diff --git a/examples/ADCP_Delft3D_TRTS_example.ipynb b/examples/ADCP_Delft3D_TRTS_example.ipynb
index 6347594ff..fcd1ab43a 100644
--- a/examples/ADCP_Delft3D_TRTS_example.ipynb
+++ b/examples/ADCP_Delft3D_TRTS_example.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "447c788c-a042-4649-81c6-532fcd464c0e",
    "metadata": {},
    "outputs": [],
@@ -81,6 +81,88 @@
       "Reading file data/river/ADCP_transect/tanana_transects_08_10_10_0_003_10-08-10_143335.PD0 ...\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for join will change from join='outer' to join='exact'. This change will result in the following ValueError: cannot be aligned with join='exact' because index/labels/sizes are not equal along these coordinates (dimensions): 'time' ('time',) The recommendation is to set join explicitly for this case.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for join will change from join='outer' to join='exact'. This change will result in the following ValueError: cannot be aligned with join='exact' because index/labels/sizes are not equal along these coordinates (dimensions): 'time_gps' ('time_gps',) The recommendation is to set join explicitly for this case.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3829104822.py:9: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  transect_1_2 = xr.merge([transect_1_raw, transect_2_raw])\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -99,9 +181,7 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
        "  --xr-font-color0: var(\n",
@@ -180,6 +260,7 @@
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -202,11 +283,18 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
        ".xr-obj-type {\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
@@ -215,6 +303,8 @@
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -225,6 +315,7 @@
        "  display: inline-block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -283,7 +374,6 @@
        ".xr-section-summary,\n",
        ".xr-section-inline-details {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -293,6 +383,7 @@
        ".xr-section-details {\n",
        "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
@@ -300,6 +391,32 @@
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
        ".xr-array-wrap {\n",
        "  grid-column: 1 / -1;\n",
        "  display: grid;\n",
@@ -567,22 +684,22 @@
        "    serialnum:                134877318\n",
        "    source_program:           WINRIVER\n",
        "    range_offset:             0.07\n",
-       "    fs:                       1.92</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-578127cd-b7cf-4d63-91f5-72d5d43d2429' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-578127cd-b7cf-4d63-91f5-72d5d43d2429' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1227</li><li><span class='xr-has-index'>time_gps</span>: 1227</li><li><span class='xr-has-index'>range</span>: 47</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3b3ff946-efbb-4554-ad63-3bd08aa87899' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b3ff946-efbb-4554-ad63-3bd08aa87899' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T14:28:15.559999942 .....</div><input id='attrs-6f699fd2-9283-4c36-a4f9-8affda9178d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f699fd2-9283-4c36-a4f9-8affda9178d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-748cabd6-4c0c-476c-a0ea-946e8174f704' class='xr-var-data-in' type='checkbox'><label for='data-748cabd6-4c0c-476c-a0ea-946e8174f704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T14:28:15.559999942&#x27;, &#x27;2010-08-10T14:28:16.319999933&#x27;,\n",
+       "    fs:                       1.92</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-25ddd7d5-97a3-452b-873d-8d7562aba1af' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-25ddd7d5-97a3-452b-873d-8d7562aba1af' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1227</li><li><span class='xr-has-index'>time_gps</span>: 1227</li><li><span class='xr-has-index'>range</span>: 47</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2dd9418a-781e-48a0-8030-25e23e89db76' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2dd9418a-781e-48a0-8030-25e23e89db76' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T14:28:15.559999942 .....</div><input id='attrs-0e3fc279-31c1-487e-8ebc-f62fb060e5b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e3fc279-31c1-487e-8ebc-f62fb060e5b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8013ee14-69bb-44ea-83d7-e94b75ad2268' class='xr-var-data-in' type='checkbox'><label for='data-8013ee14-69bb-44ea-83d7-e94b75ad2268' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T14:28:15.559999942&#x27;, &#x27;2010-08-10T14:28:16.319999933&#x27;,\n",
        "       &#x27;2010-08-10T14:28:17.069999933&#x27;, ..., &#x27;2010-08-10T14:39:59.190000057&#x27;,\n",
        "       &#x27;2010-08-10T14:39:59.910000085&#x27;, &#x27;2010-08-10T14:40:00.630000114&#x27;],\n",
-       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T22:28:17.200000047 .....</div><input id='attrs-645c471c-2cf9-4022-9054-79bc12763726' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-645c471c-2cf9-4022-9054-79bc12763726' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d99364c0-18c3-44cb-b839-8615ddedb585' class='xr-var-data-in' type='checkbox'><label for='data-d99364c0-18c3-44cb-b839-8615ddedb585' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time gps</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T22:28:17.200000047&#x27;, &#x27;2010-08-10T22:28:18.000000000&#x27;,\n",
+       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T22:28:17.200000047 .....</div><input id='attrs-aa7540f5-9220-4476-a1cc-b62400a80541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa7540f5-9220-4476-a1cc-b62400a80541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-386fcded-2195-4942-b9dd-59c88bc81dad' class='xr-var-data-in' type='checkbox'><label for='data-386fcded-2195-4942-b9dd-59c88bc81dad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time gps</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T22:28:17.200000047&#x27;, &#x27;2010-08-10T22:28:18.000000000&#x27;,\n",
        "       &#x27;2010-08-10T22:28:18.599999904&#x27;, ..., &#x27;2010-08-10T22:40:00.799999952&#x27;,\n",
        "       &#x27;2010-08-10T22:40:01.400000095&#x27;, &#x27;2010-08-10T22:40:02.200000047&#x27;],\n",
-       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.57 0.82 1.07 ... 11.82 12.07</div><input id='attrs-9dc6856a-64b6-432c-a221-71c0d9b2f0da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dc6856a-64b6-432c-a221-71c0d9b2f0da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-922d5d6d-b4d9-4676-80c5-08c0e1695166' class='xr-var-data-in' type='checkbox'><label for='data-922d5d6d-b4d9-4676-80c5-08c0e1695166' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.57,  0.82,  1.07,  1.32,  1.57,  1.82,  2.07,  2.32,  2.57,  2.82,\n",
+       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.57 0.82 1.07 ... 11.82 12.07</div><input id='attrs-d4abe76c-fa33-4c7e-95a6-5cb5efc66937' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4abe76c-fa33-4c7e-95a6-5cb5efc66937' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b809b69-a55e-4ca7-be14-dc020035dd2a' class='xr-var-data-in' type='checkbox'><label for='data-2b809b69-a55e-4ca7-be14-dc020035dd2a' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.57,  0.82,  1.07,  1.32,  1.57,  1.82,  2.07,  2.32,  2.57,  2.82,\n",
        "        3.07,  3.32,  3.57,  3.82,  4.07,  4.32,  4.57,  4.82,  5.07,  5.32,\n",
        "        5.57,  5.82,  6.07,  6.32,  6.57,  6.82,  7.07,  7.32,  7.57,  7.82,\n",
        "        8.07,  8.32,  8.57,  8.82,  9.07,  9.32,  9.57,  9.82, 10.07, 10.32,\n",
-       "       10.57, 10.82, 11.07, 11.32, 11.57, 11.82, 12.07], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-599765d6-77ae-4a61-a905-2324ac5d1f78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-599765d6-77ae-4a61-a905-2324ac5d1f78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-964ea246-25bb-44f0-901d-04ffbd82c4ac' class='xr-var-data-in' type='checkbox'><label for='data-964ea246-25bb-44f0-901d-04ffbd82c4ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27; &#x27;err&#x27;</div><input id='attrs-64de5b59-19c4-4851-b272-5ff3de8aed9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64de5b59-19c4-4851-b272-5ff3de8aed9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbbe2bd1-daae-44ea-94e2-d537e74eb20c' class='xr-var-data-in' type='checkbox'><label for='data-dbbe2bd1-daae-44ea-94e2-d537e74eb20c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>ship</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;, &#x27;err&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-b205455b-7133-47f7-85fe-ecca371d709c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b205455b-7133-47f7-85fe-ecca371d709c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9330cc18-117d-49b7-ba6a-d3a71969f4c0' class='xr-var-data-in' type='checkbox'><label for='data-9330cc18-117d-49b7-ba6a-d3a71969f4c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-1066768f-a31d-4528-a7e9-97f8496ef754' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1066768f-a31d-4528-a7e9-97f8496ef754' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56d38387-e9d7-4749-95f4-5a160110d81c' class='xr-var-data-in' type='checkbox'><label for='data-56d38387-e9d7-4749-95f4-5a160110d81c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-38d98e31-61ae-4207-b70d-6fabcb89b255' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38d98e31-61ae-4207-b70d-6fabcb89b255' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f82843d4-052d-4df7-a07a-220c972fe11e' class='xr-var-data-in' type='checkbox'><label for='data-f82843d4-052d-4df7-a07a-220c972fe11e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-c44f7188-bc78-4278-bf17-a7f81e65bea1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c44f7188-bc78-4278-bf17-a7f81e65bea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04a3d2fc-1128-4c10-a5ed-0e3c2fa7708f' class='xr-var-data-in' type='checkbox'><label for='data-04a3d2fc-1128-4c10-a5ed-0e3c2fa7708f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86463766-f297-4ebf-8c1a-1f5100ba6d0f' class='xr-section-summary-in' type='checkbox'  ><label for='section-86463766-f297-4ebf-8c1a-1f5100ba6d0f' class='xr-section-summary' >Data variables: <span>(36)</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>number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.652e+03 3.653e+03 ... 4.904e+03</div><input id='attrs-97ddec0b-186d-4a85-8f26-ed11197cb843' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97ddec0b-186d-4a85-8f26-ed11197cb843' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c33d8902-2e4c-47f6-ba42-1eee9bc54c58' class='xr-var-data-in' type='checkbox'><label for='data-c33d8902-2e4c-47f6-ba42-1eee9bc54c58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([3652., 3653., 3654., ..., 4902., 4903., 4904.], shape=(1227,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>builtin_test_fail</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-b2e55464-b80c-4679-99e7-12c356690b22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2e55464-b80c-4679-99e7-12c356690b22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82a21a40-e3bd-4842-95ae-6bc74eadf327' class='xr-var-data-in' type='checkbox'><label for='data-82a21a40-e3bd-4842-95ae-6bc74eadf327' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Built-In Test Failures</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.466e+03 1.466e+03 ... 1.466e+03</div><input id='attrs-6fbd405f-7f47-43c5-9751-03b9666ef931' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fbd405f-7f47-43c5-9751-03b9666ef931' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0d08dc1-0342-42b5-ae7b-e49152c3710e' class='xr-var-data-in' type='checkbox'><label for='data-d0d08dc1-0342-42b5-ae7b-e49152c3710e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1466., 1466., 1466., ..., 1466., 1466., 1466.],\n",
-       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ba550007-9240-4057-9406-e444918287cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba550007-9240-4057-9406-e444918287cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69d6ab2b-06a0-40f2-b269-1b7ceeae35e2' class='xr-var-data-in' type='checkbox'><label for='data-69d6ab2b-06a0-40f2-b269-1b7ceeae35e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.1 -0.09 -0.21 ... 0.2 0.07 0.56</div><input id='attrs-bd690552-6634-4c77-9cdf-23167bb2efec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd690552-6634-4c77-9cdf-23167bb2efec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47d52669-fed0-4c63-8a27-466ae8f6b301' class='xr-var-data-in' type='checkbox'><label for='data-47d52669-fed0-4c63-8a27-466ae8f6b301' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.1 , -0.09, -0.21, ...,  0.2 ,  0.07,  0.56],\n",
-       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.33 3.32 3.33 ... 2.24 2.65 2.33</div><input id='attrs-8d2385a3-41bf-472e-a0c0-862100cf2c5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d2385a3-41bf-472e-a0c0-862100cf2c5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ca0aa5-f45f-474c-bf3c-8490d40f796a' class='xr-var-data-in' type='checkbox'><label for='data-57ca0aa5-f45f-474c-bf3c-8490d40f796a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([3.33, 3.32, 3.33, ..., 2.24, 2.65, 2.33],\n",
-       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>154.6 154.5 155.0 ... 145.8 147.8</div><input id='attrs-8dd0c484-23c0-42ae-beeb-06c6c937fcb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dd0c484-23c0-42ae-beeb-06c6c937fcb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059aac28-3f57-48de-9c0b-5d09ab084401' class='xr-var-data-in' type='checkbox'><label for='data-059aac28-3f57-48de-9c0b-5d09ab084401' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([154.65, 154.49, 155.01, ..., 146.32, 145.75, 147.81],\n",
-       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.2 15.25 15.23 ... 15.21 15.22</div><input id='attrs-4617d5f5-9ccd-4b39-b273-6cc0df68d8da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4617d5f5-9ccd-4b39-b273-6cc0df68d8da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9883e43f-bc11-446d-b45c-20456e967c8e' class='xr-var-data-in' type='checkbox'><label for='data-9883e43f-bc11-446d-b45c-20456e967c8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([15.2 , 15.25, 15.23, ..., 15.2 , 15.21, 15.22],\n",
-       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-a4e2f45e-f579-462c-a1f2-3f6be538ec04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4e2f45e-f579-462c-a1f2-3f6be538ec04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a0f8759-a484-4206-ad9c-82731899e954' class='xr-var-data-in' type='checkbox'><label for='data-0a0f8759-a484-4206-ad9c-82731899e954' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>psu</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_preping_wait</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-11cad2d2-671a-460b-8aee-db407f9afb43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11cad2d2-671a-460b-8aee-db407f9afb43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bea08cd-80ec-41aa-8fbb-ffc75fc3ee7a' class='xr-var-data-in' type='checkbox'><label for='data-1bea08cd-80ec-41aa-8fbb-ffc75fc3ee7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Minimum Pre-Ping Wait Time Between Measurements</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ab02f46a-4482-40b1-a718-e0c75a36162d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab02f46a-4482-40b1-a718-e0c75a36162d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10a46c39-a816-438b-b58a-90309262c89a' class='xr-var-data-in' type='checkbox'><label for='data-10a46c39-a816-438b-b58a-90309262c89a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f916e17c-0566-4775-9823-bdd787fb8b89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f916e17c-0566-4775-9823-bdd787fb8b89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f940130b-baa8-4ded-9871-a4aab5c8d1c7' class='xr-var-data-in' type='checkbox'><label for='data-f940130b-baa8-4ded-9871-a4aab5c8d1c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-405e2f6f-7b11-4a08-8537-8ab17ece385f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-405e2f6f-7b11-4a08-8537-8ab17ece385f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-180d019c-0ce7-4470-b3ab-ef7253e9a9e6' class='xr-var-data-in' type='checkbox'><label for='data-180d019c-0ce7-4470-b3ab-ef7253e9a9e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-41000da5-77e5-4aa9-82a2-198f45e78775' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41000da5-77e5-4aa9-82a2-198f45e78775' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-149a6432-df9b-40bc-bef6-f041e0bf878b' class='xr-var-data-in' type='checkbox'><label for='data-149a6432-df9b-40bc-bef6-f041e0bf878b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-bf175105-ef42-47b5-b53c-6f66438aeaea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf175105-ef42-47b5-b53c-6f66438aeaea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aac5104a-4b37-4268-84e8-52c7470d4b73' class='xr-var-data-in' type='checkbox'><label for='data-aac5104a-4b37-4268-84e8-52c7470d4b73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.057 -0.192 0.121 ... nan nan nan</div><input id='attrs-350671e0-1533-40cb-a5f3-8187f36b2c54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-350671e0-1533-40cb-a5f3-8187f36b2c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4f334ad-743e-4fb3-80d3-3d9928096c82' class='xr-var-data-in' type='checkbox'><label for='data-e4f334ad-743e-4fb3-80d3-3d9928096c82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.057, -0.192,  0.121, ..., -0.01 , -0.629,  0.134],\n",
+       "       10.57, 10.82, 11.07, 11.32, 11.57, 11.82, 12.07], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-f510e1bb-1251-4216-b402-1e12cb344e4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f510e1bb-1251-4216-b402-1e12cb344e4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7538266c-d450-4a0a-81c0-f10f65da4969' class='xr-var-data-in' type='checkbox'><label for='data-7538266c-d450-4a0a-81c0-f10f65da4969' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27; &#x27;err&#x27;</div><input id='attrs-cf128421-713f-4d57-b418-d8e305386049' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf128421-713f-4d57-b418-d8e305386049' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dbb1eca-068b-42d3-aaef-d6f2f823904d' class='xr-var-data-in' type='checkbox'><label for='data-8dbb1eca-068b-42d3-aaef-d6f2f823904d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>ship</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;, &#x27;err&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-e57977fe-7b3e-415d-88a7-a5a7399c0c6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e57977fe-7b3e-415d-88a7-a5a7399c0c6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce05e48c-60bf-4826-8e70-66ba4acfa20e' class='xr-var-data-in' type='checkbox'><label for='data-ce05e48c-60bf-4826-8e70-66ba4acfa20e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-5bab41dd-69f4-4919-b267-7a8b72b7fdf9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bab41dd-69f4-4919-b267-7a8b72b7fdf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ae692d0-dd68-495c-a412-f203b7b7badc' class='xr-var-data-in' type='checkbox'><label for='data-6ae692d0-dd68-495c-a412-f203b7b7badc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-5b453d5a-f191-4f02-9626-4306fe928647' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b453d5a-f191-4f02-9626-4306fe928647' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c55255d-1625-46cb-a899-7b1a8bae5a1c' class='xr-var-data-in' type='checkbox'><label for='data-7c55255d-1625-46cb-a899-7b1a8bae5a1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-67962d92-d689-4e06-8679-09c6a67969ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67962d92-d689-4e06-8679-09c6a67969ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e0be781-0223-45cc-9585-0548d684750c' class='xr-var-data-in' type='checkbox'><label for='data-1e0be781-0223-45cc-9585-0548d684750c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d49104a8-d6a0-4708-b7a1-4eab423d7e17' class='xr-section-summary-in' type='checkbox'  ><label for='section-d49104a8-d6a0-4708-b7a1-4eab423d7e17' class='xr-section-summary' >Data variables: <span>(36)</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>number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.652e+03 3.653e+03 ... 4.904e+03</div><input id='attrs-51e41cb6-b204-4851-a110-f5d15ff66626' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51e41cb6-b204-4851-a110-f5d15ff66626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ace78ad3-c3f5-48cc-840a-ec13930a6c31' class='xr-var-data-in' type='checkbox'><label for='data-ace78ad3-c3f5-48cc-840a-ec13930a6c31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([3652., 3653., 3654., ..., 4902., 4903., 4904.], shape=(1227,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>builtin_test_fail</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-dd245927-f784-4894-b95b-3d2b181a4d7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd245927-f784-4894-b95b-3d2b181a4d7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f762310a-8120-41c7-b176-4422394e8a1a' class='xr-var-data-in' type='checkbox'><label for='data-f762310a-8120-41c7-b176-4422394e8a1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Built-In Test Failures</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.466e+03 1.466e+03 ... 1.466e+03</div><input id='attrs-91252847-bce4-4830-a952-9dcb31c335d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91252847-bce4-4830-a952-9dcb31c335d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e143171f-a3e1-4279-bbbd-1ff33220baa2' class='xr-var-data-in' type='checkbox'><label for='data-e143171f-a3e1-4279-bbbd-1ff33220baa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1466., 1466., 1466., ..., 1466., 1466., 1466.],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-2e90f01d-1ea2-4e8a-ade4-6ef861e641d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e90f01d-1ea2-4e8a-ade4-6ef861e641d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea00cf8a-46be-4732-adf7-a815d2274502' class='xr-var-data-in' type='checkbox'><label for='data-ea00cf8a-46be-4732-adf7-a815d2274502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.1 -0.09 -0.21 ... 0.2 0.07 0.56</div><input id='attrs-b8ad03de-91f3-4a1f-bb75-59f29fd99562' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8ad03de-91f3-4a1f-bb75-59f29fd99562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf4b7eac-edba-4219-a479-20c26cec8a20' class='xr-var-data-in' type='checkbox'><label for='data-cf4b7eac-edba-4219-a479-20c26cec8a20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.1 , -0.09, -0.21, ...,  0.2 ,  0.07,  0.56],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.33 3.32 3.33 ... 2.24 2.65 2.33</div><input id='attrs-6d51fc19-7a27-4486-9c5b-3e643cd904a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d51fc19-7a27-4486-9c5b-3e643cd904a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3f96d3f-61f6-4797-b7bf-d8d23fc3b918' class='xr-var-data-in' type='checkbox'><label for='data-c3f96d3f-61f6-4797-b7bf-d8d23fc3b918' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([3.33, 3.32, 3.33, ..., 2.24, 2.65, 2.33],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>154.6 154.5 155.0 ... 145.8 147.8</div><input id='attrs-4ed848be-bfe2-4de3-a630-f78caaccf164' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ed848be-bfe2-4de3-a630-f78caaccf164' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccc9b45a-81b7-4f76-8d97-4506de5695fe' class='xr-var-data-in' type='checkbox'><label for='data-ccc9b45a-81b7-4f76-8d97-4506de5695fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([154.65, 154.49, 155.01, ..., 146.32, 145.75, 147.81],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.2 15.25 15.23 ... 15.21 15.22</div><input id='attrs-d41ef968-b2cd-447d-93dc-bedbdbcd5350' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d41ef968-b2cd-447d-93dc-bedbdbcd5350' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6da43dac-f31b-43a6-b754-f3ed923f7b0a' class='xr-var-data-in' type='checkbox'><label for='data-6da43dac-f31b-43a6-b754-f3ed923f7b0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([15.2 , 15.25, 15.23, ..., 15.2 , 15.21, 15.22],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-c0755f7e-a799-4596-9ce9-b357260103f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0755f7e-a799-4596-9ce9-b357260103f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-297b7cce-d8c3-43d0-af2c-aee82b220670' class='xr-var-data-in' type='checkbox'><label for='data-297b7cce-d8c3-43d0-af2c-aee82b220670' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>psu</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_preping_wait</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-d5a9fab0-116c-4b6d-b2b4-6aaacad03e03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5a9fab0-116c-4b6d-b2b4-6aaacad03e03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d30a6206-3473-4720-8f6e-43e772308ed7' class='xr-var-data-in' type='checkbox'><label for='data-d30a6206-3473-4720-8f6e-43e772308ed7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Minimum Pre-Ping Wait Time Between Measurements</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-2cfc0e66-7eca-4941-bb5e-161861b87387' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cfc0e66-7eca-4941-bb5e-161861b87387' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43eb1a0d-14a0-4612-896d-866c9ec81209' class='xr-var-data-in' type='checkbox'><label for='data-43eb1a0d-14a0-4612-896d-866c9ec81209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-054198d1-5961-4bad-90ce-4d0059bced5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-054198d1-5961-4bad-90ce-4d0059bced5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d83698ca-8a48-484e-b821-5d7d27fdbc00' class='xr-var-data-in' type='checkbox'><label for='data-d83698ca-8a48-484e-b821-5d7d27fdbc00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-28d8aaec-1e83-430f-afbf-768224f18d51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28d8aaec-1e83-430f-afbf-768224f18d51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-005cb1c2-c859-42a1-90f7-3c9f7cb180e9' class='xr-var-data-in' type='checkbox'><label for='data-005cb1c2-c859-42a1-90f7-3c9f7cb180e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f4e2aa11-2f12-433c-a084-2d6cd8063b3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4e2aa11-2f12-433c-a084-2d6cd8063b3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d65d4cb-77f1-4c4b-8f43-df8687c9e855' class='xr-var-data-in' type='checkbox'><label for='data-4d65d4cb-77f1-4c4b-8f43-df8687c9e855' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-066a1fc6-b704-4b87-83e7-b1c4b5e0357f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-066a1fc6-b704-4b87-83e7-b1c4b5e0357f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bb18e0c-8162-4e85-b20e-1fcd6f477a7f' class='xr-var-data-in' type='checkbox'><label for='data-0bb18e0c-8162-4e85-b20e-1fcd6f477a7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.057 -0.192 0.121 ... nan nan nan</div><input id='attrs-a6ae1b32-f60b-40ac-9335-b2db017bd345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6ae1b32-f60b-40ac-9335-b2db017bd345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32536040-34a6-49ea-be15-3a4f04a6b856' class='xr-var-data-in' type='checkbox'><label for='data-32536040-34a6-49ea-be15-3a4f04a6b856' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.057, -0.192,  0.121, ..., -0.01 , -0.629,  0.134],\n",
        "        [-0.058, -0.131, -0.119, ...,  0.048, -0.146, -0.078],\n",
        "        [ 0.059,  0.406,  0.067, ..., -0.123, -0.524,  0.057],\n",
        "        ...,\n",
@@ -613,7 +730,7 @@
        "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
        "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
        "        [   nan,    nan,    nan, ...,    nan,    nan,    nan]]],\n",
-       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>224.0 229.0 225.0 ... 63.0 49.0</div><input id='attrs-4f37e2fe-ed0f-4d69-93b4-88f8608f406a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f37e2fe-ed0f-4d69-93b4-88f8608f406a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50afdb7d-af77-4499-a825-23388b176da8' class='xr-var-data-in' type='checkbox'><label for='data-50afdb7d-af77-4499-a825-23388b176da8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[224., 229., 225., ..., 228., 229., 227.],\n",
+       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>224.0 229.0 225.0 ... 63.0 49.0</div><input id='attrs-80710f22-84b8-47df-ad20-8f5bd134a1fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80710f22-84b8-47df-ad20-8f5bd134a1fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e3bd448-182b-4f07-aeda-9e69b7038ac5' class='xr-var-data-in' type='checkbox'><label for='data-4e3bd448-182b-4f07-aeda-9e69b7038ac5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[224., 229., 225., ..., 228., 229., 227.],\n",
        "        [222., 222., 221., ..., 222., 228., 226.],\n",
        "        [217., 215., 214., ..., 223., 219., 224.],\n",
        "        ...,\n",
@@ -644,7 +761,7 @@
        "        [ 51.,  57.,  52., ...,  61.,  92.,  49.],\n",
        "        [ 52.,  58.,  53., ...,  59.,  66.,  52.],\n",
        "        [ 51.,  60.,  50., ...,  74.,  63.,  49.]]],\n",
-       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>124.0 118.0 130.0 ... 16.0 8.0 21.0</div><input id='attrs-f567a3e8-0fac-4a0c-ba63-3880a9cfafab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f567a3e8-0fac-4a0c-ba63-3880a9cfafab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca3e4071-d4a1-46e7-a3a8-2ee3287fbbb3' class='xr-var-data-in' type='checkbox'><label for='data-ca3e4071-d4a1-46e7-a3a8-2ee3287fbbb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[124., 118., 130., ..., 125., 111., 133.],\n",
+       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>124.0 118.0 130.0 ... 16.0 8.0 21.0</div><input id='attrs-5871fb56-493d-41ae-a88d-83d2718c3cca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5871fb56-493d-41ae-a88d-83d2718c3cca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f24c3860-7900-4010-a22c-6da26beea12c' class='xr-var-data-in' type='checkbox'><label for='data-f24c3860-7900-4010-a22c-6da26beea12c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[124., 118., 130., ..., 125., 111., 133.],\n",
        "        [128., 140., 128., ..., 118., 140., 120.],\n",
        "        [132., 119., 119., ..., 109., 116., 122.],\n",
        "        ...,\n",
@@ -675,7 +792,7 @@
        "        [ 19.,  13.,  17., ...,  18.,  23.,  11.],\n",
        "        [  7.,  25.,  16., ...,  26.,  23.,  27.],\n",
        "        [ 13.,  25.,  13., ...,  16.,   8.,  21.]]],\n",
-       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-1d21a950-b980-45d7-b2eb-5b092f6c9168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d21a950-b980-45d7-b2eb-5b092f6c9168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5de435e-8387-4b8d-b170-2f524765f536' class='xr-var-data-in' type='checkbox'><label for='data-e5de435e-8387-4b8d-b170-2f524765f536' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
+       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-9a1057a8-8f29-426a-a645-dfd79c23547b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a1057a8-8f29-426a-a645-dfd79c23547b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ce531fd-a664-48f9-9ac8-bac99b6e173c' class='xr-var-data-in' type='checkbox'><label for='data-8ce531fd-a664-48f9-9ac8-bac99b6e173c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        ...,\n",
@@ -706,43 +823,43 @@
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.]]],\n",
-       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.95 3.95 3.96 ... 1.93 1.95 2.0</div><input id='attrs-18d28ea0-92c5-476d-8ce2-18edc604e463' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18d28ea0-92c5-476d-8ce2-18edc604e463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6fafaf1-cff5-4704-907f-0db346fb8b66' class='xr-var-data-in' type='checkbox'><label for='data-e6fafaf1-cff5-4704-907f-0db346fb8b66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Bottom Track Measured Depth</dd></dl></div><div class='xr-var-data'><pre>array([[3.95, 3.95, 3.96, ..., 2.65, 2.64, 2.6 ],\n",
+       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.95 3.95 3.96 ... 1.93 1.95 2.0</div><input id='attrs-669def73-782b-4ee0-b500-b3377e1d3870' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-669def73-782b-4ee0-b500-b3377e1d3870' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ea93d85-2aee-470e-82e7-56d30fa704f5' class='xr-var-data-in' type='checkbox'><label for='data-4ea93d85-2aee-470e-82e7-56d30fa704f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Bottom Track Measured Depth</dd></dl></div><div class='xr-var-data'><pre>array([[3.95, 3.95, 3.96, ..., 2.65, 2.64, 2.6 ],\n",
        "       [2.55, 2.56, 2.57, ..., 1.61, 1.59, 1.59],\n",
        "       [3.31, 3.32, 3.33, ..., 1.93, 1.95, 1.96],\n",
        "       [2.87, 2.88, 2.88, ..., 1.93, 1.95, 2.  ]],\n",
-       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_bt</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.063 -0.004 0.039 ... 0.01 0.0</div><input id='attrs-0a14f9ef-3b14-4690-acae-a60c465076ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a14f9ef-3b14-4690-acae-a60c465076ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8688af7-56f3-44b9-b1f1-97077730fb2c' class='xr-var-data-in' type='checkbox'><label for='data-e8688af7-56f3-44b9-b1f1-97077730fb2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 6.3000e-02, -4.0000e-03,  3.9000e-02, ...,  2.3000e-02,\n",
+       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_bt</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.063 -0.004 0.039 ... 0.01 0.0</div><input id='attrs-2ff489e1-3d18-4791-a01a-85817ecfad50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ff489e1-3d18-4791-a01a-85817ecfad50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1677c15d-8d9a-4581-8101-e2615ebdb8b5' class='xr-var-data-in' type='checkbox'><label for='data-1677c15d-8d9a-4581-8101-e2615ebdb8b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 6.3000e-02, -4.0000e-03,  3.9000e-02, ...,  2.3000e-02,\n",
        "         2.7000e-02,  0.0000e+00],\n",
        "       [-3.7000e-02,  1.9000e-02,  1.5000e-02, ..., -6.9000e-02,\n",
        "        -1.0100e-01, -1.5800e-01],\n",
        "       [ 9.0000e-03,  0.0000e+00,  8.0000e-03, ...,  0.0000e+00,\n",
        "        -1.7000e-02, -2.0000e-03],\n",
        "       [-1.0000e-02, -6.0000e-03, -3.2768e+01, ...,  7.0000e-03,\n",
-       "         1.0000e-02,  0.0000e+00]], shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>231.0 243.0 242.0 ... 249.0 246.0</div><input id='attrs-ed1ddc5b-2f3a-4742-bac6-facf5e7402ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed1ddc5b-2f3a-4742-bac6-facf5e7402ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83911fb1-feeb-40b8-bf83-c3d4550b73eb' class='xr-var-data-in' type='checkbox'><label for='data-83911fb1-feeb-40b8-bf83-c3d4550b73eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[231., 243., 242., ..., 252., 255., 248.],\n",
+       "         1.0000e-02,  0.0000e+00]], shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>231.0 243.0 242.0 ... 249.0 246.0</div><input id='attrs-1311c312-80fc-447d-bd9d-b20d79ae2b0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1311c312-80fc-447d-bd9d-b20d79ae2b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0054624-eb4e-4795-89a6-7f127f57ac5a' class='xr-var-data-in' type='checkbox'><label for='data-b0054624-eb4e-4795-89a6-7f127f57ac5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[231., 243., 242., ..., 252., 255., 248.],\n",
        "       [229., 239., 246., ..., 249., 248., 254.],\n",
        "       [223., 237.,   0., ..., 249., 252., 251.],\n",
        "       [248., 244., 250., ..., 248., 249., 246.]],\n",
-       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.0 40.0 36.0 ... 40.0 64.0 49.0</div><input id='attrs-93bc6915-f449-4344-ab4e-92149df0d5d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93bc6915-f449-4344-ab4e-92149df0d5d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798c2c32-d456-4bb6-98e8-b36c003ae1dd' class='xr-var-data-in' type='checkbox'><label for='data-798c2c32-d456-4bb6-98e8-b36c003ae1dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[40., 40., 36., ..., 64., 66., 51.],\n",
+       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.0 40.0 36.0 ... 40.0 64.0 49.0</div><input id='attrs-71b847b6-32ce-4a9d-9b4c-d34c71d704d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71b847b6-32ce-4a9d-9b4c-d34c71d704d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc6caf13-5338-424a-814b-34b326b20241' class='xr-var-data-in' type='checkbox'><label for='data-cc6caf13-5338-424a-814b-34b326b20241' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[40., 40., 36., ..., 64., 66., 51.],\n",
        "       [45., 38., 47., ..., 78., 78., 83.],\n",
        "       [40., 43., 36., ..., 55., 71., 71.],\n",
        "       [55., 54., 52., ..., 40., 64., 49.]],\n",
-       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 100.0 ... 100.0 100.0 100.0</div><input id='attrs-1408b995-55a5-4be7-b140-1e5705f9c189' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1408b995-55a5-4be7-b140-1e5705f9c189' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-678b44f7-d6f3-4bc0-a050-af367c37baa0' class='xr-var-data-in' type='checkbox'><label for='data-678b44f7-d6f3-4bc0-a050-af367c37baa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[  0.,   0., 100., ...,   0.,   0.,   0.],\n",
+       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 100.0 ... 100.0 100.0 100.0</div><input id='attrs-3ccdf401-8887-42cf-af2c-b5d40765da25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ccdf401-8887-42cf-af2c-b5d40765da25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fca4264-dbed-42b5-9c7a-1ba7f2969946' class='xr-var-data-in' type='checkbox'><label for='data-2fca4264-dbed-42b5-9c7a-1ba7f2969946' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[  0.,   0., 100., ...,   0.,   0.,   0.],\n",
        "       [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "       [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "       [100., 100.,   0., ..., 100., 100., 100.]],\n",
-       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>64.56 64.56 64.56 ... 64.56 64.56</div><input id='attrs-4955270e-8fce-45c7-a350-67eee8d83063' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4955270e-8fce-45c7-a350-67eee8d83063' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74a7d630-4ef4-4ee0-a26f-a11a9ec78455' class='xr-var-data-in' type='checkbox'><label for='data-74a7d630-4ef4-4ee0-a26f-a11a9ec78455' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([64.560905, 64.560905, 64.560905, ..., 64.56094 , 64.56094 ,\n",
-       "       64.56094 ], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-149.1 -149.1 ... -149.1 -149.1</div><input id='attrs-4304c44f-dfea-43b9-b5b6-e59f00833f93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4304c44f-dfea-43b9-b5b6-e59f00833f93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d23bdbd6-8fad-4a08-b5b3-91a02baaab04' class='xr-var-data-in' type='checkbox'><label for='data-d23bdbd6-8fad-4a08-b5b3-91a02baaab04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-149.06679, -149.06679, -149.06679, ..., -149.06682, -149.06682,\n",
-       "       -149.0668 ], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fix_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.0 9.0 9.0 ... 9.0 9.0 9.0 9.0</div><input id='attrs-48a1e437-d460-421d-9720-f09a8c23c220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48a1e437-d460-421d-9720-f09a8c23c220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80f8d7a4-e81a-41db-8f2f-1326bd2cb1e9' class='xr-var-data-in' type='checkbox'><label for='data-80f8d7a4-e81a-41db-8f2f-1326bd2cb1e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>GPS Fix</dd></dl></div><div class='xr-var-data'><pre>array([9., 9., 9., ..., 9., 9., 9.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_sat_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.0 9.0 9.0 ... 10.0 10.0 10.0</div><input id='attrs-953512d9-4ba2-47ef-9011-53c88a53fa55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-953512d9-4ba2-47ef-9011-53c88a53fa55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beacc763-8095-4709-b13b-e5336494c2ac' class='xr-var-data-in' type='checkbox'><label for='data-beacc763-8095-4709-b13b-e5336494c2ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>count</dd><dt><span>long_name :</span></dt><dd>Number of Satellites</dd></dl></div><div class='xr-var-data'><pre>array([ 9.,  9.,  9., ..., 10., 10., 10.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.9 0.9 0.9 0.9 ... 0.8 0.8 0.8 0.8</div><input id='attrs-aa04ea87-18a5-42f5-9dad-ff98e2d976a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa04ea87-18a5-42f5-9dad-ff98e2d976a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd8671b9-2d9e-4fa8-9cb8-e121e1af71e6' class='xr-var-data-in' type='checkbox'><label for='data-bd8671b9-2d9e-4fa8-9cb8-e121e1af71e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Horizontal Dilution of Precision</dd></dl></div><div class='xr-var-data'><pre>array([0.9, 0.9, 0.9, ..., 0.8, 0.8, 0.8], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>108.5 108.2 108.2 ... 108.6 108.6</div><input id='attrs-fcc07695-d8f4-4f7d-a4f9-58f1d8bbad58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcc07695-d8f4-4f7d-a4f9-58f1d8bbad58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31ac4442-7df8-46dd-bfa9-a8f100eb36c8' class='xr-var-data-in' type='checkbox'><label for='data-31ac4442-7df8-46dd-bfa9-a8f100eb36c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Elevation above MLLW</dd></dl></div><div class='xr-var-data'><pre>array([108.483, 108.196, 108.208, ..., 108.644, 108.629, 108.606],\n",
-       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rtk_age_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 6.0 2.0 3.0 ... 6.0 2.0 3.0 3.0</div><input id='attrs-1ffe38ff-187d-4a46-96ca-ba105be74222' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ffe38ff-187d-4a46-96ca-ba105be74222' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35efa5b0-985c-424e-b510-667668d3e6f2' class='xr-var-data-in' type='checkbox'><label for='data-35efa5b0-985c-424e-b510-667668d3e6f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Age of Received Real Time Kinetic Signal</dd></dl></div><div class='xr-var-data'><pre>array([5., 6., 2., ..., 2., 3., 3.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.03549 0.03395 ... 0.1096 0.1672</div><input id='attrs-aab67b6c-2791-4215-be33-56398b668fb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aab67b6c-2791-4215-be33-56398b668fb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7500a405-dc71-4b65-8672-8959c959c1c3' class='xr-var-data-in' type='checkbox'><label for='data-7500a405-dc71-4b65-8672-8959c959c1c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Platform Speed over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_speed_wrt_ground</dd></dl></div><div class='xr-var-data'><pre>array([0.03549382, 0.03395062, 0.03858025, ..., 0.11985596, 0.1095679 ,\n",
-       "       0.16718106], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dir_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>115.5 94.08 6.931 ... 160.5 182.1</div><input id='attrs-01872462-a56d-402f-8d58-c0b320652190' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01872462-a56d-402f-8d58-c0b320652190' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-635a854c-72b9-49f5-b10a-bb6b13d2a24c' class='xr-var-data-in' type='checkbox'><label for='data-635a854c-72b9-49f5-b10a-bb6b13d2a24c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Platform Direction over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_course</dd></dl></div><div class='xr-var-data'><pre>array([115.501,  94.077,   6.931, ..., 140.963, 160.468, 182.087],\n",
-       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdwtime_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T14:28:15.559999942 .....</div><input id='attrs-854f6af0-9af6-4929-85d8-b15d3e7b834c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-854f6af0-9af6-4929-85d8-b15d3e7b834c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-467689e9-7155-43f2-8079-ce0b5295f27e' class='xr-var-data-in' type='checkbox'><label for='data-467689e9-7155-43f2-8079-ce0b5295f27e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T14:28:15.559999942&#x27;, &#x27;2010-08-10T14:28:16.319999933&#x27;,\n",
+       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>64.56 64.56 64.56 ... 64.56 64.56</div><input id='attrs-2d90770d-7b7e-44c6-91a0-7f328a7dcae2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d90770d-7b7e-44c6-91a0-7f328a7dcae2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2da53a73-ee67-4614-b203-d72ba7187c0d' class='xr-var-data-in' type='checkbox'><label for='data-2da53a73-ee67-4614-b203-d72ba7187c0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([64.560905, 64.560905, 64.560905, ..., 64.56094 , 64.56094 ,\n",
+       "       64.56094 ], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-149.1 -149.1 ... -149.1 -149.1</div><input id='attrs-a8c7527a-2941-45f2-823a-1d00ebed1611' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8c7527a-2941-45f2-823a-1d00ebed1611' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34692369-d06a-4124-a709-e3b6ab9c6561' class='xr-var-data-in' type='checkbox'><label for='data-34692369-d06a-4124-a709-e3b6ab9c6561' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-149.06679, -149.06679, -149.06679, ..., -149.06682, -149.06682,\n",
+       "       -149.0668 ], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fix_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.0 9.0 9.0 ... 9.0 9.0 9.0 9.0</div><input id='attrs-42fdb31d-21b6-48fd-9a85-533fe9ea9713' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42fdb31d-21b6-48fd-9a85-533fe9ea9713' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-867b712b-1225-4b09-ab64-15a4b9add827' class='xr-var-data-in' type='checkbox'><label for='data-867b712b-1225-4b09-ab64-15a4b9add827' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>GPS Fix</dd></dl></div><div class='xr-var-data'><pre>array([9., 9., 9., ..., 9., 9., 9.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_sat_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.0 9.0 9.0 ... 10.0 10.0 10.0</div><input id='attrs-a70f4de9-7483-4af0-b407-ca8dc6fdf121' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a70f4de9-7483-4af0-b407-ca8dc6fdf121' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3efb8257-554a-408d-a05e-4a17c88a43df' class='xr-var-data-in' type='checkbox'><label for='data-3efb8257-554a-408d-a05e-4a17c88a43df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>count</dd><dt><span>long_name :</span></dt><dd>Number of Satellites</dd></dl></div><div class='xr-var-data'><pre>array([ 9.,  9.,  9., ..., 10., 10., 10.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.9 0.9 0.9 0.9 ... 0.8 0.8 0.8 0.8</div><input id='attrs-ed575bad-a1c7-4009-9ee5-dcf6bccfbd32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed575bad-a1c7-4009-9ee5-dcf6bccfbd32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dfeeb16-aa66-4a3f-8482-a948982831a5' class='xr-var-data-in' type='checkbox'><label for='data-4dfeeb16-aa66-4a3f-8482-a948982831a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Horizontal Dilution of Precision</dd></dl></div><div class='xr-var-data'><pre>array([0.9, 0.9, 0.9, ..., 0.8, 0.8, 0.8], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>108.5 108.2 108.2 ... 108.6 108.6</div><input id='attrs-1f54f449-d8c2-406a-87be-0cddd7e23f09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f54f449-d8c2-406a-87be-0cddd7e23f09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8d74ede-bcb5-4b90-8729-8c7686a18297' class='xr-var-data-in' type='checkbox'><label for='data-c8d74ede-bcb5-4b90-8729-8c7686a18297' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Elevation above MLLW</dd></dl></div><div class='xr-var-data'><pre>array([108.483, 108.196, 108.208, ..., 108.644, 108.629, 108.606],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rtk_age_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 6.0 2.0 3.0 ... 6.0 2.0 3.0 3.0</div><input id='attrs-b265d98f-470d-4808-9a99-2c8983eb1f11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b265d98f-470d-4808-9a99-2c8983eb1f11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa851a95-3a06-4c85-bfc6-a6660b3309e1' class='xr-var-data-in' type='checkbox'><label for='data-aa851a95-3a06-4c85-bfc6-a6660b3309e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Age of Received Real Time Kinetic Signal</dd></dl></div><div class='xr-var-data'><pre>array([5., 6., 2., ..., 2., 3., 3.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.03549 0.03395 ... 0.1096 0.1672</div><input id='attrs-00dfbf15-d1ab-4385-a6c8-d0ee6d80d8e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00dfbf15-d1ab-4385-a6c8-d0ee6d80d8e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dd7d72d-bad5-4ba7-bab6-b1248ea5b341' class='xr-var-data-in' type='checkbox'><label for='data-9dd7d72d-bad5-4ba7-bab6-b1248ea5b341' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Platform Speed over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_speed_wrt_ground</dd></dl></div><div class='xr-var-data'><pre>array([0.03549382, 0.03395062, 0.03858025, ..., 0.11985596, 0.1095679 ,\n",
+       "       0.16718106], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dir_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>115.5 94.08 6.931 ... 160.5 182.1</div><input id='attrs-67b0840f-bc1a-459b-b30f-13f6922e8dc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67b0840f-bc1a-459b-b30f-13f6922e8dc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-239098e6-618c-46c8-9df6-0f5da0343acc' class='xr-var-data-in' type='checkbox'><label for='data-239098e6-618c-46c8-9df6-0f5da0343acc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Platform Direction over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_course</dd></dl></div><div class='xr-var-data'><pre>array([115.501,  94.077,   6.931, ..., 140.963, 160.468, 182.087],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdwtime_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T14:28:15.559999942 .....</div><input id='attrs-4206d19d-df6f-49d6-a236-eed15159ac2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4206d19d-df6f-49d6-a236-eed15159ac2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-197e8816-c5bf-4f88-8bc5-25ec01955cb8' class='xr-var-data-in' type='checkbox'><label for='data-197e8816-c5bf-4f88-8bc5-25ec01955cb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T14:28:15.559999942&#x27;, &#x27;2010-08-10T14:28:16.319999933&#x27;,\n",
        "       &#x27;2010-08-10T14:28:17.069999933&#x27;, ...,\n",
        "       &#x27;2010-08-10T14:39:59.190000057&#x27;, &#x27;2010-08-10T14:39:59.910000085&#x27;,\n",
        "       &#x27;2010-08-10T14:40:00.630000114&#x27;],\n",
-       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.462 -1.462 0.0 ... -1.034 -1.034</div><input id='attrs-0113a027-7e0f-426c-a742-872ad1713938' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0113a027-7e0f-426c-a742-872ad1713938' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a03d6ad-4c2a-48c4-84f9-2644cd1a919d' class='xr-var-data-in' type='checkbox'><label for='data-9a03d6ad-4c2a-48c4-84f9-2644cd1a919d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.4619021 , -1.4619021 ,  0.        ,  0.        ],\n",
+       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.462 -1.462 0.0 ... -1.034 -1.034</div><input id='attrs-7fe05cb3-fef4-45c7-b81d-1757c42588cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fe05cb3-fef4-45c7-b81d-1757c42588cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e46f0578-241f-4487-9ccd-6dc1e5e04eeb' class='xr-var-data-in' type='checkbox'><label for='data-e46f0578-241f-4487-9ccd-6dc1e5e04eeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.4619021 , -1.4619021 ,  0.        ,  0.        ],\n",
        "       [ 0.        ,  0.        , -1.4619021 ,  1.4619021 ],\n",
        "       [ 0.26604444,  0.26604444,  0.26604444,  0.26604444],\n",
        "       [ 1.033721  ,  1.033721  , -1.033721  , -1.033721  ]],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9037 -0.9025 -0.9064 ... 1.0 1.0</div><input id='attrs-66663342-742c-4476-8963-b17dfc7bcc92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66663342-742c-4476-8963-b17dfc7bcc92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b36e2e53-263a-4699-b309-3d6dd3524d9a' class='xr-var-data-in' type='checkbox'><label for='data-b36e2e53-263a-4699-b309-3d6dd3524d9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.9037092 , -0.9025101 , -0.9063815 , ..., -0.8321477 ,\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9037 -0.9025 -0.9064 ... 1.0 1.0</div><input id='attrs-5ce604a0-caed-4ebf-b3d1-2f3094e8c30e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ce604a0-caed-4ebf-b3d1-2f3094e8c30e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ad9ec95-21e5-4041-97d1-52bf7aa0664d' class='xr-var-data-in' type='checkbox'><label for='data-8ad9ec95-21e5-4041-97d1-52bf7aa0664d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.9037092 , -0.9025101 , -0.9063815 , ..., -0.8321477 ,\n",
        "         -0.8265897 , -0.84628606],\n",
        "        [-0.4281468 , -0.43066862, -0.42246014, ..., -0.554554  ,\n",
        "         -0.56280506, -0.53272873],\n",
@@ -761,65 +878,7 @@
        "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [ 1.        ,  1.        ,  1.        , ...,  1.        ,\n",
-       "          1.        ,  1.        ]]], shape=(3, 3, 1227), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-715323bb-8a05-4f9d-9ed8-9c97206fb923' class='xr-section-summary-in' type='checkbox'  ><label for='section-715323bb-8a05-4f9d-9ed8-9c97206fb923' class='xr-section-summary' >Indexes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e5cbe076-06f8-4d0e-b075-481c131181e2' class='xr-index-data-in' type='checkbox'/><label for='index-e5cbe076-06f8-4d0e-b075-481c131181e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-10 14:28:15.559999942&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:16.319999933&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:17.069999933&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:17.799999952&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:18.529999971&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:19.279999971&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:20.029999971&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:20.779999971&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:21.539999961&#x27;,\n",
-       "               &#x27;2010-08-10 14:28:22.299999952&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2010-08-10 14:39:54.119999885&#x27;,\n",
-       "               &#x27;2010-08-10 14:39:54.849999904&#x27;,\n",
-       "               &#x27;2010-08-10 14:39:55.569999933&#x27;,\n",
-       "               &#x27;2010-08-10 14:39:56.289999961&#x27;,\n",
-       "               &#x27;2010-08-10 14:39:57.019999980&#x27;,\n",
-       "                  &#x27;2010-08-10 14:39:57.750000&#x27;,\n",
-       "               &#x27;2010-08-10 14:39:58.480000019&#x27;,\n",
-       "               &#x27;2010-08-10 14:39:59.190000057&#x27;,\n",
-       "               &#x27;2010-08-10 14:39:59.910000085&#x27;,\n",
-       "               &#x27;2010-08-10 14:40:00.630000114&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1227, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_gps</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2886a302-36d8-4d56-8351-9f96f18e870c' class='xr-index-data-in' type='checkbox'/><label for='index-2886a302-36d8-4d56-8351-9f96f18e870c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-10 22:28:17.200000047&#x27;,\n",
-       "                         &#x27;2010-08-10 22:28:18&#x27;,\n",
-       "               &#x27;2010-08-10 22:28:18.599999904&#x27;,\n",
-       "               &#x27;2010-08-10 22:28:19.400000095&#x27;,\n",
-       "               &#x27;2010-08-10 22:28:20.200000047&#x27;,\n",
-       "               &#x27;2010-08-10 22:28:20.799999952&#x27;,\n",
-       "               &#x27;2010-08-10 22:28:21.599999904&#x27;,\n",
-       "               &#x27;2010-08-10 22:28:22.400000095&#x27;,\n",
-       "               &#x27;2010-08-10 22:28:23.200000047&#x27;,\n",
-       "               &#x27;2010-08-10 22:28:23.799999952&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2010-08-10 22:39:55.599999904&#x27;,\n",
-       "               &#x27;2010-08-10 22:39:56.400000095&#x27;,\n",
-       "               &#x27;2010-08-10 22:39:57.200000047&#x27;,\n",
-       "               &#x27;2010-08-10 22:39:57.799999952&#x27;,\n",
-       "               &#x27;2010-08-10 22:39:58.599999904&#x27;,\n",
-       "               &#x27;2010-08-10 22:39:59.400000095&#x27;,\n",
-       "                         &#x27;2010-08-10 22:40:00&#x27;,\n",
-       "               &#x27;2010-08-10 22:40:00.799999952&#x27;,\n",
-       "               &#x27;2010-08-10 22:40:01.400000095&#x27;,\n",
-       "               &#x27;2010-08-10 22:40:02.200000047&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_gps&#x27;, length=1227, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0250f22b-7f25-4d4d-a112-eaaad29de8c5' class='xr-index-data-in' type='checkbox'/><label for='index-0250f22b-7f25-4d4d-a112-eaaad29de8c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5699999928474426, 0.8199999928474426, 1.0700000524520874,\n",
-       "       1.3200000524520874, 1.5700000524520874, 1.8200000524520874,\n",
-       "        2.069999933242798,  2.319999933242798,  2.569999933242798,\n",
-       "        2.819999933242798,  3.069999933242798,  3.319999933242798,\n",
-       "        3.569999933242798,  3.819999933242798,  4.070000171661377,\n",
-       "        4.320000171661377,  4.570000171661377,  4.820000171661377,\n",
-       "        5.070000171661377,  5.320000171661377,  5.570000171661377,\n",
-       "        5.820000171661377,  6.070000171661377,  6.320000171661377,\n",
-       "        6.570000171661377,  6.820000171661377,  7.070000171661377,\n",
-       "        7.320000171661377,  7.570000171661377,  7.820000171661377,\n",
-       "        8.069999694824219,  8.319999694824219,  8.569999694824219,\n",
-       "        8.819999694824219,  9.069999694824219,  9.319999694824219,\n",
-       "        9.569999694824219,  9.819999694824219, 10.069999694824219,\n",
-       "       10.319999694824219, 10.569999694824219, 10.819999694824219,\n",
-       "       11.069999694824219, 11.319999694824219, 11.569999694824219,\n",
-       "       11.819999694824219, 12.069999694824219],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a60c54d3-df25-42a7-8ef8-2253c9892a87' class='xr-index-data-in' type='checkbox'/><label for='index-a60c54d3-df25-42a7-8ef8-2253c9892a87' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b5a9ace4-c990-46a0-abfd-93d43eade1ff' class='xr-index-data-in' type='checkbox'/><label for='index-b5a9ace4-c990-46a0-abfd-93d43eade1ff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;, &#x27;err&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-08b08bc7-5644-4126-858f-18dffc747812' class='xr-index-data-in' type='checkbox'/><label for='index-08b08bc7-5644-4126-858f-18dffc747812' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a18de09c-9449-4985-b77e-86c4cdfb3f2b' class='xr-index-data-in' type='checkbox'/><label for='index-a18de09c-9449-4985-b77e-86c4cdfb3f2b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;x2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-444909e6-dced-4025-a032-e062865e9f49' class='xr-index-data-in' type='checkbox'/><label for='index-444909e6-dced-4025-a032-e062865e9f49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bf421842-96bc-4c53-9d66-2f033ee263fc' class='xr-index-data-in' type='checkbox'/><label for='index-bf421842-96bc-4c53-9d66-2f033ee263fc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-735e5954-b400-49c9-8606-fc374e7b760d' class='xr-section-summary-in' type='checkbox'  ><label for='section-735e5954-b400-49c9-8606-fc374e7b760d' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>firmware_ver :</span></dt><dd>10.16</dd><dt><span>inst_make :</span></dt><dd>TRDI</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;vel_bt&#x27;]</dd><dt><span>has_imu :</span></dt><dd>0</dd><dt><span>inst_model :</span></dt><dd>Rio Grande</dd><dt><span>beam_angle :</span></dt><dd>20</dd><dt><span>carrier_freq :</span></dt><dd>1200</dd><dt><span>beam_pattern :</span></dt><dd>convex</dd><dt><span>orientation :</span></dt><dd>down</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>n_cells :</span></dt><dd>47</dd><dt><span>pings_per_ensemble :</span></dt><dd>1</dd><dt><span>cell_size :</span></dt><dd>0.25</dd><dt><span>blank_dist :</span></dt><dd>0.25</dd><dt><span>profiling_mode :</span></dt><dd>12</dd><dt><span>min_corr_threshold :</span></dt><dd>64</dd><dt><span>n_code_reps :</span></dt><dd>4</dd><dt><span>min_prcnt_gd :</span></dt><dd>0</dd><dt><span>max_error_vel :</span></dt><dd>1.5</dd><dt><span>sec_between_ping_groups :</span></dt><dd>0.09</dd><dt><span>coord_sys :</span></dt><dd>ship</dd><dt><span>use_pitchroll :</span></dt><dd>yes</dd><dt><span>use_3beam :</span></dt><dd>yes</dd><dt><span>bin_mapping :</span></dt><dd>yes</dd><dt><span>heading_misalign_deg :</span></dt><dd>0.0</dd><dt><span>magnetic_var_deg :</span></dt><dd>0.0</dd><dt><span>sensors_src :</span></dt><dd>01011101</dd><dt><span>sensors_avail :</span></dt><dd>00011101</dd><dt><span>transmit_pulse_m :</span></dt><dd>0.3</dd><dt><span>water_ref_cells :</span></dt><dd>[0, 5]</dd><dt><span>false_target_threshold :</span></dt><dd>50</dd><dt><span>transmit_lag_m :</span></dt><dd>0.08</dd><dt><span>bandwidth :</span></dt><dd>0</dd><dt><span>power_level :</span></dt><dd>128</dd><dt><span>serialnum :</span></dt><dd>134877318</dd><dt><span>source_program :</span></dt><dd>WINRIVER</dd><dt><span>range_offset :</span></dt><dd>0.07</dd><dt><span>fs :</span></dt><dd>1.92</dd></dl></div></li></ul></div></div>"
+       "          1.        ,  1.        ]]], shape=(3, 3, 1227), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e45794d7-75d3-4e4d-aad3-696084359922' class='xr-section-summary-in' type='checkbox'  ><label for='section-e45794d7-75d3-4e4d-aad3-696084359922' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>firmware_ver :</span></dt><dd>10.16</dd><dt><span>inst_make :</span></dt><dd>TRDI</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;vel_bt&#x27;]</dd><dt><span>has_imu :</span></dt><dd>0</dd><dt><span>inst_model :</span></dt><dd>Rio Grande</dd><dt><span>beam_angle :</span></dt><dd>20</dd><dt><span>carrier_freq :</span></dt><dd>1200</dd><dt><span>beam_pattern :</span></dt><dd>convex</dd><dt><span>orientation :</span></dt><dd>down</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>n_cells :</span></dt><dd>47</dd><dt><span>pings_per_ensemble :</span></dt><dd>1</dd><dt><span>cell_size :</span></dt><dd>0.25</dd><dt><span>blank_dist :</span></dt><dd>0.25</dd><dt><span>profiling_mode :</span></dt><dd>12</dd><dt><span>min_corr_threshold :</span></dt><dd>64</dd><dt><span>n_code_reps :</span></dt><dd>4</dd><dt><span>min_prcnt_gd :</span></dt><dd>0</dd><dt><span>max_error_vel :</span></dt><dd>1.5</dd><dt><span>sec_between_ping_groups :</span></dt><dd>0.09</dd><dt><span>coord_sys :</span></dt><dd>ship</dd><dt><span>use_pitchroll :</span></dt><dd>yes</dd><dt><span>use_3beam :</span></dt><dd>yes</dd><dt><span>bin_mapping :</span></dt><dd>yes</dd><dt><span>heading_misalign_deg :</span></dt><dd>0.0</dd><dt><span>magnetic_var_deg :</span></dt><dd>0.0</dd><dt><span>sensors_src :</span></dt><dd>01011101</dd><dt><span>sensors_avail :</span></dt><dd>00011101</dd><dt><span>transmit_pulse_m :</span></dt><dd>0.3</dd><dt><span>water_ref_cells :</span></dt><dd>[0, 5]</dd><dt><span>false_target_threshold :</span></dt><dd>50</dd><dt><span>transmit_lag_m :</span></dt><dd>0.08</dd><dt><span>bandwidth :</span></dt><dd>0</dd><dt><span>power_level :</span></dt><dd>128</dd><dt><span>serialnum :</span></dt><dd>134877318</dd><dt><span>source_program :</span></dt><dd>WINRIVER</dd><dt><span>range_offset :</span></dt><dd>0.07</dd><dt><span>fs :</span></dt><dd>1.92</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 4MB\n",
@@ -988,7 +1047,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAHnCAYAAAAfNuEtAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAl8tJREFUeJztnQW4FHUXxg/dId3dXSIpoKSAKAgqLQrSpSghjYRIScOHSEioSEsLEioN0t2dl+75nvc/zN7ZvXsvNzZmd9/f8wy7OzvsTuydeef8z3lPNE3TNCGEEEIIIR4juue+ihBCCCGEUIARQgghhHgBRsAIIYQQQjwMBRghhBBCiIehACOEEEII8TAUYIQQQgghHoYCjBBCCCHEw1CAEUIIIYR4GAowQgghhBAPQwFGCCGEEOJhKMAszsaNG6V27dqSLl06iRYtmixatCjCn4FuU99//73kypVL4sSJI+nTp5dvv/3WLetLCCGEkFcTMxzLEC9y//59KVy4sLRo0ULq1q0bqc/o1KmTrF69WomwggULys2bN9VECCGEEO8Qjc24fQdEwBYuXCjvvfeebd7jx4+lV69eMnfuXLl9+7YUKFBAhg0bJhUrVlTvHzp0SAoVKiT79++X3Llze3HtCSGEEGLAIUgfp3379vLPP//IvHnz5L///pP69etL9erV5dixY+r9pUuXSrZs2WTZsmWSNWtWyZIli3z22WeMgBFCCCFehALMhzl79qxMnz5dfv31Vylfvrxkz55dvvzySylXrpyaD06ePClnzpxRy8ycOVN++ukn2blzp3zwwQfeXn1CCCEkYGEOmA+zb98+ef78uUquN4NhyeTJk6vnL168UK8hvozlpk2bJsWLF5cjR45wWJIQQgjxAhRgPsy9e/ckRowYKqKFRzMJEyZUj2nTppWYMWPaibS8efPaImjMCyOEEEI8DwWYD1O0aFEVAbt69aoagnRG2bJl5dmzZ3LixAk1RAmOHj2qHjNnzuzR9SWEEEKIDqsgfSDKdfz4cZvgGjlypFSqVEmSJUsmmTJlksaNG8uWLVtkxIgR6v1r167JunXrVOVjzZo11RDk66+/riJio0ePVq/btWsniRMnVtYUhBBCCPE8FGAWZ8OGDUpwOdKsWTOVUP/06VMZNGiQyvG6cOGCpEiRQkqVKiX9+/dXnl/g4sWL0qFDByW4EiRIIDVq1FCCDSKOEEIIIZ6HAowQQgghxMPQhoIQQgghxMMwCd+CIE8Lw4aJEiVS7veEEEIIsT7ovXz37l3Vvzl69LBjXBRgFgTiK2PGjN5eDUIIIYREgnPnzkmGDBnCXIYCzIIg8mUcQFQrEkIIIcT63LlzRwVQjOt4WFCAWRBj2BHiiwKMEEII8S3Ckz7EJHxCCCGEEA9DAUYIIYQQ4mEowAghhBBCPAxzwHwY9IGEEz4hriRWrFghmrsTQghxLRRgPuozcvnyZbl9+7a3V4X4KUmTJpU0adLQh44QQgJFgGXJkkXOnDkTYn7btm1l/PjxIeYfOHBA+vTpIzt37lT/b9SoUdK5c+cQy6FP4tdffy0rVqyQBw8eSI4cOWT69OlSokQJ9f7vv/8ukyZNUp9z8+ZN2b17txQpUsTuM6ZMmSJz5syRXbt2KaO1W7duqQuVmaNHj0q3bt1Ug+wnT56optgDBw502s8xshjiK1WqVBI/fnxeJIlLxT3+Pq5evapep02blnuXEEICQYBt375dDa0Z7N+/X6pUqSL169d3ujwuFtmyZVPvd+nSxekyEEply5ZVIggCLGXKlHLs2DF57bXXbMvcv39fypUrJw0aNJCWLVuG+l3Vq1dXU48ePZwuU6tWLcmZM6f8+eefEi9ePBk9erSad+LECRVRiCrYN4b4Sp48eZQ/jxBH8LsFEGH4nXE4khBCAkCAQRyZGTp0qGTPnl0qVKjgdPnXX39dTaB79+5Olxk2bJgyRkPEyyBr1qx2yzRp0kQ9nj59OtR1MyJrGzZscPr+9evXlbCbNm2ainwZ6z9hwgQlJEMTYI8fP1aT2cgtNIycL0S+CHEXxu8LvzcKMEIICbAqSAzhzZ49W1q0aBGlYbYlS5aooUZEyXBHX7RoUZk6daq4GkSkcufOLTNnzlQRtWfPnsnkyZPVdxYvXjzU/zdkyBBJkiSJbQpPGyL2iCTuhL8vQggJYAG2aNEiNdzWvHnzKH3OyZMnZeLEiWpocNWqVdKmTRvp2LGjzJgxQ1x90Vq7dq3KH0Mbgrhx48rIkSNl5cqVdsOdjmA4MygoyDahBREhhBBC/BfLDUGawVBejRo1VFfxqPDixQsVARs8eLB6jQgYhgSRdN+sWTOXJjC3a9dORbw2bdqkcmn+97//Se3atVVuW2gJzXHixFETIYQQQgIDy0bAUNGIaNJnn30W5c+C8MmXL5/dvLx588rZs2fFlSDxftmyZTJv3jyV9F+sWDGV/wUh5upoGyGEEEJ8F8sKMCTMI5JUs2bNKH8WxNCRI0dC2EVkzpxZXAmqJEH06Pa7Fa8RhQt0YJ/RqVMnZQGC4dnUqVOrY4PhYWPfGVYkGM7FlCBBAiVkf/31V9v7WBbDtijOwOegcANFGosXL3b6vRUrVrR9nrMJ7/siGJp/7733Xrncxo0bVRQWkWRsL4b2CSGEeBdLDkFCrECAYXgwZkz7VWzatKmkT59eJa4bifoHDx60PYff1549eyRhwoTqQg9gT1GmTBk1BAmbiW3btilPL0wG8P5CROzixYvqtSHYULloVC9CQGA6fvy4er1v3z6V65UpUyZJliyZlC5dWuV6Yb3hTYbIF5L9T5065RIh6csgDw9iC75pOA4FCxZUw67YhzgOOKbvvvuubfkBAwYoOxBUhI4YMUI+/PBDtQyOY+vWrWXr1q0yduxYFdm8ceOG/P333+rRGfB4w28DIL+uZMmSKrqaP39+NS927Nh2y6PyD27w/gIKQgoXLqyKWerWrevt1SGEEAI0C7Jq1SoNq3bkyJEQ71WoUEFr1qyZ7fWpU6fUso4TljOzdOlSrUCBAlqcOHG0PHnyaFOmTLF7f/r06U4/p2/fvrZl8NzZMvi/Btu3b9eqVq2qJUuWTEuUKJFWqlQp7Y8//ojQ9gcFBanPxaMjDx8+1A4ePKgeDV680LR79zw/4XvDS7Vq1bQMGTJo9/AfnfDC9GGZM2fWRo0aZXv99OlTLX78+Fr37t3V6yRJkmg//fSTFhmM38vu3btt8/B6woQJWu3atdX34Dg/e/ZMa9GihZYlSxYtbty4Wq5cubTRo0fbfRZ+h3Xq1NGGDx+upUmTRh3ztm3bak+ePLEtM378eC1Hjhzqd5cqVSqtXr16tveeP3+uDR482PYdhQoV0n799Ve779i/f79Ws2ZN9VtKmDChVq5cOe348eNOf4vr169/5fZjuYULF75yOWe/M0II8WVWr16tRY8eXbt27ZrbviOs67cjlhRggU5EBRg0DaS0p6dQtFQIrl+/rkWLFk0bMmRIuJZ3FGCG6Oratat6njt3bq1BgwbanTt3NFcJMIijH3/8UTtx4oR25swZJaL69OmjBPXJkye12bNnK3E2f/58OwGWOHFirXXr1tqhQ4eUyMcyhrjH/40RI4Y2Z84c7fTp09quXbu0MWPG2P7/oEGD1M3AypUr1fdCyEOobdiwQb1//vx5Jerq1q2rPgs3JFjHw4cPa3fv3lX7oHr16tqlS5fU9Pjx41duPwUYISTQePLkida4cWPbzWrFihUtIcAsOQRJ/AsM2eLaD480MylSpJBHjx6p56gehWGuIxg6xBAk7DneeustNQ9Dlo0aNVK+axhaQweDDz74QA1xRpaGDRvKJ598Yjevf//+dsa9//zzj/zyyy9qGNsAQ87jxo1TZqV58uRRQ83r1q1Tw6cY0kYOGzohYKgaOYeowAUw3sVQLIZCMXQN0NFh8+bNyjsOOW1ovQVfOBR1GEOiuXLlsn03hrjxOa7osEAIIf7Iv//+K61atVLpLgY9e/YUK0AB5gfAtPzePe98b1RALh7y/SCmzJ0AAPp2fvPNN0qgIZ8PHQWMPLo333xT5ZThDwu5XxA8Y8aMUYKpd+/ekVoXoyeoGQigH3/8UQmphw8fKjHo2B8UeWRmp3hU3Bp/6GihBdEFYWW0sHr//feVyzxEKYoJsIwZfIch0pDLWL58eb/KRyOEEE9w9uxZu0I73LD369dP9ZV2LJTzFhRgfgCaBCRIIJYFxRCovnOsRIUwMfceNIOG5qjyg/hCtaSjMztECcQJJoi1QYMGqcR9PHdMqg8PiFSZQdTpyy+/VNE3RKgQwRo+fLhK/ndcDzNYT6PiFf8HjdvRumr16tWqMAMnAHjC3XupmJcvX66KC8wYnnDO9gshhJDQwWgLRjRwDjdAYdz333+vRl2sBAUYcTu480CkB0N1HTp0CCF2nIE/FKOKNTygGhKtnxAxi4wAc2TLli2q4hJ3SwZoqB5RUMVbuXJlNfXt21dVgcIvDvsDQgt3aaH1OUU/UfjHhVaVie00N64nhJBAZvPmzeqm3EyvXr3UDboVoQAjHgGGtMjRwlAfokAQFwgDIxp0+PDhMHtlOgLfro8//lh9FsQdbEgwpl+pUiVJnDixS9YXbavQ0xOtq5D/NWvWLLWujk3cwwKmvBgqxZApcsX++OMPFR1DLhyiY4iwwSIF85DHhjw3CD9sA+7Y2rdvr6w2PvroI+V7hnwwDLvCRgOfAb80rB8ii9gPeN+ZUEO0zbBOAbBFwfAmrFNgoUIIIb7M48ePVX6so7n6rVu31E2vVbHGQCjxe2Caih6ZiARBTCB5HgIKAgNCZODAgeH+rGrVqqnIUNWqVVVHA0TVMA8J8q7i888/V55Z8B974403lMeYORoWHvCHDw8yFA9gPdH6au7cuTb/MWwzctbgaYf3kSOGIUlD5EFUIVoGAYUoGUQqfOUMkYVEfwgx7EeY0UK8OWPHjh0qr8zILevatat6jiFRQgjxZXbt2qUMuc3iC8bdGIq0svgC0VAK6e2VIPbAfBTRDEREHCM6GGJDBAMXafzoCHEH/J0RQqzMvXv31E0kCrCMvFvcjGKUwFwYZaXrtyOMgBFCCCHEZ2jTpo1K4xg1apQSXxipuHTpkkoT8ab4iijMASOEEEKIT7BixQqVzmGA3NoaNWqIL0IBRgghhBDL8uLFCzWhqhz5vohyoQL86tWrKv/VV+EQJCGEEEIsyfz585XgMgq1UD2Pqkekr/uy+AIUYIQQQgixFA8fPlQV87DhATDaxjzgS3leYcEhSEIIIYRYhrVr16pEe7N/IXrx+lt3EAowQgghJLLAf2rpUr0fXOHCIi/99gTWCIjYoGmuQys14hyYahs+iQBt2uAViR66/giHIAkhhJDI0rixSPv2Ip98IjJzZvD8ixdFEiZEc1eRVKlEcufGOBr3sxNevHihzKbN4uuTTz5RgsxfxRegACOEEEIiw5kzIps26RGuqlVFChYMfu/2bf3x6VORa9dEjh4V6d9f5MYN7muHlm0xYsSQjRs32uahb/CPP/7ostZyVoVDkIQQQkhkMNqfVaggsmqV/XuI5ty9i4aE+vTxxxhjg3GVSJMmAb+/7927p3rkPnv2zLYvUqVKJadPn/a7XK/QYASMeIzLly9Lp06dJEeOHKqNUurUqVWD7okTJ8qDBw9sy6HJdLRo0dSUIEECKVasmOrtZYBlUR2D/pL4HJQiI3y9ePHiUJt3G5/nbML7vkjz5s3lvffee+Vy6DX5+uuvK+donODwf9DAmxASRebN0x9fVurZgagYhiAzZhQpVEjEGEpDvliAs2nTJilYsKCd+ELf2ytXrgSM+AIUYMQjnDx5UjWAXr16tQwePFg15kZVy1dffaVC0Kh6MYOSY7SWwHIQD2g18ffff6v3WrdurZpcIznz8OHDsnLlSvnggw9Uw2xnYFl8FqZt27apefg+Yx7eN/MUQwZ+xF9//SXt2rVTPdLWrFmjtg+NzO/fv+/tVSPEN3DWMhlDirt2icSMKVKv3qs/4913dTGWLZsEKuhj/Omnn8qbb76pIl0AN4TIAatUqZIEHGjGTaxFUFAQ/trVoyMPHz7UDh48qB5DcO9e6JPj8mEt++DBq5eNINWqVdMyZMig3Qvl/7548cL2PHPmzNqoUaNsr58+farFjx9f6969u3qdJEkS7aefftIiw6lTp9S+3b17t20eXk+YMEGrXbu2+p6+fftqz54901q0aKFlyZJFixs3rpYrVy5t9OjRdp/VrFkzrU6dOtrw4cO1NGnSaMmSJdPatm2rPXnyxLbM+PHjtRw5cmhx4sTRUqVKpdWrV8/23vPnz7XBgwfbvqNQoULar7/+avcd+/fv12rWrKklSpRIS5gwoVauXDnt+PHjah2x3uZp/fr14doHV69eVcv/9ddfoS4T5u+MkEBiwwZNS5xY0+LH17T06TWtTRt9/vbtmlahgqbVrBm+z8E5znSeCyRwfq9fv77d+aply5bazZs3Qyx78aKmlSqlafnza1q5cpo2cKDmN9dvR5gD5k8g3B0a77wjsnx58GtU5ZiG/exAPsOGDcGvs2QRuX791XeEoYDIlBH5wpCiMzAUGBpoPxErVix58uSJep0mTRrV/6tu3bpqWM0V9OvXT4YOHSqjR49W34c7sgwZMqihz+TJk6voW6tWrSRt2rTSoEED2/9bv369modHeNYgUlekSBFp2bKl7NixQzp27CizZs2SMmXKyM2bN1Xo3Tw0OHv2bNXXLGfOnCoJtXHjxrYh1QsXLqg7RQyRIjyPhNQtW7aosP2XX34phw4dkjt37sj06dPV5yVLlixc2xoUFBSh5QkJaNatE7lzR3+Oc6bxvEQJ/TxpGkYLE/M5bto0/f8mTSry2mshp5IlReLGFX8BaSPmNBJE5d98881Qd/e//wa/dmnAENctJPsXKCCSPLl4Gwow4nYgTBBoyo0ybBMpUqSQR48eqecYIhs2bFiI/wvRNWLECCUa3nrrLTVvypQp0qhRIyWMChcuLOXKlVNDkMgniywNGzZUZc9m+qNi6SVZs2ZVQ6a//PKLnQBDEikqdlDFkydPHqlZs6asW7dOCbCzZ88qwVmrVi0lFDNnzqyGYQFaaUCQYii0dOnSal62bNlk8+bNMnnyZCXAxo8fL0mSJJF58+YpAQpy5cpl+27kSuBzIEjDC4Rl586d1b4qgJMQISR0IK6uXtWf4/zQrl3IG10MQUaULVtEZs8O/f1z50QyZPDpIwPX+vPnz6ubS5wHFy1apG5I9+/fH+aN89OXGSCvvy7y1VfwAnPhSq1eLVK9ukimTHoFq5ehAPMn7t0L/T3H1g3GScUZ0R1SA1+O1bsa5GNBEEBMQUiY+frrr+Wbb75RAi1hwoQqOgVxA3DnhJwy5DQhMgXBM2bMGCWYevfuHal1KYG7WQcggFAKDSGFkwnEIKJbZuBbY26LgWjYvn371PMqVaoo0QVhVb16dTXB0yZ+/PhKlOKuEMuYwXcYIm3Pnj1Svnx5m/hyBRC6OAFC6BFCXgGE0OTJ+nPc6BQv7ppd1rChSL58wRWSsKwwnmNCFMxYDuvwww/BBq8+AG5KO3TooJ7fvXtXncPr1Kmjplfx7GVAMW1akQ8+cPGKXb4cbJ6LSKaXbS4owPyJUIb3PLqsE1D1iCFGx8o7CBPgrOqlW7duqsoPf7iolnQcooQogTjBBLE2aNAglbiP57Fjx47wOjoOjSLqhGE+RN8QocId2/Dhw2Xr1q0h1sMM1hOiEuD/7Nq1SzZs2KCGYPv06aOGOrdv365KsMHy5cuV27OZODBuDGW/RIX27durggcMdWJ4lRDyCmAjgb9xTK5MEq9cWZ9exY4dIseOifhIwQxSInr16qUEmAFuSI0of3h49izygcVX0qwZcj9EcC2CbUj9+uJNKMCI28FQISI9xl1RaHlgjsOTEG7hJV++fCo3ChGzyAgwR5Brhbyttm3b2uadOHEiwp+DfLLKlSurqW/fvpI0aVKVz4X9AaGF6BqGG51RqFAhmTFjhqpadBYFw3Y+f/78leuA4V/s94ULFyoxiOFUQkg4gH0EovO4qfJGA2hjZAA5vMhZQmQMeWPwH0uRQn/vzz91kQZLmtSpxVtgtAA3mchdNThw4IA6N9uYM0dk926RNm2Ck7uw/PnzatueJnxNnj5Iiltb9+3u2rV1AQY7EAowEghMmDBB5R1hqA9RIIiL6NGjq2gQrCSKRyC0j6T0jz/+WH0WxB3aVfTs2VOVMbvKORl5CzNnzpRVq1YpwYJEeqxrRMQLok0YKsWQKXLFUDiA6Bhy4RAdQ4StS5cuah7y2JDnBuGHbWjWrJmKWMFq46OPPlK+Z8gHw7BryZIl1WfALw3rh8gi9gPedybUMOw4Z84c5ZOG74UfG8DygeS5Q0iEwLAgku6Rr+SiYp8IU768yM8/65E4TEY6yMsouWLiRJHfftOLrJYs8fgq4pxkjnDBnxGFRbjptAMGtI0a6c8hfLK9FGBz52LIQz3F2asj0u0koTxe/ppI4ddEpk7VixIARiCQx+VYtGAUM0CUhhU6Q4IZ0j6+/17fXwi3uSXUFk48UZZJPGRDYXEuXryotW/fXsuaNasWK1YsZatQsmRJZeNw//79UG0oHIF1Q+nSpZXtA+wbsmXLpnXs2FG7fv16pG0oFi5caLfco0ePtObNmyvLi6RJk2pt2rRRNhiFCxcOYUNhplOnTloFlKZrmrZp0yb1/LXXXtPixYunbCbmz59vV5oNa4vcuXOr/ZEyZUpl12G2h9i7d69WtWpVZY8BK4ry5ctrJ06csNlJVKlSRe3HsGwoHO0qjGn69Omh7idf/p0R4hK6dsUfj6Z9/bX3dihsKy5f1rRDhzTt7781bflyTZs9297OokoVfT1Ll/boqj1+/FhZ7JjPKW+88Yb2wNHGCFy9qmmpU+vrWbWqpl26FPze2LE46WuP4yXW33ectm4NXvb7750vY0wrVwYv+/vvuo9F7dqa1rQpTs6aVqOG/fIbN3rVhiIa/vGe/COhjaMjOoGIiGNEB0NsMLNDJAYu8IS4A/7OSMDTqZOe/N6rl8igQdbdHTCShhFsuXJ6X0oPgNQI5OiamTt3rorW24C0gIURUk4QSUTVIdozIa/N4dp15ozI//4nMmTQM6n7VpCMG3hLUsV6WZBQqlRwsjzythYssC9YMCbY68C/woiWIcr1MrIWKm44tmFdvx1hDhghhBDi0WxwF2LkgTpWr7sBdM9AYZQZVIYjPQP5rnZAdKHa0AC5uRhOdRBfW7fqGksnpkRPmVxSlQnFo6taNX1yxsviJxto/QQB6FhpCj/Jxo31fDkMXXoRi/+yCCGEEC/gawLMzUUCK1assCtKAjt37lS9ep2CPDW0ajIEEDwMCxYMsdiRI8GrD22Hjk2RwlGAZs+uTxbG4r8sQgghxMOg4hm9Hn1BgBmRH3Nnk0uX9NdITE+SJEribO/evarDh2EjBNscvIZFT6igO8fDh3qy/Sv8y168XH0EtszNWgIBNuMmhBBCzCDXyGjH5gJbG7dirN/AgcHz0FUENj6wrkBlNIbaUMGNykTDjDQcoP0ahhgN8dW1a1dVtR6m+AI9e+pdAy5etNIIajCoFu3TBz2RxJtYXNoTQgghHgZ2Bkhqh7iBv5aVQWudrl11ywoz8ePrUTAkwyNBHRNsLL78Muw2SCZgn2MAH8H3YBfUpYuumuCnhTwrgO+BYDVsIYx+QuFQVc89M4Jqz7Jlur0Fjm8oPoyegAKMEEIIMYN+r6aer5YGiVOOEanRo/UJCedG8vnBg3pvH3QVCcX/6vbt26rjBzqLoKvHwIEDVas1mEirDh3oj4tyRYBuGoYAQ7L9y1ZxdoShqjRN10Foi+nxCJghEF3Y5i0yUIARQgghBjdv6vlLyJ1ybLztayDCkyqVPuXOree1GYnpBw6ITJlii1r9sH27dEKVoohkjhlTWn3xhcROnFgGDx4c/HkwgwVly9q3UkIiF5Lxse8MSwgk3Rtmq07YsME+4d6jWsgiBRYUYIQQQojBV1+JTJsmAuHRo4d/7RdzVeChQ8rn7LqIfAlvL9NitwcMEEmXTuTzz/UZCFN9+qkurkDFiiJvvhn8H9BuaOdO+3FFRNrCCGtdfJkehvQ0dHxq3Vo8ByNghBBCiMUwoiPe6P3oQV7kyCHN8ueXFSdOyI1HjySaiBSLH18WpkolGe/dC+41CZC4b/hFgIwZw/7wcOy7py9HAdHFCF2KPAoFGCEhezyi4mY0chcs/JmEED/GIsNT7vb0egcNvl9SoEABmTJlil1PRzsQ8Vq/Xo+AQVwh8d9FGiimN3YzBRgJJNC2AgmeixYtEqsAcfZXGGXIFSpUkA1GKboPYcV9TYjP4McCDE72qVKlkgcmz7BMmTLJrl27JFZYSViws4AIc8NujhUrcAUYfcBIwPL777/LpUuX1LRt2zY1b+3atbZ5eN/MU+OPlhDiv/ipAEN1I9oImcXXqlWr5MyZM2GLL3/QQDNnijRrBi8N/fXYsSI455uigN6AAszP7m4iOj0zTjbqvPNMzXuICqBXfK4r1rVp06bqhJA2bVqnxn6PHz+WL7/8UjkvJ0iQQN544w27iNSNGzfk448/Vu/Hjx9fChYsqBrChpdkyZJJmjRp1JQyZUo1L3ny5LZ5eD5x4kR599131fd/++238vz5c/n0009VM/R48eJJ7ty5ZcyYMSEiUO+99558//33atvwOe3atbMTcBMmTJCcOXOqhuqpU6eWD1Ae/pIXL17IkCFDbN9RuHBh+e233+y+48CBA1KrVi3V7DVRokRSvnx5OXHihPTr1081yl28eLEqI8fki1E8QryGV4yp3Aei4ThPfIXigpfUqFFDnWeqVq3qtfV66kkBhibdEGHdu4v07q1Xa77+usjL87638C+JH+A4NkkND7/88ovUR7uIl0Z7DRo0CDH0liVLFrl+HbUywWgwcYkC3bp1U8N/EAoIiffs2VOFwZGvZdC+fXs5ePCgzJs3T9KlS6fWr3r16rJv3z4lXh49eiTFixeXr7/+Wp1gli9fLk2aNJHs2bNLyZIlxRVA0AwdOlTlkKHZLE5aGTJkkF9//VUJq7///lu5RUNoYd8ZrF+/Xs3D4/Hjx1XrDmxby5YtZceOHdKxY0eZNWuWlClTRm7evCmbNm2y/V+Ir9mzZ8ukSZPUdm7cuFEaN26sRCKOzYULF+TNN99UQ6h//vmn2vYtW7YoAQ3BeujQIblz545MRzuQl0KTEBJYETCco3Ge6tSpk9w17CMEThRH1XkloHbzs5dfBhuOQYN0DzMXXSOigm//wohPcu/ePZk2bZoSGW+//baah6gNhI3B2bNnlYDAI8QXgLhYuXKlmg9vGkS+MM+gQ4cOKqQOUekqAdawYUP55JNP7Ob1hxnhSxCl+ueff9R3mgUYHKTHjRunTAzz5MkjNWvWlHXr1ikBhm1CRA0RLESvMmfOLEVf9ktD1A/bhqFQIyE2W7ZssnnzZpk8ebISYOPHj5ckSZIoYWoMHeSC585LEDXD5yCKRwiJIGhKmDatSJ48PrvrcNOGc4UBzg+4kXzfME61AE89GQEzjfR47ktfDQWYnwmbiKLcjV+CP058RnQH75bTaF/hQjBU9uTJEzWkaIAoDYbzDBDlwnCfWVgACAtEngDeh1iB+EFUCJ+J9zEc6SpKlCgRYh4E0I8//qiEFIZr8b3myB3Inz+/El8GiIZhm0CVKlWU6IKwQkQPE/Y91hvRMuRoYBkz+A5DpO3Zs0cNOXojb4MQv6d9e/FlkK5gjGqAPn36SI8ePVS6g5V46k0BZpHopjXWgrgERFWiAobYMLn6cyMDhCAEzM6dO+2EjHmoFUmlyL/C8CDyv7CenTt3VmLFVThuO6JOiLohZw0RKkSwsB5bt261W85RHCEXC8OXAP8Hw60Y5l29erU6QWKoc/v27TYRjeFURPiciWVEuAghxBm4OTNAIZGVol5mnlKAUYARz4McLQgUiBaUQINbt26p3AQjbI5oDyJcV69etTuhmEHeU506dVR+FIDAwWfkgyuzm8B3Im+rbdu2dhG9iAKhW7lyZTWhz1rSpElVPhciXxBaiK6ZhxDMFCpUSA3ZIqnfWRQsduzYat8RQiIAnNx/+EFvUggneNwA+UCUGRXbOOf98ccf6qYQRT0o0sGIguPNqze5ckWkZUsUYGHEQ+TwYS/kgBkwAkYCFUSwUEmIRHwMJyIJv1evXnZDnxh6bNSokaqURLQJguzatWsqjwoCBDlVSCRFuB2J8Mi5GjlypFy5csWtAgzfOXPmTJVrhvwvJNIjcoXn4WXZsmVy8uRJlUiP9caJE+IRJ0xExxBh69Kli5pXrlw5CQoKUsIPyfbNmjVTxQljx46Vjz76SA0tIB/s33//VXlv+AwUTWD9jhw5ovYv3udwJSGvYNgwkV9/1Z/PmgVPGpGXOapWBUn2Ru4pbgxRiAPR5c5zYGRZvlxk6dKQ8197zQNfTgFGSDAYtsNwW+3atZXo+OKLL5TQMINk+0GDBqn3kOOVIkUKKVWqlEpeB998840SMtWqVVP5U6hGhP2D4+e4ks8//1x2796tqhoxrAgbDETD4CwdXhDtwtAAhh1RyQlRB/sM5I2BgQMHqopHVENi+7B8sWLFVKUogKhCtAwCFlEynHCRg1YWDXIFd5kt1fAm8tewj1GJiYpJQkgYGP5Y6BCNHEwL/83gfIhKarNXYffu3S0V8XLk8WP9sXhxWPXAHkN/bbSbdCuwoIC9EhqI//efZSJgKFW1FJkzZ4a/QYipbdu2Tpffv3+/VrduXdv/GzVqlNPlzp8/rzVq1EhLliyZFjduXK1AgQLa9u3bbe8vWLBAq1Klinofn7N79+4QnzF58mStQoUKWqJEidQyt27dsnt//fr1Ttcd07Zt28K9D4KCgtT/waMjDx8+1A4ePKgeCXEX/J2RgKN6dQw+atpPP2lW5cmTJ1q+fPls15UYMWJoHTt21K5du6ZZnR9+0HdvgwZeXIkjRzRtxw5Nc7h2u5Kwrt+OWM6IFcM5hhM5pjVr1qj55qoOM6gYQzUZSmxDK7tHfhGiAxiGQaQC3lIY1sLwj9kYFMM9wxCGDgV8FyrWjEiEIwgBm9cd02effaaGp5xV0xFCCLEIL4tkrGrA+vPPP6v8Tly/wOuvv66KeVCIhNEBq2MJf9tcufQQXNKkYgUsEocLxnAkN4CwQtJ2aAnJ+BFiMkKwzoCoypgxo82YEjjm7MDA81WWC6iwA6E5i+OPwywCkSQNo1H4U2G4ihBCiAVAmgLOyYkS6Y9mheBgw+Nt0PHDUWAhxwt5ob6U22mkYVlU33oFa/3SHICdAMw6W7RoESUBs2TJEhWBQhQNCd9I6J46dapL1zW078Ufj6ORpyPwroJzuXkihBDilhOzXoaXJIle5WiMriC3tFUrVNpYZrejIMdRfKFvLaocfUV8wXw+c2bk7OqvrZJ+ZQUsLcAWLVqk+liht15UQCIzevoh2RnVYW3atFEJjCjldydwe0eCuNnh3RlItkalmjEhWkcIIcRNfQGN4UZEvYyQDEY4Jk/WewR6GdjvoHDG3CMXleNoL2SM+PgKGDA6ezY4Cd/BWzugsbQWhYBB01CjFU1kQTk/ImBwTQeIgO3fv1/12kNZvzs4f/68rS3Oq4CVQNeuXW2vEQF7lQgzTD0JcQf8fRG/BO6fRppJx44iaFBtofQQCCx02UCFM3KXDS5evKi6afgixqWqUiWRUaNEChb09hpZB8sKsDNnzqh+eOYy28iCH66jL0revHllwYIF4i6Qbwa7gHdR0vwKYLxpbgkUFsgzg18W/iCRL4fXzC8jrrwAYOgfnmv4neH3RYjfADfQuXP154kT62arFgH5wrDRMUCgYMqUKT5fwGWk1qGDXOHC3l4ba2FZAQYBg3wtGG5GFVRAwpTSDBzT0Y/PXRcxrD9MRF09To+LIgoIUGEJEUaIO4CvGroUOPYFJcTnQMQL53+MpKAfIkYXMOz40k/QCmDI0Sy+MPSINBlnreF8NQLGU0lIYlp1+AMCBsODjj9AiBr0yEPeFMDdulGWi+cwqEOzYrit58iRQ82HqzgsIjAECddgJDHizgKTwc2bN1X7F0PUGIINVY1GZePly5fVhIbJAM2VYSKKCxWaSRvAJPPUqVPKgsIdICqB74TrMVvOEFcDM0f83TGySvyChQtFkOLRsCG8HPSEJIuAczj+1hBsQGcL9JpFlX1oVf++xo0bItev689Z/egEzYKsWrVKGZkdgWmaAzBCbdasme31qVOnnBqfYjkzS5cuVearceLE0fLkyaNNmTLF7v3p06c7/Zy+ffvalsFzZ8vg/5r5+OOPtTJlynjEyI0QQkgYDB+uO4A2aWKZ3XTs2DF1jsc1yWyy+uLFC81fgM959Oj6rsfUuLEWEARF4PodDf84E2bEeyAJH9WQaKmD/n+EEEIiyXffiXz9tQgKrn76yau7EZfbOXPmSOPGjW3z/vvvPynoh5np06aJGINAGMhCgWmLFuL33InA9duSQ5CEEEK8xOXLIsi9hVkpuoWYp+HDdfNSXzGggo3DoUOWGAM7ceKEskAyursYJuG+LL6+/VZk717dWN7xp7J7t75M3boi8+bplmvEHgowQggh9sZNu3Y53yMjR/rOnkKurinPV+LH98pqoIUdrJQQEQGoeO/du7eymvDlKuNLl4LNVcMCdQ8UX86hACOEEBKyZwzsD/r2RTNdfbp9WyRePFQZwZhKpFQpkZeFTl7h5k2Ra9eCQy6OV/ns2UX699fXHdv0spWcJxk9erQqAjPAkNSOHTuUKbiv8/Ch/ggN2atX8M/EPMF2rVEjb6+pdaEAI4QQEkyhQiIDBohky+bcqgFumsuWiaCdmzcFGHwc0TrIIEECXYjhyo/evk2bivTp45VVO3funKpUN1O5cmVlzu0v1i6GvxciXF7azT4PBRghhBB7AYbpVVdeT3tUoV4MzvXI5cLYF9YDSc5G79z79/UJTJqkO9yXLu3hVdRUgj0S7c0cOnRI8uTJI/6E4e9Fe4nIQwFGCCGByp49aDsSMosa0aTQWvQYQ5SevvLiiv/99/rzbt1EWrfWJwgx5FeZx74ePdJ733gQ+Ei2b99eli5dapvXq1cvGTRokPgT0MH4aRg63E8Cel6BAowQQgK12hF5XsaV1My4cSJoo+asJ62x/JYtulCrUUPPDQNPnui5WO7or2gIP0fxh+cwwjaZYXuSx48fy3fffacqGu8bETiBDrwlSSFs/Uh4YUR69WpdrxuHnBGwyEMBRgghgQjK2CCmMJSIhHVz5nT79nqW9Zdfhvx/RuUejJ0wwe7cuBp36qTnhpkjaubngwcHC6X9+/XejOb3kyQJPaRiFooWadEzY8YMad68uV3bO3RYcew97A88fizyxx/6c8PdHjikupEIYI1fMSGEEM+4Y27dKgKBADPQH3+EL4LepscIczx4oF9tQ4soIUE/VSr9KgzBBtFkgNcQShBlmByBADNH2SDgzCByhs+DGNu4USRDhuB2Qn/9FbycRcIuX5oEav/+/eWbb77xmyR7gEOMUWocEnOR6c6derodRn5LlvTmGvo2dMK3IHTCJ4S44cSiCxvkUpUvrwscV4PcK9hDOPMkgI0FkucN8QSLC1QyGu8bvgYGEHCGCGzTRk+sB1ACEIleioIdPXpUcuXKZTNXRc/h3bt3S5EiRcTfKFwYTv0h50Nj+5HOdCl0wieEEGIPBI5Ruoa2PO4AngTp0unTq4BHFyYDRN0g0gxBZs6fqlJFH+bEfCTXe0F8QWQVK1bMrqoxe/bsqvLRXzl9Wn9Mk0bk3j19eucdii9XwQiYBWEEjBDicmCemj69HoEyJ7STMHnx4oXK60IbIQM42Q/AUKyfgxoLBBtPnRLJkkX/2Vgk/c6yMAJGCCHEeRK7RfKnfIG///5btQzCo8H06dPtEu/9GUfHEYov10ItSwghvs7duyKbNunDdGY/LzTONiwhjKspr6KvBBYSyUxFCAkTJpTBgwdL27ZtJUYACVhvee4GCtythBDi67RrJzJrVsj5EAuffy4yfjwjYOGkZ8+eMmTIELt5yPnKYFRkBghIbWPQ1L1QgBFCiK8DN3uD1Kn1ZHWYouIKavh2IZP6t9+YQR0KEFmO/l1NmzZVXl+ByJEjwc+NnxBxLRRghBDi6/zyi54tnTIlxsv08AWqHiHEjKsn5ter5+01tRyoYqxXr54shNeYQ0PtQIt6GcCLF/3MQbVq9gWpxHXQyYMQQnwdRL2yZtVFFkDeV/z4etUjRBkJ1dPr7bffthNfP/zwgxJlgSq+ANpX7tihpxHCu5e4B0bACCGEBBT37t2TDz74QDZs2KB6OcaLF0/KlCmjGmnjeaAzcqT+iNRBaHjiHhgBI4QQX2fECJGvvhI5ftzba2J5ILISJUokq1atUuKrWrVqsn//flm7di3Fl/I90w1XDf9b4j4YASOEEF8HPR0PHtRtynPk8PbaWJo46H35ks8++0yZrEYzrDrcBEY4T5503p/c0S3E25h7ngeQ44ZXoAAjhBAr9X5Bp2Pz1dm4QofVfI8eX6GCfK5hw4ZJ69atJWnSpFK1alXp16+fNG7cWLUS8sQhrVs37GUgdBxFGSasXp8+er90bwgw+n+5FwowQgixCuvWISwTcj7EV5IkIrBEqF1bn7dtm54hjSv1tWv6PF4x7dizZ48ULVpUPYeb/ZIlS9TzvmgE7iHQm9xok/nWWyF7lBtuIeg9jsmR/PlFGjb02OradaliBMy9UIARQohVgPt62bL2V+hHj/TEHLOlBNi/X2TKFPv/j8pHIk+fPpXRo0erno0GZ8+eVdEwdw83hiZoYMO2fLn9e2a3EPOEnuSzZ4usWaNXI3pKgOHnZF5H6nn3QgFGCCHeAOIAIsoYb0LG8/vv65MZCDDjypwxY/B8RHb69w9+L3NmkYIFJdCZOHGijBo1So4dO6Zep02bViZPniy1jcihhwlrdNhwCzEcQ8xAc0OAYUTaU3TooEfkABxNKMDcCwUYIYR4w7keZktm5s4VqV5dT6g3g7GrtGn1yQwE2MvhNSJy4cIFO+8u9HL8/vvvVeNsT0e9XJGeV7y4/rh7ty7GwkoBdAX4DkN89ewpUrkymya4GwowQgjxNIhqAXhOIRKGCBZq/5s25bGIBJ988on89NNPIVoLpUqVyuv7M7ICLG9eXXujz/qJEyI5c4rHcr/gaIKUQ+JeKMAIIcTTGKVmGHvq0YP7P5Js3bpVSpUqZTfv66+/lqFDh1pmn0ZWgGH5woWxjfowpCcFGIcePQMFGCGEeBpEvsqVE0mcmPs+Ejx//lw6dOig8r3M3LhxQw09upvLl0X+/Vc/fGb7CLx2HCqMikNIsWK6ANu1S+Sjj8StUIB5HgowQghx95Xt/Hl9TAcTrtDo27hpE/d7JNi7d6+0atVKtsGG4yVz586Vj9ytUF6CIUEUqsJYNTS3ELOX1/37URNgwBOJ+BRgnocCjBBC3EmFCjCh0p8jGRxhElyZ9+0Lbp5NXsm1a9fscroSJ04sX331lRpyjOnBMbNOnYJd7VEX4cwtBJMjyZNH/LuMRPzt20WGDAlp1oqiV1e1rjQLMHcn/BMdCjBCCHEnMHIyGz8FBYncuUPPrghw6tQpyZYtm+11/fr1ZcyYMcpiwp389ZfIsGF6MjwED5g+XRcoixeLlC/v3C3E0dMLUbB69SL+/TBhTZBAj7qhMtGR118X+ecf1ximmodKrdIWyd+hACOEkMiAIcTu3fUrlmMPGbhutmypCy6jtv/cOZFYsfSrMgQYwwzhJqPJ/wwthRxzv9zFl1/a62cDHHaz+ArLLSQqwHcXQm/FipDC7sgRPTIG89Q2bSL/HfiJNmok8uef+msm4HuOaBqsgYmluHPnjiRJkkSCgoJUmJ0QYkFatNDDIc6AHxUEF8IKzZqJPH6stxFCOIOEK8n+rbfeUnldbV6qC0TBcD5MHpmxvEgA6wf0NYdOHjVKdwmB8MEwIGwaoKW9ybhxunEq9P6BA7rmj0zkCgFZbJNBvnz65xH3X78ZASOEkMhQv74+9oOsa3gEmMMTaJ6tzrAxRX7+mfs3Ahw8eFDyY+xNRDZu3Ch16tSRdOnSSVYULniQX37RH9G/sWNHsRytW4tMnSry338i6dLp0TIIqdSpRcaPDxmhM4MhzVOn9OXNuV9w3i9SxCOrTxgBsyaMgBFCAo1Hjx7Jt99+K8OGDVO9HEH27Nnl8OHDHk2yN4AQ2btXFznO+qNbAVhhoHkColhmkC6HLlfOEvRhQYco19Gj9vMh4BCoJZ67frPWgRBCiFfp06ePxIsXTwYNGqTEV61atWT79u1y/Phxj4svNMc+dEgXX/jqunXFssCD1kgpRHcr2FUgGoYqTRQPhJa6CPGFoVXzMOobb3hstclLKMAIISS8XLoEt089jIAsaHhRXbvG/RdJjh49qvo0Dhw4UL2OFSuW/Prrr7JkyRIpUaKEV/Kq0JwAESKA/uge8HWNEsj7woh3pky6b9jo0fp8NAN42Y/cjvnz9cfmzfWIFyo0L1wQWb/es+tNKMAIISR8fPedHl5IkUIPjeTJo4cNUKZGIgRqv6pVqya5c+e2m4+I1wcffOCV5tmIeHXtGjLPytf44AORqlV1cYUkfXOZHfK9fvtNf/7hh7p4g+DEz9oVVhYkYjAJnxBCXgW8CHr1Cr1rMgk3K1eulBo1atjNGzFihHR1VD8uBE4gSKRHANPRMcSYvv5aBKlndeqITJ6sixML9PKOMFhvRPIKFBBZtUpkwQJdlAFYTVy/rt9DoLiAeBcKMEIIcbxao4wMiTXw8kJ4YONGPXyAykdUNcJdE8k3CB/AcoK8kidPnqgEe+R7GSBJ+dKlSxIf+9GNIOoDUfUqUEGIpPuUKcWnQVEuvMoGDBBp317P+0LFI37GAIKMfl/ehz5gFoRVkIR4kSVL9DAIQLsghBIAkmQKFYpcT5kAZ8uWLfL555/LAZPB1KpVq6Qqxso8wLvviixdqifUwz3emVs9dPeYMSKVKolfgGIC/HSd9azET7liRW+slf9zhz5ghBASSXA1NjDbmvvLldmDnD592s6/K0WKFDJq1Chp1KiRx/K8ILBWrtSfI9ffSLD3d2BBAV+v338XuXkzOGgLc1m0JyXeh0OQhBBixnCmrFmT0a4oJtqbxRf6N6KFkKec7A0WLtRzu9C4OlDEl9kPDO2UiDWhACOEEDOwmAAsC4sUL168kOjRo6sI14wZM6RZs2bSt29f6devn1t+Z7BaQNNseF4aCfXIdzIe583Tl/voI7d8PSGRhgKMEBI4XLmiO1YaV2q0EXJs6mcIMGYpR4jHjx8r5/oLFy7Iw4cPJW7cuNKkSRP58MMPJU6cOOIu6tXTU/VeBWwXCLESNGIlhAQGaICHMSh4d+XKpZe6of+K4WL56FHwECTykxgBCzc7d+5UggviC4yDD4KyRIjmVvEFYC0BYEKKYUYUpTr2PK9cGW2N3LoahEQYRsAIIYHB33/r2ciIbCFDGYIM3Lunl8AZQgEhlbVrdbMkEib37t2T3r17yw8//GCbV6ZMGenSpYvL9hySyNu00Q+bM/8u4zDOmSNi9nXFIUXiOdxEMmfmgSTWw3IRsCxZsqi7JsepXbt2TpdHWXO9evVs/2+00YfBAdyZNW7cWCWAoudYwYIFZQfMFV/y+++/q5JovI/P2bNnT4jPmDJlilSsWFF512CZ2+ZqKRPLly+XN954Q33Pa6+9Ju+9916k9wchxEXAQmLKlGCPL2Rmo40QGuP9848e9TIqH9GnpW9f7vowgK1EokSJ1DkXeV8NGzaUy5cvK8uJGC6MHi5aJHL1qsjFizjfi2zerFtKzJyp20YYDaQRyDSD4CaMVFH15zjKTIgVsFwEDA1Ynxs5GIKO7vulSpUqqoLGGQ8ePJBs2bKp90O767p165aULVtWKlWqJCtWrJCUKVPKsWPHlDgyuH//vpQrV04aNGggLWG+GMp3Va9eXU09evRwusyCBQvU/x88eLC89dZb8uzZM7UNhBAvA2Fl/ttGSAVRLmeRLt40hQpuXF+HmZaDuz1aC7mzKLVzZ70w1ezdZTyH3xX8cgnxJSwnwCCOzAwdOlQldlYIxbgEJwLjZNAd1r9OgPtyxowZZfr06bZ55vJogGRRw7cmNDrjDCAiGzZscPo+xFanTp1k+PDh8umnn9rm5wu02mdCiN+B81uRIkXszFTBtWvXlL+XuzDux5HDhVwuQvwFyw1BOraumD17trRo0SJKpn1LliyREiVKqChZqlSppGjRojIV/SZczK5du9RQJ0qw8R1p06ZVPc9eFQFD9RDcc80TIcSFHD4sMmGCyMGD3K2RYM6cORIrViw78TVz5kzl9eVO8WWOgLEmgvgblouAmVm0aJHKs2qOfIwocPLkSWUAiGavPXv2VMOcHTt2lNixYyuPGleB7wHwuxk5cqTKS0OTWeSNHT16VJIlS+b0/w0ZMkT69+/vsvUghDhpL4RuyxhahDMnCRdIuxgwYIAaRTBH9JEjC0EWGYKCRDBAgKFDczK94d3lON2/r/8/uoIQf8PSAmzatGkqgpQuioP7SBBFBAx5WQDRKUSlJk2a5FIBhu8BvXr1UoUBAMOeGTJkkF9//VUlrToD+WQQhwaIgGHIlBDiItCNGJQvz10aTlCY1K1bN9uNJdi2bVuI/K+IgsT5BQsi/v8YASP+hmUF2JkzZ2Tt2rXqJBBVMBTomIeVN29elTDvSvA9wPxd8MBBkcDZs2dD/X9Yxt1eOYQEHKhixN8dQisbN+rzKMBeyaFDh+zOYbiBHD9+vLyLjtZR5MEDkbFj9ecISOI+07ExtmOCPWwmUOFYsmSUv54QS2FZAYbIEfK1aqLsJYqgAvLIkSN28zAkmNnF5jDFixdXQgrfhYpK8PTpU5XY7+rvIoS8gmXLkJgZ/DphQpEiRbjbXgHSNQwgxP79919lN+EKUAd1/TqKoEQGDQrfsCJywDSNVhLE/7CkAMNQHgQYhgdjOvyFNm3aVNKnT6/ypoxE/YMvE2vxHEnwyE9ImDCh5IABjIiyp4A5IIYgYTOBMDo8vTAZ3Lx5U0WpLsJsRsQm2NKkSaMmAI8bTMePH1ev9+3bp05MmTJlUvld8Adr3bq16nuGIUSILlREgtBsNAghbqJbN5Q162EUJB7VqMGreCgYN4kodkK+1//+9z+Vx4rzmauAkBoxQn/+xRfhz+li7hfxWzQLsmrVKg2rduTIkRDvVahQQWvWrJnt9alTp9SyjhOWM7N06VKtQIECWpw4cbQ8efJoU6ZMsXt/+vTpTj+nb9++tmXw3Nky+L8GT5480b744gstVapUWqJEibTKlStr+/fvj9D2BwUFqc/FIyEkHLRvr2lx4mhaypSaliuXpn30EXdbBM41mH7++We37rN58xDH0rTkyTXt/n0eHuKfROT6HQ3/eFsEEnuQhJ8kSRIJCgpSUTVCyCtAoY7RFBCULq23HiKh8tdff0mdOnXUeQbgnBNad4+ogqtMiRL6iHC/fmwyQPyXiFy/Le0DRgghETKL+u03KAuRkSO540IBqRYwioY9jiG+6tatqzqGuIt163TxhRacoXSVIyTgsGQOGCGERMouPX9+kTx5uPOcgMGOWrVqyR9//GGb16pVK9VtxNyWLSrgo9FG19HT66UDkHz2GXucE2JAAUYI8X1olx4ma9askapVq9rN27Rpk61a21VWa7Vq6cONofl4mewOCQl4KMAIIb4HvAx27NCtJWAmhVZDEGEvK5ZJsJM9qsbNuV3x4sWTq1evqkrxyPD0achiUnRPQztdiK9SpfSUPLOvF95v1UokSxYeGUIMKMAIIb7FL7+IwB7ByFlq1Ehk9mxvr5XlgJUP+uiawfAjuotEFpinfvcdzKPthxghsM6c0f29Vq/WjVMJIWFDAUYI8Q0QxenQIVhsZcokEju2/khsILEePW8noPn4SypXriyrVq2S6NGjR8nFfvx4/fnjx/BF1CeDaNHQoJvii5DwQgFGCLE+6EcIa4mrV0UgInr1Eundm8aqDkn26N04Z84cufTSkiNLliyyYsUKyeOCwoTly/XG2IhyrV8fsn1QrlwiLkwpI8TvoQAjhFgPRG8w1NiwoZ48dP68Lr6yZxeZNUsXY8QGOoCgZ6MBuoBMnjxZ3nrrLZftpXnz9MePPhJBZzV2VyMkalCAEUKsx7Fjup+XIbQgLiDKkOkdyeRxfwbt2wyQXI82aXHjxnXZ5yPHCxEwQ4ARQqIOBRghxLq2EkYjwGzZRNq08eoqWY3FixerSFf+/PlV79lZs2ZJnDhxItV3Fi0zYRGBKkYjsd7s5YV2u8j7wkhmwYJu2RxCAg4KMEKIdY1VYR5F7Lh3754kMpUZPnv2TGLEiCGNGzeOtNZFVGvr1lcvi+WQbE8IiToUYIQQa4HhNMcIGFEsX75c2rZtG8LryyzIXgW6D927p0e20Bpo6FBdfCVJIjJokMjdu3pSPYpOzUn2CRKIfP45DwQhroJnN0KItYCXwdSp+nNGwBS7d++W1q1by7Zt29TrZMmSqYjXmDFjIrRrjx8XKVRI5OFD/TVcPGCsCmAxAUs1QohnoAAjhITNzp268SnsHxA2+fVX15o9HTqEDtEi1arpr81NoQM84QjDi6+//rrsQYNFwSGILl27dpV+/fpJAoSkIsiMGcHiCzx5oj+i2BQTIcRzRNNgHkMsxZ07dyRJkiTKUDFx4sTeXh0SyMD4qUgRPXRivmo79qJxBKcVJIPjam+UzwFjDMtso47qxr17RaZMEWnZUv8/aDWE6Bd62gQov/zyi3z44Yd289auXStvv/12pD4PhwReXTiU8LJ9911d6+IQ58zJ0V5CPH39ZgSMEBI6X32lX7FhAzF2rJ489CrxBbDcggX6cwgqJBsZw4uPHoVcPnlyvZMzwLLo7xig3Lp1Sw0xmkG144EDByQ2xgwjya5d+qHE7q1TR3fzYMsgQrwHBRghxDlo6me0s5k+Hf1sgkMpPXuK/P67yMqVujW6IyZfKjV0afy/YcNCWqhDoHXvLpI2bcAfCSTZ1zKE6Ev+/vtvKe0C41nDSBUfTys1QrwPBRghJCQogTMaObdvHyy+AHwI/v1X5OhRkSVLRDp1Ct1GwpxIj//XsSP3thOuXbsmX3zxhfLyMmjatKnMQNJWBEFV4z//2Pt4YZo7V3+fRqqEWAMKMEICkf/9T2TjxpBXaUzvvKOPTUEsQRAgauUIEog2bAhdgDmLgBEnu+mFyunagH2pNGo0ad68uQwYMMCutVB4mTNHpEeP0N/HYcXhJYR4HybhWxAm4RO3ghwsjEGZo1Rm4MFlRK3gUeAs5+vECSQm6ctBoCFnCRWLJUro71++HDykyDofpzx58kQ51xsULFhQpk6dKm+88UakDisKSWExAZ+v5s1113rzSC/aCaHSsWnTSH08IcQqSfhPnz6Vy5cvKxPAlClThkgaJYRYFFQxGuKrWzc9Yd64SuM9s/dWaAn3aIpdoIDI/v0iX36pz4NBqCHAUMUI6OMVKkior1atmqxatUqSJ0+uPL4i078R7THnz9erGJFOB/0GGzV62BJibSIkwO7evSuzZ8+WefPmqZMF7uDgYoGwOcLlVatWlVatWinfGkKIRUH068oVXYSlSRP53jLIT0Jy/s2bunhD+MXAiOxE0jLBXxk6dKgcOnTIlts1d+5cuXjxournGBlOndItJYxgJW648ZriixA/GoIcOXKkfPvtt5I9e3apXbu2lCxZUtKlSyfx4sWTmzdvyv79+2XTpk2yaNEiFUIfO3as5IS5DIkwHIIkPg9OK4b9BJsHhrCWiIqflxmM/qKA9M03RcaNE0mdWiRVqih/LCHESkOQ27dvl40bN4Z6pwZB1qJFC5k0aZJMnz5diTEKMEICFIiu+PEl0MH97fz586Vz58528yMb8XIEQ48ALYQCvGkAIT4Hk/AtCCNgxG1ACMC/69w5kS5dENrmznYTqGysVKmS7XWePHmkb9++8pGLfCCOHdOd7ZFmh5qHFClc8rGEECsn4eMLEOVCEn7WrFmlcOHCqoInPu94CbEuDx6ImJs3L1xIAeYGHj16JJkzZ5arV6/a5vXv31++/vpru6rHyIKUu61bRZYt01/Doo3iixDfI1ICrG7durJ3716VbL906VI5cuSImo/8MIgxhNwJIRbD3AIItuglS3pzbfySCRMmSLt27ezm4Rzp6G4fFd5/X7dwM3BoF0kI8WcB9s8//6jwulHt+PjxY9m3b5/s2bNHCTNCiMUS4j/+OLg3I4xRedV2KahkTJ8+vd288uXLq/NkdBca0Z48qYsvfGS2bHqLzg8+cNnHE0KsLsAKFSokMU11zgirlyhRQk2EEAsmxKMPzcCBIn37MlvbxaDy+32EpUygKtxVifZmjMGFt94SWbPG5R9PCPEgkbo1++6776RPnz4q8kUI8RHQ23HfPr1RIIky58+fV8LLLL6++uorVfnoDvFlbqjNfo6EBGgELEuWLCoRP1++fPLhhx9KqVKlpGjRopIxY0bXryEhxDVRMIxXRaK/IAnZBaRAgQJyFM3IcRKNGVO6desm33zzjVsLkQ4eFPnvP705gUPAjRASKAKsXr16cuXKFalQoYL8/fffMnHiRCXIYDQIIbZ69WrXrykhJHKULi2CmyNYTlCARZmyZcvaxFesWLFk586dqgrcXezeLTJlisjhw/rratX01puEkAAUYMhvQCI+Kh4NTp8+Lbt375b/cItGCLEG8Pv691+RbdtEpk3z9tr4LEbLNQALHkTAkAsL8WXOh3UH/fqJLFkS/JrDj4T4B5E6c6D68f79+yGGJTE5JqMSQrzIli36Y5EiIokS8VBEArjYjxkzRk6ePKl8D5Hf9eDBA9WGzRPcvq0/1q4tUqYMBRghAZ2E36lTJ+nXr5/cNs4MhBBrsnmz/liunLfXxOdAmkXevHmV+ALmm0tPiK+VK0UqVhTZs0d//emnet9HON8TQgI0AvbBS+MZ9HrESQnNt5H7hbB87NixXb2OhJCI8uKF3nJo3Tr9NQVYBHbdC5k2bZqqaDTfZK6EIvIgP/wg8tdfwa/TpfPo1xNCrCjATp06pQxXDePVwYMHqxww5ELkzp2beWCEeJu7d0Xq1w9+XbasN9fGZ/j9999VkZFBsWLFZPLkyV7xODRcflq1EoGRPm0WCfEvIiXA0OcM07vvvmubd/fuXSXImIRPiAXA1RuiCxGct99m+OQVoHFu0qRJ7eaNHDlSOnTo4PYk+9B49kx/rFJFz/8ihPgXLjuzJEqUSLXewEQI8RKZMulNt7dvD87/ImHSt29fGTBggN28jRs3ev1cZggwL+k/QoibCfef9tmzZyUTTu7h5MKFCyF6oxFC3MyNG7oAI68EXl5ImTADY+m5c+faLCe8CQUYIf5N9IhYT3z++eeyHXfWYYTxp06dqpLxFxiNfwkhnuP5c/2RpXJhenrNmjUrhPg6c+aMzJs3zxLiC1CAEeLfhDsCdvDgQfn222+lSpUqEjduXClevLikS5dOPb9165Z6/8CBAyppFb0i33nnHfeuOSFE5+RJkXbtRF57TeTJE31e9Eg5zPg9yFHt2rWrrDOqQ0VkxIgRap7VoAAjxL+JpuF2MAI8fPhQli9fLps3b1Z3jHidIkUKZUNRrVo1Ff0iUQNtnZIkSaIiiokTJ+buJGGDXC9zvhKiX9evizgklQcyOE+lSpVK7t27p17jxrFPnz7yxRdfWNI6B2dlnErR/3H9et0PjBDiX9fvCKd3woAQPmCGFxghxMOgC0XcuMHDjDlyoD+OyK1bIjdv6q73FF92rF+/3ia+wL59+yQH9psF6dBBZNIkRsAI8XciHAFzRuvWrVUVEe4wSdRhBIyESv/+enNA5Cnh7qpGDZEZM0QsGMXxNjBRRbPsBAkSqNdlypRRd6QrVqywTJ6XIwsXitStG/wah/XUKbqIEOKP12+XJIrUqFFD5XyhPZFjj0hCiAvBeBTAfVNQkMi8eSIpUuhDjuTlrtFUNeNrr70mbdu2te2Vv//+W7nZW1V8Xb0q8vnn+vMvvtCHH8+fp/gixF9xiQCrU6eObN26VVKnTq3uMidNmqTaeRBC3FTlOHWqyOHDIv/8I3LggC7CiMpLxc3gL7/8ovbGzJkz5bFhKW9hoKfheH/tmkihQiLffiuSN69IypTeXjNCiLtwWalUjBgxpGbNmtKlSxf55ptvJF++fLJ06VJXfTwhxCzAILhgo1CqlEjGjAG/byCyhg0bJvnz57fr2YhhyDhx4lh+/8ycKbJ4sUisWCKzZon4wCoTQqKISzyWq1evLocOHZKMGTNKyZIlZezYsZIrVy6ZMGGCKvcePXq0K76GEAJ7FySPR8AU2d+B92ArhI9eAgd79G/MixCSBcFo8dixupaGc0iiRCLduunvwZAfETBCSACguYDdu3drz549c/pe7ty5I/RZmTNnRlFAiKlt27ZOl9+/f79Wt25d2/8bNWqU0+XOnz+vNWrUSEuWLJkWN25crUCBAtr27dtt7y9YsECrUqWKeh+fg21yZPLkyVqFChW0RIkSqWVu3boVrvUfMmRIhPZBUFCQ+n94JERx756mbdyoaXfucIe85NKlSyH+1n744Qft+fPnlt5HOB3og472U+nSmhbKaZQQ4iNE5PodoSHIQYMGyR9//CFXrlyxm1+kSBE1BOkMLB8R4LR/6dIl27RmzRo1v379+k6Xf/DggWTLlk2GDh0qadKkcboMjGLLli2rKqJQAQXTWJgvIknXAMUD5cqVU8MYoYHvQrSvZ8+eYW4DKkLN24CGvoREGoRKUO345psiJ05wRwqS1T+XtGnThjBZxd9adC+a0F6+LLJzp+6NC0cQY8TYDOYDRLoaNhQV1KxZU+Tnn9nAgJBAIkJDkDAuNCqIIHbgeg9HfOPRWe9HiKOIkNIh6xTCKnv27FKhQoVQWyRhAt27d3e6DEQVhkenwyvpJVmzZrVbpkmTJurx9OnToa5b586d1eOGDRte2Zg8NDFISIQZPlxk0yb9uemmIRA5fPhwiKFF5J2OHDlSrNCGE6eVR4+C5xluIThsxmScYiC6Bg/22uoSQrxMhG4VIXQgspBkD7EDB/zff/9dmbKiUTdEhytbED158kRmz54tLVq0iFLp+JIlS6REiRIqigavMrj2I2/EXUA0Jk+eXH3P8OHD5ZnRUySMBGJ4h5gnEuDgKt27tz716aPP++knkcyZJRBBVTVySvF3bObatWuWEF/g3DldfOFUFT++vVsIDufu3SJ//qlHx0Dq1F5dXUKIL0XAYDXx008/qSE4iDGc+BCdgoDYs2eP7Nq1S3bjLOMiFi1apKqYmjdvHqXPOXnypEycOFH1e8O6Y5izY8eOqgVJs2bNxJXgcxERTJYsmfId6tGjhxqGDOsiMWTIEOkPg01CDDp1wp1D8P6AO2fTpgG5f9AgG1FsnGMAjFXx92xEra2CcZ+VIYPI2bO4sUIVpt6gAJP5OTI2Gjf29hoTQrxKZJLM7t69q3Xr1k1LnDix1r17d+3+/fuaO6hatapWq1atcC+PBHhnSfixYsXSSiPD1USHDh20UqVKhVj21KlToSbhG6xfvz7UJHxHpk2bpsWMGVN79OhRqMvgPSTsGdO5c+eYhB/IIIkcv/sYMTStSRNNa9NG027c0AKNhw8farFjx7Yl2KP4ZezYsaEW/Hibf//Vk+mzZPH2mhBCfCEJP1I2FAkTJpTvvvtOWrZsqaJK6KmGYbemLrxDh6Hi2rVr1RBnVEGyLnzJzCCPZMGCBeJu3njjDTUEidyy3PBtcgJ8inzBq4h4CCSRw0MPSUXJkwfsbkcyPdIQDFA8kwHhJTeCr0Nvc0SozHlb6Gb0qiwIIwIW0yXmPoQQfyfSpwqICgw9fvzxx6oq8pNPPpFatWqpoTdXgIR55GvB3DWqoALyyJEjdvOOHj0qmT2QT4NhE1xI2CeTRJgAFF8HDhxQQ/aVK1dWKQL//POPLF68WA3TewIUQRspd2YgqtDf3CzKHCe0EgKhFIQTQkjkBRiiXPv27VMTqpHixo0rhQoVUuarKAtHA0pXJdxCgCE/K6bD7SSibCgEME7IuEPGnbHx/MKFC0r0IEqHyJxRJYUWSYMHD5YGDRrItm3bZMqUKWoyuHnzppw9e1YuXryoXhuCDYUFRkXj5cuX1XT8+HH1GvsBFY8oQIDwxMUCeXKVKlVS8/Ea3924cWM7ywtCQgUZ2/fuiTipKPb3/o3VqlWz2c5cvXpVVUSXKlVKTZ4Cja8B7iMhpJCvhcgWJhiohqflJvuiE0LCRUTGNqNFi6ZlzZpV69evn3bkyBHNXaxatUqNoTr7DhihNmvWLETOluOE5cwsXbpUma/GiRNHy5MnjzZlyhS796dPn+70c/r27WtbBs+dLYP/C3bu3Km98cYbWpIkSZTZa968ebXBgweHmf/lDBqxBhDLlmla4sSaliSJnjyUNSv+0PS8rwDh8OHD6u/V/Dd18OBBl34H0jXz5tW0pEn1XVy0qKa99Zam1aunaZ99pmndumna4MGa9vrreh7X99/r/+/FC90D99w5TfvvP0376y9NW7QI5wtNGzlS03r31rT27TWtUSNNq1lT0958U9N+/tmlq04I8SEicv2Ohn/CJ9VEeXEhunT37l1ViYToFyr+jKlAgQKhGrKS8AMbCkQTg4KCJDFMhIh/cumSSMGCeq6XI+PGibRrJ/7+O//ss8/UECOi1/HixVO5mVu2bFHRdVcC+4e33w7/8uPHi7Rt69JVIIQEAHcicP2O0BBk79695a233pITJ07Izp07le0Epp9//tnW9LZgwYJqiI8Q8tIIato0EQxtG8lCVauKpEqld12G+CpSRLdBx/AjxryQ7Y1l/Bh01cD5xKBGjRrK5ytLlixu+T4jQT5nTpEZM+wtIRwnaD+4fhBCiDuJkABDjgYSZHPmzKmmjz76yPbeqVOnZMeOHS71ASPE51m3TqRlS/t5W7boAgwdmJFfCHNRhypdfwU3b0ZupsEXX3yhDIujYrb8KoyWQGh8Xbq0276GEELcI8DCGq1Eax9MofVsJCQgmTtXfyxaVATCAyEWo00VBEeAmKvi3FG7dm1Zvnx5CJNkx7Zg7hRgzJAghFgFOtYQ4k5TKcPHDp0QKlYMyH0NkYWOGY4V1V9//bVbvg8ZEPv22VtEvCxupkcXIcR3BRhagJQrV04l3dNagZAwWL1aTzZCxKt8+YDbVU+fPlUtuMxttpBcj/6NsIlxB/fv6zr34UPn7zMCRgjxWQE2btw4dUJFvkbGjBntqiAxGZ5ZhPg9aPZ34ICeWIQwC5w6zb51GTOKfPKJ7ukVYFf+H374QTqhn+VL4I33zTffqCIed4IRXogvjO4i18ucXI+ApJ/XNhBC/FmAwakaLvhItjeqIKdOnSrnzp1TogwCDGaohPg9776rR7nMGGJs1Ci9lO7HHyXQKFGihKqSBugC8eOPPyoDZVcn2Q8apI/wwv/ZGGo0viJ+fL3WwczTpyKxYrl0FQghxDMCzDiBpkuXTk3mNkE3btxQJ134hBESEBw+rD/Ct+DRI/353bv6FH57Pb8DbckMAXbo0CHJlSuXy7/jyhWRvn3RNcP5+86aclB8EUL8sgoyefLkUrVqVTUREhDArfPOHZHq1fXIl9lcysFqwZ9By62uXbvKxo0bJVasWNKmTRvJkyePvB0R59MIsmCBLr7gY9ujh/1QI+zUEJwkhBC/EWArV650Wb9HQnyeWrXsX6dMqU8BwvPnz+Xjjz+WX3/9Vb0eMGCADBw4UA07ulN8gXnz9MdmzUQ+/titX0UIId4XYH369JHixYvbJrYeIiQwQQ5oq1atlPmyAaqjPQFSTDdv1p83aOCRrySEEO8KMFQ5Irdj+vTp8vjxY1vroTJlyihX/DfeeMP1a0iIVVm4UH/EsHuCBBIIXL16VdKnT68KcQAi4u3bt5d+/fpJTHMFaBS4dk2vXUDKqdnLy5jmz9dT7MqW1QtNCSHEF4lQM24DnHxRDQkxhmnt2rVy/PhxlXyLikh3thQJBNiM20eA6HrwAH24RNzUw9BKdOjQQdnQGHz44YcyevRol1vPfPONyLffvnq5H37AOrn0qwkhxJrNuG3/KWZMKVy4sJpatGih5q1Zs0YaNWqkomHGPEL8GqPDs4siP1YFlc1F0UrJRMeOHWXMmDFu+T4k0oMCBXRdayTXGzUO8PmC5jO1oiWEEJ/DZVeOKlWqyKBBg5RTPgUY8VuWLBHZu1c3XYWxlB8LMES6S5YsqfK9zFy5ckVSoZm4275Xf/zwQz0a5sz/Fr62frrbCSEBQnRXflipUqXUUCQhfp331acPQkDBXl+xY4u/gRQDWEqYxRcMVZGx4E7xFZ7AYpw4FF+EEN8nQveQ48ePl9dff10NPSIB35Hr16+rkzYhfkulSrqjpzEuVqqUSLJk4i88fPhQvv32W/nuu+9s89BI++DBgxLbQ0IzQEZ2CSEBToROcejlhgQz5IDly5dPtRzBlD9/frl375706NFD3nzzTfetLSGe5OefRYYN0/vaoPwueXKRWbNEmjb1y+PQq1cvGTx4sO31u+++q3o6Zs6c2aPrQQFGCAkEIiTAbt26JSdOnLBVP2JasGCB3EZ2rIhyv/7+++/dta6EeJbJk0X27Qt+jbwvP63wnTRpkp34wt/1+++/75WKZgowQkggEOEgP4YjMDUwOSCeP39enj59KlmzZnX1+hHiPYwk++7dRdDP8PlzvzoayOcyBBYqmNFCCJw9e1Yyuslg68gRvVWm4emFxhpIqDdDAUYICQRckmWRIUMGV3wMIdbCUAJweDc1nvcHli9fLrVq1ZIlS5ZI7dq1JVGiRHL37l1JmDChW74PLTO7dNENVh2BVY7ZaPXgQX0+c8AIIf4M01wJCQ0j4uVHSuDJkyfSt29fGTp0qC3PCz0d0b/RXeJr0yY9be70aX0EN106vX4BHraGOMN05oz9/0uRwi2rQwghlsB/riyEuDr6NX26Pl6WP79f7NvNmzer/o2HDh2yzUMXC4ivqNK5M/LG9DQ5czQLu3HOHN2xA7n8M2eKGHU6T54Em6s6TtCCfhZ0JISQqLciIu6FrYi8zMCBuuHq9u3iD5w6dUqyZctmew0fr5EjR0rDhg1dlmQPVxoIqtBo3lwExvmv6MxBCCE+jdtbERHi16xcKbJzp8i6dSJvvy2+Cu6tPvjgA/n9999t89CvFZXKyVzsXWakyyHahaR6I5IVFCRSoYJI9eou/TpCCPF5KMAIQXPBv/7SHe0xboZhRwSGMd9H2bBhg1SCaawJtAqD15erwa568UJ/Dr3qZqN8QgjxCyjACOndW2TEiJD7wQe7Ojx69EjZwVy+fNk2D8bJN2/eVJWO7sDszuFoKUEIIcQDvSAJ8Ung8zV7tgi8r9KmFYkbVyRTJpHixcWX2L59u3KtN4uvxYsXK48+V4uvR4/0Udpjx0SuXg2e70cFo4QQ4laYhG9BmIRvATCu5iOu9+jBOnDgQBk3bpy8eDkWWK5cOfnrr79cUuHoDAw1/vlnyPkYvXWTmwUhhPjV9ZsRMEKc4SPi67PPPpOUKVOqno0QX3C0v3LlimzatMlt4gsYThYIFhoUKqS3zSSEEPJqOGBAyP/+p/fI+fBDkRIlfGZ/XLp0SaZNm2Z7vXr1aqlSpYpbvxNmqps3i9y/r7/esUPv0gQ/LxRWulHzEUKIX0EBRgIXiC64h/70k57MVLCg5QUYXOtv374tyZMnl7Rp00rv3r3V8OONGzdcbi3hbFQWdhLYbWb/L9QqpEzp1q8mhBC/g/erJPBAntQPP4gUKSICWwaILxAvnliZmTNnqorGdOnSKY8vMGDAAPXc3eILwJcW4gui6/XXRZo1E8me3e1fSwghfgkjYCSwePxYpHZtkTVr9Nfly+vVj6gSrFFDrMiDBw+kf//+8t1339n6OR45ckTy5Mnj0fWYN09/rFtXN1wlhBASeSjASGCB8E2GDHr2+Pffi7Rta+mE+yVLlkjnzp1VOyGz3YSnxReChvPn688/+sijX00IIX4JhyCJ/7N4sUiTJnpzbYCmhLt3i7RrZ1nx9d9//6k+jXXq1FHiK2PGjEqMYbixhBfy1JB4f/GiSJIkItWqefzrCSHE72AEjPg///2nG63CI+GTT/ThRg9HkCKSZF+mTBnZtm2bbR5aCkF8JfSCwRbaYU6eLHL0aPDwI4KIhBBCogYjYMT/MXrlWLxPDppmI8neLL4mT54sf/75p1fEF+jfX+TXX0X27tVff/yxV1aDEEL8DkbAiDVBI2wME0J4oEE2WgRFts/Ns2eW7pOD/o3xHCowM2TIIMePH5c4Hgw33bql9yNHoNAYmTX8vlDxWLGiSOXKHlsdQgjxa6x5RSKBDSJWtWrZ97o5cEAkXz79+YQJIjNm6MLM2fTeeyLJkwf3xrl3z7IRsPXr10vr1q3t5qGF0JtvvunR9Rg3TqRDB/05fL2SJtV35dmz+jyk0KH9ECGEENdAAUasx8iRuvhCOAZKAKEZPBogIck0TBeCUqWCBRg+C0n3FhNgR48elfbt28ual3YYcePGlffee0/mzJmjku89CSzFIMAMnj4VuXZNnwxQOEoIIcR1UIAR64Ar/+DBIt9+Gxzp+vRTXSGYQcSoUiVdmKEHDh7NU6pUwcveuRMsvkqXFivQvHlzmYEI3ksQARsyZIgkNYtMD7Jnj26wCmeOkyf1EVvzrk2dWiR3bq+sGiGE+C0UYMT7ogtjXuDmTZF+/fTnGEZs0UJ/7hgRQgVjeKsYR4wQGTRIVxWofrQAh4xO1sohY7G8++67Xl0fw2C1Zk091Q7Am5YQQoj7YBUk8R7IzcIVv359PVKFTs7Nm4u0aiWqQbarhuKQ4O5F8QUn+3mGyhGRpUuXKn+ve/fueV18IbhorBoNVgkhxHMwAka8x7JlIjduiOzapQukxImDzVL9hAkTJkg7GL6KSObMmaV06dKSKlUqWbRokUfX48EDke7dRYKC7OsVUOWIRHsUm77zjkdXiRBCAhoKMOI9jN42H35oWUf6yBIUFCQ9evSQiRMn2ubt379fCTBvsGKFyNixob9fp45uP0EIIcQzUIAR74AhR6gCQ4D5CWgVhMbZU6ZMkUuXLql5iRMnVv0bc+XK5dbvvn5d5MKF4OgWolqGroUbB8iZU6RePfuaBQxD9uzp1lUjhBDiAAUY8Sz//iuCvKdHj0QeP9aT6QsV8oujsGnTJjv/rpw5cyone7QScjcQUqhURB2DAXxnUViJ6ckTfV7+/CJDhrh9dQghhPhaEn6WLFmUD5LjZOTROHLgwAGpV6+e7f+NHj3a6XIXLlyQxo0bS/LkyZXreMGCBWXHjh12bWCqVq2q3sfn7EFtvgOIalSsWFFFNLDMbdTph8Ljx4+lSJEioX5WQAM7dSMkA5sJHx9+xLFGs2yz+EqfPr1qqO0J8QUWLtTFFwpKYZ8GUPiJqNjx48GGqqxuJIQQa2A5AYahGgzdGJNhVFkflXKhVJhly5ZNhg4dKmnSpHG6zK1bt6Rs2bISK1YsWbFihRw8eFBGjBghr2Gc5iX379+XcuXKybBhw0JdN3xX9erVpWc4xmu++uorSZcuXTi2OMCASSq6O+/frxtQde0qvszUqVOVier58+dt8xYsWKBeY76n0+n69tWDi0iuxyrt2yeycSPsLkR++UV35CCEEOJ9LDcEmTJlSrvXEFbZs2eXChUqOF3+9ddfVxPojjIvJ0BUIUIx3VRhlzVrVrtlmqDXioicPn061HXr3LmzetywYUOY2wCRt3r1anUhxnPiACoeMRbmw8BCAtWMD9Gz8iWlSpWSzZs3SwwPO+7DsX7dOvt6BiTUY0qf3qOrQgghxFcjYGaePHkis2fPlhYtWkSpPcuSJUukRIkSKoqGi2bRokVV5MIdXLlyRVq2bCmzZs2S+OEsK8MQ1p07d+wmvwQ262ghBPNVH2bZsmWSP39+O/G1d+9e+eeffzwuvsCCBXr7zGLFRHLk8PjXE0II8TcBBq8k5FmhdUtUOHnypLIDQFL0qlWrpE2bNtKxY0e7djCuqoDDuqK1DARfeEEbmiRJktgmROv8EpirvvGGSNu24osgZxA3ArVr15azZ8+qKCp8vnDcC7mxkAAjtlmy6L3Iy5bV+5QjYNuxoz7kiI5NflZMSgghfo/lhiDNTJs2TWrUqBHlXKoXL14oQTQYfQZFVAQMnkyTJk2SZs2auWht4bM0Vu7evav8nyIClu9qyoVCBMwvRdjmzfqjRXoyRoRx48ZJhw4d7HL8+vbtG+4oZ1QYPlzkzJlXL9eggdtXhRBCiL8LsDNnzsjatWtVdWJUSZs2reRD+MBE3rx5VY6WK/nzzz/VMFScOHHs5kP8NWrUKNSIG5Z3/D9+BywnMPwIypUTX8NsoPrzzz9Lw4YNPfK9sBI7cULP61q+XASjno69xzFhlyJKRgghxDewrABDwjzytWqiQ3AUQQXkkSNH7OYdPXpUtYZxJT/88IMMMpWZXbx4UapVqybz58+XNzD0Fqj89huS43QRhiILuIFanBs3bkitWrWUSEcEtnjx4vLHH39I5cqVVTWtp9iyRX/ECGeNGh77WkIIIYEowDBkCAGG4cGYcJM00bRpU+WxhLwpI1EfthLGc/h9wXcrYcKEkuNlRnKXLl2kTJkyagiyQYMGsm3bNuXphcng5s2bKq8HogkYgg3WFoa9xeXLl9V0HMZKghL/fZIoUSLJlCmTJEuWTD2awToAVHFmyJBBAhKILrOFCEI1Fvf9QoQLnnGgSpUqymsOYDjcW6O2Phg0JIQQEhaaBVm1apWGVTty5EiI9ypUqKA1a9bM9vrUqVNqWccJy5lZunSpVqBAAS1OnDhanjx5tClTpti9P336dKef07dvX9syeO5sGfxfZxjrtnv37ghtf1BQkPp/eLQ0L144n3/unKY9f64/v31b0958U9MKFtS0vHk1bd06zapcuXJFa9iwod2x7dKli1fXqXhxNArStLlzvboahBBCXHz9joZ/wlRoxOMgCR/VkGjoDNd9y7B3r/6I9kHIWUPhAEr0jOaDxoQOA8i5mz1bJHVqsTqIuMKxfiMcSwUBumgqUjpy5EivmumiWQDaCL14IXLunEigBlEJIcQfr9+WHIIkFqV8eV0VYAg2e3Y9+/vBA31CF2gzEGjx4onVgUdcnTp1bK8LFy6sPOIMc19vsnWrLr6QqkjxRQgh/gUFGAk/aC4IDLPRMWNEevcOWZJ3757ecNtK0TsndyloRYXolwFy/dAKy5NJ9mbQy3H1al27IpCI/o6A+V+EEOJ/UICR8AO7dfWrefmzgcCysMgKDVSq9oZwNLF+/XrVaN1bwGICfclRLOoIBRghhPgfFGAk8hEwHwOVrrAFgaO9Qd26deW3336LUqurqIBg4RdfiBgFuWhRirQ5I5iYLJnIe+95ZdUIIYS4EQowoodf2rTRhZWRSA8VMGdO8N5ZtUpPSFK/Gt/62aDOZO7cucqO5OrVq3YtqhybsnsCGKu2b6+3xMRzo/87ahq+/VYkblyPrxIhhBAP41tXUuIekGyEMjtgqAEYppp56bumiB3bZ44EuhO8/fbbdh0Q0Be0QoUKXvWlXbky+DW6TqFJQqVKXlslQgghHoYCjATndrVurSfPY+zLlJyuKFZMN1BFS54kSXxirx0+fNhOfA0YMED1cPR226cnT/RHpJy1aKHvch/ZpYQQQlwEBRgJFmCwlgjN7X3kSJ8ackROV+7cuVWHAuR+LVu2zCVtrVyZSgertCZNvL02hBBCvEF0r3wrsRadOomgMTlCMT7M+fPnlfD6AlntLw1VT506pawmrCK+nBWTEkIICTwowAIJXPknThRp1Ehk/HiR3btFTp3Sm2PXrSuSK5f4KgsXLpSMSKYSkVGjRsmlS5fUczgRe6vC0U+LSQkhhLgA3oMHWrVj27b6c3OFY9GiIrt2iS9y7tw56dChgyxevNg2r2fPnpI2bVqxGps2ibz/vt5MADACRgghgQsFWCBRqpSe7R0UpL+GSDHMpnyMJ0+eqIpGWEmAmDFjytdffy3du3eXhAkTihUZNUrkxo3g1z4ccCSEEBJFKMACiVSpRG7fDn1MzEeYPn26tED54EsyZcokf/zxh+TPn1+syp07In/8oT9HsC5HDlhieHutCCGEeAsKMOIzY2EwUU0Ng1gTxYoVk61bt6oImJWB6Hr8WCR3bpHatXVHD0IIIYELk/CJT4A8L0fxtWvXLtm5c6flxReYP19//PBDii9CCCEUYMTiXL58WT744AMZN26cbV67du2U11dRFA/4AEizW706WIARQggh1g8dkIDk+fPnMmnSJOnVq5cEGUUDInLlyhVJhVw2H+LHH/W+jwUK6OarhBBCCIcgieX49ddf1bBi+/btlfgqXry4Gm5E1MvXxNehQyLffKM/b9fO22tDCCHEKlCAEUsB1/oGDRrYXg8ePFgl2fvKcKNjz0d43j56JFK1qkirVt5eI0IIIVaBAoxYAvRrBNGjR5dVq1ap55s3b5YePXpIDB+1jEfiPZoNJE8O6wxsm7fXiBBCiFVgDhjxKocPH1aGquDWrVuSNGlSqVq1qhpu9HVedkMStKFMl87ba0MIIcRK8J6ceAUIrBkzZtjEl5H75U+w6TYhhJDQoAAjHufQoUNSuXJlad68uW3e6NGjpWXLln51NNh0mxBCSGhwCJJ4jHv37knKlCnlEbLSRSRevHjSt29f6dq1q8SKFcvvjgQjYIQQQkKDETDiEb777jtJlCiRTXyB/fv3qwba/ia+7t8X+eEHkY0b9dc+WkNACCHEjTACRtzK6dOnJWvWrHbzatWqJUuWLJFoftoQcd48kU6dgl/Hi+fNtSGEEGJFKMCI25LsGzVqJHPnzrWbf+zYMcmRI4df7/UbN/RH6M6yZUX8LLWNEEKIC+AQJHFL1KtmzZp24mvgwIFKlPm7+DIn37/9tsisWSI5c3p7jQghhFgNRsCIy0B+V+fOnWXWrFny4MEDlduVJk0aleuVOHHigNnThgCLyb8uQgghocAIGHEJv/32m6pqnDx5shJfFSpUkH379snZs2cDSnwBCjBCCCGvgvfoAczIkSJ794pkySLyySf6Y2Q5evSo7XmrVq1k0qRJfptk/yoowAghhLwKCrAA5osvgp8PGCDy9GnEhs3gXF+3bl3Vq/Grr76Sf/75R4YOHSr58+eXQIYCjBBCyKvgEGSA4qzVYnj9qnbv3q2iWw0aNJBRo0apeTFjxpSlS5cGvPgCFGCEEEJeBQVYgILRQSPade6cLsheNWL4/PlzGTt2rBQrVsw2D6KL2EMBRggh5FVQgAUoEFyGUAiPET2GG0uXLi0dO3a0zfv555/lr7/+cuNa+h4w+l+3Tn+eJIm314YQQohVYQ5YgPLiRfDzsPK+rl+/rvo3GqCicciQIfL555+r3C9iT8+eIgcPiqROLdKsGfcOIYQQ5zACFqAg4f5VAgxNss3iCxw6dEjatm1L8eUEBANfpsTJtGkiDruOEEIIsUEBFqAYw4/OhiDh34UkeyPBHrRs2VI52adLl86Da+lbTJ6sP8LSo2ZNb68NIYQQK8MhyADFLMCMCNiLFy+Uh9c0hG9MXLx4UdKmTevhNfQ9Nm/WHxs18vaaEEIIsTqMgAUojkOQGFqsWLGinfiCmSqiXhRfr+bMGb2aFGlxpUq555gRQgjxHxgBC/AIWLRoQZIhQ165du2aPHv2TOLHjy/NmzeXkSNHSpw4cby9mj4X/YJDR4IE3l4bQgghVocCLKAjYPtE0wrJpUv6vJo1a8r48eMlc+bMXl4732PTJv2xfHlvrwkhhBBfgAIsALl586Y8eJBQRDLY5jVq1EhmzZoVsP0bXRUBowAjhBASHpgDFmDUrl1bkidPLpMmfScir0mCBFvk9OnTMnv2bIqvSHLjhsiBA/rzsmVdeLAIIYT4LRRgAcS6detk2bJl6vkff8xHcyGJG7cMhxyjyN9/64958tD7ixBCSPigAAsgsmfPLqVKlZJUqVLJzJn/ov12uNoQkbBZu1Z/5PAjIYSQ8EIBFkBkyZJF/vnnH7ly5YrEipXglW2IyKvZs0dk4kT9ea1a3GOEEELCBwVYgPuAUYBFnsePRZo00fdl3brIr3PV0SGEEOLvMP4RYGiaSLJkIrdv669Pn/b2GvkuffqI7N8vkioVTGvhqebtNSKEEOIrRLfiMBmsEByndu3aOV3+wIEDUq9ePdv/Gz16tNPlLly4II0bN1YVgPHixZOCBQvKjh07bO///vvvUrVqVfU+PmcPxpYcmDJlinKLT5w4sVrmtqFiTLz77ruSKVMmiRs3rnKQb9KkiWrlYxVOngwWXyRqthPDh+vPp0xh8j0hhBAfF2Dbt2+XS5cu2aY1a9ao+fXr13e6/IMHDyRbtmwydOhQSZMmjdNlbt26JWXLlpVYsWLJihUr5ODBgzJixAh57bXXbMvcv39fypUrJ8OGDQt13fBd1atXl549e4a6TKVKleSXX36RI0eOyIIFC+TEiRPywQcfiFXInl2kWzdvr4Vvc++eSLNmejSxeXOROnW8vUaEEEJ8jWgamv1ZmM6dOyvrhGPHjr3SpwpRMCyPyUz37t1ly5YtssmwKw8DeGJlzZpVdu/eLUWKFHG6zIYNG5TQgrBLmjRpmJ+3ZMkSee+99+Tx48dKADoD72EyuHPnjmTMmFGCgoJUtM0drFolUr26SNGiIrt2ueUr/JYRI0S+/FIkUyaR//4TSZLE22tECCHECuD6nSRJknBdvy0XATPz5MkTZRDaokWLKJmEQgSVKFFCRdFgwVC0aFGZOnWqeMJx/ueff5YyZcqEKr7AkCFD1AEzJogvT/WCZBJ+xDl/Xn/8+GOKL0IIIZHD0gJs0aJFKs8KzaGjwsmTJ2XixImSM2dOWbVqlbRp00Y6duwoM2bMEHfw9ddfS4IECVQ+2dmzZ2Xx4sVhLt+jRw+llo3p3Llz4m4owCLO3bsi06YFRwzjxnX1USGEEBIoWFqATZs2TWrUqCHp0qWL0ue8ePFCihUrJoMHD1bRr1atWknLli1lEkrX3EC3bt3UEObq1aslRowY0rRpUwlrpDdOnDgqVGmePCXAYsRw+1f5DRMmiHz2mcjGjfrr+PG9vUaEEEJ8FcvaUJw5c0bWrl2rqhOjCqoR8+XLZzcvb968KkneHaRIkUJNuXLlUt+DIcV///1XSpcuLVaBEbCIc+2a/pgli54799FHrj4qhBBCAgXLRsCmT5+u8rVq1qwZ5c9CBSSqEs0cPXrUIz0QEX0D5iR7K0ABFnGeP9cfIbxwX4AkfEIIIcRvImAQLRBgzZo1k5gOWeIYzkufPr1KXDcS9WErYTyH3xc8vBImTCg5cuRQ87t06aIS4TEE2aBBA9m2bZvy9MJkTphHvpbh2WUINlhbGPYWly9fVtPx48fV63379kmiRImU71eyZMlk69atykYDdhawuIAFRe/evVUPRitFv8xigkn4Ed9nHLYlhBASZTQLsmrVKiRMaUeOHAnxXoUKFbRmzZrZXp86dUot6zhhOTNLly7VChQooMWJE0fLkyePNmXKFLv3p0+f7vRz+vbta1sGz50tg/8L/vvvP61SpUpasmTJ1PdkyZJFa926tXb+/PkIbX9QUJD6XDy6i2nTkJSmabVque0r/I527fR91ru3t9eEEEKIFYnI9dvyPmCBSER8RCILgn+ffy7y3nsiCxe65St8nmPH4EOnG67CsxfNERBs7ddPpG9fb68dIYQQX75+W3IIkrgfVkGGzZMnIh9+KLJ7d8j3EiVy11EhhBASKFCABShMwrfn6lXd5wuRLjjbI8IF8YXG5ehOdecOWlrpOXNRtKUjhBBCKMACFQqwYA4c0G0lnj4NuZ/QMKFuXU8eGUIIIYGAZW0oiGcq+rZtE+ndO9jjKhCZPl0XX9Ed/hpataL4IoQQ4h44BBmgYKjNSDQfNEhvq9OrlwQcsGmbP19//ttvIrCdu31b5P59EQ/YxBFCCAlQKMAClCZNROLF09vr/P23yKNHEpBg29FcG8UqNWqIxI4tkiqVt9eKEEKIv8MhyAAlThyRRo1EChbUX2/eHDwU56s5bRhO3btX5OxZPaE+PAYr8+bpj++/z+bahBBCPAcjYAGO0VB6wwZ9QgWgLyadf/WVyKhR9vNQsZg0qT7cGtr0yy/6suzrSAghxJNQgAU4X3+tC5Fp09AAXSQoSHwS5LKBBAl0Dy9E8hAVu35dn8IieXKRt9/2yGoSQgghCgqwACd1ar0KEkOQEGC+2hvSsNVAThvy2x480H27kFCPx9AmDFVi+VixvL0FhBBCAgkfvdwSV+PrjaaN3DUIyGjR9EgYpgwZvL1mhBBCSEiYhE/8wpjV19efEEJIYEEBRuwiYKtWiaxZ43s7hQKMEEKIL0EBRhQwYgU//ihStaqeE+ZLUIARQgjxJSjAiGL4cJG2bUWKFAlOZvclKMAIIYT4EhRgRAHhNX68yP/+F9yWx5f6Q1KAEUII8SUowIgdxYuLlCihVxX+9JPv7BwKMEIIIb4EBRgJweef649TpujNqn3JhoJ+XoQQQnwBCjASArTlSZRI5PhxkfXrfWMHUYARQgjxJSjASAgSJhRp3Fh/Pnmy7xmxEkIIIVaHAoyEOQy5cKHI5cu+kwPGIUhCCCG+AAUYcUrhwiKlSunCZtQokdOn9UbdmmbNHcYhSEIIIb4EBRh5ZRTsu+9EsmYVSZpUJE4ckdGjrbfTKMAIIYT4EhRgJFQ+/FDknXdE0qTRhZchdBYvtt5OowAjhBDiS1CAkVCJF09k+XKRS5dEHj3S2xQBQ4xZCSbhE0II8SVYM0YinOi+bp1Ixowir70WPKVPL9K/v0iKFJ7fochLYxI+IYQQX4ICjEQoMR9RsYcPRc6f1yczN26IzJvn+R36/Hnwc1ZBEkII8QUowEi4KVlS5OpV3Zbi1i2R27f1xwsXRL78UmT+fJFPPxWpUsVzO3XZMpGVK4NfU4ARQgjxBSjASIRNWnPkCDn/zBmRMWNE2rUT2bfPc3liTZvqItAQX1bMTyOEEEIcYRI+cQnI/0K15LFjIsOHe26n3rkTLMRmzBCJHdtz300IIYRElmiaZlVrzcDlzp07kiRJEgkKCpLEiROLrzB3rkjDhiJx44ocOCCSLZv7vzN6dD0JH8OiqVO7//sIIYQQV1y/OQRJXEaePPojLCuyZxeZM8e+UhITzFxdlacF4WXcPkCIEUIIIb4CBRhxGX372r9GNCy0PDIIMUdxZra0gAlsggThr36MEcMFG0AIIYR4CAow4jIGDxb5449gYVSxYnClJCYjX+vePX1ytLEwg2rL7t1Dfx8J/wsWBL9mBIwQQogvQQFGXEaBAsGGqM7Ae2jobQgyZ9OKFSL79weLtdDo3Vvk7t3giBr8yQghhBBfgQKMeO7HFlMkeXJ9Cg2IKgiwQ4dEFi4MOTwJsRUtmsiDB8GNwhFpo/0EIYQQX4ICjFgKI5dr0SJ9cibikD9mDHO2aBG2oCOEEEKsCAUYsRQdO4rcvy9y5UrI4Uk03MYw5vXr+rIpU4r4kEsHIYQQYoMCjFiKXLlEpk8POR92Exh2NAuy3LnZeogQQohvQgFGfALkfcGWAlOGDN5eG0IIISRq0L6SEEIIIcTDUIARQgghhHgYCjBCCCGEEA9DAUYIIYQQ4mEowAghhBBCPAwFGCGEEEKIh6EAI4QQQgjxMBRghBBCCCGBLsCyZMki0aJFCzG1a9fO6fIHDhyQevXq2f7f6NGjnS534cIFady4sSRPnlzixYsnBQsWlB07dtje//3336Vq1arqfXzOnj17QnzGlClTpGLFipI4cWK1zO3bt+3eP336tHz66aeSNWtW9R3Zs2eXvn37ypMnT6K8XwghhBDiP1hOgG3fvl0uXbpkm9asWaPm169f3+nyDx48kGzZssnQoUMlTZo0Tpe5deuWlC1bVmLFiiUrVqyQgwcPyogRI+S1116zLXP//n0pV66cDBs2LNR1w3dVr15devbs6fT9w4cPy4sXL2Ty5MlKGI4aNUomTZoU6vKEEEIICUyiaRq67FmXzp07y7Jly+TYsWMq6hQWiIJheUxmunfvLlu2bJFNmza98vsQxUIEa/fu3VKkSBGny2zYsEEqVaqkhF3SpEnD/Lzhw4fLxIkT5eTJkxJe7ty5I0mSJJGgoCAVbSOEEEKI9YnI9dtyETAzGLqbPXu2tGjR4pXiKyyWLFkiJUqUUFG0VKlSSdGiRWXq1KniCXAQkiVLFuYyjx8/VgfNPBFCCCHEf7G0AFu0aJHKs2revHmUPgfRJ0ShcubMKatWrZI2bdpIx44dZcaMGeJOjh8/LmPHjpXPP/88zOWGDBmiFLMxZcyY0a3rRQghhBDvElMszLRp06RGjRqSLl26KH0O8rIQARs8eLB6jQjY/v37VX5Ws2bNxB0g6R/5Yoi6tWzZMsxle/ToIV27drWLmmXKlImRMEIIIcSHMEawwpPdZVkBdubMGVm7dq2qTowqadOmlXz58tnNy5s3ryxYsEDcwcWLF1WOWJkyZVTl5KuIEyeOmhwPICNhhBBCiO9x9+5dNaLlkwJs+vTpKl+rZs2aUf4sVEAeOXLEbt7Ro0clc+bM4o7IF8RX8eLF1TZEjx7xUV5E/M6dOyeJEiWKUu6bp4BghFjEOvt70QC31f/gMfU/eEz9kzs+cK1B5AviKzwjd5YUYBgyhHjB8GDMmPar2LRpU0mfPr3KmzIS9WErYTyHAIKHV8KECSVHjhxqfpcuXVQ0CkOQDRo0kG3btqnIlDk6dfPmTTl79qyKXgFDsMHawrC3uHz5spqQ2wX27dunRBKGC5Foj++GTxiE3ffffy/Xrl2zfX5oFhnOgGjLkCGD+Br4g7DqH4Wr4bb6Hzym/gePqX+S2OLXmldFvmxoFmTVqlUYPNWOHDkS4r0KFSpozZo1s70+deqUWtZxwnJmli5dqhUoUECLEyeOlidPHm3KlCl270+fPt3p5/Tt29e2DJ47Wwb/N6zPsOhudhlBQUFqG/Ho73Bb/Q8eU/+Dx9Q/CfKza43lfcCI9Qkk3zJuq//BY+p/8Jj6J3f87FpjaRsK4huggAAtl8yFBP4Kt9X/4DH1P3hM/ZM4fnatYQSMEEIIIcTDMAJGCCGEEOJhKMAIIYQQQjwMBRghhBBCCAUYIYQQQoh/wwgYIYQQQoiHoQAjXieQrOgCaVsJ8VUC6e80ULZVs+B2UoARr/9B+EK/y6gSSNtKiK8SSH+ngbKtmoW305K9IIn/c+/ePenfv79qqpo1a1apUqWKvPXWW+KPBMq2YjvHjh0rZ86cUc3oixUrph79EW6r/x3XQPk7DaRtvWfx7WQEjHic+/fvS5EiRWT//v3y/Plz2bVrl9SoUUNGjhzpd0cjULYVJzps5+rVq+XgwYOq0f27774rc+bMEX+D2+p/xzVQ/k4DaVvv+8J2ersZJQk8vv/+e61EiRLao0eP1Ovr169ro0eP1mLEiKH16dNH8ycCZVt79+6tlStXTnvw4IF6feDAAa1bt25atGjRtKlTp2r+BLfV/45roPydBtK2fu8D28khSOJxrl69qhqpGv28kidPLp06dVLzPv30U0mfPr20atXKL45MoGzr+fPnJW3atBIvXjz1Ol++fNKrVy+JHz++tG7dWpIlSyZ169YVf4Db6n/HNVD+TgNpW6/6wnZ6WwGSwGPmzJlaokSJtP3794d4r3///lratGm1gwcPav5AoGwr7jazZ8+unTlzxm7+tWvXtNatW2slS5bUzp8/r/kD3Fb/O66B8ncaSNs60we2kzlgxO08fPhQtmzZYntdunRpKVWqlAwbNkxOnjxp3Aiox3r16kmsWLFs832dQNlWJNu/9tpr8r///U+uXbtmm58iRQr54IMP5PTp03Lp0iXxB/x5W1+8eGH3WLJkSb/cVp6T/P+c9MYbb6jJyttJAUbcyp07d6RChQry448/yokTJ9S8HDlySP369VWy9vfffy9Hjx61lQjjvaRJk8rdu3d9Mjm7a9eu8vHHH0u7du3kyZMnantwofKnbX3w4IHMnDlTTcuWLVPzKlasKHXq1JHZs2fL9OnT5eLFi7blixYtqrbz9u3b4ms8evRI1qxZI4sXL5atW7fatvWdd96Rn3/+2a+2Fb/DcuXKyYoVKyR6dP3SUL58ealdu7ZfbSvPSf53Trp//76MGTNGRowYoW4WQK5cuaRJkyYqCd+y2+nV+Bvxa+7fv6/ly5dPe++997SgoCDtxYsXdu8jIRJDGDVq1NA2b96snTt3TpsyZYr22muvabt27dJ8iTt37mi5c+fW3n77ba1Vq1ZaihQptCZNmtgNW73xxhs+v63Yzpw5c2rFihXTMmbMqCVLlkyrXbu2dunSJfV+9+7dtVy5cmlt27bV9u3bp5afPHmyljJlSq+H+yMKfrPYThy3xIkTa8WLF9d++OEH2/tffvmlOub+sK1g+PDhKrk+QYIE2tKlS+3e++KLL/ziuPKc5J/npGzZsmmvv/66VrRoUS1JkiRaqVKltL///lu9P2HCBPWeFbeTAoy4jblz52qVK1e2CS+MyeOPfsCAAdqNGzfUvEWLFml169ZVJ/68efNqadKk0ebNm+dTR+XZs2fa+++/r9WrV88278cff9QaNGhgJzoXLlyolvPVbX3+/Ln20Ucfae+884725MkT7cKFC+qEliVLFnXiO3r0qFpu5MiRWqVKlVS1EQQMTnS+tJ3GhRrrXr9+fe3q1ava7t27tc6dO2vvvvuudvPmTdty2Na33nrLp7fV/Pv88MMPVZVnzJgxtSVLlti9P3HiRJ8/rjwn+dc56cWLF9rnn3+uVa9eXb2+e/euOi/h91mgQAFt5cqVav5vv/2mzs9W205WQRK3AUNOiHyEfTEst2/fPkmZMqX8999/8ttvv8moUaPUsBWGc/AelkuQIIEKHVvZvdjZMNX169ftKmquXLkiR44ckZo1a6rKwC5dush7772nhnP27t3rk9uKYamgoCA1TIX8iXTp0qlpx44dKtetcePGsmHDBrWtH374oRriAKlTp5aCBQvafgu+wKRJk9QQxYQJE1S+E363OMY4nji2yIsC2NZGjRqp37SvbqtBlixZVE7M8OHD5fLlyypPBrmbOI4xYsRQVY/vv/+++lv11W3lOcm/zknRokVTQ+D47QJsV8KECdXvtnr16vLll1+q9/BbrlWrlhw4cMBa2+ltBUj8l1GjRmlVqlRRQxZlypTRTp06pT19+lRN5cuXV3cpiKT4OvC+ypQpk9a0aVPt7NmzKtKHKEGbNm20cePGqWFW3I35erUY7jYRysd2GhjH7+LFi6qqqEWLFmH+f1+JaCKCOXjwYPVbNbh9+7aK9iEaZizn69tqBhWsGGZFhA8RwK+//lr9jhE12LZtW6jb5UvbynOS/5yTXrz83SGCh+FFA8P36/HjxypdolatWppVoQAjbgOCK378+Ep8IUQMoYJhLGPcPl68eCo07A+sWLFC5c5UrFhR5SAMHDjQ7n3sBwxX+Trz58/XUqVKpU2fPt02Dyc6AOGJPKETJ0741EXZGadPn9YePnyonhvbAsGF7du6dattORjO+jpmIYmUAWP7evToocWOHVuJsFWrVql5vn5ceU7yn3PSi5e/xX///Vdty3fffWd7zzCE3rRpk8pT3L59u2ZFWAVJXMLjx49VlcmePXvk2bNnah5Cv71795Zjx47JjRs3JHbs2GoYCyXuGLbLmzevGtrx5W19+vSpmodwN/qNzZ8/XwoVKiSVK1e2lbuj6qpw4cJqyMaXwDHauXOn/PXXX+o5QB81DFlMnDhRbSvAcQU4lqgEjRkzpuWHLkLbVgyh4nnmzJklbty46reKbcHNKoZfb926ZTvmqPhEq5NDhw7ZhjJ88bhieNHRaHbcuHEyevRoVfnYsWNH9ftGxasvHVdnv19/PSc521Z/PCdpL//OjL9LgG376quvVLoAetECwxAaJqx4DuNgS+JtBUh8H1SLlS5dWitcuLCqRkGUAFVUuAu5cuWK1qVLFzWMgepADG9gKAfJsBi2+++//zR/2FYYUwJETRD2xlCHAbYVw3NWvQsLbTuLFCmitiV58uRahgwZVNTr3r172pEjR1RyOoaQMZxhRFH+97//qQRXDMP6Eo7biupObCuGVQ0QucVySN5F1AuR2+jRo2s///yz5g/bisowMGLECHWsEyZMqC1evFjNu3XrlhpO/+OPPzRf306cf9CSxt/OSWEdU385J925c0cNNRopAMZoCjh58qSq1E2XLp1qM4T3cK6aMWOGljlzZu3YsWOaFaEAI1ECf9zot9W4cWPlOHz8+HHtgw8+UA7EgwYNUic2VDzCcgLz8McA8YITBYaz/GFbMeSIbTVOeP369VPDdLCkaNSokaoW++WXXzRfAXldWHeILIgtDMeh0giCE9YLuCBj27t27apC/wULFlRDrxiC9bVjGtq2wtUf24rXBjipo7z9s88+U+Jr9uzZtqEQXxiae9W24mZpy5YtqqLVUWwZuXC+vJ3G7xdVrTgv+cM56VXHFH+nxjkJQ3G+ek568OCByhuGaMa5de/evSGGz3Hjh2OKmwdsP24QcW6G2LQqFGAkSsCGAAmdhvgAuKvCxRgJy+a7LiyDBr6//vqrtmPHDp85oYdnW3FyN7YVJ0HkQyHvDSJl7dq1PrWtuEAhkoUSdTNDhw5Vvm49e/ZUia44KeJuFK/hj7Vx40af2s5XbSuSlHv16mWzTMGyyFvERQC/YV8SX6/a1vz586vjCMw2G75IeLYTdgX+cE4Kz+8Xf6eI5vrqOenFixcq0o78xOXLlyvbIgjIPXv2OC2GQTEJlp81a5Ytn9Gq20kBRqIEvFRwF2lUngD4QcEvCp5CeM+XK22isq2ImBh/+L50oYaPDgxGcQIDRjI6QHEBhi5Wr14d6v/3le0Mz7amT5/etq0YAunUqZPttS8d04geV/Pwjq/xqu3EMFVYv19/3lZfPSctX75cmzRpkor4QXSi8tGZCAvtd2vV7aQAI1ECDug4ASCXApEC44SAYRqA3ASMyQfKtsLE0sp/8OEFd5m4sza2wyw60dkAla3+QkS21Yic+NLFKxCPa6BsZ3i2FTmr/sCTl5Y32E5E9BxFGMQXUkOQIuErsAqSRAkYUqL/Iapv0F8L/eFgcDd16lT1fvLkye2a+Pr7tsKsE/hStZgZowkzKuBQ7VelShVbNZFR/Yd5qLRCr0tfJiLbispXAJNH4/j60jEOlOMaKNsZkW3Fb9fXtxXA/Nkw/U2bNq2MHz9e9WWtVKmSMkJGFeS7776rKpV9BQowEiXwx968eXP5888/VcNtlOYvWbJEvff8+XPlIp49e3b12pdK9QN1W40mzOnTp5cffvhBjh8/rk5waFqLEyCAyISTNE7qvrqdEd1WXNACZVt9+bgGynYG2rYamG96IMIgumCvATuYTp06Sd++fSVr1qziM3g7BEd8B3PuQHjCxdOmTVNVKEhe9zUCaVvD2q5169apxHuUtiPPDcOtsWLF0n7//XfNn+C2+t9x5TH1v2PqjG+++UYVxixbtky99qXUgGj4x9sikFgbRHdw54FQtmFwFxYwOYSRIyJEuENBX0BfIZC21dheRyNOR3D3PGDAADl79qy6s27QoIFUq1bNp3oAAm6r/x1XHtPAPKYGS5cuVf2E58yZIx999JH3eztGEAowEiYIZ6PJNByVEcrGHzPyoF4FHLSzZcsmFSpU8Jk/ikDZ1vv378vvv/8uTZo0seWSGMMZjjieuI3XvrCdgNvqf8eVxzSwj6mZtWvXqtSQ8uXL+8R2hsDbIThiXdCQF1V+qKQZMGCA1rlzZy1u3LhavXr1lPOwGaP811lzbV8ICQfKtsIdGqaTCNnDPNbAWfm2Mc/oqxbaclaF2+p/x5XHlMfU0W7DV869zqAAI6GCdisobzYbM+7cuVNLkSKFVq1aNZvnlfHD/+eff7QxY8b41Ak9kLYVghE5XOXKldM6duyotrd///62951tx99//61afDgTm1aG2+p/x5XHlMfU/Nt9/PixT4ouM6yCJKGChq3IhYL9AkAlWLFixeSff/5RjV979uxpF/Jdt26ddO7cWTUnDk/42EoEwraiCS/yKz755BPp1q2bypmYO3eu9O/fX71vNCU2c/jwYRk5cqRs375dfAluq/8dVx5THlPzb3fHjh2+NdzoDG8rQGJd0G8rZsyYNpdlYNwx//nnn6oty4IFC+zutJs0aaIaoPoa/r6tRhQEBrIYbgWI6vXt21fLkyePejSAq7T5zrJDhw4qUuYrd5vcVv87rjymPKYvfPS3GxYUYCRUEOLFDx1NTVeuXGmbjx89HMGLFy+uDR482O7/oEExXIp9jUDaVnPrDqy/cbFGw17w/fffaz169LBd9NavX69dvnxZ81W4rf53XHlMeUwv++hv10xMb0fgiDW4d++e9OnTRz1iyOKLL76QnDlzSsuWLeX06dMyePBgNXz1zjvvqLAvHMFTpkwpz549sysdbtSokVidQNlWZ9uZKVMmVTVkVEfBzPDzzz9Xy//222+yceNGWb9+vfz000+2oVW4TVsdbqv/HVceUx7T33z0txtuvK0AiTUqi7JmzapVrFhRa9SokZY/f34tU6ZM2qhRo1RfsW3btql+Y9myZdNGjBihbdiwQRs7dqwalvvrr780XyJQttXZdmbJkkUbOXKkambrmJyNSBiWRXXkokWL1DxfCe9zW/3vuPKY8phe9NHfbkSgACPawIEDtTfffNNuT7Rr1045oGNYCsNzx48fV7YFaH6aK1cuNWT1yy+/+NzeC5RtDW07CxQooJqjX79+3e69yZMnqxOd4ZrtS82mua3+d1x5THlMJ/vobzci+Eb5FnEraEyLKik8YngNwN29du3aqkpu3rx5qsdhr1695MSJEyokjH6I9evXV8MdvtRMIVC2NbTtrFmzpgrrL1u2TM3D9jx48EDWrFkj8+fPl/fff9/nDA25rf53XHlMeUzX+OhvN0J4WwES72C+k0C0J3v27CrsDzAUZ9C4cWM1HGc2bfQ1AmVbI7KdeM+8ncb7vnKXyW31v+PKY8pj+sBHf7uRhQIsAEFVX/fu3bV///3X5iqcIUMG7f3337ctYzgN4wKeLFkyVfHniwTKtkZlO33tBMdt9b/jymPKY5rMR3+7UYECLMDAjxpu6NGjR9datmxpu2D/8ccf6g+gYcOGdstfuXJFJXAvX75c8zUCZVsDZTsBt9X/jiuPKY/pFR/97UYV5oAFGBhDjx8/vrJd2L9/v8oL2r17t9SoUUPGjx+vcp4qVaqk3jt+/LisWrVKLl26JGnSpBFfI1C2NVC2E3Bb/e+48pjymK7y0d9uVIkGFebtlSCeAYcaJzskm1+7dk3y5Mkj3bt3l/z588uYMWNk+fLlKgG9ffv2cvXqVYkZM6ZqyTNixAjVtsaXCJRtDZTtBNxW/zuuPKY8pjF99LfrErwdgiOeZ9myZVqFChXU8zlz5milS5fWihYtqkp+jxw5Ymu/s2XLFu3gwYM+PSYfKNsaKNsJuK3+d1x5THlMffW3GxUowAIM/Lj37dunlSpVyjbvo48+0mLEiKFVqlRJ27Vrl+YvBMq2Bsp2Am6r/x1XHlMe00CFOWABBoarMEwVK1YsuX37tgwaNEiWLFkiXbt2Vd47aMOzc+dO8QcCZVsDZTsBt9X/jiuPKY9pwOJtBUg8D8rWcQeNYaoECRJoS5YsUfN//vlnrUiRItqePXv85rAEyrYGynYCbqv/HVceUx7TQIQCLEAZN26c6g1o9Ngy99/yNwJlWwNlOwG31f+OK48pj2mgwSrIAOX+/fuqai5Llix21UjGoz8RKNsaKNsJuK3+d1x5THlMAw0KMEIIIYQQD8MkfEIIIYQQD0MBRgghhBDiYSjACCGEEEI8DAUYIYQQQoiHoQAjhBBCCPEwFGCEEEIIIR6GAowQQgghxMNQgBFCCCGEeBgKMEIIIYQQD0MBRgghhBDiYSjACCHEYty4cUNSpUolp0+fjtLnfPTRRzJixAiXrRchxHVQgBFCAho0tU6aNKmMHTs2xHtt27aVkiVLyqpVq1Tj67Cm1atXS4UKFdTzwYMHh/iON954Q703YMCAV67Tt99+K3Xq1LE1Vo8s33zzjfqsoKCgKH0OIcT1UIARQgKaEydOKIFSokSJEO/t3LlTihcvLm+++aZcunTJNiVPnlx69+5tN+/tt9+W3bt3S+bMmWXfvn12nzNjxgy5ePGiel6sWLEw1+fBgwcybdo0+fTTT6O8bQUKFJDs2bPL7Nmzo/xZhBDXQgFGCAloILJixowpRYoUsZv/9OlT+e+//5QAixcvnqRJk0ZNz58/V0OE5cuXt83DBCF39+5dadasmZ0Aw7wePXpI8+bN1Wt8Xlj88ccfEidOHClVqpTdfAxHIoK2YMECJQixTq+//rqcPXtWNm3apJaPHz++EoK3b9+2/b/atWvLvHnzXLS3CCGuggKMECKBLsDy5cunBI2ZAwcOyKNHj0IIJkS5nEWy8DkQQB9//LEcOXJEnjx5ouYPHDhQRddSpkyphFratGnDXB+IKWcibe/evepx4sSJaojz77//litXrkjjxo1l6NChMm7cOFm/fr1abvr06bb/hyHUbdu2yePHjyO8bwgh7iOmGz+bEEIsD4RTaMOPiERhGM/Mrl27JGPGjGoY0nF+oUKFJHfu3BI3blw5fPiwEnUQTHgPuVivGn4EZ86ckXTp0oWYv2fPHkmWLJnMnz/f9t3IOdu8ebMSixB/AFGxy5cv2/4fPgtiEPMwPEoIsQaMgBFCAhqIo9AEWMGCBSVWrFghlncmpIz5GCaEEMMwZJcuXaRNmzaSM2dOWz7Zq3j48KEScI4gsvX+++/bCT8MP3744Yc28WXMy5o1q+21EdlDbhkhxDpQgBFCApZz586pfKn8+fOHeG/t2rVSpkyZEPNfJcAA8slGjx4tO3bsUMn6GMpERMz8/8qWLStbt25Vz5FwP2rUKPU8RYoUcuvWLacRMFRSOooyc64YvgfDn4ULF7bNu3nzpnrEECghxDpQgBFCAhYk2oN79+7ZzV+zZo0cO3ZMRZfMXL9+XYk2RwF28uRJJeSM+UWLFlXia8iQIZIoUSIllJ49e2YXAYMwQ+7WyJEjJXr06CpaZvzfgwcP2n3+nTt3VBI+3jM4deqUqt40z0PUDZYXiNwZ7N+/XzJkyKCEHSHEOlCAEUICFgzV5cmTR1Uprlu3TgmlKVOmSMOGDVXVomMEDFGu0BLwY8eObcsXQyXktWvXbJWP+H+IQCF3zKB69epquHD58uUyYcIE2/xq1aqpnC5zFAzrFSNGDLt8NCMnzJzXhXmwnUiYMKFdUn/VqlVdsr8IIa6DAowQErAgXwsCCKIF0S7YO0yePFlVGf7vf/8LsTwqIFOnTh0iSR4CC+LIyBfDIyJO+HzjfXOkCmzfvl0NDyZJksQuzwzRKwi8X375xU6AGcn95nmOn4l55uFHDEkuWrRIWrZsGYW9RAhxB9E0xKsJIYR4jAsXLsg777yjxFG9evVk5syZdtEtiMJu3bqp4UMMT0YWVGAuXLhQufQTQqwFI2CEEOJBUOVYv3591foIQ6AY/oRXmJmaNWtKq1atlFCLCoisOWuxRAjxPoyAEUIIIYR4GEbACCGEEEI8DAUYIYQQQoiHoQAjhBBCCPEwFGCEEEIIIR6GAowQQgghxMNQgBFCCCGEeBgKMEIIIYQQD0MBRgghhBDiYSjACCGEEEI8DAUYIYQQQoiHoQAjhBBCCBHP8n/1ScxJRxSMsAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1069,7 +1128,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\akeeste\\Documents\\Software\\GitHub\\MHKiT-Python\\mhkit\\dolfyn\\adp\\clean.py:90: UserWarning: The 'range_offset' is either already known or can be calculated from 'bin1_dist_m': 0.07000000029802322 m. If you would like to override this value with 0 m, ignore this warning. If you do not want to override this value, you do not need to use this function.\n",
+      "c:\\Users\\ashly\\anaconda3\\envs\\Updated_MHKiT\\Lib\\site-packages\\mhkit\\dolfyn\\adp\\clean.py:90: UserWarning: The 'range_offset' is either already known or can be calculated from 'bin1_dist_m': 0.07000000029802322 m. If you would like to override this value with 0 m, ignore this warning. If you do not want to override this value, you do not need to use this function.\n",
       "  warnings.warn(\n"
      ]
     }
@@ -1100,7 +1159,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x24186f3d5b0>"
+       "<matplotlib.collections.QuadMesh at 0x1dda6987dd0>"
       ]
      },
      "execution_count": 8,
@@ -1109,7 +1168,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHXCAYAAABQ5DVtAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAA+vpJREFUeJzsnQd4HNW1x/8z2/tKq95l2ZZ77xTbYFzoPQQIhJDwQigBAgFC50FooYdAEvIgJCGEJBAg4EIxxVTbuFdJVu9te9+Z992zkSI3sC3Zkqzz8zff7k69OzszOr73nt+VVFVVwTAMwzAMMwSR+7sADMMwDMMw/QUHQgzDMAzDDFk4EGIYhmEYZsjCgRDDMAzDMEMWDoQYhmEYhhmycCDEMAzDMMyQhQMhhmEYhmGGLBwIMQzDMAwzZOFAiGEYhmGYIQsHQgzzLdx9992QJAltbW39XZSjjg8//JDObde0Zs2aQ9qP0+ns3sfVV1/d5+VkGObohQMhhmF6xYoVK3D55Zdj3Lhx0Gg0KCoqOuh9/OIXv8Cf/vQnDBs2rHteY2MjbrnlFsyfPx82m42CHBE47Yvf/e53tD3DMMzBwoEQwzC94uWXX6bJ4XAgJyfnkPZx0kkn4eKLL0Zqamr3vB07duChhx5CfX09xo8f/43bn3/++bQ9wzDMwcKBEMMwveKXv/wlvF4vPv30U0ycOLHP9jt16lS0t7dj586duOGGG/psvwzDMD3hQIhhDhDRR0jUPNjtdrhcLvz0pz9FOBzea70///nP9EfcZDJRDccFF1yA2tra3db55JNPcN5556GgoAAGgwH5+fm4/vrrEQqFdlvv+9//PqxWK2pqanDqqafS+9zcXDzzzDO0fNOmTTjhhBNgsVhQWFhINTNHGlELpNPp+ny/ojmsZw0RwzDM4YADIYY5QEQQJAKfBx54ACeffDKeeuopXHHFFbutc//99+OSSy7BiBEj8Nhjj+G6667D+++/j+OPPx5ut7t7vb///e8IBoO48sor8fTTT2PRokX0Krbdk0QigSVLllCw9PDDD1MfHNEh+MUXX8TixYsxbdo0akISgYPYvrKy8lu/S2dnJwV23zaJMjIMwxzVqAzDfCN33XWXKm6V008/fbf5P/nJT2j+hg0b6HNVVZWq0WjU+++/f7f1Nm3apGq12t3mB4PBvY7zwAMPqJIkqdXV1d3zLr30UjrGL3/5y+55nZ2dqslkonVfeeWV7vnbt2+ndUV5v43CwkJa99umA9lXT0455RTa94GycuVKOo54/Sb+/ve/H9B6Yp2rrrrqgI/PMAyj7e9AjGEGC1ddddVun6+55hr85je/wTvvvIMJEybgtddeg6IoVHPUM9U+KyuLaohWrlxJ2VEC0WzWRSAQoCaxOXPmiP+YYN26ddRk1pMf/vCHu6WKl5aWory8nI7VhZgnlu3atetbv8tf/vKXvZrh9kXPLC6GYZijEQ6EGOYAEcFMT0pKSiDLMqqqquhzWVkZBTJ7rtdFz340os/PnXfeiTfffJOaqXri8Xh2+2w0GpGenr7bPJGhlZeXRynle87fc3/74phjjvnWdRiGYYYCHAgxzCGyZxAiaoPEvKVLl5JPZ09ER+euPj8iXbyjowM333wzRo0aRZ2dRZq46Bwt9tOTfe3rm+YnW4i+mdbWVirHtyHK3FVuhmGYoxEOhBjmABE1PsXFxd2fRdOUCFq6BIKihkgEIWKdkSNH7nc/ItNLpIT/8Y9/3K1z9LvvvosjxfTp01FdXf2t6911111k1mYYhjla4UCIYQ4QkbK+cOHC7s8iy0sgMroEZ599Nm699Vbcc889lELfs8ZIBEiiBkik3XfV5PSsuRHvn3zyySP2XbiPEMMwTBIOhBjmABFp6aeffjqlrH/++ecU7Fx44YXdEkFRI3TfffdRMCT6DZ155pmU0i62e/311ynV/sYbb6SmMLGueC+aw4SX6J///OcB9e3pK/qyj9DGjRupr1NXLZno4yTOg0Ccm9NOO+2Q9921ny1bttCrGEZj1apV9P7222/vg9IzDDPU4UCIYQ6Qv/3tb9TBWYx/pdVqyeXzyCOP7LaOWCaaxR5//HGqGRII/4+oSRJBVFen6bfeegvXXnstOYlEZ+izzjqL9teXZuYjxddff4077rhjt3ldny+99NJeBUJ77vf//u//ut9zIMQwTF8giRz6PtkTwzDMQSIGURWDqv7rX/+iWiqR/i+CzINFNDuK/loiu05oDn79618flvIyDHP0wWZphmH6HdGMKIKY9evXH9L2oi/TnooBhmGYA4GbxhiG6TdEU2DPbDkhhTwU3njjDcRise6mSIZhmAOFm8YYhmEYhhmycNMYwzAMwzBDFg6EGIZhGIYZsnAgxDAMwzDMkOWo7ywtUmobGhpIbLfn2FAMwzAM0xPRbdbn8yEnJ4cGVT5chMNhRKPRXu9Hr9eTi4w5dI76QEgEQZxFwjAMwxwMtbW1yMvLO2xBUHGhFU0t3z7w8beRlZVF9noOhg6doz4QEjVBXRe1GMqA2Z1zRvwM/yx7FAOds9J/iNdbn+/vYjAMM4A5u+havFb1VK/24fV66T/PXX87DgeiJkgEQZVrC2G3HXqtk9enoHhqNe2PA6FBGgh9/PHHNETB2rVr0djYSOMxCbGaQDhBhEL/nXfewa5du+BwOLBgwQI8+OCDVGV5oHQ1h4kgiAOhvdHK+kFxXrSSblCUk2GYo+N5diS6UoggqDeBENM39OsvEAgESKgmRvXek2Aw2D2GkXh97bXXsGPHju7xmhiGYRhmMJNQlV5PzCCvEVqyZAlN+0LUAPU0zgrE+EEzZsxATU0NCgoKjlApGYZhGKbvUaDS1JvtmSHWR8jj8VB1pRiYcX9EIhGaerb3MgzDMAzD7ItB0zgpetnffPPN+O53v/uNbcAPPPAA1SZ1TZwxxjAMwwxElD74xwyRQEh0nD7//PPJ7/Dss89+47q33nor1Rx1TSJbjGEYhmEGGglV7fXEDIFAqCsIqq6upj5D35YRYDAYujPEOFPs21nauHdH9cPFQt0Fh7zt8vBfut8vslxCr0uyr+qTcjHMQGZJwXX7nH+SfN5B7edg1x9sLLZfhh1PFPdqH4uMF/VZeZjBg3YwBEFlZWVYuXIlXC5XfxeJYRiGYfoE7iw9MOjXQMjv96O8vLz7s7Bjrl+/HqmpqcjOzsa5555LqfP//ve/kUgk0NTUROuJ5UIrzjAMwzCDFRHIJDgQGtqB0Jo1azB//vzuzzfccAO9Xnrppbj77rvx5ptv0udJkybttp2oHZo3b94RLi3DMAzD9B1cIzQw6NdASAQzogP0/vimZQzDMAzDMEd1HyGGYRiGOVrpbeYXZ431DRwIMQzDMEw/ICxAvTEBsUVoiKTPMwzDMAzDHC44EGJ6zZLMKw9ovRWxV/rkeMsDLyWPqSj79YkwzNHC0pondvNmCR/X4rQroJkwmhxDB+oHelf5OwYqvfH3dH3/Zd4XkLlUjxNOfLBPfGVHApEx1tvpYPj4449x2mmnIScnh4ar+te//rXXOtu2baPBzcXIDBaLBdOnT6fxPXuO8nDVVVeRzsZqteKcc85Bc3MzBjMcCDEMwzBMP5BQez8dDIFAABMnTsQzz+xbpFtRUYFjjz0Wo0aNwocffoiNGzfijjvugNFo7F7n+uuvx1tvvYW///3v+Oijj9DQ0ICzzz4bgxnuI8QwDMMwQ4AlS5bQtD9uu+02nHzyyXj44Ye755WUlHS/F8NW/eEPf8DLL7+ME044gea98MILGD16NL744gvMmjULgxGuEWIYhmGYfuws3ZtJ4PV6d5sikcjBl0VR8Pbbb2PkyJFYtGgRMjIyMHPmzN2az9auXUsjPixYsKB7nqg9KigowOeff47BCgdCDMMwDNMPKJCQ6MUkthfk5+dTn56u6YEHHjjosrS0tNBoDw8++CAWL16MFStW4KyzzqJmL9EEJhCjO4hRHZxO527bZmZmdo/8MBjhpjGGYRiGGcTU1tbuNsC4GHz8UGqEBGeccQb1A+oa1eGzzz7Dc889h7lz5+JohQMhhmEYhukHFDU59WZ7gQiCegZCh0JaWhq0Wi3GjBmz23zR/2fVqlX0PisrC9FoFG63e7daIZE1JpYNVrhpjGEYhmH6gd40i3VNfYVer6dU+R07duw2f+fOnSgsLKT3U6dOhU6nw/vvv9+9XKwv0utnz56NwQrXCO3HQyOcFMyBsbT52cPqF9mX26PrmMIh0tOPIlwrajR62MrDMEeCxSk/hFJagBVf3Jn8nHYFFI+X5veVj2tf909/0Rt/T8/y2/76Bar/dw4GC70NZg52W9EHqLy8vPtzZWUl1q9fj9TUVOrwfNNNN+E73/kOjj/+eBoQfdmyZZQqL1LpBaL/0eWXX04DpIttRC3UNddcQ0HQYM0YE3AgxDAMwzBDgDVr1lCA04UIaASXXnopXnzxReocLfoDic7W1157LUpLS/HPf/6T3EJdPP7445BlmUSKIjtNZJj95je/wWCGAyGGYRiG6QcUVaKpN9sfDPPmzYP6LQO1/uAHP6Bpfwi5ohAy7k/KOBjhQIhhGIZhhkDTGLNvuLM0wzAMwzBDFq4RYhiGYZh+IAGZpkPfnukLOBBiGIZhmH5A7WUfIbE903u4aWwfDITUeZHWejQc49tS43ubVrtX6q+idG9zIPtnmIFw7ywZfStpO+j98JuA/CxoPCEsHntbUufR9jtKm1/W+XyfHvdIp84v1F1wQOstslxy0PsW93t8wTTsvO363X4f8X5J5pUHvT9m6MA1QgzDMAzTD3Bn6YEBB0IMwzAM0w8kVJmmQ9++T4szZOGmMYZhGIZhhixcI8QwDMMw/YACCUov6iMUcJVQX8CBEMMwDMP0A9xHaGDAgRDDMAzDDMo+Qlwj1BdwHyGGYRiGYYYsHAj1M0uKk6P/7ol2WNF+t1mc8sNDOtaeXp0uh8iSvGv7xbnzbY6gb2PP8yC+w9LmZ79x/+RoYQaVY+lQnD2H8/uJ8hysf2d/HpslBdfRM0CpqYeUkYYlI29GoroWyzbeh6XbHsCyLff3uqxHmm86pnAhHQjLAy8d9HHF/R61a+h5Jn6frnKI9z2fC33xLO3bPkK9m5jew01jDMMwDNMPKL0cYoM7S/cNXCPEMAzDMMyQhWuEGIZhGKYf4M7SAwMOhBiGYRimn5rG2CPU/3DTGMMwDMMwQxauEWIYhmGYfiChSjT1Znum93AgxDAMwzD9QKKXWWMJbhrrE7hp7DCzJPsqel1sv4y8FsIbIt4vTruC5qs283/XzbuWlguWlj+yXzfHss7n9zpO1/72fN917C7Pxr78Kmo4/M3foeC6/XpZusrVdYxFU+/qnvdtDpOu77rn+wOl53kQ51l8v/0dc9rlj2HuyQ/vdV77gv5wtfS3w+lIcrDOnsP9/Q6lPDAY9pp1wokPYmnNEwiPyIR/yQS6NpfufIhcO0tG39rtuFnmfeGwlFU4i/rz/BzKPd/1HFpkuYSeOQt1F+y2/NN/3Iiya4qxYO4voZkw+lufy/t6ljJDD64RYhiGYZh+QFFlmg59e64R6gs4EGIYhmGYfoCbxgYGHAgxDMMwTD+g9LLDs9ie6T3cR4hhGIZhmCEL1wgxDMMwzKAUKnJdRl/AgRDDMAzDDMohNjgQ6gv4LDIMwzAMM2ThQKiPEf6PnixtfKbbBaJJTQFiMXq/rO13WDzpTkgd3uR2w2+CGghCdXu6fRnClUHLsq/ap5tDeDiEe2jxhNtpf130fN917C7fzb78KmL9b/KuCNeJpNdj/sKH9lrWVS5xDFEeubmze554FWXb53kqvoH22/MYXQg/yJLMK3EwKJ1uOmc9z5M4RhfpnzTD+MGG/ZanN9D3/I9HiRla7Os6Ffe1cHmJ61Hcc3RtWEzJ9cX9+p9rRdFJdD2uXHEz+W96snTbAwfluPkml9W+lokyCGdRV5l6esKEv6jL8SM8PeK7HI77puc93/Ws29/nLsRzSJR1eeCl5DM1O2uvdbKmNqL8+1qgrmm3+fRbjL2N9t31XN4XZzj2fezDgQKp1xPTe7hpjGEYhmH6AW4aGxjwWWQYhmEYZsjCNUIMwzAMMyiFilyX0RdwIMQwDMMw/YCiSjT1Znum93A4yTAMwzDMkIVrhBiGYRimHxBCxN40b7FQsW/gQIhhGIZhBuXo8xwI9QV8FvuILjeI8H90z/uPS0S4hbpcPx0LSjD35IdpuW+4HZBlnDj/ASSqa2lefPII8gbJ2Zk0CZ+HGovtdTyxX+HhUFPtWLbxvu75wvuxL4SnKLZwGpVj7mmPUNno1X4ZTppzH0469v69t/mPS0Ssp5YWwVjVgSUjb6bvs6cvacaljwF6HWovLE76OtKu+O95GHvbXr6VpZWPkW9lXwg/yNLmZ+n9Mef+qvv89nSd7IVGQy4k4UDpeQxyLQ2/CfEMO5Qpo9E5MTVZnuyr0Jd0uZqYo4P9eWz2RFyne667bP29kJwOyMUFiJw2A4kJwxHOd9L13nxaMV0r4v4wfLgRki9I2/S8brvouscWTb3rWx1B+/KMdbGvZaIMXdsm8tK6PWLiWPEJwxAtyaTPmuHFQE4G0NDSve2hOoV63r/inuz5nYUXiOaPvDnpOwu8tJv/qOcxWy+d9t/vHonQPd61rih/53vZSP9YC8lo3KsMUiSK0ILxyWdY3rX7fF6+4UmW5UiQgNTriek9HAgxDMMwDDNk6ddA6OOPP8Zpp52GnJwcSJKEf/3rX7stV1UVd955J7Kzs2EymbBgwQKUlZX1W3kZhmEYpq+bxnozMb2nX89iIBDAxIkT8cwz+9adP/zww3jqqafw3HPP4csvv4TFYsGiRYsQDoePeFkZhmEYpi9J9Lp5jBn0naWXLFlC074QtUFPPPEEbr/9dpxxxhk076WXXkJmZibVHF1wwb77wjAMwzAMwxwoA7ZerbKyEk1NTdQc1oXD4cDMmTPx+eef73e7SCQCr9e728QwDMMwAw1uGhsYDNizKIIggagB6on43LVsXzzwwAMUMHVN+fn5h72sDMMwDHOog672ZmJ6z1F3Fm+99VZ4PJ7uqbY2mZbOMAzDMAwzaAKhrKwsem1ubt5tvvjctWxfGAwG2O323aZ9IfwRXe6fw+WRaT1rNL2qOg1NwnWRsqGj23dh/6oOnln5kCMJqDPHI3RsKRIGDSJj8tF6Qh4CYzPIFSScJHv5OzQa8mfEXBZy4pCz6D++EfFZLFs0/Z7u1ZUUK/TuCL3X+uPwnFQK8Z8JUe72iRY6bk9vh0AcW+DP0iJh1sE7IR2hYamI5Dh28yUJvvrjDVha/ghstQqii6dD0miwcNa9tEyKxRGcMYx8ReKci2n+woegbq/Y65h7YqlLelaQn0Ouk671e3qKhA9E0uuTv0Pn83t9B1GuzlITok49TK0xuCelQslJw4K5v0x6kYpv+MYyMEOPLq/NtyGuR7kgl17FM0Vc18JRI+5dBELQeeN4d9VtMG6qIb/Q189eT9sJp5i4nlWbebfrtsutJe4RcY+J63z52uR93HXt97xnNKkp+3QQCfZ05NBzIfuqbp+PetxkWkdT/d9nLD2rZAkxe7L7aOeUNHrtegaJe1hq7ez2gvV8hu7rXu7p/+pyFYnnguIw73aviv2K9RSbEfEFSd+Z8B+J55jYR5crTcpIg7FTSXrPZk2E+4ThUNo7aBvyrzW04eJL3oUcB5TMlN18TOJciWeBHFHQMS0dS+ueoucUrTP8pu6ynFOS/I2OBCokKL2YxPbMURwIFRcXU8Dz/vvvd88T/X1E9tjs2bP7tWwMwzAM01u4aWxg0K9ZY36/H+Xl5bt1kF6/fj1SU1NRUFCA6667Dvfddx9GjBhBgdEdd9xBzqEzzzyzP4vNMAzDMMxRQr8GQmvWrMH8+fO7P99wQ7KJ4tJLL8WLL76In//85+QauuKKK+B2u3Hsscdi2bJlMO5Dnc4wDMMwgwlFlWjqzfZM7+nXerV58+aRL2jPSQRBAmGbvvfeeylLTEgU33vvPYwcObI/i8wwDMMwfYIYeb63U1+O5tCTH//4x7SO8Pn1pKOjAxdddBH1v3U6nbj88supdWcwww2MDMMwDNOPNUK9mfpyNIcuXn/9dXzxxRcUMO2JCIK2bNmCd999F//+978puBKtNoOZfm0aYxiGYRim/0dz6KK+vh7XXHMNli9fjlNOOQU92bZtG3VPWb16NaZNm0bznn76aZx88sn41a9+tc/AaTDANUIMwzAM0w8okHs9CfYcTUGMsHBI5VEUfO9738NNN92EsWPH7rVcjOogmsO6giCBGP1BlmXK6B6sDNlASDO8GGpi7yHrupwTfYGlJY747HFon5qKlllOaEuHwzM2BbWLNahd4kRgci4SBgnBHCN5btrGahG1a2CoboeilxBKTbp91PZO2l/C+t9O4pLJCPcx+eT7CE4uROeSUYg5TYieOBnxkhxya7hH2WjdE+c/gJoldvgLLAiUONExxoiO0TLtX7g3wi6gZaqBvB3CRUTujkl3dnuInOVhNM00wVukgc4fxwfv30J+E+H+6GLO+Y9i3uKH4FzbAk1EAVKdSJh0iKeYERidRu4ObWeg27dkqPdCslromHsivChdnhLPCCu9LttyP/02Xd4P4WHp8pYIH4gaCmHuyQ/T9+lC+EqOP+MRenWt98C8rQnGpgCcmzqhmPSIOnVon52J2vPy6bserE/o2xxIzODnm35jcT16vjcbzfMy6N6WHXa6fxWrEYEMLRCLoXWSEbO/+yiWNiabIoS7Stw74hpfZLmEngddiM/Klp30PnTsaLqWxXXehbhXhFNnt3smL4uufSpP3rVYPPa27kUrYq8k5//nXkq0tUNNT0n6uObch1C6nvxb4fEFyeMbL0Igzww5pkBSks+NqFVGzakutM7NTpbhs9sRmFEEJS+DnhMoyu3+Xvu6l8X93uUPIvePePZWNkLduIOcS13IsQQSM8ciWGCDP1dH3iDyI1lM5BCj8k2/B9H8VLRMl6DoZHSMtUDvSdB3MJY1J7+H0YD3rzwWLTNUhDMttF2X8yzR0YlZFz2KsEtLDjVyII0dTudNdXvo3InfwDunGEeKhCr1ehKIERR6jqggRlg4FB566CFotVpce+21+1wu+utmZGTsNk+sLzK9v2nEh4EON40xDMMwzCBGjKDQUx4sxMIHy9q1a/Hkk0/i66+/pk7SQ4khWyPEMAzDMEdDZ+k9R1M4lEDok08+QUtLCzn8RC2PmKqrq/Gzn/0MRUVFtI6QHIt1ehKPxymT7JtGfBjocI0QwzAMw/QDai9HkBfb9xXf+973qL9PTxYtWkTzL7ssOZSKGNVBOP1E7dHUqVNp3gcffEB9i2bOnInBCgdCDMMwDDME+LbRHFwu127r63Q6qukpLS2lz6NHj8bixYvxox/9CM899xxisRiuvvpqXHDBBYM2Y0zAgRDDMAzD9AMJSDT1Zvu+HM3hQPjLX/5Cwc+JJ55I2WLnnHMOnnrqKQxmOBBiGIZhmH5AUXs3TIbY/lBGczhQqqqq9ponao9efvllHE0Mqc7SPdOj1fomLA+8tNvyhboLEN9R/s37yNt3WuG+CDs1SBhkJIzJCz2eYoGilZCySYY2AEop7xwlwdgeg3eYhILXmhA3yWg+KQfeIiCQLVFKqhqNJo8rS5R6e8y5v0LLeaNh6IwjZpbhHqGDsT0O93ADOkv1aB9vpu/m3OrBkuE3oeE4IxwVCgLZMhpnaWBtSED8R8K1tgNKpxvBbAX+QoXSZ0VauUj7DeVY0TbJTim0lacbaLkuANSdYKbjB48ZAY0/TN9LbGdwx9EyTY/mE7NgKGuCZ3wapfaLtHvzyq2UVhwsTqYKC22Be1Iq4mOL9j6/BddB0usAWzJt3tIQpbR88dvFXdbulGCR5k/pxBNu797W9PlOqJn/rdoV6fs6bxyahg54R9oRz3WhZXYKgsUOaLyh5LKoCv+4CJrm2KGk2nDCiQ8e8O+7r3ThrvPB9C99pTbo+o17ajXENSquR3E9G7wKDG4F0Sw71FAY2oiK9gk2ZCyvppT5tI0R2Hd6kynzaVcgYdDQesjPAsaUwPlB8nkjrmPZlQpp0mj6TAoK/CfFu8c11aWf6CrTsvX3Qi4uSKbo1z0FKRii7961vngV95uY5z9rOiR3ciiEqMuAzpEahE8YD0+JnhQT0eMnwPbhTrSPNaNxlhado4z0F6LwpUrofSqlv0+4/nEYm0NomOvE0ponutP/3/voF/SMEs+bnmnxXWUQ6fXymq3J+9VmJX2JeCaQpmPqXWidZoccicNS4YapI5HUYORnkYoglpF8FoTTTZAUFaZGCcEsPbzD/nOA/CxEhmXAsLoMqs0MbVsAEyZXouqcpAKgJ863NkOVJcTNEp1LqawG0RHZpCmQOtyk55D3tqowRzlcI8QwDMMw/YDSy87SvdmW+S8cCDEMwzBMP6BAoqk32zO9hwMhhmEYhukHetqhD3V7pvdwvRrDMAzDMEMWrhFiGIZhmH6A+wgNDDgQYhiGYZj+6iPUm/R57iPUJ3AgxDAMwzDMEaOsrAwrV66kccvE8Bw9ufPOO3GkkdSDsSsNQrxeLxwOBzweT/fovMIDEhyXA9NX5UA0tpubY38ID4dwigjXkHBgLN32Xz/F3JMfxkfv/Lz7s3BlqDsrUXnHNNiqgGA2ECqOIfMDLZpnqbCXy4jZgJTtCpqOBWwVMrzTw9DUG5G6VYU/V4KpFQjkAqlbk/6ftI1h8nqkf+VG1GVGx2g9jB0q3CMlWBqSx40bAW0YUE7tQPyjVOg9gKoFOsclYKnWQFKA3PfdWL72Hpw05z5c9tJb+NA9Cp/UDkOoxYKc9yX48mToQkAgB7DUAY7KKBp+HIHxIxt8JQoUgwrbDg2sjQq5i3TeCFZ8cScm/vRxKBpxQQG22gSCmZru8yGOa2xXoPMnYFq1DZLFDM9xw8gJYiv3kguFHE8aDaDTkuNJLS1CNMUIrT+GYI4R9i3tUMx68gHZ39wAuSAXCIQAiwlwe6HkpAGyDLnTD7W9E2oohIafTEPOb9chMW0U6ueakPNJGIpOeJf0yPykHb6RTlirA9hxtRGGGj1ELXPKdpXcRYpOwofLbv7Ga0JcC11eowO5dpjBj/DkRApdqJ9rwLC/tkBtbUd4WglMWxvJqyN8PsEMGZookPl2NdzH5EPvV2BoDSGSaoSpzgspHENoWCpMuzoAjzfpvqqqp+eQ2P/S8kfIf6PfXodEYSY0bT7A54cqnlWdz3eXZfwNjyPnEy9kfxjN8zLw9bPXkwfrg/dvof2oZgNiLgv0Fc1UtiUjb6bjGlqDaJnlhBQXbh4g94MAOsZaYKuNImbVIJSuQdQG6H1AzAJ4R8dQ9E8hN5IQTNMgapeQvj5E7qCZ33sMqavqEBydBW0oAfdwI1ybfNB4QuT9enfVbVTWqT96HKnbgtC2eBHNdUKOKdBWNAAWM5pPzEHKzjBaphphblbJn5b9WZBcX+IejdpkyHHA4E0goZPos7dYgqoBok4FhcsUmNdUAU47gsNTEbHL9BuE04DCpUFo69uxtPIx8hnJKU4EZhShbbwWhS9WQmlrR+d3psK5ww9NZSMazx8JNRjGxv/7xW5/Mw7X36Xz3r8EOov+kPcTC0Tx9xNfOqxl7Wt+//vf48orr0RaWhoN39FzpHvx/uuvv8aRhmuEGIZhGKYf6DmC/KFuP9i47777cP/99+Pmm7/5P5pHEu5pxTAMwzDMEaGzsxPnndc35ve+ggMhhmEYhunHrLHeTION8847DytWrMBAgpvGGIZhGKYfGIpNY8OHD8cdd9yBL774AuPHj4dOp9tt+bXXHvh4nn0FB0IMwzAMwxwRfve738FqteKjjz6iqSeiszQHQgzDMAwzRBiKY41VVlZioMGBEMMwDMP0A0OxaawnXfaenin0/cHg62l1iJyV/kN6XTL6ViiZKTCvq4ZamA0pI22f6y9Ou2K3z10emMjiqeQO6fLDCMyVneSU6UJy+yEX5CGaF0X7tARiNhUIC3+NBOgVRFKBhAFomygDCQnReV4YdxphagZajk1AEwbcJ4ag8wJhl0wuj6aZRsSEMmeMA2GXFmEXEHFKMLUAHeMUdI5REElJ+n88u5ywVylwlkeQ9UYlRj3rhu64DsSsgPPXzdhQk497/vp/eO5n52LDkxNhf82GmRPK4fhkF3Kf34j0F9bAUa7S9oJYtRXhuT4qq7FRg2CuCr0nAV++Du3jbeQz8YxUyEkiymB7fzscFVFowiqMHcI3pMBXIMNXqKPzHZpYQG4kQSjfRn4P1WaGd1Im+ZmETyWYZ4FhdRnaJ1rgLdJAqamHqtPA+eEuSCUFSDhMgNEAz/jk7ye8KN7hVuy6NIdcK3JGOoydKpp/MBmqnPQyhV06tEzRw58HtM50wbZiC+JWPUZdVwZ9J+g7e0okBHL00LsjWGy/bLffeU+6HEJd6+2PwewQEr9NfyFcOof7e+3vtxUennmLH0q+H30r5i98CMec+yu0zM9B7QIDeW2CxSlIjCqEzh9H9cWFmHD94/AWyuTTEr6v5iWF8GfL5N6Ry+ug98YQLHagfXYmNBEF7qkZUIRHSKeBlJWBJXnXIp7h6C6DGouR20Z4hZY2Pwt1WC450ETZBOZWFapGQvU5GeTAEg4j/YZdWDT1Luy6JAdSczv09W6oPj99z7pTs2g74SETz4+EEdAEQfeYP1/cu1o0T9fAVwD4SmO0bigdkA0J6AJxxA0SXP/cBEujguapJiweextS17Si7qx88qi1TTCifZKC+nk2xFMsVDbBgrm/JJdS0wwzPJMy6P5qmWpG9eXD0XxSDv0VEtumr4sgZpboOdU4xwzVoINnmAb2qgi5i4RDKG6S0TpDIYeQwFnkRkeplnxL0Sw72kdryQOWfWY1bFPa6Jji2SLKoEwbAygK3MO05FBqXVQE3+lTYG6OIWHSITBLuM2AztG7C/6ORCDUm2kw8tJLL1H/IJPJRNOECRPwpz/9qd/KwzVCDMMwDMMcER577DHqLH311VfjmGOOoXmrVq3Cj3/8Y7S1teH666/HkYYDIYZhGIbpB4Zi09jTTz+NZ599Fpdcckn3vNNPPx1jx47F3XffzYEQwzAMwwwVhmIg1NjYiDlz5uw1X8wTy/qDIdNHiGEYhmGY/vcIvfrqq3vN/9vf/oYRI0b0S5m4RohhGIZh+gG1lynwg3HE9HvuuQff+c538PHHH3f3Efr000/x/vvv7zNAOhJwIMQwDMMw/cBQbBo755xz8OWXX+Lxxx/Hv/71L5o3evRofPXVV5g8eXK/lIkDIYZhGIZhjhhTp07Fn//8ZwwUJLXLaHSU4vV64XA44PF4cO7CJ6Bp8yGan4rOUUY4d0YgxxW899EvutcXrhDr0o2QTEYsa/vdXvubc/6jcKyuh5Jqg9zQBjUQROt3JmDt7//b033czx9H7vtu7LjCBpji0OgVJDx6mGs1COYnoHFEMSqnCTs/LUbCqMJQ5COXT/McBZItBtWngyYlCsNGU/c+jZ1JR1DuhxF4i/ToGAfYqiS4J8Qw6jcBhPKs+PiNmzDj0sfQcUoQxxVXwKUP4oa0TxBQgdN/dxPMs9vgX50Gay3gGakinhmFyRZBJKSDrtyEVT/8FU5ceznm55VhRdUo3DH+HTxatgCxuAap5iCqtmcDthjSV+rhWtsBKRhB9fk5sFcraJskIXdlHFGHBo0nKDBXa5H7YRDtY83kQYrZQY6O9A1xNE/VIm5RkbJVuH0k5L9SjcDkXPKtOHZFoK9oRrQkE6F0PbQRBdWnq8hbKsNSF0T1yTYMe74aoTHZMDb4yMti/rQMkSkl8BTrkb7GTedLbu5E9SXFSNsSh2VdPRrOKiT3i7UhAV+eBnqvCkdZENqKejSdOxLm5gQUnQRbZQCVZ9ig9wCBkjhG31mF6Kg8vL/y1m7/zPLwX7DIcgniM0Z3z98Xi1N+SE6jPVmSfRWWNj7Ti6uaORiEQ0eTmgLVH6DfrifChbNsy/3dn8nRE44AOi3d4+rGHXBfOAO6gELXXzjThIaLIkh7w0TXc8Qpw9KUQMsUDdI2JBBO0ZCTJ21jApqYitaJWmR/GoHOG8Hy1Xdh6o8eR+qWAOJWHRImGaY6P+RglLxYqlaDqF2H9nE66AKAvSYOQ2sIUiyBcKYFoXQtUrb6yH2l/Wob5OxMKmfLsRlIe3kdQgvG03cwvbcJ7rMnwVYThm5nPeouGg5rvQJFKyGcKsFfoCLzSxVNxwLGJhnaEBA3AenrY6ifq4OxHbA0qohZJfKTISWK22a8g9a4DZXBdFyV8QG+t+EyTM2qxU1ZK/Dr1vnIN3bgH9WT4Q8Z4DCHcX/p62iIpyCgGPDczuOQe5UX/ql50Hnj5PMS97WhNQJ/oQmf//VnmH7ZY9CGVTTPBhRHHJJGgabRAJ1PIuePKE9CLyGUCSg6IOurOKrPVYCoDG2HFpoIoPeC7lvhVBI+s+GnVqDO54DmFRdta6uNwrSuCnUXjYS9JgF/rgZpG8P0tyBtnQ9tE23wFwBhQxC1N91OfzPsdvth/bs0799XQmsxHPJ+4oEIPjz12cNa1r76vl3lE++/if74HlwjxDAMwzD9wFBpGktJSaGMsIyMDDidzn2apEWdjJifSCSOePk4EGIYhmEY5rDxwQcfIDU1ld6vXLkSAw0OhBiGYRimHxgqNUJz587tfl9cXIz8/Py9aoVEjVBtbW0/lI49QgzDMAzTL6iq1OtpsFFcXIzW1ta95nd0dNCy/oBrhBiGYRimHxAOod54hHqzbX+h/qcv0J74/X4YjcZ+KRMHQgzDMAzDHFZuuOEGehVBkBh01Ww2dy8THaSFW2jSpEnoDzgQYhiGYZh+YKj0ERKsW7euu0Zo06ZN0Ov16EK8nzhxIm688Ub0B0PGI7Rg2E8RL8mGvj0I9xgH+TTCLiDrixjMlZ1QzHpICRUts1Pw9bN7j367pPgGLK18DPMXPgTj9kaowSCkdBe8Y13kCxEOn8X2yyBlpKF1bjY5a5qPj9O2kiJhbGktylrSodMlENplh94jkYck6lBhbJWgOaaTfD3hgB6aeiPiGTHoG3RI2a4ilCYhYQAMnWJ9kKNI2lWPwLxRqD8vihfmvIi/dczA/6R9BJ2k4PtbLoX6tzSkbvTAX2xDxxgNXMc3wvt2NixLmtHhNyPiM8CV4UV7qw0IaVE6qg4dLxbAWyzBXqmidZaCgndUNM3QAqV+aDQKEgmZyv/EhL+hSOfBjVVno+2JIpibIii7XAeEZQz7RxwtUw3wj47B5AxBWm0nJ0r8BA8i2x0wNQNpm6OoPEcDOSKh5NUw4mYtDF9XoOaK0Sh4dgukrHRUnZcBa62K9skqkJDg2gjofQpC6Rq4NgVpm7BLi1CaDGdZFN5iPRQt6L2Yb68IoOo0G1ybFTjXtqDppCxyBwWyJZhaVdqXfX0zlpY/glkXPQpvoYz0TXHEDRKM7TFUXKCj8pU+Xo+6c/LJw2Lf6YUUjnd7Z2Z+7zE4XvkKK2Kv7HatCNeQEo1Cm5kBJScNikmPd1fdhv5ifz6jA6XLnbTXfu2XYZn3BQwET9C7yt93+6yxWhGfPAK6Rg8QiyGR6UQk1YiP3vk5luRdS/OWNj+LKf/zODLfroTS1g7POVPgXL4DoRnDEbNqkDBIcG73oXmWHaH0pKcGChC3gtxYqVuTbpu4GfCMSWD03ZVoOXU4dEEF/lyZrhlLQxS7LgeG/QHQesNoOtYJZ1kMkRQN5Big8ycQyNbCtdEHRadB1GVA1CrD1BKDZ5iB9p39XhsUm4F8QuJakkNRSGU1qLxxAvLfDyFh0CCQraNnWu6yFrgnpSFulMinY62PQTHI5DMSz5DsT2NonaRDxKVC0auwVsoI5qpw7Ew6esT3Sl+v4Jb7/4Q7tpyOM4o24auOQuzYngdztQbhLAWKBjC0ywjnxmCpTHqHPMNVIDcMtcUAS5WMaApgbAOt6ytRsHDmRsxzbsfGYD5e/XA21NQo7KuNKDqvgn6zDVuKYGjRoOhNL+Q2D7xTc2i+cP0IxHPPVq3CnyshbgF5gxIT/MAOa3J5TgypX+kQNwKZXwZQs9gCw+ROBEN6aLZbyFuW9blCzyptkxt1pyf3L55FHaP1iNmAqA2IOxOQ2qKouv22I+IRmvH6T3vtEfrqrCcHvEeoJ5dddhmefPLJAVVerhFiGIZhGOaI8MIL/f+fpz3hQIhhGIZh+oGh1DTWkzVr1tAAqzU1NYhGo7ste+2113Ck4fR5hmEYhukHhmL6/CuvvII5c+Zg27ZteP311xGLxbBlyxaSLormwv6AAyGGYRiGYY4Iv/zlL2nk+bfeeos6SYv+Qtu3b8f555+PgoIC9AccCDEMwzBMPyBqdJReTIOxRqiiogKnnHIKvReBUCAQoJT666+/Hr/73d4DnR8JOBBiGIZhmH5ApGyLvO1DnjD4SElJgc/no/e5ubnYvHkzvXe73QgGg/1SJu4szTAMwzDMEeH444/Hu+++i/Hjx+O8887DT3/6U+ofJOadeOKJ6A+GjEfohIk3o+JH6SgZU49gTA//skyYW1U0z1KhDUrI/FKB3ptAMFOH1DWtiOQ4oF+9gzwpwqPiPm8qvvzTDRj+0OPIXRVHxC7DWhdBMNOAhhNVZH0sIeWDXai7aDh0PqBjggJrtYzoDD+iQR20jQbYq0CuG+EkGfbXFsQybNC2BQCNBFTVQxlTjBVf3InFaVcgPK0EzTP0SN8QR81iCdZKDYLZKvmJzlz4JaZYqvBu5xisbcqH8kkKOUv0fgU1pwK2bVrEj/NiVl4VMg0+LKsdjUJHJxr9doRjWqSag+gImuH3GVGQ2UHnyaCNo7LVRb4g/Yd2hOf6EPHr4Ur3we2xID+9A1U1GdCY4ki0G2CrkOErTeB7cz7FJSlf4pGmkxBTZUy3V+H/Hj4Naas7sP1/UqDzyuRb6RgjIWFRoBhUmOo1COUmgLgEc70MS2Pyewn3ifCe6L0xNE83Q9EBMSsQKYyi+C9Aw3F6FP+zE1IwguBwF4KZWsTNEnR+FSmbPfCOtKNtgkzuJVtNBJpQDJF0EwytIXLICMQ29qoIYnYtTMvXQy4ugH+UC9YtrYin2RC36mBo8sEzNgWeEpm8R7HTO+F83oZAhhYZq1qgWI1YvvouTLrmcWSs9kHeWvmNPp2Fs+6FtKkcywMvoT/9On3hFDoSLBl+EyDLQDCEpXVP7bfsdF7X7wDGj4RUXgvF74c6czw5mxZNvQvBAhuCaRq6L4SbRxtWkbqyihxC8ZJcaOvaEBqTDV++Hqnbkm6qtnF68ukIx44clRBLi8G5UQd/gQopLsFal3TtFLzRgqXbHsD4Gx+Hr1iBvVym61WKA5lrQ2g4xoT09TFyWjXPUZC6UUPLhGNI+MP0ngS5iuxbOlB3ShpstQo6xsiwVakwtyXgz9EikANkfRWHFFMgx1RoA1HELXpIikpOovoTnbRPS7NCHixjJ5D+lRttU5z0jImmqLDWJH1Czl1x1M0TZZDIE9Q4T4Vsi0JVJKgxDbJXaNA6RYKpUcKZl32Mv6w8FtZiD8ZnNOKzsmHQ1hoRzYhB0qrIzu6EO2CCTptARDxPrEHYDWG0BS2w6SPwRQ3QaxJIKDJaalPgynPDs9mFtPUqonaZfD2hTBWWkW4Et6Yg+4sEgld0Is/mwWh7Ey5J+Rz/8EzFP38/HzmvV0NJd0BuaMOuK4dD7wY2PZb0vJ005z5E0g3wZ2nRMScKU4UelnoVnWPE7yBBN9KLYIcJBf+WUbsQ9AwVzxpjexy+fB0CuUAkPwZTpY7OkXAN6TslhA1hVN79iyPiEZr4j59BYz50j1AiGMGGcx8dVB6hjo4OhMNh5OTkQFEUPPzww/jss88wYsQI3H777VRjNCBrhLrU2AeD+EKpqamHUiaGYRiGOerpbebXYOwjlNojLpBlGbfccgv6mwMKhJ544gnMnj17NyX2N7Fq1SpcffXVvQ6ExPgjd999N/785z+jqamJIsjvf//7FGTta9A2hmEYhhksiA7P0hDwCHm9Qst+YPRHzdYB9xES+f4ZGRkHtK7NZkNf8NBDD+HZZ5/FH//4R4wdO5YkTELPLaoUr7322j45BsMwDMMwhw+n0/mtlRddo9KLCpABGQgJJfbBiI5++9vfIjMzE71FtBueccYZ3al2RUVF+Otf/4qvvvqq1/tmGIZhmP6kK/urN9sPBlauXImBzAEFQpdeeulB7fTCCy9EXyDsk8IrsHPnTowcORIbNmygZrfHHntsv9tEIhGaDqVKjmEYhmGOFEOlj9DcuXMxkOmVR8jv91Og0XPqS0QnqgsuuACjRo2CTqfD5MmTcd111+Giiy7a7zYPPPAA1V51Tfn5+X1aJoZhGIZhDp1PPvkEF198MVV21NfX07w//elPVNExKAKhyspKaqqyWCwUaIhUNzGJNsC+TnsTg7L95S9/wcsvv4yvv/6a+gr96le/otf9ceutt1IqYddUW1vbp2ViGIZhmL5gKI419s9//hOLFi2CyWSiv+tdLTji77UYfmNQeISOOeYY6tQkJEiiH9CeHaD6sgpM1OaIWqGrrrqqe959991HWWRibJKD8TWM/skvERynQ+p6DYovLcPxqTvRFrPhr+8cD8WkIGWTjLALyP3Qh5jdAK0/hnc/ux2LJ9xOjh8My0c4y4LOUj0SBiBvRSftP1hoQ83JEtK/kOH65yZEp5eidoEBSnEIuX/VoW2sFpEJIfLwjHS0wqSJYqZtF+aZalGf0MMpR7E1moEx+hakyxrEoCD2n5/knUAJHt64EDkpHlyc/yXeapmI7S0Z0Hxtg71SQcspEejKTVDH+GGzhJFl8SHD5MOn1cOwpGQrvmotRIbZT/tqCVrJ9VHRnIY8lxvNXhtkWYXdFIZBEyevULrVj1a/NXneOiyYObIS6+pz6fh17U4kohoYyo0IZycgRyQgLQIlpkHuWxo0nRuFtsyESGEETx77Cp6qPgHqfRl0LmI5UWjadEikxiEb4rCsMSGQr5KrRfiIok7AWgv48wFNGLDWq/CUSHSe41lRcZUCIS1cazSIpAD2KgXG9hgiKVoEM5N+ls5xCjK/ADpLZdgrVXLGtJ8ThPFTKzlUmqdqad+ubQnysLSN16LguW1Qi3NRP98OQ6c4bgyqTiLnTDBDRjgN0ESAkPi+MQmGNpmOVfh60iXUeKwdOj+w9vdJr0kXS4pvwNLK/zbfnnTs/WiaYUb2Zz7I5XVY1nZkNPKLpt9DvqPuz8aLIKc4oQaS9tZvch/1xj20L3/RgWyjcTjoOOKeW7bxvn36hZTGZkh6PdRQCPHZ46DrDAHVDQgdW4pAhga+QgmWeiDqAP2mHXPDkFoN0PmS15OpNfmbuqdH4FhrQNqZtajYmov0NRI6xgEJq0LXtjYnCKXCSvsI5Sj025sbAceuCPl8VFlG52gzIg4JqhZIGJOOsJhTgXObTG4rf64EbQgI5KkwN0jIfasBVRfmIGNNDJ2lOhjcKrzFEtI2JhA3yYhZJXoGZayJIpSuQzBdgjYCxA2ga8dXbMHnf/0Zjj/jEZjq/OQLkuMqFK0Eg0dB2CWTeyjj6wTaR2vIzSV8PaZmCbnvu1G7yIlgYQKOrRooGsA3IoHMz2Q4t/sge4KAx4vUN+NYs3wMIpkJut/apyVQMrIBlY1pOHvsBjSG7WgLW2DUxJFqCGDltlLAp8P8GZvxUdkIpKQEEAzryWHWErJhV7sLBamdaPbbEAzpkeH0obFNPEtkcpQJd1A4keyhUfVxIXmUxH3oLdQj/3sVeLb4NegkCTrIeNk3HCdZdiD2nz/+b/gmQicl8Iftc1D0007oX46j/N8liE3zQ9pqxaSTdmBOSjlydJ24/eWLoY72w7LSCl8BYN8F+IqSr/4CwNgGeEYlAEscCW8UddfcdUQ8QqUv39Jrj9COCx884LJ+/PHHeOSRR7B27Vo0NjZSEtSZZ55Jy8TgpyIj+5133sGuXbuofAsWLMCDDz5IGds9PUDXXHMNjRUm0t/POeccGi/Mak3+7fg2RMuOGE7jkksuocQq0eVl2LBhWLduHZYsWUIZ4gPeLC0KLU5iaWkpDjdCty1OdE80Gg1JmBiGYRiGOXDEuF4TJ07ED37wA5x99tl7/b39+uuvcccdd9A6nZ2dVOFx+umnU8Z2F6JrigiihAlaBE8ik/uKK66glpsDYceOHWSX3hMReIlhNvqDgw6Epk+fTs1NRyIQOu2003D//ffTiLQifV5EjKKjtPgRGYZhGGYwc6SzxkSNi5j2hcPhoOCmJ7/+9a8xY8YM1NTU0N/hbdu2YdmyZVi9ejWmTZtG6zz99NM4+eSTqdtKz5qj/ZGVlYXy8nLKAu+J6B8kaoYGRSD0/PPP48c//jF1cBo3bhx1Yu7JhAkT+qxw4gSL6PQnP/kJWlpa6CT/z//8D+68884+OwbDMAzD9F8g1JusseTrnolKBoOBpt4imtxE9xfRB1jw+eef0/uuIEggms9Ey82XX36Js84661v3+aMf/Yhqmv7v//6P9t3Q0ED7vfHGG+nv/aAIhFpbW1FRUUHVYV2IL3M4ZEii/VBYrcXEMAzDMMze7Jkdfdddd9GoDL0hHA7j5ptvxne/+93u/kei/86eYmWtVkujSBxo3x7R71d0bxEDrIrmONFMJoI2EQiJvkeDIhASzVKis5MQG+6rszTDMAzDMEfOIyS6q/TsLN3b2qBYLIbzzz+fKjjE6A59hago+fTTTykB6qabbqImMqHhGTNmzAF3th4QgVB1dTXefPNNDB8+/PCUiGEYhmGGAKJlqzdy6K5tRRDUVxlusf8EQeJv/QcffLDbfkX/HtFNpSfxeJwyycSyb0MkOy1cuJD6GokmNhEADQQOOhA64YQTKHNssAVC4QxAikvomBZHR3kB2nPNqNmSDakwBMcnJkqBDeQrsD3WhA2fjICjXI8T5z8AfTCCxJhitE20Uaqqd7gCY6uMtqlO6IIqfPkyoCRgdCuovGkCIqkKtOkBDLtgAy7eUY+YqoE7YcbvXluMqtQs6Ds0WJaYgad2KAhkyvAXKyj9gwf1J6TAVqfAnytDUoC4KZm2KwL+mloLnnntbKgndyAe1aLopGpUtrqQYgkjOD6GU4ZtxVsVYwGLD+ta8jA6qxlVARcKbG74Y3oMt7ViZ2s6nYd0px9jUpJVmL6oAY2NKdAYEshI9aKu04lJ2Q1YU10ARGXEVRlGQ4xS9/+MmWjodCAyPAyLLYKAxwiTKQal3ATjVbVY5GiBp9iELX8eg5dHzEJCkXHPH/4Pl678IZxr9JT2rm3UIZKig8GjIpwuwdApwVeagNatoVTW1K0KOsbIlHYcKYrAUGWAploPx4xWBD9IRzA7mV6vCymoOlUPY6uEtC1xNM3Qwl4uw1sMSp1P6CWYOhJIVFsoBR67AEeFilC6hM4Rmv+k34PS2BfOuhcxK+ica8sTiOm0iBsluLZEUXkRYKjWQ+uXoRvuQwRWGJtkVFycQSnYMQvg3KlS6rd2WBGWlj9C14zObOpO94bPj3ebn8X8hQ+h8gwbhj+lw5KC67C0pu+ae5eMvnWveUu3PYCmOckH2KKpdwFbyhE7Zjw+eP8WLLJcIv5rtt/U+MVpVyTPje4CrIi9QsuXZF4JxR+AbLVgafPe/0MU30nJTIFUXgvNuINPpJA0GjrOzO89hi833pc8d+EIltY9hcX2yyCZjGKoakgjigBFQTjPTvejUFq4jxsLvVelVHaRvt6Rn0B2bgfa1mYic6kevvO9CHSY4crywL86DTGbCo1eoRTylrfygUkRtE/WQeeWkTBKkDIiiLabYPYk0+KF4iFrlRu+kuT57Byd/J+rrTYG90g9YmkxaAIaSrtXU6PwFhlhaZCQ964bLbOcyFitQtGpaFySQ/qF+rk6mJqBqD35v3lFJyFlQwcqz3XRddk8Q49ImkjjB/SdEhImoHGODXofMP2yxxAt0sLUINP9EElToWhUOLfLCKUDelFmnYRwevJPpKVWgm9iFJUOJ7QBQOuV4Z8dQsYbBqRUqGgfLSHrf1pJr3F50WY89uLZwDQfvjdiHd4rLsVUqxdaSYE7JYB/rJqB0nG1qPqoEIbJnajTOlCU2waNrKDS68KI3BbUdKTQ82hNWz49L8ymKAIxPeZkV2JjRw4suiguHfcF6sIpqPC5UNmZiqlZtdjemYm8Y2tRNSINbllBVmo7tnxagjN/fyNaZiS/X+ekGB5vOR06H2BuFvoNCR2zopA0Cux/C+PL7cNQsC2ONtlKCo5N4VKszhqBERNr8aNzl+E3yxbRObLVAIoeSFgUuE+MAPVGeKbEUFrUiPI1BTDmeDBUzdKx/wRBZWVlNCyGy+XabbkYfF1kdonM8alTp9I8ESyJpq6ZM2ce0DFE32KRnl9cXIyBgvZQMrmEA2DTpk0YP378Xp2lRaodwzAMwzADC9EMJZqjegqS169fT318srOzce6551IK/b///W9qxurq9yOW6/V6jB49GosXL6YOz8899xwFTldffTWNAHEgGWNdLkDRH+h///d/KZgScuZBM/p8FyJjTHDvvffutay/Ro5lGIZhmCHbNnaACB/Q/Pnzuz/fcMMN3eOJis7VotuLYNKkSbttJ2qH5s2bR+/FaA8i+BGdnbuEik899dQBl0Gk2ndVmvTsYzzgR5/vCcsMGYZhGKYP6O0wGQe5rQhmvmkwCfUAxESiduhA5YmDZST6gw6EGIZhGIZhDhbRlCZak0Sz2ogRIzBQOKBBV0W1l3AKHCjiS/p8vt6Ui2EYhmGGhFm6N9NgQqfTYePGjRhoHFAgJDpHH0xg8/Of/5zEiwzDMAzD7JuhOPr8xRdfjD/84Q8YdE1jot1QdIwSBskDIRQK9bZcDMMwDMMcZcTjcRpe47333ttn1pgYT/RIc0CRjdB1HwxnnHEGdagaSMQtCnTpEShxCWpAh9rWVPKGaFpMkKOAe5QKQ7uMnf8YgZIzqrEzNxPuE1WY1+QgY20YgTzAUZ50EQnXR+cYwL5LJp+MZIuho9QIW5UKKSGjcFwbdrwwDb+9bRZapsqIOxOQi0Pk3TEXRZFmDiAwR4/zsrbj0/ZhCD+qxTBDBzbX5UBviCEa0SE7zY10SUGm2Y+NTdmwjA+gvikFZuHwienpO8XiGnoVDqFRGS1oD5th0sWQagjgq4ZCpFiCKLR1oi6UgvmFZWiPWDHS2oxPWkrI8ZHv6kQ77JhWWEPuIJfTT/sbnt2CSLoWRk0Mo9Na8NSO+QhHdChOb4dRE0d5exrsKUE6ft5xtUgzBrF85xjEA1pM+k4F1r9bivjIEC5vvwQvn/Acnhy1EI0PlkDvjmHXOQYE/uNKyvkkDPfP/XD7XXBsB5oWJICgcLdIMG83IDopAFWR4QsZECyNwVivQ8wFaIIJKKkKwtCho1SLuE2BYWo7QmvTEcyUoMpAxzTAWC8hlB+DP0sHg1eBb4QCjV9UgsrQBZLXhRyMwrVZQcIgQY4Jd4sCS6MK4/ZGaAwZSBhEeVTEG6yASUGwUIUpI4BISAclooU6wYO63DnIX5GsMY06ddCtbSBXz/LAS+QYEiRMMorf8GHXlcOR9UWM3DiCZd4XyD30/spbad13lb8f9LWtmJPXw54I55Jg+dp7sGTkzdC5QzjurF9BN2sMtIEoLQvNHrmbCygwORfmvCzMW/wQDBpNt6co3toGbU42YDRQOTWpKclje7zkGkrkpGLFF3d2u4kOFk12FhaPvQ3WDGvSi6TTQqlroGVSRhr84zMhKSoCGVq4NvnIISR8O+LeVbRA1CbR9aKVVcQazQgszYTWBhjb42hqsEKOSfDZjDC3ivMiQVtjQigDsE9qg/OdNERtSWeQIO1tI1qnAOEshZ4RwjcUtxthcMcRs2sRTpGQ85EbbVOcULUqjA06uuaE3yacryJuTyAc0aBhrhOhzOSzwlIrI5SpQo5KkOPJY/mzFZgaZXgLZTTPSIU234/wNivCOTGYanVQJ/iANTbyawWyZHJxKVoJwcIENFE7OYmE1ypYHEdCJyNuURFxqQhmS+QPiqYAwVwVjq/1UHSAd3QMBkcESoUVrVNUXLLkE3gTJrz2QdL/8tjHZyPjxHp6NpL/LGBC/K8ZaD0+DnuanxxCTn0Yo06owMaqXORmdWJiaj0+ahiOBXk7sa4jF8EOE9bY88ktZLOE4fZY4G63IBTT0XMvEtfijdrx0EgqUkxBjHC1ocyTTsfKyfTSs0jw5eYSlM6qRgUKoU0PQl8SxCSrF425dmRbvfBEjOgImqEN6ZFoNqHc7YK5TIe674RgWaOFt1iCMs4PqdqCNGMAv161AP+z6D38ds3xCKfr4Ngpkx/KY9NCG5egbdGh3JABJTOCI4qo0TmCnaUHAps3b8aUKVPo/c6dO3db1l8jVRyWQIhhGIZhmIE1+vxAYOUAzBo7oD5CDMMwDMMwfUldXR1N/Q0HQgzDMAzTn0LF3kyDDEVRKIXe4XCgsLCQJjHumDBN95enkD1CDMMwDNMPDLSxxo4Et912G2WNPfjggzjmmGNo3qpVq8hsLTQ9999/P440HAgxDMMwTH8xCGt1esMf//hHPP/887uNSzphwgTk5ubiJz/5Sb8EQofcNBaNRrFjxw5KhWMYhmEYhvk2Ojo6MGrUqL3mi3liWX9w0IFQMBjE5ZdfDrPZjLFjx6KmJpnueM0111BVF8MwDMMw385QFCpOnDgRv/71r/eaL+aJZf3BQTeN3XrrrdiwYQM+/PBDLF68uHv+ggULqI3vlltuwYDEGsetU97DH6qOwaiUZnywcTRcM1oxO7MSoYQeWjmBpVvHIhLQYcfmfECrwrxDg3A64B5uhCYEcoUIb0jHlDi0bi0iDkA7uxNKixWOXQpapkuIZ0Sh3JqGM55Zj6+yC5GniaNuTS4WTt+KCl8atJKC1qAV0YSGfBrBsB5nlGzCO9VjYLOF4DILF4pCvo1IQguXwQ+bKQKXMQivwwhvkw3jxjXBoovSOi5TEM1BK/l9hKsjoSRj26KUDpS3pZF7yGiJIuzSYle7C+F0LcakNCEYE99Zgd4SRY3PiYxUL23njhoxLz3pdljeOAYGbRzZdi9yzB7kGd3kINJpE+QJEQ4Qf0yPL3cW49xJX2Nlw3ByGJ1/1sd4r7EUk9PqcNGbV0FOj+DKB9/DrlA6at+eSn4b4X7505+fxoWX/xTxUgnhFEDXpEM8PwxjrZE8Q0qTiRwusaARhqiESHYc+pQw3COtgF8hJ0vMBlrH91k6DH7AV5qAtUIDbYcWMZtKv5NYp2OKClWrkKMomKfCuU3G4gm3o2NyKkLpEnldYlYzlUuUr2NUIQwbAUelgsZjAXO9jIhLgrVGQtaoTuzYlYMzJq3DqqZhiKQpaJtsw6yLHoUhoqDlogkwtSawyHgRNKNH0mvou1MRM1uQ/34ItT+NIwujYehIin6EQ0iwL4fQ4rQrsKztd994afuLbXvNO2nOfTDlGMn5E1s4DYFjMhHITTqWTK0amFq19P016VYcf8Yj0IQUqGOyYWiPonO8Eymb3MCoEiwZfhNa5ucgIxAix5CnSItQRg7S1yUQN8lw/n0tHS+SavyvC8Rm3W/5RXkkjYbcQ4IlmVciUZyNtiWFyHy/Af48A6yw0z3nzLLTOoGxGfDlaZCx2gd/VvK7Ns3SI5SdoP/KOfM8+E7xWnzYOhLby3Ng8MrwjkzAsV2Dxjl6QE5ADsnQfm2FvSaOmlMBjVeDtPVAu5yG+IQ4UtZr4XWpMLbKaJoXg6RIyPxYg4RepfPWMcYIW10cYacM/5QwGuCEoge0AQmR9AT5xSJZCWgajbA2Sog6kq6geGocUJLXl7FVQjhdhd4nkccqlBKFrtwIz5Qoct7WosFogmWCB7rtDoSzE9BUWgEj0DJNbK/A0CHT9WlsTPqd4pbkM0l8t8gcP7TbrEhYFSTS4lCb9eQjkmRAv1kLz3CJPFz5L8VReWUIo3Ka8Ke35mPYnGooriiKctvILSaeOddO+gBPv3UyrGM64TklALshRs8A8ewSTrOC1E7IWhW5Fi/KfelwGkNoDNvp2WNNC9KzodTRgne2Tib3kKC8MQM2fdLRI1xnnQEzWipctNwbMmJkeivsuhBc+iDty5IRQGWrC7qRXhj0ceTb3ajypJC7LNfsRkswD8GQHpeP+wz/sE1Ge4sdo/9cjbKrCumZrehVSBVWJLKi+PTrUuSNaKHrw2SLQOh+FZ0BgYJkx9z09Qpazohg0cit5EML1fz3+j3aRp8fCDz88MM45ZRTSKg4e/Zsmvf555+jtrYW77zzzuCoEfrXv/5Fkduxxx67m/xI1A5VVFT0dfkYhmEYhjlKmDt3LokUzzrrLLjdbprOPvts6mpz3HHHDY4aITGGWEZGxl7zA4FAv1khGYZhGGbwIf5m9ubv5uD8m5uTk9MvnaL7rEZo2rRpePvtt7s/dwU/ohd4VzUXwzAMwzDfwhDyCJWVleG73/0uvN5kN4yeeDweXHjhhdi1a9fgqBH65S9/iSVLlmDr1q2UMfbkk0/S+88++wwfffTR4SklwzAMwzCDlkceeQT5+fmw25N9/3oi5IpimVjn2WefHfg1QqJv0Pr16ykIGj9+PFasWEFNZaKzkxhJlmEYhmGYA2AI1Qh99NFHOO+85CDU++L888/HBx98gP7gkISKJSUl+P3vf9/3pWEYhmGYocIQGn2+pqZmn/2Lu0hLS6PMsUERCO2rfa+rr5DBYIBer++LcjEMwzAMc5TgcDgos1yMLbYvysvL99lsdiSQVFU9qMo1WZa/MTssLy8P3//+93HXXXfRuv2NCNzEDzDs9l9CKQU09UY4JrSRv2eYqx2by/JgbNBB7wF8JQno00MwfWKFr0SBrUJGzJLcTzRFRdyuAHGJXCOR4WHoqo1IGFVkjG+B58NMBEujgF9LbowdVdmYObKStl33QSkyZjaSZ6Pda8H0/Bp8WV2IrFQvGrZmQs4KId3pJ7eQ8GGI7So9qWhpdOIH01eRb6hzu4t8HyeP24z2iBXr6nNhs4TJq2ExRGHQxNHosSPb4UVtayoSURl5OR3kHypvT0PQZ8CMkmpyDjV7bTAbo+hot6Iwux25Vg+qfSlobHNieHYLdlRnweIIQ1EkLCzajpV1I6jseU43uYuEhyiU0CGqaKGX4/iqoRDpVj98UQPybB5sqMhHdnYnmrdkIHNsCzLMflR2psLbYSFv0YpZv8FzHcfglbUzUDqsAfIFMbQtLkE4RULEBaRuVeDPlcm9kjAA0bQEoFdgqNVB7wZ5XJSIBpZKHeImIJoRh9YRQSKqgX6XEZooECyMw1ypJW+L8A0JFJNCzpW4VYHWL8NRDoTSk79vOEtByath1Cw0w9KYnC+O7dypon0CoOpUIC0C00YTwpNCSEQ0UOMSNKY4dBUm2Hep0ERVdI4SsikgY10CwTQNrPUxxM0yEgYJnhIZRS83oO24bPK/pH/WCgRCWFrzBJVh0fR7sHz1Xbtdv8K7o01Pw9LmZzH7u4/C+rcv9rrGKx+Ys9c8cQ7MTcC6Z65H0R8fAqIyjPVaaIPClwRyMonzY25WYW5LQIqp8BbrEDcA2gjouhf+pdwPI2g8xoBIigqdT6Jzo/Or0AuPU0RB6xQdtt53PfmAgjOGwbytBarVBKmxFYo/APfZk/Dln26gMgkn0dLyR7rLuKT4Bqg+P6ITh6FmsR4ld6+HOn445K2VcJ82DqosIfW9CoQmFsC0tRHeGXloPF5C4b/jaJ6mR8aJ9aiqT6P/FUsaha4J4Y0ytEsIFibItyPOs3BS6byAb0QCtjINfBOjyF6mRcQpI35yJzwtVth26Gjd4Lgw1IAOMCZg3qGH3gfIccBbBLpe4mbQOQxm/+ccpcUgBzUwNcoIjQlD6tAjZZMM7zAgZldgL5eT16gDyP4igcZZGqRPbqZ7Q1yXpmYg6gTSNibQMUaDqEOlZ4omnLxm9R4JoeIYZLcWtqqkN0uOivlA2AVYGlW0zknA0KRFQg/EsyNwrDXAX6DSeRCuIU0EyFgTQ/s4HS76/rt4fuOx5CzTaxIIRPQY7mqDVRdBWzj5sNuxMxeqrCItx0PrCC+QeI4IR9japnx65giXULPfhlBEh3S7HyMcreQAKvOl0778MQOqPSnwdJrpGSO8ZZ6gEdNya+k44jlSbG9HmScdmWY/2kNm1LU7EW81wVnkJjfRtLRa1AZT8FVFIeaM2IVsoxfrOnIxztmIqoALnoiR9jvW1YiGoAM3FizHVWsvRHF6O8rXFJDbKW9aParq0qExJFCU2YbKTbn0DFALQ0j4dNC6NdCU+BFpN0EyxZHq8sMU8+LTM35NHXgP1x/mrr9Leb++B7Lpvw6ug0UJhVF39V2Htax9hWj6isVieP311/e5/IwzzqCKlL//fW+f2oCrEXrxxRdp0DQR7MyYMYPmffXVVzR+yO23307p9b/61a+odugXv/jF4SgzwzAMwwx+hpBQ8dZbb6XM8nPPPRc///nPUVpaSvO3b99OksXly5dT0lV/cNCBkAh4Hn30UYruujjttNOo4/Rvf/tbvP/++ygoKCBHAAdCDMMwDLMfhlAfocmTJ+Mf//gHfvCDH+xVK+RyufDqq69iypQpgyMQEhHbc889t88vKTLHujLLusYgYxiGYRiGOfXUU1FdXY1ly5ZRnyDRM2fkyJFYuHAhjV/aXxx0ICRy/f/whz/sNcCqmCeWCdrb25GSktJ3pWQYhmGYowxJTU692X6wYTKZaHiNgcRBB0Ki/49wASxduhTTp0+neWvWrKF2PlHtJVi9ejW+853v9H1pGYZhGOZoYQj1ERrIHHQgdPrpp1PQI/oDiYHTBMI0LQZjLSoqos9XXnll35eUYRiGYRhmIAgVi4uL92oaYxiGYRjmIBhCnaWPukDI7XZTynxLSwsURdlt2SWXXIKBiHCBTBpWi03G7KQ/Y4MdFbDDKLQvGiCSAji2a2AoDqFljAkZhR0ont6BHR3pcFc5k/uwRaG2GxBxKTCUGxHOjUPfokVjkxMojaIorxWR57OxU8lH0ZhGhBNaTHDUo/i0NmzzZkErK+T7EeS53OTnSBvZjna3FeNTG1EfdGD4yHZyCOXb3RiR0koOIZMuhmPnrUZT2IFhplZ82VwIoyFGzh7h0nCZgthQlwODIbnv/PQOcgm1+yzk6IjFNJhQVI/6gJ18IItKt+LNugk4Z/x6mOQodvozyQNS35SCNGMAleYYTivejHfrS8khJLwhmXYfnPowmuNWVPjSUNnqQr6rE5GEFsfkVmLFprGYOKIGLUErOYSEOwhjgVEpzfj44/HQFvvJ5ZGotmB+442QXBGMLanDtrosPP7pqzBLH+D1zql4b+lUxI0SNj98PU469n60TDXDWqNB6vYoVDmBytMNcH5uhHt2GAm9jjw/oXYt4lYt4pnJazE8JgRdtYn8MZZ6Cd7RMTg36qAu8JAPKdUQhXttOgK5gK1aRSBXQsKSQNysRaQwKuwt0PmBUH4c8TotzPWAb3QCiGlon8LRJL6LtsaEWEkc8jgvUo/vRHljBozrTQiMjqItroelXoV7hA5pG8OIOnVI3ZpA+zHZ0IZVXH7Pv/Cbx8+Go0IcD/Rd3+3hEOpy7ryr/B1Trnyc5uk9CciTx+zj4t67flw4mDzDgak/ehxFF7TS72QYFkfjZ7mYtGAHyt0udFos0M4OI/RpCgL5CrRBFdqABG9+DMZ6HWJWFRUXypCDChRzAlJ2HG6ngfaf8aWEluO15LSZ+b3H4P2fkbDWqsDIdKg6CZHxTqR+3ojW/ySBLJp6F6SWtu7yzVv8EAwtbZBdqaifZ4Bc7Kf3dXNsyIkVoHWKhLz34whOK0LVmSrM1YXk05H8GoRv7MTMlGbyV9nXGeAvVmBskRHMVqBmh6F6TOSHEYh7FdY4Ym06aIIyTO0qEmV6uEcAkTQFJkWGxquFIp6EMqBpNCCRHYHcbKBz6HcAhk6gYEUE1Sfryc0jXD6h3ARSv9bAuFaDhpMUBHPFyJF6yDGJniXCIWRolxE5zgdZUiFvtaN+vgTFFEfz9nQoqTFIbTokjMKdpaBtogaRvBiV2bpNh9CUEOIBLRS9BnpbBNYvdPAOV2Fslcht5BmpkvcomgJobDHot2kRygRcnxjIE5UwKyj8dQOUxmZsf2Yixp65EXZtGC/tmAmp1ghfPnDtpA/wh7I5qPM5MNzZTve3wJQWRLDDRPe9eMZsrMpF4YhOfFo9jO554fGpC6VgfN5OrGwYjkJbJyq9LjRoHfRcOH3EZqzsGI5IVIsxBY1oCthoX+I8CO9YW9BC9+Gk3Fry/9h1IcxOqcBqczHKbS5yvBVntOP1bRNxaukmqD4dNrVk49POEnKjfdVamHSTVWVAZ4tiu5yJlg47/vexH2DnGzdh0tt3ALlhJOISOdPs6/UIzg6Qc+i+k1/FKu8IrKwegZBHj1hqHKVpbdgezYLTEaC/DTXVaThicNPY4AyE3nrrLVx00UXw+/0kcOopVxTvB2ogxDAMwzDMkce7nxEp9kV/iCEPOhD62c9+Rh4AMQp9f6a7MQzDMMygZojUCDmdzm8ckUIgUunFOolEAgM+EKqvr8e1117LQRDDMAzD9IYhEgitXLkSA5mDDoQWLVpE6fLDhg07PCViGIZhGOaoYe7cuTiqAqFTTjkFN910E7Zu3UrDauh0ur3S6xmGYRiG+RaGcNZYMBikESii0WTCSBcTJkwY+IHQj370I3q9995791rWX+17DMMwDDPYGIpm6dbWVlx22WUkZd4X+4shzj777IM+lhgOLCMj41vXkw92xyJdfn8TB0EMwzAMc5B9hHozDTKuu+46UvB8+eWXNNyGGHdMDOY+YsQIvPnmm/vdTkib9Xo9HA7HAU1vv/02ZbcfCJIqumof5Wl74qTkP/q/MBaosFnCaG+xQ9IosK82Iu+cSmzZkQ9zpRahbAWpwzugkVS4/SYoFVbah1oYgnanCY4ZrfCFDEgkZESDOuhrDdCM8eKMkk14p3oMAgEjVAWYO6IMHREL+TmEE8NpCcFuCJOnQ7iFjJo4moNWVNWlk4/GYgljdFoL1tXnUvmEY6PQ0Ql/TI8Cayc+qSxBYXoH0oxB2l5MFc1pcNhCyLL4EFdlFFo6UR1IoWMI95HY3qqLoC1swSh7M7xxI0yaGKr9qajqTMWSom3wxox4v7wUGale5Fi9WP/5cGSMb0GH34xshxcNnQ6MzmrGztZ0mI1R8hk1tjlxfEkZtrRnIxzTQpZVWA0ReEPGbt+QwGFIOknEOZiQ1oCVa8bCVdQJpzGEuk4nIj4DSosa0ey3wVvhhLVShmdcDGdOWYdj7TsxydCIN3zj8euvToBtgx45n3ixfPVdKHr2V9B6NVBMCuxlMtzTI9A2GuCY0IaOdivUkBaIytC7ZcTNKvQeCfExAcRDOsCvpfmaCBCzALpA8lV4WbQBQO9V0TFRhb5DJr9Q6ywFjq0aWkf8lyGhBxIGIJEbhqxVocQlmDcbESiNQXZrkTO+CVpJQdX2bHL76NxJ75DYdzhLgdYnQ0qAHEXCTXPlTa/h2UfORtrL68R/g7A8/Jfdrt3FE27Hso33ofTex5GzKgo5ppCPaE+CaZq952UDUhzIX+5G07FOBHKSLi3hXQpmSojO8AM7rIhkxaC1xMktE0+Lw5XlQZo5QK4XcT6lDj0UWwJaSxSa7eJEAJH0BFxfa8iHJPYlzqfwCSl6wD0uAft2Dbm5UsriqDtRxq5rf4YlmVdiafOzWJzyQ6AwB6F8G+SIAvcIPXwFoH2kblOgaCX4CiXy+oSzE3QetR1axLOimD9mO0ZamrHZl4svKovp/NscIXjr7OTv0bllqGP8kLZaoXcDwVwVqk78DjIi+TEgLsFaJnw7KuSoBFWbfPSJ722o1VGZbVWA/rxmNDam0DMi9SMjQulJL5M4f8JlJMpk3aJHYELyGtfoEzCboojEtHCYw2jb6YKlRoavNOn10joiiLcbAUWCakzAWqYj15TYv/AV6Yf5EN9uh6JXoSkMoDi9HTt25ZA/KNppJFeZ3pP8PaPOpPcsmpaAHJSRslVC26wYHJt1MLcoiNpl6L3J14k/2Iibslbgt23H443PpiF7RCta3VacMKwMH9WUwKCL0z0r7m1xj25syyGPTqoxiFqPExZDFIGIHvkON4zaOKo8KXRNXDzxK7xVM448aGL9yWl1MGti+PuaqXQtTSqoQ1yRsWFLEQzpQUT8ephsEToXs/Kq8FHZCCwetRX1QScqO1NpvjhWgc2NGp+TnkVf78qH3hxDut1PPrSQ10h+MrMuivagBZMz6hCMG7CxKRsFqZ3kP6v3O3BqziZ80j6C9imcZg21qUjL8dBzWOzD4gxhbn45NnbkoK7eRc9ftcUAc52MsAv0HGndYULt9XfA4/EctlTurr9LBQ/dB9lkPOT9KKEwam6+/bCWta/Jzs7GG2+8gRkzZlCZRZ9jMfCqCIIefvhhrFq1ap/bybKMpqamA6rhEdhsNmzYsOGA+jMfklAxEAjgo48+2mf7nsgoYxiGYRiG2Vf80BXMiMHZRVOZCIREn+Ovv/4a35R5lpqaDJwPBNH0lpsrTKfo+0Bo3bp1OPnkk6mjk/hComBtbW2UTi++HAdCDMMwDPPtiK7OveojhMFHaWkpduzYQWOTTpw4kcYtFe9Ffx5RW9RXmWfHHnvsAa970H2Err/+epx22mno7Oyk9r0vvvgC1dXVmDp1Ko1MzzAMwzAMsy9++tOforGxkd7fddddVHNTUFCAp556ikTNvSEej1NL1cFy0DVC69evpwhOtNdpNBpEIhFqgxNte5deeukh9exmGIZhmCHHEEyfv/jii7vfiwoUUZGyfft2CobS0no3ztuWLVswZcqUg07cOugaIeENEkGQQDSFdUVfouNXbW3twe6OYRiGYYYmQzBrbE9EtxoRvPQ2COoNB10jNHnyZKxevZpS3USb3Z133kl9hP70pz9h3Lhxh6eUDMMwDMMMehKJBF588UW8//77aGlpIfVOTz744IP9bisCpm8iFAodmUBItOH5fMkU6fvvv59Gm7/yyispMPrDH/5wSIVgGIZhmCHHEBlrbM8+QiIQEqNUiMqTbxuMtSdiRIsLLrgAxcXF+1wu+h7t3LkTB8uQ8QgN++MvoGot0OoT5OoR/gztOynkCBGemJJZ1di5MZ98NpE5fmQ4fQjFdGhvciS9NG0a2Ka0kTdDOIG8ESN5L7a1ZcBuCqO+KQWVF9+KopcexMwRVeQJEtS2psLpCKCtwYGc/A5EExq016Rg4tiqbteFOM53itfiw9aRKG/MwA8nrMI/qicjz+ahdTwRI2alV+FvG6finPHrURtMwVhbA3RyHLXhVHJiOPRhNAVsmJpeh5gqoyVkw5SUWmz1ZZOPSHhhhCtkbVM+uX9KU1vRFjZjnLMRVQEXuY2Ef8jdboE9JYipWbUo86QnvTg1GTA5Q+TrqHM7kVjnQNZx9fBFDeScyTF7sHLrKGRkepBvd9M2wgciHCTiWOJcj0pvQXvIjLp2Jzl9JpbUkg+py60kfENiX5NT6vDKuunk/LEXeOBtsOPk6etxWdoqfBQoxe+2HIv0V0ywVvqgGHSALKHuBDOUST5ovrbB1Az4ikC+nuiwMKxfG+EbkYCqV6B1a8nZEqm1ImFJwNCkQ9ymQMqIQKNNkDfK3AQEZia9UcIBFLOpSKRHkfmuHs0LYuQd0ZaZ4JjeipbqVEhRCeYCH0LVNoyeVI0tlbmQO3SYPWcbuW7kShMy1iZQP1+CuV4m146iw24uI2eZAlNLDB+8fwtOmnMf3v3sdpx07P3QNrmRSLOh4jwrhr/iQ+1JduR8uvf/eDpHmvZ57YtrO/+xNaj436nQBiRy/YTTVXLP6IIKTK1xGHe1oeGUHKSvD6FxtgnO8gTsW9qx67vJ9Fa9F4jaAXtl0hkk9iEa1OVY0qkUzE66mmKjQih5Ig5Vq0HrZDMSOsDYqSKQK6HgmS1AfhYUsx6ahg40nlEIR0UMwUwtIme7oVmWAvcoBcZWGcHCGHLe06BpTjKbxlrsQXFKB12fX349AiVj6slDFa+0Qlvsx6iMFmxpyEJWqhdN67KQshXkN2pYkIAkA/aNOsRsoirbi3BAD+NOI2xzWuFem46U7SpaFkWgJmRYtukRNwOWRqBjUtJfJPw9qgxoR3mBdXaEhkegqzcglpqAuVaD4IgobRfKSTqilBEBut6j7SZy/JAXKCwh7ozDUqFDfIofRkMM3hoH5ATouhPotpuQMqcZ7aszEc1L7lNcJ5pw0s2kCSb9QZpocp61XqHfT4qp+Oidn2Pk/Y9j9NwKfCdrNaYba/EP7yR80TEM2z4qIYeV6bg2+EMGcp8tGbMF7+0qxekjNuO9upFwmQNocDvIdSacO5NKa7C5LgfDs1sw3tmIT5uTf3DEM+/kzM34oG0UuX/EPS22MWji9IwbldNE97FVF6V7WSB8bOJ5sKkhGxGPAcVFLTBo42j02uHtNMPiCCPP6SYnWp7LTduI5YLKVheWlGzFZnc2tHKyxmBnbSZURcLJ4zYjlNDRsywc0UGjUejZJJ6d6W8Z0DFGRtSpQJ8ZhPZrK9TpXoR8BvIS5aR46Ll1ZcnHeHTLAkRCOlhtYdqPyx6gZ/oU3XbcMv2jI+IRKrr/fsjGXniEwmFU3XbboPIIpaWl4aWXXqLs84Nl2rRpuPzyy6nyZX99mEW/o8PeR2h/iPz/U089ta92xzAMwzBHN0Owj5Ber8fw4cMPadtjjjmGUu+/SaJ4/PHHH/R+DyoQWr58OW688Ub84he/wK5du2ie6O195plnYvr06Xu19fUF9fX11Mvc5XJRur6QLgkTJcMwDMMwg4uf/exnePLJJ3EojVFiuyeeeGK/y0tKSki8eNgCIdH/Z8mSJdS299BDD2HWrFn485//jNmzZyMrKwubN2/GO++8g75EuIpEBCgy1YRrQLQPPvroo2SjZBiGYZhBzRGuEfr444/JA5iTk0N9c8T4XT0RwYlIgBJiQ1HxsGDBApSVle22TkdHBy666CJqinM6ndRUdaBjegnEEBp/+ctfKGgRZRHKnZ7T/ti4ceNBVbaIVHrhFerTQEhEYiIAEhlir776Kr3+5je/waZNm8gIOXr0aPQ14nj5+fl44YUXaFwS0UFq4cKFdAIZhmEY5mgYfb4308EgRoMQNudnnnlmn8sffvhhEhuKv+liUFSLxYJFixYhHE6OqycQQZAIMt599138+9//puDqiiuuOOAyiODprLPOoqxz0V9oz8FSvyljvb29/YCPIyppDlSueMBZYxUVFTjvvPPovYjatFotHnnkEeTl5eFwIQZhEz+COK4Y20yMG/KTn/wEP/rRj/a7jRA8iqlnpzSGYRiGGeqIVh0x7QtVVanZ6fbbb8cZZ5xB80Sn5szMTKo5Etla27Zto9HihUJHdFwWPP3009TxWYwsIWqavg1RsXEoiPLdcccd5B06EPYcB7VPAiGRn99VAFGlZjAYvnFckL5A9EN69tlnccMNN1C/JHHyxVhmorOVsFjviwceeAD33HPPYS0XwzAMwwwUs/Se/+EXf5/FdDBUVlbS6O6iOawLUUMzc+ZMfP755xQIiVdRo9MVBAnE+kKyLGqQRE3P4UJ0gv6mjtL7qhESzXt97hF6/vnnYbUm08JF25voL7SnDbIvB10V7YHihHeNPyKqxkRfJFFtt79A6NZbb6XAqQtxgYjmNYZhGIY5Gj1Ce/6NE2N43X333Qe1q6amJnoVNUA9EZ+7lonXrpHjuxCtQ2Lw9a51vg3xd3xf7iAxz2g0UkbZ97//fcyfP3+35R9++CEOFwccCIlxQH7/+993fxYdpIVNes8v0peBkKhxGjNmzG7zRF+kf/7zn/vd5psi4eLsNmSZfdjekQFPixXauSEocQmpLj85hArHNKLKmInsf5pRN8+EjMIOaExxJIIGxArD8H2dhkRJCIb0ODk/movtmFxUh7giI/RRNt49ZjScrnPJ29MRNCPVHCSHkHAPDR/WhIQio6XDjpkTysk/FAgYMS6vgcq2vHEMTs3ZhDRjgBwgYptqTwr8AQMcthA6ohZkZXjwZtk4jM5qRmXIhUyDD9X+VFh0URi1cdj0EXhiJvIDpRmD+LozH1s3FGLGtDLysATjBkRiWlhNETh0IbijRrxXU4p4QiYfi0kXQ8QWgU6bwMcVI3B8SRk+qy3GBVO/whsV48njIc6V7bh68n1U1aUjrSiAldtKoTXFMC2jBhW+NMxL30n7TjUGsb0hCy6nHw1+O3KsXmTm+7GuPpdcJYtGbsWXzYU4NmsXtrdk0Ovft05BSUEzKnbmwGKIIn90DVbVD8MXv52K0GIvfXfPlUbcOOxtbI3k4vX6SdC/aQY+skHnV+EsDyPjvTZEhmVAXSWj/go/9DusiKaBfC4JrxGmfD8SCRkJSxzaChO0TSaEsxRAr8I7IQ60G+CsTfqIhEcGPh2C6RL0liiiPgMko0oeGuRFoca10H7ggPY4P51rQ24A2XmtCCd05FbZHMhD20QdzPUgL5Fw74hXQycQHhnGs8e8hFvv+xHkFC1mfu8xfPnZ7cl76ctNiBw/Cb4CPcyNEnZ83wq9G2g4Zu//4Yj97Qtzs4ro8RNgqZOgaAC9D4imAFEbEHXIiNr1iDizqEwJgwYGD9AyVYNP//EAxv38cWR/FkT1EjNyPomi6hwZlgot+WziFhWmZgmmVhW+0jhUnw6aChPkrRux/dExMDQmfTx6nwK/rEF45ggY2sNwl1qhKbYg+70WlH0/A3nT6hFanotoOqDvlOE6vhEJtw2Nx5sg2WLQmWOYlV2NT+uLEaizQTUmUL0mD3GzCjk7jGyHF+t3FECKy2iIy1CsCjynhBDtNEJSJHIBeUcmAEVChilC/ii9B2jfkoZ4Vgzu0WEgqIOmTYdAcQLQK8hZpUI51Q+NrMDTkYa4M4FYmxnIVGDebki6gDZbERoVhq7WiMCYCGS3jrxKNluIfGGGNg2i+RFIsgqEDVSWQEkM1jVWGOe1ItYswzCzA+4aB/QdGjr//hWZ0MuAzqeHHAccuxTELBJStvkRSTfBn6WF+p+nteGHjeQVy9a78caul3HMwsn4oq4Id390AflzLjjuc2ysykXhrHqMdLSiOWxDZTwVskVFc9gOsymKNW359GzxdJphtETpXvWmGVFkacc2bSZKbG30rIlHNZheXINiSxuCip6WC6/P2rJCZNp9cBjCMGS3IK7K5BASxxX7E8vEvDG2RpQZ0uDKDyCS0MJlCiLb7kUwpEegyYrKmAYLh2/HurY8KkOhtQMrq0eQQ2hZ5WikWoNwB0y0v8LsZP8QsdxmimB+XhlW1o2gecKTlpHqheEHcSjtTthNUTpO5RQg0mGCzhYlj1Jmrh+163PwcGAhos1m/HLRq7j9qzOQk5H0GAn/24qI6O/6EQYTYnirnh6hg60NOpIsXryYWnpEBrjo+ysQrT2iM7QIgERSlKhleu2117qb6A43BxwIVVUlBYBHkn05A4Q1srCw8IiXhWEYhmH6kkPp8Lzn9gIRBPVWqJiVlUWvzc3Nu3V7EZ8nTZrUvY4YFqMnonVIZJJ1bf9tiEQrkUIv+vv05L777qMBWFesWEE1Wv/7v/97xAKhPhMqHg6uv/56fPHFF9Q0Vl5ejpdffhm/+93vcNVVV/V30RiGYRjmqBEqFhcXUzAjxgDr2bVE9P0R/W0E4tXtdmPt2rW7jQ0murGIvkQHgsg6/+53v7vXfNEHSSwTiOUH0x+otxz0WGNHEiFpfP3116nfz7333ks/lOjVLtL3GIZhGIY5cITvR1Qq9OwgvX79eurjI7q/XHfddVQzI8YOFX9vRa2NyAQT0uSurimiaUtkbou+urFYDFdffTUFMQeSMSYQ/YA+++yzvezSYp5YJhCBVdd7DPVASCCG7eChOxiGYZijjl42jR1sjZAYlaFnJ+SuxKJLL72Ukp9+/vOfk2tIeIFEzc+xxx5L6fI9gxIhQxTBz4knnkjZYueccw65hw6Ua665Bj/+8Y+pVklUdnT1ERLJWCI7vGsUi67muCPBgA+EGIZhGOao5AiPPj9v3rxvHNpCkiRqfRHT/hC1R6KbyqEiPEWitunXv/51d8JVaWkpJWNdeOGF9FkESvsbWPVwwIEQwzAMwwyBQGigcNFFF31jF5cD9f/0FZJ6CCOfCcu0sEOKVzH0hvAKiLHARBvj2LFjMZAQnb1ICvWva9HUkYuighZKbRfp87IhgfQ0H5rrnbBU6hA3AZGsGIwpYUoxb9+VSim1rjw32mtSIFliOGnUdnywawSm5tdRCry3zo7SMXWof7sQUhwIzQpAp0tQemdClRAM6xFqsWDi2CrU+RxwGkOYk1aJ9xpL0e61wGYJY2ZmNcyaGKW0NnttyHF60OB2YFZeFb5qKES61Y8TMndiacMYjE9txIp141A0rJlSYz+qKUGRqwNl9RlISQkgHNXR+mNSmvBZYzEyrT449WHU+JywG5Ka9EBMj0yzH+s/H45xM3dRymtuVidcxiCsugjiItdapF9rI6j0uuh8jU5rIS2ASNMXVHS66FWkwYrv4AsYcfGo1agLp6A6kIIcswfbOzMpDdmgiWNJ1hbsCqVj+c4xyE5zY35mGT5rK6ZU3Kr2VDjMYWRbvWgPm3FKzmb8tWIa6QcaPXacWLSTjrW1M4v2J86N5nM7og4gmheldPtgTI/vFX4JvRSHWY7CIkfwia8UG66agM5SEzomqjC0yVAm+ehYvs/SofUDvtIE9CLdOS0BXYcGppZkinsoU4ViVJLpBMYE0j7WwVcoIVIYpXRsaBTYU4II7HRCyYxAX2lEpCAKZ5ofkaiWfkPx3XZUZ8FQZaB9aiKAogOlWovPqlZFwqzgngWv4amHz6O0dkuzAk1ERTAz+RsEcoCYXYG5XkZ8hg+JMtte17jYx55YamUEJoRR8LIGDcfpYG4CvMMVWCtlBIqS61uqZMiJ5PqecXGYK7UIZSsY9loUtQuMpA7QhpLLQ5kK9JlB6L+yImYDFC0QzYhB69YiY42KlmkSRj5Zg+2/zISakGGq0NM6ItVeikuwVwF6rwJVltA6BTht3mq8vWIGLA3J9H8xdU6KQ5VVyEENkBKFpt6IeFYU2dmd6PCbIUsqgiIVuk0HtTCEeEALrSV5PRpNUUS2O2i+VG2icyK0EZveKqXrJHViKzo2pUFT4qdzKPYryhgqjgFBDTSuCFBvhByVqCwiRV8TlqALgFLl1ZgGsi/5mygGFVrxjPjEAu8IBWpqlJarCuDI8MPnMUFqNUBxRaFtNCBuTwDmBPR1euTPqUVlYxp05SZAEunygL1KgcEdR918PbSjvHDZApiVXo1RpgZMNNbCnTDh88AIvFE7HkWOTrgjRtS2pyDeYIY2J0hp+uIZ0BlKCm/FPSJSzk8ZthWVARfaQ2bkWj1oC1tQ3ph0wAhtyJRhtfh6bQlsRR5MzapFQ9BB+o7VtQUwGmL0HBH3VSCip/v/i63DYHBEaJksqyh0dKLRb0c4pkW+w41aj0hbD0MrKfTMWFiwA29VjKXfTax/RekqvFIzDRlmP6Xhb3ZnY3JqPT5uLMFYVyPKPOnwRwwoTW2lNPa6didmFlaj3u+AL2qARlLR7rZieHYL6txOjEpvQZUnhZ559CzyGTAstxUVtRkY/gcFLTeGqTx19S7YUwMoTumgZ3CWxUfrn5+9Bp96RpBeoPpvJfAdE8KkgjqEfHEsXfx7eDyeXmdifdvfpWG3/RKaXvSFSYTD2HX/Lw5rWYcCB501Joa6EPn/oie5yPPvGmxtw4YNlPLGMAzDMMzAG2usv0hNTaW0eYEYNF183t/UHxx009gtt9xCvcpFJyub7b//Oz3hhBOozY9hGIZhGKaLxx9/vDteEJnfA42DDoTEaPP76iglmse6Ij6GYRiGYRhBzyGx9jc81qAKhMSAa42NjdTruyfr1q2j0eEZhmEYhjkAhlBn6Xg8jkQisdvwH8JaLXxEImX/9NNPp3T9QdFHSIiTbr75ZhpgTaTaCfHRp59+ihtvvBGXXHLJ4SklwzAMwxxlDJU+QgIhYew5FqnP5yOP0DPPPEPeIOE3eueddzAoAiEx3MWoUaNotFvRUVoMinr88cdjzpw55AdgGIZhGIbpiagwEfLFLl566SWqISorK6NkK9Hv+JFHHsGgaBrT6/UkPhLq7c2bN1MwNHnyZFJyMwzDMAxzEAyiWp3eUF9fv1ucIMY0E4GR0Ah09R0SWp7+4JA8QoOJLl/DWe9ehs3uYsTCWozMb0Zb0II0cwA7tueRH0jgSAmSF2dybj2+LCvCxGF15LWpq02DPc2PQMCIjFQv+XjE9qNSW/BFZTFSlxthubgBNRtzYB/ZiZKUdpS1pyHb7iVvxw8nrMKqtuHk74kktOQPOn3EZry9awzMxij8IQMirWbklbSg0NZJHo+ozwBJl4DOGEe+q5PcI2g2Qk2PQNaqcDoCGO5sJ9ePSRPDOxsmICPbTS6ezXU5VM6edPl8hBdEuIjWtubR9xfeEPEdhD9DeILKNuQjc1QrcqxebG3OxISsRqypLoDL6YfNEIZVF8Ws1F14YetsOk/1ATssuigdY0dVNkqLGhGJa8kBIlxL6+pzsaRkKzZ05CKuykgoyUrIrjLoNQk6n5WtLkQ8BhQXtZA7KNUaxOl5G7HeUwCtnIBejqMjYsGGDcWwF3hg1MXJKZKf3kHntKnFASWigWuNDh3jFJSOq8WOzfnIHtGKaEID58NWaP1R1M+3w1+SgKleQ54bOQ4YOoHgXD9sy63wDAes4zrg85mQ8OjJISTrEjBuNSE+yY94rYX8QvoODRLGpCMn7kxA65UxdnbSySQ8OnpLlPwu2Q4vFmVvpfMVbTZDG5SgG+5DdJeNnDWKXoWhXcLz//M0rrnvGoRdgBwVbiBAPztZDuHLkbQqNG06WOqkva5xXdJgsRud45OuIOFOiqSoMBT5oGy2kx9HeI/UkBZatyZZfqsCfacMVQPyKeUvAzpHaOArjUGKylRvrHeFyCEUnBSCEtABURkZX0nwnOGn36p1XSZGvNiCbbc4IXm1VGWvCcjkxYlGdFBbDMj+WEXbBA3OP+tjvP6n4xGzApookDG/HtWNLqgePSZOrMRoexNeWTed/EHiXlEqLVTOGSdsxZa2LJh0Mfq9u+6Dzk4L1HYDuYeSX14Pa7WMqB2IDgvTdzU0ahHJTDqjlBEBWs3yqQWeKVFoTUkvjukTK9wTkt9ZHE94lGwVMnwlCqBV6XyJ31qOSEl3kE8HQ5uGvFBiXTGJ49lXG8ld5CxToA2rsFS4sWzjfViSfRVCEwtgaA9D7vRj2x1pdL88XPxPBFQdVoeG4aXKmeQeSySS90k8qqFrT5yjhk4HpufX4NPtw1GU10quHvGM8UaMdH/XlWVQOTPyOzEipZXu6xOH78BOTzqOy6jAWzXj4NueAik/iFR7kPw/whUk7klxz0X8ejhdAXg9JuRkuGm+cP6EE1p6TmocUSQiGjq2QRtP+oLCZrp/xfvWoBVuvwkjMlpR3paGvBQ3eYIE/1g1g+5Fsy5Kfq0SWxsqfGl032s0yWt1TGYz7WdHRzo9Q4UjyO2x0LNHbFfbmopphTX0TBHP8TEFjfQ8agub0eq3IhzR0fkR14i7yglDuwzT1A6ExPWnSDCbojRfPD+ER044n8TzXjynzkhfhwe3LSJ3mSYSwAenPHdEPELDb/4lNIZeeIQiYZQ/NDg8Qi6XC5988gm1IgnE2GSiBqhLrLhr1y6MGzcOwWBwYNYIdY1HciA89thjvSkPwzAMwzBHGZMmTaIhNR544AEKiERHaaHd6UIImg904NZ+CYRERtiBIDpPMwzDMAzz7fS2w/Ng6ix95513YsmSJXj11Vcp8/z73/8+srOzu5e//vrrOOaYYwZuILRy5crDXxKGYRiGGUoMofT5uXPn0ojzK1asQFZWFs4777y9aoxmzJjRL2XjQVcZhmEYph8YSjVCgtGjR9O0L6644gr0FwcUCJ199tl48cUXqTOWeP9NiPHHGIZhGIZhBgMHFAiJ3u1d/X9EMMR9gRiGYRimlwyhprFBHwidddZZMBqTKX6iZohhGIZhmF7CgdCA4IA8QhqNhobUSE9Pp/eix7cYZHUw0OVrKPrDHTCmaBAJ6ZAIaSEbEijMbicHjfBg1HSk0Po2UwSBiB6hgJ58KVpHBDpdAooqQZZUOC0hWl84gU7J2YwXt8/Cryf/FbftOAstjU7k5bdhhKMVZZ50ehWQ/6Yiv9u/IXw7V8/4AKvdxSh3u8jnI9henoOc/A7y3pyctxXv1I2BTR+hMgqfx6avizF+SiWMmji5NEZnNSPX7Ea1P5W8HQLh7qiozYDeHCP/kFh3S2Uuzp30NSoDLrSHzOQjET6cnPFNVMaGoIO+j0A4geKKnPQd+SwIeY3kOhHzdtWnY86IXeQ5Kk5v7z5nOm0CPo8JWRkeDHO0k7/EZgthcf42vPL5LEwcW0XlfHvdBEhhDblO7Dle2E1hOoZBH8c9o9/CI7sWojNgpv1lWn3kGMlJ8VCZjNo4ld0dNpH7Q1EknDJiC+pCKWjw2+n8FFna0Ra1YpfHRR6kWq8TwQ/SEZgQxkmjtiNVH4BDG8SfXj4JWZ9HYGjwoPa0DERn+CFttSJhUskFI8j9OIGak6XucjZuyIKmMEDnTZA5tgXN29NhyPfTOZI9yf9TiM/CDdVek0LunUS1BcbhXkQiWjw1/RVc+cElkBQJth1a8uiEh0VgKjdAEwZeuPpJXPziTxG3qFB1Ku47+VW82zkGKzePhqZDC71HQsHDa/a6xqWSor3m7bjVCk29EYpJoTIJl0+iUw/ZEYXUaEQiLQY1LkHXpoPeB2hndsJb44CcAEwNMgLFSeeO8AohLkFSgMIxjajamZU8gFaFbZsWUScQHxmC3hCD9lM7pLmd5NvSlplgmNQJv89IDhf9LiPuuvAV/KVhJrZsKoRqTEDSKUj93IDi75XRb9hYmQZDepDWFx4tgyNMjiLhs2nrsEKJaQC/Fqac5Dlua3AgLccDz2YXNCV+cjYJH45+lQ2+iVFIGoWcTgLhgtJWmhArDJNvSorL9L30bpm8QgZDHAG3ifxIMCRgF06xrSlQC0NQ4hJSXX60V6VAjklQHHFaz1ypRf5yN2pOcSJ9QxyG9ig03hCqznYhOjIEgymGW8Yth05KoEjXCp2kYHWoGJOM1aiKpeOP9bPJAdS+OpOcVo4JbXS/i2dEs9+G0tRWuKNG7NiZi6JhzaiqS+/2dIn7UzwnhIfLGzJS2WeOrESNz0m+MLEP8TyxpgURaLDCnuel32VUThO2N2Th1NJNCCX0aA4nRwQXDrG6Tif5eeyOEKyGCOZnlpF7yGUOoKo5LelsKkslh9IVUz4hx5fL4Edc0eCT2mE4ZdhWesasLSukZ12mOSm42taW/FshnEeCru+2uSUr+ezwmWCxhHFs7i6EEjpsbMuh75VqDGJzWR6mja5COK4lD9nklDp81DScvELiWTA/pxxvlo2D0RDDGUWb8F5jKUalNCMYN6DSk0rXzfiCBnKrie8uHEbimfhVRSE9/4WbSKNNQKmwJn1gKWEo2zSovPvwunm6/i6NvKH3HqGdjw0Oj9BA5oCG2BAB0BdffEHvRdzETWMMwzAM0zuG0lhjXaxevRpffvkl9kTMW7Nm7//oDZhA6Mc//jHOOOMMqg0SQZBIfRPv9zUxDMMwDHMQTWO9mQYZV111FWpra/c5BIdYNmD7CN1999006nx5eTlOP/10Gg/E6XQe/tIxDMMwDHPUsHXrVkyZMmWv+WLMUrFsQHuExIjzYrrrrrtIhGQ2mw9vyRiGYRjmaGYIdpY2GAw0vMawYcN2my/6Hmu12oHbNNYTEQiJIKi1tRWrVq2iSbxnGIZhGObAGYp9hBYuXIhbb72VOnh34Xa78Ytf/AInnXRSv5TpoMMvMTLs1VdfTYOnJRIJmif6Bl1yySV4+umnuaaIYRiGYZh98qtf/QrHH388CgsLqTlMsH79emRmZlJcMShqhK6//np89NFHePPNNymKE9Mbb7xB8372s58dnlIyDMMwzNHGEOwsnZubi40bN+Lhhx/GmDFjMHXqVDz55JPYtGkT8vPzB65HqCdpaWn4xz/+gXnz5u01MOv5558/4JrJunwNY175OWIaKzmB8pxu5Jg9+GDjaEhaFdnZnWhsTCHXCNoM5F0RviDh6dBIKtx+E6LtJvKOuIZ1IM/mgSdiJJeHf1km4sd5EewwkZtoZH4z2oIWOI0hNLgdKEjt7PZmCMeHQDhvWoJWtHstSMQ1GJ7dQvOF38Kqi6AtbCG/iECUS7hDhOensc2Josw21LankMen0WvHwoIdWFFTinyHG00BGzrarVB9OujTQ8hw+sjBI7DqoqjodGFOdiW5Or6oK8LI9FZUdqZ2+z2En0S4OUy6GNwBEzmXRuS2YLyzEW/vGoMxmc3ke+nwm8nZ0hFMOn86Oy3ITPOS26PA2kkeEOH3WNkwnM5DRVUWOWGspghicQ1OLtyKjqgFXzQWkjtk+c4x5CgRCDeMcJ9Myqrv9hWVN2ZAbTEAaRHkZLhRaOvEp+tLIVli5EtaXLwNGzpyyZ2ilRTU+xxw1ziQXdJGThHhcdrWlEmuEeEgKs1qwXX572JDuAB/fWwx0r52w19sQ+tkDWxVgK8IiObEoCqAI8OPwE4nOYSirSZogjKQG6byCleQQLiG1JUp8E2KoCi3jXwn9U3/8VI5QuRY0lUbEbcpOHvuV1haNRqBJitgilP5hcNE1ark6rlkyYfQyXFyz7xUPhOp5iDq2p3Q7LAgdWYzgm9n7nWNh117X/fRYWFy99grVfjzJQQLYzDV6mBqBTonJGCt0EAbAjonxcjtJLw+rq908BcAkawYHJt18A1ToFoTkN1aKKkxcviUjqvFztpMaPQJKPVmWIe7EY7oEA3qoG00IJ4ap/vEXKtBsDgOjS2GRFTG9yZ/iT+tmQ29LUKOIIST/wcb/Wgztl2f1e0GEtee8OLQvdtphnm7Aep0L8J1VsjpEaDeSL+F8DPlT2ogJ5bZlLzG/QEDnadIXgyQVegtUbpvhZsoIo6pSORjEo4mxaACxsR/2xYCWvptE1aFPmr8MhL2BLRuDTl+TMO8CHiMkDw6yFEJCbMCrV+GY1w75FddOO1nHyKmJrNmCw1teL1pMpyGEFa/NwbGCZ3w1iUdL8Ln422ywZQWxJScOjQFbfBFjMi3u7F20zCYMgJ0X1Z7UuhaFd+t0NFJHhxxPSpxGZKsktdIuHYmp9Xhs8Zier6YtRG699pb7PTMmJe+EzsDmeQJE88Kca+KbYQHbZS9GU1hB9ZUF5BXTdyfc/IrUel10TPgtILN+Ou2qVg4fDsqfGn0bNqwKw8avYJJBXXkHRL3Wq3HSc+espZ0chAJN4945oj5Xc8Vcf8KhMcn1+yBXRem58nCou3wxo34cMdITCiqp3VG2FqxyZ1N19jFE7/CJy0lcJmCdDzhLUskZNgsYQx3tmOsrQF/3j6dnnPCWSRcZ43+5Hn2f5yOQEmMnsm5WZ10TU3NqqVngXjmCTeV8IMJZ5xRF8f3iz9HY9SJXcE00eyBl094+Yh4hEZf3XuP0LZfs0eoX5rGRBXWngjBoljGMAzDMMwBMEQ6S7/55ptYsmQJdDodvf8mRGb6gA+EZs+eTR2mX3rppe5hN0KhEO655x5axjAMwzAM08WZZ55Jo1OIChPxfn8IT2FX3+MBHQg98cQTWLx4MfLy8jBx4kSat2HDBgqKli9ffjjKyDAMwzBHH0OkRkhRlH2+HygcdGfp8ePHo6ysDA888AAmTZpE04MPPkjzxo4de3hKyTAMwzBHGVIfTIONl156CZFIZK/50WiUlvUHB1UjFIvFSKr473//Gz/60Y8OX6kYhmEYhjnquOyyy6hVac+B230+Hy0TKp4BHQiJjk7hcPjwlYZhGIZhhgpDpGmsJ/sbuL2uro4y6fqDg+4jJAZFe+ihh/D888/3mw6bYRiGYQY7vbVDDyaz9OTJkykAEtOJJ564W/wgOkhXVlZSTdGg8AidddZZeP/992G1Wqm/kMVi2W35a6+9hoFEl6+h+N77YZ8SwuSMOoy0NGOzLxefVxSTJ0c4NrwdFkgahdwVL2+ZDpfTD42sIBTTwWqIoLY6DebUELk4zijahHfqxsDwQiqef/QxPNG8ACu2jobJFqHlAuHHWdeWR9uL/Qh/jpj33q5SjM5qxvaWDDjMYSRUidweLR12ZKe58dPi9/F83XHk9Gn22shDVPZ1AQomNKC2NRWJdgNmTinDtrYM8l+MdTVie2cmDJo4HUc4SYodHSi2tGGTOwfhhJacGcLtIfw6Xo+JvCGnTN5I/p5ERAODLUJeoK513FXO7mMIRqe1oNztIieQ8Im0Bq3kVhKOE+E6EV6TdLufXB3CKSKcRw5DGO1hM1q9VvxkzMd4euM8Wv+S4V/i3ZbR5Fry+EwwGOIoSumgfYrvIvxG8wvLUO1Ppf0Ip0muzYPtDVnISPXS9xT+JuE5kmWVXCAC4WTSaBTkOD3kb5qQ1YgsowfbvZnkUGoPmVFsbyenSlV7KqL1FtiKPPRbOrRBfN5ZgoanS2CtDqH+BAsCxTFIigREZWhcETrvkiMKNaAj/4zGkIASlyA3G6At9iPSaoaqVchLJXm1UGWgZEw9qprTyP8yvbgG7qiRjn3RyDV45ZX5iIwLQbfdhHC6AnO9TL6cUJsZGq+GXEU2WwieTjPUkBayJQa51gRr3d7XuObktr3mBcN6RHfZEHfGkZbjgU0fwcX5X+L+r05GepqPfr+cFA98UQOtL65P4TuaMqwWX+/Kh9kWQSymQTyqgSSDvoPVFqZzLrw2wsViMUS7f4dAnQ22ChnBGSHaX1x4eQJJr47OLcMwuRMjXG1YW1ZIfh/xW4UDelquJmS4Mrzkv9GaYuTKUWIamifKXVWeRevJEQnanCCiAT3dq7StTwdVnzzvhmo9Hcdf6YDiiMNUoUd4ZBhShx66vABdo8LFRQ6oFjtkjxYFYxvJ0ySXWRB1KrBWy0jMTnrBhg9rQl2nk35b6BXIPg2QkODcLpHzKf1vG9H6txzyim2oyMfJ4zfhnU3j6Tvkudx0D4j7T1Bia6NrWvh1hBtKOLjEfSWWi3MpEPeOuM5F+dLMAfL07Gp3IeQ1khdIIJ4L4vovsLnx5eYSuPLccHssGJfXgA3bCnH9ccvxzOa5cNkDaGxyorSwqdtJlmP1YnNTFqbl1iKc0GGaswp/rZhGzyzxHUQ5xmc0ot7vQDCmp/uxzJOOpg47xuY0kdtIPBv0mUFMzq2n61n42KKKlrYRz6tMu498XjNSq/FZWzEqajJRUtBM7jOnNYRsqxd1Pke3i01cf+I3Fh6gkdZmrGwegYYWJy4avxr/KJ9E5e7yKrnMAXoenJS7A29UjKfrZ86IXSjrTKfrWTjZxDNiQfYObPVl0/d74fWTyOMkzrl4vm2pzIU9Neksi6xLQcyZdHv9Y9UMGLKT18h08w48ccybR8QjNPbHvfcIbXlucHiE7rnnnu5XIV8WMUQXer0eRUVFOOecc+j9keagq3TEqPOisAzDMAzD9IIh1DR211130asIeC644AIafHWgcNCB0AsvvHB4SsIwDMMwQ41BFMz0BSeccAKNQCEUPIKvvvoKL7/8Mg23ccUVV2BAp8+L3H/RN+iYY47B9OnTccstt5BIkWEYhmEY5kC48MILaUgugZAsLliwgIKh2267Dffeey8GdCB0//334xe/+AW164lB08QgaaLjNMMwDMMwh95ZujfTYGPz5s2YMWMGvX/11Vepr/Fnn32Gv/zlL3jxxRcHdiAkREe/+c1vyB79r3/9C2+99RYVfCBaIhmGYRhmwDMER5+PxWLd/YPee++97rHFhKOwsTGZFDBgA6GamhqcfPLJ3Z9FdZZIg2toaDhcZWMYhmGYo5ahWCM0duxYPPfcc/jkk0/w7rvvdqfMi1jC5XIN7EAoHo93D7LaU7AoojuGYRiGYZhvQ/Q1/u1vf4t58+bhu9/9bveYpWJU+q4mswHrEZJlGUuWLNkt5U00j4ke4D1dQgPVI7Qg44fwnzAKvnwZtloFmsub0bghC8bWpOEyUBKHPiWMRLUFCWuyuc9V0Ek+D+HO2VSTg+LsNizI3I7ljWNQtT0bL5/6DO6pOh0VzWnkUslw+hCNaxGI6MnNcVnJ59gRzMKXzYXkzUg1BMj5I3w74zKa0By0kk9ErDvc2Q6zNoJ1LXnkOvLGTFhfk4fh2S1IMwawy+MiJ9FpBZvxXmMpOvxmGHRxKqfY/pLiL7GiZQy0skJTOK79//bOAzzSqt7/32R6JplJMum9brZ3dlmWtoCAcKWoKAoI2FBBFAtiAztYQb1cvXgV5a9SpYj03pftNbvZ9F4nyWSSTJ/5P99fnJhtsJBlk938Ps8zm5133nL6nDnvOZ8XTQPpGBm0ibel0DUg+za0ZyLRGIPBGBGfEf0n9GqsKajFI9sXwdBrRizXj7NnV+OpF5bi5JO3Y6c7F6uyG9Hld6LFmyrOnsqsXqRafBgOWZBsCuD12jJ8/binsX6oBD2+FHEANbVlYuWsRrgsw9jQU4SeehcWLWrEtqZ8vG/2bsyyd+FfHQvEJTIwYEd2xpD4UYZDZuxsysP5C7bihbZK8a3E4fWXZLSJo4n79vmTkGrxyzHxdGL603HDv8wXpueLa+cjp6pXfE30tCzM6MBrzWUIei1I3mWSc9Mf88elf8GVG69A9l9siJgS0D/XIC6auK/GaA9L2lktIWQmD6PtlUJEbDEYysdcQssWNGDTxnJYexPlOJM1LM6cMpcbtT2Z4lIadNux5Yzbsezu62CtGEJgtxOmWUMI1aUgnBQTh9OG12dh+Ql7sLk9H7HaZERLfYh4TUj07f/b5ZRVO/bb5jD6YUqM4pPpr2IkZsI/PUvQMJqBtY2lWFzUhnA0EVt3lqCkokucMf6nMhFIGzs2NncY0WgCwgNWpNQa4K0Ko7yiU/xMdNrwZ2iiMYqo14xrTn5GvFA1uwvEo0TXFv1FicEEhPKCyM0dkPLCslZid+OR15fDXuDFSEcyCip7MBywIPpsOko/VC/hmTenRVwvWdke9A8liY+nqT0DluQgAsNmIJKIqrIONLxejGBWCIYhIxJy/TBbQggGTOLFordm4L5CDM6JivfH7Bnz/tDXlFadiP7jg4DfgMTRRETTQ1I/Ah6L5C2Pj4UTYGs1ITTbB3O1DcbhsV/emVv8MPUMY2CpC0OlCfjqpQ/iGfc81PRnimOLfh/WUZspJGUv1zGElv40RGMJOKGwEQXWQWzz5GNXVzaC3UlYvXy31J9sqxfd/hTJE8J66/eZxVOV7hqWsko2dhVKuWb5f72zVK5J/w59PjyW+djROuYkOn5uA7Z15cLntWBlZRMaPenw+iywmMPITvZiT2u2lE3Cssyw0lE0y9mLdd1F4orqGU2Ge302Ck9olbi5rKPi/qEviG4feod4HD1dG3eWYuX8emnT4r4utos56UNo35WNsvnt0i7Ft7ENpNsn7gRLsQWQZhsVLxHdRefm7cDa/jLs6c0UNxEdaTw+1eyX+LJNpluI7eH/27wSjrRRcWFxX/qC6Gu6Z/Ef0R524IbdHxRn2criZmxoL0R68qjkD6+fn+zB7v4suHtTJC0uXrwB926Yi6ZP/fCIeIQWfOonMJgn4REK+rH9j0eHR2giFCgyDdLS/t3osNw3NSEpKWm/R29Mq+Xzl19++X7bLr300sMdHkVRFEWZEcwks/REDAbDXp2guF9oqjjkjpD6gxRFURRFmQylpaUHfNZYnIaGBhxp9GFhiqIoijIVzCCzdJwvf/nLmAjnGW/evBlPPvkkvv71r2Mq0I6QoiiKokwFM7Aj9KUvfemA22+//XZs2LAB03rVmKIoiqIoynsBF2P94x//wFSgI0KKoiiKMgXM1MnSB+KBBx5AevrYqscjjY4IKYqiKMoMMEtz2fp3v/tdmbBss9lQXl6OH/7wh5ho0eH/b7zxRuTm5so+lCfX1tYetigvWbIES5cuHX/xPa/FR3jxNa09QkcrcV/D7zYdh7rEUpzm2ImmYCYeWZSHwYuWoWdFDNaCYYz222AYMaDyL0NoOTcVYRvE3UKXh7HViqg5hqwFPeLpcG0wYfmntmDz7Ysw/wvbYTOE4IuYMBq2YEtnHhbndsAfMYp76LjSFuRYPdjany/h6R1OxojHiqriLtQ05WJReat4eOi/SbeOIj/Jg+fqqrC8uEX2rxt0wd3jQFVJJ4wJUWTZvOLoGApYYTGE0elxiBeDno3eoWSUuPrFp5NrHcITTXOwOr8RezyZsh/Dtba6DAnGmPh9Fjtb4A6lYENfISzGMOo6s+S6s5K7x9IubMW63mJ09ThRlt+LKmcPHt++AOcs2C4en6ykYfGX0CNCfwgdJM/uqUIsmoCcLI/4Tl5orhS3B90idCjNTuvGK43l+NicjQjFDHhwzyKYTBHMzuxBZXIP1ruLcJyrRf7SeZSUEhCnDV1Jhc5B9PuTxAV01qxq1Hiy0Pp6IUzzPbCaQ+J8GvAlodTZL+Fpc6eiKqdn3M3CNLKawnAPJks8jYkR+CMmdAw70NmZBoMlgliPBXlzu5FvH8LlOa/i5q9cgagB6FtkQMQaQ/qCPvEhGVwBGOptCOaFYOwzIrF0BHlpHjQ2ZcHSZULK0j7JN54zJcUnjpMcxxCaujNgt/sxWp2Guy7+Lb79mavQeAngyhqCd8SKSNiAiM8IQ78R9lmD4kEa7EuGoc+ESFIUCSn7C0wTev/j9oqTPa8HnbWZSEwLSH5Y7UH4R8xYWNIuZZMemYTEGFaVN8Jp8qHQ2o9Mo1deveEU+fuKtwrGhAhMCRH4omY0jrjET8Xji5IHpBzursvD8jlN4nGKO15qGvJQUNiHto50mNotSKzyItRmRzQlgqzcQUn/uCNndnoPzkzfiTtbTpAySjeWpycZ9noTRitDcGQMS5719SeL24pOLneXEwgmwtxnGM97eqiiASNStpswUhKFvSURZg+QWudH2GYADAkYzTAgfZsHnSenihNouMCC5LYAEkNRNL8/CfkvBRGxJYpzyNYwCF+xE30LTXA2RDFYkYiVF27DamedpM3D7iXj9ZBOGpbNcCxR0mB9YxEstrF8YrmNRBMx0pYCJIfxyWWv4fnuWeIPI6Vp/eLmoU+HvqXgqAl2px8Om1/KJOs924mSgl7Z32nxo83rFFcRXTxMqwuWbsajuxaIFyjZFhBnTrpjFN+e9Tj+p/VU8QMtSm/Hv2oWYHZelzi+GFa2F3Qz0dF0eske1HszJPysOyzHeYX9WJ3diO2DuViQ2il/O4cc4jIKRgzodycjxTn24O24r4seNJZ1UpA6KOnjsPilbaG/yu1LkriybCY4g9JO0AdGF9NQfSrmLG7G0rRWvN5XOt5eLsjqRO1AJiKxBIkbmpMwb1WDeNnizqxnW6qkHBCzISJtU+3j5cjeEIR1axNGl5Wi5zM+8Sqx3JuTQpid1YOFznbc/fjJSJo7gKG+ZLhyPBgZjKLm47ccEY/Qok9M3iO09a5D9wj95Cc/wa9+9Sv85S9/EcMz5+RceeWV8izRa6+9dlx4ePPNN8s+7DCx47R9+3ZUV1fvJ1V+N3z/+9/fz1GYmZkpgkU+ZmMq0FtjiqIoijIFJMRi8prM8e8EPtz0/PPPx7nnnjvu7rn77rvl6e+E4yK33XYbvvOd78h+8eeMZmdnyzNGL774YkyWm266CdONo+rW2C233CL+gX2X3ymKoijKTL01xhGmia9AIHDAy51wwgl47rnnsGfPHnm/detWvPrqqzJRmTQ2NqKrq0tuh8XhyNXKlSvxxhtvHLZo8xYdJ0b/6Ec/ktdDDz0k26aKo2ZEaP369fJ8koULF051UBRFURRl2kyWLiws3G/U5Xvf+95++99www3SUeItKNqd2fn48Y9/jEsuuUQ+ZyeIcARoInwf/2yy1NXVyQPc29vbUVVVJdt4K45xeOyxx2Te0pHmqOgIDQ8PS0b94Q9/kN6joiiKoihjtLa27jVHaOIzQSdy33334W9/+xv+/ve/yxyhLVu2yB2WvLy8Az5G672Ac5HY2Vm7du34KjG32y2P7OJn7AwdaY6KjtDVV18t9zQ5XPd2HSEOCU4cFmTvV1EURVGOVaEiO0GHMlma5maOCsXn+ixYsADNzc0yIsOOUE5Ojmzv7u6WlVxx+H7x4sU4HLz00kt7dYKIy+WSqS+rV6/GVDDt5wjdc8892LRpk2TUocD9eE8z/tp3yFBRFEVRptOtscm83gmjo6OySmsiBoMB0WhU/s9VYuwMcR7RxMGEN998E6tWrTosceZoldfrPeCdH7PZjKlgWi+f53Df8uXL8cwzz4zPDeISO/ZMObP9UEeE2Bkq/PmPYEqwwdadgNDxXvx1+Z8QiiXip63nID9pUJZgNo5k4KeFj+DCm6+HaTgG17peBHMcMLlHMLAoHYnhGIqurcWnc17Cp5+/UpaXdvc5cEplLYJRIwYDNlneXuvJxPGZzbIsvWPAiSX57RKW9hGHLKXlEnIuJU8yBWFMjMqy+Lq+DHykcjM2DRSOL8N1WYbxanuZLFflMtb48mWrMYwNu0rgzBqW81W4+mQZO8/VOexAmm0U5Sl9cmxm8jAyrKN4c0c51iyuxsv1lTAYIzi1pA7ruouQavXJsvJBvw3upjQsWjC2FJpL4vmX8P9cssulq0m2IM4v2Y7ugANb+vLhDxlxTnE1dg3ljC8Dji9v5/UZPi7h94Rsslz//7adiKz0IVlSW2wfkHMsz2qRZfnlRd2y/2jIjMFhG4JuGxbNa5J4ZVuHZOn0wowO9AfssnQ2ri3o8aWgticTxxW2YFNHAYrSByQ9mnvTZfl8cXI/qgdy4A1a0NfhRFK6DxZzWOLG+Ltso9jekYtgrw0ls7pwWvYe5JkH8KPXPoBzFm2DyzQiS2xT98Tg2uZFJMmEpnNssLqB7PV+1F9sknMlBhJkqX3MGhFFQYIhKsvWXSkjEi+mSzzuXObvezYTD37553jfI18FzFG4CgbhD5pkGTCXRzvNftEFdLanA+EE2DJGD1jmI9X7/xIMFgRhT/XJ8u2q9F5s68pFii0gy555bi7VPqOsBu5AMho9Y2qB/uEkWX5tSIhhcUY7nt45F0ZbSJZGp9j9qEh1o8WbOq5qoLaB5ynNdMsyacaRx73SWgbfkBWIJmBZVROaPGmy1JpLpXl9LnVPSAROr6gRtUNTXQ6MaX5YLGE5N5cxc9m8pCn1AW47EEuAzeFHsCEF81c2YPumUsTSg4h5TXKd9B2JsAxGYfZE0LPchKAzhtS5bny18lnRAdzfthS+kEnKjykhKmFcU1yLqqQuvD5QIXV6g68UToMP+aYBjETHbi286S2TtqFhNANdoykYuqsAlku60LUxF8UrWlFfl4vyik60utNEfZCQGMWZFbslXZOMAWzry0OO3Yt2rxP5KR45Z9dIiqRBcopfls9TR9A9nCLLz13mUTywZSmuW/kM1nlKpTwnWYMY9ZslLwMhIwK9SZg3h/qHKHpGk2Upem5lr+g0uCz/xPwG0VuwfUm1+GXJOtsh1nmHyY9dnmw5tnEgXc5ZkOKB25+E4YAFgaBR8i4r2wOzMSx5yuXtLCN8z32yk71YktaG2uEsOEw+rOsoxkh/EmypPszN7h6/npTDEbOUww+U7sCjjfOlrcq2ekVd8XTdbDj/rZZgOjA8bOt4PaYJ63CD2yX6jeZO1/iSd+43J6NnfJ94XHgsy4/75VxgyRCW5rVJnv2j6n48MlKIv155LlrOTEL68d2iBEk0xhCLAlGvGQXlPaIjGBmxotzZgmfO+d8jsnx+6cd+POnl85vu/vYhh/WKK67As88+K/NteWuMz/j67Gc/i09+8pOybJ7wL0dnJi6f37Zt22FbPv+JT3xCBjf++Mc/YsWKFbKNHa3PfOYzWLZsGf785z/jSDOtb41t3LgRPT09Il2Kw8ldL7/8Mv77v/9bOjzsze7b2zzY/VFFURRFmanPGvvtb38rHZsvfOEL8t3KuUFXXXWVCBTjXH/99RgZGZEO0uDgIE488UR5IOrh6ASR3/zmN3IbjiNMJtPYj8hwOIzzzjsPv/71rzEVTOuO0Omnny4ip4lQ/sQZ79/4xjf26wQpiqIoytHCkX7ERkpKitxNOdgdFUJFzQ9+8AN5vRekpqbikUcekdVju3btkm1z5sxBRUUFpopp3RFips2fP3+vbXa7XSZW7btdURRFUZSjg4qKiint/BxVk6UVRVEU5ZjkCD9rbDrwoQ99aHw+0kR+9rOf4aKLLpqSME3rEaED8eKLL051EBRFURTlsHAsPUH+UOAc3wPJHmm3/uUvf4mpQEeEFEVRFEU5IhxsmTwnTk+V9087QoqiKIoyFdBeM9nXUcaCBQtw7733HtAZOHfu3CkJ07T2CB1OX8P7n/wM6ryFyEvzoLU3HWnPWNG3IgqDM4iK3B40vlqMkCOKmDWKy45/XXw+1xc9idREH3YG88RFEooZ8NeGFfAFTDihsFHOT6fNHEcX1vaWiEOns9mFRXOaxbtDZ01pSQ86PQ5UZPSJ76LZk4YTchtR781A55BDHBr0ZNhNQXiCYy4WMhIw48yiGoxGTOLboJvkpdYKnFJYhxpPlpwrEDZieUYrnm2bJY6SpoF0FKQOip9ne38uspKGsb0lT5wtVltQPuMxcW8Q/Sb01NDtwnA4bH7xhPD/0WgCFuZ0ivuIniN6QVyOEdmXTphYyICSoh5x8JDdvVkIhQwodA2IUyU12Qf3YDLsdr+4T3KdQxLWh3YtQqpzBCfmNKDOm4ldbTmozO8Rb9Jg0IrGXpd4l96sLYEtJYBIJFHOJV6hURMcaaM4PrcZzSNp4jeix2SnOxfzXJ1oHHKhqSUL5SVd4iJiGla6+rCjK0f8I7u6suXcTpMPr7WXYkVe83gebt1VjJXz67GhuQiRATPWLN+JtW0l4lkZ6nDA7PJhdXEDvpD9An7RfjY2P18Fow9IP7UTsd9nwVNmQCANSAwD80+vxZaWAiQ027Ds5Bq8ua1CPCWdfanicAr5jeLUodfG05CKS099FU+1z5G0Db+UjuHyCIyDBhj+rcMK5IalTBF6Y/ZlRebYZxN5umm2OHgYx5dqK7GgqEO2073S0p+GaCxB0pPhoN+IMGx04RjNYQQ67SiZ3YnjM5vwSP0CcRD1dDsxr7QdIyEzuodSxNPS4HGhqyYTSUVecVY5LX5YDWEcl9qIP995NkYLoojaxhxJ3hGreGVycwfEeUM3TFl+r5TlvlH7WLmKJCLRY0TEHkFajlfKoddjk+30MpkbrTIvImYAktuATb+7Dmcd933UXGPFOQu2I8kQgsPgw1DEhs39+ahryIFhxIAFSxulTrp7U1Be2CPln9cztlphnuuRsPc8l4/R4ghixigsTqo5opL/9Gb9v9dXjzutOoYdkmbLi1uwtrpMfF5WU1j8XYxHijkgcYt5zHAUeeAPmMTDROhwomPshY4KcVixnHa0pss56P95as9cnFxeK2Uv1e6T6xRm9ku60pnT6kkVX87giE0+d6/PRsrSPpgNEZyRW4NnO6vEU0WvU17yEGrdGVKvK529mJfcgRd6q8SrRScXw8K6bTGGJdzvy6/B481zx3xQOe1Yk7YbHcE03F27TNqoWc5e8RDRj8Z6T7dX83A6ekeTpewmWwLIThoWjxDPWbOjUOLP/Etx+iTcce8UPVo9PQ6srGySes92ie1kQdqgeM+6R5PH21OWHTqsuB/9RQ/uWSRtEePJNpTl3Ou1ibss3jaSDOsIwlED2n5Zgc7ViXjoQ7fi0tu+gnAyYBwFbGf0Ijd5CFt3lsCSOQqbJSR50rDTidYv3XhEPELLP/wjGE3vfll6OOTHhge+856G9XDz6KOP4oMf/CA+/vGP47TTTpNtFDjefffduP/++3HBBRfgSHPUzRFSFEVRlGOCI+wRmg584AMfwMMPP4yf/OQneOCBB2Cz2USYTNHjKaecMiVh0o6QoiiKoihHDD47lK992bFjx5SocXSOkKIoiqJMAQnRyb+OdrxeL+644w553MaiRYumJAzaEVIURVGUqWAGeoQmLqPnc8f4lPtf/OIXMl+IT6WfCvTWmKIoiqIo7zldXV3yUFU+cJUTxj/ykY/IM0M5Z2iqVowRHRFSFEVRlCl81thkXkfTJOmqqip5kj2fddbR0SEPgZ0O6IiQoiiKokwFk3UBHUX2myeeeALXXnstPv/5z6OyshLTiRkzIkT3B10epQ63+D/ci2Nw5A0hFgVqdhdg9ZnbcMKK3Sgo7cWjLfPF13H5U5/F+U98Cd986UO49bn34/aNp4pjh06c15rLxPWRnzSI+6uXig/k4qINWLO4Wnwl3C+vsB+L0tvhH7CKe4Xb6RCiD4ieDzpK6AIJxxLF7UF/x5KMNvH/0I3ROpqGx7cvgMEYRfVADmZn9uDxHfPFn0PvDl0e9JHQtcLw+n1m5CV5UO91oShlUPwYp1TWIit9SDwpdZ1Z4iuiZ4NOl4qUXvGRDHrsWJDVKZ/RkcRwEbo83F67OEzOKKsRV4g/aEJ+zgBWzm6Q/be25aHANoD5WV3i2clJ8or3h04VuotK0/olvegE2T6YK2lPz8ob3aXitGH4uodT5HqM13GFLVi/vhJr5tSIu4TOEfprxMPiM4qLhPFjPOlV6Rh1IhJLgM0QQv9oEhZVtqC+NUviT3/LcMiM+Tld4t9ZkNeJukEXQrFEcR7RO/RC9WykW0bgyPGKE+b40kaUzOrCuo5i8SjNyehBaoFHvCW7B7Jx2Z++JOny0fNexvs+tA759iG0nxVDxAqU3+1G6a93oe13FTDusQGJMWzpzAOsEXEGkaDbhswMr/yf7pOUEo+4X+h2+v6cRzE8Lyj7Rwt9CKXEkNIcQ26xG40D6RIHupH2fQ2Frfu9ggGTOIQYj7NnV6POnSHXZBn4cMUWLM9vFbdLZ1eqeGrCI0bxG7lShxEOGhGzRiTf6WzhuZjGrqwhccYw38tcbmzqKEBnYwZmLWwVV01Te4Zch/n3TM8cXHjZy+LvgSUizh46ph4/7TcSV+YVYb2hQ6a/Lh2J3RZxBcXSg0i0RJCd7JWyOLeoU/YtuteIguf9yHs1CNeOKMLnDOCRhsW46YG/oLyoWxw8a3uL8cc3T8RjDXPhHrXLcWmV/eKQKnEOICExJvVs2GfBhxZsQax8RK7BOJlX96N8VgdmV3RIOWTdLHQOYl1/sZyHTi56eZjmdNZsbs8XTxhdVf1DSeLNWZbZJukW85pQNb9Vzs36uTK7Wco9y0utN1PqEcPBcsrrsZ64A8kwW0J4ftsczMrsRZIpiIvmbhInz86OHLR7nVIeef2Az4TV2Y2IlPvEB8Rte4azxR1UntIn7QjbPHE7mf2o9WSKQ4h1nw4zhqetJhu9w8moacqVsNUOZ8Hb5BTf2M6+HNzfuQwv9VaIQ4oeMdYbhp151zqUis19BdJ2nZW/S/JKfGPDDqnrrJ+pJYMS/6VlY+nQ1eOE1RJCkztdHGN0COVYPWN1IWiRdoIOIXq+CMsWHUJ0JNHFRdeRKSGCrFSv1Ce6iJbltEr6zi/oEKeROIiGU+QzOq5Y/wq+WocPnLoe1159LU66dKOcO+dXr8OzLhNbGwpgHEpEJJwocaBj7usnPn4Ev5lmDq+++qpMjF62bBlWrlyJ//7v/0ZfXx+mAzOmI6QoiqIo04mZdGvs+OOPxx/+8Ad0dnbiqquuEpN0Xl4eotEonnnmGekkTRXaEVIURVGUqWAGrhqz2+345Cc/KSNE27dvx1e/+lXccsstyMrKwnnnnTclYdKOkKIoiqIoRxxOnv7Zz36GtrY2ecTGVKGTpRVFURRlCpjs7a2j6dbYW2EwGOQZY1PxnDGiHSFFURRFmQpm0Kqx6Yx2hBRFURRlCtARoemBzhFSFEVRFGXGkhCLHdtja9R4O51OlP3lW/j4kmq4Q3Zs6CkSRwc5LXsP/rTpBJTk94n/g66Q0tw+8Zs4LH7xYbzeWopMx7DsLx6ehKj4OKyGsLhwKlN64Q4miZeGjg26bggdIbv6ssSxUpwygC1d+WNeD4tfvCZ5aZ5xnwqdN/TcWA0htA87xWdx4ZyteGDLUtgcfvEK0SPU5EkTR1F60qj4RegTKkwaEL9H57ADg8M2ce/QJfJsQxWW5LeL74QeHh5HXxD9Gowb3UYu66jEgY4O+jrcQ3YYDFFEIv/pI/N83DcYNqLU2S8eneZOF9Jdw3IMvUeMD30emanDmJ3WjY1dhRLmJVlt6A/YxYFDZxH9KEw3l2VYPCQ9/Q7xHHXWZyC3vE/SjG4SXoPpTFcS3SRlTje29+TixPwG8aDQ/XT+io34586FSDTGxE9EZxB9TPSJNLhd4st5oG6x+GB4nUjAIL4ZulPoh/EErVjmasUj65bBVTSANXl1eLqlSpwn9KrQbTLqN2NNca34m+xOPypcfeKDykgaQU1zDixNFpSc0izpxHhdlf8i3hiuxPPfX43+S0bgShkRF5Ovyw4kRbCovBXbmvJRnOsW30rvYDLCPpP4c7iNnquPZ6xFdSAfrQEXltqb5Nzfeu4iXHXS87i3cdl+ZZxpvC/0IyUMGVE2vx0nZDSKY4YultzSPkkLuoOyk4albNDtQuioaR9xwGIIY9BvE/fPt1c8jj+3rELP4JjrSeJpCUk5bhtIBXakwLJkAFZTGJlJw+gaSRE/zqDbLs6ehSXt+EDWVmwfKcAj2xfBnBSSMkm/C31Fqc4R8Vh9euGruLN6lZQ9lpvAbicytkWR9lIzgpW5GKywwnvWsPh9rs55Dr9sP1s8UltqimCwhZGXNYjeoWScXboLz7ZUwWoOSR4tSO2Uullqc+PprtkSNvqw3D0OnLNgO7b152FheoeURTp4tnfkiu9ofmonmkZcUq7pkDojtwbbPPninUk0RlGU3Y/uoRQpY3/feRwqcnsk/9kulNrd2OXJljrD+s36T+9NX4cT8yvbpHyzfrKcLk1rxV3rVolLiPu3d6WJd4e+Ik9PMj636kU81TlXjmFZjceNdZnnXZNdi0eaFkiZZVllWWf8COsm40vPT5s/Vdonljl6nOgzosOHcdxaXwizPSgeqZwsD7p3ZuHsUzZJmtBVVGhx4/HusSeC72nNRlJKYLwdy7Z68WJTBQIeC7LzBzHP1SmONdQkI+/4dqlr9Fn1+FLEE8b2kO0j24wdbXniY3p860LxkrV4U6X9mZPTLT6q6+Y8Jy6jFenN0m6/2V0s/qWRQRtSM4bh3Z2G/zptPZ6onyt5Vt+UA1uqD3mpHrS606ScslymWPxItfilfaCzivX6ltkP4tPPX4mqP/hR8xkrLF0mBPNCSNluwvsveg4/P+ExeDweOBwOvJffS6vO/gGMJuu7Pk845McbT974noZ1JqC3xhRFURRlCtBbY9MDvTWmKIqiKMqMRUeEFEVRFGUqiMbGXpM5Xpk02hFSFEVRlKlgsnZo7QcdFvTWmKIoiqIoMxYdEVIURVGUKSBhkhOeebwyebQjpCiKoihTgZqlpwUzxiM0/76vY1b+MKq7s8W5QZ9On98u7g66Njo703DVcS9hbX+ZeF4wYMasha2o68yC2RJCtsMr3pU395RiXmk78pM8yLYM4fG2ufhE6Zu4p2U5lmS0iRvEagyLD+i1bbNgzxpBSVq/eECGAla5Do+vbslFzGOGq6wf/XXpKJ7bKY4PunbonqF3ZHZ6jziF6HQZ6EqBM2sYuY4hiRd9GPSBlKe4saUvHz3dTnx48SbxCdGXQadJu9cp+7gb0rFoUSMaB9LFP+LrS0J5RSeWpLfjhY6KcScJHUn0m9DTQ7qHU5Cf4kFFSi/qvJniAZmV3I0HGxaJT2dnRw4WFXSIeyUSNkg6kVDIgGg4EZX5PeI5qht0iZeHbqY026hso7uI4aSLho4mOoN4/Z31BXDleFCR6pZ9GA+6R9YU1OJfNQvEOUQHSdx9lGP3Srh4LqYxtxkSYliV3YgX2irx4dIteKBxMbweG2YVdo97lx7YugTLKlowGLCifnceFi1owtbtJfKXNHvSMDxiQXl2H+q7MzAvr0u2L3S2S/xHvRY400bFl4MRIxBNQMwaQYIxJn6abr8DHb8tRyA1EQlhIJwEBE8dgn/EjGjAgARTFAZzVNIsyRoUr40rawjDPgsi4UTEoom4aO4mPLhnkTig6PTh9U7Jq9uvjDOe+1Ka1i+eFkJnEH0ugYBR4uH2J4lHhq6mpt25qJrfKunS6ElHT49DfElp9lEpg94Rq/iuOgacci6Ghd4fQgcM3S4LMzrwSmM5IkEDYpFErJzVKD4iwnydWK5bmzOwfE4TdnTloCBtUMp2qtUn3pdgdxLMg4koedSL2mtMWFHejJ8WPoL1gTw83LdU6mvjq8VImOPF6SV74DKNoNqbK54p+qe2deXCN2SFJSWAgMeKy5a/If6cYNSI2oFMFDoGJb/do3ZkJ3vH2oeAFSsym/FSR4X4mIZCtvE2gi96a8jinHbs7s+SskVfEsta00vFcK7ohT9klHp5fs5W/HrnGnEZ0W9FLxTrH8tnid0tzp49vZni0qK7B8FEFJT2iqMpHDSIC2luajdeailHsi0gZYIU5LvFL0THD31aO/tyMNjmRGqBB6M+s7iXTipskHagty9F9os70Ha6c8UjxDJLn8/ugWxJq3X1xcjM8CI3eQiegFXyiOde4mjBvzoWiM+K3iG6ypiHbCfpTYpUO+A6rlvaTPdgMiLBRKS6RiQPmSZ0UNW3ZIsvi3WXvijWbzrImE5sa84tqxZfF8/BMK3rKJb0HQ5ZxAuVV9g/7rJi+hWkeJCfNIjqgZyxsu1wYzRskXaF1+V+PC9ZltOKWk+mhJllnu0a2wvGkU4nurLYhqevNSPhvD7cWPUYvvvby7Ht1uvwvsSLMHTJKoSifmz++7ePiEdo9enfg9E4CY9Q2I/XnvueeoQmiY4IKYqiKMoUoB6h6YF2hBRFURRlKtBVY9MC7QgpiqIoyhSQEIvJazLHK5NHl88riqIoijJj0REhRVEURZkKov9+TeZ4ZdJoR0hRFEVRpgC9NTY90FtjiqIoiqLMWGZMR2jYa4U/bMTCnE5xSZgTw+LGoLume3cmUjOG8WLvLPHTJKUEsGJ5rTiEUp0j+OLcF+ENWsSNQkdKbU8mRiJm/H3nceJUuXXdGeKqoDvGmBgVh8X2nlyk5g2Jj4XXoafFPWSX6zS4XeL6oL/FbIjAlDuK5RmtCEYM4itye+1Ymd0sDiH6W6LRBJSW9CA9aRThaCLaBlPF2VG/Jw+ekE38HBIub6aEn24euabVh4EBO8rntksaVLr65Px03rS9UognmuaIf8Pb5ERbYyZ21+WhfTQVA74kOZ4Oj5quLLzaVSYuFKfJJz6SkUGbOELSHaPiZaFbxm73IxgwiXuF7p2Iz4gV6c3iAKJDiA6P5VktqNldIOm4taEAzb3p4sxxWUeRn+yRtMvKHZT96froHHaI8ycxMYZHdy2AK3VY0pCeJ6ZBiXMAXSMp4hPJsnnR5E4X7w1dRbuHsvGx8g14vnsWRkasWFrWKnFo8abi2bZZOHPuLjhMPlQ5e+AoGBK3jqtkQFxLdI4wzS2WMPa0ZiMzdRjplhHZJxQziBeKeL02FOT1o2RWFz584jrMr2xDVUknNvcV4OT0PSi6thY/vf4O3PKtO8QhlPimAzmPmZGx1gRXphcl2X1ItfvQ1+GEzeGXvxdWbEOSLYhIwIAHnliN1cUN4q4RZ1Ffsvic9n0NdTj2ezEO1i028byw7BakDsKw2y4em46eVEkzkuAMorHXJemSYvHD3GqRuFc6e8Xl5HKMYFF6O4IjZvEJuXtTEO21IOIx4/E3F4sL5oXq2SjO7EeiMSYuJPp86MZh3eK16YSJxhKkXhjtYSxPbYLFFEZTd4aUf5ZXeqicexKx59vX4SN3PYMrF7+B9Y1FeP8d1+PGbedJfSWRcp/8ZV2jI6jWnYHOxgy8z7VTygmdO3QILapsgS9qxku1lVKPSLZ1SFxW8zK6cE72DnFxDfmseGTLEinrL9dXYktLgXidWNdYlnwNDqzOb8SmjgLx2dBPtbMxHy39achd3S7+Lrp8+P73e04Sv9ET9XPFFcUyX9eQIz4nOoSYJ3RssZyb7UFkFQ6IN4j1JxoyYGAkSVxAs7N6JLz0UTE+wwGLtBeMJ+PC/Pnw8eul7ob8RilD7qAdpc6xPDglp07qAp1I/UNJeLRlvuT/xq5CqT9sI1ie6Izi/xnO2WndCEcN4hA6Kase/QE7mlqyxEXEOtZUlyNtJ6qGkW4dxeCwTdrGRWVt4mTq9DiwML1D0pfhJqzny/NbxVlF9xLTdE1xLdb2Fovnyl/nQMeoU1xC81PapU05beEuCSOv7RlIwjkF1VLv6GdjW0ivEfOppj9TrtHQnintRSBklHzo8aVImWzudOGDZVslXowjj81MHpb2iHU0fM6A7Hf9Xy/HmVesRbRrFn7d/Dq+cOMD+NY3/t+RXzU2mZcyafTWmKIoiqJMBWqWnhbMmBEhRVEURVGUfdERIUVRFEWZAtQsPT3QjpCiKIqiTAV6a2xaoLfGFEVRFEWZseiIkKIoiqJMAQnRsddkjlcmj3aEFEVRFGUq0Ftj04KEWOzYTsmhoSE4nU6U/eVbWF7pFn8K3RTV3lxx8uzqysacnO5x/w89E7mOIfHdLFvQgE0NhUh3Dcu56BFp9qQh2RJAZ1+qeDSGfRZkOobFlWFMjGBnXw5sphC6epzi/hgJmOV8dBItL27BYNAq3hb6Yei8oKMjx+4VNwn9G88+sQyGuUPweS3i6KBLh14cenNGvRbxf9C9kmQKSpjoAKE/g+4Z4vXYxGGyfE4TNtYVYWVlEzY0F8lns/O6xA/U5k6FM8Un/g1CxwbDs3sgW5wvTosf+UmDeGrPXJgtIfGU0HE06jdjbnY3Su1ujEZMeHzHfCyraJF0m5XcjVd6ypGdNCwembxUD9yj9vG0ogOouz0VCcFE8e7Qm0SPEZ1Ei8pbxSHCvDm/cDu6gg7xxLQOpSISSxBfylC/HVnZHol373CyhJvuF7pBGjszsKCoQxxA9JPwL11RPB9dSjw3HUI7enJk/461+eKjYdzoPToxv0HcPx2t6Vhc1YI6dwYqXH3iK2IeMk0CEaN4R+hPObWkDs0jaXJeumHOyq3GP9sWoszpFtfSE9Xz8P65O+Wc9EttqSlCWo4XVem9+Fb+Yzj/xasx5wdu7PpuBgyWCOYXdKDjj2XoP8sv3p+S/D7xstCxtKigQ9w2Ea8JifYQLLtt+5Vx41jx3AuLJwajPwarOwxDIIqwzYDBShOiBsCXG0M4IwxjnxGubUAoKQH+TGCkNISE8NjdcrPLJ+6gRI8RiTk+OF6wI7kjjM7jjUjqAgxBwJ8GBNNicO5JwHAREEyNwtqbCF9FAI60UckDOly2bi2FwRVAVvoQOjvTxI3TvT4X+SvaxTPEcpX1BxvaPhHGr1bchy+98jGsmbsbL+yYAyTGUFLQK/t5vDaYTBEpj8yLnuZ0lFR0SXk4o6gGG92F8IVMUk9ZBobrUlG5tGUsjRKi4u9KsQWwOKMdL7WUS72lo4fliD4w1k96q3Z5sqW8dI8mi0Npa38+WrrTUZrbJ9eij8tlHhkvAywnswq70T2cghNyG8XFFa8/Fbk94iuam9aFJ3fPhbHRJi6eJfnt0tY4bH6pM3QcPb59gThuhgJWaT9M1rDEl2VxOGRGfUu2+MJ43FBfMmypPolPoWNQfEp2S1DSJTeZbVEUG7eXwZI5ihJXP/pG7dJuLMlow7b+PLkm02TrU1UIVPjFP2S1BaV+0AVENxnr3LzSdslDOp9YTntGk8W9xGsx3Xi93r4UpDh9sv+HF2/Chr5CaUPo3qr1ZErbyLTk/jwPHVksA/QmcRvrIMsA2wymL9/3u5MlTdlWsn3e3ZMl+cVws91mnfrK6qdxT8tydNZnSJvS2JQFizMAqyUkLq2C8h65Bt1N3GY1hSVebE94jULnoOTzls48GNelIJbIuhFFWXkdnj/39/B4PHA4HO/p99Kpx30bRqP1XZ8nHPbjxfU/fk/DOhPQOUKKoiiKosxY9NaYoiiKokwB+qyx6YGOCCmKoijKVM4RmszrHdLe3o5LL70ULpcLNpsNCxYswIYNGyYEKYYbb7wRubm58vkZZ5yB2tpaHMtoR0hRFEVRZgADAwNYvXo1TCYTnnjiCVRXV+OXv/wl0tLSxvf52c9+ht/85jf4/e9/jzfffBN2ux1nnXUW/H4/jlX01piiKIqiTAUc0JnMEvh3OCD005/+FIWFhbjzzjvHt5WWlv7ndLEYbrvtNnznO9/B+eefL9vuuusuZGdn4+GHH8bFF1+MYxEdEVIURVGUKZwjNJlXfBXaxFcgMLYieF/++c9/Yvny5bjooouQlZWFJUuW4A9/+MP4542Njejq6pLbYXG4um3lypV44403cKwyYzpCH521Ce0jDmQkjeDvO4+T5ahcCkpK7G5sbiqQZaJcFppq9sORNyT7EC5v5/JOLpXmcvCewRRZalriHMDy/FbYTUEkGQPo89txSl4derZnyTJ3Lp3nEt6W/jTkZgxic3u+nFuWgw5kyxLXgQE7rMYwyou64Q4kY84p9UiyBgGfEW1ep1yfy2lX5zeiONcNr9cmy2W5xNViDEt40x2jY0tDAyZcufgNZOR5JOxchs04p6T4ZGlw00C6LAM3GKOylLapLVPOn5AYQ3/APr5MnPHk8vWIx4zjC5pkuSyXJLtSRtDkSZPlxVyCy6XzvA6Xpe8ZzkZTXQ76/EmypLfK2SPLxX335krcl2e14Pi5DUh0ji27faGjQsLO5bZufxJavKmSFn/ZcTw29BTJEtkUi1+WvzNeXGLPpa9Ma7/PLMtfzy/ZLkv0LbaQLGEmcxxdcl4u53f3OJBhHZVl0zy/rzkF3UMpWLBm7H73rMxeWeL9anuZLANm+LKtXvmMfxeWtEseljrcspyZGoPzKndgjydTdAhUL3CZ8J3Vq2TZ9u7+LLzeWYqlZa2SPjwn85DnZTyYThdv/LTkyx9f+hts9WZkPWLB1p0lsnSe+YRYguRroDdJ8onpazBGkOBPlKXXxf/o2+911uVr93t96KvP4n9vvg3X/c/d+H9//S1yvtuAT37+MZR9sF6Wzufm9yOxdAR95/oRPa8fvtl+pG4zIXWHAYbhRNheSUbKVjOilhgSE2OwXNiNJd/bJEveSy6qR8QM+Of7MG9VAz583TNYfEYNYskRpO2OImm3RfLnzT2l2NObKUvno+GEsbpW0IvW5gzY5g+g57l8ZH0xCPOLDvzm979FxGfEt39/ueyzuacA5pQAEgxR0T1QU7GssA1Rpo8hjL49LsSMUSmvXF5e583EmuxaUUJw6TzrwxXve1GWrvOVZRvLV+Zh9WA2YtEEtHelSb7wdeGcrVJGHnxphSgtGHYu1V7XWzy21NwcwaDfJkvnq7uzsbG3QM4n5dTpw4LUTqlTXDLPciH5HzCgpilXluDXeLKknJrme2TpPNsChrFza46c47mmWbCkBFCe0if1w2oPIi/NgxV5zdi6vQTegFXqANsZpi2XznNZOOPTMezAgqxOUTXMc3XKsnWqJy5etRYfKN+JDOsIlmW2wWHxS91i++X2JUn4o/OHcd68bbLMn+dlee7fniFKjtWz62R5edeQAyeX18p5WR8YPl5nRWYzKtN6pbwvy2mV8LlMXlkCT33Auo5iWaJPjQU1H8296QiPGEWDwbQnXK7P5ezZjrH8oWpkoDYdsRETOoccCPmNoihwJvlxYlYDtnfkYkdbHqxpfrzcP0viZEgLSh012sNIsftFoQFrRMoZ9QRsb3kNhp1lme0r96nryxDdBfUCw5UhmFf3Y+GSRmlvjzY4ysMOS/x18803H3C/hoYG/O53v0NlZSWeeuopfP7zn8e1116Lv/zlL/I5O0GEI0AT4fv4Z8ciemtMURRFUaYCDuhMSqg49qe1tXUvj5DFYjng7tFoVEaEfvKTn8h7jgjt2LFD5gNdfvnlmKnMmBEhRVEURTkWV42xEzTxdbCOEFeCzZ07d69tc+bMQUvLmHg0JydH/nZ3d++1D9/HPzsWmdYdIQ7vHXfccUhJSZH7mRdccAFqamqmOliKoiiKctTBFWP7fofu2bMHxcXF4xOn2eF57rnnxj/nnCOuHlu1ahWOVaZ1R+ill17C1VdfjbVr1+KZZ55BKBTCmWeeiZGRkakOmqIoiqJMjuhheL0DrrvuOvk+5a2xuro6/P3vf8cdd9wh37MkISEBX/7yl/GjH/1IJlZv374dn/jEJ5CXlycDEccq03qO0JNPPrnX+z//+c8yMrRx40acfPLJUxYuRVEURTnazNK8w/LQQw/hm9/8Jn7wgx/ICNBtt92GSy65ZHyf66+/XgYbPvvZz2JwcBAnnniifBdbrUffJPJjoiO0L3ywHElPTz/oPlw2OHHpIIf1FEVRFGXaMQVPn/+v//oveR2MhIQE6STxNVOY1rfG9p3tziE73uOcP3/+W84rmriMkMsKFUVRFEVRDkRCjCrJowD6DqgEf/XVV1FQMObwONQRIXaG3vf4VUhLBbpHk5GfTM+ORTxCdG9srS8Ud0nHgFPcHXRpbN1VjHOWbcXmvgLZh46hYvsAmkfSxEtyVm417q5fjvSkUTR3usR7MW9OC6pbcuX/a5buFFcQyUseEq8H3Rs9vhT0jiaLI4fQc8MwHZ/ZhFZfGsyJYbxUWynekuCIGVUlneK16GhNhzNrWPweZF5el4SpZneBOI/oABGHSlMWKsq6xGFD3wd9Q4QOjXOKq7F5oECcJ481zEVR+gDquzOARjvKj28Wh8nj2xfAYInAbvej0DmIfn+SnKd3KBmzs3rEebJ7fQlS57rF05ObO4AF6Z14eudcScO5aV3i5RnqSoE5zS9eDzqIdnbkoCqnB+FYIpaktWG9u2gsvyJGVDp7sbGrUOIwO70H61uLxNfzz9r5OKOsBo+vXyz7phaMjQiekNuIJENoPA7Ml1day8R5RF6ur5Twi1spZMSaglp0+Z3Y0FwkPpTR8NiKijdrS1BV3CX56Q1axAOzOrsRnX6HOKG4nXS+lo9AdgT2nGHMzhxLA0n76gKYM30wmSKYnzXm2DAmRvDqjkrJA0KfEh0xBWmD4mShE4XhiHVaUbq4DTeW/gvX/ObzcLREEExJhKciAZFynzicElOCSEiE+HWyXjSi++Qwtr7/t/uV+ZM3XrnftmGvVbw1o/02OHaaMLLcJ+6cnCyPlAX6VehkYhzpVvpwxRY821mF8wq2yfFr+8vEGzMUsok3JxJJRLjVDpM3AYkLhjA3e2xVyaaGQiT0WnDc8XvEw/O50pcRihlw/yVnIHHYj9orsoDEGGKFfnEJpb1mRdbLfdh9TRquP+0xzLO048GBZdjany9l9xMr3sADdYuxprgWpoQIHt21ANkZY6O6LIedfanivBE/UMSIdOuopCvTfVNHgThiWN7ob7KZQhJH5hfz4YXmSpxbVo2nW6qkfox6LYiFDDDYwkh1jsDdm4Jz5u+QsrV9MFfqA2EbsCSjDeGoAaVJvfi/bSeKd2hDX6G0FXRO8fpvdheLC4vpflJhA9pHndjZlAeDOSrnX5NXh1pvpnjLhsJWvFA9G2vm7sZrzWXiumF7VOZ0Y3tP7rhj55TMOmwcLEKyKSDbh/rt4hwj5SluaY/oCdrQXij1k94mXpvb6zYUIWtBj3jAotEESRuW8dlpY8eX2tyoGckWlw7T2hcxS5jpUWI8ol4zckv7JEztw06Ja507A6cU1qF5OF08aHQmRcIGRAbMOGflFnGh0cFUkO+WtCc1zTmSBvRkMQ+ZL8RlHZUy9sKOOZhX3gZP0CphpQeMbYrD5MdQyIrn6qoQ8Zqwcn69eNF43pGQWcpCYWa/1F3mZyA4Vl/ZjrB881xMQ8J2MMkURFHyAFqG09DUnYGzZlVLXj/07Eppz5g2dFStSNqNn5/wmNyBmLgk/XDC7yX+UD997tdgNBx4hdehEI4E8Fz1L97TsM4EjopbY9dccw3+9a9/4eWXX37LThDhssGDLR1UFEVRlJl8a0w5yjpCHKz64he/KJO7Xnzxxb2eiaIoiqIoinJMd4S4pI/L+x555BFxCcUV3xxStNlsUx08RVEURXn3cPl7wiSPV47tydJ8JgrvfZ566qlixIy/7r333qkOmqIoiqJMi4euKsfwiNBRMo9bURRFUZSjlGndEVIURVGUYxadLD0t0I6QoiiKokwF0Rjvj03ueGXmeIQm62v41EsfweaRClSkusUBQy/Hzr4cVKX3itflVyvuw62NZ4hTZVZmL+Y4urC2t0TOcVr2Htxdu0ycNXSWNHnScE5BNfYMZ2Ntddm4M4ZeDXp5nCk+cVIEIwaszG5G9UAOeoeTxVuSaIwhN2NQvBf0XNBLQ09LNJaAEwobxS1ic/gRDJjEkUF3R+NAurgxRv1m+LwWmJNC4tmxJQbxeNtcOddgXzIWlbfC7U8SPwd9NW/uKMfqhXtQaBsQf1BLf5rEjftkJw2jY9iBwZGxSed08NR6MtHelYZZhd3Y05qNzAwvlme1SPjJoN+GYueA/J9hOjGfrpRUbK0twsrZDRgMWsW9sq0/D2012SiZ3SnXafSkIzd5SNKczhL6VvrdyXKd2Y5uvNFdKl6lVncaXI4RcbbUeLLQ3JsuaTnss8BmCWFeRhfeqC8Vx1Jppht5SR40DrnEC9XgcaGrxynpS1/JYIsTueV94i3pGUxBwGNBQjARjoIh5DrGvDR00dBJwjyt6c+UuG1vyUNlfo+4m+K+lWDUiLWNpchKH8JXyp/B9W98GMW5bnE/PVK/QPKK3p9F85owHDIjJ8krzhd6jM7K34VXesphMYZR15mFkuw+KQv0nLQ2ZyAhnIiSii78pOJBvD5aiTsePBtRI1Bx9wDcS1Ll+u6FwAdOXY+mERe2b9p/5WTsAJMtMyvdqEzrlXRhOfSOWPHRqk24a90qJFoiMFnDktZMs3TXMDxeGyJBA1KcPilPK/Kax31WdGsx/CxT9L6YEqIIxRLH04XpTYdLmcuNVIsPbzYX44Kq7bjG9TLO3XgVwludCKZFEbNGcd1JT6HS0oUbdnwQfqZb2ACDMSJhZp7SD0NYbra0FCASMEi5ph8nXv+yrUNSJrnPjp4cqUOVyT3ipmobSMXZpbvwWO08FGf2S53kMaV2N55omoNQyACrJSSOLF6LZZp1x2wPSjzpuWL94fUSE2PitCl19mNjawHCPhMsKWOOsoqMPjme7rHjClvEMUQfWEt3OhYUdYzXkdHdqVh24phjyRcy4YyCPWgccUmdT7X70NmVikVlbdjWlC9pz+uxXYiHMxAyIuC1iJfHafZL+GqaciVNOocduLBoC57qnIuTsurH2wJPQyouPfVV/HXrCiTQ4RRNwIryZmn36PNaWdwsniyeh+4vQhcP69ni3A6px3Qv0XHW50+CN2CVMpRsCUjYmG/0EoU9FrgKBpGRNCJusPurl4oviR6n3T1ZyHUOjbvMeHyq1Sf5kWoec6jxOqyDyzNaMRoxiYOIYWz2pCE72SthWJLeLj6xJFsQrqQRaRMWFXTA7UtC/2gSStP6Ja9YBxcXtWFj7djDQ+OuJdY15iPrNv1N9FWxvaCrjJ62Dy/ehJc7y8UxxLSlR2iZdc8R8widUf7lSXuEnq2/TT1Ck0RHhBRFURRlKtBbY9MC7QgpiqIoypQwyY4Qj1cmjXaEFEVRFGUq0BGhacG09ggpiqIoiqK8l+iIkKIoiqJMBbLqS1eNTTXaEVIURVGUqSAWHXtN5nhl0uitMUVRFEVRZiwzxiO08uFrYU02iS9jYMCOoux+tLlTsaywDbv6snBmUQ1e6KhAijkgfpgHdi9GVU4PWj2p4stpHk4Xz0TnkAOhsAHnllWL+8IXMcFmCKHQ2o/HOubD7bWP+UHqM4CkCFxZQ2P+n0EbllU1wZgQFd8I4fULXINyXvpsFqR2Shj8QRMyk4fRtDsX8xY0wxO0ioOEYaOXo741C8sqWuRc9HzQheJuS4U9awQVrj7xlxC6h+hNoidpfkGH+DZ8PXbx1tA78vedx6Eitwd9o3asyatD62iaeHDavU6ckNuIem+GOD/qBl04MacBL3VUiHPFnuoTDw6dIr4Rs3h3vlb0FK7e+HFcOns9Gn0urOsoRklaP3Y25uOypWvFaUL3TiBixEjAjAVZnRgOWcRNQ4cPvSRM5w09RYjEEiSuLtuoxIM+GVfqsMSdx9JjQsfKsNeK5BQ/vB4bPrRgCzb0FYoHypUyIu4g+l1e21KFlQvrUGrvE+fP0rw22WbPGUaw2omEymHx6TAuafZRccjs6srGGWU1cu1X28skv08qbMDzDZXi2olFEsW/QocM3TO8Zl6qR3w1+UmDctzj2xegqqRT8qg4uR9PVM8TX4/NFJKwfXDWVvHe0KvS2OuSY7JSvci3D8FlGUaSIQRjQgRO4yjW9pdJnopTpjdpvzJOf9G+MBx0PFWm9Ipv6tnOKtl+Sk4dHm2cL2nJdFxd3IBtfXmS3sybFZnNeLWrTFwwdAbRLcW0YX4zLvQJ0UfFdBwM2FDbkyk+FoMlIn4sllN3QzrK57ajoT0TVntQ6spr3aUoShkUdwzjyzSnW4bXYRnY1pWLJGsQg9UulCxtE6cU80A8VfWFY5EaNYy7oTr7UsUbw/JK5xP9L8yHNcW1Uic7Rp3iqKFrhnWbdTWeL3TMsM4szOiAyzwqnpqQ34hTq/bgxZpZkk+DHruUOc+oFeGgEVZbUMoz6yLzKMfqkXrPaz1RPxdL8tuRZAxIWrK+011DF9CpJXWwGYIos/Xit9tOlTCwzrONoaeM4RsZsSLWaUXabPeYE6wnBfPK26Tt4Xno35mb1oVnG6qkTNsNQbzYVCFhpg+KZZBOJaahb8gq5Y55xPRk/WcbcUb2bvENsf1j3JYXt4jbKJ5u0mY1u1Be0Qn3qF3S2JAQk/jTLcRz1O/JA6wRcRotc7Wiy++UtpP1gw6prpEUyU96yhgPerhYftgmnlmyGy+0VUqZi+c904ltFOsvXUeOtFF5z7JI91DfHhdOWbUDrzWXwWSKSJiYl0+3VGFORo+UpZrmHPGqMQ1Yfhkf/k23jkp43D0OcT8l/ltauDCnU7xmdGzRJcQ0ZLtE2AYzzb29QNOnfnhkPEKFn4cxcRIeoWgAz7aOPZNTPULvHr01piiKoihTgc4RmhborTFFURRFUWYsOiKkKIqiKFOBeoSmBdoRUhRFUZSpQO6MTaYjdDgDM3PRW2OKoiiKosxYdERIURRFUaYCvTU2LdCOkKIoiqJMBVEKEaOTPF6ZLDPm1lie3SsOjXMKqpGQCHFN0NFBDwk9GA/XLBBvBl0S92xZjiRbEBUpvXIsXSa7O3LE81PoHBQ/hzuYBE/IhmDUKK6Z/91wsrg8ZmWOHeMoGBKHkLs3RTwqdAht2FUiTp/mTpe86KShw4NeErpQHq2fh5XZzeJsoUejan4rGtwu9A4li1ujtTdd/BzzSjqwqaFQ/D58TzeNq2BQvCV0eKwpqIXdEpTtWzrzkJU+hJ7RZHx13rNw5A2Jj+OBusXjDiG6P3htukL6/UnIT/Gg2+8QhxAdP7PTe/DPnQvluEXlrZif1SW+mNmZPeJj4jm+XXOhuHH+1TYfuweyxYOUn+SRtHCH7JhV2C1xykoaFpcIXT70x2xuz4fXa5N0pZOF12C4mVe17gxs78hFqnMESaYgBkdskld0itA3Qn/R0EASEhJjcJm8cmwkMuZkSk32ocreDeOgQRxCdLYcX9CETR0F4lHivsbZQ4iEDRIXqzmEtpps8fXQCeMOJOP1zlJJR+bpM7tn44Kq7YiNmLByVqOUH5YR5nlR+gCqnD3wBKzik+KxH168CbMd3fBHjHizu1jcNCxfC9M7xC3TH7QjwzqKPa3Z4jIxGKLiMKJjisfXejNxzxvHY89INnb3ZEk+ZzqG4Soa2O/Fz/d9MS07hx14ubMc6/qLxb/UO5iM+3YslbiLnybZJ96bjKQRcSIxjemKYnlguDd2FUra0PkUdwiRudndaB92Sp04r3KHeFro9OE+dPPYC7ziVMrJGsv/7YO5yEsegjExgpo9+bKNTiHmJcslywDrCONYsbxFPDYM29N1s+Xz3NwBKXfnrNwiZYD5e3J5LXb3Zkka01dDRvtteKG5UsLNMtnUlinlhGWDeUAv1rLKZnEU8dr002zuz5dySxcNnTcxn1H8Nna7X1wzzBuWP/qy6Ezitdc3FokTZ9dQDp5umo2r578k128ccsFsiEh5yHEMIcXuR/uoE+t6i/F0z1ykO0axqKBD0v/ebcskfPThmC0hGAtHpHwM70jH+Ys3i1+K6cFw/lfedvFKlWa6UTuQiVAsUerA3KJO8WldmL9FXGTBgAlmexAjIbOEh/WW9Z958X/bTkTvcLLkNdsD+n/OyK0Zz9OuHqc4hFhHi50DyLF7UersR0HamPuJrFm6E2vm7pYywfhzu9UUljTOsnnFWSQesl3ZY/kRtoiDivXuje6xusR8ZVngvty+LKcVSSkBlOT3IdcxJOWHbiWvz4KieZ2SpkzHwB4HKl2sx8XSPjIPdtfloSCvH3NyxvKf0A/FckyHEM9FTi/ZI3nCesz2zLMuE/6IScKSaIxJGrC9YXke6kpBkj2AIz4iNJmXMmlmTEdIURRFURRlX/TWmKIoiqJMBTpHaFqgHSFFURRFmQrULD0t0FtjiqIoiqLMWHRESFEURVGmgFgsKq/JHK9MHu0IKYqiKMpUwDk+k7m9pXOEDgt6a0xRFEVRlBnLjOkIrUxtEBfPC92VuGjuJnF0hIMG8XXQMVGc2Y+Tc+vlfXlhj7hu6AgZ9ZmRbhlBYWY/Ov0O7KwvwIk5DeK36PPb5ZWeNIrzF2xFqd0tLhl6MujWoFPk1yfeg8YRFza/WYHSkh4sL24Rvwpfly5aB4s5jMKkAXFf0HdBD0q6dRSRWAKa3OniBrlk1gbx1dBnMjetS1w/KU4f3C1psr3S2SveDzpFjs9sRp03U3w9JDBsRmd7OhwWP370zAXiLaEDh+Ej7oZ0cedYTGF4A1YJR01XFnY8VylukiGPTZwbVntQ/DJx78speXXivKGDg74Oz6gVjSMZyE0eQpnTPXbuoF0cMJv7ClDTlIuebieyrV7x/lx10vPiHAl2J8FoDku60ztCxANk9Yl3hHGi+4TuFaYFXTH8/7LMNtR3Z0i6Z6QP46nOuegYdGJ1cYM4mVZlN+KR1gU4830bsX0wT44ZCtnwscqN4gyhn2VhTidm53VhsCdF4rZmxQ6cWVQjjhJCnwrdO4wz84v5SAdRizdV8oi+I6ZBTXOOOGoIw8H0eaGjAk/UzxWHC8/N8sa0oRuneSRN0obpW5zrRll+r8RtjqMLJ2XVw2nyocTuRlZxP/oDdikXBamD4m/pdyfv98p1Du33shlCCEYM8IeMaBtMFUcL3TOnVNbi/JLtErf+oSRJ27rOLMkT+ns8A0mSL0vS2nB8bjPcPQ50jaZImjFdB3xJ4ndiHl27+Hn8Y/tiJCbEZBv3cRj9GO5LQrMnTTw12Q6vOH3C0bGmxugMwOUYwdanqjDU5sCSjDZ8dt6reGDrEizJasNqVwNOyG3E8qwWzMvrgsUQFncQ/Vj0dUmZDhvxwq4qcfXQZ2NKiIo/xmgP43NzXhHPDOs6fU/xPGFdpLeK+cUyUprWjzPKatDcm46XuiokzViX1iyuljpJ1rcWIdEYlWPnFHSJU4hxSU7xS51nOpF1nlLxg/F4ln2md0N7pviImP+rs8fCwbyga4nbmO8MH89xZsluLMjrRKFjUMor48Qytzq/UeK4frBUPF/MJ+Iyj8LvM2NBaofU49aAC/UDLnErMW173swVL9MKZyMCnXbxSvEzurLKU/rgHrJL/B9vm4vGXpf4dd43ezfaBlKl/rd5neIiolcs1eKXdGPbQD8Y2z2WZx7P7WynvJ4xnxrdU11DDuRU9UpZ5fsPl27BK43l4gViPrIeVndn483dZeJ7YvlmXJraM8Spxfcn5jfAP2IeqwsW/5hDa1GPOMV43oGRJCwo6sC5S7ahrTYLW3eWSNpzP7bXTHu2DwxreVE3XmqtkDrKtpl11rmiF9nWIZiNYcR6LBJXbo+GE2BO8yMUPoJfi+oRmhborTFFURRFmQpohk6YxDwfnSN0WJgxI0KKoiiKoij7oiNCiqIoijIVyK0tnSw91WhHSFEURVGmgFg0itgkbo3p8vnDg3aEFEVRFGUq0BGhaYHOEVIURVEUZcaiI0KKoiiKMhVQppigI0JTzYwZEXq6Z474c87N2yG+m95aF5aUtIlbgm6PJentcAeT8IGsraivzkdpphtlLjcslrC4LfKTPXKe3Px+8XyUOtw4P2eruDQWpbdjKGxF62ia/L9j2CEeE7p8bth6oTgrzj5lE/pHk8RjQp8G/Sm+qBnZyV482zYLoyEzHl+/GCcVNsATtKIi1Y1TiuoR7bXg7tpl4uSgK+jxrQvFpUFXii1rRLaTVIsPn5v1Cl7uLJdtDFfvUDIuW/ImLl62Di39acgqd2NXXxYWpneIT+ic7B2Yt6BZnEl0xtBzc5yrBRdUbccJZ28Txwl9HQxzknXML0KvS2VWL0yJUQkH3Sb086Qnj4p3hNfd2ZcjXg+6TegNOa9gG65Z8TwuXrIeoVgibJYQtniKxFdEd88HyneKy4h+nq6eMYdJXUOOXIu+n1SzXzwxEZ8R8yvbkJk8DE/IhrDPhEe2LRaXCaF7pGPUiaKUQTSNuCS93IFkNDxbigzrKHZ05aBmJBsrs5uRl+qReO1sysOiyhYJN/P56ZYqmKxhCQu30S9FBxPDEnfhzE7rFpcT87d7KEW8U/yMZYIuHYMhKvnPv8yLPb2Zkg5MP7pxZju6xaWyyNEm26ucPeIwcRpHEYoZUD2YjX/uXCj5UZnSCx89ToYwzimoxocWbNnvRafPvi96jei/SUyMYcRjxRcKX0SKLYBaTyb2DGdLPMqz+9A55BDHDD06dDedN2+bXOuR+gV4ZvdsuLKGcEb2bpQ4B/CDykcwz9Up5YXep1tfOUv8M6wn9BTRSdTmS0N2/iDOKNiDR5oWSJ2jd2t7S554eRj//uEkLDqrBosWNOHpZ5Zh53AecnMGxfsyFLGhxpOF1ztLsasrW9KXLiLWW8Jy2OlxYM2cGimrawpq8WJThXhgFhe14fm+2eKToYPG/+9rdww4xe1FN5PbaxcfzkJnu6QRyzrzM15vWQbozqIniOU8PGDFSGDMF7SivFnSiJ4b5hvbiTXFteJZYhqybtUOZErd5L7087gsw9IuLE1rlXPSHUWXFq/H/3eNpODRV5Zja1se3L4khKMGuRb9VVv68qXcNnrScUJhozivTsvbI86l8IhR2hxDQkzOz3aEDieW/Tmn1GM0YsJdjSulfhkNUUlbunWah9OxJL9d2j3m6ZycbnEWPb1zLmZn9UjaDfsskmb8/8aaEimDdFLRMcTyzWNZpsmHizdLmXm9tgzJpoDUTdYXhofp/2DzIjhTfEi1+8SV1dmVKuliTx/FUN+YoysrfQgl+X1I6LUgP2lQ6iw9Sw/ULZZrMc1XZDYjyRaEd8Qq1+V21gsYYzh/xUZpe+hhYp3jsXs8mVIPmtcVosLVh3klHZLmPf0OKcPto6nSPp6yaoe0Y9x3aVkrUpN9mJ3Ve+S+mMQFFJ3ESztCh4MZ0xFSFEVRFEXZF701piiKoihTQCwaQ2wSt8ZiOiJ0WNCOkKIoiqJMBbL8Xc3SU43eGlMURVEUZcaiI0KKoiiKMgXorbHpgXaEFEVRFGUq0Ftj04JjviMU7zGHR4MIGwLwD4cQGgki6vPL37DPgJAphIAhhGAwCJ8hLJ+FRwLSU4+MBhCyBhEyBcfOMxJAJBhDcDgIX2JYzhGIhRAMJyIUSUAgHJJ9DLL0O4jIqB/B4ZAcy3OFYwFERk2yzWAY2zcyakbYGJLr8rzcFkIQQeNYOHkOCWs08d/vAwiHeZxBtgdDQYRiUfiMYURGxj4LRcauHWB8Y1H5fyQaRCQckWuHgkH4TWPhHwtjEKFAEIHEEMKxqKRF2BdAyBBB2G9AJDxW4cKhwHh6MRzRf4eBYQ6bgv++bgAhy9hfhoVpHkmIIhAKIRj4z+fRf183YB47Vzg8Fl+eK/6XaTUWxrFtcq1IRK7D9+PpGvrPMSGmRyQq6cV8iwTGjounY9D473T/d3pym+RXhOkUQCQURjghgFBiUOKOxCjC4YjkZziQgCC38zqxsXOGTQEJRxBj4Yz4o1LWGPawJfDvtB8rAzwnywu3MV14nqBhLO/5Pl5+4uEKJIyVy1DQJGkeiI6pAibC/faFSoGIz4BI1ICoz4BRb2SsbBjDUrYkfSIRCW+8zBHmRTytor6x9IjXmREvy85/0jFeXkO+4LjCgGWD12G5i+dpwBaSvGbZm5gPDEPU/58yzzwIWP+dNz4jIr4oYBhLSylDowEkGiOI+MbqH8tTIML898s+oYSxOhIeHbt+vG7GrxeIjP0/jLHPeZ2AcSx9+Teepwyr/N80li9SXo1jbQDLA/MrnkfBeB7G894YRiwxKvnM+AWjY3WNNVrOGQ3K9RINIYT8/y7Xfj/Aa1pZFuJlPYoI42QMSnoGLUHZn+HkdWOhRLk2P+M+4WCC1F3W4fh143kr+ZAwdm7mlcSDcYr+J73G61YgQdIoFk1A2Dy2fTwv2XYFEqWuxeul3xxCIDAWpng7YDCG/1MOR81j9ScxIukaDSVOqM+J4+WOeT9WFsbSi2GS+mP4T7mU9PVFkWiKjsUncawMxvMy3t7Gj5H88/+n7WRdirc5rE/xdi8et3jZDRmCR2y0hbVxMmJpOV6ZNAmxY3xsra2tDYWFhVMdDEVRFOUoorW1FQUFBe/Juf1+P0pLS9HV1TXpc+Xk5KCxsRFW65hjSXnnHPMdoWg0io6ODqSkpCAhYf9f08cSQ0ND0uljBXY4HFMdHEXZCy2fytFSRqurq1FVVYXExPduPRE7QxwdnCxms1k7QZPkmL81xoL8XvXqpyv8ktEvGmW6ouVTme7k5+e/p50gws6LdmCmB7p8XlEURVGUGYt2hBRFURRFmbFoR+gYwmKx4KabbpK/ijLd0PKpTHe0jM5MjvnJ0oqiKIqiKAdDR4QURVEURZmxaEdIURRFUZQZi3aEFEVRFEWZsWhHSFEURVGUGYt2hKYJL7/8Mj7wgQ8gLy9PDNgPP/zwQff93Oc+J/vcdtttb3nOV199FatXr4bL5YLNZsPs2bNx66237rff7bffjpKSEpF7rVy5EuvWrTsscVKOHaaqfH7ve9+Tc018cT9FORJldCKvvfYajEYjFi9evN9n2oYe3WhHaJowMjKCRYsWSYV6Kx566CGsXbtWKvvbYbfbcc0110gDsWvXLnznO9+R1x133DG+z7333ouvfOUrsmR006ZNEoazzjoLPT09hyVeyrHBVJVPMm/ePHR2do6/2IFSlCNRRuMMDg7iE5/4BE4//fT9PtM29BiAy+eV6QWz5aGHHtpve1tbWyw/Pz+2Y8eOWHFxcezWW299x+e+8MILY5deeun4+xUrVsSuvvrq8feRSCSWl5cXu/nmmycRA+VY5kiWz5tuuim2aNGiSYdZmVkc7jL60Y9+NPad73zngOVR29CjHx0ROooeHnvZZZfh61//uvxCPhCnnnoqrrjiioOeY/PmzXj99ddxyimnyHs+8G/jxo0444wzxvfh83X4/o033ngPYqEcq7wX5TNObW2t/HovKyvDJZdcgpaWlsMefuXY592W0TvvvBMNDQ0y4rMv2oYeG2hH6Cjhpz/9qdyfvvbaaw+6T1FREXJzc/fbzofO0pS6fPlyXH311fj0pz8t2/v6+hCJRJCdnb3X/nzf1dX1HsRCOVZ5L8on4XyLP//5z3jyySfxu9/9Do2NjTjppJPg9Xrfs7goxybvpoyyE37DDTfgr3/9qxy7L9qGHhsc80+fPxbgL45f//rXcv+ZE/wOxl133XXA7a+88gqGh4flvjgrdUVFBT72sY+9hyFWZhLvZfl8//vfP77fwoULpWNUXFyM++67D5/61Kfeg9goxyLvpoyyg/Pxj38c3//+9zFr1qwjFFJlKtCO0FEAvyg48Y6/ViZW0q9+9auy6qGpqektjy8tLZW/CxYsQHd3t6zE4RdNRkYGDAaDbJsI3+fk5LxHsVGONd6r8nkgUlNT5Uuprq7uMMdCOZZ5N2WUo44bNmyQW7ac1B+/vcYpSBwdevrpp3HiiSdqG3oMoLfGjgJ4X3vbtm3YsmXL+ItzJniv+6mnnnpH52JFDgQC8n+z2Yxly5bhueee2+tzvl+1atVhj4dybPJelc8DwZGj+vr6A95iU5TDWUYdDge2b9++1zFcdl9VVSX/5+iktqHHBjoiNE1gAz/xVy7nQrCypaeny68YulYmYjKZ5BcHK2UcLu/Mz8/HzTffLO+5jJTHxr0rXKb8i1/8Yq975Fz2efnll8v8jBUrVsivIy5DvfLKK49ArJWjhakqn1/72tfEDcPbYR0dHTJhlb/A9dau8l6XUU56nj9//l7HZGVliSto4nZtQ49+tCM0TeAQ7Jo1a/aqXIQVjJNFDwWupmHlnfjL5Jvf/KY0CBzKLS8vlwmDV1111fg+H/3oR9Hb24sbb7xRJvdRFsaJqftO/lNmNlNVPtva2qTT43a7kZmZKbciOJeI/1eU97qMHgrahh79JHAN/VQHQlEURVEUZSrQOUKKoiiKosxYtCOkKIqiKMqMRTtCiqIoiqLMWLQjpCiKoijKjEU7QoqiKIqizFi0I6QoiqIoyoxFO0KKoiiKosxYtCOkvKdcccUVuOCCC474dSlQ48MV+fryl7+MoxnGhc/Yei8oKSkRE65y9EDr8XHHHYeUlBQxHbN+1dTU7LWP3+/H1VdfLTbl5ORkfOhDH9rveVg0ePPxEBaLRSSAB4KPpTjppJPEplxYWIif/exn7yictID//Oc/x5HkjjvuwKmnniqPyGD9Hxwc3G+f/v5+XHLJJbIP6xYf4EsztTIz0Y6Q8q6JdzQO9uLDM/nE50O1uh5u2Mh1dnbihz/84ZRc/2hg/fr1+OxnPzulYeCjNfgYDT77ieXm4Ycf3m8ffomzU819kpKScPbZZ6O2tnb8cz4082Dl8P7779/LHHzuuefKOdiJ4LOmwuHw24aR5+CjQNgh4MNhH3/88b0+f/DBB3HmmWdKx4PX5KMdDoW3+0Jmh4bx5jVp32an56WXXpJODg3bzzzzDEKhkFybj3WIc9111+HRRx+VcHN/Pp7kgx/84H7X/+QnPylm5AMxNDQk5+XjTfj0dnZoWKfZ0TgU/vSnP+H666+Xv0eS0dFRKR/f+ta3DroP03znzp2Sfv/617+kDE51PVCmEJqlFeXd0NnZOf667bbbYg6HY69tXq93ysJ25513xpxOZ+xY4FiKy4F4/PHHY9/+9rdjDz74IC33sYceemivz6PRaOz444+PnXTSSbF169bFdu/eHfvsZz8bKyoqig0PD8s+4XB4r7LH1/e///1YcnLyeDnkPvPnz4+dccYZsc2bN8t1MzIyYt/85jffMnyvvfZazGAwxH72s5/FqqurY9/5zndiJpMptn379vF97rrrLrneH/7wB4kDz38onH322bFFixbF1q5dG3vllVdiFRUVsY997GPjnzN+n/vc52J33HFH7Kyzzoqdf/75+52jp6dHrvnSSy/J+8HBQQnf/fffP77Prl27ZJ833nhjv+NvuukmCcO+/M///E8sLS0tFggExrd94xvfiFVVVb1tvF588cVYfn5+LBgMxvLy8iQNJ3L55ZfvF5cvfelLsVNOOWX8/dDQUOzjH/94LCkpKZaTkxP71a9+JZ9zv0PhhRdekDgPDAzstZ15yO3r168f3/bEE0/EEhISYu3t7Yd0buXYQjtCynv6Zb1vg8eG7JprrpHGLDU1NZaVlSWNPBv8K664Qr64ysvL5UtqIvzS4ZeG3W6XYy699NJYb2/vOw7P7bffLl82FotFzvOhD31o/LNIJBL7yU9+EispKYlZrdbYwoUL9/oyITt27Iide+65sZSUFAnriSeeGKurqxs/nl+G/AIwm83y5cIGNk5jY6M0wP/4xz9ip556asxms8k1Xn/99f3CXlhYKJ9fcMEFsV/84hd7xWXLli1yPK/PcCxdunSvRn3fTgS/6Hg+hik3Nzf2xS9+cfzz4uLi2K233jr+nuHjlzmvy+szrR555JFDTgPC42fPni1pzC9NpvmhcqCOUE1NjWzndeMwrTMzM+VaB2Px4sWxT37yk+PvWaYSExNjXV1d49t+97vfSQd+4pf9vnzkIx+R+E5k5cqVsauuumq/feN5fCgdoXf6hXygzgOpra2V88Q7Zs8999wBOwDsOLIzcagdocsuu2y/6z3//PNy7v7+/reMG4/92te+Jv//6le/ulc+HGpH6NOf/rSUz2effVbiduGFF0qZm2xH6I9//KO0PRMJhULS2WVnXJl56K0x5Yjzl7/8BRkZGVi3bh2++MUv4vOf/zwuuuginHDCCdi0aZMMx1922WUyxE14j/+0007DkiVL5MGKfKAhb5V85CMfeUfX5bGcF/GDH/xA5lTwPCeffPJecxruuusu/P73v5dhc95euPTSS+XWAmlvb5f9Oafi+eefl9sFvLUQv7XC24C//OUv5QnqnFtx1lln4bzzztvrFg759re/LU9V5+2TWbNmyUNF4+d488035fbINddcI5/zIZI/+tGP9hvWLygokNtaDMMNN9wgT9I+EP/4xz9w66234n//938lHLztxNssb8X3v/99SVvG4ZxzzpHr8RbOoaTB3/72N3n45I9//GPs2rULP/nJT/Dd735X8vzdEggE5C9vS8XhgzEZhldfffWAxzBcTD+mZZw33nhD4j7xYZjMI94CYn4fDB53xhln7LWNx3H7ZODxvB3Gp5bH4XUYN5aDQ4EPruUcuNWrV48/EZ0P/jSbzfvNK2O8+dmhwn33fXBo/P1bnYfp+cADD0jdIfx73333vaM5OF6vV8oM69Lpp58ucbvzzjsRiUQwWRh23hadCG878in17yR9lGOIqe6JKTNvRIgjCHF4u4KjPPwFGYe3NSYO4//whz+MnXnmmXudt7W1VfbhaMGhhocjMfz1zyH3ffH7/TIEv+/ozKc+9anxWxW8hVJaWirD/QeCtwB+/OMf77XtuOOOi33hC1/Ya7Tg//7v/8Y/37lzp2zjrQvCa51zzjl7neOjH/3oXnHhr+I///nPsUPhl7/8ZWzWrFkHDfOBRoR46ycOR+q4LT6y9XZpwNG8v//973ttY/6tWrXqXY8I8VoczbjoootkJIKjN7fccovsu2+5iPP5z38+NmfOnL22feYzn9lv/5GRETnPviOQE+Ftpn3jxFEujihOZkSIZYV5sy8c6eJtqX050CgKb5sxD1kf4vztb3+T0b99YVm8/vrrD3lE6H3ve5/cgpxIvLxyNOvll1+Wuht//fWvf5V9fv/738styInMmzdvr3L/diNCHPXkdZqbm/faZ8mSJeMjQky/idffd9+DjQi903RXjn10REg54ixcuHD8/1xVwgmmE0cp4r86e3p65O/WrVvxwgsvyOqX+IsTV0l9ff0hX/d973ufTPwsKyuTESeOXsRHnerq6uT/3GfidThCFL8GRxi4guZAoy/8FcwJqfxlPhG+58jIweKfm5u7V1y578qVK/faf9WqVXu9/8pXvoJPf/rTMnpwyy23vGUacKTN5/NJnD/zmc/goYceetvJwRPDZ7fbZSJvPHxvlQacrMuwcBRmYhpyROud5NO+8FqcjLxnzx751c6JziwP73//+2X0ZF8Y37///e97jQYdCpxIPTHcHM06XHzuc5/b69yHA44acqIv04IjhHFycnIQDAb3Wy3FUVR+dqhw331XmsXf8zOOZLE8xF8c/SR//OMfZYSNoyzxV3V19V6TpplvY/3e/8BJ3+80TSdenxPpDzVe8fIch3WCo57vJH2UYwfjVAdAmXns+yXKVTYTt/F9fNifcEidq4p++tOf7neueEfiUOByY956e/HFF/H000/LLRyuguEtpviw/WOPPYb8/Py9juMtGGKz2XA4eKu4HgoM88c//nEJ6xNPPIGbbroJ99xzDy688ML99uWSZ94GfPbZZ2WFzBe+8AVZ/cPbfQe7nXag/ImH763SIJ6Gf/jDH/brzLHDOxm4zJtfdh6PR77kMzMz5RoTbyvF4W0Zdmo/8YlP7LWdX3K8HXuwL3Z+kU5c7cVO11t1CN7JlyZvx/J26OH4QmYHgreU2allWS4tLd0vrZiHzz33nCybJywD7Ojt26l+K7gvb+OygxIvEyxDVVVVSEtLk/cVFRV7HbN9+3a5Bc1wxdOPME5c0r579275EcP827Fjx17HMu3j12HHnf9n3SwqKpJtzHt2huO3s3n+idd4J/FiJ5G3T5lWhLd5Wcb3LbfKzEBHhJRpz9KlS+UXJp03bHgnvjhi8U7gr1OOpNCHwjkwXHbNRnDu3LnS4eGXxb7XYGciPlLyyiuvHPCXK0dN+EX62muv7bWd73nuQ2XOnDn7zQ/hMul94dwizmFih47Lojl/4mCw88KO5G9+8xv5guLcFH5hvRveKg04ksc0aGho2C8N9/2yfrc4nU75EuV8J37hnn/++fvtwxEJjk5wv32/ABnviZ0PfrEz75hHLBsTwxz/kuVx7FRMhMe9k04F56RMPPe+X8hxDuULmUvn//rXv8qoFzv3nNfCF0fC4mnE0TCOHHK0iOe/8sor5XrHH3/8+Hk4CsrOR/zY+MgKO5qEnW3ONeK5WP/uvfdemQfH8x4Mpv2KFSuks8J5PfEX39N9xM8J5/wx/zjiyrxkZ35ix4jxuvzyy0VvwDjw+gwHR5LiPx4OBuPDeDB+hHnO9/F5bqxjXF7PEVJ2jFlHObp28cUXH/KoknKMMdX35pSZN0do31Uf+85T2XeuCFfQ8P79hz/8YVk+zRVKTz75pKwy4xyjQw3Po48+Gvv1r38t8zeamppkPgBXEcVXI3EJt8vlkvk3vMbGjRtjv/nNb8bn4/T19cnnH/zgB2Wlz549e2TZNJdzE8aBc5Duuece2calxpxfwv0ONn+E8xe4jfMZCOdFMUw///nP5bjf/va3ssIlHpfR0dHY1VdfLfszDq+++qrMyznQ3I94OnBuBlfd1NfXy/wfrgZjXA6U9geao8Nr8zyHkgZcxcXzM505f2vbtm2xP/3pTzJX6WBweTvThC9enyub+P+Jcz7uu+8+iTPj8PDDD0u4GYYDraDiqquJq/XixJfPc54Q56CwDLFcHcryeaPRKKv3OJeLc2r2XT7vdrslzI899pjEgWWA7znf7a3gSkjOe3nzzTclLysrK/daPh+fl8NzfeADH5DVgjz/gV7xPCI+n0/mpnH5O+e+ccXVvmFhXTzQeVhO42zdulXm9HEFIFdDcm7WweDcLZYNagYOxE9/+lOZVxWfX3bjjTfGsrOzpXxdd911spr07ZbPr1ixInbDDTe8ZZoyf94ufZhfTGeuemSdvfLKK6dU96FMLdoRUqZ9R4jwC5eNOTsF/KLl8uwvf/nLsjz8UMNDTwuvzy+H+NL1e++9d/xznos+JC755hcdvyTpbon7WeJfDPwiZePMSct02/DLOb6k+3vf+558YfD4gy2ff6uOUHx5b0FBgYSRX34Tl8/zy+biiy8eXw7PCdr8AuEX34FgGnKpNxt7Tiilj4fLkQ+W9m/XEXq7NIhP1uXSdYaPaX3yySe/5bLk+KTWfV8sO3HYsWKaMF05cZodugMteWenhmnDvDgQ7Dy+//3vl7SlQ4hLu7l0+u1gR4wTbBknTvxlh2ciTJ8DxYFfym/FoXwhM48OdO6ZBifusyxOnHStKIeDBP4z1aNSinK4oc2ay4oPpNdXFGX6s3nzZplTxFttnB/EeVa8tctbXtRvKMrhQucIKccsbDy5Qucb3/jGVAdFUZR3AT1CixYtknl9XJXI+WnaCVIONzoipByTUMgWX+lDsZw2noqiKMqB0I6QoiiKoigzFr01piiKoijKjEU7QoqiKIqizFi0I6QoyhGFYkxK8fjSVX2Kokw12hFSlCnm9ttvl84Bn65Oo/DEx0D4/X4xCfN5bFwBx0cm7Pu4hwNx//33y6MMeE4+x+3xxx/f63NODeQjRviIEpqnuSqHht+3g8uXafqmhZuGZGoK3kl8CB+b8I9//ONtr6UoinIk0I6QokwhfGwBH1nARwzwOWhcKnzWWWeNPwaCj9F49NFHpWPD54Pxwa58pMZb8frrr+NjH/uYPJKALpYLLrhAXhMfYcBHjPCRG7///e/lkR58VAmvy47XwWhsbMS5556LNWvWyCML6Gniw1+feuqpQ44P4aMv3s0zohRFUd4TDouWUVGUdwUfGcBHZsShEZm26Jtvvjk2ODgoJuX7779//HM+4oHVlo/iOBgf+chHYueee+5e22iXvuqqq8YN2nxkAR/jEYfX4mMU7r777oOel4/xoFV5Ih/96EfFvn0o8TmQTZpmbUVRlKlER4QUZYrgwy35QEzelorDh0ryPR+Mys/4cNOJn/N2F5/Gzc/j8DYUn0gfh59NPIZwVCZ+DEd2+GDKifvwQZ28jTXxvHxa+BVXXHHI5327+CiKokxHtCOkKFNEX18fIpGIPLV9Inwff6I4n/5NIeSBPo9TXl6+lzCSnx3snPHP49ve6rzscHEO0dudd2hoSJ5e/nbxURRFmY4YpzoAiqJMjueee+49Oe9dd931npxXURRlOqEjQooyRXAUx2Aw7LcKjO9zcnLkxdtN+y4xj39+MPjZwc4Z/zy+7XCc1+FwyMqzt4uPoijKdEQ7QooyRfC217Jly/Ya0YlGo/J+1apV8pnJZNrr85qaGrS0tMjnB4Of7TtK9Mwzz4wfU1paKh2Tifvw9hZXj03mvG8XH0VRlGnJlE7VVpQZzj333COrtf785z/HqqurY5/97Gdjqampsa6uLvn8c5/7XKyoqCj2/PPPxzZs2BBbtWqVvCZy2mmnxX7729+Ov3/ttddiRqMx9otf/EJWmd10002y+mz79u3j+9xyyy1ynUceeSS2bdu22Pnnnx8rLS2N+Xy+8X0uu+yy2A033DD+vqGhIZaUlBT7+te/Lue9/fbbYwaDIfbkk08ecnzi6KoxRVGmC9oRUpQphp0YdnbMZrMsP1+7du34Z+yYfOELX4ilpaVJJ+TCCy+MdXZ27nV8cXGxdHYmct9998VmzZol5+SS98cee2yvz7mE/rvf/W4sOztbOi6nn356rKamZq99TjnllNjll1++Xwdm8eLFct6ysrLYnXfe+Y7iM/E82hFSFGU6oE+fVxTliENDNcWMAwMD+62KUxRFOZLoqjFFUY4o8+bNQ0NDw1QHQ1EURdARIUVRjijNzc0iiiRlZWUiXVQURZkqtCOkKIqiKMqMRX+KKYqiKIoyY9GOkKIoiqIoMxbtCCmKoiiKMmPRjpCiKIqiKDMW7QgpiqIoijJj0Y6QoiiKoigzFu0IKYqiKIoyY9GOkKIoiqIomKn8f6/QQs2Mw/ExAAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1399,9 +1458,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\akeeste\\AppData\\Local\\Temp\\ipykernel_31724\\3234039020.py:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0. 0. 0. ... 1. 1. 1.]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3234039020.py:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0. 0. 0. ... 1. 1. 1.]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
       "  ADCP_points.loc[:,[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)\n",
-      "C:\\Users\\akeeste\\AppData\\Local\\Temp\\ipykernel_31724\\3234039020.py:9: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0.         0.         0.         ... 1.48387099 1.48387099 1.48387099]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3234039020.py:9: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0.         0.         0.         ... 1.48387099 1.48387099 1.48387099]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
       "  ADCP_ideal_points.loc[:,[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)\n"
      ]
     }
@@ -1497,7 +1556,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x440 with 2 Axes>"
       ]
@@ -1555,7 +1614,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x241a4281280>"
+       "<matplotlib.legend.Legend at 0x1ddc2782960>"
       ]
      },
      "execution_count": 14,
@@ -1564,7 +1623,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1637,7 +1696,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x440 with 1 Axes>"
       ]
@@ -1710,7 +1769,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\akeeste\\AppData\\Local\\Temp\\ipykernel_31724\\3546222366.py:4: SettingWithCopyWarning: \n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\3546222366.py:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -1738,7 +1797,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\akeeste\\AppData\\Local\\Temp\\ipykernel_31724\\14157162.py:29: SettingWithCopyWarning: \n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_37088\\14157162.py:29: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -1861,7 +1920,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAG/CAYAAADSENFhAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAiU5JREFUeJzt3QeYE9XXBvCz9I70Ll0QQZoFUJqiCChFRZSuFEVFEP4qRZogoAjYkSKIIlUUkaIi1QJIbwIK0rv0XnbzPe/1m5jsZnczs5lMe38+85Bk2+yYTeade869MT6fzydEREREREQmSGXGNyUiIiIiIgIGDiIiIiIiMg0DBxERERERmYaBg4iIiIiITMPAQUREREREpmHgICIiIiIi0zBwEBERERGRaRg4iIiIiIjINAwcRERERERkGgYOIiIiIiIyjWMCx5tvvik1atSQTJkyyU033WT17hARERERmWrMmDFy++23S7Zs2dRWvXp1WbhwYZJfM2vWLClbtqxkyJBBKlSoIAsWLBCrOSZwXLt2TZo3by5dunSxeleIiIiIiExXuHBhGT58uKxbt07Wrl0r9913nzRp0kS2bdsW8vN/++03eeqpp6RDhw6yYcMGadq0qdq2bt0qVorx+Xw+cZDPPvtMunfvLmfOnLF6V4iIiIiIoipnzpwyYsQIFSria9GihVy8eFHmzZvnf6xatWpSqVIl+eSTT8QqacTFrl69qjZNXFycnDp1SnLlyiUxMTGW7hsRERERJYRr4efPn5eCBQtKqlT2K8a5cuWKqrxJye8XE+88NH369GpLSmxsrCqXQqBAaVUoK1eulB49egQ9Vr9+fZkzZ45YydWBY9iwYTJo0CCrd4OIiIiIdDpw4IAqKbJb2CheNIscPR5r+HtkyZJFLly4EPTYgAEDZODAgSE/f8uWLSpg4Gfja7/55hspV65cyM89evSo5MuXL+gx3Mfjng0cvXr1krfeeivJz9m+fbtqfDGid+/eQSnv7NmzcvPNN6snMBpvKLIqfPKBuEGukqei8nPqFdxp+s8omv4fcYPrvtRR+TmpY+LEDQ5fyyFucPp6pqj8nFQxjqosTtSVWPPf0g9eis5z62psdP7mzXbwlDsmubl+KmPUftbWZ16Sc+fOSZEiRSRr1qxiNxjZQNjYt66YZMuqf/Tl3Pk4KVp1b4Jz0aRGN8qUKSMbN25U57FfffWVtGvXTpYvX55o6LAjSwNHz549pX379kl+TokSJQx//8SGp7ROf4qsVBkziBucPlzQfzt3qZOm/Zz0WdKa9r2Lpz/hqkHMNCYGjuCQYb+heyPSXzPvuRVN+eV6VMKHWwJH4Lvd5VhzngNpY9JJNEKTO165RFJfccf7YupM//2NXDtpbvgIPD+zc/l7lqwxatMrTmJ0n4umS5dOSpUqpW5XrVpV1qxZI++9956MHTs2wefmz59fjh07FvQY7uNxK1n6N50nTx61EdnVP7tymR48zAka5JXRDK/IkfZSVEc9nC5j6uumBg8njs5QZKTLdTkqwcPuYn1xEusz9nUphZ7kwB7lQCi9Wrx4sZpgSbNo0aJEez6ixTF/4fv371cN3/gXTTMYWgIkPtSzEXk1eDBk6MOg4WwMHu4JHgwZzub14BEnPrUZ+Tq97QENGjRQLQFopJ86daosW7ZMfvjhB/Xxtm3bSqFChVTfMnTr1k1q164tI0eOlEaNGsn06dPVdLrjxo0TKznmr71///4yefJk//3KlSurf5cuXSp16tSxcM/Ii8HDDuGDQSN8DBnuowUPYPgIP3jYIXwwaLgzeHg5fJjp+PHjKlQcOXJEsmfPrhYBRNh44IEH1MdxIT5wJi8sko1Q8vrrr0ufPn2kdOnSaoaq8uXLW/hbOHAdjpRAExL+Z6Hphj0ckVf8g5HiRUaDx0OFtxv6Oi8GDaNN414NGgev5RQvMho83NLDoZeR4HHgovGmcS8GjQMn3TGBg14pCR57n/2frc/XtH07vLOw4abxgmUO2vJ3M5P3/vqJHFhu5cWQkRJeDRpex3Ir+5VbeTFkkDfKrWJ9PrUZ+Tov4isBkY3LrRg0wseQQRqWW1lfbsWgQW4vt4pWD4db8BWByIajHgwa4WPQoKRw1CO6ox4MGuSVUQ8Eh1gGjrDxlYHIJqMeDBnhY8ggvTjqYd6oB0MGeTl4UHj4KkFk8agHg0b4GDQoEjjqEZlRDwYN8nK5FUuq9OGrBZFFwaN4SQaNcDFokJnB4+wN553sWBk8GDQo0pw46sGmcX34qkERE3MjxupdsL3MJc/6b++4XMB/u2zGIxbtkTOkjYm1ehcchccrecevZfXfjvPpn9rSq85fT2/1LjhKbCyfW8lJveu/kca9fV8Wp8BlMCOXwuLEmxg4iKIcNELRwgeDR9LrcPBEmiIZNEify3FpJX3qG+r2VY5yUASDBrkfXzGILAwa8TF4JI3Bg4xi0EhZ0IhPCx7A8EFeDBqxBmepimUPBxFZETJCYblV+CuPM3xQYhgyIhsyEsNRD/JKyAgU6/t3M/J1XsRXByIbBY1QOOqRNI56UHwMGtEJGvExeJAXgoaGPRz68FWByIYhIxQGj6Rx1IMYNKIfMkJhuZW3uTlkkHF8JSCyedCIj+VWyeOoh3cwZNgnaITCUQ/v8FrQiJMYiZUYQ1/nRXwFIHJI0AiFox5JY/BwLwYNeweN+Bg83MtrQUMT5/t3M/J1XsS/fCKHhYxQGDySxnIr92DQcE7ICIXlVu7g1ZARKNbgCEcsRziIyGlBIz6WWyWPox7Ow5Dh/KARCkc9nIdBg4ziXzmRC4JGKBz1SBqDh/0xaLgzaMTH4GF/DBoJcYRDH/51E7koZITC4JE0llvZD4OG+0NGKCy3sheGjKTF+WLUZuTrvIh/0eRpbg4a8bHcKnkc9bAOQ4Z3g0YoHPWwDoNGeDjCoQ//kslzvBQyEsNRj6Rx1CN6GDSMcWPICIXBIzoYMshs/Asmz2DQSIijHsnjqIc5GDSM8UrQiI/lVuZg0DAuVlKpTf/XeRP/asn1GDTCw1GPpDF4pBxDhnFeDRqhcNQj5Rg0Us5nsIfDxx4OIvdgyDCOwSNpLLfSj0HDGIaMpDF46MOQEVns4dCHf6XkKgwakcNyq+Rx1CNpDBrGMGjow3KrpDFokB3wL5NcgUHDXBz1SBqDx38YMoxj0Eg5jnr8h0HDXLG+VGrT/3XiSfyLJMdiyIg+Bo+kebncikHDGIYMc3g1eDBkRE+cxEicgabxOPFm4vDWXyK5AoOG9VhulTyvjHowaBjDoBEdXim3YtCIPvZw6OPevz5yFYYM++Koh/dGPRgyjGHIsJbbRj0YMshJ3PFXR67FoOEcDB7uH/Vg0DCGQcNenB48GDSc3sPhEy9y5l8buR6DhnOx3Mp9wYNBwxgGDXtzWrkVg4Ydezj0l0fFsaSKyFoMGe7DUQ/nllsxZBjDkOFMdh31YMiwrziDK43HsWmcyBoMGu7H4OGcUQ8GDWMYNNzBLsGDQYPchoGDLMOg4T0st7Jv8GDQMIZBw52sKrdi0HAO9nDow8BBUcWQQRqOelhfbsWQYQxDhreYPerBkJFQt27dxAklVVyHI3z6j5QF9u7dKx06dJDixYtLxowZpWTJkjJgwAC5du2a1btGOoIGwwYlFjwCRz4odPgIDCCRCBoMG8aCBsOGt4OHtkUqaDBsJLRq1Sr57LPPxO5ifTGGNy9yxAjHjh07JC4uTsaOHSulSpWSrVu3SqdOneTixYvyzjvvWL17lAgGDNKD5Vbmj3owZOjHgEGRHPVgwEjauXPnpHr16lbvBnk1cDz00ENq05QoUUJ27twpY8aMYeCwIQYNSimWW0Wu14MhwxgGDYpk8GDQSJ4vLk6yZ88uThFrcJaqWI+WVDkicIRy9uxZyZkzZ5Kfc/XqVbUFJmcyD4MGmRU8bsl41OpdcVzwYNAwhkGDItlkzqARvr3vDPHffvHFF+XDDz8UO4vzpVKb/q/ziRc5MnDs2rVLPvjgg2RHN4YNGyaDBg2K2n553flDPMFJTtZC5/23s6T+LwxT0s7E/vemffYG38CTc+Y6j5EeDBn6HD7rnKvQVostdUn9u7tFX6t3xbbOnz8v2bJl898vWrSovPnmm7YPHBzhcFDTeK9evSQmJibJDf0bgQ4dOqTKq5o3b676OJLSu3dvNRKibQcOHDD5NyJKPGgEhg04eu2/F1gKX/Y0l9RGlFKxEqM2StzFG+mCttwZLkrWDFfVRslD0GDYSNyECROCwgbs3r3bsv0hl45w9OzZU9q3b5/k56BfQ3P48GGpW7eu1KhRQ8aNG5fs90+fPr3aiKwSP2TEp4WO/OlY7qeXFjo44kF6MWQkD+EiOVroOH+F77OkDyb9yZIlS9BjTz31lEydOlWcIu7/Z6oy8nVeZGngyJMnj9rCgZENhI2qVavKpEmTJFUqR8zoSx6UXMgIhcHDuMDRDoYPSgxDRmRCRigMHqTHF198IW3btg16bNu2bVKuXDlxEuPrcKQSL3JEDwfCRp06dVRdH/o2Tpw44f9Y/vz5Ld03opQEjfgCy6wYPvTjqAfFx6BhXtCIL7DMiuGDQmnTpo1MmTLFf//xxx+XWbNmibdWGk8lXuSIwLFo0SLVKI6tcOHCQR/zebTbn9wVNELhqIdxDB7EoBG9oBEKRz0ovlatWgWVTG3ZskXKly9v6T5R9DgiZqHPA8Ei1EZkp0Zws4IHG8yNYYO597ARPHlaA3g0sMGcoHPnzkFh4/Lly44PG3ESY3jzIkcEDiK7hYxoBI3EggfDh/HgwfDh7pDBoJG4wJmmrKAFD4YP71mwYIGMHz/ef//KlSuSIUMGcTqtpMrIpgeWeLjzzjsla9askjdvXmnatKla/Dopn332WYJZX60+5o4oqSKymhUBIykstzKO5VbuwYCRPKsCRlJYbuUdCBeNGjXy3z9z5oxrZg81vg5HKl2fv3z5cnnhhRdU6Lhx44b06dNHHnzwQfnjjz8kc+bMiX4dphsODCYIHVZi4CByUNCIj8HDOAYP52LQcGbQiI/Bw/1y5Mjhv7169WrJnt09i0bG+WLUZuTr9Pj+++8TjF5gpGPdunVSq1atRL8OAcNOEysxcBA5LGSEwtmtjOO0us7AkOGOkBEKg4c7vf/++2qEAxo0aCB33XWX1bvkCmfPnlX/5syZM8nPu3DhgprdNS4uTqpUqSJDhw6V2267TazCwEHk4KARCkc9jOOoh/0waLg3aMTHaXXdYd++fWph59mzZ/sfmz9/vrhNnMGSqrj//5pz587pXqwa4aF79+5yzz33JNl0X6ZMGZk4caLcfvvtKqBgSQksmo31TuLP9hotDBzkeW4JGvExeBjH4GE9Bg3vBI1QOOrhTMuWLZP69evLtWvX/I/9+eeflvcPmCHOl0ptRr4OihQpIoEGDBggAwcOlKSgl2Pr1q3yyy+/JPl51atXV5sGYePWW2+VsWPHyuDBg8UKDBzkSW4NGaGw3Mo4lltFF0OGt0NGKAwezoGlCl566aWgsIFRjtKlS4sbGZ0ZL/b/v+bAgQOqsVuT3OjGiy++KPPmzZMVK1boHqVImzatVK5cWa1nZxUGjmRoa324MZ17kZeCRigc9TCOox7mYdBInteCRnwMHvb39ddfq8X8NFh349FHH7V0n+wsW7ZsQYEjqfPQrl27yjfffKNGkIoXL677Z8XGxqr/Nw0bNhSrMHAkI1Wqf4e+qlWrJr/++qv/PjmL14NGfAwexjF4RA6DRvK8HjTiY5+HfT3++OP+2z/99JPcf//94mYpLakKF8qoEN6+/fZbtRbH0aNH1eOY8Stjxozqdtu2baVQoUJqzQ5444031HlrqVKl1FTEI0aMUL01HTt2FKswcIRp1apVkjp1ann33XelW7duVu8OhYEhI3kstzKO5VbGMWgkjSEjPBz1sI8PP/zQf3vOnDmuDxsQa/C1LFbn548ZM0b9W6dOnaDHJ02aJO3bt1e39+/fH3RB/PTp09KpUycVTjA1cdWqVeW3336TcuXKiVUYOJJx9epVKVGihBw6dEjdx+wAWGjFypRISWPQMIajHsZx1CN5DBnJY9AwhsHDeij50TRp0kS8IFojHL7/L+1PCkqtAo0ePVptdsL6oGSkS5dODh48KIMGDfI/htRI9gwaDBuRCR6BIx+kL3gEjnyQ8cZKrwUNho3IBA9to+gJnP4WfRxEoTBwhOnVV1+1ehcoEQwa5gYPhg/9GDwYNMLBoGEeBg9rejeaNWsmXhHrS2V48yKWVOmYUiwQpn3D6AdZgwEjulhuZYzX+jwYMJLHgBFdLLcy1/fff++/PW7cOPESn8RInIHXPJ9HXye9GbN0QLCYMGGCWjAlEB6j6ONohrU46mGcm0c9OJqRPI5mWIsjHuZo0KCBZ8vNOcKhjzd/a51zF/ft21f++uuvRBukyHwMGvbD4GGMm4IHg0byGDTshX0ekYNpWjWjRo2ydF/I/lhSlQzMcYxl4M+fPy9PP/205MqVSz0eFxdn9a65HgOGM7DcylvlVgwYyWPAcAaWW6VM06ZN/bdffvll8Zo4X4zajHydFzFwhKFz585W74KnMGg4E9f0cPe0ugwayWPQcCYGD/1wIVYzffp08aJYSaU2I1/nRQwcKYBRDq48HjkMGu7BUQ/3BA8GjeQxaLgDg0d49uzZI/379/ffb9GihXgRRzj04dmyTtOmTQs5OwOlrDeDYcOd2OeRsj4Pq3o9tN4Mho3kezMYNtyHfR6Ju3DhgloMWYMVronCwcChU/Pmzf23GzVqZOm+OBlDhrdwdivjohk8GDKSx5DhLQwe/7ly5YpkzZrVf79Xr15SpEgR8ao4SWV48yKWVOmUOnVqq3fB0RgyiOVW9iu3YshIHkOGt3m93Grfvn1SrFgx//369evL0KFDxctifTFqM/J1XuTNmEVRxxENio8jHtaPeHBEI3kc0SCvl1stX748KGxgxk6UlMfEePu1Q+vhMLJ5EUc4DI5yYH0O4IrjiWPAoHBwdqvoTqvLgJE8BgwKhxdGPT755BPp0qWL//7EiRNV4CDSiyMcBixZssR/+4svvrB0X+yIoxlkFEc9zBv14GhG8jiaQUa4dcQDwSIwbKxatYphI4DPl0riDGw+j640zhEOA2rVquW/3bFjR+nQoYOl+2MXac6nloxlz1i9G+QC7PMwRgsdZ67/N+LBkJE8hozwXbju3qv5KeWWEY+9e/dK8eLFgx47ePCgFCpUyLJ9siOjF3FiPfqazMBBEXV5x03q37SleaKYnGxprvhv50vL4xXuC/SBKzkt3RcnOHcjg9W7YHtnrmX0304V47N0X+zuRlzwFdlV9YdZti9kjuvXr8v8+fPVQscnTpwI+tilS5ckY8b//l7oX3E+Y2tqxHn05cab4zoRENi3ganiKNj1v7L5N0resevZ1EbJK5LhlNqIjAaNwLBBSQeN+GGD3OXIkSPSpEkTdU7TrFmzoLDx+OOPqwWOGTYoEvhKEoEFAFeuXGnpvtgdg0f4GDzCx+BBejBohI9Bw/3OnDkjuXPnloIFC8rcuXODPla1alXZvn27zJo1y/MzUSXFSP9G3P9vXuTN3zoCKlas6L/NFcfDw+ChP3gwfIQfPBg+KLGQwaARfshg0HA3n8+nGr9z5MghJ0+eDPrY559/rlYSX7t2rZQtW9ayfXSKOIkxvHkRX1kMKlGihP/222+/bem+OA3LrfRh8AgfgwcBQ0b4GDK8Az0aqVKlks8++yxoEhwEDwSRNm3aSObMmS3dRycu/Gdk8yI2jRuEYcY777xT1qxZo+7jqkCWLFms3i3H0UIHm8yTp4UONpgnLzB0sMncGxgw9GHI8A6ECZRPnToVfDHm6NGjki9fPsv2i7yFrzgp8Mwzz/hvcz2OlOGIR/hYbqUPRz3cjaMZ4WPZlPeg6RujGoFhY8GCBSqEMGykDHs49OEIRwrUrVvXf/ull14KWiCHjAkMHRz1SB5HPcKnhQ6OeLgDQ0b4GDC8q0yZMkH3r127JmnTprVsf9xE9WMYmRZXvFlS5ZhXocaNG8vNN98sGTJkkAIFCqhaw8OHD1u6T7fccov/9o0bNyzdFzfiqEf4OOIRPjaZOxebwPXhaIa3de/eXXbt2qVu49wpNjaWYSOCfAYbxn0MHPYfTZg5c6bs3LlTZs+eLbt371ZzRFvdx1G6dGn/fQxRUuQxeISP5Vb6MHg4A0NG+Fg2RfDTTz/Je++9579/8eJFVVpFZBXHPPtefvllqVatmhQtWlRq1KghvXr1klWrVqnVMa3Uo0cP/+2//vrL0n1xO85upQ+DR/gYPOyJQSN8DBmkwRoaDzzwgP/+uXPnGDZMgHIqo5sXObKHA81PX375pQoeVg8PPvjgg/7bixYtCiqzIvNwdqvwsc8jfJzdynoMGPowZFCgzZs3B60ThguzWbNmtXSf3MpoA3icR5vGHfVbv/baa2qO6Fy5csn+/fvl22+/TfLzr169qpJ94BZpxYsX998eMWJExL8/JY0jHuFjuZU+HPWILo5mhI9lUxTK+PHjg8IGVgq/++67Ld0nN+MIhz6WvlqhLAp9EEltO3bs8H/+K6+8Ihs2bJAff/xRUqdOLW3btk2yb2LYsGGSPXt2/1akSJGI/w7Yx5tuuknd3rdvX8S/P4WH5Vb6MHiEj03m5mETuD4MGRQKZp7COU7nzp39j02ZMsXyPlci25RU9ezZU9q3bx/2it5YuAYbypZuvfVWFSAwXFi9evWQX9u7d++gHguMcJgROvr27avCEJw9e1b94ZN1WG4VvsDQwZKr5HFq3chgwNCHIYMSs2zZsqAp+gEzU5UsWdKyffIKbdYpI1/nRZYGjjx58qjN6GI2WtlUYtKnT682sz355JP+wLFw4UJ1n6zH4KEPez3Cx+BhDING+BgyKLlzoPvvv18FDk3t2rVl6dKlqvKCzGe0PCqOJVX2tXr1avnwww9l48aNqmxpyZIl8tRTT6kEn9joRjQVLlzYf7t///6W7gslxHIrfVhuFT6WWyWPZVP6sGyKkoIy8o8++kiVlQeGDUyDi/sMG9HDHg4XzlKVKVMm+frrr2XAgAFqLmks/PfQQw/J66+/HpURjHBky5ZNlWxxalx746hH+FhupQ9HPYIxYOjDkEFJOXPmjDz88MPy66+/Bj2OEu5jx47Z5lyIyNGBo0KFCmpUw87efPNN6dq1q7+BK126dFbvEiWBwUMflluFz+vBg0EjfAwZlJzz589L/fr1ZeXKlQk+Nm/ePGnUqJEl+0UsqdKLr3YR0qlTp6Cp6cgZWG6lD8utwuelciuWTenDsilKzokTJ6ROnTqqeiIwbKDi47vvvlOlVQwb1mJJlT58xYuQwOHMF1980dJ9IWMYPMLHNT30cWvwYMgIH9fOoHDNnz9f8ubNK8uXL/c/hlJylE6hrBylVWQ9X8BMVXo2n3iTI0qqnKJmzZry888/W70blEIst9KH5VbeWsmcAUMfBgwK16lTp6RUqVJy+vTpoB6NLVu2mDKlP6UMS6r04SthBH388cf+26HqLclZWG6lD0c83D3qwdEMfTiaQeFasGCBml0qV65cQWHj22+/Vc3iDBvkBnw1jKDy5cv7bz/33HOW7gtFFoNH+Fhu5a7gwaARPpZNUbhraMycOVPy5cungkb8XgxUS9y4cUMaN25s2T5S8tjDoQ9LqiIMw59YbXzz5s1W7wqZgOVW+rDcypnlVgwY+jBgUDgWLVokDz74YKIfb9mypUyYMEEyZuTfnxOwpEofvkpGGF4sNIcPH7Z0X8g8LLfShyMezhj14GiGPhzNoHAtXrw4ZNgoU6aMbNq0Sc069eWXXzJsOAhHOPThK2WEPfbYY/7bL7/8sqX7QtHB4BE+llvZM3gwaISPZVOkF2aXqlevnv8+Fi5GJQRCxo4dO+T222+3dP+IooGvmBGGekwNajTJOxg89GHwsHZND66doQ9DBhmBXoz8+fP773/xxReycOFCtb4GOZvPF2N48yK+eppg+PDh/tuXLl2ydF8o+lhupQ+Dhz4pDR4MGfowaFBKtG3b1n+7e/fu0rp1a0v3hyLHyBoccf+/eRFfRU3Qo0cP/+3Bgwdbui9kLQaP8LHcyrxRD45m6MOyKYqE8ePHy7Rp0/z3R44caen+UGSxh0MfvpqaIG3atCFHO8i7OOqhD4OHPokFD4YMfRgyKFL69esnnTt39t/HzJWpUvG5Rd7FZ79JAl9oMOc2kYbBI3wMHsaCB4OGPgwaFClHjhxRvZxDhgzxP7Zq1SqpUKGCpftFzu3hGDZsmNx5552SNWtWyZs3rzRt2lR27tyZ7NfNmjVLypYtKxkyZFDPPywwaSWuw2ESjGyMGzdO3f7000+lU6dOVu8S2UxqYRBNStmM/00rfdX336ghJa9Y5pOy6VQhq3fDEc5c+S+YZUl/1dJ9IefCjFPPPvusKqOKH0ACm8bJPaK1Dsfy5cvlhRdeUKEDkxD06dNHTbH8xx9/SObMmUN+zW+//SZPPfWUCisPP/ywTJ06VQWV9evXBy1SHU0xPvyVeMS5c+f8C/NFY4aIwBmrvHCYy/UdbfUu2NalMtcSPNaq0mpL9sVJQUPDwBGeLRcL+29vPVNAYnnVXlfgCMTwkbhf6r1t9S7YynvvvacawgO99tprLKl20PmakX2rOvtlSZM5ve6vv3Hxqqx7bLTh3+3EiRNqpANBpFatWiE/p0WLFnLx4kWZN2+e/7Fq1apJpUqV5JNPPhErcITDRJh3+6effrJ6N8hmQYPCDxqkP2gESp3q31E0Bg/9Llz990SCwYMSM2XKFGnTpk2Cx48ePSr58uWzZJ/IOc6dOxd0P3369GpLDoIK5MyZM9HPWblyZdAERlC/fn2ZM2eOWEV34NizZ4/8/PPPsm/fPjXla548eaRy5cpSvXp1VSdG//n444/llltuUbcRPAIX/iH3YsgIH0NG5ENGUsEDGD70YfCg+ObPn6/KVOJbsmSJ1K1b15J9oujzGSyp8v3/1xQpUiTo8QEDBsjAgQOT/Fr0BGM07Z577kmyNCpU6MV9PG77wPHll1+qYcO1a9eqnS5YsKBkzJhRTp06Jbt371Zho1WrVmoYsWjRoubutUOULl3af/uZZ56R/fv3W7o/ZC4GjfAxaEQnaITCUY+UBQ9g+PCmX375RWrWrJngcVw1btKkiSX7RNZBobyRannf//974MCBoJKqcEY30MuxdetW9Vx0mrACB0Yw0qVLJ+3bt5fZs2cnSGVXr15VwzfTp0+XO+64Q13Zb968uVn77CglSpSQv//+Wz2xyJ0YNMLHoGFNyAiFox7GcdTDW7Zt2xbyavKkSZPUeRF5Exbww39Gvg4QNvT0cLz44ouqJ2PFihVSuHDS7wmYqODYsWNBj+G+lRMYhPUug8an1atXy/PPP58gbGiprE6dOqoRZceOHeokm/41ceJE/20cG3JPyNA2Sj5kaBvpDxpmhI1Q4SMwgFD4wSNw5IPc5cyZM3LTTTclCBtYwA8TwTBsUDT4fD4VNr755htVtle8ePFkvwZtDosXLw56bNGiRepxW49woNEkXLly5VIb/at27dr+288995wsW7bM0v2hlGHACB8DhnHRCBmhsNzKGJZbue8ED83gKCUPhJJxTDMaOAMleZeRNTVA79egjArT2n777bdqLQ6tDwMzZaG1Adq2bSuFChVSz0/o1q2bOv9EOG7UqJGqQEJLhLZcg6NmqTp+/Lja4i9qd/vtt0div1wJU5iR8zBk6MOg4ayQEQrLrYxjuZX7Zp7CmgdoFE+ThhN70n/QMB4ThXU4xowZo/5FJVFiJX3oEQ5cyb5GjRoqpLz++utq3Q70FKPXyKo1OED3X8+6deukXbt2sn37dv/aEkj7uI1/Y2NjzdhPR8OT4umnn1a3//nnH8mdO7fVu0RhYNAIH0OGO4JGKBz1MIbBw1lwTlOuXLkEj6PuHWseEMWHU2BDTeM+vZ+f/BeEqp5BL7Wd+ql1v4NgtiVM9YpVDNEMjWlyA/+lhBDQNK+++qql+0LJY29G+NibYf/+jEhhn0fK+jzY62FPWBytWLFiCcIGTuBwosewQWTRCAdCBWaqKlWqVIR2wf0C6z0x2hHYSE72wIChD0OGMU4KGIlhuZVxHPWwD4SJnj17yujRo4MeHzx4sCpDIbJLD4dnA8f9998vmzZtYuDQqV+/fuqFTJtGOJz5lsl8DBrhY8jwdtAIheVWxjB4WL/CMxpuA911111qUWMsAUAUDgYOkwPHhAkTVIkQFh5B80natGmDPt64cWO939IT0LSjBY4RI0bwCorFGDTCx6BhjFtDRigc9TCGs1tZM7IRP2zs27dPbr75Zsv2iZwpWk3jng0cWODv119/lYULFyb4GJvGE4eV2ANHOxg4oo8hI3wMGcZ5KWiEwlEPYzjqER2BM/lg2lCsaxD4GJFX5cyZU9fn45x//fr1UrRoUXMCR9euXaV169bqpDlfvnx6v9zTWrVq5Z/XW5vVi8zHoBE+Bg3jvB404mPwMIajHubB2gSasmXLcl0scsQsVdFc6PLdd99NMAIYCs5hsRi4nkEG3YHj5MmT8vLLLzNsGIAFWLTAMW3aNGnZsqXVu+RqDBrhY9AwhiEjeSy3Mo6jHpGDyoz333/ff/+PP/6wdH/ILYHDSA+H2NaTTz4Z9sxsGIDQQ3fgePTRR2Xp0qVSsmRJvV/qeYEhDetyMHBEHkNG+BgyjGPQMIajHsYweKTMgQMH5N577w26kssKA0optzWNx8VbyDs558+fNzdwYA2O3r17yy+//CIVKlRI0DT+0ksv6f2WnlK9enXVB3PtGk+MI4lBI3wMGsYxaEQGg4cxLLfS79ChQ0EN4ejZCKdkhIhsMEtVlixZZPny5WoLhCsGDBxJGzt2rNx+++3qNhZPxPLzZByDRvgYNIxhyDAPy62M46hH8rDmVYcOHfz3+/fvL3Xr1rV0n8g9UBllpDrKJ/Y3efJkyZ07tzRq1Mi/aPW4cePUAploCQi3UTxFgQMripNxGBXSdOzYkXWkBjBkhI8hwzgGjejiqIcxDB4JnT59OsGMO7169ZKBAwdatk/kPm4rqQo0dOhQGTNmjLqNqpyPPvpILZI5b9481cf99ddfi+mBgyLTy3Hs2DHZvn271bviKAwa4WPQMIYhw3oc9TCG5Vb/+vbbb6Vp06ZBj61evVot7EcUUS4e4jhw4IB/ge85c+bIY489Jp07d5Z77rlH6tSpY+h7hvVqPnz4cLl8+XJY3xB/2PPnzxezYJXuSpUqqfKtjRs3ilOHeTV79+61dF+cEjQYNsIPGgwbxoIGw4Y9w0dgAKHww0dgAPGKzz77LChsoIoAjbAMG0T6oHUCs9LCjz/+KA888IB/Tblw84ChEQ6U/aDpqnnz5vLII4/IHXfcIXny5FEfu3Hjhvo4msinTJkihw8fls8//1zMgjqyggULyqZNm8SpGjZs6L/94osvqiEqCsaAET4GDOMYMpyB5VbGeKnc6p133pFXXnnFf//nn38OmpmKKOIMllSJA0qqEDAQ2CtXrix//vmn/7x127ZtUqxYMfMCBwIETvA//PBDNZXruXPnJHXq1JI+fXq5dOmS+hzsFHauffv2QatqRxJWN0fSmj17dsiVzp3IzNEgJ2LQCB+DhjEMGc7Fcitj3F5u9b///U+tc6XZsWOHlClTxtJ9Ivdz28J/gdCz8frrr6vSKpxz58qVSz2+bt06eeqpp8SIsHs4KlasKOPHj1ezLG3evFn27dunhlXQxY4SJ/xrJvQ8dOrUSdWSZcqUKezyK2waBCW7+OCDD/yLpmC/smXLJl7GoBE+Bg1jGDTchaMexrht1KNFixYyc+ZM//2jR49yYWKKCjc2jU+cOFEaN26szukxyBDfoEGDDH9v3a/UqVKlUgGjSZMmakXCevXqmR42sIQ6Rk6ee+45Vc4VrmHDhqn5trWtSJEiYhddunTx30aK9HJvBsNG+L0ZDBv6sT/D3djn4c0+D5wX3H333UFhAwuRMWwQGYfWiMKFC6slG9566y01Whgpll4awjR1aP5OasMvi9EAvJBgwUE98Plnz571bxgasguUpGnw+3kJQ0b4GDJSFjIYNLwXPBg+jAUPJ4UPTLaCi5+///67/zFUM6DRlShqMFJhdLMpLIx55MgRef7551X5FCZcKF26tPTs2VNWrFihezVy20yLi18AIxdJKVGihDoAmAcYPSOBMNrRqlUrtUBJKPj8+F9jJ5jLGPMaa833adK4e5ZihozwMWQYw4BBwHIrd5ZbYbFcTMsZ3/Xr113//kn249Yejhw5ckjr1q3Vdu3aNXUOPnfuXHW+jVYKNJCj7KpBgwaSOXPmsL9vjA/jkja3f//+oP4LzIRVv359+eqrr9SQKoZ/woHvgdIqjHbYoWfiwoULkjVrVnUbwaN79+7iZOX6/hueAjFkJK5VpdVB9xkyEnfVlzbRjzFkhLb1TAGrd8FWEgsfZ65kjPq+OEViweOXem9HdT8weoHSbbxnBsJ7pnbRjtzFbudrofat6Ph+kiqT/kmS4i5dkX2dBtvyd0vO2rVrVfjAejePP/649OvXL+yvdcQlAUzJG0gbNi1ZsmTYYcOOAod/Mdrh9MARiEEjfAwaxjBokB4c9XDm7FZbt26VChUqBD2G6eRHjRoladMmfiGCiCIPlUXY3njjDTWyqIcjAoebofkeSdEtGDTCx6BhDIMGpQSDh3PKrbCQcLVq1fz3a9asKUuXLg3qgSSyihtnqdKg+AlVRPh7O378eFDvBvqrMVWu3sCvO3BcvHhRrTy+ePHiBDsBf//9t5gNi444oBIsLGgY1wIH/kUAcaq4NCIZdqezejds7Xq5f9etgSPXc1i6L05y6OpNVu8CuTR4XL7Kq+R6ROt4Yfr9wLDx9ddfS7NmzaLys4nC5o5T0QRQcYNlMOrWratmfkPISCndgQOL+y1fvlzatGkjBQoUiMhOeFngVL3PPPOMfyl5cm/QiH8SXSj9GQv2yFlypb1o9S7Y1l8X8yZ47O9z/y7SdOUGB7EpsnY+2t/0n7Flyxa19pcG5xy1atUy/ecS6eHmEY4vvvhChXxthfFI0P1uhBW+sTp2qJkiyBjUp+IF9tSpU1bvCkUhaMTH4EGRChrxZUhzQ/3L4EF2DxmB5xiBJzk//PADwwbZd3TD585RkezZs6tZYiMplZHpsnLmzBnRnfC6CRMm+G9v2LDB0n2hyIQMbdMDwYOlQ5RcyNA2PRA8tI1IT9CIVthAmfTDDz8cFDbmzZsnDz74YFR+PhH9Z+DAgWpVcUyDGym6L3sNHjxY+vfvr9a+yJQpU8R2xMuwsIqmc+fOsmbNGkv3h4zRGzASExg6OOpBoDdgJIWjHmSnEQ2YOHGidOjQIcGaG9WrV4/qfhDpg9IoI+VRMWJ3TzzxhEybNk3y5s2r+qbjN4ivX79e9/cM6x2ncuXKQb0au3btUk0kkdoJErV4ChryMccxeTNohMJyK2+LZNCIj8GDrAwZWM145MiRaguEc41Lly5Jhgz61zcgiioXl1S1a9dOrTSOxf+i2jTetGnTFP8gStqkSZNUotQWNixYsKDVu0QWBo34GDy8xcygER+Dh7dFM2jExsZKp06d1PtdKD/99JPcf//9UdsfohRxceCYP3++6p+69957I/Y9w3qHGTBgQMR+IIWGFRsDFwGcMWOGpftD1oeMUFhu5V7RDBmhBPZ3MHy4X7RHNFALnlgZ9tSpU+Wpp56K6v4QUdIzqEZ6FXTdTePoWg81deuZM2ci3tHuJYHDVTNnzrR0XyghI03gZmOTuTsYaQI3GxvM3d0EHu2wcfDgwQRhAw3hWKkYzeIMG+RImN7W6GZzKHV89dVXZe/evRH7nrovY+GHY1g0vqtXr6oXFTIOCyr26tVL3UYNK5vyrWe3kBEKy62cyW4hIxSWW7lDtANG/HOG4sWL++83b95cjeBzDS9yOqw/bWQNap8DSqrQu4Hz0JIlS6pz0fj92kaWcQj7XWTu3Ln+26jrwhy9GgQQrDwe+KJC+vXo0cMfODAb2LBhw6zeJU9yQsgIhcHD/pwQMkJhuZXzWBkyNLt375ZSpUr5748fP14tHkzkCi7u4Xj33Xcj/j3DfufQGsdxVQLd64GQfDBjVfzZJkifwASJ0Q4GjuhyatCIj30e9uPUoBEKRz3szQ5B49q1a/Lee++pkozA9abiT31LRPYU/zw/EsJ+x4iLi1P/YhQD60Tkzp074jtD/67DMW7cOP8xT5VKd5sNeTRohMJRD2u5KWjEx+BhL3YIGtpMU127dpUdO3b4H+vTpw/DBrmP0X4Mnz3LCc+dO6erUfz8+fOSNWvWsD9f9zvFnj179H4J6YCRDS1wfPrpp2oKQYo8N4eMUBg8osfNISMUlltZxy4hA/766y+pUqWKXLhwIcGV0iFDhli2X0RmifH9uxn5OjvKkSOHWh8Hi/2Fo1ChQrJx48awJ4wy9O6Afo3Ro0fL9u3b1f1bb71VunfvLvXq1TPy7Sje//DA0Q4GjsjyWtCIj8HDPF4LGqFw1MN7QQOLg91xxx0hP9aqVSv55JNP1Gg9ZqQiCiwhT506tTiay3o4fD6fKn3MkiVLWJ+v929a97vCxx9/LN26dVPrRuBfWLVqlTRs2FCFkBdeeEHvt6R4ENwwLE2R4/WgER/7PCKHQSMhBg/3Bw04evRogrBRrVo16d27t1rAD7Pb4HMwbT5RfDfddJPkz5+fM5bZxM0336wmdggX/t/Fn70qKbrfDYYOHaqCxYsvvuh/7KWXXpJ77rlHfYyBI+XGjBkjpUuX9o8mceVVYxgywsNRD/0YMsLD4OG+kBF4NbRAgQL++w8++KAsXLgwqO8Q5RkIGyjRQPjgiSVpzx1MuXr8+HF1P/B55Cgu6+HYG8E1N0LR/S6AF4+HHnooweN4sXnttdcitV+eFjiN4NNPPy379++3dH+chkHDGAaP5DFoGMM+D/cEDW0WqsqVK/vv46LY999/HxQoMF2+FjZy5cpl0Z6SXWXMmFH9i9CB54gjy6tcVlJlNt1TIDVu3Fi++eabBI9/++238vDDD0dqvzxPa8I5cOCA1bviGHZcDdyJtBXMuYq5vVcDdyquYp40K1YC13NleubMmZI+fXr5448//I8vWrQoweiFVt/NBWwpMdpzw7H9Pb4UbB6k+1JTuXLl5M0335Rly5ZJ9erV/T0cv/76q/Ts2VPef//9oFIrMmbixIlSp04ddRvTC5YtW9bqXbIthgzzeH3UgyHDPCy3+o9dA0b8UY0HHnhAVqxYEfQ4ThaTKpViGRUlhs8Nb9H9So+pWjGTEq5uBF7hQPMPPhb4RGLgMK527dr+25i/HIGO/sOQEV1eCh4MGdHl5XIrJwQNOH36tOTMmTPosYoVK8p3330nadJ46/8ZkR9LqnThOhw2liFDBrly5Yr89ttvVu+KbTBoWMvNwYNBw3peGfVwStAANIJjFspAmEXxvvvu89QVavyuKCdv2rRpVH8uqknq1q2rQh8u7JKNuKxp3GypUjK8unPnTrlxg7W4Zpk6dar/tteDHvsz7MVNfR7sz7AfN/Z5aL0ZTgkbeM/BSXZg2Pjf//6n1tRAk7iXwoY241aDBg3EqYoVKybvvvtu0GOfffYZQ0wEFv4zsjnh+fLGG29EdNIi3YEDU5mhxAfNPrfddpt/Z7p27apWyabICbyS8uyzz4pXQwaDhr05MXhoIYNBwxnBw8nhw0khAxcSP/jgAxUm4q8ejCvtI0aM8FzQwDHR1hxAs7xZMKsXwhxRKOideuSRR6RgwYLqb3DOnDmSFPy94vPib1gXJxxYzPvrr79WrwPo3Zo+fbpcvXpVoho4sKDPpk2b1C+Dkp/AxepmzJiRop2hYIEv7JgFxCsYMpzJCcGDIcO5nBY8nBQ00COI9xucUMfvvcTFLpwIB/YVuhkma8E6Yzjhyp07t9SvX189HniSV6NGjQTLAJw4cUItgqY11ePkDCNChQoVksyZM8vdd9+tzpvijy7MnTtXTcaDY5/U1WT8P7r99tvVeRcWV9y6dWvQx2fPnq0uAuP74Or0yJEjg36nffv2ycsvv+w/8cS+YNr9s2fP+h8bOHCg+nyUb7Vt21b16+LiMkZ2/vrrrwT7Pm/ePClTpoz6HCwGjQvSkydPVj8fX4vnEoKUa0VxlqqLFy+qvqmPPvpI19ehEgmjc9qGKYjDgef/xo0b5ffff5dbb71VDSpgvRT8baxfvz46PRz4g0OwwBM+8IQYT/Tdu3cb2glK3Oeff67+8AEvGEWLFhW3YshwBzv2eTBkuIed+zycEjA06BG85ZZbQk6/jvedCRMm6FpJONwF36JN76KDOGnu0qVLopO1tGrVSt5++21V1aF9X5wX4epzzZo11X2cmGFiHVwZxuPo/8AaZlu2bPEv7Itj8dZbb6njjLVKkjoZfOWVV+S9995TIy19+vRRV7v//PNP9f9n3bp18sQTT6jA0KJFC9X3+fzzz6vv2b59e3WlGiernTt3lk6dOqnvh0kAUGLVv39/dVIKWbJkUf/iaxAwEIayZcumwhVK6/D7aM8H7DtmJcXvd/78eXn00UelWbNmKogsWLBA/v77b3nsscfUotDYJ0oZhD4jJX14TqWkbK5KlSpqQ4D9+OOP1XMBi1NXqFBBBUqE1nD/tnS/YiPFh/qjQPry2lBrNDz11FP+wNGtW7dkh9GchiHDvQJHO6wIHwwZ7man2a2cFjRQJoT3lfhVCTjJfuedd0xbOwMnqdpJbTRduHBBjTKEC4EAgSIxOLnHFeBffvnFHzDQc4n3a5wHYaRi0qRJ6l+EDcBoBxZHxONDhw71TymMkziEgeQMGDBAlbZogahw4cIqxGBfRo0apfpq+vXrpz6OEIlwgBI4hAeECyyslzVrVhVYNNmzZ1f7G/iYFjQQtjCSA19++aUUKVJEnX80b97cv+848SxZsqS6jxGOL774Qo4dO6b+H2PUBs3uS5cudW3gwBmvkX6MGImeSpUqqdG28uXLq0CKAKgH/j/jeYbnLSptMNiAtoqDBw+q4IsJJAL7jZOi+1X6jjvukPnz56vhFdBCBhK6ti4HRU7glINYXNEtGDS8JZqjHgwa3mPVqIfTgsbhw4dVaQ9OFgLdddddqhTIzB4FJ6latWqSH8+TJ488+OCD6kQcgQMN9itXrpSxY8eqj2MUA6VEOPEPhBO/wFXX06VLp8qkwhF4foUAgVKm7du3q/v4t0mTJkGfjxNLjGBgP/Ss4o3vhfMOPE802OfAnwcIpVrYgHz58qlSqsBAicewkjiFdu7cuaD7+PuL1N8gyp8++eQTdc6O5x3O0VFat3r1ajVikRyUTSFkTJs2TVKlSqUuUIwePTpoTTiMaN15551h75PuV2ckcwzrID1jhioM8eE2hvCWL1+u99tRGKZMmSKtW7dWtzH0iT98p2LQ8DYzgweDBkUjeDgtZABqt3HF+cyZhH93OImM1sKyOEnFaEO06R2xCWc0BGVVKClBkz2u8KLEBBvgd8RJPkqd4p/sB56QZ8yY0bGVIfFL7fB7hHrM1Y3wKZwWt0iRIglGsbQ+mpTCeWLguSJGq9D2gNCAkajkIEhgRA2jWJjAKFRpZfHixeXJJ58Me590vyrfe++9qpEEtYv44/rxxx9VWkK61/7YKLLwP1QLHHhjQB2s0zBokFnlVgwaFI3g4cSggZEMvGeg5DlUpUK4DaSRghNQPaVNdoYRBfREoEwKgUMrfYbKlSurkQVc3ddKrlJq1apVcvPNN/ubutG/gWZewL/x+01wHyMsWuDBaEr8Bu5Qj+F74WIyroRrJVUnT55UFzsRWilyC/8dOHBA9chozB5hxEgmygDDgR6c5HqG8beMUZBwGXo1xjDa+PHjjXwpGaBnONROGDLIrFEPhgyKVp+HE4MG6q5RgoMr7IFwxRKlubiyTimDky1c+UXfBEaJ0L+hwYk+RkAQQtBsiwCC/tfFixerEqpGjRrp/nlYEwGlTShT6tu3r5pBS5s6v2fPnuqK9ODBg1W/BC4Af/jhh6o/RINyJ5TN4QImTmzx9XgMozHYL/SRYCQI/SsIU2guR4kY+j569eqlZtuKX7bleSkMHNmyZQsKHGbDYAFKrcKB/ps1a9YElQACRkkxyIBAopfuV2BMoYbGkb179/rn6kazUjQPmhfhitTatWvVbTTpYBpiu2LQILOCB4MGRWPUw4khQ4OLgbjyHghl0Gj4xRVtihyECszeVKtWLf/ogwZXfocMGaLCwKFDh9QJPhpuH374YUM/C1UlmDgGTd1oBP7uu+/8/z9xAjhz5kw14xRCB04qEVDQMK7BfUxxjAvGqOlHpQRGMJ577jkVUjCKoZX0YN/xs7CvmGAAvx9mnorkjGWkD4Lhrl27/PfRN4QAgX4ePPewZAWeZ5jZFNC/g5InzCCL2ejQw7FkyRJVlRQOnOOHmtIYzx38HCNifDrqc9BLgKne4je6YKYDNKfYfSYC7Df2FaHJaQEJQ5yBf+x2LKsqOeNNq3fBUR4ru9HqXXCETKmvye6LeazeDUf5+1zwVSlKSAseFy6nd0XQwLTpuGIdCPexXoMVZUw4ycFJEU56AtfsIgrnOWLn8zVt34q9+aakMvDcjrtyRfb27avrd8O6KRh1iK9du3ZqXRSES4QEba0XzLI2btw4FQ4wcoWRNQTSUN8jEGYoA4yeYTY0/J4aBBCMhmHQQZtK2ZQRDnSsY75dJHosHqP1EqBhHEmqTZs26rFwpnejlM1WZVsHOUyvS3T6NB0dNDQXbnD2HD0uX+eVyHBlznBN1jccIk6FplwsToeR7/jv2SjlISJ7llTpgRmmkrrQjNAR6NVXX1WbXlqZHiqYEGYC4aJ3/EUlTVlpHDMxYEfwSyFUoAYQiRRDeRjCady4sZqxisyDBYI0GKolZ8p+6ym1UeJBIzBswE3pLqmNKJIQNJwcNtCsjB6/wLCB0gqcmDBsELlnpfFoXsDAhjItTHqg3ceGciqMbBgtCww7cGDGA9T/JQZ1gOF2vxuBVIXEFbihptFLAtOqtsAPOQeDRvIhI37QiI/BgyLB6UEDddh4D0TFgQarCWPFZ21ROSIio1Dqhr6jSEqjZ8Gg+IvYBMLHjDaShAtNT5g5QYPZE7xM74I+FH0MGElLLmAkJjB0nLlmzqrI5C5OCxgYpUBfBlZqxoaGz8TeY3GxT+8KwkSUMlhl3NBK4z6xpffff19NOIHqJdxOCtagMS1wXLp0KcnGL5RYoQHITAgY+fPnFy/D9Hva3NsY8TBaS0fmYtAwJ2gkFT4YPMjJQQOzBOm9oojRDExZaufF4+w4wQnZg+OfGylc+M9usCggRk1xro/bicHrjamBA3744YegjvVAoVYwjTSUUGHKN9SWtWzZUjWvJ9VMjXozbJr4s2s5UeCKsKNGjWLgsBGGjOiFjFA46kFOCxmAmWqwxkH8xfkSg15KjPbbfaFdbVZFXKzkuh8UCp4b4NjpdqPYNB6tMqpQtyNFV+CI37Een5lXWZCm0KCOOYd/++031Rh35MgRddKdmGHDhsmgQYPEbbBwk7YAD1Y5feihh6zeJU9j0LA2aITCUQ9vclLQAMyNH1gmrMGMkHhdr127tlrozYlQ7ou+EjSeAqbmtPNIDEV3ZANhA88NPEdYGu4NutbhiDQMBQfOvJRYCVHgVX3NxIkTVRM7FkNJbDn4UCMcRYoUseW8znrgf1mqVKlsNyxZcmTi4c+NUho06hXSP4+1V4PGvss5U/T1Xgse+87mEC9xWtDA+xJGNVBGpUHAwMUkNy3Oh/emo0ePRqUCgpwHYQNl8qGCqBPW4SgxYKjhdTj+HtTHlr+b5rHHHpO77rpLXnvttaDHsb4HViCfNWuW7u9p6eIOWIEzcCXMULCSeSh33323WgwPC52UKVMm5OcgiCQWRpwMf5yYFQyLLQKWmE/sOFFkcTTDfqMZ4WC5lfs4LWRoli9frubUD/Tnn39K6dKlxY3vVVj1Om/evHL9+nWrd4dsBGVUjh/ZcFlJVaAVK1aoVefja9CggeFSfksDR548edRmBJZ0x1V+vJB5EcrFtMDRrFkz2bRpk9W75GoMGs4MGqGw3MrZnBo0MKrxxBNP+FfyherVq6sp591eaoQTS8efXBLFZ3CWKnFA4ED1UKjRVgRFo/3QDli+WmTlypWyevVqtSQ7ZqrCfTSMt27dWnLk8FbpQOBQJKYixpWxzZs3q6H5XLlyWb1brsOg4Z6gER+Dh7M4NWjA/PnzEyyWhfIpLJhLRGQ3mJRixowZ0r9//6DHp0+fLuXKlXNv4EBZFH5JDO/gKlHx4sVV4OjRo4d42bJly6RgwYLqdqNGjWTVqlVW75IrMGS4N2SEwnIr+3JyyED/AmZVHDBgQIKPnThxIuKLahFRlLm4pKpfv37y6KOPyu7du+W+++5Tjy1evFimTZtmqH9Dd+DAQnMY/r399tvVFfZowexUPJlOCLWxRYsWVYtDYQQIsz5gJhAyhkHDW0EjFI562IOTg8aBAwfkwQcflB07diT42Lvvvitdu3YNmvSDiBzKxYHjkUcekTlz5qi1fr766is1tTXO/X/66Sc1e57pgQM1mHghxcxR0QwclLi1a9f6+2AwywkafSh8DBnJ80LQiI+jHtHn9NGMsWPHSpcuXUJ+fNKkSclOkEJEzuK2lcbjQ+UMtkjRXVJVvnx5NSsSyprIehiWx7RymHrw559/lsuXL3ORpTAwaCTNiyEjMRz1MJdTgwbKez/88EP53//+F/LjmM79xx9/VFOxExE50bp169QgA9x2221SuXLl6AWOIUOGqBdY1KZWrVpVMmfOHPRxu84p7GaYsQuhA9CEuGjRIqt3ybYYNJLGoJE4Bg9vBw2sJYGS4tGjR6ta5sRgFfDXX3/d9TNPEZF7HT9+XJ588knVK6xVNOE1EJM3oafayAyzugNHw4YN/Se2gS+oGFLGffR5UHRhJVrM1nX69GlVX4cnBUve/sOQkTwGjfCx3Mo7IQMjxy+88IJ8/fXXSX4eZqBCSZU2iQcReYCLezi6du0q58+fl23btsmtt96qHvvjjz+kXbt28tJLL6nmcdMDx9KlS3X/EDIfpsbVhu7R2LN//37xOgaNpDFkpBxHPdwVNHDhDFf0RowYIQsXLkzyc4cPHy7dunWTDAZWGiYi53NzD8f333+vLmBrYQMwHe5HH32kermN0B04jHank7kKFy6sFpHCGiWYJWXnzp2JrsDudgwaSWPQiDwGD2cGDfRhfPrppyo84HUzKaNGjZKWLVuqEWUiIsUB4cGIuLg4tchffHgMH4vaOhxoTsbwMZrHMR9voUKF5IsvvlCN5Pfee6+hHaGUw+iTdrUNDYu4WucVDBnJY9AwH8ut7B8yYObMmdKiRYtkP++VV16RPn36sESViDzlvvvuUyO4KJ3SSkUPHTqk1sC7//77DX1P3ZOBz549W+rXr69mQlq/fr26QgRnz55V8/WStQsk9u7dO+j/lReCBsNG0iFD2yj64SMwgHghaNg9bOD9ChOdhAobCBVvvvmmnDx5Ul2swfb2228zbBBR0j0cRjabwwx8586dk2LFiknJkiXVhkEFPPbBBx9Eb5aqTz75RNq2bas61TX33HOP+hhZC6Fv2LBh6vbjjz+uhr7cNlsKA0byGDDsw82jHnYPGIGwOGq1atWCHhs0aJC89tpr6mINEZEebu7hKFKkiBpUQB+Htogp+jnq1atn+HvqDhzoDahVq1aCx7Nnz65mRyLrYVrcBx54QN1GMES5mxswaCSPQcPe3NLr4aSggZEKvA5OmTLF/xhKAn744Qe1mC0RkSEunqUKcLEa55La+WRK6Q4cWO9h165dapgl0C+//CIlSpSIyE5RygQmULzJvvvuu5IrVy5xKgaNpDFkOI9Tg4eTggZgEo0aNWoEPYagYXSWFSIit3r//ffD/lxMjWt64OjUqZNqJJk4caJKP4cPH1Yv6lgMsF+/frp3gMxx4sQJ/8IsWI3caQ3kDBnJY9BwPieUWzktZMCpU6fUxbHr168HPY7646xZs1q2X0TkHm4rqRo9enRYn4dz/6gEjl69eqm+AAxJX7p0SZVXof4VgQMLhZA9IGQ88cQTajYWbQYrrBBpdwwayWPQcCe7jXo4MWjA559/rhanCoS+w2effdayfSIiF3JZSdWePXtM/f66AweSTd++fdV0gSitunDhgloMJEuWLObsIRmGpn4tcGCKMzuPcjBoJI0hwzusDh5ODRraCPyECRP895s2bapm60uVSveEjEREngocoVy7dk0FEcxSlSaNoZU0/HS/Cj/zzDNqufN06dKpoHHXXXepsHHx4kX1MbIPhEMtcABGoew4pS3DRuI4pa13adPqRmNqXW1KWyeHjZo1awaFjW3btsk333zDsEFEpBMqmDp06CCZMmWS2267Tfbv368eRyUTFko1Qvcr8eTJk+Xy5csJHsdjGMome2nevLn/9siRI1UwtBpDRvIYNCiQWcHD6SFDg1lUMHFJ4JslLogREZndw2Fkszus6bZp0yZZtmyZf0FpbVKiGTNmGPqeYY+PoNlOWwwJIxyBOxAbGysLFiyQvHnzGtoJMte+ffukaNGi6napUqXkyJEjUd8HBozkMWBQNJrM3RAwAmFkHXPFa27cuMHpbonIfC4uqZozZ44KFli7KHAtN4x27N6929zAgdVW8UOx3XLLLQk+jsexiBLZz80336ya+1esWCFHjx5Vi7lUqVIlKj+bQSN5DBoUjV4PtwUNGDBggEyaNMl/n2GDiKLGxYHjxIkTIQcRUCVjdDHpsAMHZjnC6Aaaj9GElzNnTv/H0M+BK+gFCxY0tBNkviVLlvgbfqpWrWp6AzmDRvIYNCgawcONQQMwDfuQIUOCynoZNoiIUu6OO+6Q+fPn+2ef1UIG+uSqV69ubuCoXbu2+hfd6ljynI14zoI34o8//lief/55df+dd96JeBP5zZUPRfT7EWnWHyts9S44zqaHB4sbYVr2SpUqyZYtW/yPnT59OqjMl4jIbG5bhyPQ0KFDpUGDBvLHH3+okeP33ntP3f7tt99k+fLlYoTu1ICRDIQNNOXt2LFDNm/eHLSRfXXp0sV/G9Max18UK6X27MqvNgpP+lTXE2zX41JzC7HlzHRJbeRtWCUcF08Cw8bJkydVyS8RkSUlVUY2m9q6dav6995775WNGzeqsFGhQgX58ccfVYkVFvpGlYwRaYzUdT399NOycOHCkB9HAznZ+8lUvnx5dRvNQOvWrYv4z9BCR/FSRyP+vd0uS5or6t8LN3i1NpTA0HHqkj0WyCPzrVq1KsEwfrZs2dTIBkfbicgKbhzhuP322+XOO++Ujh07ypNPPinjx4+P2PfW/UrdvXt3OXPmjKxevVoyZswo33//vZoqt3Tp0jJ37tyI7RiZAzMMYKYqQPP43r17TftZ2ogHRz2MBQ9to9A46uF+eJ/Be0v8sPHll1/K2bNnGTaIiCII5VI4T+zZs6cUKFBA2rdvLz///HNEvncqI83Ho0aNUg0leLFHiVXr1q3l7bfflmHDhkVkp8hcgeUIeDOPBgYP4xg8ksbg4S5Y2Xbq1KlqBBbbrl27/B8bPXq0mvCiZcuWlu4jEZEbS6pq1qwpEydOVMsnfPDBB6pvGz3cmJ32rbfeUjOdRi1wYEosbaqsHDlyqBIrQI0XrpiT/aG5skmTJuo26vNS8gTSi6MexjF4hBc8GD6cCX2BmHWqcOHC0qpVKzW6ocFEJRjRwAg7EZEtuDBwaDJnzqzaJzDi8eeff6pFpD/66CO1zELjxo0lKoGjTJkysnPnTnW7YsWKMnbsWDl06JB88sknaviFnCFwpUjU61mBwcMYllslj8HDWY4dO6ZKPTHVrXYRK3CtDZR+omeDiMguYlKwOQlem/v06SOvv/66ZM2aVU2XG5Wm8W7duvlXqsYbwUMPPaTqabEWx2effWZoJyj60qdPr8oV0Ix58OBBOX78uGUrxbPJ3Dg2mSdNCx1sMLevb7/9Vpo2bRr0WPbs2eXZZ5+VTp06+XvOiIgourBgNEqssP4e2iieeOIJ6dChg7mBA3VcxYsXV/0aGkyNtW/fPjU9LoZZcufObWgnyBpIqbly5VK3GzZsKGvXrrV0fwJHOxg+9GHwSBpnt7KnFi1ayMyZM/33CxUqpNYIatasmbooQkRkWy5dafzw4cNqAAEbeuhq1Kgh77//vgobKLUyKuzAUbJkSdUgXrduXbXaeJ06dVStbaZMmaRKlSqGd4Csg9XiixUrpsoVMD0uFtSyy6wvHPUwJrDMiuEjNI562KOEKn/+/AmupKFhkYjICdw4LW6DBg3kp59+UgMIbdu2lWeeeUa1UkRCKj2zU7Vr107+/vtvNcyN8IEZjjDsPX36dPUGQs6zYMGCoEW17IZ9HsaxzyNp7POwBmYzjB82MBkJwwYROYoLm8bTpk0rX331lSq1x6xUkQobukY4MKKBDa5cuaKWN1+2bJnasA4HVq0uW7asbNu2LWI7R+a79dZbg6acRLq1I5ZbGcdyq6Sx3Co6sH4TZjYMhD7AgQMHWrZPRET0HzPX09PdNK5Nq4qyKix9jhIrrDqO2arQy0HOtWjRInECllsZw3Kr5LHcyhyYxbBLly5Bj+3fv19Nd0tE5Fg2Hq2wmzR6F2TCrEZLly5VIxuYJx1vGLVq1ZIPP/xQLQ5CzlO/fn1bllMlh6MexnHUI2kMHpFx4cIFNY1ioM6dO6sLVERETubGHg5bBA6MaCBgYKYqBAv0bmA1WK694Xy48qgFjqtXrzpydhiOehjDUY+ksdzKuM8//1z1/QXCGk5YsZaIyPFcOkuVWcJuGv/555/VFKoIHvfff7888MADUQ8bmMb17rvvlowZM6pa4Phzt5MxjRo18t/GXMtOxiZz49hknjQ2mYcHDeAouw0MG5hO0efzMWwQERmAWfweeeQRKViwoMTExMicOXOS/RpUImEWWVxExnpGVq+Vl0pPw9+4cePUNLjoXMcvXaFCBXnxxRdVR3v81WEjDSfCbdq0UUutb9q0SX799Vdp2bKlqT/TK9KkSRNUa+0GWvBg+NCPwSNpDB6hIVB07dpVsmTJokZKNZs3b5YZM2ZYum9ERGaVVBnZjFzIqVixonz00Udhr52Hi8nos964caN0795dOnbsaGn5fNglVVjsA6uKY4Pz58/LL7/8ovo53n77bWnVqpWaJnfr1q0R38kbN26oFc5HjBgRtMJhuXLlIv6zvA4jWW7DcitjWG6VNJZb/Qvr9/Tq1Uu9PgfCjHcYlcbVOCIi14liSVWDBg10zSKKi8dogRg5cqR/RlKcs2M2UvTtWsHwKm8IIFg4DhvKm3CVfPv27WKG9evXy6FDh9SidJUrV1alXDjwyYUbXGU7d+5c0EahYWVft+OIh3Ec9UiaF0c98HqKCUNSp06dIGz89ddfao0fhg0icquUjnCci3d+GjgynFIrV66UevXqBT2GoIHHrZJKz1Ws33//XY1m4GT/pptuUsudf/zxx2oRJwzzYFFAM2jfF/O1v/766zJv3jwVcrAuyKlTp5JcYCp79uz+jVMwJu65554LmlnGzVhuZRyDR9K8EDyOHz+u6oHxmhp/RBRX0FBahY8TEVHiihQpEnSOinPWSDl69Kjky5cv6DHcR7C5fPmy2LqkCgEDNWQIF6gJw7AMTvhLlixp+IdjGB79IEnBqAnCDvTt21cee+wxdXvSpElSuHBhmTVrlpoxK5TevXtLjx49/PdxoBk6QgtMwlg5HrV+XsByK2NYbuXNcqvnn39exowZE/RY7ty51cUoDN8TEXlGCkuqDhw4INmyZfM/7MQZQk0JHBgyR9CI5CwjPXv2lPbt2yf5OSVKlJAjR44k6NnA/xh8DItHJQaf4/b/gZGCcrXA2j+vBA4N1/Qwjmt6uH9ND0yJXq1ataDHUN6KmVPQJE5E5DkpDBzZsmULChyRhMGBY8eOBT2G+/h5mOnV1oEjsVGElMiTJ4/aklO1alUVHDCHO1Y3h+vXr8vevXulaNGiEd8vr1u3bp14GUc9jGHwcF/wiI2NVesuYVbAQOipw0yFREReZeeF/6pXr6766AItWrRIPW4Vw03j0YREhh6DAQMGyI8//qiCBxarg+bNm1u9e66BmcboP+zzSFmfB3s9ku7zsHuvB6Y6x2QggWFj4sSJqkeDYYOIKHouXLigprfFpk17i9talQ9aCNq2bev/fJwzo//51VdflR07dqh+65kzZ8rLL79s2e8Q9giH1VDShTc/rMWBhhcsALhkyRLVPE6RgSfol19+qW6fPn2ax/b/sdzKOI56OHPUY/ny5apHT4P1l9CEmDVrVkv3i4jIi9Pirl27VrU1aLT+ZCywigX90HoQ2GKAnjpMS46A8d5776me5wkTJlg2Ja6jAkfatGnlnXfeURuZ45577vHf/uKLL+Sll16ydH/siOVWxjB4OKfJfOjQoWqCDk2fPn3kzTfftHSfiIjsJsbnU5uRr9MLF4AwupyYUKuI42s2bNggduGYwEHmC5wzH43jDByJY/AwhrNb2XvU46677pI1a9YETXMbeCGCiIiiP8LhBgwcFJJZizi6DcutjOOoh32Cx6VLl9RirvF7ODDlLREROatp3I4c0TRO0eO16XAjiU3mxrDB3Nomc1xcCAwbmBEQs1MxbBARUaQwcFCiK45jRWHSjyuZG8PZrZIX6eDx6aefBq1v9Mwzz8iVK1eC1uUhIqIkSqqMbB7EdxVKsOaJBqu5U8oweBjD4GFu8MAIRpUqVYJGNGfNmqUCCBERhV9SZWTzIgYOStSYMWOs3gXXYPAwhsEjsuVW586dk06dOqkpxgNnL9m9e7c8/vjjJu4pEZHLcIRDFzaNU8gpiLGS+759+3R9XbYC503bJ7c4efHfWvnb8hyVzecKW707jnHo1E1W74JjYTRj4MCBMmTIkAQfQ58G1tdInTq1JftGRETewBEOSkBbxV2vS9u5UGC4tp3IL5dupLN6N8iF/ny8n9qwymz27NnVaEaosPHDDz+omagYNoiI9GNJlT4MHJTAs88+67998OBB3aGDwSN8CB0MHhTJoIEQgTV1SpYsqUqoAmEBv2vXrqkFpB588EHL9pWIyPFYUqULS6oogcBZayZMmKDKMfQKDB2Zbj0dsX1zKy10ZEpzzepdIQdBwAjUv39/GTx4cNBj9913n2oIz5kzZ5T3jojI3bw6WmEEAwcl2zhuJHCECh8MHskLHO1g+KBwg8b58+clW7ZsQY+hCXzatGmqpIqIiMhKfCeikFD7ffbs2YiuxcHgoQ9HPSi5oKGVPRYpUiTosSNHjkj+/JwVjYjIND7fv5uRr/MgBg5KdAHAt956y5TvzXIrfTjq4W2hQoYGs8kFho0OHTqoMkgiIjKX0QbwGG/mDTaNU/KN47t27TLt57DJXB82mXuvCTwxaPxOl+6/58Lw4cMZNoiIooVN47owcFBIxYsX998eN26c6T+PwUMfBg/vBg1N69at/bcfeeQRee2110zeMyIiImNYUkXJ+uSTT+Ttt9+Oys9iuZU+LLdyh3ACRqDffvtNpk6d6r8/d+5cE/aKiIgSExP372bk67yIgYMSVaBAAdV8ihlwrMAmc33YZO7+oAFYR+Oee+7x38fkDkREFGVGy6N84kksqaIkG8ftQCu3YslVeFhu5Z6yqVBy587tvz1v3rwE0+ESEZH5uNK4PhzhoERhxpsBAwao21u3bpXy5ctbvUsc9dCB5Vb2YjRgBPrwww/9I461a9eWRo0aRWDPiIiIzMURDkpUoUKFgvo47IQjHvpw1MM6KRnNCITyxq5du/rvL126NMXfk4iIUrgOh5HNgzjCQWFB4MDVVbthk7k+7POInkiEjEAFCxb0396zZ4/ExMRE9PsTEVH4uA6HPgwclKQSJUrI33//LbGxsWJ3LLcKH8utnBEyNO3bt/ffHjx4sBQrVsyUn0NERGFi07guLKmiJHXp0kWchuVW+rDcyj5lU6H89ddfMnnyZP/9119/3ZSfQ0REZBYGDgr7yuqaNWvESTi7lT4MHvYKGtpq4rfccov//rlz50z7WUREFD7OUqUPS6oo7Ck40cdx5513ihOx3Cp8LLdKmpkBI76JEyf6b7/55puSNWvWqP1sIiJKgtEGcJ83EwdHOMjQyY9TccRDH456RG80I9ToRseOHf33e/fuHbWfTURESeMIhz4c4aBkYf0NrMPhJpzdSh8vz24VzZARCCMagQv8cVYqIiJyKgYOStarr74qbdu2VbfPnDkjN910k7gJy63C55VyK6tChub69evSr99/+8AF/oiIbIazVOnCkipKVsuWLf23hw4dKm7Fcit93FhuFe2yqcRUrlzZf3vt2rWW7gsRESXEkip9GDgoWalTp/bfHjFihLgdZ7fyXvCwS9CAOXPmyLZt29TtkiVLStWqVa3eJSIiii/OZ3zzIJZUUVieeOIJmTlzprp94cIFyZIli3gBy63cW25ll4ARCH9jLVq08N9fvXq1pftDREQUCRzhoLC8+OKL/tvz588Xr+GIh3tGPew0moFeja+++kqqV6+umsIDw8bs2bMlV65clu4fEREl08NhZPMgjnBQWALX30AfR+CJkZdwdivnzm5lh5CBqW4//fRT6dSpU5Kfh74NllIREdkX5g000o8RI97EwEFhyZAhgyqjQjnV5s2b5eLFi5I5c2bxMpZb2b/cyg4hA86ePatmmvr111+T/LxRo0ZJt27dJFUqDj4TEdkaF/7TxRHvasuWLVPlBqG2NWvWWL17nvHSSy/5by9YsMDSfbETllvZr9zKLmVT58+fl0qVKqmppOOHjbvvvltmzJghV69eVSMf2F5++WWGDSIich1HjHDUqFFDjhw5EvQY5qhfvHix3HHHHZbtl9fUrFnTfxt1582bN7d0f+yG5VbWllvZIWAEWrJkidx///1Bj+XJk0dWrFghZcuWtWy/iIgo5YxOcRvjzQEOZwSOdOnSSf78+YMaLb/99lvp2rUrV9+NcvALnE1n0qRJkilTJkv3ya5YbhW9ciu7BQ0YPXq09OjRw3+/VKlSsm7dOsmWLZul+0VERBHChf90ceTY/dy5c+XkyZPy9NNPJ/l5KFU4d+5c0EbG4WQpcEGy77//3tL9cQKWW5lXbmWXsqn4+vTpExQ2MNLx119/MWwQEblIjM9nePMiR4xwxIdZXurXry+FCxdO8vOGDRsmgwYNitp+eUGtWrVkw4YN6vasWbPk0UcftXqXHFlu5fPFyN+nOOWpXnYMGIGmTZumXnc0x44dk7x581q6T0RERFaL8aFT0SK9evWSt956K8nP2b59e1C988GDB6Vo0aKqpOexxx5LdoQDmwYjHEWKFFEzxvBqozHLly+XOnXq+O9funRJMmbMaOk+OVGFuQOs3gVHOfdPZtn3zKtiZ0ePHpUCBQoEvVYVKlTI0n0iInIinK9lz57dludr2r7VrDVA0qTJoPvrb9y4Ij+vGGTL381Mlo5w9OzZU9q3b5/k55QoUSLoPvoGsBhW48aNk/3+6dOnVxtFTu3ataVu3bqydOlSdf+HH36Qpk2bWr1b5OKg4RR9+/b1337llVcYNoiIXMxoeVQMS6qiDzO2YAsXBmMQONq2bStp06Y1dd8ocVj4Dysjw5AhQxg4yLMhQ7Nx40aZOHGi//5rr71m6f4QEZHJ2DTu3qZxNF/u2bNHOnbsaPWueFq1atX8QREz71y5csXqXSKXBA0nhg1cCOnevbv/fv/+/dUoLBERETkwcKBZHFOzcg576y1atMh/++2337Z0X8jZnBo0AmfNQ2+TBov3ERGRR1YaN7J5kKMCx9SpUxOs1kvWqFixov/2gAFsgCZjIcPJQQOuXbsmzZo1898fMWKEWlWciIi8sfCfkc2LHBU4yF4WLlwYdKJFlBw3hIz4C/xpE/1VqVKFoxtERF4R5RGOjz76SIoVKyYZMmSQu+++W37//fdEP/ezzz5TC2MHbvg6KzFwkGEPPfSQ//arr77qP/EicnvQgH/++UdN7a2ZM2eOpE6d2tJ9IiKi6IiJM77pNWPGDLWgLCpK1q9fr6pMsB7d8ePHE/0aTLl75MgR/7Zv3z6xEgMHpcjKlSuDpgIlclvZVGIwPXTgmwHW+CEiIoq0UaNGSadOneTpp5+WcuXKySeffCKZMmUKmh0xPoxq5M+f37/ly5dPrMTAQSmesUozcuRIiYszEN3JVdwcMjToJdu6dat/raAnnnjC6l0iIiIXllRdu3ZNzQhar149/2OpUqVS9wMv+sZ34cIFtVA2LoY1adJEtm3bJlZi4KAUC3wSP/nkk5buC1nHC0EDEKrvvfde//1NmzZZuj9ERGThOhxGNvl3xfLA7erVq4mW78bGxiYYocD9o0ePhvyaMmXKqNGPb7/9VqZMmaLetzDL68GDB8UqDByUYhje08yaNUs2b95s6f5Q9Li9bCqUqlWrBpVSZcmSxdL9ISIi61YaN7IBRh6yZ8/u34YNGyaRgsWZsUh2pUqVpHbt2vL111+r9dPGjh0rnlxpnNzjzJkz/ulA0cx0/fp1SZOGTy+38lLACLRgwQK1qjjcfPPNLKUiIiJDDhw4oBq7NenTpw/5eblz51YTkhw7dizocdxHb0Y40qZNK5UrV5Zdu3aJVTjCQRGBdD59+nT//fLly1u6P2QOr41mxK+jbdSokf/+33//ben+EBGRc3s4smXLFrQlFjjSpUunRtYXL17sfwwlUriPkYxwoCRry5YtUqBAAbEKAwdFTIsWLeSWW25Rt3fu3KlmSGjZsqWsXbvW6l2jFPBi2VQoBQsW9N/++eefOQUuEZGXITfEGdh8+n8UpsQdP368TJ48WbZv3y5dunSRixcvqlmrAOVTvXv39n/+G2+8IT/++KO6MIZpdFu3bq2mxe3YsaNYhTUvFFHo3whcXGbatGlqA8yygMXRNJcvX1ZNUvgazPRTuHBhS/aZQvN6wAiEXo2TJ0+q2zVr1gxqGiciIu8J7MfQ+3VGLuieOHFC+vfvrxrF0Zvx/fff+xvJ9+/fr2au0pw+fVpNo4vPzZEjhxoh+e2334J6bqMtxueh1dowCwBKf86ePRtUN0eRderUKXn00Udl+fLlCT6G41+6dOmQox7t2rVTsyoE/tG4VYW5A8Su7Bo09j3zqiU/99KlS5I5c+agoWyM3hERkffO17R9u69yL0mTWv/q3Tdir8iSDcNt+buZyf1ndhR1OXPmlGXLlqmVx7FhGFCDP7DESqwwVIgyFcx0RdHFsqnEBYaNDRs2MGwQEdH/T3FrpIdDPImBg0yHmkGUTsVvJO/atassXLhQPv3006DHMfMPTuqwqM2ePXuivLfewpCRtMApBDFqh2FsIiKiaC385xbs4aCowCwLmCHhyJEjqscDNYWBnnnmGZk7d65aDVODGRjQ25E3b175/fff1YqZFBkMGeFN9fzcc8/578+ePdvS/SEiIhtBA3iMwa/zII5wUFRhSrb4YUPTuHFjVYK1cuVKFVA0x48fl2LFiqlaR454GMeyKX0Cn6eYdY2IiIiMYeAg26lWrZoqwcLsCrVq1fI/fv78eTXigXKrb775RoUTSh5Dhn6YCUTTuXNn/3TPREREkVhp3GsYOMi2MN0bZrpCaUv9+vWDPoZ6esxmhfCBqd+wKBsFY9AwBqMZgwcPDtnHQUREpLCHQxcGDrI9TD+H+aZv3LghL730UoKPT5gwQa3QiWbz69evi5exbCplMLJWtmxZ//1jx45Zuj9ERGRTDBy6MHCQY2DK3Pfee0+VUv3zzz/y6qvB6zJgOl30fmDUo02bNmqlTcyQhdmuSpUqJWnTplUfa9SokVoUx00YMiIjcNFKrAmDCQuIiIgoZThLFTlSrly55K233lIbGsnR2xFoypQpiX7tggUL1FohsH79eqlcubI4FUNG5AQ+h5o3by5PP/20pftDREQ2ZnS0wscRDiJHKl68uBr1OHz4sBrZ0KNKlSpq1GPNmjXiFCybiiw8dzACps2AljFjRpk5c6bVu0VERHYWl4LNgxg4yFVT7n7++ef+Fc4T2y5fvixt27YN+tq77rpLBY9Ro0ZJXJw9Xw0YMiLv0qVLavKB3bt3+x+7ePGipftERET2x1mq9GHgIE/W6U+ePFkFi9deey3oYz179lS9ImhUX7ZsmdgBg4Y5sJBf5sz/HddMmTKp5wSCJxEREUUOAwd5Fk4shw8frk4yR4wYEfSxc+fOSd26ddXnYPQj2rMVsWzKPPh/if+vjz/+uP+xhx9+WC5cuMCwQURE4eEsVbowcJDn4STzf//7nyq32rdvn9SoUSPo4+jvyJ8/v/q8jRs3mrovDBnmwf/fVq1aqf+XgVasWCHfffcdwwYREYUvzmd88yAGDqIAN998s/z666/q5HTlypWSJ0+eoI9jRqsGDRpEdJVzjmaYb+HChapXY+rUqUEriOP/Y82aNS3dNyIiciCOcOjCwEGUiGrVqsnx48fVYoLdunXzP45FCHHyunbtWsPfmyEjOjBzGUYuGjZsGPT4yZMnuYI4ERFRlMT4Inmp1uZQl49m4LNnz0q2bNms3h1ymCtXrshNN92kVqPWYIXzAwcOJBgJIWuhH6Nq1ary559/JliDBSNURERkX3Y+X9P2rV6JlyRNqvS6v/5G3FX56e/3bfm7mYkjHEQ6ZrdC6Jg0aZL/MYQPrEbdokWLiJZZkTE3btxQDeBZs2YNChudOnVSkwMwbBARUUSwpEoXBg4indq3b69ObJs0aeJ/DAvFoczqxIkTlu6bV8XGxkrr1q0lbdq0Mn/+fP/jlSpVUutqjBs3jk3hREQUOWwa14WBg8gArNUxZ84cOX36dNDjGO1YsmSJZfvlNVjE8cEHH5Q0adLIl19+6X8c62ug/2bDhg1qfQ0iIiKyDgMHUQqgpwOlVAMGDPA/dv/997Mh2STTpk2T3Llzq9EKbAgTixYtCvoclFKhh4N9NUREZBpfnPHNgxg4iCJg4MCBsnr1av/95557Tvr372/pPrnJtWvXVLhr2bKlmmEqPjTwYQ0VhL/SpUtbso9EROQh7OHQhYGDKEKwIvmhQ4f89wcPHiz9+vWzdJ/cAGVrmA0MM3rE98gjj6gAcubMGbWGChERUVSwh8OdgQNlEmjSRTkFphG79957ZenSpVbvFlGQggULBvV1DBkyRD7++GNL98lpvv32W1UqpZVN5cyZ0/8x/P2jMR9N+xjNmDt3btDHiYiIyH4cEzgw1SVOMtCQu27dOqlYsaJ67OjRo1bvGlEQlP7girvmhRdekBUrVli6T07w3XffqYDRtGlT1QweaiFG/L0jdKBpn4iIyDIsqXJf4Pjnn3/kr7/+kl69esntt9+uarSHDx8uly5dkq1bt1q9e0QhewoCy6tq164tBw8etHSf7L4aeOPGjRN8rHDhwurYbdmyRVauXMmgQURE9oDcYChwiCc5InDkypVLypQpI59//rmaUx8jHZgFCFOQYjXhxGBRNqwIGbgRRbO8CifJmiJFiqjmZ/oXpqzF33ChQoWCHseaJiiXwoZV3JctWybly5e3bD+JiIgS4AiHLmnEAXD186efflKlFlhBGAus4UTl+++/lxw5ciT6dcOGDZNBgwZFdV+J4pcBjRkzRrp06aLuo/kZi9ThOex2WJUdgQFBHw3f6L3A3/Hs2bMTrF8CHTp0kPHjx3OBPiIiIpeJ8eEyokVQIvXWW28l+Tnbt29XoxsIG9evX5e+fftKxowZZcKECaphdM2aNVKgQIFERziwaXDig6vMOPlB4zlRtLRq1UqmTp2qbiNsYJTOzSfWCxculBYtWsj58+fDmt0Lq4OjN4OIiAjnayhNtuP5mrZv9fJ2lDSp0un++htx1+Sn4xNs+bu5NnDgimeoOfUDlShRQn7++We1mjCuigb+z0EvB66KIrg4/QlM7nfrrbfKjh07/PfdONKBIJU/f/5k/65RRoUSyfvuuy9q+0ZERM7giMCRp4PxwHHiU1v+bq4tqcJKwOGsBozmcIh/cob7cXHeXLGRnOePP/5Qz3ftZBwN0Ajdbrmyj4kdbrnllgQzduFiARq/69Spo0KXm0d2iIjII4z2Y/i82cPhiMur1atXV70a7dq1k02bNqk1OV555RXZs2ePNGrUyOrdIwoLTrQx4xqezxoEEK3UysnQexEYNipUqKAC1qlTp2TGjBny/PPPS7ly5Rg2iIiIPMgRgQNXgNEgfuHCBVWCcccdd8gvv/yiFgjDehxETvLbb7+pVcgD+ztQHphcGZJd1ahRQzp37uy/P3nyZNm8eTNHM4iIyL240rhzejiizc41geTNaWGrVKkS9Nhnn30mbdu2dcSJOnqq4q/yjVHHYsWKWbZPRETkfHY+X9P27f4c7Qz3cCw+PdmWv5t4fYSDyI0qV66s1pUpWrSo/7H27dur3iTM8IQZ2OwCL4wojfrf//6nFujDi2Rg2MiSJYtqGGfYICIiT/AZHN3weeY6fxAGDiILZcqUSY0KDB8+PMHid5guFiMdixYtivp+YeDzq6++UuvdYB/Q/P3kk0/KyJEj5bvvvgua7vbVV19V97kKOBEReQYX/tOFgYPIYjihf+2112T16tWqnCo+zPKEGZ7MnpEN0/Ri/QwEB4yyNG/eXM2ilRiMaqAxPLm1dIiIiMjbGDiIbAIjGmi4xugCTv6HDh3q/9jy5ctVEMDaFWbAz3z44YelYcOGCYINFt7EIpvHjx9XC2nic7FhVAON4URERJ6D90qjmwcxcBDZEEYYevfurU7q0ZymwdTQGBF54okn1KxtkdK3b181E5wGIyoIGAgWWKzwkUceUVP4pkunv0GOiIjIdVhSpQsDB5GNoWzpzJkzqtQp0KxZsyRr1qwqfGB66JQYO3asDBs2zH//119/laVLl4a1KCcREZEX+eLiDG9exMBB5AAPPfSQGm1AEIivadOmKngEhoZw4PuhWf25557zP3bo0CG1rgYRERFRpDBwEDkISp0QFC5fviwdO3YM+lifPn3UFLv79+9P9vvg69GzgbItzcaNG6VgwYKm7DcREZGrsKRKFwYOIgfKkCGDjB8/PsGoB8JGmzZt1OOJ2blzp5qOd8GCBf7H3nnnHalYsaLp+01EROQKXGlcFwYOIheMegSOaqxYsUJmz54dclTjgQcekLJlywY9PmfOHOnRo0dU9pWIiMgV1GhFnIHNJ17EwEHkAkWKFJEpU6b472MNDaxiDn///bfky5dPjWr89NNP/s8ZPHiwWh28SZMmqgeEiIiIyAxpTPmuRBR1LVu2lOnTp8u8efP8M1yFgtmt9u3bJzly5IjyHhIREbmDL84nvhj9oxU+jnAQkZNhlAILB2KBwFCqV68u//zzj5w7d45hg4iIKCUMlVPF/bsZ8NFHH0mxYsVUD+fdd98tv//+e5Kfj+nzUUKNz69QoUJQ36YVGDiIXCRnzpxy/fp1qV27tv+xDh06qMd+++03yZUrl6X7R0RE5JoRDoObXjNmzFC9lgMGDJD169erSV7q16+vFugNBe/3Tz31lHr/37Bhg5o+H9vWrVvFKjE+D43t4MouVm0+e/asZMuWzerdITINVihftmyZ1KtXTzJmzGj17hAREbnifE3btzoxzSRNTFrdX3/Dd12W+b7R9bthROPOO++UDz/8UN2Pi4tTvZtdu3aVXr16Jfj8Fi1aqD5OrcQaqlWrJpUqVZJPPvlErOCpHg4tW+HJQuR2GOXAyAY2IiIip9DO0+x8TfyG76qh8qgbcj3kuWj69OnVFt+1a9dk3bp1QetmpUqVSl1QXLlyZcifgcfjzz6JERHMSmmVNF676gtIhURERERk7/M2jCbYSbp06SR//vzyy1HjPRFZsmRJcC6KcqmBAwcm+Fz0XsbGxqrZJgPh/o4dO0J+/6NHj4b8fDxuFU8FDqyifODAATVLjxOmAUX6xRMS+2y3IUW34DE2F4+v+XiMzcXjaz4eY3M58fhiZANhA+dtdoMm7D179qiRh5T8fjHxzkNDjW64iacCB4agChcuLE6DFwinvEg4FY+xuXh8zcdjbC4eX/PxGJvLacfXbiMb8UMHtmjInTu3mn3y2LFjQY/jPkZaQsHjej4/GjhLFRERERGRDaVLl06qVq0qixcv9j+GpnHcx3T3oeDxwM+HRYsWJfr50eCpEQ4iIiIiIifp0aOHtGvXTu644w6566675N1331WzUD399NPq423btpVChQrJsGHD1P1u3bqpiWNGjhwpjRo1UosCr127VsaNG2fZ78DAYWOo50MTkdvr+qzEY2wuHl/z8Ribi8fXfDzG5uLxdb4WLVrIiRMnpH///qrxG9Pbfv/99/7G8P3796u2AU2NGjVk6tSp8vrrr0ufPn2kdOnSaoaq8uXLW/Y7eGodDiIiIiIiii72cBARERERkWkYOIiIiIiIyDQMHEREREREZBoGDiIiIiIiMg0DBxERERERmYaBw8U4AZm5eHyJiKzH12Jz8fhSJDBwuPjFISYmxupdcSUeXyIi6/G12Fw8vhRJXPjPZS5cuCCDBg2SAwcOSPHixeWBBx6Q++67z+rdcg0e3+gc4w8++ED27dsnVatWlSpVqqh/KXJ4jM3F42s+vhabi8eXIo0L/7kIlrmvWLGiWlEyS5Yscu7cOVm2bJla6r5Hjx5W757j8fhG500OK6gWKVJEYmNj5fLly3L48GEZMWKEtGzZ0urdcwUeY3Px+JqPr8Xm4vElM3CEw0U++eQTyZEjh1q+Pn369HLy5EmZMmWK9OzZU86ePauuVpBxPL7me/vtt6VAgQKyYMECyZgxo/zxxx/y2WefSevWreXSpUvSsWNHq3fR8XiMzcXjaz6+FpuLx5fMwMDhIsePH5ds2bKpFwjIlSuXdOvWTT3WoUMHKVSokHTu3Nnq3XQsHl/zHTx4UJ2s4UQNypUrJ3379pVMmTLJc889Jzlz5pRHH33U6t10NB5jc/H4mo+vxebi8SVToKSK3OHzzz/3Zc2a1bd169YEHxs0aJCvQIECvj/++MOSfXMDHl/zvfPOO76SJUv69u3bF/T4iRMnfM8995zvrrvu8h08eNCy/XMDHmNz8fiaj6/F5uLxJTNwlioHQ23wr7/+6r9fvXp1qVatmrz11lvy999/q8e0Fp3HHntM0qZN63+c9OPxNR8aazGUP2HCBDlx4oT/8dy5c8vjjz8ue/fulSNHjli6j07HYxxZcXFxQf/eddddPL4Rxve66OLxJTMwcDgUmrhq164tEydOlN27d6vHSpUqJc2bN1c1w++88478+eef/uns8LGbbrpJzp8/b/GeO6fxE81xTz31lLzwwgty7do1dQxxwsDjGxmoZ//888/VNm/ePPVYnTp1pEmTJqpeeNKkSarZVlO5cmV1jM+cOWPhXjvLlStXZNGiRfLtt9/K6tWr/ce4YcOG8uWXX/IYpxD+3u+9915ZuHChpEr179tpzZo15ZFHHuHxjRC+15mL73UUNaaMm5CpLl686CtXrpyvadOmvrNnz/ri4uKCPv7uu++qYfsGDRr4fvnlF9+BAwd848aN8+XIkcO3fv16y/bbKc6dO+crU6aM7/777/d17tzZlzt3bl+bNm2CSibuvvtuHt8UHuPSpUv7qlSp4itSpIgvZ86cvkceecR35MgR9fFevXr5brnlFt/zzz/v27Jli/r8sWPH+vLkycOh/DDhtQHHF8/VbNmy+apWrep7//33/R//3//+p57nPMbGjRgxwhcTE+PLnDmz77vvvgv6WM+ePfkcTiG+15mL73UUTQwcDjRt2jRfvXr1/C++qLfEC8Mbb7zhO3nypHpszpw5vkcffVS9Gd56662+/Pnz+6ZPn27xntvfjRs3fM2aNfM99thj/scmTpzoe+KJJ4Le7L755hv1eTy++sXGxvqefPJJX8OGDX3Xrl3zHTp0SL2ZFStWzFe5cmXfn3/+qT5v1KhRvrp16/pSp06tTpzxJsdjHP6JGo5Z8+bNfcePH/dt2LDB1717d1/jxo19p06d8n8ejvF9993HY2wQXgdatGjh69evny9NmjS+uXPnBn18zJgxfA6nAN/rzMP3Ooo2zlLlQFhMCmERQ5wYBt2yZYvkyZNHNm/eLF999ZWMHj1alaWgbAIfw+dlzpxZbrnlFq4cGkYJyj///BM0A8exY8dk586d0qhRIzXzzMsvvyxNmzZVZRObNm3i8dUJpSeYWhGlKKgFLliwoNrWrl2raocxfSjmfMdxbtGihRrWh3z58kmFChX8z31KelpLlD18/PHHqncArw94XuM5jOczegwAx7hVq1bqtQN4jPUpVqyYqmXHGhtHjx5V9e3oNcBzNnXq1GpWqmbNmqnXYeDx1Yfvdebhex1FGwOHA2GqOpy0bd26Vfbv36/q3wsXLqw+hpVAX3nlFVm1apU6mcMKt4H44pA0HFe8yaHuHXXDOPF9/fXX1YvybbfdpvoNunTpIt9//72aGpDHVz+8UeGNDm9smuvXr6upF5cvX66aml988UX59NNP/WGEwofF5hAo6tWrp0KH5s4771THEica2ufhpDhv3rzqc0k/THGLZnEsjvbuu++q+wjNeEzrmeHxNY7vdebhex1FXdTHVCjF9uzZ48uUKZOvRo0avoceesh36dIlVaai1WRmzJjR99VXX1m9m461cOFCVZNdp04dX/bs2X2DBw8O+jiOPUpRyLgZM2b48ubN65s0aZL/satXr/rLJlD7vnv37gQ12xSevXv3+i5fvqxua8cQJRQ4rqtXr/Z/3rZt2yzbRyfDsdSg5Ec7pr179/alS5dOlVD98MMP6jE+h43je525+F5H0cRZqmzu6tWraoaIjRs3yo0bN/zD+P369ZO//vpLrQCaLl06dbUCV9Vw9fLWW29Vw86k7/jiKjs89NBDcuDAAZkxY4bcfvvt/quTmJoRM6ZUrFhRlUZQePCcXLdunRq90K6u4+okhunHjBmjjjPgeQx47mLmlDRp0vAqms5jjKuUuF20aFHJkCGDek3AMcSoEsrYTp8+7X+eYyawSpUqyfbt2/3lERTecxgjQ4Gw2N+HH36oRjkwM9VLL72kXkdwRZ7PYeOvE3yvM/f48r2OooklVTaGP3i8IGD6UExBhxOwkSNHyv333y/PPPOMWg0Ub3DPP/+8DB8+XL0QL168WJWraDXapP/4Yv5x1L3jRRl12Riyx2OoacX0ophHHzWsFP6UlhcvXpRTp06pYzh48GA1peWrr76qhvDffvtt9ZzFlIwo8zl06JBkz56dJ2oGjzFWtH7jjTekfv36asVrQKDA8xsnynhtmD17trRr106++OILddJG+o8vTs5Q3tOgQQO1CjOmup0+fbo0btxY9RSECiak7xijP6NTp058rzP5Ocz3OoqKqI6nUNhQDnHHHXf4WrdurVb73LVrl+/xxx9Xq38OGTLEd+bMGTVLB6YFxGNFixb1VaxY0ZcrVy5VrkLGji+GlXF8Mf0fDBw4UJX+YNrAVq1aqVlmZs6cafXuOwJmoMJxw0xJO3fuVGU+zz77rK9EiRJqStbTp0+r496jRw81dF+hQgU1tI8hfj6HU3aMsdI1jjHua1CKUq1aNV/Hjh19qVKl8k2ZMsVf8sOyH2PH99ixY75ff/1Vza62YMGCoK+9fv26+pfHNmWvE5hlDe93fK8z5zmM12DtvQ5TNvO9jszCwGFTmCYU84trJ76wZs0adTKG6UNHjx7tfxyfM378eN+sWbN8a9euVY/xTc748cUbnXZ88eKMngLUD+PE+KefflKP8/gmDycKmEYR0yoGGj58uJpbv0+fPr4rV66oumxM24r7WCdixYoV6vN4jFN2jMuXL+/r27evf/pQfC5q3jG9JV4rgGHD+PG97bbb1HMWAqcapsgf4/Pnz6vH+F5nzmsEXoMPHz7M9zoyFUuqbAo1waitDqxPRZkJ6t4RFPv376/KUjB7BIZEO3bsGPT1LEeJzPFFLXybNm3U1KE4plo9PCUPvQKotUY/BmDYHn0Fr732mvoYpmzFqtcPPPCA6iXARpE9xpgeF6UUOMZ4HDPQYMpL3Oe0lpF9DuPztNXGKXLHGH1e2jHme505x1d7jeB7HZkpBqnD1J9AhqCeEi+yeCEYNmyYepFAo23NmjVl/PjxUrlyZVUrPGjQIKt31bXHF+EDda6cM984rEuAYLdt2zZ1DNGkj6kuAesToDYb6xZQdI4xTjowhSvDRvj4HDYfj7G1xxfrb/z2229W7ya5HC/H2BQa4Xr06KFmlChVqpQ6AUbzFk6GAWsWnDhxwurddPXxRUMi8KRMP1xRAzR64ioarp4B3uS0WZLwGILetWvXLN1XLxxjnGAAwgZoVzApcXwOm4/H2B7HF68PPL5kNgYOm8ILQvv27WXJkiUyceJENYXl3Llz1ccwkw9mUSpZsqS6z0Eq/Xh8zaWVlqDk7/3335ddu3ZJ3bp11WxgWKQLEOiwai3e6HiMzT3GOLngMdaHz2Hz8Ribi8eX7IQlVTagleyEU7qDEwdMZYmr8/Pnz5d77rknavvpVDy+1sIx/fnnn6Vr167qja5GjRqSNWtWmTx5spr/HUP6lDI8xubi8TUfj7G5eHzJagwcFsKVdK2eEvNeJweLH2FxKVyNR7NiixYtorKfTsXjG91jndyaA7iChp6Y/fv3q6trTzzxhForgj0y4eExNhePr/l4jM3F40t2xsBhEVxhwIwxWOUTw5n4g8dV9eRgFdsSJUqoZmc2fiaOx9d8WETq66+/VjObQFKz9MR/MwscdQIe49B4jM3F42s+HmNz8fiSU7CHwwJY2frOO+9UjXA4ES5Xrpz07dtXHn/8cdmzZ0/Ipi+twevpp59WJ8MavkAkxOMbnTc5lJthteo333xTPYY3Oe14BsJjOI6XL1/2Pxb4BsdjHBqPsbl4fM3HY2wuHl9yEgYOCyxcuFC9KKBZuV+/fjJ69Gg15R9mTOrSpYscOnTI/2KAz1u1apWaKxsCX0j4AhEaj6+5EM66d++u6n9RD/zll1+qIfrE3uzw2MqVK9X/Cy3Ycb2CpPEYm4vH13w8xubi8SWn4bPNAufOnVN9BZiaFfDHX6VKFfVisG7dOunTp0/QCe/ixYvVCwvm0eYLRPJ4fM2FK2SoFcZo0CuvvCJPPvmkTJs2zb8mTKg3ux07dsioUaNkzZo1Fu21s/AYm4vH13w8xubi8SXHMXchcwpl06ZNvjRp0vi++OIL/2PXrl1T/y5ZssSXMWNG3+zZs/0fi42N9bVp08Y3efJkS/bXaXh8zYNjBSdPnvRdvHhR3T548KBvwIABvrJly6p/NTdu3PDFxcX573ft2tU3aNCgoMcoIR5jc/H4mo/H2Fw8vuREaawOPF5UtmxZVdqDUp88efKoPgPMFoESH/QeoOdg586d/s/HlQp8DlbCpuTx+JpHGwHKmTOn+hdX2DDH+7PPPqvuY3pFjBwNGDBALTZ18uRJGTJkiPq6Rx99VG699VaWqiWDx9hcPL7m4zE2F48vOREDh8kuXLgg/fv3V//ij71nz55SunRp6dSpk+zdu1eGDh2qXiwaNmyoXgCwEjBOkm/cuBE0zV2rVq2s/lVsicfXmmN88803q8UTtRlOChQo4H+z++qrr2TFihWydOlS+eyzz/xvjnXq1LH4N7EvHmNz8fiaj8fYXDy+5HhWD7G42YULF3zFixf31alTx9eqVSvfbbfd5rv55pt9o0eP9l25csX3+++/+x599FFfiRIlfCNHjvQtW7bM98EHH6iSn+XLl1u9+7bH42vNMS5WrJhv1KhRvuPHjwcN78Phw4fV58bExPjmzJmjHuPQfdJ4jM3F42s+HmNz8fiSGzBwmGjw4MG+WrVqBT32wgsv+MqVK+cbOHCg7+rVq75du3b5hgwZ4suRI4fvlltuUfWXM2fOtGyfnYTH17pjXL58eV///v19//zzT9DHxo4dq97kvv76a/+bHN/oksZjbC4eX/PxGJuLx5fcgFPymAjrQGCWCPyL0h3AStaPPPKImk1i+vTpUrJkSbVGxO7du9Xw55IlS6R58+ZqeJRrMiaNx9e6Y9yoUSM1ZD9v3jz1GI4l1j9ZtGiRqh9u1qwZF5MKE4+xuXh8zcdjbC4eX3IFqxOP2wReRcCV9ZIlS6rhUECZj6Z169aq1OfSpUuW7KdT8fja6xjjY4HHWPs4r6gljcfYXDy+5uMxNhePL7kNA0cEnT9/3terVy/fqlWr1P3Lly/7Chcu7GvWrJn/c/AY4IUjZ86cvilTpli2v07D42vvY8w3t/DwGJuLx9d8PMbm4vElN2JJVYQgvL388svy9ttvy6effiqrV6+WDBkyyLhx49QK19osSHgMLl68qGaU0Bano6Tx+Nr/GGPInsP2SeMxNhePr/l4jM3F40tuxcARIfgDz5Qpk5qSdevWraq+csOGDdKgQQP56KOPVP9A3bp11cd27dolP/zwgxw5ckTy589v9a47Ao+v+XiMzcdjbC4eX/PxGJuLx5fcKgbDHFbvhNPhEOJFAo3KJ06cUAvP9erVS2677TZ57733ZP78+ap5+cUXX5Tjx49LmjRp5Pr16zJy5Eh58sknrd592+PxNR+Psfl4jM3F42s+HmNz8fiSm3HhvwjQhi+zZcsmY8eOlWXLlqmVPT/44AOpV6+ebNy4UXbs2CHr169Xi/BgoR4Mf2K1T84gkTweX/PxGJuPx9hcPL7m4zE2F48vuRkDR4Tgj71o0aJy9epVdf+pp56SuXPnyqxZs9TKnqizBAyFBuKLQ3h4fM3HY2w+HmNz8fiaj8fYXDy+5Fbs4YgQ/LFj+DNt2rRy5swZGTJkiHqR6NGjh5o7e+jQobJu3Tqrd9OxeHzNx2NsPh5jc/H4mo/H2Fw8vuRWHOGIoBs3bqiayoYNG8rmzZvVwnNYhG7q1KkyYsQI9TEyjsfXfDzG5uMxNhePr/l4jM3F40uuZPW8vG7z4Ycf+ooVK+abM2dO0OOHDx+2bJ/chMfXfDzG5uMxNhePr/l4jM3F40tuw1mqIgz1lZhdolixYkGzTmj/Usrw+JqPx9h8PMbm4vE1H4+xuXh8yW0YOIiIiIiIyDRsGiciIiIiItMwcBARERERkWkYOIiIiIiIyDQMHEREREREZBoGDiIiIiIiMg0DBxERERERmYaBg4iIiIiITMPAQUREREREpmHgICIiIiIi0zBwEBF5yMmTJyVv3ryyd+/eFH2fJ598UkaOHBmx/SIiIvdi4CAiigCfzyc33XSTfPDBBwk+9vzzz8tdd90lP/zwg8TExCS5/fjjj1K7dm11e+jQoQl+xt13360+9sYbbxjazzfffFOaNGkixYoVk5R4/fXX1fc6e/Zsir4PERG5HwMHEVEE7N69W51833HHHQk+tm7dOqlatarUqlVLjhw54t9y5col/fr1C3rs/vvvlw0bNkjRokVly5YtQd9n8uTJcvjwYXW7SpUquvfx0qVL8umnn0qHDh0kpcqXLy8lS5aUKVOmpPh7ERGRuzFwEBFFAEJFmjRppFKlSkGPX79+XTZv3qwCR8aMGSV//vxqi42NVeVNNWvW9D+GDcHl/Pnz0q5du6DAgcd69+4t7du3V/fx/fRasGCBpE+fXqpVq+Z/rE6dOtK1a1fp3r275MiRQ/Llyyfjx4+XixcvytNPPy1Zs2aVUqVKycKFCxN8v0ceeUSmT5+uez+IiMhbGDiIiCIUOMqVK6dCRaBt27bJlStXEgQEjGKEGqnA98mUKZM89dRTsnPnTrl27Zp6fPDgwWr0JE+ePCqYFChQQPc+/vzzzyGDCkZOcufOLb///rsKH126dJHmzZtLjRo1ZP369fLggw9KmzZt1AhJIJSJ4WuuXr2qe1+IiMg7GDiIiCIAQSGxciqMKqAEKRBO5IsUKaLKquI/fvvtt0uZMmUkQ4YMsmPHDvnrr79kzJgxMmrUKPVxI+VUsG/fPilYsGCCxytWrKh6MkqXLq1GUfBzEUA6deqkHuvfv78ajcFITSB8LwSio0ePGtofIiLyBgYOIqIIQBBILHBUqFBB0qZNm+DzQwUH7XE0hiN4oKzq5ZdfVqMOOPnX+kGMuHz5sgoT8eHnaFKnTq1CEPZZgzIrOH78eNDXaaM58Uc+iIiIAjFwEBGl0IEDB+TMmTNy2223JfjYTz/9pEqT4ksucAD6Qd59911Zu3atai5HaRZGPAK/7p577pHVq1er22gGHz16dKL7iVGL06dPJ3g8fhhC2Al8DPchLi4u6PNOnTql/kWZFxERUWIYOIiIUgiN4XDhwoWgxxctWqTKoVq0aBH0+D///KNCSvzA8ffff6vgoj1euXJlFTaGDRummrc3bdokN27cCBrhQBAZPny4KrdKlSqVGg1JDL7fH3/8IZGydetWKVy4sAoyREREiWHgICJKoeLFi0vZsmVV/8PixYtVMBg3bpy0bNlSzSoVf4QDoxiJNYynS5fO3++BmapOnDjhn5kKX4fRBPR+aB566CHZv3+/zJ8/Xz7++OMk97N+/fqqiT3UKIcRaEJHQzkREVFSGDiIiFIIJUc44ce6FBjNwHobY8eOVQv3TZgwIcHnY4Yq9EXEb+BGoEDY0MqZ8C9GD7SSJnwcoxSB1qxZo0qbsmfPnqA0Kj70ZSDkzJw5M8W/M8q75syZoxrLiYiIkhLjw9K1RETkOIcOHZKGDRuqE//HHntMPv/88wSzYcWHYPTKK6+ociiUYBmFWbO++eYbtTI6ERFRUjjCQUTkQJhxCmtlfPDBB6qkC+VcWKsjOY0aNZLOnTursJISGE3BzyYiIkoORziIiIiIiMg0HOEgIiIiIiLTMHAQEREREZFpGDiIiIiIiMg0DBxERERERGQaBg4iIiIiIjINAwcREREREZmGgYOIiIiIiEzDwEFERERERKZh4CAiIiIiItMwcBARERERkWkYOIiIiIiIyDQMHEREREREJGb5P4YocaEs76C3AAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x440 with 2 Axes>"
       ]
@@ -1937,7 +1996,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAGwCAYAAABSN5pGAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAkQRJREFUeJztnQecFPX5/x+u90o9ehMEARFFabGDiAYTNZo/iVhji4gxGv3F8os1ElssMUZjR42oMT+NsZdIkaogoiC9t+MaV7nyf32+t9+52bmZ3dl2u3v3eb9ey7G7s7Ozc7czn3mez/M8nZqampqEEEIIIYQYJLT8lxBCCCGEAAokQgghhBALFEiEEEIIIRYokAghhBBCLFAgEUIIIYRYoEAihBBCCLFAgUQIIYQQYiHJ+gBxR2Njo+zcuVOys7OlU6dO0d4cQgghhLgA7R8rKiqkqKhIEhKc40QUSEECcdS7d+9obwYhhBBCgmDbtm3Sq1cvx+cpkIIEkSO9g3NycqK9OYQQQghxQXl5uQpw6PO4ExRIQaLTahBHFEiEEEJIfOHPHkOTNiGEEEKIBQokQgghhBALFEiEEEIIIRboQSKEEBIQDQ0NcujQoWhvBiG2JCcnS2JiooQKBRIhhBDX/WN2794tpaWl0d4UQnySl5cn3bt3D6lPIQUSIYQQV2hx1LVrV8nIyGCTXBKTIr6qqkr27t2r7vfo0SPodVEgEUIIcZVW0+KosLAw2ptDiCPp6enqJ0QS/l6DTbfRpE0IIcQv2nOEyBEhsY7+Ow3FK0eBRAghxDVMq5GO8ndKgUQIIYQQEmsCCRN1Z8+eLX379lV5w/Hjx8vSpUt9vuazzz6To446SlJTU2XQoEHy3HPPtVrm8ccfl379+klaWpoce+yxsmTJEq/na2pq5Oqrr1a59KysLDn77LNlz549Yf98hBBCCIk/oi6QLr30Uvnwww/lxRdflG+++UYmT54sp5xyiuzYscN2+U2bNsm0adPkxBNPlK+//lqJK6zj/fffN5b5xz/+Ib/5zW/k9ttvlxUrVsioUaNkypQphqsdXHfddfL222/LvHnz5PPPP5edO3fKT3/60zb5zIQQQoiZCy+8UM4666yQ1oFgAcrb2/p9ffG///u/0q1bN5XyeuuttySuaIoiVVVVTYmJiU3vvPOO1+NHHXVU0+9//3vb19x4441Nw4cP93rsvPPOa5oyZYpxf+zYsU1XX321cb+hoaGpqKio6d5771X3S0tLm5KTk5vmzZtnLPPdd981YXcsWrTI1baXlZWp5fEzlqmqrY/2JhBC2gHV1dVNa9asUT/jkV27djXNmjWraeDAgU2pqalNXbt2bRo/fnzTX/7yl6bKyspob17TzJkzm6ZPnx7yOXXPnj1hf9++ffs2PfTQQwFvD/5ecJ785z//qfZ/TU1NUzhws82+/l7dnr+jGkGqr69XpaNIg5lBqm3+/Pm2r1m0aJGKMJlBdAiPg7q6Olm+fLnXMgkJCeq+XgbPw9luXmbo0KHSp08fYxkrtbW1Ul5e7nWLdV5ctFmO+N/35dPvWyJnhBDS0di4caOMHj1aPvjgA7nnnnvkq6++Usf6G2+8Ud555x356KOPpD2AcyfK2mOFDRs2qJ/Tp09XTRthi4knoiqQsrOzZdy4cXLnnXeqFBfE0ksvvaT+cHft2uXYqAzhOjO4D8FSXV0t+/fvV+uxWwav1etISUlpFYo0L2Pl3nvvldzcXOPWu3dviXWWbC6RhsYm+Xobu94SQiLUlK+uPio3vLdbrrrqKklKSpJly5bJz372Mzn88MNlwIAB6sT973//W84880xj2QcffFBGjBghmZmZ6jiP1x48eNBrfU899ZR6DqXkP/nJT9RrrOeTf/3rX8oriwAA3usPf/iDCgr44/7771fNDeGPhU/WXKaOC/Xf/va30rNnT7V98NfCk+srxXbXXXcp0YTzLewoN910kxx55JGu3/eEE06QLVu2KFsK0mRuq8OQWtP7FUEK8+uefvpp9TvAvkFw4i9/+YvXa7dt26Z+T/gsBQUF6ve0efNmY73PP/+82r96e8z7IJxEvVEkvEcXX3yx+oWjmRP+oH7+85+rKE8scfPNNytfkwaCLNZFUll18x94TX1DtDeFENIOqT7UIMNua/F/tiVr7pgiGSn+T2HFxcVG5Aiiwg7zyRsn80ceeUT69++vIk8QSIg06ZP4ggUL5IorrpD77rtPfvzjH6vo06233uq1vi+++EIuuOACtZ5JkyapSMqvfvUr9Ry8sU58+umnSqTg5/r16+W8885TYuayyy5Tz//617+WNWvWyKuvvipFRUXyz3/+U0477TTl3x08eHCr9c2dO1fuvvtute0TJkxQr3vggQfUZ3P7vm+++aby8WL79Xa4AUIOhVIXXXSRV8AD23TbbbfJY489pqJ6iOZhvfjdzJw5UwkzZIUQPMF+hLCFyMPnXLVqlVrvd999p87Bzz77rFonRFS7FEgDBw5UJunKykr1gfFLwi8HitsOhOms1Wa4n5OTo8KLEFm42S2D1+p1IBWHrrBmtW1exgpCg/EWHjQEUh0FEiGkY4ITPqJNQ4YM8Xq8c+fOqpoZIGICwQNQ+KPBCR4nZwgiLZAeffRRmTp1qjpRg8MOO0wWLlyoUnUaRIsQqcEJH+B8hkwJhJYvgZSfn6+EA85hiKygIOnjjz9WAmLr1q1KEOAnxBHANrz33nvqcQhAK9jWSy65RIkUAGECsWiNiPl634KCAvU4IlBO50c7UB2uz6/m1+HzQ6TpoiiINYi+J598Uu0vFFk1NjaqKJMWrvh8WBciRSjkwrke0bRAticuBZIG6hG3kpISVZE2Z84c2+WgKt99912vx1AFh8cBUmdjxoxRv1ztzMfOxn2ob4DnMe0Xj6G8H6xdu1b94en1tAfKquqMqzxCCAk36cmJKpITrfcOBbR+wblhxowZ6mSrQUQIlorvv/9eXbQjLQYhhfleSKnhXIG0mpmxY8d6CaSVK1eqSBOiNxpYP8zrsWP48OFeYzEQMEB0COAn1gFBZgbb7jT6BduKCJh1Wz/55BPX7xtOEAhBNA2izRyNwj6GdUXvO4haCDIz2Hfa09RWRF0gQQxpdY+dcsMNNygFqxUvUlso+X/hhRfUfSh5KF0ocaTm8It+7bXXVB5Zg1QYlOjRRx+t/hgefvhh9YvR68QvAr8gLAd1jOjTNddco8TRcccdJ+0FHUGqPtQY7U0hhLRDcIXvJs0VTdArD9sJsWBGZyn03C4An8sZZ5whV155pRI3OD+gYAjnC2Qd3I5ZQYQGUSS71jHWoiQzuHA3g+2GiNPrhIiB/cQ6WwzRmlDw9b7hREeu4OGCf8qM/kxYBkEMpOKsdOnSRdqSqP9ll5WVKRG0fft29ceIiA7+MPUvDLlLRHY0CMdBDMEw9uc//1l69eqlQnHIWWqQotu3b58KJ8J0jVwqwpBm4/ZDDz2kcs14PyhwvN5qFItnIDrLa5oNgdVMsRFCOiiIrpx66qnqwhoXwk4+JADxAWGAFBDODwAX4GZwMW9tZmy9Dy8tBBnEWbiAXwcRJPTzg6/JDXpb4Ydy2lY3pKSkqPcOFZyDkR6EtwuROzuw75Bmg7EcwYtIbk/MCyQ41XFzwq5LNlz1MHb5Auk0nVKzAyoe3bZxa48crK1XFWygliZtQkgHRpuUkVVAFdTIkSOVAIJYQCoNEQsAQQOTMLw7qMBCmuyvf/2r17ogsn70ox+pyjUsgyzGf/7zHy+jNy7OEYlC65hzzjlHvRdSR6tXr1aepmBAag2iAmIHAg6CCYEAWEXweeAbsoJtRSoLnxtTKiA8YHR28vg60a9fP/nvf/8r559/vvLiwr8VLIiszZo1S2VyYLxGgALVhbDXIKuDz/inP/1JVa7dcccdKgiCKjqYxZE5wn1sD7JPEKEQwFiXNQrWLjppk8im1wAjSISQjgyKgXBRjd53yFigKguiAUIIRmcYqAEeh/CBYfuII45QaR74kcxAaEE0YTksj+wEMhrm1BkyEvAkwRB9zDHHKOsGshYYqRUKMCtDIF1//fUqOgSfLUQehJgdEBv4vPiMiMxgEgU6Z/tK89lxxx13qPQj9qM5zQVRaBfE8AVaDSDrg8+CdgrHH3+8WoeurEMaE2IMnwkpSrQDQIoTHiQdUYLow+fH7xDbAyEbCTqhW2RE1tzOgXkPqhUpQqcwYDT5dmeZTHukudnm8KIc+fcsdyFZQgixAyconGBxIgv0BNvewQkbkSiUpcc6SDei+gstdkJh06ZNKqqFCjS7FgOx/Pfq9vwd9RQbiQxlVaYIEqvYCCEkbKCpIoQG/ExIr6FxYSx6WFExh2gXIlowQb/yyiuqSg+V36Hy7rvvqt5IsSiOwgUFUgdIsbEPEiGEhA+0CEArmoqKCuXnQUNIpI5iDaTAIGRQ+ISICtJSb7zxRqtxXcFw9dVXS3uHAqkjeJAYQSKEkLBhrWyLVdDCoL3MmYsGNGl3hAgS+yARQsIEbauko/ydUiB1kAgSD2qEkFDQZdTwtRAS6+i/01DK/5li6wACCdTWN0paiK35CSEdF5h8MQ8LjQp1Obbbye6EtBUIBkAc4e8Uf6/WruOBQIHUTim1CCT0QqJAIoSEgh4OqkUSIbEKxFGow2wpkOKAzfsrZeP+g3LS0JZRKf4otwqkQw2SH4FtI4R0HBAxwiBTjIFAx2lCYhGk1UKJHGkokOKAWa9+Jau2l8n7s38kQ7p7Tzh2m2KrYSUbISRM4OQTjhMQIbEMTdpxwKb9lernzrJq16+xCiSW+hNCCCHuoUCKcTBotqKmXv2/srb5pxsYQSKEEEKChwIpxik+WGf8/6BHKPmjsbHJ8CAVZKaon9V17IVECCGEuIUCKcbZf7DW+P9BlxGkitp6afS0PeqW0zykjyk2QgghxD0USHEkkHSqzR86epSWnCC56c0+fKbYCCGEEPdQIMU4+ytaUmxuPUjaf5Sbnizpnt5HjCARQggh7qFAinH2BZFiMwsk3RySESRCCCHEPRRI8ZRiC1Ag5aWntESQ6iiQCCGEELdQIMU4+4OoYtMCKQcRpBSm2AghhJBAoUCKcfZX1AbsQSqtau1BqjnEMn9CCCHELRRI7bDM386kTQ8SIYQQ4h7OYmuHZf5mgZSU2En9nx4kQgghxD0USDFMfUOjlHjSZYFEkHQfJPRAqvd0jKQHiRBCCHEPU2wxzIHKFoO29iA1NXlaZLupYstIkXSatAkhhJCAoUCKgx5IGR6Rg2hQbb1/s3VpdbOwogeJEEIICQ4KpDgo8e9TkBGQD8lc5k+BRAghhAQOBVIclPh3yU6VrNQk1z6kMlOZP/sgEUIIIYFDgRQHFWyds1oEkr9eSI2NTUbHba9ZbKxiI4QQQlxDgRQHAqkwM0Wy0pJcpdjwvPZxe89iY6NIQgghxC0USHHgQeqcnSqZLlNs2n8EY3dKUkJLBIkpNkIIIcQ1FEhxkmLLNgRSS18kfxVsgCZtQgghJHAokGKYfR6TduesFJNJu8F1F22QlpJgRJDc9FAihBBCCAVSTFPsaRSpTNoeD9JBPx4kc4m/OYIEbeSmhxIhhBBCoiyQGhoa5NZbb5X+/ftLenq6DBw4UO68806/kY7HH39cDj/8cPWaIUOGyAsvvOD1/AknnCCdOnVqdZs2bZqxzIUXXtjq+dNOO01iBVSj6U7a3mX+hwKLIHkEEmCajRBCCImDWWz33XefPPHEE/L888/L8OHDZdmyZXLRRRdJbm6uzJo1y/Y1WP7mm2+Wp556So455hhZsmSJXHbZZZKfny9nnnmmWubNN9+UurqWMR3FxcUyatQoOffcc73WBUH07LPPGvdTU1MlViipqpMGzxy1AlSxpQYWQcrzCKTkxARJSuikunAjzZYX8S0nhBBC4p+oCqSFCxfK9OnTjchOv3795JVXXlGix4kXX3xRLr/8cjnvvPPU/QEDBsjSpUuV2NICqaCgwOs1r776qmRkZLQSSBBE3bt3d7WttbW16qYpLy+Xtqhgy89IViLHSLEF6EHSaTb0RmKpPyGEEOKOqKbYxo8fLx9//LGsW7dO3V+5cqXMnz9fpk6d6vgaiJS0tDSvx5Bqg6g6dMg+/fT3v/9dzj//fMnMzPR6/LPPPpOuXbuqNN2VV16pIk1O3HvvvSqypW+9e/eWtqpgA65TbKYu2hqjmzabRRJCCCGxL5BuuukmJVyGDh0qycnJMnr0aJk9e7bMmDHD8TVTpkyRp59+WpYvX668SkjL4T7E0f79+1stD+G0evVqufTSS1ul1+BdgkBD9Onzzz9Xwgy+KDuQ1isrKzNu27ZtkzZpEpmVon66HTViRJAyvCNIgL2QCCGEkDhIsb322msyd+5cefnll5UH6euvv1YCqaioSGbOnGn7Gpi6d+/eLccdd5wSSN26dVPLzpkzRxISEmyjRyNGjJCxY8d6PQ5hpsHzI0eOVCZxRJVOPvnkVutBOq4tPUotJf6WCJJLD5I1xQZo0iaEEELiIIJ0ww03GFEkiJRf/vKXct1116l0lhNIpz3zzDNSVVUlmzdvlq1btyrvUnZ2tnTp0sVr2crKSuU/uuSSS/xuC7xMnTt3lvXr10tMddHWAsnwIAVW5g/Skj29kJhiI4QQQmI/ggSRY436JCYmSmOjfzMxUnK9evVS/4cIOuOMM1qta968ecqz9Itf/MLv+rZv3648SD169JBYQKfYUOIfTIpNV7EBYx5bPQUSIYQQEvMCCVVnd999t/Tp00el2L766it58MEH5eKLL/by/uzYscPodQRDN3xFxx57rJSUlKjl4TFCqwC79NpZZ50lhYWFXo8fPHhQ/vCHP8jZZ5+tqtg2bNggN954owwaNEh5nGKBFpN2SugpNpq0CSGEkPgRSI8++qjyFF111VWyd+9e5T1CCf9tt91mLLNr1y6VRtPARP3AAw/I2rVrVRTpxBNPVO0CkGYzg+dREffBBx+0el9EqVatWqVEVWlpqXrfyZMnqyaVsdILqVUVmyfFVlnXoJpIJiR0avUa9E2q8AgoepAIIYSQOBVI8A09/PDD6ubEc88953UfHbQRafIHSvedOnLDx/T+++9LLFNs9SB5Ikigsq5estNaBJCm3BM9snqQWMVGCCGEBAZnscUgEHaGQPJ4kFKTEiQ5sZNPH5JOr2WmJKrmkq37ILFRJCGEEOIGCqQYpLy6XuoamsVMYWazBwmz4jL9+JAMg3ZG82s0aUk0aRNCCCGBQIEUg+zz+I+yU5O8hs3qNBvGhrgt8QfpKSzzJ4QQQgKBAikGMQzanvSaVSBV+hFIuene1jKatAkhhJDAoECKQawl/prsNN8ptlKbEn+go1A0aRNCCCHuoECKQfZbxoxoMv2k2ModBBL7IBFCCCGBQYEUg1jHjGj8NYu0axJpNmkzgkQIIYS4gwIpBrE2ibSm2Bw9SFX2VWw6glR7iGX+hBBCiBsokGLapO0tdPzNY3OsYqMHiRBCCAkICqQ4SrH58yA5ptgokAghhJCAoECKoyo2f2X+TlVsNGkTQgghgUGBFINjRvx5kJxM2o5VbOyDRAghhAQEBVKMUVnXIDUeM3XrKrZkVym2vFYptuZfMwUSIYQQ4g4KpBjtgYSoj/YcaTJTEx0jSPUNjYZ52ymCBA8SIlSEEEII8Q0FUpxUsHmV+de1FkjlJtFkrWJL83iQGpvEGIJLCCGEEGcokGIMJ/+ROcVmF0HS6TUMuE1M6GQbQQI1dRRIhBBCiD8okGKMfQ4l/iDLE0Gy8yCVVtXZRo9AcmKCJHlEE0v9CSGEEP9QIMXJHDaQldIskOrqG9XNTQ+kYCrZGhub5LO1e+VAZbPocmJnabWs2l7qd32EEEJIvEGBFKMpti6WHkhmk7ZdL6RSY8yIvUBKDaBZ5Kdr98qFzy6V/3nzG8dlYPa+6Nml8pO/LJTN+yv9rpMQQgiJJyiQYozDumXLCUO6yGHds1s9l5SYYESCrONGdpRWq59Feem2601PSXAtkLYUV6mf//1hX6tIlXmZtXsqpKGxSdbsKve7TkIIISSe8K4jJ1Fn5vh+6uYEfEgQORUWo/aW4uYoTp+CDN8pNhfdtHW6rqquQVZsLZHjBhS2WuaL9ftbCSpCCCGkvcAIUpzhNLB264EqVwLJTQRJCySwwCSEzMz/YV+r9yaEEELaCxRIcYbTPLZtB5pTbH0K7QWSHliru3T7Qo8sAV/80FogIa22cEOx6b0pkAghhLQvKJDiVCCZS/1r6xtkZ1m1zwhSWpARJFSplXkM4ObHzCm+LQdo0iaEENK+oECKM3QvJHOzyB0l1YIJIhkpiVKY2br6LZQUG7pvL9rYEi0C8z1RpdF98tTPnaU1cogdugkhhLQjKJDi1oN0yNZ/1KmTdxdtTXqKe5N2qUcgHdYtS/2cv77Fb2Q2aP90dE9JTUpQKTf0RCKEEELaCxRIcSuQGlwbtINNsU0bUeQVMdLep6+2lqj/TxrcRXp73pNGbUIIIe0JCqR2kGLbWuxfIAXSSVsLpClHdFNz3TYXVxlG7CWbDsihhibplZ8ufQszjPekQCKEENKeoEBqRyk2CBYn3DaKhIDSzSF75qXL6N55XuX+uqpt0uDOKp1nCCT2QiKEENKOoEBqB32QtEDS6S470pLcRZB09AiRI7zXhEGdvXxH2o+kH2cEiRBCSHuEAinOPUiYiebGg6RN2tV17gRSTlqSihAhUgQWrt8vu8tqZN2egwIf+ISBFEiEEELaLxRIcetBahYy+w/WqZEgEC298kM3aeuht7npzUNvR/XOU6KspOqQ/O2/G9VjRxTlSr6nnYBO6yHFBrFGCCGEtAcokOKMbEuKTUduinLTJSXJ+dfZ0gep0VUESQuk5MQEYxbbS19uUT8neqJKQIsyNK7U4ooQQgiJd6IqkBoaGuTWW2+V/v37S3p6ugwcOFDuvPNOv5GIxx9/XA4//HD1miFDhsgLL7zg9fxzzz2n0kPmW1pamtcyeI/bbrtNevToodZzyimnyA8//CCxTmaqdxXbNhfpNa8+SC49SDkegQQmDmoWSHWeZpATPf4jvd6u2anq/0yzEUIIaS80n22jxH333SdPPPGEPP/88zJ8+HBZtmyZXHTRRZKbmyuzZs2yfQ2Wv/nmm+Wpp56SY445RpYsWSKXXXaZ5Ofny5lnnmksl5OTI2vXrjXuWxsozpkzRx555BH13hBoEGpTpkyRNWvWtBJTMZli80SQtrgo8QdpyQkBCaS8jJaO3BMHdzH+j8aQY/rme70Gaba9FbWy5UCVSskRQggh8U5UBdLChQtl+vTpMm3aNHW/X79+8sorryjR48SLL74ol19+uZx33nnq/oABA2Tp0qVKbJkFEgRR9+7dbdeB6NHDDz8st9xyi3p/gChUt27d5K233pLzzz+/1Wtqa2vVTVNeXi7RTrF5GbR9lPh7eZBcmrRz01v+NAZ2yZQeuWmyq6xGxvYvMNalQfXc0s0lrofWrt5RJnMXb5HrTj1MumbHrhglhBDScYlqim38+PHy8ccfy7p169T9lStXyvz582Xq1KmOr4FIsUZ4kCKDqDp0qMUDc/DgQenbt6/07t1biaBvv/3WeG7Tpk2ye/dulVbTIGp17LHHyqJFi2zf995771XL6BvWG80IEmakwXDtOsXm0qRdbvEgabF56rBu6v+TPT/NBNoL6YnPNsgrS7bJW1/tcLU8IYQQ0qEE0k033aSiNUOHDpXk5GQZPXq0zJ49W2bMmOH4GqTBnn76aVm+fLmKoCAth/sQR/v3N/fqgS/pmWeekX/961/y0ksvSWNjoxJj27dvV89DHAFEjMzgvn7OCtJ6ZWVlxm3btm0SDSB0EjzZQviQthyoDKsHqbSqrpVAAjdNHSrPXzxWZhzbt9VrdCWb3hZ/bC5uXm5PeUtEjhBCCIkloppie+2112Tu3Lny8ssvKw/S119/rQRSUVGRzJw50/Y18ApBxBx33HFKIEHUYFl4ihISmvXeuHHj1E0DcQRT95NPPqlM4MGQmpqqbtEG0RwYtStq6lWJvxYZbiNINQFWsWkyUpLk+MNavEhm9HtvO+B/YK1KC3oiTfsPUiARQgiJTaIaQbrhhhuMKNKIESPkl7/8pVx33XUqneUE0mmIDlVVVcnmzZtl69atyruUnZ0tXbrYn8B1dGr9+vXqvvYm7dmzx2s53HfyLcUS2of0/e5mH1R2WpLkZXgLGl8pNl9Vgi0CqcWk7Q/dwXtnWbXU1vvvs4SWAKD4YHO0ihBCCIk1oiqQIHJ01EeTmJioUmL+gOjp1auXWv7VV1+VM844o9W6zO0EvvnmG1XSD1C1BiEE/5PZdL148WKvyFOson1I3+4sNyI41io9K6kegdTQ2KSGzQYaQfJFl6xUJcCgu3aU+I4imVsBMIJECCEkVolqig1VZ3fffbf06dNHpdi++uorefDBB+Xiiy/28v7s2LHD6HUEQzcM2TBUl5SUqOVXr16tyvU1d9xxh0rBDRo0SEpLS+VPf/qTbNmyRS699FL1PMQEUnl33XWXDB482CjzR2rvrLPOknjphbTGJJD8oSNIOork1FSyrLo+YIGkh9au3VOhBNCALlmOy6IVgIYCiRBCSKwSVYH06KOPKmFy1VVXyd69e5VAQQk/Gjhqdu3apdJo5mjQAw88oHocIYp04oknqnYBSLNpIJzQGwleJfRHGjNmjFpm2LBhxjI33nijVFZWyq9+9SsloiZOnCjvvfdeTPdAss5jW7Or3FWJP0hO7KQG0CKCBKO2nQBC6s2oYvOTsrOCbdACyRfmVgAHKuvU9mC7CCGEkFgiqgIJviH0I8LNCXTFNgOzNSJNvnjooYfUzV/UA5Em3OINeI7M6TA3ESR8XkSR0D/JqZINkSXdLTuQCFIgpf7m59GqACKpi6cTNyGEEBIrcBZbHKIjSBo3AsnNwFotuBDRyfS0BQhYIPmJIFlbATDNRgghJBahQIpDtAdJ07cg09Xr0lMSfHbTNsaMpCf7NX1b0Wk+/ym2ZhO3Xj0FEiGEkFiEAikO0WX+OtrTI8+dbyotyU8EqSrwCja7CJJTG4G6+kbVCgAc1jVb/aRAIoQQEotQIMUhuswfFOWlSXKiu1+jv27aOoKUE4RA6pWfrqJCVXUNUlxp399oewnEU3NF3ZDuHoFUwV5IhBBCYg8KpDgkKzU54PSa2YPk1E07mB5ImtSkROmR0xzJ2uJg1DYG6xZkGMZsRpAIIYTEIhRIcUhmamKrLtZuMLpp+/EgBSOQzNtiLuU3ox/Hcp2zmgXSPgokQgghMQgFUhyiy/wDqWCzjhvxadIOsAeSdVucjNo6soThtp2zmkeZcNwIIYSQWIQCKd5TbC6aRGrSkhNceZCCjSDpbXGTYtMRJKbYCCGExCIUSHHeBymgCFJKdFNsFEiEEELihah20iahC6RAPEhuG0UGU8VmFmubi72bQQKU/hsCqTBDMjxiDSm2xsYmSeC4EUIIIfEskDZt2iRffPGFGv5aVVUlXbp0kdGjR8u4cePiYo5ZewB9jwZ0yZSu2akBRXvSI1jFBgZ1zVJ9mfZW1KqS/l75LeINpf9oAYBWAD3z0iXB0ymyvrFJvW9+ZrMniRBCCIkrgTR37lz585//LMuWLZNu3bqpwbLp6ely4MAB2bBhgxJHM2bMkN/97nfSt2/fyG51Bwd9jz687ngJNOji16QdQqNIkJ2WLKN65cqKraWyYP1+Oe+YPsZz2pfUPSfNiGThfSCOkGajQCKEEBJ3HiREiB555BG58MILVeRo165dsnz5cpk/f76sWbNGysvL5V//+pc0NjbK0UcfLfPmzYv8lndwEKkJdByI20aRwVaxgYmDu6ifX/yw3+tx7Usye6Z0JRtL/QkhhMSlQPrjH/8oixcvlquuukp69+7d6vnU1FQ54YQT5K9//at8//33MmDAgEhsKwmRVB99kOARCjXFBiYO6qx+LtxQrLxFdgZtTYtRm6X+hBBC4jDFNmXKFNcrLCwsVDcSe/hKscEfBD9QqAJpdJ88yUxJlAOVdbJmV7kc0TO3VQ8kTWfdTbuCESRCCCFxXuafmJgoe/fubfV4cXGxeo7ELi0m7dYCSUePkhM7GcsF6486bkCzQJ6/fr9tF21NF5b6E0IIaS8CyWlSe21traSk0Ggby6SnODeKNKfXAvU2WZngSbPBqO0rxVboMWZTIBFCCInbKjaYtAFOnk8//bRkZWUZzzU0NMh///tfGTp0aGS2koQFX32QSkOsYDMzaXCzQFqy6YAhxnaX16iffQszW6fY6EEihBASrwLpoYceMiJIMGOb02mIHPXr1089TuLTgxQOg7a5H1K3nFTZU14ryzaXSPfcVKPBZb6pQk6btIsZQSKEEBKvAgkNIsGJJ54ob775puTn50dyu0gkI0h1rRtFlodRICHKiDTbmyt2KB/S2P75hv/InL7TZf6MIBFCCIl7D9Knn36qxFFdXZ2sXbtW6uvrI7NlJGIRpNoIR5DMabb56/e1VLBZxqLoCBL6IDl52wghhJC4EEjV1dVyySWXSEZGhgwfPly2bt2qHr/mmmtUvyQSuxjDattAIGmj9rc7y2XltlJjBpuZLh4PUl19o1TUUmgTQgiJY4F00003ycqVK+Wzzz7zmr12yimnyD/+8Y9wbx+JQIoN/Y4ONTRGVCB1zU6Tod2zBYGh/6zebTtYF9ujB++yFxIhhJC4FkhvvfWWPPbYYzJx4kQvPwmiSZjJRmIXc38jaxSpVAukjPC1atBRpNr6RtsUG6APiRBCSLsQSPv27ZOuXbu2eryysjLk/jkksqAJpB5wW2MZNxLuCBKY6PEhacw9kFqPG2EEiRBCSBwLJAyj/fe//23c16IIvZHGjRsX3q0jYQW/q5Zu2pFNsYFj+xcoUQYgzHrmp7daptCIIFEgEUIIicMyf80999wjU6dOlTVr1qgKtj//+c/q/wsXLpTPP/88MltJwmrUrqxraJViC2eZvyYjJUmO6pMvizcdkKK8dDWGxDGCFEcepO92lUtBZop0y2nx4BFCSHvmu13lkp+RIt1zO85xL+AIErxHX3/9tRJHI0aMkA8++ECl3BYtWiRjxoyJzFaSsFey6YhRJCNI5nJ/85Ba+1L/+PAg7SqrljMfnS8zn1kS7U0hhJA2YduBKvnxY/PlgmcWS0ci4AgSGDhwoDz11FPh3xoScYZ2z5FtB6pl2ZYDMrZ/gXoMPYi0QMozdboOB788rp9sLq6Snx3d2/b5lnEj8RFB+n5XhaoCXL/3oDQ2NkmCNnURQkg75dO1e+VQQ5MxU7OjEHAECaBa7ZZbbpH/9//+n+zdu1c99p///Ee+/fbbcG8fCTNGA8cfWgbJHqytl4bGpohEkHIzkuX+c0cZYsxKlzjzIG0prlQ/IZKsUThCCGmPzPecL+BdtbaIac8ELJDgM0JqbfHixfLGG2/IwYMH1ePojXT77bdHYhtJGNGl95iRVu2pZNMn+pSkBKNXUlvRMo8tPlJsWw9UG/+PF1FHCCHBUt/QKIs2FBv3KztQU9+gGkXedddd8uGHH6ohtZqTTjpJvvzyy3BvHwkzAzpnSlFumtQ1NMrSzQci6j9yQ7yV+W890BxB0iNSCCGkPbNye5nXpANkHDoKAQukb775Rn7yk5+0ehxG7f37W9I2JHZL/XV/IgySjbpA8niQquoapKou9r945hw8m1sSQto7CzznCQ0Fkg/y8vJk165drR7/6quvpGfPngGtq6GhQW699Vbp37+/pKenK/P3nXfe6Xdw6eOPPy6HH364es2QIUPkhRde8HoeBvJJkyapobq4YQzKkiXeVUcXXnihEgvm22mnnSYdKc32hSevHIkSf7dkpiRKWnLzn+H+itgWHPi79BJIcdSagBBCgmG+ya8KDtZ0HIEUcBXb+eefL7/73e9k3rx5SlQ0NjbKggUL5Le//a1ccMEFAa3rvvvukyeeeEKef/55Napk2bJlctFFF0lubq7MmjXL9jVY/uabb1Yi6JhjjlHC57LLLlNC6Mwzz1TLYE7cz3/+cxk/fryaF4f3mTx5sjKRm0UcBNGzzz5r3E9NbY5mdBSBhL4W+ypqWyrYoiCQ8DeENNv2kmqVsrIOtI0lsK/MDTbjJS1ICCHBcLC2XlZsLVH/z89IlpKqQx1qsHhQjSKvvvpq6d27t4oADRs2TP1ERRsq2wIBzSWnT58u06ZNU/f79esnr7zySqtoj5kXX3xRLr/8cjnvvPPU/QEDBsjSpUuVCNICae7cuV6vQZdvGMo//vhjLxEHQdS9e3fpaECQDOuRI2t2lcvCDfultCp6ESRQ6BFIsS44rCWu8WIsJ4SQYFi8sVhV7GJMVI/cNNX0lyZtC+Xl5cb/YcxG9Gbjxo3yzjvvyEsvvSTff/+9Ei6JiYFVQCHCA9Gybt06oxJu/vz5qlO3E7W1tSoqZAapNoiqQ4fsy66rqqrUcwUF3qXmiDTBO4U03ZVXXinFxcU+3xf7wXxrL+X+OoKUEyWBFC+l/laBFOvbSwghoTDf4z9C1iE7LanDpdhcCSSkr3S/I1SrlZaWqgjS6aefLj/72c9k8ODBQb05KuKQshs6dKgkJyfL6NGjZfbs2TJjxgzH10yZMkVFhJYvX648IUjL4T4EkJNJHCnBoqIi5UUyp9fgXYJAQ/QJ7QsgzBANs+Pee+9VqT99w+dvD2k2fAGiadL2HjcS2xGZLcXNAqlznAg6QggJh/9o0uDOkpnqEUiMIHmTlZVlRFcQdXGK1ATKa6+9ptJhL7/8sqxYsUJ5ke6//3710wmYuiFkjjvuOCWqkKKbOXNm84dJaP1x/vjHP8qrr74q//znP70iTxBmP/7xj1VPp7POOktFw5Cqw+ezA76nsrIy47Zt2zaJZ9C4EX2PdpXVyFdbS2NDIMW44EC7fTC6T776ySo2Qkh7ZXdZjfyw96BgHv34gYWS5RFIFR0oguTKg4TIy4knnqgqxwDK/M09kMx88sknrt/8hhtuMKJIAGJly5YtKlqjRY8VpNOeeeYZefLJJ2XPnj3So0cP+dvf/ibZ2dnSpUsXr2UhtiCQPvroIxk5cqTPbYGXqXPnzrJ+/Xo5+eSTWz0Pv1J7MnGjIeQx/fJlwfpi5UWKxJgRt3SOsxTbmL758uGaPcpUjigmjOaEENIey/tH9MyVvIwUyfKk2DqSB8mVQILPCFEdjBhBKgoVZxkZoVcbwRtkjfrAx4TKOH8getSrVy/1f0SIzjjjDK91zZkzR+6++255//335eijj/a7vu3bt6soGQRXRwFpNggkTdQiSHEyj22LRyAd5Ykg1dU3qoqOnLTo7DdCCIm0/2iix46R3QFTbK4EEqI2V1xxhfo/PD/w7KAfUqig6gwipk+fPkp0oZfSgw8+KBdffLFXamvHjh1GryMYumHIPvbYY6WkpEQtv3r1aq+0HLbvtttuU6k7VMbt3r3bSBXihvEof/jDH+Tss89WVWwQfjfeeKMMGjRIeZw6CpMGdZE5sta4H/0UW+ymrDCWBWX+4LBuWSrcjAMFeiFRIBFC2hOIjM/XAslT0KM9SB2pzD/gRpGffvppWMQRePTRR+Wcc86Rq666SqXv0EsJJfxoFqlBU8qtW7ca92GifuCBB2TUqFFy6qmnSk1NjWoXACFk7pVUV1en1o2IkL4h5aajVKtWrVIepMMOO0wuueQSGTNmjHzxxRftKo3mj+FFOV5ptegLpNiNIG0raY4eoZID+6klLRi7oo4QQoJh3Z6D6oIQTXxhKQDag9SRqthcRZDg40HjRjdpNQyxRTWZ7m3kC/iGHn74YXVz4rnnnvO6DyGFSJMvNm/e7DcihtRbRychoZNMGNhZ/v3NrqgKpC4egQTzX82hhjYfmBtIBVvfwgyjueXm4qqYFnWEEBIMX/ywT/0c279QUpOaj8e6zJ8eJAtr1qyRvn37yrnnnqvSYvD0aEN0fX29eh79i+BV2rlzZ6vRHyR2QfhUC6Ro9UHKSU+S5MROcqihSWa/+rWkekaPgMnDusu0kc6+sPV7D8qbK7bL1ScOMkLAduHi5xdult4FGXLy4d1CMmijYVq8RL0IISQYdHptksd/BLJSm88P9CBZgOBBE8fHHntMdcxGk0SkqZCOgtEaoIfRpZdeqmacWRs5ktjlR4d1kcSETqqNfLQiN4jI9C3MVGLnvW+b/WKaj7/bK6eP6O5YKfbwR+vknVW7VMuC2accZrvMV9tK5X/fXiM5aUmy8vbJQVWd6RJ/iCzQOduTYuM8NkJIO+Ob7WXq57EDWporZ6Y2nx9Y5m8DPD/ooI3yevh3UI5fXV2tSuOPPPJI9ZPEHz3z0uWlS441wqfR4q+/GCP/XbdPmkxRn3ve/U5draCcvmu2vejeuK/SaGjmJJC+WOcZyltTLwcq69Rok0DZUtz8Pn0LMr0jSJX0IBFC2g849pZ6mgd3y2k57hqdtBlBcgal9BBEuJH2wbiBhdHeBBnUNUvdzDy3cLOa0ba1uMpWIOGLrFNfiBJV1BySbJuKsvnrm/PpulQ/GIFkTbHpdTCCRAhpT0AANTQ2X6qafak6xQYPUkfp/xZwFRshbYUWI9YZaBpMltZXM/hCf7nxQKtl8LzuFG5OlQVCY2OTbCupNkza8TQ/jhBCAkGPnkpJSvCyXehGkRheW1vvv1dhe4ACicQsWow4CSTr47rzq5kvNzRPozZe46lGC4Q9FTWqKSS8WphoHS+9mwghJFBKq5oFUp6laCfDJJY6ig+JAonELNoQ7SRqtC8oKaGTV2mqXTWGXkZ3ww4E/f7wayUlNn9lWMVGCGmPlDsML09I6NTSC6mD+JAokEjcpth0uuzEoV0F+mfDvkrZVdacCrMKpFOHdfO5Ll9oUaUjWubxKFV1DVJV1zEOFoSQjpNiy7Vp+6IFUkfphRSwQMIYkIqKilaPV1ZWeo0IISRUdMWYU9RHN28c2TNXRvTKM6rZNBBLaB0A8XTeMb2D9iBZS/xBZkqi6jIL9lcwzUYI6QACKc0zboQpNnsw8wzl/VbwGBtEkkhEkNDyHrPQHCvLCjOMhmY6YmQWSxBPmEgNdpfXqG7doVSwAd1NW20f02yEkA4gkDKZYrMHzSHLyspUeR8iSLivbxga++6770rXrl0ju7WkQ5GbkayaO5pnoTlFdvRARRi1UXWm/w8mDiqUgswUFfVpahLVOiCoMSMmgQToQyKEtDd0D6QcG4GUbQik5mXaO677IGFALa6accOAVyt4/A9/+EO4t490cNBh+5sdZUqkHNYt23i8tr5BdpXXNC9TkKFCv+nJiaqqbO2eChnaPVvmry9Wz08c1EX9ffYpzJTvdpUrYWXtuRRoig1QIBFC2msEKc80yFzTYtIOLArf7gXSp59+qqJHJ510krzxxhtSUNDSgjwlJUXNaisqKorUdpIOCtJaEEhWczWiQIgGISqE6BAEENrif7Z2n5Fag3CBaDqqb7M/qU9BuhJIuvrNDQglF3u6ZSOVZ6azpxdSMUv9CSEdyIN0sIN4kFwLpOOPP1793LRpk/Tu3Vt11CYk0uiojdVcrQUTntcdXScO6qwE0hfr94tu8jq2f4ExjbqlKq464BJ/zKrLsXTpZgSJENJRyvy9I0hMsdmCSFFpaaksWbJE9u7dK42N3h01L7jggnBuH+ng6NJ6a9RHCxdz6f2kwV1E5DtZsqlYDnk6vU7yeJMAUmzqtQcqQzJoWyNIFEiEkI5U5n+QESR73n77bZkxY4YcPHhQcnJyvOax4P8USKQteiHZCZfDumVJl+xUVfW2aKPHf2QWSH76KtmhI1daXJnRvZBY5k8I6VApttqO4UEKOE92/fXXq35HEEiIJKGCTd8OHGg9C4uQUNCiBrPQdHWad4l/ppdAR5rNnAIbYjJ26yo0vBZ+Ojds8USb4F+ywhQbIaTdjhrJYIotYIG0Y8cOmTVrlmRktE45EBJuMPsMY0IwCw0z0awpNmvqyyyQUN5vjnAW5aWrppE1hxpVlMkN2q9kn2JjHyRCSPsBF6HlNc5l/lkdrA9SwCm2KVOmyLJly2TAgAGR2SJCTGD2Wc/8dFXmD1HUIzddRX+cvEHmlNpE5UlqAdOp8fodpdXq9V1zmgfPav6xdKvc9e/v5FBDi69OT63u4+nqbaaLRyChqyyaT5onX9tx0xur5PvdFfKPy48zjONWVu8ok6vmrpDrJx8m04/s6XN9hJC25/7318rCDfvluYvHtirciHXW7amQS59fJr8+aZD87Ojm6QJmKmrrVXVwW3uQvt1ZJhc/t9RI75n5w4+Hy3nH9JGYFUj/93//Z/x/2rRpcsMNN8iaNWtkxIgRkpzsvRN//OMfh38rSYcGIggCCSNHjh1QqCI21YcaVDQIA2TNdMtJkxOGdJHVO8rlxCHeAkmburVAOrpfS6sK8OyCzbYt9NFGYFhRTqvHc9KTJCUxQeoaGlUrAOu2mIHoem3ZNkGWcM3OchndJ992uQ++3a227Z1VuyiQCIlBXli0Wcpr6uWjNXvkp0f1knjiwzV71PHl7ZU7bQWSrmDDGKVUm4u4Fg9SeAXSB9/ukT3l9pF40/VqbAqks846q9Vjd9xxR6vHkM5oaOgY5i0SBR+SJ2qkfyIahKiQlWdmHiONTU0q+mS3roUbio3u2Bqk3BDdAf+5dpJkew4EOpVmFx3C33thVorsKquR/RW1PgXSztJqJY4ADlBOAklHxuhrIiT2KKs6pMQRQL+1eBNI+tiJhrqBGrQjmWLT23XF8QPlF8d5R4vyMpqrhWNWIFlL+QlpS6zVZ75K70FCQidJkBbvkZu+SnosyfCiHDm8R+tokRMQT0og+RE0ZkGm/VO2y3m2i80nCYk9dNGGnvuIdL/Z5xjr6OOQ0/HKtUCqCa9A0se9I3rmSK/82PE3s9sjiaNeSFXes9Esna0DWZe11F8PuTV7mNyACJIbQWN+P19tBlqu8BhBIiTWMH9391bUyro9ByUet/9AZZ1XVXCrCrZ0+6iNTrFV1jXYvj7U7XK66I0bk/Yjjzxi+zhUdFpamgwaNEh+9KMfSWKib8MqIW6xRn3MXbQDRX8B9RULwFWgHk9iroJzg9tKtm0uBFJlbb0R+q6qa5CqunrJSAn4K0oIiRB2F1ZDure0EollUAm8q6y5KrehsUkNpYW/0i6CZFfBZo4ggcq6eskOg0m9uq7BqCqOe4H00EMPyb59+6Sqqkry85t9FOiBhLL/rKws1V0bFW6Y3YaRJISEiv7SwAiN3LfRvDEEgYQvJL6Y6SmJsmHfQdldXqP8TMdYjNv+cNsLySvF5iCQrI+jAWWfQgokQmIF8+ihkqpDMv+HfXLJxP4SD6A4xRz0wTHLSSA5pdhSkxIkObGTHGpoUsficAgkfdzLSUuKqt8oLCm2e+65R4455hj54YcfpLi4WN3WrVsnxx57rPz5z3+WrVu3Svfu3eW6666LzBaTDge+hPqLjANUKCk2fAHxRQTbSprXo6NHY/sV+C3Vdx434j7FBjGGtgC+lgHsr0RIbKG/o+eMaTZnL950QEVm4oHWF2Ctjy/+BFKnTp0kM8w+pJamv7EVPQpKIN1yyy0qijRw4EDjMaTV7r//frn55pulV69eMmfOHFmwYEG4t5V0YHQ6DX08kPsPJRyrv4haaGn/0YQA02sAo02cDjbmFJ45xYY+I7ias2I1jtOHREhsoU/mk4d3l8LMFJUKX7G1ROIBNxdg/gSSOc2Gnknt2X8UlEDatWuX1Ne33jF4bPfu3er/RUVFUlHRXDJNSDjQXx40aAMow/f1JXazLnwx0Z/oy40HWg22DWeKDaF4fTAZ0DnTsZLN2nqAAomQ2AGRIrTr0NFrfUGlK2Bjna2Wgd92Ue+y6ubHctOdU/taIMEzGc7tsmvGG3cC6cQTT5TLL79cvvrqK+Mx/P/KK6+Uk046Sd3/5ptvpH//+MjLkvhAz1HT6TAcoIItr9VfRERsVm4rVbl0eAqGBVDeH4hA0ldI3XJSZXC3LK/H7JbTvZ04BJeQ2EH3MktPTlRd9HXF6xeeY1Ks0+r44iOC5MsLlJ0WoRRbe4gg/f3vf5eCggIZM2aMpKamqtvRRx+tHsNzAGbtBx54IBLbSzoo+suzs6wm5C+TUclWXGkc3MYP6qz6JwWK9iAhSmQeUeJ0ALD2dLJLsY3smat+MoJESOxg/h6bB2Ov2l6qGkjGOnqupHF8CcKDBDIjlGILxlMaaQIukYEB+8MPP5Tvv/9embPBkCFD1M0cZSIknFhL+oMp8deYRYruijspCP8RyM9IUSNPcGWJ3iIYdeIrhNynMNM2nYayW20aP6pvvizbUkKBREgMoVuD6GMPhl8P6JIpG/dVyqKN++W0I3pIrKLmV3qOQ76OL/7K/MPdLBK9lLaVOA8EjzZB1xAPHTpU3QhpC6xXF31DyFfrdW07UC2bPUIl0AaRGkSdCjJT1cEGrQNsBZJNBMlqyEZlG0pnUUJ7BCNIhMQcdu1FcGEFgYRIdCwLJFy8obkjOLJ3nrMHqcp/BClbN4sMQwRpT0WN8nYlJXSSHrmtj51xIZB+85vfyJ133imZmZnq/7548MEHw7VthBhAeOjBsKFebeCLmJjQyVhX/86ZIbW3R5oNYgZ9mnyXsaZ7Ra/MYwqQ7gPYjq6eyjiOGyEkdtCFFeaLtYmDu8jzi7bEvFFbH4O656QZMyOtF2CIYuu0mZsqtoNhEEh6n/bMT7ednRltXG0RTNiHDh0y/u90+/rrrwN6cwy2vfXWW5WhOz09XbUOgBDDicMXjz/+uBx++OHqNUjtvfDCC62WmTdvnopwobv3iBEj5N133/V6Hu9x2223SY8ePdR6TjnlFNXbicQmEDS98luGwYYikPBFNA+WnTCoMKRt81fqrw8CSLHhfZGSqz7U4FVma746ddudmxDS9ik287Hn2AEF6tiESLQ1KhxLmHsNdTZdgJnPtRU1h1QLkrb0IG2JYYO26wgSumLb/T9U7rvvPnniiSfk+eefl+HDh8uyZcvkoosuktzcXJk1a5bta7A8+i099dRTqmHlkiVL5LLLLlNdvc8880y1zMKFC+XnP/+53HvvvXLGGWfIyy+/LGeddZasWLFCjjjiCLUMejVhbAreGwINQm3KlCmyZs0aJapI7IEv98b9leqAVJQX2u8IV4H6oDFxUJeQ1uWrkq22vkF2lbcYy1FB0iM3XfVBwgG1a3ZaqzScNn5X1NSrhpKBNq8khIQXcy8zs/8xJy1ZpayWbylR/dR+PtZ7En2s0HKRlqH6NwFE0OHB1GJI+48yUhKNSrdIe5Ds9mksEXRMa/369fL+++9LdXWzwcpf1McOCJnp06fLtGnTpF+/fnLOOefI5MmTlehx4sUXX1RtBs477zw10uT888+XX/3qV0psadDR+7TTTpMbbrhBRZoQlTrqqKPkscceM7b14YcfVk0v8f4jR45UUaidO3fKW2+9FdT+IJFHX2UgChNqOFZ/IRHNGTcwtAhSSzft1gJpR0m1uirDQUcv11JF13LFqf+P53DAghcJOKXtCCHhB2OH7Lrco0oVKSVkxM2RbKCr2f69apd8vm6fcUPaDeOMYgHzBRguuLI9Isd8zHJTwRZuD5IxFaG9CCSMFjn55JPlsMMOk9NPP101jgSXXHKJXH/99QGta/z48fLxxx8b1XArV66U+fPny9SpUx1fU1tb2yrCgxQZRJVOAy5atEilzMwgOoTHwaZNm1RTS/MyiFphXIpexu59y8vLvW6kbdHCIhzloPoLObJXXtANJ60RpN3ltT5DyNpvpLffXOpvpNg8/Z0KM/136CaEhA8ImpMf+FxueWt1q+e0RxAeHmtEVxd4III085klxm3G04vl9299I7GAPg7pY49Os5mPL24FUlZqcthSbLHcAykogYQZa8nJyWrmGgbUahDRee+99wJa10033aQiQPAKYZ2jR4+W2bNny4wZMxxfA6Hz9NNPy/Lly1UkCGk53Ic42r+/2SgH8dOtWzev1+G+7vStf/paxgrSdRBR+sZBvG3PGSOL5KShXcMyHPLsMb1k8rBucuNpLe0pgmVYUXODyaWbDrSKpNqFkPX/zQLJeqDonO0clSKEhJ93VjVf7L+/erfUW3qabfWRCjqqT7787OheMrwox7gd5mkI+9GaPcr8HG2sxyG7GZJuSvxBZmpih0mxBVzm/8EHH6jUGmaumRk8eLBs2bIloHW99tprMnfuXOURggcJJm8IJIwqmTlzpu1r4BWCiDnuuOPUyQiiBsvCU5SQEDkXPHxP5go+RJAoktqW7rlp8syFx4RlXYj6/O2Co8OyrmP6FaicPUr1EaIf1DXbZwjZiCB5niuvOaRC+F4CyUWHbkJI+Ji/fp8RGVm5vVTG9C1oXcFmcyKHJ3LOOaO8HoMoGn3HB8rjg0aSo/vkS7RAyhDHJvP22x1fAk2xHQwxgoTXawtBLA6qBQErisrKSq/IkebAgQOqq3YgwCOko0ioNPvlL3+pIlSI1jiBdNozzzwjVVVVsnnzZhXJgn8pOztbunTpYjSz3LNnj9frcB+P6+f1Y07LWMFny8nJ8boRAhByH9uvwGsUiq9J1dZu2vrgi6s6XSHScgCjB4mQSIPvIPqiaeb/UBxSKgiiafzA2JjVtt3jg8xMSZQCj0HbTiCVuuiBZE6xhepB0sc9jHmC2b1dCKRJkyZ5ldXDL9HY2KgiOIF20IbIsUZ9EhMT1fr8gZQcolhY/tVXX1XVanpd48aNU94mM+j+jccBqtYghMzLICK0ePFiYxlCAkEProQPwV8IWR9k91bUKhOn3TJGqT89SIREnC880SM9bUhHk3xd6PhjQozMajMfX7QP0k4gles5bP4EUlp4yvxj3X8UVIoNQggmbXh/6urq5MYbb5Rvv/1WRZAWLFgQ0LpQln/33XdLnz59VIoNvZTQaPLiiy/2Sm3t2LHDEGUwdMOQDUN1SUmJWn716tWqXF9z7bXXyvHHH6/mwaFCDgIK2/u3v/1NPY8/EqTy7rrrLpUa1GX+SO2hHQAhgTJpcGe57z2RLzceUDPZkhMTmtv7a3Ok6SCAQZA5aUkq/I7xIoaB0ksg0YNESFuhI7/njOklry3bLl9tbR5irUvagzmZ6/FFK7aWqGiLjg63NXazzrTHcZ9pILZrk3ZK8+dAB2y0MUlNCq4NydYDnhFMnvFL7SKChD5CECkTJ05UJfJIuf30pz9V4gaNHgPh0UcfVaX9V111lSrH/+1vf6tK+FGWr0GVHNJo5uaSED6jRo2SU089VWpqalS7AKTZzNVx8DVBEGG5119/XZXv6x5IAMLummuuUS0C0E/p4MGDymTOHkgkGIb1yFGhYhxUV24rNdJjVXUNqjQYnWLN6CtRhJntDr66+SS7aRMSWeAXWrihOaV23jF9lJCob2ySLz2PmT08gQgkrActATBCaMnmAxItzC1END49SBnuTNqgsjb4NgYtxz3vY2MsEZSkRRXX73//+5DfHL4h9CPCzYnnnnvO6z6EFMSYP84991x1cwJRpDvuuEPdCAkVzGQbP6iz6oWCkPrR/QqMA0CPnLRWV1k4WK3eUa6iRzoXb06xGWX+jCARElFW7yhT4gDm41G9clVfoy3FW1W6/JRh3VRTV6uHxw04x2Bdry7dpiJUJw7pKtHA7gLMLkLtNoKUlJgg6cmJahoAImOB7BPv7YrdIbUBC6RVq1a5Wg5NFwnpiEzyCCQcWK879TBTCLn1AQBjR7Q/oCUE3hJqZpk/IW2D9g2OG1CoTv5Il89d3CyQvLpQF2YaHh63oEeSFkjRoqXHWqZtBEnPhNQmbX9l/tqHBIGEbv/BstXTW0ofC+NaIB155JFqJ+o+L/oPxdz3BY8hBUZIR0Q3jPt6W6maa7S12PkKST+G0Sm4QrUupw9gKP/XniZCSPjR4kV/f8cN6KzM2uv3HpRdZdUhpYImDOysUuxr91TI3vIa6ZrTthYOsw/S7vhSc6hRKusalNeqzKVJG2B5FJAEW+qPtCaq62K5xB+4Puqi+/TGjRuNnyi3x1w23Nc3PE5IR6VXfob0K8xQX36YtX0ZO/VjyzcfUMujj1JXj+8I5GekGBU1BzhuhJCIgCpSzFEzjwyBB2dErzxDPNlFeN2Sn5kiRxTlqv8v2ND2USQMvEakB8cS84BuGMaRJgPFnii1rmJzM1kgS89jq21+TaBAeMLnlZKYoLqTx71A6tu3r3GDIRrRIpTZmx/HjZCOjDF24Id9Pqs0dEUJrt7UMgUZysdk7qNS4PEhsdSfkMiweFOxGtoK8dC/c2arCjSk2bTJOdhuz7oFSDTK/XV6DQOyrQNozWl8XKTpsv3ABFJDUNul05YwseNYF6swbk9IGJk4qItxYPUVQeqRmyZJpgOD3TIs9ScksugmjhMGFXr5i7SowfPGhU6QAgmeJh2NCmaoe7gr2Fr3WqszokeBeJBCGTfia3RLLEGBREgYGTewUIWzN+yrlD2e4bV24wlgBjWX/tsdwHSpP7tpExIZdFRn4uDmCxvNUX3zVAoK3711ew6GNHF+TN98SU1KUI1h4WuKdg8kO6O29h+hUs+N3zErxBSbr+1qNwIpUEc/Ie0dhKdHevwLIDs1SfIc+oqYRZGvKzxGkAgJP0hdf7+7Qv1/wsBCr+fQlmNs/5ZZbLjoKTJ5eAIeReRZV1un2XxFaszHl9IA/EdeAinICJJujhvLJf4BVbGNHj3aSxBVV1erTtgpKd49EFasWBHeLSQkzkBIHZVs1vb+VuzGj9im2OhBIiRi6bXhRTlS6BEL1u/x5+v2OXp4AgEGcIgjpN4vnthf2gqjRYFdhNqUwm9pEumup1FWWmgeJLvxSnEtkKwjONBFmxBifzB89JP1fkPI5pC9rxC4nnhNCAkfus+Rrl5zKrgIRypIres/GEVUrEZ0hCK2wpZi0yn8ijpTk0h3kiArxBSb9kbFeorNtUC6/fbbI7slhLQTRvfJl4yURDVmxFcI2fwcWgRYKQwwxfbKkq0ysEuWV2rADR+u2SN7K2pkxrGBVaF+t6tcPlqzRy770QCVRnAL2hY8v3CzmnsV61eQ8Ux9Q6M8+d+NSgCM6t2S9o0Xlmw6IF9tLZHLJg3wqvC0fsZnFmxSXaoHd8t2vW6Ypa39j6wM6ZatLlLw/Qs1FXR49xwpzExRFzv4TMcO8E7p+aKqrl7+8ukGOVDlfaGEzt9XnTDIMS2GFgbwPQG77Td363fbRbu1QPJOsa3fWyEvLtoihxqdzeiNjU3G+/W2Oe7FEtGZnkdIOwZXhzgpfbBmjwzp7nzQ1gf03gXpkp6S6Jhic1Pmj3EJN7/5jWr7v/T3p7guncXB9+qXV6ir2pOHdpPuue57ksx573v5dO0+6d8lU84YWeT6dS8v3iJ//vgHdZD83x8Pd/06Ehifrd0nf3p/rXy+dp+8dsU4iTdufWu1arAIkzNG99jx0Xd75J53v5d3v9ktb109wfW6N+w7qOar4bt6jMO6kRo/cUgXmbd8u8/vcSCjiN5euVOl9gIRSHjNY582R6StZKYkyayTB9s+t25PhSF67ISPuUo2kB5IZoFk7aR95zvfGWlJf6C1QrQG+LoltreOkDjl7p+MkCnDu8v0I3s6LjOoa5Y8/v+Ocgwzt5go/afYdHUMojPf7izzMor7YvGmA0ocNb9PbUACaWdpTVCNLFHhB0osV8QkvEAEgOLK+PSw7Sxr7rS8s6z578wO3Y155fZSKa2qkzyXHhodPTqmX77P6OctZwyTY/oXyPQj3V8AOIHeShA7X6zfL7+ZPCTg7wu2dZKn2g7CsXnu4z5HgaQbUyKibOeDNFJsB80ptuSAPEiVpggShvoihQh+9aMBhohy4oQh3pWDsQgFEiERACX6Z4/p5Xe5aSN7+FwHOFDZ3MjNV1RIew20t8KtQFpgqqrRB0m36NRfoPOY9LaGMgmcuN/PwY6DiCY42eq/K19FCugUDdBeaNGGYpk6wvn7ZO8/8n2ShmD42dG9JRxM8KTyVm4rlfKaQ5KTlhyQ0fr0ET3kogn9DZMzBNJXW0vV79dOjGgRqPswOV2A4fW7PSLUrUDKtkmxrdhSIrX1jWoiwM1Th7aLKnf2QSIkRtFTspHO9xdt8RJIAZQS6xNFoAIJ3g/tiTBfRbpBb2v1ofg7cccT8SxEzYUJvjx4MBhrEJlxA2YbYhSQL/EQCZBSGtA5U32fIebcYtdwFt493Me4jsWeqI3Vf7Rsc4lX00srOWlJatQH2Lj/YEBVbJk2Zf56/8Ne0B7EUcgCqabGOfRJCAkNNGzL9/RQ8mfU1leZAAdGHCD9AWO27gMD9DRvN0Ac6abAgUQo4HnSnqp4PHHHawQJxth4whw18imQTM+5vTBACw7sE3y3hvXIkeiMItof8LBZayper8uut9LSzQfUCJWi3DQlyuyAiNE+pI2eNF6gKbYK03ffn+m9QwikxsZGufPOO6Vnz56SlZVlDKi99dZb5e9//3sktpGQDovhQzJdKduhD6LIwuHAiAOkPxau977yDCSCZN6eQJrFbTvQ7BkBbkQcCQ5E+HZ4/Dmgsi6+onVm4ePLg2deDt8B84WCE/pEDtO0U3VcpNAtBXQPJn+UVB0yLkCsla7meXGOKcTBvqM52oeEittgUmyVtfVKxJVU1snqnWXN7+kQseoQAumuu+6S5557TubMmePVJPKII46Qp59+OtzbR0iHxk03bfg1UJEDThra1fGgaUVfeerjZ0ACybQ95qtIf5hTgfF20o4ndpXVqPSLJt58SN4CyX8ECekit3/3ehktMNqS4wYWKi/hxv2VsqO0RcD6+750y0ltZSbHWCN8d1GgoT1E1u+2U3rNenzRBBpBamxCqrxBFm4oVhHlw7plSdcc94Ue7U4gvfDCC/K3v/1NZsyYIYmJLb+wUaNGyffffx/u7SOkQ9NSaeJ8kthe0nwQhVHzzFFFrkYaqD4w65vLcY/pWxCSQArEg7SluDmUDxhBihxmIRqMTyzamKNGxQ4RJKQN9XPTPG0m9N+0EzBH6y730UgFwZg9qleu+v/8H/a5/r70LWidJkPF3sieua2EIb6b6FHmTiClBCWQ0pMTVbRaR5D1fvdnem/3AmnHjh0yaNAg29TboUPBddUkhNiD5nLmah1/85b0AREHSF/9k3DViWG6GKJ58uHNUSfzRG9/mE9agUQn9IgBwAhS2wmkQCsNo435bxd/+xD0ViDodZRMl+EvWF+sKj6d+HJD8/P9O2faNmdtC/RgXDdz2fyN5GjxNLWILZ2+g7/KGiGyYh2x4lYgderUyTBqI4KsP0tbmt5jUiANGzZMvvjii1aPv/7662peGyEkfOhSf6eraKB9FxhdggOiNp4u9PRBsUNfcaJJXtec5vcora4LKoIUiAfJfOKuOdQYd+bheBVI8ZxiQ58uuzSuOb12dN985YuBaEIfMCe0eJgwyH2jxnCjPTpIS/n7+/c39V5HbOavR4qrKWCztFVA6VRlID6kNTvLVT+q5MROAXfxb3d9kG677TaZOXOmiiQhavTmm2/K2rVrVertnXfeicxWEtJBMXe79TsZ23MQxYFxza5ydaB0alRpPojqq8ZAUmzmiFYgJ1+9rRr4F2K9m248YjUrx1uKzXpBgKo2a98gnYZDGjopMUH5ezA2B9EMpz5gLaXo0UsFje6TJ5kpiarBKr6nR3jSZL5mljmNOjmqb55Kd+H4gOaRGI/ib8acU4oNggf70S1ZEFNlIu+t3u35XPnt7rsccAQJQ2rffvtt+eijjyQzM1MJpu+++049duqpp0ZmKwnpoLgxaVvD8PrAiAOlXWqiuQ9MsbFsMALJ7BFxK5BwtbzdVMUGmGaLDOaqxnhMsVn/3u0q2fQy+jsyyU8J/c7SalXOjn0Cg3M023cc5xk14s9U7i/FlpqUaERt8Llh/oZBHyNU3ERzupgiSDku02saLYY+Xbs3aqb3mOyDNGnSJPnwww9l7969UlVVJfPnz5fJkyeHf+sI6eC4KfM3wvCegygOjDhA4kCJA6YVmFQr6xpUI0qk4wyBFEAfJHOfGpQI+/J9aFBphxYESQmdJC25+dBDo3Zk0H8TGF4czyk2DGQ137dbRp/k9YXB8i32fcC0GEF0ya3XJlLo9Jevcv/a+gbZ5alO9TX1XgtDRM7cjlCxFoGAQPdJlkcg6RYB7an/kYadtAmJYfQBDPO07KJB5kZyOgyPAyM8GU5X09pQOX5goeoDk5veHGaHz8OtJ8h6wnJzAtbb2Ss/XbI96RI2iww/ELo6GjisKCdgn1i0QYQT/X/A4d1z/AoknSaC8RqNESHCl9j0AfM3eqMt0WJuyaYDqk2HHfD14CufkZJoFGvYoQszFm8qlo+/3+uqes3OgxSoQMo2+ZXw/xE+UoUdRiDl5+dLQUFBq1thYaFqHnn88cfLs88+G5mtJaSDoQ+MhxqabFNgqPaB2Rlpg5756a667OqKF32i0AdGHIzdpGJUebVlQK0bj4u52g4HfcBxI+FH72ec/HR05WAcpTL18GP0CxrYNctxHpuOquqTPCqr7Kq69N+sjtbEQiNDDKpGbyPMLtMjQayYL3x8NXsc2j1b7QMcB/67zvPddumxyktPNmY8BhtB0hdbgfiX4oWAPxE8RwkJCTJt2jT5wx/+oG74Px67+uqr5bDDDpMrr7xSnnrqqchsMSEdCESDfKUZ9EG0KC9deRs0+gAJrxGuyM19YFZuL/MqN0Y6DkZPt5VspdWHjJSa3dBKf8ZhHPAzUnQnXkaQwo258kk39IunCJIu8UcKGINP1WO+PEimNJFTCf13u8uVqIcwh5k42igxZ1Sg7ffpP3IyaHuvq8VTlZeRLMM9kUN/IIKsL8LwukDINAkkvd/bGwFbzuE3QjftK664wuvxJ598Uj744AN54403ZOTIkfLII4/IZZddFs5tJaRDgigAIjv7KupkUHPLIr9VLkit4ICH+WqrtpfKGE8zSHMfGAzP1ODqERVlboza+sSE1+SkJ6nUnJvIk/nErWfAaf8CCR9bDlQafxP6Kj+ePEhm87WvRqlWk7aOZAD8fUFo6TYZOnp0rMefFwtMHFwob6zY7mmyODTgCjYzSKm99fXO5v8PDGyECvbf3orawFNsqSaBFANRuUgQ8F/K+++/L6ecckqrx08++WT1HDj99NONGW2EkNAo9FHq79QnBWFzHCjBzGeWytF3fahus//xte0BLZBKNp3ugPcjKzU5YA9ScwQp0Rhe2xbcMG+lXP7isrD0XfrtvJVy9dwVtp6wWMBc+WQIJB8C9ukvNsr0xxcEZNJ34q+fb5CzsC4ff0d7y2vkjEe/kJe+3GL7vFG+n5UiXTx/+8W2AqllOY25D9jJD3xm/N0/8MG6mIt0aJ/QtzvLjbSi7ffFh0FbM8n0uQI1S+vjS6BVbFme6CQutPq52MYOIZDgN0JJvxU8hudAZWWlZGdnh2cLCeng9PGMGUDPFF++HitnjOxhiBecTHDTEZtpnueCEkiegzlORuahlcF4kNoiggQT7Lzl2+X9b/fINs9YlmBBivL15dvl39/sauXDihXMVY1Gis3H7+fVpdtk5bZSV3PM/PHy4q2qSnLRBu9ByGY++X6vrN5Rrpa1w1yd1tLmwntfQ5zqXlzWZod63E55TcvfPbw+iBxNHtZNYoWu2WnKPwSdbVfNZk5J+6N7bppqfonv8cmeeYxuOcqTcgzUZD2sR66xv315pDpUiu3WW29VHqNPP/1Uxo4dqx5bunSpvPvuu/LXv/5V3UcLAJi1CSGhg7QBQvG2B1EfPoWpI3rI/N+d2OrkmJeeog6oZnIzgoggZaca5dT+PC4VNYeMq2SzB6ktIkhIM5r3V9/C1nOt3GI2C0MU+hvlEA2M1ExhhiFcfQkkvX+s3beDQYsb80gZK/p9sAyEjvXkav77cuoDhrQuOmyr5Sy/gyuOHyCnHdFdlcmbgeCyjtaINojkIh2I77YWdk7Vqf545sJjVDGH2TzthtmnDJaZ4/spz1dA2z64syy/5RTJzwjsde1aIMFXhHEjjz32mOqiDYYMGSKff/65jB8/Xt2//vrrw7+lhHRQdCj+mx1lUlJZJ/mmA1lLtMD+pO923lRAESTTFb6OotiNgjCzzdMgEgdhlPi3ZQTJ/JkgHiYNDn5d5khGLDZfhCEfDRH1iVUPMnYSSDgR6xl8Wz3epWCB2NW/T+2D8tVNHX8zKOe3npj13xfMw9qDhPVi/VpYaxGFjtTpnr8lDQQXPHbxwITBneXp+ZuUqdwsFvF3Bk9gJ0t1qi/QNDKYRtZ4z0DFkSbWBGe4Caov+IQJE9SNEBJ5EO0Z3DVLfth7UM1v0ukxnDB0xY/bq0wnAmkWae4/g9SFmwiSPvnq7YyWQPIV2Qh4Bl0MGp8hjmCzwhBiVICVVNX5/P2gNBx9g8IRQTI3M91q6Zhuxvw7wHtaT85adCMyBAGEpqLYTqy/T2GS9zKmCrZ4RJnGExNkR2m1bC6uMoSd/r4U5aYr4UOiQ0h2/pqaGikvL/e6EULCj9HfxZRm01EZiBudIguWwCJIJg+Sx+Pib2SINV3Qlik282faGkaBFIvzzcyVT4gM+KtiC+e+Mc/nc5Nic3pPLfohfvAZdArNvP6WQoH4Fkj4HmCemvW73eLXcxc9IjEikDBa5Ne//rV07dpVzWJD40jzLRAaGhqUp6l///6Snp4uAwcOlDvvvNNvdcjcuXNl1KhRkpGRIT169JCLL75YiotbTIEnnHCC+mJZb+jXpLnwwgtbPX/aaacFujsIaROMOVOqJLiZQD0K4U6x4eSkT8D+0k2tBVL0UmyhYPYgxWIEyVrVmO2pMkSkT3t2nPbNztIar55ZoYhHpPbsxs/g/bw8YcWVfqvTdBrHvH5rF+14xpidaGpuubW4JU1K4kgg3XDDDfLJJ5/IE088IampqfL000+rZpFFRUXywgsvBLSu++67T60HfiYMvMX9OXPmyKOPPur4mgULFsgFF1wgl1xyiXz77bcyb948WbJkiVfPJXijdu3aZdxWr14tiYmJcu6553qtC4LIvNwrr7wS6O4gpE0Y279QzTBD1GiL56Sif7a5QDJd4etmcf7Egtk4DDL0HKfatk+xhVKevy/GPUjW4aaZqYk+I16lnhQcgKDR/qVgMAsYmIV3lbVelzWyZI0gYRsOVHrPWNOl/ub1699DvEeQzK0HkD6v9whU7eEKpaCARMGDhHJ+CCFEaS666CI1uHbQoEHSt29fFdmZMWOG63UtXLhQpk+fbkR2+vXrp0QKBI8TixYtUsvNmjVL3Uf06fLLL1fiSqPbDWheffVVFW2yCiQIvO7du7veXkKiBSI1KMfFjCkYOnHgNDrthqEHidsqNogL8xW+0Um7xvfrrF2BMzydu6sc5lCFkzKTCIAxGBEMs9G9PXmQrM0FMf5Be3iwvdbPbf19h1LlZx2ojDJ1a5GANYJnvQ/PFAJPMCdrb5LdwGa7JpHxCsrrcYGC3wUKMdDp2yp0SZxEkA4cOCADBgxQ/8/JyVH3wcSJE+W///1vQOtC1dvHH38s69Y1N/FauXKl6tQ9depUx9eMGzdOtm3bptoK4GC9Z88eef3111VzSif+/ve/y/nnn69SgmY+++wzlSpEFR5aF5jTdFZqa2vptyIxNQE8Gik29JbRpl6VYtMeJB+RIEQFMHjTvK06slHVBiLD+pl0FVV79CDZNQ711cyz1b4JIQVpLcW38xfpx3RjQWtESa8DpeN6tpcWQRjYbBfFjHfQ1FV3ANcDdcP53SZtKJAgjjZt2qT+P3ToUHnttdeMyFJeXrPZzC033XSTEi5YT3JysowePVpmz57tMwqF6jlEqs477zxJSUlREaDc3Fx5/PHHbZdHNAoptksvvbRVeg2RMAg0RJ/QpgDCDL4oO+699171PvrWu3fvgD4rIeESSAjFQ3RsMTUEDBW3VWz6BIaIFubEGR4kH2IBaZv6xiZVrdM9p7n/Urph0m7bFFuoZmSzCIi1FBsuGO3md2kjvRuBtC0M+yY5sZNfgaRbV+wqr/HqV9QygNbcHbt1iq2l1UT8e5DM++OL9ftVY9M95bVh+26TNhRISKsh0qMFDoRJWlqaXHfddcqfFAgQVxA7L7/8sqxYsUKef/55uf/++9VPJ9asWSPXXnutGpq7fPlyee+992Tz5s2tZsOZo0cjRowwmlpqIMx+/OMfq+fOOusseeedd1TDS0SV7Lj55pulrKzMuCGKRUhbMrJnrjrZ4aS2cnupbPdUsfUOo0CC0LEz19qNGQEtHiRnYaVPur0K0o0ZUSjfbusqNni4nIzBbik2eZBiLcWGnkJaqJpTW77GjZRb9k0oESS9b47wdGS2i9Tp8nWkkWDUhx1MRxedUmfGPDavFFtdu+rDo4swvtpaYswpRPo60AGyJMoeJAghDWayff/990qowIeEIbWBAEGlo0gAYmXLli0qWjNz5kzb1+A5RJG0GMN7InUGLxSG6KKqTYORJ/Af3XHHHa4iY507d5b169eruXJW4FfCjZBogZTDuAGF8sGaPfLmiu0q1YUTWw9LV+xgMA+qxEnTyaNj7lEDsl2k2LbYRDV0c7+2jCAd1i1bjWsJNoJkboTopvdTW6M/F6J0iO5pdDrTVwQp1H1jFjfwyn21tdQ2GmVOAeLvAWIAjw3skuUskHxWsbWPYzJ8XyjpRxHG68u3Gd7C9jrCI14IeawxzNk//elPAxZHumVAQoL3JqDarLGxMeDXAGt1Circ4B36xS9+4Xdbtm/frjxIZoFFSKxeab71VfPk7l756YZXIxSSExOM0ntfPiTricnNMFTzbDBNSx+kthNII3s1RzaCFQFWE7K/3k9tjZNvxZcHqdSyb0Kp8tN9isb0zbfdz81dvmuMbdTbaRZSeh16gCqw9kEyC9X2UOZvLffX3236j+IoglRdXa38OmeccYaRcoL4MIsU9DBCus0tZ555ptx9993Sp08fGT58uHz11Vfy4IMPqr5GGrzPjh07jBYCeA1K+tEeYMqUKao8H74lpNDQasCaXkP6rLCw2QCnOXjwoGpNcPbZZysP04YNG+TGG29UUTCsk5BYLwnWJ7twVrnkpSerE49PgWSYY71TbIhmwUti1/XXbqCuOcVmN48rnOjPo1I/S7cZQ0ADxdyoMBY9SDp1aK1qNDxINtur981wz75xGv/hD/hm9P4Y3afZi4pqQaxfRyfhRUP6Vnf51kZyc1qvxYPUEhnS5f5YP95HL4P1BDp3LJaZOKiLvLJkm/HdpkCKPq7/uuAL+ve//20IJPQugqhBg0eAVBsEijkF5w/0O0KjyKuuukr27t2rXo+SffiLNBBAW7du9WrwWFFRod4fM99gDD/ppJO8yvzB2rVrVUXcBx980Op9IeZWrVqlPlNpaal638mTJyuBxzQaiWVQ/dMzL12NJgj3QTQnPVl2ltX4FEjW/jPmExROwKlZia6mkusUG+xOaGJoTgmFE4gvawRJG4MDHeFgrdKKNQ+SUwRJp9jsjPR630CwdMtJVeZgu/Ef/tCpVxi0keLD3wf2F6JDuR5Pknn7IIj1dpojTeY5f5qc9CRl8IcIx/tooYr3aE8pKFSy4ePoAF442neQNhJIMFMjymIG5mpd8v/SSy8pw3YgAik7O1sefvhhdXPiueeea/XYNddco26+QOm+U6gYou799993vZ2ExAo4ISAU/49l21qVc4eKvtLXaRc3KTaUKCM1h8gTfEiFzVYSB99JZqsUG8BrIyWQMPATTQvBgC5ZxrbuKKlW9wNBf3YIgN3lNTHrQXJKsVX6EEj43WPgsRZIR/YOrCJZRxYLM5tFS5+CdLW/EB0yTNsWoawjiuaIni7l1xFKgPUh5barrEa9T/HB9lPibwa+vyOKclUvJMAIUvRxbV6AeRkmag1SaWYvEFJcqDAjhLRNuX+4D6JueiHZmWNbSv1bvw5tA/T6zHOlIKyQIgm0km1vRY1UB+BbMlewIa2n95dTLyQ17NWhik+ndvp1zghbH6SKmkOuBgS7wYjUBZBi01Vs+N23CJbAq/yMvwuPsNFi2BwdsjY2NS+jL2btUmzm+3gfXcHWXkr82+K7TSIskJCKMnuO9u3bpzpaa2CsNj9PCIlczxSdWegdAYGkT5p2GCkQ0xW+L6P2tpIq4wRnjhqZ/Utujdob9x2USfd9KlfNXS5uMUdIEInQ+8uuwmresm0y/o+fyNzFW3x+9n6eE/vBunpHMeUGjJWY9sh8Of2RL5S3JhQwZw2pQ9Db0r3aaWAtRImei4ZycruUl1uswtkQWzaDafX7IFWMv2NE+SB6sD1GBMkikLRpG60E2lsFm5lJHqM2LiCK8jioNm4EUq9evVTDRSfg6cEyhJDIAn/IrJMGyzljesnh3XPaNoJkc4Wvu2nbeXL2eE7adq0I0vW4EZcC6f1v9yi/0mfr9nnNEPOFjs7oz6Yr6eyM2m99vcNoxGmHPjHryAeCHqGMSoEogGiAn2zp5uaJBMFSXnPI8K4UWvxDWohaPUjY72jgaaTYCkMRSN5/Fy1iqyUaZU2xpSQlSFFuurEc/u50OtRcxWZeL/xH7Vkgje1foL7X1548WFWWkuji+jeAUR4wT9fUNB/wrBVuqArTM9UIIZHlulMPk/vPHWU0XgwHuimdU8oHKSVc7Vsb9DlFKPxNXQ903Mj89c3TziEEnESMFe2nggHdnN6xptgQwVm6ucSnQNCNEJEqxBU+CMWHZDZ96xETwaK3A78L69+E/v1YU4JaCMNYDbFq5wlyyz6jgWjz34VVbJm7fJt9c+aold4fOWlJrQz03ik257+peActO/C9nnXy4GhvCglEIP3P//yPmrsG8/Of/vQn+de//qVuc+bMUY+VlJSoZQgh8Ym/CJI+MWH4qS7T9y+QnKeuBzJuxCxgAAb2BppiA04ptiWbDqg0lRYIdgUe5siFr88cTNsAt5/HCb0ddmXvTh4ka/pRixXr+A83tDQQbRYtel3oe4T+R6UOXb4NgVRcLft0dNLGfN0ybqTOiGK2ly7apB1UsXXr1k0WLlyohrqi+7U+gOCLdeqpp8pf/vIXtQwhJD7RUZbSavv0lVnsmMurfXmQjMiCzUlPiyw3DReRgtICxjyw1x9mE7JXis1jDNafw7w+nMhxQrd2EzeXl+MzQ2CEIpDMY0vQxRrVWcGe9HUPIp3uNOMk5rRA0r93iJBgq/x0FVsXz+8ZZfow4SMlCuM7eisBtBIwVyy2RPQqZUCX5tRl58zW+0CvF+/TnlNsJLYIKMnZv39/NfsMBu0vv/xS3fB/PKbL/Qkh8R5Bsj/pO52YfHmQfJ3MdOduN1VpOgV1+ojuqiINAsdNKkiLAJ0+7JnfbAyGCNCCzy6CY02zmRsh4uTvpoN4oH2VFrhMG9qh02fab2TrQarxjgxqg7b+vZujSE5Vfk5Yf89I85nTZy3d1FtaPQBzN21rJZxTim2fTaEAIZEgKBdYQUGBKuvHDf8nhMQ//qrYHAVSkB4knWKrdCGQtICZMry70an5C48nKZAUG7wtPXKaDeP6pI1tRAQHDOicaSsQdAoJDQvRuLBFFAZfoq+jLjoYN/8H/5/HCb3vMeDUijEvr67BK3Wof8/ooK7xVeXnC7u/DbNA0uuzVl3aeZDsxLR+DL2QtFBlBIlEGtrkCSHuPEge74f1yt0QCzWBeZB0iq3aT4rNLGDQ4gAjGdwam60CyZzW0SdtnV47vEeOHOkRX1aBYDRCzEpRkZYWURhKFVut0UFZf55g56BV+PAg6ccw5qPmUKPPfeOrys8JeIx0Cs0shM2m7y2e3krWxqb6PhpUbi+p9iGQUryEIIzl5u0mJBJQIBFCFHkZLSch9OgJbwTJzqStPUi+RYauWIOAwXp0Mz08jpO+L3QaSftsgJFGKvYWSBgErFNAVoHgPKQ3hAiSRzxOPaKHikxhzMum/YE3aWzeDmcPElKZOkplbubpSzwGkmI74ImuoXgu3/M3ZK1kc+ryjffWEa6vtpY6/q1gvbpy0Nyxm5BIQoFECDHKqzXlttGgwASSrl5yLPP3pNj8eZB06mnioOZIy6heuSqVhBP8as9YBidsRYDFqK0jUYhO9SlMN0zD9p+9+XOEo4pNrxORljF985s/q0vzuZMHyS6CpCJeKa2jfHb7JpgUmzbiF2SmerUYMAtRLTitKTa7mWx2fytYr3k+nJ1PiZBwQ4FECDF6sOgTrF0jxkAFko4s4MrfHFloFUHyITLMAmbi4C7Gdh6n01J+BIWdz6aPMeKiUjbur1SRG0RwxvYrMJmGqy2f3TKk1yMm7QbAusUsunRULNhyf19l/upx7UMypQStVWx2VX6Bfg4zOoK0ubjS6PJtNzvQ+pjTjDXz3x39R6QtoEAihLjyIbWIBHcepJbIQoptQ0vtQfLVjXqTRcBokA5z40MyoiSeKjZgjljo9NrR/fKVYOvjSbHtLKv26gXUUmHlnWILdh4bUoNaQKIqDgOIwZcbim3Tm6GU+Zu315xiK7WJIDlV+fnCmI1mETa63xHWBa2FVJ+1y7ddVAn7ww7z3x0FEmkLKJAIIQY5vgSSYVR2F0Hy168mw0WKTUeIkILSESegBcXyLSWOr0cExFeKDcbgj77ba6TXmrc1RXWVxgkdvYBaPounOaHnBB9qmT/EEexTECMQkJh4j21ERGrldt9pQzt0NZ1TBEmX+tul2LT3zKnKzx9Ov2f0O0LfI/N+t/MNWX1J1jEjGkaQSFtDgUQIMchNT7IVSKoPkEcAWa/wncSCU8RJk+EZNeIrCqNTTuYp56B/50wpyk2TuoZGWbzJvn9QpWXWmCY/I9koh//C42/SESk7T4xdI0RfvZ8CERVIPSJliDTkBI/HKpixIzp15iSQWkr96x2baFqN2uY5ar5Ag0un37O575HTdHrzMogyWYca20eQ6EEikYcCiRBikJeeYtsLydoHyIwhFurqvXwrWgQ4pUyMRpEOKTakmpByMgsYDYSMFk1OgsI6a8z8Wp3WweaiieTwolwbgVDlt4pNp7aC7aJtTjnp9gVuu4S7LfM3P+7PpG0d/+EGX60czOkzJ4FkftxXZIgRJNLWUCARQvx6kKx9gOxOvmq6vSndpV/jZLrVkQKnCBJSTTjxWwWMRpu2nYzaeuhubnrrbTaflCcM7OxVQt4iEHwIJJuITCDYpaV02nDF1pKAI1O63YB/D1K9z/QjsIugBfpZrOtS/7cxaIMeeWnG/vcVGaJAIm0NBRIhxECbmXV5vqt+RsmJqgcOMJ/Y/U1d9zdqREeG0EjRLGA0EzyVbN/vrjAM4WZaBECSz8op7T9yEgh2jRBD9SBZTd/qfQsz1HsjLbh4Y3FYq9isHiQsr3tItU6xtVT5ucHXvD3zfnaKICUnJkjPvHT/ESTT+lnmT2JqWC0hpANHkHyIHd1ZGr2TcOLt5iL1YhZITo0i53tGiejUkxWYxYf1yFFdtpGWOmt0T6/nnSIk1tSPNX1nTbHZtSsItQ/SPof9ibThy4u3yl8+2yBLNh3wirb94rg+jsNsXXuQPNur9w1SpmnJ3tfJWshAeN777nfG46hEnH5kkQztnuPaa+Ymxaafw/52ijZa188IEmkLKJAIIX6r2HRfoK7ZzRVOVrLTkpsFkimiEkoVG6IbX28r9RrFYQcEBQTS0s0HWgkkJxMyGNI925i95ms+GFJRdu0KQvUg6bEt1n1z/GFdlEBCdR5uZhoaG+U3k4e0WldjY1NLBMllis3c/sCafuxfmKnEID7bk//d6PUchOj//XqipV2Bs9cMZnrsMhUlym+OEtkxqGuWSpXqSJIdPXLT1bpQaWfXV4uQcEOBRAjxG0FasKGl3N6OTE9Fml2KzalsW0eQUImGNBZOohpEbQ41NKky+F4+TqyDu2Z5jQ0xU1pd5yiQju6bLw+cO0qGFXlHQ0AvSy8gO6GnIzK19a233Q1OBvZTD+8mt0w7XPaaUoYQiogmoS2BHWYflL9GkVrA+oquQTQ9/v9GywrP6A8thP4+f5N8s6NM/W50V+uSquZ2BcDc6VqDxx77f0epNCyEjRNXnTBQiaOfHd3bcRms69GfH6U+i13KlZBwQ4FECDHIsxFI+P9KTzTHWm6vsUZUrI0Q7TCXc0OM5KYnOJbBO+HLUOxLBCBqcvaYXrbr1L2A0KCyecp86xSS9vTotJW5l1BgHiTv1yFCdemkAV6PvfjlFiWQnIYI6/RaUkInSU2y31fWlKCv6Bo47Yge6maNHiHthp9njiry+hxoneD0ezp9hPd67OiakyaX/cj7c9sxbaT/dRESLmjSJoQY6BOmucz/y43FKkowoEumFDmkQLLSkr08LtZGiHakJCWokzqoslSD+TN4W/1CO0qrW3WgbkkjBZ6OMc8k031+zEIPESMtRoJJs/lLP9r9TnREzLFJZFqS4wBXa+dvbcJ3Ekh26Co7c1sFp1QhIe0BCiRCiM3JuEUgGbPQLNVeZrItEYriSncRIJ1mM7cH8ErPZfo+8XbLTlNCCxGrnaXN8740ZdX1AYsAu0GrTqlCnWYL1KgNz5DugxSIQNKfx3HMiEN6zacHKYB9M0H3nVq/3+h3pX/PFEikPUKBRAgx0CdMCBZ4a8x9hnwJJKsHqSWy4Dt642TUNgSEj6omnZLq7fEoWdNswYgAa3m6d4rNvoN4oPPYsF26w7eTP8tfVC+QEv9APUhOHNu/QFW9IVq32eP58lXiT0i8Q4FECDEwT3bHSXR7SZUaGAtT7HE+qsmyUpO9ohluU0hO40acyuAD8SGFIpDMKTanz5Jpicq4RUddEIHyZVx2M0DYvO/cRJC0mApm30DMHtU3T/1/vmdEi79xMoTEMxRIhBADCCGdLsNJVI+9GNUrV3I8PiM7jM7StQEKJJ1is4wbCcTb0tfT2HCLpbFhWZVzFVsg63SKkgTbLHJfhW/zuhW9/RA3Oqpnm2JzKPE3bysig0hHBiseDR+S5+8iEC8VIfEGBRIhxLabNk6iLcNi7Zs1OnmQ9rkWSJ4Tt6cSy+0cN6doj5mWafXBe5BQWr+ztNo2ShKsBylQUZFjEj52abZAUmy6LUDQAsnzd7BwQ7EyxQfyeyIk3qBAIoTYG7Wr6tSJ0K7btBUj3aRTbDoC5GckRItJ26GKLTu4FBtMxGhcaf48gYCydS049Hq6hMmDFMhnAzC5m6N6VnQESws2O5DKw9BevXywAmlEz1z1GvyeV+0oC/izEBJPUCARQrzQJ81FG4pVuX5mSqIc2bvZe+LXBOwpOQ84xeZQxeYuxdZScaarq3zNGnMDyuXNozHs2hVYRaFbgklLOXU4Bwc94jLT1FfKnw/J3Ek70BSs7myO6kaW+ZP2DAUSIcQLLSje/Wa3+nncgEK/naJ1hEM3LXSbejFSbCaBFGgZfO/8DEOo6BO/MWssCbPG/Buh7TALJLt2BS2iMECBFISo8GXUPujCg2TdXiP9GIR41MN9IZBY5k/aMxRIhBDbkzHKuX11z/YlFvyNGfGVYgu0DD49JVG6egzUeuRIKBVs1iaUTlVahu+qDSJIPgWSCw9S8/Mt7QL8ddL2hU63Lt1yQI2Dcft7IiTeoEAihHhhPWn68x+BTE8kCFGcQCJAdhEkHZXIcVkGb+dDKguiU7TTOp0+h5Gysvin/OG2S3igESRfHqTm7W3el3vKa4z5aea2DoFU+PUuSBdPNjOg3xMh8QQFEiHEC7MvpVtOqgzs0jwQ1hctFV2HAooA2UWQdBl8IBGWVgIphBSSdZ1O25IZdATJXRNMM7oSTws/uwiSeT6cHVrQ7ShpjgymhpB+NDcNZXqNtFeiKpAaGhrk1ltvlf79+0t6eroMHDhQ7rzzTsNo6cTcuXNl1KhRkpGRIT169JCLL75Yioubq23Ac889p0yW5ltaWprXOvAet912m3o93vuUU06RH374IWKflZB4wRx1mTioi+N8L7uTb82hRtldXuM6smBn0g4mBaXTYVvDmWIzCSQ7oRdMmT+OO/uCKI0PS4rN08dquyd1Gsq+wd+FhgKJtFeiKpDuu+8+eeKJJ+Sxxx6T7777Tt2fM2eOPProo46vWbBggVxwwQVyySWXyLfffivz5s2TJUuWyGWXXea1XE5Ojuzatcu4bdmyxet5vM8jjzwif/3rX2Xx4sWSmZkpU6ZMkZoa73lOhHQ0zCdON+k1a/RC+4DcREh0ik2bu0EwpeNOEaRQREDP/HTxzNJ1SLElBxxBgpipq28M2LeT40Ig+U+xeUeQQtk3qGTTupkl/qS94vsbFWEWLlwo06dPl2nTpqn7/fr1k1deeUUJHicWLVqklps1a5a6j+jT5ZdfrsSVGVz1du/e3fEq7uGHH5ZbbrlFvT944YUXpFu3bvLWW2/J+eef3+o1tbW16qYpLy8P8lMTEtuYT5zjBzmPFzGDajHccPLfUlzpOrKgZ7hVH6oPLYLkIJCC8dhoULlXlJcu20uqbaM9wVSx6fQaImdaHIbLg+Q/xda8r3eWVQfdQFOTn5kiRxTlyjc7yhhBIu2WqEaQxo8fLx9//LGsW7dO3V+5cqXMnz9fpk6d6viacePGybZt2+Tdd99VQmfPnj3y+uuvy+mnn+613MGDB6Vv377Su3dvJYIQbdJs2rRJdu/erdJqmtzcXDn22GOVALPj3nvvVcvoG9ZLSHtkUNcsSUroJGP7FUjXbO/UtC90Vddmj0Byk0JKT7ZJsQXjQfKk2HaVVSuRFo4IEji6b776Oawox1FwBCaQgiuLD2cV267SmrDsm5+M7ql+jvHsI0LaG1GNIN10000qEjN06FBJTExUnqS7775bZsyY4fiaCRMmKA/Seeedp9Jh9fX1cuaZZ8rjjz9uLDNkyBB55plnZOTIkVJWVib333+/EmMQSb169VLiCCBiZAb39XNWbr75ZvnNb35j3Md2UySR9kiP3HSZ/7uTJCc9sMMDIirFlXVquK3bKi0d9TCPGglGRECMpSUnKA8U2hOUhjBmxMycc0bJDacNlZ556c4pttp6dbHmxqu1X891C7As3kkgQQzWelJ22Z7tcUJHvLSBPpToGrhoQj857Yju0iPXvYgmJJ6IagTptddeU2Ln5ZdflhUrVsjzzz+vxAx+OrFmzRq59tprlcF6+fLl8t5778nmzZvliiuu8Ioywad05JFHyvHHHy9vvvmmdOnSRZ588smgtzU1NVX5msw3Qtor3XPTAkoBgUzP8pv3ezxIbiJIxrBauxRbSlCdr5FmC6XPjxmkDe3EkVlwoGM3hFkkI0hGFZtFIJnHnOh0pb8InybUfYN9jhSkG2FISDwS1QjSDTfcoKJI2vMzYsQIZaZGOmvmzJm2r8FziCLhtQBRIhisJ02aJHfddZeqSrOSnJwso0ePlvXr16v72puE9Jx5edyHqCKEBI4WDLqKzZ1JO9EmghR4GTzoU5Ap6/YclK3FlWFLsfkiw1QijyiSFnu+2BfkZ3OKIOn0GqJn1k7fVqwepUjuG0LaA1GNIFVVVUlCgvcmINXW2NgY8GuAU3sApO6++eYbQwzB2A2RBP+TOWWGajZEnwghgWONULgyaVsaReI7HOyEeHMEqS0EUkJCJ6/5Zm3hQcJ+OtTQaOM/8v85raNIKJAIieEIErxD8Bz16dNHhg8fLl999ZU8+OCDqq+R2fuzY8cOVWWmX4OSfrQHQFk+Svhnz54tY8eOlaKiIrXMHXfcIccdd5wMGjRISktL5U9/+pOKTF166aXqeYSE8RpEnAYPHqwEE/ox4fVnnXVWlPYGIfGN9QTsJkWmoy7VhxpUB+7KunrDUxPo+Ao9tLatBBKAQIJIcVvqrz1IXQL8bNmeHkYAn00LLLcl/npbzYTqzyKkvRNVgYR+RxAmV111lezdu1cJFJTsw1+kgQDaunWrcf/CCy+UiooK1Tvp+uuvl7y8PDnppJO8yvxLSkqUiILhOj8/X8aMGaNaCgwbNsxY5sYbb5TKykr51a9+pUTUxIkTlZ/J2lCSEOKOzBAiSFokBVsGb44goQ+T4UGKsAhQorBcpKK2dXVZOCNIiQmdlAjCKJfSKpNAqnFXwWa3DCNIhMSwQMrOzlb9iHBzAl2xrVxzzTXq5sRDDz2kbr5AFAmRJtwIIdFJscE7A48vsuOIHgUrIEBvj0Bav/egMWss0iJAi0Jzo0tfBOuv0p8FAsnsQ6owxoz49z8xxUZIYHAWGyEkLJgjFJkpia5My7hQ0Wbn6rqGoMvgQa98VFS1lLFDfEV6iKoWhZhB54ZijwAszAz88+mUmI6OmavYXHmQGEEiJCAokAghYcEcoQgkQpJuGjcSSgQJg1e756S1qQAwTNouPEgQgJUeM3qwESRgjiDp9w3GgxRqHyRC2jsUSISQsHuQAhE45nEjwZbBW9NsbSaQPMJEp7p8ocUfeitZ05HBCiT9vm48SPAx6c7l5vURQuyhQCKEhAXzST+QFJl53EgoESTQt60FkuFB8i+Q9pnaFwTTXFF/Hpi0A53DZhV02OeRTj8SEu9QIBFCwp9iCyiC1JJi0x6dQMvgrZVsIDc9JaZSbKH4q8wpMXMESQszNyk2tZxnexk9IsQ/FEiEkLBgTvMEIpB0N22k2IwqryAjSHpobWym2EL7bHkeweflQQogxWbeXgokQvxDgUQICb9Ayg5cIHmZtIP0IHlHkGLLpB1q+tCXB8ltii3TY4inQCLEPxRIhJCwp9gCSZHphpDeZf7xJZDQw8kfLeIvuBSb/jz2Zf6BRZBYwUaIfyiQCCFRTbHpfkn7K2tbyuCD9OkUZKYY25GbntTuI0iBlPmr5ehBIsQ1FEiEkLCQmZKkGjWCwkBM2h6BtO1AlVEG7zYiYgXVYbrUP9JjRgL2IFXUBbxvbKvYqpvXA+hBIiRyUCARQsI23X7GsX3klMO7epXbu20UiSGzoZTBa35xXB8Z0TNXJgzsLDEVQaoMrYrN1oNUcyggD9LUI3rI4T1yZNrI7kFtAyEdiajOYiOEtC/uOmtEwK/RESQMmQ1FQGhmHNtX3dqCQPogaX8VBGAw6IhYzaFGqa1vkJTEBCMl6TbFNm5gofzn2klBvT8hHQ1GkAghUUVXsWEQaygenWigU1YQKg16Qq4NEDTlIX4++Id0YA1RJAgl/Z7BpiQJIc5QIBFCooquYtPElUAyCRNflWzFnh5ISQmdgvb/IIWZk9ZSyVbhGZAL0aRFJiEkfFAgEUKiivXkHmwZfDRITUqQ5MROftNsuoKtMCtFCZ1gMY8b0b6nLGWOD36dhBB7KJAIIVElIzV+I0gQJpkujNqhlvjbGbXRWNPaf4oQEj4okAghsRVBiiOBZE6z+Sr11yX+4RRIOsVG/xEhkYHfLEJIVGkvAslXBGlfuCJIGS0CSb8vI0iERAZGkAghMWXS7hJHHiRzib0bD1Ko/ipzBCnQJpGEkMCgQCKExFQEqTAzviJImW5SbJ4qtmB7INmZtAOdw0YICQwKJEJIzAikUMrgYznFVmyqYgsF88BaLcgokAiJDBRIhJCYSbGFWgYfzRSbTnm1VRWbUeZPDxIhEYECiRASVRITOql+QvFo0HY7bkSn2MIqkBhBIiSiUCARQmImzRaPAsmfB6m+oVFKqsIjkPIokAhpMyiQCCExk2aLR4Hkz4N0oLJOmppEkDksyAzNg5TDFBshbQYFEiEkdiJIcVbi78aDpHsgQRwhnRiWKjZGkAiJOBRIhJCYGTcSahl8NMhKTfYpkMLlPzI3iqyrbzQG4FIgERIZKJAIIVEn33Pi75GbLvGGTnGVVTWP/rCyvyI8FWzqvVKSVKoO7Citbn6MAomQiMBvFiEk6tw4ZaiM6ZMvpwzrKvFGz7w0Q7A0NTWpAbb2Jf6hpw/RAgE+JDSKNFJs9CAREhH4zSKERJ1hRTnqFo/0ys9QPyFYYMgutESKiivDl2LTlWwQSBpGkAiJDEyxEUJICKQlJ0r3nOYo0tYDVY4pNqtwChZrp3EKJEIiAwUSIYSESJ/CDEeBtC+MKTZzqb+GKTZCIgMFEiGEhEifAo9AKq5yrmLLDn8EKSUxQVKTvIf9EkLagUBqaGiQW2+9Vfr37y/p6ekycOBAufPOO5XR0Rdz586VUaNGSUZGhvTo0UMuvvhiKS4uNp5/6qmnZNKkSZKfn69up5xyiixZssRrHRdeeKEyU5pvp512WsQ+KyGkAwgkuxSbJ4IUrhYGZoGUmUpxREi7FEj33XefPPHEE/LYY4/Jd999p+7PmTNHHn30UcfXLFiwQC644AK55JJL5Ntvv5V58+Yp8XPZZZcZy3z22Wfy85//XD799FNZtGiR9O7dWyZPniw7duzwWhcE0a5du4zbK6+8EtHPSwhpn/T1pNi2WARSY2OTMm6H06RtFkhMrxESOaL67Vq4cKFMnz5dpk2bpu7369dPiRRrtMcMBA+WmzVrlrqP6NPll1+uxJU5wmTm6aefljfeeEM+/vhjJa40qamp0r17d1fbWltbq26a8vLyAD4pIaQ909sTQdpmEUiYwdbQ2BwRLwyTBynP0zPK3KSSENLOIkjjx49XomXdunXq/sqVK2X+/PkydepUx9eMGzdOtm3bJu+++65Kxe3Zs0def/11Of300x1fU1VVJYcOHZKCggKvxxFp6tq1qwwZMkSuvPJKrzSdlXvvvVdyc3ONG6JShBBiTrHtLq+RmkMNrfxHEDXJiQlhjyBls4KNkPYpkG666SY5//zzZejQoZKcnCyjR4+W2bNny4wZMxxfM2HCBBUhOu+88yQlJUVFgCBYHn/8ccfX/O53v5OioiLlRTKn11544QUl0BB9+vzzz5Uwgy/KjptvvlnKysqMG0QaIYSAwswUyUxJVENpt5c0d7j2bhIZvhEq9CAR0jZE9fLjtddeU2Ln5ZdfluHDh8vXX3+tBBLEzMyZM21fs2bNGrn22mvltttukylTpijv0A033CBXXHGF/P3vf2+1/B//+Ed59dVXVbQoLa25VwmAMNOMGDFCRo4cqUziWO7kk09utR6k43AjhBArKPJAmu373RUqzTaoa1bYu2jblflnpTHFRki7FEgQNjqKpIXKli1bVDrLSSDhOUSR8FoAYZOZmamq1u666y5V1aa5//77lUD66KOP1HK+GDBggHTu3FnWr19vK5AIIcSfURsCyVzJFs5BtbYmbabYCIkYUf12wRuUkOCd5UtMTJTGxkafr0lKSmr1GmBuD4BquLvvvlvef/99Ofroo/1uy/bt25UHySywCCEkUB/SFlMvpEik2PIyWqJR2axiI6R9epDOPPNMJWL+/e9/y+bNm+Wf//ynPPjgg/KTn/zEy/tjrjzDa958803VHmDjxo2q7B8VbWPHjlWpOQBPEforPfPMM6ribffu3ep28OBB9Tx+IgL15ZdfqveFDwnVdIMGDVJpO0IICUcvJD1mJJwpNi8PUgoFEiGRIqrfLvQ7gpC56qqrZO/evUrgoGQf/iINPEZbt271avBYUVGheiddf/31kpeXJyeddJJXmT/EU11dnZxzzjle73f77bfL//7v/6qI06pVq+T555+X0tJS9b7ok4QmlfQZEUKCoU9hZqtS/0hEkGAGT0zopNoHsA8SIZEjqt+u7Oxsefjhh9XNieeee67VY9dcc426OYGokC/QtRupN0IIiUQECel+GLcj4UHCehFFQgNKlvkTEjk4i40QQsJAz7x0SegkUn2owRhQa0SQwjSHzZpmy6RAIiRiUCARQkgYSElKkB656UaaDVGkYiOCFD4PEhjsaSMwoEtzWo8QEn54+UEIIWFMs+0orVaVbIO6ZEtdQ2PYU2zg4fOPlJ2lNUa/JUJI+GEEiRBCIuBD0mk2+ITSksPb8TojJYniiJAIQ4FECCFhok9hi0AqjpD/iBDSNlAgEUJIuCNIxVWmCrbw+o8IIW0DBRIhhIRx3IiOIOkKtsJMRpAIiUcokAghJMwRpL0VtbK9pLlhZOdsRpAIiUcokAghJIz9ifR8tK+2lkakgo0Q0jZQIBFCSBi7XOs026odZeonBRIh8QkFEiGERCDNVlcfmR5IhJC2gQKJEELCSG+PQNJ0oQeJkLiEAokQQsJI3wLv8R+MIBESn1AgEUJIBFJsGgokQuITCiRCCAkj2qQN0pMTJTOVIy8JiUcokAghJIz0yE2TxIRO6v/sgURI/EKBRAghYSQpMUF65qWr/7OLNiHxCwUSIYREKM1G/xEh8QsFEiGERKjUnyX+hMQvFEiEEBJmzhjRQ3rlp8uU4d2jvSmEkCDp1NTU1BTsizsy5eXlkpubK2VlZZKTkxPtzSGEEEJIGM/fjCARQgghhFigQCKEEEIIsUCBRAghhBBigQKJEEIIIcQCBRIhhBBCiAUKJEIIIYQQCxRIhBBCCCEWKJAIIYQQQixQIBFCCCGEWKBAIoQQQgixQIFECCGEEGKBAokQQgghxAIFEiGEEEKIBQokQgghhBALSdYHiDuamprUz/Ly8mhvCiGEEEJcos/b+jzuBAVSkFRUVKifvXv3jvamEEIIISSI83hubq7j852a/EkoYktjY6Ps3LlTsrOzpVOnTmFVthBd27Ztk5ycnLCtl/iH+z56cN9HD+776MF9Hx0geyCOioqKJCHB2WnECFKQYKf26tUrYuvHl4VfmOjAfR89uO+jB/d99OC+b3t8RY40NGkTQgghhFigQCKEEEIIsUCBFGOkpqbK7bffrn6StoX7Pnpw30cP7vvowX0f29CkTQghhBBigREkQgghhBALFEiEEEIIIRYokAghhBBCLFAgEUIIIYRYoECKMR5//HHp16+fpKWlybHHHitLliyJ9ia1O+6991455phjVBf0rl27yllnnSVr1671WqampkauvvpqKSwslKysLDn77LNlz549Udvm9sgf//hH1YV+9uzZxmPc75Fjx44d8otf/ELt2/T0dBkxYoQsW7bMeB71Orfddpv06NFDPX/KKafIDz/8ENVtbg80NDTIrbfeKv3791f7deDAgXLnnXd6zQHjvo9NKJBiiH/84x/ym9/8RpV9rlixQkaNGiVTpkyRvXv3RnvT2hWff/65Ogl/+eWX8uGHH8qhQ4dk8uTJUllZaSxz3XXXydtvvy3z5s1Ty2OszE9/+tOobnd7YunSpfLkk0/KyJEjvR7nfo8MJSUlMmHCBElOTpb//Oc/smbNGnnggQckPz/fWGbOnDnyyCOPyF//+ldZvHixZGZmquMPRCsJnvvuu0+eeOIJeeyxx+S7775T97GvH330UWMZ7vsYBWX+JDYYO3Zs09VXX23cb2hoaCoqKmq69957o7pd7Z29e/fiUq7p888/V/dLS0ubkpOTm+bNm2cs891336llFi1aFMUtbR9UVFQ0DR48uOnDDz9sOv7445uuvfZa9Tj3e+T43e9+1zRx4kTH5xsbG5u6d+/e9Kc//cl4DL+P1NTUpldeeaWNtrJ9Mm3atKaLL77Y67Gf/vSnTTNmzFD/576PXRhBihHq6upk+fLlKrRqnveG+4sWLYrqtrV3ysrK1M+CggL1E78HRJXMv4uhQ4dKnz59+LsIA4jeTZs2zWv/Au73yPF///d/cvTRR8u5556r0sqjR4+Wp556ynh+06ZNsnv3bq99j1lVSPNz34fG+PHj5eOPP5Z169ap+ytXrpT58+fL1KlT1X3u+9iFw2pjhP3796tcdbdu3bwex/3vv/8+atvV3mlsbFQeGKQfjjjiCPUYDlYpKSmSl5fX6neB50jwvPrqqyp9jBSbFe73yLFx40aV5kEK/3/+53/U/p81a5ba3zNnzjT2r93xh/s+NG666SYpLy9XYj8xMVEd5++++26ZMWOGep77PnahQCLS0aMZq1evVld0JLJs27ZNrr32WuX7QhECadsLAUSQ7rnnHnUfEST83cPzAoFEIsdrr70mc+fOlZdfflmGDx8uX3/9tbooKyoq4r6PcZhiixE6d+6sri6sFTu4371796htV3vm17/+tbzzzjvy6aefSq9evYzHsb+R8iwtLfVanr+L0EAKDQUHRx11lCQlJakbjNgwp+L/uGLmfo8MqI4aNmyY12OHH364bN26Vf1f718ef8LPDTfcoKJI559/vqoc/OUvf6mKEVBNC7jvYxcKpBgBoe4xY8aoXLX5qg/3x40bF9Vta2+gpBbi6J///Kd88sknqvzWDH4PqPYx/y7QBgAnE/4ugufkk0+Wb775Rl1B6xuiGkg16P9zv0cGpJCtrSzgienbt6/6P74DOBmb9z3SQqio4r4PjaqqKuUnNYOLYRzfAfd9DBNtlzhp4dVXX1WVC88991zTmjVrmn71q1815eXlNe3evTvam9auuPLKK5tyc3ObPvvss6Zdu3YZt6qqKmOZK664oqlPnz5Nn3zySdOyZcuaxo0bp24kvJir2AD3e2RYsmRJU1JSUtPdd9/d9MMPPzTNnTu3KSMjo+mll14ylvnjH/+ojjf/+te/mlatWtU0ffr0pv79+zdVV1dHddvjnZkzZzb17Nmz6Z133mnatGlT05tvvtnUuXPnphtvvNFYhvs+NqFAijEeffRRdYJISUlRZf9ffvlltDep3YHrArvbs88+ayyDA9NVV13VlJ+fr04kP/nJT5SIIpEVSNzvkePtt99uOuKII9RF2NChQ5v+9re/eT2PcvNbb721qVu3bmqZk08+uWnt2rVR2972Qnl5ufobx3E9LS2tacCAAU2///3vm2pra41luO9jk074J9pRLEIIIYSQWIIeJEIIIYQQCxRIhBBCCCEWKJAIIYQQQixQIBFCCCGEWKBAIoQQQgixQIFECCGEEGKBAokQQgghxAIFEiGEEEKIBQokQkiH5MILL5Szzjor2ptBCIlRkqK9AYQQEm46derk8/nbb79d/vznP6vBxYQQYgcFEiGk3bFr1y7j///4xz/ktttu85pmn5WVpW6EEOIEU2yEkHZH9+7djVtubq6KKJkfgziypthOOOEEueaaa2T27NmSn58v3bp1k6eeekoqKyvloosukuzsbBk0aJD85z//8Xqv1atXy9SpU9U68Zpf/vKXsn///ih8akJIOKFAIoQQD88//7x07txZlixZosTSlVdeKeeee66MHz9eVqxYIZMnT1YCqKqqSi1fWloqJ510kowePVqWLVsm7733nuzZs0d+9rOfRfujEEJChAKJEEI8jBo1Sm655RYZPHiw3HzzzZKWlqYE02WXXaYeQ6quuLhYVq1apZZ/7LHHlDi65557ZOjQoer/zzzzjHz66aeybt26aH8cQkgI0INECCEeRo4cafw/MTFRCgsLZcSIEcZjSKGBvXv3qp8rV65UYsjOz7RhwwY57LDD2mS7CSHhhwKJEEI8JCcne92Hd8n8mK6Oa2xsVD8PHjwoZ555ptx3332t1tWjR4+Iby8hJHJQIBFCSJAcddRR8sYbb0i/fv0kKYmHU0LaE/QgEUJIkFx99dVy4MAB+fnPfy5Lly5VabX3339fVb01NDREe/MIISFAgUQIIUFSVFQkCxYsUGIIFW7wK6FNQF5eniQk8PBKSDzTqYmtZAkhhBBCvOAlDiGEEEKIBQokQgghhBALFEiEEEIIIRYokAghhBBCLFAgEUIIIYRYoEAihBBCCLFAgUQIIYQQYoECiRBCCCHEAgUSIYQQQogFCiRCCCGEEAsUSIQQQggh4s3/B6KCcQgaWYHsAAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1947,7 +2006,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2345,7 +2404,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Updated_MHKiT",
    "language": "python",
    "name": "python3"
   },
@@ -2359,7 +2418,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.11"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,
diff --git a/examples/adcp_example.ipynb b/examples/adcp_example.ipynb
index d476e0d21..9bbcb220e 100644
--- a/examples/adcp_example.ipynb
+++ b/examples/adcp_example.ipynb
@@ -1,4186 +1,5541 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Analyzing ADCP Data with MHKiT\n",
-        "\n",
-        "The following example illustrates a straightforward workflow for analyzing Acoustic Doppler Current Profiler (ADCP) data utilizing MHKiT. MHKiT has integrated the DOLfYN codebase as a module to facilitate ADCP and Acoustic Doppler Velocimetry (ADV) data processing.\n",
-        "\n",
-        "Here is a standard workflow for ADCP data analysis:\n",
-        "\n",
-        "1. **Import Data**\n",
-        "\n",
-        "2. **Review, QC, and Prepare the Raw Data**:\n",
-        "   1. Calculate or verify the correctness of depth bin locations\n",
-        "   2. Discard data recorded above the water surface or below the seafloor\n",
-        "   3. Assess the quality of velocity, beam amplitude, and/or beam correlation data\n",
-        "   4. Rotate Data Coordinate System\n",
-        "\n",
-        "3. **Data Averaging**: \n",
-        "    - If not already executed within the instrument, average the data into time bins of a predetermined duration, typically between 5 and 10 minutes\n",
-        "\n",
-        "4. **Speed and Direction**\n",
-        "\n",
-        "5. **Plotting**\n",
-        "\n",
-        "6. **Saving and Loading DOLfYN datasets**\n",
-        "\n",
-        "7. **Turbulence Statistics**\n",
-        "    1. Turbulence Intensity (TI)\n",
-        "    2. Power Spectral Densities\n",
-        "    3. Instrument Noise\n",
-        "    4. TKE Dissipation Rate\n",
-        "    5. Noise-corrected TI\n",
-        "    6. Reynolds stresses\n",
-        "    7. TKE Production\n",
-        "    8. TKE Balance \n",
-        "\n",
-        "\n",
-        "Begin your analysis by importing the requisite tools:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [],
-      "source": [
-        "from mhkit import dolfyn\n",
-        "from mhkit.dolfyn.adp import api"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "##  1. Importing Raw Instrument Data\n",
-        "\n",
-        "One of DOLfYN's key features is its ability to directly import raw data from an Acoustic Doppler Current Profiler (ADCP) right after it has been transferred. In this instance, we are using a Nortek Signature1000 ADCP, with the data stored in files with an '.ad2cp' extension. This specific dataset represents several hours of velocity data, captured at 1 Hz by an ADCP mounted on a bottom lander within a tidal inlet. The list of instruments compatible with DOLfYN can be found in the [MHKiT DOLfYN documentation](https://mhkit-software.github.io/MHKiT/mhkit-python/api.dolfyn.html).\n",
-        "\n",
-        "We'll start by importing the raw data file downloaded from the instrument. The `read` function processes the raw file and converts the information into an xarray Dataset. This Dataset includes several groups of variables:\n",
-        "\n",
-        "1. **Velocity**: Recorded in the coordinate system saved by the instrument (beam, XYZ, ENU)\n",
-        "2. **Beam Data**: Includes amplitude and correlation data\n",
-        "3. **Instrumental & Environmental Measurements**: Captures the instrument's bearing and environmental conditions\n",
-        "4. **Orientation Matrices**: Used by DOLfYN for rotating through different coordinate frames.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Indexing data/dolfyn/Sig1000_tidal.ad2cp... Done.\n",
-            "Reading file data/dolfyn/Sig1000_tidal.ad2cp ...\n"
-          ]
-        }
-      ],
-      "source": [
-        "ds = dolfyn.read(\"data/dolfyn/Sig1000_tidal.ad2cp\")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There are two ways to see what's in a Dataset. The first is to simply type the dataset's name to see the standard xarray output. To access a particular variable in a dataset, use dict-style (`ds['vel']`) or attribute-style syntax (`ds.vel`). See the [xarray docs](http://xarray.pydata.org/en/stable/getting-started-guide/quick-overview.html) for more details on how to use the xarray format."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-              "<defs>\n",
-              "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-              "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-              "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-              "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-              "</symbol>\n",
-              "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-              "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-              "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "</symbol>\n",
-              "</defs>\n",
-              "</svg>\n",
-              "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-              " *\n",
-              " */\n",
-              "\n",
-              ":root {\n",
-              "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-              "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-              "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-              "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-              "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-              "  --xr-background-color: var(--jp-layout-color0, white);\n",
-              "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-              "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-              "}\n",
-              "\n",
-              "html[theme=\"dark\"],\n",
-              "html[data-theme=\"dark\"],\n",
-              "body[data-theme=\"dark\"],\n",
-              "body.vscode-dark {\n",
-              "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-              "  --xr-border-color: #1f1f1f;\n",
-              "  --xr-disabled-color: #515151;\n",
-              "  --xr-background-color: #111111;\n",
-              "  --xr-background-color-row-even: #111111;\n",
-              "  --xr-background-color-row-odd: #313131;\n",
-              "}\n",
-              "\n",
-              ".xr-wrap {\n",
-              "  display: block !important;\n",
-              "  min-width: 300px;\n",
-              "  max-width: 700px;\n",
-              "}\n",
-              "\n",
-              ".xr-text-repr-fallback {\n",
-              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-header {\n",
-              "  padding-top: 6px;\n",
-              "  padding-bottom: 6px;\n",
-              "  margin-bottom: 4px;\n",
-              "  border-bottom: solid 1px var(--xr-border-color);\n",
-              "}\n",
-              "\n",
-              ".xr-header > div,\n",
-              ".xr-header > ul {\n",
-              "  display: inline;\n",
-              "  margin-top: 0;\n",
-              "  margin-bottom: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-obj-type,\n",
-              ".xr-array-name {\n",
-              "  margin-left: 2px;\n",
-              "  margin-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-obj-type {\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-sections {\n",
-              "  padding-left: 0 !important;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input {\n",
-              "  display: inline-block;\n",
-              "  opacity: 0;\n",
-              "  height: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input + label {\n",
-              "  color: var(--xr-disabled-color);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:enabled + label {\n",
-              "  cursor: pointer;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:focus + label {\n",
-              "  border: 2px solid var(--xr-font-color0);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:enabled + label:hover {\n",
-              "  color: var(--xr-font-color0);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary {\n",
-              "  grid-column: 1;\n",
-              "  color: var(--xr-font-color2);\n",
-              "  font-weight: 500;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary > span {\n",
-              "  display: inline-block;\n",
-              "  padding-left: 0.5em;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:disabled + label {\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in + label:before {\n",
-              "  display: inline-block;\n",
-              "  content: \"►\";\n",
-              "  font-size: 11px;\n",
-              "  width: 15px;\n",
-              "  text-align: center;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:disabled + label:before {\n",
-              "  color: var(--xr-disabled-color);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked + label:before {\n",
-              "  content: \"▼\";\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked + label > span {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary,\n",
-              ".xr-section-inline-details {\n",
-              "  padding-top: 4px;\n",
-              "  padding-bottom: 4px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-inline-details {\n",
-              "  grid-column: 2 / -1;\n",
-              "}\n",
-              "\n",
-              ".xr-section-details {\n",
-              "  display: none;\n",
-              "  grid-column: 1 / -1;\n",
-              "  margin-bottom: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-array-wrap {\n",
-              "  grid-column: 1 / -1;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 20px auto;\n",
-              "}\n",
-              "\n",
-              ".xr-array-wrap > label {\n",
-              "  grid-column: 1;\n",
-              "  vertical-align: top;\n",
-              "}\n",
-              "\n",
-              ".xr-preview {\n",
-              "  color: var(--xr-font-color3);\n",
-              "}\n",
-              "\n",
-              ".xr-array-preview,\n",
-              ".xr-array-data {\n",
-              "  padding: 0 5px !important;\n",
-              "  grid-column: 2;\n",
-              "}\n",
-              "\n",
-              ".xr-array-data,\n",
-              ".xr-array-in:checked ~ .xr-array-preview {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-array-in:checked ~ .xr-array-data,\n",
-              ".xr-array-preview {\n",
-              "  display: inline-block;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list {\n",
-              "  display: inline-block !important;\n",
-              "  list-style: none;\n",
-              "  padding: 0 !important;\n",
-              "  margin: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list li {\n",
-              "  display: inline-block;\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list:before {\n",
-              "  content: \"(\";\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list:after {\n",
-              "  content: \")\";\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list li:not(:last-child):after {\n",
-              "  content: \",\";\n",
-              "  padding-right: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-has-index {\n",
-              "  font-weight: bold;\n",
-              "}\n",
-              "\n",
-              ".xr-var-list,\n",
-              ".xr-var-item {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-var-item > div,\n",
-              ".xr-var-item label,\n",
-              ".xr-var-item > .xr-var-name span {\n",
-              "  background-color: var(--xr-background-color-row-even);\n",
-              "  margin-bottom: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-var-item > .xr-var-name:hover span {\n",
-              "  padding-right: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-var-list > li:nth-child(odd) > div,\n",
-              ".xr-var-list > li:nth-child(odd) > label,\n",
-              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-              "  background-color: var(--xr-background-color-row-odd);\n",
-              "}\n",
-              "\n",
-              ".xr-var-name {\n",
-              "  grid-column: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-var-dims {\n",
-              "  grid-column: 2;\n",
-              "}\n",
-              "\n",
-              ".xr-var-dtype {\n",
-              "  grid-column: 3;\n",
-              "  text-align: right;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-var-preview {\n",
-              "  grid-column: 4;\n",
-              "}\n",
-              "\n",
-              ".xr-index-preview {\n",
-              "  grid-column: 2 / 5;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-var-name,\n",
-              ".xr-var-dims,\n",
-              ".xr-var-dtype,\n",
-              ".xr-preview,\n",
-              ".xr-attrs dt {\n",
-              "  white-space: nowrap;\n",
-              "  overflow: hidden;\n",
-              "  text-overflow: ellipsis;\n",
-              "  padding-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-var-name:hover,\n",
-              ".xr-var-dims:hover,\n",
-              ".xr-var-dtype:hover,\n",
-              ".xr-attrs dt:hover {\n",
-              "  overflow: visible;\n",
-              "  width: auto;\n",
-              "  z-index: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-var-attrs,\n",
-              ".xr-var-data,\n",
-              ".xr-index-data {\n",
-              "  display: none;\n",
-              "  background-color: var(--xr-background-color) !important;\n",
-              "  padding-bottom: 5px !important;\n",
-              "}\n",
-              "\n",
-              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-              ".xr-var-data-in:checked ~ .xr-var-data,\n",
-              ".xr-index-data-in:checked ~ .xr-index-data {\n",
-              "  display: block;\n",
-              "}\n",
-              "\n",
-              ".xr-var-data > table {\n",
-              "  float: right;\n",
-              "}\n",
-              "\n",
-              ".xr-var-name span,\n",
-              ".xr-var-data,\n",
-              ".xr-index-name div,\n",
-              ".xr-index-data,\n",
-              ".xr-attrs {\n",
-              "  padding-left: 25px !important;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs,\n",
-              ".xr-var-attrs,\n",
-              ".xr-var-data,\n",
-              ".xr-index-data {\n",
-              "  grid-column: 1 / -1;\n",
-              "}\n",
-              "\n",
-              "dl.xr-attrs {\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 125px auto;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt,\n",
-              ".xr-attrs dd {\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "  float: left;\n",
-              "  padding-right: 10px;\n",
-              "  width: auto;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt {\n",
-              "  font-weight: normal;\n",
-              "  grid-column: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt:hover span {\n",
-              "  display: inline-block;\n",
-              "  background: var(--xr-background-color);\n",
-              "  padding-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dd {\n",
-              "  grid-column: 2;\n",
-              "  white-space: pre-wrap;\n",
-              "  word-break: break-all;\n",
-              "}\n",
-              "\n",
-              ".xr-icon-database,\n",
-              ".xr-icon-file-text2,\n",
-              ".xr-no-icon {\n",
-              "  display: inline-block;\n",
-              "  vertical-align: middle;\n",
-              "  width: 1em;\n",
-              "  height: 1.5em !important;\n",
-              "  stroke-width: 0;\n",
-              "  stroke: currentColor;\n",
-              "  fill: currentColor;\n",
-              "}\n",
-              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 83MB\n",
-              "Dimensions:              (time: 55000, time_b5: 55000, range: 28, range_b5: 28,\n",
-              "                          beam: 4, dir: 4, dirIMU: 3, earth: 3, inst: 3, q: 4,\n",
-              "                          x1: 4, x2: 4)\n",
-              "Coordinates:\n",
-              "  * time                 (time) datetime64[ns] 440kB 2020-08-15T00:20:00.5009...\n",
-              "  * time_b5              (time_b5) datetime64[ns] 440kB 2020-08-15T00:20:00.4...\n",
-              "  * range                (range) float64 224B 0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1\n",
-              "  * range_b5             (range_b5) float64 224B 0.6 1.1 1.6 ... 13.1 13.6 14.1\n",
-              "  * beam                 (beam) int32 16B 1 2 3 4\n",
-              "  * dir                  (dir) &lt;U2 32B &#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;\n",
-              "  * dirIMU               (dirIMU) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
-              "  * earth                (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
-              "  * inst                 (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
-              "  * q                    (q) &lt;U1 16B &#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;\n",
-              "  * x1                   (x1) int32 16B 1 2 3 4\n",
-              "  * x2                   (x2) int32 16B 1 2 3 4\n",
-              "Data variables: (12/38)\n",
-              "    c_sound              (time) float32 220kB 1.502e+03 1.502e+03 ... 1.498e+03\n",
-              "    temp                 (time) float32 220kB 14.55 14.55 14.55 ... 13.47 13.47\n",
-              "    pressure             (time) float32 220kB 9.713 9.718 9.718 ... 9.594 9.596\n",
-              "    mag                  (dirIMU, time) float32 660kB 72.5 72.7 ... -195.7\n",
-              "    accel                (dirIMU, time) float32 660kB -0.00479 ... 9.729\n",
-              "    batt                 (time) float32 220kB 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
-              "    ...                   ...\n",
-              "    telemetry_data       (time) uint8 55kB 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0\n",
-              "    boost_running        (time) uint8 55kB 0 0 0 0 0 0 0 0 1 ... 1 0 0 0 0 0 0 1\n",
-              "    heading              (time) float32 220kB -12.52 -12.51 ... -12.52 -12.5\n",
-              "    pitch                (time) float32 220kB -0.065 -0.06 -0.06 ... -0.05 -0.05\n",
-              "    roll                 (time) float32 220kB -7.425 -7.42 -7.42 ... -6.45 -6.45\n",
-              "    beam2inst_orientmat  (x1, x2) float32 64B 1.183 0.0 -1.183 ... 0.0 0.5518\n",
-              "Attributes: (12/34)\n",
-              "    filehead_config:       {&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, ...\n",
-              "    inst_model:            Signature1000\n",
-              "    inst_make:             Nortek\n",
-              "    inst_type:             ADCP\n",
-              "    burst_config:          {&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass...\n",
-              "    n_cells:               28\n",
-              "    ...                    ...\n",
-              "    proc_idle_less_12pct:  0\n",
-              "    rotate_vars:           [&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, ...\n",
-              "    coord_sys:             earth\n",
-              "    fs:                    1\n",
-              "    has_imu:               1\n",
-              "    beam_angle:            25</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2bb93856-6493-4446-9b87-27cea014a081' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2bb93856-6493-4446-9b87-27cea014a081' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 55000</li><li><span class='xr-has-index'>time_b5</span>: 55000</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>range_b5</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-44ab80c1-468d-4c4c-bfd8-da385ad2911b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44ab80c1-468d-4c4c-bfd8-da385ad2911b' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.500999927 .....</div><input id='attrs-b1f3c94e-ecd6-47a7-9b10-f64197ef1000' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1f3c94e-ecd6-47a7-9b10-f64197ef1000' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0639fea1-589f-4696-98e4-db209d804809' class='xr-var-data-in' type='checkbox'><label for='data-0639fea1-589f-4696-98e4-db209d804809' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.500999927&#x27;, &#x27;2020-08-15T00:20:01.501100063&#x27;,\n",
-              "       &#x27;2020-08-15T00:20:02.500999927&#x27;, ..., &#x27;2020-08-15T15:36:37.500999927&#x27;,\n",
-              "       &#x27;2020-08-15T15:36:38.500999927&#x27;, &#x27;2020-08-15T15:36:39.500999927&#x27;],\n",
-              "      shape=(55000,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.438499927 .....</div><input id='attrs-df1333fa-8090-4b73-90a1-370ef194f0d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df1333fa-8090-4b73-90a1-370ef194f0d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bc8ccde-7a5f-478c-a30e-602041272b6a' class='xr-var-data-in' type='checkbox'><label for='data-5bc8ccde-7a5f-478c-a30e-602041272b6a' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time b5</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.438499927&#x27;, &#x27;2020-08-15T00:20:01.438499927&#x27;,\n",
-              "       &#x27;2020-08-15T00:20:02.438499927&#x27;, ..., &#x27;2020-08-15T15:36:37.438499927&#x27;,\n",
-              "       &#x27;2020-08-15T15:36:38.438499927&#x27;, &#x27;2020-08-15T15:36:39.438499927&#x27;],\n",
-              "      shape=(55000,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-f266a94c-6400-4611-b6cc-540c71e6f4f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f266a94c-6400-4611-b6cc-540c71e6f4f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3d7efda-a3b4-4fdf-a0ac-47fa75f7d90b' class='xr-var-data-in' type='checkbox'><label for='data-f3d7efda-a3b4-4fdf-a0ac-47fa75f7d90b' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
-              "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
-              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-390d0180-57eb-4119-b0c0-5b41d0402e28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-390d0180-57eb-4119-b0c0-5b41d0402e28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f61bff6-c013-498b-970d-d4673831eee5' class='xr-var-data-in' type='checkbox'><label for='data-5f61bff6-c013-498b-970d-d4673831eee5' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range b5</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
-              "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
-              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-7f44c1f4-69b3-4068-9911-0419633c6f89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f44c1f4-69b3-4068-9911-0419633c6f89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f6dd975-0fde-4e31-9eb0-bf06af4dc611' class='xr-var-data-in' type='checkbox'><label for='data-8f6dd975-0fde-4e31-9eb0-bf06af4dc611' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;</div><input id='attrs-94007066-9746-4038-8627-4e4cbdeebe51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94007066-9746-4038-8627-4e4cbdeebe51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a776caf-27ad-490d-9198-a303fe3db364' class='xr-var-data-in' type='checkbox'><label for='data-6a776caf-27ad-490d-9198-a303fe3db364' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-e523f6b1-bdc3-4fbe-87ee-36a3c64207a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e523f6b1-bdc3-4fbe-87ee-36a3c64207a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d72a7f1b-9e73-4cda-884f-9df8be1c801b' class='xr-var-data-in' type='checkbox'><label for='data-d72a7f1b-9e73-4cda-884f-9df8be1c801b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-40c67b12-a0b6-47ed-afdc-dbec63068d92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40c67b12-a0b6-47ed-afdc-dbec63068d92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007855ee-909e-4285-9c39-c36252088294' class='xr-var-data-in' type='checkbox'><label for='data-007855ee-909e-4285-9c39-c36252088294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-a13fb84b-25d1-48ff-838d-4c490f163ac5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a13fb84b-25d1-48ff-838d-4c490f163ac5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8e30837-ea9d-4cdf-86a0-be81d160c31b' class='xr-var-data-in' type='checkbox'><label for='data-b8e30837-ea9d-4cdf-86a0-be81d160c31b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-b065e21f-114d-46c5-b868-fe13351035e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b065e21f-114d-46c5-b868-fe13351035e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33f36f6d-3af7-4fc4-b563-86f1121a7203' class='xr-var-data-in' type='checkbox'><label for='data-33f36f6d-3af7-4fc4-b563-86f1121a7203' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Quaternion Vector Components</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-977e242d-bfc2-4800-9149-e7762c892688' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-977e242d-bfc2-4800-9149-e7762c892688' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a82d0ad-2e37-4d7c-adfe-d3b0cba5aa75' class='xr-var-data-in' type='checkbox'><label for='data-8a82d0ad-2e37-4d7c-adfe-d3b0cba5aa75' 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>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-c996444d-d6fd-4594-9a5f-9717e2605adb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c996444d-d6fd-4594-9a5f-9717e2605adb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ae7b00f-61dc-4a25-aaea-da2285f28319' class='xr-var-data-in' type='checkbox'><label for='data-5ae7b00f-61dc-4a25-aaea-da2285f28319' 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>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aba501e5-2078-4cac-811c-1a57021b8eed' class='xr-section-summary-in' type='checkbox'  ><label for='section-aba501e5-2078-4cac-811c-1a57021b8eed' class='xr-section-summary' >Data variables: <span>(38)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-cd161dc4-2187-41ac-9b00-be6b10bc8bbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd161dc4-2187-41ac-9b00-be6b10bc8bbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f2afe60-12d6-49fd-b1dd-14df2c4dab76' class='xr-var-data-in' type='checkbox'><label for='data-0f2afe60-12d6-49fd-b1dd-14df2c4dab76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.9, 1501.9, 1501.9, ..., 1498.4, 1498.3, 1498.3],\n",
-              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.55 14.55 14.55 ... 13.47 13.47</div><input id='attrs-c884fa59-ec0b-4529-8400-537d38bd77c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c884fa59-ec0b-4529-8400-537d38bd77c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd474941-9a8d-4e30-b7d3-7f958b6dc3a2' class='xr-var-data-in' type='checkbox'><label for='data-dd474941-9a8d-4e30-b7d3-7f958b6dc3a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.55, 14.55, 14.55, ..., 13.47, 13.47, 13.47],\n",
-              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.713 9.718 9.718 ... 9.594 9.596</div><input id='attrs-227176fc-19c4-40d5-8f74-df756d306f3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-227176fc-19c4-40d5-8f74-df756d306f3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3398794a-de4e-4e9b-bd3d-dee74335344a' class='xr-var-data-in' type='checkbox'><label for='data-3398794a-de4e-4e9b-bd3d-dee74335344a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.713, 9.718, 9.718, ..., 9.596, 9.594, 9.596],\n",
-              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.5 72.7 72.6 ... -197.2 -195.7</div><input id='attrs-c135a21c-5db2-487b-ad1f-1faff443d5db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c135a21c-5db2-487b-ad1f-1faff443d5db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c21e291-655d-4ff6-be10-951b9c9bc7a9' class='xr-var-data-in' type='checkbox'><label for='data-7c21e291-655d-4ff6-be10-951b9c9bc7a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.5,   72.7,   72.6, ...,   72.3,   72.8,   72.4],\n",
-              "       [   9.3,    9.4,    8.9, ...,    5.9,    6.3,    6.4],\n",
-              "       [-198. , -196.3, -198.1, ..., -197.4, -197.2, -195.7]],\n",
-              "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.00479 -0.01437 ... 9.685 9.729</div><input id='attrs-b957bb42-48dc-425f-b414-290a166595ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b957bb42-48dc-425f-b414-290a166595ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cf8d694-3813-4297-bae1-ed46db2b4ea8' class='xr-var-data-in' type='checkbox'><label for='data-5cf8d694-3813-4297-bae1-ed46db2b4ea8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-4.7900393e-03, -1.4370117e-02, -2.3950195e-02, ...,\n",
-              "        -1.4370117e-02, -4.7900393e-03, -2.3950195e-02],\n",
-              "       [-1.2933105e+00, -1.2549902e+00, -1.2645704e+00, ...,\n",
-              "        -1.1017090e+00, -1.0873389e+00, -1.0969189e+00],\n",
-              "       [ 9.7285690e+00,  9.7141991e+00,  9.7237797e+00, ...,\n",
-              "         9.6710892e+00,  9.6854591e+00,  9.7285690e+00]],\n",
-              "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.6 16.6 16.6 ... 16.4 16.4 15.2</div><input id='attrs-63d7dc22-a222-4368-8d64-edd3a7fb6ce9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63d7dc22-a222-4368-8d64-edd3a7fb6ce9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fcd37e5-2bb0-44a4-87ab-07894fc1d27e' class='xr-var-data-in' type='checkbox'><label for='data-5fcd37e5-2bb0-44a4-87ab-07894fc1d27e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.6, 16.6, 16.6, ..., 16.4, 16.4, 15.2],\n",
-              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-7230bc27-e7dd-4a81-bc58-ec377dfba2f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7230bc27-e7dd-4a81-bc58-ec377dfba2f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-956e2170-d3fa-4b3f-91f8-b755c892f600' class='xr-var-data-in' type='checkbox'><label for='data-956e2170-d3fa-4b3f-91f8-b755c892f600' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20. , 20. , 20. , ..., 19.5, 19.5, 19.5],\n",
-              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-2b195a2d-ca32-41c9-bbc7-a257d1a55275' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b195a2d-ca32-41c9-bbc7-a257d1a55275' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43653780-016c-44e0-bb3f-c525c0c403cd' class='xr-var-data-in' type='checkbox'><label for='data-43653780-016c-44e0-bb3f-c525c0c403cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-0309ea33-451b-4d67-9839-d11061c65111' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0309ea33-451b-4d67-9839-d11061c65111' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8613093-89dc-48c6-b2a9-ee53b27f95b1' class='xr-var-data-in' type='checkbox'><label for='data-c8613093-89dc-48c6-b2a9-ee53b27f95b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
-              "       1053557762], shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-608fbc04-d386-4fa1-8e43-de8db4cc6cb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-608fbc04-d386-4fa1-8e43-de8db4cc6cb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d316239-ef9e-4482-83b0-e7264fc944a8' class='xr-var-data-in' type='checkbox'><label for='data-8d316239-ef9e-4482-83b0-e7264fc944a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000],\n",
-              "      shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.015 -0.048 ... -0.015 -0.012</div><input id='attrs-98644f44-ab34-4c0b-a749-8d40015b89b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98644f44-ab34-4c0b-a749-8d40015b89b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-199e8e9d-f69f-4bff-9f1d-881ed213794c' class='xr-var-data-in' type='checkbox'><label for='data-199e8e9d-f69f-4bff-9f1d-881ed213794c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.015, -0.048,  0.008, ..., -0.176, -0.071, -0.027],\n",
-              "        [-0.093, -0.124, -0.031, ..., -0.175, -0.16 , -0.03 ],\n",
-              "        [-0.163, -0.135, -0.105, ..., -0.129, -0.089, -0.116],\n",
-              "        ...,\n",
-              "        [-0.169, -0.013, -0.124, ..., -0.062,  0.091, -0.009],\n",
-              "        [-0.16 , -0.043, -0.111, ...,  0.012,  0.089,  0.021],\n",
-              "        [-0.158, -0.057, -0.13 , ..., -0.055,  0.104,  0.013]],\n",
-              "\n",
-              "       [[ 0.243,  0.204,  0.234, ..., -0.163, -0.135, -0.18 ],\n",
-              "        [ 0.272,  0.248,  0.236, ..., -0.254, -0.169, -0.204],\n",
-              "        [ 0.257,  0.294,  0.333, ..., -0.299, -0.226, -0.273],\n",
-              "        ...,\n",
-              "        [ 0.305,  0.377,  0.283, ..., -0.198, -0.229, -0.121],\n",
-              "        [ 0.328,  0.33 ,  0.294, ..., -0.161, -0.292, -0.103],\n",
-              "        [ 0.31 ,  0.314,  0.226, ..., -0.219, -0.292, -0.049]],\n",
-              "\n",
-              "       [[ 0.016,  0.012,  0.01 , ...,  0.009,  0.006, -0.018],\n",
-              "        [ 0.01 ,  0.011, -0.013, ...,  0.002, -0.027,  0.009],\n",
-              "        [ 0.01 ,  0.009,  0.023, ..., -0.018, -0.002, -0.024],\n",
-              "        ...,\n",
-              "        [ 0.034, -0.061, -0.024, ..., -0.014,  0.086,  0.01 ],\n",
-              "        [ 0.032, -0.04 , -0.027, ..., -0.014,  0.063, -0.01 ],\n",
-              "        [ 0.004, -0.037, -0.018, ..., -0.001,  0.059,  0.021]],\n",
-              "\n",
-              "       [[ 0.003, -0.007,  0.046, ..., -0.032, -0.008, -0.029],\n",
-              "        [ 0.017,  0.033,  0.035, ..., -0.016, -0.022, -0.034],\n",
-              "        [ 0.016, -0.012,  0.005, ...,  0.002, -0.045, -0.026],\n",
-              "        ...,\n",
-              "        [-0.044,  0.015, -0.04 , ..., -0.015,  0.006,  0.012],\n",
-              "        [-0.013,  0.014, -0.032, ...,  0.01 ,  0.   , -0.015],\n",
-              "        [ 0.007,  0.016, -0.027, ..., -0.002, -0.015, -0.012]]],\n",
-              "      shape=(4, 28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>81.0 80.0 80.5 ... 43.5 42.5 42.0</div><input id='attrs-c32c85ac-0276-4d5f-8f1d-cbd90cec0e3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c32c85ac-0276-4d5f-8f1d-cbd90cec0e3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9723746f-29e5-49fd-85c4-09b3642f16b6' class='xr-var-data-in' type='checkbox'><label for='data-9723746f-29e5-49fd-85c4-09b3642f16b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[81. , 80. , 80.5, ..., 81.5, 81.5, 81.5],\n",
-              "        [75. , 74.5, 75. , ..., 79. , 77. , 77.5],\n",
-              "        [73.5, 72. , 72. , ..., 75. , 74.5, 75. ],\n",
-              "        ...,\n",
-              "        [54.5, 55.5, 54.5, ..., 50. , 50. , 49.5],\n",
-              "        [53.5, 54.5, 54.5, ..., 46.5, 47. , 46.5],\n",
-              "        [53. , 54. , 53. , ..., 45. , 46. , 44.5]],\n",
-              "\n",
-              "       [[81.5, 80. , 80.5, ..., 82. , 81.5, 82. ],\n",
-              "        [76. , 77. , 76.5, ..., 79. , 77.5, 77.5],\n",
-              "        [73.5, 74. , 73. , ..., 75.5, 74.5, 74.5],\n",
-              "        ...,\n",
-              "        [57. , 58.5, 58. , ..., 53.5, 52. , 52.5],\n",
-              "        [55.5, 56.5, 55.5, ..., 50. , 49. , 49. ],\n",
-              "        [52.5, 54. , 54. , ..., 47.5, 47.5, 47. ]],\n",
-              "\n",
-              "       [[80.5, 79.5, 80. , ..., 81.5, 82.5, 81.5],\n",
-              "        [76. , 75.5, 75. , ..., 77. , 78.5, 77. ],\n",
-              "        [72. , 73.5, 72. , ..., 74. , 75. , 73.5],\n",
-              "        ...,\n",
-              "        [56.5, 56.5, 57. , ..., 47.5, 47. , 48. ],\n",
-              "        [56. , 55.5, 56.5, ..., 46.5, 47. , 47. ],\n",
-              "        [54.5, 54. , 55.5, ..., 45. , 45.5, 45.5]],\n",
-              "\n",
-              "       [[80. , 81. , 80. , ..., 81. , 81. , 81.5],\n",
-              "        [75.5, 75.5, 74.5, ..., 76.5, 76.5, 77.5],\n",
-              "        [73. , 72.5, 73. , ..., 75.5, 74.5, 74. ],\n",
-              "        ...,\n",
-              "        [51.5, 51. , 51. , ..., 46. , 46. , 45. ],\n",
-              "        [51. , 50. , 51. , ..., 45. , 44. , 43.5],\n",
-              "        [50.5, 49.5, 50. , ..., 43.5, 42.5, 42. ]]],\n",
-              "      shape=(4, 28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>100 91 91 97 99 ... 96 98 98 98 96</div><input id='attrs-6cf257b5-82cd-4e7a-a64f-b3c347885ec8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cf257b5-82cd-4e7a-a64f-b3c347885ec8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81ea19f2-bf29-4aa6-a618-9941b95035be' class='xr-var-data-in' type='checkbox'><label for='data-81ea19f2-bf29-4aa6-a618-9941b95035be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[100,  91,  91, ...,  89,  90,  96],\n",
-              "        [ 74,  92,  96, ..., 100,  97, 100],\n",
-              "        [100, 100,  99, ...,  99, 100, 100],\n",
-              "        ...,\n",
-              "        [ 99, 100, 100, ...,  96,  96,  99],\n",
-              "        [ 99,  97, 100, ...,  94,  97,  96],\n",
-              "        [ 98, 100, 100, ..., 100, 100,  98]],\n",
-              "\n",
-              "       [[ 94,  90,  89, ...,  91,  92,  90],\n",
-              "        [ 94, 100, 100, ...,  96, 100,  93],\n",
-              "        [ 99,  95,  97, ...,  98,  98, 100],\n",
-              "        ...,\n",
-              "        [100, 100, 100, ...,  98,  99, 100],\n",
-              "        [ 99, 100,  99, ...,  97,  94,  94],\n",
-              "        [ 96,  99,  96, ...,  97, 100,  97]],\n",
-              "\n",
-              "       [[ 92,  93,  93, ...,  93,  97,  89],\n",
-              "        [ 99, 100,  99, ...,  96,  88,  98],\n",
-              "        [ 97, 100,  98, ..., 100,  99,  97],\n",
-              "        ...,\n",
-              "        [100, 100,  99, ...,  97,  94,  95],\n",
-              "        [100, 100, 100, ...,  96, 100, 100],\n",
-              "        [100, 100, 100, ...,  96,  95,  94]],\n",
-              "\n",
-              "       [[ 87,  97,  87, ...,  91,  87,  90],\n",
-              "        [100,  93,  93, ...,  96,  96,  97],\n",
-              "        [100, 100, 100, ..., 100, 100,  97],\n",
-              "        ...,\n",
-              "        [100,  99,  96, ...,  98, 100, 100],\n",
-              "        [100, 100, 100, ...,  94,  93,  95],\n",
-              "        [100,  98,  99, ...,  98,  98,  96]]],\n",
-              "      shape=(4, 28, 55000), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2164 ... 0.9937 0.9937</div><input id='attrs-81e10eb9-2350-4e6c-8f48-13d67572014c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81e10eb9-2350-4e6c-8f48-13d67572014c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d233e24a-daa2-474f-ad1a-512b7641dc17' class='xr-var-data-in' type='checkbox'><label for='data-d233e24a-daa2-474f-ad1a-512b7641dc17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16688156e-01, -2.16397047e-01, -2.16593146e-01, ...,\n",
-              "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
-              "        [ 9.76196468e-01,  9.76219118e-01,  9.76202846e-01, ...,\n",
-              "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
-              "        [-1.22870505e-03, -1.20262802e-03, -1.17563456e-03, ...,\n",
-              "         -1.16243958e-03, -1.02279335e-03, -9.55920666e-04]],\n",
-              "\n",
-              "       [[-9.67970312e-01, -9.68004048e-01, -9.67982769e-01, ...,\n",
-              "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
-              "        [-2.15024590e-01, -2.14734554e-01, -2.14924574e-01, ...,\n",
-              "         -2.14874983e-01, -2.15436578e-01, -2.15052128e-01],\n",
-              "        [-1.29278094e-01, -1.29190803e-01, -1.29239410e-01, ...,\n",
-              "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01]],\n",
-              "\n",
-              "       [[-1.26469478e-01, -1.26385525e-01, -1.26422524e-01, ...,\n",
-              "         -1.10033900e-01, -1.10009573e-01, -1.09963998e-01],\n",
-              "        [-2.68291458e-02, -2.68030688e-02, -2.68616974e-02, ...,\n",
-              "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02],\n",
-              "        [ 9.91607308e-01,  9.91618454e-01,  9.91612315e-01, ...,\n",
-              "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
-              "      shape=(3, 3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.00293</div><input id='attrs-17f5fa72-a0bb-4b54-b694-f4903158ec8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17f5fa72-a0bb-4b54-b694-f4903158ec8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3859ed97-6d64-4f02-b4cf-bfed1a1a9664' class='xr-var-data-in' type='checkbox'><label for='data-3859ed97-6d64-4f02-b4cf-bfed1a1a9664' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00390625,\n",
-              "        -0.00292969, -0.00195312],\n",
-              "       [ 0.00097656,  0.        ,  0.        , ...,  0.        ,\n",
-              "         0.00097656,  0.00097656],\n",
-              "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
-              "        -0.00195312, -0.00292969]], shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-fd7a2f23-54b3-48d5-bf91-aee8beac0a22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd7a2f23-54b3-48d5-bf91-aee8beac0a22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e6ffbf6-25c3-484d-b5e7-aef6638ab46e' class='xr-var-data-in' type='checkbox'><label for='data-2e6ffbf6-25c3-484d-b5e7-aef6638ab46e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6244812 , ...,  0.625     ,\n",
-              "         0.6247864 ,  0.62490845],\n",
-              "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
-              "        -0.03564453, -0.03561401],\n",
-              "       [-0.05014038, -0.05010986, -0.05014038, ..., -0.04354858,\n",
-              "        -0.04360962, -0.04360962],\n",
-              "       [ 0.77835083,  0.7782593 ,  0.7783203 , ...,  0.77856445,\n",
-              "         0.77874756,  0.7786255 ]], shape=(4, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-4ca0ab12-bd84-4097-b895-e57e3fc5de6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ca0ab12-bd84-4097-b895-e57e3fc5de6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c2d82bb-5a17-4f7e-b60c-be48ad57a5e7' class='xr-var-data-in' type='checkbox'><label for='data-0c2d82bb-5a17-4f7e-b60c-be48ad57a5e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.8, 12.8, 12.8, ..., 12. , 12. , 12. ],\n",
-              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>243 243 243 244 ... 246 246 246 246</div><input id='attrs-baf3a83f-5067-483c-b593-c804797724f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-baf3a83f-5067-483c-b593-c804797724f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c853c46c-dd38-401f-91d9-dd5a664ea07a' class='xr-var-data-in' type='checkbox'><label for='data-c853c46c-dd38-401f-91d9-dd5a664ea07a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([243, 243, 243, ..., 246, 246, 246], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.9 72.8 72.4 ... -198.0 -195.7</div><input id='attrs-a3d36048-7a04-447f-bca2-3dc7664d54d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3d36048-7a04-447f-bca2-3dc7664d54d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c06619e-5cea-454c-a150-7d12c1a7adc2' class='xr-var-data-in' type='checkbox'><label for='data-5c06619e-5cea-454c-a150-7d12c1a7adc2' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.9,   72.8,   72.4, ...,   73. ,   73. ,   72.7],\n",
-              "       [   9.5,    9.5,    9.8, ...,    6. ,    6.4,    5.9],\n",
-              "       [-197.1, -197.2, -197.7, ..., -197.4, -198. , -195.7]],\n",
-              "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01437 0.0 ... 9.709 9.724</div><input id='attrs-37b68fe2-0af0-480d-9d75-c671620f8b77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37b68fe2-0af0-480d-9d75-c671620f8b77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02b2768b-9263-4783-ba00-4ba49ef9dae1' class='xr-var-data-in' type='checkbox'><label for='data-02b2768b-9263-4783-ba00-4ba49ef9dae1' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.4370117e-02,  0.0000000e+00, -1.4370117e-02, ...,\n",
-              "        -4.7900393e-03, -1.9160157e-02, -1.4370117e-02],\n",
-              "       [-1.2693603e+00, -1.2885205e+00, -1.2837305e+00, ...,\n",
-              "        -1.0825489e+00, -1.1017090e+00, -1.1064991e+00],\n",
-              "       [ 9.7189894e+00,  9.7046194e+00,  9.7094088e+00, ...,\n",
-              "         9.7141991e+00,  9.7094088e+00,  9.7237797e+00]],\n",
-              "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-e627ec10-d8cd-429d-b603-1d9a35e02e36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e627ec10-d8cd-429d-b603-1d9a35e02e36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afdfad8b-d9e3-41af-9619-c2178b544fdb' class='xr-var-data-in' type='checkbox'><label for='data-afdfad8b-d9e3-41af-9619-c2178b544fdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-437c65d8-caa7-4bc7-85ac-2e54a22e1402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-437c65d8-caa7-4bc7-85ac-2e54a22e1402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c857bb51-3bb7-4320-8d3a-493531d3d019' class='xr-var-data-in' type='checkbox'><label for='data-c857bb51-3bb7-4320-8d3a-493531d3d019' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
-              "       1053557762], shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-5c3f5314-b091-4990-8617-32819e546950' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c3f5314-b091-4990-8617-32819e546950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0253f787-a52b-4f5c-b911-d1e23b9a3c36' class='xr-var-data-in' type='checkbox'><label for='data-0253f787-a52b-4f5c-b911-d1e23b9a3c36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000],\n",
-              "      shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.003 0.021 0.018 ... -0.048 0.09</div><input id='attrs-8ba34c98-f026-466e-9c04-ca734c6dfd9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ba34c98-f026-466e-9c04-ca734c6dfd9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-505f74df-75de-4ab9-9b6f-e2c4b1c4a275' class='xr-var-data-in' type='checkbox'><label for='data-505f74df-75de-4ab9-9b6f-e2c4b1c4a275' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.003,  0.021,  0.018, ...,  0.   , -0.044,  0.009],\n",
-              "       [ 0.057,  0.034,  0.017, ...,  0.01 ,  0.001, -0.002],\n",
-              "       [ 0.039,  0.022,  0.02 , ...,  0.039, -0.008,  0.018],\n",
-              "       ...,\n",
-              "       [-0.036, -0.013,  0.058, ...,  0.058, -0.013,  0.028],\n",
-              "       [-0.043, -0.009,  0.028, ...,  0.092, -0.021,  0.019],\n",
-              "       [-0.073,  0.003,  0.   , ...,  0.049, -0.048,  0.09 ]],\n",
-              "      shape=(28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.5 80.5 80.0 ... 46.5 46.0 47.5</div><input id='attrs-e56774a7-c9e7-476f-97ad-72b4a06d9caf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e56774a7-c9e7-476f-97ad-72b4a06d9caf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5006da4b-9fac-45f3-93cf-093f338a4b03' class='xr-var-data-in' type='checkbox'><label for='data-5006da4b-9fac-45f3-93cf-093f338a4b03' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[80.5, 80.5, 80. , ..., 82. , 81.5, 81. ],\n",
-              "       [75.5, 76.5, 76. , ..., 78.5, 78. , 77.5],\n",
-              "       [72.5, 73.5, 73.5, ..., 75. , 76.5, 75.5],\n",
-              "       ...,\n",
-              "       [57.5, 57.5, 57. , ..., 49. , 49. , 48.5],\n",
-              "       [56.5, 57. , 56. , ..., 47.5, 48. , 48. ],\n",
-              "       [55.5, 55.5, 55. , ..., 46.5, 46. , 47.5]],\n",
-              "      shape=(28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>98 100 92 93 98 ... 100 99 94 100</div><input id='attrs-404c4618-f62e-4504-b6c3-1859e03b4f70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-404c4618-f62e-4504-b6c3-1859e03b4f70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfe891f4-9f91-4567-8f93-109fc59a7b74' class='xr-var-data-in' type='checkbox'><label for='data-bfe891f4-9f91-4567-8f93-109fc59a7b74' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[ 98, 100,  92, ...,  88,  90,  91],\n",
-              "       [ 97,  94,  98, ...,  97,  95,  97],\n",
-              "       [100, 100, 100, ...,  93, 100,  96],\n",
-              "       ...,\n",
-              "       [100, 100, 100, ..., 100, 100,  98],\n",
-              "       [100, 100, 100, ...,  99, 100,  98],\n",
-              "       [100, 100, 100, ...,  99,  94, 100]],\n",
-              "      shape=(28, 55000), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2165 ... 0.9937 0.9937</div><input id='attrs-1a470afd-e700-413c-b637-a244630e9ef8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a470afd-e700-413c-b637-a244630e9ef8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19fe9666-50f5-495e-8450-dc6b3f76616f' class='xr-var-data-in' type='checkbox'><label for='data-19fe9666-50f5-495e-8450-dc6b3f76616f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16694236e-01, -2.16498137e-01, -2.16593146e-01, ...,\n",
-              "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
-              "        [-9.67967808e-01, -9.68039632e-01, -9.68030274e-01, ...,\n",
-              "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
-              "        [-1.26507595e-01, -1.26426160e-01, -1.26425594e-01, ...,\n",
-              "         -1.10033900e-01, -1.10009573e-01, -1.09963998e-01]],\n",
-              "\n",
-              "       [[ 9.76198971e-01,  9.76259708e-01,  9.76250350e-01, ...,\n",
-              "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
-              "        [-2.15024590e-01, -2.14829564e-01, -2.14924574e-01, ...,\n",
-              "         -2.14874983e-01, -2.15436578e-01, -2.15052128e-01],\n",
-              "        [-2.68766508e-02, -2.68536285e-02, -2.68591940e-02, ...,\n",
-              "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02]],\n",
-              "\n",
-              "       [[-1.19058788e-03, -1.16700679e-03, -1.17257237e-03, ...,\n",
-              "         -1.16243958e-03, -1.02279335e-03, -9.55920666e-04],\n",
-              "        [-1.29325598e-01, -1.29241347e-01, -1.29241914e-01, ...,\n",
-              "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01],\n",
-              "        [ 9.91601229e-01,  9.91612315e-01,  9.91612315e-01, ...,\n",
-              "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
-              "      shape=(3, 3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.001953</div><input id='attrs-a74a7f13-0d52-4f16-82b1-ee206ae6d5bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a74a7f13-0d52-4f16-82b1-ee206ae6d5bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3f20d95-68d7-474a-8595-d5c5021028ea' class='xr-var-data-in' type='checkbox'><label for='data-b3f20d95-68d7-474a-8595-d5c5021028ea' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00097656,\n",
-              "        -0.00195312, -0.00390625],\n",
-              "       [ 0.        ,  0.        ,  0.00097656, ...,  0.00097656,\n",
-              "         0.        ,  0.00097656],\n",
-              "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
-              "        -0.00292969, -0.00195312]], shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-26443951-1c72-4ee5-bfc4-bd88dbb72a68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26443951-1c72-4ee5-bfc4-bd88dbb72a68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cd4a141-939b-4e48-927f-83fafb7cb2e4' class='xr-var-data-in' type='checkbox'><label for='data-7cd4a141-939b-4e48-927f-83fafb7cb2e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6245117 , ...,  0.625     ,\n",
-              "         0.6247864 ,  0.62490845],\n",
-              "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
-              "        -0.03564453, -0.03561401],\n",
-              "       [-0.0501709 , -0.05014038, -0.05014038, ..., -0.04354858,\n",
-              "        -0.04360962, -0.04360962],\n",
-              "       [ 0.77835083,  0.7782898 ,  0.7783203 , ...,  0.77856445,\n",
-              "         0.77874756,  0.7786255 ]], shape=(4, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>65 65 65 65 65 ... 65 65 65 65 65</div><input id='attrs-7ec6af9a-2255-46c2-850c-93e145634ac2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ec6af9a-2255-46c2-850c-93e145634ac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61005fbb-7dfb-4d0b-9523-6eb3492ca550' class='xr-var-data-in' type='checkbox'><label for='data-61005fbb-7dfb-4d0b-9523-6eb3492ca550' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65, 65, 65, ..., 65, 65, 65], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-4e977b39-1909-48dd-8c7e-e02ad7a96c22' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e977b39-1909-48dd-8c7e-e02ad7a96c22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-baa95071-3866-471b-b363-0ef3d4385487' class='xr-var-data-in' type='checkbox'><label for='data-baa95071-3866-471b-b363-0ef3d4385487' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-93757c98-db60-44da-bdfb-1318145581a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93757c98-db60-44da-bdfb-1318145581a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d6e07b5-4397-4172-8c7b-25483bd9111e' class='xr-var-data-in' type='checkbox'><label for='data-2d6e07b5-4397-4172-8c7b-25483bd9111e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-318ea917-1899-4a34-abc4-5c37c5c15fc5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-318ea917-1899-4a34-abc4-5c37c5c15fc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b729943f-bf76-445a-ba63-1bf020bd6fb8' class='xr-var-data-in' type='checkbox'><label for='data-b729943f-bf76-445a-ba63-1bf020bd6fb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1</div><input id='attrs-e6c5b282-4fac-44ca-8c0f-2b06b9941da7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6c5b282-4fac-44ca-8c0f-2b06b9941da7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45c11eaf-5ea8-491f-a07a-6202abbd36f5' class='xr-var-data-in' type='checkbox'><label for='data-45c11eaf-5ea8-491f-a07a-6202abbd36f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 1], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.52 -12.51 ... -12.52 -12.5</div><input id='attrs-eba533fc-3add-4203-8fe9-58c745d4a428' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eba533fc-3add-4203-8fe9-58c745d4a428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66aeb13a-c38a-492a-af64-001423f96b88' class='xr-var-data-in' type='checkbox'><label for='data-66aeb13a-c38a-492a-af64-001423f96b88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-12.52    , -12.505001, -12.51    , ..., -12.490002, -12.52    ,\n",
-              "       -12.500001], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.065 -0.06 -0.06 ... -0.05 -0.05</div><input id='attrs-a4b40bd9-d7cb-4510-8ce3-3b2a91e29acc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4b40bd9-d7cb-4510-8ce3-3b2a91e29acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e4a129a-fc0f-42eb-9338-20dbed6475ad' class='xr-var-data-in' type='checkbox'><label for='data-9e4a129a-fc0f-42eb-9338-20dbed6475ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.065, -0.06 , -0.06 , ..., -0.06 , -0.05 , -0.05 ],\n",
-              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.425 -7.42 -7.42 ... -6.45 -6.45</div><input id='attrs-f7999b95-d36b-45ad-b566-08589acee982' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7999b95-d36b-45ad-b566-08589acee982' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-042bdbd3-5f68-4ce3-8795-e9b64b6ae1c1' class='xr-var-data-in' type='checkbox'><label for='data-042bdbd3-5f68-4ce3-8795-e9b64b6ae1c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.4250007, -7.42     , -7.42     , ..., -6.45     , -6.45     ,\n",
-              "       -6.45     ], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.183 0.0 -1.183 ... 0.0 0.5518</div><input id='attrs-36b40b6e-6419-46de-bbb3-b61ce161e810' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36b40b6e-6419-46de-bbb3-b61ce161e810' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-699e6c95-2bd9-4c71-8390-2bda2c0a5771' class='xr-var-data-in' type='checkbox'><label for='data-699e6c95-2bd9-4c71-8390-2bda2c0a5771' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.1831,  0.    , -1.1831,  0.    ],\n",
-              "       [ 0.    , -1.1831,  0.    ,  1.1831],\n",
-              "       [ 0.5518,  0.    ,  0.5518,  0.    ],\n",
-              "       [ 0.    ,  0.5518,  0.    ,  0.5518]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85c4917d-66c8-479f-b4e2-15d04b2bf964' class='xr-section-summary-in' type='checkbox'  ><label for='section-85c4917d-66c8-479f-b4e2-15d04b2bf964' class='xr-section-summary' >Indexes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2de81dd7-2cb8-44e4-904b-7daf8ca0d950' class='xr-index-data-in' type='checkbox'/><label for='index-2de81dd7-2cb8-44e4-904b-7daf8ca0d950' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:01.501100063&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:02.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:03.501100063&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:04.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:05.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:06.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:07.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:08.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:09.500999927&#x27;,\n",
-              "               ...\n",
-              "               &#x27;2020-08-15 15:36:30.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:31.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:32.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:33.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:34.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:35.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:36.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:37.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:38.500999927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:39.500999927&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2afce65d-6462-4849-aa74-76741c2965b9' class='xr-index-data-in' type='checkbox'/><label for='index-2afce65d-6462-4849-aa74-76741c2965b9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:01.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:02.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:03.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:04.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:05.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:06.438400030&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:07.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:08.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 00:20:09.438499927&#x27;,\n",
-              "               ...\n",
-              "               &#x27;2020-08-15 15:36:30.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:31.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:32.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:33.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:34.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:35.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:36.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:37.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:38.438499927&#x27;,\n",
-              "               &#x27;2020-08-15 15:36:39.438499927&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f033e9d7-d4fd-4979-8ac4-bfb33ff92fd8' class='xr-index-data-in' type='checkbox'/><label for='index-f033e9d7-d4fd-4979-8ac4-bfb33ff92fd8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
-              "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
-              "       12.6, 13.1, 13.6, 14.1],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-de7c4065-0bf4-4f6d-b14f-69449fc8b193' class='xr-index-data-in' type='checkbox'/><label for='index-de7c4065-0bf4-4f6d-b14f-69449fc8b193' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
-              "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
-              "       12.6, 13.1, 13.6, 14.1],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9137ad8f-4782-432a-8947-4b73692e8655' class='xr-index-data-in' type='checkbox'/><label for='index-9137ad8f-4782-432a-8947-4b73692e8655' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0263034c-e555-4e21-b001-f590f8e161ea' class='xr-index-data-in' type='checkbox'/><label for='index-0263034c-e555-4e21-b001-f590f8e161ea' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-92be0ebb-9e9c-46f7-85f7-7ccca83a0541' class='xr-index-data-in' type='checkbox'/><label for='index-92be0ebb-9e9c-46f7-85f7-7ccca83a0541' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cb6b149f-ce38-46e6-9fae-b4424b15f76d' class='xr-index-data-in' type='checkbox'/><label for='index-cb6b149f-ce38-46e6-9fae-b4424b15f76d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c06b0f97-0474-48a9-bd9d-0ed657f86896' class='xr-index-data-in' type='checkbox'/><label for='index-c06b0f97-0474-48a9-bd9d-0ed657f86896' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-36cb7aef-fbb6-4ed0-9ce5-1363e63f39ec' class='xr-index-data-in' type='checkbox'/><label for='index-36cb7aef-fbb6-4ed0-9ce5-1363e63f39ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4602e62a-bc1a-49ec-bfd7-afd8f4f8498a' class='xr-index-data-in' type='checkbox'/><label for='index-4602e62a-bc1a-49ec-bfd7-afd8f4f8498a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-dea9ba20-680e-4e92-8a42-2918492d10cb' class='xr-index-data-in' type='checkbox'/><label for='index-dea9ba20-680e-4e92-8a42-2918492d10cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4c8d97d-a580-46d6-8a37-d465412da7bf' class='xr-section-summary-in' type='checkbox'  ><label for='section-e4c8d97d-a580-46d6-8a37-d465412da7bf' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>earth</dd><dt><span>fs :</span></dt><dd>1</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd></dl></div></li></ul></div></div>"
-            ],
-            "text/plain": [
-              "<xarray.Dataset> Size: 83MB\n",
-              "Dimensions:              (time: 55000, time_b5: 55000, range: 28, range_b5: 28,\n",
-              "                          beam: 4, dir: 4, dirIMU: 3, earth: 3, inst: 3, q: 4,\n",
-              "                          x1: 4, x2: 4)\n",
-              "Coordinates:\n",
-              "  * time                 (time) datetime64[ns] 440kB 2020-08-15T00:20:00.5009...\n",
-              "  * time_b5              (time_b5) datetime64[ns] 440kB 2020-08-15T00:20:00.4...\n",
-              "  * range                (range) float64 224B 0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1\n",
-              "  * range_b5             (range_b5) float64 224B 0.6 1.1 1.6 ... 13.1 13.6 14.1\n",
-              "  * beam                 (beam) int32 16B 1 2 3 4\n",
-              "  * dir                  (dir) <U2 32B 'E' 'N' 'U1' 'U2'\n",
-              "  * dirIMU               (dirIMU) <U1 12B 'E' 'N' 'U'\n",
-              "  * earth                (earth) <U1 12B 'E' 'N' 'U'\n",
-              "  * inst                 (inst) <U1 12B 'X' 'Y' 'Z'\n",
-              "  * q                    (q) <U1 16B 'w' 'x' 'y' 'z'\n",
-              "  * x1                   (x1) int32 16B 1 2 3 4\n",
-              "  * x2                   (x2) int32 16B 1 2 3 4\n",
-              "Data variables: (12/38)\n",
-              "    c_sound              (time) float32 220kB 1.502e+03 1.502e+03 ... 1.498e+03\n",
-              "    temp                 (time) float32 220kB 14.55 14.55 14.55 ... 13.47 13.47\n",
-              "    pressure             (time) float32 220kB 9.713 9.718 9.718 ... 9.594 9.596\n",
-              "    mag                  (dirIMU, time) float32 660kB 72.5 72.7 ... -195.7\n",
-              "    accel                (dirIMU, time) float32 660kB -0.00479 ... 9.729\n",
-              "    batt                 (time) float32 220kB 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
-              "    ...                   ...\n",
-              "    telemetry_data       (time) uint8 55kB 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0\n",
-              "    boost_running        (time) uint8 55kB 0 0 0 0 0 0 0 0 1 ... 1 0 0 0 0 0 0 1\n",
-              "    heading              (time) float32 220kB -12.52 -12.51 ... -12.52 -12.5\n",
-              "    pitch                (time) float32 220kB -0.065 -0.06 -0.06 ... -0.05 -0.05\n",
-              "    roll                 (time) float32 220kB -7.425 -7.42 -7.42 ... -6.45 -6.45\n",
-              "    beam2inst_orientmat  (x1, x2) float32 64B 1.183 0.0 -1.183 ... 0.0 0.5518\n",
-              "Attributes: (12/34)\n",
-              "    filehead_config:       {\"CLOCKSTR\": {\"TIME\": \"\\\"2020-08-13 13:56:21\\\"\"}, ...\n",
-              "    inst_model:            Signature1000\n",
-              "    inst_make:             Nortek\n",
-              "    inst_type:             ADCP\n",
-              "    burst_config:          {\"press_valid\": true, \"temp_valid\": true, \"compass...\n",
-              "    n_cells:               28\n",
-              "    ...                    ...\n",
-              "    proc_idle_less_12pct:  0\n",
-              "    rotate_vars:           ['vel', 'accel', 'accel_b5', 'angrt', 'angrt_b5', ...\n",
-              "    coord_sys:             earth\n",
-              "    fs:                    1\n",
-              "    has_imu:               1\n",
-              "    beam_angle:            25"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# print the dataset\n",
-        "ds"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A second way provided to look at data is through the *DOLfYN view*. This view has several convenience methods, shortcuts, and functions built-in. It includes an alternate – and somewhat more informative/compact – description of the data object when in interactive mode. This can be accessed using"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<ADCP data object>:  Nortek Signature1000\n",
-              "  . 15.28 hours (started: Aug 15, 2020 00:20)\n",
-              "  . earth-frame\n",
-              "  . (55000 pings @ 1Hz)\n",
-              "  Variables:\n",
-              "  - time ('time',)\n",
-              "  - time_b5 ('time_b5',)\n",
-              "  - vel ('dir', 'range', 'time')\n",
-              "  - vel_b5 ('range_b5', 'time_b5')\n",
-              "  - range ('range',)\n",
-              "  - orientmat ('earth', 'inst', 'time')\n",
-              "  - heading ('time',)\n",
-              "  - pitch ('time',)\n",
-              "  - roll ('time',)\n",
-              "  - temp ('time',)\n",
-              "  - pressure ('time',)\n",
-              "  - amp ('beam', 'range', 'time')\n",
-              "  - amp_b5 ('range_b5', 'time_b5')\n",
-              "  - corr ('beam', 'range', 'time')\n",
-              "  - corr_b5 ('range_b5', 'time_b5')\n",
-              "  - accel ('dirIMU', 'time')\n",
-              "  - angrt ('dirIMU', 'time')\n",
-              "  - mag ('dirIMU', 'time')\n",
-              "  ... and others (see `<obj>.variables`)"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "ds_dolfyn = ds.velds\n",
-        "ds_dolfyn"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 2. Initial Steps for Data Quality Control (QC)\n",
-        "\n",
-        "### 2.1: Set the Deployment Height\n",
-        "\n",
-        "When using Nortek instruments, the deployment software does not factor in the deployment height. The deployment height represents the position of the Acoustic Doppler Current Profiler (ADCP) within the water column. \n",
-        "\n",
-        "In this context, the center of the first depth bin is situated at a distance that is the sum of three elements: \n",
-        "1. Deployment height (the ADCP's position in the water column)\n",
-        "2. Blanking distance (the minimum distance from the ADCP to the first measurement point)\n",
-        "3. Cell size (the vertical distance of each measurement bin in the water column)\n",
-        "\n",
-        "To ensure accurate readings, it is critical to calibrate the 'range' coordinate to make '0' correspond to either the seafloor or sea surface. This calibration can be achieved using the `set_range_offset` function. \n",
-        "\n",
-        "For those using a Teledyne RDI ADCP, the TRDI deployment software will prompt you to specify the deployment height/depth during setup in order to automatically calculate water depth. If there's a need for calibration post-deployment, the `set_range_offset` function can be utilized in the same way as described above to override the initially specified value."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x25a2a9fdc10>"
-            ]
-          },
-          "execution_count": 5,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "ds[\"vel\"][1].plot()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# The ADCP transducers were measured to be 0.6 m from the feet of the lander\n",
-        "api.clean.set_range_offset(ds, 0.6)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "So, the center of bin 1 is located at 1.2 m:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-              "<defs>\n",
-              "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-              "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-              "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-              "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-              "</symbol>\n",
-              "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-              "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-              "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "</symbol>\n",
-              "</defs>\n",
-              "</svg>\n",
-              "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-              " *\n",
-              " */\n",
-              "\n",
-              ":root {\n",
-              "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-              "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-              "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-              "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-              "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-              "  --xr-background-color: var(--jp-layout-color0, white);\n",
-              "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-              "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-              "}\n",
-              "\n",
-              "html[theme=\"dark\"],\n",
-              "html[data-theme=\"dark\"],\n",
-              "body[data-theme=\"dark\"],\n",
-              "body.vscode-dark {\n",
-              "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-              "  --xr-border-color: #1f1f1f;\n",
-              "  --xr-disabled-color: #515151;\n",
-              "  --xr-background-color: #111111;\n",
-              "  --xr-background-color-row-even: #111111;\n",
-              "  --xr-background-color-row-odd: #313131;\n",
-              "}\n",
-              "\n",
-              ".xr-wrap {\n",
-              "  display: block !important;\n",
-              "  min-width: 300px;\n",
-              "  max-width: 700px;\n",
-              "}\n",
-              "\n",
-              ".xr-text-repr-fallback {\n",
-              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-header {\n",
-              "  padding-top: 6px;\n",
-              "  padding-bottom: 6px;\n",
-              "  margin-bottom: 4px;\n",
-              "  border-bottom: solid 1px var(--xr-border-color);\n",
-              "}\n",
-              "\n",
-              ".xr-header > div,\n",
-              ".xr-header > ul {\n",
-              "  display: inline;\n",
-              "  margin-top: 0;\n",
-              "  margin-bottom: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-obj-type,\n",
-              ".xr-array-name {\n",
-              "  margin-left: 2px;\n",
-              "  margin-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-obj-type {\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-sections {\n",
-              "  padding-left: 0 !important;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input {\n",
-              "  display: inline-block;\n",
-              "  opacity: 0;\n",
-              "  height: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input + label {\n",
-              "  color: var(--xr-disabled-color);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:enabled + label {\n",
-              "  cursor: pointer;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:focus + label {\n",
-              "  border: 2px solid var(--xr-font-color0);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:enabled + label:hover {\n",
-              "  color: var(--xr-font-color0);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary {\n",
-              "  grid-column: 1;\n",
-              "  color: var(--xr-font-color2);\n",
-              "  font-weight: 500;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary > span {\n",
-              "  display: inline-block;\n",
-              "  padding-left: 0.5em;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:disabled + label {\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in + label:before {\n",
-              "  display: inline-block;\n",
-              "  content: \"►\";\n",
-              "  font-size: 11px;\n",
-              "  width: 15px;\n",
-              "  text-align: center;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:disabled + label:before {\n",
-              "  color: var(--xr-disabled-color);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked + label:before {\n",
-              "  content: \"▼\";\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked + label > span {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary,\n",
-              ".xr-section-inline-details {\n",
-              "  padding-top: 4px;\n",
-              "  padding-bottom: 4px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-inline-details {\n",
-              "  grid-column: 2 / -1;\n",
-              "}\n",
-              "\n",
-              ".xr-section-details {\n",
-              "  display: none;\n",
-              "  grid-column: 1 / -1;\n",
-              "  margin-bottom: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-array-wrap {\n",
-              "  grid-column: 1 / -1;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 20px auto;\n",
-              "}\n",
-              "\n",
-              ".xr-array-wrap > label {\n",
-              "  grid-column: 1;\n",
-              "  vertical-align: top;\n",
-              "}\n",
-              "\n",
-              ".xr-preview {\n",
-              "  color: var(--xr-font-color3);\n",
-              "}\n",
-              "\n",
-              ".xr-array-preview,\n",
-              ".xr-array-data {\n",
-              "  padding: 0 5px !important;\n",
-              "  grid-column: 2;\n",
-              "}\n",
-              "\n",
-              ".xr-array-data,\n",
-              ".xr-array-in:checked ~ .xr-array-preview {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-array-in:checked ~ .xr-array-data,\n",
-              ".xr-array-preview {\n",
-              "  display: inline-block;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list {\n",
-              "  display: inline-block !important;\n",
-              "  list-style: none;\n",
-              "  padding: 0 !important;\n",
-              "  margin: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list li {\n",
-              "  display: inline-block;\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list:before {\n",
-              "  content: \"(\";\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list:after {\n",
-              "  content: \")\";\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list li:not(:last-child):after {\n",
-              "  content: \",\";\n",
-              "  padding-right: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-has-index {\n",
-              "  font-weight: bold;\n",
-              "}\n",
-              "\n",
-              ".xr-var-list,\n",
-              ".xr-var-item {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-var-item > div,\n",
-              ".xr-var-item label,\n",
-              ".xr-var-item > .xr-var-name span {\n",
-              "  background-color: var(--xr-background-color-row-even);\n",
-              "  margin-bottom: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-var-item > .xr-var-name:hover span {\n",
-              "  padding-right: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-var-list > li:nth-child(odd) > div,\n",
-              ".xr-var-list > li:nth-child(odd) > label,\n",
-              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-              "  background-color: var(--xr-background-color-row-odd);\n",
-              "}\n",
-              "\n",
-              ".xr-var-name {\n",
-              "  grid-column: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-var-dims {\n",
-              "  grid-column: 2;\n",
-              "}\n",
-              "\n",
-              ".xr-var-dtype {\n",
-              "  grid-column: 3;\n",
-              "  text-align: right;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-var-preview {\n",
-              "  grid-column: 4;\n",
-              "}\n",
-              "\n",
-              ".xr-index-preview {\n",
-              "  grid-column: 2 / 5;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-var-name,\n",
-              ".xr-var-dims,\n",
-              ".xr-var-dtype,\n",
-              ".xr-preview,\n",
-              ".xr-attrs dt {\n",
-              "  white-space: nowrap;\n",
-              "  overflow: hidden;\n",
-              "  text-overflow: ellipsis;\n",
-              "  padding-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-var-name:hover,\n",
-              ".xr-var-dims:hover,\n",
-              ".xr-var-dtype:hover,\n",
-              ".xr-attrs dt:hover {\n",
-              "  overflow: visible;\n",
-              "  width: auto;\n",
-              "  z-index: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-var-attrs,\n",
-              ".xr-var-data,\n",
-              ".xr-index-data {\n",
-              "  display: none;\n",
-              "  background-color: var(--xr-background-color) !important;\n",
-              "  padding-bottom: 5px !important;\n",
-              "}\n",
-              "\n",
-              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-              ".xr-var-data-in:checked ~ .xr-var-data,\n",
-              ".xr-index-data-in:checked ~ .xr-index-data {\n",
-              "  display: block;\n",
-              "}\n",
-              "\n",
-              ".xr-var-data > table {\n",
-              "  float: right;\n",
-              "}\n",
-              "\n",
-              ".xr-var-name span,\n",
-              ".xr-var-data,\n",
-              ".xr-index-name div,\n",
-              ".xr-index-data,\n",
-              ".xr-attrs {\n",
-              "  padding-left: 25px !important;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs,\n",
-              ".xr-var-attrs,\n",
-              ".xr-var-data,\n",
-              ".xr-index-data {\n",
-              "  grid-column: 1 / -1;\n",
-              "}\n",
-              "\n",
-              "dl.xr-attrs {\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 125px auto;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt,\n",
-              ".xr-attrs dd {\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "  float: left;\n",
-              "  padding-right: 10px;\n",
-              "  width: auto;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt {\n",
-              "  font-weight: normal;\n",
-              "  grid-column: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt:hover span {\n",
-              "  display: inline-block;\n",
-              "  background: var(--xr-background-color);\n",
-              "  padding-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dd {\n",
-              "  grid-column: 2;\n",
-              "  white-space: pre-wrap;\n",
-              "  word-break: break-all;\n",
-              "}\n",
-              "\n",
-              ".xr-icon-database,\n",
-              ".xr-icon-file-text2,\n",
-              ".xr-no-icon {\n",
-              "  display: inline-block;\n",
-              "  vertical-align: middle;\n",
-              "  width: 1em;\n",
-              "  height: 1.5em !important;\n",
-              "  stroke-width: 0;\n",
-              "  stroke: currentColor;\n",
-              "  fill: currentColor;\n",
-              "}\n",
-              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;range&#x27; (range: 28)&gt; Size: 224B\n",
-              "array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
-              "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])\n",
-              "Coordinates:\n",
-              "  * range    (range) float64 224B 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\n",
-              "Attributes:\n",
-              "    coverage_content_type:  coordinate\n",
-              "    units:                  m\n",
-              "    long_name:              Profile Range\n",
-              "    description:            Distance to the center of each depth bin</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'range'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>range</span>: 28</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-27183307-48d8-4334-9d4f-9e05d7a24cc7' class='xr-array-in' type='checkbox' checked><label for='section-27183307-48d8-4334-9d4f-9e05d7a24cc7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.2 1.7 2.2 2.7 3.2 3.7 4.2 4.7 ... 11.7 12.2 12.7 13.2 13.7 14.2 14.7</span></div><div class='xr-array-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
-              "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></div></li><li class='xr-section-item'><input id='section-0182f8e5-1c9b-476b-af6c-5e3eaa33364e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0182f8e5-1c9b-476b-af6c-5e3eaa33364e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-8ce42991-ade4-4c8c-b8e4-ef266c967ef8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ce42991-ade4-4c8c-b8e4-ef266c967ef8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127f377e-0b3a-46b3-9028-0af3c4ab733e' class='xr-var-data-in' type='checkbox'><label for='data-127f377e-0b3a-46b3-9028-0af3c4ab733e' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
-              "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e8bb7c5-5914-419d-a3aa-7e595f1cc63d' class='xr-section-summary-in' type='checkbox'  ><label for='section-8e8bb7c5-5914-419d-a3aa-7e595f1cc63d' class='xr-section-summary' >Indexes: <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-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-eeb0489d-3509-4e51-8c17-c60ea011f3f3' class='xr-index-data-in' type='checkbox'/><label for='index-eeb0489d-3509-4e51-8c17-c60ea011f3f3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
-              "                      2.7,                3.2,                3.7,\n",
-              "                      4.2,  4.699999999999999,  5.199999999999999,\n",
-              "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
-              "        7.199999999999999,  7.699999999999999,                8.2,\n",
-              "                      8.7,                9.2,                9.7,\n",
-              "                     10.2,               10.7,               11.2,\n",
-              "                     11.7,               12.2,               12.7,\n",
-              "                     13.2,               13.7,               14.2,\n",
-              "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c09138d-d003-4599-bca4-cd197af81125' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c09138d-d003-4599-bca4-cd197af81125' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div></li></ul></div></div>"
-            ],
-            "text/plain": [
-              "<xarray.DataArray 'range' (range: 28)> Size: 224B\n",
-              "array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
-              "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])\n",
-              "Coordinates:\n",
-              "  * range    (range) float64 224B 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\n",
-              "Attributes:\n",
-              "    coverage_content_type:  coordinate\n",
-              "    units:                  m\n",
-              "    long_name:              Profile Range\n",
-              "    description:            Distance to the center of each depth bin"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "ds.range"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.2. Discard Data Above Surface Level and Surface Interference\n",
-        "\n",
-        "To reduce computational load, we can exclude all data at or above the water surface level, including that affected by surface interference. Since the instrument was oriented upwards, we can utilize the pressure sensor data along with the function `water_depth_from_pressure` to find the water depth. However, this approach necessitates that the pressure sensor was calibrated or 'zeroed' prior to deployment. If the instrument is facing downwards or doesn't include pressure data, the function `water_depth_from_amplitude` can be used to detect the seabed or water surface.\n",
-        "\n",
-        "It's important to note that Acoustic Doppler Current Profilers (ADCPs) do not measure water salinity, so you'll need to supply this information to the function. If `water_depth_from_pressure` is called after `set_range_offset`, \"depth\" represents the distance from the water surface to the seafloor. Otherwise, it indicates the distance to the ADCP pressure sensor.\n",
-        "\n",
-        "After determining the \"depth\", you can use the `remove_surface_interference` function to discard data in depth bins at or above the actual water surface. Be aware that this function will generate a new dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "api.clean.water_depth_from_pressure(ds, salinity=31)\n",
-        "ds = api.clean.remove_surface_interference(ds)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x25a2aba1090>"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "ds[\"vel\"][1].plot()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.3: Apply an Acoustic Signal Correlation Filter\n",
-        "\n",
-        "After removing data from bins at or above the water surface, we typically apply a filter based on acoustic signal correlation to the ADCP data. This helps to eliminate erroneous velocity data points, which can be caused by factors such as bubbles, kelp, fish, etc., moving through one or multiple beams.\n",
-        "\n",
-        "You can quickly inspect the data to determine an appropriate correlation value by using the built-in plotting feature of xarray. In the following example, we use xarray's slicing capabilities to display data from beam 1 within a range of 0 to 10 m from the ADCP.\n",
-        "\n",
-        "It's important to note that not all ADCPs provide acoustic signal correlation data, which serves as a quantitative measure of signal quality. Older ADCPs may not offer this feature, in which case you can skip this step when using such instruments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x25a7fe4a9d0>"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "%matplotlib inline\n",
-        "ds[\"corr\"].sel(beam=1, range=slice(0, 10)).plot()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "It's beneficial to also review data from the other beams. A significant portion of this data is of high quality. To avoid discarding valuable data with lower correlations, which could be due to natural variations, we can use the `correlation_filter`. This function assigns a value of NaN (not a number) to velocity values corresponding to correlations below 50%.\n",
-        "\n",
-        "However, it's important to note that the correlation threshold is dependent on the specifics of the deployment environment and the instrument used. It's not unusual to set a threshold as low as 30%, or even to forgo the use of this function entirely."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds = api.clean.correlation_filter(ds, thresh=50)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x25a2abaa990>"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "ds[\"vel\"][1].plot()"
-      ]
-    },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyzing ADCP Data with MHKiT\n",
+    "\n",
+    "The following example illustrates a straightforward workflow for analyzing Acoustic Doppler Current Profiler (ADCP) data utilizing MHKiT. MHKiT has integrated the DOLfYN codebase as a module to facilitate ADCP and Acoustic Doppler Velocimetry (ADV) data processing.\n",
+    "\n",
+    "Here is a standard workflow for ADCP data analysis:\n",
+    "\n",
+    "1. **Import Data**\n",
+    "\n",
+    "2. **Review, QC, and Prepare the Raw Data**:\n",
+    "   1. Calculate or verify the correctness of depth bin locations\n",
+    "   2. Discard data recorded above the water surface or below the seafloor\n",
+    "   3. Assess the quality of velocity, beam amplitude, and/or beam correlation data\n",
+    "   4. Rotate Data Coordinate System\n",
+    "\n",
+    "3. **Data Averaging**: \n",
+    "    - If not already executed within the instrument, average the data into time bins of a predetermined duration, typically between 5 and 10 minutes\n",
+    "\n",
+    "4. **Speed and Direction**\n",
+    "\n",
+    "5. **Plotting**\n",
+    "\n",
+    "6. **Saving and Loading DOLfYN datasets**\n",
+    "\n",
+    "7. **Turbulence Statistics**\n",
+    "    1. Turbulence Intensity (TI)\n",
+    "    2. Power Spectral Densities\n",
+    "    3. Instrument Noise\n",
+    "    4. TKE Dissipation Rate\n",
+    "    5. Noise-corrected TI\n",
+    "    6. Reynolds stresses\n",
+    "    7. TKE Production\n",
+    "    8. TKE Balance \n",
+    "\n",
+    "\n",
+    "Begin your analysis by importing the requisite tools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from mhkit import dolfyn\n",
+    "from mhkit.dolfyn.adp import api\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  1. Importing Raw Instrument Data\n",
+    "\n",
+    "One of DOLfYN's key features is its ability to directly import raw data from an Acoustic Doppler Current Profiler (ADCP) right after it has been transferred. In this instance, we are using a Nortek Signature1000 ADCP, with the data stored in files with an '.ad2cp' extension. This specific dataset represents several hours of velocity data, captured at 1 Hz by an ADCP mounted on a bottom lander within a tidal inlet. The list of instruments compatible with DOLfYN can be found in the [MHKiT DOLfYN documentation](https://mhkit-software.github.io/MHKiT/mhkit-python/api.dolfyn.html).\n",
+    "\n",
+    "We'll start by importing the raw data file downloaded from the instrument. The `read` function processes the raw file and converts the information into an xarray Dataset. This Dataset includes several groups of variables:\n",
+    "\n",
+    "1. **Velocity**: Recorded in the coordinate system saved by the instrument (beam, XYZ, ENU)\n",
+    "2. **Beam Data**: Includes amplitude and correlation data\n",
+    "3. **Instrumental & Environmental Measurements**: Captures the instrument's bearing and environmental conditions\n",
+    "4. **Orientation Matrices**: Used by DOLfYN for rotating through different coordinate frames.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.4 Rotate Data Coordinate System\n",
-        "\n",
-        "After cleaning the data, the next step is to rotate the velocity data into accurate East, North, Up (ENU) coordinates.\n",
-        "\n",
-        "ADCPs utilize an internal compass or magnetometer to determine magnetic ENU directions. You can use the set_declination function to adjust the velocity data according to the magnetic declination specific to your geographical coordinates. This declination can be looked up online for specific coordinates.\n",
-        "\n",
-        "Instruments save vector data in the coordinate system defined in the deployment configuration file. To make this data meaningful, it must be transformed through various coordinate systems (\"beam\"<->\"inst\"<->\"earth\"<->\"principal\"). This transformation is accomplished using the `rotate2` function. If the \"earth\" (ENU) coordinate system is specified, DOLfYN will automatically rotate the dataset through the required coordinate systems to reach the \"earth\" coordinates. Setting `inplace` to true will modify the input dataset directly, meaning it will not create a new dataset.\n",
-        "\n",
-        "In this case, since the ADCP data is already in the \"earth\" coordinate system, the `rotate2` function will return the input dataset without modifications. The `set_declination` function will work no matter the coordinate system."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading file data/dolfyn/Sig1000_tidal.ad2cp ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = dolfyn.read(\"data/dolfyn/Sig1000_tidal.ad2cp\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two ways to see what's in a Dataset. The first is to simply type the dataset's name to see the standard xarray output. To access a particular variable in a dataset, use dict-style (`ds['vel']`) or attribute-style syntax (`ds.vel`). See the [xarray docs](http://xarray.pydata.org/en/stable/getting-started-guide/quick-overview.html) for more details on how to use the xarray format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Data is already in the earth coordinate system\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 83MB\n",
+       "Dimensions:              (time: 55000, time_b5: 55000, range: 28, range_b5: 28,\n",
+       "                          beam: 4, dir: 4, dirIMU: 3, earth: 3, inst: 3, q: 4,\n",
+       "                          x1: 4, x2: 4)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 440kB 2020-08-15T00:20:00.5009...\n",
+       "  * time_b5              (time_b5) datetime64[ns] 440kB 2020-08-15T00:20:00.4...\n",
+       "  * range                (range) float64 224B 0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1\n",
+       "  * range_b5             (range_b5) float64 224B 0.6 1.1 1.6 ... 13.1 13.6 14.1\n",
+       "  * beam                 (beam) int32 16B 1 2 3 4\n",
+       "  * dir                  (dir) &lt;U2 32B &#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;\n",
+       "  * dirIMU               (dirIMU) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
+       "  * earth                (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
+       "  * inst                 (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
+       "  * q                    (q) &lt;U1 16B &#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;\n",
+       "  * x1                   (x1) int32 16B 1 2 3 4\n",
+       "  * x2                   (x2) int32 16B 1 2 3 4\n",
+       "Data variables: (12/38)\n",
+       "    c_sound              (time) float32 220kB 1.502e+03 1.502e+03 ... 1.498e+03\n",
+       "    temp                 (time) float32 220kB 14.55 14.55 14.55 ... 13.47 13.47\n",
+       "    pressure             (time) float32 220kB 9.713 9.718 9.718 ... 9.594 9.596\n",
+       "    mag                  (dirIMU, time) float32 660kB 72.5 72.7 ... -195.7\n",
+       "    accel                (dirIMU, time) float32 660kB -0.00479 ... 9.729\n",
+       "    batt                 (time) float32 220kB 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
+       "    ...                   ...\n",
+       "    telemetry_data       (time) uint8 55kB 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0\n",
+       "    boost_running        (time) uint8 55kB 0 0 0 0 0 0 0 0 1 ... 1 0 0 0 0 0 0 1\n",
+       "    heading              (time) float32 220kB -12.52 -12.51 ... -12.52 -12.5\n",
+       "    pitch                (time) float32 220kB -0.065 -0.06 -0.06 ... -0.05 -0.05\n",
+       "    roll                 (time) float32 220kB -7.425 -7.42 -7.42 ... -6.45 -6.45\n",
+       "    beam2inst_orientmat  (x1, x2) float32 64B 1.183 0.0 -1.183 ... 0.0 0.5518\n",
+       "Attributes: (12/34)\n",
+       "    filehead_config:       {&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, ...\n",
+       "    inst_model:            Signature1000\n",
+       "    inst_make:             Nortek\n",
+       "    inst_type:             ADCP\n",
+       "    burst_config:          {&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass...\n",
+       "    n_cells:               28\n",
+       "    ...                    ...\n",
+       "    proc_idle_less_12pct:  0\n",
+       "    rotate_vars:           [&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, ...\n",
+       "    coord_sys:             earth\n",
+       "    fs:                    1\n",
+       "    has_imu:               1\n",
+       "    beam_angle:            25</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f28a974d-02c1-4c0e-b0aa-35a34cb04614' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f28a974d-02c1-4c0e-b0aa-35a34cb04614' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 55000</li><li><span class='xr-has-index'>time_b5</span>: 55000</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>range_b5</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-73f6a664-08f3-4fbb-9c79-c107bd2e17a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73f6a664-08f3-4fbb-9c79-c107bd2e17a5' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.500999927 .....</div><input id='attrs-2bf135d0-099b-454c-92c3-0fc84896aa43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bf135d0-099b-454c-92c3-0fc84896aa43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b43dbf41-e9ef-4950-984f-d8f6138b0ddb' class='xr-var-data-in' type='checkbox'><label for='data-b43dbf41-e9ef-4950-984f-d8f6138b0ddb' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.500999927&#x27;, &#x27;2020-08-15T00:20:01.501100063&#x27;,\n",
+       "       &#x27;2020-08-15T00:20:02.500999927&#x27;, ..., &#x27;2020-08-15T15:36:37.500999927&#x27;,\n",
+       "       &#x27;2020-08-15T15:36:38.500999927&#x27;, &#x27;2020-08-15T15:36:39.500999927&#x27;],\n",
+       "      shape=(55000,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.438499927 .....</div><input id='attrs-603893bf-5abc-410a-8c19-ef29f6982588' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-603893bf-5abc-410a-8c19-ef29f6982588' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c572de15-bf56-4083-bb69-eb0ea27440df' class='xr-var-data-in' type='checkbox'><label for='data-c572de15-bf56-4083-bb69-eb0ea27440df' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time b5</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.438499927&#x27;, &#x27;2020-08-15T00:20:01.438499927&#x27;,\n",
+       "       &#x27;2020-08-15T00:20:02.438499927&#x27;, ..., &#x27;2020-08-15T15:36:37.438499927&#x27;,\n",
+       "       &#x27;2020-08-15T15:36:38.438499927&#x27;, &#x27;2020-08-15T15:36:39.438499927&#x27;],\n",
+       "      shape=(55000,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-2a25848e-bb35-49a4-b9fb-672be8314b0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a25848e-bb35-49a4-b9fb-672be8314b0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d260546b-dc09-4642-9764-acbe5209a5bb' class='xr-var-data-in' type='checkbox'><label for='data-d260546b-dc09-4642-9764-acbe5209a5bb' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+       "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
+       "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-6c42074c-a1d8-4c94-8018-7c1e5846fcbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c42074c-a1d8-4c94-8018-7c1e5846fcbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fb7f816-ba22-412a-a090-0d23c8c09ee0' class='xr-var-data-in' type='checkbox'><label for='data-7fb7f816-ba22-412a-a090-0d23c8c09ee0' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range b5</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+       "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
+       "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-8f9f0671-99ea-4543-a37b-d94c1ac426d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f9f0671-99ea-4543-a37b-d94c1ac426d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5acdfbec-8b28-4849-8321-02b4f256cc65' class='xr-var-data-in' type='checkbox'><label for='data-5acdfbec-8b28-4849-8321-02b4f256cc65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;</div><input id='attrs-fe64bed1-065d-4572-820c-a546674bc23b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe64bed1-065d-4572-820c-a546674bc23b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91cb97d1-6a16-4001-9fe4-7c770edad6a5' class='xr-var-data-in' type='checkbox'><label for='data-91cb97d1-6a16-4001-9fe4-7c770edad6a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-a282b85b-1f3d-41db-9220-93b48867f2a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a282b85b-1f3d-41db-9220-93b48867f2a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37a55f67-7a4c-4fe9-8eb4-4e30a9092209' class='xr-var-data-in' type='checkbox'><label for='data-37a55f67-7a4c-4fe9-8eb4-4e30a9092209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-a9a65575-08ac-4c58-94a7-e351c187dcff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9a65575-08ac-4c58-94a7-e351c187dcff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27ad0f22-1689-4e27-85a9-03077cf5af87' class='xr-var-data-in' type='checkbox'><label for='data-27ad0f22-1689-4e27-85a9-03077cf5af87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-69f85873-b714-4894-bcdf-1e1100f23400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69f85873-b714-4894-bcdf-1e1100f23400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81d1c39c-d2f5-46d2-8b2d-13206e10bed1' class='xr-var-data-in' type='checkbox'><label for='data-81d1c39c-d2f5-46d2-8b2d-13206e10bed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-a14bc8fc-bfb6-4a07-ae07-0c00765ca633' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a14bc8fc-bfb6-4a07-ae07-0c00765ca633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bd0f72c-78f5-4b0b-af0b-0b8a580840cb' class='xr-var-data-in' type='checkbox'><label for='data-3bd0f72c-78f5-4b0b-af0b-0b8a580840cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Quaternion Vector Components</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-cc997752-660a-4a7e-92db-5dc162d10c73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc997752-660a-4a7e-92db-5dc162d10c73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fccb9774-d78a-413b-b35e-56e61fa4fc88' class='xr-var-data-in' type='checkbox'><label for='data-fccb9774-d78a-413b-b35e-56e61fa4fc88' 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>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-18671300-7253-40b7-b0f9-f82e20574edb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18671300-7253-40b7-b0f9-f82e20574edb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55b56463-9c00-4b38-a15f-b5bc9114fc0f' class='xr-var-data-in' type='checkbox'><label for='data-55b56463-9c00-4b38-a15f-b5bc9114fc0f' 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>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b40bdc2f-5ba2-43b8-88e0-ab62a460bc56' class='xr-section-summary-in' type='checkbox'  ><label for='section-b40bdc2f-5ba2-43b8-88e0-ab62a460bc56' class='xr-section-summary' >Data variables: <span>(38)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-11d0c1f6-a1d8-414c-83fe-3e3c06a05ca7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11d0c1f6-a1d8-414c-83fe-3e3c06a05ca7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c481825-6aa6-41dc-9aa8-05b64183f645' class='xr-var-data-in' type='checkbox'><label for='data-3c481825-6aa6-41dc-9aa8-05b64183f645' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.9, 1501.9, 1501.9, ..., 1498.4, 1498.3, 1498.3],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.55 14.55 14.55 ... 13.47 13.47</div><input id='attrs-276d2650-d81c-4c34-9775-78a2e2e1e367' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-276d2650-d81c-4c34-9775-78a2e2e1e367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41ff6085-7e2b-4f24-9fd7-bb2485b84cf1' class='xr-var-data-in' type='checkbox'><label for='data-41ff6085-7e2b-4f24-9fd7-bb2485b84cf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.55, 14.55, 14.55, ..., 13.47, 13.47, 13.47],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.713 9.718 9.718 ... 9.594 9.596</div><input id='attrs-5d688586-aa74-4f0d-ad5e-fbd7d9488d60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d688586-aa74-4f0d-ad5e-fbd7d9488d60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47260f77-e611-4f40-8321-6331a6f1564a' class='xr-var-data-in' type='checkbox'><label for='data-47260f77-e611-4f40-8321-6331a6f1564a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.713, 9.718, 9.718, ..., 9.596, 9.594, 9.596],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.5 72.7 72.6 ... -197.2 -195.7</div><input id='attrs-e6322c76-1557-4aa8-815e-7930d416ce20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6322c76-1557-4aa8-815e-7930d416ce20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3394cdc-3ef0-47f7-9e63-fb198b8e9f1c' class='xr-var-data-in' type='checkbox'><label for='data-b3394cdc-3ef0-47f7-9e63-fb198b8e9f1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.5,   72.7,   72.6, ...,   72.3,   72.8,   72.4],\n",
+       "       [   9.3,    9.4,    8.9, ...,    5.9,    6.3,    6.4],\n",
+       "       [-198. , -196.3, -198.1, ..., -197.4, -197.2, -195.7]],\n",
+       "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.00479 -0.01437 ... 9.685 9.729</div><input id='attrs-14a9fcb0-6690-4c66-9888-93437bec3f33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14a9fcb0-6690-4c66-9888-93437bec3f33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f26e634c-ad73-4b8e-8d60-0a562215bfed' class='xr-var-data-in' type='checkbox'><label for='data-f26e634c-ad73-4b8e-8d60-0a562215bfed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-4.7900393e-03, -1.4370117e-02, -2.3950195e-02, ...,\n",
+       "        -1.4370117e-02, -4.7900393e-03, -2.3950195e-02],\n",
+       "       [-1.2933105e+00, -1.2549902e+00, -1.2645704e+00, ...,\n",
+       "        -1.1017090e+00, -1.0873389e+00, -1.0969189e+00],\n",
+       "       [ 9.7285690e+00,  9.7141991e+00,  9.7237797e+00, ...,\n",
+       "         9.6710892e+00,  9.6854591e+00,  9.7285690e+00]],\n",
+       "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.6 16.6 16.6 ... 16.4 16.4 15.2</div><input id='attrs-ce4bd203-6f47-4d75-877f-b2d6458ffa2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce4bd203-6f47-4d75-877f-b2d6458ffa2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ff83154-4207-4313-9bea-3642e3d9693a' class='xr-var-data-in' type='checkbox'><label for='data-8ff83154-4207-4313-9bea-3642e3d9693a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.6, 16.6, 16.6, ..., 16.4, 16.4, 15.2],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-638397a6-8e5b-4462-b526-34e79ade8cdf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-638397a6-8e5b-4462-b526-34e79ade8cdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a687b8c7-daa8-4804-9913-5308745abcff' class='xr-var-data-in' type='checkbox'><label for='data-a687b8c7-daa8-4804-9913-5308745abcff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20. , 20. , 20. , ..., 19.5, 19.5, 19.5],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-d5e19c7f-ae28-4acf-9f57-8ccf921b9bcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5e19c7f-ae28-4acf-9f57-8ccf921b9bcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1b22bf5-0482-46ba-add2-be69c364880c' class='xr-var-data-in' type='checkbox'><label for='data-d1b22bf5-0482-46ba-add2-be69c364880c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-6343e252-273c-431b-823b-eb2bde09e714' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6343e252-273c-431b-823b-eb2bde09e714' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3397a186-35a2-46ac-a040-b32afaf9a17e' class='xr-var-data-in' type='checkbox'><label for='data-3397a186-35a2-46ac-a040-b32afaf9a17e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
+       "       1053557762], shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-5bc2999c-15fc-4cb5-94a4-67caadb4c7af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bc2999c-15fc-4cb5-94a4-67caadb4c7af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7569e9e-c73e-4915-9ec2-524b7fab94ff' class='xr-var-data-in' type='checkbox'><label for='data-a7569e9e-c73e-4915-9ec2-524b7fab94ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000],\n",
+       "      shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.015 -0.048 ... -0.015 -0.012</div><input id='attrs-7fe81551-7fd9-44c5-8f10-a69e53b22908' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fe81551-7fd9-44c5-8f10-a69e53b22908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48264037-1850-4629-940b-afe003baed01' class='xr-var-data-in' type='checkbox'><label for='data-48264037-1850-4629-940b-afe003baed01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.015, -0.048,  0.008, ..., -0.176, -0.071, -0.027],\n",
+       "        [-0.093, -0.124, -0.031, ..., -0.175, -0.16 , -0.03 ],\n",
+       "        [-0.163, -0.135, -0.105, ..., -0.129, -0.089, -0.116],\n",
+       "        ...,\n",
+       "        [-0.169, -0.013, -0.124, ..., -0.062,  0.091, -0.009],\n",
+       "        [-0.16 , -0.043, -0.111, ...,  0.012,  0.089,  0.021],\n",
+       "        [-0.158, -0.057, -0.13 , ..., -0.055,  0.104,  0.013]],\n",
+       "\n",
+       "       [[ 0.243,  0.204,  0.234, ..., -0.163, -0.135, -0.18 ],\n",
+       "        [ 0.272,  0.248,  0.236, ..., -0.254, -0.169, -0.204],\n",
+       "        [ 0.257,  0.294,  0.333, ..., -0.299, -0.226, -0.273],\n",
+       "        ...,\n",
+       "        [ 0.305,  0.377,  0.283, ..., -0.198, -0.229, -0.121],\n",
+       "        [ 0.328,  0.33 ,  0.294, ..., -0.161, -0.292, -0.103],\n",
+       "        [ 0.31 ,  0.314,  0.226, ..., -0.219, -0.292, -0.049]],\n",
+       "\n",
+       "       [[ 0.016,  0.012,  0.01 , ...,  0.009,  0.006, -0.018],\n",
+       "        [ 0.01 ,  0.011, -0.013, ...,  0.002, -0.027,  0.009],\n",
+       "        [ 0.01 ,  0.009,  0.023, ..., -0.018, -0.002, -0.024],\n",
+       "        ...,\n",
+       "        [ 0.034, -0.061, -0.024, ..., -0.014,  0.086,  0.01 ],\n",
+       "        [ 0.032, -0.04 , -0.027, ..., -0.014,  0.063, -0.01 ],\n",
+       "        [ 0.004, -0.037, -0.018, ..., -0.001,  0.059,  0.021]],\n",
+       "\n",
+       "       [[ 0.003, -0.007,  0.046, ..., -0.032, -0.008, -0.029],\n",
+       "        [ 0.017,  0.033,  0.035, ..., -0.016, -0.022, -0.034],\n",
+       "        [ 0.016, -0.012,  0.005, ...,  0.002, -0.045, -0.026],\n",
+       "        ...,\n",
+       "        [-0.044,  0.015, -0.04 , ..., -0.015,  0.006,  0.012],\n",
+       "        [-0.013,  0.014, -0.032, ...,  0.01 ,  0.   , -0.015],\n",
+       "        [ 0.007,  0.016, -0.027, ..., -0.002, -0.015, -0.012]]],\n",
+       "      shape=(4, 28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>81.0 80.0 80.5 ... 43.5 42.5 42.0</div><input id='attrs-6a311971-8539-4391-959b-5a2e29c55984' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a311971-8539-4391-959b-5a2e29c55984' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72821c48-0bc3-4a04-a089-cc29f3bd3eeb' class='xr-var-data-in' type='checkbox'><label for='data-72821c48-0bc3-4a04-a089-cc29f3bd3eeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[81. , 80. , 80.5, ..., 81.5, 81.5, 81.5],\n",
+       "        [75. , 74.5, 75. , ..., 79. , 77. , 77.5],\n",
+       "        [73.5, 72. , 72. , ..., 75. , 74.5, 75. ],\n",
+       "        ...,\n",
+       "        [54.5, 55.5, 54.5, ..., 50. , 50. , 49.5],\n",
+       "        [53.5, 54.5, 54.5, ..., 46.5, 47. , 46.5],\n",
+       "        [53. , 54. , 53. , ..., 45. , 46. , 44.5]],\n",
+       "\n",
+       "       [[81.5, 80. , 80.5, ..., 82. , 81.5, 82. ],\n",
+       "        [76. , 77. , 76.5, ..., 79. , 77.5, 77.5],\n",
+       "        [73.5, 74. , 73. , ..., 75.5, 74.5, 74.5],\n",
+       "        ...,\n",
+       "        [57. , 58.5, 58. , ..., 53.5, 52. , 52.5],\n",
+       "        [55.5, 56.5, 55.5, ..., 50. , 49. , 49. ],\n",
+       "        [52.5, 54. , 54. , ..., 47.5, 47.5, 47. ]],\n",
+       "\n",
+       "       [[80.5, 79.5, 80. , ..., 81.5, 82.5, 81.5],\n",
+       "        [76. , 75.5, 75. , ..., 77. , 78.5, 77. ],\n",
+       "        [72. , 73.5, 72. , ..., 74. , 75. , 73.5],\n",
+       "        ...,\n",
+       "        [56.5, 56.5, 57. , ..., 47.5, 47. , 48. ],\n",
+       "        [56. , 55.5, 56.5, ..., 46.5, 47. , 47. ],\n",
+       "        [54.5, 54. , 55.5, ..., 45. , 45.5, 45.5]],\n",
+       "\n",
+       "       [[80. , 81. , 80. , ..., 81. , 81. , 81.5],\n",
+       "        [75.5, 75.5, 74.5, ..., 76.5, 76.5, 77.5],\n",
+       "        [73. , 72.5, 73. , ..., 75.5, 74.5, 74. ],\n",
+       "        ...,\n",
+       "        [51.5, 51. , 51. , ..., 46. , 46. , 45. ],\n",
+       "        [51. , 50. , 51. , ..., 45. , 44. , 43.5],\n",
+       "        [50.5, 49.5, 50. , ..., 43.5, 42.5, 42. ]]],\n",
+       "      shape=(4, 28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>100 91 91 97 99 ... 96 98 98 98 96</div><input id='attrs-56aad773-9465-4433-bb11-a09cfcd4df6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56aad773-9465-4433-bb11-a09cfcd4df6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8543c267-4dac-4f97-8328-ebb8ff57392b' class='xr-var-data-in' type='checkbox'><label for='data-8543c267-4dac-4f97-8328-ebb8ff57392b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[100,  91,  91, ...,  89,  90,  96],\n",
+       "        [ 74,  92,  96, ..., 100,  97, 100],\n",
+       "        [100, 100,  99, ...,  99, 100, 100],\n",
+       "        ...,\n",
+       "        [ 99, 100, 100, ...,  96,  96,  99],\n",
+       "        [ 99,  97, 100, ...,  94,  97,  96],\n",
+       "        [ 98, 100, 100, ..., 100, 100,  98]],\n",
+       "\n",
+       "       [[ 94,  90,  89, ...,  91,  92,  90],\n",
+       "        [ 94, 100, 100, ...,  96, 100,  93],\n",
+       "        [ 99,  95,  97, ...,  98,  98, 100],\n",
+       "        ...,\n",
+       "        [100, 100, 100, ...,  98,  99, 100],\n",
+       "        [ 99, 100,  99, ...,  97,  94,  94],\n",
+       "        [ 96,  99,  96, ...,  97, 100,  97]],\n",
+       "\n",
+       "       [[ 92,  93,  93, ...,  93,  97,  89],\n",
+       "        [ 99, 100,  99, ...,  96,  88,  98],\n",
+       "        [ 97, 100,  98, ..., 100,  99,  97],\n",
+       "        ...,\n",
+       "        [100, 100,  99, ...,  97,  94,  95],\n",
+       "        [100, 100, 100, ...,  96, 100, 100],\n",
+       "        [100, 100, 100, ...,  96,  95,  94]],\n",
+       "\n",
+       "       [[ 87,  97,  87, ...,  91,  87,  90],\n",
+       "        [100,  93,  93, ...,  96,  96,  97],\n",
+       "        [100, 100, 100, ..., 100, 100,  97],\n",
+       "        ...,\n",
+       "        [100,  99,  96, ...,  98, 100, 100],\n",
+       "        [100, 100, 100, ...,  94,  93,  95],\n",
+       "        [100,  98,  99, ...,  98,  98,  96]]],\n",
+       "      shape=(4, 28, 55000), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2164 ... 0.9937 0.9937</div><input id='attrs-9b9c2cc3-8d21-4715-9999-c6d82daf0381' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b9c2cc3-8d21-4715-9999-c6d82daf0381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-370f5f57-f122-4594-b089-ec9ae3c4b3aa' class='xr-var-data-in' type='checkbox'><label for='data-370f5f57-f122-4594-b089-ec9ae3c4b3aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16688156e-01, -2.16397047e-01, -2.16593146e-01, ...,\n",
+       "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
+       "        [ 9.76196468e-01,  9.76219118e-01,  9.76202846e-01, ...,\n",
+       "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
+       "        [-1.22870505e-03, -1.20262802e-03, -1.17563456e-03, ...,\n",
+       "         -1.16243958e-03, -1.02279335e-03, -9.55920666e-04]],\n",
+       "\n",
+       "       [[-9.67970312e-01, -9.68004048e-01, -9.67982769e-01, ...,\n",
+       "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
+       "        [-2.15024590e-01, -2.14734554e-01, -2.14924574e-01, ...,\n",
+       "         -2.14874983e-01, -2.15436578e-01, -2.15052128e-01],\n",
+       "        [-1.29278094e-01, -1.29190803e-01, -1.29239410e-01, ...,\n",
+       "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01]],\n",
+       "\n",
+       "       [[-1.26469478e-01, -1.26385525e-01, -1.26422524e-01, ...,\n",
+       "         -1.10033900e-01, -1.10009573e-01, -1.09963998e-01],\n",
+       "        [-2.68291458e-02, -2.68030688e-02, -2.68616974e-02, ...,\n",
+       "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02],\n",
+       "        [ 9.91607308e-01,  9.91618454e-01,  9.91612315e-01, ...,\n",
+       "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
+       "      shape=(3, 3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.00293</div><input id='attrs-777fa3a5-705e-4db6-8040-d98d483332a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-777fa3a5-705e-4db6-8040-d98d483332a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1832c48-85c6-4601-9fd1-5a6d47409bb7' class='xr-var-data-in' type='checkbox'><label for='data-f1832c48-85c6-4601-9fd1-5a6d47409bb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00390625,\n",
+       "        -0.00292969, -0.00195312],\n",
+       "       [ 0.00097656,  0.        ,  0.        , ...,  0.        ,\n",
+       "         0.00097656,  0.00097656],\n",
+       "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
+       "        -0.00195312, -0.00292969]], shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-b3df1677-f6ee-4361-9744-d264f633c86d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3df1677-f6ee-4361-9744-d264f633c86d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0922c989-b7fb-402e-b5da-d2bb0d2c1cf7' class='xr-var-data-in' type='checkbox'><label for='data-0922c989-b7fb-402e-b5da-d2bb0d2c1cf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6244812 , ...,  0.625     ,\n",
+       "         0.6247864 ,  0.62490845],\n",
+       "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
+       "        -0.03564453, -0.03561401],\n",
+       "       [-0.05014038, -0.05010986, -0.05014038, ..., -0.04354858,\n",
+       "        -0.04360962, -0.04360962],\n",
+       "       [ 0.77835083,  0.7782593 ,  0.7783203 , ...,  0.77856445,\n",
+       "         0.77874756,  0.7786255 ]], shape=(4, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-369b7ac7-3c76-4829-bd5a-e24553418a5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-369b7ac7-3c76-4829-bd5a-e24553418a5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca75ac4f-4c6f-4bf2-9533-9288632ae70f' class='xr-var-data-in' type='checkbox'><label for='data-ca75ac4f-4c6f-4bf2-9533-9288632ae70f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.8, 12.8, 12.8, ..., 12. , 12. , 12. ],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>243 243 243 244 ... 246 246 246 246</div><input id='attrs-9f0ad4f2-aaa7-42b0-917b-c9f7c5b115c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f0ad4f2-aaa7-42b0-917b-c9f7c5b115c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce54ca4-b578-404d-84f7-cbf3032c8853' class='xr-var-data-in' type='checkbox'><label for='data-7ce54ca4-b578-404d-84f7-cbf3032c8853' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([243, 243, 243, ..., 246, 246, 246], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.9 72.8 72.4 ... -198.0 -195.7</div><input id='attrs-6e7cb7a1-075d-4116-af50-af4ebeb85739' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e7cb7a1-075d-4116-af50-af4ebeb85739' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d97d916-ae98-48c8-a287-d25c690a0daf' class='xr-var-data-in' type='checkbox'><label for='data-4d97d916-ae98-48c8-a287-d25c690a0daf' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.9,   72.8,   72.4, ...,   73. ,   73. ,   72.7],\n",
+       "       [   9.5,    9.5,    9.8, ...,    6. ,    6.4,    5.9],\n",
+       "       [-197.1, -197.2, -197.7, ..., -197.4, -198. , -195.7]],\n",
+       "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01437 0.0 ... 9.709 9.724</div><input id='attrs-f403d93b-53cf-4afb-8397-8afdcf14e667' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f403d93b-53cf-4afb-8397-8afdcf14e667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8492deda-94a2-4ba0-9972-e35a250f6ed3' class='xr-var-data-in' type='checkbox'><label for='data-8492deda-94a2-4ba0-9972-e35a250f6ed3' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.4370117e-02,  0.0000000e+00, -1.4370117e-02, ...,\n",
+       "        -4.7900393e-03, -1.9160157e-02, -1.4370117e-02],\n",
+       "       [-1.2693603e+00, -1.2885205e+00, -1.2837305e+00, ...,\n",
+       "        -1.0825489e+00, -1.1017090e+00, -1.1064991e+00],\n",
+       "       [ 9.7189894e+00,  9.7046194e+00,  9.7094088e+00, ...,\n",
+       "         9.7141991e+00,  9.7094088e+00,  9.7237797e+00]],\n",
+       "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-8753e491-8667-4bdc-85f3-06c43ba85c61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8753e491-8667-4bdc-85f3-06c43ba85c61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3643a4a9-9e2e-427e-893c-d7c598d80254' class='xr-var-data-in' type='checkbox'><label for='data-3643a4a9-9e2e-427e-893c-d7c598d80254' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-193f34be-d8d4-4977-a71f-bb63445659b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-193f34be-d8d4-4977-a71f-bb63445659b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ae9e849-6118-49a4-b4db-ebbc4bcf4eac' class='xr-var-data-in' type='checkbox'><label for='data-1ae9e849-6118-49a4-b4db-ebbc4bcf4eac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
+       "       1053557762], shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-bac83806-7e0d-4f0c-ae41-d3838df691c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bac83806-7e0d-4f0c-ae41-d3838df691c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03de88eb-7398-4de1-90b2-529099f32800' class='xr-var-data-in' type='checkbox'><label for='data-03de88eb-7398-4de1-90b2-529099f32800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000],\n",
+       "      shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.003 0.021 0.018 ... -0.048 0.09</div><input id='attrs-62c5f014-9241-42d8-ad8b-134f3b0c2f25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62c5f014-9241-42d8-ad8b-134f3b0c2f25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97cfe342-8196-4402-ba27-a1c80696ed1e' class='xr-var-data-in' type='checkbox'><label for='data-97cfe342-8196-4402-ba27-a1c80696ed1e' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.003,  0.021,  0.018, ...,  0.   , -0.044,  0.009],\n",
+       "       [ 0.057,  0.034,  0.017, ...,  0.01 ,  0.001, -0.002],\n",
+       "       [ 0.039,  0.022,  0.02 , ...,  0.039, -0.008,  0.018],\n",
+       "       ...,\n",
+       "       [-0.036, -0.013,  0.058, ...,  0.058, -0.013,  0.028],\n",
+       "       [-0.043, -0.009,  0.028, ...,  0.092, -0.021,  0.019],\n",
+       "       [-0.073,  0.003,  0.   , ...,  0.049, -0.048,  0.09 ]],\n",
+       "      shape=(28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.5 80.5 80.0 ... 46.5 46.0 47.5</div><input id='attrs-4cc9ef2a-f41a-4ab0-9776-aa0ad0ea04d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cc9ef2a-f41a-4ab0-9776-aa0ad0ea04d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acdc68ed-ef5f-4caa-a112-66b2593f4492' class='xr-var-data-in' type='checkbox'><label for='data-acdc68ed-ef5f-4caa-a112-66b2593f4492' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[80.5, 80.5, 80. , ..., 82. , 81.5, 81. ],\n",
+       "       [75.5, 76.5, 76. , ..., 78.5, 78. , 77.5],\n",
+       "       [72.5, 73.5, 73.5, ..., 75. , 76.5, 75.5],\n",
+       "       ...,\n",
+       "       [57.5, 57.5, 57. , ..., 49. , 49. , 48.5],\n",
+       "       [56.5, 57. , 56. , ..., 47.5, 48. , 48. ],\n",
+       "       [55.5, 55.5, 55. , ..., 46.5, 46. , 47.5]],\n",
+       "      shape=(28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>98 100 92 93 98 ... 100 99 94 100</div><input id='attrs-76a59e93-8a68-4c75-9552-dc6c6546a3b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76a59e93-8a68-4c75-9552-dc6c6546a3b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b472c615-dea5-49b7-a2ee-7540f0a413d1' class='xr-var-data-in' type='checkbox'><label for='data-b472c615-dea5-49b7-a2ee-7540f0a413d1' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[ 98, 100,  92, ...,  88,  90,  91],\n",
+       "       [ 97,  94,  98, ...,  97,  95,  97],\n",
+       "       [100, 100, 100, ...,  93, 100,  96],\n",
+       "       ...,\n",
+       "       [100, 100, 100, ..., 100, 100,  98],\n",
+       "       [100, 100, 100, ...,  99, 100,  98],\n",
+       "       [100, 100, 100, ...,  99,  94, 100]],\n",
+       "      shape=(28, 55000), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2165 ... 0.9937 0.9937</div><input id='attrs-c18e2035-c4f9-45f7-ac74-820e5d113539' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c18e2035-c4f9-45f7-ac74-820e5d113539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-056471b0-c180-4158-8790-a13facc490cc' class='xr-var-data-in' type='checkbox'><label for='data-056471b0-c180-4158-8790-a13facc490cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16694236e-01, -2.16498137e-01, -2.16593146e-01, ...,\n",
+       "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
+       "        [-9.67967808e-01, -9.68039632e-01, -9.68030274e-01, ...,\n",
+       "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
+       "        [-1.26507595e-01, -1.26426160e-01, -1.26425594e-01, ...,\n",
+       "         -1.10033900e-01, -1.10009573e-01, -1.09963998e-01]],\n",
+       "\n",
+       "       [[ 9.76198971e-01,  9.76259708e-01,  9.76250350e-01, ...,\n",
+       "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
+       "        [-2.15024590e-01, -2.14829564e-01, -2.14924574e-01, ...,\n",
+       "         -2.14874983e-01, -2.15436578e-01, -2.15052128e-01],\n",
+       "        [-2.68766508e-02, -2.68536285e-02, -2.68591940e-02, ...,\n",
+       "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02]],\n",
+       "\n",
+       "       [[-1.19058788e-03, -1.16700679e-03, -1.17257237e-03, ...,\n",
+       "         -1.16243958e-03, -1.02279335e-03, -9.55920666e-04],\n",
+       "        [-1.29325598e-01, -1.29241347e-01, -1.29241914e-01, ...,\n",
+       "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01],\n",
+       "        [ 9.91601229e-01,  9.91612315e-01,  9.91612315e-01, ...,\n",
+       "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
+       "      shape=(3, 3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.001953</div><input id='attrs-939e8beb-5806-47c7-bbac-a5b77d8d2d5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-939e8beb-5806-47c7-bbac-a5b77d8d2d5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c11138d-4e5f-4e3c-b8f3-bac261b7e7cb' class='xr-var-data-in' type='checkbox'><label for='data-3c11138d-4e5f-4e3c-b8f3-bac261b7e7cb' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00097656,\n",
+       "        -0.00195312, -0.00390625],\n",
+       "       [ 0.        ,  0.        ,  0.00097656, ...,  0.00097656,\n",
+       "         0.        ,  0.00097656],\n",
+       "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
+       "        -0.00292969, -0.00195312]], shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-9b0ac6e1-bea8-4307-9dc7-9863e4b221ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b0ac6e1-bea8-4307-9dc7-9863e4b221ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f43d76ae-75fc-43b6-97f7-e76dcaa21183' class='xr-var-data-in' type='checkbox'><label for='data-f43d76ae-75fc-43b6-97f7-e76dcaa21183' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6245117 , ...,  0.625     ,\n",
+       "         0.6247864 ,  0.62490845],\n",
+       "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
+       "        -0.03564453, -0.03561401],\n",
+       "       [-0.0501709 , -0.05014038, -0.05014038, ..., -0.04354858,\n",
+       "        -0.04360962, -0.04360962],\n",
+       "       [ 0.77835083,  0.7782898 ,  0.7783203 , ...,  0.77856445,\n",
+       "         0.77874756,  0.7786255 ]], shape=(4, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>65 65 65 65 65 ... 65 65 65 65 65</div><input id='attrs-7509e529-273f-4169-9c6c-138a57766fde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7509e529-273f-4169-9c6c-138a57766fde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e92d63e-8f07-41a5-b463-5e374259309c' class='xr-var-data-in' type='checkbox'><label for='data-0e92d63e-8f07-41a5-b463-5e374259309c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65, 65, 65, ..., 65, 65, 65], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-53521fd9-798b-4a9c-b11c-d52775a96263' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53521fd9-798b-4a9c-b11c-d52775a96263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f11ec6fb-60b9-4bd8-b625-51f371434f74' class='xr-var-data-in' type='checkbox'><label for='data-f11ec6fb-60b9-4bd8-b625-51f371434f74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-a8944bce-70c7-436a-91bb-c4abb1fd9507' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8944bce-70c7-436a-91bb-c4abb1fd9507' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bcf6f94-248e-459c-b751-fabed7292479' class='xr-var-data-in' type='checkbox'><label for='data-7bcf6f94-248e-459c-b751-fabed7292479' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-6757c338-4e8c-4dc9-b50e-1bbe018e9825' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6757c338-4e8c-4dc9-b50e-1bbe018e9825' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e3b3997-2deb-4ece-ba76-3daf7370642f' class='xr-var-data-in' type='checkbox'><label for='data-0e3b3997-2deb-4ece-ba76-3daf7370642f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1</div><input id='attrs-0c05add4-068c-49a7-9ac2-3704a77417e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c05add4-068c-49a7-9ac2-3704a77417e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df4fa6f3-587c-4280-a100-85a4e60f465e' class='xr-var-data-in' type='checkbox'><label for='data-df4fa6f3-587c-4280-a100-85a4e60f465e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 1], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.52 -12.51 ... -12.52 -12.5</div><input id='attrs-331cc7f8-0610-4c67-9d3e-515fc3d73aee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-331cc7f8-0610-4c67-9d3e-515fc3d73aee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-561108d0-676b-4d20-96ef-3b87a24bcc0b' class='xr-var-data-in' type='checkbox'><label for='data-561108d0-676b-4d20-96ef-3b87a24bcc0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-12.52    , -12.505001, -12.51    , ..., -12.490002, -12.52    ,\n",
+       "       -12.500001], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.065 -0.06 -0.06 ... -0.05 -0.05</div><input id='attrs-0f345601-b07b-474e-a2a0-49a2ec0708f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f345601-b07b-474e-a2a0-49a2ec0708f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cfc3192-7bda-4cec-a282-6c400ee02a2e' class='xr-var-data-in' type='checkbox'><label for='data-4cfc3192-7bda-4cec-a282-6c400ee02a2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.065, -0.06 , -0.06 , ..., -0.06 , -0.05 , -0.05 ],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.425 -7.42 -7.42 ... -6.45 -6.45</div><input id='attrs-45ec3d74-0628-4889-a8fb-529b86508120' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45ec3d74-0628-4889-a8fb-529b86508120' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5daaec6-28f0-4f8d-93a6-a5b68f6598c3' class='xr-var-data-in' type='checkbox'><label for='data-c5daaec6-28f0-4f8d-93a6-a5b68f6598c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.4250007, -7.42     , -7.42     , ..., -6.45     , -6.45     ,\n",
+       "       -6.45     ], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.183 0.0 -1.183 ... 0.0 0.5518</div><input id='attrs-8c09064d-3cdc-41fc-a42f-590b2be4e59d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c09064d-3cdc-41fc-a42f-590b2be4e59d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e624ebaf-c843-4311-9779-b513fd032d37' class='xr-var-data-in' type='checkbox'><label for='data-e624ebaf-c843-4311-9779-b513fd032d37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.1831,  0.    , -1.1831,  0.    ],\n",
+       "       [ 0.    , -1.1831,  0.    ,  1.1831],\n",
+       "       [ 0.5518,  0.    ,  0.5518,  0.    ],\n",
+       "       [ 0.    ,  0.5518,  0.    ,  0.5518]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b62067c6-93e4-4617-b952-0abf39e01c72' class='xr-section-summary-in' type='checkbox'  ><label for='section-b62067c6-93e4-4617-b952-0abf39e01c72' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>earth</dd><dt><span>fs :</span></dt><dd>1</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd></dl></div></li></ul></div></div>"
       ],
-      "source": [
-        "dolfyn.set_declination(ds, 15.8, inplace=True)  # 15.8 deg East\n",
-        "dolfyn.rotate2(ds, \"earth\", inplace=True)"
+      "text/plain": [
+       "<xarray.Dataset> Size: 83MB\n",
+       "Dimensions:              (time: 55000, time_b5: 55000, range: 28, range_b5: 28,\n",
+       "                          beam: 4, dir: 4, dirIMU: 3, earth: 3, inst: 3, q: 4,\n",
+       "                          x1: 4, x2: 4)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 440kB 2020-08-15T00:20:00.5009...\n",
+       "  * time_b5              (time_b5) datetime64[ns] 440kB 2020-08-15T00:20:00.4...\n",
+       "  * range                (range) float64 224B 0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1\n",
+       "  * range_b5             (range_b5) float64 224B 0.6 1.1 1.6 ... 13.1 13.6 14.1\n",
+       "  * beam                 (beam) int32 16B 1 2 3 4\n",
+       "  * dir                  (dir) <U2 32B 'E' 'N' 'U1' 'U2'\n",
+       "  * dirIMU               (dirIMU) <U1 12B 'E' 'N' 'U'\n",
+       "  * earth                (earth) <U1 12B 'E' 'N' 'U'\n",
+       "  * inst                 (inst) <U1 12B 'X' 'Y' 'Z'\n",
+       "  * q                    (q) <U1 16B 'w' 'x' 'y' 'z'\n",
+       "  * x1                   (x1) int32 16B 1 2 3 4\n",
+       "  * x2                   (x2) int32 16B 1 2 3 4\n",
+       "Data variables: (12/38)\n",
+       "    c_sound              (time) float32 220kB 1.502e+03 1.502e+03 ... 1.498e+03\n",
+       "    temp                 (time) float32 220kB 14.55 14.55 14.55 ... 13.47 13.47\n",
+       "    pressure             (time) float32 220kB 9.713 9.718 9.718 ... 9.594 9.596\n",
+       "    mag                  (dirIMU, time) float32 660kB 72.5 72.7 ... -195.7\n",
+       "    accel                (dirIMU, time) float32 660kB -0.00479 ... 9.729\n",
+       "    batt                 (time) float32 220kB 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
+       "    ...                   ...\n",
+       "    telemetry_data       (time) uint8 55kB 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0\n",
+       "    boost_running        (time) uint8 55kB 0 0 0 0 0 0 0 0 1 ... 1 0 0 0 0 0 0 1\n",
+       "    heading              (time) float32 220kB -12.52 -12.51 ... -12.52 -12.5\n",
+       "    pitch                (time) float32 220kB -0.065 -0.06 -0.06 ... -0.05 -0.05\n",
+       "    roll                 (time) float32 220kB -7.425 -7.42 -7.42 ... -6.45 -6.45\n",
+       "    beam2inst_orientmat  (x1, x2) float32 64B 1.183 0.0 -1.183 ... 0.0 0.5518\n",
+       "Attributes: (12/34)\n",
+       "    filehead_config:       {\"CLOCKSTR\": {\"TIME\": \"\\\"2020-08-13 13:56:21\\\"\"}, ...\n",
+       "    inst_model:            Signature1000\n",
+       "    inst_make:             Nortek\n",
+       "    inst_type:             ADCP\n",
+       "    burst_config:          {\"press_valid\": true, \"temp_valid\": true, \"compass...\n",
+       "    n_cells:               28\n",
+       "    ...                    ...\n",
+       "    proc_idle_less_12pct:  0\n",
+       "    rotate_vars:           ['vel', 'accel', 'accel_b5', 'angrt', 'angrt_b5', ...\n",
+       "    coord_sys:             earth\n",
+       "    fs:                    1\n",
+       "    has_imu:               1\n",
+       "    beam_angle:            25"
       ]
-    },
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print the dataset\n",
+    "ds"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A second way provided to look at data is through the *DOLfYN view*. This view has several convenience methods, shortcuts, and functions built-in. It includes an alternate – and somewhat more informative/compact – description of the data object when in interactive mode. This can be accessed using"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To rotate into the principal frame of reference (streamwise, cross-stream, vertical), if desired, we must first calculate the depth-averaged principal flow heading and add it to the dataset attributes. Then the dataset can be rotated using the same `rotate2` function. We use `inplace=False` because we do not want to alter the input dataset here."
+     "data": {
+      "text/plain": [
+       "<ADCP data object>:  Nortek Signature1000\n",
+       "  . 15.28 hours (started: Aug 15, 2020 00:20)\n",
+       "  . earth-frame\n",
+       "  . (55000 pings @ 1Hz)\n",
+       "  Variables:\n",
+       "  - time ('time',)\n",
+       "  - time_b5 ('time_b5',)\n",
+       "  - vel ('dir', 'range', 'time')\n",
+       "  - vel_b5 ('range_b5', 'time_b5')\n",
+       "  - range ('range',)\n",
+       "  - orientmat ('earth', 'inst', 'time')\n",
+       "  - heading ('time',)\n",
+       "  - pitch ('time',)\n",
+       "  - roll ('time',)\n",
+       "  - temp ('time',)\n",
+       "  - pressure ('time',)\n",
+       "  - amp ('beam', 'range', 'time')\n",
+       "  - amp_b5 ('range_b5', 'time_b5')\n",
+       "  - corr ('beam', 'range', 'time')\n",
+       "  - corr_b5 ('range_b5', 'time_b5')\n",
+       "  - accel ('dirIMU', 'time')\n",
+       "  - angrt ('dirIMU', 'time')\n",
+       "  - mag ('dirIMU', 'time')\n",
+       "  ... and others (see `<obj>.variables`)"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_dolfyn = ds.velds\n",
+    "ds_dolfyn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One way to visualize this data is to plot the velocity in a histogram. This will highlight any outliers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds.attrs[\"principal_heading\"] = dolfyn.calc_principal_heading(ds[\"vel\"].mean(\"range\"))\n",
-        "ds = dolfyn.rotate2(ds, \"principal\", inplace=False)"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x1200 with 4 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vel = ds[\"vel\"]\n",
+    "\n",
+    "vel_dim = [d for d in vel.dims if d in (\"dir\", \"earth\", \"inst\", \"beam\")][0]\n",
+    "labels = vel.coords[vel_dim].values\n",
+    "\n",
+    "fig, axes = plt.subplots(len(labels), 1, figsize=(8, 3 * len(labels)), sharex=True)\n",
+    "\n",
+    "if len(labels) == 1:\n",
+    "    axes = [axes]\n",
+    "\n",
+    "for ax_i, label in zip(axes, labels):\n",
+    "    values = vel.sel({vel_dim: label}).values.ravel()\n",
+    "    values = values[np.isfinite(values)]\n",
+    "\n",
+    "    ax_i.hist(values, bins=50, color=\"steelblue\", edgecolor=\"black\", alpha=0.8)\n",
+    "    ax_i.set_title(f\"Velocity histogram: {label}\")\n",
+    "    ax_i.set_ylabel(\"Count\")\n",
+    "\n",
+    "axes[-1].set_xlabel(\"Velocity [m/s]\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Initial Steps for Data Quality Control (QC)\n",
+    "\n",
+    "### 2.1: Set the Deployment Height\n",
+    "\n",
+    "When using Nortek instruments, the deployment software does not factor in the deployment height. The deployment height represents the position of the Acoustic Doppler Current Profiler (ADCP) within the water column. \n",
+    "\n",
+    "In this context, the center of the first depth bin is situated at a distance that is the sum of three elements: \n",
+    "1. Deployment height (the ADCP's position in the water column)\n",
+    "2. Blanking distance (the minimum distance from the ADCP to the first measurement point)\n",
+    "3. Cell size (the vertical distance of each measurement bin in the water column)\n",
+    "\n",
+    "To ensure accurate readings, it is critical to calibrate the 'range' coordinate to make '0' correspond to either the seafloor or sea surface. This calibration can be achieved using the `set_range_offset` function. \n",
+    "\n",
+    "For those using a Teledyne RDI ADCP, the TRDI deployment software will prompt you to specify the deployment height/depth during setup in order to automatically calculate water depth. If there's a need for calibration post-deployment, the `set_range_offset` function can be utilized in the same way as described above to override the initially specified value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 3. Average the Data\n",
-        "\n",
-        "As this deployment was configured in \"burst mode\", a standard step in the analysis process is to average the velocity data into time bins. \n",
-        "\n",
-        "However, if the instrument was set up in an \"averaging mode\" (where a specific profile and/or average interval was set, for instance, averaging 5 minutes of data every 30 minutes), this step would have been performed within the ADCP during deployment and can thus be skipped.\n",
-        "\n",
-        "To average the data into time bins (also known as ensembles), you should first initialize the binning tool `ADPBinner`. The parameter \"n_bin\" represents the number of data points in each ensemble. In this case, we're dealing with 300 seconds' worth of data. The \"fs\" parameter stands for the sampling frequency, which for this deployment is 1 Hz. Once the binning tool is initialized, you can use the `bin_average` function to average the data into ensembles."
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x400 with 6 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ds[\"vel\"][i].plot(ax=ax)\n",
+    "    ax.set_title(ds[\"vel\"].coords[\"dir\"].values[i])\n",
+    "    ax.tick_params(axis=\"x\", labelrotation=45)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The ADCP transducers were measured to be 0.6 m from the feet of the lander\n",
+    "api.clean.set_range_offset(ds, 0.6)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, the center of bin 1 is located at 1.2 m, with a constant a 0.5m bin size. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [],
-      "source": [
-        "avg_tool = api.ADPBinner(n_bin=ds.fs * 300, fs=ds.fs)\n",
-        "ds_avg = avg_tool.bin_average(ds)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Statistic  Value\n",
+      "0           min    1.2\n",
+      "1           max   14.7\n",
+      "2  mean spacing    0.5\n",
+      "3   max spacing    0.5\n",
+      "4   min spacing    0.5\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-              "<defs>\n",
-              "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-              "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-              "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-              "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-              "</symbol>\n",
-              "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-              "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-              "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-              "</symbol>\n",
-              "</defs>\n",
-              "</svg>\n",
-              "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-              " *\n",
-              " */\n",
-              "\n",
-              ":root {\n",
-              "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-              "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-              "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-              "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-              "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-              "  --xr-background-color: var(--jp-layout-color0, white);\n",
-              "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-              "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-              "}\n",
-              "\n",
-              "html[theme=\"dark\"],\n",
-              "html[data-theme=\"dark\"],\n",
-              "body[data-theme=\"dark\"],\n",
-              "body.vscode-dark {\n",
-              "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-              "  --xr-border-color: #1f1f1f;\n",
-              "  --xr-disabled-color: #515151;\n",
-              "  --xr-background-color: #111111;\n",
-              "  --xr-background-color-row-even: #111111;\n",
-              "  --xr-background-color-row-odd: #313131;\n",
-              "}\n",
-              "\n",
-              ".xr-wrap {\n",
-              "  display: block !important;\n",
-              "  min-width: 300px;\n",
-              "  max-width: 700px;\n",
-              "}\n",
-              "\n",
-              ".xr-text-repr-fallback {\n",
-              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-header {\n",
-              "  padding-top: 6px;\n",
-              "  padding-bottom: 6px;\n",
-              "  margin-bottom: 4px;\n",
-              "  border-bottom: solid 1px var(--xr-border-color);\n",
-              "}\n",
-              "\n",
-              ".xr-header > div,\n",
-              ".xr-header > ul {\n",
-              "  display: inline;\n",
-              "  margin-top: 0;\n",
-              "  margin-bottom: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-obj-type,\n",
-              ".xr-array-name {\n",
-              "  margin-left: 2px;\n",
-              "  margin-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-obj-type {\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-sections {\n",
-              "  padding-left: 0 !important;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input {\n",
-              "  display: inline-block;\n",
-              "  opacity: 0;\n",
-              "  height: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input + label {\n",
-              "  color: var(--xr-disabled-color);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:enabled + label {\n",
-              "  cursor: pointer;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:focus + label {\n",
-              "  border: 2px solid var(--xr-font-color0);\n",
-              "}\n",
-              "\n",
-              ".xr-section-item input:enabled + label:hover {\n",
-              "  color: var(--xr-font-color0);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary {\n",
-              "  grid-column: 1;\n",
-              "  color: var(--xr-font-color2);\n",
-              "  font-weight: 500;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary > span {\n",
-              "  display: inline-block;\n",
-              "  padding-left: 0.5em;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:disabled + label {\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in + label:before {\n",
-              "  display: inline-block;\n",
-              "  content: \"►\";\n",
-              "  font-size: 11px;\n",
-              "  width: 15px;\n",
-              "  text-align: center;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:disabled + label:before {\n",
-              "  color: var(--xr-disabled-color);\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked + label:before {\n",
-              "  content: \"▼\";\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked + label > span {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary,\n",
-              ".xr-section-inline-details {\n",
-              "  padding-top: 4px;\n",
-              "  padding-bottom: 4px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-inline-details {\n",
-              "  grid-column: 2 / -1;\n",
-              "}\n",
-              "\n",
-              ".xr-section-details {\n",
-              "  display: none;\n",
-              "  grid-column: 1 / -1;\n",
-              "  margin-bottom: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-array-wrap {\n",
-              "  grid-column: 1 / -1;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 20px auto;\n",
-              "}\n",
-              "\n",
-              ".xr-array-wrap > label {\n",
-              "  grid-column: 1;\n",
-              "  vertical-align: top;\n",
-              "}\n",
-              "\n",
-              ".xr-preview {\n",
-              "  color: var(--xr-font-color3);\n",
-              "}\n",
-              "\n",
-              ".xr-array-preview,\n",
-              ".xr-array-data {\n",
-              "  padding: 0 5px !important;\n",
-              "  grid-column: 2;\n",
-              "}\n",
-              "\n",
-              ".xr-array-data,\n",
-              ".xr-array-in:checked ~ .xr-array-preview {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              ".xr-array-in:checked ~ .xr-array-data,\n",
-              ".xr-array-preview {\n",
-              "  display: inline-block;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list {\n",
-              "  display: inline-block !important;\n",
-              "  list-style: none;\n",
-              "  padding: 0 !important;\n",
-              "  margin: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list li {\n",
-              "  display: inline-block;\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list:before {\n",
-              "  content: \"(\";\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list:after {\n",
-              "  content: \")\";\n",
-              "}\n",
-              "\n",
-              ".xr-dim-list li:not(:last-child):after {\n",
-              "  content: \",\";\n",
-              "  padding-right: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-has-index {\n",
-              "  font-weight: bold;\n",
-              "}\n",
-              "\n",
-              ".xr-var-list,\n",
-              ".xr-var-item {\n",
-              "  display: contents;\n",
-              "}\n",
-              "\n",
-              ".xr-var-item > div,\n",
-              ".xr-var-item label,\n",
-              ".xr-var-item > .xr-var-name span {\n",
-              "  background-color: var(--xr-background-color-row-even);\n",
-              "  margin-bottom: 0;\n",
-              "}\n",
-              "\n",
-              ".xr-var-item > .xr-var-name:hover span {\n",
-              "  padding-right: 5px;\n",
-              "}\n",
-              "\n",
-              ".xr-var-list > li:nth-child(odd) > div,\n",
-              ".xr-var-list > li:nth-child(odd) > label,\n",
-              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-              "  background-color: var(--xr-background-color-row-odd);\n",
-              "}\n",
-              "\n",
-              ".xr-var-name {\n",
-              "  grid-column: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-var-dims {\n",
-              "  grid-column: 2;\n",
-              "}\n",
-              "\n",
-              ".xr-var-dtype {\n",
-              "  grid-column: 3;\n",
-              "  text-align: right;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-var-preview {\n",
-              "  grid-column: 4;\n",
-              "}\n",
-              "\n",
-              ".xr-index-preview {\n",
-              "  grid-column: 2 / 5;\n",
-              "  color: var(--xr-font-color2);\n",
-              "}\n",
-              "\n",
-              ".xr-var-name,\n",
-              ".xr-var-dims,\n",
-              ".xr-var-dtype,\n",
-              ".xr-preview,\n",
-              ".xr-attrs dt {\n",
-              "  white-space: nowrap;\n",
-              "  overflow: hidden;\n",
-              "  text-overflow: ellipsis;\n",
-              "  padding-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-var-name:hover,\n",
-              ".xr-var-dims:hover,\n",
-              ".xr-var-dtype:hover,\n",
-              ".xr-attrs dt:hover {\n",
-              "  overflow: visible;\n",
-              "  width: auto;\n",
-              "  z-index: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-var-attrs,\n",
-              ".xr-var-data,\n",
-              ".xr-index-data {\n",
-              "  display: none;\n",
-              "  background-color: var(--xr-background-color) !important;\n",
-              "  padding-bottom: 5px !important;\n",
-              "}\n",
-              "\n",
-              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-              ".xr-var-data-in:checked ~ .xr-var-data,\n",
-              ".xr-index-data-in:checked ~ .xr-index-data {\n",
-              "  display: block;\n",
-              "}\n",
-              "\n",
-              ".xr-var-data > table {\n",
-              "  float: right;\n",
-              "}\n",
-              "\n",
-              ".xr-var-name span,\n",
-              ".xr-var-data,\n",
-              ".xr-index-name div,\n",
-              ".xr-index-data,\n",
-              ".xr-attrs {\n",
-              "  padding-left: 25px !important;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs,\n",
-              ".xr-var-attrs,\n",
-              ".xr-var-data,\n",
-              ".xr-index-data {\n",
-              "  grid-column: 1 / -1;\n",
-              "}\n",
-              "\n",
-              "dl.xr-attrs {\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "  display: grid;\n",
-              "  grid-template-columns: 125px auto;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt,\n",
-              ".xr-attrs dd {\n",
-              "  padding: 0;\n",
-              "  margin: 0;\n",
-              "  float: left;\n",
-              "  padding-right: 10px;\n",
-              "  width: auto;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt {\n",
-              "  font-weight: normal;\n",
-              "  grid-column: 1;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dt:hover span {\n",
-              "  display: inline-block;\n",
-              "  background: var(--xr-background-color);\n",
-              "  padding-right: 10px;\n",
-              "}\n",
-              "\n",
-              ".xr-attrs dd {\n",
-              "  grid-column: 2;\n",
-              "  white-space: pre-wrap;\n",
-              "  word-break: break-all;\n",
-              "}\n",
-              "\n",
-              ".xr-icon-database,\n",
-              ".xr-icon-file-text2,\n",
-              ".xr-no-icon {\n",
-              "  display: inline-block;\n",
-              "  vertical-align: middle;\n",
-              "  width: 1em;\n",
-              "  height: 1.5em !important;\n",
-              "  stroke-width: 0;\n",
-              "  stroke: currentColor;\n",
-              "  fill: currentColor;\n",
-              "}\n",
-              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 379kB\n",
-              "Dimensions:         (time: 183, dirIMU: 3, dir: 4, range: 28, beam: 4,\n",
-              "                     earth: 3, inst: 3, q: 4, time_b5: 183, range_b5: 28)\n",
-              "Coordinates:\n",
-              "  * time            (time) datetime64[ns] 1kB 2020-08-15T00:22:30.001030683 ....\n",
-              "  * dirIMU          (dirIMU) &lt;U10 120B &#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27;\n",
-              "  * dir             (dir) &lt;U10 160B &#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert1&#x27; &#x27;vert2&#x27;\n",
-              "  * range           (range) float64 224B 1.2 1.7 2.2 2.7 ... 13.2 13.7 14.2 14.7\n",
-              "  * beam            (beam) int32 16B 1 2 3 4\n",
-              "  * earth           (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
-              "  * inst            (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
-              "  * q               (q) &lt;U1 16B &#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;\n",
-              "  * time_b5         (time_b5) datetime64[ns] 1kB 2020-08-15T00:22:29.93849515...\n",
-              "  * range_b5        (range_b5) float64 224B 1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7\n",
-              "Data variables: (12/38)\n",
-              "    c_sound         (time) float32 732B 1.502e+03 1.502e+03 ... 1.498e+03\n",
-              "    temp            (time) float32 732B 14.49 14.59 14.54 ... 13.62 13.56 13.5\n",
-              "    pressure        (time) float32 732B 9.712 9.699 9.685 ... 9.58 9.584 9.591\n",
-              "    mag             (dirIMU, time) float32 2kB 3.534 3.565 ... -197.1 -197.1\n",
-              "    accel           (dirIMU, time) float32 2kB -1.261 -1.263 ... 9.714 9.712\n",
-              "    batt            (time) float32 732B 16.48 16.48 16.48 ... 16.25 16.26 16.26\n",
-              "    ...              ...\n",
-              "    heading         (time) float32 732B 3.287 3.261 3.337 ... 3.331 3.352 3.352\n",
-              "    pitch           (time) float32 732B -0.05523 -0.07217 ... -0.04288 -0.0429\n",
-              "    roll            (time) float32 732B -7.414 -7.424 -7.404 ... -6.433 -6.436\n",
-              "    water_density   (time) float32 732B 1.023e+03 1.023e+03 ... 1.023e+03\n",
-              "    depth           (time) float32 732B 10.28 10.26 10.25 ... 10.14 10.15 10.15\n",
-              "    U_std           (range, time) float32 20kB 0.04232 0.04293 ... nan nan\n",
-              "Attributes: (12/41)\n",
-              "    fs:                        1\n",
-              "    n_bin:                     300\n",
-              "    n_fft:                     300\n",
-              "    description:               Binned averages calculated from ensembles of s...\n",
-              "    filehead_config:           {&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;...\n",
-              "    inst_model:                Signature1000\n",
-              "    ...                        ...\n",
-              "    has_imu:                   1\n",
-              "    beam_angle:                25\n",
-              "    range_offset:              0.6\n",
-              "    declination:               15.8\n",
-              "    declination_in_orientmat:  1\n",
-              "    principal_heading:         11.1898</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-40afcbec-82fa-4ea2-8864-207bc81a7a2f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-40afcbec-82fa-4ea2-8864-207bc81a7a2f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 183</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>time_b5</span>: 183</li><li><span class='xr-has-index'>range_b5</span>: 28</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6a2d51fd-0c35-44ba-9e74-4c2f260aa17a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a2d51fd-0c35-44ba-9e74-4c2f260aa17a' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:30.001030683 .....</div><input id='attrs-8c6cc862-7bbd-406d-95d2-977701a314a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c6cc862-7bbd-406d-95d2-977701a314a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3388b96d-e832-465d-ba2d-8a2ee5e60b51' class='xr-var-data-in' type='checkbox'><label for='data-3388b96d-e832-465d-ba2d-8a2ee5e60b51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:30.001030683&#x27;, &#x27;2020-08-15T00:27:30.001019239&#x27;,\n",
-              "       &#x27;2020-08-15T00:32:30.001010656&#x27;, &#x27;2020-08-15T00:37:30.001015901&#x27;,\n",
-              "       &#x27;2020-08-15T00:42:30.001019954&#x27;, &#x27;2020-08-15T00:47:30.001018524&#x27;,\n",
-              "       &#x27;2020-08-15T00:52:30.001013994&#x27;, &#x27;2020-08-15T00:57:30.001026630&#x27;,\n",
-              "       &#x27;2020-08-15T01:02:30.001023769&#x27;, &#x27;2020-08-15T01:07:30.001024723&#x27;,\n",
-              "       &#x27;2020-08-15T01:12:30.001034259&#x27;, &#x27;2020-08-15T01:17:30.001035451&#x27;,\n",
-              "       &#x27;2020-08-15T01:22:30.001024961&#x27;, &#x27;2020-08-15T01:27:30.001025438&#x27;,\n",
-              "       &#x27;2020-08-15T01:32:30.001025676&#x27;, &#x27;2020-08-15T01:37:30.001031875&#x27;,\n",
-              "       &#x27;2020-08-15T01:42:30.001029014&#x27;, &#x27;2020-08-15T01:47:30.001030683&#x27;,\n",
-              "       &#x27;2020-08-15T01:52:30.001033067&#x27;, &#x27;2020-08-15T01:57:30.001029014&#x27;,\n",
-              "       &#x27;2020-08-15T02:02:30.001028299&#x27;, &#x27;2020-08-15T02:07:30.001020908&#x27;,\n",
-              "       &#x27;2020-08-15T02:12:30.001016378&#x27;, &#x27;2020-08-15T02:17:30.001012802&#x27;,\n",
-              "       &#x27;2020-08-15T02:22:30.001014709&#x27;, &#x27;2020-08-15T02:27:30.001018524&#x27;,\n",
-              "       &#x27;2020-08-15T02:32:30.001022577&#x27;, &#x27;2020-08-15T02:37:30.001020193&#x27;,\n",
-              "       &#x27;2020-08-15T02:42:30.001002311&#x27;, &#x27;2020-08-15T02:47:30.001003503&#x27;,\n",
-              "       &#x27;2020-08-15T02:52:30.001002550&#x27;, &#x27;2020-08-15T02:57:30.001000165&#x27;,\n",
-              "       &#x27;2020-08-15T03:02:30.001000642&#x27;, &#x27;2020-08-15T03:07:30.001000404&#x27;,\n",
-              "       &#x27;2020-08-15T03:12:30.000999450&#x27;, &#x27;2020-08-15T03:17:30.001003026&#x27;,\n",
-              "       &#x27;2020-08-15T03:22:30.001004695&#x27;, &#x27;2020-08-15T03:27:30.001004219&#x27;,\n",
-              "       &#x27;2020-08-15T03:32:30.001001834&#x27;, &#x27;2020-08-15T03:37:30.001005887&#x27;,\n",
-              "       &#x27;2020-08-15T03:42:30.001003742&#x27;, &#x27;2020-08-15T03:47:30.001000642&#x27;,\n",
-              "       &#x27;2020-08-15T03:52:30.001000404&#x27;, &#x27;2020-08-15T03:57:30.001001834&#x27;,\n",
-              "       &#x27;2020-08-15T04:02:30.001001834&#x27;, &#x27;2020-08-15T04:07:30.001001358&#x27;,\n",
-              "       &#x27;2020-08-15T04:12:30.001004695&#x27;, &#x27;2020-08-15T04:17:30.001001358&#x27;,\n",
-              "       &#x27;2020-08-15T04:22:30.001009464&#x27;, &#x27;2020-08-15T04:27:30.001006603&#x27;,\n",
-              "       &#x27;2020-08-15T04:32:30.001002550&#x27;, &#x27;2020-08-15T04:37:30.001004695&#x27;,\n",
-              "       &#x27;2020-08-15T04:42:30.001004934&#x27;, &#x27;2020-08-15T04:47:30.001004934&#x27;,\n",
-              "       &#x27;2020-08-15T04:52:30.001004457&#x27;, &#x27;2020-08-15T04:57:30.001002788&#x27;,\n",
-              "       &#x27;2020-08-15T05:02:30.001008749&#x27;, &#x27;2020-08-15T05:07:30.001017570&#x27;,\n",
-              "       &#x27;2020-08-15T05:12:30.001015186&#x27;, &#x27;2020-08-15T05:17:30.001005887&#x27;,\n",
-              "       &#x27;2020-08-15T05:22:30.001001358&#x27;, &#x27;2020-08-15T05:27:30.001011610&#x27;,\n",
-              "       &#x27;2020-08-15T05:32:30.001020431&#x27;, &#x27;2020-08-15T05:37:30.001011848&#x27;,\n",
-              "       &#x27;2020-08-15T05:42:30.001020908&#x27;, &#x27;2020-08-15T05:47:30.001013517&#x27;,\n",
-              "       &#x27;2020-08-15T05:52:30.001021146&#x27;, &#x27;2020-08-15T05:57:30.001010656&#x27;,\n",
-              "       &#x27;2020-08-15T06:02:30.001004219&#x27;, &#x27;2020-08-15T06:07:30.001012563&#x27;,\n",
-              "       &#x27;2020-08-15T06:12:30.001021862&#x27;, &#x27;2020-08-15T06:17:30.001018524&#x27;,\n",
-              "       &#x27;2020-08-15T06:22:30.001016139&#x27;, &#x27;2020-08-15T06:27:30.001016616&#x27;,\n",
-              "       &#x27;2020-08-15T06:32:30.001012325&#x27;, &#x27;2020-08-15T06:37:30.001016378&#x27;,\n",
-              "       &#x27;2020-08-15T06:42:30.001021862&#x27;, &#x27;2020-08-15T06:47:30.001023769&#x27;,\n",
-              "       &#x27;2020-08-15T06:52:30.001024007&#x27;, &#x27;2020-08-15T06:57:30.001014709&#x27;,\n",
-              "       &#x27;2020-08-15T07:02:30.001006126&#x27;, &#x27;2020-08-15T07:07:30.001019477&#x27;,\n",
-              "       &#x27;2020-08-15T07:12:30.001013994&#x27;, &#x27;2020-08-15T07:17:30.001016855&#x27;,\n",
-              "       &#x27;2020-08-15T07:22:30.001019239&#x27;, &#x27;2020-08-15T07:27:30.001024961&#x27;,\n",
-              "       &#x27;2020-08-15T07:32:30.001011610&#x27;, &#x27;2020-08-15T07:37:30.001024484&#x27;,\n",
-              "       &#x27;2020-08-15T07:42:30.001018524&#x27;, &#x27;2020-08-15T07:47:30.001029968&#x27;,\n",
-              "       &#x27;2020-08-15T07:52:30.001009464&#x27;, &#x27;2020-08-15T07:57:30.001014232&#x27;,\n",
-              "       &#x27;2020-08-15T08:02:30.001011610&#x27;, &#x27;2020-08-15T08:07:30.001028299&#x27;,\n",
-              "       &#x27;2020-08-15T08:12:30.001027107&#x27;, &#x27;2020-08-15T08:17:30.001024723&#x27;,\n",
-              "       &#x27;2020-08-15T08:22:30.001022577&#x27;, &#x27;2020-08-15T08:27:30.001018047&#x27;,\n",
-              "       &#x27;2020-08-15T08:32:30.001019477&#x27;, &#x27;2020-08-15T08:37:30.001017808&#x27;,\n",
-              "       &#x27;2020-08-15T08:42:30.001018762&#x27;, &#x27;2020-08-15T08:47:30.001015186&#x27;,\n",
-              "       &#x27;2020-08-15T08:52:30.001020431&#x27;, &#x27;2020-08-15T08:57:30.001026630&#x27;,\n",
-              "       &#x27;2020-08-15T09:02:30.001021146&#x27;, &#x27;2020-08-15T09:07:30.001016378&#x27;,\n",
-              "       &#x27;2020-08-15T09:12:30.001025676&#x27;, &#x27;2020-08-15T09:17:30.001024246&#x27;,\n",
-              "       &#x27;2020-08-15T09:22:30.001026630&#x27;, &#x27;2020-08-15T09:27:30.001017570&#x27;,\n",
-              "       &#x27;2020-08-15T09:32:30.001015901&#x27;, &#x27;2020-08-15T09:37:30.001016139&#x27;,\n",
-              "       &#x27;2020-08-15T09:42:30.001018762&#x27;, &#x27;2020-08-15T09:47:30.001014709&#x27;,\n",
-              "       &#x27;2020-08-15T09:52:30.001016378&#x27;, &#x27;2020-08-15T09:57:30.001024961&#x27;,\n",
-              "       &#x27;2020-08-15T10:02:30.001021146&#x27;, &#x27;2020-08-15T10:07:30.001022100&#x27;,\n",
-              "       &#x27;2020-08-15T10:12:30.001017808&#x27;, &#x27;2020-08-15T10:17:30.001013994&#x27;,\n",
-              "       &#x27;2020-08-15T10:22:30.001018762&#x27;, &#x27;2020-08-15T10:27:30.001021385&#x27;,\n",
-              "       &#x27;2020-08-15T10:32:30.001016855&#x27;, &#x27;2020-08-15T10:37:30.001021385&#x27;,\n",
-              "       &#x27;2020-08-15T10:42:30.001015186&#x27;, &#x27;2020-08-15T10:47:30.001020193&#x27;,\n",
-              "       &#x27;2020-08-15T10:52:30.001021623&#x27;, &#x27;2020-08-15T10:57:30.001021862&#x27;,\n",
-              "       &#x27;2020-08-15T11:02:30.001018047&#x27;, &#x27;2020-08-15T11:07:30.001021146&#x27;,\n",
-              "       &#x27;2020-08-15T11:12:30.001011848&#x27;, &#x27;2020-08-15T11:17:30.001015901&#x27;,\n",
-              "       &#x27;2020-08-15T11:22:30.001014232&#x27;, &#x27;2020-08-15T11:27:30.001015663&#x27;,\n",
-              "       &#x27;2020-08-15T11:32:30.001012325&#x27;, &#x27;2020-08-15T11:37:30.001004457&#x27;,\n",
-              "       &#x27;2020-08-15T11:42:30.001018524&#x27;, &#x27;2020-08-15T11:47:30.001023292&#x27;,\n",
-              "       &#x27;2020-08-15T11:52:30.001009464&#x27;, &#x27;2020-08-15T11:57:30.001002311&#x27;,\n",
-              "       &#x27;2020-08-15T12:02:30.001004695&#x27;, &#x27;2020-08-15T12:07:30.001007556&#x27;,\n",
-              "       &#x27;2020-08-15T12:12:30.001012086&#x27;, &#x27;2020-08-15T12:17:30.001007080&#x27;,\n",
-              "       &#x27;2020-08-15T12:22:30.001011371&#x27;, &#x27;2020-08-15T12:27:30.001014471&#x27;,\n",
-              "       &#x27;2020-08-15T12:32:30.001016378&#x27;, &#x27;2020-08-15T12:37:30.001012563&#x27;,\n",
-              "       &#x27;2020-08-15T12:42:30.001015901&#x27;, &#x27;2020-08-15T12:47:30.001010417&#x27;,\n",
-              "       &#x27;2020-08-15T12:52:30.001009464&#x27;, &#x27;2020-08-15T12:57:30.001010894&#x27;,\n",
-              "       &#x27;2020-08-15T13:02:30.001016855&#x27;, &#x27;2020-08-15T13:07:30.001020908&#x27;,\n",
-              "       &#x27;2020-08-15T13:12:30.001018762&#x27;, &#x27;2020-08-15T13:17:30.001018762&#x27;,\n",
-              "       &#x27;2020-08-15T13:22:30.001018047&#x27;, &#x27;2020-08-15T13:27:30.001013517&#x27;,\n",
-              "       &#x27;2020-08-15T13:32:30.001015424&#x27;, &#x27;2020-08-15T13:37:30.001009225&#x27;,\n",
-              "       &#x27;2020-08-15T13:42:30.001011133&#x27;, &#x27;2020-08-15T13:47:30.001008749&#x27;,\n",
-              "       &#x27;2020-08-15T13:52:30.001019477&#x27;, &#x27;2020-08-15T13:57:30.001022338&#x27;,\n",
-              "       &#x27;2020-08-15T14:02:30.001019716&#x27;, &#x27;2020-08-15T14:07:30.001026868&#x27;,\n",
-              "       &#x27;2020-08-15T14:12:30.001021623&#x27;, &#x27;2020-08-15T14:17:30.001034021&#x27;,\n",
-              "       &#x27;2020-08-15T14:22:30.001024961&#x27;, &#x27;2020-08-15T14:27:30.001032352&#x27;,\n",
-              "       &#x27;2020-08-15T14:32:30.001023292&#x27;, &#x27;2020-08-15T14:37:30.001024484&#x27;,\n",
-              "       &#x27;2020-08-15T14:42:30.001044034&#x27;, &#x27;2020-08-15T14:47:30.001032352&#x27;,\n",
-              "       &#x27;2020-08-15T14:52:30.001019239&#x27;, &#x27;2020-08-15T14:57:30.001032829&#x27;,\n",
-              "       &#x27;2020-08-15T15:02:30.001021146&#x27;, &#x27;2020-08-15T15:07:30.001027345&#x27;,\n",
-              "       &#x27;2020-08-15T15:12:30.001029014&#x27;, &#x27;2020-08-15T15:17:30.001026630&#x27;,\n",
-              "       &#x27;2020-08-15T15:22:30.001026153&#x27;, &#x27;2020-08-15T15:27:30.001022577&#x27;,\n",
-              "       &#x27;2020-08-15T15:32:30.001030683&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27;</div><input id='attrs-d4114ee4-4fd2-4656-a755-e1677ad95cbf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4114ee4-4fd2-4656-a755-e1677ad95cbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07f9c86c-b27f-44bc-a342-95d51837989d' class='xr-var-data-in' type='checkbox'><label for='data-07f9c86c-b27f-44bc-a342-95d51837989d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; ... &#x27;vert2&#x27;</div><input id='attrs-3ff426e7-7498-47ac-b74d-288024071044' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ff426e7-7498-47ac-b74d-288024071044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea33e978-7438-4f80-9375-612b056787b0' class='xr-var-data-in' type='checkbox'><label for='data-ea33e978-7438-4f80-9375-612b056787b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-358893ed-0b0c-41bf-8c7c-493855cc2ee4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-358893ed-0b0c-41bf-8c7c-493855cc2ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da36c9fa-4417-4a9b-8660-d95c849a25e7' class='xr-var-data-in' type='checkbox'><label for='data-da36c9fa-4417-4a9b-8660-d95c849a25e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
-              "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-3e6e203d-6844-430c-817a-055331cd2e13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e6e203d-6844-430c-817a-055331cd2e13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-834b2c68-401c-4879-bae7-bfd0f58002ba' class='xr-var-data-in' type='checkbox'><label for='data-834b2c68-401c-4879-bae7-bfd0f58002ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-24652350-689b-4f90-91ca-12b788f20e20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24652350-689b-4f90-91ca-12b788f20e20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5585fda-1e78-4e83-a0e6-3c5a5eda53be' class='xr-var-data-in' type='checkbox'><label for='data-c5585fda-1e78-4e83-a0e6-3c5a5eda53be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-f0203aca-7838-436a-88d3-fe64978fe082' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0203aca-7838-436a-88d3-fe64978fe082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d33f4c6b-3f93-4b82-97ce-4cd4e91aebd8' class='xr-var-data-in' type='checkbox'><label for='data-d33f4c6b-3f93-4b82-97ce-4cd4e91aebd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-9a254a54-000c-4a40-8c99-d7d9d1ebf2a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a254a54-000c-4a40-8c99-d7d9d1ebf2a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed47564f-b71a-4934-b7ae-18f156b1af82' class='xr-var-data-in' type='checkbox'><label for='data-ed47564f-b71a-4934-b7ae-18f156b1af82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:29.938495159 .....</div><input id='attrs-0f8d92ee-d75c-4067-96be-8ccf57a9b599' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f8d92ee-d75c-4067-96be-8ccf57a9b599' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8ee951d-8ed4-4b8a-8d8d-cdc25ea61095' class='xr-var-data-in' type='checkbox'><label for='data-d8ee951d-8ed4-4b8a-8d8d-cdc25ea61095' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:29.938495159&#x27;, &#x27;2020-08-15T00:27:29.938490867&#x27;,\n",
-              "       &#x27;2020-08-15T00:32:29.938487529&#x27;, &#x27;2020-08-15T00:37:29.938496828&#x27;,\n",
-              "       &#x27;2020-08-15T00:42:29.938495397&#x27;, &#x27;2020-08-15T00:47:29.938496589&#x27;,\n",
-              "       &#x27;2020-08-15T00:52:29.938497781&#x27;, &#x27;2020-08-15T00:57:29.938495397&#x27;,\n",
-              "       &#x27;2020-08-15T01:02:29.938493013&#x27;, &#x27;2020-08-15T01:07:29.938492536&#x27;,\n",
-              "       &#x27;2020-08-15T01:12:29.938490867&#x27;, &#x27;2020-08-15T01:17:29.938497781&#x27;,\n",
-              "       &#x27;2020-08-15T01:22:29.938496112&#x27;, &#x27;2020-08-15T01:27:29.938498497&#x27;,\n",
-              "       &#x27;2020-08-15T01:32:29.938496589&#x27;, &#x27;2020-08-15T01:37:29.938497781&#x27;,\n",
-              "       &#x27;2020-08-15T01:42:29.938495635&#x27;, &#x27;2020-08-15T01:47:29.938497066&#x27;,\n",
-              "       &#x27;2020-08-15T01:52:29.938498735&#x27;, &#x27;2020-08-15T01:57:29.938496112&#x27;,\n",
-              "       &#x27;2020-08-15T02:02:29.938496589&#x27;, &#x27;2020-08-15T02:07:29.938494920&#x27;,\n",
-              "       &#x27;2020-08-15T02:12:29.938497304&#x27;, &#x27;2020-08-15T02:17:29.938496828&#x27;,\n",
-              "       &#x27;2020-08-15T02:22:29.938496112&#x27;, &#x27;2020-08-15T02:27:29.938496828&#x27;,\n",
-              "       &#x27;2020-08-15T02:32:29.938496112&#x27;, &#x27;2020-08-15T02:37:29.938497304&#x27;,\n",
-              "       &#x27;2020-08-15T02:42:29.938495159&#x27;, &#x27;2020-08-15T02:47:29.938495635&#x27;,\n",
-              "       &#x27;2020-08-15T02:52:29.938490867&#x27;, &#x27;2020-08-15T02:57:29.938492059&#x27;,\n",
-              "       &#x27;2020-08-15T03:02:29.938489675&#x27;, &#x27;2020-08-15T03:07:29.938488245&#x27;,\n",
-              "       &#x27;2020-08-15T03:12:29.938485622&#x27;, &#x27;2020-08-15T03:17:29.938485622&#x27;,\n",
-              "       &#x27;2020-08-15T03:22:29.938488245&#x27;, &#x27;2020-08-15T03:27:29.938485145&#x27;,\n",
-              "       &#x27;2020-08-15T03:32:29.938486099&#x27;, &#x27;2020-08-15T03:37:29.938482046&#x27;,\n",
-              "       &#x27;2020-08-15T03:42:29.938489437&#x27;, &#x27;2020-08-15T03:47:29.938489437&#x27;,\n",
-              "       &#x27;2020-08-15T03:52:29.938485860&#x27;, &#x27;2020-08-15T03:57:29.938491582&#x27;,\n",
-              "       &#x27;2020-08-15T04:02:29.938488006&#x27;, &#x27;2020-08-15T04:07:29.938491106&#x27;,\n",
-              "       &#x27;2020-08-15T04:12:29.938492059&#x27;, &#x27;2020-08-15T04:17:29.938487052&#x27;,\n",
-              "       &#x27;2020-08-15T04:22:29.938489675&#x27;, &#x27;2020-08-15T04:27:29.938491582&#x27;,\n",
-              "       &#x27;2020-08-15T04:32:29.938491106&#x27;, &#x27;2020-08-15T04:37:29.938493251&#x27;,\n",
-              "       &#x27;2020-08-15T04:42:29.938489675&#x27;, &#x27;2020-08-15T04:47:29.938492059&#x27;,\n",
-              "       &#x27;2020-08-15T04:52:29.938491582&#x27;, &#x27;2020-08-15T04:57:29.938495397&#x27;,\n",
-              "       &#x27;2020-08-15T05:02:29.938491106&#x27;, &#x27;2020-08-15T05:07:29.938499212&#x27;,\n",
-              "       &#x27;2020-08-15T05:12:29.938491821&#x27;, &#x27;2020-08-15T05:17:29.938493490&#x27;,\n",
-              "       &#x27;2020-08-15T05:22:29.938495397&#x27;, &#x27;2020-08-15T05:27:29.938490390&#x27;,\n",
-              "       &#x27;2020-08-15T05:32:29.938493967&#x27;, &#x27;2020-08-15T05:37:29.938493728&#x27;,\n",
-              "       &#x27;2020-08-15T05:42:29.938491582&#x27;, &#x27;2020-08-15T05:47:29.938493013&#x27;,\n",
-              "       &#x27;2020-08-15T05:52:29.938495159&#x27;, &#x27;2020-08-15T05:57:29.938494920&#x27;,\n",
-              "       &#x27;2020-08-15T06:02:29.938493013&#x27;, &#x27;2020-08-15T06:07:29.938496589&#x27;,\n",
-              "       &#x27;2020-08-15T06:12:29.938494920&#x27;, &#x27;2020-08-15T06:17:29.938495397&#x27;,\n",
-              "       &#x27;2020-08-15T06:22:29.938493728&#x27;, &#x27;2020-08-15T06:27:29.938495397&#x27;,\n",
-              "       &#x27;2020-08-15T06:32:29.938492536&#x27;, &#x27;2020-08-15T06:37:29.938495159&#x27;,\n",
-              "       &#x27;2020-08-15T06:42:29.938496112&#x27;, &#x27;2020-08-15T06:47:29.938497304&#x27;,\n",
-              "       &#x27;2020-08-15T06:52:29.938496112&#x27;, &#x27;2020-08-15T06:57:29.938493728&#x27;,\n",
-              "       &#x27;2020-08-15T07:02:29.938497304&#x27;, &#x27;2020-08-15T07:07:29.938494920&#x27;,\n",
-              "       &#x27;2020-08-15T07:12:29.938492059&#x27;, &#x27;2020-08-15T07:17:29.938493490&#x27;,\n",
-              "       &#x27;2020-08-15T07:22:29.938493967&#x27;, &#x27;2020-08-15T07:27:29.938497304&#x27;,\n",
-              "       &#x27;2020-08-15T07:32:29.938494443&#x27;, &#x27;2020-08-15T07:37:29.938494920&#x27;,\n",
-              "       &#x27;2020-08-15T07:42:29.938495635&#x27;, &#x27;2020-08-15T07:47:29.938493967&#x27;,\n",
-              "       &#x27;2020-08-15T07:52:29.938492298&#x27;, &#x27;2020-08-15T07:57:29.938489675&#x27;,\n",
-              "       &#x27;2020-08-15T08:02:29.938493013&#x27;, &#x27;2020-08-15T08:07:29.938493728&#x27;,\n",
-              "       &#x27;2020-08-15T08:12:29.938497066&#x27;, &#x27;2020-08-15T08:17:29.938497781&#x27;,\n",
-              "       &#x27;2020-08-15T08:22:29.938495159&#x27;, &#x27;2020-08-15T08:27:29.938492774&#x27;,\n",
-              "       &#x27;2020-08-15T08:32:29.938495397&#x27;, &#x27;2020-08-15T08:37:29.938494920&#x27;,\n",
-              "       &#x27;2020-08-15T08:42:29.938494920&#x27;, &#x27;2020-08-15T08:47:29.938494682&#x27;,\n",
-              "       &#x27;2020-08-15T08:52:29.938494443&#x27;, &#x27;2020-08-15T08:57:29.938491821&#x27;,\n",
-              "       &#x27;2020-08-15T09:02:29.938497066&#x27;, &#x27;2020-08-15T09:07:29.938495397&#x27;,\n",
-              "       &#x27;2020-08-15T09:12:29.938495397&#x27;, &#x27;2020-08-15T09:17:29.938494920&#x27;,\n",
-              "       &#x27;2020-08-15T09:22:29.938498020&#x27;, &#x27;2020-08-15T09:27:29.938495397&#x27;,\n",
-              "       &#x27;2020-08-15T09:32:29.938495159&#x27;, &#x27;2020-08-15T09:37:29.938498735&#x27;,\n",
-              "       &#x27;2020-08-15T09:42:29.938493728&#x27;, &#x27;2020-08-15T09:47:29.938495635&#x27;,\n",
-              "       &#x27;2020-08-15T09:52:29.938500642&#x27;, &#x27;2020-08-15T09:57:29.938496589&#x27;,\n",
-              "       &#x27;2020-08-15T10:02:29.938497781&#x27;, &#x27;2020-08-15T10:07:29.938500881&#x27;,\n",
-              "       &#x27;2020-08-15T10:12:29.938504457&#x27;, &#x27;2020-08-15T10:17:29.938501358&#x27;,\n",
-              "       &#x27;2020-08-15T10:22:29.938507556&#x27;, &#x27;2020-08-15T10:27:29.938496112&#x27;,\n",
-              "       &#x27;2020-08-15T10:32:29.938495635&#x27;, &#x27;2020-08-15T10:37:29.938500165&#x27;,\n",
-              "       &#x27;2020-08-15T10:42:29.938496112&#x27;, &#x27;2020-08-15T10:47:29.938493251&#x27;,\n",
-              "       &#x27;2020-08-15T10:52:29.938493728&#x27;, &#x27;2020-08-15T10:57:29.938492536&#x27;,\n",
-              "       &#x27;2020-08-15T11:02:29.938492774&#x27;, &#x27;2020-08-15T11:07:29.938492774&#x27;,\n",
-              "       &#x27;2020-08-15T11:12:29.938493967&#x27;, &#x27;2020-08-15T11:17:29.938494443&#x27;,\n",
-              "       &#x27;2020-08-15T11:22:29.938496589&#x27;, &#x27;2020-08-15T11:27:29.938496112&#x27;,\n",
-              "       &#x27;2020-08-15T11:32:29.938495397&#x27;, &#x27;2020-08-15T11:37:29.938493967&#x27;,\n",
-              "       &#x27;2020-08-15T11:42:29.938493013&#x27;, &#x27;2020-08-15T11:47:29.938495635&#x27;,\n",
-              "       &#x27;2020-08-15T11:52:29.938493967&#x27;, &#x27;2020-08-15T11:57:29.938498735&#x27;,\n",
-              "       &#x27;2020-08-15T12:02:29.938496589&#x27;, &#x27;2020-08-15T12:07:29.938498020&#x27;,\n",
-              "       &#x27;2020-08-15T12:12:29.938494443&#x27;, &#x27;2020-08-15T12:17:29.938495159&#x27;,\n",
-              "       &#x27;2020-08-15T12:22:29.938495635&#x27;, &#x27;2020-08-15T12:27:29.938497066&#x27;,\n",
-              "       &#x27;2020-08-15T12:32:29.938493967&#x27;, &#x27;2020-08-15T12:37:29.938495635&#x27;,\n",
-              "       &#x27;2020-08-15T12:42:29.938496112&#x27;, &#x27;2020-08-15T12:47:29.938495635&#x27;,\n",
-              "       &#x27;2020-08-15T12:52:29.938494443&#x27;, &#x27;2020-08-15T12:57:29.938496589&#x27;,\n",
-              "       &#x27;2020-08-15T13:02:29.938494205&#x27;, &#x27;2020-08-15T13:07:29.938494205&#x27;,\n",
-              "       &#x27;2020-08-15T13:12:29.938492536&#x27;, &#x27;2020-08-15T13:17:29.938495397&#x27;,\n",
-              "       &#x27;2020-08-15T13:22:29.938493251&#x27;, &#x27;2020-08-15T13:27:29.938495397&#x27;,\n",
-              "       &#x27;2020-08-15T13:32:29.938494920&#x27;, &#x27;2020-08-15T13:37:29.938497066&#x27;,\n",
-              "       &#x27;2020-08-15T13:42:29.938497304&#x27;, &#x27;2020-08-15T13:47:29.938494920&#x27;,\n",
-              "       &#x27;2020-08-15T13:52:29.938493490&#x27;, &#x27;2020-08-15T13:57:29.938492774&#x27;,\n",
-              "       &#x27;2020-08-15T14:02:29.938496112&#x27;, &#x27;2020-08-15T14:07:29.938489675&#x27;,\n",
-              "       &#x27;2020-08-15T14:12:29.938489913&#x27;, &#x27;2020-08-15T14:17:29.938494443&#x27;,\n",
-              "       &#x27;2020-08-15T14:22:29.938495397&#x27;, &#x27;2020-08-15T14:27:29.938492774&#x27;,\n",
-              "       &#x27;2020-08-15T14:32:29.938491582&#x27;, &#x27;2020-08-15T14:37:29.938493251&#x27;,\n",
-              "       &#x27;2020-08-15T14:42:29.938492536&#x27;, &#x27;2020-08-15T14:47:29.938492774&#x27;,\n",
-              "       &#x27;2020-08-15T14:52:29.938491106&#x27;, &#x27;2020-08-15T14:57:29.938495635&#x27;,\n",
-              "       &#x27;2020-08-15T15:02:29.938493013&#x27;, &#x27;2020-08-15T15:07:29.938493967&#x27;,\n",
-              "       &#x27;2020-08-15T15:12:29.938494920&#x27;, &#x27;2020-08-15T15:17:29.938493251&#x27;,\n",
-              "       &#x27;2020-08-15T15:22:29.938486099&#x27;, &#x27;2020-08-15T15:27:29.938484191&#x27;,\n",
-              "       &#x27;2020-08-15T15:32:29.938490390&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-d5375742-8ba6-436b-b6d3-12a79f5c02df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5375742-8ba6-436b-b6d3-12a79f5c02df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5dcd2ea-cf12-4db4-bf1d-7f1fc9630097' class='xr-var-data-in' type='checkbox'><label for='data-d5dcd2ea-cf12-4db4-bf1d-7f1fc9630097' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
-              "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a932f57a-5d21-4646-bc9d-3c6f245535a4' class='xr-section-summary-in' type='checkbox'  ><label for='section-a932f57a-5d21-4646-bc9d-3c6f245535a4' class='xr-section-summary' >Data variables: <span>(38)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-0e9bf684-5b1f-4b1f-b9bd-c62e5914a05e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e9bf684-5b1f-4b1f-b9bd-c62e5914a05e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a887c99f-97d9-4fcf-a09d-d5a3c45c3342' class='xr-var-data-in' type='checkbox'><label for='data-a887c99f-97d9-4fcf-a09d-d5a3c45c3342' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.6843, 1502.032 , 1501.8505, 1501.5714, 1501.8607, 1502.4133,\n",
-              "       1502.8121, 1502.7344, 1502.8704, 1502.9467, 1502.883 , 1502.7606,\n",
-              "       1502.6915, 1502.8214, 1502.8864, 1502.7937, 1502.6268, 1502.6473,\n",
-              "       1502.4198, 1502.3577, 1501.9543, 1501.729 , 1501.32  , 1500.9684,\n",
-              "       1500.6763, 1500.2716, 1500.1096, 1499.8009, 1499.5637, 1499.3373,\n",
-              "       1499.2163, 1499.0955, 1498.9806, 1498.8579, 1498.7637, 1498.6821,\n",
-              "       1498.6133, 1498.5066, 1498.5637, 1498.7421, 1498.8367, 1498.984 ,\n",
-              "       1498.9717, 1498.9644, 1499.0553, 1499.21  , 1499.2789, 1499.2766,\n",
-              "       1499.265 , 1499.2623, 1499.3181, 1499.2903, 1499.3444, 1499.2922,\n",
-              "       1499.2983, 1499.195 , 1499.2087, 1499.212 , 1499.2494, 1499.2227,\n",
-              "       1499.1742, 1499.1772, 1499.1937, 1499.2306, 1499.2719, 1499.3043,\n",
-              "       1499.4073, 1499.3677, 1499.4554, 1499.5647, 1499.6683, 1499.7489,\n",
-              "       1499.8341, 1499.8446, 1499.8396, 1499.9257, 1499.781 , 1499.8274,\n",
-              "       1499.7506, 1499.8904, 1499.7473, 1499.7411, 1499.7415, 1499.7079,\n",
-              "       1499.508 , 1499.7562, 1499.5327, 1499.4186, 1499.4877, 1499.1946,\n",
-              "       1499.1821, 1499.466 , 1499.5144, 1499.7024, 1499.8877, 1499.8373,\n",
-              "       1499.8597, 1500.1703, 1500.0886, 1500.082 , 1500.0476, 1499.9003,\n",
-              "       1499.8434, 1498.8927, 1497.6433, 1496.299 , 1495.386 , 1495.2273,\n",
-              "       1494.9376, 1494.7117, 1494.9341, 1495.1063, 1495.0103, 1494.7747,\n",
-              "       1494.5977, 1494.4548, 1494.4042, 1494.3356, 1494.229 , 1494.0669,\n",
-              "       1493.7662, 1493.4755, 1493.3303, 1493.1544, 1492.8477, 1492.7877,\n",
-              "       1492.8064, 1493.0189, 1493.5558, 1494.0146, 1494.7506, 1495.2681,\n",
-              "       1496.2244, 1496.8036, 1497.5472, 1498.0507, 1498.3839, 1498.663 ,\n",
-              "       1498.8971, 1499.136 , 1499.3384, 1499.535 , 1499.5739, 1499.5245,\n",
-              "       1499.4437, 1499.4467, 1499.5509, 1499.6014, 1499.5997, 1499.5935,\n",
-              "       1499.525 , 1499.4967, 1499.4364, 1499.4977, 1499.503 , 1499.5526,\n",
-              "       1499.5083, 1499.5023, 1499.474 , 1499.4028, 1499.436 , 1499.4973,\n",
-              "       1499.5286, 1499.5846, 1499.6293, 1499.6262, 1499.6666, 1499.6971,\n",
-              "       1499.6998, 1499.7269, 1499.7843, 1499.6895, 1499.7037, 1499.6376,\n",
-              "       1499.4894, 1499.0636, 1499.0073, 1498.8967, 1498.8627, 1498.961 ,\n",
-              "       1498.8287, 1498.6285, 1498.472 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.49 14.59 14.54 ... 13.56 13.5</div><input id='attrs-e389d35a-f683-44a6-99bd-93380cd821b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e389d35a-f683-44a6-99bd-93380cd821b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a25e171-6518-412c-9a9c-35b718000676' class='xr-var-data-in' type='checkbox'><label for='data-9a25e171-6518-412c-9a9c-35b718000676' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.4871   , 14.592732 , 14.5369   , 14.450967 , 14.540566 ,\n",
-              "       14.712167 , 14.838034 , 14.812099 , 14.855833 , 14.879432 ,\n",
-              "       14.860366 , 14.821934 , 14.799266 , 14.840433 , 14.8621   ,\n",
-              "       14.833766 , 14.782434 , 14.788966 , 14.716966 , 14.695799 ,\n",
-              "       14.572268 , 14.502767 , 14.376032 , 14.268402 , 14.178566 ,\n",
-              "       14.055    , 14.005532 , 13.911533 , 13.838566 , 13.7688675,\n",
-              "       13.7339325, 13.696598 , 13.661633 , 13.626367 , 13.597433 ,\n",
-              "       13.572568 , 13.552067 , 13.520534 , 13.538133 , 13.592899 ,\n",
-              "       13.620734 , 13.665668 , 13.661699 , 13.658334 , 13.686766 ,\n",
-              "       13.7354   , 13.754033 , 13.754901 , 13.7525   , 13.751934 ,\n",
-              "       13.767399 , 13.760633 , 13.776802 , 13.761433 , 13.7625   ,\n",
-              "       13.731066 , 13.7371   , 13.737367 , 13.748701 , 13.741634 ,\n",
-              "       13.726335 , 13.726666 , 13.7323   , 13.743701 , 13.756766 ,\n",
-              "       13.766968 , 13.797334 , 13.7852   , 13.812601 , 13.847366 ,\n",
-              "       13.878734 , 13.903067 , 13.928333 , 13.931099 , 13.930567 ,\n",
-              "       13.957533 , 13.912533 , 13.9264   , 13.9032   , 13.947433 ,\n",
-              "       13.902601 , 13.900833 , 13.900665 , 13.890833 , 13.830032 ,\n",
-              "       13.9046335, 13.8364   , 13.801933 , 13.8241   , 13.735    ,\n",
-              "       13.732666 , 13.818233 , 13.8298   , 13.888301 , 13.944167 ,\n",
-              "       13.928867 , 13.9361   , 14.0331   , 14.008999 , 14.006901 ,\n",
-              "       13.992832 , 13.946533 , 13.9282   , 13.643267 , 13.266466 ,\n",
-              "       12.865967 , 12.595367 , 12.5469   , 12.462267 , 12.3959   ,\n",
-              "       12.461133 , 12.512366 , 12.482166 , 12.411167 , 12.3605995,\n",
-              "       12.3201   , 12.303568 , 12.284267 , 12.252732 , 12.203699 ,\n",
-              "       12.118533 , 12.031001 , 11.988433 , 11.939434 , 11.8505   ,\n",
-              "       11.8341   , 11.837234 , 11.8984995, 12.0561   , 12.189    ,\n",
-              "       12.403767 , 12.5546   , 12.837867 , 13.010934 , 13.233933 ,\n",
-              "       13.384068 , 13.485199 , 13.565999 , 13.639567 , 13.711732 ,\n",
-              "       13.774034 , 13.829665 , 13.838365 , 13.829668 , 13.8013   ,\n",
-              "       13.805167 , 13.833833 , 13.855499 , 13.857698 , 13.844232 ,\n",
-              "       13.831333 , 13.816167 , 13.802168 , 13.811266 , 13.820599 ,\n",
-              "       13.834399 , 13.828466 , 13.826566 , 13.810034 , 13.792601 ,\n",
-              "       13.800402 , 13.820232 , 13.830032 , 13.8446665, 13.8607   ,\n",
-              "       13.860667 , 13.870465 , 13.876833 , 13.878232 , 13.884268 ,\n",
-              "       13.907367 , 13.875599 , 13.875534 , 13.861733 , 13.8139   ,\n",
-              "       13.689401 , 13.665034 , 13.632333 , 13.6227665, 13.651935 ,\n",
-              "       13.6164665, 13.5551   , 13.503766 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.712 9.699 9.685 ... 9.584 9.591</div><input id='attrs-2a458a4b-e91c-4f24-9662-4a6f05dc33e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a458a4b-e91c-4f24-9662-4a6f05dc33e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-455ce3f4-c117-4153-b79f-fd02f0721f52' class='xr-var-data-in' type='checkbox'><label for='data-455ce3f4-c117-4153-b79f-fd02f0721f52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.712007 , 9.699074 , 9.685317 , 9.670219 , 9.65324  , 9.640334 ,\n",
-              "       9.62429  , 9.608537 , 9.594397 , 9.576526 , 9.56048  , 9.541069 ,\n",
-              "       9.524289 , 9.505187 , 9.486143 , 9.464264 , 9.44223  , 9.416344 ,\n",
-              "       9.393453 , 9.367396 , 9.339387 , 9.305754 , 9.278394 , 9.245257 ,\n",
-              "       9.218047 , 9.186557 , 9.157551 , 9.123754 , 9.09348  , 9.059697 ,\n",
-              "       9.026804 , 8.994499 , 8.95571  , 8.92198  , 8.886473 , 8.853367 ,\n",
-              "       8.813594 , 8.782229 , 8.74448  , 8.708926 , 8.674907 , 8.637604 ,\n",
-              "       8.602573 , 8.560874 , 8.524677 , 8.48187  , 8.4449835, 8.400766 ,\n",
-              "       8.365373 , 8.322662 , 8.28713  , 8.248011 , 8.21333  , 8.178404 ,\n",
-              "       8.14623  , 8.109827 , 8.072543 , 8.039574 , 7.9980125, 7.963136 ,\n",
-              "       7.926347 , 7.8918767, 7.8531103, 7.824253 , 7.7964296, 7.7649107,\n",
-              "       7.7383432, 7.7130632, 7.6841335, 7.65858  , 7.633538 , 7.609003 ,\n",
-              "       7.592057 , 7.573613 , 7.5537395, 7.533187 , 7.5146875, 7.497063 ,\n",
-              "       7.48256  , 7.472039 , 7.454433 , 7.4437304, 7.4306965, 7.422947 ,\n",
-              "       7.4138427, 7.4127507, 7.407543 , 7.4021435, 7.40189  , 7.4006333,\n",
-              "       7.401152 , 7.3961263, 7.4010367, 7.4031525, 7.414211 , 7.4199567,\n",
-              "       7.4243903, 7.4346695, 7.4523306, 7.4677606, 7.47785  , 7.4955306,\n",
-              "       7.507134 , 7.52034  , 7.540297 , 7.5592594, 7.583973 , 7.6029363,\n",
-              "       7.623276 , 7.6508303, 7.67267  , 7.6968994, 7.7202635, 7.7465167,\n",
-              "       7.7706933, 7.795326 , 7.820833 , 7.8509097, 7.8781033, 7.9117236,\n",
-              "       7.9442267, 7.9752865, 8.007596 , 8.042522 , 8.077546 , 8.108029 ,\n",
-              "       8.1417265, 8.17497  , 8.210143 , 8.243827 , 8.281553 , 8.317181 ,\n",
-              "       8.35565  , 8.390187 , 8.42794  , 8.465797 , 8.50642  , 8.54146  ,\n",
-              "       8.581511 , 8.618688 , 8.65223  , 8.69048  , 8.72701  , 8.758973 ,\n",
-              "       8.795943 , 8.832204 , 8.870703 , 8.8993225, 8.936847 , 8.969017 ,\n",
-              "       9.001257 , 9.036253 , 9.063161 , 9.095617 , 9.1287565, 9.157017 ,\n",
-              "       9.187741 , 9.216694 , 9.244237 , 9.274386 , 9.300383 , 9.32181  ,\n",
-              "       9.35049  , 9.37106  , 9.393917 , 9.40948  , 9.428807 , 9.441616 ,\n",
-              "       9.459889 , 9.474596 , 9.488944 , 9.495657 , 9.510249 , 9.520061 ,\n",
-              "       9.530423 , 9.537613 , 9.546494 , 9.554764 , 9.561024 , 9.57158  ,\n",
-              "       9.579874 , 9.583782 , 9.591404 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.534 3.565 3.547 ... -197.1 -197.1</div><input id='attrs-6675907a-764b-4c6d-9b2b-1bfce60030a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6675907a-764b-4c6d-9b2b-1bfce60030a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f35fda8-28ce-4760-a49c-abf3e3fe2445' class='xr-var-data-in' type='checkbox'><label for='data-7f35fda8-28ce-4760-a49c-abf3e3fe2445' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.53383756e+00,  3.56493616e+00,  3.54652500e+00,\n",
-              "         3.60746217e+00,  3.56795740e+00,  3.58543777e+00,\n",
-              "         3.56357908e+00,  3.57667398e+00,  3.60632110e+00,\n",
-              "         3.56949067e+00,  3.57479167e+00,  3.59876704e+00,\n",
-              "         3.60281253e+00,  3.59981132e+00,  3.59064198e+00,\n",
-              "         3.59985065e+00,  3.58579111e+00,  3.57373047e+00,\n",
-              "         3.56544852e+00,  3.58114982e+00,  3.58714032e+00,\n",
-              "         3.55944085e+00,  3.58880138e+00,  3.53171992e+00,\n",
-              "         3.53358531e+00,  3.49057865e+00,  3.49481201e+00,\n",
-              "         3.49612260e+00,  3.45677161e+00,  3.42318940e+00,\n",
-              "         3.46062589e+00,  3.41326380e+00,  3.41770315e+00,\n",
-              "         3.41921234e+00,  3.43796873e+00,  3.42292237e+00,\n",
-              "         3.41392589e+00,  3.38415337e+00,  3.38218069e+00,\n",
-              "         3.43648434e+00,  3.38592434e+00,  3.34218621e+00,\n",
-              "         3.38532996e+00,  3.37350154e+00,  3.37754154e+00,\n",
-              "         3.35902476e+00,  3.34339881e+00,  3.35676146e+00,\n",
-              "         3.35692763e+00,  3.32560086e+00,  3.27195191e+00,\n",
-              "         3.19083190e+00,  2.95882225e+00,  2.40057945e+00,\n",
-              "         2.16090226e+00,  1.92318320e+00,  1.78795314e+00,\n",
-              "         1.62216699e+00,  1.56376326e+00,  1.46418226e+00,\n",
-              "...\n",
-              "        -1.96982498e+02, -1.97061203e+02, -1.96969849e+02,\n",
-              "        -1.97076538e+02, -1.97007187e+02, -1.97028168e+02,\n",
-              "        -1.97006195e+02, -1.97027512e+02, -1.97029175e+02,\n",
-              "        -1.96980515e+02, -1.97019150e+02, -1.96939850e+02,\n",
-              "        -1.96979843e+02, -1.97032181e+02, -1.97038193e+02,\n",
-              "        -1.96942871e+02, -1.96959534e+02, -1.96986191e+02,\n",
-              "        -1.97056839e+02, -1.97049850e+02, -1.97026855e+02,\n",
-              "        -1.97041199e+02, -1.97016525e+02, -1.97084503e+02,\n",
-              "        -1.97077499e+02, -1.97032166e+02, -1.97061508e+02,\n",
-              "        -1.97079834e+02, -1.97101852e+02, -1.97092865e+02,\n",
-              "        -1.97062164e+02, -1.97118851e+02, -1.97044510e+02,\n",
-              "        -1.97032166e+02, -1.97112183e+02, -1.96995499e+02,\n",
-              "        -1.97103195e+02, -1.97131195e+02, -1.97147202e+02,\n",
-              "        -1.97079849e+02, -1.97053513e+02, -1.97125504e+02,\n",
-              "        -1.97132843e+02, -1.97114838e+02, -1.97074829e+02,\n",
-              "        -1.97085831e+02, -1.97115845e+02, -1.97091156e+02,\n",
-              "        -1.97044174e+02, -1.97063873e+02, -1.97090515e+02,\n",
-              "        -1.97129501e+02, -1.97007523e+02, -1.97017838e+02,\n",
-              "        -1.97019165e+02, -1.97129166e+02, -1.97051849e+02,\n",
-              "        -1.97113190e+02, -1.97104507e+02, -1.97053848e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.261 -1.263 -1.26 ... 9.714 9.712</div><input id='attrs-5660f62f-4bc7-4437-9336-fc2125a12873' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5660f62f-4bc7-4437-9336-fc2125a12873' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae198a49-8e0c-43e2-84cf-05b46db4c47b' class='xr-var-data-in' type='checkbox'><label for='data-ae198a49-8e0c-43e2-84cf-05b46db4c47b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2608225 , -1.2632061 , -1.2602278 , -1.2620431 , -1.2610018 ,\n",
-              "        -1.2613868 , -1.2610568 , -1.2621608 , -1.2631991 , -1.2639122 ,\n",
-              "        -1.2618904 , -1.258823  , -1.2596216 , -1.258702  , -1.2583325 ,\n",
-              "        -1.2578007 , -1.2583394 , -1.2576339 , -1.2579147 , -1.2534361 ,\n",
-              "        -1.2568558 , -1.2559422 , -1.2557042 , -1.2571892 , -1.2556211 ,\n",
-              "        -1.2540294 , -1.2548382 , -1.2532483 , -1.2525105 , -1.2511989 ,\n",
-              "        -1.2522614 , -1.2561746 , -1.2530836 , -1.2531874 , -1.2519903 ,\n",
-              "        -1.2518218 , -1.2511966 , -1.2544034 , -1.2561861 , -1.2536184 ,\n",
-              "        -1.2504781 , -1.2535783 , -1.2560768 , -1.2559297 , -1.257113  ,\n",
-              "        -1.2519604 , -1.2501055 , -1.2515699 , -1.2483989 , -1.2508361 ,\n",
-              "        -1.2479837 , -1.2430646 , -1.2314413 , -1.2083213 , -1.1935245 ,\n",
-              "        -1.1852744 , -1.1747023 , -1.1735051 , -1.1686733 , -1.1616861 ,\n",
-              "        -1.1579937 , -1.1570411 , -1.1541711 , -1.1540251 , -1.1536013 ,\n",
-              "        -1.1516274 , -1.147889  , -1.1449583 , -1.1449729 , -1.14322   ,\n",
-              "        -1.1418625 , -1.1392603 , -1.1384715 , -1.136121  , -1.1344364 ,\n",
-              "        -1.132704  , -1.1329331 , -1.1329101 , -1.130972  , -1.1331373 ,\n",
-              "        -1.1304799 , -1.1283726 , -1.1294136 , -1.132449  , -1.1283008 ,\n",
-              "        -1.1297874 , -1.1291262 , -1.1277461 , -1.1284393 , -1.1265595 ,\n",
-              "        -1.1229205 , -1.1222471 , -1.1207403 , -1.1196108 , -1.1196036 ,\n",
-              "        -1.1186633 , -1.1185873 , -1.1168755 , -1.1150851 , -1.1146263 ,\n",
-              "...\n",
-              "         9.716357  ,  9.717475  ,  9.7163725 ,  9.718897  ,  9.715463  ,\n",
-              "         9.711327  ,  9.713148  ,  9.713642  ,  9.712924  ,  9.715384  ,\n",
-              "         9.715974  ,  9.716772  ,  9.718337  ,  9.714473  ,  9.717203  ,\n",
-              "         9.714393  ,  9.71425   ,  9.713914  ,  9.711695  ,  9.708918  ,\n",
-              "         9.711455  ,  9.70863   ,  9.711487  ,  9.71433   ,  9.710337  ,\n",
-              "         9.711295  ,  9.7138815 ,  9.70997   ,  9.713227  ,  9.717092  ,\n",
-              "         9.713419  ,  9.719773  ,  9.71832   ,  9.716453  ,  9.717108  ,\n",
-              "         9.715989  ,  9.7141695 ,  9.711615  ,  9.71741   ,  9.715814  ,\n",
-              "         9.71428   ,  9.717347  ,  9.712972  ,  9.7162285 ,  9.713419  ,\n",
-              "         9.712238  ,  9.714361  ,  9.716245  ,  9.713563  ,  9.718161  ,\n",
-              "         9.713195  ,  9.715207  ,  9.71591   ,  9.713914  ,  9.7165    ,\n",
-              "         9.7136755 ,  9.717347  ,  9.719837  ,  9.718688  ,  9.713898  ,\n",
-              "         9.717618  ,  9.715941  ,  9.710864  ,  9.71433   ,  9.717777  ,\n",
-              "         9.716262  ,  9.716644  ,  9.719199  ,  9.718911  ,  9.715112  ,\n",
-              "         9.714042  ,  9.710752  ,  9.714153  ,  9.716006  ,  9.715191  ,\n",
-              "         9.716421  ,  9.718177  ,  9.717875  ,  9.718033  ,  9.713227  ,\n",
-              "         9.714537  ,  9.714841  ,  9.714376  ,  9.71848   ,  9.7165    ,\n",
-              "         9.715717  ,  9.711741  ,  9.710465  ,  9.712621  ,  9.71337   ,\n",
-              "         9.715622  ,  9.715989  ,  9.711598  ,  9.71219   ,  9.71021   ,\n",
-              "         9.712366  ,  9.7141695 ,  9.711616  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.48 16.48 16.48 ... 16.26 16.26</div><input id='attrs-cc0755d1-eac2-491e-928f-87d0c37109ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc0755d1-eac2-491e-928f-87d0c37109ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-567a13b3-7eb6-4554-92ac-a39a2c00f691' class='xr-var-data-in' type='checkbox'><label for='data-567a13b3-7eb6-4554-92ac-a39a2c00f691' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.481167, 16.478   , 16.478   , 16.477167, 16.481   , 16.484167,\n",
-              "       16.477167, 16.479834, 16.479166, 16.482666, 16.478167, 16.481668,\n",
-              "       16.479668, 16.475334, 16.479668, 16.479   , 16.474   , 16.475334,\n",
-              "       16.477667, 16.473167, 16.472168, 16.468334, 16.472334, 16.471169,\n",
-              "       16.466002, 16.476501, 16.464167, 16.464666, 16.462667, 16.460833,\n",
-              "       16.462164, 16.460167, 16.459501, 16.459002, 16.454332, 16.449501,\n",
-              "       16.444332, 16.4325  , 16.422   , 16.412834, 16.400167, 16.397667,\n",
-              "       16.385334, 16.382334, 16.380499, 16.383333, 16.381332, 16.379833,\n",
-              "       16.382   , 16.379168, 16.380499, 16.378834, 16.374832, 16.384499,\n",
-              "       16.374166, 16.373333, 16.377167, 16.376   , 16.38    , 16.370333,\n",
-              "       16.376501, 16.382   , 16.3775  , 16.3815  , 16.378   , 16.3785  ,\n",
-              "       16.378   , 16.383667, 16.374668, 16.3845  , 16.353   , 16.372334,\n",
-              "       16.377668, 16.378168, 16.372334, 16.372665, 16.363167, 16.367167,\n",
-              "       16.372833, 16.363333, 16.355165, 16.367002, 16.368334, 16.372665,\n",
-              "       16.360834, 16.367834, 16.367   , 16.360834, 16.358   , 16.342833,\n",
-              "       16.341167, 16.328001, 16.317   , 16.305332, 16.295   , 16.2915  ,\n",
-              "       16.279333, 16.281166, 16.280834, 16.276167, 16.279333, 16.279333,\n",
-              "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
-              "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
-              "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
-              "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
-              "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
-              "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
-              "       16.275   , 16.278334, 16.276834, 16.271   , 16.2725  , 16.27    ,\n",
-              "       16.265165, 16.268333, 16.268   , 16.264   , 16.268   , 16.268   ,\n",
-              "       16.264   , 16.268   , 16.268   , 16.264   , 16.268   , 16.268   ,\n",
-              "       16.264   , 16.268   , 16.268   , 16.264   , 16.268   , 16.263334,\n",
-              "       16.258333, 16.262335, 16.262169, 16.2575  , 16.261   , 16.260332,\n",
-              "       16.25633 , 16.257998, 16.258333, 16.253334, 16.257998, 16.257332,\n",
-              "       16.253166, 16.256998, 16.256998, 16.252666, 16.256998, 16.256998,\n",
-              "       16.252666, 16.256998, 16.256998], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-0ea2a008-c7ed-404d-82e3-22b0a9cf1efd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ea2a008-c7ed-404d-82e3-22b0a9cf1efd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bbdbfa5-d964-49a4-9ac2-26a5234ec875' class='xr-var-data-in' type='checkbox'><label for='data-7bbdbfa5-d964-49a4-9ac2-26a5234ec875' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20.      , 20.      , 20.      , 20.043333, 20.25    , 20.25    ,\n",
-              "       20.25    , 20.25    , 20.263334, 20.446667, 20.5     , 20.5     ,\n",
-              "       20.5     , 20.5     , 20.5     , 20.5     , 20.5     , 20.5     ,\n",
-              "       20.5     , 20.5     , 20.5     , 20.5     , 20.5     , 20.31    ,\n",
-              "       20.25    , 20.25    , 20.163334, 20.      , 20.      , 20.      ,\n",
-              "       19.766666, 19.75    , 19.75    , 19.726667, 19.553333, 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.606667, 19.746666, 19.75    , 19.75    , 19.75    ,\n",
-              "       19.75    , 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
-              "       19.75    , 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
-              "       19.556667, 19.5     , 19.5     , 19.5     , 19.626667, 19.75    ,\n",
-              "       19.75    , 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
-              "       19.75    , 19.75    , 19.75    , 19.54    , 19.413334, 19.233334,\n",
-              "       19.      , 18.91    , 18.75    , 18.746666, 18.5     , 18.5     ,\n",
-              "       18.5     , 18.5     , 18.366667, 18.25    , 18.25    , 18.25    ,\n",
-              "       18.25    , 18.2     , 18.      , 18.      , 18.      , 18.      ,\n",
-              "       17.873333, 17.75    , 17.75    , 17.75    , 17.863333, 18.      ,\n",
-              "       18.      , 18.17    , 18.25    , 18.44    , 18.513334, 18.75    ,\n",
-              "       18.783333, 19.      , 19.      , 19.196667, 19.25    , 19.25    ,\n",
-              "       19.25    , 19.433332, 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.53    ,\n",
-              "       19.696667, 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
-              "       19.75    , 19.75    , 19.71    , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-404cc268-b828-4be2-9f0e-f5ea092a8e62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-404cc268-b828-4be2-9f0e-f5ea092a8e62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae2d2613-dfc7-49eb-90ec-4cc77daf5d9c' class='xr-var-data-in' type='checkbox'><label for='data-ae2d2613-dfc7-49eb-90ec-4cc77daf5d9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-07b270e1-76b1-4d7a-bb68-bd0976704d2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07b270e1-76b1-4d7a-bb68-bd0976704d2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f5c59a0-1323-4827-b779-7dc9bc5a7064' class='xr-var-data-in' type='checkbox'><label for='data-9f5c59a0-1323-4827-b779-7dc9bc5a7064' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355693e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "...\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355693e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2358 0.266 0.3007 ... nan nan nan</div><input id='attrs-80f9429f-e37d-463a-b260-bc3dce95522f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80f9429f-e37d-463a-b260-bc3dce95522f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc2060a2-f49a-4655-9ed5-6966a6b9a0c8' class='xr-var-data-in' type='checkbox'><label for='data-dc2060a2-f49a-4655-9ed5-6966a6b9a0c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.23582192,  0.2660148 ,  0.30069652, ..., -0.179669  ,\n",
-              "         -0.18383324, -0.17606331],\n",
-              "        [ 0.25633767,  0.28601688,  0.3117814 , ..., -0.19580415,\n",
-              "         -0.19696543, -0.20011853],\n",
-              "        [ 0.26504937,  0.28428233,  0.31169885, ..., -0.2103943 ,\n",
-              "         -0.21627884, -0.21236075],\n",
-              "        ...,\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan],\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan],\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan]],\n",
-              "\n",
-              "       [[ 0.02619691,  0.04430203,  0.04002393, ...,  0.05932099,\n",
-              "          0.06400961,  0.07232199],\n",
-              "        [ 0.06181316,  0.07802678,  0.06236525, ...,  0.0768801 ,\n",
-              "          0.08184617,  0.0892335 ],\n",
-              "        [ 0.08793733,  0.10133781,  0.08016948, ...,  0.08136398,\n",
-              "          0.08639657,  0.10118616],\n",
-              "...\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan],\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan],\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan]],\n",
-              "\n",
-              "       [[-0.00417333, -0.01056667, -0.01045333, ..., -0.02584564,\n",
-              "         -0.02752333, -0.02575667],\n",
-              "        [ 0.01358667,  0.00760333,  0.00634667, ..., -0.01625333,\n",
-              "         -0.01776333, -0.01595   ],\n",
-              "        [ 0.01556187,  0.00811371,  0.00740333, ..., -0.00842   ,\n",
-              "         -0.01147667, -0.01189667],\n",
-              "        ...,\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan],\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan],\n",
-              "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>79.95 80.28 80.69 ... nan nan nan</div><input id='attrs-835a0dc2-bd02-46dc-b855-d47c12c9c279' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-835a0dc2-bd02-46dc-b855-d47c12c9c279' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d2b5c66-6ad4-4add-af31-56e1b8ffedc7' class='xr-var-data-in' type='checkbox'><label for='data-1d2b5c66-6ad4-4add-af31-56e1b8ffedc7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[79.94666 , 80.28167 , 80.68667 , ..., 81.721664, 81.80334 ,\n",
-              "         81.68667 ],\n",
-              "        [75.535   , 76.043335, 76.66167 , ..., 77.62666 , 77.73    ,\n",
-              "         77.528336],\n",
-              "        [72.81167 , 73.391304, 74.26833 , ..., 74.525   , 74.39667 ,\n",
-              "         74.253334],\n",
-              "        ...,\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan],\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan],\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan]],\n",
-              "\n",
-              "       [[80.706665, 80.93833 , 81.255   , ..., 82.19833 , 82.225   ,\n",
-              "         82.17    ],\n",
-              "        [76.80666 , 76.996666, 77.44833 , ..., 78.38333 , 78.41167 ,\n",
-              "         78.06    ],\n",
-              "        [74.31834 , 74.64667 , 75.903336, ..., 75.16333 , 75.26833 ,\n",
-              "         74.875   ],\n",
-              "...\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan],\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan],\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan]],\n",
-              "\n",
-              "       [[80.185   , 80.55    , 80.885   , ..., 81.888336, 81.945   ,\n",
-              "         81.85833 ],\n",
-              "        [75.78    , 76.291664, 76.76833 , ..., 77.97667 , 78.03833 ,\n",
-              "         77.87666 ],\n",
-              "        [72.67725 , 73.55666 , 74.44334 , ..., 74.75833 , 74.665   ,\n",
-              "         74.47    ],\n",
-              "        ...,\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan],\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan],\n",
-              "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>92.59 92.61 92.47 ... 0.0 0.0 0.0</div><input id='attrs-286a83dd-1148-46cb-9de7-e2ae8ece0f95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-286a83dd-1148-46cb-9de7-e2ae8ece0f95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3d168c9-4293-4ab6-a0b1-2e9f011125be' class='xr-var-data-in' type='checkbox'><label for='data-d3d168c9-4293-4ab6-a0b1-2e9f011125be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[92.58667 , 92.613335, 92.47    , ..., 91.933334, 91.96667 ,\n",
-              "         92.06333 ],\n",
-              "        [96.236664, 96.526665, 96.206665, ..., 96.00667 , 96.3     ,\n",
-              "         96.03333 ],\n",
-              "        [97.166664, 96.246666, 95.94    , ..., 96.45333 , 97.206665,\n",
-              "         97.263336],\n",
-              "        ...,\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ],\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ],\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ]],\n",
-              "\n",
-              "       [[93.333336, 93.61    , 93.31333 , ..., 91.6     , 91.49    ,\n",
-              "         91.14667 ],\n",
-              "        [96.57    , 96.39    , 96.12666 , ..., 96.27    , 96.56333 ,\n",
-              "         96.44334 ],\n",
-              "        [97.37666 , 97.09333 , 96.87666 , ..., 96.17333 , 97.30334 ,\n",
-              "         97.32667 ],\n",
-              "...\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ],\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ],\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ]],\n",
-              "\n",
-              "       [[92.78333 , 93.223335, 92.63    , ..., 92.22    , 92.34    ,\n",
-              "         92.61    ],\n",
-              "        [96.75    , 96.34333 , 96.34    , ..., 96.53    , 96.69    ,\n",
-              "         96.27333 ],\n",
-              "        [96.346664, 97.05    , 97.24    , ..., 96.66333 , 96.89333 ,\n",
-              "         97.41    ],\n",
-              "        ...,\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ],\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ],\n",
-              "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05743 0.05697 ... 0.9937 0.9937</div><input id='attrs-f571b318-e797-496a-b128-02af5fbd8d46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f571b318-e797-496a-b128-02af5fbd8d46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cb3b78f-ae14-4dba-b173-83f31056980d' class='xr-var-data-in' type='checkbox'><label for='data-2cb3b78f-ae14-4dba-b173-83f31056980d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[ 5.74317947e-02,  5.69723397e-02,  5.82992062e-02, ...,\n",
-              "          5.81927113e-02,  5.85613623e-02,  5.85512854e-02],\n",
-              "        [ 9.98300374e-01,  9.98323679e-01,  9.98248577e-01, ...,\n",
-              "          9.98254061e-01,  9.98234987e-01,  9.98234689e-01],\n",
-              "        [-1.05408905e-03, -1.34927384e-03, -1.13080605e-03, ...,\n",
-              "         -8.26976204e-04, -8.38353124e-04, -8.28948745e-04]],\n",
-              "\n",
-              "       [[-9.89950538e-01, -9.89953220e-01, -9.89922285e-01, ...,\n",
-              "         -9.91938353e-01, -9.91944909e-01, -9.91939366e-01],\n",
-              "        [ 5.68153895e-02,  5.63202463e-02,  5.76671995e-02, ...,\n",
-              "          5.77317439e-02,  5.80984168e-02,  5.80891557e-02],\n",
-              "        [-1.29125342e-01, -1.29299909e-01, -1.28952131e-01, ...,\n",
-              "         -1.12354711e-01, -1.12130061e-01, -1.12175331e-01]],\n",
-              "\n",
-              "       [[-1.28852651e-01, -1.29014045e-01, -1.28667876e-01, ...,\n",
-              "         -1.12116747e-01, -1.11889169e-01, -1.11934915e-01],\n",
-              "        [ 8.45469162e-03,  8.69723968e-03,  8.63243733e-03, ...,\n",
-              "          7.35414820e-03,  7.39390310e-03,  7.38611678e-03],\n",
-              "        [ 9.91627276e-01,  9.91604209e-01,  9.91649806e-01, ...,\n",
-              "          9.93667483e-01,  9.93692815e-01,  9.93687749e-01]]],\n",
-              "      shape=(3, 3, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.000807 0.0008203 ... -0.002598</div><input id='attrs-f5f72cc0-816a-4bd5-91a4-3554e0011ccd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5f72cc0-816a-4bd5-91a4-3554e0011ccd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53b865f0-230e-4714-ba59-680e4cf00b61' class='xr-var-data-in' type='checkbox'><label for='data-53b865f0-230e-4714-ba59-680e4cf00b61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00080699,  0.00082028,  0.00085015,  0.0008093 ,  0.00077764,\n",
-              "         0.00076247,  0.00084005,  0.00080013,  0.00073725,  0.00069978,\n",
-              "         0.00068869,  0.000708  ,  0.00070397,  0.00064101,  0.0007507 ,\n",
-              "         0.00071757,  0.00060209,  0.00062077,  0.00062501,  0.0005675 ,\n",
-              "         0.00054653,  0.00055442,  0.00072552,  0.00056834,  0.00065792,\n",
-              "         0.00086152,  0.00080709,  0.00096736,  0.00085591,  0.00094166,\n",
-              "         0.00090368,  0.00095632,  0.00094047,  0.00091472,  0.00092718,\n",
-              "         0.00097144,  0.00090388,  0.00098592,  0.00099217,  0.00104627,\n",
-              "         0.00101094,  0.00108953,  0.00094413,  0.0010326 ,  0.00105636,\n",
-              "         0.00098609,  0.00101084,  0.00101927,  0.00101796,  0.00096841,\n",
-              "         0.00102785,  0.00113951,  0.00098156,  0.00111134,  0.00100472,\n",
-              "         0.00104199,  0.00104198,  0.00100612,  0.00103637,  0.00099132,\n",
-              "         0.00102272,  0.00102712,  0.00100425,  0.0010416 ,  0.00097589,\n",
-              "         0.00090605,  0.00108258,  0.00090169,  0.00096689,  0.00081777,\n",
-              "         0.0009423 ,  0.00087804,  0.00102183,  0.00083111,  0.0009242 ,\n",
-              "         0.00094747,  0.00110261,  0.00097415,  0.00097692,  0.00113317,\n",
-              "         0.00105406,  0.00104935,  0.00102722,  0.00093711,  0.00103575,\n",
-              "         0.00095867,  0.0009583 ,  0.00104538,  0.00100466,  0.00095809,\n",
-              "         0.00101549,  0.00097118,  0.00090704,  0.00094464,  0.00096578,\n",
-              "         0.00105039,  0.00093799,  0.00100518,  0.00088882,  0.00095506,\n",
-              "...\n",
-              "        -0.00263344, -0.00258787, -0.00256183, -0.00256183, -0.00250324,\n",
-              "        -0.00268227, -0.00268227, -0.00259113, -0.00262693, -0.00257485,\n",
-              "        -0.00259113, -0.00252602, -0.00267576, -0.00253904, -0.00256509,\n",
-              "        -0.00242511, -0.00249998, -0.00250975, -0.00252277, -0.00261066,\n",
-              "        -0.00257485, -0.00265298, -0.00264647, -0.00261391, -0.00260089,\n",
-              "        -0.00270181, -0.0027604 , -0.00278318, -0.00288084, -0.00274412,\n",
-              "        -0.00280272, -0.00289386, -0.0030729 , -0.0029492 , -0.0029199 ,\n",
-              "        -0.00302407, -0.00299803, -0.00296222, -0.00288084, -0.00299477,\n",
-              "        -0.00304034, -0.00298175, -0.00296222, -0.00296873, -0.00305337,\n",
-              "        -0.00302081, -0.00299803, -0.00304034, -0.0030729 , -0.0029199 ,\n",
-              "        -0.00295571, -0.00287433, -0.00287433, -0.0028548 , -0.00275714,\n",
-              "        -0.00271157, -0.0027018 , -0.00278644, -0.0026074 , -0.00264972,\n",
-              "        -0.00273761, -0.00259764, -0.00258787, -0.00256834, -0.00263344,\n",
-              "        -0.00255207, -0.00265623, -0.00261391, -0.00255207, -0.00249673,\n",
-              "        -0.00258787, -0.00256183, -0.00253579, -0.00264321, -0.00268553,\n",
-              "        -0.00255858, -0.00264321, -0.00262693, -0.0025423 , -0.00256834,\n",
-              "        -0.00263345, -0.00258136, -0.00250975, -0.00261717, -0.00264321,\n",
-              "        -0.00260741, -0.00261066, -0.00250649, -0.00248371, -0.0026074 ,\n",
-              "        -0.00260415, -0.00260415, -0.00250324, -0.00259764, -0.002513  ,\n",
-              "        -0.00256509, -0.00253904, -0.00259764]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-45a6a8d5-3fef-4a8f-866f-3a995f2a7b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45a6a8d5-3fef-4a8f-866f-3a995f2a7b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0994d618-54bb-41b5-a0d3-170b89de0800' class='xr-var-data-in' type='checkbox'><label for='data-0994d618-54bb-41b5-a0d3-170b89de0800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245074 ,  0.624316  ,  0.6248463 ,  0.6245108 ,  0.6247046 ,\n",
-              "         0.62467265,  0.62460417,  0.62476116,  0.6245991 ,  0.6246464 ,\n",
-              "         0.624686  ,  0.6249721 ,  0.6249648 ,  0.6250649 ,  0.62494904,\n",
-              "         0.62500936,  0.6250884 ,  0.6250855 ,  0.6253171 ,  0.6254035 ,\n",
-              "         0.6253574 ,  0.6254711 ,  0.6251542 ,  0.6249595 ,  0.62512624,\n",
-              "         0.62510735,  0.62479544,  0.6250753 ,  0.624999  ,  0.6248949 ,\n",
-              "         0.6248343 ,  0.6245227 ,  0.6248399 ,  0.624795  ,  0.6250311 ,\n",
-              "         0.62494355,  0.6249766 ,  0.6244582 ,  0.62417775,  0.6244494 ,\n",
-              "         0.6248647 ,  0.6245337 ,  0.62416995,  0.6242157 ,  0.62425923,\n",
-              "         0.6247039 ,  0.6248129 ,  0.6246122 ,  0.6248121 ,  0.6246928 ,\n",
-              "         0.6245991 ,  0.6248212 ,  0.6247573 ,  0.6243741 ,  0.6243043 ,\n",
-              "         0.6244282 ,  0.6243682 ,  0.6240724 ,  0.62418926,  0.6241515 ,\n",
-              "         0.6239765 ,  0.62391806,  0.62415963,  0.62387216,  0.6239287 ,\n",
-              "         0.62390673,  0.6236841 ,  0.62378633,  0.6236643 ,  0.6235926 ,\n",
-              "         0.62364197,  0.6237233 ,  0.62374234,  0.62387735,  0.62402254,\n",
-              "         0.62393117,  0.62393445,  0.62373453,  0.62396324,  0.62340415,\n",
-              "         0.62373024,  0.62379295,  0.62355286,  0.6231889 ,  0.62350035,\n",
-              "         0.6232749 ,  0.6232579 ,  0.6233172 ,  0.6232117 ,  0.62328154,\n",
-              "         0.6234021 ,  0.62359464,  0.6235321 ,  0.62355274,  0.62340564,\n",
-              "         0.6233349 ,  0.6232732 ,  0.62326413,  0.62335676,  0.6233057 ,\n",
-              "...\n",
-              "         0.77982664,  0.7798403 ,  0.77980125,  0.7798833 ,  0.7798343 ,\n",
-              "         0.77974474,  0.7795985 ,  0.77965385,  0.77964175,  0.7797572 ,\n",
-              "         0.7798193 ,  0.77987343,  0.77987754,  0.7798154 ,  0.7798561 ,\n",
-              "         0.77976704,  0.77973217,  0.77971977,  0.77958   ,  0.7794924 ,\n",
-              "         0.77951276,  0.77934176,  0.77977145,  0.77983665,  0.7796758 ,\n",
-              "         0.77951133,  0.77975136,  0.7796223 ,  0.7795445 ,  0.7798783 ,\n",
-              "         0.7796854 ,  0.78009176,  0.77983886,  0.77976817,  0.7798124 ,\n",
-              "         0.77966744,  0.77980477,  0.77957195,  0.7797228 ,  0.7795519 ,\n",
-              "         0.77965647,  0.7795511 ,  0.7795689 ,  0.77951413,  0.77942085,\n",
-              "         0.7791843 ,  0.7793043 ,  0.7793574 ,  0.7790914 ,  0.77922   ,\n",
-              "         0.7788722 ,  0.77907705,  0.7790985 ,  0.778842  ,  0.7789314 ,\n",
-              "         0.77889466,  0.7789852 ,  0.7792381 ,  0.77916646,  0.7788889 ,\n",
-              "         0.77894676,  0.7789039 ,  0.7785506 ,  0.7786492 ,  0.7788344 ,\n",
-              "         0.7786969 ,  0.77884805,  0.7789083 ,  0.77894056,  0.7786727 ,\n",
-              "         0.77858806,  0.7783844 ,  0.7785528 ,  0.7787677 ,  0.77864134,\n",
-              "         0.77870125,  0.7787834 ,  0.7788154 ,  0.7788442 ,  0.7785659 ,\n",
-              "         0.7785521 ,  0.77869254,  0.77849793,  0.7787016 ,  0.77872854,\n",
-              "         0.77860415,  0.7784391 ,  0.7783881 ,  0.7785563 ,  0.77852774,\n",
-              "         0.77856874,  0.77847373,  0.7784385 ,  0.7782882 ,  0.778279  ,\n",
-              "         0.778462  ,  0.77835196,  0.77835387]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-80807e6c-4ad1-4cb5-a7b4-432d16d0cb72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80807e6c-4ad1-4cb5-a7b4-432d16d0cb72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d10a642e-69bf-4c03-a5c3-06929c1a6332' class='xr-var-data-in' type='checkbox'><label for='data-d10a642e-69bf-4c03-a5c3-06929c1a6332' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.800002 , 12.800002 , 12.800002 , 12.800002 , 12.800002 ,\n",
-              "       12.966666 , 13.       , 13.050666 , 13.199998 , 13.199998 ,\n",
-              "       13.199998 , 13.199998 , 13.199998 , 13.199998 , 13.199998 ,\n",
-              "       13.199998 , 13.199998 , 13.199998 , 13.176665 , 13.       ,\n",
-              "       13.       , 12.941335 , 12.800002 , 12.631333 , 12.598    ,\n",
-              "       12.411335 , 12.400001 , 12.230666 , 12.199998 , 12.119332 ,\n",
-              "       12.       , 12.       , 12.       , 11.957999 , 11.800002 ,\n",
-              "       11.800002 , 11.800002 , 11.800002 , 11.800002 , 11.800002 ,\n",
-              "       11.800002 , 11.800002 , 11.800002 , 11.800002 , 11.834001 ,\n",
-              "       11.999333 , 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.104667 , 12.193999 , 12.199998 , 12.199998 ,\n",
-              "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
-              "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.086667 ,\n",
-              "       12.113999 , 12.192665 , 12.032667 , 12.078666 , 12.03     ,\n",
-              "       12.       , 12.111999 , 12.199998 , 12.199998 , 12.199998 ,\n",
-              "       12.202665 , 12.219333 , 12.274    , 12.367333 , 12.203999 ,\n",
-              "       12.217332 , 12.199998 , 12.199998 , 12.119332 , 11.845334 ,\n",
-              "       11.496667 , 11.219998 , 11.       , 10.952001 , 10.800002 ,\n",
-              "       10.800002 , 10.800002 , 10.800002 , 10.751334 , 10.599999 ,\n",
-              "       10.599999 , 10.599999 , 10.599999 , 10.5546665, 10.400001 ,\n",
-              "       10.400001 , 10.334666 , 10.2      , 10.2      , 10.191999 ,\n",
-              "       10.029333 , 10.       , 10.009334 , 10.198667 , 10.243333 ,\n",
-              "       10.415334 , 10.599999 , 10.772668 , 11.       , 11.186666 ,\n",
-              "       11.398002 , 11.528    , 11.599999 , 11.746667 , 11.800002 ,\n",
-              "       11.876667 , 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.       , 12.000667 , 12.006    ,\n",
-              "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.001333 , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.024667 , 12.173999 , 12.199998 ,\n",
-              "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
-              "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
-              "       12.1119995, 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.       ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>244.0 244.0 244.0 ... 246.0 246.0</div><input id='attrs-da38a2c5-c0d7-45bf-ace1-9a9ae7cc141c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da38a2c5-c0d7-45bf-ace1-9a9ae7cc141c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90901631-17a9-41e8-b887-81a76cadf770' class='xr-var-data-in' type='checkbox'><label for='data-90901631-17a9-41e8-b887-81a76cadf770' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([244.04333, 243.96   , 244.04666, 244.12334, 244.30667, 244.32   ,\n",
-              "       244.04   , 243.95667, 243.72   , 243.45334, 243.46   , 243.49333,\n",
-              "       243.49667, 243.69667, 243.80333, 243.78334, 243.81667, 244.     ,\n",
-              "       244.27333, 244.39667, 244.58333, 244.73334, 244.95334, 245.     ,\n",
-              "       244.98334, 244.94   , 244.86333, 244.82666, 244.79333, 244.84666,\n",
-              "       244.76666, 244.73334, 244.82666, 244.75   , 244.86333, 244.83   ,\n",
-              "       244.84334, 244.91333, 244.96666, 245.08   , 245.25333, 245.30667,\n",
-              "       245.4    , 245.51   , 245.61333, 245.61   , 245.65666, 245.76666,\n",
-              "       245.73   , 245.79666, 245.83333, 245.85333, 245.81   , 245.85667,\n",
-              "       245.90334, 245.89333, 245.90666, 245.90334, 245.89333, 245.90666,\n",
-              "       245.93   , 245.91333, 245.91667, 245.92334, 245.92334, 245.92   ,\n",
-              "       245.96   , 245.95   , 245.94333, 245.96   , 245.94667, 245.97   ,\n",
-              "       245.95   , 245.93666, 245.93   , 245.95667, 245.94   , 245.95667,\n",
-              "       245.95   , 245.95334, 245.96666, 245.98   , 245.96666, 245.97   ,\n",
-              "       245.97   , 245.98   , 245.97333, 245.97   , 245.98666, 245.97667,\n",
-              "       245.97667, 245.98334, 245.97667, 245.99333, 245.97667, 245.98334,\n",
-              "       245.99667, 245.99667, 245.99   , 245.97333, 245.98334, 245.98666,\n",
-              "       245.97   , 245.99667, 245.98666, 245.98   , 245.85667, 245.44667,\n",
-              "       244.92334, 244.41667, 244.16333, 244.00667, 244.06   , 244.07333,\n",
-              "       243.87334, 243.77333, 243.75   , 243.69   , 243.76334, 243.79666,\n",
-              "       243.55   , 243.5    , 243.37666, 243.20334, 242.92334, 242.87666,\n",
-              "       242.83   , 242.83333, 243.07666, 243.41   , 243.88333, 244.20334,\n",
-              "       244.40334, 244.45334, 244.48666, 244.41   , 244.38   , 244.43333,\n",
-              "       244.6    , 244.72333, 244.90666, 244.9    , 244.97667, 245.03667,\n",
-              "       245.22667, 245.36   , 245.4    , 245.42   , 245.48334, 245.62666,\n",
-              "       245.67   , 245.71333, 245.75667, 245.77   , 245.84   , 245.87   ,\n",
-              "       245.87666, 245.89   , 245.87666, 245.94333, 245.93   , 245.91333,\n",
-              "       245.95667, 245.94   , 245.93   , 245.95667, 245.95   , 245.95334,\n",
-              "       245.94667, 245.96   , 245.96333, 245.97333, 245.96666, 245.96   ,\n",
-              "       245.97667, 245.99   , 245.98   , 245.98666, 245.98666, 245.98334,\n",
-              "       245.99   , 245.98334, 245.99   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.589 3.576 3.611 ... -197.1 -197.1</div><input id='attrs-2aa34c31-3361-49b7-a9c5-8ab6c267b27f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2aa34c31-3361-49b7-a9c5-8ab6c267b27f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-731bdd99-c0c2-4995-8df5-99a7b6b8cba5' class='xr-var-data-in' type='checkbox'><label for='data-731bdd99-c0c2-4995-8df5-99a7b6b8cba5' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.5890043e+00,  3.5764265e+00,  3.6108000e+00,  3.5779481e+00,\n",
-              "         3.6116719e+00,  3.5757926e+00,  3.5947008e+00,  3.6243417e+00,\n",
-              "         3.6057498e+00,  3.5963907e+00,  3.5682683e+00,  3.5830538e+00,\n",
-              "         3.6165168e+00,  3.5789921e+00,  3.5747981e+00,  3.5690453e+00,\n",
-              "         3.5796013e+00,  3.6261466e+00,  3.6063533e+00,  3.6014795e+00,\n",
-              "         3.6353865e+00,  3.6033041e+00,  3.6023455e+00,  3.5552027e+00,\n",
-              "         3.5689840e+00,  3.5028169e+00,  3.5047083e+00,  3.5002160e+00,\n",
-              "         3.4591196e+00,  3.4518204e+00,  3.4781299e+00,  3.4272485e+00,\n",
-              "         3.4080899e+00,  3.4372404e+00,  3.4341383e+00,  3.3986638e+00,\n",
-              "         3.4168522e+00,  3.4093900e+00,  3.3794670e+00,  3.3934760e+00,\n",
-              "         3.4050257e+00,  3.4207287e+00,  3.3710022e+00,  3.3771679e+00,\n",
-              "         3.3508575e+00,  3.3733418e+00,  3.3524203e+00,  3.3316767e+00,\n",
-              "         3.3258898e+00,  3.3680136e+00,  3.2842207e+00,  3.1945534e+00,\n",
-              "         3.0031645e+00,  2.4369471e+00,  2.1365104e+00,  1.9519800e+00,\n",
-              "         1.7708881e+00,  1.6470156e+00,  1.5729071e+00,  1.4625059e+00,\n",
-              "         1.3881445e+00,  1.3379683e+00,  1.3052688e+00,  1.2468668e+00,\n",
-              "         1.2023062e+00,  1.1269653e+00,  1.1022494e+00,  1.0541168e+00,\n",
-              "         1.0267943e+00,  9.7805512e-01,  9.2956728e-01,  9.2703992e-01,\n",
-              "         9.1599506e-01,  8.9450091e-01,  8.5012424e-01,  8.2220656e-01,\n",
-              "         7.9883534e-01,  7.8205061e-01,  7.3997599e-01,  7.3860037e-01,\n",
-              "...\n",
-              "        -1.9736919e+02, -1.9725951e+02, -1.9730052e+02, -1.9728384e+02,\n",
-              "        -1.9724889e+02, -1.9722919e+02, -1.9715518e+02, -1.9727151e+02,\n",
-              "        -1.9724452e+02, -1.9721884e+02, -1.9717354e+02, -1.9723384e+02,\n",
-              "        -1.9719716e+02, -1.9714218e+02, -1.9723254e+02, -1.9710817e+02,\n",
-              "        -1.9708250e+02, -1.9709784e+02, -1.9708986e+02, -1.9712950e+02,\n",
-              "        -1.9721385e+02, -1.9720052e+02, -1.9717422e+02, -1.9710918e+02,\n",
-              "        -1.9715718e+02, -1.9714487e+02, -1.9715251e+02, -1.9714418e+02,\n",
-              "        -1.9714218e+02, -1.9706715e+02, -1.9705818e+02, -1.9705084e+02,\n",
-              "        -1.9708583e+02, -1.9708820e+02, -1.9714618e+02, -1.9709253e+02,\n",
-              "        -1.9707854e+02, -1.9714384e+02, -1.9717952e+02, -1.9718620e+02,\n",
-              "        -1.9718388e+02, -1.9716652e+02, -1.9712685e+02, -1.9720485e+02,\n",
-              "        -1.9718315e+02, -1.9713019e+02, -1.9714951e+02, -1.9724086e+02,\n",
-              "        -1.9716119e+02, -1.9719583e+02, -1.9724786e+02, -1.9716052e+02,\n",
-              "        -1.9725883e+02, -1.9720285e+02, -1.9717485e+02, -1.9725154e+02,\n",
-              "        -1.9714687e+02, -1.9711821e+02, -1.9714719e+02, -1.9711786e+02,\n",
-              "        -1.9714052e+02, -1.9714351e+02, -1.9715118e+02, -1.9712117e+02,\n",
-              "        -1.9710718e+02, -1.9718983e+02, -1.9715218e+02, -1.9712483e+02,\n",
-              "        -1.9706255e+02, -1.9714519e+02, -1.9712418e+02, -1.9705786e+02,\n",
-              "        -1.9708685e+02, -1.9711749e+02, -1.9707716e+02, -1.9707919e+02,\n",
-              "        -1.9719186e+02, -1.9713850e+02, -1.9714883e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.262 -1.263 -1.26 ... 9.714 9.713</div><input id='attrs-8a8419d8-2c69-41fe-8e75-29534c04e382' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a8419d8-2c69-41fe-8e75-29534c04e382' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26ead8e4-f4aa-4e9f-9c2b-b791e066a12a' class='xr-var-data-in' type='checkbox'><label for='data-26ead8e4-f4aa-4e9f-9c2b-b791e066a12a' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2623771 , -1.2625197 , -1.2600151 , -1.2631888 , -1.2615377 ,\n",
-              "        -1.2616537 , -1.262574  , -1.2627327 , -1.2632962 , -1.2634871 ,\n",
-              "        -1.2617764 , -1.2599101 , -1.2592376 , -1.2577692 , -1.259011  ,\n",
-              "        -1.2584039 , -1.2585176 , -1.2577502 , -1.2569212 , -1.2552022 ,\n",
-              "        -1.2558354 , -1.2547812 , -1.2564313 , -1.2570257 , -1.2564919 ,\n",
-              "        -1.2552761 , -1.2551885 , -1.254373  , -1.2521615 , -1.253256  ,\n",
-              "        -1.2520183 , -1.2556639 , -1.2544196 , -1.2537049 , -1.2514051 ,\n",
-              "        -1.251719  , -1.2501887 , -1.2550075 , -1.2560523 , -1.2542702 ,\n",
-              "        -1.2513357 , -1.2537422 , -1.25587   , -1.2554086 , -1.2568508 ,\n",
-              "        -1.2513126 , -1.251984  , -1.2514541 , -1.2487588 , -1.249171  ,\n",
-              "        -1.2487395 , -1.2432916 , -1.2308283 , -1.2085198 , -1.1925628 ,\n",
-              "        -1.1850357 , -1.175814  , -1.1718632 , -1.1669031 , -1.1620393 ,\n",
-              "        -1.1585126 , -1.1573663 , -1.1550487 , -1.1537894 , -1.1520277 ,\n",
-              "        -1.1507337 , -1.1474868 , -1.1443372 , -1.1442864 , -1.1430473 ,\n",
-              "        -1.1418384 , -1.141045  , -1.1385593 , -1.1344901 , -1.134764  ,\n",
-              "        -1.1317551 , -1.1333026 , -1.1325271 , -1.1293766 , -1.1324316 ,\n",
-              "        -1.1310244 , -1.1295373 , -1.1287975 , -1.1317044 , -1.1290994 ,\n",
-              "        -1.1286312 , -1.1276112 , -1.1271322 , -1.128308  , -1.1257491 ,\n",
-              "        -1.1227695 , -1.1230273 , -1.119395  , -1.1190095 , -1.1182653 ,\n",
-              "        -1.1186818 , -1.1167871 , -1.1160123 , -1.1153725 , -1.1143075 ,\n",
-              "...\n",
-              "         9.716884  ,  9.718464  ,  9.717347  ,  9.717715  ,  9.714792  ,\n",
-              "         9.714857  ,  9.712732  ,  9.712222  ,  9.713994  ,  9.715383  ,\n",
-              "         9.71674   ,  9.716182  ,  9.717108  ,  9.716772  ,  9.715446  ,\n",
-              "         9.714425  ,  9.7126045 ,  9.713626  ,  9.7121105 ,  9.711023  ,\n",
-              "         9.711408  ,  9.709459  ,  9.712988  ,  9.7141695 ,  9.710418  ,\n",
-              "         9.713371  ,  9.713164  ,  9.711615  ,  9.713355  ,  9.714377  ,\n",
-              "         9.714473  ,  9.719391  ,  9.71805   ,  9.71583   ,  9.716421  ,\n",
-              "         9.71658   ,  9.71583   ,  9.71334   ,  9.7177305 ,  9.716677  ,\n",
-              "         9.71476   ,  9.716166  ,  9.713404  ,  9.715751  ,  9.715623  ,\n",
-              "         9.712237  ,  9.714856  ,  9.7166605 ,  9.713179  ,  9.7175865 ,\n",
-              "         9.710929  ,  9.71484   ,  9.715895  ,  9.712062  ,  9.71591   ,\n",
-              "         9.714346  ,  9.716932  ,  9.718177  ,  9.718816  ,  9.713802  ,\n",
-              "         9.717826  ,  9.715128  ,  9.711727  ,  9.713515  ,  9.718353  ,\n",
-              "         9.716836  ,  9.716278  ,  9.717762  ,  9.718656  ,  9.714761  ,\n",
-              "         9.713004  ,  9.710816  ,  9.71409   ,  9.716276  ,  9.713579  ,\n",
-              "         9.716166  ,  9.718401  ,  9.716804  ,  9.718209  ,  9.71428   ,\n",
-              "         9.714553  ,  9.715957  ,  9.7148075 ,  9.717874  ,  9.716309  ,\n",
-              "         9.715032  ,  9.7090435 ,  9.711168  ,  9.7130995 ,  9.713355  ,\n",
-              "         9.715256  ,  9.714936  ,  9.7148075 ,  9.711726  ,  9.711886  ,\n",
-              "         9.713675  ,  9.714042  ,  9.712781  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-c1489d78-e4f8-4ed9-a39b-16d34eef0607' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1489d78-e4f8-4ed9-a39b-16d34eef0607' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-979eb0b5-c11d-4941-91e7-eaed1428ac1b' class='xr-var-data-in' type='checkbox'><label for='data-979eb0b5-c11d-4941-91e7-eaed1428ac1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-52723004-2e23-4fbc-95bf-e29b29e5059f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52723004-2e23-4fbc-95bf-e29b29e5059f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dabc074-db2c-43f3-a21f-ba8e89d48e8c' class='xr-var-data-in' type='checkbox'><label for='data-4dabc074-db2c-43f3-a21f-ba8e89d48e8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355693e+09, 1.05355686e+09, 1.05355693e+09,\n",
-              "       1.05355699e+09, 1.05355686e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355693e+09,\n",
-              "       1.05355693e+09, 1.05355693e+09, 1.05355693e+09, 1.05355693e+09,\n",
-              "       1.05355693e+09, 1.05355693e+09, 1.05355693e+09, 1.05355693e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355693e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355693e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "...\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0006333 -0.004143 ... nan nan</div><input id='attrs-ac1abc66-620f-46c9-9a93-3075662d1ab6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac1abc66-620f-46c9-9a93-3075662d1ab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7234876-f33b-4775-8583-4d0c8554cecb' class='xr-var-data-in' type='checkbox'><label for='data-f7234876-f33b-4775-8583-4d0c8554cecb' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00063333, -0.00414333, -0.00776667, ..., -0.01952   ,\n",
-              "        -0.02035667, -0.01573333],\n",
-              "       [ 0.02268667,  0.01564333,  0.01504   , ..., -0.00309667,\n",
-              "        -0.00658667, -0.00238   ],\n",
-              "       [ 0.02739334,  0.01897667,  0.01496333, ...,  0.00196667,\n",
-              "        -0.00096667,  0.00083946],\n",
-              "       ...,\n",
-              "       [        nan,         nan,         nan, ...,         nan,\n",
-              "                nan,         nan],\n",
-              "       [        nan,         nan,         nan, ...,         nan,\n",
-              "                nan,         nan],\n",
-              "       [        nan,         nan,         nan, ...,         nan,\n",
-              "                nan,         nan]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.08 80.44 80.75 ... nan nan nan</div><input id='attrs-1207ec72-09c2-4b85-ac67-d272dcc3b499' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1207ec72-09c2-4b85-ac67-d272dcc3b499' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3d3b874-816d-4bc9-b9c1-cac5234ab087' class='xr-var-data-in' type='checkbox'><label for='data-d3d3b874-816d-4bc9-b9c1-cac5234ab087' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[80.085   , 80.43833 , 80.75166 , ..., 81.761665, 81.86    ,\n",
-              "        81.745   ],\n",
-              "       [76.05    , 76.60333 , 77.066666, ..., 78.23    , 78.21667 ,\n",
-              "        78.045   ],\n",
-              "       [73.58833 , 74.355   , 75.083336, ..., 75.265   , 75.098335,\n",
-              "        74.92976 ],\n",
-              "       ...,\n",
-              "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "              nan],\n",
-              "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "              nan],\n",
-              "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "              nan]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>93.52 94.19 93.97 ... 0.0 0.0 0.0</div><input id='attrs-03ad1167-0875-4cf7-975a-6bbb0d2a724a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03ad1167-0875-4cf7-975a-6bbb0d2a724a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ed62442-4022-403a-b87a-5c6c1de5cf10' class='xr-var-data-in' type='checkbox'><label for='data-8ed62442-4022-403a-b87a-5c6c1de5cf10' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[93.51667 , 94.18667 , 93.97    , ..., 92.276665, 92.09    ,\n",
-              "        92.61667 ],\n",
-              "       [96.75    , 96.403336, 96.32    , ..., 96.433334, 96.89    ,\n",
-              "        96.57667 ],\n",
-              "       [97.19    , 97.32333 , 97.136665, ..., 96.08667 , 97.16    ,\n",
-              "        96.96667 ],\n",
-              "       ...,\n",
-              "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "         0.      ],\n",
-              "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "         0.      ],\n",
-              "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "         0.      ]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2166 -0.217 ... 0.9937 0.9937</div><input id='attrs-5e4433c1-a5c9-42b2-9362-7d86df0c9815' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e4433c1-a5c9-42b2-9362-7d86df0c9815' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83522526-79fc-4223-8c0c-267c8e027356' class='xr-var-data-in' type='checkbox'><label for='data-83522526-79fc-4223-8c0c-267c8e027356' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16565624e-01, -2.17020363e-01, -2.15714216e-01, ...,\n",
-              "         -2.15817824e-01, -2.15457186e-01, -2.15469748e-01],\n",
-              "        [-9.68014121e-01, -9.67880666e-01, -9.68221545e-01, ...,\n",
-              "         -9.70180333e-01, -9.70283389e-01, -9.70276892e-01],\n",
-              "        [-1.26286745e-01, -1.26509830e-01, -1.26157105e-01, ...,\n",
-              "         -1.09885700e-01, -1.09676003e-01, -1.09719723e-01]],\n",
-              "\n",
-              "       [[ 9.76216435e-01,  9.76112664e-01,  9.76405323e-01, ...,\n",
-              "          9.76383269e-01,  9.76462007e-01,  9.76460576e-01],\n",
-              "        [-2.14884892e-01, -2.15368673e-01, -2.14055121e-01, ...,\n",
-              "         -2.14541376e-01, -2.14190111e-01, -2.14200497e-01],\n",
-              "        [-2.69518793e-02, -2.67608669e-02, -2.67287735e-02, ...,\n",
-              "         -2.34543420e-02, -2.33496260e-02, -2.33698264e-02]],\n",
-              "\n",
-              "       [[-1.05265796e-03, -1.35056104e-03, -1.13043608e-03, ...,\n",
-              "         -8.24717747e-04, -8.40478169e-04, -8.31306970e-04],\n",
-              "        [-1.29126370e-01, -1.29302278e-01, -1.28952518e-01, ...,\n",
-              "         -1.12357967e-01, -1.12130813e-01, -1.12177916e-01],\n",
-              "        [ 9.91627216e-01,  9.91603792e-01,  9.91649806e-01, ...,\n",
-              "          9.93667185e-01,  9.93692756e-01,  9.93687451e-01]]],\n",
-              "      shape=(3, 3, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0008123 0.0008472 ... -0.002637</div><input id='attrs-d7173144-8398-40be-bd9d-dde1b7389737' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7173144-8398-40be-bd9d-dde1b7389737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb4b2283-0ff2-44f1-b251-ed0b82c4cbce' class='xr-var-data-in' type='checkbox'><label for='data-bb4b2283-0ff2-44f1-b251-ed0b82c4cbce' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00081228,  0.00084717,  0.00085335,  0.00075436,  0.00084513,\n",
-              "         0.00085451,  0.00073719,  0.00067843,  0.00075248,  0.00074199,\n",
-              "         0.00070569,  0.00065896,  0.0006914 ,  0.00069842,  0.00074138,\n",
-              "         0.0006676 ,  0.00060215,  0.00059847,  0.0006675 ,  0.0006136 ,\n",
-              "         0.00056599,  0.00061758,  0.00068752,  0.00066373,  0.00073918,\n",
-              "         0.00085721,  0.00084523,  0.0009018 ,  0.00089635,  0.00084324,\n",
-              "         0.00086276,  0.00089614,  0.00096862,  0.00108153,  0.00095481,\n",
-              "         0.00098828,  0.00098117,  0.00097845,  0.00099421,  0.00095727,\n",
-              "         0.00108126,  0.00108415,  0.00096381,  0.00100037,  0.00098756,\n",
-              "         0.00103554,  0.00096841,  0.00107755,  0.00104051,  0.0009601 ,\n",
-              "         0.00106976,  0.0010707 ,  0.00102194,  0.0009954 ,  0.00100513,\n",
-              "         0.0010245 ,  0.00102471,  0.00096594,  0.0010335 ,  0.00105437,\n",
-              "         0.00105971,  0.00095506,  0.00102042,  0.00097918,  0.00088768,\n",
-              "         0.0009018 ,  0.00096684,  0.00101125,  0.00096008,  0.0008592 ,\n",
-              "         0.00082965,  0.00095935,  0.00094485,  0.00081024,  0.00092011,\n",
-              "         0.00098175,  0.00094213,  0.00095046,  0.00099598,  0.00104662,\n",
-              "         0.00101681,  0.0010222 ,  0.00099111,  0.001044  ,  0.00103549,\n",
-              "         0.00097536,  0.00099907,  0.00097684,  0.00104176,  0.00094763,\n",
-              "         0.0009761 ,  0.00097227,  0.00089143,  0.00096118,  0.00097306,\n",
-              "         0.00096506,  0.00094653,  0.00092565,  0.00091383,  0.0009503 ,\n",
-              "...\n",
-              "        -0.00262043, -0.00258136, -0.00261717, -0.00257811, -0.00261066,\n",
-              "        -0.00255532, -0.00266274, -0.00254881, -0.00255858, -0.00259764,\n",
-              "        -0.00258462, -0.00252602, -0.00258787, -0.00256183, -0.00260415,\n",
-              "        -0.00251626, -0.00250975, -0.00243488, -0.00254881, -0.00264321,\n",
-              "        -0.00260089, -0.00263996, -0.0026367 , -0.00271157, -0.00256183,\n",
-              "        -0.00271808, -0.00277993, -0.00284178, -0.00276365, -0.00277016,\n",
-              "        -0.00279946, -0.00295896, -0.00294594, -0.00302732, -0.00302407,\n",
-              "        -0.00301756, -0.00298501, -0.00291339, -0.00296548, -0.00294269,\n",
-              "        -0.00311196, -0.00294594, -0.0030143 , -0.00297198, -0.00296873,\n",
-              "        -0.002985  , -0.0030729 , -0.0030143 , -0.00306638, -0.00288084,\n",
-              "        -0.00289386, -0.00281899, -0.00278644, -0.0028255 , -0.00276365,\n",
-              "        -0.00277993, -0.002666  , -0.00267576, -0.002666  , -0.00268227,\n",
-              "        -0.00268878, -0.00263019, -0.00254881, -0.00262368, -0.00263019,\n",
-              "        -0.00260415, -0.00257811, -0.0026074 , -0.00249673, -0.00254881,\n",
-              "        -0.00258787, -0.00263019, -0.00255532, -0.00269204, -0.00258136,\n",
-              "        -0.00259113, -0.00272133, -0.00258462, -0.00263019, -0.00261066,\n",
-              "        -0.00265949, -0.00270831, -0.00258787, -0.0025423 , -0.00262368,\n",
-              "        -0.00264647, -0.00268879, -0.00251626, -0.00256183, -0.00255207,\n",
-              "        -0.00251626, -0.00251626, -0.00249998, -0.00248371, -0.0024479 ,\n",
-              "        -0.00254881, -0.00254881, -0.0026367 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-b39bd1a7-c5aa-4350-abf6-a52d0d9b0342' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b39bd1a7-c5aa-4350-abf6-a52d0d9b0342' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2805d9de-f37e-4513-91bb-60faa10fcdd8' class='xr-var-data-in' type='checkbox'><label for='data-2805d9de-f37e-4513-91bb-60faa10fcdd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245025 ,  0.62430865,  0.62484366,  0.62450564,  0.624701  ,\n",
-              "         0.62466866,  0.62460196,  0.6247584 ,  0.62459546,  0.62464297,\n",
-              "         0.6246826 ,  0.62496835,  0.6249581 ,  0.6250585 ,  0.62494546,\n",
-              "         0.6250052 ,  0.62508506,  0.62507963,  0.6253132 ,  0.6254015 ,\n",
-              "         0.6253524 ,  0.6254653 ,  0.62515104,  0.62495625,  0.62512195,\n",
-              "         0.6251015 ,  0.624793  ,  0.6250713 ,  0.6249948 ,  0.62489176,\n",
-              "         0.6248301 ,  0.62451917,  0.6248372 ,  0.62479085,  0.62502736,\n",
-              "         0.6249378 ,  0.62497205,  0.62445515,  0.62417245,  0.6244453 ,\n",
-              "         0.6248608 ,  0.62453014,  0.62416404,  0.6242139 ,  0.62425303,\n",
-              "         0.62470126,  0.6248098 ,  0.62460905,  0.62480783,  0.6246901 ,\n",
-              "         0.6245936 ,  0.6248162 ,  0.624754  ,  0.62437105,  0.62430066,\n",
-              "         0.6244233 ,  0.6243633 ,  0.62407166,  0.6241861 ,  0.6241456 ,\n",
-              "         0.6239719 ,  0.62391376,  0.62415487,  0.6238682 ,  0.6239241 ,\n",
-              "         0.62390107,  0.6236789 ,  0.6237806 ,  0.6236614 ,  0.62358916,\n",
-              "         0.6236376 ,  0.62371755,  0.6237382 ,  0.6238726 ,  0.6240171 ,\n",
-              "         0.6239264 ,  0.623931  ,  0.6237308 ,  0.62395936,  0.6234018 ,\n",
-              "         0.6237265 ,  0.6237913 ,  0.6235492 ,  0.62318647,  0.62349266,\n",
-              "         0.62327045,  0.6232548 ,  0.6233126 ,  0.6232084 ,  0.6232786 ,\n",
-              "         0.62339824,  0.62359285,  0.6235284 ,  0.6235471 ,  0.6234016 ,\n",
-              "         0.6233313 ,  0.6232729 ,  0.6232614 ,  0.62335205,  0.6232998 ,\n",
-              "...\n",
-              "         0.7798304 ,  0.77984285,  0.7798039 ,  0.7798881 ,  0.77983624,\n",
-              "         0.779747  ,  0.7796003 ,  0.7796562 ,  0.77964693,  0.77976   ,\n",
-              "         0.77982175,  0.77987325,  0.7798803 ,  0.77981853,  0.7798599 ,\n",
-              "         0.77976817,  0.7797355 ,  0.7797223 ,  0.77958274,  0.7794968 ,\n",
-              "         0.77951866,  0.7793451 ,  0.77977306,  0.7798404 ,  0.7796792 ,\n",
-              "         0.77951556,  0.7797541 ,  0.7796244 ,  0.77954704,  0.779882  ,\n",
-              "         0.7796897 ,  0.78009367,  0.77984214,  0.77977324,  0.77981466,\n",
-              "         0.77967215,  0.77980614,  0.7795769 ,  0.77972573,  0.77955645,\n",
-              "         0.7796588 ,  0.77955323,  0.7795721 ,  0.7795167 ,  0.7794243 ,\n",
-              "         0.77918714,  0.77930677,  0.7793621 ,  0.77909505,  0.7792217 ,\n",
-              "         0.7788761 ,  0.7790808 ,  0.7791028 ,  0.7788446 ,  0.7789332 ,\n",
-              "         0.7788981 ,  0.7789904 ,  0.779241  ,  0.7791693 ,  0.77889454,\n",
-              "         0.7789502 ,  0.77890694,  0.7785553 ,  0.77865255,  0.7788394 ,\n",
-              "         0.7787025 ,  0.7788493 ,  0.77891165,  0.7789452 ,  0.77867603,\n",
-              "         0.7785905 ,  0.7783878 ,  0.77855736,  0.778772  ,  0.7786441 ,\n",
-              "         0.77870494,  0.77878624,  0.77881914,  0.77884936,  0.77857053,\n",
-              "         0.77855694,  0.778695  ,  0.77849764,  0.7787041 ,  0.77873075,\n",
-              "         0.7786052 ,  0.7784392 ,  0.7783923 ,  0.77855927,  0.77852917,\n",
-              "         0.7785722 ,  0.77847767,  0.77844226,  0.77829295,  0.7782816 ,\n",
-              "         0.77846414,  0.778354  ,  0.77835685]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>65.0 65.0 65.0 ... 65.0 65.0 65.0</div><input id='attrs-ac9904c0-7c54-436e-ae3f-01105ec4b6ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac9904c0-7c54-436e-ae3f-01105ec4b6ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0044892b-bf52-4cea-a990-44d519ed1609' class='xr-var-data-in' type='checkbox'><label for='data-0044892b-bf52-4cea-a990-44d519ed1609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-530600cd-d334-468f-b609-aa2995780efd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-530600cd-d334-468f-b609-aa2995780efd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4201c682-780a-4c9b-bcdb-91bea8d76fa6' class='xr-var-data-in' type='checkbox'><label for='data-4201c682-780a-4c9b-bcdb-91bea8d76fa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-e880d741-352f-4824-8fdf-574661b1157a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e880d741-352f-4824-8fdf-574661b1157a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97f17196-9934-41d3-84eb-09808b171b41' class='xr-var-data-in' type='checkbox'><label for='data-97f17196-9934-41d3-84eb-09808b171b41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-5421ce6b-1e96-439f-8a08-d278b40a3c44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5421ce6b-1e96-439f-8a08-d278b40a3c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc9df836-e7c1-47d7-a6dc-665c75d077b6' class='xr-var-data-in' type='checkbox'><label for='data-cc9df836-e7c1-47d7-a6dc-665c75d077b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1267 0.1333 0.13 ... 0.22 0.22</div><input id='attrs-e6d26a7e-dbf1-4e8b-b5c5-e7e625fbf25b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6d26a7e-dbf1-4e8b-b5c5-e7e625fbf25b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49a0c93a-7544-4e43-a887-660a21e44663' class='xr-var-data-in' type='checkbox'><label for='data-49a0c93a-7544-4e43-a887-660a21e44663' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.12666667, 0.13333334, 0.13      , 0.13333334, 0.13      ,\n",
-              "       0.13      , 0.13      , 0.11666667, 0.12333333, 0.11666667,\n",
-              "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11666667,\n",
-              "       0.11      , 0.11333334, 0.11333334, 0.11333334, 0.12333333,\n",
-              "       0.12      , 0.12333333, 0.12      , 0.11      , 0.11      ,\n",
-              "       0.11666667, 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
-              "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
-              "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11333334,\n",
-              "       0.12      , 0.11666667, 0.11666667, 0.11666667, 0.11333334,\n",
-              "       0.12      , 0.11666667, 0.11      , 0.12333333, 0.13333334,\n",
-              "       0.13333334, 0.13      , 0.13333334, 0.12666667, 0.13333334,\n",
-              "       0.13      , 0.13      , 0.15333334, 0.13333334, 0.15333334,\n",
-              "       0.14333333, 0.13666667, 0.11666667, 0.13666667, 0.13333334,\n",
-              "       0.13666667, 0.14666666, 0.14      , 0.15666667, 0.13333334,\n",
-              "       0.13333334, 0.15      , 0.15      , 0.12666667, 0.13666667,\n",
-              "       0.15      , 0.13      , 0.13666667, 0.13      , 0.13666667,\n",
-              "       0.13666667, 0.14333333, 0.13666667, 0.12666667, 0.13666667,\n",
-              "       0.12333333, 0.12333333, 0.13333334, 0.12      , 0.11666667,\n",
-              "       0.11      , 0.11      , 0.11666667, 0.12333333, 0.11      ,\n",
-              "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
-              "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
-              "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11      ,\n",
-              "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
-              "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
-              "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11      ,\n",
-              "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
-              "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
-              "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11      ,\n",
-              "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
-              "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
-              "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11      ,\n",
-              "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
-              "       0.11      , 0.20333333, 0.22666667, 0.22      , 0.22      ,\n",
-              "       0.22666667, 0.22      , 0.22      , 0.22666667, 0.22      ,\n",
-              "       0.22      , 0.22666667, 0.22      , 0.22      , 0.22666667,\n",
-              "       0.22      , 0.22      , 0.22666667, 0.22      , 0.22      ,\n",
-              "       0.22666667, 0.22      , 0.22      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.287 3.261 3.337 ... 3.352 3.352</div><input id='attrs-ee6c7c13-5318-4806-ad24-22ffe79846c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee6c7c13-5318-4806-ad24-22ffe79846c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d46d7a3-9016-4cc3-aaa6-71f6a1560d55' class='xr-var-data-in' type='checkbox'><label for='data-1d46d7a3-9016-4cc3-aaa6-71f6a1560d55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([3.287083 , 3.2605155, 3.3369133, 3.2892823, 3.3165808, 3.3119812,\n",
-              "       3.3027983, 3.3258302, 3.3029814, 3.3100154, 3.3152144, 3.3549793,\n",
-              "       3.3538957, 3.3682969, 3.3522127, 3.3599124, 3.3716784, 3.370829 ,\n",
-              "       3.404896 , 3.4170458, 3.4101129, 3.4259632, 3.3813803, 3.352863 ,\n",
-              "       3.3772287, 3.3739138, 3.3298807, 3.368963 , 3.357279 , 3.3420138,\n",
-              "       3.3332636, 3.2887158, 3.3332634, 3.3267972, 3.3609293, 3.34788  ,\n",
-              "       3.3524635, 3.2797818, 3.2383478, 3.2774158, 3.3357968, 3.2894654,\n",
-              "       3.237099 , 3.2444148, 3.250231 , 3.3133483, 3.3285973, 3.3003979,\n",
-              "       3.3281136, 3.3106983, 3.2960656, 3.3239977, 3.3058155, 3.231132 ,\n",
-              "       3.2188826, 3.2362165, 3.2269833, 3.1857452, 3.2025456, 3.1971133,\n",
-              "       3.1716278, 3.1624265, 3.196963 , 3.1550453, 3.162944 , 3.158162 ,\n",
-              "       3.1250577, 3.1385927, 3.1210082, 3.1101758, 3.116926 , 3.1283762,\n",
-              "       3.1307604, 3.149876 , 3.1709106, 3.1579425, 3.157861 , 3.1293783,\n",
-              "       3.1623626, 3.0823452, 3.1290247, 3.1371257, 3.103561 , 3.0512455,\n",
-              "       3.0958426, 3.0634787, 3.061212 , 3.0691292, 3.0543785, 3.0647104,\n",
-              "       3.0809944, 3.1082118, 3.099077 , 3.1025605, 3.0808105, 3.070144 ,\n",
-              "       3.061945 , 3.0596778, 3.0728438, 3.0654464, 3.082978 , 3.0888774,\n",
-              "       3.090909 , 3.1166098, 3.1326916, 3.1294093, 3.1606429, 3.0821111,\n",
-              "       3.0704782, 3.099461 , 3.1288106, 3.0858114, 3.1094916, 3.1244771,\n",
-              "       3.0631604, 3.098195 , 3.0228472, 3.0709112, 3.0849445, 3.0772102,\n",
-              "       3.10411  , 3.0789108, 3.122175 , 3.0939767, 3.126108 , 3.1068432,\n",
-              "       3.1269426, 3.124676 , 3.133493 , 3.1521947, 3.1957629, 3.174427 ,\n",
-              "       3.1647441, 3.2137322, 3.1900635, 3.2544334, 3.2174487, 3.2122307,\n",
-              "       3.2605999, 3.244882 , 3.2509172, 3.234432 , 3.1874464, 3.200428 ,\n",
-              "       3.251467 , 3.2418153, 3.24985  , 3.3141315, 3.2959988, 3.2620163,\n",
-              "       3.2882996, 3.2601004, 3.2490656, 3.242116 , 3.2917488, 3.306215 ,\n",
-              "       3.3455298, 3.3139143, 3.274265 , 3.2986312, 3.2870982, 3.271833 ,\n",
-              "       3.2660322, 3.2617817, 3.3116815, 3.314548 , 3.289132 , 3.3249977,\n",
-              "       3.287216 , 3.282166 , 3.306248 , 3.3363798, 3.3452635, 3.314365 ,\n",
-              "       3.3198478, 3.3122149, 3.3298814, 3.3361466, 3.3632295, 3.3652124,\n",
-              "       3.331114 , 3.352297 , 3.351696 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.05523 -0.07217 ... -0.0429</div><input id='attrs-c5a9fc28-31a5-4b78-bf31-d79210ec1dd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5a9fc28-31a5-4b78-bf31-d79210ec1dd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-549b7c47-68e6-4efb-a142-3cdbc96f31c2' class='xr-var-data-in' type='checkbox'><label for='data-549b7c47-68e6-4efb-a142-3cdbc96f31c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.05523333, -0.07216667, -0.05975001, -0.0719    , -0.06111666,\n",
-              "       -0.05995   , -0.07213333, -0.07148333, -0.07696667, -0.08181667,\n",
-              "       -0.0731    , -0.0579    , -0.05488333, -0.05605   , -0.05533333,\n",
-              "       -0.05278333, -0.047     , -0.0521    , -0.05088333, -0.04305   ,\n",
-              "       -0.04223333, -0.0388    , -0.05338333, -0.0568    , -0.0611    ,\n",
-              "       -0.0627    , -0.07021666, -0.05368333, -0.0487    , -0.0493    ,\n",
-              "       -0.05311666, -0.06443334, -0.04586667, -0.04441666, -0.04498333,\n",
-              "       -0.03575   , -0.03521667, -0.07056666, -0.07609999, -0.0662    ,\n",
-              "       -0.04191666, -0.06681666, -0.06818333, -0.07636666, -0.07111666,\n",
-              "       -0.05511666, -0.0493    , -0.05571667, -0.04891667, -0.04571667,\n",
-              "       -0.04198333, -0.00336667,  0.08048334,  0.25323334,  0.22161667,\n",
-              "        0.19196668,  0.15959999,  0.11676668,  0.09488333,  0.07456666,\n",
-              "        0.05981667,  0.06043334,  0.06175001,  0.05643333,  0.04858334,\n",
-              "        0.06248334,  0.06495   ,  0.07498333,  0.06741667,  0.07038333,\n",
-              "        0.06546666,  0.06541666,  0.06608333,  0.07325   ,  0.0566    ,\n",
-              "        0.054     ,  0.061     ,  0.0511    ,  0.05655   ,  0.02896667,\n",
-              "        0.02978333,  0.04755   ,  0.01526667,  0.00998333,  0.0108    ,\n",
-              "        0.00458333,  0.00831667,  0.00378333, -0.00401667, -0.00796667,\n",
-              "        0.00555   ,  0.01133333,  0.00541667, -0.0047    , -0.0019    ,\n",
-              "       -0.00158333, -0.01193333, -0.00146667, -0.00791667, -0.00841667,\n",
-              "       -0.00898333, -0.00903333, -0.002     ,  0.00205   ,  0.00393333,\n",
-              "       -0.00233333,  0.01098333, -0.01495   , -0.01253333,  0.0059    ,\n",
-              "        0.01675   , -0.0048    ,  0.00353333,  0.00155   , -0.01018333,\n",
-              "        0.00053333, -0.0111    , -0.01913333, -0.02155   , -0.02396666,\n",
-              "       -0.01793333, -0.02523333, -0.0194    , -0.02291666, -0.02203333,\n",
-              "       -0.0282    , -0.02571667, -0.03851666, -0.0208    , -0.03573333,\n",
-              "       -0.02388333, -0.03908334, -0.04381667, -0.031     , -0.04036666,\n",
-              "       -0.02826666, -0.04583333, -0.0394    , -0.03658333, -0.04336667,\n",
-              "       -0.04073333, -0.05656667, -0.0553    , -0.05688334, -0.04293333,\n",
-              "       -0.05556666, -0.05011667, -0.02433333, -0.03963333, -0.04716667,\n",
-              "       -0.05826667, -0.05943333, -0.06553333, -0.04911667, -0.04304999,\n",
-              "       -0.023     , -0.03935   , -0.03393333, -0.04798333, -0.05141667,\n",
-              "       -0.04823333, -0.05695   , -0.05431667, -0.07600001, -0.0419    ,\n",
-              "       -0.04428333, -0.05206667, -0.04676667, -0.05188334, -0.0574    ,\n",
-              "       -0.06316667, -0.0484    , -0.04308333, -0.04601667, -0.0499    ,\n",
-              "       -0.05471666, -0.05536667, -0.04616667, -0.02825   , -0.03388334,\n",
-              "       -0.0423    , -0.04288333, -0.0429    ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.414 -7.424 ... -6.433 -6.436</div><input id='attrs-6c2edf8f-5c91-42ea-89ad-21b62c2beb53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c2edf8f-5c91-42ea-89ad-21b62c2beb53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c567dd3-4ec1-4bd5-ac93-c615acab80af' class='xr-var-data-in' type='checkbox'><label for='data-0c567dd3-4ec1-4bd5-ac93-c615acab80af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.414066 , -7.424116 , -7.4041324, -7.422417 , -7.4120665,\n",
-              "       -7.4167333, -7.4193   , -7.4148664, -7.4301662, -7.419133 ,\n",
-              "       -7.4179006, -7.402567 , -7.4040666, -7.394583 , -7.403933 ,\n",
-              "       -7.394567 , -7.39615  , -7.3910007, -7.386317 , -7.3802505,\n",
-              "       -7.3838835, -7.3748665, -7.3916   , -7.391783 , -7.3853164,\n",
-              "       -7.377949 , -7.3945003, -7.3738666, -7.373867 , -7.369084 ,\n",
-              "       -7.372767 , -7.3854327, -7.3678336, -7.3722672, -7.3625836,\n",
-              "       -7.3666334, -7.360083 , -7.3809   , -7.3875995, -7.3742666,\n",
-              "       -7.3516665, -7.369966 , -7.387017 , -7.386867 , -7.379284 ,\n",
-              "       -7.361167 , -7.3493667, -7.3615665, -7.344417 , -7.350366 ,\n",
-              "       -7.3399496, -7.300467 , -7.236433 , -7.0768504, -6.997516 ,\n",
-              "       -6.949867 , -6.899017 , -6.8814178, -6.8526497, -6.8263836,\n",
-              "       -6.813383 , -6.8076496, -6.78375  , -6.781251 , -6.7671165,\n",
-              "       -6.7577667, -6.7516494, -6.728033 , -6.72715  , -6.716767 ,\n",
-              "       -6.708234 , -6.69495  , -6.6866503, -6.6791   , -6.6603007,\n",
-              "       -6.659384 , -6.6542172, -6.659967 , -6.6461835, -6.667401 ,\n",
-              "       -6.6473837, -6.6366506, -6.6365337, -6.649917 , -6.6360173,\n",
-              "       -6.638784 , -6.6400666, -6.62885  , -6.628683 , -6.617567 ,\n",
-              "       -6.6096334, -6.59625  , -6.589217 , -6.5807333, -6.5850334,\n",
-              "       -6.5764165, -6.5684667, -6.5721836, -6.55585  , -6.5561   ,\n",
-              "       -6.5420327, -6.5459166, -6.5430665, -6.5388503, -6.5341506,\n",
-              "       -6.5321503, -6.52225  , -6.539833 , -6.5372   , -6.528584 ,\n",
-              "       -6.5262337, -6.5375338, -6.5275497, -6.51875  , -6.5324664,\n",
-              "       -6.525267 , -6.548233 , -6.5326   , -6.5178337, -6.5193667,\n",
-              "       -6.505567 , -6.5125165, -6.4989333, -6.508083 , -6.49675  ,\n",
-              "       -6.50315  , -6.4925   , -6.485383 , -6.492067 , -6.4811997,\n",
-              "       -6.4708   , -6.4739504, -6.472566 , -6.4622664, -6.4698668,\n",
-              "       -6.4504333, -6.4621997, -6.4667   , -6.45215  , -6.4554167,\n",
-              "       -6.454716 , -6.461316 , -6.4738665, -6.473217 , -6.458534 ,\n",
-              "       -6.46535  , -6.4565167, -6.4414663, -6.4526324, -6.4590497,\n",
-              "       -6.4487834, -6.4628167, -6.46505  , -6.4660163, -6.4543834,\n",
-              "       -6.4556837, -6.4339333, -6.443283 , -6.4565334, -6.4484835,\n",
-              "       -6.4508667, -6.4604836, -6.4581666, -6.4554834, -6.4470496,\n",
-              "       -6.4438   , -6.450166 , -6.4428334, -6.45625  , -6.455867 ,\n",
-              "       -6.44465  , -6.436783 , -6.430967 , -6.4454665, -6.441799 ,\n",
-              "       -6.4480996, -6.4399   , -6.4372997, -6.4309335, -6.4305167,\n",
-              "       -6.4460497, -6.4332495, -6.4359174], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_density</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.023e+03 1.023e+03 ... 1.023e+03</div><input id='attrs-6950b35b-71bd-4e77-af5a-edfa6d09ff1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6950b35b-71bd-4e77-af5a-edfa6d09ff1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b5fb35a-3179-4627-941c-9092dcc52a7d' class='xr-var-data-in' type='checkbox'><label for='data-9b5fb35a-3179-4627-941c-9092dcc52a7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Water Density</dd><dt><span>standard_name :</span></dt><dd>sea_water_density</dd><dt><span>description :</span></dt><dd>Water density from linear approximation of sea water equation of state</dd></dl></div><div class='xr-var-data'><pre>array([1023.2506 , 1023.2348 , 1023.2431 , 1023.2559 , 1023.2423 ,\n",
-              "       1023.21655, 1023.1977 , 1023.2015 , 1023.19476, 1023.19116,\n",
-              "       1023.194  , 1023.1996 , 1023.20294, 1023.19666, 1023.19336,\n",
-              "       1023.19763, 1023.205  , 1023.20404, 1023.2148 , 1023.2179 ,\n",
-              "       1023.23627, 1023.24646, 1023.2652 , 1023.2814 , 1023.2947 ,\n",
-              "       1023.3131 , 1023.32043, 1023.33435, 1023.3452 , 1023.3554 ,\n",
-              "       1023.3606 , 1023.366  , 1023.37103, 1023.37616, 1023.38043,\n",
-              "       1023.38403, 1023.3869 , 1023.3915 , 1023.38855, 1023.3802 ,\n",
-              "       1023.3759 , 1023.3691 , 1023.3694 , 1023.3698 , 1023.3653 ,\n",
-              "       1023.3579 , 1023.3548 , 1023.35455, 1023.3548 , 1023.35455,\n",
-              "       1023.3522 , 1023.3529 , 1023.3504 , 1023.3525 , 1023.3523 ,\n",
-              "       1023.3569 , 1023.35583, 1023.35565, 1023.35364, 1023.35455,\n",
-              "       1023.35675, 1023.3567 , 1023.35565, 1023.35364, 1023.35144,\n",
-              "       1023.3499 , 1023.3452 , 1023.347  , 1023.3427 , 1023.3374 ,\n",
-              "       1023.3326 , 1023.32874, 1023.3249 , 1023.3244 , 1023.3244 ,\n",
-              "       1023.3202 , 1023.3271 , 1023.32477, 1023.3281 , 1023.3215 ,\n",
-              "       1023.3281 , 1023.3284 , 1023.3283 , 1023.3299 , 1023.33887,\n",
-              "       1023.3277 , 1023.3379 , 1023.343  , 1023.33966, 1023.35315,\n",
-              "       1023.35333, 1023.34064, 1023.33875, 1023.33   , 1023.3218 ,\n",
-              "       1023.32404, 1023.32294, 1023.30853, 1023.3122 , 1023.3125 ,\n",
-              "       1023.3147 , 1023.3218 , 1023.32446, 1023.3674 , 1023.42395,\n",
-              "       1023.4842 , 1023.5248 , 1023.5321 , 1023.545  , 1023.555  ,\n",
-              "       1023.5453 , 1023.53784, 1023.5425 , 1023.5531 , 1023.56085,\n",
-              "       1023.5671 , 1023.5697 , 1023.5727 , 1023.5777 , 1023.5851 ,\n",
-              "       1023.598  , 1023.61127, 1023.61774, 1023.6253 , 1023.63873,\n",
-              "       1023.6415 , 1023.64105, 1023.6321 , 1023.6085 , 1023.58875,\n",
-              "       1023.55664, 1023.53424, 1023.4919 , 1023.4661 , 1023.4329 ,\n",
-              "       1023.4105 , 1023.3956 , 1023.38354, 1023.3727 , 1023.36206,\n",
-              "       1023.3528 , 1023.34467, 1023.34357, 1023.345  , 1023.34937,\n",
-              "       1023.34894, 1023.3448 , 1023.3418 , 1023.3417 , 1023.3436 ,\n",
-              "       1023.3458 , 1023.3482 , 1023.3505 , 1023.34937, 1023.348  ,\n",
-              "       1023.34607, 1023.3471 , 1023.3476 , 1023.3501 , 1023.3529 ,\n",
-              "       1023.35187, 1023.34894, 1023.3476 , 1023.3455 , 1023.3432 ,\n",
-              "       1023.3432 , 1023.34186, 1023.34094, 1023.3408 , 1023.34   ,\n",
-              "       1023.33655, 1023.34143, 1023.34155, 1023.34357, 1023.3508 ,\n",
-              "       1023.3694 , 1023.37335, 1023.3781 , 1023.3796 , 1023.3752 ,\n",
-              "       1023.3806 , 1023.3899 , 1023.3975 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.28 10.26 10.25 ... 10.15 10.15</div><input id='attrs-69c71a5f-e445-4ea7-8f0c-4ea2adeba256' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69c71a5f-e445-4ea7-8f0c-4ea2adeba256' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c304da2-da73-4cf8-8348-f522cbcc6fc6' class='xr-var-data-in' type='checkbox'><label for='data-1c304da2-da73-4cf8-8348-f522cbcc6fc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Water Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([10.275155 , 10.262421 , 10.248638 , 10.233478 , 10.216689 ,\n",
-              "       10.204074 , 10.188269 , 10.172538 , 10.158512 , 10.140744 ,\n",
-              "       10.124731 , 10.10534  , 10.088593 , 10.069619 , 10.050677 ,\n",
-              "       10.028841 , 10.00682  ,  9.981041 ,  9.958139 ,  9.932152 ,\n",
-              "        9.90408  ,  9.8704815,  9.843054 ,  9.8099   ,  9.782674 ,\n",
-              "        9.751141 ,  9.722181 ,  9.688391 ,  9.658139 ,  9.624396 ,\n",
-              "        9.591585 ,  9.559362 ,  9.5206785,  9.487036 ,  9.4516325,\n",
-              "        9.418625 ,  9.378983 ,  9.347703 ,  9.310125 ,  9.274782 ,\n",
-              "        9.240932 ,  9.203833 ,  9.168937 ,  9.127398 ,  9.091378 ,\n",
-              "        9.048801 ,  9.012082 ,  8.96804  ,  8.932783 ,  8.890241 ,\n",
-              "        8.854867 ,  8.815892 ,  8.781367 ,  8.746559 ,  8.714514 ,\n",
-              "        8.678216 ,  8.641088 ,  8.608247 ,  8.566862 ,  8.532115 ,\n",
-              "        8.495453 ,  8.461118 ,  8.42251  ,  8.39378  ,  8.36608  ,\n",
-              "        8.334696 ,  8.308269 ,  8.283072 ,  8.254289 ,  8.228873 ,\n",
-              "        8.203963 ,  8.179552 ,  8.162701 ,  8.144332 ,  8.124536 ,\n",
-              "        8.104092 ,  8.085614 ,  8.068075 ,  8.053603 ,  8.043172 ,\n",
-              "        8.025586 ,  8.014922 ,  8.00194  ,  7.9942083,  7.985075 ,\n",
-              "        7.9840665,  7.978806 ,  7.97339  ,  7.9731627,  7.971814 ,\n",
-              "        7.972329 ,  7.9674144,  7.9723177,  7.9744887,  7.9855638,\n",
-              "        7.991272 ,  7.9956956,  8.006041 ,  8.023605 ,  8.038973 ,\n",
-              "        8.049008 ,  8.066569 ,  8.078107 ,  8.090951 ,  8.110414 ,\n",
-              "        8.1288595,  8.1531725,  8.172004 ,  8.192166 ,  8.219532 ,\n",
-              "        8.241356 ,  8.265544 ,  8.288776 ,  8.314842 ,  8.338862 ,\n",
-              "        8.363347 ,  8.388729 ,  8.418659 ,  8.4457035,  8.479128 ,\n",
-              "        8.511397 ,  8.542225 ,  8.57435  ,  8.609074 ,  8.643846 ,\n",
-              "        8.674181 ,  8.707741 ,  8.740917 ,  8.776131 ,  8.809834 ,\n",
-              "        8.847663 ,  8.883326 ,  8.921983 ,  8.956591 ,  8.994466 ,\n",
-              "        9.032357 ,  9.072943 ,  9.107945 ,  9.147928 ,  9.18505  ,\n",
-              "        9.218539 ,  9.25671  ,  9.293107 ,  9.324933 ,  9.361722 ,\n",
-              "        9.397845 ,  9.436231 ,  9.464766 ,  9.502145 ,  9.534171 ,\n",
-              "        9.566268 ,  9.601108 ,  9.627889 ,  9.660232 ,  9.6932535,\n",
-              "        9.72142  ,  9.752015 ,  9.780852 ,  9.808266 ,  9.838272 ,\n",
-              "        9.864178 ,  9.885547 ,  9.914127 ,  9.934637 ,  9.957427 ,\n",
-              "        9.972928 ,  9.992192 , 10.00496  , 10.023165 , 10.037822 ,\n",
-              "       10.052144 , 10.058786 , 10.073322 , 10.083076 , 10.093331 ,\n",
-              "       10.100321 , 10.109131 , 10.117323 , 10.123545 , 10.1341   ,\n",
-              "       10.142311 , 10.14612  , 10.153638 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_std</span></div><div class='xr-var-dims'>(range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.04232 0.04293 0.04402 ... nan nan</div><input id='attrs-6f8b823e-8ebc-4098-ac88-ef9e421e2501' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f8b823e-8ebc-4098-ac88-ef9e421e2501' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aee946c4-df3b-4a9d-9731-0c3b3e11590c' class='xr-var-data-in' type='checkbox'><label for='data-aee946c4-df3b-4a9d-9731-0c3b3e11590c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([[0.04231985, 0.04293143, 0.04401674, ..., 0.04380286, 0.04201568,\n",
-              "        0.04634431],\n",
-              "       [0.04002878, 0.04084466, 0.03949426, ..., 0.04000184, 0.04028782,\n",
-              "        0.03873245],\n",
-              "       [0.04025545, 0.03514179, 0.04270846, ..., 0.0391249 , 0.03997382,\n",
-              "        0.04237254],\n",
-              "       ...,\n",
-              "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-              "               nan],\n",
-              "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-              "               nan],\n",
-              "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-              "               nan]], shape=(28, 183), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39822272-7344-4f67-9029-9ac3bdaf315f' class='xr-section-summary-in' type='checkbox'  ><label for='section-39822272-7344-4f67-9029-9ac3bdaf315f' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-22f50f61-d724-40f0-ba78-70482d1b5863' class='xr-index-data-in' type='checkbox'/><label for='index-22f50f61-d724-40f0-ba78-70482d1b5863' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:30.001030683&#x27;,\n",
-              "               &#x27;2020-08-15 00:27:30.001019239&#x27;,\n",
-              "               &#x27;2020-08-15 00:32:30.001010656&#x27;,\n",
-              "               &#x27;2020-08-15 00:37:30.001015901&#x27;,\n",
-              "               &#x27;2020-08-15 00:42:30.001019954&#x27;,\n",
-              "               &#x27;2020-08-15 00:47:30.001018524&#x27;,\n",
-              "               &#x27;2020-08-15 00:52:30.001013994&#x27;,\n",
-              "               &#x27;2020-08-15 00:57:30.001026630&#x27;,\n",
-              "               &#x27;2020-08-15 01:02:30.001023769&#x27;,\n",
-              "               &#x27;2020-08-15 01:07:30.001024723&#x27;,\n",
-              "               ...\n",
-              "               &#x27;2020-08-15 14:47:30.001032352&#x27;,\n",
-              "               &#x27;2020-08-15 14:52:30.001019239&#x27;,\n",
-              "               &#x27;2020-08-15 14:57:30.001032829&#x27;,\n",
-              "               &#x27;2020-08-15 15:02:30.001021146&#x27;,\n",
-              "               &#x27;2020-08-15 15:07:30.001027345&#x27;,\n",
-              "               &#x27;2020-08-15 15:12:30.001029014&#x27;,\n",
-              "               &#x27;2020-08-15 15:17:30.001026630&#x27;,\n",
-              "               &#x27;2020-08-15 15:22:30.001026153&#x27;,\n",
-              "               &#x27;2020-08-15 15:27:30.001022577&#x27;,\n",
-              "               &#x27;2020-08-15 15:32:30.001030683&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-68d0fa06-2aea-4d10-accd-011823a35690' class='xr-index-data-in' type='checkbox'/><label for='index-68d0fa06-2aea-4d10-accd-011823a35690' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e234f08d-8c57-434a-a27b-ceaea3df8e75' class='xr-index-data-in' type='checkbox'/><label for='index-e234f08d-8c57-434a-a27b-ceaea3df8e75' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9f63e293-4cea-4995-829a-d6f2ce67b99d' class='xr-index-data-in' type='checkbox'/><label for='index-9f63e293-4cea-4995-829a-d6f2ce67b99d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
-              "                      2.7,                3.2,                3.7,\n",
-              "                      4.2,  4.699999999999999,  5.199999999999999,\n",
-              "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
-              "        7.199999999999999,  7.699999999999999,                8.2,\n",
-              "                      8.7,                9.2,                9.7,\n",
-              "                     10.2,               10.7,               11.2,\n",
-              "                     11.7,               12.2,               12.7,\n",
-              "                     13.2,               13.7,               14.2,\n",
-              "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-34366b69-9c8f-4bf6-989d-1da3a72d15ab' class='xr-index-data-in' type='checkbox'/><label for='index-34366b69-9c8f-4bf6-989d-1da3a72d15ab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-36fbb1ab-b6cc-4322-b1c4-be9a69d327bd' class='xr-index-data-in' type='checkbox'/><label for='index-36fbb1ab-b6cc-4322-b1c4-be9a69d327bd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-235eb738-3773-4f61-a25e-957043df7788' class='xr-index-data-in' type='checkbox'/><label for='index-235eb738-3773-4f61-a25e-957043df7788' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-77db153b-8d36-4e42-8aa1-a218f8b28f07' class='xr-index-data-in' type='checkbox'/><label for='index-77db153b-8d36-4e42-8aa1-a218f8b28f07' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-42bff68f-07ca-47ad-82d7-da6c885d3a03' class='xr-index-data-in' type='checkbox'/><label for='index-42bff68f-07ca-47ad-82d7-da6c885d3a03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:29.938495159&#x27;,\n",
-              "               &#x27;2020-08-15 00:27:29.938490867&#x27;,\n",
-              "               &#x27;2020-08-15 00:32:29.938487529&#x27;,\n",
-              "               &#x27;2020-08-15 00:37:29.938496828&#x27;,\n",
-              "               &#x27;2020-08-15 00:42:29.938495397&#x27;,\n",
-              "               &#x27;2020-08-15 00:47:29.938496589&#x27;,\n",
-              "               &#x27;2020-08-15 00:52:29.938497781&#x27;,\n",
-              "               &#x27;2020-08-15 00:57:29.938495397&#x27;,\n",
-              "               &#x27;2020-08-15 01:02:29.938493013&#x27;,\n",
-              "               &#x27;2020-08-15 01:07:29.938492536&#x27;,\n",
-              "               ...\n",
-              "               &#x27;2020-08-15 14:47:29.938492774&#x27;,\n",
-              "               &#x27;2020-08-15 14:52:29.938491106&#x27;,\n",
-              "               &#x27;2020-08-15 14:57:29.938495635&#x27;,\n",
-              "               &#x27;2020-08-15 15:02:29.938493013&#x27;,\n",
-              "               &#x27;2020-08-15 15:07:29.938493967&#x27;,\n",
-              "               &#x27;2020-08-15 15:12:29.938494920&#x27;,\n",
-              "               &#x27;2020-08-15 15:17:29.938493251&#x27;,\n",
-              "               &#x27;2020-08-15 15:22:29.938486099&#x27;,\n",
-              "               &#x27;2020-08-15 15:27:29.938484191&#x27;,\n",
-              "               &#x27;2020-08-15 15:32:29.938490390&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2a0b257d-1e4d-415d-9dca-9988fdbf08bb' class='xr-index-data-in' type='checkbox'/><label for='index-2a0b257d-1e4d-415d-9dca-9988fdbf08bb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
-              "                      2.7,                3.2,                3.7,\n",
-              "                      4.2,  4.699999999999999,  5.199999999999999,\n",
-              "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
-              "        7.199999999999999,  7.699999999999999,                8.2,\n",
-              "                      8.7,                9.2,                9.7,\n",
-              "                     10.2,               10.7,               11.2,\n",
-              "                     11.7,               12.2,               12.7,\n",
-              "                     13.2,               13.7,               14.2,\n",
-              "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cea8b836-b914-44c4-b99f-da6161f6f2c3' class='xr-section-summary-in' type='checkbox'  ><label for='section-cea8b836-b914-44c4-b99f-da6161f6f2c3' class='xr-section-summary' >Attributes: <span>(41)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>fs :</span></dt><dd>1</dd><dt><span>n_bin :</span></dt><dd>300</dd><dt><span>n_fft :</span></dt><dd>300</dd><dt><span>description :</span></dt><dd>Binned averages calculated from ensembles of size &quot;n_bin&quot;</dd><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>principal</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd><dt><span>range_offset :</span></dt><dd>0.6</dd><dt><span>declination :</span></dt><dd>15.8</dd><dt><span>declination_in_orientmat :</span></dt><dd>1</dd><dt><span>principal_heading :</span></dt><dd>11.1898</dd></dl></div></li></ul></div></div>"
-            ],
-            "text/plain": [
-              "<xarray.Dataset> Size: 379kB\n",
-              "Dimensions:         (time: 183, dirIMU: 3, dir: 4, range: 28, beam: 4,\n",
-              "                     earth: 3, inst: 3, q: 4, time_b5: 183, range_b5: 28)\n",
-              "Coordinates:\n",
-              "  * time            (time) datetime64[ns] 1kB 2020-08-15T00:22:30.001030683 ....\n",
-              "  * dirIMU          (dirIMU) <U10 120B 'streamwise' 'x-stream' 'vert'\n",
-              "  * dir             (dir) <U10 160B 'streamwise' 'x-stream' 'vert1' 'vert2'\n",
-              "  * range           (range) float64 224B 1.2 1.7 2.2 2.7 ... 13.2 13.7 14.2 14.7\n",
-              "  * beam            (beam) int32 16B 1 2 3 4\n",
-              "  * earth           (earth) <U1 12B 'E' 'N' 'U'\n",
-              "  * inst            (inst) <U1 12B 'X' 'Y' 'Z'\n",
-              "  * q               (q) <U1 16B 'w' 'x' 'y' 'z'\n",
-              "  * time_b5         (time_b5) datetime64[ns] 1kB 2020-08-15T00:22:29.93849515...\n",
-              "  * range_b5        (range_b5) float64 224B 1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7\n",
-              "Data variables: (12/38)\n",
-              "    c_sound         (time) float32 732B 1.502e+03 1.502e+03 ... 1.498e+03\n",
-              "    temp            (time) float32 732B 14.49 14.59 14.54 ... 13.62 13.56 13.5\n",
-              "    pressure        (time) float32 732B 9.712 9.699 9.685 ... 9.58 9.584 9.591\n",
-              "    mag             (dirIMU, time) float32 2kB 3.534 3.565 ... -197.1 -197.1\n",
-              "    accel           (dirIMU, time) float32 2kB -1.261 -1.263 ... 9.714 9.712\n",
-              "    batt            (time) float32 732B 16.48 16.48 16.48 ... 16.25 16.26 16.26\n",
-              "    ...              ...\n",
-              "    heading         (time) float32 732B 3.287 3.261 3.337 ... 3.331 3.352 3.352\n",
-              "    pitch           (time) float32 732B -0.05523 -0.07217 ... -0.04288 -0.0429\n",
-              "    roll            (time) float32 732B -7.414 -7.424 -7.404 ... -6.433 -6.436\n",
-              "    water_density   (time) float32 732B 1.023e+03 1.023e+03 ... 1.023e+03\n",
-              "    depth           (time) float32 732B 10.28 10.26 10.25 ... 10.14 10.15 10.15\n",
-              "    U_std           (range, time) float32 20kB 0.04232 0.04293 ... nan nan\n",
-              "Attributes: (12/41)\n",
-              "    fs:                        1\n",
-              "    n_bin:                     300\n",
-              "    n_fft:                     300\n",
-              "    description:               Binned averages calculated from ensembles of s...\n",
-              "    filehead_config:           {\"CLOCKSTR\": {\"TIME\": \"\\\"2020-08-13 13:56:21\\\"...\n",
-              "    inst_model:                Signature1000\n",
-              "    ...                        ...\n",
-              "    has_imu:                   1\n",
-              "    beam_angle:                25\n",
-              "    range_offset:              0.6\n",
-              "    declination:               15.8\n",
-              "    declination_in_orientmat:  1\n",
-              "    principal_heading:         11.1898"
-            ]
-          },
-          "execution_count": 16,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;range&#x27; (range: 28)&gt; Size: 224B\n",
+       "array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+       "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
+       "       13.2, 13.7, 14.2, 14.7])\n",
+       "Coordinates:\n",
+       "  * range    (range) float64 224B 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\n",
+       "Attributes:\n",
+       "    coverage_content_type:  coordinate\n",
+       "    units:                  m\n",
+       "    long_name:              Profile Range\n",
+       "    description:            Distance to the center of each depth bin</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;range&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>range</span>: 28</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0025dbc2-44e5-46bc-869d-1190c9602df2' class='xr-array-in' type='checkbox' checked><label for='section-0025dbc2-44e5-46bc-869d-1190c9602df2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.2 1.7 2.2 2.7 3.2 3.7 4.2 4.7 ... 11.7 12.2 12.7 13.2 13.7 14.2 14.7</span></div><div class='xr-array-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+       "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
+       "       13.2, 13.7, 14.2, 14.7])</pre></div></div></li><li class='xr-section-item'><input id='section-96787b9e-7530-4068-862d-b28bdb67e527' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96787b9e-7530-4068-862d-b28bdb67e527' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-39f56f30-fd1a-4676-84cb-9afcfd108d49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39f56f30-fd1a-4676-84cb-9afcfd108d49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b45e0c4-31f4-4b55-b573-dfb44456c555' class='xr-var-data-in' type='checkbox'><label for='data-5b45e0c4-31f4-4b55-b573-dfb44456c555' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+       "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
+       "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fb59eb1-0655-463e-9953-677ac4364339' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8fb59eb1-0655-463e-9953-677ac4364339' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div></li></ul></div></div>"
       ],
-      "source": [
-        "ds_avg"
+      "text/plain": [
+       "<xarray.DataArray 'range' (range: 28)> Size: 224B\n",
+       "array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+       "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
+       "       13.2, 13.7, 14.2, 14.7])\n",
+       "Coordinates:\n",
+       "  * range    (range) float64 224B 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\n",
+       "Attributes:\n",
+       "    coverage_content_type:  coordinate\n",
+       "    units:                  m\n",
+       "    long_name:              Profile Range\n",
+       "    description:            Distance to the center of each depth bin"
       ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 4. Calculate Speed and Direction\n",
-        "\n",
-        "Two additional variables that might be of interest, which are not automatically provided, are the magnitude of the horizontal velocity (referred to as `U_mag` or speed) and its direction (`U_dir`). These can be accessed via \"shortcut\" functions through the keyword `velds`, as demonstrated in the subsequent code block.\n",
-        "\n",
-        "For a comprehensive list of these \"shortcut\" functions, you can refer to the [DOLfYN documentation](https://dolfyn.readthedocs.io/en/latest/apidoc/dolfyn.shortcuts.html)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [],
-      "source": [
-        "ds_avg[\"U_mag\"] = ds_avg.velds.U_mag\n",
-        "ds_avg[\"U_dir\"] = ds_avg.velds.U_dir"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Plotting"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plotting can be accomplished through the user's preferred package. Matplotlib is shown here for simplicity, and flow speed and direction are plotted below with a blue line delineating the water surface level."
-      ]
-    },
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "range_da = ds[\"range\"]\n",
+    "spacing = range_da.diff(\"range\")\n",
+    "\n",
+    "range_stats = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Statistic\": [\"min\", \"max\", \"mean spacing\", \"max spacing\", \"min spacing\"],\n",
+    "        \"Value\": [\n",
+    "            float(range_da.min().values),\n",
+    "            float(range_da.max().values),\n",
+    "            float(spacing.mean().values),\n",
+    "            float(spacing.max().values),\n",
+    "            float(spacing.min().values),\n",
+    "        ],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "print(range_stats)\n",
+    "ds.range"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Discard Data Above Surface Level and Surface Interference\n",
+    "\n",
+    "To reduce computational load, we can exclude all data at or above the water surface level, including that affected by surface interference. Since the instrument was oriented upwards, we can utilize the pressure sensor data along with the function `water_depth_from_pressure` to find the water depth. However, this approach necessitates that the pressure sensor was calibrated or 'zeroed' prior to deployment. If the instrument is facing downwards or doesn't include pressure data, the function `water_depth_from_amplitude` can be used to detect the seabed or water surface.\n",
+    "\n",
+    "It's important to note that Acoustic Doppler Current Profilers (ADCPs) do not measure water salinity, so you'll need to supply this information to the function. If `water_depth_from_pressure` is called after `set_range_offset`, \"depth\" represents the distance from the water surface to the seafloor. Otherwise, it indicates the distance to the ADCP pressure sensor.\n",
+    "\n",
+    "After determining the \"depth\", you can use the `remove_surface_interference` function to discard data in depth bins at or above the actual water surface. Be aware that this function will generate a new dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api.clean.water_depth_from_pressure(ds, salinity=31)\n",
+    "ds = api.clean.remove_surface_interference(ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.colorbar.Colorbar at 0x25a2de98c50>"
-            ]
-          },
-          "execution_count": 18,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 1000x600 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 84MB\n",
+       "Dimensions:              (time: 55000, time_b5: 55000, range: 28, range_b5: 28,\n",
+       "                          beam: 4, dir: 4, dirIMU: 3, earth: 3, inst: 3, q: 4,\n",
+       "                          x1: 4, x2: 4)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 440kB 2020-08-15T00:20:00.5009...\n",
+       "  * time_b5              (time_b5) datetime64[ns] 440kB 2020-08-15T00:20:00.4...\n",
+       "  * range                (range) float64 224B 1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7\n",
+       "  * range_b5             (range_b5) float64 224B 1.2 1.7 2.2 ... 13.7 14.2 14.7\n",
+       "  * beam                 (beam) int32 16B 1 2 3 4\n",
+       "  * dir                  (dir) &lt;U2 32B &#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;\n",
+       "  * dirIMU               (dirIMU) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
+       "  * earth                (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
+       "  * inst                 (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
+       "  * q                    (q) &lt;U1 16B &#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;\n",
+       "  * x1                   (x1) int32 16B 1 2 3 4\n",
+       "  * x2                   (x2) int32 16B 1 2 3 4\n",
+       "Data variables: (12/40)\n",
+       "    c_sound              (time) float32 220kB 1.502e+03 1.502e+03 ... 1.498e+03\n",
+       "    temp                 (time) float32 220kB 14.55 14.55 14.55 ... 13.47 13.47\n",
+       "    pressure             (time) float32 220kB 9.713 9.718 9.718 ... 9.594 9.596\n",
+       "    mag                  (dirIMU, time) float32 660kB 72.5 72.7 ... -195.7\n",
+       "    accel                (dirIMU, time) float32 660kB -0.00479 ... 9.729\n",
+       "    batt                 (time) float32 220kB 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
+       "    ...                   ...\n",
+       "    heading              (time) float32 220kB -12.52 -12.51 ... -12.52 -12.5\n",
+       "    pitch                (time) float32 220kB -0.065 -0.06 -0.06 ... -0.05 -0.05\n",
+       "    roll                 (time) float32 220kB -7.425 -7.42 -7.42 ... -6.45 -6.45\n",
+       "    beam2inst_orientmat  (x1, x2) float32 64B 1.183 0.0 -1.183 ... 0.0 0.5518\n",
+       "    water_density        (time) float32 220kB 1.023e+03 1.023e+03 ... 1.023e+03\n",
+       "    depth                (time) float32 220kB 10.28 10.28 10.28 ... 10.16 10.16\n",
+       "Attributes: (12/35)\n",
+       "    filehead_config:       {&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, ...\n",
+       "    inst_model:            Signature1000\n",
+       "    inst_make:             Nortek\n",
+       "    inst_type:             ADCP\n",
+       "    burst_config:          {&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass...\n",
+       "    n_cells:               28\n",
+       "    ...                    ...\n",
+       "    rotate_vars:           [&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, ...\n",
+       "    coord_sys:             earth\n",
+       "    fs:                    1\n",
+       "    has_imu:               1\n",
+       "    beam_angle:            25\n",
+       "    range_offset:          0.6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3b60b78a-3f53-4327-82a3-47a9d97f6e5a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3b60b78a-3f53-4327-82a3-47a9d97f6e5a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 55000</li><li><span class='xr-has-index'>time_b5</span>: 55000</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>range_b5</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3f52280c-ae51-4cbf-8f2d-6ff8e1f0fe68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f52280c-ae51-4cbf-8f2d-6ff8e1f0fe68' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.500999927 .....</div><input id='attrs-d32184a9-e33e-415d-a267-542d575c5d56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d32184a9-e33e-415d-a267-542d575c5d56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c042bb9-eec0-4f21-a04d-80f9c3b639a9' class='xr-var-data-in' type='checkbox'><label for='data-3c042bb9-eec0-4f21-a04d-80f9c3b639a9' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.500999927&#x27;, &#x27;2020-08-15T00:20:01.501100063&#x27;,\n",
+       "       &#x27;2020-08-15T00:20:02.500999927&#x27;, ..., &#x27;2020-08-15T15:36:37.500999927&#x27;,\n",
+       "       &#x27;2020-08-15T15:36:38.500999927&#x27;, &#x27;2020-08-15T15:36:39.500999927&#x27;],\n",
+       "      shape=(55000,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.438499927 .....</div><input id='attrs-10c3d059-5fe5-4ed8-b774-8ecde1198f83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10c3d059-5fe5-4ed8-b774-8ecde1198f83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f4aefa7-2f3b-4387-a78d-fb6723e950a4' class='xr-var-data-in' type='checkbox'><label for='data-0f4aefa7-2f3b-4387-a78d-fb6723e950a4' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time b5</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.438499927&#x27;, &#x27;2020-08-15T00:20:01.438499927&#x27;,\n",
+       "       &#x27;2020-08-15T00:20:02.438499927&#x27;, ..., &#x27;2020-08-15T15:36:37.438499927&#x27;,\n",
+       "       &#x27;2020-08-15T15:36:38.438499927&#x27;, &#x27;2020-08-15T15:36:39.438499927&#x27;],\n",
+       "      shape=(55000,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-5123f094-c771-40ba-a455-89a460000c29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5123f094-c771-40ba-a455-89a460000c29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f100808e-89c5-4611-974e-89ef10c1e3be' class='xr-var-data-in' type='checkbox'><label for='data-f100808e-89c5-4611-974e-89ef10c1e3be' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+       "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
+       "       13.2, 13.7, 14.2, 14.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-d2b47560-9f68-4ddb-9a71-3ae9f877d158' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2b47560-9f68-4ddb-9a71-3ae9f877d158' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ca236a9-821c-46ff-9074-9bddcaa9797b' class='xr-var-data-in' type='checkbox'><label for='data-1ca236a9-821c-46ff-9074-9bddcaa9797b' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range b5</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+       "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
+       "       13.2, 13.7, 14.2, 14.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-c439419b-bb03-4daa-9197-a89297d44810' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c439419b-bb03-4daa-9197-a89297d44810' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-528c857c-7fe8-424c-96b7-5d14b78688a6' class='xr-var-data-in' type='checkbox'><label for='data-528c857c-7fe8-424c-96b7-5d14b78688a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;</div><input id='attrs-f1876829-2a3b-4840-9014-19e6e4fd0fc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1876829-2a3b-4840-9014-19e6e4fd0fc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dcce187-8c0c-4dd4-b3fc-6db7314b2437' class='xr-var-data-in' type='checkbox'><label for='data-0dcce187-8c0c-4dd4-b3fc-6db7314b2437' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-aeb36c82-1db9-4419-a089-2f58994b6a0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aeb36c82-1db9-4419-a089-2f58994b6a0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d01d9df4-a49c-4a85-b93f-74a933c67353' class='xr-var-data-in' type='checkbox'><label for='data-d01d9df4-a49c-4a85-b93f-74a933c67353' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-9468b964-5319-40c6-9699-6a3dbed6dcc5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9468b964-5319-40c6-9699-6a3dbed6dcc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e43fe00-ab16-4cba-8535-8c975cc836bc' class='xr-var-data-in' type='checkbox'><label for='data-2e43fe00-ab16-4cba-8535-8c975cc836bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-ad5d3f7c-a552-483c-a8a2-dfcabfad2057' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad5d3f7c-a552-483c-a8a2-dfcabfad2057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11104ddf-2608-4dbe-a625-314637bc4e76' class='xr-var-data-in' type='checkbox'><label for='data-11104ddf-2608-4dbe-a625-314637bc4e76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-f405fe50-ee5c-4064-b0fe-fcac0cea48ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f405fe50-ee5c-4064-b0fe-fcac0cea48ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88b0b5d0-6b0e-4181-a479-40472eabdfdb' class='xr-var-data-in' type='checkbox'><label for='data-88b0b5d0-6b0e-4181-a479-40472eabdfdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Quaternion Vector Components</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-e00a9ca5-e0f8-49da-89ce-f4c7b4e16384' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e00a9ca5-e0f8-49da-89ce-f4c7b4e16384' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c82fe9d4-d230-49cf-89ea-fb7df701c364' class='xr-var-data-in' type='checkbox'><label for='data-c82fe9d4-d230-49cf-89ea-fb7df701c364' 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>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-2bf2d2de-3964-4652-b831-2d5fee0968e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bf2d2de-3964-4652-b831-2d5fee0968e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83556ad2-333b-4d99-9c32-d8f485a2ef34' class='xr-var-data-in' type='checkbox'><label for='data-83556ad2-333b-4d99-9c32-d8f485a2ef34' 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>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da3721f7-23f0-41c7-befb-6a8e5d98d95f' class='xr-section-summary-in' type='checkbox'  ><label for='section-da3721f7-23f0-41c7-befb-6a8e5d98d95f' class='xr-section-summary' >Data variables: <span>(40)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-04b9eee2-42a0-4d5e-a5d0-de155c7b5c8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04b9eee2-42a0-4d5e-a5d0-de155c7b5c8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b05b2969-59cc-44c3-b89f-d4699f952d17' class='xr-var-data-in' type='checkbox'><label for='data-b05b2969-59cc-44c3-b89f-d4699f952d17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.9, 1501.9, 1501.9, ..., 1498.4, 1498.3, 1498.3],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.55 14.55 14.55 ... 13.47 13.47</div><input id='attrs-9b5a6f5d-a7e6-45a6-9f2c-78360b5409ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b5a6f5d-a7e6-45a6-9f2c-78360b5409ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9859d01e-5e61-4d51-9e26-010178406e63' class='xr-var-data-in' type='checkbox'><label for='data-9859d01e-5e61-4d51-9e26-010178406e63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.55, 14.55, 14.55, ..., 13.47, 13.47, 13.47],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.713 9.718 9.718 ... 9.594 9.596</div><input id='attrs-e360df77-6724-4a52-9fae-09981e6dae73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e360df77-6724-4a52-9fae-09981e6dae73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-210923cc-88d8-4b0e-b8dc-4eb97348da0f' class='xr-var-data-in' type='checkbox'><label for='data-210923cc-88d8-4b0e-b8dc-4eb97348da0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.713, 9.718, 9.718, ..., 9.596, 9.594, 9.596],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.5 72.7 72.6 ... -197.2 -195.7</div><input id='attrs-8a7a903a-315c-4813-a33b-c1632e40fbc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a7a903a-315c-4813-a33b-c1632e40fbc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96153423-1386-4f28-90bc-0bd9ab38e2a5' class='xr-var-data-in' type='checkbox'><label for='data-96153423-1386-4f28-90bc-0bd9ab38e2a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.5,   72.7,   72.6, ...,   72.3,   72.8,   72.4],\n",
+       "       [   9.3,    9.4,    8.9, ...,    5.9,    6.3,    6.4],\n",
+       "       [-198. , -196.3, -198.1, ..., -197.4, -197.2, -195.7]],\n",
+       "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.00479 -0.01437 ... 9.685 9.729</div><input id='attrs-9ed21429-a052-4aa8-815a-950403f7096b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ed21429-a052-4aa8-815a-950403f7096b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64c500dd-9770-449a-8680-ccf78a2d64b0' class='xr-var-data-in' type='checkbox'><label for='data-64c500dd-9770-449a-8680-ccf78a2d64b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-4.7900393e-03, -1.4370117e-02, -2.3950195e-02, ...,\n",
+       "        -1.4370117e-02, -4.7900393e-03, -2.3950195e-02],\n",
+       "       [-1.2933105e+00, -1.2549902e+00, -1.2645704e+00, ...,\n",
+       "        -1.1017090e+00, -1.0873389e+00, -1.0969189e+00],\n",
+       "       [ 9.7285690e+00,  9.7141991e+00,  9.7237797e+00, ...,\n",
+       "         9.6710892e+00,  9.6854591e+00,  9.7285690e+00]],\n",
+       "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.6 16.6 16.6 ... 16.4 16.4 15.2</div><input id='attrs-6ce9ff5b-9dfb-4f40-b945-955d259824d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ce9ff5b-9dfb-4f40-b945-955d259824d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc3cca79-0257-4108-80db-6152c622a3d7' class='xr-var-data-in' type='checkbox'><label for='data-cc3cca79-0257-4108-80db-6152c622a3d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.6, 16.6, 16.6, ..., 16.4, 16.4, 15.2],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-98553162-8a44-4687-b531-88b59e511042' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98553162-8a44-4687-b531-88b59e511042' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-facb0350-434c-40d8-80ec-e03b7a36b532' class='xr-var-data-in' type='checkbox'><label for='data-facb0350-434c-40d8-80ec-e03b7a36b532' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20. , 20. , 20. , ..., 19.5, 19.5, 19.5],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-7058c137-8eb9-439f-9a9c-0a1bb3e7dd69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7058c137-8eb9-439f-9a9c-0a1bb3e7dd69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33e065bd-98a4-4713-bf62-adb0604678d7' class='xr-var-data-in' type='checkbox'><label for='data-33e065bd-98a4-4713-bf62-adb0604678d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-7bcdef99-2f89-4fd7-9b7a-d7a70bc15cac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bcdef99-2f89-4fd7-9b7a-d7a70bc15cac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0237d5dc-01db-489e-ade7-2917e695935e' class='xr-var-data-in' type='checkbox'><label for='data-0237d5dc-01db-489e-ade7-2917e695935e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
+       "       1053557762], shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-3d316c65-e7b9-4c3b-b6fb-82f11f6bf5ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d316c65-e7b9-4c3b-b6fb-82f11f6bf5ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c38da9c-25a1-4552-b304-7a478e4d0fc5' class='xr-var-data-in' type='checkbox'><label for='data-6c38da9c-25a1-4552-b304-7a478e4d0fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000],\n",
+       "      shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.015 -0.048 0.008 ... nan nan nan</div><input id='attrs-5eb905c4-9c12-48f9-90ee-048dc17c61f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5eb905c4-9c12-48f9-90ee-048dc17c61f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef4d0a60-551a-4545-8c7b-64f1af4ed635' class='xr-var-data-in' type='checkbox'><label for='data-ef4d0a60-551a-4545-8c7b-64f1af4ed635' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.015, -0.048,  0.008, ..., -0.176, -0.071, -0.027],\n",
+       "        [-0.093, -0.124, -0.031, ..., -0.175, -0.16 , -0.03 ],\n",
+       "        [-0.163, -0.135, -0.105, ..., -0.129, -0.089, -0.116],\n",
+       "        ...,\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan]],\n",
+       "\n",
+       "       [[ 0.243,  0.204,  0.234, ..., -0.163, -0.135, -0.18 ],\n",
+       "        [ 0.272,  0.248,  0.236, ..., -0.254, -0.169, -0.204],\n",
+       "        [ 0.257,  0.294,  0.333, ..., -0.299, -0.226, -0.273],\n",
+       "        ...,\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan]],\n",
+       "\n",
+       "       [[ 0.016,  0.012,  0.01 , ...,  0.009,  0.006, -0.018],\n",
+       "        [ 0.01 ,  0.011, -0.013, ...,  0.002, -0.027,  0.009],\n",
+       "        [ 0.01 ,  0.009,  0.023, ..., -0.018, -0.002, -0.024],\n",
+       "        ...,\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan]],\n",
+       "\n",
+       "       [[ 0.003, -0.007,  0.046, ..., -0.032, -0.008, -0.029],\n",
+       "        [ 0.017,  0.033,  0.035, ..., -0.016, -0.022, -0.034],\n",
+       "        [ 0.016, -0.012,  0.005, ...,  0.002, -0.045, -0.026],\n",
+       "        ...,\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan]]],\n",
+       "      shape=(4, 28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>81.0 80.0 80.5 80.5 ... nan nan nan</div><input id='attrs-3a562ab9-056c-4a92-b02c-d12df5163082' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a562ab9-056c-4a92-b02c-d12df5163082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fa31852-2f6d-4bb3-ae20-b9dd4edea08b' class='xr-var-data-in' type='checkbox'><label for='data-9fa31852-2f6d-4bb3-ae20-b9dd4edea08b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[81. , 80. , 80.5, ..., 81.5, 81.5, 81.5],\n",
+       "        [75. , 74.5, 75. , ..., 79. , 77. , 77.5],\n",
+       "        [73.5, 72. , 72. , ..., 75. , 74.5, 75. ],\n",
+       "        ...,\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
+       "\n",
+       "       [[81.5, 80. , 80.5, ..., 82. , 81.5, 82. ],\n",
+       "        [76. , 77. , 76.5, ..., 79. , 77.5, 77.5],\n",
+       "        [73.5, 74. , 73. , ..., 75.5, 74.5, 74.5],\n",
+       "        ...,\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
+       "\n",
+       "       [[80.5, 79.5, 80. , ..., 81.5, 82.5, 81.5],\n",
+       "        [76. , 75.5, 75. , ..., 77. , 78.5, 77. ],\n",
+       "        [72. , 73.5, 72. , ..., 74. , 75. , 73.5],\n",
+       "        ...,\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
+       "\n",
+       "       [[80. , 81. , 80. , ..., 81. , 81. , 81.5],\n",
+       "        [75.5, 75.5, 74.5, ..., 76.5, 76.5, 77.5],\n",
+       "        [73. , 72.5, 73. , ..., 75.5, 74.5, 74. ],\n",
+       "        ...,\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]],\n",
+       "      shape=(4, 28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>100 91 91 97 99 87 ... 0 0 0 0 0 0</div><input id='attrs-519d184f-8ef3-45d7-b919-9e3120f09459' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-519d184f-8ef3-45d7-b919-9e3120f09459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7f62e0a-8031-4949-984f-8c851c9ef55f' class='xr-var-data-in' type='checkbox'><label for='data-e7f62e0a-8031-4949-984f-8c851c9ef55f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[100,  91,  91, ...,  89,  90,  96],\n",
+       "        [ 74,  92,  96, ..., 100,  97, 100],\n",
+       "        [100, 100,  99, ...,  99, 100, 100],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[ 94,  90,  89, ...,  91,  92,  90],\n",
+       "        [ 94, 100, 100, ...,  96, 100,  93],\n",
+       "        [ 99,  95,  97, ...,  98,  98, 100],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[ 92,  93,  93, ...,  93,  97,  89],\n",
+       "        [ 99, 100,  99, ...,  96,  88,  98],\n",
+       "        [ 97, 100,  98, ..., 100,  99,  97],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[ 87,  97,  87, ...,  91,  87,  90],\n",
+       "        [100,  93,  93, ...,  96,  96,  97],\n",
+       "        [100, 100, 100, ..., 100, 100,  97],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]]],\n",
+       "      shape=(4, 28, 55000), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2164 ... 0.9937 0.9937</div><input id='attrs-fac58596-809d-4cd2-842d-938f8aa3b158' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fac58596-809d-4cd2-842d-938f8aa3b158' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c97b617-4b4a-4164-a3f1-a73476706a9c' class='xr-var-data-in' type='checkbox'><label for='data-3c97b617-4b4a-4164-a3f1-a73476706a9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16688156e-01, -2.16397047e-01, -2.16593146e-01, ...,\n",
+       "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
+       "        [ 9.76196468e-01,  9.76219118e-01,  9.76202846e-01, ...,\n",
+       "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
+       "        [-1.22870505e-03, -1.20262802e-03, -1.17563456e-03, ...,\n",
+       "         -1.16243958e-03, -1.02279335e-03, -9.55920666e-04]],\n",
+       "\n",
+       "       [[-9.67970312e-01, -9.68004048e-01, -9.67982769e-01, ...,\n",
+       "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
+       "        [-2.15024590e-01, -2.14734554e-01, -2.14924574e-01, ...,\n",
+       "         -2.14874983e-01, -2.15436578e-01, -2.15052128e-01],\n",
+       "        [-1.29278094e-01, -1.29190803e-01, -1.29239410e-01, ...,\n",
+       "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01]],\n",
+       "\n",
+       "       [[-1.26469478e-01, -1.26385525e-01, -1.26422524e-01, ...,\n",
+       "         -1.10033900e-01, -1.10009573e-01, -1.09963998e-01],\n",
+       "        [-2.68291458e-02, -2.68030688e-02, -2.68616974e-02, ...,\n",
+       "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02],\n",
+       "        [ 9.91607308e-01,  9.91618454e-01,  9.91612315e-01, ...,\n",
+       "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
+       "      shape=(3, 3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.00293</div><input id='attrs-f36da3b8-6c54-471a-977b-a2b10de919ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f36da3b8-6c54-471a-977b-a2b10de919ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43b7c30c-4401-4da9-a124-fcc8d894aa8a' class='xr-var-data-in' type='checkbox'><label for='data-43b7c30c-4401-4da9-a124-fcc8d894aa8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00390625,\n",
+       "        -0.00292969, -0.00195312],\n",
+       "       [ 0.00097656,  0.        ,  0.        , ...,  0.        ,\n",
+       "         0.00097656,  0.00097656],\n",
+       "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
+       "        -0.00195312, -0.00292969]], shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-ea338374-0cc4-47a6-9dd3-24e7a1907d42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea338374-0cc4-47a6-9dd3-24e7a1907d42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f238500-68d7-47bb-a9f9-614c1bbdb632' class='xr-var-data-in' type='checkbox'><label for='data-3f238500-68d7-47bb-a9f9-614c1bbdb632' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6244812 , ...,  0.625     ,\n",
+       "         0.6247864 ,  0.62490845],\n",
+       "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
+       "        -0.03564453, -0.03561401],\n",
+       "       [-0.05014038, -0.05010986, -0.05014038, ..., -0.04354858,\n",
+       "        -0.04360962, -0.04360962],\n",
+       "       [ 0.77835083,  0.7782593 ,  0.7783203 , ...,  0.77856445,\n",
+       "         0.77874756,  0.7786255 ]], shape=(4, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-49e97b68-5f8b-4e1a-b389-b7cf4c92156b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49e97b68-5f8b-4e1a-b389-b7cf4c92156b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f25b32ea-65d7-4c86-950d-3cb3f06879af' class='xr-var-data-in' type='checkbox'><label for='data-f25b32ea-65d7-4c86-950d-3cb3f06879af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.8, 12.8, 12.8, ..., 12. , 12. , 12. ],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>243 243 243 244 ... 246 246 246 246</div><input id='attrs-208a71ef-adba-43f9-a333-ca1de21ca2b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-208a71ef-adba-43f9-a333-ca1de21ca2b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cf8e297-7cf9-4173-8e1f-a242c5c4275a' class='xr-var-data-in' type='checkbox'><label for='data-6cf8e297-7cf9-4173-8e1f-a242c5c4275a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([243, 243, 243, ..., 246, 246, 246], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.9 72.8 72.4 ... -198.0 -195.7</div><input id='attrs-988dff58-bb78-4b41-b1fc-2145643890a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-988dff58-bb78-4b41-b1fc-2145643890a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28973660-cf4b-4d55-8108-3e0425072873' class='xr-var-data-in' type='checkbox'><label for='data-28973660-cf4b-4d55-8108-3e0425072873' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.9,   72.8,   72.4, ...,   73. ,   73. ,   72.7],\n",
+       "       [   9.5,    9.5,    9.8, ...,    6. ,    6.4,    5.9],\n",
+       "       [-197.1, -197.2, -197.7, ..., -197.4, -198. , -195.7]],\n",
+       "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01437 0.0 ... 9.709 9.724</div><input id='attrs-15d6f924-114e-4bf5-be69-8f81b4640295' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15d6f924-114e-4bf5-be69-8f81b4640295' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7986dd8-1046-4e66-acb4-1672df68237d' class='xr-var-data-in' type='checkbox'><label for='data-f7986dd8-1046-4e66-acb4-1672df68237d' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.4370117e-02,  0.0000000e+00, -1.4370117e-02, ...,\n",
+       "        -4.7900393e-03, -1.9160157e-02, -1.4370117e-02],\n",
+       "       [-1.2693603e+00, -1.2885205e+00, -1.2837305e+00, ...,\n",
+       "        -1.0825489e+00, -1.1017090e+00, -1.1064991e+00],\n",
+       "       [ 9.7189894e+00,  9.7046194e+00,  9.7094088e+00, ...,\n",
+       "         9.7141991e+00,  9.7094088e+00,  9.7237797e+00]],\n",
+       "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-c7ccfee5-07eb-4fd7-bfb8-7be1b0262b62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7ccfee5-07eb-4fd7-bfb8-7be1b0262b62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a141cd7-21aa-48f5-9dc7-3c1a46a6469d' class='xr-var-data-in' type='checkbox'><label for='data-5a141cd7-21aa-48f5-9dc7-3c1a46a6469d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-c18b3666-f050-4de7-b8d1-3df520752621' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c18b3666-f050-4de7-b8d1-3df520752621' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42765d09-47dd-49ed-a8c5-9aa5b63de351' class='xr-var-data-in' type='checkbox'><label for='data-42765d09-47dd-49ed-a8c5-9aa5b63de351' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
+       "       1053557762], shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-2297a980-4a57-41c7-97a4-a0d166f7cd2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2297a980-4a57-41c7-97a4-a0d166f7cd2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32b27170-8cb1-42a9-a1a3-116a4f2dd113' class='xr-var-data-in' type='checkbox'><label for='data-32b27170-8cb1-42a9-a1a3-116a4f2dd113' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000],\n",
+       "      shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.003 0.021 0.018 ... nan nan nan</div><input id='attrs-14ccc856-4363-425d-944d-65678f7370b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14ccc856-4363-425d-944d-65678f7370b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9305015-3a1d-40be-b68d-e6a0dc00cada' class='xr-var-data-in' type='checkbox'><label for='data-c9305015-3a1d-40be-b68d-e6a0dc00cada' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.003,  0.021,  0.018, ...,  0.   , -0.044,  0.009],\n",
+       "       [ 0.057,  0.034,  0.017, ...,  0.01 ,  0.001, -0.002],\n",
+       "       [ 0.039,  0.022,  0.02 , ...,  0.039, -0.008,  0.018],\n",
+       "       ...,\n",
+       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan]],\n",
+       "      shape=(28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.5 80.5 80.0 80.5 ... nan nan nan</div><input id='attrs-e7b1334d-b840-4797-a987-e2141dd2cef4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7b1334d-b840-4797-a987-e2141dd2cef4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e71dd98-e434-4165-b485-bbc4c3c9bce9' class='xr-var-data-in' type='checkbox'><label for='data-0e71dd98-e434-4165-b485-bbc4c3c9bce9' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[80.5, 80.5, 80. , ..., 82. , 81.5, 81. ],\n",
+       "       [75.5, 76.5, 76. , ..., 78.5, 78. , 77.5],\n",
+       "       [72.5, 73.5, 73.5, ..., 75. , 76.5, 75.5],\n",
+       "       ...,\n",
+       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
+       "      shape=(28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>98 100 92 93 98 100 ... 0 0 0 0 0 0</div><input id='attrs-1f747258-8108-4ae0-9c50-202c7e96b587' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f747258-8108-4ae0-9c50-202c7e96b587' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd3b67b-c20e-45dc-8c6b-bb769c506bf2' class='xr-var-data-in' type='checkbox'><label for='data-6cd3b67b-c20e-45dc-8c6b-bb769c506bf2' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[ 98, 100,  92, ...,  88,  90,  91],\n",
+       "       [ 97,  94,  98, ...,  97,  95,  97],\n",
+       "       [100, 100, 100, ...,  93, 100,  96],\n",
+       "       ...,\n",
+       "       [  0,   0,   0, ...,   0,   0,   0],\n",
+       "       [  0,   0,   0, ...,   0,   0,   0],\n",
+       "       [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "      shape=(28, 55000), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2165 ... 0.9937 0.9937</div><input id='attrs-6aa40778-97f4-43c9-ae4c-f41d70238179' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6aa40778-97f4-43c9-ae4c-f41d70238179' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8026d2f-c8d4-4aee-b62b-eaf6ca504e0a' class='xr-var-data-in' type='checkbox'><label for='data-c8026d2f-c8d4-4aee-b62b-eaf6ca504e0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16694236e-01, -2.16498137e-01, -2.16593146e-01, ...,\n",
+       "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
+       "        [-9.67967808e-01, -9.68039632e-01, -9.68030274e-01, ...,\n",
+       "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
+       "        [-1.26507595e-01, -1.26426160e-01, -1.26425594e-01, ...,\n",
+       "         -1.10033900e-01, -1.10009573e-01, -1.09963998e-01]],\n",
+       "\n",
+       "       [[ 9.76198971e-01,  9.76259708e-01,  9.76250350e-01, ...,\n",
+       "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
+       "        [-2.15024590e-01, -2.14829564e-01, -2.14924574e-01, ...,\n",
+       "         -2.14874983e-01, -2.15436578e-01, -2.15052128e-01],\n",
+       "        [-2.68766508e-02, -2.68536285e-02, -2.68591940e-02, ...,\n",
+       "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02]],\n",
+       "\n",
+       "       [[-1.19058788e-03, -1.16700679e-03, -1.17257237e-03, ...,\n",
+       "         -1.16243958e-03, -1.02279335e-03, -9.55920666e-04],\n",
+       "        [-1.29325598e-01, -1.29241347e-01, -1.29241914e-01, ...,\n",
+       "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01],\n",
+       "        [ 9.91601229e-01,  9.91612315e-01,  9.91612315e-01, ...,\n",
+       "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
+       "      shape=(3, 3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.001953</div><input id='attrs-3e99d3c9-0836-45b6-8c19-5a576c25383e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e99d3c9-0836-45b6-8c19-5a576c25383e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b20520f-b4ac-42f0-9873-501e3df0f90a' class='xr-var-data-in' type='checkbox'><label for='data-5b20520f-b4ac-42f0-9873-501e3df0f90a' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00097656,\n",
+       "        -0.00195312, -0.00390625],\n",
+       "       [ 0.        ,  0.        ,  0.00097656, ...,  0.00097656,\n",
+       "         0.        ,  0.00097656],\n",
+       "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
+       "        -0.00292969, -0.00195312]], shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-cc7659cd-127d-486e-ad9b-4b48e25bccbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc7659cd-127d-486e-ad9b-4b48e25bccbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbbae1d3-38b0-44e0-bf12-c4fa3b75e199' class='xr-var-data-in' type='checkbox'><label for='data-cbbae1d3-38b0-44e0-bf12-c4fa3b75e199' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6245117 , ...,  0.625     ,\n",
+       "         0.6247864 ,  0.62490845],\n",
+       "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
+       "        -0.03564453, -0.03561401],\n",
+       "       [-0.0501709 , -0.05014038, -0.05014038, ..., -0.04354858,\n",
+       "        -0.04360962, -0.04360962],\n",
+       "       [ 0.77835083,  0.7782898 ,  0.7783203 , ...,  0.77856445,\n",
+       "         0.77874756,  0.7786255 ]], shape=(4, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>65 65 65 65 65 ... 65 65 65 65 65</div><input id='attrs-b97d1d40-0461-49a0-bdfc-858bf0e2107b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b97d1d40-0461-49a0-bdfc-858bf0e2107b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23eb80ae-40f4-458c-86de-996156fedae8' class='xr-var-data-in' type='checkbox'><label for='data-23eb80ae-40f4-458c-86de-996156fedae8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65, 65, 65, ..., 65, 65, 65], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-db59adea-502a-4e93-b075-862d98d07eca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db59adea-502a-4e93-b075-862d98d07eca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb33cc15-3200-45c2-801c-97bd960b6d6e' class='xr-var-data-in' type='checkbox'><label for='data-eb33cc15-3200-45c2-801c-97bd960b6d6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-ec918346-f32c-49af-a738-a2176994dfcd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec918346-f32c-49af-a738-a2176994dfcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14c6370c-8658-4a0f-89d1-f8414e1af492' class='xr-var-data-in' type='checkbox'><label for='data-14c6370c-8658-4a0f-89d1-f8414e1af492' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-01031b8b-3f17-4723-9007-125ece6930f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01031b8b-3f17-4723-9007-125ece6930f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e56225-bd35-4432-aa43-b40cff669b8d' class='xr-var-data-in' type='checkbox'><label for='data-85e56225-bd35-4432-aa43-b40cff669b8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1</div><input id='attrs-97a64000-ff17-4b02-9bd3-3910113c801e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97a64000-ff17-4b02-9bd3-3910113c801e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bba3ff1c-4b69-4066-ac3c-d43a5be0bc21' class='xr-var-data-in' type='checkbox'><label for='data-bba3ff1c-4b69-4066-ac3c-d43a5be0bc21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 1], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.52 -12.51 ... -12.52 -12.5</div><input id='attrs-50cb20ba-8ec8-4267-81cb-d7ef32bb0b4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50cb20ba-8ec8-4267-81cb-d7ef32bb0b4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7783dd55-36bd-42bd-888d-994093bed229' class='xr-var-data-in' type='checkbox'><label for='data-7783dd55-36bd-42bd-888d-994093bed229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-12.52    , -12.505001, -12.51    , ..., -12.490002, -12.52    ,\n",
+       "       -12.500001], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.065 -0.06 -0.06 ... -0.05 -0.05</div><input id='attrs-2a60a58f-c1cb-4eb8-8c3c-58a6a6500ec7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a60a58f-c1cb-4eb8-8c3c-58a6a6500ec7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b80099b0-39db-4ae0-8da0-e5b94f8cdf41' class='xr-var-data-in' type='checkbox'><label for='data-b80099b0-39db-4ae0-8da0-e5b94f8cdf41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.065, -0.06 , -0.06 , ..., -0.06 , -0.05 , -0.05 ],\n",
+       "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.425 -7.42 -7.42 ... -6.45 -6.45</div><input id='attrs-ee7339f8-c6b7-4eb1-a036-bbc09bd2e808' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee7339f8-c6b7-4eb1-a036-bbc09bd2e808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a88c998-b634-4f80-b754-68f424c8f2e4' class='xr-var-data-in' type='checkbox'><label for='data-6a88c998-b634-4f80-b754-68f424c8f2e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.4250007, -7.42     , -7.42     , ..., -6.45     , -6.45     ,\n",
+       "       -6.45     ], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.183 0.0 -1.183 ... 0.0 0.5518</div><input id='attrs-0e526501-0f6f-4ee2-9d4a-e25e81ded0bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e526501-0f6f-4ee2-9d4a-e25e81ded0bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-795ecf6d-5e93-45c9-a54e-6a4ab24ec042' class='xr-var-data-in' type='checkbox'><label for='data-795ecf6d-5e93-45c9-a54e-6a4ab24ec042' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.1831,  0.    , -1.1831,  0.    ],\n",
+       "       [ 0.    , -1.1831,  0.    ,  1.1831],\n",
+       "       [ 0.5518,  0.    ,  0.5518,  0.    ],\n",
+       "       [ 0.    ,  0.5518,  0.    ,  0.5518]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_density</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.023e+03 1.023e+03 ... 1.023e+03</div><input id='attrs-016e0ef0-2457-44cb-b074-88412c61be7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-016e0ef0-2457-44cb-b074-88412c61be7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea536dce-62be-4334-8a4f-ec284ea5ea34' class='xr-var-data-in' type='checkbox'><label for='data-ea536dce-62be-4334-8a4f-ec284ea5ea34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Water Density</dd><dt><span>standard_name :</span></dt><dd>sea_water_density</dd><dt><span>description :</span></dt><dd>Water density from linear approximation of sea water equation of state</dd></dl></div><div class='xr-var-data'><pre>array([1023.2412 , 1023.2412 , 1023.2412 , ..., 1023.40265, 1023.40265,\n",
+       "       1023.40265], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.28 10.28 10.28 ... 10.16 10.16</div><input id='attrs-acaba087-0262-4545-8841-a7c3b4528202' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acaba087-0262-4545-8841-a7c3b4528202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c149786-bee8-4e55-91c6-288e974957ba' class='xr-var-data-in' type='checkbox'><label for='data-9c149786-bee8-4e55-91c6-288e974957ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Water Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([10.276234, 10.281216, 10.281216, ..., 10.158169, 10.156177,\n",
+       "       10.158169], shape=(55000,), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3aa7b58-2604-474f-b39a-1f7654920867' class='xr-section-summary-in' type='checkbox'  ><label for='section-d3aa7b58-2604-474f-b39a-1f7654920867' class='xr-section-summary' >Attributes: <span>(35)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>earth</dd><dt><span>fs :</span></dt><dd>1</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd><dt><span>range_offset :</span></dt><dd>0.6</dd></dl></div></li></ul></div></div>"
       ],
-      "source": [
-        "%matplotlib inline\n",
-        "from matplotlib import pyplot as plt\n",
-        "import matplotlib.dates as dt\n",
-        "\n",
-        "ax = plt.figure(figsize=(10, 6)).add_axes([0.14, 0.14, 0.8, 0.74])\n",
-        "# Plot flow speed\n",
-        "t = dolfyn.time.dt642date(ds_avg[\"time\"])\n",
-        "plt.pcolormesh(t, ds_avg[\"range\"], ds_avg[\"U_mag\"], cmap=\"Blues\", shading=\"nearest\")\n",
-        "# Plot the water surface\n",
-        "ax.plot(t, ds_avg[\"depth\"])\n",
-        "\n",
-        "# Set up time on x-axis\n",
-        "ax.set_xlabel(\"Time\")\n",
-        "ax.xaxis.set_major_formatter(dt.DateFormatter(\"%H:%M\"))\n",
-        "\n",
-        "ax.set_ylabel(\"Altitude [m]\")\n",
-        "ax.set_ylim([0, 12])\n",
-        "plt.colorbar(label=\"Speed [m/s]\")"
+      "text/plain": [
+       "<xarray.Dataset> Size: 84MB\n",
+       "Dimensions:              (time: 55000, time_b5: 55000, range: 28, range_b5: 28,\n",
+       "                          beam: 4, dir: 4, dirIMU: 3, earth: 3, inst: 3, q: 4,\n",
+       "                          x1: 4, x2: 4)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 440kB 2020-08-15T00:20:00.5009...\n",
+       "  * time_b5              (time_b5) datetime64[ns] 440kB 2020-08-15T00:20:00.4...\n",
+       "  * range                (range) float64 224B 1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7\n",
+       "  * range_b5             (range_b5) float64 224B 1.2 1.7 2.2 ... 13.7 14.2 14.7\n",
+       "  * beam                 (beam) int32 16B 1 2 3 4\n",
+       "  * dir                  (dir) <U2 32B 'E' 'N' 'U1' 'U2'\n",
+       "  * dirIMU               (dirIMU) <U1 12B 'E' 'N' 'U'\n",
+       "  * earth                (earth) <U1 12B 'E' 'N' 'U'\n",
+       "  * inst                 (inst) <U1 12B 'X' 'Y' 'Z'\n",
+       "  * q                    (q) <U1 16B 'w' 'x' 'y' 'z'\n",
+       "  * x1                   (x1) int32 16B 1 2 3 4\n",
+       "  * x2                   (x2) int32 16B 1 2 3 4\n",
+       "Data variables: (12/40)\n",
+       "    c_sound              (time) float32 220kB 1.502e+03 1.502e+03 ... 1.498e+03\n",
+       "    temp                 (time) float32 220kB 14.55 14.55 14.55 ... 13.47 13.47\n",
+       "    pressure             (time) float32 220kB 9.713 9.718 9.718 ... 9.594 9.596\n",
+       "    mag                  (dirIMU, time) float32 660kB 72.5 72.7 ... -195.7\n",
+       "    accel                (dirIMU, time) float32 660kB -0.00479 ... 9.729\n",
+       "    batt                 (time) float32 220kB 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
+       "    ...                   ...\n",
+       "    heading              (time) float32 220kB -12.52 -12.51 ... -12.52 -12.5\n",
+       "    pitch                (time) float32 220kB -0.065 -0.06 -0.06 ... -0.05 -0.05\n",
+       "    roll                 (time) float32 220kB -7.425 -7.42 -7.42 ... -6.45 -6.45\n",
+       "    beam2inst_orientmat  (x1, x2) float32 64B 1.183 0.0 -1.183 ... 0.0 0.5518\n",
+       "    water_density        (time) float32 220kB 1.023e+03 1.023e+03 ... 1.023e+03\n",
+       "    depth                (time) float32 220kB 10.28 10.28 10.28 ... 10.16 10.16\n",
+       "Attributes: (12/35)\n",
+       "    filehead_config:       {\"CLOCKSTR\": {\"TIME\": \"\\\"2020-08-13 13:56:21\\\"\"}, ...\n",
+       "    inst_model:            Signature1000\n",
+       "    inst_make:             Nortek\n",
+       "    inst_type:             ADCP\n",
+       "    burst_config:          {\"press_valid\": true, \"temp_valid\": true, \"compass...\n",
+       "    n_cells:               28\n",
+       "    ...                    ...\n",
+       "    rotate_vars:           ['vel', 'accel', 'accel_b5', 'angrt', 'angrt_b5', ...\n",
+       "    coord_sys:             earth\n",
+       "    fs:                    1\n",
+       "    has_imu:               1\n",
+       "    beam_angle:            25\n",
+       "    range_offset:          0.6"
       ]
-    },
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualizing the Depth from the ADCP pressure sensor. This measurement includes any previously applied range offsets. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "scrolled": false
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 1000x600 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "ax = plt.figure(figsize=(10, 6)).add_axes([0.14, 0.14, 0.8, 0.74])\n",
-        "# Plot flow direction\n",
-        "plt.pcolormesh(t, ds_avg[\"range\"], ds_avg[\"U_dir\"], cmap=\"twilight\", shading=\"nearest\")\n",
-        "# Plot the water surface\n",
-        "ax.plot(t, ds_avg[\"depth\"])\n",
-        "\n",
-        "# set up time on x-axis\n",
-        "ax.set_xlabel(\"Time\")\n",
-        "ax.xaxis.set_major_formatter(dt.DateFormatter(\"%H:%M\"))\n",
-        "\n",
-        "ax.set_ylabel(\"Altitude [m]\")\n",
-        "ax.set_ylim([0, 12])\n",
-        "plt.colorbar(label=\"Horizontal Vel Dir [deg CW from true N]\");"
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x249c4ff40e0>]"
       ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Saving and Loading DOLfYN datasets\n",
-        "Datasets can be saved and reloaded using the `save` and `load` functions. Xarray is saved natively in netCDF format, hence the \".nc\" extension.\n",
-        "\n",
-        "Note: DOLfYN datasets cannot be saved using xarray's native `ds.to_netcdf`; however, DOLfYN datasets can be opened using `xarray.open_dataset`."
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds[\"depth\"].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Updated water velocity plots with data above the surface removed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uncomment these lines to save and load to your current working directory\n",
-        "# dolfyn.save(ds, 'your_data.nc')\n",
-        "# ds_saved = dolfyn.load('your_data.nc')"
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABccAAAGGCAYAAAC+ITn5AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3QeUJGW5PvCnYsfJmwMbyBkEQcALCFxW5IKoF8ELEkVBMaF/BCVLEEEkyAUFAVEQvAoYUIIgIIIgIEkFAXfZZdm8EztV/J/37Zlxhw307PbsdE8/v3PqzHR1deWup+rrqu8z4jiOQURERERERERERETUQMzRngEiIiIiIiIiIiIioo2NheNERERERERERERE1HBYOE5EREREREREREREDYeF40RERERERERERETUcFg4TkREREREREREREQNh4XjRERERERERERERNRwWDhORERERERERERERA2HheNERERERERERERE1HBYOE5EREREREREREREDYeF40RERERERERERETUcFg4TlRlt9xyCwzDWGv35z//meuciIiohjM8mUxi4cKFq72/7777YrvtthuVeSMiIqJ/O++88zSzly9fvsbVInktuT3guuuuw+GHH45NNtlEP3fcccdxdRKRsst/iKjaLrjgAsyaNWu1/pttthlXNhERUQ0rlUr41re+hWuuuWa0Z4WIiIiq4NJLL0Vvby922203LFq0iOuUiAaxcJxohBx00EHYdddduX6JiIjqzE477YQbbrgBZ555JqZMmTLas0NEREQb6NFHHx28azybzXJ9EtEgVqtCRERERLSKr3/96wjDUO8eJyIiovo3Y8YMLRgnInonFo4TjZDu7m6t/2zVbsWKFVzfRERENU6qRTvmmGP07vG33357tGeHiIiIiIhGCAvHiUbIAQccgPHjxw/ppk6dyvVNRERUB77xjW8gCAKto5SIiIiIiMYm1jlONEKuvfZabLHFFkP6WZbF9U1ERFQHZs+ejU9+8pP4wQ9+gDPOOAOTJ08e7VkiIiIiIqIqY+E40QiRVrDZICcREVH9Ouuss/DjH/9Y6x6/6qqrRnt2iIiIaBhYxzgRVYLVqhARERERreXu8aOPPlrvHl+0aBHXERERUY1IJpP6t1AorPH9fD4/OAwR0bqwcJyIiIiIaB13j7PucSIiotoyY8YM/fvqq6+usWB8wYIFg8MQEa0LC8eJiIiIiNZi00031bvHv//972Px4sVcT0RERDVg//33h+u6uO666xBF0ZD35Ikv+WH7oIMOGrX5I6L6wTrHiUbI7373O7zyyiur9d9zzz31MW0iIiKqD9/4xje07nG5O23bbbcd7dkhIiJqeBMmTMA555yjT3jtvffeOPTQQ5FOp/HEE0/gpz/9KQ488EAccsghg+vp17/+NV544QX93/d9vPjii7jwwgv1tXx2hx12aPh1StSoWDhONEIkqNfk5ptvZuE4ERFRHdlss8307vEf/ehHoz0rREREtMqP1zNnzsT3vvc9XHDBBXq3+KxZs3D++efja1/7Gkzz35Ul/OIXvxiS43/961+1E9OmTWPhOFEDM+I4jkd7JoiIiIiIiIiIiIiINibWOU5EREREREREREREDYeF40RERERERERERETUcFg4TkREREREREREREQNh4XjREREREREtMEuueQSvPe970VTUxMmTJiAww47DK+++uq7fu7//u//sNVWWyGZTGL77bfHb3/7W24NIiIi2ihYOE5EREREREQb7NFHH8XnPvc5/PnPf8aDDz4I3/dx4IEHIpfLrfUzTzzxBD7xiU/gxBNPxF//+lctUJfu5Zdf5hYhIiKiEWfEcRyP/GSIiIiIiIiokSxbtkzvIJdC87333nuNwxxxxBFaeP6b3/xmsN/73vc+7LTTTrj++us34twSERFRI7IxxkVRhLffflsf7TMMY7Rnh4hoo5HfPnt7ezFlyhSYZnUeFCoWi/A8b1ifcV1XH5MmWh/McSJqVGMhx7u7u/Vve3v7Wod58skncdpppw3pN2fOHNxzzz3rNU2qLcxxImpUYyHHG8WYLxyXgvHp06eP9mwQEY2aBQsWYNq0aVUJ4o5UFnmEw/rcpEmTMHfuXAYyrRfmOBE1ulrI8RdeeGFIjicSCe3erVD0S1/6Evbaay9st912ax1u8eLFmDhx4pB+8lr6U/1jjhNRo6uFHOf1eIMXjssd4wM7Y3Nz82jPDhHRRtPT06M/Dg4cBzeU/EItQXwMpsKtsMkKDxFuXbxQP8tfq2l9MMeJqFHVUo6/s/D63HPPxXnnnbfOz0rd41Jv+OOPP75B8031jTlORI2qlnKc1+MNXjg+UJWKFIyzcJyIGlG1q5RKGRZco7IwtmIDYMsWtAGY40TU6Gohx995o9G73TV+6qmnah3ijz322LveLSd3tC1ZsmRIP3kt/an+MceJqNHVQo7TulWn0hsiImoYpgFYFXYyLBEREdV3jg/caDTQra1wXOpXlYLxu+++Gw8//DBmzZr1rvOzxx574KGHHhrS78EHH9T+REREtOE5Tg1+5zgREVWXZRjaVTQsmMZERESNkuNSlcrtt9+OX/7yl/oY+UC94S0tLUilUvr/Mcccg6lTp+KSSy7R11/84hexzz774Dvf+Q4OPvhg3HHHHXjmmWfwgx/8YNjLRkRENNbxerz6eOc4ERENS6W/Ug90RERE1Bg5ft1116G7uxv77rsvJk+ePNjdeeedg8PMnz8fixYtGny95557aoG6FIbvuOOO+PnPf4577rlnnY14EhERNSpej1cf7xwnIiIiIiKiDSbVqrybRx55ZLV+hx9+uHZEREREGxsLx4mIaFj4GBcREVH9Yo4TERHVL+Z49bFwnIiIhmU4j1lbXLdEREQ1hTlORERUv5jj1cc6x4mIaL1+qa60G67HHnsMhxxyCKZMmQLDMLTe0Uoe0X7Pe96DRCKBzTbbDLfccstqw1x77bWYOXMmkskkdt99dzz99NPDnjciIqJ6N9I5TkRERCOHOV59LBwnIqJhMfrDo5JufS6pc7mcNsglhdmVmDt3Lg4++GB84AMfwPPPP48vfelL+NSnPoX7779/cBhpCOy0007Dueeei+eee07HP2fOHCxdunQ95pCIiKh+jXSOExER0chhjlcfq1UhIqKaquPsoIMO0q5S119/PWbNmoXvfOc7+nrrrbfG448/ju9+97taAC6uuOIKnHTSSTj++OMHP3PvvffipptuwhlnnDHseSQiIqpXrKuUiIiofjHHq493jhMR0XrVcVZpJ3p6eoZ0pVKpamv9ySefxAEHHDCknxSKS3/heR6effbZIcOYpqmvB4YhIiJqFOuT40RERFQbmOPVx8JxIiJajzCutK7S8memT5+OlpaWwe6SSy6p2lpfvHgxJk6cOKSfvJZC+EKhgOXLlyMMwzUOI58lIiJqJOuT40RERFQbmONjrHB8OI2unXzyyTrMlVdeuVHnkYiINvyX6gULFqC7u3uwO/PMM7laxwDmOBFR/eEdZzSAOU5EVH+Y42OscLzSRtfuvvtu/PnPf9ZCdCIiqr/WsZubm4d0iUSiavMzadIkLFmyZEg/eS3TSaVSGDduHCzLWuMw8llaf8xxIqLGyHEam5jjRET1hzk+xhrkrKTRtYULF+Lzn/887r//fhx88MEbbd6IiGjNzGHUQboxfoHdY4898Nvf/nZIvwcffFD7C9d1scsuu+Chhx7CYYcdpv2iKNLXp5566kaYw7GLOU5EVH9qLcdp9DDHiYjqD3N8jBWOvxspvPjkJz+J//f//h+23Xbbij4jjbyt2tCb1DlLRET10zp2X18fXn/99cHXc+fOxfPPP4/29nZssskmWiWL/HB66623Dla79b3vfQ+nn346TjjhBDz88MP42c9+hnvvvXdwHKeddhqOPfZY7Lrrrthtt920ii65W+r4448f9vxR5ZjjRESNl+M0djDHiYhqD3O8+mr6ZoBLL70Utm3jC1/4QsWfkUbeVm30TRqBIyKi+qnj7JlnnsHOO++s3UDBtvx/zjnn6OtFixZh/vz5g8PPmjVLC8LlbnGpqus73/kObrzxRsyZM2dwmCOOOAKXX365jmOnnXbSwvb77rtvtUY6qbqY40REtYd1lVKlmONERLWHOd5Ad44/++yzuOqqq/Dcc89pQ5yVkjsKpSBl1TvHWUBORFQ9wyn0ttZj/Pvuuy/iOF7r+7fccssaP/PXv/51neOVKlRYjcrGwxwnImrMHKexgTlORFSbmOMNdOf4H//4RyxdulQfoZe7x6V788038ZWvfAUzZ85c6+ekkbd3NvxGRETVwwZAqBLMcSKi2sQcp0owx4mIahNzvIHuHJe6xg844IAh/eQReenPOmKJiEaP3EVW8R1na78BnMY45jgRUW1ijlMlmONERLWJOT7GCsffrdG1jo6OIcM7joNJkyZhyy23HIW5JSIiYQ6jIS8ZlsYu5jgRUf1hjtMA5jgRUf1hjo+xwnFpdO0DH/jA4OuBusKPPfbYNdYpS0REdVbHGcvGxzTmOBFR/WGO0wDmOBFR/WGOj7HC8XdrdO2d5s2bN6LzQ0RElddxVolKh6P6xBwnIqo/zHEawBwnIqo/zPEGqnOciIhqE3+pJiIiql/McSIiovrFHK8+Fo4TEdGw8JdqIiKi+sUcJyIiql/M8epj4TgREQ27AZBKG9pkg5xERES1hTlORERUv5jj1cfCcSIiGhbDMmCYlRWOG6xznIiIqKYwx4mIiOoXc7z6zBEYJxERjWGmZQyrIyIiosbJ8cceewyHHHIIpkyZoj+S33PPPesc/pFHHtHh3tktXrx4A5aSiIhobOL1ePXxznEiIhoey4RhVvjbqhFz7RIRETVQjudyOey444444YQT8NGPfrTiz7366qtobm4efD1hwoRhT5uIiGjM4/V41bFwnIiIhkWqVJFHuSoaFrxznIiIqJFy/KCDDtJuuKQwvLW1ddifIyIiaiS8Hq8+VqtCRETDCw5Wq0JERFS3ajXHd9ppJ0yePBn/+Z//iT/96U8bbbpERET1pFZzvJ7xznEiIhoWeRS70sexjZjVqhAREdV7jvf09Azpn0gktKsGKRC//vrrseuuu6JUKuHGG2/Evvvui6eeegrvec97qjINIiKisYLX49XHO8eJiGh4wcFfqomIiBoqx6dPn46WlpbB7pJLLqna/Gy55Zb4zGc+g1122QV77rknbrrpJv373e9+t2rTICIiGis2xvX4tddei5kzZyKZTGL33XfH008/XdHn7rjjDm1U+7DDDkM94Z3jREQ0LFJPKescJyIiapwcX7BgwZDGMqt11/ja7Lbbbnj88cdHdBpERET1aKSvx++8806cdtpp+lSXFIxfeeWVmDNnjjacva7GsufNm4evfvWr+I//+A/UG945TkRE6xHGZoUd6zgjIiKq9xyXgvFVu5EuHH/++ee1uhUiIiLa8BwfjiuuuAInnXQSjj/+eGyzzTZaSJ5Op/XJrrUJwxBHHXUUzj//fMyePbvuNhnvHCciomEZzuNZ5nr8Uk1ERET1m+N9fX14/fXXB1/PnTtXC7vb29uxySab4Mwzz8TChQtx66236vtyR9qsWbOw7bbbolgsap3jDz/8MB544IFhT5uIiGisG8kc9zwPzz77rGb14DhMEwcccACefPLJtX7uggsu0LvKTzzxRPzxj39EvWHhOBEREREREVXFM888gw984AODr+XRbHHsscfilltuwaJFizB//vwhF+Jf+cpXtMBc7kzbYYcd8Pvf/37IOIiIiGj9Vdqw9vLly/Uu8IkTJw7pL69feeWVNY5bqkH74Q9/qD+E1ytWq0JERMMiDWwYZoWdMfINgOy7777leXpHd/DBBw8Oc9xxx632/gc/+EFueSIiajgjneOSy3Ecr9ZJwbiQv4888sjg8KeffrreaV4oFLBixQr84Q9/YME4ERFRFXN8pBrW7u3txSc/+UnccMMNGDduXN1uM945TkREw2JapnYVDRubI94AyF133aV3nQ2QC+sdd9wRhx9++JDhpDD85ptvHnw90vWlEhERNWKOExERUW3leKUNa48bNw6WZWHJkiVD+svrSZMmrTb8G2+8oQ1xHnLIIYP9oijSv7Zt6zX8pptuilrHwnEiIhq51rHjDWsAREgh+b333qsNgJxxxhmrDS91mK7qjjvu0Mey31k4LicAawp0IiKiRjLSOU5ERES1leMDDWq/G9d1scsuu+Chhx7CYYcdNljYLa9PPfXU1Ybfaqut8NJLLw3pd9ZZZ+kd5VdddZXesV4PWDhOREQjHsaV1nG2vg2ArErqOzvyyCORyWSG9JdHuOXO87a2Nuy333648MIL0dHRUdE4iYiIxgoWjhMREdWvkc7x0047TdsJ2XXXXbHbbrvpk9y5XG7w5rVjjjkGU6dO1apZpBrU7bbbbsjnW1tb9e87+9cyFo4TEdGIP8b1zl+Mzz33XJx33nlVaQBkVVI3+csvv6wF5O+sUuWjH/0oZs2apY9+ff3rX8dBBx2kBe7y2BgREVGjYLUqRERE9Wukc/yII47AsmXLcM4552Dx4sXYaaedcN999w1eo0uj2nID21jCwnEiIhqeYfxSjf5fqiut42xDSaH49ttvr79wr0ruJB8g7++www5a95ncTb7//vuPyLwQERGNlRwnIiKixsnxU089dY3VqIhVG9Vek4EGuOvJ2CrqJyKiEWcaBkyzws4YWsfZQFetBkBWJY96SX3jJ5544rsuw+zZs3Var7/++rCWnYiIqBFznIiIiGoDc7z6WDhORETDYljmsLrhWLUBkAEDDYDsscce6/zs//3f/6FUKuHoo49+1+m89dZbWLFiBSZPnjys+SMiIqp3I5njRERENLKY49XHalWIiGhYTMvQrqJho5FtAOSdVapIi9rvbGSzr68P559/Pj72sY/p3edS5/jpp5+OzTbbDHPmzBn2/BEREdWzkc5xIiIiGjnM8epj4TgREY1c69jrcVG9Pg2AvPrqq3j88cfxwAMPrDY+qablxRdfxI9+9CN0dXVhypQpOPDAA/HNb35zxOo+JyIiatQcJyIiopHDHK8+Fo4TEdGwDOcxayMyN0oDIFtuuSXiOF7j8KlUCvfff/96zQcREdFYszFynIiIiEYGc7z6WDhORETDYlrlR7kqGjbiyiUiIqolzHEiIqL6xRyvPhaOExHRsBimoV2lwxIREVHtYI4TERHVL+Z49Y3qc3KPPfYYDjnkEK3/1TAM3HPPPYPv+b6Pr33ta9h+++2RyWR0GGmE7e233x7NWSYianhS37dpVdi9o25wGluY40RE9Yc5TgOY40RE9Yc5Xn2jWmqRy+Ww44474tprr13tvXw+j+eeew5nn322/r3rrru0wbVDDz10VOaViIiGNgBSaUdjF3OciKj+MMdpAHOciKj+MMfHWLUqBx10kHZr0tLSggcffHBIv+9973vYbbfdMH/+fGyyySYbaS6JiGi9GwCpcDiqT8xxIqL6wxynAcxxIqL6wxxv8DrHu7u7tfqV1tbWtQ5TKpW0G9DT07OR5o6IqDEYpqldpcMSDWCOExGNPuY4rS/mOBHR6GOOV1/dlFoUi0Wtg/wTn/gEmpub1zrcJZdconedD3TTp0/fqPNJRDTWVVzfeH9HJJjjRES1gTlO64M5TkRUG5jjDXrnuDTO+fGPfxxxHOO6665b57BnnnkmTjvttCF3jrOAnIioioZRrYoM2wjW5ymldf3QO9Ywx4mIakiD5TgzesMxx4mIaghzvOrX2na9BPGbb76Jhx9++F0XMJFIaEdERCP4GFeldY43SLUqUt2XVPtVKRn2n//8J2bPno2xjjlORFRbGi3HmdEbhjlORFRbmOPVv9a26yGIX3vtNfzhD39AR0fHaM8SEVHDYx1na/bzn/8c7e3t77p/yFNQH/rQhxpiP2KOExHVnkbMcWb0+mGOExHVHuZ49a+1R7VwvK+vD6+//vrg67lz5+L555/XwoXJkyfjv//7v/Hcc8/hN7/5DcIwxOLFi3U4ed913VGccyKiRm8d26pw2BCNYMaMGdh7770r/hFXfsV2HAf1jjlORFR/Gi3HGzWjK8EcJyKqP8zx6uf4qBaOP/PMM/jABz4w+HqgrvBjjz0W5513Hn71q1/p65122mnI5+Qu8n333Xcjzy0REf07jCu842wM1FVaCflxdzhefvlljAXMcSKi+tNoOd6oGV0J5jgRUf1hjlc/x0e1cFwKuOWW97VZ13tERDQ6TNPUrtJhaexijhMR1Z+RzvHHHnsMl112GZ599lksWrQId999Nw477LB1fuaRRx7RG6X+9re/Yfr06TjrrLNw3HHHDXvaNDzMcSKi+sPr8epjqQUREa3XL9WVdlS2ZMkSXHDBBVwdREQ0pnM8l8thxx13xLXXXlvxnd0HH3ywPlEsVWx+6Utfwqc+9Sncf//92FiY0UREVC94PV79HGepBRERDQvDeP1Iuxnnn38+9zYiIhrTOX7QQQfhwgsvxEc+8pGKhr/++usxa9YsfOc738HWW2+NU089Vdue+u53v4uNhRlNRET1gtfj1c/xUa1WhYiI6o9hmNpCdqXDNooXX3xxne+/+uqrG21eiIiI6iXHn3zySRxwwAFD+s2ZM0fvIK8WZjQREY0VtZbjG8NI5zgLx4mIaFgarQGQSknj0YZhrLG9jIH+8peIiKjecrynp2dI/0QioV217vaaOHHikH7yWqZZKBSQSqU2eBrMaCIiGisa8Xp8pxG+1mbhOBERDUsjhnEl2tvb8e1vfxv777//Gt+XRsYOOeSQjT5fREREG5rj0kjmqs4991ycd955dbNimdFERDRWNOL1ePsIX2uzcJyIiIbFtEztKh12fUgjXpdddpneTSaNel1zzTXYbbfd1jjsLbfcguOPP35IP7mbrVgsDr6WX5LlQv6GG25AV1cX9tprL1x33XXYfPPNUS277LIL3n77bcyYMWON78t01/RLNxERUa3n+IIFC9Dc3DzYv1p3jYtJkyZpQ1qrktcyvWrcNS6Y0URENFZsjOvxWjPSOT421hIREW00hmloHWeVdcN/tOnOO+/EaaedpoXZzz33nBaOS92jS5cuXetn5AJ60aJFg92bb7455H35lfnqq6/WRr+eeuopZDIZHeeqBegb6uSTT8bMmTPX+v4mm2yCm2++uWrTIyIi2lg5Ljm7alfNwvE99tgDDz300JB+Dz74oPavFmY0ERGNFSN9PV6LRjrHeec4ERHVlCuuuAInnXTS4N3gUqB977334qabbsIZZ5yxxs9I/WJy59mayC/IV155Jc466yx8+MMf1n633nqr1md6zz334Mgjj6zKfH/kIx9Z5/ttbW049thjqzItIiKiWtXX14fXX3998PXcuXPx/PPP6yPRcvF65plnYuHChZrFAxe83/ve93D66afjhBNOwMMPP4yf/exnmv3VwowmIiKqXx8Z4Wtt3jlORETrVcdZpd1weJ6HZ599FgcccMC/g8o09fWTTz65zgtxecRK6kSVAnCpc2zVi3KpnmXVcba0tGD33Xdf5zir4U9/+hNKpdKIToOIiKhWclw888wz2HnnnbUT8jSY/H/OOefoa3nCa/78+YPDz5o1SwvC5W5xeVrsO9/5Dm688UZ9wmskMaOJiKgejXSO14tq5jjvHCciohFvAKSnp2dIf3kce02PZC9fvhxhGOpd3auS16+88soap7HlllvqXeU77LADuru7cfnll2PPPffUAvJp06ZpwfjAON45zoH3RspBBx2kd8vNnj17RKdDRERUKw157bvvvuus91PaClnTZ/76179iY2JGExFRPWrEBjlHOsfH7loiIqIRYRiV1m9m6rBC7uiWu7UHuksuuaRq8yN1kh5zzDHYaaedsM8+++Cuu+7C+PHj8f3vfx+jjQ1wEhHRWMjxsYgZTURE9Yg5Xv0cr+jOcXkUbrikblepV46IiMYWw7JgWlbFw4oFCxZoA14D1taQ17hx42BZFpYsWTKkv7xeW53i7+Q4jj6+PVDf6cDnZByTJ08eMk4pUG8EzHEiItqQHKfRxRwnIqIBzPHqq6hwXBoykzvzXNetaKSPP/44Tj31VBaOExGNQevzGJcUjK9aOL42kjO77LILHnroIRx22GHaL4oifS25UgmpluWll17Chz70ocG6TKWAXMYxUBgu1bw89dRTOOWUUzCS5O71d1bnMhqY40RENICPY9dWRleCOU5ERAOY49XP8YrrHL/77rsxYcKEioZtamrakHkiIqIGDmO5O0pamt51112x22676QVhLpfD8ccfr+9LFSpTp04drJrlggsuwPve9z5sttlm6OrqwmWXXYY333wTn/rUp8rzYBj40pe+hAsvvBCbb765FpafffbZmDJlymAB/Ej5n//5Hy2Iv+eee7Ru9K233hqjhTlORESCF9W1l9GVYI4TEZFgjlc/xysqHL/55pu1jtix+Cs8ERENz0A9pJUOO1xHHHEEli1bhnPOOUcbzJS7ve+7777BXJk/fz7MVcbb2dmJk046SYdta2vTO8+feOIJbLPNNoPDnH766VrA/ulPf1oL0N///vfrOJPJJKrt4x//OPbee2+9071QKGgh/7x587ROtDvuuAMf+9jHsLExx4mIaGPleC2rxYyuBHOciIgGMMf3rnqOV3S2I3fwra1+2LWV3mcyGe65RERj+JfqSrv1IWEnd3+XSiWt/mT33XcffO+RRx7BLbfcMvj6u9/97uCwUkB+7733ap3jQ+bZMPQOc3m/WCzi97//PbbYYguMhMceewz/8R//MXiXlwS1FMhfffXVevf6aGCOExHRxszxWlWLGV0J5jgREQ1gjv9H1XN8g852+vr69Bb2VTsiIhrbDNOoPJBNA42mu7t7sM0NuTtdfr1Op9M4+OCD8dprr6GWMMeJiBpPI+d4PWV0JZjjRESNhzneXvUcH3bh+Ny5c3Wicme4VLUij7BL19raqn+JiKgxHuOqtGs006dPx5NPPqnVuEhgH3jggYPVv4xENS7DxRwnImpsjZzjtZ7RlWCOExE1Nub4k1XP8Yob5Bxw9NFH623rN910k9b/Ko+qExFR4zBMS7tKh2000vjnUUcdhWw2ixkzZmDfffcdfJR7++23H+3ZY44TETW4Rs7xWs/oSvB6nIiosW2MHL/22mtx2WWXabWkO+64I6655hrstttuaxz2hhtuwK233oqXX35ZX0sbYBdffPFah6/FHB924fgLL7yAZ599VlsCJSKiBiQBW2nIjrGL6kp89rOf1TrSpeHQ//zP/xxsPHT27Nk1UZ8pc5yIqME1cI7XekZXgjlORNTgRjjH77zzTpx22mm4/vrrNTOvvPJKzJkzB6+++iomTJiw2vDSJtgnPvEJ7Lnnnnr39qWXXqp3dP/tb3/D1KlTUQ85PuzC8fe+971YsGABC8eJiBqVBFClj1mPscexKyW/lku3KqmSrBYwx4mIGlyD53gtZ3QlmONERA1uhHP8iiuuwEknnYTjjz9eX0sh+b333qs1iJxxxhmrDX/bbbcNeX3jjTfiF7/4BR566CEcc8wxqIccH3bhuCzkySefjIULF2K77baD4zhD3t9hhx02aIaIiKi2GZalXaXDUm1hjhMRNTbmeH1jjhMRNbaRzHHP87S2kDPPPHOwn9ydfcABB2ibHZXI5/PwfX+wAex6MOzC8WXLluGNN94Y/AVBSL3jUg+5/A3DsNrzSEREtaSBH8ceC5jjREQNjjle15jjREQNbj1yvKenZ0jvRCKh3TstX75cy3WljclVyetXXnmlokl+7Wtfw5QpU7RAfcwWjp9wwgnYeeed8dOf/pQNchIRNexjXFbDPo5d75jjREQNjjle15jjREQNbj1yfPr06UN6n3vuuTjvvPOqPmvf+ta3cMcdd2g95FL/+JgtHH/zzTfxq1/9CpttttnIzBEREdU0wzS1q3RYqi3McSKixsYcr2/McSKixrY+OS5tRzY3Nw/2X9Nd42LcuHGwLAtLlizBquT1pEmTsC6XX365Fo7//ve/r7sqt4ddOL7ffvtpC9ksHCcialDGMB7jkmEbULFYxIsvvoilS5ciiqIh7x166KEYTcxxIqIG1+A5XssZXQnmOBFRg1uPHJeC8VULx9fGdV1t7FIa0zzssMO0n2SlvD711FPX+rlvf/vbuOiii3D//fdj1113Rb3l+LALxw855BB8+ctfxksvvYTtt99+tQY5hzMjjz32GC677DKt7H3RokW4++67B1e+kHrM5Vb/G264AV1dXdhrr71w3XXXYfPNNx/ubBMRUbWwrtJ1uu+++7RVbqmv7Z1qoW0O5jgRUYNr4Byv9YyuBHOciKjBjXCOn3baaTj22GO1kHu33XbDlVdeiVwuN9j2pOTo1KlTcckll+jrSy+9FOeccw5uv/12zJw5E4sXL9b+2WxWu3rI8WEXjp988sn694ILLtjgGZGVu+OOO2q9aR/96EfX+MvD1VdfjR/96EeYNWsWzj77bMyZMwd///vf66ruGiKisYSPY6/b5z//eRx++OF6gvDOhkxqAXOciKixNXKO13pGV4I5TkTU2EY6x4844ght/FmyUgq6d9ppJy2UHsjN+fPnw1xlvHITs+d5+O///u8Rr9d8pHJ82IXj77xlfUMcdNBB2q2J3DUuv06cddZZ+PCHP6z9br31Vl34e+65B0ceeWTV5oOIiIahge84q4TUxya/ttfqRTdznIiowTVwjtd6RleCOU5E1OA2Qo6feuqpa61GRRrbXNW8efNQ7zles7cCzJ07V3+hOOCAAwb7tbS0YPfdd8eTTz45qvNGRNTQBlrHrqir2ZgZMfKL+TtPGBoRc5yIqEY1cI4zoyvHHCciqlHMcVRbRXeOS9Umn/70pyuuyuT666/HUUcdhaampvWesYE6at75a4C8HnhvTUqlknYDenp61nseiIhodYZlaVeJSocbS773ve/po15//OMf19g2xxe+8IWNPk/McSIiGtDIOV6LGV0J5jgREQ1gjh9e9RyvqHBcGuD8xCc+UXHh+Omnn44DDzxwgwrH15dUCH/++edv9OkSETXWL9UV3kk2xu44q8RPf/pTPPDAA5qZcge5tMcxQP4fjQtv5jgREQ1q4ByvxYyuBHOciIgGMcdR7RyvqHBc6v/ef//9YduVVVFeKBSwoSZNmjRYn8zkyZMH+8trqQx+bc4880ytf2bVO8enT5++wfNDRET9Griu0kp84xvf0B9pzzjjjCENlYwm5jgREW3MHL/22mtx2WWX6RO/O+64I6655hrstttuaxz2lltuwfHHHz+kXyKRQLFYbIiMrgRznIiIBjXw9fg3RijHKyrtlhZGh0Ma0Gxvb8eGmDVrlhaQP/TQQ4OF4VLQ/dRTT+GUU05Z6+fkREo6IiIaGYZpaVfpsI1GWuqWFr5r6aKbOU5ERBsrx++88069WUmq2pT2oq688krMmTMHr776KiZMmLDGzzQ3N+v7g9Nd5U6wsZ7RlWCOExHRgEa+HvdGKMdHpHC8Un19fXj99deHNPrx/PPPa8H6Jptsgi996Uu48MILsfnmm2th+dlnn40pU6bgsMMOG5H5ISKiChjDeBxbhm0wxx57rBYMfP3rX0etYI4TEdHGyvErrrgCJ5100uDd4FJIfu+99+Kmm27SO73WOBnDGHxyuNEyuhLMcSIiGtTA1+PHjlCOV1ZPygh55pln8IEPfGDw9UB1KLKw8nid1F2ey+W0MdCuri68//3vx3333Vdx3edERFSfv1QP53HsG264Abfeeitefvllfb3LLrvg4osvHjL8cccdhx/96EdDPid3sUmmVFsYhvj2t7+N+++/HzvssMNqjYRIocFYwRwnIqo/I5njckfXs88+q1VdDpC7uw444AA8+eST67xpasaMGYiiCO95z3s0x7fddltUWyNldCWY40RE9aeR7xwPRyjHR7VwfN9999X609ZG7iC44IILtCMiolpqAKTSOs7MEX8cWxrikEaj99xzT/3x9NJLL9VGof/2t79h6tSpg8N98IMfxM033zz4eqSq4HrppZew88476/8DBfYj/Zj4aGGOExE1Ro5L9ZaVVGW5fPlyvXCdOHHikP7y+pVXXlnjJLbccku9q1wucru7u3H55ZdrpkuOT5s2DdXUSBldCeY4EVEdGuHr8Vo2Ujk+qoXjRERUh0a4dezhPo592223DXl944034he/+IW2WXHMMccM9peL+I3xyPYf/vCHEZ8GERHRxszx6dOnr1bNx3nnnVeVjbDHHntoN0AKxrfeemt8//vfxze/+U1UEzOaiIjq3ghfj9eykcpxFo4TEdGwGJalXaXDbozHsVeVz+fh+/5qDUPLHeZy53lbWxv2228/bdOio6NjWPNHRETUiDm+YMECbTTz3Z6+GjduHCzLwpIlS4b0l9eV/kAtj0jLXWGrtk1FREREI3893qjW+ycEKcCQR9yDIKjuHBERUW2TR7iG0/U/jr1qVyqV1jjqdT2OLfWPV+JrX/uaNt4sBeqrVqki9ZLL3eRS7cqjjz6Kgw46SKdVDR/96EdXe+R8XY466igsXboUo4k5TkTUoNYjx6VgfNVubYXjrutq2x+StwOkHnF5verd4esi2SyPTU+ePLlhM7oSzHEioga1Hjlezz66EXJ82IXjckfeiSeeiHQ6rY2kzJ8/X/t//vOfx7e+9a3hjo6IiBqAPI7d0tIy2F1yySUjMh3JoTvuuAN33333kMabjzzySBx66KHYfvvtcdhhh+E3v/kN/vKXv+jd5NXwy1/+EsuWLVvtR4A1dVKf6q9//WttfGw0MMeJiGgkSbsh0li2NIT9j3/8A6eccgpyudxgdWlS5dmqT4hJ+1IPPPAA/vWvf+G5557D0UcfjTfffBOf+tSnGi6jK8EcJyKiRvLLjZDjw65WRU5kXnjhBS1QkDvxBsgdelLv3JrqgyUiojFkOL9AmxvvcWxpwEsKx3//+99ro17rMnv2bJ2WPLK9//77Y0NJ49JbbLEF6gFznIiowa1Hjg/HEUccoRex55xzjj71tdNOO+G+++4bfCpMbq6SKtMGdHZ2alsjMqxUfSZ3nj/xxBPYZpttUA31lNGVYI4TETW4Ec7xWrMxcnzYheP33HMP7rzzTrzvfe8b0hKo3EX+xhtvVHv+iIioxhimqV2lw4qBx7DfzaqPY8sd3qs+jn3qqaeu9XPf/va3cdFFF+H+++/Hrrvu+q7Teeutt7BixYqqPbK9Pg2DTJ06FaOBOU5E1NjWJ8eHSzJ7bbn9zqe2vvvd72o3UuopoyvBHCciamwbI8drycbI8WEXjstdANKg2TvJo3KrFpYTEdEYZQzjl2oZdj0exz722GO1kHu33XbDlVdeudrj2BJ2A1WzSB3icnfa7bffjpkzZw7WTZ7NZrWTR6rOP/98fOxjH9O7z+WH3NNPPx2bbbYZ5syZg2rYZ599UC+Y40REDW6Ec7zW1FNGV4I5TkTU4JjjVTfsnxCksOLee+8dfD1QIH7jjTdW3MgKERHVMTnuG2aFnbFej2NLFSlS4C2PYj///POrPY69aNGiweGvu+46bZTqv//7v/VO8IFOxiGkmpYXX3xR6xyXx7Gk3Qy5O/2Pf/zjWqt3GcuY40REDW6Ec5xGFnOciKjBMcerbth3jl988cU46KCD8Pe//x1BEOCqq67S/6VeuEcffbT6c0hERLVl4IK50mFH+HHsefPmrXNcqVRKq1uhMuY4EVGD2wg5TiOHOU5E1OCY41U37LOd97///XoXnxSMb7/99tqyuFSz8uSTT+qdeERENLbFhjmsjmoLc5yIqLExx+sbc5yIqLExx2vgznGx6aab4oYbbqj+3BARUe3jL9V1jzlORNTAmON1jzlORNTAmONVN+xb+np6etbY9fb2ap2vRETUCHWcDaNrMOeeey7efPNN1CrmOBFRg2vgHK/1jK4Ec5yIqMExxzHqheOtra1oa2tbrZP+Uq/rjBkz9KQjiqKqzywREdUA0xxe12B++ctf6h1d+++/P26//XaUSiXUEuY4EVGDa+Acr/WMrgRznIiowTHHUe0cH/bZzi233IIpU6bg61//Ou655x7t5P+pU6fiuuuuw6c//WlcffXV+Na3vlWVGSQiotrCOs7WTdrl+Mtf/oJtt90WX/ziFzFp0iSccsop2q8WMMeJiBpbI+d4rWd0JZjjRESNjTn+l6rnuBHHcTycD0jp/Gc+8xl8/OMfH9L/Zz/7Gb7//e/joYcewo9//GNcdNFFeOWVV1ALj521tLTgH/PexuRxreguhXDM8uOBWdeEF5YXP+dHaElY6C2FsC0DWceEvNNdDCH3wA98RqRsA63Z9KgtExE1jkVdOUxuzWBxd06PX5MyDpIIgChEwXBRCGI9dplxiAAmbAMohjFSURE9vX2YMG0Guru70dzcXLXj6Yp/PI3mpmxln+ntQ8fWu1VtHuqN7/v49a9/jZtvvhn3338/ttpqK5x44ok47rjjdF2OhnrN8RWP34WWqbNghJ7Wsxd1L4eRysBwEoj6usoDB772i3I9MGxXe8lr+b7EhRwir4g41wN70iawttt/dBeMiBrC3xf3IIhi2KaBMI7hWuVrChMGLBNYtdISud6Q65C8HyFhmejt6cHWM6cwxxsoo8dyji996004LeOQsE2E8p0wAD8GEn4OsWkjthP6Wt6T89t2J4LhFxAlmhDEQJ8fIWkZSPs96LaaMb45jWU9ef1LRDTSCsWi5rkev0wDTljS63GRNGMYoQ+YFhBHKBouEiawsrsHUydPYo7XQY4P+1aAJ554AjvvvPNq/aXfk08+OdiC9vz584c7aiIiqqcGQCrtGpj8/iyhLW1yyP9SDdn3vvc9TJ8+HXfeeeeozBNznIiowTHHazajK8EcJyJqcMzxquf4sEstZCI//OEPV+sv/eQ9sWLFCp0pIiIagxjG7+rZZ5/FqaeeismTJ+PLX/6y/oD8j3/8A48++ihee+01vZvrC1/4AkYDc5yIqME1eI7XckZXgjlORNTgmOOodo7bw90Gl19+OQ4//HD87ne/w3vf+17t98wzz+gjWz//+c/1tdT1csQRRwx31EREVAdiw6i4DlIZttFsv/32mokHHnig/nB8yCGHwLKsIcN84hOf0DrSRgNznIiosTVyjtd6RleCOU5E1NiY469UPceHXTh+6KGH6gmF1Gf2z3/+U/sddNBB2jDnzJkz9bVUhk5ERGPUcO4kG4N3nL0bqQP0hBNO0Iaq12bcuHGIImnRYuNjjhMRNbgGzvFaz+hKMMeJiBoccxzVzvFhF46LWbNm4Vvf+tb6fJSIiOqd3EVW6Z1kY+yOs0oM1Hf2ToVCAZdddhnOOeccjDbmOBFRA2vgHK+HjK4Ec5yIqIExx1HtHF+vwvGuri48/fTTWLp06Wql8cccc8x6zQgREdWJBv6luhLnn38+Tj75ZKTT6SH98/m8vlcLF97McSKiBtbAOV4PGV0J5jgRUQNjjqPaOT7swvFf//rXOOqoo9DX14fm5mYYq9xNIP+zcJyIaGyTekorr6t0bF1UV3pX2qrZOOCFF15Ae3s7RhtznIiosTVyjtd6RleCOU5E1NiY40bVc3zYheNf+cpXtJ62iy++eLWSeiIiagByoWw25h1n6yKPacsFt3RbbLHFkIvvMAz1R2W5W220MceJiBpcA+Z4vWR0JZjjREQNjjmOauf4sAvHFy5ciC984Qt1VzAexjESRgTHNNDkmuj1IvR5EVoSFrpLIVK2id5SiDAGfL88XMY2kHJMeGGMMIoxUIFM1jTx6tKe8ngj6Dje7vWQcS3ManWxuM/X9+Z3l5B2LOT9EFt0pGCbBlK2odNucwEfJuZ3+1pd0LiUhUIQY3k+gGsbWNLnYWLWhRfESNomuks+xqdd/KuzgPaUA7+/OptiEGG/zcaP2nolqldzl/ci61oIoli/l3I8KIYxpqeBvsiCHGqXFQK48r11TP3rRXIsAJK2AdcysCwf6LiaXPmeR2hNWjqejgTQG8h3PdT3HBP6/Zb3IsQYl7KRsWIsLcb90zcRxUPvaMo4Bnq88vd8YWcOLQk5RkV6HIsNW+dP5iXrmnp8g2GhuxhinO0jggvfTgFx+ThVdQ38GNe6XHnllbr95AdkeaSrpaVl8D3XdbXR6j322AOjrV5z3Exlyv/IHY+mDTPThKiQA6IQCHwYmWZAWiqPQh029n0YjoO4VICRSAGmpcPFkp+2i+iNpxGuXAxEEWIZh4zadhB7RR02XDJfh5PPm01tg+/J36iYR1zIweqYhHDZQkSBj8gLEBY9OM1p/Suvc4tXIDW+FamJ4xEW8joNK5HQ4YNcEVbSRRxGaD7+gtFctUR16YhbntYMbU27mLe0DzvOaNPzdemakjZ6iwHSrqXD5r0QBT9EyrHQmna0nwxnmYYON609pf06Mi7+tSwH1zaRci3tLMNAW8rRc/pFvSX9P4pjjEu7er2QsE30eaGeUzimqeftcu7vWOWLNslqy5RzBxPF8mnDEHLtIeORcdqWgYIf6TWDZLt8bkQ0YI7XS0aP5RyXi175vpheHqbloBBZSMFH5KSQDw2k4whuHCG2bD1nlqyPE00oRYBpAJn+63Ih29Jb+TYyqXb4S+ai1DQJTliCbyWQXvw3BB0zgShA7GZQiC39TiW75qPYugmcyINZ6EZXYpyeo8s5eCYuogdJrCgEaE9a+n1e0OvptNoSFpod6DxmDF/ny4gCeKaLRH4F3HHTRnnFEtWffKEIL4zgWuVrWTcOgDiC1f02oux4hIksrGIPIjeDADJM+XOS+wnLgN1fOtfpla+H3chDZCdg9y5F7CQRWy5CO1ked+TBCEqInRR8w8aKQogJGVvL/1rjHIJEM5yeRYiy4/R4kTJCRJaj77fYEYqwNafNOIQbBegObR1Gjml+VD42LS8EGJd0NDPl+CDHqCA20CMHsJHAHEe1c3zYheNz5szBM888g9mzZ6/3RImIqI41YBhX4thjjx1sJGvPPfeE45QLYGoNc5yIqME1YI7XS0ZXgjlORNTgmOOodo4Pu3D84IMPxv/7f/8Pf//737H99tuvNkOHHnpoNeePiIhqTQOG8bvp6enRdjjEzjvvrK1lS7cmA8ONFuY4EVGDa7Acr6eMrgRznIiowTHHUe0cH3bh+EknnaR/L7hg9UeApSoAqeuFiIjGrtgwhtGQ1+qNZYxFUpfpokWLMGHCBLS2tq6xsa+BKnNGOyeZ40REja3RcryeMroSzHEiosbGHDeqnuPDLhyP+uu63hhkoc477zz85Cc/weLFizFlyhQcd9xxOOuss9Z4UkNERBtBg/1SXYmHH354sHVs+b+WM4o5TkTU4Bosx+spoyvBHCcianDMcVTbsAvHN6ZLL70U1113HX70ox9h22231brOjz/+eK14XRohISKiUSAXlZVeWK7nBei1116Lyy67TH8Y3XHHHXHNNddgt912W+vw//d//4ezzz4b8+bNw+abb6758aEPfWjIL8nnnnsubrjhBnR1dWGvvfbSfJFhq2GfffYZ/H/fffetyjjHAuY4EVENqsMc3xDM6PXHHCciqkHM8dooHM/lcnj00Ucxf/58eF65FecB1Sy0fuKJJ/DhD39Y61UT0vroT3/6Uzz99NNVmwYREdXWL9V33nknTjvtNFx//fXYfffdceWVV2rjU6+++qo+Er2mrPjEJz6BSy65BP/1X/+F22+/HYcddhiee+45bLfddjrMt7/9bVx99dX6Y6s0xiUX4DJOaT8jmUyimm6++WZks1kcfvjhq1345/P5wUbBRhNznIiogdVhjjdSRleCOU5E1MCY46h2jg/7bOevf/0rNttsMz2BOfXUU3HhhRfiS1/6Er7+9a/riU81SUviDz30EP75z3/q6xdeeAGPP/44DjrooLV+plQqaaMrq3ZERFQ9Uk/pcLrhuuKKK7Q+TXlSaJttttGL63Q6jZtuummNw1911VX44Ac/qI1Fb7311vjmN7+J97znPfje975Xnt841nySKrnkB9cddtgBt956K95++23cc889qDa5uB83btxq/aVA4OKLL8ZoY44TETW2esvxRsroSjDHiYgaG3N8XNVzfNhnO1/+8pdxyCGHoLOzE6lUCn/+85/x5ptvYpdddsHll1+OajrjjDNw5JFHYquttoLjONq6uBTEH3XUUes84ZFqVwa66dOnV3WeiIga3sAv1ZV2wGo/WsoPmWsiTyM9++yzOOCAAwb7maapr5988sk1fkb6rzq8kDvUBoafO3euPta96jCSD3I329rGuSHkqSq5O/2dZsyYoe+NNuY4EVGDq7Mcb6SMrgRznIiowTHHUe0cH3a1Ks8//zy+//3v60mOZVl6YjR79mx9ZF1uX//oRz+KavnZz36G2267TR+tkzrHZdpSOC4Nc67tVvkzzzxTH+MbICdvUkAexjHyoQE/ipD3y42K+lGMnB8hYRkohbG+tkwDQQikbBOlKMbKQgjXMtCatNBbClEIYvR5EXqKIRzLgGuZOr6EbaKr6COOXYQx0F0MdZoi7Vg6bvmcmSr/H8DSaVtmeT5kvCnbQEvSKn82imHCwN+X9WKfma3wIwsrCr6OS6b1yuIcNu9IY3zGxR0vLETeD/GvZTlkkzY2aU3pMPe88Dbev/l4NLkWNu/IYH53AaUgQkfaxcSsizdW5nXe567I4bBtJ+GKR97AB7ediAVdBew3exzufWWJjq/ghbpezvhAdermJVqbfKE4WLVlEMV4u8/HJs0uuoqhfk+9KEZ71IvFcVa/lxnHhB/GSDmmDi8fXZwLMC5lIRV7mFcwIV/Dmc1O+TsfA8vzAdpTNhz5rvcfA5pcG2nHRCEI0RNaOmwzilga2zqNPj9CNm2Xv9t+gOaEg0SxE1HcBMsATAOI4hhv9fjoSFsw/QJgpJGwTH1PW02OIj2OLM0FKOhxx4IXBjp+IX/KRyYDpSCGF0o/Az1eiKxj6XdVllmCMIIB07SRRQAPti63rLeMY8Dwi4htF07koVTqHcHWsSurg3RguHf+UCn1f0uDy++0fPlybYx54sSJQ/rL61deeWWN05CC7zUNL/0H3h/ot7Zhqkl+tX7xxRe1KrBVydNPHR0dGG31muNx4CN2EjByORhWhCjXC7O5HbFfAuzydzwuFRFHIcxMM8xMEmH3ChimBSOVgSRy7HuwOyYBUYiorwtxMY+okAMCD1bHZGnlTDszmUHkJgGzfDwwTBNGIonYK+prM5lGmOtBXMzpuIPFi+E0peHnCvpeqbMXTiYFryeH1PhWHcYMPAT5IooruvU9wzIRFj2Eno9/ffl/0L71TLhtrVj0+HNIjW/T90pdvejYdraOt2/hMmQmdyAolKuzi6MIVtKFk0li8VOvYOKuWyAoeoi8AIVlnTr+ZHuL/tXXponZ3729atuWaE3ef+kfkEjZCMMIuZ4SbMeCm7BQzPlwU7a+lqd5upfnkW5KwE3a6O0soGNiFkEQ6eemj8/g7ZUFJFwLKddCRzaBN5fndPxyPipdT28JpmVi2rg0Cl4AQ8IWQF8pgBeEaE078MIIXiHCtLbU4Pnsou4i0q6FroKP1pQD1zb1MzKdvmKAqP9aQF57QaTvdxcl210UgwideQ9tKUfzXc69s66l5yGS15Zh6LmAnP+bRvkaQeZB+rmmiWIg44YO54UxYiOGERt6nmCbhp5jhBH0r1m+hNDXI6HecryRMnos57ico5qhr/08w0ZSroNjF66fg4cUkrYF28shTDSjKWHBCD0977UMG7acb5d6YSebYRSKcNPNQAhYkY/YcvR7Y1sOEj1vI2yaICGpnzVKvXBT7TDjEGaxfF7cGdpoS2QHv3vydQuNNNyo/MWT76dcH0iZQHfJ13KBZjlG+CFcyd2oPF9u5MEIfSzszKEjAURG/3V+FCNr+DCKvVhmtWF83I040QSj0A04SRTsDLLLXoU/cUsYQQlLfBtTSovgtU7HykKArGsiXVwJL92hyyXn/+7iVxAlm+BM2rRq25ZoTZb15NES9gCmDbNvGfpaZiBpxrqvwnLQF5XLsbL917BpK8aKEtCRNPU7u7hk6TVvKsgBfhFRpgPFUK5tY7TGOURuRsdlxxEsw0RfnETGNlCMbSSjAowoAKIARhyj4DQhYQAFv1xW5sQBYttGEEPHETspuFYMNw70u+gZLiwniVCOE5LFiLX8QMi4krGH2IDOn+nl0WJZiJHQrJbjh9yNLRkdG7aW/cl3LzQdJCCZDZQiE6liL0K7TY8xvumiJNfqromOtK1lisk40HUkx4AYNjy5thgBzPEXq57jwy4clzu4JYgHTi6kZF4ef5O78BYsWIBqkkfrBu4eF9tvv73epS53h68tjBOJhHZERDQy5AeH/t/+KhpWSD40NzcP9h/Lx2mpdkza32hqasLee++t/aSdji9+8YuDeTaamONERI2tkXO81jO6EsxxIqLGxhz/QtVzfNiF41K1yV/+8hdtRVxa/j7nnHP0DoEf//jHVW8wRSpTHyiIHyC/jkcj9OsLERG9O7kLTrpKDAwnF9SrXlSvjdQDKsf5JUuWDOkvrydNmrTGz0j/dQ0/8Ff6TZ48ecgwO+20E6pN6kqdN28e9t9/f9h2OWYlt4455piaqM+UOU5E1NjqLccbKaMrwRwnImpszPF5Vc/xYdc5LhMbKFy46KKL0NbWhlNOOQXLli3Tx7uqSeo2l2nce++9ehJz9913awMvH/nIR6o6HSIiqg2u62obFtIY8wAJO3m9xx57rPEz0n/V4cWDDz44OLzULSoX2KsOI4/4PvXUU2sd54Yuw5133qmPj8ujyHfddRfeeOMNbYhM3httzHEiIqqnHG+kjK4Ec5yIiEaK26A5Puw7x3fdddfB/6Valfvuuw8j5ZprrsHZZ5+Nz372s1i6dKnWbfaZz3xG71YnIqLRoXU3D2PY4ZJ6KqXqLMmb3XbbDVdeeSVyuRyOP/54fV9+FZ46dapWsSXkESp5kuk73/kODj74YNxxxx145pln8IMf/EDflzrfpX7MCy+8UJ96ksJyyRbJlMMOOwwjZYstttDpDcxDrWCOExE1tnrL8UbK6Eowx4mIGhtzHFXP8WHfOb42zz33HP7rv/4L1SR1yMjJlNQzXigU9NcAKdyol1/1iYjGImnXZDjdcB1xxBG4/PLL9YdQqfZEGn+SH2IHGuuSti4WLVo0OPyee+6pDUXJRfSOO+6In//857jnnnuGVPV1+umn4/Of/zw+/elP473vfS/6+vp0nMlkEiPh1ltv1XYyUqmUdjvssINWP1bLmONERI2hHnO80TO6EsxxIqLGwBy/teo5Pqw7x++//369NV4Kpz/1qU9pq9hyK7s0mvnrX/8ac+bM2aCZISKi2hfHsXaVDrs+Tj31VO3W5JFHHlmt3+GHH67d2sivyRdccIF2I02q/5I702X+99prL+33+OOP4+STT9Y2Or785S9jtDDHiYioHnO8ETK6EsxxIiJijp9d9RyvuHD8hz/8IU466SS0t7ejs7MTN954o55cyJ14cnfAyy+/jK233pp7KRHRGDecO8nW546zeidVgl133XX62PiAQw89FNtuuy3OO++8UbvwZo4TEVGj53itZnQlmONERCSY49dVPccrrlblqquuwqWXXqol8T/72c/07//+7//ipZdewvXXX8+CcSKiBqzn7N26RiSPissj4u8k/VZ9jHxjY44TEVGj53itZnQlmONERDSAOV7dHK+4cFzq+x541O2jH/0obNvGZZddhmnTpq33xImIqP6MdB1n9W6zzTbTH5HfSVrVHmg0ZDQwx4mIqNFzvFYzuhLMcSIiEszxn1U9xyuuVkUaxEyn04N1tyYSCUyePLlu9kzXNJH3IwRRjNA0EEYxso6J7lKIcWkbpTBGEAGWWe4W53xIe6dhHMOPoJ/z+s8OC0EExzJQDCJkXQteGMMyDPhhjB4v1NdLcyU4lomEbaKz4KMU2PBlHGGMCSkLK4oRWhKmvpb5ksZVO0shghBYnvdQCiN0l3z9fMYx0eeZeKunhD4vQGsqi63GZdDrBZjenMCSvhKStqXztu/sDrSlbLy6PI8dpreiJWljalMSeT9EKYgwtTkJ0zDQXQwQxtD5n9KSxIwWFzM60nhjeQ4p18IbnXl4QYTWlAPLNPT/b/3hNfznZuP1vb8u6NLhPrHjFCzoKeI3f1uC1rSDbNLGfrM7cOOf52PP2e0ohhG2GZ9FxrXw8xcX4T9md+CR15djm0lN+p7MU18pQFvKwT+X9GGnaS2YuzKPb+y/xSjvMSR68wX9rsg+sLIQYkLGhhHHMEIPsWnDiAIs923dl+XbUfAjtJWWYYHZod+JJtfU78pTC/uww4Q0CkGMtGOi1wsxPm3rOJfnAzQnLIxPW0gHfQgSzbAiH2FsQaq5lH2kJWHpd0sYfhGwszot+Q7L99IMIg0ImU/5jByjIjuJpBcg65Z/A5TvnGmUf2GVv5EcFwzAtQydL/muy3df6u+S711oplHsLSKCBVeOGTH0u2rCgCOfNky0JS0s7PXRnrL1/ZRjoLcUoTWbwsq+AI5MCPIdho4zact44sHWlEtBjJaETD/SmZJ1nXJMxFZ5/mSwpGXqPMrnojjGgt5A17f080I5xpTnWZbdtUxEZhNShgn4IaJ0e93WcVbPzj//fK1u7LHHHhusB+1Pf/oTHnrooTVekG8s9Z7jRiKjf+MoggEfhuPIC8SFHGCaMGwHsWnBTGVgJNJA6MNsagOiELFXhCH9ixnAdsojlPG4SRhhWP58Mg0Evv4fFXP6HmwXyPfAbOlAlOuFkcwg6lqq7wX5ApxEEnGpiKBYgpV0YVqWTkv4uQLattgEhmXCkHH6AYJCSd8LPR92KqHvhUUPdjKhny8sWYauf62A25TRz1vJBFKbbo7gby/r50zHRtjZBz9f0Nctm06Fk0nByST0r58rT7t55mT0Llii60q6yAuQ7GjB3485BOnJ7QiLJWSnjkfP3EXoW9SDRHMCHdvN1Nfdb3bCyboo9ZQwa86OyC1eiTiUIxJQXNGt47Gcf58+RjKNMNL3ZJyhH6Dr9cXY6Rf3b8zdg9bitF++jO6Cj2zC1hySTJ3QnMAri3pgmSZcybsoRsELNEfy/Vkr+ooB8jkPtmMh8ENM6Ejr+WBPb3k/bm5KoKu7iCiMUSr4aGpLSTSiVAj0r5xvmqaBwI/gpmwU+jx0TMzC80JYdjmbozBCX1cRreMziOR8Q74TUYyEa6HkhTp/fUUfQRBpv46sixV9no5zUltKl8ELAoSSowC68h7SrqXLIZ+b1JLS4WXZZLyit1ReVv1fzoejWJdVli3n/3v55ZxWtGdcNLmWnve3yHmxZLOstxhocuX8PoJjlM93ekshko6p/VosuUYo579O2wQKpVjP5cdnXNj95yzRKncsyfm/nDvoOYlR/jsSGjnHazWjGyHHYyeJ2DBhWPI9MmBEPvKBiYSXRyqdgRmHCJPNeg2u1+x2Qr93aStCPjCQjQKYoa/n4FowlGqD4RdgSD9ZJ/J+qQ/BuNmwOucjSrZopsv3KIQJx0nA9XNodVKwOpcgapqhxwI5LhbCSL+LEnf6V68HDLTA0WvxonzhAThhSadTsDNI+d36f3PChOn1AokmvbZvCbrQ57SipdCJbHsH4rC83F66Awk/h5TfCyMo57VcU5mwEaXbYJd6kLCzSMFH7KbhRB5Cw9X5c9yUTitc8BLC5skoORmdVpPfBbOUA5bOhbfF3lq20SbzU+pF7KT0HCi0k3qtIdcOS3IBZpbmI2ificiw0FkMkbLlGsKAEwcwCt2IMh2IDQOpEWq0nipX6u1C0UwgiUC3ZeQk9RpYtpfsy7GdgJlbod8tIds8tBJYlg90/5XvWbPh6T4gV+tGUEIOLjJmCDPfiVJmPJJd8+G3z4ARhciHhu4PMdzy/pPyNKtMr0+v/UPTgRVHuu85yf7r2diAaUS6f8aWi5RdzjGdt1IfShGQNOU61oCR64XvNsGVkwT5vnp5hG4CcoUdyvmknSh/b+V9nY6BZOQhaSfkW6LLKvtnCBtuLF/WAAnLBuRcXl7LdzfZDNMvojtytNxB8z6MkYQHz3S1zE7Wn0xL5tko9iKRHaffOTmWuKaFyHR0ueU4K8cCGYUce3QtO0m0m3IcsnSe+g8NmvPSeZaNrKxzK6HHNKf/ur/amONHVD3Hh9Ugp9Qzns1m9f8gCHDLLbdg3LhxQ4b5whe+sN4zQ0REtU9OPaJhDNtoPvaxj+Gpp57Cd7/7Xdxzzz3aT9rkePrpp7HzzjuP6rwxx4mIqJFzvJYzuhLMcSIiYo4/VfUcr7hwfJNNNsENN9ww+HrSpEn48Y9/PGQY+WWFheNERGOb3ERW6Y1kY+yGs4rtsssu+MlPfoJawhwnIiLR6DleixldCeY4EREJ5vguVc/xigvH582bx72QiIgaunXstenp6al42ObmZowG5jgRETVijtdDRleCOU5ERII5Xv0cH1a1KkRERI1cx9natLa2DtYlv651oXXXSb14REREo6TRcpwZTUREYwlzvPrX2iwcJyKiYWnkOs7W5g9/+MNozwIREVFFGi3HmdFERDSWMMerj4XjREQ0LHIPWcV1lTbIut1nn31GexaIiIgq0mg5zowmIqKxhDlefeYIjJOIiMawKI6H1TWiP/7xjzj66KOx5557YuHChdpPGrF+/PHHR3vWiIiowTV6jjOjiYionjHH/1j1a20WjhMR0fB/qR5G12h+8YtfYM6cOUilUnjuuedQKpW0f3d3Ny6++OLRnj0iImpwjZzjzGgiIqp3zPE5Vb/WXq9qVd544w3cfPPN+veqq67ChAkT8Lvf/Q6bbLIJtt12W9QiqZy9GErjM+WWXeV/y5TOgBfGWFkIYVtA3pdK3MufKfjlWvbaU+XV5JoG3uguYlZbEmEE9Hkhpjcn4IUhIsRIOxaCMEbWNZGwTZSCCMvznv4N4xhv95bQlkwjF8h0ge5ShFIQw49iWFJxfAT0egGaEhaiYnl8PUUfacdEs2vqXz804QWxjk/I3/aUq9Nb7lroLPhoTdrYrD2t/0/OJtDnBci6NkzT0OE3aUngL2/3IggjFPzy9JNRCZNakljcXcSKPg+tKQftWRe9pQBbjctiRd7DzLYUXNtA0jaxIuch7I3xr84C/tWZ13mZ0Z7W+Zd1OmNcGn9f3Iv3zWzHtCYX83vK41/aV8Kk1iSKYaTzlHZimLLsMdCSdmBbJlKuhZuemY+lvSVsN6kZLQlbl6EYRPj7kl5s0pbW5d20LY15XXm8b1oL/vxWN/61Mo9swsZ+s9uxoLuEHSakcfcryzG5KYEJGRebtyfx29dWYmZrCu0pB39b1qfbRtbrpu1ptCQsuJYJxwTe7vMxLmXjibd6sEVHGrNaE+gqhnhpaU6Hn9maLK//CLBNwDGNcuX/cQzXMnTfaU5YSMj/QYykbaCnFKEjAczrDXUfnJSx4VjlnS2RXwEjjhCl24A4gpnvhN80CVZQROQkddqy/DLOPq+8P6VsU7dd1jGR9yO0JC3dj19Znse4tIttxyVgRAF6QlkuA/2TwsI+H32lCNObHWRdC8vyAVqTFlJeN5ahSedVxiv7siyrzGOi1A3DyMKQ/S6OEFoJFIIIWZQbdLIR6fyHlgUj9JFyTeT8SKdrG8DErKvjyvkBgqi8z4VRjI6UpfMkr1cWQ6QSsq94mJU19PuxpM/T5Us5QG8pQnvKQuwkYcbAQDML+l0MI90nUv0LKevDMkydxyaZlyBGrxfq+Ka3yHci0mOBLd8JP0LKNrCoL0RLwtTptpvl9ZVxLP0FUfZP2QZyrGhKmOj0gA7D1OWTTtaBaQAy9Z5SCGRN3QfchKXvFQLocUH2ARle1oGQ44bMoxwHTBv63ZF1IZ3sUzJ/CRs63QG6Lfvfk31uoKEsWZ8yWBAZMBAhbScQm/6IHE8brXXs4brwwgtx/fXX45hjjsEdd9wx2H+vvfbS92pBXea4YcIIPBhOAlFfF4xECnFQ3sflfxgmzOZ2xIVePU7FXhEwLSAKEfs+zFQTrKZWxGEII5nV96J8D6yWDh0fAl/Hp5+zHcB2YSSSCFcsghyp5H8Zl7diBZzWVlhJF2ZzB+JiDnZ3r86H9PO6+sr/uw7sTBJ+Tx5B5zKUunphJxMIix4Cv6h/RegHaJ41GVYqDfTkkGpLwuvNwXQcOJkkivPeKC9/FMHrycOwTP2b7GjRv36uCDvlIvR8eL15BLkCkh0zEHkB4jDSecqvyOk4Sj0lNM9ydZhIDp6r6J2/RIeL5JzEMREUApiOjfT4VvTMWwQrmdBpR76vnYx7YPxOJqXzqq8dG6EXYd7XjkVxRXd5Xch4JnWguLIbPW+uRNuWk2GaJlb8YyE2/fD70LdwGYJcUYc1XRteTw4d283Gsr/+Uz+XaM0i8gNdfzKfbVvPQPcbC9Gx8za6PfJLO2EnXQRFDy2zp8FsmwB/8QI4E6boNjPcJMLOZbCnbYpwyXyYrRPK+43jwLBdWB2TdLvHbVNhBEUgDBEnMkAU6H4lnex/0i92Uigarp4/yBqU47N0bUlLc08Oyd2lUDN6cZ+n53uOaWJJztNzGTm36y7K33KG95UCnZeEbWF+Zx5vdRYwrS2FWe1pLMt78IIIK/s8bD4xq8Mt6i7CtU095kt/mY/JrUn0FQPtl03a6M776Cr4mNGRRtq10F3wNTeEF0b6vo6rq4DNJzYhNGN4gaHT0iyJYp2GfMayTfR2FuAkbH1f15tkVSksZ5ZknmxjOX9L2sjL+XbOh5Mo57vklGUb8EohbDlPDiK4rqXDCxmvjC8MIrRkXXT1lrBoZQHtzQkUigEKXqjLEPgh0kkbKdk/gqKOf3JLCm915vUzTRlXl1POBSyzfB4u8yfLIp+X9daeccvrQM5fErZm8bT2lL6W5ZW/A8vYkXF1Haac8nLIOVjBD/X8y7FMPXeVc175K+eUXcXydpRzJplmk2vrsHLOLNtZrmJl35D35ZxIesl5hifnD7GBUhjpdUbCMvVzhpznxP8+36+2Rs7xesjosZrjIjIs2F4ORctCxojQ7NqIQ1vPhxNO+XxWrh3keCrn0rZlIGNGMA0LUapNr23C7Hi4WjIUwSx0I0q1lM/F+5bD8AvwDRtOvguxk0ZsJuAY5Wv/yEkjcjM6jJ4zGIApeQxXj+cr8uXvuJyXy7WEnGvLubrMy8REjFCOB6ac28u5ewS4LUiZdvk7IlkRR1qW0Bp6gANEyRY9n5cX+dBAyjF0+mah89/rw80gGxmI9QMhWryVev4h+SPnPEFqgl5PGjKfzRNhlHLwnIx+Vo4tMoysgziR0rIJGVaWNZtbgaBtE1i5pTBSLWiS6yMjXb5ukXUg150w9bovbcUwvT4EiWZYiSx6vAgpx8SirhzGecuw1Bmv12BGFOr2c3oXIzZtvFTIYmqzo+ur9Z5vIXnk13S+7AXP41/tO2GTDPSattPtQGuc0/yUZZR+cs0b2kmd5/bcW/qeDNdi+ojthK5Lq3cp8tlJSIYF7dfjAy12pNsvTDbDDH0drgAHmdwSxG4KUaIJsW4jlPcpy4ATFDTbZf/R80PT0v3MiQN9Ldeush2snsUImyfpPhZZji6LLHMxMpDtXQi/bTrMoFQ+mMv5pXy22Is4kUVsOSjFph7XZf+VbZAywsHrZjMOEcAcLBdIdc7T7VDKjEevV55PeU/Wc28pRHv+beSapyEh49VrSgdWUNLckt1Nrvl0PYW+bgvZP2I3jchO6HmcXHfq/hUDObhw5fuBSMsVUnK96gFtQUmve13ZH6IQXb5c+5bLZcpLD5SSbXCDQnnbSdaHJRiGq+UYMh9uHMDwcnCSrUD07/NKy8/Dt1NwpGxD1mPoI4ic8rmVfHX716+ZW4EW+f7qvihzCBjpf28nuVr2rYSWlRl+Sb9nsm08WWduRoeT9S3rwvDzSMYejJKPwM3KCY++J9vTCD1dBvk2ylfSiTx4pisnnXCTTbDyK3V7yPrT+Qp9pG0bQX/+yecHtnuYKJeJxP2FhmmnfO4k20aWwZQ1I/u5Ger89vaXKVb9WMocR7VzfNh3jj/66KPYfvvt8dRTT+Guu+5CX1/5IvCFF17Aueeeu94zQkREdaL/Ir+SbszdclaBV199FXvvvfdq/VtaWtDV1YXRxhwnImpwDZzjtZ7RlWCOExE1OOY4qp3jwy4cP+OMM7Q0/sEHH4Trlu/AEPvttx/+/Oc/r/eMEBFRfZC7aIbTNZpJkybh9ddfX62/1IE2e/ZsjDbmOBFRY2vkHK/1jK4Ec5yIqLExx1+veo4Pu3D8pZdewkc+8pHV+sujXMuXL1/vGSEiovpQ6d1mg3edNZiTTjoJX/ziF/UJK6lS4O2338Ztt92Gr371qzjllFNGe/aY40REDa6Rc7zWM7oSvB4nImpszPEvVj3Hh13neGtrKxYtWoRZs2YN6f/Xv/4VU6dOXe8ZISKi+tDIdZxVekdXFEXYf//9kc/n9fHtRCKhgf35z39+tGePOU5E1OAaOcdrPaMrwetxIqLGxhyPqp7jw75z/Mgjj8TXvvY1LF68WEvp5eTiT3/6k86IVIhORERjWyP/Ur0uu+66qzby1dvbi2984xtYuXIlXn75Za1ybNmyZfjmN7+JWsAcJyJqbI2Y4/WS0ZVgjhMRNTbm+DeqnuPDLhy/+OKLsdVWW2H69OnaGOc222yjJfV77rknzjrrrA2aGSIiqn2NXMfZuuy44444/fTTMXnyZP2x+IknntCM3G233ZDNZlErmONERI2tEXO8XjK6EsxxIqLGxhw/puo5PuzCcWmE84YbbsAbb7yB3/zmN/jJT36CV155BT/+8Y9hWdYGzxAREdW2WvqlWn4xPuqoo9Dc3KyPGZ944on6w+26hpfHrbbcckukUilssskm+MIXvoDu7u4hw8mTUe/s7rjjjnXOyw9/+EN9quraa6/F/Pnz9VGvzTbbTC9iFy5ciFrBHCciamy1lOMbS71kdCWY40REjY05Pr/qOT7swvEBUqDwoQ99CB//+Mex+eabb/CMEBFRfYjieFjdSJKC8b/97W948MEH9Qfbxx57DJ/+9KfXOrw02CHd5Zdfro9h3XLLLbjvvvu0UP2dbr75Zm1jY6A77LDD3nV+0uk0jjvuODzyyCP45z//qY8+f//738fMmTNx8MEH46677kKtYI4TETWmWsrxjameMroSzHEiosbEHH+k6jleUYOcp512WsUjvOKKK9Z7ZoiIqPaFUbmrdNiR8o9//EMLtv/yl79oXaLimmuu0R9upfB7ypQpq31mu+22wy9+8YvB15tuuikuuugiHH300QiCALb971iUO9EnTZq03vMn477wwgu1/jOZ5mc+8xmd3zAMsbExx4mIqNZyfDTVUkZXgjlOREQDmOOoeo5XdOf4X//614q6559/vmb3VtsykLQM2CbQ64XwwhiFIEIYxbBNeWQesPTReSDrWPp+xrGQckwdRl6HMWAaBoIoRtI20ZKwYZnQ145poBhE8KPyeCdmEjrdtGOhuxTouGU8MkzKNgcfUZTPu5aBMI51PPO6CtpfppV1y9XUmKGv4x2fcTEu7aIlaWF+dxGOaeoyJWwTBT9ENmnrtKTl2qRtYHZbGm0pR+ch74foLgb6v2UamJhxdRoy390FH31w9b2Ua8GT5QhjzGhNoa8YYNP2lM5He8pB0Y90HUxuSaIl5fTPa4wJzQnsPLkZK/s8Xb6dp7Tgj39fAsuArsPeUojpLSld96+83avzPnAnisyjDDerPY0gjDCtJYWkZWJcxkV7yoYfRTqvE7MuuvO+Lovo8wJMbU7qOhQy3xMyLkpBjKV9JSwvhNi0Pa3bSZZN1otjGfr5RX0l3YaTmxLaldenga5iCL//gqEQxBiXdnR5EybQWQwwrTmh21LI+GTa8jkZ1jGhn5fpT06bus3yfoRY77oBMo6BLl+GCdCWLG9b6S/7FiwbUaoFCH0YfgEwygsV2wndb1oSls5Hzpf9x9B5kP1M/npRrFU+FPwIaae8TNmEiUAehTXt8v4aeyiF5X1M5nd6s6PrVBZF9n/pD9PW/UbmW8Yr10LyGXk/djO6z8o4jTjCsnyAorwAkAsiFCMDkeWgGMY6z+Xvj4leL9J+XhDrZ4JI9mtTv2OuZWJRn3w3oPuxLFvkJPX7sKwkyyjDynes/N0RWcPH0rhcn5T0kfUu61jmQda/EYW6fhJWeZv0f0zXi/ST8SUsU+dBxi+flfVqI9Jjg04rjnUe5PNC/sqw0n9S1kZvKSpvN7dJ10Fr0tL1L+tJlknWbSk2dVwyBtk+A9ta59Eoz8/cbk+3RXvS0nHL9pD1Jute+us+3f9aXso2knE2uaZ+x2xDHqcqH5v0ONG/T8h0pFdsWohQHk8t/FLd09MzpCuVShs8H08++aQWYA8UjIsDDjgApmniqaeeqng8UqWKVMuyasG4+NznPodx48ZpPWY33XSTru/hkrvT5C416SSoTzrpJIyGsZDjcuyBaQLyV44BuR4g8LRfXMiVj5s6jKXHszgMYdiO/lWhr+8Zjouod6W+Z7hJGKmM/o28Ynk46W87MDNNiEtFxF4RUfcKIPB1OCvpwkxlEEflacWBDzuTROQH2hn9oSR/C0s7EYUhIi9AorUJQbFU/hyAoOgh9AMkW5vg9eS0X2FZl/51mzLl+TdNlLp6dVzyeZFf2tn/Ogs76Wo/03Hg9eYRFks6Xvnfac7oezJPxc6ifibZltR+Mq44jJBb2gsn4yAKI4ReUB6XacBOOcgtzSG3cDmSHS36WRm3k0kiNb4NXk9ePyuf83Pl9Sbj8/MF/Zud3IRSZy8ir7zu5fOyHDpMMUCQK2iD7tnJzQiLXv/6KMHrzekyyzh75y+B11MYXJ+ZaZMHl12WKbdoBfJvvQ0nm0Hf/CXav3nmZOSXLEfU21le9u4Vun1kGxqJZHk/kXkt5mA1tZa3r+xD0q+Qg1nohlGSfcmAERRhyv9RoPuV7H+GDBvHek4gOeH2H6vl0DCQN3IcHjx+m0DCKp8HyrlIOX8szYBSEKGz4CPXf06jORPF6JDzMzlfDKPyeWj/uWA5G8rZIt1A/9Z0+VxscDjTwKzxGWQTtp4XybhSjqV/816IpsS/c9XzQs0fmabVP+6u3pK+J+dbJS/UfAuDSPcLeV/GGXgRnISFghfqX9spT1s+Z1kmjP71In8t20BzUwKxnItkHBRlvKXyvhb151rUP/6ObAKlQgC/FKJLzieT5WPykq4inET5/9b+c89EwoZrm0i75enLeFb0leDa5f9lWeX/Zb3l/bMr7+m8F7xAP9eUtHV+5bxVxiHLIv3ls/K/nAfrMsXl86YVeU/Pt5tcW/NWp5f39FxUzptkeDnfHdhGA/V0D1xfSD85T5D/5a+cb8kQsg/JNGSNaXbHse5P8r/sL3ED3HE23OrRxL777rta1Wcnn3xy3WZ0w+S435+zgafXYYhCvU4wwvK1sunlytcnUaDXBuXrcNlhQ31dkv/jSP86QQGBFGVIDjupwfNhyf/B6YX9x3fDQCqSY3pf+frITiC23MGzZPlOyvdczunl+CzfW+lk15drXzmuv12AXqvIdVropPU6SY/3UaDn6YaX1/ken7ZhhL4un5+doOMpwtbM0OvMga+TYcIsdOqwmfxSXUZZDjhJXU+GX0KU6UBTlNdz/nD+P7S/le+EE3nl633LQOwkETsJINlULj/wu/R6RJbPLPUiSrfp8iL0sCSvawzF7ESdBSeWsgETRlDS9SrHsqKZKF/TxAE65bo2O1GvYSWzTL8AKyzBzK3UTOws+mgzfT2GpT72hXJWBiU9h5JjoFzPRclm/X7K/PiGrBsPfvNk3W5Lcj56vKg8f3IN55gwiz3lbSbna34efV75XCsfyjUq0BmYMIq9MP2ijm9g++m2lm1vmPoZy8+Xl6PYiR4kESVbyvMW+rD6lpev8+IIoZVAbJi67eJERvdHGa9V7NFpyHzI+gybJ2mZjO43oYfuQC7qTITZcQjtpI5X12kk5wnl6zGdvyiE7fXpeLpLke6n0sm2DjMdeoyXbSB/ZT0PlE1FTkrPNWSLyb5myLViIqvX+7KdEkak194636kWhNnxOl25XpRlG7h21nXZny9Wz2KdtiyX5k5Q1Gti2K5OR/ange+RbBNZF7IPyHCyjmRZ5Esh49bllPny+s9fpfzIsPW7pstjJ/Q76puuzpPMp5YtJFp039Jzdjlf799nZF+VcjItZolCXZ8yn80o6vLIe31GUr9Tsn0lK3P91+pyvWvmVsDwCvo5+S6Wy+VQPr7I/m2YWi4i8y37Y9Fwy+tSDziybJFuW6HrIAp1vJLdTlhCZCe0nEL6S/nBwLGlvL7/fd5nxqEeY4pWqnze6Bc078d6jo+mauZ4RXeO/+EPf1jvCRAR0dgiASsnVZUOK6QR51Wde+65OO+88zZoPqTu0AkTJgzpJwXc7e3t+l4lli9frr82v7MqlgsuuAD77befPoL9wAMP4LOf/axerEv95O/mrbfe0upapPvXv/6F//iP/8D//u//4vDDD9d6zkcDc5yIiDYkx0eKFIxL1WVSPZrv+zj++OM1k2+//fZ1fk4ugCWrB0heV6IWM7oSzHEiIqrFHN/YRirHKyocJyIiGiA3I1QasgM3LixYsEDvChuQSJSfrlmTM844A5deeum7VqmyoeQOdqmbTFq5fmdB/dlnnz34/84774xcLofLLrtsnYXjP/vZz/QO84ceekgL7Y899liccMIJ2lAIERFRPed4rVSPtmph+HCqPmNGExHRWFErOb4xjXSOV1Q4/tGPflRL5aVgQ/5fl3pryISIiEa+jjPJj1ULx9flK1/5ij4atS6zZ8/Wi+KlS5cO6S/1hssj2u92wdzb24sPfvCDaGpqwt133w3HKT+qvza777673mEu1cGsrWBf6i2XwnYZn1zYS/UutYI5TkREG5Lj8oPyqiQL1/VDdzWqR/vIRz6y1s/edttt+MlPfqJ5f8ghh+iP2uu6e7yWM7oSzHEiImrkOsePHuEcr6hwvKWlRevmEVK4MfD/xrBw4UJ87Wtfw+9+9zvk83n9VeDmm28echJFREQbz3DqLlufx7jGjx+v3bvZY4890NXVhWeffRa77LKL9nv44Ye1LmMpzF4bucCfM2eOXtT/6le/QjJZrod5XaQOz7a2tnUWBMgjXu+s5qVWMMeJiGhDcryWqkf7n//5H8yYMUPvLH/xxRf1WvHVV19d501atZzRlWCOExHRxroer0UjneMVFY7Lr/YDhQdyB/nG0tnZib322gsf+MAHtHBcCktee+01LaAgIqLRUW5AtLKQrXS49bH11lvr3d9S7+j111+vdZWeeuqpOPLIIwcfxZYfWPfff3/ceuut2rCmFIwfeOCB+mOr3HE20ECokIyxLAu//vWvsWTJErzvfe/T7JN6UC+++GJ89atfXef81PJFN3OciIg2JMdrqXq0VdsJ2X777TF58mTN+jfeeAObbrpp3WV0JZjjRERUa9fjG9NI53jFhePy6/1AwYE0mrIxTjDkpEruUpA7xQfMmjVrxKdLRET1QR6rlgJxuSiWR6s+9rGP4eqrrx58XwrM5W4yKQwXzz33nD6qLd5ZP9ncuXMxc+ZMrWLl2muvxZe//GVtDV2Gu+KKKzao9evRxhwnIqINUWvVo61q4Gmx119/fa2F4/WOOU5ERDTKheNSKP7nP/9Z63OTgoKNVa2KPO4uj75Lq6OPPvoopk6dis9+9rPrLKCQ+mClG/DO+vGIiGjDRMNo2GOkqziTR69vv/32tb4vhd2SWwP23XffIa/XRO5Gl24sYY4TEdHGyvGRrh5tTVWfCbmDfKxijhMRUS1ej48VFdVgfvLJJ+PDH/6w3jUuBePyS778v6aumv71r3/huuuuw+abb477778fp5xyCr7whS/gRz/60Vo/c8kll2idbAPdO+vHIyKiDRNG8bC6RhKGIR577DG92K8lzHEiIqq1HF+1erSnn34af/rTn9ZYPdpWW22l7wupOkUayJYC9Xnz5unNVMcccwz23ntv7LDDDnWZ0ZVgjhMRUa3l+MY2kjle0Z3j0tiKnKTIo2qHHnqoVnMiLYuPNLlrQBrelLpexc4774yXX35Z65Y99thj1/iZM888E6eddtqQO8elgFxaaHWt8h3vXhQj65gIohh9fgTHDOGFESzTRMY2dTjHNGCbQJ8fwzIM9HkR/ChGS9JGMYixPO9p3T0mXKQdE3IjolR0310KUQwiNLk28n6IrGtjZmsKxTCCYxk6bRMxcn6EloSFZfkQpmEgjCKdTimI4Icx/DDScYYxYBS6YZmtyDgm3uwK0J5yUPBDdBZ9AElMa07AMU2d3sKeos6XbZoYn3GwLOfr/ExpTujn5L2uYojuUoCtxmX0dcI2kbAMTMomdBwtUx1s2p7Got6SfpG6iyGakw6aXAtv9ZQwqzWFv8zvxOwJGew4KYMXF/dgaksKfV6AlrSDcSkL3UUHu2w2TsftheX1Nq+rgHFpB90FD6UgRJ8X6/pZWSghaVuY1JTA35f1YY9pbfCjCMvzvi6X6RhIOxYmZBxkk//eZed3FzG5KYFSEKMt5Wg/WS5Zr7Lepjc7eHV5DluOy6DZLc9HW7I8nGxfWe9Z10KfF2r/Yhgj7Ri6DV3b0P1gSlNC/5Yi+YyJXi/AxKyr62JZXraFhZTsKAh1myZsA7KbBbKV4wiGKfuSbKUAKwMTTa6p45H1WohlnwSStoHYzMAo9SK2k9ohLv++58eAgwi5QPZHmWegKWHBMiPdD2W/zZgmWpMmerxI9+lpTQ66SiEKQayvU7YBIyjBdlx9Ld8CWRfyvuy7y/I+pja5iC0HYSjDl9fVQCf7RtJ24IU+er0I7VYI13J0eYtuAkkr1B/NrPxKlOJmhJkO2CUM7uMDx+Ll+QDj0jbcyNPx9XjyvZP9T/b7WPf3FfkArmnoMi7NBSiFsl4tJPu/u0bo67podi09eC3O+Zje5Oj3aGUxxOSsrK1Al1PWV1fRR1vSQjbqg2MmdXwrCrJtHbQkLV2HhSBCPjRgmYbOs2xbIXcXy/ejNSnf5Qgdst4NGSaEZQJBZOhnZV+S6Zn9n+nxYrTLtoKhxwyZV9ne8t0e+NVWtmObrJv+1zJemZb8lXHJd1ZWm2wfWUeyj8i0srbsg5Huk6v+WizDGEaMNjtCd2Dqssj0rEiOEdUny1lpwx7vdpf2WCM/Ekud5lKX6sbIyUqNhRyXY1Rs2oCdhOEUYaQygGkBxRziKELsFYAohOEmgSiA4biICjkYiZT2l//NTLP+H5eKiLpXwDAtGE4CcRTCampFlOtFnOuBOX4qDMtC2LlMh4m9ImC7QDEPK9Ok0zVME3G+B0HXSliZrL4O8kXEYYSw6MFOJeBkUroMMn+GZcJOJsqfiyKEXgC/Jw+3KY1SVx+SHX362SiM4OcKcJszMF0bfq6IoOChbasZ6Jm3CF5vEcnWNLzePOykq59xMkntTMdBsbMTbZYJJ53U+RB20obXU0Chs4jJe4xH7/wliPwAXs5HLMf6nI/0uBhO0kZmQtOQ7VHq6oXblEHfwmW6DInWQKeVX15AIcwhO7kFoR9g8fOL0Da7FSUrB7c5BdOxdV69nAc76SPZltF+ieZyPcWRVz4+yTLqvPQvj8yzrJ/iim74xUCXT9ZPanw7/HwRYbFUXk9+oMOGpZKOw3IcmE1tiBev0O2l0+/uhZkv6Pumm0ScLE9T3pdtHgfl11GuB5F8JijpNjcnzUJsuUCuC0hkdL8z4gixYQKGHH/Lx2w5xsshTk4DtO7H/vPLlWGo401YkoExJvefX8m5lZwLybmPH/n6V86d5JxFzpfkHOdfS3OY1p7Sc0H5bMEL9dxKPq/nLUkb3Xkfk1uSeqyXTrh2+dwiJZnTf24nr8c3J9BbDNCadrCiz8OKnIeUU8659uYE+oo+ZnSkB8djWaYOK5/p6S0hmXJgu+WbY/qKAfxSUM7HIAISgO1YyPeUn9iU6SVcCyXLRFNTAr0o9/eCCKmsq/87co4WlM+P0v39TNvEisW92G56KzLNCRT6PIRhhJTr6vLLCpblTst5aGdePz9tQhbdBR8taRdLe0poTcuwQXkZTDmvjof8L538n+0/DxQd2f7x95PlKw9j63mrKAYhQlNyunxOLpkv5+iSz9Nbyt/vgWnJviDnBwMkm8vnm+XPyl/bKue85L1uN8tAwoJmvuxPUf+wA/WDjtSzurWU48OtHs11Xfz+97/HlVdeiVwup/kgnznrrLPqNqMbJcejRFYOoYCTLF9HOymEcg1gu+Xr9Kj8XSvGNhJ2+Tuk+59poUuuQaJexG4GCTkUex48IwnXdmHmVqA504HIaINZ6oObX6F5bqSaETtpGHJuEBQBs/wFtbveQpgdj6RtwigWEdquTku+v/JXyDWWHHPlGjQI5bzd1GuLGC7MONTze/nOwgMcmfVUCwwvBxs5/bxcU8j5uxPLeEw4sRyfTPSUIoy3XM0Ve/lcBOM3Rdg0EWaxG7AcwC8CoY/YTen4YieFXBBDkjlKt8HMrYRR7EXBadNrj5SbgeG/pePT9ZVs0vlK9i1DaFqIEk2AYcJPtsHr9pCxUZ4HowCz0I1cdiqyQRFW7zJY41vQXSrPt+RdHIeY2+3ptZYwvLxuO52PYjfSTgZWzyIUrakwi8sQdMzW7Aw6ZiITl8tUYjsBR65TwgAeXDimrdveya0AzA4tN7AWL9Zxdsq1XOjrdbAXmZCpyrAyTisCWhIGVhTK29Lw87CcJGTj2HIMlfNDvwgzjpBJNCMy0jAloxNNcKQ8JY7gWwnYspMlm2DF5c+Ur79MFGILaVlPUf/x122C65e3pV3s0vUo1/dOFCBMNiMt1359y4BUqy7jwLW7Z5ZzLdajumxyC5GZ0HyQ9ao5qetF9gELThTqfimZIPljhr42StyHdiCI9RpU8lzGL1eXMoznRzDsGEZQzrwibKS8Xh1Gtq1c+8p1dq4UYkoK8A27nPFGeZvoKpB5sFwkjEj7O5EHW7ZdLOUpEXr1Gtgsl3NICMoy6Xm4haAUwnPKyymflX10eS5A1rEwPu6GkWpDEJe3X0nKtSTbnBRajXJ5RSIuoGBnkbTK51ZGKaf7u2ZlFMMzE7BkW+msGkjJsaI/MyW3pGxjZTHC+ISlZQPy/dLxhJ6Wv8RWOb+lDEY/46Rg+gUt67FMF3IGJOUgsi7k3Eb3nWT53Nf0i4iN8rJZfr68beWcP4513ci8uTIrsYmCHyFtG1pG5wQFpJ0UjLBU3i+jGLnQQjYuoaf07/OMsZrjG9NI5nhFheNCfrGXTloml2pO0uk0Rpo8GrfNNtusdofBL37xi7V+RhqHWVcDMUREtGHKF+6VD9totttuO33yqdbayGCOExFRreX4cKtHk0JWqW5zrGV0JZjjRERUazm+sY1UjldcOD5ACsfFsmXL9Fd8seWWW1ZUr9xw7bXXXoPTGPDPf/4TM2bMqPq0iIioMtEwfqmudLix5MILL8RXv/pVfexb6lDNZDJD3q+0QbORwhwnImpsjZzjtZ7RlWCOExE1Nub4V6ue48MuHJdH2uTRtx//+Mda38vAre1S19s111xT1TvKv/zlL2PPPffUx7g+/vGPa11zP/jBD7QjIqLRMZy6y8ZSHWeV+tCHPqR/5bHnVRuwHmjQeiA7RwtznIiosTVyjtd6RleCOU5E1NiY46h6jtvrU2Atj7JJ4ydyZ7d4/PHHtaHMr3zlK9qAZrW8973vxd133631ll1wwQV627zUL3fUUUdVbRpERDQ8jfxLdSX+8Ic/oJYxx4mIGlsj53itZ3QlmONERI2NOV59wy4cl/q+f/7zn2Pfffcd8gt8KpXSu7urWTgu/uu//ks7IiKqDY1cx1kl9tlnH9Qy5jgRUWNr5Byv9YyuBHOciKix1UqOr1y5Ep///Ofx61//erBR7auuugrZbHatw0vVYA888ADmz5+v1XMfdthhWkVKS0vLqOb4Km2qV/4Y18SJE1frP2HChMFWxImIaOz/Ul1p14j++Mc/4uijj9aqwRYuXKj9pDoyedJqtDHHiYgaW6PneC1ndCWY40REja1Wcvyoo47C3/72Nzz44IP4zW9+g8ceewyf/vSn1zr822+/rd3ll1+Ol19+Gbfccgvuu+8+nHjiiaOe48MuHN9jjz20pL9YLA72KxQKOP/88/U9IiIa26IoHlbXaOSOrjlz5ugTVc899xxKpZL27+7u1jY0RhtznIiosTVyjtd6RleCOU5E1NhqIcf/8Y9/aMH2jTfeiN133x3vf//7tR3KO+64QwvA12S77bbTHD7kkEOw6aabYr/99sNFF12kd54HQTCqOT7swnGp8/tPf/oTpk2bhv3331+76dOn44knntDb54mIaGyTfA0r7MbYNXVFLrzwQlx//fW44YYb4DjOYH9pp0MCfLQxx4mIGlsj53itZ3QlmONERI1tfXK8p6dnSDdQqLy+nnzySbS2tmLXXXcd7HfAAQdo9SpPPfVUxeORQu3m5mbYtj2qOT7sOse33357vPbaa7jtttvwyiuvaL9PfOITeju9lNwTEdHY1sgNgFTi1Vdfxd57771af6lHraurC6ONOU5E1NgaOcdrPaMrwRwnImps65PjclPzqqRGkPPOO2+952Hx4sVavfaqpIC7vb1d36vE8uXLtb7xdVXFsrFyfFiF477vY6utttK6ZE466STUk1wQIhvG+suJ7Bt5P4JhyK3zBtKOiWJvhKxroRDE8KIQlmHAi2K0JCx0l8qvRd4PkbRN+P0/v/R4EZKWgVdWFNCWdFAMIjQnbIRxjKxrw48iLM8H+hl5r8m1Uco4+n5XMUTGsdBTClEKIjiWAceU+ZFpBtpfPh+n2oES0OdFaE/ZOg8px0JfKUBr1IvJ2QxMw4Af2ToOWb5SEMM2DUzIOliRLy/zpm0JLOzzMbezgIRlYknOw65Tslia8+BEHqY0JzCvq4BNWpK6TsT4pgRc28CKvIeUXZ4/WY5JLUlMb0np64IXYlzaQW8pxLTmJIphjElZB2nXwpSmhM63LJN0M1tT2GeL8bod5PN+KOs9haaErdN9flEPMq6Jt3t9Hbcs6/iMi6RtIOuYcG0TPUUfu05pwd+X9ek2y/kR/DBGyrWQtEw0uSZakjbcOIBjmVjcV8L4CRld5wPa044O05a08cKSPl1eWQ+bt6cwvzuv07QMYEmfhy3HpXR/kW0h2ylhGcg4JgquiZQtnwv08zFiZPr3jSCK9fMy3zIfRdiwzRil/tYQZN8yECNpWzrvrgnEThpGFADSmbbun0U/RmgaOi7ZX8anLVjFHhhWVscdxgYiAL1+jIRtohBESDkmFucCpOxYx2GZBsJkM5wYSEYldMPSfjI/Mp+yT+nmNmTegSbLgGnI/gbdBjKsGYe6/kLZ7y3oX1dmSg4iup0iNHW+Ba9pa5RiC11FHxMytq4nu9QDx0rIBBBEQNFwdYcc2JdkvO1JmSdT94uWhKnzJesxYVn63opCqPtfbJYPWbJcMv1lOU+/o0K2gVXqgxcmdF8RU5sSOp4olUFO96l/PyzjhbHOt/yN4whF2RcdS9eFbDM4Npqi8joo78MmrLCElQVf9xtZblnfsWXqOKRL2AbGpSx9T+bv7d4SnJZE+XgSxmhNWij0T1O2k6xCOeZYZvkXXdssz5esp0j2qBi6LXR8sh1j6DRkL5Lh5DMyX31+hDiO0RmaOr+yfRKOid7h//5ZEd0XKgzjSocbSyZNmoTXX38dM2fOHNJf6kCbPXs2RlM957jSY0AJhu0ApqXHLSPVhDjsQlwqwMw0Iw788v+pDIxEGgh9RLkeHT7q7YKRSMJwHMSlUPvJccVqakXs+zCSaSAnx9jyccUwTcRRiNgrwkhmgEQSYa4XTnMHDBlfIQe7uUX/hkVPh7eSLvxcAcUV3chOn4jID+D15GCFkb4XFEvwevJwMkkUlnXqdNKT2vV/rzeHRHMKpuvAzxXhNqVhWiaCgofcwmX6v5NxEfoBEv3Hs1JXH8Ji+c4PGd6wTBRW9MBybP0/KHoIigFKPSX0vNVTnn5vDlZSjstA6IUodBaRaHZhyfnHm53ITm7S92Q4pzmt05NxCBlforUJqZ4Cehf1oWPrJpS6evU9y7V0uUydtoXWTSdj0TNzERQCdL/Zg5YZzTpc36JuxGGM7ORmRF6AOIwGxy3zJeNwmzLofatLl6Fv/hKkOlp0+Xo7exF6PhKtWcRRhMLSzvKw85cgMWGcjkv2g1L/SbmsfzudAmQ7FnOA7er+IdtPtqv0l3mVfSNum6DbG4Ue3ZZINZdzuT975LMyfGT++7gmx+Q+r3y81ryVv/3njHJOJtnXXfI174qB2X/eYw1erBT8EBnX1vM4+fxAp59f5XVLwoZpGugs+JjWmtLMzyZtPd4PDC/Z2VcM8OaKvJ4nTmhOlPO6f1g5X5P/ZZrS35NzGtscHEfeC+GVAj3X0X0jKOe+zptdnobsg7I2DNPQ8y4dX58HyzYH51XySPfrjIvWtNM/fQcvz12J1pZkeT7kXDmMtJP//WKI7oKvnzdkehKMgH5+aU9Jzyld20JB9pf+8U9oSmBRdxG9KwuYPKsd/1rWp/PUV/Thplwdl0xX/sqyNiVtPXeWZZJO5kOGF/JXOukvf+UR5LaUg6W5SP8OrBNZrzI+PW83jf71B5RkWaLydYKc/8t+Ia/lXED++pDzwvI6lL/aPy6fO8iiDmS8nLPIe3Es29zE0vJkq66Rc7yWM7oRctyQY6xfRAEpOcjpdy62k0jIVz4K9Duh18Ryna7nzwY8w4IpZ8Z2EoZfQJ+VRdaS60M5gbblYIV8ECMbenqNYeVWIAo86BE49GCU+mD6Bc17OZRpvyiAXL1FiSZk4hBBJNdR5e9fdzHUeQ0Rw7DlekyuVyyEcv6eTMLuWQw/O7F8Ph8Geh3WEhbgJdvghCW9Nsu6pl4/FGKg2YkQWQ5yeR8TMzJCD7Gb0nkwAk/n3U20INk5D4ZfzvOgaQLMfKcum5Vshz1lNnrgIJNphxH6WCllG66JVr8bUbIJhlfQ6eVcGxnD03HEtqvDxnZCl3vLtI+loaWfgw8YxV6kW2JEdhvszreQ6FsC0x2PJr8L1spliNyZWJn3sZXdjaV2B5KZDjhLX4M/cUuYS/+p2ydKtyEZ6lVteXn8gs5Ld5TG7DYXVu9SWJnxElC6XY1iEY6TQpzIIgNTy1HC7HjEqRa0OxZipFCMZMvFiLLjUfJimMVulMwmtNhyPQ7EbgZGGMCIY92WBTOJjCywk5STGnhhhJQRQjaerHdfruOiAgI7CzfI6TaPgxi5UK6xZfw9SDRNAIpFhE4KzWaAlZ6NDkSwuxbCb5mq18Jy7JfyFtkPZL9Nhn75PFKO7XZC+7ty/VUK9fpM1o+sf9knI6t83SfvS3lAQvbPOEYpNpGMA8SGrbniWg4sP48gLp+jSR7KNb+sD5mOnDfIPirfCRfl8zI5BZBtGSebkLHL16dSFiTzG1m2XLrD6luu20oO50ZQhGc6+n2Q74XZu0TPp3OtM3W5mhMmekqR7g+l1ARYfct0nS8uGpiYjrXsRbaFZ9iILRdGUEIYueX58D2UIsk5wCx0w0gndRuXYgOxZK9c2+ZWwGprQiD/z/8HjM3eq+MvBjFaTSmLk2w19H0nKqEntNFmBeVzfsvR751c/5teHmapT5dL93cnBQSe7kspy9Tr/FTsITYSg2UIA9fPzVao+4aMA3H/OX8UoA8uEnIe4gf6vZHt51sJOEEJpp3UZZSclrKdtHyVw1jnuwkeTCeNyEnqdpVNFstKkHXRf45WCzm+YMECvUN7QCJR3s/e6YwzzsCll176rlWqbCi5e/3ggw/GNttsM6xC+pHK8WGVnMgt66vWNU5ERI1nOHWXjbW6SishF6tf/OIXcdNNN+lJrdS5Jo+dffWrX8XZZ589qvPGHCciokbO8VrO6Eowx4mIaH1yXArGVy0cX5uvfOUrOO6449Y5jBRCSyH10qVLh/SXesNXrlyp761Lb28vPvjBD6KpqQl33333kOpRRivHh31b4ec+9zn9FUEqXa+0ThgiIho75H6a/ocgKhq20civ7VEUaZsc+XxeH/uSX+YlsD//+c+P9uwxx4mIGlwj53itZ3QleD1ORNTYRjLHx48fr10ljUNLNSbPPvssdtllF+338MMPa8ZKA53rumNcGtSU7P3Vr36FZDJZEzk+7NLtv/zlL3jooYfwwAMPaH1nmYw+xDLorrvuWu+ZISKi2tfIdZVWQn7B/sY3voH/9//+nz7y1dfXp4+LZbNZ1ALmOBFRY2vkHK/1jK4Ec5yIqLHVQo5vvfXWeve33MktDWRKtV+nnnoqjjzySEyZMkWHWbhwoRZi33rrrdhtt920YPzAAw/UQu2f/OQng42DCimQt/qrthyNHB924bi0Rvqxj31sgyZKRET1q5HrKq3ECSecgKuuukofE5OgHpDL5fTXbHkEbDQxx4mIGlsj53itZ3QlmONERI2tVnL8tttu0wJxKQA3TVPLiq+++urB96XAXBrQlMJw8dxzz+Gpp57S/zfbbLMh45o7d+5q9YhvzBwfduH4zTffvF4TIiKisUHqLdPGWSscttH86Ec/wre+9S0N7FUVCgX91Xy0L7yZ40REja2Rc7zWM7oSzHEiosZWKzne3t6O22+/fa3vS2H3QEPqYt999x3yupZyvOLCcanT5bLLLtM6YTzP018Gzj33XKRSqfWaMBER1adwGGFc6XBjgTwSJmEvnTQysmr9aWEY4re//S0mTJgwavPHHCciokbN8VrP6Eowx4mISDDHe6ue42alA1500UX4+te/rvW4TJ06VW9jl8ZAiIioMcO40m4kSWvYRx11lLa8LY8Zn3jiiVrv2LrIL9ZSV9mq3cknnzxkmPnz5+Pggw9GOp3WkJU6zaT17XWR6cuv5zK+LbbYAm1tbYPduHHj9BGw0cxN5jgREdVajm8stZ7RlWCOExGRYI5vUfUcr/jOcbk9/X//93/xmc98Rl///ve/14KDG2+8UeuWISKixhBGld9JJsOOJCkYX7RoER588EGt0+z444/Hpz/96XU+3iWk4ZALLrhg8LUUgq/6y7Pk26RJk/DEE0/o+I855hg4joOLL754reP8wx/+oHek7bfffvjFL36hF+EDXNfFjBkzBhsnGQ3McSIiqrUc31hqPaMrwRwnIiLBHP9F1XO84sJxuYvuQx/60ODrAw44QH95f/vttzFt2rSa30MTlgnLNNBZCNDsWuguhUjYBtKOof3TjgXLAFKuiRX5EJZZ3uH6ogheEMO1Adcy0Jq0YRkGVho+pjQlkLJlvIBjGsj7Ifwo6h+Xge5SgMnZBJK2CT+KsTTnaUuxU5pcSDU7ywoe5nUW0JF2MbkpgVIQYVJTAgm7/GPDor4SHNPEkpKhr/++rA+7TW3Ginyg8zquKQHEvs5/W9LCP1eUkLAsXS6pdN+CgSAEZrUmdXiZj6xj6vzJ/C7tLuqjA+PSDjzTRVchj7aUo/Mtw8j/C7sLOux2E5pgGtD+GdfU151FX0+sXdtEMYiw/cQsluelH9DrhXjfzHZdDxFi+GGMloSt63DLcVldVy1JW+fp0Xkr8Z7JLZiUsZHqX3ctCUfXpby/LOdhk5aELufm4zJoSTqYmLGxsNdGnxdiUW8Jm7SkMD6T0PGWQpmenPX7umzyfkvUB89t0vHJuFKOiWfe7sPUJhebtad1eyzJlcr7im0i45hoSVq6vF4YI4hijEvJ/Ju6Piw/j/ZkSuepI2VhRSHUvzKcfD6MAbmWkOsOz7B1H5L1Vwziwf2hJVHeD5tcC5HlwPILiJ0UzNwKGKEPRAFiqwV+ZKDJkX3Pgh8By5BBi2XANAwdT96P4JoGMo6NgtS15EfIuuVpyHK4cYBiaOv0PcOFFwZoScj+GeuyyTAy3oTrII4D3Q+TlqHzmfMjjEcAHzZynq/jHJd04JUi/U4lTKA7jtHnR5hgJ3S6so3lOyHjkXEL6SfrohBEus8syQWY3eLoNFYWfP0eyT6bsIA+L0J7ykaza2JxXwkxXBRDWSYbRlBEEJYLMWU/GZd2dXxTm2T95RGbjo5LtkN3ScYj6yxGBAMZ20Qy8+8f8qT/K8vz2KIjpdvBMIA380Vs0pxA6DqwDWBBTwmulSw3eBHFMPwc/LB82JRLyoHqsmQ9ybRk3cu6lHdl/OMzLgp+jLQDFMMYRqm8XmV9yPjSsg/kAthm+bWQ/+X7KvMs/bz+fUXWbc6PdVtLfxm/bCdZVtndu4MITa6pw8tSyj441h/j+sc//oH77rsPf/nLX7Drrrtqv2uuuUaz6vLLL19nOEphuBR+r8kDDzyAv//97/oj8MSJE7HTTjvhm9/8Jr72ta/hvPPO0/Bdk3322WewMZHp06fX3A/H9Z7jiELEhglNRMtBbNow4gixJ0c+wEikANMGkkmYTgJRsgmGXwIsF3GwDGZTRsdhuOVH8KJcL4ykAyP0ADepr82WDsReUYeR11G+F2amWV8bqQzCZQv/PT+2I8+4IyyWG5jRebBMmLYD07ERhxG631iI1s03kaME3JYm+H052MkEeuYuguXa6HxtGdq3moliVy+CXFE/YyVdWI6NksyPa6PU2Yew6MFOuQgKHvych/SEZoR+AK83Dzvpomf+ciRay/XulXpKKCztRGpCm+6DMq70uBSCQgDLtVBc0aPjMUwToRfCbUvCSZaPa6Ef6jz4uRKapzUjKHo6vCyXYRqIo1inJ8vnFwNYjgmvNwcrmUCpx4PbnIafKyLT2qTjkfdk3N1v9aKwPI/slCwSzQn9Xz4vy2BnkrrMK15ZofOpq7Z/GoZknWWic+5KjN/ZRKTLXESQL+pwMox0Mp6wWAICH6WuPqR7u3ScMt+985fA7WhHmOuD09IBw3aQX7AAzjQflmzvMETs+7qN40IO9oTpiP0SYsuF6fcijm3Ekj9RAMMvIk63af7K+ePKQqgZLPkxcFyU93Rd9geEnJfIOZJmfxhrlslhUc4lfKN8Djog41iY1p7C9NYUlvSV0Jp0dJwdGVfPLeS474WRjjvrJpBN2ljcVUR71kXBCwezZPb4DFb2efD6MzftWjodGV7+arYE5RJPed1XDDC5JYmCF6A3Yeu4mpI2wpYECsXyEzPJlIMgiOCXQjgJC1EYYevJzegtBli6uE/7yfmgjNe0DP3cjAlZdOU9bDW5WcdpmoYOI+NeIfNXCmA7lg6fzDhY0VfChOYElgI6r5NbUijIPhkVMaMjg9eW9CLvhUgkyvvrpuMz+MeiHv0/5Zanv+qyyTnlwDLK+KQLO+PB/jL8W50FXXb5X9ZZS9rRc1D9nJw3abaWrxFk28p5fBRKrjtIO3JOaOq2lf/l3Ee2ddqJUQrQv1+Uz8n1WsKKEUflcyLdVv1BLfuD/CvDy36h+1J/PzmHHss5vjHVekY3Qo6X4v51bpp6nSTn8vLd8ZCAE0cwwgDJlAkr8vXawzHkuCjnxQaaHSA2EoBpISPHzcjR4SInBcO0kbZiOblHlG6FIddUxTyiRBPMQnd5mGIvokw7YsPQc4nIzSCIAdswERoWXCvS8/lSEMOxytf1Mt23ukuY2pzUc2y53rNKfbC6FqLHGY+JuXmI0m3l+nONclmDUcwjtpNIvPU8jKk7Ii+HUJlmXD6myNcpdtJAoRth8yREqRakTb2qgFnK6eqJnUR5GWy3fF2Y6tBzHrluy/oFRJkOmCVgfNqG0VVC2DIF9tLX9FoxG+V12cIVixFP3lY/HzZNLC+3acOKDb1+GziHks5aOQ/BuNmwVs5HaI/TdRZ3LYE7eZZea5kr/oHmTcbDCEuahb7M7oQtYC0rwiz2IpFsQpieVM5JXc8d8Htk3DHMZf+CK+vIcnV7xokmWH3LdNna/GXIWROQSrehO06gJZacLcGz4vK69ItwrTQMrwAj1YxcZEFPE/pCROlmmIVOXWcpO6nrH35Rt0dSjtWGA8+PdBum5KKuGCBlhMjbWaTkvA8OsvBQNBL6Gcny5tBHARFaHVuvu+Cber4px+YIltbb3FUMdT07tqn7VWiWc9oLy8duM/TR55dzpynKoeRkkOxbgl53fPk6TbahGcIISsgFsc5DH1xkzQC+bJ+wpMui13v9185SfuDY0HED5fIAW960bCRk99LvlKVlB0LyRbIq61h6Xd1seIjdNIpmAqbsiMlWRF4Iv2Uq7FKP7gN++0y9uC1fx8v+GsPMd6HojNdtJ/tLMfThxxYKQYzQdOH6ef2sBJtcA0sGjotCJMMCVkYJuMnyeakcowZyTdaBuTKn60G++3GpCFO2R+ij1U3pOXnakmt+D6ZsVz03MhEbLvJBrOccUuYi1/mSvWFW9kuvfI3gpvS7ZxS6sdRoxaREqNs3GXo6jOUmNHdl2rFj63fZczJwIk+XT7/DUoYm68/N6P4cSxmNrA8joedfsm6FZHUg2R7GkOKO2CzPa3kbla9ZHDuBvCXlFuXrlGpjjk+veo5XXDguj5OvWqeLkLvo5E49IiJqHLUSxk8++aQ+Jj1QMD5woShBKa1gf+QjH1lny9o/+clPtID8kEMOwdlnnz1497iMd/vtt9eC8QFz5szBKaecgr/97W/Yeeed1zlf8qu1kFa55UJW2ulY1Q477IDRwBwnIqJayvHRUKsZXQnmOBERCeY4qp7jFReOy69Hxx13HBIJ+X2qrFgsaj2tmUxmsN9dd93FvZWIaAxbn9axpSGsVUmWrJon62Px4sWrNbph27Y+YiXvrc3//M//DD529eKLL+od4a+++upgfslnVy0YFwOv1zXeAcuWLdPqXX73u9+t8X2ptmU0MMeJiGh9c3ysqNWMrgRznIiIBHP8+KrneMWF48cee+xq/Y4++mjumUREDWagmplKhxXyCPOqzj33XK2iZE3OOOMMXHrppe9apcr6kjrJB8gd4pMnT8b++++PN954A5tuuik21Je+9CV0dXXp3evS+Ofdd9+NJUuW4MILL8R3vvMdjBbmOBERrW+OjxW1mtGVYI4TEZFgjndVPccrLhy/+eabuRcSEdF6Pca1YMECNDc3D/Zf113jX/nKV/RJpXWZPXu2VomydKnUTjv0keOVK1eutT7xNdl999317+uvv66F4/LZp59+esgwEriikvE+/PDD+OUvf6nVvUgVL3KX+n/+53/q8l9yySXa2OdoYI4TEVGjP45dqxldCeY4EREJ5vgvq57j9dcSCRER1UQYV9oJCatVu3UVjo8fPx5bbbXVOjtpFHOPPfbQu7+effbZIRe9URQNFnhX4vnnn9e/cge5kPG+9NJLQwreH3zwQZ3vbbbZ5l3Hl8vlBqt7aWtr00e4B+5Sf+655yqeLyIiolrJ8ZFy0UUXYc8999R2P6QdkUqrFznnnHM0t1OplLY38tprr1X0WWY0ERHVu1rK8Y1tpHKcheNERDQsQRQPqxspW2+9NT74wQ/ipJNO0ju9//SnP+HUU0/FkUceqfWJi4ULF2ph+sCd4FJ1yje/+U0tUJ83bx5+9atf4ZhjjsHee+892HjHgQceqIXgn/zkJ/HCCy/g/vvvx1lnnYXPfe5zFdWTvuWWW2od5mLHHXfE97//fZ2P66+/frAAnoiIqNFzXEhDWocffrg2el2pb3/727j66qs1V+Wxamn/ShrOlvaw3g0zmoiI6l0t5fjGNlI5XnG1KkRERLX2GNdtt92mBeJSZ7g8VvWxj31ML5gH+L6v4SmtWQu54/z3v/89rrzySv3VWepCl89I4fcAy7Lwm9/8Ri/U5S5yueiWej4vuOCCiubpi1/8IhYtWjRYt7oU4Mt8yrRvueWWqq8DIiKies3x888/X/9Wmo9y17hkuOT2hz/8Ye136623asPZ99xzj/5Avi7MaCIiqne1lOMb20jlOAvHiYioblvHbm9vx+23377W92fOnKkX0gOkMPzRRx991/FK3WW//e1vhzUvc+fOxaxZs4Y0Vr3LLrvgzTffxCuvvIJNNtkE48aNG9Y4iYiIaiHHe3p6hvSXJ6kqeZqq2iRrFy9erFWpDGhpadHq1J588sm1Fo4zo4mIaKyopevxjWWkc7xhqlXpKYWQfcKEgSiOkXZMWIaBMC4/kiD6/Ah9XgTXMlAMIizqK8E0ANc20FUI4IUxDAPI+SGaXBstCQtJ24CUu0hrsX4UYUpTArZpoCVhYmLGRWuq/PuDH0ZwLAN+FMMxodPI+xGKYYS8H8IPYyQdEy0JR+dNpj+rNYUJGVfnT3b8rGvruFuTNjZtT8MxTdnTsbIQwA0Kulwpx9T5FDIeWYY2F0jYJiw/j/FpG21JB+1pB5u0JFEIysPKeMufsfSvDN+SsLHl+KzOS58X6LoyDQMJnfcQS/tKyLoWJjQl9HOyDma2JnS5lvR5On1Z3r5ShM6ij+ktCczKxJiUdbHt+DSmNycwMeMgaVu6bmReZram0Jq00J6ykHHL8/LS0l4dLgsPW43LYmLWxQQzr8P2lgKUwgjZhKmvp7eksLIQYtsJWcBy0JywsSLv6Xh03m0TeT9Ge9ity5SyDUxtcvT9iZkELEO2VYzOYqD7R1PCQncpxNKcr/Ml2zuSR0DtlG4XIwphmQYsE1iaD9BdKu8/TUEPSkGMhAkdj/STccty2RZ0G8mXT9ZRKZBtGCJMNgOhjyjZjCjZBMMr6PZstkIYoafzIeMZ55T3Rdnera6JMOo/6MUxml2ZR1OHk31Gpml4OZ0P+Uwq9pCyTV2X05ocfV+2p2w7mQ+Znq6r3Ardb/S7EUcohbHOuyyLEfo6ruaEqcM5pqHLGjZP1OmafhHLcj68SJYxhtW7RD8n6yhhG1hRCNHsmsgF5e+TrGP5HmTlO2mW15Xt9aElaWHTthSK/ftoR9yLUqJF15/MpawPGZ98b+1SD2DI9yHE+Lhb16t8B4Usr5DxFoIIkxLh4Hde1pnM/6LeEnJeqOtSVqfMt3yvZf+RdST95SsSJ5owuSmh+3V3sTy8jFf2o66ijLc8X/J9kn1FljnrlpdLBAPbROrK8svbXcj6kc/KOtCWp2PofNm6HWXbRfr9yDjl8bqWiaQcp8JYt4Ues8JI1/nA93ckfyUuz2PlXaOQxjwlsE844QT85P+3dx5glhRV3/93vGnyzM7mZckgQYJEQUFQgq8YMaGIIqCCiqKICcEXX0AwIgZ8FeRT5DVhFkWiIpKUvCBIWFg2787MzZ3qe87pueMO7C6zMDtz78z/9zw9d253ddWpU9V16tStrvrhD/HUU0/peVlHdY899uDA+DggbaG0j8bx9bvxMkCmAPTOHRVOr9vDv/9bFuzOXliuB7i+thXGzcIkMey2Lm03YDvpdQnuZ5FUy7A8DyYMYGULMLUyEIX63Sm0I6kUYYKa3psEEWzXg0kSOJkM4nodbi6DOIzg+K6mI9+tTBbBUFkP2/dQX1uC4zuIwxAmTmBJgyHPVy1A29wZ+j0s1xBWahqXhJdrbtaFZdtws76m7RayaZ6lDZC4bQtBOYSXzyLT3QavIw/Hc+DmXFTX1jSc7dgal3xKfHEYo7KqquG8Nh9muO3wClmVQ+5Z++gAomqEqBYgKFb0/zhMVEYJYzuWhouqoYZJZS5rumE5gFfw4Rc8OJ6LfH8bMh0ZZLra4RTaUB8oISgHiINYr/vtBZSWrISJjcYvRLX6qDL2CjlNV3Tb0EV9zaDKIIeUhZ3Np7rNpGGlvOz2btWJiQItaynnRMpXyj5XQDKwUvVr14pAVIcV1bTepfXKhbFd2CaGZYy28dL+SnstdqHRBjf6dWKjhLQ/4ep56XukfUJb+1DSnksfKu2bOugt+MhIG+862m8RpN/WcG7k/GAlVDtRqkVY2FdAECWoBjF62nx05j34Yuezrn5KGDnXYEF3HjnfQakWYqsZbXBsW6/LUaxFcF1b49uiN423vysLL+PqPbHoUvLsO+juzGKgGqK/I4Ncu49MxkXXcDpRmCAZ7pxIHA8uHVI7KPLL97ash3k9eVRLgcblZ1y4nqNhxG414slLmolBe97T/FSCWM9FUaJhu3OeXneHw0m8gnxK3quh9BOkn2T0vqfWVDFQCbDtrDaVvTHrSvS2oDOn98xsy2ifVOPxXeSG+8VSvkLGdfR/6WcI0udSWyf9B5OWtfwv/QDxOaQI5ZBwWobDJjHtD6TXG/0Uff6NvP78n3ON/nkz2HH5cVkGoRuHbHo1GcjAuCAzxddFvjeurQ/a6MlH2kpjOzBeXvux0t+WZtJDgkiemLCqz1douep7iAsQSh/cxKjEFuzqWr0mvon4TIJdWauf4p+IX2xcX/sAzrzt1AdCEqXXw6q8dqD/G78AK6qnvn0cwElCbSs6MqmfJs+ftNfim0s7I+22Z1K/0aoVEXfM0udC7YTtantshbXUNwzr6ifK+QQW8g07EFa0vZc2xwor2g9RuaI6nOIK2GEViZ+DUX9G8pGB8XIaJosIlkm03RH/sO63j+jUKa2EM7RM+zHFINb7JU9i30RPkm/57hZXqD3TNJNIz0kfKhB9rnwSVnUQcfe8EZ/FclK/T/wY09aDTFDU+FQ+QH2Q/oKHJNeJUii+aqh6l7hVLmkD60WYeg1WvagNYT1Jy0HyLGGTQu/IuEXDdxG55JS2sSbBLD+VtRTEKCC9ZjIFJH4+LUs3A7uW/nCX5Lu1HolOxB8tuJbWHbWltqt1R+QWQyb+r/ixUh5yXv2zqKZ1QOqoyCVxiwwNv1HqgOhDrmnb2Nann5nKavXtOt3U7oneqmFahvLZQMZIRC9aF4fbd3fgSU27ahz49cHh/quDfFxReyHpPRO1H/ZwGo6v+ZPxA/FTLUvGpqSvYKPg2TruVLMzCN2c9lfE/3TLq1LfXfIg9w2XqbjO4len/RsLUc8CTd+uDsJYMr7lwI/r6ovK+FCjfmpZGoMesdPtc+AOLhmxdeL3i45ljEHSV/m75mjZyOPo9s9VHZetbFp3kjj9tCy41TVaVuoni32VfoQ8d5aFRasqad89iZC4mfSeOErrdiLjOPqaEbIm0HZAZJV45NkWOTS8SbQ+SThtP6RdkTYlDrWONmQR+aXuNuqojuskobZRIlNj/ESqmTxPieOhDB+h5G+4X7M5mI7++Nab2dfmzHFCCCFkHJDNQG+44QY9fvzjH+s6qltttRVe8YpX4OCDD9bjmc48IYQQ0go8+eSTujF1g43NGj/jjDNw/vnnbzS+RYsW6Z4gEwVtNCGEENK6XLeZfW0OjhNCCNkkpvMaZxvjoIMO0kOQTcH+9re/jRjwH/zgB7r+uQwE3H///ZMtKiGEkGnM87HjMjC+7uD4xjjttNNw3HHHbTSMOLTPh1mzZunn8uXLR228Jd932223Dd5HG00IIWSqMB398YM2s6/NwXFCCCGbxHQ0xptKNpvVX7EPOOAA/RX7D3/4g+6kLeuhEUIIIVPZjs+YMUOPzYG8Ui0D5Ndee+3IYLish37rrbfqRtpjgTaaEEJIKzPd/fHsZvC1W2rN8fPOO0/XUTr11FMnWxRCCMF0N8ZjPaYT8nrXTTfdhLPPPlsNdVdXF973vvdh7dq1+MY3vqEbiUxnaMcJIWTyaSY7vnjxYtx11136Gcex/i9HqVQaCSMzwa666ir9v+ELnnPOOfj1r3+Ne++9F8ceeyzmzJmD173udRtNizb6hUM7Tgghk08z2fGJZHPa8ZaZOX777bfrLwG77rrrZItCCCHTmlg2MEmSMYedLsiv1zJzTWa1vfzlL8dJJ52EK664YtRr39MZ2nFCCGkOmsmOn3nmmfo6dIPdd99dP6+//vqR16cfeughDA4OjoQ5/fTTUS6XceKJJ2JgYEBnjl199dU6k2xD0Ea/cGjHCSGkOWgmOz5RbG473hIzx2XmwDHHHIPvfve76O7unmxxCCFkWpNswq/UEna68Je//AW9vb1quA855BC88pWv5MD4MLTjhBDSPDSTHb/ssstgjHnW0RgYF+T7umuYy+zxz3/+81i2bJmuO/rnP/8Z22233UbToY1+YdCOE0JI89BMdnyi2Nx2vCUGx08++WS8+tWvxqGHHjrZohBCyLRnur7G9VzI7LVLLrkE+Xwe559/vr7ivcsuu+CUU07Bz372M6xcuRLTFdpxQghpHqajHaeNfmHQjhNCSPNAO37+uPvaTb+sypVXXol//OMf+hrXWKjX63o0kA1aCCGEjB9RAlhjdJYl7HShUCjg8MMP10MoFov461//qq+Gf/GLX9Q3oLbddlvcd999mE7QjhNCSHMxHe04bfTzh3acEEKaC9pxjLuv3dQzx5988kl8+MMfxo9+9KONriG3Lueeey46OztHjvnz5292OQkhZDoxHX+pfr6OeE9Pjx6yJJjruli0aBGmE7TjhBDSfNCO00aPFdpxQghpPmjHMe6+dlMPjt95551YsWIF9thjD82oHDfeeCO+/vWv6/+yo/kz+eQnP6kbtjQOMehCzrNRj9OpD7EBCp6N7qwDxwLCxKAeJfBsC2Z4HKdYj/V7m2fj6aE6FnRm9DPr2JBoOobvdW0LlgX05Dxs05ODY1nw5YIo17I0/Oz2DNp8F50ZF2FssKYWw7fTMIKkXwljDFQj1OMYOdfG2mqI2Bg4tqXfRZZ230E1SpD3bJUv7zlI8t2oRQmsJMIWnb7G151x9Jzkd1UlgF1Zq/cJlTBRXXT4DhZ2ZZFzLXi2jSA2aM84KseLZrShFMSY3e5j+74C+nKuxteGADMLLqqRQWfWRTmM4ZZXob+QQV/eRzVMVF+dGRs9eQ9ZV/53NE9LhmqYkXexrO7AtYG+nKSVoH/gYc3HDr1ZLZMV5QCd8RA6Mg5sWKrrlUN12DCI3azGLfFInjKupfoRXc0qeNh1Zh6lIMLych0yHmdVB9NyznlAEsGtrkHBd+DYgPEL2Lo7DxcJcmFR893m2xgY1quUk+rfSuuA6EN0JPJIvUnLDVheNcjYQE/WQW/O1bKJEgPj+FoPrLCKxKQDhLI+opRbm5fmTUpEykHCSflYSQy7NgTYDmDZMNl2eMPVROITRA5n8GkUTE3LASbR+iN6kHodxInKXawnaPMdZG0Dq16GFQdpnSyvVhml0ko97PDTJkD0JPeLnFmpv44Lp7RS654z8BREDN+x8dCqMtzVj2NlOdT7rDhEd8bWvCeZdhQcA+NmVJ9rqzGsqA6UB7TMpV7K89CVddCXtdAelzR9qVtSv3vzruqu001gbBd+VNUylnLZPh+qTqQ8RHcdXqoXKQ/Vc60I43j6HFhxBC8JVCeClI/KalmqKwkr+ZRyfdGMAmqxwYtnFfQ5uG9FUc9L3qXOPbq2inpsVGYp79j2ND055DkKkwS9VlWfdYlb5JW6Ld9FzyKfnJN8SrlLeyF6brQPUpdqcaJ1sr/gIu+mbZDcI+1KPTL6jMo98j0tY6lLEsbWBlzkFyR+0WMp+M8Ur3WamXGFxnj9JEmC2267TX+5PuKII3T37P333x/f/OY3MWvWLFx88cV49NFHMZ0YTzsu6DNuUpsn7au0Ffp9YBksLwNIm6NtUwArqmkb2SApDgBJPNKeKiaBkc1sLBtwxVbEsNu7YOcKMLH83w270A4rW4CpV2F5Pky9BlMe0jANokoVXltB2++oXENt9SCSIILfXtBwUbWu19xsBl4hBzfrw3JsdG7Zh7Bc0+9eIYuoFuj56upBvVfiqq8tpnlybES1uoZLwggmTpDr70J1xVqEpUDviWsBvDYfSRBrGMu20TZ3BpI4gZt10bd9L1bd96TGJWQ6Ul1UVlVhORZs30F9qI44iPXIdLXDy2cRlasaLtud1XQlTTfnIigF6Nt1a5VRrtXWluEVMohrdTieq3FF1QjZrqzqWb6LLPK/yCj5Hilbx9bDLeQQVqpwfFdlsD0XXtbVvLUvmIlcbxvqa0uad9Gn6Fn0HZbrqh/bd+FkMnDnbQ0rM6xr20Zl6eq0yKNAv1uuj6Q8hHj1MiAKkZQGgCi1lXAz6afjAbY7Uo8suTeqa51TvYWJ9gXlkJY4Gq7O0tcQxAaIbZF+iNgvabc9x05tRpzo/2K35Xxn1oMt/U7f1fNiX+S+3oKPmW2+9k2kuZfzOd9Jv4v9t4COjItqkCa+ppTmYU5nFr5ro6fNV3sYRIl+XzeM9jFHZE37KrO7cpjdlUVn3kNvm4+etgy8jIP2bPqyqZ9x0d+R0XslTxLvzO6cxiP/R1Gi+XA9B20ZV+Mo1iKsKNYRSb+vI53s0pnzUOhI9Wy7NrIFT+9vy6YGXmSROLfozSM/HHcjD+uunblFbwHZvKfyDlYC1ddWMwp6v/wv/Yq2rIvZnTl05T1NX/ozszuzGKiEKoeElTJZ0JXT/qz837DVYlelTyPnBOlnShgp20ZfUS5Juchjlfbd0/ukryB2WMImSPuUYtMbiD1PP9P6pPdoP0HkkVlhm+/H5elox2mjJ9+Opz5rDKte0jZEXBm151Fd+9viQ8hzIW1rRp8faWek7a2nfeE4gjxCkfSAxVeS9tnLInQyKMOH8fPaTks8Sa4TEHtv2Wmb7XgwXgZObQjGy8IKKvqcrYlcVI0DOw61rotPKU2l+KTiB/XmPH3WE8dL/a32fo1PfL+k0IvAb0dZnYQQHhL1zcV/iwu96kskSPMZujmIC5YXX8nLa8feOK76ZtI/EJ3YQRWoDul5YTB2kbTN0PwbK7Udkh8/CTR98b2DR+5J+zJeVn0YwS6vgeV56rOJPCsDR/s3Fa8DvckgasOLB1hhHbXIIKmlvmKU6RjWdwC4PmYVXLT7tsogGC+n8flxXX0aac8Sv6BlJr65XRuEteYpLQ/xLbWP1tWPit+F0PaRRaR2VnSj5WfZ6i+J/5tN6ggsV/tokqb4Q8b1VTdx+0wdGxAdSZloGct1L5/GJW1aez/ssAovriMWeS0L9QR6TtQmeRP9NcZgRGaT69R2OvWtLK2LbVao40KSP9GZyCn/2ybWOiC+dcM+iAxSN8Rnl3gkv+IrSp5KYYJyIrbaRpLt0LEJiUfa+arXrvFqumFd65X0A5JsZ1ou9aLqpsOJ1a/TcNLXDcpah0Q/GSsd02n4+FK/dexj4HG1cfL8SL2UfIne5fmS+qLxJ5GWnZSj+o2Orz69XR/ub4ZVdPtpeWs+wzrc4gqV3RlcgoyT5k/TTiI4xeVqAztQS/tDtqtySt0QnzaQvkq9hGyc9iWTXLfWFbF9Sbmo/SwZNxL9St+9Ck/ri+ojiUfGYRo+rhPVdBxL9CH6dqoDWg9cGbOoFfW5lPEDaQe0nrlZfUbUPsf11D9OYtT9tNwkH1IHje0gH1e03OS7FVZUF2KLRYeiP2l/LJO2B6JjOUSuqpX2Z6UNkO9SptoOGOmjpf2s8YZ2/Ihx97WbelkVWWT93nvvHXXu3e9+N3bYYQd84hOfgOM8u6JlMhk9CCGEbB50IGGMzvJUcarHghjocrmsxvnggw/GV77yFd1QbOutt8Z0hXacEEKaj+lox2mjnx+044QQ0nzQjh887r52Uw+Ot7e3Y+edd37W1HnZofSZ5wkhhEzs7thjDTtduOCCC3RQfLvttptsUZoG2nFCCGk+pqMdp41+ftCOE0JI80E7Pv409eA4IYSQ5kOXCZpmM87GwkknnTTZIhBCCCHPyXS047TRhBBCpgq04+NPyw2O33DDDZMtAiGETGuMMTBjdJYlLCHrQjtOCCGTC+04eSHQjhNCyORCOz7+tNzgOCGEkMl/jSuZZq9jE0IIIVMF2nFCCCGkdaEdH384OE4IIWTTf6ke44xwzhwnhBBCmgvacUIIIaR1oR0ffzg4TgghZJOQJVXGvKwKZ44TQgghTQXtOCGEENK60I6PPxwcJ4QQsknwNS5CCCGkdaEdJ4QQQloX2vHxx8Y0oc2zEcQGsTEoeDba/PS7kHNtZFwbrm0h41poz9iYUfD0nGNb8BxLww3WI73emXXg2xZ8x0I1SpB1LHi2jb6ci9ltLrqzDoLEoBREI+m3ZxyUghhrqgGeGKjh32treHh1Wa8N1UKNO0wSVMJEX5EoBxHyno3OjDucjlF5Cm5aZL15T+WoxBbmd2RgvBwsy0KUpOE6Mg4cy8K2PQVYJtGClk+Jx7GhcfbkHIQJ0Jd3UY8S1KIEPTkPCzo8LBmqYVYhTTsBsLA7C+N4mg/JX2/Ow+z2LJJCL+Z1ZNCdc1RXS0uRyjGrIPFkND95z0EYJyrPzLyj+he6so5+zu3IYFU1xtpajFltGfkZTHffTWCQ82z9364XESYGa2oxepIi7Oog5rR5uHfpELbvK2gYKaYFnTlNT/JoRXUtn/5CBna9DDg+1lRC9OZcrIlcDWfXBoFEytXGYD2GlLSkaVsW/LiuMsr3vrynskr5Sxg/qqIYxKofka0UJFq3SmEaR+zltT6JzqWeSU3LIkKb76AeJ7AsaB0qh4nqrcO3sboOxG0zYJfXqA6sWlHzIP+7A0+p3iSeuGuelrekHcLGnDbJiw3PtrQuZmpr8eRQDe1emr6UUeJmtJ7D8bTuG9vV73ZQ1rzWIqNyi6zlyCDKdmnakifjZlGwQq2Xol+zegkSY+BZgBUHiAxU37XEgh1WVU8ryoHqzSmtRLxyicre6UpZuOhMShq3U1yucs9p97VM3MGlWF2JYFfWwi6v1jLuQRUZK4G7/CEYx4dtYqyuhnCKKzSvnRkHWceG8XNYXjWwSythPf2g6nBlJdJ8GcvSupV1Lc2zs+ox9GUtrZtSRvLM6DPsWJjfmUN9uF2oJ8AWXVkN08ANSiqz1GCpj5JHY9lajqL/xvO3vByqLmfkXY1bnjPRpVR9LXvH0k85erKuPhvZpI4E6Xn5HidAu2/r8yRpSX3JOJbKI/U9rXOpXBKf1Cu5Jv/LdclfIy/jjUk27dicrFmzBscccww6OjrQ1dWF448/HqVSaYPhH3/8cdXp+o6f/vSnI+HWd/3KK6/cvJkhG8V4GekJaqWSNsz4BW2ftLzynXpOcRyYegWIY20DrbAKy/VgFTqAQjesJNK2y+2dBSRxekg4J7VJlu2kNtXz4XT2wm7rgt3eBSuTg51vBySuTBbR0KB+Wo4Nb8ZMxPU6TBQgLFeR7e2EV0hlM2GIJIwQrFyRipf19YjDSNM0caL/J2InPRdxEGHgX09pPKUlK2FL/IWshgvLNU1PcAtp2hK3dJDdnA+3kEPb7E69LvdLOnLfqofWwPZcZDp8BOUQUS2A7bswsUFYDmE5FhzfQRLE8LIuCv3tqK2tpen7LoaeGlQ5JF9JGOp9jthnaWfWlpDpbkeuO6vf5X+Rsz5QRBIb1Ibqeq9l28h25xCW6yNlKmEqS5ZqnPZwXyvf3wXb81Tu+lCgnxKPm88iCSLNR5IkcLOZ4TRiONnMSJxtc2ek/0hdSWKV3+7uh0kSJNUynO7+tFyiAMnQasB20vO9s2CkzhQ60vpl2UgyBe0jWFGQ1js/v06FTJAR/YtdNlB7o+2vLjsFVMJYg/lumi8ptnY/7Xukfcu0HH3Rq/RDnbTvIf0++d6T9fR/QfqYEn5luY6XL+xBznPUDvuujbaMC9u20NPm44nVFT0nh8gURGkD3DEcTzVIZZrRkVF7JbZCzkm4nO8g7zvYakYBW/QW9PuszhzaMy52mt8F33V0ltLWs9rRU8igvyOLlcUa2sSG2Rbmdef1c05PTtNoz3v6XeIWeZYOVOE4qWzCYDXErN48oihBoSODQsFHsVhHqRbq9VIt0jxpHrOe5k37erat+RU5M66DrryHmV1ZvSZ9WfkUmbboy2sexC6KHFv3t2HnOR1pHfMcDSNlJffnhvupYr9XlOraLx+ujqpHCS/9QNGFlJP0l6V8pbykHKQfIv3nKE7DSvk1ylyQPpDY9bQs5Z7U/jeui6WW9OS82vthO659hc1kF5rJjpPpQ8NvMtl2bTMbGDejdd64vvaVBXm+xPOR/nni59NnJNOG2jp9W/FjpK32TJT667YjRlxtv/gG4ldZQVXvk/bcFt8qqKiPYXKdyIdD2kbkrLRtlP+lHVG/W9tkOQe0ib0cTlb8FJHXr6xGkkvt7dx2T/sVci3xsiiGRmURn0raCLnVi6roMmUY24EV1WAybWpb5DBeFlYcwgRVmHpN+zbiy6ytx5ofSU/il7i08Uhi9aukrZH+jV1apf2W2UOPwK6XtL9kumYB5bUouR1qp0Qf4o84Ty9S3yrOdSHqXYica2l/SPwsaRPVpnXM1HyJnVD/OKig6rWrbyRxi/zSropPJP6f+va2o/4jXF/LTvQncku6+aik59K+Wx52bQirotS3Fn9J/EIJG8YGQ/mZ6u+LLGLHreqg6lR8ONGZE9VQyad2XuKpm7TCROKdaZ+vor5paPsj7bgdh3DiuravojOpM1K2kqYgeVF3L0n7l+K7yffAySAu9KpPJXXNSmItP9GZhwROWEEBgZaHxCHpaxmpnbHVT/TiOgI3p+29VF23ukZ9OKmPjaUnfRNp3I0GN873jMg22xrCDDOI2PYA29W62RYOaH3JBEVEmQ44SajXxefHqifhFZep/FJ+ooPActW+iF9YlzqZ7UBJKoMR/zUdf5D6hSjQvMs4xpOl9LqMpYQL9tA6+vCaGqzSGj03UIvTcYo4UlnFBkr/Ojv4lNYlrXOWlfq40sccWqrPpTwH7trFWv9Uz529sOplrdbLSiGqmW7tF4nsEp/IIONEkp+CZ2n5SX2Q65L2kPRrpL/m5/V5TNr6kDhe6j/L4xLV07wN61p8AvGn07ICnHoJSaYdiENU1WEPNJzoSNoOea7Ul7fctI5IHFJfJXnzHx032g6pH9LOia0fafOG7f94Qzs+/nDmOCGEkJZd40wGxpcuXYprrrkGYRji3e9+N0488URcccUV6w0/f/58Db8ul1xyCS644AIcccQRo85feumlOPzww0e+y+A7IYQQ0uo0kx0nhBBCyKZBOz7+cHCcEEJIS77GtWjRIlx99dW4/fbb8ZKXvETPXXTRRTjyyCNx4YUXYs6cOc+6x3EczJo1a9S5q666Cm9+85vR1tY26rwMhj8zLCGEENLqNIsdJ4QQQsimQzs+/kybZVUIIYSM7wYgYz02F7fccosOYDcGxoVDDz0Utm3j1ltvHVMcd955J+666y5djuWZnHzyyejr68Pee++N73//+5w9RwghZErQLHacEEIIIZsO7fj4w5njhBBCNo1NcZaHww0NDY06nclk9HghLFu2DP396frBDVzXRU9Pj14bC9/73vew4447Yv/99x91/vOf/zxe8YpXIJ/P409/+hM+8IEP6FrmH/rQh16QzIQQQkgr2nFCCCGENAm04+MOZ44TQgjZJGSzmU05Gmt9d3Z2jhznnnvuBuM/44wzNrhpZuN48MEHX3CpVatVXZt8fbPGP/vZz+KlL30pdt99d3ziE5/A6aefruuSE0IIIdPRjhNCCCGkOWgWO75mzRrdA6yjo0Pf6Ba/WiaUjXXddNnzS3z7X/7yl5hsOHOcEELIpm8AkmzaRl5PPvmkGs0GG5s1ftppp+G4447baLxbbbWVrge+YsWKUeejKFIjPZa1wn/2s5+hUqng2GOPfc6w++yzD/77v/8b9Xr9Bc94J4QQQlrNjhNCCCGkOWgWO37MMcdg6dKluOaaaxCGId797nfjxBNP1Aloz8VXv/pVHRhvFjg4TgghZJPYlDVIG+FkYHzdwfGNMWPGDD2ei/322w8DAwO6bviee+6p56677jokSaKD2WNZUuWoo44aU1qyLnl3dzcHxgkhhExLO04IIYSQ5qAZ7PiiRYtw9dVX4/bbbx/ZA+yiiy7CkUceiQsvvBBz5szZqG/9pS99CXfccQdmz56NZoCD44QQQjaJJAGsMRpZCbu5kLXCDz/8cJxwwgn49re/rb9Wn3LKKXjrW986YoyXLFmCQw45BJdffrlurNngkUcewU033YTf//73z4r3N7/5DZYvX459990X2WxWfwn/n//5H3zsYx/bfJkhhBBCppkdJ4QQQsjE2PHx3gPslltu0aVUGgPjwqGHHgrbtnHrrbfi9a9//Xrvkze33/72t+Piiy8e09veEwXXHCeEELLpr3FtwrE5+dGPfoQddthBB8DlV+oDDjgAl1xyych1GTB/6KGH1Aivy/e//33MmzcPr3rVq54Vp+d5aqxlZvpuu+2G73znO/jyl7+Mz33uc5s1L4QQQsh0s+OEEEII2fx2fFP2ABsLy5YtQ39//6hzruuip6dHr22Ij3zkI9h///3x2te+Fs3EtBkcjxPAtS10ZR39PlRP9PvMgofV1QiOZSHrWKoQz07Xvcm6qXo824ZjAQs6sxom59royDjIIUSjvxgbg3KYIDaA71iQGBZ05jQ9ud+2LHRkXMxuz8JzLFTCWO+RdHvzPtZWQw0XJwZhYjSspDm/M/0lJ4gN8p6DcpTAsdP05FzBEhkMrKiOrGuNLLY/u83T/3futmBcHx0eYAUVzMi76M446EUZmdpaFINYw8taP11ZF/0FX/O1sCun5zszjuaxw3cwEAK5sKh5yns2enIelldi+I6NnqyD+e0eanGClZUIUWIwI2OQcS2NY9veAlZVI0RGZJdfuSK0+al+5QcvyY/obccZeZhMO7pMWb/LvXO7c0ASoRImei7IdCIu9CK/+hFsO6MNL5qRR961NM2ZbR7mtfvwbQuWSVSXs9p8JPluFO08unMecq6FoSBGe8aGFUewwrrKIWXRk3PR4dta9iX46LDTMpbvecdo3RAN2/WS6qDdt+EUl2s6kpusY2v+nCTU8jOWDce2tK416oncN7OQvrQh+ZFrbb7UE8AOq0hynWmZJCJbBVYcIm6bobps9x3Y5dXDddogE5a17DI24MR1rQd2vYwZBR/1BIhtD05pJew4hD/wJJJsh+bH2A464yHYpZVwoprmc6Aej17zyUrLx2QKWBu7Wl5torPeuZjbkcHqWqJ6FaI4rY/O2qdg1waxbW9e9Szl5vTOQqcdwlm7GO7gUtWX5q06pM/CrGQAoh6RU+qgcVzgiXthV9ZqnV0bACaoyftAmr+dZ+RgRTX05Ry9T+QyfkF1a0UhLM9H2DFbn4E+p65yNZ61emRg6lU4xRVYW4/1em/ORSlI69ZOM/LIOJbmS+qxPOvVKMFATeq5hbpXQHfWQZAYrWNSH0ROqaflIG1TpB5KeUq6Ips8H6JfaTukLvSgimqU1nenskafX9FV6GQg6pfykbog8UhdMsOyS149E2m5yzUJI7KJDHKuFhnUh7+LXrXsNpM/a5JNOzYnYnxlTbNisYjBwUEd9G5raxu5vnDhQtXhQQcdNOo+mQm+ePFi/WX7mchs9H/+858ap2woIq99nXTSSesNSyYO42ZgvCysOEi/exnYQUnbGa1o0mbZDozt6udI5ZPzlg3LywBShiaBqVdgojC9nu8CkhgmTL/bnb1peNeDXWiHnSuMXDNJAruQLg/k9sxAbelyOJkMTHkISRjBcn1ke9M23Mn6SOIYJgr0mtdWQFiuIiiWEZVryHS1wcQxgmL6w43t2LAcG27OR6a7oOeiWgSvo4Cu7ebrckFxLYTteem1cg3BUAVxECHTkYGJE70/N6MblmOprBJ3aclKZDr8NKv9nXA8G2sfXavfvYIHN+fCL6Rx+h05jUtk796qC0kQo7piLfw2D7a0Z+15TTMoB8h05GBLf2bZaiRBqGlKPPKciBy1gYrKL/kqzMxrHKWni6isrqqsgsjuFXJIggjZ7qyez3S1q15sP7WT+f5u5LqzqCxbg+rqQbjZDOJaHW4hi+rqoRH9SfpRLUBm1izE9brGkZSHYEmZJ7GWS1wuabm6M+bCzhZg6jW1CVrO2VTnEt4Kyv+xgW4Wxs+PfI/l+3Cdkv6YtM+NPox8lz6NtOXyv7TVQWQQSmMuefEcFOuR9gME6afNastonzBMElTDWO1Ed9ZDRvodnqP3rqoE2s7PKGRgQ+yBpdc6cx6SxOjhu6m9mdmewbKBml5vy7oaX8ZN/5cwmk4QI+87+n1edw49bWn90DhcGzM6MtoP3aIvjwV9ebRnXbRlXBQKvobvynuY3ZnF3O48SrXULs3ryWlabVkPM/rymN2Vfp/RnsXOczs1TKE9Tefhp9OZSxK2q83XcBJnb3d6T2feR2feQ8FzNP6lA1XNaxwlKNZCtOc9bNmT1zhE1nnDcuw4u0N13pPzMa8zjUvkX9CVU33IIeek3y062GF2u+ZnsBqiM+tqP1P0LnmVIpN+7tY9edW32NfOjKtx6LPjWKpLiU/KW/qJ8n12m6/fpYylTyD9Q+m/SR9d6ol8l/6A2Gf5X2y2hJP0xM7XpS9lWZAnRMp++FGZ0nb8C1/4gjrJ+XxeZ6CNBdmX5JkbdYvtJs2NlwTapkp/Xv0eaT9tD4mVPptwfH0W5Dl0q2sQio8oSwcYoC2pqP8gfqq4GA17L+dCy1WfSAJKP0HabPENrHoJcNJnNsl2Im6fOfx/OwLLBWIZAwASx4O76lEUTE2fPXlexY9XmW0Lg/W0D9BmavopvorkQdKXPHlrnkDcMVt9TzuopL55UNZ2wIuqahfgeLCrg+qTWfUyAq+Q2hqR2fFTv8e21edJ2vs1fzomEQewwirijlnw4rqGX2syQByq7yd9meDR+9N+jJdJ7ZXoUeTJ5FEKE8z0ojQNC7By7apvkTny2+DX1sJk2lDvmIPcwGJ0+TasKIDp6Fe5l5YClVt8IfE70vz76sOIr5SPSmjzbNjlNWrTTLWMUhCrDlXXhV4N7w0thZFnVXzD0ir1fcTHkzbPLq3S88GwPyP5Ft/MZNvVrxSdubUB1OyMHrnqavW1RS867hLV4dcHEfjtSHLdWiZ+WNa61NCf2GytYnJfHCIfDGjjJuWTsRLNq/jJUoYDQaLtsAcpz7K21cZL64PIIvU2go2akxvpfzq1IQ0r6UrdnunHcIeWpWkgUZ9S7BNsV3Uj9TPn2Yg60lmzks+6sVGJU19SfPVS4qTjFNKvHUbra64bxvG0/yt2ReqvjgNIXntma11wh3UoPqcntiVK/VQJL3VL6mbs5TEoAwbSL7Zd1XcDsVshbC0L0Zf0wdulX5YpjNgt8dNF9/JcaTkEFSSZgsYjYwPy7EpecyYAygP6jImMUdc8vTYUJEgGVyPqWQAribGqEqn/nFnxEAqL/qzjD/rMu8PjJcUl6E0GVT4ZE5G6Lnqt++2av/+MocTIItK6pD6BjAk5GfhxHUmmXeuclFljwNhdu1g/5XSU69F6IvVM8mzViun4imVpH0vqcBp2OH92OrYjcYodkvohZSRjTaJakWtzraj9fOy47AEm/nLj+OQnP7neuM8444xn2dhnHg8++ODzkvvXv/61LoMq6403G1xWhRBCyCYhAzFjf42LM84IIYSQZqKZ7HgQBDj66KP1bS3ZC2SsyGD4pZdeOvKdm2UTQgiZLjwfOz7WPcBOO+00/RF6Y2y11Va6JMqKFStGnY+iCGvWrNngcikyMP7vf//7WT+Gv/GNb8SBBx6IG264AZMFB8cJIYS03AYghBBCCGl9O3722Wfr52WXXbZJ98lgeDOtVUoIIYRMBTs+Y8YMPZ4L+VF7YGAAd955J/bcc8+RwW9523WfffbZ4Kz09773vaPO7bLLLvjKV76C17zmNZhMODhOCCGkZZ1qQgghhGx+Oz7eG3m9UGR2max12t3djVe84hU455xz0NvbO2nyEEIIIdPJH99xxx31La4TTjgB3/72t3Wvr1NOOQVvfetbMWfOHA2zZMkS3Rvs8ssvx957760/aq/vh+0FCxZgyy23xGTCBVQJIYRsErKm4KYchBBCCGltOz7eG3m9EMQZF0f72muvxfnnn48bb7wRRxxxBOI43UuJEEIImco0iz/+ox/9CDvssIMOgB955JE44IADcMkll4xclwHzhx56CJVKukdQM8OZ44QQQgghhBBCNohs5LXuWqUbmzUur03LoPXGWLRokTrUzweZlbbu69i77rortt56a51NLg46IYQQQjY/PT09uOKKKzZ4feHChSObn26I57o+UXBwnBBCSMu9xkUIIYSQibPjY93Ia1M28xovJK6+vj488sgjHBwnhBAy5aE/Pv5wcJwQQsgmIb/ujtmpbpJfggkhhBAyMXZ8rJt5jRdPPfUUVq9ejdmzZ09YmoQQQshkQX98/OGa44QQQjYJcaiTMR6cOU4IIYQ0F81kxxcvXoy77rpLP2XNcPlfjlKpNBJGll+56qqr9H85//GPfxx///vf8fjjj+u646997WuxzTbb4LDDDtusshJCCCHNQDPZ8anCtBkc92wgNgaxVBCT/tJiW+m5jGPDdQBHTgDIuTZ8x4Jn27AsoL/Ng2VZ6M15COLRFSvjWAgToL/gosOJ4Q3HIZ85z0LWtdCesfXenpyHdt9BGBvMbk/X6QvjBNUwRl/eQ5gkmN+ZRTUyyHsOlpdD+LYFx4LG0ZV1NN4oNipfX85BzfKR92xY1UHNW3c2fRnAMgYdGQfmbz9DnOuCsR1YUR22iVEOE1hhDSvQgTbPxsyCh3Y3jbPNtzVcZ9aFXx9ENUpUR0NBjJWVSON2iys0Xzv05uDaFrbJBcghRKGyAmsqYZp314JdXIGZXqTxL+zK6vl6LPpOrznVARgvgxWluuZT9CcYy4ZdXo357T46MjaWDdZgMu2q+56so3JZJkHUORcHbdGJghSuSZC1jabRnnEwJwfEhV4U65HKMmjlNX8irxShlHFnxkGtMANJvlvLeU01RJSkeujNO6hFBlYcqv4lrJVEWFONkQnLSDJtkIk0XlRFnOtGzrO1Hoie2j0LxnaRdwxgpXVJ4h2KHTiW1CsLbaaGRk2SuujEda1/xs3Aimowjg8kkd4vn+6aJ9ATD6rekcSwg3Ia3na13N2Bp2AFZVhhBYgDdEvZG5OWdVRDaLnpp5OBv/YJOJU1QBwh7pqn+ZL04kT0Yqm8oo8k244ZeRfGzaLLA3JJDbMKHop922lZSFi5zw+K6Mw6Wg/lHmfgacxp97VcrKAKK9um+ZK82NVBjc+ql2EybcgMl2XBtZCUBtK6G0ew/KyGT+7/i8pj4ljDxV5edW6FdZW7f/ARvd54bqOeBTAdMxAmBhkbcB67A7nqatQjg+ySu9L4e2YDtoM5bR7sylp0oKblJ8/X7IKr9UP0WgkTzb80knnPQsaSZzXRvEt9lfKV59nkOtGddfT57cu5qnN5joRSkGidkHxL3WvIKs2E1APjF5BxbUixukhgR3UNI3U540pdNSqbxKd11/ZUPvl/KEg0zw1bp8+ulZad1DMpH9HDZvulehMOQsYF29G2XnEcbV+0Pno5bWPEnki7IW2D5XraZmg7God63tQr2nZIHFYmrwekzZUwEncybIMyBaQPkqdtj2B5Hky9mrZNw+HkM9Pfl95rO3DzWSRBDV53NyzbRra3EyZJEFVqafAo1M+4FiAOh+1pNgPHc5Hv74btuXrIvZmudiRhBL89q9eX3XIfkiCE7Ttwsz5qa8vwOvIwcQI356dxFXL6GRQraJvdiWxPJ8KhiobPdmQ0nMTZtVUv2mYWYHseYmnTSiG6tuhEW39B05U0REZLbIHYa9+DZVuYvddCWI6tMhafLqE2IHGnfQ6J18QGXiGreUuCGFE1QqbDR/vsNnQs6EXf9r0atrKqqjJlOjJwfBf1tUWE5Tocz0FYizT+trkz4LcX0mL3XeT6u5Cf1ZPqL4zQsXC2yij3du3yIsS1OjJdbaorKQsnl4cldcT1tRzkU+J1Cm0wQQ1WJgcThbA7e2G3d8POFmB19es1OS9ln9atvNYFsVlWUNH65ISVtI5FdbWbWj+0XYTaBukviM2Xflyjzc+6qU2Qvqacty3pbzlqv4fqkdoDsTliDyphrPakHiX62V/wtd0Xmy/3rKoEI4+ExCXXbO37uOjvyGi7L/FKP25eRxbVIFb73F/I6P8SvzCjPYtiLcLc7hz68j6yjo0tegvozvsYrISoxQkKvosgSrCgN48gTtDfkdXvGn/ewxZ9eeR8B515Xz9918Hszqxel0+xVbO7spjVlYXv2thhdge2mlFQeXOeg96Cr3EKkt/Ddp6lYXaY3Y553XmVQehty6hu2vIeSrVI5VzYndP+WdaVdO1UP5LnnpzmV67J+c5MWpZyTvTclfMws+BrPDv1t2N2Wwb97Rnt83fnPNWF6FzSK/gOZrb5KAXRyHfZ1Er67O2+q/cIjb5/oz5IXRg+hSgGButxasOlnyV91uGNtMRWa3M2/CkmW+RMwxp40h8YrkNT2Y6feeaZ2H333fG5z31OB77lfznuuOOOkTCymdfg4KD+7zgO7rnnHhx11FHYbrvtcPzxx2PPPffEX/7yl42ug04mn9gRO20h8QvaZ3WSUNss9TsMEHgFfXalDYXt6jnpE8t1uSZ+hxxWWIVVL6IEX/vs4ouobY9qGrf2FcSHctPw+r+01dJ+S58gSeCJma+u1XZa+utR31aw6yX1X9T/FhdM2nXHwoyCrz60ID6Y9D+c8mrYtSH1JezSKrU9RtLzcuh008ZAfAZnaKl+JtKncDNwVz2a9k9Unrr6b+L3it8qfoHTNwc1ebnfJOjNuepDqN/k5zWuuKNf+//qL9ou7PYu1Jcs1j6QxCfnrFoR3spHkGREn5bGL2lK+xL1LlRfJX7gb3CDksqP8lr1RURXzuDTmpbkQ9qxVZVQr0u59KCKQbdL0xa5dOzEJDp2ET3yT/VlzJztIbmT8MXIUlmsOFD5pJ1HFKhcq6ux+l7S3iXlopaptJHi6zbawcTNYLBzSy1r0bn4dlkTwCmt0jyp7NJ2ZttVDknfKS7XMhY95CLRW2GkL9hoTVdFntYvqRehm1PdSDknhR617eL3CjJ+kmTa1ReU8Qm1/fWi2kNJS+qNlHvgZFIfvlZExcmn9aS8Ji3jqAantBK5sKhjIKJXGQeQ6zKmILrWfNiu1hOp+1LvpE+gY1bD9UbKTPIh4xzlKB1jqGe7saQYwg4qWh4l4+m4xFBhNgbr0qewNE6ps3Kf6FbqjjyHD62qqh1Sf354/KFsPDw+GMArrVA5xO9ts0JY9RLWRK6Wl/Fzep+Ov4he3AxEHCk7KcMk161loX0Rkz5D+tz5ORipn34B5djS+iZIfRe9ioxiZ0UeKQOhx5T1U/o+xSDRMnCKK7UeyRhV4Kb9XpFT2gnIsy42ecXD2kcXPzqtIDKelfoRknabHaNq+fAdG1U37WvKNakD0iZJXFJ3pP2Al0XiZfX5F1tuiQ9gEn2uJMoIttYFKVuVxTj6XOi4RGKQs+IRXU1lOz5V4LIqhBBCNgmucUYIIYS0Ls1kxy+77DI9NirDOo59LpfDH//4x80qEyGEENLMNJMdnypwcJwQQsgmIa9njUxZH0tYQgghhDQNtOOEEEJI60I7Pv5wcJwQQsgmYZJYj7GGJYQQQkjzQDtOCCGEtC6049NszfFzzz0Xe+21F9rb29Hf34/Xve51uuYcIYSQyTfGYz3I9IV2nBBCmg/acTJWaMcJIaT5oB2fZoPjN954I04++WTdjfyaa65BGIZ41atehXI5XZyfEELIxCObDI7dIA/vUEamJbTjhBDSfNCOk7FCO04IIc0H7fg0W1bl6quvHvVdNmuRGeR33nknXvayl02aXIQQMp0xcazHWMOS6QvtOCGENB+042Ss0I4TQkjzQTs+zQbHn8ng4KB+9vT0TLYohBAybTFmE9YcNxwcJ/+BdpwQQiYf2nHyfKEdJ4SQyYd2fBoPjidJglNPPRUvfelLsfPOO28wXL1e16PB0NDQBElICCHTA24AQp4PtOOEENIc0I6T5wPtOCGENAe049NszfF1kbXH77vvPlx55ZXPuWlIZ2fnyDF//vwJk5EQQqYD3ACEPB9oxwkhpDmgHSfPB9pxQghpDmjHp+ng+CmnnILf/va3uP766zFv3ryNhv3kJz+pr3s1jieffFLPx0Yya+mn4NgWfMdCnBh0Zmz0ZF1Uo0TPB3GCjGPr+TgBujMOYmPg2ul9Bc9CRu51MijYMTw5b1ka71AQ6/99OQftvqPn2jwbCQz68j5CSS/rYk01RLvvojvnIec5qIQSj42heoSca8G2Lcxp9zW9MAEKno0OX3IA5D0bW7Q5+ilkRTAAOSvWc5KvBGn+/G1eDKc2BEs2xrNdwLLRl3dhHBdtvo2urAO3ns6u78k6Kl+SacesNg+wXRgDdGUc5Fxb9QeTwArSDVH7shZ6M8AAcigZD3H7TLxoRl5lyNfWwJKwYQX+wJPIuJbqV+T3HAt2vaRxCS9f2I2ZBQ92eTWC2MBb+QisweVIjIGfBJjTlQOSGGtrETqdCM7QcsQdszQvnVYdiwdD2PUi3LWL0eOEGqdVHUTVzqJYj1UPotNMWEbWlfI1ehQGHkc5TOAUl2sZ9bd5cG0LOc9GVsrXGFhBBdXIoNvXd1e0TjhDS2FFNa07sZeHybaj26prGlL2kks7KCOCjdDJqDy1yGj+Rb9Sd0Q2+d+2oGlW4Wl8iePJtAwYN6P6t4Iq7OogjOvDWfFvZIKiXov8trReu1lIlZYwdr2sR5Lt1LKVupMGiuElAVAeQEkqk2XD+AXAcbEqsOGsWaz6k7xJfRY9VUUvQ8tTXSYRnPJqLbOMlai8Uk+DxKgeEAUarh6bVO7qEGZmDLrCtYg7ZyHJtiM00DKVupM8cqfGB9uGXVkLuzYIu7oW0dLH0Z6xYdWLwMwtYcUBoqWPaX2yMjlY9RLc2kAqU1CGkXysWQo/rmvZ9AUrUbKySPLdqV7XLka8diWsqI6enAMT1ODIo1JcrWnkV/4L9ponVR/6rNiWptmZlGCFVWzTnUEuLGp6c50ynOIKlUXqjNQR1Vk8BON4sGHQm/O0TKXu2HGoz2wpiDVeqesFU9O4Er8Ax4I+UyJbvrISRuqN5eh3qR8ik9wvSHryHErb5SQhOpxY/5f7EyNZN4gSo+HlMxpu5ORD8rU5oDEmk2HHLWlr5LkXWyb10Muk7aWfT9uTWlGvp4HTcMbx9dk3QRUmDPRZlHNqf+IQplYCorqGsTJZWJl8entY1futzn69ZmcLsDt74bR3wcp3AHZq3y3Ph4kCWJ4Hy3Zg4kTXALQce2QzWifr6xEOVfS8ZdsarvFdrjWwHEfvC8u1dc7ZiGoB3GwGma52BMUyMh05ZLva9XpuRjfaZnfCtm04notMVxviINL7bN/V+LyCp+Hk/vYFM9G+YAbqA0XYjoWOeZ3o2KIHTtYblaabdeEVskiCEFEtgiv58F3YKk+EbFceue4sagMVzU8SG2R7O/V+r5CBm3MRVSNUVlU0Tdt3UJhZwNy95iLX3w2/I6fySF4z3QWNyy/4mnamu13jLC4twfZc1amby6g8sw7YA35HAX5vL3K9nbCzedTWlkfSToprYRfaYepVLRt3xlzY7V1IwghOZ29qF7J5bccsP6vlYRU6gHpZyzApD2ndgPTrpJ5IeXkZGC+rdln1UyuqbZT2V/oKtmVpcLEBck7spPQf5RC7I30gb9gmz+nIaH9PjrznIOPa6Mx66Mt76C/4+im2oyY6NQaz2zPan5zXkfYnBuuR9h0FubcexciqcQN810apHmHrGQV0Zz14jo1t+goqn6Sf8x0NO1gJsVV/AfN6ctrnFTnmdmT1uhBEicon6XZkXL3WQNJoy7rax6gGMbbuK2Bed06/Sz9Gzm07sx2deQ/1KNHPUi3S+xr3z+nJqZwzOjLoynkan+hs+74Clg7U9FxX3kNnJo3jgG16se3MNj0n9/d3ZDRPom/RxfYz21VmOUReuba0lL5FuqAzq3qSPIo+JL5iEKGnzUdnJi1P0ZXoOowTtPnuSL+6Omx/pXxEFrGxEof09WrqM6R9fCl7sd3Sv9Znfvi66Fz62R2+o+ckvPQRrOHyEKT+1ONE42jcK2UihLH5T19unKEdJ5Nhx6W/LEj7mRk21/Ipflm7Z6mfIv6stCVig/U5sWz1Y138Z4N38T+Mm9V2Q32RJFJ/pNFP0LY7iVF02tQ/VVxfr0u/XeOIA8RtM7S/LP5h1ThIcp363EtK0teX518+BfHrxN+S+0yuEyasp/2EOESS71LfU/3kqK59CEkri0jv7bACuANLYC9/GGbVU0AcwJVHW2S1HY0Tjoeob0tE3fOQi8rq20j+xX9ySith1cswXk59Qzkf53tQiS04vbNR2G0/tU9x1xzNn8kUtK9jB9V0XKE6CGdgCSyxWdkOlSlevUx9K5Eh6Zmvfo298lHtS4mNE/8sH1c0rWTGVlhTixFn2jBYj1HNdGv8fbm0nyXlJ7ZVfC/1G7W9s9QeSvlU3DZEuR7UxFfMtiMp9KpcjXK3O/vU5xZfVPSfq69Vv1n8qfY4lVEaSfGXnIf++p+KZslYjUGc61KfMoStfmCjjLW/KIUo5ZREqvPQSsdARtpCrU91TUvLrjqIVTUZK0g0bimbtJ7VEdhpHZLvdWOrv6nxi920076ctO9ZWxxfK+2fSr8k26GytIUDqa8vOi/0AGENoZvTuOWcyCU6Ep2vqcbq25lMGxI3k/qFcaTnxfZF4hdaMsYhPmlJy0vHEgq9WFuL1a7INbkt8ArIBYPIllfqmIb4lvM7M+q7d3hp3RYdFRDAl77j8odHxkSkLkh9F5+yN2uj1r0QczIxSsHw82jbaTlmbCRtM3QMROqbPFeN8jWZdoT92yKevaP2r0TOSpT2lVIFRQj8dmzR6audjdv7tT8mdULqvNQFqS/a905SP1h1LX0K8eEdS/OQyPNRL6Z9NhkviQ0SL4tKtkfjLSXpWJcmKdXCxMiaQNuIJNeVtknS/5NiHfYn6l5B64bUrW4JIP6Fm9F7RX4dq0M67pL2CaG6kb6GIGluJjNOOz7dBselgokhvuqqq3Dddddhyy23fM57MpkMOjo6Rh2EEELGsW2WAUn5wW1Mx3+cmc3BF77wBey///7I5/Po6uoam/zG4Mwzz8Ts2bORy+Vw6KGH4uGHHx4VZs2aNTjmmGPUhki8xx9/PEql0mbKxdSFdpwQQpqPZrLjpLmhHSeEkOaDdnyaDY7Lq1s//OEPccUVV6C9vR3Lli3To1qtTrZohBAybUmSeJOOzUkQBDj66KPx/ve/f8z3fPGLX8TXv/51fPvb38att96KQqGAww47DLXaf2brysD4/fffj2uuuUZnSt1000048cQTN1Mupi6044QQ0nw0kx0nzQ3tOCGENB+049NsQ85vfetb+nnQQQeNOn/ppZfiuOOOmySpCCFketNMG4CcffbZ+nnZZZeNTR5j8NWvfhWf+cxn8NrXvlbPXX755Zg5cyZ++ctf4q1vfSsWLVqEq6++Grfffjte8pKXaJiLLroIRx55JC688ELMmTNnM+ZoakE7TgghzUcz2XHS3NCOE0JI80E7Ps0Gx2UQgxBCSOsb46GhdG+DdZfAkmOieeyxx/QNJFlKpYFs3rzPPvvglltu0cFx+ZSlVBoD44KElzWdZab561//+gmXu1WhHSeEkOaDTjUZc12hP04IIU0H7fg0W1aFEEJIExLHukHeWA4JK8yfP18HoRvHueeeOymiy8C4IDPF10W+N67JZ39//6jrruuip6dnJAwhhBAynew4IYQQQpoE2vHpNXOcEEJI82FMrLuFjzksgCeffHLUBskbmzV+xhln4Pzzz99ovLL0yQ477DBmmQkhhBDy/O04IYQQQpoD2vHxh4PjhBBCNnl37DE71RIW0IHxdQfHN8Zpp532nPtKbLXVVng+zJo1Sz+XL1+O2bNnj5yX77vttttImBUrVoy6L4oirFmzZuR+QgghZDrZcUIIIYQ0B7Tj4w8HxwkhhGwSuo74ZtzIa8aMGXpsDrbccksd4L722mtHBsNlPXRZS/z973+/ft9vv/0wMDCAO++8E3vuuaeeu+6665Akia5NTgghhLQym9uOE0IIIWTzQTs+/kybNcd9x0LesxHEBt1ZB50ZB2Fs4NoWqpGBZwO+bSFMDCzL0ntknkRPzkGYAI5lYVabNxKHawGJbFASh8jHFb3fWDY824INAyuq6zmJKUoM+nIuwiTBwq4c6lGCWW0ZtPkO+gsZLOjMwbYsdGZdTUfozLgaVylINL2MY2EoSDAnB2RdG8Z2kCkuQy4sangriWAFZXQmJeSCQbjlVcgERRjbRcVtg10vwngZRAbIDSyGXR3UvHtRFcbNwA7KaCsuwRbtHoxlobH3SsZN9TMzY1CLEsS5LiSFXtREZysfgVNaie5gNdrsGE+VInRVl6NXVkvwsjCOByQJ7Mpa5FwbPVkHecfAQwIrDuEMLlO5JXxm1cNIsh0aRj6lw54pr4S34mG8eFa7ylisx/o9fOgO2KVVKMGHu2Yxtuj0YAVVJJl22LUh5ItPww6ryCU15D1H5cuufRwIa2j3HeRcC7YF2PWy1gOsfAI9OVfz7FhQvWRto7LYtUEND8uGFdXg2BYSL6/6k3ucuK7/i3xCh2/rOcQBPBPBr6xGnBjNo3xKPXSH02gbrkuxkfppI+fZWlcEd+ApGMcHpD6YRP83bT2aluTNCSvIxlWtaxJvnO8ekcMyCdoGn9C61O5ZSLLtcAafBnLprF2pp4KU3epqBNiO1hPRkxVWYQUVlVPKQMpK9CZlErfN0HoidWZ2m6/1U5C65VlAm6nB+HmNz131KNzBp1XHVliDH1VhZdsAx0NSLcPUSkie/reWv1mzFM6Kf8NyPfSvuh9J2wwtz9W//jEyB70llbmjX58pyaOUQ9w9X+tftP2BKMOHM7RU67/UQ6e8Gtmhp4Hlj8Fp74IdVlAQpee70J93kfTMh8m0I8m0wfKzsMurVVcFO9Y4nIGntR45Ut6DS9GbdzUPdlBCKUz0WW6zQiCOVIfu6sfhFJdrPZG6l49KWmZSt6IEqEsZt89E7OWRG3wKVhyk9cgYWPUyjJeFHVTgBiUtv4IVIjRAprJa64joWZ5nqRtSTrHt6eZIcvRm/9OEyzMm7ZeWHaBth7Qbm+uX6k05NieLFy/GXXfdpZ9xHOv/cpRKpZEwsvzKVVddpf9L+37qqafinHPOwa9//Wvce++9OPbYYzFnzhy87nWv0zA77rgjDj/8cJxwwgm47bbbcPPNN+OUU07RzTolHJkkpC2UNi4KtI1Y97yuu+fn0vYtkuczhHE9oLwWKHTD8nOwPF/DSjtnvNx/bvdyMGEAuL7ep+fcrLalYjetbAFJrQy7vRtwPdiFdm2vtP3It8MudKTr8toOLMeGnSvAxAks24bjebD9rMZp+2563XfhZn1kutuQ6+/Sa/W1Rb0n09UG2/Pgt+eRm9GNtrkz9H8Jn+3tgOO56Nx6Lrp33AJO1odXyOo1CSdxu4WsnivM7oXju6gPpH2Ezi1n6fmwXIXtucj1iswJktgg29uJJIj0nqgW6PdMVzvcnAcnm0FQrGgcHQtn671ybfae6VsXub48HM9B/0t2QL4vh9yMLsS1OnL93fAKPpLEwGvz9b7CrF6V3R6Wq7a2rHK7OV/jTuIE/S9eoPnwuro0f4X+vIaVcPLdzWYQDQ2qvELXdvNh5Qpom9enuitIuO5+LRstD9dHUilq+br59FxSHkr7Jvl2Davlnm2HJWWdyWp5ws3AuL7aG7XFtpvWM+lrxUFaNywbrmNpmyvtedaRvkXa5kq7LX01+RoPN4EZx1ZbI+HlmhyeY2l/L4wTeLaNdt/VOAZrofZROzMeCr6D7pw3Ul/78p72ZeS+tdUQbRkXW/XkNW6xLdv0FjC3I4v2jKNxdWQ9eI6tfcyc72j4njYffXkfXVLGtqX9iDY/vS7M6spq/6m/LYP5nTlNa153Dj0FP41juL/amfOwoDOLveZ3qew7zG7H6nKA7We1oyfnoyvvab668z76O7LI+w52m9WBl2zZg9mdWZVlbk9Orx24/QysqoRoz7raN96+vw39BR9b9OXVtokMW/QW8OaXzEdn3sOKcqDnRI8Z6WO2+Sr7wu78SH86iBLVseRF8qv9sTZf75nflfa9lwylfTsJI+lIX0x0L2VY8G3tr81s8/V8V9bR/6U/L2l0ZVP9BcPhpWylzKXvLPZf/nds8Reg/U4JU9Ow0L6f2G25Jv6HyCt2PetakJ5gYbhPKDJNdTtOpg8JLIS2j9jJqF8r/dpiaLCsnNreDj/1SeR5E99rbS1GPQFqiaVH4mW1LY7zPWq7tW+caQeknXZ99UOkD131O9UPlf6/FVb0ED/RZNrUp6nmehHYPoJMpz6X8pjJsy7OoHzKWEGb5+g4gFyT51GedbEhUc9C7Ysk3fNUZpFXZBAfyYqjkbTi9hmw6kUk+W71dyQ/SbkIq2e29jGeKIYwmQKingXqJ4hNWRun8xbFj1J/IQlhumYj7N8OSVufyid+jb/mMdhhDYEK1wW7sxdWrZimYTnqt8BxkBTXpHov9ADVIfXZ7DjU9sjpnaX+uKQtMiIKkPQtTBsu8Q3drOZN2knxVcSv8Vc+rLrJmkD7XJllD6hvt4VXhnfQW2GFdcTt/SgGMXyT2um4cy7y4RD8gSfVr1GbqmMMYhMBd+3i1L9pn6k+pvhd7tqnNB27uEKvib8laJ5fdIj6tpIX8U1dJKjKYI3EGZZVR+IzCjU7o36ilEfRacOqagw/KGq6ck51I/XFLSCRcYsoQNg5V2UT31xaX0lf7Ij4cVI3pI5JXcgmdY1H6qOOqUjtNmLLrZH0xW+Ncj2pPqWv6eXh1Eup/+v4iNv64CUBap3z0v6pMWpPpNxlzEn8v9jN6hiIWx/S8G2+rTZD/UuT+n2iBzlEbq2TBujNyVgS0OGJ/bFg14o6ruEMLVO5t6g+rmnJmJLoUOSTMRuJz3TNUpsktlCemdByVR55FotBOrazvFzXZ0niXV2N0Vtdpn0kZ2CJPh8iWwdqWk4r6+lYSsXJq48taYhO22urtL7INT8sq412hnVsInk+2lRe6V9J3hrjWaL2OW2e3qPDccNmUvQuOk4az+PwmFYOoeZV2gN5lsWXljGpUEZSpBykrjuejpOJHobCYd/Ay6lMayMbKysRBoJE2yspvwi2junJ9VWVSOPXspPHx4KOGYgvvjmhHR9/OHOcEEJIy/5SfeaZZ+IHP/jByPfdd99dP6+//nocdNBB+v9DDz2EwcHBkTCnn346yuUyTjzxRJ0hfsABB+Dqq69GNpsOYgo/+tGPdED8kEMOgW3beOMb34ivf/3rmzUvhBBCyHSz44QQQgjZNGjHxx8OjhNCCGlZY3zZZZfpsVEZGq/CDCMzCT7/+c/rsSF6enpwxRVXjJuchBBCSLPQTHacEEIIIZsG7fj4w8FxQgghm0SSxLDoVBNCCCEtCe04IYQQ0rrQjo8/02bNcUIIIeODrFUsayyP7eBapYQQQkgz0Sx2/PHHH8fxxx+vm2XncjlsvfXW+NznPocgCDZ6X61Ww8knn4ze3l60tbXp0mfLly/fbHISQgghzUSz2PGpBGeOE0II2SSM2YTXsSUsIYQQQpqGZrHjDz74IJIkwXe+8x1ss802uO+++3QzbNkX5MILL9zgfR/5yEfwu9/9Dj/96U/R2dmpe4S84Q1v0A20CSGEkKlOs9jxqQQHxwkhhGz6GmcW1yolhBBCWpFmseOHH364Hg222mor3UT7W9/61gYHx2WD7e9973u6L8grXvEKPXfppZdixx13xN///nfsu+++m01eQgghpBloFjs+leCyKoQQQjYJMbCbchBCCCGkeWhmOy6D37Ip9oa48847EYYhDj300JFzO+ywAxYsWIBbbrllgqQkhBBCJo9mtuOtCmeOE0II2ST4SzUhhBAyvez40NDQqPOZTEaP8eSRRx7BRRddtNElVZYtWwbf99HV1TXq/MyZM/UaIYQQMtWhPz7+TPnBcWOMfhaLRXiRQSmIMWT5eq4WJfAdC7XIwLWBcpBAlqq3Mo7+HxuDTOSiWE07hW7o6GepHgO+jcgAXlSFZQwqToTANqjEFmLXghXVUbV8uLaFapjAsS2UigFsy0K5GiGXeKiUyignARLPRqVUQ8mLUI8SZGIXlVIVxaEE5TBB7NqwMjaKtRi5jEFse7AswC0WYeoJogzgF0tIQhuwbHlSgCQCHB9OsYSiP4QoLOr5IHAQFEv6f80ZUvlhO7CiAHathMgegrEsTcuzaigihKzf7/sJSsUYQ5kATq2IoomRlzSjNM0kclCsAkNJEbHJwg5rsIISjO3CFRnyQ1oWoiNB5DUiq+sh9IbgFUuIrCGUrQCmWoRdKiPJF1X+sjOEoWyIcrGKIZQQlKtwikUUoxzsYgnV7BAypSKM48GSvMehlkkceygXgSGUYQUVJAFQ9FyEroViNcJQsYT60BA8KYehIZTKIYaMp2VlJTEi2AhFdgxpeduSb8vArxdhxSECdwhOXNfzkpaUvZR1xo81PZMxsGpFBJEHFwmK1QT5RGQ0WneEIDaanmdbWt+EjORf7nc8SEFbYS3NW1TXMhUkPa3fXohikGiZOJUSYuT0vKQv8tkwqiuRV8q4aA/BqxWRJL6WS0l0G5YQOUOwgzKMK+FqqMcZzXuYG4JdTeuZcTOw61KmDoqRD+PZMFERdrWEyB2CFVbT56RUBiJb5U1iT2WR+KWMtQ6Wq6rzpFzR8jOlMqwwQSTnpR54abkXa3X9DBv3DiPlaCUR7EoJYWZI9Y7huGNnSPOVeDEskSNJYOVKCP00jiFvCKZSBES3cQinVNb6GlpDcJIQVr2kaSaxo8+TxBXkJM6i5j2tC46Wu4atljQNeQ6Kjo9cWIQJAOPWETsZlEqh6snJ2Ehgab2X9ALbhyVlXRrWrTyDxsA4aZMs16UeSN0pBQm8uIhS3dW2ReIJ4gRhbLReSvsk7VjiyzMYIVznf6teHtUOjhtxiDHHGIfjmzaZdjTq71CpBONltc3R9iUwsOIIxotgBVXAcYAorW/SjiWxO9wOpLbB1MuwvFh2sNHn1JI2T8JH9kg7ZNnSrtnSyGo7I2nY1TKicgVOpgxTr8IEdcSVKmA5sNwISbUGEwZpWxDU4GaqCKp1OEmin5lKDdVaHa5tI6rVVb5qPYBfc+HAIKgFcIefqKgWoi7XvP90zzzHQjEIYdXqqNUDxMNxeK6L+vD/JjEI6gFcx9bwEs5zbJTqIUwt0GthrY5iPQRqdSRhjFIQIggjePUA9SBEJGHqASzHVtkrcr0eIAhC1MIIQ9U6glpdZa8GIRyxs6HoKNZrEl+uWldZJZ5yEKIURQhDaY/qqNQDSC+qKjJIHywM9b6wFsCOY5TCELaEqdRgJ9LnCVCOIrUFSRAhqtQQVkWPFiKRZfh7UK6gWA8kYj3n+FUtF+nbxJWajv+JzalXanDLUn4SpgJLDHGSIC5XtK234gCmVk77RKHYGpP2p2w37VtJfRjuZ0nYpJ6gaNKBwWqUwLMsSG8xcixtt8W+S19N+gV26CBybFTjBIn0AWKgEsYoB7G250Ipqes1OSqlOqrVEKWigwQG8ghIO18pVVA2Pir1CKXYQ7VcREb6Ok6IaqmCWjVAxYuRuDZKqKNS+s+mhsa1USsXUbFD1MpVVPwY1TBGNYpRsUKUY1+v26GLMEngh572yeLEIHbk3hLqlQA1LwRCB45kDtB+lshTLQeolaUMyqiWXViug3q5jKoXabigUkLdSL8sp+nUK3VUs7H2feRatWyhYgLUKyVUSj4qtRDZRGQqafoV4+s1MbtS/6Tf7QYuKkGk/XnJU60Wo1zy9LXYarmMWln6jv/RV7kWaXx6TvvdMaqlIioIUDKirxpKuQTlcqjpl7IxEgOVUfIr9lz8iLL6AY7WRTkXJAahbWl/LpQbhvt3qKcv6EoR21IX4rRPr/LFBq5l6Tm515L4pW54NopBrOWl9aJYHNUOTqYdnz9//qjTsnHmWWedtd5bzjjjDJx//vkbjXbRokU647vBkiVLdImVo48+WtcdJ1PTH5c2Tv3hIPWnS6FBqR6pDyZ9XPFdQmnz4jqKkYfEt0fuz4hPLLMgh/vNlcggsWP1cbS9tmzUpQ00QMZKv4sfIyShk/pXloWaE+ozKa2YyCLPrWMBXlxHGX5qd+uJ+ozyXBdrEQqJB7dUVJ9XsMvDvqc8H2o3atofEd9X0xpOV3F8WPUiMOz7iN9QsjMYiktqS4wbqB9VdB24cTH1ZSIHsfgn6jvkNA1JU/0120WMIRQTDxmxX/WK+hcmtBH5CTwJV0t9LPlRy9RS3zoeKqrvWE4cJOpPl0Z8Sklf+lZ2vZz2nyJp/zMol0IM5YBiEmGoUlJfMrFC9e/k/ig7pP+LXbTLJUT+EIp1G0MhUBR/0A6H4y2hZg3BtZD6VHaqXxmXkD6c+JwiQ+JHmqdyYQiB1JfISfUh3azQRuwG8MQv07BG893wBROTT+uGtG+Oh5qdURsWuZbWFbHHWbuKwJf2N/Una8PDYWJjnXIRUeyjIuMvnq1tv9TKELbaADF74nPHAXSsQ/RvWXWVWeql+o32EJywAlvHC2R8KB0vMFIHorqG0/rox1oX7aiO0PbhByUkGYNiOcJQBBQDG5kklUeeFdtUNF2xLZInQeqpjFmJnynjEVXfQsYGSqUI2dgdGXtJLEd9UO0bezGKcQGW+NpG/H9HZZJ+stSvou1jSMonHtLyiREgdDL6HEjaokM/KKJcNBjK2nDKJZTcIR0DEp/WadRvuBgKUr1I3clkDKrGQVwtohxl0/jEJ5f+uSvPkIuSHWmeMkEJVrkCM5SOMxSjIZiMMzJ+NGQNIXBtuEFZ67v0AeqOlY5rSR8tKMvAHYpJCCdI/fZi5KqepG2RsrRNrOXqWUjbHZOO/Ui9kLEVaZukfojM9VjamtR+a9/HDrXMRBdqs6sR3NBFIH1B8cONxBGP9BvKpeax42TjWGbcS6m5eOqpp57VmSOEkOnEk08+iXnz5r3geGq1GrbccstNnpk1a9YsPPbYY8hmsy9YBjL9oB0nhEx3msGO33333aPs+MZmjq9cuRKrV6/eaJyyvrjMABeefvppHHTQQbpe+GWXXQbb3vDKn9dddx0OOeQQrF27dtTs8S222AKnnnqqbtZJmgvacULIdKcZ7Dj98Wk+OC47oEuHq729XWdlTCbyy60M1MuD0dHRgWaFclKfzVo/WTc3DWneZZbOnDlzNupobqpBDoL/zEYcC+L8cmCcPF9oxzcdtpXTT5+tIKNAOTeN6WDHZcb4wQcfjD333BM//OEP4chbQM+xJvmMGTPw4x//GG984xv1nGziKbPQZc1xbsjZfNCObzpsK6efPltBRoFybhrTwY5PFab8sipSAcfjF5rxRBq7Zm7wGlBO6rNZYd0cO52dneOqezGqNKxkIqEdf/6wrZx++mwFGQXKOXamsh2XgXGZMS6zvmWdcZlxvu4st0YYmSl++eWXY++991Z9HH/88fjoRz+qG3dKXfrgBz+I/fbbjwPjTQrt+POHbeX002cryChQzrEzle34VGLKD44TQgghhBBCCGkurrnmGt2EU45nTmZqvNwchqHODK9UKiPXvvKVr+iAq8wcr9frOOyww/DNb35zwuUnhBBCyNSAg+OEEEIIIYQQQiaU4447To+NsXDhwmdtZCYz5i6++GI9CCGEEEJeKOOz6A0ZE7JpjezsvqHNa5oFykl9Niusm4QQtkFsK2l7aB/Z3yCE0JeYfOibUZfNSqvUTdI8TPkNOQkhhBBCCCGEEEIIIYSQZ8KZ44QQQgghhBBCCCGEEEKmHRwcJ4QQQgghhBBCCCGEEDLt4OA4IYQQQgghhBBCCCGEkGkHB8cJIYQQQgghhBBCCCGETDs4ON6ktMI+qa0go0A5CSGE0O7QPrK/QQghhP7jxEN/nBDS7HBwvImo1WqIokj/tywLzUgryCiUy2UMDg4iDEPKuZlgJ2d66pMQ0tr2sVXkpB3f/LSK3aGchJCJoFXsY6vISTu++aF9nJ76JJsHDo43Cffddx/+67/+CwceeCB23313/PCHP8STTz452WK1nIwNOQ855BC8/OUvx3bbbYdzzjkHDzzwAJqNVpFTePrpp/H3v/8df/zjH1EqlUY6Y0mSoJmgnISQyaCV7GOryNkK9rFV5BRoH6enPgkhU88+toqcrWAfW0XOVrI7lJO0KpbhzyOTzqOPPoo999wTRx99NPbaay/cdNNNuO2227DffvvhtNNOwy677DLZIraEjMITTzyhcr7lLW/BEUccgRtvvFFlzefz+MIXvoD9998fzUCryCncc889OPLII9Hb24t7770X++67L4466ih84hOfGDHItj35v7NRTkLIZNAq9rFV5GwV+9gqcgq0j9NTn4SQqWUfW0XOVrGPrSJnK9kdyklaGhkcJ5PLBRdcYF75yleOOnfJJZeYAw880LzpTW8yixYtMpNNK8goXH755SpTHMcj537729+ao446yrzkJS8xt956q2kGWkXO1atXmx133NF87GMfM0uWLDGLFy82J5xwgtlrr73M8ccfb5Ik0XCNT8o5NfRJCJl69rFV5GwV+9gqcraK3aGchJDJolXsY6vI2Sr2sVXkpH2cnvokE8/k/7xEEMcxlixZomtkNzjhhBP0kPOXXXaZrtk1mbSCjEIQBHjwwQexbNmykXOvfvWr8aEPfQizZs3Cl7/8ZSxfvhyTTavIKfKJrMceeyzmzJmD+fPn44tf/CLe+ta34s4778Spp57aFOvdUU5CyGTRKvaxVeRsFfvYKnLSPk5PfRJCpp59bBU5W8U+toqcrWJ3KCdpdTg4Pok0VrSZO3euGrlHHnlEvzc22HjnO9+pr8v87//+L1atWkUZx6DLrbfeGj09Pfjb3/42av0tWUtMXkH7y1/+gsWLF0+KLltJzgaFQkHro7wi1ZC/q6sLJ554osop8v/mN7+ZbDEpJyFkwmkFG96Kcja7fWwVORvQjk9PfRJCpp59bBU5m90+toqcrWZ3KCdpeSZhtvq0R17RWPf1HUFe6dl5553NmjVr9HsYhiPXZs+ebS666KIJ1VsryCgEQaDHurz+9a83c+fONf/4xz+eFX7rrbc2n/3sZ81E0ypyPpOBgQFz8MEHm9e97nVmxYoVo64Vi0Wzxx576GtIkw3lJIRMFK1iH1tFzlaxj60i5zOhfZye+iSEtL59bBU5W8U+toqcrWp3KCdpdThzfIJZtGiRvqoju0yfd955uPrqq/X8lVdeqb8IHnroofpalOu6er5YLGL27Nn6ag9lHM3999+P97znPbq79Ac/+EH84Ac/0PO/+MUvsHDhQrzpTW/SX1Ibv6qHYai/uM+bN29Cy7xV5BQGBgZ0NsKKFSt0F+zOzk59besPf/gDPve5z2l9bNDW1qavnj388MMjslPO1tQnIWTq2PBWkrNV7GOryNlKdodyEkImg1axj60iZ6vYx1aRU6B9nJ76JE3AZI/OTyceeOAB09XVZY4++mjz9re/XX/l22mnncx5552n1x966CHz4he/2Gy55ZbmW9/6lrnqqqvMGWecYXp7e82jjz5KGddBdCW6fM973mNOPfVU89rXvtbMmDHDfOhDH9Lr1WrVHHTQQfoL+umnn66blXzkIx/Re/71r39NWJm3ipzC3XffbXbZZRezzTbbmIULF5o3vOEN5q677tJrv/rVr0wmkzHHHXecefDBB0fuecc73mHe+ta3miiKKGeL6pMQMnVseCvJ2Sr2sVXkbCW7QzkJIZNBq9jHVpGzVexjq8gp0D5OT32S5oCD4xOEvBL14Q9/2LztbW8b2flWGtuzzjpLDdnnP/95PSev+hx77LFm9913V4O39957r/c1n+kqY4Ozzz7bHHnkkSOvmq1cudJ873vfM9ls1rzvfe8bCScdhSOOOMJsv/325pBDDjH//Oc/Ked6eOqpp8ysWbO0I3DbbbeZb3zjG6q3jo4Oc9NNN2mYP//5z6avr8+89KUv1Q6E1JO2tjZzzz33TJhOKSchZDJoFfvYKnIKtOPjC+3j9NQnIWRq2cdWkVOgHZ+edodykqkKB8cnkFe/+tX6C/C6LF++3Jx77rlm3rx5+stvg2XLlum1tWvXTqSILSGjIL/wydpb61Kv180VV1xh8vn8qPXB5NdgkbFUKlHODXD99debPffc06xatWrk3L///W9zzDHH6A8Ot9xyy0jn7Gtf+5rq/xOf+IS5//77J1SnlJMQMlm0in1sFTlpx8cX2sfpqU9CyNSzj60iJ+349LQ7lJNMVTg4PoF88Ytf1E005NWedXnyySd1E4XDDz98VGM4GbSCjIIMgssmGTfffPOzNqU455xz9PWzZ+ZhMmgVOeV1PNu2zdNPPz3qvHx/y1veojMSHnnkkVHXGrMZJhLKSQiZLFrFPraKnK1iH1tFTtrH6alPQsjUs4+tImer2MdWkbNV7A7lJFMVbsg5gbzkJS/BU089hSuuuAJr1qwZOS8bPbzlLW/BNddcg8WLF0+kSC0po7DTTjvphiOymcZDDz00ahOFI444Ag8++CDl3AT2339/7LXXXvja176GoaGhkfOysctHP/pR9PX16SYlQhzH+mlZ1vgVKOWcFH0SQqaefWwVOWnHxxfa8empT0LI1LOPrSIn7fj0tDuUk0xVODi+mWk0XMLBBx+MD3/4wzjnnHPw7W9/G08//fTIte233x4vetGLNrc4LSvjM+XcddddcdJJJ+nO3d/4xjdwzz33jFzbdtttVdYkSSjnGOnv79fdu//0pz/h5z//OarV6si1vffeW3V5880363fHcSZFr5STEDLRtKJ9bBU5acfHF9rx6alPQsjUs4+tIift+PS0O5STTFXcyRZgqiMNlyxf89e//hUHHnigGjoxKmeddRaefPJJHHXUUdhll13w9a9/HQMDA/rLIGUcjehPfhVt6FKMxZve9Ca8853vVCNx3nnn4YknntBzu+++Oy6//HLtRExWx6FV5JR6KLKKbLZt4/zzz8ejjz6KL3/5y6hUKnj3u9+NfD6vYRcuXIg5c+ZQzimgT0LI1LLhrSAn7fj0tjuUkxAyWTS7fWwVOWnHNw+0j9NTn6RJmex1XaYyYRjq53ve8x6zzTbbmL/+9a8j1/7f//t/5pWvfKXuLrzzzjubBQsWTNgO0w25GjtiN6OMwooVK561jtanP/1pM3fuXPOLX/xi5Nzvf/973ZCiUCiYnXbayWy33XYTvlv3M2kVOUW/Iuu11147ck5klJ3PX/GKV+jGL1I32tvbzQMPPEA5p4g+CSGta8PXlU2gHd880I5PT/vYKnISQp4b2vEXDv3xzU+r2B3KSaY6HBwfR2RjjEWLFo3sJNxANnh43/ve96xdpMXY3HvvvebOO+/UnaYnggcffFB3NX744YebVkZB0tx+++3NxRdfPOL4C0888YQ566yzzJo1a0aFr9fr5qmnnjKPPvrohG5QIjtIf+UrXzEf/ehHzV/+8hdTqVRG5Dz77LObRs4N8ac//cnMmjVLdVqtVkcN/Bx77LFmv/32M294wxvM3XffPaly/vnPf246OVevXq3PR7PLSQiZOjZcoB0fX2jHJ4ZmtI+044RMLWjHxxf64xMD/fHnD+04GU8s+TPZs9enAvfeey/e+973YnBwEGvXrtVlM2Q97AaNVzsmCynmWq2m61jdcccdul73pz71KcyfP39EtsbrUpONbKYpGz3Iay/yStmCBQvW+7pMM5T5oYceij333FM3BRU9/vKXv8SLX/zipijzdfnXv/6F733ve1ixYgV22203HHnkkbo2u3Dttddijz32QHd397PqQL1eV1277sSswPTYY4+pDmUTGllbTTadaXDDDTeobptBTnk965WvfCWOOeYYvO997xv1SlYzyUkImRo2XKAdH39ox8cf2nFCyGRAOz6+0B8ff+iPjy/0x8m4M65D7dMUmcXV19dnzjjjDJ1x9sc//tFstdVW5pOf/OR6w3/96183P/vZz8xk8KlPfcq8+93vNrlczrztbW8zjz32WFPJKLPETzzxRJWx8f2mm24y3//+93X23jNn7n35y182F1xwwYTL+fTTT5sdd9xRZ0BFUaTnXvSiF5lvfvOb6w0/WXIK999/v+ns7DSHH364eeMb36j/H3rooebb3/72esM//vjjZjK45557zLx588whhxxi9t9/f2PbtvniF7+4wfCTJacgurMsS193+8IXvmCWLl3alHISQqaWDRdox8cH2vHxh3acEDIZ0I6PL/THxx/64+MP/XEy3nBw/AVSLBbNm9/8ZvOBD3xglEH54Ac/aI466qj1vvqx5ZZb6kCl3DtRNJYm+fCHP6xLlUgDnclk9FXWcrmsA7eNQbzJklGQgeYDDjjA/OAHP9DvL3/5y82ee+6pA7pbb721Oemkk8zixYv12sDAgA7yHnTQQc9avmRzI2vPyjqz//rXv0bOveUtbzEf+9jHzDve8Q4dzG/IKQP6kyWnLOMi8pxwwgkj52RJHZF13333NV/72tdGhb/wwgtV1jvuuGNC5ZS6J2v6nn766SN19Xvf+56ZOXPmKB1PtpwN5LXvd73rXeacc84xc+bMMf/93//9rB9umkFOQsjUsOENuQTa8fGBdnx8oR0nhEwGtOPjD/3x8YX++OaB/jgZb5pjzYcWp1AojCylIcir1wcccIC+WhoEAcIw1PPyY0RPTw+uu+46XHzxxWhra5twWQ8//HD84x//wIte9CL85S9/wf/93/9hp512wle/+lV9bVwQGa+//vpJkVGWnJgxY4buwn3mmWcik8mojKtWrcIHP/hBfWXu0ksvVVk7Oztx+eWX4//9v/+nS1hMJPLa/fLly/Hvf/9bl8q44IIL8Itf/EL/X716Nb71rW/puVKphK6urkmT0/d9lbOxtIfUwW222QZf/OIXscMOO+BnP/sZfvvb346E7+vr0zo7c+bMCZNRyvLKK69UuWSpn8bSBXvttRc8zxupl+syGXKui+jxb3/7m8orSxRdcskl+MEPfoA3vOEN+PSnP900chJCppYNF2jHxwfa8fGDdpwQMpnQjo8v9MfHF/rjmwf642TcGffh9mmCzOBq7IC97rIJsouv8H//939ml112GXVPqVSacBkbS36su+GDbHTZ2DjyiCOO0OUr5HNjS0NMpJwyK05mZR9zzDHmO9/5zqiwMjNbljMJgmBE15Mlp8wcnDFjhi4DIrPw//CHP4xcO++888yCBQt0Y87JQmQVPckSNW9605tMrVZTnTVmH8omZLIBlswiX5fBwcEJl/XGG2/UJQ3WReRcuHChuf7669d7z2TIuS6vetWrRpYlkuVfCoWCvuEgSzI0k5yEkNa04QLt+ObVJ+34+EE7TgiZSGjHx1+f9MfHH/rjmxf642Q84czx58EDDzyAY489VmdvnXzyybjvvvtGbRbZmHm27ozX0047DW9961tHrk+UjIcddhje//7343e/+53Ks99++2HLLbdELpfDe97zHp2J/f3vf19nkcsM2CVLlkyIfOuTU9K/6aab8IUvfEFnW19xxRV44oknRoV/1atepb++lshFWvoAAB+DSURBVMvlCd08dF05TzjhBJ01/Ic//AF//vOfVb8777yzznSOokjDv+xlL1M5ZRPUiaZRx+RXf5l5/a53vQtXXXUVvvOd76jOpG5KmK222grnnnsufvrTn+L+++8fqa8dHR0TKmdDXyKLsO4ewSJvY9ZmY/PQZcuWTZqc6yIzSqW+CrIhq+hbnqu7774bTz/99Ei4iZKTEDJ1bPi6ctKOj78+acfHB9pxQshkQDu+efRJf3z8oD8+vtAfJxMBB8c3ERkE23///fUBlcHQm2++GWeddRY+8pGP6HXXdfUzn8+PDJTK0guyzIYstyADaBMt4y233KIynn766bpMiQyY9vf34/e//70Omsrgqfx/6623TuiA8zPllPQ//OEPq6yy7IcM5MtSJH/84x91MFyQ/2XgXAaeJ0vO22+/HR/4wAfw8Y9/HLvuuitmz56NSqWC3t7ekfKXJVZETlkiZqJ3wZYlcpYuXTpy7uUvfznOP/98raP/+7//q+ca9bC9vR3bb7+9vo7YWM5ksuRsDIpLHZRnp1qtqpyNwWV5jl75yleud5mViZSzMVi/zz77qM4+9KEP6Q8ld911l/4v9VeWiZnIQTRCyNSx4euTk3Z8fPVJO/7CoR0nhEwGtOObV5/0x1849MfHF/rjZMIY13noUxx53fpTn/qUbt7VYGhoSDfl22233UZteviLX/xCNzyU8L7vmzvvvHPSZZRXxGWJjU9/+tPm1a9+tbntttv0euMVqmq1OiEybkxO2dhwjz32MMcff7y55557dHPOefPmmRe/+MXmNa95jenq6jJ33XXXpMsp+hSZTjnlFN2IcdtttzX777+/+exnP6uy9/b2TqicjY02e3p6jGVZ5pOf/KRZuXLlyDXZdPXss8/Wa5/5zGfMP/7xD91YTpYykY0wV6xY0RRyrvtqn9RH2YRVNrP8/Oc/r8uWNOpsM8gpm67KtdmzZ5vbb7995Pz555+/3k1ECSGTSyvY8OeSk3Z8fPVJO/78oB0nhEwGtOMTo0/6488f+uPjC/1xMpFwcHwTOe6448zLXvayUefEiFx44YXmJS95iTn33HNH1iuVgbPu7m4d3GsGGS+44AJz0EEHmQ996ENmyZIlz7pvotfw3pgu99prL/P1r39dz333u981Z555pq7j/dBDD02ojBuSU3ZGFzllIP9LX/qSue+++8zBBx+sa3gfffTR5v77759QGWUt3Pe85z0q68UXX6x17+Mf//ioQW8ZcP7BD35gZs2aZebOnWt22GEHM2fOnAkd9NmQnOsbIBd23313rQsyOLXuAHQzyCl1UX5o+Oc//6nfG2u5E0Kal1aw4RuTk3Z8/PRJO/78oB0nhEwmtOMTp0/645sG/fHxhf44mWg4OD5GGgPHMmD70pe+1Dz44IOjrq9Zs0Znncns4Xq9bh555BFz4IEH6uznZpFRZgq/973vVbnEKWx2Xe69997P2lC0GeWUa42BUSl7OSYa2WBVBnGvvPLKUQM7zxwgF2QTSdk4SzYQfeqpp5pGznUHnqXcpb7KBpeO40zoc7QpcsqM/Mn6cYkQMrVs+FjkpB0fX33Sjm86tOOEkMmAdnxy9El/fOzQHx9f6I+TiYaD45uIOMx9fX06q7QxwNwwLosXL9YBNBl0lMFS+bWrmWWcbMYi5+9///tJH3wci5y//e1vzWTzzPomA7si28c+9rGRAd0wDM0TTzxhmlXOVatWjcgpMl999dU6K7/Z5Gz84CDP+aOPPjop8hFCpqYN3xQ5Jxva8fGFdnzi9Ek7TkhrQjs+8fqkPz52aMfHF9pxMpFwcPx5cN1115lMJmNOPvnkUbNIly5dqutX3nzzzabZZfzb3/5mmgHKOf7IrOtGp+bHP/7xyIxnWUrnIx/5iHnDG96ghmayZzo/l5yvf/3rR83MbmZ9NoOchJCpY8MF2sfpqU+Bdnzi9Uk7TkjrQDs+sfpsFvvYKnIKtOMTr0/acfJC4eD48+TXv/61Ns7yIMpMlAceeEA3N5TN+Z588knTDLSCjALlHH/EeDSWepGy9zzPbL/99sZ13ZE1sptZTllKpRXkbDZ9EkLGBu3O+EJ9Tl+7QztOCJkMaHeoz2Ye3xBox6enPknrYskfkOfFP/7xD3z0ox/F448/Dtd14TgOrrzySuy+++5No9FWkFGgnONP49G2LAuHHHII7rrrLtxwww3YZZdd0ExQTkLIZEC7Q302c79IoH2cnvokhIwN2vHxhfqcvnaHchICcHD8BTI0NIQ1a9agWCxi9uzZ6Ovra7p61QoyCpRz/InjGB//+Mfx1a9+VY3xrrvuimaEchJCJgPaHeqzmftFAu3j9NQnIWRs0I6PL9Tn9LU7lJNMd9zJFqDV6ejo0KOZaQUZBcq5edhpp510JkCzGuIGlJMQMtHQ7lCfrQDt4/TUJyHkuaEdH1+oz+ltdygnmc5w5jghUxx5TUpe5Wp2KCchhBBC+8j+BiGEkKkE/dzpqU/SWnBwnBBCCCGEEEIIIYQQQsi0w55sAQghhBBCCCGEEEIIIYSQiYaD44QQQgghhBBCCCGEEEKmHRwcJ4QQQgghhBBCCCGEEDLt4OA4IYQQQgghhBBCCCGEkGkHB8cJIYQQQgghhBBCCCGETDs4OE4ImVTOPfdc7LXXXmhvb0d/fz9e97rX4aGHHhoVplar4eSTT0Zvby/a2trwxje+EcuXLx+5fvfdd+Ntb3sb5s+fj1wuhx133BFf+9rXnpXWDTfcgD322AOZTAbbbLMNLrvssjHLedJJJ8FxHPz0pz/FRPKFL3wB+++/P/L5PLq6utYbxrKsZx1XXnnlhMpJCCFkekI7vnFoxwkhhDQztOMbh3Z8esDB8SnAcccdpwOKE40MLDYG4k499VS0MpKXDQ08vlAWLlyIr371q5sl7qnAjTfeqAPff//733HNNdcgDEO86lWvQrlcHgnzkY98BL/5zW90YFrCP/3003jDG94wcv3OO+/UgfUf/vCHuP/++/HpT38an/zkJ/GNb3xjJMxjjz2GV7/61Tj44INx1113aZ1973vfiz/+8Y/PKWOlUtHB5tNPPx3f//73MZEEQYCjjz4a73//+zca7tJLL8XSpUtHjsloEwghzw/a8RcO7fjkQTu+cWjHCZn60I6/cGjHJw/a8Y1DOz5NMKSpkSLa2PG5z33ODAwMmLVr1064bJdeeqnp6OgwS5cuNUNDQ6aVkbx0dnZulrhXrFhhyuWymUxuvPFG81//9V9m9uzZWm+uuuqqZ4VZtmyZede73qVhcrmcOeyww8y//vWvkeuPPfbYBuvhT37yk5FwTzzxhDnyyCM1jhkzZpiPfexjJgzD55RR4th+++2N7/sa5/nnn6/npX57nqfxvPKVrzQ9PT0j6d5yyy0bjO8DH/iAOfjgg83q1avN29/+do3Xtm3znve8xxSLRQ3zlre8ReOUfO+8887GcRzz2te+9llxXXbZZWbfffdVWfL5vFm8ePGo6y9/+cvNhz/84VHnJB6Jt8HTTz+teslms2bhwoXmRz/6kdliiy3MV77yFfNC6+iGypQQMvnQjk8MtOO047TjhJDNAe34xEA7TjtOO04mE84cb3LWnQkqs487OjpGnfvYxz6Gzs7OzTbr+bmQWeOzZs3SJTHI+pkxY4YuiTGZyCzsF7/4xbj44ovXe136fTLT+NFHH8WvfvUr/POf/8QWW2yBQw89dGQGtyxZsm7dk+Pss8/WZU6OOOIIDRPHsc7Oll9X//a3v+EHP/iBzgI488wzNyqfhJVlUY4//nhNX5DZ3/fdd5/OCpfZ5FtvvTUOOOAAnH/++Xpd6t0tt9yywTgHBwfR09ODY445RmeTv+hFL9LlWG666SaceOKJGuawww7DrbfeqkuxfOhDH9L8ro/vfe97eMc73qHPmuR1U5ZjaXDsscfqjHdZ2uXnP/85LrnkEqxYsQLjhcy+7+vrw957762z29O+PCFksqEdb31ox2nHBdpxQqYntOOtD+047bhAO042yqQOzZNx+TVVZqeuO9tVZrGecsopOpO1q6vL9Pf3m0suucSUSiVz3HHHmba2NrP11lub3//+96Piuffee83hhx9uCoWC3vOOd7zDrFy5cpPlufjii80222xjMpmMxvPGN75x5Focx+Z//ud/dOaszKDdddddzU9/+tNR9993333m1a9+tWlvb1dZDzjgAPPII4+M3H/22WebuXPn6kzgF7/4xeYPf/jDs2Y3//znPzcHHXSQzl6WNP72t789S/b58+fr9de97nXmwgsvHJWXu+66S++X9EWOPfbYw9x+++3r1UOSJDqDX+ITmWTm9Qc/+MGR68+cHSzyffe739V0JX3R1a9+9asx60CQ+3fYYQfVscy2Fp2PlfXNMn7ooYf0vKTbQHQtM78lrQ2x22676UzsBlKnZHa2zEJv8K1vfUvfMKjX6xuM581vfrPmV9KUz5e+9KVmn332MSeddJLOsBa9PrOMd9ppJ3P66aevN76bb77ZuK6rsktYKbttt91W657UF8uyzJIlS8zvfvc7vV6pVNb7LAkye15mrjeeBdHdlltuqeU+1pnjixYtGpGjwcMPP6znxmPm+Oc//3nz17/+1fzjH/8w5513ntaLr33ta2OKlxAycdCO0443oB2nHV8X2nFCWgPacdrxBrTjtOPrQjve+nDm+BRFZuzKLNLbbrsNH/zgB3W9Ylm3WDb2+8c//qFrOr/zne/UtZSFgYEBvOIVr8Duu++OO+64A1dffbVuePjmN795k9KVe2UG7uc//3ndVFHiednLXjZqs4fLL78c3/72t3U2r6wlLTNyZZ0rYcmSJRpeNky87rrrdNbwe97zHkRRpNdlk8UvfelLuPDCC3HPPffozN+jjjoKDz/88Cg5ZNaxzKqXtaW32247nZXciENmCssM5VNOOUWvyxrU55xzzqj7ZbbxvHnzcPvtt6sMZ5xxBjzPW2+eZRbwV77yFXznO99ROX75y19il1122aieZMa16FbycOSRR2p6a9asGZMOfvSjH+lMbNkYYtGiRfif//kffPazn9Uyf77U63X9zGazI+ds21YZ/vrXv673HpFL9Ce6bCAzuSXvM2fOHDknZTQ0NKTlvSHkPpm1LbOfZba4rO8t921sZviGkPtf+9rX4nOf+5zmQd6qeMlLXjJyXdKR81IP1kX0Ksdvf/tbnQ3/l7/8Rc/LLGyRRZ4nQcpLZqVL2YwVeRZc19XNQBvIhqDd3d0j39/3vvdpuo1jU5Dyf+lLX6rP7yc+8QldG/2CCy7YpDgIIc0F7Tjt+KZAO047TghpLmjHacc3Bdpx2nEyyUz26DzZPL9Uy0zjBlEU6Wzwd77znSPnZJ3wddds/u///m/zqle9alS8Tz75pIaRWcVjlUdmbMss4fWtQV6r1XS95mfO4j7++OPN2972Nv3/k5/8pM7KDYJgvWnOmTPHfOELXxh1bq+99tL1pdedVfy///u/I9fvv/9+PSezdwVJS9Z+XhdZe3rdvMiMbVlneix86UtfMtttt90GZV7fzPHPfOYzI99lRr+ca8yAfy4dyKz/K664YtQ5Kb/99tvvec8cl7QWLFhgjj76aLNmzRqd5S0zkCXsM+tFg/e///1mxx13HHXuhBNOeFZ4WW9d4nnmmwrrIjOzZe3vefPmmUcffVTPyWx4efPg2muv1fsb6+o3ynjWrFnmy1/+8qh4pKzlnk996lP6XeqKlI1w4IEHjszulhnx3/zmN833v/99ra+C1NnXv/715pBDDtFZ3TKbXJ4deRNAZprLeuSNQ9KXdcwbyNrmH/rQh0bJInWsMXP8l7/8pc5kl5nx69Ld3T1SN5YvX67pNo4Xsg7fb3/7W5VRnjlCSPNAO047LtCO044/F7TjhDQntOO04wLtOO34c0E73npw5vgUZddddx3533Ec9Pb2jprN3JjZ21jz+O6778b1118/aubqDjvsoNf+/e9/jzndV77ylbpW9VZbbaUz02UmbmN2+iOPPKL/S5h105GZ5I00ZCbygQceuN5Z2jL7WNZslhmy6yLfZQb1hvI/e/bsUXmVsPvss8+o8Pvtt9+o7x/96Efx3ve+V2cZn3feeRvVgczIr1armucTTjgBV1111cgs7w2xrnyFQkHXkm/ItzEdyPrfIovM1l5XhzLzfVPK6ZlIWr/4xS/wr3/9S9fpljXSpT7I+toyy/qZSH6vuOKKUbPGx8LixYtHyS2z3mW8XtYql7cOZDb2lltuOeqePffcU+W79tprR51ftmzZqHKTmfg77bQT1q5dq28YPBMJ+8w4rrnmmpE4ZN18KQeRS2Z1yzrkv//971EsFnUNdimXxvHjH/9Y9SVvXDTWsZP1CBtIfmQGe4Ptt99e64TE00CeB5G1QX9/v6bbOF4IIqPMSpeZ/4SQ1oR2nHZ8U6Adpx0nhDQXtOO045sC7TjtOJlc3ElOn2wmnjmwKhtnrntOvgtJkuhnqVTCa17zmpHNDtelMbg8FmSAUZZtkU0H//SnP+nyH2eddZYuTyJpCL/73e8wd+7cUfc1BvFkQHI82Fhex4LI/Pa3v11l/cMf/qBLdMhSH69//eufFVY2qpRlM/785z/rYOsHPvABXdJClorZ0FIs6yufhnwb00FDh9/97nefNcAvP4K8EGQQWgZVZckQ2VBTBnwljXWXJGnws5/9TH/okE0t1kU2yZSlfNZFludpXJszZ46m0UAG4mUpFRkgF31L/ZFBb+Gpp57Se2QTTBmElx8sJHxDB9Lh3HffffV/GYiWgXVZTkWWFREkHvnhofGjgyxb8o1vfEOX21m9erUOVP/kJz/RMt4QshGnbDAqm5mui2zuKUsCyY8/Ir8sSSTySVyyceiXv/zlkYFzQX5okh9aZCPQb33rW1r+p512mpZ1o35u7AcFWXJHPmXQvaE/GUCXgfzf/OY3qmPRhSyLI3VQfnSQfBJCWhfacdrxTYV2/NnQjhNCJgvacdrxTYV2/NnQjpMJY7KnrpPN8xrXMzcHfObSHs9cXkOWoZCNHcMwfMHyrIssGSLLSchyK7JshWwUePnll28w/FlnnfW8llU5+eSTRy258c9//nPkuizHIeeuv/76DS6r8ta3vnWjeZHrr3nNa8xYePDBBzW9O++8c4PLqjxzWRNJW/Q5Vh3Ihg/Pl/Wlvz5kI0rZXPOPf/zjs65JHVt3o9VnbsgpS4Q0+M53vqNLl2xoiQ+RZ32HvK4mG3IK1WpVl86RZUhkE1O5fs0114zEIRuiri8O0ZV83nHHHRpO6oDEK+ekXBo6X9+zJJuKSt39yU9+ssFlZXbffXf9X8pKvvf09OiyLueee+6oDTmFp59+2hxxxBH6DEjasjSOhP32t7+90XKQONaXt0Z9luV4ZGNU2bhVlk+STWolzmcu4UIImXxox2nHBdpx2nHacUJaE9px2nGBdpx2nHZ86sHB8RZicxrjJUuW6DrMb3rTm8xtt91mHnnkEXP11Veb4447TtddHqs8v/nNb8zXvvY1HZx+/PHHdV1nGSy977779PqnP/1p09vbq+t5SxoygPz1r399ZH3vVatW6fU3vOEN5vbbb9cBWhlMlwFnQfIgA61XXnmlnvvEJz6h61VLuLEOjss66yLTBRdcoPdddNFFpqurayQvsta0DLZLeMnDX//6Vx1QPf300zeoB1nj/N577zX//ve/dT1xGcCVvDyfwfHn0oHsjC3xi55lPfh77rlH186Wtc83RLFYVJ3IIenLWt3y/xNPPDESRgaBJc+SB1kjW+QWGZ6JrIcta3A31khfF6krO++8s647ftddd2kdknol66hvjJtvvlkHoi+88EJdG14Gu6VcRacNVq9erTL/7ne/0zxIHZDvsn7+xjj88MN1EPvWW2/Vstx2221H1rhfd61yiUt+ADnooINGdLU5aazp/+c//3mzpkMIaR5ox2nHBdpx2nFCSGtCO047LtCO046TqQcHx1uIzWmMBRmElQ0JZaBYBl932GEHc+qpp5okScYsz1/+8hdNvzHDd9dddzX/93//N3Jd4vrqV7+qs9Rl8FMGTg877DBz4403joS5++67dXBVNu+UjTFlI0UZsBVkNqzMrJ47d67eL7Nk1x2kHcvguPC9731PN38UGWVAVAZlG3mRzShlpvj8+fON7/v6y/App5yis5fXh+hwn3320UF7mbm77777jhrw3NTB8efSgfCjH/1IZwuLfKLrl73sZeYXv/iF2RCS9/XNPl53ZrMMtotORK+yOacM8osunokMdItuNjQzWX5QkBnSotu+vj5z2mmnjemNBBmcl80zJU877bSTDoKvi+hnfXmQgfSNIYPqMhguM6uljN797nfrjwXrImW0vrjHE9lY9Fe/+pVuOCo/Brz0pS81Cxcu3OAbAoSQqQftOO14A9px2nFCSOtBO0473oB2nHacTC0s+TNxi7iQqcRll12GU089ddTayoSQ9fPHP/5R1xl/9NFHdW31/fffH1/96ld1A1tCCJkMaMcJGTu044SQZoN2nJCxQztONgY35CQvCNm8UTYFlE0J17eZJyEk5bDDDtODEEKaCdpxQsYG7TghpBmhHSdkbNCOk43BmePkeVMsFrF8+XL9v6urC319fdQmIYQQ0iLQjhNCCCGtC+04IYSMDxwcJ4QQQgghhBBCCCGEEDLtsCdbAEIIIYQQQgghhBBCCCFkouHgOCFTkIULF8KyLD24YSohhBDSWtCOE0IIIa0L7TghrQUHx8m04OKLL1YDlc1msc8+++C2224buVar1XRD0d7eXt1c9I1vfOPIWuob46c//Sl22GEHjXOXXXbB73//+1HXjTE488wzMXv2bORyORx66KF4+OGHnzPeG264AXvssQcymQy22WYb3YV8U/Ij3H777fj5z3/+nGkRQgghrQDtOCGEENK60I4TQpoaQ8gU58orrzS+75vvf//75v777zcnnHCC6erqMsuXL9fr73vf+8z8+fPNtddea+644w6z7777mv3333+jcd58883GcRzzxS9+0TzwwAPmM5/5jPE8z9x7770jYc477zzT2dlpfvnLX5q7777bHHXUUWbLLbc01Wp1g/E++uijJp/Pm49+9KMa70UXXaTpXH311WPOT4Prr7/eyCO+du3aF6A9QgghZHKhHacdJ4QQ0rrQjtOOE9LscHCcTHn23ntvc/LJJ498j+PYzJkzx5x77rlmYGBAB7V/+tOfjlxftGiRDirfcsstG4zzzW9+s3n1q1896tw+++xjTjrpJP0/SRIza9Ysc8EFF4xcl7QymYz58Y9/vMF4Tz/9dLPTTjuNOveWt7zFHHbYYWPKz7pwcJwQQshUgHacTjUhhJDWhXacdpyQZofLqpApTRAEuPPOO3VJkwa2bev3W265Ra+FYTjquiyVsmDBAr3eQJYwOeuss0a+y7V17xEOO+ywkXsee+wxLFu2bFSYzs5OXQJl3XgPOuggHHfccWOO97nyQwghhEwlaMcJIYSQ1oV2nBDSCnBwnExpVq1ahTiOMXPmzFHn5bsMXsvh+z66urrWe73B1ltvjb6+vpHvcm1DcTauN85tLF4ZhJc1yZ8r3qGhIVSr1efMDyGEEDKVoB0nhBBCWhfacUJIK+BOtgCEtALXXnvtZon38ssv3yzxEkIIIeQ/0I4TQgghrQvtOCFkc8KZ42RKI7O9HcfB8uXLR52X77NmzdJDXvUaGBhY7/UNIdc2FGfjeuPceMTb0dGBXC73nPkhhBBCphK044QQQkjrQjtOCGkFODhOpjSyZMqee+456pfmJEn0+3777afXPM8bdf2hhx7C4sWL9fqGkGvP/PX6mmuuGblnyy231MHqdcPI0ii33nrrC4r3ufJDCCGETCVoxwkhhJDWhXacENISTPaOoIRsbq688kqTyWTMZZddZh544AFz4oknmq6uLrNs2TK9/r73vc8sWLDAXHfddeaOO+4w++23nx7r8opXvMJcdNFFI99vvvlm47quufDCC82iRYvM5z73OeN5nrn33ntHwpx33nmazq9+9Stzzz33mNe+9rVmyy23NNVqdSTMO9/5TnPGGWeMfH/00UdNPp83H//4xzXeiy++2DiOY66++uox56fB9ddfb+QRX8vdsQkhhLQwtONrJ7sICCGEkOcN7TjtOCHNDgfHybRABrZlANz3fbP33nubv//97yPXZLD6Ax/4gOnu7taB6de//vVm6dKlo+7fYostdAB8XX7yk5+Y7bbbTuPcaaedzO9+97tR15MkMZ/97GfNzJkzdTD7kEMOMQ899NCoMC9/+cvNu971rmcNau+2224a71ZbbWUuvfTSTcrPuvFwcJwQQshUgHacEEIIaV1oxwkhzYwlfyZ79johZPy54YYbcPDBB2Pt2rXo6uqiigkhhJAWgnacEEIIaV1oxwlpHdzJFoAQMv7stNNOePTRR6laQgghpAWhHSeEEEJaF9pxQloLzhwnZAryxBNPIAxD/X+rrbaCbXPvXUIIIaRVoB0nhBBCWhfacUJaCw6OE0IIIYQQQgghhBBCCJl2cDopIYQQQgghhBBCCCGEkGkHB8cJIYQQQgghhBBCCCGETDs4OE4IIYQQQgghhBBCCCFk2sHBcUIIIYQQQgghhBBCCCHTDg6OE0IIIYQQQgghhBBCCJl2cHCcEEIIIYQQQgghhBBCyLSDg+OEEEIIIYQQQgghhBBCph0cHCeEEEIIIYQQQgghhBAy7eDgOCGEEEIIIYQQQgghhBBMN/4/eTHW07IT/r4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x400 with 6 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ds[\"vel\"][i].plot(ax=ax)\n",
+    "    ax.set_title(ds[\"vel\"].coords[\"dir\"].values[i])\n",
+    "    ax.tick_params(axis=\"x\", labelrotation=45)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3: Apply an Acoustic Signal Correlation Filter\n",
+    "\n",
+    "After removing data from bins at or above the water surface, we typically apply a filter based on acoustic signal correlation to the ADCP data. This helps to eliminate erroneous velocity data points, which can be caused by factors such as bubbles, kelp, fish, etc., moving through one or multiple beams.\n",
+    "\n",
+    "You can quickly inspect the data to determine an appropriate correlation value by using the built-in plotting feature of xarray. In the following example, we use xarray's slicing capabilities to display data from beam 1 within a range of 0 to 10 m from the ADCP.\n",
+    "\n",
+    "It's important to note that not all ADCPs provide acoustic signal correlation data, which serves as a quantitative measure of signal quality. Older ADCPs may not offer this feature, in which case you can skip this step when using such instruments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 7. Turbulence Statistics\n",
-        "\n",
-        "The next section of this jupyter notebook will run through the turbulence analysis of the data presented here. There was no intention of measuring turbulence in the deployment that collected this data, so results depicted here are not the highest quality. The quality of turbulence measurements from an ADCP depend heavily on the quality of the deployment setup and data collection, particularly instrument frequency, samping frequency and depth bin size.\n",
-        "\n",
-        "Read more on proper ADCP setup for turbulence measurements in: Thomson, Jim, et al. \"Measurements of turbulence at two tidal energy sites in Puget Sound, WA.\" IEEE Journal of Oceanic Engineering 37.3 (2012): 363-374.\n",
-        "\n",
-        "Most functions related to turbulence statistics in MHKiT-DOLfYN have the papers they originate from referenced in their docstrings.\n",
-        "\n",
-        "### 7.1 Turbulence Intensity\n",
-        "For most users, turbulence intensity (TI), the ratio of the ensemble standard deviation to ensemble flow speed given as a percent, is all most will need. In MHKiT, this can be simply calculated as `<raw dataset>.velds.I`, but be aware that this will be a conservative estimate. Another function, `turbulence_intensity`, is capable of subtracting instrument noise from this parameter and is discussed below. The noise-subtracted TI is more accurate and typically 1-2% lower than the non-noise-subtracted estimation.\n",
-        "\n"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x800 with 8 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 8), sharex=True, sharey=True)\n",
+    "\n",
+    "for i, ax in enumerate(axes.ravel()):\n",
+    "    ds[\"corr\"].isel(beam=i).sel(range=slice(0, 10)).plot(ax=ax)\n",
+    "    ax.set_title(f\"Beam {int(ds['beam'].isel(beam=i).values)}\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's beneficial to also review data from the other beams. A significant portion of this data is of high quality. To avoid discarding valuable data with lower correlations, which could be due to natural variations, we can use the `correlation_filter`. This function assigns a value of NaN (not a number) to velocity values corresponding to correlations below 50%.\n",
+    "\n",
+    "However, it's important to note that the correlation threshold is dependent on the specifics of the deployment environment and the instrument used. It's not unusual to set a threshold as low as 30%, or even to forgo the use of this function entirely."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = api.clean.correlation_filter(ds, thresh=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Updated velocity plots filtered for beam correlation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x25a3bd609d0>"
-            ]
-          },
-          "execution_count": 21,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Turbulence Intensity\n",
-        "ds_avg[\"TI\"] = ds_avg.velds.I\n",
-        "ds_avg[\"TI\"].plot(cmap=\"Reds\", ylim=(0, 11))"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x400 with 6 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ds[\"vel\"][i].plot(ax=ax)\n",
+    "    ax.set_title(ds[\"vel\"].coords[\"dir\"].values[i])\n",
+    "    ax.tick_params(axis=\"x\", labelrotation=45)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 Rotate Data Coordinate System\n",
+    "\n",
+    "After cleaning the data, the next step is to rotate the velocity data into accurate East, North, Up (ENU) coordinates.\n",
+    "\n",
+    "ADCPs utilize an internal compass or magnetometer to determine magnetic ENU directions. You can use the set_declination function to adjust the velocity data according to the magnetic declination specific to your geographical coordinates. This declination can be looked up online for specific coordinates.\n",
+    "\n",
+    "Instruments save vector data in the coordinate system defined in the deployment configuration file. To make this data meaningful, it must be transformed through various coordinate systems (\"beam\"<->\"inst\"<->\"earth\"<->\"principal\"). This transformation is accomplished using the `rotate2` function. If the \"earth\" (ENU) coordinate system is specified, DOLfYN will automatically rotate the dataset through the required coordinate systems to reach the \"earth\" coordinates. Setting `inplace` to true will modify the input dataset directly, meaning it will not create a new dataset.\n",
+    "\n",
+    "In this case, since the ADCP data is already in the \"earth\" coordinate system, the `rotate2` function will return the input dataset without modifications. The `set_declination` function will work no matter the coordinate system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.2 Power Spectral Densities (Auto-Spectra)\n",
-        "\n",
-        "Other turbulence parameters include the TKE power- and cross-spectral densities (i.e the power spectra), turbulent kinetic energy (TKE, i.e. the variances of velocity vector components), Reynolds stress vector (i.e. the co-variances of velocity vector components), TKE dissipation rate, and TKE production rate. These quantities are primarily used to inform and verify hydrodynamic and coastal models, which take some or all of these quantities as input.\n",
-        "\n",
-        "The TKE production rate is the rate at which kinetic energy (KE) transitions from a useful state (able to do \"work\" in the physics sense) to turbulent; TKE is the actual amount of turbulent KE in the water; and TKE dissipation rate is the rate at which turbulent KE is lost to non-motion forms of energy (heat, sound, etc) due to viscosity. The power spectra are used to depict and quantify this energy in the frequency domain, and creating them are the first step in turbulence analysis.\n",
-        "\n",
-        "We'll start by looking at the power spectra, specifically the auto-spectra from the vertical beam (\"auto\" meaning the variance of a single vector direction, e.g. $\\overline{u'^2}$, vs \"cross\", meaning the covariance of two directions, e.g. $\\overline{u'w'}$). This can be done using the `power_spectral_density` function from the `ADPBinner` we created (\"avg_tool\"). We'll create spectra at the middle water column, at a depth of 5 m, and use a number of FFT's equal to 1/3 the bin size."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data is already in the earth coordinate system\n"
+     ]
+    }
+   ],
+   "source": [
+    "dolfyn.set_declination(ds, 15.8, inplace=True)  # 15.8 deg East\n",
+    "dolfyn.rotate2(ds, \"earth\", inplace=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To rotate into the principal frame of reference (streamwise, cross-stream, vertical), if desired, we must first calculate the depth-averaged principal flow heading and add it to the dataset attributes. Then the dataset can be rotated using the same `rotate2` function. We use `inplace=False` because we do not want to alter the input dataset here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.attrs[\"principal_heading\"] = dolfyn.calc_principal_heading(ds[\"vel\"].mean(\"range\"))\n",
+    "ds = dolfyn.rotate2(ds, \"principal\", inplace=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Updated velocity histograms and contour plots for rotated velocity. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "rng = 5  # m\n",
-        "vel_up = ds[\"vel_b5\"].sel(range_b5=rng, method=\"nearest\")  # vertical velocity\n",
-        "U = ds_avg[\"U_mag\"].sel(\n",
-        "    range=5, method=\"nearest\"\n",
-        ")  # flow speed, for plotting in the next block\n",
-        "\n",
-        "ds_avg[\"auto_spectra_5m\"] = avg_tool.power_spectral_density(\n",
-        "    vel_up, freq_units=\"Hz\", n_fft=ds_avg.n_bin // 2\n",
-        ")"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x1200 with 4 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vel = ds[\"vel\"]\n",
+    "\n",
+    "vel_dim = [d for d in vel.dims if d in (\"dir\", \"earth\", \"inst\", \"beam\")][0]\n",
+    "labels = vel.coords[vel_dim].values\n",
+    "\n",
+    "fig, axes = plt.subplots(len(labels), 1, figsize=(8, 3 * len(labels)), sharex=True)\n",
+    "\n",
+    "if len(labels) == 1:\n",
+    "    axes = [axes]\n",
+    "\n",
+    "for ax_i, label in zip(axes, labels):\n",
+    "    values = vel.sel({vel_dim: label}).values.ravel()\n",
+    "    values = values[np.isfinite(values)]\n",
+    "\n",
+    "    ax_i.hist(values, bins=50, color=\"steelblue\", edgecolor=\"black\", alpha=0.8)\n",
+    "    ax_i.set_title(f\"Velocity histogram: {label}\")\n",
+    "    ax_i.set_ylabel(\"Count\")\n",
+    "\n",
+    "axes[-1].set_xlabel(\"Velocity [m/s]\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In the auto-spectra, we're primarly looking for three components: the energy-producing region, the isotropic turbulence region (so-called \"red noise\"), and the instrument noise floor (termed \"white noise\"). \n",
-        "\n",
-        "The block below organizes and plots the power spectra by the corresponding ensemble speed, averaging them by 0.1 m/s velocity bins. Note that if an ensemble is missing data that wasn't filled in, a power spectrum will not be calculated for that ensemble timestamp."
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x400 with 6 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ds[\"vel\"][i].plot(ax=ax)\n",
+    "    ax.set_title(ds[\"vel\"].coords[\"dir\"].values[i])\n",
+    "    ax.tick_params(axis=\"x\", labelrotation=45)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Average the Data\n",
+    "\n",
+    "As this deployment was configured in \"burst mode\", a standard step in the analysis process is to average the velocity data into time bins. \n",
+    "\n",
+    "However, if the instrument was set up in an \"averaging mode\" (where a specific profile and/or average interval was set, for instance, averaging 5 minutes of data every 30 minutes), this step would have been performed within the ADCP during deployment and can thus be skipped.\n",
+    "\n",
+    "To average the data into time bins (also known as ensembles), you should first initialize the binning tool `ADPBinner`. The parameter \"n_bin\" represents the number of data points in each ensemble. In this case, we're dealing with 300 seconds' worth of data. The \"fs\" parameter stands for the sampling frequency, which for this deployment is 1 Hz. Once the binning tool is initialized, you can use the `bin_average` function to average the data into ensembles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "avg_tool = api.ADPBinner(n_bin=ds.fs * 300, fs=ds.fs)\n",
+    "ds_avg = avg_tool.bin_average(ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dateframe, velocity histogram, and contour plot for 5 minute averaged velocity. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[Text(0.5, 0, 'Frequency [Hz]'),\n",
-              " Text(0, 0.5, 'PSD [m2 s-2 Hz-1]'),\n",
-              " (0.01, 1),\n",
-              " (0.0005, 0.1)]"
-            ]
-          },
-          "execution_count": 33,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 600x500 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 379kB\n",
+       "Dimensions:         (time: 183, dirIMU: 3, dir: 4, range: 28, beam: 4,\n",
+       "                     earth: 3, inst: 3, q: 4, time_b5: 183, range_b5: 28)\n",
+       "Coordinates:\n",
+       "  * time            (time) datetime64[ns] 1kB 2020-08-15T00:22:30.001030683 ....\n",
+       "  * dirIMU          (dirIMU) &lt;U10 120B &#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27;\n",
+       "  * dir             (dir) &lt;U10 160B &#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert1&#x27; &#x27;vert2&#x27;\n",
+       "  * range           (range) float64 224B 1.2 1.7 2.2 2.7 ... 13.2 13.7 14.2 14.7\n",
+       "  * beam            (beam) int32 16B 1 2 3 4\n",
+       "  * earth           (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
+       "  * inst            (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
+       "  * q               (q) &lt;U1 16B &#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;\n",
+       "  * time_b5         (time_b5) datetime64[ns] 1kB 2020-08-15T00:22:29.93849515...\n",
+       "  * range_b5        (range_b5) float64 224B 1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7\n",
+       "Data variables: (12/38)\n",
+       "    c_sound         (time) float32 732B 1.502e+03 1.502e+03 ... 1.498e+03\n",
+       "    temp            (time) float32 732B 14.49 14.59 14.54 ... 13.62 13.56 13.5\n",
+       "    pressure        (time) float32 732B 9.712 9.699 9.685 ... 9.58 9.584 9.591\n",
+       "    mag             (dirIMU, time) float32 2kB 3.534 3.565 ... -197.1 -197.1\n",
+       "    accel           (dirIMU, time) float32 2kB -1.261 -1.263 ... 9.714 9.712\n",
+       "    batt            (time) float32 732B 16.48 16.48 16.48 ... 16.25 16.26 16.26\n",
+       "    ...              ...\n",
+       "    heading         (time) float32 732B 3.287 3.261 3.337 ... 3.331 3.352 3.352\n",
+       "    pitch           (time) float32 732B -0.05523 -0.07217 ... -0.04288 -0.0429\n",
+       "    roll            (time) float32 732B -7.414 -7.424 -7.404 ... -6.433 -6.436\n",
+       "    water_density   (time) float32 732B 1.023e+03 1.023e+03 ... 1.023e+03\n",
+       "    depth           (time) float32 732B 10.28 10.26 10.25 ... 10.14 10.15 10.15\n",
+       "    U_std           (range, time) float32 20kB 0.04232 0.04293 ... nan nan\n",
+       "Attributes: (12/41)\n",
+       "    fs:                        1\n",
+       "    n_bin:                     300\n",
+       "    n_fft:                     300\n",
+       "    description:               Binned averages calculated from ensembles of s...\n",
+       "    filehead_config:           {&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;...\n",
+       "    inst_model:                Signature1000\n",
+       "    ...                        ...\n",
+       "    has_imu:                   1\n",
+       "    beam_angle:                25\n",
+       "    range_offset:              0.6\n",
+       "    declination:               15.8\n",
+       "    declination_in_orientmat:  1\n",
+       "    principal_heading:         11.1898</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-94cd68dd-7aac-478a-80b1-3a6f39b8452a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-94cd68dd-7aac-478a-80b1-3a6f39b8452a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 183</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>time_b5</span>: 183</li><li><span class='xr-has-index'>range_b5</span>: 28</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3bbb6d59-0298-4fdb-a533-98ba028cbc81' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3bbb6d59-0298-4fdb-a533-98ba028cbc81' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:30.001030683 .....</div><input id='attrs-83f5cfe5-8633-46aa-ae9d-9ed27402e9a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83f5cfe5-8633-46aa-ae9d-9ed27402e9a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4595c08c-025a-4bd3-9186-a759a86a2e83' class='xr-var-data-in' type='checkbox'><label for='data-4595c08c-025a-4bd3-9186-a759a86a2e83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:30.001030683&#x27;, &#x27;2020-08-15T00:27:30.001019239&#x27;,\n",
+       "       &#x27;2020-08-15T00:32:30.001010656&#x27;, &#x27;2020-08-15T00:37:30.001015901&#x27;,\n",
+       "       &#x27;2020-08-15T00:42:30.001019954&#x27;, &#x27;2020-08-15T00:47:30.001018524&#x27;,\n",
+       "       &#x27;2020-08-15T00:52:30.001013994&#x27;, &#x27;2020-08-15T00:57:30.001026630&#x27;,\n",
+       "       &#x27;2020-08-15T01:02:30.001023769&#x27;, &#x27;2020-08-15T01:07:30.001024723&#x27;,\n",
+       "       &#x27;2020-08-15T01:12:30.001034259&#x27;, &#x27;2020-08-15T01:17:30.001035451&#x27;,\n",
+       "       &#x27;2020-08-15T01:22:30.001024961&#x27;, &#x27;2020-08-15T01:27:30.001025438&#x27;,\n",
+       "       &#x27;2020-08-15T01:32:30.001025676&#x27;, &#x27;2020-08-15T01:37:30.001031875&#x27;,\n",
+       "       &#x27;2020-08-15T01:42:30.001029014&#x27;, &#x27;2020-08-15T01:47:30.001030683&#x27;,\n",
+       "       &#x27;2020-08-15T01:52:30.001033067&#x27;, &#x27;2020-08-15T01:57:30.001029014&#x27;,\n",
+       "       &#x27;2020-08-15T02:02:30.001028299&#x27;, &#x27;2020-08-15T02:07:30.001020908&#x27;,\n",
+       "       &#x27;2020-08-15T02:12:30.001016378&#x27;, &#x27;2020-08-15T02:17:30.001012802&#x27;,\n",
+       "       &#x27;2020-08-15T02:22:30.001014709&#x27;, &#x27;2020-08-15T02:27:30.001018524&#x27;,\n",
+       "       &#x27;2020-08-15T02:32:30.001022577&#x27;, &#x27;2020-08-15T02:37:30.001020193&#x27;,\n",
+       "       &#x27;2020-08-15T02:42:30.001002311&#x27;, &#x27;2020-08-15T02:47:30.001003503&#x27;,\n",
+       "       &#x27;2020-08-15T02:52:30.001002550&#x27;, &#x27;2020-08-15T02:57:30.001000165&#x27;,\n",
+       "       &#x27;2020-08-15T03:02:30.001000642&#x27;, &#x27;2020-08-15T03:07:30.001000404&#x27;,\n",
+       "       &#x27;2020-08-15T03:12:30.000999450&#x27;, &#x27;2020-08-15T03:17:30.001003026&#x27;,\n",
+       "       &#x27;2020-08-15T03:22:30.001004695&#x27;, &#x27;2020-08-15T03:27:30.001004219&#x27;,\n",
+       "       &#x27;2020-08-15T03:32:30.001001834&#x27;, &#x27;2020-08-15T03:37:30.001005887&#x27;,\n",
+       "       &#x27;2020-08-15T03:42:30.001003742&#x27;, &#x27;2020-08-15T03:47:30.001000642&#x27;,\n",
+       "       &#x27;2020-08-15T03:52:30.001000404&#x27;, &#x27;2020-08-15T03:57:30.001001834&#x27;,\n",
+       "       &#x27;2020-08-15T04:02:30.001001834&#x27;, &#x27;2020-08-15T04:07:30.001001358&#x27;,\n",
+       "       &#x27;2020-08-15T04:12:30.001004695&#x27;, &#x27;2020-08-15T04:17:30.001001358&#x27;,\n",
+       "       &#x27;2020-08-15T04:22:30.001009464&#x27;, &#x27;2020-08-15T04:27:30.001006603&#x27;,\n",
+       "       &#x27;2020-08-15T04:32:30.001002550&#x27;, &#x27;2020-08-15T04:37:30.001004695&#x27;,\n",
+       "       &#x27;2020-08-15T04:42:30.001004934&#x27;, &#x27;2020-08-15T04:47:30.001004934&#x27;,\n",
+       "       &#x27;2020-08-15T04:52:30.001004457&#x27;, &#x27;2020-08-15T04:57:30.001002788&#x27;,\n",
+       "       &#x27;2020-08-15T05:02:30.001008749&#x27;, &#x27;2020-08-15T05:07:30.001017570&#x27;,\n",
+       "       &#x27;2020-08-15T05:12:30.001015186&#x27;, &#x27;2020-08-15T05:17:30.001005887&#x27;,\n",
+       "       &#x27;2020-08-15T05:22:30.001001358&#x27;, &#x27;2020-08-15T05:27:30.001011610&#x27;,\n",
+       "       &#x27;2020-08-15T05:32:30.001020431&#x27;, &#x27;2020-08-15T05:37:30.001011848&#x27;,\n",
+       "       &#x27;2020-08-15T05:42:30.001020908&#x27;, &#x27;2020-08-15T05:47:30.001013517&#x27;,\n",
+       "       &#x27;2020-08-15T05:52:30.001021146&#x27;, &#x27;2020-08-15T05:57:30.001010656&#x27;,\n",
+       "       &#x27;2020-08-15T06:02:30.001004219&#x27;, &#x27;2020-08-15T06:07:30.001012563&#x27;,\n",
+       "       &#x27;2020-08-15T06:12:30.001021862&#x27;, &#x27;2020-08-15T06:17:30.001018524&#x27;,\n",
+       "       &#x27;2020-08-15T06:22:30.001016139&#x27;, &#x27;2020-08-15T06:27:30.001016616&#x27;,\n",
+       "       &#x27;2020-08-15T06:32:30.001012325&#x27;, &#x27;2020-08-15T06:37:30.001016378&#x27;,\n",
+       "       &#x27;2020-08-15T06:42:30.001021862&#x27;, &#x27;2020-08-15T06:47:30.001023769&#x27;,\n",
+       "       &#x27;2020-08-15T06:52:30.001024007&#x27;, &#x27;2020-08-15T06:57:30.001014709&#x27;,\n",
+       "       &#x27;2020-08-15T07:02:30.001006126&#x27;, &#x27;2020-08-15T07:07:30.001019477&#x27;,\n",
+       "       &#x27;2020-08-15T07:12:30.001013994&#x27;, &#x27;2020-08-15T07:17:30.001016855&#x27;,\n",
+       "       &#x27;2020-08-15T07:22:30.001019239&#x27;, &#x27;2020-08-15T07:27:30.001024961&#x27;,\n",
+       "       &#x27;2020-08-15T07:32:30.001011610&#x27;, &#x27;2020-08-15T07:37:30.001024484&#x27;,\n",
+       "       &#x27;2020-08-15T07:42:30.001018524&#x27;, &#x27;2020-08-15T07:47:30.001029968&#x27;,\n",
+       "       &#x27;2020-08-15T07:52:30.001009464&#x27;, &#x27;2020-08-15T07:57:30.001014232&#x27;,\n",
+       "       &#x27;2020-08-15T08:02:30.001011610&#x27;, &#x27;2020-08-15T08:07:30.001028299&#x27;,\n",
+       "       &#x27;2020-08-15T08:12:30.001027107&#x27;, &#x27;2020-08-15T08:17:30.001024723&#x27;,\n",
+       "       &#x27;2020-08-15T08:22:30.001022577&#x27;, &#x27;2020-08-15T08:27:30.001018047&#x27;,\n",
+       "       &#x27;2020-08-15T08:32:30.001019477&#x27;, &#x27;2020-08-15T08:37:30.001017808&#x27;,\n",
+       "       &#x27;2020-08-15T08:42:30.001018762&#x27;, &#x27;2020-08-15T08:47:30.001015186&#x27;,\n",
+       "       &#x27;2020-08-15T08:52:30.001020431&#x27;, &#x27;2020-08-15T08:57:30.001026630&#x27;,\n",
+       "       &#x27;2020-08-15T09:02:30.001021146&#x27;, &#x27;2020-08-15T09:07:30.001016378&#x27;,\n",
+       "       &#x27;2020-08-15T09:12:30.001025676&#x27;, &#x27;2020-08-15T09:17:30.001024246&#x27;,\n",
+       "       &#x27;2020-08-15T09:22:30.001026630&#x27;, &#x27;2020-08-15T09:27:30.001017570&#x27;,\n",
+       "       &#x27;2020-08-15T09:32:30.001015901&#x27;, &#x27;2020-08-15T09:37:30.001016139&#x27;,\n",
+       "       &#x27;2020-08-15T09:42:30.001018762&#x27;, &#x27;2020-08-15T09:47:30.001014709&#x27;,\n",
+       "       &#x27;2020-08-15T09:52:30.001016378&#x27;, &#x27;2020-08-15T09:57:30.001024961&#x27;,\n",
+       "       &#x27;2020-08-15T10:02:30.001021146&#x27;, &#x27;2020-08-15T10:07:30.001022100&#x27;,\n",
+       "       &#x27;2020-08-15T10:12:30.001017808&#x27;, &#x27;2020-08-15T10:17:30.001013994&#x27;,\n",
+       "       &#x27;2020-08-15T10:22:30.001018762&#x27;, &#x27;2020-08-15T10:27:30.001021385&#x27;,\n",
+       "       &#x27;2020-08-15T10:32:30.001016855&#x27;, &#x27;2020-08-15T10:37:30.001021385&#x27;,\n",
+       "       &#x27;2020-08-15T10:42:30.001015186&#x27;, &#x27;2020-08-15T10:47:30.001020193&#x27;,\n",
+       "       &#x27;2020-08-15T10:52:30.001021623&#x27;, &#x27;2020-08-15T10:57:30.001021862&#x27;,\n",
+       "       &#x27;2020-08-15T11:02:30.001018047&#x27;, &#x27;2020-08-15T11:07:30.001021146&#x27;,\n",
+       "       &#x27;2020-08-15T11:12:30.001011848&#x27;, &#x27;2020-08-15T11:17:30.001015901&#x27;,\n",
+       "       &#x27;2020-08-15T11:22:30.001014232&#x27;, &#x27;2020-08-15T11:27:30.001015663&#x27;,\n",
+       "       &#x27;2020-08-15T11:32:30.001012325&#x27;, &#x27;2020-08-15T11:37:30.001004457&#x27;,\n",
+       "       &#x27;2020-08-15T11:42:30.001018524&#x27;, &#x27;2020-08-15T11:47:30.001023292&#x27;,\n",
+       "       &#x27;2020-08-15T11:52:30.001009464&#x27;, &#x27;2020-08-15T11:57:30.001002311&#x27;,\n",
+       "       &#x27;2020-08-15T12:02:30.001004695&#x27;, &#x27;2020-08-15T12:07:30.001007556&#x27;,\n",
+       "       &#x27;2020-08-15T12:12:30.001012086&#x27;, &#x27;2020-08-15T12:17:30.001007080&#x27;,\n",
+       "       &#x27;2020-08-15T12:22:30.001011371&#x27;, &#x27;2020-08-15T12:27:30.001014471&#x27;,\n",
+       "       &#x27;2020-08-15T12:32:30.001016378&#x27;, &#x27;2020-08-15T12:37:30.001012563&#x27;,\n",
+       "       &#x27;2020-08-15T12:42:30.001015901&#x27;, &#x27;2020-08-15T12:47:30.001010417&#x27;,\n",
+       "       &#x27;2020-08-15T12:52:30.001009464&#x27;, &#x27;2020-08-15T12:57:30.001010894&#x27;,\n",
+       "       &#x27;2020-08-15T13:02:30.001016855&#x27;, &#x27;2020-08-15T13:07:30.001020908&#x27;,\n",
+       "       &#x27;2020-08-15T13:12:30.001018762&#x27;, &#x27;2020-08-15T13:17:30.001018762&#x27;,\n",
+       "       &#x27;2020-08-15T13:22:30.001018047&#x27;, &#x27;2020-08-15T13:27:30.001013517&#x27;,\n",
+       "       &#x27;2020-08-15T13:32:30.001015424&#x27;, &#x27;2020-08-15T13:37:30.001009225&#x27;,\n",
+       "       &#x27;2020-08-15T13:42:30.001011133&#x27;, &#x27;2020-08-15T13:47:30.001008749&#x27;,\n",
+       "       &#x27;2020-08-15T13:52:30.001019477&#x27;, &#x27;2020-08-15T13:57:30.001022338&#x27;,\n",
+       "       &#x27;2020-08-15T14:02:30.001019716&#x27;, &#x27;2020-08-15T14:07:30.001026868&#x27;,\n",
+       "       &#x27;2020-08-15T14:12:30.001021623&#x27;, &#x27;2020-08-15T14:17:30.001034021&#x27;,\n",
+       "       &#x27;2020-08-15T14:22:30.001024961&#x27;, &#x27;2020-08-15T14:27:30.001032352&#x27;,\n",
+       "       &#x27;2020-08-15T14:32:30.001023292&#x27;, &#x27;2020-08-15T14:37:30.001024484&#x27;,\n",
+       "       &#x27;2020-08-15T14:42:30.001044034&#x27;, &#x27;2020-08-15T14:47:30.001032352&#x27;,\n",
+       "       &#x27;2020-08-15T14:52:30.001019239&#x27;, &#x27;2020-08-15T14:57:30.001032829&#x27;,\n",
+       "       &#x27;2020-08-15T15:02:30.001021146&#x27;, &#x27;2020-08-15T15:07:30.001027345&#x27;,\n",
+       "       &#x27;2020-08-15T15:12:30.001029014&#x27;, &#x27;2020-08-15T15:17:30.001026630&#x27;,\n",
+       "       &#x27;2020-08-15T15:22:30.001026153&#x27;, &#x27;2020-08-15T15:27:30.001022577&#x27;,\n",
+       "       &#x27;2020-08-15T15:32:30.001030683&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27;</div><input id='attrs-ee9453dd-0e1f-4609-800f-b7785def0997' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee9453dd-0e1f-4609-800f-b7785def0997' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e987dc0-2769-47d7-9980-0cffd6452fb4' class='xr-var-data-in' type='checkbox'><label for='data-7e987dc0-2769-47d7-9980-0cffd6452fb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; ... &#x27;vert2&#x27;</div><input id='attrs-823e073a-d75c-463c-a4df-34580897ee81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-823e073a-d75c-463c-a4df-34580897ee81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aab03f98-74f0-4e0d-adac-6d2c5c9837ed' class='xr-var-data-in' type='checkbox'><label for='data-aab03f98-74f0-4e0d-adac-6d2c5c9837ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-9e796c46-5807-46ff-bcee-dd007ae9a509' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e796c46-5807-46ff-bcee-dd007ae9a509' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88900068-2151-4243-8ce7-50c91e53ffcf' class='xr-var-data-in' type='checkbox'><label for='data-88900068-2151-4243-8ce7-50c91e53ffcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+       "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
+       "       13.2, 13.7, 14.2, 14.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-129a6633-70c5-4877-971e-cbae6aef4cd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-129a6633-70c5-4877-971e-cbae6aef4cd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-274de313-22c3-46a9-89be-05e30df2f539' class='xr-var-data-in' type='checkbox'><label for='data-274de313-22c3-46a9-89be-05e30df2f539' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-07471bd2-3fff-4550-ab2c-3f3f935b6b8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07471bd2-3fff-4550-ab2c-3f3f935b6b8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ea841b9-c6fd-4485-ade0-d809c64b4430' class='xr-var-data-in' type='checkbox'><label for='data-6ea841b9-c6fd-4485-ade0-d809c64b4430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-41aafcf3-92b7-41ed-91fe-e8fd7e853019' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41aafcf3-92b7-41ed-91fe-e8fd7e853019' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c331539-ea72-4303-9589-b1a9a3d1b226' class='xr-var-data-in' type='checkbox'><label for='data-8c331539-ea72-4303-9589-b1a9a3d1b226' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-96ef30a6-3706-45d0-b9ff-4bcc47822f51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96ef30a6-3706-45d0-b9ff-4bcc47822f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f00da1e9-f04e-4fc0-b8c7-58f069ddffbb' class='xr-var-data-in' type='checkbox'><label for='data-f00da1e9-f04e-4fc0-b8c7-58f069ddffbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:29.938495159 .....</div><input id='attrs-ae39105f-724b-4ffb-ad5e-72022d2c834a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae39105f-724b-4ffb-ad5e-72022d2c834a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67133c30-1d3c-442b-b10a-a21e9ef43c2f' class='xr-var-data-in' type='checkbox'><label for='data-67133c30-1d3c-442b-b10a-a21e9ef43c2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:29.938495159&#x27;, &#x27;2020-08-15T00:27:29.938490867&#x27;,\n",
+       "       &#x27;2020-08-15T00:32:29.938487529&#x27;, &#x27;2020-08-15T00:37:29.938496828&#x27;,\n",
+       "       &#x27;2020-08-15T00:42:29.938495397&#x27;, &#x27;2020-08-15T00:47:29.938496589&#x27;,\n",
+       "       &#x27;2020-08-15T00:52:29.938497781&#x27;, &#x27;2020-08-15T00:57:29.938495397&#x27;,\n",
+       "       &#x27;2020-08-15T01:02:29.938493013&#x27;, &#x27;2020-08-15T01:07:29.938492536&#x27;,\n",
+       "       &#x27;2020-08-15T01:12:29.938490867&#x27;, &#x27;2020-08-15T01:17:29.938497781&#x27;,\n",
+       "       &#x27;2020-08-15T01:22:29.938496112&#x27;, &#x27;2020-08-15T01:27:29.938498497&#x27;,\n",
+       "       &#x27;2020-08-15T01:32:29.938496589&#x27;, &#x27;2020-08-15T01:37:29.938497781&#x27;,\n",
+       "       &#x27;2020-08-15T01:42:29.938495635&#x27;, &#x27;2020-08-15T01:47:29.938497066&#x27;,\n",
+       "       &#x27;2020-08-15T01:52:29.938498735&#x27;, &#x27;2020-08-15T01:57:29.938496112&#x27;,\n",
+       "       &#x27;2020-08-15T02:02:29.938496589&#x27;, &#x27;2020-08-15T02:07:29.938494920&#x27;,\n",
+       "       &#x27;2020-08-15T02:12:29.938497304&#x27;, &#x27;2020-08-15T02:17:29.938496828&#x27;,\n",
+       "       &#x27;2020-08-15T02:22:29.938496112&#x27;, &#x27;2020-08-15T02:27:29.938496828&#x27;,\n",
+       "       &#x27;2020-08-15T02:32:29.938496112&#x27;, &#x27;2020-08-15T02:37:29.938497304&#x27;,\n",
+       "       &#x27;2020-08-15T02:42:29.938495159&#x27;, &#x27;2020-08-15T02:47:29.938495635&#x27;,\n",
+       "       &#x27;2020-08-15T02:52:29.938490867&#x27;, &#x27;2020-08-15T02:57:29.938492059&#x27;,\n",
+       "       &#x27;2020-08-15T03:02:29.938489675&#x27;, &#x27;2020-08-15T03:07:29.938488245&#x27;,\n",
+       "       &#x27;2020-08-15T03:12:29.938485622&#x27;, &#x27;2020-08-15T03:17:29.938485622&#x27;,\n",
+       "       &#x27;2020-08-15T03:22:29.938488245&#x27;, &#x27;2020-08-15T03:27:29.938485145&#x27;,\n",
+       "       &#x27;2020-08-15T03:32:29.938486099&#x27;, &#x27;2020-08-15T03:37:29.938482046&#x27;,\n",
+       "       &#x27;2020-08-15T03:42:29.938489437&#x27;, &#x27;2020-08-15T03:47:29.938489437&#x27;,\n",
+       "       &#x27;2020-08-15T03:52:29.938485860&#x27;, &#x27;2020-08-15T03:57:29.938491582&#x27;,\n",
+       "       &#x27;2020-08-15T04:02:29.938488006&#x27;, &#x27;2020-08-15T04:07:29.938491106&#x27;,\n",
+       "       &#x27;2020-08-15T04:12:29.938492059&#x27;, &#x27;2020-08-15T04:17:29.938487052&#x27;,\n",
+       "       &#x27;2020-08-15T04:22:29.938489675&#x27;, &#x27;2020-08-15T04:27:29.938491582&#x27;,\n",
+       "       &#x27;2020-08-15T04:32:29.938491106&#x27;, &#x27;2020-08-15T04:37:29.938493251&#x27;,\n",
+       "       &#x27;2020-08-15T04:42:29.938489675&#x27;, &#x27;2020-08-15T04:47:29.938492059&#x27;,\n",
+       "       &#x27;2020-08-15T04:52:29.938491582&#x27;, &#x27;2020-08-15T04:57:29.938495397&#x27;,\n",
+       "       &#x27;2020-08-15T05:02:29.938491106&#x27;, &#x27;2020-08-15T05:07:29.938499212&#x27;,\n",
+       "       &#x27;2020-08-15T05:12:29.938491821&#x27;, &#x27;2020-08-15T05:17:29.938493490&#x27;,\n",
+       "       &#x27;2020-08-15T05:22:29.938495397&#x27;, &#x27;2020-08-15T05:27:29.938490390&#x27;,\n",
+       "       &#x27;2020-08-15T05:32:29.938493967&#x27;, &#x27;2020-08-15T05:37:29.938493728&#x27;,\n",
+       "       &#x27;2020-08-15T05:42:29.938491582&#x27;, &#x27;2020-08-15T05:47:29.938493013&#x27;,\n",
+       "       &#x27;2020-08-15T05:52:29.938495159&#x27;, &#x27;2020-08-15T05:57:29.938494920&#x27;,\n",
+       "       &#x27;2020-08-15T06:02:29.938493013&#x27;, &#x27;2020-08-15T06:07:29.938496589&#x27;,\n",
+       "       &#x27;2020-08-15T06:12:29.938494920&#x27;, &#x27;2020-08-15T06:17:29.938495397&#x27;,\n",
+       "       &#x27;2020-08-15T06:22:29.938493728&#x27;, &#x27;2020-08-15T06:27:29.938495397&#x27;,\n",
+       "       &#x27;2020-08-15T06:32:29.938492536&#x27;, &#x27;2020-08-15T06:37:29.938495159&#x27;,\n",
+       "       &#x27;2020-08-15T06:42:29.938496112&#x27;, &#x27;2020-08-15T06:47:29.938497304&#x27;,\n",
+       "       &#x27;2020-08-15T06:52:29.938496112&#x27;, &#x27;2020-08-15T06:57:29.938493728&#x27;,\n",
+       "       &#x27;2020-08-15T07:02:29.938497304&#x27;, &#x27;2020-08-15T07:07:29.938494920&#x27;,\n",
+       "       &#x27;2020-08-15T07:12:29.938492059&#x27;, &#x27;2020-08-15T07:17:29.938493490&#x27;,\n",
+       "       &#x27;2020-08-15T07:22:29.938493967&#x27;, &#x27;2020-08-15T07:27:29.938497304&#x27;,\n",
+       "       &#x27;2020-08-15T07:32:29.938494443&#x27;, &#x27;2020-08-15T07:37:29.938494920&#x27;,\n",
+       "       &#x27;2020-08-15T07:42:29.938495635&#x27;, &#x27;2020-08-15T07:47:29.938493967&#x27;,\n",
+       "       &#x27;2020-08-15T07:52:29.938492298&#x27;, &#x27;2020-08-15T07:57:29.938489675&#x27;,\n",
+       "       &#x27;2020-08-15T08:02:29.938493013&#x27;, &#x27;2020-08-15T08:07:29.938493728&#x27;,\n",
+       "       &#x27;2020-08-15T08:12:29.938497066&#x27;, &#x27;2020-08-15T08:17:29.938497781&#x27;,\n",
+       "       &#x27;2020-08-15T08:22:29.938495159&#x27;, &#x27;2020-08-15T08:27:29.938492774&#x27;,\n",
+       "       &#x27;2020-08-15T08:32:29.938495397&#x27;, &#x27;2020-08-15T08:37:29.938494920&#x27;,\n",
+       "       &#x27;2020-08-15T08:42:29.938494920&#x27;, &#x27;2020-08-15T08:47:29.938494682&#x27;,\n",
+       "       &#x27;2020-08-15T08:52:29.938494443&#x27;, &#x27;2020-08-15T08:57:29.938491821&#x27;,\n",
+       "       &#x27;2020-08-15T09:02:29.938497066&#x27;, &#x27;2020-08-15T09:07:29.938495397&#x27;,\n",
+       "       &#x27;2020-08-15T09:12:29.938495397&#x27;, &#x27;2020-08-15T09:17:29.938494920&#x27;,\n",
+       "       &#x27;2020-08-15T09:22:29.938498020&#x27;, &#x27;2020-08-15T09:27:29.938495397&#x27;,\n",
+       "       &#x27;2020-08-15T09:32:29.938495159&#x27;, &#x27;2020-08-15T09:37:29.938498735&#x27;,\n",
+       "       &#x27;2020-08-15T09:42:29.938493728&#x27;, &#x27;2020-08-15T09:47:29.938495635&#x27;,\n",
+       "       &#x27;2020-08-15T09:52:29.938500642&#x27;, &#x27;2020-08-15T09:57:29.938496589&#x27;,\n",
+       "       &#x27;2020-08-15T10:02:29.938497781&#x27;, &#x27;2020-08-15T10:07:29.938500881&#x27;,\n",
+       "       &#x27;2020-08-15T10:12:29.938504457&#x27;, &#x27;2020-08-15T10:17:29.938501358&#x27;,\n",
+       "       &#x27;2020-08-15T10:22:29.938507556&#x27;, &#x27;2020-08-15T10:27:29.938496112&#x27;,\n",
+       "       &#x27;2020-08-15T10:32:29.938495635&#x27;, &#x27;2020-08-15T10:37:29.938500165&#x27;,\n",
+       "       &#x27;2020-08-15T10:42:29.938496112&#x27;, &#x27;2020-08-15T10:47:29.938493251&#x27;,\n",
+       "       &#x27;2020-08-15T10:52:29.938493728&#x27;, &#x27;2020-08-15T10:57:29.938492536&#x27;,\n",
+       "       &#x27;2020-08-15T11:02:29.938492774&#x27;, &#x27;2020-08-15T11:07:29.938492774&#x27;,\n",
+       "       &#x27;2020-08-15T11:12:29.938493967&#x27;, &#x27;2020-08-15T11:17:29.938494443&#x27;,\n",
+       "       &#x27;2020-08-15T11:22:29.938496589&#x27;, &#x27;2020-08-15T11:27:29.938496112&#x27;,\n",
+       "       &#x27;2020-08-15T11:32:29.938495397&#x27;, &#x27;2020-08-15T11:37:29.938493967&#x27;,\n",
+       "       &#x27;2020-08-15T11:42:29.938493013&#x27;, &#x27;2020-08-15T11:47:29.938495635&#x27;,\n",
+       "       &#x27;2020-08-15T11:52:29.938493967&#x27;, &#x27;2020-08-15T11:57:29.938498735&#x27;,\n",
+       "       &#x27;2020-08-15T12:02:29.938496589&#x27;, &#x27;2020-08-15T12:07:29.938498020&#x27;,\n",
+       "       &#x27;2020-08-15T12:12:29.938494443&#x27;, &#x27;2020-08-15T12:17:29.938495159&#x27;,\n",
+       "       &#x27;2020-08-15T12:22:29.938495635&#x27;, &#x27;2020-08-15T12:27:29.938497066&#x27;,\n",
+       "       &#x27;2020-08-15T12:32:29.938493967&#x27;, &#x27;2020-08-15T12:37:29.938495635&#x27;,\n",
+       "       &#x27;2020-08-15T12:42:29.938496112&#x27;, &#x27;2020-08-15T12:47:29.938495635&#x27;,\n",
+       "       &#x27;2020-08-15T12:52:29.938494443&#x27;, &#x27;2020-08-15T12:57:29.938496589&#x27;,\n",
+       "       &#x27;2020-08-15T13:02:29.938494205&#x27;, &#x27;2020-08-15T13:07:29.938494205&#x27;,\n",
+       "       &#x27;2020-08-15T13:12:29.938492536&#x27;, &#x27;2020-08-15T13:17:29.938495397&#x27;,\n",
+       "       &#x27;2020-08-15T13:22:29.938493251&#x27;, &#x27;2020-08-15T13:27:29.938495397&#x27;,\n",
+       "       &#x27;2020-08-15T13:32:29.938494920&#x27;, &#x27;2020-08-15T13:37:29.938497066&#x27;,\n",
+       "       &#x27;2020-08-15T13:42:29.938497304&#x27;, &#x27;2020-08-15T13:47:29.938494920&#x27;,\n",
+       "       &#x27;2020-08-15T13:52:29.938493490&#x27;, &#x27;2020-08-15T13:57:29.938492774&#x27;,\n",
+       "       &#x27;2020-08-15T14:02:29.938496112&#x27;, &#x27;2020-08-15T14:07:29.938489675&#x27;,\n",
+       "       &#x27;2020-08-15T14:12:29.938489913&#x27;, &#x27;2020-08-15T14:17:29.938494443&#x27;,\n",
+       "       &#x27;2020-08-15T14:22:29.938495397&#x27;, &#x27;2020-08-15T14:27:29.938492774&#x27;,\n",
+       "       &#x27;2020-08-15T14:32:29.938491582&#x27;, &#x27;2020-08-15T14:37:29.938493251&#x27;,\n",
+       "       &#x27;2020-08-15T14:42:29.938492536&#x27;, &#x27;2020-08-15T14:47:29.938492774&#x27;,\n",
+       "       &#x27;2020-08-15T14:52:29.938491106&#x27;, &#x27;2020-08-15T14:57:29.938495635&#x27;,\n",
+       "       &#x27;2020-08-15T15:02:29.938493013&#x27;, &#x27;2020-08-15T15:07:29.938493967&#x27;,\n",
+       "       &#x27;2020-08-15T15:12:29.938494920&#x27;, &#x27;2020-08-15T15:17:29.938493251&#x27;,\n",
+       "       &#x27;2020-08-15T15:22:29.938486099&#x27;, &#x27;2020-08-15T15:27:29.938484191&#x27;,\n",
+       "       &#x27;2020-08-15T15:32:29.938490390&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-f53e9720-6d63-4f51-8a7a-f8bf4ed3af70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f53e9720-6d63-4f51-8a7a-f8bf4ed3af70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7e9300c-f075-4671-b07c-6ee5e31d03ca' class='xr-var-data-in' type='checkbox'><label for='data-b7e9300c-f075-4671-b07c-6ee5e31d03ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+       "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
+       "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b67361b7-a489-4710-b7cb-1eaa91d2b631' class='xr-section-summary-in' type='checkbox'  ><label for='section-b67361b7-a489-4710-b7cb-1eaa91d2b631' class='xr-section-summary' >Data variables: <span>(38)</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>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-63952b1d-c12b-4529-9de6-757bb7fc4022' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63952b1d-c12b-4529-9de6-757bb7fc4022' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b9837e3-8b6d-4623-b540-df60aaa29fd6' class='xr-var-data-in' type='checkbox'><label for='data-6b9837e3-8b6d-4623-b540-df60aaa29fd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.6843, 1502.032 , 1501.8505, 1501.5714, 1501.8607, 1502.4133,\n",
+       "       1502.8121, 1502.7344, 1502.8704, 1502.9467, 1502.883 , 1502.7606,\n",
+       "       1502.6915, 1502.8214, 1502.8864, 1502.7937, 1502.6268, 1502.6473,\n",
+       "       1502.4198, 1502.3577, 1501.9543, 1501.729 , 1501.32  , 1500.9684,\n",
+       "       1500.6763, 1500.2716, 1500.1096, 1499.8009, 1499.5637, 1499.3373,\n",
+       "       1499.2163, 1499.0955, 1498.9806, 1498.8579, 1498.7637, 1498.6821,\n",
+       "       1498.6133, 1498.5066, 1498.5637, 1498.7421, 1498.8367, 1498.984 ,\n",
+       "       1498.9717, 1498.9644, 1499.0553, 1499.21  , 1499.2789, 1499.2766,\n",
+       "       1499.265 , 1499.2623, 1499.3181, 1499.2903, 1499.3444, 1499.2922,\n",
+       "       1499.2983, 1499.195 , 1499.2087, 1499.212 , 1499.2494, 1499.2227,\n",
+       "       1499.1742, 1499.1772, 1499.1937, 1499.2306, 1499.2719, 1499.3043,\n",
+       "       1499.4073, 1499.3677, 1499.4554, 1499.5647, 1499.6683, 1499.7489,\n",
+       "       1499.8341, 1499.8446, 1499.8396, 1499.9257, 1499.781 , 1499.8274,\n",
+       "       1499.7506, 1499.8904, 1499.7473, 1499.7411, 1499.7415, 1499.7079,\n",
+       "       1499.508 , 1499.7562, 1499.5327, 1499.4186, 1499.4877, 1499.1946,\n",
+       "       1499.1821, 1499.466 , 1499.5144, 1499.7024, 1499.8877, 1499.8373,\n",
+       "       1499.8597, 1500.1703, 1500.0886, 1500.082 , 1500.0476, 1499.9003,\n",
+       "       1499.8434, 1498.8927, 1497.6433, 1496.299 , 1495.386 , 1495.2273,\n",
+       "       1494.9376, 1494.7117, 1494.9341, 1495.1063, 1495.0103, 1494.7747,\n",
+       "       1494.5977, 1494.4548, 1494.4042, 1494.3356, 1494.229 , 1494.0669,\n",
+       "       1493.7662, 1493.4755, 1493.3303, 1493.1544, 1492.8477, 1492.7877,\n",
+       "       1492.8064, 1493.0189, 1493.5558, 1494.0146, 1494.7506, 1495.2681,\n",
+       "       1496.2244, 1496.8036, 1497.5472, 1498.0507, 1498.3839, 1498.663 ,\n",
+       "       1498.8971, 1499.136 , 1499.3384, 1499.535 , 1499.5739, 1499.5245,\n",
+       "       1499.4437, 1499.4467, 1499.5509, 1499.6014, 1499.5997, 1499.5935,\n",
+       "       1499.525 , 1499.4967, 1499.4364, 1499.4977, 1499.503 , 1499.5526,\n",
+       "       1499.5083, 1499.5023, 1499.474 , 1499.4028, 1499.436 , 1499.4973,\n",
+       "       1499.5286, 1499.5846, 1499.6293, 1499.6262, 1499.6666, 1499.6971,\n",
+       "       1499.6998, 1499.7269, 1499.7843, 1499.6895, 1499.7037, 1499.6376,\n",
+       "       1499.4894, 1499.0636, 1499.0073, 1498.8967, 1498.8627, 1498.961 ,\n",
+       "       1498.8287, 1498.6285, 1498.472 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.49 14.59 14.54 ... 13.56 13.5</div><input id='attrs-95a9e011-df19-4e04-a3e8-3b06c28ec021' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95a9e011-df19-4e04-a3e8-3b06c28ec021' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6c6987d-f6a0-48a7-96cc-2f19ae146351' class='xr-var-data-in' type='checkbox'><label for='data-e6c6987d-f6a0-48a7-96cc-2f19ae146351' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.4871   , 14.592732 , 14.5369   , 14.450967 , 14.540566 ,\n",
+       "       14.712167 , 14.838034 , 14.812099 , 14.855833 , 14.879432 ,\n",
+       "       14.860366 , 14.821934 , 14.799266 , 14.840433 , 14.8621   ,\n",
+       "       14.833766 , 14.782434 , 14.788966 , 14.716966 , 14.695799 ,\n",
+       "       14.572268 , 14.502767 , 14.376032 , 14.268402 , 14.178566 ,\n",
+       "       14.055    , 14.005532 , 13.911533 , 13.838566 , 13.7688675,\n",
+       "       13.7339325, 13.696598 , 13.661633 , 13.626367 , 13.597433 ,\n",
+       "       13.572568 , 13.552067 , 13.520534 , 13.538133 , 13.592899 ,\n",
+       "       13.620734 , 13.665668 , 13.661699 , 13.658334 , 13.686766 ,\n",
+       "       13.7354   , 13.754033 , 13.754901 , 13.7525   , 13.751934 ,\n",
+       "       13.767399 , 13.760633 , 13.776802 , 13.761433 , 13.7625   ,\n",
+       "       13.731066 , 13.7371   , 13.737367 , 13.748701 , 13.741634 ,\n",
+       "       13.726335 , 13.726666 , 13.7323   , 13.743701 , 13.756766 ,\n",
+       "       13.766968 , 13.797334 , 13.7852   , 13.812601 , 13.847366 ,\n",
+       "       13.878734 , 13.903067 , 13.928333 , 13.931099 , 13.930567 ,\n",
+       "       13.957533 , 13.912533 , 13.9264   , 13.9032   , 13.947433 ,\n",
+       "       13.902601 , 13.900833 , 13.900665 , 13.890833 , 13.830032 ,\n",
+       "       13.9046335, 13.8364   , 13.801933 , 13.8241   , 13.735    ,\n",
+       "       13.732666 , 13.818233 , 13.8298   , 13.888301 , 13.944167 ,\n",
+       "       13.928867 , 13.9361   , 14.0331   , 14.008999 , 14.006901 ,\n",
+       "       13.992832 , 13.946533 , 13.9282   , 13.643267 , 13.266466 ,\n",
+       "       12.865967 , 12.595367 , 12.5469   , 12.462267 , 12.3959   ,\n",
+       "       12.461133 , 12.512366 , 12.482166 , 12.411167 , 12.3605995,\n",
+       "       12.3201   , 12.303568 , 12.284267 , 12.252732 , 12.203699 ,\n",
+       "       12.118533 , 12.031001 , 11.988433 , 11.939434 , 11.8505   ,\n",
+       "       11.8341   , 11.837234 , 11.8984995, 12.0561   , 12.189    ,\n",
+       "       12.403767 , 12.5546   , 12.837867 , 13.010934 , 13.233933 ,\n",
+       "       13.384068 , 13.485199 , 13.565999 , 13.639567 , 13.711732 ,\n",
+       "       13.774034 , 13.829665 , 13.838365 , 13.829668 , 13.8013   ,\n",
+       "       13.805167 , 13.833833 , 13.855499 , 13.857698 , 13.844232 ,\n",
+       "       13.831333 , 13.816167 , 13.802168 , 13.811266 , 13.820599 ,\n",
+       "       13.834399 , 13.828466 , 13.826566 , 13.810034 , 13.792601 ,\n",
+       "       13.800402 , 13.820232 , 13.830032 , 13.8446665, 13.8607   ,\n",
+       "       13.860667 , 13.870465 , 13.876833 , 13.878232 , 13.884268 ,\n",
+       "       13.907367 , 13.875599 , 13.875534 , 13.861733 , 13.8139   ,\n",
+       "       13.689401 , 13.665034 , 13.632333 , 13.6227665, 13.651935 ,\n",
+       "       13.6164665, 13.5551   , 13.503766 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.712 9.699 9.685 ... 9.584 9.591</div><input id='attrs-3afeb9ef-5d73-4f4c-b904-34b07796c1de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3afeb9ef-5d73-4f4c-b904-34b07796c1de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30163c0a-6f68-479b-b273-e60ae4eaa390' class='xr-var-data-in' type='checkbox'><label for='data-30163c0a-6f68-479b-b273-e60ae4eaa390' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.712007 , 9.699074 , 9.685317 , 9.670219 , 9.65324  , 9.640334 ,\n",
+       "       9.62429  , 9.608537 , 9.594397 , 9.576526 , 9.56048  , 9.541069 ,\n",
+       "       9.524289 , 9.505187 , 9.486143 , 9.464264 , 9.44223  , 9.416344 ,\n",
+       "       9.393453 , 9.367396 , 9.339387 , 9.305754 , 9.278394 , 9.245257 ,\n",
+       "       9.218047 , 9.186557 , 9.157551 , 9.123754 , 9.09348  , 9.059697 ,\n",
+       "       9.026804 , 8.994499 , 8.95571  , 8.92198  , 8.886473 , 8.853367 ,\n",
+       "       8.813594 , 8.782229 , 8.74448  , 8.708926 , 8.674907 , 8.637604 ,\n",
+       "       8.602573 , 8.560874 , 8.524677 , 8.48187  , 8.4449835, 8.400766 ,\n",
+       "       8.365373 , 8.322662 , 8.28713  , 8.248011 , 8.21333  , 8.178404 ,\n",
+       "       8.14623  , 8.109827 , 8.072543 , 8.039574 , 7.9980125, 7.963136 ,\n",
+       "       7.926347 , 7.8918767, 7.8531103, 7.824253 , 7.7964296, 7.7649107,\n",
+       "       7.7383432, 7.7130632, 7.6841335, 7.65858  , 7.633538 , 7.609003 ,\n",
+       "       7.592057 , 7.573613 , 7.5537395, 7.533187 , 7.5146875, 7.497063 ,\n",
+       "       7.48256  , 7.472039 , 7.454433 , 7.4437304, 7.4306965, 7.422947 ,\n",
+       "       7.4138427, 7.4127507, 7.407543 , 7.4021435, 7.40189  , 7.4006333,\n",
+       "       7.401152 , 7.3961263, 7.4010367, 7.4031525, 7.414211 , 7.4199567,\n",
+       "       7.4243903, 7.4346695, 7.4523306, 7.4677606, 7.47785  , 7.4955306,\n",
+       "       7.507134 , 7.52034  , 7.540297 , 7.5592594, 7.583973 , 7.6029363,\n",
+       "       7.623276 , 7.6508303, 7.67267  , 7.6968994, 7.7202635, 7.7465167,\n",
+       "       7.7706933, 7.795326 , 7.820833 , 7.8509097, 7.8781033, 7.9117236,\n",
+       "       7.9442267, 7.9752865, 8.007596 , 8.042522 , 8.077546 , 8.108029 ,\n",
+       "       8.1417265, 8.17497  , 8.210143 , 8.243827 , 8.281553 , 8.317181 ,\n",
+       "       8.35565  , 8.390187 , 8.42794  , 8.465797 , 8.50642  , 8.54146  ,\n",
+       "       8.581511 , 8.618688 , 8.65223  , 8.69048  , 8.72701  , 8.758973 ,\n",
+       "       8.795943 , 8.832204 , 8.870703 , 8.8993225, 8.936847 , 8.969017 ,\n",
+       "       9.001257 , 9.036253 , 9.063161 , 9.095617 , 9.1287565, 9.157017 ,\n",
+       "       9.187741 , 9.216694 , 9.244237 , 9.274386 , 9.300383 , 9.32181  ,\n",
+       "       9.35049  , 9.37106  , 9.393917 , 9.40948  , 9.428807 , 9.441616 ,\n",
+       "       9.459889 , 9.474596 , 9.488944 , 9.495657 , 9.510249 , 9.520061 ,\n",
+       "       9.530423 , 9.537613 , 9.546494 , 9.554764 , 9.561024 , 9.57158  ,\n",
+       "       9.579874 , 9.583782 , 9.591404 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.534 3.565 3.547 ... -197.1 -197.1</div><input id='attrs-2fb56565-ef29-4887-835e-41ad0b301dd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fb56565-ef29-4887-835e-41ad0b301dd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9195dd1f-c8d4-46c5-b7db-5eb6171eac67' class='xr-var-data-in' type='checkbox'><label for='data-9195dd1f-c8d4-46c5-b7db-5eb6171eac67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.53383756e+00,  3.56493616e+00,  3.54652500e+00,\n",
+       "         3.60746217e+00,  3.56795740e+00,  3.58543777e+00,\n",
+       "         3.56357908e+00,  3.57667398e+00,  3.60632110e+00,\n",
+       "         3.56949067e+00,  3.57479167e+00,  3.59876704e+00,\n",
+       "         3.60281253e+00,  3.59981132e+00,  3.59064198e+00,\n",
+       "         3.59985065e+00,  3.58579111e+00,  3.57373047e+00,\n",
+       "         3.56544852e+00,  3.58114982e+00,  3.58714032e+00,\n",
+       "         3.55944085e+00,  3.58880138e+00,  3.53171992e+00,\n",
+       "         3.53358531e+00,  3.49057865e+00,  3.49481201e+00,\n",
+       "         3.49612260e+00,  3.45677161e+00,  3.42318940e+00,\n",
+       "         3.46062589e+00,  3.41326380e+00,  3.41770315e+00,\n",
+       "         3.41921234e+00,  3.43796873e+00,  3.42292237e+00,\n",
+       "         3.41392589e+00,  3.38415337e+00,  3.38218069e+00,\n",
+       "         3.43648434e+00,  3.38592434e+00,  3.34218621e+00,\n",
+       "         3.38532996e+00,  3.37350154e+00,  3.37754154e+00,\n",
+       "         3.35902476e+00,  3.34339881e+00,  3.35676146e+00,\n",
+       "         3.35692763e+00,  3.32560086e+00,  3.27195191e+00,\n",
+       "         3.19083190e+00,  2.95882225e+00,  2.40057945e+00,\n",
+       "         2.16090226e+00,  1.92318320e+00,  1.78795314e+00,\n",
+       "         1.62216699e+00,  1.56376326e+00,  1.46418226e+00,\n",
+       "...\n",
+       "        -1.96982498e+02, -1.97061203e+02, -1.96969849e+02,\n",
+       "        -1.97076538e+02, -1.97007187e+02, -1.97028168e+02,\n",
+       "        -1.97006195e+02, -1.97027512e+02, -1.97029175e+02,\n",
+       "        -1.96980515e+02, -1.97019150e+02, -1.96939850e+02,\n",
+       "        -1.96979843e+02, -1.97032181e+02, -1.97038193e+02,\n",
+       "        -1.96942871e+02, -1.96959534e+02, -1.96986191e+02,\n",
+       "        -1.97056839e+02, -1.97049850e+02, -1.97026855e+02,\n",
+       "        -1.97041199e+02, -1.97016525e+02, -1.97084503e+02,\n",
+       "        -1.97077499e+02, -1.97032166e+02, -1.97061508e+02,\n",
+       "        -1.97079834e+02, -1.97101852e+02, -1.97092865e+02,\n",
+       "        -1.97062164e+02, -1.97118851e+02, -1.97044510e+02,\n",
+       "        -1.97032166e+02, -1.97112183e+02, -1.96995499e+02,\n",
+       "        -1.97103195e+02, -1.97131195e+02, -1.97147202e+02,\n",
+       "        -1.97079849e+02, -1.97053513e+02, -1.97125504e+02,\n",
+       "        -1.97132843e+02, -1.97114838e+02, -1.97074829e+02,\n",
+       "        -1.97085831e+02, -1.97115845e+02, -1.97091156e+02,\n",
+       "        -1.97044174e+02, -1.97063873e+02, -1.97090515e+02,\n",
+       "        -1.97129501e+02, -1.97007523e+02, -1.97017838e+02,\n",
+       "        -1.97019165e+02, -1.97129166e+02, -1.97051849e+02,\n",
+       "        -1.97113190e+02, -1.97104507e+02, -1.97053848e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.261 -1.263 -1.26 ... 9.714 9.712</div><input id='attrs-a899fa25-7d18-4765-afe3-04bc22fe5d71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a899fa25-7d18-4765-afe3-04bc22fe5d71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b02d62d4-7b2a-4b71-b7be-ca78eb28e340' class='xr-var-data-in' type='checkbox'><label for='data-b02d62d4-7b2a-4b71-b7be-ca78eb28e340' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2608225 , -1.2632061 , -1.2602278 , -1.2620431 , -1.2610018 ,\n",
+       "        -1.2613868 , -1.2610568 , -1.2621608 , -1.2631991 , -1.2639122 ,\n",
+       "        -1.2618904 , -1.258823  , -1.2596216 , -1.258702  , -1.2583325 ,\n",
+       "        -1.2578007 , -1.2583394 , -1.2576339 , -1.2579147 , -1.2534361 ,\n",
+       "        -1.2568558 , -1.2559422 , -1.2557042 , -1.2571892 , -1.2556211 ,\n",
+       "        -1.2540294 , -1.2548382 , -1.2532483 , -1.2525105 , -1.2511989 ,\n",
+       "        -1.2522614 , -1.2561746 , -1.2530836 , -1.2531874 , -1.2519903 ,\n",
+       "        -1.2518218 , -1.2511966 , -1.2544034 , -1.2561861 , -1.2536184 ,\n",
+       "        -1.2504781 , -1.2535783 , -1.2560768 , -1.2559297 , -1.257113  ,\n",
+       "        -1.2519604 , -1.2501055 , -1.2515699 , -1.2483989 , -1.2508361 ,\n",
+       "        -1.2479837 , -1.2430646 , -1.2314413 , -1.2083213 , -1.1935245 ,\n",
+       "        -1.1852744 , -1.1747023 , -1.1735051 , -1.1686733 , -1.1616861 ,\n",
+       "        -1.1579937 , -1.1570411 , -1.1541711 , -1.1540251 , -1.1536013 ,\n",
+       "        -1.1516274 , -1.147889  , -1.1449583 , -1.1449729 , -1.14322   ,\n",
+       "        -1.1418625 , -1.1392603 , -1.1384715 , -1.136121  , -1.1344364 ,\n",
+       "        -1.132704  , -1.1329331 , -1.1329101 , -1.130972  , -1.1331373 ,\n",
+       "        -1.1304799 , -1.1283726 , -1.1294136 , -1.132449  , -1.1283008 ,\n",
+       "        -1.1297874 , -1.1291262 , -1.1277461 , -1.1284393 , -1.1265595 ,\n",
+       "        -1.1229205 , -1.1222471 , -1.1207403 , -1.1196108 , -1.1196036 ,\n",
+       "        -1.1186633 , -1.1185873 , -1.1168755 , -1.1150851 , -1.1146263 ,\n",
+       "...\n",
+       "         9.716357  ,  9.717475  ,  9.7163725 ,  9.718897  ,  9.715463  ,\n",
+       "         9.711327  ,  9.713148  ,  9.713642  ,  9.712924  ,  9.715384  ,\n",
+       "         9.715974  ,  9.716772  ,  9.718337  ,  9.714473  ,  9.717203  ,\n",
+       "         9.714393  ,  9.71425   ,  9.713914  ,  9.711695  ,  9.708918  ,\n",
+       "         9.711455  ,  9.70863   ,  9.711487  ,  9.71433   ,  9.710337  ,\n",
+       "         9.711295  ,  9.7138815 ,  9.70997   ,  9.713227  ,  9.717092  ,\n",
+       "         9.713419  ,  9.719773  ,  9.71832   ,  9.716453  ,  9.717108  ,\n",
+       "         9.715989  ,  9.7141695 ,  9.711615  ,  9.71741   ,  9.715814  ,\n",
+       "         9.71428   ,  9.717347  ,  9.712972  ,  9.7162285 ,  9.713419  ,\n",
+       "         9.712238  ,  9.714361  ,  9.716245  ,  9.713563  ,  9.718161  ,\n",
+       "         9.713195  ,  9.715207  ,  9.71591   ,  9.713914  ,  9.7165    ,\n",
+       "         9.7136755 ,  9.717347  ,  9.719837  ,  9.718688  ,  9.713898  ,\n",
+       "         9.717618  ,  9.715941  ,  9.710864  ,  9.71433   ,  9.717777  ,\n",
+       "         9.716262  ,  9.716644  ,  9.719199  ,  9.718911  ,  9.715112  ,\n",
+       "         9.714042  ,  9.710752  ,  9.714153  ,  9.716006  ,  9.715191  ,\n",
+       "         9.716421  ,  9.718177  ,  9.717875  ,  9.718033  ,  9.713227  ,\n",
+       "         9.714537  ,  9.714841  ,  9.714376  ,  9.71848   ,  9.7165    ,\n",
+       "         9.715717  ,  9.711741  ,  9.710465  ,  9.712621  ,  9.71337   ,\n",
+       "         9.715622  ,  9.715989  ,  9.711598  ,  9.71219   ,  9.71021   ,\n",
+       "         9.712366  ,  9.7141695 ,  9.711616  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.48 16.48 16.48 ... 16.26 16.26</div><input id='attrs-de29e6fa-0f56-4d87-b171-ef3a1091f762' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de29e6fa-0f56-4d87-b171-ef3a1091f762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5546337-3ff6-4986-a386-f2b1901fbdcd' class='xr-var-data-in' type='checkbox'><label for='data-f5546337-3ff6-4986-a386-f2b1901fbdcd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.481167, 16.478   , 16.478   , 16.477167, 16.481   , 16.484167,\n",
+       "       16.477167, 16.479834, 16.479166, 16.482666, 16.478167, 16.481668,\n",
+       "       16.479668, 16.475334, 16.479668, 16.479   , 16.474   , 16.475334,\n",
+       "       16.477667, 16.473167, 16.472168, 16.468334, 16.472334, 16.471169,\n",
+       "       16.466002, 16.476501, 16.464167, 16.464666, 16.462667, 16.460833,\n",
+       "       16.462164, 16.460167, 16.459501, 16.459002, 16.454332, 16.449501,\n",
+       "       16.444332, 16.4325  , 16.422   , 16.412834, 16.400167, 16.397667,\n",
+       "       16.385334, 16.382334, 16.380499, 16.383333, 16.381332, 16.379833,\n",
+       "       16.382   , 16.379168, 16.380499, 16.378834, 16.374832, 16.384499,\n",
+       "       16.374166, 16.373333, 16.377167, 16.376   , 16.38    , 16.370333,\n",
+       "       16.376501, 16.382   , 16.3775  , 16.3815  , 16.378   , 16.3785  ,\n",
+       "       16.378   , 16.383667, 16.374668, 16.3845  , 16.353   , 16.372334,\n",
+       "       16.377668, 16.378168, 16.372334, 16.372665, 16.363167, 16.367167,\n",
+       "       16.372833, 16.363333, 16.355165, 16.367002, 16.368334, 16.372665,\n",
+       "       16.360834, 16.367834, 16.367   , 16.360834, 16.358   , 16.342833,\n",
+       "       16.341167, 16.328001, 16.317   , 16.305332, 16.295   , 16.2915  ,\n",
+       "       16.279333, 16.281166, 16.280834, 16.276167, 16.279333, 16.279333,\n",
+       "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
+       "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
+       "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
+       "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
+       "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
+       "       16.275333, 16.279001, 16.279001, 16.275333, 16.279001, 16.279001,\n",
+       "       16.275   , 16.278334, 16.276834, 16.271   , 16.2725  , 16.27    ,\n",
+       "       16.265165, 16.268333, 16.268   , 16.264   , 16.268   , 16.268   ,\n",
+       "       16.264   , 16.268   , 16.268   , 16.264   , 16.268   , 16.268   ,\n",
+       "       16.264   , 16.268   , 16.268   , 16.264   , 16.268   , 16.263334,\n",
+       "       16.258333, 16.262335, 16.262169, 16.2575  , 16.261   , 16.260332,\n",
+       "       16.25633 , 16.257998, 16.258333, 16.253334, 16.257998, 16.257332,\n",
+       "       16.253166, 16.256998, 16.256998, 16.252666, 16.256998, 16.256998,\n",
+       "       16.252666, 16.256998, 16.256998], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-7246ebc5-63f8-46e8-a0eb-6e66b537fdeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7246ebc5-63f8-46e8-a0eb-6e66b537fdeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b2b71af-ff51-449a-a230-50c42525f9e8' class='xr-var-data-in' type='checkbox'><label for='data-5b2b71af-ff51-449a-a230-50c42525f9e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20.      , 20.      , 20.      , 20.043333, 20.25    , 20.25    ,\n",
+       "       20.25    , 20.25    , 20.263334, 20.446667, 20.5     , 20.5     ,\n",
+       "       20.5     , 20.5     , 20.5     , 20.5     , 20.5     , 20.5     ,\n",
+       "       20.5     , 20.5     , 20.5     , 20.5     , 20.5     , 20.31    ,\n",
+       "       20.25    , 20.25    , 20.163334, 20.      , 20.      , 20.      ,\n",
+       "       19.766666, 19.75    , 19.75    , 19.726667, 19.553333, 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.606667, 19.746666, 19.75    , 19.75    , 19.75    ,\n",
+       "       19.75    , 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
+       "       19.75    , 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
+       "       19.556667, 19.5     , 19.5     , 19.5     , 19.626667, 19.75    ,\n",
+       "       19.75    , 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
+       "       19.75    , 19.75    , 19.75    , 19.54    , 19.413334, 19.233334,\n",
+       "       19.      , 18.91    , 18.75    , 18.746666, 18.5     , 18.5     ,\n",
+       "       18.5     , 18.5     , 18.366667, 18.25    , 18.25    , 18.25    ,\n",
+       "       18.25    , 18.2     , 18.      , 18.      , 18.      , 18.      ,\n",
+       "       17.873333, 17.75    , 17.75    , 17.75    , 17.863333, 18.      ,\n",
+       "       18.      , 18.17    , 18.25    , 18.44    , 18.513334, 18.75    ,\n",
+       "       18.783333, 19.      , 19.      , 19.196667, 19.25    , 19.25    ,\n",
+       "       19.25    , 19.433332, 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.53    ,\n",
+       "       19.696667, 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
+       "       19.75    , 19.75    , 19.71    , 19.5     , 19.5     , 19.5     ,\n",
+       "       19.5     , 19.5     , 19.5     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-cb14a4c6-5017-4bda-ac21-2fcc0f72702e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb14a4c6-5017-4bda-ac21-2fcc0f72702e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae80c9f2-edee-4981-90d7-b281d205f328' class='xr-var-data-in' type='checkbox'><label for='data-ae80c9f2-edee-4981-90d7-b281d205f328' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-cc0cf513-bd59-407d-adc4-aec84aade42c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc0cf513-bd59-407d-adc4-aec84aade42c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a0b89f6-b56d-405b-9bc5-d0c028b38012' class='xr-var-data-in' type='checkbox'><label for='data-0a0b89f6-b56d-405b-9bc5-d0c028b38012' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355693e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "...\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355693e+09, 1.05355699e+09, 1.05355699e+09,\n",
+       "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
+       "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
+       "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
+       "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
+       "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2358 0.266 0.3007 ... nan nan nan</div><input id='attrs-8c6a894f-c99f-4c61-8e63-9b0744f354b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c6a894f-c99f-4c61-8e63-9b0744f354b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbec152b-b3d5-455e-801a-5671c162591a' class='xr-var-data-in' type='checkbox'><label for='data-dbec152b-b3d5-455e-801a-5671c162591a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.23582192,  0.2660148 ,  0.30069652, ..., -0.179669  ,\n",
+       "         -0.18383324, -0.17606331],\n",
+       "        [ 0.25633767,  0.28601688,  0.3117814 , ..., -0.19580415,\n",
+       "         -0.19696543, -0.20011853],\n",
+       "        [ 0.26504937,  0.28428233,  0.31169885, ..., -0.2103943 ,\n",
+       "         -0.21627884, -0.21236075],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[ 0.02619691,  0.04430203,  0.04002393, ...,  0.05932099,\n",
+       "          0.06400961,  0.07232199],\n",
+       "        [ 0.06181316,  0.07802678,  0.06236525, ...,  0.0768801 ,\n",
+       "          0.08184617,  0.0892335 ],\n",
+       "        [ 0.08793733,  0.10133781,  0.08016948, ...,  0.08136398,\n",
+       "          0.08639657,  0.10118616],\n",
+       "...\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[-0.00417333, -0.01056667, -0.01045333, ..., -0.02584564,\n",
+       "         -0.02752333, -0.02575667],\n",
+       "        [ 0.01358667,  0.00760333,  0.00634667, ..., -0.01625333,\n",
+       "         -0.01776333, -0.01595   ],\n",
+       "        [ 0.01556187,  0.00811371,  0.00740333, ..., -0.00842   ,\n",
+       "         -0.01147667, -0.01189667],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>79.95 80.28 80.69 ... nan nan nan</div><input id='attrs-dcef319d-3809-4efc-ac3d-4bdfe1b0f3a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcef319d-3809-4efc-ac3d-4bdfe1b0f3a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d35a990-7c06-46e7-9f16-aa3349287c64' class='xr-var-data-in' type='checkbox'><label for='data-7d35a990-7c06-46e7-9f16-aa3349287c64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[79.94666 , 80.28167 , 80.68667 , ..., 81.721664, 81.80334 ,\n",
+       "         81.68667 ],\n",
+       "        [75.535   , 76.043335, 76.66167 , ..., 77.62666 , 77.73    ,\n",
+       "         77.528336],\n",
+       "        [72.81167 , 73.391304, 74.26833 , ..., 74.525   , 74.39667 ,\n",
+       "         74.253334],\n",
+       "        ...,\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan]],\n",
+       "\n",
+       "       [[80.706665, 80.93833 , 81.255   , ..., 82.19833 , 82.225   ,\n",
+       "         82.17    ],\n",
+       "        [76.80666 , 76.996666, 77.44833 , ..., 78.38333 , 78.41167 ,\n",
+       "         78.06    ],\n",
+       "        [74.31834 , 74.64667 , 75.903336, ..., 75.16333 , 75.26833 ,\n",
+       "         74.875   ],\n",
+       "...\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan]],\n",
+       "\n",
+       "       [[80.185   , 80.55    , 80.885   , ..., 81.888336, 81.945   ,\n",
+       "         81.85833 ],\n",
+       "        [75.78    , 76.291664, 76.76833 , ..., 77.97667 , 78.03833 ,\n",
+       "         77.87666 ],\n",
+       "        [72.67725 , 73.55666 , 74.44334 , ..., 74.75833 , 74.665   ,\n",
+       "         74.47    ],\n",
+       "        ...,\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>92.59 92.61 92.47 ... 0.0 0.0 0.0</div><input id='attrs-e8e09ea2-5181-492c-9abb-4dae602329d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8e09ea2-5181-492c-9abb-4dae602329d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8efeaaf4-1cc0-461c-90da-afcc0082ce62' class='xr-var-data-in' type='checkbox'><label for='data-8efeaaf4-1cc0-461c-90da-afcc0082ce62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[92.58667 , 92.613335, 92.47    , ..., 91.933334, 91.96667 ,\n",
+       "         92.06333 ],\n",
+       "        [96.236664, 96.526665, 96.206665, ..., 96.00667 , 96.3     ,\n",
+       "         96.03333 ],\n",
+       "        [97.166664, 96.246666, 95.94    , ..., 96.45333 , 97.206665,\n",
+       "         97.263336],\n",
+       "        ...,\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ],\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ],\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ]],\n",
+       "\n",
+       "       [[93.333336, 93.61    , 93.31333 , ..., 91.6     , 91.49    ,\n",
+       "         91.14667 ],\n",
+       "        [96.57    , 96.39    , 96.12666 , ..., 96.27    , 96.56333 ,\n",
+       "         96.44334 ],\n",
+       "        [97.37666 , 97.09333 , 96.87666 , ..., 96.17333 , 97.30334 ,\n",
+       "         97.32667 ],\n",
+       "...\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ],\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ],\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ]],\n",
+       "\n",
+       "       [[92.78333 , 93.223335, 92.63    , ..., 92.22    , 92.34    ,\n",
+       "         92.61    ],\n",
+       "        [96.75    , 96.34333 , 96.34    , ..., 96.53    , 96.69    ,\n",
+       "         96.27333 ],\n",
+       "        [96.346664, 97.05    , 97.24    , ..., 96.66333 , 96.89333 ,\n",
+       "         97.41    ],\n",
+       "        ...,\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ],\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ],\n",
+       "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "          0.      ]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05743 0.05697 ... 0.9937 0.9937</div><input id='attrs-9b2c757c-c921-4588-8c5b-3a44841f1715' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b2c757c-c921-4588-8c5b-3a44841f1715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f51a2dc2-78d5-40bf-b90c-b2d939d5d5a2' class='xr-var-data-in' type='checkbox'><label for='data-f51a2dc2-78d5-40bf-b90c-b2d939d5d5a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[ 5.74317947e-02,  5.69723397e-02,  5.82992062e-02, ...,\n",
+       "          5.81927113e-02,  5.85613623e-02,  5.85512854e-02],\n",
+       "        [ 9.98300374e-01,  9.98323679e-01,  9.98248577e-01, ...,\n",
+       "          9.98254061e-01,  9.98234987e-01,  9.98234689e-01],\n",
+       "        [-1.05408905e-03, -1.34927384e-03, -1.13080605e-03, ...,\n",
+       "         -8.26976204e-04, -8.38353124e-04, -8.28948745e-04]],\n",
+       "\n",
+       "       [[-9.89950538e-01, -9.89953220e-01, -9.89922285e-01, ...,\n",
+       "         -9.91938353e-01, -9.91944909e-01, -9.91939366e-01],\n",
+       "        [ 5.68153895e-02,  5.63202463e-02,  5.76671995e-02, ...,\n",
+       "          5.77317439e-02,  5.80984168e-02,  5.80891557e-02],\n",
+       "        [-1.29125342e-01, -1.29299909e-01, -1.28952131e-01, ...,\n",
+       "         -1.12354711e-01, -1.12130061e-01, -1.12175331e-01]],\n",
+       "\n",
+       "       [[-1.28852651e-01, -1.29014045e-01, -1.28667876e-01, ...,\n",
+       "         -1.12116747e-01, -1.11889169e-01, -1.11934915e-01],\n",
+       "        [ 8.45469162e-03,  8.69723968e-03,  8.63243733e-03, ...,\n",
+       "          7.35414820e-03,  7.39390310e-03,  7.38611678e-03],\n",
+       "        [ 9.91627276e-01,  9.91604209e-01,  9.91649806e-01, ...,\n",
+       "          9.93667483e-01,  9.93692815e-01,  9.93687749e-01]]],\n",
+       "      shape=(3, 3, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.000807 0.0008203 ... -0.002598</div><input id='attrs-b485eef9-a704-4048-8ddb-75f9f1d992a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b485eef9-a704-4048-8ddb-75f9f1d992a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ded8637c-a800-4d74-b9aa-9f557aa69155' class='xr-var-data-in' type='checkbox'><label for='data-ded8637c-a800-4d74-b9aa-9f557aa69155' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00080699,  0.00082028,  0.00085015,  0.0008093 ,  0.00077764,\n",
+       "         0.00076247,  0.00084005,  0.00080013,  0.00073725,  0.00069978,\n",
+       "         0.00068869,  0.000708  ,  0.00070397,  0.00064101,  0.0007507 ,\n",
+       "         0.00071757,  0.00060209,  0.00062077,  0.00062501,  0.0005675 ,\n",
+       "         0.00054653,  0.00055442,  0.00072552,  0.00056834,  0.00065792,\n",
+       "         0.00086152,  0.00080709,  0.00096736,  0.00085591,  0.00094166,\n",
+       "         0.00090368,  0.00095632,  0.00094047,  0.00091472,  0.00092718,\n",
+       "         0.00097144,  0.00090388,  0.00098592,  0.00099217,  0.00104627,\n",
+       "         0.00101094,  0.00108953,  0.00094413,  0.0010326 ,  0.00105636,\n",
+       "         0.00098609,  0.00101084,  0.00101927,  0.00101796,  0.00096841,\n",
+       "         0.00102785,  0.00113951,  0.00098156,  0.00111134,  0.00100472,\n",
+       "         0.00104199,  0.00104198,  0.00100612,  0.00103637,  0.00099132,\n",
+       "         0.00102272,  0.00102712,  0.00100425,  0.0010416 ,  0.00097589,\n",
+       "         0.00090605,  0.00108258,  0.00090169,  0.00096689,  0.00081777,\n",
+       "         0.0009423 ,  0.00087804,  0.00102183,  0.00083111,  0.0009242 ,\n",
+       "         0.00094747,  0.00110261,  0.00097415,  0.00097692,  0.00113317,\n",
+       "         0.00105406,  0.00104935,  0.00102722,  0.00093711,  0.00103575,\n",
+       "         0.00095867,  0.0009583 ,  0.00104538,  0.00100466,  0.00095809,\n",
+       "         0.00101549,  0.00097118,  0.00090704,  0.00094464,  0.00096578,\n",
+       "         0.00105039,  0.00093799,  0.00100518,  0.00088882,  0.00095506,\n",
+       "...\n",
+       "        -0.00263344, -0.00258787, -0.00256183, -0.00256183, -0.00250324,\n",
+       "        -0.00268227, -0.00268227, -0.00259113, -0.00262693, -0.00257485,\n",
+       "        -0.00259113, -0.00252602, -0.00267576, -0.00253904, -0.00256509,\n",
+       "        -0.00242511, -0.00249998, -0.00250975, -0.00252277, -0.00261066,\n",
+       "        -0.00257485, -0.00265298, -0.00264647, -0.00261391, -0.00260089,\n",
+       "        -0.00270181, -0.0027604 , -0.00278318, -0.00288084, -0.00274412,\n",
+       "        -0.00280272, -0.00289386, -0.0030729 , -0.0029492 , -0.0029199 ,\n",
+       "        -0.00302407, -0.00299803, -0.00296222, -0.00288084, -0.00299477,\n",
+       "        -0.00304034, -0.00298175, -0.00296222, -0.00296873, -0.00305337,\n",
+       "        -0.00302081, -0.00299803, -0.00304034, -0.0030729 , -0.0029199 ,\n",
+       "        -0.00295571, -0.00287433, -0.00287433, -0.0028548 , -0.00275714,\n",
+       "        -0.00271157, -0.0027018 , -0.00278644, -0.0026074 , -0.00264972,\n",
+       "        -0.00273761, -0.00259764, -0.00258787, -0.00256834, -0.00263344,\n",
+       "        -0.00255207, -0.00265623, -0.00261391, -0.00255207, -0.00249673,\n",
+       "        -0.00258787, -0.00256183, -0.00253579, -0.00264321, -0.00268553,\n",
+       "        -0.00255858, -0.00264321, -0.00262693, -0.0025423 , -0.00256834,\n",
+       "        -0.00263345, -0.00258136, -0.00250975, -0.00261717, -0.00264321,\n",
+       "        -0.00260741, -0.00261066, -0.00250649, -0.00248371, -0.0026074 ,\n",
+       "        -0.00260415, -0.00260415, -0.00250324, -0.00259764, -0.002513  ,\n",
+       "        -0.00256509, -0.00253904, -0.00259764]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-ecc277c0-5ffc-479d-8499-8da5fe58c508' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ecc277c0-5ffc-479d-8499-8da5fe58c508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69e77445-8f9a-4232-803d-89f14e29e3ff' class='xr-var-data-in' type='checkbox'><label for='data-69e77445-8f9a-4232-803d-89f14e29e3ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245074 ,  0.624316  ,  0.6248463 ,  0.6245108 ,  0.6247046 ,\n",
+       "         0.62467265,  0.62460417,  0.62476116,  0.6245991 ,  0.6246464 ,\n",
+       "         0.624686  ,  0.6249721 ,  0.6249648 ,  0.6250649 ,  0.62494904,\n",
+       "         0.62500936,  0.6250884 ,  0.6250855 ,  0.6253171 ,  0.6254035 ,\n",
+       "         0.6253574 ,  0.6254711 ,  0.6251542 ,  0.6249595 ,  0.62512624,\n",
+       "         0.62510735,  0.62479544,  0.6250753 ,  0.624999  ,  0.6248949 ,\n",
+       "         0.6248343 ,  0.6245227 ,  0.6248399 ,  0.624795  ,  0.6250311 ,\n",
+       "         0.62494355,  0.6249766 ,  0.6244582 ,  0.62417775,  0.6244494 ,\n",
+       "         0.6248647 ,  0.6245337 ,  0.62416995,  0.6242157 ,  0.62425923,\n",
+       "         0.6247039 ,  0.6248129 ,  0.6246122 ,  0.6248121 ,  0.6246928 ,\n",
+       "         0.6245991 ,  0.6248212 ,  0.6247573 ,  0.6243741 ,  0.6243043 ,\n",
+       "         0.6244282 ,  0.6243682 ,  0.6240724 ,  0.62418926,  0.6241515 ,\n",
+       "         0.6239765 ,  0.62391806,  0.62415963,  0.62387216,  0.6239287 ,\n",
+       "         0.62390673,  0.6236841 ,  0.62378633,  0.6236643 ,  0.6235926 ,\n",
+       "         0.62364197,  0.6237233 ,  0.62374234,  0.62387735,  0.62402254,\n",
+       "         0.62393117,  0.62393445,  0.62373453,  0.62396324,  0.62340415,\n",
+       "         0.62373024,  0.62379295,  0.62355286,  0.6231889 ,  0.62350035,\n",
+       "         0.6232749 ,  0.6232579 ,  0.6233172 ,  0.6232117 ,  0.62328154,\n",
+       "         0.6234021 ,  0.62359464,  0.6235321 ,  0.62355274,  0.62340564,\n",
+       "         0.6233349 ,  0.6232732 ,  0.62326413,  0.62335676,  0.6233057 ,\n",
+       "...\n",
+       "         0.77982664,  0.7798403 ,  0.77980125,  0.7798833 ,  0.7798343 ,\n",
+       "         0.77974474,  0.7795985 ,  0.77965385,  0.77964175,  0.7797572 ,\n",
+       "         0.7798193 ,  0.77987343,  0.77987754,  0.7798154 ,  0.7798561 ,\n",
+       "         0.77976704,  0.77973217,  0.77971977,  0.77958   ,  0.7794924 ,\n",
+       "         0.77951276,  0.77934176,  0.77977145,  0.77983665,  0.7796758 ,\n",
+       "         0.77951133,  0.77975136,  0.7796223 ,  0.7795445 ,  0.7798783 ,\n",
+       "         0.7796854 ,  0.78009176,  0.77983886,  0.77976817,  0.7798124 ,\n",
+       "         0.77966744,  0.77980477,  0.77957195,  0.7797228 ,  0.7795519 ,\n",
+       "         0.77965647,  0.7795511 ,  0.7795689 ,  0.77951413,  0.77942085,\n",
+       "         0.7791843 ,  0.7793043 ,  0.7793574 ,  0.7790914 ,  0.77922   ,\n",
+       "         0.7788722 ,  0.77907705,  0.7790985 ,  0.778842  ,  0.7789314 ,\n",
+       "         0.77889466,  0.7789852 ,  0.7792381 ,  0.77916646,  0.7788889 ,\n",
+       "         0.77894676,  0.7789039 ,  0.7785506 ,  0.7786492 ,  0.7788344 ,\n",
+       "         0.7786969 ,  0.77884805,  0.7789083 ,  0.77894056,  0.7786727 ,\n",
+       "         0.77858806,  0.7783844 ,  0.7785528 ,  0.7787677 ,  0.77864134,\n",
+       "         0.77870125,  0.7787834 ,  0.7788154 ,  0.7788442 ,  0.7785659 ,\n",
+       "         0.7785521 ,  0.77869254,  0.77849793,  0.7787016 ,  0.77872854,\n",
+       "         0.77860415,  0.7784391 ,  0.7783881 ,  0.7785563 ,  0.77852774,\n",
+       "         0.77856874,  0.77847373,  0.7784385 ,  0.7782882 ,  0.778279  ,\n",
+       "         0.778462  ,  0.77835196,  0.77835387]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-2a830c4a-30b3-44a0-bf5d-015ab205796c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a830c4a-30b3-44a0-bf5d-015ab205796c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2451ce69-7a5d-4d87-91a9-a73a177b0ae6' class='xr-var-data-in' type='checkbox'><label for='data-2451ce69-7a5d-4d87-91a9-a73a177b0ae6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.800002 , 12.800002 , 12.800002 , 12.800002 , 12.800002 ,\n",
+       "       12.966666 , 13.       , 13.050666 , 13.199998 , 13.199998 ,\n",
+       "       13.199998 , 13.199998 , 13.199998 , 13.199998 , 13.199998 ,\n",
+       "       13.199998 , 13.199998 , 13.199998 , 13.176665 , 13.       ,\n",
+       "       13.       , 12.941335 , 12.800002 , 12.631333 , 12.598    ,\n",
+       "       12.411335 , 12.400001 , 12.230666 , 12.199998 , 12.119332 ,\n",
+       "       12.       , 12.       , 12.       , 11.957999 , 11.800002 ,\n",
+       "       11.800002 , 11.800002 , 11.800002 , 11.800002 , 11.800002 ,\n",
+       "       11.800002 , 11.800002 , 11.800002 , 11.800002 , 11.834001 ,\n",
+       "       11.999333 , 12.       , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.104667 , 12.193999 , 12.199998 , 12.199998 ,\n",
+       "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
+       "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.086667 ,\n",
+       "       12.113999 , 12.192665 , 12.032667 , 12.078666 , 12.03     ,\n",
+       "       12.       , 12.111999 , 12.199998 , 12.199998 , 12.199998 ,\n",
+       "       12.202665 , 12.219333 , 12.274    , 12.367333 , 12.203999 ,\n",
+       "       12.217332 , 12.199998 , 12.199998 , 12.119332 , 11.845334 ,\n",
+       "       11.496667 , 11.219998 , 11.       , 10.952001 , 10.800002 ,\n",
+       "       10.800002 , 10.800002 , 10.800002 , 10.751334 , 10.599999 ,\n",
+       "       10.599999 , 10.599999 , 10.599999 , 10.5546665, 10.400001 ,\n",
+       "       10.400001 , 10.334666 , 10.2      , 10.2      , 10.191999 ,\n",
+       "       10.029333 , 10.       , 10.009334 , 10.198667 , 10.243333 ,\n",
+       "       10.415334 , 10.599999 , 10.772668 , 11.       , 11.186666 ,\n",
+       "       11.398002 , 11.528    , 11.599999 , 11.746667 , 11.800002 ,\n",
+       "       11.876667 , 12.       , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.       , 12.       , 12.000667 , 12.006    ,\n",
+       "       12.       , 12.       , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.001333 , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.       , 12.024667 , 12.173999 , 12.199998 ,\n",
+       "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
+       "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
+       "       12.1119995, 12.       , 12.       , 12.       , 12.       ,\n",
+       "       12.       , 12.       , 12.       ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>244.0 244.0 244.0 ... 246.0 246.0</div><input id='attrs-2f827c3d-1b05-44f9-af16-7d6e6e5751ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f827c3d-1b05-44f9-af16-7d6e6e5751ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-205c009a-da96-4bd6-b2a8-2b72ee9626d5' class='xr-var-data-in' type='checkbox'><label for='data-205c009a-da96-4bd6-b2a8-2b72ee9626d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([244.04333, 243.96   , 244.04666, 244.12334, 244.30667, 244.32   ,\n",
+       "       244.04   , 243.95667, 243.72   , 243.45334, 243.46   , 243.49333,\n",
+       "       243.49667, 243.69667, 243.80333, 243.78334, 243.81667, 244.     ,\n",
+       "       244.27333, 244.39667, 244.58333, 244.73334, 244.95334, 245.     ,\n",
+       "       244.98334, 244.94   , 244.86333, 244.82666, 244.79333, 244.84666,\n",
+       "       244.76666, 244.73334, 244.82666, 244.75   , 244.86333, 244.83   ,\n",
+       "       244.84334, 244.91333, 244.96666, 245.08   , 245.25333, 245.30667,\n",
+       "       245.4    , 245.51   , 245.61333, 245.61   , 245.65666, 245.76666,\n",
+       "       245.73   , 245.79666, 245.83333, 245.85333, 245.81   , 245.85667,\n",
+       "       245.90334, 245.89333, 245.90666, 245.90334, 245.89333, 245.90666,\n",
+       "       245.93   , 245.91333, 245.91667, 245.92334, 245.92334, 245.92   ,\n",
+       "       245.96   , 245.95   , 245.94333, 245.96   , 245.94667, 245.97   ,\n",
+       "       245.95   , 245.93666, 245.93   , 245.95667, 245.94   , 245.95667,\n",
+       "       245.95   , 245.95334, 245.96666, 245.98   , 245.96666, 245.97   ,\n",
+       "       245.97   , 245.98   , 245.97333, 245.97   , 245.98666, 245.97667,\n",
+       "       245.97667, 245.98334, 245.97667, 245.99333, 245.97667, 245.98334,\n",
+       "       245.99667, 245.99667, 245.99   , 245.97333, 245.98334, 245.98666,\n",
+       "       245.97   , 245.99667, 245.98666, 245.98   , 245.85667, 245.44667,\n",
+       "       244.92334, 244.41667, 244.16333, 244.00667, 244.06   , 244.07333,\n",
+       "       243.87334, 243.77333, 243.75   , 243.69   , 243.76334, 243.79666,\n",
+       "       243.55   , 243.5    , 243.37666, 243.20334, 242.92334, 242.87666,\n",
+       "       242.83   , 242.83333, 243.07666, 243.41   , 243.88333, 244.20334,\n",
+       "       244.40334, 244.45334, 244.48666, 244.41   , 244.38   , 244.43333,\n",
+       "       244.6    , 244.72333, 244.90666, 244.9    , 244.97667, 245.03667,\n",
+       "       245.22667, 245.36   , 245.4    , 245.42   , 245.48334, 245.62666,\n",
+       "       245.67   , 245.71333, 245.75667, 245.77   , 245.84   , 245.87   ,\n",
+       "       245.87666, 245.89   , 245.87666, 245.94333, 245.93   , 245.91333,\n",
+       "       245.95667, 245.94   , 245.93   , 245.95667, 245.95   , 245.95334,\n",
+       "       245.94667, 245.96   , 245.96333, 245.97333, 245.96666, 245.96   ,\n",
+       "       245.97667, 245.99   , 245.98   , 245.98666, 245.98666, 245.98334,\n",
+       "       245.99   , 245.98334, 245.99   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.589 3.576 3.611 ... -197.1 -197.1</div><input id='attrs-ae0eb9cf-4aef-4228-821f-8ce74f6dc8bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae0eb9cf-4aef-4228-821f-8ce74f6dc8bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9483a782-9e95-4403-9163-7f1a77ac2ebd' class='xr-var-data-in' type='checkbox'><label for='data-9483a782-9e95-4403-9163-7f1a77ac2ebd' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.5890043e+00,  3.5764265e+00,  3.6108000e+00,  3.5779481e+00,\n",
+       "         3.6116719e+00,  3.5757926e+00,  3.5947008e+00,  3.6243417e+00,\n",
+       "         3.6057498e+00,  3.5963907e+00,  3.5682683e+00,  3.5830538e+00,\n",
+       "         3.6165168e+00,  3.5789921e+00,  3.5747981e+00,  3.5690453e+00,\n",
+       "         3.5796013e+00,  3.6261466e+00,  3.6063533e+00,  3.6014795e+00,\n",
+       "         3.6353865e+00,  3.6033041e+00,  3.6023455e+00,  3.5552027e+00,\n",
+       "         3.5689840e+00,  3.5028169e+00,  3.5047083e+00,  3.5002160e+00,\n",
+       "         3.4591196e+00,  3.4518204e+00,  3.4781299e+00,  3.4272485e+00,\n",
+       "         3.4080899e+00,  3.4372404e+00,  3.4341383e+00,  3.3986638e+00,\n",
+       "         3.4168522e+00,  3.4093900e+00,  3.3794670e+00,  3.3934760e+00,\n",
+       "         3.4050257e+00,  3.4207287e+00,  3.3710022e+00,  3.3771679e+00,\n",
+       "         3.3508575e+00,  3.3733418e+00,  3.3524203e+00,  3.3316767e+00,\n",
+       "         3.3258898e+00,  3.3680136e+00,  3.2842207e+00,  3.1945534e+00,\n",
+       "         3.0031645e+00,  2.4369471e+00,  2.1365104e+00,  1.9519800e+00,\n",
+       "         1.7708881e+00,  1.6470156e+00,  1.5729071e+00,  1.4625059e+00,\n",
+       "         1.3881445e+00,  1.3379683e+00,  1.3052688e+00,  1.2468668e+00,\n",
+       "         1.2023062e+00,  1.1269653e+00,  1.1022494e+00,  1.0541168e+00,\n",
+       "         1.0267943e+00,  9.7805512e-01,  9.2956728e-01,  9.2703992e-01,\n",
+       "         9.1599506e-01,  8.9450091e-01,  8.5012424e-01,  8.2220656e-01,\n",
+       "         7.9883534e-01,  7.8205061e-01,  7.3997599e-01,  7.3860037e-01,\n",
+       "...\n",
+       "        -1.9736919e+02, -1.9725951e+02, -1.9730052e+02, -1.9728384e+02,\n",
+       "        -1.9724889e+02, -1.9722919e+02, -1.9715518e+02, -1.9727151e+02,\n",
+       "        -1.9724452e+02, -1.9721884e+02, -1.9717354e+02, -1.9723384e+02,\n",
+       "        -1.9719716e+02, -1.9714218e+02, -1.9723254e+02, -1.9710817e+02,\n",
+       "        -1.9708250e+02, -1.9709784e+02, -1.9708986e+02, -1.9712950e+02,\n",
+       "        -1.9721385e+02, -1.9720052e+02, -1.9717422e+02, -1.9710918e+02,\n",
+       "        -1.9715718e+02, -1.9714487e+02, -1.9715251e+02, -1.9714418e+02,\n",
+       "        -1.9714218e+02, -1.9706715e+02, -1.9705818e+02, -1.9705084e+02,\n",
+       "        -1.9708583e+02, -1.9708820e+02, -1.9714618e+02, -1.9709253e+02,\n",
+       "        -1.9707854e+02, -1.9714384e+02, -1.9717952e+02, -1.9718620e+02,\n",
+       "        -1.9718388e+02, -1.9716652e+02, -1.9712685e+02, -1.9720485e+02,\n",
+       "        -1.9718315e+02, -1.9713019e+02, -1.9714951e+02, -1.9724086e+02,\n",
+       "        -1.9716119e+02, -1.9719583e+02, -1.9724786e+02, -1.9716052e+02,\n",
+       "        -1.9725883e+02, -1.9720285e+02, -1.9717485e+02, -1.9725154e+02,\n",
+       "        -1.9714687e+02, -1.9711821e+02, -1.9714719e+02, -1.9711786e+02,\n",
+       "        -1.9714052e+02, -1.9714351e+02, -1.9715118e+02, -1.9712117e+02,\n",
+       "        -1.9710718e+02, -1.9718983e+02, -1.9715218e+02, -1.9712483e+02,\n",
+       "        -1.9706255e+02, -1.9714519e+02, -1.9712418e+02, -1.9705786e+02,\n",
+       "        -1.9708685e+02, -1.9711749e+02, -1.9707716e+02, -1.9707919e+02,\n",
+       "        -1.9719186e+02, -1.9713850e+02, -1.9714883e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.262 -1.263 -1.26 ... 9.714 9.713</div><input id='attrs-46b1204c-9611-4b1c-ac05-95b954782894' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46b1204c-9611-4b1c-ac05-95b954782894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c86a487e-4d9c-46fd-ad8a-56fc4ee29a89' class='xr-var-data-in' type='checkbox'><label for='data-c86a487e-4d9c-46fd-ad8a-56fc4ee29a89' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2623771 , -1.2625197 , -1.2600151 , -1.2631888 , -1.2615377 ,\n",
+       "        -1.2616537 , -1.262574  , -1.2627327 , -1.2632962 , -1.2634871 ,\n",
+       "        -1.2617764 , -1.2599101 , -1.2592376 , -1.2577692 , -1.259011  ,\n",
+       "        -1.2584039 , -1.2585176 , -1.2577502 , -1.2569212 , -1.2552022 ,\n",
+       "        -1.2558354 , -1.2547812 , -1.2564313 , -1.2570257 , -1.2564919 ,\n",
+       "        -1.2552761 , -1.2551885 , -1.254373  , -1.2521615 , -1.253256  ,\n",
+       "        -1.2520183 , -1.2556639 , -1.2544196 , -1.2537049 , -1.2514051 ,\n",
+       "        -1.251719  , -1.2501887 , -1.2550075 , -1.2560523 , -1.2542702 ,\n",
+       "        -1.2513357 , -1.2537422 , -1.25587   , -1.2554086 , -1.2568508 ,\n",
+       "        -1.2513126 , -1.251984  , -1.2514541 , -1.2487588 , -1.249171  ,\n",
+       "        -1.2487395 , -1.2432916 , -1.2308283 , -1.2085198 , -1.1925628 ,\n",
+       "        -1.1850357 , -1.175814  , -1.1718632 , -1.1669031 , -1.1620393 ,\n",
+       "        -1.1585126 , -1.1573663 , -1.1550487 , -1.1537894 , -1.1520277 ,\n",
+       "        -1.1507337 , -1.1474868 , -1.1443372 , -1.1442864 , -1.1430473 ,\n",
+       "        -1.1418384 , -1.141045  , -1.1385593 , -1.1344901 , -1.134764  ,\n",
+       "        -1.1317551 , -1.1333026 , -1.1325271 , -1.1293766 , -1.1324316 ,\n",
+       "        -1.1310244 , -1.1295373 , -1.1287975 , -1.1317044 , -1.1290994 ,\n",
+       "        -1.1286312 , -1.1276112 , -1.1271322 , -1.128308  , -1.1257491 ,\n",
+       "        -1.1227695 , -1.1230273 , -1.119395  , -1.1190095 , -1.1182653 ,\n",
+       "        -1.1186818 , -1.1167871 , -1.1160123 , -1.1153725 , -1.1143075 ,\n",
+       "...\n",
+       "         9.716884  ,  9.718464  ,  9.717347  ,  9.717715  ,  9.714792  ,\n",
+       "         9.714857  ,  9.712732  ,  9.712222  ,  9.713994  ,  9.715383  ,\n",
+       "         9.71674   ,  9.716182  ,  9.717108  ,  9.716772  ,  9.715446  ,\n",
+       "         9.714425  ,  9.7126045 ,  9.713626  ,  9.7121105 ,  9.711023  ,\n",
+       "         9.711408  ,  9.709459  ,  9.712988  ,  9.7141695 ,  9.710418  ,\n",
+       "         9.713371  ,  9.713164  ,  9.711615  ,  9.713355  ,  9.714377  ,\n",
+       "         9.714473  ,  9.719391  ,  9.71805   ,  9.71583   ,  9.716421  ,\n",
+       "         9.71658   ,  9.71583   ,  9.71334   ,  9.7177305 ,  9.716677  ,\n",
+       "         9.71476   ,  9.716166  ,  9.713404  ,  9.715751  ,  9.715623  ,\n",
+       "         9.712237  ,  9.714856  ,  9.7166605 ,  9.713179  ,  9.7175865 ,\n",
+       "         9.710929  ,  9.71484   ,  9.715895  ,  9.712062  ,  9.71591   ,\n",
+       "         9.714346  ,  9.716932  ,  9.718177  ,  9.718816  ,  9.713802  ,\n",
+       "         9.717826  ,  9.715128  ,  9.711727  ,  9.713515  ,  9.718353  ,\n",
+       "         9.716836  ,  9.716278  ,  9.717762  ,  9.718656  ,  9.714761  ,\n",
+       "         9.713004  ,  9.710816  ,  9.71409   ,  9.716276  ,  9.713579  ,\n",
+       "         9.716166  ,  9.718401  ,  9.716804  ,  9.718209  ,  9.71428   ,\n",
+       "         9.714553  ,  9.715957  ,  9.7148075 ,  9.717874  ,  9.716309  ,\n",
+       "         9.715032  ,  9.7090435 ,  9.711168  ,  9.7130995 ,  9.713355  ,\n",
+       "         9.715256  ,  9.714936  ,  9.7148075 ,  9.711726  ,  9.711886  ,\n",
+       "         9.713675  ,  9.714042  ,  9.712781  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-66d06c2c-bd2e-4996-b5b5-ba589e57b5fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66d06c2c-bd2e-4996-b5b5-ba589e57b5fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5ddc8ed-095e-4d98-b4e4-696af069547d' class='xr-var-data-in' type='checkbox'><label for='data-c5ddc8ed-095e-4d98-b4e4-696af069547d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-15c9c44d-d3c5-4a48-8ce4-6d873cffbb20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15c9c44d-d3c5-4a48-8ce4-6d873cffbb20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3efbacfb-7d62-4858-b87c-821787f8223e' class='xr-var-data-in' type='checkbox'><label for='data-3efbacfb-7d62-4858-b87c-821787f8223e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355693e+09, 1.05355686e+09, 1.05355693e+09,\n",
+       "       1.05355699e+09, 1.05355686e+09, 1.05355699e+09, 1.05355699e+09,\n",
+       "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
+       "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
+       "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355693e+09,\n",
+       "       1.05355693e+09, 1.05355693e+09, 1.05355693e+09, 1.05355693e+09,\n",
+       "       1.05355693e+09, 1.05355693e+09, 1.05355693e+09, 1.05355693e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355693e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355693e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "...\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+       "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0006333 -0.004143 ... nan nan</div><input id='attrs-297b8fbf-740d-4abd-9031-0598d65507a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-297b8fbf-740d-4abd-9031-0598d65507a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a67f119-6cdb-46e7-a66e-d907e680773a' class='xr-var-data-in' type='checkbox'><label for='data-2a67f119-6cdb-46e7-a66e-d907e680773a' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00063333, -0.00414333, -0.00776667, ..., -0.01952   ,\n",
+       "        -0.02035667, -0.01573333],\n",
+       "       [ 0.02268667,  0.01564333,  0.01504   , ..., -0.00309667,\n",
+       "        -0.00658667, -0.00238   ],\n",
+       "       [ 0.02739334,  0.01897667,  0.01496333, ...,  0.00196667,\n",
+       "        -0.00096667,  0.00083946],\n",
+       "       ...,\n",
+       "       [        nan,         nan,         nan, ...,         nan,\n",
+       "                nan,         nan],\n",
+       "       [        nan,         nan,         nan, ...,         nan,\n",
+       "                nan,         nan],\n",
+       "       [        nan,         nan,         nan, ...,         nan,\n",
+       "                nan,         nan]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.08 80.44 80.75 ... nan nan nan</div><input id='attrs-538241e5-a10c-4c59-8268-c21f482db45c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-538241e5-a10c-4c59-8268-c21f482db45c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5027d2b2-8a7b-431b-9ba2-4cefabf900e8' class='xr-var-data-in' type='checkbox'><label for='data-5027d2b2-8a7b-431b-9ba2-4cefabf900e8' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[80.085   , 80.43833 , 80.75166 , ..., 81.761665, 81.86    ,\n",
+       "        81.745   ],\n",
+       "       [76.05    , 76.60333 , 77.066666, ..., 78.23    , 78.21667 ,\n",
+       "        78.045   ],\n",
+       "       [73.58833 , 74.355   , 75.083336, ..., 75.265   , 75.098335,\n",
+       "        74.92976 ],\n",
+       "       ...,\n",
+       "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "              nan],\n",
+       "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "              nan],\n",
+       "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "              nan]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>93.52 94.19 93.97 ... 0.0 0.0 0.0</div><input id='attrs-a65f35cd-777d-4e00-b3a4-2b3b223bfae8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a65f35cd-777d-4e00-b3a4-2b3b223bfae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f69b5dac-a215-4e37-a4cc-a214d0c865c6' class='xr-var-data-in' type='checkbox'><label for='data-f69b5dac-a215-4e37-a4cc-a214d0c865c6' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[93.51667 , 94.18667 , 93.97    , ..., 92.276665, 92.09    ,\n",
+       "        92.61667 ],\n",
+       "       [96.75    , 96.403336, 96.32    , ..., 96.433334, 96.89    ,\n",
+       "        96.57667 ],\n",
+       "       [97.19    , 97.32333 , 97.136665, ..., 96.08667 , 97.16    ,\n",
+       "        96.96667 ],\n",
+       "       ...,\n",
+       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "         0.      ],\n",
+       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "         0.      ],\n",
+       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
+       "         0.      ]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2166 -0.217 ... 0.9937 0.9937</div><input id='attrs-4bec0e51-ca20-4b23-9298-9aa49c02fed7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bec0e51-ca20-4b23-9298-9aa49c02fed7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd70347f-70ee-4b01-9d6c-7d045c455cb2' class='xr-var-data-in' type='checkbox'><label for='data-cd70347f-70ee-4b01-9d6c-7d045c455cb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16565624e-01, -2.17020363e-01, -2.15714216e-01, ...,\n",
+       "         -2.15817824e-01, -2.15457186e-01, -2.15469748e-01],\n",
+       "        [-9.68014121e-01, -9.67880666e-01, -9.68221545e-01, ...,\n",
+       "         -9.70180333e-01, -9.70283389e-01, -9.70276892e-01],\n",
+       "        [-1.26286745e-01, -1.26509830e-01, -1.26157105e-01, ...,\n",
+       "         -1.09885700e-01, -1.09676003e-01, -1.09719723e-01]],\n",
+       "\n",
+       "       [[ 9.76216435e-01,  9.76112664e-01,  9.76405323e-01, ...,\n",
+       "          9.76383269e-01,  9.76462007e-01,  9.76460576e-01],\n",
+       "        [-2.14884892e-01, -2.15368673e-01, -2.14055121e-01, ...,\n",
+       "         -2.14541376e-01, -2.14190111e-01, -2.14200497e-01],\n",
+       "        [-2.69518793e-02, -2.67608669e-02, -2.67287735e-02, ...,\n",
+       "         -2.34543420e-02, -2.33496260e-02, -2.33698264e-02]],\n",
+       "\n",
+       "       [[-1.05265796e-03, -1.35056104e-03, -1.13043608e-03, ...,\n",
+       "         -8.24717747e-04, -8.40478169e-04, -8.31306970e-04],\n",
+       "        [-1.29126370e-01, -1.29302278e-01, -1.28952518e-01, ...,\n",
+       "         -1.12357967e-01, -1.12130813e-01, -1.12177916e-01],\n",
+       "        [ 9.91627216e-01,  9.91603792e-01,  9.91649806e-01, ...,\n",
+       "          9.93667185e-01,  9.93692756e-01,  9.93687451e-01]]],\n",
+       "      shape=(3, 3, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0008123 0.0008472 ... -0.002637</div><input id='attrs-bc7b5757-d49e-4310-8739-c8fe662b1c7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc7b5757-d49e-4310-8739-c8fe662b1c7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fa91cc2-2039-4555-a442-bb831c9e93b0' class='xr-var-data-in' type='checkbox'><label for='data-7fa91cc2-2039-4555-a442-bb831c9e93b0' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00081228,  0.00084717,  0.00085335,  0.00075436,  0.00084513,\n",
+       "         0.00085451,  0.00073719,  0.00067843,  0.00075248,  0.00074199,\n",
+       "         0.00070569,  0.00065896,  0.0006914 ,  0.00069842,  0.00074138,\n",
+       "         0.0006676 ,  0.00060215,  0.00059847,  0.0006675 ,  0.0006136 ,\n",
+       "         0.00056599,  0.00061758,  0.00068752,  0.00066373,  0.00073918,\n",
+       "         0.00085721,  0.00084523,  0.0009018 ,  0.00089635,  0.00084324,\n",
+       "         0.00086276,  0.00089614,  0.00096862,  0.00108153,  0.00095481,\n",
+       "         0.00098828,  0.00098117,  0.00097845,  0.00099421,  0.00095727,\n",
+       "         0.00108126,  0.00108415,  0.00096381,  0.00100037,  0.00098756,\n",
+       "         0.00103554,  0.00096841,  0.00107755,  0.00104051,  0.0009601 ,\n",
+       "         0.00106976,  0.0010707 ,  0.00102194,  0.0009954 ,  0.00100513,\n",
+       "         0.0010245 ,  0.00102471,  0.00096594,  0.0010335 ,  0.00105437,\n",
+       "         0.00105971,  0.00095506,  0.00102042,  0.00097918,  0.00088768,\n",
+       "         0.0009018 ,  0.00096684,  0.00101125,  0.00096008,  0.0008592 ,\n",
+       "         0.00082965,  0.00095935,  0.00094485,  0.00081024,  0.00092011,\n",
+       "         0.00098175,  0.00094213,  0.00095046,  0.00099598,  0.00104662,\n",
+       "         0.00101681,  0.0010222 ,  0.00099111,  0.001044  ,  0.00103549,\n",
+       "         0.00097536,  0.00099907,  0.00097684,  0.00104176,  0.00094763,\n",
+       "         0.0009761 ,  0.00097227,  0.00089143,  0.00096118,  0.00097306,\n",
+       "         0.00096506,  0.00094653,  0.00092565,  0.00091383,  0.0009503 ,\n",
+       "...\n",
+       "        -0.00262043, -0.00258136, -0.00261717, -0.00257811, -0.00261066,\n",
+       "        -0.00255532, -0.00266274, -0.00254881, -0.00255858, -0.00259764,\n",
+       "        -0.00258462, -0.00252602, -0.00258787, -0.00256183, -0.00260415,\n",
+       "        -0.00251626, -0.00250975, -0.00243488, -0.00254881, -0.00264321,\n",
+       "        -0.00260089, -0.00263996, -0.0026367 , -0.00271157, -0.00256183,\n",
+       "        -0.00271808, -0.00277993, -0.00284178, -0.00276365, -0.00277016,\n",
+       "        -0.00279946, -0.00295896, -0.00294594, -0.00302732, -0.00302407,\n",
+       "        -0.00301756, -0.00298501, -0.00291339, -0.00296548, -0.00294269,\n",
+       "        -0.00311196, -0.00294594, -0.0030143 , -0.00297198, -0.00296873,\n",
+       "        -0.002985  , -0.0030729 , -0.0030143 , -0.00306638, -0.00288084,\n",
+       "        -0.00289386, -0.00281899, -0.00278644, -0.0028255 , -0.00276365,\n",
+       "        -0.00277993, -0.002666  , -0.00267576, -0.002666  , -0.00268227,\n",
+       "        -0.00268878, -0.00263019, -0.00254881, -0.00262368, -0.00263019,\n",
+       "        -0.00260415, -0.00257811, -0.0026074 , -0.00249673, -0.00254881,\n",
+       "        -0.00258787, -0.00263019, -0.00255532, -0.00269204, -0.00258136,\n",
+       "        -0.00259113, -0.00272133, -0.00258462, -0.00263019, -0.00261066,\n",
+       "        -0.00265949, -0.00270831, -0.00258787, -0.0025423 , -0.00262368,\n",
+       "        -0.00264647, -0.00268879, -0.00251626, -0.00256183, -0.00255207,\n",
+       "        -0.00251626, -0.00251626, -0.00249998, -0.00248371, -0.0024479 ,\n",
+       "        -0.00254881, -0.00254881, -0.0026367 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-249663e1-6f7d-4320-b41d-dd342f1539e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-249663e1-6f7d-4320-b41d-dd342f1539e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62887a06-1b0e-4cba-a74c-c54451e0a3d8' class='xr-var-data-in' type='checkbox'><label for='data-62887a06-1b0e-4cba-a74c-c54451e0a3d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245025 ,  0.62430865,  0.62484366,  0.62450564,  0.624701  ,\n",
+       "         0.62466866,  0.62460196,  0.6247584 ,  0.62459546,  0.62464297,\n",
+       "         0.6246826 ,  0.62496835,  0.6249581 ,  0.6250585 ,  0.62494546,\n",
+       "         0.6250052 ,  0.62508506,  0.62507963,  0.6253132 ,  0.6254015 ,\n",
+       "         0.6253524 ,  0.6254653 ,  0.62515104,  0.62495625,  0.62512195,\n",
+       "         0.6251015 ,  0.624793  ,  0.6250713 ,  0.6249948 ,  0.62489176,\n",
+       "         0.6248301 ,  0.62451917,  0.6248372 ,  0.62479085,  0.62502736,\n",
+       "         0.6249378 ,  0.62497205,  0.62445515,  0.62417245,  0.6244453 ,\n",
+       "         0.6248608 ,  0.62453014,  0.62416404,  0.6242139 ,  0.62425303,\n",
+       "         0.62470126,  0.6248098 ,  0.62460905,  0.62480783,  0.6246901 ,\n",
+       "         0.6245936 ,  0.6248162 ,  0.624754  ,  0.62437105,  0.62430066,\n",
+       "         0.6244233 ,  0.6243633 ,  0.62407166,  0.6241861 ,  0.6241456 ,\n",
+       "         0.6239719 ,  0.62391376,  0.62415487,  0.6238682 ,  0.6239241 ,\n",
+       "         0.62390107,  0.6236789 ,  0.6237806 ,  0.6236614 ,  0.62358916,\n",
+       "         0.6236376 ,  0.62371755,  0.6237382 ,  0.6238726 ,  0.6240171 ,\n",
+       "         0.6239264 ,  0.623931  ,  0.6237308 ,  0.62395936,  0.6234018 ,\n",
+       "         0.6237265 ,  0.6237913 ,  0.6235492 ,  0.62318647,  0.62349266,\n",
+       "         0.62327045,  0.6232548 ,  0.6233126 ,  0.6232084 ,  0.6232786 ,\n",
+       "         0.62339824,  0.62359285,  0.6235284 ,  0.6235471 ,  0.6234016 ,\n",
+       "         0.6233313 ,  0.6232729 ,  0.6232614 ,  0.62335205,  0.6232998 ,\n",
+       "...\n",
+       "         0.7798304 ,  0.77984285,  0.7798039 ,  0.7798881 ,  0.77983624,\n",
+       "         0.779747  ,  0.7796003 ,  0.7796562 ,  0.77964693,  0.77976   ,\n",
+       "         0.77982175,  0.77987325,  0.7798803 ,  0.77981853,  0.7798599 ,\n",
+       "         0.77976817,  0.7797355 ,  0.7797223 ,  0.77958274,  0.7794968 ,\n",
+       "         0.77951866,  0.7793451 ,  0.77977306,  0.7798404 ,  0.7796792 ,\n",
+       "         0.77951556,  0.7797541 ,  0.7796244 ,  0.77954704,  0.779882  ,\n",
+       "         0.7796897 ,  0.78009367,  0.77984214,  0.77977324,  0.77981466,\n",
+       "         0.77967215,  0.77980614,  0.7795769 ,  0.77972573,  0.77955645,\n",
+       "         0.7796588 ,  0.77955323,  0.7795721 ,  0.7795167 ,  0.7794243 ,\n",
+       "         0.77918714,  0.77930677,  0.7793621 ,  0.77909505,  0.7792217 ,\n",
+       "         0.7788761 ,  0.7790808 ,  0.7791028 ,  0.7788446 ,  0.7789332 ,\n",
+       "         0.7788981 ,  0.7789904 ,  0.779241  ,  0.7791693 ,  0.77889454,\n",
+       "         0.7789502 ,  0.77890694,  0.7785553 ,  0.77865255,  0.7788394 ,\n",
+       "         0.7787025 ,  0.7788493 ,  0.77891165,  0.7789452 ,  0.77867603,\n",
+       "         0.7785905 ,  0.7783878 ,  0.77855736,  0.778772  ,  0.7786441 ,\n",
+       "         0.77870494,  0.77878624,  0.77881914,  0.77884936,  0.77857053,\n",
+       "         0.77855694,  0.778695  ,  0.77849764,  0.7787041 ,  0.77873075,\n",
+       "         0.7786052 ,  0.7784392 ,  0.7783923 ,  0.77855927,  0.77852917,\n",
+       "         0.7785722 ,  0.77847767,  0.77844226,  0.77829295,  0.7782816 ,\n",
+       "         0.77846414,  0.778354  ,  0.77835685]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>65.0 65.0 65.0 ... 65.0 65.0 65.0</div><input id='attrs-a7e823ec-24f5-4377-8a95-1a95da2b173e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7e823ec-24f5-4377-8a95-1a95da2b173e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92536e33-550f-4745-bbf1-ee46c2c42b1b' class='xr-var-data-in' type='checkbox'><label for='data-92536e33-550f-4745-bbf1-ee46c2c42b1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "       65.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-611825d7-5271-4d27-a72d-ff4b5e390601' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-611825d7-5271-4d27-a72d-ff4b5e390601' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b3bd447-85f3-4be3-84d1-3031537131a9' class='xr-var-data-in' type='checkbox'><label for='data-6b3bd447-85f3-4be3-84d1-3031537131a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-4448bbd9-b593-40b2-b58c-072b98492615' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4448bbd9-b593-40b2-b58c-072b98492615' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-673aba38-c31c-4c3c-9918-8990d37228a2' class='xr-var-data-in' type='checkbox'><label for='data-673aba38-c31c-4c3c-9918-8990d37228a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ff482eee-9029-43c4-93f5-66e8228b90cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff482eee-9029-43c4-93f5-66e8228b90cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31618f71-8540-44d9-85ff-e6866de2fed7' class='xr-var-data-in' type='checkbox'><label for='data-31618f71-8540-44d9-85ff-e6866de2fed7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1267 0.1333 0.13 ... 0.22 0.22</div><input id='attrs-b3c849c7-37f8-4509-bd76-c64ecac873aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3c849c7-37f8-4509-bd76-c64ecac873aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3a64d6c-df81-491b-a306-b02af38afc75' class='xr-var-data-in' type='checkbox'><label for='data-d3a64d6c-df81-491b-a306-b02af38afc75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.12666667, 0.13333334, 0.13      , 0.13333334, 0.13      ,\n",
+       "       0.13      , 0.13      , 0.11666667, 0.12333333, 0.11666667,\n",
+       "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11666667,\n",
+       "       0.11      , 0.11333334, 0.11333334, 0.11333334, 0.12333333,\n",
+       "       0.12      , 0.12333333, 0.12      , 0.11      , 0.11      ,\n",
+       "       0.11666667, 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
+       "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
+       "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11333334,\n",
+       "       0.12      , 0.11666667, 0.11666667, 0.11666667, 0.11333334,\n",
+       "       0.12      , 0.11666667, 0.11      , 0.12333333, 0.13333334,\n",
+       "       0.13333334, 0.13      , 0.13333334, 0.12666667, 0.13333334,\n",
+       "       0.13      , 0.13      , 0.15333334, 0.13333334, 0.15333334,\n",
+       "       0.14333333, 0.13666667, 0.11666667, 0.13666667, 0.13333334,\n",
+       "       0.13666667, 0.14666666, 0.14      , 0.15666667, 0.13333334,\n",
+       "       0.13333334, 0.15      , 0.15      , 0.12666667, 0.13666667,\n",
+       "       0.15      , 0.13      , 0.13666667, 0.13      , 0.13666667,\n",
+       "       0.13666667, 0.14333333, 0.13666667, 0.12666667, 0.13666667,\n",
+       "       0.12333333, 0.12333333, 0.13333334, 0.12      , 0.11666667,\n",
+       "       0.11      , 0.11      , 0.11666667, 0.12333333, 0.11      ,\n",
+       "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
+       "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
+       "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11      ,\n",
+       "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
+       "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
+       "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11      ,\n",
+       "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
+       "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
+       "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11      ,\n",
+       "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
+       "       0.11      , 0.11      , 0.11333334, 0.11      , 0.11      ,\n",
+       "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11      ,\n",
+       "       0.11      , 0.11333334, 0.11      , 0.11      , 0.11333334,\n",
+       "       0.11      , 0.20333333, 0.22666667, 0.22      , 0.22      ,\n",
+       "       0.22666667, 0.22      , 0.22      , 0.22666667, 0.22      ,\n",
+       "       0.22      , 0.22666667, 0.22      , 0.22      , 0.22666667,\n",
+       "       0.22      , 0.22      , 0.22666667, 0.22      , 0.22      ,\n",
+       "       0.22666667, 0.22      , 0.22      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.287 3.261 3.337 ... 3.352 3.352</div><input id='attrs-b8a3d627-053f-4298-ac79-4a0c3c3d1f55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8a3d627-053f-4298-ac79-4a0c3c3d1f55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25036bcc-c32c-45ef-8197-8c0061ffc31e' class='xr-var-data-in' type='checkbox'><label for='data-25036bcc-c32c-45ef-8197-8c0061ffc31e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([3.287083 , 3.2605155, 3.3369133, 3.2892823, 3.3165808, 3.3119812,\n",
+       "       3.3027983, 3.3258302, 3.3029814, 3.3100154, 3.3152144, 3.3549793,\n",
+       "       3.3538957, 3.3682969, 3.3522127, 3.3599124, 3.3716784, 3.370829 ,\n",
+       "       3.404896 , 3.4170458, 3.4101129, 3.4259632, 3.3813803, 3.352863 ,\n",
+       "       3.3772287, 3.3739138, 3.3298807, 3.368963 , 3.357279 , 3.3420138,\n",
+       "       3.3332636, 3.2887158, 3.3332634, 3.3267972, 3.3609293, 3.34788  ,\n",
+       "       3.3524635, 3.2797818, 3.2383478, 3.2774158, 3.3357968, 3.2894654,\n",
+       "       3.237099 , 3.2444148, 3.250231 , 3.3133483, 3.3285973, 3.3003979,\n",
+       "       3.3281136, 3.3106983, 3.2960656, 3.3239977, 3.3058155, 3.231132 ,\n",
+       "       3.2188826, 3.2362165, 3.2269833, 3.1857452, 3.2025456, 3.1971133,\n",
+       "       3.1716278, 3.1624265, 3.196963 , 3.1550453, 3.162944 , 3.158162 ,\n",
+       "       3.1250577, 3.1385927, 3.1210082, 3.1101758, 3.116926 , 3.1283762,\n",
+       "       3.1307604, 3.149876 , 3.1709106, 3.1579425, 3.157861 , 3.1293783,\n",
+       "       3.1623626, 3.0823452, 3.1290247, 3.1371257, 3.103561 , 3.0512455,\n",
+       "       3.0958426, 3.0634787, 3.061212 , 3.0691292, 3.0543785, 3.0647104,\n",
+       "       3.0809944, 3.1082118, 3.099077 , 3.1025605, 3.0808105, 3.070144 ,\n",
+       "       3.061945 , 3.0596778, 3.0728438, 3.0654464, 3.082978 , 3.0888774,\n",
+       "       3.090909 , 3.1166098, 3.1326916, 3.1294093, 3.1606429, 3.0821111,\n",
+       "       3.0704782, 3.099461 , 3.1288106, 3.0858114, 3.1094916, 3.1244771,\n",
+       "       3.0631604, 3.098195 , 3.0228472, 3.0709112, 3.0849445, 3.0772102,\n",
+       "       3.10411  , 3.0789108, 3.122175 , 3.0939767, 3.126108 , 3.1068432,\n",
+       "       3.1269426, 3.124676 , 3.133493 , 3.1521947, 3.1957629, 3.174427 ,\n",
+       "       3.1647441, 3.2137322, 3.1900635, 3.2544334, 3.2174487, 3.2122307,\n",
+       "       3.2605999, 3.244882 , 3.2509172, 3.234432 , 3.1874464, 3.200428 ,\n",
+       "       3.251467 , 3.2418153, 3.24985  , 3.3141315, 3.2959988, 3.2620163,\n",
+       "       3.2882996, 3.2601004, 3.2490656, 3.242116 , 3.2917488, 3.306215 ,\n",
+       "       3.3455298, 3.3139143, 3.274265 , 3.2986312, 3.2870982, 3.271833 ,\n",
+       "       3.2660322, 3.2617817, 3.3116815, 3.314548 , 3.289132 , 3.3249977,\n",
+       "       3.287216 , 3.282166 , 3.306248 , 3.3363798, 3.3452635, 3.314365 ,\n",
+       "       3.3198478, 3.3122149, 3.3298814, 3.3361466, 3.3632295, 3.3652124,\n",
+       "       3.331114 , 3.352297 , 3.351696 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.05523 -0.07217 ... -0.0429</div><input id='attrs-f62001ed-6467-4693-967f-e4902c176c26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f62001ed-6467-4693-967f-e4902c176c26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5929ec33-17f1-4434-97f7-63721111aae1' class='xr-var-data-in' type='checkbox'><label for='data-5929ec33-17f1-4434-97f7-63721111aae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.05523333, -0.07216667, -0.05975001, -0.0719    , -0.06111666,\n",
+       "       -0.05995   , -0.07213333, -0.07148333, -0.07696667, -0.08181667,\n",
+       "       -0.0731    , -0.0579    , -0.05488333, -0.05605   , -0.05533333,\n",
+       "       -0.05278333, -0.047     , -0.0521    , -0.05088333, -0.04305   ,\n",
+       "       -0.04223333, -0.0388    , -0.05338333, -0.0568    , -0.0611    ,\n",
+       "       -0.0627    , -0.07021666, -0.05368333, -0.0487    , -0.0493    ,\n",
+       "       -0.05311666, -0.06443334, -0.04586667, -0.04441666, -0.04498333,\n",
+       "       -0.03575   , -0.03521667, -0.07056666, -0.07609999, -0.0662    ,\n",
+       "       -0.04191666, -0.06681666, -0.06818333, -0.07636666, -0.07111666,\n",
+       "       -0.05511666, -0.0493    , -0.05571667, -0.04891667, -0.04571667,\n",
+       "       -0.04198333, -0.00336667,  0.08048334,  0.25323334,  0.22161667,\n",
+       "        0.19196668,  0.15959999,  0.11676668,  0.09488333,  0.07456666,\n",
+       "        0.05981667,  0.06043334,  0.06175001,  0.05643333,  0.04858334,\n",
+       "        0.06248334,  0.06495   ,  0.07498333,  0.06741667,  0.07038333,\n",
+       "        0.06546666,  0.06541666,  0.06608333,  0.07325   ,  0.0566    ,\n",
+       "        0.054     ,  0.061     ,  0.0511    ,  0.05655   ,  0.02896667,\n",
+       "        0.02978333,  0.04755   ,  0.01526667,  0.00998333,  0.0108    ,\n",
+       "        0.00458333,  0.00831667,  0.00378333, -0.00401667, -0.00796667,\n",
+       "        0.00555   ,  0.01133333,  0.00541667, -0.0047    , -0.0019    ,\n",
+       "       -0.00158333, -0.01193333, -0.00146667, -0.00791667, -0.00841667,\n",
+       "       -0.00898333, -0.00903333, -0.002     ,  0.00205   ,  0.00393333,\n",
+       "       -0.00233333,  0.01098333, -0.01495   , -0.01253333,  0.0059    ,\n",
+       "        0.01675   , -0.0048    ,  0.00353333,  0.00155   , -0.01018333,\n",
+       "        0.00053333, -0.0111    , -0.01913333, -0.02155   , -0.02396666,\n",
+       "       -0.01793333, -0.02523333, -0.0194    , -0.02291666, -0.02203333,\n",
+       "       -0.0282    , -0.02571667, -0.03851666, -0.0208    , -0.03573333,\n",
+       "       -0.02388333, -0.03908334, -0.04381667, -0.031     , -0.04036666,\n",
+       "       -0.02826666, -0.04583333, -0.0394    , -0.03658333, -0.04336667,\n",
+       "       -0.04073333, -0.05656667, -0.0553    , -0.05688334, -0.04293333,\n",
+       "       -0.05556666, -0.05011667, -0.02433333, -0.03963333, -0.04716667,\n",
+       "       -0.05826667, -0.05943333, -0.06553333, -0.04911667, -0.04304999,\n",
+       "       -0.023     , -0.03935   , -0.03393333, -0.04798333, -0.05141667,\n",
+       "       -0.04823333, -0.05695   , -0.05431667, -0.07600001, -0.0419    ,\n",
+       "       -0.04428333, -0.05206667, -0.04676667, -0.05188334, -0.0574    ,\n",
+       "       -0.06316667, -0.0484    , -0.04308333, -0.04601667, -0.0499    ,\n",
+       "       -0.05471666, -0.05536667, -0.04616667, -0.02825   , -0.03388334,\n",
+       "       -0.0423    , -0.04288333, -0.0429    ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.414 -7.424 ... -6.433 -6.436</div><input id='attrs-0629cd39-673d-4d21-998a-2ab00fc8783c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0629cd39-673d-4d21-998a-2ab00fc8783c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94dd308d-41fb-47db-887f-d873e67e2fa4' class='xr-var-data-in' type='checkbox'><label for='data-94dd308d-41fb-47db-887f-d873e67e2fa4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.414066 , -7.424116 , -7.4041324, -7.422417 , -7.4120665,\n",
+       "       -7.4167333, -7.4193   , -7.4148664, -7.4301662, -7.419133 ,\n",
+       "       -7.4179006, -7.402567 , -7.4040666, -7.394583 , -7.403933 ,\n",
+       "       -7.394567 , -7.39615  , -7.3910007, -7.386317 , -7.3802505,\n",
+       "       -7.3838835, -7.3748665, -7.3916   , -7.391783 , -7.3853164,\n",
+       "       -7.377949 , -7.3945003, -7.3738666, -7.373867 , -7.369084 ,\n",
+       "       -7.372767 , -7.3854327, -7.3678336, -7.3722672, -7.3625836,\n",
+       "       -7.3666334, -7.360083 , -7.3809   , -7.3875995, -7.3742666,\n",
+       "       -7.3516665, -7.369966 , -7.387017 , -7.386867 , -7.379284 ,\n",
+       "       -7.361167 , -7.3493667, -7.3615665, -7.344417 , -7.350366 ,\n",
+       "       -7.3399496, -7.300467 , -7.236433 , -7.0768504, -6.997516 ,\n",
+       "       -6.949867 , -6.899017 , -6.8814178, -6.8526497, -6.8263836,\n",
+       "       -6.813383 , -6.8076496, -6.78375  , -6.781251 , -6.7671165,\n",
+       "       -6.7577667, -6.7516494, -6.728033 , -6.72715  , -6.716767 ,\n",
+       "       -6.708234 , -6.69495  , -6.6866503, -6.6791   , -6.6603007,\n",
+       "       -6.659384 , -6.6542172, -6.659967 , -6.6461835, -6.667401 ,\n",
+       "       -6.6473837, -6.6366506, -6.6365337, -6.649917 , -6.6360173,\n",
+       "       -6.638784 , -6.6400666, -6.62885  , -6.628683 , -6.617567 ,\n",
+       "       -6.6096334, -6.59625  , -6.589217 , -6.5807333, -6.5850334,\n",
+       "       -6.5764165, -6.5684667, -6.5721836, -6.55585  , -6.5561   ,\n",
+       "       -6.5420327, -6.5459166, -6.5430665, -6.5388503, -6.5341506,\n",
+       "       -6.5321503, -6.52225  , -6.539833 , -6.5372   , -6.528584 ,\n",
+       "       -6.5262337, -6.5375338, -6.5275497, -6.51875  , -6.5324664,\n",
+       "       -6.525267 , -6.548233 , -6.5326   , -6.5178337, -6.5193667,\n",
+       "       -6.505567 , -6.5125165, -6.4989333, -6.508083 , -6.49675  ,\n",
+       "       -6.50315  , -6.4925   , -6.485383 , -6.492067 , -6.4811997,\n",
+       "       -6.4708   , -6.4739504, -6.472566 , -6.4622664, -6.4698668,\n",
+       "       -6.4504333, -6.4621997, -6.4667   , -6.45215  , -6.4554167,\n",
+       "       -6.454716 , -6.461316 , -6.4738665, -6.473217 , -6.458534 ,\n",
+       "       -6.46535  , -6.4565167, -6.4414663, -6.4526324, -6.4590497,\n",
+       "       -6.4487834, -6.4628167, -6.46505  , -6.4660163, -6.4543834,\n",
+       "       -6.4556837, -6.4339333, -6.443283 , -6.4565334, -6.4484835,\n",
+       "       -6.4508667, -6.4604836, -6.4581666, -6.4554834, -6.4470496,\n",
+       "       -6.4438   , -6.450166 , -6.4428334, -6.45625  , -6.455867 ,\n",
+       "       -6.44465  , -6.436783 , -6.430967 , -6.4454665, -6.441799 ,\n",
+       "       -6.4480996, -6.4399   , -6.4372997, -6.4309335, -6.4305167,\n",
+       "       -6.4460497, -6.4332495, -6.4359174], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_density</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.023e+03 1.023e+03 ... 1.023e+03</div><input id='attrs-f29e11ee-40ef-48ea-8152-093e958ca80e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f29e11ee-40ef-48ea-8152-093e958ca80e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a43a2aeb-935a-406f-843a-629a2e3b816f' class='xr-var-data-in' type='checkbox'><label for='data-a43a2aeb-935a-406f-843a-629a2e3b816f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Water Density</dd><dt><span>standard_name :</span></dt><dd>sea_water_density</dd><dt><span>description :</span></dt><dd>Water density from linear approximation of sea water equation of state</dd></dl></div><div class='xr-var-data'><pre>array([1023.2506 , 1023.2348 , 1023.2431 , 1023.2559 , 1023.2423 ,\n",
+       "       1023.21655, 1023.1977 , 1023.2015 , 1023.19476, 1023.19116,\n",
+       "       1023.194  , 1023.1996 , 1023.20294, 1023.19666, 1023.19336,\n",
+       "       1023.19763, 1023.205  , 1023.20404, 1023.2148 , 1023.2179 ,\n",
+       "       1023.23627, 1023.24646, 1023.2652 , 1023.2814 , 1023.2947 ,\n",
+       "       1023.3131 , 1023.32043, 1023.33435, 1023.3452 , 1023.3554 ,\n",
+       "       1023.3606 , 1023.366  , 1023.37103, 1023.37616, 1023.38043,\n",
+       "       1023.38403, 1023.3869 , 1023.3915 , 1023.38855, 1023.3802 ,\n",
+       "       1023.3759 , 1023.3691 , 1023.3694 , 1023.3698 , 1023.3653 ,\n",
+       "       1023.3579 , 1023.3548 , 1023.35455, 1023.3548 , 1023.35455,\n",
+       "       1023.3522 , 1023.3529 , 1023.3504 , 1023.3525 , 1023.3523 ,\n",
+       "       1023.3569 , 1023.35583, 1023.35565, 1023.35364, 1023.35455,\n",
+       "       1023.35675, 1023.3567 , 1023.35565, 1023.35364, 1023.35144,\n",
+       "       1023.3499 , 1023.3452 , 1023.347  , 1023.3427 , 1023.3374 ,\n",
+       "       1023.3326 , 1023.32874, 1023.3249 , 1023.3244 , 1023.3244 ,\n",
+       "       1023.3202 , 1023.3271 , 1023.32477, 1023.3281 , 1023.3215 ,\n",
+       "       1023.3281 , 1023.3284 , 1023.3283 , 1023.3299 , 1023.33887,\n",
+       "       1023.3277 , 1023.3379 , 1023.343  , 1023.33966, 1023.35315,\n",
+       "       1023.35333, 1023.34064, 1023.33875, 1023.33   , 1023.3218 ,\n",
+       "       1023.32404, 1023.32294, 1023.30853, 1023.3122 , 1023.3125 ,\n",
+       "       1023.3147 , 1023.3218 , 1023.32446, 1023.3674 , 1023.42395,\n",
+       "       1023.4842 , 1023.5248 , 1023.5321 , 1023.545  , 1023.555  ,\n",
+       "       1023.5453 , 1023.53784, 1023.5425 , 1023.5531 , 1023.56085,\n",
+       "       1023.5671 , 1023.5697 , 1023.5727 , 1023.5777 , 1023.5851 ,\n",
+       "       1023.598  , 1023.61127, 1023.61774, 1023.6253 , 1023.63873,\n",
+       "       1023.6415 , 1023.64105, 1023.6321 , 1023.6085 , 1023.58875,\n",
+       "       1023.55664, 1023.53424, 1023.4919 , 1023.4661 , 1023.4329 ,\n",
+       "       1023.4105 , 1023.3956 , 1023.38354, 1023.3727 , 1023.36206,\n",
+       "       1023.3528 , 1023.34467, 1023.34357, 1023.345  , 1023.34937,\n",
+       "       1023.34894, 1023.3448 , 1023.3418 , 1023.3417 , 1023.3436 ,\n",
+       "       1023.3458 , 1023.3482 , 1023.3505 , 1023.34937, 1023.348  ,\n",
+       "       1023.34607, 1023.3471 , 1023.3476 , 1023.3501 , 1023.3529 ,\n",
+       "       1023.35187, 1023.34894, 1023.3476 , 1023.3455 , 1023.3432 ,\n",
+       "       1023.3432 , 1023.34186, 1023.34094, 1023.3408 , 1023.34   ,\n",
+       "       1023.33655, 1023.34143, 1023.34155, 1023.34357, 1023.3508 ,\n",
+       "       1023.3694 , 1023.37335, 1023.3781 , 1023.3796 , 1023.3752 ,\n",
+       "       1023.3806 , 1023.3899 , 1023.3975 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.28 10.26 10.25 ... 10.15 10.15</div><input id='attrs-a229de0a-42e4-4718-8da1-7173c1238db9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a229de0a-42e4-4718-8da1-7173c1238db9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27910dde-2db5-4d77-818f-4dad1e0be671' class='xr-var-data-in' type='checkbox'><label for='data-27910dde-2db5-4d77-818f-4dad1e0be671' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Water Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([10.275155 , 10.262421 , 10.248638 , 10.233478 , 10.216689 ,\n",
+       "       10.204074 , 10.188269 , 10.172538 , 10.158512 , 10.140744 ,\n",
+       "       10.124731 , 10.10534  , 10.088593 , 10.069619 , 10.050677 ,\n",
+       "       10.028841 , 10.00682  ,  9.981041 ,  9.958139 ,  9.932152 ,\n",
+       "        9.90408  ,  9.8704815,  9.843054 ,  9.8099   ,  9.782674 ,\n",
+       "        9.751141 ,  9.722181 ,  9.688391 ,  9.658139 ,  9.624396 ,\n",
+       "        9.591585 ,  9.559362 ,  9.5206785,  9.487036 ,  9.4516325,\n",
+       "        9.418625 ,  9.378983 ,  9.347703 ,  9.310125 ,  9.274782 ,\n",
+       "        9.240932 ,  9.203833 ,  9.168937 ,  9.127398 ,  9.091378 ,\n",
+       "        9.048801 ,  9.012082 ,  8.96804  ,  8.932783 ,  8.890241 ,\n",
+       "        8.854867 ,  8.815892 ,  8.781367 ,  8.746559 ,  8.714514 ,\n",
+       "        8.678216 ,  8.641088 ,  8.608247 ,  8.566862 ,  8.532115 ,\n",
+       "        8.495453 ,  8.461118 ,  8.42251  ,  8.39378  ,  8.36608  ,\n",
+       "        8.334696 ,  8.308269 ,  8.283072 ,  8.254289 ,  8.228873 ,\n",
+       "        8.203963 ,  8.179552 ,  8.162701 ,  8.144332 ,  8.124536 ,\n",
+       "        8.104092 ,  8.085614 ,  8.068075 ,  8.053603 ,  8.043172 ,\n",
+       "        8.025586 ,  8.014922 ,  8.00194  ,  7.9942083,  7.985075 ,\n",
+       "        7.9840665,  7.978806 ,  7.97339  ,  7.9731627,  7.971814 ,\n",
+       "        7.972329 ,  7.9674144,  7.9723177,  7.9744887,  7.9855638,\n",
+       "        7.991272 ,  7.9956956,  8.006041 ,  8.023605 ,  8.038973 ,\n",
+       "        8.049008 ,  8.066569 ,  8.078107 ,  8.090951 ,  8.110414 ,\n",
+       "        8.1288595,  8.1531725,  8.172004 ,  8.192166 ,  8.219532 ,\n",
+       "        8.241356 ,  8.265544 ,  8.288776 ,  8.314842 ,  8.338862 ,\n",
+       "        8.363347 ,  8.388729 ,  8.418659 ,  8.4457035,  8.479128 ,\n",
+       "        8.511397 ,  8.542225 ,  8.57435  ,  8.609074 ,  8.643846 ,\n",
+       "        8.674181 ,  8.707741 ,  8.740917 ,  8.776131 ,  8.809834 ,\n",
+       "        8.847663 ,  8.883326 ,  8.921983 ,  8.956591 ,  8.994466 ,\n",
+       "        9.032357 ,  9.072943 ,  9.107945 ,  9.147928 ,  9.18505  ,\n",
+       "        9.218539 ,  9.25671  ,  9.293107 ,  9.324933 ,  9.361722 ,\n",
+       "        9.397845 ,  9.436231 ,  9.464766 ,  9.502145 ,  9.534171 ,\n",
+       "        9.566268 ,  9.601108 ,  9.627889 ,  9.660232 ,  9.6932535,\n",
+       "        9.72142  ,  9.752015 ,  9.780852 ,  9.808266 ,  9.838272 ,\n",
+       "        9.864178 ,  9.885547 ,  9.914127 ,  9.934637 ,  9.957427 ,\n",
+       "        9.972928 ,  9.992192 , 10.00496  , 10.023165 , 10.037822 ,\n",
+       "       10.052144 , 10.058786 , 10.073322 , 10.083076 , 10.093331 ,\n",
+       "       10.100321 , 10.109131 , 10.117323 , 10.123545 , 10.1341   ,\n",
+       "       10.142311 , 10.14612  , 10.153638 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_std</span></div><div class='xr-var-dims'>(range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.04232 0.04293 0.04402 ... nan nan</div><input id='attrs-7cc0e12c-ac1d-465f-b919-ff4396f1f4fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cc0e12c-ac1d-465f-b919-ff4396f1f4fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c229b4ba-1f27-4846-9eb7-379c42e5b345' class='xr-var-data-in' type='checkbox'><label for='data-c229b4ba-1f27-4846-9eb7-379c42e5b345' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([[0.04231985, 0.04293143, 0.04401674, ..., 0.04380286, 0.04201568,\n",
+       "        0.04634431],\n",
+       "       [0.04002878, 0.04084466, 0.03949426, ..., 0.04000184, 0.04028782,\n",
+       "        0.03873245],\n",
+       "       [0.04025545, 0.03514179, 0.04270846, ..., 0.0391249 , 0.03997382,\n",
+       "        0.04237254],\n",
+       "       ...,\n",
+       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
+       "               nan],\n",
+       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
+       "               nan],\n",
+       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
+       "               nan]], shape=(28, 183), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ce677c8-4493-441f-8813-1154a1999004' class='xr-section-summary-in' type='checkbox'  ><label for='section-3ce677c8-4493-441f-8813-1154a1999004' class='xr-section-summary' >Attributes: <span>(41)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>fs :</span></dt><dd>1</dd><dt><span>n_bin :</span></dt><dd>300</dd><dt><span>n_fft :</span></dt><dd>300</dd><dt><span>description :</span></dt><dd>Binned averages calculated from ensembles of size &quot;n_bin&quot;</dd><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>principal</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd><dt><span>range_offset :</span></dt><dd>0.6</dd><dt><span>declination :</span></dt><dd>15.8</dd><dt><span>declination_in_orientmat :</span></dt><dd>1</dd><dt><span>principal_heading :</span></dt><dd>11.1898</dd></dl></div></li></ul></div></div>"
       ],
-      "source": [
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "import matplotlib as mpl\n",
-        "\n",
-        "plt.rcParams.update({\"font.size\": 18, \"font.family\": \"Times New Roman\"})\n",
-        "\n",
-        "\n",
-        "def plot_spectra_by_color(auto_spectra, U_mag, ax, fig, cbar_max=4.0):\n",
-        "    U = U_mag.values\n",
-        "    U_max = U_mag.max().values\n",
-        "\n",
-        "    # Average spectra into 0.1 m/s velocity bins\n",
-        "    speed_bins = np.arange(0.5, U_max, 0.1)\n",
-        "    time = [t for t in auto_spectra.dims if \"time\" in t][0]\n",
-        "    S_group = auto_spectra.assign_coords({time: U}).rename({time: \"speed\"})\n",
-        "    group = S_group.groupby_bins(\"speed\", speed_bins)\n",
-        "    count = group.count().values\n",
-        "    S = group.mean()\n",
-        "\n",
-        "    # define the colormap\n",
-        "    cmap = plt.cm.turbo\n",
-        "    # define the bins and normalize\n",
-        "    bounds = np.arange(0.5, cbar_max, 0.1)\n",
-        "    norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
-        "    colors = cmap(norm(speed_bins))\n",
-        "\n",
-        "    # plot\n",
-        "    for i in range(len(speed_bins) - 1):\n",
-        "        ax.loglog(auto_spectra[\"freq\"], S[i], c=colors[i])\n",
-        "    ax.grid()\n",
-        "\n",
-        "    # create a second axes for the colorbar\n",
-        "    cax = fig.add_axes([0.8, 0.07, 0.03, 0.88])\n",
-        "    # cax, _ = mpl.colorbar.make_axes(fig.gca())\n",
-        "    sm = mpl.colorbar.ColorbarBase(\n",
-        "        cax,\n",
-        "        cmap=cmap,\n",
-        "        norm=norm,\n",
-        "        spacing=\"proportional\",\n",
-        "        ticks=bounds,\n",
-        "        boundaries=bounds,\n",
-        "        format=\"%1.1f\",\n",
-        "        label=\"Velocity [m/s]\",\n",
-        "    )\n",
-        "\n",
-        "    # Add -5/3 slope line\n",
-        "    m = -5 / 3\n",
-        "    x = np.logspace(-1, 0.5)\n",
-        "    y = 10 ** (-3) * x**m\n",
-        "    ax.loglog(x, y, \"--\", c=\"black\", label=\"$f^{-5/3}$\")\n",
-        "    ax.legend()\n",
-        "\n",
-        "    return ax, sm\n",
-        "\n",
-        "\n",
-        "# Set up figure\n",
-        "fig, ax = plt.subplots(1, 1, figsize=(6, 5))\n",
-        "fig.subplots_adjust(left=0.2, right=0.75, top=0.95, bottom=0.1)\n",
-        "\n",
-        "# Plot spectra by color\n",
-        "plot_spectra_by_color(ds_avg[\"auto_spectra_5m\"], U, ax, fig, cbar_max=2.0)\n",
-        "# Set axes\n",
-        "ax.set(\n",
-        "    xlabel=\"Frequency [Hz]\",\n",
-        "    ylabel=\"PSD [m2 s-2 Hz-1]\",\n",
-        "    xlim=(0.01, 1),\n",
-        "    ylim=(0.0005, 0.1),\n",
-        ")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In the figure above, we can see the energy-producing turbulent structures below a frequency of 0.2 Hz (one tick to the right of \"10^-1\"). The isotropic turbulence cascade, seen by the dashed f^(-5/3) slope (from Kolmogorov's theory of turbulence) begins at around 0.2 Hz and continues until we reach the Nyquist frequency at 0.5 Hz (1/2 the instrument's sampling frequency, 1 Hz). The instrument's noise floor can't be seen here, but will show up as the flattened part of the spectra at the highest frequencies. For this instrument (Nortek Signature1000), the noise floor typically varies around 10^-3, depending on flow speed and range distance."
+      "text/plain": [
+       "<xarray.Dataset> Size: 379kB\n",
+       "Dimensions:         (time: 183, dirIMU: 3, dir: 4, range: 28, beam: 4,\n",
+       "                     earth: 3, inst: 3, q: 4, time_b5: 183, range_b5: 28)\n",
+       "Coordinates:\n",
+       "  * time            (time) datetime64[ns] 1kB 2020-08-15T00:22:30.001030683 ....\n",
+       "  * dirIMU          (dirIMU) <U10 120B 'streamwise' 'x-stream' 'vert'\n",
+       "  * dir             (dir) <U10 160B 'streamwise' 'x-stream' 'vert1' 'vert2'\n",
+       "  * range           (range) float64 224B 1.2 1.7 2.2 2.7 ... 13.2 13.7 14.2 14.7\n",
+       "  * beam            (beam) int32 16B 1 2 3 4\n",
+       "  * earth           (earth) <U1 12B 'E' 'N' 'U'\n",
+       "  * inst            (inst) <U1 12B 'X' 'Y' 'Z'\n",
+       "  * q               (q) <U1 16B 'w' 'x' 'y' 'z'\n",
+       "  * time_b5         (time_b5) datetime64[ns] 1kB 2020-08-15T00:22:29.93849515...\n",
+       "  * range_b5        (range_b5) float64 224B 1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7\n",
+       "Data variables: (12/38)\n",
+       "    c_sound         (time) float32 732B 1.502e+03 1.502e+03 ... 1.498e+03\n",
+       "    temp            (time) float32 732B 14.49 14.59 14.54 ... 13.62 13.56 13.5\n",
+       "    pressure        (time) float32 732B 9.712 9.699 9.685 ... 9.58 9.584 9.591\n",
+       "    mag             (dirIMU, time) float32 2kB 3.534 3.565 ... -197.1 -197.1\n",
+       "    accel           (dirIMU, time) float32 2kB -1.261 -1.263 ... 9.714 9.712\n",
+       "    batt            (time) float32 732B 16.48 16.48 16.48 ... 16.25 16.26 16.26\n",
+       "    ...              ...\n",
+       "    heading         (time) float32 732B 3.287 3.261 3.337 ... 3.331 3.352 3.352\n",
+       "    pitch           (time) float32 732B -0.05523 -0.07217 ... -0.04288 -0.0429\n",
+       "    roll            (time) float32 732B -7.414 -7.424 -7.404 ... -6.433 -6.436\n",
+       "    water_density   (time) float32 732B 1.023e+03 1.023e+03 ... 1.023e+03\n",
+       "    depth           (time) float32 732B 10.28 10.26 10.25 ... 10.14 10.15 10.15\n",
+       "    U_std           (range, time) float32 20kB 0.04232 0.04293 ... nan nan\n",
+       "Attributes: (12/41)\n",
+       "    fs:                        1\n",
+       "    n_bin:                     300\n",
+       "    n_fft:                     300\n",
+       "    description:               Binned averages calculated from ensembles of s...\n",
+       "    filehead_config:           {\"CLOCKSTR\": {\"TIME\": \"\\\"2020-08-13 13:56:21\\\"...\n",
+       "    inst_model:                Signature1000\n",
+       "    ...                        ...\n",
+       "    has_imu:                   1\n",
+       "    beam_angle:                25\n",
+       "    range_offset:              0.6\n",
+       "    declination:               15.8\n",
+       "    declination_in_orientmat:  1\n",
+       "    principal_heading:         11.1898"
       ]
-    },
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.3 Instrument Noise\n",
-        "\n",
-        "The next thing we want to do is calculate the instrument's Doppler noise floor from the spectrum we calculated above. (We are making the assumption that the noise floor of the vertical beam is the same as the noise floor of the other 4 beams). This gives us a timeseries of the noise floor, which varies by instrument and with flow speed, at that depth bin.\n",
-        "\n",
-        "We can do this using the `doppler_noise_level` function. The two inputs for this function are the power spectra and \"pct_fN\", the percent of the Nyquist frequency that the noise floor exists. Because in this particularly dataset we can't see the noise floor, we'll just use 90% or pct_fN=0.9 as an example. If the noise floor began at 0.4 Hz and ran til our maximum frequency of 0.5 Hz, we'd use pct_fN = 0.4 Hz / 0.5 Hz = 0.8."
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x1200 with 4 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vel = ds_avg[\"vel\"]\n",
+    "\n",
+    "vel_dim = [d for d in vel.dims if d in (\"dir\", \"earth\", \"inst\", \"beam\")][0]\n",
+    "labels = vel.coords[vel_dim].values\n",
+    "\n",
+    "fig, axes = plt.subplots(len(labels), 1, figsize=(8, 3 * len(labels)), sharex=True)\n",
+    "\n",
+    "if len(labels) == 1:\n",
+    "    axes = [axes]\n",
+    "\n",
+    "for ax_i, label in zip(axes, labels):\n",
+    "    values = vel.sel({vel_dim: label}).values.ravel()\n",
+    "    values = values[np.isfinite(values)]\n",
+    "\n",
+    "    ax_i.hist(values, bins=50, color=\"steelblue\", edgecolor=\"black\", alpha=0.8)\n",
+    "    ax_i.set_title(f\"Velocity histogram: {label}\")\n",
+    "    ax_i.set_ylabel(\"Count\")\n",
+    "\n",
+    "axes[-1].set_xlabel(\"Velocity [m/s]\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds_avg[\"noise_5m\"] = avg_tool.doppler_noise_level(ds_avg[\"auto_spectra_5m\"], pct_fN=0.9)"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x400 with 6 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ds_avg[\"vel\"][i].plot(ax=ax)\n",
+    "    ax.set_title(ds_avg[\"vel\"].coords[\"dir\"].values[i])\n",
+    "    ax.tick_params(axis=\"x\", labelrotation=45)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Calculate Speed and Direction\n",
+    "\n",
+    "Two additional variables that might be of interest, which are not automatically provided, are the magnitude of the horizontal velocity (referred to as `U_mag` or speed) and its direction (`U_dir`). These can be accessed via \"shortcut\" functions through the keyword `velds`, as demonstrated in the subsequent code block.\n",
+    "\n",
+    "For a comprehensive list of these \"shortcut\" functions, you can refer to the [DOLfYN documentation](https://dolfyn.readthedocs.io/en/latest/apidoc/dolfyn.shortcuts.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "ds_avg[\"U_mag\"] = ds_avg.velds.U_mag\n",
+    "ds_avg[\"U_dir\"] = ds_avg.velds.U_dir"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting can be accomplished through the user's preferred package. Matplotlib is shown here for simplicity, and flow speed and direction are plotted below with a blue line delineating the water surface level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.4 TKE Dissipation Rate\n",
-        "\n",
-        "Because we can see the isotropic turbulence cascade (0.2 - 0.5 Hz) at this depth bin (5 m altitude), we can calculate the TKE dissipation rate at this location from the spectra itself. This can be done using `dissipation_rate_LT83`, whose inputs are the power spectra, the ensemble speed, the frequency range of the isotropic cascade, and the instrument's noise."
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x249c4fdac30>"
       ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Frequency range of isotropic turubulence cascade in same units as PSD frequency vector\n",
-        "f_rng = [0.1, 0.5]\n",
-        "# Dissipation rate\n",
-        "ds_avg[\"dissipation_rate_5m\"] = avg_tool.dissipation_rate_LT83(\n",
-        "    ds_avg[\"auto_spectra_5m\"], U, freq_range=f_rng, noise=ds_avg['noise_5m']\n",
-        ")"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib.dates as dt\n",
+    "\n",
+    "ax = plt.figure(figsize=(10, 6)).add_axes([0.14, 0.14, 0.8, 0.74])\n",
+    "# Plot flow speed\n",
+    "t = dolfyn.time.dt642date(ds_avg[\"time\"])\n",
+    "plt.pcolormesh(t, ds_avg[\"range\"], ds_avg[\"U_mag\"], cmap=\"Blues\", shading=\"nearest\")\n",
+    "# Plot the water surface\n",
+    "ax.plot(t, ds_avg[\"depth\"])\n",
+    "\n",
+    "# Set up time on x-axis\n",
+    "ax.set_xlabel(\"Time\")\n",
+    "ax.xaxis.set_major_formatter(dt.DateFormatter(\"%H:%M\"))\n",
+    "ax.lines[-1].set_label(\"Water surface\")\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "ax.set_ylabel(\"Altitude [m]\")\n",
+    "ax.set_ylim([0, 12])\n",
+    "plt.colorbar(label=\"Speed [m/s]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We have just found the spectra and dissipation rate from a single depth bin at an altitude of 5 m from the seafloor, but typically we want the spectra and dissipation rates from the entire measurement profile. If we want to look at the spectra and dissipation rates from all depth bins, we can set up a \"for\" loop on the range coordinate and merge them together:"
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAIACAYAAABO0sn2AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAhBhJREFUeJzt3QecU1Xa+PEnyTSYygwMdehIb6IiqCjCgogogq9dsLzY9a/Y9xWxrGLZtbu661pXXayoi4oiKqj03kEQpXeZoc0wk9z/55xhMvfemYQk3CGTye/r58gk9+bckuQmT845z3EZhmEIAAAAANQA7mjvAAAAAAA4hQAHAAAAQI1BgAMAAACgxiDAAQAAAFBjEOAAAAAAqDEIcAAAAADUGAQ4AAAAAGoMAhwAAAAANQYBDgAAAIAagwAHAAAAQI0R1QBn2rRpMmTIEGnUqJG4XC759NNP/cuKi4vlnnvukc6dO0tqaqpeZ8SIEbJ58+Zo7jIAAACAaiyqAc7+/fula9eu8tJLL1VYduDAAZk/f76MGTNG//vJJ5/IqlWr5Nxzz43KvgIAAACo/lyGYRhSDagWnAkTJsjQoUMDrjNnzhw56aST5Pfff5emTZse0/0DAAAAUP0lSAzJz8/XgVBWVlbAdYqKinQp4/P5ZPfu3ZKTk6MfCwAAgJpN/X6/d+9ePcTB7a6eQ84LCwvl0KFDjteblJQkKSkpEs9iJsBRLwI1JueSSy6RjIyMgOuNGzdOHnrooWO6bwAAAKh+NmzYIE2aNJHq+L1W9UbasWOH43U3aNBA1q1bF9dBTkx0UVMJB4YPHy4bN26UH374IWiAY2/BUa0+6gWkXuDBHgcAAICaoaCgQPLy8mTPnj2SmZkp1XH/1H7NmjpT0tLSHKt337590vP0k/X333j+3lvtW3BUcHPhhRfqcTfffffdEZ+s5ORkXezU4+L5iQYAAIg31X14QlpaqqQ7GOCIVIt2i6hLiIXg5pdffpHvv/9ej6MBAAAAagTVkcrJzlTVo2NWfAc4qhltzZo1/tuqv+DChQslOztbGjZsKBdccIFOET1x4kTxer2ydetWvZ5argZQAQAAALFKhSNOhiSEN9UgwJk7d6707dvXf3v06NH635EjR8qDDz4on3/+ub7drVs3y+NUa84ZZ5xxjPcWAAAAcBIhTo0LcFSQEizHQTXJfwAAAABUAQKcuBuDAwAAANRYjMGpEtVz5iMAAACghjP0fz4HS3i9n15++WXp0qWLP9twr1695KuvvrL0tlKZ6Mzl+uuvt9Sxfv16GTx4sNSuXVtyc3PlrrvukpKSEokmWnAAAACAOOyh1qRJE3n88celTZs2emjIW2+9Jeedd54sWLBAOnbsqNcZNWqUPPzww/7HqECmjEoCpoIbNbno9OnTZcuWLTJixAhJTEyUxx57TKKFAAcAAACIwwhnyJAhltuPPvqobtWZOXOmP8BRAY0KYCrzzTffyPLly+Xbb7+V+vXr68RgjzzyiNxzzz06YVi0sh7TRQ0AAACIAtVq4nRRCgoKLKWoqEiORLXGjB8/Xvbv36+7qpV59913pW7dutKpUye577775MCBA/5lM2bMkM6dO+vgpszAgQP1NpctWybRQgsOAAAAUIOSDOTl5VnuHjt2rG5RqcySJUt0QFNYWChpaWkyYcIE6dChg1526aWXSrNmzaRRo0ayePFi3TKzatUq+eSTT/RyNUelObhRym6XzV8ZDQQ4AAAAQA3qorZhwwadNKBMcnJywEe0bdtWFi5cKPn5+fLRRx/p+SinTp2qg5xrr73Wv55qqWnYsKH069dP1q5dK61atZLqigAHAAAAiALD8OniZH1KWVa0UKhxMq1bt9Z/9+jRQ+bMmSPPPfec/OMf/6iwbs+ePfW/a9as0QGOGpsze/Zsyzrbtm3T/wYat3MsMAYHAAAAiAYVkDhdjpLP5ws4Zke19CiqJUdRXdtUF7ft27f715k8ebIOrsq6uUUDLTgAAABAFJgTAzhVXzhU0oBBgwZJ06ZNZe/evfLee+/JDz/8IF9//bXuhqZun3322ZKTk6PH4Nx+++3Sp08fPXeOMmDAAB3IXHHFFfLkk0/qcTf333+/3HTTTUG7xVU1AhwAAAAgGhxqdbHUFwbV8qLmrVHz12RmZurARQU3f/rTn/Q4HpX++dlnn9WZ1VTiguHDh+sApozH45GJEyfKDTfcoFtzUlNT9Rge87w50UCAAwAAAMThPDivvfZawGUqoFHJBo5EZVn78ssvpTohwAEAAADisItaTUWAAwAAAMRhF7WaigAHAAAAiALD5xPD53W0PhDgAAAAANFBC06VoAUHAAAAiApD/+dkfSDAAQAAAKKDFpwqQQsOAAAAEAVkUasaBDgAAABAVDicRU3VBwIcAAAAIBoMw6eLk/WBAAcAAACIDpUi2sE00Y7WFcPoogYAAABEAS04VYMABwAAAIgGwygtTtYHAhwAAAAgelnUnByDQ4Cj0IIDAAAARAPz4FQJAhwAAAAgCpgHp2oQ4AAAAADRQAtOlSDAAQAAAKLA8JXo4mR9IMABAAAAooMsalWCFhwAAAAgCgyfTxcn6wMBDgAAABAdjMGpErTgAAAAAFFAFrWqQYADAAAARAMtOFWCAAcAAACIAkP9p4IcB+sDAQ4AAAAQHT5vaXGyPtCCAwAAAESDar1xtAXHwbpiGV3UAAAAgGhgDE6VIMABAAAAosFnlBYn6wMBDgAAABANdFGrGrTgAAAAANFAF7UqQYADAAAARIHh8+riZH0gwAEAAACigxacKkELDgAAABANKquzo0kGnKsqlhHgAAAAAFFAkoGqQYADAAAARINhlBYn6wMBDgAAABAVjMGpErTgAAAAAFFgGIYuTtYHAhwAAAAgOnwlIt4SZ+sDLTgAAABAVDAGp0rQRQ0AAACIArqoVQ0CHAAAACAaSDJQJQhwAAAAgGhQk3w6OtEnSQYUAhwAAAAgCpjos2oQ4AAAAADRoBpcHJ3o07mqYlncBDh/m7xK2uXVl2Y5taV5Tqo0yEgRt9sV7d0CAABAvPJ5S4uT9SF+Apw3fvpN3Mnb/beTEtzSLLu2NMtJleY5taVVbpq0yU2T1rlpklU7Kar7CgAAgJrP8Bm6OFkf4ijAuaxnU9ly0CW/7zogG3YfkEMlPvll+z5d7OqlJ+tgRwc89dP9f+ekJUdl3wEAAFADkUWtSsRNgHPf2e0lIyND/13i9cnmPYXy++798tuuA/Lbzv2yZvs+XTbtOSg79hbpMn3tLksddWonSpvcdGldvzTgUX+3qZ8muenJ4nLR3Q0AAABhYKLPKhE3AY5ZgsctTXNq63JaG+uyfUUlsvZwy84v2/fKmm2lf2/444D8caBYZv+2Wxez9JQES8Cjurm1qZ8ujTJTCHwAAACOocJir3hjpauWYejJPp2sD3Ea4ASTlpwgXfOydDE7eMgra3eUtvKowOeXbaV//777gOwtLJH56/foYpaa5NHBTuvDgU9ZENSkTi0SHAAAAIQQrKheNdsKCmW76d89Bw7p72YHi71y4JBX9hwolt37D+mi7vv02u6xcW59vtLiZH0gwAlVrSSPdGqcqYtZUYlXftt5wBL0qL/X7dwv+w95ZdHGfF3MUhLd0rZBhnRqlCEdG6k6M+S4+umSkujhJQkAAGos9b1p175DsnNfUWnZe0h2HP5b3b/nYLHkHyyWgoPFsmtfkRQUlkS0nT/2H5KYGYPjZFCi6gMBztFKTvBI2wbpupgVe306ocGaw4FPWUID1QpUWOyTRRv26FImwe3S3do6NioNfFQg1b5hhqQm08gGAACqL9XFSgUlZWOYt/v/LbTcVoGMamkJV3KCW+pnpOgxz7kZyZKbniLZqUlSO8mjf4CuleiRrNqJkp2aLNm1kyQ7LUm8hfslVs6dk13UHO3uFsOi+u152rRp8tRTT8m8efNky5YtMmHCBBk6dKjlSRo7dqy8+uqrsmfPHjnllFPk5ZdfljZtbANnqqFEj/tw97Q0OatT+f2qT+jvu/bL8i0FsnRTgSzbnC9LN+Xr8T0rthTo8tG80nVV3oKWdVOlW14d6dY0S7rnZelAStUNAABwLKjMs+t379cJmnQLy4FDsmv/If1D7q879smvO/bL3qLQW1oSPS7JSU2WuulJUjct2VSSpE7tJMmslSgZtVTAkij10lMkIyUh7DHNBUUxkvwpykkGXn75ZV1+++03fbtjx47ywAMPyKBBg/TtwsJCueOOO2T8+PFSVFQkAwcOlL///e9Sv359fx3r16+XG264Qb7//ntJS0uTkSNHyrhx4yQhISE+A5z9+/dL165d5eqrr5Zhw4ZVWP7kk0/K888/L2+99Za0aNFCxowZo0/s8uXLJSUlRWKRx+2SlvXSdDmnSyN/ILc5v1CWbcqXpZsLDv+bL9sKimTtjv26fDx/o797W+fGmdItL0u6N60jJzSrI7kZsXkuAABA9ZF/oFhWbdtbGrTs3K+TLql/1+8+ENKgfRWYqFYWNd1G2b+lf6f4/66XlqxbW8g+Wz3G4DRp0kQef/xx3Xigvo+q79znnXeeLFiwQAc7t99+u3zxxRfy4YcfSmZmptx88836O/vPP/+sH+/1emXw4MHSoEEDmT59um6wGDFihCQmJspjjz0m0eIyqklblnqhm1tw1G41atRIR4133nmnvi8/P19HjG+++aZcfPHFIdVbUFCgnxD12LI00bFCNecu3rhHFm4oLyqhgV2znNpyQrNs6d0qR05rU5eABwAAVEp9v1ID8VWSJDUvoBpHvHxLvizbXCAb/zgY8KypxEl52bV1C4sKUFRpUqe2tKqXqn+0bZpdu1qNJa7u3//K9m/yI9dJaopz8yzuLyySP435x1Edd3Z2tu5hdcEFF0i9evXkvffe038rK1eulPbt28uMGTPk5JNPlq+++krOOecc2bx5s79V55VXXpF77rlHduzYIUlJSRIN1XaAx7p162Tr1q3Sv39//33qhdCzZ099UgMFOKr5TBXzCyhWqV86+rWvr4vi8xn6l5QF6//Qwc683//Qv7SoJmJVylp52tZPl1Pb1NXBTs8WObp/KgAAiD9qHMziDfmyeFO+LNm4R5Zsyped+wIPwG+cVUta5ab5A5dWdUv/rZ/BnH9VQjUzONpFrfQf+/ff5ORkXYJRrTGqpUb1sOrVq5ceQlJcXGz5Lt6uXTtp2rSpP8BR/3bu3NnSZU31tlJd1pYtWybdu0cnm121DXBUcKOYT1jZ7bJllVF9/h566CGpidxul39cz/+ckKfvKygslvm//yGz1+2Wn9bs1BcuFfSo8tpP6yTJ45YTmteR09rU0wFPh4YZpKgGAKAGUgP9l+jsrXt0D5DFG/NlS35hhfXUcJYGGSm61UUVNb5XZXXt0ChDdzND7I/Bycsr/Z5YRo1pf/DBByt9yJIlS3RAo8bbqDE0qkdVhw4dZOHChboFJisrK+B3cfVvZd/Vy5ZFS7UNcCJ13333yejRo/23VQRrf5JrkoyURDmjba4udx9Oi/jz2p3y0y875cdfdsqmPQdl+tpdujwxSXTWkVNal7buqNIws1a0DwEAAESQbrk0mMn3BzNqiorKgpnW9dKkS5Ms6dIkUxeVpbU6dSeLZ4bXp4uT9SkbNmywdFEL1nrTtm1bHcyobm0fffSRThIwdepUiWXVNsBRg5WUbdu2ScOGDf33q9vdunUL+LhQmuBqsjqpSTp5gSqqn63q0lYa7OyQGWt36X63/120WRdFZ3nr2ECGdm+kJyQFAADVk5oX5vtVO+Tb5dv057qab88uL7uWDma66mAmS087oSYxR3y14GRkZIQ8Bke10rRu3Vr/3aNHD5kzZ44899xzctFFF8mhQ4d0JmNzK476Ll72PV39O3v2bEt9annZsmiptq94lTVNnZgpU6b4AxrVGjNr1izdrw+hJW5opfrP1kuTkb2b67l5FqzfIz/9skOm/bJT/+KjJiZ9cfsaefH7NXoOniFdG0m/drk68CHDCQAA0aOmlZj56y495lYVlVXVTKVVVllVy1tnsnRPDcSQKKeJrozP59Pj2VWwo7Khqe/iw4cP18tWrVql00KrLm2K+vfRRx+V7du3S25urr5v8uTJOrhS3dziMsDZt2+frFmzxpJYQDWRqewNagDTbbfdJn/5y1906rqyNNEqs5p5rhyETs2fc1KLbF1GD2ir00H+sHq7fL5ws0xdvUNnUFHl8a9W6kGGfdvVk0GdGsrJLXN0emsAAFB1Dhwq0d3Lp63eof9V6Znt1I+RKvnQn9rXl06NM/gxMsYZPkMXJ+sLd2iHmvNGfe/eu3evzpj2ww8/yNdff62Te11zzTV66If6bq6ClltuuUUHNSrBgDJgwAAdyFxxxRV6ehc17ub++++Xm266Kao9qqIa4MydO1f69u3rv102dkb1/VOpoO+++26dyeHaa6/VzWOnnnqqTJo0KWbnwKluMmsnynndGuuiuq59sWSLTF6+Tf9apMbuvDNzvS4ql73u9ta1oXRtkkWwAwCAQ9TY2Skrt8vXy7bqwKaoxGeZELN7Xh2dLKhHszp6/jtaaGqYKLfgbN++Xc9bo+avUQFNly5ddHDzpz/9SS9/5plnxO126xYc80SfZTwej0ycOFH3rlKBT2pqqv4e//DDD0s0VZt5cOI1D3p1/QVJjddRwc5XS7fqrCxl1IX19OPqyRlt60nfdrk6yQEAAAidSgagxtFMXrFN5v62W8w/uqsxNGe2zZU+x9WTni1zGD9TQ7//le3f1/eOlNQU57oV7i88JAMff6vaHrfE+xgcRE/tpAT//DsPn9dJ/6L02aLN8sPK7bqlZ8KCTbqoFNTqAnxOl4bSv0N9LsIAAFTC6zNk/vo/5NsV23RgYx9L065Bugzo2EAn/WnfMJ1uZ3HEUP852Nag6gMBDo4gKcGtgxdVVJICNcjx+1Xb/RdofbFesU2v17dtPRncpTRJQSoZWwAAcWx/kRpPs0MmL9+uPzfVD4RlEtwuPb61f/tc/WNiXnbtqO4rosfwGro4WR8IcBBmkgJ1QVbl3rPayept++SLxZtl4uItOh3118u26ZKS6JYz2+XqcTt92+ZKrSRy7QMAar4t+Qdlyort+oe/6Wt2ySHT/CYZKQn6s1EFNKe3rUcXb1TbLGo1AV3UEBGVQlrNfNy2QVu5/U/HyYote+WLJaXBzu+7DsiXS7bqUjvJoy/mgzs31ON2mFgMAFCTqKQ8Xy7eIhOXbJFFG/ZYljXLqS3929fXRSUKUD8UAhYEOFWCAAeOBDsdGmXocueAtjrV9H8Xb5YvFm+RjX8c9E8smprkkT91qK+7sfU5rq4kJ9CyAwCIzZYa9SOe6sUwf315UONyiRzftM7hoIY55XBkavyNo2NwaMHRCHDgeLCjZk1WRXVjW7QxX38AqGBnc36hfLpwsy7pyQnyp471ZUiXRnJK67p6DA8AANXVtoJC+WrJFj2lwpzf/rAENSc1z9YJdwZ2aiC56UxlgTCoFHoOzoPjaF0xjAAHVRrsqBmWVblvUHtZsGGPDnS+XLJFthYUyifzN+mi+iUP7NhAzunaSHq3yqEJHwBQLezYWySTlm6R/y5WQc1uy/CGE5vX0d2vz+7cUHIzCGoQmXjtoXb88ceH/Z3y888/l8aNG4e0PgEOjgm326UnKVPl/sHtZd76P3Swo34JUx8gH87bqEud2olyVqcGMrhzIzm5ZbYk0F8ZAHAM7dpXJJOWbZWJi7bIrHW7LD+IH980SyfQGdS5gTTMrMXzgqMXpxHOwoUL5Y477pC0tLSQut09/vjjeqLRUDHRJ6I+N4D6VWzi4s3y1ZKtssuURrNeerJcdEKeXNKzqTTO4oMEAFA1/th/SAc16oe3Gb/u0p9NZbrmZcmQLg1lUOeGfBbFkFiZ6HPiTRdJarKDE30WHZJzXnq/2h53GbfbLVu3bpXc3NyQ1k9PT5dFixZJy5YtQ1qfFhxElefwXACqPDiko8xet1t3BVBdAlTLzovfr5G//7BGp9a8oEee9G1Xj+QEAICjln+gWL5WLTVLtsjPa3ZagpouTTL93c+YowZVKk5bcNatWyf16tULef3ly5dLo0aNQl6fAAfVhuqO1rt1XV0ePq+jTF6+Td6Z+btMX7tLvtXzCmyXzFqJMrhLQzm/e2Pp0bSO7voGAEAoVBAzbfUO+c/s9XryzWLTpIgdG2XozxcV2DTLSeWE4hjGN05mUXOsqirVrFmzsNbPy8sLa30CHFRLaq4A9cuZKmt37JP352yQzxZukm0FRfLerPW6NKlTS4Z2ayxDuzeW1rlH7sMJAIg/6svj8i0FeiLqj+Zu0Bk9y7RrkK6zn6nPmpb1+BxBNF6gh4uT9cWI9evXh7Re06ZNw66bAAfVXqt6afLns9vLPWe1k5m/7pIJCzbJpKVb9Rw7qgubKp0bZ+pWnSFdG+mxOwCA+A5qVJdnNfn0lBXbLEFNVu1EGda9iVx8Up4cVz89qvsJxPM8OM2bN9fZ0So7hrL71b8lJSVh102Ag5gar6PmzFHlkfM6ybcrtsmnCzbJ1NU7ZMmmfF0e/XKF9GlTV67o1UxOPy5XPwYAEB/U2M2P52/Urf7rdu7335+S6JZTW9eTIV0b6mkJUhKZaBrVhIpHfA7XFyMWLFhQ6f0qwBk/frw8//zzIWVZqwwBDmJSrSSPbq1RRaX0VOmmVcvOgvV75PtVO3TJy64ll/dsplt2mKMAAGrwuJpfdsj7szfoH75KDicLSE3y6DE1auqB3q3qEtSgWjJ8Pl2crC9WdO3atcJ93377rdx7772yevVqufvuu3Uq6UgQ4CDm5aQly4hezXX5dcc+PXhU/Xq3YfdBGffVSnl80ko5sVm2nN25gU7zWZ8J2QAg5m3ac1A+mLNBPrSNq1GTS19yUp6eryY1ma85qN7iNIlaBfPnz5d77rlHfvzxR/nf//1f+fLLL0NOIV0Z3vmoUdQg0f8b3EFG/6mtfL5ok/xn9gZZuGGPzP5tty4PT1wuZ7TNlQtPyNOpp5MS3NHeZQBAiAqLvfLdyu36RyzValP2ZU5l2FSt9WpcTbsG1XfuD6CCOI9w1q5dK3/+85/l448/lgsvvFCngw51rptgCHBQY7uwXXRiU13Ur3xfLdmiu7GpLmzqw1GVnNQkGXZ8Y7noxDxpnctAUwCojopKvDJt9U75YvFmPX3A/kNe/7JeLXN0UMO4GsQqw1danKwvVtx4443y2muvSd++fWXu3LnSrVs3x+p2GbGUbqEGzmSLY0ulnP5g7gb5eN4m2bmvyH//8U2z5H9OyNOpQtUvgQCA6FqxpUBfr1UymT8OFPvvb5SZIud2K/1xqkVd5qtBbH7/K9u/CSOHSWqSc9879h8qlvPf+qTaHreZ2+2WlJQUadeu3RG7r4WLFhzEXcrp+wa1lzsHtJUfVu3QH56qNWf++j26jP1sme66pubWoQsbABxb+4pK5POFm2X8nPWyeGO+//76GckyuHMjOadrQ+nWJItJnlFjxHOa6LFjx1ZZ3QQ4iNuJRP/Uob4u2/cWyifzN8mE+Ztk1ba9MmnZVl1UF7bhPZrIxSfmMQEcAFQR9YVMBTMqQcznizbLgcNd0BI9Lunfvr5ceGKe9GlTj7T/qJnieKLPsQQ4QNXJTU+R609vpYvqEqG6Q6iU09v3Fsk/p/2qS88W2XLJSU11ulHmTwCAo1dQWCyfLShNBrN8S4H//pZ1U/X1Vo2RVFkygZrM8Bm6OFkfaMEBLNo3zNDlroFt9Vw642evl+9XbZdZ63brkvXf0kw96sOXGbABIPzWGtUdWLXWTFy8WQqLS0dEq4yWZ3dqoK+tJ7XIrnR2c6AmiuckA1WJLmpAZW8MUxe2LflqroWNeryOysj2xs+/6aISE6gPYzXXgsraBgCoXP6BYvlkwUYZP3uD7gpcpk1umr+1Jqt2EqcP8cfhMTixlia6qhDgAEfQMLOW/L/+beTmM1vreRdUq863K8oTEzz83+VyXvdG+kO6Y6NMzicAHG6tmfPbH/qaqdL0F5WU/rSckujWCQMu7ZknxzetQ2sN4lscj8GpSgQ4QIg8bpf0bZury/aCQvlw3kY92dz63QfknZnrdenSJFNG9mouQ7o2YhJRAHHpj/2H5OP5G3U3tLU79vvvb9cgXS7t2VTO69aYdPzAYXE+z2cFhYWFOnX00SLAASKQm5EiN/VtLTec3kpm/LpLf5B/vWyrzgR0x4eL5MmvV8qVvVvIpSc1lczazKsDoOa31qhroeqCNmnpVjnkLW2tqZ3kkSFdGsklPZtK1yaZtNYAFd89DkclsRfh+Hw+efTRR+WVV16Rbdu2yerVq6Vly5YyZswYad68uVxzzTVh10mAAxwFt9slp7Suq8uufUUyfs4GeWv6b7KtoEiemLRSnpuyWk8eetEJeQycBVDjqAmTP563UV/71u0sb63p3DhTLj4pT87t2kjSU/iRBwiEFhyRv/zlL/LWW2/Jk08+KaNGjfKfm06dOsmzzz5LgANEk0pnqlp1/ve0FvLfRVvkXz/+Kiu37tVz7KiiZty+sndzufCEPJISAIhZPp8hP6/dqVtrvlm+VYq9pb8YpyUnyLndGsklJzaVzk0YjwiEwvAaYrgdTBN9+P0YS95++2355z//Kf369ZPrr7/ef3/Xrl1l5cqVEdVJCw7gsOQEj1zQo4kMP76xLNiwRz6Ys0H+u2iz/nVz7OfL5Lkpv+hxOiN6NZM6qWQNAhAbysYejp+zXjbsPui/v2tellx6Up7OKJmazNcKIBy04Ihs2rRJWrduXWnXteLi4oheUFyJgCqi5nFQGYJUGXNOB/lk/kb554+/6i8Gz3y7Wl76YY1OQ62CodNa19WpqQGgOjlU4tNzgaluaFNWbhfv4UkE01MS9JxgF5/YVDo0yoj2bgIxiwBHpEOHDvLjjz9Ks2bNLOfmo48+ku7du0d0XglwgGNA/ap5Ra/mOpX0l0u3yj+mrpVlmwvki8VbdMlNT9bZha44uRkzdwOIuiUb8+XDeaWtz38cKP8FtUezOvo6NrhzQ7raAk4gTbQ88MADMnLkSN2So1ptPvnkE1m1apXuujZx4sSITqvLcHR2oeqnoKBAMjMzZcns+ZKeliYSZHJklwT+Bd0QX+B1XfZJHk3r2k6vvR7bQr9m7dsEXg8xT73tVIDz0byN8tnCTf4vEMkJbhneo4lcfUpzaZ2bHu3dBBBHCgqL5bOFm/W8Ner6VEb9ADO0e2Pd2nxcfa5LCO7cev39f5cY1u88hwxv+TJbtq9i0zKfbZm5FvvXOK+rfF3DKE9o4TVKZOGeGZKfny8ZGRnV9vvpe+eeI7UTnUvEcaC4WC79fGK1Pe5AVAvOww8/LIsWLZJ9+/bJ8ccfrwOfAQMGRFQfLThAlLqvdWqcqcufz24vk5Zt1UkJVJrp92at16V70yz9hUL1a8+sRRYiAFXzY4uasFiluletyQeLS79kJiW45ayODfQ1SGWJVPOAAaiK9yDz4CinnXaaTJ48WZxCgANEmfoioVKpDunSUGav2y3/+mmdfLdyuyxYv0eXh/67XM7p3FAu79VMuudlMY8EgKO258Ahnd1RJQxYvW2f//42uWm6C5oaX0MSFOAYoItalSDAAapRq07Pljm6bN9bKJ8t2Kz7wKsvH58s2KRLh4YZctnJTeWczo2YQBRA2OmdZ63bLe/PWa/HAqoEAkpKolu3FF9yUp5OiqKuRQCODZ9XxOd2tr5Y43a7g153vN7wD4oAB6iGctNTZFSflnpOnYUb9sg7M9fLfxdvluVbCuT/JizVrTr92+fKsO5N5PS29SSRDGwAAli9ba98umCTHl+zaU95euf2DTN0eudzuzWmGywQJXRRE5kwYYLlnKjU0AsWLNCTfz700EMRnVcCHKAaU79odG9aR5f7B7fXSQlUWbVtr3y5ZKsuOalJMqRrIxl2fGM9ezi/vgLYVlAony/cLBMWbNI/jJRJT06Qc7o21OmduzThegFEHRGOnHfeeRVOywUXXCAdO3aU999/X6655pqwTysBDhAjVH/4slYd9YVF9Z9Xv8ju3Fckb07/TZfWuWk60BnarbE0yqoV7V0GcAztLSyWr5dt0601P6/d6U/imehxyRltc/V1oV/7XElJtGf+BBAtxDeBnXzyyXLttddKJAhwgBijWmg6NsrU5b5B7eTHNTt1sPPNsq2yZvs+eXLSKnnq61XSq2WODDu+iZzVqYGkMbs4UCMVe30ybfUO+XThZpm8fKsUFpcn1D2hWR2d3lnNWUPCAKB6IsCp3MGDB+X555+Xxo0bSyQIcIAYluBxS9+2ubqoOSwmLdkqH8/fqAcST1+7S5cxny6VgR3r62CHdK9AzUgWsGDDHj2H1sTFW2T3/kP+ZS3rpcr53RrLed0aS9Oc2lHdTwBHRoAjUqeONbmJSl+/d+9eqV27trzzzjsRvYwIcIAaIiMlUS48MU+XDbsP6C8/qmXn15379a+7qtTPSNbdVM4/vrG0axA7E4AB8a7E69Np5NWcWV8v2yrbCor8y+qmJetU8yq1c6fGGYzDA2ItwPE5W1+sefbZZytkVatXr5707NlTBz+RIMABaqC87Npy85lt5Ka+rXUWNhXoqCxs6kvRP6b9qotKOa3G65zbrZHO2gag+lmhx9tt1D9Q7NhbHtSobqcqk+L5qmW2VY5uzQUQe+K9BaekpER+//13ufrqq6VJkyaO1UuAA8RJFrYx53SQ71dt11+W1ESiKlHB8i8KZNxXK6Vv23py1SktpHerHH79BaJMzYOlMqB9PH+TDnDKZNVOlAEd6utxdb1b1SVZAFADxHuAk5CQIE899ZSMGDHC2XodrQ1AtZWU4JaBHRvo8sf+QzJxyRYd7CxYv0e+XbFdl7b10+WqU5rrVp3aSVwegGOZAW3Kiu26a+m0X3aK12f4M6D1a6fG0DXWmdDU+xhAzRHvAY5y5plnytSpU6V58+biFL7BAHFIZVS64uRmuqjMa/+e8Zt8eHh+nXs/WSKPTFwuZ3duKMN7NJGTmmeL283M5kBVBTUqUcC0X3bIoZLyjvjdm2bpxCBDujSUrNpJnHygplIBiZNBSQwGOIMGDZJ7771XlixZIj169JDU1FTL8nPPPTfsOglwgDin5s556LxOMnpAW/lgzgb598zfZf3uAzrgUSUvu5YM695Ehh/fhKxMQBUGNa3qpeqUziq1c8t6aZxrIA7QgiNy44036nPx9NNPV9rV3uv1hn1eCXAAaJm1Ev0Tic757Q/5eN5G+WLJFtmw+6A8N+UXXU5qkS0X9GiiW3eYWwcIjeoSOnn5Nvlq6Rb5ec0uOeS1BTVdGunA5rj6aYyBA+KNw13UYrEFx+dzMI3cYQQ4ACr8WqICGVUePLejTkmr5tb5ac1OnaZWlbGfLZNBnRrIBSc00ROKmvPXAyhNFPDNstKgZuavu/1jaspaTdWPBAQ1ANR3eye/31dBrFDl3n77bbnoooskOTnZcv+hQ4dk/PjxESUgcBlqNp0arKCgQDIzM2XJ7PmSnpYmEuR7mEsCD940xBd4XZfHtrZpXdvptddjW+jXrH2bwOsBUbB5z0GZsGCTbtlRc+uYJxa8rGczGX58Y8YKQOL9PTJp6VZd5vy+23L5V2nZ1Y8Cgzo3kNa56dHcTaBKnVuvv//vEtsEL4eM8q5GJbamhmLTMp9tmbkW+9c4r6t8XcNILL/fKJGFe2ZIfn6+ZGRkVNvvpy/3Hiy1Esr3+2gdLCmWG6Z/UW2PuzIej0e2bNkiubm5lvt37dql76OLWhCG4dPFHsQYPlO/Prct1jPPqmoPiU1vqArtgZagJnD8GCy2/G3ZSuvm3OX77SspCrifwbgTrJGx22V6Q7nswZ0ROEgzXYSMCsceOIDzeYtNq1n7U/qKD/r/9h4q//Js17bXmQGXoWo1yqql59W58YxWehb1D+dulM8XbpJfd+zXSQmenLRSTmtTV/q1ry/92uVKbgZz66Dm+33Xfvlq6VZdFm3YY1nWLS9LBzUqrXOzHOugWVQfvbJ7+/+2f4KZPxmDL7N+FrqD/JpqXtO+VrBfnF1B1jNvL9g3Ak+QH3IrrGv6bpFge5z5eF1BtuG2fT9xmdZ2247CuszKHeTYzd+lSly+0H5Qrk5IMiDqOaysJ8jGjRt1EBgJuqgBCIu6CB3ftI4u/ze4vXy6YJO8M/N3Wbl1rz/dtNK1SaYOdvq3ry/tG6bTjQ01QmGxV+b9/of8vGanfL9qh2WeGvX5fGKzbB3QqKJ+FACA6pxkYNy4cfLJJ5/IypUrpVatWtK7d2954oknpG3btv51zjjjDJ3G2ey6666TV155xX97/fr1csMNN8j3338vaWlpMnLkSF23mucmkO7du+vvBqr069fPsq5qtVm3bp2cddZZEgkCHAARU4kGLj+5mVzWs6ms2LJXpqzYJt+u3K5/yV60MV+XpyevlkaZKaXBTof6cnLLbElOsHfrBKqnEq9PFm/Kl+lrduoEAfPW/2HJfOZxu/Q4NBXQDOhYX3LTabkEEDsBztSpU+Wmm26SE088UUpKSuTPf/6zDBgwQJYvX25J1zxq1Ch5+OGH/bdr165tCUYGDx4sDRo0kOnTp+vuZmrcTGJiojz22GMBtz106FD978KFC2XgwIE6MCqTlJSk58UZPny4RIIAB8BRU7++dGiUocst/drI9oJC+W6las3ZppMTbM4v1OmnVUlN8shpberpYKdv23qSk2btOglEk89nyOrte3Uwo4KaWet2y76iEss69TOS5ZRWdeWU1nXlzHa5el4pAKhOAU5BQXnrsqIG8NsH8SuTJk2y3H7zzTf1uJd58+ZJnz59LAGNCmAq88033+iA6Ntvv5X69etLt27d5JFHHpF77rlHHnzwQR2sVGbs2LH6XxXIqCQDKSnO/UBEgAPAcWr8zcUnNdXl4CGv7s4zZeU2Pf/H9r1FMmnZVl1Ulx7V1a1Pm3pyapsc6dokSxI8zNSOY9v3W837NH3tLv06nbF2l+zaf6hCCnXVSnNK6xzp3bqutKybSpdLANU6wMnLy6sQTKhg40hUcgIlOzvbcv+7774r77zzjg5yhgwZImPGjPG34syYMUM6d+6sg5syqkVGdVlbtmyZ7ooWjOrO5jQCHABVqlaSR7fWqKJ+HV+yKb+0K9uK7bJ8S4Eez6DKM9+KpCcnSO/WOXrcjvplnNYdVAXVwjjj19KARrXUbNpz0PqaTfTIiS2y5ZRWKqipK+0bZuiuaAAQK2miN2zYYMmiVlnrTcXH+uS2226TU045RTp16uS//9JLL5VmzZpJo0aNZPHixbplZtWqVXrsjrJ161ZLcKOU3VbLooEAB8Ax43a7pGteli6jB7TVXyx/WLXd/0Uz/2CxfL1smy7m1h31y7l6TCKtO4iAep3N+nWXnsNJdTlbZ0pzriR6XNI9r470OhzQqOxnSQm0JAI4RknUnGzBOfxvRkZG2Gmi1VicpUuXyk8//WS5/9prr/X/rVpqGjZsqJMCrF27Vlq1aiXVEQEOgKhpnFVLz6GjipoIcdnmfD12R836vmyztXVHjd3pqbsJqbEPOdK2PpnZULltBYUydfUOmbl2lw5o7C00KnhW89Ko11LvVjl6UtvaSXwcAoi/JANlbr75Zpk4caJMmzZNmjRpIsH07NlT/7tmzRod4Khua7Nnz7ass23bNv1voHE7VY0rOoBqQXUB6tIkS5fb+h+nJ038ftV2ma4Ge6/dKX8cKNbBjypK3bRk/eX0VPUltXWONKlTntEF8eWP/Yd0prM563br14wKju2vrc6NM6Vni2zp2TJbejTL1uNqACDeAxzDMOSWW26RCRMmyA8//CAtWrQ44mNU1jNFteQovXr1kkcffVS2b9/un6xz8uTJugWpQ4cOEg0EOACqpUam1h01dkeN11GBzk9rdukvsjv3FcnnizbrotfPTCnN5NYwQ4+ZUH/n1amtu8Wh5lAZzZZuypfFG0tTkat/N+yu2EKjAuXTWtfVAY3q6piazMcdgOon2gHOTTfdJO+995589tlnkp6e7h8zoybYVPPiqG5oavnZZ58tOTk5egzO7bffrjOsdenSRa+r0kqrQOaKK66QJ598Utdx//3367pDGfujgqyPPvpIz6GjgiQ1FsisbKxPOLjiA6j2VJDSqXGmLtf2aSVFJV5ZsH6PTuOr0lCrL7oqFbUqZRONls3T065BunRslCGddetQprSql8aA8RgLaFRAq4JblRhAtc5U9gHeom6qnly2z3H1dFEtfABQ3UU7wHn55Zf9k3mavfHGG3LllVfqFM8q/fOzzz4r+/fv19nZ1Nw0KoAp4/F4dPc2lTVNteao+XNUZjTzvDnBqMQG//jHP6Rv3746OYGaeuJoEeAAiDlqotCTW+boopIVqC/ByzcXyPLN+bqlR5XVW/fp++f+/ocuIr/7M2SVBjyZutuSCnpa1CXoqQ72F5XoAEZl2itrpfl15/4KH9iqtU610KjnUKUWV89jZm26nAGI0SxqDnY0CDcjm3GEiEgFNGoy0CNRWda+/PJLicS///1v3UqjWomcQoADIOaplho1UFyVMsVen/y6Y78s36K+LBfIko35snRzvhw45DUFPaVqJ3mkU6NMf9Cj/m2Rk0r3tiqgnpctewplwx8HZMPuA/rf9bsPyootBbJ2x75Kf33My66lJ9ZUWc5UUFs/w7nJ4AAgnltwqgPVHa5ly5aO1lmtAxyv16snJVITC6n+fCr/tmouU81iTjRfAai5VErptg3SdTn/8BxjKlPbup37ZPHGfN1KoIIe1WKggp7Zv+3WxRw0qce2qpcqLeul6S5QKutbw8wUyU5N4hoUQmuMCl5U4KJa11Zs2avTM2/JPyi+IB/ADTJSdFfELoeDTfV3vXS6mwGouWIwJnGU+q7/0EMPyeuvv67H/dT4AOeJJ57QfQPfeust6dixo8ydO1euuuoqHendeuut0d49ADFGZdNqnZuuy7Djm/iDHtVyoIOejXt04KOCHtW9rSxNtZ2aI6VZdm1/MgM1zqdJnVq6ZSE9JX66ShUWe/W5W7V1b2nZtlc2/nFQtuUXyt6ikoCPU+dPnS+VBEK1zqh/29RP08FMbjqtMwDiBy04IhdeeKH85z//0RnYmjdvLomJ1s/R+fPn16wAZ/r06XLeeefJ4MGD9W110OoE2HNtA8DRBD3H1U/X5YIepUFPidcna3bsk9Xb9smvO1TZL7/tUq0PhbJjb5EcKvHJL9v36VKWxc3c8lM/I1kaZKZIg4xa0iBT/V1Lt0yo1h8VBOWkJlX77m8q8FNBnir5B4r13DLq+FULTFlQ89uuA3q9QNJTEqR9A5XVLl0HgirBQ152bamXllztjx8AjgUCHNEJCebNmyeXX355fCQZ6N27t/zzn/+U1atXy3HHHSeLFi3Ss6s+/fTTAR9TVFSkS5mCAut8CABwJAket7RroFpmKs4CrYIb9WV/zfZ9/oQGv2zbq7/87y0sDQj27SiRtTv2B6w/0ePSLRWq61WSx63TGqtAKyXRo4MCFSSplqDyvxN0q4dLSi/6an31V+lngMt02yUet5oUNUGnRVaPUw4We+XgIa8UFvt0q0vZbZVqW803tGlPoWzfWyj7Ckt0y4vqXqa67YUiq3ainnS1rDtg85xUHcSpAE/tOwAgMAIckS+++EK+/vprOfXUUx17qVTrT597771XByjt2rXTKejUmBw1kdBll10W8DHjxo3T/fgAoCqoQEO1QqjSt13phGZlVGCwtaBQd9FSAY/++3DLh/p3q2oB2lckxV5DNunAwjp/S3WkAjAVKNU/3ALVMCtFmmWn+gOa3PRkxiMBQIQIcERnalOTgjqpWgc4H3zwgbz77rt6giE1BkfNnKpyZatkA6o5qzL33XefjB492n9bBUjqxAFAVVOtJqoblirBsoipbm4q6FEtKKqLl88w9L+qdUW1ApW1BO0tLPbfPuT1+UeiGuo/lXnncIrP0n9Lb3t9PjlQ5NWPVwGXatVJSXTr1iFVVJrsstsqWYKaUFUlT1Bd6DJqJepWF11SVCuQR6fkBgBUDdXLN1jilUjqizV/+9vf5O6775ZXXnlFD0ep8QHOXXfdpVtxLr74Yn27c+fO8vvvv+tWmkABjpoxtdJZUw315cBXId+3YZi6Ydhzh5v7AKrHWx5nXs8II0ef5YEBlxm27Zn3zfDZB++G2FfRk2Tdmmm/XfbjC3bLvm+WZYGP3eUyfVFyW9dzucuXuTzWwWXmvpi/zJ1u26BpX1xu6+PM58W2LGAd9nWDLYuQ+ViVVt1POOo6EVvZ3VRQoQoQi3pm9w5pvdLQO8A1OYjAn4yVLQu8NNRe/KrzZ+Vbs+6zK4zjCX6sQT4ng+5p4PVcQbZtPkfB6g827qHicxl4ez7Tup4g+2IEeR7sy8x1Vjx20+NcvpC+q1QntOCIHntz4MABadWqldSuXbtCkoHdu8sznNaIAEcdrNtt/UKpuqr5wp3FCAAAAKhmaMERefbZZx0/r9U6wBkyZIgec9O0aVPdRW3BggU6wcDVV18d7V0DAAAAjgotOBKwV1aNDXBeeOEFGTNmjNx4442yfft2PfbmuuuukwceeCDauwYAAAAcFT2G0sFzGINDcDSVSOzTTz+VFStW6NuqYePcc8/VPbdqXICTnp6um62qoukKAAAAiCZacETWrFkjZ599tmzatEnatm2rz4sab6+ShKkU0mpsTriOfsQ0AAAAgIgDHCdLrLn11lt1ELNhwwaZP3++LuvXr5cWLVroZTWuBQcAAACoqVQuX6+DQUloUzRXL1OnTpWZM2dKdna2/76cnBx5/PHH5ZRTTomoTgIcAAAAIArooiZ6epe9e/dWODf79u2TpCTrFCehoosaAAAAEOVEA06UWHTOOefItddeK7NmzSqdvNowdIvO9ddfrxMNRIIABwAAAIgCxuCIPP/883oMTq9evSQlJUUX1TWtdevW8txzz0V0XumiBgAAAERBvHdRMwxDCgoKZPz48TqLWlma6Pbt2+sAJ1IEOAAAAEAU+A4XJ+uLtQBHBTLLli2TNm3aHFVQY0YXNQAAACAK4r2Lmtvt1oHNrl27nK3X0doAAAAAhMRnGI6XWKPSQd91112ydOlSx+qkixoAAAAQBfHeRU0ZMWKEHDhwQLp27arTQteqVUvMdu/eLeEiwAEAAACiIN6TDCjPPvusOI0ABwAAAIgCp+eviZX4ZvTo0fLII49IamqqtGjRQnr37i0JCc6FJYzBAQAAAKLAZzhfYsELL7wg+/bt03/37ds3om5owcRNC45heHURcdkWmHorutwB2/lKHxuo8mA9HkN/pRm+IPW4TfviC7IvQeu3Pc58vPZjN58n2/EZptsuVxjn07yuYX2cy13+UnSb/rYvs7PuS+B43eVJqJCW0K/CeTHvmydgnRVfL0GOPYhfFy0IaT3LPtt3xfY8mNet8BxVBVf5eWrZpUvVbw+oRnpl97bcNr9TK75ry+/xuQJe5is8zmO6JtuXmW/b3+2uED+NKj4utOtG8LVs1/mQagy+nn2ZYbon2LGHKlgd7iA1hrfto78mh/N8mZcF+6w40jYCsZ+XYB851v00Ap9r22eo+bO+4vNQvsxwlX+eG67YGI0Sry04zZs31xN8DhgwQL8OZ8yYIXXq1Kl03T59+oRdf9wEOAAAAEB14nTms1jJovbUU0/J9ddfL+PGjdOB9/nnn1/pemqZ1xv+D/sEOAAAAEAUGA53K4uR+EaGDh2qi+qmlpGRIatWrZLc3FzH6ifAAQAAAKIgXruolUlLS5Pvv/9eJxpwMskAAQ4AAAAQBcbh/5ysL9acfvrpjtdJgAMAAABEgdOZz2Ili1pVI8ABAAAAoiDeu6hVFQIcAAAAIApUiuRg6bojqQ9M9AkAAABEhVcMx0useOONN+T333+vkrppwQEAAACiQDW4ONnoEksNODfeeKMcOnRImjVrJn379vWXxo0bH3XdBDgAAABAFPgOFyfrixV79uyR6dOny9SpU3Wq6Pfee08HPK1bt/YHO2eccYbUr18/7LoJcAAAAIAoiOc00cnJyf5A5sEHH5TCwkKZMWOGDnZ++OEHeeutt6S4uFhKSkrCrpsABwAAAIiCeO6iZud2u3VxuVy6qIQJTZs2lUgQ4AAAAABR4BNDFyfrixWqO9rMmTN1a813330ns2bN0uNx+vTpI6NGjZJ33nlH8vLyqi7AKSgoCLvijIyMSPYHAAAAiAvxPA9OZmam5ObmypAhQ+Smm26S8ePHS4MGDRypO6QAJysrSzcVhUqtu3r1amnZsuXR7BsAAABQY/kMEa+D/cpUfbGia9eusmDBApk2bZq/e5pKKpCTk3PUdYfcRe2jjz6S7OzsI66n+sudffbZR7tfAAAAQI0Wz0kGZs6cKfv27ZOffvpJJxZ48skn5ZJLLpHjjjtOBzqnn366LqqVp0oCnLL+cKFGVKrlJjExMeydAQAAAOJFPHdRU9LS0uSss87SRdm7d6/8+OOPMnnyZD0ORwVAVZZFbd26dWFVunTp0rB3BAAAAIgnqueTKk7WF4t8Pp/MmTNHJxxQrTk///yz7N+/XzeyRCIh3vLwGYZtCiTz7SDjjAxf4KmTgo1PqvBCs2zfuszwecvrdHsCPsy8XjgMw/o4l1H+9KscHkEeabtZvq5hWI/dfH5dLnfg8+SyHp+4TY9LSA78uAq7ZoS2nm17LjGdC4/9bWCuJ9iFwrY9y/ZDH7MmtvMUcDV7lZbXru1cm5/PEOs/GuZz/+uihbaFpu3b339B9s1cp/19FHSZ27w9I/BJrLAs8H4awV5nIb4+Q15P70rgFnBfSWHl+6yWHdrv/zsxrW7Aa9jOOR9bli17bY7/7ymriyzLVh36w/93TkIny7KtviX+v2v56liWFbrKH5doWPczScrfj8Xm96KIpLjK34+HbNesYFym91ywLELB3pnB3u1e21JPkJpCvoLYX4JBHhlsWaiCHbu1/iOtG9p64Ww/1Cttxf0M9lyHtneRHo9E8XHh1GNUwesjWDco87r2tdymZea/j7QD5nWDbdsT+CtWtRXPE33Onj1bBzSqqG5qqrWmSZMmunva888/r+fHad68+bELcFSEpaKr7du364jL7Omnn45oRwAAAIB4Es9jcE4++WSdNU0FMip+UP+2atXKkbrDDnAee+wxuf/++6Vt27ZSv359y6+Z4WRaAwAAAOJZPI/BWbFihY4nqkLYAc5zzz0nr7/+ulx55ZVVskMAAABAPFApot0OjptxMuV0VVPZ0V544QUZOXJkhfkz8/Pz5e233650WSjC7pyvclSfcsopYW8IAAAAQMUuak7+FytefPFFPQdOZQGMmgRUZVNTAVAkwg5wbr/9dnnppZci2hgAAAAAaxc1J0us+Pjjj+X6668PuPy6667T83AekwDnzjvvlFWrVulBQEOGDJFhw4ZZCgAAAIAj8xmG4yUc48aNkxNPPFHS09N1l7GhQ4fq7/lmhYWFctNNN+n5MNW8NcOHD5dt27ZZ1lm/fr0MHjxYateureu56667jjh/zdq1a6VNmzYBl6tlap1jEuDceuutOoOammVUHahqQjIXAAAAANW/BWfq1Kk6eJk5c6aeXLO4uFgGDBig56Ax997673//Kx9++KFef/PmzZZGDa/Xq4ObQ4cOyfTp0+Wtt96SN998Ux544IGg2/Z4PLquQNQyNTTmmCQZUDutmpTUgQAAAACIzTTRkyZNstxWgYlqgZk3b5706dNHD/Z/7bXX5L333pMzzzxTr/PGG29I+/btdVCkUj1/8803snz5cvn22291huVu3brJI488Ivfcc488+OCDkpSUVOm2u3fvLp9++qmuozITJkzQ60Qi7LAoOzvbsRzVAAAAQLxSAYnPwVIW4BQUFFhKUZF1EudAVEBT9n1fUYGOatXp37+/f5127dpJ06ZNZcaMGfq2+rdz5846uCkzcOBAvd1ly5YF3NbNN98sf/vb33SyAdUKVEb9rZILPPPMM7p16ZgEOCoSGzt2rBw4cCCiDQIAAAA43K3McLAcPql5eXmWISRqrM2R+Hw+ue2223S25E6dOun7tm7dqltgsrKyLOuqYEYtK1vHHNyULS9bFogay3P33Xfr4S8qoFKtNaqov9V+jB49Wi644IJj00Xt+eef1wN+1I43b95cEhMTLcvnz58f0Y4AAAAA8aSs5cXJ+pQNGzZY0i8nJyfLkajWkqVLl8pPP/0kx8qjjz4q5513nrz77ruyZs0aMQxDTj/9dLn00kvlpJNOirjesAMclV0BAAAAQPUcg5ORkRHWBJmqu9jEiRP1vDRNmjTx39+gQQOdPGDPnj2WVhyVRU0tK1tn9uzZlvrKsqyVrROMCmSOJphxJMBR3dMAAAAAHB2n564Jty7DMOSWW27RA/p/+OEHadGihWV5jx49dG+tKVOm6C5likojrdJC9+rVS99W/6qWmO3bt+sEBYrKyKYCrA4dOkg0hB3gAAAAAIj9LGo33XSTzpD22Wef6blwysbMqHE7tWrV0v9ec801ejyMGhujghYVEKmgpiz7mUorrQKZK664Qp588kldx/3336/rDqVrXFUIKcmAOqCdO3eGXKnKrPD7778fzX4BAAAANVq058F5+eWXdea0M844Qxo2bOgv77//vn8dlc3snHPO0S04KnW06nb2ySefWOazUd3b1L8q8Ln88stlxIgR8vDDD0u0hNSCo/rdffXVVyFP5Llr1y5LujcAAAAAVj7D4SQDKpVamF3UjiQlJUVeeuklXQJp1qyZfPnll1JdhNxFbeTIkVW7JwAAAEAcifYYnJoqpABH5cWOdYavRBcRl/V+w3RsXrXcxGXqwWcEbpGqGPyat2EEjpR93sD7YqvU5THVaV7Pvi+27blM+2LYjs9wJ1a6Xukd5u3ZtmHavst8jmxcriB12pa53OUvRZf92E3r2n9psFZp216gFXVFQdYNxnJeHLqMWF5nvsiOIZxl9ufa+sCguxpk58r/sr8kLNsP0ivWfj5NFblcRuDt2Za5XJ7yKsX+vjWfF/tulm+vwtYs27C9di0HbLu+mLZv3i/7VtTvd5Y63aZ9qXAtSAz4enEnpgTcF8v5tT3OY/ok8AR5Hx30/mG9w7SqV4oDLapwXTL/Wmk/19YzbT+fgV+fkfZhD/YoV4jvmmD7WfFxgWuynrPQr1Hh7Fuojwt1e8HqD3Z2Kz7OfM4i27NwrnRO1B/q0nCONdTXsTvC7VVYN9jnZhDmbVT4qDf9bW+ZCNZSEfibk3WZ2/YhY/7O4DZd24xIP+fjbAxOdaDmvqnstajuU61HrVu3liuvvFL69u1bdRN9AgAAAIj9MTjVwVlnnSW//vqrpKam6iBGlbS0ND3v5oknnihbtmyR/v3760QIoSKLGgAAABAFtOCITmR2xx13yJgxYyzn5i9/+YtOWvbNN9/oaWoeeeQRPSloKGjBAQAAABAVH3zwgVxyySUV7r/44ov1MkUtV/PvhIoABwAAAIgCuqiJHmczffr0CudG3aeWleUDKPs7FHRRAwAAAKJAJV5wNE10DI7CueWWW+T666+XefPm6TE3ypw5c+Rf//qX/PnPf9a3v/76a+nWrVvVBjhq0M8bb7yh/33uueckNzdXz5OjJvjs2LFjJFUCAAAAcSf2QhJn3X///dKiRQt58cUX5d///re+r23btvLqq6/KpZdeqm+rAOiGG26oui5qU6dOlc6dO8usWbP0LKb79u3T9y9atEgPAAIAAAAQCjqpKZdddpnMmDFDdu/erYv6uyy4UWrVqhVWF7WwA5x7771XZzWYPHmyJCUl+e8/88wzZebMmeFWBwAAAMQlwptSe/bs8XdJUwGOMn/+fNm0aZNEIuwuakuWLJH33nuvwv2qm5pK8wYAAADgyNR8pE7OSRqL3d0WL16s57nJzMyU3377Tf73f/9XsrOzdU+x9evXy9tvvx12nWG34GRlZekJd+wWLFggjRs3DnsHAAAAAMSn0aNHy5VXXim//PKLpRva2WefLdOmTYuozrADHJWT+p577pGtW7eKy+XSadt+/vlnufPOO2XEiBER7QQAAAAQrxN9OvlfrFEZ06677roK96uGExVvHJMA57HHHpN27dpJXl6eTjDQoUMH6dOnj/Tu3VtnQQAAAABwZIzBEUlOTpaCgoIK52b16tVSr169YxPgqMQCKm2bShE9ceJEeeedd2TlypU6rZvH4xGnqcFFl19+ueTk5OgMCiqD29y5cx3fDgAAAHDMAxyXgyUGn75zzz1XHn74YSkuLta3VQ8xNfZG9RgbPnx4RHVGPNGnmvNGlar0xx9/yCmnnCJ9+/bV8+yoKE71z6tTp06VbhcAAABA1fvb3/4mF1xwgU5YdvDgQTn99NN117RevXrJo48+WnUBjhr8E6qnn35anPLEE0/ornBqUtEyaiIgAAAAINY5nkXNwbqOFZU9TU0/89NPP+mMamoIzPHHH68zq0UqpABHZUgzU3mpS0pK9CyjZX3kVPe0Hj16iJM+//xzGThwoPzP//yPnmBUDTa68cYbZdSoUQEfU1RUpEuZyvr0AQAAANVjDI5zHctisYtamVNPPVUXJ4QU4Hz//feWFpr09HR56623/F3FVFeyq666Sk477TRx0q+//iovv/yybkFSE/+oLAu33nqrHgc0cuTISh8zbtw4eeihhxzdDwAAAMBpqsHFpf/vXH2x4Pnnnw95XfXdP1wuwzDCCvZUK8o333wjHTt2tNy/dOlSGTBggGzevFmcogKZE044QaZPn245SBXozJgxI+QWHNXNbeFP0yQ9La3CU28YPv/fLpct54L5tuENY8/N27CeXsvp9nlD3heXJ7H8YcUHA27Z/iuA+U3jTqxlWWa+rQZ0WR9oum17hfi8hwLup/kY3KZ9ttdp+MrXO3yHqf7SQWaV7Vuwl2uFYwiw7cMVSUQs58Wh30ksrzNfaNu2bz+cZUEvf5EekyvwMQR7Xiybtm3b8toyQn6PuVzlyU6MCu/bUB9nfx7M61qPx/oesF9fvJXWb6/Tvj3ze8f+mjd8JeYbAZd5ktOty7zly3bO+ciybOXb8/x/T1lR/v5WlheVziit1HZZ5zrLd230/53kU9fXciWufeX7YuszkSjl56JErMeQ4ir/3a3Efnwhvj6DreWKcF2vbU13kPeReT+D1W9fZn4l2V+BHtP2wvm1N9gXp0i/CPlM2w9Wf7D9tD/OvG44X/as+yIhP0eh1BdOHcGuUOEca6jPbbD9Clanx/49J8A+2297guSk8tiu8eZ9s59Pr+l6Zl8W7H3jMz3O/llvXlYo5dc5r1Ei8/fMlfz8fMnIyJDqRn0/VV2zutQ5UTzuiIfEV+D1lcjiP+ZU2+MONORkx44dcuDAAT3fprJnzx6pXbu2HpejGjzClRDJE6J2wk7dt3fvXnFSw4YNdRpqs/bt28vHH38cNNWcKna+kmLxlRyq+IXc/GUhwfY487JgKlwwQgtiKn75CrKJUFe0fyEw7Zv9S5Tl2G1fvlxu8+OMkL+Yhfp1wVy/fbddbtu+BA0AzBe9YFn87Bd88xfPwBdu+/GZ1zVsX0FCrafieuYvLhLyl2dxGZEti1hkXy6DnRfrike8I8BWAgfnFWsIHOCY38dh/Zpm3p49mLO8ru0/BgT5QmfeF8tzaX+vWC/hLndiaPuZlGLbFwm4Lz7TzWLD+uOKYTokn+k9ZWeuo3R7oX2RORbdMCL9Yh3klWSrx/4lLlid5tduZMHBsRDpr83W46u4NLI6q5Z5n4MHbM5vzy7U5zlYEO8KI3AwggRU9uAk0OOCvZ3D2U9zEFXxeln+Z6LpouQKP1FwlDg8CCdG2nDWrVvn//u9996Tv//97/Laa6/5h7+sWrVKD0mpbH6cUIT97J9//vm6O9onn3wiGzdu1EUFHNdcc40MGzZMnKQyqKkDNFPjfZo1a+bodgAAAIDodFFztsSaMWPGyAsvvOAPbhT19zPPPBPxHJtht+C88sorcuedd8qll17qz1edkJCgA5ynnnpKnHT77bfrCUTV5KIXXnihzJ49W/75z3/qAgAAAMQ2p8OS2AtxtmzZopOX2Xm9Xtm2bduxacFR/eFUM9KuXbt0djVVdu/ere9LTU0VJ5144okyYcIE+c9//iOdOnWSRx55RJ599lm57LLLHN0OAAAAcKy5DJfjJdb069dPd0VTWZrLzJs3T2644YaIU0VHPKpJBTNdunSRqnbOOefoAgAAANQstOC8/vrrOjuySiyWmFg6llS16KipYv71r38dmwCnb9++QbNVfffddxHtCAAAABBPVNIEZ9NEx14LTr169eTLL7+UX375RVasWKHva9eunRx33HER1xl2gNOtWzfLbTUOZ+HChTpNdKC5aQAAAABYqWxvTmZ8i53scRW1adNGFyeEHeCojAaVefDBB2XfvvJ5DwAAAAAEFq8tOKNHj9Zj60Mdv3/ffffJXXfdJdnZ2SGt71iYd/nll+s+dAAAAABCEZ+Jop977jk9sWeoXnrpJT35Z6gcmzp1xowZkpJinTwOAAAAQCDOtuDESoBjGIYeYxNsXL/Z/v37w6o/7ADHPpmn2kGVv3ru3Ll6oh4AAAAAoYjPLGpvvPFG2I+pX79+1QU4GRkZlmjL7Xbr2UYffvhhGTBgQLjVAQAAAHEpXsfgjKzixGRhBzhvvvlm1ewJAAAAEEfiNcCpamEnGWjZsqXs2rWrwv1q4I9aBgAAACDUNNEeB0vspomOagvOb7/9Jl6vt8L9RUVFsmnTJqf2CwAAAKjRaMGJcoDz+eef+//++uuvJTMz039bBTxTpkyR5s2bO7+HAAAAQA1EgBPlAGfo0KH6X5VgwD4wKDExUQc3f/vb35zfQwAAAKDGdlFzrltZrHVRKy4ullq1asnChQulU6dOxz7A8fl8+t8WLVrInDlzpG7duo7tBAAAABB/4jNNtLmRpGnTppUOfzmmY3DWrVsnMclXLIavWMTlsdxtGOUn1OWznlzD8JUvcwWJiE11HF65fJGvJPDDbNuzTHZk20/rirZ9seyn7fjEMO1msfVxCUnlywzbfvrML43yOg6vbN7pwMtsbzJzZg9bjeJyuwO+LC3nxRf4jWuto3SOpvI6bOfMF+HjLPvlDrif5jrs61ac1Mp02wh8Pl1u+2vCfhZD5QpYh/XYw7lImo/BdHKPQrDtB9/PIM+D6bkO9hyJK5xza37f2paYX1u2/XQFe1zQ150n8GsixOfMfs4Syy8FFX77M5+KEimyLjS9Xs3XGv0400H5bMvMt332cx3py9pSReB9qbhu4Fvmxzn1lcHl+LvWfm114ARGmf0cHcsjCicD1bE+79bXo3U/7e+xQI+rsCys63wk9Qe5dgd5plWbRqBteCpUWv6Z4zNdwYwYacmI9xYc5f/+7//kz3/+s/z73/+W7OxsOWYBzvPPPy/XXnutpKSk6L+DufXWWx3ZMQAAAKAmc7s8ujhWn/kX3Bjx4osvypo1a6RRo0bSrFkzSU1NtSyfP39+1QQ4zzzzjFx22WU6wFF/B/slgAAHAAAAODKSDIh/nL+TEsLtlhazXdQAAACAakV1KXOyW1nsdVEbO3as43WGfRYefvhhOXDgQIX7Dx48qJcBAAAACL0Fx8n/EEGA89BDD8m+ffsq3K+CHrUMAAAAQIhJBlwOlhhpwcnOzpadO3fqv+vUqaNvByrHJIuayoBRWdaNRYsWOZb5AAAAAKjp4nUMzjPPPCPp6en672effdbx+kMOcFR0pQIbVY477jhLkKNyV6tWneuvv97xHQQAAABqotLwxh13Ac7IkSMr/fuYBzgqulKtN1dffbXuipaZmelflpSUJM2bN5devXo5voMAAABATeR4mmgH6zpW8vPzZfLkyfLbb7/pBpSWLVtKv379JCMjo+oDnLLoqkWLFtK7d2898ygAAACASMV3FrV33nlHbr75ZikoKLDcrxpSXnnlFbnooouq7iyojZaV7t2764xp5vvMBQAAAMCROZpg4HCJFWoCz6uuukrPg7NgwQIdX6ikZXPnzpUhQ4bIFVdcocf4V1kLTlZWVqWJBSpLPqDG4wAAAAA4EqdTO8fGGBzlhRde0MHNm2++abn/+OOPl7ffflsHO88995y8/vrrVRPgfP/992FXDAAAAOAIaaIdTTIQOy04P//8s/z9738PuFwlL7vxxhsjqjukAOf0008PqbKlS5dGtBMAAABAvHG6W1m4dU2bNk2eeuopmTdvnmzZskUmTJigW1XKXHnllfLWW29ZHjNw4ECZNGmS//bu3bvllltukf/+97/idrtl+PDhuuUlLS0t6LY3b96sMzMHopZt2rRJInHUZ3Tv3r3yz3/+U0466STp2rXr0VYHAAAAxNU8OE7+F479+/fr7+8vvfRSwHXOOussHfyUlf/85z+W5ZdddpksW7ZMZ0KbOHGiDpquvfbaI25bdUFLSUkJuDw5OVkKCwvlmEz0WUbt/GuvvSYff/yxNGrUSIYNGxb05AAAAAAo53Il6OIUl8sX1vqDBg3SJRgVaDRo0KDSZStWrNCtOXPmzJETTjjBP7bm7LPPlr/+9a86Rgjm66+/tkw9Y7Znzx6JVFhndOvWrXogkApsVMa0Cy+8UIqKiuTTTz+VDh06RLwTAAAAQLxRCbqc7aJW2oJjz2ysghRVIvHDDz9Ibm6u1KlTR84880z5y1/+Ijk5OXrZjBkzdDKysuBG6d+/v+6qNmvWLDn//POD1n2kST6PlOQskJDPqErX1rZtW1m8eLGe9FP1m1MRGgAAAIBIAxxni5KXl6dbRsrKuHHjJBKqe5rKaDZlyhR54oknZOrUqbrFpyxrsmr8UMGPWUJCgmRnZ+tlwfh8viOWSLMzh9yC89VXX8mtt94qN9xwg7Rp0yaijQEAAACo2ok+N2zYIBkZGf57I229ufjii/1/d+7cWbp06SKtWrXSrTr9+vWT6irkAOenn37SXdN69Ogh7du315PvmA+6uvP5isXnLRaXyxYJmpoFDV+JZZEhhumGtU+jy51gqduyzOUpf5jhDb3ZzbQvwZorKywLcgxm9n0xvIHXlfJDqKSe8vPichkBl1VgOl7bw6x5211BFrmD7JjtfFr3zbbMXI/9aQi2LyGqMMjPfNMI8nzat2c5hEhz2wd7XOjnzLYzgWsMp6k9aJWBT1qwJmtLikx3kNdghTpN7yP7kxTh826Y+kKbrwul2wjWT9p0LbBvwx3kOmG59ljf3+Z13cm1LcsSk8u3Yt8rw7QDJcYha52mvbMfj3m/vW7bdcJ0KbKfa1+Qc29eYn9c8GcstNeuvY5gdUb2Cqn6xxlhPTK0a0rFK4HpMyCM8xnkMmh7Pl1Vfj5Drz+0LRz9WQ5PONcoJ86t/X1pvh3skyLYVwI782vJY7t4B/v4SzCtawT9DIuvLGoZGRmWAMcpLVu2lLp168qaNWt0gKPG5mzfvt2yTklJic6sFmjczrEQ8hk9+eST5dVXX9XZE6677joZP368Hjikmo9U1gSVTQ0AAABAbGRRC9fGjRtl165d0rBhQ327V69eOhmASjNd5rvvvtPxQc+ePSVawg4ZU1NT5eqrr9YtOkuWLJE77rhDHn/8cd3/7txzz62avQQAAABqGtXi4nQJw759+2ThwoW6KOvWrdN/r1+/Xi+76667ZObMmfLbb7/pcTjnnXeetG7dWs+Fo6heXWqczqhRo2T27Nl68s6bb75Z9/I6Uga1qnRUbWIq6cCTTz6pozl7TmwAAAAAQb6Iuz2Ol3DMnTtXunfvrosyevRo/fcDDzwgHo9HJxdTDRhq0s1rrrlGD1X58ccfLWN63n33XWnXrp3usqbSQ5966ql6jsxociTxtjoBatZT88ynAAAAAIKIoNUlqDDrOuOMM4KOn1bz1ByJypj23nvvSXXi3MxCAAAAAEKmEuNYkuMcJSfrqkpqTp1Q57hRCQvCRYADAAAARIF57hqn6osFak7NqkSAAwAAAESDCkgc7aIWGwHOyJEjq7T+2GjHAgAAAGpoC46TJRatXbtW7r//frnkkkv88+p89dVXsmzZsojqI8ABAAAAokBNPO50iTVTp06Vzp07y6xZs+STTz7R6amVRYsWydixYyOqkwAHAAAAiMN5cKqDe++9V/7yl7/I5MmTJSkpyX//mWeeqefgiQRjcAAAAICoUGNwnOxWFntd1JYsWVJpmunc3FzZuXNnRHXGXpgHAAAA1AAul9vxEmuysrJky5YtFe5fsGCBNG7cOKI6Y+8sAAAAADUmi5rDJcZcfPHFcs8998jWrVt1kgSfzyc///yz3HnnnTJixIiI6iTAAQAAAKKAFhyRxx57TNq1ayd5eXk6wUCHDh2kT58+0rt3b51ZLRIEOAAAAEA0xHELzgUXXCCTJk2SxMREefXVV+XXX3+ViRMnyjvvvCMrV66Uf//73+LxRJYVjiQDAAAAQBS43G5dHKvPiJ22iz/++EMGDx4sjRo1kquuukqXs88+25G6Y+csAAAAADWJ2yWiAhzHSuy04EyZMkW32lxzzTW61aZ169Y6NbTKqFZUVHRUdcdNC47hLdbFnh/c5UksX0cM62N8XtOK1heMy3TqLOvpOoM0p5m3b/isi1ymx9kmanJ5zNsrCVK97Sk1rWsY1v0MVk/Afbbvt2E7L+bzVOE9Zl5mX1h+7l32uNu0riG2cxasTsMIfAwu63MdaDctdRxJsP20bN9Wp2mZ5Xjs61bIjBJk3yzHHsbFLtLHWZ4zXxh1hri9YM9DhcdZnkDbqp7A+2l5HsJ43i3bs18nzLdt154ghxv89RL4NW+dwdq6PUPK3/+upBTLssSU8sd5gj0NLts1xLRvhst2Pk3L3Ib1euYzP67CowKfe/PjgqlYpwR8bq3bcwU+viNuJZQlVSP4fgbmCnJu7a9k6/YCLQn9ubVf6+zPSqiCren08xDsNRDs/AV7PVa4RpkeaX8vVPx8MNdiPu+hX7sN07XVvp/m60mF70cRvoeDvXYsrw/bwqDnxbRusum7medYvxEj5XS3shjqoqY0a9ZMHnzwQV2+++47ef3112XUqFFy8803yyWXXCJXX3219OjRQ8JFCw4AAAAQBSqQdLrEKtV6o1pyVDa1cePGyfjx46Vnz54R1RU3LTgAAABAtRLnLTh269atkzfffFOX/Px86d+/v0SCAAcAAACIBgIcKSwslI8++kh3T5s2bZpOF63G5aikA+rvSBDgAAAAANGgxlxWGGN7lPXFiNmzZ+ug5v3339dBzvnnn6/TRvfr1++ou9oR4AAAAADR4HGLy+NkUBI7Ac7JJ58sXbt2lUceeUQuu+wyqVOnjmN1E+AAAAAA0RDHXdTmzp0rxx9/fJXUTYADAAAARIXDAU4YacKjraqCG4UABwAAAIiGOG7BqUoEOAAAAEAUuNwuXZysDwQ4AAAAQHTQglMlaMEBAAAAosHtLi1O1gcCHAAAACAqVI+yOMwx0L1795Dnupk/f37NbsF5/PHH5b777pP/9//+nzz77LPR3h0AAAAgcmrMjJPjZmJkDM7QoUOrtP6YCXDmzJkj//jHP6RLly7R3hUAAADg6MXpGJyxY8dWaf0x0VFv3759eobTV1991dFZTgEAAIDodlFzOVh4LmMmwLnppptk8ODB0r9//yOuW1RUJAUFBZYCAAAAVDdqHIrTJdZ4vV7561//KieddJI0aNBAsrOzLaVGBjjjx4/Xg4vGjRsX0vpqvczMTH/Jy8ur8n0EAAAAIh6D42SJMQ899JA8/fTTctFFF0l+fr6MHj1ahg0bJm63Wx588MGaF+Bs2LBBJxR49913JSUlJaTHqCQE6uSUFVUHAAAAUO0Q4Ij6nq+Godxxxx2SkJAgl1xyifzrX/+SBx54QGbOnFnzkgzMmzdPtm/fLscff7ylGWvatGny4osv6u5oHo/H8pjk5GRd7AyfTwyft0JIZ45zXS5rXeLyRbTfLlf5RgzT36XbM23RnWhd5im/7XJbnxqXKyHgMhHDsoVAfCUl1kd5iwOuq89VJftVui/l2zAMI+Ay+76Yj936KNu6tkOw3Aza9Br4gfZHGaF2Uo3whxB7E7Hl2F1B1rWdT/O69jrN577C8QU99mA7bq7Ttj3zahUeZz4Gd+j7EvT8GiE97/b9DLqu+b1Z4fUZ+FaAvaq4ZoXnyB3k+XOH+KzY98wd5DcqV4D1bNswrNe2pNrl1z6vbVc8piepxOO11WiuM/DvZS7bE+0zXVt9tof5TDvgrvAaDPYqNAKeTfPm3baFPvPr0/b+81m2YXtvWs61RLSfFd9jQY4h6LJgtwIfgy/gPgd/nPm2L4x9MZ9PV4U1Q/scq/hOCXxezPsW7GiCf6qYP7eMMD7TAvOZ1vYEfY1LwGX21461TvvjTOvZP2OCvVJN65rfJ6XbC22fgwnncebNu4P8Nu8K+P6txuI0yYDZ1q1bpXPnzvrvtLQ03UChnHPOOTJmzBipcQFOv379ZMmSJZb7rrrqKmnXrp3cc889FYIbAAAAIHY4HODESmBn0qRJE9myZYs0bdpUWrVqJd98841u3FAZlCtrtIj5ACc9PV06depkuS81NVVycnIq3A8AAADEFLfDA0aq9eCTyp1//vkyZcoU6dmzp9xyyy1y+eWXy2uvvSbr16+X22+/XWpcgAMAAADUWHRRk8cff9x/OlSigWbNmsn06dOlTZs2MmTIkPgIcH744Ydo7wIAAABw9Ahw9Nj63r176wQDysknn6xLSUmJXtanT594aMgCAAAAaspEnw6XGNO3b1/ZvXt3hftVsgG1LC5acAAAAIAaweMuLU6xp6aMASpjX2UTlO7atUuPvY8EAQ4AAAAQDU63usRQC86wYcP0vyq4ufLKKy0Z09S0MIsXL9Zd1yJBgAMAAABEQxyPwcnMzPS34KjMybVq1fIvS0pK0uNwRo0aFVHdBDgAAABANMRxC84bb7yh/23evLnceeedEXdHqwwBDgAAABAFqntWZeNPjqa+WDN27FjH64y9kUgAAABATeB2OV9izLZt2+SKK66QRo0a6VTRHo/HUiJBCw4AAAAQDXHcRa2MSjCwfv16GTNmjDRs2NCRVigCHAAAACAanG51icEWnJ9++kl+/PFH6datm2N1EuAAAAAA0RDHWdTK5OXl6UxqTmIMDgAAABANrsPfxp0qsRffyLPPPiv33nuv/Pbbb47VSQsOAAAAEA204MhFF10kBw4ckFatWknt2rUlMTHRcop2794d9mklwAEAAACigSQDolpwnJYQX3nGK+mRZ7rPnZBkWeQt9pbfMHyB63Z7AvZ/dLmsy1ye8qjU5baefpe7fF9cLtsyU5o8l2GNbC37VqHvZXmfRpfXusznK6nscA5XYz4G63kzzMvs6fvMy+ztpJZlFbYYcF8sa9mfB8vxBj52+7LQW3DtfUJdR70s6LG7jJD3M1iWkaCPs9wKdgzhnLNgxxDp8xCkP665r25l72t//fYtmN9jkfX3tR5P6T0B17Ussh27eWGF60uQM2O6pthfA4b5dVbhvJTvd0LtLMuS1MZp/r+TXActyzymOj223fSZtm/YzqflWTeCvOpsy3ympW7befBZbob+/Bmmdc37XGG9CpcQI+Ayl3lZkHrM69mXuSv0OS9f6LO/PkM8XOs5Cv5A8/btj7Pvd6jHF4z5NVLh8IKcl8BXkMqO17TMVE/Fa0Hl9QdbGuxdan6NVawh8GvcLtLeRUaQfTHf9gbZXuBvOdbrgP1x3grPV+DjC/b+DlWwT3qP6f0d7L1erdCCIyNHjnT8tDIGBwAAAIgGdxWUMEybNk2GDBmi56BRP5p9+umnluVq8P8DDzyg0zfXqlVL+vfvL7/88kuFLmSXXXaZZGRkSFZWllxzzTWyb9++cHZDvF6vfPzxx/KXv/xFlwkTJuj7IkWAAwAAAMThRJ/79++Xrl27yksvvVTp8ieffFKef/55eeWVV2TWrFmSmpoqAwcOlMLCQv86KrhZtmyZTJ48WSZOnKiDpmuvvTbkfVizZo20b99eRowYIZ988okul19+uXTs2FHWrl0rkYibLmoAAABAPIzBKSgosNydnJysi92gQYN0qYxqvVHjY+6//34577zz9H1vv/221K9fX7f0XHzxxbJixQqZNGmSzJkzR0444QS9zgsvvCBnn322/PWvf9UtQ0dy66236gQDM2fOlOzsbH3frl27dJCjln3xxRdhnwZacAAAAICojRF3tpTNLZOZmekv48aNk3CtW7dOtm7dqrullVF19ezZU2bMmKFvq39Vt7Sy4EZR67vdbt3iE4qpU6fqlqKy4EbJycmRxx9/XC+LBC04AAAAQA1qwdmwYYMeE1OmstabI1HBjaJabMzU7bJl6t/c3FzL8oSEBB2slK1zJGrf9u7dW+F+NY4nKcmaACxUtOAAAAAA0QxwnCwiOrgxl0gCnGPlnHPO0WN2VIuP6haniuqudv3118u5554bUZ0EOAAAAEA06KDE5WBxbtcaNGig/922bZvlfnW7bJn6d/v27ZblJSUlOrNa2TpHopIYqDE4vXr1kpSUFF1OOeUUad26dcRz5NBFDQAAAIgGj6u0OFmfQ1q0aKGDlClTpki3bt38yQtUS8sNN9ygb6ugZM+ePTJv3jzp0aOHvu+7774Tn8+nx+qEQo3h+eyzz3Q2NZW0QFFZ1VSAEykCHAAAAKAGjcEJlRrnogILc2KBhQsX6jE0TZs2ldtuu03PS9OmTRsd8IwZM0ZnRhs6dKg/EDnrrLNk1KhROpV0cXGx3HzzzTrDWigZ1BSVVrpdu3Y6oDEHNaoulcSgT58+Ei4CHAAAACBanAxwwjR37lzp27ev//bo0aP1vyNHjpQ333xT7r77bj1Xjhojo1pqTj31VJ0WWnUjK/Puu+/qoKZfv346e9rw4cN1t7NQnXHGGTpxgZrc8+STT/bfr7q5qX2LZMJPAhwAAAAgCsypnZ2qLxwquFCD+oPV9/DDD+sSiGrtee+99+RoqBYfFSCpCUevvPJK//3B9i0YAhwAAAAgDruoVQcqiLrvvvvktNNOkxEjRsjixYvlb3/7m39ZJMiiBgAAANSgNNGxpKyVZtiwYfLjjz/KRx99JIMGDdJd4iJFgAMAAABEg6Mpog+XGNa9e3eZPXu2Dm5Ul7VIEeAAAAAAUeDyOF9ijUpoUKtWLf9tlZp66tSpOsBRmdwiwRgcAAAAIBoYgyNvvPFGhdOSnJwsb731VsSnlQAHAAAAiAanu5XFSBe1xYsXS6dOnXRaafV3MF26dAm7fgIcAAAAIBritAWnW7dusnXrVsnNzdV/q2xp5pTQZbfVv8yDE4TLk6iLnduTZFnHssxXPrGQz3vI/sDyxxk+67bcprjRZR3m5E4wbc9ljS9dbnfACNxl6lTptu2n4bNu3yJIX0zDWxxwe+b9NlwltkXB4mJXkF8RXEf/jrSdT0fqDL5BR5YZQR5lzfBue94DrufUNSyyWoJlpXdVqLMKngdXiOfF9nqxPg+hP0dB98WJdW3vFfM7usIr3rJu6Mdn3hd3YvkEbUpSbrap/p22R5U/zmNYt+dzmfbUsJ1P06pun3VZiad8Tz0+T8A67dMfGKYjDOf9YN6821Zp0PdmsNdZiE+t7bTY9su60Lxv5mOtbN2AdQa9ogTevn17wfbbvKbbtl/2eqzbMz0uxP2y12l/XduPVwLsW7D9chlBjtu8zLbIPPWg/XGWde2vOfPrqsLrMdgzFvgYzMdnPyeh3wq8Nfu3DJ95v83XgSN8bnlNj6v42jHXYf8sdAV5jZuubQH+rs7itAFH1q1bJ/Xq1fP/7TRacAAAAIBoiNMWnGbNmul/i4uL5aGHHpIxY8ZIixYtHKufLGoAAABANMR5mujExET5+OOPHa+XAAcAAACIAtWT2ukSa4YOHSqffvqpo3XSRQ0AAACIhjjtombWpk0befjhh+Xnn3+WHj16SGpqqmX5rbfeKuEiwAEAAACiQCWOsSaPOfr6Ys1rr70mWVlZMm/ePF3sx0OAAwAAAMSS2ItJHEUWNQAAAKCGcHrcTCyOwTErS51+tC1RMX4aAAAAgBgfg+NkiUFvv/22dO7cWWrVqqVLly5d5N///nfE9TEGBwAAAIgCl9uli5P1xZqnn35az4Nz8803yymnnKLv++mnn+T666+XnTt3yu233x52nQQ4AAAAQDSQRU1eeOEFefnll2XEiBH+03LuuedKx44d5cEHHyTAAQAAAGKF03NzxmASNdmyZYv07t27wv3qPrUsEozBAQAAAKKBMTjSunVr+eCDDyqcmvfff1/PkRMJuqgBAAAAUUALjshDDz0kF110kUybNs0/BkdN+jllypRKA59QEOAAAAAA0UCEI8OHD5dZs2bJM888I59++qk+Le3bt5fZs2dL9+7dIzqtBDgAAABAFBDflOrRo4e888474hQCHAAAACAK4nmiz4KCgpDWy8jICLtuAhwAAAAgCuK5BScrK0tcQXbYMAy93Ov1hl133AQ47sRU8SSlimFYT5I7Idn/t8udaFnmcpeUr2cYtmWBT53LU16P/WlzJySZ17Q+zlVeZ9DXp33bLl/53/b9NCXKsx+fuIoDPs6Q8jrt58zlMh2f7YXpsvx0YP8ZIciLWEIV+jvXCLGWUNc70rrBHuczPdJlW2oEXSYOnLOqF+wYgp3r4MtCe66Dn4djez7DOz4J6TXhsy1zm95XRhivM7XUX0diLWudqWn+vxNs72m3qZ4EW52mK0iFrbl8HtOy8r8Vj6/8GuY2rNclr7vQtMxnPQLTRnwu69G7jSDXF/PjHHoNBFvXvG/B9ivYq8J8rHqJ7Rpd+aNEfLbLrvk0VTxngZdZ17PujHndILtlOe/6tuV4A58X+7Ga67F/FpuP11q/dd/cYeynmTfIMvMir+38WW7a6rCuG+GnjO08mOuscI5Mfwf7cd9+zSgxXzNsJ8lyzTdCbzIwX6PMf5fWGRqjWn8aRiCO58H5/vvvLcHM2WefLf/617+kcePGR1133AQ4AAAAQHWifigO1ooRSX2x4vTTT7fc9ng8cvLJJ0vLli2Pum4CHAAAACAaHO6iFkstOFWJAAcAAACIFoISxxHgAAAAAFEQz0kGqrKLHQEOAAAAEAVud2lxsr5YMWzYMMvtwsJCuf766yU1NdVy/yeffBJ23QQ4AAAAQDTEcRNOZmam5fbll1/uWN0EOAAAAEAUxHF8I2+88UaV1U2AAwAAAERBPAc4VYkABwAAAIgCApyqQYADAAAARAEBTtUgwAEAAACiwOUuLU7WBwIcAAAAIGrzvjg190tZfRCp1nHeuHHj5MQTT5T09HTJzc2VoUOHyqpVq6K9WwAAAIBjXdScLKjmAc7UqVPlpptukpkzZ8rkyZOluLhYBgwYIPv374/2rgEAAABHhQAnDsfgTJo0yXL7zTff1C058+bNkz59+kRtvwAAAICjRZKBOAxw7PLz8/W/2dnZAdcpKirSpUxBQcEx2TcAAAAgHAQ4cdhFzczn88ltt90mp5xyinTq1CnouJ3MzEx/ycvLO6b7CQAAAISCLmpxHuCosThLly6V8ePHB13vvvvu0y09ZWXDhg3HbB8BAACAULldLnG7HSxkGYidLmo333yzTJw4UaZNmyZNmjQJum5ycrIudu7EFHEn1hIxvJb7XZ7E8r/dHtuyhICJxd2mxxm2x7k9SabHWffD5TJtr8KLMLR402Wr1GV6nOEybGuX33YnJFlbxbyHytfyldj20x04qbplmW1fTOfCfnzmPbPvZaQMU0328+JzYCv2Z8hwYD+D1VJxe5Ft0X4uAtVpXy/YslDZ99lci/1oQl0WfHuBHxfsNRHsuXVHeF4qPl+ugK9H6zYCb89+hL4Qt++zLbNsz/beTMgo7/ab4gl2zgJv22VYrxMuKb8WeGwfNW7Tuub1lBJXof9vb4XrWfnjPD7bfrrLr+0V9sW8rq1Ow+UL+LhA6x2+I2CdHl/58frsj7PsmPVxXtO5NuyvXXfgeizPi3m/bK8ej88T8JyZ99nO/ix4THcEOz6XbV/MtyucT9O5sD3Mwmv/TDW/luzHbjo+8+MqvKzMj6lQf+B1zWfG/jjLbZ/tPRyk0lD3zb6a+SVeYltqvS5Yd9RtWTXwfpYY1ufL8t607Uyw64T5euI1ghysrYpg127z9szrOfEd4Figi1ocBjiGYcgtt9wiEyZMkB9++EFatGgR7V0CAAAAHENm5zgLcFS3tPfee08+++wzPRfO1q1b9f1qbE2tWrWivXsAAABAxGjBicMxOC+//LIeR3PGGWdIw4YN/eX999+P9q4BAAAAR4UkA3HaRQ0AAACosUkGHEwMQJKBGAhwAAAAgJqKLmpx2EUNAAAAqKncbudLOB588EGd9dZc2rVr519eWFiox8Tn5ORIWlqaDB8+XLZt2ybVHQEOAAAAEKdjcDp27Chbtmzxl59++sm/7Pbbb5f//ve/8uGHH8rUqVNl8+bNMmzYMKnu6KIGAAAAREFZq4mT9YUrISFBGjRoUOF+lejrtdde0xmNzzzzTH3fG2+8Ie3bt5eZM2fKySefLNUVLTgAAABADWrBKSgosJSioqKA+/DLL79Io0aNpGXLlnLZZZfJ+vXr9f3z5s2T4uJi6d+/v39d1X2tadOmMmPGDKnOCHAAAACAKHC7nC9KXl6enjeyrIwbN04q07NnT3nzzTdl0qRJenqWdevWyWmnnSZ79+7V808mJSVJVlaW5TH169f3z01ZXdFFDQAAAKhBWdQ2bNggGRkZ/vuTk5MrXX/QoEH+v7t06aIDnmbNmskHH3wgtWrVklhFCw4AAABQg7qoZWRkWEqgAMdOtdYcd9xxsmbNGj0u59ChQ7Jnzx7LOiqLWmVjdqoTAhwAAAAgCjwul+PlaOzbt0/Wrl0rDRs2lB49ekhiYqJMmTLFv3zVqlV6jE6vXr2kOqOLGgAAABANDndRU/WF484775QhQ4bobmkqBfTYsWPF4/HIJZdcosfuXHPNNTJ69GjJzs7WLUG33HKLDm6qcwY1hQAHAAAAqEFjcEK1ceNGHczs2rVL6tWrJ6eeeqpOAa3+Vp555hlxu916gk+ViW3gwIHy97//Xao7AhwAAAAgCtwuly5O1heO8ePHB12ekpIiL730ki6xhAAHAAAAiMMWnJoqbgKchKTakpCUKj5fieV+l7s8z4LbZT0dhjvRtJ4EfJzL1uHR5fIEXCamZT7bPhpiBH6cuQpbbgjzi9llqsPOnWBN9+fxlp8LnzfwBFDm4ym97Q64zPrOsh6Dec98tv00H7td4CVHWjNInUbgtcpyyFd2DD7zA4OwP3tGkGXmc+G2by/E10TF7Yd2Pt0u63pe0/GFM1Ax2GlxcobmyrdtBPn1KvDrzH4+ra9Bd5DHBdkX2/bM6/oqvOMD53gxr2vfT/Oj7Kfd/Pqs+Cte+W13gjWbTnJuc//ftRMCnxf769Nau/Va4DF9vHgkKcjjrHUW+8of53Vbr9ceb/kyt217LtP1zP4smdf1GV7LMsNVfq49RqJ1mel5MP9t34a5Dr09o3w/XUbFK32gxxmm96PHdB5Kl1n3u/Ia1bZt12TLHtueP58ryLEHu9aZ3g9B3vzB3mOGfV8s9QS+gprPUen23QGP3Wc6vUGv3KY6bdWrDQZ8vszH5/bZrhmmdb1u63Pn8SUGPM/m46t4PSnfXomtTvOxl7ht+2mqxv58mQ6v4vXEcg2x1uk1LTPEdnym5yTY552ded1D9vdpkOuQx3JVLP+7JMTP62hTR+NogONcVTEtbgIcAAAAoDpxIvOZvT4Q4AAAAABRoXqNWHuOHH19IMABAAAAooIxOFWDLmoAAABA1AIc55pd6KFWigAHAAAAiAK6qFUNAhwAAAAgCuiiVjUIcAAAAIA4nOizpiLAAQAAAKLURc1DFjXHEeAAAAAAUaASDDibZIAWHIUABwAAAIgCkgxUDQIcAAAAIApowakaBDgAAABAFLgPFyfrAwEOAAAAEBW04FQNWnAAAACAKGAMTtUgwAEAAACiwON26eJkfSDAAQAAAKJChSNOhiSEN6VowQEAAACiwO1y6eJkfSDAAQAAAKJCxSNOxiTEN3HWgpN3XEvJyMiI9m4AQDXQPOCSh9aea719DPYGAJxWUFAgmZmZ1f7EusTZFhxVH+IowAEAAACqE8bgVA0CHAAAACAKGINTNQhwAAAAgCggTXTVIMABAAAAokCNmXFy3AxjcEoR4AAAAABRoObldHJuTub5LEWAAwAAAESBy+XSxcn6QIADAAAARIX7cHGyPhDgAAAAAFFBC07VoIsaAAAAEAUel0sXJ+sDAQ4AAAAQHQ6PwVH1gQAHAAAAiAqyqFUNuqgBAAAAUcA8OFWDAAcAAACIAtWjjB5qziPAAQAAAKKAFpyqQYADAAAARAFjcKoGAQ4AAAAQBW6XS9wut6P1gQAHAAAAiArG4FQNWnAAAACAKGAMTtUgwAEAAACiQHUoc7JTGR3UShHgAAAAAFHgcrl0cbI+EOAAAAAAUUELTtWgBQcAAACIAlpwqgYBDgAAABAFbnHp4mR9IMABAAAAooaQxHm04AAAAABRwDw4VYMABwAAAIgK0gxUBQIcAAAAIAoIb6oGAQ4AAAAQBa7D/zlZHwhwAAAAgKigBadq0IIDAAAARAEtOFXDLTHgpZdekubNm0tKSor07NlTZs+eHe1dAgAAAFANVfsA5/3335fRo0fL2LFjZf78+dK1a1cZOHCgbN++Pdq7BgAAABx1FzUnC2IgwHn66adl1KhRctVVV0mHDh3klVdekdq1a8vrr78e7V0DAAAAUM1U6zE4hw4dknnz5sl9993nv8/tdkv//v1lxowZlT6mqKhIlzL5+fn634KCgmOwxwAAAIi2su99hmFIdbZ/3z5HW11UfajmAc7OnTvF6/VK/fr1Lfer2ytXrqz0MePGjZOHHnqowv15eXlVtp8AAACofvbu3SuZmZlS3SQlJUmDBg2k5+k9Ha+7QYMGuv54Vq0DnEio1h41ZqeMz+eT3bt3S05Ojrhc9Ew0/7Khgr4NGzZIRkZGlJ6tmoPzyfmsznh9cj6rM16fnMuqoFpuVHDTqFEjqY5U4qx169bp3kpOS0pK0vXHs2od4NStW1c8Ho9s27bNcr+6raLTyiQnJ+tilpWVVaX7GctUcEOAw/msrnh9cj6rM16fnM/qitdmqerYcmOmgpB4D0TiMsmAikB79OghU6ZMsbTIqNu9evWK6r4BAAAAqH6qdQuOorqbjRw5Uk444QQ56aST5Nlnn5X9+/frrGoAAAAAEFMBzkUXXSQ7duyQBx54QLZu3SrdunWTSZMmVUg8gPCobnxqbiF7dz5EhvPpLM4n57M64/XJ+ayueG0CpVxGdc+fBwAAAAA1YQwOAAAAAISDAAcAAABAjUGAAwAAAKDGIMABAAAAUGMQ4MSol156SZo3b64niOrZs6fMnj1b379792655ZZbpG3btlKrVi1p2rSp3HrrrZKfn3/EOj/88ENp166drrNz587y5ZdfWparfBQqm13Dhg113f3795dffvlFavL5tB//oEGDxOVyyaeffnrEOjmfgc/njBkz5Mwzz5TU1FQ9IV2fPn3k4MGDQc/nDz/8IMcff7zOEtS6dWt58803I3oeY1Gw41LZJa+44go9+bE6n+ocffzxx0esMx7P57Rp02TIkCF6ZvPK3seRXuPi8Vwe6XwWFxfLPffcoz9L1OtSrTNixAjZvHnzEevlfFb++jS7/vrr9Tpq6gzOJ1AJlUUNsWX8+PFGUlKS8frrrxvLli0zRo0aZWRlZRnbtm0zlixZYgwbNsz4/PPPjTVr1hhTpkwx2rRpYwwfPjxonT///LPh8XiMJ5980li+fLlx//33G4mJibq+Mo8//riRmZlpfPrpp8aiRYuMc88912jRooVx8OBBo6aeT7Onn37aGDRokMo6aEyYMCFonZzPwOdz+vTpRkZGhjFu3Dhj6dKlxsqVK43333/fKCwsDHg+f/31V6N27drG6NGj9evzhRde0K/XSZMmhf08xpojHdef/vQn48QTTzRmzZplrF271njkkUcMt9ttzJ8/P2Cd8Xo+v/zyS+P//u//jE8++aTS93Ek17h4PZdHOp979uwx+vfvr9/b6j0+Y8YM46STTjJ69OgRtE7OZ+DXZxm1vGvXrkajRo2MZ555hvMJVIIAJwapD4mbbrrJf9vr9eoLnfrCWJkPPvhAf7gWFxcHrPPCCy80Bg8ebLmvZ8+exnXXXaf/9vl8RoMGDYynnnrK8gGWnJxs/Oc//zFq+vlcsGCB0bhxY2PLli0hBTicz8DnU72uVAAdjrvvvtvo2LGj5b6LLrrIGDhwYFjPYyw60nGlpqYab7/9tuUx2dnZxquvvhqwzng+n2Xs7+NIr3Gcy8rPZ2Vmz56t1/v99985n2G+Psts3LhRfxapH4eaNWt2xACH1yfiFV3UYsyhQ4dk3rx5uutEGbfbrW+rbj+VUd3TVDeghITyeV1VV4kHH3zQf1s91lynMnDgQH+d69at011hzOtkZmbqrhaBtltTzueBAwfk0ksv1V1MVDegynA+Qzuf27dvl1mzZklubq707t1bT9h7+umny08//WQ5n2eccYZceeWVIb8+I3lfxIJQjkudx/fff193T/X5fDJ+/HgpLCzU57AM5/PIQr3GcS4jpz6LVLeqrKwszmcE1PtbdUe96667pGPHjpWuw+sTKEWAE2N27twpXq9XfzE0U7fVh3Nl6z/yyCNy7bXXWu5v1aqV1K1b139bPTZYnWX/hrrdmnQ+b7/9dv0l8rzzzgtYD+cztPP566+/6tsquB41apRMmjRJj13o16+fZayDGjumxkEc6fVZUFCgx+6E+76IFaEc1wcffKDHO+Tk5OgxINddd51MmDBBjwUpw/k8slCvcZzLyKigW43JueSSS/QPbpzP8D3xxBP6h0o1rjYQXp9AqfKf9FHjqC9/gwcPlg4dOlhaa5QpU6ZEbb9iyeeffy7fffedLFiwIOh6nM/Qf4FU1Jfwq666Sv/dvXt3ff5ef/11GTdunL7v7bffPspnLn6MGTNG9uzZI99++63+0UINTL7wwgvlxx9/1AO8Fc6ncziX4VMBuHpNqp5XL7/8MuczAqol97nnnpP58+frVjBen0BwtODEGPUFxuPxyLZt2yz3q9vm7lN79+6Vs846S9LT0/WvuYmJiUHrVY8NVmfZv0fabk07nyq4Wbt2re5SoX45K+vmN3z4cEsXIDvOZ+Xns6xVRgXdZu3bt5f169eHfT7VL8Eq21Wo74tYc6TjUq/NF198UQeHqhWsa9euMnbsWDnhhBN0l8pA4vV8BhPpNY5zGVpw8/vvv8vkyZMtrTecz9CpHyxUF1/VQlP2WaTO6R133KG7SHM+ASsCnBiTlJQkPXr0sLQYqF/F1e1evXr5W24GDBig11UtECot6ZGox9pbIdSHUVmdLVq00B/k5nXUdtR4irJ1auL5vPfee2Xx4sWycOFCf1GeeeYZeeONNwLWy/ms/HyqD2KVLnbVqlWW87V69Wpp1qxZxOczlPdFLDrScanxYWXjcsxUcFLWWlaZeD2fwUR6jeNcHjm4Ud1PVQuj6kZ5JJzPyqmxN/bPInUtVeNxvv76a84nYBftLAcIn0o5qjL7vPnmmzot6bXXXqtTjm7dutXIz8/XWao6d+6s00SrrF9lpaSkxF/HmWeeqdOZmtMaJyQkGH/961+NFStWGGPHjq00TbTazmeffWYsXrzYOO+882pMmuhA5zPU7Dacz9DPp8r6o9JEf/jhh8Yvv/yiM6qlpKTo12uZK664wrj33nsrpI6966679OvzpZdeqjQVbzjPY6wIdlyHDh0yWrdubZx22mk6TbQ6h+o97HK5jC+++MJfB+ez1N69e3VGRFXU+1ilfld/l2X1CuUax7kM7Xyq16ZKs92kSRNj4cKFls+ioqIizmcEr0+7yrKo8foEShHgxCgVnDRt2lSnf1bpXGfOnKnv//777/WFsbKybt06y4VRBTH2dNLHHXecrlOlkDV/QSpLozpmzBijfv36+gtXv379jFWrVhk1+XyGGuBwPsM7nyrVsPrio4KWXr16GT/++KNl+emnn26MHDnScp96bXfr1k3X2bJlS+ONN944qucxlgQ7rtWrV+u5r3Jzc/X57NKlS4W00ZxPI+j1sey1Fso1jnMZ2vlUnzeBPovU4zif4b8+QwlweH0CpVzqfxWadQAAAAAgBjEGBwAAAECNQYADAAAAoMYgwAEAAABQYxDgAAAAAKgxCHAAAAAA1BgEOAAAAABqDAIcAAAAADUGAQ4AAACAGoMABwBi2JVXXilDhw6N9m4AAFBtJER7BwAAlXO5XEFPzdixY+W5554TwzA4hQAAHEaAAwDV1JYtW/x/v//++/LAAw/IqlWr/PelpaXpAgAAytFFDQCqqQYNGvhLZmambtEx36eCG3sXtTPOOENuueUWue2226ROnTpSv359efXVV2X//v1y1VVXSXp6urRu3Vq++uory7aWLl0qgwYN0nWqx1xxxRWyc+fOKBw1AABHhwAHAGqYt956S+rWrSuzZ8/Wwc4NN9wg//M//yO9e/eW+fPny4ABA3QAc+DAAb3+nj175Mwzz5Tu3bvL3LlzZdKkSbJt2za58MILo30oAACEjQAHAGqYrl27yv333y9t2rSR++67T1JSUnTAM2rUKH2f6uq2a9cuWbx4sV7/xRdf1MHNY489Ju3atdN/v/766/L999/L6tWro304AACEhTE4AFDDdOnSxf+3x+ORnJwc6dy5s/8+1QVN2b59u/530aJFOpipbDzP2rVr5bjjjjsm+w0AgBMIcACghklMTLTcVmN3zPeVZWfz+Xz633379smQIUPkiSeeqFBXw4YNq3x/AQBwEgEOAMS5448/Xj7++GNp3ry5JCTwsQAAiG2MwQGAOHfTTTfJ7t275ZJLLpE5c+bobmlff/21zrrm9XqjvXsAAISFAAcA4lyjRo3k559/1sGMyrCmxuuoNNNZWVnidvMxAQCILS6DKbABAAAA1BD8NAcAAACgxiDAAQAAAFBjEOAAAAAAqDEIcAAAAADUGAQ4AAAAAGoMAhwAAAAANQYBDgAAAIAagwAHAAAAQI1BgAMAAACgxiDAAQAAAFBjEOAAAAAAkJri/wPZHrZqGpLIFQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.figure(figsize=(10, 6)).add_axes([0.14, 0.14, 0.8, 0.74])\n",
+    "# Plot flow direction\n",
+    "plt.pcolormesh(t, ds_avg[\"range\"], ds_avg[\"U_dir\"], cmap=\"twilight\", shading=\"nearest\")\n",
+    "# Plot the water surface\n",
+    "ax.plot(t, ds_avg[\"depth\"])\n",
+    "\n",
+    "# set up time on x-axis\n",
+    "ax.set_xlabel(\"Time\")\n",
+    "ax.xaxis.set_major_formatter(dt.DateFormatter(\"%H:%M\"))\n",
+    "\n",
+    "ax.set_ylabel(\"Altitude [m]\")\n",
+    "ax.set_ylim([0, 12])\n",
+    "plt.colorbar(label=\"Horizontal Vel Dir [deg CW from true N]\");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving and Loading DOLfYN datasets\n",
+    "Datasets can be saved and reloaded using the `save` and `load` functions. Xarray is saved natively in netCDF format, hence the \".nc\" extension.\n",
+    "\n",
+    "Note: DOLfYN datasets cannot be saved using xarray's native `ds.to_netcdf`; however, DOLfYN datasets can be opened using `xarray.open_dataset`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment these lines to save and load to your current working directory\n",
+    "# dolfyn.save(ds, 'your_data.nc')\n",
+    "# ds_saved = dolfyn.load('your_data.nc')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Turbulence Statistics\n",
+    "\n",
+    "The next section of this jupyter notebook will run through the turbulence analysis of the data presented here. There was no intention of measuring turbulence in the deployment that collected this data, so results depicted here are not the highest quality. The quality of turbulence measurements from an ADCP depend heavily on the quality of the deployment setup and data collection, particularly instrument frequency, samping frequency and depth bin size.\n",
+    "\n",
+    "Read more on proper ADCP setup for turbulence measurements in: Thomson, Jim, et al. \"Measurements of turbulence at two tidal energy sites in Puget Sound, WA.\" IEEE Journal of Oceanic Engineering 37.3 (2012): 363-374.\n",
+    "\n",
+    "Most functions related to turbulence statistics in MHKiT-DOLfYN have the papers they originate from referenced in their docstrings.\n",
+    "\n",
+    "### 7.1 Turbulence Intensity\n",
+    "For most users, turbulence intensity (TI), the ratio of the ensemble standard deviation to ensemble flow speed given as a percent, is all most will need. In MHKiT, this can be simply calculated as `<raw dataset>.velds.I`, but be aware that this will be a conservative estimate. Another function, `turbulence_intensity`, is capable of subtracting instrument noise from this parameter and is discussed below. The noise-subtracted TI is more accurate and typically 1-2% lower than the non-noise-subtracted estimation.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import xarray as xr\n",
-        "\n",
-        "spec = [None] * len(ds.range)\n",
-        "e = [None] * len(ds.range)\n",
-        "n = [None] * len(ds.range)\n",
-        "\n",
-        "for r in range(len(ds[\"range\"])):\n",
-        "    # Calc spectra from each depth bin using the 5th beam\n",
-        "    spec[r] = avg_tool.power_spectral_density(\n",
-        "        ds[\"vel_b5\"].isel(range_b5=r), freq_units=\"Hz\"\n",
-        "    )\n",
-        "    \n",
-        "    # Calculate doppler noise from spectra from each depth bin\n",
-        "    n[r] = avg_tool.doppler_noise_level(spec[r], pct_fN=0.9)\n",
-        "    \n",
-        "    # Calc dissipation rate from each spectra\n",
-        "    e[r] = avg_tool.dissipation_rate_LT83(\n",
-        "        spec[r], ds_avg.velds.U_mag.isel(range=r), freq_range=f_rng, noise=n[r]\n",
-        "    )\n",
-        "\n",
-        "ds_avg[\"auto_spectra\"] = xr.concat(spec, dim=\"range\")\n",
-        "ds_avg[\"noise\"] = xr.concat(n, dim=\"range\")\n",
-        "ds_avg[\"dissipation_rate\"] = xr.concat(e, dim=\"range\")\n",
-        "\n",
-        "del spec, n, e  # save memory"
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x249daa0dd30>"
       ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that we have a profile timeseries of dissipation rate, we need apply some quality control (QC). Since we can't look at each individual spectrum to ensure we can see the isotropic turbulence cascade, we want to QC the output from `dissipation_rate_LT83` to make sure what was calculated actually falls on a f^(-5/3) slope. We can do this using the function `check_turbulence_cascade_slope`, which uses linear regression on the log-transformed LT83 equation (ref. to Lumley and Terray, 1983, see docstring) to calculate the spectral slope for the given frequency range. \n",
-        "\n",
-        "In our case, we're calculating the slope of each spectrum between 0.2 and 0.5 Hz. We'll use a cutoff of 20% for the error, but this can be lowered if there still appear to be erroneous estimations from visual inspection of the spectra."
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Turbulence Intensity\n",
+    "ds_avg[\"TI\"] = ds_avg.velds.I\n",
+    "ds_avg[\"TI\"].plot(cmap=\"Reds\", ylim=(0, 11))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.2 Power Spectral Densities (Auto-Spectra)\n",
+    "\n",
+    "Other turbulence parameters include the TKE power- and cross-spectral densities (i.e the power spectra), turbulent kinetic energy (TKE, i.e. the variances of velocity vector components), Reynolds stress vector (i.e. the co-variances of velocity vector components), TKE dissipation rate, and TKE production rate. These quantities are primarily used to inform and verify hydrodynamic and coastal models, which take some or all of these quantities as input.\n",
+    "\n",
+    "The TKE production rate is the rate at which kinetic energy (KE) transitions from a useful state (able to do \"work\" in the physics sense) to turbulent; TKE is the actual amount of turbulent KE in the water; and TKE dissipation rate is the rate at which turbulent KE is lost to non-motion forms of energy (heat, sound, etc) due to viscosity. The power spectra are used to depict and quantify this energy in the frequency domain, and creating them are the first step in turbulence analysis.\n",
+    "\n",
+    "We'll start by looking at the power spectra, specifically the auto-spectra from the vertical beam (\"auto\" meaning the variance of a single vector direction, e.g. $\\overline{u'^2}$, vs \"cross\", meaning the covariance of two directions, e.g. $\\overline{u'w'}$). This can be done using the `power_spectral_density` function from the `ADPBinner` we created (\"avg_tool\"). We'll create spectra at the middle water column, at a depth of 5 m, and use a number of FFT's equal to 1/3 the bin size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng = 5  # m\n",
+    "vel_up = ds[\"vel_b5\"].sel(range_b5=rng, method=\"nearest\")  # vertical velocity\n",
+    "U = ds_avg[\"U_mag\"].sel(\n",
+    "    range=5, method=\"nearest\"\n",
+    ")  # flow speed, for plotting in the next block\n",
+    "\n",
+    "ds_avg[\"auto_spectra_5m\"] = avg_tool.power_spectral_density(\n",
+    "    vel_up, freq_units=\"Hz\", n_fft=ds_avg.n_bin // 2\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the auto-spectra, we're primarly looking for three components: the energy-producing region, the isotropic turbulence region (so-called \"red noise\"), and the instrument noise floor (termed \"white noise\"). \n",
+    "\n",
+    "The block below organizes and plots the power spectra by the corresponding ensemble speed, averaging them by 0.1 m/s velocity bins. Note that if an ensemble is missing data that wasn't filled in, a power spectrum will not be calculated for that ensemble timestamp."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Quality control dissipation rate estimation\n",
-        "slope = avg_tool.check_turbulence_cascade_slope(\n",
-        "    ds_avg[\"auto_spectra\"], freq_range=f_rng\n",
-        ")\n",
-        "\n",
-        "# Check that percent difference from -5/3 is not greater than 20%\n",
-        "mask = abs((slope[0].values - (-5 / 3)) / (-5 / 3)) <= 0.20\n",
-        "\n",
-        "# Keep good data\n",
-        "ds_avg[\"dissipation_rate\"] = ds_avg[\"dissipation_rate\"].where(mask)"
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 0, 'Frequency [Hz]'),\n",
+       " Text(0, 0.5, 'PSD [m2 s-2 Hz-1]'),\n",
+       " (0.01, 1),\n",
+       " (0.0005, 0.1)]"
       ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we plot the dissipation rate below in a colormap, we can see that the profile map has a lot of missing data. One of the reasons is that the 1 Hz sampling rate doesn't provide enough information needed to make dissipation rate estimations, and the other part is that turbulence measurements push the boundaries of what ADCPs are capable of.\n",
-        "\n",
-        "Also, 1x10^-4 to 3x10^-4 $m^2/s^3$ is reasonable for a dissipation rate estimate for the 1 - 1.5 m/s current speeds measured here. They can be a magnitude greater for faster flow speeds, typically increase closer to the seafloor, and depend heavily on bathymetry and regional hydrodynamics."
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 18, \"font.family\": \"Times New Roman\"})\n",
+    "\n",
+    "\n",
+    "def plot_spectra_by_color(auto_spectra, U_mag, ax, fig, cbar_max=4.0):\n",
+    "    U = U_mag.values\n",
+    "    U_max = U_mag.max().values\n",
+    "\n",
+    "    # Average spectra into 0.1 m/s velocity bins\n",
+    "    speed_bins = np.arange(0.5, U_max, 0.1)\n",
+    "    time = [t for t in auto_spectra.dims if \"time\" in t][0]\n",
+    "    S_group = auto_spectra.assign_coords({time: U}).rename({time: \"speed\"})\n",
+    "    group = S_group.groupby_bins(\"speed\", speed_bins)\n",
+    "    count = group.count().values\n",
+    "    S = group.mean()\n",
+    "\n",
+    "    # define the colormap\n",
+    "    cmap = plt.cm.turbo\n",
+    "    # define the bins and normalize\n",
+    "    bounds = np.arange(0.5, cbar_max, 0.1)\n",
+    "    norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
+    "    colors = cmap(norm(speed_bins))\n",
+    "\n",
+    "    # plot\n",
+    "    for i in range(len(speed_bins) - 1):\n",
+    "        ax.loglog(auto_spectra[\"freq\"], S[i], c=colors[i])\n",
+    "    ax.grid()\n",
+    "\n",
+    "    # create a second axes for the colorbar\n",
+    "    cax = fig.add_axes([0.8, 0.07, 0.03, 0.88])\n",
+    "    # cax, _ = mpl.colorbar.make_axes(fig.gca())\n",
+    "    sm = mpl.colorbar.ColorbarBase(\n",
+    "        cax,\n",
+    "        cmap=cmap,\n",
+    "        norm=norm,\n",
+    "        spacing=\"proportional\",\n",
+    "        ticks=bounds,\n",
+    "        boundaries=bounds,\n",
+    "        format=\"%1.1f\",\n",
+    "        label=\"Velocity [m/s]\",\n",
+    "    )\n",
+    "\n",
+    "    # Add -5/3 slope line\n",
+    "    m = -5 / 3\n",
+    "    x = np.logspace(-1, 0.5)\n",
+    "    y = 10 ** (-3) * x**m\n",
+    "    ax.loglog(x, y, \"--\", c=\"black\", label=\"$f^{-5/3}$\")\n",
+    "    ax.legend()\n",
+    "\n",
+    "    return ax, sm\n",
+    "\n",
+    "\n",
+    "# Set up figure\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(6, 5))\n",
+    "fig.subplots_adjust(left=0.2, right=0.75, top=0.95, bottom=0.1)\n",
+    "\n",
+    "# Plot spectra by color\n",
+    "plot_spectra_by_color(ds_avg[\"auto_spectra_5m\"], U, ax, fig, cbar_max=2.0)\n",
+    "# Set axes\n",
+    "ax.set(\n",
+    "    xlabel=\"Frequency [Hz]\",\n",
+    "    ylabel=\"PSD [m2 s-2 Hz-1]\",\n",
+    "    xlim=(0.01, 1),\n",
+    "    ylim=(0.0005, 0.1),\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the figure above, we can see the energy-producing turbulent structures below a frequency of 0.2 Hz (one tick to the right of \"10^-1\"). The isotropic turbulence cascade, seen by the dashed f^(-5/3) slope (from Kolmogorov's theory of turbulence) begins at around 0.2 Hz and continues until we reach the Nyquist frequency at 0.5 Hz (1/2 the instrument's sampling frequency, 1 Hz). The instrument's noise floor can't be seen here, but will show up as the flattened part of the spectra at the highest frequencies. For this instrument (Nortek Signature1000), the noise floor typically varies around 10^-3, depending on flow speed and range distance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.3 Instrument Noise\n",
+    "\n",
+    "The next thing we want to do is calculate the instrument's Doppler noise floor from the spectrum we calculated above. (We are making the assumption that the noise floor of the vertical beam is the same as the noise floor of the other 4 beams). This gives us a timeseries of the noise floor, which varies by instrument and with flow speed, at that depth bin.\n",
+    "\n",
+    "We can do this using the `doppler_noise_level` function. The two inputs for this function are the power spectra and \"pct_fN\", the percent of the Nyquist frequency that the noise floor exists. Because in this particularly dataset we can't see the noise floor, we'll just use 90% or pct_fN=0.9 as an example. If the noise floor began at 0.4 Hz and ran til our maximum frequency of 0.5 Hz, we'd use pct_fN = 0.4 Hz / 0.5 Hz = 0.8."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x25a30084250>"
-            ]
-          },
-          "execution_count": 28,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Statistic</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>mean</td>\n",
+       "      <td>0.024059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>std</td>\n",
+       "      <td>0.007032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>min</td>\n",
+       "      <td>0.013010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>max</td>\n",
+       "      <td>0.046780</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "ds_avg[\"dissipation_rate\"].plot(cmap=\"turbo\", ylim=(0, 11))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.5 Noise-Corrected Turbulence Intensity\n",
-        "\n",
-        "Now that we've calculated the noise floor for each ping, we can recalculate TI and include subtracting instrument noise using the `turbulence_intensity` function. If we subtract this from the non-noise corrected function, we can see there's a large difference\n",
-        "at slower slow speeds, but the average difference is about 0.008 (0.8%). Notice this will also remove measurements where noise is \n",
-        "high."
+      "text/plain": [
+       "  Statistic     Value\n",
+       "0      mean  0.024059\n",
+       "1       std  0.007032\n",
+       "2       min  0.013010\n",
+       "3       max  0.046780"
       ]
-    },
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_avg[\"noise_5m\"] = avg_tool.doppler_noise_level(ds_avg[\"auto_spectra_5m\"], pct_fN=0.9)\n",
+    "\n",
+    "noise_stats = pd.DataFrame({\n",
+    "    \"Statistic\": [\"mean\", \"std\", \"min\", \"max\"],\n",
+    "    \"Value\": [\n",
+    "        float(ds_avg[\"noise_5m\"].mean().values),\n",
+    "        float(ds_avg[\"noise_5m\"].std().values),\n",
+    "        float(ds_avg[\"noise_5m\"].min().values),\n",
+    "        float(ds_avg[\"noise_5m\"].max().values),\n",
+    "    ]\n",
+    "})\n",
+    "\n",
+    "noise_stats\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.4 TKE Dissipation Rate\n",
+    "\n",
+    "Because we can see the isotropic turbulence cascade (0.2 - 0.5 Hz) at this depth bin (5 m altitude), we can calculate the TKE dissipation rate at this location from the spectra itself. This can be done using `dissipation_rate_LT83`, whose inputs are the power spectra, the ensemble speed, the frequency range of the isotropic cascade, and the instrument's noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Frequency range of isotropic turubulence cascade in same units as PSD frequency vector\n",
+    "f_rng = [0.1, 0.5]\n",
+    "# Dissipation rate\n",
+    "ds_avg[\"dissipation_rate_5m\"] = avg_tool.dissipation_rate_LT83(\n",
+    "    ds_avg[\"auto_spectra_5m\"], U, freq_range=f_rng, noise=ds_avg['noise_5m']\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have just found the spectra and dissipation rate from a single depth bin at an altitude of 5 m from the seafloor, but typically we want the spectra and dissipation rates from the entire measurement profile. If we want to look at the spectra and dissipation rates from all depth bins, we can set up a \"for\" loop on the range coordinate and merge them together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "spec = [None] * len(ds.range)\n",
+    "e = [None] * len(ds.range)\n",
+    "n = [None] * len(ds.range)\n",
+    "\n",
+    "for r in range(len(ds[\"range\"])):\n",
+    "    # Calc spectra from each depth bin using the 5th beam\n",
+    "    spec[r] = avg_tool.power_spectral_density(\n",
+    "        ds[\"vel_b5\"].isel(range_b5=r), freq_units=\"Hz\"\n",
+    "    )\n",
+    "    \n",
+    "    # Calculate doppler noise from spectra from each depth bin\n",
+    "    n[r] = avg_tool.doppler_noise_level(spec[r], pct_fN=0.9)\n",
+    "    \n",
+    "    # Calc dissipation rate from each spectra\n",
+    "    e[r] = avg_tool.dissipation_rate_LT83(\n",
+    "        spec[r], ds_avg.velds.U_mag.isel(range=r), freq_range=f_rng, noise=n[r]\n",
+    "    )\n",
+    "\n",
+    "ds_avg[\"auto_spectra\"] = xr.concat(spec, dim=\"range\")\n",
+    "ds_avg[\"noise\"] = xr.concat(n, dim=\"range\")\n",
+    "ds_avg[\"dissipation_rate\"] = xr.concat(e, dim=\"range\")\n",
+    "\n",
+    "del spec, n, e  # save memory"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have a profile timeseries of dissipation rate, we need apply some quality control (QC). Since we can't look at each individual spectrum to ensure we can see the isotropic turbulence cascade, we want to QC the output from `dissipation_rate_LT83` to make sure what was calculated actually falls on a f^(-5/3) slope. We can do this using the function `check_turbulence_cascade_slope`, which uses linear regression on the log-transformed LT83 equation (ref. to Lumley and Terray, 1983, see docstring) to calculate the spectral slope for the given frequency range. \n",
+    "\n",
+    "In our case, we're calculating the slope of each spectrum between 0.2 and 0.5 Hz. We'll use a cutoff of 20% for the error, but this can be lowered if there still appear to be erroneous estimations from visual inspection of the spectra."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quality control dissipation rate estimation\n",
+    "slope = avg_tool.check_turbulence_cascade_slope(\n",
+    "    ds_avg[\"auto_spectra\"], freq_range=f_rng\n",
+    ")\n",
+    "\n",
+    "# Check that percent difference from -5/3 is not greater than 20%\n",
+    "mask = abs((slope[0].values - (-5 / 3)) / (-5 / 3)) <= 0.20\n",
+    "\n",
+    "# Keep good data\n",
+    "ds_avg[\"dissipation_rate\"] = ds_avg[\"dissipation_rate\"].where(mask)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we plot the dissipation rate below in a colormap, we can see that the profile map has a lot of missing data. One of the reasons is that the 1 Hz sampling rate doesn't provide enough information needed to make dissipation rate estimations, and the other part is that turbulence measurements push the boundaries of what ADCPs are capable of.\n",
+    "\n",
+    "Also, 1x10^-4 to 3x10^-4 $m^2/s^3$ is reasonable for a dissipation rate estimate for the 1 - 1.5 m/s current speeds measured here. They can be a magnitude greater for faster flow speeds, typically increase closer to the seafloor, and depend heavily on bathymetry and regional hydrodynamics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Text(0.5, 1.0, 'TI Difference')"
-            ]
-          },
-          "execution_count": 29,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "ds_avg[\"turbulence_intensity\"] = avg_tool.turbulence_intensity(ds.velds.U_mag, noise=ds_avg[\"noise\"])\n",
-        "\n",
-        "(ds_avg[\"TI\"] - ds_avg[\"turbulence_intensity\"]).plot(cmap=\"Greens\", ylim=(0, 11))\n",
-        "plt.title(\"TI Difference\")"
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x249c4a40f50>"
       ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.6 Reynolds Stress Components\n",
-        "\n",
-        "The next parameters we'll find here are the Reynolds normal and shear stresses (-$\\overline{u_iu_j}$). Since we're using the vertical beam on the ADCP, we can directly measure the vertical TKE component from the along-beam velocity using the `turbulent_kinetic_energy` function. This function is capable of calculating TKE for any along-beam velocity.\n",
-        "\n",
-        "We can also use the so-called \"beam-variance\" equations to estimate the Reynolds stress tensor components (i.e. $\\overline{u'^2}$, $\\overline{v'^2}$, $\\overline{w'^2}$, $\\overline{u'v'}$, $\\overline{u'w'^2}$, $\\overline{v'w'^2}$), which define the normal and shear stresses acting on an element of water. These equations are built into the functions `stress_tensor_5beam` and `stress_tensor4beam`. \n",
-        "\n",
-        "Both of these functions will give comparable results, but `stress_tensor_5beam` takes into account instrument tilt, and `stress_tensor_4beam` does not. Both will throw a tilt warning if tilt is greater than 5 degrees.\n",
-        "\n",
-        "#### Quick 5-beam ADCP lesson before we dive in:\n",
-        "\n",
-        "There are a couple caveats to calculating Reynolds stress tensor components:\n",
-        "  1. Because this instrument only has 5 beams, we can only find 5 of the 6 components (6 unknowns, 5 knowns)\n",
-        "  2. Because the ADCP's instrument (XYZ) axes weren't aligned with the flow during deployment, we don't know what direction these components are aligned to (i.e. the 'u' direction is not necessarily the streamwise direction)\n",
-        "  3. It is possible to rotate the tensor, but we'd need to know all 6 components to do so properly (\"coupled ADCPs\")\n",
-        "  4. Measurements close to the seafloor can be suspect due to increased vertical flow. ADCPs operate under the \"assumption of homogeneity\", which means that they can only accurate measure consistent horizontal currents with relatively little vertical motion.\n"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_avg[\"dissipation_rate\"].plot(cmap=\"turbo\", ylim=(0, 11))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.5 Noise-Corrected Turbulence Intensity\n",
+    "\n",
+    "Now that we've calculated the noise floor for each ping, we can recalculate TI and include subtracting instrument noise using the `turbulence_intensity` function. If we subtract this from the non-noise corrected function, we can see there's a large difference\n",
+    "at slower slow speeds, but the average difference is about 0.008 (0.8%). Notice this will also remove measurements where noise is \n",
+    "high."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As an example, we'll calculate the Reynolds stresses from the `stress_tensor_5beam` function, which calculates the individual Reynolds stress tensor components and takes the same inputs: the raw dataset in \"beam\" coordinates, the instrument Doppler noise, the ADCP's orientation and its beam angle. It outputs both the normal stress (\"tke_vec\") and the shear stress (\"stress_vec\") vector. Note, this function will drop at least one warning every time it's run, primarily the coordinate system warning. This function also requires the input raw data to be in beam coordinates, so we'll create a copy of the raw data and rotate it to 'beam'. If you do not, this function will do so automatically."
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'TI Difference')"
       ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "C:\\Users\\mcve343\\MHKiT-Python\\mhkit\\dolfyn\\adp\\turbulence.py:422: UserWarning:     The beam-variance algorithms assume the instrument's (XYZ) coordinate system is aligned with the principal flow directions.\n",
-            "  warnings.warn(\n"
-          ]
-        }
-      ],
-      "source": [
-        "dolfyn.rotate2(ds, \"beam\", inplace=True)\n",
-        "ds_avg[\"tke_vec\"], ds_avg[\"stress_vec\"] = avg_tool.stress_tensor_5beam(\n",
-        "    ds, noise=ds_avg[\"noise\"], orientation=\"up\", beam_angle=25\n",
-        ")"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_avg[\"turbulence_intensity\"] = avg_tool.turbulence_intensity(ds.velds.U_mag, noise=ds_avg[\"noise\"])\n",
+    "\n",
+    "(ds_avg[\"TI\"] - ds_avg[\"turbulence_intensity\"]).plot(cmap=\"Greens\", ylim=(0, 11))\n",
+    "plt.title(\"TI Difference\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.6 Reynolds Stress Components\n",
+    "\n",
+    "The next parameters we'll find here are the Reynolds normal and shear stresses (-$\\overline{u_iu_j}$). Since we're using the vertical beam on the ADCP, we can directly measure the vertical TKE component from the along-beam velocity using the `turbulent_kinetic_energy` function. This function is capable of calculating TKE for any along-beam velocity.\n",
+    "\n",
+    "We can also use the so-called \"beam-variance\" equations to estimate the Reynolds stress tensor components (i.e. $\\overline{u'^2}$, $\\overline{v'^2}$, $\\overline{w'^2}$, $\\overline{u'v'}$, $\\overline{u'w'^2}$, $\\overline{v'w'^2}$), which define the normal and shear stresses acting on an element of water. These equations are built into the functions `stress_tensor_5beam` and `stress_tensor4beam`. \n",
+    "\n",
+    "Both of these functions will give comparable results, but `stress_tensor_5beam` takes into account instrument tilt, and `stress_tensor_4beam` does not. Both will throw a tilt warning if tilt is greater than 5 degrees.\n",
+    "\n",
+    "#### Quick 5-beam ADCP lesson before we dive in:\n",
+    "\n",
+    "There are a couple caveats to calculating Reynolds stress tensor components:\n",
+    "  1. Because this instrument only has 5 beams, we can only find 5 of the 6 components (6 unknowns, 5 knowns)\n",
+    "  2. Because the ADCP's instrument (XYZ) axes weren't aligned with the flow during deployment, we don't know what direction these components are aligned to (i.e. the 'u' direction is not necessarily the streamwise direction)\n",
+    "  3. It is possible to rotate the tensor, but we'd need to know all 6 components to do so properly (\"coupled ADCPs\")\n",
+    "  4. Measurements close to the seafloor can be suspect due to increased vertical flow. ADCPs operate under the \"assumption of homogeneity\", which means that they can only accurate measure consistent horizontal currents with relatively little vertical motion.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, we'll calculate the Reynolds stresses from the `stress_tensor_5beam` function, which calculates the individual Reynolds stress tensor components and takes the same inputs: the raw dataset in \"beam\" coordinates, the instrument Doppler noise, the ADCP's orientation and its beam angle. It outputs both the normal stress (\"tke_vec\") and the shear stress (\"stress_vec\") vector. Note, this function will drop at least one warning every time it's run, primarily the coordinate system warning. This function also requires the input raw data to be in beam coordinates, so we'll create a copy of the raw data and rotate it to 'beam'. If you do not, this function will do so automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There is one other important thing to note on Reynolds stress measurements by ADCPs: the minimum turbulence length scale that the ADCP is capable of measuring increases with range from the instrument. This means the instrument is only capable of measuring the stress transported by larger and larger turbulent structures as the beams travel farther and farther from the instrument head. One of the benefits of calculating w'w' from the vertical beam is that it isn't limited by this beam spread issue, though on its own it may not be particularly useful."
-      ]
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ashly\\anaconda3\\envs\\Updated_MHKiT\\Lib\\site-packages\\mhkit\\dolfyn\\adp\\turbulence.py:407: UserWarning:     The beam-variance algorithms assume the instrument's (XYZ) coordinate system is aligned with the principal flow directions.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "dolfyn.rotate2(ds, \"beam\", inplace=True)\n",
+    "ds_avg[\"tke_vec\"], ds_avg[\"stress_vec\"] = avg_tool.stress_tensor_5beam(\n",
+    "    ds, noise=ds_avg[\"noise\"], orientation=\"up\", beam_angle=25\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is one other important thing to note on Reynolds stress measurements by ADCPs: the minimum turbulence length scale that the ADCP is capable of measuring increases with range from the instrument. This means the instrument is only capable of measuring the stress transported by larger and larger turbulent structures as the beams travel farther and farther from the instrument head. One of the benefits of calculating w'w' from the vertical beam is that it isn't limited by this beam spread issue, though on its own it may not be particularly useful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.7 TKE Production\n",
+    "\n",
+    "Though it can't be found from this deployment, we'll go over how to estimate the TKE shear production rate. Note that we're assuming here that the buoyancy production is negligible and we're in a well-mixed tidal channel. There isn't a specific function in MHKiT-DOLfYN for production, but all the necessary variables are. \n",
+    "\n",
+    "It is possible to estimate production rates from either an ADV or an ADCP aligned with the flow direction (so \"X\" would align with the principal flow direction). The following estimation for shear production rates takes into account both along- and cross-stream shear:\n",
+    "\n",
+    "$P = -\\overline{u'w'}\\frac{du}{dz} - \\overline{v'w'}\\frac{dv}{dz}$\n",
+    "\n",
+    "Note that the signs can get tricky but are important here. We found the Reynolds shear stresses -$\\overline{u'w'}$ and -$\\overline{v'w'}$ above using the Reynolds stress equations. If ADV data is available, those estimations are preferred because the ADV's point measurement does not have the assumptions that ADCP measurements have.\n",
+    "\n",
+    "The velocity shear components can be found from the aptly named functions `dudz` and `dvdz` in ADPBinner. These functions, which are useful alone in their own right, approximate the shear in the velocity vector between respective depth bins. There is always correlation between velocity measurements in adjacent depth bins, based on ADCP operation principles, which is why \"approximate\" is also used here for velocity shear.\n",
+    "\n",
+    "The velocity shear functions operate on the raw velocity vector in the principal reference frame and need to be ensemble-averaged here. This can be done by nesting the `d*dz` function within the ADPBinner's `mean` function. With the ensemble shear known, we can put all the components together to get a production estimation. If using ADV data, take the mean again of the \"range\" dimension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.7 TKE Production\n",
-        "\n",
-        "Though it can't be found from this deployment, we'll go over how to estimate the TKE shear production rate. Note that we're assuming here that the buoyancy production is negligible and we're in a well-mixed tidal channel. There isn't a specific function in MHKiT-DOLfYN for production, but all the necessary variables are. \n",
-        "\n",
-        "It is possible to estimate production rates from either an ADV or an ADCP aligned with the flow direction (so \"X\" would align with the principal flow direction). The following estimation for shear production rates takes into account both along- and cross-stream shear:\n",
-        "\n",
-        "$P = -\\overline{u'w'}\\frac{du}{dz} - \\overline{v'w'}\\frac{dv}{dz}$\n",
-        "\n",
-        "Note that the signs can get tricky but are important here. We found the Reynolds shear stresses -$\\overline{u'w'}$ and -$\\overline{v'w'}$ above using the Reynolds stress equations. If ADV data is available, those estimations are preferred because the ADV's point measurement does not have the assumptions that ADCP measurements have.\n",
-        "\n",
-        "The velocity shear components can be found from the aptly named functions `dudz` and `dvdz` in ADPBinner. These functions, which are useful alone in their own right, approximate the shear in the velocity vector between respective depth bins. There is always correlation between velocity measurements in adjacent depth bins, based on ADCP operation principles, which is why \"approximate\" is also used here for velocity shear.\n",
-        "\n",
-        "The velocity shear functions operate on the raw velocity vector in the principal reference frame and need to be ensemble-averaged here. This can be done by nesting the `d*dz` function within the ADPBinner's `mean` function. With the ensemble shear known, we can put all the components together to get a production estimation. If using ADV data, take the mean again of the \"range\" dimension."
-      ]
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ashly\\anaconda3\\envs\\Updated_MHKiT\\Lib\\site-packages\\mhkit\\dolfyn\\rotate\\api.py:72: UserWarning: You are attempting to rotate into the 'principal' coordinate system, but the dataset is in the inst coordinate system. Be sure that 'principal_heading' is defined based on the earth coordinate system.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Vertical shear gradients\n",
+    "upwp_ = ds_avg[\"stress_vec\"][1]\n",
+    "vpwp_ = ds_avg[\"stress_vec\"][2]\n",
+    "\n",
+    "# Find shear velocity and and ensemble-average it\n",
+    "ds.velds.rotate2(\"principal\", inplace=True)\n",
+    "dudz_raw = avg_tool.dudz(ds[\"vel\"], orientation=\"up\")\n",
+    "dudz = avg_tool.mean(dudz_raw.values)\n",
+    "\n",
+    "dvdz_raw = avg_tool.dvdz(ds[\"vel\"], orientation=\"up\")\n",
+    "dvdz = avg_tool.mean(dvdz_raw.values)\n",
+    "\n",
+    "# Calculate Production\n",
+    "P = -upwp_ * dudz - vpwp_ * dvdz\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.8 TKE Balance \n",
+    "We can plot the production rates and the ratio of production rates to dissipation rates to get an understanding of the TKE balance.We always expect production to be greater than 0, though negative values can give us an indication of uncertainty. In a well mixed coastal environment, we expect production and dissipation to be approximately equal. Our production estimates are possibly high because our stress components aren't aligned with the flow (1.3x10^-3 $m^2/s^3$ is quite large), but if this weren't the case, we would conclude that TKE is produced (kinetic energy is lost to turbulence) but not dissipated (turbulent energy is lost to entropy) here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "C:\\Users\\mcve343\\MHKiT-Python\\mhkit\\dolfyn\\rotate\\api.py:72: UserWarning: You are attempting to rotate into the 'principal' coordinate system, but the dataset is in the inst coordinate system. Be sure that 'principal_heading' is defined based on the earth coordinate system.\n",
-            "  warnings.warn(\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Vertical shear gradients\n",
-        "upwp_ = ds_avg[\"stress_vec\"][1]\n",
-        "vpwp_ = ds_avg[\"stress_vec\"][2]\n",
-        "\n",
-        "# Find shear velocity and and ensemble-average it\n",
-        "ds.velds.rotate2(\"principal\", inplace=True)\n",
-        "dudz_raw = avg_tool.dudz(ds[\"vel\"], orientation=\"up\")\n",
-        "dudz = avg_tool.mean(dudz_raw.values)\n",
-        "\n",
-        "dvdz_raw = avg_tool.dvdz(ds[\"vel\"], orientation=\"up\")\n",
-        "dvdz = avg_tool.mean(dvdz_raw.values)\n",
-        "\n",
-        "# Calculate Production\n",
-        "P = -upwp_ * dudz - vpwp_ * dvdz\n"
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'TKE Balance')"
       ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.8 TKE Balance \n",
-        "We can plot the production rates and the ratio of production rates to dissipation rates to get an understanding of the TKE balance.We always expect production to be greater than 0, though negative values can give us an indication of uncertainty. In a well mixed coastal environment, we expect production and dissipation to be approximately equal. Our production estimates are possibly high because our stress components aren't aligned with the flow (1.3x10^-3 $m^2/s^3$ is quite large), but if this weren't the case, we would conclude that TKE is produced (kinetic energy is lost to turbulence) but not dissipated (turbulent energy is lost to entropy) here."
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Text(0.5, 1.0, 'TKE Balance')"
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "image/png": "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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Remove estimations below 0\n",
-        "P = P.where(P > 0)\n",
-        "# Plot production rate\n",
-        "P.plot(cmap=\"turbo\", ylim=(0, 11))\n",
-        "plt.title(\"TKE Production\")  # remove bogus title\n",
-        "\n",
-        "\n",
-        "# Plot difference between production and dissipation rate\n",
-        "plt.figure()\n",
-        "balance = P - ds_avg[\"dissipation_rate\"].values\n",
-        "balance.plot(ylim=(0, 11))\n",
-        "plt.title(\"TKE Balance\")"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "work",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.10"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "# Remove estimations below 0\n",
+    "P = P.where(P > 0)\n",
+    "# Plot production rate\n",
+    "P.plot(cmap=\"turbo\", ylim=(0, 11))\n",
+    "plt.title(\"TKE Production\")  # remove bogus title\n",
+    "\n",
+    "\n",
+    "# Plot difference between production and dissipation rate\n",
+    "plt.figure()\n",
+    "balance = P - ds_avg[\"dissipation_rate\"].values\n",
+    "balance.plot(ylim=(0, 11))\n",
+    "plt.title(\"TKE Balance\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Updated_MHKiT",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index ac0f49f73..b6c08031a 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -388,7 +388,7 @@ def get_layer_data(
     elif isinstance(data, xr.Dataset):
         layer_percentages = cord_sys.values  # accumulative
 
-    if layer_dim == "FlowLink_xu FlowLink_yu":
+    if layer_dim in ("FlowLink_xu FlowLink_yu", "mesh2d_edge_x mesh2d_edge_y"):
         # interpolate
         if isinstance(data, netCDF4.Dataset):
             x_laydim = np.ma.getdata(data.variables[coords[0]][:], False)
@@ -421,13 +421,13 @@ def get_layer_data(
     waterdepth = []
 
     if dimensions == 2:
-        if layer_dim == "FlowLink_xu FlowLink_yu":
+        if layer_dim in ("FlowLink_xu FlowLink_yu", "mesh2d_edge_x mesh2d_edge_y"):
             z = [bottom_depth_wdim]
             waterlevel = water_level_wdim
         else:
             z = [bottom_depth]
     else:
-        if layer_dim == "FlowLink_xu FlowLink_yu":
+        if layer_dim in ("FlowLink_xu FlowLink_yu", "mesh2d_edge_x mesh2d_edge_y"):
             z = [bottom_depth_wdim * layer_percentages[layer_index]]
             waterlevel = water_level_wdim
         else:
@@ -651,6 +651,34 @@ def variable_interpolation(
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
+    # Avoid Quhall errors by filling in bad sigma values
+    if "mesh2d_interface_sigma" in data.variables:
+        sigma = data["mesh2d_interface_sigma"].values
+        bad = ~np.isfinite(sigma) | (np.abs(sigma) > 0.1)
+        if bad.any():
+            # Check neighboring values to determine if sigma should be -1 or 0
+            for idx in np.where(bad)[0]:
+                neighbors = []
+                if idx > 0:
+                    neighbors.append(sigma[idx - 1])
+                if idx < len(sigma) - 1:
+                    neighbors.append(sigma[idx + 1])
+
+                if neighbors:
+                    avg_neighbor = np.mean(neighbors)
+                    if np.abs(avg_neighbor - (-1.0)) < np.abs(avg_neighbor):
+                        sigma[idx] = -1.0  # sea bed
+                    else:
+                        sigma[idx] = 0.0  # water surface
+                else:
+                    sigma[idx] = -1.0
+
+            data["mesh2d_interface_sigma"] = xr.DataArray(
+                sigma,
+                dims=data["mesh2d_interface_sigma"].dims,
+                attrs=data["mesh2d_interface_sigma"].attrs,
+            )
+
     data_raw = {}
     for var in variables:
         var_data_df = get_all_data_points(data, var, time_index=-1, to_pandas=True)