load_VH_VV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0228491-8fa3-4a69-b093-8fdf0a6b02a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import boto3\n",
    "from datetime import datetime as dt\n",
    "\n",
    "# EASI defaults\n",
    "easinotebooksrepo = '/home/jovyan/easi-notebooks'\n",
    "if easinotebooksrepo not in sys.path: sys.path.append(easinotebooksrepo)\n",
    "from easi_tools import unset_cachingproxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e23de1d-750c-4426-a972-a82320869248",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
       "  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n",
       "  var reloading = false;\n",
       "  var Bokeh = root.Bokeh;\n",
       "  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\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",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    if (!reloading) {\n",
       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\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() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
       "      require([\"jspanel\"], function(jsPanel) {\n",
       "\twindow.jsPanel = jsPanel\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-modal\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-tooltip\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-hint\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-layout\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-contextmenu\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-dock\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"gridstack\"], function(GridStack) {\n",
       "\twindow.GridStack = GridStack\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"notyf\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      root._bokeh_is_loading = css_urls.length + 9;\n",
       "    } else {\n",
       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
       "    }\n",
       "\n",
       "    var existing_stylesheets = []\n",
       "    var links = document.getElementsByTagName('link')\n",
       "    for (var i = 0; i < links.length; i++) {\n",
       "      var link = links[i]\n",
       "      if (link.href != null) {\n",
       "\texisting_stylesheets.push(link.href)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
       "\ton_load()\n",
       "\tcontinue;\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",
       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    var existing_scripts = []\n",
       "    var scripts = document.getElementsByTagName('script')\n",
       "    for (var i = 0; i < scripts.length; i++) {\n",
       "      var script = scripts[i]\n",
       "      if (script.src != null) {\n",
       "\texisting_scripts.push(script.src)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\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",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\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",
       "      var url = js_exports[name];\n",
       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\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",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var js_exports = {};\n",
       "  var css_urls = [];\n",
       "  var 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 (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }\n",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "\tvar NewBokeh = root.Bokeh;\n",
       "\tif (Bokeh.versions === undefined) {\n",
       "\t  Bokeh.versions = new Map();\n",
       "\t}\n",
       "\tif (NewBokeh.version !== Bokeh.version) {\n",
       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "\t}\n",
       "\troot.Bokeh = Bokeh;\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",
       "      root._bokeh_is_initializing = false;\n",
       "      root._bokeh_onload_callbacks = undefined;\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",
       "      Bokeh = root.Bokeh;\n",
       "      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "      root._bokeh_is_initializing = true\n",
       "      root._bokeh_onload_callbacks = []\n",
       "      if (!reloading && (!bokeh_loaded || is_dev)) {\n",
       "\troot.Bokeh = undefined;\n",
       "      }\n",
       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "\trun_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  var force = true;\n  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\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      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\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() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\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    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\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      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\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      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\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  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var 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 (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\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      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\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      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_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": {
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       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
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       "    var nchildren = toinsert.length;\n",
       "    var html_node = toinsert[nchildren-1].children[0];\n",
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       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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"
    }
   ],
   "source": [
    "import time\n",
    "import rasterio\n",
    "import datacube\n",
    "from datacube.utils.cog import write_cog\n",
    "import rasterio.warp\n",
    "from utils import load_data_geo\n",
    "import geopandas as gpd\n",
    "from deafrica_tools.areaofinterest import define_area\n",
    "from datacube.utils.geometry import Geometry\n",
    "import xarray as xr\n",
    "import rioxarray\n",
    "import os, sys\n",
    "os.environ['USE_PYGEOS'] = '0'\n",
    "from IPython.display import Markdown\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_rows\", None)\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from dask.distributed import Client, LocalCluster\n",
    "easinotebooksrepo = '/home/jovyan/easi-notebooks'\n",
    "if easinotebooksrepo not in sys.path: sys.path.append(easinotebooksrepo)\n",
    "from easi_tools import EasiDefaults, xarray_object_size, notebook_utils\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48aca2d8-b908-41c1-ac77-108679ee35f7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# VH_file = \"Sen1_VH_10.21-10.22_update_new.tif\"\n",
    "# VV_file = \"Sen1_VV_10.21-10.22_update.tif\"\n",
    "\n",
    "from osgeo import gdal\n",
    "gdal.UseExceptions()\n",
    "gdal.PushErrorHandler('CPLQuietErrorHandler')\n",
    "\n",
    "def load_ctu_s1_data():\n",
    "    vvpath = 's3://easi-asia-dc-data/staging/ctu/sentinel-1/vv-0922_0923-full_ST.tif'\n",
    "    vhpath = 's3://easi-asia-dc-data/staging/ctu/sentinel-1/vh-0922_0923-full_ST.tif'\n",
    "    with unset_cachingproxy():\n",
    "        vv = rioxarray.open_rasterio(vvpath)\n",
    "        vh = rioxarray.open_rasterio(vhpath)\n",
    "        return vv,vh\n",
    "    \n",
    "ds, dsvh = load_ctu_s1_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bbe312c-5da7-4c26-a32c-feccf3394786",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds_vhvv = xr.concat([ds, dsvh], dim='band')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "846729df-2483-4f23-bf09-6276d98aa1ba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "del ds\n",
    "del dsvh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c61cb34-0bf5-410d-b43d-629117b46825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Water1</td>\n",
       "      <td>POINT (620223.628 1072681.101)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Water1</td>\n",
       "      <td>POINT (621057.521 1071074.602)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Water1</td>\n",
       "      <td>POINT (621891.602 1069430.621)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Water1</td>\n",
       "      <td>POINT (622762.564 1068048.689)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water1</td>\n",
       "      <td>POINT (623256.484 1066777.765)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name                        geometry\n",
       "0  Water1  POINT (620223.628 1072681.101)\n",
       "1  Water1  POINT (621057.521 1071074.602)\n",
       "2  Water1  POINT (621891.602 1069430.621)\n",
       "3  Water1  POINT (622762.564 1068048.689)\n",
       "4  Water1  POINT (623256.484 1066777.765)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path = \"train/Soc Trang_Traning.shp\"\n",
    "train = load_data_geo(train_path)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9092318-4400-4a6d-936c-fd9f4563a27b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "loaded_datasets = {}\n",
    "for idx, point in train.iterrows():\n",
    "    key = f\"point_{idx + 1}\"\n",
    "    try:\n",
    "        loaded_datasets[key] = {\n",
    "            \"data\": ds_vhvv.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values,\n",
    "            \"label\": point.Name\n",
    "                               }\n",
    "    except Exception as e:\n",
    "        # loaded_datasets[key] = None\n",
    "        print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d53e4a4f-e73a-4cb3-9799-a33dc8b2f95d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Fit and transform the labels\n",
    "labels = train.Name.values\n",
    "numeric_labels = label_encoder.fit_transform(labels)\n",
    "label_mapping = dict(zip(labels, numeric_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc37cf82-9a2e-47fd-bc52-177e249f4b4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Water1': 2,\n",
       " 'Water': 1,\n",
       " 'Urban1': 0,\n",
       " 'tomlua': 9,\n",
       " 'rice12': 8,\n",
       " 'rice11': 7,\n",
       " 'mia': 6,\n",
       " 'cln1': 5,\n",
       " 'cln': 4,\n",
       " 'chn1': 3}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a59306e8-0dc5-4ef2-85c1-5f60dd013560",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = []\n",
    "for k, v in loaded_datasets.items():\n",
    "    X.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7a936cc-9bc4-4260-9559-ea023f3a2a09",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_new = []\n",
    "lb_new = []\n",
    "for i in range(len(X)):\n",
    "    if X[i] is not None:\n",
    "        x_new.append(X[i]['data'])\n",
    "        lb_new.append(numeric_labels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1ba4f40-3d59-4925-ad34-6a819943b0b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x_new, lb_new, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "09659bd6-51dc-43cd-92a5-611eeb8a7095",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'classifier__criterion': 'entropy', 'classifier__max_depth': 6, 'classifier__n_estimators': 300}\n",
      "Accuracy: 0.6424050632911392\n"
     ]
    }
   ],
   "source": [
    "# Tạo RandomForestClassifier mặc định để sử dụng làm mô hình ban đầu trong pipeline\n",
    "base_model = RandomForestClassifier(random_state=42, n_jobs=-1)\n",
    "\n",
    "# Tạo pipeline\n",
    "pipeline = Pipeline([\n",
    "    # ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('classifier', base_model),\n",
    "])\n",
    "\n",
    "# Thiết lập các tham số bạn muốn tối ưu hóa\n",
    "param_grid = {\n",
    "    'classifier__n_estimators': [100, 300, 500],\n",
    "    'classifier__max_depth': [2, 4, 6],\n",
    "    'classifier__criterion': ['gini', 'entropy'],\n",
    "}\n",
    "\n",
    "# Sử dụng GridSearchCV để tìm bộ tham số tốt nhất\n",
    "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# In ra bộ tham số tốt nhất\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Best Parameters:\", best_params)\n",
    "\n",
    "# Dự đoán trên tập kiểm tra\n",
    "y_pred = grid_search.predict(X_test)\n",
    "\n",
    "# Đánh giá kết quả\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "41defb10-c565-46cc-881f-c8c80d88a3e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "5000\n",
      "6000\n",
      "7000\n",
      "8000\n"
     ]
    }
   ],
   "source": [
    "tmp = -1 * np.ones((8874, 9902))\n",
    "result = []\n",
    "for i in range(ds_vhvv.shape[1]):\n",
    "    result.extend(ds_vhvv.isel(y=i).values.T)\n",
    "    if i % 1000 == 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3a6307a6-9119-45d0-8302-f56f3d24064e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "num_parts = 5\n",
    "split_arrays = np.array_split(result, num_parts, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6537dd62-3248-43ab-b38b-90bca5e61722",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.65073561668396\n",
      "89.57659387588501\n",
      "88.50649929046631\n",
      "87.9844377040863\n",
      "88.20552492141724\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_pred = []\n",
    "t = time.time()\n",
    "for d in split_arrays:\n",
    "    kq = grid_search.predict(d)\n",
    "    y_pred.append(kq)\n",
    "    print(f\"{time.time() - t}\")\n",
    "    t = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd4f243f-3ea0-4a98-91ac-d01d66d80fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_label = y_pred[0]\n",
    "for i in y_pred[1:]:\n",
    "    final_label = np.concatenate((final_label, i), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "47d47376-f9a2-4827-9d02-ec3bb491f259",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_label = final_label.reshape(8874, 9902)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c2f11692-c5f0-4cdd-ac12-c0b92cafddf7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from shapely.geometry import Point, Polygon\n",
    "from pyproj import CRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c838f418-afae-4c4a-9ec4-d4da4a234779",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_xarray = xr.DataArray(final_label, dims=(\"y\", \"x\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "235ede30-fa81-44d2-b504-15157f31ddf3",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "\n",
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       "\n",
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       "}\n",
       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 8874, x: 9902)&gt;\n",
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])\n",
       "Dimensions without coordinates: y, x</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span>y</span>: 8874</li><li><span>x</span>: 9902</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-960caf91-7a8f-47db-9025-9b7a4d98178b' class='xr-array-in' type='checkbox' checked><label for='section-960caf91-7a8f-47db-9025-9b7a4d98178b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</span></div><div class='xr-array-data'><pre>array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])</pre></div></div></li><li class='xr-section-item'><input id='section-1b56eb10-d9db-4c9e-b0bf-cf94d8241769' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1b56eb10-d9db-4c9e-b0bf-cf94d8241769' class='xr-section-summary'  title='Expand/collapse section'>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-17fdfb39-398f-495a-a413-3c9877949e01' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17fdfb39-398f-495a-a413-3c9877949e01' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <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-c65a2ee4-7341-46a5-8e19-9f940dd577b0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c65a2ee4-7341-46a5-8e19-9f940dd577b0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray (y: 8874, x: 9902)>\n",
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])\n",
       "Dimensions without coordinates: y, x"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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  {
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  padding-right: 10px;\n",
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       "}\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",
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       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "  word-break: break-all;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 8874, x: 9902)&gt;\n",
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])\n",
       "Coordinates:\n",
       "    spatial_ref  int64 0\n",
       "Dimensions without coordinates: y, x</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span>y</span>: 8874</li><li><span>x</span>: 9902</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e3fd4734-68f6-442f-bd6c-e4ea5925e906' class='xr-array-in' type='checkbox' checked><label for='section-e3fd4734-68f6-442f-bd6c-e4ea5925e906' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</span></div><div class='xr-array-data'><pre>array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])</pre></div></div></li><li class='xr-section-item'><input id='section-4c85d729-8e18-4d14-8b64-27bc474a13bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c85d729-8e18-4d14-8b64-27bc474a13bc' class='xr-section-summary' >Coordinates: <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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-6c7cf3cc-f406-49a5-a981-aa1e6676e47f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c7cf3cc-f406-49a5-a981-aa1e6676e47f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f91ad68-f797-4431-bafa-5e13dd533088' class='xr-var-data-in' type='checkbox'><label for='data-3f91ad68-f797-4431-bafa-5e13dd533088' 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>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 48N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32648&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 48N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>105.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 48N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32648&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45f83fa3-60ba-4a9f-bcd5-e9997e0d5adf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45f83fa3-60ba-4a9f-bcd5-e9997e0d5adf' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <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-7f1691a0-7ce8-4d66-afeb-30b7d35ad399' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7f1691a0-7ce8-4d66-afeb-30b7d35ad399' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (y: 8874, x: 9902)>\n",
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])\n",
       "Coordinates:\n",
       "    spatial_ref  int64 0\n",
       "Dimensions without coordinates: y, x"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_xarray.rio.write_crs(ds_vhvv.rio.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "80321f6e-df70-472a-aad4-71be717cb4be",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_values = ds_vhvv.x.values\n",
    "y_values = ds_vhvv.y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "52267724-60d0-47b2-ba28-deea92b9dcb4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_array = xr.DataArray(final_xarray,\n",
    "                          coords={'x': x_values, 'y': y_values},\n",
    "                          dims=['y', 'x'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e3c64ab7-5748-4735-b4d2-3bc82c3d4b81",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "  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",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 8874, x: 9902)&gt;\n",
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])\n",
       "Coordinates:\n",
       "  * x            (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "  * y            (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "    spatial_ref  int64 0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ec19986d-c7c8-44c0-a77a-c60f53ce8e9f' class='xr-array-in' type='checkbox' checked><label for='section-ec19986d-c7c8-44c0-a77a-c60f53ce8e9f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</span></div><div class='xr-array-data'><pre>array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])</pre></div></div></li><li class='xr-section-item'><input id='section-780ee3ae-13cb-406b-8ced-7c4696fdfd60' class='xr-section-summary-in' type='checkbox'  checked><label for='section-780ee3ae-13cb-406b-8ced-7c4696fdfd60' class='xr-section-summary' >Coordinates: <span>(3)</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 class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.548e+05 5.548e+05 ... 6.538e+05</div><input id='attrs-cf776cc6-4eee-4b71-ba93-290742dc8b28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf776cc6-4eee-4b71-ba93-290742dc8b28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8930abe-8f21-4d01-9989-c83d8817259b' class='xr-var-data-in' type='checkbox'><label for='data-d8930abe-8f21-4d01-9989-c83d8817259b' 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([554795., 554805., 554815., ..., 653785., 653795., 653805.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.106e+06 1.106e+06 ... 1.017e+06</div><input id='attrs-4c1b88cd-e2fc-4c72-b0fb-8ed791cd7902' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c1b88cd-e2fc-4c72-b0fb-8ed791cd7902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b41f493d-c5a0-4c8c-aed8-5a22e2e9bb63' class='xr-var-data-in' type='checkbox'><label for='data-b41f493d-c5a0-4c8c-aed8-5a22e2e9bb63' 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([1105735., 1105725., 1105715., ..., 1017025., 1017015., 1017005.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-a95e0e96-8672-46a6-accf-1567fe62f8aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a95e0e96-8672-46a6-accf-1567fe62f8aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c607a2e2-6761-4b5a-bdbf-7135e83ed4b3' class='xr-var-data-in' type='checkbox'><label for='data-c607a2e2-6761-4b5a-bdbf-7135e83ed4b3' 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>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 48N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32648&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 48N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>105.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 48N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32648&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b587e13-5a58-477d-9454-d7d3a385f302' class='xr-section-summary-in' type='checkbox'  ><label for='section-8b587e13-5a58-477d-9454-d7d3a385f302' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d80ba937-43c7-410f-88dc-348516f7d9a0' class='xr-index-data-in' type='checkbox'/><label for='index-d80ba937-43c7-410f-88dc-348516f7d9a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([554794.9999999984, 554804.9999999984, 554814.9999999984,\n",
       "       554824.9999999984, 554834.9999999984, 554844.9999999984,\n",
       "       554854.9999999984, 554864.9999999984, 554874.9999999984,\n",
       "       554884.9999999984,\n",
       "       ...\n",
       "       653714.9999999984, 653724.9999999984, 653734.9999999984,\n",
       "       653744.9999999984, 653754.9999999984, 653764.9999999984,\n",
       "       653774.9999999984, 653784.9999999984, 653794.9999999984,\n",
       "       653804.9999999984],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=9902))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9adf3e5-f279-413a-9306-c2c779368b0c' class='xr-index-data-in' type='checkbox'/><label for='index-e9adf3e5-f279-413a-9306-c2c779368b0c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1105735.0000000005, 1105725.0000000005, 1105715.0000000005,\n",
       "       1105705.0000000005, 1105695.0000000005, 1105685.0000000005,\n",
       "       1105675.0000000005, 1105665.0000000005, 1105655.0000000005,\n",
       "       1105645.0000000005,\n",
       "       ...\n",
       "       1017095.0000000005, 1017085.0000000005, 1017075.0000000005,\n",
       "       1017065.0000000005, 1017055.0000000005, 1017045.0000000005,\n",
       "       1017035.0000000005, 1017025.0000000005, 1017015.0000000005,\n",
       "       1017005.0000000005],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=8874))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9a96855-6e67-468c-b09a-f47a981b9452' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c9a96855-6e67-468c-b09a-f47a981b9452' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (y: 8874, x: 9902)>\n",
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])\n",
       "Coordinates:\n",
       "  * x            (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "  * y            (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "    spatial_ref  int64 0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_array.rio.write_crs(ds_vhvv.rio.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "dcbaaeb5-bf7c-419b-b991-586c6159fb6b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = data_array.to_dataframe(name='value').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e5d79d3c-1faa-4eba-a8a1-6ceeb5afc71c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1105735.0</td>\n",
       "      <td>554795.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1105735.0</td>\n",
       "      <td>554805.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1105735.0</td>\n",
       "      <td>554815.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1105735.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "           y         x  value\n",
       "0  1105735.0  554795.0      0\n",
       "1  1105735.0  554805.0      0\n",
       "2  1105735.0  554815.0      0\n",
       "3  1105735.0  554825.0      0\n",
       "4  1105735.0  554835.0      0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ae9a9582-b1ad-4560-9b08-b6f6ffcf753e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "geometry = [Point(x, y) for x, y in zip(df['x'], df['y'])]\n",
    "gdf = gpd.GeoDataFrame(df[\"value\"], geometry=geometry, crs=\"EPSG:4326\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38b4a5ed-66ce-4325-af7c-b97230eb225b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5eaea8a0-fb16-4014-8b2a-183a90a4fb5b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f098cdd0c10>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "colors = ['red', 'green', 'blue', 'orange', 'purple', 'brown', 'pink', 'gray', 'cyan', 'yellow']\n",
    "cmap = ListedColormap(colors)\n",
    "data_array.plot(cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b5a364b9-fe69-4369-a4b0-3cc5f3550a5b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Water1': 2,\n",
       " 'Water': 1,\n",
       " 'Urban1': 0,\n",
       " 'tomlua': 9,\n",
       " 'rice12': 8,\n",
       " 'rice11': 7,\n",
       " 'mia': 6,\n",
       " 'cln1': 5,\n",
       " 'cln': 4,\n",
       " 'chn1': 3}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "469a4fb1-0f27-4f4d-bf32-ed1274630f5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}