diff --git a/book/_toc.yml b/book/_toc.yml
index d591499..876fa81 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -32,6 +32,15 @@ parts:
             - file: tutorials/Data_access/earthdata_search.md
             - file: tutorials/Data_access/earthaccess_snowex.ipynb
             - file: tutorials/Data_access/earthaccess_icesat2.ipynb
+        - file: tutorials/snowex_database/index
+          title: Introduction to the SnowEx Database
+          sections:
+            - file: tutorials/snowex_database/1_getting_started_example
+            - file: tutorials/snowex_database/2_database_structure
+            - file: tutorials/snowex_database/3_forming_queries
+            - file: tutorials/snowex_database/4_get_spiral_example
+            - file: tutorials/snowex_database/5_plot_raster_example
+            - file: tutorials/snowex_database/6_wrap_up
 - caption: Projects
   chapters:
     - file: projects/index
diff --git a/book/tutorials/snowex_database/1_getting_started_example.ipynb b/book/tutorials/snowex_database/1_getting_started_example.ipynb
new file mode 100644
index 0000000..55142c3
--- /dev/null
+++ b/book/tutorials/snowex_database/1_getting_started_example.ipynb
@@ -0,0 +1,535 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SnowEx Database Introduction\n",
+    "\n",
+    " \n",
+    "__Tutorial Author Micah'__: [Micah Sandusky](https://github.com/micah-prime)\n",
+    "\n",
+    "__Tutorial Author Micah_o__: [Micah Johnson](https://github.com/micahjohnson150)\n",
+    "\n",
+    "[SnowEx](https://snow.nasa.gov/campaigns/snowex) has introduced a unique opportunity to study SWE in a way thats unprecedented, but with more data comes new challenges. \n",
+    "\n",
+    "![examples](./images/data_examples.png)\n",
+    "\n",
+    "\n",
+    "<!-- \n",
+    "<img src=\"https://snowexsql.readthedocs.io/en/latest/_images/gallery_overview_example_12_0.png\" alt=\"Grand Mesa Overview\" width=\"1000px\"> -->\n",
+    "\n",
+    "**The SnowEx database is a resource that shortcuts the time it takes to ask cross dataset questions**\n",
+    "\n",
+    "      \n",
+    "- Standardizing diverse data\n",
+    "- Cross referencing data\n",
+    "- Provenance!\n",
+    "- Added GIS functionality\n",
+    "- Connect w/ ArcGIS or QGIS!\n",
+    "- **CITABLE** \n",
+    "\n",
+    "    * [*2022- Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)*](https://tc.copernicus.org/articles/17/1997/2023/tc-17-1997-2023-discussion.html)\n",
+    "    * [*2024 - Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign*](https://tc.copernicus.org/articles/18/2257/2024/)\n",
+    "      \n",
+    "      \n",
+    "\n",
+    "## What's in it?\n",
+    "\n",
+    "* Snow pits - Density, hardness profiles, grain types + sizes\n",
+    "* Manual snow depths - TONS of depths (Can you say spirals?)\n",
+    "* Snow Micropenetrometer (SMP) profiles - (Subsampled to every 100th)\n",
+    "* Snow depth + SWE rasters from ASO Inc.\n",
+    "* GPR\n",
+    "* Pit site notes\n",
+    "* Camera Derived snow depths\n",
+    "* Snow off DEM from USGS 3DEP \n",
+    "* And almost all the associated metadata\n",
+    "\n",
+    "## Technically, what is it?\n",
+    "\n",
+    "* PostgreSQL database\n",
+    "* PostGIS extension\n",
+    "* Supports vector and raster data\n",
+    "* And a host of GIS operations\n",
+    "* AND NOW WITH API!\n",
+    "\n",
+    "\n",
+    "### So whats the catch?\n",
+    "New tech can create barriers...\n",
+    "\n",
+    "```{figure} ./images/pits_not_bits.jpg\n",
+    ":scale: 20 %\n",
+    ":alt: pits not bits\n",
+    "```\n",
+    "\n",
+    "### TL;DR Do less wrangling, do more crunching. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How do I get at this magical box of data ?\n",
+    "* [SQL](https://www.postgresql.org/docs/13/tutorial-sql.html) \n",
+    "* [snowexsql](https://github.com/SnowEx/snowexsql/) <span style=\"font-size:20pt;\"> **&#8592; 😎**</span>\n",
+    "\n",
+    "\n",
+    "### Welcome to API Land"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>version_number</th>\n",
+       "      <th>equipment</th>\n",
+       "      <th>value</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>northing</th>\n",
+       "      <th>easting</th>\n",
+       "      <th>elevation</th>\n",
+       "      <th>utm_zone</th>\n",
+       "      <th>geom</th>\n",
+       "      <th>...</th>\n",
+       "      <th>date</th>\n",
+       "      <th>time_created</th>\n",
+       "      <th>time_updated</th>\n",
+       "      <th>id</th>\n",
+       "      <th>doi</th>\n",
+       "      <th>date_accessed</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>observers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>39.04496</td>\n",
+       "      <td>-108.06311</td>\n",
+       "      <td>4.325871e+06</td>\n",
+       "      <td>754172.639132</td>\n",
+       "      <td>3253.169922</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (754172.639 4325871.377)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-03-12</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41824</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>39.04563</td>\n",
+       "      <td>-108.19593</td>\n",
+       "      <td>4.325583e+06</td>\n",
+       "      <td>742673.504400</td>\n",
+       "      <td>3048.699951</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (742673.504 4325582.611)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-30</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41825</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>39.00760</td>\n",
+       "      <td>-108.14791</td>\n",
+       "      <td>4.321491e+06</td>\n",
+       "      <td>746962.448982</td>\n",
+       "      <td>3087.709961</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (746962.449 4321490.615)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-29</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41826</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>39.02144</td>\n",
+       "      <td>-108.16401</td>\n",
+       "      <td>4.322983e+06</td>\n",
+       "      <td>745520.203184</td>\n",
+       "      <td>3099.639893</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (745520.203 4322983.253)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-09</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41827</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>39.03404</td>\n",
+       "      <td>-108.19103</td>\n",
+       "      <td>4.324309e+06</td>\n",
+       "      <td>743137.395316</td>\n",
+       "      <td>3055.590088</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743137.395 4324309.223)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41828</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>39.03596</td>\n",
+       "      <td>-108.20975</td>\n",
+       "      <td>4.324472e+06</td>\n",
+       "      <td>741510.229315</td>\n",
+       "      <td>3030.070068</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (741510.229 4324472.430)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41919</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>39.03126</td>\n",
+       "      <td>-108.18948</td>\n",
+       "      <td>4.324005e+06</td>\n",
+       "      <td>743281.122092</td>\n",
+       "      <td>3060.439941</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743281.122 4324004.792)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-05</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41920</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>39.01843</td>\n",
+       "      <td>-108.15596</td>\n",
+       "      <td>4.322671e+06</td>\n",
+       "      <td>746227.685533</td>\n",
+       "      <td>3103.750000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (746227.686 4322670.914)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-01</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41921</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>39.01437</td>\n",
+       "      <td>-108.14158</td>\n",
+       "      <td>4.322259e+06</td>\n",
+       "      <td>747487.040217</td>\n",
+       "      <td>3100.860107</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (747487.040 4322259.296)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-29</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41922</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>39.03918</td>\n",
+       "      <td>-108.00313</td>\n",
+       "      <td>4.325399e+06</td>\n",
+       "      <td>759385.731498</td>\n",
+       "      <td>-9999.000000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (759385.731 4325399.285)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-03</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41923</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    version_number equipment  value  latitude  longitude      northing  \\\n",
+       "0                1     ruler   83.0  39.04496 -108.06311  4.325871e+06   \n",
+       "1                1     ruler  100.0  39.04563 -108.19593  4.325583e+06   \n",
+       "2                1     ruler  117.0  39.00760 -108.14791  4.321491e+06   \n",
+       "3                1     ruler   98.0  39.02144 -108.16401  4.322983e+06   \n",
+       "4                1     ruler   92.0  39.03404 -108.19103  4.324309e+06   \n",
+       "..             ...       ...    ...       ...        ...           ...   \n",
+       "95               1     ruler   92.0  39.03596 -108.20975  4.324472e+06   \n",
+       "96               1     ruler   35.0  39.03126 -108.18948  4.324005e+06   \n",
+       "97               1     ruler  101.0  39.01843 -108.15596  4.322671e+06   \n",
+       "98               1     ruler  102.0  39.01437 -108.14158  4.322259e+06   \n",
+       "99               1     ruler  115.0  39.03918 -108.00313  4.325399e+06   \n",
+       "\n",
+       "          easting    elevation  utm_zone                            geom  ...  \\\n",
+       "0   754172.639132  3253.169922        12  POINT (754172.639 4325871.377)  ...   \n",
+       "1   742673.504400  3048.699951        12  POINT (742673.504 4325582.611)  ...   \n",
+       "2   746962.448982  3087.709961        12  POINT (746962.449 4321490.615)  ...   \n",
+       "3   745520.203184  3099.639893        12  POINT (745520.203 4322983.253)  ...   \n",
+       "4   743137.395316  3055.590088        12  POINT (743137.395 4324309.223)  ...   \n",
+       "..            ...          ...       ...                             ...  ...   \n",
+       "95  741510.229315  3030.070068        12  POINT (741510.229 4324472.430)  ...   \n",
+       "96  743281.122092  3060.439941        12  POINT (743281.122 4324004.792)  ...   \n",
+       "97  746227.685533  3103.750000        12  POINT (746227.686 4322670.914)  ...   \n",
+       "98  747487.040217  3100.860107        12  POINT (747487.040 4322259.296)  ...   \n",
+       "99  759385.731498 -9999.000000        12  POINT (759385.731 4325399.285)  ...   \n",
+       "\n",
+       "          date                     time_created time_updated     id  \\\n",
+       "0   2020-03-12 2022-06-30 22:56:52.635035+00:00         None  41824   \n",
+       "1   2020-01-30 2022-06-30 22:56:52.635035+00:00         None  41825   \n",
+       "2   2020-01-29 2022-06-30 22:56:52.635035+00:00         None  41826   \n",
+       "3   2020-02-09 2022-06-30 22:56:52.635035+00:00         None  41827   \n",
+       "4   2020-01-28 2022-06-30 22:56:52.635035+00:00         None  41828   \n",
+       "..         ...                              ...          ...    ...   \n",
+       "95  2020-01-28 2022-06-30 22:56:52.635035+00:00         None  41919   \n",
+       "96  2020-02-05 2022-06-30 22:56:52.635035+00:00         None  41920   \n",
+       "97  2020-02-01 2022-06-30 22:56:52.635035+00:00         None  41921   \n",
+       "98  2020-01-29 2022-06-30 22:56:52.635035+00:00         None  41922   \n",
+       "99  2020-02-03 2022-06-30 22:56:52.635035+00:00         None  41923   \n",
+       "\n",
+       "                                     doi date_accessed  instrument   type  \\\n",
+       "0   https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "1   https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "2   https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "3   https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "4   https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "..                                   ...           ...         ...    ...   \n",
+       "95  https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "96  https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "97  https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "98  https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "99  https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "\n",
+       "   units observers  \n",
+       "0     cm      None  \n",
+       "1     cm      None  \n",
+       "2     cm      None  \n",
+       "3     cm      None  \n",
+       "4     cm      None  \n",
+       "..   ...       ...  \n",
+       "95    cm      None  \n",
+       "96    cm      None  \n",
+       "97    cm      None  \n",
+       "98    cm      None  \n",
+       "99    cm      None  \n",
+       "\n",
+       "[100 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from snowexsql.api import PointMeasurements\n",
+    "\n",
+    "df = PointMeasurements.from_filter(type=\"depth\", instrument='pit ruler', limit=100)\n",
+    "df.plot(column='value', cmap='jet', vmin=10, vmax=150)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Old Ways / Advanced Users \n",
+    "Advanced queries can be made using SQL or SQAlchemy under the hood. \n",
+    "\n",
+    "See previous presentations\n",
+    "\n",
+    "Engine objects, session objects, and a crash course in ORM, oh my! \n",
+    "* [Hackweek 2021](https://snowex-2021.hackweek.io/tutorials/database/index.html)\n",
+    "* [Hackweek 2022](https://snowex-2022.hackweek.io/tutorials/database/index.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/book/tutorials/snowex_database/2_database_structure.ipynb b/book/tutorials/snowex_database/2_database_structure.ipynb
new file mode 100644
index 0000000..f4bb469
--- /dev/null
+++ b/book/tutorials/snowex_database/2_database_structure.ipynb
@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8d563d3c",
+   "metadata": {},
+   "source": [
+    "# How is the Database Structured?\n",
+    "\n",
+    "The goal of the database is to hold as much of the SnowEx data in one place and make it easier to \n",
+    "do research with. With that in mind follow the steps below to see how the the data base is structured.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9f0a9c7",
+   "metadata": {},
+   "source": [
+    "### Where do datasets live (i.e. tables)?\n",
+    "\n",
+    "Data in the database lives in 1 of 4 places. \n",
+    "\n",
+    "\n",
+    "```{figure} ./images/structure.png\n",
+    ":scale: 50 %\n",
+    ":alt: Structure of the snowex db\n",
+    "\n",
+    "Layout of the database tables\n",
+    "\n",
+    "```\n",
+    "\n",
+    "The 4th table is a table detailing the site informations. Lots and lots of metadata for which the API has not been written yet.\n",
+    "\n",
+    "So how does this look in python?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "979fad96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from snowexsql.api import PointMeasurements, LayerMeasurements, RasterMeasurements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07bf71eb",
+   "metadata": {},
+   "source": [
+    "### How are tables structured?\n",
+    "Each table consists of rows and columns. Below are the available columns!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8fd4e693",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "These are the available columns in the table:\n",
+      " \n",
+      "* version_number\n",
+      "* equipment\n",
+      "* value\n",
+      "* latitude\n",
+      "* longitude\n",
+      "* northing\n",
+      "* easting\n",
+      "* elevation\n",
+      "* utm_zone\n",
+      "* geom\n",
+      "* time\n",
+      "* site_id\n",
+      "* site_name\n",
+      "* date\n",
+      "* time_created\n",
+      "* time_updated\n",
+      "* id\n",
+      "* doi\n",
+      "* date_accessed\n",
+      "* instrument\n",
+      "* type\n",
+      "* units\n",
+      "* observers\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import the class reflecting the points table in the db\n",
+    "from snowexsql.api import PointMeasurements as measurements\n",
+    "\n",
+    "# Grab one measurment to see what attributes are available\n",
+    "df = measurements.from_filter(type=\"depth\", limit=1)\n",
+    "\n",
+    "# Print out the results nicely\n",
+    "print(\"These are the available columns in the table:\\n \\n* {}\\n\".format('\\n* '.join(df.columns)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba2df485",
+   "metadata": {},
+   "source": [
+    "**Try this:** Using what we just did, but swap out PointMeasurements for LayerMeasurements.\n",
+    "\n",
+    "\n",
+    "**Question:** Did you collect any data? What is it? What table do you think it would go in?\n",
+    "\n",
+    "For more detail, checkout the readthedocs page on [database structure](https://snowexsql.readthedocs.io/en/latest/database_structure.html) to see how data gets categorized. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d106e6e",
+   "metadata": {},
+   "source": [
+    "## Bonus Step: Learning to help yourself\n",
+    "[snowexsql](https://github.com/SnowEx/snowexsql/) has a host of resources for you to  help your self. First when you are looking for something be sure to check the snowexsql's docs.\n",
+    "There you will find notes on the database structure. datasets, and of course our new API! \n",
+    "\n",
+    "### Database Usage/Examples\n",
+    "* [snowexsql Code](https://github.com/SnowEx/snowexsql/) \n",
+    "* [snowexsql Documentation](https://snowexsql.readthedocs.io/en/latest/) \n",
+    "\n",
+    "### Database Building/Notes\n",
+    "* [snowex_db Code](https://github.com/SnowEx/snowex_db/) \n",
+    "* [snowex_db Documentation](https://snowex_db.readthedocs.io/en/latest/) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbe9ae13",
+   "metadata": {},
+   "source": [
+    "## Recap \n",
+    "You just explored the database structure and discussed how they differ.\n",
+    "\n",
+    "**You should know:**\n",
+    "* Which table a dataset might live in\n",
+    "* What columns you can work with (or how to get the available columns)\n",
+    "* Some resources to begin helping yourself.\n",
+    "\n",
+    "If you don't feel comfortable with these, you are probably not alone, let's discuss it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e260d71e-c4d8-4f4f-b170-4f64f8fa56ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/book/tutorials/snowex_database/3_forming_queries.ipynb b/book/tutorials/snowex_database/3_forming_queries.ipynb
new file mode 100644
index 0000000..e6d170e
--- /dev/null
+++ b/book/tutorials/snowex_database/3_forming_queries.ipynb
@@ -0,0 +1,1522 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Forming Queries through the API!\n",
+    "\n",
+    "Get familiar with the tools available for querying the database. The simplest way is to use the api classes \n",
+    "* [`snowexsql.api.PointMeasurements`](https://github.com/SnowEx/snowexsql/blob/830fa76de8cf13c5101e1b4b663c1b399f81d7e6/snowexsql/api.py#L185)\n",
+    "* [`snowexsql.api.LayerMeasurements`](https://github.com/SnowEx/snowexsql/blob/830fa76de8cf13c5101e1b4b663c1b399f81d7e6/snowexsql/api.py#L262)\n",
+    "\n",
+    "* Each class has to very useful functions\n",
+    "  1. [`from_filter`](https://github.com/SnowEx/snowexsql/blob/830fa76de8cf13c5101e1b4b663c1b399f81d7e6/snowexsql/api.py#L192)\n",
+    "  2. [`from_area`](https://github.com/SnowEx/snowexsql/blob/830fa76de8cf13c5101e1b4b663c1b399f81d7e6/snowexsql/api.py#L210)\n",
+    "\n",
+    "## `from_filter`\n",
+    "\n",
+    "Use the from filter function to find density profiles\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                    value\n",
+      "site_id                  \n",
+      "Banner Open    235.500000\n",
+      "Banner Snotel  216.666667\n",
+      "Bogus Upper    260.625000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import in our two classes to access the db\n",
+    "from snowexsql.api import LayerMeasurements\n",
+    "from datetime import datetime \n",
+    "\n",
+    "# Find some density pit measurements at the Boise site in december 2019.\n",
+    "df = LayerMeasurements.from_filter(\n",
+    "    type=\"density\",\n",
+    "    site_name=\"Boise River Basin\",\n",
+    "    date_less_equal=datetime(2020, 1, 1),\n",
+    "    date_greater_equal=datetime(2019, 12, 1),\n",
+    ")\n",
+    "\n",
+    "# Plot Example!\n",
+    "df.plot()\n",
+    "\n",
+    "# Show off the dataframe\n",
+    "df\n",
+    "\n",
+    "# Analysis Example - Find the bulk density \n",
+    "df['value'] = df['value'].astype(float)\n",
+    "print(df[['site_id', 'value']].groupby(by='site_id').mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `from_area`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>depth</th>\n",
+       "      <th>site_id</th>\n",
+       "      <th>pit_id</th>\n",
+       "      <th>bottom_depth</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>sample_a</th>\n",
+       "      <th>sample_b</th>\n",
+       "      <th>sample_c</th>\n",
+       "      <th>value</th>\n",
+       "      <th>flags</th>\n",
+       "      <th>...</th>\n",
+       "      <th>date</th>\n",
+       "      <th>time_created</th>\n",
+       "      <th>time_updated</th>\n",
+       "      <th>id</th>\n",
+       "      <th>doi</th>\n",
+       "      <th>date_accessed</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>observers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>84.0</td>\n",
+       "      <td>1C5</td>\n",
+       "      <td>COGM1C5_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>45.6</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 19:52:45.154374+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2346713</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>79.0</td>\n",
+       "      <td>1C5</td>\n",
+       "      <td>COGM1C5_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>38.2</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 19:52:45.154374+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2346714</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>74.0</td>\n",
+       "      <td>1C5</td>\n",
+       "      <td>COGM1C5_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 19:52:45.154374+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2346715</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>69.0</td>\n",
+       "      <td>1C5</td>\n",
+       "      <td>COGM1C5_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 19:52:45.154374+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2346716</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>64.0</td>\n",
+       "      <td>1C5</td>\n",
+       "      <td>COGM1C5_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>22.4</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 19:52:45.154374+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2346717</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1C1</td>\n",
+       "      <td>COGM1C1_20200131</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-31</td>\n",
+       "      <td>2024-08-15 19:52:49.654026+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2349018</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Juha Lemmetyinen &amp; Ioanna Merkouriadi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1C1</td>\n",
+       "      <td>COGM1C1_20200131</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-31</td>\n",
+       "      <td>2024-08-15 19:52:49.654026+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2349019</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Juha Lemmetyinen &amp; Ioanna Merkouriadi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1C1</td>\n",
+       "      <td>COGM1C1_20200131</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-31</td>\n",
+       "      <td>2024-08-15 19:52:49.654026+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2349020</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Juha Lemmetyinen &amp; Ioanna Merkouriadi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1C1</td>\n",
+       "      <td>COGM1C1_20200131</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-31</td>\n",
+       "      <td>2024-08-15 19:52:49.654026+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2349021</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Juha Lemmetyinen &amp; Ioanna Merkouriadi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1C1</td>\n",
+       "      <td>COGM1C1_20200131</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-31</td>\n",
+       "      <td>2024-08-15 19:52:49.654026+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2349022</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Juha Lemmetyinen &amp; Ioanna Merkouriadi</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>160 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     depth site_id            pit_id bottom_depth comments sample_a sample_b  \\\n",
+       "0     84.0     1C5  COGM1C5_20200212         None     None     None     None   \n",
+       "1     79.0     1C5  COGM1C5_20200212         None     None     None     None   \n",
+       "2     74.0     1C5  COGM1C5_20200212         None     None     None     None   \n",
+       "3     69.0     1C5  COGM1C5_20200212         None     None     None     None   \n",
+       "4     64.0     1C5  COGM1C5_20200212         None     None     None     None   \n",
+       "..     ...     ...               ...          ...      ...      ...      ...   \n",
+       "155   28.0     1C1  COGM1C1_20200131         None     None     None     None   \n",
+       "156   23.0     1C1  COGM1C1_20200131         None     None     None     None   \n",
+       "157   18.0     1C1  COGM1C1_20200131         None     None     None     None   \n",
+       "158   13.0     1C1  COGM1C1_20200131         None     None     None     None   \n",
+       "159    8.0     1C1  COGM1C1_20200131         None     None     None     None   \n",
+       "\n",
+       "    sample_c value flags  ...        date                     time_created  \\\n",
+       "0       None  45.6  None  ...  2020-02-12 2024-08-15 19:52:45.154374+00:00   \n",
+       "1       None  38.2  None  ...  2020-02-12 2024-08-15 19:52:45.154374+00:00   \n",
+       "2       None  24.5  None  ...  2020-02-12 2024-08-15 19:52:45.154374+00:00   \n",
+       "3       None  23.5  None  ...  2020-02-12 2024-08-15 19:52:45.154374+00:00   \n",
+       "4       None  22.4  None  ...  2020-02-12 2024-08-15 19:52:45.154374+00:00   \n",
+       "..       ...   ...   ...  ...         ...                              ...   \n",
+       "155     None  13.1  None  ...  2020-01-31 2024-08-15 19:52:49.654026+00:00   \n",
+       "156     None  10.1  None  ...  2020-01-31 2024-08-15 19:52:49.654026+00:00   \n",
+       "157     None  10.6  None  ...  2020-01-31 2024-08-15 19:52:49.654026+00:00   \n",
+       "158     None  10.5  None  ...  2020-01-31 2024-08-15 19:52:49.654026+00:00   \n",
+       "159     None  13.2  None  ...  2020-01-31 2024-08-15 19:52:49.654026+00:00   \n",
+       "\n",
+       "     time_updated       id                                   doi  \\\n",
+       "0            None  2346713  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "1            None  2346714  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "2            None  2346715  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "3            None  2346716  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "4            None  2346717  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "..            ...      ...                                   ...   \n",
+       "155          None  2349018  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "156          None  2349019  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "157          None  2349020  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "158          None  2349021  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "159          None  2349022  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "\n",
+       "     date_accessed     instrument                   type units  \\\n",
+       "0       2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "1       2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "2       2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "3       2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "4       2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "..             ...            ...                    ...   ...   \n",
+       "155     2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "156     2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "157     2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "158     2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "159     2022-06-30  IS3-SP-11-01F  specific_surface_area  None   \n",
+       "\n",
+       "                                 observers  \n",
+       "0                                Kate Hale  \n",
+       "1                                Kate Hale  \n",
+       "2                                Kate Hale  \n",
+       "3                                Kate Hale  \n",
+       "4                                Kate Hale  \n",
+       "..                                     ...  \n",
+       "155  Juha Lemmetyinen & Ioanna Merkouriadi  \n",
+       "156  Juha Lemmetyinen & Ioanna Merkouriadi  \n",
+       "157  Juha Lemmetyinen & Ioanna Merkouriadi  \n",
+       "158  Juha Lemmetyinen & Ioanna Merkouriadi  \n",
+       "159  Juha Lemmetyinen & Ioanna Merkouriadi  \n",
+       "\n",
+       "[160 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import our api class\n",
+    "from snowexsql.api import LayerMeasurements\n",
+    "from datetime import datetime\n",
+    "\n",
+    "# import some gis functionality \n",
+    "from shapely.geometry import Point \n",
+    "\n",
+    "# Find some SSA measurements within a distance of a known point\n",
+    "df = LayerMeasurements.from_area(pt=Point(740820.624625,4.327326e+06), crs=26912, buffer=500,\n",
+    "    type='specific_surface_area')\n",
+    "\n",
+    "# plot it up\n",
+    "df.plot()\n",
+    "\n",
+    "# TODO: plot the point \n",
+    "\n",
+    "# show off the dataframe\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How do I know what to filter on?\n",
+    "We got tools for that! Each class has a host of functions that start with `all_*` these function return the unique value in that column. \n",
+    "\n",
+    " * `all_types` - all the data types e.g. depth, swe, density...\n",
+    " * `all_instruments` - all instruments available in the table\n",
+    " * `all_dates` - all dates listed in the table\n",
+    " * `all_site_names` - all the site names available in the table. e.g. Grand Mesa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Available types = two_way_travel, snow_void, density, swe, depth\n",
+      "\n",
+      "Available Instruments = Mala 1600 MHz GPR, None, Mala 800 MHz GPR, pulse EKKO Pro multi-polarization 1 GHz GPR, pit ruler, mesa, magnaprobe, camera\n",
+      "\n",
+      "Available Dates = 2020-05-28, 2020-01-09, 2021-03-19, 2020-05-23, 2020-01-04, 2019-11-29, 2019-10-20, 2019-11-30, 2021-01-28, 2020-04-17, 2021-02-19, 2020-02-19, 2020-02-26, 2020-02-03, 2020-05-05, 2019-10-05, 2019-12-29, 2020-06-02, 2019-10-28, 2020-01-30, 2020-05-22, 2020-03-09, 2019-12-09, 2019-12-28, 2020-02-24, 2020-03-17, 2021-03-18, 2020-04-01, 2020-05-14, 2019-10-29, 2019-10-14, 2019-10-02, 2020-01-31, 2020-04-18, 2020-04-29, 2020-04-26, 2019-10-12, 2021-03-03, 2021-01-15, 2020-02-23, 2020-01-22, 2020-01-01, 2019-11-21, 2020-05-10, 2023-03-13, 2020-02-12, 2020-05-06, 2019-11-19, 2019-10-25, 2019-11-02, 2020-02-08, 2020-04-14, 2020-04-02, 2019-11-16, 2020-04-07, 2021-03-21, 2021-04-21, 2023-03-15, 2020-11-25, 2019-12-27, 2021-01-27, 2019-10-01, 2020-04-16, 2020-06-08, 2019-12-13, 2019-10-17, 2019-10-22, 2021-01-22, 2020-04-21, 2020-01-03, 2019-12-12, 2019-12-08, 2021-03-05, 2020-01-25, 2020-02-29, 2019-11-24, 2019-10-18, 2021-03-04, 2021-03-24, 2021-03-16, 2020-05-09, 2020-03-22, 2019-11-06, 2019-12-16, 2020-01-15, 2019-11-22, 2019-10-13, 2019-11-10, 2019-12-06, 2020-02-04, 2020-03-07, 2019-10-31, 2020-04-06, 2020-05-03, 2019-12-10, 2020-05-26, 2019-12-02, 2021-02-09, 2020-02-14, 2020-02-13, 2020-05-11, 2019-12-01, 2020-01-19, 2019-11-28, 2020-01-17, 2019-12-17, 2021-02-17, 2021-01-07, 2021-03-31, 2019-12-25, 2019-12-14, 2019-10-24, 2020-02-01, 2020-03-11, 2021-03-23, 2020-02-09, 2020-05-12, 2020-05-25, 2020-03-29, 2020-04-24, 2019-12-11, 2020-01-10, 2020-06-05, 2020-11-20, 2019-10-10, 2020-04-13, 2020-03-23, 2020-04-23, 2020-05-24, 2019-11-08, 2021-05-05, 2021-04-06, 2019-12-26, 2019-12-15, 2020-05-07, 2021-01-20, 2020-02-28, 2019-11-03, 2020-04-04, 2019-11-27, 2021-01-14, 2020-03-15, 2020-01-16, 2019-11-23, 2023-03-14, 2019-10-08, 2019-11-14, 2020-02-15, 2020-02-11, 2023-03-12, 2019-11-13, 2020-04-30, 2019-10-26, 2020-03-06, 2021-03-17, 2020-05-31, 2020-03-04, 2021-02-24, 2019-10-04, 2020-05-16, 2020-04-03, 2019-10-06, 2021-02-25, 2019-10-09, 2020-03-12, 2019-11-12, 2019-11-01, 2020-03-10, 2020-02-21, 2019-10-30, 2020-12-17, 2023-03-07, 2020-06-01, 2020-03-20, 2020-03-03, 2019-11-07, 2020-01-06, 2021-02-11, 2019-12-22, 2020-01-11, 2019-11-11, 2019-11-05, 2020-01-13, 2023-03-16, 2019-12-18, 2019-12-30, 2020-05-04, 2020-04-20, 2021-04-14, 2023-03-09, 2023-03-08, 2020-02-22, 2020-12-18, 2020-05-08, 2020-01-24, 2019-12-24, 2020-04-22, 2020-03-31, 2020-01-08, 2019-11-04, 2020-02-06, 2021-02-18, 2020-03-05, 2021-05-27, 2020-03-14, 2021-02-04, 2020-06-09, 2021-01-21, 2020-02-20, 2020-11-23, 2020-04-05, 2021-05-07, 2020-06-03, 2019-10-16, 2020-04-15, 2021-01-26, 2019-12-03, 2020-05-30, 2019-11-09, 2021-02-16, 2020-04-28, 2020-01-12, 2020-05-20, 2023-03-10, 2020-05-02, 2020-02-05, 2020-01-28, 2020-01-21, 2019-12-19, 2019-10-07, 2020-03-28, 2020-02-10, 2021-04-28, 2020-03-02, 2019-09-29, 2019-11-15, 2020-01-02, 2020-05-27, 2020-02-18, 2019-10-11, 2019-12-21, 2021-03-10, 2020-04-09, 2019-09-30, 2020-01-05, 2019-10-27, 2020-04-10, 2021-04-23, 2020-03-16, 2020-03-21, 2020-02-02, 2020-04-08, 2020-02-25, 2020-01-29, 2021-03-22, 2019-12-04, 2021-02-10, 2021-02-03, 2019-11-26, 2020-03-19, 2020-01-20, 2020-02-27, 2019-12-31, 2020-03-30, 2020-04-25, 2020-01-26, 2020-01-14, 2020-12-08, 2020-03-01, 2020-02-17, 2021-03-02, 2020-05-21, 2019-10-23, 2020-04-11, 2019-10-21, 2020-12-16, 2019-11-25, 2020-04-12, 2020-03-13, 2021-05-20, 2020-05-01, 2021-01-13, 2020-03-08, 2020-05-19, 2020-03-27, 2019-11-17, 2020-04-19, 2020-01-23, 2021-02-23, 2020-05-15, 2020-02-16, 2019-10-19, 2020-05-29, 2020-03-24, 2019-12-07, 2020-02-07, 2020-03-18, 2020-05-17, 2020-05-13, 2019-12-20, 2019-12-23, 2020-06-07, 2020-01-07, 2021-05-17, 2021-04-07, 2020-05-18, 2019-12-05, 2019-11-20, 2020-06-06, 2020-12-09, 2023-03-11, 2021-02-02, 2019-11-18, 2020-06-10, 2020-01-27, 2020-11-16, 2020-01-18, 2020-06-04, 2020-04-27, 2020-12-01, 2020-03-25, 2019-10-15, 2020-03-26, 2021-03-09, 2019-10-03\n",
+      "\n",
+      "Available sites = American River Basin, Central Ag Research Center, Senator Beck, Fairbanks, None, Fraser Experimental Forest, Boise River Basin, Little Cottonwood Canyon, East River, North Slope, Jemez River, Grand Mesa, Cameron Pass, Sagehen Creek, Mammoth Lakes, Niwot Ridge\n"
+     ]
+    }
+   ],
+   "source": [
+    "from snowexsql.api import PointMeasurements\n",
+    "\n",
+    "# Instatiate the class to use the properties!\n",
+    "measurements = PointMeasurements()\n",
+    "\n",
+    "# Get the unique data names/types in the table\n",
+    "results = measurements.all_types\n",
+    "print('Available types = {}'.format(', '.join([str(r) for r in results])))\n",
+    "\n",
+    "# Get the unique instrument in the table\n",
+    "results = measurements.all_instruments\n",
+    "print('\\nAvailable Instruments = {}'.format(', '.join([str(r) for r in results])))\n",
+    "\n",
+    "# Get the unique dates in the table\n",
+    "results = measurements.all_dates\n",
+    "print('\\nAvailable Dates = {}'.format(', '.join([str(r) for r in results])))\n",
+    "\n",
+    "# Get the unique site names in the table\n",
+    "results = measurements.all_site_names\n",
+    "print('\\nAvailable sites = {}'.format(', '.join([str(r) for r in results])))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### More specific filtering options\n",
+    "Sometimes we need a bit more filtering to know more about what I can filter on. Questions like \"What dates was the SMP used?\" are a bit more complicated than \"Give me all the dates for snowex\"\n",
+    "\n",
+    "The good news is, we have tool for that! `from_unique_entries` is your friend!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[datetime.date(2020, 2, 4),\n",
+       " datetime.date(2020, 2, 3),\n",
+       " datetime.date(2020, 1, 30),\n",
+       " datetime.date(2020, 2, 1),\n",
+       " datetime.date(2020, 2, 6),\n",
+       " datetime.date(2020, 1, 31),\n",
+       " datetime.date(2020, 2, 12),\n",
+       " datetime.date(2020, 2, 8),\n",
+       " datetime.date(2020, 2, 5),\n",
+       " datetime.date(2020, 1, 28),\n",
+       " datetime.date(2020, 2, 11),\n",
+       " datetime.date(2020, 2, 10),\n",
+       " datetime.date(2020, 1, 29)]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import layer measurements\n",
+    "from snowexsql.api import LayerMeasurements\n",
+    "\n",
+    "# Query dates where SMP was used\n",
+    "LayerMeasurements.from_unique_entries(['date'], instrument='snowmicropen')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query Nuances\n",
+    "### Limit size \n",
+    "To avoid accidental large queries, we have added some bumper rails. By default if you ask for more than 1000 records then an error will pop up unless you explicitly say you want more. \n",
+    "\n",
+    "Try doing a large query. Something like the following to see the error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed query for PointData\n"
+     ]
+    },
+    {
+     "ename": "LargeQueryCheckException",
+     "evalue": "Query will return 2296512 number of records, but we have a default max of 1000. If you want to proceed, set the 'limit' filter to the desired number of records.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mLargeQueryCheckException\u001b[0m                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msnowexsql\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PointMeasurements\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Query db using a vague filter or on a huge dataset like GPR\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mPointMeasurements\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_filter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtwo_way_travel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Show the dataframe\u001b[39;00m\n\u001b[1;32m      8\u001b[0m df\n",
+      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/snowexsql/api.py:246\u001b[0m, in \u001b[0;36mPointMeasurements.from_filter\u001b[0;34m(cls, **kwargs)\u001b[0m\n\u001b[1;32m    244\u001b[0m         session\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m    245\u001b[0m         LOG\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed query for PointData\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 246\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    248\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df\n",
+      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/snowexsql/api.py:241\u001b[0m, in \u001b[0;36mPointMeasurements.from_filter\u001b[0;34m(cls, **kwargs)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    240\u001b[0m     qry \u001b[38;5;241m=\u001b[39m session\u001b[38;5;241m.\u001b[39mquery(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mMODEL)\n\u001b[0;32m--> 241\u001b[0m     qry \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextend_qry\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    242\u001b[0m     df \u001b[38;5;241m=\u001b[39m query_to_geopandas(qry, engine)\n\u001b[1;32m    243\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/snowexsql/api.py:139\u001b[0m, in \u001b[0;36mBaseDataset.extend_qry\u001b[0;34m(cls, qry, check_size, **kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not an allowed filter\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_size:\n\u001b[0;32m--> 139\u001b[0m     \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_size\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m qry\n",
+      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/snowexsql/api.py:80\u001b[0m, in \u001b[0;36mBaseDataset._check_size\u001b[0;34m(cls, qry, kwargs)\u001b[0m\n\u001b[1;32m     78\u001b[0m count \u001b[38;5;241m=\u001b[39m qry\u001b[38;5;241m.\u001b[39mcount()\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m count \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mMAX_RECORD_COUNT \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlimit\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[0;32m---> 80\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LargeQueryCheckException(\n\u001b[1;32m     81\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuery will return \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcount\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m number of records,\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     82\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m but we have a default max of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mMAX_RECORD_COUNT\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     83\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m If you want to proceed, set the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlimit\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m filter\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     84\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m to the desired number of records.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     85\u001b[0m     )\n",
+      "\u001b[0;31mLargeQueryCheckException\u001b[0m: Query will return 2296512 number of records, but we have a default max of 1000. If you want to proceed, set the 'limit' filter to the desired number of records."
+     ]
+    }
+   ],
+   "source": [
+    "# Import PointMeasurements\n",
+    "from snowexsql.api import PointMeasurements\n",
+    "\n",
+    "# Query db using a vague filter or on a huge dataset like GPR\n",
+    "df = PointMeasurements.from_filter(type='two_way_travel')\n",
+    "\n",
+    "# Show the dataframe\n",
+    "df\n",
+    "\n",
+    "# Throws an exception, try adding the limit keyword arg in the function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have added this on the db to allow you to explore without accidentally pulling the entire SnowEx universe down. If you know you want a large query (defined as > 1000) then use the `limit = ####` option in the `from_filter` or `from_area` function.\n",
+    "\n",
+    "**Warning** - It is better to filter using other things besides the limit because the limit is not intelligent. It will simply limit the query by the order of entries that were submitted AND fits your filter. So if you encounter this then consider how to tighten up the filter.\n",
+    "\n",
+    "### List of Criteria\n",
+    "You can use lists in your requests too!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>depth</th>\n",
+       "      <th>site_id</th>\n",
+       "      <th>pit_id</th>\n",
+       "      <th>bottom_depth</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>sample_a</th>\n",
+       "      <th>sample_b</th>\n",
+       "      <th>sample_c</th>\n",
+       "      <th>value</th>\n",
+       "      <th>flags</th>\n",
+       "      <th>...</th>\n",
+       "      <th>date</th>\n",
+       "      <th>time_created</th>\n",
+       "      <th>time_updated</th>\n",
+       "      <th>id</th>\n",
+       "      <th>doi</th>\n",
+       "      <th>date_accessed</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>observers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>91.0</td>\n",
+       "      <td>2C12</td>\n",
+       "      <td>COGM2C12_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>45.02</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.019334+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407735</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>reflectance</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>86.0</td>\n",
+       "      <td>2C12</td>\n",
+       "      <td>COGM2C12_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>39.82</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.019334+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407736</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>reflectance</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>81.0</td>\n",
+       "      <td>2C12</td>\n",
+       "      <td>COGM2C12_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.019334+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407737</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>reflectance</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2C12</td>\n",
+       "      <td>COGM2C12_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>35.11</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.019334+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407738</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>reflectance</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>71.0</td>\n",
+       "      <td>2C12</td>\n",
+       "      <td>COGM2C12_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>34.86</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.019334+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407739</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>reflectance</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>72.0</td>\n",
+       "      <td>2C13</td>\n",
+       "      <td>COGM2C13_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>40.5</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.225144+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407830</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>2C13</td>\n",
+       "      <td>COGM2C13_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>22.6</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.225144+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407831</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>62.0</td>\n",
+       "      <td>2C13</td>\n",
+       "      <td>COGM2C13_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.225144+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407832</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>2C13</td>\n",
+       "      <td>COGM2C13_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.225144+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407833</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2C13</td>\n",
+       "      <td>COGM2C13_20200212</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>2024-08-15 20:03:26.225144+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2407834</td>\n",
+       "      <td>https://doi.org/10.5067/SNMM6NGGKWIT</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>IS3-SP-11-01F</td>\n",
+       "      <td>specific_surface_area</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kate Hale</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    depth site_id             pit_id bottom_depth comments sample_a sample_b  \\\n",
+       "0    91.0    2C12  COGM2C12_20200212         None     None     None     None   \n",
+       "1    86.0    2C12  COGM2C12_20200212         None     None     None     None   \n",
+       "2    81.0    2C12  COGM2C12_20200212         None     None     None     None   \n",
+       "3    76.0    2C12  COGM2C12_20200212         None     None     None     None   \n",
+       "4    71.0    2C12  COGM2C12_20200212         None     None     None     None   \n",
+       "..    ...     ...                ...          ...      ...      ...      ...   \n",
+       "95   72.0    2C13  COGM2C13_20200212         None     None     None     None   \n",
+       "96   67.0    2C13  COGM2C13_20200212         None     None     None     None   \n",
+       "97   62.0    2C13  COGM2C13_20200212         None     None     None     None   \n",
+       "98   57.0    2C13  COGM2C13_20200212         None     None     None     None   \n",
+       "99   52.0    2C13  COGM2C13_20200212         None     None     None     None   \n",
+       "\n",
+       "   sample_c  value flags  ...        date                     time_created  \\\n",
+       "0      None  45.02  None  ...  2020-02-12 2024-08-15 20:03:26.019334+00:00   \n",
+       "1      None  39.82  None  ...  2020-02-12 2024-08-15 20:03:26.019334+00:00   \n",
+       "2      None  37.85  None  ...  2020-02-12 2024-08-15 20:03:26.019334+00:00   \n",
+       "3      None  35.11  None  ...  2020-02-12 2024-08-15 20:03:26.019334+00:00   \n",
+       "4      None  34.86  None  ...  2020-02-12 2024-08-15 20:03:26.019334+00:00   \n",
+       "..      ...    ...   ...  ...         ...                              ...   \n",
+       "95     None   40.5  None  ...  2020-02-12 2024-08-15 20:03:26.225144+00:00   \n",
+       "96     None   22.6  None  ...  2020-02-12 2024-08-15 20:03:26.225144+00:00   \n",
+       "97     None   26.6  None  ...  2020-02-12 2024-08-15 20:03:26.225144+00:00   \n",
+       "98     None   24.3  None  ...  2020-02-12 2024-08-15 20:03:26.225144+00:00   \n",
+       "99     None   26.0  None  ...  2020-02-12 2024-08-15 20:03:26.225144+00:00   \n",
+       "\n",
+       "    time_updated       id                                   doi  \\\n",
+       "0           None  2407735  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "1           None  2407736  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "2           None  2407737  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "3           None  2407738  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "4           None  2407739  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "..           ...      ...                                   ...   \n",
+       "95          None  2407830  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "96          None  2407831  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "97          None  2407832  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "98          None  2407833  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "99          None  2407834  https://doi.org/10.5067/SNMM6NGGKWIT   \n",
+       "\n",
+       "    date_accessed     instrument                   type units  observers  \n",
+       "0      2022-06-30  IS3-SP-11-01F            reflectance  None  Kate Hale  \n",
+       "1      2022-06-30  IS3-SP-11-01F            reflectance  None  Kate Hale  \n",
+       "2      2022-06-30  IS3-SP-11-01F            reflectance  None  Kate Hale  \n",
+       "3      2022-06-30  IS3-SP-11-01F            reflectance  None  Kate Hale  \n",
+       "4      2022-06-30  IS3-SP-11-01F            reflectance  None  Kate Hale  \n",
+       "..            ...            ...                    ...   ...        ...  \n",
+       "95     2022-06-30  IS3-SP-11-01F  specific_surface_area  None  Kate Hale  \n",
+       "96     2022-06-30  IS3-SP-11-01F  specific_surface_area  None  Kate Hale  \n",
+       "97     2022-06-30  IS3-SP-11-01F  specific_surface_area  None  Kate Hale  \n",
+       "98     2022-06-30  IS3-SP-11-01F  specific_surface_area  None  Kate Hale  \n",
+       "99     2022-06-30  IS3-SP-11-01F  specific_surface_area  None  Kate Hale  \n",
+       "\n",
+       "[100 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import layer measurements\n",
+    "from snowexsql.api import LayerMeasurements\n",
+    "\n",
+    "# Grab all the data that used the one of these instruments (hint hint SSA)\n",
+    "ssa_instruments = [\"IS3-SP-15-01US\", \"IRIS\",  \"IS3-SP-11-01F\"]\n",
+    "\n",
+    "# Query the DB (throw a limit for safety)\n",
+    "LayerMeasurements.from_filter(instrument=ssa_instruments, limit=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Greater than or Less than\n",
+    "Sometimes we want to isolate certain ranges of value or even dates. The `greater_equal` and `less_equal` terms can be added on to `value` or `dates`. \n",
+    "\n",
+    "* `date_greater_equal`\n",
+    "* `date_less_equal`\n",
+    "* `value_greater_equal`\n",
+    "* `value_less_equal`\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>version_number</th>\n",
+       "      <th>equipment</th>\n",
+       "      <th>value</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>northing</th>\n",
+       "      <th>easting</th>\n",
+       "      <th>elevation</th>\n",
+       "      <th>utm_zone</th>\n",
+       "      <th>geom</th>\n",
+       "      <th>...</th>\n",
+       "      <th>date</th>\n",
+       "      <th>time_created</th>\n",
+       "      <th>time_updated</th>\n",
+       "      <th>id</th>\n",
+       "      <th>doi</th>\n",
+       "      <th>date_accessed</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>observers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>101.096736</td>\n",
+       "      <td>39.034358</td>\n",
+       "      <td>-108.190907</td>\n",
+       "      <td>4.324345e+06</td>\n",
+       "      <td>743146.962029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.962 4324344.879)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320356</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>101.096736</td>\n",
+       "      <td>39.034358</td>\n",
+       "      <td>-108.190907</td>\n",
+       "      <td>4.324345e+06</td>\n",
+       "      <td>743146.933029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.933 4324344.839)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320357</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>103.532801</td>\n",
+       "      <td>39.034350</td>\n",
+       "      <td>-108.190913</td>\n",
+       "      <td>4.324344e+06</td>\n",
+       "      <td>743146.462029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.462 4324343.986)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320378</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>104.750834</td>\n",
+       "      <td>39.034350</td>\n",
+       "      <td>-108.190913</td>\n",
+       "      <td>4.324344e+06</td>\n",
+       "      <td>743146.454029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.454 4324343.945)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320379</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>104.750834</td>\n",
+       "      <td>39.034350</td>\n",
+       "      <td>-108.190913</td>\n",
+       "      <td>4.324344e+06</td>\n",
+       "      <td>743146.447029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.447 4324343.904)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320380</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>109.622966</td>\n",
+       "      <td>39.034313</td>\n",
+       "      <td>-108.190909</td>\n",
+       "      <td>4.324340e+06</td>\n",
+       "      <td>743146.897029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.897 4324339.877)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320471</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>109.622966</td>\n",
+       "      <td>39.034313</td>\n",
+       "      <td>-108.190909</td>\n",
+       "      <td>4.324340e+06</td>\n",
+       "      <td>743146.915029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.915 4324339.839)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320472</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>108.404933</td>\n",
+       "      <td>39.034313</td>\n",
+       "      <td>-108.190909</td>\n",
+       "      <td>4.324340e+06</td>\n",
+       "      <td>743146.934029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.934 4324339.802)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320473</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>108.404933</td>\n",
+       "      <td>39.034312</td>\n",
+       "      <td>-108.190909</td>\n",
+       "      <td>4.324340e+06</td>\n",
+       "      <td>743146.953029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.953 4324339.764)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320474</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>108.404933</td>\n",
+       "      <td>39.034312</td>\n",
+       "      <td>-108.190909</td>\n",
+       "      <td>4.324340e+06</td>\n",
+       "      <td>743146.971029</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (743146.971 4324339.727)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-07-05 16:45:41.402741+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1320475</td>\n",
+       "      <td>https://doi.org/10.5067/Q2LFK0QSVGS2</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>Tate Meehan</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   version_number equipment       value   latitude   longitude      northing  \\\n",
+       "0            None      None  101.096736  39.034358 -108.190907  4.324345e+06   \n",
+       "1            None      None  101.096736  39.034358 -108.190907  4.324345e+06   \n",
+       "2            None      None  103.532801  39.034350 -108.190913  4.324344e+06   \n",
+       "3            None      None  104.750834  39.034350 -108.190913  4.324344e+06   \n",
+       "4            None      None  104.750834  39.034350 -108.190913  4.324344e+06   \n",
+       "..            ...       ...         ...        ...         ...           ...   \n",
+       "95           None      None  109.622966  39.034313 -108.190909  4.324340e+06   \n",
+       "96           None      None  109.622966  39.034313 -108.190909  4.324340e+06   \n",
+       "97           None      None  108.404933  39.034313 -108.190909  4.324340e+06   \n",
+       "98           None      None  108.404933  39.034312 -108.190909  4.324340e+06   \n",
+       "99           None      None  108.404933  39.034312 -108.190909  4.324340e+06   \n",
+       "\n",
+       "          easting elevation  utm_zone                            geom  ...  \\\n",
+       "0   743146.962029      None        12  POINT (743146.962 4324344.879)  ...   \n",
+       "1   743146.933029      None        12  POINT (743146.933 4324344.839)  ...   \n",
+       "2   743146.462029      None        12  POINT (743146.462 4324343.986)  ...   \n",
+       "3   743146.454029      None        12  POINT (743146.454 4324343.945)  ...   \n",
+       "4   743146.447029      None        12  POINT (743146.447 4324343.904)  ...   \n",
+       "..            ...       ...       ...                             ...  ...   \n",
+       "95  743146.897029      None        12  POINT (743146.897 4324339.877)  ...   \n",
+       "96  743146.915029      None        12  POINT (743146.915 4324339.839)  ...   \n",
+       "97  743146.934029      None        12  POINT (743146.934 4324339.802)  ...   \n",
+       "98  743146.953029      None        12  POINT (743146.953 4324339.764)  ...   \n",
+       "99  743146.971029      None        12  POINT (743146.971 4324339.727)  ...   \n",
+       "\n",
+       "          date                     time_created time_updated       id  \\\n",
+       "0   2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320356   \n",
+       "1   2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320357   \n",
+       "2   2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320378   \n",
+       "3   2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320379   \n",
+       "4   2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320380   \n",
+       "..         ...                              ...          ...      ...   \n",
+       "95  2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320471   \n",
+       "96  2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320472   \n",
+       "97  2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320473   \n",
+       "98  2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320474   \n",
+       "99  2020-01-28 2022-07-05 16:45:41.402741+00:00         None  1320475   \n",
+       "\n",
+       "                                     doi date_accessed  \\\n",
+       "0   https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "1   https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "2   https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "3   https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "4   https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "..                                   ...           ...   \n",
+       "95  https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "96  https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "97  https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "98  https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "99  https://doi.org/10.5067/Q2LFK0QSVGS2    2022-06-30   \n",
+       "\n",
+       "                                     instrument   type units    observers  \n",
+       "0   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "1   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "2   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "3   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "4   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "..                                          ...    ...   ...          ...  \n",
+       "95  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "96  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "97  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "98  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "99  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  Tate Meehan  \n",
+       "\n",
+       "[100 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import the point measurements class\n",
+    "from snowexsql.api import PointMeasurements\n",
+    "\n",
+    "# Filter values > 100 cm from the pulse ecko GPR\n",
+    "df = PointMeasurements.from_filter(value_greater_equal=100, type='depth', instrument='pulse EKKO Pro multi-polarization 1 GHz GPR', limit=100)\n",
+    "\n",
+    "# Show off the dataframe\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Recap \n",
+    "You just came in contact with the new API tools. We can use each API class to pull from specific tables and filter the data. \n",
+    "**You should know:**\n",
+    "* How to build queries using `from_filter`, `from_area`, `from_unique_entries`\n",
+    "* Determine what values to filter on\n",
+    "* Manage the limit error\n",
+    "* Filtering on greater and less than\n",
+    "  \n",
+    "If you don't feel comfortable with these, you are probably not alone, let's discuss it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/book/tutorials/snowex_database/4_get_spiral_example.ipynb b/book/tutorials/snowex_database/4_get_spiral_example.ipynb
new file mode 100644
index 0000000..45d9140
--- /dev/null
+++ b/book/tutorials/snowex_database/4_get_spiral_example.ipynb
@@ -0,0 +1,669 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Excercise: Visualize a Manual Depth Spiral\n",
+    "\n",
+    "During the SnowEx campaigns a TON of manual snow depths were collected, past surveys for hackweek showed an overhelming interest in the manual \n",
+    "snow depths dataset. This tutorial shows how easy it is to get at that data in the database while learning how to build queries\n",
+    "\n",
+    "**Goal**: Visualize a small subset of snow depth, ideally a full spiral (mostly cause theyre cool!)\n",
+    "\n",
+    "**Approach**: \n",
+    "1. Determine the necessary details for isolating manual depths\n",
+    "2. Find a pit where many spirals were done. \n",
+    "3. Buffer on the pit location and grab all manual snow depths"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Process\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from snowexsql.api import LayerMeasurements\n",
+    "data_type = 'depth'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: Find a pit of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>depth</th>\n",
+       "      <th>site_id</th>\n",
+       "      <th>pit_id</th>\n",
+       "      <th>bottom_depth</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>sample_a</th>\n",
+       "      <th>sample_b</th>\n",
+       "      <th>sample_c</th>\n",
+       "      <th>value</th>\n",
+       "      <th>flags</th>\n",
+       "      <th>...</th>\n",
+       "      <th>date</th>\n",
+       "      <th>time_created</th>\n",
+       "      <th>time_updated</th>\n",
+       "      <th>id</th>\n",
+       "      <th>doi</th>\n",
+       "      <th>date_accessed</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>observers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1N1</td>\n",
+       "      <td>COGM1N1_20200208</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-7.5</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-08</td>\n",
+       "      <td>2024-08-15 19:56:43.640672+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2367521</td>\n",
+       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>None</td>\n",
+       "      <td>temperature</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   depth site_id            pit_id bottom_depth comments sample_a sample_b  \\\n",
+       "0   67.0     1N1  COGM1N1_20200208         None     None     None     None   \n",
+       "\n",
+       "  sample_c value flags  ...        date                     time_created  \\\n",
+       "0     None  -7.5  None  ...  2020-02-08 2024-08-15 19:56:43.640672+00:00   \n",
+       "\n",
+       "   time_updated       id                                   doi  date_accessed  \\\n",
+       "0          None  2367521  https://doi.org/10.5067/DUD2VZEVBJ7S     2022-06-30   \n",
+       "\n",
+       "  instrument         type units observers  \n",
+       "0       None  temperature  None      None  \n",
+       "\n",
+       "[1 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pick the first one we find\n",
+    "site_id = LayerMeasurements().all_site_ids[0]\n",
+    "\n",
+    "# Query the database, we only need one point to get a site id and its geometry\n",
+    "site_df = LayerMeasurements.from_filter(site_id=site_id, limit=1)\n",
+    "\n",
+    "# Print it out \n",
+    "site_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: Collect Snow Depths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>version_number</th>\n",
+       "      <th>equipment</th>\n",
+       "      <th>value</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>northing</th>\n",
+       "      <th>easting</th>\n",
+       "      <th>elevation</th>\n",
+       "      <th>utm_zone</th>\n",
+       "      <th>geom</th>\n",
+       "      <th>...</th>\n",
+       "      <th>date</th>\n",
+       "      <th>time_created</th>\n",
+       "      <th>time_updated</th>\n",
+       "      <th>id</th>\n",
+       "      <th>doi</th>\n",
+       "      <th>date_accessed</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>observers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CRREL_C</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>39.03636</td>\n",
+       "      <td>-108.22098</td>\n",
+       "      <td>4.324487e+06</td>\n",
+       "      <td>740536.699426</td>\n",
+       "      <td>3030.000000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740536.699 4324487.049)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>5552</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>magnaprobe</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CRREL_C</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>39.03636</td>\n",
+       "      <td>-108.22097</td>\n",
+       "      <td>4.324487e+06</td>\n",
+       "      <td>740537.565112</td>\n",
+       "      <td>3030.000000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740537.565 4324487.075)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>5553</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>magnaprobe</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CRREL_C</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>39.03637</td>\n",
+       "      <td>-108.22095</td>\n",
+       "      <td>4.324488e+06</td>\n",
+       "      <td>740539.262551</td>\n",
+       "      <td>3029.700000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740539.263 4324488.238)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>5554</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>magnaprobe</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CRREL_C</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>39.03637</td>\n",
+       "      <td>-108.22092</td>\n",
+       "      <td>4.324488e+06</td>\n",
+       "      <td>740541.859610</td>\n",
+       "      <td>3032.000000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740541.860 4324488.318)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>5555</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>magnaprobe</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CRREL_C</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>39.03638</td>\n",
+       "      <td>-108.22110</td>\n",
+       "      <td>4.324489e+06</td>\n",
+       "      <td>740526.243320</td>\n",
+       "      <td>3028.000000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740526.243 4324488.951)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-01-28</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>5594</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>magnaprobe</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CRREL_A</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>39.03427</td>\n",
+       "      <td>-108.21925</td>\n",
+       "      <td>4.324260e+06</td>\n",
+       "      <td>740693.559835</td>\n",
+       "      <td>3031.700000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740693.560 4324259.641)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-11</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>33950</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>magnaprobe</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CRREL_A</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>39.03434</td>\n",
+       "      <td>-108.21924</td>\n",
+       "      <td>4.324267e+06</td>\n",
+       "      <td>740694.187866</td>\n",
+       "      <td>3031.000000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740694.188 4324267.437)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-11</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>33951</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>magnaprobe</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CRREL_A</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>39.03436</td>\n",
+       "      <td>-108.21924</td>\n",
+       "      <td>4.324270e+06</td>\n",
+       "      <td>740694.119957</td>\n",
+       "      <td>3031.000000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740694.120 4324269.657)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-11</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>33952</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>magnaprobe</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>39.03462</td>\n",
+       "      <td>-108.22145</td>\n",
+       "      <td>4.324293e+06</td>\n",
+       "      <td>740501.915674</td>\n",
+       "      <td>3029.909912</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740501.916 4324292.667)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-08</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41832</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ruler</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>39.03441</td>\n",
+       "      <td>-108.21963</td>\n",
+       "      <td>4.324274e+06</td>\n",
+       "      <td>740660.187468</td>\n",
+       "      <td>3028.929932</td>\n",
+       "      <td>12</td>\n",
+       "      <td>POINT (740660.187 4324274.175)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-11</td>\n",
+       "      <td>2022-06-30 22:56:52.635035+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41907</td>\n",
+       "      <td>https://doi.org/10.5067/9IA978JIACAR</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>pit ruler</td>\n",
+       "      <td>depth</td>\n",
+       "      <td>cm</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>392 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     version_number equipment  value  latitude  longitude      northing  \\\n",
+       "0                 1   CRREL_C   81.0  39.03636 -108.22098  4.324487e+06   \n",
+       "1                 1   CRREL_C   96.0  39.03636 -108.22097  4.324487e+06   \n",
+       "2                 1   CRREL_C   93.0  39.03637 -108.22095  4.324488e+06   \n",
+       "3                 1   CRREL_C   88.0  39.03637 -108.22092  4.324488e+06   \n",
+       "4                 1   CRREL_C   98.0  39.03638 -108.22110  4.324489e+06   \n",
+       "..              ...       ...    ...       ...        ...           ...   \n",
+       "387               1   CRREL_A  101.0  39.03427 -108.21925  4.324260e+06   \n",
+       "388               1   CRREL_A  105.0  39.03434 -108.21924  4.324267e+06   \n",
+       "389               1   CRREL_A  107.0  39.03436 -108.21924  4.324270e+06   \n",
+       "390               1     ruler   67.0  39.03462 -108.22145  4.324293e+06   \n",
+       "391               1     ruler  112.0  39.03441 -108.21963  4.324274e+06   \n",
+       "\n",
+       "           easting    elevation  utm_zone                            geom  \\\n",
+       "0    740536.699426  3030.000000        12  POINT (740536.699 4324487.049)   \n",
+       "1    740537.565112  3030.000000        12  POINT (740537.565 4324487.075)   \n",
+       "2    740539.262551  3029.700000        12  POINT (740539.263 4324488.238)   \n",
+       "3    740541.859610  3032.000000        12  POINT (740541.860 4324488.318)   \n",
+       "4    740526.243320  3028.000000        12  POINT (740526.243 4324488.951)   \n",
+       "..             ...          ...       ...                             ...   \n",
+       "387  740693.559835  3031.700000        12  POINT (740693.560 4324259.641)   \n",
+       "388  740694.187866  3031.000000        12  POINT (740694.188 4324267.437)   \n",
+       "389  740694.119957  3031.000000        12  POINT (740694.120 4324269.657)   \n",
+       "390  740501.915674  3029.909912        12  POINT (740501.916 4324292.667)   \n",
+       "391  740660.187468  3028.929932        12  POINT (740660.187 4324274.175)   \n",
+       "\n",
+       "     ...        date                     time_created time_updated     id  \\\n",
+       "0    ...  2020-01-28 2022-06-30 22:56:52.635035+00:00         None   5552   \n",
+       "1    ...  2020-01-28 2022-06-30 22:56:52.635035+00:00         None   5553   \n",
+       "2    ...  2020-01-28 2022-06-30 22:56:52.635035+00:00         None   5554   \n",
+       "3    ...  2020-01-28 2022-06-30 22:56:52.635035+00:00         None   5555   \n",
+       "4    ...  2020-01-28 2022-06-30 22:56:52.635035+00:00         None   5594   \n",
+       "..   ...         ...                              ...          ...    ...   \n",
+       "387  ...  2020-02-11 2022-06-30 22:56:52.635035+00:00         None  33950   \n",
+       "388  ...  2020-02-11 2022-06-30 22:56:52.635035+00:00         None  33951   \n",
+       "389  ...  2020-02-11 2022-06-30 22:56:52.635035+00:00         None  33952   \n",
+       "390  ...  2020-02-08 2022-06-30 22:56:52.635035+00:00         None  41832   \n",
+       "391  ...  2020-02-11 2022-06-30 22:56:52.635035+00:00         None  41907   \n",
+       "\n",
+       "                                      doi date_accessed  instrument   type  \\\n",
+       "0    https://doi.org/10.5067/9IA978JIACAR    2022-06-30  magnaprobe  depth   \n",
+       "1    https://doi.org/10.5067/9IA978JIACAR    2022-06-30  magnaprobe  depth   \n",
+       "2    https://doi.org/10.5067/9IA978JIACAR    2022-06-30  magnaprobe  depth   \n",
+       "3    https://doi.org/10.5067/9IA978JIACAR    2022-06-30  magnaprobe  depth   \n",
+       "4    https://doi.org/10.5067/9IA978JIACAR    2022-06-30  magnaprobe  depth   \n",
+       "..                                    ...           ...         ...    ...   \n",
+       "387  https://doi.org/10.5067/9IA978JIACAR    2022-06-30  magnaprobe  depth   \n",
+       "388  https://doi.org/10.5067/9IA978JIACAR    2022-06-30  magnaprobe  depth   \n",
+       "389  https://doi.org/10.5067/9IA978JIACAR    2022-06-30  magnaprobe  depth   \n",
+       "390  https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "391  https://doi.org/10.5067/9IA978JIACAR    2022-06-30   pit ruler  depth   \n",
+       "\n",
+       "    units observers  \n",
+       "0      cm      None  \n",
+       "1      cm      None  \n",
+       "2      cm      None  \n",
+       "3      cm      None  \n",
+       "4      cm      None  \n",
+       "..    ...       ...  \n",
+       "387    cm      None  \n",
+       "388    cm      None  \n",
+       "389    cm      None  \n",
+       "390    cm      None  \n",
+       "391    cm      None  \n",
+       "\n",
+       "[392 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We import the points measurements because snow depths is a single value at single location and date\n",
+    "from snowexsql.api import PointMeasurements \n",
+    "\n",
+    "# Filter the results to within 100m within the point from our pit\n",
+    "df = PointMeasurements.from_area(pt=site_df.geometry[0], type=data_type, buffer=200)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Plot it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": [
+     "nbsphinx-gallery",
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(128.66274298237227, 0.5, 'Northing [m]')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CBJ566iluu+02rFZr2HTvv/8+paWlvPfee4ESMPjGwp8si8UCgMPhCNpePaD89NNP/PDDDyxYsICxY8cGtu/evfukrx2O0WhkypQpPPPMM/z000/1Pj4hIYHDhw+HbPd35EpMTAxsGzx4MB988AGrV6/G6/UyaNAgoqKiSEtLY8WKFaxcuZIBAwaE9JMId82cnJyQ7dW3+a/97rvvBv38auJ2u8nPzw8KBv5z1iVA1MeAAQMYMGAAHo+H77//njlz5nDvvfeSnJzMddddF/YY/9/NwYMHQ2o+/Pz5rOlnUvXnURf+znmffvpp2P1RUVF1zltDXMf/+XJycoIe9P0/u9q89dZbGI1Gli1bFvhbBM74kMHp06fTpUsXpk2bdkbz8XshJfHT4IsvvkCn09GqVauQfU2aNKFbt27ExMTw4osvUlpayp133hmUZvny5fzhD3/gggsu4P3336/1i/5ErFYrjzzyCJdffjl//etfa0znL+VXvZZSipdffvmkr+3v4Vq9496HH35Y67UB/v3vf5/0tcN9scPxqnl/6bk+Bg8ezKpVq0J6X7/66qtERETQt2/fwLYhQ4Zw5MgRnn32Wfr27Rv4Yh48eDBLliwhMzOz1qp0gIsuuojPP/88qFe7x+MJGes+fPhwDAYDv/zyC7179w77qu71118Pev/GG28ABPVAP9nalXD0ej3nnXdeoHZn48aNNaYdNmwYer2eF154ocY0/fr1w2q1hnSmPHjwYKDpoz4uu+wy8vPz8Xg8Ye9f+/bt65w3qPne1fU6/p9D9Z/T//73P9xud62fR9M0DAYDer0+sK28vJzXXnutznk9HTp06MAtt9zCnDlz2L9//69yzd8zKYmfgr/85S9ER0dz7rnnkpycTF5eHu+88w5vv/02f//73wNP7EAgGLZu3ZrCwkI++eQT5s+fz7Rp0zjnnHMC6b7++mv+8Ic/kJKSwuTJk0NKwp06dar3RC0TJ05k4sSJJ0wzdOhQTCYT119/PQ888AAVFRW88MILp1Tl1adPH9q3b8/f/vY33G43cXFxLFmyhK+//jooXYcOHWjdujUPPfQQSini4+NZunRpoP34ZAwfPpxmzZpx+eWX06FDB7xeL5s3b+b//u//iIyM5J577qn3OadMmcKyZcu46KKLeOSRR4iPj+f111/no48+YsaMGcTExATS+mfFW758OY8++mhg+5AhQwK1DXUJ4v/4xz/48MMPufjii3nkkUeIiIjg+eefp7S0NChdixYteOyxx3j44YfZs2cPl1xyCXFxcRw5coTvvvsOm80WlA+TycT//d//UVJSQp8+fVi7di1PPPEEl156KRdccEEgXdeuXfnyyy9ZunQpqampREVFBYJMXbz44ousWrUqMLNhRUVFYOjeiT5/ixYtmDx5Mo8//jjl5eVcf/31xMTEsG3bNvLy8nj00UeJjY3ln//8J5MnT+amm27i+uuvJz8/n0cffRSLxcKUKVPqnE+A6667jtdff50RI0Zwzz33cO6552I0Gjl48CBffPEFV1xxBVdeeWWd8ua/d++99x4vvPACvXr1QqfT0bt37zpfp2PHjtx44408++yzGI1GhgwZwk8//cTMmTPr9B0wcuRIZs2axZgxY/jLX/5Cfn4+M2fODFso6Nq1K2+99RZvv/02rVq1wmKx0LVr1xrPvW/fPjIzMwHfyBIgMKtkixYtwj40VjV16lRef/11vvjiC2w2W9C+o0ePsnr1agC2bNkC+PoGJSUlkZSUxMCBA2v97GeVM92zrjF75ZVX1IABA1RiYqIyGAwqNjZWDRw4UL322mshaf/973+rjh07qoiICBUZGakGDBig3n///ZB0/l60Nb1q6tHtV703aE3C9ZxdunSp6t69u7JYLKpp06bq73//u/rkk09Crjtw4EDVuXPnkHOOHTs25Jw7d+5Uw4YNU9HR0SopKUnddddd6qOPPgo557Zt29TQoUNVVFSUiouLU1dffbXav3+/AtSUKVMC6eraO/3tt99WY8aMUW3btlWRkZHKaDSqjIwM9ac//Ult27YtKG3z5s3VyJEjQ84RbjKQLVu2qMsvv1zFxMQok8mkunfvrv773/+GzUPPnj0VoL755pvAtkOHDikgpHfwiXzzzTeqb9++ymw2q5SUFPX3v/9dvfTSS2Hvw/vvv68uuugiFR0drcxms2revLn64x//qFauXBlI4+8R/OOPP6pBgwYpq9Wq4uPj1V//+ldVUlISdL7Nmzer/v37q4iICAUE7kdNk71UH3mwbt06deWVV6rmzZsrs9msEhIS1MCBA0Mm/ajJq6++qvr06aMsFouKjIxUPXv2DLnf//nPf1S3bt2UyWRSMTEx6oorrlBbt24NSlPXyV5cLpeaOXNm4O8gMjJSdejQQd1+++1q165d9crbsWPH1B//+EcVGxurNE0Lun5dr+NwONT999+vmjRpoiwWi+rbt69at25dnSd7eeWVV1T79u2V2WxWrVq1UtOnT1fz588P+d3Zu3evGjZsmIqKilJArZPU+H/+4V5V83Wi76PJkycrIKR3uv+YcK/TOTlPY6UpVWXUvxDid2/cuHG8++67lJSUnOmsCCFOkbSJCyGEEI2UBHEhhBCikZLqdCGEEKKRkpK4EEII0UhJEBdCCCEaKQniQgghRCMlk738xni9XrKzs4mKijqpudKFEOK3QClFcXExaWlpp22N8IqKilqXtK0rk8kUND1tYyFB/DcmOzv7pOZjFkKI36IDBw6c9GIxJ1JRUYE1Og5cFQ1yvpSUFLKyshpdIJcg/hvjn2P7wIED9Z5eVQghfivsdjvp6emB77SG5nQ6wVWB1nMU6I2ndjKPi5xNH+J0OiWIi1Pjr0KPjo6WIC6EaPROe7OgwYxmOLUgrrTG2z3sN5Pz6dOno2ka9957b2Db1KlT6dChAzabjbi4OIYMGcL69esD+48dO8Zdd91F+/btiYiIICMjg7vvvpuioqKw13A4HPTo0QNN00IWFtE0LeT14osvBqXZsmULAwcOxGq10rRpUx577DGqD7NfvXo1vXr1wmKx0KpVq5BzCCGEaECa1jCvRuo3URLPzMzkpZdeolu3bkHb27Vrx9y5c2nVqhXl5eU888wzDBs2jN27d5OUlER2djbZ2dnMnDmTTp06sW/fPsaPH092dnZgRZ2qHnjgAdLS0vjhhx/C5uO///0vl1xySeB91VWp7HY7Q4cO5aKLLiIzM5OdO3cybtw4bDYb999/PwBZWVmMGDGC2267jUWLFvHNN99wxx13kJSUxFVXXdUQt0oIIURVmganWpJuxEH8jJfES0pKuOGGG3j55ZeJi4sL2jdmzBiGDBlCq1at6Ny5M7NmzcJutwfWp+7SpQuLFy/m8ssvp3Xr1lx88cU8+eSTLF26NGS93U8++YTly5czc+bMGvMSGxtLSkpK4GW1WgP7Xn/9dSoqKliwYAFdunRh9OjRTJ48mVmzZgVK4y+++CIZGRk8++yzdOzYkT//+c/ccsstJ7ymEEKIxuWrr77i8ssvJy0tDU3TeP/99wP7XC4XDz74IF27dsVms5GWlsZNN91EdnZ20DkcDgd33XUXiYmJ2Gw2Ro0axcGDB+udlzMexCdMmMDIkSNrXVvZ6XTy0ksvERMTQ/fu3WtMV1RURHR0NAbD8UqGI0eOcNttt/Haa68RERFR47F33nkniYmJ9OnThxdffBGv1xvYt27dOgYOHBi0Fu/w4cPJzs5m7969gTTDhg0LOufw4cP5/vvvcblcJ/x8QgghToKma5hXPZSWltK9e3fmzp0bsq+srIyNGzfyz3/+k40bN/Lee++xc+dORo0aFZTu3nvvZcmSJbz11lt8/fXXlJSUcNlll+HxeOqVlzNanf7WW2+xcePGwOLy4SxbtozrrruOsrIyUlNTWbFiBYmJiWHT5ufn8/jjj3P77bcHtimlGDduHOPHj6d3796BgFvd448/zuDBg7FarXz++efcf//95OXl8Y9//AOAnJwcWrRoEXRMcnJyYF/Lli3JyckJbKuaxu12k5eXR2pqash1HQ4HDocj8N5ut9d4L4QQQlTTEG3a9Tz+0ksv5dJLLw27LyYmhhUrVgRtmzNnDueeey779+8nIyODoqIi5s+fz2uvvRYowC5atIj09HRWrlzJ8OHD65yXMxbEDxw4wD333MPy5ctP2KX/oosuYvPmzeTl5fHyyy9zzTXXsH79epo0aRKUzm63M3LkSDp16sSUKVMC2+fMmYPdbmfSpEknzI8/WAP06NEDgMceeyxoe/Velv5q9Krb65KmqunTp/Poo4+eMG9CCCFOv+qFKLPZHFT7erKKiorQNI3Y2FgANmzYgMvlCqq5TUtLo0uXLqxdu7ZeQfyMVadv2LCB3NxcevXqhcFgwGAwsHr1ambPno3BYAhUKdhsNtq0aUPfvn2ZP38+BoOB+fPnB52ruLiYSy65hMjISJYsWYLReHy4wapVq/j2228xm80YDAbatGkDQO/evRk7dmyN+evbty92u50jR44AvokAcnJygtLk5uYCx0vkNaUxGAwkJCSEvc6kSZMoKioKvA4cOFDrvRNCCFGpAavT09PTiYmJCbymT59+ytmrqKjgoYceYsyYMYFhwzk5OZhMppB+YMnJySExpDZnrCQ+ePBgtmzZErTt5ptvpkOHDjz44IPo9fqwxymlQqqfhw8fjtls5sMPPwwp1c+ePZsnnngi8D47O5vhw4fz9ttvc95559WYv02bNmGxWAJPTv369WPy5Mk4nU5MJhMAy5cvJy0tLVDN3q9fP5YuXRp0nuXLl9O7d++gB4uqGupJTwghzkon0aYd9hyETrJ1qt/NLpeL6667Dq/Xy7x582pNr5Sq97j6MxbEo6Ki6NKlS9A2m81GQkICXbp0obS0lCeffJJRo0aRmppKfn4+8+bN4+DBg1x99dWArwQ+bNgwysrKWLRoEXa7PVAdkpSUhF6vJyMjI+gakZGRALRu3TowFeDSpUvJycmhX79+WK1WvvjiCx5++GH+8pe/BH6IY8aM4dFHH2XcuHFMnjyZXbt2MW3aNB555JHATR8/fjxz585l4sSJ3Hbbbaxbt4758+fz5ptvnr4bKYQQokE05CRbLpeLa665hqysLFatWhV03pSUFJxOJwUFBUGl8dzcXM4///x6Xec3MU48HL1ez44dO1i4cCF5eXkkJCTQp08f1qxZQ+fOnQFflbx/8hd/NblfVlZWSEe0mhiNRubNm8fEiRPxer20atWKxx57jAkTJgTS+DsrTJgwgd69exMXF8fEiROZOHFiIE3Lli35+OOPue+++3j++edJS0tj9uzZZ8UY8ZzCcjbsyWdPbglepWgaF0HvVgm0aBJ5prMmhPgd03Qamu4UO7bpNFTtqerMH8B37drFF198EdKc2qtXL4xGIytWrOCaa64B4PDhw/z000/MmDGjXtfSVPUpx8QZZbfbiYmJCQyVawy2Hijk402HQAP/b5NW+e/z2yVxQYfgToglDjcfbT/Cp9uPYHe4aREXwZXdUjm/RTy6RjzpghDiuNP9XeY/v+7Cm9EMplM6l3I78X713zrntaSkhN27dwPQs2dPZs2axUUXXUR8fDxpaWlcddVVbNy4kWXLlgWNWIqPjw80x/71r39l2bJlLFiwgPj4eP72t7+Rn5/Phg0bamxODuc3WxIXjUNhqZOPNx/yPcVWeRz0B/O1O4/SLCGCFkm+EvmhonLGv/MDuSWOQPJDReWsycpnSLskHr+kI/pTfaoWQojT6Pvvv+eiiy4KvPfXyI4dO5apU6fy4YcfAsdHOvl98cUXDBo0CIBnnnkGg8HANddcQ3l5OYMHD2bBggX1CuAgQVycos17j51wv6bBhj35tEiKRCnF35duJa/UEVR15VWgAZ/vPEq7xEjGnZtR0+mEECJYA3Zsq6tBgwaFrJtRVV0quC0WC3PmzGHOnDn1unZ1EsRFnTncHr4/UEixw03TaAtdUqM5eKyM6r+vBp2GRa+h0zQ8SpF9rAyATYeK2J1XGkin16BltIXmkRYsBh0epcjcncfwdomkxtY8s15VJ9ObUwjxO3IGJnv5LZEgLmqllOLdH7JZ8N1+ylzHpwRsGmOhR2Jwx7Vokx6zXlflSdS3ItzXW3PYYS9HXxnYDRr0TYkm2ni86kivaSRaDMxfvYeb+rckI9EWNj8bDhQyb80ePtuRi8vrpWNyFH/p14LrzmmGrp5V8SUONwqINOnJL3VS7HCTYDMRbTnF9YmFEL+OM1AS/y2RIC5q9cbGg/zn230h27OLKjhsr+C8pChizQYijTpMlUG0eul4V7YdzXT8D6VdbARRRn1IOp2m4fEq3vluP/de0iGkfXzJj9n89X+b0SrTAfycW8LE93/ihbVZ/Pf6c2iTdOIe8UopPtp2hAWZ+/k5twSTXiPJZgpU8es0OL9lArec15ymMdYTnksIIc6kxvv4IX4VxRVuFny3P+w+f9D7uagcHWDR605YtW1wKzQUeg3SI80n7Ile4nCzMyd4CsQjxQ7ufPcHvAo8XoVO0zDofb/Cmga7j5Zy4ZyvmfrJdrze0DYph9vDml1HueqV9Uz6aBs/55ZgNuhIiDDirdIm4FWwNiufuxf/wMHC8hrzKIT4DfAvRXpKL6lOF79Tq3/Jwx0mIPp5FRxzuPFQe9u016vokRzFrmNlGKqVsJVSeBX4143Ta5CVW0rHtONrur+54UCg9K3TtMDfXfXrvrRuHwk2M3dd2Cqw7YcDBTyzahdrDxRiNh3/tY+1GMKew6ugzOXhxbVZPDGiU9C+Y6VO1uw+So69AqtRT+/m8XRMiZK2eSHOBGkTF6Jmx8qc6HXHq65r0jkjjqNHS8Puq7oIzJ/Pa87jq3YF7fcqhafy9AZNI95iQK/Blv0FpCdE0DU9FoAfs+1B49CrBk2rUUes1YjZqMOrYGHmPq7t2ZQmUWZ2HSlm9qrdrN5XgMmgD3SGMxt0JxzO5lWQub+AoyUOkiJ9M/ct35bDWxsO4D/K7VG8uzmbprFWpozoSEp0zYv5CCFEQ5MgLk4o0WaqNYADtE2OCgriSil2FpWzJb+MvAoXmgYZkWY6tEngv2PO4fkVu3C5fZ3kPJVDzNrEWEiPCq5mz9p3jBizgYwmkZj04UvfiZEmEmymQHBWSoFBx7g3NzD7ym58tvUwO4+VohTodVrgWINOq1Pv9hx7BUmRZjL3HePN730L1Li8ip15pRwqduBV8H22nWU7chl3bgaTh7bDaqzfWE8hxEk6yzu2Nd6ci1/Fha0TMelr/jXRadCzaQztU6NJiDKj4Qvgqw4VsepQEUcrXCh8pdr9xQ7u+eAn1uw5xsjuaWiahv/5oGN8BBlRoe3kkUY9u/ceo9zhZkj7JlR/nogyG0iw+WZA8gdjTfMF6gqXl78v3cquI8UcLPaNTVdKBWoG6jpZYY7dwX/W7uE/32QBvpqDDdl2DtgdQfnxeBWvrN/HuNc34vZ4azibEKJhNcQKZo03FDbenItfRaTZwG39mofdp9N8pdnbz28JwMCuqZiMOnbbK9hZFNohzKjXsOp1TF/5MylxVgZ1bIIXiDTqSLOZwpaIdZqGphT7s+1c3iWFpjEWdBxPF28z1hiMFVBY7qLQ6QkE26rt+xXu2gNtrEnPS9/s4fMdR3FWps8pdlJY4Q5/TQVr9uTz6Y7cWs8thBCnSoK4qNUfuzfl/kFtiLUGj51ulWDjmT90o33lIicxNhNX9G3OruIKqobjOLOBjnFWOsZF0D7OSqf4CGZ/sYveLRNIijKTajMF9Q738yqFy+PFq+BIXikmvY53bzmX5ChTZeBWWMIMUwMwaGAz6Igy6ihze4ky+aq3XW5voETuVVDq9NT4EGDAV7rWa1rQH8oBe8UJ75deg0Xf17wuvFcpduTYWftLHj8eLJRSuxCnwt+x7VRfjZS0iYs6uaxzCpd0aMK6PfnklzhIjrJwXquEkMlVbBYjh4qPT6vaxGokzWYKCpQ6TSOv2MG/PttBz6YxOOyOoKDv8njJK3dR7Doe3GwGHUcKy2mdGMm3EwfxwjdZPPvlLyH51IAoow5T0IQz0DU5koIyF3llLo5VuEiMNNE6xooODYfXQ4TRgE6DwyUO8ivcVDg9dKx8ONEBaMfbz2srwXsUHCgIPzRty6FCFq3fR0GZ6/hnM+kZ3bMZA9omnfC8QogwzvI2cQniok6OFJWz/MfDHK4MTjspYsMveVzQvgk9WsQHpTXoNNxehUmnkRrhK72HKy3nFldQ7o7C6fGVjjXA6fGyv9gR0vZd6vayLPMgI3s3IzU+gvsGteEPXVO5490fKHd5AuePMuowVplwxqTTSLOZiTTqIc53jRiTnlizAZ2mUehwcczhYb+9gq8PFuJRvqFw0SY9XuUrhfvP7VWgQ2HUa9RQmw74mhmSIoNXVapwelj2wyGW/xxazV7q9PDa+n14lWJguyYh+4UQoiaN9/FD/GqO2it44+sscqqVLsscHpb/eJjvducFbR/QKgF95VCxE/EP4erYPC7QoS23zBUSwI+nV3yx5XCghN0ywcY9F7Y+3ttcA1OVCWdMOo3W0VZshuO/5k2sRuIqA7jd6eaYw8PRMidfHSgMDHMrc3rCDj3zKF87e9Moc62f6+qeTSkqd7HxQAFrf8nj+ZU/s2rn0RMe996mQ4F2d4C1e/IZt2gD58xYxYXPfcVzX/5CYbnrBGcQ4ix0yhO9NEBJ/gySkrio1Vfbj+D2Kmrqy71mey7dMuKwVLY733BOMz7fdRST7vgfhgbYjHoiDDrf/OleRanbQ5nDTfcW8Wz35nG0oJyyWqqqSyrcHMovo1nlvOrDOzRh+5FiPtyaE5iz3R/Ek60mdFXGk5v1GrbKoV9KKY45fMXpLUdLgq7h9ngpdrjRaVqg/dx/DrcXUiLN7C+qoNzlDbknep1G6wQb2YXljF30PR6l6BgbgVEH7lp6w5e7PGw5VMg5GXFMWrqNWV/sRq8ReLhYv6+Amat2sXxCfzomR53wXEKcNc7yyV4a7+OH+FWUOtz8cqQkZKWyqjxKsT27KPC+U0o0j13SEX/Y12m+EnC0UY9B861uZtBpxJgMJFmNuD2KHu2akBAXfuUyh8fLkTInWcUVZBVXsGTTIXYfKQZ8Afrega156rJOpESbA8FWp/kWY6lajR9l1AdK8RWVHeY8XsWhEmdQMNY0jcN2By6P1zckjeDhaJqm0SMlmpgwNQ39W8TRKdnG13vy8CiFzaDDZtTjVXX7kiiqcDPj813M+mJ35b09vk8pyCt1ctm/10lnOCH8zvKSeOPNufhVlJ6o8beSToOSatW8w9o34aGh7dE0jTizMdC2XH0st1GnY/mWbDRNIz1M6bLY5eFgqZMSty/oehUcKizn5a/2sGJrTuBc5zWP57qezQLHGatc63g+j7/3V9l7wjydWE16vAo2HypGAU6vL2BWDeRmg45eadFc1CqeyUPa8q/LO7H6rgu4sXc6BwrLA+ePrHxwOMFQ+yAlDhfTlv9c436PV3GgoJyllZ9dCHF2kyAuTijCXPvMY14FEebQUmnvjDjaJkXWujDK1kNFlFS4SIyxBF3P5fWSG6YN2B9KV247wq7KEjlA/9aJmCvbv8NVXVedec7f+c2o07BUi7BGvYbJoHG01Mm6vYXkFjtxer148bXLK6UwG3Rc2jmFudf04M4LW/OnPhm0TYrk423hg6uhsvahOq9SlLk9lLjcFDvdvLbxEOWuE5eyDTqNL3flnTCNEGeNs7wkLm3i4oQiLUYyEm0cyCsNqnJWSuGsbCc36zU6NI0JOVbTNAa2TeKTH7JD9nmVwuVVgclXlm/NYUS3NHq3SeSrrUcAsDs9IcdVpdPg611HaVtZgrca9dw1sA2zVu1EAcVON5FVxpEXuzzEVD5smPQ6zDoNhxfaxUew5WhJ4PNpmkaczURhqYuiCjcbD9kx6jWU8k1+85/re3Bpp5SQ/JQ43GRXm+SmuErP+ViTgbwKV+D+5Zb7hruVOD2UVHgq73ftD02+Me51m21OiN89neZ7neo5GikJ4qJWAzs24fWv90Jl8Ch0usmrcAdKuwadxoc/ZnNlj6ZYqs0ZbjaEPuG6vN6gDmw6DXYcLGRfTjEjejTl3HaJZO7Ko7yGdt8Sl4e8ChcOj5csewVdm8dxTrNYdJrGeS3ieXRkZ97bfJAdh4sD1dmapuH0KoqcbqIrA3ui1Uh2qZNOiREcLK6goMJdZU1xjcQoEw63l/4t4mmdGEnn1Cj+0DU16DO6vV5+PGTnl6MlHMwvRfNNPBvYX+b2UuJyYzPoMet1JFqMFFS4yCpxUOL2UuzwUOrwBXC9zre0qsmgC+qlXp1HQb+W8TXur87p8ZJb7MBi1JFoO3HPeiFE4yJBXNQqNS6Ca89vziebDvFzfmmgV7ef26tYueMIu3JLeGh4B0xVAneLyl7kfh6lggJ4q2gLLaIsgarmrH0FRNlMjD4vg/+u3cvREmcgrVKKrOIK8qq009vx8PcPt9I8zso1PZrSOz2ODslRTB7ekQqXh91HilnxUw72ymr5/Ao3XgXxFgNmvY6mNhPHHG6GtYjnp7xSdh4rw1lZO9CjaQwTLmjFwDaJYe/LZzuO8N9v91JaWWPQNMJEpFFPoTP4/vxir6B9bARmnW/Ym07TKHF7fT30HcdrG/yl66RoM4eO1byOeZTFwFXd02rc71fqdPPCN1m8vekQxZU/s84pUdzRvyWDZTy6+J3w9a851clepCQufufSE2wM79mUdR9tC7vfq2Bvfilf7MxleJWq5pgIE52aRrO9chlRZ5XSdftYKxmR5pD28uJSJ5t/PkrP9FhWbM8NlGuzy5xBARyOl3n3FZTz9Be70fCNU/eNH4f2qdF0bhrDnqMl5Jc4MRt0tEuJxqTXyLNX4PEqYiPNKKCkwoXRoMPp8U3nWn2a2aqW/XSYf6/NCtqm0zQijXqKXe6gXuUur2LbsVISLL4e+j8dK0UpRbkruLlAKXB5FAk2I2UONwWlof0BdBq8Pa4PZsOJq91LnW6uW5jJ7mrNINuPFDNh8Y/8Y2h7buydfsJzCNEoyIxtQtTNmt156DRqnIxFAV/8HBzEAS7v2YyisiwOFZTjqjzYatDRPKrmtbfdbi9JRj1GvQ6Xx4tHKXLKnDWm93N6vLz3YzavZu7Hq3zV+Vd2TWXCgFac2yohKG1ytSFttjCd8/zs5S5yiysoc3k5bK9g/rd7Q9J4lMKgaaRYzeRVOHFUuVFeoMjpZktuCXqDr9rcG6bGvNzpIdpqID3eSkyEkbxiJxVODzqdRmyEkbsubMWQ9rWXou95bws7j5aEPCD5szRt5c8Mbd+E5FomrhFC/LZJEBd1dqS4osYA7ne0xBGyzWLUc/OFrfn5sJ0Fa7NwexVpEb5FT6ovPeqngLxjZYzt34KF3+zFXu4MKt2G4/R4ya4yvAvA4fbyzg/ZfLL9CH86N4NtOcU4PF46J0dxVfc0OjQ58aQpx0qdLNl4kB8PFvJzQRm55S6sBh1xFkNIgCxxeYg1GTDoNFIizDg9XhxeLyhFaYWLQ3YnhRUeYm1G9EqF7Uvj8Srs5W5sZj2xESZiI3zTt9pMem4+N4M/9aq99Pzp9hzW7MmrdZ30JT9mM75/y1rPJ8Rv2lk+2YsEcVFnNpPhhCVxIKRjm59ep9GpaQztkqPYkWPHXIeB014F6XERPDCiA29vOMj2zYdOmP5omDnXwff3aTLpeadKL/ms/DI+2JrDhP4tGdsno/J6in25JezNLcHtVUSY9Xy0LYdSp4dNR0sodh3vgBZOsctDpFGPgcp52/U6copKWbfnCEXlTtKSE9HrdVQ4PZisBqwmPSWO0B74/kBu0uvolhbNHRe05NyMuBrvbXVz12TVGsAB9uSX1ul8QvymSXW6EHVzbot41u89VuN+DejXMqHG/QAD2iWx7bA9qG28xvNpYDToMOv0jO7ZlDd/OFTjA4TT7cURpke3BsRFmKge0vyTvDz/TRatEiJoHhPB6i2HsZe70DRQXsX2wjKMOo3cMmcggEPwePOqFJBT5iTeYiRCr2NffjHLtx8M7NdXjpcvd3mJMCsMOo0Ik56yMEPpdBrodfDYiI60S4o84X2qKq/EwY7cEvS68IvOVM2rzSR//kI0do338UP86ro3i6V5fESNQyoVkF/qILuw5p7V7VOiGdS+Cdllzhqr0qFytbEYC98fKODbvccoc3polWCjwuXB4faGrAFe00OBxaSvrG0Lfy29Bo98sp3Xv95DYWWbu1LwS3EFBp1v1rfs0uC2+HJ36Jzpfl4gr8JFXrmTr38JnvjFU6URvKDUhdOtiLLosZlC/wxbJ9p440+96xXAgcCDTPWpYqtTyjfvvBCNnkz2IkTd6HUa9w9px7zVv7CjykxpVf14qIhth4t5cHh7WiWGD0CXd0+jRaKNbXvyiQkzm5vHq/gqp4iffirD5fFS6vRQ7PAEz2+Ob+IV/zj0mh4sTCeotjfofOdpYjESXaVU6lWKrOJyWkZZAd/c7VUpwO7wEHuCVdqKSh2UVRtqVlpaTnSUDU3T8CooKHNh0Plmh7MadTjcXm4+tzkjOiXTLS26TlXiVbm9XjYdKgwsBavXBS/eEsi/UmTERXBe87h6nV+I36SzvE288T5+iDMiymLkweEd6JgSFVJFDb52bJfXy4tf7alxVjFN0+jWLJZrB7SidbMYdFUisFcpPjpwjB/ySnF7FaVOD/ZqARx8gbTY4Q6UPC1Gfdj8hN/oC/paZV7SI81BeS11e6hwq0BJtupqbH4lLg8FFa5AGqV8s8+5vQqTXkeJM3TO+eLSMjze4FoEt1dR5vQ1BfRtHs+DQ9rSvWlMnQO4y+Nla46dzP3HuPG1Ddy75KfALHj+gr+qnCrWf129pvHaDb3q/ZAgxG+SlMSFqJ+ichc7coprrFJWytdLfUdOMZ1So2s8j6ZptGseT6umseQXlePxKnbkl/LLFl8HNK9SFIfp+BU4vvK/OhRoGjFWY8h62y63N+yscVVL7tU7qinlqxYvdnmIMupJtZkoDtNEUOryUupyEmXSY618iFCA26OCJrzx83oVR44eIyEuBovZFJSXP3RN5dFLO56wiaEqj1fx3+/2sWjDAYoq3JQ7Pbirdd9XgMdbWVChsklBwbxrupMSXfPwPiFE4yFBXNTb4aLyGgO4nwYcLCw7YRD3Mxh0JCf4ZnZ7ecOBQA94xwnangF0Oo0yl4enL+8Gmq/E+eGWw3y07QgWow6P8i2lqggtkAeCGlDocNOsynSktsolU49WuIg06km2GjlU4qA0TMc5s17D6l+jvMr2JlERmAx6nO7ghxCPx0tuXgFGg57E6AievbYPF7RKICmy7uO1lVJM/Ww7y7YdCbyvHsCD0/vydnGbBO6+sDVd6vAzEaLROMt7pzfenIszJlwpszrFiduja5JbcnyYWE090SNMehIijMRZfa/pn+9kf0E5EUY9uSUOWiVEkBZtIT3GQqfkSEa0b4JJrxFp0BFj8q9p7psC1aBp/GKv8OW5SnVzyygLFR7FvhIH5R4v7WIjaB5lxlYZsDV8Jfgoc+isbi7lRa/X0T09/HStAC63hydHdeXKbmn1CuCF5U7e33I4EMCh5t7y1V3WOUUCuPj98beJn+qrkZKSuKi35vE2YqxGisIsExqgFJ3T6h8wEmwm9uSX4lW+nuPVRVsMGHXBa4WXOj3859u9RBh0ISVur4JD+aXc2jkVg/K1Zf+YXxI0favbq1iXY+f8lOjABDTtYq0UOFx4FZRVto9HGPWkG/QcKnVQ4VGV7eqh5XxPZb+A5onRKOCH/UdxVekcZzLo+VO/Ntx4Xqs635cjxRW8tHYv3+zND2kyqKs6xnohRCMiQVzUm16ncVnXVF7/bn/IPnuFm115pWQXO1jy+ErSY638uV9z/tK/ZaDa+USGtmvC+n0FgG/K1KqTy5gNupAA7mfWab6SdLV96ZFm+jaJQnkVJW4vaw4XBTp+BT6PBi6l+DK7kEijntbRViwGHTq0QHz2XzO33ElFZdX1iYJisctDjNFAi8RoMhKiyCkqw+FyYzEaGNgumUmXdKj1XvjlFju4+70fsVe4UIqQmet0NXXNr6ZnmOVihWj0pHe6EPU3uH0TLuuaChxfzjev1MkXe/I5aK8IBMoDheU8+ukOLvv3OkrD9Niu7vyWCXRKjvKVcjWNmCrDuCw1VOP7q7arB3eDBn2SoirzqLG9wNfjvXrs9R+naZqvpH6slKNuT0hDutursFdbtMTtVWHHY3uBQpebMo+X2AgTndPiuKRLMx4b1Y1/jOhY6wIm4KveP1xUzpw1v2CvcAUeGqrHbJ2mBVaBC0evaQxsnUDz+Iga0wjReGkN9Kq7r776issvv5y0tDQ0TeP9998P2v/ee+8xfPhwEhMT0TSNzZs3h5zD4XBw1113kZiYiM1mY9SoURw8eDAkXW0kiIuTomkaV/Vsxowru3FZ1zR6Z8SxMdsOhJZQvQo2Hyzk6c931XpevU7jiZGduLBVIpoGVqOeOKsBvRY+UEPNf37pkRb0lQ8DTo+Xw2Wu2jvkVZ7/YEF5yOcod4f2lHd4Qsdh+yl8JfwnLu/M89f1ZMrITgxok4ghzJC16r7be4wHlmxh4uIf+W5/QVBewj3MWEy6sGPlNSAjzsq/Lu9c6zWFEHVTWlpK9+7dmTt3bo37+/fvz1NPPVXjOe69916WLFnCW2+9xddff01JSQmXXXYZHk/NI3LCkep0cUqSosxc2aMp724+FFhXOxyPgle+3cekoe1qLYXaTAYmDW3Pn0ta8GN2EV4FHZpEcu/7WzhWFtoe7J+drHowjTLq8QJ6oLwO07yeDJdXUeH2YjHogvKg943m4qHB7UisR8c1h9vDq9/u48tdeUD4KnuzXodB5w1qFtA0jQizHo/HN1Y90mQgOcrM1T2aclX3NCJPsEKbEI1bQ3RMq9/xl156KZdeemmN+//0pz8BsHfv3rD7i4qKmD9/Pq+99hpDhgwBYNGiRaSnp7Ny5UqGDx9e57zIX7ZoED9mF2HUaYGlRsOxV7g5VFhBq0Rbnc6ZFGlmcLvjU4OO7JTC6xsOhAQ2he8hQU9wIHcrFfjTNNWx3bjqOasfUdOiLeVuLy6vwqzXMOgg1mKkX8t4/tA1jVYJdfusAIcKypj3xW72FlUEtoUtXWsasRYDRRXuwP3WfDtonxzJ05d3pmU9ritEo6ZpDTDE7NdtE9+wYQMul4thw4YFtqWlpdGlSxfWrl0rQVz8+kx6fa1V1UDYiVfq6g9dUvnwp8MUO9whgdztVRiqdYQ7UOKgS7wvmFkNeuLNBo45am+XB8Iuk2rS64gw6CgLM17c3zbev2UCM0d1qfdnK3e6efHL3eSXB8/TrtN8w+Dc1drddZpGnNWI2+vF6VFc26Mp/Vsm0KMes70JIYLZ7fag92azGbO57jVpdZWTk4PJZCIuLnjq4+TkZHJycmo4KjxpExcNYniHJiG9vqvSgI7JkaTFnPxMYfE2E89d2Y1mMb45zfXa8ZJqx5RoHh/ZiQ7Jx9cHL3Z5OFIlKHaMi6h7pZkGMVZDSPoUqwmjpoWdPCYtxsrkIe3q85ECMvceo9TpIVytv+0EDz5GnY7RXdO4c0BrejaLlQAuzj4NOE48PT2dmJiYwGv69Om/6kcJ1yxYGymJiwbROyOWvi3iyNxfGDL5SMcEG5e0iqdbkyiWfbOXhBgLbZrF0CSu/r2lm8dHsGDMOWw+VMTWnGL0Oo1e6bGB1b56NI3lUGE5OcUV2EwGWsZH8O2OXPbnlpBoMXJechSbjpbgqGXQtFJw70Vt+flIMZ9tP0JBZVt8q0QbfxnQkgNFFXy49TD5pS4SbCb+0CWVK7um1rvtOa/UyZHiCjbu9w2rC/f3a9TpiDJAifv4HPJa5f+N6JjMHf3rPt5ciN+f+vcuD38OOHDgANHRx+e3OB2lcICUlBScTicFBQVBpfHc3FzOP//8ep1LgrhoEJqmsehPvblq/np+yLZj0Gl4vIpLWydwTYdk3xMmvtnFjhaUk1tQTou0aJweL/YyFyajjpbJUTRNtNU6f7imafRsFkvPZrFh9zeNtdI01hp4P7BrKoUlDvbnluDyeBnRvSlFbg9bsu2s2pmLRylUleFbCrhjQGvaJUfRLjmKkV1TsZe7fDO0WY7P0Da2T8ZJ3699x8p4ds0vrM06hgK6xkcQYzZg0esodYWb3lWHSafhUop4m4kRXVLp3yK+Xp3mhPhdasBx4tHR0UFB/HTp1asXRqORFStWcM011wBw+PBhfvrpJ2bMmFGvc0kQFw0mMdLMqrsGsPLnXN7fchiDVzE4zTfBSNUqIq9SVHi8bMo6Flg0RNPgl8PFJEZbGHZOU8x1mBimPmIjzcRWC3h9Wybwx55N+fznXDYfLMSjFB2ToxnaMZnUKguE6DSN2AhT9VOetKz8Usa9tZEK1/G54UtcHqJNegw6DYteC0woU5WmaVh0GhMvakv7Ks0GQohfV0lJCbt37w68z8rKYvPmzcTHx5ORkcGxY8fYv38/2dm+xZx+/vlnwFcCT0lJISYmhltvvZX777+fhIQE4uPj+dvf/kbXrl0DvdXrSlPhZqoQZ4zdbicmJoaioqJf5YnwdNqwI5eDR0uo/hvm8HjDdg4DX6VWakIEw89pdvozeAa4PV7+uOA7sosdQdsteh29knxrjSulsDs9IYHcYtQxYWBrzkkP7gwjxG/R6f4u859fP+b/0EzW2g84AeUsx/PG/XXO65dffslFF10Usn3s2LEsWLCABQsWcPPNN4fsnzJlClOnTgWgoqKCv//977zxxhuUl5czePBg5s2bR3p6er3yLkH8N+b3FMRXfLef0org3uBKKYqcoeuDV3dF3+bER/3+qopf/mYPL2ceCLsvJcJImxhroGe8x6tweLx4gU4pUdwxqA2mOsz0JsRvwa8XxGc1UBCf2Ci/d6U6XZw2WphBzl5F3ZYxzSttlEG8pMLFTweLKK5wYTMbaJ5oY3XWMb7fX4BXKXIKymo8NqfMRbnbS1ObiXiLEYNOo2ViFBe2S6JnRpz0PBdChJAgLk6b1PgIdpcVBQXtOlX7aMeX1yxzujlW6sRq1JPwG+7EpZTi651HWfNzLlSuw5Jb6mT1oSJcXl+nvmiTnvRahtgVOT0UOct56OKmXNk1TQK3ELU5yxdAkSAuTpuWadH8csgetEBIuOVFq1MKTCYdC77JYmOVecObxVm5rFsaXeqxGldhqZMKp4dIi4FIa+ja33Xh8Sp2HrazZX8BxRVuoiwGumXE0S41OrCC2Pd7jvHVjtzAMWUuD18eLApM0qIANN9qa2addsIhbjoNLmqTJAFciLqQIC7E6RFhMdK3SzLrtx4JlKw1TcOs13CE6X0Nvqp0nV7H65kHKKk2M9uhgnJeXP0LV/RI47sDRSz8bj/5pU5SYyzc0rc5f+7XnOjKIWAH80tZ//NR8qt0IGsaH0Hf9kkkRNd9whmHy8M73+4ju6A80JP+WLGDvUdLaRYfwR/7ZqDXaazZmRt03O7CcjzVupuUVQ4daxJh5EBJ8MxsVV3ROZW4BuwNL4T4/ZIgLk6rJnERDDs3g305xeQVlQMQG2li5+FiCkqDA5lWuVJZuZ6QAA6+AFrscHPDaxupcHsC62pn5Zfx6CfbeWfzIS7t1IRIvY7IMGuxfPlzDlM/+YF9haXodTqGdEzl7sEd6NMiscb8r/jxMIcLygPXr/rfQ8fK+PynHDo2jaG82uIvh0qcIU0HLq+ioMJNnMVAU5uJw6VOqvfRbxMfwQMXt60xP0KI6hpuspfGSIK4OO3MJj3tMmJpR2xgW9uMOLbvL2THwUJKK9wY9RqtUqJpnRbNtE92hF29C+CrvQWUuYJ7t0dZDHRKjcSg01ibdYwR6fEoffCypZ/tzGbptkNBc6sv3riP9zYf4IperUiNjSA5ysxV3ZsSH2Fk1c9H+fFAIREoQidZ9VHA1gOFNA2zTre3hkEf++0OrEYdMSY9UUYLxS7f4il6HaRHW5g6sjP6ei7WIsRZTarThfj1GfU6urWMp1vL+KD5gg+HqYb2O1buIrda6d2k1+iSFoWuct3wZKsxZL3t3XnFLN12CAhe2tMWaSMiwsrXewvQ6wp9s85lHqBbcpRv7nSjnuZRJ6569ypwhhnzHm8xUBxmKJ1bKXbkl9EkwkR6tIUkg54oi4EBbZIY3KFJ0IxwQghRGwni4oyrWmK2mGoeB51XGtqOnBpjCQRwAJtBH7KIwJd7jgSVwAGiIm1YrZbA9b0KjBp0SorEq2oufYcTbTHSLD6CQwVlgYlt2sZGsNfuCJveo+BwqZMnL+9MnwyZuEWIU6LpGmAp0sa7FthvJufTp09H0zTuvffewLapU6fSoUMHbDYbcXFxDBkyhPXr1wf2Hzt2jLvuuov27dsTERFBRkYGd999N0VFRWGv4XA46NGjB5qmsXnz5rBp8vPzadasGZqmUVhYGNi+d+9eNE0LeX366adBx69evZpevXphsVho1aoVL7744knfk7NRXISJFgnhVxsLN6d6QqQpKGA7vaGrAO3JLwkK4JqmYbNZQ9IlR5qDHghK3V5qmwtJA1LjrIzonoZRrwvUyiVYjXStXDe96lX8NeV/7ttcArgQDUJroFfj9JsI4pmZmbz00kt069YtaHu7du2YO3cuW7Zs4euvv6ZFixYMGzaMo0ePApCdnU12djYzZ85ky5YtLFiwgE8//ZRbb7017HUeeOAB0tLSTpiXW2+9NSQfVa1cuZLDhw8HXhdffHFgX1ZWFiNGjGDAgAFs2rSJyZMnc/fdd7N48eK63goBXNY9/M8oNczkL9Wbjw+XOUNWUase+80mY9jhW7GW4Iopl9c3/WlNgVzToF1aNFFWI0nRFm4Z2JoOadGB63VOsHFdlxS6p0Wjq1w2tUdaDM9e2ZV7Lmwd9pxCiPoJV7g6mVdjdcar00tKSrjhhht4+eWXeeKJJ4L2jRkzJuj9rFmzmD9/Pj/++CODBw+mS5cuQQGydevWPPnkk9x444243W4MhuMf75NPPmH58uUsXryYTz75JGxeXnjhBQoLC3nkkUdqTJOQkEBKSkrYfS+++CIZGRk8++yzAHTs2JHvv/+emTNnctVVV9V6L4RPh5Robr6gJW+s30eFy4te0/CisJn0nJsew/cHiwIl6xKHB5NBFyilu5Vie2EZXeJtgfN1To7l2/1HA8fU9Aerhdl3oNRBa70Fi14XcmxilJnh3Y4/cCREmhndOwOHy0Opw43VZMBa2TzgfxBozF8WQojfnjMexCdMmMDIkSMZMmRISBCvyul08tJLLxETE0P37t1rTOef+7ZqAD9y5Ai33XYb77//PhER4dew3rZtG4899hjr169nz549NZ5/1KhRVFRU0LZtW+677z7++Mc/BvatW7eOYcOGBaUfPnw48+fPx+VyYTSGdlpyOBw4HMfbTu12e43XPpuckxFHl7QYNh8oILfYgcWop0d6LBajnj++8h1rfslHr8HhogoSI4PHVP9cVI6mQYfYCHTAoNZNWLf/aGC/y+0mnCKHm2izISjQehTsLqogzmwgwWLAatCTHGOhW0YcXdJjMRpCK7PMRn3IKmwSvIU4TaR3+pnz1ltvsXHjRjIzM2tMs2zZMq677jrKyspITU1lxYoVJCaGH9ebn5/P448/zu233x7YppRi3LhxjB8/nt69e7N3796Q4xwOB9dffz1PP/00GRkZYYN4ZGQks2bNon///uh0Oj788EOuvfZaFi5cyI033ghATk4OycnJQcclJyfjdrvJy8sjNTU15LzTp0/n0UcfrfHzn81MBh3ntkwI2f7R7f1Y8XMub3x/kCPFFUQa9ZS4PGgagY5lPxeV84u9gj90SOYP7ZJIiLXw4PsbUQrcbg9OpwujMThg55Q4aBZtgWod47xAvsNNvsPNQ8Pa07UeM8YJIU4zCeJnxoEDB7jnnntYvnw5FkvNw3guuugiNm/eTF5eHi+//DLXXHMN69evp0mTJkHp7HY7I0eOpFOnTkyZMiWwfc6cOdjtdiZNmlTjNSZNmkTHjh0DwTicxMRE7rvvvsD73r17U1BQwIwZM4KOq17iqq0addKkSUycODHoc9R3KbqzjV6ncUnHZC7p6HtgUkrxxe48Fv9wiN15pQC0S4zkjz2acmFr3wNfj1YJjDonnZfX7GLNrlw8wBGHb6Uw/6QxTo9i17Ey2sVHBPVm9//7jz2bSgAXQvymnLEgvmHDBnJzc+nVq1dgm8fj4auvvmLu3Lk4HA70ej02m402bdrQpk0b+vbtS9u2bZk/f35QUC4uLuaSSy4hMjKSJUuWBFVbr1q1im+//RazObhTVO/evbnhhhtYuHAhq1atYsuWLbz77rvA8cCbmJjIww8/XGNJuW/fvvznP/8JvE9JSSEnJycoTW5uLgaDgYSE0BIlgNlsDsmbqB9N07i4bRIXt03C6fGN2TbpQ6u5mydE8sQfegbe7y8o4/k1e3jvx8M4PV4sBh3DOyQzulsqmw8UsulgIV6vol1yFMM6JtMptXEtUSjE2UFmbDsjBg8ezJYtW4K23XzzzXTo0IEHH3wQvT78eGGlVEgb8vDhwzGbzXz44YchpfrZs2cHtbVnZ2czfPhw3n77bc477zwAFi9eTHl5eSBNZmYmt9xyC2vWrKF165p7EW/atCmoirxfv34sXbo0KM3y5cvp3bt32PZw0fDCBe+aZMRF8K9RXXhiZCdKnW4izQYMOt/xvWT4lxCNg1SnnxlRUVF06dIlaJvNZiMhIYEuXbpQWlrKk08+yahRo0hNTSU/P5958+Zx8OBBrr76asBXAh82bBhlZWUsWrQIu90e6BiWlJSEXq8nIyMj6BqRkZGAryd7s2bNAv+uKi8vD/D1Lo+NjQVg4cKFGI1GevbsiU6nY+nSpcyePZt//etfgePGjx/P3LlzmThxIrfddhvr1q1j/vz5vPnmmw1018TpYNTriLXKgiNCiMbnjPdOr4ler2fHjh0sXLiQvLw8EhIS6NOnD2vWrKFz586Ar0reP/lLmzZtgo7PysqiRYsWDZqnJ554gn379qHX62nXrh2vvPJKUHt4y5Yt+fjjj7nvvvt4/vnnSUtLY/bs2TK8TAghThff2NBTP0cjpanapqQSvyq73U5MTExgqJwQQjRGp/u7zH9+w60vo5nCDx2uK+Uswz3/tkb5vfubmLFNCCGEEPX3m61OF0IIIWolHduEEEKIRkqCuBBCCNFY6Tj1luHG27LceHMuhBBCnOWkJC6EEKLxkup0IYQQopE6y4O4VKcLIYQQjZSUxIUQQjRisgCKEEII0Ug1QHV6Iw7iUp0uhBBCNFJSEhdCCNF4neUd2ySICyGEaMTO7jZxqU4XQgghGikpiQshhGi8zvLqdCmJCyGEaLR0mtYgr/r46quvuPzyy0lLS0PTNN5///2g/Uoppk6dSlpaGlarlUGDBrF169agNA6Hg7vuuovExERsNhujRo3i4MGD9f/89T5CCCGE+I3wF8RP9VUfpaWldO/enblz54bdP2PGDGbNmsXcuXPJzMwkJSWFoUOHUlxcHEhz7733smTJEt566y2+/vprSkpKuOyyy/B4PPXKi1SnCyGEEPVw6aWXcumll4bdp5Ti2Wef5eGHH2b06NEALFy4kOTkZN544w1uv/12ioqKmD9/Pq+99hpDhgwBYNGiRaSnp7Ny5UqGDx9e57xISVwIIUSj1ZDV6Xa7PejlcDjqnZ+srCxycnIYNmxYYJvZbGbgwIGsXbsWgA0bNuByuYLSpKWl0aVLl0CaOn/+eudQCCGE+I3QaQ3zAkhPTycmJibwmj59er3zk5OTA0BycnLQ9uTk5MC+nJwcTCYTcXFxNaapK6lOF0IIIYADBw4QHR0deG82m0/6XFq1hnalVMi26uqSpjopiQshhGi0NE1rkBdAdHR00OtkgnhKSgpASIk6Nzc3UDpPSUnB6XRSUFBQY5q6kiAuhBCi0WrI6vSG0LJlS1JSUlixYkVgm9PpZPXq1Zx//vkA9OrVC6PRGJTm8OHD/PTTT4E0dSXV6UIIIUQ9lJSUsHv37sD7rKwsNm/eTHx8PBkZGdx7771MmzaNtm3b0rZtW6ZNm0ZERARjxowBICYmhltvvZX777+fhIQE4uPj+dvf/kbXrl0DvdXrSoK4EEKIRqtqdfgpnKReyb///nsuuuiiwPuJEycCMHbsWBYsWMADDzxAeXk5d9xxBwUFBZx33nksX76cqKiowDHPPPMMBoOBa665hvLycgYPHsyCBQvQ6/X1y7pSStXrCHFa2e12YmJiKCoqCupgIYQQjcnp/i7znz/uvv+hmSNO6VzKUUbBM9c0yu9daRMXQgghGimpThdCCNF4NUB1umrEC6BIEBdCCNFoNUjv8sYbwyWICyGEaLwaomPbKXeMO4OkTVwIIYRopKQkLoQQotGS6nQhhBCikfKtB36q1ekNlJkzQKrThRBCiEZKSuJCCCEaLalOF0IIIRop6Z0uhBBCiEZJSuJCCCEaLR0Nu5RoYyNBXAghRKOl0zR0v/IqZr8lUp0uhBBCNFJSEhdCCNFo+caJn/o5GisJ4kIIIRqts706XYK4EEKIRutsL4lLm7gQQgjRSElJXAghRKOlNUB1umrERXEJ4kIIIRotqU4XQgghRKMkJXEhhBCNVkP0TpfqdCGEEOIMkOp0IYQQQjRKUhIXQgjRaEl1uhBCCNFISXW6EEIIIRolKYkLIYRotM726vTfTEl8+vTpaJrGvffeG9g2depUOnTogM1mIy4ujiFDhrB+/frA/mPHjnHXXXfRvn17IiIiyMjI4O6776aoqCjsNRwOBz169EDTNDZv3hw2TX5+Ps2aNUPTNAoLC4P2bdmyhYEDB2K1WmnatCmPPfYYSqmgNKtXr6ZXr15YLBZatWrFiy++eFL3QwghRO381emn+mqsfhNBPDMzk5deeolu3boFbW/Xrh1z585ly5YtfP3117Ro0YJhw4Zx9OhRALKzs8nOzmbmzJls2bKFBQsW8Omnn3LrrbeGvc4DDzxAWlraCfNy6623huQDwG63M3ToUNLS0sjMzGTOnDnMnDmTWbNmBdJkZWUxYsQIBgwYwKZNm5g8eTJ33303ixcvru8tEUIIUQf+kvipvhqrMx7ES0pKuOGGG3j55ZeJi4sL2jdmzBiGDBlCq1at6Ny5M7NmzcJut/Pjjz8C0KVLFxYvXszll19O69atufjii3nyySdZunQpbrc76FyffPIJy5cvZ+bMmTXm5YUXXqCwsJC//e1vIftef/11KioqWLBgAV26dGH06NFMnjyZWbNmBUrjL774IhkZGTz77LN07NiRP//5z9xyyy0nvKYQQghxss54EJ8wYQIjR45kyJAhJ0zndDp56aWXiImJoXv37jWmKyoqIjo6GoPheHP/kSNHuO2223jttdeIiIgIe9y2bdt47LHHePXVV9HpQm/LunXrGDhwIGazObBt+PDhZGdns3fv3kCaYcOGBR03fPhwvv/+e1wuV9jrOhwO7HZ70EsIIUTd6LSGeTVWZzSIv/XWW2zcuJHp06fXmGbZsmVERkZisVh45plnWLFiBYmJiWHT5ufn8/jjj3P77bcHtimlGDduHOPHj6d3795hj3M4HFx//fU8/fTTZGRkhE2Tk5NDcnJy0Db/+5ycnBOmcbvd5OXlhT3v9OnTiYmJCbzS09PDphNCCBFK07QGeTVWZyyIHzhwgHvuuYdFixZhsVhqTHfRRRexefNm1q5dyyWXXMI111xDbm5uSDq73c7IkSPp1KkTU6ZMCWyfM2cOdrudSZMm1XiNSZMm0bFjR2688cYT5rn6D9pfjV51e13SVL92UVFR4HXgwIET5kEIIcSZV1xczL333kvz5s2xWq2cf/75ZGZmBvYrpZg6dSppaWlYrVYGDRrE1q1bGzwfZyyIb9iwgdzcXHr16oXBYMBgMLB69Wpmz56NwWDA4/EAYLPZaNOmDX379mX+/PkYDAbmz58fdK7i4mIuueQSIiMjWbJkCUajMbBv1apVfPvtt5jNZgwGA23atAGgd+/ejB07NpDmnXfeCeRj8ODBACQmJgYeCFJSUgIlbj//w4S/9F1TGoPBQEJCQtj7YDabiY6ODnoJIYSoG60BqtJPpiD+5z//mRUrVvDaa6+xZcsWhg0bxpAhQzh06BAAM2bMYNasWcydO5fMzExSUlIYOnQoxcXFDfr5z9g48cGDB7Nly5agbTfffDMdOnTgwQcfRK/Xhz1OKYXD4Qi8t9vtDB8+HLPZzIcffhhSqp89ezZPPPFE4H12djbDhw/n7bff5rzzzgNg8eLFlJeXB9JkZmZyyy23sGbNGlq3bg1Av379mDx5Mk6nE5PJBMDy5ctJS0ujRYsWgTRLly4Nuv7y5cvp3bt30IOFEEKIhtEQvcvre3x5eTmLFy/mgw8+4MILLwR8Q6Lff/99XnjhBR5//HGeffZZHn74YUaPHg3AwoULSU5O5o033ghq8j1VZyyIR0VF0aVLl6BtNpuNhIQEunTpQmlpKU8++SSjRo0iNTWV/Px85s2bx8GDB7n66qsBXwl82LBhlJWVsWjRoqCOYUlJSej1+pA27sjISABat25Ns2bNAv+uyt9+3bFjR2JjYwFfT/lHH32UcePGMXnyZHbt2sW0adN45JFHAlXl48ePZ+7cuUycOJHbbruNdevWMX/+fN58880GvHNCCCHOJLfbjcfjCSk0Wq1Wvv76a7KyssjJyQnq6Gw2mxk4cCBr16799YP4yfSYPtVqYb1ez44dO1i4cCF5eXkkJCTQp08f1qxZQ+fOnQFflbx/8hd/NblfVlZWoITcEGJiYlixYgUTJkygd+/exMXFMXHiRCZOnBhI07JlSz7++GPuu+8+nn/+edLS0pg9ezZXXXVVg+VDCCHEcTpOvXe5v125eqwzm81BI5L8oqKi6NevH48//jgdO3YkOTmZN998k/Xr19O2bdtAs2q4js779u07tcxWU6cgHhsbW6/ee5qmsXPnTlq1alWvzHz55ZeBf1ssFt57770Tph80aFDIjGm1adGiRa3H1HTerl278tVXX53w2IEDB7Jx48Z65UkIIcTJacjq9Oqjg6ZMmcLUqVPDHvPaa69xyy230LRpU/R6Peeccw5jxowJ+v4P19G5oXvC17k6/d133yU+Pr7WdEopRowYcUqZEkIIIepCx6n30PYff+DAgaBa5HClcL/WrVuzevVqSktLsdvtpKamcu2119KyZUtSUlIA37Dj1NTUwDG5ubkhpfNTVacg3rx5cy688MIae1hX16pVK+nIJYQQolE5mRFCNpsNm81GQUEBn332GTNmzAgE8hUrVtCzZ0/AN2HZ6tWr+de//tWgea5TEM/KyqrXSX/66aeTyowQQghRHw0xWcvJHP/ZZ5+hlKJ9+/bs3r2bv//977Rv356bb745sJjXtGnTaNu2LW3btmXatGlEREQwZsyYU8prdbIUqRBCiEarIaZNPZnji4qKmDRpEgcPHiQ+Pp6rrrqKJ598MlAL/cADD1BeXs4dd9xBQUEB5513HsuXLycqKurUMluNpurbMwz47rvv+PLLL8nNzcXr9Qbtq7qql6g/u91OTExMYA54IYRojE73d5n//BfO/BSD1XZK53KXl/LV3y5plN+79S6JT5s2jX/84x+0b9+e5OTkE045KoQQQpxOZ6ok/ltR7yD+3HPP8corrzBu3LjTkB0hhBCi7s7EjG2/JfXuma/T6ejfv//pyIsQQggh6qHeQdw/G5kQQghxpuka6NVY1bs6/W9/+xsjR46kdevWdOrUKWQ8eG2zrAkhhBANRWuA6vTG3J+r3kH8rrvu4osvvuCiiy4iISGhUX94IYQQojGrdxB/9dVXWbx4MSNHjjwd+RFCCCHqTHqn11N8fHzI0p1CCCHEmSC90+tp6tSpTJkyhbKystORHyGEEKLOpGNbPc2ePZtffvmF5ORkWrRoEdKxTZbhFEIIIX4d9Q7if/jDH05DNoQQQoj6O9ur0+sdxKdMmXI68iGEEELU29nesa0xNwUIIYQQZ7U6BfH4+Hjy8vLqfNKMjAz27dt30pkSQggh6sJfnX6qr8aqTtXphYWFfPLJJ8TExNTppPn5+Xg8nlPKmBBCCFGbs706vc5t4mPHjj2d+RBCCCFEPdUpiHu93tOdDyGEEKLefOO8T7F3esNk5Yyod+90IYQQ4rdCa4Dq9EbcJN6oH0CEEEKIs5qUxIUQQjRaMtmLEEII0UhJ73QhhBCikZKSeD3Z7faw2zVNw2w2YzKZTjlTQgghhKhdvYN4bGws2gmeWpo1a8a4ceOYMmUKOp30mxNCCHH6aJWvUz1HY1XvIL5gwQIefvhhxo0bx7nnnotSiszMTBYuXMg//vEPjh49ysyZMzGbzUyePPl05FkIIYQApE283kF84cKF/N///R/XXHNNYNuoUaPo2rUr//73v/n888/JyMjgySeflCAuhBBCnEb1ru9et24dPXv2DNnes2dP1q1bB8AFF1zA/v37Tz13QgghxAnoaIAFUBpxhXq9g3izZs2YP39+yPb58+eTnp4O+BZAiYuLO/XcCSGEECfgr04/1VdjVe/q9JkzZ3L11VfzySef0KdPHzRNIzMzkx07dvDuu+8CkJmZybXXXtvgmRVCCCHEcfUO4qNGjeLnn3/mxRdfZOfOnSiluPTSS3n//fdp0aIFAH/9618bOp9CCCFECB2nXh3emKvTT2qylxYtWvDUU081dF6EEEKIepHe6SehsLCQ7777jtzc3JBlSm+66aYGyZgQQgghTqzeQXzp0qXccMMNlJaWEhUVFTTxi6ZpEsSFEEL8arQGmHb1RBOY/dbVu3f6/fffzy233EJxcTGFhYUUFBQEXseOHTsdeRRCCCHCOtt7p9c7iB86dIi7776biIiI05EfIYQQos5OeYz4SZTk3W43//jHP2jZsiVWq5VWrVrx2GOPBTUvK6WYOnUqaWlpWK1WBg0axNatWxv649c/iA8fPpzvv/++wTMihBBCNAb/+te/ePHFF5k7dy7bt29nxowZPP3008yZMyeQZsaMGcyaNYu5c+eSmZlJSkoKQ4cOpbi4uEHzUu828ZEjR/L3v/+dbdu20bVrV4xGY9D+UaNGNVjmhBBCiBPRcRKl0TDnqI9169ZxxRVXMHLkSMA3YuvNN98MFHCVUjz77LM8/PDDjB49GvBNWZ6cnMwbb7zB7bfffoo5Pq7eQfy2224D4LHHHgvZp2kaHo/n1HMlhBBC1IGmaafcMc1/fPWlts1mM2azOST9BRdcEJgrpV27dvzwww98/fXXPPvsswBkZWWRk5PDsGHDgs41cOBA1q5de2aDePUhZUIIIcTvgX/qcL8pU6YwderUkHQPPvggRUVFdOjQAb1ej8fj4cknn+T6668HICcnB4Dk5OSg45KTk9m3b1+D5vmkxokLIYQQvwUNOdnLgQMHiI6ODmwPVwoHePvtt1m0aBFvvPEGnTt3ZvPmzdx7772kpaUxduzYQLrqNQRKqQYfzlanID579mz+8pe/YLFYmD179gnT3n333Q2SMSGEEKI2WuXrVM8BEB0dHRTEa/L3v/+dhx56iOuuuw6Arl27sm/fPqZPn87YsWNJSUkBfCXy1NTUwHG5ubkhpfNTVacg/swzz3DDDTdgsVh45plnakynaZoEcSGEEL9rZWVl6HTB3eH0en2gublly5akpKSwYsWKwNLdTqeT1atX869//atB81KnIJ6VlRX230IIIcSZdDLjvMOdoz4uv/xynnzySTIyMujcuTObNm1i1qxZ3HLLLYCvQHvvvfcybdo02rZtS9u2bZk2bRoRERGMGTPmlPJanbSJCyGEaLTOxBCzOXPm8M9//pM77riD3Nxc0tLSuP3223nkkUcCaR544AHKy8u54447KCgo4LzzzmP58uVERUWdYm6DaUopVZ8DPB4PCxYs4PPPPw+7AMqqVasaNINnG7vdTkxMDEVFRXVqmxFCiN+i0/1d5j//A69+gzki8pTO5SgrYcZN/Rvl9269S+L33HMPCxYsYOTIkXTp0qVRTxwvhBCicdM03+tUz9FY1TuIv/XWW/zvf/9jxIgRpyM/QgghRJ2d7auY1TuIm0wm2rRpczryIoQQQtRLQw4xa4xOainS5557jno2pQshhBCigdUpiI8ePTrw+uabb3j99ddp3bo1l19+edA+/0TvJ2P69OmBbvl+U6dOpUOHDthsNuLi4hgyZAjr168P7D927Bh33XUX7du3JyIigoyMDO6++26KiorCXsPhcNCjRw80TWPz5s2B7fn5+VxyySWkpaVhNptJT0/nzjvvDJpHd+/evYE5equ+Pv3006BrrF69ml69emGxWGjVqhUvvvjiSd8TIYQQJ3YmliL9LalTdXpMTEzQ+yuvvLJBM5GZmclLL71Et27dgra3a9eOuXPn0qpVK8rLy3nmmWcYNmwYu3fvJikpiezsbLKzs5k5cyadOnVi3759jB8/nuzsbN59992Q6zzwwAOkpaXxww8/BG3X6XRcccUVPPHEEyQlJbF7924mTJjAsWPHeOONN4LSrly5ks6dOwfex8fHB/6dlZXFiBEjuO2221i0aBHffPMNd9xxB0lJSVx11VUNcauEEEJUcbZ3bKv3ELOGVlJSwjnnnMO8efN44okn6NGjR2AlmOr8QwpWrlzJ4MGDw6Z55513uPHGGyktLcVgOP6M8sknnzBx4kQWL14cGJzfo0ePGvM1e/Zsnn76aQ4cOAD4SuItW7Y84XEPPvggH374Idu3bw9sGz9+PD/88APr1q078Y2o9hkb41AHIYTw+7WGmP3jjXVYTnGIWUVZCU+M6dcov3fr3SZ+8cUXU1hYGLLdbrdz8cUX1zsDEyZMYOTIkQwZMuSE6ZxOJy+99BIxMTF07969xnT+H0LVAH7kyBFuu+02XnvtNSIiImrNU3Z2Nu+99x4DBw4M2Tdq1CiaNGlC//79Q0r769atC1p6DmD48OF8//33uFyusNdyOBzY7faglxBCiLrRNdCrsap33r/88kucTmfI9oqKCtasWVOvc7311lts3LiR6dOn15hm2bJlREZGBuZtX7FiBYmJiWHT5ufn8/jjjwet1aqUYty4cYwfP57evXufMD/XX389ERERNG3alOjoaP7zn/8E9kVGRjJr1izeffddPv74YwYPHsy1117LokWLAmlycnLCLj3ndrvJy8sLe83p06cTExMTeFVfCk8IIUTNpE28jn788cfAv7dt2xZYLxV8s7h9+umnNG3atM4XPnDgAPfccw/Lly/HYrHUmO6iiy5i8+bN5OXl8fLLL3PNNdewfv16mjRpEpTObrczcuRIOnXqxJQpUwLb58yZg91uZ9KkSbXm6ZlnnmHKlCn8/PPPTJ48mYkTJzJv3jwAEhMTue+++wJpe/fuTUFBATNmzODGG28MbA+39Fy47X6TJk1i4sSJQZ9DArkQQoi6qHMQ9/fq1jQtbLW51Wplzpw5db7whg0byM3NpVevXoFtHo+Hr776irlz5+JwONDr9dhsNtq0aUObNm3o27cvbdu2Zf78+UFBubi4mEsuuYTIyEiWLFmC0WgM7Fu1ahXffvttyLqwvXv35oYbbmDhwoWBbSkpKaSkpNChQwcSEhIYMGAA//znP4OWkquqb9++QaX1lJSUoIcb8C09ZzAYSEhICHsOs9lc45q1QgghTuxsHyde5yCelZWFUopWrVrx3XffkZSUFNhnMplo0qQJer2+zhcePHgwW7ZsCdp2880306FDBx588MEaz6WUwuFwBN7b7XaGDx+O2Wzmww8/DCnVz549myeeeCLwPjs7m+HDh/P2229z3nnn1Zg/fwm66rWq27RpU1CA79evH0uXLg1Ks3z5cnr37h30YCGEEKJh6DTf61TP0VjVOYg3b94cl8vFTTfdRHx8PM2bNz+lC0dFRdGlS5egbTabjYSEBLp06UJpaSlPPvkko0aNIjU1lfz8fObNm8fBgwe5+uqrAV8JfNiwYZSVlbFo0aKgjmFJSUno9XoyMjKCrhEZ6evF2Lp1a5o1awbAxx9/zJEjR+jTpw+RkZFs27aNBx54gP79+9OiRQsAFi5ciNFopGfPnuh0OpYuXcrs2bOD1oYdP348c+fOZeLEidx2222sW7eO+fPn8+abb57SvRJCCCHCqde0q0ajkQ8++CBoubXTRa/Xs2PHDhYuXEheXh4JCQn06dOHNWvWBMZpb9iwITD5S/WpYLOysgIBuDZWq5WXX36Z++67D4fDQXp6OqNHj+ahhx4KSvfEE0+wb98+9Ho97dq145VXXglqD2/ZsiUff/wx9913H88//zxpaWnMnj1bxogLIcRp4m/mPdVzNFb1Hid+880307Vr16DOWKLhyDhxIcTvwa81Tnza2+sbZJz45GvPa5Tfu/VeAKVNmzY8/vjjrF27ll69emGz2YL233333Q2WOSGEEOJEzvaSeL2D+H/+8x9iY2PZsGEDGzZsCNqnaZoEcSGEEOJXUu8gnpWVdTryIYQQQtSb9E4/BbVNZCKEEEKcThqnPm1qY45gJ/XZX331Vbp27YrVasVqtdKtWzdee+21hs6bEEIIIU6g3iXxWbNm8c9//pM777yT/v37o5Tim2++Yfz48eTl5QVNTSqEEEKcTtKxrZ7mzJnDCy+8wE033RTYdsUVV9C5c2emTp0qQVwIIcSvpiFWITurVjE7fPgw559/fsj2888/n8OHDzdIpoQQQghRu3oH8TZt2vC///0vZPvbb79N27ZtGyRTQgghRF1ogKad4utMf4hTUO/q9EcffZRrr72Wr776iv79+6NpGl9//TWff/552OAuhBBCnC4NsR54Y15PvN4l8auuuor169eTmJjI+++/z3vvvUdiYiLfffcdV1555enIoxBCCCHCOKlx4r169WLRokUNnRchhBCiXmQ9cSGEEKKROtur0+scxHU6Xa1j6TRNw+12n3KmhBBCiLqQaVfraMmSJTXuW7t2LXPmzKGeq5oKIYQQ4hTUOYhfccUVIdt27NjBpEmTWLp0KTfccAOPP/54g2ZOCCGEOJGzvU38pCaqyc7O5rbbbqNbt2643W42b97MwoULycjIaOj8CSGEEDXyt4mf6quxqlcQLyoq4sEHH6RNmzZs3bqVzz//nKVLl9KlS5fTlT8hhBBC1KDOQXzGjBm0atWKZcuW8eabb7J27VoGDBhwOvMmhBBCnNApz9ZW+aqPFi1aBBZeqfqaMGEC4Fume+rUqaSlpWG1Whk0aBBbt249DZ++Hm3iDz30EFarlTZt2rBw4UIWLlwYNt17773XYJkTQgghTuRMrCeemZmJx+MJvP/pp58YOnQoV199NeAr9M6aNYsFCxbQrl07nnjiCYYOHcrPP/9MVFTUKeY2WJ2D+E033dSol2sTQgghGkJSUlLQ+6eeeorWrVszcOBAlFI8++yzPPzww4wePRqAhQsXkpyczBtvvMHtt9/eoHmpcxBfsGBBg15YCCGEOFW+6vBTXU/85I91Op0sWrSIiRMnomkae/bsIScnh2HDhgXSmM1mBg4cyNq1a89cEBdCCCF+axpyPXG73R603Ww2YzabT3js+++/T2FhIePGjQMgJycHgOTk5KB0ycnJ7Nu37xRzGqoxr4UuhBBCNJj09HRiYmICr+nTp9d6zPz587n00ktJS0sL2l69dkApdVqapKUkLoQQotHy9ww/1XMAHDhwgOjo6MD22krh+/btY+XKlUEdulNSUgBfiTw1NTWwPTc3N6R03hCkJC6EEKLRasghZtHR0UGv2oL4f//7X5o0acLIkSMD21q2bElKSgorVqwIbHM6naxevZrzzz+/wT+/lMSFEEI0Wg3ZJl4fXq+X//73v4wdOxaD4Xgo1TSNe++9l2nTptG2bVvatm3LtGnTiIiIYMyYMaeY01ASxIUQQoh6WrlyJfv37+eWW24J2ffAAw9QXl7OHXfcQUFBAeeddx7Lly9v8DHiIEFcCCFEI9aQbeL1MWzYsBpX7tQ0jalTpzJ16tRTylddSBAXQgjRaJ3MtKnhztFYSRAXNfJ4FYVlTjQg1mZq1Cv9CCHE75EEcRHC41Ws3XWUzD35lDt98wNHWQyc1yaRc1slhB3/6Puv7986XWj1ltPtocDuQClFbJQZi0l+9YQQp+5MdWz7rZBvUhHEqxSLv9vPriPFQduLK9ys/CmHb34+il4H0REmWsbbwOWmsNgJgM2kJ8FmIjbCSGysldi4CBSKn/YcY99hO97K5iMNSEuy0b1NIiaj/lf+hEKI35Mz1Sb+WyFBXATZfqgoJIBXVe7ylcxLHOVkF5Rj0Wu0iLSg12mUOj2UOsupcHnweBSHj5ayKduOy+PFotdh1mvoNA0F7D9Swre/5DP/p8Nc1yud2/u3IC7C9Ct9SiGE+H1ozLUI4jTYuPdYvZblq/AossucQdtyS5yUuzwYNUiKMOLyKopdHo5VuHFXFsf1Oo2mUWbaxFiY8flOBs1eQ469ogE/iRDibKA10KuxkiAuAkrKnFi9is7xEXSMs5JkNdbpF8Tu8uDyeoO2HSv1Bfam0ZbANi9Q5HQHDcsYmB6HV8HBwgrufe/HhvgYQoiziE5rmFdjJUFcUFHhYldWPht+yiHBbCDGbCDObKBdrJUeSZGY9bX/hpe7g4N4mcuLpmmYDTr0VdqbPAqclaVxnaYRbzFWbld8uj2XeWv2NOAnE0KI3zdpEz+L2YsdHMq2U1buAsBm1ONRCofbi7cy8Jr1Gh3jIticV1qvc/ufbJVSKIInRHB6FGa9b1+R0x2074EPtlLu8nL/xW1O8lMJIc4m0rFNnJUKiyr4JetY0DZN09ADEUY9ZW4PXuUrLduMemJMeooqh5uFE2EI7mUeZTHgVYqCclegV3p1ClhzoDBk+5SPt5MUaaRLajTdm8aib8x1XUKI06oh2rQb8zeMBPGzjFIKr8fL4cP2sPs1TUMphVmvC1SRe5UizmyoMYjHGPUYqgRanQaxFgMasL8otLOaQafh8Sryy12s3l8QlDeF7w/quS9+oUOTSGIsBm45vyW9m8fV+tl+yi5ize6jAAxok0SXtJhajxFCNG5aA7RpN+KCuATxs4VSCleFG5fDDQoykqPweLwUFDvIqxZoNU3DoGloeH1BVdOwVo7n1iAQaBVg1etItR0fGqbTICPWikGvY/vRUgorgqvLNcCi19iWV8rLP2RTVvmgoJQKKrGXVD4wFFW4eXbVLh4c1p6uTcMH5cNF5fxpwXq+2n008EStgAFtEnltXF/SYqwndc+EEOK3ToL4WUAphaPUiccV3PlMr9eREGPBbNJz6Ghom7dO0/AohQb07dCEthUuNu8roLjCTbTVSJemMVh1GoXFDjwehc2kJz7SRHZBORv3F+KpEpU1fE+7+4sd/N/6fRwucQRK/b7/EhTEDdUerd/6/kDYIF7icDP4uS/Zm+/Lf9Wa+3V78hn83JdkPjiUSLP8qgvxeyRt4uJ3z+Py4nZ68Hh9AbnqtKiaphEVYSIqwklxmSvoOH+HNKNBR0qCjTSdRtf00Gptr8dLaYmTslInyqtoHheB1WpkT14phaVODHqN1inRdM6IJcpqZMPREg6XOPEqBco3zasKui40jbEEvd97rIycogpSqmwHeP27ffxytIRwze5ur2LP0RJe/24ftw9ofRJ3TgjxW3e2t4nLELPfOa/HS1FBGSVlLsor3JRVuCkpc+FweqrMea6IiTQHjvFVbfuqt3U6jS5tkzhW6mDPkWIOF5ThcropK/UF7fIyJ0dziiktdqCqFKWbWIyc1yyGgS3j6Z0WQ6wGhcfKcTg8PDWqCzrNN9e6RwWXnjUg3mokMcIY8lnWZeWHbHvtu7213oNX19eeRgghGiMpif+Oeb2K/KOleKqN4QZwujx4lcJqNqBpGiaj73nO3wbu8nppmhyJyWJk2aZD5FaZTc1m0tMjNZqMuIgTZ0CBUafD6fU9MOQeLeFwbglNEm289+fzGPvaBvLLXIGObgpItJnokx4Ttnrr3U2HiDDpGd4pJbAt114RthReJQtszS5kd66dNk2iT5xfIUSj0xCTtTTmATASxH/HykocYQO4n9vtxWPwotNpeD3Hp0NNbxpDXJyFjb8c4/vth0D5xou7vQqPglKnh2/2FeBR0DK+5kCuaRoGg4ZOp6E8CpvFQHG5m9y8UszAN3f2Z322nR8OFbFi+xESbSZiLKEl8KoWfbefvi0TiLH60rVKjGR/QVnNw9iUoszh5PwnP2Ttw6MkkAvxO6NV/u9Uz9FYSXX671hZqbPWNC5/kNdrtMiIpVvnZCJsRpZ+u5+f9xdg0jT0Og29pmEx6INmb9twMLjzWk30lW3wGmAy6HzV+F7F7qxj2EqdjGwexyND29cawMHXfj5l2VbyKz/bLee3qjGAQ+WQObcLe4WLv/9vfa3nF0KIxkSC+O+QUorth4pYtv0Ib/6YzZJtOWzNLcblCS2Ve72+9b/jE2zEx1k5lFvC+h8PE2vQ0SzKTKtYC6k2E8bK+ia9pgX+7fIqDtVz0RKD3hfQ/dVXZoOO/MIKvMUOruyccuKDKx0pdvDPZVspcbgZ3aMpF7VrEnacp1IK5XGD143Hq/joxwPkFJXVK79CiN82TWuYV2MlQfx3xuP18ta3+/jfd/s5WFSB3eEmt9TJ1/sKeGfrYUqqTXOq02lYosy4PV527MojL7eEGLMBq1GPTvOVZCONOtKjzegr3/uDuAaUVTtfdUop3JWlfX9pPPBvzbc0qf8PSF9x4nMFzgnklThYseMIBr2O92+/gL7NY3095apcF48LXOVVtsHevJI6XUMI0TjoaIAFUM70hzgFjTnvIowvt+eyK8e3Hnj1WuYSh4cVu/OCttliLJSUOtnx81E8Tjcmgw6jXofVoCPWYsRYWXI2aBpxFl8XCn9JWgHmatOtVqWUwunyBvKhlPJ1YKvs/e7nD+xGnUab+IgTdjLRNK2y57xi6ZbDAFhNesb0SgdHCcpRhnKUgaME3I6Q42NlzXIhxO+IBPHfEZfHS+ae/Bp7aysgt9TJ0cr2ZJNJj8fr5eDBIox6DaNBj1Gvw6jXMOh9vxpRJkOgRB5jCu4Hqdc0mlUbtw0Ehq65PV4qqpTUNU3D4fatbuau0pBdNb9to8w1djLxB39/D/rCchfrKud/v6JnRuU0ch7fK4yYCDPtU2QqViF+T7QG+l9jJUH8dyTXXoHjBL3RwRfnsosrMFsNRMdHcPiQHbNRj9lowGTQY9DrAiVtY2Ugt1SWtvU636+6f4rUrqlRgTTVlZY7KausHvcH9TKnG68Ct9eLR1WWyL3B063GmgyMap8UWPREVaYD33rk7ipj211eL8999YvvvabDEmE74WdX5giWbcs5YRohToVSioIyJ8UVLpRS5Ngr2HesjDKnmyP2CrZlF7E3r5Rf8kp4a+NB3t50kH3HpJ/GqTjb28RliNnvSe0dxQGwRZmJS7CRk23HaNAHpj4F0BTodXqcbg9KgU5pGPUauAhUY3sV9EyLoX1SaNA0Ww2g0+Fwe3F73Ch8M6dVuNw4Pb7qdE+VwOyo1tlOp0GkghvPacr87/YHpmatWv3ul1/mYvvRMvJLnWw8UIgtKhqlFBVl1b4UNY2o6BjMFiv//Hg7bq/iyq5pdbtZQgA7j5bwxoaDZO4vwOtVGDTYdugYJQ43HZKj+csFrSj3wKuZB8iu7OwZazGQFGEi3mpEwzdMM8qkR6dplDo9/HCkhP32CjRgaPskpl/WCU3TiLUasRhrbqYSwRoiCDfmIP6bKYlPnz4dTdO49957A9umTp1Khw4dsNlsxMXFMWTIENavPz5M6NixY9x11120b9+eiIgIMjIyuPvuuykqKgp7DYfDQY8ePdA0jc2bNwe25+fnc8kll5CWlobZbCY9PZ0777wTuz14pa8tW7YwcOBArFYrTZs25bHHHgsEI7/Vq1fTq1cvLBYLrVq14sUXXzz1m1NHTaItmAwn/pEqoGVSJGVlThyVJeWqE6v4/22qDO46zVfVpJTC7vQQYzPxp0Gt6NexCfFJNpJSo0hMiSShSSRNUqOIS7ARF2elRct42rVPon37JJqkRuMCnB5fCdyfjwq3F0+12OzPib7MRYRZj9vrpXrdgqpc8zyvcprYEoe78g9ZIyomlvgmydiio4mIjCQqJpbEJslYIo6PZ39m9W6OFNevV704e7254SAj/72ONzce5OfcEnbllbIttwSMJsrdivV787n5tUzuX7yJQ0XHO1IWVrjZdayMQ8W+CYkqPIr8CjdepYgw6jg/PYZWsVYUsOLno/R8+ktaP7aClH98wp/f3MQveaHrGQhR3W8iiGdmZvLSSy/RrVu3oO3t2rVj7ty5bNmyha+//poWLVowbNgwjh71LTeZnZ1NdnY2M2fOZMuWLSxYsIBPP/2UW2+9Nex1HnjgAdLSQktgOp2OK664gg8//JCdO3eyYMECVq5cyfjx4wNp7HY7Q4cOJS0tjczMTObMmcPMmTOZNWtWIE1WVhYjRoxgwIABbNq0icmTJ3P33XezePHihrhNtTIadPTMiCXGpCfBYiDObAj0JAdfKTc11kq8xUBJYQVWswGLSR9o//bzB3KDXoeGr61dAa2bxXJ1/xZEWk2YrUbMFiN6vQ6DQY/RpEcX5jyaptEkPoK+3dPo0bEJOoOOMpeHMldoAPf3fgdfT/JYnZ68MldQKVwpRWGFm5355XiV75jiMid9MuICVfB6vZ4IWyS2qGgsERFouuP5slTOTLd0q1Sri9ptOljII59sR0Hwgj6Vv6eJsbZAMa7c4aa4LLQz5UG7gzKXr5+GV0GJyxuoYeqZEom+spOoTuer9XJ5FP/bdIj+z3zFTzUsGSyO06E1yKux0lT1ouSvrKSkhHPOOYd58+bxxBNP0KNHD5599tmwae12OzExMaxcuZLBgweHTfPOO+9w4403UlpaisFwvLXgk08+YeLEiSxevJjOnTuzadMmevToUWO+Zs+ezdNPP82BAwcAeOGFF5g0aRJHjhzBbPbNM/7UU08xZ84cDh48iKZpPPjgg3z44Yds3749cJ7x48fzww8/sG7dujrdD/9nLCoqIjq6frOLbd1XwKZf8lDqeDu0pmkUO90crXATYzUyumsqETpdldXD/G3Mvi8hP397tcPlocTtoUPrRKJsp96z+1BuCT/uOori+IgwTfOvcnZ86Jp/gpgJn+9EKd8KaRoaZS4PrsovUw3oEB/BpW0SGT+0Hfct2cLiH7JrnPzFZtaTHO372V3YKoFpIzuf8ucRv19KKa5bmMnavcfw+udEqgy0xx82FUWlFdhLfcFbp9NomhgdMm1wis1E81jfkrgakGQ1BNKsO1jEvqKKwOiNUocv4Os1yIiLYMqlHeidEUvLhBP3+fitOZXvsvqcf/mX27BFRp3SuUpLihk2qNNpy+vpdMZL4hMmTGDkyJEMGTLkhOmcTicvvfQSMTExdO/evcZ0/h9C1QB+5MgRbrvtNl577TUiImqZ7xtfCf+9995j4MCBgW3r1q1j4MCBgQAOMHz4cLKzs9m7d28gzbBhw4LONXz4cL7//ntcruAVwhra7uwiNu7OqxIYj3/RRJoM9EiL5k990omoLJVWXcVMqxyrbTZVa4fTwBphpHfX1AYJ4AApiRFYTAZ0/pngdP6x4sE1BgadRvM4K8NbxOFRYHd4KHK4gwK4zaind0oUJRVu9h8t5cmRnTinWWzY65oNOpIiTYHzm2tpdhBnN6UUD3y4lTV7juHx+krK/v4d5S5vUKncUmXUhterws5i6C+JU3kefxORv2odQpfU9CjIOlbGuNc30mX6Kq58+Vty7BV4vIr8UmetczSIs8MZ7dj21ltvsXHjRjIzM2tMs2zZMq677jrKyspITU1lxYoVJCYmhk2bn5/P448/zu233x7YppRi3LhxjB8/nt69ewcCbjjXX389H3zwAeXl5Vx++eX85z//CezLycmhRYsWQemTk5MD+1q2bElOTk5gW9U0brebvLw8UlNTQ67pcDhwOI5XwVVvh6+LohIHu/YVEGc24PYqSt2eoNKoBhSXOiktcWIvd3HUXoHL48Wg15EYaaZJtAWDXodBr+HUPMcfBHQazZvHNehau3qdjvO6prJm48Gw/fD8VeoxViM6nY5eydF4vLDhSDFHy12BNG1irfRJicZa2XO+qNxJiyaRLL7lXK5/NZONB4vweBV6nUa0xYDNrA98Dl/PehlqJmr2xoaDvL7hYI37HW4vVqOu8neq9r8PXbXJD7Qq/62oHFFSUwdOv8935dFn5pc0jbdS6vQ9FPRrEcdf+rWgd5glgs8WdfsJ1H6OxuqMFUcOHDjAPffcw6JFi7BYQsca+1100UVs3ryZtWvXcskll3DNNdeQm5sbks5utzNy5Eg6derElClTAtvnzJmD3W5n0qRJtebpmWeeYePGjbz//vv88ssvTJw4MWh/9WBWtcq6Pmmqmj59OjExMYFXenp6rfn0c3u8/PhzLpu2HcGi07AZdcSY9aTZTEQag3+0SsGWg4Xk2yswaBpmvQ6X20t2YTnbDhVV9kb3BT1/VXuzZuFXEztVERYDXVsloK82a5I/gNtMeqIr51HXaZARbeHKtknc0DGZa9olcVOnFAamxxFRpQdveWUnPaNeR4uECFJjzDSLs5AWayHSYgiq/vR4vTKsR9RIKcWLa7Nq/WL3T1zkqFIiNuh1gb4ZVcVXWRfAVFn7BL4HyoN230O8pmnH1zKo4XrHylwcKjjeeW79vgJufWsTn+44UpeP9rukq5z58VRfjdUZC+IbNmwgNzeXXr16YTAYMBgMrF69mtmzZ2MwGPB4fE+aNpuNNm3a0LdvX+bPn4/BYGD+/PlB5youLuaSSy4hMjKSJUuWYDQe/4NZtWoV3377LWazGYPBQJs2bQDo3bs3Y8eODTpPSkoKHTp04IorruDf//43L7zwAocPHw7sy8kJ7gzlf5jwl75rSmMwGEhISAh7HyZNmkRRUVHg5W+Dr4sde/I5VuTrZX28Wtz3irMYsVarMjbrdcSYDUSZ9MSYDTSxmYgw6HB6vOw5enw6Uk2n0SQ1CqPp9FXUNEmMICPeRlyECZNeh0GnYTXpaRJloUmUBU3zLU9a7vTgrByGZjPqK2eRq9aBDjiUU4yzsspy88EiiquMUa861tyjFEXlblb/EjxznRDgG+nw5saD7MotDRoKGY5/pEVJ+fGatOgIc8iDr0mvkRBx/Dup6gP21qMluCofBtweL67qvT3DsFeZntirfA/o//x4O2t+yWPVzlx+OFS3hYnE78MZq04fPHgwW7ZsCdp2880306FDBx588EH0+vDjJJVSIdXPw4cPx2w28+GHH4aU6mfPns0TTzwReJ+dnc3w4cN5++23Oe+882rMX2Acc+W1+vXrx+TJk3E6nZhMvrbV5cuXk5aWFqhm79evH0uXLg06z/Lly+ndu3fQg0VVZrM5qJ29rkrLXeRVeSI36XVEmPToNQ2PUpQ53USb9JRXebKPthqwWY14K+czd3sUMRYjqsJFSYWbCpcHm81EdIwFw2kep2ow6ImNsWDQaSTYQj+/UooDReVklzvJLnXSOtpa47nizQaUgn1HimnbLBa3V+H0KI6VujAbfQ8ICnC6j39JuuVLTlShlOKFb7KYsXLX8fbryl8RnY4aS2p5RWV4K3+XoiPM2KzBfUcseh3tE22VQzUhxuSbFdHt9fJTbik78st80xO7vVS4TjxRk1/1AO0frvnop9uJqyzxx0cY+fP5LenTPL5uN6Axa4jJWhpvQfzMBfGoqCi6dOkStM1ms5GQkECXLl0oLS3lySefZNSoUaSmppKfn8+8efM4ePAgV199NeArgQ8bNoyysjIWLVqE3W4PtCknJSWh1+vJyMgIukZkZCQArVu3plmzZgB8/PHHHDlyhD59+hAZGcm2bdt44IEH6N+/fyBAjxkzhkcffZRx48YxefJkdu3axbRp03jkkUcCT97jx49n7ty5TJw4kdtuu41169Yxf/583nzzzQa/f3kFx6uD4yonh6ja49xqNBNp9nCswoXL6ysNxFb+gWv4hqMZ9L4e6VEmA+VuJxUeL+mJv14P2KQmkbhcHpwOD97KMen+/x4tdfL94SK2V37OJIuR/ApX0JhxDYgzG4iu7JBXUFkteU6zWD77ORePV4X9YtTrNHqnx57mTycak+e/zuKxT3eE3ef1KggTyO0lFehQJMdYSYu3EWU1UuLwBGZNTLOZSI8yo9Aw6HwBXadpmPQaLo9vuGPrBCu5dgcHy+veSc1Qw+ICDvfx4H6szMXTK3fy0LD2nPM7by8/29cT/83O2KbX69mxYwcLFy4kLy+PhIQE+vTpw5o1a+jc2Tc0aMOGDYHJX/zV5H5ZWVkhHdFqYrVaefnll7nvvvtwOBykp6czevRoHnrooUCamJgYVqxYwYQJE+jduzdxcXFMnDgxqN28ZcuWfPzxx9x33308//zzpKWlMXv2bK666qpTvBuhPB6FpvnmNvf3tK7a4xx8pfOMGCt7CsppHR8R6IV+/LFTYTbqcbq9GHUaEfXogV5Y7GD/kWJKy1wYDBoJhzSaX5yEvoZpWMPR6TSaNovh/z7eTocEGwkWI+VuLzvzS9h0tIRjlUPedECUSY/NqKPM7cVd2WHNZtAFvlg1jnceurF3Oh9vr7mN0OtVXH9O3fseiN+fnbkl7DhSjNWop3vTaJ7+fOcJ03u9Ck1H0AyC5Q4XLZKiaNnk+PCmaMvxr9RmURaiqzVJWfS+9leDpmE26tEbdMRajTSLs5Jjd2CvcHOs7MQjWaIs4b+2q8d2Bbz0TRbzrolBp5PRGL9XZ3ycuAhW17GVR/JK2bEnn+So0Da4qpRSOLyKeJspMC+6v/3N4fLg9SrKHR6Olbvo1zMtaLhMTefbuiefvYeLffOoA4ZPS7A8mQ+PJ9PvwXaY61EVr5Ri6pItbCuouaNZpEHHpc0T0KpMDxtOr/ZJtEz13bPZX/3CvG+ywqYb2yedSUPa1zmP4vdj99ES7luyhfX7CgLbzAZdrWsOAIGFgJRSFJWU4/F4iTAZOK9tk6B0Gr7FgTrHRQTNfWCuDOBKKfIdbvaGmTVQKcXmQ8UU1BDIDXqNZnHWkN7uAM2jzVjD/O1FWo08N7pboLPor+XXGif+5ZodRJ7iOPGSkmIGDehQr7we+v/23jxOrrLK/38/d6u9qvct6WwQkpAQggSJoAKyizAz4IbLjDoyIiCizoCOfhVRw+hPERVFQYbBFUXFQRRZhlVBgYRA2Jfs6XQ6vVV1rbfuvc/vj1tVXdVd3QkkId3J8+ZVJH3r7rl9z3POc87nbN3KZZddxh133EEul+OQQw7hhhtu4MgjjwT8f8svf/nLXHfddQwNDXH00Ufz/e9/v+KE7inU8Gya0tIUJmTpNVnXZQ/Bq0rIEULQWEqqyeYdRrKjjUnCAaNUpw1NDcGdGnCADdtSbNhW1erUkVj/PQyAd+0Aj6/d9SxZT0puXbWFkK4xPx5CL72Xql9PMVPnkIYwUps4w7/iGZWyhD0p2TCQZmY8QMTUKtnvUUunOx7g7+sHeVn1FT/g2DyU44wfPcLjm4Zrlu+KAQdwizbZnM1QKotbSrZ0vdptdeFHwL5w8iHMSIQwNUFQ9z966flNF102TSD7K4RgaVeMrkRgXIA3bOnMmMCAhwyN4ATaBy/3p/nU/66dNElvOrO7jU9ei/b60NAQxx57LKZpcscdd/Dss8/yrW99i4aGhso63/jGN7jqqqu45ppreOyxx+jo6ODkk09mZGRkj17/lA2nKyZH1wQz2+OMJHOVrmLVSCh5rpC3XZxSQpeuCYQGuYKLEBC0dNJ5h8UH16+9r9mnlLyyJVmzzLgng7bNTwTSehxGbh0geUgziejOk/UeXz/I6o1DBDRBwtI5vDnKUMEh67hoQtBoGUTKnoUE2/Mwq6YMKjkAgO1JNvdnOHR2E09sGealHRlCpl7XMwHJb9ds5TLljR9QXH3/y6QKTiWr/NWSfGkNsn0+wvCnnXQhWNARpzFkMpQrYuqCEw5u5V3LZjCrMczRc5t5cdsIazYOkswViYdMpC745ZqtvtGY4DR0TbCwPcrc5hA7MjaOByFL59QFbfzxue047mgpqCt9Az5zkoic7UpWb0nyZE+SZTMaXtO1T2X2xZz417/+dbq7u7nxxhsry6qnb6WUXH311Xz+85/n7LPPBuCmm26ivb2dX/ziFzVaJruLMuLTmNaWMKnh7IQyo7L0v/IvfSxsYVUZNdfzKNguc2clxumn1yOdK5K3q3p1l7xwKfzuZ1KA9d9J+j6S3akRl1Lylxd9DXzbk5iaQANagiZQG/ab2RRGur7WuicluhBopTeg641qsBdKSWwPrx9AE0x4X1wJj6wfqIjBKPZ/bMfj12u2Tlp6JSeYrpFS4mRTeHYOkR2GuB8+d6XkijOW8PYlnb54kjZWeVCwsCvOwq7a8OyhXQluWbOFp3omF3YKGDrxkFl5vu97pZ+vv2MxvSMFXunPEDQ1Ht8wCJOcd8GV2J5EE/Dtu5/mg0d0csKSua8qd+VAYqzY1kTVQ7fddhunnnoq73rXu3jggQeYMWMGF1xwAeeddx7g52T19vbWKHgGAgGOO+44Hn744T1qxNW/5DTGNHXEToyQxA8lN8aDmGPCbZoQhIMm0q2/7bh9jXn/lb1wUVZ4k743nvnfofEbj2FoxGYwY1fOMet4FaNbXdsdCxmcs2IWWsnzkPg9xW3P/7hV25TryXNFd0IDXsYt9TVXHBiMFBwKjkc4oNMWD9DVGKSzMUhjxMTQa6M71ZR/zvZu8Bd4bsVn++ej53D64g4cz2PLcI4Ng9m6z9RTPUl+9vhmfvXEFral8iyb2cDX3rGEf1o6cTtcKSVFr7ZJkJSSL9/5PNtTec4/Zg6fPfEQPnncwZOed39J5dBxPe5/dhOnX/EzDvr4d/jz6pd29dZNeaoFo3bnA9Dd3V0jvnXllVfWPea6deu49tprmT9/PnfeeSfnn38+F198MT/5yU8AKnoh9RQ8x2qJ7C7KE5/GFIsubtVveTmsLkRtOYxp6jUNRsqUfy4W/AS3enNt1USC/hy668lxXnjlHARkvrMd76I5aBPM0b2yNcnDY7LHPSDjeuhe6dwleEgWNUcIGDqZoosxycSVEILBXJFUvsjWUjOJyebQNU1gKW/kgEEXgmWdMaKWge159OWKFD1JyNIJWTqDaZucPX4067ouuUwGt9z7wAwxpznCJW87hI8eM48b/r6R/3l0E/2lAWlzxOKfl3fzrytms3Ewx0W/eZInqzxuTcDZS7v4rzMX88GjZrFlOMdjm4YqkaOy8fUkZMaUR5af5+sf2cAvVm/hkmNnceujL7B1OE9XZwemPvq8255kR7ZIvjSwFUJQyPrJoz2DKf7xyl9yxxc/yAmHzd1Dd3jf8VrmtOvtA3wl0erEtok0PDzPY/ny5axcuRKAI444gmeeeYZrr72Wf/7nf67ab+2JTfZeeq0oIz6NKSfXSCnJFv361LI91YUgbOpYhoY+prFCPQq5IqGdlJjpusbsjhjrelI1c+HVCAnFDQX6bu6j4wMd477fsC3F6hd3kC66GAKcMR6zK6mZs5zX5tf1+y81l0hVPXz52oUQpGyHoaLLv/5iFQPZYqWpxESkCi6D2SLNe6ixi2Lq8tj6QX63ajPL2mJ+rgiwCFifyvNKKaekMWpRGMqXuuv5z5Rt22TTfgKkhqQlEeXRb72PGQ1+E6V/v+1p/vhs7WB0IGNz9QOvsHrLMH9dN0gyX1v/7Un43VM97EgX+MU/L+cLpy7k0Y1D3Pn8dp7elvTrzF0Pe5JQkgek8kW++Ofn2fqKXxq3NV2gu6sTQxc4nh9GL1OOag3v2F762f8de9+3f8uPL/onTls2F12VoAEQj8d3KTu9s7OTQw89tGbZokWLKm2nOzr8d19vb29Nz4y+vr5x3vnuov7lpjGG4Ru0ZN4hX2XAwf8lHbEdckUXXfdbj2bzRfqGsmzdkaanP81gKk/RKfU53gW5R4BDZjeSCFkVL7wuGmz40ga8MVm/nid5ev2g/3fEaNLaBJiaYFGX36hkZkuElO0yXHAqamuyFFYfzBcZyDs8vj1FwfHwgJHi6ACnGikljifJOh5FV4XT93ee3DzMbx7fjOv5hrncMU8TgoMSIebFfYlfAYQD/vNYHiCapkkwFALXQXML/PiSd2EZOo4neeCVgXEGvIwEHnhlgHTBqTsH70n/+/N+/QQfv2UN//v0Nk5d2M65R3YzUnQnNODlVqX+Iy0wTItQxB/k9m/vYzidJW274wy4EIKedS/hOrUDiv5khn/8xm844tIb2TwwffuWiz3036vh2GOP5YUXXqhZ9uKLLzJ79mzA1wzp6Ojg7rvvrnxv2zYPPPAAxxxzzO5fdBXKE5/GGIaGp4lJs22zRRdNg6GRPLlCVTvEUv/wXMGhKR4kEts16VdD1zg0GWJtHS+8ggf5dXmSf0nSePyoWtRAKk/ediuGNaRrOIYkU6fERwBzE8FKFm5D6QWbc7waKdky27K2H47EV7RK2b4KXMTQKmFGT0pyjke66JEIGrQoL3y/ZvNwlqvue4mRvEPE1GkPW+MSGecmQmwaKVCU0s8ZqfodEUIQCARo94aZc8wKzr7pCTz5BNGAwezWONLTJs1J0XUBkwyO73lhB0FTR9cEt67dxjFzGgnoGoVJBpeZmt9hSSAUJpdJ43ke6196idaOdpqam9FLrZizIyl2bNlEeniCPBUpeWnbEKd97des+caHMY29K7e8VxC87m3MPvWpT3HMMcewcuVK3v3ud/Poo49y3XXXcd111/m7E4JLLrmElStXMn/+fObPn8/KlSsJh8O8733v282TrUUZ8WmMlJJ0YedyjTtSeYQ3uk35tVKeNx9M5Wnv2nWxhMZjGzj014fiFSZ+2WgBjcSbatt95vKjAhaWJrA9iFsGQd0j47g4JW8pqGuEDY32WJChkQJ/WbsN23ZpCxn0TSBPmbSdynUFdcEIUHAlBddFw4WqbHVNwDsP79qljHzF9MN2PL569wvcutZvXlQRJdIES1sidFRp9etC0Boy6ckU6u5LaBq9xNm6ebQKJF1weGbLIIau0dXWOOFzpO9kCqvspZf/fHjDEJYuKops1VNG5evKjpm3rx4ieJ7H9p5tbO/ZhiFA2tlx3vf4C/TD7y9uG+Lbf/gbl/7TsZOvrwDgqKOO4tZbb+Vzn/scV1xxBXPnzuXqq6/m/e9/f2WdSy+9lFwuxwUXXFARe7nrrruIxXZPmGYsyohPYzzPb2RSJmjqtDeGaAhbICCVLbJ9OIdd9LA0QcH1asLuuoCgoWPpGslkgdbWXXsctIBG27va6n4npaRou7iOR9FzsTwBAnLZIm7JAAvht021S16FpWvjkswsTdCaCPB/q7fguhJDEyQsg6CuM1Qoki1dd8jQaAwYPDkwuq0QgkRAZ7i0fw8qbzsBLO1M8KGjZu/StSqmH5ff+Ry3V4W6y8+740lW96U5ukOjOeSXMUrplzcKIfA8SaDU1a86uOVRv2GO43oMDKdpb64/h7qzCap6TVVsVzKccwiZWkVO2fUkGdslZ49PdMtn6osWOZ6EnRpwzf8ASMl//uwenHyW/zz35J2c+dRiTya2vRre8Y538I53vGOSfQouv/xyLr/88td+YruAMuLTmOpktaZogPmletTy8oCp05YIsmlHmu1DOWzXw3Zl5YVklpJgQqZONmcDu9f8xM47pJP5SlcnAE/6L0RN+H3CIwGdTMFFl35Hp2SdjGBTE8RMDduRFZGaipdtaHQataF/AcxJBHm6P1NpkBLQNZqDgkzRJV/aR9TSOW/FHM5e2lV5QSr2LzYOZvnDM5OrBr44lOVNIT9KJIQg67i4nqRY6kdQlmGVpWxxx5l46iiTK+C6Xt26a3snSnDWBM+g60nSBZdUzplYA0JK7HyeQm5iuWJ0C1x78u/L+wNA8MWf3MHSeV284+g9Kw26NznQG6CoN9k0RtMEzQ0hAqbGwWMMOJTCiBK6WyLohkay4JJzPIqepOhJskWPVMElY7sUd1F6sh7Sk2RSeVJDObxSb2TX83AcvwTOKycVaRqHtMdLJXBgCEGTpRM3dUK639CkMaATMzTmdsTZ0DsqT1hOTKpHLGDw5q4Gxl6BoQkSAYP2sElbyOAH5xzOuW+YqQz4fsydL/SNawQylqGCQ8H1SmIoHn3ZIiOleupKUpuuIfCfoY7mOE3x8ITCQEV3TIhbShzXrcn/GIuhi52epxDVU7WjyZxSSpyizY6tGyfZWoJugF5HL11oYAT8HqvVeA5CwDdvuXfyE1NMKZQnPs3p7owzuCNdUwdeKLoMJHOMZIq+2IsmaAhb9I0U6sqzpm2XtL3rrRCr8VyPTDJfCY2Xm6sYuoZmCApFryLUIoQgETI5oruRV3akGc7aeAhMAabmJ9QkIhYzWyM8s6E2EUfihwiNqn2B7zV1lJLyTp3dxJ0bB9GgYtDLf3/Hwa0sbN+zc1GKqcdI3qm0tJ2MouthaTqrelMMj2k2IkqKgJrm9x3QNEEsHKC5Icq2/iTpbO38ebmpSXnbYrFI75YtOB5EWzsJmEbNANQyBAFD22nZpxACXS/pP3iCfHoE1y2STQ6RdUBOFC6XEkqiM1Iz/L9LF2EESiODCQaxhSxSwl+eWc/QSIbG2OvXlnh32Ffh9KmCMuLTnFjEwnRHuyXlCg6bto/Uzul5Es92mREPsjWVr2vIk5nJ2x9ORG6kgOeOzrN7Ulb6ldtFd/QAVcRDJkfMaiRnu9iOi6b5JT8NzWHiUYt7n9haSUaqxil5+eV+yqYmOKQ1il10kcDbuhuZEQ3w0NZh1iVzAMyLh3jzjARL2pQBPxDobgyVyrAktuORs0d71YcsXzdBE4K847ExlaclYtEetXyPPG3Tl7FLgkmCRNioDZNLSVdLgo29gxRsp2S4JTv6BgiFgwggl8+TyxWg6IJ0GRnYQSbS4Ce/SWhNBHfJgFcjhADpsO251aMLdRMRjvvetltlzD0PpFezrfRchOcQi0YZsT3fyI/RWSCXqtnu8zfdyQ8uOvvV3v59gtgFHYxd2cd0RRnx/QBd16D04tq6I1NjwP3wGyB8o9ccttiRsWuy1AEyhVdvxF3HT2CrGTC4EtPURo9LSe+8zpxTWS0LQGiCeNTCdjx2DNfv8AQlMRhXIpC0N4bQdQ3heBVPaEFjmAWN4XHbma+iPapi+nL6onb+6/9epC9ZwK4p75IUHI+AobFsZpwh2yFgjvajD+ga3YkgTSGT5/szeJJx4fOyvGlTPMy2/lTJQHrk7SL5QpV3LkuhbMcFO4sXilMsnUoya9MSD6Gx68pdUkpSfT21C90icmSArrZWtqXre+TSn9T31wXymRHesXwRtz+xHkTp98GxkYU0OLVz5z+5dxX/9ZG3Ew8Hd+kc9yX7oMJsSqEmB/cDyvKmmbyDU6ox9aQkX3TJFf2M9HzR87WjDa3S/cir+mRtl607MhUVuF3BLZYlHUeX+VrtJUGNqpdgea58IsJRCyEEmQn6KI/FKpWglb3+ndHYGNql/SqmN7GAwaxEaIwBH6XgeGwbzpf0sqvyR0rPbMTSmREPllr0jn+1CyGIhqoMW3l+uTpEPTZkXTVn7riSZMaXeC1HDDxPkrfrP/dSejh2gaGezXW/7xlOYwQj5ZVLf8hKlIBCprJu2DK48aKzMLIDyOFe5PA2yAyOM+BoOnnb4bnNfXWPqZhaKE98P0AzdTzbpVDK9PY8Sb5OoprtSpIFB1PXMEo6647nSzw6Eu59yh/tz2gOc9jcJloTu2b4/BfgqOddlq40dEGx5CS4ngTh186ONeaOBlv60zi9HqlUoW4ovRpLF7SEzUpiULkn+kSZvMGgQTw+9T0Kxe7Tly7w940TCJuU2DycZ2YiWKM1XkYIQVvEYig/cY5ItW2vNB8xLChOEEEacxgJjOQcUlWaB9lMmngkSEMkgGGOlr+lB/vp3/AynjPB4NYM4loRKBZ9j1rTK943TpHyb5Kha5xzzBIaoiE+ctqb+NHtf5ngXLXKCVvTRPhFzYkrpj1CE+hBA1FSTrTreNMFxyNVlbxWTt4JGDqe41U8eAEMJ/P8fW0vjVGL9qYwM9pihILjHxW9KkSt6wLH8Ut0XNdDK837WYZWKbVxXYnn+nPgCF8adutQjkyuiBBgahoaELN0UnVKz8okAv65uJ4/UBBCELQMCkV3nMxlIKAze3bjTpu7KPYPHl43MGm7USjlgOSdCRX7dE3geR4Z228vahl6JbQupaQwJgm0Ysg1AzynlFhWen6F5i8voWmC4timRa6L9DySqRGGX1hNIBJF0zTsXBa3OEmJGCBKpXKE4xj5YaRdwK3TSc1xPX7512cQus5ZRy/mR3/6mz8HXpkHF362eimC0NEY47A543sfTEUO9DlxFU7fT9BMnURzCK/kYVfjSlljwKvxpCSoCwzN/7SFTWKWTkAX5PIOG3pSrHm2l81bk2QzNrJq57qhoZcajWhCYOgCQ9dGa7ulxDC00tyjv43EN76BoMGWwSyZUmmPlP7DKISgIWhWktfG0hw2K/W16YJDdVZw0DIIBwwCpk7A1AkFDGbObFC9kw8g6omyvBa80nRy0ZVkCg52qVZcCEGm4BAKBTGMMQNbTRvt1+uVft+CsYqB0DSBqAqzV5TYCgV/IDC8HaRHIZ0ilxr2Dbg+sTSwiLcjDLN0XhpuoovOJj+B0yiVyI2em07Odrjp3jW891u/YfHcGb7Xrpulj1EzBfCOYw7H0KeHJ36gozzx/Yhg0CIUMskXa0tgxobWPekn+VRPG+rC94CrS9WiAYPWWABD03DzDju2pxGaoKExSCQaQEoIRgPkUnmKtluaX/drxJNpl3DI8Af7UqLrAksTaIaGZRkMpPJkq0KWWtVxdU3QFQ0wlC+Stt1KaL01bNIcthgpDUhGCkUiAQNDG91WlAYTEohGLQIB9YgfSMxuDJLP5ykU7FLLWY1AwMKyrBpvKxqob6DKteNjf2fyRQ9NCIquR9EDy7IIBAK4rks6nRlNIgNfYEV6YEXALE3j2FmsUrOScka4lBK7kMdzCsjkdsgma09GM8AKgmv44XHX8UNlVgQRaUBYtdNdwrA4863HctJBDXz11/ezZl1vKetrNBve8Ty8ouSFXBCsENi58Tch0sidG3K71J5Yse9Rb7j9jM6OGEOpWiNe3a3L9WTdBiKe9NtzNgQNdAGRgEFnIlTj6fqhP0n/jiz9O3ylKE33E4AKtku2UKx44eGAUSPQEgzohIImnifJZooMjMlAH+s/6ZqgJWzRFJKV/cTGvHilhO2pHI3hAGFLrxzL8yRGUKe5dXrUuSr2DOv607zzhw+Sz48+/57nkcv5Rj0WiyKEoDlsEpxgvlcIwbaR+jrqmYJDvujVDAY0TSMajTAykkbgYRgCIxRDWEEK6RGcgY14ye3IXIqMpmHE2xBWCBmM4+bTfti9KvmsBrM08DBMCEYQ4YZJr19KyQ/vfx7JQp7rGQa9Xk0IeJqBMIKIljmQTyNzSf88DAsR9gcHW4dz3PfCNk5c1DXpMacCak5csV8RjVgELL2S5FaNlPUT3lxPUizVd48UHBpCJq3RwLgG9lKON7aeK3E8j5GcXXFEIkHDV7wql+9YOoloANf1sIuj2fM158ZojXnNS1IINF1g6aPzXqYmKJbCpp6EgUyBoayvsgV+TsDRc2ZM63kuxavn/Tf8lR1p3wCPTXT0PI9cNkdzIsohOxncjRTq52NICelMDiEEpqFjlkRcdF3HNAw8XMKRGKFwSbchHiO5bpB0fsQfBDtFnIEtEGlESAHZYT+kXQ/drJlLx7GRngcTzP+OJosKfvTQK1CcpMpDq2qwEoohQvU1FNbvSPuN1xVTGmXE9zOEEHR3xXm5SvHM0jWKnltp1VnG8fy58kLV226oAK6AgzUxxoDLCTPG80W3YsDLiUDVREJmpRdymYCpQ672ReN4clwjFPA13qtrdkOmjlNwas7Hk37yHsDBsxp3qexMsf+wauMA6/vTnLGwi8O7GrF0jZFCkcc2D/LXjTsoOB6OU+TwjmjdZ6yaicZ+EonjOICgWHQQhSLRSLCSD5LNFBi2bTKZLM0tzWiaRnz2ItI965CuA2bQ96aDMf8ghsk/Hb2ApzZs55Vtg6MHNwK+Vzz2RPIpCCXqDK7LZSEemKEa0Zb6F7JrZaTTRp54DyS2TWdXXBnx/ZDmpjCuJ9m4JYnnSUKGRrbo1ni/rpQM5Ivj9MYl0JO2ebpvhMPaR7szTdaIoVAc9VwsU695yeiaqBhUt2oSvjESYEeqNqTuSontepglaVUX3zsPmeY47zwWMMgV3YpH7i+H2V0JOtuiu3KbFPsRD7+8gwvfNB9D0yoDvljA5PiD2ljSkeD6R18hV3QZztm4eZe+jI3jegQNnfaYRUvYf8bK+SJjkVJi28UaAyg9STqdIxYNIZB+iZlhUbRtUskUDY0NoGkEW7rIZ7KISEPNfLgIxljbm+Wer34UUxd84eYH+Z8HnpnYILmOP4ddNRdeqQcvq7BpOlBKWHMn8MbdIlJ6wCTGT0oyuTrz5VMQJfai2C9pa4nwhsM6mDe7gZldcRZ0N9QkqaSL7jgDDhDQ/f7KyVyRHen8pAIt9Rj7TpjoHWEZGp0N4+vQXSnJOS7ZUnLRRD2Z/a5oBvGAQdTSiVkGzdEAMzqUvOqBhpSSfM7FrDLgZTQhaA4HOO2QTgCe7cvwfF+aZK5IoaSb8GJ/lmf6MrieR89QmqHBYbLZbM2z7wsRjZkrlx7Sc/1GP15Zpa0Anks2k8HzPEAi9QCER7umVf+5cUeSc676Pe0NUb71LyciJgqvI/xadOmC5yJLn1LmaGkVbbTcqlx6VndXGhQLo6Vx9VbxbAbTEysnKqYOyhPfj9F1jdbm0fm/tpYId/x9E54cn9ymATNjARIBoxI6H84WSeWKdMRDBHdRttTzJFStWlal8l8uo+8bgOZYENPQ2JHMU3Q9DOFnlWeLoxnpOwuTaWJUWat7ZoOaBz8A2bgjg/SYMJNa1wSHdzVwxwvbSOVdEP6zb2iCWMhA1zSSeYeX+7O8vHGrnwiayZDSkjQ2N2FZFql0lqLjwjgpIt9Dd23bz0TXTd8j13SKdhHTMrG9iZ9jx5M8sX47f31hK29eOJPjl87jgWc3+c1NpC+QhGb4HvarkGnFCkEo7muij0XTwHOQxTwYgfGG3LHBtelomB6JoWKyiMKr2Md0RXniBxDxiMX87gSlAFwNs+IB4mUd8yrD6EnoSeYqdbJjKQu6lLEdb8x8HZWWjEadObawZWBoAlPzvYiiJ8fN2+/KL+js2Q1EJhDvUOzfbE/ld2rfDE1jRlOUWS1hZreE6WwIEjA1hjPFSq7GjqxdMz3jeR6D/QPsGEyRLbi+IdV0X3e8qqbacZ3SQFXzjbgV9j1kARZu7ci17rkJ7n16IwCfPXM5UuhgBvzyMjNYquEuXeBYSddqvDGtT0MJSHRAIOrvxwpDtNkvffNc31gX0v6gw7HByfs/uzaWqXPOUQdNflOnCOXs9N39TFeUET/AWHZQC/M6a0POIUMjZhl1jWXB9ejPFXlsa5JVW5NsGs7VlKwBBC2jMo51PX+OvPplks4WS52kxrcw3pHMVcrSgIpyXBnb9Sre/ETE4wHiMSWreqBiaGJynd4SDdEQhu6H3IOmRnsiSEs8QM4uC7loGEZt/20pJY49JowuSrOwZWPqyYolEOV5ac3ANE3OP2bOzk9MiMpA4sTF3Vz74RMmXA8zjNA0Yg2N6GPFWISOKA2GK1ZJNyHSCLFW34CbIT/kX54vl9IXpnHtmm5o718xn0Q4sPNznwIc6EZchdMPMDRNsOLQDnZkiryyfQQJNJRC6GON+HDeYbAw+oudczyG8kXWD2WZlQiiaxqBkv50vuiWkuh0wMHzdIKl2m3PkwwmCzTEAlimjutKRnI2fcM57DF1t/Xexcl8kcZShvvYczQMjc6OeJ2tFAcK89qi/N8k30spyToew/Zoz/vyn9GAXhl0+s9WnSfQdcCqSp8qh7kRJY9bgGZWJFeFEKAbXHnGYt79hhn8f7fcx1Bm4vllx/V40yGj9djnvHE+X753PcP9/eRzpe10A7TRgbZpWTS2tlEs2rhFByEEeVfguB4ByyDvWL6xHktZDlZK3/vWzVoLJiWa59CVCPKj+5/nugeeZ33/CA1hiw+sOJgL3raIjsT4LoGKfYcy4gcob17Uxsb+DI7nUW8qMVN0awx4GU34L79NyTymEBUjLoQ/q5QUDjObwrS3R4jFgniuREiJaepoGhRsl6HBHOt6RxACAmO8CU3A2AZURU8ymCsSsXQCejl5BxobQrS1RVWb0QOcxmiAQzpjvNiTYtB22JErUvA8TCFoDpo0B02e6k/7ksRerZkWQCJkkrVdPM/DKU7Q+EQ3odwdTAM8ifAc3xsXWmlgUE768A25pXlYhs4Fpx7Blbf+bZw2Avjz9bNa4pyydG5l2c8e30xR6nTNnEE2m2c4NUKh1CrY0HUMHQrZNKFoHMsKgBXA9TzSI3kQglgkSDBgkBoamrhxCvgDEKcwGqKX0l+mCX7y2GY2D73orwaM5It8489Pcf2Dz3PfpWewsLPhVfwL7V384dXuzolPX5QRP0BpjAZ49zGzueOJrXXbNg5PYMDL8qi6gGCprWnZO5jXFmVOa7QyRy7zju+NaxpF22XHcI5C0WVjf5rSK28clq7VVZRzPEky7yCAw+a30NIYVpKQigonLe3knlf62Z4ZbRhSQJJOF9iULvDiUJZ6XXYl5QGoJFcoQqA8X1wYLSfTdGSxAMVcTWMTaQYAAbnhMV6vQJoBUjn/XP7zn97Eoy9v4+6nNqCVytjAN+CJcIDffuafap7lv7wyQCbvEA8ZhMNBIpEQnufheR6927aTyxbQNI1gxJ8WE1XhePA9e0PXaWppIZ/NkstkcEuhcjMUxrEzft165SZIP+u9hGeE2DKcGxeTcD3JUNbmXdf+H099+eypk0R6gNeYKSN+ANOWCPHPxx3Exh1pXl43WFnu60ePN+zV+uYBrdaAH9bdwIzGUOVnTRMESolyrifpG8zieZJ03qnUnPtNJmpD5LoQGELgTDAH3tkaobUpPHVeIIopwa1P9tCXqd/xy5OS7niQlwcnrns2dY2s7SJ0A6nppdaiOT/kLDQ/4asa6fk120IDp4C0c5AZGtUiD4T5zUMJPvqWQ7AMnf/9j7O5+eHn+OHda3hp2xCJsMX73ryY809eRmdjra5B0fV7jA+M2LTErIoGfDKZolDwBwue55Ec2EGiuRWotUG5QpFoyBeLCUUihCKRUflkoKB5pPq3T3wzzeCEuhCuJ3l+W5KHXuzlrQs6J96H4nVDGfEDHCEEc9piCFfyUqkPc73f3+p8NAE19bgNYZOZTbXzZJY5mumeyRT80jMYl+XuSIlBbf1s0NAoer7wS/lcggGd2Z0JZrZHlQFX1JAvutzz/HbmxYLMjQUJGRq2J9kwkmfdSJ6iB7GAQdDQ6soOA5WEt2q9f6wwUgpw62upA36tuJ2HgU21y3Mp7rn/Qb4xv4VL33UCpqHzwbcu4YNvXbLT6wnpfoVGvujRmywQCxqELJ1UsrZczLELDPZuJRCOYFrBitRsoehiGi5BazTXxc9N8cgmB8iOpNGCEbz8eM12EYggd1rWCX9ft2PKGPE9kZg2nV8pyogrAJjdGSdg6qzbMkw279TokwOThptmNoVL2edlQ1xr5DNV8qpjDbDjSYSGXyNe5X1bukZLQ5A5JZEay9SV8VbU5ZUdaVa0xmmoKpE0NcnixjDz4kHu70mSdVxilj6hERdCEDB0dEMjbBlomsD1PLI5nfSwPTrnLUZrkivPqzlBaaOm84Wb/sRpyxcyq72ZdQNZQqbOgrbopFNBmWyusn/HhaFMkcG0XZJ8rUVKST6TppDNEIgkKJRe6emcjV10CQUMvxWvhIQoMJBOgxBYje0gPZxMEunYCE1HD8cRhkV+ZHjS+13uVjhVEKX/dncf0xVlxBUVOloitDeHyRUcXupJ8feX+ivfyapJbEltGDwcMCoGHMYb6uqxQNjSGc7WHrfoSRzPxdD8X0bDECw8qIVYtI5+tEIxhi3bRkhYtYO8Svc8XeONbTHu7xmufGfpAlPXcKUkXxw16vGSzsCoZLBGNBwgZLXRv6O/8txXH8OXT40jY60lyVOJrhmEmjswG/xQ9/E/WgWM/h7Magxx6YmH8IGjusddy7ZUnr+uH8DNFdBDscrv2byWMC8lRV3jKTSNhtZ2dN3AclzSpWZEtuNiOy4BQ+OzJx/CNX/8e2UbTdcRmoVu1ZZm+ipzO6c4Ubxd8bqj6sQVNQghCAdNls5tYsEMv3SruvCm/BIrVtVu245X83IZW9Nt6KMv16Cp122sIEv7tD2PubMaiccCyoArdkrBdhlO5WsGkdVoQtASNElYBrYnmdkQoLsxREc8wIxEkNlNIWIBvablbjVCCHRDJ9GQmPB7ABFrQQi/DWl83pKKAS+6Ek/6zUTmNYc5Ymac5rDJN+99ka/e+XzttTgu/3rzE3iagXQdnEwSz87THjWY2xymozlR11+MJhrRdb/8LGAaNMVCxMIWkaBJPBzgDbObOX1hC/1VMqrS8ybQXpATd1aruubh3ARZ/PsAVSeuUNRBCMFbF3cwqzXKM5uG6E8VEFCZ2y54siSyIdk2lKvRQZcSXNdDK3VCi4YtBpP5yn47GkJsG85h1wltHjSrgdYmVYeq2DWG0oWKIJrrSTKO3xRHE4KwoWGVwtbtYYumiDnOCOoC2mIBJDCUrV+OJYQgGAqipUbqeqpCCKQVhmySSMesSsi9LFzUFrM4pDWChMpgIxLQeWT9AH96tpe3HtTCfS/t4P9e2sHm4RzhUJCR1AgaksXNQbq7ErjA3M4WegeStdEATSMQqk30LBvzMi/1Z3h620jNOTv5DGZkvL66KIvYiCpN9rH3zDB3WYb59aCiF7+b+5iuKCOumBAhBHPbY8xtH1V429g3wqqXB0hmbDKOR0AT9KXyDGds4mGz8pKyHY+g5Xs44aBBJjfa49zQNGY2hsnaLplCEaEJGhpCdLVGCQbUI6nYdcqv3pGiy9CYssiRous39AmaRIJGXW+9/PJujVgM54oTKqQKITBMA7swmgGvmQE0K4jQSp58IAi6VQmzexIill7pXz5+yknyg4de4Qd/XYfjSgZLg4hYyCQdS/ChpR20x8P8dbuf0BYLBzlywWzWvLyZouP6Am5jOvxNdI+yriBo6uRLHQeLuTRGKAramGkITUM3jJJ4m/RL6sr3RPNr4l0JZx0+fipAsW9Qb0zFq2J2W4xZrVGSGRvb8YiGTAKmTr7gIIouslSa5nmSvO0SMHU0TdDaGCKZtkln7VLXREEsZNLZHiWeCE7rkbBi39EUD5J3vXEGvEzBlfTni6zurdMIpAoh/Az2VH50P4LRMKvnT4hXvtNDMYRuVG0vwAqTL3oEDI1yy/KuRLDGAwc/ibOplC3/cipfUTdyq+rH37u0i5kNIewxc8/NiSgnHLGA3sEUqUwOTzPYWcNQiT8w+PBbFvHDe5/2bbKU5If6CMSb0K1gTYvUhW1Rnt2W9O/AmN7ruiY45qBWjprTvJOjvn4c4GXiyogrXj1CCBqitbrKkZAJIRPP9XBLyUKaIRBaSU9JShqaw8SbQjilMLppqYxzxe5hGhq5nWRK5+toHtTdV1XGuGVoaGLUU5dSEgmHsO2i733rdXoNlH4uOB4h0/97S8TE1PzufJ4ESxN0hS1aQxZrB2tLvExdQxeSI1ujzG/wNRcCmr9NtTHXNI2ulga6WhrwpOSpnpFxKofVaAJOOLiVf1jcwXV//juOWZqu8lzywzsQmoFmGMiBzTC4hUXHLuFj7zmVT9/yeKVNucCvJDlqdjO3fOy4KfV7q0rMFIo9iKZraHq9fMmSCAwCq+73CsWrZyRfZCRf64XrAqxSp51CSWugNWT5sqQTIISoeMIBUyt54bXh74ZEFMPQGcrtPIM7Yhks7ogSsapesVISNDQaLAPH88i7nq/gCgQ0wbKWMEe1xUtz+17luDMjAdZNcO6GJpiZCLJxeOJrW9QeoysRJFNwcDc+iYy3IRo6kWYQXAeZ2oQ71OML2wADwyN8/PgF/MOybm565BVe6E0RC5qcfcQsjl/QPqUMOKg5cWXEFQrFtKVYlRypAY0Bg7Ch1XjQGcej3bPYMJKfsNmZocHijjh/3zQ0zoCXEUIQi4bJunlsR1ZtK+hKBGiJWAjhe+IzE8FKiHZGxGJBIkxryCRTdNmcKZB3JYc3RwFJ3NRpDpqVSMDY6q1Z0QBJ22FgzJSBJnw9heWzGtma6sWpU/alCdgwmGFdf5oP/nQVnhGEoR7kUE/9+6BrzO1q57a127jmwXU8unEIXROcuqiNSEiVfE5FlEukUCimLbGQian7Uh1tIbPGgINveCOGxsKq6ol6NIQsfvqBI2kOT26odAGRquTLWEDnTXMbOKglTCJkkAiZdJcMuBCCJU1h3tyRoDlokLIdXkrlK+F9DeiOBGgJmuhVioXGGCEYTQgOa4qwqCFM3NRLkQbB6YvaueqflnLfy/1YhkbQ0DA0v6+BrgkChkagdD8++bu1rNo8jGg/eNL74Lge2baFvPO/H+WhV/rJFl1GCg6/f2obb/nOQ/zk0U2Tbq94/VFGXKFQTFtMXWPprEaipoap1Q+rCiEQHswek8dhaoIFiRCnzGzgjU0R/vj4FmJm7StRE/78eNDUCFk6pqHRGrUwhG/QD58Z90WKSiFdo8oYtwQNFjf6mekC2JSulW9NWHrlnKtP29BEzfy8fx6CzrDF8tYYx3c1cN6RszjvmLls2JGuqNBpmsAyNAIlLQZdEzQETU7sbuAvLw/4bc/bD4ZwAxOlcp12+un86ukBoLaboONJpISP3byG9QPj5Vr3JfuiTvzyyy+v/JuXPx0dHZXvpZRcfvnldHV1EQqFOP7443nmmWf28JX7KCOuUCimNccuaCM+QWli+d3sScnB8VFvPG7qnDm7ieWtUVqDJk1Bk+GRAh86tJMTuxsA35sdm+AGgCbobArRHg+MGzjoVeseHA9VRJCStoszJtodt+okx5WIWnrdFsEATVGLGS1h/vu+V3jo2e0TZlY3Bw3efUgrnZEAmVJpmdANtEPfhmg7yK8HL2MGOPotx+O0HFSJCkzE9Q9vmPT7A4XFixezbdu2ymft2rWV777xjW9w1VVXcc011/DYY4/R0dHBySefzMjIyCR7fG2oOXGFQjGtCVo6IVPHLlVFWLogaukES6Fkx5NkbJek7c8pC+CEGQ0E9TGh99KfKzoT9OWKvJT0i7fqybm2hk3ilkHc1Mk4Hh6Mm0tvqpIjLtTpg1odNi9ngZfRhaAhYJB3/AQ4Kf15+8aGMC/2jbDh8S0ENIGhacyJB9mQGj/f/7ZZjf4xxigkCsNCzFuOnHU45JKgaRiRRlasOIj/eXRjJcGvHq6UPFpqlDRV2FeJbYZh1HjfZaSUXH311Xz+85/n7LPPBuCmm26ivb2dX/ziF3zsYx/brXMdi/LEFQrFtCdUygIPGRotYbNiwMH3juMBne54EF34iWYxU6/09h6xHbZnbbZlbPqyNiO2w5s6SpLDY17uYUPj6PYYx81s5Ii2GPMSYQ5titARGt8EpTocXU9optpYjpU1Lm8TNnWagibNIZNEPMRTW5MUir7IUvnc3tAeG+e1t4RM2sIWmvCrQeY3hsZ57MIwfbnYSBMugn9Y2lnTuGgiplp1yb4IpwO89NJLdHV1MXfuXN773veybt06ANavX09vby+nnHJKZd1AIMBxxx3Hww8/vKcuu8LU+tdQKBSK18C8rjiagMaQb8zHes9CCOKWzlFtMTrCFp6UuJ5kR65IuuhVMsJdCemiX5aWsGqlRQO6xjGdCZqCZs1yXQjaQiYzIoEaI7w1U6iE08fuC2DEdmvWL59DeZnjSfKuR9Hz6GoJ8/Q2X7DGGBPCbw6ZnDGvmWhpPl/gZ+lXc8Lcpgnvna4JVsxuJJnO0xUNTCp8IgScuqh9kjWmN6lUquZT7t8+lqOPPpqf/OQn3HnnnVx//fX09vZyzDHHMDAwQG9vLwDt7bX3qb29vfLdnkQZcYVCMe2Z1xmnMewb18lCo0e1xyoNfYYKzrhyrjKuhCNaYzXLDkoEMTUxzqsOGxrd0QBvaImyoCFM2Vy/ksrjSoknJZau0RSoNeTDtn/8stGWpeMWXI+eTIFN6QI9GZvNaZvHNicn9RY7IgHOXdjO2+c2saIzziGNtdn4BzWGec/i9krinSaoeO9vnNXAafObeeDFHRzWHhu76wqagHjQ4J/fOLUkV8Ue+g+gu7ubRCJR+Vx55ZV1j3n66adzzjnncNhhh3HSSSfxxz/+EfDD5pXzGvMPVt35cU+i5sQVCsW0xzQ0ZjRH6B8eFSENGBqhUtjclZJc0SUhBFnHxZM7b6fZGbYI6Rq50nx2dzRYY8B1AQcnQjQGDKSUSKAjbHJUW5SnB7NsThd4cFuSt3QkMDWYEbaQ0mao1ENAAj1Zm/aQiVXq9Ge7HttzxXHz26n8qK67U+ogWK+j2sxYkJklO6yJ2przIzriLGiOsHpbiu0ZG0vX+MI7FpFK2/zl5R1ICW1Ri5MObuaelwcq5wi+dx8LGvzxY2+iMTxB//R9xJ5UbNu8eTPxeLyyPBAITLBFLZFIhMMOO4yXXnqJf/zHfwSgt7eXzs7Oyjp9fX3jvPM9gTLiCoViv8A0tIqOdmPYwjK0Gk3wUKkByFf+4TB++dD6cdsHdEFnJEDU1PGkZCDv0B422TBSQBeMq9+enwhVwuRC+PXZ5Vrt42Y2AH4Tls05mzmNfo33HCEwLJ3BfJGC49EUsVja3UAm7zCQynPv8311r636yBKwPYmlTSRKAwe1RugdzlMY0ykwbOq8eVajf780weLWKN9e+1KNsV/QEqErFuCZvjS9IwU04c+X/7/TF9EQqp1K2N+Ix+M1RnxXKRQKPPfcc7zlLW9h7ty5dHR0cPfdd3PEEUcAYNs2DzzwAF//+tf39CkrI65QKPYPmhpC9PZniJcEYGDUyJX/DJg62VSBJbMbebDKYLaHLRaMCUG3hEw+sWwm/9+qzezIF3HK7XeBiKHRUDXvrAm/Y9nYDPWYqbPECtHVEMItuniexNQE82YkaCjpowNEgya9wzkGMjb10DWBU5Upl3E8dFNHZ3wf9LZYgIAEHTAE40rbwB+QWJrg5e0jdVsCxwIGK8qldgKOnds0pQ34ng9ST86///u/c+aZZzJr1iz6+vr46le/SiqV4l/+5V8QQnDJJZewcuVK5s+fz/z581m5ciXhcJj3ve99e/xclBFXKBT7BU2JIJGgUZOZPhYBJJM5Fs1M8NDzfZUEtrIBH7tdxND50oo5PDiYYSRtI0sGrzlo4klZCa8HjfF66+Aby4aQSSFXrHxn2y5929OMjBRo74jTO5Dhpa1JXtyRnvDadCHQhaxkvEsgWXQJaIKArqEjaU8EObQtyvBwnozt+g1UDB1TSj8EX7p+ozSvLwBd23lalMQXvJmy7Ml4+i6yZcsWzj33XPr7+2ltbWXFihX87W9/Y/bs2QBceuml5HI5LrjgAoaGhjj66KO56667iMUmzjl4rSgjrlAo9guEEMzpijPQn51wnaClE7AMTMfjbQvbeOCFHcyMjW8XqmuCsGUQtHSEgPc2hglGLX78yAZyRRe96p1fNoz1Bg6JoDmhFvtQKs8LPSkc12N7tli3DK2acjczj9GuqAVP0hgzOffoWeRSBTZtS1EsRQzKCXx+mdn4fUtgTmuU2c1hNg1kJ9SV9yQsndEw6bntS8pTKLu7j1fDzTffPPn+hODyyy/n8ssvf83ntKtMmeHVlVdeWQlDlLn88stZuHAhkUiExsZGTjrpJP7+979Xvh8cHOQTn/gECxYsIBwOM2vWLC6++GKSyWTdYxQKBZYtW4YQgjVr1lSWP/nkk5x77rl0d3cTCoVYtGgR3/nOd2q23bBhwziZPSEEf/7zn2vWe+CBBzjyyCMJBoPMmzePH/7wh7t/cxQKxS5h6ONLuQB0XdDeFKGlIUw0ZIIHh7bF+Mib5jAzUZuwZuqC5liAcEBHL3mtnifJpQp8cNkMZjaGyble5cUvxiq6lbB0X/q03neuJ9k+UsD1JHnX95Ktku75RAjhi9j8x2kLec8buzn7yJlc+LaDueTkQ2gImmzZPoJTsu6aEIQn8J6llKSLLgO2w+/XbCViGRMacCFgQUeMmU3hiU9MsU+ZEp74Y489xnXXXcfSpUtrlh9yyCFcc801zJs3j1wux7e//W1OOeUUXn75ZVpbW+np6aGnp4dvfvObHHrooWzcuJHzzz+fnp4efvOb34w7zqWXXkpXVxdPPvlkzfJVq1bR2trKz372M7q7u3n44Yf5t3/7N3Rd56KLLqpZ95577mHx4sWVn5uaRusv169fz9vf/nbOO+88fvazn/HXv/6VCy64gNbWVs4555w9casUCsUkBEPjX2lCQGtDuCJkUm1UA4bGQV0JXtwyXOmI1hAJjG9FWvrTK3q85/AuslKy+jl/Tn0igTNTFxOWFaWrytvKoi9CCBosY1y3smresqCN5miA5jE68H0D46MPcUun6MmaXuSOJ+nJ2hQ9iQCG8w6a8MP1HhKqlOM8CfPbYvzLMXMnPJ8pwb5wxacQQspJNPZeB9LpNG94wxv4wQ9+wFe/+lWWLVvG1VdfXXfdVCpFIpHgnnvu4cQTT6y7zi233MIHPvABMpkMhjH6C33HHXfw6U9/mt/+9rcsXryYJ554gmXLlk14XhdeeCHPPfcc9957L+B74nPnzp10u8suu4zbbruN5557rrLs/PPP58knn+SRRx6Z/EaMucZkMvmasiQVigOdLZuHyeWKlZ8jQZOGWGDCeXIpJf3JPNsGswRMjcZI/bKicNAgYOmV/XhS8sr2EXqGckQsvUY3HfxEt7Cp1z3utlS+0rgkW3RJlsrOwM9oHy4447zjo+c1c+rSznH7y+WKvLh+gFS2iOdJRqr2JaUk53hkHI+i67E1a1e6qNW7D0tmJAgHDAKmxtKZDcxujtRdd1fY2++y8v43vryVeGz39p8aSTH74BnT8r27z8PpF154IWeccQYnnXTSpOvZts11111HIpHg8MMPn3C98j9CtQHfvn075513Hj/96U8Jh3ctLJRMJmu87DJnnXUWbW1tHHvsseO8/UceeaRGag/g1FNP5fHHH6dYLFKPQqEwTiVIoVC8djo6YhhVoeTQBM1RpJS4rofjSuJhf+7a0jXq+TXxiFVjwMEPWc/viLNoZqJikKu3LTjexAOHqr8Hx4S9Y6bOjIhFU8AgYek0BQwObY1y6lK/5jidsRkazpFOF9jWm2LTxiHcqk5m1bsTJenW1pBJzNInNODldbcO5zj7yJmcefiM3TLgitePfRpOv/nmm1m9ejWPPfbYhOvcfvvtvPe97yWbzdLZ2cndd99NS0tL3XUHBgb4yle+UiMwL6XkQx/6EOeffz7Lly9nw4YNOz2vRx55hF//+tcVFR6AaDTKVVddxbHHHoumadx222285z3v4aabbuIDH/gA4Bf315PacxyH/v7+msL/MldeeSVf/vKXd3pOCoVi1zBMnVmzG0km86SSObQ689KO62Hbbo0xnd0eI5MbP9gOWjq6XrsPrzQAkBKawxbHHzGDwZE8m7YmcVw/VO14kqLrYdbRGg8YWqWGWxOCmKkzUhz1oDUhiJqj8/srFrSSTObZ3JPCtl1cT1L0PJojfv/zoKGTKXngIdP/e3Xtt6EJ8qUQ+mSh1+FskYG0TWts10ROpgL7qgHKVGGfGfHNmzfzyU9+krvuuotgMDjheieccAJr1qyhv7+f66+/nne/+938/e9/p62trWa9VCrFGWecwaGHHsqXvvSlyvLvfe97pFIpPve5z+3SeT3zzDP8wz/8A1/84hc5+eSTK8tbWlr41Kc+Vfl5+fLlDA0N8Y1vfKNixKG+1F695WU+97nP8elPf7rmOrq7p5asoUIx3dB1jaamME1NYeyMjVscrYV2XI9CVci5so0miEcsCraLU9V1LFCley6lpGC7lfnz8rK87dDaFqWzJcrAcI58wUHXNRpiAbZv8w1vNfGAQSo/OvcdKemeVxty8Ovaj1nURtTUeWWD3z3Mk5Ks4xIPjr6+dU0QMnVyRdcfAFg6RVdiu74ufCJgsC1bvwZ9LN6+nWFVvEr2mRFftWoVfX19HHnkkZVlruvy4IMPcs0111AoFNB1nUgkwsEHH8zBBx/MihUrmD9/PjfccEONUR4ZGeG0004jGo1y6623YpqjogT33nsvf/vb38bJ5y1fvpz3v//9NVq3zz77LG9729s477zz+MIXvrDTa1ixYgU//vGPKz93dHSME7jv6+vDMAyam5vr7iMQCOyytJ9CoXj1GAEDt+gbMCkldnG8Aa/uGR6wNJzcqJGu9uTzBaciuuK4HsNZm5F8EU+Ctn2E9pYIs7oStFZlc8+Z28TISIGRVB7XkwQsg0RDkEQyzzOvDPg5VSXDGzY1Cq5HQyzA7I4YDSELIWDT5tGKG9stt1ytrYcv9yDP2i4IgWX4/dBDlo7rSBoDJpJRWdp6BE2NpsjUklVVTM4+M+InnnhiTRN1gA9/+MMsXLiQyy67DH2CUhEpZU1nmVQqxamnnkogEOC2224b59V/97vf5atf/Wrl556eHk499VR+9atfcfTRR1eWP/PMM7ztbW/jX/7lX/ja1762S9fwxBNP1ITI3/SmN/GHP/yhZp277rqL5cuX1wwsFArF64dmaBgBHafglhqOjH4nBBilUHk5aqbrOrqukc7YlfWFoDJ/DlB0PLYMZWu8Vk/C1r4063tHMC2dQtHD86Q/CNAEmYJDtuACfkmZrgnaEiGCukY2ayOlpDEeZFZ7jORIgc1bkmySfulZwBh9H5Y138f6y0IIIpZB2NSxS6H+gKljBAwGhnJ0hE1Cuub3J69znwSwYl5z3fD/VGYfaL1MKfaZEY/FYixZsqRmWSQSobm5mSVLlpDJZPja177GWWedRWdnJwMDA/zgBz9gy5YtvOtd7wJ8D/yUU04hm83ys5/9rCYxrLW1FV3XmTVrVs0xotEoAAcddBAzZ84EfAN+wgkncMopp/DpT3+64k3ruk5rayvgd6cxTZMjjjgCTdP4wx/+wHe/+90aLdzzzz+fa665hk9/+tOcd955PPLII9xwww388pe/3At3UKFQ7CpG0EDoGm66NqRsVBVmV3u1uiZIxALkih6uJxGCmhB6Xyo3LuxsOx7D+VJWec4PlUspybkerix3DhstO3NcybZBvzTszUs6iAZNXtgyzHPrBytVU4YmMKpU1cYmzpl15vvLSm0AiYYQI9liZfmKzjgP9SRxSwpu1cxuCXPS4o6d3supx4FdYzYl6sTroes6zz//PDfddBP9/f00Nzdz1FFH8dBDD1XqtFetWlURfzn44INrtl+/fj1z5szZpWPdcsst7Nixg5///Of8/Oc/ryyfPXt2TSLcV7/6VTZu3Iiu6xxyyCH893//d818+Ny5c/nTn/7Epz71Kb7//e/T1dXFd7/7XVUjrlDsY0Sp8UggapIv1WHXqxsHf044nS2SzNh+G1FLJxowK7rptuNWstHLuJ5kKD++vrvg+VKpglFFuOrjSXzD/Jeneyl6kpil0xw0KusYdQx0OTktaztELX3SFpfhkElqZDRy2RgwOGlmAy8nc2xOF3A8ScTQWdga4R9WzMaYZl64YgrUiStqUXXiCsXeZWhHBtfxMI3xXqzrSbbuSFMojm8KYhkajbEAmYJDXypf81264JAZs42UknTJ2Jcd/rHHawybREydDUP+XHVnxCJQCu/rQlQ87ertC45bEXCxdI2mMX3Uy0a9IREkkyn6HdmqDHk9DprTSMseVmV7verEt6zftkfqxGfO7ZyW71017FIoFAcU0cTEiaR9Q9m6BhygUHQZHinglfp5VzPWM4faXt4w3oC3RCyOmJGgJzVqYKvD49Wre5LKvL2la5Xgr+169KULpAsOtuNhOx7C0JgxM0Em44fRNSFIhCfOyWlvjdA8poObYvowZcPpCoVCsTcwLYNEU4hsqlATii46Hunc+JC4lL50ad7xldTKGFptVvurZVF7zDe+VeVsrpRoFYNNZapW4kcJyscLmzoFx8OREk9C2nbJux7dnXFmtUXp68vUHMsydJqigpztUij69fFBy2DWzASJ+MRqdtMBXyJ39/cxXVFGXKFQHHCYAYNQVJKv6t+ds+trluccj4I73tN2SkbV0HyddHdM426tyjJIgKoBQ3PYImjq4zz4EdulqVT/7UqJSe38uStByNFQemPUorM9iib8GnfN8cCRFOror+uaRjSoEQ36XrlpajQkJtboUEwPlBFXKBQHJGbQoGi7uHXqxss4Xn0DXsaT/ids6uSdWsMphBhtHyr9efGy5x8qJaRFA35tdzn0nrZd4gEdo7S940nMMa3NykMFTRPM7W4gHCqFyotuVd3ZzrTZmN51VVWI0n+7u4/pipoTVygUByRCCMLxAIGwWTKs432aenPdYzEMjY7mCF1N4+eVLU1gVHUFA9+QF11fV93QNGY2jG7nAdvSNoVyPbrnYbvuuDn4SNjk0ENaCIdMcgWHp9cNUKjKjg/X6eY2lsgk8+TTCrGHPtMU5YkrFIoDFiEEgbCFFTKRnmQoa5OsSjTbFQlS15McerDfz2H7YJYXtwzTN5RDSl/+NaoLAgEDTwgKtotEUvA8PCnRhGBBa5ThXLEiw+pK2JaxsTRBxNJZPLuRzpYIruPhupJg0Kh43yNZm4ee7MEUgiVdico5JaIWqfTEMqtCQEyF0vcLlBFXKBQHPEIIhC6YM6uRF1/uJ1en5nsi9Kpwd3tTmPamMFJKhkYKbN4+Qi7vELB0ZrRFCVg6z29JsrEvzf0v9xMLGMxpCnP0rEY2D+fYPJwjV3QxNMHc9hhL5zUTDdX3mKWUPP5cH8Wih2bWKlyahk57c5jtdfqMCwHtHTFMs74q5nTjwJZ6UUZcoVAoKhiGxsJDWhkYytI/kMWRHiOFiefMAdpLLTullGTyDo7rsW5Lkp7+TGVmWgDre0dI2S6IUelX27Hpz9jMbgyxqD3G3OaI7yU3hAjX0TDPZAoMD+XI5x2ytkuylJiXL7qM5IpEq4RiIiGTWZ0xRjI2uVKiWyhsEUsEa1q1TnsOcCuujLhCoVBUoWmC1uYIrc0RHMfj4Se3UizW1xvXNcHMtigvbE3yxLoBUtkiMVMnavrNScrbuFKSLHcyq9pR+a8bh3J0NIfpbomQdSQZx8NyvRoFtf7+NEODow1MsmOy6V/aluIN82obLRm6RmM8SCNQapi+3yS0KXyUEVcoFIoJMAyNIw/t4Innt5MvuBX7JyWYhsayBW2s3TzMmnWDgG8nI6Y2ru46P0mGe5mnNg2zev0gxVJSm2loLOpuYNm8ZvI5u8aA+8eqPcam/gyRgMGCGYnKfHv5z/3ZgB/o2enKiCsUCsUkREImxyybQf9QjsFkHpAkYgHamyIMZQoVAw6+4ppWx1A6Y+Xb6pAb03O86Hg8u2GQV7YkOagpRCxg1AwOooHxc9rPbU2ydTDLnLYo0aCB60naWyJohrZfGnBQXcyUEVcoFIqdoAlBW1OYtjH64s9vSSKq5rj3pDEIaL5/6LoeYcsY592bukYiaJAck4SXyhV5auMQAAtmNdC5nySwKeqzH2U3KBQKxevLcMau6U9erKOrDmBqr+5Va5UMuBAlLfUJSt264kEiVq2RLpv6We1RFs5ufFXHVUw/lCeuUCgUr5FyM5KyifUk5FyPkF47Lx7UBZniTjXUgFLbUmo12ZN5h6aSKE01miaY3RgiY7vkpAQhCAcNZrfHaIwfGHXglYHObu5juqKMuEKhULxG5nXEWN+XrlmWKriYIVF5uZaNTENAZ7jg1hjyek522QOvpi9doCls1u0dLoQgFjRZOq8JXfUDP+BQRlyhUCheI3PaYzRGBhjOjobVPaA/5xAxNSKGjiHAMnUObk/Q2Rph044MG/vSuK6kJRHANDSe2Tg86XGyRY8NgznmNIXGGXIhBDNmJpQBP0BRRlyhUCheI7omOOOobu5cvZUdqTzlai5PgisEK5Z20t5Qq6l+WCTAYXOaKj/bRZcN29NkCw5S+oOAeh73YK5IutehJWIRDRi0JILEogHiieABbcBVdrpCoVAoXjPhgME/rpjF9uEcm/szuJ6kLRFiTlsUTdu5dbBMnbcv7+bep3oYSBX8jHQJOuMNue1KelIF5nRYzJqlktbggBdsU0ZcoVAodhchBB2NYToawztfuQ7RkMmZb5xFfypP71AO6Ul6B7MMlox6WbpVAm0NIZYvaN2DZ6+YzigjrlAoFFMAIQStiRCtCT/8ftjcJrYNZFm3LUW24BCyDOZ1xulsCdcVlDlgOcDj6cqIKxQKxRRECEFXS4Sulsi+PpUpzYEeTj9wsyEUCoVCoZjmKE9coVAoFNOXA9wVV564QqFQKKYtYg/991q58sorEUJwySWXVJZJKbn88svp6uoiFApx/PHH88wzz+yBqx2PMuIKhUKhULwGHnvsMa677jqWLl1as/wb3/gGV111Fddccw2PPfYYHR0dnHzyyYyMjOzxc1BGXKFQKBTTlnJy+u5+Xi3pdJr3v//9XH/99TQ2jtbsSym5+uqr+fznP8/ZZ5/NkiVLuOmmm8hms/ziF7/Yg1fuo4y4QqFQKBRAKpWq+RQKhQnXvfDCCznjjDM46aSTapavX7+e3t5eTjnllMqyQCDAcccdx8MPP7zHz1kZcYVCoVBMW/akJ97d3U0ikah8rrzyyrrHvPnmm1m9enXd73t7ewFob2+vWd7e3l75bk+istMVCoVCoQA2b95MPB6v/BwIBOqu88lPfpK77rqLYHDidq9jJXPr6eHvCZQRVygUCsU0Zs/VmMXj8RojXo9Vq1bR19fHkUceWVnmui4PPvgg11xzDS+88ALge+SdnZ2Vdfr6+sZ553sCFU5XKBQKxbTl9U5sO/HEE1m7di1r1qypfJYvX8773/9+1qxZw7x58+jo6ODuu++ubGPbNg888ADHHHPMHr9+5YlPMWSpKXEqldrHZ6JQKBSvnfI7rPxO29vHeb32EYvFWLJkSc2ySCRCc3NzZfkll1zCypUrmT9/PvPnz2flypWEw2He97737fa5jkUZ8SlGuY6wu7t7H5+JQqFQ7D4jIyMkEok9vl/Lsujo6KB7/tw9sr+Ojg4sy9oj+7r00kvJ5XJccMEFDA0NcfTRR3PXXXcRi8X2yP6rEXJvD5MUrwrP8+jp6SEWi+2VJIg9QSqVoru7e1wSyIGEugfqHhzo1w+T3wMpJSMjI3R1daFpe2fmNp/PY9v2HtmXZVmTJqpNVZQnPsXQNI2ZM2fu69PYJXYlCWR/R90DdQ8O9OuHie/B3vDAqwkGg9PS8O5JVGKbQqFQKBTTFGXEFQqFQqGYpigjrnjVBAIBvvSlL9UVQjhQUPdA3YMD/fpB3YOpgEpsUygUCoVimqI8cYVCoVAopinKiCsUCoVCMU1RRlyhUCgUimmKMuIKhUKhUExTlBHfD5kzZw5CiHGfCy+8cNy6H/vYxxBCcPXVV9csLxQKfOITn6ClpYVIJMJZZ53Fli1bdnqcz372szXrbNq0iTPPPJNIJEJLSwsXX3zxOIWltWvXctxxxxEKhZgxYwZXXHHFbuktT6Xrr3ceP/zhD/fq9b+e9wDgj3/8I0cffTShUIiWlhbOPvvsmu/3xTMw1e7B/vwc3H///XWPIYTgscceq6y3r56D/R6p2O/o6+uT27Ztq3zuvvtuCcj77ruvZr1bb71VHn744bKrq0t++9vfrvnu/PPPlzNmzJB33323XL16tTzhhBPk4YcfLh3Hqawze/ZsecUVV9Qca2RkpPK94zhyyZIl8oQTTpCrV6+Wd999t+zq6pIXXXRRZZ1kMinb29vle9/7Xrl27Vr529/+VsZiMfnNb35z2l+/lFIC8sYbb6xZJ5vN7tXrfz3vwW9+8xvZ2Ngor732WvnCCy/I559/Xt5yyy2V7/fVMzCV7oGU+/dzUCgUao6xbds2+dGPflTOmTNHep4npdy3z8H+jjLiBwCf/OQn5UEHHVT5hZJSyi1btsgZM2bIp59+Ws6ePbvmF3d4eFiapilvvvnmyrKtW7dKTdPkn//858qysduN5U9/+pPUNE1u3bq1suyXv/ylDAQCMplMSiml/MEPfiATiYTM5/OVda688krZ1dVVc767w766fin9l/ett9464fevx/VLuXfuQbFYlDNmzJA//vGPJzzuVHkGpNx390DK/fs5GItt27KtrU1eccUVlWVT6TnY31Dh9P0c27b52c9+xkc+8pFKQxXP8/jgBz/If/zHf7B48eJx26xatYpiscgpp5xSWdbV1cWSJUt4+OGHa9b9+te/TnNzM8uWLeNrX/taTXjskUceYcmSJXR1dVWWnXrqqRQKBVatWlVZ57jjjqsRizj11FPp6elhw4YN0/r6y1x00UW0tLRw1FFH8cMf/hDP8yrf7e3rh713D1avXs3WrVvRNI0jjjiCzs5OTj/9dJ555pma69vXz8C+vgdl9tfnYCy33XYb/f39fOhDH6q5vqnwHOyPKCO+n/P73/+e4eHhml+or3/96xiGwcUXX1x3m97eXizLorGxsWZ5e3s7vb29lZ8/+clPcvPNN3Pfffdx0UUXcfXVV3PBBRfU7Ke9vb1mH42NjViWVdlPvXXKP1cf67WyL68f4Ctf+Qq33HIL99xzD+9973v5zGc+w8qVK2uOtTevH/bePVi3bh0Al19+OV/4whe4/fbbaWxs5LjjjmNwcHDC63u9nwHYt/cA9u/nYCw33HADp556ak075anyHOyPqC5m+zk33HADp59+emUEvGrVKr7zne+wevXqV93qVEpZs82nPvWpyt+XLl1KY2Mj73znOyveKVD3GGP3M3YdWUpk2ROtWPf19X/hC1+orLNs2TIArrjiiprle/P6Ye/dg7In+fnPf55zzjkHgBtvvJGZM2dyyy238LGPfWzC63g9nwHY9/dgf34OqtmyZQt33nknv/71r8d9NxWeg/0R5Ynvx2zcuJF77rmHj370o5VlDz30EH19fcyaNQvDMDAMg40bN/KZz3yGOXPmANDR0YFt2wwNDdXsr6+vb9xIuZoVK1YA8PLLL1f2M3YEPTQ0RLFYrOyn3jp9fX0Akx5rV9jX1z/ROqlUiu3bt1eOtbeuH/buPejs7ATg0EMPrXwfCASYN28emzZtmvD6Xs9nAPb9PajH/vQcVHPjjTfS3NzMWWedVbN8KjwH+y37ZCZe8brwpS99SXZ0dMhisVhZ1t/fL9euXVvz6erqkpdddpl8/vnnpZSjySy/+tWvKtv19PRMmswipZR/+MMfJCA3btwopRxNZunp6amsc/PNN49LZmloaJCFQqGyzn/913/tkWSWfX399fje974ng8FgJXlnb16/lHv3HiSTSRkIBGqSuspJTT/60Y+klPv+GZgK96Ae+9NzUMbzPDl37lz5mc98Ztzxp8JzsL+ijPh+iuu6ctasWfKyyy7b6br1sqzPP/98OXPmTHnPPffI1atXy7e97W01ZSUPP/ywvOqqq+QTTzwh161bJ3/1q1/Jrq4uedZZZ1X2US4rOfHEE+Xq1avlPffcI2fOnFlTVjI8PCzb29vlueeeK9euXSt/97vfyXg8vttlJVPh+m+77TZ53XXXybVr18qXX35ZXn/99TIej8uLL754r1//63EPpPSznWfMmCHvvPNO+fzzz8t//dd/lW1tbXJwcFBKuW+fgalyDw6E50BKKe+55x4JyGeffXbcfvf1c7A/o4z4fsqdd94pAfnCCy/sdN16v7i5XE5edNFFsqmpSYZCIfmOd7xDbtq0qfL9qlWr5NFHHy0TiYQMBoNywYIF8ktf+pLMZDI1+9m4caM844wzZCgUkk1NTfKiiy6qKSGRUsqnnnpKvuUtb5GBQEB2dHTIyy+/fLdH3lPh+u+44w65bNkyGY1GZTgclkuWLJFXX311jTe0t65fyr1/D6T0vc7PfOYzsq2tTcZiMXnSSSfJp59+umadffUMSDk17sGB8BxIKeW5554rjznmmAn3vS+fg/0Z1YpUoVAoFIppikpsUygUCoVimqKMuEKhUCgU0xRlxBUKhUKhmKYoI65QKBQKxTRFGXGFQqFQKKYpyogrFAqFQjFNUUZcoVAoFIppijLiCsV+xvHHH88ll1yy148zZ84chBAIIRgeHt6tfR1//PGVfa1Zs2aPnJ9CcSCgjLhCsZf40Ic+VDFM1Z/TTjttj+z//vvvr2tAf/e73/GVr3xljxxjZ1xxxRVs27aNRCKxW/v53e9+x6OPPrqHzkqhOHBQrUgVir3Iaaedxo033lizLBAI7NVjNjU17dX9VxOLxejo6Njt/TQ1NZFKpfbAGSkUBxbKE1co9iKBQICOjo6aT2NjY+X7q666isMOO4xIJEJ3dzcXXHAB6XS68v3GjRs588wzaWxsJBKJsHjxYv70pz+xYcMGTjjhBAAaGxsRQvChD30IGB9OnzNnDitXruQjH/kIsViMWbNmcd1119Wc58MPP8yyZcsIBoMsX76c3//+968ptP0///M/NDQ0cPvtt7NgwQLC4TDvfOc7yWQy3HTTTcyZM4fGxkY+8YlP4Lruq7uZCoViHMoTVyj2IZqm8d3vfpc5c+awfv16LrjgAi699FJ+8IMfAHDhhRdi2zYPPvggkUiEZ599lmg0Snd3N7/97W8555xzeOGFF4jH44RCoQmP861vfYuvfOUr/Od//ie/+c1v+PjHP85b3/pWFi5cyMjICGeeeSZvf/vb+cUvfsHGjRt3a049m83y3e9+l5tvvpmRkRHOPvtszj77bBoaGvjTn/7EunXrOOecc3jzm9/Me97zntd8HIVCoYy4QrFXuf3224lGozXLLrvsMv7f//t/ADXGcu7cuXzlK1/h4x//eMWIb9q0iXPOOYfDDjsMgHnz5lXWL4fN29raaGhomPQ83v72t3PBBRdUjv/tb3+b+++/n4ULF/Lzn/8cIQTXX389wWCQQw89lK1bt3Leeee9pmsuFotce+21HHTQQQC8853v5Kc//Snbt28nGo1y6KGHcsIJJ3DfffcpI65Q7CbKiCsUe5ETTjiBa6+9tmZZ9Zz1fffdx8qVK3n22WdJpVI4jkM+nyeTyRCJRLj44ov5+Mc/zl133cVJJ53EOeecw9KlS1/1eVRvI4Sgo6ODvr4+AF544QWWLl1KMBisrPPGN77xVR+jTDgcrhhwgPb2dubMmVMzmGlvb68cX6FQvHbUnLhCsReJRCIcfPDBNZ+yEd+4cSNvf/vbWbJkCb/97W9ZtWoV3//+9wHfmwX46Ec/yrp16/jgBz/I2rVrWb58Od/73vde9XmYplnzsxACz/MAkFIihKj5fnc6FNc71mTHVygUrx1lxBWKfcTjjz+O4zh861vfYsWKFRxyyCH09PSMW6+7u5vzzz+f3/3ud3zmM5/h+uuvB8CyLIDdThBbuHAhTz31FIVCoebcFArF1EcZcYViL1IoFOjt7a359Pf3A3DQQQfhOA7f+973WLduHT/96U/54Q9/WLP9JZdcwp133sn69etZvXo19957L4sWLQJg9uzZCCG4/fbb2bFjR01W+6vhfe97H57n8W//9m8899xz3HnnnXzzm98EGOehKxSKqYUy4grFXuTPf/4znZ2dNZ83v/nNACxbtoyrrrqKr3/96yxZsoSf//znXHnllTXbu67LhRdeyKJFizjttNNYsGBBJeltxowZfPnLX+azn/0s7e3tXHTRRa/pHOPxOH/4wx9Ys2YNy5Yt4/Of/zxf/OIXAWrmyRUKxdRDyN2Z/FIoFPslP//5z/nwhz9MMpmcsHRtzpw5XHLJJXtM4nXDhg3MnTuXJ554gmXLlu2RfSoU+zvKE1coFPzkJz/hL3/5C+vXr+f3v/89l112Ge9+97snrT0Hv1wtGo2STCZ36/inn346ixcv3q19KBQHIqrETKFQ0Nvbyxe/+EV6e3vp7OzkXe96F1/72tcm3eaBBx6oZNHHYrHdOv6Pf/xjcrkcALNmzdqtfSkUBxIqnK5QKBQKxTRFhdMVCoVCoZimKCOuUCgUCsU0RRlxhUKhUCimKcqIKxQKhUIxTVFGXKFQKBSKaYoy4gqFQqFQTFOUEVcoFAqFYpqijLhCoVAoFNMUZcQVCoVCoZim/P8fAJIUR0+shwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the Matplotlib Axes object from the dataframe object, color the points by snow depth value\n",
+    "ax = df.plot(column='value', legend=True, cmap='PuBu')\n",
+    "site_df.plot(ax=ax, marker='^', color='m')\n",
+    "\n",
+    "# Use non-scientific notation for x and y ticks\n",
+    "ax.ticklabel_format(style='plain', useOffset=False)\n",
+    "\n",
+    "# Set the various plots x/y labels and title.\n",
+    "ax.set_title(f'{len(df.index)} Manual Snow depths collected at {site_id}')\n",
+    "ax.set_xlabel('Easting [m]')\n",
+    "ax.set_ylabel('Northing [m]')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Try This:**\n",
+    "\n",
+    "A. Go back and add a filter to reduce to just one spiral. What would you change to reduce this?\n",
+    "\n",
+    "B. Try to filtering to add more spirals. What happens?\n",
+    "\n",
+    "\n",
+    "## Recap \n",
+    "You just plotted snow depths and reduce the scope of the data by using `from_area` on it\n",
+    "\n",
+    "**You should know:**\n",
+    "\n",
+    "* Manual depths are neat.\n",
+    "* filter using from area is pretty slick.\n",
+    "* We can use LayerMeasurements to get site details easily. \n",
+    "\n",
+    "\n",
+    "If you don't feel comfortable with these, you are probably not alone, let's discuss it!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/book/tutorials/snowex_database/5_plot_raster_example.ipynb b/book/tutorials/snowex_database/5_plot_raster_example.ipynb
new file mode 100644
index 0000000..e55fbda
--- /dev/null
+++ b/book/tutorials/snowex_database/5_plot_raster_example.ipynb
@@ -0,0 +1,338 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Forming Queries with Rasters\n",
+    "Querying the database with rasters is essentially the same as with the other two tables. A primary difference however is they are returned as a [rasterio dataset](https://rasterio.readthedocs.io/en/stable/api/rasterio.io.html#rasterio.io.DatasetReader) instead of a dataframe.\n",
+    "   \n",
+    "## Grab a whole raster. \n",
+    "Grabbing whole rasters can be done (albeit with caution) using the `from_filter` function. \n",
+    "\n",
+    "**Note**: snowexsql will throw an error if you try to pull more than one dataset at a time. This is because this function is merging tiles together based on the query and if the dataset grids dont match the database throws a cryptic error. So we took the liberty ahead of time. \n",
+    "\n",
+    "**Try this** : To see the error in action, remove the date from the query and run it. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(50.000101089121436, 50.000101089121436)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import in the raster measurments class\n",
+    "from snowexsql.api import RasterMeasurements\n",
+    "from datetime import datetime \n",
+    "\n",
+    "# Pick a date\n",
+    "dt = datetime(2020, 2, 13)\n",
+    "\n",
+    "# Query db filtering to swe on a certain date surveyed by ASO\n",
+    "ds = RasterMeasurements.from_filter(observers='ASO Inc.', date=dt, type='swe')\n",
+    "\n",
+    "# Plot it up!\n",
+    "show(ds[0], vmin=0.1, vmax=0.4, cmap='winter')\n",
+    "\n",
+    "# Note the resolution!\n",
+    "ds[0].res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Let's get part of raster dataset centered on a point\n",
+    "\n",
+    "More reasonably, we often want chucks of rasters given an point or area of interest. Below is an example of how to do this off of a point. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3.000000000007304, 3.000000000007304)\n",
+      "[datetime.date(2020, 2, 2), datetime.date(2020, 2, 13)]\n"
+     ]
+    },
+    {
+     "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>depth</th>\n",
+       "      <th>site_id</th>\n",
+       "      <th>pit_id</th>\n",
+       "      <th>bottom_depth</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>sample_a</th>\n",
+       "      <th>sample_b</th>\n",
+       "      <th>sample_c</th>\n",
+       "      <th>value</th>\n",
+       "      <th>flags</th>\n",
+       "      <th>...</th>\n",
+       "      <th>date</th>\n",
+       "      <th>time_created</th>\n",
+       "      <th>time_updated</th>\n",
+       "      <th>id</th>\n",
+       "      <th>doi</th>\n",
+       "      <th>date_accessed</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>observers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1N3</td>\n",
+       "      <td>COGM1N3_20200211</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>242.5</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-02-11</td>\n",
+       "      <td>2024-08-15 19:24:19.860106+00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2308829</td>\n",
+       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>None</td>\n",
+       "      <td>density</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   depth site_id            pit_id  bottom_depth comments sample_a sample_b  \\\n",
+       "0   67.0     1N3  COGM1N3_20200211          57.0     None     None     None   \n",
+       "\n",
+       "  sample_c  value flags  ...        date                     time_created  \\\n",
+       "0     None  242.5  None  ...  2020-02-11 2024-08-15 19:24:19.860106+00:00   \n",
+       "\n",
+       "   time_updated       id                                   doi  date_accessed  \\\n",
+       "0          None  2308829  https://doi.org/10.5067/DUD2VZEVBJ7S     2022-06-30   \n",
+       "\n",
+       "  instrument     type units observers  \n",
+       "0       None  density  None      None  \n",
+       "\n",
+       "[1 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import in the Raster and Layer measurement classes\n",
+    "from snowexsql.api import RasterMeasurements, LayerMeasurements\n",
+    "from datetime import date\n",
+    "from rasterio.plot import show\n",
+    "\n",
+    "# Pick a site ID\n",
+    "site_id = '1N3'\n",
+    "df_site = LayerMeasurements.from_filter(site_id=site_id, limit=1)\n",
+    "\n",
+    "# Grab available dates\n",
+    "dates = RasterMeasurements.from_unique_entries([\"date\"], observers='ASO Inc.', type='depth')\n",
+    "dt = dates[0]\n",
+    "\n",
+    "# Subset a raster on our buffered point!\n",
+    "ds = RasterMeasurements.from_area(pt=df_site.geometry[0], buffer=100, observers='ASO Inc.', type='depth', \n",
+    "                                  date=dt)\n",
+    "\n",
+    "# Plot it up!\n",
+    "show(ds, vmin=0, vmax=1, cmap='winter')\n",
+    "\n",
+    "# Note the resolution\n",
+    "print(ds.res)\n",
+    "\n",
+    "# Show the site df and available dates. \n",
+    "print(dates)\n",
+    "\n",
+    "# Show off the location dataframe\n",
+    "df_site"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lidar snow depth = 0.74m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Demo a useful function from rasterio! \n",
+    "\n",
+    "# grab the xy as a tuple e.g. (x,y)\n",
+    "xy = (df_site.geometry[0].x,  df_site.geometry[0].y)\n",
+    "\n",
+    "# Use the rasterio sample function and the grab the sample\n",
+    "sd = [s[0] for s in ds.sample([xy])][0]\n",
+    "\n",
+    "# Print it out nice and neat!\n",
+    "print(f\"Lidar snow depth = {sd:0.2f}m\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from snowexsql.api import RasterMeasurements, LayerMeasurements\n",
+    "from shapely.geometry import Polygon\n",
+    "import geopandas as gpd\n",
+    "\n",
+    "\n",
+    "# Lets form a triangle using site IDs\n",
+    "site_id = ['2S6', '2C2', '8N45']\n",
+    "\n",
+    "# Grab the unique locations for these \n",
+    "locations = LayerMeasurements.from_unique_entries(['easting', 'northing'],\n",
+    "                                                site_id=site_id)\n",
+    "\n",
+    "# Form a polygon object\n",
+    "triangle = Polygon(locations)\n",
+    "\n",
+    "# Query the db for raster data in the triangle\n",
+    "ds = RasterMeasurements.from_area(shp=triangle, observers='ASO Inc.', type='depth',\n",
+    "                                  date=date(2020, 2, 2))\n",
+    "# plot it up!\n",
+    "show(ds, vmin=0, vmax=1, cmap='winter')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Recap\n",
+    "Isolating raster datasets can enable users to build out workflows using only data of interest! No more downloading massive datasets (unless you want to!) \n",
+    "\n",
+    "**You should know something about**\n",
+    "* How `RasterMeasurements.from_*` differ from `PointMeasurements.from*` or `LayerMeasurements.from*`\n",
+    "* Raster with too coarse of filtering were error out due to too many datasets.\n",
+    "* Rasterio datasets offer a useful sample function for point extraction\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/book/tutorials/snowex_database/6_wrap_up.ipynb b/book/tutorials/snowex_database/6_wrap_up.ipynb
new file mode 100644
index 0000000..4bd370d
--- /dev/null
+++ b/book/tutorials/snowex_database/6_wrap_up.ipynb
@@ -0,0 +1,70 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "approximate-selling",
+   "metadata": {},
+   "source": [
+    "# Wrap up\n",
+    "\n",
+    "1. SnowEx database is going to save you time and frustration.\n",
+    "2. Its structured into 4 tables, points, layers, rasters and site data\n",
+    "3. Forming queries is best done using the new API tools `PointMeasurements`, `LayerMeasurements`, `RasterMeasurements` and their functions `from_filter` and ` from_area`\n",
+    "\n",
+    "\n",
+    "## Community Software\n",
+    "\n",
+    "* Open Source software means you can participate! Checkout the repos involved:\n",
+    "  1. [snowexsql](https://github.com/SnowEx/snowexsql) - Access tool for querying the database.\n",
+    "  2. [snowex_db](https://github.com/SnowEx/snowex_db) - Source code for managing the db\n",
+    "  3. [insitupy](https://github.com/M3Works/insitupy) - **NEW** python package for reading insitu measurements files\n",
+    "     \n",
+    "* Can I work locally? Yep! Checkout the [python installation on RTD](https://snowexsql.readthedocs.io/en/latest/installation.html#python) If you are just doing python development you do not need to install the database.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "compact-mills",
+   "metadata": {},
+   "source": [
+    "## Acknowlegdments\n",
+    "\n",
+    "Big thanks to all the dedicated scientists who went out and collected these invaluable datasets. \n",
+    "\n",
+    "A huge thanks to HP Marshall who originally gave us the opportunity to build this a couple years ago. And thanks to Joe Meyer who pursued funding to develop it futher. Buy them a beer or better yet volunteer for field work to hang out with them! \n",
+    "\n",
+    "❄️ Go forth and snow science! ❄️"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df844503-99b5-4f74-9eea-2b0753062012",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/book/tutorials/snowex_database/images/structure.png b/book/tutorials/snowex_database/images/structure.png
new file mode 100644
index 0000000..3e36466
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diff --git a/book/tutorials/snowex_database/index.md b/book/tutorials/snowex_database/index.md
new file mode 100644
index 0000000..b28875c
--- /dev/null
+++ b/book/tutorials/snowex_database/index.md
@@ -0,0 +1,4 @@
+# SnowEx Database 
+
+```{tableofcontents}
+```
diff --git a/conda/environment.yml b/conda/environment.yml
index 80a90db..ff006f7 100644
--- a/conda/environment.yml
+++ b/conda/environment.yml
@@ -57,6 +57,7 @@ dependencies:
   # JupyterBook Addons
   - sphinx~=7.3
   - sphinxcontrib-bibtex
+
   # dashboards
   - voila~=0.5
 
@@ -142,7 +143,7 @@ dependencies:
   - itslive~=0.3.2
   - is2view~=0.0.8
   - sliderule~=4.5
-  - snowexsql~=0.4
+  - snowexsql~=0.5
 
   # Desktop tools whose versions are more recent on conda-forge than ubuntu
   #- qgis~=3.38.0