +++++++++----------
 test/grid/geometry/test_intersections.py      | 10 +-
 uxarray/grid/intersections.py                 | 11 +--
 4 files changed, 56 insertions(+), 58 deletions(-)

diff --git a/ci/environment.yml b/ci/environment.yml
index 02f4616d9..d2bfb5a28 100644
--- a/ci/environment.yml
+++ b/ci/environment.yml
@@ -13,6 +13,7 @@ dependencies:
   - healpix
   - holoviews
   - hvplot
+  - tornado<6.5.7
   - hypothesis
   - matplotlib-base
   - matplotlib-inline
diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index b57a7a5f9..a8830b73f 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "id": "title-cell",
    "metadata": {},
-   "source": "# Accurate Spherical Geometry\n\nCross products are at the heart of nearly every geometric test on the sphere \u2014 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 \u2014 the physically meaningful result \u2014 can lose all significant digits to floating-point cancellation. UXarray guards against this throughout its geometry stack using **compensated arithmetic** \u2014 algorithms built on error-free transformation (EFT) primitives that track every rounding residual exactly.\n\nThis guide covers:\n\n1. The problem: catastrophic cancellation\n2. How UXarray handles it\n3. Seeing it on a real mesh: point-in-polygon\n4. Where it is used in UXarray"
+   "source": "# Accurate Spherical Geometry\n\nCross 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 **compensated arithmetic** — algorithms built on error-free transformation (EFT) primitives that track every rounding residual exactly.\n\nThis guide covers:\n\n1. The problem: catastrophic cancellation\n2. How UXarray handles it\n3. Seeing it on a real mesh: point-in-polygon\n4. Where it is used in UXarray"
   },
   {
    "cell_type": "code",
@@ -28,7 +28,7 @@
    "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 \u2014 and floating-point rounding can reduce it to zero."
+    "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."
    ]
   },
   {
@@ -79,7 +79,7 @@
     "ax.text(\n",
     "    0.5,\n",
     "    0.96,\n",
-    "    f\"|a \u00d7 b| = {area1:.3f}\",\n",
+    "    f\"|a × b| = {area1:.3f}\",\n",
     "    ha=\"center\",\n",
     "    fontsize=12,\n",
     "    color=\"steelblue\",\n",
@@ -88,7 +88,7 @@
     "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  \u2014  large, well-conditioned cross product\", fontsize=11)\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",
@@ -114,7 +114,7 @@
     "ax.text(\n",
     "    0.5,\n",
     "    0.96,\n",
-    "    f\"|a \u00d7 b| = {area2:.4f}  \u2190  tiny!\",\n",
+    "    f\"|a × b| = {area2:.4f}  ←  tiny!\",\n",
     "    ha=\"center\",\n",
     "    fontsize=12,\n",
     "    color=\"#d62728\",\n",
@@ -124,13 +124,13 @@
     "ax.set_ylim(-0.1, 1.1)\n",
     "ax.set_aspect(\"equal\")\n",
     "ax.set_title(\n",
-    "    \"Nearly-parallel  \u2014  tiny cross product, catastrophic cancellation\", fontsize=11\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 \u2014 digits cancel\",\n",
+    "    \"Small area means two nearly equal numbers are subtracted — digits cancel\",\n",
     "    fontsize=12,\n",
     ")\n",
     "plt.show()"
@@ -140,7 +140,7 @@
    "cell_type": "markdown",
    "id": "9a3dc8b0",
    "metadata": {},
-   "source": "## 2. How UXarray Handles It\n\nUXarray uses **compensated arithmetic** \u2014 a family of algorithms that prevent catastrophic cancellation by representing floating-point operations as exact `(hi, lo)` pairs. There are two distinct layers:\n\n- **Error-free transformations (EFT)** \u2014 `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** \u2014 `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\nThe 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.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\u00d7b)\u00b7q`: +1, \u22121, or 0 |\n| `on_minor_arc(q, a, b)` | `grid.arcs` | True if `q` lies on the minor arc from `a` to `b` |\n\nMost users will never call these directly \u2014 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."
+   "source": "## 2. How UXarray Handles It\n\nUXarray uses **compensated arithmetic** — a family of algorithms that prevent catastrophic cancellation by representing floating-point operations as exact `(hi, lo)` pairs. 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\nThe 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.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\nMost 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",
@@ -159,33 +159,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "North Pole:      orient3d = +1  \u2192 left  of A\u2192B (northern hemisphere)\n",
-      "South Pole:      orient3d = -1  \u2192 right of A\u2192B (southern hemisphere)\n",
-      "On great circle: orient3d =  0  \u2192 collinear, not a crossing\n"
+      "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\u00d7B)\u00b7Q.\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 \u2192 B (above the plane by right-hand rule)\n",
-    "#   -1  Q is on the RIGHT of the directed arc A \u2192 B (below the plane)\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\u00b0E on the equator\n",
-    "B = np.array([0.0, 1.0, 0.0])  # 90\u00b0E on the equator\n",
-    "# A\u2192B defines the equatorial great circle; right-hand normal points to the North Pole.\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 \u2014 on the great circle itself\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",
@@ -193,13 +193,13 @@
     "\n",
     "\n",
     "print(\n",
-    "    f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  \u2192 left  of A\u2192B (northern hemisphere)\"\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))}  \u2192 right of A\u2192B (southern hemisphere)\"\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))}  \u2192 collinear, not a crossing\"\n",
+    "    f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  → collinear, not a crossing\"\n",
     ")"
    ]
   },
@@ -210,7 +210,7 @@
    "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 \u2014 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."
+    "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."
    ]
   },
   {
@@ -245,7 +245,7 @@
    "id": "pip-setup-text",
    "metadata": {},
    "source": [
-    "Query points are placed at 50 log-spaced distances from the midpoint of edge V0\u2192V1 on face 0, stepping inward toward the face centroid. The sign of the naive orient3d flips once the distance drops below $\\sim \\varepsilon_\\text{machine} / |V0 \\times V1|$."
+    "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 $\\sim \\varepsilon_\\text{machine} / |V0 \\times V1|$."
    ]
   },
   {
@@ -265,12 +265,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Face 0 edge V0\u2192V1:  |V0 \u00d7 V1| = 0.04851\n",
-      "Naive sign flips below \u03b5 \u2248 4.5e-15 rad  (2.89e-05 mm on Earth)\n",
+      "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 \u2014 correct answer is always 'inside'.\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  \u2190 8 misclassified as outside near the edge\n"
+      "  Naive (raw cross product):  42/50 correctly classified as inside  ← 8 misclassified as outside near the edge\n"
      ]
     }
    ],
@@ -306,10 +306,10 @@
     "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 \u2192 mm on Earth\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 \u2014 the expected answer for every point is \"inside\".\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",
@@ -325,16 +325,16 @@
     "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\u2192V1:  |V0 \u00d7 V1| = {cross_mag:.5f}\")\n",
+    "print(f\"Face 0 edge V0→V1:  |V0 × V1| = {cross_mag:.5f}\")\n",
     "print(\n",
-    "    f\"Naive sign flips below \u03b5 \u2248 {flip_threshold:.1e} rad  ({flip_mm:.2e} mm on Earth)\"\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 \u2014 correct answer is always 'inside'.\")\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\"  \u2190 {n - naive_ok} misclassified as outside near the edge\"\n",
+    "    f\"  ← {n - naive_ok} misclassified as outside near the edge\"\n",
     ")"
    ]
   },
@@ -343,7 +343,7 @@
    "id": "pip-interp",
    "metadata": {},
    "source": [
-    "When the query is close enough to the edge, the naive orient3d value rounds to the wrong sign \u2014 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."
+    "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."
    ]
   },
   {
@@ -377,7 +377,7 @@
     "fig = plt.figure(figsize=(14, 5.5))\n",
     "fig.subplots_adjust(wspace=0.08)\n",
     "\n",
-    "# \u2500\u2500 Left: zoomed face \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
+    "# ── Left: zoomed face ──────────────────────────────────────────────────────\n",
     "ax = fig.add_subplot(1, 2, 1)\n",
     "\n",
     "face_lons = np.append(lons, lons[0])\n",
@@ -391,7 +391,7 @@
     "    color=\"#d62728\",\n",
     "    linewidth=3.5,\n",
     "    zorder=3,\n",
-    "    label=\"Test edge  V0 \u2192 V1\",\n",
+    "    label=\"Test edge  V0 → V1\",\n",
     ")\n",
     "\n",
     "for i, (lo, la) in enumerate(zip(lons, lats)):\n",
@@ -416,7 +416,7 @@
     "    color=\"#ff7f0e\",\n",
     "    marker=\"*\",\n",
     "    zorder=6,\n",
-    "    label=\"Edge midpoint \u2014 sweep origin\",\n",
+    "    label=\"Edge midpoint — sweep origin\",\n",
     ")\n",
     "\n",
     "ax.annotate(\n",
@@ -428,7 +428,7 @@
     "ax.text(\n",
     "    (em_lon + cen_lon) / 2 + 0.06,\n",
     "    (em_lat + cen_lat) / 2 + 0.18,\n",
-    "    \"50 query points\\n(\u03b5 from 10\u207b\u00b3 \u2192 10\u207b\u00b9\u2076)\",\n",
+    "    \"50 query points\\n(ε from 10⁻³ → 10⁻¹⁶)\",\n",
     "    fontsize=9,\n",
     "    color=\"#555\",\n",
     "    style=\"italic\",\n",
@@ -442,7 +442,7 @@
     "    edgecolors=\"#d62728\",\n",
     "    linewidths=2,\n",
     "    zorder=7,\n",
-    "    label=f\"Naive sign wrong below \u03b5 \u2248 {flip_threshold:.0e} rad (\u2248 0.03 mm)\",\n",
+    "    label=f\"Naive sign wrong below ε ≈ {flip_threshold:.0e} rad (≈ 0.03 mm)\",\n",
     ")\n",
     "\n",
     "q_far = normalize(\n",
@@ -451,7 +451,7 @@
     "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",
-    "    \"\u03b5 = 10\u207b\u00b3\\nboth correct\",\n",
+    "    \"ε = 10⁻³\\nboth correct\",\n",
     "    (qf_lon, qf_lat),\n",
     "    textcoords=\"offset points\",\n",
     "    xytext=(7, -18),\n",
@@ -459,16 +459,16 @@
     "    color=\"#1f77b4\",\n",
     ")\n",
     "\n",
-    "ax.set_xlabel(\"Longitude (\u00b0)\", fontsize=11)\n",
-    "ax.set_ylabel(\"Latitude (\u00b0)\", fontsize=11)\n",
-    "ax.set_title(\"Face 0 \u2014 query sweep toward centroid\", fontsize=11)\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",
-    "# \u2500\u2500 Right: global context \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\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",
@@ -506,7 +506,7 @@
     "    zorder=5,\n",
     "    transform=ccrs.PlateCarree(),\n",
     ")\n",
-    "ax_global.set_title(\"Global context \u2014 highlighted face in red\", fontsize=11)\n",
+    "ax_global.set_title(\"Global context — highlighted face in red\", fontsize=11)\n",
     "\n",
     "plt.show()"
    ]
@@ -515,7 +515,7 @@
    "cell_type": "markdown",
    "id": "3138ae9a",
    "metadata": {},
-   "source": "## 4. Where It Is Used in UXarray\n\nCompensated 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\u2013arc intersection (remapping, antimeridian) | `grid/intersections.py` | `accucross`, `accucross_pair`, `on_minor_arc` |\n| Arc\u2013latitude 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\nIf you extend UXarray with custom geometry \u2014 for example, a new remapping kernel or a spatial predicate \u2014 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."
+   "source": "## 4. Where It Is Used in UXarray\n\nCompensated 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\nIf 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": {
diff --git a/test/grid/geometry/test_intersections.py b/test/grid/geometry/test_intersections.py
index 94b5be7a9..8d3b36821 100644
--- a/test/grid/geometry/test_intersections.py
+++ b/test/grid/geometry/test_intersections.py
@@ -4,7 +4,7 @@
 from uxarray.constants import ERROR_TOLERANCE
 from uxarray.grid.arcs import extreme_gca_z
 from uxarray.grid.coordinates import _lonlat_rad_to_xyz, _xyz_to_lonlat_rad,_xyz_to_lonlat_rad_scalar
-from uxarray.grid.intersections import gca_gca_intersection, gca_const_lat_intersection, _gca_gca_intersection_cartesian, get_number_of_intersections
+from uxarray.grid.intersections import gca_gca_intersection, gca_const_lat_intersection, get_number_of_intersections
 
 def test_get_GCA_GCA_intersections_antimeridian():
     GCA1 = _lonlat_rad_to_xyz(np.deg2rad(170.0), np.deg2rad(89.99))
@@ -16,7 +16,7 @@ def test_get_GCA_GCA_intersections_antimeridian():
         _lonlat_rad_to_xyz(np.deg2rad(70.0), 0.0),
         _lonlat_rad_to_xyz(np.deg2rad(179.0), 0.0)
     ])
-    res_cart = _gca_gca_intersection_cartesian(GCR1_cart, GCR2_cart)
+    res_cart = gca_gca_intersection(GCR1_cart, GCR2_cart)
 
     assert len(res_cart) == 0
 
@@ -30,7 +30,7 @@ def test_get_GCA_GCA_intersections_antimeridian():
         _lonlat_rad_to_xyz(np.deg2rad(175.0), 0.0)
     ])
 
-    res_cart = _gca_gca_intersection_cartesian(GCR1_cart, GCR2_cart)
+    res_cart = gca_gca_intersection(GCR1_cart, GCR2_cart)
     res_cart = res_cart[0]
 
     assert np.allclose(np.linalg.norm(res_cart, axis=0), 1.0, atol=ERROR_TOLERANCE)
@@ -47,7 +47,7 @@ def test_get_GCA_GCA_intersections_parallel():
         _lonlat_rad_to_xyz(0.5 * np.pi, 0.0),
         _lonlat_rad_to_xyz(-0.5 * np.pi - 0.01, 0.0)
     ])
-    res_cart = _gca_gca_intersection_cartesian(GCR1_cart, GCR2_cart)
+    res_cart = gca_gca_intersection(GCR1_cart, GCR2_cart)
     res_cart = res_cart[0]
     expected_res = np.array(_lonlat_rad_to_xyz(0.5 * np.pi, 0.0))
 
@@ -65,7 +65,7 @@ def test_get_GCA_GCA_intersections_perpendicular():
         _lonlat_rad_to_xyz(*[0.5 * np.pi - 0.01, 0.0]),
         _lonlat_rad_to_xyz(*[-0.5 * np.pi + 0.01, 0.0])
     ])
-    res_cart = _gca_gca_intersection_cartesian(GCR1_cart, GCR2_cart)
+    res_cart = gca_gca_intersection(GCR1_cart, GCR2_cart)
 
     # rest_cart should be empty since these two GCAs are not intersecting
     assert(len(res_cart) == 0)
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index f4558911a..e94d4be51 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -308,13 +308,6 @@ def faces_within_lat_bounds(lats, face_bounds_lat):
     return candidate_faces
 
 
-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)
-
-    return gca_gca_intersection(gca_a_xyz, gca_b_xyz)
-
-
 @njit(cache=True, inline="always")
 def _accux_gca(w0, w1, v0, v1):
     """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_gca``).
@@ -547,6 +540,10 @@ def _try_gca_const_lat_intersection(gca_cart, const_z):
 @njit(cache=True)
 def _snap_const_lat_endpoint(point, x1, x2, const_z):
     """Snap a candidate point to an arc endpoint when the endpoint lies on the latitude."""
+    # 1e-14 is distance² in Cartesian between candidate and endpoint; corresponds
+    # to ~1e-7 in arc length (unit sphere). Candidates within this distance are
+    # snapped to the exact endpoint to avoid sub-ulp drift when the arc ends
+    # exactly on the latitude circle.
     snap_sq = 1e-14
     out = np.empty(3)
     out[0] = point[0]

From f3c861895a6ec603d7bba2a86d8e982ddf9b36f3 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Wed, 10 Jun 2026 14:33:26 -0500
Subject: [PATCH 13/51] Trim computing.py docstring; drop inline=always from L1
 kernels

---
 uxarray/grid/intersections.py |  4 +--
 uxarray/utils/computing.py    | 56 ++++++++---------------------------
 2 files changed, 14 insertions(+), 46 deletions(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index e94d4be51..2a5b9a138 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -308,7 +308,7 @@ def faces_within_lat_bounds(lats, face_bounds_lat):
     return candidate_faces
 
 
-@njit(cache=True, inline="always")
+@njit(cache=True)
 def _accux_gca(w0, w1, v0, v1):
     """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_gca``).
 
@@ -450,7 +450,7 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     return res[:count]
 
 
-@njit(cache=True, inline="always")
+@njit(cache=True)
 def _accux_constlat(x1, x2, const_z):
     """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_constlat``).
 
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index 3222e1bf5..baaba8aac 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -1,49 +1,17 @@
 """Compensated floating-point primitives for accurate spherical geometry.
 
-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.
-
-Naming note
------------
-The term "error-free transformation" (EFT) strictly applies to ``two_sum``
-and ``two_prod``, which capture their rounding errors exactly so that
-``hi + lo`` equals the mathematical result with zero information loss.
-``diff_of_products``, ``accucross``, and ``accucross_pair`` use those EFT
-building blocks as compensated algorithms that are roughly twice as accurate
-as direct floating-point cross products, but they are not zero-error
-transformations.
-
-All functions are ``@njit``-compiled and use the portable Veltkamp-splitting
-form of ``two_prod`` (no FMA dependency), making them suitable for use inside
-Numba-compiled geometry kernels.
-
-These primitives are a Python/Numba port of 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 — a compensated-arithmetic 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 first tier. The compensated routines are roughly twice as accurate as
-direct floating-point equivalents while retaining the same vectorizable
-operation structure; robustness against all degenerate inputs would require
-adding an adaptive predicate or exact-arithmetic fallback tier.
+Cross products over nearly-parallel unit vectors suffer catastrophic
+cancellation. ``two_sum`` and ``two_prod`` are true error-free transformations
+(EFT): ``hi + lo`` equals the mathematical result exactly. The higher-level
+functions (``diff_of_products``, ``accucross``, ``accucross_pair``) compose
+those EFTs into compensated algorithms that are roughly twice as accurate as
+direct floating-point equivalents. All functions are Numba-compiled using the
+portable Veltkamp-splitting form of ``two_prod`` (no FMA dependency).
+
+Port of AccuSphGeom (Chen 2026, SIAM J. Sci. Comput.
+https://doi.org/10.1137/25M1737614; https://github.com/hongyuchen1030/AccuSphGeom).
+Only the compensated-arithmetic tier is implemented; the full library also has
+Shewchuk adaptive and exact-arithmetic fallback tiers.
 """
 
 import math

From 59a8020e3c15096ed7676193b5177c2cc27e0162 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Wed, 10 Jun 2026 15:09:40 -0500
Subject: [PATCH 14/51] Revert tornado pin; root cause is openssl 3.6.3 on
 Windows

---
 ci/environment.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ci/environment.yml b/ci/environment.yml
index d2bfb5a28..02f4616d9 100644
--- a/ci/environment.yml
+++ b/ci/environment.yml
@@ -13,7 +13,6 @@ dependencies:
   - healpix
   - holoviews
   - hvplot
-  - tornado<6.5.7
   - hypothesis
   - matplotlib-base
   - matplotlib-inline

From 35c85a83f3a01554041c900bdc12a8365024c1ad Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 22 May 2026 06:58:34 -0500
Subject: [PATCH 15/51] Port AccuSphGeom EFT algorithms and add spherical
 geometry user guide

Closes #1509
---
 .../spherical-geometry-accuracy.ipynb         | 1666 +++++++++++++++++
 docs/userguide.rst                            |    4 +
 uxarray/grid/_eft.py                          |  167 ++
 uxarray/grid/arcs.py                          |  108 ++
 uxarray/grid/bounds.py                        |  365 +++-
 uxarray/grid/intersections.py                 |  263 ++-
 uxarray/grid/point_in_face.py                 |  232 ++-
 7 files changed, 2656 insertions(+), 149 deletions(-)
 create mode 100644 docs/user-guide/spherical-geometry-accuracy.ipynb
 create mode 100644 uxarray/grid/_eft.py

diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
new file mode 100644
index 000000000..c8f6e6aff
--- /dev/null
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -0,0 +1,1666 @@
+{
+ "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": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "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",
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+       "  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",
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+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
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+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
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+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks;\n",
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+       "    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",
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+       "      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",
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+       "    const links = document.getElementsByTagName('link')\n",
+       "    for (let i = 0; i < links.length; i++) {\n",
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+       "        existing_stylesheets.push(link.href)\n",
+       "      }\n",
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+       "    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",
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+       "        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",
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+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
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+       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n",
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+       "          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",
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+       "          on_load();\n",
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+       "        continue;\n",
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+       "      var element = document.createElement('script');\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
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+       "      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\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.8.10/dist/panel.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 = 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  // 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\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.8.10/dist/panel.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",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        })\n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "  if (id !== undefined) {\n",
+       "    var nchildren = toinsert.length;\n",
+       "    var html_node = toinsert[nchildren-1].children[0];\n",
+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
+       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
+       "    for (var i in nodelist) {\n",
+       "      if (nodelist.hasOwnProperty(i)) {\n",
+       "        scripts.push(nodelist[i])\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
+       "      var newScript = document.createElement(\"script\");\n",
+       "      var attrs = [];\n",
+       "      var nodemap = oldScript.attributes;\n",
+       "      for (var j in nodemap) {\n",
+       "        if (nodemap.hasOwnProperty(j)) {\n",
+       "          attrs.push(nodemap[j])\n",
+       "        }\n",
+       "      }\n",
+       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
+       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var 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",
+       " */\n",
+       "function 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",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function 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",
+       "\n",
+       "if (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"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((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        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        })\n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var 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"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='511425db-17a9-43d0-9390-9143fd70fc44'>\n",
+       "  <div id=\"a5581328-170e-4d70-a959-f524dc20052d\" data-root-id=\"511425db-17a9-43d0-9390-9143fd70fc44\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"57699642-6e25-4f82-8f94-98775ebd7625\":{\"version\":\"3.7.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"511425db-17a9-43d0-9390-9143fd70fc44\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"f62f5983-225d-46af-8a2f-f3902bfcedb1\",\"attributes\":{\"plot_id\":\"511425db-17a9-43d0-9390-9143fd70fc44\",\"comm_id\":\"376348b687e7487291ac0ae5732287a4\",\"client_comm_id\":\"722736ecfe324560808c4506f3e031ad\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"start\",\"kind\":\"Any\",\"default\":0},{\"name\":\"end\",\"kind\":\"Any\",\"default\":100},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"holoviews.plotting.bokeh.raster.HoverModel\",\"properties\":[{\"name\":\"xy\",\"kind\":\"Any\",\"default\":null},{\"name\":\"data\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"57699642-6e25-4f82-8f94-98775ebd7625\",\"roots\":{\"511425db-17a9-43d0-9390-9143fd70fc44\":\"a5581328-170e-4d70-a959-f524dc20052d\"},\"root_ids\":[\"511425db-17a9-43d0-9390-9143fd70fc44\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  async function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t  for (const child of root_el.children) {\n",
+       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
+       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
+       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
+       "\t  }\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(version);\n",
+       "    } else if (root.Bokeh.version === version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "511425db-17a9-43d0-9390-9143fd70fc44"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(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",
+       "    },\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",
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+       "          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",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
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+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
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+       "              var message = result.value;\n",
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+       "        })\n",
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+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
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+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
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+       "    return\n",
+       "  }\n",
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+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
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+       "        scripts.push(nodelist[i])\n",
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+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
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+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
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+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
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+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var 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",
+       " */\n",
+       "function 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",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function 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",
+       "\n",
+       "if (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"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((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        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        })\n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var 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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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "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([np.array([0,0]), a1, a1+b1, b1], alpha=0.25, color=\"steelblue\", zorder=0)\n",
+    "ax.add_patch(para1)\n",
+    "ax.annotate(\"\", xy=a1, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2))\n",
+    "ax.annotate(\"\", xy=b1, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2))\n",
+    "ax.text(*a1*1.08, r\"$\\mathbf{a}$\", fontsize=13, color=\"#1f77b4\")\n",
+    "ax.text(*b1*1.08, r\"$\\mathbf{b}$\", fontsize=13, color=\"#d62728\")\n",
+    "area1 = abs(a1[0]*b1[1] - a1[1]*b1[0])\n",
+    "ax.text(0.5, 0.96, f\"|a × b| = {area1:.3f}\", ha=\"center\", fontsize=12, color=\"steelblue\",\n",
+    "        transform=ax.transAxes)\n",
+    "ax.set_xlim(-0.1, 1.8); ax.set_ylim(-0.1, 1.1)\n",
+    "ax.set_aspect(\"equal\")\n",
+    "ax.set_title(\"Well-separated  —  large, well-conditioned cross product\", fontsize=11)\n",
+    "ax.axis(\"off\")\n",
+    "\n",
+    "# --- Right panel: nearly-parallel vectors ---\n",
+    "ax = axes[1]\n",
+    "eps = 0.04\n",
+    "a2 = np.array([0.8 + eps, 0.6]); b2 = np.array([0.8, 0.6 + eps])\n",
+    "a2 /= np.linalg.norm(a2); b2 /= np.linalg.norm(b2)\n",
+    "para2 = plt.Polygon([np.array([0,0]), a2, a2+b2, b2], alpha=0.5, color=\"#d62728\", zorder=0)\n",
+    "ax.add_patch(para2)\n",
+    "ax.annotate(\"\", xy=a2, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2))\n",
+    "ax.annotate(\"\", xy=b2, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2))\n",
+    "ax.text(*(a2*1.06 + [0.01, 0.03]), r\"$\\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(0.5, 0.96, f\"|a × b| = {area2:.4f}  ←  tiny!\", ha=\"center\", fontsize=12,\n",
+    "        color=\"#d62728\", transform=ax.transAxes)\n",
+    "ax.set_xlim(-0.1, 1.8); ax.set_ylim(-0.1, 1.1)\n",
+    "ax.set_aspect(\"equal\")\n",
+    "ax.set_title(\"Nearly-parallel  —  tiny cross product, catastrophic cancellation\", fontsize=11)\n",
+    "ax.axis(\"off\")\n",
+    "\n",
+    "fig.suptitle(\"Cross product = parallelogram area\\n\"\n",
+    "             \"Small area means two nearly equal numbers are subtracted — digits cancel\",\n",
+    "             fontsize=12)\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",
+    "def fmt(v):\n",
+    "    return f\"{v:+d}\" if v != 0 else \" 0\"\n",
+    "\n",
+    "print(f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  → left  of A→B (northern hemisphere)\")\n",
+    "print(f\"South Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, south_pole))}  → right of A→B (southern hemisphere)\")\n",
+    "print(f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  → collinear, not a crossing\")"
+   ]
+  },
+  {
+   "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),\n",
+    "                     np.cos(lat) * np.sin(lon),\n",
+    "                     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(f\"Naive sign flips below ε ≈ {flip_threshold:.1e} rad  ({flip_mm:.2e} mm on Earth)\")\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(f\"  Naive (raw cross product):  {naive_ok}/{n} correctly classified as inside\"\n",
+    "      f\"  ← {n - naive_ok} misclassified as outside near the edge\")"
+   ]
+  },
+  {
+   "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": [
+       "<Figure size 1400x550 with 2 Axes>"
+      ]
+     },
+     "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([lons[0], lons[1]], [lats[0], lats[1]], \"-\", color=\"#d62728\",\n",
+    "        linewidth=3.5, zorder=3, label=\"Test edge  V0 → V1\")\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(f\"V{i}\", (lo, la), textcoords=\"offset points\",\n",
+    "                xytext=(6, 4), fontsize=11, fontweight=\"bold\")\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(em_lon, em_lat, s=200, color=\"#ff7f0e\", marker=\"*\", zorder=6,\n",
+    "           label=\"Edge midpoint — sweep origin\")\n",
+    "\n",
+    "ax.annotate(\"\", xy=(cen_lon, cen_lat), xytext=(em_lon, em_lat),\n",
+    "            arrowprops=dict(arrowstyle=\"-|>\", color=\"#555\", lw=1.5))\n",
+    "ax.text((em_lon + cen_lon) / 2 + 0.06, (em_lat + cen_lat) / 2 + 0.18,\n",
+    "        \"50 query points\\n(ε from 10⁻³ → 10⁻¹⁶)\", fontsize=9, color=\"#555\", style=\"italic\")\n",
+    "\n",
+    "ax.scatter(em_lon, em_lat, s=700, facecolors=\"none\", edgecolors=\"#d62728\",\n",
+    "           linewidths=2, zorder=7,\n",
+    "           label=f\"Naive sign wrong below ε ≈ {flip_threshold:.0e} rad (≈ 0.03 mm)\")\n",
+    "\n",
+    "q_far = normalize(normalize(vertices[0] + vertices[1]) + 1e-3 * normalize(vertices.sum(axis=0)))\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(\"ε = 10⁻³\\nboth correct\", (qf_lon, qf_lat),\n",
+    "            textcoords=\"offset points\", xytext=(7, -18), fontsize=8.5, color=\"#1f77b4\")\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]; la_ = node_lat[verts_g]\n",
+    "    if lf.max() - lf.min() > 180:\n",
+    "        continue\n",
+    "    ax_global.plot(np.append(lf, lf[0]), np.append(la_, la_[0]), \"-\",\n",
+    "                   color=\"steelblue\", linewidth=0.3, alpha=0.5,\n",
+    "                   transform=ccrs.PlateCarree(), zorder=2)\n",
+    "ax_global.fill(face_lons, face_lats, alpha=0.8, color=\"#d62728\", zorder=4,\n",
+    "               transform=ccrs.PlateCarree())\n",
+    "ax_global.scatter(em_lon, em_lat, s=40, color=\"#ff7f0e\", marker=\"*\", zorder=5,\n",
+    "                  transform=ccrs.PlateCarree())\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 <user-guide/spherical-geometry-accuracy.ipynb>`_
+ How UXarray uses error-free transformations to avoid catastrophic cancellation in cross-product and point-in-polygon operations
+
 `Working with HEALPix Grids <user-guide/healpix.ipynb>`_
  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..4b609664e 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 +11,7 @@
     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 +19,333 @@
     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]
+    max_nodes = face_node_connectivity.shape[1]
+    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 +413,30 @@ 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..0b7c7d242 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -1,15 +1,16 @@
+import math
+
 import numpy as np
 from numba import njit, prange
 
 from uxarray.constants import ERROR_TOLERANCE, INT_DTYPE, MACHINE_EPSILON
+from uxarray.grid._eft import accucross
 from uxarray.grid.arcs import (
     extreme_gca_z,
     in_between,
+    on_minor_arc,
     point_within_gca,
 )
-from uxarray.grid.utils import (
-    _angle_of_2_vectors,
-)
 
 
 @njit(parallel=True, nogil=True, cache=True)
@@ -292,6 +293,19 @@ 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 +315,200 @@ 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
-
-        return res[:count, :]
+    if n1_mag == 0.0 or n2_mag == 0.0:
+        return res[:count]
 
-    # Normalize the cross_norms
-    cross_norms = cross_norms / np.linalg.norm(cross_norms)
-    x1_xyz = cross_norms
-    x2_xyz = -x1_xyz
+    # 2. Intersection direction: cross product of the two plane normals.
+    vx, vy, vz, vn = _normalize_pair(
+        *accucross(n1x, n1y, n1z, n2x, n2y, n2z)
+    )
 
-    # 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..b969cb7b5 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 _orient3d_on_sphere_value, 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)

From 6c1497e976130e4ed4e45157b8cd56c25b204033 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 22 May 2026 07:54:45 -0500
Subject: [PATCH 16/51] Fix pre-commit: remove unused imports and variable

---
 uxarray/grid/bounds.py        | 7 +++++--
 uxarray/grid/intersections.py | 7 ++-----
 uxarray/grid/point_in_face.py | 2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
index 4b609664e..bff921a8e 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -11,7 +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.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,
@@ -315,7 +319,6 @@ def _construct_face_bounds_array_gca(
         [[lat_min, lat_max], [lon_min, lon_max]] in radians per face.
     """
     n_face = face_node_connectivity.shape[0]
-    max_nodes = face_node_connectivity.shape[1]
     bounds_array = np.empty((n_face, 2, 2), dtype=np.float64)
     deg_to_rad = math.pi / 180.0
 
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 0b7c7d242..00b8d8b42 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -3,13 +3,12 @@
 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,
     on_minor_arc,
-    point_within_gca,
 )
 
 
@@ -356,9 +355,7 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
         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)
-    )
+    vx, vy, vz, vn = _normalize_pair(*accucross(n1x, n1y, n1z, n2x, n2y, n2z))
 
     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.
diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py
index b969cb7b5..36b06f674 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -7,7 +7,7 @@
 from numba import njit, prange
 
 from uxarray.constants import INT_DTYPE, INT_FILL_VALUE
-from uxarray.grid.arcs import _orient3d_on_sphere_value, on_minor_arc, orient3d_on_sphere
+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 57373d0dc23430eaa4b90b1e6c1b263d348053f2 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 22 May 2026 08:02:26 -0500
Subject: [PATCH 17/51] Fix pre-commit: split semicolons in notebook cells

---
 .../spherical-geometry-accuracy.ipynb         | 240 +++++++++++++-----
 1 file changed, 175 insertions(+), 65 deletions(-)

diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index c8f6e6aff..99d51ab9a 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -1264,16 +1264,30 @@
     "ax = axes[0]\n",
     "a1 = np.array([0.6, 0.8])\n",
     "b1 = np.array([0.8, 0.2])\n",
-    "para1 = plt.Polygon([np.array([0,0]), a1, a1+b1, b1], alpha=0.25, color=\"steelblue\", zorder=0)\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(\"\", xy=a1, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2))\n",
-    "ax.annotate(\"\", xy=b1, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2))\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(0.5, 0.96, f\"|a × b| = {area1:.3f}\", ha=\"center\", fontsize=12, color=\"steelblue\",\n",
-    "        transform=ax.transAxes)\n",
-    "ax.set_xlim(-0.1, 1.8); ax.set_ylim(-0.1, 1.1)\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",
@@ -1281,25 +1295,45 @@
     "# --- Right panel: nearly-parallel vectors ---\n",
     "ax = axes[1]\n",
     "eps = 0.04\n",
-    "a2 = np.array([0.8 + eps, 0.6]); b2 = np.array([0.8, 0.6 + eps])\n",
-    "a2 /= np.linalg.norm(a2); b2 /= np.linalg.norm(b2)\n",
-    "para2 = plt.Polygon([np.array([0,0]), a2, a2+b2, b2], alpha=0.5, color=\"#d62728\", zorder=0)\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(\"\", xy=a2, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#1f77b4\", lw=2))\n",
-    "ax.annotate(\"\", xy=b2, xytext=[0,0], arrowprops=dict(arrowstyle=\"->\", color=\"#d62728\", lw=2))\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(0.5, 0.96, f\"|a × b| = {area2:.4f}  ←  tiny!\", ha=\"center\", fontsize=12,\n",
-    "        color=\"#d62728\", transform=ax.transAxes)\n",
-    "ax.set_xlim(-0.1, 1.8); ax.set_ylim(-0.1, 1.1)\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(\"Nearly-parallel  —  tiny cross product, catastrophic cancellation\", fontsize=11)\n",
+    "ax.set_title(\n",
+    "    \"Nearly-parallel  —  tiny cross product, catastrophic cancellation\", fontsize=11\n",
+    ")\n",
     "ax.axis(\"off\")\n",
     "\n",
-    "fig.suptitle(\"Cross product = parallelogram area\\n\"\n",
-    "             \"Small area means two nearly equal numbers are subtracted — digits cancel\",\n",
-    "             fontsize=12)\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()"
    ]
   },
@@ -1363,20 +1397,28 @@
     "#\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 = 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",
+    "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",
+    "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",
-    "print(f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  → left  of A→B (northern hemisphere)\")\n",
-    "print(f\"South Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, south_pole))}  → right of A→B (southern hemisphere)\")\n",
-    "print(f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  → collinear, not a crossing\")"
+    "\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",
+    ")"
    ]
   },
   {
@@ -1458,9 +1500,7 @@
     "\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),\n",
-    "                     np.cos(lat) * np.sin(lon),\n",
-    "                     np.sin(lat)])\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",
@@ -1473,7 +1513,7 @@
     "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",
+    "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",
@@ -1492,7 +1532,7 @@
     "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",
+    "_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",
@@ -1500,16 +1540,20 @@
     "    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",
+    "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(f\"Naive sign flips below ε ≈ {flip_threshold:.1e} rad  ({flip_mm:.2e} mm on Earth)\")\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(f\"  Naive (raw cross product):  {naive_ok}/{n} correctly classified as inside\"\n",
-    "      f\"  ← {n - naive_ok} misclassified as outside near the edge\")"
+    "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",
+    ")"
    ]
   },
   {
@@ -1558,35 +1602,80 @@
     "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([lons[0], lons[1]], [lats[0], lats[1]], \"-\", color=\"#d62728\",\n",
-    "        linewidth=3.5, zorder=3, label=\"Test edge  V0 → V1\")\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(f\"V{i}\", (lo, la), textcoords=\"offset points\",\n",
-    "                xytext=(6, 4), fontsize=11, fontweight=\"bold\")\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(em_lon, em_lat, s=200, color=\"#ff7f0e\", marker=\"*\", zorder=6,\n",
-    "           label=\"Edge midpoint — sweep origin\")\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(\"\", xy=(cen_lon, cen_lat), xytext=(em_lon, em_lat),\n",
-    "            arrowprops=dict(arrowstyle=\"-|>\", color=\"#555\", lw=1.5))\n",
-    "ax.text((em_lon + cen_lon) / 2 + 0.06, (em_lat + cen_lat) / 2 + 0.18,\n",
-    "        \"50 query points\\n(ε from 10⁻³ → 10⁻¹⁶)\", fontsize=9, color=\"#555\", style=\"italic\")\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(em_lon, em_lat, s=700, facecolors=\"none\", edgecolors=\"#d62728\",\n",
-    "           linewidths=2, zorder=7,\n",
-    "           label=f\"Naive sign wrong below ε ≈ {flip_threshold:.0e} rad (≈ 0.03 mm)\")\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(normalize(vertices[0] + vertices[1]) + 1e-3 * normalize(vertices.sum(axis=0)))\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(\"ε = 10⁻³\\nboth correct\", (qf_lon, qf_lat),\n",
-    "            textcoords=\"offset points\", xytext=(7, -18), fontsize=8.5, color=\"#1f77b4\")\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",
@@ -1603,17 +1692,38 @@
     "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]; la_ = node_lat[verts_g]\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(np.append(lf, lf[0]), np.append(la_, la_[0]), \"-\",\n",
-    "                   color=\"steelblue\", linewidth=0.3, alpha=0.5,\n",
-    "                   transform=ccrs.PlateCarree(), zorder=2)\n",
-    "ax_global.fill(face_lons, face_lats, alpha=0.8, color=\"#d62728\", zorder=4,\n",
-    "               transform=ccrs.PlateCarree())\n",
-    "ax_global.scatter(em_lon, em_lat, s=40, color=\"#ff7f0e\", marker=\"*\", zorder=5,\n",
-    "                  transform=ccrs.PlateCarree())\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()"

From 3a55e5d3b9ec55430bf592f426e56f63bdaa8194 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 28 May 2026 10:19:35 -0500
Subject: [PATCH 18/51] Address Hongyu's review: port AccuSphGeom compensated
 arithmetic, rewrite intersections, add 241 baseline testsgit status! - most
 came from accusphere

---
 docs/api.rst                                  |   19 +-
 .../spherical-geometry-accuracy.ipynb         | 1332 +----------------
 docs/userguide.rst                            |    2 +-
 .../gca_constlat_cases_with_baseline.csv      |  201 +++
 ..._pairs_seed20251104_N100_with_baseline.csv |   32 +
 .../geometry/test_accusphgeom_baseline.py     |  208 +++
 uxarray/grid/_eft.py                          |  167 ---
 uxarray/grid/arcs.py                          |   10 +-
 uxarray/grid/bounds.py                        |   65 +-
 uxarray/grid/intersections.py                 |  247 +--
 uxarray/grid/point_in_face.py                 |    4 +-
 uxarray/utils/computing.py                    |  936 +++++-------
 12 files changed, 1071 insertions(+), 2152 deletions(-)
 create mode 100644 test/grid/geometry/data/accusphgeom/gca_constlat_cases_with_baseline.csv
 create mode 100644 test/grid/geometry/data/accusphgeom/gca_gca_pairs_seed20251104_N100_with_baseline.csv
 create mode 100644 test/grid/geometry/test_accusphgeom_baseline.py
 delete mode 100644 uxarray/grid/_eft.py

diff --git a/docs/api.rst b/docs/api.rst
index 9cdaf9992..11a4c17a5 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -584,13 +584,24 @@ Arcs
    grid.arcs.in_between
    grid.arcs.point_within_gca
    grid.arcs.extreme_gca_latitude
+   grid.arcs.orient3d_on_sphere
+   grid.arcs.on_minor_arc
 
 
-Accurate Computing
-------------------
+Compensated Arithmetic
+----------------------
+
+Numba-compiled primitives used throughout the geometry stack to avoid
+catastrophic cancellation in cross-product and dot-product operations.
+``two_sum`` and ``two_prod`` are true error-free transformations (EFT);
+the higher-level functions are compensated algorithms built on top of them.
 
 .. autosummary::
    :toctree: generated/
 
-   utils.computing.cross_fma
-   utils.computing.dot_fma
+   utils.computing.two_sum
+   utils.computing.two_prod
+   utils.computing.diff_of_products
+   utils.computing.accucross
+   utils.computing.accucross_pair
+   utils.computing.acc_sqrt_re
diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index 99d51ab9a..321bd1824 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -4,22 +4,11 @@
    "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"
-   ]
+   "source": "# Accurate Spherical Geometry\n\nCross products are at the heart of nearly every geometric test on the sphere \u2014 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 \u2014 the physically meaningful result \u2014 can lose all significant digits to floating-point cancellation. UXarray guards against this throughout its geometry stack using **compensated arithmetic** \u2014 algorithms built on error-free transformation (EFT) primitives that track every rounding residual exactly.\n\nThis guide covers:\n\n1. The problem: catastrophic cancellation\n2. How UXarray handles it\n3. Seeing it on a real mesh: point-in-polygon\n4. Where it is used in UXarray"
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "imports-cell",
    "metadata": {
     "execution": {
@@ -29,1198 +18,8 @@
      "shell.execute_reply": "2026-05-22T11:58:09.059089Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "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",
-       "  // 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",
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-       "      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",
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-       "      console.error(\"failed to load \" + (src_el.href || src_el.src));\n",
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-       "      root._bokeh_is_loading = css_urls.length + 0;\n",
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-       "      }\n",
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-       "      element.onload = on_load;\n",
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-       "      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\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.8.10/dist/panel.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 = 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  // 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\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.8.10/dist/panel.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",
-       "        comm_promise.then((comm) => {\n",
-       "          window.PyViz.comms[comm_id] = comm;\n",
-       "          if (msg_handler) {\n",
-       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
-       "            function processIteratorResult(result) {\n",
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-       "              var content = {data: message.data};\n",
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-       "            return messages.next().then(processIteratorResult);\n",
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-       "        })\n",
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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"holoviews.plotting.bokeh.raster.HoverModel\",\"properties\":[{\"name\":\"xy\",\"kind\":\"Any\",\"default\":null},{\"name\":\"data\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
-       "  var render_items = [{\"docid\":\"57699642-6e25-4f82-8f94-98775ebd7625\",\"roots\":{\"511425db-17a9-43d0-9390-9143fd70fc44\":\"a5581328-170e-4d70-a959-f524dc20052d\"},\"root_ids\":[\"511425db-17a9-43d0-9390-9143fd70fc44\"]}];\n",
-       "  var docs = Object.values(docs_json)\n",
-       "  if (!docs) {\n",
-       "    return\n",
-       "  }\n",
-       "  const version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
-       "  async function embed_document(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
-       "\tconst id_el = document.getElementById(root_id)\n",
-       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t  for (const child of root_el.children) {\n",
-       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
-       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
-       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
-       "\t  }\n",
-       "\t}\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  function get_bokeh(root) {\n",
-       "    if (root.Bokeh === undefined) {\n",
-       "      return null\n",
-       "    } else if (root.Bokeh.version !== version) {\n",
-       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(version)) {\n",
-       "\treturn null\n",
-       "      }\n",
-       "      return root.Bokeh.versions.get(version);\n",
-       "    } else if (root.Bokeh.version === version) {\n",
-       "      return root.Bokeh\n",
-       "    }\n",
-       "    return null\n",
-       "  }\n",
-       "  function is_loaded(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
-       "  }\n",
-       "  if (is_loaded(root)) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (is_loaded(root)) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 200) {\n",
-       "          clearInterval(timer);\n",
-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ]
-     },
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "511425db-17a9-43d0-9390-9143fd70fc44"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(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",
-       "    },\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));"
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-       "  if (id !== undefined) {\n",
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-       "    var scripts = [];\n",
-       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
-       "    for (var i in nodelist) {\n",
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-       "    output_area._hv_plot_id = id;\n",
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-       "    var bk_div = document.createElement(\"div\");\n",
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-       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
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-       "  delete PyViz.plot_index[id];\n",
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-       "    );\n",
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-       "\n",
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-       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
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-       "if (window.Jupyter !== undefined) {\n",
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-     },
-     "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\")"
-   ]
+   "outputs": [],
+   "source": "import warnings\n\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport uxarray as ux\nfrom uxarray.grid.point_in_face import _point_in_polygon_sphere\n\nwarnings.filterwarnings(\"ignore\")"
   },
   {
    "cell_type": "markdown",
@@ -1229,7 +28,7 @@
    "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."
+    "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 \u2014 and floating-point rounding can reduce it to zero."
    ]
   },
   {
@@ -1280,7 +79,7 @@
     "ax.text(\n",
     "    0.5,\n",
     "    0.96,\n",
-    "    f\"|a × b| = {area1:.3f}\",\n",
+    "    f\"|a \u00d7 b| = {area1:.3f}\",\n",
     "    ha=\"center\",\n",
     "    fontsize=12,\n",
     "    color=\"steelblue\",\n",
@@ -1289,7 +88,7 @@
     "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.set_title(\"Well-separated  \u2014  large, well-conditioned cross product\", fontsize=11)\n",
     "ax.axis(\"off\")\n",
     "\n",
     "# --- Right panel: nearly-parallel vectors ---\n",
@@ -1315,7 +114,7 @@
     "ax.text(\n",
     "    0.5,\n",
     "    0.96,\n",
-    "    f\"|a × b| = {area2:.4f}  ←  tiny!\",\n",
+    "    f\"|a \u00d7 b| = {area2:.4f}  \u2190  tiny!\",\n",
     "    ha=\"center\",\n",
     "    fontsize=12,\n",
     "    color=\"#d62728\",\n",
@@ -1325,13 +124,13 @@
     "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",
+    "    \"Nearly-parallel  \u2014  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",
+    "    \"Small area means two nearly equal numbers are subtracted \u2014 digits cancel\",\n",
     "    fontsize=12,\n",
     ")\n",
     "plt.show()"
@@ -1341,24 +140,7 @@
    "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."
-   ]
+   "source": "## 2. How UXarray Handles It\n\nUXarray uses **compensated arithmetic** \u2014 a family of algorithms that prevent catastrophic cancellation by representing floating-point operations as exact `(hi, lo)` pairs. There are two distinct layers:\n\n- **Error-free transformations (EFT)** \u2014 `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** \u2014 `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\nThe 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.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\u00d7b)\u00b7q`: +1, \u22121, or 0 |\n| `on_minor_arc(q, a, b)` | `grid.arcs` | True if `q` lies on the minor arc from `a` to `b` |\n\nMost users will never call these directly \u2014 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",
@@ -1377,33 +159,33 @@
      "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"
+      "North Pole:      orient3d = +1  \u2192 left  of A\u2192B (northern hemisphere)\n",
+      "South Pole:      orient3d = -1  \u2192 right of A\u2192B (southern hemisphere)\n",
+      "On great circle: orient3d =  0  \u2192 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",
+    "# orient3d_on_sphere(A, B, Q) returns the sign of the scalar triple product (A\u00d7B)\u00b7Q.\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",
+    "#   +1  Q is on the LEFT  of the directed arc A \u2192 B (above the plane by right-hand rule)\n",
+    "#   -1  Q is on the RIGHT of the directed arc A \u2192 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",
+    "A = np.array([1.0, 0.0, 0.0])  # 0\u00b0E on the equator\n",
+    "B = np.array([0.0, 1.0, 0.0])  # 90\u00b0E on the equator\n",
+    "# A\u2192B 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",
+    "on_equator = np.array([0.0, 1.0, 0.0])  # same as B \u2014 on the great circle itself\n",
     "\n",
     "\n",
     "def fmt(v):\n",
@@ -1411,13 +193,13 @@
     "\n",
     "\n",
     "print(\n",
-    "    f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  → left  of A→B (northern hemisphere)\"\n",
+    "    f\"North Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, north_pole))}  \u2192 left  of A\u2192B (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",
+    "    f\"South Pole:      orient3d = {fmt(orient3d_on_sphere(A, B, south_pole))}  \u2192 right of A\u2192B (southern hemisphere)\"\n",
     ")\n",
     "print(\n",
-    "    f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  → collinear, not a crossing\"\n",
+    "    f\"On great circle: orient3d = {fmt(orient3d_on_sphere(A, B, on_equator))}  \u2192 collinear, not a crossing\"\n",
     ")"
    ]
   },
@@ -1428,7 +210,7 @@
    "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."
+    "Point-in-polygon on the sphere works by casting a ray from the query point and counting edge crossings \u2014 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."
    ]
   },
   {
@@ -1463,7 +245,7 @@
    "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 $\\sim \\varepsilon_\\text{machine} / |V0 \\times V1|$."
+    "Query points are placed at 50 log-spaced distances from the midpoint of edge V0\u2192V1 on face 0, stepping inward toward the face centroid. The sign of the naive orient3d flips once the distance drops below $\\sim \\varepsilon_\\text{machine} / |V0 \\times V1|$."
    ]
   },
   {
@@ -1483,12 +265,12 @@
      "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",
+      "Face 0 edge V0\u2192V1:  |V0 \u00d7 V1| = 0.04851\n",
+      "Naive sign flips below \u03b5 \u2248 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",
+      "All 50 query points are inside face 0 \u2014 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"
+      "  Naive (raw cross product):  42/50 correctly classified as inside  \u2190 8 misclassified as outside near the edge\n"
      ]
     }
    ],
@@ -1524,10 +306,10 @@
     "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",
+    "flip_mm = flip_threshold * 6.371e6 * 1e3  # radians \u2192 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",
+    "# All 50 are strictly inside the face \u2014 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",
@@ -1543,16 +325,16 @@
     "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(f\"Face 0 edge V0\u2192V1:  |V0 \u00d7 V1| = {cross_mag:.5f}\")\n",
     "print(\n",
-    "    f\"Naive sign flips below ε ≈ {flip_threshold:.1e} rad  ({flip_mm:.2e} mm on Earth)\"\n",
+    "    f\"Naive sign flips below \u03b5 \u2248 {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\"All {n} query points are inside face 0 \u2014 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",
+    "    f\"  \u2190 {n - naive_ok} misclassified as outside near the edge\"\n",
     ")"
    ]
   },
@@ -1561,7 +343,7 @@
    "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."
+    "When the query is close enough to the edge, the naive orient3d value rounds to the wrong sign \u2014 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."
    ]
   },
   {
@@ -1595,7 +377,7 @@
     "fig = plt.figure(figsize=(14, 5.5))\n",
     "fig.subplots_adjust(wspace=0.08)\n",
     "\n",
-    "# ── Left: zoomed face ──────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Left: zoomed face \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "ax = fig.add_subplot(1, 2, 1)\n",
     "\n",
     "face_lons = np.append(lons, lons[0])\n",
@@ -1609,7 +391,7 @@
     "    color=\"#d62728\",\n",
     "    linewidth=3.5,\n",
     "    zorder=3,\n",
-    "    label=\"Test edge  V0 → V1\",\n",
+    "    label=\"Test edge  V0 \u2192 V1\",\n",
     ")\n",
     "\n",
     "for i, (lo, la) in enumerate(zip(lons, lats)):\n",
@@ -1634,7 +416,7 @@
     "    color=\"#ff7f0e\",\n",
     "    marker=\"*\",\n",
     "    zorder=6,\n",
-    "    label=\"Edge midpoint — sweep origin\",\n",
+    "    label=\"Edge midpoint \u2014 sweep origin\",\n",
     ")\n",
     "\n",
     "ax.annotate(\n",
@@ -1646,7 +428,7 @@
     "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",
+    "    \"50 query points\\n(\u03b5 from 10\u207b\u00b3 \u2192 10\u207b\u00b9\u2076)\",\n",
     "    fontsize=9,\n",
     "    color=\"#555\",\n",
     "    style=\"italic\",\n",
@@ -1660,7 +442,7 @@
     "    edgecolors=\"#d62728\",\n",
     "    linewidths=2,\n",
     "    zorder=7,\n",
-    "    label=f\"Naive sign wrong below ε ≈ {flip_threshold:.0e} rad (≈ 0.03 mm)\",\n",
+    "    label=f\"Naive sign wrong below \u03b5 \u2248 {flip_threshold:.0e} rad (\u2248 0.03 mm)\",\n",
     ")\n",
     "\n",
     "q_far = normalize(\n",
@@ -1669,7 +451,7 @@
     "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",
+    "    \"\u03b5 = 10\u207b\u00b3\\nboth correct\",\n",
     "    (qf_lon, qf_lat),\n",
     "    textcoords=\"offset points\",\n",
     "    xytext=(7, -18),\n",
@@ -1677,16 +459,16 @@
     "    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.set_xlabel(\"Longitude (\u00b0)\", fontsize=11)\n",
+    "ax.set_ylabel(\"Latitude (\u00b0)\", fontsize=11)\n",
+    "ax.set_title(\"Face 0 \u2014 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",
+    "# \u2500\u2500 Right: global context \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\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",
@@ -1724,7 +506,7 @@
     "    zorder=5,\n",
     "    transform=ccrs.PlateCarree(),\n",
     ")\n",
-    "ax_global.set_title(\"Global context — highlighted face in red\", fontsize=11)\n",
+    "ax_global.set_title(\"Global context \u2014 highlighted face in red\", fontsize=11)\n",
     "\n",
     "plt.show()"
    ]
@@ -1733,23 +515,7 @@
    "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."
-   ]
+   "source": "## 4. Where It Is Used in UXarray\n\nCompensated 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\u2013arc intersection (remapping, antimeridian) | `grid/intersections.py` | `accucross`, `accucross_pair`, `on_minor_arc` |\n| Arc\u2013latitude 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\nIf you extend UXarray with custom geometry \u2014 for example, a new remapping kernel or a spatial predicate \u2014 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": {
diff --git a/docs/userguide.rst b/docs/userguide.rst
index a442a7c15..e96d4c9a1 100644
--- a/docs/userguide.rst
+++ b/docs/userguide.rst
@@ -95,7 +95,7 @@ Supplementary Guides
 These user guides provide additional details about specific features in UXarray.
 
 `Accurate Spherical Geometry <user-guide/spherical-geometry-accuracy.ipynb>`_
- How UXarray uses error-free transformations to avoid catastrophic cancellation in cross-product and point-in-polygon operations
+ How UXarray uses compensated arithmetic to avoid catastrophic cancellation in cross-product and point-in-polygon operations
 
 `Working with HEALPix Grids <user-guide/healpix.ipynb>`_
  Use UXarray with HEALPix
diff --git a/test/grid/geometry/data/accusphgeom/gca_constlat_cases_with_baseline.csv b/test/grid/geometry/data/accusphgeom/gca_constlat_cases_with_baseline.csv
new file mode 100644
index 000000000..ce456ea88
--- /dev/null
+++ b/test/grid/geometry/data/accusphgeom/gca_constlat_cases_with_baseline.csv
@@ -0,0 +1,201 @@
+case_id,a0_x_sig,a0_x_exp,a0_y_sig,a0_y_exp,a0_z_sig,a0_z_exp,a1_x_sig,a1_x_exp,a1_y_sig,a1_y_exp,a1_z_sig,a1_z_exp,z0_sig,z0_exp,baseline_x_sig,baseline_x_exp,baseline_y_sig,baseline_y_exp
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diff --git a/test/grid/geometry/data/accusphgeom/gca_gca_pairs_seed20251104_N100_with_baseline.csv b/test/grid/geometry/data/accusphgeom/gca_gca_pairs_seed20251104_N100_with_baseline.csv
new file mode 100644
index 000000000..8941026a4
--- /dev/null
+++ b/test/grid/geometry/data/accusphgeom/gca_gca_pairs_seed20251104_N100_with_baseline.csv
@@ -0,0 +1,32 @@
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diff --git a/test/grid/geometry/test_accusphgeom_baseline.py b/test/grid/geometry/test_accusphgeom_baseline.py
new file mode 100644
index 000000000..e704f2aff
--- /dev/null
+++ b/test/grid/geometry/test_accusphgeom_baseline.py
@@ -0,0 +1,208 @@
+"""Baseline regression tests ported from the AccuSphGeom C++ test suite.
+
+Test data and expected results are taken directly from:
+    https://github.com/hongyuchen1030/AccuSphGeom
+
+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
+the EFT tier, so the tolerances here reflect what double-precision EFT can
+achieve:
+
+    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
+import os
+
+import numpy as np
+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(
+    _DATA_DIR, "gca_gca_pairs_seed20251104_N100_with_baseline.csv"
+)
+_GCA_CONSTLAT_CSV = os.path.join(
+    _DATA_DIR, "gca_constlat_cases_with_baseline.csv"
+)
+
+
+def _sigexp(sig, exp):
+    return math.ldexp(int(sig), int(exp))
+
+
+def _parse_vec3(fields, start):
+    return np.array(
+        [
+            _sigexp(fields[start], fields[start + 1]),
+            _sigexp(fields[start + 2], fields[start + 3]),
+            _sigexp(fields[start + 4], fields[start + 5]),
+        ]
+    )
+
+
+def _parse_scalar(fields, start):
+    return _sigexp(fields[start], fields[start + 1])
+
+
+# ── GCA-GCA intersection ──────────────────────────────────────────────────────
+
+
+def _load_gca_gca():
+    rows = []
+    with open(_GCA_GCA_CSV) as f:
+        next(f)
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            fields = line.split(",")
+            assert len(fields) == 32
+            pair_id = int(fields[0])
+            a0 = _parse_vec3(fields, 2)
+            a1 = _parse_vec3(fields, 8)
+            b0 = _parse_vec3(fields, 14)
+            b1 = _parse_vec3(fields, 20)
+            baseline = _parse_vec3(fields, 26)
+            rows.append((pair_id, a0, a1, b0, b1, baseline))
+    return rows
+
+
+@pytest.fixture(scope="module")
+def gca_gca_rows():
+    return _load_gca_gca()
+
+
+def test_gca_gca_row_count(gca_gca_rows):
+    assert len(gca_gca_rows) == 31
+
+
+@pytest.mark.parametrize("idx", range(31))
+def test_gca_gca_intersection_baseline(gca_gca_rows, idx):
+    pair_id, a0, a1, b0, b1, baseline = gca_gca_rows[idx]
+    result = gca_gca_intersection(np.stack([a0, a1]), np.stack([b0, b1]))
+    assert result.shape[0] >= 1, f"pair_id={pair_id}: expected intersection, got none"
+    err = float(np.linalg.norm(result[0] - baseline))
+    assert err < 1e-15, f"pair_id={pair_id}: err={err:.3e} ≥ 1e-15"
+
+
+# ── GCA-const-lat intersection ────────────────────────────────────────────────
+
+
+def _load_gca_constlat():
+    rows = []
+    with open(_GCA_CONSTLAT_CSV) as f:
+        next(f)
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            fields = line.split(",")
+            assert len(fields) == 19
+            case_id = int(fields[0])
+            a0 = _parse_vec3(fields, 1)
+            a1 = _parse_vec3(fields, 7)
+            z0 = _parse_scalar(fields, 13)
+            bx = _parse_scalar(fields, 15)
+            by = _parse_scalar(fields, 17)
+            rows.append((case_id, a0, a1, z0, bx, by))
+    return rows
+
+
+@pytest.fixture(scope="module")
+def gca_constlat_rows():
+    return _load_gca_constlat()
+
+
+def test_gca_constlat_row_count(gca_constlat_rows):
+    assert len(gca_constlat_rows) == 200
+
+
+@pytest.mark.parametrize("idx", range(200))
+def test_gca_constlat_intersection_baseline(gca_constlat_rows, idx):
+    case_id, a0, a1, z0, bx, by = gca_constlat_rows[idx]
+    result = gca_const_lat_intersection(np.stack([a0, a1]), z0)
+    assert not np.all(np.isnan(result[0])), f"case_id={case_id}: no intersection returned"
+    dx = result[0, 0] - bx
+    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/_eft.py b/uxarray/grid/_eft.py
deleted file mode 100644
index 4a5ec5f97..000000000
--- a/uxarray/grid/_eft.py
+++ /dev/null
@@ -1,167 +0,0 @@
-"""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 481b2415c..7363f44a9 100644
--- a/uxarray/grid/arcs.py
+++ b/uxarray/grid/arcs.py
@@ -4,14 +4,14 @@
 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
+from uxarray.utils.computing import diff_of_products, two_sum
 
 # Tolerance used to classify orient3d results as zero.  For double-precision
-# unit-vector inputs this covers rounding error in the EFT cross product.
+# unit-vector inputs this covers rounding error in the compensated cross product.
 _PREDICATE_ZERO_TOL = 1e-15
 
 # Default tolerance for the on_minor_arc collinearity and interval tests.
@@ -376,11 +376,11 @@ def compute_arc_length(pt_a, pt_b):
 
 @njit(cache=True)
 def _orient3d_on_sphere_value(a, b, q):
-    """Return the EFT-accurate value of the orient3d-on-sphere predicate.
+    """Return the accurately computed 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
+    For unit vectors all coordinates are in [-1, 1], so the compensated 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
@@ -411,7 +411,7 @@ def _orient3d_on_sphere_value(a, b, q):
 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
+    Evaluates the sign of ``(a x b) . q`` using compensated arithmetic 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.
diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
index bff921a8e..2d07a601a 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -86,31 +86,30 @@ def _face_location_info(face_vertices, polar_cap_z):
         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.
+        # Parameter along the arc at which z is extremal (matches C++ get_face_location_info).
         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
+        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
+
+        # Mask-based selection: use z_ext only when the extremum is interior (a_raw in (0,1)).
+        use_ext = 1 if (0.0 < a_raw < 1.0) else 0
+        z_max_candidate = use_ext * z_ext + (1 - use_ext) * z_edge_max
+        z_min_candidate = use_ext * z_ext + (1 - use_ext) * 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
 
     if z_max >= polar_cap_z:
         return _FACE_LOC_NORTH_POLAR, z_min, z_max
@@ -199,11 +198,11 @@ def _generate_lat_lon_bounds_local(face_vertices, z_min, z_max, snap_tol_deg):
     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
+    # 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
 
@@ -273,10 +272,10 @@ def _generate_lat_lon_bounds_pole(face_vertices, label, z_min, z_max, snap_tol_d
     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
+    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:
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 00b8d8b42..f71bf3d4f 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -4,11 +4,17 @@
 from numba import njit, prange
 
 from uxarray.constants import ERROR_TOLERANCE, INT_DTYPE
-from uxarray.grid._eft import accucross
-from uxarray.grid.arcs import (
-    extreme_gca_z,
-    in_between,
-    on_minor_arc,
+from uxarray.grid.arcs import on_minor_arc
+from uxarray.utils.computing import (
+    _cdp2,
+    _cdp4,
+    _sum_sq_c2,
+    _sum_sq_c3,
+    acc_sqrt_re,
+    accucross,
+    accucross_pair,
+    two_prod,
+    two_sum,
 )
 
 
@@ -292,19 +298,6 @@ 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)
@@ -316,7 +309,7 @@ def _gca_gca_intersection_cartesian(gca_a_xyz, gca_b_xyz):
 def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     """Find intersection point(s) of two great-circle arcs using compensated arithmetic.
 
-    Uses ``accucross`` (error-free cross products) and ``on_minor_arc`` (EFT-based
+    Uses ``accucross`` (compensated cross products) and ``on_minor_arc`` (compensated
     arc membership) to avoid the catastrophic cancellation that affects naive
     cross product implementations when arcs are nearly parallel.
 
@@ -340,24 +333,56 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     v0 = gca_b_xyz[0]
     v1 = gca_b_xyz[1]
 
-    # 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])
+    # 1. Plane normals via accurate cross products — keep compensated (hi, lo).
+    n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = 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])
+    n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo = accucross(
+        v0[0], v0[1], v0[2], v1[0], v1[1], v1[2]
     )
 
     res = np.empty((2, 3))
     count = 0
 
-    if n1_mag == 0.0 or n2_mag == 0.0:
+    # Degenerate check: collapsed (zero-length) input arc.
+    n1x = n1x_hi + n1x_lo
+    n1y = n1y_hi + n1y_lo
+    n1z = n1z_hi + n1z_lo
+    n2x = n2x_hi + n2x_lo
+    n2y = n2y_hi + n2y_lo
+    n2z = n2z_hi + n2z_lo
+    if (
+        n1x * n1x + n1y * n1y + n1z * n1z == 0.0
+        or n2x * n2x + n2y * n2y + n2z * n2z == 0.0
+    ):
         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))
-
-    if vn == 0.0 or not (math.isfinite(vx) and math.isfinite(vy) and math.isfinite(vz)):
+    # 2. Intersection direction: compensated cross of the two plane normals.
+    vx_hi, vy_hi, vz_hi, vx_lo, vy_lo, vz_lo = accucross_pair(
+        n1x_hi,
+        n1y_hi,
+        n1z_hi,
+        n1x_lo,
+        n1y_lo,
+        n1z_lo,
+        n2x_hi,
+        n2y_hi,
+        n2z_hi,
+        n2x_lo,
+        n2y_lo,
+        n2z_lo,
+    )
+    vx = vx_hi + vx_lo
+    vy = vy_hi + vy_lo
+    vz = vz_hi + vz_lo
+    vn = math.sqrt(vx * vx + vy * vy + vz * vz)
+
+    if vn == 0.0 or not (
+        math.isfinite(vx)
+        and math.isfinite(vy)
+        and math.isfinite(vz)
+        and math.isfinite(vn)
+    ):
         # Parallel (coplanar) arcs: check whether endpoints of one lie on the other.
         if on_minor_arc(v0, w0, w1):
             res[count, 0] = v0[0]
@@ -372,14 +397,15 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
         return res[:count]
 
     # 3. Two antipodal candidate intersection points; keep those on both arcs.
+    inv = 1.0 / vn
     pos = np.empty(3)
-    pos[0] = vx
-    pos[1] = vy
-    pos[2] = vz
+    pos[0] = vx * inv
+    pos[1] = vy * inv
+    pos[2] = vz * inv
     neg = np.empty(3)
-    neg[0] = -vx
-    neg[1] = -vy
-    neg[2] = -vz
+    neg[0] = -pos[0]
+    neg[1] = -pos[1]
+    neg[2] = -pos[2]
 
     if on_minor_arc(pos, w0, w1) and on_minor_arc(pos, v0, v1):
         res[count, 0] = pos[0]
@@ -400,11 +426,20 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
 def gca_const_lat_intersection(gca_cart, const_z):
     """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.
+    Implements the ``accux_constlat`` algorithm from AccuSphGeom
+    (gca_constlat_intersection.hpp) using compensated arithmetic throughout
+    to achieve near-machine-precision accuracy even for arcs nearly tangent
+    to the latitude circle.
+
+    The algorithm:
+    1. Compute the arc's plane normal n = a × b via ``accucross`` (compensated).
+    2. Compute s2 = nx² + ny² and s3 = |n|² using compensated sum-of-squares
+       on the (hi, lo) pairs from ``accucross``.
+    3. Compute the discriminant planar_sq = s2 − s3·z₀² using compensated
+       arithmetic; take its accurate square root via ``acc_sqrt_re``.
+    4. Compute the two candidate intersection points using compensated 2-term
+       dot products for the x and y numerators, divided by s2.
+    5. Retain each candidate that is finite and lies on the minor arc.
 
     Parameters
     ----------
@@ -425,72 +460,108 @@ def gca_const_lat_intersection(gca_cart, const_z):
     x1 = gca_cart[0]
     x2 = gca_cart[1]
 
-    # 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_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_z:
-        res[0, 0] = x1[0]
-        res[0, 1] = x1[1]
-        res[0, 2] = x1[2]
-        return res
-    elif x2_at_z:
-        res[0, 0] = x2[0]
-        res[0, 1] = x2[1]
-        res[0, 2] = x2[2]
-        return res
+    # 1. Plane normal via compensated cross product (keeps hi, lo residuals).
+    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]
+    )
 
-    # 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")
-    if not in_between(z_min, const_z, z_max):
+    # 2. s2 = nx²+ny²  (compensated, on hi/lo pairs — matches sum_of_squares_c<2>).
+    s2_hi, s2_lo = _sum_sq_c2(nx_hi, nx_lo, ny_hi, ny_lo)
+    denom = s2_hi + s2_lo
+    if denom == 0.0:
         return res
 
-    # 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]
+    # 3. s3 = |n|² = nx²+ny²+nz²  (compensated — matches sum_of_squares_c<3>).
+    s3_hi, s3_lo = _sum_sq_c3(nx_hi, nx_lo, ny_hi, ny_lo, nz_hi, nz_lo)
+
+    # 4. zsq = z₀² exactly (two_prod replaces two_prod_fma; same exact result).
+    zsq_hi, zsq_lo = two_prod(const_z, const_z)
+
+    # 5. d = s3 · zsq  via 4-term compensated dot product matching C++:
+    #    compensated_dot_product({s3_hi, s3_hi, s3_lo, s3_lo},
+    #                            {zsq_hi, zsq_lo, zsq_hi, zsq_lo})
+    d_hi, d_lo = _cdp4(
+        s3_hi,
+        zsq_hi,
+        s3_hi,
+        zsq_lo,
+        s3_lo,
+        zsq_hi,
+        s3_lo,
+        zsq_lo,
     )
+    # Note: Numba doesn't allow negative sign in function args, so negate d_hi explicitly.
+    neg_d_hi = -d_hi
+
+    # 6. planar_sq = s2 − d  (compensated two_sum on the high parts + low correction).
+    e_hi, e_lo = two_sum(s2_hi, neg_d_hi)
+    planar_sq = e_hi + (e_lo + s2_lo - d_lo)
+
+    if planar_sq < 0.0:
+        return res
+
+    # 7. Accurate square root of discriminant.
+    s_root, s_corr = acc_sqrt_re(planar_sq)
+
+    # Collapse compensated values to scalars for the final formula.
     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
+    planar = s_root + s_corr
+
+    # 8. Numerators via 2-term compensated dot products (matches C++ accux_constlat).
+    #    x_pos = -(nx*nz*z₀  + (−ny)*planar) / denom
+    #    y_pos = -(ny*nz*z₀  +   nx *planar) / denom
+    #    x_neg = -(nx*nz*z₀  +   ny *planar) / denom
+    #    y_neg = -(ny*nz*z₀  + (−nx)*planar) / denom
+    xp_hi, xp_lo = _cdp2(nx * nz, const_z, -ny, planar)
+    yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
+    xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
+    yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
 
-    # 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[0] = -(xp_hi + xp_lo) * inv_denom
+    p1[1] = -(yp_hi + yp_lo) * inv_denom
     p1[2] = const_z
 
     p2 = np.empty(3)
-    p2[0] = cx - (-ny * s)
-    p2[1] = cy - (nx * s)
+    p2[0] = -(xn_hi + xn_lo) * inv_denom
+    p2[1] = -(yn_hi + yn_lo) * inv_denom
     p2[2] = const_z
 
-    # 5. Keep candidates that lie on the minor arc.
+    # 9a. Snap computed (x, y) to any arc endpoint that lies exactly on the latitude.
+    #     Adjacent edges sharing such an endpoint would otherwise return slightly
+    #     different coordinates; snapping gives them the same exact value so that
+    #     deduplication in the caller works correctly.  Matches Hongyu's suggestion
+    #     of mask-selection to snap after computing rather than branching out early.
+    _snap_sq = 1e-14  # distance² ≈ (1e-7)² — well above algorithm error (~1e-15)
+    for xe in (x1, x2):
+        if abs(xe[2] - const_z) <= ERROR_TOLERANCE:
+            dx = p1[0] - xe[0]
+            dy = p1[1] - xe[1]
+            if dx * dx + dy * dy < _snap_sq:
+                p1[0] = xe[0]
+                p1[1] = xe[1]
+            dx = p2[0] - xe[0]
+            dy = p2[1] - xe[1]
+            if dx * dx + dy * dy < _snap_sq:
+                p2[0] = xe[0]
+                p2[1] = xe[1]
+
+    # 9b. Retain each candidate that is finite and lies 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)
 
+    # When both candidates are valid but nearly identical (tangent/endpoint case),
+    # treat as a single intersection — same as the C++ scalar gca_constlat_intersection
+    # which returns only one point when status==0 (exactly one candidate lies on the arc).
+    if p1_ok and p2_ok:
+        dx = p1[0] - p2[0]
+        dy = p1[1] - p2[1]
+        if dx * dx + dy * dy < _snap_sq:
+            p2_ok = False
+
     if p1_ok and p2_ok:
         res[0, 0] = p1[0]
         res[0, 1] = p1[1]
diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py
index 36b06f674..7a10934ae 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -85,7 +85,7 @@ def _counts_as_crossing(A, B, q, R):
 
     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
+    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.
     """
@@ -127,7 +127,7 @@ def _point_in_polygon_sphere(q, polygon):
 
     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
+    (compensated) 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.
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index ca5ca5183..5f99ac010 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -1,628 +1,426 @@
-import sys
-
-import numpy as np
-
-
-def _fmms(a, b, c, d):
-    """
-    Calculate the difference of products using the FMA (fused multiply-add) operation: (a * b) - (c * d).
-
-    This operation leverages the fused multiply-add operation when available on the system and rounds the result only once.
-    The relative error of this operation is bounded by 1.5 ulps when no overflow and underflow occur.
-
-    Parameters
-    ----------
-    a (float): The first value of the first product.
-    b (float): The second value of the first product.
-    c (float): The first value of the second product.
-    d (float): The second value of the second product.
-
-    Returns
-    -------
-    float: The difference of the two products.
-
-    Example
-    -------
-    >>> _fmms(3.0, 2.0, 1.0, 1.0)
-    5.0
-
-    Reference
-    ---------
-    Claude-Pierre Jeannerod, Nicolas Louvet, and Jean-Michel Muller, Further
-    analysis of Kahan’s algorithm for the accurate computation of 2 x 2 determinants,
-    Mathematics of Computation, vol. 82, no. 284, pp. 2245-2264, 2013.
-    [Read more](https://ens-lyon.hal.science/ensl-00649347) (DOI: 10.1090/S0025-5718-2013-02679-8)
-    """
-    import pyfma
-
-    cd = c * d
-    err = pyfma.fma(-c, d, cd)
-    dop = pyfma.fma(a, b, -cd)
-    return dop + err
-
-
-def cross_fma(v1, v2):
-    """Calculate the cross product of two 3D vectors utilizing the fused
-    multiply-add operation.
-
-    Parameters
-    ----------
-    v1 (np.array): The first vector of size 3.
-    v2 (np.array): The second vector of size 3.
-
-    Returns
-    -------
-    np.array: The cross product vector of size 3.
-
-    Example
-    -------
-    >>> v1 = np.array([1.0, 2.0, 3.0])
-    >>> v2 = np.array([4.0, 5.0, 6.0])
-    >>> cross_fma(v1, v2)
-    array([-3.0, 6.0, -3.0])
-    """
-    x = _fmms(v1[1], v2[2], v1[2], v2[1])
-    y = _fmms(v1[2], v2[0], v1[0], v2[2])
-    z = _fmms(v1[0], v2[1], v1[1], v2[0])
-    return np.array([x, y, z])
-
-
-def dot_fma(v1, v2):
-    """Calculate the dot product of two vectors using the FMA (fused multiply-
-    add) operation.
-
-    This implementation leverages the FMA operation to provide a more accurate result. Currently the ComptDot product
-    algorithm is used, which provides a relative error of approvimately u + n^2u^2cond(v1 dot v2), where u is 0.5 ulps,
-    n is the length of the vectors, and cond(v1 dot v2) is the condition number of the naive dot product of v1 and v2.
-    This operatin takes approvimately 3 + 10 * n flops, where n is the length of the vectors.
-
-    Parameters
-    ----------
-    v1 : list of float
-        The first vector.
-    v2 : list of float
-        The second vector. Must be the same length as v1.
-
-    Returns
-    -------
-    float
-        The dot product of the two vectors.
-
-    Raises
-    ------
-    ValueError
-        If the input vectors `v1` and `v2` are not of the same length.
-
-    Examples
-    --------
-    >>> dot_fma([1.0, 2.0, 3.0], [4.0, 5.0, 6.0])
-    32.0
-
-    References
-    ----------
-    S. Graillat, Ph. Langlois, and N. Louvet. "Accurate dot products with FMA." Presented at RNC 7, 2007, Nancy, France.
-    DALI-LP2A Laboratory, University of Perpignan, France.
-    """
-    if len(v1) != len(v2):
-        raise ValueError("Input vectors must be of the same length")
-
-    s, c = _two_prod_fma(v1[0], v2[0])
-    for i in range(1, len(v1)):
-        p, pi = _two_prod_fma(v1[i], v2[i])
-        s, signma = _two_sum(s, p)
-        c = c + pi + signma
-
-    return s + c
-
-
-def _two_prod_fma(a, b):
-    """Error-free transformation of the product of two floating-point numbers
-    using FMA, such that a * b = x + y exactly.
+"""Compensated floating-point primitives for accurate spherical geometry.
+
+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.
+
+Naming note
+-----------
+The term "error-free transformation" (EFT) strictly applies to ``two_sum``
+and ``two_prod``, which capture their rounding errors exactly so that
+``hi + lo`` equals the mathematical result with zero information loss.
+``diff_of_products``, ``accucross``, and ``accucross_pair`` use those EFT
+building blocks to achieve near-double precision for cross products, but they
+are compensated algorithms, not zero-error transformations.
+
+All functions are ``@njit``-compiled and use the portable Veltkamp-splitting
+form of ``two_prod`` (no FMA dependency), making them suitable for use inside
+Numba-compiled geometry kernels.
+
+These primitives are a Python/Numba port of 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.
+"""
+
+import math
+
+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
-        The floating-point numbers to be multiplied.
+        Input values.
 
     Returns
     -------
-    tuple of float
-        The product and the error term.
-
-    Examples
-    --------
-    >>> _two_prod_fma(1.0, 2.0)
-    (2.0, 0.0)
-
-    Reference
-    ---------
-    Stef Graillat. Accurate Floating Point Product and Exponentiation.
-    IEEE Transactions on Computers, 58(7), 994–1000, 2009.10.1109/TC.2008.215.
+    s : float
+        Rounded sum fl(a + b).
+    e : float
+        Rounding error term; s + e = a + b exactly.
     """
-    import pyfma
+    s = a + b
+    bp = s - a
+    e = (a - (s - bp)) + (b - bp)
+    return s, e
 
-    x = a * b
-    y = pyfma.fma(a, b, -x)
-    return x, y
 
+@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.
 
-def _err_fmac(a, b, c):
-    """Error-free transformation for the FMA operation. such that x =
-    FMA(a,b,c) and a * b + c = x + y + z exactly. Thhis function is only
-    available in round to the nearest mode and takes approximately 17 flops.
-
-    Parameters
-    ----------
-    a, b, c : float
-        The operands for the FMA operation.
-
-    Returns
-    -------
-    tuple of float
-        The result of the FMA operation and two error terms.
-
-    References
-    ----------
-    Graillat, Stef & Langlois, Philippe & Louvet, Nicolas. (2006). Improving the compensated Horner scheme with
-    a Fused Multiply and Add. 2. 1323-1327. 10.1145/1141277.1141585.
-
-    Ogita, Takeshi & Rump, Siegfried & Oishi, Shin’ichi. (2005). Accurate Sum and Dot Product.
-    SIAM J. Scientific Computing. 26. 1955-1988. 10.1137/030601818.
-    """
-    if sys.float_info.rounds == 1:
-        import pyfma
-
-        x = pyfma.fma(a, b, c)
-        u1, u2 = _fast_two_mult(a, b)
-        alpha1, alpha2 = _two_sum(c, u2)
-        beta1, beta2 = _two_sum(u1, alpha1)
-        gamma = (beta1 - x) + beta2
-        y, z = _fast_two_sum(gamma, alpha2)
-        return x, y, z
-    else:
-        raise ValueError(
-            "3FMA operation is only available in round to the nearest mode. and the current mode is "
-            + str(sys.float_info.rounds)
-        )
-
-
-def _two_sum(a, b):
-    """Error-free transformation of the sum of two floating-point numbers such
-    that a + b = x + y 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
-        The floating-point numbers to be added.
+        Input values.
 
     Returns
     -------
-    tuple of float
-        The sum and the error term.
-
-    Examples
-    --------
-    >>> _two_sum(1.0, 2.0)
-    (3.0, 0.0)
-
-    Reference
-    ---------
-    D. Knuth. 1998. The Art of Computer Programming (3rd ed.). Vol. 2. Addison-Wesley, Reading, MA.
+    p : float
+        Rounded product fl(a * b).
+    e : float
+        Rounding error term; p + e = a * b exactly.
     """
-    x = a + b
-    z = x - a
-    y = (a - (x - z)) + (b - z)
-    return x, y
-
-
-def _fast_two_mult(a, b):
-    """Error-free transformation of the product of two floating-point numbers
-    such that a * b = x + y exactly.
-
-    This function is faster than the _two_prod_fma function.
+    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 : float
-        The floating-point numbers to be multiplied.
+    a, b, c, d : float
+        Input scalars; computes a*b - c*d.
 
     Returns
     -------
-    tuple of float
-        The product and the error term.
-
-    References
-    ----------
-    Vincent Lefèvre, Nicolas Louvet, Jean-Michel Muller, Joris Picot, and Laurence Rideau. 2023.
-    Accurate Calculation of Euclidean Norms Using Double-word Arithmetic.
-    ACM Trans. Math. Softw. 49, 1, Article 1 (March 2023), 34 pages. https://doi.org/10.1145/3568672
+    hi : float
+        High-order part of the accurate result.
+    lo : float
+        Low-order correction term; hi + lo equals the accurate value.
     """
-    import pyfma
+    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
 
-    x = a * b
-    y = pyfma.fma(a, b, -x)
-    return x, y
 
+@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.
 
-def _fast_two_sum(a, b):
-    """Compute a fast error-free transformation of the sum of two floating-
-    point numbers.
-
-    This function is a faster alternative to `_two_sum` for computing the sum
-    of two floating-point numbers `a` and `b`, such that a + b = x + y exactly.
-    Note: |a| must be no less than |b|.
+    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
     ----------
-    a, b : float
-        The floating-point numbers to be added. It is required that |a| >= |b|.
+    a0, a1, a2 : float
+        Components of vector a.
+    b0, b1, b2 : float
+        Components of vector b.
 
     Returns
     -------
-    tuple of float
-        The rounded sum of `a` and `b`, and the error term. The error term represents the difference between the exact sum and the rounded sum.
-
-    Raises
-    ------
-    ValueError
-        If |a| < |b|.
-
-    Examples
-    --------
-    >>> _fast_two_sum(2.0, 1.0)
-    (3.0, 0.0)
-
-    >>> _fast_two_sum(1.0, 2.0)
-    Traceback (most recent call last):
-        ...
-    ValueError: |a| must be greater than or equal to |b|.
-
-    Reference
-    ---------
-    T. J. Dekker. A Floating-Point Technique for Extending the Available Precision.
-    Numerische Mathematik, 18(3), 224–242,1971. 10.1007/BF01397083.
-    Available at: https://doi.org/10.1007/BF01397083.
+    x_hi, y_hi, z_hi, x_lo, y_lo, z_lo : float
+        High and low parts of each cross-product component.
     """
-    if abs(a) >= abs(b):
-        x = a + b
-        b_tile = x - a
-        y = b - b_tile
-        return x, y
-
-    else:
-        raise ValueError("|a| must be greater than or equal to |b|.")
-
-
-def _comp_prod_fma(vec):
-    """Compute the compensated product using Fused Multiply-Add (FMA).
-
-    This function computes the product of elements in a vector using a
-    compensated algorithm with Fused Multiply-Add to reduce numerical errors.
-
-    Parameters
-    ----------
-    vec : list of float
-        The vector whose elements are to be multiplied.
-
-    Returns
-    -------
-    float
-        The compensated product of the elements in the vector.
-
-    Examples
-    --------
-    >>> _comp_prod_fma([1.1, 2.2, 3.3])
-    7.986000000000001
-
-    Reference
-    ---------
-    Takeshi Ogita, Siegfried M. Rump, and Shin'ichi Oishi. 2005. Accurate Sum and Dot Product.
-    SIAM J. Sci. Comput. 26, 6 (2005), 1955–1988. https://doi.org/10.1137/030601818
+    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
+
+
+@njit(cache=True, inline="always")
+def _cdp8(
+    a0,
+    a1,
+    a2,
+    a3,
+    a4,
+    a5,
+    a6,
+    a7,
+    b0,
+    b1,
+    b2,
+    b3,
+    b4,
+    b5,
+    b6,
+    b7,
+):
+    """Compensated sum of 8 exact products: Σ ai*bi, i=0..7.
+
+    Uses ``two_prod`` + ``two_sum`` accumulation (Ogita-Rump-Oishi style)
+    so the result has error bounded by one ulp of the true value regardless
+    of cancellation in intermediate sums.
     """
-    import pyfma
-
-    p1 = vec[0]
-    e1 = 0.0
-    for i in range(1, len(vec)):
-        p_i, pi = _two_prod_fma(p1, vec[i])
-        ei = pyfma.fma(e1, vec[i], pi)
-        p1 = p_i
-        e1 = ei
-    res = p1 + e1
-    return res
-
-
-def _sum_of_squares_re(vec):
-    """Compute the sum of squares of a vector using a compensated algorithm.
-
-    This function calculates the sum of squares of the elements in a vector,
-    employing a compensation technique to reduce numerical errors.
+    s, lo = two_prod(a0, b0)
+    p, e = two_prod(a1, b1)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a2, b2)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a3, b3)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a4, b4)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a5, b5)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a6, b6)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a7, b7)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    return s, lo
+
+
+@njit(cache=True, inline="always")
+def accucross_pair(
+    ax_hi,
+    ay_hi,
+    az_hi,
+    ax_lo,
+    ay_lo,
+    az_lo,
+    bx_hi,
+    by_hi,
+    bz_hi,
+    bx_lo,
+    by_lo,
+    bz_lo,
+):
+    """Compensated cross product of two compensated vectors.
+
+    Computes (a_hi + a_lo) × (b_hi + b_lo) using a compensated 8-term dot
+    product for each component, matching the two-argument ``accucross`` overload
+    in the AccuSphGeom C++ library.  This is more accurate than collapsing
+    (hi, lo) to a single float before the cross product.
 
     Parameters
     ----------
-    vec : list of float
-        The vector whose elements' squares are to be summed.
+    ax_hi, ay_hi, az_hi : float
+        High parts of vector a.
+    ax_lo, ay_lo, az_lo : float
+        Low  parts of vector a (rounding residuals from a prior compensated operation).
+    bx_hi, by_hi, bz_hi : float
+        High parts of vector b.
+    bx_lo, by_lo, bz_lo : float
+        Low  parts of vector b.
 
     Returns
     -------
-    float
-        The compensated sum of the squares of the elements in the vector.
-
-    Examples
-    --------
-    >>> _sum_of_squares_re([1.0, 2.0, 3.0])
-    14.0
-
-    Reference
-    ---------
-    Stef Graillat, Christoph Lauter, PING Tak Peter Tang,
-    Naoya Yamanaka, and Shin’ichi Oishi. Efficient Calculations of Faith-
-    fully Rounded L2-Norms of n-Vectors. ACM Transactions on Mathemat-
-    ical Software, 41(4), Article 24, 2015. 10.1145/2699469. Available at:
-    https://doi.org/10.1145/2699469.
+    x_hi, y_hi, z_hi, x_lo, y_lo, z_lo : float
+        Compensated cross-product components.
     """
-    P, p = _two_square(vec)
-    S, s = _two_sum(P[0], P[1])
-    for i in range(2, len(vec)):
-        H, h = _two_sum(S, P[i])
-        S, s = _two_sum(H, s + h)
-    sump = sum(p)
-    H, h = _two_sum(S, sump)
-    S, s = _fast_two_sum(H, s + h)
-    return S + s
-
-
-def _vec_sum(p):
-    """Compute the sum of a vector using a compensated summation algorithm.
-
-    This function calculates the sum of the elements in a vector using a
-    compensated summation algorithm to reduce numerical errors.
-
-    Parameters
-    ----------
-    p : list of float
-        The vector whose elements are to be summed.
-
-    Returns
-    -------
-    float
-        The compensated sum of the elements in the vector.
-
-    Examples
-    --------
-    >>> _vec_sum([1.0, 2.0, 3.0])
-    6.0
-
-    Reference
-    ---------
-    Takeshi Ogita, Siegfried M. Rump, and Shin'ichi Oishi. 2005. Accurate Sum and Dot Product.
-    SIAM J. Sci. Comput. 26, 6 (2005), 1955–1988. https://doi.org/10.1137/030601818
+    # x = (ay*bz) - (az*by), expanded over all four hi/lo cross-terms
+    x_hi, x_lo = _cdp8(
+        ay_hi,
+        ay_hi,
+        ay_lo,
+        ay_lo,
+        -az_hi,
+        -az_hi,
+        -az_lo,
+        -az_lo,
+        bz_hi,
+        bz_lo,
+        bz_hi,
+        bz_lo,
+        by_hi,
+        by_lo,
+        by_hi,
+        by_lo,
+    )
+    # y = (az*bx) - (ax*bz)
+    y_hi, y_lo = _cdp8(
+        az_hi,
+        az_hi,
+        az_lo,
+        az_lo,
+        -ax_hi,
+        -ax_hi,
+        -ax_lo,
+        -ax_lo,
+        bx_hi,
+        bx_lo,
+        bx_hi,
+        bx_lo,
+        bz_hi,
+        bz_lo,
+        bz_hi,
+        bz_lo,
+    )
+    # z = (ax*by) - (ay*bx)
+    z_hi, z_lo = _cdp8(
+        ax_hi,
+        ax_hi,
+        ax_lo,
+        ax_lo,
+        -ay_hi,
+        -ay_hi,
+        -ay_lo,
+        -ay_lo,
+        by_hi,
+        by_lo,
+        by_hi,
+        by_lo,
+        bx_hi,
+        bx_lo,
+        bx_hi,
+        bx_lo,
+    )
+    return x_hi, y_hi, z_hi, x_lo, y_lo, z_lo
+
+
+# ---------------------------------------------------------------------------
+# Compensated dot products and sum-of-squares (fixed small sizes)
+#
+# These port accusphgeom::numeric::compensated_dot_product and
+# accusphgeom::numeric::sum_of_squares_c from eft.hpp, using our Veltkamp-
+# splitting two_prod instead of FMA.  Fixed-size variants are used because
+# Numba does not support generic runtime-length accumulations inside @njit.
+# ---------------------------------------------------------------------------
+
+
+@njit(cache=True, inline="always")
+def _cdp2(a0, b0, a1, b1):
+    """Compensated dot product of 2 pairs: a0*b0 + a1*b1."""
+    s, lo = two_prod(a0, b0)
+    p, e = two_prod(a1, b1)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    return s2, lo
+
+
+@njit(cache=True, inline="always")
+def _cdp4(a0, b0, a1, b1, a2, b2, a3, b3):
+    """Compensated dot product of 4 pairs: Σ ai*bi, i=0..3."""
+    s, lo = two_prod(a0, b0)
+    p, e = two_prod(a1, b1)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a2, b2)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    s = s2
+    p, e = two_prod(a3, b3)
+    s2, e2 = two_sum(s, p)
+    lo += e + e2
+    return s2, lo
+
+
+@njit(cache=True, inline="always")
+def _sum_sq_c2(h0, l0, h1, l1):
+    """Compensated sum of squares for 2 (hi, lo) pairs: h0²+l0²+h1²+l1².
+
+    Mirrors sum_of_squares_c<T, 2> from accusphgeom/numeric/eft.hpp, which
+    constructs lhs = rhs = [h0, l0, h1, l1] and calls compensated_dot_product.
+    Used to compute nx²+ny² accurately from the compensated normal (hi, lo).
     """
-    pi_1 = p[0]
-    sigma_i1 = 0
-
-    for i in range(1, len(p)):
-        pi, qi = _two_sum(pi_1, p[i])
-        sigma_i = sigma_i1 + qi
-        pi_1 = pi
-        sigma_i1 = sigma_i
+    return _cdp4(h0, h0, l0, l0, h1, h1, l1, l1)
 
-    res = pi_1 + sigma_i1
-    return res
 
+@njit(cache=True, inline="always")
+def _sum_sq_c3(h0, l0, h1, l1, h2, l2):
+    """Compensated sum of squares for 3 (hi, lo) pairs: h0²+l0²+h1²+l1²+h2²+l2².
 
-def _norm_faithful(x):
-    """Compute the faithful norm of a vector.
-
-    This function calculates the faithful norm (L2 norm) of a vector,
-    which is a more numerically stable version of the Euclidean norm.
-
-    Parameters
-    ----------
-    x : list of float
-        The vector whose norm is to be computed.
-
-    Returns
-    -------
-    float
-        The faithful norm of the vector.
-
-    Examples
-    --------
-    >>> _norm_faithful([1.0, 2.0, 3.0])
-    3.7416573867739413
+    Mirrors sum_of_squares_c<T, 3> from accusphgeom/numeric/eft.hpp, which
+    constructs lhs = rhs = [h0, l0, h1, l1, h2, l2] and calls a 6-term CDP.
+    We use _cdp8 with two zero-padding pairs (adding zero products).
+    Used to compute |n|² = nx²+ny²+nz² accurately from the compensated normal.
     """
-    return _norm_l(x)
-
+    # lhs = rhs = [h0, l0, h1, l1, h2, l2, 0, 0]
+    return _cdp8(h0, l0, h1, l1, h2, l2, 0.0, 0.0, h0, l0, h1, l1, h2, l2, 0.0, 0.0)
 
-def _norm_l(x):
-    """Compute the L2 norm (Euclidean norm) of a vector using a compensated
-    algorithm.
-
-    This function calculates the L2 norm of a vector, employing a compensation
-    technique to reduce numerical errors during the computation. It involves
-    computing the sum of squares of the vector elements in a numerically stable way.
-
-    Parameters
-    ----------
-    x : list of float
-        The vector whose L2 norm is to be computed.
-
-    Returns
-    -------
-    float
-        The compensated L2 norm of the vector.
-
-    Examples
-    --------
-    >>> _norm_l([1.0, 2.0, 3.0])
-    3.7416573867739413
-
-    Reference
-    ---------
-    Vincent Lef`evre, Nicolas Louvet, Jean-Michel Muller,
-    Joris Picot, and Laurence Rideau. Accurate Calculation of Euclidean
-    Norms Using Double-Word Arithmetic. ACM Transactions on Mathemat-
-    ical Software, 49(1), 1–34, March 2023. 10.1145/3568672
-    """
-    P, p = _two_square(x)
-    S, s = _two_sum(P[0], P[1])
-    for i in range(2, len(x)):
-        H, h = _two_sum(S, P[i])
-        S, s = _two_sum(H, s + h)
-    sump = sum(p)
-    H, h = _two_sum(S, sump)
-    S, s = _fast_two_sum(H, s + h)
-    res = _acc_sqrt(S, s)
-    return res
-
-
-def _norm_g(x):
-    """Compute the compensated Euclidean norm of a vector.
-
-    This function calculates the Euclidean norm (L2 norm) of a vector,
-    using a compensated algorithm to reduce numerical errors.
-
-    Parameters
-    ----------
-    x : list of float
-        The vector whose norm is to be computed.
-
-    Returns
-    -------
-    float
-        The compensated Euclidean norm of the vector.
-
-    Examples
-    --------
-    >>> _norm_g([1.0, 2.0, 3.0])
-    3.7416573867739413
-
-    Reference
-    ---------
-    Stef Graillat, Christoph Lauter, PING Tak Peter Tang,
-    Naoya Yamanaka, and Shin’ichi Oishi. Efficient Calculations of Faith-
-    fully Rounded L2-Norms of n-Vectors. ACM Transactions on Mathemat-
-    ical Software, 41(4), Article 24, 2015. 10.1145/2699469. Available at:
-    https://doi.org/10.1145/2699469.
-    """
-    S = 0
-    s = 0
-    for x_i in x:
-        P, p = _two_prod_fma(x_i, x_i)
-        H, h = _two_sum(S, P)
-        c = s + p
-        d = h + c
-        S, s = _fast_two_sum(H, d)
-    res = _acc_sqrt(S, s)
-    return res
-
-
-def _two_square(Aa):
-    """Compute the square of a number with a compensation for the round-off
-    error.
-
-    This function calculates the square of a given number and compensates
-    for the round-off error that occurs during the squaring.
-
-    Parameters
-    ----------
-    Aa : float
-        The number to be squared.
-
-    Returns
-    -------
-    tuple of float
-        The square of the number and the compensated round-off error.
-
-    Examples
-    --------
-    >>> _two_square(2.0)
-    (4.0, 0.0)
-
-    Reference
-    ---------
-    Siegfried Rump. Fast and accurate computation of the Euclidean norm of a vector. J
-    apan Journal of Industrial and Applied Mathematics, 40, 2023. 10.1007/s13160-023-00593-8
-    """
-    P = Aa * Aa
-    A, a = _split(Aa)
-    p = a * a - ((P - A * A) - 2 * a * A)
-    return P, p
-
-
-def _acc_sqrt(T, t):
-    """Compute the accurate square root of a number with a compensation for
-    round-off error.
-
-    This function calculates the square root of a number, taking into account
-    a compensation term for the round-off error.
-
-    Parameters
-    ----------
-    T : float
-        The number whose square root is to be computed.
-    t : float
-        The compensation term for round-off error.
-
-    Returns
-    -------
-    float
-        The accurate square root of the number.
-
-    Examples
-    --------
-    >>> _acc_sqrt(9.0, 0.0)
-    3.0
-
-    References
-    ----------
-    Vincent Lef`evre, Nicolas Louvet, Jean-Michel Muller,
-    Joris Picot, and Laurence Rideau. Accurate Calculation of Euclidean
-    Norms Using Double-Word Arithmetic. ACM Transactions on Mathematical Software, 49(1), 1–34, March 2023. 10.1145/3568672
-
-    Marko Lange and Siegfried Rump. Faithfully Rounded
-    Floating-point Computations. ACM Transactions on Mathematical Soft-
-    ware, 46, 1-20, 2020. 10.1145/3290955
-    """
-    P = np.sqrt(T)
-    H, h = _two_square(P)
-    r = (T - H) - h
-    r = t + r
-    p = r / (2 * P)
-    res = P + p
-    return res
 
+@njit(cache=True, inline="always")
+def acc_sqrt_re(value, error=0.0):
+    """Accurate square root: return (root, correction) s.t. root+correction ≈ sqrt(value+error).
 
-def _split(a):
-    """Split a floating-point number into two parts: The rounded floating point
-    presentation and its error. This can be utlized to substitute the FMA
-    operation on the software level.
+    Mirrors accusphgeom::numeric::acc_sqrt_re from eft.hpp.  Computes
+    root = fl(sqrt(value)), measures the rounding error of root*root via
+    two_prod, then recovers a correction term from the residual.  When
+    ``error`` is provided (e.g. the ``lo`` half of a compensated sum),
+    it is folded into the residual so the correction accounts for the
+    full compensated input.
 
     Parameters
     ----------
-    a : float
-        The number to be split.
+    value : float
+        Non-negative scalar (the ``hi`` part of a compensated value).
+    error : float, optional
+        Low-order correction to ``value`` (default 0.0).
 
     Returns
     -------
-    tuple of float
-        The high and low precision parts of the number.
-
-    Examples
-    --------
-    >>> _split(12345.6789)
-    (12345.67578125, 0.00311875)
-
-    Reference
-    ---------
-     T. J. Dekker. A Floating-Point Technique for Extending the Available Precision.
-     Numerische Mathematik, 18(3), 224–242,
-    1971. 10.1007/BF01397083. Available at: https://doi.org/10.1007/
-    BF01397083.
-    27
+    root : float
+        Rounded sqrt, fl(sqrt(value)).
+    correction : float
+        Additive correction; root + correction ≈ sqrt(value + error) to ~1 ulp.
     """
-    y = (2**27 + 1) * a
-    x = y - (y - a)
-    y = a - x
-    return x, y
+    root = math.sqrt(value)
+    if root == 0.0:
+        return 0.0, 0.0
+    sq_hi, sq_lo = two_prod(root, root)
+    residual = (value - sq_hi) + (error - sq_lo)
+    correction = residual / (2.0 * root)
+    return root, correction

From 683bc9a59bbea906b06ce81986640c31acbb4fcc Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 29 May 2026 18:10:59 -0500
Subject: [PATCH 19/51] Fix RTD: remove RST-invalid numbered list from
 gca_const_lat_intersection docstring

---
 uxarray/grid/intersections.py | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index f71bf3d4f..7ccaace12 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -431,15 +431,10 @@ def gca_const_lat_intersection(gca_cart, const_z):
     to achieve near-machine-precision accuracy even for arcs nearly tangent
     to the latitude circle.
 
-    The algorithm:
-    1. Compute the arc's plane normal n = a × b via ``accucross`` (compensated).
-    2. Compute s2 = nx² + ny² and s3 = |n|² using compensated sum-of-squares
-       on the (hi, lo) pairs from ``accucross``.
-    3. Compute the discriminant planar_sq = s2 − s3·z₀² using compensated
-       arithmetic; take its accurate square root via ``acc_sqrt_re``.
-    4. Compute the two candidate intersection points using compensated 2-term
-       dot products for the x and y numerators, divided by s2.
-    5. Retain each candidate that is finite and lies on the minor arc.
+    Computes the plane normal via ``accucross``, forms the discriminant using
+    compensated sum-of-squares and ``acc_sqrt_re``, solves for the two candidate
+    intersection points with compensated dot products, and retains only those
+    that are finite and lie on the minor arc.
 
     Parameters
     ----------

From 3ae95f31e4ce8a34038549198590d6bcd6eb221a Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sun, 31 May 2026 21:33:20 -0500
Subject: [PATCH 20/51] Fix RTD notebook kernel metadata

---
 docs/user-guide/spherical-geometry-accuracy.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index 321bd1824..b57a7a5f9 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -520,9 +520,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "uxarray_env3.12",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "uxarray_env3.12"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

From 7e35cee26c10f2564e7b39aa61331dce0e52d66c Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 4 Jun 2026 13:20:45 -0500
Subject: [PATCH 21/51] Address AccuSphGeom review feedback

---
 uxarray/grid/bounds.py        |  22 ++--
 uxarray/grid/intersections.py | 229 +++++++++++++---------------------
 uxarray/utils/computing.py    |  10 +-
 3 files changed, 105 insertions(+), 156 deletions(-)

diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
index 2d07a601a..84a941ebc 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -27,11 +27,6 @@
 # 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.
@@ -111,11 +106,16 @@ def _face_location_info(face_vertices, polar_cap_z):
         if z_min_candidate < z_min:
             z_min = z_min_candidate
 
-    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
+    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)
@@ -423,7 +423,7 @@ def _populate_face_bounds(
             grid.node_x.values,
             grid.node_y.values,
             grid.node_z.values,
-            _POLAR_CAP_Z,
+            math.sin(80.0 * math.pi / 180.0),
             _SNAP_TOL_DEG,
         )
     else:
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 7ccaace12..e87816056 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -312,18 +312,6 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     Uses ``accucross`` (compensated cross products) and ``on_minor_arc`` (compensated
     arc membership) to avoid the catastrophic cancellation that affects naive
     cross product implementations when arcs are nearly parallel.
-
-    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.
-
-    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")
@@ -333,7 +321,6 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     v0 = gca_b_xyz[0]
     v1 = gca_b_xyz[1]
 
-    # 1. Plane normals via accurate cross products — keep compensated (hi, lo).
     n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = accucross(
         w0[0], w0[1], w0[2], w1[0], w1[1], w1[2]
     )
@@ -344,20 +331,6 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     res = np.empty((2, 3))
     count = 0
 
-    # Degenerate check: collapsed (zero-length) input arc.
-    n1x = n1x_hi + n1x_lo
-    n1y = n1y_hi + n1y_lo
-    n1z = n1z_hi + n1z_lo
-    n2x = n2x_hi + n2x_lo
-    n2y = n2y_hi + n2y_lo
-    n2z = n2z_hi + n2z_lo
-    if (
-        n1x * n1x + n1y * n1y + n1z * n1z == 0.0
-        or n2x * n2x + n2y * n2y + n2z * n2z == 0.0
-    ):
-        return res[:count]
-
-    # 2. Intersection direction: compensated cross of the two plane normals.
     vx_hi, vy_hi, vz_hi, vx_lo, vy_lo, vz_lo = accucross_pair(
         n1x_hi,
         n1y_hi,
@@ -383,7 +356,8 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
         and math.isfinite(vz)
         and math.isfinite(vn)
     ):
-        # Parallel (coplanar) arcs: check whether endpoints of one lie on the other.
+        # Coplanar overlap is outside the AccuXGCA intersection kernel. Preserve
+        # the historical UXarray behavior by detecting shared endpoints here.
         if on_minor_arc(v0, w0, w1):
             res[count, 0] = v0[0]
             res[count, 1] = v0[1]
@@ -396,7 +370,6 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
             count += 1
         return res[:count]
 
-    # 3. Two antipodal candidate intersection points; keep those on both arcs.
     inv = 1.0 / vn
     pos = np.empty(3)
     pos[0] = vx * inv
@@ -423,58 +396,19 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
 
 
 @njit(cache=True)
-def gca_const_lat_intersection(gca_cart, const_z):
-    """Find intersection point(s) of a great-circle arc and a constant-latitude line.
-
-    Implements the ``accux_constlat`` algorithm from AccuSphGeom
-    (gca_constlat_intersection.hpp) using compensated arithmetic throughout
-    to achieve near-machine-precision accuracy even for arcs nearly tangent
-    to the latitude circle.
-
-    Computes the plane normal via ``accucross``, forms the discriminant using
-    compensated sum-of-squares and ``acc_sqrt_re``, solves for the two candidate
-    intersection points with compensated dot products, and retains only those
-    that are finite and lie on the minor arc.
-
-    Parameters
-    ----------
-    gca_cart : np.ndarray, shape (2, 3)
-        Cartesian coordinates of the two endpoints of the great-circle arc.
-    const_z : float
-        The constant z-coordinate (= sin(latitude)) of the latitude line.
-
-    Returns
-    -------
-    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)
-
+def _try_gca_const_lat_intersection(gca_cart, const_z):
+    """AccuSphGeom-style GCA/constant-latitude kernel."""
     x1 = gca_cart[0]
     x2 = gca_cart[1]
 
-    # 1. Plane normal via compensated cross product (keeps hi, lo residuals).
     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]
     )
 
-    # 2. s2 = nx²+ny²  (compensated, on hi/lo pairs — matches sum_of_squares_c<2>).
     s2_hi, s2_lo = _sum_sq_c2(nx_hi, nx_lo, ny_hi, ny_lo)
     denom = s2_hi + s2_lo
-    if denom == 0.0:
-        return res
-
-    # 3. s3 = |n|² = nx²+ny²+nz²  (compensated — matches sum_of_squares_c<3>).
     s3_hi, s3_lo = _sum_sq_c3(nx_hi, nx_lo, ny_hi, ny_lo, nz_hi, nz_lo)
-
-    # 4. zsq = z₀² exactly (two_prod replaces two_prod_fma; same exact result).
     zsq_hi, zsq_lo = two_prod(const_z, const_z)
-
-    # 5. d = s3 · zsq  via 4-term compensated dot product matching C++:
-    #    compensated_dot_product({s3_hi, s3_hi, s3_lo, s3_lo},
-    #                            {zsq_hi, zsq_lo, zsq_hi, zsq_lo})
     d_hi, d_lo = _cdp4(
         s3_hi,
         zsq_hi,
@@ -485,93 +419,108 @@ def gca_const_lat_intersection(gca_cart, const_z):
         s3_lo,
         zsq_lo,
     )
-    # Note: Numba doesn't allow negative sign in function args, so negate d_hi explicitly.
-    neg_d_hi = -d_hi
 
-    # 6. planar_sq = s2 − d  (compensated two_sum on the high parts + low correction).
-    e_hi, e_lo = two_sum(s2_hi, neg_d_hi)
+    e_hi, e_lo = two_sum(s2_hi, -d_hi)
     planar_sq = e_hi + (e_lo + s2_lo - d_lo)
-
     if planar_sq < 0.0:
-        return res
-
-    # 7. Accurate square root of discriminant.
-    s_root, s_corr = acc_sqrt_re(planar_sq)
+        root_arg = np.nan
+    else:
+        root_arg = planar_sq
+    s_root, s_corr = acc_sqrt_re(root_arg)
 
-    # Collapse compensated values to scalars for the final formula.
     nx = nx_hi + nx_lo
     ny = ny_hi + ny_lo
     nz = nz_hi + nz_lo
     planar = s_root + s_corr
 
-    # 8. Numerators via 2-term compensated dot products (matches C++ accux_constlat).
-    #    x_pos = -(nx*nz*z₀  + (−ny)*planar) / denom
-    #    y_pos = -(ny*nz*z₀  +   nx *planar) / denom
-    #    x_neg = -(nx*nz*z₀  +   ny *planar) / denom
-    #    y_neg = -(ny*nz*z₀  + (−nx)*planar) / denom
     xp_hi, xp_lo = _cdp2(nx * nz, const_z, -ny, planar)
     yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
     xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
     yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
 
     inv_denom = 1.0 / denom
-    p1 = np.empty(3)
-    p1[0] = -(xp_hi + xp_lo) * inv_denom
-    p1[1] = -(yp_hi + yp_lo) * inv_denom
-    p1[2] = const_z
-
-    p2 = np.empty(3)
-    p2[0] = -(xn_hi + xn_lo) * inv_denom
-    p2[1] = -(yn_hi + yn_lo) * inv_denom
-    p2[2] = const_z
-
-    # 9a. Snap computed (x, y) to any arc endpoint that lies exactly on the latitude.
-    #     Adjacent edges sharing such an endpoint would otherwise return slightly
-    #     different coordinates; snapping gives them the same exact value so that
-    #     deduplication in the caller works correctly.  Matches Hongyu's suggestion
-    #     of mask-selection to snap after computing rather than branching out early.
-    _snap_sq = 1e-14  # distance² ≈ (1e-7)² — well above algorithm error (~1e-15)
+    pos = np.empty(3)
+    pos[0] = -(xp_hi + xp_lo) * inv_denom
+    pos[1] = -(yp_hi + yp_lo) * inv_denom
+    pos[2] = const_z
+
+    neg = np.empty(3)
+    neg[0] = -(xn_hi + xn_lo) * inv_denom
+    neg[1] = -(yn_hi + yn_lo) * inv_denom
+    neg[2] = const_z
+
+    pos_valid = (
+        math.isfinite(pos[0]) and math.isfinite(pos[1]) and on_minor_arc(pos, x1, x2)
+    )
+    neg_valid = (
+        math.isfinite(neg[0]) and math.isfinite(neg[1]) and on_minor_arc(neg, x1, x2)
+    )
+
+    pos_mask = 1.0 if pos_valid and not neg_valid else 0.0
+    neg_mask = 1.0 if neg_valid and not pos_valid else 0.0
+    point = np.empty(3)
+    point[0] = pos_mask * pos[0] + neg_mask * neg[0]
+    point[1] = pos_mask * pos[1] + neg_mask * neg[1]
+    point[2] = pos_mask * pos[2] + neg_mask * neg[2]
+
+    both = 1 if pos_valid and neg_valid else 0
+    none = 1 if (not pos_valid and not neg_valid) else 0
+    status = both + none * 2
+    return point, status, pos, neg
+
+
+@njit(cache=True)
+def _snap_const_lat_endpoint(point, x1, x2, const_z):
+    snap_sq = 1e-14
+    out = np.empty(3)
+    out[0] = point[0]
+    out[1] = point[1]
+    out[2] = point[2]
     for xe in (x1, x2):
         if abs(xe[2] - const_z) <= ERROR_TOLERANCE:
-            dx = p1[0] - xe[0]
-            dy = p1[1] - xe[1]
-            if dx * dx + dy * dy < _snap_sq:
-                p1[0] = xe[0]
-                p1[1] = xe[1]
-            dx = p2[0] - xe[0]
-            dy = p2[1] - xe[1]
-            if dx * dx + dy * dy < _snap_sq:
-                p2[0] = xe[0]
-                p2[1] = xe[1]
-
-    # 9b. Retain each candidate that is finite and lies 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)
-
-    # When both candidates are valid but nearly identical (tangent/endpoint case),
-    # treat as a single intersection — same as the C++ scalar gca_constlat_intersection
-    # which returns only one point when status==0 (exactly one candidate lies on the arc).
-    if p1_ok and p2_ok:
-        dx = p1[0] - p2[0]
-        dy = p1[1] - p2[1]
-        if dx * dx + dy * dy < _snap_sq:
-            p2_ok = False
-
-    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]
+            dx = out[0] - xe[0]
+            dy = out[1] - xe[1]
+            if dx * dx + dy * dy < snap_sq:
+                out[0] = xe[0]
+                out[1] = xe[1]
+    return out
+
+
+@njit(cache=True)
+def gca_const_lat_intersection(gca_cart, const_z):
+    """Find intersection point(s) of a great-circle arc and a constant-latitude line.
+
+    The core computation follows AccuSphGeom's status-code kernel; endpoint
+    snapping and UXarray's NaN-filled result packaging are isolated in this wrapper.
+    """
+    res = np.empty((2, 3))
+    res.fill(np.nan)
+
+    point, status, pos, neg = _try_gca_const_lat_intersection(gca_cart, const_z)
+    x1 = gca_cart[0]
+    x2 = gca_cart[1]
+
+    if status == 0:
+        point = _snap_const_lat_endpoint(point, x1, x2, const_z)
+        res[0, 0] = point[0]
+        res[0, 1] = point[1]
+        res[0, 2] = point[2]
+    elif status == 1:
+        pos = _snap_const_lat_endpoint(pos, x1, x2, const_z)
+        neg = _snap_const_lat_endpoint(neg, x1, x2, const_z)
+        dx = pos[0] - neg[0]
+        dy = pos[1] - neg[1]
+        if dx * dx + dy * dy < 1e-14:
+            res[0, 0] = pos[0]
+            res[0, 1] = pos[1]
+            res[0, 2] = pos[2]
+        else:
+            res[0, 0] = pos[0]
+            res[0, 1] = pos[1]
+            res[0, 2] = pos[2]
+            res[1, 0] = neg[0]
+            res[1, 1] = neg[1]
+            res[1, 2] = neg[2]
 
     return res
 
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index 5f99ac010..bd0a8f78b 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -38,11 +38,11 @@
 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.
+exact-arithmetic fallback. This port implements only the EFT tier. The
+compensated cross-product routines are roughly twice as accurate as direct
+floating-point cross products while retaining the same vectorizable operation
+structure; callers that need the full robustness stack should add an adaptive
+predicate or exact-arithmetic fallback.
 """
 
 import math

From 1f844e19970a6f2353f2d4fceb62d07fe1d6ef61 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 4 Jun 2026 14:28:34 -0500
Subject: [PATCH 22/51] Separate intersection kernels into three layers:
 numerical core, status/mask, dispatcher

---
 uxarray/grid/intersections.py | 249 +++++++++++++++++++++-------------
 1 file changed, 157 insertions(+), 92 deletions(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index e87816056..1d139b535 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -305,32 +305,24 @@ def _gca_gca_intersection_cartesian(gca_a_xyz, gca_b_xyz):
     return gca_gca_intersection(gca_a_xyz, gca_b_xyz)
 
 
-@njit(cache=True)
-def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
-    """Find intersection point(s) of two great-circle arcs using compensated arithmetic.
+@njit(cache=True, inline="always")
+def _accux_gca(w0, w1, v0, v1):
+    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_gca``).
 
-    Uses ``accucross`` (compensated cross products) and ``on_minor_arc`` (compensated
-    arc membership) to avoid the catastrophic cancellation that affects naive
-    cross product implementations when arcs are nearly parallel.
-    """
-    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")
-
-    w0 = gca_a_xyz[0]
-    w1 = gca_a_xyz[1]
-    v0 = gca_b_xyz[0]
-    v1 = gca_b_xyz[1]
+    Computes the two antipodal candidate intersection points of the great-circle
+    arcs w0-w1 and v0-v1.  No branching, no validity filtering.
 
+    Returns
+    -------
+    pos, neg : np.ndarray, shape (3,)
+        Two antipodal candidate unit vectors.
+    """
     n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = accucross(
         w0[0], w0[1], w0[2], w1[0], w1[1], w1[2]
     )
     n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo = accucross(
         v0[0], v0[1], v0[2], v1[0], v1[1], v1[2]
     )
-
-    res = np.empty((2, 3))
-    count = 0
-
     vx_hi, vy_hi, vz_hi, vx_lo, vy_lo, vz_lo = accucross_pair(
         n1x_hi,
         n1y_hi,
@@ -349,28 +341,9 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     vy = vy_hi + vy_lo
     vz = vz_hi + vz_lo
     vn = math.sqrt(vx * vx + vy * vy + vz * vz)
-
-    if vn == 0.0 or not (
-        math.isfinite(vx)
-        and math.isfinite(vy)
-        and math.isfinite(vz)
-        and math.isfinite(vn)
-    ):
-        # Coplanar overlap is outside the AccuXGCA intersection kernel. Preserve
-        # the historical UXarray behavior by detecting shared endpoints here.
-        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]
-
-    inv = 1.0 / vn
+    # Use np.inf safely when vn==0 (coplanar arcs): the resulting pos/neg
+    # will be non-finite, so the status layer marks them invalid without branching.
+    inv = 1.0 / vn if vn != 0.0 else np.inf
     pos = np.empty(3)
     pos[0] = vx * inv
     pos[1] = vy * inv
@@ -379,98 +352,192 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     neg[0] = -pos[0]
     neg[1] = -pos[1]
     neg[2] = -pos[2]
+    return pos, neg
 
-    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 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
+@njit(cache=True)
+def _try_gca_gca_intersection(w0, w1, v0, v1):
+    """Layer 2 — batch/status layer (mirrors AccuSphGeom ``try_gca_gca_intersection``).
 
-    return res[:count]
+    Calls the pure numerical kernel, applies integer mask arithmetic to determine
+    validity, selects the output point without if/else branching in the hot path.
+
+    Status codes mirror AccuSphGeom:
+        0  exactly one candidate is valid
+        1  both candidates are valid
+        2  neither candidate is valid  (includes coplanar/parallel case)
+    """
+    pos, neg = _accux_gca(w0, w1, v0, v1)
+
+    pos_fin = (
+        1
+        if math.isfinite(pos[0]) and math.isfinite(pos[1]) and math.isfinite(pos[2])
+        else 0
+    )
+    neg_fin = (
+        1
+        if math.isfinite(neg[0]) and math.isfinite(neg[1]) and math.isfinite(neg[2])
+        else 0
+    )
+    pos_on_a = 1 if (pos_fin and on_minor_arc(pos, w0, w1)) else 0
+    pos_on_b = 1 if (pos_fin and on_minor_arc(pos, v0, v1)) else 0
+    neg_on_a = 1 if (neg_fin and on_minor_arc(neg, w0, w1)) else 0
+    neg_on_b = 1 if (neg_fin and on_minor_arc(neg, v0, v1)) else 0
+
+    pos_valid = pos_fin * pos_on_a * pos_on_b
+    neg_valid = neg_fin * neg_on_a * neg_on_b
+
+    pos_mask = pos_valid * (1 - neg_valid)
+    neg_mask = neg_valid * (1 - pos_valid)
+
+    point = np.empty(3)
+    point[0] = pos_mask * pos[0] + neg_mask * neg[0]
+    point[1] = pos_mask * pos[1] + neg_mask * neg[1]
+    point[2] = pos_mask * pos[2] + neg_mask * neg[2]
+
+    both = pos_valid * neg_valid
+    none = (1 - pos_valid) * (1 - neg_valid)
+    status = both + none * 2
+    return point, status, pos, neg
 
 
 @njit(cache=True)
-def _try_gca_const_lat_intersection(gca_cart, const_z):
-    """AccuSphGeom-style GCA/constant-latitude kernel."""
-    x1 = gca_cart[0]
-    x2 = gca_cart[1]
+def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
+    """Layer 3 — dispatcher / convenience API.
 
+    Calls the batch/status layer and packages results into UXarray's existing
+    array-returning API (0, 1, or 2 rows).  Coplanar/shared-endpoint handling
+    lives here, outside the numerical core.
+    """
+    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")
+
+    w0 = gca_a_xyz[0]
+    w1 = gca_a_xyz[1]
+    v0 = gca_b_xyz[0]
+    v1 = gca_b_xyz[1]
+
+    point, status, pos, neg = _try_gca_gca_intersection(w0, w1, v0, v1)
+
+    res = np.empty((2, 3))
+    count = 0
+    if status == 0:
+        res[0, 0] = point[0]
+        res[0, 1] = point[1]
+        res[0, 2] = point[2]
+        count = 1
+    elif status == 1:
+        res[0, 0] = pos[0]
+        res[0, 1] = pos[1]
+        res[0, 2] = pos[2]
+        res[1, 0] = neg[0]
+        res[1, 1] = neg[1]
+        res[1, 2] = neg[2]
+        count = 2
+    else:
+        # status == 2: no candidate on both arcs.
+        # Check for coplanar overlap (shared endpoints) outside the kernel.
+        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]
+
+
+@njit(cache=True, inline="always")
+def _accux_constlat(x1, x2, const_z):
+    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_constlat``).
+
+    Computes the two candidate intersection points between the great-circle arc
+    defined by unit vectors *x1*, *x2* and the constant-latitude plane z = const_z.
+    No branching, no validity filtering — all operations follow the exact compensated
+    sequence from AccuSphGeom so that the error bound holds.
+
+    Returns
+    -------
+    pos, neg : np.ndarray, shape (3,)
+        Two antipodal candidate points.  Invalid inputs propagate as non-finite
+        coordinates; the caller uses masks/status to identify validity.
+    """
     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]
     )
-
     s2_hi, s2_lo = _sum_sq_c2(nx_hi, nx_lo, ny_hi, ny_lo)
     denom = s2_hi + s2_lo
     s3_hi, s3_lo = _sum_sq_c3(nx_hi, nx_lo, ny_hi, ny_lo, nz_hi, nz_lo)
     zsq_hi, zsq_lo = two_prod(const_z, const_z)
-    d_hi, d_lo = _cdp4(
-        s3_hi,
-        zsq_hi,
-        s3_hi,
-        zsq_lo,
-        s3_lo,
-        zsq_hi,
-        s3_lo,
-        zsq_lo,
-    )
-
+    d_hi, d_lo = _cdp4(s3_hi, zsq_hi, s3_hi, zsq_lo, s3_lo, zsq_hi, s3_lo, zsq_lo)
     e_hi, e_lo = two_sum(s2_hi, -d_hi)
     planar_sq = e_hi + (e_lo + s2_lo - d_lo)
-    if planar_sq < 0.0:
-        root_arg = np.nan
-    else:
-        root_arg = planar_sq
-    s_root, s_corr = acc_sqrt_re(root_arg)
-
+    s_root, s_corr = acc_sqrt_re(planar_sq)
     nx = nx_hi + nx_lo
     ny = ny_hi + ny_lo
     nz = nz_hi + nz_lo
     planar = s_root + s_corr
-
     xp_hi, xp_lo = _cdp2(nx * nz, const_z, -ny, planar)
     yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
     xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
     yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
-
     inv_denom = 1.0 / denom
     pos = np.empty(3)
     pos[0] = -(xp_hi + xp_lo) * inv_denom
     pos[1] = -(yp_hi + yp_lo) * inv_denom
     pos[2] = const_z
-
     neg = np.empty(3)
     neg[0] = -(xn_hi + xn_lo) * inv_denom
     neg[1] = -(yn_hi + yn_lo) * inv_denom
     neg[2] = const_z
+    return pos, neg
 
-    pos_valid = (
-        math.isfinite(pos[0]) and math.isfinite(pos[1]) and on_minor_arc(pos, x1, x2)
-    )
-    neg_valid = (
-        math.isfinite(neg[0]) and math.isfinite(neg[1]) and on_minor_arc(neg, x1, x2)
-    )
 
-    pos_mask = 1.0 if pos_valid and not neg_valid else 0.0
-    neg_mask = 1.0 if neg_valid and not pos_valid else 0.0
+@njit(cache=True)
+def _try_gca_const_lat_intersection(gca_cart, const_z):
+    """Layer 2 — batch/status layer (mirrors AccuSphGeom ``try_gca_constlat_intersection``).
+
+    Calls the pure numerical kernel, computes integer validity masks (0 or 1)
+    for each candidate using finiteness and arc-membership tests, then selects
+    the output point via integer arithmetic — no if/else branching in the hot path.
+
+    Status codes mirror AccuSphGeom:
+        0  exactly one candidate is valid  (normal case)
+        1  both candidates are valid
+        2  neither candidate is valid
+    """
+    x1 = gca_cart[0]
+    x2 = gca_cart[1]
+    pos, neg = _accux_constlat(x1, x2, const_z)
+
+    pos_fin = 1 if math.isfinite(pos[0]) and math.isfinite(pos[1]) else 0
+    neg_fin = 1 if math.isfinite(neg[0]) and math.isfinite(neg[1]) else 0
+    pos_on = 1 if (pos_fin and on_minor_arc(pos, x1, x2)) else 0
+    neg_on = 1 if (neg_fin and on_minor_arc(neg, x1, x2)) else 0
+
+    pos_valid = pos_fin * pos_on
+    neg_valid = neg_fin * neg_on
+
+    pos_mask = pos_valid * (1 - neg_valid)
+    neg_mask = neg_valid * (1 - pos_valid)
+
     point = np.empty(3)
     point[0] = pos_mask * pos[0] + neg_mask * neg[0]
     point[1] = pos_mask * pos[1] + neg_mask * neg[1]
     point[2] = pos_mask * pos[2] + neg_mask * neg[2]
 
-    both = 1 if pos_valid and neg_valid else 0
-    none = 1 if (not pos_valid and not neg_valid) else 0
+    both = pos_valid * neg_valid
+    none = (1 - pos_valid) * (1 - neg_valid)
     status = both + none * 2
     return point, status, pos, neg
 
 
 @njit(cache=True)
 def _snap_const_lat_endpoint(point, x1, x2, const_z):
+    """Snap a candidate point to an arc endpoint when the endpoint lies on the latitude."""
     snap_sq = 1e-14
     out = np.empty(3)
     out[0] = point[0]
@@ -488,18 +555,17 @@ def _snap_const_lat_endpoint(point, x1, x2, const_z):
 
 @njit(cache=True)
 def gca_const_lat_intersection(gca_cart, const_z):
-    """Find intersection point(s) of a great-circle arc and a constant-latitude line.
+    """Layer 3 — dispatcher / convenience API.
 
-    The core computation follows AccuSphGeom's status-code kernel; endpoint
-    snapping and UXarray's NaN-filled result packaging are isolated in this wrapper.
+    Calls the batch/status kernel, applies endpoint snapping, and packages the
+    result in UXarray's NaN-filled (2, 3) format.  All UXarray-specific branching
+    lives here so the numerical core and status layers stay uniform.
     """
     res = np.empty((2, 3))
     res.fill(np.nan)
-
     point, status, pos, neg = _try_gca_const_lat_intersection(gca_cart, const_z)
     x1 = gca_cart[0]
     x2 = gca_cart[1]
-
     if status == 0:
         point = _snap_const_lat_endpoint(point, x1, x2, const_z)
         res[0, 0] = point[0]
@@ -521,7 +587,6 @@ def gca_const_lat_intersection(gca_cart, const_z):
             res[1, 0] = neg[0]
             res[1, 1] = neg[1]
             res[1, 2] = neg[2]
-
     return res
 
 

From ec81e160a238bf0f2d45ce91d6199ad9925cc130 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 4 Jun 2026 14:44:15 -0500
Subject: [PATCH 23/51] Fix NaN/Inf propagation in intersection kernels for
 denom=0 and planar_sq<0

---
 uxarray/grid/intersections.py | 4 +++-
 uxarray/utils/computing.py    | 4 ++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 1d139b535..a3530e136 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -484,7 +484,9 @@ def _accux_constlat(x1, x2, const_z):
     yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
     xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
     yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
-    inv_denom = 1.0 / denom
+    # denom == 0 means the arc is vertical (normal has no x/y component).
+    # Produce inf so the isfinite mask in the status layer rejects candidates.
+    inv_denom = 1.0 / denom if denom != 0.0 else np.inf
     pos = np.empty(3)
     pos[0] = -(xp_hi + xp_lo) * inv_denom
     pos[1] = -(yp_hi + yp_lo) * inv_denom
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index bd0a8f78b..6ce5e0db2 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -417,6 +417,10 @@ def acc_sqrt_re(value, error=0.0):
     correction : float
         Additive correction; root + correction ≈ sqrt(value + error) to ~1 ulp.
     """
+    # Negative value means no real intersection; return NaN so that the
+    # isfinite mask in the status layer rejects this candidate without a branch.
+    if value < 0.0:
+        return math.nan, 0.0
     root = math.sqrt(value)
     if root == 0.0:
         return 0.0, 0.0

From c0f281da1dbfdeabcb9472c9858c5726d8a42e29 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 19 Jun 2026 14:28:27 -0500
Subject: [PATCH 24/51] computing: use FMA two_prod where available (portable
 Veltkamp fallback)

Add an LLVM fma intrinsic and route two_prod through a single fused
multiply-add for its error term on hardware that supports it, selected at
import time and validated to be bit-exact against the Veltkamp split. Falls
back to the portable Veltkamp form otherwise, so there is no hard FMA
dependency.

The FMA path is ~2x faster in the compensated geometry kernels (each
two_prod drops from ~17 flops to one FMADD) and is numerically identical:
all 241 AccuSphGeom baseline cases pass unchanged.
---
 uxarray/utils/computing.py | 159 +++++++++++++++++++++++++++++++------
 1 file changed, 136 insertions(+), 23 deletions(-)

diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index 6ce5e0db2..f53d4ccb3 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -18,9 +18,11 @@
 building blocks to achieve near-double precision for cross products, but they
 are compensated algorithms, not zero-error transformations.
 
-All functions are ``@njit``-compiled and use the portable Veltkamp-splitting
-form of ``two_prod`` (no FMA dependency), making them suitable for use inside
-Numba-compiled geometry kernels.
+All functions are ``@njit``-compiled. ``two_prod`` uses a single fused
+multiply-add (FMA) for its error term on hardware that supports it (selected at
+import time and validated to be bit-exact), falling back to the portable
+Veltkamp split otherwise — so there is no hard FMA dependency, but FMA is used
+when available (~2x faster in the compensated kernels).
 
 These primitives are a Python/Numba port of the AccuSphGeom C++ library:
 
@@ -49,6 +51,76 @@
 
 from numba import njit
 
+# ---------------------------------------------------------------------------
+# Fused multiply-add (FMA) support.
+#
+# ``two_prod`` needs the exact rounding error of ``a * b``. On hardware with an
+# FMA instruction this is a single op: ``e = fma(a, b, -p)`` where ``p = a*b``.
+# Without FMA we fall back to the portable Veltkamp split (no hardware
+# dependency). We expose an LLVM ``fma`` intrinsic through Numba and validate at
+# import time that it both compiles and yields a bit-exact error-free transform;
+# if anything fails (older toolchain, unsupported target, or a non-exact FMA),
+# ``_HAS_FMA`` stays False and the Veltkamp path is used. This keeps the
+# library's "no FMA dependency" guarantee while using FMA where it is available.
+# ---------------------------------------------------------------------------
+try:
+    from numba.core import types as _nb_types
+    from numba.extending import intrinsic as _nb_intrinsic
+
+    @_nb_intrinsic
+    def _fma(typingctx, a, b, c):
+        sig = _nb_types.float64(
+            _nb_types.float64, _nb_types.float64, _nb_types.float64
+        )
+
+        def codegen(context, builder, signature, args):
+            return builder.fma(*args)
+
+        return sig, codegen
+
+    _FMA_INTRINSIC_OK = True
+except Exception:  # pragma: no cover - toolchain without intrinsic support
+    _FMA_INTRINSIC_OK = False
+
+
+def _validate_fma() -> bool:
+    """Return True iff the FMA intrinsic compiles and is a bit-exact EFT."""
+    if not _FMA_INTRINSIC_OK:
+        return False
+    try:
+        import numpy as _np
+
+        @njit(cache=False)
+        def _tp_fma(a, b):
+            p = a * b
+            return p, _fma(a, b, -p)
+
+        @njit(cache=False)
+        def _tp_vk(a, b):
+            p = a * b
+            f = 134217729.0
+            a_hi = f * a - (f * a - a)
+            a_lo = a - a_hi
+            b_hi = f * b - (f * 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
+
+        rng = _np.random.default_rng(20260101)
+        for _ in range(20000):
+            a = float(rng.standard_normal() * rng.integers(1, 1 << 20))
+            b = float(rng.standard_normal() * rng.integers(1, 1 << 20))
+            pf, ef = _tp_fma(a, b)
+            pv, ev = _tp_vk(a, b)
+            if pf != pv or (pf + ef) != (pv + ev):
+                return False
+        return True
+    except Exception:  # pragma: no cover
+        return False
+
+
+_HAS_FMA = _validate_fma()
+
 
 @njit(cache=True, inline="always")
 def two_sum(a, b):
@@ -79,27 +151,12 @@ def two_sum(a, b):
 
 
 @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.
+def _two_prod_veltkamp(a, b):
+    """Portable TwoProd via Veltkamp splitting (no FMA dependency).
 
-    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.
+    Decomposes each operand into a high and low half using the splitting
+    constant 2**27 + 1, then reconstructs the exact rounding error from the
+    four partial products. Works on every Numba target.
     """
     p = a * b
     factor = 134217729.0  # 2**27 + 1
@@ -111,6 +168,62 @@ def two_prod(a, b):
     return p, e
 
 
+if _HAS_FMA:
+
+    @njit(cache=True, inline="always")
+    def _two_prod_fma(a, b):
+        """TwoProd via a single hardware FMA: e = fma(a, b, -p)."""
+        p = a * b
+        return p, _fma(a, b, -p)
+
+    @njit(cache=True, inline="always")
+    def two_prod(a, b):
+        """Dekker TwoProd: return (p, e) with p = fl(a*b) and p + e = a*b exactly.
+
+        Uses a single fused multiply-add for the error term on hardware that
+        supports it (selected at import time via ``_HAS_FMA``), falling back to
+        the portable Veltkamp split otherwise. The FMA path is ~2x faster in the
+        compensated geometry kernels and is bit-for-bit identical to the
+        Veltkamp result (validated at import).
+
+        Parameters
+        ----------
+        a, b : float
+            Input values.
+
+        Returns
+        -------
+        p : float
+            Rounded product fl(a * b).
+        e : float
+            Rounding error term; p + e = a * b exactly.
+        """
+        return _two_prod_fma(a, b)
+
+else:  # pragma: no cover - exercised only on FMA-less toolchains
+
+    @njit(cache=True, inline="always")
+    def two_prod(a, b):
+        """Dekker TwoProd: return (p, e) with p = fl(a*b) and p + e = a*b exactly.
+
+        Portable Veltkamp-split implementation (no FMA available on this
+        toolchain/target).
+
+        Parameters
+        ----------
+        a, b : float
+            Input values.
+
+        Returns
+        -------
+        p : float
+            Rounded product fl(a * b).
+        e : float
+            Rounding error term; p + e = a * b exactly.
+        """
+        return _two_prod_veltkamp(a, b)
+
+
 @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.

From 3438b90c8fd7aa1fb8e0f2a7fc3614b98b7bf040 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 19 Jun 2026 15:25:19 -0500
Subject: [PATCH 25/51] intersections: add allocation-free scalar const-lat L1
 kernel

Add _accux_constlat_scalar, which takes the arc endpoints as six scalars and
returns the candidate coordinates as scalars instead of two np.empty(3)
arrays. _accux_constlat now wraps it so the array API is unchanged.

Returning scalars lets Numba keep the candidates in registers, so a batch
loop over many edges does no per-point heap allocation. On a 16M-point
const-lat sweep this is ~2.7x faster than the array-returning path and drops
the AccuX/FP64 cost ratio from ~19x to ~7x. Bit-identical results; all 241
AccuSphGeom baseline cases pass.
---
 uxarray/grid/intersections.py | 58 +++++++++++++++++++++++++----------
 1 file changed, 42 insertions(+), 16 deletions(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index cf632254c..5457a17c0 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -547,23 +547,23 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
 
 
 @njit(cache=True, inline="always")
-def _accux_constlat(x1, x2, const_z):
-    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_constlat``).
+def _accux_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z):
+    """Layer 1 (scalar) — allocation-free numerical kernel.
 
-    Computes the two candidate intersection points between the great-circle arc
-    defined by unit vectors *x1*, *x2* and the constant-latitude plane z = const_z.
-    No branching, no validity filtering — all operations follow the exact compensated
-    sequence from AccuSphGeom so that the error bound holds.
+    Same compensated AccuSphGeom sequence as :func:`_accux_constlat`, but takes
+    the two arc endpoints as six scalars and returns the two candidate points as
+    six scalars (``pos`` xy and ``neg`` xy; the z of both candidates is
+    ``const_z``). Returning scalars instead of ``np.empty(3)`` arrays lets Numba
+    keep everything in registers, so a batch loop over many edges does no
+    per-point heap allocation. This is the preferred entry point for hot loops.
 
     Returns
     -------
-    pos, neg : np.ndarray, shape (3,)
-        Two antipodal candidate points.  Invalid inputs propagate as non-finite
-        coordinates; the caller uses masks/status to identify validity.
+    px, py, nx_out, ny_out : float
+        ``pos = (px, py, const_z)`` and ``neg = (nx_out, ny_out, const_z)``.
+        Invalid inputs propagate as non-finite coordinates.
     """
-    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_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo = accucross(a0, a1, a2, b0, b1, b2)
     s2_hi, s2_lo = _sum_sq_c2(nx_hi, nx_lo, ny_hi, ny_lo)
     denom = s2_hi + s2_lo
     s3_hi, s3_lo = _sum_sq_c3(nx_hi, nx_lo, ny_hi, ny_lo, nz_hi, nz_lo)
@@ -583,13 +583,39 @@ def _accux_constlat(x1, x2, const_z):
     # denom == 0 means the arc is vertical (normal has no x/y component).
     # Produce inf so the isfinite mask in the status layer rejects candidates.
     inv_denom = 1.0 / denom if denom != 0.0 else np.inf
+    px = -(xp_hi + xp_lo) * inv_denom
+    py = -(yp_hi + yp_lo) * inv_denom
+    nxo = -(xn_hi + xn_lo) * inv_denom
+    nyo = -(yn_hi + yn_lo) * inv_denom
+    return px, py, nxo, nyo
+
+
+@njit(cache=True, inline="always")
+def _accux_constlat(x1, x2, const_z):
+    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_constlat``).
+
+    Array-returning wrapper around :func:`_accux_constlat_scalar`. Computes the
+    two candidate intersection points between the great-circle arc defined by
+    unit vectors *x1*, *x2* and the constant-latitude plane z = const_z. No
+    branching, no validity filtering. For allocation-free hot loops call
+    :func:`_accux_constlat_scalar` directly.
+
+    Returns
+    -------
+    pos, neg : np.ndarray, shape (3,)
+        Two antipodal candidate points.  Invalid inputs propagate as non-finite
+        coordinates; the caller uses masks/status to identify validity.
+    """
+    px, py, nxo, nyo = _accux_constlat_scalar(
+        x1[0], x1[1], x1[2], x2[0], x2[1], x2[2], const_z
+    )
     pos = np.empty(3)
-    pos[0] = -(xp_hi + xp_lo) * inv_denom
-    pos[1] = -(yp_hi + yp_lo) * inv_denom
+    pos[0] = px
+    pos[1] = py
     pos[2] = const_z
     neg = np.empty(3)
-    neg[0] = -(xn_hi + xn_lo) * inv_denom
-    neg[1] = -(yn_hi + yn_lo) * inv_denom
+    neg[0] = nxo
+    neg[1] = nyo
     neg[2] = const_z
     return pos, neg
 

From 69c6d48d3b3d57bf8dd07bf5f51606a4ae418d49 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Tue, 23 Jun 2026 21:54:20 -0500
Subject: [PATCH 26/51] style: apply ruff formatting to computing.py

---
 uxarray/utils/computing.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index f53d4ccb3..a0866bbd1 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -69,9 +69,7 @@
 
     @_nb_intrinsic
     def _fma(typingctx, a, b, c):
-        sig = _nb_types.float64(
-            _nb_types.float64, _nb_types.float64, _nb_types.float64
-        )
+        sig = _nb_types.float64(_nb_types.float64, _nb_types.float64, _nb_types.float64)
 
         def codegen(context, builder, signature, args):
             return builder.fma(*args)

From 07ac029a3ee3f25dd338d27235d968a2e9992fbb Mon Sep 17 00:00:00 2001
From: Rajeev Jain <1466114+rajeeja@users.noreply.github.com>
Date: Tue, 7 Jul 2026 14:41:37 -0500
Subject: [PATCH 27/51] Clean up spherical geometry review items

---
 .../spherical-geometry-accuracy.ipynb         |  4 +-
 uxarray/grid/intersections.py                 | 93 -------------------
 uxarray/grid/point_in_face.py                 |  4 +-
 3 files changed, 4 insertions(+), 97 deletions(-)

diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index a8830b73f..1402df88f 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "id": "title-cell",
    "metadata": {},
-   "source": "# Accurate Spherical Geometry\n\nCross 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 **compensated arithmetic** — algorithms built on error-free transformation (EFT) primitives that track every rounding residual exactly.\n\nThis guide covers:\n\n1. The problem: catastrophic cancellation\n2. How UXarray handles it\n3. Seeing it on a real mesh: point-in-polygon\n4. Where it is used in UXarray"
+   "source": "# Accurate Spherical Geometry\n\nCross 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\nThis guide covers:\n\n1. The problem: catastrophic cancellation\n2. How UXarray handles it\n3. Seeing it on a real mesh: point-in-polygon\n4. Where it is used in UXarray"
   },
   {
    "cell_type": "code",
@@ -140,7 +140,7 @@
    "cell_type": "markdown",
    "id": "9a3dc8b0",
    "metadata": {},
-   "source": "## 2. How UXarray Handles It\n\nUXarray uses **compensated arithmetic** — a family of algorithms that prevent catastrophic cancellation by representing floating-point operations as exact `(hi, lo)` pairs. 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\nThe 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.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\nMost 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."
+   "source": "## 2. How UXarray Handles It\n\nUXarray 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\nThe 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\nMost 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",
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 5457a17c0..a8b7d974f 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -358,99 +358,6 @@ def _accux_gca(w0, w1, v0, v1):
     return pos, neg
 
 
-@njit(cache=True)
-def _try_gca_gca_intersection(w0, w1, v0, v1):
-    """Layer 2 — batch/status layer (mirrors AccuSphGeom ``try_gca_gca_intersection``).
-
-    Calls the pure numerical kernel, applies integer mask arithmetic to determine
-    validity, selects the output point without if/else branching in the hot path.
-
-    Status codes mirror AccuSphGeom:
-        0  exactly one candidate is valid
-        1  both candidates are valid
-        2  neither candidate is valid  (includes coplanar/parallel case)
-    """
-    pos, neg = _accux_gca(w0, w1, v0, v1)
-
-    pos_fin = int(
-        math.isfinite(pos[0]) and math.isfinite(pos[1]) and math.isfinite(pos[2])
-    )
-    # neg = -pos exactly, so neg is finite iff pos is finite.
-    neg_fin = pos_fin
-    # on_minor_arc must be guarded by the finiteness mask — calling it with inf
-    # inputs is undefined. The guard is the only unavoidable branch in L2.
-    pos_on_a = pos_fin * int(on_minor_arc(pos, w0, w1)) if pos_fin else 0
-    pos_on_b = pos_fin * int(on_minor_arc(pos, v0, v1)) if pos_fin else 0
-    neg_on_a = neg_fin * int(on_minor_arc(neg, w0, w1)) if neg_fin else 0
-    neg_on_b = neg_fin * int(on_minor_arc(neg, v0, v1)) if neg_fin else 0
-
-    pos_valid = pos_fin * pos_on_a * pos_on_b
-    neg_valid = neg_fin * neg_on_a * neg_on_b
-
-    pos_mask = pos_valid * (1 - neg_valid)
-    neg_mask = neg_valid * (1 - pos_valid)
-
-    point = np.empty(3)
-    point[0] = pos_mask * pos[0] + neg_mask * neg[0]
-    point[1] = pos_mask * pos[1] + neg_mask * neg[1]
-    point[2] = pos_mask * pos[2] + neg_mask * neg[2]
-
-    both = pos_valid * neg_valid
-    none = (1 - pos_valid) * (1 - neg_valid)
-    status = both + none * 2
-    return point, status, pos, neg
-
-
-@njit(cache=True, inline="always")
-def _accux_gca(w0, w1, v0, v1):
-    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_gca``).
-
-    Computes the two antipodal candidate intersection points of the great-circle
-    arcs w0-w1 and v0-v1.  No branching, no validity filtering.
-
-    Returns
-    -------
-    pos, neg : np.ndarray, shape (3,)
-        Two antipodal candidate unit vectors.
-    """
-    n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = accucross(
-        w0[0], w0[1], w0[2], w1[0], w1[1], w1[2]
-    )
-    n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo = accucross(
-        v0[0], v0[1], v0[2], v1[0], v1[1], v1[2]
-    )
-    vx_hi, vy_hi, vz_hi, vx_lo, vy_lo, vz_lo = accucross_pair(
-        n1x_hi,
-        n1y_hi,
-        n1z_hi,
-        n1x_lo,
-        n1y_lo,
-        n1z_lo,
-        n2x_hi,
-        n2y_hi,
-        n2z_hi,
-        n2x_lo,
-        n2y_lo,
-        n2z_lo,
-    )
-    vx = vx_hi + vx_lo
-    vy = vy_hi + vy_lo
-    vz = vz_hi + vz_lo
-    vn = math.sqrt(vx * vx + vy * vy + vz * vz)
-    # Use np.inf safely when vn==0 (coplanar arcs): the resulting pos/neg
-    # will be non-finite, so the status layer marks them invalid without branching.
-    inv = 1.0 / vn if vn != 0.0 else np.inf
-    pos = np.empty(3)
-    pos[0] = vx * inv
-    pos[1] = vy * inv
-    pos[2] = vz * inv
-    neg = np.empty(3)
-    neg[0] = -pos[0]
-    neg[1] = -pos[1]
-    neg[2] = -pos[2]
-    return pos, neg
-
-
 @njit(cache=True)
 def _try_gca_gca_intersection(w0, w1, v0, v1):
     """Layer 2 — batch/status layer (mirrors AccuSphGeom ``try_gca_gca_intersection``).
diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py
index 07719cb7a..a1f0b988f 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -37,8 +37,8 @@ def _ray_endpoint(q):
 
     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 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.

From dba02064c6f1c9d110b5cf7029193c984b7fd0b1 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 10 Jul 2026 23:23:22 -0500
Subject: [PATCH 28/51] Scalarize constant-latitude intersection dispatcher to
 remove per-call heap allocations

---
 benchmarks/geometry_samebody.py | 415 ++++++++++++++++++++++++++++++++
 uxarray/grid/arcs.py            |  46 +++-
 uxarray/grid/intersections.py   | 111 ++++++---
 3 files changed, 524 insertions(+), 48 deletions(-)
 create mode 100644 benchmarks/geometry_samebody.py

diff --git a/benchmarks/geometry_samebody.py b/benchmarks/geometry_samebody.py
new file mode 100644
index 000000000..5f9e96d46
--- /dev/null
+++ b/benchmarks/geometry_samebody.py
@@ -0,0 +1,415 @@
+"""Same-body FP64-vs-AccuX diagnostic for the GCA / constant-latitude path.
+
+Purpose (PR #1513)
+------------------
+This is *not* a standalone benchmark. It is a diagnostic that isolates the
+engineering overhead of the UXarray AccuX wiring (wrapper / status layer /
+dispatcher / masking) from the cost of the compensated-arithmetic (EFT) kernel
+itself.
+
+Method
+------
+We build a second implementation of the exact same three-layer stack used by the
+real AccuX path in ``uxarray.grid.intersections``:
+
+    L1  kernel      pure numerical core, returns two candidate points
+    L2  try/status  finiteness + on-minor-arc masks, branchless point select
+    L3  dispatcher  endpoint snapping, UXarray (2, 3) NaN-filled output
+
+The only difference is the L1 body: here it is the *direct FP64* formula taken
+verbatim from the AccuSphGeom reference
+
+    tests/performance_test/gca_constLat/fp64_GCAconstLat.hh
+
+    nx = a1*b2 - a2*b1;  ny = a2*b0 - a0*b2;  nz = a0*b1 - a1*b0
+    denom   = nx^2 + ny^2
+    norm_n2 = denom + nz^2
+    s       = sqrt(denom - norm_n2 * z^2)
+    pos = ( -(z*nx*nz - s*ny)/denom, -(z*ny*nz + s*nx)/denom, z )
+    neg = ( -(z*nx*nz + s*ny)/denom, -(z*ny*nz - s*nx)/denom, z )
+
+Because L2/L3 here are byte-for-byte the same logic as the real AccuX L2/L3, the
+comparison factors cleanly:
+
+    real AccuX time  -  same-body FP64 time  ==  cost of EFT math only
+    same-body FP64 time  -  direct FP64 time ==  cost of L2/L3 plumbing only
+
+Acceptance (interpret ``main()`` output)
+    1. same-body FP64 dispatcher output/status == direct FP64 output/status
+       (exact match: the plumbing does not change results)
+    2. same-body FP64 dispatcher output/status == real AccuX output/status
+       within tolerance (same algorithm, EFT only tightens rounding)
+    3. plumbing overhead (L3 - L1) is small and comparable for both bodies
+    4. remaining real-AccuX-vs-same-body gap is attributable to EFT math, not
+       to wrappers/dispatch
+
+Run directly:  ``python benchmarks/geometry_samebody.py``
+This module is import-safe for asv (timing classes at the bottom).
+"""
+
+import math
+import time
+
+import numpy as np
+from numba import njit
+
+from uxarray.grid.arcs import _on_minor_arc_xyz, on_minor_arc
+from uxarray.grid.intersections import (
+    _accux_constlat_scalar,
+    _snap_const_lat_endpoint_xy,
+    gca_const_lat_intersection,
+)
+
+# ---------------------------------------------------------------------------
+# L1 (FP64 body) — direct double-precision kernel, verbatim from
+# fp64_GCAconstLat.hh. Scalar in / scalar out so Numba keeps it in registers,
+# mirroring _accux_constlat_scalar.
+# ---------------------------------------------------------------------------
+
+
+@njit(cache=True)
+def _fp64_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z):
+    nx = a1 * b2 - a2 * b1
+    ny = a2 * b0 - a0 * b2
+    nz = a0 * b1 - a1 * b0
+
+    denom = nx * nx + ny * ny
+    norm_n2 = denom + nz * nz
+    s = math.sqrt(denom - norm_n2 * const_z * const_z)
+
+    inv_denom = 1.0 / denom if denom != 0.0 else np.inf
+    px = -(const_z * nx * nz - s * ny) * inv_denom
+    py = -(const_z * ny * nz + s * nx) * inv_denom
+    nxo = -(const_z * nx * nz + s * ny) * inv_denom
+    nyo = -(const_z * ny * nz - s * nx) * inv_denom
+    return px, py, nxo, nyo
+
+
+@njit(cache=True, inline="always")
+def _fp64_constlat(x1, x2, const_z):
+    px, py, nxo, nyo = _fp64_constlat_scalar(
+        x1[0], x1[1], x1[2], x2[0], x2[1], x2[2], const_z
+    )
+    pos = np.empty(3)
+    pos[0] = px
+    pos[1] = py
+    pos[2] = const_z
+    neg = np.empty(3)
+    neg[0] = nxo
+    neg[1] = nyo
+    neg[2] = const_z
+    return pos, neg
+
+
+# ---------------------------------------------------------------------------
+# L2 (FP64 body) — identical logic to _try_gca_const_lat_intersection, only the
+# L1 call differs. Branchless integer masks; status codes 0/1/2 as in AccuSphGeom.
+# ---------------------------------------------------------------------------
+
+
+@njit(cache=True)
+def _fp64_try_gca_const_lat_intersection(gca_cart, const_z):
+    x1 = gca_cart[0]
+    x2 = gca_cart[1]
+    pos, neg = _fp64_constlat(x1, x2, const_z)
+
+    pos_fin = int(math.isfinite(pos[0]) and math.isfinite(pos[1]))
+    neg_fin = int(math.isfinite(neg[0]) and math.isfinite(neg[1]))
+    pos_on = pos_fin * int(on_minor_arc(pos, x1, x2)) if pos_fin else 0
+    neg_on = neg_fin * int(on_minor_arc(neg, x1, x2)) if neg_fin else 0
+
+    pos_valid = pos_fin * pos_on
+    neg_valid = neg_fin * neg_on
+
+    pos_mask = pos_valid * (1 - neg_valid)
+    neg_mask = neg_valid * (1 - pos_valid)
+
+    point = np.empty(3)
+    point[0] = pos_mask * pos[0] + neg_mask * neg[0]
+    point[1] = pos_mask * pos[1] + neg_mask * neg[1]
+    point[2] = pos_mask * pos[2] + neg_mask * neg[2]
+
+    both = pos_valid * neg_valid
+    none = (1 - pos_valid) * (1 - neg_valid)
+    status = both + none * 2
+    return point, status, pos, neg
+
+
+# ---------------------------------------------------------------------------
+# L3 (FP64 body) — identical dispatcher to gca_const_lat_intersection, reusing
+# the production _snap_const_lat_endpoint so only the numerical body differs.
+# ---------------------------------------------------------------------------
+
+
+@njit(cache=True)
+def _fp64_gca_const_lat_intersection(gca_cart, const_z):
+    # Mirrors the production scalar dispatcher exactly (same allocation profile:
+    # one (2, 3) array), only the L1 body differs. This keeps the same-body
+    # comparison honest: any timing gap is the EFT math, not plumbing.
+    res = np.empty((2, 3))
+    res.fill(np.nan)
+
+    a0 = gca_cart[0, 0]
+    a1 = gca_cart[0, 1]
+    a2 = gca_cart[0, 2]
+    b0 = gca_cart[1, 0]
+    b1 = gca_cart[1, 1]
+    b2 = gca_cart[1, 2]
+
+    px, py, nx, ny = _fp64_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z)
+
+    pos_fin = math.isfinite(px) and math.isfinite(py)
+    neg_fin = math.isfinite(nx) and math.isfinite(ny)
+    pos_valid = pos_fin and _on_minor_arc_xyz(px, py, const_z, a0, a1, a2, b0, b1, b2)
+    neg_valid = neg_fin and _on_minor_arc_xyz(nx, ny, const_z, a0, a1, a2, b0, b1, b2)
+
+    if pos_valid and not neg_valid:
+        sx, sy = _snap_const_lat_endpoint_xy(px, py, a0, a1, a2, b0, b1, b2, const_z)
+        res[0, 0] = sx
+        res[0, 1] = sy
+        res[0, 2] = const_z
+    elif neg_valid and not pos_valid:
+        sx, sy = _snap_const_lat_endpoint_xy(nx, ny, a0, a1, a2, b0, b1, b2, const_z)
+        res[0, 0] = sx
+        res[0, 1] = sy
+        res[0, 2] = const_z
+    elif pos_valid and neg_valid:
+        psx, psy = _snap_const_lat_endpoint_xy(px, py, a0, a1, a2, b0, b1, b2, const_z)
+        nsx, nsy = _snap_const_lat_endpoint_xy(nx, ny, a0, a1, a2, b0, b1, b2, const_z)
+        dx = psx - nsx
+        dy = psy - nsy
+        if dx * dx + dy * dy < 1e-14:
+            res[0, 0] = psx
+            res[0, 1] = psy
+            res[0, 2] = const_z
+        else:
+            res[0, 0] = psx
+            res[0, 1] = psy
+            res[0, 2] = const_z
+            res[1, 0] = nsx
+            res[1, 1] = nsy
+            res[1, 2] = const_z
+    return res
+
+
+# ---------------------------------------------------------------------------
+# Test inputs
+# ---------------------------------------------------------------------------
+
+
+def _unit(v):
+    return v / np.linalg.norm(v)
+
+
+def _make_cases(n, seed):
+    """Random great-circle arcs paired with a latitude their arc actually crosses."""
+    rng = np.random.default_rng(seed)
+    cases = []
+    while len(cases) < n:
+        a = _unit(rng.standard_normal(3))
+        b = _unit(rng.standard_normal(3))
+        if abs(np.dot(a, b)) > 0.999:  # near-degenerate arc, skip
+            continue
+        # pick a latitude strictly between the two endpoints' z so an
+        # intersection is likely to exist
+        zlo, zhi = sorted((a[2], b[2]))
+        if zhi - zlo < 1e-6:
+            continue
+        const_z = zlo + (zhi - zlo) * rng.uniform(0.2, 0.8)
+        cases.append((np.stack([a, b]), float(const_z)))
+    return cases
+
+
+# ---------------------------------------------------------------------------
+# Batched drivers — the timing loop lives *inside* njit, mirroring the
+# AccuSphGeom C++ benchmark (2M points looped in-kernel). Timing a Python-level
+# per-call loop would drown the signal in interpreter overhead; batching in
+# Numba measures true kernel throughput, which is also how UXarray actually
+# calls these paths (once per edge over a whole grid).
+# ---------------------------------------------------------------------------
+
+
+@njit(cache=True)
+def _batch_accux_kernel(A, B, Z):
+    """Real AccuX L1 (EFT) kernel over a batch; accumulate to defeat DCE."""
+    acc = 0.0
+    for i in range(A.shape[0]):
+        px, py, nxo, nyo = _accux_constlat_scalar(
+            A[i, 0], A[i, 1], A[i, 2], B[i, 0], B[i, 1], B[i, 2], Z[i]
+        )
+        acc += px + py + nxo + nyo
+    return acc
+
+
+@njit(cache=True)
+def _batch_fp64_kernel(A, B, Z):
+    """Same-body FP64 L1 kernel over a batch; accumulate to defeat DCE."""
+    acc = 0.0
+    for i in range(A.shape[0]):
+        px, py, nxo, nyo = _fp64_constlat_scalar(
+            A[i, 0], A[i, 1], A[i, 2], B[i, 0], B[i, 1], B[i, 2], Z[i]
+        )
+        acc += px + py + nxo + nyo
+    return acc
+
+
+@njit(cache=True)
+def _batch_accux_dispatch(gcas, Z):
+    """Real AccuX full L1+L2+L3 dispatcher over a batch."""
+    acc = 0.0
+    for i in range(gcas.shape[0]):
+        res = gca_const_lat_intersection(gcas[i], Z[i])
+        v = res[0, 0]
+        if v == v:  # not NaN
+            acc += v
+    return acc
+
+
+@njit(cache=True)
+def _batch_fp64_dispatch(gcas, Z):
+    """Same-body FP64 full L1+L2+L3 dispatcher over a batch."""
+    acc = 0.0
+    for i in range(gcas.shape[0]):
+        res = _fp64_gca_const_lat_intersection(gcas[i], Z[i])
+        v = res[0, 0]
+        if v == v:  # not NaN
+            acc += v
+    return acc
+
+
+def _time_batch(fn, args, repeat=7):
+    """Best-of-`repeat` wall-time for one batched call (compile excluded)."""
+    fn(*args)  # warm / compile
+    best = math.inf
+    for _ in range(repeat):
+        t0 = time.perf_counter()
+        fn(*args)
+        best = min(best, time.perf_counter() - t0)
+    return best
+
+
+def _pack(cases):
+    """Turn the case list into contiguous arrays for the batched kernels."""
+    A = np.array([c[0][0] for c in cases])
+    B = np.array([c[0][1] for c in cases])
+    Z = np.array([c[1] for c in cases])
+    gcas = np.array([c[0] for c in cases])
+    return A, B, Z, gcas
+
+
+# ---------------------------------------------------------------------------
+# Diagnostic driver
+# ---------------------------------------------------------------------------
+
+
+def main():
+    base_cases = _make_cases(200, seed=20251104)
+
+    # ---- correctness (on the 200 baseline cases) ----
+    max_out_diff = 0.0
+    status_mismatch = 0
+    n_with_result = 0
+    for gca, z in base_cases:
+        fp64_res = _fp64_gca_const_lat_intersection(gca, z)
+        accux_res = gca_const_lat_intersection(gca, z)
+        fp64_rows = int(np.isfinite(fp64_res[0, 0])) + int(np.isfinite(fp64_res[1, 0]))
+        accux_rows = int(np.isfinite(accux_res[0, 0])) + int(
+            np.isfinite(accux_res[1, 0])
+        )
+        if fp64_rows != accux_rows:
+            status_mismatch += 1
+        if fp64_rows > 0 and accux_rows > 0:
+            n_with_result += 1
+            d = np.nanmax(np.abs(fp64_res - accux_res))
+            if np.isfinite(d):
+                max_out_diff = max(max_out_diff, d)
+
+    # ---- timing: replicate the 200 cases into a large batch (~200k points) ----
+    reps = 1000
+    big = base_cases * reps
+    A, B, Z, gcas = _pack(big)
+    n = A.shape[0]
+
+    t_direct = _time_batch(_batch_fp64_kernel, (A, B, Z))
+    t_accux_k = _time_batch(_batch_accux_kernel, (A, B, Z))
+    t_fp64_d = _time_batch(_batch_fp64_dispatch, (gcas, Z))
+    t_accux_d = _time_batch(_batch_accux_dispatch, (gcas, Z))
+
+    ns_per = lambda t: t / n * 1e9  # noqa: E731
+
+    print("=" * 70)
+    print("Same-body FP64-vs-AccuX diagnostic — GCA/ConstLat (PR #1513)")
+    print("=" * 70)
+    print(f"baseline cases: {len(base_cases)}   with-result: {n_with_result}")
+    print(f"timing batch  : {n} points ({reps}x replication), best of 7")
+    print()
+    print("CORRECTNESS (same-body FP64 dispatcher vs real AccuX dispatcher)")
+    print(f"  status mismatches : {status_mismatch}")
+    print(f"  max output diff   : {max_out_diff:.3e}")
+    print()
+    print("TIMING (ns per point, in-kernel batch)")
+    print(f"  L1  FP64  kernel            : {ns_per(t_direct):8.2f} ns")
+    print(f"  L1  AccuX kernel            : {ns_per(t_accux_k):8.2f} ns")
+    print(f"  L1+L2+L3 FP64  dispatch     : {ns_per(t_fp64_d):8.2f} ns")
+    print(f"  L1+L2+L3 AccuX dispatch     : {ns_per(t_accux_d):8.2f} ns")
+    print()
+    print("DECOMPOSITION")
+    plumb_fp64 = t_fp64_d - t_direct
+    plumb_accux = t_accux_d - t_accux_k
+    eft_kernel = t_accux_k - t_direct
+    eft_dispatch = t_accux_d - t_fp64_d
+    print(
+        f"  plumbing L2/L3 over FP64  body : {ns_per(plumb_fp64):8.2f} ns/pt"
+        f"  ({100 * plumb_fp64 / t_fp64_d:.1f}% of FP64 dispatch)"
+    )
+    print(
+        f"  plumbing L2/L3 over AccuX body : {ns_per(plumb_accux):8.2f} ns/pt"
+        f"  ({100 * plumb_accux / t_accux_d:.1f}% of AccuX dispatch)"
+    )
+    print(
+        f"  EFT math cost (kernel level)   : {ns_per(eft_kernel):8.2f} ns/pt"
+        f"  ({t_accux_k / t_direct:.2f}x FP64 kernel)"
+    )
+    print(
+        f"  EFT math cost (dispatch level) : {ns_per(eft_dispatch):8.2f} ns/pt"
+        f"  ({t_accux_d / t_fp64_d:.2f}x FP64 dispatch)"
+    )
+    print()
+    print("INTERPRETATION")
+    print("  - plumbing overhead should be ~equal for both bodies (body-independent)")
+    print("  - AccuX/FP64 ratio at dispatch ~= ratio at kernel => plumbing adds no")
+    print("    EFT-dependent overhead; measured cost is the EFT math, wired correctly")
+    print("=" * 70)
+
+
+# ---------------------------------------------------------------------------
+# asv timing classes (batched; Numba warmed in setup)
+# ---------------------------------------------------------------------------
+
+
+class SameBodyConstLat:
+    """asv: same-body FP64 vs real AccuX at kernel (L1) and dispatch (L3) levels."""
+
+    def setup(self):
+        cases = _make_cases(200, seed=20251104) * 100
+        self.A, self.B, self.Z, self.gcas = _pack(cases)
+        _batch_fp64_kernel(self.A, self.B, self.Z)
+        _batch_accux_kernel(self.A, self.B, self.Z)
+        _batch_fp64_dispatch(self.gcas, self.Z)
+        _batch_accux_dispatch(self.gcas, self.Z)
+
+    def time_fp64_kernel(self):
+        _batch_fp64_kernel(self.A, self.B, self.Z)
+
+    def time_accux_kernel(self):
+        _batch_accux_kernel(self.A, self.B, self.Z)
+
+    def time_fp64_dispatch(self):
+        _batch_fp64_dispatch(self.gcas, self.Z)
+
+    def time_accux_dispatch(self):
+        _batch_accux_dispatch(self.gcas, self.Z)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py
index 2ee53edb8..c6d367024 100644
--- a/uxarray/grid/arcs.py
+++ b/uxarray/grid/arcs.py
@@ -401,12 +401,24 @@ def _orient3d_on_sphere_value(a, b, q):
     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]
+    return _orient3d_on_sphere_value_xyz(
+        a[0], a[1], a[2], b[0], b[1], b[2], q[0], q[1], q[2]
+    )
+
+
+@njit(cache=True, inline="always")
+def _orient3d_on_sphere_value_xyz(a0, a1, a2, b0, b1, b2, q0, q1, q2):
+    """Scalar-argument form of :func:`_orient3d_on_sphere_value`.
+
+    Takes the nine vector components directly so hot loops can call it without
+    materializing ``(3,)`` arrays.
+    """
+    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)
+    p0 = (x_hi + x_lo) * q0
+    p1 = (y_hi + y_lo) * q1
+    p2 = (z_hi + z_lo) * q2
     s, e = two_sum(p0, p1)
     s, e2 = two_sum(s, p2)
     return s + (e + e2)
@@ -466,19 +478,29 @@ def on_minor_arc(q, a, b, tol=_ON_MINOR_ARC_TOL):
     bool
         True if q lies on the minor arc ab, False otherwise.
     """
+    return _on_minor_arc_xyz(q[0], q[1], q[2], a[0], a[1], a[2], b[0], b[1], b[2], tol)
+
+
+@njit(cache=True, inline="always")
+def _on_minor_arc_xyz(q0, q1, q2, a0, a1, a2, b0, b1, b2, tol=_ON_MINOR_ARC_TOL):
+    """Scalar-argument form of :func:`on_minor_arc`.
+
+    Same logic, but takes the nine vector components directly so hot loops can
+    test arc membership without allocating ``(3,)`` arrays for the query point.
+    """
     # Coincident endpoints: degenerate arc, no interior.
-    if a[0] == b[0] and a[1] == b[1] and a[2] == b[2]:
+    if a0 == b0 and a1 == b1 and a2 == b2:
         return False
     # Antipodal endpoints: a×b = 0, so every point on the great circle passes
     # the collinearity test and the interval conditions degenerate to 0 >= -tol,
     # causing false positives for all points on the great circle.
-    if a[0] == -b[0] and a[1] == -b[1] and a[2] == -b[2]:
+    if a0 == -b0 and a1 == -b1 and a2 == -b2:
         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:
+    if abs(_orient3d_on_sphere_value_xyz(a0, a1, a2, b0, b1, b2, q0, q1, q2)) > 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]
+    qa = a0 * q0 + a1 * q1 + a2 * q2
+    qb = b0 * q0 + b1 * q1 + b2 * q2
+    ab = a0 * b0 + a1 * b1 + a2 * b2
     return (qb - ab * qa) >= -tol and (qa - qb * ab) >= -tol
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 5457a17c0..fb8588a01 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -4,7 +4,7 @@
 from numba import njit, prange
 
 from uxarray.constants import ERROR_TOLERANCE, INT_DTYPE
-from uxarray.grid.arcs import on_minor_arc
+from uxarray.grid.arcs import _on_minor_arc_xyz, on_minor_arc
 from uxarray.utils.computing import (
     _cdp2,
     _cdp4,
@@ -666,55 +666,94 @@ def _snap_const_lat_endpoint(point, x1, x2, const_z):
     # to ~1e-7 in arc length (unit sphere). Candidates within this distance are
     # snapped to the exact endpoint to avoid sub-ulp drift when the arc ends
     # exactly on the latitude circle.
-    snap_sq = 1e-14
+    sx, sy = _snap_const_lat_endpoint_xy(
+        point[0], point[1], x1[0], x1[1], x1[2], x2[0], x2[1], x2[2], const_z
+    )
     out = np.empty(3)
-    out[0] = point[0]
-    out[1] = point[1]
+    out[0] = sx
+    out[1] = sy
     out[2] = point[2]
-    for xe in (x1, x2):
-        if abs(xe[2] - const_z) <= ERROR_TOLERANCE:
-            dx = out[0] - xe[0]
-            dy = out[1] - xe[1]
-            if dx * dx + dy * dy < snap_sq:
-                out[0] = xe[0]
-                out[1] = xe[1]
     return out
 
 
+@njit(cache=True, inline="always")
+def _snap_const_lat_endpoint_xy(px, py, a0, a1, a2, b0, b1, b2, const_z):
+    """Scalar-argument form of :func:`_snap_const_lat_endpoint`.
+
+    Returns the (possibly snapped) x, y of the candidate; z is always ``const_z``
+    so it is not returned. Allocation-free for use in hot loops.
+    """
+    snap_sq = 1e-14
+    ox = px
+    oy = py
+    if abs(a2 - const_z) <= ERROR_TOLERANCE:
+        dx = ox - a0
+        dy = oy - a1
+        if dx * dx + dy * dy < snap_sq:
+            ox = a0
+            oy = a1
+    if abs(b2 - const_z) <= ERROR_TOLERANCE:
+        dx = ox - b0
+        dy = oy - b1
+        if dx * dx + dy * dy < snap_sq:
+            ox = b0
+            oy = b1
+    return ox, oy
+
+
 @njit(cache=True)
 def gca_const_lat_intersection(gca_cart, const_z):
     """Layer 3 — dispatcher / convenience API.
 
-    Calls the batch/status kernel, applies endpoint snapping, and packages the
-    result in UXarray's NaN-filled (2, 3) format.  All UXarray-specific branching
-    lives here so the numerical core and status layers stay uniform.
+    Runs the numerical kernel, validity masks, endpoint snapping, and packaging
+    into UXarray's NaN-filled (2, 3) format entirely on scalars, so the only heap
+    allocation is the returned array. All UXarray-specific branching lives here so
+    the numerical core stays uniform. See ``_try_gca_const_lat_intersection`` for
+    the array-returning form used by the layer benchmarks.
     """
     res = np.empty((2, 3))
     res.fill(np.nan)
-    point, status, pos, neg = _try_gca_const_lat_intersection(gca_cart, const_z)
-    x1 = gca_cart[0]
-    x2 = gca_cart[1]
-    if status == 0:
-        point = _snap_const_lat_endpoint(point, x1, x2, const_z)
-        res[0, 0] = point[0]
-        res[0, 1] = point[1]
-        res[0, 2] = point[2]
-    elif status == 1:
-        pos = _snap_const_lat_endpoint(pos, x1, x2, const_z)
-        neg = _snap_const_lat_endpoint(neg, x1, x2, const_z)
-        dx = pos[0] - neg[0]
-        dy = pos[1] - neg[1]
+
+    a0 = gca_cart[0, 0]
+    a1 = gca_cart[0, 1]
+    a2 = gca_cart[0, 2]
+    b0 = gca_cart[1, 0]
+    b1 = gca_cart[1, 1]
+    b2 = gca_cart[1, 2]
+
+    px, py, nx, ny = _accux_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z)
+
+    pos_fin = math.isfinite(px) and math.isfinite(py)
+    neg_fin = math.isfinite(nx) and math.isfinite(ny)
+    pos_valid = pos_fin and _on_minor_arc_xyz(px, py, const_z, a0, a1, a2, b0, b1, b2)
+    neg_valid = neg_fin and _on_minor_arc_xyz(nx, ny, const_z, a0, a1, a2, b0, b1, b2)
+
+    if pos_valid and not neg_valid:
+        sx, sy = _snap_const_lat_endpoint_xy(px, py, a0, a1, a2, b0, b1, b2, const_z)
+        res[0, 0] = sx
+        res[0, 1] = sy
+        res[0, 2] = const_z
+    elif neg_valid and not pos_valid:
+        sx, sy = _snap_const_lat_endpoint_xy(nx, ny, a0, a1, a2, b0, b1, b2, const_z)
+        res[0, 0] = sx
+        res[0, 1] = sy
+        res[0, 2] = const_z
+    elif pos_valid and neg_valid:
+        psx, psy = _snap_const_lat_endpoint_xy(px, py, a0, a1, a2, b0, b1, b2, const_z)
+        nsx, nsy = _snap_const_lat_endpoint_xy(nx, ny, a0, a1, a2, b0, b1, b2, const_z)
+        dx = psx - nsx
+        dy = psy - nsy
         if dx * dx + dy * dy < 1e-14:
-            res[0, 0] = pos[0]
-            res[0, 1] = pos[1]
-            res[0, 2] = pos[2]
+            res[0, 0] = psx
+            res[0, 1] = psy
+            res[0, 2] = const_z
         else:
-            res[0, 0] = pos[0]
-            res[0, 1] = pos[1]
-            res[0, 2] = pos[2]
-            res[1, 0] = neg[0]
-            res[1, 1] = neg[1]
-            res[1, 2] = neg[2]
+            res[0, 0] = psx
+            res[0, 1] = psy
+            res[0, 2] = const_z
+            res[1, 0] = nsx
+            res[1, 1] = nsy
+            res[1, 2] = const_z
     return res
 
 

From a4639134a552180524dc1c4c2190aba11ec8946b Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Mon, 13 Jul 2026 18:44:00 -0500
Subject: [PATCH 29/51] Import benchmark kernels inside setup to avoid asv
 collection failures

Move the uxarray kernel imports from module level into each benchmark
class's setup, so a stale or mismatched environment build only errors the
affected benchmark instead of aborting collection of every benchmark in
the directory. Thanks @cmdupuis3 for catching the asv import failure.
---
 benchmarks/geometry_kernels.py | 158 +++++++++++++++++++++------------
 1 file changed, 100 insertions(+), 58 deletions(-)

diff --git a/benchmarks/geometry_kernels.py b/benchmarks/geometry_kernels.py
index f5a8bd2de..d7e777bd6 100644
--- a/benchmarks/geometry_kernels.py
+++ b/benchmarks/geometry_kernels.py
@@ -6,29 +6,15 @@
 
 All Numba functions are warmed (compiled) during ``setup`` so that benchmark
 timings reflect steady-state throughput, not JIT compilation.
+
+The ``uxarray`` kernels are imported inside each ``setup`` (not at module
+import) so that if a symbol is missing in the environment asv built, only the
+affected benchmark errors out rather than aborting collection of every
+benchmark in this directory.
 """
 
 import numpy as np
 
-from uxarray.grid.arcs import on_minor_arc, orient3d_on_sphere
-from uxarray.grid.intersections import (
-    _accux_constlat,
-    _accux_gca,
-    _try_gca_const_lat_intersection,
-    _try_gca_gca_intersection,
-    gca_const_lat_intersection,
-    gca_gca_intersection,
-)
-from uxarray.grid.point_in_face import _point_in_polygon_sphere
-from uxarray.utils.computing import (
-    acc_sqrt_re,
-    accucross,
-    accucross_pair,
-    diff_of_products,
-    two_prod,
-    two_sum,
-)
-
 
 def _unit(v):
     return v / np.linalg.norm(v)
@@ -67,6 +53,18 @@ class EFTPrimitives:
     diff_of_products, and acc_sqrt_re."""
 
     def setup(self):
+        from uxarray.utils.computing import (
+            acc_sqrt_re,
+            diff_of_products,
+            two_prod,
+            two_sum,
+        )
+
+        self.two_sum = two_sum
+        self.two_prod = two_prod
+        self.diff_of_products = diff_of_products
+        self.acc_sqrt_re = acc_sqrt_re
+
         # Warm Numba
         two_sum(1.0, 1e-16)
         two_prod(1.23456789, 9.87654321)
@@ -74,47 +72,66 @@ def setup(self):
         acc_sqrt_re(1.0 - 1e-15)
 
     def time_two_sum(self):
-        two_sum(1.23456789012345678, 9.87654321098765432e-16)
+        self.two_sum(1.23456789012345678, 9.87654321098765432e-16)
 
     def time_two_prod(self):
-        two_prod(1.23456789012345678, 9.87654321098765432)
+        self.two_prod(1.23456789012345678, 9.87654321098765432)
 
     def time_diff_of_products(self):
-        diff_of_products(1.23456789, 9.87654321, 1.23456788, 9.87654322)
+        self.diff_of_products(1.23456789, 9.87654321, 1.23456788, 9.87654322)
 
     def time_acc_sqrt_re(self):
-        acc_sqrt_re(1.0 - 1e-15)
+        self.acc_sqrt_re(1.0 - 1e-15)
 
 
 class AccucrossKernels:
     """Benchmark the compensated cross-product kernels."""
 
     def setup(self):
-        accucross(
-            _W0[0], _W0[1], _W0[2],
-            _W1[0], _W1[1], _W1[2],
-        )
+        from uxarray.utils.computing import accucross, accucross_pair
+
+        self.accucross = accucross
+        self.accucross_pair = accucross_pair
+
+        accucross(_W0[0], _W0[1], _W0[2], _W1[0], _W1[1], _W1[2])
         accucross_pair(
-            1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-            0.0, 1.0, 0.0, 0.0, 0.0, 0.0,
+            1.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            1.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
         )
 
     def time_accucross(self):
-        accucross(
-            _W0[0], _W0[1], _W0[2],
-            _W1[0], _W1[1], _W1[2],
-        )
+        self.accucross(_W0[0], _W0[1], _W0[2], _W1[0], _W1[1], _W1[2])
 
     def time_accucross_pair(self):
-        n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = accucross(
+        n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo = self.accucross(
             _W0[0], _W0[1], _W0[2], _W1[0], _W1[1], _W1[2]
         )
-        n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo = accucross(
+        n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo = self.accucross(
             _V0[0], _V0[1], _V0[2], _V1[0], _V1[1], _V1[2]
         )
-        accucross_pair(
-            n1x_hi, n1y_hi, n1z_hi, n1x_lo, n1y_lo, n1z_lo,
-            n2x_hi, n2y_hi, n2z_hi, n2x_lo, n2y_lo, n2z_lo,
+        self.accucross_pair(
+            n1x_hi,
+            n1y_hi,
+            n1z_hi,
+            n1x_lo,
+            n1y_lo,
+            n1z_lo,
+            n2x_hi,
+            n2y_hi,
+            n2z_hi,
+            n2x_lo,
+            n2y_lo,
+            n2z_lo,
         )
 
 
@@ -122,74 +139,99 @@ class OrientPredicates:
     """Benchmark the orient3d and on_minor_arc predicates."""
 
     def setup(self):
+        from uxarray.grid.arcs import on_minor_arc, orient3d_on_sphere
+
+        self.orient3d_on_sphere = orient3d_on_sphere
+        self.on_minor_arc = on_minor_arc
+
         orient3d_on_sphere(_W0, _W1, _V0)
         on_minor_arc(_V0, _W0, _W1)
 
     def time_orient3d_on_sphere(self):
-        orient3d_on_sphere(_W0, _W1, _V0)
+        self.orient3d_on_sphere(_W0, _W1, _V0)
 
     def time_on_minor_arc(self):
-        on_minor_arc(_V0, _W0, _W1)
+        self.on_minor_arc(_V0, _W0, _W1)
 
 
 class GCAGCAIntersection:
     """Benchmark all three layers of the GCA-GCA intersection stack."""
 
     def setup(self):
-        gca_a = np.stack([_W0, _W1])
-        gca_b = np.stack([_V0, _V1])
+        from uxarray.grid.intersections import (
+            _accux_gca,
+            _try_gca_gca_intersection,
+            gca_gca_intersection,
+        )
+
+        self._accux_gca = _accux_gca
+        self._try_gca_gca_intersection = _try_gca_gca_intersection
+        self.gca_gca_intersection = gca_gca_intersection
+
+        self.gca_a = np.stack([_W0, _W1])
+        self.gca_b = np.stack([_V0, _V1])
         _accux_gca(_W0, _W1, _V0, _V1)
         _try_gca_gca_intersection(_W0, _W1, _V0, _V1)
-        gca_gca_intersection(gca_a, gca_b)
-        self.gca_a = gca_a
-        self.gca_b = gca_b
+        gca_gca_intersection(self.gca_a, self.gca_b)
 
     def time_accux_gca_kernel(self):
         """Layer 1: pure numerical kernel."""
-        _accux_gca(_W0, _W1, _V0, _V1)
+        self._accux_gca(_W0, _W1, _V0, _V1)
 
     def time_try_gca_gca_intersection(self):
         """Layer 2: batch/status layer."""
-        _try_gca_gca_intersection(_W0, _W1, _V0, _V1)
+        self._try_gca_gca_intersection(_W0, _W1, _V0, _V1)
 
     def time_gca_gca_intersection(self):
         """Layer 3: dispatcher (full public API)."""
-        gca_gca_intersection(self.gca_a, self.gca_b)
+        self.gca_gca_intersection(self.gca_a, self.gca_b)
 
 
 class GCAConstLatIntersection:
     """Benchmark all three layers of the GCA / constant-latitude intersection stack."""
 
     def setup(self):
-        gca_cart = np.stack([_X1, _X2])
+        from uxarray.grid.intersections import (
+            _accux_constlat,
+            _try_gca_const_lat_intersection,
+            gca_const_lat_intersection,
+        )
+
+        self._accux_constlat = _accux_constlat
+        self._try_gca_const_lat_intersection = _try_gca_const_lat_intersection
+        self.gca_const_lat_intersection = gca_const_lat_intersection
+
+        self.gca_cart = np.stack([_X1, _X2])
         _accux_constlat(_X1, _X2, _CONST_Z)
-        _try_gca_const_lat_intersection(gca_cart, _CONST_Z)
-        gca_const_lat_intersection(gca_cart, _CONST_Z)
-        self.gca_cart = gca_cart
+        _try_gca_const_lat_intersection(self.gca_cart, _CONST_Z)
+        gca_const_lat_intersection(self.gca_cart, _CONST_Z)
 
     def time_accux_constlat_kernel(self):
         """Layer 1: pure numerical kernel."""
-        _accux_constlat(_X1, _X2, _CONST_Z)
+        self._accux_constlat(_X1, _X2, _CONST_Z)
 
     def time_try_gca_const_lat_intersection(self):
         """Layer 2: batch/status layer."""
-        _try_gca_const_lat_intersection(self.gca_cart, _CONST_Z)
+        self._try_gca_const_lat_intersection(self.gca_cart, _CONST_Z)
 
     def time_gca_const_lat_intersection(self):
         """Layer 3: dispatcher (full public API)."""
-        gca_const_lat_intersection(self.gca_cart, _CONST_Z)
+        self.gca_const_lat_intersection(self.gca_cart, _CONST_Z)
 
 
 class PointInPolygonSphere:
     """Benchmark the spherical point-in-polygon kernel."""
 
     def setup(self):
-        # Warm Numba
+        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):
-        _point_in_polygon_sphere(_Q_INSIDE, _POLY)
+        self._point_in_polygon_sphere(_Q_INSIDE, _POLY)
 
     def time_point_outside(self):
-        _point_in_polygon_sphere(_Q_OUTSIDE, _POLY)
+        self._point_in_polygon_sphere(_Q_OUTSIDE, _POLY)

From 465fd4c7e2bfe89822b3339372b12333b7a348ce Mon Sep 17 00:00:00 2001
From: Christopher Dupuis <45972964+cmdupuis3@users.noreply.github.com>
Date: Thu, 16 Jul 2026 17:22:40 -0500
Subject: [PATCH 30/51] Cmd/accusphere (#1579)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Honor quadrature kwargs in calculate_total_face_area

calculate_total_face_area accepted quadrature_rule, order and
latitude_adjusted_area but ignored them, always returning the cached
default-parameter face_areas. As a result the gaussian/corrected path
produced the same total as the triangular one.

Keep the cached fast path for the default parameters (which also preserves
the equal-area values used for HEALPix grids) and route any non-default
quadrature settings through _compute_face_areas so the requested rule,
order and latitude adjustment actually take effect.

* Restore matplotlib backend after HoloViews matplotlib plot (#1538)

* Restore matplotlib backend after HoloViews matplotlib plot

plot(backend='matplotlib') calls hv.extension('matplotlib'), which switches
the active matplotlib backend and clobbers the IPython inline display hook,
silently breaking subsequent native matplotlib/xarray .plot() calls. Restore
the original matplotlib backend right after the HoloViews extension switch;
HoloViews objects still display via Store.current_backend, so this is safe.

Closes #1537

* Address review: capture backend at switch, accurate docstring, effective test

* Reconfigure IPython inline display hook when restoring backend

mpl.use() restores the matplotlib backend name but does not re-register
IPython's inline display integration that hv.extension('matplotlib')
clobbers. In a Jupyter kernel without an explicit %matplotlib inline,
native matplotlib/xarray .plot() calls after a uxarray matplotlib plot
still failed to render. Re-run configure_inline_support when the restored
backend is inline so the display hook is reinstated. See #1537.

* Restore matplotlib backend via IPython shell reactivation to fix inline display

---------

Co-authored-by: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com>

* Allow SCRIP reader to respect units w/r/t radians (#1433)

* Allow SCRIP reader to respect units wrt to radians

* Add test file and unit test for SCRIP radian coordinate handling

* minor formatting changes for scrip radians fix

Moves meshfiles/scrip/scrip_radians.nc to meshfiles/scrip/scrip_radians/scrip_radians_grid.nc to match style of other meshfiles naming schemes.

Renames the new _scrip._convert_to_degrees() to _scrip._values_in_degrees(). It doesn't always convert; and it also does more than just converting, because it gives numpy array from DataArray. Clarified docstring.

ran the following, so it should now pass ruff checks: pre-commit run --all-files

---------

Co-authored-by: Sam Evans <s7evans11@gmail.com>
Co-authored-by: Sam Evans <47793072+Sevans711@users.noreply.github.com>
Co-authored-by: Rajeev Jain <rajeeja@gmail.com>

* Support Python 3.14 and upgrade YAC to v3.18 (with DNN remapping) (#1563)

* Upgrade YAC to v3.18, expose DNN remapping, drop pathlib backport

- upgrade YAC CI to v3.18.0 on Python 3.14 (numba>=0.63, py3.14 classifier,
  cython>=3.1 via conda for the bindings build)
- fix add_average: reduction_type -> weight_type (renamed in YAC v3.18)
- expose distance-nearest-neighbour (yac_method='dnn', new in YAC v3.15) + test
- remove the obsolete 'pathlib' backport dependency: it shadows the stdlib and
  breaks tools that import pathlib on Python 3.10+ (broke YAC's Cython build)

* Reference #1561 in numba version pin comment

---------

Co-authored-by: Christopher Dupuis <45972964+cmdupuis3@users.noreply.github.com>

* Bump actions/download-artifact in the actions group across 1 directory (#1572)

Bumps the actions group with 1 update in the / directory: [actions/download-artifact](https://github.com/actions/download-artifact).


Updates `actions/download-artifact` from 7 to 8
- [Release notes](https://github.com/actions/download-artifact/releases)
- [Commits](https://github.com/actions/download-artifact/compare/v7...v8)

---
updated-dependencies:
- dependency-name: actions/download-artifact
  dependency-version: '8'
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: actions
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* [pre-commit.ci] pre-commit autoupdate (#1565)

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.15.20 → v0.15.21](https://github.com/astral-sh/ruff-pre-commit/compare/v0.15.20...v0.15.21)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Accusphere: gca_gca benchmarks and better arc sampling

* Accusphere: fix sum_of_squares accuracy bug + tests

* Accusphere: numba fma refactor and fix validation tautology

* Accusphere: scalarized _counts_as_crossing

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: vakudo <127726617+vakudo@users.noreply.github.com>
Co-authored-by: Sam Evans <47793072+Sevans711@users.noreply.github.com>
Co-authored-by: Rajeev Jain <rajeeja@gmail.com>
Co-authored-by: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com>
Co-authored-by: zarzycki <colin.zarzycki@gmail.com>
Co-authored-by: Sam Evans <s7evans11@gmail.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 .../workflows/asv-benchmarking-comment.yml    |   2 +-
 .github/workflows/yac-optional.yml            |  11 +-
 .pre-commit-config.yaml                       |   2 +-
 benchmarks/geometry_samebody.py               |  41 ++-
 benchmarks/geometry_samebody_gcagca.py        | 338 ++++++++++++++++++
 ci/environment.yml                            |   1 -
 pyproject.toml                                |   5 +-
 test/grid/geometry/test_eft_accuracy.py       | 170 +++++++++
 test/grid/grid/test_areas.py                  |  23 ++
 test/io/test_scrip.py                         |  19 +
 .../scrip/scrip_radians/scrip_radians_grid.nc | Bin 0 -> 12916 bytes
 test/test_plot.py                             |  86 +++++
 test/test_remap_yac.py                        |  17 +
 uxarray/grid/arcs.py                          |  31 +-
 uxarray/grid/grid.py                          |  15 +-
 uxarray/grid/intersections.py                 |   7 +-
 uxarray/grid/point_in_face.py                 |  87 ++++-
 uxarray/io/_scrip.py                          |  76 +++-
 uxarray/plot/utils.py                         |  66 +++-
 uxarray/remap/accessor.py                     |  10 +-
 uxarray/remap/yac.py                          |  31 +-
 uxarray/utils/computing.py                    | 135 ++++---
 22 files changed, 1016 insertions(+), 157 deletions(-)
 create mode 100644 benchmarks/geometry_samebody_gcagca.py
 create mode 100644 test/grid/geometry/test_eft_accuracy.py
 create mode 100644 test/meshfiles/scrip/scrip_radians/scrip_radians_grid.nc

diff --git a/.github/workflows/asv-benchmarking-comment.yml b/.github/workflows/asv-benchmarking-comment.yml
index 0b6befbd4..a6852600f 100644
--- a/.github/workflows/asv-benchmarking-comment.yml
+++ b/.github/workflows/asv-benchmarking-comment.yml
@@ -24,7 +24,7 @@ jobs:
     if: ${{ github.event.workflow_run.conclusion == 'success' }}
     steps:
       - name: Download benchmark results artifact
-        uses: actions/download-artifact@v7
+        uses: actions/download-artifact@v8
         with:
           name: asv-benchmark-results-Linux
           path: asv-results
diff --git a/.github/workflows/yac-optional.yml b/.github/workflows/yac-optional.yml
index 68b166bbb..ccfa606db 100644
--- a/.github/workflows/yac-optional.yml
+++ b/.github/workflows/yac-optional.yml
@@ -10,13 +10,13 @@ on:
 
 jobs:
   yac-optional:
-    name: YAC core v3.14.0_p1 (Ubuntu)
+    name: YAC core v3.18.0 (Ubuntu)
     runs-on: ubuntu-latest
     defaults:
       run:
         shell: bash -l {0}
     env:
-      YAC_VERSION: v3.14.0_p1
+      YAC_VERSION: v3.18.0
       YAXT_VERSION: v0.11.5.1
       MPIEXEC: /usr/bin/mpirun
       MPIRUN: /usr/bin/mpirun
@@ -36,7 +36,7 @@ jobs:
         with:
           activate-environment: uxarray_build
           channel-priority: strict
-          python-version: "3.11"
+          python-version: "3.14"
           channels: conda-forge
           environment-file: ci/environment.yml
           miniforge-variant: Miniforge3
@@ -65,8 +65,9 @@ jobs:
           mpif90 --version
       - name: Install Python build dependencies
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install cython wheel
+          # Cython and wheel are needed to build YAC's Python bindings from source.
+          conda install --yes -c conda-forge "cython>=3.1" wheel
+          python -m cython --version
       - name: Build and install YAXT
         run: |
           set -euxo pipefail
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index edcf0ed53..838b780f7 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -14,7 +14,7 @@ repos:
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: v0.15.20
+  rev: v0.15.21
   hooks:
     - id: ruff
       name: lint with ruff
diff --git a/benchmarks/geometry_samebody.py b/benchmarks/geometry_samebody.py
index 5f9e96d46..7712d1c7a 100644
--- a/benchmarks/geometry_samebody.py
+++ b/benchmarks/geometry_samebody.py
@@ -201,21 +201,28 @@ def _unit(v):
     return v / np.linalg.norm(v)
 
 
-def _make_cases(n, seed):
-    """Random great-circle arcs paired with a latitude their arc actually crosses."""
+def _make_cases(n, seed, frac_cross=0.7):
+
     rng = np.random.default_rng(seed)
     cases = []
     while len(cases) < n:
-        a = _unit(rng.standard_normal(3))
-        b = _unit(rng.standard_normal(3))
-        if abs(np.dot(a, b)) > 0.999:  # near-degenerate arc, skip
-            continue
-        # pick a latitude strictly between the two endpoints' z so an
-        # intersection is likely to exist
-        zlo, zhi = sorted((a[2], b[2]))
-        if zhi - zlo < 1e-6:
-            continue
-        const_z = zlo + (zhi - zlo) * rng.uniform(0.2, 0.8)
+        mid = _unit(rng.standard_normal(3))
+        tan = _unit(np.cross(mid, _unit(rng.standard_normal(3))))
+        half = 0.5 * math.radians(10.0 ** rng.uniform(math.log10(0.2), math.log10(8.0)))
+        ca, sa = math.cos(half), math.sin(half)
+        a = _unit(mid * ca - tan * sa)
+        b = _unit(mid * ca + tan * sa)
+        zlo, zhi = (a[2], b[2]) if a[2] <= b[2] else (b[2], a[2])
+        if rng.random() < frac_cross and zhi - zlo > 1e-9:
+            const_z = zlo + (zhi - zlo) * rng.uniform(0.15, 0.85)
+        else:
+            # a latitude the arc does not span -> empty-return path
+            room_above = 0.999 - zhi
+            room_below = zlo + 0.999
+            if room_above >= room_below:
+                const_z = zhi + min(room_above, rng.uniform(0.02, 0.3))
+            else:
+                const_z = zlo - min(room_below, rng.uniform(0.02, 0.3))
         cases.append((np.stack([a, b]), float(const_z)))
     return cases
 
@@ -324,10 +331,8 @@ def main():
             if np.isfinite(d):
                 max_out_diff = max(max_out_diff, d)
 
-    # ---- timing: replicate the 200 cases into a large batch (~200k points) ----
-    reps = 1000
-    big = base_cases * reps
-    A, B, Z, gcas = _pack(big)
+    # ---- timing: a large batch of DISTINCT cases (no replication) ----
+    A, B, Z, gcas = _pack(_make_cases(100_000, seed=20251105))
     n = A.shape[0]
 
     t_direct = _time_batch(_batch_fp64_kernel, (A, B, Z))
@@ -341,7 +346,7 @@ def main():
     print("Same-body FP64-vs-AccuX diagnostic — GCA/ConstLat (PR #1513)")
     print("=" * 70)
     print(f"baseline cases: {len(base_cases)}   with-result: {n_with_result}")
-    print(f"timing batch  : {n} points ({reps}x replication), best of 7")
+    print(f"timing batch  : {n} distinct points, best of 7")
     print()
     print("CORRECTNESS (same-body FP64 dispatcher vs real AccuX dispatcher)")
     print(f"  status mismatches : {status_mismatch}")
@@ -391,7 +396,7 @@ class SameBodyConstLat:
     """asv: same-body FP64 vs real AccuX at kernel (L1) and dispatch (L3) levels."""
 
     def setup(self):
-        cases = _make_cases(200, seed=20251104) * 100
+        cases = _make_cases(20_000, seed=20251104)
         self.A, self.B, self.Z, self.gcas = _pack(cases)
         _batch_fp64_kernel(self.A, self.B, self.Z)
         _batch_accux_kernel(self.A, self.B, self.Z)
diff --git a/benchmarks/geometry_samebody_gcagca.py b/benchmarks/geometry_samebody_gcagca.py
new file mode 100644
index 000000000..348f55859
--- /dev/null
+++ b/benchmarks/geometry_samebody_gcagca.py
@@ -0,0 +1,338 @@
+"""Same-body FP64-vs-AccuX diagnostic for the GCA x GCA path.
+
+Companion to ``geometry_samebody.py``, which only covers the GCA/constant-latitude
+stack.  T
+
+gca_gca is the kernel invoked per edge from the njit point-in-polygon crossing
+loop (``uxarray/grid/geometry.py`` ``_check_intersection``), so its allocation
+profile is on the hot path of ``get_faces_containing_point`` and pole detection.
+
+Factoring (identical to ``geometry_samebody.py``)::
+
+    real AccuX time  -  same-body FP64 time  == EFT math only
+    same-body FP64   -  direct FP64 kernel   == L2/L3 plumbing only
+
+"""
+
+import math
+import time
+
+import numpy as np
+from numba import njit
+
+from uxarray.grid.arcs import on_minor_arc
+from uxarray.grid.intersections import _accux_gca, gca_gca_intersection
+
+
+@njit(cache=True, inline="always")
+def _fp64_gca(w0, w1, v0, v1):
+    """
+    L1 (FP64 body) -- plain double-precision cross-product triple, the direct
+    analogue of _accux_gca (intersections.py) with accucross/accucross_pair
+    replaced by naive FP64 cross products.  Same allocation shape (two np.empty(3))
+    so the twin's allocation profile matches the real kernel exactly.
+    """
+
+    n1x = w0[1] * w1[2] - w0[2] * w1[1]
+    n1y = w0[2] * w1[0] - w0[0] * w1[2]
+    n1z = w0[0] * w1[1] - w0[1] * w1[0]
+    n2x = v0[1] * v1[2] - v0[2] * v1[1]
+    n2y = v0[2] * v1[0] - v0[0] * v1[2]
+    n2z = v0[0] * v1[1] - v0[1] * v1[0]
+
+    vx = n1y * n2z - n1z * n2y
+    vy = n1z * n2x - n1x * n2z
+    vz = n1x * n2y - n1y * n2x
+
+    vn = math.sqrt(vx * vx + vy * vy + vz * vz)
+    inv = 1.0 / vn if vn != 0.0 else np.inf
+    pos = np.empty(3)
+    pos[0] = vx * inv
+    pos[1] = vy * inv
+    pos[2] = vz * inv
+    neg = np.empty(3)
+    neg[0] = -pos[0]
+    neg[1] = -pos[1]
+    neg[2] = -pos[2]
+    return pos, neg
+
+
+@njit(cache=True)
+def _fp64_try_gca_gca_intersection(w0, w1, v0, v1):
+    """
+    L2 (FP64 body) -- byte-for-byte identical logic to _try_gca_gca_intersection
+    (intersections.py), only the L1 call differs.
+    """
+    pos, neg = _fp64_gca(w0, w1, v0, v1)
+
+    pos_fin = (
+        1
+        if math.isfinite(pos[0]) and math.isfinite(pos[1]) and math.isfinite(pos[2])
+        else 0
+    )
+    neg_fin = (
+        1
+        if math.isfinite(neg[0]) and math.isfinite(neg[1]) and math.isfinite(neg[2])
+        else 0
+    )
+    pos_on_a = 1 if (pos_fin and on_minor_arc(pos, w0, w1)) else 0
+    pos_on_b = 1 if (pos_fin and on_minor_arc(pos, v0, v1)) else 0
+    neg_on_a = 1 if (neg_fin and on_minor_arc(neg, w0, w1)) else 0
+    neg_on_b = 1 if (neg_fin and on_minor_arc(neg, v0, v1)) else 0
+
+    pos_valid = pos_fin * pos_on_a * pos_on_b
+    neg_valid = neg_fin * neg_on_a * neg_on_b
+
+    pos_mask = pos_valid * (1 - neg_valid)
+    neg_mask = neg_valid * (1 - pos_valid)
+
+    point = np.empty(3)
+    point[0] = pos_mask * pos[0] + neg_mask * neg[0]
+    point[1] = pos_mask * pos[1] + neg_mask * neg[1]
+    point[2] = pos_mask * pos[2] + neg_mask * neg[2]
+
+    both = pos_valid * neg_valid
+    none = (1 - pos_valid) * (1 - neg_valid)
+    status = both + none * 2
+    return point, status, pos, neg
+
+
+@njit(cache=True)
+def _fp64_gca_gca_intersection(gca_a_xyz, gca_b_xyz):
+    """
+    L3 (FP64 body) -- identical dispatcher to gca_gca_intersection
+    (intersections.py), same np.empty((2, 3)) + res[:count] slice profile.
+    """
+    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")
+
+    w0 = gca_a_xyz[0]
+    w1 = gca_a_xyz[1]
+    v0 = gca_b_xyz[0]
+    v1 = gca_b_xyz[1]
+
+    point, status, pos, neg = _fp64_try_gca_gca_intersection(w0, w1, v0, v1)
+
+    res = np.empty((2, 3))
+    count = 0
+    if status == 0:
+        res[0, 0] = point[0]
+        res[0, 1] = point[1]
+        res[0, 2] = point[2]
+        count = 1
+    elif status == 1:
+        res[0, 0] = pos[0]
+        res[0, 1] = pos[1]
+        res[0, 2] = pos[2]
+        res[1, 0] = neg[0]
+        res[1, 1] = neg[1]
+        res[1, 2] = neg[2]
+        count = 2
+    else:
+        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]
+
+
+# ---------------------------------------------------------------------------
+# Case generation -- a LARGE set of DISTINCT, SHORT arcs (matching real grid
+# edges, ~0.2-8 deg / median ~1.5 deg) with a controlled intersect / no-intersect
+# mix.  Short arcs put a x b in the near-parallel cancellation regime the EFT
+# targets; the mix exercises both the status 0/1 (allocating) and status 2
+# (empty-return) dispatcher branches.
+# ---------------------------------------------------------------------------
+
+
+def _unit(v):
+    return v / np.linalg.norm(v)
+
+
+def _short_arc(rng, mid=None):
+    """A short great-circle arc (~0.2-8 deg, median ~1.5 deg) around ``mid`` (a
+    random point if not given), matching real unstructured-grid edge lengths."""
+    if mid is None:
+        mid = _unit(rng.standard_normal(3))
+    tan = _unit(np.cross(mid, _unit(rng.standard_normal(3))))
+    half = 0.5 * math.radians(10.0 ** rng.uniform(math.log10(0.2), math.log10(8.0)))
+    ca, sa = math.cos(half), math.sin(half)
+    return _unit(mid * ca - tan * sa), _unit(mid * ca + tan * sa)
+
+
+def _make_gca_cases(n, seed, frac_intersect=0.6):
+    rng = np.random.default_rng(seed)
+    cases = []
+    while len(cases) < n:
+        if rng.random() < frac_intersect:
+            # two short arcs sharing a midpoint p -> p is the midpoint of both
+            # minor arcs, so they intersect there (status 0/1).
+            p = _unit(rng.standard_normal(3))
+            a0, a1 = _short_arc(rng, mid=p)
+            b0, b1 = _short_arc(rng, mid=p)
+        else:
+            # two independent short arcs -> their great circles cross off at
+            # least one minor arc, exercising the status 2 / empty-return branch.
+            a0, a1 = _short_arc(rng)
+            b0, b1 = _short_arc(rng)
+        cases.append((np.stack([a0, a1]), np.stack([b0, b1])))
+    return cases
+
+
+def _pack_gca(cases):
+    wa = np.ascontiguousarray([c[0][0] for c in cases])
+    wb = np.ascontiguousarray([c[0][1] for c in cases])
+    va = np.ascontiguousarray([c[1][0] for c in cases])
+    vb = np.ascontiguousarray([c[1][1] for c in cases])
+    ga = np.ascontiguousarray([c[0] for c in cases])  # (n, 2, 3)
+    gb = np.ascontiguousarray([c[1] for c in cases])
+    return wa, wb, va, vb, ga, gb
+
+
+# ---------------------------------------------------------------------------
+# Batched in-kernel drivers -- the timing loop lives inside njit (mirrors
+# geometry_samebody.py).  Accumulate a scalar to defeat dead-code elimination.
+# ---------------------------------------------------------------------------
+
+
+@njit(cache=True)
+def _batch_accux_gca_kernel(wa, wb, va, vb):
+    acc = 0.0
+    for i in range(wa.shape[0]):
+        pos, neg = _accux_gca(wa[i], wb[i], va[i], vb[i])
+        acc += pos[0] + pos[1] + pos[2]
+    return acc
+
+
+@njit(cache=True)
+def _batch_fp64_gca_kernel(wa, wb, va, vb):
+    acc = 0.0
+    for i in range(wa.shape[0]):
+        pos, neg = _fp64_gca(wa[i], wb[i], va[i], vb[i])
+        acc += pos[0] + pos[1] + pos[2]
+    return acc
+
+
+@njit(cache=True)
+def _batch_accux_gca_dispatch(ga, gb):
+    acc = 0.0
+    for i in range(ga.shape[0]):
+        res = gca_gca_intersection(ga[i], gb[i])
+        if res.shape[0] > 0:
+            acc += res[0, 0]
+    return acc
+
+
+@njit(cache=True)
+def _batch_fp64_gca_dispatch(ga, gb):
+    acc = 0.0
+    for i in range(ga.shape[0]):
+        res = _fp64_gca_gca_intersection(ga[i], gb[i])
+        if res.shape[0] > 0:
+            acc += res[0, 0]
+    return acc
+
+
+def _time_batch(fn, args, repeat=7):
+    """Best-of-`repeat` wall-time for one batched call (compile excluded)."""
+    fn(*args)  # warm / compile
+    best = math.inf
+    for _ in range(repeat):
+        t0 = time.perf_counter()
+        fn(*args)
+        best = min(best, time.perf_counter() - t0)
+    return best
+
+
+def main(n_cases=100_000, seed=20251104):
+    """
+    Standalone diagnostic driver (prints ns/edge-pair). Mirrors geometry_samebody.main.
+    """
+    cases = _make_gca_cases(n_cases, seed=seed)
+    wa, wb, va, vb, ga, gb = _pack_gca(cases)
+    n = wa.shape[0]
+
+    # correctness: FP64 twin vs real AccuX dispatcher (row count + max diff)
+    row_mismatch = 0
+    max_out_diff = 0.0
+    n_check = min(n, 5000)
+    for i in range(n_check):
+        r_fp = _fp64_gca_gca_intersection(ga[i], gb[i])
+        r_ax = gca_gca_intersection(ga[i], gb[i])
+        if r_fp.shape[0] != r_ax.shape[0]:
+            row_mismatch += 1
+        elif r_fp.shape[0] > 0:
+            max_out_diff = max(max_out_diff, float(np.max(np.abs(r_fp - r_ax))))
+
+    t_fp64_k = _time_batch(_batch_fp64_gca_kernel, (wa, wb, va, vb))
+    t_accux_k = _time_batch(_batch_accux_gca_kernel, (wa, wb, va, vb))
+    t_fp64_d = _time_batch(_batch_fp64_gca_dispatch, (ga, gb))
+    t_accux_d = _time_batch(_batch_accux_gca_dispatch, (ga, gb))
+
+    def ns(t):
+        return t / n * 1e9
+
+    print("=" * 70)
+    print("Same-body FP64-vs-AccuX diagnostic -- GCA x GCA")
+    print("=" * 70)
+    print(f"distinct cases : {n}   (best of 7, in-kernel batch)")
+    print(
+        f"correctness    : row mismatches {row_mismatch}/{n_check}"
+        f"   max |diff| {max_out_diff:.3e}"
+    )
+    print()
+    print("TIMING (ns per edge-pair)")
+    print(f"  L1  FP64  kernel        : {ns(t_fp64_k):8.2f} ns")
+    print(f"  L1  AccuX kernel        : {ns(t_accux_k):8.2f} ns")
+    print(f"  L1+L2+L3 FP64  dispatch : {ns(t_fp64_d):8.2f} ns")
+    print(f"  L1+L2+L3 AccuX dispatch : {ns(t_accux_d):8.2f} ns")
+    print()
+    print("DECOMPOSITION")
+    print(f"  plumbing (FP64  body)   : {ns(t_fp64_d - t_fp64_k):8.2f} ns/edge")
+    print(f"  plumbing (AccuX body)   : {ns(t_accux_d - t_accux_k):8.2f} ns/edge")
+    print(
+        f"  EFT math (kernel)       : {ns(t_accux_k - t_fp64_k):8.2f} ns/edge"
+        f"  ({t_accux_k / t_fp64_k:.2f}x)"
+    )
+    print(
+        f"  EFT math (dispatch)     : {ns(t_accux_d - t_fp64_d):8.2f} ns/edge"
+        f"  ({t_accux_d / t_fp64_d:.2f}x)"
+    )
+    print("=" * 70)
+
+
+class SameBodyGcaGca:
+    """
+    asv timing class (Numba warmed in setup, distinct cases)
+    same-body FP64 vs real AccuX gca_gca at kernel (L1) and dispatch (L3).
+    """
+
+    def setup(self):
+        cases = _make_gca_cases(100_000, seed=20251104)
+        self.wa, self.wb, self.va, self.vb, self.ga, self.gb = _pack_gca(cases)
+        _batch_fp64_gca_kernel(self.wa, self.wb, self.va, self.vb)
+        _batch_accux_gca_kernel(self.wa, self.wb, self.va, self.vb)
+        _batch_fp64_gca_dispatch(self.ga, self.gb)
+        _batch_accux_gca_dispatch(self.ga, self.gb)
+
+    def time_fp64_kernel(self):
+        _batch_fp64_gca_kernel(self.wa, self.wb, self.va, self.vb)
+
+    def time_accux_kernel(self):
+        _batch_accux_gca_kernel(self.wa, self.wb, self.va, self.vb)
+
+    def time_fp64_dispatch(self):
+        _batch_fp64_gca_dispatch(self.ga, self.gb)
+
+    def time_accux_dispatch(self):
+        _batch_accux_gca_dispatch(self.ga, self.gb)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ci/environment.yml b/ci/environment.yml
index 2049e965e..44897b982 100644
--- a/ci/environment.yml
+++ b/ci/environment.yml
@@ -20,7 +20,6 @@ dependencies:
   - numba
   - numpy
   - pandas
-  - pathlib
   - pre_commit
   - polars
   - pyarrow
diff --git a/pyproject.toml b/pyproject.toml
index 3ee69940c..0596956c6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,7 @@ classifiers=[
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
   "Programming Language :: Python :: 3.13",
+  "Programming Language :: Python :: 3.14",
 ]
 
 dynamic = ["version"]
@@ -30,7 +31,7 @@ dependencies = [
   "matplotlib<3.11",  # matplotlib 3.11.0 breaks some plots but <3.11 is fine; see issue #1542.
   "matplotlib-inline",
   "netcdf4",
-  "numba",
+  "numba>=0.63",  # 0.63 is the first release supporting Python 3.14 (3.10 <= py < 3.15). See #1561.
   "numpy",
   "pandas",
   "pyarrow",
@@ -50,7 +51,7 @@ dependencies = [
 
 [project.optional-dependencies]
 complete = ["uxarray[dev]"]
-dev = ['pathlib', 'pre_commit', 'pytest', 'pytest-cov', 'ruff', 'asv']
+dev = ['pre_commit', 'pytest', 'pytest-cov', 'ruff', 'asv']
 
 [project.urls]
 Documentation = "https://uxarray.readthedocs.io/"
diff --git a/test/grid/geometry/test_eft_accuracy.py b/test/grid/geometry/test_eft_accuracy.py
new file mode 100644
index 000000000..58f831b4d
--- /dev/null
+++ b/test/grid/geometry/test_eft_accuracy.py
@@ -0,0 +1,170 @@
+"""Accuracy regression tests for the compensated EFT primitives in
+``uxarray.utils.computing`` — specifically the compensated sum-of-squares used by
+the GCA / constant-latitude intersection kernel.
+
+An earlier revision computed the naive ``Σ h² + Σ l²`` instead of the
+correct compensated squared norm ``Σ (h + l)² = Σ h² + 2·Σ h·l`` (matching
+AccuSphGeom's ``numeric::sum_of_squares_c``. The correct compensated result
+reaches ~1e-30 relative accuracy on well-conditioned inputs; the naive form
+only reaches ~1e-15.
+"""
+
+from fractions import Fraction
+
+import numpy as np
+import pytest
+
+from uxarray.utils.computing import (
+    _HAS_FMA,
+    _sum_of_squares_c,
+    _two_prod_veltkamp,
+    accucross,
+    two_prod,
+)
+
+# Correct compensated result: ~1e-30 rel err.  Naive Σh²+Σl²: ~1e-15.
+_SUM_SQ_REL_TOL = 1e-24
+
+
+def _unit(v):
+    return v / np.linalg.norm(v)
+
+
+def _make_normals(n, seed):
+    """Compensated cross products (hi, lo) of well-separated arc pairs.
+
+    ``|dot(a, b)| < 0.999`` keeps ``|n|`` away from the noise floor so the
+    compensated squared norm is meaningful. (Extreme near-parallel arcs lose
+    accuracy for *any* algorithm and are out of scope for this primitive test.)
+    """
+    rng = np.random.default_rng(seed)
+    hi = np.empty((n, 3))
+    lo = np.empty((n, 3))
+    i = 0
+    while i < n:
+        a = _unit(rng.standard_normal(3))
+        b = _unit(rng.standard_normal(3))
+        if abs(np.dot(a, b)) > 0.999:
+            continue
+        xh, yh, zh, xl, yl, zl = accucross(a[0], a[1], a[2], b[0], b[1], b[2])
+        hi[i] = (xh, yh, zh)
+        lo[i] = (xl, yl, zl)
+        i += 1
+    return hi, lo
+
+
+def _rel_err_vs_exact(res, hi_tup, lo_tup):
+    """|(res_hi + res_lo) − Σ (h + l)²| / Σ (h + l)², computed exactly."""
+    true = sum((Fraction(h) + Fraction(l)) ** 2 for h, l in zip(hi_tup, lo_tup))
+    if true == 0:
+        return 0.0
+    return abs(float((Fraction(res[0]) + Fraction(res[1]) - true) / true))
+
+
+@pytest.fixture(scope="module")
+def normals():
+    return _make_normals(2000, seed=20260716)
+
+
+@pytest.mark.parametrize("ncomp", [2, 3])
+def test_sum_of_squares_compensated_accuracy(normals, ncomp):
+    """The generic keeps the 2·Σh·l cross term for each tuple length used by the
+    const-lat kernel (N=2 -> denominator, N=3 -> |n|²). A naive Σh²+Σl² would
+    regress to ~1e-15 and fail this bound."""
+    hi, lo = normals
+    worst = 0.0
+    for i in range(hi.shape[0]):
+        h = tuple(float(x) for x in hi[i, :ncomp])
+        lo_t = tuple(float(x) for x in lo[i, :ncomp])
+        worst = max(worst, _rel_err_vs_exact(_sum_of_squares_c(h, lo_t), h, lo_t))
+    assert worst < _SUM_SQ_REL_TOL, (
+        f"N={ncomp}: _sum_of_squares_c max rel err {worst:.2e} ≥ "
+        f"{_SUM_SQ_REL_TOL:.0e} (naive Σh²+Σl² regressed the cross term?)"
+    )
+
+
+def test_sum_of_squares_keeps_cross_term(normals):
+    """Directly assert the result tracks the compensated ``Σh² + 2·Σh·l`` and
+    NOT the naive ``Σh² + Σl²``, on cases where the two formulas are exactly
+    distinguishable."""
+    hi, lo = normals
+    checked = 0
+    for i in range(hi.shape[0]):
+        h = (float(hi[i, 0]), float(hi[i, 1]))
+        lo_t = (float(lo[i, 0]), float(lo[i, 1]))
+        big_h = [Fraction(x) for x in h]
+        big_l = [Fraction(x) for x in lo_t]
+        correct = sum(big_h[k] * big_h[k] for k in range(2)) + 2 * sum(
+            big_h[k] * big_l[k] for k in range(2)
+        )
+        naive = sum(big_h[k] * big_h[k] + big_l[k] * big_l[k] for k in range(2))
+        # The two formulas differ by ~2·Σh·l ≈ 1e-16 relative — that IS the bug
+        # we test. Skip only cases where the gap falls below the compensated
+        # accuracy floor (so the two are genuinely indistinguishable there).
+        if correct == 0 or abs(float((naive - correct) / correct)) < 1e-20:
+            continue
+        res = _sum_of_squares_c(h, lo_t)
+        val = Fraction(res[0]) + Fraction(res[1])
+        assert abs(val - correct) < abs(val - naive)
+        checked += 1
+    assert checked > 100, "test did not exercise enough distinguishable cases"
+
+
+def test_sum_of_squares_generic_any_n():
+    """The single generic works for any tuple length — e.g. N=4, which no
+    hand-written ``_sum_sq_cN`` exists for — with the same compensated accuracy.
+    This is the point of one N-generic primitive instead of per-N helpers.
+    """
+    rng = np.random.default_rng(7)
+    worst = 0.0
+    for _ in range(2000):
+        h = tuple(float(x) for x in rng.standard_normal(4))
+        # residual-scale low parts (~1 ulp of each high part)
+        lo_t = tuple(x * 1e-16 * float(rng.standard_normal()) for x in h)
+        worst = max(worst, _rel_err_vs_exact(_sum_of_squares_c(h, lo_t), h, lo_t))
+    assert worst < _SUM_SQ_REL_TOL, (
+        f"N=4: _sum_of_squares_c max rel err {worst:.2e} ≥ {_SUM_SQ_REL_TOL:.0e}"
+    )
+
+
+def _random_operands(rng, n):
+    for _ in range(n):
+        yield (
+            float(rng.standard_normal() * rng.integers(1, 1 << 20)),
+            float(rng.standard_normal() * rng.integers(1, 1 << 20)),
+        )
+
+
+def test_two_prod_error_term_is_the_exact_residual():
+    """``two_prod`` must return the EXACT rounding error of ``a * b``.
+
+    This is the property ``computing._validate_fma`` exists to guarantee at
+    import time, asserted here directly against exact rational arithmetic so it
+    cannot silently lapse. A non-fused FMA lowering (or a non-compliant FMA)
+    yields ``e = 0.0``, which fails this outright.
+
+    Note this is deliberately checked as ``p + e == a*b`` **exactly, over the
+    rationals** — NOT as the float expression ``p + e``, which rounds straight
+    back to ``p`` (since ``|e| <= ulp(p)/2``) and would make the assertion
+    vacuously true for any ``e`` whatsoever.
+    """
+    rng = np.random.default_rng(20260716)
+    for a, b in _random_operands(rng, 20000):
+        p, e = two_prod(a, b)
+        assert Fraction(p) + Fraction(e) == Fraction(a) * Fraction(b), (
+            f"two_prod({a!r}, {b!r}) = ({p!r}, {e!r}) is not an exact "
+            f"error-free transform"
+        )
+
+
+@pytest.mark.skipif(not _HAS_FMA, reason="no FMA path on this toolchain")
+def test_two_prod_fma_matches_veltkamp_bit_for_bit():
+    """The FMA and Veltkamp paths must agree bit-for-bit, so ``_HAS_FMA`` can
+    never change results. The exact residual is unique and representable, so
+    two correct implementations have no freedom to differ."""
+    rng = np.random.default_rng(20260717)
+    for a, b in _random_operands(rng, 20000):
+        p, e = two_prod(a, b)
+        pv, ev = _two_prod_veltkamp(a, b)
+        assert np.float64(p).view(np.int64) == np.float64(pv).view(np.int64)
+        assert np.float64(e).view(np.int64) == np.float64(ev).view(np.int64)
diff --git a/test/grid/grid/test_areas.py b/test/grid/grid/test_areas.py
index 9f74d7d72..e3f1a54c6 100644
--- a/test/grid/grid/test_areas.py
+++ b/test/grid/grid/test_areas.py
@@ -30,6 +30,29 @@ def test_face_areas_calculate_total_face_area_triangle(mesh_constants):
     nt.assert_almost_equal(area_gaussian, mesh_constants['CORRECTED_TRI_AREA'], decimal=3)
 
 
+def test_calculate_total_face_area_respects_quadrature_kwargs(mesh_constants):
+    """calculate_total_face_area must honor its quadrature_rule/order/
+    latitude_adjusted_area kwargs instead of always returning the cached
+    default-parameter face areas."""
+    verts = [
+    [[0.02974582, -0.74469018, 0.66674712],
+    [0.1534193, -0.88744577, 0.43462917],
+    [0.18363692, -0.72230586, 0.66674712]]
+    ]
+
+    grid_verts = ux.open_grid(verts, latlon=False)
+
+    area_default = grid_verts.calculate_total_face_area(
+        quadrature_rule="triangular", order=4)
+    area_corrected = grid_verts.calculate_total_face_area(
+        quadrature_rule="gaussian", order=5, latitude_adjusted_area=True)
+
+    # the corrected result must actually differ from the uncorrected one
+    assert not np.isclose(area_default, area_corrected)
+    nt.assert_almost_equal(area_default, mesh_constants['TRI_AREA'], decimal=6)
+    nt.assert_almost_equal(area_corrected, mesh_constants['CORRECTED_TRI_AREA'], decimal=6)
+
+
 def test_face_areas_compute_face_areas_geoflow_small(gridpath):
     """Checks if the GeoFlow Small can generate a face areas output."""
     grid_geoflow = ux.open_grid(gridpath("ugrid", "geoflow-small", "grid.nc"))
diff --git a/test/io/test_scrip.py b/test/io/test_scrip.py
index 57b844802..4b30ddb0c 100644
--- a/test/io/test_scrip.py
+++ b/test/io/test_scrip.py
@@ -101,6 +101,25 @@ def test_open_multigrid_mask_active_value_default(gridpath):
     assert grids["atm"].n_face == expected_atm
 
 
+def test_scrip_radians_units(gridpath):
+    """SCRIP files with coordinates in radians are converted to degrees on load."""
+    # scrip_radians.nc has a 2-cell grid whose lat/lon are stored in radians.
+    # The expected degree values are: face_lon=[10, 20], face_lat=[30, 40].
+    grid_file = gridpath("scrip", "scrip_radians", "scrip_radians_grid.nc")
+    grid = ux.open_grid(grid_file)
+
+    expected_face_lon = np.array([10.0, 20.0])
+    expected_face_lat = np.array([30.0, 40.0])
+    # 7 unique nodes: the two cells share only one corner point (15, 35)
+    expected_node_lon = np.array([5., 5., 15., 15., 15., 25., 25.])
+    expected_node_lat = np.array([25., 35., 25., 35., 45., 35., 45.])
+
+    nt.assert_allclose(np.sort(grid.face_lon.values), np.sort(expected_face_lon), atol=1e-10)
+    nt.assert_allclose(np.sort(grid.face_lat.values), np.sort(expected_face_lat), atol=1e-10)
+    nt.assert_allclose(np.sort(grid.node_lon.values), np.sort(expected_node_lon), atol=1e-10)
+    nt.assert_allclose(np.sort(grid.node_lat.values), np.sort(expected_node_lat), atol=1e-10)
+
+
 def test_open_multigrid_mask_active_value_per_grid_override(gridpath):
     """Per-grid override supports masks with different active values."""
     grid_file = gridpath("scrip", "oasis", "grids.nc")
diff --git a/test/meshfiles/scrip/scrip_radians/scrip_radians_grid.nc b/test/meshfiles/scrip/scrip_radians/scrip_radians_grid.nc
new file mode 100644
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HcmV?d00001

diff --git a/test/test_plot.py b/test/test_plot.py
index 60c8a8d96..470cd63a7 100644
--- a/test/test_plot.py
+++ b/test/test_plot.py
@@ -249,6 +249,92 @@ def test_to_raster_pixel_ratio(gridpath, r1, r2):
     d = np.array(raster2.shape) - f * np.array(raster1.shape)
     assert (d >= 0).all() and (d <= f - 1).all()
 
+def test_matplotlib_backend_restored_after_switch(monkeypatch):
+    """Regression test for #1537: switching HoloViews to matplotlib must restore
+    the Matplotlib backend captured beforehand.
+
+    A bare pytest can't reproduce the Jupyter inline-hook flip, so we simulate
+    hv.extension flipping the backend and assert assign() restores it.
+    """
+    import holoviews as hv
+
+    from uxarray.plot.utils import HoloviewsBackend
+
+    original = matplotlib.get_backend()
+    try:
+        matplotlib.use("svg")  # the "user's" backend before plotting
+
+        # Simulate hv.extension("matplotlib") flipping the active backend to agg.
+        monkeypatch.setattr(hv.Store, "current_backend", "bokeh", raising=False)
+        monkeypatch.setattr(hv, "extension", lambda *a, **k: matplotlib.use("agg"))
+
+        be = HoloviewsBackend()
+        be.assign("matplotlib")
+
+        # assign() must restore the backend that was active before the switch.
+        assert matplotlib.get_backend() == "svg"
+    finally:
+        matplotlib.use(original)
+
+
+def test_inline_backend_reactivated_via_shell(monkeypatch):
+    """Inside IPython, restoring an inline backend must re-run the shell's own
+    backend activation (the public equivalent of ``%matplotlib inline``), which
+    rebuilds the full display integration that ``hv.extension`` tore down.
+
+    Simply calling ``mpl.use`` is not enough: it restores the backend name but
+    leaves last-line figure auto-display broken (see #1537 / #1538).
+    """
+    import sys
+    import types
+
+    from uxarray.plot.utils import HoloviewsBackend
+
+    inline_backend = "module://matplotlib_inline.backend_inline"
+    calls = []
+
+    class FakeShell:
+        def enable_matplotlib(self, gui):
+            calls.append(("enable_matplotlib", gui))
+
+    ipython = types.ModuleType("IPython")
+    ipython.get_ipython = lambda: FakeShell()
+
+    monkeypatch.setitem(sys.modules, "IPython", ipython)
+    monkeypatch.setattr(
+        matplotlib, "use", lambda backend: calls.append(("use", backend))
+    )
+
+    be = HoloviewsBackend()
+    be.matplotlib_backend = inline_backend
+    be.reset_mpl_backend()
+
+    # The module:// inline backend must be mapped to the "inline" gui name and
+    # restored through the shell, without falling back to mpl.use.
+    assert calls == [("enable_matplotlib", "inline")]
+
+
+def test_reset_backend_falls_back_to_mpl_use_outside_ipython(monkeypatch):
+    """Outside IPython (get_ipython() is None), restoration falls back to
+    ``mpl.use`` with the stored backend."""
+    import sys
+    import types
+
+    from uxarray.plot.utils import HoloviewsBackend
+
+    calls = []
+    ipython = types.ModuleType("IPython")
+    ipython.get_ipython = lambda: None
+    monkeypatch.setitem(sys.modules, "IPython", ipython)
+    monkeypatch.setattr(matplotlib, "use", lambda backend: calls.append(("use", backend)))
+
+    be = HoloviewsBackend()
+    be.matplotlib_backend = "svg"
+    be.reset_mpl_backend()
+
+    assert calls == [("use", "svg")]
+
+
 def test_plot_with_features(gridpath, datasetpath):
     """ensure can render a multiplot layout with geoviews features.
     Regression test for issue #1542.
diff --git a/test/test_remap_yac.py b/test/test_remap_yac.py
index 51f2b9e33..3efcd7c11 100644
--- a/test/test_remap_yac.py
+++ b/test/test_remap_yac.py
@@ -140,6 +140,23 @@ def test_yac_bilinear_face_remap(gridpath):
     assert out.size == dest.n_face
 
 
+def test_yac_dnn_face_remap(gridpath):
+    # distance-nearest-neighbour (YAC >= 3.15); the default CELL_AREA search
+    # distance requires a face-centered target.
+    mesh_path = gridpath("mpas", "QU", "mesh.QU.1920km.151026.nc")
+    uxds = ux.open_dataset(mesh_path, mesh_path)
+    dest = ux.open_grid(mesh_path)
+
+    out = uxds["latCell"].remap(
+        destination_grid=dest,
+        remap_to="faces",
+        backend="yac",
+        yac_method="dnn",
+    )
+
+    assert out.size == dest.n_face
+
+
 def test_yac_bilinear_rejects_non_average_method(gridpath):
     mesh_path = gridpath("mpas", "QU", "mesh.QU.1920km.151026.nc")
     uxds = ux.open_dataset(mesh_path, mesh_path)
diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py
index c6d367024..1d0c5592d 100644
--- a/uxarray/grid/arcs.py
+++ b/uxarray/grid/arcs.py
@@ -8,7 +8,7 @@
     _normalize_xyz_scalar,
 )
 from uxarray.grid.utils import _angle_of_2_vectors
-from uxarray.utils.computing import diff_of_products, two_sum
+from uxarray.utils.computing import accucross, two_sum
 
 # Tolerance used to classify orient3d results as zero.  For double-precision
 # unit-vector inputs this covers rounding error in the compensated cross product.
@@ -406,6 +406,24 @@ def _orient3d_on_sphere_value(a, b, q):
     )
 
 
+@njit(cache=True, inline="always")
+def _normal_dot_value(nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo, q0, q1, q2):
+    """Compensated dot of an already-computed ``(hi, lo)`` normal with ``q``.
+
+    This is the second half of :func:`_orient3d_on_sphere_value_xyz`, split out
+    so that callers holding a normal that is invariant across many queries (e.g.
+    the ray plane ``q x R`` in the spherical point-in-polygon kernel, which is
+    the same for every edge of a face) can compute the cross product once and
+    reuse it, paying only this dot per query.
+    """
+    p0 = (nx_hi + nx_lo) * q0
+    p1 = (ny_hi + ny_lo) * q1
+    p2 = (nz_hi + nz_lo) * q2
+    s, e = two_sum(p0, p1)
+    s, e2 = two_sum(s, p2)
+    return s + (e + e2)
+
+
 @njit(cache=True, inline="always")
 def _orient3d_on_sphere_value_xyz(a0, a1, a2, b0, b1, b2, q0, q1, q2):
     """Scalar-argument form of :func:`_orient3d_on_sphere_value`.
@@ -413,15 +431,8 @@ def _orient3d_on_sphere_value_xyz(a0, a1, a2, b0, b1, b2, q0, q1, q2):
     Takes the nine vector components directly so hot loops can call it without
     materializing ``(3,)`` arrays.
     """
-    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)
-    p0 = (x_hi + x_lo) * q0
-    p1 = (y_hi + y_lo) * q1
-    p2 = (z_hi + z_lo) * q2
-    s, e = two_sum(p0, p1)
-    s, e2 = two_sum(s, p2)
-    return s + (e + e2)
+    nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo = accucross(a0, a1, a2, b0, b1, b2)
+    return _normal_dot_value(nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo, q0, q1, q2)
 
 
 @njit(cache=True)
diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py
index f4f4d96ff..f79c6586a 100644
--- a/uxarray/grid/grid.py
+++ b/uxarray/grid/grid.py
@@ -1951,8 +1951,21 @@ def calculate_total_face_area(
         -------
         Sum of area of all the faces in the mesh : float
         """
+        # Default parameters match the cached ``face_areas`` property, which also
+        # preserves the equal-area values used for HEALPix grids; reuse it to avoid
+        # recomputation. Any non-default quadrature settings require a fresh
+        # geometric computation so the requested rule/order actually take effect.
+        if (
+            quadrature_rule == "triangular"
+            and order == 4
+            and not latitude_adjusted_area
+        ):
+            return np.sum(self.face_areas.values)
 
-        return np.sum(self.face_areas.values)
+        face_areas, _ = self._compute_face_areas(
+            quadrature_rule, order, latitude_adjusted_area
+        )
+        return np.sum(face_areas)
 
     def compute_face_areas(
         self,
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index eaebb827b..94f8974ec 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -8,8 +8,7 @@
 from uxarray.utils.computing import (
     _cdp2,
     _cdp4,
-    _sum_sq_c2,
-    _sum_sq_c3,
+    _sum_of_squares_c,
     acc_sqrt_re,
     accucross,
     accucross_pair,
@@ -471,9 +470,9 @@ def _accux_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z):
         Invalid inputs propagate as non-finite coordinates.
     """
     nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo = accucross(a0, a1, a2, b0, b1, b2)
-    s2_hi, s2_lo = _sum_sq_c2(nx_hi, nx_lo, ny_hi, ny_lo)
+    s2_hi, s2_lo = _sum_of_squares_c((nx_hi, ny_hi), (nx_lo, ny_lo))
     denom = s2_hi + s2_lo
-    s3_hi, s3_lo = _sum_sq_c3(nx_hi, nx_lo, ny_hi, ny_lo, nz_hi, nz_lo)
+    s3_hi, s3_lo = _sum_of_squares_c((nx_hi, ny_hi, nz_hi), (nx_lo, ny_lo, nz_lo))
     zsq_hi, zsq_lo = two_prod(const_z, const_z)
     d_hi, d_lo = _cdp4(s3_hi, zsq_hi, s3_hi, zsq_lo, s3_lo, zsq_hi, s3_lo, zsq_lo)
     e_hi, e_lo = two_sum(s2_hi, -d_hi)
diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py
index a1f0b988f..3199c97d2 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -7,8 +7,13 @@
 from numba import njit, prange
 
 from uxarray.constants import INT_DTYPE, INT_FILL_VALUE
-from uxarray.grid.arcs import on_minor_arc, orient3d_on_sphere
+from uxarray.grid.arcs import (
+    _normal_dot_value,
+    _PREDICATE_ZERO_TOL,
+    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
@@ -69,8 +74,27 @@ def _ray_endpoint(q):
     return r
 
 
-@njit(cache=True)
-def _counts_as_crossing(A, B, q, 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
@@ -79,12 +103,19 @@ def _counts_as_crossing(A, B, q, R):
     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)
+    # 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
@@ -95,20 +126,27 @@ def _counts_as_crossing(A, B, q, R):
     # 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)
+    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)
+    )
 
-    # 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
+    # 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
 
-    return 1 if s_qR_A != s_qR_B 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)
@@ -163,13 +201,26 @@ def _point_in_polygon_sphere(q, polygon):
     # 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, B, q, R)
+            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
diff --git a/uxarray/io/_scrip.py b/uxarray/io/_scrip.py
index 3cc5b4d5c..ca4c513fb 100644
--- a/uxarray/io/_scrip.py
+++ b/uxarray/io/_scrip.py
@@ -9,6 +9,33 @@
 from uxarray.grid.connectivity import _replace_fill_values
 
 
+def _values_in_degrees(data_array):
+    """Return data_array.values, converting to degrees if necessary,
+    i.e., if data_array.attrs["units"] in ["radians", "radian", "rad"].
+
+    Parameters
+    ----------
+    data_array : xr.DataArray
+        Array containing values to be extracted and converted to degrees.
+        Values assumed to be in degrees already unless data_array.attrs["units"]
+        is present and is one of ["radians", "radian", "rad"].
+
+    Returns
+    -------
+    numpy.ndarray
+        data_array.values, converted to degrees if necessary.
+    """
+    values = data_array.values
+    units = data_array.attrs.get("units", "").lower()
+
+    # Check if units indicate radians
+    if units in ["radians", "radian", "rad"]:
+        return np.rad2deg(values)
+
+    # Otherwise assume degrees (or no units means degrees for SCRIP)
+    return values
+
+
 def _to_ugrid(in_ds, out_ds):
     """If input dataset (``in_ds``) file is an unstructured SCRIP file,
     function will reassign SCRIP variables to UGRID conventions in output file
@@ -21,8 +48,9 @@ def _to_ugrid(in_ds, out_ds):
     if any(key in in_ds for key in ["grid_imask", "grid_rank", "grid_area"]):
         # Create node_lon & node_lat variables from grid_corner_lat/lon
         # Turn latitude and longitude scrip arrays into 1D
-        corner_lat = in_ds["grid_corner_lat"].values.ravel()
-        corner_lon = in_ds["grid_corner_lon"].values.ravel()
+        # Convert to degrees if needed
+        corner_lat = _values_in_degrees(in_ds["grid_corner_lat"]).ravel()
+        corner_lon = _values_in_degrees(in_ds["grid_corner_lon"]).ravel()
 
         # Use Polars to find unique coordinate pairs
         df = pl.DataFrame({"lon": corner_lon, "lat": corner_lat}).with_row_count(
@@ -62,14 +90,15 @@ def _to_ugrid(in_ds, out_ds):
         )
 
         # Create face_lon & face_lat from grid_center_lat/lon
+        # Convert to degrees if needed
         out_ds[ugrid.FACE_COORDINATES[0]] = xr.DataArray(
-            in_ds["grid_center_lon"].values,
+            _values_in_degrees(in_ds["grid_center_lon"]),
             dims=[ugrid.FACE_DIM],
             attrs=ugrid.FACE_LON_ATTRS,
         )
 
         out_ds[ugrid.FACE_COORDINATES[1]] = xr.DataArray(
-            in_ds["grid_center_lat"].values,
+            _values_in_degrees(in_ds["grid_center_lat"]),
             dims=[ugrid.FACE_DIM],
             attrs=ugrid.FACE_LAT_ATTRS,
         )
@@ -465,12 +494,17 @@ def _extract_single_grid(
         grid_corner_lat = grid_corner_lat.rename({corner_dim: "grid_corners"})
         grid_corner_lon = grid_corner_lon.rename({corner_dim: "grid_corners"})
 
-    grid_corner_lat = grid_corner_lat.copy()
-    grid_corner_lon = grid_corner_lon.copy()
+    # Convert to degrees if needed and create copies with correct units
+    grid_corner_lat_values = _values_in_degrees(grid_corner_lat)
+    grid_corner_lon_values = _values_in_degrees(grid_corner_lon)
 
     result = xr.Dataset()
-    result["grid_corner_lat"] = grid_corner_lat
-    result["grid_corner_lon"] = grid_corner_lon
+    result["grid_corner_lat"] = xr.DataArray(
+        grid_corner_lat_values, dims=grid_corner_lat.dims, attrs={"units": "degrees"}
+    )
+    result["grid_corner_lon"] = xr.DataArray(
+        grid_corner_lon_values, dims=grid_corner_lon.dims, attrs={"units": "degrees"}
+    )
 
     n_cells = grid_corner_lat.sizes["grid_size"]
 
@@ -481,26 +515,40 @@ def _extract_single_grid(
     computed_lat_lon = None
 
     if center_lat and center_lat in ds:
-        center_lat_da = _stack_cell_dims(
+        stacked_lat = _stack_cell_dims(
             ds[center_lat],
             _resolve_cell_dims(metadata, ds[center_lat].dims),
             "grid_size",
-        ).copy()
+        )
+        center_lat_da = xr.DataArray(
+            _values_in_degrees(stacked_lat),
+            dims=["grid_size"],
+            attrs={"units": "degrees"},
+        )
     else:
         if computed_lat_lon is None:
             computed_lat_lon = grid_center_lat_lon(result)
-        center_lat_da = xr.DataArray(computed_lat_lon[0], dims=["grid_size"])
+        center_lat_da = xr.DataArray(
+            computed_lat_lon[0], dims=["grid_size"], attrs={"units": "degrees"}
+        )
 
     if center_lon and center_lon in ds:
-        center_lon_da = _stack_cell_dims(
+        stacked_lon = _stack_cell_dims(
             ds[center_lon],
             _resolve_cell_dims(metadata, ds[center_lon].dims),
             "grid_size",
-        ).copy()
+        )
+        center_lon_da = xr.DataArray(
+            _values_in_degrees(stacked_lon),
+            dims=["grid_size"],
+            attrs={"units": "degrees"},
+        )
     else:
         if computed_lat_lon is None:
             computed_lat_lon = grid_center_lat_lon(result)
-        center_lon_da = xr.DataArray(computed_lat_lon[1], dims=["grid_size"])
+        center_lon_da = xr.DataArray(
+            computed_lat_lon[1], dims=["grid_size"], attrs={"units": "degrees"}
+        )
 
     result["grid_center_lat"] = center_lat_da
     result["grid_center_lon"] = center_lon_da
diff --git a/uxarray/plot/utils.py b/uxarray/plot/utils.py
index e97919d6c..e5c8d3f8b 100644
--- a/uxarray/plot/utils.py
+++ b/uxarray/plot/utils.py
@@ -2,36 +2,68 @@
 
 
 class HoloviewsBackend:
-    """Utility class to compare and set a HoloViews plotting backend for
-    visualization."""
+    """Compare and set the HoloViews plotting backend."""
 
     def __init__(self):
         self.matplotlib_backend = None
 
     def assign(self, backend: str):
-        """Assigns a backend for use with HoloViews visualization.
-
-        Parameters
-        ----------
-        backend : str
-            Plotting backend to use, one of 'matplotlib', 'bokeh'
-        """
-
-        if self.matplotlib_backend is None:
-            import matplotlib as mpl
-
-            self.matplotlib_backend = mpl.get_backend()
-
+        """Assign a HoloViews backend, one of 'matplotlib', 'bokeh'."""
         if backend not in ["bokeh", "matplotlib", None]:
             raise ValueError(
                 f"Unsupported backend. Expected one of ['bokeh', 'matplotlib'], but received {backend}"
             )
         if backend is not None and backend != hv.Store.current_backend:
-            # only call hv.extension if it needs to be changed
+            import matplotlib as mpl
+
+            # Capture the live backend now (not once at init) so a backend the
+            # user set later is what we restore.
+            self.matplotlib_backend = mpl.get_backend()
             hv.extension(backend)
 
+            if backend == "matplotlib":
+                # hv.extension("matplotlib") switches the active Matplotlib
+                # backend (e.g. to agg in Jupyter), breaking subsequent native
+                # matplotlib/xarray .plot() calls. HoloViews renders through
+                # hv.Store.current_backend, so restoring is safe. See #1537.
+                self.reset_mpl_backend()
+
     def reset_mpl_backend(self):
-        """Resets the default backend for the ``matplotlib`` module."""
+        """Restore the Matplotlib backend captured before the last switch.
+
+        ``hv.extension("matplotlib")`` does not just change the active backend;
+        in an IPython/Jupyter kernel it also tears down the display integration
+        that auto-renders figures at the end of a cell. Simply calling
+        ``mpl.use`` puts the backend name back but leaves that integration
+        broken, so subsequent native ``matplotlib``/``xarray`` ``.plot()`` calls
+        silently produce no output unless ``plt.show()`` is called explicitly.
+
+        Inside IPython we therefore re-run the shell's own backend activation
+        (the public equivalent of the ``%matplotlib`` magic), which rebuilds the
+        full integration in one step. Outside IPython we fall back to
+        ``mpl.use``.
+        """
+        if self.matplotlib_backend is None:
+            return
+
+        try:
+            from IPython import get_ipython
+
+            shell = get_ipython()
+        except ImportError:
+            shell = None
+
+        if shell is not None:
+            # Map the stored backend to the gui name enable_matplotlib expects.
+            gui = self.matplotlib_backend
+            if gui.startswith("module://") and "inline" in gui:
+                gui = "inline"
+            try:
+                shell.enable_matplotlib(gui)
+                return
+            except Exception:
+                pass
+
         import matplotlib as mpl
 
         mpl.use(self.matplotlib_backend)
diff --git a/uxarray/remap/accessor.py b/uxarray/remap/accessor.py
index bb0f176ce..6415aa144 100644
--- a/uxarray/remap/accessor.py
+++ b/uxarray/remap/accessor.py
@@ -85,8 +85,10 @@ def nearest_neighbor(
         backend : {'uxarray', 'yac'}, default='uxarray'
             Remapping backend to use. When set to 'yac', requires YAC to be
             available on PYTHONPATH.
-        yac_method : {'nnn', 'average', 'conservative'}, optional
+        yac_method : {'nnn', 'dnn', 'average', 'conservative'}, optional
             YAC interpolation method. Defaults to 'nnn' when backend='yac'.
+            ``'dnn'`` (distance-nearest-neighbour, YAC >= 3.15) averages all
+            source points within a search distance of each target.
         yac_options : dict, optional
             YAC interpolation configuration options.
 
@@ -206,10 +208,12 @@ def to_rectilinear(
             remapping before reshaping the result to latitude/longitude axes.
             The YAC backend uses YAC's rectilinear grid support directly and can
             be faster for large targets when YAC is installed.
-        yac_method : {'nnn', 'average', 'conservative'}, optional
+        yac_method : {'nnn', 'dnn', 'average', 'conservative'}, optional
             YAC interpolation method. When ``backend='yac'``, defaults to ``'nnn'``
             because nearest-neighbor works for node-, edge-, and face-centered
-            source data. ``'conservative'`` requires face-centered source data.
+            source data. ``'dnn'`` (distance-nearest-neighbour) averages source
+            points within a search distance. ``'conservative'`` requires
+            face-centered source data.
         yac_options : dict, optional
             YAC interpolation configuration options forwarded to the selected
             YAC method.
diff --git a/uxarray/remap/yac.py b/uxarray/remap/yac.py
index 285a89a65..956ff175b 100644
--- a/uxarray/remap/yac.py
+++ b/uxarray/remap/yac.py
@@ -90,10 +90,11 @@ def _import_yac():
 def _normalize_yac_method(yac_method: str | None) -> _YacOptions:
     if not yac_method:
         raise ValueError(
-            "backend='yac' requires yac_method to be set to 'nnn', 'average', or 'conservative'."
+            "backend='yac' requires yac_method to be set to 'nnn', 'dnn', "
+            "'average', or 'conservative'."
         )
     method = yac_method.lower()
-    if method not in {"nnn", "average", "conservative"}:
+    if method not in {"nnn", "dnn", "average", "conservative"}:
         raise ValueError(f"Unsupported YAC method: {yac_method!r}")
     return _YacOptions(method=method, kwargs={})
 
@@ -232,6 +233,30 @@ def __init__(
                 max_search_distance=yac_kwargs.get("max_search_distance", 0.0),
                 scale=yac_kwargs.get("scale", 1.0),
             )
+        elif yac_method == "dnn":
+            # Distance-nearest-neighbour (YAC >= 3.15): interpolate each target
+            # from all source points within a search distance around it.
+            weight_type = _coerce_enum(
+                yac_core.yac_interp_dnn_weight_type,
+                yac_kwargs.get("weight_type", yac_kwargs.get("reduction_type")),
+            )
+            if weight_type is None:
+                weight_type = (
+                    yac_core.yac_interp_dnn_weight_type.YAC_INTERP_DNN_WEIGHT_DIST
+                )
+            dnn_kwargs = {"weight_type": weight_type}
+            # search_distance_type default (CELL_AREA) requires a face-centered
+            # target; only forward it when explicitly provided.
+            if "search_distance_type" in yac_kwargs:
+                dnn_kwargs["search_distance_type"] = _coerce_enum(
+                    yac_core.yac_interp_dnn_search_distance_type,
+                    yac_kwargs["search_distance_type"],
+                )
+            if "search_distance" in yac_kwargs:
+                dnn_kwargs["search_distance"] = yac_kwargs["search_distance"]
+            if "scale" in yac_kwargs:
+                dnn_kwargs["scale"] = yac_kwargs["scale"]
+            stack.add_dnn(**dnn_kwargs)
         elif yac_method == "average":
             reduction_type = _coerce_enum(
                 yac_core.yac_interp_avg_weight_type,
@@ -242,7 +267,7 @@ def __init__(
                     yac_core.yac_interp_avg_weight_type.YAC_INTERP_AVG_ARITHMETIC
                 )
             stack.add_average(
-                reduction_type=reduction_type,
+                weight_type=reduction_type,
                 partial_coverage=yac_kwargs.get("partial_coverage", False),
             )
         elif yac_method == "conservative":
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index a0866bbd1..09ce972f1 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -81,45 +81,6 @@ def codegen(context, builder, signature, args):
     _FMA_INTRINSIC_OK = False
 
 
-def _validate_fma() -> bool:
-    """Return True iff the FMA intrinsic compiles and is a bit-exact EFT."""
-    if not _FMA_INTRINSIC_OK:
-        return False
-    try:
-        import numpy as _np
-
-        @njit(cache=False)
-        def _tp_fma(a, b):
-            p = a * b
-            return p, _fma(a, b, -p)
-
-        @njit(cache=False)
-        def _tp_vk(a, b):
-            p = a * b
-            f = 134217729.0
-            a_hi = f * a - (f * a - a)
-            a_lo = a - a_hi
-            b_hi = f * b - (f * 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
-
-        rng = _np.random.default_rng(20260101)
-        for _ in range(20000):
-            a = float(rng.standard_normal() * rng.integers(1, 1 << 20))
-            b = float(rng.standard_normal() * rng.integers(1, 1 << 20))
-            pf, ef = _tp_fma(a, b)
-            pv, ev = _tp_vk(a, b)
-            if pf != pv or (pf + ef) != (pv + ev):
-                return False
-        return True
-    except Exception:  # pragma: no cover
-        return False
-
-
-_HAS_FMA = _validate_fma()
-
-
 @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.
@@ -148,6 +109,14 @@ def two_sum(a, b):
     return s, e
 
 
+if _FMA_INTRINSIC_OK:
+
+    @njit(cache=True, inline="always")
+    def _two_prod_fma(a, b):
+        p = a * b
+        return p, _fma(a, b, -p)
+
+
 @njit(cache=True, inline="always")
 def _two_prod_veltkamp(a, b):
     """Portable TwoProd via Veltkamp splitting (no FMA dependency).
@@ -166,13 +135,36 @@ def _two_prod_veltkamp(a, b):
     return p, e
 
 
-if _HAS_FMA:
+def _validate_fma(n_samples=2000) -> bool:
+    """Return True iff the FMA intrinsic compiles and is a bit-exact EFT."""
+    if not _FMA_INTRINSIC_OK:
+        return False
+    try:
+        import numpy as _np
 
-    @njit(cache=True, inline="always")
-    def _two_prod_fma(a, b):
-        """TwoProd via a single hardware FMA: e = fma(a, b, -p)."""
-        p = a * b
-        return p, _fma(a, b, -p)
+        rng = _np.random.default_rng(20260101)
+        for _ in range(n_samples):
+            a = float(rng.standard_normal() * rng.integers(1, 1 << 20))
+            b = float(rng.standard_normal() * rng.integers(1, 1 << 20))
+            pf, ef = _two_prod_fma(a, b)
+            pv, ev = _two_prod_veltkamp(a, b)
+            # Compare the residuals *directly*. Do not compare ``pf + ef``
+            # against ``pv + ev``: both sums round straight back to the product
+            # (|e| <= ulp(p)/2 by construction), so that predicate collapses to
+            # ``pf != pv`` -- a tautology, since both are fl(a*b). The exact
+            # residual is unique and representable, so a correct FMA and the
+            # Veltkamp split must agree bit-for-bit.
+            if pf != pv or ef != ev:
+                return False
+        return True
+    except Exception:  # pragma: no cover
+        return False
+
+
+_HAS_FMA = _validate_fma()
+
+
+if _HAS_FMA:
 
     @njit(cache=True, inline="always")
     def two_prod(a, b):
@@ -480,27 +472,52 @@ def _cdp4(a0, b0, a1, b1, a2, b2, a3, b3):
 
 
 @njit(cache=True, inline="always")
-def _sum_sq_c2(h0, l0, h1, l1):
-    """Compensated sum of squares for 2 (hi, lo) pairs: h0²+l0²+h1²+l1².
+def _fast_two_sum(a, b):
+    """Fast TwoSum: (x, y) with x = fl(a + b), x + y = a + b exactly.
 
-    Mirrors sum_of_squares_c<T, 2> from accusphgeom/numeric/eft.hpp, which
-    constructs lhs = rhs = [h0, l0, h1, l1] and calls compensated_dot_product.
-    Used to compute nx²+ny² accurately from the compensated normal (hi, lo).
+    Requires ``|a| >= |b|`` (or ``a == 0``) for the error term to be exact —
+    the callers here satisfy this because ``a`` is a running non-negative sum.
     """
-    return _cdp4(h0, h0, l0, l0, h1, h1, l1, l1)
+    x = a + b
+    return x, (a - x) + b
 
 
 @njit(cache=True, inline="always")
-def _sum_sq_c3(h0, l0, h1, l1, h2, l2):
-    """Compensated sum of squares for 3 (hi, lo) pairs: h0²+l0²+h1²+l1²+h2²+l2².
+def _sum_non_neg(a_hi, a_lo, b_hi, b_lo):
+    """Add two non-negative compensated values (mirrors accusphgeom ``sum_non_neg``)."""
+    hh, h = two_sum(a_hi, b_hi)
+    d = h + (a_lo + b_lo)
+    return _fast_two_sum(hh, d)
 
-    Mirrors sum_of_squares_c<T, 3> from accusphgeom/numeric/eft.hpp, which
-    constructs lhs = rhs = [h0, l0, h1, l1, h2, l2] and calls a 6-term CDP.
-    We use _cdp8 with two zero-padding pairs (adding zero products).
-    Used to compute |n|² = nx²+ny²+nz² accurately from the compensated normal.
+
+@njit(cache=True, inline="always")
+def _sum_of_squares_c(hi, lo):
+    """Compensated squared norm ``Σ (hi[i] + lo[i])²`` of a compensated vector.
+
+    Faithful port of ``sum_of_squares_c<T, N>`` from
+    accusphgeom/numeric/eft.hpp: a compensated ``Σ hi²`` plus the cross-term
+    correction ``2·Σ hi·li``.
+
+    Parameters
+    ----------
+    hi, lo : tuple of float
+        Equal-length tuples of the high and low parts of each vector component.
+        Numba specializes this per tuple length at compile time and keeps the
+        tuples register-resident (no allocation) — the direct analog of the C++
+        template. Used for both nx²+ny² (denominator) and nx²+ny²+nz² (|n|²).
     """
-    # lhs = rhs = [h0, l0, h1, l1, h2, l2, 0, 0]
-    return _cdp8(h0, l0, h1, l1, h2, l2, 0.0, 0.0, h0, l0, h1, l1, h2, l2, 0.0, 0.0)
+    n = len(hi)
+    s_hi = 0.0
+    s_lo = 0.0
+    for i in range(n):  # compensated Σ hi²
+        ph, pl = two_prod(hi[i], hi[i])
+        s_hi, s_lo = _sum_non_neg(s_hi, s_lo, ph, pl)
+    r_hi, r_lo = two_prod(hi[0], lo[0])  # accurate Σ hi·li
+    for i in range(1, n):
+        p, e = two_prod(hi[i], lo[i])
+        r_hi, e2 = two_sum(r_hi, p)
+        r_lo += e + e2
+    return _fast_two_sum(s_hi, (2.0 * (r_hi + r_lo)) + s_lo)
 
 
 @njit(cache=True, inline="always")

From 0d131d7b384fe140104bde9f0fb0ba0e50bf7165 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 16 Jul 2026 17:51:51 -0500
Subject: [PATCH 31/51] Add thread-scaling benchmark for FP64 vs AccuX constlat
 dispatcher

---
 benchmarks/thread_scaling_constlat.png | Bin 0 -> 75884 bytes
 benchmarks/thread_scaling_constlat.py  | 123 +++++++++++++++++++++++++
 2 files changed, 123 insertions(+)
 create mode 100644 benchmarks/thread_scaling_constlat.png
 create mode 100644 benchmarks/thread_scaling_constlat.py

diff --git a/benchmarks/thread_scaling_constlat.png b/benchmarks/thread_scaling_constlat.png
new file mode 100644
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diff --git a/benchmarks/thread_scaling_constlat.py b/benchmarks/thread_scaling_constlat.py
new file mode 100644
index 000000000..cccbe8190
--- /dev/null
+++ b/benchmarks/thread_scaling_constlat.py
@@ -0,0 +1,123 @@
+"""End-to-end thread-scaling benchmark for the GCA / constant-latitude path.
+
+Unlike the bare-kernel micro-benchmarks, this drives the *real* UXarray
+dispatcher (`gca_const_lat_intersection`) over the edges of a real mesh, and a
+same-body FP64 dispatcher built from the identical L1/L2/L3 structure, sweeping
+the Numba thread count. It answers: as threads increase, does the compensated
+(AccuX/EFT) path scale the same way as plain FP64 when hit through the full
+UXarray path (dispatch + masks + snapping + output packaging), not just the
+raw kernel?
+
+Run:  python benchmarks/thread_scaling_constlat.py [grid] [n_lat] [repeats]
+Prints CSV to stdout: threads,fp64_ns,accux_ns,accux_over_fp64
+(ns are per edge-x-latitude evaluation.)
+"""
+
+import math
+import sys
+import time
+
+import numpy as np
+from numba import njit, prange, set_num_threads, get_num_threads
+
+import uxarray as ux
+from uxarray.grid.intersections import gca_const_lat_intersection
+
+# same-body FP64 dispatcher: identical structure, FP64 body instead of EFT.
+from benchmarks.geometry_samebody import _fp64_gca_const_lat_intersection
+
+
+@njit(cache=False, parallel=True)
+def _batch_accux(edges, zs, acc):
+    """Real AccuX dispatcher over every (edge, latitude) pair, in parallel."""
+    n_edge = edges.shape[0]
+    for i in prange(n_edge):
+        s = 0.0
+        for k in range(zs.shape[0]):
+            res = gca_const_lat_intersection(edges[i], zs[k])
+            v = res[0, 0]
+            if v == v:  # not NaN
+                s += v
+        acc[i] = s
+
+
+@njit(cache=False, parallel=True)
+def _batch_fp64(edges, zs, acc):
+    """Same-body FP64 dispatcher over every (edge, latitude) pair, in parallel."""
+    n_edge = edges.shape[0]
+    for i in prange(n_edge):
+        s = 0.0
+        for k in range(zs.shape[0]):
+            res = _fp64_gca_const_lat_intersection(edges[i], zs[k])
+            v = res[0, 0]
+            if v == v:
+                s += v
+        acc[i] = s
+
+
+def _build_edges(grid):
+    """Extract each edge as a (2, 3) Cartesian arc from a real grid."""
+    en = grid.edge_node_connectivity.values
+    x = grid.node_x.values
+    y = grid.node_y.values
+    z = grid.node_z.values
+    n_edge = en.shape[0]
+    edges = np.empty((n_edge, 2, 3), dtype=np.float64)
+    edges[:, 0, 0] = x[en[:, 0]]
+    edges[:, 0, 1] = y[en[:, 0]]
+    edges[:, 0, 2] = z[en[:, 0]]
+    edges[:, 1, 0] = x[en[:, 1]]
+    edges[:, 1, 1] = y[en[:, 1]]
+    edges[:, 1, 2] = z[en[:, 1]]
+    return edges
+
+
+def main():
+    grid_name = sys.argv[1] if len(sys.argv) > 1 else "outCSne30"
+    n_lat = int(sys.argv[2]) if len(sys.argv) > 2 else 40
+    repeats = int(sys.argv[3]) if len(sys.argv) > 3 else 5
+
+    grid = ux.tutorial.open_grid(grid_name)
+    edges = _build_edges(grid)
+    # latitudes spanning the sphere (as z = sin(lat)); avoid exact poles
+    zs = np.linspace(-0.95, 0.95, n_lat)
+    n_eval = edges.shape[0] * n_lat
+    acc = np.empty(edges.shape[0], dtype=np.float64)
+
+    max_threads = get_num_threads()
+    counts = []
+    t = 1
+    while t < max_threads:
+        counts.append(t)
+        t *= 2
+    counts.append(max_threads)
+    counts = sorted(set(counts))
+
+    # warm-up compile
+    set_num_threads(max_threads)
+    _batch_accux(edges[:8], zs, acc[:8])
+    _batch_fp64(edges[:8], zs, acc[:8])
+
+    sys.stderr.write(
+        f"grid={grid_name} n_edge={edges.shape[0]} n_lat={n_lat} "
+        f"evals/pass={n_eval} max_threads={max_threads} counts={counts}\n"
+    )
+
+    def best(fn, nt):
+        set_num_threads(nt)
+        b = math.inf
+        for _ in range(repeats):
+            t0 = time.perf_counter()
+            fn(edges, zs, acc)
+            b = min(b, (time.perf_counter() - t0) / n_eval * 1e9)
+        return b
+
+    print("threads,fp64_ns,accux_ns,accux_over_fp64")
+    for nt in counts:
+        f = best(_batch_fp64, nt)
+        a = best(_batch_accux, nt)
+        print(f"{nt},{f:.4f},{a:.4f},{a / f:.4f}")
+
+
+if __name__ == "__main__":
+    main()

From b9f5bb3065a0c08509fbe0c2ac34413bcc3b6f64 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 16 Jul 2026 17:56:56 -0500
Subject: [PATCH 32/51] Cap thread-scaling sweep at performance cores to avoid
 E-core artifact

---
 benchmarks/thread_scaling_constlat.png | Bin 75884 -> 74699 bytes
 benchmarks/thread_scaling_constlat.py  |  10 +++++++++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/benchmarks/thread_scaling_constlat.png b/benchmarks/thread_scaling_constlat.png
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diff --git a/benchmarks/thread_scaling_constlat.py b/benchmarks/thread_scaling_constlat.py
index cccbe8190..c720b23bb 100644
--- a/benchmarks/thread_scaling_constlat.py
+++ b/benchmarks/thread_scaling_constlat.py
@@ -84,7 +84,15 @@ def main():
     n_eval = edges.shape[0] * n_lat
     acc = np.empty(edges.shape[0], dtype=np.float64)
 
-    max_threads = get_num_threads()
+    # Cap the sweep at the number of fast performance cores. On heterogeneous
+    # CPUs (e.g. Apple M-series: performance + efficiency cores) adding the slow
+    # efficiency cores makes the parallel loop wait on the slowest chunk, so
+    # times go *up* past the P-core count — a scheduling artifact, not a
+    # property of the kernels. Override with the PERF_CORES env var if needed.
+    import os
+
+    perf_cores = int(os.environ.get("PERF_CORES", "8"))
+    max_threads = min(get_num_threads(), perf_cores)
     counts = []
     t = 1
     while t < max_threads:

From f61555b33cc0ffaeb4e46831340ea3b96a296d6c Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 16 Jul 2026 17:59:24 -0500
Subject: [PATCH 33/51] Fix thread-scaling plot ticks to show only 1,2,4,8

---
 benchmarks/thread_scaling_constlat.png | Bin 74699 -> 75489 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/benchmarks/thread_scaling_constlat.png b/benchmarks/thread_scaling_constlat.png
index 7ebd313c701890035a76fcf99a4e1ba604424503..11fb3ac3854d1f354006148001d3942e9e7a285d 100644
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From 146672c5e723baad98002e9bc9e874a5b8ffdb4a Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 16 Jul 2026 18:01:31 -0500
Subject: [PATCH 34/51] o Fix pre-commit

---
 uxarray/grid/point_in_face.py | 58 ++++++++++++++++++++++++++---------
 1 file changed, 43 insertions(+), 15 deletions(-)

diff --git a/uxarray/grid/point_in_face.py b/uxarray/grid/point_in_face.py
index 3199c97d2..071a6caa1 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -8,8 +8,8 @@
 
 from uxarray.constants import INT_DTYPE, INT_FILL_VALUE
 from uxarray.grid.arcs import (
-    _normal_dot_value,
     _PREDICATE_ZERO_TOL,
+    _normal_dot_value,
     on_minor_arc,
 )
 from uxarray.grid.utils import _get_cartesian_face_edge_nodes
@@ -88,12 +88,24 @@ def _sign_from_value(v):
 
 @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,
+    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.
 
@@ -127,10 +139,14 @@ def _counts_as_crossing(
     # 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)
+        _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)
+        _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
@@ -214,12 +230,24 @@ def _point_in_polygon_sphere(q, polygon):
             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,
+                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

From e9b394fae9441114f1d9fa63c47d6ff3e389b3a2 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 16 Jul 2026 18:39:02 -0500
Subject: [PATCH 35/51] o doc fixes

---
 docs/api.rst                                  |   1 +
 .../spherical-geometry-accuracy.ipynb         | 123 +++++++++++++-----
 uxarray/grid/arcs.py                          |  10 +-
 uxarray/grid/bounds.py                        |   4 -
 uxarray/grid/intersections.py                 | 115 +++++++++++-----
 uxarray/utils/computing.py                    |  89 ++++---------
 6 files changed, 207 insertions(+), 135 deletions(-)

diff --git a/docs/api.rst b/docs/api.rst
index 84c856050..2addaabd8 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -594,6 +594,7 @@ Intersections
 
    grid.intersections.gca_gca_intersection
    grid.intersections.gca_const_lat_intersection
+   grid.intersections.get_number_of_intersections
 
 
 Arcs
diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
index 1402df88f..e75a9909e 100644
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ b/docs/user-guide/spherical-geometry-accuracy.ipynb
@@ -4,22 +4,45 @@
    "cell_type": "markdown",
    "id": "title-cell",
    "metadata": {},
-   "source": "# Accurate Spherical Geometry\n\nCross 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\nThis guide covers:\n\n1. The problem: catastrophic cancellation\n2. How UXarray handles it\n3. Seeing it on a real mesh: point-in-polygon\n4. Where it is used in UXarray"
+   "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": null,
+   "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"
+     "iopub.execute_input": "2026-07-16T23:25:18.320022Z",
+     "iopub.status.busy": "2026-07-16T23:25:18.319857Z",
+     "iopub.status.idle": "2026-07-16T23:25:20.085333Z",
+     "shell.execute_reply": "2026-07-16T23:25:20.084604Z"
     }
    },
    "outputs": [],
-   "source": "import warnings\n\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport uxarray as ux\nfrom uxarray.grid.point_in_face import _point_in_polygon_sphere\n\nwarnings.filterwarnings(\"ignore\")"
+   "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",
@@ -37,16 +60,16 @@
    "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"
+     "iopub.execute_input": "2026-07-16T23:25:20.087254Z",
+     "iopub.status.busy": "2026-07-16T23:25:20.087061Z",
+     "iopub.status.idle": "2026-07-16T23:25:20.343006Z",
+     "shell.execute_reply": "2026-07-16T23:25:20.342290Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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8Ak0xpverpVgVPTqf/AF4op4matXWVGQK5FXWCvR1LiS7jIZSxZB6meh8Va64cth1PGodUrme6FpwxhlnuIo3VSCpokzL0Hms41rd+dVtXO/Vv9V1XN+tKdgaOigiACADtPRw6ADQUn788cfQ4YcfHurXr5+bIkhTdm200Uah66+/PmK6IF0qjz322JjL+Pzzz93UTO3atQuVlJSENt9889AHH3wQ8Z5///vfoVGjRoXKy8tDxcXFocGDB7vpgDTFksyZM8ctX6+3bds2VFZWFho9enTokUceSWrqL31G04+NGTPGrYOmTNI0RTU1NXWmTYo3vdhrr73mtl3rp+mZdthhBzetV7S33347NGLECFdemo7qlltuCU+J5Kcpjg499FC3LSrXPfbYw035FD31l2jKI00BpimdioqK3HJVHv7poG6//Xb3ujfNWLLTgDWWt20qi/Hjx7tt6dChQ+i4446LmBZLnnnmmdDaa6/tpkfTMXXZZZeF7rrrLvd5lb9HU2LFmwou2WUkM/WX6LhQ2Xbv3j1UUFAQ6tWrV2jcuHGhxx57LO7UX56HH344NHz4cLdPOnbsGNp3331D06ZNq7PODz30kDt29T5NF6Zt2G233dxryRx/mo7s4osvduWiZeg7Nc2Yju1Y04dFL8Nbf00RF2sKuFjTyvlpm3bZZRd3fup43X333d0UftHHqncseNPpRXv88cdDG2+8sTsf9aPt13E8ceLE8Ht0HG211VbuetG5c2d3/dH0cfGmygMAZLYc/aelA34AQMOoBV6t5E3dApit1LKvFn11u1bXeCRHvRHUq4J8ZABANiNnGwAANIi6XiudwE/zxauLulInAADIZuRsAwCABlHusUY732+//dyAaRrBXvnWmlLtqKOOolQBAFmNYBsAADSIpnnTwH+aO15d7TVyvAY50+B4nTp1olQBAFmNnG0AAAAAAAJGzjYAAAAAAAEj2AYAAAAAIGAE28i6aXxycnIiXuvXr5+bPglozf7v//7PHfuTJ0+2bKFRsbXN+n+mrrumdQNl3pzS9Vqh0e39I9xr/bSeWt+G0Gf1TIDUyh1Aagi20aS+/vprGz9+vPXt29fatGljvXr1sq233tquv/56Sh6BeeCBB+yaa66hRAFkxTVn2bJlLlDMxIqkdPXBBx+4Ml2wYEFLrwqAVoTRyNGkN67NN9/c+vTpY4cffribCua3336zjz76yK699lo7/vjjKX0E9uD7zTff2AknnECJAmj11xwF2+eff777d7a2OqoSv6KiwgoKChr0eX02Pz8/4plFZaqebuXl5QGuKYBsRrCNJnPRRRdZWVmZffrpp3VuXLNmzcqakq+trbXKykrXsg80t6VLl7rpmIB0Oz6y5dhcvny5FRYWWm4unQmDpG7gjbmvck8G0By48qPJ/PLLLzZkyJCYNcRdu3atc9M87rjj7NFHH7U111zTiouLbYMNNnDd0OXWW2+1gQMHupujavGjc8neffdd23333V0relFRka2yyip24oknuprroFx55ZW24YYburljtX6aWzZWPqW3Lffff7/bfq3PSy+95P72+++/2yGHHGLdunVzr+vvd911V8TnFZife+65bvmqrNDD6CabbGJvvvlmUuupHPRx48a57oUjR4506zp06NBwd8MnnnjC/a6y1Hd88cUXdZbxww8/uO7/HTt2dO/Tcp555pmI98ybN89OPvlkt6x27dpZ+/btbdttt7UJEybEzD195JFHXAVM79693TK33HJL+/nnnyPe+9NPP9luu+3mekHoPXrvXnvtZQsXLoy7vToenn/+eZsyZYr7Hv2oDEKhkHXu3Nn++c9/RlR86HjMy8uL6Cp42WWXuRaOJUuWhF974403XLmr/PWZnXbayb7//vuE5Z/K9srHH39s22yzjdvXJSUlttlmm9n7778f8R5t2zHHHGOrr7662586BnW8R58HXq7l22+/7d6v80zfH8uBBx7oyqeqqqrO38aMGeO+K5Fk1l3ee+89W2+99Vw5rLrqqu58jh4/ob78y+jcymTLI1neumj/eK1a2qaDDz7YtSA2ZB29Zf7444+23377ueV16dLFzjnnHHdsqpePjimdNzre//Of/8Rct5qaGjvzzDPde3Qs7rjjju6zDdkX3jp99913ts8++7g5sjfeeGP3tz/++MNtr44XXZt69Ojh1i9RmX711VeuzAYMGOD2r9ZT17i5c+cm/d1y3333ueuR9qeuOzrvY21ntMWLF7vWZZ3zWm8d80pV+vzzzxOOyxEvFzVRmce75vjP/4ceesjOPvtslzql/bFo0aKkr5legK4yGzRokCtX7Y9dd93V3Ve1T3QsiVpivXXwH3/JXMPl22+/tS222MKVu/b9v//9b3edbGm33Xabu1ZovUaNGuXu8dHinY/ec4S2e6211rInn3zS7X9vH3n8Zab/n3LKKe7f/fv3D5epd/y/+uqr7njVtUH7TtceHSPJ0LGtbdBxoON+0003tVdeeSX896efftrNTd+zZ093DGu7L7zwQncc+um40/boHFKvQS1Px9fll1+e0vHj0X5WKoSeQ/QePZcceeSRNn/+/KS2C0ByaNlGk3bx+vDDD11XO90gEtHNVA8Dxx57rPv9kksucUHjqaeeajfddJN7uNZNQDcWPcwpGPLfXPVQfPTRR7sH708++cTlhU+bNs39LQjq+q6Hrn333dcFxHqY0gP+c889526Uflo3BVsKuhXQ6CY/c+ZMW3/99cPBuB6WXnzxRTv00EPdg5jXHVH/vuOOO2zvvfd23e/1MHnnnXfa2LFj3Xats846CddVQYMeaHXj1IO+Kgp22GEHu+WWW9wDgsrSK+M99tjDJk6cGG510cPXRhtt5G7ip59+unvY1LbsvPPO9vjjj9suu+zi3vfrr7/aU0895cpADyfaPgVResjXw4AeHPwuvfRS9x162FTwrP2oslSQICpTbeOKFStcioEedFU5ofJVYKwgIpazzjrLLU/7+uqrr3av6WFI5azteOeddyICA71X66FAxNtvOvaGDx/uPievvfaaewhWAKEHFlXa6HjS8vQQH/3QFkui7fWOE32PgozzzjvPvf/uu+92D79aJz2giXqHqIujAhA9EOsB8Oabb3YPXyprPXT5af/q+FKljVoPY9l///3t3nvvtZdfftmdZx4FXVovrU99kl13VZgpeNf6qCyrq6vd+/Vg11CplkeydC7oWNZ5of2s81DBmypjGmrPPfe0NdZYwx0PCtAUzCgA0rmistKyVTGn40QVEnoQ91OFjY7l0047zfUI0sPxVlttZV9++aULRFLZFx6ds6uttppdfPHFLvAXVXLp3Ne5p+Nb36UAY+rUqfUe73qPrgUK1HXOahkKlPR/pQxFD0gZ67u1jaqEUPkfdthhNnv2bHe+qSxUGVhfl96jjjrKVXrqmqoAS0G+KndUMbbuuus2YI8lLvN41xw/BUtqzdZ+1TVN/9axmcw1U0GWzsnXX3/dHeP/+Mc/3H1AZa37qdZFx7vud7oeK4iStddeO6VruM51BW06J733ad95x1VL0f1O9y5Vbuu+qONL916dN6pIr4/OMZ1zqtDQeaxnBt1jVRb1URmqYuzBBx90+1T3bdF1S+Wp/aHyveCCC1xArHtsrIrFaKoM0XVP26LP6jjQPUDnrK6LosoCHT+qGNb/9Tddu/UscMUVV0QsT9ujSjWtr84XHfs6TrW9ugYkc/womBeVsb5b5+7f//53mzRpkt1www3unNO2NbR7PoAoIaCJvPLKK6G8vDz3s8EGG4ROPfXU0MsvvxyqrKys814dikVFRaFJkyaFX7v11lvd6927dw8tWrQo/PoZZ5zhXve/d9myZXWWeckll4RycnJCU6ZMCb923nnnuc/69e3bN3TggQcm3J7o79B2rLXWWqEtttiizrbk5uaGvv3224jXDz300FCPHj1Cc+bMiXh9r732CpWVlYWXX11dHVqxYkXEe+bPnx/q1q1b6JBDDkm4ntoercMHH3wQfk3lrteKi4sjysMr4zfffDP82pZbbhkaOnRoaPny5eHXamtrQxtuuGFotdVWC7+mv9fU1ER8t/aJ9uMFF1wQfk3L1nesscYaEdt17bXXute//vpr9/sXX3zhfn/00UdDqdp+++3ddke74oor3PHnHT/XXXede9+oUaNCp512mntN21BeXh468cQTw59bZ511Ql27dg3NnTs3/NqECRPcfj3ggAPqXZdkt1dlqvIcO3as+7dHx0H//v1DW2+9dcRr0T788EO3vHvvvTf82t133+1e23jjjd1x5Of9zTtvtN29e/cO7bnnnhHvu+qqq9x58+uvv8bdxlTWfeeddw61adMm4rj77rvv3H7xn4taL/2u9Yym13Xuploe3r7wH9+xeNeF6PNrl112CXXq1KlB6+gt84gjjgi/pn2iMlf5XnrppRHnt85N/3XIW/devXpFXP8eeeQR97qOp1T3hbdOe++9d8S66/v1us6XVMXaFw8++KBb3jvvvJPwuydPnuyOhYsuuijidZ0n+fn5dV6PpmvnscceW+974l3jN9tsM/eTapnXd83xljFgwIA6ZZPsNfOuu+5yy9C5GM3bx7Nnz65zzKV6DT/hhBPcMj7++OPwa7NmzXJlGn2PbS66r+raq2uw//p52223uXXy769Y56O2W+fY4sWLw6+99dZb7n3R+yu6/HT8x9ruq6++2r2uMk/FTz/95O4Zuo5E7/foczXakUceGSopKYnYh9r26GucykjPSLvttltKx8+7777r3nP//fdH/P2ll16q83r0eQIgNXQjR5NRVz61bKtGWt3k1LKnlkvVMMfqzqZutv4WlNGjR4dbXEpLS+u8rtpuj78mXi15c+bMcTXJup/G6ibdEP7vUO2yWjbUzdjfXdGjlgq1sni0HmpRUOuy/q31835UJlqWtxx1cVbtt9fNS10P1fKgboCxvisWfbe64UeXmVq61NU+Xlnqu1Srrhpz1YR766jWIq2nunmrtVlUu++1hqsmXe/xutfFWk/VnnvbJSo7/3d7LddqafV33W0MfYfWTa2golY+vaYfr1uiavrVcu6tz4wZM1wLlrodqiXFo1YNHdMvvPBCUt+daHv1HSpP9UBQ2XllreNX54Ja5L3unP5jT92+9X6lVajFL1ZZq0eEjqP6aN+ppV3nova1R62sOnfU8hZPsuuustf+VIua/7hTS6+Op4ZKtTySpVZSP+0zLVstTA2lllqP9onOY10D1Nrm0XrrvPFf0zwHHHBAxPVPXYPVJdQ7DlM5juJtp8pTx6q6QKfahdS/L9R1Vd+tHjwSa19Ef7fSWrR+uub4r4tqJVcLeKL0GZWdWgqnT59uQUlU5slQmkZ0C3Gy10zdK9SyGmsQ0eieAtFSuYZre7Sv/D0f1JKr60JL+eyzz1xvAh0n/uunrsfxejd5dAyoJ432n7+nge7HavltKK9nhbp7p9LFXr0Y9H61Ukfn6/v3o/848faZrj26DyodwE/bpd5qHpWR9p//2pHM8aMefypP3dP85516x+g7kk1bA5AY3cjRpNQtUg9T6iKsgFu5U+qipYcXPST6A1L/w7h4N9bobmPe6/6HQnV11A1NgUP0w2J9+b6pUHdmdQHVeqtbYH0PP9GBirpFKqBTFz39xOIfNO6ee+5xOZy60fpzausLgPwaWpbqGqdAQF069RNvPVVhoocIda1XF391P/Pnl6krf6J1Uu6a/7u1bepGd9VVV7mATw8bqqjx8l0bQt1I1aVYgbUeNPV/devTg7y6qSo48IJuL39UeZgSK2dZQaKCx2QGdkq0vXro9R7K49Gxq8+pG7u6RKprsB6Uve633nuiJXuc6KFU3Zh1XurfSif43//+59IN6pPsuus80boraIqm8k0lePFLtTySVd8+U35tEMvUsaz8SK+bqv/16DxniS47XW9UseDlkqZyHMU7PhQE6jg46aSTXPd+BWDqhqpjQudKouBO55TSaqIHvkzm2NT6a//FOkYkUVdWVeJq23VtU6Cw3XbbufVWCkhDJSrzZMQ6B5O9ZiqvVueHf6TsZKVyDde1zqtw9UtmvAadgw091xRcxrume9ff6H2g4yDRPvU+q30VTa81tCJO3dKVUqKKM3W3VyWWunHrOaa+Qe+0H/V3/3NOLOqmrvx+VZJEV+xFl7HSZqKfOXRuK0UqleNH552WHT1+TjYOYgs0NYJtNAvVvirw1o8G7FCrn2pW/Xmh8Vri4r3uPWDrgUW1s3roU+7S4MGDXSCkh3DVhgcx2IsCMgV+yiHUg5JaOXTz18O+poCJFt2i4a2DAsd4D8Vevp0GU9F6qzVQA7boZqgyUHDhH9ykPg0tS289lWcYr+XRe5BRzqUe5pQ/r/xEtQLrwUI5drHKPNF3iyoYtO1qQdAAMsoj03Yr9zPeQF/10T7Sw6Ra9/QQqhxFBfEKKFSJoRYx7VsdM96AQ0FJtqyVkxcvD99rnVELhY41la16LOhBVQ9cyseLVdbJ5lzqIVABio45BSj6v85VtYrVJ9l191dKJRKvxS56kKCGlEdQ+yyVdaxvmcmcC8lK5Tiq7/hQWarnjVrjVKGkc1vnngIAjWcQj44V9RzRtUrfr+/SOimvNJljU+9RuWr8iljlEr3usb5f57QqjHTNUDmo4kCVvF4Oa337LVEPkIaKVcapXjMbIpVreGM8/PDD7j7eELoHxhpkMF1pX+oeotZe5YRrwFNtv3qK6ZhrzDGkSni1vKsyTzndyqdWZZwqBvQ8E31cBHXt0HL1bKGK7ViCvh8C2YxgG81O3Si97rpBULcxDWyi1mAFDB4NBhIUdcvSDVAPoWoF8uiBPxm6calboh7uNLhNfTTgiWrw9bDof0hMNGBVELyWAwWpyaynBtfRYDbRDw/RrXapUHc//aimXw/xGuhHLa3qVRBPfV0r9SCuh28Neqb1UmCt92sEVgXa+vEPEKaB/UStvNHU00DLCGK6Im+QGj1kJVPWekD1j1itVnn/iOoNpXNGPQp0PqriSIPG+VtBG7PuOu71oOq1vvpFl6/3ndHb5LVWNVd51CeVdQxKdNnpoVoVR17lXCrHUSJallq39aPvVfCsMlYlTCxq8dcgTGrZVs+ieOuc6Du1TWoJVkVsQ6jyU4MC6kctcurRokHOvGBb+y3WsaH9Fqu1NFGZJ9OduzHXTJWJKgJVIRivZT/e96dyDde1LplzMxYF8g29x0YPnhm9TqL1UjDrUVmoN8CwYcMSfjbWrA+xXotW3z5VpYhatPWj3leqONFAeQrA45Wz9qOCWg1+F68iTKkb6tGi+71/cERta0Mlc/zoPbon6v7a0gPiAa0dOdtoMroJxapt9bqOJtNVLRleTa//u/RvddcLir5DN2J/C5a6FKoVKNnPK/dcQbtyhKOpm7n/vdHboxun8t+bmmq6NaKzRsiNVRkSvZ7R+1e9Fbx8wFSp+5xy0/0UdOshJ1ELqYLfeF0aFWzr8xpRWF3FvQcqvf7f//7X5fl5+dTeg7sejFR5439A135TK4a6qQZBLcp64NFI8f4px5Ita3WDr69FNVka9V5lohFrlffnzwds7LprvfVQrvNEqR4ejRStiis/BYsKOPyjx4t6kkRryvKoTyrrGBSNGO/PqVfApnPTCyRTOY7iUW6oKiv8tExVENZ37sW6VonOtWSpO66Wo4A9ejn6PVbXeo/2d/R5r2uYgjn/emtb1DtG6Uz+tKB4U4slKvNE15x4kr1m6l6h/FmNDB3N+7w34n50JUIq13Bdy1QumuXC//d4rZ1+uk4qyGzIT33dqlUZr0o6VbD695dawhNVpmm/a+YT7T//uaCpEL1pROvjVaJGf496zUXzguf6zg/1TtP9Sy3W0S3U3n6MdQ5puxtzTUnm+FGPEJ0/6mERTffhpq64BLIJLdtoMurqqYc4TTOi1kTdQNRSqe5XGgitoV3QomnZephStzk9tOiBWEFtkHNFqrVPtdnqGqmBiNR6cuONN7rueP5cqfpo6h9VQKhbswaw0gOHbuLqLqYaZu+GrlZW1XKr3PS9quHWg4feH+thOmjaLgWlCnS1nmop0RQ1CvY11Y03J6zWUw8R2o8aUEsPM3pIa2iupLqravoeTYujFi7d8BUMexUV9VHAoeNKLbRKVVDXU3WJFXUzVu6aWmuOOOKI8GfUiqDpc8QfbIu6ourBWp/VQFbe1F/qruyfy7Yx9BCmPEB9j1rZVY7Ko9QxrONEx/Gzzz4bLmuVhb5fx4H2hY6ZWLnxqdKDrY5rPfRrIKDoaewau+4KotTtUmWslkftV5WlPhd97ignUueJ/q+HbgW16rUSrSnLI5Fk1zEo6mqs81FlrPNQgayuOzo3U90X8Wj91WKnB3CVp84XdcvW96lrfjxats4j5U2rFU3fqwqpVFrldO1Wr5UzzjjDVWAqQFGQr2VoHXTO6toeiwJipZcod1YtnjrvdRxoajh/rwftKwXMOs61jUrHUWu91ysg1TJPdM2JJ9lrpnqbKGDUshUI69zROBHaNp1Dmv9crZHaV1oHXS+1zgo09ZPsNVzTauo8Urmoss2b+kstxMne14KmllgdD5qWSi3bypfWsaBeZMncW9TirPJRi63KWc8BCjpVLonun9qnohZrHfdaF+1T7TOd57o2qmx0/1cwrGPPP1d8NB0zWpYCWu1DVSypZ5yOT1UMKE1Dx4F6XqinjtKmVPGpfdKQlJJUjh91XVcZax00Do2mIdP2qkeB7gVqrNB5BSAAKY5eDiTtxRdfdFPpDB48ONSuXbtQYWFhaODAgaHjjz8+NHPmzIj36lCMnr7Fm9Yjejoab2oV/xRRmkpoq622ct/TuXPn0OGHH+6maoqeFqQxU3/deeedbtoUTdOibdJyYy0v1rZ4tN362yqrrBIqKChwU3ZomhZNa+KfmuPiiy9266XvGj58eOi5555z6xhrqploeo+mpYmWShn/8ssvboorrZ/WU1PhjBs3LvTYY4+F36MpSU466SQ3nZmmLdpoo43c9EvxptOJntIretoWTTWl42XVVVd1U0V17NgxtPnmm4dee+21hNu8ZMmS0D777OOm8Io1xct6661XZ4qbadOmude0L2LR92qbtG3t27cP7bDDDu44SyTZ7fVoyrNdd93VTTGl/a1132OPPUKvv/56xNRMBx98sDu2dYxrmqcffvihzrHrTe/16aef1lmv6Km//LypjfzTVCUjmXWXt99+OzRixAh3DdCUSLfcckvMc0dT4GiKPE09VFpa6palqYiip+hJtjxSnforemqfWGWW7DrGW6bWr23btnXWQefMkCFD6qy7ptHSdIeaDknHos5t/zRqqeyLeOuk6Qh1bdB1TeumbRs9erQ7LhLReaSpjXTu6XO77757aPr06UmXh+fxxx93U9bp+/WjddE6TZw4Me53a9qjU045JTRs2DC3L/Q5/fumm26q897//Oc/7jqmstF5/dlnn8W9ViVT5vGuOfHO/1Sumd5xdtZZZ7np27x7xfjx49212aPpHb3zKrq8k7mGy1dffeW+W9dcvefCCy9097qWmvrLo32obdf+GjlypJtGLrqc4l1TH3roIXf86LOanvOZZ55xU2PpNb9YU6dp+1UOmrLLKwOdQzvttFOoZ8+erqz1f01h9+OPPya1LZqKS/dxrU+HDh3cNrz66qvhv7///vuh9ddf3x0TWrY3TWr0tSv6GuGJ9WyQzPEjevbQMaTv1jmkqdP0/TqH/d/L1F9Aw+XoP0EE7QCAzKQB6dSiqNab6Fb+pqIeArG6DgNA0NTtW714ghzLBQCSQc42AGS522+/3XXRrK9LJACkO6UzRI/9oUHI1HVeuewA0NzI2QaALKW5kZWbqelslKPXkBGWASBdaKwCDcKmgR6VF60ZJDTmieaLP+qoo1p69QBkIYJtAMhSGolcAztpEDgNnAMAmUyDjWmgMw0aqJHVNeibBjbToIbNMYAiAEQjZxsAAAAAgICRsw0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAICAEWwDAAAAABAwgm0AAAAAAAJGsA0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAICAEWwDAAAAABAwgm0AAAAAAAJGsA0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAICAEWwDAAAAABAwgm0AAAAAAAJGsA0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAICAEWwDAAAAABAwgm0AAAAAAAJGsA0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAICAEWwDAAAAABAwgm0AAAAAAAJGsA0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAICAEWwDAAAAABAwgm0AAAAAAAJGsA0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAICAEWwDAAAAABAwgm0AAAAAAAJGsA0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAICAEWwDAAAAABAwgm0AAAAAAFoq2L7rrrssJyfHfvrpp4jXr7/+evf6eeedF/H6vHnzLDc31y6//PKkV6Zfv3523HHHhX8/6KCDbK211rJs89Zbb9nFF18c6DKvueYat5/S0b/+9S9r165dS69GWvrb3/5m48aNy8iy2nnnnd36x1v3yZMnu9emT59e5/jXsfrZZ59ZuikvL3frjPS+Li1YsMDtp++++85aE22TymvTTTet87cTTjjB3UObm9bnyiuvtNYk+tnj//7v/9x2zpkzp1HLSSdPPfWU3XTTTRm1ztmutV8vm+LZN122MZnrZEOvM63BWzGe+xpyb/nyyy/dfl62bFlalW3SwfZGG23k/v/BBx9EvP7+++9bSUlJndf1eygUso033jiodc0aTXnBAVrKYYcdZm+++WZEsH3++efXCbbXXXdd+/DDD22NNdZogbVEa6AHKx1bLf3w2FTeffddd58Agg62zznnHHvggQco2CySLtfLpg6202Eb67P99tu7Zx9V6qNhFGxrP0cH2y1dtkkH26uvvrp16dLFBdd++l01oR999JHV1NREvN6mTRsbOXJksGucoSoqKlp6FVq9FStWWG1tbUuvBuLo3bu3rbfeegnLp3379rb++utb27Zts6YsM/X6kKnrncl0XowaNcouvPDCFl2PTNv3mba+LWXVVVe1tdde29JZpu7LTF3vbNNS+0kxlp598vPzW+T7W7MuLVy2KeVsq3XbH2xPnTrVpk2bZv/4xz9s+fLl9tVXX4X/pvcp0C4sLHS/63377befde7c2YqLi103uP/973+BbMQzzzzjvktdVFVroX+/8MILdboQ6AaiCoBevXrZWWedFVE54HUxUKXBFlts4Vrr1SVP3ef9VDOy4447Ws+ePd1DzzrrrGP//e9/Y3aHeP755238+PEueNh9993d3+69917X2t+xY0fr0KGD62b7ySefhD+r7g+qlVm6dKlbhn78XXG///5722mnnaysrMx9v2prfvnll4jvX7RokR1wwAFWWlrqDrBTTz3VqqurLVNo25VOoAoebz8cddRRtnDhwphpB0pV6Nu3rzuulL6gHhUXXHCBde/e3R0TKvvXXnvNlaW/NUjvUxeVQYMGWVFRkQ0YMMCuvvrqlNdX+1+f91+ghw4d6k5q7QvPBhtsYMcee2xETesxxxxjPXr0cJ8fMWKEvfLKKxYUHX86Z1WG3rH2xRdfhP8+ZcoUd3x6x9LYsWPt66+/jlnGN954oytjvVfdw2fPnh3xPh2Xm222mTu/9LB2zz331Fkffzdy7YfNN9/c/VsBuHesx+tOpOvLP//5T3fe6Tt03j355JMxuz/q88OHDw8HJdHXmWT3+9NPP22DBw9236flfPrpp0mXvc5zrYM+q2vedttt58rbf63RtWTrrbd263nKKae4v6n8tR/0mspa+0fXWT9dk4YMGeKO906dOrnriX/dEv09mnoYaH20zw499FD3vbo+qbz9143Grncy16V4Xb20v7V//bQeY8aMcddXLXP06NH26quvuu3p37+/e4/Ofe/Y0uuthVof33jjjTo9yqIlc43RdUL7s2vXrq4sVY4vvfRSxHvq2/fRaWW63vive971QZ+Pvi/Huj7oWNX5pnNHvVuee+65QNf3P//5j7vm6DjVMpSm8+OPP1pDKnfPPPNMd11U2WpdM6VFWOeSzvdvv/02fH5451e8LvS6d2y77bauLFdbbTV3jQtivwvXy8y/Xv7+++9ufbt16+buPbp3XnvttRH7uKHPvj/88IPttddetsoqq7jjbM0113TncXTjyqWXXmoDBw501w6V2VZbbWWTJk2qdxu9+5/K8vDDD3f3TF1/RM+ThxxySDhu2XDDDe2dd96Jmeqn7dOzj96n1yZOnFinjLS+2k6VkZZ58MEHu+2tb3/qOnP22We75xRdZ9RoEb1vU33+S/UZO9Hzn+4zxx9/vFs3raPK+owzzqizPqNHj3blo31z9NFHR2x7MhJd+1V+KlPRd6gsvdSqWGWbyv597LHHXHnpHqUYMTruCjzY1sVz/vz54YBaB78eWocNGxYOxKuqqtwN0+tCrvfr32re10X58ccfdxdsrfCsWbOsMbTBerDTw6Uevh9++GHbY489wusoV111levCqofBZ5991k477TS77rrrXMAdTSe0dqaWpWBAD5/+nakHZpXDHXfc4Za12267uffECi6OOOIId/JpWSeffLJ7TSe2LkiPPvqouzH36dPHVTx4N3utp5anHa+Lo368rl6//vqrOxh0gOjA0ed10G+55ZbuhPTo4NF36sKj9VK3GeX6ZAp1/1BFyEUXXWQvvvii/fvf/7a3337bneTRdCzpYUwXdQVHOq50jOmCpgvSE0884faByjWaKonOPfdcO/DAA91JrPfr2LjllltSWl/tv8rKSldRI3PnznUPMQUFBeFzQtukoM/LtdT7dZxp3bWdqjDSDUSVJ9EBb0PoPNhhhx3cRUnHyf333++OW90QZfHixeGLr7b3vvvuc+ut9fvtt98ilqV1048uuCpn7QtdWP2BsG7iM2fOdBUPOu70U1+Ap67iWp7cfffd4WM9nn333dduvfVW98Ch7o8qK517Wi+/P/74w/7+97+7B+tHHnnErdsuu+zirkmp7Hddq7R8PVTqGNJ7dV3xn2fxXHHFFe79Cmz02TvvvNMtJ/oGtc8++7hroI6B/fff35W7yl/7QftD6/P555+7SgztL9GNQNcHBe96gNUNXue/bnbJ/L0+Chz0MKByU/npPNJNPlpD1jvo65LOKx2/2h+6Fus6oEpIPbAqsFS5i7okeseWXm8tdPNXZY4eTuNJ9hqjh1FdK3Tuqhx1ndDxE6ubevS+j6YKdVVmPfjggxGvqwJIldy6B9dH5+mee+7pzh/tQz046/wNcn1V8a8HSN0vdOzomPfuq6nQ9UDXpJNOOsktf5tttnHbr3tWJlTWqMz0AO+dH3qtProG6zqv66+OPV039TzY2P3O9TLzr5e69qsxQeegrjW6r5544onh543GPvtqOQp09Lvua3q2VoOKv3eP7nU6hr1ndm2nKh1UaZHMNio49I5hHZN6BlXlkp7zL7vsMrfeCrR0TY2uwNf+u+SSS9y+0nrMmDHDHfPRzws33HCDG/dK+1PPICqHRD2U9ByiGEbHg8pV65YoSE30/JfKM3ai5z9to66x+g49N2h5ev72B7WPPfaYa6RUI5SOaTWQaX9oX6Ui0bVf9zbvmUXHgPZxdKOMJ5X9q+dBlbv2r2Kvn3/+2V3zUhJKwYcffhjSR55//nn3+3HHHRfac8893b+PP/740N577+3+/dFHH7n3Pffcc+73c889N1RWVhaaOXNmeFnLly8P9enTJ3TKKaeEX+vbt2/o2GOPDf9+4IEHhoYMGVLvOj366KPuuxYtWhTz73q9Xbt2oTPOOCPi9ZtvvjlUXFwcmjNnjvv97rvvdss555xzIt636aabhtZff/2Yy66trQ1VVVWFjjjiiNAGG2wQfv3NN990yzrqqKPqXfeamhr3+dVXXz1i/c4777xQ27Zt67z/gAMOCA0YMCBUUVERfm3WrFlu+2688Ub3+7fffhvKyckJ3XnnneH3VFdXh/r37+/WKR3F216Pyui9995z6z9x4sSI46VTp06hJUuWRGxrjx49QoccckjEMg499FD3ee0b+fnnn1053XrrrRHvO+2000Ldu3d3+yYVOpb/9a9/uX8/+eSToV69eoV22WUXtzx59dVX3fdPnz7d/X7XXXeF8vPz3f7yGz16dGj33XcP/77ZZpuFtt9++6TLyjsue/fuHRo7dmzc91x77bVu+7/77rvwa3PnznXL/uc//xlRxlqWzlf/OhQUFITLSOdSbm5u6Mcffwy/56effnKvaf3jrbt3nnz66acR6xb9+oQJE9zvt9xyS8T7dM6tu+66EdcLbdM333xTZ1nvvvtuSvtd1zWdMzqePDqntCxtRzwLFiwIlZSUuGtCPN615tJLL414/cQTT3Tlo/3g+f777936Xnfdde73K664ItSxY8e4y07091gmTZrk1meTTTaJeF3XQm3LvHnzGr3eyV6XvO+YPXt2xHcMGzbM7V/PhhtuGFpzzTUj9k+sbdL9oTXxn0OPP/6428aPP/7Y/f6Pf/zDna+eZK8xse5JY8aMCd/P69v3otd13Hn222+/0KhRo8K/a3ndunULnXnmmQm3TcuKdYzstddega2vn5a/bNkydw/1XxOinz2ij8s33njD/f7yyy9HLE/XjfXWWy/uctJJvHWLt+3eM4bonqtrw4UXXtio/c71snVcL7WPi4qK3HKSkeqzb6zn7osuusg963kUO/ifB5LdRu/1bbbZJuL1p59+2r3+0ksvhV+rrKx0z3q77rpr+DU948R7/vE/s2hZ/vNDtI9WXXXVuPvzlVdecb8/8MAD9ZZJqs9/qTxjJ3r+u+2229znPvjgg7jr07dv34jrs7z44osRz2uxngej7y2pXPujz4no11PZvzomFWtFL+u3334LJSullm211KjWyWut0/9VIyyq1fK/ruZ672/qtqZWYnUfUTcY/eTl5bmavFS6Znqf9X5EXcO1LNVgq4YiuhuEutktWbLEdR3xf1bdS9Tt95tvvol4v2rRo2uVVMvhdTlXi7laztSlQi2X+rnttttidkNTLUs01QTrO9SNROutz6u7STLd2FSOqh1S92RvO9Q9RLXMXjnq/zpG/duh74lVY9WQMk/mJ4i8adVcabtU06Qy8npJRJeTamv9ub1qtVCtosrJT7W4fupW7u3f6ONCraPRrbuJqIbW636i/+t3Hd+qBfReUyuNV5OqfalaPvUK8X+/atVSOSd0XEafEzqeVA6qCa1vgCV1FfQPQqbzU9//3nvvRbxX26GuQR61jqkFyuuV8vHHH7tlqfXWo21Vb5cgaF3FS8XwqAVMLfP+Wl51M1cvF/+6isojlf2ubVINqs4dj3rQJKKaVNUaJ1NjG3190Haqhlj7waOueCpHb5+oR4Ba4NSqpC6A0YOAJPp7faKvfdpefT66p0VD1jvI65LWSb1I1Prp3z/ZRmWp804tPLEke43RuaGyVAuk7i263uqzyd7ToqkrprqHqnePqCVK1wrveqT7g399Vj5TRW5X9DGi8zGo9dWxozJQd1F9Xt0o9YyQSldyfZ+Odx330WWra5I/RS1VKo+Wuu/WR63aHt1z9QzkXVeT2e+xcL1sHdfL119/3Z0L9c2G0JhnX/VQ04xHeq7Qs4g+q56petbTuevd+3TuKf1J5ejvzZaMWPc1dVX298rQ9+666651npHiPf/4r1ui64Ofnk/851CsctX1ST1uk5XM818qz9iJnv+0jnqOVAwYi5Y3ZcoU1xPIf73ScjVjVSqzzqRy7U8klf2rHhLqlu4vA299kpVSsK0VUa6Tgmkd4MrR9gJq/d/L4dbftTIKBEXdCdT1yAtOvR/t7FSCmujPix4k1IVLQbZOZBWIAi0v/8XryqAT0f9Z78SI/n51u/DThUEHlrccPcSqm4m6hWsn66Kog1oXg2j6rJ+6COmGpQNP3UK0s/V5nZSxPh9N66BuRNHloOV426GLj17zyj7euiRD3X6ivyuZn0QneSLq9qHuRsqbUZdW3Si8riDR5RS9Xdp+8Z8YsfarylI3M+Vq+NfduximGmzrwqH11LHiBdveuAS62Xmv+b9fN4boslN3nlS+W13k/Z/XPlOXLi/wjEeVRrGOCb0W3Z0yevRGbxwGb1+ozKPL11tWELSu2jZ/MOctX/vQ30U60bomu99jbZMuzMoFq08yZe9f/1T3iR5odN3UA61uEtoOnSvJ/r0+sa59/nOqMesd5HVJ36fAIpkybs1Uoa0HTnUtVDfGaMlcY1SOul/q4UJBu2YL0D1J3euSuafFouucunwqfcLrSqzXdK0S3R/86+NPwYp3jHjHYGPXV88FugcrGFYXcD2r6PM69pO5B/vLVsd2dNmqK6weJKPPmVSogrYh9914lS5BiXVt9ZdZov0eC9fL1nG91H6sb/mNffZVmpe68apCR5U4+qzXXdj7vJ7NNfbKyy+/bJtssol7BlTKWLKDncW6r8V7rom+n8Z7X/R1INY5VF9qmspVDTSpTPeWzDmVyjN2omeqRPvei5122WWXiOuVKhF0HU72eTfVa38iqezfRGWQjJSHZVPthwI+bbBqOxTxi2o5dVDo5qXWZH9Loh6Slc8UKzfBX2OSSLwWPy1bP8rNUD995YooSV41Lt4DuvIDlF8ezRs0waPaGtWaeJSHqgNDD60qWAX2ulj4cxbi1ShHnyCqxVVlhJbhb/VTRYEGFkhE26LaNw14E00DXoj2gQI+HUj+C7W2I1U6gVJpZfWorBpDuRM6rvQw5PFaiBOVsddyHJ0fGz02gMpSn9Vx7J04fnpoSIUeKhRU6wKg/A79rtpqXVD0mmo4/YNa6PvVK8N7MGko9ebwX6y1z7wLQPSUWn76/lgDeOg4iQ5qE1GZx3rY17IUoDaW1ifeMa19mMpUDsnud21T9DGj60uii6tay7yyT3RORx+7WrdYY1hoO1Wp6FGukH50E1Peqa53ukZ5x1Kiv8cT/d3eNSM6d68h653sdcmrzFC+sZ9/DA7tb9WI13d8Zwu1Fig/TvdW3YP9krnGKPdMAbkqw/337HgPqMk+9CnoVF6eWplUGeAfaFTrq5zpWPfgeMeIdww2dn31fKCGAj0PeNcNBcep5murbPUwH2/gr1gPcan0IGzIfTcdKp/q2++xcL1sHddL7cf6lt/YZ189Ex555JEu6Pbo+PLTNiq41o9ykx966CE7/fTT3fNoovEIUr2vRT8jxXufFx81plwVsKuBINlrr/+cCuIZO5nv8w+OHc0rqxtuuMENaNbQ61aq1/5EUtm/QUipZdsLthVUqODUyu0fRl2t22pVUXdMb15uURdNDe6grgYaKdz/o25uyYr+bDQ92OvhQ10uvIE71LVBAY9O9OjP68c7MD3RyfRKwtfNT91evKml/A/pqrGLHqQpHu+g8H9eFRPRoz7Gq+1SOarbu7p+RG+HFyR4Uyv5t0O1RzpAU6X1iFVmiX7q60qUbDlFB0IafCEZunBrFHIFGX7R269Bo7xauVjb4FVeJEs3SX2vBt/QiaqeHbr463xRjayCNH/LtvalBrzThSbW9ydL54//cyo3HQsqBw08Fo/WS92D/QG3btDqZu11J0qWakd1XOpi6NG/J0yYUO/nkq0d9NZHNwg//e6NOp6sZPe7tkkVGf7uoBrkIxHvelNf2de3naog9D8oaf/oRhZrn+ghQt3V1SrvXe9S+Xu06Guftlfbkugancx6J3td8h68/Ourf/trv7W/Vc4aiCZed92G1DxnIl1j1Lqt6130A08y15hY9yS1PkVP8ZkqdfXTg7QG1dIx5E/B0P0h2Xuwd4x4D2mNXV99Xg+tXs84UctOqrN1qGxVoRvvHhmrIi9ZugY15L6barAd3TIdhPr2eyxcL1vH9VLng2ZHiB5RPahn3+hnQm2Hgul41GCmgQtV2eiVTarbqH2hCnb/7A26TmifRN+P4z3/xAouUy1XxVu6RiUrmee/xjxjx1pHlXF0l3mPGp169+7t7kWNuW4le+1P5bky2f0bhJRbtnXS6gavGl3VGkX/zZtew7+yquXUjlRXW9U6aRRC3ai0c1TQanlpKNXMqNZMLduqDdRodRoR18svUq2euhxoFGMF3MrxVeCsHa8HFAXTuil4dEFSXrq6netkVvdfrwZNw97rIqgR6VSrrYoG/VuvJzOquuZ4U36Epn9S2an2TXko/pZ0UaWEdrpG/lMFhioRdAJp5Fl9v7qHajRGdXdQxYZqpNRtZu+993ZBnrprnHDCCe5g04ONRnCMrvlMZwoOVEZqrdExpWNND/PJ0L7VqJLafpWPxgpQy7KXq6tj1wuO9R0aoVbHrC6KqklW7ofe793UvCkjtJ/UIlMf7QMFgMr58CjAVm2sLjYa+dWjLjw6dnU8KiVB66Pu0Kq5077SyJYNpYdJTW2l40G5yfou9SDReaLjRyMZq+eHulypp4S6laqGXCNT6phW2aVCLfZahpbr9V7RSJuqfKiPtln7S60f+l79xKpo0A1TZarriC64Ohd0jutmHV2pkkiy+13np8pKOXLqSaLrhco0UTdyXQt0rGifq2JONbD6v5at/VFfRYqug7pB6tqlAErnr7rK6Xrp9YrQslVRoONGrWeqMFFrncommb8nmtlBx4UqK73RVbVO0V0ZG7LeyV6XtD/UA0nL1PfrZqhrbHRAptfUZV43eu0fraPWWRUM6qasY0/XfqX86PzV8a/jqDFBULrSeCW6N+gY87duJ3ON8R6EdLzrAVatvrHuSanS/VHHvtcipXtqMrR/dC3RMaL9pmNEgYN3XjZ2fXXMiI5zrZfSLTSFUCq9Y7x7lMZ00HOHni10bGnsCC1PD9oaCTnd6TlD116dI0qr07nT2IryVPc718v0v17q+uFNkRWPvl/PznreUSuynnV0z9R9VSM9N/bZV+fb7bff7spF66yyiA7Kdbxpu/Rd+r+CMAW8Xk/QeNsYj56NVOmuXmIqPz1PaoYOtTRr5g4//U3XAy+VQ2WgbUtmiq76aH9ptG3tI92ftb/VC0cV4RpxvKHPf415xo6mZyntD5WX9qny17V/FTtpPCutz1VXXeXuU7pG6n2qAFKgrNhKDVT+nnvxJHvt98Yh0ujpen6L12CQyv4NRKgBhg4d6kZie/bZZyNe12h0er1nz551PjNjxgw3IrRGDywsLHQj3I0fPz70/vvvN2o0cn2nRmv2lquR5DQqa/To5A8++KAbJVQjkLdv3z40fPhwN9quRrPzjy6n5Wn0uTZt2rhlaaQ9P40yuMUWW7iROFdZZRU3Ul6yoyx7I/Bpm7T8tddeO/TCCy/UGXFa63TMMce4kTw1Wp9/RGeNeLjHHnu4Ubg1+mO/fv3cKOX+EZjnz58f2nfffd066X0aXVrrmSmjkWvEzJNOOinUpUuXUGlpqTtOvBHu/SNJRh8v/tEPNTJ4165d3X7acccdQw8//LD7/Jdffhnxvuuvvz601lpruWNHozhrhOurrroq/B6Vqz6nEbcTueGGG9x7r7nmmvBr3npHj8QoCxcudCM56zjT6I46hrfbbrvwKP4NHY3c88wzz7iRh3WslZeXu+P2iy++CP998uTJbtRFlbHKaeuttw599dVXEcuIVcYabV3b5B95VOWk0axVjhoxVSMh77TTTvWORi4arVMj7GvUZO/4jHX+aMTgE044wY0Yru/QuaPRmP1iXS90LmhZOr9T2e/yxBNPhAYNGuTOsxEjRrh9qVkV6huN3KPt13VSy9c5OG7cuNCUKVPqHS3TG3ld+0H7Q/tF+0f7yaNr7pZbbunODa2XRjLV+njXsUR/j8UbjVXrpTLU9+p40XVUo3N6GrPeqVyXPvvss/C1WmX42muv1RldV3Tv2HzzzcPfqVkj9F7/cbrGGmu4cog+XjNVvPP/jjvucNvoH4082WvMJ5984spb14nVVlstdM899yQcjdsv3oixGkHXP1p6stum80znm84djVasUWP9Gru+9957r7vm6PM6ZrS8RM8esZa3YsWK0Pnnn+/WQeuqc07Ho5YfbznpRMeGRnnXuaht886vZMsy1jnZkP0uXC/T93o5cuRI9xyRyNSpU9366n6qc2vw4MHh0dUb++z7xx9/hHbeeWe33vqbZg+5/fbbI47L//u//wtttNFG4e/X6Ov+74+3jfWNxK7Zig466CC3TH1GzwlvvfVWxHu8bdAxrOdxvU+zGPlneol3nbz66qsTji6v2YdOP/308DVcsVP0bDupPv815hk71vOfZiw5+uijw89nur6eddZZEZ975ZVXwiN760fHgtZBMxIkOxp5Mtd+0fO/ykkjwnv3xFhlm8r+9VM5+mc3SkbOnxuU9bzJ0NXi3ticY6Qf1TSqBUOtfsm2sojyHVWTplo4fw8IoLXwem+oNSqZEdeBZKhFRa3o0aPZx6OeQ2qR8UYXRnbs90yTTddL9SRTa7DSQ5WiibrU8q9We+WjA4F1IwfSnfJH1M1Y3ZDUDUoT3ush7uijj04p0BZ1RVIXKQJtAEhMQZYGiVQalroXIjuw31sfDdSnLuGtvVIBaGoE22h1FBgrP+Xmm292A9gpp0P5uYlyrmNJNJoqAOAvyl1UDzENltXYaSCROdjvrY9ysJMZXBNA/ehGDgAAAABAS0/9BQAAAAAA6kewjbRzwHVv2H/f/tHS0SsTfrOxFz5vP05fkPC9EybPde/9Y8GyZlk3AAB+3mJLm339DWlZEAueeNK+H7yGVXz9TcL3Lv34E/feymm/N8u6If1MP/0MdzwDmYycbaAVmjp7sd3y6vf27dR5lp+Xa6NX62pHbL2GlbctSqqyY+bCijqvb7duH/vH9n/NV/jFpDn2xte/27e/zbc5iyqsQ7siW6dfZzvgb4OsU2nd+ai//W2e3fn6D/bzjIVWUlRgm67Zww7eYnUrLuQyBABIHyt++cVmXnKpLfv8c8spKLB2m21q3U4/3fI7dkzq84vfeMNm33CDVf78i+V16mTlu+xinY852nLy8yMqHmbEmdN3tXffsfwuXWL+rXLqVPt13A4Wqqy0fo8+asVD12r0MltS1cxZtuCRR6x0qy2tzZ/zJLdEUL/sk09s4BsNm28aqA9PuUArM3tRhZ1870dWUpTvgtmKyhp77MNfbdKsxXbdoRtZQV7iDi2rdmtvu63fP+K1Xp3aRfyuwHlxRaVtskYP69Wprc2Yv8ye/XSKffzTLLvpiI2tY7u/Au5f/lhop9/3sfXp3M6OGLOmzVm03K3T7/OW2kX7jApw6wEAaLiqP/6wKfvtb7mlpdb1hBOsdtkym3v33bbix5+s/yMPW05hYb2fX/LOOzbt2OOsZNQo63b2We5zc265xarnzbUeMQZq7fz3462wd++I13Lbt4+7fFUCmIL2ysq470l1mS2petYsm3PjjVbQq1edYLvHhRdoImprjSqnTLGFzzxjKyb+aHldu7rjLFRVaaWbb26lY8ZYboLjDJmDYBtZYXFFlU2YPMc2XqNHzL+/+c3vtsGgbtamFbSyPvTez7a8stpuOGxj61q2cqqz1XuW2xn3f2yvTpjmWqgT6dS+jW25duSNOtqRW69hQ/p0tNycnPBr663axQX6z3w6xQ7afPXw63e/OdHatSmwyw9Y39oWFbjXupUX2zXPfW3/+2W2jVg1/WrbAQAr1SxcaEs//tjajxkTs0gWPve8lW6xueWWlGR8kc259Varraiw/o8/ZgU9e7rXitcealMPOdQWPPmUddiz/jmnZ15+uRWtvrr1ufOOcEt2bru2NvfW26zjAQdY0YABEe9vt8mmEa3T9Vny7nu29L33rONhh9rcm2+J+75UlpnO1KvgryeM1iFUXW1LP/jAFr3yqtUuXmQFffta1bRplldeZmW77WrFI0daju+5CpmPnG1khA8m/mHnPPiJ7X31azbu4hftoBvetPvf+clqapOr8Xz2s8l20eNf2Ps//FHnby99MdUuffJLe/WraUmvz4qqGrv2+a9t/JWv2C6XvWyXP/WlC+jTwXs//GGjVusWDrRl3QGdrXfHtvb2d9OTXk5VTa0L2uMZ2rdTRKDtvVZaXGBT5ywJv7Z0RZV9/usc23Jor3CgLVut3duKC/Psne9mpLB1AIBULX79dZt65JH20yab2g9D17aftx5js2+6yUI1NUl9fv6DD9rvJ/7TFr36ap2/LXjsMZt+8sm24Kmnkl6f0PIKm3Huefbj6PVt4oiRNv2001xAnw4Wv/Kqtfvb38KBtrTdcEMr7NfPFr30Yr2fXfHzz67rePkeu0d0Ge+w9z5moZAtfvnlmJ+rWbI04b4IVVXZzIsvtg4H7G+FqySuNE9mmS1NefmTd9/d/Vvd35Wjrx91h4+Vs638ff197p132fyHH3HHsY7nSeN3t4qvvw6/b8HjT7j3Lf/uuzrfOeeWW+37NYdY1cyZ1tyq5861efff79ZPx0NBr96uhbugzyrW8aCDrGS99Qi0W6HMb8ZDVlCLrFqdd12/v8vx/XLSXLv37R9tWWW1Hb5V4hyfPTda1SZOX2iXPPGFXbj3eja8f2f3+nvfz7Brn//GBYLjRvRNen1ufOlba9umwPbfdDX7be5Se/5/U2zWwgq74oD1G3ShXLq8yqqTqDgozM+tN8dZ3bMXLK20QT3L6vxt9V7l9snPs5JanwmT5tiOl7xktaGQdSsrtl1G93c/iVRUVtvyyhorK/mr+9PkWYtdpchqPSLXSd3ZB3Rrbz//kR4PWADQWi148knX6qwH+ty2Jbb0o49tznXXW+2Spdbt1FMSfr7TYYdZxVdf2/STTra8W2+xthts4F5f9MorNuO8f1n7HXewDnvvnfT6/HHhvy23fal1Pu44q5w0yeY/9JBV/T7d+vz33gbdQ2sWL3Ythomoa25u27Zx/64ArGbuXCtea0idv7VZe6gtffudepe//M95qYvXimxVLujW1fK7d7fl39Wdt3rqgQe6LsRqxW278cbW7bRTXWAfbd6991rNokXW+eijXYVAfZJdZirUfb3NkDWtbMcdLShFqw5wXd51LJbvsYeVjBzhXi8ePrzezy18/jmrXbp0ZS+DnBybe8edNu34v9vAV19x21w6dqz9ceGFtvDZ56zNmmtGfva5Z10X/4Ju3ay5qCv88q+/tkXPPW+V06db4SqrWO2KFVY17TcrGbGule28s+V36NBs64PmRbCNjHD6LsOtqCAv/LsCY7UsP/vZFDvwb4OsMP+vv8WSl5trZ+023M564BM7/5HP7NL9RrtcZrVorzewi52049op3eA16Nhl+412/xcFpHe8/oN99OMs22D11C/g/3rkM/tqyryE79t67d528k7D4v593pLl7v8d29UdCE2vqfW9srqm3vLq37XUhvTpa707tXXvf2XCNLvlle9s7uLldliCio0nP57kWsQ3W/Ov7vrzFq9Y+f0xBk1TXrcGTgMANJ1eV15puW3+ugZ32Gsvm3FemWux7nLCPxLmh6qVttfVV9lvhx3u8pH73H2XC+amn3yKtdtkE+t58cUp3UMVEPW9+273fyno1dNmXXGlLXnzTSvdYouUt2/aMcfask8/Tfg+BTU9L70k7t+rZ812/481kJheU+t7bWVl3PJK9HnlJ3tyi9tY2S67WMnoUZbXrp1VfPutzfu/e2zy3vtY/ycet4Ief91Hq2fPtjk33WxdTz3VvTeeVJaZipmXX2Hz7rnHup1ztgUpv3Nn1+VdwXbxOuskHchXT59hq778kuWVrazEL+zf3x0DS957z+U857Vra6VbbmmLnn/eup5ysuXkrnxWU0u3eh50OuRQay46T9QjRN3/LWRWNHCgGxfA5WePGUN+dhYg2EZG8Afay1ZUu4BuaJ+O9sLnU+23OUtt1e6JB/5QgPmvPUfaqfd+ZGc/+KlV19Ta4F7ldtZu67pgPBXKe/YCbRk3sq/LS1bLcUOC7SO2XjOpbuidSusfTXxFda37f6xB0AryV75WWV1bb7B9/l7rRfw+ZlhvO+vBT+2JjyfZTqP6WZf2f3VP9/t6yly7752f3Cjj6/zZc2DlOtXEXSe11KtLPgCg6fgDbde9uKrStSIuePhhq/z1V2szeHDiZRQVWe+bb7KpBxxovx1xpOvWXLz22tbrmqsjukwnQ62YXqDtBf+zrr7Glrz9ToOC7a6nnWa1ixL3ksrv2rXev4dWrKywjjUImrbfvWf5crM4wXZtPZ/PKSp0PQk87bfd1v14SrfaytptvLEbnE1dnXuc/9dgarOu/I8VrLKKle8+vt71T2WZyZp1zTU27667rMuJJ1r5+PGuRTaunJxmGdir/XbbhgNtKRmxskW86re/0gHLdt7JBdvLPv443BNDLd05bdq4ALe5B0HL79bNDbqnnhzkZ2cXgm1kBHVFvuetifbl5Lku2PZbtiL5XGnlDB++9Rp22n8/dr8fNWbNiEA+WT07Rg4Co67dajmONWVWMqK7WDdU0Z8BtSojolX9GYgrwE2FWit2Hd3fDWT21eS5MQdOU472+Y/+z/p1KbUTx60dtU55cddJgX9Dyh8AkLwVP/1ks6691pZ99LHVLvlrTA2pXbw46eWotVStq1MPOsj93u3MMyIC+WQV9o1M21LXbrX8Vv3esDm1Y3X7boicopXbomm1onlBpoK1eHLr+XxoRWU4YI9HQaMqMJZ++GH4tYovv3QBWx/1BEixYSDeMpOldIO5t9zq/j376qvdT32U594c02flR7XQe4G3utn78+x1TCnAVrAdqq11wbcqc9Ty3ZyDoKnlXfnzyukv7N/Pynfd1Yr6J07NQ+tAsI20t2R5lZ1y74duKqsDNhtkPTqUuJZZ5fpq+qkkx0hzND3VZU9+aat0ausCvQsf+59dddCGMeeFbk6LKipdS3siClyVKx6PN93WvCV1a571mgYvS9TlPpYu7Vcud/HyuhUbylU/8/6PrW1RvsuH136KWKc/W+PnLV4eY52Wx+zyDgAIhgKQKfsfYLnt2lmXvx/vWkgV9KlLrVpMQyncRCt/+82mn3KKFQ4Y4Fp4p/39H9b3gfutIEGLcVOrWbDAtbQnokA5r7Q07t/zu3YJd9uOptcU1NXXcuv/fHSXbb1WPHRoUoHkismTw7/PvPJKFzAX9O7tBgiTmvnzw8usmj49YjC3ZJaZLOVot1lrLVv+zTfW8cAD3Sjr9Wmu0ehzcuM8x/imCcvJy7P248bZgkcftdrzzrWKzz933fg1vkBTD4K28LnnrOLzL9w5V7jqQHd81syZTX52liLYRtpTa+qiiio7d/cRbrRrzx8LlqW0HOUca/ordf++ZL/RVllVa/+85wM78/5P7IoD17f2xcl3fZo+b5mt0y9yYDAFs+sNbNgDx4WP/i+QnO3O7du4wcl+nF63O93E3xe4+bMbQpUU4h/4TBYtq3SBtlqtL9svdqWFWrvzcnPspxkLbbMhfz0Q6DO/zlzkup0DAJrGsk8+cQ/7va+/zo127KmclvwMHFI1a5ab/soK8q3PXXe6YHvyvvvZb4ceZn3/e6/llZen1L227fqjw79rsCsFju023dQaQoNjBZGzrUGz8jp2tIpvvq3zt+VffW1FUfNAR/O641d8841rTfZUzZxl1X/8YUV7rBx5uz5Vv/0WMViW8pMVUP+y1VZ13jvtmGNc1+TVP/0kpWUmSxUTmsJsykEH28Jnn7W+u493OceBasJprtSVfN7dd7uxAJa8867bt+pWH62+Y6Ixg6DlFBe7fUd+dnYj2Ebay81deSH2170rUHvusylJL0P50BocTUHxVQduGM47vnifUXbKvR/ZuQ9+6gZNS3aebeWKK5fZy9vWumjEbQ221hBB5WzLxmt0t9cmTHMtzt70X19MmmPT5i21Xdb/q9uSWtKnz1/mWqS9IFkt7Opqr+DY/75HPvjF5VwP6/dXZYemBVPu+9zFK9z82b06xe6WpZZ4jf7++te/2z6brBZu+X79q2lukLpN4sx9DgAIwJ+tgAoGPOrmPP/Bh5JehAYG0+BoGuyp3/33WUH37u71PnfcblOUw33kUW7QtGRbNhc88oiV77pLOG9bo5FbdbW123QTa4igcraldMzWtvCpp61qxoxw67S6YFdOnmwdDzow/D61pKulX62XXst+0WqruVb/BY88ah323NO1rq7cvgddUNl+7Njw56vnzbP8jh0jvnvJ22/b8m+/tQ777x9+rfsFF7ip0qK7d8+/7z7Xpb9wQP+Ul5kKtearckW5+vPuu896/Cv1vO/65JasfE5Rd+ugtVl9ddcav+DRx6xiwgRX2RJrfAEXEFdXW2GfxFOqJTsImlT+8gv52SDYRvpbs3cHa9emwK58eoIboEthoAK3FHqPu3m2Zy+qsMv33yAiKFy1e5ldsNd6rnVW82zvMDK5qTEUgJ5238euVXba3CUu2B6ySgfbYFC3Fs3Zlr02GmjvfjfDTv3vR7bLqH4uoH30w1/dKOOqIPDMWbzcDr/57YjW8o9+nGkPvPuzC4C7lxe7CoA3v5luk2cvtoM3Xz3cTV00kvvE6Qts7Dq9bersxe7Hn8O+4eCVD2Ny0Oar24l3f+DSAbZdt4+bouzxj361EQM6N7g3AAAgseLh61huWZnNOP2MlQFXjrkcYH+X20Q0arlGUO577z0RU0i1WWMNW+WWm23qoYe5ebY77rNPUstToDrl4IOt/Tbbrpz668EHrXjECGvXgMHRgszZls5HHmmLX3rZphx4kHXcf38XSM296y4rGjTIynbdNaK1+tfttq/TWq7RrzUytspEA3mt+Olnm3///W5wsaJVVw2/b/Lee1ubNdZ0657brtR161/wxBOuy3fnI48Iv6/dxhvVWceaRSvvt+qpUDx0rZSXmSq1ive59556R0JvKLUA57Zvb/Mfetjl7qs1uHjYMCvsXXd8mIYo22knm3X55Sv/HacL+fTTTnfjBTQk3zx6EDT18KitqLDKqVPJz4ZDyzbSXvuSQrtgr5F2+6vf2z1vTrR2xQW2xVq9XGvpmQ/U33XKP8/2hqt3t35d6+ZqrdWno11zyEYuGE3WsdsMsTe+mW73vvWj1dTW2t+G9LSjtxnSoPlBg6bW7CsO2MBue/U7u/ONiVaQl2OjBnZ1reeJ8rX7d21vfTu3sze+/t0WLqu0/LwcNxe2RmyP7u6tLuDy8pfT3I+fpkLzB9uqTFDPAeXY3/rKdy4YH7vOKnbIFolHwAUAWKMCpVVuvtlmXXaZzb72Wstr394FHSXrb2C/HXZYUsvQPNuaSkktt9GUT9zv4YdcMJqs7uec7Qaumn399WZVVdZ+++2t+1lnpsU9VK3Z6hY/89LLbNZVV7nW93abbebmqk5mpG1NPaUu+7NvvMlm/vsi13W505FHWJdjjol4n0YN1+jrS99/32qXL7f8Lp3daONdjj3WTYnVEE2xTE9TzQOt8lVlhcp6xr/Odz0celx8cXDB9g7jbNZ//uOCen/X/qYYBE2j0FfPn09+NiLkhPz9ioA0cMB1b9jWw3rb/pslf+NORxMmz3Wty/ccv7l1L2+eQUMAANnt5y22dHMtdzn+OMtkSz/+xKYeeKCt+tprVti7V0uvDjKUgt+fNtnUOh9zdJ0Kj6AGQVOLtoTzszffnPmzEUbLNgAAAIBWZ+ETT5rV1FjZjjs1ySBoGqdA03oxfzbiIdgGAAAA0Gos/egjW/HzLzbn1lutdKstG907ItYgaBoAj/xsJEKwDQAAAKDVmHPjTbbsyy+tZPhw63b22Y1aVqxB0IT8bCSDnG0AAAAAqG8QtD593SBo6k5OfjaSRbANAAAAAPUMgqbR8iPys8eNs+KRI9NiFH2kL7qRAwAAAMh68QZBE/Kz0RC0bAMAAADIatGDoBX07u0GQfPnZxcPH25lO+/cZPOOo/Uh2AYAAACQteINgkZ+NhqLYBsAmkFNba0tXV5tbdsUWF4u+V0AAKTrIGjub+RnIwAE2wDQhMH1kuVVtnBZpc1bstyqa0K2es9y695hZf4XAABIr0HQhPxsBIUB0gCgiYLrisoaq6qptbzcXKuorLZeHUusY2kR5Q0AQBoOgibMn40g0bINAE0QXBcX5FlxYb4V5OfagqUrzHLM1unX2cpKVnZPAwAA6TMIGvnZaAq0bANAQMF1eUmRC679llfV2IqqGhvSpyOBNgAAaTYIWp387N12Zf5sBIZgGwACDK4jlxGyeYuXW/9updaDPG0AAFp+ELT+/cODoAn52WhKdCMHgBS7hSdr5oJlLkd77b6drDB/ZTc1AADQ8oOgub8zfzaaGC3bALJSEC3X9VGedlFhnq3Wo5xAGwCANBoELSI/e8wY95Pra+0GgkKwDSArNHVw7UeeNgAA6TEIWtHAgeFB0IT8bDQngm0ArVJzBteR30ueNgAA6TYImpCfjeZGsA2gVWip4DranEUV1qWsjQ3o1t5yfXlhAACgZQZBE+bPRktggDQAGakpBjRrLObTBgAgvQZBY/5stCRatgFkhHRpuY6HPG0AANJnEDT3HubPRgsj2AaQltI9uI5cV/K0AQBIl0HQ3HsqKqxy6lQr7N/Pynfd1Yr692cHodkRbANIC5kUXEcjTxsAgPQYBE3Iz0a6IGcbQItIx5zrhiBPGwCAFhgErU/fOoOgkZ+NdEOwDaBZtJbgOjpPe+HSFTakT0fr1bFtS68OAABZOQhanfzsceOseOTIOu8BmhvdyAE0iUzuFp4M8rQBAGj5QdCE/GykK4JtAIFo7cF1NPK0AQBo2UHQhPxspDOCbQANkm3BdXSedlFhnq3Wo9wK8+ve+AEAQNMOghaRnz1mjPvJjcrhBloawTaApGRzcO3HfNoAADTTIGj9+9cZBM29j/mzkSEItgHERHAdq0yYTxsAgOYYBK1w1YExBzgjPxuZhGAbgENwnRh52gAAtMwgaEJ+NjINU3+hVftq2gK7671J9v2MxTZ7yQpbVFFlRfm51qdTW9t6zW525KYDrG1RdtY5tcapuJoS82kDANC0g6AV9O4dcxA05s9GpiLYRqt230dT7Oynvon796G9yuzJYza0/LzWH1ASXDcc82kDAND8g6AJ82cjkxFso1X735R59sXUBbZKxxJrV5RvuTk5Nn9Zpd369i82YdpC954b91nXtl+7h7U2BNdBlWPIZi5YZv27ldqgnuXuGAIAAAENgtanb8xB0IT8bGS67Ow/i6wxrHe5ffP7Irv9nV/tp1lLbPHyKqsNRb7ni6nzW0WwTc510yBPGwCA5h0Ezb13/nyrmTPbSkasa2U772z5HTqwG5BxCLbRqp362Ff2xBe/1/uehRVVlokIrpse82kDANC8g6AxfzZaE4JttFp/LFweEWgfslF/23xwF2tTkGcPfjLVnvh85d+iW7rTFcF182I+bQAAgh8ErWjgwJiDoAnzZ6O1IdhGqzV9YUX43x1KCuzcHdYM/37Naz9auiO4bsmyZz5tAACaaxA0IT8brRHBNlqtPh3/6p40f1mV3fDGTza0d7m9+PUMe//nuZZuCK7TB3naAAAEOAha//5xB0ET8rPRWhFso9Xq3K7IdhjW056dMN39fuUrK1uz83JzbFS/jvbJ5HlpF1wv/3Oe69w/57kuLylinutmRp42AADNMwga+dlo7Qi20apdtttQ69KuyF78Zoab8mtw9/b2z60H2f+mzG/2YDuZ4LqM4LpFkacNAEAjB0F7/nmr/L3+QdDc+2tqrHLSJMsrL7Oy3Xa14pEj4wblQKZinm2gBYPr4sJ8Wq7TBPNpAwAQzCBoBb17xx0Ezb2/osIqp061wv79rHzXXa2of3+KHq0SLdtAQGi5zmzkaQMA0LSDoAn52cgmBNtAAxFctx7kaQMA0LSDoJGfjWxEN3IgSXQLb7152guXrrAhfTpar45tW3p1AABIe9GDoKlFu75864j87HHjyM9G1qBlG4iDluvWj/m0AQBoukHQhPxsZDOCbeBPBNfZhzxtAAAaNgha0cCB9Q6CJuRnI9sRbCNrEVxnN/K0AQBomkHQyM8GViLYRtYguIaH+bQBAAh+EDT3GebPBsIIttFqEVwj9nERsnmLl1v/bqXWo0P9eWYAAGSr6EHQClcdWO8gaEJ+NhCJYButBsE1kkGeNgAAwQ6CJuRnA3Ux9RcyFlNxoSF52pZjtk6/zlZWUn83OAAAsn0QtILevRMOghaRn7355lY6ZozlJuhqDmQLWraRMWi5RmOQpw0AQHCDoAn52UD9CLaREWbMX2a/zlxoyytrrKqm1nJzc624IM/KSoqsID+3pVcPaY48bQAAghsETcjPBhIj2EbaW7is0n6asdCqa2oJrtEg5GkDABDMIGjuc/PnW82c2VYyYl0r23lny+/QgeIFYiDYRlqrrK5xgfbyymrrVs7I0Ugd82kDABDMIGjMnw2khmAbaas2FLJJMxfZ7IUVBNpoEPK0AQCofxC0ooEDEw6CJuRnA6kj2EbambtkhZ3xxNf21sRZNqRHqZ0+ZpDl5Sbu0gT4kacNAEDjB0ET8rOBhiHYRloF2be9+6v93/uTbUV1rXvty2mLrE0hhylSR542AAC+QdA+/NAWvfxKSoOgCfnZQMMRxSBtgux7P5hiFVU1EX8rbcMhitSRpw0AQOMGQSM/G2g8Ihm0qFmLltu469+zWYtXxPz7sN5lzb5OyGzkaQMA0PBB0IT8bCAYBNtoUdMWVIQD7fzcHKuuDUX8fUDnti20ZshE5GkDANDwQdCE/GwgOATbaFHDVym3i3ZZyybOWGwPfDKlzt/7d2K6LySPPG0AQLZr6CBoQn42ECyCbbQo5QztMKyn3fr2e/bnmGjWuV2hzVlS6f7dj2AbSSJPGwCQzRozCBr52UDTINhGi9LF/fTHv7Kp85a531fr0jY8Enl5cYF1KEnuJoHsRp42ACCbNXQQNCE/G2g6BNtoUfd9NMVe+PoP9++2hXl2ytar2ZfTFtrjX0y3PUb0Yu8gIfK0AQDZqjGDoAn52UDTygnpLAVawDe/L7RdbnrfqmpWHoJnjh1ko/t3ZF8gJTMXLLOOpUW2dt9OVpif3OAvAAC0tkHQCnr3TnoQNH9+dvHw4Va2886W36FDk64vkI1o2UaLWLS8yo65//NwoL3j2t0JtJEy8rQBANmoMYOgkZ8NNB+CbaRFnvYBo/uwJ5AS8rQBANmmMYOguc/X1FjlpEmWV15mZbvtasUjRyad2w0gdQTbSIs87YK8XPYEkkaeNgAg2zRmEDQhPxtofgTbaPY87Que+y78+z82X9W6tW/DXkBKmE8bAJAtGjsImjB/NtAyCLbRbMjTRhDI0wYAtJTZ199gc2680f1bg4r1vPSSZh0ErWjgwJQGQSM/G2hZBNtoFuRpIwjkaQMAskVjBkET8rOBlkewjWZBnjYaizxtAEA2aOwgaEJ+NpAeCLbR5MjTRhDI0wYAtHaNHQTNP392yYh1mT8baGEE22hS5GkjCORpAwDSUcWECTbr6mus4quvLCc/39ptvJF1PeUUK+jRo9kHQSM/G0g/BNtoMuRpIwjkaQMA0lHFN1/bov1fsFBlpfs9pEaGF160ZZ9/Yf0ff8zyO3VqlkHQ3HczfzaQlpjcGE2GPG0Elafdp0s769EhtRp+AACaUuXPv1jbjTe23rfcbN3OPjvcEl39xx82+5prklvGlCk29847bcmrr1le+zIr7Ns35UBb+dkrfv7ZCvqsYh0POshK1lsv5a7nAJoGLdtoEuRpIwjkaQMA0lV+167W+5qrw4OXqYV71uWXu39rcLPu559vObm5TTYImpCfDaQ3gm0EjjxtBIE8bQBAOisetnZEgKwByTy1ixZZzfz5MbuSxxsE7bvKQntwSZn9VFVoc2vzbHFtrhXlhKxXfpVt1maZ7d9uoZXkqrM6+dlApiDYRqDI00YQyNMGALQ2iQZB+76qyF6qaBfxmWWhHPupqsj9vL+8xO7qMt3yamusctIkyysvs7LddrXikSPpNg6kKYJtBIo8bTQW82kDADJBxVdfW6iqynIKCtzvGhjNk1taankdOqQ0CNrA/Eo7of1c65VfbSU5tZaXY7agNtfuXVxu31UVuWD8zUX5tskfE62wfz8r33VXK+rfvxm3GECqCLYRGPK0EQTytAEAmaB65kybduKJVj5+vFX9/rvNufHG8N9Kx44J52trELSFzzxjKyb+aPndulleeXnM5Q0pXGETqwrtviVlNqmqwJaEcq3WIgc6mzC/xsYyfzaQMQi2EQjytBEE8rQBAJmioE8fW/LW27bktdcjXldA3fWEE1IeBO3C+Z3thYrSer9zea++1mHfMZbbgMHUADQ/pv5Co5GnjSDztAd2L7OyEh4iAADprWTdda3PnXda8cgRllNc7LqOl267jfV74H6znBybd//9tuCxx/WgtHIQtHoC5Fk1eRGB9t5tF9oNnWbYbR2n2TbV08Ov5/fuTaANZBBattFo5GmjscjTBgBkgi7HH+d+/NqOvi/pQdDimVnz1yN5WW6N/bN8nps/u3LqVJvbYROzKu+vzJ8NZBKCbTQKedoIAnnaAIBMl8wgaPH0ygtH07awNs/umFVkgxbPtXd7bWafLixuwrUG0JQIttFg5GkjCORpAwAyXbKDoMXTMa/WxhQvsVf+nPrr1qqeZm16Wt7iHBvVr4N9MnleE605gKZEsI0GIU8bQWA+bQBAJkt1ELT6nFU608oXz7W38rvborwiG9yz3P659SD735T5BNtAhsoJKWoCUvTfDyfbOU9/6/7dtjDPrh4/1Lq1b0M5IqU87ZkLlln/bqU2qGe55eaQhwYAyBzVc+fawuees4rPv7Dcdu1ci3ZOA+9lXn4282cDrQst20gZedoIAnnaAIBM1NBB0OKpnj/faubMthLmzwZaHYJtpIQ8bQSBPG0AQLYNghYraK+aPt1CVZVWOmaM+2H+bKB1IdhG0sjTRhDI0wYAZKKq33+3BU8+aSt+mNigQdD8QjU1VjlpkuWVl1nZbrta8ciRDe6CDiB9EWwjacynjcZiPm0AQEZ2G//mG1v41FNWPWt2owZBE/KzgexBsI2kkKeNIJCnDQDItNHGF7/1li1+9VXVGFuhuo3n5jZ4eeRnA9mFYBsJkaeNIJCnDQBIZ7UrVtjy776z5V99ZUve/8CWffyxmrWteOQIK1ylj+V37tzgZZOfDWQngm3UizxtBIE8bQBAurZcz73rblv88su2fOJEs+rqOu+pmb/A8oev2/DvID8byFoE26gXedpoLPK0AQDpaun779vsq66q9z3Fw4c3ePnkZwPZjWAbcZGnjSCQpw0ASFeaukujitcsWGA5JSUWWrYs8u+DBll+hw4NWjb52QAaPsIDWjXytBEE8rQBAOmsoFcv6/Pfey2/d+86gbYUr7tug1LwKn//3WoWLnBzZ3fYd98GB+wAMhst26iDPG0EgTxtAEC6W/zaa/b7qaf9FWjn54fztgv69bP8Tp1SWh752QD8CLZRB3naaCzytAEA6SxUW2uzr7/B5t58c/i1vI4dzfLyrGb2bPd7SYq52uRnA4hGsI1687T/vvmq1q19G0oJKSFPGwCQrmoWL7bfTz7Flr79dvg1zZ9dusUWtvDZZ8Pdy/N79Ei6R2DNnDkuR7tkxLpWtvPOdBsH4BBsI26e9g5Du9v6/TtSQkgJedoAgHS14pdf7Lejj7GqqVNXvpCTYyUbbGDF66xjOTk5Vjp2rFVNm2aF/fu735OZOqxyyhTLLSm2sp13stJNNrGcwsKm3xAAGYFgGzHztAd2aWsHrt+H0kFKyNMGAGRKfnZOUZELrgtXWSX8nry2bS1v9dVjfl5tETNq8m1SVYG9vbzEVlRW20kLPrOyVftb2Q7j3MjmAOBHsI2Yedqnbr2aFeQxWD2SR542ACCT8rPbb7+95bVvH/9zIbOXKtraO8tLbHJVoU2tzrfK6Il8VtnCbjxsC8srLW3KTQCQoQi2QZ42AkGeNgAgk/KzcwoK6v3st1VFdu78rvW+p//agwi0AcRFsJ3lyNNGEMjTBgBkWn52Il3yqq1tTq0tDeVajoVs5Yg2f32ua2mRnTQmdpdzABD6CWcx8rQRZJ72wO5lVlbCoDAAgPTIz560+x7hQFv52e132MFN55VMoC3d8mrs9i7TbdW8FRZyQXbk587dYU3LJ+UOQD1o2c5i5GmjscjTBgC0hvzsOssJmT27rJ1duaCjVVheuIWq9s+/D+nZ3rZbK7mpwQBkL4LtLMV82ggCedoAgNaQn+23qDbXLprfyd5Y3i78Wv+OxbaiJmTTFy53v5+6zWDLzU2uhRxA9qIbeRYiTxtBIE8bAJBO+dmTdhv/V6Ct/OwNN7TSMWNSCrT/t6KN7T2zZ0SgvefI3vbEsRvZjEUrA+3R/Tvapqt1Dn4jALQ6tGxnGfK0EQTm0wYAZNL82YlUh8xuWVRu9y4p/zM/26ysKM8uHT/Mth26srv4leOH2aeT59k/tx6UdN43gOyWE1L0hazx3w8n2zlPfxueT/vq8UOtW/s2Lb1ayLA87ZkLlln/bqU2qGe55fLAAQDI4PxszZ999twu9n31X89DG/TvYFftNdx6lBUHvt4Asgct21mEPG0EgTxtAEBryM+ONQhafo7ZSWMH2xGbDrA8crIBNBLBdpYgTxtBIE8bAJDp82fXNwjadfuMsKG9y5pq1QFkGYLtLECeNoJAnjYAoDXkZ2sQtHPndbZZtQURg6Cdt+MQKynk0RhAcLiiZAHm00ZjMZ82ACDT87OTGQQNAIJEsN3KkaeNIJCnDQDI5PxsBkED0BIItlsx8rQRBPK0AQCZmp/NIGgAWhLBditFnjaCQJ42ACBT87MX1ubaxQyCBqAFEWy3UuRpo7HI0wYAZGp+NoOgAUgHBNutEHnaCAJ52gCATMvPZhA0AOmEYLuVIU8bQSBPGwCQafnZDIIGIN0QbLci5GkjCORpAwAyKT+bQdAApCuC7VaEPG00FnnaAIBMys9mEDQA6Yxgu5UgTxtBIE8bAJAp+dkMggYg3RFstwLkaSMI5GkDADIhP5tB0ABkCoLtDEeeNoJAnjYAIBPysxkEDUAmIdjOcORpo7HI0wYApHt+NoOgAchEBNsZjDxtBIE8bQBAOudnMwgagExFsJ2hyNNGEMjTBgCkc342g6AByGQE2xmIPG0EgTxtAEC65mdXhcxuXVRu9y4pt5CtDMjLivLs0vHDbNuhPdhxADICwXYGIk8bjUWeNgAgXfOzGQQNQGtBsJ1hyNNGEMjTBgCkW342g6ABaG0ItjMIedoIAnnaAIB0y89mEDQArRHBdoYgTxtBIE8bAJBu+dkMggagtSLYzhDkaaOxyNMGAKRTfjaDoAFo7Qi2MwB52ggCedoAgHTJz2YQNADZgGA7zZGnjSCQpw0ASIf8bAZBA5BNCLbTGHnaCAJ52gCAdMjPZhA0ANmGYDuNkaeNxiJPGwCQDvnZDIIGIBsRbKcp8rQRBPK0AQAtmZ/NIGgAshnBdhoiTxtBIE8bANCS+dkMggYg2xFspxnytBEE8rQBAC2Vn80gaACwEsF2miFPG41FnjYAoKXysxkEDQD+QrCdRsjTRhDI0wYAtER+NoOgAUAkgu00QZ42gkCeNgCgufOzGQQNAGIj2E4D5GkjCORpAwCaOz+bQdAAID6C7TRAnjYaizxtAEBz5mczCBoAJEaw3cLI00YQyNMGADRXfjaDoAFAcgi2WxB52ggCedoAgObKz2YQNABIHsF2CyFPG0EgTxsA0Bz52QyCBgCpI9huIeRpo7HI0wYANEd+NoOgAUDDEGy3APK0EQTytAEATZmfzSBoANA4BNvNjDxtBIE8bQBAU+ZnMwgaADQewXYzIk8bQSBPGwDQlPnZDIIGAMEg2G5G5GmjscjTBgA0VX42g6ABQLAItpsJedoIAnnaAICmyM9mEDQACB7BdjMgTxtBIE8bABB0fjaDoAFA0yHYbmLkaSMI5GkDAILOz2YQNABoWgTbTYw8bTQWedoAgKDzsxkEDQCaHsF2EyJPG0EgTxsAEFR+NoOgAUDzIdhuIuRpIwjkaQMAgsrPZhA0AGheBNtNgDxtBIE8bQBAEPnZDIIGAC2DYLsJkKeNxiJPGwAQRH42g6ABQMsh2A4YedoIAnnaAIDG5mczCBoAtCyC7QCRp40gkKcNAGhMfjaDoAFAeiDYDgh52ggCedoAgMbkZzMIGgCkD4LtgJCnjcYiTxsA0ND8bAZBA4D0Q7AdAPK0EQTytAEADcnPZhA0AEhPBNuNRJ42mjJPWy0Zc2680f27bOedreell1DgAJAlksnPZhA0AEhfBNuNQJ42gkCeNgAg1fxsBkEDgPRHsN0I5GmjscjTBgCkmp/NIGgAkBkIthuIPG0EgTxtAECy+dkMggYAmYVguwHI00ZLzaddMWGCzbr6Gqv46ivLyc+3dhtvZF1POcUKevRgpwBAK87PZhA0AMg8BNspIk8bLZWnXfHN17Zo/xcsVFm58lhUxc8LL9qyz7+w/o8/ZvmdOrFzAKAV5mczCBoAZKbcll6BTEOeNoLK0+7TpZ316FCS9Ocqf/7F2m68sfW+5WbrdvbZlluy8rPVf/xhs6+5hh0DABmYnz3r2uts2nHHhwNt5WeX77GHC7Q1CNoNC8vt6DndbVbtymm+yory7OZ917XLxg+zkkLaTAAgnXGVTgF52mjJPO38rl2t9zVXW07hypZwtXDPuvxy9+9FL79i3c8/33JyqT8DgNaQn80gaACQ+Qi2k0SeNloqT9tTPGztcKAtJSPWDf+7dtEiq5k/n67kAJDh+dlmOfbM0nZ25YKOVmEr7xP5OWYnjR1sR2w6wPJyk6+kBQC0LILtJJCnjSAwnzYAoL78bAZBA4DWhWA7CeRpIx3m06746msLVVW57oWigdE8uaWlltehAzsKADJ0/mwGQQOA1odgOwHytJEu82lXz5xp00480crHj7eq33+3OTfeGP5b6dgx5GsDQAbmZ1fnF9jNC8vt3iXlFrKc8CBol44fZtsOZVpHAMhkBNv1IE8bLZ2n7VfQp48teettW/La6xGv53frZl1POCGANQUANGd+9m81BXb2rC72fXWb8Ps36N/BrtpruPUoK2ZnAECGY+jiOMjTRpB52gO7lyU9n3Y8Jeuua33uvNOKR46wnOJi13W8dNttrN8D91t+587sMABIw/zsSbvvEQ60lZ/dfocdrHid4fbsslLbb2bPcKCtQdBO22aw3Xf4BgTaANBK5IQUVaKO/3442c55+lv377aFeXb1+KHWrf1fNc/IUjpdkuwGrjztmQuWuTztQT3LG9x9HADQevKzl7Qrt4vnd7I3lrcL/61/x2K7bp8RNrR3WQutMQCgKdCNPAbytBEtZ/Kvln/GCWYrVlho8JoWWmMtq11jiIVWX9OstH3Ee1V/VVlda/OXrmh0njYAoPXkZ39eW2rnzuxss2pXDnQpe47sbeftOMRKCnkkA4DWhit7FPK0EUvOV19Yzvx5K//9+admn3/65+ynZqG2ba169SE2/7QLbUUox2pDZoX5udahbWGj87QBAJmfn50/bB27cXEHBkEDgCxDsO1Dnjbiqd18a6v55kvL/d8nlrN0acTf9HvB559Y24/esR477GDtSwqsXZsCKynKp0UbALJ8/uw/evS3s2czCBoAZCOCbR/m00ZEbvbvv1muWrT//MmdN7eeMynfhu64pRX1IN8OALJJvPzs0u22txfye9qVMztaxZ99oTQI2kljB9sRmw6wvFzSiwCgtSPY/hN52lku1eA6akoujRJe2IP5UAEgm8TLz67921Z21pLu9sYSBkEDgGxGsE2ednZKMbjWVFslI0eYhcyWvvde+PW2m25iva662vLatW2mFQcApHN+9vdrrG/nzuvCIGgAAIJt8rSzRAOD65JRo6ztqFHWZs01rWbJEvtp403C7+mwz97W7cwzLSefOisAyPb87OIxY+2usqF279xyC9nKLuJlRXl26fhhtu1Qej4BQDbK+iiBPO1WKoDgOqfgr6lZJK+szIqHDrUVP/1kXf5+vHU44ADLYUovALBsz89eMnZHO7FqoH2/pE349Q36d7Cr9hpuPcqKW2htAQAtLSekpt0sztPe5ab3rapmZRGcMXaQrd+/Y0uvFtIkuI79NSELVVVZbmEh+wkAski8/Ox3Ru9gly/tziBoAIA6srZlm/m0M1wzBdd1lpOTYzkE2gBg2Z6fnbv+hnZRn63sjaWl4ff171hs1+0zwob2ZmYKAECWBtvkaWegFgquAQDZLVZ+9h+bj7NTS0barBV/3Vf2HNnbzttxiJUUZuWjFQAghqy8I5CnnQEIrgEAaZafnduxoz296d52s/WzUC2DoAEA6pd1wTbzaacpgmsAQBrnZ1evOsjOHrqXTbD24dcYBA0AUJ+sCrbJ004jBNcAgAzJz548YjP7Z69trOLPx6b8HLOTxg62IzYdYHm5K1u4AQDI2mCbPO0W3wHkXAMAMio/2wqL7JEN97C7y4eF38MgaACAZGVNsE2edjMjuAYAZHB+dmWHznbGqIPtu+Ju4dcYBA0AkIqsCLbJ024GBNcAgFaSnz2l75p2wtB9bXl+kfu9rCjPLh0/zLYd2qMF1xQAkGlafbBNnnYTIbgGALSy/OyQ5dizQ8fazQO2dLnawiBoAICGatXBNnnagRYmOdcAgFabn11V2MYuHrmvfdR1jfAgaCdvM9gO34RB0AAADdOqg23ytBuB4BoAkCX52bPKutmpow6xmW07ud8ZBA0AEIRWG2yTp50igmsAQBbmZ3/Ue227dJ09bcWf+dkMggYACEqrDLbJ004CwTUAIIvzs2stx+4asp09PvBvLj+bQdAAAEFrdcE2edpxC4acawBAVorOz15aWGwXjdzfvug6yP3OIGgAgKbQ6oJt8rT/RHANAMhysfKzJ7fvYf8afZDLz2YQNABAU2pVwXZW52kTXAMAUG9+9lu91rFrhu/u8rMZBA0A0NRaTbCddXnaBNcAAKSQn729PT5wM5efzSBoAIDm0CqC7azI0ya4BgAg5fzsRQUldul6+7n8bAZBAwA0p1YRbLfKPG2CawAAGpWf/Wv7HnbBn/nZDIIGAGhuGR9st5o8bYJrAAACz8+uKSiy07cZbIdvMsDycnMoYQBAs8noYDuj87QJrgEACDw/u8Zy7O4/87P7dyqx6/YZYUN7l1HSAIBml7HBdsblaRNcAwDQbPnZDIIGAGhpGRtsp32eNsE1AADNnp+9vGNXu2X3YbbNWj0ofQBAi8rIYDst87QJrgEAaNH87HVX625X7TXcepQVsycAAC0u44LttMnTJrgGACAt8rOfXm0zO3nbNRgEDQCQVjIq2G7RPG2CawAAWjY/+5TTLFQRmZ+9YPAwe5JB0AAAaSijgu1mzdMmuAYAIK3zs/+22TA7b8chVlKYUY8zAIAskTF3pybP0ya4BgAg7fKzp510si17552I/Oy719/LLth7FIOgAQDSWn7W5mkTXAMAkNb52b8eebTZtN8i8rOnb72zPb33ugyCBgBIe/lZk6dNcA0AQEZY9OqrNvWU0yxvecXK3wtK7IpR+9lWB+xol28ywPJyc1p6FQEAyPxgu8F52gTXAABkXH72b1dda0vvuM3yfPnZd489ys47cqwN7V3WwmsIAEArCbZTytMmuAYAIKPzs7895h9W8OmHEfnZ0w49we4dP4JB0AAAGSc/Y/O0Ca4BAGgVlkz8yb4/9EhrN2dGOD/7wWE72CZn/8OOHtqzpVcPAIDWFWyf89Q3kXnao1cxmzbVcr/6wnL+/MmdNzfu53OKi61k5AgrGTXK2o4aZW3WXNNyCgqacQsAAEAiPz/5vC0+92xrV7U8nJ/9zA5H2XGn7ccgaACAjJYT0ghkaaa6ptbWPPclNzDKmD8m2P5t5ljx918RXAMA0IrM/2WyTdthnOXX1rjfJ7XvYbNOu9D232VDBkEDAGS8tGzZXrqi2rZfq7ttc/1p1mfetJjvoeUaAIDMFaqutkUffWw5tbXu90/7j7Dh111p263WvaVXDQCA1htsz1ywzHYc0tW65lWFXyO4BgCg9aiaPt1K5s2230873+YVtrPdxm9lbYtI9wIAtB5p2Y18RVWNVdXUWv6Mabb88y+saOCq5FwDANDK1CxZYnnt2rX0agAAkD3BNgAAAAAAmSy3pVcAAAAAAIDWhmAbAAAAAICAEWwDAAAAABAwgm0AAAAAAAJGsA0AAAAAQMAItgEAAAAACBjBNgAAAAAAASPYBgAAAAAgYATbAAAAAAAEjGAbAAAAAACCbQAAAAAA0hst2wAAAAAABIxgGwAAAACAgBFsAwAAAAAQMIJtAAAAAAACRrANAAAAAEDACLYBAAAAAAgYwTYAAAAAAAEj2AYAAAAAIGAE2wAAAAAABIxgGwAAAACAgBFsAwAAAAAQMIJtAAAAAAACRrANAAAAAEDACLYBAAAAAAgYwTYAAAAAAAEj2AYAAAAAIGAE2wAAAAAABIxgGwAAAACAgBFsAwAAAABgwfp//X9i3bpcnOAAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1200x500 with 2 Axes>"
       ]
@@ -140,7 +163,27 @@
    "cell_type": "markdown",
    "id": "9a3dc8b0",
    "metadata": {},
-   "source": "## 2. How UXarray Handles It\n\nUXarray 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\nThe 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\nMost 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."
+   "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",
@@ -148,10 +191,10 @@
    "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"
+     "iopub.execute_input": "2026-07-16T23:25:20.345008Z",
+     "iopub.status.busy": "2026-07-16T23:25:20.344852Z",
+     "iopub.status.idle": "2026-07-16T23:25:20.652953Z",
+     "shell.execute_reply": "2026-07-16T23:25:20.652492Z"
     }
    },
    "outputs": [
@@ -219,10 +262,10 @@
    "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"
+     "iopub.execute_input": "2026-07-16T23:25:20.654640Z",
+     "iopub.status.busy": "2026-07-16T23:25:20.654484Z",
+     "iopub.status.idle": "2026-07-16T23:25:21.118617Z",
+     "shell.execute_reply": "2026-07-16T23:25:21.118100Z"
     }
    },
    "outputs": [
@@ -254,10 +297,10 @@
    "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"
+     "iopub.execute_input": "2026-07-16T23:25:21.120318Z",
+     "iopub.status.busy": "2026-07-16T23:25:21.120124Z",
+     "iopub.status.idle": "2026-07-16T23:25:22.758428Z",
+     "shell.execute_reply": "2026-07-16T23:25:22.757817Z"
     }
    },
    "outputs": [
@@ -352,16 +395,16 @@
    "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"
+     "iopub.execute_input": "2026-07-16T23:25:22.760072Z",
+     "iopub.status.busy": "2026-07-16T23:25:22.759945Z",
+     "iopub.status.idle": "2026-07-16T23:25:24.562916Z",
+     "shell.execute_reply": "2026-07-16T23:25:24.562151Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1400x550 with 2 Axes>"
       ]
@@ -515,7 +558,23 @@
    "cell_type": "markdown",
    "id": "3138ae9a",
    "metadata": {},
-   "source": "## 4. Where It Is Used in UXarray\n\nCompensated 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\nIf 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."
+   "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": {
@@ -534,7 +593,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,
diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py
index 1d0c5592d..c5da3e439 100644
--- a/uxarray/grid/arcs.py
+++ b/uxarray/grid/arcs.py
@@ -14,12 +14,10 @@
 # unit-vector inputs this covers rounding error in the compensated cross product.
 _PREDICATE_ZERO_TOL = 1e-15
 
-# Default tolerance for the on_minor_arc collinearity and interval tests.
-# Intentionally tighter than AccuSphGeom's 1e-8 default: using 1e-10 keeps
-# borderline near-endpoint candidates out of the valid set, which produces
-# better accuracy on the AccuSphGeom baseline suite (err < 1e-15 vs ~1e-10
-# with the looser C++ default). The C++ tolerance was tuned for SIMD batch
-# throughput; the scalar Python path is more sensitive to spurious candidates.
+# Tolerance for the on_minor_arc collinearity and interval tests. Tighter than
+# AccuSphGeom's 1e-8 default: 1e-10 keeps borderline near-endpoint candidates
+# out of the valid set, which is more accurate on the baseline suite for the
+# scalar path here.
 _ON_MINOR_ARC_TOL = 1e-10
 
 
diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
index 289551496..7dedb36c8 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -23,9 +23,7 @@
     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
@@ -41,9 +39,7 @@
 _SOUTH_POLE = np.array([0.0, 0.0, -1.0])
 
 
-# ---------------------------------------------------------------------------
 # Per-face GCA bounds helpers (accurate path).
-# ---------------------------------------------------------------------------
 
 
 @njit(cache=True)
diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 94f8974ec..45b4d62cd 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -16,15 +16,9 @@
     two_sum,
 )
 
-# ---------------------------------------------------------------------------
-# Edge screeners (pre-existing, unrelated to the EFT intersection kernels below).
-#
-# These two functions are fast O(n) passes used by Grid.get_edges_at_constant_*
-# to identify candidate edges before the expensive GCA intersection is computed.
-# "no_extreme" means arc z-extrema along the great circle are not considered —
-# only the endpoint z/lon values are checked.  They are not part of the
-# AccuSphGeom-derived EFT stack.
-# ---------------------------------------------------------------------------
+# Edge screeners: fast O(n) passes used by Grid.get_edges_at_constant_* to
+# identify candidate edges before the expensive GCA intersection. "no_extreme"
+# means arc z-extrema along the great circle are not considered.
 
 
 @njit(parallel=True, nogil=True, cache=True)
@@ -309,11 +303,20 @@ def faces_within_lat_bounds(lats, face_bounds_lat):
 
 @njit(cache=True, inline="always")
 def _accux_gca(w0, w1, v0, v1):
-    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_gca``).
+    """Compute the candidate intersection points of two great-circle arcs.
+
+    Pure numerical kernel (mirrors AccuSphGeom ``accux_gca``).
 
     Computes the two antipodal candidate intersection points of the great-circle
     arcs w0-w1 and v0-v1.  No branching, no validity filtering.
 
+    Parameters
+    ----------
+    w0, w1 : np.ndarray, shape (3,)
+        Cartesian endpoints of the first arc.
+    v0, v1 : np.ndarray, shape (3,)
+        Cartesian endpoints of the second arc.
+
     Returns
     -------
     pos, neg : np.ndarray, shape (3,)
@@ -359,7 +362,9 @@ def _accux_gca(w0, w1, v0, v1):
 
 @njit(cache=True)
 def _try_gca_gca_intersection(w0, w1, v0, v1):
-    """Layer 2 — batch/status layer (mirrors AccuSphGeom ``try_gca_gca_intersection``).
+    """Select the valid great-circle intersection and report a status code.
+
+    Batch/status layer (mirrors AccuSphGeom ``try_gca_gca_intersection``).
 
     Calls the pure numerical kernel, applies integer mask arithmetic to determine
     validity, selects the output point without if/else branching in the hot path.
@@ -405,11 +410,24 @@ def _try_gca_gca_intersection(w0, w1, v0, v1):
 
 @njit(cache=True)
 def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
-    """Layer 3 — dispatcher / convenience API.
+    """Return the intersection points of two great-circle arcs.
+
+    Dispatcher / convenience API. Calls the batch/status layer and packages
+    results into UXarray's existing array-returning API. Coplanar/shared-endpoint
+    handling lives here, outside the numerical core.
+
+    Parameters
+    ----------
+    gca_a_xyz : numpy.ndarray
+        First great-circle arc as two Cartesian endpoints, shape ``(2, 3)``.
+    gca_b_xyz : numpy.ndarray
+        Second great-circle arc as two Cartesian endpoints, shape ``(2, 3)``.
 
-    Calls the batch/status layer and packages results into UXarray's existing
-    array-returning API (0, 1, or 2 rows).  Coplanar/shared-endpoint handling
-    lives here, outside the numerical core.
+    Returns
+    -------
+    numpy.ndarray
+        Intersection points, shape ``(2, 3)``, with unused rows filled with NaN
+        (0, 1, or 2 valid rows).
     """
     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")
@@ -454,12 +472,13 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
 
 @njit(cache=True, inline="always")
 def _accux_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z):
-    """Layer 1 (scalar) — allocation-free numerical kernel.
+    """Compute the constant-latitude intersection candidates, scalar in/out.
 
-    Same compensated AccuSphGeom sequence as :func:`_accux_constlat`, but takes
-    the two arc endpoints as six scalars and returns the two candidate points as
-    six scalars (``pos`` xy and ``neg`` xy; the z of both candidates is
-    ``const_z``). Returning scalars instead of ``np.empty(3)`` arrays lets Numba
+    Allocation-free numerical kernel. Same compensated AccuSphGeom sequence as
+    :func:`_accux_constlat`, but takes the two arc endpoints as six scalars and
+    returns the two candidate points as six scalars (``pos`` xy and ``neg`` xy;
+    the z of both candidates is ``const_z``). Returning scalars instead of
+    ``np.empty(3)`` arrays lets Numba
     keep everything in registers, so a batch loop over many edges does no
     per-point heap allocation. This is the preferred entry point for hot loops.
 
@@ -498,14 +517,22 @@ def _accux_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z):
 
 @njit(cache=True, inline="always")
 def _accux_constlat(x1, x2, const_z):
-    """Layer 1 — pure numerical kernel (mirrors AccuSphGeom ``accux_constlat``).
+    """Compute the two constant-latitude intersection candidates as arrays.
 
-    Array-returning wrapper around :func:`_accux_constlat_scalar`. Computes the
+    Pure numerical kernel (mirrors AccuSphGeom ``accux_constlat``). An
+    array-returning wrapper around :func:`_accux_constlat_scalar`. Computes the
     two candidate intersection points between the great-circle arc defined by
     unit vectors *x1*, *x2* and the constant-latitude plane z = const_z. No
     branching, no validity filtering. For allocation-free hot loops call
     :func:`_accux_constlat_scalar` directly.
 
+    Parameters
+    ----------
+    x1, x2 : np.ndarray, shape (3,)
+        Cartesian endpoints of the great-circle arc.
+    const_z : float
+        Constant-latitude plane, given as the Cartesian z value ``sin(lat)``.
+
     Returns
     -------
     pos, neg : np.ndarray, shape (3,)
@@ -528,7 +555,9 @@ def _accux_constlat(x1, x2, const_z):
 
 @njit(cache=True)
 def _try_gca_const_lat_intersection(gca_cart, const_z):
-    """Layer 2 — batch/status layer (mirrors AccuSphGeom ``try_gca_constlat_intersection``).
+    """Select the valid constant-latitude intersection and report a status code.
+
+    Batch/status layer (mirrors AccuSphGeom ``try_gca_constlat_intersection``).
 
     Calls the pure numerical kernel, computes integer validity masks (0 or 1)
     for each candidate using finiteness and arc-membership tests, then selects
@@ -609,13 +638,27 @@ def _snap_const_lat_endpoint_xy(px, py, a0, a1, a2, b0, b1, b2, const_z):
 
 @njit(cache=True)
 def gca_const_lat_intersection(gca_cart, const_z):
-    """Layer 3 — dispatcher / convenience API.
+    """Return the intersection points of a great-circle arc and a latitude.
+
+    Dispatcher / convenience API. Runs the numerical kernel, validity masks,
+    endpoint snapping, and packaging into UXarray's NaN-filled (2, 3) format
+    entirely on scalars, so the only heap allocation is the returned array. All
+    UXarray-specific branching lives here so the numerical core stays uniform.
+    See ``_try_gca_const_lat_intersection`` for the array-returning form used by
+    the layer benchmarks.
 
-    Runs the numerical kernel, validity masks, endpoint snapping, and packaging
-    into UXarray's NaN-filled (2, 3) format entirely on scalars, so the only heap
-    allocation is the returned array. All UXarray-specific branching lives here so
-    the numerical core stays uniform. See ``_try_gca_const_lat_intersection`` for
-    the array-returning form used by the layer benchmarks.
+    Parameters
+    ----------
+    gca_cart : numpy.ndarray
+        Great-circle arc as two Cartesian endpoints, shape ``(2, 3)``.
+    const_z : float
+        Constant-latitude plane, given as the Cartesian z value ``sin(lat)``.
+
+    Returns
+    -------
+    numpy.ndarray
+        Intersection points, shape ``(2, 3)``, with unused rows filled with NaN
+        (0, 1, or 2 valid rows).
     """
     res = np.empty((2, 3))
     res.fill(np.nan)
@@ -665,7 +708,19 @@ def gca_const_lat_intersection(gca_cart, const_z):
 
 @njit(cache=True)
 def get_number_of_intersections(arr):
-    """Returns the number of intersection points for the output of the gca-const-lat intersection."""
+    """Return the number of intersection points in a gca-const-lat result.
+
+    Parameters
+    ----------
+    arr : numpy.ndarray
+        Output of :func:`gca_const_lat_intersection`, shape ``(2, 3)`` with
+        unused rows filled with NaN.
+
+    Returns
+    -------
+    int
+        Number of non-NaN intersection points (0, 1, or 2).
+    """
     row1_is_nan = np.all(np.isnan(arr[0]))
     row2_is_nan = np.all(np.isnan(arr[1]))
 
diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index 09ce972f1..de5af6529 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -1,68 +1,32 @@
 """Compensated floating-point primitives for accurate spherical geometry.
 
-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.
-
-Naming note
------------
-The term "error-free transformation" (EFT) strictly applies to ``two_sum``
-and ``two_prod``, which capture their rounding errors exactly so that
-``hi + lo`` equals the mathematical result with zero information loss.
-``diff_of_products``, ``accucross``, and ``accucross_pair`` use those EFT
-building blocks to achieve near-double precision for cross products, but they
-are compensated algorithms, not zero-error transformations.
-
-All functions are ``@njit``-compiled. ``two_prod`` uses a single fused
-multiply-add (FMA) for its error term on hardware that supports it (selected at
-import time and validated to be bit-exact), falling back to the portable
-Veltkamp split otherwise — so there is no hard FMA dependency, but FMA is used
-when available (~2x faster in the compensated kernels).
-
-These primitives are a Python/Numba port of the AccuSphGeom C++ library:
+Cross and dot products over nearly-parallel unit vectors suffer catastrophic
+cancellation in double precision, which degrades GCA-GCA and constant-latitude
+intersections and the point-in-polygon ray test. These ``@njit`` primitives
+recover near-double precision using error-free transformations (``two_sum``,
+``two_prod``) and compensated algorithms built on them. ``two_prod`` uses a
+hardware FMA when one is available (validated bit-exact at import time) and
+falls back to the Veltkamp split otherwise, so there is no hard FMA dependency.
+
+Python/Numba port of the AccuSphGeom C++ library (EFT tier only; the adaptive
+Shewchuk predicate and exact-arithmetic fallback tiers are not ported):
 
     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. The
-compensated cross-product routines are roughly twice as accurate as direct
-floating-point cross products while retaining the same vectorizable operation
-structure; callers that need the full robustness stack should add an adaptive
-predicate or exact-arithmetic fallback.
+    Operations. EGUsphere preprint egusphere-2026-636.
+    Chen, H. Great Circle Arc Intersection and Constant Latitude Intersection
+    on the Sphere. SIAM J. Sci. Comput. https://doi.org/10.1137/25M1737614
+    Reference implementation: https://github.com/hongyuchen1030/AccuSphGeom
 """
 
 import math
 
 from numba import njit
 
-# ---------------------------------------------------------------------------
-# Fused multiply-add (FMA) support.
-#
-# ``two_prod`` needs the exact rounding error of ``a * b``. On hardware with an
-# FMA instruction this is a single op: ``e = fma(a, b, -p)`` where ``p = a*b``.
-# Without FMA we fall back to the portable Veltkamp split (no hardware
-# dependency). We expose an LLVM ``fma`` intrinsic through Numba and validate at
-# import time that it both compiles and yields a bit-exact error-free transform;
-# if anything fails (older toolchain, unsupported target, or a non-exact FMA),
-# ``_HAS_FMA`` stays False and the Veltkamp path is used. This keeps the
-# library's "no FMA dependency" guarantee while using FMA where it is available.
-# ---------------------------------------------------------------------------
+# Fused multiply-add (FMA) support. ``two_prod`` needs the exact rounding error
+# of ``a * b``; with hardware FMA this is ``e = fma(a, b, -p)``, otherwise we
+# fall back to the portable Veltkamp split. The LLVM ``fma`` intrinsic is
+# validated at import time; if it is unavailable or non-exact, ``_HAS_FMA``
+# stays False and the Veltkamp path is used.
 try:
     from numba.core import types as _nb_types
     from numba.extending import intrinsic as _nb_intrinsic
@@ -433,14 +397,8 @@ def accucross_pair(
     return x_hi, y_hi, z_hi, x_lo, y_lo, z_lo
 
 
-# ---------------------------------------------------------------------------
-# Compensated dot products and sum-of-squares (fixed small sizes)
-#
-# These port accusphgeom::numeric::compensated_dot_product and
-# accusphgeom::numeric::sum_of_squares_c from eft.hpp, using our Veltkamp-
-# splitting two_prod instead of FMA.  Fixed-size variants are used because
-# Numba does not support generic runtime-length accumulations inside @njit.
-# ---------------------------------------------------------------------------
+# Compensated dot products and sum-of-squares, fixed small sizes (Numba does not
+# support generic runtime-length accumulations inside @njit).
 
 
 @njit(cache=True, inline="always")
@@ -505,6 +463,11 @@ def _sum_of_squares_c(hi, lo):
         Numba specializes this per tuple length at compile time and keeps the
         tuples register-resident (no allocation) — the direct analog of the C++
         template. Used for both nx²+ny² (denominator) and nx²+ny²+nz² (|n|²).
+
+    Returns
+    -------
+    tuple of float
+        The compensated squared norm as a ``(hi, lo)`` pair.
     """
     n = len(hi)
     s_hi = 0.0

From a7d8e8871dbf20a691d5fd7a5ebeab2c276fa73d Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Thu, 16 Jul 2026 19:08:59 -0500
Subject: [PATCH 36/51] o Remove benchmark image from repo

---
 benchmarks/thread_scaling_constlat.png | Bin 75489 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 benchmarks/thread_scaling_constlat.png

diff --git a/benchmarks/thread_scaling_constlat.png b/benchmarks/thread_scaling_constlat.png
deleted file mode 100644
index 11fb3ac3854d1f354006148001d3942e9e7a285d..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From a5fd60308a08929dad998b7db18940f2f8be806b Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Fri, 17 Jul 2026 16:52:53 -0500
Subject: [PATCH 37/51] Split out point-in-face and lat-lon bounds to a
 separate PR

---
 .../geometry/test_accusphgeom_baseline.py     |  73 ----
 uxarray/grid/bounds.py                        | 372 +-----------------
 uxarray/grid/point_in_face.py                 | 312 +++------------
 3 files changed, 55 insertions(+), 702 deletions(-)

diff --git a/test/grid/geometry/test_accusphgeom_baseline.py b/test/grid/geometry/test_accusphgeom_baseline.py
index e704f2aff..94565c2c6 100644
--- a/test/grid/geometry/test_accusphgeom_baseline.py
+++ b/test/grid/geometry/test_accusphgeom_baseline.py
@@ -6,8 +6,6 @@
 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
@@ -16,7 +14,6 @@
 
     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
@@ -26,13 +23,6 @@
 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(
@@ -143,66 +133,3 @@ 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 7dedb36c8..626946244 100644
--- a/uxarray/grid/bounds.py
+++ b/uxarray/grid/bounds.py
@@ -1,5 +1,3 @@
-import math
-
 import numpy as np
 import pandas as pd
 import xarray as xr
@@ -11,11 +9,6 @@
     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,
@@ -23,336 +16,6 @@
     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,
@@ -420,30 +83,17 @@ def _populate_face_bounds(
     """
     grid.normalize_cartesian_coordinates()
 
-    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_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 071a6caa1..a622eb8dc 100644
--- a/uxarray/grid/point_in_face.py
+++ b/uxarray/grid/point_in_face.py
@@ -1,303 +1,79 @@
 from __future__ import annotations
 
-import math
 from typing import TYPE_CHECKING
 
 import numpy as np
 from numba import njit, prange
 
-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
+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
 
 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 _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.
-    """
-    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.
+def _face_contains_point(face_edges: np.ndarray, point: np.ndarray) -> bool:
     """
-    # 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.
+    Determine whether a point lies within a face using the spherical winding-number method.
 
-    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.
+    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.
 
     Parameters
     ----------
-    q : np.ndarray, shape (3,)
-        Query point (unit vector).
-    polygon : np.ndarray, shape (n, 3)
-        Polygon vertices on the unit sphere, ordered.
+    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.
 
     Returns
     -------
-    int
-        Location code: _LOC_OUTSIDE (0), _LOC_INSIDE (1),
-        _LOC_ON_VERTEX (2), _LOC_ON_EDGE (3).
+    inside : bool
+        True if the point is inside the face or lies exactly on a node/edge; False otherwise.
     """
-    n = polygon.shape[0]
+    # 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
 
-    # 1. Vertex coincidence check.
-    for i in range(n):
-        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
+    n = face_edges.shape[0]
 
-    # 2. Edge membership check.
+    total = 0.0
+    p = point
     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
+        a = face_edges[i, 0]
+        b = face_edges[i + 1, 0] if i + 1 < n else face_edges[0, 0]
 
+        vi = a - p
+        vj = b - p
 
-@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.
+        # check if you’re right on a vertex
+        if np.linalg.norm(vi) < ERROR_TOLERANCE or np.linalg.norm(vj) < ERROR_TOLERANCE:
+            return True
 
-    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.
+        ang = _small_angle_of_2_vectors(vi, vj)
 
-    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.
+        # 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
 
-    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
+        total += sign * ang
+
+    return np.abs(total) > np.pi
 
 
 @njit(cache=True)

From e87873f33e46c9c71199b025b9c952b206e6edd1 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 02:54:45 -0500
Subject: [PATCH 38/51] o computing: match AccuSphGeom acc_sqrt_re exactly
 (residual order, branch-free)

---
 uxarray/utils/computing.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/uxarray/utils/computing.py b/uxarray/utils/computing.py
index de5af6529..b41bd9c68 100644
--- a/uxarray/utils/computing.py
+++ b/uxarray/utils/computing.py
@@ -483,7 +483,7 @@ def _sum_of_squares_c(hi, lo):
     return _fast_two_sum(s_hi, (2.0 * (r_hi + r_lo)) + s_lo)
 
 
-@njit(cache=True, inline="always")
+@njit(cache=True, inline="always", error_model="numpy")
 def acc_sqrt_re(value, error=0.0):
     """Accurate square root: return (root, correction) s.t. root+correction ≈ sqrt(value+error).
 
@@ -508,14 +508,14 @@ def acc_sqrt_re(value, error=0.0):
     correction : float
         Additive correction; root + correction ≈ sqrt(value + error) to ~1 ulp.
     """
-    # Negative value means no real intersection; return NaN so that the
-    # isfinite mask in the status layer rejects this candidate without a branch.
-    if value < 0.0:
-        return math.nan, 0.0
+    # Branch-free, matching AccuSphGeom acc_sqrt_re exactly. Negative value
+    # yields nan via math.sqrt and root==0 yields nan via the 0/0 correction,
+    # both under error_model="numpy"; the isfinite mask in the status layer
+    # rejects such candidates.
     root = math.sqrt(value)
-    if root == 0.0:
-        return 0.0, 0.0
     sq_hi, sq_lo = two_prod(root, root)
-    residual = (value - sq_hi) + (error - sq_lo)
+    # Residual accumulation order matches AccuSphGeom acc_sqrt_re exactly:
+    # (value - square.hi) - square.lo + error.
+    residual = (value - sq_hi) - sq_lo + error
     correction = residual / (2.0 * root)
     return root, correction

From 4af6ed1a919c1f31c1b2bc4f084ee06580f7df9f Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 02:54:45 -0500
Subject: [PATCH 39/51] o intersections: compensated norm in AccuXGCA kernel,
 branch-free mask validity per AccuSphGeom

---
 uxarray/grid/intersections.py | 58 +++++++++++++++++++----------------
 1 file changed, 31 insertions(+), 27 deletions(-)

diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py
index 45b4d62cd..f923bd1bc 100644
--- a/uxarray/grid/intersections.py
+++ b/uxarray/grid/intersections.py
@@ -301,7 +301,7 @@ def faces_within_lat_bounds(lats, face_bounds_lat):
     return candidate_faces
 
 
-@njit(cache=True, inline="always")
+@njit(cache=True, inline="always", error_model="numpy")
 def _accux_gca(w0, w1, v0, v1):
     """Compute the candidate intersection points of two great-circle arcs.
 
@@ -345,10 +345,13 @@ def _accux_gca(w0, w1, v0, v1):
     vx = vx_hi + vx_lo
     vy = vy_hi + vy_lo
     vz = vz_hi + vz_lo
-    vn = math.sqrt(vx * vx + vy * vy + vz * vz)
-    # Use np.inf safely when vn==0 (coplanar arcs): the resulting pos/neg
-    # will be non-finite, so the status layer marks them invalid without branching.
-    inv = 1.0 / vn if vn != 0.0 else np.inf
+    # Compensated norm: sum_of_squares_c over the (hi, lo) vector, then acc_sqrt_re
+    # folding the low part into the root, matching AccuSphGeom accux_gca. n = root.hi.
+    sum_hi, sum_lo = _sum_of_squares_c((vx_hi, vy_hi, vz_hi), (vx_lo, vy_lo, vz_lo))
+    vn, _ = acc_sqrt_re(sum_hi, sum_lo)
+    # vn==0 (coplanar arcs) yields inf via IEEE division under error_model="numpy",
+    # so pos/neg become non-finite and the status layer masks them out. Branch-free.
+    inv = 1.0 / vn
     pos = np.empty(3)
     pos[0] = vx * inv
     pos[1] = vy * inv
@@ -360,7 +363,7 @@ def _accux_gca(w0, w1, v0, v1):
     return pos, neg
 
 
-@njit(cache=True)
+@njit(cache=True, error_model="numpy")
 def _try_gca_gca_intersection(w0, w1, v0, v1):
     """Select the valid great-circle intersection and report a status code.
 
@@ -377,19 +380,19 @@ def _try_gca_gca_intersection(w0, w1, v0, v1):
     pos, neg = _accux_gca(w0, w1, v0, v1)
 
     pos_fin = (
-        1
-        if math.isfinite(pos[0]) and math.isfinite(pos[1]) and math.isfinite(pos[2])
-        else 0
+        int(math.isfinite(pos[0]))
+        * int(math.isfinite(pos[1]))
+        * int(math.isfinite(pos[2]))
     )
     neg_fin = (
-        1
-        if math.isfinite(neg[0]) and math.isfinite(neg[1]) and math.isfinite(neg[2])
-        else 0
+        int(math.isfinite(neg[0]))
+        * int(math.isfinite(neg[1]))
+        * int(math.isfinite(neg[2]))
     )
-    pos_on_a = 1 if (pos_fin and on_minor_arc(pos, w0, w1)) else 0
-    pos_on_b = 1 if (pos_fin and on_minor_arc(pos, v0, v1)) else 0
-    neg_on_a = 1 if (neg_fin and on_minor_arc(neg, w0, w1)) else 0
-    neg_on_b = 1 if (neg_fin and on_minor_arc(neg, v0, v1)) else 0
+    pos_on_a = pos_fin * on_minor_arc(pos, w0, w1)
+    pos_on_b = pos_fin * on_minor_arc(pos, v0, v1)
+    neg_on_a = neg_fin * on_minor_arc(neg, w0, w1)
+    neg_on_b = neg_fin * on_minor_arc(neg, v0, v1)
 
     pos_valid = pos_fin * pos_on_a * pos_on_b
     neg_valid = neg_fin * neg_on_a * neg_on_b
@@ -408,7 +411,7 @@ def _try_gca_gca_intersection(w0, w1, v0, v1):
     return point, status, pos, neg
 
 
-@njit(cache=True)
+@njit(cache=True, error_model="numpy")
 def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     """Return the intersection points of two great-circle arcs.
 
@@ -470,7 +473,7 @@ def gca_gca_intersection(gca_a_xyz, gca_b_xyz):
     return res[:count]
 
 
-@njit(cache=True, inline="always")
+@njit(cache=True, inline="always", error_model="numpy")
 def _accux_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z):
     """Compute the constant-latitude intersection candidates, scalar in/out.
 
@@ -505,9 +508,10 @@ def _accux_constlat_scalar(a0, a1, a2, b0, b1, b2, const_z):
     yp_hi, yp_lo = _cdp2(ny * nz, const_z, nx, planar)
     xn_hi, xn_lo = _cdp2(nx * nz, const_z, ny, planar)
     yn_hi, yn_lo = _cdp2(ny * nz, const_z, -nx, planar)
-    # denom == 0 means the arc is vertical (normal has no x/y component).
-    # Produce inf so the isfinite mask in the status layer rejects candidates.
-    inv_denom = 1.0 / denom if denom != 0.0 else np.inf
+    # denom == 0 (vertical arc) yields inf via IEEE division under
+    # error_model="numpy", so the isfinite mask in the status layer rejects the
+    # candidates. Branch-free.
+    inv_denom = 1.0 / denom
     px = -(xp_hi + xp_lo) * inv_denom
     py = -(yp_hi + yp_lo) * inv_denom
     nxo = -(xn_hi + xn_lo) * inv_denom
@@ -553,7 +557,7 @@ def _accux_constlat(x1, x2, const_z):
     return pos, neg
 
 
-@njit(cache=True)
+@njit(cache=True, error_model="numpy")
 def _try_gca_const_lat_intersection(gca_cart, const_z):
     """Select the valid constant-latitude intersection and report a status code.
 
@@ -572,10 +576,10 @@ def _try_gca_const_lat_intersection(gca_cart, const_z):
     x2 = gca_cart[1]
     pos, neg = _accux_constlat(x1, x2, const_z)
 
-    pos_fin = int(math.isfinite(pos[0]) and math.isfinite(pos[1]))
-    neg_fin = int(math.isfinite(neg[0]) and math.isfinite(neg[1]))
-    pos_on = pos_fin * int(on_minor_arc(pos, x1, x2)) if pos_fin else 0
-    neg_on = neg_fin * int(on_minor_arc(neg, x1, x2)) if neg_fin else 0
+    pos_fin = int(math.isfinite(pos[0])) * int(math.isfinite(pos[1]))
+    neg_fin = int(math.isfinite(neg[0])) * int(math.isfinite(neg[1]))
+    pos_on = pos_fin * on_minor_arc(pos, x1, x2)
+    neg_on = neg_fin * on_minor_arc(neg, x1, x2)
 
     pos_valid = pos_fin * pos_on
     neg_valid = neg_fin * neg_on
@@ -636,7 +640,7 @@ def _snap_const_lat_endpoint_xy(px, py, a0, a1, a2, b0, b1, b2, const_z):
     return ox, oy
 
 
-@njit(cache=True)
+@njit(cache=True, error_model="numpy")
 def gca_const_lat_intersection(gca_cart, const_z):
     """Return the intersection points of a great-circle arc and a latitude.
 

From ff2d6e0dfa3e4de2f47fa6d96c2800d57ee27b27 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 02:54:45 -0500
Subject: [PATCH 40/51] o arcs: branch-free on_minor_arc int mask at tol 1e-8
 matching AccuSphGeom

---
 uxarray/grid/arcs.py | 106 +++++++++++++++++++++----------------------
 1 file changed, 52 insertions(+), 54 deletions(-)

diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py
index c5da3e439..765276878 100644
--- a/uxarray/grid/arcs.py
+++ b/uxarray/grid/arcs.py
@@ -10,15 +10,15 @@
 from uxarray.grid.utils import _angle_of_2_vectors
 from uxarray.utils.computing import accucross, two_sum
 
-# Tolerance used to classify orient3d results as zero.  For double-precision
-# unit-vector inputs this covers rounding error in the compensated cross product.
+# Magnitude below which orient3d_on_sphere classifies a result as zero. For
+# double-precision unit-vector inputs this covers rounding error in the
+# compensated cross product.
 _PREDICATE_ZERO_TOL = 1e-15
 
-# Tolerance for the on_minor_arc collinearity and interval tests. Tighter than
-# AccuSphGeom's 1e-8 default: 1e-10 keeps borderline near-endpoint candidates
-# out of the valid set, which is more accurate on the baseline suite for the
-# scalar path here.
-_ON_MINOR_ARC_TOL = 1e-10
+# Tolerance for the on_minor_arc collinearity and interval tests. Matches
+# AccuSphGeom's gca_*_minor_arc_tol (1e-8) so the Python predicate accepts the
+# same candidate set as the C++ reference.
+_ON_MINOR_ARC_TOL = 1e-8
 
 
 def _to_list(obj):
@@ -377,33 +377,6 @@ def compute_arc_length(pt_a, pt_b):
     return rho * abs(delta_theta)
 
 
-@njit(cache=True)
-def _orient3d_on_sphere_value(a, b, q):
-    """Return the accurately computed 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 compensated 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.
-    """
-    return _orient3d_on_sphere_value_xyz(
-        a[0], a[1], a[2], b[0], b[1], b[2], q[0], q[1], q[2]
-    )
-
-
 @njit(cache=True, inline="always")
 def _normal_dot_value(nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo, q0, q1, q2):
     """Compensated dot of an already-computed ``(hi, lo)`` normal with ``q``.
@@ -424,15 +397,31 @@ def _normal_dot_value(nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo, q0, q1, q2):
 
 @njit(cache=True, inline="always")
 def _orient3d_on_sphere_value_xyz(a0, a1, a2, b0, b1, b2, q0, q1, q2):
-    """Scalar-argument form of :func:`_orient3d_on_sphere_value`.
+    """Accurate value of the orient3d-on-sphere predicate: ``(a x b) . q``.
 
     Takes the nine vector components directly so hot loops can call it without
-    materializing ``(3,)`` arrays.
+    materializing ``(3,)`` arrays. Uses a compensated cross product, so for
+    unit-vector inputs it provides roughly double the effective precision of a
+    naive evaluation. Positive when q is left of the directed arc a->b, negative
+    when right, near zero when q is on the great circle through a and b.
     """
     nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo = accucross(a0, a1, a2, b0, b1, b2)
     return _normal_dot_value(nx_hi, ny_hi, nz_hi, nx_lo, ny_lo, nz_lo, q0, q1, q2)
 
 
+@njit(cache=True)
+def _orient3d_on_sphere_value(a, b, q):
+    """Array form of :func:`_orient3d_on_sphere_value_xyz`: value of ``(a x b) . q``.
+
+    Convenience wrapper taking three ``(3,)`` unit vectors. Positive when q is
+    left of the directed arc a->b, negative when right, near zero when q is on
+    the great circle through a and b.
+    """
+    return _orient3d_on_sphere_value_xyz(
+        a[0], a[1], a[2], b[0], b[1], b[2], q[0], q[1], q[2]
+    )
+
+
 @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.
@@ -440,7 +429,9 @@ def orient3d_on_sphere(a, b, q, tol=_PREDICATE_ZERO_TOL):
     Evaluates the sign of ``(a x b) . q`` using compensated arithmetic 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.
+    and b the point q lies on. This is a public spatial predicate (used by
+    point-in-face, bounds, antimeridian handling and available for custom
+    geometry code); it is not part of the AccuXGCA/AccuXConstLat batch kernels.
 
     Parameters
     ----------
@@ -467,7 +458,7 @@ def orient3d_on_sphere(a, b, q, tol=_PREDICATE_ZERO_TOL):
 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
+    Uses ``_orient3d_on_sphere_value_xyz`` (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
@@ -484,32 +475,39 @@ def on_minor_arc(q, a, b, tol=_ON_MINOR_ARC_TOL):
 
     Returns
     -------
-    bool
-        True if q lies on the minor arc ab, False otherwise.
+    int
+        1 if q lies on the minor arc ab, 0 otherwise. Returned as an integer
+        mask (not bool) so callers can multiply it into branch-free validity
+        products, mirroring AccuSphGeom's ``on_minor_arc_tol_ptr``.
     """
     return _on_minor_arc_xyz(q[0], q[1], q[2], a[0], a[1], a[2], b[0], b[1], b[2], tol)
 
 
 @njit(cache=True, inline="always")
 def _on_minor_arc_xyz(q0, q1, q2, a0, a1, a2, b0, b1, b2, tol=_ON_MINOR_ARC_TOL):
-    """Scalar-argument form of :func:`on_minor_arc`.
+    """Scalar-argument form of :func:`on_minor_arc`, returning a 0/1 int mask.
 
     Same logic, but takes the nine vector components directly so hot loops can
     test arc membership without allocating ``(3,)`` arrays for the query point.
     """
-    # Coincident endpoints: degenerate arc, no interior.
-    if a0 == b0 and a1 == b1 and a2 == b2:
-        return False
-    # Antipodal endpoints: a×b = 0, so every point on the great circle passes
-    # the collinearity test and the interval conditions degenerate to 0 >= -tol,
-    # causing false positives for all points on the great circle.
-    if a0 == -b0 and a1 == -b1 and a2 == -b2:
-        return False
-    # Collinearity check: q must lie on the great circle through a and b.
-    if abs(_orient3d_on_sphere_value_xyz(a0, a1, a2, b0, b1, b2, q0, q1, q2)) > tol:
-        return False
-    # Interval check: q must lie on the minor-arc side of both endpoints.
+    # Branch-free mask form, mirroring AccuSphGeom on_minor_arc_tol_ptr: the
+    # result is a product of 0/1 masks so the hot path has no data-dependent
+    # branches. Coincident-endpoint degeneracy is folded in as (1 - degenerate),
+    # exactly as in the C++ reference.
+    coincident = 1 if (a0 == b0 and a1 == b1 and a2 == b2) else 0
+    # Antipodal endpoints (a = -b): a×b = 0 so the collinearity test passes for
+    # every point on the great circle and the interval conditions degenerate to
+    # 0 >= -tol, yielding false positives. Not present in the C++ tol path (which
+    # assumes non-antipodal mesh edges); kept here as a mask factor, not a branch.
+    antipodal = 1 if (a0 == -b0 and a1 == -b1 and a2 == -b2) else 0
+    orient_ok = (
+        1
+        if abs(_orient3d_on_sphere_value_xyz(a0, a1, a2, b0, b1, b2, q0, q1, q2)) <= tol
+        else 0
+    )
     qa = a0 * q0 + a1 * q1 + a2 * q2
     qb = b0 * q0 + b1 * q1 + b2 * q2
     ab = a0 * b0 + a1 * b1 + a2 * b2
-    return (qb - ab * qa) >= -tol and (qa - qb * ab) >= -tol
+    s1_ok = 1 if (qb - ab * qa) >= -tol else 0
+    s2_ok = 1 if (qa - qb * ab) >= -tol else 0
+    return (1 - coincident) * (1 - antipodal) * orient_ok * s1_ok * s2_ok

From 3e3a972f55262e5b1920034786a84edb14e97aa9 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 02:54:45 -0500
Subject: [PATCH 41/51] o benchmarks: drop point-in-polygon kernel bench, moved
 to point-in-face PR

---
 benchmarks/geometry_kernels.py | 30 ------------------------------
 1 file changed, 30 deletions(-)

diff --git a/benchmarks/geometry_kernels.py b/benchmarks/geometry_kernels.py
index d7e777bd6..08aea236e 100644
--- a/benchmarks/geometry_kernels.py
+++ b/benchmarks/geometry_kernels.py
@@ -35,18 +35,6 @@ 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,
@@ -217,21 +205,3 @@ 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)

From 5c80ff78ef8fa9170a9c0a236479bcb08a5f4733 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 02:54:45 -0500
Subject: [PATCH 42/51] o benchmarks: drop thread-scaling constlat, moved to
 separate scaling issue

---
 benchmarks/thread_scaling_constlat.py | 131 --------------------------
 1 file changed, 131 deletions(-)
 delete mode 100644 benchmarks/thread_scaling_constlat.py

diff --git a/benchmarks/thread_scaling_constlat.py b/benchmarks/thread_scaling_constlat.py
deleted file mode 100644
index c720b23bb..000000000
--- a/benchmarks/thread_scaling_constlat.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""End-to-end thread-scaling benchmark for the GCA / constant-latitude path.
-
-Unlike the bare-kernel micro-benchmarks, this drives the *real* UXarray
-dispatcher (`gca_const_lat_intersection`) over the edges of a real mesh, and a
-same-body FP64 dispatcher built from the identical L1/L2/L3 structure, sweeping
-the Numba thread count. It answers: as threads increase, does the compensated
-(AccuX/EFT) path scale the same way as plain FP64 when hit through the full
-UXarray path (dispatch + masks + snapping + output packaging), not just the
-raw kernel?
-
-Run:  python benchmarks/thread_scaling_constlat.py [grid] [n_lat] [repeats]
-Prints CSV to stdout: threads,fp64_ns,accux_ns,accux_over_fp64
-(ns are per edge-x-latitude evaluation.)
-"""
-
-import math
-import sys
-import time
-
-import numpy as np
-from numba import njit, prange, set_num_threads, get_num_threads
-
-import uxarray as ux
-from uxarray.grid.intersections import gca_const_lat_intersection
-
-# same-body FP64 dispatcher: identical structure, FP64 body instead of EFT.
-from benchmarks.geometry_samebody import _fp64_gca_const_lat_intersection
-
-
-@njit(cache=False, parallel=True)
-def _batch_accux(edges, zs, acc):
-    """Real AccuX dispatcher over every (edge, latitude) pair, in parallel."""
-    n_edge = edges.shape[0]
-    for i in prange(n_edge):
-        s = 0.0
-        for k in range(zs.shape[0]):
-            res = gca_const_lat_intersection(edges[i], zs[k])
-            v = res[0, 0]
-            if v == v:  # not NaN
-                s += v
-        acc[i] = s
-
-
-@njit(cache=False, parallel=True)
-def _batch_fp64(edges, zs, acc):
-    """Same-body FP64 dispatcher over every (edge, latitude) pair, in parallel."""
-    n_edge = edges.shape[0]
-    for i in prange(n_edge):
-        s = 0.0
-        for k in range(zs.shape[0]):
-            res = _fp64_gca_const_lat_intersection(edges[i], zs[k])
-            v = res[0, 0]
-            if v == v:
-                s += v
-        acc[i] = s
-
-
-def _build_edges(grid):
-    """Extract each edge as a (2, 3) Cartesian arc from a real grid."""
-    en = grid.edge_node_connectivity.values
-    x = grid.node_x.values
-    y = grid.node_y.values
-    z = grid.node_z.values
-    n_edge = en.shape[0]
-    edges = np.empty((n_edge, 2, 3), dtype=np.float64)
-    edges[:, 0, 0] = x[en[:, 0]]
-    edges[:, 0, 1] = y[en[:, 0]]
-    edges[:, 0, 2] = z[en[:, 0]]
-    edges[:, 1, 0] = x[en[:, 1]]
-    edges[:, 1, 1] = y[en[:, 1]]
-    edges[:, 1, 2] = z[en[:, 1]]
-    return edges
-
-
-def main():
-    grid_name = sys.argv[1] if len(sys.argv) > 1 else "outCSne30"
-    n_lat = int(sys.argv[2]) if len(sys.argv) > 2 else 40
-    repeats = int(sys.argv[3]) if len(sys.argv) > 3 else 5
-
-    grid = ux.tutorial.open_grid(grid_name)
-    edges = _build_edges(grid)
-    # latitudes spanning the sphere (as z = sin(lat)); avoid exact poles
-    zs = np.linspace(-0.95, 0.95, n_lat)
-    n_eval = edges.shape[0] * n_lat
-    acc = np.empty(edges.shape[0], dtype=np.float64)
-
-    # Cap the sweep at the number of fast performance cores. On heterogeneous
-    # CPUs (e.g. Apple M-series: performance + efficiency cores) adding the slow
-    # efficiency cores makes the parallel loop wait on the slowest chunk, so
-    # times go *up* past the P-core count — a scheduling artifact, not a
-    # property of the kernels. Override with the PERF_CORES env var if needed.
-    import os
-
-    perf_cores = int(os.environ.get("PERF_CORES", "8"))
-    max_threads = min(get_num_threads(), perf_cores)
-    counts = []
-    t = 1
-    while t < max_threads:
-        counts.append(t)
-        t *= 2
-    counts.append(max_threads)
-    counts = sorted(set(counts))
-
-    # warm-up compile
-    set_num_threads(max_threads)
-    _batch_accux(edges[:8], zs, acc[:8])
-    _batch_fp64(edges[:8], zs, acc[:8])
-
-    sys.stderr.write(
-        f"grid={grid_name} n_edge={edges.shape[0]} n_lat={n_lat} "
-        f"evals/pass={n_eval} max_threads={max_threads} counts={counts}\n"
-    )
-
-    def best(fn, nt):
-        set_num_threads(nt)
-        b = math.inf
-        for _ in range(repeats):
-            t0 = time.perf_counter()
-            fn(edges, zs, acc)
-            b = min(b, (time.perf_counter() - t0) / n_eval * 1e9)
-        return b
-
-    print("threads,fp64_ns,accux_ns,accux_over_fp64")
-    for nt in counts:
-        f = best(_batch_fp64, nt)
-        a = best(_batch_accux, nt)
-        print(f"{nt},{f:.4f},{a:.4f},{a / f:.4f}")
-
-
-if __name__ == "__main__":
-    main()

From 66b907b3ec924c501afd3afa1d8171b45a37b6fd Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 02:54:45 -0500
Subject: [PATCH 43/51] o docs: move spherical-geometry notebook out, depends
 on split-out point-in-face

---
 .../spherical-geometry-accuracy.ipynb         | 601 ------------------
 1 file changed, 601 deletions(-)
 delete mode 100644 docs/user-guide/spherical-geometry-accuracy.ipynb

diff --git a/docs/user-guide/spherical-geometry-accuracy.ipynb b/docs/user-guide/spherical-geometry-accuracy.ipynb
deleted file mode 100644
index e75a9909e..000000000
--- a/docs/user-guide/spherical-geometry-accuracy.ipynb
+++ /dev/null
@@ -1,601 +0,0 @@
-{
- "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": "2026-07-16T23:25:18.320022Z",
-     "iopub.status.busy": "2026-07-16T23:25:18.319857Z",
-     "iopub.status.idle": "2026-07-16T23:25:20.085333Z",
-     "shell.execute_reply": "2026-07-16T23:25:20.084604Z"
-    }
-   },
-   "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": "2026-07-16T23:25:20.087254Z",
-     "iopub.status.busy": "2026-07-16T23:25:20.087061Z",
-     "iopub.status.idle": "2026-07-16T23:25:20.343006Z",
-     "shell.execute_reply": "2026-07-16T23:25:20.342290Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "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": "2026-07-16T23:25:20.345008Z",
-     "iopub.status.busy": "2026-07-16T23:25:20.344852Z",
-     "iopub.status.idle": "2026-07-16T23:25:20.652953Z",
-     "shell.execute_reply": "2026-07-16T23:25:20.652492Z"
-    }
-   },
-   "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": "2026-07-16T23:25:20.654640Z",
-     "iopub.status.busy": "2026-07-16T23:25:20.654484Z",
-     "iopub.status.idle": "2026-07-16T23:25:21.118617Z",
-     "shell.execute_reply": "2026-07-16T23:25:21.118100Z"
-    }
-   },
-   "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": "2026-07-16T23:25:21.120318Z",
-     "iopub.status.busy": "2026-07-16T23:25:21.120124Z",
-     "iopub.status.idle": "2026-07-16T23:25:22.758428Z",
-     "shell.execute_reply": "2026-07-16T23:25:22.757817Z"
-    }
-   },
-   "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": "2026-07-16T23:25:22.760072Z",
-     "iopub.status.busy": "2026-07-16T23:25:22.759945Z",
-     "iopub.status.idle": "2026-07-16T23:25:24.562916Z",
-     "shell.execute_reply": "2026-07-16T23:25:24.562151Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x550 with 2 Axes>"
-      ]
-     },
-     "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": "3.11.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 8e6c88a288dc6fa78d72c8c5fc3642c2fc049572 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 02:54:45 -0500
Subject: [PATCH 44/51] o docs: drop spherical-geometry notebook from
 user-guide toctree

---
 docs/userguide.rst | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/docs/userguide.rst b/docs/userguide.rst
index 73e154f13..d185a4d59 100644
--- a/docs/userguide.rst
+++ b/docs/userguide.rst
@@ -97,9 +97,6 @@ Supplementary Guides
 
 These user guides provide additional details about specific features in UXarray.
 
-`Accurate Spherical Geometry <user-guide/spherical-geometry-accuracy.ipynb>`_
- How UXarray uses compensated arithmetic to avoid catastrophic cancellation in cross-product and point-in-polygon operations
-
 `Working with HEALPix Grids <user-guide/healpix.ipynb>`_
  Use UXarray with HEALPix
 
@@ -134,7 +131,6 @@ 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

From 64ecad4ab77567954089cade8a61b17dbb5f7045 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 02:54:45 -0500
Subject: [PATCH 45/51] o test_plot: drop to_raster_auto_extent, unrelated to
 AccuXGCA/AccuXConstLat scope

---
 test/test_plot.py | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/test/test_plot.py b/test/test_plot.py
index 470cd63a7..24cf9de43 100644
--- a/test/test_plot.py
+++ b/test/test_plot.py
@@ -127,25 +127,6 @@ def test_to_raster_with_extra_dims(gridpath):
 
     assert isinstance(raster, np.ndarray)
 
-def test_to_raster_auto_extent(gridpath):
-    fig, ax = plt.subplots(
-        subplot_kw={'projection': ccrs.Robinson()},
-        constrained_layout=True,
-    )
-
-    xlim0, ylim0 = ax.get_xlim(), ax.get_ylim()
-
-    mesh_path = gridpath("mpas", "QU", "oQU480.231010.nc")
-    uxds = ux.open_dataset(mesh_path, mesh_path)
-
-    raster = uxds['bottomDepth'].to_raster(ax=ax, pixel_ratio=0.5)
-
-    xlim1, ylim1 = ax.get_xlim(), ax.get_ylim()
-    assert not (np.allclose(xlim0, xlim1) and np.allclose(ylim0, ylim1))
-
-    finite = raster[np.isfinite(raster)]
-    assert finite.size > 0
-    assert finite.std() > 0
 
 
 def test_to_raster_reuse_mapping(gridpath, tmpdir):

From f6d6142e17a035f95148dfbb86277befd3cfc059 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 03:27:52 -0500
Subject: [PATCH 46/51] o point_in_face: AccuSphGeom spherical point-in-polygon
 via ray-casting orient3d

---
 uxarray/grid/point_in_face.py | 312 +++++++++++++++++++++++++++++-----
 1 file changed, 268 insertions(+), 44 deletions(-)

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)

From 4d18baed4ed0510ef148141a7702d5986a52a85c Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 03:27:52 -0500
Subject: [PATCH 47/51] o bounds: GCA face-bounds path with compensated
 interior arc z-extrema

---
 uxarray/grid/bounds.py | 372 +++++++++++++++++++++++++++++++++++++++--
 1 file changed, 361 insertions(+), 11 deletions(-)

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,

From 8fad6e9a342fc347c4906473a3ea645d33c80609 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 03:27:53 -0500
Subject: [PATCH 48/51] o test: restore point-in-polygon baseline cases for
 point-in-face path

---
 .../geometry/test_accusphgeom_baseline.py     | 73 +++++++++++++++++++
 1 file changed, 73 insertions(+)

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}"

From d80330b6452401b5c21b710c7735ccb95e25a5b1 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 03:27:53 -0500
Subject: [PATCH 49/51] o benchmarks: point-in-polygon sphere kernel benchmark

---
 benchmarks/geometry_kernels.py | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

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)

From 3a246b405424f4bb5a460f1d966de23a7a95ab01 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 03:27:53 -0500
Subject: [PATCH 50/51] o docs: spherical geometry accuracy user guide notebook

---
 .../spherical-geometry-accuracy.ipynb         | 601 ++++++++++++++++++
 1 file changed, 601 insertions(+)
 create mode 100644 docs/user-guide/spherical-geometry-accuracy.ipynb

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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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "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": [
+       "<Figure size 1400x550 with 2 Axes>"
+      ]
+     },
+     "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
+}

From 8cc2168e23d839fd2b3ef8d3b481b6f9f2a20ba8 Mon Sep 17 00:00:00 2001
From: Rajeev Jain <rajeeja@gmail.com>
Date: Sat, 18 Jul 2026 03:27:53 -0500
Subject: [PATCH 51/51] o docs: add spherical geometry notebook to user-guide
 toctree

---
 docs/userguide.rst | 4 ++++
 1 file changed, 4 insertions(+)

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 <user-guide/spherical-geometry-accuracy.ipynb>`_
+ How UXarray uses compensated arithmetic to avoid catastrophic cancellation in cross-product and point-in-polygon operations
+
 `Working with HEALPix Grids <user-guide/healpix.ipynb>`_
  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