{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99564fab-c321-4116-8229-b16eefa1536e",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.148443,
     "end_time": "2025-10-10T23:06:37.542126",
     "exception": false,
     "start_time": "2025-10-10T23:06:37.393683",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Compute land-atmosphere coupling indices \n",
    "This notebook takes in a series of CESM simulations, computes the land-atmosphere coupling index (CI; \n",
    "terrestrial leg only currently), and plots those seasonal means. <br>\n",
    "- Note: Built to use monthly output; ideally, CI should be based on daily data. \n",
    "- Optional: Comparison against FLUXNET obs\n",
    "<br><br>\n",
    "Notebook created by mdfowler@ucar.edu; Last update: 11 Dec 2024 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "750da831-1c5c-4b41-947e-a9e57a62a820",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:06:37.551393Z",
     "iopub.status.busy": "2025-10-10T23:06:37.551075Z",
     "iopub.status.idle": "2025-10-10T23:06:59.913179Z",
     "shell.execute_reply": "2025-10-10T23:06:59.912575Z"
    },
    "papermill": {
     "duration": 22.368172,
     "end_time": "2025-10-10T23:06:59.914496",
     "exception": false,
     "start_time": "2025-10-10T23:06:37.546324",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "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 py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
       "  const reloading = false;\n",
       "  const Bokeh = root.Bokeh;\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, 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",
       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\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/1.7.5/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/1.7.5/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",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "        var NewBokeh = root.Bokeh;\n",
       "        if (Bokeh.versions === undefined) {\n",
       "          Bokeh.versions = new Map();\n",
       "        }\n",
       "        if (NewBokeh.version !== Bokeh.version) {\n",
       "          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "        }\n",
       "        root.Bokeh = Bokeh;\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 = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\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, function() {\n",
       "        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\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 py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\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, 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      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\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/1.7.5/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/1.7.5/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      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\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 = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\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, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\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='0b55a82d-7a5f-442a-9d94-68a3036f178f'>\n",
       "  <div id=\"b510bd2d-c779-4aa1-a02a-4f3fffff158d\" data-root-id=\"0b55a82d-7a5f-442a-9d94-68a3036f178f\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"595888de-5a44-4290-917a-0c15a13d2ca6\":{\"version\":\"3.7.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"0b55a82d-7a5f-442a-9d94-68a3036f178f\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"6665d961-eb5f-4814-9903-187172d01766\",\"attributes\":{\"plot_id\":\"0b55a82d-7a5f-442a-9d94-68a3036f178f\",\"comm_id\":\"c368265c7f584fa1959f4b3b93a78a38\",\"client_comm_id\":\"bf0c3fc5c18645d390c19b93aa4f54f0\"}}],\"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\":\"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\":\"595888de-5a44-4290-917a-0c15a13d2ca6\",\"roots\":{\"0b55a82d-7a5f-442a-9d94-68a3036f178f\":\"b510bd2d-c779-4aa1-a02a-4f3fffff158d\"},\"root_ids\":[\"0b55a82d-7a5f-442a-9d94-68a3036f178f\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_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 !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_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": "0b55a82d-7a5f-442a-9d94-68a3036f178f"
      }
     },
     "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 py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
       "  const reloading = true;\n",
       "  const Bokeh = root.Bokeh;\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, 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",
       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\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/1.7.5/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",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "        var NewBokeh = root.Bokeh;\n",
       "        if (Bokeh.versions === undefined) {\n",
       "          Bokeh.versions = new Map();\n",
       "        }\n",
       "        if (NewBokeh.version !== Bokeh.version) {\n",
       "          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "        }\n",
       "        root.Bokeh = Bokeh;\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 = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\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, function() {\n",
       "        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\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 py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = true;\n  const Bokeh = root.Bokeh;\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, 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      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\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/1.7.5/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      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\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 = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\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, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\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"
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from datetime import timedelta\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "# Plotting utils\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import uxarray as uxr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "774bd269-ce50-4449-b32f-83246b74b73c",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.014536,
     "end_time": "2025-10-10T23:06:59.937875",
     "exception": false,
     "start_time": "2025-10-10T23:06:59.923339",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## 1. Modify this section for each run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f8f2d17-c653-4ad1-9dc3-c49bf836ceb6",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:06:59.949505Z",
     "iopub.status.busy": "2025-10-10T23:06:59.949123Z",
     "iopub.status.idle": "2025-10-10T23:06:59.952935Z",
     "shell.execute_reply": "2025-10-10T23:06:59.952518Z"
    },
    "papermill": {
     "duration": 0.010396,
     "end_time": "2025-10-10T23:06:59.953723",
     "exception": false,
     "start_time": "2025-10-10T23:06:59.943327",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "## - - - - - - - - - - - - - - - - - - - - - -\n",
    "##  Settings for case locations + names\n",
    "## - - - - - - - - - - - - - - - - - - - - - -\n",
    "## Where observations are stored\n",
    "# '/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data/lnd/analysis_datasets/ungridded/timeseries/FLUXNET2015/'\n",
    "obs_data_dir = \"\"\n",
    "obsDir = \"\"\n",
    "\n",
    "CESM_output_dir = os.path.join(\n",
    "    os.path.sep,\n",
    "    \"glade\",\n",
    "    \"campaign\",\n",
    "    \"cesm\",\n",
    "    \"development\",\n",
    "    \"cross-wg\",\n",
    "    \"diagnostic_framework\",\n",
    "    \"CESM_output_for_testing\",\n",
    ")\n",
    "\n",
    "ts_dir = None\n",
    "\n",
    "## Full casenames that are present in CESM_output_dir or ts_dir and in individual filenames\n",
    "# caseNames = [\n",
    "#             'b.e23_alpha16b.BLT1850.ne30_t232.054',\n",
    "# 'b.e30_beta02.BLT1850.ne30_t232.104',\n",
    "#             ]\n",
    "case_name = \"b.e30_beta02.BLT1850.ne30_t232.104\"\n",
    "\n",
    "# clmFile_h = '.h0.'\n",
    "\n",
    "start_date = \"0001-01-01\"\n",
    "end_date = \"0101-01-01\"\n",
    "\n",
    "## - - - - - - - - - - - - - - - - - - - - - -\n",
    "##  Optional settings for notebook\n",
    "## - - - - - - - - - - - - - - - - - - - - - -\n",
    "\n",
    "## If comparison against FLUXNET desired\n",
    "# fluxnet_comparison = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0770ef5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-10T23:06:59.964852Z",
     "iopub.status.busy": "2025-10-10T23:06:59.964669Z",
     "iopub.status.idle": "2025-10-10T23:06:59.967944Z",
     "shell.execute_reply": "2025-10-10T23:06:59.967565Z"
    },
    "papermill": {
     "duration": 0.009843,
     "end_time": "2025-10-10T23:06:59.968714",
     "exception": false,
     "start_time": "2025-10-10T23:06:59.958871",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "case_name = \"b.e30_beta02.BLT1850.ne30_t232.104\"\n",
    "base_case_name = \"b.e23_alpha17f.BLT1850.ne30_t232.092\"\n",
    "CESM_output_dir = \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing\"\n",
    "start_date = \"0001-01-01\"\n",
    "end_date = \"0101-01-01\"\n",
    "base_start_date = \"0001-01-01\"\n",
    "base_end_date = \"0101-01-01\"\n",
    "obs_data_dir = (\n",
    "    \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data\"\n",
    ")\n",
    "ts_dir = None\n",
    "lc_kwargs = {\"threads_per_worker\": 1}\n",
    "serial = False\n",
    "clmFile_h = \"h0\"\n",
    "fluxnet_comparison = True\n",
    "obsDir = \"lnd/analysis_datasets/ungridded/timeseries/FLUXNET2015/\"\n",
    "subset_kwargs = {}\n",
    "product = \"/glade/work/richling/CUPid_pr_test/CUPiD/examples/key_metrics/computed_notebooks//lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5724aa30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-10T23:06:59.989164Z",
     "iopub.status.busy": "2025-10-10T23:06:59.988983Z",
     "iopub.status.idle": "2025-10-10T23:06:59.991679Z",
     "shell.execute_reply": "2025-10-10T23:06:59.991199Z"
    },
    "papermill": {
     "duration": 0.018607,
     "end_time": "2025-10-10T23:06:59.992417",
     "exception": false,
     "start_time": "2025-10-10T23:06:59.973810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set some parameter defaults\n",
    "if ts_dir is None:\n",
    "    ts_dir = CESM_output_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0014712f-d094-4dae-b583-740bf7a9789c",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:00.003728Z",
     "iopub.status.busy": "2025-10-10T23:07:00.003548Z",
     "iopub.status.idle": "2025-10-10T23:07:00.012117Z",
     "shell.execute_reply": "2025-10-10T23:07:00.011714Z"
    },
    "papermill": {
     "duration": 0.015091,
     "end_time": "2025-10-10T23:07:00.013000",
     "exception": false,
     "start_time": "2025-10-10T23:06:59.997909",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## - - - - - - - - - - - - - - - - - - - - - -\n",
    "##  Settings for computing coupling index\n",
    "## - - - - - - - - - - - - - - - - - - - - - -\n",
    "\n",
    "# Set up directory for aux output like coupling index file\n",
    "if \"SCRATCH\" in os.environ:\n",
    "    cupid_temp = os.path.join(os.path.sep, os.environ[\"SCRATCH\"], \"CUPiD_scratch\")\n",
    "    os.makedirs(cupid_temp, exist_ok=True)\n",
    "else:\n",
    "    cupid_temp = \".\"\n",
    "\n",
    "startYrs = [start_date.split(\"-\")[0]]\n",
    "endYrs = [f\"{int(end_date.split('-')[0])-1:04d}\"]\n",
    "\n",
    "caseNames = [\n",
    "    case_name,\n",
    "    # base_case_name,\n",
    "]\n",
    "\n",
    "shortNames = [case.split(\".\")[-1] for case in caseNames]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d70024c7-0af2-48b9-9041-893f40e613ec",
   "metadata": {
    "papermill": {
     "duration": 0.006589,
     "end_time": "2025-10-10T23:07:00.024977",
     "exception": false,
     "start_time": "2025-10-10T23:07:00.018388",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2. Read in model data and compute terrestrial coupling index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "304ce8d0-6aab-4fcb-9635-2a78e270f3c7",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:00.036296Z",
     "iopub.status.busy": "2025-10-10T23:07:00.036068Z",
     "iopub.status.idle": "2025-10-10T23:07:00.039639Z",
     "shell.execute_reply": "2025-10-10T23:07:00.039259Z"
    },
    "papermill": {
     "duration": 0.010233,
     "end_time": "2025-10-10T23:07:00.040413",
     "exception": false,
     "start_time": "2025-10-10T23:07:00.030180",
     "status": "completed"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Inputs: xname -- controlling variable \n",
    "        yname -- responding variable\n",
    "        ds    -- dataset containing xname and yname data \n",
    "        \n",
    "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
    "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def compute_couplingIndex_cesm(xname, yname, xDS, yDS):\n",
    "    xday = xDS[xname].groupby(\"time.season\")\n",
    "    yday = yDS[yname].groupby(\"time.season\")\n",
    "\n",
    "    # Get the covariance of the two (numerator in coupling index)\n",
    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
    "        \"time.season\"\n",
    "    ).sum() / xday.count()\n",
    "\n",
    "    # Now compute the actual coupling index\n",
    "    couplingIndex = covarTerm / xday.std()\n",
    "\n",
    "    return couplingIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c35ae8d-dfff-44b0-a854-dfc2b5b030c0",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:00.051406Z",
     "iopub.status.busy": "2025-10-10T23:07:00.051220Z",
     "iopub.status.idle": "2025-10-10T23:07:00.066040Z",
     "shell.execute_reply": "2025-10-10T23:07:00.065599Z"
    },
    "papermill": {
     "duration": 0.021308,
     "end_time": "2025-10-10T23:07:00.066856",
     "exception": false,
     "start_time": "2025-10-10T23:07:00.045548",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using previously computed coupling index saved in file  /glade/derecho/scratch/richling/CUPiD_scratch/b.e30_beta02.BLT1850.ne30_t232.104_TerrestrialCouplingIndex_SHvsSM.nc\n"
     ]
    }
   ],
   "source": [
    "for iCase in range(len(caseNames)):\n",
    "    ## Check first if coupling index has already been created:\n",
    "    TCI_filePath = os.path.join(\n",
    "        cupid_temp, f\"{caseNames[iCase]}_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
    "    )\n",
    "\n",
    "    if os.path.exists(TCI_filePath):  # Use previously computed TCI\n",
    "        print(\"Using previously computed coupling index saved in file \", TCI_filePath)\n",
    "    else:  # Compute TCI\n",
    "\n",
    "        # Get list of necessary time series files\n",
    "        soilWater_file = np.sort(\n",
    "            glob.glob(\n",
    "                ts_dir\n",
    "                + \"/\"\n",
    "                + caseNames[iCase]\n",
    "                + \"/lnd/proc/tseries/\"\n",
    "                + caseNames[iCase]\n",
    "                + clmFile_h\n",
    "                + \"SOILWATER_10CM.\"\n",
    "                + startYrs[iCase]\n",
    "                + \"??-\"\n",
    "                + endYrs[iCase]\n",
    "                + \"??.nc\"\n",
    "            )\n",
    "        )\n",
    "        if len(soilWater_file) == 0:\n",
    "            print(\"Soil moisture file not found!\")\n",
    "        elif len(soilWater_file) > 1:\n",
    "            print(\n",
    "                \"More than one file matches requested time period and case for soil moisture.\"\n",
    "            )\n",
    "        elif len(soilWater_file) == 1:\n",
    "            soilWater_DS = xr.open_dataset(soilWater_file[0], decode_times=True)\n",
    "\n",
    "        sh_file = np.sort(\n",
    "            glob.glob(\n",
    "                ts_dir\n",
    "                + \"/\"\n",
    "                + caseNames[iCase]\n",
    "                + \"/lnd/proc/tseries/\"\n",
    "                + caseNames[iCase]\n",
    "                + clmFile_h\n",
    "                + \"FSH_TO_COUPLER.\"\n",
    "                + startYrs[iCase]\n",
    "                + \"??-\"\n",
    "                + endYrs[iCase]\n",
    "                + \"??.nc\"\n",
    "            )\n",
    "        )\n",
    "        if len(sh_file) == 0:\n",
    "            print(\"Land-based SHFLX file not found!\")\n",
    "        elif len(sh_file) > 1:\n",
    "            print(\"More than one file matches requested time period and case for SH.\")\n",
    "        elif len(sh_file) == 1:\n",
    "            shflx_DS = xr.open_dataset(sh_file[0])\n",
    "\n",
    "        # If years start at 0000, offset by 1700 years for analysis\n",
    "        yrOffset = 1850\n",
    "        if shflx_DS[\"time.year\"].values[0] < 1500:\n",
    "            shflx_DS[\"time\"] = shflx_DS.time + timedelta(days=yrOffset * 365)\n",
    "        if soilWater_DS[\"time.year\"].values[0] < 1500:\n",
    "            soilWater_DS[\"time\"] = soilWater_DS.time + timedelta(days=yrOffset * 365)\n",
    "        # Convert times to datetime for easier use\n",
    "        shflx_DS[\"time\"] = shflx_DS.indexes[\"time\"].to_datetimeindex()\n",
    "        soilWater_DS[\"time\"] = soilWater_DS.indexes[\"time\"].to_datetimeindex()\n",
    "\n",
    "        # Add case ID (short name) to the DS\n",
    "        shflx_DS = shflx_DS.assign_coords({\"case\": shortNames[iCase]})\n",
    "        soilWater_DS = soilWater_DS.assign_coords({\"case\": shortNames[iCase]})\n",
    "\n",
    "        ## Compute coupling index and save to netCDF file\n",
    "        ## - - - - - - - - - - - - - - - - - - - - - - - - -\n",
    "        xname = \"SOILWATER_10CM\"  # Controlling variable\n",
    "        yname = \"FSH_TO_COUPLER\"  # Responding variable\n",
    "\n",
    "        xDS = soilWater_DS\n",
    "        yDS = shflx_DS\n",
    "\n",
    "        couplingInd = compute_couplingIndex_cesm(xname, yname, xDS, yDS)\n",
    "        couplingInd.to_netcdf(TCI_filePath)\n",
    "        print(\"File created: \", TCI_filePath)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8fba2a-98d2-4e94-9d71-3b2625e16032",
   "metadata": {
    "papermill": {
     "duration": 0.005182,
     "end_time": "2025-10-10T23:07:00.077783",
     "exception": false,
     "start_time": "2025-10-10T23:07:00.072601",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.1 Read in FLUXNET data if requested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdf20c82-5a01-4ab9-8881-d9388b1b2356",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:00.089718Z",
     "iopub.status.busy": "2025-10-10T23:07:00.089507Z",
     "iopub.status.idle": "2025-10-10T23:07:00.096393Z",
     "shell.execute_reply": "2025-10-10T23:07:00.095955Z"
    },
    "papermill": {
     "duration": 0.013758,
     "end_time": "2025-10-10T23:07:00.096988",
     "exception": false,
     "start_time": "2025-10-10T23:07:00.083230",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# --------------------------------------------------------\n",
    "# Function to read requested variables from FLUXNET file.\n",
    "# - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
    "#\n",
    "# Inputs:  fileName --> Full path to FLUXNET data file\n",
    "#          varNames --> An array of variable names to be\n",
    "#                       retrieved from said data file.\n",
    "#                       NOTE: If you wish to retrieve *all*\n",
    "#                       variables, pass the string 'ALL'.\n",
    "#\n",
    "# Outputs: fluxnetID --> ID string used to identify station\n",
    "#          fluxnetDS --> An x-array dataset containing the\n",
    "#                        requested variables.\n",
    "#                        Missing values will be set to NaN.\n",
    "#\n",
    "# --------------------------------------------------------\n",
    "\n",
    "\n",
    "def readFLUXNET_var(fileName, varNames):\n",
    "    # Get ID of station\n",
    "    startID = fileName.find(\"FLX_\")\n",
    "    fluxnetID = fileName[startID + 4 : startID + 10]\n",
    "\n",
    "    # If this is taking a long time or you just want to know where in the stations you are, uncomment print statement\n",
    "    # print('Reading in site - ', fluxnetID)\n",
    "\n",
    "    # Read in CSV file containing data\n",
    "    dataDF = pd.read_csv(fileName)\n",
    "\n",
    "    # Return ALL variables from dataDF if requested\n",
    "    if varNames == \"ALL\":\n",
    "        fluxnetDF = dataDF\n",
    "\n",
    "        # Set any value that's missing to NaN (not -9999)\n",
    "        fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
    "\n",
    "        # If time has been requested, reformat to pandas date index\n",
    "        fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
    "            fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
    "        )\n",
    "        fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
    "\n",
    "        # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
    "        #   NOTE: since current implementation doesn't use the pre-formatted CoMeT, might not need this step now\n",
    "        fluxnetDS = fluxnetDF.to_xarray()\n",
    "\n",
    "    # Reduce returned DF to contain only variables of interest\n",
    "    else:\n",
    "\n",
    "        # Check that requested variables are available in specific file\n",
    "        errCount = 0  # Initialize flag for error\n",
    "        colNames = dataDF.columns.values  # Available variables in file\n",
    "\n",
    "        for iVar in range(len(varNames)):  # Check each variable individually\n",
    "            if (varNames[iVar] in colNames) == False:\n",
    "                # Turn on print statement for more verbose output\n",
    "                # print('** ERROR: %13s   not contained in file for %8s  **' %(varNames[iVar], fluxnetID))\n",
    "\n",
    "                # If any variable is not conatined in file, return a NaN\n",
    "                fluxnetDS = -999\n",
    "                errCount = errCount + 1\n",
    "\n",
    "        # If all the variables *are* available, isolate those in DF and return that\n",
    "        if errCount == 0:\n",
    "            fluxnetDF = dataDF[varNames]\n",
    "\n",
    "            # Set any value that's missing to NaN (not -999)\n",
    "            fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
    "\n",
    "            # If time has been requested, reformat to pandas make index\n",
    "            if (\"TIMESTAMP\" in varNames) == True:\n",
    "                fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
    "                    fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
    "                )\n",
    "                fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
    "\n",
    "            # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
    "            fluxnetDS = fluxnetDF.to_xarray()\n",
    "\n",
    "    return (fluxnetID, fluxnetDS)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Inputs: xname -- controlling variable \n",
    "        yname -- responding variable\n",
    "        ds    -- dataset containing xname and yname data \n",
    "        \n",
    "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
    "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def compute_couplingIndex_FLUXNET(xname, yname, ds):\n",
    "    xday = ds[xname].groupby(\"TIMESTAMP.season\")\n",
    "    yday = ds[yname].groupby(\"TIMESTAMP.season\")\n",
    "\n",
    "    # Get the covariance of the two (numerator in coupling index)\n",
    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
    "        \"TIMESTAMP.season\"\n",
    "    ).sum() / xday.count()\n",
    "\n",
    "    # Now compute the actual coupling index\n",
    "    couplingIndex = covarTerm / xday.std()\n",
    "\n",
    "    return couplingIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9acb032f-f106-4783-8c57-8e0d3a44eb3e",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:00.112565Z",
     "iopub.status.busy": "2025-10-10T23:07:00.112324Z",
     "iopub.status.idle": "2025-10-10T23:07:23.539566Z",
     "shell.execute_reply": "2025-10-10T23:07:23.538944Z"
    },
    "papermill": {
     "duration": 23.437807,
     "end_time": "2025-10-10T23:07:23.540467",
     "exception": false,
     "start_time": "2025-10-10T23:07:00.102660",
     "status": "completed"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Prr\n",
      "No data for station:   US-Tw4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   AU-Cum\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   ES-Ln2\n",
      "No data for station:   US-Ha1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   BR-Sa1\n",
      "No data for station:   FI-Lom\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Cop\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Atq\n",
      "No data for station:   US-Wi4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   CZ-wet\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   GL-NuF\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-GBT\n",
      "No data for station:   US-Wi5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Los\n",
      "No data for station:   DE-Akm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   NL-Hor\n",
      "No data for station:   US-Wi6\n",
      "No data for station:   BE-Bra\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Ivo\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   DE-SfN\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   AR-Vir\n",
      "No data for station:   US-ORv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Wi3\n",
      "No data for station:   CA-Obs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Wi2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Wi9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   DE-Spw\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   FR-Pue\n",
      "No data for station:   US-Ne3\n",
      "No data for station:   DE-RuS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   DE-Zrk\n",
      "No data for station:   CA-Man\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Twt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Wi7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   IT-La2\n",
      "No data for station:   US-Wi1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Wi8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   GL-ZaF\n",
      "No data for station:   US-Myb\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-WPT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   DE-RuR\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   FR-Fon\n",
      "No data for station:   US-Ne2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   CG-Tch\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   RU-Che\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Tw1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   RU-Cok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-Wi0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   FI-Let\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   SJ-Adv\n",
      "Number of FLUXNET stations with CI calculated: 150\n",
      "Minimum number of months used for JJA mean CI: 1 \n",
      "Maximum number of months used for JJA mean CI: 57 \n"
     ]
    }
   ],
   "source": [
    "if fluxnet_comparison == True:\n",
    "    ## Metadata files\n",
    "    siteInfoFile = os.path.join(obs_data_dir, obsDir, \"SiteList.csv\")\n",
    "    siteInfoDF = pd.read_csv(siteInfoFile)\n",
    "\n",
    "    metadataFile = os.path.join(\n",
    "        obs_data_dir,\n",
    "        obsDir,\n",
    "        \"FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.csv\",\n",
    "    )\n",
    "    metadataDF = pd.read_csv(metadataFile)\n",
    "\n",
    "    ## List of all station files\n",
    "    dataFiles = glob.glob(os.path.join(obs_data_dir, obsDir, \"FLX_*/*SUBSET_DD*\"))\n",
    "\n",
    "    # Set up a few empty arrays to save data into\n",
    "    terraCI_fluxnetConverted = np.full(\n",
    "        [len(dataFiles), 4], np.nan\n",
    "    )  # CI using kg/m2 soil water content  [nStations, seasons]\n",
    "\n",
    "    # Also save out some data on each station\n",
    "    startTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
    "    endTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
    "    lat_fluxnet = np.full([len(dataFiles)], np.nan)\n",
    "    lon_fluxnet = np.full([len(dataFiles)], np.nan)\n",
    "    SWCdepth = np.full([len(dataFiles)], np.nan)\n",
    "\n",
    "    stationID = []\n",
    "    stationID_converted = []\n",
    "\n",
    "    allStationID = []\n",
    "\n",
    "    # Variables I want returned:\n",
    "    varNames = [\"TIMESTAMP\", \"H_F_MDS\", \"SWC_F_MDS_1\", \"SWC_F_MDS_1_QC\"]\n",
    "\n",
    "    # Loop over each station (data file)\n",
    "    for iStation in range(len(dataFiles)):\n",
    "\n",
    "        # Read in data\n",
    "        # ----------------------------------------------------------\n",
    "        fluxnetID, fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
    "\n",
    "        # Save lat and lon for this station\n",
    "        # ----------------------------------------------------------\n",
    "        indStation = int(np.where(fluxnetID == siteInfoDF[\"SITE_ID\"])[0][0])\n",
    "        lat_fluxnet[iStation] = siteInfoDF[\"LOCATION_LAT\"].values[indStation]\n",
    "        lon_fluxnet[iStation] = siteInfoDF[\"LOCATION_LONG\"].values[indStation]\n",
    "        allStationID.append(fluxnetID)\n",
    "\n",
    "        # Check that there was data saved for this particular site:\n",
    "        # ----------------------------------------------------------\n",
    "        if type(fluxnetDS) == int:\n",
    "            print(\"No data for station: %8s\" % fluxnetID)\n",
    "\n",
    "        elif (np.all(np.isnan(fluxnetDS[\"H_F_MDS\"])) == True) | (\n",
    "            np.all(np.isnan(fluxnetDS[\"SWC_F_MDS_1\"])) == True\n",
    "        ):\n",
    "            print(\"No data for station: %8s\" % fluxnetID)\n",
    "\n",
    "        # If data is present:\n",
    "        # ----------------------------------------------------------\n",
    "        else:\n",
    "            # Only consider where data is actually present for selected vars\n",
    "            iReal = np.where(\n",
    "                (np.isfinite(fluxnetDS[\"SWC_F_MDS_1\"]) == True)\n",
    "                & (np.isfinite(fluxnetDS[\"H_F_MDS\"]) == True)\n",
    "            )[0]\n",
    "            fluxnetDS = fluxnetDS.isel(TIMESTAMP=iReal)\n",
    "\n",
    "            stationID.append(fluxnetID)\n",
    "\n",
    "            # Convert units from volumetric (%) to mass (kg/m2)\n",
    "            # -------------------------------------------------\n",
    "            #   Step 1: Convert from % to fraction\n",
    "            fracSM = (fluxnetDS[\"SWC_F_MDS_1\"].values) / 100.0\n",
    "\n",
    "            #    Step 2: Need to use depth of obs in conversion\n",
    "            metaData_station = metadataDF[metadataDF.SITE_ID == fluxnetID]\n",
    "            iSWC = np.where(metaData_station.DATAVALUE == \"SWC_F_MDS_1\")[0]\n",
    "            #  Some locations (5) have two depths\n",
    "            if len(iSWC) > 1:\n",
    "                for iDepth in range(len(iSWC)):\n",
    "                    SWC_DF = metaData_station[iSWC[iDepth] : iSWC[iDepth] + 4]\n",
    "\n",
    "                    depth = np.asarray(\n",
    "                        SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
    "                    ).astype(float)\n",
    "                    depthDay = np.asarray(\n",
    "                        SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_DATE\"].DATAVALUE.values[0]\n",
    "                    ).astype(int)\n",
    "                    depthDay = int(\n",
    "                        str(depthDay)[:8]\n",
    "                    )  # Some weird ones have time attached; don't want that\n",
    "                    depthDay = pd.to_datetime(depthDay, format=\"%Y%m%d\")\n",
    "\n",
    "                    # Keep deepest level as the depth for station\n",
    "                    if iDepth == 0:\n",
    "                        SWCdepth[iStation] = depth\n",
    "                        convertSM = fracSM * 1000.0 * np.abs(depth)\n",
    "                    else:\n",
    "                        # Use date as break point for getting kg/m2 SWC\n",
    "                        #  Eq: SWC_kgm2 = SWC_vol [m3/m3] * 1000 [kg/m3] * depth [m]\n",
    "                        dateArr = pd.DatetimeIndex(fluxnetDS.TIMESTAMP.values)\n",
    "                        iTime = int(np.where(dateArr == depthDay)[0][0])\n",
    "                        convertSM[iTime::] = (fracSM[iTime::]) * 1000.0 * np.abs(depth)\n",
    "\n",
    "                        # Keep deepest level as the depth for station\n",
    "                        if depth < SWCdepth[iStation]:\n",
    "                            SWCdepth[iStation] = depth\n",
    "\n",
    "                stationID_converted.append(fluxnetID)\n",
    "\n",
    "            # If station only has one level recorded, things are a bit easier:\n",
    "            else:\n",
    "                SWC_DF = metaData_station[iSWC[0] : iSWC[0] + 4]\n",
    "                SWCdepth[iStation] = np.asarray(\n",
    "                    SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
    "                ).astype(float)\n",
    "                convertSM = fracSM * 1000.0 * np.abs(SWCdepth[iStation])\n",
    "\n",
    "                stationID_converted.append(fluxnetID)\n",
    "\n",
    "            # Save converted soil moisture to dataset\n",
    "            fluxnetDS[\"SWC_F_MDS_1_convert\"] = ((\"TIMESTAMP\"), convertSM)\n",
    "\n",
    "            # Save first and last time used for computing CI\n",
    "            # ----------------------------------------------\n",
    "            startTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[0]\n",
    "            endTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[-1]\n",
    "\n",
    "            # Compute terrestrial coupling metric\n",
    "            # -----------------------------------\n",
    "            terraLeg = compute_couplingIndex_FLUXNET(\n",
    "                \"SWC_F_MDS_1_convert\", \"H_F_MDS\", fluxnetDS\n",
    "            )\n",
    "\n",
    "            # If there's less than one full year of data, don't use station\n",
    "            #   (i.e., as long as all 4 seasons are defined, save values)\n",
    "            if np.shape(terraLeg)[0] == 4:\n",
    "                terraCI_fluxnetConverted[iStation, :] = terraLeg\n",
    "\n",
    "    seasons = terraLeg.season\n",
    "\n",
    "    ## Print some useful information\n",
    "    print(\n",
    "        \"Number of FLUXNET stations with CI calculated: %i\"\n",
    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
    "    )\n",
    "\n",
    "    # How many months go into each calculation of CI for JJA?\n",
    "    nMonths = np.full([len(dataFiles)], np.nan)\n",
    "\n",
    "    for iSt in range(len(dataFiles)):\n",
    "        if np.isfinite(terraCI_fluxnetConverted[iSt, 1]):\n",
    "            dateRange = pd.date_range(\n",
    "                start=startTime_fluxnet[iSt], end=endTime_fluxnet[iSt], freq=\"ME\"\n",
    "            )\n",
    "            nMonths[iSt] = len(\n",
    "                np.where((dateRange.month >= 6) & (dateRange.month <= 8))[0]\n",
    "            )\n",
    "\n",
    "    print(\n",
    "        \"Minimum number of months used for JJA mean CI: %i \" % int(np.nanmin(nMonths))\n",
    "    )\n",
    "    print(\n",
    "        \"Maximum number of months used for JJA mean CI: %i \" % int(np.nanmax(nMonths))\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd29ba9d-bbbb-4f59-9829-250018215b53",
   "metadata": {
    "papermill": {
     "duration": 0.006604,
     "end_time": "2025-10-10T23:07:23.555974",
     "exception": false,
     "start_time": "2025-10-10T23:07:23.549370",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Make some choices on limiting which stations are used*\n",
    "- Let's limit usage to depths less than 20 cm (arbitrary, but I don't want us using non-surface soil moisture for this application). This will eliminate 11 stations.\n",
    "- It would also be good to put some time limits on this. So let's say the observations need to have at least 9 months of data for JJA means (3-years). Otherwise, set terraCI to np.nan again so we don't use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "13afbfb4-2040-403c-9d82-3e370f47ec5d",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:23.570385Z",
     "iopub.status.busy": "2025-10-10T23:07:23.569988Z",
     "iopub.status.idle": "2025-10-10T23:07:23.575161Z",
     "shell.execute_reply": "2025-10-10T23:07:23.574762Z"
    },
    "papermill": {
     "duration": 0.013254,
     "end_time": "2025-10-10T23:07:23.575834",
     "exception": false,
     "start_time": "2025-10-10T23:07:23.562580",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of FLUXNET stations to use with reasonable depths of SWC: 139\n",
      "Number of FLUXNET stations to use with 3+ years of JJA data: 115\n"
     ]
    }
   ],
   "source": [
    "if fluxnet_comparison == True:\n",
    "\n",
    "    # Get stations with SWC from below 20 cm (or equal to zero)\n",
    "    iLimit = np.where((SWCdepth == 0.0) | (SWCdepth < -0.2))[0]\n",
    "\n",
    "    # Set the terrestrial leg of CI to missing so we don't consider those\n",
    "    terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
    "\n",
    "    print(\n",
    "        \"Number of FLUXNET stations to use with reasonable depths of SWC: %i\"\n",
    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
    "    )\n",
    "\n",
    "    # Get stations with less than 9 months used for JJA terrestrial CI\n",
    "    iLimit = np.where(nMonths < 9)[0]\n",
    "\n",
    "    # Set to missing so we don't consider stations with less than three years of data going into the average\n",
    "    terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
    "\n",
    "    print(\n",
    "        \"Number of FLUXNET stations to use with 3+ years of JJA data: %i\"\n",
    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc387253-cdd7-4a36-956b-8ce548e963bd",
   "metadata": {
    "papermill": {
     "duration": 0.006524,
     "end_time": "2025-10-10T23:07:23.589043",
     "exception": false,
     "start_time": "2025-10-10T23:07:23.582519",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Make plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67253fd1-d2f7-45fe-a59f-215303b93c06",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:23.602864Z",
     "iopub.status.busy": "2025-10-10T23:07:23.602642Z",
     "iopub.status.idle": "2025-10-10T23:07:26.200717Z",
     "shell.execute_reply": "2025-10-10T23:07:26.200264Z"
    },
    "papermill": {
     "duration": 2.60707,
     "end_time": "2025-10-10T23:07:26.202635",
     "exception": false,
     "start_time": "2025-10-10T23:07:23.595565",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
       "  );\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "  border: 2px solid transparent !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0) !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;uxarray.Grid&gt;\n",
       "Original Grid Type: ESMF\n",
       "Grid Dimensions:\n",
       "  * n_node: 48602\n",
       "  * n_face: 48600\n",
       "  * n_max_face_nodes: 4\n",
       "Grid Coordinates (Spherical):\n",
       "  * node_lon: (48602,)\n",
       "  * node_lat: (48602,)\n",
       "  * face_lon: (48600,)\n",
       "  * face_lat: (48600,)\n",
       "Grid Coordinates (Cartesian):\n",
       "Grid Connectivity Variables:\n",
       "  * face_node_connectivity: (48600, 4)\n",
       "Grid Descriptor Variables:\n",
       "  * n_nodes_per_face: (48600,)\n",
       "</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>uxarray.Grid</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dd7c777c-c68a-42dd-ba7e-5e291e97196e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd7c777c-c68a-42dd-ba7e-5e291e97196e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>n_node</span>: 48602</li><li><span>n_face</span>: 48600</li><li><span>n_max_face_nodes</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-17c52fdd-e1ac-433d-b7ee-0c8aa79c787f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17c52fdd-e1ac-433d-b7ee-0c8aa79c787f' class='xr-section-summary' >Spherical Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>node_lon</span></div><div class='xr-var-dims'>(n_node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-159.9 -162.3 ... 170.2 167.8</div><input id='attrs-8a248cef-51ad-4d0c-a08e-798e14c839b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a248cef-51ad-4d0c-a08e-798e14c839b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20a0d00b-913a-49b3-9a13-998e6d725b30' class='xr-var-data-in' type='checkbox'><label for='data-20a0d00b-913a-49b3-9a13-998e6d725b30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-159.89124086, -162.26235981, -164.68737511, ...,  172.60921368,\n",
       "        170.18081007,  167.78169785], shape=(48602,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>node_lat</span></div><div class='xr-var-dims'>(n_node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a2e108ec-fff4-4088-86f0-8dabaf11bd33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2e108ec-fff4-4088-86f0-8dabaf11bd33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01e4c770-fa53-49ea-a895-6302d2b76458' class='xr-var-data-in' type='checkbox'><label for='data-01e4c770-fa53-49ea-a895-6302d2b76458' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long name :</span></dt><dd>Latitude of the corner nodes of each face</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[48602 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>face_lon</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-44.5 -43.5 -42.5 ... 136.0 135.0</div><input id='attrs-804af301-6a06-4708-bf7a-2384987048d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-804af301-6a06-4708-bf7a-2384987048d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff1bec1d-469a-427b-b3ea-9493a7ac3617' class='xr-var-data-in' type='checkbox'><label for='data-ff1bec1d-469a-427b-b3ea-9493a7ac3617' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-44.5       , -43.5       , -42.5       , ..., 137.00152343,\n",
       "       136.00045705, 135.        ], shape=(48600,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>face_lat</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-279e8cc2-3498-4f15-957e-79ea3fa1d729' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-279e8cc2-3498-4f15-957e-79ea3fa1d729' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66ac268e-9b0e-4d30-a8c3-8d22f768783e' class='xr-var-data-in' type='checkbox'><label for='data-66ac268e-9b0e-4d30-a8c3-8d22f768783e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long name :</span></dt><dd>Latitude of the center of each face</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[48600 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b77633d9-2c77-4d1c-9903-9c5092576751' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b77633d9-2c77-4d1c-9903-9c5092576751' class='xr-section-summary'  title='Expand/collapse section'>Cartesian Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5aade721-0001-4e76-9a4a-51d2cbfdde1c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5aade721-0001-4e76-9a4a-51d2cbfdde1c' class='xr-section-summary' >Connectivity: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>face_node_connectivity</span></div><div class='xr-var-dims'>(n_face, n_max_face_nodes)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>6985 6986 6987 ... 36983 38576</div><input id='attrs-ab0b794d-2aec-48f5-98b6-a5a6be5ec720' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab0b794d-2aec-48f5-98b6-a5a6be5ec720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82ca3bd0-271f-4986-a586-f20d7e829760' class='xr-var-data-in' type='checkbox'><label for='data-82ca3bd0-271f-4986-a586-f20d7e829760' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>face_node_connectivity</dd><dt><span>long name :</span></dt><dd>Maps every face to its corner nodes.</dd><dt><span>start_index :</span></dt><dd>0</dd><dt><span>_FillValue :</span></dt><dd>-9223372036854775808</dd></dl></div><div class='xr-var-data'><pre>array([[ 6985,  6986,  6987,  6988],\n",
       "       [ 6986,  6989,  6990,  6987],\n",
       "       [ 6989,  6991,  6992,  6990],\n",
       "       ...,\n",
       "       [41616, 41619, 38577, 38578],\n",
       "       [41619, 41620, 38576, 38577],\n",
       "       [41620, 36982, 36983, 38576]], shape=(48600, 4))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da2969d8-fc6c-47f7-aa40-9c40e5bfb80d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da2969d8-fc6c-47f7-aa40-9c40e5bfb80d' class='xr-section-summary' >Descriptors: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>n_nodes_per_face</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4</div><input id='attrs-b28b41d3-9144-4f09-abf4-56eac10b8adb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b28b41d3-9144-4f09-abf4-56eac10b8adb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-443d7b0b-849f-47a5-bcb6-bf0d34ec1193' class='xr-var-data-in' type='checkbox'><label for='data-443d7b0b-849f-47a5-bcb6-bf0d34ec1193' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>n_nodes_per_face</dd><dt><span>long name :</span></dt><dd>Number of nodes per face</dd></dl></div><div class='xr-var-data'><pre>array([4, 4, 4, ..., 4, 4, 4], shape=(48600,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d9ee935-c54f-4be1-bb05-f037b3aba80e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d9ee935-c54f-4be1-bb05-f037b3aba80e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source_grid_spec :</span></dt><dd>ESMF</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<uxarray.Grid>\n",
       "Original Grid Type: ESMF\n",
       "Grid Dimensions:\n",
       "  * n_node: 48602\n",
       "  * n_face: 48600\n",
       "  * n_max_face_nodes: 4\n",
       "Grid Coordinates (Spherical):\n",
       "  * node_lon: (48602,)\n",
       "  * node_lat: (48602,)\n",
       "  * face_lon: (48600,)\n",
       "  * face_lat: (48600,)\n",
       "Grid Coordinates (Cartesian):\n",
       "Grid Connectivity Variables:\n",
       "  * face_node_connectivity: (48600, 4)\n",
       "Grid Descriptor Variables:\n",
       "  * n_nodes_per_face: (48600,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Load coupling index with uxarray\n",
    "gridFile = (\n",
    "    \"/glade/p/cesmdata/cseg/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc\"\n",
    ")\n",
    "uxgrid = uxr.open_grid(gridFile)\n",
    "uxgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43206b67-1313-4b61-94ea-b50b85a3d50c",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:26.227286Z",
     "iopub.status.busy": "2025-10-10T23:07:26.227077Z",
     "iopub.status.idle": "2025-10-10T23:07:26.293868Z",
     "shell.execute_reply": "2025-10-10T23:07:26.293461Z"
    },
    "papermill": {
     "duration": 0.078847,
     "end_time": "2025-10-10T23:07:26.295231",
     "exception": false,
     "start_time": "2025-10-10T23:07:26.216384",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coupling index is now ready to go\n"
     ]
    }
   ],
   "source": [
    "for iCase in range(len(caseNames)):\n",
    "    TCI_filePath = os.path.join(\n",
    "        cupid_temp, f\"{caseNames[iCase]}_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
    "    )\n",
    "\n",
    "    couplingIndex_case = uxr.open_dataset(gridFile, TCI_filePath)\n",
    "    # Rename the variable:\n",
    "    couplingIndex_case = couplingIndex_case.rename(\n",
    "        {\"__xarray_dataarray_variable__\": \"CouplingIndex\"}\n",
    "    )\n",
    "\n",
    "    # Assign case coord\n",
    "    couplingIndex_case = couplingIndex_case.assign_coords(\n",
    "        {\"case\": couplingIndex_case.case.values}\n",
    "    )\n",
    "\n",
    "    # Return all the cases in a single dataset\n",
    "    if iCase == 0:\n",
    "        couplingIndex_DS = couplingIndex_case\n",
    "        del couplingIndex_case\n",
    "    else:\n",
    "        couplingIndex_DS = uxr.concat([couplingIndex_DS, couplingIndex_case], \"case\")\n",
    "        del couplingIndex_case\n",
    "\n",
    "print(\"Coupling index is now ready to go\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9e22cd0-870e-47cb-b5c7-2e57bbd9af16",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:26.320424Z",
     "iopub.status.busy": "2025-10-10T23:07:26.320227Z",
     "iopub.status.idle": "2025-10-10T23:07:26.325674Z",
     "shell.execute_reply": "2025-10-10T23:07:26.325302Z"
    },
    "papermill": {
     "duration": 0.016889,
     "end_time": "2025-10-10T23:07:26.326474",
     "exception": false,
     "start_time": "2025-10-10T23:07:26.309585",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def make_cmap(colors, position=None, bit=False):\n",
    "    \"\"\"\n",
    "    make_cmap takes a list of tuples which contain RGB values. The RGB\n",
    "    values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
    "    True when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
    "    a cmap with equally spaced colors.\n",
    "    Arrange your tuples so that the first color is the lowest value for the\n",
    "    colorbar and the last is the highest.\n",
    "    position contains values from 0 to 1 to dictate the location of each color.\n",
    "    \"\"\"\n",
    "\n",
    "    import matplotlib as mpl\n",
    "    import numpy as np\n",
    "\n",
    "    bit_rgb = np.linspace(0, 1, 256)\n",
    "    if position == None:\n",
    "        position = np.linspace(0, 1, len(colors))\n",
    "    else:\n",
    "        if len(position) != len(colors):\n",
    "            sys.exit(\"position length must be the same as colors\")\n",
    "        elif position[0] != 0 or position[-1] != 1:\n",
    "            sys.exit(\"position must start with 0 and end with 1\")\n",
    "\n",
    "    if bit:\n",
    "        for i in range(len(colors)):\n",
    "            colors[i] = (\n",
    "                bit_rgb[colors[i][0]],\n",
    "                bit_rgb[colors[i][1]],\n",
    "                bit_rgb[colors[i][2]],\n",
    "            )\n",
    "\n",
    "    cdict = {\"red\": [], \"green\": [], \"blue\": []}\n",
    "    for pos, color in zip(position, colors):\n",
    "        cdict[\"red\"].append((pos, color[0], color[0]))\n",
    "        cdict[\"green\"].append((pos, color[1], color[1]))\n",
    "        cdict[\"blue\"].append((pos, color[2], color[2]))\n",
    "\n",
    "    cmap = mpl.colors.LinearSegmentedColormap(\"my_colormap\", cdict, 256)\n",
    "    return cmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6e3e7b6f-98a8-4e21-8378-adfd8b399823",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:26.347278Z",
     "iopub.status.busy": "2025-10-10T23:07:26.347103Z",
     "iopub.status.idle": "2025-10-10T23:07:26.350182Z",
     "shell.execute_reply": "2025-10-10T23:07:26.349825Z"
    },
    "papermill": {
     "duration": 0.014414,
     "end_time": "2025-10-10T23:07:26.351049",
     "exception": false,
     "start_time": "2025-10-10T23:07:26.336635",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Create a list of RGB tuples for terrestrial leg (SM, SHFLX)\n",
    "colorsList_SMvSHF = [\n",
    "    (124, 135, 181),\n",
    "    (107, 109, 161),\n",
    "    (51, 82, 120),\n",
    "    (49, 114, 127),\n",
    "    (97, 181, 89),\n",
    "    (200, 218, 102),\n",
    "    (255, 242, 116),\n",
    "    (238, 164, 58),\n",
    "]  # This example uses the 8-bit RGB\n",
    "my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "882b1c92-e5b9-4cd0-aa55-4e04aca3c50c",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:26.371573Z",
     "iopub.status.busy": "2025-10-10T23:07:26.371400Z",
     "iopub.status.idle": "2025-10-10T23:07:26.378533Z",
     "shell.execute_reply": "2025-10-10T23:07:26.378175Z"
    },
    "papermill": {
     "duration": 0.018463,
     "end_time": "2025-10-10T23:07:26.379382",
     "exception": false,
     "start_time": "2025-10-10T23:07:26.360919",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def plotTCI_case(seasonstr, caseSel=None):\n",
    "\n",
    "    transform = ccrs.PlateCarree()\n",
    "    projection = ccrs.PlateCarree()\n",
    "\n",
    "    if caseSel:\n",
    "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
    "        collection = (\n",
    "            couplingIndex_DS[\"CouplingIndex\"]\n",
    "            .sel(season=seasonstr)\n",
    "            .isel(case=caseSel)\n",
    "            .to_polycollection()\n",
    "        )\n",
    "\n",
    "        collection.set_transform(transform)\n",
    "        collection.set_antialiased(False)\n",
    "        collection.set_cmap(my_cmap_SMvSHF)\n",
    "        collection.set_clim(vmin=-20, vmax=5)\n",
    "\n",
    "        fig, ax = plt.subplots(\n",
    "            1,\n",
    "            1,\n",
    "            figsize=(12, 5),\n",
    "            facecolor=\"w\",\n",
    "            constrained_layout=True,\n",
    "            subplot_kw=dict(projection=projection),\n",
    "        )\n",
    "\n",
    "        ax.coastlines()\n",
    "        ax.add_collection(collection)\n",
    "        ax.set_global()\n",
    "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
    "        ax.set_title(\n",
    "            seasonstr\n",
    "            + \" Coupling Index: \"\n",
    "            + str(couplingIndex_DS.case.isel(case=caseSel).values)\n",
    "        )\n",
    "\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "\n",
    "    else:\n",
    "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
    "        collection = (\n",
    "            couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).to_polycollection()\n",
    "        )\n",
    "\n",
    "        collection.set_transform(transform)\n",
    "        collection.set_antialiased(False)\n",
    "        collection.set_cmap(my_cmap_SMvSHF)\n",
    "        collection.set_clim(vmin=-20, vmax=5)\n",
    "\n",
    "        fig, ax = plt.subplots(\n",
    "            1,\n",
    "            1,\n",
    "            figsize=(12, 5),\n",
    "            facecolor=\"w\",\n",
    "            constrained_layout=True,\n",
    "            subplot_kw=dict(projection=projection),\n",
    "        )\n",
    "\n",
    "        ax.coastlines()\n",
    "        ax.add_collection(collection)\n",
    "        ax.set_global()\n",
    "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
    "        ax.set_title(\n",
    "            seasonstr + \" Coupling Index: \" + str(couplingIndex_DS.case.values)\n",
    "        )\n",
    "\n",
    "        if fluxnet_comparison == True:\n",
    "            ## Add FLUXNET obs\n",
    "            iSeason = np.where(seasons == seasonstr)[0]\n",
    "            iStations = np.where(\n",
    "                np.isfinite(terraCI_fluxnetConverted[:, iSeason]) == True\n",
    "            )[0]\n",
    "            norm_CI = matplotlib.colors.Normalize(vmin=-20, vmax=5)\n",
    "\n",
    "            ax.scatter(\n",
    "                lon_fluxnet[iStations],\n",
    "                lat_fluxnet[iStations],\n",
    "                c=terraCI_fluxnetConverted[iStations, iSeason],\n",
    "                cmap=my_cmap_SMvSHF,\n",
    "                norm=norm_CI,\n",
    "                edgecolor=\"k\",\n",
    "                s=30,\n",
    "                marker=\"o\",\n",
    "                transform=ccrs.PlateCarree(),\n",
    "            )\n",
    "\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6003435f-f16d-4c8b-80da-40f9cb571560",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:26.399882Z",
     "iopub.status.busy": "2025-10-10T23:07:26.399710Z",
     "iopub.status.idle": "2025-10-10T23:07:26.406798Z",
     "shell.execute_reply": "2025-10-10T23:07:26.406395Z"
    },
    "papermill": {
     "duration": 0.018414,
     "end_time": "2025-10-10T23:07:26.407671",
     "exception": false,
     "start_time": "2025-10-10T23:07:26.389257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plotScatter(seasonstr, caseSel=None):\n",
    "    node_lats = uxgrid.face_lat.values\n",
    "    node_lons = uxgrid.face_lon.values\n",
    "\n",
    "    predictions = []\n",
    "\n",
    "    fig, axs = plt.subplots(1, 1, figsize=(8, 8))\n",
    "\n",
    "    iSeason = np.where(seasons == seasonstr)[0]\n",
    "    iStations = np.where(np.isfinite(terraCI_fluxnetConverted[:, iSeason]) == True)[0]\n",
    "\n",
    "    for iPoint in range(len(iStations)):\n",
    "        this_lon = lon_fluxnet[iStations[iPoint]]  # lon1\n",
    "        this_lat = lat_fluxnet[iStations[iPoint]]  # lat1\n",
    "        obs_point = np.array((this_lon, this_lat))\n",
    "\n",
    "        # Get subset of relevant points\n",
    "        i = np.where(\n",
    "            (node_lats >= (this_lat - 2))\n",
    "            & (node_lats <= (this_lat + 2))\n",
    "            & (node_lons >= (this_lon - 2))\n",
    "            & (node_lons <= (this_lon + 2))\n",
    "        )[0]\n",
    "\n",
    "        minDistance = 100\n",
    "        for iSelClose in range(len(i)):\n",
    "            # Find point in uxarray? Use euclidian distance\n",
    "            distance = np.linalg.norm(\n",
    "                obs_point - np.array((node_lons[i[iSelClose]], node_lats[i[iSelClose]]))\n",
    "            )\n",
    "\n",
    "            if (distance < minDistance) & (\n",
    "                np.isfinite(\n",
    "                    couplingIndex_DS[\"CouplingIndex\"]\n",
    "                    .sel(season=\"JJA\")\n",
    "                    .values[i[iSelClose]]\n",
    "                )\n",
    "                == True\n",
    "            ):\n",
    "                minDistance = distance\n",
    "                selLoc = i[iSelClose]\n",
    "\n",
    "        predictions = np.append(\n",
    "            predictions,\n",
    "            couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).values[selLoc],\n",
    "        )\n",
    "        axs.plot(\n",
    "            terraCI_fluxnetConverted[iPoint, iSeason],\n",
    "            couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).values[selLoc],\n",
    "            \"bo\",\n",
    "            alpha=0.5,\n",
    "        )\n",
    "\n",
    "    axs.set_xlabel(\"FLUXNET CI Value\", fontsize=12)\n",
    "    axs.set_ylabel(\"CESM CI Value\", fontsize=12)\n",
    "    axs.set_title(\n",
    "        \"Individual station CI vs. nearest gridcell CI: \" + seasonstr, fontsize=14\n",
    "    )\n",
    "    axs.set_xlim([-25, 25])\n",
    "    axs.set_ylim([-25, 25])\n",
    "    axs.plot(np.arange(-25, 26), np.arange(-25, 26), \"k--\")\n",
    "\n",
    "    rmse = np.sqrt(\n",
    "        ((predictions - terraCI_fluxnetConverted[iStations, iSeason]) ** 2).mean()\n",
    "    )\n",
    "    axs.text(0.05, 0.95, \"RMSE: \" + str(rmse), transform=axs.transAxes)\n",
    "\n",
    "    return axs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d43b6525-aa30-47a4-8b66-ca70a805a13a",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T23:07:26.428915Z",
     "iopub.status.busy": "2025-10-10T23:07:26.428684Z",
     "iopub.status.idle": "2025-10-10T23:07:37.756676Z",
     "shell.execute_reply": "2025-10-10T23:07:37.756165Z"
    },
    "papermill": {
     "duration": 11.339478,
     "end_time": "2025-10-10T23:07:37.757423",
     "exception": false,
     "start_time": "2025-10-10T23:07:26.417945",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if len(caseNames) == 1:\n",
    "    plotTCI_case(\"JJA\", None)\n",
    "    plotTCI_case(\"DJF\", None)\n",
    "\n",
    "    plotScatter(\"JJA\", None)\n",
    "    plotScatter(\"DJF\", None)\n",
    "else:\n",
    "    for iCase in range(len(caseNames)):\n",
    "        plotTCI_case(\"JJA\", iCase)\n",
    "        plotTCI_case(\"DJF\", iCase)\n",
    "\n",
    "        plotScatter(\"JJA\", iCase)\n",
    "        plotScatter(\"DJF\", iCase)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cupid-analysis",
   "language": "python",
   "name": "cupid-analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "papermill": {
   "duration": 64.081075,
   "end_time": "2025-10-10T23:07:40.294496",
   "exception": null,
   "input_path": "/glade/derecho/scratch/richling/tmp/tmpqkzzxdke.ipynb",
   "output_path": "/glade/work/richling/CUPid_pr_test/CUPiD/examples/key_metrics/computed_notebooks/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb",
   "parameters": {
    "CESM_output_dir": "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing",
    "base_case_name": "b.e23_alpha17f.BLT1850.ne30_t232.092",
    "base_end_date": "0101-01-01",
    "base_start_date": "0001-01-01",
    "case_name": "b.e30_beta02.BLT1850.ne30_t232.104",
    "clmFile_h": "h0",
    "end_date": "0101-01-01",
    "fluxnet_comparison": true,
    "lc_kwargs": {
     "threads_per_worker": 1
    },
    "obsDir": "lnd/analysis_datasets/ungridded/timeseries/FLUXNET2015/",
    "obs_data_dir": "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data",
    "product": "/glade/work/richling/CUPid_pr_test/CUPiD/examples/key_metrics/computed_notebooks//lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb",
    "serial": false,
    "start_date": "0001-01-01",
    "subset_kwargs": {},
    "ts_dir": null
   },
   "start_time": "2025-10-10T23:06:36.213421"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}