From 1e37b74e6006ed260afcf5ecb5a3da9b92bd9ae8 Mon Sep 17 00:00:00 2001
From: Meg Fowler <mdfowler@ucar.edu>
Date: Wed, 16 Oct 2024 11:21:36 -0600
Subject: [PATCH 1/5] Updates to work with latest CUPiD

---
 environments/cupid-analysis.yml               |    1 +
 examples/key_metrics/config.yml               |   19 +-
 .../lnd/LandAtm_CouplingIndex_V2.ipynb        | 3569 +++++++++++++++++
 3 files changed, 3578 insertions(+), 11 deletions(-)
 create mode 100755 examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb

diff --git a/environments/cupid-analysis.yml b/environments/cupid-analysis.yml
index 7486bbe..3a82150 100644
--- a/environments/cupid-analysis.yml
+++ b/environments/cupid-analysis.yml
@@ -20,6 +20,7 @@ dependencies:
   - numpy
   - pip
   - python==3.11.4
+  - uxarray
   - xarray
   - yaml
   - zarr
diff --git a/examples/key_metrics/config.yml b/examples/key_metrics/config.yml
index e559ee7..66584a9 100644
--- a/examples/key_metrics/config.yml
+++ b/examples/key_metrics/config.yml
@@ -68,7 +68,7 @@ timeseries:
     level: 'lev'
 
   lnd:
-    vars: []
+    vars: ['SOILWATER_10CM','FSH_TO_COUPLER']
     derive_vars: []
     hist_str: 'h0'
     start_years: [1,1]
@@ -142,16 +142,13 @@ compute_notebooks:
 #            endyr2: 305
 #            nyears: 25
 
-#    lnd:
-#      land_comparison:
-#        parameter_groups:
-#          none:
-#            cases:
-#              - ctsm51d159_f45_GSWP3_bgccrop_1850pAD
-#              - ctsm51d159_f45_GSWP3_bgccrop_1850pSASU
-#            type:
-#              - 1850pAD
-#              - 1850pSASU
+    lnd:
+      LandAtm_CouplingIndex_V2:
+        parameter_groups:
+          none:
+            clmFile_h: '.h0.'
+            fluxnet_comparison: True
+            obsDir: '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/'
 
 #    ocn:
 #      ocean_surface:
diff --git a/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb b/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb
new file mode 100755
index 0000000..c04b0b5
--- /dev/null
+++ b/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb
@@ -0,0 +1,3569 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "99564fab-c321-4116-8229-b16eefa1536e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Compute land-atmosphere coupling indices \n",
+    "This notebook takes in a series of CESM simulations, computes the land-atmosphere coupling index (CI; \n",
+    "terrestrial leg only currently), and plots those seasonal means. <br>\n",
+    "- Note: Built to use monthly output; ideally, CI should be based on daily data. \n",
+    "- Optional: Comparison against FLUXNET obs\n",
+    "<br><br>\n",
+    "Notebook created by mdfowler@ucar.edu; Last update: 2 Aug 2024 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "750da831-1c5c-4b41-947e-a9e57a62a820",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "  const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
+       "  const reloading = false;\n",
+       "  const Bokeh = root.Bokeh;\n",
+       "\n",
+       "  // Set a timeout for this load but only if we are not already initializing\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "    if (js_modules == null) js_modules = [];\n",
+       "    if (js_exports == null) js_exports = {};\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      // Don't load bokeh if it is still initializing\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
+       "      // There is nothing to load\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "    window._bokeh_on_load = on_load\n",
+       "\n",
+       "    function on_error(e) {\n",
+       "      const src_el = e.srcElement\n",
+       "      console.error(\"failed to load \" + (src_el.href || src_el.src));\n",
+       "    }\n",
+       "\n",
+       "    const skip = [];\n",
+       "    if (window.requirejs) {\n",
+       "      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n",
+       "      root._bokeh_is_loading = css_urls.length + 0;\n",
+       "    } else {\n",
+       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
+       "    }\n",
+       "\n",
+       "    const existing_stylesheets = []\n",
+       "    const links = document.getElementsByTagName('link')\n",
+       "    for (let i = 0; i < links.length; i++) {\n",
+       "      const link = links[i]\n",
+       "      if (link.href != null) {\n",
+       "        existing_stylesheets.push(link.href)\n",
+       "      }\n",
+       "    }\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const escaped = encodeURI(url)\n",
+       "      if (existing_stylesheets.indexOf(escaped) !== -1) {\n",
+       "        on_load()\n",
+       "        continue;\n",
+       "      }\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }    var existing_scripts = []\n",
+       "    const scripts = document.getElementsByTagName('script')\n",
+       "    for (let i = 0; i < scripts.length; i++) {\n",
+       "      var script = scripts[i]\n",
+       "      if (script.src != null) {\n",
+       "        existing_scripts.push(script.src)\n",
+       "      }\n",
+       "    }\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const escaped = encodeURI(url)\n",
+       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n",
+       "        if (!window.requirejs) {\n",
+       "          on_load();\n",
+       "        }\n",
+       "        continue;\n",
+       "      }\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    for (let i = 0; i < js_modules.length; i++) {\n",
+       "      const url = js_modules[i];\n",
+       "      const escaped = encodeURI(url)\n",
+       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n",
+       "        if (!window.requirejs) {\n",
+       "          on_load();\n",
+       "        }\n",
+       "        continue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      element.type = \"module\";\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    for (const name in js_exports) {\n",
+       "      const url = js_exports[name];\n",
+       "      const escaped = encodeURI(url)\n",
+       "      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n",
+       "        if (!window.requirejs) {\n",
+       "          on_load();\n",
+       "        }\n",
+       "        continue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.type = \"module\";\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      element.textContent = `\n",
+       "      import ${name} from \"${url}\"\n",
+       "      window.${name} = ${name}\n",
+       "      window._bokeh_on_load()\n",
+       "      `\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    if (!js_urls.length && !js_modules.length) {\n",
+       "      on_load()\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n",
+       "  const js_modules = [];\n",
+       "  const js_exports = {};\n",
+       "  const css_urls = [];\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {} // ensure no trailing comma for IE\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
+       "      for (let i = 0; i < inline_js.length; i++) {\n",
+       "        try {\n",
+       "          inline_js[i].call(root, root.Bokeh);\n",
+       "        } catch(e) {\n",
+       "          if (!reloading) {\n",
+       "            throw e;\n",
+       "          }\n",
+       "        }\n",
+       "      }\n",
+       "      // Cache old bokeh versions\n",
+       "      if (Bokeh != undefined && !reloading) {\n",
+       "        var NewBokeh = root.Bokeh;\n",
+       "        if (Bokeh.versions === undefined) {\n",
+       "          Bokeh.versions = new Map();\n",
+       "        }\n",
+       "        if (NewBokeh.version !== Bokeh.version) {\n",
+       "          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
+       "        }\n",
+       "        root.Bokeh = Bokeh;\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    }\n",
+       "    root._bokeh_is_initializing = false\n",
+       "  }\n",
+       "\n",
+       "  function load_or_wait() {\n",
+       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
+       "    // versions of Bokeh and its dependencies at the same time.\n",
+       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
+       "    // to determine whether there is an ongoing attempt to initialize\n",
+       "    // bokeh, however for backward compatibility we also try to ensure\n",
+       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
+       "    // before older versions are fully initialized.\n",
+       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
+       "      // If the timeout and bokeh was not successfully loaded we reset\n",
+       "      // everything and try loading again\n",
+       "      root._bokeh_timeout = Date.now() + 5000;\n",
+       "      root._bokeh_is_initializing = false;\n",
+       "      root._bokeh_onload_callbacks = undefined;\n",
+       "      root._bokeh_is_loading = 0\n",
+       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
+       "      load_or_wait();\n",
+       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
+       "      setTimeout(load_or_wait, 100);\n",
+       "    } else {\n",
+       "      root._bokeh_is_initializing = true\n",
+       "      root._bokeh_onload_callbacks = []\n",
+       "      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n",
+       "      if (!reloading && !bokeh_loaded) {\n",
+       "        if (root.Bokeh) {\n",
+       "          root.Bokeh = undefined;\n",
+       "        }\n",
+       "        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "      }\n",
+       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
+       "        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "        run_inline_js();\n",
+       "      });\n",
+       "    }\n",
+       "  }\n",
+       "  // Give older versions of the autoload script a head-start to ensure\n",
+       "  // they initialize before we start loading newer version.\n",
+       "  setTimeout(load_or_wait, 100)\n",
+       "}(window));"
+      ],
+      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
+       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
+       "}\n",
+       "\n",
+       "\n",
+       "    function JupyterCommManager() {\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        comm_manager.register_target(comm_id, function(comm) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        });\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        });\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
+       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "          function processIteratorResult(result) {\n",
+       "            var message = result.value;\n",
+       "            console.log(message)\n",
+       "            var content = {data: message.data, comm_id};\n",
+       "            var buffers = []\n",
+       "            for (var buffer of message.buffers || []) {\n",
+       "              buffers.push(new DataView(buffer))\n",
+       "            }\n",
+       "            var metadata = message.metadata || {};\n",
+       "            var msg = {content, buffers, metadata}\n",
+       "            msg_handler(msg);\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "          return messages.next().then(processIteratorResult);\n",
+       "        })\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (comm_id in window.PyViz.comms) {\n",
+       "        return window.PyViz.comms[comm_id];\n",
+       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
+       "        if (msg_handler) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        }\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
+       "        comm.open();\n",
+       "        if (msg_handler) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        }\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        }) \n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "  if (id !== undefined) {\n",
+       "    var nchildren = toinsert.length;\n",
+       "    var html_node = toinsert[nchildren-1].children[0];\n",
+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
+       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
+       "    for (var i in nodelist) {\n",
+       "      if (nodelist.hasOwnProperty(i)) {\n",
+       "        scripts.push(nodelist[i])\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
+       "      var newScript = document.createElement(\"script\");\n",
+       "      var attrs = [];\n",
+       "      var nodemap = oldScript.attributes;\n",
+       "      for (var j in nodemap) {\n",
+       "        if (nodemap.hasOwnProperty(j)) {\n",
+       "          attrs.push(nodemap[j])\n",
+       "        }\n",
+       "      }\n",
+       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
+       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
+       "  if (server_id !== null) {\n",
+       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
+       "    return;\n",
+       "  } else if (comm !== null) {\n",
+       "    comm.send({event_type: 'delete', 'id': id});\n",
+       "  }\n",
+       "  delete PyViz.plot_index[id];\n",
+       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
+       "    var doc = window.Bokeh.index[id].model.document\n",
+       "    doc.clear();\n",
+       "    const i = window.Bokeh.documents.indexOf(doc);\n",
+       "    if (i > -1) {\n",
+       "      window.Bokeh.documents.splice(i, 1);\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle kernel restart event\n",
+       " */\n",
+       "function handle_kernel_cleanup(event, handle) {\n",
+       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
+       "  window.PyViz.plot_index = {}\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle update_display_data messages\n",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function register_renderer(events, OutputArea) {\n",
+       "  function append_mime(data, metadata, element) {\n",
+       "    // create a DOM node to render to\n",
+       "    var toinsert = this.create_output_subarea(\n",
+       "    metadata,\n",
+       "    CLASS_NAME,\n",
+       "    EXEC_MIME_TYPE\n",
+       "    );\n",
+       "    this.keyboard_manager.register_events(toinsert);\n",
+       "    // Render to node\n",
+       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "    render(props, toinsert[0]);\n",
+       "    element.append(toinsert);\n",
+       "    return toinsert\n",
+       "  }\n",
+       "\n",
+       "  events.on('output_added.OutputArea', handle_add_output);\n",
+       "  events.on('output_updated.OutputArea', handle_update_output);\n",
+       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
+       "  events.on('delete.Cell', handle_clear_output);\n",
+       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
+       "\n",
+       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "    safe: true,\n",
+       "    index: 0\n",
+       "  });\n",
+       "}\n",
+       "\n",
+       "if (window.Jupyter !== undefined) {\n",
+       "  try {\n",
+       "    var events = require('base/js/events');\n",
+       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  } catch(err) {\n",
+       "  }\n",
+       "}\n"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        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+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  async function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t  for (const child of root_el.children) {\n",
+       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
+       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
+       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
+       "\t  }\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
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+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
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+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
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+       "    embed_document(root);\n",
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+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
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+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "p1002"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import numpy as np \n",
+    "import xarray as xr\n",
+    "import datetime\n",
+    "from datetime import date, timedelta\n",
+    "import dask\n",
+    "import pandas as pd\n",
+    "import sys\n",
+    "\n",
+    "# Plotting utils \n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt \n",
+    "import cartopy\n",
+    "import cartopy.crs as ccrs\n",
+    "import uxarray as uxr\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "774bd269-ce50-4449-b32f-83246b74b73c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 1. Modify this section for each run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7f8f2d17-c653-4ad1-9dc3-c49bf836ceb6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "##  Settings for case locations + names \n",
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "## Where observations are stored \n",
+    "# obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data \n",
+    "\n",
+    "## Where CESM timeseries data is stored  \n",
+    "CESM_output_dir = '/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/'\n",
+    "\n",
+    "\n",
+    "## Full casenames that are present in CESM_output_dir and in individual filenames\n",
+    "# caseNames = [\n",
+    "#             'b.e23_alpha16b.BLT1850.ne30_t232.054',\n",
+    "            # 'b.e30_beta02.BLT1850.ne30_t232.104',\n",
+    "#             ] \n",
+    "case_name =  'b.e30_beta02.BLT1850.ne30_t232.104'\n",
+    "\n",
+    "# clmFile_h = '.h0.'\n",
+    "\n",
+    "start_date= '0001-01-01'\n",
+    "end_date  = '0101-01-01'\n",
+    "\n",
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "##  Optional settings for notebook \n",
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "\n",
+    "## If comparison against FLUXNET desired \n",
+    "# fluxnet_comparison = True \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0014712f-d094-4dae-b583-740bf7a9789c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['0100']\n",
+      "['104']\n"
+     ]
+    }
+   ],
+   "source": [
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "##  Settings for computing coupling index\n",
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "startYrs = [start_date.split('-')[0]]\n",
+    "endYrs   = [f\"{int(end_date.split('-')[0])-1:04d}\"]\n",
+    "\n",
+    "caseNames = [case_name, \n",
+    "            #base_case_name, \n",
+    "            ]\n",
+    "\n",
+    "shortNames = [\n",
+    "    case.split('.')[-1] for case in caseNames\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eda751c0-6b42-47fd-a94e-9e4ab9f7e1c6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d70024c7-0af2-48b9-9041-893f40e613ec",
+   "metadata": {},
+   "source": [
+    "## 2. Read in model data and compute terrestrial coupling index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "304ce8d0-6aab-4fcb-9635-2a78e270f3c7",
+   "metadata": {
+    "editable": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "Inputs: xname -- controlling variable \n",
+    "        yname -- responding variable\n",
+    "        ds    -- dataset containing xname and yname data \n",
+    "        \n",
+    "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
+    "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
+    "'''\n",
+    "\n",
+    "def compute_couplingIndex_cesm(xname,yname,xDS,yDS):\n",
+    "    xday = xDS[xname].groupby('time.season')\n",
+    "    yday = yDS[yname].groupby('time.season')\n",
+    "\n",
+    "    # Get the covariance of the two (numerator in coupling index)\n",
+    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby('time.season').sum() / xday.count()\n",
+    "\n",
+    "    # Now compute the actual coupling index \n",
+    "    couplingIndex = covarTerm/xday.std()\n",
+    "\n",
+    "    \n",
+    "    return couplingIndex\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3c35ae8d-dfff-44b0-a854-dfc2b5b030c0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using previously computed coupling index saved in file  /glade/derecho/scratch/mdfowler/b.e23_alpha16b.BLT1850.ne30_t232.054_TerrestrialCouplingIndex_SHvsSM.nc\n"
+     ]
+    }
+   ],
+   "source": [
+    "for iCase in range(len(caseNames)):\n",
+    "    ## Check first if coupling index has already been created:\n",
+    "    TCI_filePath = '/glade/derecho/scratch/mdfowler/'+caseNames[0]+'_TerrestrialCouplingIndex_SHvsSM.nc'\n",
+    "\n",
+    "    if os.path.exists(TCI_filePath):  # Use previously computed TCI \n",
+    "        print('Using previously computed coupling index saved in file ', TCI_filePath)\n",
+    "    else:   # Compute TCI\n",
+    "        \n",
+    "        # Get list of necessary time series files\n",
+    "        soilWater_file = np.sort(glob.glob(CESM_output_dir+'/'+caseNames[iCase]+'/lnd/proc/tseries/'+caseNames[iCase]+clmFile_h+'SOILWATER_10CM.'+startYrs[iCase]+'??-'+endYrs[iCase]+'??.nc'))\n",
+    "        if len(soilWater_file)==0:\n",
+    "            print('Soil moisture file not found!')\n",
+    "        elif len(soilWater_file)>1: \n",
+    "            print('More than one file matches requested time period and case for soil moisture.')\n",
+    "        elif len(soilWater_file)==1: \n",
+    "            soilWater_DS = xr.open_dataset(soilWater_file[0], decode_times=True)\n",
+    "         \n",
+    "        sh_file = np.sort(glob.glob(CESM_output_dir+'/'+caseNames[iCase]+'/lnd/proc/tseries/'+caseNames[iCase]+clmFile_h+'FSH_TO_COUPLER.'+startYrs[iCase]+'??-'+endYrs[iCase]+'??.nc'))\n",
+    "        if len(sh_file)==0:\n",
+    "            print('Land-based SHFLX file not found!')\n",
+    "        elif len(sh_file)>1: \n",
+    "            print('More than one file matches requested time period and case for SH.')\n",
+    "        elif len(sh_file)==1:     \n",
+    "            shflx_DS = xr.open_dataset(sh_file[0])\n",
+    "    \n",
+    "    \n",
+    "        # If years start at 0000, offset by 1700 years for analysis\n",
+    "        yrOffset = 1850\n",
+    "        if shflx_DS['time.year'].values[0]<1500: \n",
+    "            shflx_DS['time'] = shflx_DS.time + timedelta(days=yrOffset*365)\n",
+    "        if soilWater_DS['time.year'].values[0]<1500: \n",
+    "            soilWater_DS['time'] = soilWater_DS.time + timedelta(days=yrOffset*365)\n",
+    "        # Convert times to datetime for easier use\n",
+    "        shflx_DS['time']     = shflx_DS.indexes['time'].to_datetimeindex() \n",
+    "        soilWater_DS['time'] = soilWater_DS.indexes['time'].to_datetimeindex() \n",
+    "\n",
+    "        # Add case ID (short name) to the DS\n",
+    "        shflx_DS     = shflx_DS.assign_coords({\"case\":  shortNames[iCase]})\n",
+    "        soilWater_DS = soilWater_DS.assign_coords({\"case\":  shortNames[iCase]})\n",
+    "\n",
+    "        ## Compute coupling index and save to netCDF file \n",
+    "        ## - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+    "        xname = 'SOILWATER_10CM'    # Controlling variable \n",
+    "        yname = 'FSH_TO_COUPLER'    # Responding variable \n",
+    "        \n",
+    "        xDS = soilWater_DS\n",
+    "        yDS = shflx_DS\n",
+    "\n",
+    "        couplingInd = compute_couplingIndex_cesm(xname,yname,xDS,yDS)\n",
+    "\n",
+    "        filePath = '/glade/derecho/scratch/mdfowler/'+caseNames[0]+'_TerrestrialCouplingIndex_SHvsSM.nc'\n",
+    "        couplingInd.to_netcdf(filePath)\n",
+    "        print('File created: ', filePath)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8396cb31-b438-4e7f-8ef5-83f4f915e332",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c54b2c9-62a9-46ef-86eb-411f66fc9119",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f8fba2a-98d2-4e94-9d71-3b2625e16032",
+   "metadata": {},
+   "source": [
+    "### 1.1 Read in FLUXNET data if requested"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fdf20c82-5a01-4ab9-8881-d9388b1b2356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# --------------------------------------------------------\n",
+    "# Function to read requested variables from FLUXNET file. \n",
+    "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
+    "# \n",
+    "# Inputs:  fileName --> Full path to FLUXNET data file \n",
+    "#          varNames --> An array of variable names to be \n",
+    "#                       retrieved from said data file. \n",
+    "#                       NOTE: If you wish to retrieve *all* \n",
+    "#                       variables, pass the string 'ALL'. \n",
+    "#  \n",
+    "# Outputs: fluxnetID --> ID string used to identify station\n",
+    "#          fluxnetDS --> An x-array dataset containing the \n",
+    "#                        requested variables.\n",
+    "#                        Missing values will be set to NaN. \n",
+    "# \n",
+    "# --------------------------------------------------------\n",
+    "\n",
+    "def readFLUXNET_var(fileName, varNames): \n",
+    "    # Get ID of station \n",
+    "    startID   = fileName.find('FLX_')\n",
+    "    fluxnetID = fileName[startID+4:startID+10]\n",
+    "    \n",
+    "    # If this is taking a long time or you just want to know where in the stations you are, uncomment print statement\n",
+    "    # print('Reading in site - ', fluxnetID)\n",
+    "    \n",
+    "    # Read in CSV file containing data \n",
+    "    dataDF = pd.read_csv(fileName)\n",
+    "    \n",
+    "    # Return ALL variables from dataDF if requested\n",
+    "    if varNames=='ALL':\n",
+    "        fluxnetDF = dataDF\n",
+    "        \n",
+    "        # Set any value that's missing to NaN (not -9999)\n",
+    "        fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
+    "\n",
+    "        \n",
+    "        # If time has been requested, reformat to pandas date index\n",
+    "        fluxnetDF['TIMESTAMP'] = pd.to_datetime(fluxnetDF['TIMESTAMP'].values, format='%Y%m%d')\n",
+    "        fluxnetDF = fluxnetDF.set_index(['TIMESTAMP'])\n",
+    "        \n",
+    "        # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
+    "        #   NOTE: since current implementation doesn't use the pre-formatted CoMeT, might not need this step now\n",
+    "        fluxnetDS = fluxnetDF.to_xarray()\n",
+    "            \n",
+    "    # Reduce returned DF to contain only variables of interest  \n",
+    "    else:\n",
+    "        \n",
+    "        # Check that requested variables are available in specific file\n",
+    "        errCount = 0                       # Initialize flag for error \n",
+    "        colNames = dataDF.columns.values   # Available variables in file \n",
+    "        \n",
+    "        for iVar in range(len(varNames)):  # Check each variable individually\n",
+    "            if (varNames[iVar] in colNames)==False:\n",
+    "                # Turn on print statement for more verbose output\n",
+    "                # print('** ERROR: %13s   not contained in file for %8s  **' %(varNames[iVar], fluxnetID))\n",
+    "                \n",
+    "                # If any variable is not conatined in file, return a NaN \n",
+    "                fluxnetDS = -999\n",
+    "                errCount  = errCount+1\n",
+    "                \n",
+    "        # If all the variables *are* available, isolate those in DF and return that\n",
+    "        if errCount == 0: \n",
+    "            fluxnetDF = dataDF[varNames]\n",
+    "            \n",
+    "            # Set any value that's missing to NaN (not -999)\n",
+    "            fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
+    "            \n",
+    "            # If time has been requested, reformat to pandas make index\n",
+    "            if ('TIMESTAMP' in varNames)==True:\n",
+    "                fluxnetDF['TIMESTAMP'] = pd.to_datetime(fluxnetDF['TIMESTAMP'].values, format='%Y%m%d')\n",
+    "                fluxnetDF = fluxnetDF.set_index(['TIMESTAMP'])\n",
+    "        \n",
+    "            # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
+    "            fluxnetDS = fluxnetDF.to_xarray()\n",
+    "    \n",
+    "    return(fluxnetID, fluxnetDS)\n",
+    "\n",
+    "\n",
+    "'''\n",
+    "Inputs: xname -- controlling variable \n",
+    "        yname -- responding variable\n",
+    "        ds    -- dataset containing xname and yname data \n",
+    "        \n",
+    "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
+    "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
+    "'''\n",
+    "\n",
+    "def compute_couplingIndex_FLUXNET(xname,yname,ds):\n",
+    "    xday = ds[xname].groupby('TIMESTAMP.season')\n",
+    "    yday = ds[yname].groupby('TIMESTAMP.season')\n",
+    "\n",
+    "    # Get the covariance of the two (numerator in coupling index)\n",
+    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby('TIMESTAMP.season').sum() / xday.count()\n",
+    "\n",
+    "    # Now compute the actual coupling index \n",
+    "    couplingIndex = covarTerm/xday.std()\n",
+    "\n",
+    "    \n",
+    "    return couplingIndex\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9acb032f-f106-4783-8c57-8e0d3a44eb3e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Prr\n",
+      "No data for station:   US-Tw4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   AU-Cum\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   ES-Ln2\n",
+      "No data for station:   US-Ha1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   BR-Sa1\n",
+      "No data for station:   FI-Lom\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Cop\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Atq\n",
+      "No data for station:   US-Wi4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   CZ-wet\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   GL-NuF\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = depth\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-GBT\n",
+      "No data for station:   US-Wi5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = depth\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Los\n",
+      "No data for station:   DE-Akm\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   NL-Hor\n",
+      "No data for station:   US-Wi6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   BE-Bra\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Ivo\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   DE-SfN\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   AR-Vir\n",
+      "No data for station:   US-ORv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = depth\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Wi3\n",
+      "No data for station:   CA-Obs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Wi2\n",
+      "No data for station:   US-Wi9\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   DE-Spw\n",
+      "No data for station:   FR-Pue\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Ne3\n",
+      "No data for station:   DE-RuS\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   DE-Zrk\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   CA-Man\n",
+      "No data for station:   US-Twt\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = depth\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Wi7\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   IT-La2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Wi1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Wi8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   GL-ZaF\n",
+      "No data for station:   US-Myb\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = depth\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:95: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = depth\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-WPT\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   DE-RuR\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   FR-Fon\n",
+      "No data for station:   US-Ne2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   CG-Tch\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   RU-Che\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Tw1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   RU-Cok\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   US-Wi0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   FI-Let\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:137: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
+      "  dateRange      = pd.date_range(start=startTime_fluxnet[iSt],end=endTime_fluxnet[iSt],freq='M')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No data for station:   SJ-Adv\n",
+      "Number of FLUXNET stations with CI calculated: 150\n",
+      "Minimum number of months used for JJA mean CI: 1 \n",
+      "Maximum number of months used for JJA mean CI: 57 \n"
+     ]
+    }
+   ],
+   "source": [
+    "if fluxnet_comparison==True: \n",
+    "    # obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data \n",
+    "\n",
+    "    ## Metadata files\n",
+    "    siteInfoFile = obsDir+'SiteList.csv'\n",
+    "    siteInfoDF = pd.read_csv(siteInfoFile)\n",
+    "    \n",
+    "    metadataFile = obsDir+'FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.csv'\n",
+    "    metadataDF = pd.read_csv(metadataFile)\n",
+    "    \n",
+    "    ## List of all station files \n",
+    "    dataFiles = glob.glob(obsDir + 'FLX_*/*SUBSET_DD*')\n",
+    "\n",
+    "    # Set up a few empty arrays to save data into \n",
+    "    terraCI_fluxnetConverted   = np.full([len(dataFiles), 4], np.nan)   # CI using kg/m2 soil water content  [nStations, seasons]\n",
+    "    \n",
+    "    # Also save out some data on each station \n",
+    "    startTime_fluxnet = np.zeros(len(dataFiles), dtype='datetime64[s]')\n",
+    "    endTime_fluxnet   = np.zeros(len(dataFiles), dtype='datetime64[s]')\n",
+    "    lat_fluxnet       = np.full([len(dataFiles)], np.nan)\n",
+    "    lon_fluxnet       = np.full([len(dataFiles)], np.nan)\n",
+    "    SWCdepth          = np.full([len(dataFiles)], np.nan)\n",
+    "    \n",
+    "    stationID           = []\n",
+    "    stationID_converted = []\n",
+    "    \n",
+    "    allStationID        = []\n",
+    "        \n",
+    "    # Variables I want returned:\n",
+    "    varNames = ['TIMESTAMP','H_F_MDS','SWC_F_MDS_1','SWC_F_MDS_1_QC']\n",
+    "    \n",
+    "    # Loop over each station (data file)\n",
+    "    for iStation in range(len(dataFiles)):\n",
+    "        \n",
+    "        # Read in data \n",
+    "        # ----------------------------------------------------------\n",
+    "        fluxnetID,fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
+    "        \n",
+    "        # Save lat and lon for this station \n",
+    "        # ----------------------------------------------------------\n",
+    "        indStation            = np.where(fluxnetID==siteInfoDF['SITE_ID'])[0]\n",
+    "        lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
+    "        lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+    "        allStationID.append(fluxnetID)\n",
+    "        \n",
+    "        # Check that there was data saved for this particular site: \n",
+    "        # ----------------------------------------------------------\n",
+    "        if (type(fluxnetDS)==int):\n",
+    "            print('No data for station: %8s' % fluxnetID)\n",
+    "        \n",
+    "        elif ( (np.all(np.isnan(fluxnetDS['H_F_MDS']))==True) | (np.all(np.isnan(fluxnetDS['SWC_F_MDS_1']))==True) ):\n",
+    "            print('No data for station: %8s' % fluxnetID)\n",
+    "            \n",
+    "        # If data is present: \n",
+    "        # ----------------------------------------------------------\n",
+    "        else:        \n",
+    "            # Only consider where data is actually present for selected vars\n",
+    "            iReal      = np.where((np.isfinite(fluxnetDS['SWC_F_MDS_1'])==True) & \n",
+    "                                  (np.isfinite(fluxnetDS['H_F_MDS'])==True))[0]\n",
+    "            fluxnetDS  = fluxnetDS.isel(TIMESTAMP=iReal)\n",
+    "            \n",
+    "            stationID.append(fluxnetID)\n",
+    "            \n",
+    "            # Convert units from volumetric (%) to mass (kg/m2)\n",
+    "            # -------------------------------------------------\n",
+    "            #   Step 1: Convert from % to fraction\n",
+    "            fracSM = (fluxnetDS['SWC_F_MDS_1'].values)/100.0\n",
+    "            \n",
+    "            #    Step 2: Need to use depth of obs in conversion \n",
+    "            metaData_station    = metadataDF[metadataDF.SITE_ID==fluxnetID]\n",
+    "            iSWC                = np.where(metaData_station.DATAVALUE=='SWC_F_MDS_1')[0]\n",
+    "            #  Some locations (5) have two depths \n",
+    "            if len(iSWC)>1:             \n",
+    "                for iDepth in range(len(iSWC)): \n",
+    "                    SWC_DF   = metaData_station[iSWC[iDepth]:iSWC[iDepth]+4]\n",
+    "                    \n",
+    "                    depth    = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+    "                    depthDay = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_DATE'].DATAVALUE.values).astype(int)\n",
+    "                    depthDay = int(str(depthDay[0])[:8])  # Some weird ones have time attached; don't want that\n",
+    "                    depthDay = pd.to_datetime(depthDay, format='%Y%m%d')\n",
+    "            \n",
+    "                    # Keep deepest level as the depth for station \n",
+    "                    if iDepth==0:\n",
+    "                        SWCdepth[iStation] = depth \n",
+    "                        convertSM = fracSM*1000.0*np.abs(depth)\n",
+    "                    else: \n",
+    "                        # Use date as break point for getting kg/m2 SWC\n",
+    "                        #  Eq: SWC_kgm2 = SWC_vol [m3/m3] * 1000 [kg/m3] * depth [m]\n",
+    "                        dateArr = pd.DatetimeIndex(fluxnetDS.TIMESTAMP.values)\n",
+    "                        iTime   = int(np.where(dateArr==depthDay)[0])\n",
+    "                        convertSM[iTime::] = (fracSM[iTime::])*1000.0*np.abs(depth)\n",
+    "                        \n",
+    "                        # Keep deepest level as the depth for station \n",
+    "                        if depth<SWCdepth[iStation]: \n",
+    "                            SWCdepth[iStation] = depth\n",
+    "                            \n",
+    "                stationID_converted.append(fluxnetID)\n",
+    "                    \n",
+    "            # If station only has one level recorded, things are a bit easier:             \n",
+    "            else: \n",
+    "                SWC_DF             = metaData_station[iSWC[0]:iSWC[0]+4]\n",
+    "                SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
+    "                convertSM          = fracSM*1000.0*np.abs(SWCdepth[iStation])\n",
+    "                \n",
+    "                stationID_converted.append(fluxnetID)\n",
+    "    \n",
+    "            # Save converted soil moisture to dataset \n",
+    "            fluxnetDS['SWC_F_MDS_1_convert'] = (('TIMESTAMP'), convertSM)\n",
+    "            \n",
+    "            \n",
+    "            # Save first and last time used for computing CI \n",
+    "            # ----------------------------------------------\n",
+    "            startTime_fluxnet[iStation] = fluxnetDS['TIMESTAMP'].values[0]\n",
+    "            endTime_fluxnet[iStation]   = fluxnetDS['TIMESTAMP'].values[-1]\n",
+    "                    \n",
+    "            # Compute terrestrial coupling metric\n",
+    "            # -----------------------------------    \n",
+    "            terraLeg = compute_couplingIndex_FLUXNET('SWC_F_MDS_1_convert', \n",
+    "                                                      'H_F_MDS',\n",
+    "                                                      fluxnetDS)\n",
+    "            \n",
+    "            # If there's less than one full year of data, don't use station\n",
+    "            #   (i.e., as long as all 4 seasons are defined, save values)\n",
+    "            if np.shape(terraLeg)[0]==4:\n",
+    "                terraCI_fluxnetConverted[iStation,:] = terraLeg\n",
+    "\n",
+    "    seasons = terraLeg.season\n",
+    "\n",
+    "    ## Print some useful information     \n",
+    "    print('Number of FLUXNET stations with CI calculated: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n",
+    "    \n",
+    "    # How many months go into each calculation of CI for JJA? \n",
+    "    nMonths = np.full([len(dataFiles)], np.nan)\n",
+    "    \n",
+    "    for iSt in range(len(dataFiles)):\n",
+    "        if np.isfinite(terraCI_fluxnetConverted[iSt,1]):\n",
+    "            dateRange      = pd.date_range(start=startTime_fluxnet[iSt],end=endTime_fluxnet[iSt],freq='M')\n",
+    "            nMonths[iSt]   = len(np.where((dateRange.month>=6) & (dateRange.month<=8))[0])\n",
+    "            \n",
+    "    print('Minimum number of months used for JJA mean CI: %i ' % int(np.nanmin(nMonths)) )\n",
+    "    print('Maximum number of months used for JJA mean CI: %i ' % int(np.nanmax(nMonths)))\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd29ba9d-bbbb-4f59-9829-250018215b53",
+   "metadata": {},
+   "source": [
+    "*Make some choices on limiting which stations are used*\n",
+    "- Let's limit usage to depths less than 20 cm (arbitrary, but I don't want us using non-surface soil moisture for this application). This will eliminate 11 stations.\n",
+    "- It would also be good to put some time limits on this. So let's say the observations need to have at least 9 months of data for JJA means (3-years). Otherwise, set terraCI to np.nan again so we don't use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13afbfb4-2040-403c-9d82-3e370f47ec5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of FLUXNET stations to use with reasonable depths of SWC: 139\n",
+      "Number of FLUXNET stations to use with 3+ years of JJA data: 115\n"
+     ]
+    }
+   ],
+   "source": [
+    "if fluxnet_comparison==True: \n",
+    "\n",
+    "    # Get stations with SWC from below 20 cm (or equal to zero)\n",
+    "    iLimit = np.where((SWCdepth==0.0) | (SWCdepth<-0.2))[0]\n",
+    "    \n",
+    "    # Set the terrestrial leg of CI to missing so we don't consider those \n",
+    "    terraCI_fluxnetConverted[iLimit,:] = np.nan\n",
+    "    \n",
+    "    print('Number of FLUXNET stations to use with reasonable depths of SWC: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n",
+    "    \n",
+    "    # Get stations with less than 9 months used for JJA terrestrial CI \n",
+    "    iLimit = np.where(nMonths<9)[0]\n",
+    "    \n",
+    "    # Set to missing so we don't consider stations with less than three years of data going into the average \n",
+    "    terraCI_fluxnetConverted[iLimit,:] = np.nan\n",
+    "    \n",
+    "    print('Number of FLUXNET stations to use with 3+ years of JJA data: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acb41b70-cab5-46cc-962b-2e7243696e9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc387253-cdd7-4a36-956b-8ce548e963bd",
+   "metadata": {},
+   "source": [
+    "## 2. Make plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67253fd1-d2f7-45fe-a59f-215303b93c06",
+   "metadata": {},
+   "outputs": [
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+       ".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;uxarray.Grid&gt;\n",
+       "Original Grid Type: ESMF\n",
+       "Grid Dimensions:\n",
+       "  * n_node: 48602\n",
+       "  * n_face: 48600\n",
+       "  * n_max_face_nodes: 4\n",
+       "  * n_nodes_per_face: (48600,)\n",
+       "Grid Coordinates (Spherical):\n",
+       "  * node_lon: (48602,)\n",
+       "  * node_lat: (48602,)\n",
+       "  * face_lon: (48600,)\n",
+       "  * face_lat: (48600,)\n",
+       "Grid Coordinates (Cartesian):\n",
+       "Grid Connectivity Variables:\n",
+       "  * face_node_connectivity: (48600, 4)\n",
+       "Grid Descriptor Variables:\n",
+       "  * n_nodes_per_face: (48600,)\n",
+       "</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>uxarray.Grid</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8d0312db-2e38-44c9-8f16-3c517704f99e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8d0312db-2e38-44c9-8f16-3c517704f99e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>n_node</span>: 48602</li><li><span>n_face</span>: 48600</li><li><span>n_max_face_nodes</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5e46ee84-9ede-4e6a-ac78-5edcd66447a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e46ee84-9ede-4e6a-ac78-5edcd66447a1' class='xr-section-summary' >Spherical Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>node_lon</span></div><div class='xr-var-dims'>(n_node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-159.9 -162.3 ... 170.2 167.8</div><input id='attrs-3d72e0de-c61a-46cb-ab1f-c5d211c6a25d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d72e0de-c61a-46cb-ab1f-c5d211c6a25d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e12e846-0138-46cd-a610-b352cab6003c' class='xr-var-data-in' type='checkbox'><label for='data-7e12e846-0138-46cd-a610-b352cab6003c' 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>longitude</dd><dt><span>long name :</span></dt><dd>Longitude of the corner nodes of each face</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-159.89124086, -162.26235981, -164.68737511, ...,  172.60921368,\n",
+       "        170.18081007,  167.78169785])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>node_lat</span></div><div class='xr-var-dims'>(n_node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-67.77 -68.05 -68.3 ... 67.7 67.54</div><input id='attrs-b3d3831b-1a35-4b76-b885-f052b1d509d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3d3831b-1a35-4b76-b885-f052b1d509d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1f027ff-0e70-456e-bc5f-54ba26c43780' class='xr-var-data-in' type='checkbox'><label for='data-f1f027ff-0e70-456e-bc5f-54ba26c43780' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long name :</span></dt><dd>Latitude of the corner nodes of each face</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-67.76610373, -68.049425  , -68.29778632, ...,  67.83347501,\n",
+       "        67.70475883,  67.54027826])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>face_lon</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-44.5 -43.5 -42.5 ... 136.0 135.0</div><input id='attrs-75ed936c-4739-49ff-8bd2-70e5b60ea06e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75ed936c-4739-49ff-8bd2-70e5b60ea06e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7852748-7ebd-4b71-ad92-528ae024f286' class='xr-var-data-in' type='checkbox'><label for='data-b7852748-7ebd-4b71-ad92-528ae024f286' 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>longitude</dd><dt><span>long name :</span></dt><dd>Longitude of the center of each face</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-44.5       , -43.5       , -42.5       , ..., 137.00152343,\n",
+       "       136.00045705, 135.        ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>face_lat</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-35.03 -35.48 -35.92 ... 36.2 35.74</div><input id='attrs-6921a611-f548-49b5-b86b-cf3b4e7f0034' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6921a611-f548-49b5-b86b-cf3b4e7f0034' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee89dbcf-6c05-4479-b115-beee30f25366' class='xr-var-data-in' type='checkbox'><label for='data-ee89dbcf-6c05-4479-b115-beee30f25366' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long name :</span></dt><dd>Latitude of the center of each face</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-35.0269697 , -35.48216087, -35.92409689, ...,  36.65844396,\n",
+       "        36.20454323,  35.73717077])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba22bedd-1c6f-47fe-80fb-a1fa88e4c0bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba22bedd-1c6f-47fe-80fb-a1fa88e4c0bf' class='xr-section-summary'  title='Expand/collapse section'>Cartesian Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-2a453a4f-5b9c-4c9c-b85c-fb50464e5dd9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a453a4f-5b9c-4c9c-b85c-fb50464e5dd9' class='xr-section-summary' >Connectivity: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>face_node_connectivity</span></div><div class='xr-var-dims'>(n_face, n_max_face_nodes)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>6985 6986 6987 ... 36983 38576</div><input id='attrs-73b195ba-1a84-4cd5-a576-282b91fcdf59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73b195ba-1a84-4cd5-a576-282b91fcdf59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3e46941-9ee1-48c9-9598-772ffbd068af' class='xr-var-data-in' type='checkbox'><label for='data-b3e46941-9ee1-48c9-9598-772ffbd068af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>face_node_connectivity</dd><dt><span>long name :</span></dt><dd>Maps every face to its corner nodes.</dd><dt><span>start_index :</span></dt><dd>0</dd><dt><span>_FillValue :</span></dt><dd>-9223372036854775808</dd></dl></div><div class='xr-var-data'><pre>array([[ 6985,  6986,  6987,  6988],\n",
+       "       [ 6986,  6989,  6990,  6987],\n",
+       "       [ 6989,  6991,  6992,  6990],\n",
+       "       ...,\n",
+       "       [41616, 41619, 38577, 38578],\n",
+       "       [41619, 41620, 38576, 38577],\n",
+       "       [41620, 36982, 36983, 38576]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83c819c9-a799-4c21-a6e0-d36379f8e206' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83c819c9-a799-4c21-a6e0-d36379f8e206' class='xr-section-summary' >Descriptors: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>n_nodes_per_face</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4</div><input id='attrs-f54caae1-ffbb-4a08-b288-53b052592893' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f54caae1-ffbb-4a08-b288-53b052592893' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67e569fb-3437-4b22-b15b-1be14d4dee15' class='xr-var-data-in' type='checkbox'><label for='data-67e569fb-3437-4b22-b15b-1be14d4dee15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>n_nodes_per_face</dd><dt><span>long name :</span></dt><dd>Number of nodes per face</dd></dl></div><div class='xr-var-data'><pre>array([4, 4, 4, ..., 4, 4, 4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6fd24b3-72df-40ae-bec0-79f394c57309' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f6fd24b3-72df-40ae-bec0-79f394c57309' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<uxarray.Grid>\n",
+       "Original Grid Type: ESMF\n",
+       "Grid Dimensions:\n",
+       "  * n_node: 48602\n",
+       "  * n_face: 48600\n",
+       "  * n_max_face_nodes: 4\n",
+       "  * n_nodes_per_face: (48600,)\n",
+       "Grid Coordinates (Spherical):\n",
+       "  * node_lon: (48602,)\n",
+       "  * node_lat: (48602,)\n",
+       "  * face_lon: (48600,)\n",
+       "  * face_lat: (48600,)\n",
+       "Grid Coordinates (Cartesian):\n",
+       "Grid Connectivity Variables:\n",
+       "  * face_node_connectivity: (48600, 4)\n",
+       "Grid Descriptor Variables:\n",
+       "  * n_nodes_per_face: (48600,)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Load coupling index with uxarray \n",
+    "gridFile = \"/glade/p/cesmdata/cseg/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc\"\n",
+    "uxgrid = uxr.open_grid(gridFile)\n",
+    "uxgrid\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "43206b67-1313-4b61-94ea-b50b85a3d50c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coupling index is now ready to go\n"
+     ]
+    }
+   ],
+   "source": [
+    "for iCase in range(len(caseNames)):\n",
+    "    filePath = '/glade/derecho/scratch/mdfowler/'+caseNames[iCase]+'_TerrestrialCouplingIndex_SHvsSM.nc'\n",
+    "    couplingIndex_case = uxr.open_dataset(gridFile, filePath)\n",
+    "    # Rename the variable:\n",
+    "    couplingIndex_case = couplingIndex_case.rename({'__xarray_dataarray_variable__': 'CouplingIndex'})\n",
+    "    \n",
+    "    # Assign case coord\n",
+    "    couplingIndex_case     = couplingIndex_case.assign_coords({\"case\":  couplingIndex_case.case.values})\n",
+    "\n",
+    "    # Return all the cases in a single dataset\n",
+    "    if iCase==0:\n",
+    "        couplingIndex_DS    = couplingIndex_case\n",
+    "        del couplingIndex_case\n",
+    "    else: \n",
+    "        couplingIndex_DS = uxr.concat([couplingIndex_DS, couplingIndex_case], \"case\") \n",
+    "        del couplingIndex_case\n",
+    "    \n",
+    "print('Coupling index is now ready to go')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e9e22cd0-870e-47cb-b5c7-2e57bbd9af16",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def make_cmap(colors, position=None, bit=False):\n",
+    "    '''\n",
+    "    make_cmap takes a list of tuples which contain RGB values. The RGB\n",
+    "    values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
+    "    True when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
+    "    a cmap with equally spaced colors.\n",
+    "    Arrange your tuples so that the first color is the lowest value for the\n",
+    "    colorbar and the last is the highest.\n",
+    "    position contains values from 0 to 1 to dictate the location of each color.\n",
+    "    '''\n",
+    "    \n",
+    "    import matplotlib as mpl\n",
+    "    import numpy as np\n",
+    "    \n",
+    "    bit_rgb = np.linspace(0,1,256)\n",
+    "    if position == None:\n",
+    "        position = np.linspace(0,1,len(colors))\n",
+    "    else:\n",
+    "        if len(position) != len(colors):\n",
+    "            sys.exit(\"position length must be the same as colors\")\n",
+    "        elif position[0] != 0 or position[-1] != 1:\n",
+    "            sys.exit(\"position must start with 0 and end with 1\")\n",
+    "    \n",
+    "    if bit:\n",
+    "        for i in range(len(colors)):\n",
+    "            colors[i] = (bit_rgb[colors[i][0]],\n",
+    "                         bit_rgb[colors[i][1]],\n",
+    "                         bit_rgb[colors[i][2]])\n",
+    "    \n",
+    "    cdict = {'red':[], 'green':[], 'blue':[]}\n",
+    "    for pos, color in zip(position, colors):\n",
+    "        cdict['red'].append((pos, color[0], color[0]))\n",
+    "        cdict['green'].append((pos, color[1], color[1]))\n",
+    "        cdict['blue'].append((pos, color[2], color[2]))\n",
+    "\n",
+    "    cmap = mpl.colors.LinearSegmentedColormap('my_colormap',cdict,256)\n",
+    "    return cmap\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6e3e7b6f-98a8-4e21-8378-adfd8b399823",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Create a list of RGB tuples for terrestrial leg (SM, SHFLX)\n",
+    "colorsList_SMvSHF = [(124,135,181), \n",
+    "                     (107,109,161),\n",
+    "                     (51,82,120),\n",
+    "                     (49,114,127),\n",
+    "                     (97,181,89),\n",
+    "                     (200,218,102),\n",
+    "                     (255,242,116),\n",
+    "                     (238,164,58)] # This example uses the 8-bit RGB\n",
+    "my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "882b1c92-e5b9-4cd0-aa55-4e04aca3c50c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotTCI_case(seasonstr, caseSel=None):\n",
+    "\n",
+    "    transform = ccrs.PlateCarree()\n",
+    "    projection = ccrs.PlateCarree()\n",
+    "\n",
+    "    \n",
+    "    if caseSel: \n",
+    "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
+    "        collection = couplingIndex_DS['CouplingIndex'].sel(season=seasonstr).isel(case=caseSel).to_polycollection()\n",
+    "        \n",
+    "        collection.set_transform(transform)\n",
+    "        collection.set_antialiased(False)\n",
+    "        collection.set_cmap(my_cmap_SMvSHF)\n",
+    "        collection.set_clim(vmin=-20, vmax=5)\n",
+    "        \n",
+    "        fig, ax = plt.subplots(1, 1, figsize=(12,5), facecolor=\"w\",\n",
+    "                               constrained_layout=True,\n",
+    "                               subplot_kw=dict(projection=projection))\n",
+    "        \n",
+    "        ax.coastlines()\n",
+    "        ax.add_collection(collection)\n",
+    "        ax.set_global()\n",
+    "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
+    "        ax.set_title(seasonstr+' Coupling Index: '+str(couplingIndex_DS.case.isel(case=caseSel).values))\n",
+    "\n",
+    "        plt.show()\n",
+    "        plt.close()\n",
+    "\n",
+    "    else:\n",
+    "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
+    "        collection = couplingIndex_DS['CouplingIndex'].sel(season=seasonstr).to_polycollection()\n",
+    "        \n",
+    "        collection.set_transform(transform)\n",
+    "        collection.set_antialiased(False)\n",
+    "        collection.set_cmap(my_cmap_SMvSHF)\n",
+    "        collection.set_clim(vmin=-20, vmax=5)\n",
+    "        \n",
+    "        fig, ax = plt.subplots(1, 1, figsize=(12,5), facecolor=\"w\",\n",
+    "                               constrained_layout=True,\n",
+    "                               subplot_kw=dict(projection=projection))\n",
+    "        \n",
+    "        ax.coastlines()\n",
+    "        ax.add_collection(collection)\n",
+    "        ax.set_global()\n",
+    "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
+    "        ax.set_title(seasonstr+' Coupling Index: '+str(couplingIndex_DS.case.values))\n",
+    "\n",
+    "        if fluxnet_comparison==True:\n",
+    "            ## Add FLUXNET obs         \n",
+    "            iSeason = np.where(seasons==seasonstr)[0]\n",
+    "            iStations = np.where(np.isfinite(terraCI_fluxnetConverted[:,iSeason])==True)[0]\n",
+    "            norm_CI = matplotlib.colors.Normalize(vmin=-20, vmax=5)\n",
+    "            \n",
+    "            ax.scatter(lon_fluxnet[iStations], lat_fluxnet[iStations], c=terraCI_fluxnetConverted[iStations,iSeason], cmap=my_cmap_SMvSHF, norm=norm_CI,\n",
+    "                            edgecolor='k', s=30, marker='o', transform=ccrs.PlateCarree())\n",
+    "            \n",
+    "\n",
+    "        plt.show()\n",
+    "        plt.close()\n",
+    "        \n",
+    "    return "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d43b6525-aa30-47a4-8b66-ca70a805a13a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if len(caseNames)==1:\n",
+    "    plotTCI_case('JJA', None)\n",
+    "    plotTCI_case('DJF', None)\n",
+    "else:\n",
+    "    for iCase in range(len(caseNames)):\n",
+    "        plotTCI_case('JJA', iCase)\n",
+    "        plotTCI_case('DJF', iCase)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8ff07ee-af88-4342-88de-ef1f2fdfa7bc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c51c912b-e2d4-4a82-a27a-ac7003cbe2df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60d2dfe0-791f-40ed-8f19-6fc36b72c8b7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:cupid-analysis]",
+   "language": "python",
+   "name": "conda-env-cupid-analysis-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 6f312708e4bac2ea282103f7f0dcc61a348cf744 Mon Sep 17 00:00:00 2001
From: Meg Fowler <mdfowler@ucar.edu>
Date: Thu, 17 Oct 2024 09:15:14 -0600
Subject: [PATCH 2/5] Update notebook

---
 .../lnd/LandAtm_CouplingIndex_V2.ipynb        | 3682 +++--------------
 1 file changed, 544 insertions(+), 3138 deletions(-)

diff --git a/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb b/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb
index c04b0b5..826a2f0 100755
--- a/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb
+++ b/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "750da831-1c5c-4b41-947e-a9e57a62a820",
    "metadata": {
     "editable": true,
@@ -31,622 +31,11 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "  ),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  const force = true;\n",
-       "  const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
-       "  const reloading = false;\n",
-       "  const Bokeh = root.Bokeh;\n",
-       "\n",
-       "  // Set a timeout for this load but only if we are not already initializing\n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "    if (js_modules == null) js_modules = [];\n",
-       "    if (js_exports == null) js_exports = {};\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      // Don't load bokeh if it is still initializing\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
-       "      // There is nothing to load\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "    window._bokeh_on_load = on_load\n",
-       "\n",
-       "    function on_error(e) {\n",
-       "      const src_el = e.srcElement\n",
-       "      console.error(\"failed to load \" + (src_el.href || src_el.src));\n",
-       "    }\n",
-       "\n",
-       "    const skip = [];\n",
-       "    if (window.requirejs) {\n",
-       "      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n",
-       "      root._bokeh_is_loading = css_urls.length + 0;\n",
-       "    } else {\n",
-       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
-       "    }\n",
-       "\n",
-       "    const existing_stylesheets = []\n",
-       "    const links = document.getElementsByTagName('link')\n",
-       "    for (let i = 0; i < links.length; i++) {\n",
-       "      const link = links[i]\n",
-       "      if (link.href != null) {\n",
-       "        existing_stylesheets.push(link.href)\n",
-       "      }\n",
-       "    }\n",
-       "    for (let i = 0; i < css_urls.length; i++) {\n",
-       "      const url = css_urls[i];\n",
-       "      const escaped = encodeURI(url)\n",
-       "      if (existing_stylesheets.indexOf(escaped) !== -1) {\n",
-       "        on_load()\n",
-       "        continue;\n",
-       "      }\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error;\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }    var existing_scripts = []\n",
-       "    const scripts = document.getElementsByTagName('script')\n",
-       "    for (let i = 0; i < scripts.length; i++) {\n",
-       "      var script = scripts[i]\n",
-       "      if (script.src != null) {\n",
-       "        existing_scripts.push(script.src)\n",
-       "      }\n",
-       "    }\n",
-       "    for (let i = 0; i < js_urls.length; i++) {\n",
-       "      const url = js_urls[i];\n",
-       "      const escaped = encodeURI(url)\n",
-       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n",
-       "        if (!window.requirejs) {\n",
-       "          on_load();\n",
-       "        }\n",
-       "        continue;\n",
-       "      }\n",
-       "      const element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error;\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "    for (let i = 0; i < js_modules.length; i++) {\n",
-       "      const url = js_modules[i];\n",
-       "      const escaped = encodeURI(url)\n",
-       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n",
-       "        if (!window.requirejs) {\n",
-       "          on_load();\n",
-       "        }\n",
-       "        continue;\n",
-       "      }\n",
-       "      var element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error;\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      element.type = \"module\";\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "    for (const name in js_exports) {\n",
-       "      const url = js_exports[name];\n",
-       "      const escaped = encodeURI(url)\n",
-       "      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n",
-       "        if (!window.requirejs) {\n",
-       "          on_load();\n",
-       "        }\n",
-       "        continue;\n",
-       "      }\n",
-       "      var element = document.createElement('script');\n",
-       "      element.onerror = on_error;\n",
-       "      element.async = false;\n",
-       "      element.type = \"module\";\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      element.textContent = `\n",
-       "      import ${name} from \"${url}\"\n",
-       "      window.${name} = ${name}\n",
-       "      window._bokeh_on_load()\n",
-       "      `\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "    if (!js_urls.length && !js_modules.length) {\n",
-       "      on_load()\n",
-       "    }\n",
-       "  };\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n",
-       "  const js_modules = [];\n",
-       "  const js_exports = {};\n",
-       "  const css_urls = [];\n",
-       "  const inline_js = [    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "function(Bokeh) {} // ensure no trailing comma for IE\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
-       "      for (let i = 0; i < inline_js.length; i++) {\n",
-       "        try {\n",
-       "          inline_js[i].call(root, root.Bokeh);\n",
-       "        } catch(e) {\n",
-       "          if (!reloading) {\n",
-       "            throw e;\n",
-       "          }\n",
-       "        }\n",
-       "      }\n",
-       "      // Cache old bokeh versions\n",
-       "      if (Bokeh != undefined && !reloading) {\n",
-       "        var NewBokeh = root.Bokeh;\n",
-       "        if (Bokeh.versions === undefined) {\n",
-       "          Bokeh.versions = new Map();\n",
-       "        }\n",
-       "        if (NewBokeh.version !== Bokeh.version) {\n",
-       "          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
-       "        }\n",
-       "        root.Bokeh = Bokeh;\n",
-       "      }\n",
-       "    } else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    }\n",
-       "    root._bokeh_is_initializing = false\n",
-       "  }\n",
-       "\n",
-       "  function load_or_wait() {\n",
-       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
-       "    // versions of Bokeh and its dependencies at the same time.\n",
-       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
-       "    // to determine whether there is an ongoing attempt to initialize\n",
-       "    // bokeh, however for backward compatibility we also try to ensure\n",
-       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
-       "    // before older versions are fully initialized.\n",
-       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
-       "      // If the timeout and bokeh was not successfully loaded we reset\n",
-       "      // everything and try loading again\n",
-       "      root._bokeh_timeout = Date.now() + 5000;\n",
-       "      root._bokeh_is_initializing = false;\n",
-       "      root._bokeh_onload_callbacks = undefined;\n",
-       "      root._bokeh_is_loading = 0\n",
-       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
-       "      load_or_wait();\n",
-       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
-       "      setTimeout(load_or_wait, 100);\n",
-       "    } else {\n",
-       "      root._bokeh_is_initializing = true\n",
-       "      root._bokeh_onload_callbacks = []\n",
-       "      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n",
-       "      if (!reloading && !bokeh_loaded) {\n",
-       "        if (root.Bokeh) {\n",
-       "          root.Bokeh = undefined;\n",
-       "        }\n",
-       "        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "      }\n",
-       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
-       "        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "        run_inline_js();\n",
-       "      });\n",
-       "    }\n",
-       "  }\n",
-       "  // Give older versions of the autoload script a head-start to ensure\n",
-       "  // they initialize before we start loading newer version.\n",
-       "  setTimeout(load_or_wait, 100)\n",
-       "}(window));"
-      ],
-      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
-       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
-       "}\n",
-       "\n",
-       "\n",
-       "    function JupyterCommManager() {\n",
-       "    }\n",
-       "\n",
-       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
-       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
-       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
-       "        comm_manager.register_target(comm_id, function(comm) {\n",
-       "          comm.on_msg(msg_handler);\n",
-       "        });\n",
-       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
-       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
-       "          comm.onMsg = msg_handler;\n",
-       "        });\n",
-       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
-       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
-       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
-       "          function processIteratorResult(result) {\n",
-       "            var message = result.value;\n",
-       "            console.log(message)\n",
-       "            var content = {data: message.data, comm_id};\n",
-       "            var buffers = []\n",
-       "            for (var buffer of message.buffers || []) {\n",
-       "              buffers.push(new DataView(buffer))\n",
-       "            }\n",
-       "            var metadata = message.metadata || {};\n",
-       "            var msg = {content, buffers, metadata}\n",
-       "            msg_handler(msg);\n",
-       "            return messages.next().then(processIteratorResult);\n",
-       "          }\n",
-       "          return messages.next().then(processIteratorResult);\n",
-       "        })\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
-       "      if (comm_id in window.PyViz.comms) {\n",
-       "        return window.PyViz.comms[comm_id];\n",
-       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
-       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
-       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
-       "        if (msg_handler) {\n",
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-       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
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-       "    var Bokeh = get_bokeh(root)\n",
-       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
-       "\tconst id_el = document.getElementById(root_id)\n",
-       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t  for (const child of root_el.children) {\n",
-       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
-       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
-       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
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-       "\t}\n",
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-       "      return null\n",
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-       "\treturn null\n",
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-       "\t  var Bokeh = get_bokeh(root)\n",
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-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ]
-     },
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "p1002"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import glob\n",
-    "import numpy as np \n",
+    "import numpy as np\n",
     "import xarray as xr\n",
     "import datetime\n",
     "from datetime import date, timedelta\n",
@@ -654,12 +43,12 @@
     "import pandas as pd\n",
     "import sys\n",
     "\n",
-    "# Plotting utils \n",
+    "# Plotting utils\n",
     "import matplotlib\n",
-    "import matplotlib.pyplot as plt \n",
+    "import matplotlib.pyplot as plt\n",
     "import cartopy\n",
     "import cartopy.crs as ccrs\n",
-    "import uxarray as uxr\n"
+    "import uxarray as uxr"
    ]
   },
   {
@@ -678,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "7f8f2d17-c653-4ad1-9dc3-c49bf836ceb6",
    "metadata": {
     "editable": true,
@@ -692,38 +81,38 @@
    "outputs": [],
    "source": [
     "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "##  Settings for case locations + names \n",
+    "##  Settings for case locations + names\n",
     "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "## Where observations are stored \n",
-    "# obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data \n",
+    "## Where observations are stored\n",
+    "# obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data\n",
     "\n",
-    "## Where CESM timeseries data is stored  \n",
-    "CESM_output_dir = '/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/'\n",
+    "## Where CESM timeseries data is stored\n",
+    "CESM_output_dir = \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/\"\n",
     "\n",
     "\n",
     "## Full casenames that are present in CESM_output_dir and in individual filenames\n",
     "# caseNames = [\n",
     "#             'b.e23_alpha16b.BLT1850.ne30_t232.054',\n",
-    "            # 'b.e30_beta02.BLT1850.ne30_t232.104',\n",
-    "#             ] \n",
-    "case_name =  'b.e30_beta02.BLT1850.ne30_t232.104'\n",
+    "# 'b.e30_beta02.BLT1850.ne30_t232.104',\n",
+    "#             ]\n",
+    "case_name = \"b.e30_beta02.BLT1850.ne30_t232.104\"\n",
     "\n",
     "# clmFile_h = '.h0.'\n",
     "\n",
-    "start_date= '0001-01-01'\n",
-    "end_date  = '0101-01-01'\n",
+    "start_date = \"0001-01-01\"\n",
+    "end_date = \"0101-01-01\"\n",
     "\n",
     "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "##  Optional settings for notebook \n",
+    "##  Optional settings for notebook\n",
     "## - - - - - - - - - - - - - - - - - - - - - -\n",
     "\n",
-    "## If comparison against FLUXNET desired \n",
-    "# fluxnet_comparison = True \n"
+    "## If comparison against FLUXNET desired\n",
+    "# fluxnet_comparison = True"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "0014712f-d094-4dae-b583-740bf7a9789c",
    "metadata": {
     "editable": true,
@@ -732,46 +121,22 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['0100']\n",
-      "['104']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "## - - - - - - - - - - - - - - - - - - - - - -\n",
     "##  Settings for computing coupling index\n",
     "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "startYrs = [start_date.split('-')[0]]\n",
-    "endYrs   = [f\"{int(end_date.split('-')[0])-1:04d}\"]\n",
+    "startYrs = [start_date.split(\"-\")[0]]\n",
+    "endYrs = [f\"{int(end_date.split('-')[0])-1:04d}\"]\n",
     "\n",
-    "caseNames = [case_name, \n",
-    "            #base_case_name, \n",
-    "            ]\n",
+    "caseNames = [\n",
+    "    case_name,\n",
+    "    # base_case_name,\n",
+    "]\n",
     "\n",
-    "shortNames = [\n",
-    "    case.split('.')[-1] for case in caseNames\n",
-    "]\n"
+    "shortNames = [case.split(\".\")[-1] for case in caseNames]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eda751c0-6b42-47fd-a94e-9e4ab9f7e1c6",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "d70024c7-0af2-48b9-9041-893f40e613ec",
@@ -782,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "304ce8d0-6aab-4fcb-9635-2a78e270f3c7",
    "metadata": {
     "editable": true,
@@ -790,125 +155,144 @@
     "slideshow": {
      "slide_type": ""
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [],
    "source": [
-    "'''\n",
+    "\"\"\"\n",
     "Inputs: xname -- controlling variable \n",
     "        yname -- responding variable\n",
     "        ds    -- dataset containing xname and yname data \n",
     "        \n",
     "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
     "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
-    "'''\n",
+    "\"\"\"\n",
+    "\n",
     "\n",
-    "def compute_couplingIndex_cesm(xname,yname,xDS,yDS):\n",
-    "    xday = xDS[xname].groupby('time.season')\n",
-    "    yday = yDS[yname].groupby('time.season')\n",
+    "def compute_couplingIndex_cesm(xname, yname, xDS, yDS):\n",
+    "    xday = xDS[xname].groupby(\"time.season\")\n",
+    "    yday = yDS[yname].groupby(\"time.season\")\n",
     "\n",
     "    # Get the covariance of the two (numerator in coupling index)\n",
-    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby('time.season').sum() / xday.count()\n",
+    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
+    "        \"time.season\"\n",
+    "    ).sum() / xday.count()\n",
     "\n",
-    "    # Now compute the actual coupling index \n",
-    "    couplingIndex = covarTerm/xday.std()\n",
+    "    # Now compute the actual coupling index\n",
+    "    couplingIndex = covarTerm / xday.std()\n",
     "\n",
-    "    \n",
-    "    return couplingIndex\n"
+    "    return couplingIndex"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "3c35ae8d-dfff-44b0-a854-dfc2b5b030c0",
    "metadata": {
     "editable": true,
     "slideshow": {
      "slide_type": ""
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using previously computed coupling index saved in file  /glade/derecho/scratch/mdfowler/b.e23_alpha16b.BLT1850.ne30_t232.054_TerrestrialCouplingIndex_SHvsSM.nc\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for iCase in range(len(caseNames)):\n",
     "    ## Check first if coupling index has already been created:\n",
-    "    TCI_filePath = '/glade/derecho/scratch/mdfowler/'+caseNames[0]+'_TerrestrialCouplingIndex_SHvsSM.nc'\n",
+    "    TCI_filePath = (\n",
+    "        \"/glade/derecho/scratch/mdfowler/\"\n",
+    "        + caseNames[0]\n",
+    "        + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
+    "    )\n",
+    "\n",
+    "    if os.path.exists(TCI_filePath):  # Use previously computed TCI\n",
+    "        print(\"Using previously computed coupling index saved in file \", TCI_filePath)\n",
+    "    else:  # Compute TCI\n",
     "\n",
-    "    if os.path.exists(TCI_filePath):  # Use previously computed TCI \n",
-    "        print('Using previously computed coupling index saved in file ', TCI_filePath)\n",
-    "    else:   # Compute TCI\n",
-    "        \n",
     "        # Get list of necessary time series files\n",
-    "        soilWater_file = np.sort(glob.glob(CESM_output_dir+'/'+caseNames[iCase]+'/lnd/proc/tseries/'+caseNames[iCase]+clmFile_h+'SOILWATER_10CM.'+startYrs[iCase]+'??-'+endYrs[iCase]+'??.nc'))\n",
-    "        if len(soilWater_file)==0:\n",
-    "            print('Soil moisture file not found!')\n",
-    "        elif len(soilWater_file)>1: \n",
-    "            print('More than one file matches requested time period and case for soil moisture.')\n",
-    "        elif len(soilWater_file)==1: \n",
+    "        soilWater_file = np.sort(\n",
+    "            glob.glob(\n",
+    "                CESM_output_dir\n",
+    "                + \"/\"\n",
+    "                + caseNames[iCase]\n",
+    "                + \"/lnd/proc/tseries/\"\n",
+    "                + caseNames[iCase]\n",
+    "                + clmFile_h\n",
+    "                + \"SOILWATER_10CM.\"\n",
+    "                + startYrs[iCase]\n",
+    "                + \"??-\"\n",
+    "                + endYrs[iCase]\n",
+    "                + \"??.nc\"\n",
+    "            )\n",
+    "        )\n",
+    "        if len(soilWater_file) == 0:\n",
+    "            print(\"Soil moisture file not found!\")\n",
+    "        elif len(soilWater_file) > 1:\n",
+    "            print(\n",
+    "                \"More than one file matches requested time period and case for soil moisture.\"\n",
+    "            )\n",
+    "        elif len(soilWater_file) == 1:\n",
     "            soilWater_DS = xr.open_dataset(soilWater_file[0], decode_times=True)\n",
-    "         \n",
-    "        sh_file = np.sort(glob.glob(CESM_output_dir+'/'+caseNames[iCase]+'/lnd/proc/tseries/'+caseNames[iCase]+clmFile_h+'FSH_TO_COUPLER.'+startYrs[iCase]+'??-'+endYrs[iCase]+'??.nc'))\n",
-    "        if len(sh_file)==0:\n",
-    "            print('Land-based SHFLX file not found!')\n",
-    "        elif len(sh_file)>1: \n",
-    "            print('More than one file matches requested time period and case for SH.')\n",
-    "        elif len(sh_file)==1:     \n",
+    "\n",
+    "        sh_file = np.sort(\n",
+    "            glob.glob(\n",
+    "                CESM_output_dir\n",
+    "                + \"/\"\n",
+    "                + caseNames[iCase]\n",
+    "                + \"/lnd/proc/tseries/\"\n",
+    "                + caseNames[iCase]\n",
+    "                + clmFile_h\n",
+    "                + \"FSH_TO_COUPLER.\"\n",
+    "                + startYrs[iCase]\n",
+    "                + \"??-\"\n",
+    "                + endYrs[iCase]\n",
+    "                + \"??.nc\"\n",
+    "            )\n",
+    "        )\n",
+    "        if len(sh_file) == 0:\n",
+    "            print(\"Land-based SHFLX file not found!\")\n",
+    "        elif len(sh_file) > 1:\n",
+    "            print(\"More than one file matches requested time period and case for SH.\")\n",
+    "        elif len(sh_file) == 1:\n",
     "            shflx_DS = xr.open_dataset(sh_file[0])\n",
-    "    \n",
-    "    \n",
+    "\n",
     "        # If years start at 0000, offset by 1700 years for analysis\n",
     "        yrOffset = 1850\n",
-    "        if shflx_DS['time.year'].values[0]<1500: \n",
-    "            shflx_DS['time'] = shflx_DS.time + timedelta(days=yrOffset*365)\n",
-    "        if soilWater_DS['time.year'].values[0]<1500: \n",
-    "            soilWater_DS['time'] = soilWater_DS.time + timedelta(days=yrOffset*365)\n",
+    "        if shflx_DS[\"time.year\"].values[0] < 1500:\n",
+    "            shflx_DS[\"time\"] = shflx_DS.time + timedelta(days=yrOffset * 365)\n",
+    "        if soilWater_DS[\"time.year\"].values[0] < 1500:\n",
+    "            soilWater_DS[\"time\"] = soilWater_DS.time + timedelta(days=yrOffset * 365)\n",
     "        # Convert times to datetime for easier use\n",
-    "        shflx_DS['time']     = shflx_DS.indexes['time'].to_datetimeindex() \n",
-    "        soilWater_DS['time'] = soilWater_DS.indexes['time'].to_datetimeindex() \n",
+    "        shflx_DS[\"time\"] = shflx_DS.indexes[\"time\"].to_datetimeindex()\n",
+    "        soilWater_DS[\"time\"] = soilWater_DS.indexes[\"time\"].to_datetimeindex()\n",
     "\n",
     "        # Add case ID (short name) to the DS\n",
-    "        shflx_DS     = shflx_DS.assign_coords({\"case\":  shortNames[iCase]})\n",
-    "        soilWater_DS = soilWater_DS.assign_coords({\"case\":  shortNames[iCase]})\n",
+    "        shflx_DS = shflx_DS.assign_coords({\"case\": shortNames[iCase]})\n",
+    "        soilWater_DS = soilWater_DS.assign_coords({\"case\": shortNames[iCase]})\n",
     "\n",
-    "        ## Compute coupling index and save to netCDF file \n",
+    "        ## Compute coupling index and save to netCDF file\n",
     "        ## - - - - - - - - - - - - - - - - - - - - - - - - -\n",
-    "        xname = 'SOILWATER_10CM'    # Controlling variable \n",
-    "        yname = 'FSH_TO_COUPLER'    # Responding variable \n",
-    "        \n",
+    "        xname = \"SOILWATER_10CM\"  # Controlling variable\n",
+    "        yname = \"FSH_TO_COUPLER\"  # Responding variable\n",
+    "\n",
     "        xDS = soilWater_DS\n",
     "        yDS = shflx_DS\n",
     "\n",
-    "        couplingInd = compute_couplingIndex_cesm(xname,yname,xDS,yDS)\n",
+    "        couplingInd = compute_couplingIndex_cesm(xname, yname, xDS, yDS)\n",
     "\n",
-    "        filePath = '/glade/derecho/scratch/mdfowler/'+caseNames[0]+'_TerrestrialCouplingIndex_SHvsSM.nc'\n",
+    "        filePath = (\n",
+    "            \"/glade/derecho/scratch/mdfowler/\"\n",
+    "            + caseNames[0]\n",
+    "            + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
+    "        )\n",
     "        couplingInd.to_netcdf(filePath)\n",
-    "        print('File created: ', filePath)\n"
+    "        print(\"File created: \", filePath)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8396cb31-b438-4e7f-8ef5-83f4f915e332",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c54b2c9-62a9-46ef-86eb-411f66fc9119",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "5f8fba2a-98d2-4e94-9d71-3b2625e16032",
@@ -919,1892 +303,308 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "fdf20c82-5a01-4ab9-8881-d9388b1b2356",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "# --------------------------------------------------------\n",
-    "# Function to read requested variables from FLUXNET file. \n",
-    "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
-    "# \n",
-    "# Inputs:  fileName --> Full path to FLUXNET data file \n",
-    "#          varNames --> An array of variable names to be \n",
-    "#                       retrieved from said data file. \n",
-    "#                       NOTE: If you wish to retrieve *all* \n",
-    "#                       variables, pass the string 'ALL'. \n",
-    "#  \n",
+    "# Function to read requested variables from FLUXNET file.\n",
+    "# - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+    "#\n",
+    "# Inputs:  fileName --> Full path to FLUXNET data file\n",
+    "#          varNames --> An array of variable names to be\n",
+    "#                       retrieved from said data file.\n",
+    "#                       NOTE: If you wish to retrieve *all*\n",
+    "#                       variables, pass the string 'ALL'.\n",
+    "#\n",
     "# Outputs: fluxnetID --> ID string used to identify station\n",
-    "#          fluxnetDS --> An x-array dataset containing the \n",
+    "#          fluxnetDS --> An x-array dataset containing the\n",
     "#                        requested variables.\n",
-    "#                        Missing values will be set to NaN. \n",
-    "# \n",
+    "#                        Missing values will be set to NaN.\n",
+    "#\n",
     "# --------------------------------------------------------\n",
     "\n",
-    "def readFLUXNET_var(fileName, varNames): \n",
-    "    # Get ID of station \n",
-    "    startID   = fileName.find('FLX_')\n",
-    "    fluxnetID = fileName[startID+4:startID+10]\n",
-    "    \n",
+    "\n",
+    "def readFLUXNET_var(fileName, varNames):\n",
+    "    # Get ID of station\n",
+    "    startID = fileName.find(\"FLX_\")\n",
+    "    fluxnetID = fileName[startID + 4 : startID + 10]\n",
+    "\n",
     "    # If this is taking a long time or you just want to know where in the stations you are, uncomment print statement\n",
     "    # print('Reading in site - ', fluxnetID)\n",
-    "    \n",
-    "    # Read in CSV file containing data \n",
+    "\n",
+    "    # Read in CSV file containing data\n",
     "    dataDF = pd.read_csv(fileName)\n",
-    "    \n",
+    "\n",
     "    # Return ALL variables from dataDF if requested\n",
-    "    if varNames=='ALL':\n",
+    "    if varNames == \"ALL\":\n",
     "        fluxnetDF = dataDF\n",
-    "        \n",
+    "\n",
     "        # Set any value that's missing to NaN (not -9999)\n",
     "        fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
     "\n",
-    "        \n",
     "        # If time has been requested, reformat to pandas date index\n",
-    "        fluxnetDF['TIMESTAMP'] = pd.to_datetime(fluxnetDF['TIMESTAMP'].values, format='%Y%m%d')\n",
-    "        fluxnetDF = fluxnetDF.set_index(['TIMESTAMP'])\n",
-    "        \n",
+    "        fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
+    "            fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
+    "        )\n",
+    "        fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
+    "\n",
     "        # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
     "        #   NOTE: since current implementation doesn't use the pre-formatted CoMeT, might not need this step now\n",
     "        fluxnetDS = fluxnetDF.to_xarray()\n",
-    "            \n",
-    "    # Reduce returned DF to contain only variables of interest  \n",
+    "\n",
+    "    # Reduce returned DF to contain only variables of interest\n",
     "    else:\n",
-    "        \n",
+    "\n",
     "        # Check that requested variables are available in specific file\n",
-    "        errCount = 0                       # Initialize flag for error \n",
-    "        colNames = dataDF.columns.values   # Available variables in file \n",
-    "        \n",
+    "        errCount = 0  # Initialize flag for error\n",
+    "        colNames = dataDF.columns.values  # Available variables in file\n",
+    "\n",
     "        for iVar in range(len(varNames)):  # Check each variable individually\n",
-    "            if (varNames[iVar] in colNames)==False:\n",
+    "            if (varNames[iVar] in colNames) == False:\n",
     "                # Turn on print statement for more verbose output\n",
     "                # print('** ERROR: %13s   not contained in file for %8s  **' %(varNames[iVar], fluxnetID))\n",
-    "                \n",
-    "                # If any variable is not conatined in file, return a NaN \n",
+    "\n",
+    "                # If any variable is not conatined in file, return a NaN\n",
     "                fluxnetDS = -999\n",
-    "                errCount  = errCount+1\n",
-    "                \n",
+    "                errCount = errCount + 1\n",
+    "\n",
     "        # If all the variables *are* available, isolate those in DF and return that\n",
-    "        if errCount == 0: \n",
+    "        if errCount == 0:\n",
     "            fluxnetDF = dataDF[varNames]\n",
-    "            \n",
+    "\n",
     "            # Set any value that's missing to NaN (not -999)\n",
     "            fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
-    "            \n",
+    "\n",
     "            # If time has been requested, reformat to pandas make index\n",
-    "            if ('TIMESTAMP' in varNames)==True:\n",
-    "                fluxnetDF['TIMESTAMP'] = pd.to_datetime(fluxnetDF['TIMESTAMP'].values, format='%Y%m%d')\n",
-    "                fluxnetDF = fluxnetDF.set_index(['TIMESTAMP'])\n",
-    "        \n",
+    "            if (\"TIMESTAMP\" in varNames) == True:\n",
+    "                fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
+    "                    fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
+    "                )\n",
+    "                fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
+    "\n",
     "            # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
     "            fluxnetDS = fluxnetDF.to_xarray()\n",
-    "    \n",
-    "    return(fluxnetID, fluxnetDS)\n",
     "\n",
+    "    return (fluxnetID, fluxnetDS)\n",
     "\n",
-    "'''\n",
+    "\n",
+    "\"\"\"\n",
     "Inputs: xname -- controlling variable \n",
     "        yname -- responding variable\n",
     "        ds    -- dataset containing xname and yname data \n",
     "        \n",
     "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
     "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
-    "'''\n",
+    "\"\"\"\n",
+    "\n",
     "\n",
-    "def compute_couplingIndex_FLUXNET(xname,yname,ds):\n",
-    "    xday = ds[xname].groupby('TIMESTAMP.season')\n",
-    "    yday = ds[yname].groupby('TIMESTAMP.season')\n",
+    "def compute_couplingIndex_FLUXNET(xname, yname, ds):\n",
+    "    xday = ds[xname].groupby(\"TIMESTAMP.season\")\n",
+    "    yday = ds[yname].groupby(\"TIMESTAMP.season\")\n",
     "\n",
     "    # Get the covariance of the two (numerator in coupling index)\n",
-    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby('TIMESTAMP.season').sum() / xday.count()\n",
+    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
+    "        \"TIMESTAMP.season\"\n",
+    "    ).sum() / xday.count()\n",
     "\n",
-    "    # Now compute the actual coupling index \n",
-    "    couplingIndex = covarTerm/xday.std()\n",
+    "    # Now compute the actual coupling index\n",
+    "    couplingIndex = covarTerm / xday.std()\n",
     "\n",
-    "    \n",
-    "    return couplingIndex\n"
+    "    return couplingIndex"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "9acb032f-f106-4783-8c57-8e0d3a44eb3e",
    "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Prr\n",
-      "No data for station:   US-Tw4\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   AU-Cum\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   ES-Ln2\n",
-      "No data for station:   US-Ha1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   BR-Sa1\n",
-      "No data for station:   FI-Lom\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Cop\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Atq\n",
-      "No data for station:   US-Wi4\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   CZ-wet\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   GL-NuF\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = depth\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-GBT\n",
-      "No data for station:   US-Wi5\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = depth\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Los\n",
-      "No data for station:   DE-Akm\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   NL-Hor\n",
-      "No data for station:   US-Wi6\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   BE-Bra\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Ivo\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   DE-SfN\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   AR-Vir\n",
-      "No data for station:   US-ORv\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = depth\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Wi3\n",
-      "No data for station:   CA-Obs\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Wi2\n",
-      "No data for station:   US-Wi9\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   DE-Spw\n",
-      "No data for station:   FR-Pue\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Ne3\n",
-      "No data for station:   DE-RuS\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   DE-Zrk\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   CA-Man\n",
-      "No data for station:   US-Twt\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = depth\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Wi7\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   IT-La2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Wi1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Wi8\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   GL-ZaF\n",
-      "No data for station:   US-Myb\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:84: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = depth\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:90: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  iTime   = int(np.where(dateArr==depthDay)[0])\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:95: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = depth\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-WPT\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   DE-RuR\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   FR-Fon\n",
-      "No data for station:   US-Ne2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   CG-Tch\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   RU-Che\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Tw1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   RU-Cok\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   US-Wi0\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   FI-Let\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:102: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
-      "/glade/derecho/scratch/mdfowler/tmp/ipykernel_13813/1925081246.py:137: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
-      "  dateRange      = pd.date_range(start=startTime_fluxnet[iSt],end=endTime_fluxnet[iSt],freq='M')\n"
-     ]
+    "editable": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No data for station:   SJ-Adv\n",
-      "Number of FLUXNET stations with CI calculated: 150\n",
-      "Minimum number of months used for JJA mean CI: 1 \n",
-      "Maximum number of months used for JJA mean CI: 57 \n"
-     ]
-    }
-   ],
+    "tags": [
+     "hide-input",
+     "hide-output"
+    ]
+   },
+   "outputs": [],
    "source": [
-    "if fluxnet_comparison==True: \n",
-    "    # obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data \n",
+    "if fluxnet_comparison == True:\n",
+    "    # obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data\n",
     "\n",
     "    ## Metadata files\n",
-    "    siteInfoFile = obsDir+'SiteList.csv'\n",
+    "    siteInfoFile = obsDir + \"SiteList.csv\"\n",
     "    siteInfoDF = pd.read_csv(siteInfoFile)\n",
-    "    \n",
-    "    metadataFile = obsDir+'FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.csv'\n",
+    "\n",
+    "    metadataFile = obsDir + \"FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.csv\"\n",
     "    metadataDF = pd.read_csv(metadataFile)\n",
-    "    \n",
-    "    ## List of all station files \n",
-    "    dataFiles = glob.glob(obsDir + 'FLX_*/*SUBSET_DD*')\n",
-    "\n",
-    "    # Set up a few empty arrays to save data into \n",
-    "    terraCI_fluxnetConverted   = np.full([len(dataFiles), 4], np.nan)   # CI using kg/m2 soil water content  [nStations, seasons]\n",
-    "    \n",
-    "    # Also save out some data on each station \n",
-    "    startTime_fluxnet = np.zeros(len(dataFiles), dtype='datetime64[s]')\n",
-    "    endTime_fluxnet   = np.zeros(len(dataFiles), dtype='datetime64[s]')\n",
-    "    lat_fluxnet       = np.full([len(dataFiles)], np.nan)\n",
-    "    lon_fluxnet       = np.full([len(dataFiles)], np.nan)\n",
-    "    SWCdepth          = np.full([len(dataFiles)], np.nan)\n",
-    "    \n",
-    "    stationID           = []\n",
+    "\n",
+    "    ## List of all station files\n",
+    "    dataFiles = glob.glob(obsDir + \"FLX_*/*SUBSET_DD*\")\n",
+    "\n",
+    "    # Set up a few empty arrays to save data into\n",
+    "    terraCI_fluxnetConverted = np.full(\n",
+    "        [len(dataFiles), 4], np.nan\n",
+    "    )  # CI using kg/m2 soil water content  [nStations, seasons]\n",
+    "\n",
+    "    # Also save out some data on each station\n",
+    "    startTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
+    "    endTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
+    "    lat_fluxnet = np.full([len(dataFiles)], np.nan)\n",
+    "    lon_fluxnet = np.full([len(dataFiles)], np.nan)\n",
+    "    SWCdepth = np.full([len(dataFiles)], np.nan)\n",
+    "\n",
+    "    stationID = []\n",
     "    stationID_converted = []\n",
-    "    \n",
-    "    allStationID        = []\n",
-    "        \n",
+    "\n",
+    "    allStationID = []\n",
+    "\n",
     "    # Variables I want returned:\n",
-    "    varNames = ['TIMESTAMP','H_F_MDS','SWC_F_MDS_1','SWC_F_MDS_1_QC']\n",
-    "    \n",
+    "    varNames = [\"TIMESTAMP\", \"H_F_MDS\", \"SWC_F_MDS_1\", \"SWC_F_MDS_1_QC\"]\n",
+    "\n",
     "    # Loop over each station (data file)\n",
     "    for iStation in range(len(dataFiles)):\n",
-    "        \n",
-    "        # Read in data \n",
+    "\n",
+    "        # Read in data\n",
     "        # ----------------------------------------------------------\n",
-    "        fluxnetID,fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
-    "        \n",
-    "        # Save lat and lon for this station \n",
+    "        fluxnetID, fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
+    "\n",
+    "        # Save lat and lon for this station\n",
     "        # ----------------------------------------------------------\n",
-    "        indStation            = np.where(fluxnetID==siteInfoDF['SITE_ID'])[0]\n",
-    "        lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
-    "        lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
+    "        indStation = int(np.where(fluxnetID == siteInfoDF[\"SITE_ID\"])[0][0])\n",
+    "        lat_fluxnet[iStation] = siteInfoDF[\"LOCATION_LAT\"].values[indStation]\n",
+    "        lon_fluxnet[iStation] = siteInfoDF[\"LOCATION_LONG\"].values[indStation]\n",
     "        allStationID.append(fluxnetID)\n",
-    "        \n",
-    "        # Check that there was data saved for this particular site: \n",
+    "\n",
+    "        # Check that there was data saved for this particular site:\n",
     "        # ----------------------------------------------------------\n",
-    "        if (type(fluxnetDS)==int):\n",
-    "            print('No data for station: %8s' % fluxnetID)\n",
-    "        \n",
-    "        elif ( (np.all(np.isnan(fluxnetDS['H_F_MDS']))==True) | (np.all(np.isnan(fluxnetDS['SWC_F_MDS_1']))==True) ):\n",
-    "            print('No data for station: %8s' % fluxnetID)\n",
-    "            \n",
-    "        # If data is present: \n",
+    "        if type(fluxnetDS) == int:\n",
+    "            print(\"No data for station: %8s\" % fluxnetID)\n",
+    "\n",
+    "        elif (np.all(np.isnan(fluxnetDS[\"H_F_MDS\"])) == True) | (\n",
+    "            np.all(np.isnan(fluxnetDS[\"SWC_F_MDS_1\"])) == True\n",
+    "        ):\n",
+    "            print(\"No data for station: %8s\" % fluxnetID)\n",
+    "\n",
+    "        # If data is present:\n",
     "        # ----------------------------------------------------------\n",
-    "        else:        \n",
+    "        else:\n",
     "            # Only consider where data is actually present for selected vars\n",
-    "            iReal      = np.where((np.isfinite(fluxnetDS['SWC_F_MDS_1'])==True) & \n",
-    "                                  (np.isfinite(fluxnetDS['H_F_MDS'])==True))[0]\n",
-    "            fluxnetDS  = fluxnetDS.isel(TIMESTAMP=iReal)\n",
-    "            \n",
+    "            iReal = np.where(\n",
+    "                (np.isfinite(fluxnetDS[\"SWC_F_MDS_1\"]) == True)\n",
+    "                & (np.isfinite(fluxnetDS[\"H_F_MDS\"]) == True)\n",
+    "            )[0]\n",
+    "            fluxnetDS = fluxnetDS.isel(TIMESTAMP=iReal)\n",
+    "\n",
     "            stationID.append(fluxnetID)\n",
-    "            \n",
+    "\n",
     "            # Convert units from volumetric (%) to mass (kg/m2)\n",
     "            # -------------------------------------------------\n",
     "            #   Step 1: Convert from % to fraction\n",
-    "            fracSM = (fluxnetDS['SWC_F_MDS_1'].values)/100.0\n",
-    "            \n",
-    "            #    Step 2: Need to use depth of obs in conversion \n",
-    "            metaData_station    = metadataDF[metadataDF.SITE_ID==fluxnetID]\n",
-    "            iSWC                = np.where(metaData_station.DATAVALUE=='SWC_F_MDS_1')[0]\n",
-    "            #  Some locations (5) have two depths \n",
-    "            if len(iSWC)>1:             \n",
-    "                for iDepth in range(len(iSWC)): \n",
-    "                    SWC_DF   = metaData_station[iSWC[iDepth]:iSWC[iDepth]+4]\n",
-    "                    \n",
-    "                    depth    = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-    "                    depthDay = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_DATE'].DATAVALUE.values).astype(int)\n",
-    "                    depthDay = int(str(depthDay[0])[:8])  # Some weird ones have time attached; don't want that\n",
-    "                    depthDay = pd.to_datetime(depthDay, format='%Y%m%d')\n",
-    "            \n",
-    "                    # Keep deepest level as the depth for station \n",
-    "                    if iDepth==0:\n",
-    "                        SWCdepth[iStation] = depth \n",
-    "                        convertSM = fracSM*1000.0*np.abs(depth)\n",
-    "                    else: \n",
+    "            fracSM = (fluxnetDS[\"SWC_F_MDS_1\"].values) / 100.0\n",
+    "\n",
+    "            #    Step 2: Need to use depth of obs in conversion\n",
+    "            metaData_station = metadataDF[metadataDF.SITE_ID == fluxnetID]\n",
+    "            iSWC = np.where(metaData_station.DATAVALUE == \"SWC_F_MDS_1\")[0]\n",
+    "            #  Some locations (5) have two depths\n",
+    "            if len(iSWC) > 1:\n",
+    "                for iDepth in range(len(iSWC)):\n",
+    "                    SWC_DF = metaData_station[iSWC[iDepth] : iSWC[iDepth] + 4]\n",
+    "\n",
+    "                    depth = np.asarray(\n",
+    "                        SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
+    "                    ).astype(float)\n",
+    "                    depthDay = np.asarray(\n",
+    "                        SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_DATE\"].DATAVALUE.values[0]\n",
+    "                    ).astype(int)\n",
+    "                    depthDay = int(\n",
+    "                        str(depthDay)[:8]\n",
+    "                    )  # Some weird ones have time attached; don't want that\n",
+    "                    depthDay = pd.to_datetime(depthDay, format=\"%Y%m%d\")\n",
+    "\n",
+    "                    # Keep deepest level as the depth for station\n",
+    "                    if iDepth == 0:\n",
+    "                        SWCdepth[iStation] = depth\n",
+    "                        convertSM = fracSM * 1000.0 * np.abs(depth)\n",
+    "                    else:\n",
     "                        # Use date as break point for getting kg/m2 SWC\n",
     "                        #  Eq: SWC_kgm2 = SWC_vol [m3/m3] * 1000 [kg/m3] * depth [m]\n",
     "                        dateArr = pd.DatetimeIndex(fluxnetDS.TIMESTAMP.values)\n",
-    "                        iTime   = int(np.where(dateArr==depthDay)[0])\n",
-    "                        convertSM[iTime::] = (fracSM[iTime::])*1000.0*np.abs(depth)\n",
-    "                        \n",
-    "                        # Keep deepest level as the depth for station \n",
-    "                        if depth<SWCdepth[iStation]: \n",
+    "                        iTime = int(np.where(dateArr == depthDay)[0][0])\n",
+    "                        convertSM[iTime::] = (fracSM[iTime::]) * 1000.0 * np.abs(depth)\n",
+    "\n",
+    "                        # Keep deepest level as the depth for station\n",
+    "                        if depth < SWCdepth[iStation]:\n",
     "                            SWCdepth[iStation] = depth\n",
-    "                            \n",
+    "\n",
     "                stationID_converted.append(fluxnetID)\n",
-    "                    \n",
-    "            # If station only has one level recorded, things are a bit easier:             \n",
-    "            else: \n",
-    "                SWC_DF             = metaData_station[iSWC[0]:iSWC[0]+4]\n",
-    "                SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
-    "                convertSM          = fracSM*1000.0*np.abs(SWCdepth[iStation])\n",
-    "                \n",
+    "\n",
+    "            # If station only has one level recorded, things are a bit easier:\n",
+    "            else:\n",
+    "                SWC_DF = metaData_station[iSWC[0] : iSWC[0] + 4]\n",
+    "                SWCdepth[iStation] = np.asarray(\n",
+    "                    SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
+    "                ).astype(float)\n",
+    "                convertSM = fracSM * 1000.0 * np.abs(SWCdepth[iStation])\n",
+    "\n",
     "                stationID_converted.append(fluxnetID)\n",
-    "    \n",
-    "            # Save converted soil moisture to dataset \n",
-    "            fluxnetDS['SWC_F_MDS_1_convert'] = (('TIMESTAMP'), convertSM)\n",
-    "            \n",
-    "            \n",
-    "            # Save first and last time used for computing CI \n",
+    "\n",
+    "            # Save converted soil moisture to dataset\n",
+    "            fluxnetDS[\"SWC_F_MDS_1_convert\"] = ((\"TIMESTAMP\"), convertSM)\n",
+    "\n",
+    "            # Save first and last time used for computing CI\n",
     "            # ----------------------------------------------\n",
-    "            startTime_fluxnet[iStation] = fluxnetDS['TIMESTAMP'].values[0]\n",
-    "            endTime_fluxnet[iStation]   = fluxnetDS['TIMESTAMP'].values[-1]\n",
-    "                    \n",
+    "            startTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[0]\n",
+    "            endTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[-1]\n",
+    "\n",
     "            # Compute terrestrial coupling metric\n",
-    "            # -----------------------------------    \n",
-    "            terraLeg = compute_couplingIndex_FLUXNET('SWC_F_MDS_1_convert', \n",
-    "                                                      'H_F_MDS',\n",
-    "                                                      fluxnetDS)\n",
-    "            \n",
+    "            # -----------------------------------\n",
+    "            terraLeg = compute_couplingIndex_FLUXNET(\n",
+    "                \"SWC_F_MDS_1_convert\", \"H_F_MDS\", fluxnetDS\n",
+    "            )\n",
+    "\n",
     "            # If there's less than one full year of data, don't use station\n",
     "            #   (i.e., as long as all 4 seasons are defined, save values)\n",
-    "            if np.shape(terraLeg)[0]==4:\n",
-    "                terraCI_fluxnetConverted[iStation,:] = terraLeg\n",
+    "            if np.shape(terraLeg)[0] == 4:\n",
+    "                terraCI_fluxnetConverted[iStation, :] = terraLeg\n",
     "\n",
     "    seasons = terraLeg.season\n",
     "\n",
-    "    ## Print some useful information     \n",
-    "    print('Number of FLUXNET stations with CI calculated: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n",
-    "    \n",
-    "    # How many months go into each calculation of CI for JJA? \n",
+    "    ## Print some useful information\n",
+    "    print(\n",
+    "        \"Number of FLUXNET stations with CI calculated: %i\"\n",
+    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
+    "    )\n",
+    "\n",
+    "    # How many months go into each calculation of CI for JJA?\n",
     "    nMonths = np.full([len(dataFiles)], np.nan)\n",
-    "    \n",
+    "\n",
     "    for iSt in range(len(dataFiles)):\n",
-    "        if np.isfinite(terraCI_fluxnetConverted[iSt,1]):\n",
-    "            dateRange      = pd.date_range(start=startTime_fluxnet[iSt],end=endTime_fluxnet[iSt],freq='M')\n",
-    "            nMonths[iSt]   = len(np.where((dateRange.month>=6) & (dateRange.month<=8))[0])\n",
-    "            \n",
-    "    print('Minimum number of months used for JJA mean CI: %i ' % int(np.nanmin(nMonths)) )\n",
-    "    print('Maximum number of months used for JJA mean CI: %i ' % int(np.nanmax(nMonths)))\n",
-    "    \n"
+    "        if np.isfinite(terraCI_fluxnetConverted[iSt, 1]):\n",
+    "            dateRange = pd.date_range(\n",
+    "                start=startTime_fluxnet[iSt], end=endTime_fluxnet[iSt], freq=\"ME\"\n",
+    "            )\n",
+    "            nMonths[iSt] = len(\n",
+    "                np.where((dateRange.month >= 6) & (dateRange.month <= 8))[0]\n",
+    "            )\n",
+    "\n",
+    "    print(\n",
+    "        \"Minimum number of months used for JJA mean CI: %i \" % int(np.nanmin(nMonths))\n",
+    "    )\n",
+    "    print(\n",
+    "        \"Maximum number of months used for JJA mean CI: %i \" % int(np.nanmax(nMonths))\n",
+    "    )"
    ]
   },
   {
@@ -2819,47 +619,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "13afbfb4-2040-403c-9d82-3e370f47ec5d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of FLUXNET stations to use with reasonable depths of SWC: 139\n",
-      "Number of FLUXNET stations to use with 3+ years of JJA data: 115\n"
-     ]
-    }
-   ],
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
    "source": [
-    "if fluxnet_comparison==True: \n",
+    "if fluxnet_comparison == True:\n",
     "\n",
     "    # Get stations with SWC from below 20 cm (or equal to zero)\n",
-    "    iLimit = np.where((SWCdepth==0.0) | (SWCdepth<-0.2))[0]\n",
-    "    \n",
-    "    # Set the terrestrial leg of CI to missing so we don't consider those \n",
-    "    terraCI_fluxnetConverted[iLimit,:] = np.nan\n",
-    "    \n",
-    "    print('Number of FLUXNET stations to use with reasonable depths of SWC: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n",
-    "    \n",
-    "    # Get stations with less than 9 months used for JJA terrestrial CI \n",
-    "    iLimit = np.where(nMonths<9)[0]\n",
-    "    \n",
-    "    # Set to missing so we don't consider stations with less than three years of data going into the average \n",
-    "    terraCI_fluxnetConverted[iLimit,:] = np.nan\n",
-    "    \n",
-    "    print('Number of FLUXNET stations to use with 3+ years of JJA data: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n"
+    "    iLimit = np.where((SWCdepth == 0.0) | (SWCdepth < -0.2))[0]\n",
+    "\n",
+    "    # Set the terrestrial leg of CI to missing so we don't consider those\n",
+    "    terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
+    "\n",
+    "    print(\n",
+    "        \"Number of FLUXNET stations to use with reasonable depths of SWC: %i\"\n",
+    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
+    "    )\n",
+    "\n",
+    "    # Get stations with less than 9 months used for JJA terrestrial CI\n",
+    "    iLimit = np.where(nMonths < 9)[0]\n",
+    "\n",
+    "    # Set to missing so we don't consider stations with less than three years of data going into the average\n",
+    "    terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
+    "\n",
+    "    print(\n",
+    "        \"Number of FLUXNET stations to use with 3+ years of JJA data: %i\"\n",
+    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
+    "    )"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "acb41b70-cab5-46cc-962b-2e7243696e9c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "bc387253-cdd7-4a36-956b-8ce548e963bd",
@@ -2870,493 +667,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "67253fd1-d2f7-45fe-a59f-215303b93c06",
-   "metadata": {},
-   "outputs": [
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-       " *\n",
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-       "\n",
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-       "  border-bottom: solid 1px var(--xr-border-color);\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
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-       ".xr-section-details {\n",
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-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
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-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
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-       "\n",
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-       "  color: var(--xr-font-color3);\n",
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-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
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-       "\n",
-       ".xr-array-data,\n",
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-       "  display: none;\n",
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-       "  display: inline-block;\n",
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-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
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-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
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-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
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-       "\n",
-       ".xr-dim-list:after {\n",
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-       "\n",
-       ".xr-has-index {\n",
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-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
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-       "\n",
-       ".xr-var-item > div,\n",
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-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
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-       "\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",
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-       ".xr-var-dims {\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
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-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
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-       "\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",
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-       "\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;uxarray.Grid&gt;\n",
-       "Original Grid Type: ESMF\n",
-       "Grid Dimensions:\n",
-       "  * n_node: 48602\n",
-       "  * n_face: 48600\n",
-       "  * n_max_face_nodes: 4\n",
-       "  * n_nodes_per_face: (48600,)\n",
-       "Grid Coordinates (Spherical):\n",
-       "  * node_lon: (48602,)\n",
-       "  * node_lat: (48602,)\n",
-       "  * face_lon: (48600,)\n",
-       "  * face_lat: (48600,)\n",
-       "Grid Coordinates (Cartesian):\n",
-       "Grid Connectivity Variables:\n",
-       "  * face_node_connectivity: (48600, 4)\n",
-       "Grid Descriptor Variables:\n",
-       "  * n_nodes_per_face: (48600,)\n",
-       "</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>uxarray.Grid</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8d0312db-2e38-44c9-8f16-3c517704f99e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8d0312db-2e38-44c9-8f16-3c517704f99e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>n_node</span>: 48602</li><li><span>n_face</span>: 48600</li><li><span>n_max_face_nodes</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5e46ee84-9ede-4e6a-ac78-5edcd66447a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e46ee84-9ede-4e6a-ac78-5edcd66447a1' class='xr-section-summary' >Spherical Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>node_lon</span></div><div class='xr-var-dims'>(n_node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-159.9 -162.3 ... 170.2 167.8</div><input id='attrs-3d72e0de-c61a-46cb-ab1f-c5d211c6a25d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d72e0de-c61a-46cb-ab1f-c5d211c6a25d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e12e846-0138-46cd-a610-b352cab6003c' class='xr-var-data-in' type='checkbox'><label for='data-7e12e846-0138-46cd-a610-b352cab6003c' 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>longitude</dd><dt><span>long name :</span></dt><dd>Longitude of the corner nodes of each face</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-159.89124086, -162.26235981, -164.68737511, ...,  172.60921368,\n",
-       "        170.18081007,  167.78169785])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>node_lat</span></div><div class='xr-var-dims'>(n_node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-67.77 -68.05 -68.3 ... 67.7 67.54</div><input id='attrs-b3d3831b-1a35-4b76-b885-f052b1d509d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3d3831b-1a35-4b76-b885-f052b1d509d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1f027ff-0e70-456e-bc5f-54ba26c43780' class='xr-var-data-in' type='checkbox'><label for='data-f1f027ff-0e70-456e-bc5f-54ba26c43780' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long name :</span></dt><dd>Latitude of the corner nodes of each face</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-67.76610373, -68.049425  , -68.29778632, ...,  67.83347501,\n",
-       "        67.70475883,  67.54027826])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>face_lon</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-44.5 -43.5 -42.5 ... 136.0 135.0</div><input id='attrs-75ed936c-4739-49ff-8bd2-70e5b60ea06e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75ed936c-4739-49ff-8bd2-70e5b60ea06e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7852748-7ebd-4b71-ad92-528ae024f286' class='xr-var-data-in' type='checkbox'><label for='data-b7852748-7ebd-4b71-ad92-528ae024f286' 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>longitude</dd><dt><span>long name :</span></dt><dd>Longitude of the center of each face</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-44.5       , -43.5       , -42.5       , ..., 137.00152343,\n",
-       "       136.00045705, 135.        ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>face_lat</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-35.03 -35.48 -35.92 ... 36.2 35.74</div><input id='attrs-6921a611-f548-49b5-b86b-cf3b4e7f0034' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6921a611-f548-49b5-b86b-cf3b4e7f0034' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee89dbcf-6c05-4479-b115-beee30f25366' class='xr-var-data-in' type='checkbox'><label for='data-ee89dbcf-6c05-4479-b115-beee30f25366' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long name :</span></dt><dd>Latitude of the center of each face</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-35.0269697 , -35.48216087, -35.92409689, ...,  36.65844396,\n",
-       "        36.20454323,  35.73717077])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba22bedd-1c6f-47fe-80fb-a1fa88e4c0bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba22bedd-1c6f-47fe-80fb-a1fa88e4c0bf' class='xr-section-summary'  title='Expand/collapse section'>Cartesian Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-2a453a4f-5b9c-4c9c-b85c-fb50464e5dd9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a453a4f-5b9c-4c9c-b85c-fb50464e5dd9' class='xr-section-summary' >Connectivity: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>face_node_connectivity</span></div><div class='xr-var-dims'>(n_face, n_max_face_nodes)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>6985 6986 6987 ... 36983 38576</div><input id='attrs-73b195ba-1a84-4cd5-a576-282b91fcdf59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73b195ba-1a84-4cd5-a576-282b91fcdf59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3e46941-9ee1-48c9-9598-772ffbd068af' class='xr-var-data-in' type='checkbox'><label for='data-b3e46941-9ee1-48c9-9598-772ffbd068af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>face_node_connectivity</dd><dt><span>long name :</span></dt><dd>Maps every face to its corner nodes.</dd><dt><span>start_index :</span></dt><dd>0</dd><dt><span>_FillValue :</span></dt><dd>-9223372036854775808</dd></dl></div><div class='xr-var-data'><pre>array([[ 6985,  6986,  6987,  6988],\n",
-       "       [ 6986,  6989,  6990,  6987],\n",
-       "       [ 6989,  6991,  6992,  6990],\n",
-       "       ...,\n",
-       "       [41616, 41619, 38577, 38578],\n",
-       "       [41619, 41620, 38576, 38577],\n",
-       "       [41620, 36982, 36983, 38576]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83c819c9-a799-4c21-a6e0-d36379f8e206' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83c819c9-a799-4c21-a6e0-d36379f8e206' class='xr-section-summary' >Descriptors: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>n_nodes_per_face</span></div><div class='xr-var-dims'>(n_face)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4</div><input id='attrs-f54caae1-ffbb-4a08-b288-53b052592893' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f54caae1-ffbb-4a08-b288-53b052592893' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67e569fb-3437-4b22-b15b-1be14d4dee15' class='xr-var-data-in' type='checkbox'><label for='data-67e569fb-3437-4b22-b15b-1be14d4dee15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>n_nodes_per_face</dd><dt><span>long name :</span></dt><dd>Number of nodes per face</dd></dl></div><div class='xr-var-data'><pre>array([4, 4, 4, ..., 4, 4, 4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6fd24b3-72df-40ae-bec0-79f394c57309' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f6fd24b3-72df-40ae-bec0-79f394c57309' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<uxarray.Grid>\n",
-       "Original Grid Type: ESMF\n",
-       "Grid Dimensions:\n",
-       "  * n_node: 48602\n",
-       "  * n_face: 48600\n",
-       "  * n_max_face_nodes: 4\n",
-       "  * n_nodes_per_face: (48600,)\n",
-       "Grid Coordinates (Spherical):\n",
-       "  * node_lon: (48602,)\n",
-       "  * node_lat: (48602,)\n",
-       "  * face_lon: (48600,)\n",
-       "  * face_lat: (48600,)\n",
-       "Grid Coordinates (Cartesian):\n",
-       "Grid Connectivity Variables:\n",
-       "  * face_node_connectivity: (48600, 4)\n",
-       "Grid Descriptor Variables:\n",
-       "  * n_nodes_per_face: (48600,)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
    "source": [
-    "## Load coupling index with uxarray \n",
-    "gridFile = \"/glade/p/cesmdata/cseg/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc\"\n",
+    "## Load coupling index with uxarray\n",
+    "gridFile = (\n",
+    "    \"/glade/p/cesmdata/cseg/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc\"\n",
+    ")\n",
     "uxgrid = uxr.open_grid(gridFile)\n",
-    "uxgrid\n"
+    "uxgrid"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "43206b67-1313-4b61-94ea-b50b85a3d50c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coupling index is now ready to go\n"
-     ]
-    }
-   ],
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
    "source": [
     "for iCase in range(len(caseNames)):\n",
-    "    filePath = '/glade/derecho/scratch/mdfowler/'+caseNames[iCase]+'_TerrestrialCouplingIndex_SHvsSM.nc'\n",
+    "    filePath = (\n",
+    "        \"/glade/derecho/scratch/mdfowler/\"\n",
+    "        + caseNames[iCase]\n",
+    "        + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
+    "    )\n",
     "    couplingIndex_case = uxr.open_dataset(gridFile, filePath)\n",
     "    # Rename the variable:\n",
-    "    couplingIndex_case = couplingIndex_case.rename({'__xarray_dataarray_variable__': 'CouplingIndex'})\n",
-    "    \n",
+    "    couplingIndex_case = couplingIndex_case.rename(\n",
+    "        {\"__xarray_dataarray_variable__\": \"CouplingIndex\"}\n",
+    "    )\n",
+    "\n",
     "    # Assign case coord\n",
-    "    couplingIndex_case     = couplingIndex_case.assign_coords({\"case\":  couplingIndex_case.case.values})\n",
+    "    couplingIndex_case = couplingIndex_case.assign_coords(\n",
+    "        {\"case\": couplingIndex_case.case.values}\n",
+    "    )\n",
     "\n",
     "    # Return all the cases in a single dataset\n",
-    "    if iCase==0:\n",
-    "        couplingIndex_DS    = couplingIndex_case\n",
+    "    if iCase == 0:\n",
+    "        couplingIndex_DS = couplingIndex_case\n",
     "        del couplingIndex_case\n",
-    "    else: \n",
-    "        couplingIndex_DS = uxr.concat([couplingIndex_DS, couplingIndex_case], \"case\") \n",
+    "    else:\n",
+    "        couplingIndex_DS = uxr.concat([couplingIndex_DS, couplingIndex_case], \"case\")\n",
     "        del couplingIndex_case\n",
-    "    \n",
-    "print('Coupling index is now ready to go')"
+    "\n",
+    "print(\"Coupling index is now ready to go\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "e9e22cd0-870e-47cb-b5c7-2e57bbd9af16",
    "metadata": {
     "editable": true,
     "slideshow": {
      "slide_type": ""
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [],
    "source": [
     "def make_cmap(colors, position=None, bit=False):\n",
-    "    '''\n",
+    "    \"\"\"\n",
     "    make_cmap takes a list of tuples which contain RGB values. The RGB\n",
     "    values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
     "    True when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
@@ -3364,60 +755,80 @@
     "    Arrange your tuples so that the first color is the lowest value for the\n",
     "    colorbar and the last is the highest.\n",
     "    position contains values from 0 to 1 to dictate the location of each color.\n",
-    "    '''\n",
-    "    \n",
+    "    \"\"\"\n",
+    "\n",
     "    import matplotlib as mpl\n",
     "    import numpy as np\n",
-    "    \n",
-    "    bit_rgb = np.linspace(0,1,256)\n",
+    "\n",
+    "    bit_rgb = np.linspace(0, 1, 256)\n",
     "    if position == None:\n",
-    "        position = np.linspace(0,1,len(colors))\n",
+    "        position = np.linspace(0, 1, len(colors))\n",
     "    else:\n",
     "        if len(position) != len(colors):\n",
     "            sys.exit(\"position length must be the same as colors\")\n",
     "        elif position[0] != 0 or position[-1] != 1:\n",
     "            sys.exit(\"position must start with 0 and end with 1\")\n",
-    "    \n",
+    "\n",
     "    if bit:\n",
     "        for i in range(len(colors)):\n",
-    "            colors[i] = (bit_rgb[colors[i][0]],\n",
-    "                         bit_rgb[colors[i][1]],\n",
-    "                         bit_rgb[colors[i][2]])\n",
-    "    \n",
-    "    cdict = {'red':[], 'green':[], 'blue':[]}\n",
+    "            colors[i] = (\n",
+    "                bit_rgb[colors[i][0]],\n",
+    "                bit_rgb[colors[i][1]],\n",
+    "                bit_rgb[colors[i][2]],\n",
+    "            )\n",
+    "\n",
+    "    cdict = {\"red\": [], \"green\": [], \"blue\": []}\n",
     "    for pos, color in zip(position, colors):\n",
-    "        cdict['red'].append((pos, color[0], color[0]))\n",
-    "        cdict['green'].append((pos, color[1], color[1]))\n",
-    "        cdict['blue'].append((pos, color[2], color[2]))\n",
+    "        cdict[\"red\"].append((pos, color[0], color[0]))\n",
+    "        cdict[\"green\"].append((pos, color[1], color[1]))\n",
+    "        cdict[\"blue\"].append((pos, color[2], color[2]))\n",
     "\n",
-    "    cmap = mpl.colors.LinearSegmentedColormap('my_colormap',cdict,256)\n",
-    "    return cmap\n"
+    "    cmap = mpl.colors.LinearSegmentedColormap(\"my_colormap\", cdict, 256)\n",
+    "    return cmap"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "6e3e7b6f-98a8-4e21-8378-adfd8b399823",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "### Create a list of RGB tuples for terrestrial leg (SM, SHFLX)\n",
-    "colorsList_SMvSHF = [(124,135,181), \n",
-    "                     (107,109,161),\n",
-    "                     (51,82,120),\n",
-    "                     (49,114,127),\n",
-    "                     (97,181,89),\n",
-    "                     (200,218,102),\n",
-    "                     (255,242,116),\n",
-    "                     (238,164,58)] # This example uses the 8-bit RGB\n",
-    "my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)\n"
+    "colorsList_SMvSHF = [\n",
+    "    (124, 135, 181),\n",
+    "    (107, 109, 161),\n",
+    "    (51, 82, 120),\n",
+    "    (49, 114, 127),\n",
+    "    (97, 181, 89),\n",
+    "    (200, 218, 102),\n",
+    "    (255, 242, 116),\n",
+    "    (238, 164, 58),\n",
+    "]  # This example uses the 8-bit RGB\n",
+    "my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "882b1c92-e5b9-4cd0-aa55-4e04aca3c50c",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "def plotTCI_case(seasonstr, caseSel=None):\n",
@@ -3425,124 +836,119 @@
     "    transform = ccrs.PlateCarree()\n",
     "    projection = ccrs.PlateCarree()\n",
     "\n",
-    "    \n",
-    "    if caseSel: \n",
+    "    if caseSel:\n",
     "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
-    "        collection = couplingIndex_DS['CouplingIndex'].sel(season=seasonstr).isel(case=caseSel).to_polycollection()\n",
-    "        \n",
+    "        collection = (\n",
+    "            couplingIndex_DS[\"CouplingIndex\"]\n",
+    "            .sel(season=seasonstr)\n",
+    "            .isel(case=caseSel)\n",
+    "            .to_polycollection()\n",
+    "        )\n",
+    "\n",
     "        collection.set_transform(transform)\n",
     "        collection.set_antialiased(False)\n",
     "        collection.set_cmap(my_cmap_SMvSHF)\n",
     "        collection.set_clim(vmin=-20, vmax=5)\n",
-    "        \n",
-    "        fig, ax = plt.subplots(1, 1, figsize=(12,5), facecolor=\"w\",\n",
-    "                               constrained_layout=True,\n",
-    "                               subplot_kw=dict(projection=projection))\n",
-    "        \n",
+    "\n",
+    "        fig, ax = plt.subplots(\n",
+    "            1,\n",
+    "            1,\n",
+    "            figsize=(12, 5),\n",
+    "            facecolor=\"w\",\n",
+    "            constrained_layout=True,\n",
+    "            subplot_kw=dict(projection=projection),\n",
+    "        )\n",
+    "\n",
     "        ax.coastlines()\n",
     "        ax.add_collection(collection)\n",
     "        ax.set_global()\n",
     "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
-    "        ax.set_title(seasonstr+' Coupling Index: '+str(couplingIndex_DS.case.isel(case=caseSel).values))\n",
+    "        ax.set_title(\n",
+    "            seasonstr\n",
+    "            + \" Coupling Index: \"\n",
+    "            + str(couplingIndex_DS.case.isel(case=caseSel).values)\n",
+    "        )\n",
     "\n",
     "        plt.show()\n",
     "        plt.close()\n",
     "\n",
     "    else:\n",
     "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
-    "        collection = couplingIndex_DS['CouplingIndex'].sel(season=seasonstr).to_polycollection()\n",
-    "        \n",
+    "        collection = (\n",
+    "            couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).to_polycollection()\n",
+    "        )\n",
+    "\n",
     "        collection.set_transform(transform)\n",
     "        collection.set_antialiased(False)\n",
     "        collection.set_cmap(my_cmap_SMvSHF)\n",
     "        collection.set_clim(vmin=-20, vmax=5)\n",
-    "        \n",
-    "        fig, ax = plt.subplots(1, 1, figsize=(12,5), facecolor=\"w\",\n",
-    "                               constrained_layout=True,\n",
-    "                               subplot_kw=dict(projection=projection))\n",
-    "        \n",
+    "\n",
+    "        fig, ax = plt.subplots(\n",
+    "            1,\n",
+    "            1,\n",
+    "            figsize=(12, 5),\n",
+    "            facecolor=\"w\",\n",
+    "            constrained_layout=True,\n",
+    "            subplot_kw=dict(projection=projection),\n",
+    "        )\n",
+    "\n",
     "        ax.coastlines()\n",
     "        ax.add_collection(collection)\n",
     "        ax.set_global()\n",
     "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
-    "        ax.set_title(seasonstr+' Coupling Index: '+str(couplingIndex_DS.case.values))\n",
-    "\n",
-    "        if fluxnet_comparison==True:\n",
-    "            ## Add FLUXNET obs         \n",
-    "            iSeason = np.where(seasons==seasonstr)[0]\n",
-    "            iStations = np.where(np.isfinite(terraCI_fluxnetConverted[:,iSeason])==True)[0]\n",
+    "        ax.set_title(\n",
+    "            seasonstr + \" Coupling Index: \" + str(couplingIndex_DS.case.values)\n",
+    "        )\n",
+    "\n",
+    "        if fluxnet_comparison == True:\n",
+    "            ## Add FLUXNET obs\n",
+    "            iSeason = np.where(seasons == seasonstr)[0]\n",
+    "            iStations = np.where(\n",
+    "                np.isfinite(terraCI_fluxnetConverted[:, iSeason]) == True\n",
+    "            )[0]\n",
     "            norm_CI = matplotlib.colors.Normalize(vmin=-20, vmax=5)\n",
-    "            \n",
-    "            ax.scatter(lon_fluxnet[iStations], lat_fluxnet[iStations], c=terraCI_fluxnetConverted[iStations,iSeason], cmap=my_cmap_SMvSHF, norm=norm_CI,\n",
-    "                            edgecolor='k', s=30, marker='o', transform=ccrs.PlateCarree())\n",
-    "            \n",
+    "\n",
+    "            ax.scatter(\n",
+    "                lon_fluxnet[iStations],\n",
+    "                lat_fluxnet[iStations],\n",
+    "                c=terraCI_fluxnetConverted[iStations, iSeason],\n",
+    "                cmap=my_cmap_SMvSHF,\n",
+    "                norm=norm_CI,\n",
+    "                edgecolor=\"k\",\n",
+    "                s=30,\n",
+    "                marker=\"o\",\n",
+    "                transform=ccrs.PlateCarree(),\n",
+    "            )\n",
     "\n",
     "        plt.show()\n",
     "        plt.close()\n",
-    "        \n",
-    "    return "
+    "\n",
+    "    return"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "d43b6525-aa30-47a4-8b66-ca70a805a13a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
     },
-    {
-     "data": {
-      "image/png": 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QCAR/EkL8EAgEgr8xo0aNIiYmhltuuYVnn332z65OhyAwMJBXX32V3NxcTCYTqampPPTQQ/z3v//9s6smEAgEAoFA8LdFuL0IBAKBQCAQCAQCgUAg6NCIaGECgUAgEAgEAoFAIBB0YB5//HFkMpnHKz4+/s+u1hlFuL0IBAKBQCAQCAQCgUDQwenRo4dHWnmFQvEn1ubMI8QPgUAgEAgEAoFAIBAIOjhKpfJvZ+3hTpvFD6PRiNlsPp11EQgEAoFAIBAIBALB3xi1Wo1Wq/2zq3HKOJ3jaEmSkMlkHss0Gg0ajcbr9ocOHSIxMRGNRsOQIUN49tlnyczMPC11OxtpU8BTo9FIRkYGpaWlZ6JOAoFAIBAIBAKBQCD4GxIfH8+xY8c6hABiNBpJjw+lrM5yWsoPDg6moaHBY9ljjz3G448/3mLbRYsWYTAYyM7OpqysjKeffpqcnBz27t1LVFTUaanf2UabxA+dTkdYWBgFBQWEhoaeiXoJBAKBQCAQCAQCgeBvhE6nIyUlhbq6ug4x7nSOo3e/OYiQgFMbX6O+0UavOza3GKP7s/xwR6/Xk5WVxYMPPsi99957Sut2ttKumB+hoaEd4iYUCAQCgUAgEAgEAoHgTBASoCA08PSE2zzRMXpQUBC9evXi0KFDp6FWZyci1a1AIBAIBAKBQCAQCASnCZlcdlpeJ4PJZGL//v0kJCScorM8+xHih0AgEAgEAoFAIBAIBB2Y+++/n99//51jx46xceNGLr74YnQ6Hddee+2fXbUzhkh1KxAIBAKBQCAQCAQCwelCLne8TnWZ7aCwsJArrriCyspKYmJiGDp0KBs2bCAtLe3U1ussRogfAoFAIBAIBAKBQCAQdGC+/fbbP7sKfzpC/BAIBAKBQCAQCAQCgeA0IVPIkClOLkaHtzIF7UPE/BAIBAKBQCAQCAQCgUDQoRGWHwKBQCAQCAQCgUAgEJwuzoKYHwJh+SEQCAQCgUAgEAgEAoGggyMsPwQCgUAgEAgEAoFAIDhNyORyZKfYUuNUl/d3QIgfAoFAIBAIBAKBQCAQnC7kMsfrVJcpaBdCLhIIBAKBQCAQCAQCgUDQoRGWHwKBQHCGKS+764T2i417/RTXxBN/9TrdxxYIBAKBQCDoqAi3l7MDIX4IBAKfnOggvT3IZQ6TvejY1077sc4Ep/Oa+StbkiT251SxZ88w6uvrqa76CZPZhtlsw2yyYbbYMZtsmMw2LBYbcrkMhUKOUilHqZChUDb73LROoZShUspRKIc1rXcsUyrkRERejlKpRKlUEhwcTHp6OhkZGURGRiKTnbwppvN8hfAiEAgEAoFAIDhZhPghEAi8UlJ8B1VVjdTVmajTmdDVmajTmTEZrdjsEjarHZtNwmpzvNttEjabnZSUUJKSQ2ioN6PTmdDVm6nXmdHVm9DpzOgbzCiUcjQaBRq1Aq1W6fisGYlarUCrUaDRKl2f1RoFWo3ju0br2EejUaLVKggOURMUqGox0LZKEvGnecBc6kWIOBX6u90ukZ9fx759VezfX0lxSQNyuUNskCtkKBQyFHKHAKFQyFHIZZRXGFixIpeiogbU6m8JDlajVisc10yjcH12vVRyJAmsNjtW5+9otWOz2puWOX5Lq9Xzs2N907Y2O1brGqxWe4tzCApSkZoaSkpKqMd7aorjc1iYpl3XxF30EUKIQCAQCASCvxyy0xDz4xRMNP3dEOKHQPAXp7L87jZv29BgprbWSGCgisBAFRqNwiUcmExW3nhzC2v/KKSwoJ7ikgavA1sncrkMpXMArnC8y2RQW2tqsW1IiJrQUDUhIRqCg1XYrBJmsw2jyYrZbMNksmE0Hv8sSW0/f4VC5ig/RENIqJromECyMsPJzBxDZlY4nbIiSE4JaZclgrtw4k3k8IXvq3Ucd4Gkrs7Enj0V7N9fxb79lezfX0lOTjUGgwWAyEgtqSmhSBLY7A4hwm6zY7M7BAi7TcJmlwgOUjFtWicmjE9j6NAktNoz92iXJAm7XcJQbyG/QMfR3FoKCuopKNCRn69jzeoC8gt0NDZaXfuEhWlISQkhNTXM8Z4SxtChifTsGdPq8U6FNYi/3/R0i2YCgUAgEAgEgj8HmSS1PszQ6XSEhYVRV1dHaGjomaiXQCBoI77ED4PBQlVVI4eP1LB2bSFr1xawc1c5Ntvxv3xYmIbh5yQzbGgic77P4cCBKqZOzSI1JZTk5BASEoOJCNcSGqohLExNaKgGrVaJQiHzKSZUVBioqDQQGqIhNFRNcLAaeZPSLW+DACFJEhaLHbPZRmNjkyBitmFqEkeMJocbR6PRir7BTJ3ORG2d08LEREmpnmNHazl4sNpV5v/+N54rruzRjqt6Yhj0FnbtLmfHjjLKSvVkZIbTqVMEnTtHEh0d4HHNysr0XHTRXI4crkGtlpOdHUn3btF07x5Nt25RdOsWTWxs4ClxH2kPKjf/UXuTyGKx2LFabVitEhZrk0WIxY6laZnVYnMttzVZANntYLPZMTdtU16hJze3jtxjdeQcqOLQoRqP4/brF8fiRZe1q65tEZtOBCGACAQCgUDw59DRxp3O8ymaO5HQINWpLVtvIenC3zrMtToTCMsPgeAvxp49e9izZw/Hjh0jNzeXgvxlLkFAbzBTVdVIVWUjBreZ9piYQEYMT+aqK3uQnBKKwWDBYLCQm1vH2j8KefLpP0hPD2PxosvaNPvuj5iYQGJiAl3f2yJ4uCOTyVzuGcHBaq/bWJs028ZGq+N8qxopLW0gJ6eKmhojzTXdRqOVZb8do7ikgZKSBmprjDQ0WGjQm9E3WGhoMNPQYEavt6DXW7Ba7UgSrnKOvzssXvr0iWXcuDT694/n8OEaduxwCB4HD1Zjt0toA5TExgRSVFTvEpvCwjR06hRBVmY44eFafl14mKKiBgDGjk3jmqt7MmJ4CkGtNIw2ezvMYppQtMPM0o7ERRfP4/fV+e0+TnvQahVMnZpF375xDOgfT+9esW26VywWG9U1RqoqG2lstBIYoiIsTENYmAaN5tQ0aaVldwkBRCAQCAQCgaCDISw/BII/iRMJjKnXm8nMeg+A0FA1aWlhxMUGuWJhBAaqiI4OJCoqgOioAKKiA0hJDiUrK9yvBYHRaEWtVsApNDJor+jhj9IyPe++t43164vYn1OF0U3YcScsTOOymti2rZScnCpMJtvxOsllxMcHERGhJShYTUiQiqBgNcHBKkJC1AQHqQkKUqFUOawfZDIZMhnIkLncKs1mG+s3FLFmTSEGgwWlUk63blH07RtH376x9OsbR58eMSiVcsxmG0eP1bL/QDWHDldz6FANubl16OrN5OXVebiCuDNqZAo//jCzVauPxmZuSQHKUxP1e8GCQ1x/40Kf64MCVQQFqVziVFm5Hr3e0qayO3WK4NixWgAefGAo994zmPp6E0eP1lJWpqeq2kh1daNL1KqqaqS62khllYHqaiN1dS3dqpxotQrCwrQuMWTUqBTuvmcwyhO8LkIAEQg6Lrqqe05JObZTaIIWEfPqqStMIPiL0tHGnS7Lj58nnx7Lj/OXdJhrdSYQlh8CwVlEdXUj77y7jfAwLXZJwmJ2uBNYzHYiI7Wcf35nYmICqagwoFTKSU0N5bxzOzP93E4oFL4HeDbAXyANpUaBnVMTsNOJXZJOWgBxWnjcePNCNm0s9rrN++9PITUtDIvFRnFxA3l5ddRUG9m5s9yx7419mHlhNgkJwSTGBbc6ELa3IeDIrbf2x2SycvRoHV07RRAQ4P1RqlYr6Noliq5donyWpas3sXd/Fb/8eoSvvt5LTY2R/HydS/hoLnD4w9u2JyKIXHBeNhdUZPvdxublOtntEpWVBoqKGygqrKeouB6NRkl2diSdO0UQEqJm5oVzOdxkDfPc8+v5+OOdlFcYPMoJCVETFRlAVFQAkZFasjqFM3hwQtN3h7AXGRlAYKASXb2ZqhojdXVG6urM1NUa2bOngk2bitm8uYT164v4ds4FqFSKdl8HgSfGmvv8rtdGvHKGanLmOBPpn70dQwT2PXuwWGyUlukpKWmguERPaWkDNTVG+vaJY+jQJEJD2xfA2RdC+BAIOjYyuQzZKQ54eqrL+zsgLD8Egj8J9w7vvv2VfPnlHj75ZJdrWXi4w4xfpZSjUsspLdVjsdiZMiWDwYMSKSvXs2lTCZs3l5CVFc5VV/WkT59YevSMaXc2DTi1wsfJYJMcg+i8vDpycqo4kFPF6tX5bNjgED80WgV9+sSSlBSCWq2gpLiBXbvKXYFWIyK0REQ4Zv8bDVZef20C/fvHn5K6mdym+ELUp38wrTPbWt+ojSQnvOHxvbby1Mx6gnchxBsLFx3hmmt/ASA9LYyBA+LJzHQEpc3MDCchIRhtiBqNpm3XtqLCQM7+Sg4cqGb//ipycio5eKDaZVETEaGlX784Pv5k+gkHgRXWH62LHu3hbBVI2mqJJ0kSDQ1myssNlJcb0NWbsNkkJLsj8G+v3rEEBijZu28iu3fv5tixY0SE55CZFU5mRjjdukUTEaFtd/1ORAzxFdhX3NMO8ovv8Lq8prqRdeuKWLe2kK1bSti/v8pjvUajIChIRXW1EblcRv9+cYwckcLoUakMPyfJ70SEL4TwIRAcp6ONO53nU7xgymmx/Eg8d3GHuVZnAiF+CAQnyYm4rzgpKNDx5FN/MH/+IRISgujdJ4683DqmTcvigQeHemxbV2fiu+/28/lnuzhypBZomiGPCsCgt1BV3eiKLxEfH0SvXrEEBCixOQNW2pxBKo8HrLTZJQIClAQHqwgOUmNotLB/XxUXXtSFRx4+54TP60SwSZCTU8Vbb25h0aIjrgFseLiGLl2iCA5RU1VpIDe3ziV0JCQG07tXLH36OF69e8cS6xZvxMmJWKCYTtKW+XSLI82FkebiRns5UTFEZ27puhOi8i001DeYCQ5qmZ64OQar4/zsdgm93kJ9vSMuS0F+HXPm7GfjhmKqqhoBhyCW3TmSrt2i6No1iq5do+nSNZLEhOATChjbUQaHlrr7271PYVE9efl1lJUbKC3XOwb5FQbKKhyfGxrMZGSE061LFN26RmG3SSxaepRlK/IAiI8LIiEhiPg45yuYlOQQenaPpseAj1Aozi4LnPY8v+PiT+4/5qSs9M4T3jdAqUBvPXWiaEe5108Ep/ghSRJr1xSwZnUBf6wtZO+eCiQJMjPDOWdEMp2zI0lLCyM+Poj4+CDCI7SEqpXk5en4fU0+q9cUsGZtIRUVBlJSQrjhut5cfVVPoqICWq1DeLQQPQSC5nS0cadL/Ph12ukRP6Yv7DDX6kwgxA+B4BRwIgLIH38UMuvqBYSFqbn//iFccmm3Npnm2+0Su3aVk5tbR2GBjsLCeoqK6inI13mkFNVoFNjtjswpJ0J56Z2nPdOIXZKwSbB9eylvvL6FJUuOkpQUwjXX9KRX71i6dYtmzZp87rzjN9c+F17YhenTOzFwUIIrsKriFFTTmQSnLW4vrZHUihBxor7moVFnrqPsa1a0vfgTQpqzP6eK4aO+bHW7rl2jmD4ty5UZJz09rNXZ1r+TG4FT9KitNXIsr47ikgaGD00iPNzT4uCP9YUcOVqLuSm7Up3OxOPP/OHykFMq5cTEBBAXE0RsTCCxsYEEBao4crSW/QeqKCp2BOwd0D+OKRMyCAhUUVqqp7RMT2lZAyWlekrLLOj1egC0Wi3du3fHaDTy1ltvMXbs2DN3UdqBr+d5z54fUlHZSHi4xiOld7duUVx0YRf27a9k7tyDHvsEBakcA+ama//QQ0MZPSrVa/kByrYJQ6dS/HDydxRBnM+45ctyuaHJIg0csaGefX4MV1zlO0NYqNrzuSZJElu3lfLJp7uY+5PjHrjwgmxuurEP/ft5tzwUwodA4J2ONu4U4sfZhYj5IRD8Sag1ChQKGUqlAqVCTn2dicjollYLzZHLZU3BNeNcy5RNIoUkSdTWmjh2rJb7H1zB4cM1BAepqKk10b1bFCNGpBARoXVYfljtNDRYyMuvo67WRJ8+sQwZksiQwYkkJASf8vMtLdOzZk0BpaUNjgCWlQYqm7K07NlTSadOEbz+2gQuuqiLhwhU0zS772Tu3APMm3eAuLggLGYbH3wwlREjUk6oTjYvOkdrViLexJHWxI7mnEkR40RJTXzT4/uJiiH1FocYp26DKbg60HeTFB0dwGefziAiQttqAF9vlJfd9ZcSQEy17XM1WfLbMVavLeBYno7cvDqO5dZRU2t0rU+ID+LWm/uhUsmx2ewsXZbL6j8KAZDJQKNRYrdLSBKolHIsVrvreaLXW8gv1HHxRV256cY+hAQ70l6bzTYsFjtRUQE+XZ8kSaKoqJ7lK/J49rl1bNu2DYD337+NXj0mubaLjn2tnVfo9OHrPtm/7/g919ho5fkX1rN7dwX79lfy7HPrSU4OYczoVAYPTuDyy7oTFRXgN3uTzS61WfBwJ6jZPicrhvwdhQ93xo1P44efLuLokRpyj9WyaWMJj/xnFdoAJTMv7NJi+xCV0ksILRkD+icwoH8CTz4+itlf7eHjz3bxzZz9DBmcyNzvLyQgQInJfroSdAsEgrMdEfPj7EBYfgj+lrTHUmPnznLWri0gPEJLeJiG8HAt4RFaUpJDTjrQWV5eHQ88uILVqwsA6NcvjsiIACRJ4sjRWnJz65g6NZPPPp3htxxvA3a93sLd9yzjp589ZyITE4N59pnRTJ/W6YTrbXV7bChlMnbsKGPi5G89thk5IhmjyYbRaMVgsHD0aC2S5MhS4wxcGRXleI0bm8b06Vk+Z+/r6hyCzroNRaxZXcDatQWYzY5O5OzZ5zF+QnqrdZbjXexoLwnxf++BAjiEkJMJZqtsQ2M959t9PPzw74SGqnnq6dFMn56FTCZrd2yav5Lg4aS9wgdA78GfcvBQDX16x9KzVwxpaaGkpYbRs3MkQUFqHnt6LSt+z0chlyGXy0hJDmHW5T247R/9MNjtyJFhsdjYvKWElavyWbj4CAcOVGG3O6zIoqMDXKmZnahUckJDNYQEqwkOUaNUyjAYrDQ2WhzvBotHyu3AQBUzZnTi0ou7MmJEcov/+9kkgLSHyvK7sFrtbbLci4p5zee6+qp7T7ouTiHk7y5otAVfgq7VauffD6zg++9yGD8hnUGDExg4KIFevWPRapVttmaz2ew89J9VzP56L/tzbsFgsLBkyVFyj9VRVdVIRISWjMxwevaIoUfPaL/3j/g9BX8nOtq403k+JYumnxbLj4Spv3aYa3UmEJYfgr88voK6wXEXkcICHbFxQWSkhREbG9jmWWOj0cqkZoN6dyIitKSlhZGeHkZaWijpaWFkZoYzeHAicrcBXlVVIwsXHUGllKPRKtFqFcTFBtGlSxTfzZlJeYWB5cty+X11Pkajld9+y8XalLmjutro6/A+qa01Mue7/ezPqXQtU6nkWCx2iosbyMmpOmHxw9pML7VKEsfy6lpsV1hUz6BBiWi1SrQBCu7oFs2EiRlERrb0gzYarRw6UktxoY78/OOvvPw68vN1HibmQUEqOnWKJCU1lKtn9WxV+GhPp7GktOW9JMSOlrhbhRSWtD9+gdXuuIe8iSAWi43HH1vDJ5/s4soru/P4EyMJCTkuMjafNxWDAgcffTSNmRfOxWqzc+edA0lPC/NY/8FH07zuZ2iaibYjoVDJGTosiaHDkvjPf4ah11vYvqOMzZuL2bS5hJoaIwaDFZnMIaL26R1L375x1Nebqa83Y7NJBAYqCQxUERDgeA8MUBEYqCQkVMPwc5JbWEL8VQUPd6JjT809GBL1P4/v3sSQ5tu0WH9KavL3RqmU8+Ir4+neI4Zlvx3jjde2YDBYUKvldMqKJCEhiISEYOLjgkl0fo4PJiRYTX2Dmfp6EzqdGZ3OxPYdZQwZksiGDUXcdedvVFcbSUwMJiYmkJoaI7m5jrZTq1XQp28cAwckMGBgPAMHHnfrFM84gaCDIJc7Xqe6TEG7EJYfgr8c/sQOJwaDhX/csohly3J9bjNv7oWcc05yq2Vde90vLF581PX9yiu7M3lyJqUlDVRUONJ65uXWkZdfR3FxA5IEAwbE89JL4+jRPRqAzz7bxUP/XtWi7IAAJf36xjFhQjozZ2aTmOjoun7++W4efGglY8emct11vRkzOpXAAN9qsV2SkCSJbdvL+OLz3cydd8AV68P5D4+NDSQ9PYyBAxL4v/8ObzXlqzeaCx/N+eTjnTzyyO8AzJ13EcOGJXnU4dDBajZtLqYgX0dBQT35BXUUFtRTVqZ3laFQyEhKCiE1LYzUlFDS0kIdn1NDSU0NIzJS6yFeedOxRGfxz+FEhBDwFEHef387Tz25lmeeHcO11/Zq0/4d7fc+EcuPOpOVnJwqLr3sJ8rL9UyenMnNN/Vh+PBkr2Kvog3WN5Ik8eprm/l9dQGHDlVTXn48JXFSUgiTJmdw0019HVlPdCaGD08mMNDzOaXycpyOIHgIOi7NLUKsVjv791eyZXMJpXk6iood8WyKSxooLdNjt/tuF7UaBUOHJrHq93zGj0vjrdcnERcX5FpvMlnZtbuCzVtK2LylhE2bSygpcVhYffHFuVx99fzTc5ICwVlMRxt3uiw/lpx7eiw/Ji/oMNfqTCDED8FfFn8iyJIlR7nOLYCZN3755RIGDUxo9Th791UybtzXXtepVHK6dYuid+9Y+vSOJTMznCNHannxpQ1UVjbywftTOP/8bHbsKGPylDmcf35n7rxjILFxQRQU6Fi/roiPPt5BSYmeiRPTmf3leQCYjDa+nL2Hb77dx549FYDD9Dw0VENYmIawUA0BAUqMRiuNjVb0BgsNDWYqKxtJTg6hsLDeVUetVsGwYUnMmNGJSy7uikZzYgZf3twcJEmirEzPnr2V7N1bwdatpSxfkYfVaufxx0Zw6z/7Y2nqGFZVNdKr54eAY9Y4JSWU1NRQkpveU1JCSU0JJT4h+ISEGRBWGmcLJyOCvPjCBubM2cfWbTe0aZ+OJny0F6n+AY/vBoOF2XP28eZ729i7v4rePWO445/9mTYpg4R4RyyfaqOlTWW/9/52/vt/q5kxPYvOXaLo1CkCtUrBzTcv9Lr9gAHxPPjQMDp1iiA+PshDdBH/TcFfHW/Zk2w2O2XlBkpKG6ivNxMaqiE0VE1oiIaS0gau/+ci9uyt5Kore3DZJV2pqzNRU2uittZIba2RmhojtXUmamqMjnU1RvLy6wgPj2Dnzp2kpJxYPCuB4K9MRxt3usSP3847PeLHxPkd5lqdCYT4IfjLYLPZKCsro6ioiPT0dCT70y22Wfl7Ppdf9pPru0IhIyREjVIpp7LSM3BmcdHtrWaIAMcAf/UaR0wOyS5htUpYbXZsVjulpXp27a5g165yDhyocqWadWfL5utISQnlp58Ocvc9y2hstJKSHILBYKGqyaVFJoO+fWMJC9Mil8mIjw9i1MgUZszoxKHDNezbV0ldnQmdzkRdneNlMFhdpuWOdyV9+8axcmUeH3600+f5XH9dLx5++Jx2xytpLn78/ns+d979G8VNGR9CQtT06BHN9KlZzLywC3GxQR7bb9lSwtTp3wEQEx1A9+7RdO8eTY8eMWRmhBMeriEk1CHsaLUKZDKZSzhpDWfV/u4D4bMFd/chX4Ew3VG7WQe8+tpmPvxwB/v23gz8NWN2nCmaCx8e6ySJ5avyeePdrfy65CiS5EhD27NnNAMGJHD7bQMICGgphNbXm1iy9Bhbt5by2ee7ufnGvjz95CjX+gMHqxk24gsAMrPCee65scTFBlJdbeTCC3/0KGvmzC688+5kQPw3BX992pM6uqbWSEb3DzAYvAuNISFqwsM1RIRrCQvToNUosdklNBoF3bpfwKxZs+jXr9+pqrpA8Jeio407nedTuvyC0yJ+xI//qcNcqzOBED8EZw1Go5HffvuNefPmsXTp95iMNuQKR3A+u12iqqrRJS7MmJ7Fxx9Pb1HGw4/8zscfOwb+crmM4cOTaWgwc+RIDTqd2bXdSy+N45qre55wXeUyWYusH42NVo4eq6W2aQanstKARjuTmRccQK12BDJraDCzZm0BG9YXExauYdvWEn5blufzOC88P4brruvdan1Ubj5/b769lceeWMM/bulLbEwgBw/WcPBQNdt3lHnsU1pyR5tjnyjkMiorG1m/oYht20rZtq2MDRuLGDkyheuv7U2PHtGkpoa2Wl5ubh179lawb18l+/ZVsndvJble4oX444Xnx3DD9X1adcFpjhh8nVq8xUfxR1vTEc+Zs5877/qN887rzCMPDyM9PVwIIG3AnxBSWFTP7Dn7ePjxNQBERmr5feVVLbI6SZLE+TN/ZN26IjIzwhk7JpVnnx7tEYjxg4928O+HV7m+x8YEkpgYTGFRfQuB+f77h3Df/UNarbv4bwo6GsUld/JTU8rbsDANEREOkSM8XEtFhYE/1haSc6CKnP2V5ORU0dDgXSTp3z+OuT9cSHCw2rXMX9BcgaAj0NHGnUL8OLsQ4ofgT6Gq4m4kyWGivWjREX5ddITly/MwGCxkZYUzeVImYWEaJEly+dPGxAQSFx/E/fev4LzzOvPM06O9ll1XZ2LHjjK2bitl165ywkI1ZHWKoFOnCDplRZCeHoZSKcdstqHVes58tjeDRVt915tnl7Fa7dz6ryXMn38IpVKOUilDoZCjUMjQaJSkpIQwdkwa11/fm9BQtUdaPZVK7mGxotdb+PSTnRw+UkthoY68pmCho0elMu+HC13b2e0SxcX1LFuVR3R0AJMnZbap7s7YACNHz2b//iqPdVde0Z3nnh3jEcSwrR2zyvK7OXy4hkcfW80f64p8zpA15+KLu/LmW5Na39ANMbg6PbRXAAHvIogkSRQU1LNnTwWHj9Tw7bf7OHKkFqVSzq239uOpR0cgk8n+EimCz0Z0Vfew9o9Crr3xVyorGwkJVjN6dAoTx6czYXw6UZEBNBqtLF5ylFtvX8qcb89n4rgMr2VJkkRJqZ78vDry8h1pdQuKdCQkBNOte3STEBrmEfC5NcT/88/FZDKRn59PTU0NNTU1HD32GrW1RlJTQhk7Lq1NIrlwazqOL5fc9euLePutrSxfnotaLadz50i6douiR9dounWPonu3aL7+Zh8vvLjBY78xo1Pp0iWSWVf1oEe3mHbVJUyjQB768gmfS0enyIeLZnvT1wtOLR1t3OkSP1ZeSGjwKRY/GizEj53bYa7VmUCIH4LTRlXF3T7XlZQ08NHHO/n8i93U1pro2zeWaVOzmD6tE9nZka7tSksb2J9ThU5npqLCwOyv9rB3byUffzSNvn3jKCvXk5wUQkxMoNfOtlPMcFppFBfXM3v2Xr7+Zi8lJXqiIrUkJ4fy+OMjPIKfnmgaT4vFxt69lWzeUsK2baWYLXYCApRYzDbmLzjMY4+N4Ibre/PxJzt59NE1qNVy+vaNo2vXKDQaJRq1gqrqRlatyqOkRO/1GIGBKs47txOzZvVk0KAEtm0rZeq075DJICM9nKyscDplRTBxQgaDBia0yK5gacruYLHYaGiwUF9vpkFvRqNWEB8f7LG9e1DEzz7fzf0PrGhRn6WLL6N//3igbcJHbeU9rs9bt5Uy65oFlDYFPM3MCOeuOwYyYngySUnBHvFJjLbmeT5aRwyqzhztFUKCVHLWrC3ghZc3sntPhSujT2iImsBAFWq1HLVaSedOEXwz+1zXAEwIIO1DV3X8/2a3S+zcVc6y5bksW5HHps0lLYI1Tp2cybdfndfu69yWQNTuiP/mmUWSJEoK7qC+3kxNrZFdux0xmrZuK2HX7gpXgGwnTovLc85J4rXXJpKc0ra+319JBKmpuKf1jZpoi/uek8o6IwX5OmRyGQqFDLlMxqFD1dx840K6doviln/047zzs9FoFC6XP2dbe+hQNbf8YzEBAUri44MwmW3U1Zo4erSWkBA106dlsXpNAWXlBmQykMlkyGSO30uG47tGq6BXjxhGDElkyKAE+vSMaVOsr44skvgSOdqKEEPOLB1t3CnEj7MLIX4ITjmtiR6Tp86huLiB4GA1s67qwU039iEtLZSaGiMFhfVs3VrKpk3FbNpUQn6BzrWvTAajR6USFx/Ejp1lHMipdq1TqxWOIJrJISQnh5KcHEKnrAjGjUsjPFzr2m7EyC85cLCaCRPSmTG9EyWlDSxZfJSi4gaW/XY58fHBmM02zBY7IUEqvzNeMhl89fVeXnxpAyqVAo1GQX6+jsZGK2q1gt69YggOVqM3WDAYLMhkMvbsqaB3rxjef2cKn8/ew6rf88nJ8bSm0GoVhARrqKpupFevGK68ortHqs/c3Fq++HIPpaV6du+8kYgILT17f+SRDtadgAAlUVEBhIZqaDQcFzuMRpvX7UND1SQkBJMQH0x8QhAx0YEcOlTDli0lVFY5zNo7d45g0MAEhg1L4uKLuqJUyttkBeMufDix2eysXlPAd9/nsOCXw+gNFnr2iGbs2DRGj0whMESNyWjFbLZhNNlcn80WR9wVq9WOzS41fZaw2ewolXJHil2NEm2AI7VwRLiW3r1jCQ3ViGwTJ4i+umXqzea0NU4LwMJFR3jgoZUUN2U36Nsnlquv6sGVl3dvkTXEHSGAtB93EcRJTa2RtX8U0miwog1QEqBVMO28bwgIaJmOWnD2YrFY2LdvHytX3s+ePRXk5dVR32Cmod6MTmemocGRitiZPt1JVmY4AwbEM3BAAp07RaDXm6muNlJdY6S8XM/sr/ei05l57fWJXHpptzbVpSOJH8UlDVRVGtA3Wh3BxY2Od6PRikql4JxzkoiLDaK8XM/GTcVs2FDM+g1F7N1b6TUDzMCBCfz400WuiRp1O6yjdu2pYNrUOUREaBk5KoW01DAkCSQkFMhcVrKSBBajla3by9i+qxyz2YZaraBf71jGjEzhXzf3JTnJe0LkjiR+NBc76upMHDtWS2mJIzCtQilHqZChVMlRKuQolHLUKjlJSSGkpIaiUfsWi/5K9/hflY427nSeT9nvF50W8SNu9I8d5lqdCYT4IThteBNBjhytYchQR7A8pVJO1y6R1NQaKS83uGaelEo5vXrG0H9gPAMGJdCrdyzh4Rry8+q4565l5OXVMWNGJ8ZPyCA9I4yS4gbKS/QUFuooKKynqLCegkId5eUGVCo5o0enct65nZk6JZOtu8p44J7lVFUbeezR4cy6phcVFQamTvmWkhI93bpFcehQjauTGBam4ccfZtK3T5zXc3ziybV8+tlurr+uF3qDhbBQDUOGJDJieLLXmZYVK/O47PKfmD/vIkYMd0RxLy3Tc/hwNTqdmfIKPQaDFb3eQs6BKhb8chiNRsHzz43h8su6u8q5+ZZF/PTzQSIjtfTuFcvQIYkMG5ZEaIjaIQyYbRiNNqprjFRVGqisakSnM6MNUCKXyzCbrUgSjpgqMhkSEg31FnQ6E0aTDUNT9hi93oJBbyE9PYxBgxIZONDRUY6I0Hqc18mKCU5RRK+3sHjpUZYvz2XlqnyXRYgv5HKZy2VIqXS4DSkUcqxWO0ajFZPJU+CRySA7O5IB/ePpNyCeAQPiyc6O9HAjEjPS3mmL8OFOW0WQvfsqeO6FDaxeW4DJaMNisXHpxV354N0pKFqxwAqK/F+76iQQ/NXJz7uN/fur2L27nF27K9i9q5z9OVWYTDZkMsjMiiArM5zQUA0hIWpCQtWEBKsJCVETFKxGV2eiqsKAUimnttbE0WO15Dalane2wQqFjLTUMPr1i+O2W/vTtWdLV4s/YwDYFuuiULWi1W2cVOlMlJXpaWiw0KB3iEQNDY62L+dAFe+/v6PVMuLiglyp2lNTQxk8JJHBgxPJ7uKwYLXbJeyShN0m0bdvHAFNom5bhQ93uUqnMxESovY5KaNsttxksrJ3byVbtpaybVspy5bnYjBY+NfNffnfc2Nd2/3VRQ9DjWfbVGO0uj6vW1fIHbctdf1GbUGhkJGWFkZGRjiZmeFkZDreMzPCSUwK8bAydv8ftOX+FP2LttHRxp1C/Di7EOKH4LTSXACx2ewcOVLLwYPVHDhYTXFxPZGRAcTFBREbG0h8XDA9ekR7zPrqLVY++2QXzz2zjozMcN58exLZXaJaHCtIpXDEEbE6BrxlpQ0sXHiEhb8cYfOmYpRKOSkpoUiSxLFjjiCbY8em8eXscykp0TNv3gHy83X06B5NaJgGvd7CM0//wcyZ2bz84jiv5/f2O9t47PE1aDQK10BbLpexfev1JCa2nF2Zv+AQN960kF9/vYSBAxIoLq7nm2/2sWdvJXv2VJCfryMyUsull3Tjuut6ERCg5K67l1FS3MDq1bNc5dTXm1i/vpjdu8vZvqOM1asLXMfXaBVoNEoCA5SENHWAg4NVVFY0kpdXh17fMraGUiknMFCJTCbDYLB4mEAnJQVz4cwu3HB9b5KTW/7/T6UVhfv9IkkSh4/UYjHbUGsUaDWO81Kp5ajVCtRqR0YYfy5KdruEyWTDaLRSXq5n2/aypo5gCfv2VWG3SwQFqbj00m48+9yYFvur3Do5Isicg5MVQdzNx5evyGXWNb+gDgkgrl8WAPmr92K1WJn//YWMG5vWavlCABF0RJyCcFVVI2s3FLJpYwkbNxWza1c5FosdhUJGly5R9O4VQ69esfTuHUOPHtEEBKmxWGzk5FRx+HANRw7XcPhwDUeP1HLocLXL4k+jUZDuHOBlON579XmITp06kZqaikp1ajvoJ0t73Kp8CSBGo5WNm0v4fXUBq9cWsHlraQtrGCchIWr+cUtfJk3KQKl2WA9qtUrXq6HBzJo1BezfV0mPnjEMHpxIWrJ3i4rmtNWDpj1BvZsLH94oK9dz/vk/oFDK2bD2Gp/bRcSc3ZZ1zcUOXxQV1XPO2K/IzAzn2ut6k5EZTs/OkQQFqhxZ+6x2LNbjFqSNRiv5BTqOHKlh/6Fqjh6r5ejRWvLzdK77RKtV8NJzY7n26l4sXnqUjz/d5QhmG6ElKjqAyEgtsTFBxMUHkZAQTFRUgFeXbCGC+KejjTtd4seaS06P+DHy+w5zrc4EQvwQnDL8ubv4oi1t+xdf7Oa+plgTAwbG0zU7EqVSjsVix2RyWDmYzDYsTe9lZXqOHKn1KGPT1utYvPgoBfk6Gg1WqqoaKSlpQKWU8+STozA0Oiwc9HqHi4reYGHN6gKWLj3GM0+P5qab+ngdZJeX6/n+hxw0GiVBQSoKCnS89PJG7rxjAEMGJRIdHUB9vdmRnlZn4uX/baJXzxg+/3wGO3eWM+vqBRiNVvr2jaNnD0fq1/37q/h2zj6qq418990FGAwWrrvuV775+jzGjUv3eo3Kqgys/r2AhgazyyzXYLBSX2+iod5MfYOZ6KhAsjLCSUsLJT09jMjIAAIDVQQGKj2yOQCYLDYMBis5OVXMm3uA+fMPERMTyIqVV542K4n23j/+xIjK8pZlNe9I6vVmdu4s5803t7B1SykvvzyOMWPTCA3VeIgeJ1qHjkh7hQ93msdsaWy00qvPx2hS4uh/y2S2vLuI2twyLAZHVqaICC2H997cqq+6XYKQKCGACP6aVFXc7dXC6ZPPdvHBhzs4cNDh3pmYGMyQIYkMGZxI375xdOsW5RGwu7LWyMoVefy29BgrV+S6spvFxgaS3SmS7M4RdO4cSXZnx+eU5FCvg7Kz+b/UVgEkVK2grFzP8y9tZOeucswWGyaTjSNHazGZbERFahk1IoVh5yTRqXMkwcEqQoLVBAc7JgoCA1Wuds7sJX29N9RtTGfVVj2jrWHHmrdV7vWtqTGyfUcZ27aVsmZtAZs3l2Cx2Hn/nSlccnHXNpV/NgkhbRU9nDz86Gpef2srI4Ync8UV3ZkyOZOYCE+XPkmS0NU7/itymSO7oFzuiKOiUjo+W6wSO3eXc/HlPyHZJRbMu5jszpHEpbzZah2USjlKlRxjo5WRI1P4bs4FHv87kcnMOx1t3CnEj7OL1iMgCQSnCbvd4aOqVMo9OgQ6s9Vju1GT0nnSPJrCQh2FBTr251RhtUpo1ArUGgVqlRy1RsHKVflej9OzVwzr1hVRVqLn2NFaDh2oJi//eCyRqVPntNgnIEBJdFQAn38+gymTHVlR7JLUQgCJjQ3itn8NcH0vKWlg5co8PvlkF2+8udVjW7lcRnpaGC88OwaNUsE7726josJASIgaudwxK3XwUDVh4Rouu7Qb7763nZpqI+ef35lRo1K48qr5xMcHuWKaJCeHkJri+BwRE8igwQmER2hbZLBxRy2Todc7/L91OhPFJQ001DvMfQMDVcTGBhITG4QEHD1aS+6xWkJCHQEoDx6sJi9PR2ZmOHDqZy7chYTWhJDWRIfWrFFKy+4iKEjNOeckI5fL+O8jv/OPfyx2uEmNSuGVl8eT5MMv+u/EyYgdzdE2DSacIsgvvx6mttbI2PuGo1ApCE+PpWJ/AXKlnE5TB3BwwWYWLDzChRd0abXs+qp7z+pBm0AA7RN4164t4MDBal5+cSwTJ2SQ2hRwtNFqRyEDs13iaH4dy5YeY+mSY6xfX4TVaqdHj2huvKkvI0el0Dk7krTY4FaO5MnZ/l8K9xOLARzt6Muvbebl/21CoZQxdXImAVolSpWCa2b1ZNSIFLp3i0Yul7UpiKlM1ja1wtYGNz9F06C6tcOeiPBhNtvYsbOMrdscme62bivj6LFaAMLDNJwzLIlnnhzFuDFpZHeOxNpKfc8m0cNJYMTx+9KbEGJoFrT3X7cNIComkAULDnPb7UtdbtDdukaRl69z9XEavFjDeiMmJpCff7iQnj1isNnsPPvUaH5bfoxduyuoaoqHFhMTiM1qp77BjMXisChxWo0cPVqD3S55iB/OTIBCBPl7IJPLkLUj1k9byxS0D2H5ITgtVJbfxRNP/sGxY7Xomywq9HpHDAlnAFCDwYpWqyAqKsDlTxkWFYBK5VDKVSoFapUCpUpOgFbJgEHxJDW5XQSqFC1mPF54aSMvvXQ8RVxgoAqj0eoKPJaUFEK3rlF06xpF1y5RxMQGEhyoIjBIRVCQCk2AksBApcesT3NaywLjnP2x2SXKygxUVTcSFqYhLExDcLMAqnq9hc1bStiypYQt20opKqqnvsEhShgbrUyYkMHtt/ZHFaiksdHK+vWF6HRmiosaHEJQYT0lJQ3Yms1MBQaq0GgUKBQyVwA0m82OzSqhb2M6WXeSkoI5Z1gyN9/Ux5XVpS2zV2dzUFFvwR/zC3T8uvgob7+zFYvFzrdfn09PL77uwtrjxGh0My8P1yh5+oX1vPj2dqJ6pCJXyFEFaqg6VELtsTLkSjkKpYL/3DeY++8Z3OZjnM2DNsHfl7aKHlVVjfz88yHmfLePrdvKiIjQsmTpFSS7uVNIksSPP+Tw3vvb2be3EpVKztBhSUyanMnEiRkk+XC9CPMjGvyV/jfGmvv8rn/g4VW89d42xoxK4bOPZxDZLD6VO20RP9qSZcx7b6ElijYMUuT438aOo87O/k9FhYHLZ81ny9ZSwBH8vU/vGAb0j2dAv3gG9I8nKzO81XTFEp7X4lQElXYGHT0TmVIMNfe2ED/cKS6u55dfjzB/wSGKiupJT3e4emWkh5HUFMdDo5BhtzsmupyTc86XZJcYPy6NFC+uv5IkkV+gY/OWUvbnVFGnMzkmmOrN2Gx2Jk1IZ8LkTKKjA1s9DyGCOOho407n+ZSvv4zQYPWpLbvBTOywOR3mWp0JhPghOGW4d/DsdonzZ/7I+vVFAEyflkVioiONquOlJrBpUF9aqufoUYdvZVV1I1arHbPZhsVidynnTrp1i2L69E5cdnl3V4fQeQfX1hqZPXsPkuQwNVTIZQQFqeicHUmXLlHERfrPYOBMAduWhKqBSt/dndo6Iz/PP4xSKad7t2iysyPRaBTodCZqaozU1plQKmT06hXr2ufQ4Wpee30LK1flUVLqOzCXSiUnOTmElJRQUlJDmTwpk27doykv1zui9Fc1UlNjxGSyYreDJNnRNwVQNTVaSUwMplvXKEcgvGANoU3xQIKD1eh0JrbvLKe2zkh0kyCVlBRCYWE9lVWNBLt+OxWBgWqCglSo/QSXO1vFD2/ChztlZXouueJnjh6t5cvPZzB2dKrP4J1/JyGkNRHE3dy6LYOKz7/YzQMPrURqdm3D0mKRyaEut5z3Xp/EDdf0cgknf6VBmkDQHF8iyOzZe/n8i93s2l2OTCZj4oR0LrukG6PGpXlY8hUV1fPQgytYsSKPSZMzmDmzC2ObXPX8/eeST2Lw6S1DlzfCo0+tpYAkSdTV1ZGfn+96VVZWIpmXolDKUCkVjokSpRwrEg0NjoDdO3eWsWx5HgCP/98I7rxjoM9jtOU55c0lyWRvlha4DedzKoQPOC5+OPnkk508+O9Vx4+jkNGjRwzdu0bRo1s0CqWcbdtLqa0z8drL410WRBaLjfIKAyWlDVgsdldsMKf7T/O2XelWf3cLjOaUlJRw+MiDREYFEBam8Sm6nG5BxN311d7K73zgQBUHDlRTU+PIdBQYqGTyuHR6dItqVTRqDw1m7xn2hODRko427hTix9mFED8EJ42vDp3NZuellzfy8iubmDQpg69nn9dqWd5MR22SRF2didW/57P0t2P8+usRTEYrY8akce99g+nbN66F64wvAvyIFk5++fUwd927DI1Ggdlsx2SyEhqqYcyYVK65tjd9+jhEi+YCyP6cKt5+dxs/zjuA0WhzWZw4TRybp7576YWx3HRDH1b9ns811/9CeLiWadOzOOecZFJTQx0pXK0SVpsNq1WiXmeioEDHunVF/PLLYQCuvbYXz78wFjkOc99fFx5hwYJDFBToKC3VU1nZ6HHM8ePTmf3VeeTl1bF3V4Wj0T9YzcFD1Rw5UovZbEMul3HTjX3490NDuf/BFcyde9DntYqPD2Lq1CymT83inHOSXLFDzlbhw0lrAkhDg5nrblrIylX5vPa/8Vx+eXe/27uLI3/1QGbtETlao/ngorRMj71pJrWmxsiEyd8SnpWAxWCmLr+CoLhwxjx+Obu/Xk35loNs334jYWEajzJEmkHBXxm77n4AakxW12A30u15+dYbE7ny8h4e+zRabXw1ey9PPrmG4GA1L7w4jokTMzy2OdH/RWvPwnZkrm5VADHV+rfagOPPlyuuns+vi464liuVcqIitdglx8DdEbDS5kpvHhysJizMIeiHhjqsLc+b0Zkr/Dy7T1T8aC9tOU5bjtVc+HBSUWGgqLie4qIG8vJ0HDhYxf6cKg7kVGGx2undK5bCQh0xMYEkJASzbVsZlVUGv30mtVrRNEmiYtDABD5+b4pPi1g4nmFlQP9PKGuawFEq5URGaomMCiA0xDHgkySQyyA4WM3kiRmcN6MziQkO16zTJW6X+4gVM2fOfl7530by8hxu0HK5jIgILXq9GaPRRmxsICNHpfDyS4tJT0/3WkZb45D4E4wELelo407n+VRsvOK0iB8xQ77pMNfqTCDED8Epo7kIsm59ETfdtBBk8MlH0xg6NMnrfq35yup0JlavLuC3ZcdYtjyXygrHgN7pO6vVKujaNZoZ07P41+2OWR693kxRYT2paWEtYmAYDBYMOpPLfHHvvkreeXcbOQeqKC3VY7HaqGg6RmpqKFfN6sHPPx1i375KzjuvM+9/MBWrXWqRqm7ipG/YtauCYUMTef/9qQQFqdh/oIqcnCpsNonICC3791fx9ttbMZls3HhjH55+ZjQXzvyRxkYr330/k2AvD0XHeUoUFTXw3Xf7efONzYSHa3ns8ZGcf35nAN56cwvvvrudmhojQ4Yk0rVLFPHxQcTHB1FeYeC559YzYEA8n39xLr8sOMTDD/+O3S4RExNI56ZAeF2yI+mcHcnuXeW8/MomgoJUhISoOXq0lquu6sHll3VD1hQzRK+30NBgYe++Chb+eoSCwnrCwzVMm5bF00+NJijId0Cns3GWw31209lRtVrtPPDQSr78cg8PPjCEB+4f0mIWqC3pXP9qYkhb3VzaKoI4Z0kXzD/ELbcs8rmdNjyItNE9qD5QREVOEc8/P5Zrru3ldVshgAj+KjjFDm/sP1DF2x9s592PdgIQEx3I7yuvIj4uyLXNvv2V3P/gCjZsLObqWT158rERhIVpT1lMhtbED3C00bm5dWzYWIxGoyAuLoj4uCDSk0I9nvWnapLcbJOYMPVbNGoFj/3fCJKTQoiLDTwehNTeFleU1ivTmihxJoWPE8W9is3P2T2u2q7d5Uyd8R0J8cFcdmlXEuKDXb+jSq1oSvN7PNWvrsGMUW+hvMLAO+9v57wZnUhKDEarVRKgVaINcLxrNEoCAhwvjUbJJVf8xF23D2BA/3gqKxspKtdTVdWIUW9BJgOZzBH3pKRUz5q1BVgsdoYMTuCimV246fo+KJsmlax26bTEHXEXQuLiPa1PgoNVhDaJ7cVFDa7lF17Uhbffnuxqywub3Hn8cTLWVoKON+4U4sfZhRA/BKcM9wHk2j8KuOTynxg4IIEP3p9CbGyQnz0dOEUQSZLIyalm+fJcli49yuYtpdjtkkvsUCpldO8ezfDhyfTpE8fCXw8zf4HDEmLgwHhyc+tcFg9hYRrOPbczF13UhbT0MD77dBdffLGb2loT/frGcfml3TDb7fzff1czeHACQ4YmkXuslo0biykvNwAOVxOLxc7MC7vw7PNjPASKfbvK+eetiwkMVFFd3UhJiWPGQ6NRkJ93G+DpRjN40GcUFOgIC9Nw9TU9mTgxg+eeXYfdDnPnXeQRCEuSJL7+ai9ffbWXQ4eq0estqFRybrmlH/fcO4igIEc93n9/O48/toYbbujNTTf2ISsrwuO67t9fxQUzfyAsTMOo0al8+cUebrq5L/feO5gIN39o9zmd4uJ6Hnt8LfPnHyIuNpDqGiNWq53x49OprGyksFBHVVWjI9WsXObhmvT2W5O42E8k+bNJ/GjNpFuSJF59fTNPPbOOoUMT+d/L48noFOF3H3f+asKHO+2J9aG3eDfnBTCZrOzaVUFCUjCffbabDz/cgbHRyuTJGdx992D27a/ks892sXtXBQB9+sVx150DmTLFkfpWCB2CvzLexA9Jkrjk6gXMW+B4vl59dU+uuaYXyQnH43U0NJh56ZWNvPv+djLSw3jphXGMGpni9RgnO0hsLoDI3AbRDQ1mZsz8gW3by7zuGxKsJiEhiG5donj/rcmEu1lq3XjrYpb8dowB/eL4+rNz/Yri7phtEldeMx+jycqPc2aewBl5noMvTlb8aC1oaFuOcarwl23GGbakrExPRITGbwYtb/V9/oX1LF+R58gk12jFaLLS2Oh4mUwtn/0L5l7E6FGpAHgzFnHWp7bOyKIlR/l5/iEWLz3Gg/cP4cH7h/qs28ne5+7Cx9GjtXz+xW6WLjlKVbWRgAAlarXDlUqtVqDRKByijlbJoIEJjByeTESElsjIACLCtVjamN1HiCAnRkcbd7rEj81Xnh7xY9DXHeZanQmE+CE4YXwNHLdsLWHmRXMZODCBb2af57K8aK0ToJbL2bmrnP/83++s/aMQtVoOOIJ2jhqZzNgxaQwelECf3rHI3FxOnnt+Pa+9tpmePaPp1jWa9PQwsjLCiYwNZO3aAn784QBFRfUABAWpuGpWD/r2jWP+/EP8tvQYl1zajQMHqqipMbL0tysIDHR00PLy6vjko5188+0+IiO0rP7j6hYpYefNPcCdty91fR8wIJ6tW0sZMiSR+T9fjFYpR+8WhKumxsjKlXmsXJHLqlX5VFU18uRTo3j0/1bzxBMjufmWfoDDjPW++5ax7Ldcpk3LYuCAeLKzI+ndO5Y4t1nBRYuPcP31v3Lrv/rzn0eG+7y2JQU6brjxV/bureTJJ0dxy819Pdb7mkf7/PPd/Puhlbz22gRHYNYtpciAktIGamtNhIdpMFvsGJoCqUZFaRk1KpXx49KYODGD8HDPYHNnk/ABbfdn/+iTnTzw0EomjE/nq68c7lv+5h7/yqKHNzNeSYJ6PwIHePd7X7++iAtm/uj6npISSlJSMBs2FAOwc8v1ZKSHA6CrNwEQGnJ88CRifAg6Ek4hZH9+Hb37fMy11/TkiSdGthiMHj1ay8WXzKOqysB99wzh9n/19ztgbe+g0Jc1it5LwMhb71jCT/MP8d5bkxkzOhW1XEZJqZ7SsgaKS/WUluopKW3g3Q938OIzo7n15n7IZI4Ax5de8RObNpdgs9oZNCiB77+9AIVCjs1oZe2mYnJyqjCarFjMdkxmR9p6u01iwIB4Vq8p4Psfcvj+2ws4Z5h3q1F/nA3ihx8vkeN1aMWIpS3iSVvT7PrCLrVdpGm+md0uOUQRoxW7xY4kQXJSSJvOHY6f/wMPr+TDj3ayeuVVdO8W3aZ9W7vvmwfH1ZkdAfBnXvgjGzYUExCgZOKEdLKzI6mta4rJVmukptpIba2J6ppG6upMXt2DtFoFkREBhIZriIjQEh6uJdzjs5bwSC2dOjksa4UI0j462rhTiB9nF+0SPw4c/AchIcL/WnCc5oPHbdtLueCiuXTKimDBTxe3mOlxNrBquWfLWFXVyP89sYavvtlLp6wIBvSP59vv9jNjehbPPDXaFaSrOW1prxutNjZsKOLYsVpmzOjsEUPgm6/3cu+9y7no4i78+MMB7rl3MPfdP8Rj/507yzh3+vc8/PA53Hb7AI91DQ1mPvxwB8uWHmPb9jLkchlDBicwdXImU6dkkt05EvDeqVy8+Ag3XP8rW7Zez/vvbePjj3cSHKxBq1Wg11tQqxW89vpEJk7MwFff5oorfqasXM/CJZd7WI00Ry2XYTRaKSjQ0bmpTu746n999uku/vOfVa7vcrmM1NRQhgxNYsaMTowdm4ZMBrm5dU3iSAmbN5Vw4EAVSqWcsWPTOP+CbKZPzSIosG2zfn92vBD3e9pss7NiRS63/GMxSUkhfPH5DNLSwrzu5y0T0J99Lu2hLb7LOh8B29xx/2f//NNBbvnnYp/b7tl+o8//NoC6pBJNzy9aPaZAcLbibkG1eOlR7n1wJfkFjhgDvqzk1qwt4OKL57F43sVMGp/uWl5rtJ707Lc/Nxw43lZZLDbe+2AHjzy2mnffnMRVVzjikEhIqOVyj+xNv6/O58qr53PhzC68+dpEdPUmfv75EG+/u5VDh2u47db+vPXONmZd2YO9+yrZtbscm01yza5rmmbaVSoFdkni2LE6V9kBAUpmfzaDCW7Xoa04BRBfIkVrXi2nW/w4WeFD2azNP9GMl21x32xLX8vb9dIoWy5rft6r1xRw6VU/kRAfzC8/XUxSYojXa1tQoEOnM9G9e7TXQKRtiekGMOnc79iyrZTfV13lEt99YbPZaWwwU1NjpKbWSHW10fW5pqbpe9Pn8qpGamuN1NYY0enMgOMeu/Vf/bn3/qFoNAqXCFLqIw4J/LUnT04VHVX8qNw267SIH9H9Z3eYa3UmaJf4ccklXQkMdGTP6N07lu7do12z5H9lEaS5yafdLqHQPkVIiPeUcQLv1Fbew+tvbuHxJ9cCkJEexuDBiQwfmsjQwYl06xrlMUB3bwD//cgqPp+9hyceHUFRUT2vvrGFfn1iefXFcaRnhRMW5jtdHbTeMPuLzv7YY2t47/3tREZq+fzLc+nXL77F/k89sZbPP9/FuvXXktAUnKs5uspGFi89yuIlx1i1Op/GRisTJ6Tz6CPn0L1bNCbJIZZs3VrKxo3FLFp4mKqqRub9dDH//vdK/lhbiFarwGg8PsAcMyaVm2/uy7mTMr2KGy++spH/vb6ZnP23tIhtAo7Zt+axSbzhqw9WXFzPooVHSU4JITMzgtTUUK8ZXpr3Q8rK9CxYcIif5h1k69ZSzj+vM/fcPYjCwnoKCuspLNSRmBjCddf28psxxsmZEBG8BUXbvKWE8877Aa1WyWuvjkevt3Ast4642CAuvDCb6KjWU9e5c7aLIW0N3uYugnjrbtbXm6gob6RTk4tQdXUj+/ZV8se6Qj79bDdVVY2MGpHCW69PJN2LmKQuqfT4LgSQs5OcJtc+d7qmvd3ucvKK7mixLC3pzROq09lEc9ex9z7cwf3/Xsl11/Tkysu6M2xIIgarHbVCTpibZYdebyYi5S3u+ld/nnl0ZItnpCzkpZOqly8BpNpoxWy28e23+3jjzS3k5eu4+cY+vPT8mBaDTWf7Pfvrvdx7/3JGjkjmnrsG8eXsvfyy8DBGo5VxY9K47tpePP7kWo4crQVg2tRMJk7IYOiQRLI6RaDxMmDNy69j0eKjLFx0lLXrCtFqleQfvrWF5WVr4kBr/YKTET9aEzZaE0ZOtfDRFprv0hbRI1jlvX2ud2sD2hobxblZ8+1XryngiqvnExCo5IVnxnDeeZ1b3G+bt5Qw8+IfMRisxMcFMXZsGuPHpZEUH8SBg9WuV2mZnvS0MLpmR9I1O4rszhFotUq+/X4/m7aUEhGhxWazs3jpMUack8yCeRdhbWO9tW00Z6kxWbFa7dTVmZj/fQ5PPbeOTlkRzLqiO5MnZtAlO5IGays3AC1FkOZx9TpqtrmcnByKi4sZP358hxnQC/Hj7KJd4kfv3jFYLHYOHqzG1hTsbubMLrz9zuS/rPjhFD4kSWLnrgp+mHuAeT8dpLConogILZkZYWSkhzN0WBJXXdkDpVJ+QgMYa51nZ0MZ9vKpqP5ZyaE9N7NhUzHrNxazYVMxu3ZXYLNJDB6YwFefzUBvsLB7TwW791Y43vdUUlhUz8jhySxdcCkPPrKKH386SHFJg89jhISoyT/6L9d3lVzmEYDRWxtl9NHY2Gx2PvtsNxMnZpCQ7F3wqq83M3jgp1x7XS8e8eJeIge0bp04g8HCwsVHeea59Rw9VotMBuHhWnQ6EzabRESElsGDE0hMDuHr2XuJiwviuWfHMGFCOgaDhaKierZuLeXjT3Y6gqgOSWT2ZzOIaxY7ZX9OFYOGf0G/fnFIksSOHeWMH5fGnG8v8NjOX3DKU5jJzStvvLaZ555f7/quUslJSgyhoFBHVlYELz4/luHDk/2WcbpFA1/R4CsqDDzxxFpWrMilqtoIQGJiMOXlBuRyGZMnZ3D5pd0YOzatRce8LZytYog3EaR5Z73R6tsK5O23t/LkU38wcEA8//n3MEaOTHF1ZgPtEp9+tYdnX9lEanIIKz8e36Z0gkIAObvwJny4U1WupzBfh0otR6Vy+NJnJD9OeXk5hYWFFBQUsD/nK0xmG/36xzFwUAIZGeEt7gVzswFa55S3Tvm5nG6cIkhefh2DR3yJXu9wEwwMVJKZEc70aVn8+8FhHgLIv+75jfc+3kl8XBD/vLEP/7ihD7ExnmKrPPTk+hHu6UCtVjtffLmbN97cQnFxA+ed25mH7h9Cj+6+XRAMjVYys9/DarXz5Wfn8va7W1mztpD+/eL48rMZJCeFsGN3OaPHfk1AgCO1/fXX9eKlF8e5ylC1MoivrTVRVFRPzx4xLdadTvGjtQH9yYgff4bw0RrNj+lL9HCnvpkloLdr1pb+hUImI7+onv88sooFvxxm1MgULjivMyNHppCZEU7OgWpmnP892Z0jeeC+Iaxanc/KlXns3ecQyZVKOVkZ4XTpEkl8XBDHcus4eKiavHyd6xiRkVrGjEqlocFCRaWBykoDFoudDWuvIdIt9llbgun6EkGai0nOe3vX7nKefHYdq1bnYzTaSE8LZfLETCZPTKfv4EQCAhz/e/cMgmazDb3BQl19U4D5pgw0YaFqoqIDiYrUuvocHUEEMRqNvPjii8yZM4d9+/YxevRofv/99w4zoHeJH9uvdmU+OmVl15uJ7vdlh7lWZ4J2iR9dukRiNFopKWnAbHY8IP55a38efXTEWSl+ePPnLyjUsWFjMRs2FrNxUzHGRisREVpqaowcOVpLVFQAF5zXmcGDEigorCc3t5ZDR2rZtKmYXr1iSUoMZveeCrRaJVqtEoVCRmS4ltmfnetIC9bMR7256OELs93eaiosi5eyVGdQRPF2fH91MNTci9kmoddb2LixiNvv/s0VEBQgPi6Inj1j6NUjmh7dYxg/OtUjyv1vy3M575K5LcqVy2W8+vJ4Zl3ejUajlUaDIwBXWJiWiHCN31gM4FsEAVDJ5Vh8NH6PP76Gd9/bTs+e0UyYkMHEien06xvnkf5N22wWy2y2ser3fEpK9ZRXGAgO1zBkSCKdOkUgk8m4+CLH+X355ble1WBJkliztpDbb1+CSqngu6/Oo3evWI/1Tz67jpf+t8m1rE+fWJYtvdw1iGgtK8fJiB+tzYPIZTIa9GZWrconLjaIlJQQYmODkMtl7N1bwQMPrmTzlhLS08Pokh1JdnYkXbpE0SU7kk6dI0jPeOfEK3eCeBNCbDY7BYX1rPujkJ/nH6K0VE9OTpXHNv37xfHvfw9j7Ji0Nh/rbBU/nNRXtW4J4k0EycutY/DQz13fB/aL49GHhjFjSiYymQydzsRnX+/lrodWsvKXSxmWpmlRRnOE+HHm2H70n37XByh8D4xqqhv54O2tfPXFHixe3P2chISqXVZ0hw5WI0kQFRXAwEEJDBqcQJ+BCXTvEd2qqPhXE0MqSu4kL1/H4SM1HDlSy46dZfzw4wGuu7YXcbGBBASoiAxRExSk4lhuHZ/O3kNRsWMiICkxmE2rZnm0k+BdBJHqH/D47staxCmALPjlEDfcuJAZMzrxn4eGkZ3tcI9UKxwTC75cCrZsLeWRx1azbUcZvy+/klde3cSc73MYcU4Sr/9vAhkZYXw39wC7mlKrZ2VF8Owzo137tyZ+OF1XfG3W6Kc9j/RiDdnWfU+mXTwZq4/WhI/WXDvMNumkJzRaE0Kaix7e8GXl0Zzmv+uixUd5+dVNbN9Rhs0mkZgYjNlsIy4uiF9/vtjDCri4pIGGBjNdssI9nhPO62swWDh0uIaaWiPnDE3yamUq+Ugf3JpVjDOzTluutfMeNxgsrPmjkIVLjrJ02TEKCuoJCFCS3dkxtnKKHHq9xe+z00lYmIbY2EBmnp/NDTf3bZEW3p3T6Urjr5/Q1rhd27dvp3///q7vDz74IC+++GKHGdA7x9FVO689LeJHVJ/PO8y1OhO0S/y48qoermA+A/rH06dvnEuxPNvEj9rKeygr0/PjvAMcO1ZHQaGOPXsrXYEvsztHMGRwIuERWmqqjShVcmZMy2L0qFRXqi21Qoah6QG0dWspDz+yCpPZxvhx6VitdhqNVubNPUBtnSNQX2xsIEkJwQQHqykrN1BerickRE12pwiyO0fSOSuCLp0jSU8LJTREQ3CQisBAFXK5zKfarJK3zcwOwNIGAQV3c9dWZo3cZ4U0kkN0cDYem7aUcOf9y0lJDmHEsGRuvr43YQmv+w0gWVqqZ9GSI2RlhNOrRwzR0d5dBpwNRU2tkW59P6ZO57i+Go0ChUKGTCajsdERuMofUVEBrPztctJSW5rVtzbz4k0AsVhsLPjlMMuW5bJiRR41NUaiIrWMGJnCyKZXUkooIX7cOAzNOltjx3zF8OHJPP3MaL+dwOLiemZd8wuHD1fzzluTmTGjk8f6Ged+z4aNxSQnhzB+bBqxsUHExgQSGxtETEIQ6elhLYKPOjld4oe3GBju2CVHGr5ffz3Mli2lHDxYzcGD1RQ2/UcBBg9K4I7bBjB1SpaH24/Fbj+twVObm5fa7BLbt5cx49zv6d8/jsTEEOxNgsj2ZlkQ5v10EUOHJqH0cf5nu+DhDV+dm+bPrfoGM59+uosPPtxBaameq67szuDBibz+xhaOHq0lOFiFzSbR2Hjc0PjFJ0fxwF2DKG90zIafbUFx/274Ez5iAhydtiqdiUMHqzEarWg0CjRaR6aEpQuP8OF720GS+Me/+jN5ahZWqx2LxY7ZbMNothEVFUBsfJBH1ixTg4Xt20rZsrmE9esK2bqlFHDEe5g8LYsXX53gs05no/jh/L/46/g720pJkrjvwRWsXVOIodGCwWDF0GjxmkUDYMH3M5k2KdP13TmAD2yj5Zk3EaSq4m627yhj4qRvefmlcVx7TcsU0/4G3ZVVjXTr8xH33jWIB+8fwm/LjnHJFT8zdkwq876/EItdIlTtW4jwZ0UG/gfQZ6P4YfAzcLX76XK31ma2Rfjwh7Nfo/ZhuXCyljTQPjHAH7p6E+vXF7N6TQElZXqefmIU8fGtZw1siwuOU5xqb1BWf3g7bPP7y/33kySJAwerWfrbMY4cqSUoSEVwsMrxHuQQP52vwEDHd41GQV2dicrKRiqrGqmqNHA0t47vf9iPSqXg40+mM9JHRijwLYDUVNzTatBcf/G+WrMSaosAIkkS//nPf9i4cSNBQUEMGTKERx99tMMM6IX4cXZx0gFPnZxN4seubTfw6htb+ObbfchkkJkZQXJSCNnZkQwbmsiQQYlERQX4LaMt0bNtdseM8Jo/Ciko0FFU3EBxcT26enNT/vRAdDozhw7XcOhwDUeO1mJu9gCRyRwZSKIiA+jaJYro6AAKCuspKNBRUtpAWmoY/frE0rdPHH16x9Kts8Osr3n8B2+DdZcQ0kqAM8AlhFSW3015uZ4VK/PYvbvCMTt1uIb8Ah1RkQE8/9RoEhOC+O8Ta6msNNC5UwSr/ygkOiqARx4cxnmXdEGlUqCwS8z5LofX39zCFZd34967Bzuumc3O7j0V2GQyYsI0hIdriWgalDefLdEoHZleFvx6mDV/FPLuBztaP49mLJx/McPP8e5S4a9BsyO5Uu96w2ixsW1rKStW5LF2bQE7d5Rjt0ukpIRy+819uf+uQR7bV1U38u9HV7N4WS79+scxclQqY8ekcsEFP5CYGMK99w1m1KhUAvwIJ4cOVTNh0rcYDBb2773ZQzySSbBhYzGff7Gbg4eqKSs3UFFh8EhBGx6uITExhLBwDclJIURFBxITE0BiQjCRUQGEhmgIDdMQGqomNFTjELrsEjqdyREBvcbEtm2lvPnWFuJig7jppj4MG5ZMeLiGoCCVh7l6W4QPXzQ0mDl2pJZ9+6v45tt9rN9QRJ/esXz3zQXENJl9+7LOgZMbPDcXPdy5977lrPo9nw3rrkHR1Ilx1kKvN3PsWB07d5YRGRngivDuxNtM4NkY/V1ffe9JDQiefGotb7y5tc3H27jxWtJSQ1u4OggB5M/Bm/BhsdjI2VtJwYEadu+qYPeucg4cqHK5vrqjUsm58uqe3HbnwBZtbGszqc6BwSP/WcXsL/YA0KNnDBMmZ3DZlT3YvbOcgYMTCAnVnNWChy98DQBqK+/xOniw2ewuIcRgsKKQQ3R8kIeloTsBSjk5B6sJDFCSEB/sdZa7wWzza9Z/z73L+XHuAVatvNJrIMjmg2/333TC5G/Iyorg7bcm8+//rOKLL3czb+5FDBmcCODh0uMNfwLIiYof4F8AOR3ix4kKH+C/3fS2b5DbfeNP+PDXXoLnJJvdhyVEayMF52/ka3/AZ5+qucVsa7QhVIlHvdqSktj9b+UtQD34j/lhbItCgm8By71L31p1m/8WhUX19O73MS+/MJarru3lczvwfw5tEUAkSeLNN7Ywf/4hIqMCiI4OICY6kMQ4x4RbdHQgMTGBhIVqsFptGI02lAG3YTKZMJvNpKam0qlTJ1Qq/wHwO2rA06rd158e8aPXpx3mWp0JOpz4UV58JwOGfIbJZOMfN/flxut7+5zx9sXJpA2z2X2ryU7T+fwCHbX1Zhoamnz5GiyUles5eKCamhojKckhpKaGEhcbxJGjDrPY3XsrXIEwNRoFqcmhpKWFkp4W1vQKpUf3aDo3uVO443zwh6oUWCw2Nm8rZcXqAo7m1hIVEUBM08Nrz9Eali/LZefOcmQyyMqKoFNWBJ06RZCZGc7atQXMnXcQgOTkED59dyo9B8WTl1fHSy9tYO6PB0hLC+OSS7vx/Xf7yc2tIzxcQ6esCJ58fCR5eXV8+91+fl9d4FG/Ll0iGTsmjbFjUhk+LNkVRNeJRinj89l7+NedvwEwY3oWw4clk5gYTEJCMGqVHJPJhtHksAZJSQohJSXUawBQdyx2yWW66K3B9id+OPffurWU3bvKqa83sXxZHlu2lDBgQDybVlyFJEls3V7GNz/k8MXXe7HZJa6+vDsbt5eybWspNptEenoYRqOF0lIDkZFa/nFLP/51a3+XRZWTNWsLuPyKnzGZbMTGBnLTjX1ITAh2WJskhbjOwx27XXK4c+XXkZtbS15uHR9/vJPycoPf83Ki1SowmWweDahMBhde2IXq6kZWrsx3Lc/ICOPxx0cyeVJGq/EbFM2Eu+bXubm104YNRVx/06+ER2j54YcLW/i9u3MqBs2+BJAnnlzLN9/u4803JjJiWDLBwWokSaKwqJ4Vv+fz22/HWLkyH4PBwsUXd+XVNya2eqw/WwBpHpARWh9MgO+BQWOjlV17K7BaJWxWO1abnfIyPb/8cpjFi496bHvHHQN45OFzfN4vQgA5sxzI94zh0VBv5quv9vD1p7spK9WjUsnp2jWKXr1j6dU7lp69YggJVmM0WmloslRITQ0lMckzblLzgX2NqWV4QfcBQX5eHY/+dzUrV+QxdFgiMpmMjRuKsdsltFoF06dmccVl3Rg7Js1lpRkadXIZUE4E53OieeYyXzS4pYr21V9qi5usycsgq7KqkX/duZSFbv+x6OgAEuKDiY8PIiHO8Z6REUb/gfFkeomtArBiRR6XXv4Tzz83huuv602oF8HC2yBPkiTSO73LXXcMJCMznBtuXMhLL47lumt7e2znTwBptNp8DvpUchmBfgbVZX4EB18WeOBfjPAn1vmzXPD39JQkfGZua6/wcapozbrY2TfyVYXWLC1am0jyVg9/FhnNbz9fh29uxeKtj+ReRyetWc+AQ0Boq9jRHOf4ojXrl9YumfvxjxypYcTwLxk2LIlJkzPo2zcenc7EkabJy8rKRmw2O1arHatNArtEZGQA8fFBJMY7+tHR0QGYzTYaDRYaGsw0NDgsMS+7uCtxcUE0Wu1YJYn33tvGE4+v5fzzOyNJUFnpmGirrGykpsbYpmugUsnJzAynS3Yk/fpfRY8ePejevTvZ2dkuUUSIH+0oW4gf7aZd4sfZcGF9pYeqqzOx5NcjfPXNXjZvKWHdmqvp2iXqlB33ZPOoO2ntwequULt3CK1WOwcPV5ObW0duno68/Dry8nTkNr3XNzjSanXKCmfalCxmTMtkwIAEV+cw50AVr72xhQW/HKZBbyE0VE3XzpFU1xgprzSg05kJC9MwenQq4yakM3ZsGlFRAWiatUI7d5UjyaBHj5apxvbvr+T559azdOkxpkzN5P77h7B8eS7PPXs82GVaWiiPPjqSuPgg9HoLFeV6/lhXyO+r8ikubkCplJORHkbnppnzjKxwOmdH0iM7kq3byrjn/uVUVBiYdWUPGhrMlJXpycgIp1/fOPr2iaVb1yjXOTux2f3PRjgxmawcPFjD4SM16HQm6uvNWK120tPD6NQpkszMMDRunTezzU5K0vEZyPAILcFBKu65axCXXtWD+T8f5LZblwCQmhbK7K/OJzEphLKSBjQaBdu3l/HuO1vZtq2Mc4YnkZ4Wxvff5xAfH8Tjj43kvHM7u8rOza3jnXe3UlTcQElxA8UlDVRVNTJjehaffTqDtWsK+Gn+Ia64rBuDByV6nJfZrZEcM/YrevWM4dZ/9aeh3kxtrZHCwnry83UUlzRw8GA1+/dXodEo6NEjhvNmdCI1LZSIcC3hEVqXK41dksjNq+PY0Vpqao3MmbOfVavyGTc2jddfn0BsrHfz1ObCR3O8dTg1cjl791YwadockpND+PWXS736tp6uwXJt5T3YJIk1awu47falFBc3oFLJycqKoKiwnvoGMzKZI+bHqJGpvPr6Zp56ZjTXXd/bZ5l/tugB3oUPd1oTQaw+mg5JkjhypJZlvx1j2bJc1q8vAmDo0ESmTe/E1KlZJCQEuwYBQuQ4M+zO/ZfPde4ZocrK9Hz5yS7mfL0XY6OVGednc8VVPejeMwaNxlPIUCtkyGUyj2eMO62ZQ7v/35sPVlatzOON/20iNFTDhRdkM2RwIouXHOWbOfvZn1NFbGwg3btGERMd6HguxQQS0zTrmJL1H3r37k1AgH8Lz/bizzKsuQjiLnb4wl0EMdbc5/rsDGhptUtoI17xuq+++l7XwPO7H3O49uaFxEQHcMetA4iLDaSwxNFOlJbqKSl1vDuDiEdFBTBoUALjxqYxYVIG8fGO2Cu337aEuXMPuI5x1eXd+fDtyS3a+uYDv+KSBnr0/oivvjyXQ4drePqZdSxcdCkJCcGEh2s92mR/gz5/A+kwPwPiMyl+nIzw4Qt/+kH7bCI88dfe+hMDoG19ptbK8OZG0/yatya+KORtczuBttW5rbRFBGmNtowb/N1Pslaur77JWspksvLmG1v4449Cduwoc02UBgQoycwKJyE+GKVSjkIhQ6GUI5PJqK1upKRUT2lpgystr+u4MggKUmO12pDJZNx4Qx9uv20AUVEBfPrFbh58YAWTp2TyxhsTCQ093hczmqxUVxuprDBQpzOhUStQqxWoNQqCA5TI5XIKCnQcPFjNgQNVHDhYzaFDZioqKgAICwtj2rRpXHDBBZxzzjmkpKScFePOU4FzHF2994bTIn5E9vikw1yrM0G7xI9BA+O58cY+zLwgG7nbA8vbTISTpFPYyS8tu4uDB6t59pk/qKtzZM2wWOxYrTYOHarBYrEzYmQy11/fmylTsjz2dW8AT0WKsPaW5c3szn1Ra2Z5JpudcB+zJpIkUV1jZNPmEn5eeJglS45RUWFApZITEx1IWJiG/TlVJCUGc/21vRg3No0u3aNdHRKLXcJstjkejM3EjtYGq95obLS6LBcaGsxs2lRMUmIIKamhBAR4N3WTI3HoUA1/rC/i8OEaDh+u5tDBGleMFnAEd0pKDKax0UppmWMmUqmUI5fLqKpqRJIgOzuSb2efT3q6I86H3S7RoDdTX2+mts6Re11Xb3K860zU1BjJOVDF7l0VHD1a65qhkMlArVYgl8tc8QnkchlpaaEkJYU4TPuiA9DrLXz9zT7i44PYteNGj05ilc7IF5/tZuHCI+zYXoZKJcdqtSNJjvKzsyMZMDCBjRuLOXK4hsefGMnEiRk8cP9y1q0rYuOGa8nMCHeV17yDNOuaBZSV6UlKDOaXX48QHq6httbEjOlZ/N8jw8lucrtwH5gMHPQZF16YzcP/Ocfn75ebV8ePP+Tw7nvbufKK7jz91GiP9d46amvWFjDzQkfw1okT0nngviF06RJFiJtfv7+OibeOZm5uLfPmHWTdH4Vs2VKC0eiIF/DbosvIynRcF6P19MX+8Ba/RpIkDh+pYdXvBezPqSQtNYzs7Ej69ovDYLDwxBNrWbL0KGvXXE2aWwrX5hkrzgbxw0lrIgj4N+m12B0xPNatK2TF8lyWL88lL0+HRqPgnHOSmTo1i3OnZ9G1+wenstqCdtAW4ePQwWo++WAHv/x0EI1WyWVXdue6G/u4BsYt9mulYy+XyTxElT17Kpg79wA1tUY6dY7k4ou7EunD/dT5bwn1FqBQkti3p5IffzpIXr6OikoD5eUGKioNVFU3ugaZAQFKJoxJ5dwpWcy45HMSEhL81tcX7qKEvpXYFE7aMnCy2O2UlTaQs72CdRuLqK01UlZu4PmnRtO7Z8vMJtqIV9ib+y90OhMb1haybk0BydGB3H37QGJjAln82zHuvG85hUX1fPrBVC6/uJvHc7XSaKGu1siO7WVs21rKlk3FbN5UgtVqp3//OKZMzWLUyFQaGy1UVTXyySc7WbeuiGFDEnniv8MZOTzFo6xglYJQu+N6rNtcwsgZP3DNZV154LYBnDP9e+rrHQOq0FA1f6y7xuWmearEj1q3n8JfP6zGZPG5ztfA218dm4u+7iWciPhxou41zUWd5qLCifTd2oo/Sw6VXN5q7JC2CCC+ymh+j/jarvl19VVnb9epeZFtSQns73HY2m/Rmojk7/5u/kyyW+wcOFhNZISWhITgFi7yTtyvj15vobLSgEarJDhIRUCAIxZhba2Rd97dxgcf7gDglpv7EhkdwIcf7CA/X8esq3vy0kvHMze5X+Pm5+xL4oqNe52Kigr27dvHqlWr+Omnn9ixYwdjxoxh1apVHWZAL8SPs4t2iR9OFAoZXbtEceml3bj+ul7I/cQpOJXix5qVV3H+xXMJCVYzcFACKqUchVKOSiknPSOMCy7I9tlR86f+u3fivD3j/D23vD0UPSw2WnlouvsW+mpwfYlL7mKI3m2WyW6X2La9lF07y6msbKS2xki/vnGcPzPbqx+waz+OP7za03B6u4Naa9Bbm81wnrFBb+HIUUdU/MICHUVN8VAsFjvh4RqsVokVK3MxmWz06xtLaZkBXZ2JkBA19fVmGvS+Oz5yuYywUA1qtZzyCkOL8wgL01DXFMx24MB4xoxORa+3UFqmp7LSYeZXUWEACT76aFqL2CLuA8aDB6t46ok/KCrUUVFhIDRUQ2CgCp3OhN0m8dD9Q7j26l4olXIun/UzR4/VsmHt1X5dSC6/aj6LlhwlIT6Ixx4bwcwLsvnxxwM898J6Skv1bNtyvaPxa2pY53y3n1tvX8IH707h4ou6epTV3Cd49ep8br5lEefO6Mz/Xhnvsc5blXbvrmDSlG9bRChPTwtj0sQMJk/JcAQB9eLv6n6vuf+fbv3nYn76yeFmpdUq+MfNfbnztgEesU5Ol7m7v8C9TiRJ4ru5B1ixIo/164soKNChUsn54P2pTJt2XHxtLnw0588UQtzP0/n88TVws0l2KisbKSh0/A8Lixyvw4dqWLe+iMZGKykpIUyckMGECemMGN7Sha0jpOT7q+FP+FDJYMMfRXz64Q5Wr8onLj6Ia27ozWVX9iAs1H8GHl/iR3PzfZvNzn8eWsk3X+8jJlxNcpSGvfl65AoZb709mSlTj/9XWuuMqOUytEq5T1cTq9WOvs5IYVEDy3/P45clR/mjyWVmQN84zp2ayblTsug38otW3fOqi+5k2NjZKBVyUpJDSE5yvFKSQ0lOCiY6IZi0tJbxaprj/D+ZbTbycuvYuLGYzRuL2bSphLzcuhbbf/j2ZDplRfDJt3sxGW1YbXbsNgmz1UZxUQO7m7JgZGSFU1luwGqzc9etAyivMPDpl7vp0iWKTz+a5mH56s3VCKCu1siKZbksW3KMlSvzMBqtdO4cwYTJmUyYnEFNtZHHH/6dwqJ65nw/k3OapSRPbOpO2Gx2Xv9gB8+8ugWbzc49/+xHdr94brppIT26R/PTzxd7PPvjA1RYffzYbckk0pw/U/xw4q9P48+6wY7UpsF1c/z1ab3Rls1bs05pzX2lNSGhLe47rcULcy+nLdueyuO6/05Bqpa/qb9MggCtaaL+4nE473GdueV/ubmFdlvxdcoOl/DjVFU18s472/j4k53YbHYGD0pk1MgULru0G6kpxwfb/iZJ/NWw+QRWbm4uhYWFjBw5ssMM6F3ix/6bTo/40e2jDnOtzgTtEj/27LqR777P4Ycfcsg5UI3dLhEUpOKyy7tz2WXd6OFltuJUiR+1lffwv9c28dQz6wDokh3J0KGJXHl59xZm/k6cf8S2Ch/Nad4IN8/i4a/Bar4usNmgz1dAJTiuILfWIAYqj9entYBWrdGevZ0NYGuNRVvapZPJFgISS5Ye45NPdrFiZR4AD9w/xLVvcLCagGAlwUFqgkPUhIVpsFhsVBbriYoIYMqkdB55fA2vvbWVoCAVt/+zP9NndCIwQElMbCChIRrKyvQsWXqMN97awtFjtZx/fjZRkVpqao2YTTYGD0hg1MgUeveO8RqIzmm2uHFzMROnzmHEOckMHZpIYWE9m7eUcORoLQD/9/A57NlbgQwZ8+Yf5I1XJ3DNrJ5+z/7nBYfIyanitlv7o3EbZD740Ep++fUwO7ffgEblEMg2bCjigovncsnFXXnj1Ql+O+zfztnHv+5YyqiRKbz7zmTi3NIq+ur8/L46nwfuX0Fevg6AkBA1fXrHkpYaxopVeZSUNBAZqeW8czvz6KMjCAo6Xl9fQpvJZGX9+mJWrspj5co89u2vQiaDfn3jGDwogZTUMDIywshID2fg4I9RKv3HeDkZmoshRpudt97cwjPPrKNHj2iGnZPMsGFJDB2aSGSkYza7LUHWzrTwUV91r99ZOUmS2LK1lG/n7Gf3ngokScIuSdTVmigsqvfIQBEQoCS5KT7RyBEpTBifTpcuka0OBoUAcmbJL74DgDq39sxitrH4l8PM/ngX+/dV0qVbFNff3Idp53ZGrVb4nYH3FZzQLkkYLHaCm7WTb725hZdeWM/L12cxa0w8SoWMSp2F+z89zOJtNSxddgWdmqzU/P1j1D6eE84sIlofbXlVdSOLl+Uyf9ERlizLpU5nIjEhmGmTMzl/eiemX/SD6551T+cuSRI337aEL77ei1qtoEe3KAoK66msanRtc8nFXXnzjYleRV2bzc7ufZVsWF/Mxo1FbNhYTEW5AZkMunWPZvCQRAYPTiQoWMX11/yC3S4REaElLSOMHdvKSEoOISo6AKXCMcmjVMgIDdcydHgyw0elkJgcQmO9mQ/e2cYXn+5Co1HyyL+HccP1vb3WBzxFkKBm25TVGfljTQFLFx9j5bJj1NaaiIsLorxcz/jx6bz17mSCglp22p0CiGSxUVHVSI/RX5OWHMLT/x7K9Fm/8OhDQ3ni4eEt9vMlfsCpFUDOZvHjRIKD2mm/8OHE324nElbEKSq0NlnWXgHEHWc/8GSCxbrjLMdXn9fXtXV/JramNRitdr9CgD8xzCmAeLs3/IkrrQkgvsYT7XG5amgwo1DICQv2PXj3dd7tET/g7Aq3cCoQ4sfZRbvEj4OHjgc8tdvtzJt7kO07yljw8yHKKwz06hnDVVd05+KLuvrNpuKrY+UvGjo4OiN5eTo2bCpm4ybHoCgvT8eggfE8cN8QJk7IaLGvrz+iViH3a9Hhr/Ft/pBtLqD4Ey3cHwDeVGB/pnPOct1Fj5bbtE3GcDZWrQksThcNf4p/i5k+L7eUr46JzS61GvjJV6O2eXMx02Z87/oeGamlT+9YAgJUGAwW9AYLer0Fg+H4yxnECWD0yBSe+O9wPvtyD5/NdmQX+HnuRYzykirMYrHxxew9fPTxTpRKORHhWmQyGVu2lWAwWAkLVXPOsGSiowNc/tUlpXpMRqurI1pbZ+Kzj6Zx4QVdXOWWljaQ3fNDwBE0NC0llJBQDR+9N6XVgK0e9XP7ffoN+AStVsl3315ASlIoH3y0gyefXsuA/vH8+N2Ffq1/AFb/UcCsqxcQFxfI7C/O9cha4u0+KCqqZ/yEb+jaNYrLL+tGUmIIa9YW8PobW/jfK+O5dlZPtu8o49eFR/jwox106RrFvLkXotEofXaavD2VSksbWLkqn5Ur89i7r5K8vDrXYDw1JZSXXx7H6NGpHvucCpcYb24h3/50gJtuXMjd9wzioYeG+d2/uQhyJgUPXxkovP1H/1hXyH0PrODAwWoSEoIZNSIFhdIR0yEsTOOY/U4OITkplPjEYCIjtT6FDudiIXT8uTiFD3f27a3kn7csIi+3jhGjU7ju5r4MHZ7k+i0VMpnPNtpfVoYGL22mxWJjwjlfML13GK/c6Jme22Sx0/vOLcy4qCvPPDPGa5k2SUJG++IsxDdzrXQfZFssNv5YX8Qvi4/y6+IjHD5ay/Dhybz76gS6ZreMESZJEqMmfcOOXeWUHP0XdpWcffsq+XbOfn5bdoxDh2q46sruvP6aI7jx3r0VLF+exx/ri9i8uRidzoxKJadv3ziGDE1k6JAkevePIyxM47rGBoOFH77PoaSkgYoKAzqdiYlTs5g2o5OHybq7RWnzeDx1tUYUSrkrfXC02zVoHnvEl4gEx9NZWq12tmwqZvlvuSQlh3DN9b3JCA+gxuhdSIiyWrHZ7Dzy/EZefnc7K364gJFDErj0liXMW3SUn785n/Omef7+rYkf4V5m1gHy9Wavy1tztfCGr/TAvu5yf4N8X30pzSkUPvzV4cRjivjom7XhenoTJNzr11oR7bXIaKtw4qvP6G5F3ZpgY7NLfvvajjLct2+5vt7i3eIKjgsgvqw9/N0frVmYeMOfOORrla//gb+j+xM5fFnBeusTdVjx48DNp0f86PJhh7lWZ4ITFj/cUdgllq3I4+tv9rJ4yTFkMpgyKZO77xpIzx4xLQZb/maVvAkgvkzQ7XaJJb8d4823trJ+QxHXXtOTJx4b6TLX9ae8+nrgOK+GwYdvb1vVZW+b+XooOCOg1/kwTXXS0oeubb6P7g2Grwd+84bbW1G+Gh73jqk/E3+FTOZXSPHXwQ1SKVr8npIksWePYxBcWFRPQaGOwoJ6TCYrgYEqAgNVhIeoUWiUBAYqCQxUkZwcQr++cRw4UM2TT67lwMFqALp2ieSuOwZyxaXdkctlbfY3NZttbNteyto/Clm1poCGBjOJCY7I+vHxwQQFKjFZHFG2ZTK46soeLrcNhUyG0WglIeUtEuKDWPHbla489r4uha9b2v12XvtHIf/41xJ0OhPdukWxcVMJ/7ylL4//3whXLBZ/mpdCJuPwkRqGjPiCu24fyKOPOGbtfKUi/O67/dxx52988vE0EhNCqKpu5Msv97B4yVG+/GwG0906vVu3lTJ52hz++/A53NMsHbDrHCWpTbNQdrtESUkDR47U8PobW1i9poBLLu7KE0+MbCG+qt0uUHh0211lvAkfO3aWMXH6d0ydnMlnH05DLpdR34bghiFeOtpBkd4F3xOlvFlA6IBWOm82ScJul3jjrS08/ew6Bg9K5IH7BjNqZIrPlJruuAs70bGvtamOeUXHB+RpSW+2aZ+2lnmqyusIlJTe1UIMX/DzIe6/dznpmeE8979xZHf1HPD7T4PYPuEDIPdYLVPHfs28h3syumd4i/V3fXiIHRU2lvx2ZYt1rbU3rXX/nfdmfKD3jmad2cqqVXk88NBKyssN7NpxIykxnoGav5iXw03X/8rD/3cO//hnfwCuv2YBq1bmExMTSFxcEKNHpfDo/43gyJEaho/8Eo1GyeBBCQwemsiQIYn06xfveu42j2FSY/Td5stlMvQWm8/UmNBSBHGfiAlVK30GXfUlgNgkhwttWqj3DHm+xI/i4nru+tcS1m4q4emHhnDXTX24/p7lfDf/MH17RPPeh9PIyoposV9za1qPOvpYfjaKHyeSGcZXdf0N8E9E+HDvszQfUPuLNeFtIu1UWWGcCiuOtgohrU3wOa+pP8HJmwjSmvDhxF0A8XZeQX7aaH9ZaE4kA5C347d2qU8kVo2v/48QP4T4cbbQLjtxtVzuVcWWKWDq5EymTs6kstLA9z/k8Mprm1nw62HA4a8fEqIhJFhNSIia0BDH++BBCdx4fR+XWOHL8sPbYMVeficAl01M49IJqXz45V7ue3QN33y7j7Ejkjl3RjYXnd+Z6CjHQLPSrdH218Fz4kvxbWt6K+cDQyY7bkbX2sOqeRo4pxjiq8Fz9xX1ZwbZloZEJZe5Oll1Jt/Cj3MbX9H91XIZCrnMo1Pm3vj6uwQWu0RCkJrKRotH/nonzdOLyWQyevWKoVevlu5WzbMMNBeWUlJCGTMmlYULDhMaqmHyxAyPWTajzd7iPvHmIhWgVTB8WBLDhyVx372D/Z5bc2yShEaj4NprenLNrJ4u4eNkGTE8mT9WX8VD//mdDZuK+enHCxnbzCLCFwX5Or7/MYc53+dgsdgZ7WYF09yKwq5zmIhff2k3nntuPTfcuNC1rkvnCD7/ZAbT3Pz56xvMvPfBdux2CYUPEydnB9bbLdvc3Uoul5GUFEJSUgjDRyQzZ85+Hnt8DStW5DJ0WBKBAQ4BLDRUzU3X9yY11RG3yF1MbU0IcYoT7iLI0mW5NDZaqaszkV/cQGJCsMdz0eTWafQmeLijr773pAUQZwYsb081p2DlSwQprzRw+x1L+W1ZLvfePYj/PDTMp8m8k/aIR+Apdnhb15pg0VzQ8ZeFpi3l/Z1wzi7abHaef349b7+1lWnndeLJF8b4DD7tDX/Ch0Im8yngq5ru/4ZG721KQ6MNpUrucZ8abXa/1gk2CYxGK4cP12C3SXTuHEGgmxtdc0urUoPnYDk+UE1dk9/8mDFpvP/uFCZNmUNefh1hYRqPCZDk5BBi4wJ5961tZGZGMHFSBpIEiYnBPPr4CPr0jqO4QMeXs/fwy6+HCQlRs2vHTS6xQ2+xoVHKvQZuBYhosu5ziiDN22pnO+it76BVytEq5dQYrV7bJmdsAG/tv3MQ4m0iKkiloLLR4mE9cry+qhYCyGNf7ObLp9eh1ip4/6sZdOkWzd2vbeaHX47w+AtjmHFhNmmh3i2B6802vwJIe/Fliebr7vW13NeAub1BRNsrfLRG8wG6Qi5rs/ABnmKHHcljcO2+zpcFsV2SUMnlHuW6b3uqRAt/MT3asr+7TuBPsJHwfF74qr3BakMuk3mN8wHHhRD3brHT2jlC4/gf1XmJ1QGOgKXuAkhbxxhymazVa3Eqgt7KZN7vV6dVeFtRK+Scwr/6XxaZQo7sBGO0+CtT0D7aZfmRe+RWQr1Yfnj7A5wz6kv2N/no/9+jIzAZbdQ3mGmoN1OnM1Fba2T9ukI0WiXXXtebf9zS1yOQoRM53k3XK3JuZuO2MjZtK2XD1jKqqhspKtETEqwiJTGE1RuKiAjX8sWH05g4Lt21nzeLDuezr70NUmu5wf2msJL5TwPobRajeaffVwPdFn9QjUKO2W73O6vkLoL42s6bCOL+wPUX9d754E7w4kfsb4Djr3Hwd019WdYEKBU+o/j76rT6wlecB5+dKR+/lS9xyVfnwlfsGvditmwt4Y03tzB8eDKTJmQQFxdEUXE9xcWOFLdzfzrIxk3FBAWpmDEtiysu6864MWmAd2HSKX4AjuCXxfUEBaoIDlKTlhpKndus4969Fdxw00LKygz875XxzLwg27XO2UnwNXMnSRJ1OhPl5QbKyvRotEoG9I9vcU3skkR5hYH/vbKRvDwdhkYL5WUGjh6r5f13p3DpxZ5BXts7iHeKDHa7xEfv7+CxJ9bw3tuTufSSbi22bYsAcjKih6+U3601gXKZDIPBwvIVufzyy2GW/paLSiXnnbcnM2F8ulf3tPZeJ284BRC93kxFeVN2jgoDWVnh9O4Z672uOIUwzzrlHK5h0cIj7NlTgUajcFh5BSgZPjKFUaNThfiBw+rDSW2tkX/dupjVqwu47z9DufamPl4Hid6CiDpvB2/ihz8LTqfFgSRJnDfpW5I0Et8/1N3juMXVJgbeu5Xb7xnMrbcPALwPXt2FELlMxty5B7j9tiWuZTIZpKaG0q1rNF26RJKVHel47xTpSs3rrV1oaDDz6Re7eefdbZSXG1i88FIOHa7h8OEawsI0xMQEMnFCBna7xMjRs7FYbOzeezObN5Xw/HPr2LSp5Hi95DKSk0K4444BXHJlD5/XJaZZAODmj3l/6XGd7aUv1yO9l33dJxF8tan+fsfoABU5NYYWy+MCVMw5UM0Pb2xm9Y8H6DsmlazesWxacpSiQzXY7RKXX9OTBx87HusjJdi7NUmIWuHTykMye7c0KbD4jofhDV/PRV/9mvaKH+21FDkRqw+vx/Xz2/n6Xf25UzQ/vHt9WktJ62w7/GV/9FqfE1WCmtGezLStWQnJ8H9tm4sg7m4orVlbuosg7U376w1f1+9UZfuRJN9imK97zNfksa9L6q2P0VEtP2qO/tPrOPqkyq43EZH5Xoe5VmeCdokf3351Hjt2liNJ8MB9Q5DLZT5v5g8/3cXbb28hL0/HH+uuIaMpZaf7oK6stIGPPtzBV1/uxS5JzJrVk7vuHEh0zHERxPkoqKgwsHx5LmvXFrJ1WylHmwJFRkUFMGBAPIkJwXz9zT4yUkOYOjmLouJ6vp93EJkMXn1hHLfd0s+nK4s//D3HfTUiTgW9rSm4nDg7aP4ezKYmR1lTG2N7+BJC3IMj+RINfVlvuOPscPnzBZcj8xAs4gLbNuNotNldjbHOi1m1rwG/LwFEIZP5bParfJjztkf8UMvlPht+XxaVvn7rUy1+GI1Wxk36hspKAzqdGXOz66lQyBg3No0rLunG9KlZHgFJwfdA3V0Accc9sN6RozWMG/8NmRnhfPjhVLIyW5o/++PW2xbzww8HPJZNnZrJs0+PJjnZ94O+sdHK7XcuZe3a/2fvrMPjqNo2/pvdzcbdpWlSd3dXSinQlpa2aKG4FHd9cXd3d1qoUKClQIVC3d0tSeOerM73x2Z2z8zObBIo0PLlfq/3opmdOXPOmZnzPM99HjnM5vWXeCuP/FnSQ8RNN/7EwoX7WbP2YqIFt/r6vsuU41SW14j8UKB8jS6Xm+17SygtreXQgXJ+/GEfS345SE2Nk44dEzjj9Nace04HUlM9VbIaGrrSWFRVVXHGmR355eeDfr8NGJDOJZd245RTspEkidWrcpg1ayfz5+8mONhC714p9O6dSk2ti/nzd7NlSyEhIRa6dE3C5XJTXe1k+7ZCADZvv5zISOv/ewLkaK7HM3L3rmIuvmg+paW1vPb6WLr00y/3alQ9Rc8DD3zrllYuVFTY2LS9kMpKB737pGENNrPwh31ce/n3TBqQyE0TMmieGMLSLaXc99kBKl0S8xZOIy4u1HDXXksol+ZVMWbM55SU1AKQmhrBwAHpFJfUsmNHETk5lZ6+mSVaZMfQoX087drFk9U8miNHKtixq5idO4vYvacEl0umb780IiKsrFmdS1FRDWlpEVRW2ikvt2O1muncOZG1a/N47bUxnDWxrdcw3rqlgLy8SrKyYkhJj/QSLYGgeMgEIhzAQ4LEGpS2NyLqa5xu3LJs+MwCbSgE6k+hgWy898mVzHltHQCJGZEUHKmg27BMOg/M4MqxLWnZMsbvmRoZZHEGfT6RyI9/i/gwmyTDkIyGhmEr+LMJT8X50HrENqQMq5Eeqj1cXzUZLRpDeOhfH9jrw2h+lcOBqAotCSJ+t0b6HegTIAr5UN+6oUUgAqSxnNPxIECayI8m8uNEwZ8qdQuQmzMTk0mi1u7C6XT7lTS0SBKyLHPsWBXJyeFeISi+/Ha7i9yiGlb9cZQbb/iJ2honl13ejQcfHALU7bC+tYG5c3ezbl0eAJ07J9KrVyp9eqfSq2eqqtTclKnfsv9AKUEWE0FBZsxmiaAgE2NPbckdN3uqgPyZuFA9GO0MGMVQ1rrcASvLgP9iFCawy7YA2cEaSoQEii0EDwkSSEEC30Knt8sE+iSIMifRDVAOwd+zwygMB/yNftHbxoh9bgwBkhQaZCjgjB7J8SBAjif5UVxcw/nT57F2XR6njmlBu7bxZGZGYQ0yk54eQVpqJCkp4USF65NSjfFQ0IYoAJw95Rt27y5mxW8XEqHj5RMIsizTqs3rjBiRxUXTO5OcHM6WLQXce99Systs3HJLX668orvXvV7BylU5XH/DIo4cqeCZp0cydYrHO+PPGvdasuHmmxfz6SdbOeecDtz/v8FERwcWZseL9DDqlyzL5OVVsXt3Meu2FLB7ZzG7thexZ1cxtUJegS5dkzj1tJaMGduCrOwYWmS8/Lf0a/fha73/rqy0c/lF81m3Jo/rbupNtx4pJCaFEZcQxsoVR/jgnU2sX5tHZmYUbrfMkSMVpKdHMmGixzto3ZpcNmzMx2SSGD06m9PHtWTEiCxihVKsl1y+gCVLD7NqxYXeajvHw2PlZIRCfMz5dhe33vIzmc2jeOfdccSn6ZeBDzGbDOWZniEtrldOp5u339rAsqWH2b2rmNzcSu9vEZFWRp2SzWlntKKkqIYnH1lBUXGt9/euXZN4+sXRNMtS6xail0mgiklFOZ7Nk88+20ZYqIV1a2cQGmqhrMzGgb0lbNteyPYdRWzbXsT2nUXk51cTGxtCu7bxtGkdy9G8Ko7lVbJ1ayHh4UFMmdqeC6d3pkXLWELMJgoKqvl69g5mz97F6eNaMnNmL9X9lTlrSFUnbUirgvqMmUDeo0akBBgb9eCR8XpGZqDQJu294oItVFXZee6b3WQ7XBw4WEanDglMv6ATkiQZ5oD5s+RHsUYKVzlcum1lGMiXIwZ5Qo7H3rieTDZLx5f80IP2Gf6V6nvKe6h9lwPlywjkZWtE0IHHNqjPhlfu25CwJKNxi+uU0XfmDbMN3B1M9fQ5EAkSSKPW6nkNyalyvAiQ40l+6OnaRpvDemtaVPz/H/KjdP9VRNVTRr7xbduIyX7tPzNX/wQaRX5ERgZx2thWfPHldlq1iqW8zhUdICMjkrZt47nr7gF06JBgyPQuXLSPW279hbKyWqqrfUp569axXH5ZN84+u703Xra62kG//h9y7FgVcXEhvPLyKYyoC2HR+6CNFAWjxUK7sP8VLzFPXWzjBoxCNazmwDGb4nV65IWeN0tD3N3EtvTqhuuRGOJ8GTH0SgxzILJGS4IcqbR5/60XZwyehHoiy28Ue6mFfqIqySvkgzRT1didI71hGpEfRorEn0kopYXRI99/oIzJU7+lpKQGtyxjDTJTUWmndas42reLZ+CAdKZMbkdIiMXPu0bZedSSH0rOjOpqB6++vp7S0lpcLjduNzicLhxON+XldsrLbZSV2Vi//hgAP/04TRWuokBRQLTzIEme6gwDBn3EoUPlTL+wM7fe0pe4+FAqKmw8+dRK3n57I9nZ0Uyd2p4J49uQnBTOo4+t4M23NtCzRwovvjCa1q3jjqtHQ3XJTZTZnHz22Tb+d/8yIiKCeOrpkYwcmeV3rnYdPF79yBdIj48/2cqjj66guM6wtFrNZLeMoU37eNq2T6B12zjiE8JolhJOn+7vHpf7NwQKAfLL4gNccfF33uPBwWZSUiNIS4/kngcG0bJ1HJs35TPr021YzCYmTWpL7z5pqpKSFbUOZBm/5NnJdV43s7/ZyY23LMZiMXH/PYM4/7yOxP1NXiwnOvYduIYH/reMDz/YzISJbXjiyeGEh1t1QyqMKw34I8RsIiE0iGN1OTQOHy5n5tU/smHDMYaPyKJF61hato6lZes4LGaJhd/v4/v5e9i/r5Sbb+rDTTf2ZsHiA5SV2WjVKo6e3ZMNSfRAhpzW6Pp9xREmTZrNz7+cx5C2MQDUBnvei8pKu7cCSnW1g8jwICRJ4pkXVnP3/5bRpk0cF1/SlYlntSE83KqfW0MzR3rruNZo1MpgI0NRuS5QmUpFrwlEdugh1GIyDPUMZEAZkSBGBlmsQVWyQASIXlNGc2AkT/V0qhOF/DCa3vo8IPRyeuihsZ4gRv0J/B54ftPbfApEfIAx+aE3fuVQfYlJG+IVUt8ZZsk4mT2o34fGPFe7To44EXpvcHXd2heIdDT65ow29oy+lYYSIA0lgxrSn8aQH+BPgDSRH41pu4n8aCwaRX4MGJDOl19M4KfFB1i8+CDJSWGkpUdiMkns3l3CvHm7iYy0csutfenTM5WkJHUCR0mCxx7/nbfe3sBtt/YjJjqYqOhgEhLD6N4tWZVwEjy5Pqqrq3nvvfd45pln2L9/Pxdf1JlHHx3md67eQmU1S5iAozmVFBXVEBFhpWWLGMB4odXbcQ8U0gHGZadMklTvgi5CXBQC5bWob2FqCPmhPPU/k1lchMstGyo/EJgEMSoDFmYxq7xeRCWqvmehhYREhEHoimRUHUfnmPauyjmNdbvUU/b/LvKjptbJsFGfYat1cuMNvbnuhp/45adz2LevlMuu/IF2bePYvqOIsDBPjo8Rp2QzZGim11gA/SzcpYU34nS6ueCi+fzy60GaZ0ZhMkmYzCYkCYIsJiKjgomOshIVHUxUXbLja67qTnS0Ou67IVV1amudvP7WBp57fhUOh5tRI7MYP741I0dmsW9fKS++tIaFC/dTU+MkJiaY2lond905gCsu74bZbDpu5VarS/wrvxw5UsFlM3/kt+VHWLtuBsnJ4fXm3FGUSr3djkDQetVUVtq5/vpFzP9uL9OmtmfsaS1p2yaO4MRQVaWWzlmvNuo+xxMKAbJjeyHPPvEH69fmUVbmIzvvf2AwF1/Std52jNbDZCHkKC+virFnfMmBA2U88uRwJk1tT9vMV/7iCE4uLP99Oldf+T07dxRx3wNDOO/8jp6d+Lp1R+uirqewG0mejAifwrZzdzEDR35CXEwIH7x1Gklt4/zOP7C/lLlfbOfDj7bQqWMC382f4v1N9DDUromG3m1C30WDYf/+UgYO+JBFX41n+MAMz70Pl3Pz/cuZ88N+WmRHM2RwM4YPzmTo4GakJIezbXshI8Z+QedOicz7+ixqpMCu6A3ZbVWqVAWSm4rRaOQtIhIAWrKqPq9VI2+P+ggQPflolKsrEAGiF5pq1GUjA0iPAGkM+QH6BMiRKnuDwxoak4OisWRDIONfC6M5CqRv6sHo3Q30Sjckh1hjECgPiDjfemfpzZnYntE8iZVF9OZA73tqqFeFkRcE6K+pyrPR22iEwARIY+4DxnPdEA8ZEY0hQIzIGD0CpD7yo7q6mvnz51NbW8v06dP/Mwa9l/w4eM3fQ340f+U/M1f/BBpV7WX+/M3ExsYyfTpMn+45JrqDjzm1BVdc/j0XX+TZ5ctsFsXEiW247LJuJCeFI8tgs7mIiwvlisu7AxiWPlWSnIaFhXHNNdcwccIOPvxwM3ffs5TFPx+kfft42rSJo1XLWBxONyXFtRQX1+B2ywwckIHN7uLLL7ezYUM+BYW+hF05h67FbfF8fEaxsVrUOt2Gnib11dvWfuiByBCHZrHWW5T+KvGhbVKvhnpjEiWZTRLldpdhboxgi2RIgEQGWQwJkGqHW1cJqXW6DQkQu0vWVZ4q7S5dBS+32kGqXv4RuxNZ0y8XIIX7J2wzS3897tQieKIEgkuWDd8frSD8/Y+j3HnXEvbtK+XnRefw9rsbadUylq5dkujRLZkzxrUkONjCgYNlfPLpNuYu2MMXX+3AYjHRs1cKg4dkMmVKe6Kt/gY/wAMPLeeHH/fx5uuncvYkTyJRZQxOp5uKCjubNuVz1TU/Mmpkc0aPyiIkLKhBYWdaBSQkxMINM3tx4Xkd+fDjLXw7ZzeXXvY94WFBXHpJV15+8RRcLjc/LtzPuvV5XHxRF1rVlVU8XsSHETIyInn5mVF06fUee3YWkZEaYajwaxXK8iKPB019JIheKBHA11/v4Psf9vHO26dx+umtVL/pJYn+N9C62cscyplJRUktK5Yfxm4XPNnCgwgNtWC3u/w8OrRQ3m/t+19Y48DpdrNuTR6P3L+MAwfKmDC5LaNOzT7+gzmBUJh/g+pvWZaZO28PN92ymOjoYGZ9O5nOXTyJZEUjWlxnjHZvS20OYoID52WqrXVSXm7nk2eG0i8zBGp8Mnbj9iKefHMzsxceJD4uhLMnt+P8831JQLWhlYocFr+Oam0JV41MEomKmBiPIllQWOM9dv7Vi/hjbR7XXNEdt1vm12WHef/DLQC0bxdPn14pREYFs+y3I7z/xTamTuugMkCU9uszhrTrmST5V6oQDR7l30mh/gZ6rlCVJiHEf/6VvhitoTVO/QTm0cEWymxOsiP0Fe5iHULeapYMCRC9o8W1Tt3cD0aVIoxgZBzqyXWjZ1Ph8NfJGmPIBQo18uuDYR4QY5KjIbLe0KB3ybpGrDj3znqMftAnvCCwUf9niQ9QPz/FONfT78SnHMj7KzhAqJ4C5erGkkJGEL+7QOEptS63IRkQZbXovuP1rTd675m2IqFT0N/1bCq3rP/u1OcNo4We/t2QTSy9Y19+vYMZV3zP6JFZZLespqysjPnz51NVVcXkyZMb3KcmNKGxaJTnR0NYpYJj15OTU8matXn8tuIIX365HbvdRUpKuMfYOlDGsGGZfPbJeKDhbuCKAbBs+WEWLdrPrl3F7N5VwpGjFUgSxMaGEBcXit3m4tDhcgD690unfbt43n1/EwDjxrbkow/O0A0VEdlSrVJoJOD0iI8gkxRw0W6MJ4gCRaFpiIuw3q0bG9tn1H2xfK8e6ksOqkeCKGt3Y3aalFlvSDJXBaKwz9O4wCZb/NvRkh8K9AgQyenCafa1r7Smn8uj4Q/DbqCIiO+QLMts3JhPSWENVVV2qqucLF1+mFmzd9KjezJPPzGCXj1SGHP6F6SnRfLum6fptrl6Qx7r1h/D5XTz65JDLFlyiCFDmnHlpd1JT4ugXdt477kuWebRx3/n+RdXYzZLtGsbT2Wlg/IKG5UVdqpr1HMXGmqhpsZJfHwo405rycSJbejfL13lnaBAIQjq+0z27i3h08+38cpr6xgxMot33jlNlVxPT8HU82L5sxC9QCpqnWRmvcoN1/fitlv7qc6TZeNdNAV/lvx4+eW1vPTyGnbuuMJ77EQhPbQ4lDMTm83Fls35rF2Tx8cfbeHggTIAHnx4CBde1MV7rt0tG5Y6dbhlXC43u3YWs2ZVDmtW5bJ2dS7HjlXRqUsi9zwwmG6a0Kr/oveHSH4sW3aYRx5dwdp1eQwfncXzz40iOsa3TumFuxgRH8U2/dCKbnE+L8712wp58LEVzJm/h1cfGMAlUzy5WZavOcaTb27ix2VHyc6K5rYb+jD93I6ECJ6Bh8prdeWp0Rei7N7q0d3BdXLR6XTTru0bVFU5aNMmjmF9U4mMsPLxrJ04nW6O7LkKSZLIO1bFkmWHWfTrQdaty6NN23iGj2jOhAltVOSbSKzoKe/alaU+T4FAFVzMkqTaodZCIUG0Xg6BDPRQi0k3d1d6gNLGIgES4/St33kGAZ9GPdYjQIyqRRjtXOvpSEYhMXrn6r3bRqSKUb6OhkLvXTYKf26kre1pS7jGSHcwSjiqnV/lnWgs8fFXSA89aD0TGuJpIz4n48p5gX9vbHJYEYHIgUD9NyJBKgKsCfUl0NWuSUbDakhoVJU39KbhBJHR5qPeNOhdL95r9pxdXDjDFxLbp08fxo8fz5QpU0hKSvpPhr2UHb72b/H8iG728n9mrv4JHHfyQ7sjVVZm4+tZO8jPr6a21klGRiTnnduR0FAL87/bQ0azGwgODiY3N5ehQ4eSkuKfE8CwX0U3UlPjxGo1qQypvftKOZhTSZ8+qUiSxMYNx7jj9l84mlPBqjUX++0yGrmJabM1ix+tkceHdpFUFr9ApIe4Pmifht5OjkuWqXW6Ay7m9e241Pe7W5brrSEv3l670BqFwticsmF1mUDEgNOgI3oEiKIo6ZEHRveojwBxlVV5/x2UFON3rsvsL+SMK7k0jACpj/xYvy6Phx76jd9/P6r6PS0tgrtu78/553TkaE4FN9/2Mz8s3M+0Ke15+onhqkzT+/aX8tCjK5j1jaeaylVXdueCCzrx1lsbeO/9zd7zRo3M4slHh9GqZax3XPn5VXz6+TYOHCgjMiqYqEgrkRFWIiN9/09JjaB161g2bMxn7tzdzJm7m0OHyklODmfsqS24bmYvmjXzrClaAbxqdQ6zvtnF9df39pbBFhW9Wpeb+fP3cNmlC3j99VMZX1c+12hn7XiSHyKKCm7grruX8N77m5g/92x69vSsYVo38Hjhm2hsyIsChQgpL7dxypjPSUwMY97cs/3OOxFJkEM5nnK3y5cd5rxpc7zH+w1Ip1lmNKlpEaSmRZCS6vlv84xIQkODqKqys3lTAWvX5rJ6ZS5r1+ZSUW4nKMhEpy5J9OqdyqghmYwckUV88vP/0uj+HoglawFSU17wytg1a3J59LEVLFt+hB7dk7n8xt70rQv7UGBkYCrfmlY51SM/FOJj6/ZCHnzyD776dhfZmVHcfX1Pzp/Ulh17Srj27qUsX5lLx9Yx3Hn7QKac1RaLILsOldf6tQs+GaKnJBuRAorKEiYYubm5lSxffpj1q/NY8cdRdu4qBqBd2zg2/HGR97wKgzwUzrqQlUA734GqhUHDSBCj5LJ6Y42ry7lkkiTDEA+FBNGG7ARyo1dIkJwa9bNOMQh4CkSAaPOQ2dxuXZ3BaF713s/6yA9xnhtKPlQ7XbrPTk+Daoznhzgq5bpAXh9/BXrzatSu0Xevd74nPFv/+RgnZ9U/7mnP+PxA4S9hQSbDPDFiBRS9b0H7/uuScMK/A4UCBfqM/wwJEsjTx6jClQn9d7uxIUx6BIhRUQM9AsTofnoEiDJ8UVdvSHLf3NxKHnpgOXPn7mb27NlMnDgR+O/m/GgiP04MHHfyoyEozL+Bx5/4nWeeXeX3W+/eqbRuG0dGsyhGj8mmRZ37urYqgeIyDh5lqLzCTmFhDakp4diBd97ZyEsvrsFqNRMUZOLIkQratY9nzryzCQn1GSHVDv0qLEa1uo0W2UCLoviTqJQYLVjKYmm0O6e9l9aY1iNMxAWvPu8QXXfEANL7zzDfoF9i1y0bCws9AsREgGdlIND1yIfITXt0z7VmJvkd0yM/oOEEiHJ/UYA0RnBKEhSX1NKqw5u0bBHD/+4ZyKBuSYSHBREWFuTNh1PpkIlOUxv8rVrG8tMP01i4aD+ff7WdX5ccIiU5nPvvHEBZuY0771+KS5if5plRHDxUjiTBqy+dwrSpHXT7qQdttQ1H2S3IssyqNblcdf0itmwrZPLENrz2+ljdb/DWO37hzbc3ApCeHsHVV/Vk3GktSU+PVCk3F834jmW/HeaPldMJNUh493cRHwqOHb2OMyZ8zY6dRTz46FDGT2yre168DilYHxGy9cDVqr/Xr83jxSf+YOfOIhYtOoes5tF+15yI5IeCrXuu4udF+zlypJzcnErycivJOVpJbm4lxUU1qnOjo4OpqLDjdstEhAfRv0cyg/qk0mNwc3p0T/EmxxYRm/jniKUTEVryo6iwmp8W7WfO3N38+ush2reL5847+nPqqS04qpPUUc8I0vvWjPjYzMpKlu2q4rlX1rJg0X6yMqO4+5a+nH9GC8xmEy+8vZF7nlhJy+ZRPPzAUE4b04Lychvzf9hHWVktHdolMHJopiqptQJDhV32hLnoEQJG6kpYkFlliO/dV8rc+bvp2CGBocOb69+oDlpjOxABokCUh3oGm5izqqEVNKxmE5UOl5f00EKcD20eDyOvS0Xf0BpBRt4CegTIQZeHfNCTRVFW/74aETXaeQ2zmHHLst9zlmi4R0ZjPC/0knD+FfJDb5TBBiWj9fQAhdBpSMhBY4gPxWDVjqMxSU/tBiTWn4V4i2pNWFKYQfL6SrtLt+yrCCOPHgX1hXCbA1Rx+bMkSKDcQXqQJEnX09HoDtrnVVP3XhvpwXq6ZUMIEGXt0Ntg1POeNyJc9aZK2ydZlpl5zRGWLl3Khg0baNas2X+W/Cg9MvPvyfmR8dJ/Zq7+Cfwr5AfAh++fwS23LlbF6WpxxvjWPP/yKYCa/FB2EAEKCqqZNGGW14XaYjERGxtCUVEN557bkcSkMMrLbIwak03/AekqplW7CCtQyAPtx2y0qGkVCaPqFQqsZpPX3Uzrgmhk+EsETvapGNRGSc+UPhkpXvVl0laEiNGYzFJg75bG1PwG/XlwyrKhS2BjCRA9BK/frXu8PgKkrO4W0XpeOgaD1JspPSElzoMsy2zdXsThw2Ws35DPo0/+wbLF59CjewohOu+my2Lms692YLM5ycqMptbmZOI533p+c8n075fGtLPbc9E5HfjtjxyO5lRSWWknN68Km92Jw+nm8JEKBg/MYPLEtqSlRugSRyU2h64SrIWYY2fud3uYfJ5n93/UyOacf25Hhg/LJEZIiOp0ybz/4WZuvvVnVTs9uicz9tSW9O+bRteuSXz8yVbuvm8pGzdcQkqyzz2/1uX+20mPkgIfCVtebuOW237m69k7mXp2e554fBjuuspGeqSHAiPy40hdyVLF0Fn9Rw6vv7SGlSuO0qpNLHc/MITTBMPuRCY8tNgjlMJVkBpupbbWydGcSo7mVHD0aAVHcypJiTDTp1sS7VvFer0JKsJCDdv+r5EfR45U8MP3e/n++72sXJmDLMt0753KpGntGXN6K8xmk+6OaqQgW0SDsCHkh93m4o+vt/DC+1vZvLOEzm1juf6ijkw9LZvQ+EhkWeaq25fw9qfbuOHyrjx8W1+sCZE8//Ia/vfIb1TXOAmymnDY3bRsFcvLr59K23a+0DkT+uu/EQkTYjZ5lXwttCER4loUaKcZjGWW0jcjma8s1fWVhg8U8uZwy4YGg9YYEntZWKMfmiQSIKJXjM2gj1q9RZmr5mYP4aGFnpFstO7rESCGCTx1ztXPFaDTp5OA/AhEfGihd2tlKsSw4cZWb4kUCAZb3TtvdK6RzhTIUgjT0WiqCVweVtFZanXmQqtniPc2qiSj6LB68639zrTdCkSS1EeC6OnOgciRMKNNTSNPN51jRuuSnh6s1SuV71zP60TPfjAKL28oAdIQ8gMgxHo/3bp1o6amhrfffpsRI0b8J8mPkr/J8yO2yfOjUfjXyA9teMzefSXMn7+HRT8fJCTEzOChmUw4qy2xsSF+Xh8K+VFRYeeqy79n+7ZC7n9wMHFxoRw6UMbu3SVMm9aBTp0SAfVCEYiBBv2P32o2eRlm7U6a0Q6KuJshXmFYZaZO4OgpE3p3UIgQoxAKSfL0NVDohVGyWRENLZElKiZ6YzSbJIJNpka5f3rcjPX7pxd2pCz8WuUlUM15LUQCJLyPJ5FnhYGg0Zu6hhIgjSU/cnMruf6WxSz4YR8AZrNEqxax/LJoGtFRwbrkhzvI/1164/1N5B2rYsrkdmQ1j+bgoTJuvvVnFi4+oDrPbJaoKrxRdazW4farslRUVMO3P+zBEmSiZctYWraMJSTU4vcel5bWEmo1EyeQG1VVDj75ejvvfbCJteuOYTJJ9OiezPBhmQwdkkmvnqlYrWaqqhw8/exKXnhpjbdvoSEWKqscmEwSQUEmzji9Fa+9eqrqnsezvG0giAQIwJdfbeeW234mISGMd948lV49U3Wv0yM95IpbOaqzS37FZQtY8N1e2raP58qZPRk5pgUmk0THf7GSy9+BqmL9BLvBFdV+xyzNX/+7u/OPYf2+K1V/y7LM0vn7eO/dTWzalE9QkInBg5sxcFQWw0ZmEZfgI3+MDPxIHW8APcNQkXmKzKqpcTBp6Ifsyavl1CEZ3HBxR4b1TUGSJMyRnvCzNz/eytV3LOHtZ4Zz4SU9AHjvw81ccd1CJl/YiXMv7Up8Yiib1ubxwsMrKC2q4fufziU+PtTQGA8LMumSH3reKwoRYmRERqpyeOgr/yIUTwMj+aToEEbaUmNIEG2f9TxclDOM5K9IgGjPSdTJ7aGdc0VGh1r0iTMjKAazmMckLTyYEp1wKT1DTI9Y0CM/jNQEveetZ4Q11FNCz7NAz/OnodU49N5VsyT5zbHeOAIRHw2B0WOM1PGsMCrCZ0R8hOn0t8rp1iU9wN8LVmxXT1dRUB4gNa3yTP8M+QE+XT1QotOGkiAiERuoPZcsN5hkAM8Y9Db+9PplRIDokStKWd19e0s4eKCM0lIbNdUOxp3RmphYdR67hhAgRmHoemu73rfjkmUqK+2sXpWD3e4mPT2S3j1fxuFwcP7557N37142bNhATEzMf8agbyI/Tiz8a+SHHvYd8d8NBLXXh2JoLFt+mKtnLqSsrJbX3hzLkKGZuoLMaIHQJiGzmk2GSfb0hLO4QGgXPz1lJhDJoCdwQswmrGajPYz64xfFQ1qFQ6sM1hfmEkgAG6kEinKlt2jrKQhBJskwjlSPAHG49Rl30CeQ6hOYIhJ13I71CJCGkB+rCytJ1sns3zzSf/Fz6LTndLn55PNt3HrXrwQHm3nq0eGMGNyM5KQwVZ4bKfIp/4sbgFdfGM01N/xEvz5pNMuI5KvZO4mKtPLg/YO49OKunj443dx216+8/d4mEhJC+fCdcQzon8FzL67mwUd+U4XJSBJkZkbRIjuG5KRwEhJC2bAxnxW/H8VqNTHu1Jace04HThmZpSJSDh0uZ9HPB/nl14P8uvQwJSW1RIQH0a5dPK1axtKiRQwut8x7728iP7+aUaOyuPuuAWzZmM/W7YVce1VPktMivO39U8SHiH1HriW2rkrGwYOlXHrF92zYmM+cWWcxeFAz73na7y8h6Xnkilu9f+uRH9OmfMOhg+UsW3EB4cL7/W+M8++GQoCExz37L/fkn4NIfhQX1vDQ3UtY8tNBho3K4pRxLRk4vBmRkcHE6VRhsZgkv7CSxhIfIj64dR4PfXmIhf/rTLds3zcV3rONl/y46f7lvPv5dvZvu4Lo6GDcbpl23d+mRccE/vfsSFV7xYXVTB35OTNv7M0lV/aoZyZ8CLaYdGWFnqEdXfdN6I1bQbnd1aCqH1qZpZURWuOkvjBU8Ln2BzKU7G59z0Ztf4prfeM3yhkgEiCiQaK3MRHI0UFPxumRJXrvkZb8CJRcsaGVN05k8sOI+NDCaL6VuRZ/byj5YVRRLyzIRLBJa7gatKF5rsq99YgPgFrNex4se67XC/8F9Xssa77hSp2BKvNhZKEoOp2RHuiSZcPNSfA9Gz19Xfs+i99QfZVNGkoleklnzbwbeldrxmKU2Bj0N2V/WLSfiy+Ypzr29genM2Jklqd9yfj+wQG8zkUEIj+cTjcbNx5j6ZLDLFl6iHVr83Bqzg8LCyMoKIjExETWrFnznyQ/ig79PeRHfGYT+dEYnFDkR0NQUnAjGzYe45SxX9CvbxovvzCazMxoQ9dCUVFQoJd9XU+4WU36icaMQ0v0+2y0OCuLmZ47r54gVQS/XmvKPYwWKeWoUR+NymCJvwdixcW7isIkUCZyvV0gvakyScZlXvXm9lCFjU7xvvCHwrp3oDEurloCRCQ/xFdCr4753jL/UK6GECBa8kOWZW64/Wdee3MD50xpz9OPDSMuNlT3Gf8Z8sNWejPLfjvCZdf86GGOY0O45oruXHR+J6xCmMYnn23limsX0rtnCqvX5nHlpV158elRXH/rYr7+dhe/LzmfIKuZXbuL2bWrmB27izmwv4xj+VUUFFTTokUME05vRXmFnc++3M6WrYW0axPHjdf3YurkdgQJOxVHK+24XG62bSnkjxVH2L2rmIMHyji4v5TiYnXSxPnfTGbI4GZoERn/zxrNRqRttMXE+LNmc/hIOb8tPZ+QMP18JPEBqjAoRMj61bmcOf5rPv3kTEaPylad818kQP6/QSE/ZFlm4qgvqCi3cd9jQxk6Kkt1nhH5oUWZTiy8opyLolIryyoq7Azt/S5n9onn2RktDfubV2Kn1+0buO7y7jz4vyHs2FVElz7v88w7Y+mtSboKcN/1P1FZUstnX5/lPRYoEaBeRQql/3rkxzcHK1V/z+zoC7HRJh5WUJ9daVQyEnx9Dxz7LxvmM1DGojU4jUgA5T56uouWAFH6HGuQO0RPjjrdsi4p0VDyA/SJJz0i6HiTHyU2/3dd77np3Vd3V7qB+VFAbZD+FfLDSO8xak/UOwIRH4Eg9q2hhJYJf9JDgXZ+xTEZ6Y4VAXJ2BHJI0t7LKBRMgfYxNLTontEMBvrujQhJ8A9nM2pLPCR+r0ohAVmWKS21ceBgGbv3leJ2y5x+RivVhpj43hYXVDNs+Ce075jIE8+M4LIZ3+F0uJn3w1SsAZKXimgMASLLMvv3l1J1rJo9e0uZ/8sBVq04SkWFnYhIKwMHZjB4SDMGD25GZFQwR49WYKuewqFDhygoKGDGjBn/2WovTeTHiYH6A/VPICheHzfc9BMJ8aF8+dkEVRk9PcSFqJXFKqfL+xEX1S2YRqy+ngKptKf1llAWU1mWycmpJC0twrsI6ilRojBQdkjMUmCFUCQVRKNdbFthXsWFShydIjOU/ooLr/hvUTAqY1XuoxVkgfrsdPvYdzEeVpY9ipFWiBnpGWZJwmz2J6O0c3uowmMsbimqIkWT/FLGX+F1uGVdAqTUIB/M34Vfc8oBGJjqWbhkWebO+5fy2psbeOapEcyoKwVa63ITEvvMcblncMwzjBoHOwberDqufZ6jRmbRMjuG1WvzaJYRyf13DQSgW/dkXntrA0FWM7ExIfTtnUavXv4hHuK7NPOaXqxclcNzL67mimsW8sjjf/DNlxNo1zaeo5WehI1ms4nOXZPo3FWda6W0tJaD+0vZv6+MY7mVtBdyCCgorHUQ+eem47ijzOnmkadHMO6Uz7nh5p95/rlRfuuVzeUmp9JGWoS+MFS+vbxcj3EXHa0+r4n4+G8gVNgtLS2uZcYV3RgxOrveZIiNJT7AZwiYdMqsfv7Iz1TZ3Nw83p/AENHu3EGcs6qWL7/dxaP3DkSqM8qDDOLZLUEmlYcY6BtEKwuqGZYa4XdcNBqU8q8Kqa0lPgBe2lrEjDaxAccg9kbSOS6ug5LOMfDNq36eAUkl/xSIu7Va2dOQ5JdaKInRtUlQS2xOXQJEkYPasRjJY/G0EIspYO4xrV5kNUt+BIjefYptToLqKYsJHvKjMWE6fzdE3cNlqv/ZNWbDRwvxGxa9uCRJ3YAs1098gO9d09PfjDb4HLL6u1XGr0csmSSf4e5yqfPbHC/SQ0FCaFBAAkSWG+aR0RDzXtRB9Z6nLIS6NKRsslmSvM/CKIzdZnPx4Xsb2bD+GLmHKzhwsIyKCnWC6+L8aq69uqf3bzcy+flVvP/BZt6vq9z3+iunsGjJIbZsKuCTLyZgNntCDbX9FKveKM/QqtM1LeHncrlZ8tMBnn5hNWvWH/NcZzXTqWsSF1zalf6DMujYJQmLxURnIWS3exf/tsvLy3Xn4mSHjHEy27/SZhMah5OK/DhW7fnY23dOZNOn2xg88hPe/eRMOmf5Kzl6OwTa0If40CDdsBibS9b1xhCJFK2XRHWtkwXzdvP0k3+Qm1PJ1TN7csvt/b2/KwKjzObE6XRTW2pTEyR1zYmKUmxwEAU1dl0m2eGWvQuPXhE3MQO9Xib4+hZwZTHWW7uVOWtI3XQJQUHQkSx6ilAgmJBwI/vt5hXpCL68KrsfAQL+Srdbp2sOt7veTOPgmWdll1IZZ1ZkiN95R6v8Qxn2V/gfU/DQ47/z7ItreOax4V7i4+9CcIyaTBHN6+qSm0hJDmfVbxeweUsBqSkRRMUE43C76d8rDYC16/IYNSJLV3HX2/ka0C+NAf3Gs3VbIdMv+Y4xZ3xF65axnmo14UEQZOKc8zvRs7eaSImJCSGmewo9hRwahTqeXfuPenICZae/1OA5+CsQw/JELxBJkshsHs1Djw7jtpsXs2pNLrfd2Z/hg5phd7m937XJJOF2y94wID83eruLhx9bwajRWfTpnead0/9SYs8m+NCseRQHD5YB6u+nWYT/uqLnmagYRnpead5zdL7LlM17WL65mOhwC1JUOJZYX16RyNN+8P675BMP+XnocDkd6wjINi1jSEwKY9H8PXTro/5uqyrt/P7rYS65xLeO6fVsZYEnr8uvuT4yQ48IUaCEN05vHcMHu0u9x7WkhziHRuSCKQCJEeg4eOSH8i3rOdI63TJmg3K1RuS7ESKCzNS63DjdMpGa8M7oYIsfAaIQLVqZZzQaRR43NMdFfVCmQ0uY6HlrNATldqefbhdmMVFdT86VhqK+HGiNQUO9WfQgvitGZWu15ylQSDcReiSp0aaVxSR5Dd4gr0zSv7+yxijeIEpfdcs51/XV6L7e51qnroo5+ZT+i5cqQ6rP68N7vqrfPmj7o+edUeV06T5P7Tx5m6p78c0a3dYlyz4PPLFvkqSbxw5g6+YCbrvpJ/bvK6V33zQ6dk1i4sQ2NG8eTfPmUWQ1j+bpZ1bx6OMrGHNKNq1bxbFh4zHeeHMD38zZhcUiMXVKey67tBu//HKIhx9aziljWtC1T6o3F4getN9ZlcOlSloPdZtxZhOVlXa++HI7r7y2jgMHyxgxJJNvPhtP9y5JpKVGsKNMv9R5E5rwb+GkIT92HLzG++//PTqMiWe3Z9rEWWxdmUvnrFi1pwKyXx6NQIuxFqEWkyHzrYeFP+7jysu/xyEs1kcOV/id99TTK1n2y0F27yjCZnOR3iySqZPbM25Ca1q10t+lShTCJURDTxSIiqJid7l1x1lud3kJkCqdBdbhljFL+gqxjEch0CvpaxQq43XN1CiC1U6Xt+qL2E5NndtmoDkXGXG92PX40CA/AqRVjHFFCK3SoEeAKP0t1exSaM9riMKUHh6sS4DoYfGvB3n4id956L5BzLyqhzdW+t9ESIiF3oJXhwmJVi1jiIkOZtXKHMaOyvbb2QX/nUMRHTsk8PXnE3j5tXVUVNipqnJQVeVg+c9HSEsIY+KILL9rcnTKeZ5IEIkQhYSZMKktnbsm8djDvzHzqh91r2vbNo477h7IsOGZXkNK+bQ+/3Qbhw+V89lH41XrnOIJ10SC/LeQ2Tyaw4fq3/XSM3xE41dZJ+sr9yjilRs6cfodqxl3x2o+u7c7nbIjVcQHQNSQLrgqa/hjTS79eiSzdUMuHTolceUV3Xn44d9olhXNxHM7EBxi4eihcp66bxkSMhde2JlMWw02u4vKKidVNQ52O8wkJYezXSc05aOlUXykPXZO3Q6pZlGZ3jomoLGpl8fB5N148F2n9XjUkh7iLfR2k0USRNsfRWbXt/GgB+2zNgrFUDwh/XZk3f5hq2aTRLBZ0s2RpRdeYwStHqUdt94YY4MtfgSIQ5Z1vT/qSybruaf6bxf+oQ56CVjrK5UKEGupX03+s2SRnmzU6q61Lnfdt+zvVaOFkWeJ1qPAaeCxoX2vHG7Z71qFyNL7pmpdbq/nhrZtkWQQ3xGjdzksyGRYkRF8BJqeZ7V4VaAno0fEKGuLdnzKWmBEguiWDBY297TflPa7C7GYVASI3e7i49fX8+Rzq+jUIYEVP59H57oiDqDetLrnzmtZuHA/l17+PWFhQaxclUOzZpHcdns/Bo/K4vt5uxk/cRYFBdUMGZbJbfcP1J8QATaXm2CzCbdbZsvmfFxOmYF901Sbsfv2lfLB+5v44ottVFY6GHd6K157/VROGRDYc/D/M9yyrJtn8a+22YTG4aTI+VFYWMgtNwyjsKiG0tJaiivspKZGsGzpIcLDgpg4vg03XNebZs08fdPb8arUKFei8hQsLNIN8WZQFqgQi4l5c3cz85ofvYbfg08OZ9CwTBISw7znRwWZKbE5GTv4Y4KDzUw+twOp6ZFs+O0oc+ftprzcTkZGJH16p9K9VyojBzWjfbt4j0uay015hR2TSaJSWMWjNe6sWqFXKigWem6qDpUgUv+mnQNRsOhNj0mSDBVP8aMUy90a7aQpBIhW4dFrXyQllPFHGLhdaxFkkqgQhFGIUH5YRKBM/Hr9Fq/T7iDokR9assoEXHbOXOx2Fx/MmogkSfRs+e9Vtagu0a++oRhWV1+/kAU/7mP9mhmEhgZ+J0Ff2dOe16nXu4wf14pHHxyiOq4bhyrBqn0lPPXE75w2rhXD65J3/VNeHw3FwToiZPOmfAoKqr07xLIMFdUOPv9oCyv/yGHgoAzuuncgrVvHUVPtwOJwM2LM5wwf1pxXXx7j124T8XHy42hdSWMFTzz+O19+tYP5S89THc+OVnt+uNyy346sducfGkZ+ZAr5hw4fqeDMs2ezc1cRl56ZzT2XtCc+2kPCB2eleM+bec8yPp69i4pKB8MHZvD5W6fyyPOreemtjYSFBRETG8zRI5WEhVno1z+d/CPl7NhbilOToyA01MLF53Xkpmt60iIrhk/2lQAe8kOLD87xXWtkTImocrgMPQuNPPv8Q1q1f+teVtemsezUorEhLnpklyfEwexnWOmRCEoorHY6jMrVim3WGniQePrQMI8/5VpFF3PIMuEaudmQHCMN3ZjSyla9cTaI/DDInSJCj/xoiOeHXqixlvzQ2+zRL1ftH5KlN59GnheKsS/qlkZhGOK9lL4YhasoXtR6XgNaKPqbEelhMUm6YX0KjKopgke3MqJSnAaEhx6U9zFMh0wD33MUn5ERmSi+k4rO2ywimI2b87ns6h/YvrOYO27uy2039cGq48EtEiDfz5vC+Emz6NkrlUsu7Uq/fum89OJqPvhgM8gwflJbps/oQsvWcaoxA5SW1FJaUktWixiigy3UVDtYtuwwixbtZ/FPByjI93jl9eiZwh0392XY0GZcfvWPfDtnN7GxIZx7XkcuvLCz1wZLTXmhnlmsH/+23Xm8oYwn/+A1f0vOj6Tmr/xn5uqfwElBfvz47WROnTjL+3dERBCVlQ66Dkinedt4lszdTXWFnUsv7kJYeBB795Wyb18prVvFcucd/UlI83ed1Rq1wWapQcQHeBSB/ftKefXFNcz9dhcjT8nmj9+OcObkttx+3yDveeVlNg5sL2LH9kJ27Cji558OkJAYxqavx1GR5FmAamud/PrrIX7/4ygrV+awcVM+TqebiPAgJJOkiutrlhlFx86JdOqSRKfOSXTpkkhkVDAx9QjnQElbjbwKwixmw98kAy8RUAt8UZBE6pRdBZ8QFQWFkcBV2hbHo6dAGhEgWsVUT1A2hAAJtZiwOd31GhV6ipVWkdP24eC+UqaM+ZInXxnNiDEtAP5V8kMLd/ktqr/37C2hfa/3eO6J4Vx1WXfV/GnnW08viZD8D3Yb/BG9uibx6qtjvceMiI+FPx/gvMsWUFpmIyjIxC/zppDULk51nrZU9r8NhQgRCT5ZlvnlpwM88/jv7NtbqjrfajWz9vcLyc6K8X4bTaTHfwda8uPzz7Zx6y2LWb75UqzBnrVMS3yAv2Gtt86L67FRaECmTuWpmnV7ePXrvdz92lZO6ZvMN0/1Bw1ZEJyZhE2WmL/oAFff9gspiWH07p7M1k157DpUSXmVA7kuR0DHrAh6to2mY3Yk8S2SCA+zEBEWRGRyNN9+t4dnXl6LySSxeO5kOvVOU93nvE89/xWJDz2Ia4SR7LKapQaTHqq26/5rZFyZJQ8pYuTJIaFvdP4ZAkTvEr0nG24xYXfJfokK9bggxQhTQot95xqTGAqMyA/tXOmNP1CSTAUNTcapQHHX1xqnRTohktrnpdeuSGwYeQtryY/GEh+BIHrx1DjdDSI+wKCyjw6RYmTwK2NqSNiYV4/TfFt64eNgTMiKMjFc8/wqHBoPXEnUGbWVakRvLm1f1X+LoR9Gj80oz4z4jolTY5RzRSFBRC8k8btwudy8+uIaXntpLe3bxvHWq6fStbMv95m2f9qQ5T37riYkxMysWTt55KHfqKy0c+VVPbj44i5Yo6zUltp4771NxMWFkJgUTkxMMAsW7OXrL3dQW+vk/As7kZtTyfLlh7HVumjRIoaRo7MYNTqbmhonL72wmrVr8khICKW83M7TTwxnyuR2uDXfTRP54Q9lPMcO/D3kR3JWE/nRGJwUYS+jR2Sxc90MXnlrA3O+28PBQ+WkpoTTZUAGY8/tyIRLujL/g818+PEWIiOttGwRS6eOCfz8y0EGDPqQnxadQ9u2ntjkWpcbm657vv+qp43BC7GY2LG7mBefW8V3c/eQmBjGPf8bRIvsGBb9sI/zz+mIu9LO2jV5zPt4I98tzcHhcBMSYqFN2zgGD8nk1O4ewywyv9jzX+DUU1swbqwns351tYP1G46xdl2eJwdAiJmoqGBsNifbthSyZXM+r76wmupqjyBonhVNv/7pXHpld7q1S9CdvxCzJ4ynxOb0E+4KG68oi+Jirv0N6pQs2SPs9BQ9o3J2FQ4nJknyE2hmSUK7aaJNkia6Rep5hGgFcnGtg4QAFTQUBJn8FTQlJlN8H0LMkp/QaYwreWNQWuKJjcxqEWtY8uxfg4b4AFiwcD+yLFNY5Klwo7wztU63YbiLAj3iw21zMKRfGt/9dBBLjcdTxpzqIy9q1kxDapnu8fhYm8vE8+cyZEA6bz5/CmfNmM+4ad/y1TdnkZUd8ycH+fejeZ1XihjKJ0kSI0ZnM2BoM35ZdICaGgeZ8WGEhwXRulUs2VkxhMc9S7hRo004aZGe+qL330dzryMrOxpZhgP7SmnT3j+prx6OHKng23m7Off8joSFedY+rQEWZjHprlsHyn3x2FlRHpLFGmSixuZZyy85M8uP+ACoXLsLgDHx8N3/unLVi9vYsq2Q9IQQYiODOFZsw2ySiAk3U1zuYMnGYg4eq6H1kSrSuzTDZquidMUxPvtqO0FBJi48pwPN2viP96sLlD5LAXM71GdvaneeFdTnMiz+qsg8byl34Z5mSR0Kqu2OxaTOxWBXea4Y319UVxTiRmvsKU9HK9/1DWX/+5XbnQ0KL1GgNWr9wmx05lQ7fj2EWkx+u+QWk1Svp6tRvoTjDT0PDz1VQDt+8c9A1WO00M5rqMXkR3LqNWdUGU+bH05pSTsqcZx6iVH1wqDB57Wi6G/a98z7zgttGZVsVXL01fd96hFSStiZ3ppglqDCoAKUcitJgrRwfwN1X1k1lRV2iotqkGXo2SyG6KhgcmwOFeFS7XCTn1/F/Fk7+fKL7RTkV5GSHE5KcgQxCaFMPbcDg4dkAurvYuf2Il56bjXNmkUyb87ZJMepQ7e1ZIcWVRVXcM60saxZncvpZ7Ti7nsHkpbuSQFvNZt44PFfmTVrByazidoajw2RkBDKNTN74nK6efONDXTpmsStt/Vj9OhsWrSMVc3/sOGZ/PH7UT58ZxOTJrZh8qR2nvv+wwUCTma4kQPm4/qzbTahcTjhPT+0O82KobW4tNYvEegoIYEowHMvrOK551azYf0MYmJC2L27mMVLDjFlansAwsKCsLlk7wJtNZuw2VxYLJJf2VHw5IC45Jw5rFmZy+kT2/D4E8OJDA8iN6eSfr3fJyYmmNJSj8HWqVU008e34JQBqbRsFkFhWhIpJWW6Y6xO1ldwxd1dlaEky+zbU8KWzQVs2VzAjwv2UlhYzZkT23LNdT1pnhXj5w0iKkVa4SkqEnpeF4og1Xe7lQ2NXKtBOJGWAAH8CBAFem1rBaXVLOm6e+sRIKJBoOxy6Skj2t1BPSFqpMiJp2q9RvSuEXdelv16iCsv+o7PF5xNi1axSJJEr1YniOdH3bfoBtxumdvuXcJzL6/l5ut788j9g70JOxuqiIa71M9Mrnu/5v6wj7NmfM+Gn6fRpbtnF1iKeYaaNdO85x44WsmwGT+RnR7BgleHUZqVQklJLVMmzqK6ysHHX04gOzvmhPP6MMLWA1cb/tZRyIrehP8+tu+5kj7d3+XSK7oz84begP+ap3iC1NQ4eeHlNTz9/Gpqapz07ZXCG++NIzYutEG79nrrZmqYldnf7uLiyxZwz50DuO3mvp5rdxz0nuMoKPW7LrxrS2579A/e/HQ7VdVOzGaJNi1iaJYWQVqoTESYhf251ewtclJUXENwsJmQYAujR2Zx3bU9aZbh0y2U8eoZ76Ki3RBnzUq7f6I+8Vq90BRVSI02V4aBd6Onv4LRqNOuYtgbe07qyzwjJU3P8NPzXpAkqUGVJ7TvWVKolaJah9/86ZUb1kvMqYxFnEO9hJIi9EIE6iM/wF/uHA/Pj4bk8tB2Rff5NcA20eohumPUS3CsOU1LEAYZlGwOJKW1z1JPb3HWDUrbTz0d0tBjJEAVRL1322IK7KEdKMxMkiC/2v8dEJ9529gwyspsfD5rB0eOVnA0r5L8gmoKCqo5ll9NYWE1Npv/+2mxmAiPCCIiwkpEhJXgEAtbN+djtZqZNKENXTolkpdXRe6xKuZ/v5dTxrbgqWdHAXgKIJTYOHKkgqNHy3nplbVs2lxAh/YJ/PDdFJplv6I7nt2HfcnVXU43r728lldeWE1Wdgz3PTiYYUMzVecvX3aY6efP4+67B3DFld0pL7eTn19FWkYUwXUehmKlGgUi+ZGZ9s+FE/9XPT9yD1z9t3h+pGa9+p+Zq38CJzz5UR8+W38JAKPTfQUuFWP9jdfX8eSTf3D99b1ZuuQQK34/6j0nKsrKsy+M5ujRCtavy6Nlq1h+/eUQa1fnMmlqOx58Yjjgc+OrrmOhD+4v5brLfuDg/lKWfnE6fbt5XNLuf34tVYcK6DmgOd3bxdIqM1K1iIT0+AzblgtVfQ/u9OGfGvMeYdEDsNU6+fzTrbz56jqKimoYf1Zbrp7Zi4GdkgyFzuG6SiNpEf7VUOwu2dC91+F245L1XV21lyhC0chLQiFBRAVSLz+J2K4ihPXknxEBolWK9JQrf8VDrx/qAeoJYe05ejlDtIqEOP7flh7m8gvnAxAaZiE5JYLTTm/Fjbf0VV3Tqtm/YNQLROSd/1vG48+t4v67B3DnLf1Up0k5RQDYktXhJyK0xAeAXPdcysptpHV/H5vNRVJiGJPHt+aZh4cSHGyhdOsh5i05ykNvbMbthl/eHUlpK18i1oL8Ki47bx6VFXbe/3Q8LYREwjEhFlKS/7o75t8JhQRpIjz+fTz523TV3wWHyiC3gpj4UExmE9WVdirLbVx86gt06XL8KjLtO3ItN85cyI7tRcz/cSp2naU4KyqYed/t5Y57lpCTW8kNV/Vg7Ohspl48n4y0SG56+zTMmrU0KURtDHaMC1WtmWtX5/LhGxtYtSaXkpJaJo5vzftvj0OSJCLjn/WeV/HDWFU77loPiRzetSVDz55Lda2TZ+/tT88eqYSHqQnoX8s9xsfQNJ8eoedSniskNk4S2tDbYVTWXP9wDL9TAY9ccgYwxoxIbbPJOLeVWTI2Jl1uWVfmiPJDlBtG92gsAaJtJxBRFBvsm+OcKhtJoWq9QM/oFgkQJWzGP1+K/73qIz/AX0Yr5IfYvNZA14ZFaMmPhiQF1I7SojNpjfHcgD9HfADImicuPgJfuWr/trTkhx4h8Gc8V8W50Pt+bF7Csn7voxqn25CoUHQm7WWBiCitjicSMtqKcNqut6mrahVqNvHpl9u5/d4lFBXXkpYSTlJSOPEJoSQlhpGUFE5SYhgZqREkJ4ZhMkmUV9gpK7dRUfffQwVVnuTtlQ5Gj5jJ5NO3EaMJWWzR+S2qKu2EhwdRUW73K1kbFWUlIz2K1q1jeen50UREWFUbobs1+v+hA2XceuNPbFx/jKtn9uSqmb28+UE8YX4Sb721gQcfWM7AQRm8997pXu9A8OijGYL34YmCE9nu/DNQxpOz/+8hP9Kym8iPxuCkJz+0yM273vvvjRuOcfHF87HbXXRpF89l53WgrMLO1XcsoVv3ZDbU1aFOTYugvMxGVZVnkfzgi/H06uuLO1biJF0uN2+8so6Xn1vFiP5pfPHyCKIE8qBy1Q5VX0LbZxLS47O/baygJkJqa518/omHBCksrKZzx0QGDcigf980oqOCkcwSFouJEoeT8Agr0dHBJCSGYTabvCSIVsfRKj2ioW6oJDZgx1GBniKkR4Do7SY0tJJcoB04BYryISqkeuOojwARf3c4XJjNJrRmvnbeRCEvyzIbN+Zz5FA5ubmVbN1SwLxvd/Ptgil0FDJ9/+Pkh8YDa9acXVx8zY+Ehlh44uGhTDu7HZIkeYkPEbJTPQPOEk8py9D2vp0JWfM8DhwuZ+2OUtZuOMYLr6+jW+dEerSN5bN5eykttzOoVzKv3tkTubt6dwOgvKiWi8+bS0lxDe99Op7WbeKICfGP8DvRiZAm/LsQyY8Dm/N556afsNf4k3ZnnXUWs2bN8jv+Z3EoZyZLfj3IhefNIyUlnAumdeD8aR1JT41g87YCFq/JYflPB/h1yWHGjM7miUeG0qUuid2qtbn0G/UZl983iGET2njb1BIfAK2j1QrYVefPYcvOYi6a3IYeQ7IZMqgZFotJFZ6mlXEA4QM6ev992z1LePblNZw9rgVmi5lH7+pHszTPxoRCfIhoUacEJgleerk6FZ1EA0okpo0MWrNJ0jU6RaPIiAABnyEH+oapqc6lXiupRGmplTtamWOU3FuBWdIPE9GTpZ7S2epjeh6WyjnaPBYi+QH6sr3W5TYOY6iD3vPQim6rjqzVXqcts6m3GdNY8sOof6rfNX9ryY+GEB/1hfZoYZYkv3a1xAf462Y2nVwdNqdbleOloaV7G0qGiN+FeB+bzjtsNZt0iQ+tXqUQsHobRRA4v4vLbbxRF2SS/IgPBa01lQC3bS/kltt+4bffjzJpfBueemSoyhPNEv206nxHmX8IMECQ5jwAW+nN7Cyp9v79yfub2b+nhKjoYKJjgmmeFEFKSjgZ6VGkp0cQpZODSZvja/fha5Flmdlf7eDh+5cRFx/KE8+Nol9fdb4kp9PN3bf+zFdf7eCqq3pw190DyDjBksEb4WSyOxuCJvLjxMJ/mvwQkSTsNJeW2SiJCOarL7Z7c2YUFtbQt/u73PfwEKae71PmTEjk5lQw+6sdzP5yB7k5FYw7ozXZ2dEU5FeT0SyKmye1IHj3Ab97xp7323EfXyDsOXwtGRHB1NQ4mD1nN0uWHWbZiiMcOKgfbgPQq08an301wRuyoFWCwBOSUqrjVQENJ0AUAaVHbIgEiKgQ6QlOkQRRTtW+wXqkiKgEKIqftu/1JQwEffJDjH+WZZn53+7mrTc3sHVLARaLiVGnZHPNdb285EUg8sN3H89/nU43Y4Z/QvsOibz8xqne37UZ4bX9bJup7yp5vFCYfwN5eZXcfe9S5s7dzaQJbfjonXGY84pV52mJD/CRHwpM4f7JHENapXv/vXpdHpPPn4PT5eaCia258KzWtMmOpiw60u+6wrpyx8VFNcw4by6FhdXMnXs2LVrEqM5rIj6a0BA8+dt0ju4s4o3rFpLWOo7bnxhGdYWdI/tKeeX+ZSSlRbBmxU6SkpLYtP8qALpkv/aX7nkox5MQd8P6Y3z91Q7mz9lNSakvL4fFYqJjxwRuu7Ufp4zOJiKnAPC5LJ9/+3JWbilm55qLCQ0N4vP9pX7kh5b4kGWZHp3e5poLOnDvzO6e+8Try3qRAFGID4fDRVCQGYfDxevvbeKGW38GYOG8sxk8sBmldidbi2tU7bTQUQAVWSASAoocyM2pZPasHeTmVpGeHsFZk9qSlqpeA4xc4iVJfxffiAARD2vXfNFQNDLL3OgbnjKevFh6Bp0yZm2CSz2YTVK9VWnAR4Bok54anadAm9vE15/6E4Ir/VLIhwqd8Wq7qkdK1EeANJb80NMntHqNIkeVZ9xY8qOxxAcYVNDTSUwqwoj4CNR2Q5Kweu+vky8jUPvax2fkpaH33ouJ4cV8fHp6kdKu3relvB964UIKCaJ9z6sq7bz/6jpee2M92VkxPPn4MEYOb06YxexHePxVKPJBD80ifDqQUTLzo0ePsmDBApaveYXDB8o4sLeU/XtLOevsdtx1/yDC6zYwxW99/do8Jo3/muuu78Xtt/f36vkng/5zMtmdDYEyniP7r9YluP5S2xU2MprIj0bh/wX5kaTjYm9O9xiH+4/OxOVy89br63nysd9ZtnI66emRHD1azpzZu1i8aD8b1h8jJMRCerNI8nIqqax0EBlpJSs7mt27SoiPC+HnH88hNdVTVcb5w8p/nPgQUVtys/ffNU43RUU11NqcuJxunE4Zu8NFfkkN23cWceetv/DksyOZdHY77zWZkSHeMB8tFGVBK0i1rstmSaLG6dZVFvQIENCPGdarAqMnw40IED3SQ0R9BIiekNUKcHFH78nHfueNV9cxcHgmw0ZnUV5mZ/ZnWzmWU8E1M3szekwLWrePV4VEWU3+SVvF2371xTbuuvUXvph9Fh27J6vGp+DfID8UfDtnF5df8T233tyXO2/vrzpPeabSYY+XVUOID6llut8xc0k5LrsTc117esSHXx8Lqzl9/NfYbS7mzptMSorn+zwZBH8T/h2cP0sd6tIlBZ6/aB4ms4krXjqF9kmhlBRWc/s5cykpqCY0PAgJz/doNpt46OnhXHfp93+pDwr5oSDeYubHnw5QWeWgU4cEkjMjCRZyIBz9bQ9vz9rD1z8eYtzQdG67czAd+33Ao/cN4paZvXQTKmmN2H2rDtH51FkseG8MowamGxIfUpxPMbcfuZpZ8/fy0dc7WbTkMJMntOGNl8dgtZr58JMtXHndQnL3X4OsIV6UMA0jjwARyilvvrGeRx/+DWuwmdRmUeQcKsfpcHP/A4O58KLO3vVPS35o10nFmA3k9WH0k1uWDXfIjagFxSDTNmnkgWB07xqd6mJGbbhk2S9sJdB4I4V8HkabFuq++N6dCKuZ4lqnX4U1rVzV3l+3Yo1wMDzI7Ef8aO+h3QDQI4qidMqDijAiP7x/10N2aMehNdjF640egfYd1RIfeqSWqKdYTFK9xAfoE4N6o9M7TyEUquqpjKJ801pdLlyn+olbDlxqOBCRpH1OWp1MfBeO1fg8yaKClPApmQXz9vDkIyuoKLdx6819uPaqHiT+zTkt9AiQQGT5sp2Xe/99/42L+fWHfaSmR9KseTSZWVEMGJrJqae08LtOIUCOHq1gxKCPcDjchIcH0aNXCm+9PY6QUIs38fqJipPJ7mwImsiPEwsnRbWXxsCoxJLr6DW6f69amcftNy/m4IEyzhjfmrS0CGprnEw9azalJbUMHprJE8+M5OjBMl54YQ1XXtGd88/vSGWtkw/f2cSWzQWECi67kfHPwnl/3/gagpBYT0bokoIbAYiPV7v4VTldNCOGnr1SWbHsCE8//js9eqWQnR1DZqTHGFV2TUQSxOX2lazVkiPBZk8lgdJaRZh5BJfTLfsRIIoA1av7LgpNsyThcutnTtcTvCplUEdu2l1uXQJEVPDMkuSfTd2/KV3s3F7EG6+u49rb+nL+pV3ZsbWQpx9YztFDFQC8+PxqXnhuNRERVu6+dwDnnNfJe62f26vQ/6lT2vPJR1u487ZfGDwsk8MHyzhU583TrWcKPXql0qt3Ks0yo/ySVf0TmDC+Dfv3l/LoY7/Tt08aI4Y3B9QKkNwsmaDqWizx0bpt2HM94TJ6xIeltAIkCUudV5I9r5iwQs/4q1tmGPZLigjirQ9P59xJs5ky9Vs++nICSfFh7D/qMy6zT3AFoAn/Lix5FeTuKeG250bSPimU7IggTAUyrVrFEN8/jczm0URGWjl8sIwvP9mGpQEJEuuDXlK5s871/Leo4AYAvv9hHxs2HKOgoJovv9xOdJSVsgo7hSU2WmZHM2ZEc+5+aDnrNhxj1Mgspk1q543z1hIf1tJKft9QgMkk0bdHCpLFjE3yjSNY9jesXLnXct3dS3njw63065nCTVd24/k3N1JeYefu2/rx3fd76dIp0ZD4AJ+hqBg5Npfs3e23u33VxBYt3M9DDyznrOmdOO/qHoSFW6mqsPPBS2u49+4ltGwRw5C65H5i/g89Y9OIBFA9NUk/V4VJkvxkj9KcMqOi8Wvkjq+0pUdeiDvp4n3Cgkx+VeoUI9UtyySHacgOzb0tkifPiXZXXG/DQYRSEU2UwdpwhrgQi2F5eu3967tfY2A1mVQEiFHVkBMFklS/l2p9Hh/gv0FjFHIihijrERp6ZKPeYxTfGW81N5c7YH4XpY/xOuGm4J94VM8rR9SJtO+z8j5qCTEFChlyoKJWdbzc4WT/nhLeeGwlvyw9xMQzWvP0o8PIbPbPGIyN8QoUiY+Vyw6zZsVRxk9rz033DyJMQ/ZE6pB8drdMenokq9bPYMuWAuZ9s4svv9hOeYWNkND/nOl30kCu+9/xbrMJjcN/zvPDCCL54XbL/PjrIV56ZxMLlxyme88U7r5/ELW1TlYsOsA383ZTWFTDD4vP8ZbM3LW5gLGnfUm/fmnU1jjZsDGfpKQw7rilHxdf2AmzxaxKCnei4NyvLvI79uQg3zPMOVrB2ZNmc+xYFXfd0o9bru/tTZYEPo8OPeVGIUC0uyM+AkQNPbs8XGfXSU9B0hIgeooEBI7pVfqp3TXQKkxa5SJco1RZzf7l5uwuN088/BtzvtnF3CXnsW93MZefO5eE1AjOvKgLqZlRbF+Xx5z3NmN3yDhq7Dzw0BCmX9zFOx4R2mGsWp/HpefPIyY2hMzmUWRmReN0uFm/No89u4qRZUhMDGPoiOacdXY7hg3MUBEhf6e3w8GjM5FlmRFDPmbosOb876Ehfso4QFB1rc7VPrjKqvyOmTQ7mHZNWA2ASROqdTBBnWh1965izjzlc6bP6Mp9DwxW/dZEfjRBhNbzo/bHjSz4bBvv/HwuQVYz2RHqdy3IJCHLMo/et4wFc/bwy+rp9Gr35t/Wv6KCG/j4ky3ccONiUlMjSIoNZnCfVLp0iOfy25YweUIbvvtxPzV14V+SCWQ3JCaGMv/jM+jSO8OvLF5QSQWjz/uOikoHK+dOxBmj71WlkOoA7711GjMu/57XnhzGZed3RA4L4adfDjDpvDneMuzXXtuTu+8Z6L2mxumfM0Jvd1dZtxSja9JZs6iyu3jqg9NVa5osy9x4zlxSEkL55NPx3uN/JpmjnklepROGAmpZpJUlyk62kVFWYXcRprMLrqBaSOhqdJ7NJRNt1YZ06Hs1agkYPSNTHF9RrYOkUCv5NZokjFa1sWQUkqqEHYJ+tbXGIpD3h93tDkgwefoUODxU64Wqlbv1hbko74JRadf6kvDWR35oqwnp5cXQeqdq3wWxT8r1esSHHm2kDfvRCw329bX+cF4jkkx5Dn4J4/0IP+N24uqIlqNVNt2+1dY4+eGDrTz94moyM6J48ekRnDIy2+/c4x3u8lewbOflvPb0Sj57exN9B2fw0NMjiIsP1c3noyVAtO/K+x9u5uF7lrJ21+VeD9rOJ3Bi9f+C3SlCGc/h/Vf9LZ4fzbJf+8/M1T+B/yT9p3g8qGD1lGxb+utBnvjfcnbtK6Vnl0Seem4U0THBXHXJAgoKqslIj+DsiW05b1oHundJ5nBFLRFBZnr0SOGjD8/glVfXkp4awR239GPM6CyC6oRxeNzJQXwA3La8nIs6BtEjIZwWmdEsW3I+Tz+zkgcfX8EnX21n9ifjydLkSLCaJT+Bo3iHaONPleSSWhJEu5sVYjbhcst+wlrrAQL6Lsd6BIjVbMLucquT4tXDimp3jELMpkZlGleQf6ya5i1isASZeP351cQlhfPwR2cQUqcEtu6SRPfBzbh96rdEZSTw2GO/c+lFXbBazSp3UvBPjte2YwLL1l/sG5MwpPIyG1s3HGPtqlx+mL+Hr7/YTqvWsTz88FCGasqdNRZ5x/zDyESDRVG8ZBlCQoPIz/cQGNo5DzGbINLjgSRXqGP/jeC2O3Dbfcp0flIcNE8l4WCu95iW+AD8XL4/+e0osgynnd7Ke6yJ9GiCHj6e9IGXAAkLc7DfYqGm0sGSb3ZxxrkdyKtxkhbmE5sm4JXn1/DlJ9u4/d4BhBrsdB4v2O0u7r5nKeed24HnnxtFrM1jpP68/AgAX3+7i4iM5mR3603VsRzyV69AMkmYwq2MPWcu29ZdQnRUMGXlNswmCWuohc9/OsqSP3KZ/eVEaqIi0DNZReKjqvgmvvt+L316pXLBJd0pcbrB5qTXgAx2bbiUY/nVVMlusus2DsBnxIoiRFlHxLVWJDccbhlZllm7OpfLbuvn59UmSRKDxmTz6avrAs6ZaByKMkQvB4hblr2khwLF4Asxm/xIeOVvsySpXPiV9TsiyIxLllWkRrXDrSI2RA8OEco1YUEm1c59qEWPePB4Ndbn+WB3yV7iokDYfRf7oyU+wEPqKASIMs6E0CCVLBCJD+XvhhAg4rxpzw8xq7+nKoNw3D+D0tJaXDYXSUlhXj2uMVB7w/hXAtLyCy43qjgTrYysj/jQ7UM9xIcWnhBb9XeoV6lI79sIRHyAj6x0uWXdjS7tu2mWJD/ySXnPzZLkpxOBuD7463SKl4fWu6lldCgLF+3nljt+ITe3irtu6cttN/QhRFirlWk8kYiPrzdeQu6hcj57exNTruzOrTf39o6/yunyI0Aq7C4irWYV6SF+n2VFNcTGhXiJjyb8O3DL+uWw/2qbTWgc/pPkhwJZlvljZQ47dhRxMLeCI0cq+HbWTvoNSOfBp0bQtXsyy5ce4s5bf6ZFy1hmfTKePr1SvUmBAFpkeKpq5B+7nlNOyWbiuFZ+9zkRiY9AuKijR8FYV+gxVHskhHPfvYOYdFZbzj1/Lg88+TsvvHiKX7yssgui3a0LNnk8IbQCLybE4keAKMJZXH6V3RlRcCtKjrhD4XL7EyDVTpfOrpRJRYqYkFSKhkuW/ZRVUFcRcMlqpdyscYUO0fH+yGoexfKlhyjIr+L3JYe55K4BXuJDQbOWsfQa1pwdO8qprXaw/LcjjBjenPAgfwKkoYiKDmbI8OYMGd6c62/ty+o/jvLuq+uZNvVbJk5sw8hRWZw6Zi8tWrT428JiXnphNTu2F3Lb7f1IDNMqsGphK0WG+i3WUmUN5uhwr/eHSHpoUdg81e9YUl4hADXNUlTHc6ttHDjkCZG5+fpFTLugI6MntCG/+grvOX1bv1HP6Jrw/wlhYb53r8PprYkqrOLtp/7g1MltCdKsia+9vJbXX1pLuw4JpKZH4v6btRCLDFVVDkYObY4kSZSGeHaQ5vyW4z0numVrHFWVOMrLCE1MoaYgD6cLSkpr+fSLbbRtHcfEad9it7sIDjYjyzD+jFaMGpEFqIkOLYoKbiDEbKK62kFCQqifQRMcHUxMqJkYwC578kuF6RiWKgJVnDLZP0zSbDZhM/AmtNU6sWg8JBSD0GyS/IxDl1smpi5firbvXk9GnbXe2xdJnZ9J6alblokIMqtI61CLP1nivVcdASKSGiZJIsJqptLuaSOQh4jdLfvt3uuVvNW91iX7lYXXEjIKtBvsoswsrHF45xI8JYm14Qy1LrdqnmODLRQLFTgaWzb2eGD9+jyefuIPliw9DEBifCjTL+rMTTf2UZEgf3ff6gtx0YZGaXfyG0J8aMW9nuPFnyU+rCbJsE/ia29Emoh5OpTTRQJJ9ArWtiEm9SzQkHWKB0q72DCO5lRwwQ3zmTtvD8OGZjL7i4m0ahVLiGZNOpFID/AQHwArftxHSJiFM6d35kCVU+V56Cm9rb7O5nKrdDynzcVFF8xl29ZCqqsctOsQrzp/84GrT2jvj/8iZLlh5a8b22YTGof/HPmhJBRa/cdRXn5mNRvW5mE2S0iShLNOCKelRzLnm51ce+X3FBbU0L5jAs++eArJKeEcrvS4zDWPClGVrUrSCRuoKr7phCY+Pj37fe+/z/3qIi/poYWyVrZpn8CUKe15771NVFbaIcKKw+1fBk8kQUShpHyAouyMCbHgcsv+GeFREyBmnaSf4K/k6REE4q6UHhTiQ1QWXAG8QVwuN++8s5H339tIUVEtsbEhXDi9Mxdf1IWwcN9cqMkhM1OnduDVl9fy7ivrkGVIqEuAq0ViajjbNpcAUFaqHwqiVaQbCpNJou+ADMaOyOLjj7fy+mvr+OabXUArEhJDOePMNky/sBNt2/qEoFZpFkkgCFxSuKS4hocfXM6sr3dy6619OXVMC8ps6n6HhGm9eNRtSJU+TxBzdDju6lrMGuUkNzzMuBNAfkoC6bKbILtHESq3Wsmt9nzLd9w3kNPOaMWnH27huSdX8srza7jyxt5MOq8DZrOJDfvUSci6tfhr1TqacPLhm02X6h6XJImeA9P54asdXDr2S1q0j+f5l0cTWkfwHT1cTlJyOAf3l3LDlT9y3S19uPDKK+nZ8vXj3seq4psIDq6LuReM148/2cIrr6zDZLFgjYnl6JKfkMxmwpJSqSnIA6DocBnpbeL4cfEBnnx2FX37pHLhBZ0pKa6htsbJBed78g8FCt1U8o3UutzExIawcVOBn5eellC2mCTsmhKp1Q7/5J0ixDxRkiQxYlQWP83ZxcQLO6mMU7vNyeK5uxlWR9qIEI1y8CW2FI+LpWa1OazMku/cKof/jrVLlnVN1yirhXK70y8BY1iQyevJoYzdk8NDVp3rcsu6FTEUI9gkSbohC2K/Qd/IFREdbPEjQIJMpoDeBiU2p5+xVWpzqp5/lNWsGz4htiFCmxNM6y1yuNLmF+Ijjs0/bEQN7Xvwy29HmDblG1omh/D8JS1JjbWyeFMJL764hq1bCvj6E1/lu/rCYbVyU1tRJ7LO6wf8y9TWB5fwvPXu3ZAStsrcKFfaNKG74jkK9Cql6IWYKO+g+C7qkRzhQWa/3G7a5PjgIRGV+RbPV9575T6KHimSp4p+WmLzkGotokJxOt28+MpaHn/yDyLCg3jnzbFMmtiGUpuLklonJXVk6ome9PO3H/bRe1hzMmM9JHe1002KEFKs3aTT4tNPtrB2dS7X39yXmJhguvZIUW3cNREfTfj/iv9Uzo85P01j8Q/7WfzjfrZtKSA5NZy27ROoqrSzbnUusgztOyVgliQqK+0MG5HFhElt6dAxwW9HvFWzl/+lUfy9WLjtMu+/eyaG+/2+c2cRw0d+itls4tSxLbn62h60qTOURRJETJqnlY2SpC+ctQSI1SQ1KE2PkRulFlFWi26JRG1bIkSBHWQy4XS6GDniM3bvLqZjxwQ6dkqkutrBD9/vo0XLGObPP5tIIV5Pqyw899IaHnnoNyxBJk49pwPn39hH9bssy9w25VsqnFZKDxzju4XTaN8hAfBX3OJCfPOtTdylhXaoIhl0rLCaDevz+H35Eb6ZtZPi4lratIkjOjqYkBAL1mAzYWFBxMaFkJAQRo9uyYwYmeW9Xpx7RekoLqph7pxdPPP0SiRJ4p57B3LOOR3Q5o+P04QB7ClVh7y0C9JxPdfkBjGFqSvCOI558n7kpyR4j6VrkjIelfwNiNwqO0WF1bzz8jpmf7qNjl2TOO3M1rRoFUOLVrEkJocjSRIp4T7loqkyzP8PKORHVYWdDUsOUVVhwy3D8HEt2b+nhLtmLPCe+9K7YxkohJMpS8qrz67i/dc38NpHp3P5uXOPex+VKl7ZHd6gR9dkvvl8ArIMsekvEhpqwRmbiTUiiqIt68kYNoaELj3YO+cLyg/sBcGojYsLYdmv55OaEmFYVrG8SB06ajFJ3rW1uLiGZ55dxZtvbWDjlsuIjfV8n+KurSIftLvn1RoiQfE2EJN5aq/ZtPEYZ0+cRZfeaVx0fS9atItn99ZC3n9+NTs25vP5N2fRtr1nLQi1mPxCJ7SEuWJcaQ1x8BlbWqPZ045bl6CX8MgBrRw0IhK0iUsVskNPZkqSf7JKI/JDm3PBqOxvIOj1WemuImO04R1aGahtQUsiyDrePSLhriX9A5EfoJZP2lVfG1YxZMQnWKprmXdXR0Ksvt9+WFfMuc/uYNZnEzhtTAvdfgciP/SIDxHaOdPd6BEGpv1de2/t/Il3d8n+pIZeUtRozTteX5/EfoQL49UjPbRlhkF/40pcV7TQIwEVVOtck1j33f+xKodbbvuZbdsKmXFxV+6+qz9RUcFejyotTjQCZNUej1dqzpEKJg7/jAdfGMWwMercJCmanGpmyf85OJ1uRvT7kP6DMnj2xVNUv7U+Ceybk8XubCiU8ezfe6XKhjgeqKiwkd3y9UbP1auvvspTTz1Fbm4uHTt25Pnnn2fw4MH1X/gfwElNfry7ZgYup5t1vx5k8Zfb2b3eU04zJNSCw+7y5pjoOzCD0WNbMHRkFsN7v++9fs/ha3Xb/a8SHyKUXTwtXG6ZQ4fKmTV7J59+tpW83EoefHgo55zX0SvA9XYH9ASu0l5DIJ7V0NJtepVktF4LWgLEpfFW0V4z4+L5/PjjPpA8iQIBkpLCOP/Czrz2ylqmTevAo48N8/RTkvwUziCTxOKfD3Db7T+Tk1vFna+OoWMvT5iG2y0z971NfP7yWqyRoaTGW/ll+QXeawORHxCYAAlEfohKms3mYulPB/j996PU1DiprXVSU+ukqspBcXENhw+VExxsZstWT6ZxsXrS2t+nM3f+Hn5YuJ/Vaz1k4tlT2nHPvYO8FYW0Cnd95If2+bWO8bTjOubxjDEiPkQcSfSQc9kmz7PQIz607thb1uXx3ON/sGNrIfY6pSgy0soZZ7Tiuut707x5NNBEfvx/weyNl/DbD/t49+mVlBbWEBxixuWSCQ6xMPWyrpgtJma9txm3y81t9w4kMzua5tnRhEdYveSH0+nm6gvnc+hAGV/NP5v4RI+30vHaXVPIj+9+2Mvkc+fw8bvjmHJWO2687WfefG8jUnAobc69jCO/LqR01zaCwiNwVFUS36kbRVs2AHD1lT24/NKuZGZGq769SocLh8PFoYPTWLRoEYcOzKZd23g6dkhg8KAMgswmPvh4Cy+9spbtOz3fYGZmFN/OO5vExDDDxJ8itJqGXphFhTfkw2c8Od0yy5Yc4q7bfuZYri8pclp6JI88NZx+AzwVn7TGkkKCaI0uo910McRAJD/KDYwm0IRlGhjleuEoIWYTZXaXX58VeSka03pJYZX7hgpGpt6cHy8CRItABEity11vGWOtoa2V2yIB0hDyQ5wjlaeooEfs21vCqKGf8NENbRnXS+3+DzD83k207pbGR++cDsCxanU4hZhvRJtvoTHkh1EolAL/xKGea8vqnm8g4gP832vlHRB1MS3xocenaT2e9EiOKJ18ZUpFQBF676b4Cokki9ZTBAInslfOr6pycM/9S/ngw80kJ4Xz3HMjGTG8uV+Oi0q7S1dX1auy9U9DIT4AaqodjOr1ATfc1Z8zz+ngPZ5Ut65p9XDt8ykprmHiqV9SW+Pk6wVnk908RvX7iU6AnKh255/FiUZ+fPHFF1xwwQW8+uqrDBw4kDfeeIO3336bbdu2kZn513IFngw4acmPd9fMAODRGfPZu7nA7/es1rFMO68jY89oRWycr9Rrk5uXGgoJokdS1NY6mXntQlatzWXN2hmq3+xumXCLyc99WS/7en0ESCAXaJEEcePZNdIiEAHSEEVOOb+iwkaHDm8SGhbE9Ot703tIM0qKqvn2gy0s+X4fw0c0Z+UfOezbc6VKoOoRIDU1Ts45bw6//XaEtt2TSc+KZsvqXPKPeMrehoYF8fNP00jOjPbNQz3xueLY/ZLPagyJUmFXU6tsafsr7iJcd81CcnMq+XbOZMBHftgKbiA++1VPvoHhzZl8Vlv6Ds4gKcnnPaT17NEqZTVO9c6pEfGhQPvaOPceRQuF+FDQUmjjcB1ZFKisnsvl5ujhCoqPVLBpYz4ffLCZyko7v/x6Hv36fuB3vyac3Hhk6YXef7eN9iiR+3cU8cHTK9m6Jo9+o7KYcVs/mqVFUFZSy0cvr+X7r3bQoVsS0y7tyrP3LaO40EPgWYJM3PbAYCZPbedtsyC/inNOn0VNjYNuPVK47b6BTBj9+V/u94Z9V9Eu1hf61abLW5w3rQMP3jOIeQv2MOncOQCEp2WQOfp03E4HOct+xu1yUVtwDNwO2reNY8XP54GwduXkVjLvh70s+mk/vy0/QlWVg5iYYDLSI9m9pwSbzeUhQAZm8N6Hmxk2JJNTT29Frz6pZGREIkmS11AVyVqtsaM14JPDrH6hJhUakkGbw8DpdLP296Pk5lSSnh7JoMEZmM2evBl+VT00a4tiEOuRATaXvkdHIOJA8fbQwmzyJAbXu9Suk8hbXDNFA8+oeosW0ZpwT2XeG0N6KDJSnIKGXK69R6DSpw0hP7QlmEUEIg+0c2VEfqxZncuUibP47fFutM/wD6O85OWdlFrDWPDt2UBg8kMrf7UGuzjaCA15oyVGtBDls5Zk0r4WylQoBIQeQaF9TmK4rlv2Jz70vCr0vDO0OeG8YSsaYqhUsxaY0H+/lOu1IcziuaIuqfUuWfzzAc6e9q3qWEJiGAvmnU2LugT+dpdMVZUdm81FaEQQFoG0KqtbfzpnvcqxY8dYtmwZ48aNIzRUrZf83RAJkMumziE+MYzHXhrt542mt/4Ultby2UdbWLHkMGtW5+J2y7TrkMBzr51Cy+xY4MQnPRSciHbnX4Eynn17/h7yo0WrxpEfffv2pUePHrz2mi/Mu3379kyYMIHHHnvsuPbvRMQJm/Nj6qeX+B0b1ELjvmox0XVwM/ZuLqDnyCzatIkls0UMWW3iyMiO5vTO7/xT3T1pEZ/4PFu2bGHy5KGExYUQFRsKVXYmjGvNlZd2Y8zILOZ9t4fHHljONTf2Jioq2KuwVTndfgIQPB4RTrEcoJAFXEGIxVRvmTqF+BDFriRJugQI+BQBu07y1UBwyzLBZhPvv7MRl1PmoTdPpXXHRAASUsK55YlhuFwym9blUV3toKi0lqR44xwUblkmOMTMF5+PZ+7cPbzw4mpW/LAPh91FRISVcWNb8shDQ4iLC/UjZ+rLqN4QlOq4cxvBajJRLCQT3H+glOZZ0X67OcHBFt59eQyXXreQI0fKOfusttQ2Yo4VxUZU5sTH2CY2MPEBYGmZrvr7QLnaE6alhjxR9NN4wQ2+QqOImc0mOrSOg9ZxDBrenAsv7crIQR/zxmvraZV9AwAJSc8bjqsJJw9E4gOgrLiGz19Zx+LZO0nLjuGxN0+l96BmAISaJaKSwph8TnsWfbuLzJax9BvWnC+XNqeizEbRkXK+/WI7j961BEeNg3Mu6gxAYlI4H82ayKLv9/HVp1t58K4ljB8lH5dEwztKqr3/btE6li+/3cX0q3vQulcK9zwwmC8+3crunUfY/sHrBEVE4qiqVH1k1VUOPvhkK2NPbcGHn2xl7rzdbNiUj8kk0bt3Ktdd14shQzPp3DkRs9mEy+XmrnuWsGZVLst+O8KQQc14762x2AWDQdyhL651+IU8WE2SLvEBPuOlQscdXs/4D7OaGT68ueqYYtiJ5+t5WihH3LKsSuio5B9Q1iVtrgUtxKOK/AoySSqyRSFXld+VRxBkMvnF6CsEsLbPSvUWERaduSyzO4m2WrzjCK67xki2igRDoISe2spsClTrt+HV/tAr0SrOQ0M2KcQ+1Oc9oSDcYvISIM2bR2EySfy+o9yP/HC6ZFburuT08Rm67TSmykx9X7pIJPhv3EgEmfw9SuuDQr6InIzD7Q5IfCgQb6WnfwSZJFWiZyVRq5hEXhyTMld6Y/BLlm/2D3tRCDytdwp4dEk9EhCgc/80Vm6aQVFBDcXFtRQX1fD0478z/eL5PPbkcLp1Seb77/dy802Lqa7bEImKDmbmLX2Ycl5H7xq9dN3FDO35PgCPP/44t99+u+79/i70afUGK3d7CJCe/dL45rPtuN0ypTanigBxuGWCTJIqfO/1Z1fz6adbGTa8OU88OZxpUz8gI0P/nW7Cfw/l5eWqv4ODgwkO9ida7HY7a9eu5Y477lAdP+WUU1ixYsXf2scTBScs+dEQzOj1LjCDcRd3JTlErSiM6/T2v9OpkxCvfn45O3cW06VvKkXHqtixKR9TVDBXXtqNC87tSEmZjcee+oMvZ+/kvgcHM+6M1t5ry+0uTPiEr6JYagkQ8Ci7osBUmGutkNSGvJjQr0GvwFcyUW1Ya3NVaHdRRMXD5nKzbPkR2nZJ9BIfCiRJ4ozzOrB84X7MZo/XhlZJ0CatAwgKMjNpUlvOmdJede6JnJm5R89Uvp+/B7dbpk3zV1S/TTmrLa1bxtBjyMcs+fkgo8a28P5WYXepdtyMFBQj7CpRh8MoBpK2dr0CLfFR43KzpcjjEt8pPtzvd/DsuIm7bnquuGlxYVx2WTeefmYlBw6U0a1bMhdddQVhdQkuT5Zdkyb44+4hH6oIkLceWcHapYe5+LZ+jJ7cjkQhmXGNSybULLF4wT7sNhfX3Nkf8CTZrCizkZoRxa3/G0x5qY1P39/sJT8AmjWPZsaV3WnbPp6rLvqO1989neGjsry/t9N8VwqUxLuVFXZ+/WEfB/aV4pZlho3IYs++En5ZdICKchvFRTWUldgIspqoqXESEWnlwou7cOHFXbh55iJ+WrQPl6OGsPAgundP4tJremC3ufj2653MvOknpJs9ZOa4sS259IruDB/e3Ju3Q1wizWYT9z84xPu3wy1TDt7sjVFWC9STlLk+bzCR+FBCXRRyWzHkRC8LZV1R+ikad4oBLUnqBKFaA9Mpy3VJWDXhA5LkCxEQvTACeDeapLrSoUIJWfDIAGWs2hlSCBBRgihEh0KCuGSfQSnKFjHZuNKvGqfLz8hVDGiRVNCuydqwUaV0vPcKOXAyRQl/AkRdZS2w3G4s9AgU8MyT6HXi0pBGypofmR7F2FNb8PTcwwzvHEN2ckhdn2Uen3WInCIb553X0TvvKo8Dza1Fw1zrzVPrcht6u+pVUtFDkMm/dK7DLXsTp5pN/nqEXniWOC8hZhO1Trc3D4q2i7Lsqdynuj5AOd5A1WpqdTxF9GbE7pL9iMeMCH9jrcLh8nt/FZJF3KiJjg4hOjqEFni+m3atY5k6+RsmnPE1wcFmbDYXEya24dSxLSmvsLFuTR6P3LuMzWvyuOuRoYSFBxESaiE6JpiyUhtPPvkkt912299WJc8IfVt7CJAefdJ479X17N9TgiMlEnDQPc43P2JueVmW+W7BXi64sDMPPaSs208BTaG7JxLcshwwjOvPtgnQrFkz1fH777+f//3vf37nFxYW4nK5SE5OVh1PTk4mLy/vuPbtRMUJS37UVqkT+ozqrDaOZvZ7H1AIkCb8WXy35VK69EkDICk1gqmXd+Oy076iW780Fh8to31MCNMv78Ypp7finrt/5bqrf2RA/3RiEzw7J4qorHK4/Fw/g0ySn4DUlp1VhKS4c6bEz2pJkPoitKxmk1d5VGSVuqSimgDR7rAEWU3ExOm7OEbFeBSlrKwYrHUGuVFv9EosKiiocRCs0SjFBH1Wl1r5EA10k0nc3QzsMi5CjMGtdKhJCtF10mKSGD46i/ff2ciGDccItsz0/ta8zk2ve9dkunRK5LNvdqrIj0Cwa0oU17rcBAVQJpKFMJwKu8vPeNG6I9doiCi9vCJaV+N95epzwJMlHuDKK7ojyzKbNhXw5lvrWbr8MB99OYGgIDM7Dl7jPd/IiG3CyYGMPumsXHyQNl2TsASZ/Aw9iwkqSmtp2SaWsFDPN/TcfctYNG+P9xxJgtFjW2IRvk1nncHZZ1A6/Qak88zjvzNwcDOswYHd3QF+mLeb+2//Fbvd5TEuZXjvrY0AdOmaRPPm0WzbUkDv3mk89fwoIiOtqnXomZdGq9Yeca0dNDSTc8/ryN49JYw7szXWurKJTjzlIsW1MT40iOoGVJsSQ11qXe6AFaqilNj8unPyqu0kaRL3iWu8WdL3ABOXDsW41/PUUI4pMkDrZeFS3UsTXhBgfVLkhvaeSiiENs+UXgUWL7EjnKrn6Qge4z7Uoi7fru2fW/b0p9blFirlqNvReo54K36IJ0lqkkHrqaKVoRLqeVQIlIbA5pL9vjkxnESUGQ319KgPjz8+nPHjv2LAHRsY1zOOlNggftpUxq6j1fzvvkF07px0XO4j9l30bBClu9Xs8X5VyC29MAYF/qG1aj0hSPJ4ueTWeTRoPWlEgqbW6SZRIG/KnG7DZybqb2aTcdUaMdSlqFYdZqo3LL3v2uibq3W5vR4x4lwq65wyNmVDSvkubS6ZrNZxrFp3Mdu2FrJ2TS7RcaGcMb41kuRpb8rUDgwc3Iw7bv2ZLVsLufTqHjz3+O9UlNtJS48k52gxH8+exAWTZusP/G/Eh/sqccSEY7KYWPDrYUZP8+T9WF9sUxEgZuDQ4XLW/JFD/rEq0nqn/ON9bcKJgcOHD6vCXvS8PkRoST1ZPj5eqicDTkjyY/xbV/kdm9nvfV7646J/vjP/cYzr9DZ0gs33DuTNp1fx07e7ARjSNZn2dQa/3eUmNS2Chx4eysIf97PijxxOPa0lgLcsHHiUWotJUmVYV5RvUYgqglYseebSGMiKq6W4m2EU8iImfWvod6soHMGCUtCjewqvvLaO6ko7YRFqpXzlLweRJHjyyRF+bVnryA6x/6GaeNQ8jcF+oqJn71Qym0fx9OO/88WXE33PN8pX9nny6St48qU1mKsdhIZa2FejLu0YyOtDUVAcdc/RqVHSsiLV5JOW+DikSfqqKMXKgq3dhVP6I7qGKiXxRIRazOQKz2jGNT0B+OWnA1wx4zt27yr2VpRQsOPgNU0EyEmGEIvvfeo7tgUrP9/KrNfW8+BrY6h1yYQI1pjbLVNcVENRQQ1hFomKWhcrfj3EuMltGTAsk8pyO5WVdk4RvDpESJLE7fcMZMr4r7ni4vn06ptGy1ax5PSaTmJdrhwxB9X2rQXcecNiAFKyouk+IousTomsXbSPVd/vY8YlXZk0uR05RdWEhQchSZ611uVys3zZEXJzK0lKCaf/wHRvTqJKh88YtppM9BuQ4U0QKubc0Boe+Zr1KibYoqrMoI3LV77rCMX9XlgDalxuqh0uL/kBvvVQuY+yDmvzEShwqIx2YfdZ0v+3FmJZV9GbzGdse/4bHmSmxulSrd9itROxH6J3iQilzYaUnVXCW2w6BrIbtceLf8UQzz18ZIX/jcQNAaXSjtUsqUJqZNQEiNUkeQhrg+nUEiBGUDYxtGENjfUK/DMQSTkx1Cc8LoTZ86fw+adb+W7OLjbkVdOpRxpPvdKNkYPVu6Zhsu8diAyycKTKUzo9IkDOjkBhq0a1S5T5cQkTrs2bEQhiwbTUsKC6MB/f9doQ1kRNOEm0JllrqcN/DMrrovxXeYR6pEm8MPd6shbUhIj2dcit8q09sZqE6aEWk3dtqdFcqBCv2rVJNpto3yWJDl3UxJbDLSMDY05vRev28Vx35Q88dPcSYmJDmPPTNJIzIjljyCfMnb2TDt3+nvLlerjmx/O8/w4KsZDcLp7flh/xkh/gIUBiqu3cNXMRe3cWYav1jL1Zq1jad09heV4lk7s2hf2fiHBzfD3ilDYBoqKiGpTzIyEhAbPZ7OflkZ+f7+cN8l/FCUl+GEHx9mjC8cUlC86Ffs054+lovrnmewAmT5rNDz9OpUXLWMCzWxCbDgkNGgABAABJREFUFEbLVrFcf/WP9OufzqnjWjL29FYkxoWqjFTRtVKBG9lPUAabTSoCRPldVG6jNASIqMzpIchkUu16WEySX+y0+JeyYwZw4QWdeOXVdTx956/c9PBQIqKDkWWZDb/n8Onr6xk5Iovhg9VZkI1ile1uN1aTvroTZbXohlxoIcvqWHbRZTYkVD134q5RoWbnRTQoAu3MAlgsJh58bBgXnTuXzz7dynnndwKgdoNPIJ91Rivue/x3Fi87xOmn+JcFrHD4xtY1IQKAvWWBS/WCxwA7XOk7T1QyE0OthsQHeBhr8Xwjg8NikkgM9RFbevMhlvFr1SoGgNISm995TcTHyYebB3zAMyumA2CvUxhLi/y9gADefH4Ny38+yOMvjwY8ZIjZLLF3VzEzb+9HZJRvV0U09sXlqXnbWB58ajhffLyVjz/YTGlxLZ27JfHJN5NU98rNu56n7l8OwPWvjKFNT0+FqOK8SiKndKCisIbXXl3HWZPaEi4Qs8uWHuLG6xdx7JgvH0hwsJkzJ7bh2lv7EhMbQo3NyctPr2LThnySksO46a5+ZGXEqAz8QGuqGGceajH5JRrU7kprvd6qBU8P8C/TKBLQylqvLGd6JTMVcsEk6Zczrw+il1ywRjYohrJIgCjLrht/rz6XLHvXIe1uvNEa5HC7VXJKyVGlLGcqb0Qd74cap1slb7UeA0o4i/hMxRLDdpeMXjThnyElZHRIJylwPhERWs8Sz7G6sCeXrDISAlU1EcmO+gyLqOhgLr+qB5df1cMw+ahegs+M8OC69tXzJOo1blk9GFG+iMS8X5LWAO+xxeQfuuW9t+bvKk2/Q2x2xHpp7upanJVgiY/Wb88kEa3xUNPKSPE1ER+9XsJc8bkU1/oTIVaNR4oCZbgKaRkdbPFbV5QNl8OVatms6FZ+JIjSN/w9iVq0jGX6JV25+7Zf6NQ1kuzsGACGj8lm2c+HAFi790qAv50EeWXMJyoCJLNXGms/38ryo9X0T/Plqvn8m93s2V7E5Tf2JrtVLKEZkSSkhP+/2bk/WSHLxz/0vbHtWa1WevbsyaJFi5g4caL3+KJFixg/fvzx7dwJipOC/Jhz2Wv1n9SEP4VLFpzr/XdsVgxjHx3B93f9jMvmYvSwT8nPzycx0ZMDY8/ha7nutVNZ+/NB9iw7xAP3LuXXxQf44KMz/XaPap1uvxwfypqsfKhmSSLMYvbbLbFr4qcjg9TnaAkQUVF11+1KiRnLbS6XarfPjb8iJUmQnh7Je++exoxLFjB91Ge065pMSWE1h/aWMmhQBu+/My6gW2+4xaxKjCYK+8RQddlacedM/HeoRVIpqscbooeGNkxJUbDGjMhixuTWPP7Qb/RNC2Fw7xSVQrlmg6ekdGpyOFqIxIeIltEhbCysVB0Ls5ixmsx+VR/Af3ctp8qmUgCbRQarwlu052t3u0A/gZr2Oq1nUV6dUVlZYfO6jTeRHic3modbkGWZ5/63lKL8ah775Ewq69aLgsJafl98kN8W7mfTyhwuuak33YY1p7DKyXMfbCUkIZwdmwr46I0NXH1r3wbdb8wZrRhzRitqa53MmDKHlNQIv3MKCqrZsOEYsclhlByrYuvvR1j00RZ2r/PszEgmj9fbrp3FtGvnqXC0eVM+08+fx6CeScx+YRhd28ayfnsxdz+3jllfbuenhfuZcHY7Pnx7Iy7BEl/0/T5atYlj7g9TkSTJj4gVPwGtUaYlI7TXKsSE8q1qXeCtZolim8PbbrDZ5Fc5Q7Tzqp0uleeK9hwxRwao1zdRRoRaTLoGrZgoVGtfKjJHW45WgX8VLTc2l2zoGSAS5VrS3Ih4UDYSSmxOXa82T1sK+SJ5ZbDWUAwLMqnkiuKpqPbgEHKZ6N5J7Smj3eAwgjj3IWYTmNUGtd5z0YOe3P63Ecjo0JIGYjhRdLCm6o/wrmqN9kCwuFwqAiSkztOiNtjqd6672rd54CwqQ0qIUf1uxFeJ73Oty+33vSpQXqVanapGlQ6XX1JiLQEUYtHf2HLJqJKyK6WPFaJHIVgUfcto/rTfnDZ8q6rSc31EpG/uwsPUets/hVfGfMKVP3gIkGZ90ln5/kZyNuezyppKnwQPCbdx+WF69E3jnBldvNfl1DQ82X0T/n/jpptu4oILLqBXr17079+fN998k0OHDnHllVf+2137R3BCkh9NZMc/h3dO+xTwkSCpXZOZ+sF4ghdHYzabCQsL85YVBoiICWHoWW0ZelZb8qZ+Q0aGz8VKEYmiy6Y2x4dIfCgIMZtwybJXiZPq3IstmnP0CBBFMXa41Qnswiwm3ZJtonD17Vj54m5Hj8pm7eqL+PzzbWzaXEDnljGc8sBgxo1uoQrxURBkMqmUY9G9WuxPmc2pa3yfKNDuLD12ay+27y1j1IU/cMmkVjx8fTdiIq3YHS7uf2Ap43rH07o4lxpTKqnhPmWhptwzv0632+v1oQfRiAqzmP2S2ZXbXbqZ6cFDfAC0qqvusr24mhKbk9i6+dVL/Fbf3HttvLpntm1rIc8/t5ofv99LYlIYoSGevjQRHyc/Jnd9h5ufHsHviw6QmBrBnPc2ERMfyp6tBWxd7SEbOvVO5fqHhjB2oie589oVR/jpuZVYwyx0Ob01Z1/YqVH3rK5yMO30r8nLreTya3t6j6/fdyW1NU7OHf81sgwlx6r58EGPB4glyERaVhRZ7RJIyojkl292cdH0eXz//VRi40J58JHfyEgJ49tXRxBc96307pzA3NdG0m3CXApKbXz49kYSEsOYeUsfuvdKZf/eEt58aS2bN+UzYewXzPlhmqqfWmNOkiRvCUjwlYNMDfM3rkSjyOmWcSOrSGdtXiLF60IhQpR7G5XDtgvGi2hcibaYXpJQBaIRpximyhqtkA9KX8TdU7FNkVQQEzXahE4obWtJEK1Xok0THqPAVJfrRMllpM1ppPRXu2Y7hCSoSv/E6jURVrPGQ8njyaRAu6kgy8ZVY0Dfw1O53u5S7+rrJfA0um99UJ5tuiYxpvjuygaVSLTvhUi8i93TnidOg+jhI15TWKMf4gGBQ7K0Y6/VkHZGuWnMmrxfbiHEJMRmRw4LQQ4T/D4E8kNLfGhLQ4P/RoDSL+UbUbop9k85R/lvmMXkJ49ddSFcyrtoNvmXqva063cIMCbLjMhB5RXXPh2XLHsJ1b17SnjhmZXEx4dSU+XR4SKCzOB0E2w1kRASRPP0l/Q79Dfh9VM9BEhcVjQRiWEcWnWUZj1T2VNUy5L3N7FjbR6nXd/He/6ANm/8o/1rwp+DW/YvC3482mwspk6dSlFREQ8++CC5ubl06tSJBQsW0Lx58+PatxMVklxfFkn+e/WWm9A4iOSHiAcvnEtMsJk58872xpgr0ItZ1e6UiYJc+U1bIUYkQNwCQdKQNgE/5Uz0CNAqJKLCU+N0qzxZtMZ5jFXtCq4gp0rtgin2RzTAtUk5eyT5iAJxx0gcr3bHRVxAxd0O8XqtEifu1DoDtN2iphq3W+bNz3dyz7NrCA8N4pJJLZm1YB+7jlSz/OletG8WTtDIHt5r9mkqrMiyTFWlnZwjFZRZzCSk+DxF2kb7FFft3Gp3LcV+dtCUGN5eXK36W73z67lOS3zoef0cy6tk/fpjrF93jLVrclm9KhdrsBm7kE79WN51ACQlv0ATTm68smAK3328leL8KoqOVVFSUENaVhT9R2ezIrc/m9/25LgZ+eTNALhdbrZ++BkFO46p2plxQ2+mXtaVUNGIVIUqeP7rdLiZcdZs7DYnn82fjCXIzJ7tRaxdlcO7r62nrNTGiLEtuPmBQZhNEovm7+WVx/8gLjIIm8NNSYWdc67rzazX13H11T248aY+ZDd/hfuv6catl/oTMY+8toln3t9KdbWTF98+laEjsry/2WxOzjljFvv2ljD/13PJaOaT6eI6EBxgZ19bAcUkSYaJVsHfMCm3u1ReJYG0kEBe3NpQEz2YJX+5oBhn2vuKhIJiEGrJCaUtPXJdgZLUWq9/VQ6XbrJs7X20IQzgmUftdGgNVyVnhwJtWIx2ffUk3vQPP1WgR4Aocjc8yKwbriC27e2n5hmIJFFBjV11nkh0ScKIRUJcDKnQ9lt83Upq1bvhwQbeQaInpPi+aocvzqc4JK1RLibmThJCLcW51hI/4nxpZbdfdSKbr31ZQx6oSA88JZFFxNfJRKesT3xImpegxOYiWMiXpOUqFBltE8YWprN+aJ8FqMOUFHNEz5gzSghrFMpr5LGlJWOqqxxceJYnoek1M3ty0/U/8e4HpzNiZBYffbiZ/927lN/+mE5KasQ/ToAAPP/7Rcx5aTV/zN3NOfcMZN4rayk5VsXQi7oyYGoHTGYTDwz78B/v19+N/5rdqYxn+84riIz03zz4K6iosNO+7Rv/mbn6J3DibkU34YSBUlFHJEGqK2w89r9BnDt1Di+9uIZLL+9GhOABUGp3qggCUO9ESZJPQRblrLZErq0u07cCbYUWpU2FyNALvxEJEJtL9iqmbllW7S447C5DAatVKLVQxpUcZuVwhU047hPtYkiM1g25IbCaJUOX0+OJ9IhgbHW7ahdf35/xZ7Rh5j1LeeytrYw/rSVvvtGTfv3TKQug/JeX1fLc438w75vd2Ou8Ydr3SOHim3ozblCGirRS3FjL7K6AxAfA/gC5Q7TKTkKov8uqtv0lSw7xwH1L2be3FIDklHBi4zyKY6fOiURFB/Pr4oPcd+9A7zX5x65vIkBOcrz7dRsIaQOZEJUJkeGed3B1KQTpFHwymU0Mvn00K577mbxNOd7jLdvH+53rdsvk5VRiq3UiI4EE61bmUJhfha3WyTUXLWDX9kJqqp1egu3s6Z2YWVdOF2D8tPZERFp54Oaf+fWNobw37wAfvbiaXsMy+ebbXVxxXS/cLpnwUH0RHh5m8Ya6lBSpv5ngYAvTLuzEI/ct462X1vLAk8M9x80mlHD/hQv28trLaykurCE2PoRrZ/ZiyKn61Z2UtVcxotyyjBmfV4QWivEhVkcJFE4o5j5SDMeGkB7gkQXiTluQSVLtrIuhmFpPCu/OtIaVCFR5JDbYovImEPsrEu8SPk/JMJV8EozxuuNVTjehBrvaSj/NJsmXrwR1wm2HW/bKNdFAV/pW5XSpvFTEZ6HIPJE0F8+tcrhURrlb9idAJO9v6lCZCPQ9+xpa2aW41uFHgASC8g6JGzOxAjGuzH0gb5eGwoj4CNSvKGRUWTwEXScQ8eGwqufA6Za97ESw2WRIfNTdQuXhofeWldRtANicnvNCLSZE+k6U0UqpXItJUnnfKMmOxaTH+yv882jFBnvGIobLGelkynuiyH1tiWLlO1eIrAKNZ47iebZk8QH27ilhydLzad4imtlf7+SB+5cxdFgmE89qy2MPr+CrL7Zz8819yc27HoDUlH9O/t/Q/30qS6bx+5zdvHfnr2R3TeKyp0YSlBLpPef+Xy/8TxIg/0VovvLj1mYTGocm8qMJDYZCguw9eDUDTv+C/GPVxMaG8MxTK3numVW0aBVLnz6p9BuQwbARzaHOmUEhQSwmCQvqXR1AN9mb6NUgKm8KtB4dolKnJUCKa/Xj0hXo7Xxo2wk2m4gN0SZOrT+xlEj4yHLDq9Howe6SVTtSNpULtb9SqzUQGpJkVYuwWhthaRF88+5pOBwugmPUeT6U5+V0y15X+NpaJ+dduIAdu0pJ6DaIiPQs7BWlHNr4B3fPWECzL8bTpbsno7SoSCdryApF8c6ry/6uJTc8xprnWJIQm3uk0qYb9yuOv7zMxrOP/s5XX2yn34B0brm9H0NqC1i+rZwrX9/NVaemMqFfAmc8upVp0zpw9dUeD5cm0uO/gbXvPkzPGfc0+PyqY0Wsev5HqguK6HpeL9qc1pHzWsMbT/7Ba4/9Tr8hzcjJr+bo/lJyD5Z5k6mKGDOhNZvX5lFRYePSa3vSvWcK2zbn8/wTK7nsht5+5w8dk03UA1YWr87n2Ru7MW95LsX51VRU2JEkieiYYL74fj9XndtWFaYhyzKzFh6kY6dE1q3NY/fOYu655Wfcbpne/dI59YyWxCV4GJ6CArXnlCzLnDf5G9atySMuwkLn5uFsOVTGtVf9SPceycz6dhKycC+toah151XcypXT/D0D1Lvoyvk2g93wIJMJtyyrElXrocrh8gs7cbplr6GmzT+l/A7+JIhRCVuFRLe5ZJURHWbx5TFyetdHlzecxTsuHbmjrGc2ISeC1WxSeVeIEOWg6Euo5MESw1ACzVdl3XyJ8yGS/WKS7krN3Aaq/mJ3uf2SyuohMdSq8v4Aca588t9Tqt0/94unH75/q6sDNS5TiHaDRZQ7Wl5GJGpE2dwyWs2gKiFfRgnS/VAnv5RxS6JXVUW1RzcJ9id+tJsFUnUtMeLfgUi0glLEVasyKtLvHG1ZXkV3qqwbn/jtKB475XanivQoryMB44VKLlpPEqViTIXD6TfnyvXhGn0g2hv2qtZzjJLFhphNxIRYcFU5CAm10KZNHAAzZnRhxsXfcTC3kqTkcMaNb81HH27mqqt6EFanZ+TmXf+PEiARsSGcd98gqstt9D6tFSaTRInN3UR4NKEJfxJN5EcTGo2Xt5VQWLebmNIjhai9xRw8UM6eXcXs2VXMpx9vJTY2hPsfGsKocS2ocbpoFuFzw7SaTORU2bw788oOm6i8mCTJT5FW5KO5TjkRCRExqZoiiBtCaoAvfjqprj+iW6R4j2qHm7Cg+hWpljGh7K0La8mMClYJb3H3zxzmH1cOajdfj8eJOta2oXC43YTIvv5mCDHSWgJI1F1V+1RRdWEmLjdihHUgr48vvt7O5s3HaDv5UsKSPFUrSG1GTIv27PnmPV58ahXfzj4rYFlAUfFOCbd6FUtZlnn9jfV8NWsnpaW1VJTbcLrqyjJKntLLcXGhpKREkJwcRnJSOOFxIVitZgryqygsqKEgv4odWwuprXXy1FMjuOD8jlgXr+WYXeKOj/YzeUAC990/jNT+nwEwdWgqEQVl2DftoQSPB0jseb8Z9r0JJwfWvvtwvecsvu0Z2k+bxI4vPG7RktnExk/WsGP+Nr43u7HXOGk3IINfFu4nISWCFh0SGHlmazKyogmLtHr2tmWIiA5m4dc7yDlcgckEb720lqGjskhMCcdkkgiy+q8rZrOn3LfDKRMSbGZY90R+XFNAt27JxAZbmDypHW+9vZE7n1nH3Vd1ITI8iPJKOw++spHVmwrp3ScVs0Xi43c30alzIkEWEw/M2c0bL66hfacELBYTXbomYZY8+RhkGR5/8DfWrcnjrsnNuO70dKwWEw6nm1e/z+GBLw7xwP3LuO/BId4+iqECyrprZGMrhIaxd51vDoLNJu+6J1ZHUXsZyLrlLZU1VvlNRibUrCZCxPVWFeKA7F17lP4ockQseSv+bTFJVDhc3sohpULeJ+1QlWox4hzo5e8INpvUiSwtHgIkkAxQCAqlXx6iQD1fJhVJ5nlWyvi15XT1vB2jhXkWx6AuAWycPFNL4ohtiFWAtBU8/gr+Lo9JMbQr0EaIDETUeTeapQAlbA02JyS3ft9lmwNnpDoMVNxsCbGpySSF+HDXeo5LocEeDyRZxlVQqj43IQaR+hA3cLz3Ep5dhNVTIlpGVpEeCo7WbWCEWEx+379ZklA+T+U5iYnTlVeyTBOuooSFxWrCWpW1QKs/KrnJRI/b0loneQXVxMZ4tJvkYAsRdTcMriNnLrmqO9/O2sE7r69j5k2eJNf/JPEBHu8P+td7WhNOAijk/fFuswmNQxP50YRG4ZafzsccZGbcXQOZ9+AydvxykNiMSKae24EzJ7blWF4l77+zkS2bCrjh2oV0fDORmTf2ZtCwTEItZhJDrd68GIU1DpXgMkv+BIio65lQx4cqBIjy3YeYTaqkY4o7sBaKe7DoGpwkeB2I5XX1vE4UVDldXmXQgqRSVjOjgnWvMYKYIV27Q/h3o0HeKGaTd95AvRtW7fAZJTa3m1mzdhGd2dJHfNTBZLGQ0KUvv/80h2PHqmghJMvV7iDb3f4lAu12F7fc+jOffb6NKRNak5WZSUx0MM69RyhJSOS5F9cAHrf+3NxKCgqq2OjI51hBFXa7m9SUcOITQklODmfixLZcdWV30tMjCfrJc93tH+7HYoYXnh5FrwlzvPefeNViRnaJ4avbOiDLMpIkUbPGlygytNfnDZjAJpyssJVVeP8dk5lARv+22MqriQ+uYsBZ7UhoFkXzCAvL5u/B7ZYZfXpLgqxmnA4329fksvSHffy6YC92m4uoKCu339afyko7n362lV8WVmG3uVjx6yEGj8xS3XfT2jyKCmvo38UTWnMkv4aqCjvnXdQZgPvuHcT87/bwwofbeOPznbTIjGT/kUpqbS5atY5l3do8EhLD+PCjM+jQ0VOxa/++UmZcPJ8liw/icsmcNbWDyuCd/dV2+rWN5JYJzbzHgiwmrj8jg1+2lDH7qx1e8sNoR12WfYahmFTQ6FzwrOs1Ll94h9Y7UNkxF70MxFj/crtTN8Zfcb2vqUsOGWr2ry5W63IjI/t5KBgZ8AqBoA3PUEIdxHaUZc3plr25Jswmya9yiZ7XiRIao6x/yn/FsBq37PFmUeRmkMmESxifXUiCqhA+4UE+jxA9TwZlfq1mE3a3j0BJCAlSrdOibNR6SyjXUzcGqwFRYoRmEcH1EiBmzT1NSAI5pPXekITrfG2I+kJahH7paqOEqUZhsOIzbKgYl6t8oWnmcHW+DrENd4XPU0tLfIjJ5IOq1aFuksYLSgr1jVWSJCxJsd6/taGh4nsuST7yIELIvyLqBjIeMi82xKLK8aF4KSnPRS9BqbJuhAeZvSSm8r0pumJJHbmoPGqxtK2270qfPed7/qEkYVa8cYrzqkhOCveG7NTW9bl7cgQhIRayo0O47uqePP/yWi66oDNtOr/ld48mNKEJJxeayI8mNApPj/qYW346nzaDM7ngtbHk7igib2cRX3y6jZzcSqae25G3PjqDG676kTUrcyjIr+bKixfQvUcyN9zcl4GDM1TtiVU63OAX++2SqZcAEZOiJoQGqRQasfoKqL0nxBK5OVV20sLVsbmK0He5fDtz5QHyghihqNbhdeNUdv5AjNX2jOjr/WXea87M9BED4UH+yjTg3WkENYGxscinIA0N8SmQcq3vfEuUL3zF7HJhrxP8qhkQlJManXK0ekgKDaKi3I4lIkn3d2tkDAD2Kt8z0irDYtLYFlGhHDpSzlezd/LxZ9vYu6+ED14dw7mT21JWbufgnFXYusaC7OC5umvy8qq818fHhXDVjK7ccVMfSjW7pi0yXqbkE48nx7zVRcxdVcTHLwwnMSmcqy/sQH5hDf3jXEx7ZgeLN5Vyx4f7+HpFIQnxoZw+tJizT8mka9tYyuef4m0z6vSFDZqnJpw82Pf9QoqKiti9ezd9+/ZVhZgomLv5Un77bg+bVubw1ctr6NInjbW/HaGizEZyWgR2m4vpF3biqSdHeK+/8orunD31GzZvKeCZ+5cTGRlM194pSJLE9s0FPHr7r3RsFc2IXkn8sbmIVVuLGTE6ixGjsgAICbGw9NfzuWLmj/yy6ADb9pR6187S0lpcLpmnnh7hJT4AslvE8PyLozl97JeMn9yGtPQIHEo+DEmiqtLBGb3TdefhjF5xLN1aRozVQqndqUrUGGhnXZuQ0OmWVUakuJ7XuNxeUsCIlFXGKJYWF9uzmiUv4SLuIEcEeeRMuKmuVKbgGQI+z5TIIIuK+FByIkiS5Jd/CjzrV7UwRltdqEeZ3anKL2Fzun0EiE7YDfi8QGyquXWrPFuUsBqj8rhKZTRFztRozqtyqGVYjdNNqMWkCWXx/DdCIEqUMSvrtd3tJshkVv3WkHwdYhuBNheaiWSE0Kz4PhXXOokLUfQHrU/nPwdx3G4NIaMgRJA/Dc32JWvCXURY6whBu8kUMGGwOVIdguOqqkWurMEU4Z/cSFt1xWo2YcZXjUX0mqi0u7zfndXk89QSyZ/YEIt3bdC+GyIJqa3mBx4CxCGEqnnbFDbMFOIjREgcq6qIE0BXO1Bpp09iOL/8eojJk9oBUOpwU1BUQ1CQCZtZYvO2Ag4dKueSizrz4Sdbue+h5bz7xk2ExT5r2G4TmhAIshw4wfefbbMJjUMT+dGERkMhQJJaxXHLmOYAzJ2VyacfbObay76n74B0nn9tDK+8uIZvPt+OJMGBA2VMP28u3Xskc831vRg0zHOdWZIot7tUuwiS5CEGFMZfcW9UYjy1BIhT9hEgYqIxUIfDgEdJ17oOK0LzQHmtSgEMJDgVFNY6SKgjNkpqHaprxBAXlaAXFHW9sqz/BhRFSiyXJ8Jl1RBDeOZRL2lr5/bx7Ft8wOslIaLi8D5Cw4LIbBblc2cVygCLxEdBfhXXTv+OX5YcIiTETIfWsQzslcLdDy1n+tU/qtrt1SqCOXd1YOfRGj745RjbDlcjy1BUXMtTL66hwuXm1jt8fqMtMl72jDstnmUbC7n5g/2cPjKTs0/LBuDWK7pSFhmBy+XmpebN+OGHfXz7x1EmntIcp8vNO7P38u43ezn46bD6prYJ/wHEx8cTH+9LblpeXs62bdvIzc0lOzsbm91J135pbFqZQ8+BGezbWcxpU9pxymkt+XnBXhZ8vZOHHxqq+h7CwoK48/b+TJw0m+TUCK67cD6pGZHIskze0UqapYTx8JVdePaTXTz14U7atI3jnocG8/nHWykrraVlq1h6DcngyVfHUFpcw5F9ZVRU2mjRIoa1y4/yyKMrGFq3zoro2jWZlJRwkpMj1HkSZBmTSaK4Qt/9vqjSidmE3zetTSQYbbX4lThXoBgyyprsSXbqa0/cC/Yvu+sfXiCSAop3iOhpEllHeIgkCKjXZhHK+WLohqoqmeBNqCXWFdS63IYhfXaXW0XUiLlHSjTtiZVNyu1OHG5ZNSeiHFO8JivFcAEDIkD0PFEMVrvd7ZcjxXcfE4W1DoLEkKO6Z1vldOlWdlPG6ktUq/asCRaJgAZo7qJXaESQ2bDCx8kC0dNDMpu8ISlSaLDvqwkQFmoSvEPMJRWq3yTleZhMusSHAndljcrLxGbguSP+22o2UVrnGaH1tBLL13vfK2GBMUuS95sSNzwq7S4qhWwjEVaz6neFTNEj+5TNKzFpq0KyaD3TZGBLsbrK3ufLDpF3rIqRI5oTU0ei/fDTAYYN8ni+TTt/LvvrNqWsVjNffLWDa67ozuCRfl1pQhOacBKhifxowp/C06M+BmDT/qsAOHNSW86c1JYVyw5z3WXfM3LAR/Tql8Yl1/Sg4FgVn32whQlntWXZkkPce+cSlqy4EIvF5N2dqrR78odHWs3eY25ZVrmWVjndKgJEhFuW/ZRwBQoBogjFGqfPk8NhdxrWhy+3O73KdYXd5VXY7C61YC2u1b/vfx12l0x0sL/ie/VlXfnsq8/JW7WElD4+g68q7whFm1cy44IOhId7CKOCas/cldmchAeZvZnx9x8o5eKpc6istHP62JYU5FWwcn0+0ZFWzpnQis5pwcREWomJDKKqxsmdr25l/KPbeOfaNix7tBsllQ5W7qpgzZ4KXG44O8NF7JL1gC9fR8knA3nixwKe/Hg3A7vE8+LV7XAcLqC2UwufC7HZxLSpHZgypb13fC6XmyU936GkwsGFj2/kvVu7YDZLTV4f/w/w1ltvces9N1CWX+33W3hd+bpTJrahS29PyFeoWeLL9zfRpnUcISH+4rZLF4+H1EVX9/BUFvr1EHmHK3C53BzOq2biLSsIDjaTnBrOvr0ljOz/EWaTRESEldIyG1HRVp5941S69UolKT6U5UsOM+uLHWzfXojT4aay0k6UJgTPbndRXeP0xrSLSGsWxcdLjjFzXBpRYUL57FoXH/5yjGaZ0RTWrXd66SeidRINK0Z6pcOlIi+U42J5SxfGOUHAZ3Tr5VhQ7CwxfEZpK05IDqkNV5CQiLKa/UINlfY8oTu++ykEu1Zu6JmqVU4XLllWzYtC/IRaTAE9ZsrtniSpRglD7S4Zu9tt6G2heLRISKp/G/X3aJWN9PBg72ZDSB3pUR8UAkTxahGN1Ib4Yejl9xKh9FMklMTzxTm0u3xGtEgeiXMk9i9JyDMiejeKz1Y08p2Cx4oCsaqJ+A6JbYQ0Ml+XpzEhibtb9oaqSAG+D0kkotxuXGVV6hOE37XhNcFiuE6AWNiYEIuKbLC7/HPWKD+LXj7iKcrxSs3mT7DF5OcJqoSjBAufQZXTpTpP0THU/VIIVp+e2T7GN+btpbWs+e0I4aFmhia4qdmyn9BO2axYlcPdt/Zj6dwd7N9fxofvjcNuc7F+c0vWrVtHrftiw7lpQhPqg5uGe341ps0mNA5N5EcT/hK6ZL+m+RuG9t7O2fdOZ9Pa/SxdvNL72/q1ubzw0imcf+4cfv7pAKec2gK7243V5EvuVmF3qUr/iUnXimocFAEZkT5lXlQLE0ODvASIG1SuyIEgxl8frbITEyzGsta/rIjZ76scLlUSQAVaDw9FFxJ3sPon+gRzqlC9pLQB3iHiJtHCHN/uxpvHfMrdM8OF9ssFpUiIHfZmg69TOGSHk2pr42qSD+ibzoP3DeK+B5dRvncLoalZOCtLKTu0n76903jyvsGAj/jQYvOWAqad8y35dQbm/O/30uf/2DvvMKeqtYv/Tuok03uvzABD772IUpWigooVFey9e+2Na+9dRFQUsWKliPTee+/DMDNM75m0c74/Tk5ykskMg4L3Xr8sH58Jyck+NXu/e+31rrdnIu++NZxL2wVhVk3aJEni3R+PkVdQR8fWEfQblIExwUwCMK4djMwr9mpbTXxU1zt4ceYBAKY/0pW4SCMNHeRynkrsp9cIjUz6jJJArR2i44L5aVUxB+410aVNNLa912FoO+O0rlUA/1vYuHEjDpuTix/pT1xmBEmJwZQV1FJ8rAprYQ1lxfXEJoa4J0EmrYakpFDWLjuOxeLA5FOadts2+flMTAmlQ5d4uvaX0wIlSSL/SCW11VamPriUvKPVANx/a1ceuLUb0VFB7Nxbxq0PLeOmK38hIyucivIGyssaSEwIxtLgQBQlvpy5k1tu6+61zzk/7KO6ysrA89JxKF4arkf8/if6c8+UeYyZuotHJ6TSOTOE7cfqeP67PE5W2vjwdU+KlzJJNrv6u+ZS45R+TiEvauwSsSoTUvV8Rz2pcVe78JmLncpXQZns6lXTb/XkWSHEtRrBQ6gLnratPqSETZSoszu9JtFKe8khRi/i3aCS4CvbV9kcbgJEGVMsDm+1hfK61u5s0m9EEOQx0lv10Xy6iXpyLvmkhiiqFmXyeNJidx+Hr3rFLknoBY8Bt3ql36gas3UawU0SqPempAOB7FuiPmT1/VWbsjZVqePvgvq5bCrN6HQhiRIaHyJDbLB5+XBgb7oym5rgcFTUusfq5kgRTUSI17/FylqcVXVow4MbbduI+FCOxaWKknyeNYPd8+yLRoNXyhZ4k0AeE2O5DbXi158XjF4jK8jUZKdVFOXKgaqUYV8iUumLdBqZWPNNp04INlB3so7fv9zJuT3jMbqOw7LzCFFhBqqLa3h38RG6d4xh4qBE9FkBn48AzgwkSWr0GzoTbQZwegiQHwGcUYz/9EYA2ozuQmhyJGvf+h1DaBCRZi3HjlVzx20LiIsz88fCIwwcKkuybS43fwX1DhGzTsNWlXdFvGrCm19jdRMgHvmu/OMPN+rc8mGzXutFgKj9Q07UO8gM9Uzq1U7ilVanmwCxq47N6pRU6g9Z0qu0rw4U63z22RzUEl510Hg6qKuzs3pdAXa7kx7dEk65faLDE6xoVMSHZGtMRgh6HWabJ5XIGuS5ZgaNxh0cajWCuwO2OSUeuLsXg/qnMH3GdnbuKSS6lZGr/jWSyy5qjdGV4qJWzyS4ql38vPw4V131M7UuT5CLx7Xmvvt6k9tWTjkIl7wnWGu3lvDQq5uZMiGbl27OdQcxAOaBc/C2hJOh9vlQYCuqQIrwnJsoStgsDuxawV3eTkDAUmtj5foCDuyYgiAIpLd+ny9+OkSXdtEE8M9HdnY2DbV2Dm0sxOkQiYk0kt4+howOsaQHy8+1yWeVd8z4Nnz6wRZee309j/yrr0cJVWfn3y+sJjM7kvadvT1yBEEgo1UkMz/YwonjNcTEmjmndyIvPNbPvU2HttH8+sVo0rt/Sn5eDaEherKzIjh4uBKQq8W88PwaamptXDaxHXqdhjk/7OO119Yzakw22W2iGp1f34GpPPnSObz4xAomvrrX/X6wWce9UwczaEjjNBrwTDaq7Y4m0ycU1NjlfqKkwUmoXqUE0WgamT37W4D2NVBVVAAKUa5WaSj9sm9FFZsqfUWtKFS2UZex9arQ5TqgMIPWTfqAN/HuEEU3eaHu//3Fp0rfr9cI7nM1aL3HAatTbKQQ8U3f1Gk0OETRq6qNxdmYjBIlz9jVkoDZpjLiBm8j2nqH002A1PqkroKH5DHrNO7zcUqnJvJPVcbYH4LcaRme76mb0DahQVETdsq9VZdQrVbde/WiRktMW08HmiCDV8qppHqtLmmrUSmIHBW1Po34VwhpwrxHQbHS8z3JH2GpV00L1CSM3YGjTCZhtfEug9QG7zRjpyR5pan4lkBW/q3VCu7fZlOkh9cxuwgQq9g0saKG8nvxMhAWZP+1dlHB7NlbxvkXfUdoeBBvvH8BjoNH3NslhOlYtvQoa3ZXMu3FQVh2HsWycxhhYxf63VcAAQTwvwdBasEIWF1dTXh4OFVVVYSFhZ1q8wD+H0MhP0Be4Vj2758RtBpu/mAo55s1jBo+m+pqG+3bx/DNb5d4fVev0bBNlZPpO4lQEyAWp+hVocVXbaEQIDU+qolSq/e/1QSIeluF/LA4RCptngE3JdizT/UA3dQKVVPkh/rUlADYKXmrVdzBsCqoaRspBzIWi503vtjONzN3cWhfOXaX94ZWp6HPBdlMuKcXhiAdF6XLv9c5x6rdbawvlgOrGQPjvY7Ji/xQ7VNSBUC+5If7fNQrsWrzN9X5a1XvF6lc4BMMciD+4ac7uPX+xe73n3tqINfc0NltVOhLfAgGPTU1NmJavcezl6dz43Dv6jJqqBUfCj76vZCHPz/iPn6dVkDvqgpQ1yCvTGq1Al27xDN4YCqDB6Vx5z0LOXqsmovHtearF/rx9FtbeO2TnZxYNZGo7l80uf8A/hk4efIk50w5l5O7iqg4Wg4SxGZGMPK+PvTp4SEeE03e/dEP07fxxVsb6d07idHnt6Kq2spXs/dQXmHhoXdH0tpFfrQNdxkjCwJVNicThsxCb9By4lg1P352AaOHZTQ6psl3L+LLH/bhdEokZIQTkxxGcJgBQRBYO+8gAoLbhNFg1DLh0lxe/fc5bhKyoM7aSOEmSRKL5h/h0P4ysnKi6HZeupc3R7jKhNms13pNItXkh2//a3FKXn2imvzwKjnr2kY9mfWNVOw+xqnQuGqKL5lstzupr7MTGuEt+VdaCVVN4NWEuCKbD/OZ4Jv9ED2FKt8ixejanzdSaqiRo9Uq7wefIcQhSs2mxeg0soeC+r7ofRoRBLxW4v2pajzbCu4yvWaf66q+N2qViVmn9WpfnaKjPnKDxj8p4av+yK+1Ndq/emxXV/NQ3yuFhGmK/GgqylWfiz/yQ626VB+HOu3mVOpQ9cJIvMqsU63UEFVlYdXEh69nh3pPUo3FTV4ITfiugPe4rouW4wFnVZ2Xbwg09vtS1ClK+o1CfMgf+pAT0eGN9qu+thoaq5OUW1nXjGJMrxH8prg15anTlA+Pmgjct7uUWyb9RkpCCL/NGU98XDDW5dvdn499fDO1FgeHS2y0STTyw1PdCA7yXN8ACfL34J8271TOZ/OuGwgNPT019alQU2OjW/tp/5hr9XcgoPwI4Izi+2s/AmQSRNAIDLwsh5/+vYqKghrmJoVy5909ee6ZVVRX2wgz6Ki2OdyBia/TuMUpuQmQY3VOjtU56RjhIR+KLXY3AaJONzlYJQeUBj+ERIxR24gAAdmJ36Byq6+xOd0BdIRB4yZA8uvsbgJE7VTvu7KoDojUQaYSfKqD8lMl1iipQQpWrT3BhCt+pLLSSmpyKEaDFrtdZOLgeNqkmHnx24PUVli44cVzG7UliiK7ftrD0ZXHiBPhznFZPHBzZ3Q6LZLDEzhoQxtrJhqMBpDkoPJ0iQ8ApyoaVU+QrFY7F171CwsWHwPAbNYxc8YYerkqA7krOgR57r3R1VRoqIF+bUL5fUsF53QIJzJYR2x444FFIT3yShrYlVfPqO5RTB6aQGKkgYpaB5iDcDgl7HaZ9AgxaQkL1rN0SxmfLshn46YiXn1jA926xnP0WDVJCcEIidHkVzmIjAzCnhBFRck9AETGvt5o/wH8MxAfH0/Xa3oCYKuzIR4vYM0XO/jizgVU3tiVEZM6+q0Gc/HkznRtF82sGTt4dupqDEYtA4ZmMHFyJwxJcrCiEB8Kwg1aLHV2YuODOaEiL33hTtEy6ig8UoXNIWGts1Fb3kBGhxiO7iyl3+BUrrq6A127J9Am2TNRUQyGTTpNIxPJnkMzGDpKTgMrV00C1cSHU/ImOEINWrfCzpd4sLgm8v7sD3xXcBv7afiQGD7GqSaVugBcpWU1AosXHmHlqhOUFtexc3sJBSdkc8il6ycRE2tuVHK2xubE4hCJM3vfC39ldAGOVTeQHuY9iUwMNiJJHgNWkP0KVq3MZ/mK45zTO5khg9MQRYm6Wht6vRaDUYsk0ajSi0EreBEgijpQGWtEyft6nqi3k2zWN1kpR/Fa0AqC29TV075nP4r6Ug31JFUxOfVNcWgOm8vkhY3uMSb+KJDTLtUT4vZ++u3ThT9D87OJlqTFqlGl2j5cdXkli09ZXz8qDl/iA2TSQ13GVjDIz63oMjb1R3wAaMODvVQfTRIfqs80ISbEWksj4sNZVQcqbxExs/EihHxP5O9ZHKJXbBCsIq7Upr/qPkB5TJTflHKLnT7vm119k0I2KpWsFGXU7h0l3HLNr7ROC+O3T0YQEReMMeJVjGOh+udhGLMSKbJIjBiazVsT2jJi3Ldc/f4hZt3WiiAX2Vb9c0AFEkAA/+sIkB8BnBV8f+1HPLbkauzxqeiDdOxdnkffie2x90tjwnXV3DqpEwIegzxlxSk33MieKivVLml0tV2iQRX87ai0exEglVaHewWo2uag2OKf+Q83aKmyOVl+Ug4K0kPk/e6ssJITqvf7nabgHZx6fkKnSnFR48+muBQU1jJ2wg906xTLJ28PJzsrAovFwYefbee+x5bz2MRM3ru1Dde/sYfMCgtkeCY6J49V8fLkX7HU2gmPCgIBnnhlAy+8t4Ul34yla1tZyqomPiQ/wa0keXuVqIkMm5+KC75QB4wGrcB9T69yEx+5baP5YsZo4tK92esYFfGhLa1EfZcvGpPDfS9tpM+DWwF48ZpMLukfS0Swzr1SqNUIFFXYGDN1FxW1DvI+7o1WIzCmpyddRVGH1MwdCYCzpp5Pfjnq/vyWUUnc//gQWvX7ktXrCnj06RXM+mYPTz0xAIOheal/AP88GIINpPZLJatXMqtnbueXDzbjsDkZfWNXv9sPHprBYFeZ2hqVEqDQ0vTKp04nsGtrMSHBemZ+u7eR8qO6xsZ3vx7EKUJUUggXPjaA2IwIRKfIvhXH+eWl1ZjDDNRWWhk2XCYy7D5phv5Q63B5VdhFoowaYl0rn0drHdQ5nCSZGj/v6pV4pW9TSIOmJon1DokQlfGnukztrp0lrFyWR96xKkLDjDzwqJzy49uSQnrbLHZ+nrOfPXtKsdTa2LWzlEMHKxrtMyzMgE2Q/E7mleMsdvkROSWJCNVqvUK66DWCO83lmEu9kR4W5KUwsFkcbNxcxMoVx5nz5Q4Kym1EheiY9v4WLw+C9LQw5i27Eo075UbjrabQabA6RDfh4asyVHxbyq3y3xP1di+FotKeQjSov+47BJm0GixOD/GhHGNLJ/n7qhrcJBdAncPzOsw1Md1U6lF3qv1KdlXZ3ASIP/LFLopoEIh3mZQ25XOiPEtGnYDVITXa1uTjT6J8R1FzqNUe6oULoVwmIJ2Roe731ESLpgl/GX9QEx+iqgKLlzeH3eEmKJyqdCah1rtiiT9ogoOQHE53yozGx4i4OeJDUCuwfEgZyYcgFRu8Pw/K9i6T7e8q+D5z6muolLH1fd4EF5nqa45rFxvvweIQPeSfal9tI8xc/8RKslNDmf/ZSMJDDVBSib1kMqLVjjFLJm0KCmqJNUh8MXs3/TrHsHRVPm/nhvPAyPgA6RHAGUHAoeM/jwD5EcBZw8GTekBPSFIER/bX8lC8vEJ27v298Wd1ua60wc+7slO6QoCkB2uptovE+KkyAhBn0rkJkOzwIC8junBVcH6s1uEmQA7U2BsRIE4JnE6JAhWZkuQyK6y1i4S4Arkam4NQPxUO1KoUNdTxmtOPdLap9BklGJ767kYkSeLnWWOJCJevp8mk4+6bu3HwcBUffL+Hbe/0IiHKyJIFRxjeTy7ZNsleReqNv6HTaXj0g5F06CUP9Ls3FvHmw0s497Jf2LJrClqtBrMkECe0rHv2R3wAHKryBGjpoZ6V0bIGu7uCjhL03HpjF3r1SKR3n2QSE4IRBIEQVUBU4xTdbZvKvVfARYuVyy/I5F+vb8EqOhEleOjzIzz0+RG0Go+R48H3e3LZK3vIL7MiSXCw0EJ2okdSrBAfAKHnz3e//rKhP3klDUT2bE3rrAicTpG3nh3A9wuPMeub3ZwzOI1rruogtxFQfPy/wKxLPnW/HvPhrQBEDxzIiBAD8z7cTEVlA63bx6A3aLl0RAbhrhQLdfpctKr/inD1S+qfvsUhIkkSeoOOnr2iuGhCWx55cAn/mrqGB27tSlSkbHh628PLqKuX1XMTnhlMZJI8MdNoNeSek051ST2LPtyMRuvxqgB50lDaYHdPEmrtTq+yq6dCgcXb/DNJZeRab3d6TTAbnPJkxKwTqHd4Nx7iM8EVBAG7JJF/vJqrL52DXqchPDKI48eqqauz0yonklY5UeS0iUIbZiDBNRF+540NfDJtK5Z6OzltogkJNdC+QwzPv3wuTz25nN3bSxg4JI2Hnh5AfGJoIyVJkauf91a1uCbDVoebAFGvRieYDRS5Kr/YRIkDlXKflx1uYurUVXzw/macTomIYB0X9o7mikFxdG8VQl6JlRV7qtC0z6TgZB3/fn4NBQcqSPHjwaKoiBRDUTWZCx7iAyDKqHETIIpCUU1Sq58vCcld+cXXUFJNOqhJGnX5YuV6KWlLJQ1/rvRskknL8XqHezxXV+JRKPg6u8fg1V/55KZgdfznphjhOrUJqXxuXtP0ZlI9TgUxxDNu6Zqq9OPTvjqtRtBp3coSX3JDIT4kmx1HuaeErjY8GGcjjxEBjdkzthuS5IUEu91JXZ2dkDAjWtXvxbeyi0OlglUUG/7SVnz9XzSCQL2f6+dvMUlR/6aHBjH7uz1s3V7M/J8mEJIZB6WVQGPyJzXRzGuf7aHSpSCWJKiqsWPMiMe6/SqMnQLprQEE8L+OAPkRwFnBxNnXu1+HxIdSV1zDJ9udXN9Ji9Mpsnz+YR76ei/19XY6dY1n0k1d6JwcyrYKj4FWscXpJihMWoEwVX54qdXpDphqbE5CDVr3ik+EQUtSsBwUq43o/OFAtd39t3+cZyBX0m2STDovAsQf1AN2jSrYVMvBlcmArwGYL3zTZ3yxaukxxozIdBMfalw+vg3vTd/GwQILEcE6LKrj/nrhcSqqbDzwxlA69k5yv9++ZyK3PjOIF27/nU+nb2fyjV0AKJbkY8ivk9toF212h8cmSfJoxZ1Or1WplqDaFYgp+eFxKWGcnxLm/rcv8aHApNNCXKT731oBxKNFhIcaeGhSW576cBcajadCoPLVrlnBXPbKHo4WN/D1/bnc9fEhBvxrKy9NyuKaIfFexIcvUiatIkU51eO3oNVquOKm7lxxk6eCRnTsG6d1/gH8M9Ht/FZs+u0Aa3/Yx9of9gHwxXOrmHhdR266u+dpt1dUUEtxUR1PPj2Qkee3orzcwmsvr+P1D7YQEWGktKyB2DgzXXokUFRtcxMfarQ7N50/3t+EyaSjsrKBsLjG1R18UaVSpYToNFhck8iTqgmuP+KjxuZ0y9EtDrFRqogv8eGUPPsK12vcE/zqaisP3r6QyEgTc+ZfisMp8dyTy9m2uYifvt+L3UUi3ftkfy68vB0An36yjW49E3niuUEkJMqr50p/8sij/bnqsh8JMuuJT3SRQ67erNzm9OqTq+wehYSaCFHIZ990HEXtUtbgwGEXyT9WxVfLtvH+O5u4Z0wyE/rH0ibJ5FZ1AKTHBZEeFwTUc2JIO/79/BqO59cQnimr9Lx9TiQEQfDyglL7jChyfpDVCyE6j3Fqlc3hdW5Kmovynih5CGs1ASJJnlQqZUKpVnP4UzjGBmndBIhJK7i3D9NriFHVKFX4vzC9p6Rpqlnn1X5LcaoqN2qoh91SVTyQHCKndwTZPXGH2nPD6iIaDJU+E/9mYGx+iG8ExXtD0mndaaOCKLkJCnW6qPps1cSHOr1FMOjdHiC+JIjaG0SsaVyuG9fzrSY+5DecXv4jTpVaRRcl/6aOHynn6ZfXMeuH/VgsDqKiTNxwbUcevb8P+iCdl4rGV9lRrvIAUx5/3wp5EUad+3eofq59U6+U1C41GfLc2xt4/umVXDyuNYMGpHq2zZnu+WL1/QD06ZfGJ5/v4NXnh5DdKoJrpswlPenU/WYAAbQEoiQ1W9r7z7YZwOkhQH4EcFYwe+InbgIkJC6Usv1yScf8CitPXP4jJ47XkN06iqSUUOb+uJ8fZu/hX88OhL5pXquBaoVGtV3yIkBqHRKJZv+P8L5KC20iPIO1EjBeluWdTnGguqpF52MX4Vidw73/CpvTS31ibMJ13BdOqbEDelNQqyrUq29WP54lADZXsHCirIG9x+t4oGu8OwD4eVMlhiAdXQekNPpep77JmIL1/LHwiJv8ALmqTkugdTqpUfW99Q6RcNdKqU51rmUNngDN3/WyOkXMeg0W18kG2R2EALV+yBWt4PIXSY6lcsUuJg9L4KLe0Xy9uIDPF54g/6S8CqsRoLDcRmKkgU/vbMNRyYRTcK16SVKzxEejfabKZZ2jgbKSu+XXAeLj/zV+uek9AJxOJzFZyQh1spritZfOZewF2Xz0yTZeenUdeRaRlx7r6/6eosDwnQR4lVJ1kZfR0SYEQWD0mBxeeWEtI8dmk5EVSUZWBIPOS+f5J1ZQsOVko2OTJIm9y/IAWLPqBD07yYH+V79dQmxmhHs7rUuVAd7qFHU/rCY+1JPZk82s+KsNQ5X0hyDXzEY9141ytecQRSrKLNx27VwKC2r46PMxBIcYsIkiU187D7vdyfNPrOTHb+QqNDu3nOTCy9th0AjExgdzPK+aefMOUV5nx+SQGDMuhyOHK3nw3kXkdo3nelWlHLWHiXLNfSfTVXaREJ3GrfKTz8mBU5KIMuq90mY2/HGUx+5dhMV1/a4ZmsgT17VGEATEuga3F4MkSRQW17PzmOyRYKk6DkBYqoe4Unwr/HnHgKysUcYzJV3U4OW75JnsK+ONcl5OSTZxhkbWDYhIXiV+/Xlm+YNVRXQsKfJMijtGnr6HR4VNJNKgIStMJiWU9KwKq92d+ng66o8zBUuYJx1F62eiIY+zLuJCPbap0qQUCKep+tC6zDrVRIbyPJ0SalVqgw3JJiIY9P6JD2TiR2yweRQgThGtyhjVml/itX1QK3khJb+gln4XfEu11Un3y9oRnRZO/s4S3nh/M4uW5fH7z5dgcilha32qJ/mmCDklGnnW2ESRYotMUCmp0r4eRco1VtoTBHA4RF759xpmfrKdayZ34j5Xta2gyFdBWUdxkR4KoiOMXHdle26/uSuCIFBw6BY4fjKg+AgggH8QAuRHAGcFF7x7O4pw1dagR3TCuDYCj101j9Liej78fDR9B8oMfH29nWcfWca/H1vODZ+MhvQIr8C7bbievVV2bKJEqVUiRUV4FNY73ASIYg6nqC92qCrHYHG4Ayo1rs4OZ+ZBmQCJMmo4WisHk9V2iHfluSeZdByrU973JmB8EarXuvevXk1TB8pqibDiLK8+X2UQtzrFRiTBwCFpfPLhVgoKa0lKDPH6bPoXO0mOMfL8N0dJijdz8bjW7s+0WgFJlBBFCY0PlyA6JUSnhFGnJf5Anvv9XNU2RUFpAGSaT88fBTxBitoTIL/WQ6zEmZoPkkOcTkSLjW27y7jnqVVU1tnJP1FLRaV3mtSQrtH8/EJvHroyhyOF9YQboaq4hqU7q1i2q5IbPzxERbWNK0dn8uxbi0lPTz/tc1EQID0CUGPu3LlU5p1k1mejuWLSr6SmhBIfH8zj/+qHwy7yzkdbqL2rOyGncHlXJgM6jUBaehjh4UYWLTxKrz7JbHURHA8+OYBQVV824Jw0fvl+Hyf2lJKcG+N+v/pkHb+/sxEEmPrKuTx6n1xJ6cbLf6JD9wSGjWvNoOGZgKzKkI1K5b7HoOp2KmxNT9YMGsFtIK1MliMN3n2W2vehwSl5EQyKksLpFBFFidtu+Z0jRyp5csYF2JJD2VLeQHtXCep1K0+4iY8O3RMYc2V7TjY4STXrePiZgdx1/VxefXa1u+333toIQLtu8Tz+7nDMwQYvs2tlPuo5PvlvsIsIUvrkWpc6JUSvcR97uUsdYNRq2L+mkAfvXMi4vrFcPyKZ3PQQosMMiHUNjabpY57exqrdasJ9DyHBesZ1iWeLqz/zZ9ipLtteanWC1en20AC8Svz6+qw4JMlrMtngKl9rc3rGJ4VQMGoFN5nh1aZLzaFcE+U6tGTBsdQqugmzQovDiwhoUBEnqa5x3NdsFiDS2Py4c6pFBXXKzn8CatNatcWPMryHGnQIuJQ+6vQNP54W4KdErUadZuPaRk1e+ZSkVZe4VxMhos92zREfxrR4twfIky+sodrq5Kp3RxEaI7fdZlA67c7LZNad8/no0+1MntLF6/u+pIdWELyeU6V0cnmDt3K3yuag0uokOsh7+uIrHqqrs/PsvYv4beFRHn16IM89sRy/CHvFTYAUnazjtXc34XRKREabeOrxAYTGvA4x/r8aQACnC0lqWb95um3+k2C32ykqKqK+vp7Y2FiiohqnhP5VtGy5OoAA/gLqS6sxx4Qz7TcnR/eWMfnWbm7iA8Bs1vPUi+cQEmJg6cdbAah1iIxINjEiWVZvqCsh5Nd7p6GYdFq0goBWELyk1oYmnm6HjyTy6uxwxqQ2rm7SHML0GmyihMUp/19pc2ITZW8Ko1bA6BPA+ivX9mdw3Q1d0Os1DL34e5auPI4kSRSdrOO+x5cz67t9FJRaOVHp4KfPRxOkCg5uuLYTdpuT1fMPN2pz7R9HsDY4uPKKdn73eTInze/7kii5/zccL5b/VxkpqlUf6oBeTXykhwZh0mkw6TRE6wRMkoRJkghSldcVLTasVifPvrGRFesLMUtObh2TSrcsD/kzrEcMT09uiyRJrN5ZzjvfHWLoAxvocs9m7p1xiLwSK9cOjmXjN+fz+U+H/hLxEcA/H8OunHpa2//yyy/k5ESRmiIry+JV6SXXTeqI1eJg0/oCAKKCWrbmYDTqmHhlez6bsY3f5x/G4OrQamu8JyiDzksnu20U3zyyhD1Lj+F0iNRVNrDhB5koGDIsg+Fjc7jxTjlVKzLaxIlj1bz/whpPbpgfOCUPUWHQyP/Hq0o+VqhUImqVQIHFSYHFSalVpNTatLIlwaQjwqBBlOCRuxfRu+3H7N1UxKCxOaTmyMHOVd0+oWvWBwD0G5zKk28PY8zl7di15SQzP9xKg8XOJ5/v5PF/LaO+Tp4kRUSbyO4UR0p2JIIAcenh7D3Q2PhUlKDM2vj86xySF2GjoNYuulOAALrEhpK/tYQJV/3EmN6xfHRXLgPaRxAdovOatFbXO/hjYzE7opPZfKSOK6/uwPI1V7N8zdX8Onscv319ITuqreg0glfKoyRJ7jQIm4sQU39ebRepdhEzB2oc7KryniSqx0K1clCD0GwJXTXUHhzq0vNqokF974ckBNE/Tv4/TK9BK3jSGJQFBH+oVqVaKWazTcHpx9vhbMAaYjr1Rj44lQJUrfTyt6kgCPIHyv96Heh1OLRanEYXcdqEWbGb+BBFEEUEh1NWmfgQGmqIdQ1yexpNI+JDoypr76iqQxtqdv9vyvWMnzabk9k/HaTzmNZu4kNBYptosvunMnP2buyi5PW/GlVWB+UNdspUKUlWh4jVIRKs07r/r7Q6qXQRmGUNDvf/al8QvUZDWFUDN1/+E0tW5vPj5xfwzM1dEIvvbPI6nKizcaLOxrszd8i+aA/35bW3NnLDLfOpKLqrye8FEMDpQkQ6K///r6O2tpYPP/yQc845h/DwcDIyMmjXrh2xsbGkp6dzww03sGHDhjO2v4DyI4Czgt9ue4cL3r2d0hMR1JdWEZIQRUjRTkRRYtC5jSeeRqOO/ueksWrNCeJdlQRWFssrYQPifEoJmrQ4JYkcle+FQatxl8qNM+kpVg2iblM2i+2UK0gZITq3+kMdqKeYtV4BX0tzjZuCTfTIx22iJ/ddvRqSFmJwr74puw4NN/LBpxdw87W/ce6479HrNdjtojun/PLL2vLGS+cRE6IHSW6rbstBBoRBSmoo06auxm4XGXh+KwBWzT/MjBfXEBcdxCWh1fgucezPq2bqB38gijDp2o5k9pXTZiRV8GLzWRGKcMlSlXzyEKcTXAtVe1U+JGojVLOq87YIghz0AXu/3cB3ywv5ZO5xTlbYGNErlg/u78yo+9ey73gt43pFc8/YZDr3SmX6L8eY8tI29ufVkh5v4rxu0Tw1KZv+GUFEBMvtRY7/9dQ3J4D/Vzjn8mfcr/W+sqgWwm63YzbrKToppzMkxHvID7NrJdvhIgsOVnvIP7WvhFt1oZpk3nBnd/btL+fmKXPJyIpAp9Pwzcyd5ORGs2H1Cdp1imPU2Gyuu6krj923mB+eWeH+riu7i1EuBdjk27oTEmpkyLB08vOqueWa33j0lgW8NeN893dq7N79mnpSGulaidUKgpcP0qnS+HxL3PoKG0RRYvniPEIjgrht6mA69klCEAQu7yqn6Ww5fDMAGo3AuPOziMuN4dfZu9m/vZibh39NfZ2ddv1TGH1bd9LaxWB2qWIiNPDTtK38OmM7S+fsxxSsp9egVO59dhAWrcZddUJRH1TYRBJVVWxONsimrmozVwCTKg3vwKEKnE6JN6dfhGbzTnCp2MIvXuLe5q3LM3hq9jF4aQ8AXbsnkJwShsUhEpUgE7jqyaDizaGeKKtJjwSTjiKLg2N1yuq/051OtKvKTo9oo7tNnaZlvhglDU5ig+QxTjambVl1F0VJIy8GNL2dmihTl4hXm5n7g7/KRBrBUxHIolJAOPycp/otpZKImhRS/CLsqt+9wZVm6zXeq8YnxbhTXeLdoNEg/ckJiD/D9KbgNBpQhKeN1B9NQE1iqMdtdZUZsd5bRakN9/Rf9uJKr88MKbFyu8FBSBqB2nILDQ0OotPC/e4/Oj2cA/vK5Lb8KFlqfJRl6nTbWFeqjKK0MrlUWRaH5GVgr8R+GWFB7NhZwoTLf0SSYOHcy+jV1uMTJhbfiSbuLa/9nSj0kCKrVuXTr38Kt9zZg9SMcO6883fWbypkypRt3HbbT4SHh7No0SJmvHIdr9zViYSRgXgigAD+Kl5//XWmTp1KRkYGY8eO5eGHHyY5ORmTyUR5eTk7d+5kxYoVDBs2jD59+vD222+Tk5Pzl/YZID8COOPo/chjrlcRANQV1xKS1haNVpZXlpf5L9VWVlIPgsDhGgdZoZ5H8/191V5Gc6NT5BWZA1UNXgRIpdVBgUpTqsiCa+ySmwCpsNrdBIhTlNikOpaeMXK7naN0nKhrerWkKSjxorIS5pQkr6AJ8Dq+P4vuvZI4svcmnn1+NfN/P4LNITKgfwoP3NGDdors3U+Q8fvvExk58mumPbuKj5+TvS4kCZLizaz65gKE4yewFZShMRkoKrfS9bZ1WF0pMQA/fr8Ps1nPlk+HkBQrXyt1kER6gvulrQm5rjrOVVYfwxx2cN2TGqfE+o0F/LHoGL/9sIudR2oJMWmZMCiRW86LJSfJBLZ68ovreXRCGveOS2FnjZaBN69gz9EaxvSM4uWr0hmQG4YgCG5Pj4ov+5+Wv0cA//8gSRIOewNanbFJv4WmMGDAAD777FPWrjuBIEBMjGfVeM5P+9FqBTp0iTvtY9IbtLz+wQhWr8pn/s8HcDhEvpi+HYDUjHDm/nSA16euRhQlclpHctMt3diyuYhZM2XzX61Bw3OPLqOmysr5F7Xmqus7ApCcGkaQSce6lfns2lZM+87ysYXqBWrsEjbRm/hoSkWnTBAtTok6nwlzsE5N7HjeV/plZYJdcLgSa4OD+98cRvuechUqhfgA6Jr1gZsA2VVpIywyiCc/Hc2quYcwBuk4d0IbxCjvFef7+n0GwOQ+8NllE5k9bStrFx9j2bzDrF2Sx+1TB9Pz3HREyXtirpQdVgh2wE30JJl0RKnK3u4rryelaxxJiSGMHP4FvzzaHkWcW/Flf4xp8jXt3TcFZh8D4KH7ezN6dHYjwkgxaVTgK25QjLAV0iA1WK8iP2QCJ0gr0DXK0IjsUHt+KKmYakWHktZU0uAkwUX0mHUa9/1RzMWVscvfs1BtFxuZwYL8DCjPRZhe4/VMKQjSCmQE60gJ9aRyqdUd6jSZ/2YICF4+FMr1UN/L0+xWAO8qJk1l3KpJDbepqSh6GZx6ER8WqzvvS6xvkF8rlYRUY7rvwoYxJ8WtFlPSasLDjIRHGCnYU0KbQY1VooV7SsnKkIkRvUbjJkAMWg2lJfUsXHiE+noHnbvEEd860qvv9RdFdIz2KD73VcgxZTuXv9vvi48yYdIvtMqK5Nsvx5GUFIIFMDkcjUgPBcmJb3Gi8E4kSWLrlpNce30nnJLEqDHZzMkM56P3NvPEE8t58skoHKo+buzAJEYHX4R54By/7QYQgD8E0l4aY/Xq1SxZsoSOHTv6/bxXr15cf/31fPDBB0yfPp1ly5b9ZfJDkKRTX7bq6mrCw8OpqqoiLCzsVJsHEICbALHX1bHlrTfIGjOO0VdqePfKH+ndP4X3Zlzg5YC/d3cpl17wLe2GZ5Fz6TAA4pK8y5r6EiBrS+QVghFJ3vJUJUjbXWmjT6xMjlTbRa+Ar3u05zv+CBB/5IdSArDGFcCpg/1I1SqEmvzwhZr8UCs/FPgqP9TtgecaqD1EFAMwtet55LItRIzqJW+75aB83O2zAFi79gSzvtyJKMJ1o9IYPiBZ3m7TfgAcOi0JV6xAFCGrdxI9L87FHGHk4Jp81szahSBKFP12Pkaj1h0c6SI9hn2Gfu3lv1oNkqqU3i5VulJKsCfQDXPIqzobtp5k9OU/U1ZlIzJUz7kdwhl/XgrndYtBX+9t0Nb2tg1EhuhonWTit43ldEgLZsYPy8ncc4fnGgTIjgBOgZFXP4/FbqPy5BGO7lxMdelxdPogzKHRhOcYELQCoUFy2dmQiCBi08OJSQ3D4pBoqLWBBPVIIEqsfnsDllobI4dn8e2scYiixLwFh5l88zwGnZfBS28M9dr3iXq7l/JDtIvkHasiLsZMVHSQ1yRg+/ZiXnxyJTu3FWMM0iKKkrvqiSBAcIgBk0lHj14JLJx3BIeKZVTKlWZlR/L6RyMYO+QrAD5cfAVv37uIQ/vLufuRvgw9P8utXABv8iMzxKOYU8qc1jpEr0m8uj9Uk5xK/6V4aag9Qcrq7bz91EqWzT3E16uuJsis54IOHzd7z77Y7KkkdlW3T5rdVo2vtkxm6U/7+OTfa3DYRbr0S+KJd0ag1WnIr5f7094x3r5QR2rtXqk+ALEu402lDz56qIIbLv+ZrslBfPeQJ3VQIT8ATpRbuezhNYhBRn78ZYL73iqTZYNqbKtTG0JqhEZKGfVkuMEpylWw3J95TxdFydsIUkm/tPmwKwoBYnF6K13Ufi/qcaopMswfAfJHoWd8bR+hd+9HQSeXp0t6mGchQ01++EslUd5TKz/8TZTVQ3C8S4GlrvaiqEp8fVYEQVZ4KMbdXp4dDv/KD/Am/f2RH6dS4bg9cCTJbWrrj/xQqz78Eh94+38oRyWIkld5W7G+AadTZP6yfDZsL8EcGsS4kZnk5kRhLyxzb2fM8TZKV66Bcrz3PrqUDz7ZxmWvDiMhJ9q93f4Vefz49HLeemsYEy7xuIiF6DQ8M3UVb767CYdDRK/XYLOJdOwcx5vvjSA0zky0yVulmxRsaEQaKtfGKMCsb/dw3W0LGD4kna+mX4DGdb+Do16jOdjtdvbtv5VDhyq5cNx3fPHlWAaeI5M4Ztf1LCuu57d5hygvt5CWFsZjjy5l8tgMHp8sn1OAADnz+KfNO5XzWbt98im9v04XtTU2+nSa/o+5Vn8HAuRHAGcF3e58EoCTm1ZRuG4p931/MebwID69cwF5O4oZdG46197QhfjEYFYtP867r62nrt7OhdPGY7d7zG3UBEh1VRAREZ6BO0klUVYTIAUWJ7srZfKiXYR3J6MEfU2RH2tLrF6B+5AEOSBLC/YEhKdLflickjtfWh30KfGWupqAOqdckXWqSxsqW/ojPxYvPcaH729h/boCtILE0E4R3D06mW6tZGJCGNHL/Z0IgzyBWrs6j4XLjiMAaY4aRvWM5o6vC/nul0Nk90nm4qcHe03Cjmws4OuHFnPjhZm8dnenU5IfCvyVaqxscLiJj0NHq+g/6hsyk8y8dHM7OkRK7uDSl8So+LI/034vZOnOSkprHJzfLZJHZx5Erz99M9YA/v9i5NXPU1mSx67tc6ktzMMcm0T2yNbYaiw0VJRTX1KLJEmYjXK50bpyC2X51W4llD/o9BocdpG2baOpqbFy4kQtfQem8Mq7Iwj3E/CsXXeCZX8cZdvmk+zdWequ2GQK1pOQEkpiWhgGg5Zl8w6Tlh3BuGs70bF3EpVl9Txw6U9cNakjV17TnrKyBq669EcAhg9O5d4bO7F+SzFrNp1kx94y8gvldBxBI3D1VVfz6KOPsqnuRawWB9OfXcHK+UfQ6TT0GJjCtXf3JDsnyk1++CM+wHsip0xKSq1Ov8QHQKtQb6FpSYOTf9+ziPVL87j1if4Mv6j1KYmPP4Ovtkz2+ndttZWX7/ydgztK6NwniZGX5DLq3FRCQ41eE1V1ekSZqxSnuq9Wb/v27XPZerCKle8OcL8XMvw3AOpXXAQ6LYvWn2Ts3SuZentHRt3Yi9AIz2Tf94mqszu9JtfqublyratsnnvhS4A0lYqknsTbRMmL4KpVjWd/hgCpsIleJqzrS+WxWv08nC75EWPSNyplCp5rr34GlfulJgt0fhYOWkJ+gDcJpYZy6DbVcfmSSerjiTTq/B6vP6jJD99zUH/VKHn2rZAfpyI+wLvSjORwsv9QBWOu/IWDR6qIiTVjsdipq7Vz5aVtmf72CAyquAY/hJOCmhoboy78ju07Smg9MJWotHAKd5dweEMho8fl8PY7wzHqtO4qVy++so7nXljNPff24trrOxMebmT58jwe/ddSAH5beLnbs0yrEUgKNrB+YyFPPreKuFgzb71yHmFhRhwOkZ9+Pcjb729i3YZCrpnYjmlvDcPQAkPyfXm3cfRIJRMv+oHKCjntR6fTsHXbZFLjg71+K5IksXXrSfbuLmPHkv18u6SAfh2i+G1N4ypbAZwZ/NPmncr5rD5L5Ee/APlxWgiQHwGccSjEhyRJ7P78bUKS0kgfdiFjLj6Ctc7GjDsWUJZXhdM1uAgCaA1ahjx6LvEdEpBEgdpKmZwwhVqRRO/AxB8Bsr9UwGjyBDVZIZ7gTU2A+AYphSolxr7COnb8dpC9Cw9TW25Ba9CSnhnO+RPbccnYbHTNKD/UQZ+/udHZJj+++GInDz+4hJjsGJL7ZCHanRxfcZDak9XMvLsNI7pGeZEfi+Yd4tb7F1FaZkF0ShiMWmxWJxGhemotThwOkaveHE5KB2+pviRJfDz5V6zl9WzZdWMj1//kXYdaTH6o3xvcdzoVNXYWvdKLcMlzHwPqjQDOFkZc9W+W/fACNmsdmcPGE57RhuSu8mqnOdTTx+QkykSqQSPgdIhUFtViFQSCgg0gQLnFgegQyTBpCA43snP5ceoOlqPXazn//Cy6dE9AEIRG8n1JkujfdQZ6g5ZuvRLp0CWOnNxojp60UJRfTWFeNcX5NVSU1jNoTA4XXNkenauvkCSJ287/lrKTdYwYlcXr7wynbeb7mE06Xn+qH3c8vhKdTkPHjnHk5VVTWFjr3q8hOAi9IPLotFFktIkmWCdQWlTHmkVHmfvVbooLahl+UWsGXNiarHYxhOgaG136Iz7Au39ToHg6KOTHHpcx54GtJ5k6ZS43PTeIviNbMal7y1UcpwuFAFmQ77mvBVsL2fDeOiqL6oiKNvHAE/0YMTrb/blacRDlUnsU1nmX/9ZpNGTuP8YVz2xi99Ealr/dn7BgD1mkECAAdavHc/3zW/nm18NoNAJdusbz8GP96dpdThf0F4gpSkCnKJe/VV9rNfkRotd6TdaayhSptYsYNILbY0qdatMS8gNgZbF8DTJcY2yw6vloKfkxLj3C/Z5Cbqirganxv05+NJXpopyvcow2p9Ri8kPwQ4Zpnc5GVV+aIz7q6+20H/glglHLA1MH07ZTHDabkz9+PsDbz63ipms78daL53odc7nrmqiJQQUnKxqYOXMHX329h5KSetLSw7n66g5ceHFrtFqN+/gtFjt9enzKhEtzeerpgV5tHDxQzpDBX/LCq+cy/pJcUkKMHDxUwRPPrmTOzwdo1zaa/OPVREcY6JgTwZa9FRwvqmfQgBTuuKU74y+fc8qURaVMvShKXHDhdxQV1PLQY/2o02qw252c1z7Wkz6MbLx696Q5zFpwHI0GspOD6ZAZypXDkhnWI5bQkfOa3V8Afw7/tHlngPzwD4vFQnl5OcnJyV7v79q1i/bt25+1/QbIjwDOCrrd+SSSKLJj2stEd+hGcv9hjLn4CFmhOiRJYs+GQpb+uJ/6Ohvteicx8MI27KwTqa3zBF1Op8qtXkWA1FUFERrlSYPQaFQSWT8ESLArIFOCNHWAppAfFSfreOuWedSU1ON0SkTEmcnsmkBZfjV5u0rp2DuRR94ajjFIpyrP5z/oO13y40CNo9F2gNvETo2esXJuuzowO1pYw5hBs0gdkE336/u5B3/RIbL6jT+wHj7Jrje7I42UyY+5cw8x+frfEDQC3QakcN29vUjNiqDgWBWfvbGBtYuPgQQ3f3EhET7ldAG+eXgRJ7YXs/PAzX5LHhpdgVFMkGcSEKTzGNL6Iz9iEt/k8auzuf3CdMJG/974AgYQwBnGyKufp/j4HrYsm0nGeRcSldORpC6lgIf8uK6zxv07VP821fG1Qmqo5z8K8adXbWh3/UiUnuJEfjUjBn7JM+8Np/dgWWY9vN00v8f63TZv9cK8r3bzyYtrQQCdVsPT/x7EYw8uRasTcDgkzj8/i5dfG0pYmBGnU+Tbb/by4P2L0Bj0OK129MFBZLcK4+NvL3SbCu6pbAC7kx8+28mvs3dTVlxPZm40l03uzIARmQAku6Tk6gmp2jPDH/mhJon3VtnJCZPbmDF1FZuX5fHm/Ilc13OG3/NuCq+unuT1b8XjoyW49perAMgJl+9EeWEN89/bzO7lecz+dQKtcz2y/ZIGJ52iG1cCK6yzolMZce7bXcqUS+fQqVUYP0ztSczo+X73bT80hcLieuYvPc7bn+1C0mr5cf5lXtv4U4GooS5laxNFQvRq40dvAsRX6aFAfZ+aIkDAM5Yp1W8OVHvG1+bID4PG4yuSHeYJ9JUxQa1U+Svkh5roUH5qaoJITQIp+xRVg5WSdqQev5TroTbnVD/Wyl0XVf8+E+QHeIovqTN9lPNpEfnhguQ6V7l8tes7PuTHjK92c8M9i5jx2yWkZER4HduXH2xh1odbOLH3JqJcCqVydTqWD8GgJoIspzDL3bD2BJdMmMPCxVfQtm10o88vGvcdmfFmXnl1GIuWHOXmOxcSF2vmyUf6MbF7GEdO1PHke9upszhJiTdxw/hs+ly5oNl9KlCID4Bly/IYf8kcPpk5huRu8dhtTiZf8C0nC2oZ2D+Fe27pxpiRWTz5/Br+/eo63nuwC5cMTcHsUqQ4XYaxAfLj7OCfNu9UzmfVtuvPCvnRv/Mn/3PX6rvvvuOee+4hKioKSZKYNm0avXv3BqBbt25s3rz5rO07YHgawBlH9+sekwf8UC1R7bpQtmsrFz2VRqlVS6nVSa8YI+16JdGuVxJzthqITK5hZ52rMkiww02AaLWimwBxOuS/DXVyp1FTbnYTIKIoeBEgAClmHTYRIo2nrubc4JD4+oXV6BrsOJ0SQ67uyNDrO6NxRSCHNhfx2cOLmP3eZibd26vZtipsYqPqAOAJZtQrJqcKElqKBb8cRJSg46XdvVY9NDoNHSf2ZMFDc1i4tYKhI+XVjmeeWUVQuJnIcD3/ev089K7gOSk9nAdfPpe7LplD/uFKjmwsoOuY1l77stbbydteTKsmnN2bgzIhlFzO7cYaD4GVGG3krR+O8uj0/Xzx8iAmjMyQt+nw+WnvJ4AAWoL5M/+FxWLBbJ5JbKsK0voWMqa9HOD7+jycDShzK30Tkz41JnT2GIB+tul6Fv6wj57nppOcFcGPH2/j0QeXIgjQLieSnfsqWLnyBMuX5TF6TA5arYaJl7dj65YivvpqN+nndOXYsq3s2V7MrVf8TO/eSVx8SVtyW8lVET59cwOOVx1MfmU4q7/fywv3L+byQ1259IbOQOPUsiA/hG2Fn9IfexXFh2sC3b5/Csvm7GfzyuPsrb7avd2L5830+t60Dde5X9dWNrB9cxGHtxZTWVyH5JTQATPFNIJDDQwYnc3zN81F00Q5UIBPx3wBwNTl1wAQlRjKbc+fw8Ojv+HjWXt46Vk5daWkQX4WtpfJ/VSnaLOX8sMhiug0GiKNOvp0TeCTL8Zx5eU/ccXb+/mo8+1kpL7TaN/6Vh+TKE7mukvbMO27g+Qdr2bme5v5/teDDBiUyv0P90XAmwAJ1mvdBIhNFLHZRLfHhEGjweaU3MoFCQkBAQmpEQmvJiRMWsFNgCjkXbVdTpdRPFlONpzanDsuSOs18Tc0JTlpAbaWeNRJKSEe75U4szzmVzR4iBdln/48Rv5uiKgIBtX42+Aa39WKLyWV6a9auCqLDqeqtCRvK//VuwgRxS9kycp82naMa0R8AAwdk82MtzayZn0BvQZ7TExlDxrvfap9yqw+MU1qqLeHzsl6u1vxq/ejHgEw6DWcLKmnR79PKSlrICstjI1rJmEy6QmKfJXsjRP58oX+7u1NPWY3d/p+UdHgQPklJySGkBVq5J1PtlFcWMtND/Vhx5I8Lr7qZ5ISgykorOOlZwZx8x09sOw47G4jQHoEEMBfw3PPPcfmzZuJjY1l48aNTJo0iUcffZQrrriCFugy/hIC5EcAZw1CjZOI+BxKtq5jxScRnHNzDeApj7dij5zaUnQilITkGvf3jCY7ddWq/F9H0wGOIciB1aLH6RQwh1ib3A48AfmQBI+b+XdHa6gorGXfugJ65EagCQti2JQuXkFMq24J9L2oLQt/2Mf4W7qSGm70as/39d+Nk0V1hMQGYwwNavRZeEokWp2GE+WydH/37lKOHa1EH6RnyJi2buJDgVanYdhFrfnk1fUsm76VpNwY4rNlDxa71cH819bisDl5982hpIYa3UGYRhXOGV0vnRU1WMIaK0fUsNmcbNldxjmdopix4AQAVz2wwk1+BBDA2YTFIvv9aPRnn+xQIAJV5RZefX41AEF+yNJToW33BDb8cZT6Whup2ZE88sFI7hj5DUeO1zDh0lzq62zcctN85s09xGOPDyAxKYSR57fiyy92cWzpFiIigrBaHWzbVMS2TUV89N5mdh68GYNBy/xdNzCy/TRy+6eS2z+VzZ9v46v3t1BWXMc9Tw8C5BV1tVmrL0xeqjVNo0ow6SE60gamkp4bzQ/TtjHiniBisyIJ0wtuUgIgzpXWeHhnCT+/v5ndGwoBiEwIJiktHI1OQNQIaDQCB3aVsnruIaZPjaDn2By6jmzFv8d+427rxnlXuF9/NGoWjw76nEV7bgRgd4mF8BgTFcV17HL5RcWpCLBjdQ6O1cn+U0op2QKXgXNerY3O0WZ69U7iw2mjmHzdbzxyzx989t5d6HQahKjX3e3YD7gUPKLE8bwqikstvP7ORtrmRDL9o61MuCyXjMwId28qAdWq9BYFNtFDgOg1ApLkMdtUl1xVuAFlfqomQNSpFXWO5gNNRe2oLksbdxoEYbBr4q08B6UqIsPhRzXxT4eEhxxRQ0nD0baQRHJKkvs+6lUlmE91ZwSdFq1Wg9jEtVc8V6p8Up6iVP3kSav3c1nvo1DKCgvySrspdJnId+wUh8mk46cf93Pv/b29vlNwooa1a0+g02no1yOB157sT0KsmcgkT6WWP0N2KIh2eYFUHL+dtHR5Aef9dzZiCwti3bxDdDsvgw9eWAPA+sVX8u60raSnhXHf7d2BgLFpAH8dotS4mteZaPN/EXa7ndhYuXR2jx49WL58ORdffDEHDx487Yp7p4sA+RHAGcemGc/R/brHwCGh1cirNsm9CiiriKFnmmeAHJhr8SJAAKIT5QAzOKzBTYBodaKbADEY5QE3NNq7+ocv8usdVJUGk5HkMTONM/kPCUqOy/usqHXQqmey3x9d615JLP9qF9tW55M6qtUprkBjKPJjo2pSoKhAUsye41KTKMoKXIhqheRkvRxA1KsCp+j4YGpL6rDWNDQiQKryK3A6RCIH5VJvd1JeJUs1JUlC28TKi1anAQkaam3MuGkuqZ3iCI4ycXh9ATaLnQtHZzOgb4rf7/rCVC2v5tmLK2mwOvll+QlmzDvO6o0nEZBTlC0+K4yzXh3UorYDCOCvIjIyEmN4MHnrThDZtj0gP6/q1Wx/pTbVZJ+yqXqhQlm1sLn+2m1O9h+o4MjBCnbsLeP3H/bjsDu555Vzic+NpczacvLUJkr0v7ANq347xK71hVx9Xy/Co0xcc80kPvtsBrYGB+99MJKhw/bx3DMrGTxwJsOGZ1JbK/cdzz0zkFtu7IpOp+HkyTq69JxBfb2DSy/+njFXdaD/sAzm77rBvb9r7uzOnE93EBLmvYqr+EYoREedQ/KbqgdyydMYn8myJEq0H9OauS+tYcaNsjdGn4vaYAo3Yg41YAozEhdhZO28Q2xefIzU7EhueHIA7XomkpMuS3tLXdctv86BJEkc217Mhl8OsGj6Nv74eCtze6bQuU8Sta2iicqIwG5xUFNUy/WvDqVXhlzud+HPB/j16z3UVNu4/M4e7uMrbnCSFaJnV5V35a9yP/eq1jVJvPr8VpinjWLilLkYDVo+eq4fQpVMsOgyP0KfM91NgFx1UTavTtvB7ZM78a87utNpyFdcc+kc3p52Pp26xPu9jkrZWiXVRf3MGTSaJkuMazWy3wd4m5b6Q4VNJFgneBmKt/T5zFTJueNclYP8kTenAyWlQqMal5XHzKH2xnD9JmtV6R3qp1EhG9RpPsordXUZhUxQp6Oo0zr89QfqNB4FdeKfL2uvJoR8W2lJ6V/B9X2NKzUDgFDvqnjDhqTz+dd7OLy/nKzWUV6fLZizH7NZR+/eSe73YlVlnosavO+p+vpkqYxrNYDdZ2IWFKLniqva887bG0lMDGH8JW0xGLTs2VPKvXf9gcmkx9rg4NsX+hIz8BvOBrJT36FVikR2l3n88ccxnA4nTqfE/k1FvLH4SkwhBm4690t6nXvqtgII4HQguf47023+LyIuLo7t27fTqVMnAKKjo1m4cCGTJk1i+/btZ3XfAfIjgDOO7lc/6n7ttMtqDCWFZEOelnFtBAosDrJC9GT1dPDZBs9jWFYY5iZAAIKCbWi18sBaWdy0iiA2wk6ManDefszY5LY/5NVwcVoov+bXUusQEVzVDPRagariOr/fqSyWyZb3H19Btw6xJKWGEWnQuFUs6jDL38raX5EDnwrDRrfig1fWs/ObTXRTeX44HU52zN5ISKiBbVuLOJ5fzYCBqej0GkxRoSyfd5iLr+vkVXJYkiSW/XaIwQNTyW0TxfTPdnB8RzGCIGA0aHjttfO47qqO7pQd5as6jfeEUA1Jknjhk90889GuRp/16RLLKw/1JC03niCjljBH8+qdAAI4kxAEgdT+HTi6ZMtZ3c/tU+axdmU+AFGxZtp3i2fKg73RRzb2kzgVPnt2FXs3FlJfI0/KN6w4Tr/xbZk+/TNmzZrF3LmHeKSojvET2jJ8RBbvvbuJDesK2LKlCACzSU9ZmYX4+GDi44PZtXUK7Tp/TGlxPS8+uISIqCDGX9uJpFHZ6PRath+vxeEQiWoT09xhAbIy4GRBDX8syePIzlJO7C1F0AgkZYQjBukZMKEtSa0iWbaqgF9eWEX1Se/+dtfyPBDAUm3D4SIUYhKCufWZgfQemYVGq/EqlasgJVhHfp2DjM7xjBiUQk1lA8t/O8Ta5cf56p3N2G1OtEYtTqtnKqm4jJjMes65MIeRl7cjPkUmVeyiRJZrXGgfLk/id1XZ3O+BnHIRbdR6pV7sq7ExfmwOn7w9nEm3LmBgt1iuGS+nDjqO3OhVovTOSe15ddoOfpl/mM7Z4bzy1AAm3rSAKy6Zw6JlVxKfJC8IhBl0VNscXt4eytii8AHKUKMmQGxOyW3aWtOEeSfIhIpW8IxVTRFYakQaNG7SS10JraUw6bREu7wTFAWLUbVbi2rsPMvq57OGIJ1S6tb/CSgkjFGraXKbpr6nDicMLUz9UUgI0XW9J4zN5plXInnytt+566kBdOubTIPFzm/f7GX2tK3ce1t3UmJklaz63ijER35+DV98sZMF8w9x7wN9GDEyi6ggPTV2kVCXKkxNfMSZDW4C6v6H+1JW3sCDDyzmuedWERZmIP94DclxJs7rGcfPS09g1GuwbJwI/DW1hz98uF5OpevYP4VD24tp1zuJ6LQwVny7l1ULDtNnXGteXT3ptLyEAggggNPDzJkz0em8xw+DwcBXX33F7bffflb3HSA/AjirqDl5FK3BRPH2dkSkFTGujTyKKr4YoijRTczj+2U1GIKNtBkejSjpkESBoGDvFbfgSAt1FfLqhdcipNFOS6Dk8VfYRH7N9+QXJ+REEZcWht3u4MjqExQdriAhK9L9ud3qZNW3u+nVJ4nDhyqYNm0bE+/vQ3QL/ERaAmU1TysIXlJyRSKszt1W4lI1mWKOMHH7o/14/ckVVB4tI6WvXO3l2MqDVBdWgQTffrePqooGXnhuNW1zo9mzr5zaIpG3nljBtff0JCLaRFW5hS/f3cz+HSW89v14hp+XwTvPeEo3FtH8+You9tniOl5NlTyxefKdbbw03Zv4MBq1dMuN4v1n+pHbKgJdjEJsmdDEvUUAAfxdcNrsaLSnlvCrTSLVIjKTa/Lh5bfj+lvS4GDu9/tYtyqfyXf34OUnficy0tO3/BnYrA4qiuuY/MxAjGY9Hzy0hJ8/2srNfWHDhg107daF8Rd9z+NP9KdvvxRGjspi/74ybDaRjh1iuPeBxdz/0BKmfTCSCRe3ISoqiNHnt2Lr7hIyWkexbX0B019bT+z3+xj3YF/io+WV3KBgvXtirO5/nJJE6ck6Pnl/C5dM7szz9y1m344SohODadcnGZtDpCi/hkNbTmKptREaZWLFt3sACI4wotVrqS6pR6vT8NyscZgigyi2OLFbHcRLEmGRRnR6rVfVGWicapjtMlJ1ShAaEUS3i9vS7eK22K1ODm8vpvBAOfpIE1FJIYRGmYiyO6iusNC6czxBITLBkRvubURXaxeptotEGjTukqy1rhSRaKP8ECieB0FaDUadhqMWBwPH5nDVH4e5+ZEVPP/OFp65uT0XDklBF+UpCR4TGURIsI68wjom3rFYfi/WxEXj2xIbZ8bmFN0T2zBXOXNR5fWgJtcFlbmpzZ/jtgqhLqPumhYMm2tcFVsaXOfsj3hqCsUWefxWkzbKeZzplcpam9OvikNUpwC5fp9qBYmi7lDzPYp4Ru3JpSYYNJy6ZO3pQjkmZTf+TFBPB4o5aUQz2wgmA7NnX8j1k3/j4RvmYTLrsNtFRKfELZM7M/XxAW7VSa3qmaqqsvLCC6uZ+flOTCYdWa0iuO3m+cz+YizDh8rGyDWqEspBOk2jFJ/WYUa+/2Akv93Uld/nHSKoxkJSnJn3v9zL3JUFXHtxDlrFaPoMEx8AN/WawSsLLueXj7Zw3ZTOPPhoP/n9knr2zj9En3EyYRkgQAI40wikvXiQkuKtIC8qKiIhQa5+1r9/f39fOWMIkB8BnHFsmjkVkBUgtroaNHqJkFYFVBaH81kxTBooEw97d5Xy1sNLKcqrkqM3SWLbTAM9J3chZ3iOV5tBWgG7XSZAToWYIC0GU8sIEZAnLR2v78nSZxcTZNTy8Z0LGHRlR1p1S6C8oIZls3ZSdryad147l7m/HmLOT/uZeH+fJttTHPPV6SpqQzoFotS4hOSfxQWXtCU1NZSvPtnO1m83IQjgcIi07RjHAy+cQ1JaGHU1Nr79ZBtfT9tGWno4eceqWPbrQZb9dpDIGBOVpRY0GoH3nuvP8M4RUFpJTbgnWA8VlOM+vWMrKpXv2S0jExnRNYrOGcFk3LjO/blt97V/6dwDCOCv4OTWAyR2y8RgtLsn92rDUyW948/8Rn/7di+vPLGCMZe15fIpnf8y8QHQaUg6G34/QpdeSUTEmlnZO4miwxUAdOjQgV9+/pXLLruEG6d4DPmUFLennxxIpw6x3PfQEu685w9WrznB1GcGUVVt5djhKo4frcbqSkMrOVbF7MeX8cyP4zGYdPz64Ra6Z4SRke19DhanxGcfbeXX2XtYu/w4vYZmsG9HCWWFdYy9oQtCuEye/DJjB8s+9ihswhJCCA41oNVriEoKYeSluYRHm7CJkpyiaNKSZNK6yQY1FOJDK8hkh1qt4KnGpaHaLtI5zkRw7yQ6qiT86cE6r8omSl9dUO8gyaxzv/aFU/L1M2nadHPskBS++OEAB49Vc8W/1lCx/GIor0EXFYoYG8H6dQXU1jm46uoOjBiVRbBZT1b7GEKDdO6Jt5oAAXmSrCZA/FVC8YXVKbbo2T1cK59vjqsk8dYKW3Obu6EQGgDJIR7y6HjN6an4rJKHVChWlaMNcxkCq89Vef2fCvq1guBVcUVJYVWnpCjKjqbKE7cUHnWlWqEp/z0VCSOoU138bJqSEsqC+ZexYUMhW7acJChIx7BhGSQlhVLhFAlTPXuSJPHdD/v412PLqKu38+yTA5l0TQeefXENO7aXsG9fuZv8kCSJ4/k1lJdbaN8h1k0M6zQCwZLnPo7sFEvRvnJ+/KWU977cS1Kcmc0/X0ibzHCctZazQnwoaHBKOOwiYnoE807UMSo5mEsvb8dtN8zDXFhDfWJogPgIIIC/EcOHDz/r6S4KAuRHAGcNm2ZO5Zxni9jw1jE2vvcjAx652p3+cuRELf++eT4YImk9bCzBsSnY6qoo2rmK1e+sxxSmJ2uA7DLeXI5rtVWu9gLQNvzUq7cNTslddQCg3iofT1KXRJ6cNoov3tjA/u0lzP9gkzvA6NItnje+vpAuXRNYsew4DptIhEHjVdXFt0TgfwKd+iTTo59cK/vdqWtYMPcgL3xyPkaXvDg41MC1d/Uk/2gVJw5V8uZ7w5n70wEOH6kkNNTIrdd15tYbuxItNh+0qm/HBleaUNsIT55vdJCe0goL0U4nglZLbGQQIUFanpyYjsGPz4ih3ad/8cwDCODPYevWrdSX1hDTNunUG58mGhocTH9zI8PGZnPPkwPcEwB12dpIn0ov5+V+dMp2TS5fBatLfl5VZqFVh1j35yNHjqSqqoZvvvmGtWvXkpKSwi233ELnzh355NMdfPHpBbzz5lBeeW09H3y0Fa1W4I9FxxBFiWvu6cmw8W2ZN3s3s97ZRGhUEHaNhuteOpcvn1zOrRN/4p35E2kb5+0fkNVGLllZUVzP3M93ApDdPYF6rYYgV7nNbhe1cZMf/a7rTM+J7UlQ9aEpwVq/ZcIVxYfFKWHSCl6KD8WoUyE8InyUCe1cSo7sUD0HXVKHdFeaRohOQ4JJx8Ea70n+ulLZJyHVrO7fPVVSFPhTL1gdIsmuSiV/rCogLTGYJ+/sSm52BEbXvXaU12DKmU5I8DW0aR3FFzN3Umux89zLssGAbxUwxU9BIUFaQni4j6eZbdUTZ4X4aA6RRg2D4z33PdRFerTUsLTG5iTaVepWbZxa6SIEQv9G0+H/JqhJDZ1GOC0DWK0gePltwKnTYGpUJqYaQUAEevVKok9vOXZQp+AoxrTBOi0vvLiGl15dx9gx2Ux9bjDpyWH867FlfPThFl54dhC33NCFdz/YzNJleWzeepKSEjlVeOCAFM4f1YrhI7JISQlFWb6KNRu46JIf+G3BYc47J41HH+zLrVO6YK6twdB2BmcTb6y5luAI2VuoOK+KAbFB1NicZPRMRKMV2L31JGNyo/l99w1Nlh8PIIA/A0k686l8/6upgb442xVe1AiQHwGcVSx9fDo9i8rZ+N6PYC1i7JAQKmyw/Lu9WBsk2g2/HJ1RDqiMIRGk9T4fu6WGzV/uIrN/qreUXFXOtroFi0pt40T2Fnse8Zyw5n9Yud0SmPr5GB69dA6RkSYeeKQvMTFmUtPkPHBJkvh9wWFyOsY2205zUEuSlThakSHL+/C8PuAK1lPMnmAm1kVklDQ0DlajVGk4G9eeoN95GW7iQ41zR2fz7F1/MGJgOleMz3W/X1hvowQJKVjO842p8+9/0hz27y+nT8c4Ulu9j16n4d5JbXn10908eH074ietOe32AgjgbKHfo3ew4bVphKVGE9deJlqV32SBxTNBCHZNvs2qtAuDqpSq0kfZvFamJX7+fh+V5Rbee3Ue2dnZZ+y4q1xk747jtWxefpzi/Bp6nJveaLtLL72USy+91P3vp556hiuvvJLb71rIPXf25JGH+nL8eA0ffbyNsMggkrIi+Pz1DVSVNzDp3l6E5cYSES/3BVldE7jupXN5e8pc5n+1i+Aburjb1QrQ9bwM4j/cQlVFA8/OHocQG+wmugHu6/cZvaZ0A6D9xbn0uryD33PTCp7UCn8VtCxOyUfFIPenSvlcBWF6Df3jPIRssllPslmeeB91Gb8qxEu2i0wqsjg4rlJ7HK930D7cQLVKwm9xStQ5RGKMWtV78rNi0mrdxIckSSzbXMKQvolcdaHn3qsndR0j4JuZY+nc+1NycqKanPja3R5STft2NDjFZgNg9Tm0xH5qZbHVq0x8S0rGA+wu8xiRK+NX16jGVcj+DNRETnOZPWoyQZ3iolxag4rAcqcKqS6estgSolK0xKv8xCSlXK3Kv6Xe5XdlF6VmF2vq6mxUV9uIjja5ybDmjh88JJXFJhLagrLYNqforspS7qrIEmlsOtRXrlGla1uDViBIq6Gqysp3c/ZRWdnA8aPVfDV7N089PoA77+hBZOzr1JTdS4PVgSRBQnwwvQd/wcFDlZw3OJUbJ3Wkfed47HaRt9/dyL8eXcYXs3axeJFccSmyupaqIju/LTjMfTd05OVXRwAghL58yvM7E7i776e8seZaQqOCqCryxDnRQVpEp0SkWU+U61pvPHgzPbI/+FuOK4AA/j/jbFd4USNAfgRw1hGfKwfQ5QdPwhDZ22HHqnzCUtq6iQ8FgiAQ3aoLR1b8QF2ZhfiEEK9c++ZQqqoa0jau6e26RhnYUm6jfYSeY6oVLyU+vO22Htx110LWry3guimdAaivt/PaS+vYvbOUxz4cCcjBcapZ5/brUGBw5+169vln8nb/CgRBaLKMnfL+qbqZ0uBgLKqShMqKknplKF0lc/7px/3cdusCFs29DAC7Q+TF6bu5dlwWT93a8c+cRgABnDXUnSzF0WBl3BPDiE63AlagaaPk04XZrEOSYN++fWeU/AiNNaPRCnx5/x+IDnnSq00JP+X3rrjiCr7Z/BZzpm3li1m7vT6LiDXTc2gGR/eWYRUEihucdOmRwE29PJP1l5jE8Mty+f7DLWTlRtN1QKr7M1OwnpzO8ayce4j9GwoZflk7Lu86nZdWTXJvk3VOBnnrTrD7x730GZhCdvdEOkbKhMSOisZpipEGjV+iwx/SQ3TuvjzM5Zu0qlhWcKQE69zEB3jSmKrsNtqEGfjqSC3nJQahETyqkGN1DrfRqdKeQnyAq1y7Vf4sPdTzzJQ22Iky6Hjorvns21/OGw97qseoYd0pl/M1lFeSlRrKy8+vYemSY3w6ayxaVz/rqwBRQ/msuVjRKbVMkagVBDpGePrxPwpPnVoqCAJWV9umJqqGtQR5NQ1kuKqDqE1Z1alEdc2Ytf43wu4z7kpIHDpUwT0PLGbFynxEUSIkRM+USZ245e4eBEeYmmipMWpsja+FXZTcqUFNocLqMcxVjq85kubYiWquueoXDuwvJzzcSGiIgScf68+dd3ie59VLjjB9hixRv/bGefTqkcDapVfSrb28OOQoqwLgsgHn03b496SnhxNZ7fFayy+SibJXp+2gY9sorhiXjd6TZXtWcduCK9EfKuPksWrG3daD9WVWekUb+XLGDgDadYnzius2HrwZIECCBPCXISJ5eRGdqTYDOD0EyI8AzjqCwkwkdk1j6xdrmNcuiLi2MdTbRHRNDNgarfxYmnzG5iC9RINdfjMrzBMcHatTCIymH+deKZ4V3AqbSPsIfZPbXnJpWw4cLOel59cw/aOtZGSGs39fOXV1du56rB/nDJSDfoPmzBifaRD8dl6dIuWgukoV8CiKj5RgT7BaY5ffE1R0Rp+BKfz03T6m3N+bYBVBIUkSC+fsp11uNAkJwV4lPf8KYkx6li06CsDBwxUsnj+Rc0fOJiRYz7Rpo3EcLz4j+wkggDMFp1VWANSV1ROVFnbKVYdKV7obeCZqRw9VcGxvOaIo0bN3EskpoQiCgFNykpkVgcGg5Z133uGCCy5wtzOh8/S/dNxhccFc+fpwNs3Zy8BrOhGdGobQwmpSPce2psuILA5uLMReZycs2sTH9/5B3v5yZr64lqyOsfQclun3uw/2/4z9FRNJPVbN6w8u4c73R9Gjs0cF165HIivnHmLbqhP0GJLu/o4Cqz2WpK4JFO8uISTSWw2gkCAgp5j4KziimKyatFDtWz8TmQABqFCVZU1xkRmrSywcqLYzqVWY13e+OlLr9e8clz9JjotLKqyTn5E4k+f4dlc2uAkRgGM1VjcBklRWwebdZXz41V6evqsrQ/okNj4RwNjhc6w7ryEzJZQdv4zlkdc28+6svQgIaATB74S/1u0947dJ9yLBqSqLKV1+S0qmAuTXOd3j3DkJp1edKD1YxxeHPdXbnuomT/TzKhua+spfhlrZUau6jpFG+R5GqdSQQlE5AGKDJ/VJGyw/A1Kt5zmyixL6pOg/dTxbthQxfPS32KxOgoP1IEBtrZ13PtrCslX5/PbzJZhcKqSWenn4otrm9FqUsPkpvesLuyhhb6I08ocfbGHvnjK+/O4iuveUn+E2ae96bWNpcBIZYeSRe3ox7Jw0OuRG++1D5y87ztGjVUy5piOaEBNirUyutcmOIH/dFTz8wnquvW8Z+UUWHrrtZvf3dOlnlmiYOPt6oiNlybAkSXz97mZic6K4dXwOgiDwy5z9vP3iWq66oTOZLl8jNQESID4CCOCfgwD5EcBZx+FtycQNuIrqkzP5/clljHj2HBI7xXJo8T5E5zA32aGg4uguQuNCCI0Ndr8XY9R4GYg2N2m3iRL5tfLnvVJaviqV5Qqe6+widz3Qh/PHtea7b/dQUlxPx15JjLq4NYkpofhRYzdCbY2NeXMPsWdnKUEmHcPGZNO+cxy79pSx8o+j2G1OOnWJo/85adhUpIWvmd6fxaVXd2DO13t48tYF3PJIP1q1jaa8pJ7ZH21l/fLjTH39PPZXWmgd6Vl1UqSx1SpTNnX+cHP5yDfctoBvvt8HwNNTV/PcUwPR6zU8/a++aLUatBkJZ+S8AgjgTCGtdyzHl0bzw8OLadU3hQufGez2IogL8jz3ym/SrOp/Sk/W8txDy9i89oScR+D6XUTFmrnnwd70HJjCTVf8TGZOJO+9994ZPe7nz50J5wJ3NL/dCysmcWxHMRt+O0BkRBAhEUYsIlSX1lNdUk9NaT2lxz0T035jsrn28QHc0LPpfHuNVsOQB/vxx78WMWvqSpI/G0uiq/RNzvBMrjZo+em1ddx30fcM33aAnByPcbW1poFtX+0iuVcrwtMjmlX0KR9V2UWvClgKwlypgkFaLcUN3kRBsN5jWqv01UpazGeH5PPVCh6lH8CSogb33xvbRAAyqeGL/DqbF/GhQE04BLsmsh0zQxDrGtCGmPyaNxo7fA6AWHwn2uAgkpJD0fmoKNRqCH+mpfV+zGB9UeeQvDw2mqvYYtB4yJX8upYpLiqs6vHCc4zpf6IErgJ1CpnySv24GLVKlRbP/hQyp+EsySztBWXyPiNdlcn0nvOrcN0n9R1SxspLr/wZo0HLO++NYPhwmVj8fcFhHrhvMdt3FDNr9i4mX9e5yf0qQ65DRW44RKlJo101qqwtN1hV37vrp3Tm+2/38vZr65lyS1fad4yjLuRegqNec28z/IJsSkdmNWpHcjjRxLyBmH8NDWi47v5lxMWaueLSXGpqbYSGyDGHs6KWuGgT018aRGpyKE+8uoG+3eMZ1OfM+i9NnH29+3VZhRFRFMlbuJWi3SW8+NFIKmwiJ/aW8uSDS7jwkrbc/3Af9++ld86HZ/RYAggA/jkeHWcaBoPh1BudIQTIjwD+Fmj0erIuvIKiRR/z630L6XLtOThshzmy8gdSe47EYA5DdNgp3reB8qO76HdLd+pdNaHigppexegY6fmxtFTFoM7X3lzWdFWYnNZR3PtwX8CTOwueyZBRTVSo5MU71hVw/62/Y21wkJQZQVWZhdmf7yQs3Eh1lRVzqAGjSccXH24lJT2Mqe+PIC0zokXH3lIkpYTy9ifn86+7/uD2CXMIDtFjqXdgNGp58pmBDB/952T4Oo1AmcVzLaJdK6LnDExl5iy5nG1BYS3X3zSPAX2SuG1y00FdAAH8J6HRaul738UULV7KvqXHWmy21WCxc+e1czlZJK9gRuWkkdyrPYJGQ+GmPTz+wBJ69EnC2uDkjemjyMz0r6QAmLr8GvdrZSKnXvVV3lNSNZ4/d+ZpneOyWTs5sq2Y8KggaiutOBwiYTEmQmPN2CwO6qvlFe+waBPj7/CfoqHGR6NmATBp52A+f2I5VSX1JKbJWvWdy48z87FlBAXrsVoc7Nixw4v80Jv0xLZPJn/tQRZPD2LI9V04pppgRxs1jcrZqs/doBH8qh7igrTua1Zo8Z6wK/P8LlEGtpbL56q0oXAYdp+58lu75co541I9KoffC2RfgHbhKgWIyzh7XJpHq18aG0VMbBTtW4Xz4bcHGdw9DpPL4tGycWKT1SvyC2qJc/mrWBzOZk0vS61qP5rGE+ACVf/cXJUXdXpGsJ/r7g/7qjyEUM8Y+fqcDtmgpOvkRnkWNhT1QXG9R30RbvhnhKZr15yguLiej6afz6hRrdzvjzo/G1GEm2+cx/QZ27l5cpfTblu57k6fZ8XXM8QXarWHU/KvJEpJCePd90dy683zmXL1r+j1GjavnUQm9+K0i7z+9kY+/GQrQ4dkcNstXenZ2lUBSuP9PDZUNxARauBIfi1ZHaah12u4/vJc3nxuEAIgWR1IDgdP3tODVRuKuObuxWyaN4H4Lp+e9vVoCYaE1fLW48vZs+UkF1/XkZ4D5FKbm7YWI4oSj08dhCAI9M4JqDwCODtwSs17Fv3ZNv8J2Lhx49+2r3/GCBPAfzU2v/oU3e57iu3vvsDYD8v45eaPKT9YxKAHB7Py9RXs+uldzOGRNNTV4rTb6Dwhl3ajcxq1kxnqeVxPJe1V0D3KQ44oK41l1uaDtbIGO0ddqTRKgNgvzhMIF9U3TZgUF9Vy703zye4cz5SnBhIZa0Z0iqxdcISPn15BZLyZ536YgFan4ejuUj57ZgUPTJnHjF8vwRik81oNFQRXTrVqhedonbzvdpEeAkcxKnOqSshFGHX0653MopVXs2llPocPVWII0TN0RBZh4UYOuVY1DdUe+bHRtZ9gvfa086yvvrw9Eye0JTj2Dfd7H390PsaEt06rnQAC+DuhM+qJTgvHVm9HUk0i1GU+PekW8t9VP+8n/0glGr2e5D4daDvhPLfcu7awlJJdh9m4toC+56ahCf1rHiKiKOGwObHZnIDAvxZfTYxLlaK12Nm48Ah5+8pJzwyn64BUkrMiyFeZdtaUN9BQayO1dSTDr+5Ah3PSMbnS4N65dT5hXeMZf2s3Hr50NmFhYf4OwS+0bWLQ6DQs+PkApus60VBnY8/qfAAa6uxEJoaQnu5twvrTDR8jThZ5/vnneeyxx0hoFUXCKO+V41qHRKVLruFbuQXkIE+tXCj16csTTVo3meGbOtDFNRbscN3btuHeqY+7Ku1eComfjnvMO3PC5G13V9kpbXAyKN6TtvNTXg0gkyCKeeQNd/bi7rsWEn/OD3RqHcGdV7bh0hHe18NZeDsAglZD+zZRzF10jJoGeyP1B0B+vdNdztefcMOgEZol//15fzSnAOkba/S6tj9sdj3HCc2XvxUlj4Ig0+XlcdEZUjMqMOs0RLhVip5xSjnapGDPb04qrnC/1imEi1pVEto4jcfuSoVRQxPsud9CrTz26uM9JZ9jdfK1L7V4xwbLlh7DbNa5FR9qjBiZhdmsp6iosbG4msBoquxxU3CIkpsYUdJ+QpqppKM88grhpizqDBiUysbtk9m8sZDLxs/hsadWMG50Dq++sZ69+8u5ZHwbFi89xsxZuzCZdCTEmln12yXEq2KliDADW34cyw3PbmD/wQpy20Tx8Ze7CQk28O/bO3kOoraeT57tQ6/L5nHHc2uZ8dG9AF5Kk9NFRUUFM2fO5OPr3+KNTbew4ecD3Pn+JiKjTEydfj7teyRS45AI0wtotRokCbpmf/C3mi4GEEAA/xkEyI8A/hZsfvUpADQ6LW3GdOfAvC1knteFoc9fg/PoDsqPVxMUmkraoHTCEkPc+atDElpuBtYrxhP0RBqbJiiijRo3AXJRmqd9ZfK/r7r5AE+B1Sm5A4UQ1zLi59/tQwJuf3GIe5Kh0Wrod34rThyuYN7nOzmwtYi2PZLIaBfDzS+dy5OXzGHpvMOMuKh1i8+1pdDpNIwcIU8wyv1UiPGHBofoXjFUS2t3lcsrmOrUnIRgg8dATRVgvfXaeaQk/03uZQEE8Cfw6y1yDnvuqGw0Og2/vrSGLalhbPh5PwajlriMCHpckE2P8zK8vrd12XHMsVHUl5aTObyPO1g+tmQTh39fS+rALpxYs50O3RIQJZi/6wbAu3rJ5V2nU1tbS0leFZZqG1aLHUe9A6vFgc6oJT4rgh1LjrLxlwNUl3oMKFv1SSYtO4KSvGr2rjmBKEokZkaw+teDfPH6Blq1j0HUarj1vRHotQJTXh/GruV57PjjMF/8ezXCC2tIzomi9/mtMJp0VJVaaNM14bSIDwBTeBBtzstk25x9jLosl4qiOtb/epC47EgGXN+FzJ5JdO/evdH3NBoNjz76KO98O41f39pEWJcE9EE6OkU2lrtW2kQv/4424Y19mg7X2rE1OCg4Wc/gdlFen6kVg+pr3z1aHie0gqcKSn6dk3C9BmUX6SE61pd6VA4Hqu3khOndptrLT8qkcUyQlmFJ8qS63jVJDdFrufTSXNq3j2HjxkJWLjrKtY+t5fctFYwaM4bUlDByE0wYjRr2HKhg195ylq7Op7bWRmlxPQlJIRi0AodqvMkYBTbRo3KMNDQ9UTNohBavCKqvT9/YlhF2ygRbXfLX2ILJuS9MgqKk9Hw3TKX8KLbI43FzE/j/FgSrjrGozoY+yNvLwxeSJBET3fIYR0kHUiuDnNKpF4Nq7U739VOeiaY4KavqoTFoNXTvmciTj/bnsy928NMvB+nWNZ5Vi6+ke+d47HYnH0zfxo8/7WP5mgJ+nH+Ym66RKzkpaV1GYPbHd8sNChAfF8yr72+hR4doJlzQCmeV7LuTEm/m0Yf78uAjS3nh2cHExwfzV3DfffcxY8YMnnjhUaKTQji8qYgeY3IYdWt3RJUBcufoYJY7QaMREEURrfa//zkL4H8XTkk6I16Bvm3+U1BZWcn06dMpKioiMzOTLl260LlzZ4KD/1p/4IsA+RHA34q87fEEpQ5HZz7Erq9X0uuOC2h/gUflMTxJDgROpc5Q5133cQVr/gJFBWadBpNqTFPKmKklws3ht3yPMV5vF8miJgaUzmfN8uN06pfiJj7U6HleBr99uoOl3+2lbQ85rzU+LZyM9jFsXV/QIvKjo8socEe5Z0KkLOBFqMrZ2USpxeqYvwq9RqDMVRVm4sR2zJ69m4gYE3V2J2e2uwoggDMPg1lPUHgQFcerObw8j3bnpBEVF8zhrSf5Zuoq2nSNJzTKhNMhsurn/ezdUIDdLoIEG9/+GkmUkEQRW009GUN7kT2qH2W7DlFb5SFRS4rqWPT7Ebatzqe0oJYpxTOpr22aoAXQB2npOrwVGZ3iaJCgodbGpjn7qMirIjzGzIW3dqP3iCzCY8xMf2Qp6/44yqFdpQgaAdEpodUJPDliFoyA36+8gdKTdfw6/wj7NhXx/RsbkESJcy9p++eumUag12Xt2L/kKC9f/iPnXtOR0DgzpvAgMnueOme/w8R+LH78G2ZN+gmNVkACMvul0uOqjlRbYugVU8E79yyktsqKzqAlLMbMhDt7kNkhjn1VdvrHyX1w0ZFKPnlwMeUFtXyRGEJYnJm8bcU88OYwhg71KC0yXP4TR+u8+3vPOCKTGoppKshkui8BAh7FRJhBg02U+C2/lgtSQtzbKavt5tRQBqWG8uB1HZn57V7ufmw5s1y+SGoIAmSmhTHumg40hBk5Wmv3S1oo49upuvVT9fspZnns81dK2B92V9qJdCk+2oSfHkkGEBPkmWgqflJ1Ds8xxjdThvVM4dUiB11slQAcVlV46xcrj6fhoZ77FxwZRsj+PAxp8djyTv6l/Q46J423Xl3P7wsOc/4F3qmm8+cdwmJxMPlajwJCER3UuhQtIS0obQvyeK/2SREcTrfBa1NwSt6GsE15iOg0Gm6+ozs33d6Nw4cr6ZwTjU6n4fNZu3jo8WWUlsmxSHycmbjEUESjAd+WNDFvAPDMM4N484MtCALsO1QpH6vBc5yXTmjLfQ8t4fc/jnL1le0bHcvIt+5i/p1vNnteAN1vGM7mGQtBgKrCWqpP1tJuaCaj7+qJTq/l0kz5OY4J0uNwiHwyYxvjxuYEiI8AAvgP4+KLL2bHjh307NmTefPmsX//fkRRJCsriy5duvDNN9+ckf0EyI8A/nZodHpSBo3g8K9fU7jpEK+e7yE/jtX6V120Ddd75SjX+HH7V5AarOOka4XOfBpl+BTFR2dVJYJQV/Dxw7Fqv9/xhSSBpc7/OTS45Oj1PsoS0SESpBOIMGgoUZn3KQavJ1WKjdAWBkPgWR0scB2POrjpGOUyHVNdxgjXaps6BjpQeeqyh2q89vp5LFlyjC1bTjJieGMztAAC+G/Dtu/3uF+LoohGo+Hm+VeScaGVWZN+4tNnVnLeTd1Y9NZ6Dm8rJrFVBEVHqpCAqNZpGMNDEDQagiJDSeyei6W8ivqKGpLTwygvqee9f69m1cKjaHUCud0T6dwniai4YKLizCQnBhMeGYQpWE94iAGTWU9DlZW9u0ro2jMR0eWp4zZ7vrWLu0+LVE0a7ff3pqaygeMHK7jlvZFo/fR7MfHBDB7flsHj23Iyr5pNcw8y7NLcP02SRqaEcc200aybuZ2f3lgPEjTU2pEkCUEQmPC5rHhxOjXMuc7bOHDhw7P4o8f1PDTjHjRaAbvFwZ75Bzmw+AjxndJZeKQEu8VG1rntiQ+xcnBTIa/eOI9+Y3LQmvV8X2WlvtrKgc1FRCcEc+FjAzi+o5hNP+0HICjWTIRB406hUZARrHMrDPLqPOTTiGS5P9RpNOyskAkPi1Nye0opqTIWp4TF5SsSpkobUcjxeJfaxNfsM2xgGof330xpqYXj+dWUHq2g3uIgt3UUe8ODCDLp/Zpdq1NT/JEVe6s955AV4j+cC9YJhOmbnwgr2F3paa+l5Ei0itxQL0D+1ewBZRyKNxsatR1UJ49LQSqPiRqjvN2m4hr3e0uKWm74qUZt6zSiGqwY0uLlN1Q+Gcpk3VnlSVcxuNJidBZPGmlDkIH+PZPo0y+Z++9dhCTJqS4A8+Ye4oH7FpGaGsptN3dt+jhsTvcYXaiKK1JCjFhP4bVSYbV7qWUanGKzJqmK2lb5Tr1PqpQgCLRqFUmDJPLT9/u46Y4FXDy2NVdelkvXzvEkJYY0atMX2a4qKjNnjuWKUVk4a7zji6jIIPR6DZZmVKoj37oLwC8JMubDW6nKL2XLJ3+g12l4+s4upCYEs3htITN/PsxKJD6ecQE21bO0es0JjuVV88Zbw055/AEE8FcR8PxoHuvWrWPZsmX06CF7kFmtVnbt2sW2bdvYtm3bGdtPgPwI4D+Cgz9/RXynLWz9dAlrzzXRp3+K1+fqeLx1WPOBm1INIDaoZdGWuqxscxUHmoNBo0GdYm1ztdNnUCoz3ttMUV4VCWnhXt9Z8v1edHoNaa098uzj+8vI21fOLbd2O+1jcDpFNqzM59i+MszBekaPziY+4dQByJ+FySeXW41aVe51VmY4R45UnbXjCCCAswWNajIVFGpk6CMDmPf4Ug6vLwBg8vPnkNE+lmcu+QGnpKGuuILWYwejC5InXvYGK9s++QWdTsPvP+7n3efXYDRqufuZgbQfnEZwqNHt3QDeRpMKCRGWEExcgqyZqvJ142wCozrEsrJtNCePVDEyKYTWmd59z/B20wD4cP11AMSnhTGxBQanp0J4YgijHurHwCldKSpzYgg1smNdKVpTJMGeKrhcNOMmAC8SZOjQoWwausP97z7/uhPL/oUc31JGVE4CGQPbEt06gTnXfYjD4aDHpD7sXLgHrUGLOcyIOcxI5uAs4tvHo8lMp1Vua0KiTCybsY2aCnkSqviGmHRa6n18jDJC5HHlqI8Cp4OrxPiGUs9EVlGa/FHoeW+vy/C0bbjenZ4T72POvanc6ibiBUEgNtZMbKyZbyPkPnQ7NKoek2TSue+73Y+Ph1oVGeSHMFEPac0ZmaoVEA0tqBoDUKPq53MjZW8HpdR6S2ETJepVBuKFdTLZlBbqGVf+RPbMfy1++/QCLp0yj5tvnEeQqxJQg8VB2w4xvPfZaA5UyQRA64jTKyOskHjKnVN8QnyfczUanCLlDZ7nXSPIhJ8/mHUadwUd5YlbvTqfl15Yw7r1hVw0NodPp41C6zoOXfgrOKrub/aYB5+TTlZWBN98u4crRmWhDTWhCQ7CUeqJFzQawUWguva5/yZVC/IzkpZTwpRfJ1JbUk/hvgZqiqrR6rVEZESxbcYKJFHi+3eHMHxAMgCXjMpg3HlpXHjbYubPO8y5I7OwOUXqHU5SXCqQo0cDMUsAAfyn0aFDB684zGg00q1bN7p1O/05UnMIkB8B/K3Y/ObT7tdtLzmfnV/8yC2T5zJ95hg2hZvdsmN/FV70GoEQV3m5cqu6DGHTAZ46F07TzHb+UG13YHYFpuoKMYYmggWAq6Z0ZtYn23nplgVccV8vOvZLoaK4jvkzd7Lu9yMA9BieiShK7FpzgtkvrSEzO5LzhmeiFQTiVbk5JpcE06wKjhWZatXRKu6+ZT7Hj1UTEWGkrt7OOy+s5Zopnbnrwd5eZnWxrmupVo0oqzqRfiTHTvHPB5+iKJdVzCuooaDORtyfayaAAP4rUKGa7AJ898o6zruoNX0HprJyyTGqjhSw4ulpxHXOwVZroWzvUZAk4pJCCIsyMemGzky4sj0RkUHuChZxqnxz9eRWea3O5492/U6LLc2nyADcclt3fp93mBmfbOP5F4b43eamXk2XsT0dvD7si0bvXT3nGurL6vnjqYVITom0ftmIThHRIWEMM2GKDiF/WD4pKSl+WgS92YS+y1jad4EVj7/oJkwAdDodueM6kjMql5PbTlC4vYDK/EqK1+1j/7x9DHxgGIldUrGHRwMek0g1zK4Vbb0geFUoyQjRuyeOauKgd4w80VqnIkGGJsrvKSSIKEleviQKNpU3LpO7qqim0XsKcsMNbgLdFwo5oq7y4otqu+gmM/wZmTolyU1eH6trGVkRZ1KnlrZMOQIQ5VKCKF4glapVfONpKDFPhVt2VpGtWhjpHiUTVFsrPNc+SEX+KNfHorr3Xx2RFTtqs9gYo3yMN+d6FinUioljNfK9j3IRnrG25j3CIsKN/P7thWzeXsyUGQcBSOiUxHPjvdPD9lfWuwmNStW9jnKN0WmhQeTVePdH/mDWa90pMIofUXOVrBxuVUvz9+bXXw9yw5S5dOkcx/dfX8j5wzIbmYPqwl9ptg2LU2TQkHQWzDvk/b0YmaxdtqUIm82J2ez/eRMdTvb9vIr1R49QXVCD00USavRaJKfoNq3u3SnGTXwoGDkomd6dY/n+270MGJbhfn/2V7vRaAQiks/ewlEAASgIKD+ax4svvsjjjz/O999/T1BQ44XWM4UA+RHAfwzLn3id+vun0rZfFtdN+pUbv7oIfKS7DU7JHRDomyEdAGpVq6RZIU0HaxoEt/ojN9zjzaEExNWnuZKlwCmB0aznufdG8MjN83nnwSXuz3SuAFYQ4KUpc9HqNNitTjp2iePld4ajP410lqrKBm685hfi4oOZ88sEOnWJp6baymczdvD6K+sIjzAyelKnUzfkA+WanKj1BI96lQQnOUQOLpXJmW8ZRUmSePO19WzZUMjL7w4/7f0HEMB/C+osWhwNdnZ8s4P2g9O4/JnBxNnsTJu6miU/7SciIojM7EiOHqzAabNTuGE3AJGxZiY91Mctb49tpkz36UCttlKqaajJXKck0TYzAqdTJD4hBIconbLk5ZlGyfFIyvbVILkisbzVBxttk/pdKk6n02tlpyn4psqsfWcFR1fIk6bQpDBqCuRUxCeffJKtaQWAk6r8SszhRnJ6JDbbdpBWQ4NTbOSfoZSOrVORJ50i5DHiqCpNZmhiEL8XeEv26xyiW+WhFmQo6SPxQd7nrJAr/pASLI9fZX7k/2F6wX2cpyIyFOWL9TSiY+V41eSHP9y2VK6kcnFrzzM+IiW8qc2bhUmr9TLkVpdvrXL5hMw45PHdEv+LDP5KDAbKXdXTfs7zVAgqtcpkV3Gd8jDoGT0lF4BDJacfeh+s8jxvagWOTiMQdApSSRAEN7mmLAg1VQbZ1wfEpNNw/Hg19937B6NHZ/PFjAvk9kSJsOjTr8aya2cJHTrFUe46nqAGBz/OO8TaDYXMmXeY7JwohozKYmtJHZR4CNAGi4OaH+dxfPUJ2o7Mou2obCJSQrmxfyLxicG8us5K/vpjrHt3OY/f5j/+yW0VztbD1TgcImtWHueH7/axaN5hJt/WjY5d40/7XAIIIIAzi8zMTGpqasjNzeXyyy+nd+/edO3albS0tDO6nwD5EcB/FGazmXNv68FnN/7G/uV5iP1SaBVv9mteqs5ZTQ72kBvFLtPSZir3UWkTSXeZkCrB1Omgf7zZXVK2yhV4GLXqyYdn2y69k/h8wWXMensTK5cew+EQ6dQtnquu70xWqwiWLTqGzeqkQ5d4OnaJO+3Saj9/t4/qaiu/zr+MOJcjemiYkdvv6kHxyTo+/3gbwy9vj+E0CJU/A6ckUdHgZPPGQr79YS9rlh3nxPEabn+gF/ffuuCs7juAAM42ts7aSkN1A8NvkuWWkbFmHnxjKCCnJgDYbU6OHiinIL+G+iA9ud0TTvl7Lq63e6k/zgScTpFXXl1HZaWV3Hay+sHRTOnTs4XoNmkMfuZ6nDVFFGw6yuHFu9G6FC9Om4PHHnusSeJjxeMvNtu21TWvDI6LwNEgr7YndUvhqaee4qIZN2Gz6tAHB1NfZcXmQxoo5cItPj4JyiUyaDTYVL4OwToNdlHyuoYZwXovAkRBnR+ViVOCaheJEOwzMY0N0pIa3Pj+m04xge0WLZMl+6r8Kw0UxcKpeI41JfJEXa0QCWlm30kmnTudx5+i5VSIVT3rq4pkAqNzlKfCicXx95J0/w1oFevgqYVymos+yPOsds2Sr0+7CL27j2kJGnyeQYNW42WA6g9OScLQAoNPi0Nk1boCqqttHD5aycI/jjJsaMafKgkrAYUFtWS1imDXrhLWri3gtTc3cLKojozMcHr2SuT2u3oSFKQDt1cZLFxwlA/f2EDRiRremD6KIYPTcDpFXlpVyMnCWrZuKCShsI5OqWY26zXsPljF0H7J1NXbeXn6Lo4X1dFgdbJgxQlMwXqG9ptJaUk9WTmR3PlQHy6/tsNpn0sAAfwZBKq9NI/x48dTVlbGkCFDWL9+PR999BEVFRVERETQuXNnFi9efEb2EyA/AviPo0afQHTreBa8spYFgMGkY/jEdvQd1Yr4VLlcqs0podV4y5WbglOCKFcgqDYQPRWUldRYkydYO1wtB3tRxtMjEmLignn6JY/0XAlsHaLE5VfJA62y8mJxeo4xQuXQHuqSaVeq8qMrnU7WrMxn4KA0N/GhxkUT2vDlzJ1UHq2iQ2c56UQhjNQdpBIYnaz3BNKKVFydZqPXaLA4mr6GX36+g6cfX05CUgj9Bqcx8Lx0+gz0L2sPIID/FUQWl7Lvt72Mua0HvV3y91ahnt9mYrCnHGhGpAl6efLiwfN7D1MRkFq3BN2znV1s/NtSr3prlZx7r9+u0p6rxLRGw78eWcpnn+/k4Yf6MuTcjNM40zMHxYBw5Ft3EZyaTFy7ZLpc3R+rxXPdnr3l2T/dfsfLB6I3Gyk/WERMTiZp/dsRnuZJrBOdIsdW7CM8MYQTVolYk0x2l1g8fZxJnc/nc+kNGg2CIHilCOg0AkadBqsfgqOdSxGSr1JfnHSZofqal1bbRTpE+Ce8FIWOv/g1MdijTPTXD+eEekK4pkiPcqvIH4UtM66ODtLQLVre56kqrin4Yb+TJ3rJY9HswxXu9wvr5eO9zacE8elCMVRVl7JXoFSuAc/4pqS/AISqxjLF/HafyiRWGXtLqzzXscJFRjy3tdT93qhkD1mzuUx+ntSGsONSZSLj0kxPefc5eTXua/BnoFSi65Mgt5lg8zwLRU2Yqqth0Gq8+hKrU2y2FLHyicG1jZo8uWBsDjGxZt54ZR2XXP4TV13XkYefGED76BafjhvtO8Ty9azdfD1rN4IAF13chnvu7UVmZgQAyYlvcezEHQDkH6vimfsXs2t7CT36JPHEy3JM9dgDi1k0/zB1tXa+crWr0wnExgczbFQWr87YTee2Udz3wgaO5NfSqW0kpRUN1NY7aNMxlvYd4zh/bDatcqMRBIHurT44/RMJIIA/AVGCM70u8R9Y5zhr2L17N2vXrqVTJ496Ky8vjy1btrB169Yztp8A+RHAfxyCRmDwvy6g/HAp6cYqju8pY+7nO/h1xnZyuyfwxtvD/E70FSS7VpaKWpAXD95y8XJr4+oqLYWSww8+q6yu2Yn+b5adw1932PcHk04OMKsU5UudjQ/e3MjGdQXs2FrMhGs6cPvDfRicO431B29qrqkAAvivRllZGVOnTuW9txeQ1i6GQZfm/qcP6ZSwiyIarYaoqCBMwToKy+uJiDh7ubKngr8qDKPfv+0vt7vgvhlwH1zw7u2NPjOFWNk1ZweVR0uY8NpwNKpJXqzJgM0pNpL5q4kpdfftWdH2vKl4VWSq0inzXRPblGAdda6Uy1of41CnJBMfpwOnJGEU/I9FVqfoZWLqzxA1SeUbVd4MgZGnKmA2MKllY9/AOA8JcF5i4/SrlmL+CU8KS19Xudlyi2csTg5uXCr+fxGJZi21Dg85tnVvhPt1UoZMFCmVgTYeMtFSxKgWaBQvryqrg2pb82SL1Sl6xSUavElbNQxajdeCSe++ycz67iJee3kdn07byi139Wzx8arx6FMDGDe+NbGxZgb0eoeEhAQAThTeyY5yCzvKb2Bk+2ns33gt990wj3xXpT2b1cmz9y3msKtELsjxzuDzMtizswSr1UlFuYUrp3Rm07oCRkxeSGiwnivHZrF9XwUHjtZwxfWduPvhPhh1Gjplvv+njj+AAAI4e+jZsye1tbVe76WlpZGWlsa4cePO2H4C5EcA/xUQNBqWPPMTl82aTExmK96f1IFV8w/xxZsbGdl/Jnc80JuoUTnuwPTSjNBTtCgjwqBBkiRWL8vj/QVHCQnVM+Gq9sQnhaE7Bdmh1QjulaM4sycYM+nkAONM5R2Lkuy6LkpQafUQOMqqjVYVrMSa9JwzKJU3XllPSXEdsXHepNCc7/YRHW2iR6c4jK6gSMnh1aoCakXl4U9qXW8XvdQfatRUW7li/A+cyKth6MgsRl+Wy8gLPfelV/aHfr8XQAD/rXhhxSTKC2pY8fVuNs89iKAVGHF9ZwZPbOcuGdslKshrwqGG4tfjrzuwqibDTkn+zTXVb/j+3p2nsZxzxZXtmTfvEM89u4pVa0/w2rvD/5Qs/Wzh11vePWNt/XbbO43ec9gc7P11F9nD2zB+sGx0+PquCu5pH+neRpnIaQSh0XTdt6oFeO6nU5S8+mAFA+OCOFhj52C1N+lu0Ah+VRh5KoVI6zDvyb3VDzmj7LvepfhoStqsGKIqxpi1fqp9xKkIke1F8l+NrnlSRk3aKOkuavKjOXSNNHJOgnw8anVhOxcpV2ip8/u9P4tBiWHuMc2uSl+qUKkmlZK5WWEeZcgFLpGi+toqBILaDFzdTqJZ/rylpYBbih6tLIxPl+MaxV8LQCxzsVShTS8AKVAIPeV5Vc6lub5EGemVtC8zTatcBUHg8ms6MP2DLfzywz76dT7lIXkhJ/UdclJhYC84cPx2N/EBsuJjR/kN7NtRzAfPd2D1wiNYrU5y2kRxYF85e3aVMGxEJnnHqhk+MgudXsPypXm06xhL3tEqtDoHEjD5sp+wWZ0kpYehAX5ckk9mTiQvvzecwUMz6JkTiFEC+M9BPAtpL/9NHkh/FXfffTdPPfUUX3/9NZGRkaf+wp9EgPwI4D+O766Z1ui9qDgzY67pyMsP/cqdjw7nrZfW0e9oDSNu7d6iNhVzr0P7yphy2U9YrU5Ep1w+7ctPdyJJEBFp5Mopnbnmxi6ApwqD5i9OGhQ+Qd2M0tmpTQgV7xGtIJyWbG38Ze346MOtTJ70G/9+aQgdOsZSW2tj5qc7+OLznTz4cF+MLUzTUctgleBbXQpYHUgWldbz8jOrOHakitk/T6B122ivlJwAAvhfxO6Vx5n1xDKCgvWMnNSRcyfkkpvsmWgoXkF/N7QaAdFPBRjBle6CyjsjNzeGjZuu55tv9nD3XQsZN/xrBg3NoP+QC2nbPoYjhXVEx5kx6Lz7hfNyPzqtY7rq+0kADE6TSeVJnT/EaGycjvB348SKPdhqrbQ536PUURMfvpDw9HfeFcFk+E5r1ZNHJS1yTXG91zbpIToOVDfuD/2Vkd1fbXNXEAtvojoLyKSIL2KDtO6KY+XN9L+xJj0hevn72yubT5P4aaOnzGpotHxe52Q0+xW3Abl6jOga+eefBbOfss8AaS7vi1ZZHjNVxSwzWHd2fa2awyXpnj5iZYlchcVQ4bnOOyvke2Nv8ITZHdtUs2Nf2J/an5p/Cz1NPy+tRkBw3SaltK1D9E/eqNPEFHWrOiaKjTMz/Pwspr23mQfuKicq6s+lNeWkNiYxbVYH9171C+ZgAyPHZjP+ina0bR3FrE938MZLa9m+rQSHQ+SSie247qqfycyK4P03NhAeFcQnX19IRGQQC76JJ79uIeMnd0ar1ZBk0tEjO5DWEkAA/wsYP348ADk5OYwdO5Y+ffrQtWtXOnXqdEZjjQD5EcB/Fb6+Ynqj9669uydGk46Z72yi32W5hEabvVbiTjSR/1pfb+eai35AdEq07ZvMgQ2FJCSFMPaStgSHGFg07xDvvryeY4crefyFc5o9rvIGz+qeEvSpAwKTKnCra8Yj40wgMjKIqR+O5Nm7/2Dc+d8QFW2ittaG0yFy401dufnWM1sPWxQlvvtqN2+8vA6HQ+LhpwcyfuTXZ3QfAQTwn8CiRYuY9eQy2vZL4dLHBpDyFyZv/w249NJcUhJDmP31br7/ajcz3t+CIMgqhiCTjuzcaHoPTmPwyCxCEkKYs30KABd1+rjF+yjec5I3Xt7B0T2l3FzzGXEJIaRmhpOcGc6E6zoRlxjC8HaNCe2zgVHv3InodLL5q13knJtBco6JQouTRJP35NCL4PAht7WCgFOSvJQXGjypLmqfqVgf9U+fWDN2sbGKIVjvaUuZY6qzX3yPD5ovoW7Wad3n0JSRbYjLI+pUxqnnpsvbrTjZMuI62qhxEzXpoZ50qtKGlqWZ1qtOPMTl0aFO3dlYJqtKmvJE+aehYxtZyWHSyuF3T5WXSerREwDoosOwJca0qD2l8otBtZBRa3ee8jnQaTQcVxn4xvt5JhX4Lgc98Gg/xg2bzdWT+/Dcq+fSNevMkAsGo47zxmSzdkkel17dnrTMCEx6LVdd34mF8w5R7TL7vf7qn0nLimDe4iuYt+o4EVEmghNDGNJuGkO6wc87ZL+WsR1b3q8FEMDfgUCp2+Zx5MgRtm7dyrZt29i6dSsvvvgiR48eRavV0rZtW7Zv335G9hMgPwL4n8DrT/3Olx/EsmbuIXpe1r7ZbdNCDBypsTH1keU4HRLjH+jN8q9207FrPG9MH4XRJWcdf0U7pr25kenvbuaaG7uQ0CEOm1NCnfFxqgDidCBJ4FAF4TV2+XWy2f/P0B2wq4LmEpevSY/OsXy/8DLWLDtO4aFKgoMNDB2VRUJiCA6g8hSBqRIoq1cWfdeBtALU1dl56K4/WPLHUcaMb83tD/QmOsZMAAH8r+PV1ZN4ecpPpHeIZeKTAxmcbCY6SP4tqieiSkqEOlfepoo2FMmp2n9BmaCq+w9/AYp61VyZlKslrFo/IjTlY7so+i3/PXhwGoMHp3Ggoo5d24rZv6eMkBgzx49WsXdrMV+8v5npr2+gVfsYLrmhC72GpDfeiQpvr71WPi6nyMZPNrBv3l7S20Yz7PL2hEWbcJbUcfxIFUvnHmbnppNccn0n6uquxeRS0lmc0mmRK6eLqmMnaai20mFMG/d7hRYn+a6S3Skh3oSWcn1FBNRXzylJCIL3e+BZcVdzDvV2kT6xjfvBnDD5+SlQ+VdoNP7vI8hqB10TpIdBq2myHCnIqr3wFigAok16RiZ5jlUhG/xhXI96d6W16BaqBy0OEZNOg8UhusswA9T5Sb/5syiyebet/AaqVZXb/P2+1ESXUhElQpXOovw+i1V+Ycp+FhV6SK0lxz37iQmR21Gq64DH4LbKRfScrn+YPxgK5Qm8Nkl2FdWdpiLV4hC9TEv1Go1Xf+OLEL18XU4lobc6RcJjTNz/eH+eeGAJvfomw4SbzwgBMveEhbSJHVmz8gSXjfyGCyfm8vwLQ8g/XMnWTSe5/tnBvNo7gZlf7KLP4DTy6+107JrQKI0gQHoEEMD/Fmw2GwaDgfT0dNLT0738PWpqati6desZIz4gQH4E8D+Ake3lVcQ252aw7ad9dLu4rfuzhQV1pBXXkpYWRki0dz7y2mV5hEQGEZsaTkl+Dc++NMRNfCi4+qYuzP5sB7M/2kbX14ee9rH5C0eiXBVb1MPxqcrO/RnodBoGnpdO/OgceR9n2PLZ0uBkylW/cHB/OW9+PJKBQ9LP2ApPAAH8p1FeVMvJo1U8eUd3Bif/7xF6kuTpV9RKOINrktQqwkyrwRkwOIOtZfX0QVY5WOrtrFuWxw9f7uLNR5fx/m+XsvXwLXTJat4AcMUP+9g3dy/dJvXgxpvbuU1Fc1xVcPbsKuXhKXN54cElmMx6EtPCiE8KYeiEtsAUr9Kq1XaxyQnKyLfu8vq3PwNVNYq2HCQ4zMAlAxLRaDUUWrwn3fm1VuJcig3fSZK/Xll5T68RvAgtjeCtYlAwUGXGveKkPGFOMmlJdKVrbK3wJhtKrU5ahTYOvdSldkMN/kOzcKOO2lOYWirENoBZ3zSB8VQXT7rC73G1TW6nhvr6qRUxFj/VcJrDoIRgt4fO3LwqwNs4tUjlE2JopkLJfytqHSKZIfK1dwZ7rpm62tDxeplQ6V9SwqrY2NNq399zaHOKp7xWeo0Gtb4mO8zIweqmyTAlpnC6nk31s3XBRTls2VjIc48uJzbeTNes0ziBZmCONHH1jLGseXYZWzYUcbDaynez9xARGUSnwWkUGLScd2sP2oR5zuTvUpoFEMBfRUD54R9paWnccccd3HLLLY1S6UJDQxk4cCADBw48Y/sLkB8B/M/g/Ks68MqCw5xceYyF0bL8tqywltsv/g69XkO3HomkZ4Rz+109INSI0yESkxZGTYVc5q91buO6bEFBOtIyIzhxosbvPpWVVYMfwzt/Tvt/BnZJQu+zquOUJHcoqA44lfSaKFVJXCVAUa/qqFcTlXx1JWBqyszUF88/tYK9u0r5ePZYd9ncAAL4p2DVd3sxhRroe05ai7avaHAQGdR4yFRPlvF5faqg5K/6C7UU7SJVxHBEEF0ub8/oczM4b8BMfv1oCzkP92Xr4Vuo8VmtH9jG4wmy5qcDdD0njdtu70hb1cTDqBWosYu0yo3mm5VXUVxYy5LfDnOyoIZ9O0p48d4/SMmMQJAkRKdEfFIIHfokk99vAplZEW5jVoXkrjp2koKNe7FW16HT1NL+93kkdY3nuWva0dvHrHBIWiHzl25j4AWt3GTMsESZyDLpNI1SIrWC4C79rZ4+qr2Y1PdMrxGIMOr8ehsp/a56bOgcJY9LxZbG25da/ZMWiveCP5WHViO4q2wl+KmAYlZ5XihkhD+1YoLKsHu4q3TsqQx124R7FDNhzZAo/uCUJBRuxNv7Sv4bbz57KS62JtKUlN/kohOeMjdLimSvjjvbefxh9rlSKxYfU1WCC5Zf11nOjsdI/5ISAIxdsgHYX+nxk4m0iST48YXxB+XclUuuKNB0zVSey3YZwCrbOiWpye1r7U5MWvka1Dmc/OuZgZwsrOOxexdz66QWHWKzeGv4FwDcseBKTuRVM3hEJnU1Nn74fh9DRrdym0vD6fsVBRBAAP+9eOihh3j77bd5/vnnmTRpEvfccw/Z2dlnbX8B8iOA/xkkZEXQ5bwM5n20ld5DswgK1nNsTymSBNdc05HjJ2r4Y8ERfvxhH8+9MZT4pGAKDlRgchkW7tlZQvfeSV5tNlgcHD1UwYXjWrvfU6+e2MTTl+4qZEWVSpKrGLOpCZNwFQlhVyoRnPbeTh/1dhHlFP3Fv9b/Y++s46Qo/zj+3rjd2+vu4o48ju5uUEpRUURA7G5ExEbUn61YYAN2INIg3d3NkdfFdW3+/pid3dm7vYI7ct6+1htmZ555Jne+n+cbJgt558v4+7cjPDW1G+NHzbsEvZKRuXSUl5ezc1Eiw25rhqckoakodko9KURj0kWppAEcuKrFmQe6aExKhZPqwiOqws9fx0OPdeCj97Yy77cjxDX1o2OPcLr1jSKhXbCtGlaQq4rMMhNR8QGc2Z/ptC1PF6Wtr/4xPrR5UkhMfTK7lLlf7aG4UI+bRolCqeBsYi4/frSdb981ExLhSZc+UXQbEM2Bgzfz319H2TbvOFpvD9wCfdAFqEndn8HhxSdYOX0jbTsuoHe/aKKivVAoFXw5YxcATzzZgWgPNaeLHEUHacnUimK1aAxXnO8sREUMlZCGO4lTerOlSs8+H42SCGtYY2aZ/bckSzIdViHsUaNU2vqW7aR8u/TarE7AcFEqKa8mzEHaXq8gu+fTuSpyaFVaz9oNh3wqFzEecKbYYCsfXCCppKJSCIMXE5v42OaJ59Ug2Z4o/meVNNxN6q4z0dxb2PaWLLvHhK/W0QsK7FV4pFVzjuXXLk9KRdL1jiVqzdXksHGGIGxYq0lZl3dWwchxebDUEAKjVCnp0TeKXdtSa+xDXfiw3w98W+zJmtXnWLP0FAa9iaF3tCDcXcWwBDmkRebqxdQA1V7qu73LwTPPPMNTTz3F33//zccff0zz5s0ZMWIEkyZNokePHvW+PVn8kLlqMJhg2KPteW/sv8yeuZuOE9tSHuuHSqUgurEvr7zRi8JCPUMG/Mq2DUlMndabRycsYsfSRIKjvfnqw+189uMwWyy6xWLhm892Ulpi5MmnOtaqDyaLxfay7Oogklz9Dx8ph61GTo/+1ecDkJG5GlEoFJSXGgkM9bjcXal3xOeTNEdAxYFj8Xn10GPt6dY3iv17M9i3K53l/57g1+/24+mtpdeAaHzfOAiArxLKzpeSm1lMqEZh80ArdVLJREpcgI43XxVeXDQquydGVoGeAztS2b0hmU0rz/Dvz4eE7QTqGHh/W3reEc/mg/5ENcnCYrbw270L0eeV4emt5evPd1Fm9ayIbOrHlG+HEh3rA0AjD3WVrv+i8VhmMjs8u6syKkWUCkWVeRAUVK7IolEq8HEyUh9QTQ4Nkxl01XjkuSgVtqTbuioqnIhGrbM8MM6Wq63SHqxzsRnY0hwbdcVFqaDQun6JxMMowVr+dllq/Za/vRLxklwXLzYWPFG1bqG2eSlFVYegVEW5k9C3EqMZtxryjlQs4ezMA1VEGu5SYjShUQrVnn76bj8/fbsPD8/6rYil0WiY+NkQ5k3bgFe4L7t/38yBgmn1ug0ZGZkrC6VSyejRoxk9ejRbt27lww8/pG/fvrRv355JkyZx6623oqzh9622yOKHzFWFR6AHve5sybqfD9LsxsZ4BnsQFu7JmdN5AHh6aujbdySr1v7LE1O7M3JUU/795zg6Dw05qYXcPuQPht/aFA9PDSuXnOLIgSzuv78N0dE+FBtMQkk4ye+/xua+Xj/ihotSwZkiwQAIscaEF0piuKUvLOKLiXR0T3zPlsZbmywW9u1OZ8XSU6SnFVNYUE5gkBshYR6UlRpJTS3ifHYpCR1CGDG6OSFhHqisTTq6ewszS00WTNb2L5VbvozMpUSj0dCoqR+Jh7Md5meVGgjUuTiEDphtfysnOZUiNSTsX0vWqfSd85xBUhrq9tMoFbhavRl6tg+hZ/sQuLcNJpOF/fsz+X3hcVbMP0GrVq1o1zOCrNQiMpILeeWTAagkx0anVtiMafFZ4iwfgYg4Yh/urqHtyKaMHtaY5CI9xw5kkZlTSuuu4Zy0jv73aptL72BBnDJ8eSMTYj1w99CgVSooKtRTVKQnPMwTpVKBVqXkfLnjqLrUo8Olwih3mcmMu7KyiOA86ax9H/UmU5UeIzq1EoPZgtFo5qdv99GqfTBt2ofgWm7E3VNDVrnjcSmwui0EOzlcRXoTxUaTg0hTES+NysFYdnMiithCHiW/F1WJJwAeGhVRCOfIz0mIlzMsFmwVhaSeMaXWab9aJk6tiR9P5DEoTAjfcrVeg+cKy2zfB+mEfksHIqS/297WhJ7tJQm7xWlpzpJNmUKbIfbKutzd2P4P8ZwcybNvW/RYyS43VSty1RZfSVir6GEjvd6qSqArpcRodgjtqo3JUFEAqeq9R282YzRb+PbzXYRHevHOjLrnS6uJr+5egH/UeEDIBxCF7PEhc/Uj5/yoHV27duXPP//k7NmzfPrppzzwwAO88MILnDp1ql7al8UPmauOPne2ZOvCE+z4bi9TPhrAzghPlv53hk53JfBQr7lMmrSXn3/+meX/nuD9TwZxw7A4pr+2gcwyI9mZxcyZtReLBYKC3Xjr3b6MGduSUqOpWjdQZ4gjiOoKgkl6ifASLh0tER9O54pq51IMYNCbyMgoRqVWEiRJqleRlKQCxt7yDwCdu4Xh6aXlVGIuG9cl4ebuQlCwO17eWv6YfYBfv9/HO58PJqaRN7/POcjKJado0z6Y51/tgZ/E9TmisRADfe8t8xiv1xHXOJaHHnyEhx56CBeX66Mkocy1TXxCAIf2ZTrkbPDWXvk/iaKBrqrCSFFYJ01UbfxX2bZSQdu2wbRtG8zU57uy8N8TfPfNXqLCPXnv4wG0bBVIupN8FlLcXJQO1XJMFkuV5VkBIjw0RHQLtxldJ89Wzr90X4IvmlIT6SfzaJUQiFKpwMtb6yDOinmQVEpFpe1JRd7a5GpSSgQPKRWPo9FssQllhw5m8dEH2ygpNrB9qxAGEBnlRdK5Ah6Z0pWbxyfY1sspr6x4SPuY78TDQjymXtVUeLFwYUK9Ry2qxoBjmKT0GF/o2EBKiQE/67Yj3Ox9aBth98h6ZYvgEfJM+/r1LmgoxNwu4nmQVn7xkpRA9ktMEiZax9V5Gw4Cj9leAllbiyozDklrrUKlexU5Xfys+WHEXktFIoVCwc1jWvDH7IP8+fNhBnYuwMvLq077URPT+82t1/ZkZC43ZovzcPOLbfNq56WXXiI/P9/pJy8vj6KiIgoLnedmvBCu/Dc9GRkrU3rNtk2febI3P7y2gfXzjnLrox1458ElfPvCapp+eQ/uHhoGDY9jxjtb6Nsrkl79Y1jePwatdcRG+pyoaQRF/Kl3Noqoracs9FqVgnKrOlKQX86fPx9i0/ok9u9Ox2SdH98qkPAwD06fzkerVfHK9N4ktBaSkDaJ8SE8wpOu3cL5648jDm0fOvOore9ZeWW8/txqnr1/KQoFeHprGTK8MWv/O81jdy9m5i8j8PZ1JT+vjJceWY5SqcDVVcVTz3Rg9650nn76KZavWMY/8+ajVsuPDpmrlz8Wjmb1slMktAuutzalBqDoOSAdkRGNDvG7msIT6hOxH1J7XuxHVVUiXFxU3HJbcwaMcEw6FqJTO4gbKpsnRNUeHxUTKJotliq9ym6Oshu+aqXCVob04fsWs3d3Blt2TrSJwQqFgmK9kQXzjzNgUAzuVs8SYXuit4Zjv0RR2tn7oih4OIhJ1m5Kz6/ebKmUbyM5qYC1q86i0SiZ8eUQTGYLWzclk9G4mJ++3E3/EY3xsoZ4iES7q6sVK/IlXoFBusrnSHpca/v+K56n6rz6vLVqm3eFsE7d365FUfF8udSzUZiXp2+4vBxuaiUlFarPZJUYbKV3pYc715pMNrHQPiixd28Qbds6z23TEJwusHuQeDgRIQxmS63Fy3LrfpcaHQWYqsopixQbTLaSzhq10sGDR4pOraTE1jY88GRHXLVqvvtiN707/8wjjzxSq37KyMjISHnnnXdwdXVl4sSJdO7cGW9vb7y8vPDy8rJNe3t719xQLZEtGJmrks5DYjm0JYVf3t1KSJQXI5/uzOIZO3ji7sV8/O2NTH+rLzcP/Z03X13PV98Pc1hXgf1F0WSRGiPCvPoqSyt9gRBHyaTJ7cTvxVGcMydzefmx/8jKKKZ7r0heeaMXjWJ9yMwqYdWK0xQUlNOhcyj792Yw7tZ/GDW6OX7+OrZuTCYluZBFC05QWlqKTmev7NAy5kuHPvVZrueLL77Azc2NcePGserUU/Qd3ZypExfz9P1LmTl3BD9/sYu87FJemNqNd6ZvpnffKO65vw3r1pzl3gmLmDt3Lvfcc0+9HCMZmUvJoTOPsmt7Kk88sJRGsb58+tlgB28P0fiQmgqiSCE17k1OhA51bXzRJRjMZgcDVDRklZbKHh3Slp3Nu1j0TsLcxN3xcFFRZKg58bNUEBENqepsZrPF4lD6syrvELFMbe8+UezdncH/3trMRzMG2b7/9ccDvD1tE/97rx9jxrasVL5Wo1JWGc4iUlGAUCkUtlAOEYXCeW4n8by1sVbEevf9AYwc2QS92cKwkU04mVrArQN+49dZe3noha6AIHpUhcliIcA64l6xWg3UXFY2t9xouzZqYzNXFKZq6/0k/n5pJNd9TTlgaos0hKl/I+GveEwAbnlC8Ex8Z5o90eafZ4QqLh387B4iNVW0qYq9e4P49Da7KCGttGMf/LDvt7taCD+SJrUVq+WkWT1BL2Z01mC2VMotI1KbZMdGsxmF9aoQn3FVtQf2cypeawazGW8n5ZddtGoGDG/Md1/spmnTppW+l5GRccTcAAlPq8pJdTWxcuVKPvroI77//nvGjBnDpEmTSEhIqHnFC+TqK6AuI4PwQvvQm3149aeRmIClM3fzzEvdSU4qYNLDgtfC+Imt2b45heNHc/gvrYScMoPtc976qetDw0WpcIgBF9GbLLaPq0pZbax2RYwGM0v/Oc7Dt/+LRqNk3rLbmfH1DUyY2JpevaMYcXNTPvlyCN//NJKp03ox969R3PdwO3ZuS2X+30fx8HBhwMAYHnioHa6urtVuS6PR8Mwzz/DQQw/h7i6MnkY08uH1mUM4eew8c77Zy/L5Jxh3dyvG392a0FAPpr+xEYvFQp9+0fTtF8O3331Tp2MmI3MlUFRUxPNP/sfdd/xLXFM/vp0zHE8vbc0rXmUoFQqUCgVifQcxFMJksVBqNNdoPDvDw0WFh4sKjVIIaVEoapePRKkQntUalRKNUuG0ZLiIVq1Eq1biqVHh6SQMY/zEVgD8+89xcrJKUCsVWCwW3p62CYD2HUIAQbQRP5X7o7B9pIh9qzjCrlA4Jo6VUrGfQUHuBAa6sX9PuoNI4uev456H27Lgl8Mkn8mv1I63Ro23Ru20CkC4u8a2nbQSPWklzsMm/STCQHWnRatS2n6n1EpFtSVQRUqMZts65Saz7VNXCgxmsspMZJWZKDNZbJ+kEiNJJUY81Erb51pEqYBOATrbRxsTgjYmhMY+OtunNiJjRcpMZspMZtu9XWo0O+QEqwqtSolF8l9NBlm+3ki+3mh7Fogc3JsBQNu2bevcdxkZGRmA/v37s2jRIvbt24dWq6VLly7ccMMNrFq1qkG2J3t+yFzVRDfz57Evb+Cb51byzYydvPPZIB4dt4jRo/7mw9nDCfjjKBPGLuCRTwdBQGiN7YW4aziWW2L7t6c1SVpt3U7rwo70Elb+epi1fx0lP6uE22+/nW+//RZPT88a1+3wGfDZxfdhZCtrErFWsPTvFvz50yGKiw20ah2Eq07N69N789B9S1i96gwDBjYioVUA8/4+c/EblpG5RKSlP0V6ehETxi/k1Ok83vqgH8NvboqmmsSPJot99NNZmIA0+ak46cwglJoTzp4g0nnObA/ROL3QEeyqKDWabS7yzoSG2m5NoajsOQDVl8jUSEJSoOqQior9cvHT0X9gDKtXnmH6GxuZ8cVgDh2xJ6yNs+YpklKdM469VKvjfPFZb5TsgyiAOGtPFMJ1WjXjxyUwc9YeHn22M94+rjSJ/Jw9px5m7D2tmPfHMd5/cS0fzhmBp1v1eZNE8cSZEXu6oAw/q3eGv676dkQjtbZhK0pAZd3JiqEjVWEyg0opHAcPSU4L0TMku7zuBn1NfP4/weB2V9v3/+xZITfI3DlRks7Z97vHbacB2Lgg1jbPP1aIIfcOsOdYmTxUmFdqtF9/ZZJjkWQNw6prjrDaUmQwOTwXxOeQuCu1dTCreO1UFB5LSgycOpuHl7eWgEA36zYsFEhCrbw0qiqTKZ4qMmA0mFi86CRN2gTh7+9fu47JyFzHyAlPq6dZs2bMmjWL6dOn88UXX3DXXXcRGhrKpEmTGDNmDCpV/STQvjYldpnrhns7fk+/aA86tgmiqMxEergvXUe34ExiLqePn+fhTwbhHejGZ48u46+fD3HmZB4hbhrbR/TiUCkUDq6ttUHqpq03m20fhUIhlNKUjJKJo1sZpSYySk0kphfz6RMrWPztXlp1j+DAgQP8/vvvtRI+Gor882X4h3mgc1Nz+FAWAIOGxOLhqSHxeC4Ahw/nEB4ecdn6KCNTW/5dMJoBA2Lo2XMO3brOJienhN/+voXbb4/HTaO2eRp4a1S2j+gxoKljCMulxEWprJQvRBzFV1u9F1wk/74QFNi9JDQq+0dXy1F58RmoUCiq9MKw74/C6lEnLFeVbqJWKnjr7T4ALPj3BO+/u5Vf5hy0bg/eeGMjxXpTjS+XFb3yVArBkK1ozKoVCtQKRZUeLs7WmTixFUajmUV/HqVJ5Ockpz0JgE7nwkefDuTEoWzmz9qDsYKwoFUrbSPqzvI+eLioiPdzI97PzSZ8VIVOrbR9qsJDo8JkzVtiMltQUveXQel1UZvQC2e4qhRolEIpZr3ZYvuklJhsn/Z+Wtr7aW0eDrXxarjUBOnUBOnUDp4dOpUSnUpJvK/O9gnQudg+ea4X5nVmsjh6mor3aW1PQVZeKS9PXUvX9t9z25A/GNx1Lg/etZBD+53nOVEphOoz4kdk4+JEHuj1E/s2JdPthlin68rIyMhcCIGBgbz++uscOXKEW265hSeffJLY2Pp7zsieHzJXJfd2/B6AqVOn8s47wvSgRzrY/p7ccI7vP9vJ1M8Gc3jHacaOHcsH0xajUivZuGkCoWEeVbYtRXy5FROIQcUqLhcmub4/ZR1pZ/J59qsbiG0V1KCxbbUl+WQecQkBtGkVxNzZB7h1dAsio7xo1syPTRuTaNM2iDWrTvPll5Mvd1dlZKpl9uzZ3Hff38THBzBwQAzh4Z6MvKkpHn7Vh4VdClQKRb3H/FaHWqmweXlIET0rSoxmhxLbNSGWdBUR16zJJJUaZyZL9ca2eHjE568YnhgW7smLL3fnnemb+eKzXQA0b+FPXBM/5ny/n+AQdx55VPgdqC6hp6tKafPekXrVVFUeV2xKUUWbYj8jwzy5Y3RzvvhqN8HhNzJ8RGMCdRoiQmegUz3Go0914rOPtvP7L4fp0y+K+x5qR+tWQU7b9NSoUBur3ofGPjrOFZZX+b0U6bGoTWWXYoPJITzqQq7XUqtIIRWivKyJtZzlTrkeyXPVYpKEMoliZXXVkapDPM3uVq82MYxGnF9ebuShuxdx8kQuI+9uRavOYWSmFLJw7kHuv3MBX/00gm4dwwDnXkcgVLNLSy5kzv+2cvNNt9D8hhKatnV+DcvIyDgie3445+abb7ZVdykoKLD9NRqNNk/SvLy8etuewlKdf6qVgoICvL29yc/Pr/dSVjIyF8OSJUsYNmwYGq2KX1eOwS9AcN/cuDmFt5/8j24DY/Af3ROFQkGAawGzxsyj2/hW/DStV6W2xBcPaZZ70QW0KvFDRBqrK30QlVjnJ5UIbrU55WbSTufxxpj53Pdmb759ed2F7nq9c8v9bVjyy2Fm/jKCl59aRWFeOaPHtBAq0Px+BLWLgr59+rN48RI0GruXjMViqdIokJG5lKSkPUlycgH9+vzM8OGN+eDDAbhI7tdiyX0qGqxSTwCnYQ3W5aTu4LUNg5Ma1mI7UvfzmgQDcX2ph4Gz5KRSDw8xREMatiGKH9Ltif2QhjdIDR4v63FTKRU28UFs0plIIH0EiMuZcX5MxWekVABxVlXFWWhBSbmRQX1/5tzZAp6d3IUHHm6PSq3k/bc388O3+/j971G06xDqcI7cnYQ4SUOX9CYLGpXCIUm12B9pO9JrQKdWUm40O5QXdVUpSU0r4uHHlrNhQxLvfziAMXfGk1Wqx1ujxmKxsH9vJqtWnWHJwkRSUgp59Y1e3HlXS4dzKFbfycgpZdfONJRKBV26h+PioiLS0+4x4Ez8kHp8iNePtJqPgwhlFnI9OIR0WU+AM/HDseKN8H1ViWOLrGVUsyVlfX01lcUP8Vqq+AKfa60IIwomBQbpdSos3DPIntzbmXAQ7mE/VuJ9Xiz5LRf3Mb/cHvZi74+9HS9Jsk9n3lSF1tLE0mMhJo51V6ts23TmjSN9RhRb+2N0OD5m67r2a9iZOCLNXyZ+L30vUShgwZ9Heful9fxv7kiatAq0fVdeZmTqhIX4+Wr56bdRtvnitV1qNNm8Pk7mlfLk3YtITS7k+JEU2SaQaRCuNbtT3J/P19yFzqN+y3aXFul5vN/PV/WxmjhxIj4+Pvj4+ODt7e3wVzpdX+F1sueHzFXN0KFD+erboTzz+Apu7/cLCe1CuOuhtrTsFMbE5zrz1bRNRGeb6XB/D9L1vkR2a8Lm2ft5wF1D25FNeaxF5TjxmhBdzgv09hcmaSb0POuLVFXJy4ryhCzyUc2vrBjZmya2ZvX8E8ydtY9Zv9/E3Fl7+fvPo+TllqFQQut2ISxatJjjx4/z3nvvUVRUxLFjxzh8+DBLlizhxhtvvNy7IHOdkp7xFCAIcS9PXYe3j5apb/RCD1hMzkewVfVaL+Xy4mAMKR3+AHbj12SxXFDZUlu1FMTKWGI+lOrbUkrXraX3QE3lW9VqJQ892p6Xp6zlhhvjUKuVWICnn+/Crh1pPPHIchYvvwMPb8HLx7WCwSleAwazxSG8SW+yOBVgKuJpFYWKKzzfjWYLBSYjHv6u/PL7zTz68DKmTF7NgaPZPPx0R7w1ahQKBW3aBZPQNpiHH+vAtDc28PKUtWzZmMyYsfF07hyG1lXF5k3JfDpjBxvWJ9n6EtPIm0XLx4BE/Ijy1JJSJAggzkq5V0SlsJ+PEmPtcnGYLBYHMeti8NZUNv7Fa+h4gb3Cy7UwkilFFN+k4qIzUdBdrbQJILXFWT6gqt49li9MpE3XcAfhA0DrqmbE+ARmvLyezMxigoLcK62ba63A88svh9mzPY1pX9941RpaMjKXC2eJreujzaudH3/88ZJuTxY/ZK56BgxuxPrtd/P9nAPM/GA7+3ak0X5gDLc+05muj/dlx9cb0HrrSBjdjbYTeuLvD/99toN9SxLp++VgWra0vwiUGB1SGdZb2VtnWK4w1193Tw13P9uZGS+tY/jo5jz5YjcmvdQds9nCY3cvws9fh1ar5ZZbbuHEiRMO6w4dOpSZM2fy0EMPXabey1zvnD2bz1vTN7Fy5Rm+/vZGPOppdEU6KmzHbrmonBj3zu5scQ3pqLfU/hETplbMn3CheTuqQhQuRCNYGvIijjLX1vh0cZL3Qli/+gZEw0/sQ1UJXSu2LC416tbmfPbJTr76fBcffjoIvcmMSq3koy8GM+rGP3hpylo+/eoGQEhWKT7HpTkLwJ6U01lZW2dhOZ4VvP5USoXTUXiNUsEXnw3iy/gAPvhwGz6+rjz1REfrvivI1xtABS9O60XPruG889ZmFi9KRKNVERHhyamTecTHB/DOe/3IPV+Gr68rUyavYd3ac6gGN7Jvp5pkKmaLxSZ61ZTCRnr8xV+82r5QmywWWyiXs5/LqGrK+l4sGzNLaeQhtC9Widl13u4N08zLLqi09he8QqVeQBqrGCP1xrIlm5WIEO6S8y4ey7qKFLXFXa2sUJHJKmqJJa4v0MvSYoGC/HKiWwQ4/T44XMg3VlKoRx0ihAVXvLazs0qY88kOBtzchDZdwy6oHzIy1zNmy8WVva6qzauZc+fOERUVVfOCVlJSUggPD7+obcoJT2WueppEfk6nVt8y9v42/LRkNJOn9+LYjjTeHvsvaq2a2P7NObfpJGaTGaVKycinOjPu3f5knszly6WnmZ2Yz+zE/FpntxeRusKWGM22j5hoUKtS2j5iUlV3tYKQYOElLDepoF6Pw8WiUSroPzyO9l3C+PCNjZSXGYWRXqWQGV7nLhgOd9xxB927d2fixIm88847NG3aFACdTldd8zIyDUZaWhGDBv7K9h1pfPzJQG68Mc72ncFssSVKFJNrShNsSjFbP1dSckWj2WIrzX2+zECJwUyJwYzZYrF9pIjlLi+kbKaIkPRV6RAuAdXn0ZBiln6c9LHS9iQlxJ2VohVRIIhB7jo1Dz/ajgXzj5N44rzt+5BQD6a+2pMli09ywFqCU0puuaHKUq0KhXMRxmKp7AlitFhqrPah1ap55ulORMd4k55UyN7d6Tz6wFI2rD/nsFy7QTH8vuZO/ls9lhde7Ea79iHMnjOc5f+NwVBu4t13trB0sZnm8QEsmH/c6bZ0apUtWWhNx9tssVBsMNn2qy6Dhkaz2fZRWP+D+hl59NUobR8vF/unwGCmwGDGXa2wfUJ1KkJ1KpvwcbkJ1GkI1Glwc1HZPo41jQSkSWIvlFpEqldJozhfDu1Mw+zkOt+/PRWdm9ppPrS0EiNpJUbenLYJpVLBxGc6A3Drjw/aPjIyMjIXQqdOnXjggQfYvn17lcvk5+fzzTffkJCQwLx58y56m7L4IXNNERXrw/DbmvPr0tE0be7PoZ+2MHx0DGX5pfjnnOLviV8D4O7j6vC3KiI8tER4aPHSqG0fkVKjyWZI1YXgSC+CIz3ZuvxUHfeu4VEoFEx6vSeZaUU8PmERGWlFmExm0pIL8beWw3vzzTfZtGkTP/zwA1OmTKF9+/YA9O3b9zL2XOZ6Ztqbm9BoVSz9bwwjb22G3myxVeO4FlxCL5Qig0konamwl6UVP7URJZyhVCgc2hFFi5raUkqEjeoEDunyUrGqInfe2ZLoaG+ef2YVags0i/oCgBE3N6FJUz9emLSKtJQC6uo4Y6uAUqF/Fosgehgr7KdGqbB9RMpMZgxmIcQoM70YrauaRx9cxvJlp7j/nsWYCgy0i51Ju9iZgPDcbdLUj/vub8uHHw+kZfdwPvt6D6+8vB6AmGZ5DB3RmNUrT/P4Q0v5cPpm/px7kAO7M7DoqxboDGYzGpWCYqPZ9imtpcgvFUcuxFXbT6vGT6u2iWgaldI26nm+3Gz7XK+4qpWVPuJxEgXM2p6rioj3ZsXKP7feFU/q2QL+mLXHQQA5cTCLhXMPMvKWZri6uTg93zs2JrFyYSIPTurCuN4/MXuXbD7IyNQVE/akp/X2udw7dZEcOXIEb29vbrjhBoKDgxk2bBgPPPAATzzxBOPGjaN9+/YEBQXx448/8v777/PEE09c9DavDNlcRqYe6NXsa9v0qiMPMmp8Aq8+vgIPH1fi2gUz7+PtHCt5CpU2mN4dTYS38OeP19cT0SmMduNac3uM4PaZK3Fz962hrGBtyS4XHk9mC2SlFJKZXMiAsZe/wouUka2+BeAfy/28/vWNvPvsKr54bzsdu4eRe76MHn0Ft7RvdtwDQHFBOUd2Z9D98XAS7hxdJ7c1GZn6ZPWqM9x7fxu8axAznSGGeoA9n4+j4VvZ6HNIZKpyDCNxQGIfODMenYVMSF3sxXwIzhKeis3VZ65hae4iUcxQUH0eCSniOrUd2JYuJu5jVTa2TQCxni5XnZoPPh7IrTf/xa+/HOLlF+0CyPx/HmfgoC68/OI6fpg7wqGdcpPZlh+jIsoKIo5SYS/jWvH8OQtHEq8faRvDb27K3B/2A/DZl0N44tHlpKcVQVv7etLfLovFwv+e7czMT3Yy4IZYVi07RUKbIPr1iiI9pZCUpELWrTtLUlIBZWUmVCoFTZr4EdvYl+ZN/WjZ3J9mTf3QalUkpxWRnl5MckYx+nITZrMFs8mMyWjhfG4pGRnFZKYXk5FRjF5v4r0PB9B/kD2spjrE8yEN/6hvnVEaIpYjEUrc1dYkxPaoFpLNwm+sq+TiO1ts/y1PLBQ8LWMl3iLhVm9Gb400rEVYX+o5VSrxFBLfCaSC3Pr0IgB6BNtzZaiUiirDueqCuBWpF5pDUuBaPgB0aiVdOofx8DOdmPnxDjYuPUlCpzCyUgvZuyWFhDZBPDO5q8M654qESjT7TuXx9uQ1dOgRzmHvBIZ98Tiu7pB9LI30/Uk0HdrmovZRRkbm+sXPz48PPviA6dOns2TJEjZs2MCZM2coLS0lICCAu+66iyFDhtRrVUxZ/JC5ZtFohRcahQJeeacP9w6fx9m1W4kd0ptpg37mlX0GBkzpw/4/DrFsykoeSRhNo7iaE6CKL8HSF2jpi5L4UlRexTvJur+O4uappevQOOcLXAG0aB/CTXe3Yu4nO1i+KJGO3cJo1cZezq64oJxnBv4KwITXetHlxit3X2SuTVLSngTgfE4pRYV6goPdUSgUNrdwUViQGq0uEnNbNFSl4sfViMXiWLlEHOWtalz2QvZWPGq1DXuRIq5TvZxkRyqcVBQjHJdT0KlTKLeNbsFH729jwMCJhIUJAnZ8/Gc88XQnpk5ew7mz+URFezusW24yO1TPcNZfZ9ur6PUBNedHeXt6b36dexCAggIhH4W7hz33SK9mX3Mu9QksFtDrTUyZvJp5fx3jqec6M+GRtvRu+wOnT+Zxy8imvPlOX1u/DQYTh4/ksGdPOocPZ3PyRC5z5hwgO7u0Uh/c3FzQalUolQqUKsEjytdPR3CwO81bBNCnXzQHD2TyyINLmfHlEIZYn+eiYS16IGjrUBLZGeLhk+Y9LTeJ96l93rVSCrei55lUMBPDbKUiSm2FDCniva+U3GFiuJqzEJmJj7SjXedQ5v1ymNOHs/D00vLi9N50HNKIbAVk5JfTzNueVFevN/H55DVoXNVMfKM3S48K29n7Wz6nVy7FYjaRtCWRsvtm4ep6+UuJy8hcycgJT6vG1dWVW265hVtuuaXBtyWLHzLXJCoFqK1v0fPf2cRDL3QltFNrMvYcptHAnny4+W4A4vo3IqprBIueXc4TDy3jzVk30E4igOSUCUNLATr7y+r5MmcJEGvP4W0ptO0bhcb1yr79Rt3TmrGjm5GWUkR860CHRGv7NyYD4O6tZd2fR2TxQ+ayUFSkZ/qbm9BoVAy5Ifai4uFrQ8UcGGAf5XdmNNdkykjbu5AQlAtB3KL4FJMaZPUxUl0bRCNPKnRUJcrUVC3mpVd6sHlTMg/et4S/5t2Kq054rrZqLSSy3rYlxUH8MFuoFApT3aGvmAxVRDxW0vNum5Qs7+Ki4t33+/HC82tYuuQk0THeRDfy4Vyqo+uuXm/isUeWsW7NWT6YMYjhNzWhxGiiz8AYfvnhALfc0YIBnWc7rKNUP0mCtXKHeB5zc8s4nzMBo9FIWFgYoaGh5BdOta3jzNsoOvwzDAYDd911F0888jd/LL6NFs2cJ8asiNEMaqXw15nHj7RMvLKBKywdyzeQkme/p1qH2L+LdpJ4NbPUaO2XHXFQQ1oC+2JydNQFMbmy9FEini/xb20TIJ85lce3X+1m9bLTlJUZadIygJF3taTtwBjadAihTYcQh+VF71SAY/mCSLcntYR572/h3InzvPzdMDytnnUZB85wasVSfGKaovHw5vyJA7i4OCYTlpGRkblSubKtLxmZi6B91zBemzGQ2Z/t4skx/6J290BfUMTmNz8lVt2LhN5RZCcLL8Xt7h/K4VmLeOS2f/j488F06xFRY/sWHI0bf1fhx18sgSv1DMkps77alxtIPZlHnztbUnqF1vMb1frbar8vNloIjPPFK0BHQXYpxfnl5KQVXaLeycgIlJYaGTd2AQcOZPLCi93x89M5HQFxVSkpM5lxVSkdBDwxfMShroJ1fWmsvWh0u7s49xSob6TlY12diC1ibglpKEyR3m64iEaSpq7JLqpA5UQcqYsHiHj8NCplteKKupZtSvtjNFvw99cx67uh3Hbz30yZvJqPZwwCoFl8IDcMjeONVzewa2c6zz7fhSBrWILZYjfKpR4gVdm4YtekZ0M84maLxTZ6X1HAUSkVlBvN3HFnS0bc3IyffzrIW29spHnsV/TtF01ubhlarQpXVzVZWSUcPZrDrG+G0q2vPYTwuand2Lw+iWcfXs4b799JnESc99WqiQid4bDNkODK/c8vtE9Hh3/mdB9dXFz46aefCAtfxG9zDvLGW30dvvdzVTt4JmSX2WNOLrbwSWqpqcqyyacKhd/T5t5241oMbdFJTtia9LKL60QtEUscOwtPk1aHUyuF76XeQuUGc6V164rRbHE4D+KzSrwtdCoVhw9mcf/YBXh6arj3gTb4+en4b8Vp3p28hpvubc2d1spDvhIXnAKDBS8Xe7sHdqTx6cvrKcotY/TUHrw+fgEAf2+fyKHf1xAYH0lEt1s4/MdXeEc1QaW6NM9HGZmrGbOl/kt5XyOOcpcUWfyQuSbp21yIoU4pvZfXOofx73f7mf/NXgAatQ7kp9c28OgXQwDhZdgjxJev/hrF28+v5v7xC5n6Wk/uursVJQaztQqAvW0P64uL1EAShY+aKMoTRlR8g9xrWPLScPN3D9ume7Wwu0uLL1dPd/vR6Xqhcb7c/94APrp3EQAmp+VAZWTqn2RruMvTT//HwYNZ/PrnKNq0C64kfEgNWWciwvWE+KSSjvpfiIu9M8TjLpY7rQ5RuBDPRk1eHVKqC4Fp0TKAG4bGMn/ece6+rw0xVu36rff6MeeH/fw85wAb1p3jh59G0Lipv8O6pUaTTah2dkyMZksV+T2qPn7OKsG46dTcOT6B777eS25uKaWlRqKjvdDrzZSWGvDy1jLru2H07B3pcBx9/XV8PHMILz+3mtuG/E5MrA/N4wPo1T+aAUMaEREKiUmPA9A48nOn/QmvIJBUhUajYdyEVnz60XaOHszmzrtacvud8fip6v6qWOakdLO4X6mlV3uKvouj2GC/5qSCoPhOIT3/db1PS4xGXpuylugYb375YxSenkLJ7wn3tGbWl7t59+3NdBvciJhm/pXWLTBYSMwuZdnXe9j811EatQnivo8G4W8tgwtwdMEeDCXltJvYg7K8w5QX5NLhgW516qOMjIzM5UQWP2SuedQuKm59uB2NWgfx1ZQ16IsMhDX25ccpa5j06828MugX27I3rTcx8YF2TH9tA16+Wkbe1LTe+qE3W1CWC6NlPl4aW9K2KwlDuYmCzGIUSgXuXlrmbLkblVqJ2kVJsdHR8Ihs7k/vW5tRlFeOf2jl8ngyMg3F1q0pLF6YyMefDaJt+5B6C3cRjQ83SW4DsWWHJKcS49aZF0RtQ1gcEn5a25GOHjsTE2zGkLnmcqt1xdm+WCy1T6rqzFBzUSpqLBlcW08SZVUxKICnp5aAQB0tE4RwjSZWIcDzqce5Y0w84+/8l1enruPnP0ehUCjQqVUOIRngeF6lmxFFIwcvIcm0s8S0IqJwUmI0o9GoWL/tbjRKbF5I4r6XOXGfcFOrKDeZad8xlN8Wj2b5wkQSj55n7+50Fv97gu69Ivl+zqOo6lHce+zJjjRu4su//xznxclrWLH8FO9+OAB/f8dS5uK51l6CAf9ugVqSSuwCe66Qh5NewfY+uSgFz4/+0fYOhens0+K9JBWtvF2E4+YsnE2ax0tjlqxjTQosHfzoaq2EJo7o1meUjMHab+k9VCL5LS4wmAl3s7/Knzh2nmOHsvnmh2E24UPkvgfb8sP3+1g27zijn+3CaatXTXt/DRaLhe2rzvDjB9soyS9nxJOd6H5bc6b0mmNb32w2c3r1QeIGtsLN34Pji/eg83UnsEVY/e2wjMw1jJzz48pAFj9krmnGtf/eNr09exxDXu7Fvy+uRq1RYjKYKS3UOyyvUql44ZUe5OWW89Kk1QQHudOlW7jjMgoFxdYytyIlkpdo+4uU/WVFTOBWWiSIH67uV0Z8rMVsIfNgEsnbTrA9J4eclEL0pY5eHAqlglZdwuh8Qxzt+0fj5arm3o7CcX36r8vRa5nrnVlf7qZ5C39G3lxZnNSbHGPjpfH60ncE8YVBUU8eEJcaM9jKVToz3qSIwk1tvDOcIa4iGup1bcNVpbygZKkVK69URDzHehN4+2gpKzWyd18mZsVjgFD9pUnk5zSJhBmfLmHYsGFsXJ9Erz5CWIkY8qJSUCnsQrq56nbXWbhGdeE92hosY5VSgc6ajDdHElqic3Ph5jtaAIL34cb153jw7sV8/P42Jk2pv5F3tVrJsBFNGDaiCWtXnWHSMysZNug3PpgxqFbhoNWhUijIKjORJynPm2ut4iJNcioV2vuFNFwSTYvFQuKx87hYwN3NhUZxPqjrQUgyWSReHJL9kt43oqBWkzBYG1JKjBRYQ2r2JuYB0DIhsNJyarWS5s39yc0oBoT9Lysy8O+RbBZ9vYdzu9KI7RbB3l828EfKa5XWVyqVuHlpCfc3o3MrInXHSZre0BRPv/KL3gcZmesBsTxtfbcpUzeuS/Gjw4Ov2KZ3ff3mZeyJzKUmsl0IkS0CSD1xnjGv9cInuHL4yZ9ni2nzaEcOJRXw1CPLWbVmLFkSocPtIuJ1c9KFlw6vCqNol5o+b02mKC2TE/8soDA1Fxc3LYYSxxeY0ZO7EuKjpTCvnK3/nebb19YTNtuHWx/rwOg/u9E3ahz33HMPbm5ul2kvZK5HzBYLGZnFNGnqZxMuxB//hkzYWV3pSr3J7BAa4Syco77CTC4WsR/OuiPOq4u24aw90XysKo9DQzBuQgIb1ydx123/MPW1nowZ19Lh+xtvvJFu3brx6Ufb6dknypYHRqS6MBZx39QSX52K3j1alZLyagzZqkQPcW51Xjw+WrUt0bZYzadn7ygmTenKe29vIS2tiFfe6AWRVTZRaxpZc4KcS32CAQNjWLbyTp564j8enLiIvxaOpllzx3AJUVBTKRQOYVWioV8kETIuxfVwqtBArKd9cEHMrSXmB5FWmklKL2bsCLuC37t/ND/MHo5CoSDfYLZ5hlwqiq2DKCkSL5cAq2tNbSvg+FvfaQ4dzCKkgkdmSkohO3ek0aRbBF88/R8n9qRjsCY69Qp2Z+S0PsR2jSAmJobJMbMrtQ0QEufHkc3JlLi7oS/WE9tPTnYuIyNTP6xcuZKBAwc6/W7WrFk89NBD9bKd61L8KEw7S/LW/4gbNJoOj71qneno/rprzluXoWcyDc1nQ37mgz3l6PV6PD09q1xO5aLi1ld68cN9i3jrzU28/0nlm9FosVRK0ie6KYNjEqLewcLo1ZHzpWhd1fi5a1BfZNnAi6E0O5ejfy6iJDMXjzB/yvOK8Qh0I35AI7b/dgiA/QdzOBfugXuQOx2n9qLj+RL+fXktnz23CoVSwTzFNl5550VuerknIU39eaf/3Mu2PzLXD0uXnOTA/izGT2xdq+XFhKdgNxylSEdNTErHygrgvAqJWWrkVVu4tfaIrUj76CycwuaFIV25gt18sSNB1USX1AkXpaLOHh8VvT1qG0IUGOTOr3+NYvq0Tbzx8noO7Mvkz9/MKK1eFAqFglGjRvHGtJds64jXRaibxpao2rZtxCSm1W9f68RTQLrPzvpfkxAmCgc+2upf0R58uB0pSYX8PPcgI29qQsdW1S5+QQQHu9OrTyRbNiWzduUZWsYLYUWiWGSokIDzQjmeLamaIzmkG63hLNLkpkklwvtavCQJ6rkC4e+Utvbf9WP5jp6dAIlHc1AqFbjq1BRaPTHvvDuB5vEBvPbCWubOPsAE67Ml3+pN4ae0d0i8ZqTPCPE+DXazh5mI4WtKyb17tkBPRD14febqK4fGifd8WGM/YuMD+PjDbXTuGm4Lfdm3N4O7x/5LSbGBfStP4xXoxsD72nJP3xcICwujQ4cOuLq68sx/46rdduwtrVk2ZSkZ32xn9OjRaMPk8rYyMrXFbBY+9d3mtcKwYcN4/PHHeeedd9BohGdXVlYW9957L5s2bZLFj4sh5/g+SrPTObP2X5qOuhtjaQnG/EI07t4o1VdGOIJM/TNj8E8AaLVatFptDUuDm7crLYc34b9/jpKRW4aH9SUi15rcU+pqHuqmcdpGRRI6h/HbjJ3s25xMhz5RNa9QBbfNecA23TLKmkRVa+/Pv+sdyxSueeED2/Tu3bvZ8Ym9okvZ+UKC28Rx0+SW6Ly0eDYL5OjCY6TtSydxZQkmvQn3QDda3RDH4MndCffR4BWgQ19qZM7rG5jzxHL63t8Wc1+7oSEj0xD89ddfPPzAUobcGMvttze3uZaL4oAzcQOuv4SnKoWjaCMeH2c6Ql1yetjbV0imHbdRl9H9msJanM03U0H4kaDRqHjmle40SQjg9efX8MctfzBmzBjb98XFxbi5uThNYOqlUdtG3h22L1WWpKV5KxxMg9lS5T5YnOxHpb5Lq+o4OVFihRBpThgXtZLMzGKCg93p1KV+8y5EhQkeIBaLhZ/meOPm7sLNtzar9fqicCC9HkSjXerFcKzAVnT5InvsnML8cpJO5pJ9rgCDwcQ3729DLynr6uWt5dGnO+HlrWXX9lRmfbmbu8YnNEhfAJKLBcHllFV48ZJ4lzTyEI5BsKs0V0nlNkqK9OzekITKRUmzdsF4+zl6kt78fFe+emIFA/rMpWkTf04m5pJhDXVp1T8anxAPeo1piZu3ltG9Rzus+/Ggn6rtf0CTAJrd2Izjy44zbdo0mjdvXss9l5GRkame9evXM378eFauXMkvv/zCmTNnuPfee4mPj2ffvn31tp3rUvyI6jkUj+BI3IOEXA6nlv9FUepZPIIiiel5M4rr7EVZxpE3+toTfC1VjWP030d4d9pG3ny//0W33b19EM06hDD7/a3EdAy96PYADOVGTu1II+tYNmcPZZGXUUyZyRUUUJZbgGdkCMWPv4G7u+AOGxsbi1+z1hhLSwht601s/wQ0Hq6k5ML8W2bCQJjYQRj9USpNFKQWseeXA+z55xhb5x4Q5qsUTJo7kvEzhrDuu72snrmb1vuiuOmR9ky5+edqvWpkZC6UtWvX0riJL1/MupFSY/14XEgRXfilxrGxmmSWVSEaxheS50KKs1CMhqiTIdraUoNenFJXI5w4o2Kfa7tebT08wC7sVLXK8FFN+eunQ/zxh6P4ERMTQ1ZmCSUlBrw9qhespVVsqutHVaFQ4v5c6FVam/ws//x9jP+Wn+ajzwehdWuYgRuFQsG3s4dz3/iFjB39D3N+uYnwKC/7cVHCuTP5rFt9hkG3NEPXQP0oNVkItiYwzT4jDF74NJWUmy0XXme/PyG4gBRll/Dr0ysosBr9CoXwPw8vDU++3YcjuzNwcVXTpE0QJRoVJaVGeg5vzIK/j3PgcDYLTPbX44eb2sNHigyCaBEiGfBws5aIPVdYRpRn/XpC5FrLWB/KN2A2W9j6zzGWzdpDeYk9H4x/sDvdh8VxyyPtWfDdPvZtSUGpVpCdWUp2ZjIKBQREefHpO19x5513XlSeo7mj5mAaaeLWr2+ThQ8ZmToiJzytni5durBnzx4efvhhOnTogNlsZvr06Tz//PP1mp/tuhQ/9nz3DgDtn30dC+CX0Iai1LOU5KRzcJ4w2hHQdD3+ca04ttR53KPM9UFEpBcDhsSyd2e6w3xvjdrhJT+txO5e624dfZbGf/tZ43a3ZJVxyxMdeWfiIk7tz4SeF943i8XCwT92sGjdEcqLDXgH6IhOCCI6PpBjZzRYzGb8mqpJWr+TTZs2MXjwYAB8fHyIGXATAJHtM522/eOICqM/D4DRaGTr1q18tPplln+8jcXf7+eld/vSYWo3NvUI58c3NvL2hIX8z8WHxj0iaXVPX1Rq+8vp72O/u/CdlZEBUlJSCAhwE1zJq/Ew0KiUtmoN7rUMMag4fTXhzOvVmYR/McKJs5weUH0uFClScaOqqim1Xb8i0jxMomdeQtsg5v++lOLiYpvw26lTJwB+/+UwDz7Y1raO3uo3LFZccZV4EDkkqaymD3URb2qiKsFFq1KgUwuvbXNmH+ClF9cy4uYm3Di8cb1t2xnRcT7M+fNmHhy/iNtH/c27Hw+gRUIgO7el8svcg2zZmAyAxkPDDU6SEEvZlaMnq8B+vooLBGFBo7WHHeVn28WGMK/zAES41y3XVlmhnoKMYrrd3Zq47pEMaOOP2kWF1lppp8uAGAwVbpyIRj4AHDqQBfEXNjhxrlAI0/kusRBw9Nx4pKmXbfrvLcJ0XLNs27xGHlVXTcs4ncff723h7IEsut7UlAETElAoFaQdyOK7V9ezdcVp4vtG88/MPbTsEcGzT0wmPj6eoUOH8uFuoRTy2L5jL2ifKqJSqZj/yD/10paMjIyMlGPHjrFjxw4iIiJITU3l6NGjlJSU2H7H64PrUvwQ2f3R67R/9nX8W7bDP7gl+tJC0navIy/pODkn9pBzYg9G43eo1df1YbquWZuYx4K/jzFiVFObe7dY6u5CiWzmj4ePlgObknmh2XgA3h1Q93wZqbvPcnzJfrrdEU/7YU3wj/Syhb6YrWEv59bvACAkJOSi+gygVqvp2bMnC8sjKc4tY8Un23hsSDqB4Z70vyuB138fReqpPH76ZDvH157l7O7f8YsLJOHWdvg2Cqi27U+2TLRNP93tx4vuq8y1SceOHXn55fn8761NTHqxu22+xlbZRfbaqw5nniTl5sr5A6BuXisqpcIxPERCTTlRxK2LZ66mPB+11RhOHj+Pq6urw2hRy5YtmXhva957ZzOde4TTuIkfAF4aR8O6zGi2CSA1be9CRA/pGtUdZReHMELhSOXnl3P4UBbvvbuFW29vzvT3+qFQKGgW9UWd+1EXwsI9+eWvUTxw9yLuHbfQNr9122De+qAf3391ji27M4gb0AjAVn2kVGL9p5U0hN+Sc8SqZcFN/Qlo5IPaKpBVN3roH+hGv4ExfPXpTvp+NASNe+3CWeuLxcmlAMT72L1nkkuM7Fx6kj/f3UJco8asX/8321X2sNWIIZ4s+nYv0QmB7Fl1BjdPDff/rx9P95xmW0bqzSojI3P5kKu9VM///vc/XnvtNR588EHef/99Tp48ybhx42jdujU//fQT3brVT2Uz2aq3olAq0bp7E9NrJPqiAs5s/BdDSSETJ07kueeeo127dpe7izKXgT9mCOJB9161L/Enjjx6SUQSMU7b3ypODBjVlAWzD+LVIpDG3epePjAgqITTWWm4B7gRPKQnKUY4c8S+Pe+AItL2nObUkvU0vqEtrVs7Jofc9cW0ik3WmrQiaDu8Cb7hnuiPZbFuYSIzJ6/mpbkjaNIumBFv9OXw8pPkZJpI3X2O/15eQPyotlA/g04y1zFTp06lsHQx7761hW49I+nRq+byFrnlRnwliSNFY1U0uqW2kIrKlUtEwaCqJJbOvC6MTjwhxHakpS01dQilqYg0DMfcAFU0nOXFqK1AcbHUNVzIzUmuF08vLR06tKhUjer5Kd3YsD6JBycu4u572zD+ntZkWkMIgp0kPq24n5ULmdcPzpLaVqSoSM+wG3/n7Jl83NxdePr5rjSP/rKee1KZeOs2DlgeZc68Wzh5/DxnT+YRG+dDfEIgzaK+YP+OMew9sfaCt6EvV1OUK5wrldp+dM9kCALEGcmyg5oL5+jdnfZzdWsz4cwsTTGTezafFW9uwLeRD3OfXIBHNR4VFUl+9naeGvMvJz/dwhffD8VFo7LlFgLwsz5LpN5OHlZhZco8e7jn9+OEfkuL/GRKQlVqy7FtKfzxzmbaD4llw9/7cHV1pRe9bN+/sGo8sb2i2Pb7YcxGM5EdQzlSYKymRRkZGZkrk08//ZT58+dz4403AsKAxfbt25k6dSp9+/alvLx+ympf9+LH7o9erzRv2roJWB7vzfSh8/n555/5+eefCW7TnfS9my59B2UuK1EdQ2FhIp7+OpsbfZi7cNuUSV6ILHU0BO58vAOHT+SxYPoGxn9+I/ctsSsD3w39pVZtFOeU4uZXdXzxsUW7CWwRTvytnevUt9qQVgSaJiEc25PJeWtM9TdT1qIvN+Lu44q7t5bUNDOlOcJIVn6GgqysLMaNG0d6zn4CAt0wB3uQuCMVLKBzU9OkYyhtB8RQXl5eq4S0MtcfCoWCBx5qx++/HGbxv8fp3VsQP8R7Uy0RFsTQAR8nwsf1gLPQEmnYhjivphKtVXGx+UykfahNe87EKmenU0xu26iRDyuXJlb+Xqdm1vdD+fTD7bw7fROnEnN5852+tqocomhtq6JB5WMmolYoHGY6C/8Rhe+qvJJquiLF/VUpFKxccZqzZ/L57qcRREZ5ERB46cuMq9VKmsUH0Coh0GG+u7s7Rkkcyd7zQihorIf9/svTW8vf5tkTdBr1gnBQXnrxXhYnDmSy8su9nNuSjFeYB0Pe6FMn4QMgLMqbVz4dxMsPLeWJB5YyZFhjBgyPw0Vz4SXuRR6aGWybVlgrS43rWWibtz1bOGa7c4S/GYnn+e2ldTTrGs7oF7rh6ir83k/4V/AYLc4u5sSf+zm9MxWz9X0kaWcaO+bsh6EX3V0ZGZl6xtwAnh+XsKJ8g3PgwAECAhw9xV1cXHj//fcZPnx4vW3nuhc/qqI4twxjaRHBbbphNhrI2LeZ1atX07//xSe9lLl6aNU3mrgOJ3jlmVV8//cowiLqJ5GnSqXktQ/68dTNf7Pxj0P0erprndbPydRwdnsa4R2j0ZcILrLeQUW2702lheSfzSZuQCy+wcX10mdnaDw0uLi70G1ADO5eGlzdXMjJKaM4vwxPtReBCU3wCAvCt0kMM2bMYMWKFQD4B+ooKTPh6qkhvJk/ecUWln+/nyWz9vBtQizNR7TBLzaQf+6Xc+7IOKJWKsnLLSMi0qvmhS8QiwU0KkdjXBrW4cwrQmqIK3GsQgPYRo+lIRYKSZsWJ2aw+L3ebK5VXo0LRSzXKq3MUdFUv1hPB1HcqE8BSjxH4vmQetw0a+HPdzP3kJKSQnh4uMN6jRr58Mnng+nZK5Ipz6/Gx0vLK6/1pNRoQqd2NHKl59AkqejiTMoQt+/s+pCK5a7VlDlXgi0WpuIxP3wom9AwD7r3jKR5dMOGuVRHq5jK3iYWi4Uyk4Wj+XX3bqgrqxOVJO6OICYhDYCy/BJe/WA1R3ak4RHiQ9uJfYjo0oRSfd0FixtafsMNLcFUPJh3pq5jy4Zk/IN09LBWZxNFMWllIO8aShJfKBu+34troAetH+nF/izHa6YwrZAVr/yHxWwhpmcM0e2D8A73ZNnr6zix5kyD9EdGRubikBOeVk9AQAAbNmxg1qxZnDx5kr/++ovw8HDmzp1Lo0aN6m07svhRBR5+Olx9A8nYtwWFSoXSRYOXV8O9bMtcmShVSu56ozffPLyUscP+5M57WvPcEx3x8tKyKs0uKgwKq5yIJ7vM/hIourzHuNtjeQ8ZLfS/rTl/z9xD08FxBMcHVmrDGYmJiWx4fwVl+aXEDXCebd3VWxgh0jRA5v05N9nzk7zgMZ6+twt9eHfAXH7afS8FBuFB/Odaf4f1jjc/RqcHOrHjmx2Mf6gdPgNiMZssqNRKjmYqKS8oY/8fB0hccZjMg6m4+rpxpMcUWrRoYWvjhhlPOe3Tsic/re/dlLmCCY/wJDurpMrvXVVKp6VXnXkNSDUOcfS/HpOK1xvi/hRbqz94aez3tslsN8Qq5tCQzrsYlDj3zrBVilFYqsz74dCOQoHKyWI1vb/VRoyRig55sX4oVQoe/fhWWg1rYps/vZ/9+TX5OUhJ6cysr3bz9LOd8ffWUmo1al3Vykojas72vzpRSl+DYKVWKmqV8NVigZMnczmZmEtaahFRge/XYq36x5noIaJQKDDXYkgz65wPZcV2z77cdGFAwcXVHqrRvMtZ23TaSWEUMDAy1zbvyJYY23R5URkb31+CsryMJ97vz1nvxiguIpxMpM+gRgQEufPg7fPJz6ve1TrDmvD8g9vsyxXqhT64ScS0TgPtnkj5BYKni95szVdU4UIoLzFwdk86bcd1QO1a+Xf8xMpEjGVGRnw6HDd/NyxmBTu+20FRdgn9Xx5Qhz2tGx3uf8U2vevbNxtsOzIyMpefmJgYzp496zDvhRde4H//+1+DbO/vv/9m/Pjx3HXXXezZs8cW5lJYWMjbb7/NkiVL6mU7svjhhFf7CMmhvgjtSlluFkEtuhDSqhsdO3a8zD2TudRM7iF4Hux7ZRxHF+5n9tf7mPfzIT6fdQPE+F50+0PGJbB+zTmWvbSG4PgAQloFM7X4FvzDPTFZwKg3YcopISUxlxN7Mzi9J53Tx3NxC/Cg9+TB+ET5UWR9J8zP9MA7qIiy/FKSjwrZ96N7RF10H6uipKSE9OM5uOjU+IQIrsXj2n9v+/67r17GYjaTtm8DIa0KMed4EdgsAK9wXz6Zvpl+Kn/69vOxLa/1cqXt+G40HtySbV+sIfdMDgltWjH3xzmMHTsWo9FI3pl0LBYLaq0LOn8v1NpLm5BO5vLjpVHTplUQB/dn2ap8VFeOVKZ6pKEwF3MYLRbnyU0VitolKRW3XdtBLIeKM07Kwrp5aYluHcTBNWdJuNFuEL+8ZryDAPLc038x4+NoFi5KpP/IxkR52g1zpcJR8NCbLLZ+Sj05KuJM9FAq7B42tS2bbLGAwWDiremb+O67fZhMFmIaeePi0jDlZC+GFi1aMPunORw64YpSYvAXu9sroRXl1m8JdEOpnk0fLqUsv5TXf7iRsEY+nNtbf8+Clm2C6H9jLO9P20SnbmEEBtVftYGK6M2wZ5vgodSzRyoqpQIXVzXpBzOIG9SyUqJWhVKBi84FN38h9CnvbC6n1pykxYgWBLcMrtR+fWIxmyjNyqLFiPtx9Q5AoVRhMZtQqgSTYtfs6Q26fRmZq5WrMeHptGnTeOCBB2z/rmsoYV2YPn06M2fOZMKECfz222+2+d27d2fatAvPVVgRWfyohohOAwlt0xOVpn7rtstcfWg9tLS5sxNNhsRz/vdNPHTvEoY+0JY2faIICKv8Qqc3WfDT2l9Q861J9HQSd+cmnsL3b305mLXzj3N4VzqH5x3h6IJj9BrdgqPbUjh3MMtmDASEe9KuUwi339eGEwGRttGg3AzBI6l/vyQAPnt8MQWZgldKRmZXMrMUMKL+jsWwLx7HUFLGmlfnYBS9WxSwpt0xnn32WcaOHWt7USvJSSN9/0bS91duJ2nrKfZEdKAkp4hd362nvKAMjacHham5WMwW3IN8KMnK4+m3pjJ27FieeeYZtn3+h0MbzUb1IqavnIz4eiMi3JNVq8/WvGAVWCx2Y1tq3DoTUezLOW9LZQvnsM9TKwXjTxrKUupkG1KD3Wyp/L24TZWDAW6touEkjKI6g7yuiC2pnRwTsT+1dbet2IToIWKuIeOFY3ldhW3bBnPl/RT75Gd9LhYYLHQc1oQ/p29k7jMrGD65O37hlZ/VUVFR9OkfzYyPt9N1QDTnrPOb+uoqLetMHKruGLhK8nxUFe7jzFNHyuLFJ/n6671MebEbY+9qiYen5ooUP3r27IlJbyT/XA6+sUF1Xt9QpsbF1Uh0fDoHFsfa5j/zoCDkz1xoLz0b0SwTfXE5h2YtpTw7n6c+H4JHhBd3d/ieuztc/L4A5Fo9rO57sRt7R83jrZc28MnXN9hENGnlpDenC+V9n37hiG2eznruzRahHQ+XuoXgaHRqhk7qxrzX1nFy5TEaD7J7eZYXa1Bp3SjOKubfp1ZjKtdTnJGDW4AXQW07cj614UQagLS9G0nfswEAtc4dlVoDSiXxIx+qtpqOjIzM1Yenp2e9VIysDceOHaN3796V5nt5eZGXl1dv25HFj2rY9aOgXneY+PJl7onM5eb3sd/Zpttun4xL8kL+/nwXf36yA+8IT+bE+XD3g23p0CUMd3XdXnI8vLQMn9CK4RNacTy9mK+fWcmqOQdo2jmUm5/rSmycN8HR3heti14AAQAASURBVPgEuBFtTbZ6em/VRo7S6lMe2f/GBnsRKS8sswkfUd1iKS8sY/fu3YwbNw4XFxduv/120CrQ+PngFhhGSVZqpTYSlx/i9JqjaDxcKc0tBgu4W1RE9WyOxsOVrKPpKF1UJNw1EICUlJRKbeQcPSeLH9chgQFuZGeXkFmiR6FQ4G3NoyEdAVGIVV0kRqdj6dBrD1e1sloBRGp/G6z/sOA8R0VtUCkUNgHDWfhHXZKiin2rjQeIeB7FZfUmS6UcLQCN+sYwxlfHig+38u0Dixj38WDCmvlXWm7yqz249YY/+Oz9rUx+rafDc1MjuWYMkvAi0YujRJL7QaEQrkHXakouVyw37OxsmUxm5vx0iEMHs9i9J53YWB8ee+LK9jxt3749ShcVOYnp+MYGkZcpCE2efvbwUDHcRQx1AYhtKzzXlcraD18WZeSx/fNlmEtKePijgUQ6Oaf1hbevK8+91I2Xnl1N8rkCYhr5XFA7RQYTDzSx77e3NbGuNNlw2eBMAE4UCMciqlsEcQObs3fudgKaO3pzNBkUj87HjWPLT6NUq2lxS3OieyU4eN00BDG90jj4+358Y+IJbN6R8ycPkH1iT4NuU0bmWsFsqf8EpWJ7BQUFDvO1Wm29FA949913efPNN4mMjGT06NE8//zzaDQN43UdGhpKYmIiMTExDvM3btxIbGys85UuAFn8qAWiCCIjA6By0RDb/1ZaDT1G8q400g5kcv50Lk/et5R7H23Hgw+2w9W19rdWpJuapBIjTb1c8NV40vmvmzEYzKRZc2f4SoKBddYXfDfvMtu8hG5nAMi0ztJ4awkLdufcqvqJjXOGR7APfV67i2Pz15G09TQWi4VmzZpx/vx5h9KSLm4eNLv5HpSq/bj6euAe7EvaZhfUrm5EtNzOwhdWUXq+mBHvDeTfSStQKBTc9M0jAET3cxzdfOKJJ9iRfgyVqwaFQoHSxYXoPm0bbB9lrkwKDUbOphYSEOR2SUcZNTUIJ87yWBRJql+42kaCpevYV9JaGyi0jjhrqzGg6xtnpXk1TvKf1DWvmtniPA+IwWKuUYiqeGprCp/RW5Uv8Tw83dKPTw6dJ6pNMI98N4xvHlnK+u/3MuH9gZXWjYjy4qkXuvDuG5tITyni/S8HkVRYTqRnzS+ONbkci/vhUodrdepL65g9+wDxLQNwdVXz1lt97X0NnVHrdi4lGo2G0GZ+6NNTaBQVy57M+g1xASGpaubeA5xZsQqtl45nvxtGUKQXT3T9sd63NSzhW9t0aYFQXWXj9lSyPF3pFHDpvIHjb+tO5pEMtsxYT9kTZbi6utoHYyY4Ljvsi8cbvD+5p7LQF+XT78VOhLQyAfGsfa+YrKOpji51MjIyl5TIyEiHf7/22mu8/vrrF9XmU089Rfv27fH19WX79u28+OKLnD59mm+//bbmlS+Ahx56iKeeeorvv/8ehUJBamoqW7ZsYdKkSbz66qv1th1Z/JCRuUB+vPVPuFWYfmrxGDb9uJ9ZM3bxz29HuHNiKzoOjcPDS3h5buMnuFA/t8ouWjzfTXj516gUNPWyG/oKhQKNRgUGewK4umAoNeLu33AlEBc/9rn9H69DTk4OGRkZxMfHO11eoVAQ2DLG9m8XNyFeMKx1MLfMuIG8pHzCWgXZDNl/H/jKaTv9+vUjafMhbvz8SQAsZvkl63rl1MlcIiK9sFgsTg3iNWnFJPgI91Shwb6An9ZucItihp8kmWC5RRArpEkKK47UXyrKTWabV4FUnPC39ldaYULqdaFQO0nKabFQdgHlbCsihu0VG+zeDmJCWbGksPS76qgYuiJ6iRjMliqPeUW7Sgw5cRaudL7MwIQ4wQBfnV7CsPvb8sPL6zh3MBP6OC57stBAl1ua80GwO5MeXcHmdUkMuSGOpMJyGnk7Grri9WasQolRKaq3/8w4D3E5fTqPTZuT8ffXseK/09x0U1M+/GwQsRGfO1n6yiSkeQDH19ccjmYx2Q9QUZ7wW6VU2K+HoIQ82/RHPwov9IbSYs6umE/huZP4NW9N4ua1+PpefN6t2uDl40pwmAeJR3JoNiCWHdlltPaxj3ze/tBedG4u6M32Mys+a6ThafkSMbTEKORC8bSGw1R1zas0ajo+2J91b86n05g2dHuoA+dOBDpN8u3w29xAFGUKo8v+TezeNo36NufsxuNovHcQ3rH+KjLIyFxrCDk/6rvai/A3KSnJoTBHVV4fr7/+Om+88Ua1be7YsYOOHTvyzDPP2Oa1bt0aX19fbrvtNt599138/evf427y5Mnk5+fTr18/ysrK6N27N1qtlkmTJvH44/Un7srih4xMHdn1XWVPIBetmr4PtafNsMak/H2Ez97fhvLD7XQf1IiWHUJw6RFBfHyAk9bqH32xAZ3vpRuZ8vf3d/oQ3DWr5kzwD3MXgU386rS9pY9fmSOfMpeGf3ams3zZKUIaedOh+deYDGbCmwfQ6454Bg6Lu9zdu2JxVSkdXrrKjJXDgqRhKi5KpdPcGjXh7qKiUCKAiMKMQuE8f4gzREFFunx1q+ol/TRZnHvhtOkXTWisD1t/OQSPOW/nzpub8f3nu/lg2ia6dAnHx9e58CHFXZLHqTqBydk3J0/msmLlGfbty2TBv8cxWd9imzT15b6H21bZ1pWK0SOGwswj7N3mSmGScOxMRvvxUWtqJ4w5I/vATopTzxE78k68Y5pcMuEDoDC/nKz0YqJifRzmWywWfp9zkE//t5X7p3TltrEtL3gb0qpUrpILODCkkMAQDWX3tWHLrN0ExweiDgl0qHx2KaudKS1FKNVKWgS52AYs8iL9UapVlOQUXrJ+yMjIOOLl5VWrqqSPP/44Y8aMqXaZimEnIl27dgWEqpMNIX4AvPXWW7z00kscPnwYs9lMfHx8vSdZlcUPGZl6xDfCi0c+HsAz2d1Y8Ncx1i0r5qslm/nCbKZj51C8h7YkpFUICoWC/XnCiM+C3YJXiMlg4s5GeZQUG2jaMoAwXeXY3eQS4eXxsWZ2l+LzVjf5RcmlJG48R3FOCWHNL43QcrHMvOHny90FmauMtQtOYDJZyC80kjC6DS5uGpK3nePX19dTnFLATQ/WLgfMB4fyAdBKbrOScsFQ6xpi/2ls6yuM8OokHhV6SaxDUongoZXgYzeUC6xeW9Kkx6L3htRjQ5qn4uR5ISVqidHedqA1fC7Ks7KYKRUmalNi9kJwUSodkjRrnXh+1AXRW+JCAnpE7w6pwFDVaLnJgoPHRnFSMe4uCrrf3JT5M3by25YJuHloGNlKcN0d1Vr4m5b+FLPnjKBL5x9ZufwUt42Jr+RVYkuAW8XgncniKJI4E20yzpfy4H2L2bolFVdXNdEx3kx9pSe33dGCnOwSwiM8Uauvvvw03tFCUrz8s+kolKE1LF01SoWZ9L2CuGEqLSLn+D5yDu/ALcif0PZRQPWlZ+ubnVtSMJsttOkeTon13n9vUwZbZ+4kaWsyQeGe/PrVHuL6x6DVCfe8KGbozRbCdBf/qt1yZFPSD2Wx6p1NRHTPp9lNPVG7XppqZ12mCnnv8k6eJPGf3bjoXBxCDlN2J2E2mghKiMFkuvquWxmZS8WVUO0lICCAgIALsxH27BHy+4SGXvjzvTa4ubk1aIVVWfyQkakHPhj4k+OMxjCkK/CBUBL2259uZdbnu9j5+krcA9wJbhWM17AYErpHUF5QwtkNhzm9+gBLi4WwGG8fLV36RzPm4XYEhdZO8Uw/ks3SN4UM7FoPDUMf74hPiAev3PoVWVlZzJo1C4VCQUREBImJifTp04ebb765UpygjMyVisViYe2CEwD0fr4vvjGC11DTIc048Oc+FnyzF7+uEfhHebM2RUjI6+9mfzPIyLP/5GkbLjLskiOtmuLiJFeHUiEYJNLwGFEwMVksDkk760qA1djTSHKUSBOoKhUKsksNNbZjcJJ3RKWsXMJWitiudBGTxeIQyiRSbLAQ1yUCk3E729edo++wxpWWCQ35lNAQaNZsK8eP5lT63nm1F+fTIuL5kB77X346yO5d6Xz25WD6D2yEq059VYW3VIWrrwcaLzdyT6Vx/2vCvXkky37QOoYJ03N+tVcj8fApASArye7JkXdMSebBDZRkp1OQlAhAYOt4Ivv0aPB9cMb+7alotCoO78nAs0kABzYkMf+rXahd1fSb2osR3YJ5ftQ8ti0+Se/bmldaP7VUEEMLJGEveutkmcl+b7hYbyGdRBQN1alJKzWiUCgY8GIPji49yZav95J7MoUuz9yOi+7iExrWlqx9ezEbzJQbyikwWLBYLGz5bg+7/jiCf5MQPEJ8LllfZGRkGpYtW7awdetW+vXrh7e3Nzt27OCZZ55h5MiRREVF1dt2nn322Vov+9FHH9XLNmXxQ0amgXFzc2PA4Eb0HxTD07MTSdmVTNq+NL6cdAqNqxp9mRGli4qoHi149r4Y1GolW9aeY/G846xfeorn3uxNv6GCO/+rPwpq69ePZNjaD3cTXvQjS+2jYYtfX2eb/vUVIReHzkuDb4gHhctK0fm68s/8eTwz6RnajutM7ICmKJVKfhvzfYMfDxmZC2Xz5s3kZZXgFuCOT5Sj23v8TQkcW3qUQytO0fv++q8AVGq0OHh/iCQWCIZNWqm9qoXoLVCgt+f46RQgGCkqiTBwLldP24DLr8KIOU6kooVYmlOas+BoVhFtAuvufhqgc7GV6AW794bBbKnRC6Six0fFqjDOOF9m4L0DdvHirkYerEwrxTfUg7bdw/nyjU24e2gY2aryuiaTidOnT9OlZzMUCsf9F/sM9pCciuKMyWzBIMlfoVE5evAdP36eBfNP0K17BMNGCiVSG4V/VvXOXEXoyzQEtWrOuQ37yUmOxD+iZhdsZ2Qe2Er63o24BYYR1qk//o1b4dnk8pX37TUkltPHzvP+5LW2ec1ubEz7u9ui9dAQGKolIs6H1MTzDbL9UJ0aL41wvTW/swUugbFs+N8/HPnjPzo+NKhBtukMrbc3ADe93R+AgsxiNv52mOBW3Qht35ukzVaT4r5L1iUZmasKcwN4ftR39RgRrVbL77//zhtvvEF5eTnR0dE88MADTJ48uV63I3qTiOzatQuTyUSzZs0AOH78OCqVig4d6qmOObL4ISNzSWgZ8yUAw4dMhCHhAHzzior89JOofbzxCmuEi6s7P66AXXPe4pEx8OuECcx6ezNvP7+akmIDw0ZXHlGS0mdYHBGNvElLKiBToSS6VRDFuWWU55Tg6q7BK8oLpXV0tsBgobxIz6LP9rHr+y0cX3qI3lMGN+gxkJG5WH777TcA2oxpi6JCyINKo8IrzJuibGEkWaUWDNBgSeWltrF2U9tVJYRFSD0Oiq0hJ9LEhAuThfZyyu3eEaESN3adswQTdWCvtb9igkQPicByJL/c4S9A1yBh1FwajiENgRETotY2v0Zd2ZdV5FCeM9RdcL2PdLO74LvoDRg0tTNWxZbqmgTOYrF7naQW623zqxJTBoYK4YWrRo3Eu3A505/4j33bEhj/ZEcaWXN7dGw8k/T0dMrKyigoq+wN48w7JbvMPnKvc1Khp9hgIiezhP9WnmbRgkQ2bEjCP0DHM893qfW+Xk3EDO5B9pGT/PPeFu79ZDA4CcnqNizRNr31FyFBZlAnIZGmxWzh/Mm9BLdLoOmoIQCse+m9hu94NZji/Lnr0yHkZRSTfSYP70A3wpr4MbnHbAAmrRyHNtiDs6cLOGEVQ5OLhesnwt0ufnm52K+PNGu4nMkCAa51K0/rGepLu4n92PHVcpK3noAHL2r3akTM26LU6NB6aIhtH0ypyULGcUHsCWzZCaVKNidkZGrCZLE0QMLThlE/2rdvz9atWxukbSlr1qyxTX/00Ud4enoye/ZsW16n3Nxc7rnnHnr16lVv25SfVjIylwk3n2DcfIIxuzk3Utw9NTz9Vh98vLV8/NoGykoNQPVxdnHxAcTFB3AgVzAGNKEeuEcJo2/FBscRTK2Hho73d6dR3yasfGURB//aA09e/H7JyNQ3ZrOZ559/ns8/F0ID3Pwqe0sYy43kJ+cR2iaQnPIGGgq5ypGGx4heHhoHEcX+/fYMIXlhK397iEJdcNELooDC6pZfpLe37a5WUVxFqM2hg1ksXXKS0lIjTeMDGHRDLBqtypZ7pMBgIsBJWItIujXE4MXW9mRs5wrt4pFK40Lbe4cRkbqduZ/uZO+WFN7+sD/NWwayM/FhQnWvAHBkfyZpJUZbiWJwHLGLspbBTStx3L6XRjBkM7NK+PbbvaxceYZDh7JRKhV06hzKjM8HM+jGOJrHflnlPlytuHmUgQe0GdeXbTP+5ZvnMgi7sZPt+6I8wRsnPKLqMCiTwUB5fgFe0eEN3t+64hPsTmCYc88n33BPUg5nXVC72VahrdR6geXq7b/VeXnCdTYgzn7xRUcXEh0dwMn/Qkjfe/qCtnkheMc2JnXjWlZ/sROXkGCUVuE3rm828x+6dElXZWRkrk0+/PBDVqxY4ZDQ2tfXl+nTpzN48GCee+65etmOLH7IyFxCnu72o336r+qXFZPxpT85kTK1iq/+t5UxE4t4/PmulEuSiv11VjBShobrbPOkI0mNPITbfFOm3QAQR6NMFj0RAd6sc1UT1ujyu9/LyDijqKjIFuvpEezB3l/3MKBJAC7WkX+LxcKBv/ajL9bTeEBsndqWjsaG6oRpqTOHGKfvq7EvJzVO0koFw0XqLZJpNcBjPe1GuhieJq2uUiwJqYhwF/fFvu2ccuGeFZOuAqxNKwLshtLwyAsLLagt2zOLbNPJktFqETE8Jgm9g/dHTbirVZzIL7X9W22yMPnZVSxacIIAfx0+3lq+nrWH4BB3vvx2KB3aBtuWFb0tzhXprf0SzkH3IB3OcJOc4yf6F1v3IYF23cL5aOpaJt42n5em92bErc0IDw/n9ddf59333sJYQTB2hrfG/qwVhZIdO9K47/4llJUZGTgohgcfbU/vPlG0bflNrY7N1Y5/0wgC4tqRcmA9IYPbo1TV3rNBrdWg8fSgNCcXo75uHhENxUu951T7vZeLkpgWAWz79RBR5eUEhnmiN1tILDCSXGxy8P6oT+I6B3Bg8QnMZjNKZcMlGt353jTbdPuCDeyZfww4QePBQkjtieWHaTL3dvSlhez572f8/OpWwU1G5nrhSkh4eiVTUFBARkYGLVs6Vs7KzMyksLD+qknJ4oeMzBWOQqFg1GMd8Al0489Pt7Njcwo3jmqGh6eGJi38sXi7OWRerytFOaWgAJO+5hd9GZnLgZeXF2fPnmXo0KGkFCaTn5THoqf/pVHfODRuGpK2nSP7eBa97m9Lo0aCGCCWIJW6hPYIsgt8p6zeAGVX+ZvDoqQCBwFHFGsi3ew/78HWylGr0+1igxg2I3qEPZ1wcRWijuaVcTRPyHES7WEXQbwtgkBxvsxAqFvVyRnffXszK5ad5McvBjP2tuao1UqOHDvP3Y8u4+GJi5i/6k48vapP7rg5s9SWYNTbRTi/oodGRVQKaNTEl49+Gcn372zlzRfXsitPT8uB40kLyKa0xEDi/gx+1NiPy7DG0oo/wnHz1KiwWCwsWpDIgX0Z5BYb+Of3I7RuG8wnXw4hvJYJq681Qvu1JfvbPeRvScIzUEiOl+3iA0B5qf36uOPBJAA2Hbbfm7oAf4ozGiZ/RkPRqF0ICqWCQ9tS6TtKiFVv7KXmQK7BFgrTwd++39nlwvUzOtruWbUyTbg/VVr7/ewbZOB0pnNPp/DWwez45SCHDx8mISGhfneoCqKG97KKH9B0cBzleUUc/mcvsBeAwKAQ/Bu1IqhJJw4umXlJ+iQjI3NtMGrUKO655x4+/PBDW1ndrVu38vzzz3PLLbfU23Zk8UNG5iqh3+0tGNItlG8/28XXH+9Aby1xG9zEj25jE8iO9mZC95A6t3tk1WksZgsdR7eo7y7LyNQbUVFRfPrppwwcOJBRz3Xh+KFsElccw6Q3EdIikOHT+tKs25XnKl8Ri6X6ZJ2Xi08OZhPqVv0rgegRE6BV2oy3upBWIggSRdZSud4aYXv5+WX8/sthXnquCxPGxNuWb9HMj3lzRtCo3fcsnHecsRNbsTjJnlh263HB06O81I2bOzsfFTpXWE6pJEfJiULBEG3iKWzbxUXF5Gm9MJvMLHlvCxqdC3FdwwmJ9mLeL4cJmtjbabsWi4XSUiM7t6Tw20+HWLbkJKFhHujcXJh4fxuefK4zLlavmGsloWlNLH7MXrGm2ysvonJ1pSg72SZ+1Badvz8F55Lqu3sNis5TQ2zLAPZtSLKJHyKl+WWYTRZKtODmUffytI2CDGzNsoeJiV5oUS0DUKqVrFu37pKJHwDBbaLIPJDEtpmbKc0VBBv/6AR8QpuSm3qU3KSjZJ3cw/Hjz9K0adNL1i8ZmSsdMxYH78/6avNaYebMmUyaNIlx48ZhMAgenmq1mvvuu4/333+/3rYjix8yMlc4D3X+wf6PznDPrfDMf+Mwm8yc25POxlm7mf/GegBWtwumcYdQDm1OJuVwNsMmdyO1VMgGH9w019bMwPb5AKxZFsfpjfvRBoSxdXM81J+wKiNT7/Ts2RO1Rkl5qZFBz3Zl0LPCyIAz540REcJI8vYce7hXXrnRNu1h9QxJLLQny+wfIngJSPNRuFkTkEoTjEqnw62CwYZMe2UXV6tKkFpi396Xx4R7bmCoq9N9e2ef8H2wJAdQvxBhWWlITa4TD612vpVHhoskITUapRK9uX49uwKso9NJxcILioskAW1OmX2/vbU1v2Yc3J9FWZmRMaMqG0oR4Z706hbOzm2pjJ3opDyLlfnbPQH44mZhP09bz6u3i/NwADEMCYQ8IY+81pPE7DIWTN/AbW/3JyTKm/NFBp6Nt4csZJeb2bDgBPO/3kN+TqktLCYk1IOPPh/MwGFxtIq59nJ5XAgKhQL3kGBKctMrfZd5xMc2/dtpIUTCN9guXim17hhKyjCUXx2vqK/2EcJi9t7YnX/e28Li+Sdw7RgDQPa6Eyz9eDsAP6oU3PfLKNx9dbSyhrIdytfT0rvuggiAi6uaoDhfduzYcfE7UUuO/5dKxr5zAOQk5uDfPA6fpt6kbtlNztmDAER3GsbZHYsZ9fonuPoGsOuLadU1KSMjIwMI1TG//PJL3n//fU6ePInFYqFx48a4u19Y7rGquDp+WWRkZCqhVCmJ6RhGs85hlOSVk30wk+0Lj7Ph98OUWl/8F7+3hXZPVF8Kz2IyXVTYjIzMpSIzMxOj3kzfVv50ipS4yV9kxZXrAY1SyZhG3rZ/77JWmZGKKRqJgCFOni02EqC98JwFy5MFo1YvSaYqVsIQQ3TaWjdWrneeBLWs3ESJxsTSlGI2Hba/BBnKBQFDoax65CvfYHaooDM8woNFyUWVllOplEx4oxc/vrCaea+sxVBmxNtfR3GhHn25keMHsvhz9kGO7kqnXZ8oBvWLwtNTw7B+H9C2bdsLfoYuPni/0/nDEr69oPauFDZPe5vJZUY+//JbIj0CATibL1R0UVCTEKfArNfXsMyVR/uhcRxce5Ytfx+hn1X8OJ9ciGegG427hLFncSL6EgMqlRIkeXwO5Qv72sTLWr2o1C4eivekM9EToDBfzbKD++n+6lQ2T3u7IXbLAf/4OLQ+XiiUSnQBvhQkpdFt3C28PP5+Zs6cycHDh0jaswyApA3LaTLyLgaPs/drxU9TG7yPMjJXKnLOj9rh7u5O69atG6x9WfyQkbkK+XjQT5Vn3gq8Jky+uWwMr974OwDNY7cS2SqIvQfs1Q82HxdGuBXJBlR6FaV5hSiSq87A3xAkJiby3Xff0aVLF26++eZLum2Zq5OIiAh8fF05diibTn2j67XtviE6TlrzgEi9AkQP1dNF9vtDKgb4OBEGxFFdaWVU0aARE6QChOrs6z7SXDDqsyUldcWkpgGSHADx3hoO5+tp5uXCsYJLc8+KfRK9KEolb1tivhExGaq0DHBt2eGhQ+ep4du5B/nk7b4O3x06msPWHWnc/lKPKtefcZPEM8bq3dPST8eh86VOlx8e4UGhwW5gBloTRKtdVEx4qy/LZu1h019Hyc8p5Y7uc23LqdQKPvnkEx5//HFUdUji+de++wC4rc13tV7nWqBHjx68//775GScxj+4Ua3WsZjN5Bzcj3dc4wbuXf1zMM2FMpUWjc5+PZYWlOMV6IbOUwsWmH3PQpQqBYdHNGH4fW3wCb64EU1DSSlqnfNEvw3BhldnsKbXKPoPGIC+oMhWleeRRx7hkUceofvzd3Hot5UUJGdSnJ7EwbkzUONCo5Z9CItrf8n6KSMjc3WyatUqVq1aRWZmJuYKHqvff/99vWxDFj9kZK5B3Ly0TFt6Bz9OXctvz/3HsCndIdC/0nIWi4X8tETc/cIuWd9GzHqUgpQcdny+EEOZEWOpgd5PdyGuTxRJs/cSGOrBXY8KL0lX++inTP2iUCho1T6Y5f8mMubOloRay05uyxZCThJ87KLFukzB8N2bYTfUU0vs+SLERKf3NmnYaikNQby3BpPFQjPrSLE0qavogCD1hjljrYqSnWMPzXG3ekOIx8G1nrxn0kqNtmSzAGeLBJEho8wu6ni5KB28TFxc1STc0pwZs/bi6aHhyQfb4ufrypL/zvDYC2vwDvNGH9OE7SfVfDrM3rbeZOTpf6t+jWnpJxiFu7Ls591bo7aV+a1ItwAtoKXP9F68bTSxbP4JXni3L2azhdadQnHVqbmt+1N1Oh5/7buP5FN5bFhykuKes7n77rvrtP7VzIgRI/CJ9uNw4u8MGD8MxRLhd8ZFolvqi4XzabYI5yTr0CnK8/Noc8+NeAVV9tC50tEXloO7K/pS4d5MO1uAzldH1PAWuDYOQKlSknsmj53/HGbT4kRue28AEQlBgF1Uld4rudZbtkOgC4fyKoudxtJSXNwunfgB0K9fPyL69Cd57So8GzWnIN0uJG5+/2eM7xhpMmIoyeu3oC8qwCM4liM7FuAfdvUJWjIy9YnZorA96+qzzWuFN954g2nTptGxY0dCQ0MbzCtdFj9kZK5R3Ly0PPTJIH54bQMrPtlOm/tC8Y11TAhpLC/BWF6KX3TLKlqpX0pKSjjyz1ZOrdyHV5gXre5ox/aZmwELe347zN75J1CqFPQYGENo1NVnlMo0PC+80I1Rw/5g+ivrmfZuX/wD5BLN9UHFqjeiGBIsKZtdbBSWaeVrr6CSKXHRrwt6s4WsAnvbgya0QmMy8/7nu3jrw+0olQrMZgsBjYPo9vAAVBrnryuf3GS0VawBSLF6oAS7aaoUOYxmC25q+7bFBKwgCMJTHl7OlnVJ9B4Uw8ibm9K96awq92PPqYcd/t0uVqhwkZeXx7Jly5i7cCsr5x1DX2Zi3rcTmTBhgsML3bUs8CqVStqO68Tat5ZzZsNJoGaRvTAlA623B16RwTUueyXi6qMj83AaxnIjZXmlZB3Nptez3dF6aonsFAFAePsw+o9uypynVrD5x/3c/sHAGttdvN0eshYULVTCKcsvw2ww8Ob4iYwbN65hdqgKzq3+jxZ3TaDsfC57P/+YqJ0LCGgehtbTlS0zVhDaqSOu3t4c/f0vNFG+mNNPcq549yXto4zMlYbFrMBirl+Dvr7bu5zMnDmTH3/8kfHjxzfodmTxQ0bmGuS57rNt0/seGU1u9nr2zprHB58OYsjQOF5aKxgxRfmpAKg83LFonScGrCvD7xYyMqcV5mAoLSQv9Til5lRcPNzJPXYCfWEhTYZ2oCglje0zN6NyUeHqpeXE6jMAaLVqHr/1HwAee8yDTz75BLW6bo+qF1bZH5y79gTapldO+ugi907mctOsuT/T3unLi5NW06fTbO5/tB1dJrRGqXK8frPLBIM42NNu1D/Q1Nc27WU1pg+et3sFJJcIRvArM+1G2j3jhOR+0iSnBQZ7m6LDxN5suwigsl5y0rCWG8IE93bRSwWgkac97r/Qmu9ifYY9QevhRCGJp1KS06J3SyFXx7BwewnVlBL7iLDoBaKpwvCvb8RR6ghJpZhTRfZjEWAVT4J1KvbnVp3HQaFQ0Pfetkx/uiMb1pylrMyIeyNf4loKpWbFBKYFevt2XFXVP7OM1rgjaTLW4wXlNK2iZK6QVkGBf4hwbLMzijmfXQI1FKzIyS7h8IEsNq1LYvVKD0oK9ZRYw6SCIzwZdFtzyhUK9i5Nv+7yKwUnhBHdI5bdP2yh2Q2t0Xp4YzgLFl/hPLoHCh5aBceF5L7lOSosJhX/PfvxZevzhVKc70qjQZ05u/FXUneepDirCLWrmqXTVjhN2NddP59Ro0aRfCCTiFZBddpW7plc1v1vHRoPDd27d6+vXXDKE0/N5fChdeTkJNOr91189ul4FAoFR3+Zy7Zt2+i6fDFJW0+StPUkADmv5ZC+ew+nFwu5P9K378TFw4OCM2cbtJ8yMjJXN3q9vsGfZyCLHzIy1zwadw2D3+zH3FG/89Qjy9l95AHbdyajYFAo1ZWrRUg5f/48a9euxWg00r17dyIiIqpd3lBeQuLmPynIOGWb5+rni6uvLyNveB4f3xAWZ/0KJGMymFgxbQMaD2/ueKANg29tRuLhbFLO5PPl51+yaf8CnvxwAEqlAqlnvpg0cUovQeixWCzo9XoKCwvJTy/CZDRjsVgoSldQnHGe/DOp/OD/A/fcc09dDp/MFUbjyM+Z8hxEthrD6sWJfP/5bnZvTeXrWTeivEQG/9VGkbFy7pA4T0EA2HPeLsY097Y/B8TqsFJbfVu2XZi5EFpbc6GITiYxdv2Gdn5W49dkYcQtQqnQYwXVJ70UhR7paf/1iPBcaN+r6vWOF5Sz97y97WERjt5DT7/Wk8FD43jpyf/4dsZOhkvexYqLizl58iTLtk4mLbmQrWvOsX1zCmazBW8fLT2HNyYk2B1vX1fadg0nz13Y51/e3kxsbGy1+3MtYtSraDO+J6l7fyf76C4i2vTDHFT9783VTHFGHgA+0T4c+HM/0d2jq6xUMHLkSLyj/Fn64U76vnITRdHCNZmXZxfninKFkJascz4AmA0GDsw9QPrejWi9/Ghy490Nfl2VlRWxccOvmEwGuve4w+E7V1fhvu3+zA0c+H0rxRn5tG/fnnPnzoECxCqchqIiijMyGrSfMjJXOmYzmOvZU6OeC7ldVu6//35++eUXXnnllQbdjix+yMhc4/ww7GcA1rRfR/LudN5ZlYRbhDCa+tZHfrw83g+XnP+Y/M6NldY1mUxMmTKFGZ99hr5cMHyUKhVjxozh61mznL7UWSwWtv73NcWF54npOByNmze+PUJQuQgvvD7ZIQAExncgZetKAJoPuxedXwjHPVw4vlxop1mPs3Q26tj25UZevuMffALdKCnQk5ddQnmpkQH3taX7rc15a/0E0k/m8vVjy9DX4IL/7MFnZfHjGiE8yovxj7Snbecw3nl+NS+/sp63v6i+slFteWhCEl9/Jnh/zF8v5MrJS7QbyOPvSbJN77V6Mzzd0tM2T2NV6YySjKfrMoQR7nkb7N4nv6vtby0zRwtGu95s90TRaIXr2c27jIJsx3ttQXIRIyM8uJR0CxSMshJJmIm4i9KomTZWkWNfNZ4e1VFoDUMJk3jOtPEVjKwCgxGNsmYvtU0Z9uMoTTzrr1VWCvERkZbF7dY9nAce78BHb23mJnNzAoPd2bk9jUO7MzBaywirVAqatAnmnhe70aZ7BMGh7iiVClsZZYC8QsEDpLSwHPc6erBdK7joNER2jSNp00HCW/Wpcjmz0YCxrATBar46Sd2ZiGe4Hya9iaKMIro+1q3KZZVKJZ0e7Muaaf+y4sU/CW7qQ1TnMEK7tazkIWSxWMg9eojUDaswFBcS2KIjYR371ThwUR/s3rUEvV7wOMvJSXb4rrRUeK5t/niZoJRaLILwAfg1DuL8iUwAovr3wrfx9Sf+ycjI1J6ysjK+/vprVq5cSevWrXFxcXy+ffRR/XhvX5+/xDIy1yGR7UJI2ZuBm68rhxYcofHgJqhdNNz2UDs+eHYV877Zyx1fOK4zadIkPp0xg+BOvfBPaI9SrSb3+EH+/OsvzuecZ8mSxZVe0nLDT1BwPpXOjw0irINgQGac1mCpYG9kHbbH/6o0OhQKBRVNkogu0ejSMikrMWI2WwiO8MQnIJLcrBIWzdjBnuWnCI714diWZPSlRro+1AG3AB3n0zzZ+/1SANRurhhLyvBuFIG3+cJLdspcWfRt/rX1L6Qe6cEnH26n/EwRXTqFAtDYWxAr/j2bZ1vnzll2gWLWvcL8eD+7qPHMG4LAYLkCLxOvAMGYP1EgCAOxnmoWWEu2tpWUzRTLx0rt+8PWRIkR7vYdC3QVhJWqSmjWB218NThzxhHFiJxy56Vta0JvHepKsYYppUoq6HSJtLAtqXrj2VXl6EW2OFkw7IaGO3qA3DmxFSazmbnf7MNigeatA3locheatQokPMwDX38d6Xr7gVYqIcpdjVFySN3VSrJTC9m/7hwfffTMBe3v1UxepnDP+Se05dSqw3g23kVhUmfb93oPa9LeUj1HF31LeWEuOr+rM99HaFQeif5myrLNpG86iVewO+26Vh/Ocv54cxoP8CT37FHystI5+9kO/Fedp/noG3DRuVJwTrgm07atIX3XRrwbN6dx27tw9fa7FLvE2rVr2b1rEQDe3sF4ezvuT9OmTfGO9MU70o9zW08h/RGPHdiK84mrwAK3tulYb4aLjMzVipzzo3r2799P27ZtATh48KDDd/UZMiqLHzIy1wkBcX5YzBZm3zUfgBP/JTJy/i107BfN7Y+2548vd3MqpyVGl5tQKpUYSos4OO8zQrr2JaRzL0zlZRSlJuERHo3LwJEsW/wn3Yc+jk9AFEvnvmDbTsG5NAD8m4Q47cdO/WlM+jJStq3CNzaB4oxzJO1eTtzIsfiGFNqWO75USM5qiYizzbt3on3EPbJjGAfWnSP9VC6x7UMYcF9bCnyEF0XNqQCajerJsX82YiwR3PqL07J44PEn6uFIylxp3Hp7CxYtPEG3AT/zv2m9mfx055pXkqnEugx7CEysh/B6UOrES0JMfAqS0JNyxxLAdSXdKmBIN+cl8cTQ18G3t0ukBV+NfV2zc0ePSuw6bw/r+e+osP8db2zK0ntbA1BocGzIDDT1sm/HVFHhtXJoayoKpYJ77723dh25BvEMD8Iz1ItzW8/iG175/kw/sAl9cQGNB44lP/kEQQldCO/cH6VKza5v3rwMPb4wXD21lOaVc2ZXGk16RKKoRSiee2A47oHhaKPNnD92guPz/uHkorU0H30DAGk715O+ayPvvfcezz//fIP2f/C4t23TH74wgn79+gHgFxRD1wH3kVVe7LC8n58fg94exoopC3H1dGX4c53569V1BLeJIrRtNE2HtUMB/LVjOXumjWXNq780aP9lZGSuXtasWXNJtiOLHzIy1wmFyna0eySYw78spDy/iPykfB4Z8Sdd721H3Mjm9DZC4pYk9PoVRHYeQkHqKSwmM37xbchLPELqplWU5+bY2lOoVJw9uhmfnlF0uPtlYRvpZzm9fiUqjQsWlQ/gPEeAUu2Cxt0TLGZUWh1KjfMEhBX5+DNJtQBlGMqIHgTHC4+xdTtA4y6McAdG5tJkSCuCW4aSfcZA3slzpG7dwzc//MbeFE9UKheWzX2x7gdR5orE31/HvAWj+eHzXUx5dT2BATruGdcKQ1IWd3jbf+Y63Z9vmy6xDs+nF9tDMz54VfAA2Jplv26Xbxayl0pDXXLL7YZ4gKtg/FokQ55pVo+EEJ1922LJ15ICV9u80Nhs23RmiWAkPdDUXuWopJGwnSnzA2zzWrXJrLT/fhq76JBmFRFaetu9QQ47KZHZEGRbPTmKJa4PYtlb6Ty9VY2Qihu1RZq8NNhVOL7fbLMf577xZZXWkZJpTdDawd/+zJl9yFohJsb5OqvThDalnjOhOuevT3vO64l2t38XplNxZncaTRMC8fK6fitYKRQKIrvGkPjfMTrdn4xqjSCAHNNnYrFYyDi4GbfQCNxbxHByzR9YTEZKslKJHTT6Mve89hjMCpL3ZRDYPICS9EJykws4uzsNS39LrUct3YICsJhMeMeEYzIYOLd2MdmHdxPWpV+DCx9SctISad1aEP20Ok+6DXoAFxdXp8ua9SbK8stw0blwdHMyrp4auj7WBxedgtZ3tCdx9RmOLVpF8uajhC5KJqRzXxQKBbs/e+OS7Y+MzBVBA3h+cA15flwqZPFDRuY6wjs6jE7P3oOHTykFyTmkrNvG8ukbGPJyT9rf1gJDuZEts3eRd/YoHsFRABz9aRamshJcA4Lwim2KPi8XXVAIeccPk3ZmL75BMWibxZOfdIzT6+fjERZIeLe2AJQVCwZGWOMsWx/SFIIhF9SpB8lrlgDQ/u6mxPQ4g6Hc/kjKOyIkelMUmrB4XtiIsldEAG7BaqK6RBKia83u1T9SkJOCb1DMBbUnc2XSKPwzAB56+nFOpRTy2LOr6Bipo3msdw1rXh/0CxGMFqlXRZ413EUa/hHhZr/PjhYIgkmZxMsj1Pq9qp7cT3MkIpLoNeEuyZehuoDNHM4TxKw+wXZDrUiSo6S6nK0ZkvCZU/uiARjRUshfIApaQhtm3K35WrIloTt5TkKIykoM7N6Swoixl6ac+JWGtMJWzyl3oP9nPxlz3AmLFOYVpSaRtkkY7StJS6bgzEksJiNRvYaTumM15zYsxmL58KqoklOSW0r2sRyaP9OEvMOZHFx+ilPbU+mmmcFTTz3ldJ1ds6c7/HvRokWM+HwmZWkadq7+jfKC87TqeiuRTS6dN1t26gn2rPkRALXWjbhed7Dop6lVLq/x0NL7hQGsmbacg8tO0u+xjrjoNFjMZvb9sp3s44LI6xMXT/qODfg1a4PW59KE7cjIyFz53HLLLbVabt68efWyPVn8kJG5Tljzwgf0e3cSaq0GtdaIX1wICT178vVNf3B46Uma94qizU3NyEsp5Pi6VPLOHgHA1T+AsG79SdmwgpL0VNQ6HblHDwAQENqUozsXoTqwCkNZEb7RLWh1/2CUahVQfbLDgIT2KFQqXNw9iOpaWdyI6SOMcHdpYrdWVmzxt03nJgrJHhW59iSnQ25KYvnKSKfb8w+JQ6lUc3j7AvwDoxk8uhSduzf//vhcLY6ezNWAQqHgjTd7seq/0/z0byLTn+lQb23/uz6Am3oLL/GPtLAnLS03VjZ43ayGsUZSinX3PkH0e/UOuxCYLzHKZx4v4+GmlzaB6YXi5WI3RNekC/fn1n/s4WkqH7tg4u4rJERs1iaNoeG6C96m2urxMW2d/X5vFyNse3CC8PxYcbBmDzLRc+REod0bRu0inIfN+3zp3ibX6Xqhbiqyy6sPvfHRKPFQKxzO65oFiZQUGegzskmNfbvW8YoIwDs6iG3rf6L/0Cfx9A7i7LJ/UShVRPYfhndcMzJ2bcbF3RP/pm1QaVw5veovWtxxE4GtWrDhlXcv9y5US/YxwTMyKD6QbkPjGPh4J5Z9tJWpb77CSu1hlCol/z7wVbVt3HDDDbj6BHJ28yK03v50v/FxvHxDL0X3ASgrybcJH55B0TTqNAIXXfXPpV9v/wGAlV1Xct9Ld7P732Pc3S2cU/vSSVx+iMYdQ2kc35hS//7knjhI6flMWfyQuS4xmxUNUO3lyheGa8Lb+9IOVMnih4zMdcSaFz5w+Hf7iS/hHdmEpF0n+HSIEIvrHRNOZO92BCY04fiCtRQmp3Pirx9R69yIHTEGpdaVY79+g8VowDOuJQHtepN35jBqrRtBjTugOiQkjcz0shsXrsH2ShV+YfmcT/VGoVIRkNAeAKXqdL3t45CBSWzaaQ8TyNstGFxak57wFj0ozk0jLekgyaf30DihH8PG6Vn8kxwCcy0QFSZ4gHTocIbVxw+zwKBiQKA9yWlckSRe3fq+kK61h4e4uQhG9C+ni+zzPISQh5cGGYDahWddKDOPFzG9vV3gKzcJAuLkofZcOH6ayoaINNfE0hPC3+yoUts8nVWEkXo2xPsIiSaloSeJBYKw0NirYV4NlqQIfergr7H2y/7SlmENR4lws88Ld9eQUlz7ijGDE8rZe16YXp5i3/9OAbU/b5v3WYUta36Pv3baPUgGtBbCov5eGYybpyC8TBtm3052hQSuFouFJb8comOfKILCPbneWfL4F2Td8TrRLRJYseRjfOPiKc89T8K99xBUGgtFcDrxJP4tovFuUoZ3k2jyUxpzdvVGAlo2u9zdr5GsE8I1sfjZ//jfDzcS3cSP3d07cnTN32QeOEdI25ga21Cr1WxcuZS///6bqVOn4uFxaQXRjFN7AGic0I8W7W9kweyaQ20Gf/wEyZsPcnbtHkpzC3D10vLZ+H+xWKBxpzDu/WAACoWCPckFnJinxd39BE17OC//KyNzLWOxKLBY6jnhaT23dzn44YcfLun2ZPFDRuY6RqFQENt/NIWppzi9fh5ugf6otS6cW7+T88fP4B4SRFFqhrCsWkPSmiWUZqWj0mgxATrfYFw9/XBvFVin7fqF5ePWzO7RkZ3iA8CdPexGXktvwShbnWFfzz/MnrPh/DlPFPpaZjK0EtmyNwDeKg3H963k+P6VZKUeo7j4Sadle2WuTvr06cNrb6wkM6UQwq7fPAsXSmKBkVa+gjgiDUMJsua5GP+mXaCxuAvfu2TbPTI0ucK65Y0urJqLlHB3QSgZ86mwzQdvP2f7LsJN+O7nI0Z6RtXuBTDOWl1k4Ul7f4vyhETJWUe8CWyR73Q9EESP6vg3SRBHyvSCiJayM4WkU3n0e6Ize8/rGVWrHl7bBAYG0mTkeE4s/ImsgzsJ79UT99BQOCV8bzSWo/G0P4sje3dj39dzyTly4jL1uPb4xQmVUMoLy3jtgaW8+d1QfKKD0Pl7kJOYVivxA6BDhw506FB/Xmt1oXHLPkQ36YrOvfYjsWdW7+bE4i0Et4nj1le7suqrXRCgY+zLPQlr7IvBAmaz8L7hFe5DQUrV95iMjIxMQyOLHzIy1zG7frDHG1ssQlK2SVP/5OD+tSxf/CUFSemEN+5ESuIOVGYFd900nOOnzPgFRDH/j9dxTU2mc9fO7Miu/GKqKDBh8WrYmqEWjQKFJLRg4YJYAKJaptvm5euFkduP/2dXUd6a3pSWnUYQFtOazSu+pmWr7owYOQml0t7Wpx/d1aB9l2k4HnnkET754m2mPbyMvx4chc5XGHXv3sqeDPPOWEEUCdTYfwazSgUvgwg3+7weXUusU/Z688OftBtn374jhElEe9o9BLytdnhqsV3gm3uXcG3pTfblfjlZYJtWKgQhb8Zhe9jFnY2EUd/v99tFhH6xgtF+pshuvEvzW4T6C/uw/Yzg7dA5xr7cpUZ7WoXBKpjs/CbKNn9HI0G0uGPUGdu85l7C8T1VZCRUV/fnxsZzwvEb0Vjw9FqTbvfIaO+vcbqOM7KOCEafwurFkXJQIuxq7AJL8XlhO9NW2I9veKx9UYvFwv4/DxDRMpDoNtWXO73eUOvcaHrz3fz89ARathRyobR/5nXhOx9vik8WU2r1uIm/Xcnxv9zQZx2+XN2tNavfnM8TeU9wxx13cMvdI5n6wFKe+2YoZ1v7c/5cMn1aFNfcyGVm8c8v1Wn5srIyzq7bi29cGGajiaUfbiX7XD5YYM/K04Q38QOLBZPBhPZ8JqEx7uScK6BR0KVJwiwjcyUhl7q9MpDFDxkZGcCxhnZC6740a9Gdb2Y+QUriDty9AmnZ/Ta++eZL7nv0WwDad76Z1KRDHNi3Et+oeNu6ZUbrS41KEEAASl3sxkeRmzDKqlLb4+LFOPs/Pk2wzfPQCMZFqaGcng8eqM9dpVlUI2EiqhE+rhoWzvuQjPREQsOa1ut2ZC4PPj4+dHhuKOveWsz66b8SN6g9UT0Tal7xGmT7GTVK673m6Wk31E/mCPf7kUWSHDlq+zNg5MtC/oLx/7OHkFlE49+1sseVIVyNS0r9CC1ixZqsUruBNHy4EBq39ICbbd7ghGqyl1bBwmRBzDq9P9w2T3Qb1oUIwlFpeu3FEilnjwkiR6PmWaTsTSf7WDY3TuvLtH5zL6i9a5VdX0yr8juNlw/67DyHef5x/pRkFzlf4QpCqVTyxRdfAHDT2wP49ZHFrP3tMF2GN+ab51fz+/+2MP6HLAID6+YpeSlo//RrlGZn4mLZj3/TEHyihPv+n3tmVbveunXr0BeWoC8sQevth39jf7qM6E7iv5vYt/Ycwx/pgMlo5qeX13J0SwqRbYIoLaj7fSsjIyNTX8jih4yMjAMfvD2aSVP/xMVFQ6fBD2M0lKHz8EWpdByNbdthGMbyErZs/p3OXo/i4VO9S/jFsPHrVgCUtEq1zQuMFVxnpcmeCrKFEfmytdG2ed2jhBHnx6Yc4ov/2Uv1igSHCMO1f/4+jZ697qR9x2F4Bfnyyv+ESjRvThnqsLzFYuHnn3/mk08/5cD+/bh7eHDnmDFMnjyZ6OjoSu3LXB7cAz0ZOH0U+/84wImlO0hctpOg13sS3yuq5pWdcKLQQBNPu/dHYeZZMo9vo1fbJNRqJTcOacQTj3WkeTP/aloRkqDqTdUnzryaUOuEfdF5loPVwaE4X/KsOCZMRw63l/XNWCosuGClkMhx5MC0i+pDebFdrPh9t+BhU1poz7GRHCh42Eir2VSHLkSPp78gkigV9nNVXGBP2GooE16fXFztgo/RIMyzWCzsnHuAgMa+RHa8dMkqrwU0Xj4UnkrEbDKgVAn3W2F6IUEtGu73pSFw99fRfGAsWxeeoHWfKMa+3INfpm8iKCiIxm2C8L/hTtQ6wbtl42v/u8y9BUNJMSf+no2pvAylWknXJ4YQlBBR43rdunVD4+2Le3AEkX2HEtMxn7KCEnKSChj2dGcK9Ga2zTvG0S0pqLRqkvZlEjugKWeyZfND5vpD9vy4MpCfPjIyMpX44O3R1qnRlb777sv7bdO331vEoYNryEw9jsZTMPpK3ARjQFEqqfgQZA838PARjIrifMGQcPe2u6c3JP1adOSv2cK0UhIqExIZw0NPf8XubcvYuOFXyvRFDBn5IAprCMwr/1uCwVCOyWREq3Vj+cJZ7NyyEJ/YxoT07ouhuIjv587lt99/Z9PGjTRv3vyS7I9M9fw5TvBQ6pH2IgFte3F6+Up+fXU9zUcPIbR9PGeLBYO7R5A9ZOSrfwRD1Wy0V3N5b6IQLvXcB3bjK/vsLs5tW0p0E3963NoKfZmR/xYm8tf8E4z9X3+G9BaMBmki0tTSYnJSClmx9DxF6XkUZxUQ4Cp4NpjNFrILLYS2i6Tb2FibF9axAsET4cn2dtFFY61WIk1U6lhuVdjmN0PsuU76PybcaxZ/+0++LkhoWy2xzQ2F9jZ/OVXI2NhLk6RzwcpQcnqmAJBRYj9m2WX2/RK9QbS6C3eXTy4xcSLLKo7kSbw7lI4vj24+ZdQVhdKCRicc0/1LUkg/nEXfl4aQn+9aw5oyAJHxwn3m6x/O2t2bOF+ykoBm4WybeZT8pDyaDYuvoYUri02/xmB2DSOo6Ry+nbKGCbNvYvq8W0ncl8EfH20nbfavNL5pKB6hl0/U6fnGFEAQ65LXLQNgyPt3snf2BrZ/8R9DP5tQYxteXl6U5eZUKkUcOHMRRzaco/3QxkTEC14kGg8NpeVGGvWOc9aUjIyMzCVBFj9kZGQuGJVKjZdPCMV56TUvXAXF+ToWfCYYm6++dtw239VaCeLDFfb8CsO75NmmD+QKhsbZwyG2eaNuTAJg7dd1EyACg6PoO2A8Xl7+rFr+A8eObiEoOAaLxUxK0jHKy4RYbW/fIPJzMwls05aoQYNRuQhGaUjnLhz/+Scee+wxVq1aVadtyzQ8Gk8Pmt4yktNLF3Pk92VkHz5J6AstcfOre+lVQ0kRSTuWM2R0cx58qbvtpf/W+9ry5mPLWfDuZgb1uM0msGUkFbBhYSIbl50iJ6UQFArc/D1wD/RC6SF4IqgUoCgxs3fuNixFeXR/oD0K5eUfzfnlVCEfPmX3bEguEaa3Z9srsGxc3hiAwhx7OMqWV+2Ji3u8Ldy/Z3fb71NX6sfz5feVQviAodz+KuMXKniENWpuLyn8xXeCsfXYfcm1arckz9WhTWd4+AiirULpGAJkLDOw7+edRHSOJjghrFbbk7HjEexDowFtObl8NyeX78bV25UO93Ymtm/jy901AB5bLuSCkpZt/mG3/V79a8I3tmmVi4ZBk7vzw13/kLjhHO3HtMA/1IPwWF8+eGoDB777ifhxlQcYLiUWs5mTi1eQd/wQfZ7vhs7Pg8Y3tCbjQBK7f1jHY+ZhRLTw58Xec6pso6LwAdD61uasfm8zH935D93uTMDd15XAGG8mPDaJ11953ek6MjLXOmZLA5S6vQaqvYg8++yzTucrFApcXV1p3LgxN910E35+F1cqWxY/ZGRkLpgDilPknU8hKqTySI5Fp0RRennc+/1GHrNNpy2ziieSBPO5ZXbjTGX18GgR0ohWrQbh6xPOqcQ9FBZnkpWZTFRMS1q06gXAxjW/olAqydq3l5zDh/COjSO0S1c8wsMJ7taN1QsXcPbsWTn85Qpi0xvv2KYH+5URGB/J0X/W8dv9yTQZdQupvexGTEgjISxKpbJft18fEAz9VjcJOScOzjuMi4uCcU92dHiB1+rUTHi6E1PGL+T1/5VjKC7CkrKNcwcy0bq70LJvNN9/+RN9+vSpsqb9559/zhNPPIHKy4P4m+I5XSiIDYkFdm+HHSdEkcHuDfJaP3sbPaxlpY/nltLUt+7izqVAbxb2S3fY7hWxP1HIyRJ9137bvKNJ9v6HhQhiQ+GqNigG7Lug7X7xXQQWV+F+Vybb8w6YwwUvEIfqUbU8dAVZHuSmCx4yao2J5PVrKCvUs+H3tcTExFxQP69H/n3gK9t02fgy/vjjD9q3b0/Lli2vKEN5//xjpB3K4lSwjsBQD4LCPMgu8sQjxBtXL/tFIyYTb/2w8DJ/5lQM+62CPYEe/LB0JA+PWcvhn/+irdYdtyBByNs87e0G6fedf9zj8O9fb/8BQ0kpif8u5fzxRHo+3YW4vjHEepRiifXFs6g9W+cd48uHEwmM8sJr8hc89thjtd5eXJ9o/GN92fPbIVZ9uROL2cLNE1rzxpNv1PeuycjIXCPs2bOH3bt3YzKZaNasGRaLhRMnTqBSqWjevDlffvklzz33HBs3biQ+/sK9AWXxQ0ZG5oJRurhgsZghyJMif8Et3WKtnqEoM9sMDUO5/eVVrRGWO3/C7pY/+qkzABzOk7Rt9cAvL/WxzZu/1p4ozmRU0rRDUn3tio2IyHhCQ4TEp94hjuryvl3/QaAP4b16k3v8GDmHDnLkpzlEDxqMR6SQODL5/+yddZgUR/6H3x6fdXcXFpbF3SEkQCDu7ndxI/klFyEXD1GSy13c3Y1AggV3t4WFXdbdfUf790fPTs8yq7Bo+n2ePPRUd1fXbKaqqz71lYICRfw4SREEgfChKQSmxLDtgyVkfPs1WmECMeP7t3u9zWwle2sWVYcqaSyrpb6onsaKJkSbnTefWE3KoBB0ejUDRkYQnehHVIIfALmLfsfSUE/iiHAumTOevuOj0eo1nDfpvE7bd9ddd/H6L2+Q/ms6KTNTONpX9IFqSTD46TVJbPDWyvXds0YSeloX7gDl+bK7z5xASQiodhFQJgZIZbur5Yw0L98ixeFxdfG5ZbmJD6Ycu6CO4rJBANT7VQEglMriUJlZ6rOu7jHeMQ7hJK/7YpClwREfROuy8DbJolhNrSQyiXrZVai+IJ+SjesJHzNOET6OAoPBwHXXde1ycSLIWpNHVU4tRHuzblkuddUOFylBYMQ/J8FhzbaZJJHN2twEyH1Nb9Dw9leTuPncBWT9vpC0G687riLPuPsms/39zdjtNvpdcRF9zpLdwARBYPwV/RlzYV9+fnk9O5Zk8+STT/ZI/Hjv7K+kg3/CwYMHyc7OZtq0ab39NRQUTimUmB+d02rV8fHHH+PjI60R6urquPnmmxk/fjz/+Mc/uOqqq7j//vtZtGjRET9HET8UFBSOGI3BiDE4jPqiXAL7DT7uzz+wNZrACNmkY1eJ9BJIzLrMWbaQdYDkVtDKuAg588eaGimFYl3uXkbFtr8IbsXHN4iy3F0YAgKIGDOWsJGjyFu2lJxFf+LtCKwaGRnZaR0KJx6dp5GUy68gd8lidn+1kuJtWSROG0LogHAEQaAmr5K8tQfIW3sQc6MJ73AffKO8iRoRSXVuDWV7Sqmtbub793ZgtdiwWuyERnlTXS7Fs1Hr9SRddDlX3NTzrAap56eS9Vcmu77ZycD7hwFgt9mxWuxUFzeQ/tMeSnflMvi6SfgnnLwpVG9ZLrmdPHWd1D/f/Et2X9MPlHbACzbKmWSa7JI1luqrQc6ykMtdLTyOfoLnHdNMXJoU66W2Qm5P3nrp7yi6pLJttQIRoa0AchitGa0szY1k/fITHoERhMaOPeq2KpychKeFUJZRyY2zRzJ4dATNTRbeW9pIxoKdbPlgJbPTppM2WnoH3DT8IwyBQfin9Kdw1VIsd5yL1iBNu00tVtRaFQmzzmbvp19QfTCTgD7Jx+U77PxqK/t+3U3cuGhG3zYMjwAdCV5SuypcBL7q/ZXsWJKNzkPLZZdd1lF1XZKcnExy8vH5bgoKJzOK+NE5L7/8MkuWLHEKHyDFFXryySeZNm0a9957L0888cRRC6mK+KGgoHDE/PlQNVP+tFLn3UzUYGmxU1clLSpcYwDYrfLuaO4Oyfc/OF4WLXYUSrusOqOF1AD5WoCZE8tYuOr4LPI25u4FYHzfIe2eHzx8Gru3/0X5ju2EDBmKSq0mbtp0jAGB5C5ZhK9/KO9+voMX5sQdl/Yq9IxF973etmDu89z8whQWf7KLjf+Zj0+IJ4JKoLakAb2PgfgpScRNTMUrVHJT+eG698nPzycuLo7UIWE8/8k5WMw2dm0sYsuqfPZsKqK2zswD307nqanvuzegGxTuHUTkqCb2/ryUN/JA62mkOiODhlJJHNAYNFhbrGx5/y/GP3Y1KrWaW96SLTamzTwEwMoVcgrbtDE5AJwTJffJewdIx4nj5R1fvabJeWwVpT4X5BIcGJMc6+N4c2a45CLz68UbnGXCd1KmJtFfnsrYqqTFW85fsuVJn3Mk65TiLFls6S6CVQSrQwhxFUGssqWLaLeTs/JXRJuV+PEXIKi6l1VG4dRDNM/EM7CZf9+5hDH3n0NAYhjPXmbAeuFkbv9nC2899Bez/zudpIFS/xEKLYTGjqA6Yy8rv/TGVFtN2fZ1vFVeAgIMGh2JRqNifGwBsy5p3x2uN8lYmM6+X3cz+NoRDL2i4zgq9WWN7FoijSUTrh3gTOGroKCgcKyora2lrKzMzaWlvLycujopa5ufnx9m89HNRRTxQ0FB4YipqzORnl5BcKoBu9WGSnP0k/50x+KlNeDggaUu6fZcQ4i0rkP2yIEUGzWOAKRRckwP0VeqZ8SlB51lmVnSIJp0qGc7tDHxaQwdeTbb/vyDmsxMfBMTsdTXU7FrFyq1htrqUn76ei4vzLmgR/UqnDgGT4ll0OQYDmwuZsPKfOw2O14p0YQPikSlUTlTmrYSHR3NCy+8wMMPP0zW3krGTIvD3GJl3/ZSSgsbuOKZSag1qg6e1j1CBo7GZm6hLn8fdpuNgFg/+p0/EL2PgahhYZTvK2PZ00s48Os6EqYPB/y7rLM3qBWk/n1xrCyiqBwd0TXjzKVJsqDir5PuefdiufOa7Y5Ar2fL7jNBRqnvTnlI+hxmkXd+eovwxAoaHdlXcnfK44ZHpGSh01TukgGmVdxotoOv+7gW0+QHQH5TBQXbl1BflE3yhMvZ/dObvd5uhZMHlVpD4piLyN3zJRv+s5DR986CKcFotCpue2EK8+5ZzKt3/MlDL01hgeEWIASjjyS6FW9aSX1uJj7hiYSMnIXdZuHg/u1YrXayM9zTsPcmvj5minaXsfOLzQy8uB+jrkpmoL/8e89vlFzjKpsELC1W5t+/BEuLjdSLhuE5pHOLSAUFhe5hF4VeD1B6OgU8Pf/887npppt49dVXGTFiBIIgsGnTJh588EEuuOACADZt2kSfPn2O6jmK+KGgoHDE+PjomTw5hlVrCjE3mTD4yIsi78CmNtYfJyOZCetgtxQjwKyRF296j/bTUwqCwNkX3ElEdAqb1v1G3uJFaHUG+g+cyIgxF5Cft5c/fvkve/bsIS0trd06FE4u7h79iXQwBrine/c89NBDJCUl8fLLL/Hus2tRq9XMnDmT7z5/lNGjRx9Ve7a+83S3rjs3ZyCLvtmNvSCXy/57tjO7zPK/YphyRt5RteFkoERbR6KLpUVrit8df7mYz2slQeXci7OdRUZHlqhfFsmi6cFfpKwryRcUHVljaqWxwWBxEbUcXjNlB7dQdnAL0UOn4RMWf2T1K5wybPlOCkg6+DqBzEVfs/alX7kxczTxUwdy+3gdL74/g9ceXcVz9y3l+vtG8O9HBnDuwA+ZNSudhQsXEpQwmNiRZzvr8w1LZM+Ct9nwVx63P9E7AcLf3SQFN433kqf4ldk1LHp6JWFpIYy6cVBHtwKw6+udtNS2cOazF+MV2vsipIKCgkJ7vPvuu9x///1cccUVWK2SIKvRaLj++uuZN28eAH379uWDDz44quco4oeCgsIR8+7PGaxYkcewm0YREK0CWpxpvBpqPPAOlMzo63JkEaQ1NWRSPzkVZWmpdL6iwM9ZVpXjJR0YXfzwTS7ZGBybseFeclDS+CjJ1F9n1MvXlUn/bFqczMhpsvXHkSIIAoOHn8Xg4WchiqIzSJ3NYsXPL5i//vyY77//XhE/TnMuuugiLrroIqxWKyqVCpXq6Kw9espV941g9LR45lz7Gzt+P8jQ81Oc55b/FYPnIZfMEyVSNqaq8XJK6sRIKX1zlGeLs6zOLC++Bjh2haM95WlCabMkAnybI7vHeDsEilCD/P21arnPhhqkOktbZHGx1UqkuMnGNYnHb3F18JcIRMfXEUQ5hW9wreSikCdWOcs8mqULbS5DTpBRdksI8fAjN2cnBTuWMnv2bF599dVj2HKFkw21Tk/yzGsp2rKc9B/WYbfZYXx/dHoND708hS/+68Mn8zZzYHc5MxZaGDRoEAv/+IO60uw27w2dly9qrZ6mehOHthXD0N5rY01VM9kHqsjOqGLRR7sIDPPiP+9Mx9Nb6tsNVrm/+zjStvs1NfDl7/uZfm0aHorwoaDQqygxPzrHy8uL999/n3nz5nHo0CFEUSQxMREvLy/nNYMHDz7q5yjih4KCQo8oK72Xyspmfv75AG/8ZzPDxkWRNK3viW5Wh3j6S5ke4tKKaWx2xBbxkDNBPHRbIQAvfijvFDfWSG4znn5ydP72cI3Ob7PaUGu0JPcdydvvfordOAqAZ/41sxe+hcLJikZz4l6jCalBJPQPomBPWRvx41TiC4cLWisP3S1/XlUqKw/PPB0OgGCVfX3FQOlv3+iyiGvNChwcLbvU1GWG0qjq3XgluTk7WLb4XWLjhvDyyy/3at0KJy9D738SkLwuVWo1UaPOxCusmQO/b6HsthhCIrxRqQSuu2c4fdKCeeGBZVx81QDUagGdpy/mhhrqS3PxCYuT6hEE9N4BNFUVU17SyNSXHgBg2UNHLqaV5dfx+fNrObitFAC9QU1EvyAumzPBKXx0hLe/gajkADYvyeGxa9K4b+pXR9wOBQUFhSPBy8uLgQMHHrP6FfFDQUGhR4iiyA03/s727aUEJ/gz6d6RNHrLYoJWL+2otgku6LJ72u+MXEC29gCoLHYP9DZ8ViYAO1YmOstsWsfucoPodv3JgM1qo2/aWPbsWE5leSGBwUrmF4Vjw/5aC6IoUphTx+VnxDLVERDUZ5qU/rnsl+nOa9dU7gPaprLVGSWTqAN5spWUa3rYQw2SdYdWJQt8rWE9WhrlBZRJkPpi1kFpZ2bsgLZixsmCp11qc2Ojw9LFxVDH4CedS1XJ/bVJJV2nV7vELzF6IYp2tm6ez6aNP+Mf0YfAgVOPu9WPwkmASxgY75jpqHWHeOK+v7j03xPwC/NidLCeCWfGoXpuEk8/vIIRYyMxN9XjERDOwZXfEJoyiogBE1CpNfiGJ9BUVcyn35Yw4Nqjb9r279Opzq/nH09NIDEtmJAobwYEGFhU1MTWShPDAvUd3qvVqbnthck8e/18Pnl6DT6+NwFS5preZM7chc5jZYNA4e+CYvnRNcuWLWPZsmWUlZVht7d1Bfzoo94ZhxTxQ0FBoUeUVzSzaVMx//vfNCr6hwLQ2Gzr4q5exkuAOmlQrBuU5SzWNUnR60WXAbOxVJroHWiOcZa9cn2Z8/j7XCnYYWvKSoBys2T+bjXJi0HfUNm9pjMSk4ei0erZuPYXJp91rXOSp0zwFHqb8pwaWhrMpKQFd33xcWLdbh+nuxtAUqjUhywuc5hWPaXFxadkxzYpAGlm31Jn2W39ZFPX/cMkNx1/b9lNx+ApWXMs/yzWWdaa+UVokR8o13J0NDbWsOKvj8nN2Ulk6gQi+o5vY/2l8PdErdOTMO0SStZ+y1s3/845949i9JWSNeTMC/tQWtLAu/O2ANBUVYxa50FZxiZqiw4SO3Im9WXZePnoqczIxtLUjNbD2Nnj2mA2m7nrrrsIDg7mnnvuwWq1snl9EZPPimPcrLbZXKZHeFDaYsUmSv3OtU+qW91wVBAR7cPE85JZ8fOBNi46vU1hfgYfvzWbvTsfov+gSYDynlRQ+Dvz1FNP8fTTTzN8+HDCw8OP2dijiB8KCgrdYt2BWwG467r5AOh1nWd28Q1qpLbC85i362TBbpNmkuWlFaSkTmTbxoWk71zJ7Me/RqVWUl8q9C5qATLWFqAzahgwIvyI6zF6mWhu6Hgn+HSgIdBFnDVKlhz96kKdRV46abFptllpD1EUycnczLYNP6BSqZk07TaiYgfwxXt3HLtGK5yUbJv3pFvZBR/eBuiIHHkt6d8v58dn1xAW58t5Y8Kptdi54bYhJPcN5JX38ijZug+7zYwoipib6slY+jkarZqH503lhfuWUbx5LzGThnerLXa7nakXprJ+cTY6nZq5L76A3SaiUglMnXF0wXcrihoQgAPbS0kZKmdGuvI7KZiqRiv3qfQ/ZfERR7yfMRpJeDl4cCPpe1eyft2fREW5ZG4D/vz1LQCWLHifooKDTJl23VG1WUHhZMduF5xx8XqzztOFd955h08++YRrr+0FE7hOUMQPBQWFbmG3iyyZn8n27aVoDRpKkwJb5zk0mWSzb1fTet8gabfWI7HZWVZR6AeAVi9bVajVknBw7ZQaZ9lXq6UghK6++8X7A53HuhDpnkGVk51lC3PXSQca+WUQb5csNuqq5d3o3z6XXQJ8PKSF39QYk7NsWfY26V6LvKPeKmB4B7m76BzO8NEX4x8QxZrlH2M2N2Mw9tbes4KCzMF1BYQlBRDlI4sXl8VLx1dU7XOWGeKkhcqFZ8gWT4u2ugczdDWfbT12nViZm6WgiC2N8vPMjpTUrvc21MgubVFBUoaVggrZfcRmlcaLslzZmqq+XBIgzhwj1/PGFlmMGDNCavvSZbIFl7OtfvJURlUsjSv2MKmtgunoMmg0N9Wxec1XFObtIS5xOMPHXoreoPRnBXe0HnoGXDON2pwSVn21h/DkAMaFGGixw7CJMbw7Joq9WxP46ZNdbF9XiM1ixivISENFM++8tBG71UZLfTM2W/fcqL799lvWLMzi7hcmM2lSNCsWZuEXaMSvXxDqACMGl8DDrsFNne11eUxGrdRvWo2xpt47iqonV/HKHX9y/uxR3OSix1RklLHp3TV4hXiReEYipjoftJ7eqNRyP7RYTKxZ/SXpe1cC8MUXX/Cvf/3Leb6mrIziwixSUscginY2rvmZ+MRBwAXd+u4KCqciittL55jNZsaOHXvMn6OIHwoKpxDn3yAHQfv1kweO+fNuXHA1AFnLs8n8Zg/FhfUMmBLLzDuGoVKrnOazClK2F4Dd9XnSTnH2JlQqDTm5BWi1p/fOusKJobnBjLnZ0vWFf2NEvQpcUuYKuI9ZWseirdEiu9R4aA1UlGWzeukHiKKdcy98gKQ+I3ht7uXHvtEKpywqtYrYKUPY8/MqZt7VDCFt06b3HxZG/2FhFDdaePWORZQU1nPWvSOpyK7Bu08aoUNSu/2sXbt2ERzhxZjpCXjrVZx7pXRvbmP7Fkw9Yfsfmag1AnabyM8vb2AS44mfEEf+xkLWv7mKgLgAzE0WVr+6GlgtfXetDrXOgN1mYafFis0mjU3+/uHccUdbK6mKinxAZMLUKwkNi+e5x86ltrbiqNutoKBw6nLLLbfw1VdfMWfOnGP6HEX8UFBQ6JLMJYfQalV88sMFJA8IcZYvKpIsOkwuARAbqqUd3LD4SrnMZSdYb5T89FUaeSfKL8SRXcXFYkPnuM5i0jrLfKMancf1lVKdfx3a7CwbFCyZ+hq1Lu0xS23M0ckWJBVjfnIeZ38hZclIC46Tv7BjxyzCVw7aWlAlxSJQVZeTktzWl9oVURQp3L6M6py9jJ1yoyJ8KBwTLo/3Zn64J37+BnxcXNCCHW4dbz8up5IOMkh96NNMuc95+jj6brPcv1otsEC2zrCY3KcJGq28uGrtx65BUF0Dp+5Jl6y1Wvs4yBYkbXD0uXW7ZYuUxjo59kFdlWSZlTSkwFlWki3VXbNP7mP+41qo3mBEVWJxWn8cCYcObGTD6i8JDY3n3AsfwNPL74jrUji9+eXmd9p8rrqmiuBfgshZl8/BKNlKKMIo99MkXz3/fmkKN573A7sWZnLmvSOxBoYDVqCS7pCdnY0h2JOtlSZS/VwC8+qkvqt28ZdvbMfy41CD3I9nRErv0321Zg7sLGXxO9sIHRBO0lkpeAXrCekXQll6GWtfW4EgQOLQUJrrTBh0AuXZNZgazNgt0n+e4ZHYGkzQ0gCIRI88Hx+ftpZmgUGSC0zG7g0c3LcJAIPBAwWF0xnF8qNzWlpaeO+991i6dCkDBw5Eq237Dn/ttdd65TmK+KGgoNAplmYLtYV1TJkYTdrgUEw2xdoj46CUiabJKrvKVDTVAmDyaqRs/yY8I6IptdZTlbsZjcGTpqYmPDyUyZ1C71BT08KObaU89uT4E92Ukw7/0c0OMUZy92nKc0/v2WKTU9+arQ6Tf0eg5JysLaxf8Slpg6Zw1tn/5LUXrjj2jVY4bQgICKD/6EiWfbWXIZNiCAhtP/ZVWKQ3l744lSWvb+LLu/9k7D2TiR7d/VgdarXaGWuqN/nj8z1EJvox/tFpCCrBKWaue3MDAKIIexZn4R3iiUavJqRPABqdGpvFTtG+OhqLC0EQ8PKPIDptCkbvQLdn+IeEEhQSw+oVcipdX/9Qt+sUFBT+PuzatYvBgwcDsGfPnjbnejP4qSJ+KCicory+/gYAVqTLZrWH70D1Bum/7MdUb+bWe7sXhK2VkuzANtYfR4pWb2lj/XGkpBHBHoqOuh67aEcldOyTrfP2JWzUJEo2rSKrKN9Z7un5FjFnnEvYqP6IoohKrWbDs88ddXsU/p7sXFWAzWbnzOkJR1WP3mhpY/1xpBg8zW2sP46U4kNBhCccvfn7kbanuGAfG1d+Tv+Bk5lxzh1KNheFI+KyB0Yx784/eemfC5n93+mERLvH2AEI7xfE+JsG8fPjK9B69sxKsG/fvvz0+4+90dw21FWbiO8biOCS5rqltoWi7cUAXPLwaEaf14eyZkl4aXbZEFm/LA5LQz3aQh3qLqweo2P7UVGWR1RsKlfe+BR6vbw5MPvhbwHIKpUsvURRJD97B5WlWQwdPI7YhIH4OCwzlQwxCqcKoqjCLvZuanSxl+s7kSxfvvy4PEcRPxQUTiGaBkoTgVljqru4sneYFq5n6fp8xk2LZ4uohvwmIjxk092sSmlylJsuR4OPS5MmSJYWDXaH6XyrqwuA0VuylnA1sW8NqphZJ0+iLh8hHb+7QA462praEkDtMLe3RMkLt53NedKBHNsULFI9+maB1qnYrl/kBaPK4eefUSkLFfF6aVJl8JLN7lV10nc5VFvsLPPRyZM1i6/guM5ORN/xhPcZi1VowdLciMXUSN6y36g+uJfijX9hs1jwjY9jeMkqwgZHM3FI28XnQ+M+RUGhI1paWpj76kbOnBLL8MS2KZj1GneT9wO1Up9L9pFf+S2R0u8+t1yLRidZSHj6yDEv7KJ0v6XFfZpgtchjgNUsHYt2AQ8v6X6zi6uMSiX1v1Z3OICERMlKqrJIDh588fmHpOus8hiw/C/5/PLbpf5ur5f73DnFktn+jOFTnWV/7FkPgNAkjy+GFNm8vyVfam+4j/tutFhXzNplHxITO4ApU2/i5WcvcbtGQaE7aII8+eebM/j8waXMu/1P3vz5Ygxe8jjv7YhDE+6h5pef9uMb4sEdF4aj1XV/ITNy5Eha6kwEZFfSOEC2mvDXSf3PbJf7kqdjXLC4lEV7yP200iSNATYR1FoVzU1WZiQKXD/sI/75x1VkbcpzXluWVYNBJfDcpI/ZvHkzd3/wADV5VRRuyaOlppmIcWOJnjyZDc8+22n7h46ayfbNiyjITWdkf7jkkrYiRmlJNjkHtlBXXURNRT41lQUYPP349YBkgRIQFEm/tHEMq1zPeHs/530+wX7OY0UYUVBQOBxF/FBQUOiQp25eQMGhGq6+b8SJbspJgxigQajqOqCcoFKhNXqh9ZB8vtUGD+rzD+ERFkVQfDLmir1s/eggAAdGhDFsRiL9J8agMyjDskLn7N27l+ycWt59c9qJbsppgyjaWbTwfwQERjLz3HtQq5V+qNAzHn12vvPYZxL4hnjy9Htnc/t53/PTx7u4bfbIdu/rMzyc7J2l3Dz1K27+1xhW50zgWaSAf0KNnFJ2y7fPt7lv2rRppA4J5f2XN3LvR+eg1hzdDnBjg5n3n17Lvs3FXHT70DbnDq3OI2JwKBPOjOfbVzZgt4k8vCGexuICBLWA6GL9ITpcce59QHZpeePVq9yeFx6ZxNhJl7Ju5fc89srXvPH9HlZ/+yQANTWlfPXZIyAIePuG4J3kw4CbziByeAzWpkZK95ZStL2Qtcu+IznoKojp51Y/wJy5CxUBROGkQRR7P0bHqZ53YPbs2TzzzDN4enoye/bsTq9VYn4oKPwNqSyUdkI/+8GXba8+CcB9Y47d8yoqmhkyIwHvQWFUm6UJjUqQd30baqTdXEuZPOnS6iVhoLleNnnVuQRAdKbQdNmZtjtS++0slkfxLS3Sc3wC5CCnVos8ZDktP5Db02rlgcvumcoxeYzwkQO15lbLKT9TAqTAa66ZHqIDJUuW/XnZ1NeWUZC5har6MnQePgQMGIwhwBFo0UMWQQSHtYnQKE9WaZCORV81HgGhGP2CiTn7PFQaLYnDEjA3mCjckkPVlgN88/QadEYNQ6cnMMDrZnQ6NVP7vYeCwuHEx8fj7a3jyx/2kzY8nCCjvKNsr5N+iM+ukvvSqCTJYmp0sLsZur9O/r3mNMp9sq+vVGdFi3y+dX1jF2WrimZH7Iw6OfyN0xoEQGtwFwr9HQFaR4+Tg5cmeEtt+2mv3Aa/kAbn8dmfwZ/3vEGN5T5nWdlWSVj807TOWSY4ZoL2cNnlRVDJfRuzdF502QE3eBnZvfMvqquKufTKf6PVtc3QoaDQU+pWXsLzj5/LP/+4itTzU/j58z0MO78P/iFS/A8/xztKpxI4+8aBDJoYzZv3L+ONR1cy8LrhaAzGzqoHJB/4ybcO4+07/uDlm37nyicnEhLrS4ujoyZ7u4wLjixHKuT+9W2W/G4VrWZ+u+cPmqub+eyzz7jmmmucLl++djvFu8qYfvcIoqYnMtVsY9l/NqPx8CTxvKsIiChi99cb8Qr3RRc4AL/Efoxq7t/536e8RvoOIf0Ye/FjbdLkAnz4zt2I5u18+tln6EI0GPy98IuVrNwMvgZix8YSMyaGvE3l1OVlg3sGbAAs5hZmvDiDpho9Kq0Kv9ggTCukgOV/fP5wV39iBQWFY8z27duxWCzO445QYn4oKCgcF8KTAqguaez6wtOUjJ2LObBzCd6+BmIS/cg5mEnJ/vVEjJhC2OBx3apDQIAaG/EDpN0nsUkArOQ5XIXUIbGc/3IstcX17FuazeZv9jJPFHno+UnH6mspnOIEBATw4IOjeOqpNVx9TRqTR0Sc6CYdF+x1D/Z6nU0tNSxb/h7796xl4LAz+fbLfytxPhR6lUGX9GPfH5n8/v4Orn2s/fdGRKI/U5+cwW93/khtbgaBKYPdrpkzd6FbWfS4IG797wy+nLOCtd/v48IHRx9RG+tLGqgrquesp6dw7bXXtjlXfqgG0S4SlSa5oA4+tw95BanYrRbyly8gq76WoL5hjLz9DIoPdpwJrT0EQUBox8pKEATeffddEuOz2LCpiPXr97HhUA5XvDkDs0v/DO/nBWIua9X75TL/CuxWG3lr9pG1aDPN1bL4aQz0IipqDNHJozj72hcVAUThuKJke3HHNc6HEvNDQUHhhGK328nfV0F9RRNrv03nUHoFsx7t3oIfICiylopCX7dylSPWh9ol1W3rcWt6Tdey1hghAFqDLMS0ps4syJAtOlpapJ0uoU7erdaqpF1mg0beCR6fMFA+r5fuUWvl4XDHwf2U5O7iwM4lXHfXMC69eSA6nRqzycpX7+zg6/eWYwgPxS8qWf5iJmlnzVMt7xq7ZoPpCt9wb0ZfOxCvIA+WzNvIlLMTee7zCbTUmRgxOpzAaB/6+MrfwdWf+/phH3X7OQqnBzfcOJAvv9zLA7OXsmbxlRiNRxa0NMpTTYHDWmlkkGwZYnSknrW52NS2xhFx/e21UqeV+7PF7pJSV+Ow4HKxxopyxA3yd0nRG2aU+t9PuMYHkhctP17sDcCGUtkaBJsji4ZLnBB7pNRHNHp5DGiqlfukyiT1SY1Oi8nUxE/fP4/dbuX8yx4gbdBkRfhQ6HV0njqGXjWAde9uZdx5ySS4pIt3xTPYi+jRsRRuWo65sR5jQCh+vgkIgsDZQ9rP6tS49nLe/tdM1n0dQ0N5E/46FR8uk967l06UY4NpHcFLd1TJ/Wv/ejmzjL9fDQCCVk7N28qmv8pQaVRUqoOpc1h5DjhXTU1uHYd+ryNmfB9yV2e0uac9ocYVtVbq+82iud3zQ+/+NwCL5wzlttuGsnNXGWdN/4Z3L/0Bc6NsSRo1NIya/DqCXe6tySlj2wdLaKqsI2FSPH3P6YvJ5I25voW8tRlkrVvBoT0riEoeyeLFQxgwYABhYWHHvO/X1taiVqvx8nL/GysoKBw/FPFDQeEUotXV5XggCAKpoyNZ/+sB/nhrKwBDzu2DMFCeuNVXOoItukSFdxU1giKlwIaNdaeeGXluxhoGj4rg6tuHOMt0eg3X3zOMnZuLKdi+oa340Q7J/pEANLuIIPm15QDY6uW/2TZPeee+NK8KgEdv+xMAQSWw4S0R3xAPhoyNYsCoCOL6BWKy2LGYbWRUmdh46BI8/KW/8StnfnE0X1vhFOGClwMRhl7Mnu8/5s7bF/LhixOkXVR/SSSIiKhxXpteJS1Yzo2WX/keGun3V9hkI8VXOt/q2vZ34I8tv3Nw9TeoBBU33j6P99+860Q3SeEU5/nHz3Ure+9sKe6FbZqN0KVhvPPEGs5++Rw0eqkvhvnKIl10gMC59w/jx2fWUnVgA6Y6E9GDziQkqetMawGhnhzcUXrEbTc3SCKE5rCYU5mZmWQs2EXStP6otWpEu53inQUcXLyf2HHJ9LtgGOk/b+GK658mLmEQAHP/fX67z2hPELHbrNSVZmO3WrDbrSRMvgSDbyD66FAEQWDaM75se/Mphg6rJjJ1KQV7ytvcb22x0lDexGuX5BES6kl5WRPnzlxIYJAHd807j/BEfwAO1VsBLYOnDKe0dCD7F+xn3/wtTJ8+HQC9tz9+sX25ePqNBARFOuvvrXghmZmZJCdL84WXXnqJe++9F53u6LNjKZxa2O2CM8B/b9Z5KtNVnA9XlJgfCgoKxxRBELjs4TEkDwvjsydWodKo8Aw4/iKGWmNvYxHSFaIG0MsvA7NVmlyWNdU4y/w9vJ3Hdsf5Vp9DgP7RCSwqz+fqf7oHVBEEgfFnxvHR69tIMcsR9gP9pFSGostOeVVzfbfb3YpnWDBxk1KIHOJPxKBQdN56hKwKMjcXkbm9hBW/HHC7R6NXM/ySfoy4PLXHz1M4dfEIDidm6rl88cvPJMb68Nidg090kzqlr49kneLpEF70armfejusQPJcMkdptPLC8Kx3pMVZQ4183sORVSbcW854k1UuLQBtLnULHrKoY1RJ1i160UKB0Ztrb5mLf0D40XwtBYUuUavVjLlzLH88vIA181Yx8cHJqNoJUOoV6MG0Z88CYPOHW8j4/S+8Q+K6rD8owosVP+zn/2Z+i13vjaWphaYVIVz7+DjK8mrZu7YAD289XuNiUKndn1u+vxS1zkDJ+hS4Ty5fv349douNof5X4bshlN93fUvh6iUAGK3x9Bl8AY1xZn7/6XVuvG0enl5+QPuiwaHM7RTlZ2A2mzAYPPjzt/cYP/Fbsjb87HatxsML/+T+RI6XgjovXLiQgj3lCCqBM+8ZQdzwcOw2EbNK4LvbF3LetG+4874RLPojC7sIN790Br5BHm71Auh99Ay6chDhYyfRXFlHfVEFeSsLqTiwnbf2rCc2YSBDR84gJbX3AqqVlJQ4jx966CH+85//8Omnn3LGGWf02jMUTn4Utxd3Oovz4YoS80NBQeG48PmPsVTsliwRzvrnEGLi/dhbKJvX16RLQdmEZjmooY/Dcr7OJE+wXAdnL193M9cWR4BEnWtwRMexq/Dhar7e0ijtmoTFVzrLsivlhdHRotFoqK9r322lod7crWwQ0RGSRYfFJH/nxPBoAArL5V26g9sKnccCEaREX09Qoy844jiGBYcQ1gf2GLJIGlRLY10FtZ4WBLWahDGl5K7LYfN36excmMnmq0aTeEYiKrWKz87/vMffW+HUYNt/nnIeR4w/g6feWM67G735/MNkBEFg/R+y7/1ETV8Arvt+j7PswQekyXirEAGQ5if37VYNT+uyTrI4NASD2n0S4pqh0zVdrb/jRDueMicMq8VExu41hIUn4aH3x9TY0vVNCgpHSb/K6zBcPICfvn2Rpc9tJ+mCc9hXKvc5jyjpPdEa5Dtk0BD2z99PhK7tIt5qtnA4sT7/xzkXb6C6spiMvP0YDCrWL9jG5uUlWJsaUOvU2Cw2DD4Z6H0D8YmIIz422VFfC1nLCvEOjnFbYJx//vnodEb+WvoBqWmTEW1W1Do9ggg6vQeCSsXIcRfy/RdPUVKUSWKfjq1Uli74kLKSbIwePjQ31fHCC/Hs27salUqNl5c/jc31iKIdu9WCtamB8p0bifLvA8BVV13FG2s+YteX69j1ZxYJZ8STsSgLr+QUpj53OeVLN/LC02sQVAKPvHs2cRGSa0nrWOU6ZB0SpLmFKr6aUlUAHsG+CJYhRI+aQazZxvbNi/j5m5fQ6gy8/UkcvgkpnJt2PkbHpsnLz/U8Bfb48eOpq6vjiy++4I477qCgoICpU6fyzTffcPnll/e4PgWF04WO4ny0biQeC3c0RfxQUFBoF7PZTN7ShVTs3ELggKGMvqT9VHKnClXmBgJ00oSorKHGWR4VLFlvuMb80Bn1JPcdzaIfd3LRdQMwGOVzzY0WFv98kKTkUUSGhLa5B8Dc3P04H0eCwcMXg4cvNi9pghwQbyEgPoDkaX3Y+fU2NvxvI7lr85j40IRj2g6Fk4ewURMRBBVFa5bx8X9queneUzc1dXhihfPYO6DJeVyWK1l3WCvliZDNLvW1Q82ykKh3iKVm0UVIbZYtP3QaD/L3LKehoZrzLvq/3m28gkIXxCcOYdb5dzP/53l4hocSEDe2w2tr86W+EBjUQSoTF3R6IwMGTwGgZdd6APSRITRXluMbl8yIq1RUZ1ex5eNCzA215G9YhM4kUF9dTFnOTgCCEwe71evj48OosZeyY+tCtm76lT4Dz6TIbMLbJ5DKgj1MnXIRWft2AVBWXECAX7xbHa2MnnAhv33/Gs1NdQAEBQUxftIVlJXlYrOa8QsKQRShvq6SvbtWY2quJyd9NSAtgKJHJdJQXMOhZXvY9Pkutn+/D61xF6kXD+ey2aMYc2EKLQ0WkgeHdtiGwwmNlTZ3gqOl+Ch1FZ4MmTaW5JJU0heUU3sog7ylv/LWsvlERfdj4hnXdLvuw/H29mZ3upGrr32RpUvepbTkEFdccQUffLyUJX++f8T1Kpw6KJYfXfPhhx8yb948Dh48CEBycjL33Xcft9xyS689QxE/FBQU2uXVV1+lYvc2Ys6aRdDAYahU2T26X1CJiHYBQSUiqFzSSjpMbo0uWzENKvdYA63p+lz9GXUe8o6X2WHyXpbv7yyLHSYtgvL2yhYgok66X1Npp7ZFEgz8DbLbS0eMmXgJn72/kUdu+ZPr7xlKYt9ADqZX8MkbW6mvsTLrXHf/blcM3vJuXasw4kqST4Lc7sioNudsVhsGLznVYVFeEQDlllpnmUeVZPmyb2ksAP3OzGXC7PEkTU1k5YurWPzoEgb8dA86Dx+2fvpsl99X4dQmdOR4UKn46t0lRET7AF27crzyahhhY+TfVGJyjfM4wVvqX5ND5d9ha2ifKpPsjvJTniRQ3JIs96kGi9yfQxyBWJttclm9Wbo/r1EWKH7LbxU6PLts99EginbKC9IJjunPJx/cf0yfpaDQHn37j2NL0Rpyl61EmOKPf0LbjYWy5T6ETKmjZG0Nek8/vvnkgS7rdI038jwdv5vur/kGURT56fvnyNr+J37+oQwaNo2dWxcza/LFBIW4Cy3B8YNJtprZtuZbp8XjxVc9wvwfX+ejt+7n4sueJKXveFYt/4SgToSagUOnEh2XSl72HmITBnLHHTdyqOAHWp01jT7SO/OZf82kpaWFjz76iLIyOS39jze8R/rIdNIGDGD79/sIHz2MAP9adn6xjrptB7jmwZEMGh7C8EDZQrQ1YHO8l8uYZJAsTXMd4096lUt6egdeYX6EDUshbNh4LI31hJc0smv7Er7+7HH27NtEcuokBEHguw/v6fD7tkdTUy2///YaLaYGjEZvmpvrWbroA2prX8HX1z1AvILC34k5c+Ywb9487r77bsaMkdzO1q9fz/33309OTg7PPts7c1lF/FBQUHDDbDbz5ptvMmT4NGadcQcAv/wirXyEWnnBMtgjDgCri7uKTcx1HruKHic7gkogv0xyBQhq8kEn+HDeBQ+zfNkHPHyTHKgtOCSKi694gqDgGASXQK8qR1YLV9HjSLCaLQiq7sc4aWXf0liHb0EcSTMTyVz4DRkLPyHxjMuOqj0KJz+ugZDjJvXjtafWMnByX7z8JQHEc4Bk8aTJke955dXecxE7HlQclGLqCBWyADomSlo22UWXzFGO7E4HKvOdZRqVNNURRTtbVn5BU10lfYd3Ll4qKPQ2Lz1zsfNYrVPzc/1LZCz9EV3qGKafdxsGqyQg/sVmypb7YGqqRe8V0FF1R4wgCMw69z7qmyuIjEpBUKmYecEdqDUdZ4wyevgAImaTtIHg4xfM2EmXMv+HeWRnb6C0VNqlXbr4berrH8Tb232Dob04IK88374LicFg4I477nArT01NZeAtV4MAXhFhDB1dRL+zElj3v80843hPBwQaOfeCPlx1QxphkV1vdKQGqKk2SWOIn1HOJpUSJ2eXy8w3MuaiKaS/foidG3+mobaMIaN77v4iiiK1taUEBcdQUZ7nLD9w4AAjRpy6FnsK3cMuCtjFXg542sv1nUjefvtt3n//fa688kpn2XnnncfAgQO5++67FfFDQUHh2FFdXU1JSQkDhp13opuCVmtz+u02quSFT6upn8UkT9gqCvwA8A+TA41W10s7yVZPF0sTc7PzuLFemuC0F5w0IqIPV13zIqUlWZisjfj4BhESknBUPojNddIOd+suF7j7cBu8jHLQBcDbU/oOk3wGO8tWHpSCREU1BwFQYJR38I0BIaTMvIGsZd+y//cP8Q1Zin9YEnZvHXoff3wiE9j6wTNH/B0UTl4GXj2WmtwK9q37jqHTbketPbYZBS6K8WB5SduYGS+vlY9fn9baV2RBr1kl7bRGe8hTkBRfyToqr07uh6XZgb3a1srSQ5Tl7SFt3GVsXaakh1Y4cWi0Oi655jH2713Hot/e5p15tzF91u30S5NT2tptVlTdiC3VE+a9fEWP70ntk0LJofV4ePqQmjaMHRt/prGmlsCAGHQ6I+tX/whAQGAkVZWF7N69m7FjO3bnOVp2vPtZm88XfzOPf30aRuaOUmrKGhGK6vn5+/18/vEuxp8ZxyXXpTFihJzKNrte2qxpdliXemq79z5XadQMGnUhPv5hbFn7LZ7eQcx5IZlnHpnV4T0NDQ3ceeedXHTRRYwdO5ZfF/0XAMHDD8jD6BOMp28IgwcPBiAnJ4d33nmHxx9/XEmJq/C3w2azMXy4e9ygYcOGYbVa27njyFDEDwUFBTdCQ0O5/PLL+W3+d/gGp6BWa8EoTRQ84uWF+vYKaXd1sFl222gNtJg6IcdZ1ugSqLTKkcFBb5Tr0arcg5L5OCItmk9QpMTSxmpS+6QA4BcWSEOV5Kfs4Sub5beWAeg9jRwNRp+jM/ePavZtI+BEekQTc/a9ZGZtpjR7BwX712KzmkAUCR007qiepXDyctd4FRe+PYWbZn4PUSuJm5LIpBQpoO4f5XJK5YBo6bcSGCWLZhfFyIJcQZM00cisl4P1hjrMxQ81yJOQ3Ibem5B0l2ifYAB8gv0AcB0hmmqknVtPrTzmJMdIcQgW7FyIf0A4u1Z/c1zaqaDQEa1WEHPmCsQnDuKPX99m/k/zaKqvZ8YZFwBQsWMhHp4nhyvEwf2bCAmLp6lBGi+sVgtBwdFcf8ur7N61lA1rfiIqti9VlYVoNMdvaXHRja9ycM8KGl8fxJ/ffwrA3pw7uPP+Efz+ywE++XAX91wzn9SBwbzy7gwCAt3f040WkcEBkkisc7HmzKiV5yhWizT2ZXtUwuA++Bb2obRgNzXVpcx5YYFTALn0+pfZvXUB9T5q9H6BNBTlU75zA9/O/w2N3kBLZTX+Mf1orCnBOyCSAWfchEqlQquVNnHeffddXnzxRfbt28fPP/+M6gisQBVOXpSYH51zzTXX8Pbbb7ultH3vvfe4+uqre+05ivihoKDQLvfeey/ffPMNxfnpRMUNOtHN6ZKQ2CrMzdIEorJInjD6JEhWHrUFsrhQWikv+KrLpEWgUSPH5bCJ7j7AvYWrxYdc1o7w4WJdYnS40thd4iacNUjyh2yslgSY3OpSDket0RISO5CQ2IHStV5WiretpGTnWmpqavDz8zvi76Fw8hIZ60tI/1CyVx4icUriiW4OAGa7S7pZh4vYg7/IVinvXCr1ub3z45xloqc88VeXSguRKIfw0VNMpmYy0tczZsIlxyR6vILCkWIwenHBZQ8g2mDZkg+oqStEpzdSWV6A1Wpm5oV3odMZMRg8iU0YgE7fdgHfnjtJb5PcdwTrVv5ATtZOtDoDvkFBvPL8pQDc8y8jB/ZvZNf2vxg8fBrDhg075u1pJefARvZs+Z2cAxsBySS+f9xbAIxIhYEzb2bH+kLeeGQF/3ttMw8+PYEzw6W/X5Uj9tDasp4HKQ/sP5T8xT/z35dvJiY+jYSwMi655BLyDm0nY+9Kt+stjU2IVhs2i4nqvH14eQcy+cxbCA5uGyNl+vTpzJ07l99++43YKZOInjSBdU8/3+P2KSicqnz44YcsXryY0aNHA7Bhwwby8/O57rrrmD17tvO6wwWSnqCIHwoKCu3y4YcfAuBxhD7HDTXyBE2nl3eHm+slkUHrEifEy5Fu09aOkYfrTgw6+QKbwwQuxBGt/ViQfiDDaf1hapZM+73wcZ7XaE+eITTWPxStI94BQHWLtAPeLMg796oGCIocQMmONaxYsYILLrjgeDdT4TgRMyqaLR9toamqCXAPuDuiaZJ0sFMuq46f7zxudWUJN8q/qYPFkljx3ARZwLthgWSa3fe8cmeZ3ij30+d2SeLifal+R/ZFusBut2NpkX/jrcJGa78FaKyup6QoC4vFRFRUf4bdOgeAre8qrl8KJweCSsWUM25ApzOSeWArapUaq9VCQe4+CvMyEB1xbYwePowafz59+48jKCT6uLXvjBk30jdtPFaLifCoZLRaeUzx9Qvhpjvn0dRQS0BQBGq1upOaeg9RFNm3YzEALc117V6jUgkMHRfFdXcM5a2567nw6lQG9W8roI4L0bO7RhpDipvkjQ9PjSy++oZI79NiT8nNNLB/IpcM/4L9e9exa9sybrr5Zm6/427GTbmBoJA4KspyUGm0BPYfhk9sIPX5hZTv2o1KqyXxwquZENi+9eXYsWMJDQ2ltLSUglVrMAb2ruufwolFsfzonD179jB06FAAsrKyAAgODiY4OJg9e/Y4rzvaDYyTZ+auoKBwUnHWWWfx6def89ef8xh87RhG2K6Xys+THfpbzUJ//EG+T1vgEDUGHLemdopvkDRp8QuRXUL8QuSgZsVZ0mTGapYnbKnWoUf9XJvVhlrT8SRwydZ1zuOzhnXuH63RdRyILr9GioYf7RfSrXbpPf3Qe/mxdOlSRfw4DZmWKqVMzBk3iy0fwajaav71dn8ArrtKDkZcuuTof+NHyysXmLnnW0lIeWxdDQCDL6p0nt/xq2y1khIgudbFJ8Q5yw6PldMZnl5SVqhFm74jctRUNPqjc1NTUOgN3K02rmfO3IXkHtrN5x88wuRp1zFu8mVYrWbqairYtO5XVi/7mhWLP+eci+8lPmkw1dXVeHt78+AT3/DRW/fj7R1AXOIg4pIGER3TD63O0O6z239+z685HpYnHdHSJFlx+gZEtXv+woEfAPD0u4+jNuzisWdKGX27JH70i5cE3ilhHf99OmNpwQ7w9eCMGTeyb/dq1q/6kZqqbMpKDpFyzvVUZe6mat92ynea0Hh4ETZiIkH9h7Hrg5cBuPmOD9zq1Ol0vPfee5x//vkAHPz5Vx5//HFsNhuzZ88mOPjILN8UTg7sdqFNBsPeqvN0Yfny5cflOYr4oaCg0C6XXXYZX1TMZ9fXG9ny/iosQzwZOfqiY/pMtSDH+mi0upuBuMYEaXFYkzQ1yDtQKrW0OxaeWOEsq6vondSZgZHu4oLZZce59djTv+vo8iAJHku2rsPaSy42+TVlRPnKEyOdWhJMvCzyIq/WEak/IDSJxYsX98pzFU5Opp4Vx/kX9uHJx1cRf0l/9L7+Xd/UBbcPdcT8qJfNxP1CpH763mK5/pDoauexxTExa7bKbi+txlyzv++dgH5agw61wwpLdMQIaqlvcp4vrpKsUgaPvICdW+fTWFpI6qW39sqzFRSOBUsWvA+iyI7Ni7BazUw+61oCgyM5+/w7mDLtOr77/Bl+//ENAN58UbpHpVKj0xvxDwxn59YlrFv5PWq1hvikIVx05cNu7jKnAnPmLmzz2VVocd39veqGOZ3WI6jVeEXG0lCUB6S1Obe8pIVQh4VbYZW80RAZIIurBp00fvmGNFBb1nbc+vHL56mvq8Q/IBxfv2AEQcA7PBbv8Fhixs9C1ImgUrntVn/41i3ttvW8884j/swRZC/dDEBGRgY//PADc+fOZfny5UyePLnT76qgoNA5ivihoKDQIb/d8Tni7SLz5s3jgQceoCaohfqMWOf5sxKkIeSKy3OcZQv/K+3W1pTJIsCYUSXO4+wyaXJhaZGHnyCHJ8mxDG4q2gVn6l3/1XIWm5gZCwDYsld2Z6nPlnaTKutrnGVx8fL37g5qjRrRkbHFddKzYbeUpaXa1NDufT0lxj8UgLx2Yn50hH9YIulrvyU3N5fY2J59L4VTA2+dhnmvTCVtUTaqlvUkThvExo/kKOqRwZJYV1srm4t/+bq8KLhxtmRiurVSFviOB7Ge8riwo0UeD+L7xQFtRY3WtNKi2L1xo+/AqRQ05FN1cDdi07GL66OgcLRMO/dWCvP2U1xwkDXLvwURomP7ERnTF4PRiytveIrS4mxMpibMpmZMpmbMpibCIpOIieuPKIpUlOWRnbmDZX9+zLqVPzDprGvcFuCu4sKxtuDoTMg4Uh5/YQEtLY0YDJ1vcqQ2BlBtN5JXmkHD0ghUag3rlkjixth/Zvboma0uMFUF0vgTmziQ/Oy93HLPf9DrpbLDs6kNveuJHj0jedZYvCODsTa18PFHH3Pw4EF27tzJlClT+PXXX9myZQvp6em89NJLJCQk9KhuhROH4vbSOU8//XSn5594omf9qCMU8UNBQaFTBEFg9uzZzP3sE4o3bcV+YzQq9YmLQN7gsAhp9ce1uMQTMeikBU1ttbzD5eUnBTxNDJQXSPUZR/78P3bJbj+ih/x3mJk0pkf1+Ou9GD1gyJE35DBi/EOxuwSVrGiWBJwgoxz81ebwG69wTICVwI+nN56eWqZMiWH9tlz6nndsgxZ7+jTTWGdk4qiyNmbkfjpJVKy3yGKD2fEzdXU1K/xRysjS9/asI26DIAjYbO7ZZzwcmV/sNivVmXsJShqs/PYVTmpi4voTE9cfi7kFQaVm26aFrFn+DQgCwSExGIxeVJTm4eMXRHBoHH37j6H/oInOWByCIBAcGktwaCwN9dWsWf4Nm9b+iq9/CL5+wYRGJDJ89Dl4+xxZTK85cxe6ZKxZ2MXVx05Y6areOXMXYjY188u3T9HcXE+fvuMRDsugsu69JBqipEEpLF52u7u+v2ThlptVzU8/HWDDkmwsJjUhA5MIHdQHUYxDEASGjz6H/bvX8sGb93LRlQ/zwCPfAfDqC5c569r2384XdYez6L7X23zeuHEjCWedS+mm1cz+9AfylvyGpb6WH3/8kZDBowkdMRGN3sC2N5/q0XMUFE4mfv755zafLRYL2dnZaDQaEhMTFfFDQUHh+BI+chjlO3ez54cdDLy863gB9QdldxT/iS5ZG0Kl3ZZ9ubJAsV8n7eYGuVjltoobNpdd3VpT71uGVDtWYqPS5B3wXw5J6kisWbYGWbzFIXrICSo6ZfXOLaQFxwHgHxHkLB/RT8q80lk8kO7QmvllW+FBAIZGJnf73qbKQmJjY4mJien6YoVTmrNnJLBgwRKaa5potcU6oCnDq1bqbAaN/IPOCZKDB7cGPG3tHwBry6SyIIP8233yLOl4fkETIMfV6RVcsr0sKN8EwKzgkfL5diyrOkUQsFnM6L39idcovvMKJyeHL+jnPnERj7+wgOrKIvJz91GYt5+mxloSxp1PfV0lBXn72LNjOVqdgfikwYRHJBEakYCXdwBGDy9GjD0XL+8AqiqllNe1NWVsWTefDat/YsiI6YyZeAm+fsFtRIzVy76mqbGW1EETsVrMxCUOQhAELOYW8nP3YTI1YbGchVarxW63kbl/M1s2LGD0xItISOpc1K+rraCyvICWljMwGI4s3kZP2LTuN5pb6rnl7jfQaHQsWfABer0HEVFJxCcNYV9JWfvtrGnh3bkbWL4gEw9vPYMmxZBx0IuC9enkLt+K0SeYPhOvZr+lkv5Tb+LAhp/4+K3ZXHzF40THpPbqd9Dr9YQOG0uoIz5YyhU3k/XLlzSXl1K2YwOV+3fS94pbKSgooKysjCFDhigC70mIKB4Dyw/x9Pn/vH37dreyuro6brjhBi688MJee44ifigoKHSLne99zIuJffnXv/5FinoWCUlD8ezzBwC7q+Vd3Xovh5/scbYq99DbCTVIQ1qKr7z722iRFki//BDvLEudLE/yerrvZQ+Wh02dt/ycBWXS4syn+OitYszNJnRGWTxqrmtsPx2uC9sKD2JHXqgODZPEkOpm9wVpVdkhzj17ylG3U+HkJSRUigdw5VVV3H1vEDX7D1KsSTph7Wl2SeX0dbYkdl4wVY4dsi5a+r17ao9uItdcJ9Xt2l98vCQffbO5GUQ7oXolg4LCqYUgCAQERRIQFMmgYWe6na8sL2TfnjXkZO1k49pfaGlu363SPzCCgUPOYMiIGSz8+b9sWf87e3euYvbjXwGw8Jf/knVgK3U15ajUGjavlzJAnXPxveh0Rn797hWndZXuy+cZO3Ys27btpKVFiifVp9+oNuKHaLeze8cK0ga/RElRFjVVJc77czP+YPXq1b33R+qArRsWYLdZee+NOwHw8vbHyzuQfXtWAx9zwWUPElMvbQTsWyPH+Xh1z3Y2/Z7JsBtHct1NKWi0aj78OJqYCTOpL8zm0OIfKNq3muRhMzF6BzDgjBvJ2/gjS/98n2tveumYfietlzcpV/2T8m2bKFy9CFtLMwUr/yChz4dYmuv55JNPuP76649pGxQUjgc+Pj48/fTTnHPOOVx77bW9UqcifigoKHSbhx56iDf/+wkb1v5AfGLvuWzoNNKiPbtADiQWGCpN3loDoAIY1e4LIw+93a3sSLgyQbbyOO9OKX7GzU+6ZJMI7ZmlRr3ezDokE/4JZfK9PiFHFniyua6RnJJC5+cKR2q/IRHSgnZ7Udd+y5FegZjNzdRVFTFx4sQjaofCqcWqVasQ7SI6Dy3t6GBdkpse5jxuiJBcqRLCXAL9OuL0RHjIv/GCJlkU3FEtXZvq23HGou4wgT5uZTarpLCqu5lyWqPR4+kdRP6hbUyZdGyDNysoHA/aWon8A5Bi4BQWFvLUSz/T3FSP2dyMRqPDZrWwf+861q36AYu5BYPBk4HDzmTQ0DMRBIH9e9ayfdOfIAio1RqCQmIoLT4EQPqu1UTF9nMKF0YPH5qb6li3Ts5aptHoSEyR4wpVVRSx+q9v2L19GeGRSej1Hm3c0u65554efr8j49xL7qe2pgyNRotKrSEmLo3Xn7+GR575hd9+mMdP37zE8LGXEhndH0tLIxqdAUGlxuAjWaUEp4ayoVjFV5d+zB2j5HonTVGxeuWXhMUNwisgAkGlYuTYK/j1+2dYu+obrvlHHtEx/dBo25qLHul3UhW3jb+05ZvnGX7pIwSd15eSfesx+AWB2U5tYT0vvPAC1113nWL9cZIhikKvW2qcTpYfHVFTU0NtbW2v1aeIHwoKCt1GEAQMI4aQ9fPXzM+aT94e6aV+w2DZdLV8Rh4A2d/JgTR3FMmD8xV9pR3eaE9ZWNgqu9n2Kr+8Ec+k2yQBYvqFh5zlxY3yLvRtKT5u93VGdFoZ+XtCiE4ro6le/t51dmmnuV7f0Z1tsZrlSaBG13YoLiktpcHc7PycV1fOpD6di01DIpLa+DJvyN0LQKyvFBBVp5aesWHd96hUaqZNm9a9hiqc0nz00UeEJfpzxqxEfvpJ7ofbmnMAEF1+M0KxbK71lyPNbEBsL7uyuKBTye2pLJZi03y1SfZ9mxo02Hlsamzpdr3eQb5tAqN6uFiBnDn9Rn794WUK8tKBi4+g1QoKx5+eLJgFQSAqKoqwiES3c31SRzPj/DsoL8khLCIRtUYWJctKcp33BwZHERIWR9rgyQQERaLV6inMz6Bv2ji8fQJpaqyluakeP/9QQiMSCAtPIDgsDp3OgNnUzE9fv0hmxmY0Gh1arZ6WliaqK4vw9Qth2OhZDB5+FpdeeunR/2G64Jl/zWTO3PbPabQ6Lrz8//Dw8GHz2m/Z7CzXExwUQ0lJFnq9p9v7uZXBw2ZwIGM96au+InXiVXgFROAfEEna4Gls3fQ7Wzf9zqyL7mHIiOnH5ss5EG02GqoKsVqa8QyIoLbwIBkZGbzzzjvcfvvtx/TZCgq9yX/+8582n0VRpLi4mM8//5wZM2b02nMU8UNBQaFH+MQnYQwOpWTTWgadd+xdJ7IdEdUBdB6yYCIIkoAxKVZeQL33UTQAHtVH7nry4Jx4Eq6W/A4HXCov/KZESELPT9v0RKe17yPsihCiQkBq45rSfc5yUZCG3em+7cdNcV20ubLywHZmDB/v/GxullwG8ouKAAjy8m33Puf1Niur0r8nP30lMRNmKVle/iaMHTuWPxYtpLnu2GRt2VEl1ZvsLS+i9tfKIkpr8NNlxd0XLzqj9XcPOF3DmmplE3/voM77QXLKCPwDIti2+Q+g8/SYCgqnMj21MBAfPpvHX7gckNLmtvLu63dQXporiesiBAZHcvFVjxIc2jZmlCQ0LGTJgvfJPbSLcy6+j/171pCTtYuYuP7MOO824pMGt6n7eNDR38FZLgik9p9Ic1MDjfV1VFUWUlJykMlTryc1bRK6QgMUAodpNW+8cjVPzzmHvv2Gs3/Vl8SPOBcCYxg28kLGTjqP9/5zFwt++g9LFrzPrfe9ja/f0cUZ2vLN8+2WN1QV0lgp/Qdg9AuluaaUO+64g3/+85+o1cf3763QMXa7gL2XY370dn0nknnz5rX5rFKpCA4O5vrrr+eRRx7pteco4oeCgkKPEASB0JHjyVnwI1WH0ghIaP+F7jVGDiC6f12c8/ipjZIgcOcV+c6yi2Kkndn1RnmBlFHSu4HQVq6Idh6bsuWXRew4ycLiqtmR3arnoqEmftomLbrCEyuc5V7+kmiRbw3tso5pPkPIys2R2xAW0eZ8UX0l1WZ5QTe5z7Au66xoqCW/rtz5uTWzS26oJNRYVmSQv/5PgtNGEtS391yWFE5ubrrpJh5/4jG2LsgEfV8ABJMIDhcyweTiNqaT+4XOETenpVE22a4o8APAZpX7tm9Q41G38eMvYpztmR4mi4JL9mx0Ho+N7FkAQbVOFmNULsGF7TaRQUOnseqvz7npjteIjJH+Jsc6zaeCwsmOIAjtChNTz76JpQs/oKIsn4DACCrK8vnsvYepr6tyW1iXFmezffMiBgyZSu6h3WQd2Mal1z5On36yv8jJ1teefWQWMKvdc7Mf/rbTe319fbngoodZuvg9Dqz7gcayXHyDogkKiGTMmbewbvF7mE3NzP9hHpPOuobo2N4NhHr2sAk0pgzgy+wdlJVkAyDYrFx//fXs37+/V5+l0Ascg1S3nEbiR3Z29nF5jiJ+KCgo9IgbLsnFbvPguZ3e5H5RzJBLrmVV+K/O88E+krhR3lEFvYTRU9pxXlssT748IqVdYVuIXLZ175HF2IjyVGNQywvD9BrJ6qSwRoV3QPvWGYcTmSz9FfKbQ5xl03w6Fh4sJkuH5wAsLfLufVa+ZKKcLUgCTIoqvMP7SrZsJm/9YhLOHEDa5YMRhGP9f0fhZCEkJIT+U+NZ+8M+4mdNRmvsPHBubzDAXxZMljosPsqq5LLCg5JgurO0mz5i7aAz6llYIYkjUz3kNL7NdZIYo9F3nJap/4BJ7E9fzSfv/B+33P0fQsPjO7xWQeHvRHvCxJy5kJA0hF3b/2LtckkM0Or0qFTuFpa1NZLYvnv7MvQGT2ZeeBd9+o3qFcHDNRvNsRJQ5sxdiCiKtLQ0YDR689qLl3d5j1ZrYMbMu9myeT7bdyyiMHOz2zU5WTvJydrJP+55s9fb7Onlxz/v/S/rtm6gubEag4cPn34qWYncddddPPjggyQmurtAKSj8XVHEDwUFhR6jUquYfuMgvnhmDcWFB4k6gjo+XybnWRk8tASAkUHyYijKUxIe1pbIQkNJtpylwSOpZwt435AGasukgKqinzz07ayQFktn3rbbWWa2d89MVKuVzftVDjccL//mji53sqR+G9o6+d4I/7bWM3WmJiYmDZbb0yQtIDU6Lzoiw14MLsYyosMNoSVrGXlL1hE2chRplw9SAqD9DZl4/UAyNxRQuH4JcWdc0K17rJnS79l3tGyN1WoFUlHo5yyzmKS+ZI+QrUFCjEduZr1k1wbncd8A2VqrqlGqPz60e/mZrCZzuxmSPHy98MCLG+94jf/MvZ69O1co4oeCQheo1GoGDz+LX797lcLCQvz9/dt9lySnjOCya+fg5RNIWETCEbu4zJm7EIvFRE7mTpqa64lLHIiv77FPT22zWfnlm5fIOrCNW+9/q1v3+DrGpDPPvYEzz70Bi7mFqsoiqqtKKCnMYu2K7xBFO75+IQQEdc/CtLu0FYGk4zlzF2KuvIy1K77jnXfe4Z133uGRZ37h+cfP79VnK/Qc8RhYfvS6JckJpqamhg8//JB9+/YhCAL9+vXj5ptvxte3c5fWnqCIHwoKCj3i7tGfAGAbYeOXD0L5Y93/iNPf4JwI3fkPye909GQ5XsbXW10XUJLAYWo+uuwPh7NnaQKCQzvxC5FdRoKjqwEozQ50Wmy0VHdvEA0yqJxBGdfl9fwF01grBW/UestCx5L6bR1eL9olwcfq1f6zChzxPQCKG6ukA60I+vavbyjK5eDP60icNojUiweDKPDLLW/35CsonAYUNAcTMiie0v0ViJ7Sbq3QIP0mRRdXF7TyQkVo7N0YIQUZsvVTc5kkogj1LvmwVT3vX0FRUvR3fUvPXeTUag2eXv6YTb0Ti0RB4XTlcCuL6OjoDq4EQaWiT+roo7LMEEWRpX98xOa1v2Kz2QA5QLnB6MXg4WdRW1OGr19Iu+07ElqtSvbvWcu+PWsxGL1Y89c3wA09rkurMxAankBoeAJ9+49lxNhzEQQBo4fPcdt8mHjm1ajVWkqKsggMikAlHHkcNAWF48WWLVuYPn06RqORkSNHIooi8+bN4/nnn2fx4sUMHdp+rLyeoogfCgoKR4RarSb5nLFsf38+AcmH8I3uXbPKD36WdkjOOivXWVapl91C9m+UAnY2V3Vs3t4VwYm1VJikBdifW2WripKtfgDc8M+CLuuwWKQFY2REU5t/AdJ3hbR7T2CJniq7LA61ih6uWF1cYGpbGtzOt8HkmBx6yYvXpNEFLJ2zAL+4YFIvGq1YfCiAXez6mm5gs8oT6dZga0ne8m+vxSY/R3cUc+64ODmgYkO11F+sZhfXsKMMC2SxmNDqjtz1piNczfPh5ItxoKBwrOiN3/q9997LxjU/4+Xlz+jxFxEcGsuKJZ9RVHCAluYGNqz+mR1bljDrwrvpkzrarb8BPHLPZIxGY4/fe/m56fj4BROfNJiCvO7FzOjsO8+ZuxBPL78etaE3UKs1TDzzKhB7Z8xX6B3sdgFBCXjaIffffz/nnXce77//PhqNJFFYrVZuueUW7rvvPlatWtUrz1HEDwUFhSMmuH8cfgkRFG36C5+oBOdE463vonj+hmLndYMSZUGgziIt5KtdNlw3vi+ZnVsj+siFHhlH3C6tQU4ju399HADnz5RFlJ4On2ODDazKlOp0NTG06K0d3QKAoJImHq3WJwDmte2bvWaU58kfOjCKqXYRQVQ4dvCbLYhG9xVm7tpMqnMqmfLETH679Z1O26lw+lNfXIveTzLPVhWYsYc5fmQa+fds9JIzqcSOlfpvkK/8Gz+wX3I7a6w9OtUhIFESMtTbZXGwolkq89Ia272nIx6Y1siSL+XxRe8l3d+tRY8oIh6DxUFleQG7ty9nwhlXtEklqqCg0DkVFRX873//w2j04sbbXkOr01NRls8V1z/Fji2L+GvRJ0REp+Dp6cuPX72A3uBJcEg0CCqGj55FQe4+igoO8PJThSQmD+WCK/6P5x49t1vPtlrMpO9azYAhZ+AfEMbOLUv48MMPufnmm4/4+5wI4VMRWxVOVbZs2dJG+ADQaDQ89NBDDB8+vNeeo4gfCgoKR8yie//DuhFXMG7cOCqydxGUNpS3vjuSCCAds2RJrNP6w+6y4+wfJsUA8AuRFznPni0t1B75vWe7uc9+Gd5GPfeIlxaBY4O7t8jzM4r4aKX7bV2spaoc4g8uekUb4cNBab0smJQ2Ssee2vbbIzQ7LEcc/7TUVbB7+UbChyUSnBLWjW+gcDqz65caKg8UEzt8CKqCY5Py1uxivFTn8qE1k9Nf8+U+afSWRZbusC/zIH3iJMsyjU6etjww7egyzfgFhFJTXXpUdRxOS0sL33/xLBVl+ZjNzUw755+9Wr+CwunM/PnzEUUYMuJscrJ2sGzRxzQ21DjPC4KK8tI8bnzye0qKMjmwbxPlJTk0NdXzy7cvYzR6E588hJbmBtJ3ryYoJAa6KX7s3bWKpqY6ho48m4DAcDau/ZVX5n1ETrkUTFwRFRSOFiXmR+f4+PiQl5dH375925Tn5+fj7e3da89RxA8FBYWjYuzYsQSkDqZo1WJ845LR+3cvm0RfP5f0k1fnALDuN1k5mOLI4JBfVsLBLx3pY4f03BokIKKO+6Y28eLv8i6zwVNeAD77ZcdZUlzxccQLqSiU44X4GTtXOqo3SLvQfiM7fzlVtNShFmU1RGi2U6WvI8Do0+Y6191sX4P0d072iGRX2SFnubmxjoMrv0Pv68nAqyfRVN/7Zv0KJz8fbbkJgNz9FeT88Se+SX0JjB/YrXtFUaDwgNRfvIbKrl8hsVKcmcytHfv89wTbkDIGV40HID0vC4ASrRw4dV/mwW7VY/D2OKLn+wWEUVzQvWd0l23btlFRlk/a4ClsWvsrSX1H0BqIUEFBoXPq6uoQRTvNjXX8tuJbdHpH3CytnsQ+wyVRYt2vCIJAeGQy4ZHJznsryvLx9g1Er/egsryAd+bdjtGj+wum6qpiEEVUahWCSsWYiRex8Of/cnD/JpL7juz176qgoNCWyy+/nJtvvplXXnmFsWPHIggCa9as4f/+7/+48sore+05ivihoKBw1Mx7J417L8zA89CPnHXm/wD4veBr5/mZkbIpu0EtLeA3lPds97c9AiLqaHIxwZ/9vSRMpA6WXW7OjerewkilEgl0BFBstTD5x+uhzvNDzq5r9z4AT43Awt+lnenLLug8T3lrgMkAuywS1ZnaT51b1Sw9c3h8KiDFO8gsd49DMjAkAYDdBzeye9mH6PSeDB11J1qPerdrFf4+1Ne08Mb9SzH4BBE38lzw1MihA1uNM1xMlUSxd3eQNmYaaLBKv+2IGjmmTnGlZKF1Rlz3g5fVVUjWTwERcsaHz/ZJIuaR5mrR641YLEc/DrUyZ+5Cdm37C4DJ066lurKYtcu/Ax7rtWcoKJzOjB49GhDYtvkPADxCYgkNi8PcUENW5nYOZmzGw6P9gOVBIdHYbTb27V7DiiWf4+sfwpAR07v97MqyfHz9Q/H09ANgyIgZbN2wgH271yjih0KvIIpCr79ne7u+E8krr7yCIAhcd911WK3SPEGr1XL77bczd+7cXnuOIn4oKCgcNd6+em5+eDSvPrSc5PhdxCV2b4f5cPqfl+sUM5av3AnAxIA05/ltNfnO49i0YnrCw+fUUtQsBTedv1v2wx8xRbKaOHQwsN37XEn21pKSKgsV1aYuXjqOeALVu10FGHdrEfthZZ4aA3ZRRNVO3IKkYMmt6FC5lFXH38+X6hpJtMk/sAGdwYsxZ9+F0dOP+be+2OV3Ujh9+e39HZhbbCSdfSlq7ZEHBm6l1JFqujXlLUBzvdRfdxyUf8MqjXsA365IjUnE4GVsE4OjqVaOcWNq6v2sLCq1FpvV0vWFndAabLEwP4OykhyWLfyQpJQR+PqFMGz0TH77/jXufvgT/PxDFLN5BQUHFosFrVbLnLkLnf1iztyFiKKIoBIQ7SI+4QkkTLjIeU9wygj2//kxVmvHrntlpTn8+NULANzxwPtoejDulZXm0qffKKe1iSAIBARGUF9XeSRfUUHBHZH2poBHX+dpgk6n44033uCFF14gKysLURRJSkrCw+PIrDs7QhE/FBQUegVvP4cFhgBZSd+T9V6s81zf/5NTtIYbJXeX82Jky4fRwZLC+99NRzeKB4RKlhLrf09ylm33lxY3t5xX0u16Wl1FNJ7yIi7Zu/PAheOnZwKwv6aLylUChjgbFoPLhVvbr9vuWAiaHQs/nYceq9k9yKq/ny92u53ywv3MnfsC//d//9dFIxROd8wtVtYtyOSsK1IptLY1/Rb8BDQaSQi0NLm4n7n4Dremot63RXZx8eggVkdjaSXZi3bQWFqDzttIzNgkgvpFtHHTauhbJt+QJY0VO3NkN7ZRaYN7+A0hZ4/kshZ/hJ5darUGm63zoMXdZeWSLzh0cBv+gRFccPmDCIJAQrJk2VJSlImff/uZnxQU/g7MmbuQvAMH2bN7GbnZO6isKmDg0KmcefZNba7Lz9mLaLejMXgS7yJ8AOg8vAlLHU3BtqUs/v19ps64ocOAwhdd9S8CgiJ61MbGhho8PNq6moqiiNnUDMDsx7/itWev6lGdCgoKXfPXX39x1113sWHDBnx8fPDw8GDAgAEA1NbW0r9/f9555x0mTJjQK89TxA8FBYWjJs5Lg9/QEDy9tGwp+5Ih9F5U5lVVe+QPZfIujiVZGr7ULrvMVrO8kOsMTx95F9lLI7m4+IXILiIN1R1nnPj2Uzn9ZsjAWudxcp8qt2vTrstnz5cxRE+scJaV5/m7XWfXt7XwaLSZ8BINqAX3LC6tAR8tdmnx6uHrRVNtA4IgYLfb8fHxcbtH4e/HB58X09JooTGuH0jhNBD8et88dv/v6RSuWkSAt5aRiV4cyC5n7boMAvqmEjfrQsxNNT2qLydbzsoUGhjkPHZ1d+ktdDoDppamXhFAdDoDGq2em+54DYNRcvHx9PJDqzNQXdkzKzUFhdOJx19YwG/fv8bu7ZJLWHKfMaQNmcTGNT+TuX8TtTXlhEcmIdrt/Pnb2wD4RaWg1rhbbRgDpADem9f9Rk1VCZde+7hTZLXbbPz52zsEBEWS0m90j9sZHBpLeVnb4OP9B0/mxy+f58sPHyM7cwf796xl4S//63HdCgqAYvnRAa+//jr/+Mc/2p2/+vr6cuutt/Laa68p4oeCgsLJhZ+/gSuuS+OTD3YRPjACaD+lK8CGH2fx/N7Nzs9zn5HEgXP725xlFUmSi8tXH/Y8uOJolygAOzgAwMJ0WRgxeNrc7jkci0mDT5CcTWLBis6zphSVSIKJd6DsFrP781i36/qMlCZXrbvWnWETJWGnwbHzpLaY8PV1fzl4+EqLLa1OT1NT+/FDFP5eVOwvwjvCH69QP2qyun+fuV6aFmiM0m/PbJd3Vm0WRx9y6I0NhbkUrlzEnTMjePzSGPRaFaIo8tOGSm59ex8lwaGEOeLVuCLUSmJDpZf8W122bT0ACX7dC0AMEJYr9cnttr3OsiH9+nf7/rjEgSz74yPysvcA53X7PoCzrn6OJV8+ht1mY+WyL9m/dx3nXnJ/mwCLgiDgHxBOVWVRJzUpKJy+1NfX89H/7qO4ULKMnDDpOlLTJhMcFcqQkTP47rOn+frjOUyZfgMqlYqykhzUai1N1e0Lhs1VJQiCipjhZ3Ng8wJyDu0iPlEKjr5lw+8U5KZz3a0vHVGKabOpCa22rbVISupoAoOjKC/NIyllBIvmv8sHHwzhlltu6XH9JztLly7l/PPP5/333+eqqxQLF4Xjx86dO3nxxY7dtKdNm8Yrr7zSa89TxA8FBYWjZkjCOwDMm1vPrl2prHx+KamX1NDn7IEIKoG/3h3kvDYx2l0Q6CneZh0HljhS6ppl2Ttpes92WAO8bWzcJC22XOMYdIZHtNkZl0Rv7DptqK4P6I2dxxXQmtvuyFtFOw3qFrxU7qlta2sl1x5vnSS2bNm7m+H9JfNArVZPc3Nz119C4bSnJqcMn8gABJWIf39JZGiokS2avB3Zi6qKZDGtVfjoLhU7NpIQ7sHTV8Y6d18FQeDiMUGsSa/l282bKRoyHkGlQqiU+4BA92KCtIp6ADXFkt+9X3jXsXkAzM2yi47O2L5fTFWFJEp4eQd0q87DmXTxI+Rs/5383HSmTL+egUOnul0TmzCAXduWMWXa9Uf0DAWFU5E77/kUu93O8mUfUl6Wz+XX/ZuEPkMxNUhWl1azFYPemytueIqvPnqcBT/9x3mvp5cfdZXFVOem4x8ri6fmpjpK923ELyqFwJg0yvZvYM+O5ZL4IYo0N9WjUqnbpMbtCcGhcZQUtVWKVSo1N97xGmq1BpVKzZ+/vc0//vEPNuxsJDwy+bSK45OdnU1TUxNXX301KSkpDBs27EQ36fRDFJ2x4Hq1zlOc0tJStNqOBUuNRkN5eXmvPU8RPxQUFHoNb29vXnhnOh+9sYWv3t9M4ZYchlw/DndHD3fmfB3E17fKrielzZKwsGmqLGiUzu+5cBJVKO0OF2tla48howq7da/NJrudjBoj3bP8z8QOrzd6m9j9mdxGXYr7NZnbJNGm6aA8/HY05DfYpYmir5e0m6xSq6irb5vBpVX4ANBodYrlhwK7d++mNq+ClHN7b/JqaVBjESTLD8HssAopK+LscX5tYnu0Mn2IP58uL8Xa3IjWs/10k0KDjb7+kmWXT6gU0Eyt6p7rGoDosPfV9uAe572iyMY1vxCbMIDg0JiubwAmXfGU814sLWz762OspiZGn3UrHqHxbn+HP399m9zs3ZhaGikrzelxGxUUTlVEUWTJorc5lLWVcy+6l8TkYbQ0uAvzHp4+3HzXG4h2O/nZudTVlaM3eLLw15fJXvsLVTl78Q6Lw9xQS2X2LtQaHVEDpiAIAloPH0zNsnXmqPEXUF6ay49fPk//QZMYO+kSQsMTut3ekqJMQsLcc0cZDHJ8sn5p49m+6U+0WveNiVMdT0/5ey5fvlwRPxSOG5GRkezevZukpKR2z+/atYvw8O5bhXZFj8QPm61rU3EFBYW/N2q1ii/f20HCqFl88sI6lj/1K31npTPkmoHoPHUkBEtZXET7Oc575ny99YifN9ZfVhi2/iItPkLCe7YYiksrJspLXrhUOxZ36TvcB1uf4Ebqyj3xCW7Ey0+ezAmq9tX3pGH5hHjK54racUGwJx62eMxTtUlB2ub53tJCcneZI6XuQUhJll4Yot3eJluGwt+Pj7bcxI//3YLOy0DogPZdxixFAjX7JSsQTR/5vS5q5N+htcUh/HVipCFodZTXtm/VVF4nlasrRVT1ZjRWWUj00EpCR4RX11YcrRYfGp0kEVpM3cvO0pG1Ryv7dq+hqOAAV9/8XLfqAzA11VGas5OigxsxtzRImZWm34aXr3sw0+amerZs+N35ObSdRZWCwumI1WqlvDyXrMzNTJ5yA2mDp7Q5LwgqdmXuB2BoahqCICCo1Wh1BgKDpDHrzAseZtu6H8jL2kxtURYanZHA2AGEpYxCa/DCZjXTWFVEUOqo1koxGL24+OpH2b39L5Yv+pS9O1cSmzCAirJ8gkKiO7XSKC7MpKIsn2nn3Nrpd/P2kcasuroKgkJ67pJ7MjNjxgzOPPNMli5dyvr1609YO0RRpK6u7oQ9/5iixPxol5kzZ/LEE09w9tlnYzC0FRabm5v597//zTnnnNPB3T2nR+JHTk4O/v7d2cNVUFD4uzK573sApAwJ5Zkvz2fJt+l89852slfnMeyGwYy4tA9qjXsgz/Yw2UQu6Cd/XuwrZ4fY/YXDwsKTbuHpKwc5Ndvlt0VcFylzhw2TssTsy/BzlvkEN3ZwNdjDZTuOpGGH3M6PGlNIVr4nuWWuMUQ6dwOwO4Rnldr979YqfLQ0N1BfV0lqqnuMBYW/F/kHqwjuG4ynvxWw0lAtiQ2Wol4MeGoT8Y5N5ddNq3n8shiiAmWxwWy1897iUnwj4tDourdDqtE4piMuE7lW4aMzQj39qW5p6PK6ViwmC6XFh/jj17dI7jeS+KTBnV7/6LPz2bpxISsWf4rZ3IxKrSEkdiCGwAi8Q2KJDm1/Z9lg9MLL25+G+mqmTL++TSwQBYXTiVGXPAaAqamWofGJbNv0Bzu3LAEgPnEwOF3iOn7v79q/j3iX7CwVTbUMHncpdQ0VNNaW0WfSVRi8pPWHaLeRv2MJdpuVwSOmt6lHEAQGDp1K/0GTOJC+geWLP+P9N+9m5gV3MmeufN3hQkhO1k60OgNxCQM7/a5BIdF4ePqSm7WLhKQhnf9hTjECAgJYvHgxKpWKn376iZdeeomHHnrouD2/tLSU+fPn8/HHH2M0dhx0XuH04/HHH+enn36iT58+3HXXXaSkpCAIAvv27eN///sfNpuNxx57rNee1yPxIz5e2blQUFDoPhqtirOvScMyKJLNH25j7esb2PXdXgZeNoBxM39yLuZN38QB8GmmnD1lStiRm5VuKtzvPG41Rffr4p6CBhFzizQkiqL0b6vw0RHpa+UxUahw7Ei3Ez7AXydP+rZkub/UnbvsrXUZRKLsARQ2t138iS4rw4n9hrY5V1aSA8DAgZ1P3hROf4pzagkaFtfj+wSViFYvBSO1mqT+J1hdhDlr2y2m4NRh1BzYztlP7+XRi6MY29eHzJJmXvmlkH2FTSRPnyzfqrEjaqW+WN/U87g0hVVSqlyhWhZwQj073oxpqpEFEQ8/KXaI1Wph766VLF34Pv6B4Zx3yexu+ezv37uWwJBovKP64xeWhEZnoDlUQFXSsRWKIAgEh8YSHpXMuMmXdfkMBYVTGUtLI/tWfs7OP6Qd+4Sk4XgYvfHxdc/SVF1TQ7SPVL5r/74O6ywQq7nuhif4/P1/kb7ofXzCE9HqPagrOYSlpZFzLroXLUYaq+vx9G8rLqrVGvoNGE9S3xEsnv8u8394nbycvfRLG09ictt3pyiKpO9aRWz8AFTqzq1GBUEgPmkI6bvXMH7K5d3625xKCILAo48+yvPPP8/jjz/O/fff32ksht7i/fff59Zbb3Varnbk/nBacBpYavQ2oaGhrFu3jttvv51HHnnE+TsQBIHp06fz1ltvERoa2mvP65H4oVJ1b7dWQUFB4ZqhH7kcA9fD9u3bOe+281j7+jr2/7SHcdcOoO+kzuN4RHmoKWiSLB9u6yMHQPzwuhwA1n8sX6uj44lLzWpv/CbUo1bbOPBZX2d54EwptWbroq89zHYoSXeoGsbOd89Txsqp8jz0rQvHtu1qqjWi9ZbdDSxN7bc70uhwC2gnpsLhlBZno1JrSElpJ9CIwt8LQcDHYCMuSPpNB/tJwkGGRk4dW1PmEATK5N+WMbp7LiWtaAweJJ5zPflrFnDne5nOck//IBKnXoVnUCRYpEmM2M3ZhtbgEnjY9WffiXHHwSo5hk9SorslRl1tBVs3LGDbxj9pbq4jKWUEF1z+IC8/1bUoUVVZRGHePiZPu55m77aZIOxhWuIi2h+/sjN3kJezl3GTLzutgiIqKByO3Wbl4IYfsbukjG5uquO8Cx7AarZQUtj5JkIrVrM8/hSI1QD4+AZxy13/YefWpezZsZLmuioiY9JI6DsOrY/cH6uKyttNh63V6pl54d2Ehiew9I+P2LllCSFh8TQ21jDvuWsA2LBhAyVFWVx5Y/eCEg8afiZ7d65g4S//Q+uwbDud+vjTTz9NSkoK48aNO2bCx5y5C9t8XrtinXPBm5KSws0333xcrU6OG6eY28tzzz3HggUL2LFjBzqdjpqaGrdr8vLyuPPOO/nrr78wGo1cddVVvPLKK+h03Usi0EpsbCwLFy6kurqazMxMRFEkOTn5mHicKAFPFRQUjhtDhgxhyqOTqcysJOP7nfz23FoyNxRy3wvSILksSx6SDBopW0N/Xy0+2p6Z61v9ZKFWNErH4X27NqEHCPK1kl8o+dKY/TpfDE46W17wNTtidNTWuQ/4B2vl3fOm2nbMOQ/zehG91ARafKhsbuv36ukn72611DchuLjBlJfmEhQc1eMXjsLphcUuEhrjQ3VhfdcXdwNRr3KKEEKT44fq0h11Xr4kzrgKU2MtptoqNEYPjMbgdoOgOusUpL6yv6bAWRZmkiY4KeEdi6FJcXE018kBfb0CHZlqijLcrvXw8+KXZT9TtH8t1YUZaLV6+vWfyJjJF3bLV791cr709w/x9PSnT9JYAiNkV7XsQzmkG0rb3LMwYx0AEwL7s23DIvwDwhg78ZIun6WgcCpTXXyAhsoC+pxzPbUHMyg9sJHAwKg2YobbPQ5XNau961iCeoMHI8edR0BYGgBmm1zvjnwplX2/oBiqiqRsEN6Bfmj18qJdEASGjzmH4WPOIT8nnW8/fZKvPprDvt1rCAiKYsXiT/H2Deq2G0tC0hAmTr2a9at/ZKblLrTazuMLnWqo1Wquu+66Y/6cspIc5v/wOmUl2ag1WvoNmMC4yZfy3ht3UV9ff3qKH6cYZrOZSy+9lDFjxvDhhx+6nbfZbMyaNYvg4GDWrFlDZWUl119/PaIo8uabbx7RM/39/RkxYsTRNr1TFPFDQUHhuPLZ+Z8D8Oa4G1j4wQ6WfbGHLUuyGXZmHG23ejvnqnhp57rpcnnXd/+PjvS3dG2lVusvTaCMjrS1ick1nV4/7vxD2ESRgjyfTq8zN8mTrvxySUQJjmlbd2OtocMAqa4EGqVneQW0/0zRJi1GRVGktPgQQSHdy1qhcHrjE2AkL7umw/PeAY3YHAFITUb599q8Q+43Kq2Ubcke0T0xTe/th97bT/pgasdVRt29vm3w9qClvgmDtwemRjlOT1JcXIf3nD1iAht2bwdw/ltQsI8D675Hq/dk0pRr6dd/IjqdkddfubJb7WilqamGiKi+7WZ3SG0JRbS3H6+npbkeb69gmmuV1NMKpzdmVQsIKrxCo/EyhBHWdxQTYuVMIRExkoXGH7vWOssSPcLc6smozHcezxwwtt1nHW7ZtXaXFCw9ICLYKX6AHBjZVQQBiI5L5dLrnmDDqh9ZsuAD7HYboeHxXHjFQwg9sG7vN2Acq5Z9SV72XhL7DO36BgU3vvn0SfR6D4aNmkX67tVk7F3HBZc/2KlwrtAxhweJ1ev16PVHJ8w99ZSU4eyTTz5p9/zixYtJT08nPz+fiAipn7/66qvccMMNPPfcc/j4dD5fPlEo4oeCgsIJY9KlfSnIqOS9x1aS/P1++twwAa9QybphbboUpLGljxxc9ECF/FK8pX/3BvXoemnwLSmUd5is/p1bdNSVS8JKUIK8cClt6XyHKkgvTZzay0ReeFA2x22ud2+3cHhml0Y7gZH+VFZVd/g8rVGPpVmyjtm5dSmF+RmMGn9hp21UOP0parKh9jdQtbIW34YWAsO9nNmLigI6DtTbEwYhW06otdLv3jXd7EZTpts97eKSmch1N9fg7dHhLUYfD9Raaeryx5bVAPjrvdpcU1F0gANrv8UrIIqi7D1tUjj2FLtaR2VNGVkVhQwJ9nOW72sppJ8hss21qnrp7+zVx4eKinwSEpRFkcLpy6hLHkMURRoritAapT42c9hEAOrKa7q8P9NaSqC9474O8Mfm1c7js0dMcDs/bqAssri6vRyeFeqPHWvkegaPJzY+jabGOiorCoiK7oupsYWW+ia2ZcvxwsYO7Lj/Wq1S/Xq9EpjzSNFq9ZjNLdTXVWC324iK7YdarSxLj5To6LYWjf/+97958sknj+kz169fT1pamlP4AJg+fTomk4mtW7cyZcqUTu4+cSi/MgUFhRPC3aM/AeBf02HOOzP475xVVP33ILFjpXRWUeO756ZyS3896Y50m/YLJSuQqt9c4l50kfXW1CztbJdVdW+HOz9dDrqUEOfuWqDRySJJQ6E0sdMZ207Eho4pYO0PLgGkXTenam3gSDkaGNAa3d6xu3xYthetI53nvt1riE8aQupA98mhwt+PURf3Y9+yHP73wDIe+mAmaLqX+lnXXxYXtQek35a6RP7NaVSOPtKDxCWir/RsoU7uF2pRqtMmdJHlyMUkvnUx5R8R1MHVEO8TQm7BVnat/orEPkPZtW2lW9q87tLqw//l53MpztnVbgrpfS2FHNwmjTlnDR3jLC/Kz6C+roK04ZPxDvI9oucrKJzMtAofOdv+oCpnF1GDpyI0drxBsGXvbkYHye/l9XUH3K6p8pQtvRZmS6lW27MBWFC2yXk8K2Rku8873OKjPTw8ffDw7Dg7Wn2FHIDdYpYs4VoFFp1OEj2qKouIiu3nfrNCl8w4/w52bV1GbU0paYMmM3zMrBPdpGOPKLYR/XutTiA/P7+NpcXRWn10h5KSErdApP7+/uh0OkpKuhfr50SgiB8KCgonnEFjIhkwKoJtm9wFj60HPPEPk0SGUZHykNWarjazvuNApa5Yy+VplOjRPbPKOou8ONvwrUOsaGctk19sxGqRFnl6o7tViblZ6xRAho4pcDtPbdtJY5ohiuUHtzHlsKj0NSXy38crwMdppltTXUJin2EoKAB4+hk47+nJfHHPn/x3zmrmvD4VAIOn2XmNWiP9tpt3y8KIri9HxDZLDqKnXI/g4SLSmbsx0ROhUpCsUtbs3uosHhbXeYNaXXKSxUjM5mbm//oypSVZDB4xjRnn3X7EwocrfRNHkJ2+Gm1THaZG2RIs0O5JpUq2pFmybT3hnlJQ5PTda/D08iM2Pu2on6+gcLJSW3qI8pwdxI6YSVDiIGd5Y3XbTYEte3d3WMeotMHO44WH1rudHxc34OgbCowNl0WOxXXrmObT1q0mr1RKeT80XhpzXC1A2iMwOJLkfiNZvuhTNFodjz1vQ+Wwfjudgp8eS+ITBxHv8rtRODp8fHy65Wby5JNPOt1ZOmLz5s0MHz68W89tz01JFMWT2n1JET8UFBROOEneOtKSA1i/fC/qcGnALFgjZToZcElOt+sZHyPgr1PxqlmefImerQuxjgfi+ioPtAZZRAmIqO3wWqOPyXmcX9yxyWtViY8zrkd7mWRGXHTIebz5izjncfI5xbBCMl9cfnAbAGFGyQIk3r+tn7RotyPaBWqqSvAPcPehVvj78dTkzwB42HYtZ909goUvrackr5awmCOzQDC5uKMEGaU6dmhkAU8wdW690YroowaHYGlzuIccHui3I5oamrCJHV9sMjXxx4I3qKzI57KrnuSbL+Z0r+JuEB6ZgrdPELt3LqZPv7YBEQPtnlRESOOBtkjq46LdTvquVfRLG+9cDCkonOoMveffAAiOGD7m2jJytv+JR2gkF025zrnQOVz4cKWwvsJ53E8X0e41okF6X6sqHO/Mth5tNNc1MlHlntFs7a6tbVxguqI9AcSVVhGkFe8g3zYxRQCmnfNPFv78P376ai6BwVFMOusa+qWN73Yb/s78XQUiQZT+6+06e8Jdd93FFVdc0ek1cZ3E2HIlLCyMjRs3timrrq7GYrH0amra3kYRPxQUFE4KYuJ8sTY1YW1pQdPFjq2nRsWcL6SB9bErZdO6RmsnbwGbCGoB0ahCqJDFiHrPjn2Oc0vkdgSYJKGjqYOcm/mrJXP8gIHucRUsJg0arWTd0eIIUqpyUcWTzyluc/0eeyH6vmA+bPPJbHG3KsnYtwGbzUpgcNcZLBT+XqRMimXpf7ewdkEWF98uWxGdEavm+2Lpd+iaHlrvKVs26MdKv+P6td0TTYRGG6KnmonqFFZb053l430kk/A1Nekd3epksHc8OwQ5VXRTQ1MnV8MYEqioyOfrX/6FzWblwksfITwiuVvt7S5+vn7ExA6gvCyHZXnbneWBtB03LBEagiy+FBXup76ukrTBk3u1HQoKx4PhFz/S5vOWH19g0G3/orE4HwSBlvIy6vKyaCjKxRAQTPyMS9rd4VVr1GSWSAFMk4Ok2Dj5NWVu17nG4hBD3F1V1hbvZVKMZB3QXOf+bt2TIb8kW4OftieC+IYGyB/q3E7TJykRgM0eixhQ2r776OGpdP0Dwrn65mcpzM9g9bKv+emrufRNG8c9N48gJCSk3ToUFE40QUFBBAV17D7aE8aMGcNzzz1HcXEx4eHhgBQEVa/XM2xY98TI2bNnd/t5r7322hG183AU8UNBQeGkIChYEheszU3ccmUFX78mpbwMcjGu8NR0Lxr7GSmyud7Shl3SQWfCCFBV5ENYvORW0uTIAOPh2+J2nUemF1W+0m5vdkW4y5m2O9NVWd74x7cvlICUkrQrWt0QiivqCW9sG2jBZrFSXVXMb9+/RlKfkcQnDe6yPoW/Dy9OlbIqmW64k0+++oioC/pxRuzRB+criimCShdfYpW08Jng1X2/937+klCX3iRbkAz2ju/ocgC2NWUDIBbL4sgYEsg5tA273c51N72Cl3dAR7cfMaJop6z0EP4BbXeqhyZJZvRLiqUFl+gIeHzo4Ea8fYL46K3ZJ7XZr4KCK8Ouewxoax9paqzh008/Jf3z/2Ftkt5lglqDd0QsEWOnEth/KGqtjuWNe4mok0w0mi3Su9GznexI0X4hVDdJliG+ekcQ4naEiKTGQHRe0vIkNMB9kSao3PuVyWZBr+46zgeA7aDDzbSDtdnuUCnIanm6LPzO9B7tdl1taRUAEVF9uPz6f5O+azV//vY2cfHJTDvnn/zy7cvdas/xxG63M3jwYHbv3s1jjz3Go48+iodH50FnFXoR0fFfb9d5jMjLy6Oqqoq8vDxsNhs7duwAICkpCS8vL6ZNm0ZqairXXnstL7/8MlVVVTz44IP84x//6Haml+3bt3d9Ee271xwpivihoKBwwrGJIjqjNBzNGp8PBHb7XrOLiKDtbqY6l+vUmdKAGnxWRQcXQ+6ecOw2addJDGh/2PTpJwklVVntR4OsKpReBOGJ0nN0Lq4wh6e9PeeqXPz1Kr77Q15wFXvWE2STXyYWi4lff3gFTw8/Zsy6g5b6znfJFf6e3Hvvvbz11lscWJHLkMs7j6FRm+8yCXZMNFTI1kZFMUU9erY+1EbdqAUAxP3YPf/hyAb5N16q6jjbUStWsQVPbz/ee+v2Xp0czZm7EICMg2spL8tl+vm3UdXS8QAjmOyI2Mg5tI20gVMV4UPhlEIURRrL8qnPz0Gl0tBYU0xNYQY3LHoHQ2AIiedeiVpvQOvlhUqjRWiwgQWw2Ai3dmwdVtQox6nyNbhnXRod0pfdZZKwGdro73a+tKqCsqYaAKK8JcsLD4MOU0sjBqMXXo7Ao2VNNU4XvRXbNzF5SNtAqHZr59naWhnRNJ3NHos6PL9ul+SK2l4mmNSBE4hLHMgnbz/Ir9+9wt4nbqB///7deu7xZG/6PgCee+453n7nYy6++lFCw9uKz39XtxSFtjzxxBN8+umnzs9Dhkiun8uXL2fy5Mmo1WoWLFjAHXfcwbhx4zAajVx11VW88sor3X7G8uXLe73dXaGIHwoKCicFRqO0a2NubhsfY/m38ks5Y7g8kbId6l7AgOl+0mC9uGBLt64vzQ3AmiWJEapY98nYCFU8m+3ZjFDFs73EJaVnjHtdVbkOIUTjvhDStrN7dTiXnS0tNpfvMVKwva3J7brV31FTXcKNd7zGm69dz4OPft9lfQp/P/r06UPowBjWf7Wfay9LQe2wnopIkES4ygKXhUvHhkodIjqCm65y8dEyhHZvoZHqEcW+ask0vtCRJFqn6nz3VlUmizEBg4JJTBnGtk1/8OOPP3LJJZf0qO3dYfmizxg47EziEgZSlb7HWa41SMFWtVXSWGEJECgs2IvF3ExCUvsZKBQUTjYmPfcQNdn5ZC1aQ2N5AWqdEbvVjM7oTcKsGQSm9sPa5BJ4Q93xe6tYqAVfaTyoLe84rfa+2nzJDRXwVLfv4lpnksT8FpscpLmxoZqtm34hO2szdpsVjUZHfPJIBo84Dw+NniarHI9rxXYpI0yzzcTZw9u6sYg+ncfiGdE0XTqIk/5ZmLPB7Zp1u7ZhtklzFUuB9O+ZQ8fg6eXH9HNv5ZtPn+T2e55j1bKvOn3W8UalUnHNzc/x1Udz8AsIRRBUfPL2A5x/2QP0TRt3opt3+nOKWX588sknfPLJJ51eExMTw++//96rz01PTycvLw+zWe7/giBw7rnn9kr9ivihoKBwwrHaZcuPIEEkyVvDuFRJtFjSTdHCYodqsySIrEG+ZxKjOrxH9NNgapHuKc3t3GQ+WCsvEkeo2jfR9wupp36LvHgTww5Ln2sVKcmWrFoSU2X/Z18fM3195fumhntR3GRxpvAFiBpSzu4V8qIyP2sHA4ZOJSQsDoBXnr+00/Yr/H3pe95wVj73M39+vZdZ13Y/e4JgF7EHy9MEjU6KCaL1kn+HlpbuTSNym1yCBYqH/dsBtSZ5ASVYW9M9t70mJXUMQZF9uffee7ngggvQaHpnWtPS0sg3nzxJc1MdCUnSWDQ0tePsLdoqkZysrfj5h7u5yCgonCyMevRx57He0ELxlp1kLliGR3A4CedciU9MIqLNiqBSYwyS+py1C6PCYqHjAOEg9+MiU1WH1zRZTVQ2yz4wh7vN+Aoq5v/8AipBYNKZVxMSFkdR/gE2r59PcfFBhp15Cx4OK5AWa9vYWH9sWc2ZAyS3FbW6u+ahMjPjRrPwwDrpgx78TO0LNku3reesYWNJ6juCKdOvZ/miT7n8uiT6pI4+qSwpYuLTuOTqR/nu82eIjOlLdFx/fvjyecZOupTJZ12LSq0Eaj5mnGLix/Hm0KFDXHjhhezevRtBEJwp5lstKW227m2sdIUifigoKJwUtDgsPjSd+K4UbQmk3wzJ53/yPdKEymLv3jAm1MuDpj2o893lUK0fAJVZLg7J7cx3jMNkVdovxD3KvVDtyADh797Ghka5bHhE+7tpqQ5B5KusIKe7DIBot1FRno9XRD/+2r6BZzh5JlYKJx9+ccEknNGfH97ezsAxUYwcEMToYCluxwGX9Lf2LLnvtRd8sFM0gjMldWOd3FmKv2kVDDrOAgHygqXSIl/nrXOPUSK4rGtaTdDj0iazZdE7XH71v4mNG8hrL17es7a3Q8aejRTkpnPRlf+iXzs7os60nEYRbbOA3W4jN2cnqWlnHPWzFRSOFS1VVZTv3IHaYKBs22bMdQ2EjxxM6PBzpNTpooigae37DsHRxUrR+R7tyACkSbpH9HVZQLdjBCLqpLGmyWRyPwlUt0hmaAmODGcbVn8Nop2b734TH18pDkhy35GkDZ7M+2/eQ37GehIGSH2v2SbXKQZoEKpka1KbzQ6Or7Bw3zpm9us440tH1OglF9fWv4VGcBcLxk66lIP7NrJt05/0SXWPF3IikYSYmfzz+onMmnU+eoMHSSnDWb/6Rwry9nPpNY+d6CYq/E259957iY+PZ+nSpSQkJLBp0yYqKyt54IEHeuRK0xWK+KGgoHBSUJgnCQ3e4V7UWewsydkMwIxE2XIjt98P3arLlCPPzBb5SwukTvd7RFCpupbPPaZmsfkvWRDxjnS/xh4uW3u4TrpAEkNqPSUT4tBYeRdsS5HI2Ci5hRvKm4n1bDs8F2cF4SfqqassIGPnn4h2O17KDrNCNxDtAn3PH4lQWMSLd/zJx/MvgSPY3asrNBLat6ZNmV+ItEixmrtZn1VEDJYWV0NVsc7ig1WF7V5eq5UWMoJ7tmgn3v5SP/jpu+e5/6FvuteOLijM20dAYATJKaMxN0ttWFa1s91rLUaR2sJszKYm4hO7n25TQeF4Iooiu9592/k5bNgAIkYNwSsilPoSFapiM8FGP+d5jybpXZbtVdNOZYAAhigbLaUuQmlz++6oDR4Wgi1yPKxyu7uPXa1dNjExIr9HRVEk6+BGRo+/wCl8tBIYHEXa4Mns378J//7tW3mKARrKSyXLs+DQtu6jC/dJFh2CReTsgR27fczsI4kkCzPWuZ07a5gsoCzcsgq71czMEVMYMOQM/pz/Dr9+9yo5WTuJSxx0UlmATJkyhZvumsdv388jM2MLvv6h5GXv5p15t/HkQ7Pw8vLquhKFniGK0n+9Xedpwvr16/nrr78IDg5GpVKhUqkYP348L7zwAvfcc0+3g6N2hSJ+KCgonHCabSKHcmrR6tQEhLgHRWsPnWM3qsEqT7Y++EIKvCHgvlKyh2hRlVmIOrOKvJ0u+cc7eW8EGuXgi4YzMt3O1+/TOa066g86MmAcNl8QWuxQ7d6eA5tj6D/hEAAjgnRu5x/5SBY21BrpO9ZW5LN1yft4+gQxcOLV7Fz6IeMuf6LjL6Cg4EBj0PHsf6dx5ZnfsHV9IVHj2wlSE++yiGk1wPCQRbnQ+I7N1l3x9GlB5yGZaJRnOPpnN+ZnUd5BZNfKqatbhY/20Kk1znTRA6KT2eoTRF1dx0GLe4qgEkAQsFvbV118EiUXoLq9Ur+vzc/AxyeE4KAYJdipwknHzQuvomiH3Lf8Yv1JmDULAKsZVMXmjm51oio2I/pJ7ztDRCdqJLSxFmnwcE/RHqzyolzdiNhJFrYWm5k9jfnYbVZsFhOBQe3sNgCBQZFYWmThpKOg5IAkgugcbTO1ffYfu9YyOW6I87PRxz0LSqBFLquIMqEqkP5uFnMLa1d8z+7187G0NLJnwf+IiR+CSlCzb/cadm//C08vP0wt7/PSk70fm+hI8Q8I57p/zGX7lsWsWvoFADarBZPJpIgfCscdm83m/N0FBQVRVFRESkoKsbGxZGRk9NpzFPFDQUHhpKAor5bQKG9UhwUCzczJkY9r4pzHoy7J7bpSjYDWUzJNdXUbaQ+9i/l/dYu08vM3uGduad21BtpNn5vUKGeqOdRS7HbeO0CapMX0L2lT/tNqObiqoBIR7Xaay0tpKMjFXFYDgKWsBE/vAEacfScqlbTTvvbbpzv9XgoKv/5D2u3dlHkrwaGe5GdWM+XsRAD0Rnlh0lyvb/f+jgiNraLRkRZaq5frsdm6YQViE8mpk/tAlLd7SktXRL00Lug7sDAZMPgstm6aT1Nj72Q98vMLo7a6DIvVhFaj51BhPmjaX6iJokhd0SF8o/sowofCSYvF4VoaPSqWYTeNpqx9Yysnra5oXQkjgktGS7tB6p9Co7sFSFmwLGYebhUJIBpkobWlwSXQoUqNwcOXvJy9DBw61e2+3Ow9GHwCEdsJLN7qnrNdlccQe1vBN1ET4jwO8fVjQ0XbxZXFJI9pKpWAWuu+ZLJH6bBazHz10RyKCzNJG3QGkVEpVFUWsXPbYrQ6D66/9QV+/e5VSoqyKCvJcW/jCUS2RDmHlpaXWLVqFePGjcPTs3ubUAo9RIn50SlpaWns2rWLhIQERo0axUsvvYROp+O9994jISGh156jiB8KCgonHKNaoCS/nqhYH3wcMT/ip5RKJ1eFdnIntNjE1sDxCHUOZ972JkEuRPaXgy8WuYgOh1PdUs+oOEequr3SxCmb/W7XjbTFYQiQFoFF1eVtT+pU7UbIz9sbxqRR0rWtwocoipTuyKB0+36qM4uwWUwIKjU6bz8EBLSo6TtoGgEe3cufrqBwOHqDGou5e0HD1L4iNheBz9NHsnbwCeg4k4Mbjv4s6lz6gK2T3V5vl3M6F2e1evc21zgCKTY1NlFdVYJO775Te6QYfKOw220sX/4jSakT3M6btkrxSASVjZbmKsyNdfhEJPDOf2/stTYoKPQmRj/pHdXv/AEY/IzE+JWQsU56r0V5SkK/xd7F2GCSRA2tQRYvTM3u8YFET5VzUdae0OG8ztCxQ6rdV4W3w/UmLHEYu7f9RdrgycQlDHRes3/PWjL3byZm9NnyjS0uwotRrn+7SooX5lnrEFAPM2wYHZTC8uIdAEwJH+zWHpvFSqVns0uJVPeOLYspyNvPFdc8TURUH+fZgYOn8sUnj7B+1U/ccNsrvPjviykvy3Om0T6ZXGAADAYD06ZNO9HNUPgb8/jjj9PYKL3Xn332Wc455xwmTJhAYGAg3377ba89RxE/FBQUTgoK8uoYMTHardw2cRfqVdJkx6tAnshsq5R2ZQYFdB6Y0VoCoq+G/NXyznLEmI7N9y0tGkwe0qxtYoh7doeAcDnmR1KOe+aMerPL5EjnPrEbOVzK8mJ0EURsFis1S+rJ3bea6pIsJk2axEEhCq/gGDz9w5k1crLz2vSDBzpsu4JCV2g0KqxWO2GO1NIpA0qd5zb8Kq8G1L49204q3tC+iCh0si1V5dkCjh3iqmaHxYa+k+g8RhUmk7yQcpUUC/PTaW6qY1Nl7/QPD68AIuMGkZ2xnqTUCWjVGkbopJ2nzeZDba6tzz+EoFLj7e8+fikonCxk/pWNWq9F4xNBQ7Wawn3BnV5fopMsIAVz96yZjN4mGqsdQYrbETiNhbIoYRIdgVFdxAnXocLu23YciO47lsrybL54/1ES+wwjNDyOgrwM8rJ34x+TSqCLINIGRwwSwa5yWo+1cqhetjwL8fRrc2558Q5GeCY6PweEBuKGo73rtv1CQvKwNsIHgJd3AEOGTWfdmh8ITB6Hp18o61f+QGz8AAKDI50iCJx8QoiCwolg+vTpzuOEhATS09OpqqrC39+/V60qFfFDQUHhhGMx2yjKryMp0Q8/h2CgM0omsvEZF0OcdF3Jns5jDohBjiHN4jKLasfqopWiHUF495fEis5Sdnr5e7PJuET6YG1/ceZm8eHCwMuyXT5J9zfbREp2lfL7u+lUHSjFbrdi8PRn0KRrWbHiszb3z3lhgfP4x4/u7/A5Cgqd4avT4O2lw9TYhb9+B1hMUh/x8JWDFba6ixXTsQUVgGAWsQdK9zsttDrx9wcQXfquYOxYFNlcnYndYMTe1HnKzZ4i+gXQVJCOVt12bBihS8Cvv5Qae+m+jVQX7Mc7JAa1xj12j4LCyYCp3sT+hZn0vXA0Ko2761iBjyR0qMpd4nPoO3ZfM7i4ido6eCe2Ilg66eeuY0A7FpsWu2OsEgQSp1xGVfZuSg/tprDkEFpvX+LGno9/bKoUo6cV18e5FAuOGB/NdhNGVVsXv+waSQgRA9QIte7WLyVFkkgcMkAaY8p2+zrPmeubCIppX/gMDIrGbrNiNZsYNOFK9q//jk/efoBLrnmM2ITupx1XUPi7EhAQ0Ot1KuKHgoLCCaeooB6bTSQu0a9b1ze4xBdYsL7zRVcror8GQS1Nfop2dB5fwKtF2hX38neP+ZFWOoI9oZtJKx2BRdWxL3SZTyOGIHe/55wKDdYWC1s/2Un28j34J4SSOOgs/MMSWP/7f1Cp3CeSzzwyq9P2Kih0l379g9i+taT9k64uLv6SKNjtLC4uiIHy1ELrCHZoru1iuuHjeE6Dq8l6O3W7ptCstdFSX0Xpzk3Ul+YQljZeStfZC2S1lKLRG7FaTFitZoqaamhxDBsjhHi5PbV1NJTnkzL6YoyiIn4onJzU5EsWi8EpIWh00uLe4CO/R1uqO7agFH3kPucV23EQYsAZ6Nhc33F/N9ktCKKkSrQri6hdY4bI/Vml1RDUZwhBQ1wyKjmECu9U6V1cn35YH2wVTQ97FTfbTYgejvgkh2WoaR1j6hzxg3zacacLGVBL2RbJ/dTHI4SiovYtzoqLDqLWGagPVSGoPblh8Cv88MXzfPnhY5xz8b3txjBROI1RYn64MXv2bJ555hk8PT2ZPXt2p9e+9tprvfJMRfxQUFA44eQeqgEgLsHPWea5QvKzz7MWOMtcRY9OcdlBMnpLk7WWxo4XJkGRNTTWySutpnZWXbUVcgCwtNIRbuez7JI7Cz7ui68Dm2K4aHoRAMsyalj98jKaK5tJu3wcO79a1a7goaDQ26TE/I8ZZ37M91/fjKnJgre3rrPwGx1S59IXWneAB1yS4yzbtTyp8wq8uv97FxwxBlpjhggmUQowWnCIsh3rqS/NQa0z8vrrr3PPPff0qmmsl084AJu3/UFEymhat5E3i9mQlYm5qZ7SfavR6IwERvbttecqKPQ2owYFsz7ci4N/7iSwT1iH14lRLiJIXftpa0EKjmz0NlFR4OvMRgZSsG63Or0lMcFU4/7+VplE7J6O8aAdndXUJsf1YeOGVUQd3bbMO9WM2RGDxJQtjwV+yXIg5PDECvZ/I2dTE40qrEOk97PfpkBqQtpaxtWZmrA53HSCdvejYsC+NucTU8ayfsWnHNi/gT59RzvLy8ty2bltMQF9ByI4Uosbjd5cddMz/P7Df/jt+3mINkgd4B5TSOE0RRE/3Ni+fTsWi8V53BGK24uCgsJpxca9lRg9tby51cc5wEV0cU8rVZmydYbQJE1a2kt1Z/A009KoQxQFBJdA5kGRNe3WWx8u/j97dx0eV5n2cfw7Hnd3b5p66g6UAi0OWyguiy7F7WVhgcV3kUWKu1uBFilSSr3Um3qqcdeJjp/3j0kmSaMtSSW9P3vNxcjR2ebMOb/zPPdDg7FVYccOjpZrWtcX0Lc9MA86LYskP+d7njrnf2sqGln29CIcigcpl1/Ftvde6WrXhOh1Y8eORVEUtm0tZcLEqC6nDYqsJi2qpXXT9mxnwUS7vWfhhU9wPTUlzrumiWNbhpY48Gfnf92tW3aoTAfdkVUUqvZto3jLakxVZXj4hxE36iz8o1K5/fbbe7RNPaUyO3DzDiQ0IZ2crb9Tkb+Tk6ZcRFLKGL7fvoCS9auoL3MGs//5z3+47777mHzxo726DUL0FoOHjsmz0/j+5fXU5FfiExWAWtPy9+UR5LxJ0NWIT4qbmtoy540BtV5xjfR0MJtF03VXF8BH7zwuVOtM7T5TNThcI7eoDuoapzI52tXugJZRq1oXXzXEK7h7dTz6U+rsQnauiHMus6ptNxe/UuePfd7AXKJz2w8JHrRtIAAWSwUAIYkjKcjdzg/fvUBs/DCiogdSWVHA7sw/cfMOJCJxIupyG46mbrkajZYZ5/wDRbHz43cv4ubuCZzX4XYK0d8tWbKkw+d9ScIPIcRRV5xjJCzOt8Nk102rx2SzEODlS0T6Ntf7SU13k7Z2sVz/sFqqipvCkW5C4+YmrABKeMcnbnGDi6ha2nlRw0HndTz8br1VYf0uHav+uxRLo4oBl16O3ltGbBFHXmpqKgEBAaxamc/YCVEM8mu5WKg7Pdf1vKMRHDpTV+XOnj9b/i58wno2Gox3VEtxYK3OeQFSmdu+qxmAzsdB9q+/ULppI/7hySSedAaGgIg+H1r2qssf4sC+TaxbvYAf57+ITmfAajUTETmAoInn4BuRwH333QfAii8f7dNtEeJwFVVZyNxYjGJX2PbZUmY8cxpluZ13GdW52bBYmy4RurmzbK1tCSzV7p0MB+2phtZDYB+ceaicoUe7+bQqV7cUldKu7wrqg65iDO5WfIOcNYnK8nraJVaDVue8cXJw25S8GOcxsSHf2XI0rYPbMgdCSgiddRqa7RGUrd9M4bof0Bo8CB80ieCkdDQ6Z6CkLrexqnIjAOlxA5l+xk1UV5Xy54pvABmy/oSgKM5Hby+zH7BarZx22mm8+eabpKSkdD/DXyDhhxDiqCsvrCMkygdjectoEz5No6aE+ATg0VHn/4OZHa7+vf5htV1PW9fyY3FgvfNkRkX7ImdLzDvxKXYuM+7conafD/aOpiTN2QQ2JLZ9MVZ9UxG2tfvc2PPTemoKKkmadS16bx82Pf9o9/skRC9Tq9Wcd955/PzjN9x579hDmre5uKmlVTBSV9WDv02gvMDP9Vzr23lz+stmZ7ue/7iiZTSKunINpZs2EhA1kAHjLwRg9VeP92jdh2PDvKcB+L9/LyAxZRSJKaO4+JwYbp7zOLHxwwgLTe6zdQvR2xqqTexe7ryQL8ssp/JAJeAMLA3uVtRNXVds1q5r/DTXCbF0USAcDhrCVt/+4qzazQQWBToJLw0W5/wWe9suKCl/K2T395Htpm/uluob1BK8BkdXYW7Utytm3hzsRg8qJW9HSNvl+LXEHx0NnL3Dq2V0LP9WdU1UajXBQ4fiHzYMgPEeLceHNaWZHSwJ1GoNQ4aews8/ziU3N5eYmPatTIQ4Ueh0OrZv397nNzRAwg8hxDGgprKRxLRAejpWQ1pSLRpV08mVufMLqWZ6DyvW3FZveHRxgqfg6lrsU9i+eX9meZ7ruf+UunafZ21tuTOUdlJLYcnKfcWED49i+Fk2vpj9aLfbLERfufDCC3nvvffIzjKCoeU0QN2q735zjZwsY9cFDptFpJZTmBmESgcNrZrDxw93hoaVJZ23dHLztDBhWFXTq45PfNQ6He4BnlTm72LeizcTEdHTjnF/zTOPnNvm9ZpV37qe3373Z0dkG4T4q3Iz2hY5dlitrq4iXQmON1KW5dvtdFpP57FDZ3CGFR12n2k9YlMHI6q0Gfb2oMZjSqiOASfncrDmYEPn5lyvsdyTikIfIpNbRl/TudlcI9I4DhqZJnqQs1ZXwe/+hE6pafNZ65BX0atRWdqea3hMddYJCW8KXPZu7LhVaPCklvOEuuYuNk2N3mLinUP0ZmRkSPhxIpCaH1268soreffdd3nmmWf6dD0SfgghjjpjeSO+gR5two9gLz8ANvltcb03IambFh04m8lWlbQ0nW+uPt8ZdYnzc6WLoTStDhtZ88I7/bxsqw+RSR0PdRvuocHgbqW+1EhQUkKX2yLEkTBhwgRUKti4vohzZw1wvb++pPu/L2gpIgxQXer8W6sq9kbVg54yvkF1hEQ7g47WLb06ctuMatfzjzZqMM0YxtZPVxMYGNij7exrLz1/6dHeBCF6ZNTUGH55fg0Apz4yhZDUIHIPapBgcLcQNLTa9bqysPPQo3XrSmNpF3/HdqXdSCtttP7ZPegizqxzQFAHB5UOfqs1GkebWkQFe52txgaOz27T/UWtdbjC2eZRb5qVLPdBl+x8z1rZ/gaJolejqm6qK+bX/vIpeWQe5fOdhY//1O4DYLy+4+LPendnuOyhcn53JlP72idCnGgsFgvvvPMOixYtYtSoUXh6erb5XEZ7EUL0CyaTiYY6CwMivdhX0fM6AwDzfojFENnSLNZU1XmLDl0MWOqaDnmtTsY6us+sLnQ277c62h8i7bqWM7Syre3vZqeMzCPI4NyOcI+W/QlMCqZkZ3mnReKEOFL8/PxISglg9cq8NuHHwQYk1pBd2NKtJTnKeYKeb+xZs1St3u4KR5r74Xdm9RZ/ThlZzZmRLRdSuw5qdVKyNY/AlFAMhs6LMgoh2vP0c+O8u8cx//k1OKwOPAyaNmGBu3fnF9/B8UbXKC4ajTMcsJg7/q1uboHRWN1+dDWl1Shs+DUNM9tw0I+x2bkes+6gQqdVNnYvat+yQrG3LFPTVMDV6tC2GXUmOLoK/6aWKbu2t3SlMzd2PgKcLsDerrtMm+2ptlHQtKwi9yBSx3Zc7+tPyz7cK1o60Bw8Gk59XbVzG4ODEScAafnRpe3bt5Oeng7Anj1th4+W0V6EEP1GSYmzD21gkDtUtP98ROUQVip7AXDTtBz85v0Q2/3CW43j6Qo+OhKnRZXfvhlua+5NRcvq2lVqc9r8q7OP7/RzDrT7rN7ohkrrjsPeceV5IY6082el8txTf3LVzSOIS3TeGZ0R03IxsNGzprNZ2whPLKe61IuqzJY7NNqIrv+WAIqzAtvUyRkd33ELLX3T9ZnDYqJ0Zz7DLh3fo+0SQrQoN9tJOD2BmGU5bP1yB6lTOu9i0dwywuFw/t62Hsq2Iz6t6mx0VDOkuUuMrbF9iw3FQ43K1PQ73dh2PSq7AlW2dvMAoG57IaRSKyiOlvdaP29t4OAycnK8sVkO2s6mya3lGnRBnR+/FD+tq/VHa5lrnecjKefsBqBsccffr2WL8zxkhWYTAOaGGlRqNXf/38ukDdvFJ2/9o9N1C9Hfffjhh0RFRaFWtz1WKIpCXl5eJ3MdOgk/hBBHxeJdNwAw/9MdqNUq4pP88VrUcvGlCnce/FbYd/doeeYKLaqm+h+KpuuhOL1DW0aZqHfruCWG7wgz5tYnY5ltD5eKtwZDWMtJksnYdp11NgdFDS2fG/PK8Y0J6nEFeiH60uzLB/Hx+1t57IFlzH3/TDw8u251pThUrhYfRfuDXO93dce4mUNRkbszzPXaO6DzENBL33JRom/1J1VbWI1iVwhICulgLiFEd1QqFdHp4az5YAuKwxk4xA121uRpLhja0E3LxOZupJ21/ABnAVXPoJbjgrm+bQsLdz8LpuKm39OOcgY3Far6DgIXn6YDQquC5apGBwEptTTWGtq1qgC4Ic25Py+tVYgIazW6lN5O0VZn97mQ5Oo281jLnccgpWkXVQc1NGvu8hIY6eyo6xpRrpWUU1ou1Iq+i8eW2vExz+DhQ3LqJLZn/ErCAAl2+z1p+dGl+Ph4ioqKCAlp+ztfWVlJfHw8dnv3N1Z6QsIPIcRRY7XY+fLdLZw8MxG3oI5qq7eXU9sUcJQ4/2v26vowZjE2FUTz7uagGaVBqXb+ivgldXyiUuttBTrvWnPZxdkcqG1/0la420BtYRX+aeldb4MQR4jBTcsLc0/just/4I7rf+ald2aQ6ttyQZPX4Ly7mdXJHdSDRYyuoHB9IIqvBnOr2oqGxM7nL80JICze2dwrwr3rUSbK9jnvLqvdQ3u0PUKItp479RMu2HoadoudlW9sZMTfVXQ1BnxHXdVad5Vp5uXf8nvZWNtxeOLha8Jq6OC3WuOspQGg6mDITkermh+uLfVSoTQ1vgiMad9CLWVM68KoLfMXFju78FWXeuMX0lKzpHSvH/ok5wJt+2zt6nko3dR81ru1tATZ81U4KRe1HxlOm+k8v7niviwA/ngrncJa57FvSPpM9uxcTmHu9q5XJI5/En50Selk2N66ujrcOrlReTgk/BBCHDWLf9xHWXE9s68f1u6zVYXOEwH/ES1nHjkdBAttGFRQ50BlaTmAKprOT+50BpurSa+tvP10BncrjXXO2z7O4KMtc7EG90jn+5ddnN3u829/isFht5Oz6AdUKhWBaWldb78QR4iPXsuksZG89N5Mbrt2Ic8+top///ekHs0bnljuGg3mwCrnqCuKhxq6GBTCN6SOmkznyYvZ3XlBEptW3On0VWYbGZXO2js15Q0cWLSBwAHRaN0776cvhOjYc6d+AkD4oGAm3TSSlW9sJGpCLCFpzjusvkH11FW5o9W13CTorOuIw6bGo5sWX62LiWq07ad1j3D+bjaUt6/fo/hpwdD+t14xA3pVm+6sAH4hte2KlzZL8Gm+YGps8351qXdLsdVWq1L0KmhusRmkA2vbdYWXtdyksUR2PD7dnq/C2xRQb98uxCnC29nyJNDTucwAv2OjkLMQR9pdd90FOFunPfzww3h4tPyd2e121q5dy/Dhw3ttfRJ+CCGOmm9+yyFoQCB7Pd3ZW9hIjE9LU7ftxo4LiLXmYdLSugRHg1sn/YOBhOEFrudluZ13PdEGKVTlOYsuuvm3DzxUNS0nWW4D25+geWlbTnqsDfXs/eZrGooLef+997jqqqs6Xa8QR8Ow9DBuuWcMzz22isuuGULKwJYT8JEBBvQDLK7XdsV5MbTmQPeFiaPHOkc/ytvQ9Ddt6jq4HBbgvEipMrf9G967rpB5T67E7tASMv4cSrICut8pIUSHcrN98R6Ujs5zO3sX7SV8iLMLW3O3l45YzT27VHDztFBX1XkLztYjr7UrJqpXoejbr0dlV/BPcLZAqSxqW2BcpaVNC45mtU0FRr0DW1qk6Lsa1lcNk05ynm8s+eigWmI65zHP07+R+tK2d56r/nS+Vtyd0xjiOr5rXatpuYHz1twIbphT2Obzulrn6Ffubp3FJEL0b5s3bwacLT+2bduGXt9yk0Ov1zNs2DDuueeeXlufhB9CiKPikwP1GHwMVGVXdzmdcbOB5POdzUi3f9ZSRMyjg5FYOqKq77q7i63CeeKiDeq87aCpTg/uzs9bhx/NfIPqXBeECeEtJzrZP/yCubKSlJlXSvAhjjlR4S8TaryHtDmjmf/pTp57+k+efWcm4Aw+eqSpdkDrO8YRYzse9rlZ86gMpvquW3Eo6/P58N4lhAyOJmTSxeg8Or9AE0L0jEanZeAF49n68VLSzhlAUFJLoNg66LDbnEG+uoNaGtB2yOvOePo0uoa0Prh1hru3mYaajpuyD5zmDCOKs9q2hohIKmtTc6iZo3lbWxVnra3w4LntzUWVW/ar+UbI1iVJKAdlIn6DW+6mVBW3DXk9Q0xUBzjDYL+s9jdQzNkqVE1hCSoVdNKE/625EfjZWlqN1OdkoNe7Ex6R3OH0QvR3S5YsAeCaa67hpZdewsen/UiKvUnCDyHEUeMR4E5jVUtz1LKGVs1Im4a1Sj6v8ODZ2qkPdp7wqLoYTGXPL5GknFFAZaFvm5EpOirh0VzN3aRrf3EWOa3K9bxdxXhg1cpIACp376M6exdxU8/DMySy230Q4kiKCn/Z9Vyn0/DkI5O56IrvWbcijzGT2w8pWWNxUGJqGuaysWf1ecA5RCWA4tZ1EWKA7VVm1yAO+7aX8fD9y4iZlMrwK6eQlynBhxC9JWhAFAAr525DrdHgH+dF4rQUDP5hnc5jLPfC08f5e926VUVrzXVCzI3tW4c1zwtg7ui3NaW003XHphXj5tk+bNEZ2rYUc9jUOGxqdIa2qYalaXsObgGiatrMLVuDGTa0bWjbUQHVZtXxVRgynTObHPaWLjRtFuA8mKlsTXXKtB13IzqwbwPJqWPx8O3bCz5xDFCUTkOxv7TMfuL9998/IuuR8EMIcVTUVnhgMemxNtrYl2/o0RjeyecXsecXZ5DQ4Gi6qPLq5qKq6YQjKK2WysLOixLYD7Rqlq9rvy3hKR2Mw9tK6ztUdrOF/T/9hk9kAgeWfNur45ML0RfOPSuJ1JFh/O/J1Tz95XlUN/X3r7F0U2enib0KAgY4L3xyt7QUJVXTeXPz04e23GXdXtXyd6woCh/+dw0+kQEMu3wSC65/85D2RQjRsR9ufA2r1crMM88EoK6wDg//cLKX57L3l10Mvmg8iacNBcDT1/n3eXDri9Y6ugFwsOauKR3VEBk8Zb/r+cGjpgT72DF0UBcoakD7kEStdbhafwBYm0ajCXXTuEJbcIYgxlKvDrdzy9bgTvehecQaW70a9wBL+wmaDpOKXtWm5lhrKpvi6irrcKtGvcWPxvpqykpzmDD1ok7XLcSJorGxEUVRXDU/cnJy+O677xg4cCCnn356r61Hwg8hxFGTvy6LsKHRHYYDip/zpGrPslZ3oRu7vhBTfJ2HtLuuaKn2/r832re68E5sueiq29J5/QLPglYvEtt+5nCoqCppuVOjadXs/8D8VVjrG0k+dYYEH+KYpvN9zvX8ynv389Bl3/PLpzsYd8ngTucJalXoz9zovCiw27oOIVUmB45I57Smws7/JpaXmNj01U72bivj3P9MY/6Nb/doP4QQPfPUU0/xxx9/oHXzxCs4lvhxZ6E47BRsXcb2r/4kcrg3IWkhrtYSrfkEO0ddalez4yCtg46DW1CkJhupMDvfO/i4ERnrPLa4d1CovLm1SevheFt302nu8uI4aJmhbhqWbYggYVTbVqRunh2EGB18fnD3vMZK5+vZt2Wz4OXYdvMpehXB9paWauW0r0sC4BhWTf1eZ/ee55++kcTExA6nE/2IjPbSpXPPPZcLLriAm266ierqasaOHYtOp6O8vJwXXniBm2++uVfW0307VCGE6AOm6gaqs8vxTR7EgfURHFgfQbCHr+vRLYMKDCpUVsX16ErZfl/Xo1NNI/9NDBzIxMCB7T4u3uKPw6HC0cEdLLtVQ32FG0VL91GauZ6I4VMwePt1vx9CHCOikwM4fXYa376ZQdH+KsLdNejVKtfDX6/GX9/9aUNgXA0qPaj0oE9xPpqDj44M99cTaFATaFCTs76QtR9sYeTsQUQOk2FthehNNpuNV+bOJTBhGKEDxlCZswNzXRUqtYbIYSfj5htA5k+7XdN7+pgwuFtdj86o1AoqtYK7t7nDWiBpSbWkJhtJTW47QopG68DbzfloDj5a89SqXY9mHr6mdusZnlbVbt6DHdgQwYENEVjM7UOd5u4y7h2MYuPmacGRbceR3b7e17m35eBm1bgeHXFE6F2Pxr2dTOPoWQs7IfqzTZs2MXnyZADmzZtHaGgoOTk5fPTRR7z88svdzN1z0vJDCHFU1NYGg0qF3dpFFXZwptpNI0U0t+wAUDV0Xsj0xS+iXM81Ac5QxFbffrpJ44r5ZUtTy5IObkaPCE9idekuABq2tC9wFhhZ7SoGl7cjhPKtG8hb9jN+salkr/kVrVYOseL4MSvBn7OePYWJG4v58vGVDPn47G7n0Wicf4et7442j7bQGW2w829muH/bQGTFr1n8/NhyYkaHM/qKIaz9IBZ6r6WrECe8oqIiKsrLSUo7Be+QGEp2r6Nw+0rix52NSqXCJyyRqqzd6HR2dLrOf2O9/Fvqd9Qb2xct9Q+rxW53BhYxge1HYYv3coYArbukWButFG4rpbakHmuDFUujFXuDDRSF1OkJaMLCgc6H4G0OQDZtbemmsyHLeYxx97fQWNVyvLGYda7RZywNbcOQ5gCkw4LM3WW/TTdh7E39YDTq9jM07tXQqHGeG5RmFKPRu5GQkNDNgkW/0Y9aavS2hoYGvL2d3d9+++03LrjgAtRqNePGjSMnp/sRIHtKzsyFEEeFWqvFzd+HhrJKPCOc7+XVtBQci9Q5u5QUaKq7XVZzMUVVeVOQEt7+7orW0+FqfjtpXPt+xK3VmZ0ndluN2e0+a103JCjKuW12i438Zb9QlrGO4MGjKd6yBnUHJz1CHOvc3XV88NZMxkz5mHWLs4mY5GzWnddgI9St+/79BzPVOi8gVHWtLqQ66Fq/6OtMnvv3Svyj0wiIOYv1Hx/6uoQQXfP0dHbHsJrqUWt1hA0cR37GH8SMPB2NTo+1sQ6tW9tLg6iUUvZsdN4ksDcNE99R3Y2aci8sTd1QwuLb18gqrW8JLXz8nLV9yg9Ukbu+kIotJezfWorD5kCtVaP30KFz16Lz0OFotLLtx72Ej4giZkI8WoOOoAGhKCrfDoewDYysBqCiwK/N++7+HXdz0XtY24wS06x1wKMeZKBxR9tjUpXZOY/Z4dwGg7p9ixJ7Fy06FEWhKmcXPhEJaDRyvDshSLeXLiUlJTF//nzOP/98fv31V+68804ASktLe3UEGAk/hBBHRWC4Eb9oH2y1paibQwtV5/U3AFRGG4q38yShJ6NHOKxqlKYTFK1fFychPhoC6jsecg9AE6ciJNZ5Mndw0VRTTQPLnlqIxVhF1ITTCB40WoIPcVwy+D0PwOjJoNF+Sl2NhbyG9ndtO+Lp00h9jbvzRXGreYI7/5vOa7ChKApfv76Zb9/OIHbqEALDzpQ6OUL0kYCAAE455RTWbdlEQGwa3sExoCg0GsvQuXlQXbCHkVcOprrUy1VjY39G56OV5ewMQ2/o/Bixf1MU1rRizLUmcldsoaaoFpvJRoa3ltxtpRhLG9C7aUkZGcb460cQPTIcnwhvrkp03v39tbARh93BzqU5LHpvK2tfXQE4h+uNmZzKwLMH4RHkxZI/YgA4+ZSWel+BkdWuQqWtNRdW1bvZMBxU96N5WN7mUWtacx9kZ+gY593nPxcmtfvc7LCisjcduzrJMiaHOWsp7S3NoyhnK2ZjBXETum9hJ8SJ4OGHH+bSSy/lzjvv5JRTTmH8+PGAsxXIiBEjem09En4IIY4avaeB2qLqDj/Lbzr50LYaecXW2E2o4OH83GHterr9ZS2HPsWn47OUzOAsNP7tlxMUVU15vh9BUdXk7QqlYPlv2OprSb3gOtz9O68WL8Tx4sknn8Ruc+Du2XFwUZrn53putzv/fjpsIt5Kc1cXgMcvLwGgyGjlrcdWsfqXAww4ZxwJ09NRqWr56Za5f3EPhBCdefLJJ5k6dSp7l3yOb2QyAMbCfVRkbcMjwJOwUcM6nbeyyHn3tblLS0dq9rpTXxWF4nBQX5TPlowNZC3JRKUGn0gfbJYAqvLMeIQNZs6DviSNCEOn1/CPsS3DXK7ec6PruVqj5pvHV/DyzKuwmu38uk5F7spMspZsJeuP7Qy+aDSeydMBWPJHDL5Bzj6uzS1AABpLtahUKtyCnTdarPV1FK5YRfm2HdhMJjxDAoiaOJTAwemo1CqM5V6uwqpBUe1rkYyfuY+aLnrsatUt5xhnBAzll8qNbT43VhSQuelnAsOSSAxqX19M9E8qxfno7WX2F3/729+YNGkSRUVFDB8+3PX+tGnTOP/883ttPRJ+CCGOCkVRKMssIjVhCgEa592WysbWVdEN3Syg6b8dDEt7MFWJFa+4riu7V3qaUFU7m+bXB2e1+7w0JwCfoHo8vE2u7i62xgbKt24geMQ4HvmnlYuHP97ttghxrCstLUVn0DBqagxv/NwymkFIbGWP5leCWk4tfMOcIWbrERocDoVVi7J5/b9rqK02kTT2fHx1A1k458le2gMhRGfGjRvHokWLOOu82RRsWQJA8c7V+IYnM+He0eg9Wn57641uBEa0XPzXV7u3W15ZdktrSFWds4Wl8cB+9i/4FrvFjMbNjdDRYwlKGIvWzVkPSNE7f7dX7YFXbnusw+2ckPImE1JaXqtUKvRuWkKTHYQmD2TYRYl8dcWXbP9qPSc/NZyG+rY3HyoK/CjN9sW49WvKMutJOe8qTGU6VIYKMj/5BLvJQmDsEAxeftSW5pA5bzH+20pIu2QGKnXLeUV5vi+6nKZue2Pab2eAoWWI3gqlHpW9/dXoGQEjAVi6dR1Fe9eRs+V3vPxCGTy29y7ohOgP9u7dy5tvvsmBAwf4+uuviYyMZPfu3cTHx/faOiT8EEIcFd4r02isrCf+1BGdDATnZGtVQV1taOm64jB1Hnpo8lqCDkXpPBa3mrTccF0+AG8/F97uc/uBlvX5j2ls85m5thHL2u/Q6xQefzrq4FmFOG7dfffdvDL3Fdb8ng20H8qxtfIVTRcz/q1OJzqod2q3WDBXV1O1ZxfnvrIdS101fmFJpE6djptX+2LCQoi+M2XKFEaddBXb131HWcFuBp9+M25efii6Ohqaenx0VAejWVcjvyiKQuHylRi8A4gedzq+Q8NQqdVYS1tai2ya23Hg0WxCypvt3rt13AcAXPHdlQDoPfWkXXoOe777jfWvfMvYO/6G1s1A6T4/ghOMKIpCwbJfKcvYhUqjQVEUVCoV2b8tQbEqpJ32d/QezpYsIcmjqMzdSdaaBRSuH4Rf4gBwgOqgezAR7i3nI7+8msDQa/e23/8OhultZmmsJWfrYgYOmsLkKZej0chl2AlFUZyP3l5mP/HNN99wxRVXcNlll7Fp0ybMZueITrW1tTz11FMsXLiwV9Yjf3VCiCPqX884D15ZBzaj1eqIikljV5GzH61hcMt0jdVNB3RtNy07bC0HfsXQfHLVvkq9ca0eR6zzTMY/rKbNZ5nrC7FbA9DoDER6BOJZ5LxLvYeCdsvxCarHXGtmySM/ozJZefD1M/AJaH83TIjjVUxMDHq9hvKiOghyvmfLUygsCABAZWm5KLKa6qjK34Vd58BuMWEyVqD398c9KBRUkPftPux2C1WFzosEtV6Pf8xAAhPPxs8r2rWctfOk1YcQR4rZbGbFDy/gcNiZdPJsoqMHAVDF7nbT6gw29m+Owm42YzPuxNZo4sCechrKjbj5eqHo41Hr9DgsFWgLoc5YQl1JHgkn/w3P4EhXofGNb/VOy8iPz//I9fykzHvxCA5g85ufk/H+z6TfcA4AhdvUFK7+g8rMLeh9/LDUVOPQOXDY7VTs2k3UsJNdwUezgJg0Svasp2LnJmf4AShNo+laPUDX0H5btr6XTEOgc3SYwJqO64b9lrHa9bxwz1rUai3jJ1wkwYcQB3niiSd44403uPLKK/niiy9c70+YMIHHHus6MD0U8pcnhDjiFEUhI+MPAsKS2F9aiM3R+ZB6rWn1rYbFq2kqfGroPByJOL2Kgl/9AHDEdNyNZv6KRn655ze0uuWcct69WC2NKFo9KlXLXSpHsA6Hzeza9nVvrcVca+a/n53D7Wd/3aNtF+J4YjbZ+PbtDFJOG4JncJSrCKnDbsNUUURDVSk1xQeoLtiHSqVCrdHi7uaJv38E5Xv3ULZ5DQCe/mGY66sx+PuTcOZMPMPDcZQY2PjhE0dz94Q4odXW1mKzWRg9/mzGTj6PwsLyNp+XrfKiLNoPAIddjbm6kv3ffoq52tn1TWMwoPfxx1qbjc20zjWfWqNDo9UROeoU/GIHsPH9vv879wgOJuWCmez67DtKdlZhabSz56t3UWk0BA8bS9m29QSnj8Nht2NrqAPFgYd/+5ae4Dxe1ZXnoyuwYQ1ve4lk9YDvXnYWfz3/trY3Rsy11WzdtYnG0iLUWj1Vkakkx45GrW5pKdJQXUzR3nWEj5jEZnshG19+nFtu+7CXv42ja/Z1r7QpWP3523OO4tYcg2S0ly7t3r2bKVOmtHvfx8eH6urqXluPhB9CiCMuL2cnNZWFDBgxo9NpmofGVHx6fphSWZy/AhGnV3U5Xe7OMNfz+mLnPDarid++dt6Z8vAKIHDgKHyHDsFSU43BFICiKFTuzWHtS+upL8wjbuaFfLhkELdLoXbRD0179DQWP/obe377GIN3AHETz8FqqiV//e9Y6o2gUuHhH0pK+gzC4oaRHj/ENW9mcQ42qxmb1YxF62xuvvKLf8soSEIcI/z8/EhJG8eGtT+xPWMpJ59xFSNGnc4vq1ou6hVFoSpzBwUrlmIxVmHwC2DQNddj8PXDYTGgUqlQFAW1phKH3YbBzw9raUvh441v930NLMXhvNB28/cDoGzrThrKG9G4uZM6+wbKMtY6h/CtMJL5wWukzrwWVCoaq0vwDo5ut7yG6hL07k2jwRS3utlyUFe+716ORPFyjnJj257FztVfotWriRoeSn1lDVmr5lO6Zx1JU2czMmQgW0r3U7RrNW6+AYQOnehazqsvX9WbX8dRdckll/DlF18wZOSZDBpxxtHeHHEcCg8PZ9++fcTFxbV5f+XKlSQkJPTaeiT8EEIccetXLcDTJ5jgiGSKNEZoar2h1LS0zlDR9RCbzQXTVNbuY28lQEtwTLXrdfNQdwAeoWFETJhE4eqVAISOmYylpIK8DYvIW/9b08ao0Hn5YK014hEWScJ5l+Ibn9yjfRXieBQ2OIy6ujrG3/w3sn5fz+5fPgDAJz6BhKRzcfcLRa3R4q/2bDfvzx/ff4S3VghxKLRaLRdd8S9qaypZ8usHLPxuLhnrf8Pm6YcKUOv01K8poiZ7P75xKQSnjSQgZShuIU01fqxNv78qFRtfftW13JHX/+to7A6GknAiUydStHktDquNqKkzsNQYKV6/HIDqnF0ANFaX4hc1gJLda/GPSkXn7uVaRlX+buorCoifdmGH61AcDl68r4gbH1SjVmnRuQdia6xj5+ovSZgQySl3jUXn7hwhq3hnGT8+tIy8jb8x8oyBDPQNJ6PkAOljzmfd+08x8rqj8z31JaPRWRh328afMDXWEp08lvOveQGA796/62humjhO3Hjjjdx+++289957qFQqCgsL+fPPP7nnnnt4+OGHe209En4IIY6o1etXkrnjT6LGTqdY21WpUydVjY3mHMRqbNXFxavj6QHyN7Wq+h7QeThiq1ej97URffIUvOMGkvPrfEo3rCJ5+uVEDJ9KfVUhBh9/GqvKaDAW4xs/AO/EJFQqFZteeLTbbRfiePXJhc7m2OHpAwgeFE/1gUIaTYF4hoWj5LbU/Fj06YNHaxOFEH+Rt08A58y6i+GjT2fpbx9jqSrC4XBgtZhQqbRMn3kbFTG+reZQWPdM9y06jkSrD4BlD/4XgIkXP0zs0FMITx5NVeFevNKGseX1p1zThUSkUFq4F5OxnKgR09i96CN2/voOgQnDMHg6R3upys/EL34gfnGp2FQqaLrBoim2UrRlNaW71jH1UwuK4jz+afVuuPkFo9ap2gQfAGFpwYy+YjCr386gsaGGXduX4HDYiU8a5fx+3ul/I8O99NJL/PLrbygOO/v3rGXvzuXEJI0mfdLso71pxw7p9tKl++67D6PRyMknn4zJZGLKlCkYDAbuuece5szpvS5UEn4IIY6o3L3r0Gj1BCQNbfeZf3hLGFJd39TO1Nz1kV1pNdStm6+zAr25rP2hrXynt6t4akfV2D1CI4ib+TcyP34ddaCV7Z++xshrHwLAMzgSxVPDplf+3c3eCdH/aA16ggbG8esdLx7tTRFC9JLH/2+m6/m/noErb3imzeeFuYUARAPvvnZdj5Z5pEKPg6368jEmXvwwendvQhPTWfXaY8w/bTR3vfoJXmEx1K1fA+yhYud6bMUFxE84F2N2JuUHMrBbTLgFBBE19XSCI4a3qVmhKAqZ67+iNu8A4AxRkgZORqd3pyBnK/t2rcAzwB2tW/tzjtgxEax6cxOVVfvI2PQTU0+9nJS0we2m6y+Sk5OJHn4quZt+JWzAGLw0BvZu+4PBo8852psmjiNPPvkkDz74IDt37sThcJCWloaXVxd3Ow+DhB9CiCPGbreTv38jYbFD0Oha+ganKKEAlJLVzQJg6EXOabYucPb/U9y7riPg6h7Tyah9zX2G9R5W6k1lANQVFuJwOGfY+J4UZhQnLgk8hDjxtA5Gjhervmw7GsR5553HeeedB0BOTg7nzb6b+rpKCnO2UbHiG0ad+neGj7+AyoZabNHOVhuqChuYnL/99VUl5C79nsaKMlQqNRExQxh/yjWucCQkPBn/wGjWr/iU0j0VhA4IarP+hirnKDBZ+zLw9Qth0kkX9eXuHxOCE0ZgaailcMcKUoefjqI4KMrdzsgbWrr59NaoP8clafnRKavVymmnncabb75JSkoKo0aN6rN1SfghhDhiFi9ejKmhmsikUVi7mVbxbKqS7tFyZB82fV+X85gqnYe0bgbHReXo+NfCNzGRsLFjyF+6jKefflqCDyGEEP3e8Rh2HIrY2Fg2/zkPgLMve4plP73M3oxFjDzlKgI8vElWolilajm/qK8oYu+ij/GN8iIwKZL8tQUMHH5am1YhALGJo9i24Qcyvt7F6Q9Ndr2vKApbvsnE3cuP/OwdDBs1HdUJUPBZCdASPvlkbGozmRm/ArB980L8awfhERSOf0LaUd5CcazS6XRs37693d9YX5DwQwjR5+596BsslkZ+/uFVPH2D8Q2MopwG54dWheioCAD2lJb1eJlu4c5CIKYDLScUiq+m3XQqO6Bq2z1GZWsffmg0DjQaFfGnnUzVnj2Ul5e3m0YIIYQQx69CdT3+A0eSu2YhZlMdBjdnk/qJShJ7yQdgb8YSfCK9OePpU/n2+gUAeHoFtFuWSq3Gw8ufrD/zyPhmF7FjImioMrHl291kr8knbfQ57Fz/PXEJ7bv59lcqlYroiWfQWFZCQ0URdquZ0q3Ooc8ttdVHd+OONkVxPnp7mf3ElVdeybvvvsszzzzT/cR/gYQfQog+Z7NZeOOlG7BYGklOn+FspdeDUVoAUKlQVTYNKWdpH250ROPbsmx7TccpcnPdD63O3ub9wj/XYa6q5qyzzurZ9gkhhBDimDfyCmeBZp9wZ7dZTW0l0SFth9C0mOqoLcpmwm3jKN9bgbnOAkBp0R6i4oa3ndbcQHVFAcGhcax9bxur394MgF9gCOfPvh+tTs/O9d/jHxjRx3t2bNj4ZkuXltTSQnYveB+PkHDqi52hUk3efkbe8jCqspbR/CbHtg2G/vesFEg9UVksFt555x0WLVrEqFGj8PRsO5rcCy+80CvrkfBDCNHnVCoVFksjYyecj1fsiE6nm+YxjD/2bQRACWzqwlJj73R6AEN8S9BhcgUd7YMVlVVB1VQzyaF0HIjU5OWTs3gpkRPGMW3atC7XK4QQQohjX3Po0Uzv6YNHQBh/rvgCH79woiOTALA7HFitZgC8gj1dwcfAoSFs3/g93j4h+PiHo1KpcNhxd/kiAABptElEQVRtbFr9FYriYOTUq5zv1ZVSWVGAt18AlRWFbFj9A2qNlnvnnEpcXNwR3eejzTMkEt+YZOx2C/EzLqKhrIiSTavIWfIjQWGDMHgHUpW7k7d+eg1vnyCmnHQ5kVGpR3uz+5bU/OjS9u3bSU9PB2DPnj1tPuvN7jASfggh+pxGo0Ovd8fN3Ru1WkOSfwRTZ/3h+vzb95qKklq6PorvnB8DTY0/kk4tAiAvM7TT6W0mNTTVVVXrO1621ew8DFqqC9nzzfd4R0YQffLkDqcVQgghxPFl48dPtgtAzj7rbn798X8snP9fEpLHMnz0ydisFnQGL7R6A0Vbi0mYGg/AkJGh7N25g9/m/4eA4Bh8/SMozt9OY0M9UQkjqDWWsHvrYkoLdqNSqdHq9Oi0esIik5h+5vUnXPDRTOflg7kkD7/EVHxiE1Gr1JTv3EzFrs2uaZIHjGPv7jWUlmT1//BDdMpqdVYCbC542pck/BBC9Dm1Ro2Hlx91tRWMGXiIzT/trUILfdfJr6rBWaXdRjeFxRRnQTJrfS3l27dTvmMbjaUl6L29SbnwXMZbRx7aNgohhBDimLXx4ycBuPbmt13vzTjnHjI2/EhB/jZ2bXfekNG6eaIxeLLju12EDAzGN9Kbr97f5prHbivFaq0hIEhNUb4Km5sbK35+Dd+ASM6ddTcDB09Eqzcc2Z07BvklNZCzohKdhxcoCmqNlpTzR5N8zkhKd9dgNhrR+/jgUWiH3WuwePqzo+jA0d7sviUtPzolBU+FEP1OdGwaWQcyGDPmb+0+mxw0CICV+dvafdaZfb+GAxA1qcL1Xn5xy1BzDrud7J/ngaLg5ueOQ3HH1lCPtb4Wa10t1voaHFYrKo0Gv6QUpk+8griE4Wg0clgUQggh+qP3Xr/e9fxfzywkccAAFEWhrCSH39b9islYjrFoPw6bg8WPLQVArVWBAkEhHlRXmjBWmVCpVAQFx1K8czVhaROIGDyFfJWKwVrdUdqzo2/Cw/8EQHE4qD2QSXVWAcFDxraZRqVR4xEagUeo80ZY/opvcfcKwNMn+Ihvrzi2SMFTIUS/EhCUyLbNi1leuB6Dtz+8He36bHLnPVdQ/LWo6p11P1SNjpb39Z237lCV21DZbdTmZKFx02Nr9MLWaEHr4YnO0wf3wFAG+Cbi7R3InEeMePsY+O69vhtTXAghhBDHluYhfkdd6uwSExg3GIDIYScxOiSGlUu/pKT4AKigzmKm3uRA7+mFwduArb6KurpKkibPwjciybXMJ/7Ztlj6v55ZeIT25tix55v5VGbuJiAljqDB7VvS1uZmkbXwOxSbDbvZRNq484/IHf+jTaVS9fp+9qfvTQqeCiH6jeKSEooKMtFq9ai7uCsyKWoIK3OdrT/Uxc5CY4p31yO85K8MpLmXi/+gegCqMtxRa7R4BUehcncw6HJn9fBV/34agNvv+tQ1v7fPsjbLe+E/Fx/CngkhhBDieLbhsyddzx94bIHr+VkX3M7TD5/rej1u1kOu55NTh4NKhUaj5ZlHzmXU7H92uOzmgOVEUldUhM7DC4/AFFTuGlRuzgt0jcZ5A6to5S8MH5jKrFmzCAwM5Oqrr0aj6dlofsczNSrU9G5YofTy8o4mKXgqhOg3ysty2LNzOWMmzcbm7hxyJWpKpevziqCfAdj1eVS3y9LanEmHtamQaWflPRRFoaGymKDE4a7QoyOTB7zl/O9/ul21EEIIIfqx1mHHwdZ8/YTr+f/92xmSLMpezzOcy4YvnurzbTtWjf2nMxRq7jWcdM5ZFKzYSMHa3ylYt5jkv12BV1QM4OwSYzOZSUhI4L777jtamyyOQUuWLDki65HwQwjR56pLs9DpDIwacQZr6vb2eD4fnQeOxlZD2dosXU5ftcsDAJVDwW42YTM3YPDyazfdSy9c1uNtEEIIIYRo7sLy+P/N5JlHzmXkNc6L/ub/ApwUNpTnn77oqGzf0VRXVIitsZHdX3wOwLiHHsTa0EDmF1+Tu3ghA6+8Ee/AevYs3IypqoYbbrjhKG/xUdAH3V7oR91ejhQJP4QQfa68LI+AwCjU6o6bafh7OptCOiL1rvf8SrpuAqmud85jNOdRuW8HPlEJ+EYmo9ZoUBwOGo2lqLV6aoqze2cnhBBCCHFCmjztErZsWIR/YDgrl3zJ4OEnMSN5HN+smU9dcS6WeiPhBl8O1Fm5+wEoKi8D4LO3bznKW943Tnr6XgAcNju5f/xJ8bq1qNRqJv/Lg/eveoD64mI8QkPR6A1Ya4woDgdVWaVkLthA0hnDmTZt2lHeA3EsWrFiBW+++Sb79+9n3rx5REZG8vHHHxMfH8+kSZN6ZR0Sfggh+lx5eS7h4cmsyd8Fwc7DTkJcretzexdDdalVKhyKgrvWQE2gzfW+pthBY005exd9gs7Di4rMzWgM7mgNbphrjaA48AiOIHT0xD7bLyGEEEL0X82tPQpyM1FrtHh4+LBqyZcs/e0j9AZ3LOZGVGoNBg8f6hWFvNwdJCS1FPm89PpX+20AAlCyeQfF69YSOWkqpZvXsvnt+aQ89QEqjYawsWOpzTlA4nmzsTU2sPGzxfhGBzLwvBOzwLxapULdyy01ent5R9M333zDFVdcwWWXXcbmzZsxm80A1NbW8tRTT7FwYe8UD5bwQwjRp8xmM1WVhXhFD8K9h/PMuSyXRQeciUj1D6mdTqc4nGFI4sQLUev0VJTvRLHbMbj5YfALxDsirl9VwhZCCCHEkdF6pJaYuMHkHNjGRVc8RNTg0/FUajFWlxAdO4i9lUbUGi15O5ZTkZVBfW29a77+HHyYa+ooXLcFr4hIIiZMwidcy54Fy4k/bSz5KzOozdoNwP75X6DSaNG66Rh+3XmYGz27WbI4ET3xxBO88cYbXHnllXzxxReu9ydMmMBjjz3Wa+uR8EMI0WdGzf4nDZXFKIoDvYcPDdXFGIIiUalUbFgT6ZruH2eVAzD8stxul6mqbeoi46dB5xEEQH1lEcFJ6XhoQnELCMIjxDl27ob/9t7BUgghhBAnpglT/8bObSv48uOnGT9jDmZ1EDVhBnaYa3FrqvQZ7R9B0e4/AfAxeGA013PJ9XP5/O05R3PT+4Rid7Dtg3lY6xtJOPsCACLHDiZizCDM1bXkLt1I0MBYQgZNI3fVQgJThjFw1iB07oajvOVHjwx127Xdu3czZcqUdu/7+PhQXV3da+uR8EMI0Seah32rqyhApVJjzM+kLHcbbhkhxJx8Jh4RwT1ajtFc53quqm3pH6P4aFDjgWdEDHkbf6No12psDXUY/ANIu+Ym1BoNo+57WAIQIYQQQhyyx/9vJpMvftT1OjR1KtuXfURNZQHmED/X+yY3B6rgRnYs/g0PTz8URaHG0nDkN/gIOrBwO/XF5aSe93cSJxmASk4eVoep3srrc35F76Vn2KxkDD5u7PvVeLQ3VxwHwsPD2bdvH3FxcW3eX7lyJQkJCb22Hgk/hBB9qr6iEE+/MGoq8vALTaC65AB1RXmEDG0bfnhqVcR6thySPstt2ywycFoFBbtD2i0/edZVVGzfTN7iHwEwV1VSX5CHd0xc7++MEEIIIU4IrYMPAJ+gaFQqNQ0V+aRFDWS3pQgAbaSdko3bMVVUMOiMf5BZkQdAhHdgv2j1Mf2FOwFoMLoBULplGwVrfydo4Eg8gsOBSgDsNgcf/2sZFQW1TH14JgYft6O1ycckddP/enuZ/cWNN97I7bffznvvvYdKpaKwsJA///yTe+65h4cffrjX1iPhhxCiT9VXFKA3eGKuryYoaiDVJQfwDo1FcbQ01fPUtm2293tRI6eNNzlfjK9gxfbO+4c2lhVTunE5GoOWyFHRxIyPI3KUGpUqj88ver9P9kkIIYQQJ5ZY/3CKk8eyY/PPRMcNQ+tQcNht7P7tW4zF+wDw8Gm5sdMfgo/WrI2N5C5eRsmmDKLGD2Tw7NGoNVWExlaRt8Odd+9dzP5NJVz/wqlUBQcc7c095qhVvV+gVN1/er1w3333YTQaOfnkkzGZTEyZMgWDwcA999zDnDm997ck4YcQok8obs40WmNwo7YiH9RqrAYHar0Bj6AwwEZorPNuQaSH81D0U37XzUQjB5Q6W39YnHU/aktz2f/tJ3iGBjPk2ktIGmtuM/0lX10jAYgQQgghDtmKLx91Pb/0+lcBSB//N8qK9/H7z3PxCYmntiIPU20lsUmj0ekMRPoEUVhbwbIvHjlKW907Rt7Tsv1+oQ4K125j389rUOwO4mecRurMpDb1Jvb9+DtZ64tJOm026zKcXRQ2vvvEEd9ucfy59tpreemll/D29ubJJ5/kwQcfZOfOnTgcDtLS0vDy8urV9Un4IYToU4mjz8furqDYbeSvX4RXZAx4aQmNLW037ZlRHq4AxGRTcNOqWJ3pjkbrIG+Ds8uLCgeKxjm9cU8mem9Phl53KWqtlvzdEDWg/XKFEEIIIQ5X61FbhuTvJHfr79SW52K3WZhy2g2ERw0E+ldrD1NlBcYDe9ixdRPmqgqChw0hdtpU9F5eqFS1KIpC+a48tn20kZKd5aAoWBvqul/wCUoKnnbsww8/5JlnnsHb2xsADw8PRo3qu+GQJfwQQvSJjR88wfCz72T7b2+iUmvwiUykrjSf8IknA5C1LYL4IYUAPLXCwR3jnfOdHObGvR+3NBsNi68AIHpUaUsAYgdFAxoK8Y/zRa1tOZRJSw8hhBBC9JVtv7/jen7J9XNdz/tD8HHqc3ehOBSKVm+m6M9lqLRafOKSiD/rQlImqwEbUI1fSDUL719E+e4KfKKDiBidilqjZuHcZxkwYMDR3g1xHFEUpfuJepGEH0KIPtNQXYzisBOcMIK6qnwUhx2fuGSiU0sAsFk1vHKm8zCUU2vucBnFWYGExVdgbtQTMqiainxfABx2O9XZ5URPSHJ1nxFCCCGEOFL6Q+DR7NTn7sLaaGLn579QnplF8szRJJyajkavpb7GHWM5TB3vPH/bubSQ8t0VTHt4KobQNFcLBAk+OqdWqfqg5sfx3/IDjmwLFgk/hBC9Ln2Osypzna0Etc5A1MhTwU2Dw1eFSqMBStrNE+ttcAUgn99cwyWv+2CpaXuIaqioIffXn6ktrMVhs2EyNhI7MbnP90cIIYQQoj9rrDSy+a1vsDaYGHbteUSOiOxwOkVRWP3lDqKGhDBySjRQy449Pkd2Y0W/kpKS0m0AUlnZOzc6JfwQQvSZhtJCPP3DnAc0swOVRg9A3q5Qogc6AxCLXSFw+WYABgPVJ6UDzgDkwv84q4XnbgnFWrGKXfOWo/N0wzsuBRUQMmIkxto0fClm3pVvH/H9E0IIIYQ4Hk37792u5w6bnW0f/wQKjLn9UhrNMRTtd34WEFHTZr4dS7IpzKxg9nOnAvCfaR/DtCO22cctVdP/enuZ/cG///1vfH19j8i6JPwQQvS6TXMfY/CVt1FXmEvgwOHEnl7u+syhtByonz1VB0DFlBGuAMRv6SZXAPLN/ZXMma9gqjGz5LUlhKcnk3bRSWjd9Pw85+UjuEdCCCGEEP1T7vIN1BWWMWrOxbgH+tFY2PZzrc4OQFFBDb+/tZlhU6I5ZUrUUdhS0R/Nnj2bkJCQI7IuCT+EEL1q5JUP0lhdxr4/vkCjdyN4+Big6yFsD+a3dBOXV49wvS7csBsUhdQLJqN10/fyFgshhBBCnJjmXePg0TwT767QkLVoDVp3N1RuYQQNG01IghUAm9mCedcy3n9wFwYvHbNuH32Ut/r4IzU/OnakR6yR8EMI0ass9TXs+e0T7BYzsaeeg1qrI2tzOPEjigBQqxQuT3cAsK3KzknbdnW7TJVaheJQyP35N6bdMQ69p47bfrucl0/7pE/3RQghhBCiP1p83/MAVJXdya23jKK21kJRcT0rttfTWLKSkg2rKU4OQq3RYMwpx9ZoYuJFaQydlUZIuAcA14+WEfZ6Soa67ZiM9iKEOK4pioK7bxANlcXkLJoPQPSocOJHnEV6gqlpqpbWG0uHDOwwAPnEbzOXV49gcKqRwakxxKZM4o8X1/L5LQtJPTWe0AGB1E2ow8vL6wjslRBCCCFE/1JVdicAgYHuPP+ss3DHtKd9sTbUU7ZpDRqlEIfdzuDJEUy9fAj+4V48OOUj3l5/zdHcbNGPOByOI7o+CT+EEL1q+7dzGXmDP6bqCvb9/Bk2Uy1x46IIDqsHNADsrLaQ5td995VP/DbzDAkADD0ljpDkAFa+vYmtC/ZgqjEze306P87f05e7I4QQQghxQpj2tLPopM7Dk/E3pwFpAIxKbGwznbT4OHRq+qDbSz8peHokSfghhOh15poqMue/i87dk/NeOI3gZOeoLZ46NcP9W0KP4duaggu9DsVi7XBZ/1d8gDdjkgDwT/Tjvk/PprysgWnjP2Lo8FB2ZP+DQXGv9e0OCSGEEEL0M/7B/2vzetMLbT8///0bj+DWCNH3JPwQQvSa9DseAaAkcx1ag5qT/n0BFr2eVN/uC56qDgpA3AfFtXxY2/L0/T+LyPhwC2qNirPOSwaQAEQIIfrAvC1/b/P6b8PePUpbIoQ4Gr675s2jvQn9hsrZ9qPXlykOjXxjQoheZTObqNyZQeyUQejcna08Fm/1cH2eUWUBYICfO42Th6FYrK5Ha407sl3P7/C2UVVcx+f/Xs4Lly9g7foiHnlyKr5+bgASfAghRC+bt+Xv2KwONq3Io7HBeXz+ftt1rocQQghxvJGWH0KIXrPpxX+T/LercFitaPxHAXDLJOdJc2aNs9YH0Kbri2naKNwWb+hweY7aBgrjIlEUhV+f/oaSwjoeeXwK99/1C25ubn28N0IIcWL6absz3Lj34vnkH6jGy9dA2sgwIqK8GTY+kvSJUUd5C4UQ4vii6oOhbvvDaC9HmoQfQoheMe2/dwOgMThDibD4YvSGENfnqT46V/jxWVYd/x7h3umyDDEhbV7//OM+tmwu4aPPz2X8xCjyy+4hKXpub++CEEKIJg0VDeQfqOby20dRU20md28l8z/azrKfD1BZWn+0N08IIYQ4ZBJ+CCF6VWCsc7xuS52pw8+vS/YGIK/ORPLa7R1OY84tdQUghi1ZPPefNZw8LY7xcrdRCCH6VHOrD6vFOfxgxsp8NG5azCY7BnctF5576dHcPCGEOC6pVKpeb6khLT8OnYQfQoheFZ7qDD2C3GoZGNvIylKYGems+XF1onePluFz0nD2rt7Ho/PzWfDtHtRqePO9M1GrICFKWnwIIURfCwz1YMKpcdTWWdDp1Xj5eJKQFsizzz57tDdNCCGOO+qmkqe9vUxxaCT8EEL0isX3Pc/sL64FtOjcdRTsKmfIaQntprM5QNtUannv2MHtWn/4nDSc4tIGzrjrTxosdv5x60hmXzaIwEBnN5kD+XMkABFCiD5y5uB3XM/PW+T87+ebnaO+XDJCRnsRQghx/JLwQwjR69LOH8bGzzaQODaC5HFR5NTbCDS0DC41wLelWGnNSen4LN3UZv7/vZVBRZWJbUtm4wj36ZNtrKm4EwCr1U5VlYmkgW/1yXqEEOJ4J6GHEEL8NdLt5dggQ90KIXrNF7PfY3SMnctuHoiXvxvFfxYwyE/fbrrdxrb1QGpOSgfAY1gitqpaBg0IoL7Byj6HihqL3fVIiJr7l1p9VBTfxtyXpjNxfBR33fsHX361i6nTPic57W1mXzSQvLy8w162EEIIIYQQ4tgl4YcQotdlbS2lrspE6rRY13sVZofrucmusKWyEaPF5np4DEt0fX7ayEBUKhV//Hqg17bp7dfPIH3sh9x6x+8UldTx+Zc7ueEfv6JWw/3/nMCCH/YxanQqiqL02jqFEEIIIYRQNw1129sPcWgk/BBC9Kq7J3yIb5A7KrWK7TsrqLI4XI99tTZM9o7Dhd2engDYbA6qjBYSkv15+pGVnDenlGvnhnLt3FDS73r0sLfrhn/8Sm5uDSOGh5KTU4NOr2HWhQPw8nXjtbkbMJlsXDQ7jezC28gquPWw1yOEEEIIIYQ49kj4IYTodTW+Hoy6fDAZX+/ihXv/IGdneYfTZddZAbDYFYy1Fp78IZeEmfMZcvYC9u2uBCDn529prChzzXO4Acj777+PRqMhN7+G+x+awMjR4SxemovBoOH6G0fw+dfnc+c9Yw9r2UIIIYQQQnRGWn4cG6TgqRCi1716+qfcZJ2NTq9h9bsZ/HdFLikjQrnq/nH4JAcQ7t5y6ClttPH5q5v4/INtNDY4wxAPTx1mkw27XSFo+BgMfgF/eZuuvvpqJk2aRFhYGAWV93PV34e1+VyvcWbB8ZGv/OV1CSGEEEIIcbx68skn+emnn8jIyECv11NdXd1umo4Krr7++uvcdNNNR2ALD4+EH0KIPlFe6U11hRqaerns2VzCg5cs4KRzU7j0rjEkBDmHrq0obeC91zYREOSOj6+Bux4Yx8RT40CBR+/9g5VLNmEdMZb3HjAzNvnNv7RNSUlJAAzwepXdubc4n8e8+peWKYQQQgghRFdUTf/r7WX2FYvFwqxZsxg/fjzvvtv5iF/vv/8+Z5xxhuu1r69vn21Tb5DwQwjRJ+Zd+TZ/43o8grzY8PZy55sKLJ2/h5U/7WNwehhnnpfkCkcqyxt5+e0ZnHRqHAANVjsPPjWVy8+eh37b1zgcZ7N2742MTX6TZ1Zc5VrP/03+8LC2T0IPIYQQQghxJKhVatSq3q040dvLa+3f//43AB988EGX0/n5+REWFtZn29HbpOaHEKJPRY4dyOR/nofB1wODh5bgKG80WjUZawt58oHlPPlPZzAy6ZRYppzSMjqMh06Du4eO5144lS0bi7n2/G954aOdPLnkijbLbx2ECCGEEEIIcSKpqalp8zCbzUds3XPmzCEoKIjRo0fzxhtv4HA4up/pKJLwQwjRZ+Zd+TYAAQmhTP3XBQSmxVCWX4tfiAdnXDOUM64ZypARoZx/yUCefGU69TYHXjqN6xHioWfs+Eg+//p8QkM9+eqJlTx3yXf88uYmtizOoiS7miRPDd9tve4o76kQQgghhBAdU6mcNTJ69+FcdnR0NL6+vq7H008/fUT26fHHH+frr7/m999/Z/bs2dx999089dRTR2Tdh0ulKErH4062UlNTg6+vL0ajER8fnyOxXUKIfuS8d1sKH6VqC/jk5Y0UbCrCM8iDa64dwpl/S8XH1wBAuKe+w2UoisJ//vMn2zJK2b2nksrSBgA8vPW8+M0FBIV6cv7Qd/p+Z4QQQgghRJ/ob9edzftz/hX/Qad379VlWy2NfPfx/eTl5bX5rgwGAwaDod30jz76qKs7S2fWr1/PqFGjXK8/+OAD7rjjjg4Lnh7s+eef57HHHsNoNPZ8J44wqfkhhDiinrnqB4oDLqcyq4qd32fy2gvrmfvftQSHehIV68OwUeFcf/VQqtw1rnni3bS88soG3pq7CV8/A2ecmci+3Fq2/FlAVLwvvv5uAHy39ToJQIQQQgghxDGlL4ambV6ej49Pj4KiOXPmMHv27C6niYuLO+ztGTduHDU1NZSUlBAaGnrYy+lLEn4IIfrc/L+/0eb1B2d/wtU/XM6k28eTfsVwCjYVUltcS2VBLZ++v5UP39hMbKIfwRFeaLVqMv4spLHByjU3DKOivJHNG0vw8tYx/rR4rrt/HDq9ppM1CyGEEEIIIYKCgggKCuqz5W/evBk3Nzf8/Pz6bB1/lYQfQoij6qurvoGmmqVX/3A51gYrWStzqDxQCfUm6mstXHD1EE4+K5HxaW0P2Gmxrx2FLRZCCCGEEKLnjrehbnNzc6msrCQ3Nxe73U5GRgYASUlJeHl58cMPP1BcXMz48eNxd3dnyZIlPPjgg9xwww0ddrk5Vkj4IYQ4Kj44+5MO39d56Eg5Lcn1ela8s0vLmYOlO4sQQgghhBB97eGHH+bDDz90vR4xYgQAS5Ys4aSTTkKn0/Haa69x11134XA4SEhI4LHHHuOWW245WpvcI1LwVAghhBBCCCHEUdffrjub9+eiq57rk4KnX314T7/5ro4EGepWCCGEEEIIIYQQ/Zp0exFCCCGEEEIIIfqKSoWql0d7obeXdwKQ8EMIIYQQQgghhOgjfTnUreg56fYihBBCCCGEEEKIfk1afgghhBBCCCGEEH3keBvqtr+Slh9CCCGEEEIIIYTo16TlhxBCCCGEEEII0UfUKjVqVe+2O+jt5Z0I5BsTQgghhBBCCCFEvyYtP4QQQgghhBBCiD6i6oOhbnt96NwTgLT8EEIIIYQQQgghRL8mLT+EEKKVqk8nup77X7bqKG6JEEIIIYToD9QqFepebqnR28s7EUj4IYQ4oTWsuhBzdnGHnzUHIRKCCCGEEEKIw6VGhbqXh6bt7eWdCCT8EEKcUDIO3ExKUWmb9wxxYZ0GIEIIIYQQQojjn4QfQoh+LePAzT2azhAXhjExyvXabfEGQFp9CCGEEEKIv0YKnh4bpOCpEKJfG57werv39oSHsCc8BABjYpTr0Zpp2igJPoQQQgghhOgnJPwQQvR7zQFIg83R5pERHNTlfBv23cSGfTcdiU0UQgghhBD9VHPB095+iEMj3V6EEP3Wd1uva/M61E3T5rXDofDmp9tZ8usBSnJrcKjgvIsGcuGlaWg0Ldnwhn03MSrpjSOyzUIIIYQQQojeJ+GHEKJfaKi6q83rX/Nq2k1TYrJjtztQKhtZ9UcOixfsZfuOciaMiyR1QCALvt/Ls4+tIj7Jn9HjI9vM29wCREIQIYQQQghxKKTmx7FBwg8hxHFnR/Y/2r0X7+vW5vXp0T6AMwTZvbWU/NUFfPzpdmpqLK5pAgPdOe/cZLKzjSz4fi8qFdx+6yhGjYvocL0SfAghhBBCCHF8kvBDCNEvZBlNxPu6YbM5WLQ4my2ZFdTUmFn403727qnscJ6KikZ++HEfZ5+VxD9uHMG0U2IJCvJwfe4T+L8jtflCCCGEEKKfUjX9r7eXKQ6NhB9CiOPOoLjXXK0/vPXOOh719RaeeH4t7727hZLi+nbzTD8tnov/lsqUSdEEBrpjsznYuauCAH834mKdrUQk7BBCCCGEEL1NRR90e5Hw45BJ+CGEOC5kFdza5rWHToOm6ZhfXFTHhed/Q0lxPRfOSuXKq4bw5hub+eP3bO64awyzLh6Ir68BAG+d87Cn02k4adqnR3QfhBAntvLSO1zPg0JebPNZTcWdnc4nwWz/pChKn/fZb/1v7mAH/xs8VpWW3E5I6Eudfr5ixQoWLFjAddddR2pqaofTKLX3ovJ+tq82UQhxnJDwQwhx3MrOqWH+N5l88dlOVCpYvPQyYuN8AVi9Mp8rrxrCdTcMByAm4pWjuKVCiBNZRxeg5aV3YLXayc2toaKsgbLyBuw2B35+bvj7uxEe7kVIsAdarZqaijtx17aMVqXzfe4Ibr3obfv37+eOO+5g8eLFDBs2jOnTp3PZZZcxYMCAPltnfn4NGo2a8HAv13vlpXccswFIacntHb7uKAR59913+fDDD3n++ef59NNPufTSS12fKbX3dvhcghBxxKmaHr29THFIVIqiKN1NVFNTg6+vL0ajER8fnyOxXUII0U5z649tW0t57ZUN/PbLATw9dZxxZhK33TbKFXzERLzC1KlTWb58OYsXL+aUU045mpsthOiH9uXNASApem6HnzdfrKlb3dnPyq5m2bJc1qwpZOu2UrKyjNhsjk7XodGomDguklkXDOCqywbj5tb2npWEIN3Lzc2lurqaoUOHtnn/4Ivr7nTV8qCn6uvreeaZZ/jvf/9LWFgY119/PTt37uSnn36ipqaGU089laeffppRo0a1ma+j8Kyz0MJisfDNN9+wcuVKGhsbaWxsZNOm39jTVPsqPt6XWX9LZc4to3B313a5rKOhJ/+/WK127I77KC4upri4mKuuuoqLLrqI7OxsCgoKWPzryRgMzn3zNXR9n9dotgHgF9S2dZWj5p4Op1f7yN9cX+tv153N+3PDTW+iN7j36rIt5kbeeuPGfvNdHQkSfgghjmkb99/kel5aXM+7L2/gm68yiU/w44H/+y+XXXYZHh4eWCwW5s6dy5o1aygrK2P58uU4HA5uuOEG3nzzzaO4B0KI/qI58OhMUvTcDi/evvxyF2++tZkdO8rRaFQMGxrCiBFhjBx1IwMGDCAqKoqQkBC0Wi25B+6goqKR4uJ6igpq+emXAyxemsP/3T2WR/450bXMjoKP+sqWIb89A174C3vaP1RVVZGenk5ubi7PPvssd93V8v0cavgBhx+A5Ofn89xzz/HRRx/R0NDA/fffz/3334+Hh7PAtslk4uOPzuf5F9axe3cl06fHceftoxkzxjnyWGdn6s2hhdlsZtOmTXz99dd88cUXFBUVMWjQIHx8fHB3dycuLo6JE4oBWLYsl8+/2E1ERAR33nknZ511FmazGZPJREJCAn5+foe0b/v27WPevHkUFxdTUVFBaGgoqampxMfHExoaSlBQELm5uezcuZOysjIAtFotERERREVFERkZSUREBPX19WzevJktW7awdcv77D9QTWlJPQ2NNkwmGw6H80twOBSqq820vnyJjIxk48aN7Nx2J2ec+RXnnJ3E3JemuwKQZq2DkObQoyM+ek2nnzWTEKTv9Lfrzub9ufGmt/ok/HjzjRv6zXd1JEj4IYQ46gqKbuvw/eIG57C0iqLw3uubee/VTbh56Lj5ztGcd1EqYwe8haIoLFiwgP/7v/9j3759TJ06ldDQUGJiYjj//PMZM2aMjIMuhDhstRXOC+aSBks3UzovmurrrTQ0WNHp1JSWNrBoURZPPLmaU06J5dJL0jjppBg8PfVt5uvqotpqvIerb1jIvPl7OOfMRK6/6RWmT5/e5rjWOvTojMnubGESGPxit9NC2y4CBzsWuww0NDTQ0NCAj48POp2OP//8kwcffJAtW7Zw6aWX8uqrr3Lvvfdy+eWXM2jQICrKW74zRVEwm+3tWtYA2O0OHA4FnU7To/DDYrGg1zv//zWbzbz11ls8+OCD6PV6rr32Wv7xj38QFxfXbr6Ksjuw2x188+0eXnp5Pbt3VzJhQiSTJ0Wj06nRatWoVCocDoWKikYKC2spKKwjP19FYWEhiqIQGhrKrFmzuOmmmxg0aFCn25iZmcnjjz/Ol19+id1ud71vMBg455xzmD59OmPHjmXQoEFoNO2DAEVRWLZsGa+99hrffPMNnp6eREdHExAQQGFhIdnZ2Tgc7Vs0+fn5oVKpMJvNNDQ0dLhtbm5uJCcnk5KSQlRUFIqyCnc3LT6+ZwOgUqkIDg4mMjKS8PBwQkNDCQ4Opq7a+e/1u/l7uOmWXwkL82T8uEiSEv0xmWzU1lpobLRiszmw2RVsVjs1NRaMNWYslpbvQAVYrQ6sVgc6nRqDXoObmwZPTz0B/m4Ehp7C+PHjufjii13/P4ve1d+uOyX8OLZI+CGEOOo6Cz/AGYD859GVfP3JDq68YRjX3jwCL29n8dKiglpeeLSIJUuWMG3aNF588UUGDx58pDZbCNGPNYceB+ssBKkpu5bXXnuNTz/9FJPJ5HrfYNBw+ukJvDr3NPRd3FHu6sLaaDTyzjvv8P7777Njxw7S0tI488wzCQ8Px9/7d0aPDCMu1rfT+U12Bzabg02biomL8yUkxBMAdasO4/7BLc3+uwo+4MiGH7W1tfz222+sWLGCzZs34+bmRnh4OB4eHhQWFpKXl0dubi7l5eWuebRaLTabjZiYGN59911OPfVUnnrqKR555BFsNhseHh4EB2sJCHCnod5KXn4tDQ1W3Nw0BAS44+mpQ6fTYLPayc4xYrE48PLSER+fymmnnUZCQgJ79uxpc5FvNBrZu3cvRUVF+Pn5kZCQQElJCYWFhfz973/n2Wef7bJVRUXZHa7nDofCL78c4NXXNpKdU4PN5sBqbblADwx0JyLcm/iEk4iLiyMuLo6BAwcyduzYDsOKzhQUFLBz507c3d0xGAwsX76czz77jIyMDBwOhyvUCAwMJCgoCEVRqKioIDc3l7y8PFJSUrjzzju5+uqrcXNzcy3XZDJRWFhIcXExZWVlREZGMnDgQDw9PV3TGI1G8vLyKCgooKCgADc3N9LT00lOTj6kfWitutxZNDhjSwmffraTzRnFZGUb8fTU4esbi4eHBzqdDpRstFo1Pt4GfH0N6A0aDG7OVlUqlQq9Xu/6N9RYswST2UadKZWqqirKysrYtm0bYWFh/PTTT6Snpx/WtorO9bfrTlf4cXMfhR+vS/hxKCT8EEIcEw4OQDKrnRcPiqJw3tiPOOOcJB54bLLrc139P5g0aRL+/v689dZbnH766Ud0e4UQR0fzBQ6076f/V7Suq2DQqDucpr7eyopVefyxNIdt28oIDfEkNuFMli5dyubNm4mKiuKmm24iNmYtNqsdLy89Y8dGYOigRUFrPe1OoSgKK1eu5KWXXiIjI4OioiIaGhpQqeDUU+K45OKBzDgtAW/vljvSReUNLPh+L3PnbiQ7xwhAXKwvaWlBREd7ExPt43qEhnoSEOBOkFfnd7R7K/joqNtJ6++htraWxx57jJdffhmLxUJCQgKjRo3CZrNRVFREfX09kZGRREZGEhsbS0xMDN7e3tTW1lJTU8PAgQM5+eSTUatb/r9saGhg/fr1bNy4kdLSUsrKyvD09CQuLo6goCCqq6spKyujvr4eq9WKSqUiOTkZLy8vqqqq2L59O7/88gtlZWUkJiYSHx/vvJgGPDw8SElJITY2lrKyMg4cOIBareaOO+7odASSg7UOQHraQqcv1NXVsWnTJtavX09hYSHl5eWUlZWhUqkICgoiODiYs88+mylTppyQLSvfeOMNbr75Zp555hnuu+++E/I76Ev97bpTwo9ji4QfQoijYmvWzR2+X2ayt3vvsTt/J2tPJe/+MIvpg94GYM6cOXz22WdkZWXh69v5HU8hxPGtddjRmcMNQUpLbm9TkPRgDodC9v4qFv+Rw6I/sln9ZwFms52YmBjGjRtHWVkZubm5DBo0iOuvv570Eb9i6EG9AHtT/YK/WkRTURSMRiPz58/nzTffZM2aNRgMBuLj4/Hz86PGuJddmRUAnHNOMtf9fRglJfWsW1fE3r2V5OfXkpdfg+mg4663tx612vm9XPK3VF594VRUKlWvtvg4OPyor7eyfn0hK1bms2mTgQ0bNqDRaLjvvvu46qqriI+P77V1/xWKomC329FqZcDEE9Xu3bu54oorWL9+PUFBQYwdO5Zx48YxYcIExowZg5eXV/cLEZ3qb9edrvDjH29hMHj06rLN5gbefE3Cj0Mh4YcQos/s7aI4YKOtfcgBHYcfCz7bwStPrCZz1w34+zub1q5cNZULL7yQa6+9lueeew5/f//e2WghxDHl4PBDURR++S2LstIGEhP9GJASQFCQR48DkI5aHCiKwt49VWTuriArq5r9B6rZvbuCPXuqXN0hpkyZxowZM5gxYwYpKSmuu70dLU+j7jxQ6cs7+jk5OcyfP5+cnByMRiMajYYJEyZwyimnEBMTA0BVWfvvs6ysgdy8GkpLG6iobHQVlCwvb2Tuaxt58vEp/POhZYe0LV2NUNL6OyssrOW/z67lm28ysVgchIR4cNJJZzFp0iTOPfdc13YLcSxRFIXVq1ezaNEi1qxZw5o1a1x/c0OGDCE1NZXExEQSExOJi4sjJiaGqKgoDAZDh8uzWq2sXr2awYMHExgYeIT35tjS3647XeHHLW/3Tfjx6vX95rs6EiT8EEL0qe4CkIYGK3t2VbBvTyXu7lqumT3IdccRYPfuCs4//xtGjAjj00/PaTP/gu8Hc/fdd+NwOJg2bRpnnXUWF198sRynhOiHqsvvpLi4nutv+pmVq/JRq1WuESAeffRRHnrooQ5rBRS3utBu3ZmlsrKRbdvKWLo0l58W7iMnpwYAf383EuL9SEkJYOSoaxk2bBgTJ07E3b19c+WejhiiUauOajeGnjo4GAF4+NEVvP5mBr///jsnnXRSj5bTUfDRmqPp1HPt2kIunj0fD3ctN9+czmmnxTNp8sfSjUAcdxwOB7t27WL16tWsXbuWvXv3cuDAAfLz89tMFx4eTkpKCqmpqYSEhKDT6VAUhY8//ph9+/ah0WiYPHkyl112GZdcckmbOikHa6hqqUvk4d9/Rnfqb9edEn4cWyT8EEL0qa7Cj1Wr87nnlt+oqGhEo1FhtytMmRLNu+/MxMfHwJIlOdx40y9ERHjx7TcXEBDQ/uLDoTzABx98wM8//8yqVatwc3Pj2muv5b///W+bAmxCiONbfn4+p556KnV1dbz77rucdNJJ7N+/ny+++ILHH38cPz8/pk2bhq+vLxUVFdTWZjB7dhpnnpXUJlCtrGzksku/JyOjBICgIHdmzkxk5oxEhg8PZUDqoQ+N3VUI8le7thxtNpuNoUOHEhsby88//9yjeboLP4JCXmTFihVccMEFpKWl8cMPP8j5peiXTCaTqyhvXl4eWVlZ7N69m8zMTCorK7FardhsNiZOnMhdd91FZmYm33zzDYsWLcLHx4ezzjqLU089lVGjRhEcHExgYCCW2vu6Xe/xHIb0t+tOCT+OLRJ+CCF6TVFxxxcAdU1V6pOj57re27ZtGyNHjmTixIn866FwkpP9WbO2kGuv/Ymzz07m+edOYeiwd0lLC3KFIV0JCX2JgoIC3nnnHZ555hkmTZrEwoULXcXohBDHj7KyMnbu3ElBQQFFRUUsX76chQsXEhYWxpIlS0hKSmoz/YYNG/jpp5/4/fffsVgsBAUFUVtby4oVK0hK9ufCC1NJSwtk06YSfl64n7LyBp54YirDhoUQH+9HRPjLR2lPj47uWqw0BzaKopCZmclZZ53Faaedxuuvv97tsstL76CqykRurpGGRhsmk42aGguFhbXk5dWSXxDO7t27ycrKYsqUKcybN4+goKBe2S8h+ovs7Gzeffddfv75ZzZt2kTry7XAADfCwrwID/Nk4vhI7rp9NFpt2yLNEn4cOyT8OLZI+CGE6DWdhR+thYc5T6q/++47LrjgAkpKSggJCXGdjH/62Q7uumsxt85J55W5m3jzjTM477yUHq2/+YT9jz/+4NRTT+X555/nzju7L5YoxLHu559/5vHHHyc8PJwLL7wQh8PBgQMHcDgc3H777R3WvLHZbKxfv55FixaxZcsW3Nzc8PLywsfHhxtuuIGQkBA++OADtmzZQl5eHoWFhdTV1WEymTCZTKjVanQ6He7u7oSHhxMZGUlYWBi+vr74+Phw0kknMW7cuL+8b4qiUFlZyaJFi5g/fz4rV66koKDA9bmXlxeDBg3isssu49JLLz2k/vB//vknr7zyCgsWzKOhwUpgoDujx4Tz70c/Z8yYMX95249HPemqU1rWwCefBPLxxx9TWFiIwWDg999/Z9KkSV3Od+DAAc4991y2b9/e7jMPDw9iYmJISkpiwIABDB06lEsuuUQCaiG6UVFRwZ49e8jd/wwVlY2UlDZQXFJPQUEtv/x2gNGjwjn/3BTSR4QyYmgIgRHHd5jb3647m/fnpjnv9En48cbc6/rNd3UkSPghhOhV3QUgzeHHjz/+yNlnn01BQQERERFtpnniiSf417/+hb+/PxMnTuT777+nrINm1J2N0hAU8iK33norb7/9Nr/88kuP+6kLcbQpikJxcTH79u1j3759rFu3jqVLl5KZmcmkSZNobGxk48aNAISEhFBfX4+3tzevvfYa559/vms5mzZt4txzzyU/Px9fX19Gjx6NzWajrq6OnJwc7HY7VqsVk8lEeno60dHRRERE4O3tjbu7OwaDAYfDgdVqpaGhgcLCQgoKCigpKaG2tpbKykpqamq44447uO+++wgNDaWoqIjdu3djNBppbGzEZDK57lY2j5BhtVppbGwkOzubffv2kZOTQ35+PnV1dQCkp6czffp00tPTGTJkCNHR0b0yckJDQwMlJSXExcWdsPUkelqfZOOmM7jgggvQ6XRcc801zJw5k0mTJnVZewBg//79TJ48GS8vLx5//HESExPx9vbGzc0NHx8f/Pz8TtjvXoi/qrbirg7fX7Eyj0efWMWWraVYLHY8PT257rrruOWWW0hMTEStVnc6r3fgsdk6pL9dd0r4cWyR8EMI0asODj+aw46DvfLKK9xxxx3U19e3q82hKAoXX3wxCxcupL6+nqeeeooHHngA6L4veTMvn2eYMWMGy5cv58UXnWGIED3R2NjI7t27CQoKIiQkBL1e3+vrUBSF6upqsrOzycnJYe/evaxevZpVq1ZRVlbmmi4lJYWTTz6Zs88+m5kzZ6JSqSguLsbLywsvLy8KCgq4+eab+eGHH5g5cyaBgYFs3PQze/dUMmhwMI/8ezJDh4W0aRJdWdnIYw9Xkpqayq233kp4ePghb7/dbuf555/n8ccfx2Kx4ObmRk1NTZfzqFQqdDodBoOB2NhYEhMTiY+PJzo6mqioKMaNGycje/SBnoYeAHqNmh9/2scVV/9ISLAHdfWQlpbG6tWrO22hoSgKv//+O3fccQcWi4VVq1YREhLSW5svxAmvs/CiNYP3M2zbto358+fz6quvUlVVhbu7O/FxHgQHeWC3K1htdjRqNd7eery99Wg0Kmw2BavVjt6gwc/XDV9fAyHBHkRFexMT7UNSoj86Q9tC0n1dvLm/XXe6wo9b+yj8eEXCj0Mh4YcQR8Du3Fu6nWZAzKtHYEuOPIvFwsqVK1mxYgV79+5FrVbj7u7O999/z8SJE5k3b16H85WVlZGSksKgQYNYtWoVn3/+ObNnzwZ6HoD4Bz7P3XffzSuvvMKaNWsYPXp0b+2WOE4VFBSwfPly3Nzc8Pb2Jjw8nEGDBrk+37lzJxdeeCGZmZmu92655Rbmzm2pV2O328nPz2f//v3k5eVRXFxMcXExdXV1eHt74+3tzbhx45gxYwbg7Lu9evVqtm3bxo4dO8jKyiInJ4fa2lrXMt3dtQwfEcro0eEMHRpCfIIfMTG+uLtrO92XyKY6FYqi8O233/Loo4/i7e1NXIKRgWlBXHTxQNzdO75gjYl45fC+wINUV1fzwQcfYDabGThwIKmpqQQEBODm5obBYGgz+oparaag6LZ22y/6zqEGHwBWq53n/7eOktIGPvhwW9N7VrTa9v8WN27cyO23386qVatIT0/n/fffZ+jQob2z8UKITtVW3IXN0fElXF2dhVWr8zmQVU12tpGKikZ0OjVarRqbzUFdnZXaWgsOh4JWq0KjUWO22KmuNlFtNFNa2oDF4qzVptOpGTo0hFGjwhg2NJShQ4JJSvJvV2OkI4cbkvS3604JP44tEn4I0QsaGxvZv38/oaGhBAUFtWvae6KEH61bfezdU8m7727hxx8LqKysJCgoiNTUVMD5fXl6evLhhx8SFxfX6fLOPfdcqqqqiIuL46uvvmLlypWMGjXK9XlnIUhQyIuu53a7HX9/f/71r39x7733/qX9E8ev/Px8brzxRn755RccDkebzy6//HLOPvts3N3duf/++1EUhblz52I2m1m1ahVPPfUUer3e1SWktLQUi8Ximt/Pz4+wsDC8vLyoq6vDaDRSVFTE5ZdfTkZGhqv+QXR0NIMGDSIxMZHY2Fi8ff4gKtqbqCgfgoLcD7lLQFfhQW5h9y2deisAOVjrgKMnJAQ5MjoLQppDj9ays41cfOl8Cgrq+PCjz7nwwgvbTfPHH39wxhlnMGDAAJ577jlOO+006dYixBHS0bDUh8p60G9h87HA4VAoLW0gJ9fIlq2lrFtfxIYNReTkOlv3+fkZuGR2GjfdOILISO9Oly/hh1Pz/tx867t9En68/srf+813dSR0fktJCNEj77//Ptdee63rtV6vJyIiAn9/f3x8fFBps/H1NRAR6U1UtA9u7lpKiuspLalnzPhITjk17rgNPrqq7/HEk6tYvSqfq68Zyo03LGXw4MGoVCoURaGiooLc3FwCAgI6nT83N5clS5YwZ84cHnnkETIyMnj88cdZsGCBa5rWIUdnNBoNKSkp7N69+5D2TfQfW7Zs4fzzz8dms/HGG2+4amPU1taydOlS7rrrLj755BPX9G+88QbTpk0DYPiIX5gw4Ueys7Opra2lvr6esLAwEhMTSUhIIDo6Gnf3tkMwK4rCv/71Lz7++GNOOukkHn30UU455ZR2RUkLivL+0n4VFN2GTt2zi01NBxelBUW3/eXg4VCDjq6WISFI32ouCF1d3vVFk8lk44wzv6SsvJGfflrI6aef7vrM4XCQn5/PV199xYMPPsjJJ5/MDz/80Cddw4QQHeuN4KMjFntLGBIQ7E5AsDsjRoZx9TXO1lxGo5kdO8pYtCib9z/YSn5eLR99cFaHy7J3f2/9xKNqevT2MsUhkfBDiL9o06ZNAISHhzNgwAACAwMpKyujoKCA3bt309DQ0Om8H7yzpamexV/fDofDgVrdfTPE3hQe9lKnAcjgwcEs+i2LZUtzqa66hPLyKLKzs8nNzaWxsRGAqVOn8sILL1BeXs727dvZtm0b+/fvJzc3l/z8fCIjI7n33nvR6/XExsaSk5PT5fZUlN3R4Z2GiRMn8t5773HvvfcyYMCAv7zf4thntVpZsWIFr7/+OvPmzSM1NZWlS5e2qSkRFBREfHw8V1xxBQ0NDZhMJiwWC5GRkYAz3FOpVKSP/I30ka2XXgPsQaUCY43zcbA5t8KcW89rerUcs2U5xSU9335DB3fjD+bo4uTSTaPG2kmT6NZ6IwDpLRKC9L3ugg8AvV7DxRcP5MOP9jFjxgz0ej0hISF4e3uTk5Pj+k276667XK2ihBBHjn/w/9q87igMObhVB4Cu1TliR593x9fXQHp6GD//cgCTycaE8ZGdTtvXdUGEOFzS7UWITnTWbHz9ukIe+ucy4uJ9iY/3Izz0PLZs2cKGDRvIz8/Hbrcf8roqKys7HKqyJxRF4emnn+app55izpw5PPDAA/j6+h7Wsg5XRwGIzebgjz+y+eLzneTn15KUNJG4uDhiYmK46672xbs8PDwYNGgQycnJxMbGEhMTQ3p6Orm5ubz66qssXbqUjz76iJlnbOzRNrX+4a2rqyMpKYkrrriCZ5999rD3U/x1pqq7cfN/vteXa7FY2LJlC2vXrmX16tX8/PPPVFdXk5CQwD//+U+uvPLKQx5Ss7uRi/5qC39tL3QR6CoAAXoUgPRG2NAbLUAOJiFI7+lJ6NHML8h5YVVfX8+vv/5KYWEhpaWlGI1GYmNjGTBgAIMHDyY2NrbD+Yt7WGckLLTjYthCiMPT0xo/9m5+FjSd/DSZTDauvfYnVq7K56F/TuDmG0eg6SSobz6OHI7+dt3ZvD//uP29Pun28tpL1/ab7+pIkPBD9Aqj0ciOHTsYPHhwt/9GHA4HNpvtmL5b1FV/+a1bSjh75teEhnni4e4carKuro6Ghgbc3d1xc3PD3d2d2tpaV0FDNzc3xo8fz5QpU7j88svx9fVFo9G4agccXCCuuWlxbm4uxcXFFBUVodVqGT9+PEOGDEGj0aAoCvX19dxzzz28+eabnH/++fzyyy8YDAbS09MZMGAAsbGx1NbWUl5ejtFo5Mwzz2T27NkdFq7rzIEDB/jyyy+ZMWMGw4cPP6zv82AlJSXs2LGDwsJCysvLaWxsRFEUysrKyM/Pp6CggOzsbIqKigAYNmwYTzzxBGeddRYVZXcc0rqaQ5Abb7yRzz//nI0bN5KcnNwr+yF6zlR1d4+m62kw4nA4WLBgAT/88AMZGRns2LEDi8WCTqdj+PDhnHHGGZx77rmkp6cfVh2C0pLbuz1B7I3yBn81ADk4/Ag5xi8oDyUkkfCjcz0p+hwU8uIhhR7Qttl7R7r79yXBhxBHXutzVrduWg1297sG7UfpKy253RV8rFqdz+efnMtJU7semUvCjxYSfhxbJPw4geR3cNIZ1Qsnl2eeeSYLFy50vR4+fDhTp07llFNOYcqUKfj5+bWZ/vzzz2f+/PnExsaSkpJCUlISsbGxxMbGEhISQm5uLgsWLKCiooLvv/++3fydsdvtqNXqv1xwrSeFAi+9eD4VFY1s31biGs1AUZQ261YUhaqqKvbt28eKFStYuHAhy5cvZ9SoUVx88cV4enri6emJzWajoKCAgoICcnNz2b9/P/v378dsNruWpdfrXaGRl5cXBoMBo9GIzWZDo9Hw1ltvce2115Kfn89bb73Fzp072b17N3l5efj5+REUFIRGo2HdunXEx8czduxYDAaDa8SLsLAwwsPDiY6OZvDgwfj7+2O32/n666+5/fbbKS8vx+FwMHLkSIYOHUplZSUVFRU0NjYyZMgQxowZw/Tp00lKSmr3XTkcDtd3sHTpUlavXk1ubi42m63NdAEBAYSEhBAZGUlUVBRRUVGkp6czbtw4IiIi2kx7cADSUT2Dg5nt/0dYWBhvv/021113XbfTi97T0+ADehZ+7Nixg1mzZrFr1y6GDh3K6NGjGT58OKNHj2bYsGHthk4+HD29g3ashw3HKhn15fD0dKSrZtoe1oTpLvRo1tW/dwk+hDjyenLOCh0XNgY4+Crw4OCjsbGRrKwsbr75ZtatW8cPP/zAqaeeeljb2lP97brTFX7c0Ufhx4sSfhwKCT/6uY4Cj64cThhy880388Ybb3Q5zR9//MHJJ5+MoigEBQVRWVkJgE6nw2q1djpfc8uInJwcAgMDSUxMdLVaaG4psHTpUr7++msWLlxIfHw8Dz30EBdffHGvV51v/QOzeVMx5509j0svvZT//e9/BAcH92h9a9eu5eqrryY3N7dNLZCAgADXRX9SUhJJSUmu7h/h4eH4+flhMplYv349a9euxWaz4evri6+vL0OGDOnx0IIZGRm89NJL5OXlYTKZMJlMGI1G1zCdzaKjo1Gr1eTk5DBjxgzeffdd1q9fz3vvvUdJSQkBAQEEBASg1+vZsmULW7ZswW63M2vWLEaMGOEKR3JyctiwYQNGoxG1Ws2IESOYPHkyKSkproAjPDycwMDALrskdNbaoyehB0Bjo437/m8Jn3y2g127drlGnRF9r7eDj8bGRlJTUykqKuKJJ57g1ltvbVdwtDdI+CGONYcafHR1fGz9UU+Cj7/y7/zgUETCDyH+up6GHs06Cz9aa/7b3LhxIw888AAZGRmUlZUBEBgYyPfff8+ECRMOfWMPUX+77pTw49gi4Uc/0ln1Z0VROFBQQ2ZmBZk7K6iqasTPz43AIA8iIrwAKCtroKysgYryBqKjZpGQkEBCQgIhISGuFgc2m43S0lJXKwWz2UxAQACBgYEEBASgUqnIy8ujoqKC2tpa1q9f72oR8tlnnzFt2jQ0Gg27d+/ms88+Y968eZSUtK8AGBkZSVxcHNXV1ezbt69NCwiDwUBaWhparZY9e/ZgNBoBGDlyJOeeey6rV6/ml19+4bbbbuPZZ59Fr9djt9vZvn07UVFRBAYG9tr3/fHHH3PbbbdRXV2NRqPBz8+P8PBwnnnmGc4888xu53c4HJhMJlQqVZ9cvB2quro6srKy2LZtG1u3bqWqqoobbriBkSNHdjtvY2MjH3/8Mc8++ywVFRUEBgYSGBhIeHg4I0eOZMyYMYwZM6bHrXia9bSLS0cn+Tabg5LSejZsKOaxJ1ZRWFTLf58+mVvvWHRI2yD6TnMwcig1QGpra7nkkktYunQp9fX1TJ06laVLl/b6tvUk/JDgQxwLDg5FehoKO+j89M/eqlaM/DsX4thzKOFHd8HH9u1lbFg/lJ07d7Jjxw42b97M4MGDmTVrlqtl9tChQw+7Nt2h6m/Xnc37c8sd7/dJ+PHqi9f0m+/qSJDw4zh3cOBhszk4kFXNrswKtm8vI2NLCVu2llJe7hxdw9NDR3CwB1XVJoxGc5t5vby8CA4OprKy0hUqdMbLyws3NzeqqqoOq8BndyIjI5kxYwZDhw4lPj6e2NhYysrKXCOCOBwOUlJSSElJIT09vU3htddff51bbrmFuLg4xowZw++//05FRQUajYYpU6Zw7rnnMmLECEJCQggJCcHf3/+wW4kUFxfzxx9/YDQaMRqNLFmyhN9++43Zs2fz6KOPntAji/S0CTR0fSfwUAIQs9nGo4+t5Lv5eygta3A155w+fTovvvgiaWlpPd4mcWzau3cv8+bNY968eWzatIn//e9/XDw7q9PpD27CK8SJorMbIl2FHq3JaA1CHN+6Ow9TFIWvvtzFvfcuw83NjUGDBjFo0CAmTJjAlVdeeUj14XpTf7vulPDj2CLhxzHCbDaTnZ2NVqslMTGx3eclJSWsWbOGjRs3Ultbi91ux263Y6xeRnWViapqExWVjWRnGzGbnWFESLAHw4aFMGxoKEOHBDMoLYjYWF/UahX+wf/DZDKRn5+PSqUiNDQULy8v1/qqqqo4cOAAZWVlaLVa1yMoKIjIyEi8vb0B54GzpqaGiooKqqqqXI+6ujr0ej0GgwGDwYBarcZut7takDRvf/NDr9fj5uaGwWAgNjaWIUOG/KVuK9u3b+ff//43ubm5TJ8+nWnTprF3717mz5/P4sWLsVgsrmk9PT0ZP348kyZNYsiQIVgsFurr66mvr6egoID9+/ezb98+KioqXPuk1+vx9fUlIiLC9YiLiyMuLo7FixfzwAMP4O3tTVlZ2SGPMNFfHEr4AR0HIK1H29B3Un48MPhFFEVh8eLF3H333WRmZnL77beTkpJCREQEsbGxpKWl9Xo3KHFkOBwOdu7cyfLly/nss89YtWoVnp6ezJgxg1mzZjFp8spD+v9WwhAhnLoKliX4EOLY0tk51eF0I1MUhWXLlvHQQw+xatUqrr32Wt54441j5ny1v113usKPO/so/PifhB+HQsKPo0hRFO666y6+/fZb8vLyXAUzr7jiCpKSksjKyiIrK4v9+/eTl5cHQGhoKAEBAWi1WjQaDQaDwVV/ITAwkPDQLQxMDWJgaiDBwS1/YAePCX4iq6+vJz8/n9LSUkpLS9m3bx8rV65k5cqVVFdXu6YzGAyEh4eTlJREYmIiISEhWK1WzGYzZrOZ6upqCgsLKSwsJD8/v00ND61Wy8svv8zNN998FPbw2HHwj3X3R5ueMZtt1Nf9gxUrVrBs2TKWLVtGUVER48eP55VXXulRVx1xbCovL2fdunWsXbuWNWvWsGbNGmpqatBoNEybNo1rrrmGc889F3d3926Hoj2YBB9CCCGOJ4d6I0nXRZHjwOAXWbJkCXfddRcZGRkMHz6c//znP5x22ml/dTN7VX+77pTw49gi4Uc3mvvSBoW82OvLfuedd7j++uu5/vrrGTNmDImJiWRmZvLYY49ht9uJj493PZpHvYiKipI72H3E4XBQXl6Oh4cH7u7urlFcekJRFMrLy8nOziYrK4uUlJReGxb2eHeoF6jNHA6FvLwadu+uYHdmBbt3V5KVVU1BQS2lpc6gSaNRMWrUGKZOnepq4SN/H8cHq9XKvn37yMzMZPv27WzevJlNmzaRk5MDQFBQEGPHjmXChAlMmDCB0aNH4+np2WYZh/JvS4IPIYQQx4tDDT2g6+ADYNmyXC69/HtGDA/l34+9z/Tp04/Jc6b+dt3ZvD9z7vqgT8KPuS9c3W++qyPhhA4/DrVyOvRuCLJ582amTJnCrFmzeO+993ptuUIca3pykaooCitW5PHlF7vYvaeCA/urMJmcXbh8fPSkDAgkMdGfqChvIiO9iY725ozTP2rTXUscHYqiYDQaKSwspKCggLy8PEpKSjCbzVgsFsxmM/X19ZSUlFBcXExxcTH5+fmu4Y79/f1JT08nPT2dESNGMHbsWOLj44/JkzIhhBDiSOkuBGldylTTRfhRUdHIhEkfM2xoCAt/3o3BYOilLex9/e26syX8+LCPwo+r+s13dSQcnUo2x7HDbQlit9tZu3YtCxcuZN26dVRWVlJdXU1dXR0bN27s/Q09Slpf5Mqd1hNDVVUVP/30U5v3HA4H9fX11NbWUltbS329BrVajcWyDJ1ejV6nQaUCi9WB1WLHbLGTsbmEtWsLGTp0KJMnXcj116UycOBABg0aRGRkpFwI94LmPv6H0p//4LoApaX1fPvdHvbtqyI3t4acXCOFhXU0NtraTBcQEIC7u7urTo67uzthYWEMHDiQk08+mejoaAYOHMjAgQMJCQmR/3+FEEKIg3RW06OjEclaj9J0sIU/76eyspGXX55OrfF+alt91het24U4Vp3Q4UdQyIs4HA7GjYuiqsrE3y5M5YILUoiP8wOcdzPrG6wYq82UVzRSXt5AeXkjDQ1Wrr6mjODg4E6XbTQa2bx5s6vP+sqVK6moqCAgIIApU6YQHx+Pn58f/v7+TJw48Qjtcd/o7K5+Z+9LKHJ0HM6Fb0/89NNPXHHFFe3e12g0eHt74+XlhZeXFw6HA6vVisViwWKx4HA4XMVj9Xo94eHJ/PDDm5x55plyIdxLOito2F2hw4M/NxrNrF1XyPff7+Xb7/agVkNSoj8xMb5MPzWeqChvwsO9CAv1JDTUk8FD3zim7yoJIYQQx6OeDMPekdzcGkJDPQkJbt/y4OCW8BKG9A2Vyvno7WWKQ3NCd3sBZ8ChVjsbjXl46GhosBIR4UVjo46ampoOh3FVq9V4eHhw2223ERMTQ3V1NdXV1VRVVbF371527dpFUVERAN7e3owZM4bx48czY8YMxo4de0i1JI5Vh1vHoTUJQXpHT4eCbdbb4YfdbmfmzJn89ttvrvc0Gg3p6elMnTqVqVOnMm3aNNzd3Xt1vaJjh/rvoSNlZQ2sWVPAn2sK+fPPArbvKENRICbGh2uvGcrllw3Cz8+t3XwyQoQQQgjR+w71t711KxC1SsUPP+7l2r8vZNq0WF5/9Qz8/dv/hsOxEXz0t+vO5v259e6+6fbyyvPS7eVQnPDhB8A111zDhg0bWLt2LT/88ANbt27Fx8cHX19ffH198fPzIyQkhJCQEIKDg6mrq+M///kPc+fOxWKxuKbx9fUlMTGRgQMHkpaWxpAhQxg4cGC/CDs6cjgBiAQeve9wLnZ7+yJVURRKSkrIzc0lLy+PnJwcNm7cyLJlyygoKCAqKopnn32W2bNn9+p6j0cWi4UDBw6wd+9e9u/fj4eHB0lJSSQnJxMZGekKY/+q7v5dWK12srON7NlTyf4D1WRlGykoCGHfvn3k5uYCkJCQwJQpU1wPX++X27XKkcBDCCGE6Bt/5YaG9aBuMIsXZ3PLP37llGlxzJ17GupWNUK0KtUxEXxA/7vudIUf93zUN+HHc1f2m+/qSOh34UdJSQlffPEFnp6eBAcHuwKLyMjITu88f/HFF1xyySXMmTOHO+64g8TExB6ty2azodFopIn+QQ4ORSTw6FuH0wQy5DDGhe9ISUkJGRkZbNu2ja1bt1JWVtYmONRoNHz++efk5uZiMBgoKyvD29u7V9Z9PFm4cCEvvvgi69evp6amBofDAYCbm5urC1Dz62nTpnHppZdyzjnn9Hox1/z8fJ54/GyWr8hj374qrFbnen18fFxDOiclJTFkyBAmT55MVFRUr65fCCGEEN3r6tyuq8KmzQ4OPpp99eUubr99EZMnRzNqVDj+AW74+7uRmHAHaWlpxMbGHvY295bj6bqzJyT8OLb0q/Bj9erVTJw4EbVajaIotN41vV7PqaeeynnnncesWbPw8/NzfWaz2XjyySeZO3cu5eXlDBgwwHWn85xzzjmm91mIw+3/+VcDkK1btzJixAgcDgeenp4MHjyY8PBwamtrqampwWg00tjYyJgxYzj99NOZMWPGCXcxnZOTwz333MO8efNITU3lyiuvJDAwkOTkZJKTk4mIiMBms5Gdnc3evXvZuXMn3333HX/++SceHh785z//Yc6cOb22PUOHDqWgoIBZs2YxZMgQ0tLSSEtLk4KjQgghxDHgcM/pwBmKdBZ6tLZ4cTbP/ncNZWUNVFaZMLUqWL506VKmTp162NvQG46X686eat6f2+7tm/Dj5Wcl/DgU/Sr8+OOPP5g2bRre3t58+eWXpKenU1ZWRmlpKVu2bGHBggWsWLECDw8PbrjhBh566CH8/f1d8zc2NvL999+zbNkyli9fzo4dOwgJCeFf//oXqampeHt7ux6+vr54e3vLBYM46o5W+PHxxx9z5ZVXsnPnTgYMGNBr3TX6i08++YQbbrgBPz8/nn32WS699NIeHy+ys7N54okn+OCDD1i9ejVjxoz5y9vTfBz/6KOPOixQK4QQQogjr/Vwtod7JnXwOZ3FYsFoNGK1Wl2P/PxHKSioJSu7muwsI9nZRqqqAsjJyaGiogKA4cOHs3nz5sPdlV5xvFx39pSEH8eWfjXayymnnMLGjRu54447mDlzJqeffjpnnXUW06dP5+STT+bOO++ksLCQp556ipdffpmcnBzmzZvnmt/d3Z2LL76Yiy++GIDc3FweeOABbr311g7Xp9VqCQwMJDAwkKCgIHx8fDAYDBgMBtzc3DAYDPj6+jJs2DBGjhxJYmKiXCCKXqc+zACuvPSOv9S/s6ysDIAHH3yQGTNmcPbZZxMWFnbYy+sv6uvrueeee3jjjTe4+uqrefnllw+5q09cXByvv/46mzdv5oEHHmDx4sV/ebuaT2y2b9/O999/3+Yzs9lMRUUF5eXlrv9WVVVhNptdI/SYzWY8PT2ZP38+AQEBf3l7hBBCiBNVcSc3rhw9nL/5auLg0ENRFL788kvmzJnj+t0/mMFgIDExkcTEISQlRXDmmWe6ahuOHj26h1sgDp2q6dHbyxSHol+1/GimKAqfffYZb7/9NqtXr8ZqtRISEoLdbqeqqsrVv16tVlNdXd3thUnzSC61tbWuR01NDRUVFa4LhfLycmpqajCbzW0e5eXl5OXlAeDr68vIkSMZM2aMawQYuVgUf9XBQ5Qdir8SfpSXl/P666/zyy+/sGbNGvz9/dmxYwehoaGHvcy+VFxczL333kt1dTXXXnstZ511FjqdrteWn5+fz4cffshbb71FeXk5zz33HDfddNNfah324osvct9999HY2NgrhZNvv/12Xn755Q4/02g0bcJcf39/3Nzc0Ol06PV6du7cydq1a/niiy9cAbEQQggheqazwONwhHXQetdut3P55ZfzxRdfcNFFF3HJJZe4fsOb/xsdHd2rxdX7wvF23dkdV8uP+z7um5Yf/72i33xXR0K/DD9aq6urY/ny5axZswZ3d3cCAgIIDAwkICCAuLg4EhIS+nwbysvL2bhxIxs2bHCNKlNUVIRWq2XBggXMnDmzz7dB9F9HK/xoraCggCFDhlBfX8/UqVOZOXMm48aNIygoiMDAQHx9fXv1hzYjI4O6ujp0Oh1ardb1X4PBgKenJ15eXnh4eKBWq7FYLLz22ms89thjaLVa4uLiWL9+PeHh4SxZsoQBAwYc0roLCwtZsGABWVlZbR6VlZV4eHgwa9YsHnzwQZKTkw9r3yorK9m0aRMbNmzgp59+YuXKlZSXlxMYGHhYyztYRUWFKwBuptPp8PX17TKoKSy4lUsvW8DKlflcecVgbr11FJGR3bdo6a3iukIIIcTxpjcDD+g49Gj2888/M3PmTD744AOuuuqqXl3vkXQ8X3d2RMKPY0u/Dz+ORfX19bz99tvcf//9+Pj4UFpaKrVDxHEvPz+fb7/9lp9++omlS5disVhcn2k0Gvz9/QkODiYpKYkBAwa4HoMGDTrkbhQ9/XtpDkAaGhr4+9//zlNPPUVQUBA333wz7733Hnv27DmkyuaKojBq1Cg2b95MXFwc8fHxrkdSUhIzZszo0TGyrq6OnJwcsrOz2b9/P5mZma5HUVERAN7e3owcOZKpU6fyyCOPHNFjhMPhoKqqisxdd1BZaaKispHSknoyMyt4/4Ntrumys/6Bu3vf9Z6U4EQIIcTxpLfDDoDur9Rg3bpC/n7tTyQkDGbt2rXH9XVFf7vubN6f2+//pE/Cj5f+c3m/+a6OhH5V86O31NXV4XA40Ov16PX6NnesFUWhoaGBqqoqqqqqqKysxGg0Eh8fz6BBg9rd3f7/9u49qMlzzwP4lwSSECAhISThjhCpXEQt2hFbj3K2g7ettTPa6XbnTK2lPTudru1px87u/qOdHbu9saetf9S6ndY6s7ue1ukfnam2jtVaiyIsrUWlVEUxQBIu4ZILV0P2D8wrQcAEEhLi9zPzzJvLy/s+L7SR98vzPD+Xy4Xr16+jrq4OZ8+exdmzZ3HhwgW4XC6sXr0au3btmtcfUEQe6enp2LlzJ3bu3AmHw4GmpiZhapindXR04OrVq/jqq6/Q3NwsjEBYuHAhVq5cidLSUixbtgyFhYXTTkf75JNPUFFRITzXarVYsGAB7HY7uru7YbVaMTIygv7+fgDA2rVrodPp8Pnnn0Mmk+HTTz/Fa6+95ndJN4fDgcuXLwMYKw9bXFyMvLw8SKVSjIyM4Ntvv4VUKoVEIsHg4CAsFotXM5lMaG5uRldXl3BMqVSKvLw8LFq0CBUVFcjPz0dJSQkMBkNAR8sMDQ2htbUVLS0tMBqNaGlpQVtbG7q7u4XPszutG6MTVowXi6OQk5OIv9+Ui9S0BDy0IgUy2eyn4kzHs5gvQxAiIgp3gQ4+fAk9AOCdd6rx/l9rUFCgwWcHl8LS/goAIEXPfzuJJuLIjwkGBgYgl9+dykkkEsjlcvT393v9RXs8pVKJ0tJSLFmyBEajEQ0NDWhsbMTQ0BAAwGAwYNWqVVi1ahXWrFmDRYsWBfVaiMLZ4OAgmpqacOHCBVRXV6O6uhoXLlzArVtjJdeysrJQVFSE/Px8JCcnQ6VSITExUWgJCQkwm82orq7GiRMnYDKZhAWHJRKJsB0fVvb29sJut2PZsmWoqqpCbGys3/1ubW3F999/j1OnTuGHH35AW1ub0OeJoqOjodfrvVp2djays7ORlZWF7OxspKSk+LWeh9vtRktLC2pqamA2mzE6OnpXGx4ehtlsRmtrq9A8C9R6JCUlIT09HWq1GiqVyqslJSUhOTkZGo1G2KrVaqGfsynF5w+GHkREFCkmC0d8DTims37dYdTXdwAAiou1+NOfduHVV18N63U9phNp950c+RFeGH5MYv/+/aisrMS1a9e8Xtfr9SgvL8fDDz+MjIwMqFQqqNVqJCQkoLGxEVVVVaiqqsLly5eRnZ2NgoICoRUXF0Or1Yboiojmh4GBATQ2NuLSpUtCa2xsFEZYTfZxpVAokJ6ejoyMDKFlZ2cLwcnEgOPWrVsQiUQB/aXAEzh4qqIYjUbcvHkTZrMZdrt90oBCJBJBoVAITalUCtu0tDQolUqvc9TW1mLv3r2orq5Ge3s7gLFRI2KxWLgeT/OELunp6UJLS0tDZmYmMjMzkZ6ePmnIG0z+BCYMPYiIKBKZLcGYFuOG0WhDw+U/4MSJEzh8+DA2bNiAd955B4WFhQE/X7BF2n0nw4/wwvDjHtxuN3799VdUVlbi8OHDXn+VLiwsxJtvvoklS5aEuJdEkW90dBR2ux29vb1CBSaLxYKWlhaheaZ1eMIBkUiE3NxcFBUVobi4GMXFxVi8eDFycnJmVT3F6XTi+vXrwrodnu3169dx5coVOBwOAGMjPxITExEVFXVXQOFyuYTqUZNRKBRCWBEfH48jR46gsLAQjz/+OB566CGsWLGC1aKIiIjC2MTRHoEY6THeZFNbjh49ioqKCpjNZjz66KPYtm0b1q1b5/dU31CJtPtOz/W88i//HZTw4/23/jFivldzgeGHH5xOJxobG9HQ0IDLly/jiy++QFpaGs6cORPqrhHROHa7HQ0NDcLokYsXL6K+vl6Y+iGXy1FSUoKXX34ZTzzxxJSjQEZHR9Hc3Iz6+nqvdu3aNWEUSkxMDNLT5chIVyAjQ4GFC1Uw5KpgWKhCVqYS0dF3jj1ZdR2XywWHwwGbzQabzYaenh60tbXBaDQKa3OYTCZs2bIFu3btCmh5XiIiIgqcYCx46uG5Y/NlLY/h4WEcOXIE+/fvx9mzZ+FyuZCXl4c1a9Zg1apVKC0tRV5eXliuOxhp950MP8ILw48ZunTpEtauXYsHHngAVVVVoe4OEfmgvb1dCDC++eYbnDp1Cvn5+aisrMSGDRtgtVrx3Xff4fTp07f3q0N//wgAQK2WobBAg4LbzWBQITNDAa02DiLR7H95CFTZYSIiIgq+YAYdE7lGZz5kpK9vCGfPtuLH00bU1Jhx5fduuN1uqNVqrFixQmgPPvggUlNTQ75WSKTdd94JP/4HUlmAw4/Bfrz/1tMR872aCww/ZsBkMmHFihVITk7GyZMn/S7TSURzZ7q1Jv6vzoy3367Gjz+2oLBQg99+s2J01I1Fi5JQVKRBYUHyWNiRnwSdLm5O/kLCEISIiCj8BTv8mE3gMZ14+b/j/PnzqK6uRm1tLWpqatDRMbZgqkwmQ05ODnJycpCbm4vc3FyUlZWhqKgoKH2ZTKTdd3qu5y//Gpzw46//wfDDHyx1OwMffvghzGYz9uzZgytXrggVERQKRVgOHyOab3o6/zLjrx25XT7XF8tLUvC3w1vwX59cQF2dBc89twR/98cs6PXxd+3rBoSpLqIA/H/OkIOIiGj+0s9wcW5L+8vwZWyFr6NKR3wISdJSPvR6Xl5ejvLycgB3qsjV19ejqalJaMeOHcONGzcgEonw5ZdfYtOmTSEfFUI0W36FHy6XC7W1tcjMzIROp7vrvfPnz6OtrQ3Lly/HggULvN4fGhrCyZMn8fPPP6O3txd9fX13bW02GzIzM7FmzRqsXbsWjzzyCBISEmZ/lQFmt9vhdrvxwgsveL0eExMDjUaD8vJyvP766ygoKAhRD4nmh9mEHFOJmfAP873CEJEoCn9+Ydmk74n9nM7iGUc3XTaSlPy+X8ckIiKi+WviCNRAxwcxU/yu4hqXidyrykyMBChZPtaAKAAGAAYMDd3CP/35W2zevBkiURSUSilUahl02nxoNBokJSVBo9Fg9erV2LhxI6KjvW8t3W43HA4H2tvb4XA4kJWVBZVKNavrnb+ibrdAH5P84de0l5KSEtTV1QEAkpOTUVRUhMWLF6O3txdHjx5FV1eX8DWLFi3Cxo0bsXTpUhw/fhxff/01bDYb1Go1kpKSoFQqkZiYCKVSKTz2lIw9ffo0LBYLxGIxFi9ejNLSUqxcuRLLly9HWlpaWIywGBkZgdVqRWdnJ7q6uoRta2srDh06hLa2NuTn3/lg8Fz3+MdKpRJisVgoVTnxsWer0WjuCpvmuxtt/zyjr1uQti/APaFwEeggZBRTf7SNH0rqb8ARbAxHiIiI5jd/yrsHkisIM2VGRlw4deom2i1O9PQMoqdnEIODxbBarbBarTCbzWhubkZqairWrVuH3t5eWCwWtLe3w2KxoL+/3+t4KpVKmFLjaQaDAQaDASkpKbDb7RE67eV/gzTt5R8i5ns1F/wKPxYuXIgPPvgAAwMDuHjxotCkUik2bdqEzZs3w2Aw4MyZMzh69CiOHTsGk8mEwsJCbN26FVu3bkVhYeE9gwu3242rV6/i9OnTOHfuHM6dO4fGxkbhfalUCq1WC51OJzS9Xo/s7GwYDAbExR9CSmr8jBYhnDgsbCaGh4dx+PBh1NXVCR8M3d3dwranp8ev48XExMDpdM67Kg8zDTiCgaHJ/OMJQ6YLMSIRww8iIqL7g78hie8Te70FqsTuVJVmfvnlFxw4cAA1NTXCPZperxe2er0ecrkczc3NXlNrmpqa0NraKhxHLpfjySefxMGDByPmht5zH/3qvwUn/PjPNxl++MOv8OPX+gokJ/v+Q3O73ei2DiBJM7sf9Kjbjd7eQTQ2WtHZ0Y/Ozn50dd3edg6gs6sfnR39MJsdGL39F12pVIzMTAUyMhVITJRBoZBCqZQiIUEChUIKhVIKRYJkbKuQQioRw9o9gI52Jzo6+tHR4URnRz/aO5yw9ekRFRWF+Ph4ocXFxXk9H/9aWloacnJyphzW5XK50NPTg+7ubrS2tuLmzZto+O1jmM0OmM0OWMxOmM0O9PQMAgAefiQdf/viCeHrAxHQBEKreadP+/kyFzFY5ir06Op4ZU7OM5X7Yf0Ia+croe7CjDDMICIiIn/MZSWZe3G7fSuvO1MDAwO4ceMGrl27hqamJiQmJmLHjh0Rc0PP8CO8+LXmh0wm9uvgUVFRsw4+gLHFBdWqWKwqTcfoNFnN8LALrS02NN/sQ/ONPjQ396G1xYa2VjsabF2w24Zhsw3Bbh++R7+BpKRYaLVxSNbKkZbeDQBwOi3oso6gpWUETucInP0j6Hfefuwcues4CoUEGRkKZGYpkZmhwKjbDZPJAZPJDrPZgY72fiGsAQCJRAStNg5aXRxWlqZBq5VDp4vDtifzvY7b5mPoMJOQxNdAwx9TzUWcqanClFCM7gh16HE/mSxEmKtAhAEGERERBUM4BR3jzXRBV3/FxsaioKBAWCvRZrNhx44dc3LuORUVNf2icDM9JvklrKq9SMS+/ADv7DM8YWKbTBoNg0ENg2H60rMu1ygcjhHYbUPosw3B1jeEwcFbSNLIodXKodHIER3t33JEo6NuDA7egt0+DLPZAaOxDzdv2mA09qHFaMN3x68jWixCSko88haq8Yc/ZCIlNR6pqfFISYmHXheHRJUsYGuZiEVRM/owjRZF4VYIR2r4ItRTWBh4hA+GEkRERESBMVeBB1Go+BV+SMRRPgYUc2NiXyaGIVMRi0VQKsemwaQHqC8iURTk8hjI5THQ6eKwdOm9FyidahRLqBdgjA7Q+WcaoqSHybSeqfg7xYRhCRERERFNhoHD/YG1XsJDWI38mK3JghlfA5GJ5iLjEc9glMdMFzoKhYkhiicMCfdwI9Duh/U4iIiIiIhoCpz2EhYiKvyYjEQc5VXWcr6bTW3wYAQnTKuJiIiIiIgo3PkVfqg1b0fUSrKhqsEdKuODk/FBCAMMIiIiIiKi4ODAj/AQ8SM/pqO9x01/KMORe/WNiIiIiIiIiHxzX4cf98IAgoiIiIiIiGaFQz/CwmyWkCAiIiIiIiIiCnsMP4iIiIiIiIiCJCpILRiam5vx3HPPYcGCBYiNjUVubi52796N4eFhr/2MRiMee+wxxMXFQaPRYOfOnXftE2447YWIiIiIiIiI0NjYiNHRUXz88ccwGAy4dOkSnn/+eTidTrz33nsAAJfLhU2bNiE5ORk//fQTrFYrnnnmGbjdbuzbty/EVzA1hh9EREREREREwTKP1vxYv3491q9fLzzPycnB77//jo8++kgIP44fP46Ghga0tLQgNTUVAFBZWYnt27dj7969YVsh1q/ww2azBasfREREREREdB+L1PvNoaH+oB1z4vdMKpVCKpUG9Fx9fX1Qq9XC83PnzqGoqEgIPgBg3bp1GBoaQl1dHcrKygJ6/kDxKfyQSCTQ6/XIyMgIdn+IiIiIiIjoPqXX6yGRSELdjYDw3Ed/+NYzQTl+fHz8Xffou3fvxp49ewJ2jqamJuzbtw+VlZXCaxaLBTqdzms/lUoFiUQCi8USsHMHmk/hh0wmw40bN8J+ARMiIiIiIiKavyQSCWQyWai7ERDBvo92u92ImjD9ZapRH3v27MEbb7wx7fFqa2uxfPly4bnJZML69euxbds2VFRUeO078bxT9Sec+DztRSaTRcx/hERERERERETBFi730S+99BKeeuqpaffJzs4WHptMJpSVlaG0tBQHDhzw2k+v1+P8+fNer/X09GBkZOSuESHhhAueEhEREREREUUwjUYDjUbj075tbW0oKytDSUkJPvvsM4hEIq/3S0tLsXfvXpjNZqSkpAAYWwRVKpWipKQk4H0PlCi32+0OdSeIiIiIiIiIKLRMJhPWrFmDzMxMHDp0CGKxWHhPr9cDGCt1u3TpUuh0Orz77rvo7u7G9u3bsWXLlrAudcvwg4iIiIiIiIhw8OBBPPvss5O+Nz46MBqNePHFF3Hy5EnExsbi6aefxnvvvRfwSjOBxPCDiIiIiIiIiCKa6N67EBERERERERHNXww/iIiIiIiIiCiiMfwgIiIiIiIioojG8IOIiIiIiIiIIhrDDyIiIiIiIiKKaAw/iIiIiIiIiCiiMfwgIiIiIiIioojG8IOIiIiIiIiIIhrDDyIiIiIiIiKKaAw/iIiIiIiIiCiiMfwgIiIiIiIiooj2/wBia3mGZEf0AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
    "source": [
-    "if len(caseNames)==1:\n",
-    "    plotTCI_case('JJA', None)\n",
-    "    plotTCI_case('DJF', None)\n",
+    "if len(caseNames) == 1:\n",
+    "    plotTCI_case(\"JJA\", None)\n",
+    "    plotTCI_case(\"DJF\", None)\n",
     "else:\n",
     "    for iCase in range(len(caseNames)):\n",
-    "        plotTCI_case('JJA', iCase)\n",
-    "        plotTCI_case('DJF', iCase)"
+    "        plotTCI_case(\"JJA\", iCase)\n",
+    "        plotTCI_case(\"DJF\", iCase)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c8ff07ee-af88-4342-88de-ef1f2fdfa7bc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c51c912b-e2d4-4a82-a27a-ac7003cbe2df",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "60d2dfe0-791f-40ed-8f19-6fc36b72c8b7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From ba37cf73d5586e3a9a59e0cb87bc01d25057a1b4 Mon Sep 17 00:00:00 2001
From: Meg Fowler <mdfowler@ucar.edu>
Date: Wed, 23 Oct 2024 08:29:23 -0600
Subject: [PATCH 3/5] Change name of notebook

---
 ...strialCouplingIndex_VisualCompareObs.ipynb | 975 ++++++++++++++++++
 1 file changed, 975 insertions(+)
 create mode 100755 examples/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb

diff --git a/examples/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb b/examples/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb
new file mode 100755
index 0000000..826a2f0
--- /dev/null
+++ b/examples/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb
@@ -0,0 +1,975 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "99564fab-c321-4116-8229-b16eefa1536e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Compute land-atmosphere coupling indices \n",
+    "This notebook takes in a series of CESM simulations, computes the land-atmosphere coupling index (CI; \n",
+    "terrestrial leg only currently), and plots those seasonal means. <br>\n",
+    "- Note: Built to use monthly output; ideally, CI should be based on daily data. \n",
+    "- Optional: Comparison against FLUXNET obs\n",
+    "<br><br>\n",
+    "Notebook created by mdfowler@ucar.edu; Last update: 2 Aug 2024 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "750da831-1c5c-4b41-947e-a9e57a62a820",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import datetime\n",
+    "from datetime import date, timedelta\n",
+    "import dask\n",
+    "import pandas as pd\n",
+    "import sys\n",
+    "\n",
+    "# Plotting utils\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy\n",
+    "import cartopy.crs as ccrs\n",
+    "import uxarray as uxr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "774bd269-ce50-4449-b32f-83246b74b73c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 1. Modify this section for each run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f8f2d17-c653-4ad1-9dc3-c49bf836ceb6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "##  Settings for case locations + names\n",
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "## Where observations are stored\n",
+    "# obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data\n",
+    "\n",
+    "## Where CESM timeseries data is stored\n",
+    "CESM_output_dir = \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/\"\n",
+    "\n",
+    "\n",
+    "## Full casenames that are present in CESM_output_dir and in individual filenames\n",
+    "# caseNames = [\n",
+    "#             'b.e23_alpha16b.BLT1850.ne30_t232.054',\n",
+    "# 'b.e30_beta02.BLT1850.ne30_t232.104',\n",
+    "#             ]\n",
+    "case_name = \"b.e30_beta02.BLT1850.ne30_t232.104\"\n",
+    "\n",
+    "# clmFile_h = '.h0.'\n",
+    "\n",
+    "start_date = \"0001-01-01\"\n",
+    "end_date = \"0101-01-01\"\n",
+    "\n",
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "##  Optional settings for notebook\n",
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "\n",
+    "## If comparison against FLUXNET desired\n",
+    "# fluxnet_comparison = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0014712f-d094-4dae-b583-740bf7a9789c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "##  Settings for computing coupling index\n",
+    "## - - - - - - - - - - - - - - - - - - - - - -\n",
+    "startYrs = [start_date.split(\"-\")[0]]\n",
+    "endYrs = [f\"{int(end_date.split('-')[0])-1:04d}\"]\n",
+    "\n",
+    "caseNames = [\n",
+    "    case_name,\n",
+    "    # base_case_name,\n",
+    "]\n",
+    "\n",
+    "shortNames = [case.split(\".\")[-1] for case in caseNames]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d70024c7-0af2-48b9-9041-893f40e613ec",
+   "metadata": {},
+   "source": [
+    "## 2. Read in model data and compute terrestrial coupling index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "304ce8d0-6aab-4fcb-9635-2a78e270f3c7",
+   "metadata": {
+    "editable": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Inputs: xname -- controlling variable \n",
+    "        yname -- responding variable\n",
+    "        ds    -- dataset containing xname and yname data \n",
+    "        \n",
+    "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
+    "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def compute_couplingIndex_cesm(xname, yname, xDS, yDS):\n",
+    "    xday = xDS[xname].groupby(\"time.season\")\n",
+    "    yday = yDS[yname].groupby(\"time.season\")\n",
+    "\n",
+    "    # Get the covariance of the two (numerator in coupling index)\n",
+    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
+    "        \"time.season\"\n",
+    "    ).sum() / xday.count()\n",
+    "\n",
+    "    # Now compute the actual coupling index\n",
+    "    couplingIndex = covarTerm / xday.std()\n",
+    "\n",
+    "    return couplingIndex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c35ae8d-dfff-44b0-a854-dfc2b5b030c0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "for iCase in range(len(caseNames)):\n",
+    "    ## Check first if coupling index has already been created:\n",
+    "    TCI_filePath = (\n",
+    "        \"/glade/derecho/scratch/mdfowler/\"\n",
+    "        + caseNames[0]\n",
+    "        + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
+    "    )\n",
+    "\n",
+    "    if os.path.exists(TCI_filePath):  # Use previously computed TCI\n",
+    "        print(\"Using previously computed coupling index saved in file \", TCI_filePath)\n",
+    "    else:  # Compute TCI\n",
+    "\n",
+    "        # Get list of necessary time series files\n",
+    "        soilWater_file = np.sort(\n",
+    "            glob.glob(\n",
+    "                CESM_output_dir\n",
+    "                + \"/\"\n",
+    "                + caseNames[iCase]\n",
+    "                + \"/lnd/proc/tseries/\"\n",
+    "                + caseNames[iCase]\n",
+    "                + clmFile_h\n",
+    "                + \"SOILWATER_10CM.\"\n",
+    "                + startYrs[iCase]\n",
+    "                + \"??-\"\n",
+    "                + endYrs[iCase]\n",
+    "                + \"??.nc\"\n",
+    "            )\n",
+    "        )\n",
+    "        if len(soilWater_file) == 0:\n",
+    "            print(\"Soil moisture file not found!\")\n",
+    "        elif len(soilWater_file) > 1:\n",
+    "            print(\n",
+    "                \"More than one file matches requested time period and case for soil moisture.\"\n",
+    "            )\n",
+    "        elif len(soilWater_file) == 1:\n",
+    "            soilWater_DS = xr.open_dataset(soilWater_file[0], decode_times=True)\n",
+    "\n",
+    "        sh_file = np.sort(\n",
+    "            glob.glob(\n",
+    "                CESM_output_dir\n",
+    "                + \"/\"\n",
+    "                + caseNames[iCase]\n",
+    "                + \"/lnd/proc/tseries/\"\n",
+    "                + caseNames[iCase]\n",
+    "                + clmFile_h\n",
+    "                + \"FSH_TO_COUPLER.\"\n",
+    "                + startYrs[iCase]\n",
+    "                + \"??-\"\n",
+    "                + endYrs[iCase]\n",
+    "                + \"??.nc\"\n",
+    "            )\n",
+    "        )\n",
+    "        if len(sh_file) == 0:\n",
+    "            print(\"Land-based SHFLX file not found!\")\n",
+    "        elif len(sh_file) > 1:\n",
+    "            print(\"More than one file matches requested time period and case for SH.\")\n",
+    "        elif len(sh_file) == 1:\n",
+    "            shflx_DS = xr.open_dataset(sh_file[0])\n",
+    "\n",
+    "        # If years start at 0000, offset by 1700 years for analysis\n",
+    "        yrOffset = 1850\n",
+    "        if shflx_DS[\"time.year\"].values[0] < 1500:\n",
+    "            shflx_DS[\"time\"] = shflx_DS.time + timedelta(days=yrOffset * 365)\n",
+    "        if soilWater_DS[\"time.year\"].values[0] < 1500:\n",
+    "            soilWater_DS[\"time\"] = soilWater_DS.time + timedelta(days=yrOffset * 365)\n",
+    "        # Convert times to datetime for easier use\n",
+    "        shflx_DS[\"time\"] = shflx_DS.indexes[\"time\"].to_datetimeindex()\n",
+    "        soilWater_DS[\"time\"] = soilWater_DS.indexes[\"time\"].to_datetimeindex()\n",
+    "\n",
+    "        # Add case ID (short name) to the DS\n",
+    "        shflx_DS = shflx_DS.assign_coords({\"case\": shortNames[iCase]})\n",
+    "        soilWater_DS = soilWater_DS.assign_coords({\"case\": shortNames[iCase]})\n",
+    "\n",
+    "        ## Compute coupling index and save to netCDF file\n",
+    "        ## - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+    "        xname = \"SOILWATER_10CM\"  # Controlling variable\n",
+    "        yname = \"FSH_TO_COUPLER\"  # Responding variable\n",
+    "\n",
+    "        xDS = soilWater_DS\n",
+    "        yDS = shflx_DS\n",
+    "\n",
+    "        couplingInd = compute_couplingIndex_cesm(xname, yname, xDS, yDS)\n",
+    "\n",
+    "        filePath = (\n",
+    "            \"/glade/derecho/scratch/mdfowler/\"\n",
+    "            + caseNames[0]\n",
+    "            + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
+    "        )\n",
+    "        couplingInd.to_netcdf(filePath)\n",
+    "        print(\"File created: \", filePath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f8fba2a-98d2-4e94-9d71-3b2625e16032",
+   "metadata": {},
+   "source": [
+    "### 1.1 Read in FLUXNET data if requested"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fdf20c82-5a01-4ab9-8881-d9388b1b2356",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# --------------------------------------------------------\n",
+    "# Function to read requested variables from FLUXNET file.\n",
+    "# - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+    "#\n",
+    "# Inputs:  fileName --> Full path to FLUXNET data file\n",
+    "#          varNames --> An array of variable names to be\n",
+    "#                       retrieved from said data file.\n",
+    "#                       NOTE: If you wish to retrieve *all*\n",
+    "#                       variables, pass the string 'ALL'.\n",
+    "#\n",
+    "# Outputs: fluxnetID --> ID string used to identify station\n",
+    "#          fluxnetDS --> An x-array dataset containing the\n",
+    "#                        requested variables.\n",
+    "#                        Missing values will be set to NaN.\n",
+    "#\n",
+    "# --------------------------------------------------------\n",
+    "\n",
+    "\n",
+    "def readFLUXNET_var(fileName, varNames):\n",
+    "    # Get ID of station\n",
+    "    startID = fileName.find(\"FLX_\")\n",
+    "    fluxnetID = fileName[startID + 4 : startID + 10]\n",
+    "\n",
+    "    # If this is taking a long time or you just want to know where in the stations you are, uncomment print statement\n",
+    "    # print('Reading in site - ', fluxnetID)\n",
+    "\n",
+    "    # Read in CSV file containing data\n",
+    "    dataDF = pd.read_csv(fileName)\n",
+    "\n",
+    "    # Return ALL variables from dataDF if requested\n",
+    "    if varNames == \"ALL\":\n",
+    "        fluxnetDF = dataDF\n",
+    "\n",
+    "        # Set any value that's missing to NaN (not -9999)\n",
+    "        fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
+    "\n",
+    "        # If time has been requested, reformat to pandas date index\n",
+    "        fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
+    "            fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
+    "        )\n",
+    "        fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
+    "\n",
+    "        # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
+    "        #   NOTE: since current implementation doesn't use the pre-formatted CoMeT, might not need this step now\n",
+    "        fluxnetDS = fluxnetDF.to_xarray()\n",
+    "\n",
+    "    # Reduce returned DF to contain only variables of interest\n",
+    "    else:\n",
+    "\n",
+    "        # Check that requested variables are available in specific file\n",
+    "        errCount = 0  # Initialize flag for error\n",
+    "        colNames = dataDF.columns.values  # Available variables in file\n",
+    "\n",
+    "        for iVar in range(len(varNames)):  # Check each variable individually\n",
+    "            if (varNames[iVar] in colNames) == False:\n",
+    "                # Turn on print statement for more verbose output\n",
+    "                # print('** ERROR: %13s   not contained in file for %8s  **' %(varNames[iVar], fluxnetID))\n",
+    "\n",
+    "                # If any variable is not conatined in file, return a NaN\n",
+    "                fluxnetDS = -999\n",
+    "                errCount = errCount + 1\n",
+    "\n",
+    "        # If all the variables *are* available, isolate those in DF and return that\n",
+    "        if errCount == 0:\n",
+    "            fluxnetDF = dataDF[varNames]\n",
+    "\n",
+    "            # Set any value that's missing to NaN (not -999)\n",
+    "            fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
+    "\n",
+    "            # If time has been requested, reformat to pandas make index\n",
+    "            if (\"TIMESTAMP\" in varNames) == True:\n",
+    "                fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
+    "                    fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
+    "                )\n",
+    "                fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
+    "\n",
+    "            # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
+    "            fluxnetDS = fluxnetDF.to_xarray()\n",
+    "\n",
+    "    return (fluxnetID, fluxnetDS)\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "Inputs: xname -- controlling variable \n",
+    "        yname -- responding variable\n",
+    "        ds    -- dataset containing xname and yname data \n",
+    "        \n",
+    "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
+    "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def compute_couplingIndex_FLUXNET(xname, yname, ds):\n",
+    "    xday = ds[xname].groupby(\"TIMESTAMP.season\")\n",
+    "    yday = ds[yname].groupby(\"TIMESTAMP.season\")\n",
+    "\n",
+    "    # Get the covariance of the two (numerator in coupling index)\n",
+    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
+    "        \"TIMESTAMP.season\"\n",
+    "    ).sum() / xday.count()\n",
+    "\n",
+    "    # Now compute the actual coupling index\n",
+    "    couplingIndex = covarTerm / xday.std()\n",
+    "\n",
+    "    return couplingIndex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9acb032f-f106-4783-8c57-8e0d3a44eb3e",
+   "metadata": {
+    "editable": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input",
+     "hide-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "if fluxnet_comparison == True:\n",
+    "    # obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data\n",
+    "\n",
+    "    ## Metadata files\n",
+    "    siteInfoFile = obsDir + \"SiteList.csv\"\n",
+    "    siteInfoDF = pd.read_csv(siteInfoFile)\n",
+    "\n",
+    "    metadataFile = obsDir + \"FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.csv\"\n",
+    "    metadataDF = pd.read_csv(metadataFile)\n",
+    "\n",
+    "    ## List of all station files\n",
+    "    dataFiles = glob.glob(obsDir + \"FLX_*/*SUBSET_DD*\")\n",
+    "\n",
+    "    # Set up a few empty arrays to save data into\n",
+    "    terraCI_fluxnetConverted = np.full(\n",
+    "        [len(dataFiles), 4], np.nan\n",
+    "    )  # CI using kg/m2 soil water content  [nStations, seasons]\n",
+    "\n",
+    "    # Also save out some data on each station\n",
+    "    startTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
+    "    endTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
+    "    lat_fluxnet = np.full([len(dataFiles)], np.nan)\n",
+    "    lon_fluxnet = np.full([len(dataFiles)], np.nan)\n",
+    "    SWCdepth = np.full([len(dataFiles)], np.nan)\n",
+    "\n",
+    "    stationID = []\n",
+    "    stationID_converted = []\n",
+    "\n",
+    "    allStationID = []\n",
+    "\n",
+    "    # Variables I want returned:\n",
+    "    varNames = [\"TIMESTAMP\", \"H_F_MDS\", \"SWC_F_MDS_1\", \"SWC_F_MDS_1_QC\"]\n",
+    "\n",
+    "    # Loop over each station (data file)\n",
+    "    for iStation in range(len(dataFiles)):\n",
+    "\n",
+    "        # Read in data\n",
+    "        # ----------------------------------------------------------\n",
+    "        fluxnetID, fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
+    "\n",
+    "        # Save lat and lon for this station\n",
+    "        # ----------------------------------------------------------\n",
+    "        indStation = int(np.where(fluxnetID == siteInfoDF[\"SITE_ID\"])[0][0])\n",
+    "        lat_fluxnet[iStation] = siteInfoDF[\"LOCATION_LAT\"].values[indStation]\n",
+    "        lon_fluxnet[iStation] = siteInfoDF[\"LOCATION_LONG\"].values[indStation]\n",
+    "        allStationID.append(fluxnetID)\n",
+    "\n",
+    "        # Check that there was data saved for this particular site:\n",
+    "        # ----------------------------------------------------------\n",
+    "        if type(fluxnetDS) == int:\n",
+    "            print(\"No data for station: %8s\" % fluxnetID)\n",
+    "\n",
+    "        elif (np.all(np.isnan(fluxnetDS[\"H_F_MDS\"])) == True) | (\n",
+    "            np.all(np.isnan(fluxnetDS[\"SWC_F_MDS_1\"])) == True\n",
+    "        ):\n",
+    "            print(\"No data for station: %8s\" % fluxnetID)\n",
+    "\n",
+    "        # If data is present:\n",
+    "        # ----------------------------------------------------------\n",
+    "        else:\n",
+    "            # Only consider where data is actually present for selected vars\n",
+    "            iReal = np.where(\n",
+    "                (np.isfinite(fluxnetDS[\"SWC_F_MDS_1\"]) == True)\n",
+    "                & (np.isfinite(fluxnetDS[\"H_F_MDS\"]) == True)\n",
+    "            )[0]\n",
+    "            fluxnetDS = fluxnetDS.isel(TIMESTAMP=iReal)\n",
+    "\n",
+    "            stationID.append(fluxnetID)\n",
+    "\n",
+    "            # Convert units from volumetric (%) to mass (kg/m2)\n",
+    "            # -------------------------------------------------\n",
+    "            #   Step 1: Convert from % to fraction\n",
+    "            fracSM = (fluxnetDS[\"SWC_F_MDS_1\"].values) / 100.0\n",
+    "\n",
+    "            #    Step 2: Need to use depth of obs in conversion\n",
+    "            metaData_station = metadataDF[metadataDF.SITE_ID == fluxnetID]\n",
+    "            iSWC = np.where(metaData_station.DATAVALUE == \"SWC_F_MDS_1\")[0]\n",
+    "            #  Some locations (5) have two depths\n",
+    "            if len(iSWC) > 1:\n",
+    "                for iDepth in range(len(iSWC)):\n",
+    "                    SWC_DF = metaData_station[iSWC[iDepth] : iSWC[iDepth] + 4]\n",
+    "\n",
+    "                    depth = np.asarray(\n",
+    "                        SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
+    "                    ).astype(float)\n",
+    "                    depthDay = np.asarray(\n",
+    "                        SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_DATE\"].DATAVALUE.values[0]\n",
+    "                    ).astype(int)\n",
+    "                    depthDay = int(\n",
+    "                        str(depthDay)[:8]\n",
+    "                    )  # Some weird ones have time attached; don't want that\n",
+    "                    depthDay = pd.to_datetime(depthDay, format=\"%Y%m%d\")\n",
+    "\n",
+    "                    # Keep deepest level as the depth for station\n",
+    "                    if iDepth == 0:\n",
+    "                        SWCdepth[iStation] = depth\n",
+    "                        convertSM = fracSM * 1000.0 * np.abs(depth)\n",
+    "                    else:\n",
+    "                        # Use date as break point for getting kg/m2 SWC\n",
+    "                        #  Eq: SWC_kgm2 = SWC_vol [m3/m3] * 1000 [kg/m3] * depth [m]\n",
+    "                        dateArr = pd.DatetimeIndex(fluxnetDS.TIMESTAMP.values)\n",
+    "                        iTime = int(np.where(dateArr == depthDay)[0][0])\n",
+    "                        convertSM[iTime::] = (fracSM[iTime::]) * 1000.0 * np.abs(depth)\n",
+    "\n",
+    "                        # Keep deepest level as the depth for station\n",
+    "                        if depth < SWCdepth[iStation]:\n",
+    "                            SWCdepth[iStation] = depth\n",
+    "\n",
+    "                stationID_converted.append(fluxnetID)\n",
+    "\n",
+    "            # If station only has one level recorded, things are a bit easier:\n",
+    "            else:\n",
+    "                SWC_DF = metaData_station[iSWC[0] : iSWC[0] + 4]\n",
+    "                SWCdepth[iStation] = np.asarray(\n",
+    "                    SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
+    "                ).astype(float)\n",
+    "                convertSM = fracSM * 1000.0 * np.abs(SWCdepth[iStation])\n",
+    "\n",
+    "                stationID_converted.append(fluxnetID)\n",
+    "\n",
+    "            # Save converted soil moisture to dataset\n",
+    "            fluxnetDS[\"SWC_F_MDS_1_convert\"] = ((\"TIMESTAMP\"), convertSM)\n",
+    "\n",
+    "            # Save first and last time used for computing CI\n",
+    "            # ----------------------------------------------\n",
+    "            startTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[0]\n",
+    "            endTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[-1]\n",
+    "\n",
+    "            # Compute terrestrial coupling metric\n",
+    "            # -----------------------------------\n",
+    "            terraLeg = compute_couplingIndex_FLUXNET(\n",
+    "                \"SWC_F_MDS_1_convert\", \"H_F_MDS\", fluxnetDS\n",
+    "            )\n",
+    "\n",
+    "            # If there's less than one full year of data, don't use station\n",
+    "            #   (i.e., as long as all 4 seasons are defined, save values)\n",
+    "            if np.shape(terraLeg)[0] == 4:\n",
+    "                terraCI_fluxnetConverted[iStation, :] = terraLeg\n",
+    "\n",
+    "    seasons = terraLeg.season\n",
+    "\n",
+    "    ## Print some useful information\n",
+    "    print(\n",
+    "        \"Number of FLUXNET stations with CI calculated: %i\"\n",
+    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
+    "    )\n",
+    "\n",
+    "    # How many months go into each calculation of CI for JJA?\n",
+    "    nMonths = np.full([len(dataFiles)], np.nan)\n",
+    "\n",
+    "    for iSt in range(len(dataFiles)):\n",
+    "        if np.isfinite(terraCI_fluxnetConverted[iSt, 1]):\n",
+    "            dateRange = pd.date_range(\n",
+    "                start=startTime_fluxnet[iSt], end=endTime_fluxnet[iSt], freq=\"ME\"\n",
+    "            )\n",
+    "            nMonths[iSt] = len(\n",
+    "                np.where((dateRange.month >= 6) & (dateRange.month <= 8))[0]\n",
+    "            )\n",
+    "\n",
+    "    print(\n",
+    "        \"Minimum number of months used for JJA mean CI: %i \" % int(np.nanmin(nMonths))\n",
+    "    )\n",
+    "    print(\n",
+    "        \"Maximum number of months used for JJA mean CI: %i \" % int(np.nanmax(nMonths))\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd29ba9d-bbbb-4f59-9829-250018215b53",
+   "metadata": {},
+   "source": [
+    "*Make some choices on limiting which stations are used*\n",
+    "- Let's limit usage to depths less than 20 cm (arbitrary, but I don't want us using non-surface soil moisture for this application). This will eliminate 11 stations.\n",
+    "- It would also be good to put some time limits on this. So let's say the observations need to have at least 9 months of data for JJA means (3-years). Otherwise, set terraCI to np.nan again so we don't use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13afbfb4-2040-403c-9d82-3e370f47ec5d",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "if fluxnet_comparison == True:\n",
+    "\n",
+    "    # Get stations with SWC from below 20 cm (or equal to zero)\n",
+    "    iLimit = np.where((SWCdepth == 0.0) | (SWCdepth < -0.2))[0]\n",
+    "\n",
+    "    # Set the terrestrial leg of CI to missing so we don't consider those\n",
+    "    terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
+    "\n",
+    "    print(\n",
+    "        \"Number of FLUXNET stations to use with reasonable depths of SWC: %i\"\n",
+    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
+    "    )\n",
+    "\n",
+    "    # Get stations with less than 9 months used for JJA terrestrial CI\n",
+    "    iLimit = np.where(nMonths < 9)[0]\n",
+    "\n",
+    "    # Set to missing so we don't consider stations with less than three years of data going into the average\n",
+    "    terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
+    "\n",
+    "    print(\n",
+    "        \"Number of FLUXNET stations to use with 3+ years of JJA data: %i\"\n",
+    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc387253-cdd7-4a36-956b-8ce548e963bd",
+   "metadata": {},
+   "source": [
+    "## 2. Make plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67253fd1-d2f7-45fe-a59f-215303b93c06",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "## Load coupling index with uxarray\n",
+    "gridFile = (\n",
+    "    \"/glade/p/cesmdata/cseg/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc\"\n",
+    ")\n",
+    "uxgrid = uxr.open_grid(gridFile)\n",
+    "uxgrid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43206b67-1313-4b61-94ea-b50b85a3d50c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "for iCase in range(len(caseNames)):\n",
+    "    filePath = (\n",
+    "        \"/glade/derecho/scratch/mdfowler/\"\n",
+    "        + caseNames[iCase]\n",
+    "        + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
+    "    )\n",
+    "    couplingIndex_case = uxr.open_dataset(gridFile, filePath)\n",
+    "    # Rename the variable:\n",
+    "    couplingIndex_case = couplingIndex_case.rename(\n",
+    "        {\"__xarray_dataarray_variable__\": \"CouplingIndex\"}\n",
+    "    )\n",
+    "\n",
+    "    # Assign case coord\n",
+    "    couplingIndex_case = couplingIndex_case.assign_coords(\n",
+    "        {\"case\": couplingIndex_case.case.values}\n",
+    "    )\n",
+    "\n",
+    "    # Return all the cases in a single dataset\n",
+    "    if iCase == 0:\n",
+    "        couplingIndex_DS = couplingIndex_case\n",
+    "        del couplingIndex_case\n",
+    "    else:\n",
+    "        couplingIndex_DS = uxr.concat([couplingIndex_DS, couplingIndex_case], \"case\")\n",
+    "        del couplingIndex_case\n",
+    "\n",
+    "print(\"Coupling index is now ready to go\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9e22cd0-870e-47cb-b5c7-2e57bbd9af16",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "def make_cmap(colors, position=None, bit=False):\n",
+    "    \"\"\"\n",
+    "    make_cmap takes a list of tuples which contain RGB values. The RGB\n",
+    "    values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
+    "    True when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
+    "    a cmap with equally spaced colors.\n",
+    "    Arrange your tuples so that the first color is the lowest value for the\n",
+    "    colorbar and the last is the highest.\n",
+    "    position contains values from 0 to 1 to dictate the location of each color.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    import matplotlib as mpl\n",
+    "    import numpy as np\n",
+    "\n",
+    "    bit_rgb = np.linspace(0, 1, 256)\n",
+    "    if position == None:\n",
+    "        position = np.linspace(0, 1, len(colors))\n",
+    "    else:\n",
+    "        if len(position) != len(colors):\n",
+    "            sys.exit(\"position length must be the same as colors\")\n",
+    "        elif position[0] != 0 or position[-1] != 1:\n",
+    "            sys.exit(\"position must start with 0 and end with 1\")\n",
+    "\n",
+    "    if bit:\n",
+    "        for i in range(len(colors)):\n",
+    "            colors[i] = (\n",
+    "                bit_rgb[colors[i][0]],\n",
+    "                bit_rgb[colors[i][1]],\n",
+    "                bit_rgb[colors[i][2]],\n",
+    "            )\n",
+    "\n",
+    "    cdict = {\"red\": [], \"green\": [], \"blue\": []}\n",
+    "    for pos, color in zip(position, colors):\n",
+    "        cdict[\"red\"].append((pos, color[0], color[0]))\n",
+    "        cdict[\"green\"].append((pos, color[1], color[1]))\n",
+    "        cdict[\"blue\"].append((pos, color[2], color[2]))\n",
+    "\n",
+    "    cmap = mpl.colors.LinearSegmentedColormap(\"my_colormap\", cdict, 256)\n",
+    "    return cmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e3e7b6f-98a8-4e21-8378-adfd8b399823",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "### Create a list of RGB tuples for terrestrial leg (SM, SHFLX)\n",
+    "colorsList_SMvSHF = [\n",
+    "    (124, 135, 181),\n",
+    "    (107, 109, 161),\n",
+    "    (51, 82, 120),\n",
+    "    (49, 114, 127),\n",
+    "    (97, 181, 89),\n",
+    "    (200, 218, 102),\n",
+    "    (255, 242, 116),\n",
+    "    (238, 164, 58),\n",
+    "]  # This example uses the 8-bit RGB\n",
+    "my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "882b1c92-e5b9-4cd0-aa55-4e04aca3c50c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "def plotTCI_case(seasonstr, caseSel=None):\n",
+    "\n",
+    "    transform = ccrs.PlateCarree()\n",
+    "    projection = ccrs.PlateCarree()\n",
+    "\n",
+    "    if caseSel:\n",
+    "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
+    "        collection = (\n",
+    "            couplingIndex_DS[\"CouplingIndex\"]\n",
+    "            .sel(season=seasonstr)\n",
+    "            .isel(case=caseSel)\n",
+    "            .to_polycollection()\n",
+    "        )\n",
+    "\n",
+    "        collection.set_transform(transform)\n",
+    "        collection.set_antialiased(False)\n",
+    "        collection.set_cmap(my_cmap_SMvSHF)\n",
+    "        collection.set_clim(vmin=-20, vmax=5)\n",
+    "\n",
+    "        fig, ax = plt.subplots(\n",
+    "            1,\n",
+    "            1,\n",
+    "            figsize=(12, 5),\n",
+    "            facecolor=\"w\",\n",
+    "            constrained_layout=True,\n",
+    "            subplot_kw=dict(projection=projection),\n",
+    "        )\n",
+    "\n",
+    "        ax.coastlines()\n",
+    "        ax.add_collection(collection)\n",
+    "        ax.set_global()\n",
+    "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
+    "        ax.set_title(\n",
+    "            seasonstr\n",
+    "            + \" Coupling Index: \"\n",
+    "            + str(couplingIndex_DS.case.isel(case=caseSel).values)\n",
+    "        )\n",
+    "\n",
+    "        plt.show()\n",
+    "        plt.close()\n",
+    "\n",
+    "    else:\n",
+    "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
+    "        collection = (\n",
+    "            couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).to_polycollection()\n",
+    "        )\n",
+    "\n",
+    "        collection.set_transform(transform)\n",
+    "        collection.set_antialiased(False)\n",
+    "        collection.set_cmap(my_cmap_SMvSHF)\n",
+    "        collection.set_clim(vmin=-20, vmax=5)\n",
+    "\n",
+    "        fig, ax = plt.subplots(\n",
+    "            1,\n",
+    "            1,\n",
+    "            figsize=(12, 5),\n",
+    "            facecolor=\"w\",\n",
+    "            constrained_layout=True,\n",
+    "            subplot_kw=dict(projection=projection),\n",
+    "        )\n",
+    "\n",
+    "        ax.coastlines()\n",
+    "        ax.add_collection(collection)\n",
+    "        ax.set_global()\n",
+    "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
+    "        ax.set_title(\n",
+    "            seasonstr + \" Coupling Index: \" + str(couplingIndex_DS.case.values)\n",
+    "        )\n",
+    "\n",
+    "        if fluxnet_comparison == True:\n",
+    "            ## Add FLUXNET obs\n",
+    "            iSeason = np.where(seasons == seasonstr)[0]\n",
+    "            iStations = np.where(\n",
+    "                np.isfinite(terraCI_fluxnetConverted[:, iSeason]) == True\n",
+    "            )[0]\n",
+    "            norm_CI = matplotlib.colors.Normalize(vmin=-20, vmax=5)\n",
+    "\n",
+    "            ax.scatter(\n",
+    "                lon_fluxnet[iStations],\n",
+    "                lat_fluxnet[iStations],\n",
+    "                c=terraCI_fluxnetConverted[iStations, iSeason],\n",
+    "                cmap=my_cmap_SMvSHF,\n",
+    "                norm=norm_CI,\n",
+    "                edgecolor=\"k\",\n",
+    "                s=30,\n",
+    "                marker=\"o\",\n",
+    "                transform=ccrs.PlateCarree(),\n",
+    "            )\n",
+    "\n",
+    "        plt.show()\n",
+    "        plt.close()\n",
+    "\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d43b6525-aa30-47a4-8b66-ca70a805a13a",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "if len(caseNames) == 1:\n",
+    "    plotTCI_case(\"JJA\", None)\n",
+    "    plotTCI_case(\"DJF\", None)\n",
+    "else:\n",
+    "    for iCase in range(len(caseNames)):\n",
+    "        plotTCI_case(\"JJA\", iCase)\n",
+    "        plotTCI_case(\"DJF\", iCase)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:cupid-analysis]",
+   "language": "python",
+   "name": "conda-env-cupid-analysis-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ccd80b72737bb52d07db02d3a47d4731dcc3f472 Mon Sep 17 00:00:00 2001
From: Meg Fowler <mdfowler@ucar.edu>
Date: Mon, 18 Nov 2024 12:44:40 -0700
Subject: [PATCH 4/5] Reduce imports and add to website

---
 examples/key_metrics/config.yml               |   8 +-
 ...strialCouplingIndex_VisualCompareObs.ipynb |   5 +-
 .../lnd/LandAtm_CouplingIndex_V2.ipynb        | 975 ------------------
 examples/nblibrary/lnd/image.png              | Bin 0 -> 39179 bytes
 4 files changed, 5 insertions(+), 983 deletions(-)
 delete mode 100755 examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb
 create mode 100644 examples/nblibrary/lnd/image.png

diff --git a/examples/key_metrics/config.yml b/examples/key_metrics/config.yml
index 66584a9..aa1fd75 100644
--- a/examples/key_metrics/config.yml
+++ b/examples/key_metrics/config.yml
@@ -143,7 +143,7 @@ compute_notebooks:
 #            nyears: 25
 
     lnd:
-      LandAtm_CouplingIndex_V2:
+      Global_TerrestrialCouplingIndex_VisualCompareObs:
         parameter_groups:
           none:
             clmFile_h: '.h0.'
@@ -194,9 +194,9 @@ book_toc:
     #   chapters:
     #       - file: ocn/ocean_surface
 
-    # - caption: Land
-    #   chapters:
-    #     - file: lnd/land_comparison
+    - caption: Land
+      chapters:
+        - file: lnd/Global_TerrestrialCouplingIndex_VisualCompareObs
 
     # - caption: Sea Ice
     #   chapters:
diff --git a/examples/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb b/examples/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb
index 826a2f0..8ae658e 100755
--- a/examples/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb
+++ b/examples/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb
@@ -37,16 +37,13 @@
     "import glob\n",
     "import numpy as np\n",
     "import xarray as xr\n",
-    "import datetime\n",
-    "from datetime import date, timedelta\n",
-    "import dask\n",
+    "from datetime import timedelta\n",
     "import pandas as pd\n",
     "import sys\n",
     "\n",
     "# Plotting utils\n",
     "import matplotlib\n",
     "import matplotlib.pyplot as plt\n",
-    "import cartopy\n",
     "import cartopy.crs as ccrs\n",
     "import uxarray as uxr"
    ]
diff --git a/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb b/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb
deleted file mode 100755
index 826a2f0..0000000
--- a/examples/nblibrary/lnd/LandAtm_CouplingIndex_V2.ipynb
+++ /dev/null
@@ -1,975 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "99564fab-c321-4116-8229-b16eefa1536e",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "# Compute land-atmosphere coupling indices \n",
-    "This notebook takes in a series of CESM simulations, computes the land-atmosphere coupling index (CI; \n",
-    "terrestrial leg only currently), and plots those seasonal means. <br>\n",
-    "- Note: Built to use monthly output; ideally, CI should be based on daily data. \n",
-    "- Optional: Comparison against FLUXNET obs\n",
-    "<br><br>\n",
-    "Notebook created by mdfowler@ucar.edu; Last update: 2 Aug 2024 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "750da831-1c5c-4b41-947e-a9e57a62a820",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import glob\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import datetime\n",
-    "from datetime import date, timedelta\n",
-    "import dask\n",
-    "import pandas as pd\n",
-    "import sys\n",
-    "\n",
-    "# Plotting utils\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy\n",
-    "import cartopy.crs as ccrs\n",
-    "import uxarray as uxr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "774bd269-ce50-4449-b32f-83246b74b73c",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "## 1. Modify this section for each run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7f8f2d17-c653-4ad1-9dc3-c49bf836ceb6",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "parameters"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "##  Settings for case locations + names\n",
-    "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "## Where observations are stored\n",
-    "# obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data\n",
-    "\n",
-    "## Where CESM timeseries data is stored\n",
-    "CESM_output_dir = \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/\"\n",
-    "\n",
-    "\n",
-    "## Full casenames that are present in CESM_output_dir and in individual filenames\n",
-    "# caseNames = [\n",
-    "#             'b.e23_alpha16b.BLT1850.ne30_t232.054',\n",
-    "# 'b.e30_beta02.BLT1850.ne30_t232.104',\n",
-    "#             ]\n",
-    "case_name = \"b.e30_beta02.BLT1850.ne30_t232.104\"\n",
-    "\n",
-    "# clmFile_h = '.h0.'\n",
-    "\n",
-    "start_date = \"0001-01-01\"\n",
-    "end_date = \"0101-01-01\"\n",
-    "\n",
-    "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "##  Optional settings for notebook\n",
-    "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "\n",
-    "## If comparison against FLUXNET desired\n",
-    "# fluxnet_comparison = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0014712f-d094-4dae-b583-740bf7a9789c",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "##  Settings for computing coupling index\n",
-    "## - - - - - - - - - - - - - - - - - - - - - -\n",
-    "startYrs = [start_date.split(\"-\")[0]]\n",
-    "endYrs = [f\"{int(end_date.split('-')[0])-1:04d}\"]\n",
-    "\n",
-    "caseNames = [\n",
-    "    case_name,\n",
-    "    # base_case_name,\n",
-    "]\n",
-    "\n",
-    "shortNames = [case.split(\".\")[-1] for case in caseNames]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d70024c7-0af2-48b9-9041-893f40e613ec",
-   "metadata": {},
-   "source": [
-    "## 2. Read in model data and compute terrestrial coupling index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "304ce8d0-6aab-4fcb-9635-2a78e270f3c7",
-   "metadata": {
-    "editable": true,
-    "scrolled": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "Inputs: xname -- controlling variable \n",
-    "        yname -- responding variable\n",
-    "        ds    -- dataset containing xname and yname data \n",
-    "        \n",
-    "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
-    "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "def compute_couplingIndex_cesm(xname, yname, xDS, yDS):\n",
-    "    xday = xDS[xname].groupby(\"time.season\")\n",
-    "    yday = yDS[yname].groupby(\"time.season\")\n",
-    "\n",
-    "    # Get the covariance of the two (numerator in coupling index)\n",
-    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
-    "        \"time.season\"\n",
-    "    ).sum() / xday.count()\n",
-    "\n",
-    "    # Now compute the actual coupling index\n",
-    "    couplingIndex = covarTerm / xday.std()\n",
-    "\n",
-    "    return couplingIndex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3c35ae8d-dfff-44b0-a854-dfc2b5b030c0",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "for iCase in range(len(caseNames)):\n",
-    "    ## Check first if coupling index has already been created:\n",
-    "    TCI_filePath = (\n",
-    "        \"/glade/derecho/scratch/mdfowler/\"\n",
-    "        + caseNames[0]\n",
-    "        + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
-    "    )\n",
-    "\n",
-    "    if os.path.exists(TCI_filePath):  # Use previously computed TCI\n",
-    "        print(\"Using previously computed coupling index saved in file \", TCI_filePath)\n",
-    "    else:  # Compute TCI\n",
-    "\n",
-    "        # Get list of necessary time series files\n",
-    "        soilWater_file = np.sort(\n",
-    "            glob.glob(\n",
-    "                CESM_output_dir\n",
-    "                + \"/\"\n",
-    "                + caseNames[iCase]\n",
-    "                + \"/lnd/proc/tseries/\"\n",
-    "                + caseNames[iCase]\n",
-    "                + clmFile_h\n",
-    "                + \"SOILWATER_10CM.\"\n",
-    "                + startYrs[iCase]\n",
-    "                + \"??-\"\n",
-    "                + endYrs[iCase]\n",
-    "                + \"??.nc\"\n",
-    "            )\n",
-    "        )\n",
-    "        if len(soilWater_file) == 0:\n",
-    "            print(\"Soil moisture file not found!\")\n",
-    "        elif len(soilWater_file) > 1:\n",
-    "            print(\n",
-    "                \"More than one file matches requested time period and case for soil moisture.\"\n",
-    "            )\n",
-    "        elif len(soilWater_file) == 1:\n",
-    "            soilWater_DS = xr.open_dataset(soilWater_file[0], decode_times=True)\n",
-    "\n",
-    "        sh_file = np.sort(\n",
-    "            glob.glob(\n",
-    "                CESM_output_dir\n",
-    "                + \"/\"\n",
-    "                + caseNames[iCase]\n",
-    "                + \"/lnd/proc/tseries/\"\n",
-    "                + caseNames[iCase]\n",
-    "                + clmFile_h\n",
-    "                + \"FSH_TO_COUPLER.\"\n",
-    "                + startYrs[iCase]\n",
-    "                + \"??-\"\n",
-    "                + endYrs[iCase]\n",
-    "                + \"??.nc\"\n",
-    "            )\n",
-    "        )\n",
-    "        if len(sh_file) == 0:\n",
-    "            print(\"Land-based SHFLX file not found!\")\n",
-    "        elif len(sh_file) > 1:\n",
-    "            print(\"More than one file matches requested time period and case for SH.\")\n",
-    "        elif len(sh_file) == 1:\n",
-    "            shflx_DS = xr.open_dataset(sh_file[0])\n",
-    "\n",
-    "        # If years start at 0000, offset by 1700 years for analysis\n",
-    "        yrOffset = 1850\n",
-    "        if shflx_DS[\"time.year\"].values[0] < 1500:\n",
-    "            shflx_DS[\"time\"] = shflx_DS.time + timedelta(days=yrOffset * 365)\n",
-    "        if soilWater_DS[\"time.year\"].values[0] < 1500:\n",
-    "            soilWater_DS[\"time\"] = soilWater_DS.time + timedelta(days=yrOffset * 365)\n",
-    "        # Convert times to datetime for easier use\n",
-    "        shflx_DS[\"time\"] = shflx_DS.indexes[\"time\"].to_datetimeindex()\n",
-    "        soilWater_DS[\"time\"] = soilWater_DS.indexes[\"time\"].to_datetimeindex()\n",
-    "\n",
-    "        # Add case ID (short name) to the DS\n",
-    "        shflx_DS = shflx_DS.assign_coords({\"case\": shortNames[iCase]})\n",
-    "        soilWater_DS = soilWater_DS.assign_coords({\"case\": shortNames[iCase]})\n",
-    "\n",
-    "        ## Compute coupling index and save to netCDF file\n",
-    "        ## - - - - - - - - - - - - - - - - - - - - - - - - -\n",
-    "        xname = \"SOILWATER_10CM\"  # Controlling variable\n",
-    "        yname = \"FSH_TO_COUPLER\"  # Responding variable\n",
-    "\n",
-    "        xDS = soilWater_DS\n",
-    "        yDS = shflx_DS\n",
-    "\n",
-    "        couplingInd = compute_couplingIndex_cesm(xname, yname, xDS, yDS)\n",
-    "\n",
-    "        filePath = (\n",
-    "            \"/glade/derecho/scratch/mdfowler/\"\n",
-    "            + caseNames[0]\n",
-    "            + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
-    "        )\n",
-    "        couplingInd.to_netcdf(filePath)\n",
-    "        print(\"File created: \", filePath)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f8fba2a-98d2-4e94-9d71-3b2625e16032",
-   "metadata": {},
-   "source": [
-    "### 1.1 Read in FLUXNET data if requested"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fdf20c82-5a01-4ab9-8881-d9388b1b2356",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
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-    },
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-   "source": [
-    "# --------------------------------------------------------\n",
-    "# Function to read requested variables from FLUXNET file.\n",
-    "# - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
-    "#\n",
-    "# Inputs:  fileName --> Full path to FLUXNET data file\n",
-    "#          varNames --> An array of variable names to be\n",
-    "#                       retrieved from said data file.\n",
-    "#                       NOTE: If you wish to retrieve *all*\n",
-    "#                       variables, pass the string 'ALL'.\n",
-    "#\n",
-    "# Outputs: fluxnetID --> ID string used to identify station\n",
-    "#          fluxnetDS --> An x-array dataset containing the\n",
-    "#                        requested variables.\n",
-    "#                        Missing values will be set to NaN.\n",
-    "#\n",
-    "# --------------------------------------------------------\n",
-    "\n",
-    "\n",
-    "def readFLUXNET_var(fileName, varNames):\n",
-    "    # Get ID of station\n",
-    "    startID = fileName.find(\"FLX_\")\n",
-    "    fluxnetID = fileName[startID + 4 : startID + 10]\n",
-    "\n",
-    "    # If this is taking a long time or you just want to know where in the stations you are, uncomment print statement\n",
-    "    # print('Reading in site - ', fluxnetID)\n",
-    "\n",
-    "    # Read in CSV file containing data\n",
-    "    dataDF = pd.read_csv(fileName)\n",
-    "\n",
-    "    # Return ALL variables from dataDF if requested\n",
-    "    if varNames == \"ALL\":\n",
-    "        fluxnetDF = dataDF\n",
-    "\n",
-    "        # Set any value that's missing to NaN (not -9999)\n",
-    "        fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
-    "\n",
-    "        # If time has been requested, reformat to pandas date index\n",
-    "        fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
-    "            fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
-    "        )\n",
-    "        fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
-    "\n",
-    "        # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
-    "        #   NOTE: since current implementation doesn't use the pre-formatted CoMeT, might not need this step now\n",
-    "        fluxnetDS = fluxnetDF.to_xarray()\n",
-    "\n",
-    "    # Reduce returned DF to contain only variables of interest\n",
-    "    else:\n",
-    "\n",
-    "        # Check that requested variables are available in specific file\n",
-    "        errCount = 0  # Initialize flag for error\n",
-    "        colNames = dataDF.columns.values  # Available variables in file\n",
-    "\n",
-    "        for iVar in range(len(varNames)):  # Check each variable individually\n",
-    "            if (varNames[iVar] in colNames) == False:\n",
-    "                # Turn on print statement for more verbose output\n",
-    "                # print('** ERROR: %13s   not contained in file for %8s  **' %(varNames[iVar], fluxnetID))\n",
-    "\n",
-    "                # If any variable is not conatined in file, return a NaN\n",
-    "                fluxnetDS = -999\n",
-    "                errCount = errCount + 1\n",
-    "\n",
-    "        # If all the variables *are* available, isolate those in DF and return that\n",
-    "        if errCount == 0:\n",
-    "            fluxnetDF = dataDF[varNames]\n",
-    "\n",
-    "            # Set any value that's missing to NaN (not -999)\n",
-    "            fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
-    "\n",
-    "            # If time has been requested, reformat to pandas make index\n",
-    "            if (\"TIMESTAMP\" in varNames) == True:\n",
-    "                fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
-    "                    fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
-    "                )\n",
-    "                fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
-    "\n",
-    "            # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
-    "            fluxnetDS = fluxnetDF.to_xarray()\n",
-    "\n",
-    "    return (fluxnetID, fluxnetDS)\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "Inputs: xname -- controlling variable \n",
-    "        yname -- responding variable\n",
-    "        ds    -- dataset containing xname and yname data \n",
-    "        \n",
-    "This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
-    "        https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "def compute_couplingIndex_FLUXNET(xname, yname, ds):\n",
-    "    xday = ds[xname].groupby(\"TIMESTAMP.season\")\n",
-    "    yday = ds[yname].groupby(\"TIMESTAMP.season\")\n",
-    "\n",
-    "    # Get the covariance of the two (numerator in coupling index)\n",
-    "    covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
-    "        \"TIMESTAMP.season\"\n",
-    "    ).sum() / xday.count()\n",
-    "\n",
-    "    # Now compute the actual coupling index\n",
-    "    couplingIndex = covarTerm / xday.std()\n",
-    "\n",
-    "    return couplingIndex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9acb032f-f106-4783-8c57-8e0d3a44eb3e",
-   "metadata": {
-    "editable": true,
-    "scrolled": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input",
-     "hide-output"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "if fluxnet_comparison == True:\n",
-    "    # obsDir   = '/glade/campaign/cgd/tss/people/mdfowler/FLUXNET2015/' ## Need to copy into CUPiD Data\n",
-    "\n",
-    "    ## Metadata files\n",
-    "    siteInfoFile = obsDir + \"SiteList.csv\"\n",
-    "    siteInfoDF = pd.read_csv(siteInfoFile)\n",
-    "\n",
-    "    metadataFile = obsDir + \"FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.csv\"\n",
-    "    metadataDF = pd.read_csv(metadataFile)\n",
-    "\n",
-    "    ## List of all station files\n",
-    "    dataFiles = glob.glob(obsDir + \"FLX_*/*SUBSET_DD*\")\n",
-    "\n",
-    "    # Set up a few empty arrays to save data into\n",
-    "    terraCI_fluxnetConverted = np.full(\n",
-    "        [len(dataFiles), 4], np.nan\n",
-    "    )  # CI using kg/m2 soil water content  [nStations, seasons]\n",
-    "\n",
-    "    # Also save out some data on each station\n",
-    "    startTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
-    "    endTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
-    "    lat_fluxnet = np.full([len(dataFiles)], np.nan)\n",
-    "    lon_fluxnet = np.full([len(dataFiles)], np.nan)\n",
-    "    SWCdepth = np.full([len(dataFiles)], np.nan)\n",
-    "\n",
-    "    stationID = []\n",
-    "    stationID_converted = []\n",
-    "\n",
-    "    allStationID = []\n",
-    "\n",
-    "    # Variables I want returned:\n",
-    "    varNames = [\"TIMESTAMP\", \"H_F_MDS\", \"SWC_F_MDS_1\", \"SWC_F_MDS_1_QC\"]\n",
-    "\n",
-    "    # Loop over each station (data file)\n",
-    "    for iStation in range(len(dataFiles)):\n",
-    "\n",
-    "        # Read in data\n",
-    "        # ----------------------------------------------------------\n",
-    "        fluxnetID, fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
-    "\n",
-    "        # Save lat and lon for this station\n",
-    "        # ----------------------------------------------------------\n",
-    "        indStation = int(np.where(fluxnetID == siteInfoDF[\"SITE_ID\"])[0][0])\n",
-    "        lat_fluxnet[iStation] = siteInfoDF[\"LOCATION_LAT\"].values[indStation]\n",
-    "        lon_fluxnet[iStation] = siteInfoDF[\"LOCATION_LONG\"].values[indStation]\n",
-    "        allStationID.append(fluxnetID)\n",
-    "\n",
-    "        # Check that there was data saved for this particular site:\n",
-    "        # ----------------------------------------------------------\n",
-    "        if type(fluxnetDS) == int:\n",
-    "            print(\"No data for station: %8s\" % fluxnetID)\n",
-    "\n",
-    "        elif (np.all(np.isnan(fluxnetDS[\"H_F_MDS\"])) == True) | (\n",
-    "            np.all(np.isnan(fluxnetDS[\"SWC_F_MDS_1\"])) == True\n",
-    "        ):\n",
-    "            print(\"No data for station: %8s\" % fluxnetID)\n",
-    "\n",
-    "        # If data is present:\n",
-    "        # ----------------------------------------------------------\n",
-    "        else:\n",
-    "            # Only consider where data is actually present for selected vars\n",
-    "            iReal = np.where(\n",
-    "                (np.isfinite(fluxnetDS[\"SWC_F_MDS_1\"]) == True)\n",
-    "                & (np.isfinite(fluxnetDS[\"H_F_MDS\"]) == True)\n",
-    "            )[0]\n",
-    "            fluxnetDS = fluxnetDS.isel(TIMESTAMP=iReal)\n",
-    "\n",
-    "            stationID.append(fluxnetID)\n",
-    "\n",
-    "            # Convert units from volumetric (%) to mass (kg/m2)\n",
-    "            # -------------------------------------------------\n",
-    "            #   Step 1: Convert from % to fraction\n",
-    "            fracSM = (fluxnetDS[\"SWC_F_MDS_1\"].values) / 100.0\n",
-    "\n",
-    "            #    Step 2: Need to use depth of obs in conversion\n",
-    "            metaData_station = metadataDF[metadataDF.SITE_ID == fluxnetID]\n",
-    "            iSWC = np.where(metaData_station.DATAVALUE == \"SWC_F_MDS_1\")[0]\n",
-    "            #  Some locations (5) have two depths\n",
-    "            if len(iSWC) > 1:\n",
-    "                for iDepth in range(len(iSWC)):\n",
-    "                    SWC_DF = metaData_station[iSWC[iDepth] : iSWC[iDepth] + 4]\n",
-    "\n",
-    "                    depth = np.asarray(\n",
-    "                        SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
-    "                    ).astype(float)\n",
-    "                    depthDay = np.asarray(\n",
-    "                        SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_DATE\"].DATAVALUE.values[0]\n",
-    "                    ).astype(int)\n",
-    "                    depthDay = int(\n",
-    "                        str(depthDay)[:8]\n",
-    "                    )  # Some weird ones have time attached; don't want that\n",
-    "                    depthDay = pd.to_datetime(depthDay, format=\"%Y%m%d\")\n",
-    "\n",
-    "                    # Keep deepest level as the depth for station\n",
-    "                    if iDepth == 0:\n",
-    "                        SWCdepth[iStation] = depth\n",
-    "                        convertSM = fracSM * 1000.0 * np.abs(depth)\n",
-    "                    else:\n",
-    "                        # Use date as break point for getting kg/m2 SWC\n",
-    "                        #  Eq: SWC_kgm2 = SWC_vol [m3/m3] * 1000 [kg/m3] * depth [m]\n",
-    "                        dateArr = pd.DatetimeIndex(fluxnetDS.TIMESTAMP.values)\n",
-    "                        iTime = int(np.where(dateArr == depthDay)[0][0])\n",
-    "                        convertSM[iTime::] = (fracSM[iTime::]) * 1000.0 * np.abs(depth)\n",
-    "\n",
-    "                        # Keep deepest level as the depth for station\n",
-    "                        if depth < SWCdepth[iStation]:\n",
-    "                            SWCdepth[iStation] = depth\n",
-    "\n",
-    "                stationID_converted.append(fluxnetID)\n",
-    "\n",
-    "            # If station only has one level recorded, things are a bit easier:\n",
-    "            else:\n",
-    "                SWC_DF = metaData_station[iSWC[0] : iSWC[0] + 4]\n",
-    "                SWCdepth[iStation] = np.asarray(\n",
-    "                    SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
-    "                ).astype(float)\n",
-    "                convertSM = fracSM * 1000.0 * np.abs(SWCdepth[iStation])\n",
-    "\n",
-    "                stationID_converted.append(fluxnetID)\n",
-    "\n",
-    "            # Save converted soil moisture to dataset\n",
-    "            fluxnetDS[\"SWC_F_MDS_1_convert\"] = ((\"TIMESTAMP\"), convertSM)\n",
-    "\n",
-    "            # Save first and last time used for computing CI\n",
-    "            # ----------------------------------------------\n",
-    "            startTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[0]\n",
-    "            endTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[-1]\n",
-    "\n",
-    "            # Compute terrestrial coupling metric\n",
-    "            # -----------------------------------\n",
-    "            terraLeg = compute_couplingIndex_FLUXNET(\n",
-    "                \"SWC_F_MDS_1_convert\", \"H_F_MDS\", fluxnetDS\n",
-    "            )\n",
-    "\n",
-    "            # If there's less than one full year of data, don't use station\n",
-    "            #   (i.e., as long as all 4 seasons are defined, save values)\n",
-    "            if np.shape(terraLeg)[0] == 4:\n",
-    "                terraCI_fluxnetConverted[iStation, :] = terraLeg\n",
-    "\n",
-    "    seasons = terraLeg.season\n",
-    "\n",
-    "    ## Print some useful information\n",
-    "    print(\n",
-    "        \"Number of FLUXNET stations with CI calculated: %i\"\n",
-    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
-    "    )\n",
-    "\n",
-    "    # How many months go into each calculation of CI for JJA?\n",
-    "    nMonths = np.full([len(dataFiles)], np.nan)\n",
-    "\n",
-    "    for iSt in range(len(dataFiles)):\n",
-    "        if np.isfinite(terraCI_fluxnetConverted[iSt, 1]):\n",
-    "            dateRange = pd.date_range(\n",
-    "                start=startTime_fluxnet[iSt], end=endTime_fluxnet[iSt], freq=\"ME\"\n",
-    "            )\n",
-    "            nMonths[iSt] = len(\n",
-    "                np.where((dateRange.month >= 6) & (dateRange.month <= 8))[0]\n",
-    "            )\n",
-    "\n",
-    "    print(\n",
-    "        \"Minimum number of months used for JJA mean CI: %i \" % int(np.nanmin(nMonths))\n",
-    "    )\n",
-    "    print(\n",
-    "        \"Maximum number of months used for JJA mean CI: %i \" % int(np.nanmax(nMonths))\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd29ba9d-bbbb-4f59-9829-250018215b53",
-   "metadata": {},
-   "source": [
-    "*Make some choices on limiting which stations are used*\n",
-    "- Let's limit usage to depths less than 20 cm (arbitrary, but I don't want us using non-surface soil moisture for this application). This will eliminate 11 stations.\n",
-    "- It would also be good to put some time limits on this. So let's say the observations need to have at least 9 months of data for JJA means (3-years). Otherwise, set terraCI to np.nan again so we don't use it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13afbfb4-2040-403c-9d82-3e370f47ec5d",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "if fluxnet_comparison == True:\n",
-    "\n",
-    "    # Get stations with SWC from below 20 cm (or equal to zero)\n",
-    "    iLimit = np.where((SWCdepth == 0.0) | (SWCdepth < -0.2))[0]\n",
-    "\n",
-    "    # Set the terrestrial leg of CI to missing so we don't consider those\n",
-    "    terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
-    "\n",
-    "    print(\n",
-    "        \"Number of FLUXNET stations to use with reasonable depths of SWC: %i\"\n",
-    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
-    "    )\n",
-    "\n",
-    "    # Get stations with less than 9 months used for JJA terrestrial CI\n",
-    "    iLimit = np.where(nMonths < 9)[0]\n",
-    "\n",
-    "    # Set to missing so we don't consider stations with less than three years of data going into the average\n",
-    "    terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
-    "\n",
-    "    print(\n",
-    "        \"Number of FLUXNET stations to use with 3+ years of JJA data: %i\"\n",
-    "        % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc387253-cdd7-4a36-956b-8ce548e963bd",
-   "metadata": {},
-   "source": [
-    "## 2. Make plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "67253fd1-d2f7-45fe-a59f-215303b93c06",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "## Load coupling index with uxarray\n",
-    "gridFile = (\n",
-    "    \"/glade/p/cesmdata/cseg/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc\"\n",
-    ")\n",
-    "uxgrid = uxr.open_grid(gridFile)\n",
-    "uxgrid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "43206b67-1313-4b61-94ea-b50b85a3d50c",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "for iCase in range(len(caseNames)):\n",
-    "    filePath = (\n",
-    "        \"/glade/derecho/scratch/mdfowler/\"\n",
-    "        + caseNames[iCase]\n",
-    "        + \"_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
-    "    )\n",
-    "    couplingIndex_case = uxr.open_dataset(gridFile, filePath)\n",
-    "    # Rename the variable:\n",
-    "    couplingIndex_case = couplingIndex_case.rename(\n",
-    "        {\"__xarray_dataarray_variable__\": \"CouplingIndex\"}\n",
-    "    )\n",
-    "\n",
-    "    # Assign case coord\n",
-    "    couplingIndex_case = couplingIndex_case.assign_coords(\n",
-    "        {\"case\": couplingIndex_case.case.values}\n",
-    "    )\n",
-    "\n",
-    "    # Return all the cases in a single dataset\n",
-    "    if iCase == 0:\n",
-    "        couplingIndex_DS = couplingIndex_case\n",
-    "        del couplingIndex_case\n",
-    "    else:\n",
-    "        couplingIndex_DS = uxr.concat([couplingIndex_DS, couplingIndex_case], \"case\")\n",
-    "        del couplingIndex_case\n",
-    "\n",
-    "print(\"Coupling index is now ready to go\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9e22cd0-870e-47cb-b5c7-2e57bbd9af16",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "def make_cmap(colors, position=None, bit=False):\n",
-    "    \"\"\"\n",
-    "    make_cmap takes a list of tuples which contain RGB values. The RGB\n",
-    "    values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
-    "    True when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
-    "    a cmap with equally spaced colors.\n",
-    "    Arrange your tuples so that the first color is the lowest value for the\n",
-    "    colorbar and the last is the highest.\n",
-    "    position contains values from 0 to 1 to dictate the location of each color.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    import matplotlib as mpl\n",
-    "    import numpy as np\n",
-    "\n",
-    "    bit_rgb = np.linspace(0, 1, 256)\n",
-    "    if position == None:\n",
-    "        position = np.linspace(0, 1, len(colors))\n",
-    "    else:\n",
-    "        if len(position) != len(colors):\n",
-    "            sys.exit(\"position length must be the same as colors\")\n",
-    "        elif position[0] != 0 or position[-1] != 1:\n",
-    "            sys.exit(\"position must start with 0 and end with 1\")\n",
-    "\n",
-    "    if bit:\n",
-    "        for i in range(len(colors)):\n",
-    "            colors[i] = (\n",
-    "                bit_rgb[colors[i][0]],\n",
-    "                bit_rgb[colors[i][1]],\n",
-    "                bit_rgb[colors[i][2]],\n",
-    "            )\n",
-    "\n",
-    "    cdict = {\"red\": [], \"green\": [], \"blue\": []}\n",
-    "    for pos, color in zip(position, colors):\n",
-    "        cdict[\"red\"].append((pos, color[0], color[0]))\n",
-    "        cdict[\"green\"].append((pos, color[1], color[1]))\n",
-    "        cdict[\"blue\"].append((pos, color[2], color[2]))\n",
-    "\n",
-    "    cmap = mpl.colors.LinearSegmentedColormap(\"my_colormap\", cdict, 256)\n",
-    "    return cmap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e3e7b6f-98a8-4e21-8378-adfd8b399823",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "### Create a list of RGB tuples for terrestrial leg (SM, SHFLX)\n",
-    "colorsList_SMvSHF = [\n",
-    "    (124, 135, 181),\n",
-    "    (107, 109, 161),\n",
-    "    (51, 82, 120),\n",
-    "    (49, 114, 127),\n",
-    "    (97, 181, 89),\n",
-    "    (200, 218, 102),\n",
-    "    (255, 242, 116),\n",
-    "    (238, 164, 58),\n",
-    "]  # This example uses the 8-bit RGB\n",
-    "my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "882b1c92-e5b9-4cd0-aa55-4e04aca3c50c",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "def plotTCI_case(seasonstr, caseSel=None):\n",
-    "\n",
-    "    transform = ccrs.PlateCarree()\n",
-    "    projection = ccrs.PlateCarree()\n",
-    "\n",
-    "    if caseSel:\n",
-    "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
-    "        collection = (\n",
-    "            couplingIndex_DS[\"CouplingIndex\"]\n",
-    "            .sel(season=seasonstr)\n",
-    "            .isel(case=caseSel)\n",
-    "            .to_polycollection()\n",
-    "        )\n",
-    "\n",
-    "        collection.set_transform(transform)\n",
-    "        collection.set_antialiased(False)\n",
-    "        collection.set_cmap(my_cmap_SMvSHF)\n",
-    "        collection.set_clim(vmin=-20, vmax=5)\n",
-    "\n",
-    "        fig, ax = plt.subplots(\n",
-    "            1,\n",
-    "            1,\n",
-    "            figsize=(12, 5),\n",
-    "            facecolor=\"w\",\n",
-    "            constrained_layout=True,\n",
-    "            subplot_kw=dict(projection=projection),\n",
-    "        )\n",
-    "\n",
-    "        ax.coastlines()\n",
-    "        ax.add_collection(collection)\n",
-    "        ax.set_global()\n",
-    "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
-    "        ax.set_title(\n",
-    "            seasonstr\n",
-    "            + \" Coupling Index: \"\n",
-    "            + str(couplingIndex_DS.case.isel(case=caseSel).values)\n",
-    "        )\n",
-    "\n",
-    "        plt.show()\n",
-    "        plt.close()\n",
-    "\n",
-    "    else:\n",
-    "        # create a Poly Array from a 1D slice of a face-centered data variable\n",
-    "        collection = (\n",
-    "            couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).to_polycollection()\n",
-    "        )\n",
-    "\n",
-    "        collection.set_transform(transform)\n",
-    "        collection.set_antialiased(False)\n",
-    "        collection.set_cmap(my_cmap_SMvSHF)\n",
-    "        collection.set_clim(vmin=-20, vmax=5)\n",
-    "\n",
-    "        fig, ax = plt.subplots(\n",
-    "            1,\n",
-    "            1,\n",
-    "            figsize=(12, 5),\n",
-    "            facecolor=\"w\",\n",
-    "            constrained_layout=True,\n",
-    "            subplot_kw=dict(projection=projection),\n",
-    "        )\n",
-    "\n",
-    "        ax.coastlines()\n",
-    "        ax.add_collection(collection)\n",
-    "        ax.set_global()\n",
-    "        fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
-    "        ax.set_title(\n",
-    "            seasonstr + \" Coupling Index: \" + str(couplingIndex_DS.case.values)\n",
-    "        )\n",
-    "\n",
-    "        if fluxnet_comparison == True:\n",
-    "            ## Add FLUXNET obs\n",
-    "            iSeason = np.where(seasons == seasonstr)[0]\n",
-    "            iStations = np.where(\n",
-    "                np.isfinite(terraCI_fluxnetConverted[:, iSeason]) == True\n",
-    "            )[0]\n",
-    "            norm_CI = matplotlib.colors.Normalize(vmin=-20, vmax=5)\n",
-    "\n",
-    "            ax.scatter(\n",
-    "                lon_fluxnet[iStations],\n",
-    "                lat_fluxnet[iStations],\n",
-    "                c=terraCI_fluxnetConverted[iStations, iSeason],\n",
-    "                cmap=my_cmap_SMvSHF,\n",
-    "                norm=norm_CI,\n",
-    "                edgecolor=\"k\",\n",
-    "                s=30,\n",
-    "                marker=\"o\",\n",
-    "                transform=ccrs.PlateCarree(),\n",
-    "            )\n",
-    "\n",
-    "        plt.show()\n",
-    "        plt.close()\n",
-    "\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d43b6525-aa30-47a4-8b66-ca70a805a13a",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "if len(caseNames) == 1:\n",
-    "    plotTCI_case(\"JJA\", None)\n",
-    "    plotTCI_case(\"DJF\", None)\n",
-    "else:\n",
-    "    for iCase in range(len(caseNames)):\n",
-    "        plotTCI_case(\"JJA\", iCase)\n",
-    "        plotTCI_case(\"DJF\", iCase)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:cupid-analysis]",
-   "language": "python",
-   "name": "conda-env-cupid-analysis-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
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From d76d718dbafc400df5d556c79559d1bd019f842b Mon Sep 17 00:00:00 2001
From: Meg Fowler <mdfowler@ucar.edu>
Date: Mon, 18 Nov 2024 16:07:00 -0700
Subject: [PATCH 5/5] Remove extra img file

---
 examples/nblibrary/lnd/image.png | Bin 39179 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 examples/nblibrary/lnd/image.png

diff --git a/examples/nblibrary/lnd/image.png b/examples/nblibrary/lnd/image.png
deleted file mode 100644
index 8bf3667d2cab7c4fb27a060e34b304f3ea027baf..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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