read-doppler-lidar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "447361e9-2072-451a-865e-43c4838cfc14",
   "metadata": {},
   "source": [
    "# Read Data from the Doppler Lidar and ingest into PyDDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f5c563a-eacf-4515-8b4a-eb7f007e06cf",
   "metadata": {},
   "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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     "text": [
      "\n",
      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
      "## library for working with weather radar data. Py-ART is partly\n",
      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
      "## Science user facility.\n",
      "##\n",
      "## If you use this software to prepare a publication, please cite:\n",
      "##\n",
      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import utils\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cmweather\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "from datetime import datetime\n",
    "import hvplot.xarray\n",
    "from scipy.signal import convolve2d\n",
    "import holoviews as hv\n",
    "import pyart\n",
    "from datatree import DataTree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d451aa5e-7d39-4e55-870f-a3bba3ee89f8",
   "metadata": {},
   "source": [
    "## Compile a list of data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "51ccb0e1-0508-43e3-9cb3-c9e96d19c8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = sorted(glob.glob('/Users/rjackson/dl_vad_data/*.hpl'))\n",
    "#files = ['/Users/rjackson/instrument-cookbooks/notebooks/doppler-lidar/User1_240_20240127_203719.hpl']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "140581c8-16f0-4de5-aeca-960bff4bc1f1",
   "metadata": {},
   "source": [
    "## Create helper functions to read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "5266fbf5-0457-496a-87e8-2ebfc6353baf",
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   "outputs": [],
   "source": [
    "def convert_to_hours_minutes_seconds(decimal_hour, initial_time):\n",
    "    delta = timedelta(hours=decimal_hour)\n",
    "    return datetime(initial_time.year, initial_time.month, initial_time.day) + delta\n",
    "\n",
    "def read_as_netcdf(file, lat, lon, alt):\n",
    "    field_dict = utils.hpl2dict(file)\n",
    "    initial_time = pd.to_datetime(field_dict['start_time'])\n",
    "\n",
    "    time = pd.to_datetime([convert_to_hours_minutes_seconds(x, initial_time) for x in field_dict['decimal_time']])\n",
    "\n",
    "    ds = xr.Dataset(coords={'range':field_dict['center_of_gates'],\n",
    "                            'time': time,\n",
    "                            'azimuth': ('time', field_dict['azimuth']),\n",
    "                            'elevation': ('time', field_dict['elevation'])} ,\n",
    "                    data_vars={'radial_velocity':(['time', 'range'],\n",
    "                                                  field_dict['radial_velocity'].T),\n",
    "                               'beta': (('time', 'range'), \n",
    "                                        field_dict['beta'].T),\n",
    "                               'intensity': (('time', 'range'),\n",
    "                                             field_dict['intensity'].T),\n",
    "                               'spectral_width': (('time', 'range'),\n",
    "                                             field_dict['spectral_width'].T)\n",
    "                              }\n",
    "                   )\n",
    "    # Fake field for PYDDA\n",
    "    ds['reflectivity'] = -99 * xr.ones_like(ds['beta'])\n",
    "    ds['azimuth'] = xr.where(ds['azimuth'] >= 360.0, ds['azimuth'] - 360.0, ds['azimuth'])\n",
    "    diff_azimuth = ds['azimuth'].diff(dim='time').values\n",
    "    diff_elevation = ds['elevation'].diff(dim='time').values\n",
    "    unique_elevations = np.unique(ds[\"elevation\"].values)\n",
    "    \n",
    "    counts = np.zeros_like(unique_elevations)\n",
    "    \n",
    "    for i in range(len(unique_elevations)):\n",
    "        counts[i] = np.sum(ds[\"elevation\"].values == unique_elevations[i])\n",
    "    \n",
    "    if np.sum(np.abs(diff_azimuth) > 0.02) <= 2  and not np.all(ds['elevation'] == 90.0):\n",
    "        sweep_mode = 'rhi'\n",
    "        n_sweeps = 1\n",
    "    elif np.all(ds['elevation'] == 90.0):\n",
    "        sweep_mode = 'vertical_pointing'\n",
    "        n_sweeps = 1\n",
    "    else:\n",
    "        # We will filter out the transitions between sweeps\n",
    "        diff_elevation = xr.DataArray(np.pad(np.abs(diff_elevation), (1, 0), constant_values=(0, 0)), dims='time')\n",
    "        print(diff_elevation)\n",
    "        sweep_mode = \"azimuth_surveillance\"\n",
    "        ds = ds.where(diff_elevation < 0.01)\n",
    "    ds['sweep_mode'] = xr.DataArray(np.array([sweep_mode.lower()], dtype='S32'), dims=['string_length_32'])\n",
    "    ds['azimuth'] = xr.where(ds['azimuth'] < 360., ds['azimuth'], ds['azimuth'] - 360.)\n",
    "    \n",
    "    if sweep_mode == 'rhi':\n",
    "        ds['fixed_angle'] = ('sweep', np.unique(ds['azimuth'].data[np.argwhere(np.abs(diff_azimuth) < 0.01) + 1]))\n",
    "    elif sweep_mode == \"azimuth_surveillance\" or sweep_mode == \"vertical_pointing\":\n",
    "        ds['fixed_angle'] = ('sweep', np.unique(ds['elevation'].data))\n",
    "        n_sweeps = len(np.unique(ds['elevation'].data))\n",
    "    ds['sweep_number'] = ('sweep', np.arange(0, n_sweeps))\n",
    "    ds['sweep_number'].attrs[\"long_name\"] = \"sweep_index_number_0_based\"\n",
    "    ds['sweep_number'].attrs[\"units\"] = \"\"\n",
    "    ds['sweep_number'].attrs[\"_FillValue\"] = -9999\n",
    "    ds[\"latitude\"] = lat\n",
    "    ds[\"latitude\"].attrs[\"long_name\"] = 'latitude'\n",
    "    ds[\"latitude\"].attrs[\"units\"] = \"degrees_north\"\n",
    "    ds[\"latitude\"].attrs[\"_FillValue\"] = -9999.\n",
    "    ds[\"longitude\"] = lon\n",
    "    ds[\"longitude\"].attrs[\"long_name\"] = 'longitude'\n",
    "    ds[\"longitude\"].attrs[\"units\"] = \"degrees_east\"\n",
    "    ds[\"longitude\"].attrs[\"_FillValue\"] = -9999.\n",
    "    ds[\"altitude\"] = alt\n",
    "    ds[\"altitude\"].attrs[\"long_name\"] = alt\n",
    "    ds[\"altitude\"].attrs[\"units\"] = \"meters\"\n",
    "    ds[\"altitude\"].attrs[\"_FillValue\"] = -9999.\n",
    "    num_rays = ds.dims['time']\n",
    "    diff_elevation = ds[\"elevation\"].diff(dim='time').values\n",
    "    transitions = np.argwhere(np.abs(diff_elevation) > 0.01)\n",
    "    \n",
    "    print(transitions)\n",
    "    end_indicies = [0]\n",
    "    last_ind = 0\n",
    "    for i, t in enumerate(transitions):\n",
    "        if t - last_ind < 2:\n",
    "            print(t)\n",
    "        else:\n",
    "            end_indicies.append(t[0])\n",
    "            last_ind = t\n",
    "    end_indicies.append(num_rays - 1)\n",
    "    end_indicies = np.array(end_indicies)\n",
    "    \n",
    "    print(end_indicies)\n",
    "    ds[\"sweep_start_ray_index\"] = ('sweep', end_indicies[0:-1])\n",
    "    ds[\"sweep_start_ray_index\"].attrs[\"long_name\"] = \"index_of_first_ray_in_sweep\"\n",
    "    ds[\"sweep_start_ray_index\"].attrs[\"units\"] = \"\"\n",
    "    ds[\"sweep_start_ray_index\"].attrs[\"_FillValue\"] = -9999\n",
    "    ds[\"sweep_end_ray_index\"] = ('sweep', end_indicies[1:])\n",
    "    ds[\"sweep_end_ray_index\"].attrs[\"long_name\"] = \"index_of_last_ray_in_sweep\"\n",
    "    ds[\"sweep_end_ray_index\"].attrs[\"units\"] = \"\"\n",
    "    ds[\"sweep_end_ray_index\"].attrs[\"_FillValue\"] = -9999\n",
    "    ds.attrs[\"Conventions\"] = \"CF-1.7\"\n",
    "    ds.attrs[\"version\"] = \"CF-Radial-1.4\"\n",
    "    return ds"
   ]
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   "cell_type": "code",
   "execution_count": 248,
   "id": "e3d0aa08-3e17-48c0-8249-c308241460d8",
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray (time: 6)>\n",
      "array([0., 0., 0., 0., 0., 0.])\n",
      "Dimensions without coordinates: time\n",
      "[]\n",
      "[0 5]\n"
     ]
    }
   ],
   "source": [
    "dataset = read_as_netcdf(file, 41.98053299237866, -87.71662374616044, 0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "755cc0eb-b3b9-4099-8b55-23fc99d8d89e",
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       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\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 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\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: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",
       "  margin-bottom: 0;\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",
       "}\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",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\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-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",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (time: 6, range: 400, string_length_32: 1, sweep: 1)\n",
       "Coordinates:\n",
       "  * range                  (range) float64 15.0 45.0 ... 1.196e+04 1.198e+04\n",
       "  * time                   (time) datetime64[ns] 2024-03-06T16:10:00.289992 ....\n",
       "    azimuth                (time) float64 360.0 60.01 120.0 180.0 240.0 300.0\n",
       "    elevation              (time) float64 60.0 60.0 60.0 60.0 60.0 60.0\n",
       "Dimensions without coordinates: string_length_32, sweep\n",
       "Data variables: (12/13)\n",
       "    radial_velocity        (time, range) float64 0.2293 -1.032 ... -17.7 10.13\n",
       "    beta                   (time, range) float64 6.665e-05 ... -1.309e-05\n",
       "    intensity              (time, range) float64 2.184 1.819 ... 1.004 0.997\n",
       "    spectral_width         (time, range) float64 0.0382 0.0382 ... 6.115 10.36\n",
       "    reflectivity           (time, range) float64 -99.0 -99.0 ... -99.0 -99.0\n",
       "    sweep_mode             (string_length_32) |S32 b&#x27;azimuth_surveillance&#x27;\n",
       "    ...                     ...\n",
       "    sweep_number           (sweep) int64 0\n",
       "    latitude               float64 41.98\n",
       "    longitude              float64 -87.72\n",
       "    altitude               float64 0.0\n",
       "    sweep_start_ray_index  (sweep) int64 0\n",
       "    sweep_end_ray_index    (sweep) int64 5\n",
       "Attributes:\n",
       "    Conventions:  CF-1.7\n",
       "    version:      CF-Radial-1.4</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-187cb392-dbf7-4b25-95de-bc49d26e92e5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-187cb392-dbf7-4b25-95de-bc49d26e92e5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li><li><span class='xr-has-index'>range</span>: 400</li><li><span>string_length_32</span>: 1</li><li><span>sweep</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fac23ae1-f3a4-47d8-9625-0a1e20b55d40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fac23ae1-f3a4-47d8-9625-0a1e20b55d40' class='xr-section-summary' >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 class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 ... 1.196e+04 1.198e+04</div><input id='attrs-89b650ec-0497-485e-aebb-663b939d0de2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89b650ec-0497-485e-aebb-663b939d0de2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8443146-5afe-401c-aa0f-6bb5bfd8b7be' class='xr-var-data-in' type='checkbox'><label for='data-f8443146-5afe-401c-aa0f-6bb5bfd8b7be' 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([   15.,    45.,    75., ..., 11925., 11955., 11985.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-06T16:10:00.289992 ... 2...</div><input id='attrs-ebb0895d-faf8-46cb-a07c-47ce2f933003' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebb0895d-faf8-46cb-a07c-47ce2f933003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0eed898c-e517-4247-a1ee-4560246ab2b8' class='xr-var-data-in' type='checkbox'><label for='data-0eed898c-e517-4247-a1ee-4560246ab2b8' 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([&#x27;2024-03-06T16:10:00.289992000&#x27;, &#x27;2024-03-06T16:10:03.820008000&#x27;,\n",
       "       &#x27;2024-03-06T16:10:07.259988000&#x27;, &#x27;2024-03-06T16:10:10.689996000&#x27;,\n",
       "       &#x27;2024-03-06T16:10:14.729988000&#x27;, &#x27;2024-03-06T16:10:18.570000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>azimuth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>360.0 60.01 120.0 180.0 240.0 300.0</div><input id='attrs-3924bf95-a0f1-456c-97d8-2d304bb14f70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3924bf95-a0f1-456c-97d8-2d304bb14f70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38b3867a-faed-413a-9a4b-223815afde6f' class='xr-var-data-in' type='checkbox'><label for='data-38b3867a-faed-413a-9a4b-223815afde6f' 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([359.99,  60.01, 120.01, 180.  , 240.01, 300.01])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0 60.0 60.0 60.0 60.0 60.0</div><input id='attrs-0130b99a-4ddc-4fc4-ada5-3f5247e8812f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0130b99a-4ddc-4fc4-ada5-3f5247e8812f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f86a6e3d-1333-4b4a-abd7-d754eff4896c' class='xr-var-data-in' type='checkbox'><label for='data-f86a6e3d-1333-4b4a-abd7-d754eff4896c' 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([60., 60., 60., 60., 60., 60.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50bd1644-a529-425b-8d81-16d4bee44676' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50bd1644-a529-425b-8d81-16d4bee44676' class='xr-section-summary' >Data variables: <span>(13)</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>radial_velocity</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2293 -1.032 ... -17.7 10.13</div><input id='attrs-9d5afd42-3f5e-49a9-83b9-553a41ae96a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d5afd42-3f5e-49a9-83b9-553a41ae96a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a3190da-230a-44b1-8be6-a40ee7c22fdb' class='xr-var-data-in' type='checkbox'><label for='data-0a3190da-230a-44b1-8be6-a40ee7c22fdb' 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([[  0.2293,  -1.032 ,   0.9173, ...,   3.6692,  18.0783,  -1.567 ],\n",
       "       [  0.1911,  -1.3759,  -0.3058, ...,   4.0514,   0.8408,  -2.0257],\n",
       "       [  0.1147,  -1.2995,  -1.2995, ...,   9.0965, -19.3778,  14.7913],\n",
       "       [  0.1529,  -1.6817,  -0.7644, ..., -12.3834, -13.2625,  -5.4273],\n",
       "       [  0.1911,  -1.567 ,   0.8026, ..., -17.8872,  11.963 ,   5.6566],\n",
       "       [  0.1911,  -1.4142,   0.2293, ..., -16.5877, -17.6961,  10.1284]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.665e-05 4.621e-05 ... -1.309e-05</div><input id='attrs-b4be7b0b-b9fc-417a-b69f-5c058d90b62b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4be7b0b-b9fc-417a-b69f-5c058d90b62b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a6a5ce-ee15-48cc-9f3e-f2260e251221' class='xr-var-data-in' type='checkbox'><label for='data-d9a6a5ce-ee15-48cc-9f3e-f2260e251221' 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([[ 0.00007,  0.00005,  0.00027, ...,  0.00001,  0.00001, -0.00001],\n",
       "       [ 0.00008,  0.00005,  0.00027, ...,  0.     ,  0.00003,  0.     ],\n",
       "       [ 0.00007,  0.00005,  0.00025, ...,  0.00002,  0.00002,  0.00004],\n",
       "       [ 0.00008,  0.00006,  0.00028, ...,  0.00001,  0.00001,  0.00004],\n",
       "       [ 0.00008,  0.00006,  0.00026, ...,  0.00001,  0.00005, -0.00002],\n",
       "       [ 0.00008,  0.00005,  0.00023, ..., -0.00002,  0.00002, -0.00001]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.184 1.819 5.866 ... 1.004 0.997</div><input id='attrs-f57b4f74-db07-4079-9286-2dc3a5c3edda' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f57b4f74-db07-4079-9286-2dc3a5c3edda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e37e116-d174-4dce-abdc-2278beeed158' class='xr-var-data-in' type='checkbox'><label for='data-3e37e116-d174-4dce-abdc-2278beeed158' 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([[2.18353, 1.81937, 5.86555, ..., 1.00194, 1.00222, 0.99793],\n",
       "       [2.39487, 1.91448, 5.75819, ..., 1.00074, 1.00639, 1.00007],\n",
       "       [2.3054 , 1.84471, 5.49533, ..., 1.00496, 1.00583, 1.0088 ],\n",
       "       [2.43551, 1.9971 , 6.03031, ..., 1.00213, 1.00232, 1.01024],\n",
       "       [2.42855, 2.00342, 5.6063 , ..., 1.00164, 1.01098, 0.99583],\n",
       "       [2.40633, 1.93111, 5.0295 , ..., 0.99615, 1.00429, 0.99696]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectral_width</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0382 0.0382 9.861 ... 6.115 10.36</div><input id='attrs-ba4712b3-4f72-4603-aac8-eeaa581a8de5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba4712b3-4f72-4603-aac8-eeaa581a8de5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c39a089-b75e-4542-ab7b-5797e183fbd0' class='xr-var-data-in' type='checkbox'><label for='data-9c39a089-b75e-4542-ab7b-5797e183fbd0' 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([[ 0.0382,  0.0382,  9.8609, ...,  8.5232,  4.3189,  8.8289],\n",
       "       [ 0.0382,  0.0382,  9.7462, ...,  8.1792,  7.2237,  9.9755],\n",
       "       [ 0.0382,  0.0382, 10.0138, ...,  6.077 ,  9.2876,  7.109 ],\n",
       "       [ 0.0382,  0.0382,  9.6315, ...,  6.4593, 11.4661,  7.3765],\n",
       "       [ 0.0382,  0.0382, 10.1284, ...,  7.0708, 12.4599,  3.8603],\n",
       "       [ 0.0382,  0.0382,  9.8609, ...,  7.0708,  6.1153, 10.3577]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reflectivity</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-99.0 -99.0 -99.0 ... -99.0 -99.0</div><input id='attrs-25fea789-65f1-4649-bcee-f62967748994' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25fea789-65f1-4649-bcee-f62967748994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbaffc2a-4a3b-4cc2-a3f2-6f4295db7977' class='xr-var-data-in' type='checkbox'><label for='data-bbaffc2a-4a3b-4cc2-a3f2-6f4295db7977' 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([[-99., -99., -99., ..., -99., -99., -99.],\n",
       "       [-99., -99., -99., ..., -99., -99., -99.],\n",
       "       [-99., -99., -99., ..., -99., -99., -99.],\n",
       "       [-99., -99., -99., ..., -99., -99., -99.],\n",
       "       [-99., -99., -99., ..., -99., -99., -99.],\n",
       "       [-99., -99., -99., ..., -99., -99., -99.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_mode</span></div><div class='xr-var-dims'>(string_length_32)</div><div class='xr-var-dtype'>|S32</div><div class='xr-var-preview xr-preview'>b&#x27;azimuth_surveillance&#x27;</div><input id='attrs-be0d4369-91c9-4c1a-aeb2-abcf94a621dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be0d4369-91c9-4c1a-aeb2-abcf94a621dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52956f5f-3ed1-4bfd-910e-5d09a54451e0' class='xr-var-data-in' type='checkbox'><label for='data-52956f5f-3ed1-4bfd-910e-5d09a54451e0' 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([b&#x27;azimuth_surveillance&#x27;], dtype=&#x27;|S32&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fixed_angle</span></div><div class='xr-var-dims'>(sweep)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-badcbec2-1d4f-449b-989e-c7928c59071c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-badcbec2-1d4f-449b-989e-c7928c59071c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5ba9ee8-c209-4d7c-917b-72bd2dac554d' class='xr-var-data-in' type='checkbox'><label for='data-f5ba9ee8-c209-4d7c-917b-72bd2dac554d' 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([60.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>(sweep)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-5f5d83fb-9a8a-44be-9176-a205b4102299' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f5d83fb-9a8a-44be-9176-a205b4102299' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5f11957-5cfa-4f18-85ae-a01d17e49c3a' class='xr-var-data-in' type='checkbox'><label for='data-f5f11957-5cfa-4f18-85ae-a01d17e49c3a' 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>long_name :</span></dt><dd>sweep_index_number_0_based</dd><dt><span>units :</span></dt><dd></dd><dt><span>_FillValue :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>41.98</div><input id='attrs-c8a6308e-7b0f-47a4-a579-fc6f9c90c490' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8a6308e-7b0f-47a4-a579-fc6f9c90c490' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-268af12e-2984-40d8-a2c5-15b2fd3c69c3' class='xr-var-data-in' type='checkbox'><label for='data-268af12e-2984-40d8-a2c5-15b2fd3c69c3' 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>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd></dl></div><div class='xr-var-data'><pre>array(41.98053)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.72</div><input id='attrs-4e23cc4e-ba1d-4bca-889c-8374a41cb5f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e23cc4e-ba1d-4bca-889c-8374a41cb5f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9174dde5-981a-49df-83e9-8c213ce2d7e5' class='xr-var-data-in' type='checkbox'><label for='data-9174dde5-981a-49df-83e9-8c213ce2d7e5' 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>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd></dl></div><div class='xr-var-data'><pre>array(-87.71662)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-4e496688-e950-4211-918f-90dc0562bca2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e496688-e950-4211-918f-90dc0562bca2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d35aac7e-0ff7-43cb-88e0-442fb9243983' class='xr-var-data-in' type='checkbox'><label for='data-d35aac7e-0ff7-43cb-88e0-442fb9243983' 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>long_name :</span></dt><dd>0.0</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_start_ray_index</span></div><div class='xr-var-dims'>(sweep)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-578c10b1-5bbe-47fa-b88e-d305dc048826' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-578c10b1-5bbe-47fa-b88e-d305dc048826' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae26f52d-e28a-428c-ad00-5cb4c6214d3f' class='xr-var-data-in' type='checkbox'><label for='data-ae26f52d-e28a-428c-ad00-5cb4c6214d3f' 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>long_name :</span></dt><dd>index_of_first_ray_in_sweep</dd><dt><span>units :</span></dt><dd></dd><dt><span>_FillValue :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_end_ray_index</span></div><div class='xr-var-dims'>(sweep)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5</div><input id='attrs-406fd30f-b98b-4e1d-a0d2-fa8520f5d372' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-406fd30f-b98b-4e1d-a0d2-fa8520f5d372' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fa394e0-11a7-4901-b545-749faa227762' class='xr-var-data-in' type='checkbox'><label for='data-5fa394e0-11a7-4901-b545-749faa227762' 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>long_name :</span></dt><dd>index_of_last_ray_in_sweep</dd><dt><span>units :</span></dt><dd></dd><dt><span>_FillValue :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5468ed17-3333-477f-aecb-05adcddf851f' class='xr-section-summary-in' type='checkbox'  ><label for='section-5468ed17-3333-477f-aecb-05adcddf851f' 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>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-17365d32-939e-4f88-9100-6a777356b5a1' class='xr-index-data-in' type='checkbox'/><label for='index-17365d32-939e-4f88-9100-6a777356b5a1' 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(Float64Index([   15.0,    45.0,    75.0,   105.0,   135.0,   165.0,   195.0,\n",
       "                225.0,   255.0,   285.0,\n",
       "              ...\n",
       "              11715.0, 11745.0, 11775.0, 11805.0, 11835.0, 11865.0, 11895.0,\n",
       "              11925.0, 11955.0, 11985.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;, length=400))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6e7090c0-7743-4699-bc30-ee4e7c9df782' class='xr-index-data-in' type='checkbox'/><label for='index-6e7090c0-7743-4699-bc30-ee4e7c9df782' 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(DatetimeIndex([&#x27;2024-03-06 16:10:00.289992&#x27;, &#x27;2024-03-06 16:10:03.820008&#x27;,\n",
       "               &#x27;2024-03-06 16:10:07.259988&#x27;, &#x27;2024-03-06 16:10:10.689996&#x27;,\n",
       "               &#x27;2024-03-06 16:10:14.729988&#x27;, &#x27;2024-03-06 16:10:18.570000&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67e3e853-d46c-4fc4-8bfe-e5df6fc557a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67e3e853-d46c-4fc4-8bfe-e5df6fc557a5' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>version :</span></dt><dd>CF-Radial-1.4</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (time: 6, range: 400, string_length_32: 1, sweep: 1)\n",
       "Coordinates:\n",
       "  * range                  (range) float64 15.0 45.0 ... 1.196e+04 1.198e+04\n",
       "  * time                   (time) datetime64[ns] 2024-03-06T16:10:00.289992 ....\n",
       "    azimuth                (time) float64 360.0 60.01 120.0 180.0 240.0 300.0\n",
       "    elevation              (time) float64 60.0 60.0 60.0 60.0 60.0 60.0\n",
       "Dimensions without coordinates: string_length_32, sweep\n",
       "Data variables: (12/13)\n",
       "    radial_velocity        (time, range) float64 0.2293 -1.032 ... -17.7 10.13\n",
       "    beta                   (time, range) float64 6.665e-05 ... -1.309e-05\n",
       "    intensity              (time, range) float64 2.184 1.819 ... 1.004 0.997\n",
       "    spectral_width         (time, range) float64 0.0382 0.0382 ... 6.115 10.36\n",
       "    reflectivity           (time, range) float64 -99.0 -99.0 ... -99.0 -99.0\n",
       "    sweep_mode             (string_length_32) |S32 b'azimuth_surveillance'\n",
       "    ...                     ...\n",
       "    sweep_number           (sweep) int64 0\n",
       "    latitude               float64 41.98\n",
       "    longitude              float64 -87.72\n",
       "    altitude               float64 0.0\n",
       "    sweep_start_ray_index  (sweep) int64 0\n",
       "    sweep_end_ray_index    (sweep) int64 5\n",
       "Attributes:\n",
       "    Conventions:  CF-1.7\n",
       "    version:      CF-Radial-1.4"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3454fc5e-0112-4c18-a0c5-62df32b36c89",
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_azimuth = dataset['azimuth'].diff(dim='time').values\n",
    "if np.all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f265f846-4cbc-4d28-a905-a060a5ce236b",
   "metadata": {},
   "source": [
    "## Read in the datasets and merge into a single dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "12b7a222-66d3-4971-9936-46ee67c6f908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x17655f3d0>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['intensity'].T.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "4a09e837-b3f8-4fef-9d32-69132c2f61e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1845a1630>]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfWklEQVR4nO3de3CU1f3H8c+SywaQRCUSiMQQKwptvHVTJKEM1WIQrXYcfwMWS1DDjJmoXFK1RH5TgXGasaOYWgl4CVI7iBmUdrTNCNupIpooTUym1eDgCBqUhDSpJtHaBML5/eGPTNcslGdz+yZ5v2b2j5ycZ5+zZ6J58+xu1ueccwIAADBk1GAvAAAA4JsIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJgTPdgLOB3Hjx/X4cOHNW7cOPl8vsFeDgAAOA3OObW3tys5OVmjRnm7JjIkAuXw4cNKSUkZ7GUAAIAIHDp0SJMnT/Z0zJAIlHHjxkn6+gHGx8cP8moAAMDpaGtrU0pKSvfvcS+GRKCceFonPj6eQAEAYIiJ5OUZvEgWAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACY4zlQXn/9dV1//fVKTk6Wz+fTH/7wh/96zO7duxUIBBQXF6fzzz9fmzZtimStAABghPAcKF9++aUuvfRSPf7446c1/+DBg7r22ms1e/Zs1dTU6P7779eyZcv04osvel4sAAAYGTx/Fs/8+fM1f/78056/adMmnXfeeSouLpYkTZ8+XVVVVXr44Yd10003eT09AIRwzumro129uo/RMVERfVaIhfMDw1W/f1hgZWWlsrOzQ8bmzZun0tJSHT16VDExMT2O6ejoUEdHR/fXbW1t/b1MAEOQc07/s6lS1R9/1qv7yUg9S9vzMj1HwmCfHxjO+v1Fso2NjUpKSgoZS0pK0rFjx9Tc3Bz2mKKiIiUkJHTfUlJS+nuZAIagr4529ToOJKnq488iugoy2OcHhrN+v4Ii9fyYZedc2PETCgsLVVBQ0P11W1sbkQLglKr+d67GxEZ5OuZfnV3KePDPw+L8wHDT74EyceJENTY2how1NTUpOjpa48ePD3uM3++X3+/v76UBGEbGxEZpTOyA/JvL5PmB4abfn+LJzMxUMBgMGdu1a5cyMjLCvv4EAADAc6B88cUXqq2tVW1traSv30ZcW1ur+vp6SV8/PZOTk9M9Py8vTx9//LEKCgq0b98+bd68WaWlpbrnnnv65hEAAIBhx/P1yKqqKl155ZXdX594rciSJUu0ZcsWNTQ0dMeKJKWlpam8vFwrV67Uhg0blJycrMcee4y3GAMAgJPyHCg/+MEPul/kGs6WLVt6jM2ZM0fvvPOO11MBAIARis/iAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5kQUKCUlJUpLS1NcXJwCgYD27Nlzyvlbt27VpZdeqjFjxmjSpEm67bbb1NLSEtGCAQDA8Oc5UMrKyrRixQqtXr1aNTU1mj17tubPn6/6+vqw89944w3l5OQoNzdX7733nrZv366//vWvWrp0aa8XDwAAhifPgbJ+/Xrl5uZq6dKlmj59uoqLi5WSkqKNGzeGnf/WW29pypQpWrZsmdLS0vT9739fd9xxh6qqqnq9eAAAMDx5CpTOzk5VV1crOzs7ZDw7O1sVFRVhj8nKytInn3yi8vJyOed05MgRvfDCC7ruuutOep6Ojg61tbWF3AAAwMjhKVCam5vV1dWlpKSkkPGkpCQ1NjaGPSYrK0tbt27VwoULFRsbq4kTJ+rMM8/Ub37zm5Oep6ioSAkJCd23lJQUL8sEAABDXEQvkvX5fCFfO+d6jJ1QV1enZcuW6Re/+IWqq6v1yiuv6ODBg8rLyzvp/RcWFqq1tbX7dujQoUiWCQAAhqhoL5MTExMVFRXV42pJU1NTj6sqJxQVFWnWrFm69957JUmXXHKJxo4dq9mzZ+vBBx/UpEmTehzj9/vl9/u9LA0AAAwjnq6gxMbGKhAIKBgMhowHg0FlZWWFPeZf//qXRo0KPU1UVJSkr6+8AAAAfJPnp3gKCgr09NNPa/Pmzdq3b59Wrlyp+vr67qdsCgsLlZOT0z3/+uuv144dO7Rx40YdOHBAb775ppYtW6YZM2YoOTm57x4JAAAYNjw9xSNJCxcuVEtLi9atW6eGhgalp6ervLxcqampkqSGhoaQv4ly6623qr29XY8//rh+9rOf6cwzz9RVV12lhx56qO8eBQAAGFY8B4ok5efnKz8/P+z3tmzZ0mPs7rvv1t133x3JqQAAwAjEZ/EAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYE1GglJSUKC0tTXFxcQoEAtqzZ88p53d0dGj16tVKTU2V3+/Xt771LW3evDmiBQMAgOEv2usBZWVlWrFihUpKSjRr1iw98cQTmj9/vurq6nTeeeeFPWbBggU6cuSISktLdcEFF6ipqUnHjh3r9eIBAMDw5DlQ1q9fr9zcXC1dulSSVFxcrJ07d2rjxo0qKirqMf+VV17R7t27deDAAZ199tmSpClTpvRu1QAAYFjz9BRPZ2enqqurlZ2dHTKenZ2tioqKsMe89NJLysjI0K9+9Sude+65uvDCC3XPPffoq6++Oul5Ojo61NbWFnIDAAAjh6crKM3Nzerq6lJSUlLIeFJSkhobG8Mec+DAAb3xxhuKi4vT73//ezU3Nys/P1///Oc/T/o6lKKiIq1du9bL0gAAwDAS0YtkfT5fyNfOuR5jJxw/flw+n09bt27VjBkzdO2112r9+vXasmXLSa+iFBYWqrW1tft26NChSJYJAACGKE9XUBITExUVFdXjaklTU1OPqyonTJo0Seeee64SEhK6x6ZPny7nnD755BNNnTq1xzF+v19+v9/L0gAAwDDi6QpKbGysAoGAgsFgyHgwGFRWVlbYY2bNmqXDhw/riy++6B7bv3+/Ro0apcmTJ0ewZAAAMNx5foqnoKBATz/9tDZv3qx9+/Zp5cqVqq+vV15enqSvn57Jycnpnr9o0SKNHz9et912m+rq6vT666/r3nvv1e23367Ro0f33SMBAADDhue3GS9cuFAtLS1at26dGhoalJ6ervLycqWmpkqSGhoaVF9f3z3/jDPOUDAY1N13362MjAyNHz9eCxYs0IMPPth3jwIAAAwrngNFkvLz85Wfnx/2e1u2bOkxNm3atB5PCwEAAJwMn8UDAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgTkSBUlJSorS0NMXFxSkQCGjPnj2nddybb76p6OhoXXbZZZGcFgAAjBCeA6WsrEwrVqzQ6tWrVVNTo9mzZ2v+/Pmqr68/5XGtra3KycnRD3/4w4gXCwAARgbPgbJ+/Xrl5uZq6dKlmj59uoqLi5WSkqKNGzee8rg77rhDixYtUmZmZsSLBQAAI4OnQOns7FR1dbWys7NDxrOzs1VRUXHS45555hl9+OGHeuCBB07rPB0dHWprawu5AQCAkcNToDQ3N6urq0tJSUkh40lJSWpsbAx7zAcffKBVq1Zp69atio6OPq3zFBUVKSEhofuWkpLiZZkAAGCIi+hFsj6fL+Rr51yPMUnq6urSokWLtHbtWl144YWnff+FhYVqbW3tvh06dCiSZQIAgCHq9C5p/L/ExERFRUX1uFrS1NTU46qKJLW3t6uqqko1NTW66667JEnHjx+Xc07R0dHatWuXrrrqqh7H+f1++f1+L0sDAADDiKcrKLGxsQoEAgoGgyHjwWBQWVlZPebHx8fr73//u2pra7tveXl5uuiii1RbW6srrriid6sHAADDkqcrKJJUUFCgxYsXKyMjQ5mZmXryySdVX1+vvLw8SV8/PfPpp5/q2Wef1ahRo5Senh5y/IQJExQXF9djHAAA4ATPgbJw4UK1tLRo3bp1amhoUHp6usrLy5WamipJamho+K9/EwUAAOBUPAeKJOXn5ys/Pz/s97Zs2XLKY9esWaM1a9ZEcloAADBC8Fk8AADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5kQUKCUlJUpLS1NcXJwCgYD27Nlz0rk7duzQ1VdfrXPOOUfx8fHKzMzUzp07I14wAAAY/jwHSllZmVasWKHVq1erpqZGs2fP1vz581VfXx92/uuvv66rr75a5eXlqq6u1pVXXqnrr79eNTU1vV48AAAYnjwHyvr165Wbm6ulS5dq+vTpKi4uVkpKijZu3Bh2fnFxse677z5973vf09SpU/XLX/5SU6dO1csvv9zrxQMAgOHJU6B0dnaqurpa2dnZIePZ2dmqqKg4rfs4fvy42tvbdfbZZ590TkdHh9ra2kJuAABg5PAUKM3Nzerq6lJSUlLIeFJSkhobG0/rPh555BF9+eWXWrBgwUnnFBUVKSEhofuWkpLiZZkAAGCIi+hFsj6fL+Rr51yPsXC2bdumNWvWqKysTBMmTDjpvMLCQrW2tnbfDh06FMkyAQDAEBXtZXJiYqKioqJ6XC1pamrqcVXlm8rKypSbm6vt27dr7ty5p5zr9/vl9/u9LA0AAAwjnq6gxMbGKhAIKBgMhowHg0FlZWWd9Lht27bp1ltv1XPPPafrrrsuspUCAIARw9MVFEkqKCjQ4sWLlZGRoczMTD355JOqr69XXl6epK+fnvn000/17LPPSvo6TnJycvTrX/9aM2fO7L76Mnr0aCUkJPThQwEAAMOF50BZuHChWlpatG7dOjU0NCg9PV3l5eVKTU2VJDU0NIT8TZQnnnhCx44d05133qk777yze3zJkiXasmVL7x8BAAAYdjwHiiTl5+crPz8/7Pe+GR2vvfZaJKcAAAAjGJ/FAwAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYE5EgVJSUqK0tDTFxcUpEAhoz549p5y/e/duBQIBxcXF6fzzz9emTZsiWiwAABgZPAdKWVmZVqxYodWrV6umpkazZ8/W/PnzVV9fH3b+wYMHde2112r27NmqqanR/fffr2XLlunFF1/s9eIBAMDwFO31gPXr1ys3N1dLly6VJBUXF2vnzp3auHGjioqKeszftGmTzjvvPBUXF0uSpk+frqqqKj388MO66aaberf6XnDO6aujXYN2fgC996/OvvtvOJL7GuzzA31pdEyUfD7fYC+jm6dA6ezsVHV1tVatWhUynp2drYqKirDHVFZWKjs7O2Rs3rx5Ki0t1dGjRxUTE9PjmI6ODnV0dHR/3dbW5mWZp+Wro1369i929vn9AhiaMh7884g+P1C3bp7GxHq+btFvPD3F09zcrK6uLiUlJYWMJyUlqbGxMewxjY2NYecfO3ZMzc3NYY8pKipSQkJC9y0lJcXLMgGMMBmpZ2l0TJTn40bHRCkj9awhf35gOIoolb55Ccg5d8rLQuHmhxs/obCwUAUFBd1ft7W19XmkjI6JUt26eX16nwAGR6SXpn0+n7bnZfb66d7BPj/QFyKJ7P7kKVASExMVFRXV42pJU1NTj6skJ0ycODHs/OjoaI0fPz7sMX6/X36/38vSPPP5fKYuZQEYHIP9/4LBPj9glaeneGJjYxUIBBQMBkPGg8GgsrKywh6TmZnZY/6uXbuUkZER9vUnAAAAnt9mXFBQoKefflqbN2/Wvn37tHLlStXX1ysvL0/S10/P5OTkdM/Py8vTxx9/rIKCAu3bt0+bN29WaWmp7rnnnr57FAAAYFjxfF1x4cKFamlp0bp169TQ0KD09HSVl5crNTVVktTQ0BDyN1HS0tJUXl6ulStXasOGDUpOTtZjjz02qG8xBgAAtvnciVesGtbW1qaEhAS1trYqPj5+sJcDAABOQ29+f/NZPAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwJwh8RGaJ/7YbVtb2yCvBAAAnK4Tv7cj+aP1QyJQ2tvbJUkpKSmDvBIAAOBVe3u7EhISPB0zJD6L5/jx4zp8+LDGjRsnn8/XPd7W1qaUlBQdOnSIz+iJEHvYO+xf77GHvcP+9R572Dun2j/nnNrb25WcnKxRo7y9qmRIXEEZNWqUJk+efNLvx8fH80PVS+xh77B/vcce9g7713vsYe+cbP+8Xjk5gRfJAgAAcwgUAABgzpAOFL/frwceeEB+v3+wlzJksYe9w/71HnvYO+xf77GHvdNf+zckXiQLAABGliF9BQUAAAxPBAoAADCHQAEAAOYQKAAAwJwhFyifffaZFi9erISEBCUkJGjx4sX6/PPPT3nMmjVrNG3aNI0dO1ZnnXWW5s6dq7fffntgFmyM1/07evSofv7zn+viiy/W2LFjlZycrJycHB0+fHjgFm1MJD+DO3bs0Lx585SYmCifz6fa2toBWasVJSUlSktLU1xcnAKBgPbs2XPK+bt371YgEFBcXJzOP/98bdq0aYBWapOX/WtoaNCiRYt00UUXadSoUVqxYsXALdQwL3u4Y8cOXX311TrnnHMUHx+vzMxM7dy5cwBXa4+X/XvjjTc0a9YsjR8/XqNHj9a0adP06KOPej+pG2KuueYal56e7ioqKlxFRYVLT093P/rRj055zNatW10wGHQffvihe/fdd11ubq6Lj493TU1NA7RqO7zu3+eff+7mzp3rysrK3Pvvv+8qKyvdFVdc4QKBwACu2pZIfgafffZZt3btWvfUU085Sa6mpmZgFmvA888/72JiYtxTTz3l6urq3PLly93YsWPdxx9/HHb+gQMH3JgxY9zy5ctdXV2de+qpp1xMTIx74YUXBnjlNnjdv4MHD7ply5a53/72t+6yyy5zy5cvH9gFG+R1D5cvX+4eeught3fvXrd//35XWFjoYmJi3DvvvDPAK7fB6/6988477rnnnnPvvvuuO3jwoPvd737nxowZ45544glP5x1SgVJXV+ckubfeeqt7rLKy0kly77///mnfT2trq5Pk/vznP/fHMs3qq/3bu3evk3TSH87hrLd7ePDgwREXKDNmzHB5eXkhY9OmTXOrVq0KO/++++5z06ZNCxm744473MyZM/ttjZZ53b//NGfOHALF9W4PT/j2t7/t1q5d29dLGxL6Yv9uvPFG99Of/tTTeYfUUzyVlZVKSEjQFVdc0T02c+ZMJSQkqKKi4rTuo7OzU08++aQSEhJ06aWX9tdSTeqL/ZOk1tZW+Xw+nXnmmf2wStv6ag9His7OTlVXVys7OztkPDs7+6T7VVlZ2WP+vHnzVFVVpaNHj/bbWi2KZP8Qqi/28Pjx42pvb9fZZ5/dH0s0rS/2r6amRhUVFZozZ46ncw+pQGlsbNSECRN6jE+YMEGNjY2nPPaPf/yjzjjjDMXFxenRRx9VMBhUYmJify3VpN7s3wn//ve/tWrVKi1atGhEfqhWX+zhSNLc3Kyuri4lJSWFjCclJZ10vxobG8POP3bsmJqbm/ttrRZFsn8I1Rd7+Mgjj+jLL7/UggUL+mOJpvVm/yZPniy/36+MjAzdeeedWrp0qadzmwiUNWvWyOfznfJWVVUlSfL5fD2Od86FHf9PV155pWpra1VRUaFrrrlGCxYsUFNTU788noE2EPsnff2C2ZtvvlnHjx9XSUlJnz+OwTRQezhSfXNv/tt+hZsfbnyk8Lp/6CnSPdy2bZvWrFmjsrKysP84GSki2b89e/aoqqpKmzZtUnFxsbZt2+bpnNGeV9kP7rrrLt18882nnDNlyhT97W9/05EjR3p87x//+EePuvumsWPH6oILLtAFF1ygmTNnaurUqSotLVVhYWGv1m7BQOzf0aNHtWDBAh08eFB/+ctfht3Vk4HYw5EoMTFRUVFRPf6l1dTUdNL9mjhxYtj50dHRGj9+fL+t1aJI9g+herOHZWVlys3N1fbt2zV37tz+XKZZvdm/tLQ0SdLFF1+sI0eOaM2aNfrJT35y2uc2ESiJiYmn9XRLZmamWltbtXfvXs2YMUOS9Pbbb6u1tVVZWVmezumcU0dHR0Trtaa/9+9EnHzwwQd69dVXh+UvicH4GRwJYmNjFQgEFAwGdeONN3aPB4NB/fjHPw57TGZmpl5++eWQsV27dikjI0MxMTH9ul5rItk/hIp0D7dt26bbb79d27Zt03XXXTcQSzWpr34GI/qd6+kltQZcc8017pJLLnGVlZWusrLSXXzxxT3e4nnRRRe5HTt2OOec++KLL1xhYaGrrKx0H330kauurna5ubnO7/e7d999dzAewqDyun9Hjx51N9xwg5s8ebKrra11DQ0N3beOjo7BeAiDzuseOudcS0uLq6mpcX/605+cJPf888+7mpoa19DQMNDLH3An3qJYWlrq6urq3IoVK9zYsWPdRx995JxzbtWqVW7x4sXd80+8zXjlypWurq7OlZaW8jZjD/vnnHM1NTWupqbGBQIBt2jRIldTU+Pee++9wVi+CV738LnnnnPR0dFuw4YNIf/P+/zzzwfrIQwqr/v3+OOPu5deesnt37/f7d+/323evNnFx8e71atXezrvkAuUlpYWd8stt7hx48a5cePGuVtuucV99tlnIXMkuWeeecY559xXX33lbrzxRpecnOxiY2PdpEmT3A033OD27t078Is3wOv+nXhbbLjbq6++OuDrt8DrHjrn3DPPPBN2Dx944IEBXftg2bBhg0tNTXWxsbHuu9/9rtu9e3f395YsWeLmzJkTMv+1115zl19+uYuNjXVTpkxxGzduHOAV2+J1/8L9rKWmpg7soo3xsodz5swJu4dLliwZ+IUb4WX/HnvsMfed73zHjRkzxsXHx7vLL7/clZSUuK6uLk/n9Dn3/68+AwAAMMLEu3gAAAD+E4ECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADDn/wCeLK/M7Y7wxQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_inds = np.argwhere(np.isclose(dataset['azimuth'], 201., atol=0.5))\n",
    "hist, bins = np.histogram(dataset['radial_velocity'].values[target_inds, 5], bins=np.arange(-0.3, 0.3, 0.02))\n",
    "plt.step(bins[1:], hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "635ef914-3db2-452b-95db-5906ca2233d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "altitude:\n",
      "\tdata: <ndarray of type: float64 and shape: (1,)>\n",
      "\t_FillValue: -9999.0\n",
      "\tlong_name: 0.0\n",
      "\tunits: meters\n",
      "altitude_agl: None\n",
      "antenna_transition: None\n",
      "azimuth:\n",
      "\tdata: <ndarray of type: float64 and shape: (110,)>\n",
      "\t_FillValue: nan\n",
      "elevation:\n",
      "\tdata: <ndarray of type: float64 and shape: (110,)>\n",
      "\t_FillValue: nan\n",
      "fields:\n",
      "\tradial_velocity:\n",
      "\t\tdata: <ndarray of type: float64 and shape: (110, 400)>\n",
      "\t\t_FillValue: nan\n",
      "\t\tcoordinates: azimuth elevation\n",
      "\tbeta:\n",
      "\t\tdata: <ndarray of type: float64 and shape: (110, 400)>\n",
      "\t\t_FillValue: nan\n",
      "\t\tcoordinates: azimuth elevation\n",
      "\tintensity:\n",
      "\t\tdata: <ndarray of type: float64 and shape: (110, 400)>\n",
      "\t\t_FillValue: nan\n",
      "\t\tcoordinates: azimuth elevation\n",
      "\treflectivity:\n",
      "\t\tdata: <ndarray of type: float64 and shape: (110, 400)>\n",
      "\t\t_FillValue: nan\n",
      "\t\tcoordinates: azimuth elevation\n",
      "fixed_angle:\n",
      "\tdata: <ndarray of type: float64 and shape: (10,)>\n",
      "\t_FillValue: nan\n",
      "instrument_parameters: None\n",
      "latitude:\n",
      "\tdata: <ndarray of type: float64 and shape: (1,)>\n",
      "\t_FillValue: -9999.0\n",
      "\tlong_name: latitude\n",
      "\tunits: degrees_north\n",
      "longitude:\n",
      "\tdata: <ndarray of type: float64 and shape: (1,)>\n",
      "\t_FillValue: -9999.0\n",
      "\tlong_name: longitude\n",
      "\tunits: degrees_east\n",
      "nsweeps: 1\n",
      "ngates: 400\n",
      "nrays: 110\n",
      "radar_calibration: None\n",
      "range:\n",
      "\tdata: <ndarray of type: float64 and shape: (400,)>\n",
      "\t_FillValue: nan\n",
      "scan_rate: None\n",
      "scan_type: ppi\n",
      "sweep_end_ray_index:\n",
      "\tdata: <ndarray of type: int64 and shape: (1,)>\n",
      "\t_FillValue: -9999\n",
      "\tlong_name: index_of_flast_ray_in_sweep\n",
      "\tunits: \n",
      "sweep_mode:\n",
      "\tdata: <ndarray of type: |S1 and shape: (1, 32)>\n",
      "sweep_number:\n",
      "\tdata: <ndarray of type: int64 and shape: (1,)>\n",
      "\t_FillValue: -9999\n",
      "\tlong_name: sweep_index_number_0_based\n",
      "\tunits: \n",
      "sweep_start_ray_index:\n",
      "\tdata: <ndarray of type: int64 and shape: (1,)>\n",
      "\t_FillValue: -9999\n",
      "\tlong_name: index_of_first_ray_in_sweep\n",
      "\tunits: \n",
      "target_scan_rate: None\n",
      "time:\n",
      "\tdata: <ndarray of type: int64 and shape: (110,)>\n",
      "\tunits: microseconds since 2024-02-19 22:00:32.839992\n",
      "\tcalendar: proleptic_gregorian\n",
      "metadata:\n",
      "\tConventions: CF-1.7\n",
      "\tversion: CF-Radial-1.4\n",
      "\tvolume_number: 0\n",
      "\tplatform_type: fixed\n",
      "\tinstrument_type: radar\n",
      "\tprimary_axis: axis_z\n"
     ]
    }
   ],
   "source": [
    "#dataset['radial_velocity'] = 8 * xr.ones_like(dataset['radial_velocity'])\n",
    "dataset.to_netcdf('ncfile.nc')\n",
    "radar = pyart.io.read_cfradial('ncfile.nc')\n",
    "radar.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9486a504-add7-4a88-b8d2-e1b7789ead7a",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "('Sweep out of range: ', 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[59], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mfilter\u001b[39m \u001b[38;5;241m=\u001b[39m pyart\u001b[38;5;241m.\u001b[39mfilters\u001b[38;5;241m.\u001b[39mGateFilter(radar)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#filter.exclude_below('intensity', 1.02)\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mdisp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_ppi\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mradial_velocity\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msweep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbalance\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgatefilter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfilter\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlim([\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mylim([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m])\n",
      "File \u001b[0;32m~/pyart/pyart/graph/radardisplay.py:356\u001b[0m, in \u001b[0;36mRadarDisplay.plot_ppi\u001b[0;34m(self, field, sweep, mask_tuple, vmin, vmax, norm, cmap, mask_outside, title, title_flag, axislabels, axislabels_flag, colorbar_flag, colorbar_label, colorbar_orient, edges, gatefilter, filter_transitions, ax, fig, ticks, ticklabs, raster, title_datetime_format, title_use_sweep_time, **kwargs)\u001b[0m\n\u001b[1;32m    353\u001b[0m cmap \u001b[38;5;241m=\u001b[39m common\u001b[38;5;241m.\u001b[39mparse_cmap(cmap, field)\n\u001b[1;32m    355\u001b[0m \u001b[38;5;66;03m# get data for the plot\u001b[39;00m\n\u001b[0;32m--> 356\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfield\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msweep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask_tuple\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_transitions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgatefilter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    357\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_x_y(sweep, edges, filter_transitions)\n\u001b[1;32m    359\u001b[0m \u001b[38;5;66;03m# mask the data where outside the limits\u001b[39;00m\n",
      "File \u001b[0;32m~/pyart/pyart/graph/radardisplay.py:1630\u001b[0m, in \u001b[0;36mRadarDisplay._get_data\u001b[0;34m(self, field, sweep, mask_tuple, filter_transitions, gatefilter)\u001b[0m\n\u001b[1;32m   1628\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, field, sweep, mask_tuple, filter_transitions, gatefilter):\n\u001b[1;32m   1629\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Retrieve and return data from a plot function.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1630\u001b[0m     sweep_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_radar\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_slice\u001b[49m\u001b[43m(\u001b[49m\u001b[43msweep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1631\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfields[field][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m][sweep_slice]\n\u001b[1;32m   1633\u001b[0m     \u001b[38;5;66;03m# mask data if mask_tuple provided\u001b[39;00m\n",
      "File \u001b[0;32m~/pyart/pyart/core/radar.py:375\u001b[0m, in \u001b[0;36mRadar.get_slice\u001b[0;34m(self, sweep)\u001b[0m\n\u001b[1;32m    373\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_slice\u001b[39m(\u001b[38;5;28mself\u001b[39m, sweep):\n\u001b[1;32m    374\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a slice for selecting rays for a given sweep.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 375\u001b[0m     start, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_start_end\u001b[49m\u001b[43m(\u001b[49m\u001b[43msweep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    376\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mslice\u001b[39m(start, end \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/pyart/pyart/core/radar.py:371\u001b[0m, in \u001b[0;36mRadar.get_start_end\u001b[0;34m(self, sweep)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_start_end\u001b[39m(\u001b[38;5;28mself\u001b[39m, sweep):\n\u001b[1;32m    370\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the starting and ending ray for a given sweep.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 371\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_start\u001b[49m\u001b[43m(\u001b[49m\u001b[43msweep\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_end(sweep)\n",
      "File \u001b[0;32m~/pyart/pyart/core/radar.py:361\u001b[0m, in \u001b[0;36mRadar.get_start\u001b[0;34m(self, sweep)\u001b[0m\n\u001b[1;32m    359\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_start\u001b[39m(\u001b[38;5;28mself\u001b[39m, sweep):\n\u001b[1;32m    360\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the starting ray index for a given sweep.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 361\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_sweep_in_range\u001b[49m\u001b[43m(\u001b[49m\u001b[43msweep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    362\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msweep_start_ray_index[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m][sweep]\n",
      "File \u001b[0;32m~/pyart/pyart/core/radar.py:306\u001b[0m, in \u001b[0;36mRadar._check_sweep_in_range\u001b[0;34m(self, sweep)\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check that a sweep number is in range.\"\"\"\u001b[39;00m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sweep \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m sweep \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnsweeps:\n\u001b[0;32m--> 306\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSweep out of range: \u001b[39m\u001b[38;5;124m\"\u001b[39m, sweep)\n\u001b[1;32m    307\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "\u001b[0;31mIndexError\u001b[0m: ('Sweep out of range: ', 2)"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = pyart.graph.RadarDisplay(radar)\n",
    "filter = pyart.filters.GateFilter(radar)\n",
    "#filter.exclude_below('intensity', 1.02)\n",
    "disp.plot_ppi('radial_velocity', sweep=2, cmap='balance', gatefilter=filter)\n",
    "plt.xlim([-1, 1])\n",
    "plt.ylim([0, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "6ed3cb3c-c7ce-430a-901d-953709e6909b",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_spec = (2, 100, 100)\n",
    "grid_limits = ((0, 2000), (0, 2000.), (-0., 2000.))\n",
    "grid = pyart.map.grid_from_radars([radar], grid_shape=grid_spec, grid_limits=grid_limits, fields=['radial_velocity', 'intensity'],\n",
    "                                 gatefilter=filter, constant_roi=60., )\n",
    "#grid.fields['radial_velocity']['data'] = np.ma.masked_where(grid.fields['intensity']['data'] < 1.02, grid.fields['radial_velocity']['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "5499db3e-9358-4484-bef5-3afd8529195b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rjackson/pyart/pyart/graph/gridmapdisplay.py:239: UserWarning: No projection was defined for the axes. Overridding defined axes and using default axes with projection Mercator.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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8889z7Fu9erUAICIjI4UQWdfRzc1NJCQkiKSkJJGUlCR+//13AUD8+OOPIikpSe2DDwU5r9ycPn1ao8n23333ndoHBW7evCkAiICAADnNwcFBABCPHj1SyXvw4EEBQGzbtk1Ou3HjhmjYsKH8+2JmZiZWrlwpbG1tRcuWLVXKb9y4UZQtW1bO6+XlJU9g37x5sxAia/K5o6Oj6NChQ45zGDBggDAzM5Mf/CjI9Z84caIAIJYuXZrn9SJSh3OoKFfZc6cuXbqkkp6QkIAHDx7k+399NWvWxP379+W1hbJl16fp/zUeOHAApUuXRtOmTbXuWdLVwYMH0aVLF/j7+yM4OFjto+c1a9bMcY2A3M/zzp07aNasGYQQOHbsGMqXL69xe7KHY65fvw4gawmAsLAwla0wvX6sDz74QG91CyEKfWJ7REQEAKBevXoq6dbW1vDw8Mh3HpeJiQk8PDxy/b5NTExQsWJFAFlz627fvg0HBweUKVMGZcqUka/XwIEDUaZMGSQnJ+vhrHLn4+MDAwMD+fclNxERETA0NETdunVV0itWrIiyZcuqXBd1Q9NA1vcHqPYmeXh4IDQ0FHfv3sXFixeRmJiIHj164MGDB/Dz81MpP3DgQCQkJODKlSu4ceOGPPdQkiQ0bdoUQNZaUvHx8SrDktnq1auHZ8+e4fbt2wC0v/5bt27FsmXL0KtXL3z22Wd5Xi8idRhQUa7atWsHY2PjHItBZi/M2KVLlzzLd+7cWV4j5vXyJiYmKsM+eXF1dcVff/0FhUKBpk2b4saNG9qcRoEdOnQIXbp0QZMmTbBr165c51J07doV165dw5kzZ+S0jIwM/PTTT2jQoIHKkGNMTAyaNWuGzMxMHD16NN81j1537NgxAFk3KiBr3SYfHx+VrTC9fqzsdaN0FR0djb///hsNGzbUS325yf4uTp8+rZL+8OFDXL9+XaPgtmvXrjh69KjKU5lPnjzBjh070KlTJ3nIdcWKFTh27JjKtnz5cgBZC5ceO3ZMZb5QYTh+/DiUSqX8+5IbJycnZGZm5gjIr1+/jocPH6pcl+w1ofbv36+Sd9++fTAwMMgRrAKAs7MzatasCVNTUyxZsgRmZmYYOnRojnylSpVCtWrV4OHhgeTkZKxbtw6dO3eW/07KlCkDY2PjHN8fkLWWlIGBARwdHQFod/0vXrwoL4y6YcOGPK8VUW44h4pyZWNjgxkzZmDmzJmwsbGRF/YMCAjAsGHD5EmnALBp0yYMGTIEP/zwAwYMGAAg66m1oUOHYvbs2TA0NES9evVw6NAhrFu3DvPnz1c7sTc3jo6OOH78ONq2bQs/Pz8cPnxY4x6ugjh58iS6dOkCBwcHTJ8+Xe7ZyObl5SUvKjlkyBB888036NGjBxYtWgQ7Ozt8++23iIyMxJEjR+QyiYmJaN68OeLj47FhwwYkJibKSz8AQPny5eUb1+zZs/Hff//Bz88Pzs7OePz4MQ4cOID169ejR48eKvPF8pK9InT2k2fnzp2TbyIffvihnO/OnTvyzfTmzZsqZd3c3DQK1M6dOyf3DqSkpEAIIddRr149+abYqlUr+Pn5oVatWvKk9MWLF0OSJMybN0+j83rd8+fPsW/fPgD/HywdP34cDx48gJmZGdq3bw8ga0LzrFmzMGrUKNy9exd169ZFfHw8lixZgufPn2P8+PEq9ZYqVQr+/v4qq95PmjQJmzdvRseOHTF37lwoFAosWrQIL1++VFlNu3bt2rm2t3r16jme6FN3LE3P648//sD69evRqVMnuLq6Ij09HefOncOKFSvg4eGBYcOGyXUeP34cLVu2xKxZs+QFagcPHozly5fLi2VWrVoVt27dwsKFC2FmZoaRI0fK5QcPHoy1a9di9OjRePDgAby8vHDkyBF88803GD16tMr/JCxevBgODg6oUKEC/vvvP/z666/YtWsXNm/erLJEQmJiIr766is0btwYFhYWuHbtGhYvXgwDAwOVp+wUCgVGjx6NZcuWYcCAAejVqxcMDQ2xa9cubNmyBUOHDpX/XdH0+iclJaFLly5ITU3FlClT1PY+AllLUFSqVCnXOok4h4rytXLlSlGlShVhZGQkKlSoIGbPni3S0tJU8mTP5Xl9Tk1aWpqYPXu2qFChgjAyMhJVqlQRX3/9tUbHVbew5+PHj0Xjxo2FjY2NPN+jMOZQZR87t+31eSwJCQliwIABwsbGRhgbG4uGDRuKw4cPq+Q5duxYnnXOnj1bzrtnzx7RqlUrYW9vL0qVKiXMzc1F/fr1xddffy3S09Pzu3Qq55rb9ip1c7Gyt4EDB2p0rIEDB+Zax6u/FxMmTBBeXl7CwsJClCpVSjg5OYmPPvpInnukTn5zqLL3q9tcXV1V8sbHx4uxY8cKDw8PYWxsLJycnETHjh1FaGio2uunbi5YVFSU6NKli7C0tBSmpqaiZcuW4vz58/leo+zfAXULS6o7lqbndfXqVfHhhx8KV1dXYWxsLIyNjYWnp6f4/PPPcyygm92GV3/fhMia79S/f3/h5uYmFAqFqFChgujVq5faBXwfPnwoRowYIezt7UXp0qVFlSpVxJIlS1QW2xRCiDlz5ohKlSoJhUIhrK2tRbt27cSJEyfU1temTRtRrlw5Ubp0aVGhQgXxySefqF3UNzMzU6xfv174+PgIa2trYWlpKerUqSNWr16d49+l16m7/vn9XWr7d0AllyTE/wa+iYiIiKhAOIeKiIiISEcMqIiIiIh0xICKiIiISEcMqIiIiIh0xICKqJjJXgcseytVqhQcHR3Ru3fvQlmjS5IkleUAso+fvUSCNnQpq4uQkBBIkoSQkJA3Vv++fftUrhsRvdsYUBEVU0FBQQgNDcWRI0cwduxY7NmzB02aNEFSUlKhHrdjx44IDQ2VF1AkoG7duggNDVVZaXzfvn2YM2dOEbaKiN4kLuxJVEzVqFFDXnAze/X12bNnY9euXfILYgtDuXLlUK5cuUKrvziytLQs9FXeiejtxh4qondEdnD133//yWkvX77EZ599htq1a8PKygo2Njbw9fXF7t27c5RPSUnB8OHDUbZsWZibm6Ndu3Zq3wGnbtju8OHD6Ny5M8qXLw9jY2N4eHhgxIgRePDggdbnsWvXLkiSpLJieLbvvvsOkiTh4sWLctq5c+fQqVMn2NjYwNjYGHXq1MGvv/6q0bH27NkDX19fmJqawsLCAq1bt0ZoaGiOfNeuXUOfPn1gb28PhUKBChUqYMCAAUhNTQWQc8hv0KBB8grfrw7P3r59Gy1btoSnpydeXwJQCAEPDw907NhRo7YT0duFARXROyI6OhoAUKVKFTktNTUVjx49wqRJk7Br1y5s3boVTZo0Qbdu3bBp0yY5nxACXbp0webNm/HZZ59h586daNiwofxqk/zcvHkTvr6++O6773Do0CHMmjULZ86cQZMmTZCenq7Vebz//vuws7NDUFBQjn0bN25E3bp15Rf0Hjt2DI0bN8bjx4+xZs0a7N69G7Vr10avXr1yvIPydVu2bEHnzp1haWmJrVu3YsOGDUhKSkKzZs1w8uRJOd8///yDevXq4fTp05g7dy7279+PwMBApKamIi0tTW3dM2fOlF/tExoaKm+Ojo4YP348IiMjcwSM+/fvx82bNzFmzBhtLhcRvS2KdJ12ItJa9mtiTp8+LdLT08WTJ0/EgQMHhIODg/Dz88vz1TQZGRkiPT1dDB06VNSpU0dO379/vwAgVq5cqZJ/wYIFOV5Tkn386OhotcdQKpUiPT1d3LlzRwAQu3fv1rhstokTJwoTExPx+PFjOe3KlSsCgFi1apWc5unpKerUqZPjnN9//33h6Ogovwol+/Ui2a8MyszMFE5OTqJmzZoqr0t58uSJsLOzE40aNZLTWrRoIaytrUViYmKu7X29fiGEGDNmTI5X/GQfu2LFiqJz584q6e3btxeVKlUSSqUy9wtDRG8t9lARFVMNGzZE6dKlYWFhgXbt2qFMmTLYvXs3SpVSnRq5fft2NG7cGObm5ihVqhRKly6NDRs24OrVq3KeY8eOAQD69eunUrZv374atSUxMREjR46Ei4uLfIzsl+S+ehxNDRkyBC9evMC2bdvktKCgICgUCrlNUVFRuHbtmtzmjIwMeevQoQPi4+MRGRmptv7IyEjcu3cP/fv3h4HB//8zaG5uju7du+P06dN4/vw5nj9/juPHj6Nnz556mzdmYGCAsWPH4o8//kBMTAyArB6+AwcOYPTo0ZAkSS/HIaI3iwEVUTG1adMmhIWF4ejRoxgxYgSuXr2KPn36qOTZsWMHevbsCWdnZ/z0008IDQ1FWFgYhgwZgpcvX8r5Hj58iFKlSqFs2bIq5R0cHPJth1KpRJs2bbBjxw5MnjwZf/75J86ePYvTp08DAF68eKH1uVWvXh316tWTh/0yMzPx008/oXPnzrCxsQHw/3PFJk2ahNKlS6tso0ePBoBc53A9fPgQANQ+qejk5ASlUomkpCQkJSUhMzMT5cuX1/oc8jJkyBCYmJhgzZo1AIBvvvkGJiYmGDJkiF6PQ0RvDp/yIyqmqlWrJk9Eb968OTIzM/H999/jt99+k+fv/PTTT3B3d8e2bdtUej6yJ1NnK1u2LDIyMvDw4UOVoCohISHfdvz777/4559/sHHjRgwcOFBOj4qK0un8Bg8ejNGjR+Pq1au4desW4uPjVZ5etLW1BQBMmzYN3bp1U1tH1apV1aZnn2N8fHyOfffu3YOBgQHKlCkDSZJgaGiIu3fv6nQur7OyssLAgQPx/fffY9KkSQgKCkLfvn1hbW2t1+MQ0ZvDHiqid8TixYtRpkwZzJo1C0qlEkDWE2ZGRkYqwVRCQkKOp/yaN28OAPj5559V0rds2ZLvcbPrVigUKulr167V/iRe0adPHxgbG2Pjxo3YuHEjnJ2d0aZNG3l/1apVUblyZfzzzz/w8fFRu1lYWKitu2rVqnB2dsaWLVtUnrZ79uwZgoOD5Sf/TExM4O/vj+3bt2v9xGL29cith27cuHF48OABPvzwQzx+/Bhjx47Vqn4ieruwh4roHVGmTBlMmzYNkydPxpYtW/DRRx/h/fffx44dOzB69Gh8+OGHiI2Nxbx58+Do6KiyqnqbNm3g5+eHyZMn49mzZ/Dx8cHff/+NzZs353tcT09PVKpUCVOnToUQAjY2Nvj9999x+PBhnc7H2toaXbt2xcaNG/H48WNMmjRJZb4TkBW0tW/fHm3btsWgQYPg7OyMR48e4erVqwgPD8f27dvV1m1gYIDFixejX79+eP/99zFixAikpqZiyZIlePz4MRYtWiTnXbZsGZo0aYIGDRpg6tSp8PDwwH///Yc9e/Zg7dq1uQZtNWvWBAB8+eWXaN++PQwNDVGrVi0YGRkByHoas127dti/fz+aNGmC9957T6frRURFrKhnxRORdrKflAsLC8ux78WLF6JChQqicuXKIiMjQwghxKJFi4Sbm5tQKBSiWrVqYv369WL27Nk5nkB7/PixGDJkiLC2thampqaidevW4tq1axo95XflyhXRunVrYWFhIcqUKSN69OghYmJitH5C8HWHDh0SAAQAcf36dbV5/vnnH9GzZ09hZ2cnSpcuLRwcHESLFi3EmjVr5DzqnsITQohdu3aJBg0aCGNjY2FmZiZatmwp/v777xzHuHLliujRo4coW7asMDIyEhUqVBCDBg0SL1++zLX+1NRUMWzYMFGuXDkhSZLa8964caMAIH755ReNrgcRvb0kIV5bXY6IiN6I7CcKb9++jdKlSxd1c4hIBxzyIyJ6g1JTUxEeHo6zZ89i586dWLZsGYMponcAe6iIiN6g27dvw93dHZaWlujbty9Wr14NQ0PDom4WEemIARURERGRjt6pZRMCAwMhSRImTJgAAEhPT8eUKVNQs2ZNmJmZwcnJCQMGDMC9e/fyrOfy5cvo3r073NzcIEkSVqxYoTbft99+C3d3dxgbG8Pb2xt//fWXyn4hBAICAuDk5AQTExM0a9YMly9f1sepEhERFXsnTpzABx98ACcnJ0iShF27dqnsL+h9NDg4GF5eXlAoFPDy8sLOnTsL6Qz+3zsTUIWFhWHdunXyS1MB4Pnz5wgPD8fMmTMRHh6OHTt24Pr16+jUqVOedT1//hwVK1bEokWLcl0petu2bZgwYQK++OILXLhwAU2bNkX79u3lV0kAWesCLVu2DKtXr0ZYWBgcHBzQunVrPHnyRD8nTUREVIw9e/YM7733HlavXq12f0Huo6GhoejVqxf69++Pf/75B/3790fPnj1x5syZwjqNLEX3gKH+PHnyRFSuXFkcPnxY+Pv7i/Hjx+ea9+zZswKAuHPnjkZ1u7q6iuXLl+dIr1+/vhg5cqRKmqenp5g6daoQIusFsQ4ODmLRokXy/pcvXworKyuVx7mJiIhICABi586d8ueC3kd79uwp2rVrp5LWtm1b0bt3b723+VXvxFN+Y8aMQceOHdGqVSvMnz8/z7zJycmQJEmnVzykpaXh/PnzmDp1qkp6mzZtcOrUKQBAdHQ0EhISVFZ2VigU8Pf3x6lTpzBixIgc9aampuZ4JYgQgi9LJSIq4YyMjGBsbFyox3j58iXS0tJ0rkfdfUuhUOR4m0J+CnIfBbJ6qD799FOVtLZt2+Y6fUdfin1A9csvvyA8PBxhYWH55n358iWmTp2Kvn37wtLSssDHfPDgATIzM2Fvb6+Sbm9vL7/7LPu/6vLcuXNHbb2BgYGYM2eOSpq5uTmePn1a4LYSEVHx5+DggOjo6EILql6+fAlLE1OkQ/fn1NTdt2bPno2AgACt6inIfTS7XF7358JSrAOq2NhYjB8/HocOHcr3lyw9PR29e/eGUqnEt99+q5fjvx6Bq4vKNcmTbdq0aZg4caL8OSUlBS4uLoiNjdUpACQiouIr+16QlpZWaAFVWloa0iHQ0NgahjqMimQKgdNPH+e4b2nbO/Uqbe6jupTRVbEOqM6fP4/ExER4e3vLaZmZmThx4gRWr16N1NRUGBoaIj09HT179kR0dDSOHj2qc3Bia2sLQ0PDHNFuYmKiHBVnT2ZPSEiAo6Oj2jyvy61L1NLSkgEVEREVOkNJQilJl+fVsl7Mro/7VkHuo9nl8ro/F5Zi/ZRfy5YtcenSJURERMibj48P+vXrh4iICJVg6saNGzhy5AjKli2r83GNjIzg7e2d4+Wvhw8fRqNGjQAA7u7ucHBwUMmTlpaG48ePy3mIiIjeJgaQdN70paD3UV9f3xz350OHDhX6vbdY91BZWFigRo0aKmlmZmYoW7YsatSogYyMDHz44YcIDw/HH3/8gczMTDlqtbGxkd/6PmDAADg7OyMwMBBA1hd25coV+ee4uDhERETA3NwcHh4eAICJEyeif//+8PHxga+vL9atW4eYmBiMHDkSAOT1sBYuXIjKlSujcuXKWLhwIUxNTdG3b983cn2IiIi0YSgZwFCHHiptZ2A9ffoUUVFR8ufo6GhERETAxsYGFSpU0Og++vo9fPz48fDz88OXX36Jzp07Y/fu3Thy5AhOnjxZ4PPSRLEOqPJz9+5d7NmzBwBQu3ZtlX3Hjh1Ds2bNAAAxMTEwMPj/X6B79+6hTp068uelS5di6dKl8Pf3R0hICACgV69eePjwIebOnYv4+HjUqFED+/btg6urq1xu8uTJePHiBUaPHo2kpCQ0aNAAhw4dgoWFReGcMBERUTFy7tw5NG/eXP6cPY944MCB2Lhxo0b30dfv4Y0aNcIvv/yCGTNmYObMmahUqRK2bduGBg0aFOq58NUzb7GUlBRYWVkhOTmZc6iIiEqoN3EvyD5GC9NyOs2hyhBKHH1+v0Tet97pHioiIiLSnIEk6fSUn1KPc6iKGwZUREREBAA6TyzX56T04qZYP+VHRERE9DZgDxUREREB0P0pP6Ue21LcMKAiIiIiAIAhJBjqMGxXkudQcciPiIiISEfsoSIiIiIAnJSuCwZUREREBCDrXX5cNqFgOORHREREpCP2UBEREREADvnpggEVERERAeBTfrrgkB8RERGRjthDRURERACyhux06aHKLME9VAyoiIiICADnUOmCARUREREB0H0OlS5lizvOoSIiIiLSEXuoiIiICABgIEkw0GFhTw75ERERUYlnCN2G7Qz115Rih0N+RERERDpiDxUREREByO6h0q18ScWAioiIiAAABkLHZRNEyZ1DxSE/IiIiIh2xh4qIiIgA6L5SOp/yIyIiohKPc6gKjkN+RERERDpiDxUREREB4KtndMGAioiIiABkDVvpMnRVkoe9GFARERERAPZQ6aIkB5NEREREesEeKiIiIgLAp/x0wYCKiIiIAAASdBu6KrkDfhzyIyIiItIZe6iIiIgIAIf8dMGAioiIiAAwoNIFh/yIiIiIdMSAioiIiAAABpLumzbc3NwgSVKObcyYMWrzh4SEqM1/7do1PZy9bjjkR0RERADe/JBfWFgYMjMz5c///vsvWrdujR49euRZLjIyEpaWlvLncuXKaXlk/WNARUREREXi9UBo0aJFqFSpEvz9/fMsZ2dnB2tr60JsmfY45EdEREQA9Dfkl5KSorKlpqbme+y0tDT89NNPGDJkCCQp77HDOnXqwNHRES1btsSxY8f0ceo6Y0BFREREAP5/yE+XDQBcXFxgZWUlb4GBgfkee9euXXj8+DEGDRqUax5HR0esW7cOwcHB2LFjB6pWrYqWLVvixIkTBTthPeKQHxEREQEAJAMBA0kUvLzIKhsbG6syx0mhUORbdsOGDWjfvj2cnJxyzVO1alVUrVpV/uzr64vY2FgsXboUfn5+BW63PrCHioiIiPTK0tJSZcsvoLpz5w6OHDmCYcOGaX2shg0b4saNGwVtqt6wh4qIiIgAZPWyaLv0gUr5AnZuBQUFwc7ODh07dtS67IULF+Do6FiwA+sRAyoiIiICABhKWZsu5bWlVCoRFBSEgQMHolQp1bBk2rRpiIuLw6ZNmwAAK1asgJubG6pXry5PYg8ODkZwcHDBG60nDKiIiIioyBw5cgQxMTEYMmRIjn3x8fGIiYmRP6elpWHSpEmIi4uDiYkJqlevjr1796JDhw5vsslqSUKIgs8+o0KVkpICKysrJCcnq0zuIyKikuNN3Auyj7HC1gMmBgVf2vOFMhMTHkSVyPsWe6iIiIgIQMFeH/N6+ZKKT/kRERER6Yg9VERERAQAMDAQMCjoo3oADFByZxExoCIiIiIAgIFB1lbg8vprSrFTks+diIiISC/YQ0VEREQAOOSnCwZUREREBIBDfrpgQEVEREQAAEnHgKoEr5pQooNJIiIiIr1gDxUREREBAAwModscqhLcRcWAioiIiAD8b6V0XeZQldw56RzyIyIiItIVe6iIiIgIwP+G/Ar+buQS3UvDgIqIiIgAZD3lJ+nylB+H/IiIiIiooNhDRURERAA45KcLBlREREQEgEN+uijJwSQRERGRXrCHioiIiABkD/kVfHXOktxLw4CKiIiIAHDITxcMqIiIiAgAIBlIkHTooZJEyX33TEnunSMiIiLSC/ZQEREREQBAMmQPVUExoCIiIiIAgIGBBAMDHSal61C2uOOQHxEREZGO2ENFREREWXQc8gOH/IiIiKikkwwNIBkWfPBKEiV33QQO+RERERHpiD1UREREBOB/61DpMLFcl7LFHQMqIiIiAsAhP11wyI+IiIhIR+yhIiIiIgCAZKBjD5Wy5PZQMaAiIiIiAP8LqAx0CKgMGFARERFRCafzHKoS3EPFOVREREREOmIPFREREQHgkJ8u3qkeqsDAQEiShAkTJgAA0tPTMWXKFNSsWRNmZmZwcnLCgAEDcO/evXzrCg4OhpeXFxQKBby8vLBz506Nj5tt0KBBkCRJZWvYsKEup0hERFR4DCXA0ECHTbt1qAICAnLcJx0cHPIsc/z4cXh7e8PY2BgVK1bEmjVrdDljvXlnAqqwsDCsW7cOtWrVktOeP3+O8PBwzJw5E+Hh4dixYweuX7+OTp065VlXaGgoevXqhf79++Off/5B//790bNnT5w5c0aj476qXbt2iI+Pl7d9+/bpdqJERETvkOrVq6vcJy9dupRr3ujoaHTo0AFNmzbFhQsXMH36dIwbNw7BwcFvsMXqvRNDfk+fPkW/fv2wfv16zJ8/X063srLC4cOHVfKuWrUK9evXR0xMDCpUqKC2vhUrVqB169aYNm0aAGDatGk4fvw4VqxYga1bt+Z73FcpFIp8o+1sqampSE1NlT+npKRoVI6IiEgfdB/yyyr7+v1LoVBAoVCoLVOqVCmN75Nr1qxBhQoVsGLFCgBAtWrVcO7cOSxduhTdu3cvcLv14Z3ooRozZgw6duyIVq1a5Zs3OTkZkiTB2to61zyhoaFo06aNSlrbtm1x6tQprY8bEhICOzs7VKlSBcOHD0diYmKueQMDA2FlZSVvLi4u+Z4PERGRvmQ/5afLBgAuLi4q97PAwMBcj3njxg04OTnB3d0dvXv3xq1bt3LNm9v9+dy5c0hPT9fPRSigYt9D9csvvyA8PBxhYWH55n358iWmTp2Kvn37wtLSMtd8CQkJsLe3V0mzt7dHQkKCVsdt3749evToAVdXV0RHR2PmzJlo0aIFzp8/rzZSnzZtGiZOnCh/TklJYVBFRETFTmxsrMp9NrfeqQYNGmDTpk2oUqUK/vvvP8yfPx+NGjXC5cuXUbZs2Rz5c7s/Z2Rk4MGDB3B0dNTviWihWAdUsbGxGD9+PA4dOgRjY+M886anp6N3795QKpX49ttv861bklQn1gkh5DRNj9urVy/55xo1asDHxweurq7Yu3cvunXrliN/Xl2iREREhU2SDCEZGOpQPuspP0tLyzw7LrK1b99e/rlmzZrw9fVFpUqV8OOPP6p0MKgeI+f9WV36m1asA6rz588jMTER3t7eclpmZiZOnDiB1atXIzU1FYaGhkhPT0fPnj0RHR2No0eP5vslOzg4qPRGAUBiYqIcFWt63Nc5OjrC1dUVN27c0OW0iYiICoXOC3vqUBYAzMzMULNmzVzvk7ndn0uVKqW2R+tN0urMMzIy8OOPP+Y4maLSsmVLXLp0CREREfLm4+ODfv36ISIiQiWYunHjBo4cOaLRBff19c0xmf3QoUNo1KiRxsdV5+HDh4iNjS3SLkkiIqK3VWpqKq5evZrrfTK3+7OPjw9Kly79JpqYK616qEqVKoVRo0bh6tWrhdUerVhYWKBGjRoqaWZmZihbtixq1KiBjIwMfPjhhwgPD8cff/yBzMxMORi0sbGBkZERAGDAgAFwdnaWJ82NHz8efn5++PLLL9G5c2fs3r0bR44cwcmTJzU6LpD1BGBAQAC6d+8OR0dH3L59G9OnT4etrS26du1aqNeFiIioIPT1lJ+mJk2ahA8++AAVKlRAYmIi5s+fj5SUFAwcOBBA1tziuLg4bNq0CQAwcuRIrF69GhMnTsTw4cMRGhqKDRs2qDyBX1S0HvJr0KABIiIi4OrqWhjt0au7d+9iz549AIDatWur7Dt27BiaNWsGAIiJiYHBK78EjRo1wi+//IIZM2Zg5syZqFSpErZt24YGDRpofGxDQ0NcunQJmzZtwuPHj+Ho6IjmzZtj27ZtsLCw0PnciIiI9M7AMGsrcHntVkq/e/cu+vTpgwcPHqBcuXJo2LAhTp8+LccY8fHxiImJkfO7u7tj3759+PTTT/HNN9/AyckJX3/9dZEvmQAAksiezaWh7du3Y+rUqfj000/h7e0NMzMzlf25LXBJ2ktJSYGVlRWSk5M1mtxHRETvnjdxL8g+xsFBrWBmVPDp1c/SMtB245ESed/S+qplP7k2btw4OU2SJPkpuMzMTP21joiIiKgY0Dqgio6OLox2EBERURHLmkOlw7IJBko9tqZ40TqgKg5zp4iIiEh7Rb1sQnFWoDPfvHkzGjduDCcnJ9y5cwdA1vvvdu/erdfGERERERUHWgdU3333HSZOnIgOHTrg8ePH8pwpa2tr+WWFREREVPxIBoY6byWV1gHVqlWrsH79enzxxRcqC1j6+Pjg0qVLem0cERERvUGS4f8vnVCQTWJApbHo6GjUqVMnR7pCocCzZ8/00igiIiKi4kTrgMrd3R0RERE50vfv3w8vLy99tImIiIiKQPakdF22kkrrp/w+//xzjBkzBi9fvoQQAmfPnsXWrVsRGBiI77//vjDaSERERG+CrvOguGyC5gYPHoyMjAxMnjwZz58/R9++feHs7IyVK1eid+/ehdFGIiIiordagdaXHz58OIYPH44HDx5AqVTCzs5O3+0iIiKiN0zXJ/VK8sKeWg92tmjRAo8fPwYA2NraysFUSkoKWrRoodfGERER0RtkKAGGBjpsUlGfQZHRuocqJCQEaWlpOdJfvnyJv/76Sy+NIiIiojdP9x6qkrtsgsYB1cWLF+Wfr1y5goSEBPlzZmYmDhw4AGdnZ/22joiIiKgY0Digql27NiRJgiRJaof2TExMsGrVKr02joiIiN4c9lAVnMYBVXR0NIQQqFixIs6ePYty5crJ+4yMjGBnZ6eycjoREREVL5KBASQDHV6OrEPZ4k7jgMrV1RUAoFSW3Bn8REREROoUaNmE69evIyQkBImJiTkCrFmzZumlYURERPSGGRgCuow2cchPc+vXr8eoUaNga2sLBwcHSNL/PyIpSRIDKiIiomKKc6gKTuuAav78+ViwYAGmTJlSGO0hIiIiKna0DqiSkpLQo0ePwmgLERERFaGsSem69FCV3EnpWp95jx49cOjQocJoCxERERUlnVZJ/99WQmndQ+Xh4YGZM2fi9OnTqFmzJkqXLq2yf9y4cXprHBEREVFxoHVAtW7dOpibm+P48eM4fvy4yj5JkhhQERERFVOclF5wWgdU0dHRhdEOIiIiKmIMqAquwIOdaWlpiIyMREZGhj7bQ0REREXFwFD3rYTSOqB6/vw5hg4dClNTU1SvXh0xMTEAsuZOLVq0SO8NJCIiInrbaR1QTZs2Df/88w9CQkJgbGwsp7dq1Qrbtm3Ta+OIiIjozZEMDCEZ6rCV4B4qredQ7dq1C9u2bUPDhg1VVkn38vLCzZs39do4IiIienM4h6rgtO6hun//Puzs7HKkP3v2TCXAIiIiIioptA6o6tWrh71798qfs4Oo9evXw9fXV38tIyIiojfLQAIMDHTYSm7HitZDfoGBgWjXrh2uXLmCjIwMrFy5EpcvX0ZoaGiOdamIiIio+MieC6VL+ZJK6x6qRo0a4e+//8bz589RqVIlHDp0CPb29ggNDYW3t3dhtJGIiIjoraZ1DxUA1KxZEz/++KO+20JERERFiJPSC07rgGrfvn0wNDRE27ZtVdIPHjwIpVKJ9u3b661xRERE9OYwoCo4rYf8pk6diszMzBzpQghMnTpVL40iIiKiIvCGV0oPDAxEvXr1YGFhATs7O3Tp0gWRkZF5lgkJCYEkSTm2a9eu6XLmOtM6oLpx4wa8vLxypHt6eiIqKkovjSIiIqJ33/HjxzFmzBicPn0ahw8fRkZGBtq0aYNnz57lWzYyMhLx8fHyVrly5TfQ4txpPeRnZWWFW7duwc3NTSU9KioKZmZm+moXERERvWH6esovJSVFJV2hUEChUOTIf+DAAZXPQUFBsLOzw/nz5+Hn55fnsezs7GBtbV3gtuqb1j1UnTp1woQJE1RWRY+KisJnn32GTp066bVxRERE9OZIkoE8j6pAm5QVVri4uMDKykreAgMDNTp+cnIyAMDGxibfvHXq1IGjoyNatmyJY8eOFfyk9UTrHqolS5agXbt28PT0RPny5QEAd+/eRdOmTbF06VK9N5CIiIiKl9jYWFhaWsqf1fVOvU4IgYkTJ6JJkyaoUaNGrvkcHR2xbt06eHt7IzU1FZs3b0bLli0REhKSb69WYSrQkN+pU6dw+PBh/PPPPzAxMUGtWrWK9CSIiIhIDwowsTxHeQCWlpYqAZUmxo4di4sXL+LkyZN55qtatSqqVq0qf/b19UVsbCyWLl1avAIqIOt1M23atEGbNm303R4iIiIqIpKhASRDrWcDqZQviE8++QR79uzBiRMn5NEvbTRs2BA//fRTgY6tLwUKqIiIiIh0JYTAJ598gp07dyIkJATu7u4FqufChQtwdHTUc+u0w4CKiIiIALz5hT3HjBmDLVu2YPfu3bCwsEBCQgKArOlFJiYmAIBp06YhLi4OmzZtAgCsWLECbm5uqF69OtLS0vDTTz8hODgYwcHBBW63PjCgIiIioix6mkOlqe+++w4A0KxZM5X0oKAgDBo0CAAQHx+PmJgYeV9aWhomTZqEuLg4mJiYoHr16ti7dy86dOhQ8HbrAQMqIiIiKhJCiHzzbNy4UeXz5MmTMXny5EJqUcFpFFC9vkBXXrSd1U9ERERvCR2H/HTq3SrmNAqorK2tIUlSnnmEEJAkSe17/oiIiOjtl/WUny4rpRf8CcHiTqOA6m1YgZSIiIgK2RueQ/Uu0Sig8vf3L+x2EBERERVbGgVUFy9e1LjCWrVqFbgxREREVHTe9LIJ7xKNAqratWtDkqR8Z+PnN4dKk5cdvl5feHg4XF1dtSpHRERE2pMMDHWbQ8WAKm/R0dF6Odjjx4+xYsUKWFlZ5ZtXCIHRo0dzkjsRERG99TQKqPTZQ9S7d2/Y2dlplPeTTz7R23GJiIgobxzyK7gCPd+4efNmNG7cGE5OTrhz5w6ArKXgd+/enWc5pVKpcTAFAE+ePEHFihUL0kQiIiLSloGB7lsJpfWZf/fdd5g4cSI6dOiAx48fy0Ny1tbWWLFihb7bR0RERPTW0/rVM6tWrcL69evRpUsXLFq0SE738fHBpEmT8i1/4sQJjY7j5+enbdOIiIhIB5KhjpPSdShb3GkdUEVHR6NOnTo50hUKBZ49e5Zv+ddfgPiq7NXYJUlCRkaGtk0jIiIiHUgGBjrOoeKQn8bc3d0RERGRI33//v3w8vLKt3xSUpLaLS4uDp9//jkUCgU8PT21bRYRERFRkdG6h+rzzz/HmDFj8PLlSwghcPbsWWzduhWBgYH4/vvv8y3/+pIJSqUSP/zwA+bMmQMDAwN88803GDhwoLbNIiIiIl3x1TMFpnVANXjwYGRkZGDy5Ml4/vw5+vbtC2dnZ6xcuRK9e/fWqq4dO3Zg+vTpuH//PqZNm4ZPPvkECoVC2yYRERGRHnAOVcFpHVABwPDhwzF8+HA8ePBA66UQAOD48eOYMmUKLl26hPHjx2PKlCkaLfZJREREhUeSdFyHSmJApbHo6GhkZGSgcuXKsLW1ldNv3LiB0qVLw83NLc/yHTp0wJ9//onBgwdj165dcHBw0LrRRERERG8TrSelDxo0CKdOncqRfubMGQwaNCjf8gcOHIAQAtu2bYOXlxdsbGzUbkRERPRmZa+UrstWUmndQ3XhwgU0btw4R3rDhg0xduzYfMsHBQVpe0giIiJ6AziHquC0DqgkScKTJ09ypCcnJ2v0ImNtn+DbunUrOnXqBDMzM63KEREREb0pWg/5NW3aFIGBgSrBU2ZmJgIDA9GkSRO9Ng4ARowYgf/++0/v9RIREdFrDAz+f+mEAm0ld2FPrXuoFi9eDD8/P1StWhVNmzYFAPz1119ISUnB0aNH9d5AIYTe6yQiIqKcdJ0HVZLnUGkdSnp5eeHixYvo2bMnEhMT8eTJEwwYMADXrl1DjRo1CqONRERERG+1Aq1D5eTkhIULF+q7LURERFSEJEMDSIYFH7bTpWxxV6CAiogoL7Ot8n+vZ1Gbk3ylqJtA9NbhkF/BldxQkoiIiEhP3voeKldXV5QuXbqom0FERPTOYw9VwRVpQPX48WP89ttvuHnzJj7//HPY2NggPDwc9vb2cHZ2BgD8+++/RdlEIiKiEkMy0HFhTwZUb97FixfRqlUrWFlZ4fbt2xg+fDhsbGywc+dO3LlzB5s2bSqqphEREZVIkoGBjj1UJXcmkUYBVZ06dSBJkkYVhoeHa5Rv4sSJGDRoEBYvXgwLCws5vX379ujbt69GdRARERG9DTQKqLp06SL//PLlS3z77bfw8vKCr68vAOD06dO4fPkyRo8erfGBw8LCsHbt2hzpzs7OSEhI0LgeIiIi0g/OoSo4jQKq2bNnyz8PGzYM48aNw7x583LkiY2N1fjAxsbGSElJyZEeGRmJcuXKaVwPERER6YmO61ChBK9DpfWZb9++HQMGDMiR/tFHHyE4OFjjejp37oy5c+ciPT0dQNZLl2NiYjB16lR0795d22YRERERFRmtAyoTExOcPHkyR/rJkydhbGyscT1Lly7F/fv3YWdnhxcvXsDf3x8eHh6wsLDAggULtG0WERER6Sh7yE+XraTS+im/CRMmYNSoUTh//jwaNmwIIGsO1Q8//IBZs2ZpXI+lpSVOnjyJo0ePIjw8HEqlEnXr1kWrVq20bRIRERHpgWSo47IJOpQt7rTuoZo6dSo2bdqECxcuYNy4cRg3bhwuXLiAjRs3YurUqVo3oEWLFhg7diw+//xznYOpwMBASJKECRMmyGk7duxA27ZtYWtrC0mSEBERkW896enpmDt3LipVqgRjY2O89957OHDggEqeJ0+eYMKECXB1dYWJiQkaNWqEsLAwlTxCCAQEBMDJyQkmJiZo1qwZLl++rNM5EhERvWu+/fZbuLu7w9jYGN7e3vjrr7/yzH/8+HF4e3vD2NgYFStWxJo1a95QS3NXoNljPXv2xN9//41Hjx7h0aNH+Pvvv9GzZ0+t6lAqlZg3bx6cnZ1hbm6O6OhoAMDMmTOxYcMGrdsUFhaGdevWoVatWirpz549Q+PGjbFo0SKN65oxYwbWrl2LVatW4cqVKxg5ciS6du2KCxcuyHmGDRuGw4cPY/Pmzbh06RLatGmDVq1aIS4uTs6zePFiLFu2DKtXr0ZYWBgcHBzQunVrPHnyROvzIyIiKmxFMeS3bds2TJgwAV988QUuXLiApk2bon379oiJiVGbPzo6Gh06dEDTpk1x4cIFTJ8+HePGjdNqHndhKLLp+PPnz8fGjRuxePFiGBkZyek1a9bE999/r1VdT58+Rb9+/bB+/XqUKVNGZV///v0xa9YsrXq/Nm/ejOnTp6NDhw6oWLEiRo0ahbZt2+Krr74CALx48QLBwcFYvHgx/Pz84OHhgYCAALi7u+O7774DkNU7tWLFCnzxxRfo1q0batSogR9//BHPnz/Hli1btDo/IiKiNyFrYU/dNm0tW7YMQ4cOxbBhw1CtWjWsWLECLi4u8v30dWvWrEGFChWwYsUKVKtWDcOGDcOQIUOwdOlSXU9fJxqduY2NDR48eAAAKFOmDGxsbHLdNLVp0yasW7cO/fr1g+ErY661atXCtWvXtDqJMWPGoGPHjnqbf5Wamppjgv2rk/EzMjKQmZmZZ57o6GgkJCSgTZs28n6FQgF/f3+cOnUq1+OmpKSobESkX3OSr2BO8pWibgbRO+31e1lqaqrafGlpaTh//rzKvRIA2rRpk+u9MjQ0NEf+tm3b4ty5c/LKAUVBo0npy5cvl1czX7FihV4OHBcXBw8PjxzpSqVSqwvyyy+/IDw8PMf8JV20bdsWy5Ytg5+fHypVqoQ///wTu3fvRmZmJgDAwsICvr6+mDdvHqpVqwZ7e3ts3boVZ86cQeXKlQFAXpzU3t5epW57e3vcuXNH7XEDAwMxZ84cvZ0HEf0/BlFEmpD+t+lSHnBxcVFJnT17NgICAnLkfvDgATIzM9XeK3Nb5DshIUFt/oyMDDx48ACOjo46tL/gNAqoBg4cCCCrZwbICjgcHBx0OnD16tXx119/wdXVVSV9+/btqFOnjkZ1xMbGYvz48Th06JBWSzbkZ+XKlRg+fDg8PT0hSRIqVaqEwYMHIygoSM6zefNmDBkyBM7OzjA0NETdunXRt2/fHK/eef2VPUKIXF/jM23aNEycOFH+nJKSkuOXkoi0w0CKSHOZSiUylUqdygNZ92dLS0s5XaFQ5FlOm3tlbvnVpb9JWi2bUKpUKYwaNQpXr17V+cCzZ89G//79ERcXB6VSiR07diAyMhKbNm3CH3/8oVEd58+fR2JiIry9veW0zMxMnDhxAqtXr0ZqaqrKcKKmypUrh127duHly5d4+PAhnJycMHXqVLi7u8t5KlWqhOPHj+PZs2dISUmBo6MjevXqJefJDjgTEhJUouXExMQckXU2hUKR7y8dEWmGgRSR9pRCIPN/wUlBywNZSyO9GlDlxtbWFoaGhjl6o/K6Vzo4OKjNX6pUKZQtW7aALded1rPHGjRooPK0W0F98MEH2LZtG/bt2wdJkjBr1ixcvXoVv//+O1q3bq1RHS1btsSlS5cQEREhbz4+PujXrx8iIiIKFEy9ytjYGM7OzsjIyEBwcDA6d+6cI4+ZmRkcHR2RlJSEgwcPynnc3d3h4OCAw4cPy3nT0tJw/PhxNGrUSKd2EZF62fOjGEwRFQ9GRkbw9vZWuVcCwOHDh3O9V/r6+ubIf+jQIfj4+KB06dKF1tb8aL2w5+jRo/HZZ5/h7t278Pb2hpmZmcr+15ctUCcjIwMLFizAkCFDcPz4cW2bILOwsECNGjVU0szMzFC2bFk5/dGjR4iJicG9e/cAZL0rEMiKcLN7kQYMGABnZ2cEBgYCAM6cOYO4uDjUrl0bcXFxCAgIgFKpxOTJk+XjHDx4EEIIVK1aFVFRUfj8889RtWpVDB48GADk9bAWLlyIypUro3Llyli4cCFMTU3Rt2/fAp8zEeXEAIpIP5RKJZQ6DPkVpOzEiRPRv39/+Pj4wNfXF+vWrUNMTAxGjhwJIGs6TFxcHDZt2gQAGDlyJFavXo2JEydi+PDhCA0NxYYNG7B169YCt1sftA6oevXqBQAYN26cnCZJkjzemT1xO8+DliqFJUuWyHOzCtOePXvkIAcAevfuDUB1glxMTAwMXnnU8+XLl5gxYwZu3boFc3NzdOjQAZs3b4a1tbWcJzk5GdOmTcPdu3dhY2OD7t27Y8GCBSrR8eTJk/HixQuMHj0aSUlJaNCgAQ4dOiRP8Cci3TCQItKvTKVAprLgQ34FKdurVy88fPgQc+fORXx8PGrUqIF9+/bJc6zj4+NV1qRyd3fHvn378Omnn+Kbb76Bk5MTvv766yJ/D7AkhHaDpbk9oZbt9UnmuenSpQu6dOmCQYMGaXP4EiUlJQVWVlZITk7WaCya6G0x28qr0OpmEEUlzZu4F2QfI/JapE7/0//kyRNU9axaIu9bWvdQaRow5ad9+/aYNm0a/v33X7VDh506ddLLcYjo3cBAiqjw6espv5JI64Bqz549atMlSYKxsTE8PDxUnobLzahRowBkrZCqri5Nhg6J6N3HQIrozVEqBZQ6DPnpUra40zqg6tKlizxn6lWvzqNq0qQJdu3aleM1MK/SZdIbEb37GEgRUXGi9bIJhw8fRr169XD48GEkJycjOTkZhw8fRv369fHHH3/gxIkTePjwISZNmlQY7SWidxiXPSAqWplCqfNWUmndQzV+/HisW7dOZX2Ili1bwtjYGB9//DEuX76MFStWYMiQIXnW8/XXX6tNf3Xo0M/PT+e1pIjo7ccAiujtUBRP+b0rtA6obt68qXbmvqWlJW7dugUAqFy5svwy5dwsX74c9+/fx/Pnz1GmTBkIIfD48WOYmprC3NwciYmJqFixIo4dO8bXrxC9oxhIEdG7QushP29vb3z++ee4f/++nHb//n1MnjwZ9erVAwDcuHED5cuXz7OehQsXol69erhx4wYePnyIR48e4fr162jQoAFWrlyJmJgYODg44NNPP9W2iUT0FuOwHtHbS/m/p/wKupXk+dFa91Bt2LABnTt3Rvny5eHi4gJJkhATE4OKFSti9+7dAICnT59i5syZedYzY8YMBAcHo1KlSnKah4cHli5diu7du+PWrVtYvHhxkS/URUT6wQCK6O2XKXQc8tPhPYDFndYBVdWqVXH16lUcPHgQ169fhxACnp6eaN26tbzaeJcuXfKtJz4+HhkZGTnSMzIy5JceOjk54cmTJ9o2kYjeEgyiiIoXpRDyC44LWr6k0jqgArImjrdr1w7t2rUr8IGbN2+OESNG4Pvvv0edOnUAABcuXMCoUaPQokULAMClS5c0WtOKiN4uDKSIqKTReg6VvmzYsAE2Njbw9vaGQqGAQqGAj48PbGxssGHDBgCAubk5vvrqq6JqIhERUYmS9ZSfLvOo2EP1xjk4OODw4cO4du2aytBh1apV5TzNmzcvquYRERGVOFw2oeCKLKDKVrFiRUiShEqVKqFUqSJvDhEREZHWNBrymzhxIp49ewYAOHHihNrJ5Np6/vw5hg4dClNTU1SvXh0xMTEAgHHjxmHRokU6109ERETayRRC562k0iigWrVqFZ4+fQogaxju0aNHOh942rRp+OeffxASEgJjY2M5vVWrVti2bZvO9RMREZF2lP8b8ivoxpcj58PNzQ1ff/012rRpAyEEQkNDc33xsZ+fn0YH3rVrF7Zt24aGDRtCkiQ53cvLCzdv3tSoDiIiIqK3gUYB1ZIlSzBy5EgEBgZCkiR07dpVbT5JkpCZmanRge/fvw87O7sc6c+ePVMJsIiIiOjNyH5aT5fyJZVGQ35dunRBQkICUlJSIIRAZGQkkpKScmzaDAXWq1cPe/fulT9nB1Hr16+Hr6+vlqdBREREuuIcqoLT6rE6c3NzHDt2DO7u7jo/kRcYGIh27drhypUryMjIwMqVK3H58mWEhobi+PHjOtVNRERE9CZpHRX5+/sjMzMTwcHBuHr1KiRJQrVq1dC5c2cYGhpqXE+jRo3w999/Y+nSpahUqRIOHTqEunXrIjQ0FDVr1tS2WURERKQjpY4TyzkpXQtRUVHo2LEj7t69i6pVq0IIgevXr8PFxQV79+5VedlxfmrWrIkff/xR2yYQERFRIdB12I5DfloYN24cKlasiNDQUNjY2AAAHj58iI8++gjjxo1TmRf1upSUFI2PY2lpqW3TiIiISAeclF5wWgdUx48fx+nTp+VgCgDKli2LRYsWoXHjxnmWtba21vgJPk2fFiQiIiIqaloHVAqFAk+ePMmR/vTpUxgZGeVZ9tixY/LPt2/fxtSpUzFo0CD5qb7Q0FD8+OOPCAwM1LZZREREpCOljkN+Sg75ae7999/Hxx9/jA0bNqB+/foAgDNnzmDkyJHo1KlTnmX9/f3ln+fOnYtly5ahT58+clqnTp1Qs2ZNrFu3DgMHDtS2aURERKSDTKHjy5FLcECl0TpUr/r6669RqVIl+Pr6wtjYGMbGxmjcuDE8PDywcuVKjesJDQ2Fj49PjnQfHx+cPXtW22YRERERFRmte6isra2xe/duREVF4erVqxBCwMvLCx4eHlrV4+LigjVr1uCrr75SSV+7di1cXFy0bRYRERHpKPudfLqUL6kKvDqnh4eH1kHUq5YvX47u3bvj4MGDaNiwIQDg9OnTuHnzJoKDgwtcLxERERUMl00oOK2H/PSlQ4cOuHHjBjp37oxHjx7h4cOH6Ny5M65fv44OHToUVbOIiIiItKbb+2O0dPHiRdSoUQMGBllxXPny5bFgwYJc81++fBlVq1bV+TU3RERElD8O+RXcG+2hqlOnDh4+fKhxfl9fX8TExBRii4iIiCgbX45ccG+060cIgZkzZ8LU1FSj/GlpaYXcIiIiIiLdFSig+uuvv7B27VrcvHkTv/32G5ydnbF582a4u7ujSZMmuZbz8/NDZGSkxsfx9fWFiYlJQZpIREREWuKQX8FpHVAFBwejf//+6NevHy5cuIDU1FQAwJMnT7Bw4ULs27cv17IhISEFbigREREVLqXQ7Um9EhxPaT+Hav78+VizZg3Wr1+P0qVLy+mNGjVCeHi4XhtHREREb052D5UuW0mldUAVGRkJPz+/HOmWlpZ4/PixPtpEREREJLt9+zaGDh0Kd3d3mJiYoFKlSpg9e3a+c60HDRoESZJUtuy1L/VN6yE/R0dHREVFwc3NTSX95MmTqFixor7aRURERG/Y27qw57Vr16BUKrF27Vp4eHjg33//xfDhw/Hs2TMsXbo0z7Lt2rVDUFCQ/NnIyKhQ2qh1QDVixAiMHz8eP/zwAyRJwr179xAaGopJkyZh1qxZhdFGIiIiegPe1oCqXbt2aNeunfy5YsWKiIyMxHfffZdvQKVQKODg4FAo7XqV1gHV5MmTkZycjObNm+Ply5fw8/ODQqHApEmTMHbs2MJoIxERERUjKSkpKp8VCgUUCoVej5GcnAwbG5t884WEhMDOzg7W1tbw9/fHggULYGdnp9e2AAVc2HPBggV48OABzp49i9OnT+P+/fuYN2+evttGREREb1CGUui8AYCLiwusrKzkLTAwUK/tvHnzJlatWoWRI0fmma99+/b4+eefcfToUXz11VcICwtDixYt5BUK9KnAC3uamprCx8dHn20hIiKiIqTUcchP+b+ysbGxsLS0lNNz650KCAjAnDlz8qwzLCxMJd64d+8e2rVrhx49emDYsGF5lu3Vq5f8c40aNeDj4wNXV1fs3bsX3bp1y/d8tKFRQKXNQXfs2FHgxhAREVHxZ2lpqRJQ5Wbs2LHo3bt3nnlefQju3r17aN68OXx9fbFu3Tqt2+Xo6AhXV1fcuHFD67L50SigsrKykn8WQmDnzp2wsrKSI8bz58/j8ePHeo/2iIiI6M3JFECmUrfy2rC1tYWtra1GeePi4tC8eXN4e3sjKCgIBgbaz1p6+PAhYmNj4ejoqHXZ/GgUUL36uOGUKVPQs2dPrFmzBoaGhgCAzMxMjB49WqNolIiIiN5Ob+tTfvfu3UOzZs1QoUIFLF26FPfv35f3vfoEn6enJwIDA9G1a1c8ffoUAQEB6N69OxwdHXH79m1Mnz4dtra26Nq1q97bqPUcqh9++AEnT56UgykAMDQ0xMSJE9GoUSMsWbJErw0kIiKiku3QoUOIiopCVFQUypcvr7JPvBLERUZGIjk5GUBWbHLp0iVs2rQJjx8/hqOjI5o3b45t27bBwsJC723UOqDKyMjA1atXUbVqVZX0q1evQqnUoZ+QiIiIitTb+nLkQYMGYdCgQfnmezW4MjExwcGDBwulPepoHVANHjwYQ4YMQVRUlLx8++nTp7Fo0SIMHjxY7w0kIiKiN+NtHfIrDrQOqJYuXQoHBwcsX74c8fHxALJmzU+ePBmfffaZ3htIREREb0aGMmvTpXxJpXVAZWBggMmTJ2Py5MnySqicjE5EREQlWYEX9gQYSBEREb1L9LWwZ0mkdUDl7u4OSZJy3X/r1i2dGkRERERFI1Op4zpUHPLT3IQJE1Q+p6en48KFCzhw4AA+//xzfbWLiIiIqNjQOqAaP3682vRvvvkG586d07lBREREVDT4lF/Bab9uey7at2+P4OBgfVVHREREb1im0H0rqfQWUP3222+wsbHRV3VERERExYbWQ3516tRRmZQuhEBCQgLu37+Pb7/9Vq+NIyIiojcnQylgqMNq5xmFtFJ6caB1QNW5c2eVgMrAwADlypVDs2bN4OnpqdfGERER0Zuj67BdSR7y0zqgCggIKIRmEBERERVfWs+hMjQ0RGJiYo70hw8fwtDQUC+NIiIiojdP+b+XIxd0U3LIT3Mil0ciU1NTYWRkpHODiIiIqGhwyK/gNA6ovv76awCAJEn4/vvvYW5uLu/LzMzEiRMnOIeKiIioGMsUOq6UzoAqf8uXLweQ1UO1Zs0aleE9IyMjuLm5Yc2aNfpvIREREdFbTuOAKjo6GgDQvHlz7NixA2XKlCm0RhEREdGblz0XSpfyJZXWc6iOHTtWGO0gIiKiIsY5VAWnUUA1ceJEzJs3D2ZmZpg4cWKeeZctW6aXhhEREREVFxoFVBcuXEB6ejoAIDw8XGVhTyIiIno3ZCp1nJSuQ9niTqOA6tVhvpCQkMJqCxERERUhpY5DfiV4CpX2C3sOGTIET548yZH+7NkzDBkyRC+NIiIiIipOtA6ofvzxR7x48SJH+osXL7Bp0ya9NIqIiIjevOwhP122kkrjp/xSUlIghIAQAk+ePIGxsbG8LzMzE/v27YOdnV2hNJKIiIgKX6YQMMjljSiali+pNA6orK2tIUkSJElClSpVcuyXJAlz5szRa+OIiIiIigONA6pjx45BCIEWLVogODgYNjY28j4jIyO4urrCycmpUBpJREREhU+pzNp0KV9SaRxQ+fv7A8haMd3FxQUGBlpPvyIiIqK3WKYSMOCyCQWi9Urprq6uAIDnz58jJiYGaWlpKvtr1aqln5YRERHRG5UpAAOulF4gWgdU9+/fx+DBg7F//361+zMzM3VuFBEREVFxovW43YQJE5CUlITTp0/DxMQEBw4cwI8//ojKlStjz549hdFGIiIiegOUOi6ZwDlUWjh69Ch2796NevXqwcDAAK6urmjdujUsLS0RGBiIjh07FkY7iYiIqJAphY6T0kvwkJ/WPVTPnj2T15uysbHB/fv3AQA1a9ZEeHi4fltHREREVAxoHVBVrVoVkZGRAIDatWtj7dq1iIuLw5o1a+Do6Kj3BhIREdGbkSl030oqrYf8JkyYgPj4eADA7Nmz0bZtW/z8888wMjLCxo0b9d0+IiIiekMylYDEZRMKROuAql+/fvLPderUwe3bt3Ht2jVUqFABtra2em0cERERUXGg8+qcpqamqFu37lsRTAUGBkKSJEyYMEFO27FjB9q2bQtbW1tIkoSIiIh860lPT8fcuXNRqVIlGBsb47333sOBAwdU8nz33XeoVasWLC0tYWlpCV9f3xxLSQwaNEh+XU/21rBhQ32cKhERkd5lr5Suy1ZY3NzcctxTp06dmmcZIQQCAgLg5OQEExMTNGvWDJcvXy6U9mnUQzVx4kSNK1y2bFmBG6OLsLAwrFu3LsfCos+ePUPjxo3Ro0cPDB8+XKO6ZsyYgZ9++gnr16+Hp6cnDh48iK5du+LUqVOoU6cOAKB8+fJYtGgRPDw8AAA//vgjOnfujAsXLqB69epyXe3atUNQUJD82cjISNdTJSIiKhRv+5Df3LlzVe7l5ubmeeZfvHgxli1bho0bN6JKlSqYP38+WrdujcjISFhYWOi1bRoFVBcuXNCoMkmSdGpMQT19+hT9+vXD+vXrMX/+fJV9/fv3BwDcvn1b4/o2b96ML774Ah06dAAAjBo1CgcPHsRXX32Fn376CQDwwQcfqJRZsGABvvvuO5w+fVoloFIoFHBwcCjIaREREdErLCwsNL6nCiGwYsUKfPHFF+jWrRuArM4Pe3t7bNmyBSNGjNBr2zQKqI4dO6bXg+rbmDFj0LFjR7Rq1SpHQFUQqampMDY2VkkzMTHByZMn1ebPzMzE9u3b8ezZM/j6+qrsCwkJgZ2dHaytreHv748FCxbIy06oO25qaqr8OSUlRcczISIi0lymACQ9vHrm9fuXQqGAQqHQoWVZvvzyS8ybNw8uLi7o0aMHPv/881xHfqKjo5GQkIA2bdqotMPf3x+nTp0qmoDqbfbLL78gPDwcYWFhequzbdu2WLZsGfz8/FCpUiX8+eef2L17d47X6ly6dAm+vr54+fIlzM3NsXPnTnh5ecn727dvjx49esDV1RXR0dGYOXMmWrRogfPnz6v9xQoMDMScOXP0dh5ERETaEDrOgxL/K+vi4qKSPnv2bAQEBBS8YgDjx49H3bp1UaZMGZw9exbTpk1DdHQ0vv/+e7X5ExISAAD29vYq6fb29rhz545ObVGnWAdUsbGxGD9+PA4dOpSjR0kXK1euxPDhw+Hp6QlJklCpUiUMHjxYZS4UkLUmV0REBB4/fozg4GAMHDgQx48fl4OqXr16yXlr1KgBHx8fuLq6Yu/evXL346umTZumMl8tJSUlxy8lERFRYclUAtDDHKrY2FhYWlrK6bn1TgUEBOTbkRAWFgYfHx98+umnclqtWrVQpkwZfPjhh/jyyy9RtmzZXMu/Ph1JCFEoU5SKdUB1/vx5JCYmwtvbW07LzMzEiRMnsHr1aqSmpsLQ0FDresuVK4ddu3bh5cuXePjwIZycnDB16lS4u7ur5DMyMpInpfv4+CAsLAwrV67E2rVr1dbr6OgIV1dX3LhxQ+1+fXWJEhERFaXsJ+DzM3bsWPTu3TvPPG5ubmrTs5+aj4qKUhtQZc+1SkhIUFl4PDExMUevlT4U64CqZcuWuHTpkkra4MGD4enpiSlTphQomHqVsbExnJ2dkZ6ejuDgYPTs2TPP/EIIlTlQr3v48CFiY2O5ojwREb2VMgUAPcyh0pStrW2Bl13KfmAut3uqu7s7HBwccPjwYfkJ/bS0NBw/fhxffvllgY6Zl2IdUFlYWKBGjRoqaWZmZihbtqyc/ujRI8TExODevXsAIL82x8HBQY5eBwwYAGdnZwQGBgIAzpw5g7i4ONSuXRtxcXEICAiAUqnE5MmT5eNMnz4d7du3h4uLC548eYJffvkFISEh8npVT58+RUBAALp37w5HR0fcvn0b06dPh62tLbp27Vq4F4aIiKgAlDoum1BY61CFhobi9OnTaN68OaysrBAWFoZPP/0UnTp1QoUKFeR8np6eCAwMRNeuXeV1KRcuXIjKlSujcuXKWLhwIUxNTdG3b1+9t7FYB1Sa2LNnDwYPHix/zu5afHWCXExMDAwM/n+N05cvX2LGjBm4desWzM3N0aFDB2zevBnW1tZynv/++w/9+/dHfHw8rKysUKtWLRw4cACtW7cGABgaGuLSpUvYtGkTHj9+DEdHRzRv3hzbtm3T+9oXRERE7zKFQoFt27Zhzpw5SE1NhaurK4YPH67S0QFkdZokJyfLnydPnowXL15g9OjRSEpKQoMGDXDo0KFCuQ9LQogS/CrDt1tKSgqsrKyQnJys0Vg0ERG9e97EvSD7GJ5Td8JQYVbgejJTn+Haoq4l8r71zvdQERERkWbe1iG/4kDnd/kRERERlXTsoSIiIiIA+luHqiRiQEVEREQAAKWOyyYoS/CsbA75EREREemIPVREREQEAFAqJUBZ8NeyKHUoW9wxoCIiIiIAWXOgBJ/yKxAGVERERASAAZUuOIeKiIiISEfsoSIiIiIA/+thYg9VgTCgIiIiIgBAplKC4KT0AuGQHxEREZGO2ENFREREADgpXRcMqIiIiAgAIIRuQZHgSulEREREVFDsoSIiIiIAWZPKJR0mlusyob24Y0BFREREALKG+yRdhvxK8BwqDvkRERER6Yg9VERERASAPVS6YEBFREREAP43B4pzqAqEARURERFl0fHVMzqVLeY4h4qIiIhIR+yhIiIioizsoSowBlREREQEADDgpPQC45AfERERkY7YQ0VEREQAsnqnpEwdKijBPVQMqIiIiAgAYMghvwLjkB8RERGRjthDRURERACyJqUb6NDLpCzBPVQMqIiIiAgAYKgUMFCKApeXdChb3HHIj4iIiEhH7KEiIiIiAIBhpoCBgQ49VJklt4eKARUREREBAAyVShjoMBFKKsGTqBhQEREREQAGVLrgHCoiIiIiHbGHioiIiAAABkqhUw8V+JQfERERlXQGykydt8IQEhICSZLUbmFhYbmWGzRoUI78DRs2LJQ2soeKiIiI3mqNGjVCfHy8StrMmTNx5MgR+Pj45Fm2Xbt2CAoKkj8bGRkVShsZUBEREREAQBK69TIJkVU2JSVFJV2hUEChUBS4XiMjIzg4OMif09PTsWfPHowdOxaSJOVZVqFQqJQtLBzyIyIiIgCAJDJ03gDAxcUFVlZW8hYYGKjXdu7ZswcPHjzAoEGD8s0bEhICOzs7VKlSBcOHD0diYqJe25KNPVRERESkV7GxsbC0tJQ/69I7pc6GDRvQtm1buLi45Jmvffv26NGjB1xdXREdHY2ZM2eiRYsWOH/+vN7bxICKiIiIAACSMhOSDkN+2WUtLS1VAqrcBAQEYM6cOXnmCQsLU5kndffuXRw8eBC//vprvvX36tVL/rlGjRrw8fGBq6sr9u7di27duuVbXhsMqIiIiAgAICkzICkzdCqvjbFjx6J379555nFzc1P5HBQUhLJly6JTp07aNg+Ojo5wdXXFjRs3tC6bHwZUREREVCRsbW1ha2urcX4hBIKCgjBgwACULl1a6+M9fPgQsbGxcHR01LpsfjgpnYiIiAAAklBCEpk6bIX76pmjR48iOjoaQ4cOVbvf09MTO3fuBAA8ffoUkyZNQmhoKG7fvo2QkBB88MEHsLW1RdeuXfXeNvZQERERURZlRtamS/lCtGHDBjRq1AjVqlVTuz8yMhLJyckAAENDQ1y6dAmbNm3C48eP4ejoiObNm2Pbtm2wsLDQe9sYUBEREVEWkZG16VK+EG3ZsiXvw4v/f/WNiYkJDh48WKjteRWH/IiIiIh0xB4qIiIiyiIyszZdypdQDKiIiIgoy1s+h+ptxiE/IiIiIh2xh4qIiIiyiEwdJ6VzyI+IiIhKOs6hKjAO+RERERHpiD1URERElIWT0guMARUREREBAITIgNBhDpUuZYs7DvkRERER6Yg9VERERPQ/Ok5KR8mdlM6AioiIiAAAQpkJIekw5KdkQEVEREQlHZdNKDDOoSIiIiLSEXuoiIiICED2U36GOpUvqRhQEREREYDsOVQFH7YryXOoOORHREREpCP2UBEREVEWkQHoMOSn04uVizkGVERERJRFKCF0espPqb+2FDMc8iMiIiLSEXuoiIiICAAgRKZOPVQ69W4VcwyoiIiICAAglBkQUsEHr4Sy5M6h4pAfERERkY7YQ0VEREQAOOSnCwZURERElIXv8iswBlREREQEIHul9ILPg+JK6URERERUYOyhIiIiIgDZc6h0eMqPQ35ERERU0jGgKjgO+RERERHpiD1UREREBOB/PVRK9lAVBAMqIiIiAsAhP11wyI+IiIhIR+yhIiIiIgDsodIFAyoiIiIC8L+FPXUYvOLCnu+IwMBASJKECRMmyGk7duxA27ZtYWtrC0mSEBERoVFdK1asQNWqVWFiYgIXFxd8+umnePnypbw/IyMDM2bMgLu7O0xMTFCxYkXMnTsXSqVSziOEQEBAAJycnGBiYoJmzZrh8uXL+jpdIiIivRJCKb/Pr2CbMv+DFNCCBQvQqFEjmJqawtraWm2emJgYfPDBBzAzM4OtrS3GjRuHtLS0POtNTU3FJ598AltbW5iZmaFTp064e/eu1u17ZwKqsLAwrFu3DrVq1VJJf/bsGRo3boxFixZpXNfPP/+MqVOnYvbs2bh69So2bNiAbdu2Ydq0aXKeL7/8EmvWrMHq1atx9epVLF68GEuWLMGqVavkPIsXL8ayZcuwevVqhIWFwcHBAa1bt8aTJ090P2EiIqISJC0tDT169MCoUaPU7s/MzETHjh3x7NkznDx5Er/88guCg4Px2Wef5VnvhAkTsHPnTvzyyy84efIknj59ivfffx+Zmdr1tr0TQ35Pnz5Fv379sH79esyfP19lX//+/QEAt2/f1ri+0NBQNG7cGH379gUAuLm5oU+fPjh79qxKns6dO6Njx45ynq1bt+LcuXMAsnqnVqxYgS+++ALdunUDAPz444+wt7fHli1bMGLEiBzHTU1NRWpqqvw5JSVF5b9ERFTyvMl7gDIjFcKg4MN2Qpn1HsDX26xQKKBQKHRq25w5cwAAGzduVLv/0KFDuHLlCmJjY+Hk5AQA+OqrrzBo0CAsWLAAlpaWOcokJydjw4YN2Lx5M1q1agUA+Omnn+Di4oIjR46gbdu2mjdQvAMGDBggJkyYIIQQwt/fX4wfPz5HnujoaAFAXLhwId/6tm7dKqysrMSZM2eEEELcvHlTeHp6isDAQDlPYGCgcHV1FZGRkUIIISIiIoSdnZ3YsmWLXAaACA8PV6m7U6dOYsCAAWqPO3v2bAFAZTMyMsqRxo0bN27cStZmbm4uHj9+nO/9q6BevHghHBwc9NbW19Nmz56tt7YGBQUJKyurHOkzZ84UtWrVUkl79OiRACCOHj2qtq4///xTABCPHj1SSa9Vq5aYNWuWVu0q9j1Uv/zyC8LDwxEWFqa3Onv37o379++jSZMmEEIgIyMDo0aNwtSpU+U8U6ZMQXJyMjw9PWFoaIjMzEwsWLAAffr0AQAkJCQAAOzt7VXqtre3x507d9Qed9q0aZg4caL8OSUlBS4uLoiNjVUbWVPh43dQ9PgdFC1e/6KX/R1IklRoxzA2NkZ0dHS+8400IYTI0VZde6c0kZCQkOOeW6ZMGRgZGcn3ZHVljIyMUKZMGZV0e3v7XMvkplgHVLGxsRg/fjwOHToEY2NjvdUbEhKCBQsW4Ntvv0WDBg0QFRWF8ePHw9HRETNnzgQAbNu2DT/99BO2bNmC6tWrIyIiAhMmTICTkxMGDhwo1/X6L5W6X7RsuXWJWlpa8h+yIsbvoOjxOyhavP7vPmNjY73eSzUREBAgD+XlJiwsDD4+PhrVp+7+mtd9NzcFKVOsA6rz588jMTER3t7eclpmZiZOnDiB1atXIzU1FYaGhlrXO3PmTPTv3x/Dhg0DANSsWRPPnj3Dxx9/jC+++AIGBgb4/PPPMXXqVPTu3VvOc+fOHQQGBmLgwIFwcHAAkBX9Ojo6ynUnJibmiKCJiIhKorFjx8r30dy4ublpVJeDgwPOnDmjkpaUlIT09PRc77sODg5IS0tDUlKSSi9VYmIiGjVqpNFxsxXrgKply5a4dOmSStrgwYPh6emJKVOmFCiYAoDnz5/DwED1AUhDQ0MIISCEyDNP9rIJ7u7ucHBwwOHDh1GnTh0AWU8oHD9+HF9++WWB2kVERPQusbW1ha2trV7q8vX1xYIFCxAfHy93ZBw6dAgKhUKl4+VV3t7eKF26NA4fPoyePXsCAOLj4/Hvv/9i8eLFWh2/WAdUFhYWqFGjhkqamZkZypYtK6c/evQIMTExuHfvHgAgMjISQFZUmt2LNGDAADg7OyMwMBAA8MEHH2DZsmWoU6eOPOQ3c+ZMdOrUSQ7SPvjgAyxYsAAVKlRA9erVceHCBSxbtgxDhgwBAHk9rIULF6Jy5cqoXLkyFi5cCFNTU/npwfwoFArMnj37jYw9k3r8Dooev4Oixetf9PgdZImJiZHv6ZmZmfK6kh4eHjA3N0ebNm3g5eWF/v37Y8mSJXj06BEmTZqE4cOHy8PVcXFxaNmyJTZt2oT69evDysoKQ4cOxWeffYayZcvCxsYGkyZNQs2aNeWn/jSm1RT2YuD1p/yCgoLUPoXw6hMH/v7+YuDAgfLn9PR0ERAQICpVqiSMjY2Fi4uLGD16tEhKSpLzpKSkiPHjx4sKFSoIY2NjUbFiRfHFF1+I1NRUOY9SqRSzZ88WDg4OQqFQCD8/P3Hp0qVCPHsiIqJ308CBA9Xez48dOybnuXPnjujYsaMwMTERNjY2YuzYseLly5fy/uwn/l8t8+LFCzF27FhhY2MjTExMxPvvvy9iYmK0bp8kxP/GsIiIiIioQN6ZldKJiIiIigoDKiIiIiIdMaAiIiIi0hEDKiIiIiIdMaDSk+vXr6Nz586wtbWFpaUlGjdujGPHjsn7N27cCEmS1G6JiYm51puQkID+/fvDwcEBZmZmqFu3Ln777Td5/+3btzF06FC4u7vDxMQElSpVwuzZs3O8PiAmJgYffPABzMzMYGtri3HjxuXIc+nSJfj7+8PExATOzs6YO3cuitMzC4XxHdy+fTvXMtu3b1fJu3fvXjRo0AAmJiawtbWVX4oNAP/88w/69OkDFxcXmJiYoFq1ali5cqVGxzpw4IAer1LhKcrrn5SUhP79+8PKygpWVlbo378/Hj9+rFKXujrWrFmjkod/A7kLDQ1FixYtYGZmBmtrazRr1gwvXrzIkS81NRW1a9eGJEnyY+2aHru4/w0AhfcdNGvWLEf+1xfEDA8PR+vWrWFtbY2yZcvi448/xtOnT1XylIR7QZEp4NOL9BoPDw/RoUMH8c8//4jr16+L0aNHC1NTUxEfHy+EEOL58+ciPj5eZWvbtq3w9/fPs95WrVqJevXqiTNnzoibN2+KefPmCQMDA/mly/v37xeDBg0SBw8eFDdv3hS7d+8WdnZ24rPPPpPryMjIEDVq1BDNmzcX4eHh4vDhw8LJyUmMHTtWzpOcnCzs7e1F7969xaVLl0RwcLCwsLAQS5cu1f/FKiSF8R1kZGTkKDNnzhxhZmYmnjx5Iuf77bffRJkyZcR3330nIiMjxbVr18T27dvl/Rs2bBCffPKJCAkJETdv3hSbN28WJiYmYtWqVXKe7Md5jxw5onK8V5fieJsV5fVv166dqFGjhjh16pQ4deqUqFGjhnj//fdV6gIggoKCVOp6/vy5vJ9/A7k7deqUsLS0FIGBgeLff/8V169fF9u3b1d5HD3buHHjRPv27QWg+jJ6TY5d3P8GhCi878Df318MHz5cpdyrL0uOi4sTZcqUESNHjhTXrl0TZ8+eFY0aNRLdu3eX85SUe0FRYUClB/fv3xcAxIkTJ+S0lJQU+R8GdRITE0Xp0qXFpk2b8qzbzMwsRx4bGxvx/fff51pm8eLFwt3dXf68b98+YWBgIOLi4uS0rVu3CoVCIZKTk4UQQnz77bfCyspK5R/IwMBA4eTkJJRKZZ5tfBsU5nfwutq1a4shQ4bIn9PT04Wzs3Oe34k6o0ePFs2bN5c/Z99MXr0JFRdFef2vXLkiAIjTp0/LaaGhoQKAuHbtmpwGQOzcuTPXevk3kLsGDRqIGTNm5NuGffv2CU9PT3H58uV8f5fVHbs4/w0IUbjfwetrLL5u7dq1ws7OTmRmZsppFy5cEADEjRs3hBAl415QlDjkpwdly5ZFtWrVsGnTJjx79gwZGRlYu3Yt7O3tc13uftOmTTA1NcWHH36YZ91NmjTBtm3b8OjRIyiVSvzyyy9ITU1Fs2bNci2TnJwMGxsb+XNoaChq1KgBJycnOa1t27ZITU3F+fPn5Tz+/v4qK/G2bdsW9+7dw+3btzW4CkWrML+DV50/fx4REREYOnSonBYeHo64uDgYGBigTp06cHR0RPv27XH58uU863r9e8rWqVMn2NnZoXHjxirDu2+zorz+oaGhsLKyQoMGDeS0hg0bwsrKCqdOnVIpP3bsWNja2qJevXpYs2aN/Kqo7Hr4N5BTYmIizpw5Azs7OzRq1Aj29vbw9/fHyZMnVfL9999/GD58ODZv3gxTU9N825vXsYvj3wBQ+H8HP//8M2xtbVG9enVMmjQJT548kfelpqbCyMhI5ZVoJiYmACB/VyXhXlCkijqie1fcvXtXeHt7C0mShKGhoXBycsrz/7K8vLzEqFGj8q338ePHom3btgKAKFWqlLC0tBSHDh3KNX9UVJSwtLQU69evl9OGDx8uWrdunSOvkZGR2LJlixBCiNatW4vhw4er7I+LixMAxKlTp/Jt59ugsL6DV40aNUpUq1ZNJW3r1q0CgKhQoYL47bffxLlz50SfPn1E2bJlxcOHD9XWc+rUKVG6dGmV7/L+/fti2bJl4syZMyIsLEzMnDlTGBgYiM2bN2vVxqJSVNd/wYIFonLlyjnyVq5cWSxcuFD+PG/ePHHq1Clx4cIFsXTpUmFqairmzZsn7+ffgHrZvX02Njbihx9+EOHh4WLChAnCyMhIXL9+XQiR9VaIdu3ayddTk54mdccu7n8DQhTe38G6devE4cOHxaVLl8TWrVuFm5ubaNWqlbz/33//FaVKlRKLFy8Wqamp4tGjR6Jbt24CgPx3UFLuBUWFAVUeZs+erXaZ+1e3sLAwoVQqRadOnUT79u3FyZMnxfnz58WoUaOEs7OzuHfvXo56T506JQCIc+fO5duGsWPHivr164sjR46IiIgIERAQIKysrMTFixdz5I2LixMeHh5i6NChKunDhw8Xbdq0yZG/dOnSYuvWrUKIrD+ijz/+WGX/3bt3BQARGhqabzsLy9vwHWR7/vy5sLKyyjGX4OeffxYAxNq1a+W0ly9fCltbW7FmzZoc9fz777+iXLlyKjfz3IwdO1bUrFlT4zbqW3G4/gsWLBBVqlTJkd/Dw0MEBgbmWt/SpUuFpaWl/Jl/A+r9/fffAoCYNm2aSnrNmjXF1KlThRBCrFy5UjRq1EhkZGQIIfIPqLT9N7Ao/waEKPrvQJ1z584JAOL8+fNy2s8//yzs7e2FoaGhMDIyEpMmTRL29vbiyy+/FEIU73tBccCAKg/3798XV69ezXN78eKFOHLkiDAwMJDHoLPl9g/6kCFDRO3atfM9flRUlAAg/v33X5X0li1bihEjRqikxcXFiSpVqoj+/furjKELIcTMmTNFrVq1VNIePXokAIijR48KIYTo37+/6NSpk0qe8PBwAUDcunUr37YWlqL+Dl61adMmUbp0aZGYmKiSfvToUQFA/PXXXyrp9evXF9OnT1dJu3z5srCzs8uRnpuffvpJGBsba9VOfSoO13/Dhg3CysoqR34rKyvxww8/5FrfyZMnBQCRkJAghODfQG5u3bolAOToJerZs6fo27evEEKIzp07CwMDA2FoaChvAIShoaEYMGBAgY8tRNH/DQhR9N+BOkqlUpQuXVr88ssvOfYlJCSIJ0+eiKdPnwoDAwPx66+/CiGK972gOCilw2jhO8/W1ha2trb55nv+/DkAqIxdZ39+dY4GADx9+hS//vorAgMDC1yvoaGhSr1xcXFo3rw5vL29ERQUlCO/r68vFixYgPj4eDg6OgIADh06BIVCIY/r+/r6Yvr06UhLS4ORkZGcx8nJCW5ubvm2tbAU9Xfwqg0bNqBTp04oV66cSrq3tzcUCgUiIyPRpEkTAEB6ejpu374NV1dXOd/ly5fRokULDBw4EAsWLNDomBcuXJC/s6JQHK6/r68vkpOTcfbsWdSvXx8AcObMGSQnJ6NRo0a51nfhwgUYGxvD2tparod/Azm5ubnByckJkZGRKunXr19H+/btAQBff/015s+fL++7d+8e2rZti23btqnMbdP22EDR/w0ARf8dqHP58mWkp6ervTb29vYAgB9++AHGxsZo3bo1gOJ9LygWijqiexfcv39flC1bVnTr1k1ERESIyMhIMWnSJFG6dGkRERGhkvf7778XxsbG4tGjRznquXv3rqhatao4c+aMEEKItLQ04eHhIZo2bSrOnDkjoqKixNKlS4UkSWLv3r1CiP8f5mvRooW4e/euyiO12bIflW3ZsqUIDw8XR44cEeXLl1d5VPbx48fC3t5e9OnTR1y6dEns2LFDWFpaFptHZQvrO8h248YNIUmS2L9/v9rjjx8/Xjg7O4uDBw+Ka9euiaFDhwo7Ozv5GNnDfP369VP5jl7tbdm4caP4+eefxZUrV8S1a9fEkiVLROnSpcWyZct0vTyFrqivf7t27UStWrVEaGioCA0NFTVr1lRZNmHPnj1i3bp14tKlSyIqKkqsX79eWFpainHjxsl5+DeQRd13sHz5cmFpaSm2b98ubty4IWbMmCGMjY1FVFSU2rbkNeSX17GL89+AEIX3HURFRYk5c+aIsLAwER0dLfbu3Ss8PT1FnTp15GFWIYRYtWqVOH/+vIiMjBSrV68WJiYmYuXKlfL+knAvKEoMqPQkLCxMtGnTRtjY2AgLCwvRsGFDsW/fvhz5fH195W7y12X/I3Ts2DE57fr166Jbt27Czs5OmJqailq1aqk8XhsUFJTrmP6r7ty5Izp27ChMTEyEjY2NGDt2bI41ZC5evCiaNm0qFAqFcHBwEAEBAcXqMdnC+g6EEGLatGmifPnyOYZTs6WlpYnPPvtM2NnZCQsLC9GqVSuVodrc5mC4urrKeTZu3CiqVasmTE1NhYWFhfD29i5Wk3GL8vo/fPhQ9OvXT1hYWAgLCwvRr18/kZSUJO/fv3+/qF27tjA3NxempqaiRo0aYsWKFSI9PV2lHv4N5P4dBAYGivLlywtTU1Ph6+ubY4hbXR3qAqq8jl3c/waEKJzvICYmRvj5+QkbGxthZGQkKlWqJMaNG5fjoZf+/fvLeV6/V2QrCfeCoiIJweVPiYiIiHTBdaiIiIiIdMSAioiIiEhHDKiIiIiIdMSAioiIiEhHDKiIiIiIdMSAioiIiEhHDKiIiIiIdMSAioiIiEhHDKiINNCsWTNMmDDhnTruoEGD0KVLl0Kp+1W7du2Ch4cHDA0N9X4uGzdulN/Fl5uAgADUrl1br8d91e3btyFJEiIiIgAAISEhkCQJjx8/LrRjEtHbhwEV0Vtsx44dmDdvnvzZzc0NK1asKLoGFcCIESPw4YcfIjY2VuVc9KFXr164fv26Xut8F7we5BFR4StV1A0gotzZ2NgUdRN08vTpUyQmJqJt27ZwcnLSa93p6ekwMTGBiYmJXuslVenp6ShdunRRN4PorcceKqICSEpKwoABA1CmTBmYmpqiffv2uHHjhrw/eyjq4MGDqFatGszNzdGuXTvEx8fLeTIyMjBu3DhYW1ujbNmymDJlCgYOHKgyDPfqkF+zZs1w584dfPrpp5AkCZIkAVA/pLVixQq4ubnJnzMzMzFx4kT5WJMnT8brr/EUQmDx4sWoWLEiTExM8N577+G3334r8HUICQmBhYUFAKBFixaQJAkhISFq67l27RqaNGkCY2NjeHl54ciRI5AkCbt27QLw/z0uv/76K5o1awZjY2P89NNPaof8Fi1aBHt7e1hYWGDo0KF4+fJlnucAAJcvX0bHjh1haWkJCwsLNG3aFDdv3pT3BwUFoVq1ajA2Noanpye+/fbbfOvMy+PHj/Hxxx/D3t4exsbGqFGjBv744w95/6lTp+Dn5wcTExO4uLhg3LhxePbsmbzfzc0NCxcuxJAhQ2BhYYEKFSpg3bp18n53d3cAQJ06dSBJEpo1a6bRueR2nYlIA0X7bmai4sHf31+MHz9e/typUydRrVo1ceLECRERESHatm0rPDw8RFpamhBCiKCgIFG6dGnRqlUrERYWJs6fPy+qVaum8nb5+fPnCxsbG7Fjxw5x9epVMXLkSGFpaSk6d+6s9rgPHz4U5cuXF3PnzhXx8fEiPj5eCCHE7NmzxXvvvafS3uXLlwtXV1f585dffimsrKzEb7/9Jq5cuSKGDh0qLCwsVI41ffp04enpKQ4cOCBu3rwpgoKChEKhECEhIblel7yuQ2pqqoiMjBQARHBwsIiPjxepqak56sjMzBRVq1YVrVu3FhEREeKvv/4S9evXFwDEzp07hRBCREdHCwDCzc1NBAcHi1u3bom4uDgRFBQkrKys5Lq2bdsmjIyMxPr168W1a9fEF198ISwsLHJcn1fdvXtX2NjYiG7duomwsDARGRkpfvjhB3Ht2jUhhBDr1q0Tjo6O8nGDg4OFjY2N2Lhxo0rbLly4IIQQ4tixYwKASEpKUnu8zMxM0bBhQ1G9enVx6NAhcfPmTfH777+Lffv2CSGEuHjxojA3NxfLly8X169fF3///beoU6eOGDRokFyHq6ursLGxEd988424ceOGCAwMFAYGBuLq1atCCCHOnj0rAIgjR46I+Ph48fDhQ63O5fXrTET5Y0BFpIFXA5vr168LAOLvv/+W9z948ECYmJiIX3/9VQiRFVABEFFRUXKeb775Rtjb28uf7e3txZIlS+TPGRkZokKFCrkGVEJk3UiXL1+u0jZNAipHR0exaNEi+XN6erooX768fKynT58KY2NjcerUKZV6hg4dKvr06aP2mmhyHZKSkgQAcezYMbV1CCHE/v37RalSpeQAUQghDh8+rDagWrFihUrZ1wMqX19fMXLkSJU8DRo0yDOgmjZtmnB3d5eD4de5uLiILVu2qKTNmzdP+Pr6qrRN04Dq4MGDwsDAQERGRqrd379/f/Hxxx+rpP3111/CwMBAvHjxQgiR9Xvw0UcfyfuVSqWws7MT3333ndo2aXsur19nIsof51ARaenq1asoVaoUGjRoIKeVLVsWVatWxdWrV+U0U1NTVKpUSf7s6OiIxMREAEBycjL+++8/1K9fX95vaGgIb29vKJVKvbY3OTkZ8fHx8PX1ldNKlSoFHx8fedjvypUrePnyJVq3bq1SNi0tDXXq1FFbr6bXIT+RkZFwcXGBg4ODnPbqdXmVj49PnnVdvXoVI0eOVEnz9fXFsWPHci0TERGBpk2bqp0ndP/+fcTGxmLo0KEYPny4nJ6RkQErK6s825LX8cqXL48qVaqo3X/+/HlERUXh559/ltOEEFAqlYiOjka1atUAALVq1ZL3S5IEBwcH+fdLHW3OJb/rTEQ5MaAi0pJ4be7Rq+nZ85oA5LhBS5KUo+yr+fOqOy8GBgY5yqWnp2tVR3YQt3fvXjg7O6vsUygUastoeh3yo01+MzMzjevVVF6T2rOvy/r161UCRyArANb38bKPOWLECIwbNy7HvgoVKsg/q/v9yisY1+ZcCuM6E73rOCmdSEteXl7IyMjAmTNn5LSHDx/i+vXrcu9BfqysrGBvb4+zZ8/KaZmZmbhw4UKe5YyMjJCZmamSVq5cOSQkJKgEOK8+Lm9lZQVHR0ecPn1aTsvIyMD58+dVzkmhUCAmJgYeHh4qm4uLi9q26OM6AICnpydiYmLw33//yWlhYWEal39VtWrVVM4TQI7Pr6tVqxb++usvtUGovb09nJ2dcevWrRzXJXvit7Zq1aqFu3fv5rrcQ926dXH58uUcx/Pw8ICRkZFGx8jO9+rvSmGcCxH9P/ZQEWmpcuXK6Ny5M4YPH461a9fCwsICU6dOhbOzMzp37qxxPZ988gkCAwPh4eEBT09PrFq1CklJSXn21ri5ueHEiRPo3bs3FAoFbG1t0axZM9y/fx+LFy/Ghx9+iAMHDmD//v2wtLSUy40fPx6LFi1C5cqVUa1aNSxbtkxl4UkLCwtMmjQJn376KZRKJZo0aYKUlBScOnUK5ubmGDhwYKFdh9atW6NSpUoYOHAgFi9ejCdPnuCLL74AkLMHLz/jx4/HwIED4ePjgyZNmuDnn3/G5cuXUbFixVzLjB07FqtWrULv3r0xbdo0WFlZ4fTp06hfvz6qVq2KgIAAjBs3DpaWlmjfvj1SU1Nx7tw5JCUlYeLEiVq1DwD8/f3h5+eH7t27Y9myZfDw8MC1a9cgSRLatWuHKVOmoGHDhhgzZgyGDx8OMzMzXL16FYcPH8aqVas0OoadnR1MTExw4MABlC9fHsbGxrCystL7uRDR/2MPFVEBBAUFwdvbG++//z58fX0hhMC+ffu0Wq9nypQp6NOnDwYMGABfX1+Ym5ujbdu2MDY2zrXM3Llzcfv2bVSqVAnlypUDkNUr8+233+Kbb77Be++9h7Nnz2LSpEkq5T777DMMGDAAgwYNgq+vLywsLNC1a1eVPPPmzcOsWbMQGBiIatWqoW3btvj999/z7L3Qx3UwNDTErl278PTpU9SrVw/Dhg3DjBkzACDPa6FOr169MGvWLEyZMgXe3t64c+cORo0alWeZsmXL4ujRo3j69Cn8/f3h7e2N9evXy+cwbNgwfP/999i4cSNq1qwJf39/bNy4UadeneDgYNSrVw99+vSBl5cXJk+eLPcm1apVC8ePH8eNGzfQtGlT1KlTBzNnzoSjo6PG9ZcqVQpff/011q5dCycnJznALYxzIaIskijIpA0i0julUolq1aqhZ8+eel9RvLj5+++/0aRJE0RFRalM7CcieltxyI+oiNy5cweHDh2Cv78/UlNTsXr1akRHR6Nv375F3bQ3bufOnTA3N0flypURFRWF8ePHo3HjxgymiKjYYEBFVEQMDAywceNGTJo0CUII1KhRA0eOHNFqQve74smTJ5g8eTJiY2Nha2uLVq1a4auvvirqZhERaYxDfkREREQ64qR0IiIiIh0xoCIiFc2aNZNfvvzqelavCgkJgSRJKksvvMsGDRokX5PsFzYTEb2KARUR5TB8+HDEx8ejRo0aRd2UQrdx40Y5WHp1e3XJhpUrVyI+Pr4IW0lEbztOSieiHExNTVXerVdU0tPTtVrTqqAsLS0RGRmpkvbqoqJWVlYFfncfEZUM7KEionzt27cPVapUgYmJCZo3b47bt2/nyHPq1Cn4+fnBxMQELi4uGDduHJ49eybvj4+PR8eOHWFiYgJ3d3ds2bIFbm5uWLFihZxHkiSsWbMGnTt3hpmZGebPnw8A+P333+Ht7Q1jY2NUrFgRc+bMQUZGhlwuOTkZH3/8Mezs7GBpaYkWLVrgn3/+0fj8sl8u/Opmb2+v/YUiohKLARUR5Sk2NhbdunVDhw4dEBERgWHDhmHq1KkqeS5duoS2bduiW7duuHjxIrZt24aTJ09i7Nixcp4BAwbg3r17CAkJQXBwMNatW4fExMQcx5s9ezY6d+6MS5cuYciQITh48CA++ugjjBs3DleuXMHatWuxceNGLFiwAEDWy5U7duyIhIQE7Nu3D+fPn0fdunXRsmVLPHr0qHAvDhFRNkFE9Ap/f38xfvx4+fO0adNEtWrVhFKplNOmTJkiAIikpCQhhBD9+/cXH3/8sUo9f/31lzAwMBAvXrwQV69eFQBEWFiYvP/GjRsCgFi+fLmcBkBMmDBBpZ6mTZuKhQsXqqRt3rxZODo6CiGE+PPPP4WlpaV4+fKlSp5KlSqJtWvX5nu+QUFBAoAwMzNT2Vq3bp0jLwCxc+fOfOskopKHc6iIKE9Xr15Fw4YNVeYU+fr6quQ5f/48oqKi8PPPP8tpQggolUpER0fj+vXrKFWqFOrWrSvv9/DwQJkyZXIcz8fHJ0fdYWFhco8UAGRmZuLly5d4/vw5zp8/j6dPn6Js2bIq5V68eIGbN29qdI4WFhYIDw9XSTMxMdGoLBERwEnpRJQPocHav0qlEiNGjMC4ceNy7KtQoUKOCd951W1mZpaj7jlz5qBbt2458hobG0OpVMLR0REhISE59ltbW+fbdiBr1XoPDw+N8hIRqcOAiojy5OXllWPtpdOnT6t8rlu3Li5fvpxrUOLp6YmMjAxcuHAB3t7eAICoqCiN1rGqW7cuIiMjc627bt26SEhIQKlSpeDm5pZvfUREhYGT0okoTyNHjsTNmzcxceJEREZGYsuWLdi4caNKnilTpiA0NBRjxoxBREQEbty4gT179uCTTz4BkBVQtWrVCh9//DHOnj2LCxcu4OOPP4aJiYnKUKI6s2bNwqZNmxAQEIDLly/j6tWr2LZtG2bMmAEAaNWqFXx9fdGlSxccPHgQt2/fxqlTpzBjxgycO3dOo3MUQiAhISHHplQqtb9gRFQiMaAiojxVqFABwcHB+P333/Hee+9hzZo1WLhwoUqeWrVq4fjx47hx4waaNm2KOnXqYObMmXB0dJTzbNq0Cfb29vDz80PXrl0xfPhwWFhYqCygqU7btm3xxx9/4PDhw6hXrx4aNmyIZcuWwdXVFUDWkgf79u2Dn58fhgwZgipVqqB37964ffu2xksfpKSkwNHRMcem7ilEIiJ1+HJkIlLRrFkz1K5dW2V9qMJw9+5duLi44MiRI2jZsmWhHktfJEnCzp070aVLl6JuChG9ZdhDRUQ5fPvttzA3N8elS5f0VufRo0exZ88eREdH49SpU+jduzfc3Nzg5+ent2MUlpEjR8Lc3Lyom0FEbzH2UBGRiri4OLx48QJA1nCfkZGRXuo9ePAgPvvsM9y6dQsWFhZo1KgRVqxYIQ/dFZbq1avjzp07avetXbsW/fr1y7eOxMREpKSkAAAcHR1zPIlIRMSAiojeaXfu3EF6erraffb29rCwsHjDLSKidxEDKiIiIiIdcQ4VERERkY4YUBHR/7VbxwIAAAAAg/ytB7G3KAJgEioAgEmoAAAmoQIAmIQKAGASKgCAKeF2u9JoY0M4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_disp = pyart.graph.GridMapDisplay(grid)\n",
    "grid_disp.plot_grid('radial_velocity', 0, cmap='balance', vmin=-10, vmax=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "a63403e8-7104-4c00-aad5-31e2c3a6fc1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4du1aifoD5H8PffHFFwgNDcXhw4exe/duNG3aFH379kVsbKykYzHGMG7cOBw7dgybNm1CvXr1jNsK3geNRoNffvkFAwYMQP/+/fHTTz+hdu3aWLRoUZFtTpw4EWfOnMHatWvRtm3bEp8nsS0aoFioevXqyM7OxuPHj022PXjwAB4eHqL7379/32R9VlYWcnNzRfcvsHnzZtSpUwcvv/xysXWKakuj0Zis12g0APK/wM25c+cOzp8/D7VazS2urq5gjBlvdSQmJqJbt25ITk7G0qVLcezYMZw6dQorV64EADx58gQA4O7ujujoaLRp0wZz5sxBixYt4O3tjfnz51scsi10//591KpVy+QL29PTEw4ODkW+9pa0CeQP+IS8vb1N2iyqXnHtenl5mawval1xiprzodVqja+xJWJjY9G3b180btwY+/btM7k9Udxn9sGDBwCK/pw9fPgQffv2RXJyMqKiooqdN1CtWjV06NABvXr1wpw5c7Bu3Trs2bMHu3fvBpA/iBN+3m7cuCGpXwWvUbNmzeDj42Osp1AoEBwcjFu3bhnnbjVs2JA7VuHBi6Wv19ixY/Hpp5/i22+/Rd26dVG/fn1cunQJ77zzDgCgTp06Rb4W5pTkPQCA9evXG/84sLb4+HjMmzcPCxYswIcffoiePXti4MCB+Pnnn1G1alXMnDnT4rbY/25Bbd68GRERERg0aBC3veA97NKlC1xdXY3rnZ2d0aNHD5w9e9akzeXLl+Obb77BlClTMGLEiBKeJbEH4jPSCID/5p789ddf6NSpk3F9amoq7t27h5YtW4ru/8MPPyA1NZWbh1JwX1ps/wL79+/HsGHD0K1bNxw6dIj74i0tNWrUgJOTU7GTMGvUqAEgfyJwVlYWduzYwfUrLi7OZJ+C14MxhvPnzyMiIgILFy6Ek5MT3n//fcl9rF69Ok6ePAnGGDdIuXv3LnQ6nbGPUtsE8ueW1K1bl9t2+/ZtkzYtvQpWvXp1/PnnnybrU1NTJfexpGJjY9GnTx/4+Pjg4MGDJhMegfz3KDIyEjqdjpvvUdxn9uHDh+jTpw8SEhJw6NChIudOFKdg0mnBVbb27dvj1KlTXJ2Cq3+W9qthw4bchNXC2P/mTRVc1di7d69xMnXhYxWw5PUCgFmzZmH69Om4evUqXF1d4ePjgzfeeANVqlThrmpaKiAgoMi5K+a+N+7evYuffvoJAwcOLHIyqlznzp0DYwwdO3bk1qvVarRu3RrR0dEWtVMwONm4cSPWr1+P1157zaSOuc8QY8zkqtSxY8fw9ttv4+mnn8ZXX31lUT+I/aIrKBZ69tln4ejoaPLgooIHL4k97GvQoEFQKBQmk/MiIiLg5ORk0YOUAMDHxwfHjh2DVqtFt27din2wkTU999xzuH79OqpXr44OHTqYLAUT4Ap+QRf+y5Ixhv/7v/8rtm2FQoHWrVtjyZIlqFq1KvcXkZQrAr1790ZmZqZJNNWmTZuM26UquF2zefNmbv2pU6cQHx9fojYBoEePHsjIyMAvv/zCrS/uQWPWFhcXhz59+qBu3bqIiopCtWrViqz3/PPPIzMzE9u3b+fWf/PNN/D29uYG6gWDk3/++QcHDx6UfFm94HZao0aNAORHvwg/ZwVX/Cztl4ODAwYNGoT4+Hjj1Rcg/zO5f/9+NGzY0DjIDAgI4I5VeIBi6etVQKvVomXLlvDx8UFiYiK2bNmCCRMmwMnJSdJrUnCuly9f5iKTdDodNm/ejE6dOpkMpID8z3xeXl6xt3fkKjjmH3/8wa3PycnB2bNnTQbzRWGMYcKECdi4cSPWrl1rEt1YoHbt2ujcuTOOHz+O9PR04/rHjx8jOjqai85KTk7GkCFDUKNGDWzbtq3I6CtSvtAVFAt5eHhg7ty5+PDDD+Hh4YGgoCCcOnUKoaGhGD9+PPz9/Y11N23ahLFjx2LDhg3GS6wtWrTAuHHjMH/+fKhUKnTs2BEHDx7EunXr8NFHH1l8iwfI/6GNjo5GcHAwunfvjqioKIuvwJTE9OnTsX37dnTv3h0zZsxAq1atYDAYkJiYiIMHD+Ltt99Gp06d0LdvX2g0Grzyyit47733kJ2djdWrV+Phw4dcez/99BNWrVqFwYMHo0GDBmCMYceOHXj06BH69u1rrBcQEIAjR45g7969qF27NlxdXdG0adMi+zhy5EisXLkSo0aNMkZu/P777/jkk0/Qr18/9OnTR/J5N23aFK+//jqWL18OpVKJkJAQ3LhxAx9++CHq1auHGTNmSG4TAEaNGoUlS5bgtddew0cffYRGjRrhl19+wYEDBwCUfK6CJa5cuWJ8LT7++GNcvXqVG+Q2bNjQGPIbEhKCvn374s0330R6ejoaNWqEyMhI7N+/H5s3b4ZKpQKQf+suODgYsbGxCA8Ph06n43551axZEw0bNgQArF27FseOHUNQUBDq1auHrKwsHDt2DMuXL0eXLl1MLvEXxdJ+AcCiRYvwyy+/4Nlnn0VoaCjc3Nzw9ddf49y5c9i6datVX68LFy5g+/bt6NChA7RaLc6dO4dPP/0UjRs3LnauhJixY8di5cqVGDJkCD799FN4enpi1apVuHLlCn799dci91m/fj3q1auH4ODgYtstGAgK54b8+OOPAPIjagDg9OnTcHFxAQDj/K6nn34aHTt2RGhoKB4/fozu3bsjLS0Ny5cvR0JCAr799lvRY02dOhXr16/H2LFjERAQwH1etFotN8D94osv0KtXLwQHB2PWrFlQKBT48ssvce/ePePrmpubixdeeAF37tzBl19+iRs3bnCD0gJubm7cdzWxczaYmFuuLV26lDVp0oRpNBpWv359Nn/+fJMHOW3cuLHIGfC5ubls/vz5rH79+kyj0bAmTZqIRtAUKOpBbY8ePWJdu3ZlHh4exgib4iJUfHx8WP/+/U3Ww0wEQWGZmZls7ty5rGnTpkyj0TB3d3cWEBDAZsyYwVJTU4319u7dy1q3bs0cHR1ZnTp12Lvvvst++eUXBoAdPnyYMcbY5cuX2SuvvMIaNmzInJycmLu7O3vqqadYREQEd8y4uDjWtWtX5uzszABwkTBFuX//Pps4cSKrXbs2c3BwYD4+Pmz27NksOzubq2dpFA9j+ZFIixcvZk2aNGFqtZrVqFGDvfbaaywpKcniNoVRPIwxlpiYyF544QXm4uLCXF1d2Ysvvsj27dvHALDdu3cb6xUXxVPUe1nUcYQKPpvFLcLPbEZGBps6dSqrVasW02g0rFWrViwyMpKrk5CQYLbNwpFYx48fZ8899xzz9vZmGo2GOTs7s9atW7NFixZJerCdJf0q8Ndff7H+/fszV1dX5ujoyAIDA9nevXstOo6U1+vKlSuse/fuzMPDg2k0GtaoUSM2d+5clpmZafYYYj+DqampbOTIkczDw8PY/6ioqCLrHj9+nAFg8+bNM3tMHx8f5uPjU2RfilsKe/ToEfvggw9Y8+bNmbOzM/P09GQ9e/YsMqKvqGP5+PgUe5yi+nXs2DHWo0cP5uzszJydndkzzzzDjh8/btwu9hksWMR+Poh9UTBW6CEWhBCb+eSTTzB37lwkJiZadJmcEEIqMrrFQ4gNrFixAkB+hEleXh5+++03LFu2DK+99hoNTgghBDRAIcQmnJ2dsWTJEty4cQM5OTmoX78+Zs2ahblz59q6a4QQYhfoFg8hhBBC7A6FGRNCCCHE7tAAhRAbK3iWzunTpyXt9/jxY4SGhprNxlzWfH19udQAt2/fRmhoaJEP6yOEEHNoDgoh5dTjx4+xYMECAEDPnj1t25n/2blzJ9zc3Izl27dvY8GCBfD19UWbNm1s1zFCSLlDAxRCiNVQYjZCiLXQLR5C7Mzo0aPh4uKCa9euoV+/fnBxcUG9evXw9ttvG/PF3Lhxw/gE0wULFkChUEChUHC3V65evYrhw4fD09MTWq0WzZs3NyZuLHDkyBEoFApERkbigw8+gLe3N9zc3NCnTx9cuXKFqxsbG4vnnnvO2J63tzf69++PW7duGesUvsVz5MgRY76WMWPGGPsYGhqKb7/9FgqFAjExMSbnv3DhQqjVaty+fVv2a0kIKb9ogEKIHcrLy8PAgQPRu3dv7N69G2PHjsWSJUuwePFiAPnpDvbv3w8AGDduHGJiYhATE4MPP/wQAHDp0iV07NgRFy5cwJdffomffvoJ/fv3x9SpU423hQqbM2cObt68ia+//hrr1q3D1atXMWDAAOj1egD5Wbf79u2LO3fuYOXKlYiKikJ4eDjq16+PjIyMIs+hXbt22LhxIwBg7ty5xj6OHz8ew4YNQ61atUwGTDqdDmvXrsXzzz9fZJ4ZQkglYtsH2RJCCh6nXpCuYNSoUQwA27p1K1evX79+rGnTpsbyv//+ywCw+fPnm7QZHBzM6taty9LS0rj1U6ZMYY6OjuzBgweMMcYOHz7MALB+/fpx9bZu3coAsJiYGMYYY6dPn2YA2K5du8yei4+PD/do+1OnThX5CH3G8h/jr9Fo2J07d4zrtmzZwgCw6Ohos8chhFR8dAWFEDukUCgwYMAAbl2rVq1w8+ZN0X2zs7Nx6NAhPP/883B2doZOpzMu/fr1Q3Z2tkkm2oEDB5ocC4DxeI0aNUK1atUwa9YsrFmzBpcuXZJzegCAN998EwC4bNcrVqxAQEAAunfvLrt9Qkj5RgMUQuyQs7MzHB0duXVarRbZ2dmi+96/fx86nQ7Lly+HWq3mln79+gEA7t27x+1TvXp1k2MB+ZmKAcDd3R3R0dFo06YN5syZgxYtWsDb2xvz589HXl5eic7Ry8sLw4YNw9q1a6HX63H+/HkcO3YMU6ZMKVF7hJCKhaJ4CKlgqlWrBpVKhREjRmDy5MlF1vHz85PcbkBAAH744QcwxnD+/HlERERg4cKFcHJywvvvv1+ivk6bNg3ffvstdu/ejf3796Nq1ap49dVXS9QWIaRioQEKIeWU8CpHAWdnZ/Tq1QuxsbFo1aoVNBqNVY+rUCjQunVrLFmyBBERETh79qzkPhZo3749unTpgsWLF+PChQt4/fXXUaVKFav2lxBSPtEAhZByytXVFT4+Pti9ezd69+4NDw8P1KhRA76+vli6dCmefvppdOvWDW+++SZ8fX2RkZGBa9euYe/evfjtt98kHeunn37CqlWrMHjwYDRo0ACMMezYsQOPHj1C3759i92vYcOGcHJywnfffYfmzZvDxcUF3t7eXITOtGnTMGzYMCgUCkyaNKnErwchpGKhOSiElGPr16+Hs7MzBg4ciI4dOyI0NBQA4O/vj7Nnz6Jly5aYO3cugoKCMG7cOPz444/o3bu35OM0btwYVatWxWeffYaBAwdiyJAhOHv2LCIiIjBhwoRi93N2dsaGDRtw//59BAUFoWPHjli3bh1XZ/DgwdBqtQgODkbjxo0l940QUjFRNmNCiE3t3bsXAwcOxM8//2ycxEsIITRAIYTYxKVLl3Dz5k1MmzYNVapUwdmzZ6FQKGzdLUKInaBbPIQQm5g0aRIGDhyIatWqITIykgYnhBAOXUEhhBBCiN2hKyiEEEIIsTs0QCGEEEKI3aEBCiGEEFIJrV69Gq1atYKbmxvc3NzQuXNn/PLLL2b3iY6ORvv27eHo6IgGDRpgzZo1JnW2b98Of39/aLVa+Pv7Y+fOnSXqX6V7UJvBYMDt27fh6upKk/IIIYSYxRhDRkYGvL29oVSW3t/02dnZyM3Nld2ORqMxyeNVnLp16+LTTz9Fo0aNAADffPMNBg0ahNjYWLRo0cKkfkJCAvr164cJEyZg8+bNOH78OCZNmoSaNWvixRdfBADExMRg2LBhWLRoEZ5//nns3LkTQ4cOxe+//45OnTpJOxmb5VG2kaSkJAaAFlpooYUWWixekpKSSu330pMnT5gGWqv0s1atWuzJkycl7ku1atXY119/XeS29957jzVr1oxb98Ybb7DAwEBjeejQoezZZ5/l6gQHB7OXX35Zcl8q3RUUV1fX//1P8b8FSEt7yNVxd69Wtp2qZFQK/mOnZzob9UQ+4bkIiZ2b2Gsh1r5Ucl9ruf2x5ntdkT5HRBprf2ebby//d/9/vzusLzc3F7nIQWf0hoOMGxs66BCTegj37t2Dm5ubcb1WqzXmxSqOXq/Htm3bkJWVhc6dOxdZJyYmBkFBQdy64OBgrF+/Hnl5eVCr1YiJicGMGTNM6oSHh0s+n0o3QPnvto4Civ8NUAq/kQVbSOkR3lpTsPL7eovdJhQ7N7HXwtq3IeW+1nL7Y833uiJ9jog01v7ONtce+9+/ZTElwEnlDAeFusT761geoAfq1avHrZ8/f74xDYbQX3/9hc6dOyM7OxsuLi7YuXMn/P39i6ybmpoKLy8vbp2Xlxd0Oh3u3buH2rVrF1snNTVV8vlUugEKIYQQYo/UajXUMgYoCgZADyQlJZlcQSlO06ZNERcXh0ePHmH79u0YNWoUoqOjix2kCAdq7H+PUiu8vqg6JRng0QCFEEIIsQMajRpqhabE+ysYgGwYo3IsO6bGOEm2Q4cOOHXqFJYuXYq1a9ea1K1Vq5bJlZC7d+/CwcEB1atXN1tHeFXFEhRmTAghhBAA+Vc7cnJyitzWuXNnREVFcesOHjyIDh06QK1Wm63TpUsXyX2hKygAlILJdgbBZDvh9spOpRRMTjRIm5worC+nPbl9KW1l3T+57Vv7vbV2+9batzSU9ntt75/1siT3O9lev+M1DhqolTKuoBik1Z8zZw5CQkJQr149ZGRk4IcffsCRI0ewf/9+AMDs2bORnJyMTZs2AQAmTpyIFStWYObMmZgwYQJiYmKwfv16REZGGtucNm0aunfvjsWLF2PQoEHYvXs3fv31V/z++++Sz8c+3hVCCCGkklOrNdDIGKBA4gDlzp07GDFiBFJSUuDu7o5WrVph//796Nu3LwAgJSUFiYmJxvp+fn7Yt28fZsyYgZUrV8Lb2xvLli0zPgMFALp06YIffvgBc+fOxYcffoiGDRtiy5Yt0p+BgkqYLDA9PR3u7u4AlMXO/LbX0bW9sPZfcuX5Corw+GKkXj2S275UcvtTlldQ7A1dQSk/pHzHMzAABqSlpVk8r0Oqgt9LL3uNkzVAyTXk4oc760u1r2WJfvMSQgghdkCj1kCjNP+8ErMkXkGxdzRAIYQQQuyARvYtnop1Q4SieAghhBBid+gKShHsLarH1scn1iN1Tom9tS9VeZ43Ye35QcR2yst3qFqtgUZV8isoTF+xrqDY57tECCGEVDIatVreAEVZsSah0C0eQgghhNgduoJCCCGE2AG1RgO1jCsoBn3FuoJCAxRCCCHEDqgd1NA4lDxZoEGht2JvbI8GKIQQQogdcNCq4eBQ8isoDqqKNUChOSiEEEIIsTt0BcUC1g5JkxryZm8hcbZOSGfNvkhV2o/5t7fQVan9EatfnsKOxfpGj7IvPVK/I8VeO3v7Di2OWqOGWsYtHr2yYn1myse7RgghhFRwao0aajUNUArQLR5CCCGE2B26gkIIIYTYAZXGAQ7qkv9aVpWTW1mWqlhnQwghhJRTDloHOMi4xeOgzLNib2zPprd4jh49igEDBsDb2xsKhQK7du2yeN/jx4/DwcEBbdq0KbX+EUIIIcQ2bHoFJSsrC61bt8aYMWPw4osvWrxfWloaRo4cid69e+POnTul2MPSUV5mlBP5yjoqR6VSmd2u1/PPSRDWF24Xa1+svhix16dwNEZFj3KpaFFDctpXqxyt2pfywkHjAAdNyb8zHCrY7xabnk1ISAhCQkIk7/fGG29g+PDhUKlUolddcnJykJOTYyynp6dLPh4hhBBS2lRaB6hkDFAq2hyUchfFs3HjRly/fh3z58+3qH5YWBjc3d2NS7169Uq5h4QQQgiRq1wNt65evYr3338fx44dg4ODZV2fPXs2Zs6caSynp6fTIIUQQojdcdCo4aCVMUkWJd/XHpWbAYper8fw4cOxYMECNGnSxOL9tFottFptKfaMEEIIkU+lUcmag6Ji5ueglTflZoCSkZGB06dPIzY2FlOmTAEAGAwGMMbg4OCAgwcP4plnnrFxLwkhhJCSUWsdoNaW/Neyuvz8SrdIuTkbNzc3/PXXX9y6VatW4bfffsOPP/4IPz8/Se2lpT2Em5sbAPlRNVLzRlR29hx9IRZ5YM99B6RH1ciNwpFL+Hqai+qxpyiT8nC8ysTaObHy9NnG/6enp8Pd3V1W+6RkbPqbNDMzE9euXTOWExISEBcXBw8PD9SvXx+zZ89GcnIyNm3aBKVSiZYtW3L7e3p6wtHR0WQ9IYQQUt6o1Q5Qy7jFozZUrD+ObXo2p0+fRq9evYzlgsmso0aNQkREBFJSUpCYmGir7hFCCCFlRq1RQ60p+URXtYEmyVpNz549wRgrdntERITZ/UNDQxEaGmrdThFCCCHE5irW9SBCCCGknNJoVNBqSh6JozNQFA8hhBBCrMzRQQVHGdmM9ToaoFQI7u7VoIACgPwoHGF9iuqpOKRGXojVl5ubR2quHWsr66ifwq+X1NfS2rltpL531o4ssbf2SpNY34TfscLcPVLPrXDUDsB/ZzMUPw2BlC76zUkIIYTYAUe1Ck7qkl8FoSsohBBCCLE6Z7UKzjIGKIYKNkApd8kCCSGEEFLx0RUUQgghxA6oDXpoDCWf56WWsa89ogEKIYQQYgdUOh1UupJPXpazrz2iAQqsH4VDUTukpMSidEzrCyIzBFE2UtuTS+7xrdlfuRFTpU1uVI2to3LK8vhi38nCvoj1TSwKyFaUeblQqnJk7V+R0BwUQgghpBIKCwtDx44d4erqCk9PTwwePBhXrlwxu8/o0aOhUChMlhYtWhjrREREFFknOzvbTMumaIBCCCGE2IOcPPmLBNHR0Zg8eTL++OMPREVFQafTISgoCFlZWcXus3TpUqSkpBiXpKQkeHh4YMiQIVw9Nzc3rl5KSgocHaVdqbLva6CEEEJIJaHIzYNCKW2QIdxfiv3793PljRs3wtPTE2fOnEH37t2L3Mfd3R3u7u7G8q5du/Dw4UOMGTOG74tCgVq1aknqjxBdQSGEEEIqkPT0dG7JybFsXktaWhoAwMPDw+JjrV+/Hn369IGPjw+3PjMzEz4+Pqhbty6ee+45xMbGWn4C/0MDFEIIIcQOsDw9WK6u5Ete/iT1evXqGa90uLu7IywsTPzYjGHmzJl4+umn0bJlS4v6m5KSgl9++QXjx4/n1jdr1gwRERHYs2cPIiMj4ejoiK5du+Lq1auSXg+6xQPTGd/Wjuqx9Wx9W8/2F7Lm+cjNdWPt/CaStwuiVoRROeLH5/fXaLSy9hfSS3yugmlUkc7sdin7l3ZEktQIJLG8RFI/i3I/S3LrW3t/a5IbGVlu8qPl6gCFjO+k3Px9k5KS4ObmZlyt1Yp/L0yZMgXnz5/H77//bvHhIiIiULVqVQwePJhbHxgYiMDAQGO5a9euaNeuHZYvX45ly5ZZ3L6dvkuEEEIIKQk3NzdugCLmrbfewp49e3D06FHUrVvXon0YY9iwYQNGjBgBjUZjtq5SqUTHjh3pCgohhBBSHrEcPRhK/jRYliNtX8YY3nrrLezcuRNHjhyBn5+fxftGR0fj2rVrGDdunEXHiYuLQ0BAgKT+0QCFEEIIsQMsTwcm4xYPy5O27+TJk/H9999j9+7dcHV1RWpqKoD8SB0nJycAwOzZs5GcnIxNmzZx+65fvx6dOnUqcr7KggULEBgYiMaNGyM9PR3Lli1DXFwcVq5cKal/NEAhhBBCKqHVq1cDAHr27Mmt37hxI0aPHg0gfyJsYmIitz0tLQ3bt2/H0qVLi2z30aNHeP3115Gamgp3d3e0bdsWR48exVNPPSWpfzRAIYQQQuwAy5V5iydX+i0eMRERESbr3N3d8fjx42L3WbJkCZYsWSKpL0WhAQohhBBiB1iOHoyV3QDF3tEAxQLWDjuWytphwdYOOxZrT254pJz+CUNFxUJDxfaXut20vvnXQu3Az4Y3iIT5KkXChIXbhe2p1fzxhK+P2P5ixMKYxbZbkzDkWUhuYkOpny1rs3VYcGmGNUv9DsjT8zlf7DasWIDl6sGYnDkoFWuAQg9qI4QQQojdKR/DSkIIIaSCM+ToYJBxxdggMYrH3tEAhRBCCLEDLFdnMoVA0v4VbIBCt3gIIYQQYnfoCgohhBBiBwy5Mm/x6CrWFRQaoJSAWuUoa3+x2e6lncxPbrJBa7N2kjQpNGppyfWkJuMTsnbUivWjjKS1JzVyRU4UkDBxofC1FEtsKDURo1jUj2n70l5rqa+d3Oi40laW/RO2XV6jdoT0uXpZ3/96HUXxEEIIIYSUqvI5zCSEEEIqGH1uHvSGkl9l1evyrNgb26MBCiGEEGIHdDoddDKieHQSb0vaO7rFQwghhBC7Q1dQCCGEEDug0+VBx0p+i0enp1s8FUJa2kO4ubkBsH5UTmnntpG6f2kfT+7xTfYXiYYoHP0g+VxFo1QEr4XUSA6JuXHESK0vPD+p+4uxdnvWJDVCSmrUj/CzIPuzIjEiytbRdPZ8PGtH7RR+WFp6ejrc3d2t2n5xdDo98mTd4qlYUTyVdoBCCCGE2JM8XR7yWMlnXuRVsCsoNAeFEEIIIXaHrqAQQgghdiDPoEMeSn4bNa+UH/JZ1mx6BeXo0aMYMGAAvL29oVAosGvXLrP1d+zYgb59+6JmzZpwc3ND586dceDAgbLpLCGEEFKKdPo85MlYKtokWZsOULKystC6dWusWLHCovpHjx5F3759sW/fPpw5cwa9evXCgAEDEBsbW8o9JYQQQkhZsuktnpCQEISEhFhcPzw8nCt/8skn2L17N/bu3Yu2bdtKOra7ezUooAAAk/TWcmeES52dLjeHhb3l5LAlYWSE/EgLsTxBpRvVIhZ1JIyqEct1o1KpuTJjhpJ1zMLji0X9iEaumDl/sWPJyfsDFJH7R2IuH6nk5jmS2p7UnGBl/b0mpW0hqZGJ5n4HMDCJvSu5XL0eDjKieHIlfubtXbn+zWYwGJCRkQEPD49i6+Tk5CAnJ8dYTk9PL4uuEUIIIZLk6XORJ2M8lGfItV5n7EC5juL58ssvkZWVhaFDhxZbJywsDO7u7salXr16ZdhDQgghhJREuR2gREZGIjQ0FFu2bIGnp2ex9WbPno20tDTjkpSUVIa9JIQQQiyj0+XJXiqScnmLZ8uWLRg3bhy2bduGPn36mK2r1Wqh1WrLqGeEEEJIyeh0echTyNifVawBSrm7ghIZGYnRo0fj+++/R//+/W3dHUIIIYSUApteQcnMzMS1a9eM5YSEBMTFxcHDwwP169fH7NmzkZycjE2bNgHIH5yMHDkSS5cuRWBgIFJTUwEATk5OsnIlCKN2hDO6pebqkTrDXIzcXDlSZ9OXeU4OkUgVufXNtyX1vZCXS0dq3+Xm7lEolGa3G0SCeKRGxigl/skjJUpJGIViz3mBSkNZR/lUZNaO3LQWfXYO9IqSR9bp6QqK9Zw+fRpt27Y1hgjPnDkTbdu2xbx58wAAKSkpSExMNNZfu3YtdDodJk+ejNq1axuXadOm2aT/hBBCiLUY8nJlLxWJTYeNPXv2BGPFx1RFRERw5SNHjpRuhwghhBAbUTCd8flcJd2/Iil3c1AIIYQQUvHZx403QgghpJJTqRhUMuagqBgD5D0c2q7QAIUQQgixAyoHBgdFyR8lq2IMqEDzZGmAUgRrR/VYm9wom9LMkWEJa0bhSD926ebWKeuoHbEoHWsfTzy3jvk/38T2NxclJDXqRG5uHpPji+TqsTeyo3zEov2E74eNv1fMsdeoHWIevUuEEEKIHXCQeQXFwUzQSXlEAxRCCCHEDqhUgErGk2RVFWt8QlE8hBBCCLE/NEAhhBBC7ICDEnBQyVgk/kYPCwtDx44d4erqCk9PTwwePBhXrlwxu8+RI0egUChMlsuXL3P1tm/fDn9/f2i1Wvj7+2Pnzp1SXw4aoBBCCCH2QOUgf5EiOjoakydPxh9//IGoqCjodDoEBQUhKytLdN8rV64gJSXFuDRu3Ni4LSYmBsOGDcOIESNw7tw5jBgxAkOHDsXJkycl9U/yHJScnBz8+eefuHHjBh4/foyaNWuibdu28PPzk9oUIYQQQmxk//79XHnjxo3w9PTEmTNn0L17d7P7enp6omrVqkVuCw8PR9++fTF79mwAwOzZsxEdHY3w8HBERkZa3D+LBygnTpzA8uXLsWvXLuTm5qJq1apwcnLCgwcPkJOTgwYNGuD111/HxIkT4erqanEHygNhWHGePtvsdqmsHY5n7WSFpU0sHFJOWLJeLwh9lPonhgi5CevE9heGEYvtrxSpb20GxocVq1RqrqzX8w9lED8fy49d2snuxELOpYakCz+LFY1YUlOx753C9eV+Rwm/o8tLWLFKBahk/AgXTLBNT0/n1mu1Wmi1WtH909LSAAAeHh6iddu2bYvs7Gz4+/tj7ty56NWrl3FbTEwMZsyYwdUPDg5GeHi4aLuFWfRSDBo0CC+99BLq1KmDAwcOICMjA/fv38etW7fw+PFjXL16FXPnzsWhQ4fQpEkTREVFSeoEIYQQUtkpZd7eKRjX1atXD+7u7sYlLCxM9NiMMcycORNPP/00WrZsWWy92rVrY926ddi+fTt27NiBpk2bonfv3jh69KixTmpqKry8vLj9vLy8kJqaKun1sGhYGRQUhG3btkGj0RS5vUGDBmjQoAFGjRqFixcv4vbt25I6QQghhFR21rqCkpSUBDc3N+N6S66eTJkyBefPn8fvv/9utl7Tpk3RtGlTY7lz585ISkrCF198wd0WUij4eGnGmMk6MRYNUCZPnmxxgy1atECLFi0kdYIQQggh1uHm5sYNUMS89dZb2LNnD44ePYq6detKPl5gYCA2b95sLNeqVcvkasndu3dNrqqIkXXDOjMzE+np6dxCCCGEEOlUKgVUDjIWiU95Y4xhypQp2LFjB3777bcSB7vExsaidu3axnLnzp1NpnocPHgQXbp0kdSu5JlDCQkJmDJlCo4cOYLs7P8mIhVcvrH2RDVCCCGkMlCp8pcS7y+x/uTJk/H9999j9+7dcHV1NV71cHd3h5OTE4D8CJzk5GRs2rQJQH6Ejq+vL1q0aIHc3Fxs3rwZ27dvx/bt243tTps2Dd27d8fixYsxaNAg7N69G7/++qvo7SMhyQOUV199FQCwYcMGeHl5Sb6nVBGIRfWIzRgXi7IREs5oF4teEJtNb+/kRO0Io3TKOnJCat/FolrECKN2FILIEiYzoZ1Ye6JRQ4KoHrGEfcLXgxWKEirt5H9i7D05oJDY94TcPybt6Y9R4XeerRO6lherV68GAPTs2ZNbv3HjRowePRoAkJKSgsTEROO23NxcvPPOO0hOToaTkxNatGiBn3/+Gf369TPW6dKlC3744QfMnTsXH374IRo2bIgtW7agU6dOkvqnYExadiEXFxecOXOGmyRTnqSnp8Pd3R2AEgpYNrgSC3kr7ZA2qQMUIXvLMio6wCrFAYq1sxnLzV4sDMuVykGwf1kPUMQIw5ClDioYKz47srAtsZ8DuQMauQMUqYNlsZ8LqeH5ZT2gsGaYsdxHJ8j5I42BATAgLS1N0rwOKQp+Lx1o3htVZDwKIUuvQ3D8oVLta1mS/Odbx44dkZSUVBp9IYQQQiqtsn6SrL2TfDpff/01Jk6ciOTkZLRs2RJqNf8XXKtWrazWOUIIIYRUTpIHKP/++y+uX7+OMWPGGNcpFAqaJEsIIYTIoHAA5DycuqJNCZU8QBk7dizatm2LyMjISjtJlhBCCLE2lZJBpZI0LZTfX9qUUrsneYBy8+ZN7NmzB40aNSqN/pSZtLSHxklEcie1iu1f1lE19jYpVozYxFJzkwXlToq1d6K5emRul0qsPdEoH5FZb+Ymsgq3ib02YldzrR0VJEbYH41GK9gu/Oxa970rz8r6O8zA/nsv/gusIGVN8iTZZ555BufOnSuNvhBCCCGVFk2S5Uk+nQEDBmDGjBn466+/EBAQYDJJduDAgVbrHCGEEFJZKFUMShm3eJSV/RbPxIkTAQALFy402UaTZAkhhJCSURbKSFyi/a3XFbsg+aUwGIp/cBIhhBBCiDVIHnCZe0jbH3/8IaszhBBCSGWlUjHZS0Ui+QpK3759cfz4cVSvXp1bf/z4cfTv3x+PHj2yVt9KlUfVmsYQ6cIztgHrP6re2uztNprkR+9b8dH2ckl9tL3J/oK+i0WWyCWa+0Z2+9L6b2DW/SwKXz+93npXbEv7vSlrZf0oe1s+Ol/OY/MtIfwdUNW1pvH/5tItWJtSxaB0oDkoBSR/23Xr1g1BQUHIyMgwrjt69Cj69euH+fPnW7VzhBBCCKmcJA9Q1q1bBz8/P/Tv3x/Z2dk4fPgw+vfvj4ULF2LGjBml0UdCCCGkwlOq5C8VieR7GQqFApGRkejfvz969+6N8+fPIywsDFOmTCmN/hFCCCGVgtKBQUW3eIwsGqCcP3/eZN38+fPxyiuv4LXXXkP37t2NdShZICGEEELksmiA0qZNG2NCwAIF5bVr12LdunWULJAQQgiRQfaD2gyV8ApKQkJCaffDpuw9asfWpM7et+Vsf2vn3pEa+SE1X4zo8a0ctSM1SkdqewZBd4W5ecSYy4dT1rlz5BLLrSPcbv3j29frIyUSx9r5ysQiNR01VbhyFWdX4/8ZK7uEuEoHiuIpzKJvcx8fn9LuByGEEEKIkUV/nsXExFjcYFZWFi5evFjiDhFCCCGVEUXx8CwaoIwcORJ9+/bF1q1bkZmZWWSdS5cuYc6cOWjUqBHOnj1r0cGPHj2KAQMGwNvbGwqFArt27RLdJzo6Gu3bt4ejoyMaNGiANWvWWHQsQgghxJ4VPKitxEsFe5KsRQOUS5cuYdCgQZg3bx6qVauGFi1aoG/fvhgwYACefvpp1KhRA+3bt8fNmzcRFRWFESNGWHTwrKwstG7dGitWrLCofkJCAvr164du3bohNjYWc+bMwdSpU7F9+3aL9ieEEELsFV1B4Vk0B0WtVmPKlCmYMmUKzp49i2PHjuHGjRt48uQJWrdujRkzZqBXr17w8PCQdPCQkBCEhIRYXH/NmjWoX78+wsPDAQDNmzfH6dOn8cUXX+DFF1+UdGxCCCGE2C/JIQ/t2rVDu3btSqMvomJiYhAUFMStCw4Oxvr165GXlwe1Wm2yT05ODnJycozl9PR0yceVmgdCbLu1WXvGu7XJjeqRkqtHmFtHb+VID6l5g+RG9RgEeUBKOxeP6fGtnGtH0H/h+UlqS2ZUj1h9sc+O8LOWm5dTTE3L2DpHldTvObH95RAeW6xt4XZh1I5a5WidjpUyhUP+UuL9yy5tUJko2287mVJTU+Hl5cWt8/Lygk6nw71794rcJywsDO7u7salXr16ZdFVQgghRBKlg0L2UpGUqwEKAGMG4gIFD48Tri8we/ZspKWlGZekpKRS7yMhhBBC5ClXTyirVasWUlNTuXV3796Fg4MDqlevXuQ+Wq0WWq22LLpHCCGElBjd4uGVqwFK586dsXfvXm7dwYMH0aFDhyLnnxBCCCHlhkoJOMi4saEvdzdFzLLp2WRmZiIuLg5xcXEA8sOI4+LikJiYCCD/9szIkSON9SdOnIibN29i5syZiI+Px4YNG7B+/Xq88847tug+IYQQQkpJia6gHDp0CIcOHcLdu3dhMPDXlDZs2GBxO6dPn0avXr2M5ZkzZwIARo0ahYiICKSkpBgHKwDg5+eHffv2YcaMGVi5ciW8vb2xbNkym4cYW3vGuNTZ82UdNSRXWUcncMcWifIRbhcjFpGkVmvMbpca1aMo4wcdiOXukRrlIydqR4y1c8+IfVZsHSEmtr/kaDmZkTNSWTPqx9rfwYV/Lg1lmItHoVZAoS758RSGijVJVvJvtgULFmDhwoXo0KEDateuXezkVEv07NmTy5AsFBERYbKuR48eFj+plhBCCCk3HOgWT2GSByhr1qxBRESExU+LJYQQQgiRSvIAJTc3F126dCmNvhBCCCGVl4MifykpfcW6xSP5etD48ePx/fffl0ZfCCGEkMpLpYTCoeQLVJX8Fk92djbWrVuHX3/9Fa1atTIJ7/3qq6+s1jlCCCGk0nBQyJuDUsGeJCt5gHL+/Hm0adMGAHDhwgVum5wJs4QQQgghBSQPUA4fPlwa/bBrYmG8SsGj/4Qhb8LtYu2Lhd+VeTJCieGKpZ30TKUq/vzFQj/lhoaKvRZlHULNhGHSKvNhzQqZyQaZSfJCQcI+kbBjsWSBwrBrvd7ysGSx197aYcj2Tm7YsZDcsGCp33Pm5OmzubKjpkqJ2wJMX5vCPydl+me33DkoEvcNCwvDjh07cPnyZTg5OaFLly5YvHgxmjZtWuw+O3bswOrVqxEXF4ecnBy0aNECoaGhCA4ONtaJiIjAmDFjTPZ98uQJHB0tDwGvWDesCCGEkPKqIMxYziJBdHQ0Jk+ejD/++ANRUVHQ6XQICgpCVlZWsfscPXoUffv2xb59+3DmzBn06tULAwYMQGxsLFfPzc0NKSkp3CJlcAJYeAXlhRdeQEREBNzc3PDCCy+Yrbtjxw5JHSCEEEJI2du/fz9X3rhxIzw9PXHmzBl07969yH3Cw8O58ieffILdu3dj7969aNu2rXG9QqFArVq1ZPXPogGKu7u7cX6Ju7u7rAMSQgghpAhyc/H8L4onPT2dW21p0ty0tDQAgIeHh8WHNBgMyMjIMNknMzMTPj4+0Ov1aNOmDRYtWsQNYCxh0QBl48aNRf6fEEIIIVZipTko9erV41bPnz8foaGhZndljGHmzJl4+umn0bJlS4sP+eWXXyIrKwtDhw41rmvWrBkiIiIQEBCA9PR0LF26FF27dsW5c+fQuHFjy0/H4pqEEEIIsXtJSUlwc3Mzli25ejJlyhScP38ev//+u8XHiYyMRGhoKHbv3g1PT0/j+sDAQAQGBhrLXbt2Rbt27bB8+XIsW7bM4vYlD1Datm1bZDixQqGAo6MjGjVqhNGjR3NJAO3Rg0f/Gt9AYWIpuUmy5CaqIqSAMOpFSCx5oFjUjliyQoOh+OgGQDyqRw95kSOFjyc8lrBv9kYYbabXWy85Xn57pXv+ZR1daG5/4TZhZKTcYwujggpLT08vu6kNcnPx/G9fNzc3boAi5q233sKePXtw9OhR1K1b16J9tmzZgnHjxmHbtm3o06eP2bpKpRIdO3bE1atXLe4TUIIonmeffRb//PMPqlSpgl69eqFnz55wcXHB9evX0bFjR6SkpKBPnz7YvXu31KYJIYSQystBJX+RgDGGKVOmYMeOHfjtt9/g5+dn0X6RkZEYPXo0vv/+e/Tv39+i48TFxaF27dqS+id52Hnv3j28/fbb+PDDD7n1H330EW7evImDBw9i/vz5WLRoEQYNGiS1eUIIIaRSMj6yXsb+UkyePBnff/89du/eDVdXV6SmpgLID4ZxcnICAMyePRvJycnYtGkTgPzByciRI7F06VIEBgYa93FycjJeaVqwYAECAwPRuHFjpKenY9myZYiLi8PKlSsl9U/yK7F161a88sorJutffvllbN26FQDwyiuv4MqVK1KbJoQQQkgZWb16NdLS0tCzZ0/Url3buGzZssVYJyUlBYmJicby2rVrodPpMHnyZG6fadOmGes8evQIr7/+Opo3b46goCAkJyfj6NGjeOqppyT1T/IVFEdHR5w4cQKNGjXi1p84ccL4EBaDwWDRpBxCCCGE/I+VwowtxRgTrRMREcGVjxw5IrrPkiVLsGTJEkl9KYrkAcpbb72FiRMn4syZM+jYsSMUCgX+/PNPfP3115gzZw4A4MCBA5LjnQkhhJBKTaUE5KTLUNn3xHGpJA9Q5s6dCz8/P6xYsQLffvstAKBp06b4v//7PwwfPhwAMHHiRLz55pvW7amVubtXg6KYLAvCXDrWjsqxZk6KovYv61w9Yqydm8dcNIS5PD1F1hdEsYhFtUjNbyKMNFGp1MXU/N/xJebKEUbNmGwXOR/R9gTdkRrVI4wyEuYOEsvNY47w3KRG9cjd39pKO+qntFk7909pkpIvjUH8KgMpHSX6Tfbqq6/i1VdfLXZ7weQaQgghhFioBJE4JvtXIPb1pzYhhBBSWalUMm/xVMIBioeHB/7++2/UqFED1apVK/JBbQUePHhgtc4RQgghpHKyaICyZMkSuLq6AjDNZEgIIYQQK6BbPByLBiijRo0CAOh0+ROLgoODZadRJoQQQkghKpkDlMp4i8dY2cEBb775JuLj40urP3ZBOKNbyozvkrB2VI9QaUf5SJ2tLxbVY+2oHymEkRxSo3rE6ottF4t6Malv5agf4fGVwqAakcMZBPWF1Q2CFWLnV/j10ustj/CxhfIWhVPq3wt2FF0ozLVD+dLKB8lPhOnUqRNiY2NLoy+EEEJI5VUwSVbOUoFIHtJOmjQJb7/9Nm7duoX27dujSpUq3PZWrVpZrXOEEEJIZZGfi6fkgww5eXzskeQByrBhwwAAU6dONa5TKBRgjEGhUNj1w3kIIYQQu0VhxhzJA5SEhITS6AchhBBCiJHkAYqPj09p9IMQQgip3CjMmFOiadXXr19HeHg44uPjoVAo0Lx5c0ybNg0NGza0dv/sgjBqp7Sjbqzdvlh71p5db885OYS5d6SydoSRWO4dk6gaibl3hPWF7UklbM/A5L23YlFDhXPziEcs2c/nrCSkRv2U9c9ZWX4vWfs7Sew73G4oZd7ikfnzbW8kz6g5cOAA/P398eeff6JVq1Zo2bIlTp48iRYtWiAqKqo0+kgIIYSQSkbyMPL999/HjBkz8Omnn5qsnzVrFvr27Wu1zhFCCCGVBk2S5Ui+ghIfH49x48aZrB87diwuXbpklU4RQgghlY3CwUH2UpFIHqDUrFkTcXFxJuvj4uLg6elpjT4RQgghpJKTPNyaMGECXn/9dfzzzz/o0qULFAoFfv/9dyxevBhvv/12afSREEIIqfhUSpm3eCr5g9o+/PBDuLq64ssvv8Ts2bMBAN7e3ggNDeUe3laRiM34Fm4X5n2wdu6eikYs+kC4XaPRlmZ3OHKjdKTmyhEyicIRtFfaUTtiuYFMonoEkTQm/RW0LxYFVDjKSdi2VGUe9aIy/3Nvb7l6xKJyxL4HTSLcSjF6UJgfraLk1lGoHKAQ+dyI7V+RSD4bhUKBGTNmYMaMGcjIyAAAuLq6Wr1jhBBCCKm8ZA23aGBCCCGEWAddQeHZ/IbVqlWr4OfnB0dHR7Rv3x7Hjh0zW/+7775D69at4ezsjNq1a2PMmDG4f/9+GfWWEEIIKR0KpRoKlYxFqbb1KViVTQcoW7ZswfTp0/HBBx8gNjYW3bp1Q0hICBITE4us//vvv2PkyJEYN24cLl68iG3btuHUqVMYP358GfecEEIIsTKVClA5yFgq+XNQrOmrr77CuHHjMH78eDRv3hzh4eGoV68eVq9eXWT9P/74A76+vpg6dSr8/Pzw9NNP44033sDp06fLuOeEEEIIKU2yBijZ2dnilYqRm5uLM2fOICgoiFsfFBSEEydOFLlPly5dcOvWLezbtw+MMdy5cwc//vgj+vfvX+xxcnJykJ6ezi2EEEKIvSmYgyJnqUgkD1AMBgMWLVqEOnXqwMXFBf/88w+A/PDj9evXW9zOvXv3oNfr4eXlxa338vJCampqkft06dIF3333HYYNGwaNRoNatWqhatWqWL58ebHHCQsLg7u7u3GpV68eACAt7SEMTGcSrmYNSoUDt6iU/GJtUtuX2x+VSsUtpV1f6v72TKlQcotCqeIWpYJfFAoltyiVKm5RqTTcYtqe0qqLsH2T8xP0rzyxdd/1ej23lDbh90Bpf09Zk/A71toKfjcYmA5paQ+t3n5xFA5q2UtFInmA8tFHHyEiIgKfffYZNBqNcX1AQAC+/vpryR1QKBRcmTFmsq7ApUuXMHXqVMybNw9nzpzB/v37kZCQgIkTJxbb/uzZs5GWlmZckpKSJPeREEIIIWVL8tBz06ZNWLduHXr37s0NDFq1aoXLly9b3E6NGjWgUqlMrpbcvXvX5KpKgbCwMHTt2hXvvvuu8ZhVqlRBt27d8NFHH6F27dom+2i1Wmi1ZfdgL0IIIaQkKMyYJ/kKSnJyMho1amSy3mAwIC8vz+J2NBoN2rdvj6ioKG59VFQUunTpUuQ+jx8/hlLJd7ngkj9jzOJjE0IIIfYmf4AiJ9S4kg9QWrRoUeSzSrZt24a2bdtKamvmzJn4+uuvsWHDBsTHx2PGjBlITEw0XpmZPXs2Ro4caaw/YMAA7NixA6tXr8Y///yD48ePY+rUqXjqqafg7e0t9VQIIYQQYqckD7fmz5+PESNGIDk5GQaDATt27MCVK1ewadMm/PTTT5LaGjZsGO7fv4+FCxciJSUFLVu2xL59++Dj4wMASElJ4Z6JMnr0aGRkZGDFihV4++23UbVqVTzzzDNYvHix1NMghBBC7IpCKfMWj51PbpZKwUpwb+TAgQP45JNPcObMGRgMBrRr1w7z5s0zCRm2R+np6XB3d4dKoTFOxhVL7ic16ZXc7ULWTLJlDWKRNCZJw6wceSOWhI2rKzEaQxi9IdZ3YX2xZH4qFT/LXiwZoINKw28XtOcgeHKkaX/M95+JJOszCJIDCpP7GZiBb0+svmC73mD+tnDh9oTHErbFBNuFTI4tEikjrC+kNwiT46nMbjfZ38rJAq0d+WPt7x0p34vW/k4U1teo+XmJ2blZXLlw8kHGGPQsF2lpaXBzczN7nJIq+L30761NcHNzltHOY9SsO7JU+1qWSvSJCw4ORnBwsLX7QgghhBACoARzUMaMGYNDhw7RpFRCCCHEiuhBbTzJA5T79++jf//+qFu3Lt5++23ExsaWRr8IIYSQSkWpUslepAgLC0PHjh3h6uoKT09PDB48GFeuXBHdLzo6Gu3bt4ejoyMaNGiANWvWmNTZvn07/P39odVq4e/vj507d0rqG1CCAcqePXuQmpqK+fPn48yZM+jQoQP8/f3xySef4MaNG5I7QAghhBBAoVLJvIIibYASHR2NyZMn448//kBUVBR0Oh2CgoKQlZVV7D4JCQno168funXrhtjYWMyZMwdTp07F9u3bjXViYmIwbNgwjBgxAufOncOIESMwdOhQnDx5UtrrUZJJsoXdunULkZGR2LBhA65evQqdzvqPjrcmmiQrH02SLb4+TZKlSbLFbTfZnybJFru9sk6SfXj/R7i5VZHRThaqVX+pxH39999/4enpiejoaHTv3r3IOrNmzcKePXsQHx9vXDdx4kScO3cOMTExAPIjdNPT0/HLL78Y6zz77LOoVq0aIiMjLe6PrE9cXl4eTp8+jZMnT+LGjRvFPgHWHumZDgqWP0ARG5BUdqWd/0Y44LDmF7fwF7bYL53SHpCoRAYUKsGARCkyYDGtL+ifxAGa1AGGcICj0+fyx2f88ZUKaYMKLn28oG3T67/m32uVSl7ydpP2ZObrkTLQBuT/XEj9w0IuqYMIa37vCo8tzLlWeAAC2M/vAIVKCYWMz2nBvsKkuJY+UT0tLQ0A4OHhUWydmJgYk4jd4OBgrF+/Hnl5eVCr1YiJicGMGTNM6oSHh1tyGkYleiUOHz6MCRMmwMvLC6NGjYKrqyv27t1LeW4IIYSQEmJWWACgXr16XJLcsLAw8WMzhpkzZ+Lpp59Gy5Yti62XmppaZJJfnU6He/fuma1TXCLg4kgeJtatWxf3799HcHAw1q5diwEDBsDR0VF8R0IIIYSUuqSkJO4WjyVXT6ZMmYLz58/j999/F61bVJJf4XopiYCLI3mAMm/ePAwZMgTVqlWTuishhBBCimFgBpO5VlL3BwA3NzdJc1Deeust7NmzB0ePHkXdunXN1q1Vq1aRSX4dHBxQvXp1s3WkTgORfIvn9ddfNw5Obt26heTkZKlNEEIIIURAZ9BDZ9DJWKTNI2KMYcqUKdixYwd+++03+Pn5ie7TuXNnkyS/Bw8eRIcOHaBWq83WKS4RcHEkD1AMBgMWLlwId3d3+Pj4oH79+qhatSoWLVoEg6HkIz9CCCGElJ3Jkydj8+bN+P777+Hq6orU1FSkpqbiyZMnxjrCpL0TJ07EzZs3MXPmTMTHx2PDhg1Yv3493nnnHWOdadOm4eDBg1i8eDEuX76MxYsX49dff8X06dMl9U/yLZ4PPvgA69evx6effoquXbuCMYbjx48jNDQU2dnZ+Pjjj6U2We6IzQgXEs4ol0oYEmft2fb2Rk5Uj1hkhTBqRup2a0ftCKNuxMKG1Q5OfH9Mwo7NR/mIEYvKEUb1CKN+hGHNeuH+CvN/E5mLGhKei0KkL8I/v4RROGLvtbWJhR1bm9zoO2tHsgj7U5rfY1K/o+2F3qCX9TmRuu/q1asBAD179uTWb9y4EaNHjwZgmrTXz88P+/btw4wZM7By5Up4e3tj2bJlePHFF411unTpgh9++AFz587Fhx9+iIYNG2LLli3o1KmTpP5Jfg6Kt7c31qxZg4EDB3Lrd+/ejUmTJtn9LZ+CeHNACQWKnrAj9oMpfG6K8MMvdUAiGn5Xhj/YlhxfjNTnoIiFWwoHKObqCwcoUn8JqdXmf6GX9gBF7SAIfyxnAxRhfZMBimB/vT7P7PbCgwphSLJYX8r6uSmi+8scoIgN1OU+f6i0w4yt+YeWWAiz2He0lPbK8jkoSXfkJwus51VxkgVKvsXz4MEDNGvWzGR9s2bN8ODBA6t0ihBCCCGVm+QBSuvWrbFixQqT9StWrEDr1q2t0ilCCCGkstEbdLKXikTyTcbPPvsM/fv3x6+//orOnTtDoVDgxIkTSEpKwr59+0qjj4QQQkiFZ5A5yDBUsAGK5CsoPXr0wN9//43nn38ejx49woMHD/DCCy/gypUr6NatW2n0kRBCCKnw8ifJyrmCUrECKEo0Tdvb27tCR+uITcASm3AllhxQcn/sLGpHbDKetXP3mEyuKxzZIXESrFjfhJNghRMpTSe5CibNKqRN0jVJHiiSm0dsUqxazU+iFfZHbNKscFKrcFKucBKscFKtQveEKwvPR9i+sH86QfLAwrl7hJNiISgaBH9uKYVzYE2ieviy6HstktdJ7OdUajJBk/1FottKP2eWtMn6pZnPRhilI8ylU16idoh5JfoEPXz4EOvXr0d8fDwUCgWaN2+OMWPGmE0wRAghhJDi6ZkeepEM42L7VySSb/FER0fDz88Py5Ytw8OHD/HgwQMsW7YMfn5+iI6OLo0+EkIIIRUeTZLlSb6CMnnyZAwdOhSrV682XvLT6/WYNGkSJk+ejAsXLli9k4QQQgipXCQPUK5fv47t27dz9yNVKhVmzpyJTZs2WbVzhBBCSGWhZzKfJFvZb/G0a9cO8fHxJuvj4+PRpk0ba/SJEEIIqXQKHnUvZ6lILLqCcv78eeP/p06dimnTpuHatWsIDAwEAPzxxx9YuXIlPv3009LpZTlT3u8DlnY0gFxSI3cKk/roe5NH24vkkjGtL5L7RyTKRljWCKJqhFE2YlE8DiJRPOYeNQ8AasH2vDw+asckF4+Sj9rJU/D1TSJz+CAeLnePgom8d1aO6hESezS+kFjUj1xyclZZ5/i2S8FBUTqVg0UDlDZt2kChUKBw2p733nvPpN7w4cMxbNgw6/WOEEIIqST0Bgad2EhZZP+KxKIBSkJCQmn3gxBCCKnUdAYGnYxBhpx97ZFFAxQfHx/ROnq9Hnv37rWoLiGEEEKIObIf9Xf58mVs2LAB33zzDR4+fIjc3FzxnQghhBDC0RuYrNs0Fe0Wj+QoHgDIysrChg0b0LVrV7Ro0QJnz57Fxx9/jNu3b1u7f4QQQkilUHCLR85SkUi6ghITE4Ovv/4aW7duRePGjfHqq6/i5MmTWLZsGfz9/Uurj4QQQkiFpzPkL3L2r0gsHqD4+/vj8ePHGD58OE6ePGkckLz//vul1rnSlJb2EG5ubgBME02VttJMolUWxMKQheGGGo0g2Z/McMjC4ZtioZtSkwOKJQM0bZ8P2xWG8Zom9+OTA2o1LmbbE4YROzm6c2WTsGO1s9n+qFT8eyGk1+dwZeHrKwwLzlFlcGVhMkFhGLLw9RXWN+3Pf9slh+mKhB2rIC3kXK83n0xQjLXDjMUIw5CFrB2WLDfsuPD+tk6QmqfPNv4/PT0d7u7uZmqT0mLxLZ5r166he/fu6NWrF5o3b16afSKEEEIqHT0DdDIWfcW6w2P5ACUhIQFNmzbFm2++ibp16+Kdd95BbGwsFApFafaPEEIIqRTyDArZS0Vi8QClTp06+OCDD3Dt2jV8++23SE1NRdeuXaHT6RAREYG///67NPtJCCGEkEqkRFE8zzzzDDZv3oyUlBSsWLECv/32G5o1a4ZWrVpZu3+EEEJIpVAwSVbOUpGUaIBSwN3dHZMmTcLp06dx9uxZ9OzZ00rdIoQQQioXHVNAZ5CxsIp1i8dq4SRt2rTBsmXLrNVcqXN3rwYF8t9MAzM/m72iJaaydjJA06RlYpE11otiEoukEG4Xi9pRqfgoG2GyPeH+wigZtQP/WRGL8hFG8YhF7Wg1rmbLzo5VubJSEDEm7I9we17eY65sECS+FEbdaARRQ7mC/YVRPqo8/vh5OkGUj+D1LpxcUCziR4xCEMZjkvxP8FFigqgbuXElws+aMPOsWBJMsUy1Un+urPlzaAnh94K576HSTkRYOEoHMP2OL1wunIOOlC1ZV1CsYdWqVfDz84OjoyPat2+PY8eOma2fk5ODDz74AD4+PtBqtWjYsCE2bNhQRr0lhBBCSodeztUTgwL6CjZJ1qYP5NiyZQumT5+OVatWoWvXrli7di1CQkJw6dIl1K9fv8h9hg4dijt37mD9+vVo1KgR7t69C52ubNOME0IIIdaWZ8hf5Oxfkdh0gPLVV19h3LhxGD9+PAAgPDwcBw4cwOrVqxEWFmZSf//+/YiOjsY///wDDw8PAICvr29ZdpkQQgghZUDWLZ7s7GzxSsXIzc3FmTNnEBQUxK0PCgrCiRMnitxnz5496NChAz777DPUqVMHTZo0wTvvvIMnT54UWR/IvyWUnp7OLYQQQoi9yY/EkXObx9ZnYF2SBygGgwGLFi1CnTp14OLign/++QcA8OGHH2L9+vUWt3Pv3j3o9Xp4eXlx6728vJCamlrkPv/88w9+//13XLhwATt37kR4eDh+/PFHTJ48udjjhIWFwd3d3bjUq1fP4j4SQgghZSWPKWQvFYnkWzwfffQRvvnmG3z22WeYMGGCcX1AQACWLFmCcePGSWpP+CRaxlixT6c1GAxQKBT47rvvjLkRvvrqK7z00ktYuXIlnJycTPaZPXs2Zs6caSynp6ebDFKEuXg0avP5SoQzwEs7l4/U3DdSCc9XGC0gNWeHMPeOGGH0gvD4ppE2xefs0GjMR1w5CKJ0hLlyhFEkwqgeYRSMRiRqRxiVI4zicdQKcuuoBVE82qp8WRCl46h148rOztW5svD84Mi/NwYV/zeKNlfwXgvyz+hz+auVT7IfcWVhFI9pLiA+qkeZa/71L8zkXCRSKsznFRJG+egEn0NhBJdJ+4LNwtw7pj/H/GsjmldK4vmLRv2IRQ3JzNUjPF/TaL/SmzsoFqUj/M62l/xoepnPMtFX9isomzZtwrp16/Dqq69yH8BWrVrh8uXLFrdTo0YNqFQqk6sld+/eNbmqUqB27dqoU6cOl7ipefPmYIzh1q1bRe6j1Wrh5ubGLYQQQgixb5IHKMnJyWjUqJHJeoPBgLy8PIvb0Wg0aN++PaKiorj1UVFR6NKlS5H7dO3aFbdv30ZmZqZx3d9//w2lUom6detafGxCCCHE3sibf5K/VCSSBygtWrQo8lkl27ZtQ9u2bSW1NXPmTHz99dfYsGED4uPjMWPGDCQmJmLixIkA8m/PjBw50lh/+PDhqF69OsaMGYNLly7h6NGjePfddzF27Ngib+8QQggh5UVBmLGcpSKRfONt/vz5GDFiBJKTk2EwGLBjxw5cuXIFmzZtwk8//SSprWHDhuH+/ftYuHAhUlJS0LJlS+zbtw8+Pj4AgJSUFCQmJhrru7i4ICoqCm+99RY6dOiA6tWrY+jQofjoo4+kngYhhBBC7JjkAcqAAQOwZcsWfPLJJ1AoFJg3bx7atWuHvXv3om/fvpI7MGnSJEyaNKnIbRERESbrmjVrZnJbiBBCCCnv8gwKOMi4TZNXwW7xlGjqcnBwMIKDg63dF7shFhXjqKnClYUzwPWGsn2ybVlH+eTm5UjaX2r0gdpBGPkhiPQo1J4wykZILGpHpRTk3jGJGBLk0lGaj+oRi9oR5t4RRu0Ic+tIjdrRV+W352n588ly5ss5Gv4ur0rP5x1xzOGvGTs/5vvrksaX87IzubJpVJT5CC9hfS7SRjDFjamkfa6FP5VKweVwgyDbjvCzIvyyNAhy+RgE7UmN6hGL4hF+NsXqC0n9ORRtTySXT1lG6Qi/k4Xl8kLPZEbxlCBt0NGjR/H555/jzJkzSElJwc6dOzF48OBi648ePRrffPONyXp/f39cvHgRQP7FhTFjxpjUefLkCRwdLc9tJ3kOyqlTp3Dy5EmT9SdPnsTp06elNkcIIYQQG8nKykLr1q2xYsUKi+ovXboUKSkpxiUpKQkeHh4YMmQIV8/NzY2rl5KSImlwApTgCsrkyZPx3nvvoVOnTtz65ORkLF68uMjBCyGEEELMy2OASkby5LwS7BsSEoKQkBCL6xc89LTArl278PDhQ5MrJgqFArVq1ZLeoUIkX0G5dOkS2rVrZ7K+bdu2uHTpkqzOEEIIIZWVtaJ4hOldcnKk3ZaXYv369ejTp48xuKVAZmYmfHx8ULduXTz33HOIjY2V3LbkAYpWq8WdO3dM1qekpMDBwT6exkcIIYRUVvXq1eNSvBSVfNcaUlJS8MsvvxgT/hZo1qwZIiIisGfPHkRGRsLR0RFdu3bF1atXJbUveUTRt29fzJ49G7t37zZe5nn06BHmzJlToigeQgghhBQkC5S3PwAkJSVxT03XaqWlH7FUREQEqlatajKpNjAwEIGBgcZy165d0a5dOyxfvhzLli2zuH3JA5Qvv/wS3bt3h4+Pj/HBbHFxcfDy8sK3334rtblySSwqRhjVI5ytLzeqRiqxKB+pxHL3iBFGI8ipL9aWWK4duVE7Ju2ZRK0II5KEUT/OXFmjNl8WRvUwVz4q6FE1vn1hlE6mu+DbT5MLc9Jy+c+yYyb/3ns68O07ZfD1xWIp9Hrzl551+v/6J4xa0Rn4visZ/9ozQVkpyL2jh0jUjOC9FKsvRiwKR+rPhRhrR+3YkjAyUphbx9rfcbaiMyhkhQoXPEm2LNK6MMawYcMGjBgxAhqNxmxdpVKJjh07lv4VlDp16uD8+fP47rvvcO7cOTg5OWHMmDF45ZVXoFabD/kkhBBCSNHyDKbh71L3LyvR0dG4du2aRQmCGWOIi4tDQECApGOUaNJIlSpV8Prrr5dkV0IIIYTYiczMTFy7ds1YTkhIQFxcHDw8PFC/fn3Mnj0bycnJ2LRpE7ff+vXr0alTJ7Rs2dKkzQULFiAwMBCNGzdGeno6li1bhri4OKxcuVJS30o0QPn7779x5MgR3L17FwbB04nmzZtXkiYJIYSQSs0WV1BOnz6NXr16GcszZ84EAIwaNQoREREmKWcAIC0tDdu3b8fSpUuLbPPRo0d4/fXXkZqaCnd3d7Rt2xZHjx7FU089Jalvkgco//d//4c333wTNWrUQK1ataBQ/He/rODR94QQQgiRxhYDlJ49e4Kx4h+gUlTKGXd3dzx+/LjYfZYsWYIlS5ZI74yA5AHKRx99hI8//hizZs2SfXBCCCGEkKJIHqA8fPjQ5JG25ZFK4WC8+lPauXNEo34EM9CFuW7kzlAXy5khlzBaQGrOEKnnp1AU//gepZltRe0rjNQwaU/k3EyihESihoS5eYRl01w+fNSMSsNvf1iV357lxJ9ftgv/J5W2Whrfvtp8FI8uj+9fNty5sjDKRyWIkdRkC84nV3A+gvNTKp7w9Qu9Pnkir63c2Djhe6fXy5txKPysMUHuHuH3gtxoP7m5ekozd07RxyvbaMbyQC8zzFjmR9buSH5Q25AhQ3Dw4MHS6AshhBBSaeX9L8xYzlKRSP7TulGjRvjwww/xxx9/ICAgwCS0eOrUqVbrHCGEEEIqJ8kDlHXr1sHFxQXR0dGIjo7mtikUChqgEEIIISWgYwoo5TyojVXyKygJCQml0Q9CCCGkUtMZZA5QKtgtHslzUAghhBBCSluJwjtu3bqFPXv2IDExEbm5fBTAV199ZZWOEUIIIZWJ3qCQdRVEX8GuoEgeoBw6dAgDBw6En58frly5gpYtW+LGjRtgjKFdu3al0cdS8eDRv8UmUxImoiprwmR8YsTCFUX3F4QjWjvJmGlCPmmhvebCikWPLRJGbFpf2rGkty8SlixMNOkg+Cyo+P7pHPgvJL2wOxo+dFSp4t9rtSBZoErwlKhsQXPmU/sBSj3/wCeDjm/fIAjpZ8IEfoLkgQZmvVBUgyDMt3AiQqCoMGN+uzCk3JCXx5WFYcTCz21envC1kPZzK6wvNUmnkNz9xQgfb1CaYcxSvwOFCV3z9MJP+n/S09Ph7u5e7HZr0huUUBpK/n2nl7GvPZJ8NrNnz8bbb7+NCxcuwNHREdu3b0dSUhJ69OhRIZ6PQgghhNiCXq+SvVQkkgco8fHxGDVqFADAwcEBT548gYuLCxYuXIjFixdbvYOEEEIIqXwkD1CqVKmCnJz8y7De3t64fv26cdu9e/es1zNCCCGkEtEblLKXikTyHJTAwEAcP34c/v7+6N+/P95++2389ddf2LFjBwIDA0ujj4QQQkiFZ9ArYZBxm8agr+QDlK+++gqZmZkAgNDQUGRmZmLLli1o1KiRVbIXEkIIIYRIHqA0aNDA+H9nZ2esWrXKqh0qK+7u1aBA0SFZYjO8bR3lIzdqR/LxxJL9WTnqR4ww2qIwk0gLQRSIShBZYbIdfOoG04RrgsgPkWSAwvaFkSO5eXxyPLXaWXB8QeSDIBuYNocvqwTJAvNE/qISRu1o1HnF1MyXpRIkvBNEaij15iNlcvMemy0L6xeOpBG+F2IRPsK2xJi2z5+L4KUyidoREm4Xtm8a3cZ/9oRRP2I/h2LRctk5/GfN2oRROqWdpNSc7Nwss9uVCgez5cIYWLHbrE2vV0Ep4wpKpZ8k26BBA9y/f99k/aNHj7jBCyGEEEIsZ2AqGAwyFlbJByg3btwoMk12Tk4OkpOTrdIpQgghhFRuFl+D27Nnj/H/Bw4c4B5co9frcejQIfj6+lq1c4QQQkhlwfQqWZNkWQW7xWPxAGXw4MEA8jMWFzwHpYBarYavry++/PJLq3aOEEIIqSwMMgcocva1RxYPUAyG/Alffn5+OHXqFGrUqFFqnSKEEEJI5SZ5mnVCQoLJukePHqFq1arW6I9dEs7wFkb56AWRFgbGl0s76kcsqqe0Z9dLjS4QI4xe0GiKf/1M85nwkRBMsJ0phK+NIArGJApHEOUiEiVk2h9Bf0UiTxxUfO4dR6eqXDnH3YkrP6jKv5e5NfmIsyquaVzZu/ojrlzLmY9Q8HLiy0/0fFTPJad/uPLtuzW5cgqq8e2pvLiyu0La61eYQiSPUZ4gIkrYVp6O367VVBFs5187jSCiSm/gXwvhZ01Yzs3lI5SE/RV+rsV+ToTRclJzXMlly6gcIWFkpZSoHHtWMNlVzv4VieRJsosXL8aWLVuM5SFDhsDDwwN16tTBuXPnrNo5QgghpLJgepXspSKRPEBZu3Yt6tWrBwCIiorCr7/+iv379yMkJATvvvuu1TtICCGEVAYGveJ/T5Mt6VL0s73KK8nXwVJSUowDlJ9++glDhw5FUFAQfH190alTJ6t3kBBCCCGVj+QrKNWqVUNSUhIAYP/+/ejTpw8AgDFW5PNRCCGEEGIBgxLQy1gqe7LAF154AcOHD0fjxo1x//59hISEAADi4uLQqFEjq3eQEEIIqRQKBhpy9q9AJA9QlixZAl9fXyQlJeGzzz6Di4sLgPxbP5MmTbJ6B8sje8/VI4zqEZKbW0cY1SM1isekPUEkibn2hFE7wqgcIY0gkkNIq+a3C3PtCNvXaly4spOjO1eu4lSdK7tW4aNcXD39uPLfzfn2HL3uceXa1W9yZX8P/vyra/monBYefISUpwv/WXUSnO8TQa6cm2l8JMwVrxSu/Ifnv1z59n0Prsxu1OXKPjeqcmWX+7e58uPsh8b/5+RmcNuyBeWc3EyuLHwv0rNSubLwvXv85AFXFuZZepz9iN9fzUftCCkEeZ8cHc1/1oTEonSE281FQAGmP9fCn1N7itIRRkYKldcoHSKN5OGWWq3GO++8g6VLl6Jt27bG9dOnT8f48eMld2DVqlXw8/ODo6Mj2rdvj2PHjlm03/Hjx+Hg4IA2bdpIPiYhhBBibxR6peylIrFoGLpnzx6EhIRArVZzj7wvysCBAy0++JYtWzB9+nSsWrUKXbt2xdq1axESEoJLly6hfv36xe6XlpaGkSNHonfv3rhz547FxyOEEELslVKfv8jZvyKxaIAyePBgpKamwtPT0/jI+6IoFApJE2W/+uorjBs3znjlJTw8HAcOHMDq1asRFhZW7H5vvPEGhg8fDpVKhV27dll8PEIIIYSUDxZdDzIYDPD09DT+v7hFyuAkNzcXZ86cQVBQELc+KCgIJ06cKHa/jRs34vr165g/f75Fx8nJyUF6ejq3EEIIIfam4AqKnKUisdlMo3v37kGv18PLi58k6OXlhdTU1CL3uXr1Kt5//30cO3YMDg6WdT0sLAwLFiyQ3V9CCCGkNKn0+UtJiWTSKHckDVAMBgMiIiKwY8cO3LhxAwqFAn5+fnjppZcwYsQIKBTSn2In3IcxVmQ7er0ew4cPx4IFC9CkSROL2589ezZmzpxpLKenpxsfNGevbP08GeHsfrWDID+NYLtYLh6T9gXnJ8xJInb+hXOeqB2E+/JRKs6OVbmyTpBPRRilI4yMcNLyUTjCfDBVnPgoFSfB8dxdvPn6znySTQc3vv4/DfnIE9e6fFSLfy3+CmCr6vxr1aoGX65RhW+/pjP/B0Fu2n2uDP7lQQ0X/mfF3SmLK1fT8lE8asE12TgNH3UUn8t/lm7CjSv75npyZUWh90OYp8jkcyh474Xb1Q58HiNhbh5hhFauIDePMNeOkFi0mVIQ1SPM82Sax6liPbY8Ny/H1l0g5YzFAxTGGAYOHIh9+/ahdevWCAgIAGMM8fHxGD16NHbs2CFpPkiNGjWgUqlMrpbcvXvX5KoKAGRkZOD06dOIjY3FlClTAOQPmBhjcHBwwMGDB/HMM8+Y7KfVaqHVak3WE0IIIfZEZQAc5FxBMYjXKU8sHqBERETg6NGjOHToEHr16sVt++233zB48GBs2rQJI0eOtKg9jUaD9u3bIyoqCs8//7xxfVRUFAYNGmRS383NDX/99Re3btWqVfjtt9/w448/ws/Pz2QfQgghpLxQ6RlUeiZesRgGGfvaI4uDpiMjIzFnzhyTwQkAPPPMM3j//ffx3XffSTr4zJkz8fXXX2PDhg2Ij4/HjBkzkJiYiIkTJwLIvz1TMOBRKpVo2bIlt3h6esLR0REtW7ZElSrSHoJECCGE2BMHPZO9SHX06FEMGDAA3t7eUCgUondCjhw5AoVCYbJcvnyZq7d9+3b4+/tDq9XC398fO3fulNw3iwco58+fx7PPPlvs9pCQEJw7d07SwYcNG4bw8HAsXLgQbdq0wdGjR7Fv3z74+PgAyH86bWJioqQ2CSGEEGKZrKwstG7dGitWrJC035UrV5CSkmJcGjdubNwWExODYcOGYcSIETh37hxGjBiBoUOH4uTJk5KOYfEtngcPHhQ5N6SAl5cXHj58WOz24kyaNKnYR+RHRESY3Tc0NBShoaGSj0kIIYTYGwcdg4Oq5LdpmE76viEhIcacelJ4enqiatWqRW4LDw9H3759MXv2bAD5d0Oio6MRHh6OyMhIi49h8QBFr9ebDe1VqVTQ6cznT7AnaWkP4eaWH0FQ3vM6mObaEYuC4c9XNDePSDSB1JwgQg6C6Ailo+XtCaN4hPlPhMSidrRqPopGGLWjUfORIGpBWaN25soODvwEbQcNX1+n4d8Lg0jghrMD/wXkpuZnxVVzduXK1V0EUTsP+agd3aO7XFkhiKhigsgU1xq1+OM78vlrXNX8Z8nZgX8/lIIYSpMJgari3z+dPkdQNh+1I4zSERLWF77XwrxOwtw8CsFnJ08Q9SMkrA89/94xK89wFP0eEMnNY9qe+e8Jkyidchy1Y2D/nWt6ejrc3d3N1LYea81BET7vqzSCRdq2bYvs7Gz4+/tj7ty53PSPmJgYzJgxg6sfHByM8PBwSceQFMUzevToYk8yJ6f8fhgJIYSQikL4KI358+db7W5D7dq1sW7dOrRv3x45OTn49ttv0bt3bxw5cgTdu3cHAKSmpkp6xllxLB6gjBo1SrSOpRE8hBBCCOGpSjjRtUDBFZSkpCTjHQIAVr160rRpUzRt2tRY7ty5M5KSkvDFF18YByiA5c84M8fiAcrGjRslNUwIIYQQy6n0Bqh0Jb/Vp/rfbUM3NzdugFLaAgMDsXnzZmO5Vq1aFj/jzJyKlZuZEEIIIWUqNjYWtWvXNpY7d+6MqKgors7BgwfRpUsXSe2W79mhhBBCSAWRn/Cv5Ld4SpIsMDMzE9euXTOWExISEBcXBw8PD9SvXx+zZ89GcnIyNm3aBCA/QsfX1xctWrRAbm4uNm/ejO3bt2P79u3GNqZNm4bu3btj8eLFGDRoEHbv3o1ff/0Vv//+u6S+VdoBirt7NShQ9P2wPL352fhqlaPZ7VKjasqaaNSOMNpAhFiUj9ScIsKcJUpBZIcwuoKrKzGyAuDzrwgjP8QilEzzv/CvrU7HTx436Pj6gPkHDDo68v33ENxKrl2F74+nK5/7h2Xzx8+9e5PfLvwsPMngtwsmvwvr16/VkCunZ1/iynmM/xn75xH/KIJ/Xapx5RwX/gS1Of9FJT3JfsRtE0ZU5eTyfRdG3QjLWg0fsSV874URYsLPgjCKSKXko9GE+wsJc/cII9CUguvbYhFqgLzvGeH3gvB7S28oP1GaKiX/q034nS4WuVn4O56xsns6q1JvgFJf8ls8Jdn39OnTXAROQe66UaNGISIiwuR5ZLm5uXjnnXeQnJwMJycntGjRAj///DP69etnrNOlSxf88MMPmDt3Lj788EM0bNgQW7ZsQadOnST1rdIOUAghhJDKrmfPnmYHYcLnkb333nt47733RNt96aWX8NJLL8nqGw1QCCGEEDtgiyso9owGKIQQQogdUOgNUMgYZMjZ1x7RAIUQQgixB3qDyROGJe9fgVCYMSGEEELsDl1BIYQQQuwAY3qzUYqW7F+R0AClCHKTB2bnZnFlsbBkWxOGE+aCDy0VhlfKZRquyYdbCpMHChkKJVUThno6qDTC6vyxDPyxhMn+hFQi7Qm3KwXhjcJkgUoXPplfkjf/2XCse4crd6/Dvxd96vOhng1cG3DlzNNHuDJLz+TLCSlcWSEI62WP+FBbdov/LGfXusiVc5rU5sqN2/JhhL6N+fPN1vFPlzzscJUrJzj5cOXqyTX++38CtwmOGr5t4WdB+DkTvlcmIeUiYckGsS9/wUfFIEj+JwxJFwunF+4vJAx5F56vyc+1IHmfSXK/ckwYViwMiRZ+p4vVtxVmkDlAkbGvPaJbPIQQQgixO3QFhRBCCLEDdAWFRwMUQgghxA4YDAaTW3dS969I6BYPIYQQQuwOXUEhhBBC7ICB6cUnY4vsX5HQAKUUiEUBmcwgl5lMsLSTEWbnPObKGjUf+SEWTSCXMPpCYeaHUCE4tkYkYZuwba2aTyCnduCjfJwc3bmysyOf7K6KU3Wu7OLGR7nc8OXbVzXgo3a6+j3gyv0b8Mn2aoFvP3P/Tq5sOHmLK+f9y0cn3LvJn69am8aVH6bwn820h/zxq9Xkk67V9Y/nynjAt6fu3Jkr9/HlPzuuaj6SZA/4ZIbJqnrG/1d5zL/W2hT+c+DkWJUrC6NwxIhdWhf78hfuLxaFI/zsmbQv8tAtYbLBrMd8ssSKFKVTWRgMOpOEo1L3r0joFg8hhBBC7A5dQSGEEELsAEXx8GiAQgghhNgBmoPCowEKIYQQYgeYQS9rHklFu4JCc1AIIYQQYnfoCoodkpoXQhgVVNb0glG7sGySpERAKXGYrC4UmWOaP4VvTCHYLsy9oxFE6Wg1fJSNxqS+s6Av/PYqzjW48sPafL4YvXc6V25T5yFXbleDj8yo59KQK2ecjebKwqid5D/48797i4+aSb4leH2qeHLl2h7eXFmr5T9bFy/yuXOEH9VG1fmoJEVdPoGOd+1nuHLz6te5cvwjvsEHaf9FBaW58n2t9S//uRLm5hFG1Qhz9ZhE7Qje+5xcPo+RQZC7R4zUv2aFUTnZ2XweJGGUjr3kjykpe82HY0sGg17mg9oq1hUUGqAQQgghdoDmoPDoFg8hhBBC7A5dQSGEEELsgIHJfFAbq1i3yWiAQgghhNgBusXDo1s8hBBCCLE7dAWlAhDOfpea60elsm7uHDFi/dEJohmE+XVUUBe7zSSqR7DdQclHfgjzoQjri23XqPmoHpVTFa58rzpfv2pVPmqniTufb8XXnc99k303iSuzG8lc+ckt/rVMuckf7/ZdN/54jfy5cu2ABlxZVYM/H/09Pg+T2kHNlVNv/cGVvW/z5+N64zbf33p8VE+1anzkjZcTHymjUv13fnoV/9rAkY9QUj/h+64RRO3k5fHnYhBEaAn/+hTLKWWae8d81FCu4PgZmfxn4YkgaodUPhTFw6MBCiGEEGIHGDOAybhNw0QSVJY3dIuHEEIIIXaHrqAQQgghdsBg0MFgKPktdzkRQPaIBiiEEEKIHaA5KDwaoBBCCCF2gMKMeTYfoKxatQqff/45UlJS0KJFC4SHh6Nbt25F1t2xYwdWr16NuLg45OTkoEWLFggNDUVwcLDk46alPYSbm5t4RQBKhc1fpjIljLIRRvnk5uWY3V+lkvd6CSd65eVl89sL/ZWgEaT5EebiERJGZpjm8hGW+XPRCvK9qAVRPHlOfIf0Gv5cnLV8ZEc1Lf9aV69SnSsb0vioHXYjjStn/Mv3796/fKRLEz/zUTvq9vX547ny+WjUKf9y5ZoZ/Htx+W9Hrpz+L+PKLoL+6tvykStOnnx+HScHPtJFWSiKJ4PvGnKd+Igi7WP+vRFG7YhF5ehFcvXkCXLxZOfweZUyH9/nyukZD/j+ivzc2JuKlCtHzgPM0tPT4e7ubsXeEEvZdJLsli1bMH36dHzwwQeIjY1Ft27dEBISgsTExCLrHz16FH379sW+fftw5swZ9OrVCwMGDEBsbGwZ95wQQgixLva/WzwlXaQmqLR3Nr008NVXX2HcuHEYP348ACA8PBwHDhzA6tWrERYWZlI/PDycK3/yySfYvXs39u7di7Zt25ZFlwkhhJBSkX+LR86j7ivWAMVmV1Byc3Nx5swZBAUFceuDgoJw4sQJi9owGAzIyMiAh4dHsXVycnKQnp7OLYQQQgixbzYboNy7dw96vR5eXl7cei8vL6SmplrUxpdffomsrCwMHTq02DphYWFwd3c3LvXq1ZPVb0IIIaQ0sP/dppGzVCQ2f1CbQsFP6mOMmawrSmRkJEJDQ7FlyxZ4CibaFTZ79mykpaUZl6SkpGLrEkIIIbZSEMUjZ6lIbDYHpUaNGlCpVCZXS+7evWtyVUVoy5YtGDduHLZt24Y+ffqYravVaqHVak3Wu7tXgwLiAyFiSiyXjlTCBxOp1cL8N3z9wrl6FDo+qkQvyOMjLAujdBSCslokH4uDiv8sCXPxZDkLfqQ0/P1kByUf5aIWnJuLIx8tkJ1xiSsbUvjIlAcpfH9yGR/JUtPXmz9e05pc+V+3qlz5SR5//nVr8/Udm/KRKLUv8VFB/96K58peKfz7o8wxH8niIHg9nLT/1U934dt67MxH8VTL5COKlI/Nf73l5fFROVlP+KibzMd8BNOjdD6vUEbmI65c3qJ0yjNhhJEYOZGYDEy8Ujl29OhRfP755zhz5gxSUlKwc+dODB48uNj6lkTTRkREYMyYMSb7PnnyBI6Ojibri2OzKygajQbt27dHVFQUtz4qKgpdunQpdr/IyEiMHj0a33//Pfr371/a3SSEEELKhIHJi+IpyRWUrKwstG7dGitWrLCovqXRtG5ubkhJSeEWKYMTwMZRPDNnzsSIESPQoUMHdO7cGevWrUNiYiImTpwIIP/2THJyMjZt2gQgf3AycuRILF26FIGBgcarL05OThSnTgghpFyzxYPaQkJCEBISYnF9S6NpFQoFatWqJbk/hdl0DsqwYcMQHh6OhQsXok2bNjh69Cj27dsHHx8fAEBKSgr3TJS1a9dCp9Nh8uTJqF27tnGZNm2arU6BEEIIsSvCyNUckVurchQXTZuZmQkfHx/UrVsXzz33XImeV2bzR6ROmjQJkyZNKnJbREQEVz5y5Ejpd4gQQgixAYNBD4NCfi4eYbTq/PnzERoaKqdrxSoqmrZZs2aIiIhAQEAA0tPTsXTpUnTt2hXnzp1D48aNLW7b5gMUQgghhOSn+WAybvEUpAlJSkriUrkUFShiDQXRtLt37+aiaQMDAxEYGGgsd+3aFe3atcPy5cuxbNkyi9unAQohhBBiB/KvoJR85kXBFRQ3NzeLc82VlJRoWqVSiY4dO+Lq1auSjkEDFBso7aRbwvalhuTJPr7EMGS9nu+vXvCwIZWy+DBkkwRwgmKeIAw58/E9wXY+1FT414swLNnJsSpXFiaU06v40HWVhg9zFsozmN0M9pBPtpfN56PDv6l8CGRdbz+urPXj7wsbBGHDDx/z96ZzcgXvXRX+L6/6taty5RpufPsJN6SFZAo/q04q/gWp6fzf9geO/HsF8GHGhhz+vc7IusOV7z7gvxzvP0rgyhRGXLas+T1YnhMZlkeRkZEYO3YsIiMjLYqmZYwhLi4OAQEBko5DAxRCCCHEDuRH8ci4glKC20OZmZm4du2asZyQkIC4uDh4eHigfv36JYqmXbBgAQIDA9G4cWOkp6dj2bJliIuLw8qVKyX1zeZPkiWEEEKIbbIZnz59Gm3btjWGCM+cORNt27bFvHnzAJQsmvbRo0d4/fXX0bx5cwQFBSE5ORlHjx7FU089JalvdAWFEEIIqaR69uwJxoq/NVuSaNolS5ZgyZIlMntGAxRCCCHELjCmB5Nxi0dOBJA9ogEKIYQQYgcMBj0UVojiqShogFIJiM1wt/cZ8MKoHn3Of9EcwoghlUolKAsiPQR/Yej0ufx2wbFyBVE+wv2F9Wvd5UP77noKQv3q8EVhskDhuQrpcvgoobR0vgH/pxpyZVVtPgVEpoaPynHU8ecvlJ3HfzYMri5cuWpdPpO47hrfP4Wj8AT58xMmR3TV8An6nuj+a093pxq3Tfv331z58u2TXPnWnXNc+VFaMlfOepzBlSlKh2fv3wuk4qMBCiGEEGIHDEwPRRlH8dgzGqAQQgghdsDADFDIShYo8mClcobCjAkhhBBid+gKCiGEEGIHmEEPplCIVzSzf0VCAxRCCCHEDuTPQSn5AIXmoJBKR+5sfrH9hbmCxCNz/qsvjKIxyY0jaIsJ7tEKQ/r0Bj53jjCXj14Q9ZOTm8nXz3vMlRvntOXKfzv5cuV4j+tcuZOgPQetRlDmH6ik1/P9V9fgo2z01fnIl/uP+f7nLd/BlR0d+KintJFB/P7OfBRQVVdHrqwV9I9lC+6JC96PB1l3ufKOfwRZV3f8lzZeceFHbtPvyX9w5bKO0jH53FLUC5HJwPRQGGiAUoDmoBBCCCHE7tAVFEIIIcQO5D9JVsYclAp2BYUGKIQQQogdMBj0UEDGLZ4KNkmWbvEQQgghxO7QFRRCCCHEDjBmkDXRVRgEUN5V2gFKWtpDuLnl50lRKirty2AXJOcKKhSNIYykEBJGAAkvgSqV/Ha9XhDFk8dH8eTkZXHlx08eCsoPuHJaZgpXrp/ZnisfehDAH88Qz5Xff/pZrmz4/QpXrhXNvzb6dL6/t3WC3D1ZfCTLMydawJwDq/jX4+FjQSTMVcH5NeSP5xDcgCtfqu3Nleet8ePr/7SXbz7xa+P/7z9I5LYJo3TKOoqGonbsh9SIKgOz/L1LT0+Hu7u7eEUrMDADICvMuGINUOgWDyGEEELsDl06IIQQQuwAM+jBxKuZ3b8ioQEKIYQQYgcMTA85IxR6UBshhBBCSCmjKyiEEEKIHZD7HJOK9hyUSjtAcXevJuuBOMQ+SI0AEubmEaPR8LlhVEo+iidbzUeSPM5+xJUfZdzmyg/T+UiUmnf/4spXz3XlyiHP8bl5Vo4P4cqdm57mymfe3cqVG7rxuXJqd/Pnyq8Ov8GVnwj6vy6oJle+tuYgV67T4wRXdlkxgysP+oaPktKMiubK//zzDVd+8Kj4fDoUNUMKyP0sqFX8z4WwvcJRQYzJmRUiDWMGmU+SrVhRPJV2gEIIIYTYk/wwY5n7VyA0B4UQQgghdoeuoBBCCCF2ID/MuOSXUJihYl1BoQEKIYQQYgfoFg+PbvEQQgghxO7QFRRSqUid/f8k23x9sVxAwiig9Ix/ufK9hwlcOTH1LFeuea0hVw7d2ZMr3xn4PFdef/oOV64SsYUrH53N59LZee9QEb3+T4Y2jit3jZ3ClftHduLKyt58lM61hCiu/OAhH6WT+Tjd7PGJ9UjNV1OR2eu5G5geMJT8EkpFu4JCAxRCCCHEDjBmkPeo+wo2QKFbPIQQQgixO3QFhRBCCLEDzKCHQSEjiqeCXUGhAQohhBBiB/R6PRQ0QDGiWzyEEEIIsTs2H6CsWrUKfn5+cHR0RPv27XHs2DGz9aOjo9G+fXs4OjqiQYMGWLNmTRn1lBBCCCk9BoMBBoNexlKxrqDY9BbPli1bMH36dKxatQpdu3bF2rVrERISgkuXLqF+/fom9RMSEtCvXz9MmDABmzdvxvHjxzFp0iTUrFkTL774og3OgFR2YuGKwjDlJ+CTDRZOhgcAj9L4MOT7D/jkgkmpsVy5xt8NuPJ7O57lynljX+DKmy+r+Q7WMB9mrPluEFcePzyVKyfEfc2V7/x7nStTGDEhltMb9FAo5CQLLLvEhmXBpldQvvrqK4wbNw7jx49H8+bNER4ejnr16mH16tVF1l+zZg3q16+P8PBwNG/eHOPHj8fYsWPxxRdflHHPCSGEEOvS63Wyl4rEZgOU3NxcnDlzBkFBQdz6oKAgnDhxosh9YmJiTOoHBwfj9OnTyMvLK3KfnJwcpKencwshhBBCgKNHj2LAgAHw9vaGQqHArl27RPexZKrF9u3b4e/vD61WC39/f+zcuVNy32w2QLl37x70ej28vLy49V5eXkhNTS1yn9TU1CLr63Q63Lt3r8h9wsLC4O7ublzq1atnnRMghBBCrEiv18tepMrKykLr1q2xYsUKi+oXTLXo1q0bYmNjMWfOHEydOhXbt2831omJicGwYcMwYsQInDt3DiNGjMDQoUNx8uRJSX2zeZix8H4bY8zsPbii6he1vsDs2bMxc+ZMYzk9PZ0GKYQQQuyOnumgYDLmoJTgObQhISEICQmxuH7hqRYA0Lx5c5w+fRpffPGFcS5oeHg4+vbti9mzZwPI/z0cHR2N8PBwREZGWnwsmw1QatSoAZVKZXK15O7duyZXSQrUqlWryPoODg6oXr16kftotVpotf/lQ/lvEpGcpNaEWIdwUptpmZ+VL5ylL7znnJeXzZcfP+HK6Rn8rVCxn4KMDH7/3Dz+eAYD/xebSf/pp8xu0HtjucKvVcH/y2oCqjXeF+FUBuHvQTmKm2qxfv165OXlQa1WIyYmBjNmzDCpUzCosZTNBigajQbt27dHVFQUnn/+v4RnUVFRGDRoUJH7dO7cGXv37uXWHTx4EB06dIBarS5yH6GMjIKoCQZZea0JsQI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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_disp = pyart.graph.GridMapDisplay(grid)\n",
    "grid_disp.plot_longitudinal_level('intensity', 0, cmap='ChaseSpectral', vmin=1, vmax=3.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "b37f444e-775d-412f-b46a-7acd720f1e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rjackson/PyDDA/pydda/retrieval/angles.py:25: RuntimeWarning: invalid value encountered in divide\n",
      "  elev = np.arccos((Re**2 + slantrsq - rh**2) / (2 * Re * slantr))\n",
      "/Users/rjackson/PyDDA/pydda/retrieval/angles.py:25: RuntimeWarning: invalid value encountered in arccos\n",
      "  elev = np.arccos((Re**2 + slantrsq - rh**2) / (2 * Re * slantr))\n"
     ]
    }
   ],
   "source": [
    "grid_pydda = pydda.io.read_from_pyart_grid(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "e92b8643-8c21-4532-80ba-c90dac50371a",
   "metadata": {},
   "outputs": [
    {
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       "  /* 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 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\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: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",
       "  margin-bottom: 0;\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",
       "}\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",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\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-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",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (time: 1, z: 60, y: 100, x: 2, nradar: 1)\n",
       "Coordinates:\n",
       "  * time              (time) object 2023-06-29 07:18:26.129987\n",
       "  * z                 (z) float64 0.0 25.42 50.85 ... 1.475e+03 1.5e+03\n",
       "    lat               (y, x) float64 41.94 41.94 41.95 ... 42.02 42.02 42.02\n",
       "    lon               (y, x) float64 -87.72 -87.72 -87.72 ... -87.72 -87.72\n",
       "  * y                 (y) float64 -4e+03 -3.919e+03 ... 3.919e+03 4e+03\n",
       "  * x                 (x) float64 0.0 100.0\n",
       "Dimensions without coordinates: nradar\n",
       "Data variables: (12/22)\n",
       "    radial_velocity   (time, z, y, x) float64 nan nan nan nan ... nan nan nan\n",
       "    intensity         (time, z, y, x) float64 0.9999 nan 1.0 ... nan 1.006 nan\n",
       "    ROI               (time, z, y, x) float32 60.0 60.0 60.0 ... 60.0 60.0 60.0\n",
       "    projection        int64 1\n",
       "    radar_latitude    (nradar) float64 41.98\n",
       "    radar_longitude   (nradar) float64 -87.72\n",
       "    ...                ...\n",
       "    AZ                (time, z, y, x) float64 180.0 178.6 180.0 ... 0.0 1.432\n",
       "    EL                (time, z, y, x) float64 -0.01349 -0.01349 ... 20.54 20.53\n",
       "    u                 (time, z, y, x) float64 43.88 43.88 ... -71.66 -71.66\n",
       "    v                 (time, z, y, x) float64 9.967 1.505e-15 ... -2.354e-20\n",
       "    w                 (time, z, y, x) float64 nan nan nan nan ... nan nan nan\n",
       "    reflectivity      (time, z, y, x) float64 -999.0 -999.0 ... -999.0 -999.0\n",
       "Attributes:\n",
       "    first_grid_name:  radial_velocity\n",
       "    units:            m\n",
       "    long_name:        Point altitude</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-eb0e56d4-63a6-437a-9d25-cf391d7f30b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eb0e56d4-63a6-437a-9d25-cf391d7f30b9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>z</span>: 60</li><li><span class='xr-has-index'>y</span>: 100</li><li><span class='xr-has-index'>x</span>: 2</li><li><span>nradar</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7c8bde75-5ab6-42a9-9306-00da77852f82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c8bde75-5ab6-42a9-9306-00da77852f82' class='xr-section-summary' >Coordinates: <span>(6)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2023-06-29 07:18:26.129987</div><input id='attrs-08ad59e9-9cd6-4a10-95e7-ee36d57fabb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08ad59e9-9cd6-4a10-95e7-ee36d57fabb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81187d21-68ec-4055-809a-909240a98dcb' class='xr-var-data-in' type='checkbox'><label for='data-81187d21-68ec-4055-809a-909240a98dcb' 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>units :</span></dt><dd>seconds since 2023-06-29T07:18:26Z</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2023, 6, 29, 7, 18, 26, 129987, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 25.42 ... 1.475e+03 1.5e+03</div><input id='attrs-4afd1e06-2354-4618-91d2-85f8baa281bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4afd1e06-2354-4618-91d2-85f8baa281bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b4cad01-7ec3-457d-86e6-c7c11dd233ef' class='xr-var-data-in' type='checkbox'><label for='data-3b4cad01-7ec3-457d-86e6-c7c11dd233ef' 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>projection_z_coordinate</dd><dt><span>long_name :</span></dt><dd>Z distance on the projection plane from the origin</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([   0.      ,   25.423729,   50.847458,   76.271186,  101.694915,\n",
       "        127.118644,  152.542373,  177.966102,  203.389831,  228.813559,\n",
       "        254.237288,  279.661017,  305.084746,  330.508475,  355.932203,\n",
       "        381.355932,  406.779661,  432.20339 ,  457.627119,  483.050847,\n",
       "        508.474576,  533.898305,  559.322034,  584.745763,  610.169492,\n",
       "        635.59322 ,  661.016949,  686.440678,  711.864407,  737.288136,\n",
       "        762.711864,  788.135593,  813.559322,  838.983051,  864.40678 ,\n",
       "        889.830508,  915.254237,  940.677966,  966.101695,  991.525424,\n",
       "       1016.949153, 1042.372881, 1067.79661 , 1093.220339, 1118.644068,\n",
       "       1144.067797, 1169.491525, 1194.915254, 1220.338983, 1245.762712,\n",
       "       1271.186441, 1296.610169, 1322.033898, 1347.457627, 1372.881356,\n",
       "       1398.305085, 1423.728814, 1449.152542, 1474.576271, 1500.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>41.94 41.94 41.95 ... 42.02 42.02</div><input id='attrs-0ad6a5e3-0486-4c5e-bbd6-c51ba88d4caf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ad6a5e3-0486-4c5e-bbd6-c51ba88d4caf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b192112-c54a-48b5-a425-75602c9fb608' class='xr-var-data-in' type='checkbox'><label for='data-7b192112-c54a-48b5-a425-75602c9fb608' 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>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>standard_name :</span></dt><dd>Latitude</dd></dl></div><div class='xr-var-data'><pre>array([[41.94456011, 41.9445601 ],\n",
       "       [41.94528684, 41.94528683],\n",
       "       [41.94601356, 41.94601355],\n",
       "       [41.94674029, 41.94674028],\n",
       "       [41.94746701, 41.947467  ],\n",
       "       [41.94819374, 41.94819373],\n",
       "       [41.94892046, 41.94892045],\n",
       "       [41.94964719, 41.94964718],\n",
       "       [41.95037391, 41.9503739 ],\n",
       "       [41.95110064, 41.95110063],\n",
       "       [41.95182736, 41.95182735],\n",
       "       [41.95255408, 41.95255408],\n",
       "       [41.95328081, 41.9532808 ],\n",
       "       [41.95400753, 41.95400753],\n",
       "       [41.95473426, 41.95473425],\n",
       "       [41.95546098, 41.95546098],\n",
       "       [41.95618771, 41.9561877 ],\n",
       "       [41.95691443, 41.95691443],\n",
       "       [41.95764116, 41.95764115],\n",
       "       [41.95836788, 41.95836788],\n",
       "...\n",
       "       [42.0026981 , 42.00269809],\n",
       "       [42.00342483, 42.00342482],\n",
       "       [42.00415155, 42.00415154],\n",
       "       [42.00487828, 42.00487827],\n",
       "       [42.005605  , 42.00560499],\n",
       "       [42.00633173, 42.00633172],\n",
       "       [42.00705845, 42.00705844],\n",
       "       [42.00778518, 42.00778517],\n",
       "       [42.0085119 , 42.00851189],\n",
       "       [42.00923862, 42.00923862],\n",
       "       [42.00996535, 42.00996534],\n",
       "       [42.01069207, 42.01069207],\n",
       "       [42.0114188 , 42.01141879],\n",
       "       [42.01214552, 42.01214552],\n",
       "       [42.01287225, 42.01287224],\n",
       "       [42.01359897, 42.01359897],\n",
       "       [42.0143257 , 42.01432569],\n",
       "       [42.01505242, 42.01505242],\n",
       "       [42.01577915, 42.01577914],\n",
       "       [42.01650587, 42.01650587]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.72 -87.72 ... -87.72 -87.72</div><input id='attrs-99c61397-94c3-485a-b491-ad0038fed6ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99c61397-94c3-485a-b491-ad0038fed6ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34d2d440-f549-4909-ac20-c9be69c9e371' class='xr-var-data-in' type='checkbox'><label for='data-34d2d440-f549-4909-ac20-c9be69c9e371' 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>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>standard_name :</span></dt><dd>Longitude</dd></dl></div><div class='xr-var-data'><pre>array([[-87.71662375, -87.71541464],\n",
       "       [-87.71662375, -87.71541463],\n",
       "       [-87.71662375, -87.71541461],\n",
       "       [-87.71662375, -87.7154146 ],\n",
       "       [-87.71662375, -87.71541459],\n",
       "       [-87.71662375, -87.71541457],\n",
       "       [-87.71662375, -87.71541456],\n",
       "       [-87.71662375, -87.71541455],\n",
       "       [-87.71662375, -87.71541453],\n",
       "       [-87.71662375, -87.71541452],\n",
       "       [-87.71662375, -87.7154145 ],\n",
       "       [-87.71662375, -87.71541449],\n",
       "       [-87.71662375, -87.71541448],\n",
       "       [-87.71662375, -87.71541446],\n",
       "       [-87.71662375, -87.71541445],\n",
       "       [-87.71662375, -87.71541444],\n",
       "       [-87.71662375, -87.71541442],\n",
       "       [-87.71662375, -87.71541441],\n",
       "       [-87.71662375, -87.71541439],\n",
       "       [-87.71662375, -87.71541438],\n",
       "...\n",
       "       [-87.71662375, -87.71541354],\n",
       "       [-87.71662375, -87.71541352],\n",
       "       [-87.71662375, -87.71541351],\n",
       "       [-87.71662375, -87.7154135 ],\n",
       "       [-87.71662375, -87.71541348],\n",
       "       [-87.71662375, -87.71541347],\n",
       "       [-87.71662375, -87.71541345],\n",
       "       [-87.71662375, -87.71541344],\n",
       "       [-87.71662375, -87.71541343],\n",
       "       [-87.71662375, -87.71541341],\n",
       "       [-87.71662375, -87.7154134 ],\n",
       "       [-87.71662375, -87.71541339],\n",
       "       [-87.71662375, -87.71541337],\n",
       "       [-87.71662375, -87.71541336],\n",
       "       [-87.71662375, -87.71541334],\n",
       "       [-87.71662375, -87.71541333],\n",
       "       [-87.71662375, -87.71541332],\n",
       "       [-87.71662375, -87.7154133 ],\n",
       "       [-87.71662375, -87.71541329],\n",
       "       [-87.71662375, -87.71541328]])</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'>-4e+03 -3.919e+03 ... 4e+03</div><input id='attrs-3b22119f-622c-4e7d-adbe-9426dd5867a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b22119f-622c-4e7d-adbe-9426dd5867a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-034f1a62-7a45-42f4-b857-e7d7da398acc' class='xr-var-data-in' type='checkbox'><label for='data-034f1a62-7a45-42f4-b857-e7d7da398acc' 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>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>Y distance on the projection plane from the origin</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-4000.      , -3919.191919, -3838.383838, -3757.575758, -3676.767677,\n",
       "       -3595.959596, -3515.151515, -3434.343434, -3353.535354, -3272.727273,\n",
       "       -3191.919192, -3111.111111, -3030.30303 , -2949.494949, -2868.686869,\n",
       "       -2787.878788, -2707.070707, -2626.262626, -2545.454545, -2464.646465,\n",
       "       -2383.838384, -2303.030303, -2222.222222, -2141.414141, -2060.606061,\n",
       "       -1979.79798 , -1898.989899, -1818.181818, -1737.373737, -1656.565657,\n",
       "       -1575.757576, -1494.949495, -1414.141414, -1333.333333, -1252.525253,\n",
       "       -1171.717172, -1090.909091, -1010.10101 ,  -929.292929,  -848.484848,\n",
       "        -767.676768,  -686.868687,  -606.060606,  -525.252525,  -444.444444,\n",
       "        -363.636364,  -282.828283,  -202.020202,  -121.212121,   -40.40404 ,\n",
       "          40.40404 ,   121.212121,   202.020202,   282.828283,   363.636364,\n",
       "         444.444444,   525.252525,   606.060606,   686.868687,   767.676768,\n",
       "         848.484848,   929.292929,  1010.10101 ,  1090.909091,  1171.717172,\n",
       "        1252.525253,  1333.333333,  1414.141414,  1494.949495,  1575.757576,\n",
       "        1656.565657,  1737.373737,  1818.181818,  1898.989899,  1979.79798 ,\n",
       "        2060.606061,  2141.414141,  2222.222222,  2303.030303,  2383.838384,\n",
       "        2464.646465,  2545.454545,  2626.262626,  2707.070707,  2787.878788,\n",
       "        2868.686869,  2949.494949,  3030.30303 ,  3111.111111,  3191.919192,\n",
       "        3272.727273,  3353.535354,  3434.343434,  3515.151515,  3595.959596,\n",
       "        3676.767677,  3757.575758,  3838.383838,  3919.191919,  4000.      ])</pre></div></li><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'>0.0 100.0</div><input id='attrs-07961d89-dc1c-4179-84aa-9fff3a3912ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07961d89-dc1c-4179-84aa-9fff3a3912ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f51f147-6a13-4000-b808-161abb236a14' class='xr-var-data-in' type='checkbox'><label for='data-3f51f147-6a13-4000-b808-161abb236a14' 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>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>X distance on the projection plane from the origin</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([  0., 100.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-426c9c78-3ad6-4e86-b3b9-031d6b4f6e98' class='xr-section-summary-in' type='checkbox'  ><label for='section-426c9c78-3ad6-4e86-b3b9-031d6b4f6e98' class='xr-section-summary' >Data variables: <span>(22)</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>radial_velocity</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-e33ef570-ef40-4ca7-bf58-30b35e335647' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e33ef570-ef40-4ca7-bf58-30b35e335647' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47de0ea2-ff45-4685-b7e3-74be15287a81' class='xr-var-data-in' type='checkbox'><label for='data-47de0ea2-ff45-4685-b7e3-74be15287a81' 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>_FillValue :</span></dt><dd>nan</dd><dt><span>coordinates :</span></dt><dd>azimuth elevation</dd></dl></div><div class='xr-var-data'><pre>array([[[[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]],\n",
       "\n",
       "        [[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]],\n",
       "\n",
       "        [[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]],\n",
       "\n",
       "        [[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]],\n",
       "\n",
       "        [[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9999 nan 1.0 ... nan 1.006 nan</div><input id='attrs-ef065d58-cd05-4292-bd8b-9a9165070335' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef065d58-cd05-4292-bd8b-9a9165070335' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8999032-a49e-4667-bc30-edde9267cae1' class='xr-var-data-in' type='checkbox'><label for='data-d8999032-a49e-4667-bc30-edde9267cae1' 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>_FillValue :</span></dt><dd>nan</dd><dt><span>coordinates :</span></dt><dd>azimuth elevation</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.99990249,        nan],\n",
       "         [1.00025392,        nan],\n",
       "         [0.99949336,        nan],\n",
       "         ...,\n",
       "         [1.00745201,        nan],\n",
       "         [1.00174451,        nan],\n",
       "         [1.01000428,        nan]],\n",
       "\n",
       "        [[0.99990261,        nan],\n",
       "         [1.00025213,        nan],\n",
       "         [0.9994325 ,        nan],\n",
       "         ...,\n",
       "         [1.00752175,        nan],\n",
       "         [1.00174272,        nan],\n",
       "         [1.01000488,        nan]],\n",
       "\n",
       "        [[0.99997151,        nan],\n",
       "         [0.99997073,        nan],\n",
       "         [0.99786234,        nan],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [1.00334692,        nan],\n",
       "         [1.00562131,        nan],\n",
       "         [1.00493348,        nan]],\n",
       "\n",
       "        [[1.00191259,        nan],\n",
       "         [1.00115466,        nan],\n",
       "         [       nan,        nan],\n",
       "         ...,\n",
       "         [       nan,        nan],\n",
       "         [1.00397944,        nan],\n",
       "         [1.00536084,        nan]],\n",
       "\n",
       "        [[1.00244522,        nan],\n",
       "         [       nan,        nan],\n",
       "         [1.00305319,        nan],\n",
       "         ...,\n",
       "         [1.00684977,        nan],\n",
       "         [       nan,        nan],\n",
       "         [1.00570858,        nan]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ROI</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 60.0 60.0 ... 60.0 60.0 60.0</div><input id='attrs-a26e197e-459c-48a6-9696-a1bfc7f17043' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a26e197e-459c-48a6-9696-a1bfc7f17043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bdae652-3744-4fc0-9651-0dbe37598ae9' class='xr-var-data-in' type='checkbox'><label for='data-9bdae652-3744-4fc0-9651-0dbe37598ae9' 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>radius_of_influence</dd><dt><span>long_name :</span></dt><dd>Radius of influence for mapping</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>least_significant_digit :</span></dt><dd>1</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd></dl></div><div class='xr-var-data'><pre>array([[[[60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         ...,\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.]],\n",
       "\n",
       "        [[60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         ...,\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.]],\n",
       "\n",
       "        [[60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.]],\n",
       "\n",
       "        [[60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         ...,\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.]],\n",
       "\n",
       "        [[60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         ...,\n",
       "         [60., 60.],\n",
       "         [60., 60.],\n",
       "         [60., 60.]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-cd2f0635-dd18-4fbf-9e15-cbb3368e4b39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd2f0635-dd18-4fbf-9e15-cbb3368e4b39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-245ef737-432f-4dc2-bfcd-1cf230a3f793' class='xr-var-data-in' type='checkbox'><label for='data-245ef737-432f-4dc2-bfcd-1cf230a3f793' 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>proj :</span></dt><dd>pyart_aeqd</dd><dt><span>lon_0 :</span></dt><dd>[-87.71662375]</dd><dt><span>lat_0 :</span></dt><dd>[41.98053299]</dd><dt><span>_include_lon_0_lat_0 :</span></dt><dd>true</dd></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_latitude</span></div><div class='xr-var-dims'>(nradar)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>41.98</div><input id='attrs-3f016480-89b2-45df-a02b-2ddef736835d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f016480-89b2-45df-a02b-2ddef736835d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d9cf39a-25cc-420d-8f9d-8d68aabbd9df' class='xr-var-data-in' type='checkbox'><label for='data-2d9cf39a-25cc-420d-8f9d-8d68aabbd9df' 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>long_name :</span></dt><dd>Latitude of radars used to make the grid.</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([41.98053299])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_longitude</span></div><div class='xr-var-dims'>(nradar)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.72</div><input id='attrs-ced3196c-303b-42fe-83f3-447aa9f2e21a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ced3196c-303b-42fe-83f3-447aa9f2e21a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb488a96-451b-417f-90a9-8e9ecd40779f' class='xr-var-data-in' type='checkbox'><label for='data-fb488a96-451b-417f-90a9-8e9ecd40779f' 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>long_name :</span></dt><dd>Longitude of radars used to make the grid.</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-87.71662375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_altitude</span></div><div class='xr-var-dims'>(nradar)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-45113c91-ea51-4e36-b53a-44da09042b67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45113c91-ea51-4e36-b53a-44da09042b67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d87b6aad-27db-42ea-8daf-09c8745f0f50' class='xr-var-data-in' type='checkbox'><label for='data-d87b6aad-27db-42ea-8daf-09c8745f0f50' 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>long_name :</span></dt><dd>Altitude of radars used to make the grid.</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>origin_latitude</span></div><div class='xr-var-dims'>(nradar)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>41.98</div><input id='attrs-b2812690-e480-4e1d-bff2-f5eadd7bb727' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2812690-e480-4e1d-bff2-f5eadd7bb727' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a598ec56-bfdb-483c-a3b9-807c53f269cc' class='xr-var-data-in' type='checkbox'><label for='data-a598ec56-bfdb-483c-a3b9-807c53f269cc' 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>long_name :</span></dt><dd>Latitude at grid origin</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([41.98053299])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>origin_longitude</span></div><div class='xr-var-dims'>(nradar)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.72</div><input id='attrs-31ac53ac-772e-4929-9155-1326dfabd46a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31ac53ac-772e-4929-9155-1326dfabd46a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-783b3e8c-f6ca-465a-ac10-564706aae73b' class='xr-var-data-in' type='checkbox'><label for='data-783b3e8c-f6ca-465a-ac10-564706aae73b' 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>long_name :</span></dt><dd>Longitude at grid origin</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>array([-87.71662375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>origin_altitude</span></div><div class='xr-var-dims'>(nradar)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-85871d1b-aa0c-4d05-9100-bb897a48af52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85871d1b-aa0c-4d05-9100-bb897a48af52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3850c110-cbdd-47c3-a9f1-a03b3aacf4ce' class='xr-var-data-in' type='checkbox'><label for='data-3850c110-cbdd-47c3-a9f1-a03b3aacf4ce' 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>long_name :</span></dt><dd>Altitude at grid origin</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_x</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 100.0 0.0 ... 100.0 0.0 100.0</div><input id='attrs-9e44c5d5-26f6-4eff-b22d-4cf7da4f1c25' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e44c5d5-26f6-4eff-b22d-4cf7da4f1c25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3b3b035-25c3-424f-ae06-e5b5289e791a' class='xr-var-data-in' type='checkbox'><label for='data-a3b3b035-25c3-424f-ae06-e5b5289e791a' 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([[[  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        ...,\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.]],\n",
       "\n",
       "       [[  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        ...,\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.]],\n",
       "\n",
       "       [[  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.]],\n",
       "\n",
       "       [[  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        ...,\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.]],\n",
       "\n",
       "       [[  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        ...,\n",
       "        [  0., 100.],\n",
       "        [  0., 100.],\n",
       "        [  0., 100.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_y</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4e+03 -4e+03 ... 4e+03 4e+03</div><input id='attrs-1e400b31-f407-4378-a618-ec4cd7a242a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e400b31-f407-4378-a618-ec4cd7a242a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68203685-d11e-497e-b6f4-3e9bc50fc971' class='xr-var-data-in' type='checkbox'><label for='data-68203685-d11e-497e-b6f4-3e9bc50fc971' 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([[[-4000.        , -4000.        ],\n",
       "        [-3919.19191919, -3919.19191919],\n",
       "        [-3838.38383838, -3838.38383838],\n",
       "        ...,\n",
       "        [ 3838.38383838,  3838.38383838],\n",
       "        [ 3919.19191919,  3919.19191919],\n",
       "        [ 4000.        ,  4000.        ]],\n",
       "\n",
       "       [[-4000.        , -4000.        ],\n",
       "        [-3919.19191919, -3919.19191919],\n",
       "        [-3838.38383838, -3838.38383838],\n",
       "        ...,\n",
       "        [ 3838.38383838,  3838.38383838],\n",
       "        [ 3919.19191919,  3919.19191919],\n",
       "        [ 4000.        ,  4000.        ]],\n",
       "\n",
       "       [[-4000.        , -4000.        ],\n",
       "        [-3919.19191919, -3919.19191919],\n",
       "        [-3838.38383838, -3838.38383838],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 3838.38383838,  3838.38383838],\n",
       "        [ 3919.19191919,  3919.19191919],\n",
       "        [ 4000.        ,  4000.        ]],\n",
       "\n",
       "       [[-4000.        , -4000.        ],\n",
       "        [-3919.19191919, -3919.19191919],\n",
       "        [-3838.38383838, -3838.38383838],\n",
       "        ...,\n",
       "        [ 3838.38383838,  3838.38383838],\n",
       "        [ 3919.19191919,  3919.19191919],\n",
       "        [ 4000.        ,  4000.        ]],\n",
       "\n",
       "       [[-4000.        , -4000.        ],\n",
       "        [-3919.19191919, -3919.19191919],\n",
       "        [-3838.38383838, -3838.38383838],\n",
       "        ...,\n",
       "        [ 3838.38383838,  3838.38383838],\n",
       "        [ 3919.19191919,  3919.19191919],\n",
       "        [ 4000.        ,  4000.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_z</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 1.5e+03 1.5e+03</div><input id='attrs-8e9d398f-e33e-4b34-afdf-a3e1bec5fb40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e9d398f-e33e-4b34-afdf-a3e1bec5fb40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f06b303-763d-4618-801b-126d8dd00937' class='xr-var-data-in' type='checkbox'><label for='data-1f06b303-763d-4618-801b-126d8dd00937' 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([[[   0.        ,    0.        ],\n",
       "        [   0.        ,    0.        ],\n",
       "        [   0.        ,    0.        ],\n",
       "        ...,\n",
       "        [   0.        ,    0.        ],\n",
       "        [   0.        ,    0.        ],\n",
       "        [   0.        ,    0.        ]],\n",
       "\n",
       "       [[  25.42372881,   25.42372881],\n",
       "        [  25.42372881,   25.42372881],\n",
       "        [  25.42372881,   25.42372881],\n",
       "        ...,\n",
       "        [  25.42372881,   25.42372881],\n",
       "        [  25.42372881,   25.42372881],\n",
       "        [  25.42372881,   25.42372881]],\n",
       "\n",
       "       [[  50.84745763,   50.84745763],\n",
       "        [  50.84745763,   50.84745763],\n",
       "        [  50.84745763,   50.84745763],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [1449.15254237, 1449.15254237],\n",
       "        [1449.15254237, 1449.15254237],\n",
       "        [1449.15254237, 1449.15254237]],\n",
       "\n",
       "       [[1474.57627119, 1474.57627119],\n",
       "        [1474.57627119, 1474.57627119],\n",
       "        [1474.57627119, 1474.57627119],\n",
       "        ...,\n",
       "        [1474.57627119, 1474.57627119],\n",
       "        [1474.57627119, 1474.57627119],\n",
       "        [1474.57627119, 1474.57627119]],\n",
       "\n",
       "       [[1500.        , 1500.        ],\n",
       "        [1500.        , 1500.        ],\n",
       "        [1500.        , 1500.        ],\n",
       "        ...,\n",
       "        [1500.        , 1500.        ],\n",
       "        [1500.        , 1500.        ],\n",
       "        [1500.        , 1500.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_altitude</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 1.5e+03 1.5e+03</div><input id='attrs-b27e9a8e-2e9b-4f06-8270-15c19e37e7bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b27e9a8e-2e9b-4f06-8270-15c19e37e7bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a574c23f-8fa9-412a-be17-6af5e52dcb6e' class='xr-var-data-in' type='checkbox'><label for='data-a574c23f-8fa9-412a-be17-6af5e52dcb6e' 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([[[   0.        ,    0.        ],\n",
       "        [   0.        ,    0.        ],\n",
       "        [   0.        ,    0.        ],\n",
       "        ...,\n",
       "        [   0.        ,    0.        ],\n",
       "        [   0.        ,    0.        ],\n",
       "        [   0.        ,    0.        ]],\n",
       "\n",
       "       [[  25.42372881,   25.42372881],\n",
       "        [  25.42372881,   25.42372881],\n",
       "        [  25.42372881,   25.42372881],\n",
       "        ...,\n",
       "        [  25.42372881,   25.42372881],\n",
       "        [  25.42372881,   25.42372881],\n",
       "        [  25.42372881,   25.42372881]],\n",
       "\n",
       "       [[  50.84745763,   50.84745763],\n",
       "        [  50.84745763,   50.84745763],\n",
       "        [  50.84745763,   50.84745763],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [1449.15254237, 1449.15254237],\n",
       "        [1449.15254237, 1449.15254237],\n",
       "        [1449.15254237, 1449.15254237]],\n",
       "\n",
       "       [[1474.57627119, 1474.57627119],\n",
       "        [1474.57627119, 1474.57627119],\n",
       "        [1474.57627119, 1474.57627119],\n",
       "        ...,\n",
       "        [1474.57627119, 1474.57627119],\n",
       "        [1474.57627119, 1474.57627119],\n",
       "        [1474.57627119, 1474.57627119]],\n",
       "\n",
       "       [[1500.        , 1500.        ],\n",
       "        [1500.        , 1500.        ],\n",
       "        [1500.        , 1500.        ],\n",
       "        ...,\n",
       "        [1500.        , 1500.        ],\n",
       "        [1500.        , 1500.        ],\n",
       "        [1500.        , 1500.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.72 -87.72 ... -87.72 -87.72</div><input id='attrs-a45da5f9-62f7-4f7b-be92-875cfc02c926' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a45da5f9-62f7-4f7b-be92-875cfc02c926' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-405b4045-9835-4649-87bc-0e34320284c4' class='xr-var-data-in' type='checkbox'><label for='data-405b4045-9835-4649-87bc-0e34320284c4' 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>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Point longitude</dd></dl></div><div class='xr-var-data'><pre>array([[[-87.71662375, -87.71541464],\n",
       "        [-87.71662375, -87.71541463],\n",
       "        [-87.71662375, -87.71541461],\n",
       "        ...,\n",
       "        [-87.71662375, -87.7154133 ],\n",
       "        [-87.71662375, -87.71541329],\n",
       "        [-87.71662375, -87.71541328]],\n",
       "\n",
       "       [[-87.71662375, -87.71541464],\n",
       "        [-87.71662375, -87.71541463],\n",
       "        [-87.71662375, -87.71541461],\n",
       "        ...,\n",
       "        [-87.71662375, -87.7154133 ],\n",
       "        [-87.71662375, -87.71541329],\n",
       "        [-87.71662375, -87.71541328]],\n",
       "\n",
       "       [[-87.71662375, -87.71541464],\n",
       "        [-87.71662375, -87.71541463],\n",
       "        [-87.71662375, -87.71541461],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [-87.71662375, -87.7154133 ],\n",
       "        [-87.71662375, -87.71541329],\n",
       "        [-87.71662375, -87.71541328]],\n",
       "\n",
       "       [[-87.71662375, -87.71541464],\n",
       "        [-87.71662375, -87.71541463],\n",
       "        [-87.71662375, -87.71541461],\n",
       "        ...,\n",
       "        [-87.71662375, -87.7154133 ],\n",
       "        [-87.71662375, -87.71541329],\n",
       "        [-87.71662375, -87.71541328]],\n",
       "\n",
       "       [[-87.71662375, -87.71541464],\n",
       "        [-87.71662375, -87.71541463],\n",
       "        [-87.71662375, -87.71541461],\n",
       "        ...,\n",
       "        [-87.71662375, -87.7154133 ],\n",
       "        [-87.71662375, -87.71541329],\n",
       "        [-87.71662375, -87.71541328]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>41.94 41.94 41.95 ... 42.02 42.02</div><input id='attrs-6ee7f41a-6463-48e5-acfa-96711c25471f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ee7f41a-6463-48e5-acfa-96711c25471f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c6c8d76-581d-453b-9c19-19b766c3ee15' class='xr-var-data-in' type='checkbox'><label for='data-0c6c8d76-581d-453b-9c19-19b766c3ee15' 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>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Point latitude</dd></dl></div><div class='xr-var-data'><pre>array([[[41.94456011, 41.9445601 ],\n",
       "        [41.94528684, 41.94528683],\n",
       "        [41.94601356, 41.94601355],\n",
       "        ...,\n",
       "        [42.01505242, 42.01505242],\n",
       "        [42.01577915, 42.01577914],\n",
       "        [42.01650587, 42.01650587]],\n",
       "\n",
       "       [[41.94456011, 41.9445601 ],\n",
       "        [41.94528684, 41.94528683],\n",
       "        [41.94601356, 41.94601355],\n",
       "        ...,\n",
       "        [42.01505242, 42.01505242],\n",
       "        [42.01577915, 42.01577914],\n",
       "        [42.01650587, 42.01650587]],\n",
       "\n",
       "       [[41.94456011, 41.9445601 ],\n",
       "        [41.94528684, 41.94528683],\n",
       "        [41.94601356, 41.94601355],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [42.01505242, 42.01505242],\n",
       "        [42.01577915, 42.01577914],\n",
       "        [42.01650587, 42.01650587]],\n",
       "\n",
       "       [[41.94456011, 41.9445601 ],\n",
       "        [41.94528684, 41.94528683],\n",
       "        [41.94601356, 41.94601355],\n",
       "        ...,\n",
       "        [42.01505242, 42.01505242],\n",
       "        [42.01577915, 42.01577914],\n",
       "        [42.01650587, 42.01650587]],\n",
       "\n",
       "       [[41.94456011, 41.9445601 ],\n",
       "        [41.94528684, 41.94528683],\n",
       "        [41.94601356, 41.94601355],\n",
       "        ...,\n",
       "        [42.01505242, 42.01505242],\n",
       "        [42.01577915, 42.01577914],\n",
       "        [42.01650587, 42.01650587]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AZ</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>180.0 178.6 180.0 ... 0.0 1.432</div><input id='attrs-79a97a18-bfeb-48ca-a516-c4474a774248' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79a97a18-bfeb-48ca-a516-c4474a774248' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e935b55d-5843-4a47-89cf-a822ae78c6f6' class='xr-var-data-in' type='checkbox'><label for='data-e935b55d-5843-4a47-89cf-a822ae78c6f6' 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>units :</span></dt><dd>degrees from north</dd><dt><span>long_name :</span></dt><dd>Azimuth</dd><dt><span>standard_name :</span></dt><dd>Azimuth</dd></dl></div><div class='xr-var-data'><pre>array([[[[180.        , 178.56790382],\n",
       "         [180.        , 178.53838874],\n",
       "         [180.        , 178.50763175],\n",
       "         ...,\n",
       "         [  0.        ,   1.49236825],\n",
       "         [  0.        ,   1.46161126],\n",
       "         [  0.        ,   1.43209618]],\n",
       "\n",
       "        [[180.        , 178.56790382],\n",
       "         [180.        , 178.53838874],\n",
       "         [180.        , 178.50763175],\n",
       "         ...,\n",
       "         [  0.        ,   1.49236825],\n",
       "         [  0.        ,   1.46161126],\n",
       "         [  0.        ,   1.43209618]],\n",
       "\n",
       "        [[180.        , 178.56790382],\n",
       "         [180.        , 178.53838874],\n",
       "         [180.        , 178.50763175],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [  0.        ,   1.49236825],\n",
       "         [  0.        ,   1.46161126],\n",
       "         [  0.        ,   1.43209618]],\n",
       "\n",
       "        [[180.        , 178.56790382],\n",
       "         [180.        , 178.53838874],\n",
       "         [180.        , 178.50763175],\n",
       "         ...,\n",
       "         [  0.        ,   1.49236825],\n",
       "         [  0.        ,   1.46161126],\n",
       "         [  0.        ,   1.43209618]],\n",
       "\n",
       "        [[180.        , 178.56790382],\n",
       "         [180.        , 178.53838874],\n",
       "         [180.        , 178.50763175],\n",
       "         ...,\n",
       "         [  0.        ,   1.49236825],\n",
       "         [  0.        ,   1.46161126],\n",
       "         [  0.        ,   1.43209618]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EL</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.01349 -0.01349 ... 20.54 20.53</div><input id='attrs-418d2371-87e0-4dab-bc01-ed36cc8e5511' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-418d2371-87e0-4dab-bc01-ed36cc8e5511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4addb54-00c4-48e4-9ad8-325fa58e9676' class='xr-var-data-in' type='checkbox'><label for='data-f4addb54-00c4-48e4-9ad8-325fa58e9676' 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>units :</span></dt><dd>degrees from north</dd><dt><span>long_name :</span></dt><dd>Azimuth</dd><dt><span>standard_name :</span></dt><dd>Azimuth</dd></dl></div><div class='xr-var-data'><pre>array([[[[-1.34898304e-02, -1.34940454e-02],\n",
       "         [-1.32173086e-02, -1.32216104e-02],\n",
       "         [-1.29447868e-02, -1.29491791e-02],\n",
       "         ...,\n",
       "         [-1.29447868e-02, -1.29491791e-02],\n",
       "         [-1.32173086e-02, -1.32216104e-02],\n",
       "         [-1.34898304e-02, -1.34940454e-02]],\n",
       "\n",
       "        [[ 3.50666917e-01,  3.50548959e-01],\n",
       "         [ 3.58447617e-01,  3.58322394e-01],\n",
       "         [ 3.66544437e-01,  3.66411327e-01],\n",
       "         ...,\n",
       "         [ 3.66544437e-01,  3.66411327e-01],\n",
       "         [ 3.58447617e-01,  3.58322394e-01],\n",
       "         [ 3.50666917e-01,  3.50548959e-01]],\n",
       "\n",
       "        [[ 7.14793157e-01,  7.14561485e-01],\n",
       "         [ 7.30080155e-01,  7.29834042e-01],\n",
       "         [ 7.45999234e-01,  7.45737440e-01],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [ 2.06687806e+01,  2.06623545e+01],\n",
       "         [ 2.02771680e+01,  2.02711000e+01],\n",
       "         [ 1.98994991e+01,  1.98937636e+01]],\n",
       "\n",
       "        [[ 2.02207048e+01,  2.02148927e+01],\n",
       "         [ 2.06033954e+01,  2.05972472e+01],\n",
       "         [ 2.10001542e+01,  2.09936441e+01],\n",
       "         ...,\n",
       "         [ 2.10001542e+01,  2.09936441e+01],\n",
       "         [ 2.06033954e+01,  2.05972472e+01],\n",
       "         [ 2.02207048e+01,  2.02148927e+01]],\n",
       "\n",
       "        [[ 2.05405876e+01,  2.05346998e+01],\n",
       "         [ 2.09282302e+01,  2.09220029e+01],\n",
       "         [ 2.13300610e+01,  2.13234682e+01],\n",
       "         ...,\n",
       "         [ 2.13300610e+01,  2.13234682e+01],\n",
       "         [ 2.09282302e+01,  2.09220029e+01],\n",
       "         [ 2.05405876e+01,  2.05346998e+01]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.88 43.88 37.41 ... -71.66 -71.66</div><input id='attrs-ca27012b-d7b8-48b7-8fe9-2b446e63a901' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca27012b-d7b8-48b7-8fe9-2b446e63a901' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be77fc78-1b96-400e-b2cb-4ab4907283dc' class='xr-var-data-in' type='checkbox'><label for='data-be77fc78-1b96-400e-b2cb-4ab4907283dc' 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>u_wind</dd><dt><span>long_name :</span></dt><dd>meridional component of wind velocity</dd><dt><span>min_bca :</span></dt><dd>30.0</dd><dt><span>max_bca :</span></dt><dd>150.0</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 43.87728201,  43.87728201],\n",
       "         [ 37.41239655,  37.41239655],\n",
       "         [ 32.36233688,  32.36233688],\n",
       "         ...,\n",
       "         [-39.44980342, -39.44980342],\n",
       "         [-44.1517122 , -44.1517122 ],\n",
       "         [-49.1447935 , -49.1447935 ]],\n",
       "\n",
       "        [[ 17.51716218,  17.51716218],\n",
       "         [ 14.52841379,  14.52841379],\n",
       "         [ 11.99299591,  11.99299591],\n",
       "         ...,\n",
       "         [-70.99649306, -70.99649306],\n",
       "         [-67.01135391, -67.01135391],\n",
       "         [-59.51585637, -59.51585637]],\n",
       "\n",
       "        [[ 43.86009228,  43.86009228],\n",
       "         [ 37.39687273,  37.39687273],\n",
       "         [ 32.34911455,  32.34911455],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [-78.69696289, -78.69696289],\n",
       "         [-77.90164603, -77.90164603],\n",
       "         [-72.61574555, -72.61574555]],\n",
       "\n",
       "        [[ 37.34934175,  37.34934175],\n",
       "         [ 31.54853952,  31.54853952],\n",
       "         [ 27.14448481,  27.14448481],\n",
       "         ...,\n",
       "         [-23.58688071, -23.58688071],\n",
       "         [-26.43820927, -26.43820927],\n",
       "         [-28.84119558, -28.84119558]],\n",
       "\n",
       "        [[ 12.96666098,  12.96666098],\n",
       "         [ 10.3510841 ,  10.3510841 ],\n",
       "         [  8.00727565,   8.00727565],\n",
       "         ...,\n",
       "         [-78.05850674, -78.05850674],\n",
       "         [-77.21358956, -77.21358956],\n",
       "         [-71.66187727, -71.66187727]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.967 1.505e-15 ... -2.354e-20</div><input id='attrs-233ff26c-73ef-4fd0-9e81-d6940a5de4f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-233ff26c-73ef-4fd0-9e81-d6940a5de4f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5323a68-c6b9-409a-b3b7-044b7bd4702c' class='xr-var-data-in' type='checkbox'><label for='data-e5323a68-c6b9-409a-b3b7-044b7bd4702c' 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>v_wind</dd><dt><span>long_name :</span></dt><dd>zonal component of wind velocity</dd><dt><span>min_bca :</span></dt><dd>30.0</dd><dt><span>max_bca :</span></dt><dd>150.0</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 9.96660913e+00,  1.50500591e-15],\n",
       "         [ 8.87870475e+00,  3.40637111e-16],\n",
       "         [ 7.99940966e+00,  5.88050373e-17],\n",
       "         ...,\n",
       "         [ 5.76196616e+00, -5.47738314e-20],\n",
       "         [ 5.99877511e+00, -3.85409093e-20],\n",
       "         [ 6.17893811e+00, -1.01579104e-19]],\n",
       "\n",
       "        [[ 3.69857613e+00, -7.46245712e-15],\n",
       "         [ 3.41807787e+00, -5.45230888e-15],\n",
       "         [ 3.21941420e+00, -4.47845190e-15],\n",
       "         ...,\n",
       "         [ 5.09425101e+00, -7.99602895e-21],\n",
       "         [ 4.39698224e+00, -5.06171484e-21],\n",
       "         [ 3.24921724e+00,  3.58386206e-21]],\n",
       "\n",
       "        [[ 9.96730148e+00,  1.47646035e-15],\n",
       "         [ 8.87897676e+00,  3.20902888e-16],\n",
       "         [ 7.99971271e+00,  4.25683188e-17],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [ 8.22076872e+00, -2.72883996e-20],\n",
       "         [ 7.93492868e+00, -1.73349047e-20],\n",
       "         [ 7.26768416e+00, -2.34708206e-20]],\n",
       "\n",
       "        [[ 9.43481870e+00, -8.96053757e-15],\n",
       "         [ 8.54192579e+00, -7.08251334e-15],\n",
       "         [ 7.74166470e+00, -5.91457530e-15],\n",
       "         ...,\n",
       "         [ 3.45497650e+00, -3.77553197e-20],\n",
       "         [ 3.25704516e+00, -2.23049982e-20],\n",
       "         [ 2.97709684e+00, -4.54245023e-20]],\n",
       "\n",
       "        [[ 3.23677638e+00, -6.19348730e-15],\n",
       "         [ 3.06861967e+00, -4.53653240e-15],\n",
       "         [ 2.92223864e+00, -3.72129353e-15],\n",
       "         ...,\n",
       "         [ 8.20730195e+00, -2.73344776e-20],\n",
       "         [ 7.91695569e+00, -1.73624091e-20],\n",
       "         [ 7.25102697e+00, -2.35358853e-20]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-8b26a9fe-ef1f-4b44-9c28-7176a324e184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b26a9fe-ef1f-4b44-9c28-7176a324e184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e043582-1c42-463a-8924-8b7a31dc8315' class='xr-var-data-in' type='checkbox'><label for='data-6e043582-1c42-463a-8924-8b7a31dc8315' 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>w_wind</dd><dt><span>long_name :</span></dt><dd>vertical component of wind velocity</dd><dt><span>min_bca :</span></dt><dd>30.0</dd><dt><span>max_bca :</span></dt><dd>150.0</dd></dl></div><div class='xr-var-data'><pre>array([[[[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]],\n",
       "\n",
       "        [[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]],\n",
       "\n",
       "        [[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]],\n",
       "\n",
       "        [[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]],\n",
       "\n",
       "        [[nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         ...,\n",
       "         [nan, nan],\n",
       "         [nan, nan],\n",
       "         [nan, nan]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reflectivity</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-999.0 -999.0 ... -999.0 -999.0</div><input id='attrs-a3d79e35-9d06-438b-af8d-6efe9fffc04c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3d79e35-9d06-438b-af8d-6efe9fffc04c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40f38c46-cc42-41b8-9618-482eed6cdefa' class='xr-var-data-in' type='checkbox'><label for='data-40f38c46-cc42-41b8-9618-482eed6cdefa' 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([[[[-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         ...,\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.]],\n",
       "\n",
       "        [[-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         ...,\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.]],\n",
       "\n",
       "        [[-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.]],\n",
       "\n",
       "        [[-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         ...,\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.]],\n",
       "\n",
       "        [[-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         ...,\n",
       "         [-999., -999.],\n",
       "         [-999., -999.],\n",
       "         [-999., -999.]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b7732b8-e140-4f84-9fc8-7c52a3eafbc9' class='xr-section-summary-in' type='checkbox'  ><label for='section-7b7732b8-e140-4f84-9fc8-7c52a3eafbc9' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-38d13dcb-00c6-48bb-8936-bef324618b69' class='xr-index-data-in' type='checkbox'/><label for='index-38d13dcb-00c6-48bb-8936-bef324618b69' 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(CFTimeIndex([2023-06-29 07:18:26.129987],\n",
       "            dtype=&#x27;object&#x27;, length=1, calendar=&#x27;standard&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-044e5784-16c6-44dc-abc0-bc5640fdb95f' class='xr-index-data-in' type='checkbox'/><label for='index-044e5784-16c6-44dc-abc0-bc5640fdb95f' 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(Float64Index([               0.0, 25.423728813559322, 50.847457627118644,\n",
       "               76.27118644067797, 101.69491525423729, 127.11864406779661,\n",
       "              152.54237288135593, 177.96610169491527, 203.38983050847457,\n",
       "              228.81355932203388, 254.23728813559322, 279.66101694915255,\n",
       "              305.08474576271186, 330.50847457627117, 355.93220338983053,\n",
       "              381.35593220338984, 406.77966101694915, 432.20338983050846,\n",
       "              457.62711864406776,  483.0508474576271, 508.47457627118644,\n",
       "               533.8983050847457,  559.3220338983051,  584.7457627118644,\n",
       "               610.1694915254237,  635.5932203389831,  661.0169491525423,\n",
       "               686.4406779661017,  711.8644067796611,  737.2881355932203,\n",
       "               762.7118644067797,  788.1355932203389,  813.5593220338983,\n",
       "               838.9830508474577,  864.4067796610169,  889.8305084745763,\n",
       "               915.2542372881355,  940.6779661016949,  966.1016949152543,\n",
       "               991.5254237288135, 1016.9491525423729, 1042.3728813559321,\n",
       "              1067.7966101694915, 1093.2203389830509, 1118.6440677966102,\n",
       "              1144.0677966101696, 1169.4915254237287,  1194.915254237288,\n",
       "              1220.3389830508474, 1245.7627118644068, 1271.1864406779662,\n",
       "              1296.6101694915253, 1322.0338983050847,  1347.457627118644,\n",
       "              1372.8813559322034, 1398.3050847457628, 1423.7288135593221,\n",
       "              1449.1525423728813, 1474.5762711864406,             1500.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;z&#x27;))</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-9bc9d0cd-a55c-4737-b170-bd813de60482' class='xr-index-data-in' type='checkbox'/><label for='index-9bc9d0cd-a55c-4737-b170-bd813de60482' 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(Float64Index([            -4000.0,  -3919.191919191919, -3838.3838383838383,\n",
       "              -3757.5757575757575, -3676.7676767676767,  -3595.959595959596,\n",
       "               -3515.151515151515, -3434.3434343434346, -3353.5353535353534,\n",
       "               -3272.727272727273, -3191.9191919191917, -3111.1111111111113,\n",
       "              -3030.3030303030305, -2949.4949494949497,  -2868.686868686869,\n",
       "               -2787.878787878788,  -2707.070707070707, -2626.2626262626263,\n",
       "              -2545.4545454545455, -2464.6464646464647,  -2383.838383838384,\n",
       "               -2303.030303030303, -2222.2222222222226, -2141.4141414141413,\n",
       "               -2060.606060606061,   -1979.79797979798, -1898.9898989898993,\n",
       "              -1818.1818181818185, -1737.3737373737376, -1656.5656565656568,\n",
       "               -1575.757575757576, -1494.9494949494951, -1414.1414141414143,\n",
       "              -1333.3333333333335, -1252.5252525252527, -1171.7171717171718,\n",
       "               -1090.909090909091, -1010.1010101010102,  -929.2929292929293,\n",
       "               -848.4848484848485,  -767.6767676767677,  -686.8686868686873,\n",
       "               -606.0606060606065,  -525.2525252525256,  -444.4444444444448,\n",
       "              -363.63636363636397, -282.82828282828314,  -202.0202020202023,\n",
       "              -121.21212121212147,  -40.40404040404064,   40.40404040404019,\n",
       "               121.21212121212102,   202.0202020202014,   282.8282828282827,\n",
       "               363.63636363636306,  444.44444444444434,   525.2525252525247,\n",
       "                 606.060606060606,   686.8686868686864,   767.6767676767677,\n",
       "                 848.484848484848,   929.2929292929293,  1010.1010101010097,\n",
       "                1090.909090909091,  1171.7171717171714,  1252.5252525252517,\n",
       "                1333.333333333333,  1414.1414141414134,  1494.9494949494947,\n",
       "                1575.757575757575,  1656.5656565656564,  1737.3737373737367,\n",
       "                1818.181818181818,  1898.9898989898984,  1979.7979797979797,\n",
       "                 2060.60606060606,  2141.4141414141413,  2222.2222222222217,\n",
       "                2303.030303030303,  2383.8383838383834,  2464.6464646464647,\n",
       "                2545.454545454545,  2626.2626262626254,  2707.0707070707067,\n",
       "                2787.878787878787,  2868.6868686868684,  2949.4949494949487,\n",
       "                 3030.30303030303,  3111.1111111111104,  3191.9191919191917,\n",
       "                3272.727272727272,  3353.5353535353534,  3434.3434343434337,\n",
       "                3515.151515151515,  3595.9595959595954,  3676.7676767676767,\n",
       "                3757.575757575757,  3838.3838383838374,  3919.1919191919187,\n",
       "                           4000.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><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-75f1b819-2969-4b7d-8c43-ca7a2eb36b5e' class='xr-index-data-in' type='checkbox'/><label for='index-75f1b819-2969-4b7d-8c43-ca7a2eb36b5e' 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(Float64Index([0.0, 100.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9d3f3c0-6777-4e47-9f07-83bd3cc916f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9d3f3c0-6777-4e47-9f07-83bd3cc916f5' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>first_grid_name :</span></dt><dd>radial_velocity</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Point altitude</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (time: 1, z: 60, y: 100, x: 2, nradar: 1)\n",
       "Coordinates:\n",
       "  * time              (time) object 2023-06-29 07:18:26.129987\n",
       "  * z                 (z) float64 0.0 25.42 50.85 ... 1.475e+03 1.5e+03\n",
       "    lat               (y, x) float64 41.94 41.94 41.95 ... 42.02 42.02 42.02\n",
       "    lon               (y, x) float64 -87.72 -87.72 -87.72 ... -87.72 -87.72\n",
       "  * y                 (y) float64 -4e+03 -3.919e+03 ... 3.919e+03 4e+03\n",
       "  * x                 (x) float64 0.0 100.0\n",
       "Dimensions without coordinates: nradar\n",
       "Data variables: (12/22)\n",
       "    radial_velocity   (time, z, y, x) float64 nan nan nan nan ... nan nan nan\n",
       "    intensity         (time, z, y, x) float64 0.9999 nan 1.0 ... nan 1.006 nan\n",
       "    ROI               (time, z, y, x) float32 60.0 60.0 60.0 ... 60.0 60.0 60.0\n",
       "    projection        int64 1\n",
       "    radar_latitude    (nradar) float64 41.98\n",
       "    radar_longitude   (nradar) float64 -87.72\n",
       "    ...                ...\n",
       "    AZ                (time, z, y, x) float64 180.0 178.6 180.0 ... 0.0 1.432\n",
       "    EL                (time, z, y, x) float64 -0.01349 -0.01349 ... 20.54 20.53\n",
       "    u                 (time, z, y, x) float64 43.88 43.88 ... -71.66 -71.66\n",
       "    v                 (time, z, y, x) float64 9.967 1.505e-15 ... -2.354e-20\n",
       "    w                 (time, z, y, x) float64 nan nan nan nan ... nan nan nan\n",
       "    reflectivity      (time, z, y, x) float64 -999.0 -999.0 ... -999.0 -999.0\n",
       "Attributes:\n",
       "    first_grid_name:  radial_velocity\n",
       "    units:            m\n",
       "    long_name:        Point altitude"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grids[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "f27687ca-3dde-401b-a18b-48e071bf7389",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "Starting solver \n",
      "rmsVR = 8.0\n",
      "Total points: 7417\n",
      "The max of w_init is 0.0\n",
      "Total number of model points: 0\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "      0|7417.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 77.43228062959608\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     10|1501.4297|1605.9903|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 67.71547035119507\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     20|  77.8251|2343.4361|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 83.16957497820778\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     30| 145.4362|1726.3472|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 43.27752650375165\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     40| 157.3472|1700.2379|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 43.27689757781778\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     50| 157.3468|1700.2380|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 27.327376248858194\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     60| 153.5992|1567.1861|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 27.673867305325594\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     70| 153.5600|1566.8943|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 26.952554732894637\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     80| 153.5992|1566.8955|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "The gradient of the cost functions is 27.673867205534226\n",
      "Nfeval | Jvel    | Jmass   | Jsmooth |   Jbg   | Jvort   | Jmodel  | Jpoint  | Max w  \n",
      "     90| 153.5600|1566.8943|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000|   0.0000\n",
      "Done! Time = 0.9\n"
     ]
    }
   ],
   "source": [
    "grid_pydda = pydda.initialization.make_constant_wind_field(grid_pydda, (0., 0., 0.))\n",
    "grid_pydda['reflectivity'] = -999 * xr.ones_like(grid_pydda['intensity'])\n",
    "grids, _ = pydda.retrieval.get_dd_wind_field([grid_pydda], Co=1,  wind_tol=0.10, vel_name='radial_velocity', low_pass_filter=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "ad883a5a-17fa-4f35-9b4a-5daf2719163f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'PyDDA retreived winds @0.0 km'}, xlabel='X [km]', ylabel='Y [km]'>"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pydda.vis.plot_horiz_xsection_quiver(grids, colorbar_flag=False, level=0, background_field='intensity',cmap='Blues', quiverkey_len=1, vmin=0, vmax=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "62c44179-a1bc-49e6-9d32-4decf3cc5f75",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[335], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m u \u001b[38;5;241m=\u001b[39m grids[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues[\u001b[38;5;241m0\u001b[39m, :, :]\n\u001b[1;32m      4\u001b[0m v \u001b[38;5;241m=\u001b[39m grids[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mv\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues[\u001b[38;5;241m0\u001b[39m, :, :]\n\u001b[0;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquiver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/envs/pydda_env/lib/python3.10/site-packages/matplotlib/pyplot.py:2843\u001b[0m, in \u001b[0;36mquiver\u001b[0;34m(data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2841\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mquiver)\n\u001b[1;32m   2842\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mquiver\u001b[39m(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2843\u001b[0m     __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquiver\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2844\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2845\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2846\u001b[0m     sci(__ret)\n\u001b[1;32m   2847\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n",
      "File \u001b[0;32m~/mambaforge/envs/pydda_env/lib/python3.10/site-packages/matplotlib/__init__.py:1446\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1443\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1444\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1445\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1446\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1448\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1449\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1450\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m~/mambaforge/envs/pydda_env/lib/python3.10/site-packages/matplotlib/axes/_axes.py:5161\u001b[0m, in \u001b[0;36mAxes.quiver\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   5159\u001b[0m \u001b[38;5;66;03m# Make sure units are handled for x and y values\u001b[39;00m\n\u001b[1;32m   5160\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_quiver_units(args, kwargs)\n\u001b[0;32m-> 5161\u001b[0m q \u001b[38;5;241m=\u001b[39m \u001b[43mmquiver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQuiver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5162\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_collection(q, autolim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m   5163\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request_autoscale_view()\n",
      "File \u001b[0;32m~/mambaforge/envs/pydda_env/lib/python3.10/site-packages/matplotlib/quiver.py:478\u001b[0m, in \u001b[0;36mQuiver.__init__\u001b[0;34m(self, ax, scale, headwidth, headlength, headaxislength, minshaft, minlength, units, scale_units, angles, width, color, pivot, *args, **kwargs)\u001b[0m\n\u001b[1;32m    471\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    472\u001b[0m \u001b[38;5;124;03mThe constructor takes one required argument, an Axes\u001b[39;00m\n\u001b[1;32m    473\u001b[0m \u001b[38;5;124;03minstance, followed by the args and kwargs described\u001b[39;00m\n\u001b[1;32m    474\u001b[0m \u001b[38;5;124;03mby the following pyplot interface documentation:\u001b[39;00m\n\u001b[1;32m    475\u001b[0m \u001b[38;5;124;03m%s\u001b[39;00m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axes \u001b[38;5;241m=\u001b[39m ax  \u001b[38;5;66;03m# The attr actually set by the Artist.axes property.\u001b[39;00m\n\u001b[0;32m--> 478\u001b[0m X, Y, U, V, C \u001b[38;5;241m=\u001b[39m \u001b[43m_parse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaller_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mquiver\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    479\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX \u001b[38;5;241m=\u001b[39m X\n\u001b[1;32m    480\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY \u001b[38;5;241m=\u001b[39m Y\n",
      "File \u001b[0;32m~/mambaforge/envs/pydda_env/lib/python3.10/site-packages/matplotlib/quiver.py:423\u001b[0m, in \u001b[0;36m_parse_args\u001b[0;34m(caller_name, *args)\u001b[0m\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _api\u001b[38;5;241m.\u001b[39mnargs_error(caller_name, takes\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom 2 to 5\u001b[39m\u001b[38;5;124m\"\u001b[39m, given\u001b[38;5;241m=\u001b[39mnargs)\n\u001b[0;32m--> 423\u001b[0m nr, nc \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m, U\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;28;01mif\u001b[39;00m U\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m U\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    425\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m X \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    426\u001b[0m     X \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mravel()\n",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = grids[0]['point_x'].values[0, :, :]\n",
    "y = grids[0]['point_y'].values[0, :, :]\n",
    "u = grids[0]['u'].values[0, :, :]\n",
    "v = grids[0]['v'].values[0, :, :]\n",
    "plt.quiver(x, y, u, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "df27d871-6aa1-47eb-bad9-a5409cbc1237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1bf699f30>"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4XXF24pXni7F7sDqfd/pJH1v5z4D3fI00/RSufmL6iemn0aefCv0n7MkPr7G+vj6bNGlSeEMxJf6kBrVT7SXTtRBnAFMLaV6+7b6GtzyBY668kcofeAcw3syP92dADVjjLdX2/UOf5ABGHcsApjYDGM+A41TXrXaZBPXXtOe4JFHQEgAApAKDGgAAkAoMagAAQCqQqRmn4uwZozIv9VCLUHAtNPqeNIqnBIJ3f5d6lT+IE1p25zjc5Q9i5Gfk+xxj/xl3JqlsnxryM+5+6vOpdlmDpM/nyc/IjmOtTAIAAEA9MKgBAACpwPRTlcQpTTAWqeXlaS2LEGf5drwppGSXb1e6/8wpz1e+xLcG5Q/SUmk7zlRTpdNFpzpW7nHjOLZeU03qGnLqpQZTTYWi77pjbarJTMwieaaaVD+WdAMAAIyOQQ0AAEgFBjUAACAVyNSkTJwsT7XLLqSlplMcSS/f1suSKythcPJenLkQmSkp7VeL8gfuJd3ekggx6jd58zPuvIszPzNcYQbm1Net7Ngkz3WqtnrlZ7ylCRopP1MU+Z5KSxuojvreRj+Xuq8k8aQGAACkAoMaAACQCgxqAABAKpCpwR/VKCURGkkj7UmjeMsfqMyGvpfK9sKpRfkDeax7L5fK8zPDkTPHIu5l2Lnfjt5HJ9ncityTpsLMi9yTpsHzM7J0gjMrknR+phjjXirda+ZU/Tz7zXjvI9iXhn1qAAAARsegBgAApALTT3VWiHxLsKu93Pp0NNK9VMo9hRRj3F+v5dvyXrzLq+VS5cpKDDR6+QPvVJNcvh6r0nblU01xpouSnGo6eb7Ry2LUa6pJL8GOMTXknGpSZRIapazBqfq5ShuIe3FNNcnjxI0liCc1AAAgFRjUAACAVGBQAwAAUoFMzThQqPYk5imuUZ4WaqSSCHGMxeXb3mO9GR1PRqXRyx948zN6GbU3P+PLhSSdn/HmduJkXsrPl+b8jLdMQr3yM3KrhAqzMqfqV56Xkf+seJaCUyYBAABgdAxqAABAKjCoAQAAqUCmZpxKw14zcXj3pPFu6z8W96Txlj/wZn4875V73xZnVsZbrsD/+r3lFJz5EXF/3oxOnPxMPkauqNJyB/I4bybEmWNxn8+ZHfHmr5LOuxS9+9mEl010r5lTn89xrPtcYVM18aQGAACkAoMaAACQCgxqAABAKpCpGcOKckeXyql9ZBqZf3+TyvdFGIt70nhrOineukme3Eqcmk7ebI+/vpRvbxjVz5vb0TmgpDMwjZGfUcc2Un5Gve9J7xeT9N419crPyH1jPMdWmKlhnxoAAAAHBjUAACAVmH5KQNLTQI1ELf32lEQwC6ezki6JEEec5dv6fNVfvu0tO6B4l297yh947yVO+QPvNJj3c4wzheTtN+ycbnOXbGjgqSbVT009qPv1TwOpKR/fdFasMgnuqbDw2KSnmtRUTaLLsk9xM65pJPfy7fJ+TD8BAACMikENAABIBQY1AAAgFcjU1FAh8mVv4pQwUHmX8cS7pb1XrHIF3vICMZZge5dvJ13+wLM0O075A+8yctkW4zW4czHucg++nMlYy8+YhZmKRs/PeEsTxMnPeF9bXcoanOq6ziXWYb8KSyJU+Z8ontQAAIBUYFADAABSgUENAABIBTI140CcjE49qD1pvHue+K9R+fm8e9LovItz7xrnnjRyG36Z20mu/MHJttEzJWpvGHVN7x4ycfJI3vIM3vzMcNF3z978jH/vmsbIz5iFr81dwsBZXsCbn1HZG+9+MXGyNzUpa+B8ryrda+Z0+gW5HU8fC+83KrBPDQAAwKgY1AAAgFRg+mmMqEVF7iRLIpw81nNcso8i9TRVsuUKFPWoXPeLM13inX6pbvmDU99f6etIutJ2Xk5nqSkV0U9cV/WLM9WU/DLvxp1qUv2SnmpKugp2nGmqNJQ1OJ3r6hc8+nGNsKMIT2oAAEAqMKgBAACpwKAGAACkApkaNBzv8m01h6800vJtWcLAmW1plPIHp2orP9a7jNx7bzp7VPnPir6/yo+VGZ0Gz894l/XrjErZ5+3MWPivGR7ruY/TOda7zLteZQ1kCYM4y7LV33sq7CjzQmVnEvem2srPnxms7rMUntQAAIBUYFADAABSgUENAABIBTI1Y5jaQ6aR6axDsudr9D1pZD9n6QTv+apd/uBUbeXX9d6HavOWTvD2i7PHDfkZnVspPzbp/EzRva9OeKw3KzOuyhqcRj99z5k/9q2ZmWWy4sDyt6S5uv9u8aQGAACkAoMaAACQCkw/naakyxUkXUFbljpI+Bqekggn76XS87N8O+nl20mWPzjVvZRPBbmni2IsN2eqKd5Uk14O7Vw6H0w/OZfDO8sVxJmmilNB23t/SZc18C7flsuyVT9nyQbVLyNr2pT2y4hq27JtuOz7E+HvU5J4UgMAAFKBQQ0AAEgFBjUAACAVyNRUSaF8T+kGI7M3YsK12q/Cm3fRxya7fNudWfGez5ljkVvzq2O995Jw+YPhYvjXhCcDIl+/aBuWORGV2Ynxfjo/23rlZ7xZmVrkZwoiY6HuufxYdZy3NEGcY2WMxX0+caw3PyP+ckx8WXaCZQ1OReZnHKKm8IZl9LGltF+x0gs68aQGAACkAoMaAACQCgxqAABAKlQlU/PMM8/YTTfdZD/4wQ9sYGDAXvnKV9o3v/lNW7hwoZmZRVFkq1atsk2bNtnhw4ftoosusltuucXmzZtXjdsZc5LeC6deyvMZ3pIISe81E6+fN7fjzbFUnu/x7A1zqmvEKX/gzfKUX9e7X4w37xMnZ+PNwMi2OHvIxMjPyNIEznupdn5GHestr+DNsXj3rhmbZQ28JQxUm++1Zbz73uR9+81kh0vbskNhn6YT4embj5d+XxgcY/vUHD582N7whjdYS0uL/eAHP7Bf/epX9uUvf9le9rKXjfRZt26drV+/3jZu3Gg7d+607u5uu+yyy+zo0aNJ3w4AABgnEn9Ss3btWpsxY4bdfvvtI22zZ88e+e8oimzDhg22YsUKu+KKK8zM7I477rCuri7bvHmzLVmyJOlbAgAA40DiT2ruueceu/DCC+0973mPnXnmmbZgwQK77bbbRv5879691tvba4sXLx5py+VytmjRItu+fbs85+DgoB05cqTkCwAA4KUSf1Lz1FNP2a233mrLli2zz3zmM7Zjxw77xCc+Yblczj7ykY9Yb2+vmZl1dXWVHNfV1WX79u2T51yzZo2tWrUq6VsdU9QeMjW5bh3qPOl+MWre1GlPGqWRajrJPE6MLE+wT42z3pL6GfDWZYqTn/HXfhLXqFN+RvaLsReM9xqeHEyc/Iz3NdSiVpO3BlOsvWa8uR1vLkbdizpWkNkbddmyj7fYqvapEb8D7WXHidxNkhJ/UlMsFu11r3udrV692hYsWGBLliyxv/3bv7Vbb721pF+m7MVHURS0vWD58uXW19c38nXgwIGkbxsAAIxxiQ9qzjrrLHv1q19d0nbeeefZ/v37zcysu7vbzGzkic0LDh06FDy9eUEul7NJkyaVfAEAALxU4tNPb3jDG+w3v/lNSdvjjz9us2bNMjOzOXPmWHd3t23dutUWLFhgZmZDQ0O2bds2W7t2bdK301BUaYKkz6emi+pVEqF8CXf9lmXHmOKSU1fJLt+uV/mDSqeVTtVWfqx3ab53GmhY9GOqyT/t4/2ZUscWHffiPU7Onsht/isva5D0FJJ7qilGP+9ScjldpMhyCuJ8akl3PuzXcqy0X3N/2Cf3fNg2obf0ZPnhvO0Vt5aUxAc1n/zkJ+3iiy+21atX23vf+17bsWOHbdq0yTZt2mRmJ6edli5daqtXr7aenh7r6emx1atXW0dHh1155ZVJ3w4AABgnEh/UvP71r7e7777bli9fbp///Odtzpw5tmHDBvvgBz840ufGG2+0gYEBu+aaa0Y239uyZYt1dnYmfTsAAGCcyERRnZbVxHDkyBGbPHmyHX58rk3qrO7uhOW8u/2qKt3e6Sd1DT1dVP3ppzirn4aDqs2+6Qi1S6yaypH91JSK+Vb5qH7DUTjuV9NPsp+8Z3UN5/3JY9V1R58aMtPVt73TT57X4T2O6Semn5h+qsL0k5hWUufLDoWfUXWnn07YL374T9bX11eVfGxVyiSkRVrKFSRND35G/0XTW9/7fkHd/RJevq14l1Z7l28nXf5AD0wq33Jfns+xxN6dz3EOYLzZHgYwp5HJci6H9hzb8GUN3Eu/xfliLMuWAxiZdxHZlhjXkEu61QlVkxgZFFtKv893iNMXwrZCrvRkhaHqDjsoaAkAAFKBQQ0AAEgFBjUAACAVyNQkQIWCvRopt+MNBfvPV1nOxpsdkfmUeu1JI7Mi9Sl/EKskgvN9GZYZkGzZ9yKLIgPLvvyMCgWnJT/jztQ4ywnEyc+ocgqea9SkrIH467ImYV/FGWSOdY0Yf//KPI6456wIBWeHwn6550q/bzkeHtd5YDho69jxVMn3+eKQPSpuLSk8qQEAAKnAoAYAAKQC008NKOmK3EmXRNBLs0c/rtGXb8sl2N6pIeeeNHHKH+hl3skuG1fTQ94puPJ9g/zn8k01eZele6eLkp5qUvesp3eSnWryThd5l4NXurdMvfaVcVeyTnpZdoxj5dSQknBJhKhJNYq/L9rCfkOTS+8lK5ZvZ9T0YPeU0u8Lg2bPhf2SwpMaAACQCgxqAABAKjCoAQAAqUCmZoyIU+cp3nUr51vS3UDLt53LqOtV/kCWRIhRv8mb41DLplXdrfL3ynsub35GL+l21n6KcWy98jNxMjBxajrJWIgjt6OWanuzKPFqNcXInQhRwXc+dw0m75LurPPvbnVZUQIx8tYabA37NQ2EF5lwsPT7E1OCLnZsWvj3UcuPHi/5vhCFy76TxJMaAACQCgxqAABAKjCoAQAAqUCmpkqSzrYkLU5JBLUnjd67JnPafU55zRrsSSPzM848TpzyBzoDU/k+Leo9VdfwZm/05+bYp8ZZwqBe+RnVpjIbcfIzag8Zb+mEOPkZbykC+ftXaWmDpMsayMxKDfaaUeKUNVCHOveVkf3EowhV1qDYLE4o2nK94bEDc4eCtufOKv3+5Ttbgz5TvrMrvI/wLqqKJzUAACAVGNQAAIBUYPrp/2ukatlJq/aybG8/NfUi+zmXb3unfLzLt71TQ97l297yB96SCLJNTPG4p8yc01TeJd3l/XQZAt+S7kaaatJTQ75yBe6fiypX0D5Vv0RLGzinkNzVstXfKXLaxvd3lFyqLeoVZPJqfkfcYF4tyxYXFuUE5OsVx2bEPUct4ZtQ6BCvY1hU5J4YLqeOXjsQtLU93Bm0veK+0n7Zn20Pz3/2nKDtD28unbcqDJ0wu+Pfgn5J4UkNAABIBQY1AAAgFRjUAACAVCBTc5oKao2ik8rtFMQEdpySCOp8Xt5XVmnOxr1c2Ll8W14zzvJtZ/kDb94lTvkDb25HXSMvsze+1xFnGXZ5P/Iz/vxMnCXdlZY1ONW9VFzaIE5Zg1osy1aSXqqt+qml1SLvIlerq7/2RJan46D4vX3t0aBt8Hi4DHvmOnHdHWFeJvBnrwnbnjseNJ3xaGlbPn9i9HPHwJMaAACQCgxqAABAKjCoAQAAqUCmZhxIuiSC7ufbMya8j8bZk6YW5Q/0XjPhr6H3WJWf8WaIZN5DvF61F47OtzQ5+lQ/P+PNtuiyBr5si/y5de81k2xJhIrLGpjFKm3gydS4yxo498tRx2ZkOQVB7fki8i5qf5eo1dmvJeyXHRCfo8rZCO3/Ex574lVhJmVgYj48dk+410zPzWFWRt6JyMs0HSndp2aoLfx76+jrpgZtz80rfQ2FE01mO9VFk8GTGgAAkAoMagAAQCow/ZSARqrIXa+SCL4yCZUv35ZTPjVYvp10+YM4lbblFIqauvGWNXCWXfBMNal+Y3GqSb2f3iXdtaig7S5r4JzikpWw3Uuzy7/3LtWufKpJHuucfcqoVdRqqq1JzTdWvvS70BFeRE1J2eSwhMHw/MGgrf2hCUHbzP99OLzuw2HFbKWpM5yminb/JmjLTC7t1yre0KaBtrBtqL3k+/xw0Z5y3VlleFIDAABSgUENAABIBQY1AAAgFcZlpkaVK2h0cUoiqOXb6h1Qy7dlDsY5iV3eL9bybWe5glos3x6OKl+CrZd0V35so+RnzMLPuxb5GZWLcWdqEl5u7V3SLbMySednnMuyEy1t4M3sKM5+mRjZFvmXXkvYlB0UP1MTCmFHUa5AXXfC78TfF68JywkM94c3c866/qCt8MhDYVt4WWt+1TlBW/43T4Qds+Hrzf7JlLBfW670mpPagy4npoZlGI7OKFvSLd7fJPGkBgAApAKDGgAAkAoMagAAQCqMy0yNV0FNTDup3I7KwMisTIx9b7zHxtmTRvcbPS8TZ08ab/mDOHmXIeex3n16vPveuDM/shRF5fvyePfC8baV52BqkZ9xZ2qc+RnvHjLefWrkVive7I3q591XJkaZBMmRZXH/dSmuKfeL8f4vd0a8AfJYZ4mJ1vBmMifCE7Y9G7b1zw73mul/ZZh4mbgj3Gtm9pfDEgYyK3P2nKAt/+Te8NgnwjYlOhHuhaPashNL77l5OHytE/rCnE3rkYkl3+fzYZmHJPGkBgAApAKDGgAAkAoMagAAQCqQqRnDvFPY3n7ePWnc/comxePsSdPoNZ2Gi5VnefQeMr59edz71CS8J43MxjhqPyWdn5HZFvUz5czPxNmnRu7fJI+NsU9NkrWaTqPNtSWWysqoc6l4jjxWbpwVahLHDor3riU8n8rttD8b/g4Mnh3mQIa7wpuZ8KCoy3Svqsv0QHhhJ5WfUTLN4b43UUG8gU3hm5BpERv4NJe9L6JP1Br+PVhoLft3QOyLkySe1AAAgFRgUAMAAFIh9dNPY7EkghKnJILiLYkgr+FYvm0WLktOevl2I5U/UNfwLoVOw1ST6leLqSZ9Pu+ybN+UVCOVNfBOP3mXakfOsgOqPEGUjUbto+afZD/1GsT/csv7aAnfvGK7uO5QeGy2LR+0FV4dLmfu2B1OK82497nwWG8Jg5nTg7b8/qdFz1C2NSxFUBwaCtqifLjkWomGwn7RYPgeZMqWcGcGw2tmj7cFbe1HS6fu8oXw3EniSQ0AAEgFBjUAACAVGNQAAIBUSH2mJmmqrEEcqqyB9xpJl0TwHutZvq2OrcXy7VqUP5A5G5l3ceZxnHmXRs7PqLZ65WfUcmtvzkbmZxqqrEHYJPMzcfopol9GvTbPNZvEyfLi9avTO4/t+L34/X7tsaBt6HiYT5m7Nsx8FHdXXsKgeOCZoM2bn1FUfkaRy7edMk3h++dSFOGw8rYY5Yc8eFIDAABSgUENAABIBQY1AAAgFVKVqYmzJ00hxjyfuq7aVyZOHqd+JREq25PGTJVJqHxPmiGZTxFbcjvLH3jzLqr8gbqGuySCMyvT6PmZ4cLo1/DmZ+Ru+AnnZxIva5D0XjNKjDyO96+aTF68jmbxQobF//+2lvbLDPtKE2QHxGch+qkSC+294c/diVeGJQwGekQJgwcmBm2z1oZZGfV3aPM5c4O2/BNPBW2FfWFWRu0Xk+3oCK/b3x+0qWyLOyuTEZ+Z+CFV54vEJTLlm5vlwz1+IpH3yZwozShFkS8TVCme1AAAgFRgUAMAAFJhTE8/FS1KTRmEl6pXSYQkl2+bhUu4vcu31bSSt/yBngYS5/NW33aXSYixLNu7zLsGU01DYlpJTRmpY8v7yemnGOUKkq6gXYuyBnFKGMQ61kuVMVDTWVn1l0hZP3W/4n0qdISNakrKJoXTNsPzw+XW7aoy9v9WJQx+LW4wpKpbq6kmeWxW/EyJfsWBcMpMibMs2710WvVzTV2JZd9yLrnsNai5rQTxpAYAAKQCgxoAAJAKDGoAAEAqjOlMTRrUoiRCPZZvq/Op4/SybF+OR5U/8L4G95JutSxbLv12Lst2llgYUlmZBs7PqLak8zOqn8zKqBIG5fmPU/SLVdZALtUWbeJeVLYlEsutVbYlI8oESCqPI9cvi2XYQ+LznlCajVBLutV717Ev/L3NX3A8aBvuD7Mt56wLlz0XHnkobAsvK0sY5J/cG7TJJdi5tqCtOOjLxSjZFpHzU8uh4yzpjkMt/S77nYyK4ZLujFoeXpbPKUbh+5skntQAAIBUYFADAABSgUENAABIhapnatasWWOf+cxn7IYbbrANGzaYmVkURbZq1SrbtGmTHT582C666CK75ZZbbN68edW+ndOi8i5xSiJ4czFxSiI0yp40ZmFeRpU6UBkYlbORe8PEKX/gzLbIa8j8jDd7U/1SBzqP48vFVJqfMQszNDXJz8iSCM6yBjFKDuh+vmyLum5GZW8Ub4kFdd0mVYrAd75iy+h7y7Q9G77H/bPD/IQqYTBxR7jXzOwvhyUM4mRl8k/tE0f7ePMzKhcTp19N8jNe5TkbsZdNJP4BymSL5Z2SvKtAVZ/U7Ny50zZt2mQXXHBBSfu6dets/fr1tnHjRtu5c6d1d3fbZZddZkePHq3m7QAAgBSr2qDm2LFj9sEPftBuu+02e/nLXz7SHkWRbdiwwVasWGFXXHGFzZ8/3+644w7r7++3zZs3y3MNDg7akSNHSr4AAABeqmrTT9dee61dfvnl9ta3vtW+8IUvjLTv3bvXent7bfHixSNtuVzOFi1aZNu3b7clS5YE51qzZo2tWrUqsXuLU5E7aXFKIjTy8u2T/UrP5y1DkHT5g6QrbburbzunkIYKYolrnaaa8nKZt3Nasqxf0hW0vVNNahpILelWItnPuRRaLi8XZ1PLy9XU0JDo1ypOqCpoO6aLzHQVbTUj1f5s+HMxeE7plMxwl5hW2hNOK834weGgrfDwA+FFndRUkyKXR+crLRFwGtcQU0jupdox7qXqnPdRXhUhGotlEu68807bvXu3rVmzJviz3t5eMzPr6uoqae/q6hr5s3LLly+3vr6+ka8DBw4kf9MAAGBMS/xJzYEDB+yGG26wLVu2WFtbuGHRCzKZ0v8diKIoaHtBLpezXC6X6H0CAIB0SfxJza5du+zQoUO2cOFCa25utubmZtu2bZt99atftebm5pEnNOVPZQ4dOhQ8vQEAAPBK/EnNW97yFnvkkUdK2v76r//azj33XLvpppts7ty51t3dbVu3brUFCxaYmdnQ0JBt27bN1q5dm/TtNJQ4JRGSnkWt9vLtk8eWnk8u6Y5R/iDW8u0YWRnVNihyO420VFvle/RS7cryM2ZhNqYW+ZlYS7Al3/m8+Rk3dX/ifzlVHicj/nKQ71+7yNmcCC+S6QyXYefPGwzaOsryMjPufS7o4y5hMHN6eM39T4ueoWxra9Cmlky7l0fHyKx4r1GLexmvEh/UdHZ22vz580vaJkyYYFOmTBlpX7p0qa1evdp6enqsp6fHVq9ebR0dHXbllVcmfTsAAGCcqEtByxtvvNEGBgbsmmuuGdl8b8uWLdbZ2VmP2wEAAClQk0HNfffdV/J9JpOxlStX2sqVK2txeQAAMA7U5UlNLXn3pPHmXbzHJl0SQR4bY++aOHvSyMyLLFlQ2k+dvyblD5wlDLxZmWGVgXHmZ7y5mHrlZ2SpiApLGyRe1sCZHZFUPxWfGRaZFbWcIu87n8q7yHtpDn+Zs2KfmsIEsefJcPiZqb9+OvaJ39vXHgvaho6HGZWzN4QZleKu0jIG3hIGxQPPBG3e/IziLTlAPmV8oKAlAABIBQY1AAAgFRjUAACAVEh9piaOoki8eGs1KQ1V58m5J82wqdpMKhszes7Gn4FRORbnsXIfGJGVEfWWVFZGtQ2pPWlqkJ/xZ2XUsb5+leZnzMKfvcTzM86MjiT3mnEeqsoDiX4ZtT+Oqukk7lkdWxR1mbL94c9A27Ph+zcwJ9xrZqBH1Gb6r4lB26wvbw/aVBql+Zy5Jd/nn3gq6FPYF2Zlonx4b9mOjvCa/f1Bm7tmEsYtntQAAIBUYFADAABSYUxPPxWiopyqaQRJl0SIs3xbTaF4l2/rJd1iGsQxJRWr/IFaWi2mY/SxvmklWerAWSahXlNNwwVVesI3hVQoVFb+wMy3XNtb1sDb5i1XIMsk5EU5ADU1NCymwlrEb6RY+m3O6aKoNeynXkbbs+Jn+ZyBoC3fHd5feQkDM7OZ9x4O2goPPxBeWMg0t4TXFdNNlSoOnHD1Y6oJo+FJDQAASAUGNQAAIBUY1AAAgFQY05maSsUpiaDEKYnQ6Mu3dRkDZw6mrC3pUgeqTeViZFbGWSZBLd9u9PxMXmRlVC5G9kuwtEGsZdkx8jOSWDMtl2qrUgcFsQRbvv7w2GJHmAHJDoSfY2ZiuMy5cO5g0NYusjIzfqCyMg+FbeHtWfPM6UGbKlkgl2Hn2kq+Lw6GuZhMVrxP4j6yLeHvmSp/wJJujIYnNQAAIBUY1AAAgFRgUAMAAFJhXGZqFG9JBPf+M+LYRtqTRuVRdPbGt09No2RqVJu3TIIsfyDeE29+ZkhmYJxZGZWBkaUOVKZG5KAcZQ3Mki1tIPefUbkYZ6mDSGRbvNkblYtR+89khsTrn6D6qeuGTR37wv1diq89GrQNHssFbWdvCC9S3BWWMJBZmbPnhMceeCZoyx/4vTjaR2Vogj4iFxOnH/kZjIYnNQAAIBUY1AAAgFRI/fRT0su3a3GNWizfdpcnkBWzxdSNo59egh0+nj8h2rxLtdUUkppqktNKqiSEmMpJeqpJLa1Wx8p+zvIH6uenqKZk1JSUt1/5NJJ3WbaafpIlr8X53OWyRT81PeYsdZATlbFPzBaVsc/JB20Tf9EZtM2ssDK2mS5XoKaa1BRP1atjZ8T/N4u17+7zO8+H8YsnNQAAIBUY1AAAgFRgUAMAAFIhVZkab7ZFLd/2nq9eJRGSXr7tzcokubx6WORdVIkAbwkD1abPp5ZgizxOjPyMt9SBysWoY9Uyam/5A+9SbbksW8VRvEuzy9ucUTNZrkBsry+XZYv/LcsMikaxfNuawhvMPSt+Rs8eCNoKXeH5OmQJg+eDtuJDu8J7ETLNYbZM5WfiKA6MvizbLMZSamfexX1+8jMYBU9qAABAKjCoAQAAqcCgBgAApMKYztQULUpsjxhV1iDOsXFKIsTZk0aVMFB7yOh+1S1ZIPepicSeNGJfGf8+NSoXU/38jCpNoEoYyOyN3H9G/AzI0gSinzif2h+mmPeWJ1DBFdHPcz5xv3JbGdGYGRb30R5mMSLRpnI2TRPDfWWi8wbDS+yaGLRN/+HzQVvxIedeM7NmBG35fQfCe8mH95fNtYXXcJQrOJVsS/i7ofazqXifGqDGeFIDAABSgUENAABIhTE9/eThXb7tP59vWqley7eLqsSC6qe28K+w/MHJtvDxdHm5AzVdNCymVNRrbfSpJvU6vEuwZb9YFbR91az1suywyd3m6JMRy6htWExJqZkscayakmr7vZgqeY2qjN0atM3eEJY1iB5wTiupythPHwza8vvDEgZe3qkmqmNjvOJJDQAASAUGNQAAIBUY1AAAgFRIfaZGUUuwvSURkl6+rTM1jb18+4S4xmAxXJpdfqx3WXZa8jPqWJWBkUv482r5tmoLmvxlDcQ1VG7FhlTZAZFvKesXNYs+x8VrEOeKxO9Ze6/4HF8VljA40R5eo+OBzqCt54thVkZFhZp7zg7a8k/sDdqKB8KsjMqsZDs6wn79/UFbrGXUGbWsX9ajSLYfUGc8qQEAAKnAoAYAAKQCgxoAAJAKYzpTU0x8F5ry88conRBj/xnZZiLbodpkLkbkVkR5AlnGQGVlRJalfE+ak8c2/9HvT9UmszIx8jODebU/jnifxPnyzvyMKmGgyhXITI3caybh/Iz3R1ldQ/2vT0HsLVP849+bmUUTRCakP3zfmyaF+8UU5oX5lLY9E4K2mfceDo99eFd4XSHbGu5dk//tk65jvYoDvr1mYu0N4827JN0PqDOe1AAAgFRgUAMAAFJhTE8/ecRZvq14SyIkvXxbtelyBb4SBmrp9wn3lFR4bN7RzzvVdKIQ3kcjTTV5yx+oaSV3BW0xvSNLHcjzhU3qGqaqaovl1dkTYipMTSOV/3yL19/2O1EV+oJjQdvQ8XAaqGddWEFbVcZWd6ZKGOSfFMuy1RJsKmMDYwZPagAAQCowqAEAAKnAoAYAAKTCmM7UFKJIZmYqIXM2MUoi1GL5trf8gV5u7cvPyGNF9qZf5WDK8jJx8jMnCiIr4yx1MJT39VN5l4LIhajyB2ppdVGcT/24RiI/o84neX/8M6IUgfpfGnXdVvETLpZhtz1besITc8KcyImzw6XaE/9rYtA268thVkb9njWfMzdoyz/xVNj21D5xtI83P6NyMXH6kZ8BTh9PagAAQCowqAEAAKnAoAYAAKTCmM7UeNRiT5phsQdInD1pvKUOvOUP/PvPjF7qwMxsUORbVF5moCwvM6SOi5GfUfeh9pDx5mfk/jPis3XnZ5xlDSTVT21JkxeN2fCHNDMo+ok9aTLi2Nz/iPe5ZyBoK3aVZkA6dodZmRk/ej48bo+vhIFlxGcm8jOKd2+YWMT9yR+CpPsBGMGTGgAAkAoMagAAQCowqAEAAKkwpjM1RYtkZual9L4yjb0njazL5NxXRh3r3X9GtQ0Uwho8Ayp7I/MypW0qK6Pa1LlkrSaRY1EZHbWvjKrLlBf72Sj5obBfRuwDU3Sez9Q+NeLHWl0jo+o3tYmf0vZwz5PMifD+miYOh/fy6rDmUtsDYV5m+pbnS74v7nHuNTNrRtCW33cg7CjyJNnW8OdTZWWKw+H+OInz5l2S7gdgBE9qAABAKjCoAQAAqTDmp5+8y7NfSk01Jb18278sW/VLtvyBausvimklUbLAu6RblTson1qKM9Wkp5XUsmzf8m21VFtNSZnop37kikW1/DZskm1NonFInE/1E0uw1bFtvxe/6hccDZpOHAt/Ls7eEE7dRDtGn1pqPntO2Ofpg0GbnGpyci/LZioHGBd4UgMAAFKBQQ0AAEgFBjUAACAVxnymZrQl3fq4kMrmDMtSB77l26qfzMVEYYZBtXnLH6isTL9Ylq2yMnL5tszKjJ6fUW1y2XdeZGpEPmVoOOxXUMuyRaZGljVQbcPhdVXZgGhYLNVuEj9VKhfTLH5ej4vrqn7innO9ooTBK8MSBifEku4JOzuDtp61YVZG/YY195wdXveJvSXfFw88E/RRGZhsR0fYr78/aMs0ic+2EL4uAOMXT2oAAEAqMKgBAACpwKAGAACkQuKZmjVr1thdd91lv/71r629vd0uvvhiW7t2rb3qVa8a6RNFka1atco2bdpkhw8ftosuushuueUWmzdv3mldqxBFcs+Zl3LnZ1SEQe5To8ofqKyMKGsgSyJUXsJA5Wfk3jXiXrx70lSanzELMzQqP+Pdf0blZwoieyPzM+J86scmEnvSRKKEgSSuoTIwkYiAZDpE43FRwmBSmEdRJQxyu8MSBjN+9Hx4e3t2hdcVVCmC/G+fdB3rofIzCvkZAKNJ/EnNtm3b7Nprr7X/+q//sq1bt1o+n7fFixfb8ePHR/qsW7fO1q9fbxs3brSdO3dad3e3XXbZZXb0aLgZGAAAgEfiT2p++MMflnx/++2325lnnmm7du2ySy+91KIosg0bNtiKFSvsiiuuMDOzO+64w7q6umzz5s22ZMmS4JyDg4M2OPji/5EeOXIk6dsGAABjXNWXdPf19ZmZ2RlnnGFmZnv37rXe3l5bvHjxSJ9cLmeLFi2y7du3y0HNmjVrbNWqVUF70fT00kupqSZV/kAuwRYPsoacU0hxKmPrpdq5oE2VPxiQS7p91bf782I6yznVNJAPr1E+3ZQX00WDYqm2mhoaFv0UVUFbkdNKqkyCKkNwQjzgnCCmRlSlbfED27Y3fN8jUcJgUJQwOOerYVXt4m5ndWxRxiD/5N6gTS7DzrWF/QZPlHwfqflbIdMc/uxE+fB1saQbwGiqGhSOosiWLVtml1xyic2fP9/MzHp7e83MrKurq6RvV1fXyJ+VW758ufX19Y18HThQea0YAACQTlV9UnPdddfZww8/bPfff3/wZ5lM6f8VR1EUtL0gl8tZLhc+qQAAAHhB1Z7UXH/99XbPPffYT3/6U5s+ffpIe3d3t5lZ8FTm0KFDwdMbAAAAr8Sf1ERRZNdff73dfffddt9999mcOaXz9nPmzLHu7m7bunWrLViwwMzMhoaGbNu2bbZ27drTutZQFNnQKEu6FVX+YFgs59XLt8M2lYHxlj/wZmXkkm6VixH5meP58Bpxlmqr/IwqT1C+XFuVJiiIpdBF8VmoY9XybZVjUf1k+QO1LFvWCFClDsLXn3s2bBucHS7BPnF2mAuZ+IuwhMHMLzuzMufMDdryT/4ubHtqnzhayIjPqCw/o6hcTKx+5GcAjCLxQc21115rmzdvtn/7t3+zzs7OkScykydPtvb2dstkMrZ06VJbvXq19fT0WE9Pj61evdo6OjrsyiuvTPp2AADAOJH4oObWW281M7M3velNJe233367ffSjHzUzsxtvvNEGBgbsmmuuGdl8b8uWLdbZGf7fKQAAgEdVpp9Gk8lkbOXKlbZy5cqkLw8AAMapqu9TU015y8h8zGj8+Rlf+QPZ5i1rIPekUdmbsE3tNePNz/SLXIzKz/QPh9cYFvmZwbwqd1DapvIz+XzYphbBFQbD82ebw0RJNCz2qWkS+ZnjYn+clvB86qer9VB4bLHneNAWnRlet2OXKGGwNdxMsrjLV8JA7d2Sf+Ip37FyfxiR0olG2w3qVBdQGSVxrqT7ARi3KGgJAABSgUENAABIhTE9/XQiylqzmDYaTeJTTaLtuFiq7Z2SUku11bTSMdHPu1RbTT+VV9U201NNurJ22FY+3aSWakeq0nbQohXFlJcqfyAnKNvD5cEZsSy7ZapYunxuWDYg90A4rTR9a1/Q5i5hMGtG0JbfF+6krZY5q6raqtRB1ZdIe6eGku4HYNziSQ0AAEgFBjUAACAVGNQAAIBUGNOZmoFii2VfksnIZsI0hspxFGSZBFEiQLXJXIzIz4iczdFiW9B2LC/aCuH5BtR1Rc4mzlLtgsi3DAyF51Pv6bBYSl1e2qCoSiKIJd3ZJpGdOBGeP9ORD/vJjE54v20HwtfVdEGYgek/Gn4+Pd8Q1/2FMytz9pyw39MHg7b8/mfE0T4qPyORUQGQMjypAQAAqcCgBgAApAKDGgAAkApjOlPzbGGiHX/J/iitmTDrkBW7nhRFxmJIZmp8+Rm1J82xQpjF6BdZGZWfOa7a8mpPGt/+M8NFta+M2KvH2U9lasrzM2bhHjSq/IHJvWtEW7PYveZ4+Fpzh8S+OnPCvWaG5oY/KxP+z+SgrefLYVZGae45O2jLP7E3aCseCLMyKgOT7egI+/X3hxemdAAAjOBJDQAASAUGNQAAIBUY1AAAgFQY05maPxQ6rT3/4ktoyw4HfZrkbiGhghjfqT1pVM5G5WeOija5/4yo33R0WORxRFZG7kkj2oZEjaT+ofC6ap+foaHwR6RJ7COTHwz7ZbKl5yuqvWZawmtGImfTeih8XZmeY0Gb/Ul4vvYHOoO26f95NLzujt3h+YRsLvx88r990nWsl8zPKORnAGAET2oAAEAqMKgBAACpMKann/Ycm2Wt9uK0xJSW40GfjqbBoK1NLP1WhqNwukS19YsppDhLtb2lDgZF23AhvL9CJJZli2XTqp9acp0fCq9hBbEMu2wmKNtWCPscD19DbupA0JZ5pVj27J5WCpdliwXi1jxrRtCW33cgaCsOhkvE1ZSU6hcV1ZVDmebwZyDKh9OrLOkGgBfxpAYAAKQCgxoAAJAKDGoAAEAqjOlMzZNHp1jzS0oUHG4Lt5af3BLmMyY2h/mM5kyY91DlAFSmZrAYvo0DBbH0WyzVHhKlCY4NhzkbVcJALcuW5Q/Eku7h4bAt2xTmPQpiGXZTe5hJUqUNMmXvX8t+UdbggueDtmNHwvfp7NvCe8v83JmVOWdu0FZ85mDQVnj69+JoQeRYVH5GkbmYGP3IzwDAi3hSAwAAUoFBDQAASAUGNQAAIBXGdqbm19Ms2/5i/mLv1DDXMLkzzNRM7Qj3s5nYEuZs2prCXIPK2RTF/i4qK6P2mjmRF3kckalR11X5mULRN05VpQiKYazILBumVPJHw/trfTZ8HYXZpe99fm6YxWn9+cuCtnO+HGZllOZXnRNe84m9QVvxQJiVkXvNdISZrMhbrsDLu69M0v0AYBzgSQ0AAEgFBjUAACAVxvT0U+dTTdaUe3Ga5/hAOH3wf6eK6taTwumTzvawnMKE1rCttSmco8k7p3xUBW1lYFhUvA5ni+zEkDifWNM8NBD2y4pK25YNL9J0KHyvcuccCY+dGja17Jxc8v0r7gun/ewXvqkmNTWU/80TrmMzTaKsg1Ac8C3LjjW94z026X4AMA7wpAYAAKQCgxoAAJAKDGoAAEAqjOlMzVn/8pg1Z17MfAxccm7Q5/meMBNybHqYMfmfM9qDttZOscw7Fy7zbhL5lGxGLIUWS7CVIZGpyWbDa+QHw6xIc2uY+WnOhUup88dFrmiKyLy8MswVFX75sqDtFdvE0ufto+dlmmfPCu/td/uCtqJYWp3NheUU1FLtqKiKJ4RU9ibKs4waAMYKntQAAIBUYFADAABSgUENAABIhTGdqSkcPWaZzIv5mLZtjwV9zsi/WhwZ5kmOF8KczXA+3LelMDEcB6qIRUtrmGMpiExNq+g3NBh+LC0iF1MU5xvqFzmbg+Hrfdn854K2I31hrmj2N8NrNP20sjIGqlxB4emwTRJvssrPKFE+zEHF6Ud+BgAaE09qAABAKjCoAQAAqTCmp5/KqWW/zVsfCNq6Dp8ftD13/sSg7dj08O0ZnCqmQZrDJcOFCWJrfrGyeHhAfASqIrcok9D8+1zQ1n7e80Hb8eZwmffwz88I2s5e55xWOrcnaCvuPRC27X+m9PuBsGK6rIwtpoEyooRDpKqKK1S8BoBxgSc1AAAgFRjUAACAVGBQAwAAUiFVmRqv7K/3Bm0Tpp4XtBVy4TLvYqtY0i1iF0MFkQERuZjM5LAUQ+a58LoT5oQlDE6cHYZKovtfFrSdvb2yEgZmOvOS//Vvfce2hkvJyxUHnMuyC94AjTrYmYshPwMAYxpPagAAQCowqAEAAKnAoAYAAKTCuMzUFI4dC9o6/rs3aIuazwrahieGb1lW7K5fyIXjxeZwmxaL/m9b0DY4ezBoO/LchKDtlbeG/aIdzr1mZs0I2vL7xF4zYu+fbC68Z2/JgnKZpnA/nyjPHjIAgNPHkxoAAJAKDGoAAEAqjMvpJykfLhluORZWxs4Uwrds6qPhNNDz/WEJg2Mzw8sOTQ2vO/374TXav//LoE1UXbDmrjODtvz/HArbxFSTl3eqqTgULlcv516qzVQTAGAUPKkBAACpwKAGAACkAoMaAACQCmRqXtAcLi3ODoU5jqLY+T9TCNMt/eFqcJtz1/NBW9QafgTRLx/W9+ig8jP1kmkuLfcQ5cO1794l3bJfnNIJAIDU4UkNAABIBQY1AAAgFRjUAACAVEhVpkZt369yF03TuoK2/vO6g7bD57YEbYXXHw2vu/aRoG3GfeH9sdNKDVBOAQDGLZ7UAACAVGBQAwAAUoFBDQAASIW6Zmq+/vWv25e+9CU7ePCgzZs3zzZs2GBvfOMbKz5f//96TdA2ODkctw13ZMJ+LxPne2VYu+i68/5P0PYjm+S7QaF55vSgLb//6YrPVwvl+8+Y6T1osm2l9a8Kx8Q+Na2+c3n3qclkw882YjsbABgX6vak5rvf/a4tXbrUVqxYYXv27LE3vvGN9hd/8Re2f//+et0SAAAYw+r2pGb9+vX28Y9/3P7mb/7GzMw2bNhgP/rRj+zWW2+1NWvWlPQdHBy0wcEXK2H39fWZmVnehktKVeeHw+rRhaFw3FZoDv9vvhAW2rbiQPik5oSo3J2PwicLbsXwwrHOVwMZUR48EvccRaXvX0H0yUbhe1yU/cLPTPXLiJVOEY9qAKAh5O3k39tRJP4hSUJUB4ODg1FTU1N01113lbR/4hOfiC699NKg/+c+97nITg5f+OKLL7744ouvMf715JNPVmV8UZcnNX/4wx+sUChYV1fpfjFdXV3W29sb9F++fLktW7Zs5Pvnn3/eZs2aZfv377fJkydX/X7xxx05csRmzJhhBw4csEmTKs8XIT4+i8bBZ9E4+CwaR19fn82cOdPOOOOMqpy/rkHhTKZ0SiGKoqDNzCyXy1kulwvaJ0+ezA9oA5k0aRKfR4Pgs2gcfBaNg8+icWSz1Yn01iUoPHXqVGtqagqeyhw6dCh4egMAAOBRl0FNa2urLVy40LZu3VrSvnXrVrv44ovrcUsAAGCMq9v007Jly+zDH/6wXXjhhfZnf/ZntmnTJtu/f79dffXVox6by+Xsc5/7nJySQu3xeTQOPovGwWfROPgsGke1P4tMFFVrXdXovv71r9u6devs4MGDNn/+fPvKV75il156ab1uBwAAjGF1HdQAAAAkhdpPAAAgFRjUAACAVGBQAwAAUoFBDQAASIUxOaj5+te/bnPmzLG2tjZbuHCh/fznP6/3LaXemjVr7PWvf711dnbamWeeae9+97vtN7/5TUmfKIps5cqVNm3aNGtvb7c3velN9thjj9XpjsePNWvWWCaTsaVLl4608VnUzjPPPGMf+tCHbMqUKdbR0WGvfe1rbdeuXSN/zmdRG/l83j772c/anDlzrL293ebOnWuf//znrVh8scgtn0X1/OxnP7N3vvOdNm3aNMtkMvb973+/5M897/3g4KBdf/31NnXqVJswYYK9613vsqeffvr0bqQqFaWq6M4774xaWlqi2267LfrVr34V3XDDDdGECROiffv21fvWUu1tb3tbdPvtt0ePPvpo9OCDD0aXX355NHPmzOjYsWMjfb74xS9GnZ2d0fe+973okUceid73vvdFZ511VnTkyJE63nm67dixI5o9e3Z0wQUXRDfccMNIO59FbTz33HPRrFmzoo9+9KPRL3/5y2jv3r3Rj3/84+iJJ54Y6cNnURtf+MIXoilTpkT/8R//Ee3duzf613/912jixInRhg0bRvrwWVTPvffeG61YsSL63ve+F5lZdPfdd5f8uee9v/rqq6NXvOIV0datW6Pdu3dHb37zm6PXvOY1UT6fd9/HmBvU/Omf/ml09dVXl7Sde+650ac//ek63dH4dOjQocjMom3btkVRFEXFYjHq7u6OvvjFL470OXHiRDR58uToG9/4Rr1uM9WOHj0a9fT0RFu3bo0WLVo0Mqjhs6idm266KbrkkktO+ed8FrVz+eWXRx/72MdK2q644oroQx/6UBRFfBa1VD6o8bz3zz//fNTS0hLdeeedI32eeeaZKJvNRj/84Q/d1x5T009DQ0O2a9cuW7x4cUn74sWLbfv27XW6q/Gpr6/PzGyk0urevXutt7e35LPJ5XK2aNEiPpsqufbaa+3yyy+3t771rSXtfBa1c88999iFF15o73nPe+zMM8+0BQsW2G233Tby53wWtXPJJZfYT37yE3v88cfNzOyhhx6y+++/397+9rebGZ9FPXne+127dtnw8HBJn2nTptn8+fNP6/Opa5Xu0/WHP/zBCoVCUPSyq6srKI6J6omiyJYtW2aXXHKJzZ8/38xs5P1Xn82+fftqfo9pd+edd9ru3btt586dwZ/xWdTOU089ZbfeeqstW7bMPvOZz9iOHTvsE5/4hOVyOfvIRz7CZ1FDN910k/X19dm5555rTU1NVigU7Oabb7YPfOADZsbvRT153vve3l5rbW21l7/85UGf0/n3fUwNal6QyWRKvo+iKGhD9Vx33XX28MMP2/333x/8GZ9N9R04cMBuuOEG27Jli7W1tZ2yH59F9RWLRbvwwgtt9erVZma2YMECe+yxx+zWW2+1j3zkIyP9+Cyq77vf/a59+9vfts2bN9u8efPswQcftKVLl9q0adPsqquuGunHZ1E/lbz3p/v5jKnpp6lTp1pTU1Mwajt06FAwAkR1XH/99XbPPffYT3/6U5s+ffpIe3d3t5kZn00N7Nq1yw4dOmQLFy605uZma25utm3bttlXv/pVa25uHnm/+Syq76yzzrJXv/rVJW3nnXee7d+/38z4vailT33qU/bpT3/a3v/+99v5559vH/7wh+2Tn/ykrVmzxsz4LOrJ8953d3fb0NCQHT58+JR9PMbUoKa1tdUWLlxoW7duLWnfunWrXXzxxXW6q/EhiiK77rrr7K677rL//M//tDlz5pT8+Zw5c6y7u7vksxkaGrJt27bx2STsLW95iz3yyCP24IMPjnxdeOGF9sEPftAefPBBmzt3Lp9FjbzhDW8ItjZ4/PHHbdasWWbG70Ut9ff3WzZb+k9aU1PTyJJuPov68bz3CxcutJaWlpI+Bw8etEcfffT0Pp+K48118sKS7m9+85vRr371q2jp0qXRhAkTot/97nf1vrVU+7u/+7to8uTJ0X333RcdPHhw5Ku/v3+kzxe/+MVo8uTJ0V133RU98sgj0Qc+8AGWS9bIS1c/RRGfRa3s2LEjam5ujm6++ebot7/9bfSd73wn6ujoiL797W+P9OGzqI2rrroqesUrXjGypPuuu+6Kpk6dGt14440jffgsqufo0aPRnj17oj179kRmFq1fvz7as2fPyHYrnvf+6quvjqZPnx79+Mc/jnbv3h39+Z//efqXdEdRFN1yyy3RrFmzotbW1uh1r3vdyLJiVI+Zya/bb799pE+xWIw+97nPRd3d3VEul4suvfTS6JFHHqnfTY8j5YMaPova+fd///do/vz5US6Xi84999xo06ZNJX/OZ1EbR44ciW644YZo5syZUVtbWzR37txoxYoV0eDg4EgfPovq+elPfyr/jbjqqquiKPK99wMDA9F1110XnXHGGVF7e3v0jne8I9q/f/9p3UcmiqIo1nMlAACABjCmMjUAAACnwqAGAACkAoMaAACQCgxqAABAKjCoAQAAqcCgBgAApAKDGgAAkAoMagAAQCowqAEAAKnAoAYAAKQCgxoAAJAK/w/Ce/oK58TtRAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(v[0], vmin=0, vmax=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "7e68a92d-b01f-481b-995d-7e1fdd65ebc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "3ddfec19-aa44-4c50-8817-b1fdde4094c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-12-11 15:26:30.230000')"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_time + timedelta(hours=72.345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "03b3d425-43ab-4634-874d-dcf7879b0215",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'hours' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[170], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pd\u001b[38;5;241m.\u001b[39mto_datetime(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m%02d\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m%02.6f\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[43mhours\u001b[49m, minutes, seconds))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'hours' is not defined"
     ]
    }
   ],
   "source": [
    "pd.to_datetime(\"%d:%02d.%02.6f\" % (hours, minutes, seconds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c7a0f9d4-e850-4b82-93ff-0b853ecadccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'7.329996'"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"%02.6f\" % 7.329996000000392"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "fb660fa9-6323-4500-9f65-e32975c620e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-12-08 15:05:28'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(['2022-12-08 15:05.47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "95b61cd5-20d1-4d48-8780-95dfde3396f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "7b8add42-ff55-4a5b-9b5f-a510aa12035d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-12-08 15:05:47.699988', '2022-12-08 15:05:49.340004',\n",
       "               '2022-12-08 15:05:50.970012', '2022-12-08 15:05:52.609992',\n",
       "               '2022-12-08 15:05:54.250008', '2022-12-08 15:05:55.880016',\n",
       "               '2022-12-08 15:05:57.519996', '2022-12-08 15:05:59.150004',\n",
       "               '2022-12-08 15:06:00.780012', '2022-12-08 15:06:02.419992',\n",
       "               '2022-12-08 15:06:04.060008', '2022-12-08 15:06:05.690016',\n",
       "               '2022-12-08 15:06:07.329996'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2afed110-364a-415b-b0b6-d20de473a3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133, 13)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "field_dict['spectral_width'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "906fcf59-bd42-431e-b8d6-37da896c6fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "field_dict['center_of_gates'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "d69e430f-e385-4020-886d-62c0e7cc82c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'filename': 'RHI_240_20221208_150544.hpl',\n",
       " 'system_id': 240,\n",
       " 'number_of_gates': 133,\n",
       " 'range_gate_length_m': 24.0,\n",
       " 'gate_length_pts': 8,\n",
       " 'pulses_per_ray': 5000,\n",
       " 'number_of_waypoints_in_file': 13,\n",
       " 'no_of_rays_in_file': 13,\n",
       " 'scan_type': 'RHI',\n",
       " 'focus_range': '65535',\n",
       " 'start_time': '20221208 15:05:48.23',\n",
       " 'resolution': '0.0382 m s-1',\n",
       " 'range_gates': array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "        130, 131, 132]),\n",
       " 'center_of_gates': array([  12.,   36.,   60.,   84.,  108.,  132.,  156.,  180.,  204.,\n",
       "         228.,  252.,  276.,  300.,  324.,  348.,  372.,  396.,  420.,\n",
       "         444.,  468.,  492.,  516.,  540.,  564.,  588.,  612.,  636.,\n",
       "         660.,  684.,  708.,  732.,  756.,  780.,  804.,  828.,  852.,\n",
       "         876.,  900.,  924.,  948.,  972.,  996., 1020., 1044., 1068.,\n",
       "        1092., 1116., 1140., 1164., 1188., 1212., 1236., 1260., 1284.,\n",
       "        1308., 1332., 1356., 1380., 1404., 1428., 1452., 1476., 1500.,\n",
       "        1524., 1548., 1572., 1596., 1620., 1644., 1668., 1692., 1716.,\n",
       "        1740., 1764., 1788., 1812., 1836., 1860., 1884., 1908., 1932.,\n",
       "        1956., 1980., 2004., 2028., 2052., 2076., 2100., 2124., 2148.,\n",
       "        2172., 2196., 2220., 2244., 2268., 2292., 2316., 2340., 2364.,\n",
       "        2388., 2412., 2436., 2460., 2484., 2508., 2532., 2556., 2580.,\n",
       "        2604., 2628., 2652., 2676., 2700., 2724., 2748., 2772., 2796.,\n",
       "        2820., 2844., 2868., 2892., 2916., 2940., 2964., 2988., 3012.,\n",
       "        3036., 3060., 3084., 3108., 3132., 3156., 3180.]),\n",
       " 'radial_velocity': array([[  0.4969,   0.2675,   0.344 , ...,   0.5351,   0.4204,   0.4204],\n",
       "        [ -0.344 ,  -0.688 ,  -0.5733, ...,  11.7337,  11.7719,  11.963 ],\n",
       "        [ 11.619 ,  11.6955,  11.8483, ...,  11.8101,  11.8483,  12.0394],\n",
       "        ...,\n",
       "        [ -1.9875, -16.5877,  -3.7074, ..., -14.7913,   0.2675, -15.4028],\n",
       "        [  7.7205,  -3.2487,   9.5551, ...,  -2.943 ,  -0.1147, -12.8038],\n",
       "        [ 19.1867,   4.854 ,  18.6133, ..., -14.7913,   0.3058, -13.4918]]),\n",
       " 'intensity': array([[1.141879, 1.49347 , 1.455399, ..., 0.844797, 0.954667, 1.001475],\n",
       "        [1.105347, 1.463778, 1.39274 , ..., 0.849536, 0.942302, 0.979837],\n",
       "        [1.021476, 1.082985, 1.061974, ..., 0.97966 , 0.985956, 1.004995],\n",
       "        ...,\n",
       "        [1.002233, 0.999644, 0.998573, ..., 1.010497, 3.379957, 0.999662],\n",
       "        [0.99991 , 0.99865 , 0.988375, ..., 1.000328, 2.219137, 0.999468],\n",
       "        [1.000787, 0.999883, 0.998965, ..., 0.997938, 1.517312, 0.997432]]),\n",
       " 'beta': array([[ 7.989228e-06,  2.778736e-05,  2.564359e-05, ..., -8.739524e-06,\n",
       "         -2.552723e-06,  8.307399e-08],\n",
       "        [ 5.938080e-06,  2.614180e-05,  2.213759e-05, ..., -8.481209e-06,\n",
       "         -3.252267e-06, -1.136548e-06],\n",
       "        [ 1.212306e-06,  4.684448e-06,  3.498434e-06, ..., -1.148210e-06,\n",
       "         -7.927657e-07,  2.819446e-07],\n",
       "        ...,\n",
       "        [ 6.606759e-07, -1.053746e-07, -4.222111e-07, ...,  3.105908e-06,\n",
       "          7.041767e-04, -9.987265e-08],\n",
       "        [-2.707911e-08, -4.045048e-07, -3.483673e-06, ...,  9.819745e-08,\n",
       "          3.653321e-04, -1.592993e-07],\n",
       "        [ 2.389713e-07, -3.556643e-08, -3.139982e-07, ..., -6.258545e-07,\n",
       "          1.569954e-04, -7.794487e-07]]),\n",
       " 'spectral_width': array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]),\n",
       " 'elevation': array([ -0.  ,  15.04,  30.03,  45.03,  60.03,  75.03,  90.03, 105.03,\n",
       "        120.03, 135.03, 150.04, 165.03, 180.03]),\n",
       " 'azimuth': array([1.0e-02, 3.6e+02, 0.0e+00, 0.0e+00, 0.0e+00, 0.0e+00, 0.0e+00,\n",
       "        3.6e+02, 0.0e+00, 0.0e+00, 0.0e+00, 0.0e+00, 0.0e+00]),\n",
       " 'decimal_time': array([15.09658333, 15.09703889, 15.09749167, 15.09794722, 15.09840278,\n",
       "        15.09885556, 15.09931111, 15.09976389, 15.10021667, 15.10067222,\n",
       "        15.10112778, 15.10158056, 15.10203611]),\n",
       " 'pitch': array([0.45, 0.45, 0.45, 0.45, 0.34, 0.34, 0.34, 0.34, 0.34, 0.45, 0.45,\n",
       "        0.34, 0.45]),\n",
       " 'roll': array([-0.65, -0.65, -0.65, -0.65, -0.65, -0.65, -0.65, -0.65, -0.65,\n",
       "        -0.65, -0.75, -0.65, -0.65])}"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "field_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "6d2cfc47-b77f-44f3-a818-e8f31a1f3a7b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5b06f8f-6b95-45dd-a4e9-d48a75044a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample_file.nc/2022/202212/20221208/RHI_240_20221208_150544_l0.nc is created succesfully\n"
     ]
    }
   ],
   "source": [
    "utils.hpl_to_netcdf(files[0], 'sample_file.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45873a8e-6475-4cf1-91bc-51e54c90b627",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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