{
 "cells": [
  {
   "attachments": {
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   "metadata": {},
   "source": [
    "# 📍 Pick Points\n",
    "\n",
    "## Extract Model Grid Data at Specific Points with Herbie\n",
    "\n",
    "**Extract weather model data at point locations | Interpolate gridded data | Sample NWP grids**\n",
    "\n",
    "Herbie's `pick_points` xarray accessor extracts values from weather and climate model grids at specific latitude/longitude locations. Whether you need to sample HRRR, GFS, or other numerical weather prediction (NWP) model output at station locations, city coordinates, or arbitrary points, this tool provides efficient nearest-neighbor and distance-weighted interpolation.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "## What you'll learn: Extract and Interpolate Model Data\n",
    "\n",
    "In this tutorial, you'll learn how to:\n",
    "- **Extract point values** from gridded model data (HRRR, GFS, NAM, etc.)\n",
    "- **Sample weather models** at specific latitude/longitude coordinates\n",
    "- Work with both **regular grids** (lat/lon) and **curvilinear grids** (Lambert Conformal, polar stereographic)\n",
    "- Use **nearest-neighbor** vs **distance-weighted interpolation** methods\n",
    "- Create **time series** from model forecasts at station locations\n",
    "- Extract **vertical profiles** (model soundings) at points\n",
    "- **Interpolate GRIB2 data** to observation sites\n",
    "- Optimize performance for extracting hundreds or thousands of points\n",
    "- Cache spatial indices (BallTree) for faster repeated queries\n",
    "\n",
    "### Common Use Cases\n",
    "- Compare model output to weather station observations\n",
    "- Extract forecast data for specific cities or locations\n",
    "- Create model soundings at radiosonde sites\n",
    "- Sample gridded forecasts along flight paths or trajectories\n",
    "- Validate NWP models against point measurements\n",
    "- Extract model data for machine learning training datasets\n",
    "\n",
    "**Requirements:** This tutorial requires the `scikit-learn` package for spatial indexing:\n",
    "```bash\n",
    "pip install scikit-learn\n",
    "# or install with all Herbie extras\n",
    "pip install 'herbie-data[extras]'\n",
    "```\n",
    "\n",
    "## How it Works: Spatial Indexing with BallTree\n",
    "\n",
    "Herbie uses **scikit-learn's BallTree algorithm** with the **haversine formula** for accurate nearest-neighbor queries on Earth's spherical surface. This approach works for any model grid projection without requiring coordinate transformations.\n",
    "\n",
    "### Advantages over other methods:\n",
    "- ✅ Works with **curvilinear/projected grids** (HRRR, NAM, WRF)\n",
    "- ✅ Handles **longitude convention differences** (0-360° vs ±180°)\n",
    "- ✅ Returns **distance to nearest grid points** (great-circle distance in km)\n",
    "- ✅ Supports **k-nearest neighbors** for interpolation and uncertainty quantification\n",
    "- ✅ **Fast queries** even for thousands of points\n",
    "- ✅ No coordinate transformation required\n",
    "\n",
    "## Background: When to Use Pick Points\n",
    "\n",
    "For regular latitude-longitude grids (e.g., GFS, IFS), you could use xarray's [advanced indexing](https://docs.xarray.dev/en/stable/user-guide/indexing.html#more-advanced-indexing) to select your points of interest. However, `pick_points` is necessary when:\n",
    "\n",
    "- **Curvilinear grids**: Models like HRRR use Lambert Conformal or other map projections\n",
    "- **Longitude conventions differ**: Model uses [0, 360) but your points are in [-180, 180)\n",
    "- **Distance matters**: You need to know how far the nearest grid point is from your location\n",
    "- **Multiple neighbors**: You want k-nearest points for interpolation or statistics\n",
    "- **Performance**: Extracting many points at once (spatial indexing is very fast)\n",
    "- **Batch processing**: Repeatedly querying the same grid (BallTree caching)\n",
    "\n",
    "<details>\n",
    "<summary><b>Development History: Why I Built This</b></summary>\n",
    "\n",
    "Picking values at nearest-neighbor points in a [curvilinear grid](https://en.wikipedia.org/wiki/Curvilinear_coordinates) has been one of my longstanding challenges. In November 2019, I asked [How to select the nearest lat/lon location with multi-dimension coordinates](https://stackoverflow.com/q/58758480/2383070) on Stack Overflow, which has had over 28k views. My initial solution found the minimum distance between points and grid cells, but it didn't scale well for many points.\n",
    "\n",
    "I iterated through several approaches:\n",
    "\n",
    "**Previous approach (deprecated):** `ds.herbie.nearest_points` used MetPy's [assign_y_x](https://unidata.github.io/MetPy/latest/tutorials/xarray_tutorial.html?highlight=assign_y_x) to transform coordinates to the model's projection (e.g., Lambert Conformal for HRRR), then selected nearest neighbors. This worked but required Cartopy coordinate transformations, and not all datasets have sufficient projection metadata.\n",
    "\n",
    "**Current approach:** Using BallTree with haversine distance:\n",
    "- ✅ No coordinate transformation needed\n",
    "- ✅ Can extract k-nearest neighbors\n",
    "- ✅ Returns actual distances (km) using haversine formula\n",
    "- ✅ Supports inverse-distance weighted interpolation\n",
    "- ✅ Much faster for bulk queries\n",
    "\n",
    "I later discovered the [xoak](https://xoak.readthedocs.io/) package, which also uses BallTree for nearest neighbors.\n",
    "\n",
    "</details>\n",
    "\n",
    "---\n",
    "\n",
    "## Getting Started\n",
    "\n",
    "To use Herbie's xarray accessors, just import Herbie (the accessor registers automatically):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "import herbie\n",
    "from herbie import FastHerbie, Herbie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Very Simple Demonstration\n",
    "\n",
    "First, I'll show how Herbie extracts nearest points from a simple grid. Let's make a 3x3 xarray Dataset with latitude/longitude coordinates and then extract data nearest two points of interest.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 120B\n",
       "Dimensions:    (latitude: 3, longitude: 3)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) int64 24B 44 45 46\n",
       "  * longitude  (longitude) int64 24B -99 -100 -101\n",
       "Data variables:\n",
       "    a          (latitude, longitude) int64 72B 0 1 2 0 1 0 0 0 0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a9eb8748-3f27-4531-b54f-db736288e4d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a9eb8748-3f27-4531-b54f-db736288e4d4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 3</li><li><span class='xr-has-index'>longitude</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dae6d767-1bc7-4b69-9a01-2d02a51ff36f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dae6d767-1bc7-4b69-9a01-2d02a51ff36f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>44 45 46</div><input id='attrs-afe37a9a-e0a9-4fc4-98a7-8cd984b4da52' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afe37a9a-e0a9-4fc4-98a7-8cd984b4da52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ea9dfbb-efe7-42c1-ad7c-22c888c02358' class='xr-var-data-in' type='checkbox'><label for='data-6ea9dfbb-efe7-42c1-ad7c-22c888c02358' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([44, 45, 46])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-99 -100 -101</div><input id='attrs-2d5e4a46-d139-4c99-9af5-69f167aa3462' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d5e4a46-d139-4c99-9af5-69f167aa3462' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d18760ab-0cef-4cd1-8e51-4daa720f4475' class='xr-var-data-in' type='checkbox'><label for='data-d18760ab-0cef-4cd1-8e51-4daa720f4475' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ -99, -100, -101])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1594a60b-c758-4bf7-a172-070df22d5516' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1594a60b-c758-4bf7-a172-070df22d5516' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>a</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 0 1 0 0 0 0</div><input id='attrs-46e575b0-a7b3-409a-9f98-1bc0d8d85725' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46e575b0-a7b3-409a-9f98-1bc0d8d85725' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d943ee9c-842b-452b-8249-d34ffddb14cf' class='xr-var-data-in' type='checkbox'><label for='data-d943ee9c-842b-452b-8249-d34ffddb14cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0, 1, 2],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 0]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e211b1a7-a386-483f-b00e-3574579fc9e8' class='xr-section-summary-in' type='checkbox'  ><label for='section-e211b1a7-a386-483f-b00e-3574579fc9e8' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5b2fba17-2956-406c-bb9a-df50b5929bde' class='xr-index-data-in' type='checkbox'/><label for='index-5b2fba17-2956-406c-bb9a-df50b5929bde' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([44, 45, 46], dtype=&#x27;int64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8d5e4d64-2297-4a0f-93bd-33ce6558d29f' class='xr-index-data-in' type='checkbox'/><label for='index-8d5e4d64-2297-4a0f-93bd-33ce6558d29f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-99, -100, -101], dtype=&#x27;int64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77f34bb7-4348-4dbd-bb9b-2a864c409e8b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-77f34bb7-4348-4dbd-bb9b-2a864c409e8b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 120B\n",
       "Dimensions:    (latitude: 3, longitude: 3)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) int64 24B 44 45 46\n",
       "  * longitude  (longitude) int64 24B -99 -100 -101\n",
       "Data variables:\n",
       "    a          (latitude, longitude) int64 72B 0 1 2 0 1 0 0 0 0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 3x3 xarray dataset\n",
    "ds = xr.Dataset(\n",
    "    {\"a\": ([\"latitude\", \"longitude\"], [[0, 1, 2], [0, 1, 0], [0, 0, 0]])},\n",
    "    coords={\n",
    "        \"latitude\": ([\"latitude\"], [44, 45, 46]),\n",
    "        \"longitude\": ([\"longitude\"], [-99, -100, -101]),\n",
    "    },\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The points you want to extract must be given as a Pandas DataFrame with columns `latitude` and `longitude` given in degrees.\n"
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-100.25</td>\n",
       "      <td>44.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-99.40</td>\n",
       "      <td>45.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude\n",
       "0    -100.25     44.25\n",
       "1     -99.40     45.40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We want to pick data closest to two points\n",
    "points = pd.DataFrame(\n",
    "    {\n",
    "        \"longitude\": [-100.25, -99.4],\n",
    "        \"latitude\": [44.25, 45.4],\n",
    "    }\n",
    ")\n",
    "points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can pick the nearest grid points with the custom Herbie xarray accessor.\n",
    "\n",
    "> Note `method='nearest'` is the default behavior.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: BallTree caching disabled - tree_name not specified.\n",
      "         Provide tree_name parameter to enable caching.\n",
      "INFO: 🌱 Growing new BallTree...🌳 Complete in 0.01s\n"
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    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 96B\n",
       "Dimensions:              (point: 2)\n",
       "Coordinates:\n",
       "    latitude             (point) int64 16B 44 45\n",
       "    longitude            (point) int64 16B -100 -99\n",
       "    point_grid_distance  (point) float64 16B 34.22 54.41\n",
       "    point_longitude      (point) float64 16B -100.2 -99.4\n",
       "    point_latitude       (point) float64 16B 44.25 45.4\n",
       "Dimensions without coordinates: point\n",
       "Data variables:\n",
       "    a                    (point) int64 16B 1 0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-320c6385-9e6b-4072-bd74-f303c6bbd66f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-320c6385-9e6b-4072-bd74-f303c6bbd66f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>point</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b1cd2061-0320-4b4e-8ad2-262af63f79ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1cd2061-0320-4b4e-8ad2-262af63f79ec' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>44 45</div><input id='attrs-26e2c594-9c33-43d7-a943-92b703a26ad5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26e2c594-9c33-43d7-a943-92b703a26ad5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da29ba2e-d018-4846-af74-5006ec190c48' class='xr-var-data-in' type='checkbox'><label for='data-da29ba2e-d018-4846-af74-5006ec190c48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([44, 45])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-100 -99</div><input id='attrs-414bc62d-b440-48ac-a9d2-64d38d8fbfc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-414bc62d-b440-48ac-a9d2-64d38d8fbfc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b04cd65f-b7e0-495f-8b9f-dc14a2681f50' class='xr-var-data-in' type='checkbox'><label for='data-b04cd65f-b7e0-495f-8b9f-dc14a2681f50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-100,  -99])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>34.22 54.41</div><input id='attrs-44e46b30-1e52-4ed3-b6c1-a407c8e18e31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44e46b30-1e52-4ed3-b6c1-a407c8e18e31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fe5217c-b6ee-40af-9d87-52385956d72c' class='xr-var-data-in' type='checkbox'><label for='data-2fe5217c-b6ee-40af-9d87-52385956d72c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array([34.21916302, 54.41057983])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-100.2 -99.4</div><input id='attrs-19815642-4c29-40f5-ad89-0d8e4cda9c62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19815642-4c29-40f5-ad89-0d8e4cda9c62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c01c2c92-7317-4a13-b502-9680a4d709cd' class='xr-var-data-in' type='checkbox'><label for='data-c01c2c92-7317-4a13-b502-9680a4d709cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([-100.25,  -99.4 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.25 45.4</div><input id='attrs-1c653175-a26c-49cd-abf9-e390098415c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c653175-a26c-49cd-abf9-e390098415c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f20a264-3aa0-45ab-b612-fbc2ae27e969' class='xr-var-data-in' type='checkbox'><label for='data-8f20a264-3aa0-45ab-b612-fbc2ae27e969' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([44.25, 45.4 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-648dd877-8722-413e-b5f3-36a7fb85bd4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-648dd877-8722-413e-b5f3-36a7fb85bd4c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>a</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 0</div><input id='attrs-86a8669a-a3a4-4c1c-9313-9b1648fba65c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86a8669a-a3a4-4c1c-9313-9b1648fba65c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25c8f0cf-d74d-46fd-b179-d92099419339' class='xr-var-data-in' type='checkbox'><label for='data-25c8f0cf-d74d-46fd-b179-d92099419339' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2921101e-0a45-4004-803f-0b34f241bada' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2921101e-0a45-4004-803f-0b34f241bada' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e633ae44-4dbe-4f24-a903-1324651b7171' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e633ae44-4dbe-4f24-a903-1324651b7171' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 96B\n",
       "Dimensions:              (point: 2)\n",
       "Coordinates:\n",
       "    latitude             (point) int64 16B 44 45\n",
       "    longitude            (point) int64 16B -100 -99\n",
       "    point_grid_distance  (point) float64 16B 34.22 54.41\n",
       "    point_longitude      (point) float64 16B -100.2 -99.4\n",
       "    point_latitude       (point) float64 16B 44.25 45.4\n",
       "Dimensions without coordinates: point\n",
       "Data variables:\n",
       "    a                    (point) int64 16B 1 0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pick the value nearest the requested points\n",
    "matched = ds.herbie.pick_points(points, method=\"nearest\")\n",
    "matched"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can get the inverse-distance weighted mean of the 4 nearest points.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: BallTree caching disabled - tree_name not specified.\n",
      "         Provide tree_name parameter to enable caching.\n",
      "INFO: 🌱 Growing new BallTree...🌳 Complete in 0.00s\n"
     ]
    },
    {
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       "Dimensions:              (point: 2, k: 4)\n",
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       "Data variables:\n",
       "    a                    (point) float64 16B 1.082 0.2588\n",
       "Attributes:\n",
       "    pick_point_method:  weighted\n",
       "    pick_point_k:       4</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3bf96973-ea8e-48ad-9e5a-b657070252ac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3bf96973-ea8e-48ad-9e5a-b657070252ac' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>point</span>: 2</li><li><span>k</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c8b73ec4-e687-42c9-af4e-64bb13f2417f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8b73ec4-e687-42c9-af4e-64bb13f2417f' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-100.2 -99.4</div><input id='attrs-517946db-1f0e-42da-afc5-60e287913387' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-517946db-1f0e-42da-afc5-60e287913387' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7634dc1-a4d2-4b92-8494-45b50045497c' class='xr-var-data-in' type='checkbox'><label for='data-b7634dc1-a4d2-4b92-8494-45b50045497c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([-100.25,  -99.4 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.25 45.4</div><input id='attrs-d199cbc2-9e57-4b8d-8936-bd62e6b0248b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d199cbc2-9e57-4b8d-8936-bd62e6b0248b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19e1b75b-42a8-4d7a-8865-65036f5e51f6' class='xr-var-data-in' type='checkbox'><label for='data-19e1b75b-42a8-4d7a-8865-65036f5e51f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([44.25, 45.4 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>44 45 44 45 45 46 45 46</div><input id='attrs-0de461b2-efdd-4a86-a447-aa38cb6f0d68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0de461b2-efdd-4a86-a447-aa38cb6f0d68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04ae9279-80f7-4197-a481-4eb02c9c366f' class='xr-var-data-in' type='checkbox'><label for='data-04ae9279-80f7-4197-a481-4eb02c9c366f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[44, 45],\n",
       "       [44, 45],\n",
       "       [45, 46],\n",
       "       [45, 46]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-100 -99 -101 ... -99 -101 -100</div><input id='attrs-388d9636-c2c9-448b-9223-c11f33ccfe82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-388d9636-c2c9-448b-9223-c11f33ccfe82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b7d9e3d-3ea1-4d31-9023-d38d7c9ba5cd' class='xr-var-data-in' type='checkbox'><label for='data-8b7d9e3d-3ea1-4d31-9023-d38d7c9ba5cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-100,  -99],\n",
       "       [-101, -100],\n",
       "       [-100,  -99],\n",
       "       [-101, -100]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>34.22 54.41 66.0 ... 102.4 81.38</div><input id='attrs-cc043c88-c479-4825-9481-00781bae3a21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc043c88-c479-4825-9481-00781bae3a21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c1764f7-36f3-4e6d-8e80-cff632839eac' class='xr-var-data-in' type='checkbox'><label for='data-9c1764f7-36f3-4e6d-8e80-cff632839eac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array([[ 34.21916302,  54.41057983],\n",
       "       [ 66.00289505,  64.71693908],\n",
       "       [ 85.71082468,  73.59410018],\n",
       "       [102.36056098,  81.37727042]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4064c7fa-0ef0-478f-8e40-4ec7582cb1af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4064c7fa-0ef0-478f-8e40-4ec7582cb1af' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>a</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.082 0.2588</div><input id='attrs-dc471526-13a4-4d85-8108-b54c2f8c9703' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc471526-13a4-4d85-8108-b54c2f8c9703' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c48b775-af77-42ce-a87f-cd05b92cf417' class='xr-var-data-in' type='checkbox'><label for='data-7c48b775-af77-42ce-a87f-cd05b92cf417' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.08177177, 0.25879479])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd0fc741-7317-42bb-8fbe-c28d1f065704' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fd0fc741-7317-42bb-8fbe-c28d1f065704' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d297e750-d2bf-4757-ad39-ed7278a128fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d297e750-d2bf-4757-ad39-ed7278a128fe' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>pick_point_method :</span></dt><dd>weighted</dd><dt><span>pick_point_k :</span></dt><dd>4</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 240B\n",
       "Dimensions:              (point: 2, k: 4)\n",
       "Coordinates:\n",
       "    point_longitude      (point) float64 16B -100.2 -99.4\n",
       "    point_latitude       (point) float64 16B 44.25 45.4\n",
       "    latitude             (k, point) int64 64B 44 45 44 45 45 46 45 46\n",
       "    longitude            (k, point) int64 64B -100 -99 -101 ... -99 -101 -100\n",
       "    point_grid_distance  (k, point) float64 64B 34.22 54.41 66.0 ... 102.4 81.38\n",
       "Dimensions without coordinates: point, k\n",
       "Data variables:\n",
       "    a                    (point) float64 16B 1.082 0.2588\n",
       "Attributes:\n",
       "    pick_point_method:  weighted\n",
       "    pick_point_k:       4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distance weighted mean\n",
    "matched_w = ds.herbie.pick_points(points, method=\"weighted\")\n",
    "matched_w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the Output\n",
    "\n",
    "The returned Dataset includes several useful coordinates:\n",
    "- `point_latitude`, `point_longitude`: Your requested coordinates\n",
    "- `point_grid_distance`: Distance (km) to the nearest grid point\n",
    "- `latitude`, `longitude`: The actual grid point coordinates\n",
    "- Any additional columns from your points DataFrame (e.g., `point_stid`)\n",
    "\n",
    "The `point` dimension indexes your requested points.\n",
    "\n",
    "That's a lot of info to digest. Let's visualize what we have done with the following figure:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=[13, 5])\n",
    "for p, ax in zip(matched_w.point, axes):\n",
    "    zz = matched.sel(point=p)\n",
    "\n",
    "    # Plot grid\n",
    "    ax.pcolormesh(\n",
    "        ds.longitude, ds.latitude, ds.a, edgecolor=\"1\", lw=1, cmap=\"Pastel2_r\"\n",
    "    )\n",
    "    x, y = np.meshgrid(ds.longitude, ds.latitude)\n",
    "    x = x.flatten()\n",
    "    y = y.flatten()\n",
    "    ax.scatter(x, y, facecolor=\"tab:red\", edgecolor=\"tab:red\", s=100, zorder=100)\n",
    "\n",
    "    # Plot requested point\n",
    "    ax.scatter(\n",
    "        zz.point_longitude,\n",
    "        zz.point_latitude,\n",
    "        color=\"yellow\",\n",
    "        ec=\"k\",\n",
    "        marker=\"p\",\n",
    "        s=300,\n",
    "        zorder=100,\n",
    "    )\n",
    "    for i in ds.latitude:\n",
    "        for j in ds.longitude:\n",
    "            z = ds.sel(latitude=i, longitude=j)\n",
    "            ax.text(j, i, f\"\\na={z.a.item()}\", ha=\"center\", va=\"top\")\n",
    "\n",
    "    # Plot path to nearest point and distance\n",
    "    # for i, j in zip(x, y):\n",
    "    #    ax.plot([i, point.longitude.values[0]], [j, point.latitude.values[0]])\n",
    "    for i in matched_w.k:\n",
    "        if i == 0:\n",
    "            kwargs = dict(lw=2, color=\"k\", ls=\"--\")\n",
    "        else:\n",
    "            kwargs = dict(color=\".5\", ls=\"--\")\n",
    "        z = matched_w.sel(k=i, point=p)\n",
    "        ax.plot(\n",
    "            [z.longitude, z.point_longitude], [z.latitude, z.point_latitude], **kwargs\n",
    "        )\n",
    "        ax.text(\n",
    "            z.longitude,\n",
    "            z.latitude,\n",
    "            f\"  {z.point_grid_distance.item():.1f} km\",\n",
    "            va=\"center\",\n",
    "            fontweight=\"bold\",\n",
    "        )\n",
    "    ax.set_xticks(ds.longitude)\n",
    "    ax.set_yticks(ds.latitude)\n",
    "    ax.set_xlabel(\"Longitude\")\n",
    "    ax.set_ylabel(\"Latitude\")\n",
    "    ax.set_title(f\"Point={p.item()}\", loc=\"left\", fontsize=\"xx-large\")\n",
    "    ax.set_title(\n",
    "        f\"nearest value:  $a={zz.a.item():.2f}$\\nweighted mean:  $a={z.a.item():.2f}$\",\n",
    "        loc=\"right\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The yellow pentagon is the requested point. The grid's nearest-neighbor point is connected by a thick dashed line. The three other nearest neighbors used to compute the distance-weighted mean are connected by a thin dashed line.\n",
    "\n",
    "**Summary:** From a simple 2D Dataset with latitude and longitude coordinates, we got the nearest value for two points of interest. We also compute the inverse-distance weighted mean from the four grids nearest our point of interest.\n"
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    "## Pick points from HRRR data\n",
    "\n",
    "The above is nice, but you might say, \"Yeah, but you can already select data from a grid using [xarray advanced selection](https://docs.xarray.dev/en/stable/user-guide/indexing.html#more-advanced-indexing).\" That is true, but it does not work for models with curvilienar grids, like the HRRR model.\n",
    "\n",
    "Here is a demonstration using real HRRR data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Mar-01 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 46MB\n",
       "Dimensions:              (y: 1059, x: 1799)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    heightAboveGround    float64 8B 2.0\n",
       "    latitude             (y, x) float64 15MB 21.14 21.15 21.15 ... 47.85 47.84\n",
       "    longitude            (y, x) float64 15MB 237.3 237.3 237.3 ... 299.0 299.1\n",
       "    valid_time           datetime64[ns] 8B 2024-03-01\n",
       "    gribfile_projection  object 8B None\n",
       "Dimensions without coordinates: y, x\n",
       "Data variables:\n",
       "    t2m                  (y, x) float32 8MB 292.5 292.5 292.4 ... 266.8 266.8\n",
       "    d2m                  (y, x) float32 8MB 287.3 287.2 287.2 ... 262.2 262.2\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
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       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240301/subset_e0ef89f...\n",
       "    search:                  :(?:TMP|DPT):2 m</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ad796b79-c7eb-4ab1-b991-f7c11cf268a8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ad796b79-c7eb-4ab1-b991-f7c11cf268a8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 1059</li><li><span>x</span>: 1799</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-53ae597c-6c73-4fa1-b73e-cf0532c064d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-53ae597c-6c73-4fa1-b73e-cf0532c064d5' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-01</div><input id='attrs-7a4925b2-ef45-49c5-93ed-7db7be0d1a8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a4925b2-ef45-49c5-93ed-7db7be0d1a8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ac4fc17-b8a1-43d0-8d70-4be591eb1a6a' class='xr-var-data-in' type='checkbox'><label for='data-2ac4fc17-b8a1-43d0-8d70-4be591eb1a6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-93dbda3b-4477-4961-9b57-0015f9c0f2d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93dbda3b-4477-4961-9b57-0015f9c0f2d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-897fa788-894e-4b3e-a4f8-2168db6b0d02' class='xr-var-data-in' type='checkbox'><label for='data-897fa788-894e-4b3e-a4f8-2168db6b0d02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heightAboveGround</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-06cb3c4f-6cff-4bb1-ab06-6cda6783b922' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06cb3c4f-6cff-4bb1-ab06-6cda6783b922' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11bde6b6-662f-4bf5-b608-f14f9e8dde1f' class='xr-var-data-in' type='checkbox'><label for='data-11bde6b6-662f-4bf5-b608-f14f9e8dde1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height above the surface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>21.14 21.15 21.15 ... 47.85 47.84</div><input id='attrs-17d81739-d966-4b3f-b081-dc1a78bec9b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17d81739-d966-4b3f-b081-dc1a78bec9b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8951e440-7231-4f7f-b62f-3b4b82b88e63' class='xr-var-data-in' type='checkbox'><label for='data-8951e440-7231-4f7f-b62f-3b4b82b88e63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[21.138123  , 21.14511004, 21.1520901 , ..., 21.1545089 ,\n",
       "        21.14753125, 21.14054663],\n",
       "       [21.16299459, 21.1699845 , 21.17696744, ..., 21.17938723,\n",
       "        21.1724067 , 21.16541921],\n",
       "       [21.18786863, 21.19486142, 21.20184723, ..., 21.20426802,\n",
       "        21.19728462, 21.19029425],\n",
       "       ...,\n",
       "       [47.78955926, 47.799849  , 47.81012868, ..., 47.81369093,\n",
       "        47.80341474, 47.79312849],\n",
       "       [47.81409316, 47.82438621, 47.8346692 , ..., 47.83823259,\n",
       "        47.8279531 , 47.81766354],\n",
       "       [47.8386235 , 47.84891986, 47.85920615, ..., 47.86277069,\n",
       "        47.85248789, 47.84219502]], shape=(1059, 1799))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>237.3 237.3 237.3 ... 299.0 299.1</div><input id='attrs-01b1656c-ffe1-4bbc-9097-66afcd723aed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01b1656c-ffe1-4bbc-9097-66afcd723aed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f46a53d-4e09-426d-8a0b-4743d1c87b19' class='xr-var-data-in' type='checkbox'><label for='data-3f46a53d-4e09-426d-8a0b-4743d1c87b19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([[237.280472  , 237.30713868, 237.3338097 , ..., 287.6569408 ,\n",
       "        287.68361332, 287.71028151],\n",
       "       [237.27297501, 237.29964881, 237.32632695, ..., 287.66442108,\n",
       "        287.69110073, 287.71777603],\n",
       "       [237.26547368, 237.2921546 , 237.31883986, ..., 287.6719057 ,\n",
       "        287.69859247, 287.7252749 ],\n",
       "       ...,\n",
       "       [225.9351904 , 225.97171577, 226.00825329, ..., 298.97907406,\n",
       "        299.0156158 , 299.05214538],\n",
       "       [225.91986142, 225.95639874, 225.99294822, ..., 298.99437498,\n",
       "        299.03092868, 299.06747022],\n",
       "       [225.90452027, 225.94106954, 225.97763099, ..., 299.00968806,\n",
       "        299.04625373, 299.08280723]], shape=(1059, 1799))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-01</div><input id='attrs-f6a04227-c0f8-4b23-964b-0b78db58b963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6a04227-c0f8-4b23-964b-0b78db58b963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b59377-e9cb-430d-83fb-681f6ffb23a8' class='xr-var-data-in' type='checkbox'><label for='data-92b59377-e9cb-430d-83fb-681f6ffb23a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-d39cbbe4-5a0e-4cbe-89e9-52fd4881651b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d39cbbe4-5a0e-4cbe-89e9-52fd4881651b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-943bd3b7-2f1f-4c5b-adbe-9ca7da66ebb6' class='xr-var-data-in' type='checkbox'><label for='data-943bd3b7-2f1f-4c5b-adbe-9ca7da66ebb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c976e4c-b99a-417a-b5c8-b17d495fe0b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c976e4c-b99a-417a-b5c8-b17d495fe0b4' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>292.5 292.5 292.4 ... 266.8 266.8</div><input id='attrs-5bda712c-0b43-4fba-ac0e-e7058f0a32e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bda712c-0b43-4fba-ac0e-e7058f0a32e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38d906b2-08d6-4613-a089-f407f8891e6a' class='xr-var-data-in' type='checkbox'><label for='data-38d906b2-08d6-4613-a089-f407f8891e6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[292.45584, 292.45584, 292.39334, ..., 298.83084, 298.83084,\n",
       "        298.83084],\n",
       "       [292.39334, 292.39334, 292.39334, ..., 298.83084, 298.76834,\n",
       "        298.76834],\n",
       "       [292.39334, 292.39334, 292.39334, ..., 298.76834, 298.76834,\n",
       "        298.76834],\n",
       "       ...,\n",
       "       [276.39334, 276.39334, 276.39334, ..., 266.51834, 266.58084,\n",
       "        266.70584],\n",
       "       [276.33084, 276.33084, 276.33084, ..., 266.58084, 266.58084,\n",
       "        266.76834],\n",
       "       [276.33084, 276.33084, 276.33084, ..., 266.70584, 266.76834,\n",
       "        266.76834]], shape=(1059, 1799), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>d2m</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>287.3 287.2 287.2 ... 262.2 262.2</div><input id='attrs-f70b08d8-f809-495e-b5f8-65b069efe1e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f70b08d8-f809-495e-b5f8-65b069efe1e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f113ad9-917e-4472-a771-f3032fd6abb2' class='xr-var-data-in' type='checkbox'><label for='data-3f113ad9-917e-4472-a771-f3032fd6abb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Dew point temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[287.2552, 287.1927, 287.1927, ..., 294.3177, 294.3177, 294.3802],\n",
       "       [287.1927, 287.0677, 287.0677, ..., 294.2552, 294.3177, 294.3802],\n",
       "       [287.1302, 287.0677, 287.0052, ..., 294.2552, 294.3177, 294.3802],\n",
       "       ...,\n",
       "       [270.6927, 270.6927, 270.6302, ..., 261.7552, 261.7552, 261.9427],\n",
       "       [270.7552, 270.7552, 270.7552, ..., 261.8802, 261.8802, 262.0677],\n",
       "       [270.8802, 270.8802, 270.8802, ..., 262.1302, 262.1927, 262.1927]],\n",
       "      shape=(1059, 1799), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-02402d67-bb85-4477-8f84-c8bf1a17ef0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02402d67-bb85-4477-8f84-c8bf1a17ef0a' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d66cfef4-7b14-4f3f-ab03-d03ca8b865b3' class='xr-section-summary-in' type='checkbox'  ><label for='section-d66cfef4-7b14-4f3f-ab03-d03ca8b865b3' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>hrrr</dd><dt><span>product :</span></dt><dd>sfc</dd><dt><span>description :</span></dt><dd>High-Resolution Rapid Refresh - CONUS</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20240301/conus/hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/hrrr/20240301/subset_e0ef89ff__hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>search :</span></dt><dd>:(?:TMP|DPT):2 m</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 46MB\n",
       "Dimensions:              (y: 1059, x: 1799)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    heightAboveGround    float64 8B 2.0\n",
       "    latitude             (y, x) float64 15MB 21.14 21.15 21.15 ... 47.85 47.84\n",
       "    longitude            (y, x) float64 15MB 237.3 237.3 237.3 ... 299.0 299.1\n",
       "    valid_time           datetime64[ns] 8B 2024-03-01\n",
       "    gribfile_projection  object 8B None\n",
       "Dimensions without coordinates: y, x\n",
       "Data variables:\n",
       "    t2m                  (y, x) float32 8MB 292.5 292.5 292.4 ... 266.8 266.8\n",
       "    d2m                  (y, x) float32 8MB 287.3 287.2 287.2 ... 262.2 262.2\n",
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       "    institution:             US National Weather Service - NCEP\n",
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       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240301/subset_e0ef89f...\n",
       "    search:                  :(?:TMP|DPT):2 m"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H = Herbie(\"2024-03-01\", model=\"hrrr\")\n",
    "ds = H.xarray(\":(?:TMP|DPT):2 m\")\n",
    "ds"
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  {
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   "metadata": {},
   "source": [
    "Let's extract the data from some points with some fruit-themed station id names.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <td>McIntosh</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.375</td>\n",
       "      <td>-100.25</td>\n",
       "      <td>Golden</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.750</td>\n",
       "      <td>-100.50</td>\n",
       "      <td>Fuji</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.125</td>\n",
       "      <td>-100.75</td>\n",
       "      <td>Gala</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "   latitude  longitude        stid\n",
       "0    44.000    -100.00    McIntosh\n",
       "1    44.375    -100.25      Golden\n",
       "2    44.750    -100.50        Fuji\n",
       "3    45.125    -100.75        Gala\n",
       "4    45.500    -101.00  Honeycrisp"
      ]
     },
     "execution_count": 8,
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   "source": [
    "points = pd.DataFrame(\n",
    "    {\n",
    "        \"latitude\": np.linspace(44, 45.5, 5),\n",
    "        \"longitude\": np.linspace(-100, -101, 5),\n",
    "        \"stid\": [\"McIntosh\", \"Golden\", \"Fuji\", \"Gala\", \"Honeycrisp\"],\n",
    "    }\n",
    ")\n",
    "points"
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   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 330 ms, sys: 414 ms, total: 744 ms\n",
      "Wall time: 764 ms\n"
     ]
    },
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       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 320B\n",
       "Dimensions:              (point: 5)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    heightAboveGround    float64 8B 2.0\n",
       "    latitude             (point) float64 40B 43.99 44.37 44.76 45.13 45.5\n",
       "    longitude            (point) float64 40B 260.0 259.8 259.5 259.3 259.0\n",
       "    valid_time           datetime64[ns] 8B 2024-03-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (point) float64 40B 0.8515 1.07 1.611 1.41 1.318\n",
       "    point_latitude       (point) float64 40B 44.0 44.38 44.75 45.12 45.5\n",
       "    point_longitude      (point) float64 40B -100.0 -100.2 -100.5 -100.8 -101.0\n",
       "    point_stid           (point) object 40B &#x27;McIntosh&#x27; &#x27;Golden&#x27; ... &#x27;Honeycrisp&#x27;\n",
       "Dimensions without coordinates: point\n",
       "Data variables:\n",
       "    t2m                  (point) float32 20B 288.0 286.7 286.1 282.3 284.8\n",
       "    d2m                  (point) float32 20B 268.8 271.8 270.5 274.1 270.5\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240301/subset_e0ef89f...\n",
       "    search:                  :(?:TMP|DPT):2 m</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-70d2d747-8ad2-449e-91d3-2249af4f911f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70d2d747-8ad2-449e-91d3-2249af4f911f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>point</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-74826596-d21f-4d7d-a3ed-3ccc7f9c2739' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74826596-d21f-4d7d-a3ed-3ccc7f9c2739' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-01</div><input id='attrs-457f5390-bac9-495c-b5c9-f149de0c5f8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-457f5390-bac9-495c-b5c9-f149de0c5f8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-403d2367-3831-4878-b97e-8c7609474192' class='xr-var-data-in' type='checkbox'><label for='data-403d2367-3831-4878-b97e-8c7609474192' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-04b0dc76-fe42-4a64-a7cf-3853acf26426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04b0dc76-fe42-4a64-a7cf-3853acf26426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5e85e92-1ef0-4bc3-8e5c-430c9ac0d556' class='xr-var-data-in' type='checkbox'><label for='data-c5e85e92-1ef0-4bc3-8e5c-430c9ac0d556' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heightAboveGround</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-d8b7c02d-9aa2-45ad-9602-dd99f103c359' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8b7c02d-9aa2-45ad-9602-dd99f103c359' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fc2b649-4660-453d-bb05-49d738b92ad3' class='xr-var-data-in' type='checkbox'><label for='data-9fc2b649-4660-453d-bb05-49d738b92ad3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height above the surface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.99 44.37 44.76 45.13 45.5</div><input id='attrs-3c3b3608-d123-43a5-940b-fc0da575d941' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c3b3608-d123-43a5-940b-fc0da575d941' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e83438b-03a2-4efc-9e96-83e90883326d' class='xr-var-data-in' type='checkbox'><label for='data-1e83438b-03a2-4efc-9e96-83e90883326d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([43.99457721, 44.36562216, 44.76275862, 45.13234174, 45.50116868])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>260.0 259.8 259.5 259.3 259.0</div><input id='attrs-9bc90ac6-bac0-4b9d-9965-cdb3fd5bc5d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bc90ac6-bac0-4b9d-9965-cdb3fd5bc5d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ca0a8e2-15ac-445c-9237-a54e7b35e5a3' class='xr-var-data-in' type='checkbox'><label for='data-8ca0a8e2-15ac-445c-9237-a54e7b35e5a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([259.99248463, 259.75304459, 259.50966517, 259.2646478 ,\n",
       "       259.01682808])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-01</div><input id='attrs-9d500a25-13c4-46e9-b9f8-9f1edac02f15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d500a25-13c4-46e9-b9f8-9f1edac02f15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea51e186-1884-4641-94e8-180e232a7ab8' class='xr-var-data-in' type='checkbox'><label for='data-ea51e186-1884-4641-94e8-180e232a7ab8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-0dc8d45c-9782-4578-96bf-7663d68635f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dc8d45c-9782-4578-96bf-7663d68635f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f29d698-8ff4-429c-bfda-6113841b166e' class='xr-var-data-in' type='checkbox'><label for='data-6f29d698-8ff4-429c-bfda-6113841b166e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8515 1.07 1.611 1.41 1.318</div><input id='attrs-b507991c-4e4e-40aa-b81d-34ba88535009' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b507991c-4e4e-40aa-b81d-34ba88535009' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5108490-808f-4213-8699-ff0875f13903' class='xr-var-data-in' type='checkbox'><label for='data-d5108490-808f-4213-8699-ff0875f13903' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array([0.8514605 , 1.07048195, 1.61093492, 1.40958307, 1.3179483 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0 44.38 44.75 45.12 45.5</div><input id='attrs-309e3b2f-a010-4832-85e6-96f2618a761b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-309e3b2f-a010-4832-85e6-96f2618a761b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e21103a-5077-4920-9763-e0ec032254d5' class='xr-var-data-in' type='checkbox'><label for='data-1e21103a-5077-4920-9763-e0ec032254d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([44.   , 44.375, 44.75 , 45.125, 45.5  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-100.0 -100.2 -100.5 -100.8 -101.0</div><input id='attrs-386f64cc-2126-4f65-a6e0-d7392a6101b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-386f64cc-2126-4f65-a6e0-d7392a6101b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e25b06cd-2a82-4ea2-b2ff-13aabca55793' class='xr-var-data-in' type='checkbox'><label for='data-e25b06cd-2a82-4ea2-b2ff-13aabca55793' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([-100.  , -100.25, -100.5 , -100.75, -101.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_stid</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;McIntosh&#x27; ... &#x27;Honeycrisp&#x27;</div><input id='attrs-bbf75cf8-b49a-4981-a63d-3635a5b07e12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbf75cf8-b49a-4981-a63d-3635a5b07e12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6988d5bb-4521-4c3c-9880-4676fd8826a7' class='xr-var-data-in' type='checkbox'><label for='data-6988d5bb-4521-4c3c-9880-4676fd8826a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point stid</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;McIntosh&#x27;, &#x27;Golden&#x27;, &#x27;Fuji&#x27;, &#x27;Gala&#x27;, &#x27;Honeycrisp&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-03c7af68-c309-478c-be7a-fe9a92676eaa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03c7af68-c309-478c-be7a-fe9a92676eaa' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>288.0 286.7 286.1 282.3 284.8</div><input id='attrs-21314c90-b3b1-4c5c-8f7b-34fcb8778840' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21314c90-b3b1-4c5c-8f7b-34fcb8778840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8e28e97-5b52-41d2-bf81-157384ea74fa' class='xr-var-data-in' type='checkbox'><label for='data-c8e28e97-5b52-41d2-bf81-157384ea74fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([288.01834, 286.70584, 286.14334, 282.26834, 284.83084],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>d2m</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>268.8 271.8 270.5 274.1 270.5</div><input id='attrs-1e470890-b62e-414e-a746-296b0e5e25fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e470890-b62e-414e-a746-296b0e5e25fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7825c91-8878-4c5a-9318-8cbb6f91b754' class='xr-var-data-in' type='checkbox'><label for='data-b7825c91-8878-4c5a-9318-8cbb6f91b754' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Dew point temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([268.7552, 271.7552, 270.5052, 274.0677, 270.5052], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97b531e2-9bd9-47dc-8732-6852622f2362' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97b531e2-9bd9-47dc-8732-6852622f2362' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ba2da030-8bcf-4827-b9c3-caeaaa8ceb07' class='xr-section-summary-in' type='checkbox'  ><label for='section-ba2da030-8bcf-4827-b9c3-caeaaa8ceb07' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>hrrr</dd><dt><span>product :</span></dt><dd>sfc</dd><dt><span>description :</span></dt><dd>High-Resolution Rapid Refresh - CONUS</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20240301/conus/hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/hrrr/20240301/subset_e0ef89ff__hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>search :</span></dt><dd>:(?:TMP|DPT):2 m</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 320B\n",
       "Dimensions:              (point: 5)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    heightAboveGround    float64 8B 2.0\n",
       "    latitude             (point) float64 40B 43.99 44.37 44.76 45.13 45.5\n",
       "    longitude            (point) float64 40B 260.0 259.8 259.5 259.3 259.0\n",
       "    valid_time           datetime64[ns] 8B 2024-03-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (point) float64 40B 0.8515 1.07 1.611 1.41 1.318\n",
       "    point_latitude       (point) float64 40B 44.0 44.38 44.75 45.12 45.5\n",
       "    point_longitude      (point) float64 40B -100.0 -100.2 -100.5 -100.8 -101.0\n",
       "    point_stid           (point) object 40B 'McIntosh' 'Golden' ... 'Honeycrisp'\n",
       "Dimensions without coordinates: point\n",
       "Data variables:\n",
       "    t2m                  (point) float32 20B 288.0 286.7 286.1 282.3 284.8\n",
       "    d2m                  (point) float32 20B 268.8 271.8 270.5 274.1 270.5\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240301/subset_e0ef89f...\n",
       "    search:                  :(?:TMP|DPT):2 m"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "matched = ds.herbie.pick_points(points)\n",
    "matched"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that a BallTree object for the HRRR model was saved with the name `<model_name>_<x_dim>_<y_dim>.pkl` in the directory you save Herbie data. This is done so it can be loaded more quickly next time you extract points from this grid.\n",
    "\n",
    "In the next cell I run the same command, and it uses the cached tree instead.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 523 ms, sys: 241 ms, total: 764 ms\n",
      "Wall time: 760 ms\n"
     ]
    },
    {
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 320B\n",
       "Dimensions:              (point: 5)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    heightAboveGround    float64 8B 2.0\n",
       "    latitude             (point) float64 40B 43.99 44.37 44.76 45.13 45.5\n",
       "    longitude            (point) float64 40B 260.0 259.8 259.5 259.3 259.0\n",
       "    valid_time           datetime64[ns] 8B 2024-03-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (point) float64 40B 0.8515 1.07 1.611 1.41 1.318\n",
       "    point_latitude       (point) float64 40B 44.0 44.38 44.75 45.12 45.5\n",
       "    point_longitude      (point) float64 40B -100.0 -100.2 -100.5 -100.8 -101.0\n",
       "    point_stid           (point) object 40B &#x27;McIntosh&#x27; &#x27;Golden&#x27; ... &#x27;Honeycrisp&#x27;\n",
       "Dimensions without coordinates: point\n",
       "Data variables:\n",
       "    t2m                  (point) float32 20B 288.0 286.7 286.1 282.3 284.8\n",
       "    d2m                  (point) float32 20B 268.8 271.8 270.5 274.1 270.5\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240301/subset_e0ef89f...\n",
       "    search:                  :(?:TMP|DPT):2 m</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0c13595f-a858-436d-a10f-f6e20088bba2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0c13595f-a858-436d-a10f-f6e20088bba2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>point</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1d40b6eb-1f38-4230-b283-1476aff3835e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d40b6eb-1f38-4230-b283-1476aff3835e' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-01</div><input id='attrs-2d685ba7-b711-42fa-8c5c-cd972d39efdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d685ba7-b711-42fa-8c5c-cd972d39efdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-728d21b1-8e9b-42a0-a771-098447de307c' class='xr-var-data-in' type='checkbox'><label for='data-728d21b1-8e9b-42a0-a771-098447de307c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-5141377a-bbd2-4de5-aa4e-435954690fcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5141377a-bbd2-4de5-aa4e-435954690fcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48eb13a0-7f6a-4a12-8700-5246a01bcfb1' class='xr-var-data-in' type='checkbox'><label for='data-48eb13a0-7f6a-4a12-8700-5246a01bcfb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heightAboveGround</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-60cc487f-783d-47b3-8d8d-f83936057f3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60cc487f-783d-47b3-8d8d-f83936057f3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87d098ae-0adc-4fbc-941e-8cdde7956030' class='xr-var-data-in' type='checkbox'><label for='data-87d098ae-0adc-4fbc-941e-8cdde7956030' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height above the surface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.99 44.37 44.76 45.13 45.5</div><input id='attrs-6b09050c-07c2-4287-b2b7-fc09824edd38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b09050c-07c2-4287-b2b7-fc09824edd38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1381efb4-13a5-42ba-81d6-eafd6969832d' class='xr-var-data-in' type='checkbox'><label for='data-1381efb4-13a5-42ba-81d6-eafd6969832d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([43.99457721, 44.36562216, 44.76275862, 45.13234174, 45.50116868])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>260.0 259.8 259.5 259.3 259.0</div><input id='attrs-5b2c7286-6d58-47af-be92-efb43637c6a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b2c7286-6d58-47af-be92-efb43637c6a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372bd930-ec22-4e0c-a791-c821a5a8dc2a' class='xr-var-data-in' type='checkbox'><label for='data-372bd930-ec22-4e0c-a791-c821a5a8dc2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([259.99248463, 259.75304459, 259.50966517, 259.2646478 ,\n",
       "       259.01682808])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-01</div><input id='attrs-b88813c6-8d9f-4447-8a01-6e3ef831b12f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b88813c6-8d9f-4447-8a01-6e3ef831b12f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f12803c-c716-4cfa-a845-9b41d83b2651' class='xr-var-data-in' type='checkbox'><label for='data-3f12803c-c716-4cfa-a845-9b41d83b2651' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-2eee5972-d5df-46fd-acfd-d511ed32afd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2eee5972-d5df-46fd-acfd-d511ed32afd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1bfb41e-2d87-429c-8ec3-7e031819ef79' class='xr-var-data-in' type='checkbox'><label for='data-c1bfb41e-2d87-429c-8ec3-7e031819ef79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8515 1.07 1.611 1.41 1.318</div><input id='attrs-a814128c-49bf-4643-a5a5-8e8a1bec5be6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a814128c-49bf-4643-a5a5-8e8a1bec5be6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26df4f7c-b0a8-4e9c-a91e-bae74c95c275' class='xr-var-data-in' type='checkbox'><label for='data-26df4f7c-b0a8-4e9c-a91e-bae74c95c275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array([0.8514605 , 1.07048195, 1.61093492, 1.40958307, 1.3179483 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0 44.38 44.75 45.12 45.5</div><input id='attrs-8d65b29e-31ac-4eb0-a008-380d273ec15d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d65b29e-31ac-4eb0-a008-380d273ec15d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6281e29-10ec-4c17-a745-96b4573098fc' class='xr-var-data-in' type='checkbox'><label for='data-e6281e29-10ec-4c17-a745-96b4573098fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([44.   , 44.375, 44.75 , 45.125, 45.5  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-100.0 -100.2 -100.5 -100.8 -101.0</div><input id='attrs-7e606b85-66c7-42a3-8134-388547a7b77c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e606b85-66c7-42a3-8134-388547a7b77c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ff16365-85fd-47d5-98a6-fc2b7965bb75' class='xr-var-data-in' type='checkbox'><label for='data-8ff16365-85fd-47d5-98a6-fc2b7965bb75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([-100.  , -100.25, -100.5 , -100.75, -101.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_stid</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;McIntosh&#x27; ... &#x27;Honeycrisp&#x27;</div><input id='attrs-72ebfe2c-df24-436c-808a-cd60a5b3ef86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72ebfe2c-df24-436c-808a-cd60a5b3ef86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbfcb7dc-41be-41bb-935e-c1334cdaee49' class='xr-var-data-in' type='checkbox'><label for='data-dbfcb7dc-41be-41bb-935e-c1334cdaee49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point stid</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;McIntosh&#x27;, &#x27;Golden&#x27;, &#x27;Fuji&#x27;, &#x27;Gala&#x27;, &#x27;Honeycrisp&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-10a2bb4b-d621-4f43-a9ae-245f9ed93bee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-10a2bb4b-d621-4f43-a9ae-245f9ed93bee' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>288.0 286.7 286.1 282.3 284.8</div><input id='attrs-4190422c-a717-406c-bd98-58270b769066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4190422c-a717-406c-bd98-58270b769066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2f175a9-24ed-4825-9685-22f15825223c' class='xr-var-data-in' type='checkbox'><label for='data-b2f175a9-24ed-4825-9685-22f15825223c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([288.01834, 286.70584, 286.14334, 282.26834, 284.83084],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>d2m</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>268.8 271.8 270.5 274.1 270.5</div><input id='attrs-22c826e5-7e28-452d-b558-d1f5a6e965ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22c826e5-7e28-452d-b558-d1f5a6e965ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-116cb58b-0439-4bb9-94ff-7be11f7e47c2' class='xr-var-data-in' type='checkbox'><label for='data-116cb58b-0439-4bb9-94ff-7be11f7e47c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Dew point temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([268.7552, 271.7552, 270.5052, 274.0677, 270.5052], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a677ac5-1d69-4c6b-9a9c-2e3a1fa3f522' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a677ac5-1d69-4c6b-9a9c-2e3a1fa3f522' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5147a3ce-5658-4565-afe6-b036f8d17643' class='xr-section-summary-in' type='checkbox'  ><label for='section-5147a3ce-5658-4565-afe6-b036f8d17643' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>hrrr</dd><dt><span>product :</span></dt><dd>sfc</dd><dt><span>description :</span></dt><dd>High-Resolution Rapid Refresh - CONUS</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20240301/conus/hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/hrrr/20240301/subset_e0ef89ff__hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>search :</span></dt><dd>:(?:TMP|DPT):2 m</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 320B\n",
       "Dimensions:              (point: 5)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    heightAboveGround    float64 8B 2.0\n",
       "    latitude             (point) float64 40B 43.99 44.37 44.76 45.13 45.5\n",
       "    longitude            (point) float64 40B 260.0 259.8 259.5 259.3 259.0\n",
       "    valid_time           datetime64[ns] 8B 2024-03-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (point) float64 40B 0.8515 1.07 1.611 1.41 1.318\n",
       "    point_latitude       (point) float64 40B 44.0 44.38 44.75 45.12 45.5\n",
       "    point_longitude      (point) float64 40B -100.0 -100.2 -100.5 -100.8 -101.0\n",
       "    point_stid           (point) object 40B 'McIntosh' 'Golden' ... 'Honeycrisp'\n",
       "Dimensions without coordinates: point\n",
       "Data variables:\n",
       "    t2m                  (point) float32 20B 288.0 286.7 286.1 282.3 284.8\n",
       "    d2m                  (point) float32 20B 268.8 271.8 270.5 274.1 270.5\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240301/subset_e0ef89f...\n",
       "    search:                  :(?:TMP|DPT):2 m"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "matched = ds.herbie.pick_points(points)\n",
    "matched"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may want to swap the dimensions to use the `point_stid` coordinate as the dimension instead. Doing this makes it possible to select data by station ID instead of point index.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 128B\n",
       "Dimensions:              ()\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    heightAboveGround    float64 8B 2.0\n",
       "    latitude             float64 8B 45.5\n",
       "    longitude            float64 8B 259.0\n",
       "    valid_time           datetime64[ns] 8B 2024-03-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  float64 8B 1.318\n",
       "    point_latitude       float64 8B 45.5\n",
       "    point_longitude      float64 8B -101.0\n",
       "    point_stid           &lt;U10 40B &#x27;Honeycrisp&#x27;\n",
       "Data variables:\n",
       "    t2m                  float32 4B 284.8\n",
       "    d2m                  float32 4B 270.5\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240301/subset_e0ef89f...\n",
       "    search:                  :(?:TMP|DPT):2 m</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-177ba76c-bf8d-4d8c-a091-a9d5e2f34d64' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-177ba76c-bf8d-4d8c-a091-a9d5e2f34d64' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-31f685c2-dbcf-436c-9d6a-01d523e27e19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31f685c2-dbcf-436c-9d6a-01d523e27e19' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-01</div><input id='attrs-bb2b9613-0c6c-4d4c-a38e-0b7c88ed1cc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb2b9613-0c6c-4d4c-a38e-0b7c88ed1cc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d9486e0-a456-45bd-a332-befebc42dcc3' class='xr-var-data-in' type='checkbox'><label for='data-7d9486e0-a456-45bd-a332-befebc42dcc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-3b96ea74-a53b-47d6-b50f-1ba2aea0e7a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b96ea74-a53b-47d6-b50f-1ba2aea0e7a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f78d818-413d-4461-9ce7-eec2d28546c9' class='xr-var-data-in' type='checkbox'><label for='data-9f78d818-413d-4461-9ce7-eec2d28546c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heightAboveGround</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-9dc9f80b-987e-474b-8f6e-6c895b0659b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dc9f80b-987e-474b-8f6e-6c895b0659b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86431dc8-1469-4634-9d3e-9f2e6acb6898' class='xr-var-data-in' type='checkbox'><label for='data-86431dc8-1469-4634-9d3e-9f2e6acb6898' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height above the surface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>45.5</div><input id='attrs-e9865399-023a-4bee-bd3d-33435a4d94f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9865399-023a-4bee-bd3d-33435a4d94f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27170c37-e9d8-4820-8dd8-55d76707e1a4' class='xr-var-data-in' type='checkbox'><label for='data-27170c37-e9d8-4820-8dd8-55d76707e1a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array(45.50116868)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>259.0</div><input id='attrs-bce47665-83e8-4600-8255-b68e1a64b01d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bce47665-83e8-4600-8255-b68e1a64b01d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecf97e47-8d51-4823-8192-51714455aa2e' class='xr-var-data-in' type='checkbox'><label for='data-ecf97e47-8d51-4823-8192-51714455aa2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array(259.01682808)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-01</div><input id='attrs-469eabb9-0f4f-4c78-bbfe-59ead53d2d6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-469eabb9-0f4f-4c78-bbfe-59ead53d2d6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7833bc89-43e5-4fa6-9691-80e6a9fc9e18' class='xr-var-data-in' type='checkbox'><label for='data-7833bc89-43e5-4fa6-9691-80e6a9fc9e18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-5afed9ff-54d0-44fc-afc0-8f8ab44cc3b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5afed9ff-54d0-44fc-afc0-8f8ab44cc3b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bb3f274-d790-49ef-b292-777bf8516f51' class='xr-var-data-in' type='checkbox'><label for='data-5bb3f274-d790-49ef-b292-777bf8516f51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.318</div><input id='attrs-bc0e3462-66e6-4f16-9a67-ee78dcb8d067' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc0e3462-66e6-4f16-9a67-ee78dcb8d067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5074554-0159-44fd-9cea-3d6cf0ba9d0e' class='xr-var-data-in' type='checkbox'><label for='data-a5074554-0159-44fd-9cea-3d6cf0ba9d0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array(1.3179483)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>45.5</div><input id='attrs-0135df75-0a01-4e6a-a186-21f811efd854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0135df75-0a01-4e6a-a186-21f811efd854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc2c4bbc-6ed6-41eb-b919-1dc69e5ce38c' class='xr-var-data-in' type='checkbox'><label for='data-cc2c4bbc-6ed6-41eb-b919-1dc69e5ce38c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array(45.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-101.0</div><input id='attrs-71053063-176f-4529-9eb3-69472d50c202' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71053063-176f-4529-9eb3-69472d50c202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1acba8bb-65dd-474a-828d-f47782d7b3f5' class='xr-var-data-in' type='checkbox'><label for='data-1acba8bb-65dd-474a-828d-f47782d7b3f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array(-101.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_stid</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;Honeycrisp&#x27;</div><input id='attrs-959928d3-a7ba-4fe1-aa77-917952c99c01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-959928d3-a7ba-4fe1-aa77-917952c99c01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f32825a3-4cec-4c56-9fcb-05c5610a4ece' class='xr-var-data-in' type='checkbox'><label for='data-f32825a3-4cec-4c56-9fcb-05c5610a4ece' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point stid</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;Honeycrisp&#x27;, dtype=&#x27;&lt;U10&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ca0a651-41bd-41d4-8ea1-de7df097d7f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8ca0a651-41bd-41d4-8ea1-de7df097d7f5' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>284.8</div><input id='attrs-e9799fef-283b-499f-92ea-76bc305ecb29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9799fef-283b-499f-92ea-76bc305ecb29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a61d3e3-d467-4249-b290-1fd452c568e8' class='xr-var-data-in' type='checkbox'><label for='data-6a61d3e3-d467-4249-b290-1fd452c568e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array(284.83084, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>d2m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>270.5</div><input id='attrs-36813b5c-c3a9-4c9a-8b71-7d99feb18f30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36813b5c-c3a9-4c9a-8b71-7d99feb18f30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7a631d0-2f2f-48f7-b735-581a3ad2d86c' class='xr-var-data-in' type='checkbox'><label for='data-d7a631d0-2f2f-48f7-b735-581a3ad2d86c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Dew point temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array(270.5052, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a003f655-feb1-4597-90f5-0baa45c50441' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a003f655-feb1-4597-90f5-0baa45c50441' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-880b4796-f8f0-4b4e-b8b2-3c11d6b8bf71' class='xr-section-summary-in' type='checkbox'  ><label for='section-880b4796-f8f0-4b4e-b8b2-3c11d6b8bf71' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>hrrr</dd><dt><span>product :</span></dt><dd>sfc</dd><dt><span>description :</span></dt><dd>High-Resolution Rapid Refresh - CONUS</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20240301/conus/hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/hrrr/20240301/subset_e0ef89ff__hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>search :</span></dt><dd>:(?:TMP|DPT):2 m</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 128B\n",
       "Dimensions:              ()\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    heightAboveGround    float64 8B 2.0\n",
       "    latitude             float64 8B 45.5\n",
       "    longitude            float64 8B 259.0\n",
       "    valid_time           datetime64[ns] 8B 2024-03-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  float64 8B 1.318\n",
       "    point_latitude       float64 8B 45.5\n",
       "    point_longitude      float64 8B -101.0\n",
       "    point_stid           <U10 40B 'Honeycrisp'\n",
       "Data variables:\n",
       "    t2m                  float32 4B 284.8\n",
       "    d2m                  float32 4B 270.5\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240301/subset_e0ef89f...\n",
       "    search:                  :(?:TMP|DPT):2 m"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matched = matched.swap_dims({\"point\": \"point_stid\"})\n",
    "matched.sel(point_stid=\"Honeycrisp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot each point on a map with the value at the nearest grid point.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-289442.889311017, -108684.3334160587, 605320.486730601, 849853.0858732163)"
      ]
     },
     "execution_count": 12,
     "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 herbie.toolbox import EasyMap, ccrs, pc\n",
    "\n",
    "ax = EasyMap(crs=ds.herbie.crs).ax\n",
    "ax.pcolormesh(\n",
    "    ds.longitude,\n",
    "    ds.latitude,\n",
    "    ds.t2m,\n",
    "    cmap=\"Spectral_r\",\n",
    "    vmax=290,\n",
    "    vmin=270,\n",
    "    transform=pc,\n",
    ")\n",
    "\n",
    "for i in matched.point_stid:\n",
    "    z = matched.sel(point_stid=i)\n",
    "    ax.scatter(z.longitude, z.latitude, color=\"k\", transform=pc)\n",
    "    ax.scatter(\n",
    "        z.point_longitude, z.point_latitude, color=\"yellow\", marker=\".\", transform=pc\n",
    "    )\n",
    "    ax.text(\n",
    "        z.point_longitude,\n",
    "        z.point_latitude,\n",
    "        f\" {z.point_stid.item()}\\n {z.t2m.item() - 273.15:.2f} C\",\n",
    "        transform=pc,\n",
    "    )\n",
    "ax.set_extent([-101, -99, 44, 46], crs=pc)\n",
    "ax.EasyMap.INSET_GLOBE()\n",
    "ax.adjust_extent()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is another example with several cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = pd.DataFrame(\n",
    "    {\n",
    "        \"latitude\": [40.7608, 41.8781, 39.7392, 44.9778, 47.6062],\n",
    "        \"longitude\": [-111.8910, -87.6298, -104.9903, -93.2650, -122.3321],\n",
    "        \"stid\": [\"Salt Lake City\", \"Chicago\", \"Denver\", \"Minneapolis\", \"Seattle\"],\n",
    "        \"elevation\": [1288, 182, 1655, 264, 56],  # Optional: add metadata\n",
    "    }\n",
    ")\n",
    "\n",
    "# ---------------\n",
    "# Pick the points\n",
    "matched = ds.herbie.pick_points(points)\n",
    "\n",
    "# -------------\n",
    "# Make the plot\n",
    "\n",
    "from herbie.paint import NWSTemperature\n",
    "\n",
    "ax = EasyMap(crs=ds.herbie.crs).STATES().ax\n",
    "ax.pcolormesh(\n",
    "    ds.longitude,\n",
    "    ds.latitude,\n",
    "    ds.t2m - 273.15,\n",
    "    **NWSTemperature.kwargs,\n",
    "    transform=pc,\n",
    ")\n",
    "\n",
    "for i in matched.point:\n",
    "    z = matched.sel(point=i)\n",
    "    ax.scatter(z.longitude, z.latitude, color=\"k\", transform=pc)\n",
    "    ax.scatter(\n",
    "        z.point_longitude, z.point_latitude, color=\"yellow\", marker=\".\", transform=pc\n",
    "    )\n",
    "    ax.text(\n",
    "        z.point_longitude,\n",
    "        z.point_latitude,\n",
    "        f\" {z.point_stid.item()}\\n {z.t2m.item() - 273.15:.2f} C\",\n",
    "        transform=pc,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pick Points: Sounding\n",
    "\n",
    "For vertical profiles, download all pressure levels and extract at a point. This creates a \"model sounding\" that can be compared to observations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Get real sounding data to compare model data to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=prs\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Mar-28 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 704B\n",
       "Dimensions:              (isobaricInhPa: 39, point: 1)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-28\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "  * isobaricInhPa        (isobaricInhPa) float64 312B 1e+03 975.0 ... 75.0 50.0\n",
       "    latitude             (point) float64 8B 40.75\n",
       "    longitude            (point) float64 8B 248.1\n",
       "    valid_time           datetime64[ns] 8B 2024-03-28\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (point) float64 8B 1.193\n",
       "    point_latitude       (point) float64 8B 40.76\n",
       "    point_longitude      (point) float64 8B -111.9\n",
       "    point_stid           (point) object 8B &#x27;KSLC&#x27;\n",
       "Dimensions without coordinates: point\n",
       "Data variables:\n",
       "    t                    (isobaricInhPa, point) float32 156B ...\n",
       "    dpt                  (isobaricInhPa, point) float32 156B ...\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 prs\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240328/subset_0befd2f...\n",
       "    search:                  (?:DPT|TMP):[0-9]* mb</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-289c0b19-51f9-415e-ac7d-0448be4bfe9c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-289c0b19-51f9-415e-ac7d-0448be4bfe9c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>isobaricInhPa</span>: 39</li><li><span>point</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b0b68b19-08e7-4263-8936-60405f874108' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0b68b19-08e7-4263-8936-60405f874108' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-28</div><input id='attrs-3c710d7d-ce04-4aeb-896c-8c893c288e87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c710d7d-ce04-4aeb-896c-8c893c288e87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f3b5eb-3453-48fd-be12-0f938aca996e' class='xr-var-data-in' type='checkbox'><label for='data-85f3b5eb-3453-48fd-be12-0f938aca996e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-28T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-e59b8a98-fbe2-4278-bef2-5b13e09b5491' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e59b8a98-fbe2-4278-bef2-5b13e09b5491' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3122b30d-51a8-4e1a-afec-c7d1bd1d7ec0' class='xr-var-data-in' type='checkbox'><label for='data-3122b30d-51a8-4e1a-afec-c7d1bd1d7ec0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaricInhPa</span></div><div class='xr-var-dims'>(isobaricInhPa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 75.0 50.0</div><input id='attrs-e9c1f55e-09da-4729-9f10-49906daeae1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9c1f55e-09da-4729-9f10-49906daeae1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ec57234-4d33-4e26-b69f-916b5bcfd4a9' class='xr-var-data-in' type='checkbox'><label for='data-9ec57234-4d33-4e26-b69f-916b5bcfd4a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
       "        750.,  725.,  700.,  675.,  650.,  625.,  600.,  575.,  550.,  525.,\n",
       "        500.,  475.,  450.,  425.,  400.,  375.,  350.,  325.,  300.,  275.,\n",
       "        250.,  225.,  200.,  175.,  150.,  125.,  100.,   75.,   50.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.75</div><input id='attrs-833a07c3-dcf1-431c-8228-076e851c3ce9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-833a07c3-dcf1-431c-8228-076e851c3ce9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2e1b15e-6ca0-4364-83d6-935afb39663e' class='xr-var-data-in' type='checkbox'><label for='data-f2e1b15e-6ca0-4364-83d6-935afb39663e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([40.75050232])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>248.1</div><input id='attrs-4124dd42-300e-4a52-b2ac-164eebb31626' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4124dd42-300e-4a52-b2ac-164eebb31626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aa3cb4a-3390-41ea-ae0c-afdf00e7e8ff' class='xr-var-data-in' type='checkbox'><label for='data-3aa3cb4a-3390-41ea-ae0c-afdf00e7e8ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([248.13040753])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-28</div><input id='attrs-357c188e-baaf-4aab-b246-6ed93bf2c6b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-357c188e-baaf-4aab-b246-6ed93bf2c6b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e520282-1e49-4012-828e-bce16c8e7ac5' class='xr-var-data-in' type='checkbox'><label for='data-0e520282-1e49-4012-828e-bce16c8e7ac5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-28T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-961a6654-03f5-46e8-bf13-c0773f86c2a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-961a6654-03f5-46e8-bf13-c0773f86c2a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff13d6aa-551a-4b16-b232-8c79d991be77' class='xr-var-data-in' type='checkbox'><label for='data-ff13d6aa-551a-4b16-b232-8c79d991be77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.193</div><input id='attrs-1f9a258b-e98a-4dfb-a6a1-c32df8d4167e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f9a258b-e98a-4dfb-a6a1-c32df8d4167e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1adf5420-088a-4431-8dc1-7fd63cffa992' class='xr-var-data-in' type='checkbox'><label for='data-1adf5420-088a-4431-8dc1-7fd63cffa992' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array([1.19310392])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.76</div><input id='attrs-74894813-00ab-4d1a-bf53-33d54464f0a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74894813-00ab-4d1a-bf53-33d54464f0a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1101c8c-7b27-40b4-8b02-62135de76926' class='xr-var-data-in' type='checkbox'><label for='data-d1101c8c-7b27-40b4-8b02-62135de76926' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([40.76])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-111.9</div><input id='attrs-3f8304a2-1e4f-4de3-afcf-54b87877b250' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f8304a2-1e4f-4de3-afcf-54b87877b250' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8329917d-3a40-4752-9f3b-7ad25b9c4783' class='xr-var-data-in' type='checkbox'><label for='data-8329917d-3a40-4752-9f3b-7ad25b9c4783' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([-111.876183])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_stid</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;KSLC&#x27;</div><input id='attrs-06e674fc-0d3c-4ef7-bfb3-07c157496d48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06e674fc-0d3c-4ef7-bfb3-07c157496d48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a1801c5-4f21-4260-a5b3-c9296e13b187' class='xr-var-data-in' type='checkbox'><label for='data-9a1801c5-4f21-4260-a5b3-c9296e13b187' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point stid</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;KSLC&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73c816de-6e6f-4075-a12f-4c88d4feafdc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73c816de-6e6f-4075-a12f-4c88d4feafdc' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(isobaricInhPa, point)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef3bcd07-458a-472a-9487-edfd43ffb97f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef3bcd07-458a-472a-9487-edfd43ffb97f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32025533-1645-42a7-b05a-c6645c2e4c18' class='xr-var-data-in' type='checkbox'><label for='data-32025533-1645-42a7-b05a-c6645c2e4c18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>[39 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dpt</span></div><div class='xr-var-dims'>(isobaricInhPa, point)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7985a8e9-d327-4b91-a5c6-6776481b27b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7985a8e9-d327-4b91-a5c6-6776481b27b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dade8c2-b94f-4d98-beb5-39877bc8e0e9' class='xr-var-data-in' type='checkbox'><label for='data-0dade8c2-b94f-4d98-beb5-39877bc8e0e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>3017</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dpt</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Dew point temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Dew point temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>dpt</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Dew point temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>[39 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a4a3e04d-dea5-4f17-8d7c-9ee5894aca68' class='xr-section-summary-in' type='checkbox'  ><label for='section-a4a3e04d-dea5-4f17-8d7c-9ee5894aca68' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>isobaricInhPa</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6e65ced9-bbf8-4d2f-8fb3-a490758c5559' class='xr-index-data-in' type='checkbox'/><label for='index-6e65ced9-bbf8-4d2f-8fb3-a490758c5559' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,\n",
       "        775.0,  750.0,  725.0,  700.0,  675.0,  650.0,  625.0,  600.0,  575.0,\n",
       "        550.0,  525.0,  500.0,  475.0,  450.0,  425.0,  400.0,  375.0,  350.0,\n",
       "        325.0,  300.0,  275.0,  250.0,  225.0,  200.0,  175.0,  150.0,  125.0,\n",
       "        100.0,   75.0,   50.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;isobaricInhPa&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cda8922a-fad5-49df-9fea-313eb1153daa' class='xr-section-summary-in' type='checkbox'  ><label for='section-cda8922a-fad5-49df-9fea-313eb1153daa' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>hrrr</dd><dt><span>product :</span></dt><dd>prs</dd><dt><span>description :</span></dt><dd>High-Resolution Rapid Refresh - CONUS</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20240328/conus/hrrr.t00z.wrfprsf00.grib2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/hrrr/20240328/subset_0befd2fd__hrrr.t00z.wrfprsf00.grib2</dd><dt><span>search :</span></dt><dd>(?:DPT|TMP):[0-9]* mb</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 704B\n",
       "Dimensions:              (isobaricInhPa: 39, point: 1)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-28\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "  * isobaricInhPa        (isobaricInhPa) float64 312B 1e+03 975.0 ... 75.0 50.0\n",
       "    latitude             (point) float64 8B 40.75\n",
       "    longitude            (point) float64 8B 248.1\n",
       "    valid_time           datetime64[ns] 8B 2024-03-28\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (point) float64 8B 1.193\n",
       "    point_latitude       (point) float64 8B 40.76\n",
       "    point_longitude      (point) float64 8B -111.9\n",
       "    point_stid           (point) object 8B 'KSLC'\n",
       "Dimensions without coordinates: point\n",
       "Data variables:\n",
       "    t                    (isobaricInhPa, point) float32 156B ...\n",
       "    dpt                  (isobaricInhPa, point) float32 156B ...\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 prs\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240328/subset_0befd2f...\n",
       "    search:                  (?:DPT|TMP):[0-9]* mb"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all pressure level data\n",
    "H = Herbie(\"2024-03-28 00:00\", model=\"hrrr\", product=\"prs\")\n",
    "ds = H.xarray(\"(?:DPT|TMP):[0-9]* mb\", remove_grib=False)\n",
    "\n",
    "slc_point = pd.DataFrame(\n",
    "    {\n",
    "        \"latitude\": [40.76],\n",
    "        \"longitude\": [-111.876183],\n",
    "        \"stid\": [\"KSLC\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "slc = ds.herbie.pick_points(slc_point)\n",
    "slc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Udu3aWTN6vfHGG6Snpzt0a7qcNnGuQ4cO5eg6leXFF1+kS5cudOzYkXvvvdf697x9+3YiIiIcFsjbtm2btUCeppwVuQQuGjQuIiVEQS+QZxiG0bx5c+u4j4+P8ffff7v82ry+H7NmzbroNReagWfp0qVGcHDweWfK8fDwMN58802Ha44fP27UqlUr1/Pbtm173lmcTp8+bbRv3/6898otxsudFepi9wCMcuXKGVu2bHG47s8//zSuuOKKC1730EMP5bjfDTfckOu5t956q0P5QgvkZXeh2aT27NnjMFNT9p9zZ03K7lIWyMvLdYZhGH369Mk1jk6dOjmUv/jiC4fr8nOBvCy7d+92qBcwhg4dai0weLlt4nwzUZ37k/13U0ZGhvH0009bC1he6EdE8k5doUSkyHv66aet7bS0NF5//fUif21+ad26NX/88QePPfYY9erVo1SpUvj4+FC1alX69u3LypUreeyxxxyuKVu2LMuXL2fIkCGEhITg6+tLZGQkEydOJCYm5rz3KlWqFAsWLOCbb77h1ltvJSwsDC8vL/z9/bnyyivp2bMnn3zyCW+//bbTr2vDhg288cYbdO7cmYiICMqXL4+npydlypShcePGPPHEE2zdupX69es7XBcREcHmzZt58cUXadKkCaVLl8bLy4uKFSvSo0cPFixYwHvvvZfjfj/++CODBw+23o+GDRvy2Wef8cEHHzj9Ws5Vo0YNVq9eTa9evQgODsbf358WLVowa9Ysp7oVXY6vvvqKV199lSuvvBJvb2+qV6/O888/z0cffeRw3rljQwpCzZo1WbZsGVdeeaW178MPP+S+++7DMIzLbhOXw9PTk1deeYUdO3bwxBNPcM0111CuXDk8PT0pXbo0kZGRjBw5krVr1zp9LxF3YjOMc6bIEBERkRIhOTkZf3//HPs/+OADHnzwQat88ODBi04mICJyMRpjISIiUkLdfffdpKamcsstt1CtWjVOnz7Nr7/+6jDLWV5mKBMRyQs9sRARESmhunfvzg8//HDe49dccw3z58+3pisWEXGGnliIiIiUUP3798dms7FhwwaOHj1Keno65cuXp1GjRvTq1Yu7777bWhVeRMRZemIhIiIiIiJO06xQIiIiIiLiNCUWIiIiIiLiNCUWIlLsLFmyBJvNZv3s27fPOjZgwABrf9u2bfNcZ9u2ba3rCnutARERkZJAiYWIOK1jx47Wh/KyZcuSmpqa63mGYXDllVda5zZu3LiQIy1cmzdvZuzYsXTq1InatWsTFBSEr68vVapUoVevXixbtuy816akpPD666/TtGlTAgMDCQgIoH79+jz33HMkJCTk672yi42NdUjazk3cLsXOnTsZNGgQ1atXx9fXlwoVKnDzzTczderUXM+fNm0ad955J/Xq1aNChQp4e3tTunRpIiIiGDRoEBs3brzkGP766y/eeecdunfvTt26dSlXrhw+Pj5UrFiRrl278uOPP5732q1bt3LvvfdSp04dAgIC8PLyIjg4mGbNmvHcc88RHx9/yfGsW7eOPn36UKlSJXx9fQkLC+O2227j559/Pu81J0+e5LnnnqN+/foEBAQQGBhI06ZNef3110lJSbnkGODS21eWn376idtuu43Q0FB8fHyoXLkyffr0Yd26dZd0/0mTJjm0sSVLluQ4J3uyX716dSDnlwp5+cm6Nru4uDjGjBlDmzZtCAsLw8fHh4CAAOrVq8egQYOYN28eGoIqchlctua3iJQY3377rQFYP9OnT8/1vF9//dXhvHffffey7rd48WKHevbu3WsdmzdvnvHGG28Yb7zxhvHdd9/luc42bdpY9fXv3/+y4jpXhw4dHOLM7ee1117Lcd3Ro0eNxo0bn/ea6tWrG3v27MmXe2V35MgRIzQ0NMd12d/fvJozZ47h5+d33lj69+9v2O12h2s6d+58wfi9vLyMGTNmXFIc991330Xfl6FDh+Yav4+PzwWvCw8PN/bv35/nWD799FPDw8PjvPW98MILOa7ZvXu3Ua1atfNe07hxY+Po0aOX9J5cTvsyDMN4/vnnz3uNh4eH8emnn+Y5hokTJzpcv3jx4hznZP83Wa1aNcMwcv7bz8tP1rVZxo0bd8G26Uy7F3F3mmNORJzWvXt3goODOXnyJABffvkl0dHROc778ssvrW1vb2/69u2b77F07NiRjh075nu9zqhXrx7t27enfPnyrF27llmzZlnHnnnmGbp160bt2rWtfffee6/17by/vz/33Xcffn5+fPrppxw7dox9+/bRp08fVq1ahYeHh1P3ym7YsGGX9S38uQ4ePMgdd9xhfZtet25d+vTpw59//sl3330HwBdffEHz5s0ZNmyYdV2pUqVo27YtDRo0IDQ0lIyMDFasWGF9m5+RkcEzzzxDVFTUJcdUo0YNOnbsSKVKlfjzzz+ZOnUqmZmZAHz44Yf06NGD9u3bW+c//vjjpKWlAeDr68s999xD5cqVWbRoEYsXLwbg8OHDfPDBB7z++usXvf/GjRt54IEHsNvtAFx33XV06dKFFStWMG/ePABGjx7NNddcQ+fOnQGw2+306dOH/fv3A1CuXDnuvfdeUlJS+Pjjj0lOTmbjxo3cf//9TJs2Lc/vxeW0r1mzZjFmzBirjo4dO3L99dczZ84cVq1ahd1u54EHHqBZs2Y0atQoz7FcqiuvvJI33njDYd/ChQv56aefrPIzzzzjsC5HUFCQtf3666/z5JNPWmVPT086d+5M06ZNsdls7Nq1iwULFhAXF1dgr0GkRHN1ZiMiJcP9999vfdPn7e2d41vUlJQUIzg42DqnR48ehmEYxrFjx4zHH3/caNeunVGtWjWjdOnShre3txEaGmq0b9/e+PLLL3N8s32hJxb9+/e39rdp0yZHnLGxsUbz5s0NPz8/IzQ01LjnnnuMuLi4Cz6xyF7nud9+Xsjo0aONZcuW5dj/wgsvOMT//vvvW8e2bt3qcOyTTz6xji1cuNDh2Ny5c526V3bTpk2zzunevbtT39w+/vjj1rVlypQxjh07Zh278847rWOVKlUyMjIyLlpf+/btrWv8/PwuKZb//e9/xqxZs3K0oQkTJji8xkcffdThuK+vr3Vs+PDh1v6MjAwjKCjIOjZgwIA8xXH77bdb19SoUcNITU21jrVq1co6ds0111j758yZ4xDjwoULrWOffPKJw7E///zTOnbuv4/sTwMut301b97c2t+qVStrf2pqqlGjRg3rWK9evfL0flzuE4vcxMTE5Km9bt261fD09LTOCw0NNTZs2JDjvLS0NOOTTz4x4uLi8vRaROQsjbEQkXyRfcBzenq69c10llmzZllPNLKf/++///LGG2/wyy+/sH//fpKSkkhPTyc+Pp6ff/6Zfv36MWjQoHyJcfz48URFRbF27VpSUlKIj49nwoQJXHfddRw5ciRf7pHd888/zw033JBj/+233+5QzvpmHMjR5z/7k5/27dsTGBiY67mXc68sR44cYejQoQDcfffddOvWLdfXk1fZ42rbti3lypWzytlfz7///nvBvvmJiYksWLCA33//3dpXr169S4pl+PDhdOnSBZvN5rD/Yu9L3bp1re0VK1awZ88eUlNTmTt3LqdOnbKO5eXpWGZmJnPnzrXKXbp0wcfHxypnfwLz22+/Wd+WZ38fAwMDHZ6onPtE8EJjRS50Xl7a1+HDh1m7dm2u8fr4+NClSxerPGfOHOupTFHzv//9z3pKBfDRRx/lOs7L29ubIUOGEBoaWpjhiZQISixEJF9ce+21REREWOXs3Z7OLYeGhnLrrbcC4OHhQUREBP379+fJJ59k7NixxMTEcNttt1kfBidOnMhvv/3mVHz//PMPI0aMsMplypThkUce4dFHH+XEiRP8+eefTtV/KbZv3+5Qvuaaa6zt7B+ig4KCHD6U22w2atSokeu5l3OvLEOHDuXIkSNUqlSJ999//+Iv4AJSU1PZuXOnVa5Zs6bD8XPLub2GK664ApvNRlBQEB07drS6ZwUHB/Pee+85FV+Wi70v7777LmXKlAHMbkxXXnklfn5+3HbbbdjtdsqVK8fbb79N7969L3qv3bt3c/r0aauc1/ck+3tTo0YNh+SoXLlyDl188tIWzj0vr+3r3LovFP/p06fZvXt3nmIpbIsWLbK2y5YtS/fu3V0XjEgJpcRCRPJN9qcWv/32m/UB8+jRo8yfP9861rdvX7y8zCFedevW5c8//2T06NE0b96cMmXKUKZMGW644QYqVapkXbNgwQKnYps8ebLDDDrff/8977zzDm+99RZz5sxxqu5LcfjwYZ544gmrfOONNzo8aTh+/Li1nf3b4yxZH3YBjh075tS9AKZMmcL06dMB+PTTTwkODs7bCzmPEydOOMymc+5ryB4/XPw1ZKlZsyY///wzrVq1cio+gKSkJOsJDUCdOnXo1auXwzmtW7dmzZo11KpVK9c6unTpkucPptn/TiHv70l+toXcYslrnZcbf1Fz8OBBa7tWrVo5xieJiPM0eFtE8s3dd9/NM888Y3U3+OqrrxgzZgzfffcd6enp1nkDBw60to8dO0b//v0v+uH+n3/+cSq27F1uwsLCuOmmm6xyy5YtqVGjBnv37s312kmTJjFp0iSn7g/m9Ku33nqrdZ+6deued+pVINfpLnPbd7n3io+PZ/jw4YD5d5L1FOli3nzzzVz3jxw58qLx5iX+559/noSEBI4ePcqiRYvYsGEDe/bsoWXLlnz22WfcfffdgNlV6pNPPslxfVBQEEOGDMm17sOHD3PbbbdZXXsqVarErFmz8PX1dThv6dKlREVFcfz4cby8vLjrrruoUaMG8+fPZ9WqVXz55ZfMmTOHlStXnjf5OJ/LeU8upS20bds23+u80Dl5bZMiUvIpsRCRfFOxYkVuueUWa5abyZMnM3r0aIduUE2aNKFBgwZWedCgQXl6YnC+tTHyKvv4jtz6ToeFhZ03scgPS5YsISoqihMnTgDQrFkz5s6dS4UKFRzOK1++vLWdvS9/bvvOvfZS7/X8889z9OhRrrjiCt555508v5bHH3881/0jR46kbNmy2Gw268Pmua/h3HJur+G+++5zKA8aNIgJEyaQlpbGfffdx80330x4eDjHjx/PNZZq1arlmlhs2bKFLl26cODAAcCcYWjBggVceeWVDuelpqbSt29f65v6559/nhdeeAEwZxyqW7cuf/31F8eOHWP06NFMnjw51/cjS/a/09zeg/O9J/nRFi4US17rvNz4L8Tb29uhnNt6HMnJydZ29jEpl6ty5cr89ddfgJl4G4aRY+yNiDhHzwFFJF9l7w61b98+Pv30U4eBn9mPnz59mtmzZ1vlm266id27d5ORkYFhGDRv3jzf4srexSe3KVULcnrJSZMmccstt1gf9Lt27cqSJUsICQnJcW7Dhg2t7YSEBIduJXa73SH5yZ6gXc69sl7zP//8Q3BwsLWgWPYnSnC2f39eFsrz9fV1mM52z549DsfP7X+f22s4V/bB5MnJyaxZs+ai15xr/vz5tGrVykoqrrvuOlatWpUjqQBz/EX2bjPZ26GXl5fDdKqbNm266L1r1qxJQECAVc7re5K9Lezdu9fhycCRI0dITEzMcc3FXE77yn7NxeIPCAjIMQYjN+e2x3OTesMwHNpbbu33UmV/SnnixAl++OEHp+sUEUdKLEQkX3Xr1s1hDvnsA6Z9fHy48847rXJCQoLDLC2dO3emZs2aeHp6smPHjjwPSM2LZs2aWdtxcXEOAzlXrlx5wacVAwYMuOAqvudjGAbPPvssAwcOtLqCPfjgg8ycOdPhg2Z2Xbt2dSjHxsZa2wsWLHD4djj7B+7LudflMgwj158st912m7W9ZMkShz762ddbqFy5svX3smvXrvMOoD/3iVbWt8zVq1fPNY5zE6APP/yQLl26WO9ddHQ0v/zyy3k/rGZvk4BDYpyRkeGQTPj7++daR3ZZayVkmT17tjULlWEY1hgXMCdBCAsLAxzfx8TERIfVubNfA45t4dzVqbOvan057SssLMxhcHv2a1JTUx3WSunSpUuexi40a9YMT09Pq/zRRx85tJOPP/7Y4QuAa6+99qJ1Xszw4cMd7vnAAw+wefPmHOelp6fz2Wef5cuaLiLuRl2hRCRf+fr6cscdd/Dhhx8CcObMGetY165dHbpVhIaGOiys99JLLxEfH09GRgYTJkxwuvtTdn379uXFF1+06uzRoweDBw/GZrMxYcKEfLtPdg899BAffPCBVa5Xrx5Vq1bl7bffdjivfv361rSldevWJTo6mhkzZgDw8MMPs23bNvz8/Pj444+ta6655hpuueUWp+7VsmVLaxB9dvv373cYk9KpUydKlSqV5wTloYceYvz48SQmJnLq1CluuOEGa4G87OM8nn76aeuD3h9//EGPHj1o0qQJrVq1omLFiiQmJrJ06VJWrVplXVOmTBlat26dpzgA3nrrLYexH5UrV+baa69l3LhxDudVqVLFmuGpfv36lC9f3vo2f8yYMezbt4/q1aszf/58qzsNQIcOHfIUx1NPPUVsbCwZGRns3buXtm3b0rlzZ5YvX+7wBOb55593qLt58+ZWYtOnTx/uu+8+UlJS+Oijj6zzevbsSZ06dfIUx+W2r+eff95KSlasWEGnTp24/vrrmT17tvUUyMvLi6eeeipPcZQrV47evXvzzTffAGY3tZo1a1K/fn2OHTvmMGuXl5cX9957b57qvZB69eoxZswYnnnmGcAcb9OsWTO6dOlC48aNcyyQl316XxHJowJfKUNE3M5vv/3msGBV1s+sWbNynPvqq6/mem79+vWNpk2b5rpo3eUukPfBBx/keq9KlSoZV199db4vkJd9ga8L/Zx7v6NHjxqNGjU67/nVqlUzdu/enS/3ys25i5dd6gJ5hmEYs2fPdlhkLrc4si9a9/3331809lKlShk//PDDJcWR/e/uQj/ntpVp06YZXl5eF7wmMjLSSEhIyHMsn3zyieHh4XHe+p5//vkc1+zevduoVq3aea9p1KhRjsUoL7RAnmFcXvsyDMN4/vnnz3uNh4eH8emnn+b5vTAMwzh+/LjRuHHjC77HXl5eF603rwvkZXnvvfcu2Dadafci7k5doUQk3zVv3jzHQmbh4eG5Lib25JNPMm7cOGrVqoW3tzfh4eEMGTKEpUuXUrp06XyNa9iwYUyfPp2mTZvi6+tLhQoVuPvuu1mzZo3D1LauVr58eVauXMmrr75K48aNCQgIwN/fn7p16/LMM8+wadOmPPVjd6XOnTvz+++/M3DgQKpUqYKPjw9ly5alXbt2TJkyhUmTJjkMnG3evDkvvPAC7dq1o2rVqpQqVQovLy/Kly9PixYtePbZZ9mxY4dD96CC1LNnT9avX8/gwYOpVasWpUqVwtPTk7Jly9KqVSvefPNNVq9eneuUreczZMgQVq9eTa9evQgPD8fb25sKFSrQuXNnFi5cyOjRo3NcU7NmTTZt2sQzzzxDREQE/v7+BAQE0LhxY1577TVWrVqVY3D1xVxu+xo9ejQLFy6kc+fOVKhQAW9vbypWrEivXr1Ys2YNgwcPvqQ4ypYty6pVqxg3bhw33ngjFSpUwMvLi1KlSlG7dm2GDBli/R3kp4ceeoi9e/fy4osvcv311xMSEmLdNyIiggceeIAlS5ZQrVq1fL2viDuwGYbmiRMREREREefoiYWIiIiIiDhNiYWIiIiIiDhNiYWIiIiIiDit2CUWy5Yto2vXrlSqVAmbzcbMmTMdjhuGwQsvvEDFihXx9/enffv2DlMDAhw/fpy+ffsSGBhIcHAwgwYNIikpqRBfhYiIiIhIyVLsEovTp08TGRmZYw7yLK+//jrvv/8+48ePZ82aNQQEBNChQwdSUlKsc/r27cvWrVv56aefmD17NsuWLcuXObJFRERERNxVsZ4Vymaz8f3339O9e3fAfFpRqVIlHnvsMWtBpISEBMLCwpg0aRJ9+vRh27Zt1K1bl7Vr11orvs6fP59bb72Vf/75p0hNOSkiIiIiUlyUqJW39+7dy+HDhx1WywwKCuLaa69l1apV9OnTh1WrVhEcHGwlFQDt27fHw8ODNWvW0KNHjxz1pqamOqwAbLfbOX78OOXLl3eYh11EREREpCQxDINTp05RqVIlPDwu3NmpRCUWhw8fBiAsLMxhf1hYmHXs8OHDhIaGOhz38vKiXLly1jnnGjt2LKNGjSqAiEVEREREir6///6bK6644oLnlKjEoqA8/fTTPProo1Y5ISGBqlWrsn///ktaddVZ10+4nq1HtubYXz+0Pr8O/BUwn6YcPXqUChUqXDSrlJLpfG3Adv31sHUr2Z+xGQD162P8+mthhykFTL8LBNQOBP79186HHyYzdKg/lSqpDbgrZ34XJCYmUq1aNcqUKXPRc0tUYhEeHg5AXFwcFStWtPbHxcXRqFEj65z4+HiH6zIyMjh+/Lh1/bl8fX3x9fXNsT84OLhQE4vRnUYTPTU65/6OowkODgbMhpOWlkZwcLD+E3FT520Do0dDdM72w+jR8F/7kZJDvwsE1A4EwE7r1lCpUjDBwWoD7sqZ3wVZ5+el+3+JamE1atQgPDycRYsWWfsSExNZs2YNLVq0AKBFixacPHmS9evXW+f88ssv2O12rr322kKP+VJERUTx2W2fWeVA30Bie8XSIyLnuBCRHKKi4LOz7YdSpSA2FnIZVyQiIiVDYCC0bp1GIX4PKm6s2CUWSUlJbNq0iU2bNgHmgO1NmzZx4MABbDYbjzzyCC+99BI//vgjW7ZsoV+/flSqVMmaOSoiIoKOHTsyZMgQfvvtN1asWMHw4cPp06dPsZgRqvPVna3tdjXaKamQSzNoENSubW6npcHNN7s2HhERKVCpqfD3355km4NGpMAUu8Ri3bp1NG7cmMaNGwPw6KOP0rhxY1544QUAnnjiCR588EHuvfdemjdvTlJSEvPnz8fPz8+q4+uvv6ZOnTrcdNNN3HrrrVx//fV88sknLnk9IoXuxhvNPzMyYPly18YiIiIF6vhxmDLFn+PHXR2JuINiN8aibdu2XGjpDZvNxujRoxk9evR5zylXrhzffPNNQYQnUvTdeCOMH29uL14MHTu6Nh4RESkUmZmZpKenuzoMcQG73U56ejopKSk5xlh4e3vj6emZL/cpdomFiDipbduz24sXuywMEREpLAZJSYfZuTPB1YGIixiGgd1u59SpU7kOwg4ODiY8PNzp9dmUWIi4m9BQqFcPtm6F9eshIQGCglwdlYiIFJB69Y6Tnn6GihVDKVWqlBb3dUOGYZCRkYGXl5fD379hGJw5c8aaMTX7rKqXQ4mFiDu68UYzsbDb4ddfoUsXV0ckIiIFIpOrr04iJCSM8uXLuzoYcZHzJRYA/v7+AMTHxxMaGupUt6hiN3hbRPJB1gBuUHcoEZESrGzZdIKDDcqUKeXqUKQIK1XKbB/OjsFRYiHijtq0gaxvLJRYiIiUeOr+JBeSX+1DiYWIOypfHho2NLc3bULzEIqIlExHj8KpUzY0GZQUBiUWIu4qqzuUYcCyZa6NRURECoRhmD8ihUGJhYi70jgLEREpgmw22wV/XnzxRVeHmO+qV6/Ou+++6+ownKZZoUTcVevW4OFhzgylxEJERIqIQ4cOWdtTpkzhhRdeYMeOHda+0qVLuyKsS2YYBpmZmXh5Fd7H7bS0NHx8fArtfufSEwsRdxUcDI0bm9tbtsCRIy4NR0REirDYWIiMBH9/88/Y2AK7VXh4uPUTFBSEzWZz2Pfdd98RERGBn58fderU4cMPP7Su3bdvHzabjalTp3LDDTfg7+9P8+bN2blzJ2vXrqVZs2aULl2aTp06cSTb/3sDBgyge/fujBo1ipCQEAIDA7n//vtJS0uzzrHb7YwdO5YaNWrg7+9PZGQk06dPt44vWbIEm83GvHnzaNq0Kb6+vixfvpzdu3fTrVs3wsLCKF26NM2bN+fnn3+2rmvbti379+9nxIgR1lMZgBdffJFGjRo5vDfvvvsu1atXzxH3yy+/TKVKlahduzYAf//9N7169SI4OJhy5crRvXt39u3blx9/PRekxELEnWXvDrV0qeviEBGRAhEUBKVKGTj1pXlsLERHm19CpaSYf0ZHF2hycT5ff/01L7zwAi+//DLbtm3jlVde4fnnn+eLL75wOC8mJobnnnuODRs24OXlxZ133skTTzzBe++9x6+//squXbt44YUXHK5ZtGgR27ZtY8mSJXz77bfExsYyatQo6/jYsWP58ssvGT9+PFu3bmXEiBHcddddLD3n/8+nnnqKV199lW3bttGwYUOSkpK49dZbWbRoERs3bqRjx4507dqVAwcOABAbG8sVV1zB6NGjOXTokMMTm7xYtGgRO3bs4KeffmL27Nmkp6fToUMHypQpw6+//sqKFSsoXbo0Xbp0cUiUCoK6Qom4sxtvhDffNLcXL4aePV0bj4iI5CsfH/DyOjvDOADNmsHhw3mvJC7O/DNrFHjWn717Q1hY3usJD4d16/J+fi5iYmJ46623iIqKAqBGjRr8+eeffPzxx/Tv3986b+TIkXTo0AGAhx9+mDvuuINFixbRqlUrAAYNGsSkSZMc6vbx8WHChAmUKlWKevXqMXr0aB5//HHGjBlDeno6r7zyCj///DMtWrQAoGbNmixfvpyPP/6YNm3aWPWMHj2am2++2SqXK1eOyMhIqzxmzBi+//57fvzxR4YPH065cuXw9PSkTJkyhIeHX/J7EhAQwGeffWZ1gZo8eTJ2u53PPvvMevoxYcIEypYty5IlS6z3pSAosRBxZzfcAJ6ekJmpcRYiIiVQUhKkpNjIzMy28/BhOHjQ+cozMvKnnjw6ffo0u3fvZtCgQQwZMiRbGBkEBQU5nNswa0p1IOy/5KdBgwYO++Lj4x2uiYyMtBaKA2jRogVJSUn8/fffJCUlcebMGYeEAcwxDY2zuhX/p1mzZg7lpKQkXnzxRebMmcOhQ4fIyMggOTnZemLhrAYNGjiMq9i8eTO7du2iTJkyDuelpKSwe/fufLnn+SixEHFnZcqY31ytWQPbtpn/2VzGtyUiIlI0JSdDWpo5T4flUn/Px8WZScS5vLwu/YmFE5KSkgD49NNPufbaax2OeXp6OpS9vb2t7axv7c/dZ3d4U/J27zlz5lC5cmWHY76+vg7lgIAAh/LIkSP56aefePPNN7nqqqvw9/enZ8+eF+2W5OHhgXHOXMG5rYx97v2SkpJo2rQpX3/9tbXPMAwyMjKoWLHiBe/pLCUWIu7uxhvNxAJgyRLo08el4YiISAG71O5IWWMsbDazG1TWn1OnQo8eBRNjLsLCwqhUqRJ79uyhb9+++V7/5s2bSU5Oxt/fH4DVq1dTunRpqlSpQrly5fD19eXAgQMO3Z7yYsWKFQwYMIAe/71XSUlJOQZS+/j4kOnwWAlCQkI4fPgwhmFYydGmTZsuer8mTZowZcoUQkNDCQwMBM4mFgU9Q5UGbxczc/6aY23/svcXYrcV/sApKWGyD+C+664Cn+1DRESKmagomDEDGjYEPz/zz9jYQk0qsowaNYqxY8fy/vvvs3PnTrZs2cLEiRN5++23na47LS2NQYMG8eeffzJ37lxiYmIYPnw4Hh4elClThpEjRzJixAi++OILdu/ezYYNG/jf//6XY+D4ua6++mpiY2PZtGkTmzdv5s4778zxtKR69eosW7aMgwcPcvToUcCcLerIkSO8/vrr7N69m3HjxjFv3ryLvo6+fftSoUIFunXrxq+//srevXtZsmQJI0aM4J9//rn8NygPlFgUI7HbYhn842CrnJiaSPTUaCUX4pz/foEB5lgLF872ISIiRVRUFGzaZPat2rTJJUkFwODBg/nss8+YOHEiDRo0oE2bNkyaNIkaNWo4XfdNN93E1VdfTevWrenduze33Xabw2J8Y8aM4fnnn2fs2LFERETQsWNH5syZc9F7v/3225QtW5aWLVvStWtXOnToQJMmTRzOGT16NPv27ePKK68kJCQEgIiICD788EPGjRtHZGQkv/32GyNHjrzo6yhVqhTLli2jatWqREVFERERweDBg0lJSbGeYBQUm3Fu5y25qMTERIKCgkhISCjwv6DsIsdHsiVuCwZn/8ps2GgY1pBN928CzDmW4+PjCQ0NxcNDeaM7uuQ2EBkJv//uuM9mM7+RysMjVyma9LtAQO1A4PDhM/z9914iImpSurS/q8MpsgYMGMDJkyeZOXOmq0MpENm7QtkcpggzpaSksHfvXmrUqIGfn5/DsUv53KvfMsXIzqM7HZIKAAODHcd2nOcKkTzYvj3nPsOAHWpXIiLFXWAg+PsbnDO2WaRAKLEoRmpVqIUNxyzTho3a5Wu7KCIpEXL79sFmg9pqVyIixV1GhtnLVf1TpDAosShGYtrE5PrEIqZNjIsikmIvKQlOn3bclzXbR4zalYhIcXfiBJw+bct1tlg5a9KkSSW2G1RhUmJRjERFRPFN1DdWOcA7gNhesfSIcM0AKikBvvrKHIgHUK6cy2f7EBERkeJLiUUxc3u9263txhUbK6mQy2cY8L//nS0vWuTy2T5ERESk+FJiUcx42s6Ovsqw67mmOGHRInO1bYAbboBGjVwajoiIiBRvSiyKGZvNZiUX6Zk5l3UXybPsTysefNB1cYiIiEiJoMSiGPLyMJdj1xMLuWx798KsWeb2FVdA9+4uDUdERApGSAgEBhp4e7s6EnEHSiyKISUW4rRx487OPfjAA+h/HBEREXGWEotiyNvT/BCoxEIuy+nT8Pnn5ravLwwZ4tp4RESkwBw/DklJmm7WlSZNmkRwcLCrwygUSiyKoawnFul2jbGQy/D113DypLl9xx3mc3IRESmRMjPBbi9+C+QNGDAAm82GzWbD29ubsLAwbr75ZiZMmIDdbndZLD4+Plx11VWMHj2ajDxma71792bnzp2XdM+2bdvyyCOPXEa0rqXEohhSVyi5bIYB779/tqxB2yIiUkR17NiRQ4cOsW/fPubNm8eNN97Iww8/TJcuXfL8oT6/Y/nrr7947LHHePHFF3njjTfydK2/vz+hoaEFHGHRoMSiGFJiIZdtyRLYutXcbtkSmjRxaTgiIlI8xG6LJXJ8JP4v+RM5PpLYbbEFfk9fX1/Cw8OpXLkyTZo04ZlnnuGHH35g3rx5TJo0yTrv5MmTDB48mJCQEAIDA2nXrh2bN28GICEhAU9PT9atWweA3W6nXLlyXHfdddb1kydPpkqVKnmKpVq1ajzwwAO0b9+eH3/8EYATJ07Qr18/ypYtS6lSpejUqRN//fWXde25XaFefPFFGjVqxFdffUX16tUJCgqiT58+nDp1CjCfkCxdupT33nvPelKyb98+Z97KQqPEohjy9tAYC7lM2aeYfegh18UhIiLFRuy2WKKnRrMlbgspmSlsidtC9NToQkkuztWuXTsiIyOJjT1779tvv534+HjmzZvH+vXradKkCTfddBPHjx8nKCiIRo0asWTJEgC2bNmCzWZj48aNJCUlAbB06VLatGlzSXH4+/uTlpYGmInAunXr+PHHH1m1ahWGYXDrrbeSnn7+Luu7d+9m5syZzJ49m9mzZ7N06VJeffVVAN577z1atGjBkCFDOHToEIcOHbpo4lNUeLk6ALl01hgLrWMhl2L/fvjhB3O7UiWIinJtPCIiUuDKlAF/fwPPs+vr0uyTZhxOOpznOuJOxwFgYDj82Xt6b8ICwvJcT3jpcNbduy7P559PnTp1+P333wFYvnw5v/32G/Hx8fj6+gLw5ptvMnPmTKZPn869995L27ZtWbJkCSNHjmTJkiXcfPPNbN++neXLl9OxY0eWLFnCE088kad7G4bBokWLWLBgAQ8++CB//fUXP/74IytWrKBly5YAfP3111SpUoWZM2dy++2351qP3W5n0qRJlClTBoC7776bRYsW8fLLLxMUFISPjw+lSpUiPDzc2berUCmxKIbUFUouy4cfmiP4AO6/X1PMioi4AT8/89e9R7Y+KoeTDnPw1EGn686wZ+RLPZfKMAxsNhsAmzdvJikpifLlyzuck5yczO7duwFo06YNn3/+OZmZmSxdupRbbrmF8PBwlixZQsOGDdm1axdt27a94D1nz55N6dKlSU9Px263c+edd/Liiy+yaNEivLy8uPbaa61zy5cvT+3atdm2bdt566tevbqVVABUrFiR+Pj4S30rihwlFsWQEgu5ZMnJ8Nln5raPD9x7r2vjERGRQnHmDKSlnf1eCcwnB5ci7nRcrp85vDy8LvmJRX7Ytm0bNWrUACApKYmKFStaXZ2yyxrX0Lp1a06dOsWGDRtYtmwZr7zyCuHh4bz66qtERkZSqVIlrr766gve88Ybb+Sjjz7Cx8eHSpUq4eXl3Edo73O+3LPZbIU+21VBUGJRDGkdC7lk33xjTmYO0Ls3hOX9PwIRESm+Tp+GlBQbmZln911qd6SsMRY2bBgY1p9Te06lR0SPfI74wn755Re2bNnCiBEjAGjSpAmHDx/Gy8uL6tWr53pNcHAwDRs25IMPPsDb25s6deoQGhpK7969mT17dp7GVwQEBHDVVVfl2B8REUFGRgZr1qyxukIdO3aMHTt2ULdu3ct+nT4+PmRm/0srJjR4uxjKvo6FUdwmppbCpylmRUTECVERUczoNYOGYQ3x8/KjYVhDYnvFFnhSkZqayuHDhzl48CAbNmzglVdeoVu3bnTp0oV+/foB0L59e1q0aEH37t1ZuHAh+/btY+XKlTz77LPWTFBgrgvx9ddfW0lEuXLliIiIYMqUKZc8cDu7q6++mm7dujFkyBCWL1/O5s2bueuuu6hcuTLdunW77HqrV6/OmjVr2LdvH0ePHi02TzOUWBRDWYkFgN0oHg1NXOjXX+G/QW5cey00b+7aeEREpNiJiohi0/2bSH42mU33byqUJxXz58+nYsWKVK9enY4dO7J48WLef/99fvjhBzz/G41us9mYO3curVu3ZuDAgdSqVYs+ffqwf/9+wrI9nW/Tpg2ZmZkOYynatm2bY9/lmDhxIk2bNqVLly60aNECwzCYO3duju5Ol2LkyJF4enpSt25dQkJCOHDggFMxFhaboa+8L1liYiJBQUEkJCQQGBhY6PdvM6kNy/YvAyDl2RR8vXytY3a7nfj4eEJDQ/HwUN7ojnK0gdtvh+nTzYNffw133unaAKVQ6HeBgNqBwP79Zzh4cC/169ckMNDf1eGIixiGQUZGBl5eXtbA9+xSUlLYu3cvNWrUwM/Pz+HYpXzu1W+ZYuh48nFru9mnzVwyj7QUA7GxULfu2aQiOBh69nRpSCIiUrh8fMDLC3L5LCmS75RYFDOx22L5I/4Pq7w1fqvLFqmRost3zhw8br8dsk91d/IkzJ7tsphERKTwBQVBqVIGTk5iJJInSiyKmVFLRzmUs2ZnGL10tIsikqKo9FtvYZz79ZTNBqPVTkRE3ElmpjmHhzq+S2FQYlHM7Dy6M8c+A4Mdx3a4IBopqrx27cJ27v8ihgE71E5ERNzJ8eNw6pSNDM1QL4VAiUUxU6tCLWw4fhNtw0bt8rVdFJEUOcuWkev/IDYb1FY7ERERkYKhxKKYiWkTg4HjN9EGBjFtYlwUkRQpCxZgu/XWnE8rbDbziUWM2omIiLsxu0Jpeno5v/xaJ0NDeYqZqIgopt0+jd7Te2M37HjaPJl6e+GvfClF0MyZ0Ls3trQ0AIzGjbFlZMBff5lPKmJioIfaiYiIO/H09OHMGRuHDx8CQvDx8cl1ulEp2c433axhGKSlpXHkyBE8PDzw8fFx6j5KLIqhnnV70umqTsz5aw6ZRib1Quq5OiRxtW++gX79zFF6QErnzvhMn47tnLmoRUTEvdhsHixbVpE77kjg33//dXU44iKGYWC32/Hw8Mg1sSxVqhRVq1Z1er0bJRbF1PVVr2fOX3MAWH5gObUrqO+82/rsM7j3XmvKD+Ouuzg5diyhTn7rICIixV9YGAwenEqlSlUAO5n/fQEl7sVut3Ps2DHKly+fI3nw9PQ878J5l0qJRTHVqkora3vF3ysY1GSQC6MRl3nvPXjkkbPl++/H+N//4OhRl4UkIiJFh4cH+PqCp6cNDw9vvL29XR2SuIDdbsfb2xs/Pz+nn0pciAZvF1PNKzfHx9P8Rnr5geUujkZc4pVXHJOKxx6DDz80/xcREREBjh2DadP8OHbM1ZGIO9AnkGLKz8uPphWbAvDX8b+IPx3v4oik0BgGPPMMPPvs2X0xMfDGG+bsTyIiIv9JS4P9+734b14PkQKlxKIYu77q9db2igMrXBiJFBq73XxKMXbs2X2vvw4vvqikQkRERFxKiUUxdu44CynhMjNhyBB4//2z+8aNg8cfd11MIiIiIv/R4O1irGWVlta2xlmUcOnp5nSy331nlj08YMIE6N/ftXGJiIiI/EdPLIqxkIAQ6lSoA8CGQxs4k37GxRFJgUhJgZ49zyYVXl7mtpIKERG5iMBAuOmmVAIDXR2JuAMlFsVcVneodHs6aw+udXE0ku/OnIHbboMffzTLvr7w/fdw++2ujUtERIqFgABo3DidgABXRyLuQIlFMecwgFvjLEqWxETo2BF++sksBwTAnDnQpYtr4xIRkWIjORn+/NOL5GRXRyLuQIlFMZd9ALfGWZQgx4/DTTfBr7+a5cBAWLjQ3CciIpJHJ0/C3Ll+nDzp6kjEHSixKOauKncVoQGhAKz8eyV2w+7iiMRpcXHQti2sW2eWy5eHxYuhZcsLXiYiIiLiSkosijmbzWY9tUhITWBr/FYXRyRO+ftvaN0atmwxy+HhsHQpNGni2rhERERELkKJRQmQfZzF8r/VHarY2r0bbrgBdu40y1Wrml2h6tVzbVwiIiIieaDEogTIPs5i5d8rXRiJXLZt28wnFfv3m+WrroJly8w/RURELpO3N1SsmIm3t6sjEXegxKIEaFyxMf5e/oBmhiqWNm2CNm3g33/Nct26ZlJRrZpLwxIRkeKvQgXo2zeZChVcHYm4AyUWJYCPpw81y9YEYH/CflpPaU3stlgXRyUXFBsLkZHmuhRNm8KRI+b+Jk3MMRUVK7o2PhEREZFLpMSiBIjdFsvWI2cHbe86uYvbp9+u5KKoio2F6GhzgHZaGtj/m8mrdm345Rf0tZKIiOSXQ4fgzTdLc+iQqyMRd6DEogQYtXQUNmxW2cDAho3RS0e7MCo5r1GjwGYDw3Dc7+0NQUGuiUlERETESUosSoCdR3di4Pgh1cBgx7EdLopILmjnzpxJBcCuXYUfi4iIiEg+UWJRAtSqUMvhiQWADRu1y9d2UURyQdWr59xns5ldoURERESKKSUWJUBMm5hcn1jEtIlxUURyQZGRjuWsblEx+vsSERGR4kuJRQkQFRHFjF4zrJmhANpVb0ePiB4ujEpyZbfDb7+dLfv6QsOG5oDuHvr7EhGR/BUSAoMGnSYkxNWRiDtQYlFCREVEsXXoVvy8/AD46/hfGLn14xfXWroU9u41tzt0gJQUcx0LJRUiIlIAvLygbFkDLy9XRyLuQIlFCeLn5Wetwv134t/sOq7BwEXOxIlnt++5x3VxiIiIWzhxAubM8eXECVdHIu5AiUUJ075Ge2v75z0/uzASySEhAaZPN7fLloXbbnNtPCIiUuKlpMC2bd6kpLg6EnEHSixKmJtq3mRt/7xXiUWRMnUqJCeb2337gp+fa+MRERERyUdKLEqYRmGNKOtbFoBf9v5Cpj3TxRGJZcKEs9sDB7ouDhEREZECoMSihPH08KRVZXOcxcmUk2w4tMHFEQkA27bB6tXmdmQkNG7s2nhERERE8pkSixLohso3WNsaZ1FEZB+0PXCguXaFiIhIAStdGlq2TKN0aVdHIu5AiUUJ1Lpya2tb4yyKgPR0+PJLc9vb2xxfISIiUgjKlDETizJlXB2JuAMlFiVQtcBqVA+uDsDyA8s5k37GtQG5u/nzIS7O3L7tNqhQwbXxiIiI20hNhb17PUlNdXUk4g6UWJRANpuNm2qYs0OlZaax4sAKF0fk5s7tBiUiIlJIjh+HGTP8OX7c1ZGIO1BiUUJlJRagcRYuFR8Ps2aZ2xUrmqtti4iIiJRASixKqHbV21nbGmfhQl9/DRkZ5na/fuDl5dp4RERERAqIEosSKiQghEbhjQDYeGgjR88cdW1A7sgwtHaFiIiIuA0lFiVY+xrtATAwWLx3sYujcUPr18Mff5jbLVtC7dqujUdERNyOpycEB9vx9HR1JOIOlFiUYO1rtre2Nc7CBbI/rbjnHtfFISIibis0FAYPPkNoqKsjEXegxKIEu77q9fh4+gAaZ1HokpPh22/N7VKloFcv18YjIiIiUsCKVWIxduxYmjdvTpkyZQgNDaV79+7s2LHD4ZyUlBSGDRtG+fLlKV26NNHR0cRlrSHwnwMHDtC5c2dKlSpFaGgojz/+OBlZA2xLkACfAFpWaQnAnhN72HNij4sjciMzZ8LJk+b27bejlYlERMQV4uJg3LgAzvkoJFIgilVisXTpUoYNG8bq1av56aefSE9P55ZbbuH06dPWOSNGjGDWrFlMmzaNpUuX8u+//xIVFWUdz8zMpHPnzqSlpbFy5Uq++OILJk2axAsvvOCKl1TgssZZACzas8iFkbgZrV0hIiJFgN0Oyck27HZXRyLuoFglFvPnz2fAgAHUq1ePyMhIJk2axIEDB1i/fj0ACQkJfP7557z99tu0a9eOpk2bMnHiRFauXMnq1asBWLhwIX/++SeTJ0+mUaNGdOrUiTFjxjBu3DjS0tJc+fIKRPZxFvfPuZ/I8ZHEbot1YUQlXGwsRETATz+Z5bAwaN3atTGJiIiIFIJilVicKyEhAYBy5coBsH79etLT02nf/uyH6Tp16lC1alVWrVoFwKpVq2jQoAFhYWHWOR06dCAxMZGtW7cWYvSF4++Ev61tu2FnS9wWoqdGK7koCLGxEB0N27ef3RcXB99/77qYRERERApJsV2ty26388gjj9CqVSvq168PwOHDh/Hx8SE4ONjh3LCwMA4fPmydkz2pyDqedSw3qamppKamWuXExEQrBnsRe7Zot9sxDMOKa8yyMQ7HDQxs2Bi1dBTda3d3QYQll23UKLDZsBmGtc+w2WDUKIzu3QstjnPbgLgntQMBtQNxbANqBu7Lmd8Fl3JNsU0shg0bxh9//MHy5csL/F5jx45l1KhROfYfOXKElJSUAr//pbDb7SQkJGAYBh4eHuw4tiPHOQYGO47uID4+3gURllxhO3Y4JBUANsPA2L69UN/rc9uAuCe1AwG1A4HMTDudO58mMzOA+Hi1AXflzO+CU6dO5fncYplYDB8+nNmzZ7Ns2TKuuOIKa394eDhpaWmcPHnS4alFXFwc4eHh1jm//fabQ31Zs0ZlnXOup59+mkcffdQqJyYmUqVKFUJCQggMDMyvl5Uv7HY7NpuNkJAQPDw8qF2+Nlvit2Bw9gOvDRt1KtQhVJNa56/atTG2bMmRXODpSWipUlC6dKGEcW4bEPekdiCgdiBmG/D1PaI24Oac+V3g5+eX53OLVWJhGAYPPvgg33//PUuWLKFGjRoOx5s2bYq3tzeLFi0iOjoagB07dnDgwAFatGgBQIsWLXj55ZeJj4+3Plj/9NNPBAYGUrdu3Vzv6+vri6+vb479Hh4eRfIfqc1ms2KLaRtD9NRoh+MGBjFtYopk7MVaTIw5xsJmg2zJhS05GVvv3vDjj+DtXSihZG8D4r7UDgTUDtxdYiIsWeJLp04eBAerDbizy/1dcCnnF6sWNmzYMCZPnsw333xDmTJlOHz4MIcPHyY5ORmAoKAgBg0axKOPPsrixYtZv349AwcOpEWLFlx33XUA3HLLLdStW5e7776bzZs3s2DBAp577jmGDRuWa/JQ3EVFRDGj1wxqBJ9Nwlpe0ZIeET1cGFUJFRUFM2ZAw4bg5we1a5uL4wHMnw/33eeQcIiIiBS006dh/Xofss3ML1JgilVi8dFHH5GQkEDbtm2pWLGi9TNlyhTrnHfeeYcuXboQHR1N69atCQ8PJzb27AxInp6ezJ49G09PT1q0aMFdd91Fv379GD16tCteUqGIiohix/AdhJQKAWD9ofWcTDnp2qBKqqgo2LTJXHl7+3YzochKWCdONJ9qiIiIiJRAxa4r1MX4+fkxbtw4xo0bd95zqlWrxty5c/MztCLP29ObOxvcyXtr3iM1M5VpW6cxpOkQV4dV8t1wA0yeDL16mU8rxoyBK66Ae+91dWQiIiIi+apYPbEQ5/SL7Gdtf/n7ly6MxM307AnvvHO2/MADMHu26+IRERERKQBKLNxI4/DG1A0xB6gvP7CcPSf2uDgiN/Lww/DYY+a23Q69e8M5s5OJiIjkt1KloFGjdGvIn0hBUmLhRmw2G/0ann1qMfn3yS6Mxg29/jr06WNunzkDnTvDrl2ujUlEREq0oCBo3z6VoCBXRyLuQImFm+nbsC82bAB89ftXeRq3IvnEwwMmTYIbbzTLR49Cx46ghQpFRKSApKdDXJwH6emujkTcgRILN3NF4BW0q9EOgF3Hd7H6n9UujsjN+PpCbCzUr2+Wd++GLl3QPIAiIlIQjh6Fr74qxdGjro5E3IESCzd0d8O7re2vfv/KhZG4qeBgmDfPnB0KYO1ac9aojAyXhiUiIiLiDCUWbigqIopS3uYoru/++I7UjFQXR+SGrrjCTC6yOr3OnWvOFqWuaSIiIlJMKbFwQ2V8y9Cjjrny9omUE8z5a46LI3JT9evDzJng42OWP/vMXOdCREREpBhSYuGmsq9poe5QLtS2LXyZbU2RmBiYMMFl4YiISMlis4GPj4HN5upIxB0osXBTN9W4iYqlKwIwZ+ccjp055uKI3Fjv3vDWW2fL995rdpMSERFxUng4PPTQacLDXR2JuAMlFm7K08OTvg36ApBuT2fK1ikujsjNPfooPPKIuZ2Zaa7WvW6dS0MSERERuRRKLNxY9u5QX27+8gJnSqF46y24/XZzO2sBvT1aHV1ERC7fkSMwcWIpjhxxdSTiDpRYuLEGYQ2IDIsEYM3BNew8ttPFEbk5Dw9zvEXr1mY5Pt5cQE//G4iIyGXKyIBjxzw0o7kUCiUWbs5hEPdmDeJ2OT8/c6aoevXM8l9/Qdeu5hMMERERkSJMiYWbu6P+HXjYzGYwectk7IbdxREJZcuag7crVTLLa9bAHXdoAT0REREp0pRYuLmKZSpyy5W3ALDv5D78X/IncnwksdtiXRyZm6tSxUwuAgPN8o8/QlgY+PtDZCTE6u9HREREihYlFkKd8nWs7TR7GlvithA9NVrJhas1bAjffw+enmb5+HFISYEtWyA6WsmFiIhcVNmy0L17MmXLujoScQdKLISf9/7sUDYwsGFj9NLRLopILO3ane0SlcUwzBWPRuvvR0RELszPD666KhM/P1dHIu5AiYWw69iuHPsMDHYc2+GCaCSH3GaFMgzYtq3wYxERkWIlKQlWr/YmKcnVkYg7UGIh1KpQCxs2h302bNQuX9tFEYmDWrXMJxTnSk+Ht98Guwbci4hI7k6dguXLfTl1ytWRiDtQYiHEtImxuj9lMTAY0mSIC6MSS0zM2e5P2RkGPPYYtG8PBw64JjYRERGR/yixEKIiopjRawYNwxriafO09k/cNJG0zDQXRiYAREXBjBnmYG4/P2jQALp1O5toLF5sHvvmG9fGKSIiIm5NiYUAZnKx6f5NJDyVYHWBWn9oPS8sfsHFkQlgJhebNkFyMvz+u7mI3i+/mNPSAiQkQN++5noXJ064MlIRERFxU0osxEGATwDfRH+Dt4c3AK+veJ3Fexe7OCrJVdu2ZpLRt+/Zfd99hy0yEp9ly1wWloiIFB1+flCrVoZmhZJCocRCcmhSsQkvt3sZMMda3P393Rw7c8zFUUmugoNh8mT47jtzG7AdPEi53r2xjRhhPuEQERG3VbYs3HZbitaxkEKhxEJy9VjLx2hXox0AB08d5L7Z92EYhoujkvPq3dtcOO+mm6xdtvffh2bNzC5UIiLiljIz4dQpG5mZro5E3IESC8mVh82DL7t/STn/cgDM2DaDCRsnuDgquaArroCFC7G/8w6Gr6+5788/4Zpr4LXX0P8qIiLuJz4ePv44gPh4V0ci7kCJhZxX5cDKfNb1M6v80PyH2Hlspwsjkovy8ICHHuLYggUYjRqZ+9LT4amn4MYbYd8+V0YnIiIiJZgSC7mgHhE9rPUszqSf4c4Zd2oK2mIgo3ZtjFWrzIQia1raX381p6X94gtzDQwRERGRfKTEQi7qnQ7vaAra4sjHB8aOhaVLoVo1c9+pUzBgANx+Oxw96tLwREREpGRRYiEXpSloi7kbbjCnpe3f/+y+GTPMhfbmz3ddXCIiIlKiKLGQPMltCtrjycddHJXkWWAgTJoE06dD+fLmvsOHoVMnGD4czpxxaXgiIlIwwsPhkUeSCA93dSTiDpRYSJ6dOwXtvbPu1RS0xU10tDktbceOZ/eNGwdNmsC6da6LS0RECoTNBl5eZ4fbiRQkJRaSZ5qCtoSoWBHmzoUPPgB/f3Pfjh3QogW89BJkZLg2PhERyTfHjsGUKf4c0zq3UgiUWMgl0RS0JYTNBsOGwYYN0LSpuS8jA55/Hlq3ht27XRufiIjki7Q0+PtvT9I0oaMUAiUWcsk0BW0JUqcOrFoFzz1nroEBZjkyEj77TNPSioiISJ4psZDLcu4UtDGLY1wckVw2b28YM8Zc56JmTXPf6dMwZAh0746WaxUREZG8UGIhl+XcKWhfW/GapqAt7lq2hE2bYPDgs/t+/NGclnb2bJeFJSIiIsWDEgu5bE0qNuGldi8BmoK2xChTBj79FGbOhJAQc198PHTtCvfdB0lJLg1PREQuTVAQ3HJLCkFBro5E3IESC3HKyJYjNQVtSdStmzktbefOZ/d98gk0bgxr1rguLhERuSSlSkHDhhmUKuXqSMQdKLEQp+Q2Ba3PSz5Ejo8kdlusi6MTp4SFwaxZ8PHHWP8j7dpldpkKDwc/P3OQd6z+nkVEiqozZ+D33720DqoUCiUW4rTKgZUZ3Phsv/wMewZb4rYQPTVayUVxZ7PBvfeaYy+uvdbcZ7dDXBykpppPNaKjlVyIiBRRCQmwcKEfCQmujkTcgRILyRfzd893KBuY3aFGzB9Bhl0LrhV7V18Ny5ebTzGyMwwz+Rg92jVxiYiISJGhxELyxc6juS+SdyDxAHU+qMPnGz7XWhfFnZcXuX7lZRjwxx/mEwwRERFxW0osJF/UqlALG7Zcj+0+sZvBswZz9f+u5sO1H5KSkVLI0Um+qVXLfEJxrsxMaNVKK3aLiIi4MSUWki9i2sRgYFjJRdafDcMaWuccSDjAsLnDqPleTd5e9Tan0067JFZxQkzM2e5P4JhkrF8PTZrA9OmuiU1ERHLw8YEqVTLx8XF1JOIOlFhIvoiKiGJGrxk0DGuIn5cfDcMaEtsrls33b2blPSvpfPXZaUsPJR3isYWPUf296oz9dSyJqYkujFwuSVQUzJgBDRuas0I1bAhvvWWOwQBITITbb4fhwyFFT6ZERFytfHno3TuZ8uVdHYm4A5uhRQcuWWJiIkFBQSQkJBAYGOjqcBzY7Xbi4+MJDQ3Fw6No5Y0bD23kpV9fyjFTVLBfMA9f+zAPXfuQNW2tXD6XtIFTp8zZo7777uy+xo1h6lS46qrCiUEcFOXfBVJ41A4kM9POoUPxVKwYiqen2oC7cuZ3waV87lULk0LTuGJjZvSawR8P/MGdDe7Ew2Y2v5MpJxm1dBTV3q3GUz8/RfzpeBdHKpesTBn45htzzQtfX3Pfxo1m16hp01wbm4iIGzt8GN59tzSHD7s6EnEHSiyk0NULrcfXUV+zbdg2BjYaiJeHFwBJaUm8tuI1qr9bnRHzR3Aw8aCLI5VLkrXmxZo15iBvMJ9k9OoFw4apa5SIiEgJp8RCXKZW+VpM6DaBvx78iweaPYCPpzmyLDkjmXfXvEvN92vywOwH2Hdyn2sDlUsTGQnr1sEdd5zd9+GH5ordu3a5Li4REREpUEosxOWqB1fnw84fsuehPTxy7SP4e/kDkJaZxvj147n6f1dzzw/38Nexv1wcqeRZmTLw9dfwySfmIG842zVqyhTXxiYiIiIFQomFFBmVAyvzTsd32PfIPp5s9SSlfUoDkGHPYOKmidQZV4e+sX3ZGr/VxZFKnthsMGRIzq5RffrA0KHqGiUiIlLCKLGQIic0IJRX27/K/kf2E9MmhmC/YADshp1vtnxD/Y/qEz01mg2HNrg2UMmbhg3NrlF9+57d99FH0KIF/KWnUCIiBSk0FO677zShoa6ORNyBEgspssr5l+PFti+y/5H9vNLuFSqUqmAdi90WS9NPmtLlmy6s/me1C6OUPClTBr76Cj777GzXqE2b1DVKRKSAeXpCmTIGnp6ujkTcgRILKfICfQN5+oan2ffwPt6+5W0qlq5oHZvz1xxafN6C9l+2Z8m+JWhZliLMZoNBg+C336B2bXNfUpLZNeqBB9Q1SkSkAJw4AT/+6MeJE66ORNyBEgspNgJ8AhjRYgR7Ht7DuFvHUSWwinVs0d5F3PjFjbSe1JoFuxYowSjKGjQwu0bdddfZfePHw3XXwc6drotLRKQESkmBnTu99N2NFAolFlLs+Hn5MbT5UHY9tIvPun7GlWWvtI4tP7Ccjl935NrPruXHHT8qwSiqSpeGL7+Ezz8/2zVq82Zo2hS+/da1sYmIiMhlUWIhxZaPpw+Dmgxi+/DtTO4xmYgKEdaxtf+updt33Wj0cSOmbp1Kpj3ThZFKrmw2uOces2tUnTrmvqQkuPNOuO8+SE52bXwiIiJySZRYSLHn5eFF34Z9+WPoH0y7fRqRYZHWsd/jfqf39N7U/6g+X23+igx7hgsjlVw1aABr18Ldd5/d98kn6holIiJSzCixkBLDw+ZBz7o92XjfRmbdMYtrKl9jHdt+dDv9Zvaj9ge1+XT9p6RlprkwUsmhdGn44guYMAH8zQUS+f13dY0SEXFSmTJw/fWplCnj6kjEHSixkBLHZrPRpVYXVg9azcK7FnJD1RusY3tO7OHe2fdy5ftX8sFvH5Ccru42RYbNBgMHmk8vIv7r1qauUSIiTildGq67Lp3SpV0dibgDJRZSYtlsNm6+8maWDVzG0gFLubnmzdaxfxL/4cF5D1Lz/Zq8tfItktKSXBipOKhXz0wu+vU7uy+ra9SOHa6LS0SkGEpJgV27PDUrlBQKJRbiFlpXa83CuxeyetBqutbqau0/nHSYkT+NpPq71Xl52cskpCS4MEqxBASYXaMmTszZNerrr10bm4hIMXLiBMyc6a91LKRQKLEQt3LtFdfy4x0/svG+jfSs2xMbNgCOJR/jucXPUe3datw+7XbqfVgP/5f8iRwfSey2WBdH7cYGDHDsGnX6tLn+xc03m4O+/f0hMhJi9XckIiLiakosxC01Cm/EtNun8cfQP+jboC8eNvOfQkJqAtP/nM6fR/4kJTOFLXFbiJ4areTClbK6Rg0YcHbfzz/DH3+Yz/i3bIHoaCUXIiIiLqbEQtxa3ZC6TI6azI7hO7in0T05jhuYC+z1n9mf0UtHs2TfEg34doWAALNb1KRJ5iDv7AzD3Dd6tEtCExEREZOXqwMQKQquKncVn3f7nK+3fE1qZmqO40lpScQsiQHA28ObaypfQ+tqrWldrTUtq7Qk0DewsEN2T/37w733Qto50wUbBmzf7pqYRESKMC8vKF/ejpc+8UkhUDMTyaZ2hdpsidtiPanITbo9nRV/r2DF3ysYu3wsHjYPGoU3onVVM9G4odoNVChVoRCjdjN16pjdn4xz/o48PGDfPqhe3RVRiYgUSSEhMHDgGUJCNN+sFDx1hRLJJqZNDAaGNag768/xncfzRfcvGNR4EFeXu9rhGrthZ8OhDby75l2ipkYR8kYI9T6sxwOzH+DbLd/yT+I/hf46SrSYmLPdn7JLToYmTWD2bNfEJSIi4uZshnHu135yMYmJiQQFBZGQkEBgYNHqAmO324mPjyc0NBQPD+WNlyN2Wyyjl45mx7Ed1C5fm5g2MfSI6OFwzuGkw/y6/1eW7V/GsgPLLvqUo2bZmubTjKo30Lpaa64seyW2cz8Y5xO3aAOxseaYih07oGpVSEiAuLizx598El56CXd+9u8W7UAuSu1A/v3XzgcfnGb48AAqVVIbcFfO/C64lM+9SiwugxILOdfx5OOsOLDCSjTW/7ueTCPzvOdXLF3RGqPRulpr6obUtWamcpZbtoGEBHPV7u+/P7uvdWv47juoWNF1cbmQW7YDyUHtQA4etPPOO2cYMaIUlSurDbirwkos3PfrPJF8VM6/HF1rd6VrbXPxvaS0JFb9vYpl+5fx64FfWf3PaodB4YeSDjFl6xSmbJ1iXZ/1NOOGqjfQuGJjvDz0zzPPgoJgxgx491144gnIyIBly6BxY/j2W7jxRldHKCIiUuLpk4tIASjtU5qbr7yZm6+8GYDUjFTW/rvWfKKxfxkr/l5BUlqSdf7x5OP8sOMHftjxg3V9yyotrQHhzSs3x8/LzyWvpdiw2WDECLj2WujVCw4eNLtHtW9vdpt6+mlzgLeIiIgUCCUWIoXA18uX66tez/VVr+eZG54hw57BpsObrETj1wO/cjz5uHV+UloSC3cvZOHuheb1nr5ce8W1VqLRokoLSvtoho9ctWwJGzeaK3QvXAh2Ozz3HKxYAV99BeXLuzpCERGREkljLC6DxlhIfrMbdv488qc5IPzAMpbuW8qhpEPnPd/T5kmTik2sMRrXV72ecv7lzLrUBkyZmfDyy/Dii2enpq1aFaZONZ9qlHBqBwJqBwKpqXa2bz9KnToV8PVVG3BXGrxdhCmxkIJmGAZ7TuyxBoMv27+MPSf2XPCaBqENzCSjyvVElIqgQY0GagMAP/0EffvCkSNm2dsb3nwTHnww55S1JYh+FwioHYjagJiUWBRhSizEFf5J/Mea4vbXA7+y9cjWC55/dbmrHaa4rR5cvcCmuC3yDh6E3r3N7lBZbr8dPvsMiti/4fyi3wUCagcCJ07YmTMngc6dgyhbVm3AXSmxKMKUWEhRcPTMUZYfWG6N09h4eCN2w37e868IvMLsOvXfOI06Feq4V6KRng7PPGM+rchy9dUwfTo0bOi6uAqIfhcIqB2IppsVk6abFZELqlCqAt3rdKd7ne4AJKYmsvLvlSzdt5RFuxex+chm0jLTrPP/SfyHb7Z8wzdbvrGuz55oNAxriKeHpyteSuHw9oY33oBWrWDAAHPti7/+MsdbfPSRuU9EREQumxILkRIi0DeQjld15Jaat/Bw/YcpU7YM6w6ts8ZprPx7JWfSz1jnHz1zlNhtscRui7Wub1WllTUgvFmlZvh4+rjq5RSc7t1h/XqzK9TGjZCSYi6u9+uv8MEH4O/v6ghFRESKJSUWIiWUv7c/baq3oU31NgCkZ6az4dAGK9H4df+vJKQmWOcnpiYyb9c85u2aZ17v5c91V1xnJRrXXXEdpbxLueS15Lsrr4SVK+GRR+Djj819EybAunVm16irr3ZpeCIiIsWREgsRN+Ht6c21V1zLtVdcy+OtHifTnskf8X9Yg8GX7V9G3Ok46/zkjGQW71vM4n2LAfDy8KJZpWZW16lWVVsR7BfsoleTD/z8YPx4uP56uO8+OHMGfv8dmjY1k4yePV0doYiI0wICoGnTNAICSsgXQ1KkafD2ZdDgbSnKLrcNGIbBX8f/sgaDL9u/jP0J+897vg0bkeGRVqJxQ7UbCA0IzY+XUPi2bjUTie3bz+57+GF4/XXwKZ7dwfS7QEDtQNQGxKRZoYowJRZSlOVnG9h/cr/1NGPZ/mXsOLbjgufXqVDHIdGoGlTVqfsXqqQkuPde+Pbbs/uuuw6mTDEX1itm9LtAQO1AICXFztatR6lXrwJ+fmoD7kqzQomIy1ULrka14Grc1fAuAOKS4s5OcXtgGZsPb8bg7HcT249uZ/vR7Xyy4RPz+qBq1hiN1tVac3W5q4vuFLelS8PXX8MNN5hjL9LSYPVqaNzY3N+xo6sjFBG5ZMeOwbfflmLECKhc2dXRSEmnxEJE8iysdBjRdaOJrhsNwMmUk6w4sMJKNNb9u44Me4Z1/v6E/Xz1+1d89ftX5vUBYQ6JRv3Q+njYitA3aDYbPPAANG9uzhq1bx8cPw633grPPgsvvgieJXhKXhEREScUof/RL+6jjz6iYcOGBAYGEhgYSIsWLZg3b551PCUlhWHDhlG+fHlKly5NdHQ0cXFxDnUcOHCAzp07U6pUKUJDQ3n88cfJyMg491YikgfBfsF0rtWZ125+jVWDVnHyyZP8fPfPvND6BdpWb4ufl5/D+XGn45j25zQenPcgkeMjKf96ebp+25U3VrzBmn/WkJ6Z7qJXco5mzWDDBuja1SwbBrz0EtxyC5zzO0VERERMxeqJxRVXXMGrr77K1VdfjWEYfPHFF3Tr1o2NGzdSr149RowYwZw5c5g2bRpBQUEMHz6cqKgoVqxYAUBmZiadO3cmPDyclStXcujQIfr164e3tzevvPKKi1+dSPEX4BPATTVv4qaaNwGQmpHK+kPrrTEayw8s51TaKev8kyknmb1zNrN3zgaglHcpWlZpaY3TuKbyNfh7+xO7LZZRS0ex8+hOalWoRUybGKIiogr2xZQtCzNnmit1P/MMZGbCL7+YXaOGDoVp02DnTqhVC2JiIKqA4xERESniiv3g7XLlyvHGG2/Qs2dPQkJC+Oabb+j53zSR27dvJyIiglWrVnHdddcxb948unTpwr///ktYWBgA48eP58knn+TIkSP45HH2Fw3elqKsKLeBTHsmm+M2O8w8dSz52HnP9/H0oWbZmmw/uh0bNgwM688ZvWYUfHKRZdky6NMHDh3KecxmM59ozJhRpJKLotwOpPCoHcihQ3Y+/PA0Q4cGULGi2oC70uDti8jMzGTatGmcPn2aFi1asH79etLT02nfvr11Tp06dahataqVWKxatYoGDRpYSQVAhw4deOCBB9i6dSuNGzfO9V6pqamkpqZa5cTERMD8S7Lb7QX0Ci+P3W7HMIwiF5cUnqLcBmzYaBTWiEZhjXjomocwDINtR7ex7MAya1D4wVMHrfPTMtPYftScAjZrkHhWcjFq6Si61+5eOIFffz2sX4/trruw/fKL4zHDwLDZYNQojO6FFE8eFOV2IIVH7UBCQuwMHZpESIg/agbuy5nfBZdyTbFLLLZs2UKLFi1ISUmhdOnSfP/999StW5dNmzbh4+NDcHCww/lhYWEcPnwYgMOHDzskFVnHs46dz9ixYxk1alSO/UeOHCElJcXJV5S/7HY7CQkJGIahb6fcVHFrAxWoQFSVKKKqRGG0NPj71N+sPrTa+tmbuDfHNQYG245sIz4+vvACtdngyy8Jq1YNW2am4yHDwNixo3DjuYji1g6kYKgdiNqAgHPt4NSpUxc/6T/FLrGoXbs2mzZtIiEhgenTp9O/f3+WLl1aoPd8+umnefTRR61yYmIiVapUISQkpEh2hbLZbISEhOgXiJsq7m0gLCyMZlc1YzjDAaj/UX22H93uMK0tQLo9nTHrx/Byu5cJ9C3Ef4f16mFs2YItWy9SA6BGDUJDi84CgcW9HUj+UDuQw4ftTJ8ewKBBAYSGqg24K2d+F/j5+V38pP8Uu8TCx8eHq666CoCmTZuydu1a3nvvPXr37k1aWhonT550eGoRFxdHeHg4AOHh4fz2228O9WXNGpV1Tm58fX3x9fXNsd/Dw6NI/qK22WxFNjYpHCWpDbzU7iWip0ZbYyuy+3Ddh/yw4wfG3TqObnW6FU5AMTEQHX12bAVgA/jnH2yrV0PLloUTRx6UpHYgl0/twL0ZBiQkeGIYagPu7nJ/F1zK+cW+hdntdlJTU2natCne3t4sWrTIOrZjxw4OHDhAixYtAGjRogVbtmxx6K7w008/ERgYSN26dQs9dhG5uKiIKGb0mkHDsIb4efnRMKwhAyIHUMq7FAAHTx2k+5TuRE+N5t9T/xZCQFHmQO2GDcHXF7K+yUlKgnbtzNmiRERE3FCxSiyefvppli1bxr59+9iyZQtPP/00S5YsoW/fvgQFBTFo0CAeffRRFi9ezPr16xk4cCAtWrTguuuuA+CWW26hbt263H333WzevJkFCxbw3HPPMWzYsFyfSIhI0RAVEcWm+zeR/Gwym+/fzMTuE9k6dCsdrzq7GnbstlgixkXw0dqPsBsFPEIxKgo2bYKUFDh8GLImjUhNhV694K23rKcZIiIi7qJYJRbx8fH069eP2rVrc9NNN7F27VoWLFjAzTffDMA777xDly5diI6OpnXr1oSHhxMbG2td7+npyezZs/H09KRFixbcdddd9OvXj9GjR7vqJYnIZaoeXJ25d87l2+hvCQ0wxzYkpiYydO5Qbph4A1vjtxZOIEFBMGcO9O9/dt/IkfDQQ+baFyIiIm6i2K9j4Qpax0KKMndsA8eTj/P4wseZsGmCtc/bw5unrn+KZ254JscK4AXCMGD0aHjxxbP7brsNvvkGAgIK/v7ncMd2IDmpHUhysp1Nm47RqFF5/P3VBtxVYa1joRYmIsVeOf9yfN7tc37p9wtXl7sa+G/WqGVjaDS+EUv3FezMcYA5mDsmBiZOBK//5sX48Ue48Ub4b5IIEZHC5usLNWpkoh7fUhiUWIhIiXFjjRv5/YHfee6G5/DyMD/c7zi2g7ZftGXIj0M4kXyi4IMYMADmzYOsb3XWroUWLWDHjoK/t4jIOU6dgpUrfbiEpQhELpsSCxEpUfy8/BjTbgwb79vIdVdcZ+3/bONnRIyLYMofUyjwHqDt28Py5XDFFWZ5714zufj114K9r4jIOZKSzMQiKcnVkYg7UGIhIiVS/dD6LB+4nA86fUAZnzIAxJ2Oo8+MPnT9tiv7T+4v2AAaNIDVqyEy0iyfOGEmHFOmFOx9RUREXESJhYiUWJ4engy7Zhh/DvuTbrXPLqA356851PuwHu+ufpdMewHO3FS5MixbBh06mOW0NOjTB157TdPRiohIiaPEQkRKvCsCr2Bmn5nE9oqlYumKAJxOP82IBSO47vPr2HR4U8HdPDAQZs2CQYPO7nvqKRg6FDIyCu6+IiIihUyJhYi4jR4RPdg2bBsPNHvA2rfu33U0+6QZT/70JGfSzxTMjb294dNPYcyYs/vGj4fu3VHHZxEpSH5+EBGRjl8hzLotosRCRNxKkF8QH3b+kOUDl1M3pC4AmUYmr698nfof1mfh7oUFc2ObDZ57Dr76ykw0wFxYr00bc/VuEZECULYsdO6cStmyro5E3IESCxFxS62qtmLjfRsZc+MYfDx9ANh7ci8dJnfg7u/v5sjpIwVz47vuggULzBW7ATZsgOuugz//LJj7iYhby8iAEyds6nkphUKJhYi4LR9PH55r/Ry/3/87rau1tvZP/n0yEeMi+HLzlwUzNe2NN8KKFVC1qlnevx9atYKlhbCQn4i4lSNH4PPPAzhSQN+ViGSnxEJE3F7tCrVZ3H8xn3X9jGC/YACOJR+j/8z+3PzVzew6viv/b1qvnjkdbePGZvnkSbj5Zvjmm/y/l4iISCFQYiEiAnjYPBjUZBDbhm2jd73e1v5FexfR4KMGvLr8VdIz0/P3phUrmtPRdupkltPToW9fGDtW09GKiEixo8RCRCSb8NLhfNfzO2bfMZuqQWZXpZSMFJ5e9DTNPm3Gbwd/y98bli4NP/4I9913dt8zz5hldYoWEZFiRImFiEguOtfqzNahWxlx3Qg8bOavyt/jfue6z67jkfmPcCr1VP7dzMsLPvoIXn317L5PP4WuXeFUPt5HRESkACmxEBE5j9I+pXm7w9usGbyGRuGNADAweG/Ne9T7sB6zd87Ov5vZbPDkk+YYCx9zlirmz4fWreHff/PvPiLiVipWhJEjk6hY0dWRiDtQYiEichHNKjXjt8G/8Xr71/H38gfg78S/6fptV1p+3pK64+ri/5I/keMjid0W69zN7rgDfvoJgoPN8qZNUL06+PpCZCTEOlm/iIhIAVFiISKSB96e3jze6nH+GPoHN9e82dq/6p9VbDu6jZTMFLbEbSF6arTzyUXr1rByJYSEmOX0dEhLgy1bIDpayYWI5NnRo/D11/4cPerqSMQdKLEQEbkENcvWZMFdC5jcYzKeNk+HYwYGNmyMXjra+RtFRECFCo77DMPsMjU6H+oXEbeQng6HDnmSns+T2onkRomFiMglstls9G3YFy8PrxzHDAx2HN2RPzfauzfnPsPQKt0iIlIkKbEQEblMtSvUxoYtx35/b//8WfOiVi3zCcW5MjLMgd0iIiJFiBILEZHLFNMmxur+lN2JlBPc9t1tnE477eQNYs52f8rOMKBbN5gzx7n6RURE8pESCxGRyxQVEcWMXjNoGNYQPy8/qgdVx9vDG4D5u+Zz05c3cezMMSduEAUzZkDDhuDnZ/7ZsqV5LC0NevSAWbPy4ZWISEkVHAy33ppiTTQnUpCUWIiIOCEqIopN928i+dlk9j6yl5/7/UyQbxAAaw6u4YaJN/B3wt9O3CDKnHI2ORk2b4alS6FPH/NYero5S9QPPzj/QkSkRPL3h7p1M/D3d3Uk4g6UWIiI5KPW1VqzbOAywkuHA7Dt6DZaTmjJtiPb8ucGXl7w1Vdw551mOT0devbUFLQikqvTp2HjRm9OO9kzUyQvlFiIiOSzhmENWXnPSq4qdxUA/yT+w/UTr2fNP2vy5wZeXvDll3D33WY5IwN69YLp0/OnfhEpMRITYdEiXxITXR2JuAMlFiIiBaBG2RqsuGcFTSo2AeB48nHafdmO+bvyaTYnT0+YOBEGDDDLmZlmF6kpU/KnfhERkUukxEJEpICEBoSyuP9i2tVoB8CZ9DN0/bYrX//+df7cwNMTPv8c7rnHLGdmml2kvv02f+oXERG5BEosREQKUKBvIHPvnEvPuj0ByLBncNf3d/He6vfy5wYeHvDppzBkiFm22+Guu2Dy5PypX0REJI+UWIiIFDBfL1++i/6O+5veb+17ZMEjPLPoGQzDcP4GHh4wfjzc/1/9dju2AQPwmzrV+bpFpFjz8YFq1TLw8XF1JOIOlFiIiBQCTw9PPuz8ITFtYqx9Y5ePZcisIWTYM5y/gYcHfPghDB0KgM0wCHrkEXMchoi4rfLl4fbbUyhf3tWRiDtQYiEiUkhsNhsvtn2RDzp9YK3W/fnGz+k5tSfJ6cn5cQP44AN48EGzaBjYhgwxx2GIiFuy2yE11fxTpKApsRARKWTDrhnGdz2/s1bp/mHHD3T8uiMnU046X7nNBu+9h/Hww2bRMGDwYPjkE+frFpFiJy4O/ve/0sTFuToScQdKLEREXKBXvV7M7TuX0j6lAVi2fxltJrXh0KlDzldus2G89Ran7z87poP77oOPPnK+bhERkfNQYiEi4iLta7Zncf/FVChVAYDf436n1YRW/HXsL+crt9k49cILGI8/fnbf0KEwbpzzdYuIiORCiYWIiAs1q9SMFfesoFpQNQD2ntzL9ROvZ8OhDc5XbrNhjB0LTz99dt/w4fD++87XLSIicg4lFiIiLlarfC1WDlpJ/dD6AMSfjqftpLb8svcX5yu32eDll+G5587ue/hheOcd5+sWERHJRomFiEgRUKlMJZYNWEarKq0AOJV2ik5fd2L6n9Odr9xmg9GjIebsVLc8+ii8+abzdYtIkRYaCkOHniY01NWRiDtQYiEiUkSU9S/LwrsX0qVWFwDSMtPoNa0X49eNd75ymw1efBFGjTq77/HH4bXXnK9bRIosT08oVcrA09PVkYg7UGIhIlKElPIuxfe9v2dAowEAGBg8MOcBRi8dnT+rdL/wArz00tnyU0/BK684X6+IFEnHj8P33/tx/LirIxF3oMRCRKSI8fLwYsJtE3ii5RPWvpglMYS8EYLfS35Ejo8kdlvs5d/g2Wdh7FjHcng4+PtDZCTEOlG3iBQpqamwe7cXqamujkTcgRILEZEiyGaz8drNr/HmzWfHQRxLPkZqZipb4rYQPTXaueTiqafg9dfPluPiICUFtmyB6GglFyIicsmUWIiIFGGPtXyMKoFVHPYZGNiwMXrpaOcqf/xxqFjRcZ9hnB3sLSIicgmUWIiIFHFHTh/Jsc/AYPvR7c5XfuJEzn2GATt2OF+3iIi4FSUWIiJFXK0KtbBhy7Hfz8uPDHuGk5XXMp9QnEtzU4qUCGXKQNu2qZQp4+pIxB0osRARKeJi2sRY3Z+yS0hN4O7v73YuuYiJOdv9Kbt//oHZsy+/XhEpEkqXhmbN0ild2tWRiDtQYiEiUsRFRUQxo9cMGoY1xM/Lj5rBNfHy8ALguz++Y+APA8m0Z15m5VEwYwY0bAh+flC+vLnfbofbb4elS/PpVYiIKyQnw44dXiQnuzoScQdKLEREioGoiCg23b+J5GeT2f3wbmb2nom3hzcAk3+fzOBZg7Eb9susPAo2bTI/gcTHwx13mPtTUqBrV1i3Ln9ehIgUupMnYdYsP06edHUk4g6UWIiIFEOda3Vm2u3TrCcXkzZN4v7Z919+cpHFwwO++AI6dzbLp05Bx46wbZuTEYuISEmnxEJEpJjqVqcb30V/h6fNE4BPN3zK8LnDnV+h29sbpk2D1q3N8rFjcPPNsG+fc/WKiEiJpsRCRKQYi64bzddRX+NhM3+df7TuIx6Z/4jzyYW/P8yaBU2amOWDB83k4vBhJyMWEZGSSomFiEgx17t+b77o/oU1a9T7v73PEz8/4XxyERgI8+dDnTpmedcu6NAh97UvRKRI8vKC0NBMvLxcHYm4AyUWIiIlwF0N72JCtwlW+e3Vb/PKb684n1yEhMDChVC1qln+/Xdz/MXp087VKyKFIiQE+vVLJiTE1ZGIO1BiISJSQgxoNIBPunxilT/Y9AGjlo5yvuIqVeDnn88umrdqlTmTVGqq83WLiEiJocRCRKQEGdJ0CONuHWeVx/w6hpeWveR8xVdfbT65CAoyywsXwl13QeZlrp8hIoXi0CF4++0ADh1ydSTiDpRYiIiUMEObD+XdDu9a5ecXP89ry19zvuLISJgzxxzYDTB9Otx3n7lyt4gUWXa7zdUhiJtQYiEiUgI9eM2DxFwXY5WfWvQUb6962/mKW7WC7783p6QF+PxzePxxJRciIqLEQkSkpLo/8n5eafeKVX5s4WO8v+Z95yvu0AG+/tpcTA/grbfglVcufI2IiJR4SixEREqwJ1s9yei2o63yw/Mf5qO1Hzlf8e23w8cfny0/9xx88IHz9YqISLGlxEJEpIR7vs3zPN/6eas8dO5QPl3/qfMVDx4Mb7xxtvzggzB5svP1iki+qVABBgw4Q4UKro5E3IESCxERNzCq7SiebPWkVb5v9n1M2jTJ+YpHjoRnnjlbHjAAfvzR+XpFJF94e0OFCnZrWJRIQVJiISLiBmw2G2NvGsuj1z0KgIHBPT/cw+Tf8+EJw0svwdCh5nZmJvTqBYsXO1+viDjt5ElYsMCXkyddHYm4AyUWIiJuwmaz8eYtb/LQNQ8BZnLRf2Z/pvwxxdmK4X//gzvvNMupqXDbbbB2rZMRi4izkpNhyxZvkpNdHYm4AyUWIiJuxGaz8W7Hd3mg2QMA2A07fWP7MuPPGc5V7OEBkyZBly5mOSkJOnaEP/90rl4RESk2lFiIiLgZm83GB7d+wODGgwHINDLpNb0X1d+tjv9L/kSOjyR2W+ylV+ztDVOnQps2Zvn4cbj+eoiIMBfVi4yE2MuoV0REigUlFiIibsjD5sHHXT9mQKMBgPnkYn/CflIyU9gSt4XoqdGXl1z4+5uDt5s1M8snTsD27ZCSAlu2QHS0kgsRkRJKiYWIiJvysHnwWdfPCPYLdthvYGDDxuilo3O/8GICA2HePPD1ddxvGOZ4jNGXWa+IXLKAALjmmjQCAlwdibgDJRYiIm7M08OT5PScozoNDHYc23H5FZ9v0nzDgB1O1CsilyQwEFq3TiMw0NWRiDtQYiEi4uZqV6iNDVuO/VeVu8rJimubTyhy2y8ihSI1Ff7+25PUVFdHIu5AiYWIiJuLaRNjdX/KztPmSYY9w4mKY852f8ruiivM/SJS4I4fhylT/Dl+3NWRiDtQYiEi4uaiIqKY0WsGDcMa4uPpg6fNE4DNcZt5bMFjTlQcBTNmQMOGOCz7O2cOvPGGk1GLiEhRo8RCRESIiohi0/2bSH0ulV/6/4KXhxcA7//2Pp+s/8SJiqNg0yZIS4OJE8/uf/JJ+Oor54IWEZEiRYmFiIg4aF2tNR91/sgqD5s7jCX7ljhf8YAB8PLLZ8v33AMLFzpfr4iIFAlKLEREJIfBTQbzyLWPAJBhzyB6ajR7TuxxvuKnn4ahQ83tjAxzXYsNG5yvV0Ry5eEBpUvb8dAnPikEamYiIpKrN255g45XdQTgePJxun7blcTUROcqtdng/fehRw+znJQEnTrBnnxIWkQkh7AwuP/+M4SFuToScQdKLEREJFdeHl58F/0ddSrUAeDPI39yx4w7yLRnOlexpyd8/TW0amWW4+OhQwc4csTJiEVExJWUWIiIyHkF+QUx645ZlPMvB8Dcv+by5M9POl+xvz/8+CNERJjlXbugc2c4fdr5ukXEEhcH48eXIi7O1ZGIO1BiISIiF3RVuauYfvt0a6aot1a9xcSNEy9yVR6UKwfz50PlymZ57Vro1QvS052vW0QAsNshKckDu93VkYg7UGIhIiIXdWONG/lfp/9Z5ftm38fyA8udr7hqVZg3D4KCzPLcuXD//VpAT0SkGFJiISIieXJ/s/sZ3nw4AOn2dHpM6cG+k/ucr7hBA5g5E3x8zPKECfDCC87XKyIihUqJhYiI5Nk7Hd+hfc32ABw9c5Tbvr2NU6mnnK+4bVuYPNmcNQrgpZdg/Hjn6xURkUKjxEJERPLMy8OLqT2nUqt8LQC2xG/hru/vwm7kQwfu22+Hd989Wx42zHySISKXrVw56N07mXLlXB2JuAMlFiIicknK+pdl1h2zCPYLBuDHHT/y7KJn86fyhx6CJ54wt+12uOMOWLEif+oWcUO+vlClSia+vq6ORNyBEgsREblktcrXYmrPqXjaPAF4dcWrfLX5q/ypfOxYuOsuczslBbp2hW3b8qduETeTmAjLlvmQ6OTaliJ5ocRCREQuy81X3sy7Hd+1yoNnDWb1P6udr9jDAz7/HG6+2SyfOAEdO8K//zpft4ibOX0afvvNR0vESKFQYiEiIpdtWPNh3N/0fgDSMtPo/l13DiQccL5iHx+YMQMaNzbLBw5Ap06QkOB83SIiUiCUWIiIyGWz2Wy83+l9bqx+IwBxp+Po9l03Tqflw9ejZcqY61rUqGGWf/8duneH1FTn6xYRkXxXrBOLV199FZvNxiOPPGLtS0lJYdiwYZQvX57SpUsTHR1N3Dnr2B84cIDOnTtTqlQpQkNDefzxx8nIyCjk6EVESgZvT2+m3T6NK8teCcCmw5voN7Nf/swUFR5urs5doYJZXrIE+vVDywiLiBQ9xTaxWLt2LR9//DENGzZ02D9ixAhmzZrFtGnTWLp0Kf/++y9RUVHW8czMTDp37kxaWhorV67kiy++YNKkSbygxZhERC5b+VLlmXXHLAJ9AwGI3RZLzOKY/Km8Vi2YPRtKlTLLU6fCY49pdW6RPPD3hwYN0vH3d3Uk4g6KZWKRlJRE3759+fTTTylbtqy1PyEhgc8//5y3336bdu3a0bRpUyZOnMjKlStZvdocULhw4UL+/PNPJk+eTKNGjejUqRNjxoxh3LhxpKWlueoliYgUexEhEUzpOQUPm/lfy0u/voTPGB8ix0cSuy3WucqvvdZMKDzNWah4912oXNn81BQZCbFO1i9SQgUHQ4cOqQQHuzoScQfFMrEYNmwYnTt3pn379g77169fT3p6usP+OnXqULVqVVatWgXAqlWraNCgAWFhYdY5HTp0IDExka1btxbOCxARKaE6XtWR/pH9rXK6PZ0tcVuInhrtfHLRuTN88snZ8qFD5nS0W7ZAdLSSC5FcpKfD0aMepKe7OhJxB16uDuBSfffdd2zYsIG1a9fmOHb48GF8fHwIPictDwsL4/Dhw9Y52ZOKrONZx3KTmppKarbBgon/TQZtt9uxF7F+vna7HcMwilxcUnjUBgRc2w7W/bvOoWxgYMPGqKWj6F67u3OVDxiA7ZlnsGUfO2cYGDYbjBqF0d3J+ksY/T6Q+Hg7Eyf688gjdipXdnU04irO/C64lGuKVWLx999/8/DDD/PTTz/h5+dXaPcdO3Yso0aNyrH/yJEjpKSkFFoceWG320lISMAwDDw8iuUDKXGS2oCAa9vBzmM7c+wzMNhxdAfx8fFO1x928mSOfTbDwNiRP/WXJPp9IEePQkqKJ0ePnsbb29XRiKs487vg1KlTeT63WCUW69evJz4+niZNmlj7MjMzWbZsGR988AELFiwgLS2NkydPOjy1iIuLIzw8HIDw8HB+++03h3qzZo3KOudcTz/9NI8++qhVTkxMpEqVKoSEhBAYGJhfLy9f2O12bDYbISEh+k/ETakNCLi2HdQuX5st8VswcBxcXc6/HKGhoflwg9oYW7ZgyzZ42wCoVSt/6i9B9PtA0tPt+PklU6GCP6GhagPuypnfBZfyZX6xSixuuukmtmzZ4rBv4MCB1KlThyeffJIqVarg7e3NokWLiI6OBmDHjh0cOHCAFi1aANCiRQtefvll4uPjrf+AfvrpJwIDA6lbt26u9/X19cXX1zfHfg8PjyL5i9pmsxXZ2KRwqA0IuK4dxLSNIXpqNDZsDslF3Ok4fvv3N6674jonbxBjjqmw2ayZoWwAQUHYbDZzv1j0+8C9eXioDYjpctvBpZxfrFpYmTJlqF+/vsNPQEAA5cuXp379+gQFBTFo0CAeffRRFi9ezPr16xk4cCAtWrTguuvM/8huueUW6taty913383mzZtZsGABzz33HMOGDcs1eRARkUsTFRHFjF4zaBjWED8vP0IDzC9x7Iad3tN7czz5uJM3iDJX5W7Y0FyhO+s/veXL4fXXnYxepOTx8NDUzFI4ilVikRfvvPMOXbp0ITo6mtatWxMeHk5stplCPD09mT17Np6enrRo0YK77rqLfv36MXr0aBdGLSJSskRFRLHp/k0kP5vMwUcPcn3V6wE4kHCAgT8MxHB2DYqoKNi0yVyFe/r0s/ufftpc80JEAKhYER599DQVK7o6EnEHNsPp3+7uJzExkaCgIBISEorkGIusbl565Ome1AYEil47+CfxHxqNb8Sx5GMAvH3L24xoMSL/bjBmDGQtdFqmDKxZAxER+Vd/MVXU2oEUPrUBAefawaV87lULExGRAndF4BV81eMrq/zkz0/y28HfLnDFJXruOejZ09w+dQpuuw1OnMi/+kWKqSNH4Msv/TlyxNWRiDtQYiEiIoWi09WdeLLVk4C5cF7v6b05kZxPH/5tNpg0yVyFG2DXLujdGzIy8qd+kWIqIwPi4z31T0EKhRILEREpNGNuHEPLKi0B2HdyH/f8eI/z4y2yBATADz9ASIhZ/uknePzx/KlbREQuSomFiIgUGm9Pb76L/o5y/uUAmLl9Jv/77X/5d4Nq1cwZo7z+m0393Xdh4sT8q19ERM5LiYWIiBSqKkFV+KL7F1Z55MKRrD24Nv9ucMMNMG7c2fL998OqVflXv4iI5EqJhYiIFLoutbowssVI4Ox4i5MpJ/PvBvfeC8OGmdtpadCjB/zzT/7VL1JMBAdD164pBAe7OhJxB0osRETEJV656RVrFe69J/cy+MfB+TfeAuCdd+DGG83tuDjo3h2Sk/OvfpFiwN8fatfOwN/f1ZGIO1BiISIiLpE13qKsX1kAZmybwbi14y5y1aXcwBumToUaNczy+vUwaBBo+SZxI0lJsG6dN0lJro5E3IESCxERcZlqwdWY1H2SVX5s4WNsOLQh/25QoYI5U1RAgFn+9lt47bX8q1+kiDt1CpYs8eXUKVdHIu5AiYWIiLjUbbVvY8R15ircaZlp9JrWi4SUhPy7QYMGMHny2fIzz8Ds2flXv4iIAEosRESkCHi1/atcU/kaAHaf2M2QWUPyd7xF9+4werS5bRhw553w55/5V7+IiCixEBER1/Px9GFKzykE+wUDMO3PaYxfNz5/b/Lcc9Czp7l96hR06wbHj+fvPURE3JgSCxERKRKqB1dnYrezi9k9suARNh7amH83sNlg0iSIjDTLu3ZB796QkZF/9xApYnx94corM/D1dXUk4g6UWIiISJHRvU53Hr72YeC/8RbTe5GYmph/NwgIMAdzh4SY5Z9/hscfz7/6RYqYcuWgR48UypVzdSTiDpRYiIhIkfL6za/TrFIzAHYd38W9s+7N3/EW1arBjBng5WWW330XJk684CUixVVmJpw5YyMz09WRiDtQYiEiIkVK1niLQN9AAKZsnUKVd6rg/5I/keMjid0W6/xNbrgBxmVbM+Pee+Gqq8zVxCIjITYf7iFSBMTHw4cfBhAf7+pIxB0osRARkSKnZtmaTLhtglU+eOogKZkpbInbQvTU6PxJLu69F4YNM7czMmD3bkhJgS1bIDpayYWIyCVSYiEiIkVSdN1oyvuXd9hnYGDDxuilo/PnJu+8c3bxPOsmhjnQe3Q+3UNExE0osRARkSIrKS0pxz4Dgx3HduTPDby9c58VyjBgRz7dQ0TETSixEBGRIqt2hdrYsOXYX6t8rXy8SW3zCUV2Npu5X0RE8kyJhYiIFFkxbWKs7k/ZVSlTJf9mioqJOdv9KYthwKBB+VO/iAuFhcGDDyYRFubqSMQdKLEQEZEiKyoiihm9ZtAwrCHeHt7W/jm75vDO6nfy6SZR5vSzDRuCR7b/Fj/+GM6cyZ97iLiIh4e5SJ6HPvFJIVAzExGRIi0qIopN928i7fk0h5miHlv4GFO3Ts2nm0TBpk2QmAj16pn7tm6FoUPNpxcixdSxYzBtmh/Hjrk6EnEHSixERKTYGNh4IC+2edEq3/393fy6/9f8u0FAAEybdnamqC++gAkTLnyNSBGWlgb793uRlubqSMQdKLEQEZFi5YU2L3BPo3sASMtMo9t33dh2ZFv+3SAiAj799Gx5+HDYvDn/6hcRKaGUWIiISLFis9kY32U8Ha7sAMCJlBN0+roTh5MO599N7rgDHnjA3E5JgZ49zW5SIiJyXkosRESk2PH29Gba7dNoFN4IgP0J++n8Tedc1724bO+8A02bmtu7dpmzRGm8hYjIeSmxEBGRYqmMbxnm3jmXqkFVAdhwaAO9pvUiw57LgneXw9fXHG8RHGyWp0+H//0vf+oWKSSBgXDTTakEBro6EnEHSixERKTYqlimIvP6ziPYLxiAebvm8cDsB/JvjYsaNcwB3FlGjoQ1a/KnbpFCEBAAjRunW/MRiBQkJRbyf/buO67Kuv/j+OtiD0GU5UjNLBBLsLTMsmzYHuohzbJsWLaH3o27MknLbP1aNsy2ZcOCrMws8y5bpmWZpIaWmqYCLkCUfa7fH5ceQLQYB64z3s/Hw0ff73UO13kfuAI+XN8hIuLVesT3YNaFswgJDAHgpV9e4oGvH3DfC5x/vlVQAFRUwNChaO1O8RYlJbBiRRAlJXYnEX+gwkJERLzegIMH8Prg6jsL478az+tLX/+Hj2igBx+E/v2t9oYNMHIkOJ3uO79IMykogDlzwigosDuJ+AMVFiIi4hOGHzGcRwY+4upf9fFVzPtznntOHhwM77wD8fFWf84cePhh95xbRMRHqLAQERGfcdtxt3Hj0TcCUOmsJH1mOr/mumkPio4d4a23wDCs/rhx8NVX7jm3iIgPUGEhIiI+wzAMnjzzSQZ3HwzAzvKdnP3W2Wwo3OCeFxg4EDIyrLbTCcOHQ64b988QEfFiKixERMSnBAYEMsMxg2MPOhaATTs3cdaMsygoLXDPC4wbB6edZrXz8qzN9CrdtMStiJsFB0P79lUEB9udRPyBCgsREfE5EcERfDT8Iw5teygAy7csZ8i7QyirLGv6yQMD4c03oUMHq//VV3DffU0/r0gziIuDESNKiIuzO4n4AxUWIiLik+Ij4/l0xKfERVi/UX217iuu/OhKnKYbVnNKSIB337WKDIBJk+DTT5t+XhERL6bCQkREfNahbQ9l9kWzCQ8KB+Ct7Le4Z/497jl5//7w0EPV/UsugfXr3XNuETfZvBkee6wVmzfbnUT8gQoLERHxaX0P6ss7F7xDgGH9yHvou4d4/sfn3XPy//wHBg2y2tu3w7BhUF7unnOLiHgZFRYiIuLzzk8+nylnTXH1b/z0Rj7K+ajpJzYMeO016NrV6i9aBHfe2fTzioh4IRUWIiLiF64/+nruPN76pd9pOhn8zmBCHwglbWoaWSuzGn/imBh47z0ICbH6Tz5ptdPSIKsJ5xUR8TIqLERExG88eOqD9O/UHwATk/KqcrLzskmfmd604qJ3b7jssup+RQVkZ0N6uooLEfEbKixERMRvBBgBFJYV1jpmYmJgMHHBxKadfNGi2n3TtIZKTWzieUWaID4eRo3aRXy83UnEH6iwEBERv7J62+o6x0xMcrblNO3Eq1bVPWaakNPE84o0QVAQtGljEhRkdxLxByosRETEryTFJWFg1DmeEJHQxBMnWXco9tWlS9POK9IEO3bAJ5+EsmOH3UnEH6iwEBERv5IxIMM1/Kmm3F25LM9f3oQTZ1QPf6qpstL6J2KD0lJYuTKY0lK7k4g/UGEhIiJ+xZHiIHNYJqmJqYQFhdE2rC0A5VXlpM9MZ2fZzkae2AGZmZCaCqGh1j+AP/+svZGeiIiPUmEhIiJ+x5HiYOm1Sym5p4S/x/5NWmIaADnbchg9ezSmaTbyxA5YutT6M/E330BgoHV8wgTruIiID1NhISIifi08OJz3h71PdGg0AO/89g7P/+SGnbmPPhr++1+rXVlpLUerXblFxIepsBAREb93aNtDeXXQq67+rXNvZfHGxU0/8fjx1tAogGXLtPSstLhWreC448pp1cruJOIPVFiIiIhgDY8ae+xYACqcFQx7bxjbS7Y37aQhITB9OgQHW/2HHoLFbihYROopKsoqLKKi7E4i/kCFhYiIyB4PDXyI4zodB8BfhX8x8oOROE1n006almbduQCoqrKGRJWUNDGpSP2UlcHatYGUldmdRPyBCgsREZE9ggODefeCd4mLiAPgk9Wf8PC3Dzf9xP/9L/TpY7V//x3uvbfp5xSph+3bITMznO1NvPkmUh8qLERERGo4KPog3nK85drnYtyX4/hq3VdNO2lQELz+evUStI8/Dt9+27Rzioh4GBUWIiIi+zit22lkDMgAwGk6Gf7+cDbv3Ny0k/boAQ88YLVNEy6/HHbtato5RUQ8iAoLERGR/Rh34jhO73Y6AHm78rgo8yIqnU3cQXvMGDj+eKv9559w551NTCki4jlUWIiIiOxHYEAgbw55k45RHQFY8NcC7v1fE+dGBAbCq69CeLjVf/ZZmD+/iUlFDiwwEGJinK69GkWakwoLERGRA4iPjGfm0JkEBQQB8NB3DzF71eymnfSww+DhGhPCr7wSioqadk6RA0hIgKuu2k1Cgt1JxB+osBAREfkHx3U6jkdPe9TVv/SDS1m7Y23TTnrDDXDyyVZ7/XoYO7Zp5xMR8QAqLERERP7FLX1vIT0lHYCC0gKGvT+MssombAwQEACvvIJrO+SXX4Y5c9yQVKS2vDx49tlI8vLsTiL+QIWFiIjIvzAMg5fPf5lD2x4KwE+bfmLsZ028y3Dwwdays3tdfTXs2NG0c4rsw+mEkhIDZxP3eRSpDxUWIiIi9dA6rDXvD32fsKAwAJ776Tneyn6raSe96io480yrvWkT3HxzE1OKiNhHhYWIiEg9pbVL49mzn3X1R388mpVbVjb+hIYBL74IrVtb/TffhA8+aGJKERF7qLAQERFpgCuPvJLLe10OwK6KXaTPTKe4vLjxJzzoIJgypbp/zTWwZUvTQoqI2ECFhYiISAM9e/az9EzoCcDKrSvp9EQnwh8IJ21qGlkrsxp+wksugUGDrPaWLdaStOHhkJYGWY04n8gesbFw0UW7iY21O4n4AxUWIiIiDRQRHMH7w6rnWxSUFlBaVUp2XjbpM9MbXlwYBrzwQvUqUYWFUFoK2dmQnq7iQhotJAQ6dnQSEmJ3EvEHKixEREQaISk2iYTI2ruOmZgYGExcMLHhJ0xMhLZtax8zTavomNiI84lg7b345Zch2oNRWoQKCxERkUbKL86vc8zEJGdbTiNPWPd8mCbkNPJ84vd27YIlS0LYtcvuJOIPVFiIiIg0UlJcEgZGrWMGBsmxyY08YZJ1h6LWCQ1IbuT5RERakAoLERGRRsoYkOEa/rSXicl/+/+3kSfMqB7+5DqhCXfc0cSkIiLNT4WFiIhIIzlSHGQOyyQ1MZUAo/pH6u9bf2/kCR2QmQmpqRBQ40f04sVNTCoi0vxUWIiIiDSBI8XB0muX8tt1vxEUEATAQ98+xJodaxp5QgcsXQqrVllLzgI8/TQsWuSewOJXIiKgV68KIiLsTiL+QIWFiIiIG6TEp3Br31sBKKsq45a5tzTthN26wYQJVts04eqroaKiaecUv9O6NQwcWOba3F2kOamwEBERcZPxA8bTIaoDALNXzWb2qtlNO+GYMXDkkVY7OxsefbSJCcXfVFRAXl6AalJpESosRERE3CQqNIrHTnvM1b9l7i2UVpY2/oRBQfDii9XzLSZOtIZIidTT1q3wxhsRbN1qdxLxByosRERE3Gj4EcMZ0GUAAGt2rOGR7x5p2gl794axY612WRmMHg1OZxNTioi4nwoLERERNzIMg2fOfoZAIxCAyd9OZu2OtU076YQJ0LWr1V6wAF5+uYkpRUTcT4WFiIiImx2RcAQ3970ZgNLKUsZ8NqZpJ4yIgBdeqO7ffjts3ty0c4qIuJkKCxERkWZw30n30a5VOwA+zPmQT1d/2rQTnnYajBxptQsL4eabm5hQ/IFhQEiIWWdDd5HmoMJCRESkGUSHRvPoadWrON0892bKKsuadtLHH4f4eKv9/vvw4YdNO5/4vHbt4Oabd9Gund1JxB+osBAREWkmI3qO4ITOJwDwx/Y/eOz7x/7lI/5FbCw8+WR1//rrrbsXIiIeQIWFiIhIM9l3IvekbybxV8FfTTvpRRfBmWda7U2b4K67mphSfNmWLfDqqxFs2WJ3EvEHTS4sSktLKStr4q1dERERH5WamMqNx9wIQEllCWM/H9u0ExoGTJ0KkZFW//nn4bvvmphSfFVlJWzbFkBlpd1JxB80uLD46quvGDNmDMcccwytWrUiMjKSiIgIoqKiOOaYY7j11lv56quvmiGqiIiId5pw0gQSIxMByFqZxWd/fNa0E3bpAg88UN2/6iprjwsRERvVq7CoqKjgmWee4ZBDDuGUU07hjTfeICYmhksuuYQ77riD22+/nYsvvpiYmBjefPNNTjnlFLp27cozzzxDhfaQFxERP9c6rDWPnFa9Ud5Nn97U9IncN90ERx9ttX//HSZPbtr5RESaKKg+Tzr00EMpLy/nsssuY9iwYRx11FH/+PwlS5bw3nvv8eCDD/LYY4+xbt06d2QVERHxWpemXsq0JdP4bsN3rN6+mscXPs5dJzRhfkRgILz0krUzd2UlPPggDB0Khx/uvtAiIg1QrzsWd999N+vWreOhhx7616ICoHfv3jz00EOsW7eOuzSpTEREBMMwePbsZwkwrB+9D3zzAOsL1zftpKmpcMcdVruiAkaPBqeziUnFl7RpA4MHl9Cmjd1JxB/Uq7C45pprCA0NbfDJQ0JCuOaaaxr8cQdy3333YRhGrX/du3d3PV5aWsoNN9xAbGwsrVq1Ij09nby8vFrnWL9+Peeccw4REREkJCRw++23U6kZTSIi0gLS2qVxfZ/rAdhdsZv/fP6fpp/03nvhsMOs9vffWxO7RfYIC4NDD60iLMzuJOIPvG652cMPP5zNmze7/n377beux8aMGcPHH3/Me++9x4IFC9i0aRMOh8P1eFVVFeeccw7l5eV8//33vP7667z22muMHz/ejrciIiJ+6P5T7ic+wtrk7v0V7xNyfwhpU9PIWpnVuBOGhcG0adX9m26yjqWlQVYjzyk+o7gYfvghmOJiu5OIP6jXHIsD+e677/j5558pLCzEuc+tV8MwuPfee5sUbn+CgoJot5/tIwsLC3n55Zd56623OOWUUwB49dVXSUlJ4YcffuDYY4/l888/Z8WKFXzxxRckJibSq1cv7r//fu68807uu+8+QkJC3J5XRESkppiwGIb1GMazPz0LQIWzguy8bNJnppM5LBNHiuNfzrAfJ50Ep54K8+dbQ6HKyiA7m4ChQwl96SW44gr3vgnxGjt3wrffhnL00RAdbXca8XWNKiy2b9/OOeecw+LFizFNE8MwME0TwNVursJi9erVdOjQgbCwMPr168fkyZPp3LkzS5YsoaKigoEDB7qe2717dzp37szChQs59thjWbhwIT179iQxMdH1nDPOOIPrrruO5cuXc+SRR+73NcvKymrt1VFUVASA0+msU1DZzel0Ypqmx+WSlqNrQEDXgaf7ev3XtfomJgYGExZMYHDy4Ead09i82fqv66QmpmHQ6vHHcV52WePDiler+b1A3w78V1N+JjTkYxpVWNx+++0sW7aMt956i759+3LIIYfw2Wef0bVrV5544gkWLlzIp59+2phT/6O+ffvy2muvkZyczObNm5kwYQInnHACv/32G7m5uYSEhBATE1PrYxITE8nNzQUgNze3VlGx9/G9jx3I5MmTmTBhQp3jW7ZsobS0tInvyr2cTieFhYWYpklAgNeNdBM30DUgoOvA063atqrOMROTnK055OfnN+qciX/+WV1U7GGYJoF//EFufr6uAz+1dSuUlgaydesugoPtTiN2acrPhJ07d9b7uY0qLObMmcM111zDhRdeyLZt2wAICAjg0EMP5dlnn8XhcHDrrbfy9ttvN+b0B3TWWWe52qmpqfTt25cuXbowc+ZMwsPD3fpaNd11112MHVu9U2pRURGdOnUiPj6eaA+7r+h0OjEMg/j4eP0Q8VO6BgR0HXi65NhksvOzMTFdxwwMusd1JyEhoZEnTcbMzsYwq89pAlWHHkpCQoKuAz9VUeEkLKyEuLhwEhJ0DfirpvxMCGvAzP9GFRYFBQUcvmed7FatWgFQXGNW0Omnn87dd9/dmFM3SExMDElJSfzxxx+cdtpplJeXU1BQUOuuRV5enmtORrt27Vi8eHGtc+xdNWp/8zb2Cg0N3e+qWAEBAR75jdowDI/NJi1D14CArgNPlnFSBukz0zEwXMWFiUnGgIzGf70yMiA9HQwD9g5PBkocDlrpOvBbERGQnFxFRISuAX/X2J8JDXl+o66wDh06uIYOhYaGkpCQwK+//up6fOPGjRjGvjdk3a+4uJg///yT9u3b07t3b4KDg5k/f77r8ZycHNavX0+/fv0A6NevH9nZ2bVuM8+bN4/o6Gh69OjR7HlFREQAHCkOModlkpqYilFjAFPH6I5NOKkDMjOtvS2Cqv9uGP7RR1BV1ZS44sXatIHzzy/VPhbSIhpVWJx44onMmzfP1b/wwgt55JFHmDRpEvfffz9PPvkkJ598sttC7nXbbbexYMEC1q1bx/fff8+QIUMIDAzkoosuonXr1owaNYqxY8fy5ZdfsmTJEq644gr69evHscceC1h3Unr06MGll17Kr7/+ymeffca4ceO44YYbGrVPh4iISGM5UhwsvXYpL573ouvYU4ueauJJHbB0KezeDUccAUDwsmXw/PNNO694raoq2LnTUG0pLaJRhcXYsWM5//zzXSsl3XfffRx77LHce++9ZGRk0Lt3b6ZMmeLWoAB///03F110EcnJyQwbNozY2Fh++OEH4uOt9cCfeOIJzj33XNLT0znxxBNp164dWTXW8A4MDGT27NkEBgbSr18/LrnkEkaOHMnEiRPdnlVERKQ+Lu55MbHhsQDMXD6TTTs3Nf2kwcG1Nsozxo2DTW44r3id/Hx44YVIGrkmgEiDGKZZY5ZXExUUFBAYGEhUVJS7TumRioqKaN26NYWFhR45eTs/P18T9fyYrgEBXQfe5u75dzP528kA3HvivUw82T1/8DKvugrj5ZetztChMHOmW84r3mPjRidPPLGbMWMi6NhR3wv8VVN+JjTk994GnXnatGmkpKQQFhZGx44dGTNmTK39HWJiYny+qBAREXG364++nkAjEICpP02lrLLsXz6ifszJk3G2bWt13nsPmmEpeBGRvepdWMyaNYtrr72Wv//+m9TUVJxOJ08//TTXXnttc+YTERHxeQdFH8QFPS4AYMvuLbzz2zvuOXFsLEUZGdX9G26w5l+IiDSDehcWjz/+ON26deOPP/5g8eLFbNiwgWHDhjFjxgzXTtQiIiLSODf3vdnVfmrRU7hrpHLp0KGYJ51kddauhUmT3HJeEZF91buwyMnJ4ZprrnHtVB0UFMRdd91FZWUlK1eubLaAIiIi/qDfQf3o06EPAL/k/sJ3G75zz4kNA/PZZ3Ftu/zoo7BihXvOLR6vXTu49dZi/mG7LhG3qXdhsWXLFjp06FDrWMeO1nrbu3VbVUREpEkMw+DmY6rvWjy96Gn3nbx7d7jzTqtdUQHXXuvaRE98m2FY25q0wPZiIg2bvN0Sm96JiIj4q2GHDyMx0hoZkLUyiw2FG9x38rvvhm7drPY338Drr7vv3OKxtm2Dd98NZ9s2u5OIPwj696dUe+yxx3j77bdd/YqKCgDuuece4uLiaj3XMAw+/PBDN0QUERHxD6FBoVzb51omLJhAlVnFcz8+x+SBk91z8vBweO45OOMMq3/bbXDuubDPz2/xLeXlsGFDIOXldicRf1DvwqJz585s376d7du31zrepUsXNm/ezObNm2sd190NERGRhru2z7U8+M2DVDgrmPbzNO4dcC8RwRHuOfnpp8Pw4fDOO9afsu+8E/bucyEi0kT1LizWrVvXjDFEREQEoF2rdgw/YjhvLHuD7SXbmbFsBlf3vtp9L/D44zBnDhQVwSuvwOWXwwknuO/8IuK3tAWjiIiIh6m59OzTi59229KzALRvDw8+WN2/9lo0TkZE3KHJhUVxcTEbNmxg/fr1df6JiIhIw/Xp0IfjOh0HwG/5v/Hlui/d+wLXXgtHH221V6yA//s/955fPEbr1nD66aW0bm13EvEHjSosSktLueuuu0hISKB169YcfPDBdO3atc4/ERERaZxmW3oWIDAQpk6FgD2/BkycCGvWuPc1xCNEREBqaiURbpqmI/JPGrQq1F7XX389r7/+OoMHD+aEE06gTZs27s4lIiLi1xwpDjpGdWTjzo18lPMRa3as4ZA2h7jvBY46Cm6+GZ58EkpL4cYb4ZNPtOGBj9m9G5YtC+K446BVK7vTiK9rVGGRlZXFVVddxQsvvODuPCIiIgIEBwZz/dHXc8//7sHE5NnFz/J/Z7h5yNLEifDee7BxI3z6KWRmwgUXuPc1xFaFhfD552EcfrgKC2l+jRoKZRgGRx11lLuziIiISA2je48mLCgMgJd/eZni8mL3vkBUFDxdY5jVLbdYq0WJiDRCowqLQYMG8cUXX7g7i4iIiNQQFxHHiJ4jACgsK6TNw21Im5pG1sos973IkCFwzjlWe9MmaNsW0tIgy42vISJ+oV6Fxd6N8fb+u/fee1mzZg2jR49myZIlbNmypc5z9t1IT0RERBquR3wPV7vSWUl2XjbpM9PdV1wYhrUD915VVZCdDenpKi5EpEHqNcciLi6uzk7apmnyyy+/8PI/7NhZVVXVtHQiIiJ+7vVfX6/VNzExMJi4YCKOFId7XuT552v3TdMqOCZOBIebXkNsERICnTpVERJidxLxB/UqLMaPH1+nsBAREZHmt2rrqjrHTExytuW48UXqvgamCTlufA2xRWwsXHhhCbGxUXZHET9Qr8Livvvua+YYIiIisj9JcUlk52VjUr37toFBcmyyG18kyRr+tO8O3wcf7L7XEFuYJlRW1v3SijSHJu+8LSIiIs0nY0BGraICrDsWGQMy3PgiGdXDn2oKCACn032vIy0uNxeefLIVubl2JxF/UK/CYvLkyezcubPBJy8qKmLy5MkN/jgRERGxOFIcTD13qqsfHRpN1rAshqQMceOLOKw9LFJTITQU14D8FStg2jT3vY6I+LR6FRZvvfUWnTt35vrrr+err776x0nZFRUVfPHFF4wePZrOnTvz9ttvuy2siIiIPxp15CgCDOtHdlJsknuLir0cDli61NqFe86c6uN33AF//+3+1xMRn1OvORbLli3jrbfe4rHHHmPq1KmEhoZyxBFH0LVrV9q0aYNpmuzYsYO1a9fy22+/UVFRQc+ePXnmmWcYMWJEc78HERERnxYUEESHqA78XfQ36wvXN/8LnnoqjBoFL78MO3fCtdfCxx/XHSolIlJDvQoLwzAYMWIEI0aM4JdffmHWrFksXLiQH374gW3btgEQGxtL9+7dufPOOxk0aJB25hYREXGjzq0783fR3+TvyqekooTw4PDmfcFHH4VPPrEG6X/yCbzzDlx0UfO+poh4tXoVFjUdeeSRHHnkkc2RRURERA6gc+vOfL/hewD+Lvqbw2IPa94XbNMGnnuueh+Lm2+G006DuLjmfV1xq4QEuOaaXSQkRNgdRfyAVoUSERHxAp2jO7vaG4o2tMyLDhli7cANsHUr3Hpry7yuuE1gIERFmQQG2p1E/IEKCxERES/QqXUnV7tF5lns9cwzEBNjtWfMqD2xWzzejh3w0Udh7NhhdxLxByosREREvEDn1tV3LFq0sGjXDh5/vLp/7bXWhG7xCqWlsGpVEKWldicRf6DCQkRExAvYVlgAXH45DBxotTdsgLvuatnXFxGvoMJCRETEC9QsLFpsjsVehmFtlBexZwLws8/Ct9+2bAYR8XgqLERERLxAm7A2RARbv9i3+B0LgK5dYdKk6v5VV6HxNSJSU5MKi40bN/L222/z1FNP8feeXTmrqqrYvn37P+7OLSIiIg1jGIbrrsX6wvWYptnyIW66Cfr2tdo5OXD//S2fQRokKgr69y8jKsruJOIPGlVYmKbJ2LFj6dq1KyNGjGDs2LGsWrUKgOLiYg4++GCmTJni1qAiIiL+bm9hsbtiN9tLtrd8gMBAazfu4GCr//DDsHRpy+eQemvVCo49toJWrexOIv6gUYXFo48+ylNPPcVtt93GvHnzav3VpHXr1jgcDjIzM90WUkRERGzay2Jfhx8O99xjtauqYNQoqKy0J4v8q9JS+OOPQI1akxbRqMLixRdfZOTIkTz44IP06tWrzuOpqamuOxgiIiLiHrbtZbGvu+6yCgyAn3+uvRyteJQdO2DWrHDtYyEtolGFxYYNGzjuuOMO+HhkZCRFRUWNDiUiIiJ12brkbE0hIdaQKMOw+hkZsHq1fXlExCM0qrBISEhgw4YD34JdsmQJnTt3PuDjIiIi0nAeU1iANYn7llusdmkpXH01OJ32ZhIRWzWqsHA4HEydOpU1a9a4jhl7/mrx+eef89prrzF06FD3JBQRERHA5r0s9ueBB+Dgg632ggXw0ku2xhERezWqsJgwYQLt27enV69ejBw5EsMwePjhh+nfvz9nnXUWqamp3H333e7OKiIi4tcOij7I1X73t3dJm5pG1sos+wJFRsKLL1b3r7sOwsIgLQ2ybMwlLkFBEBvrJCjI7iTiDxpVWLRu3ZoffviBO+64g40bNxIWFsaCBQsoKCggIyODb775hoi9u3OKiIiIW8xZPcfVNjHJzssmfWa6vcXFwIFw8slW2+mEsjLIzob0dBUXHiA+Hq64Yjfx8XYnEX/Q4Pq1tLSUadOm0atXL8aNG8e4ceOaI5eIiIjsY8KCCbX6JiYGBhMXTMSR4rApFZCfX7tvmtbE7okTwWFjLhFpUQ2+YxEWFsadd95JTk5Oc+QRERGRA1i1te5S7iYmOdts/pn85591j5mmtTu32Co3F55+OpLcXLuTiD9o1FCoI444gnXr1rk5ioiIiPyTpLikOscMDJJjk21IU0NSUvXSszUl25xLME0oLzeosZexSLNpVGExadIkXnjhBb744gt35xEREZEDyBiQUatvYGBi1jne4jIyqoc/1TR8uD15RMQWjVoj4JlnnqFt27acccYZdO3ala5duxIeHl7rOYZh8OGHH7olpIiIiIAjxcFt/W7jsYWPAZDYKpHnzn6OISlDbA7mgMxMa07F8uVQWWkdnzED/vMfCA62N5+ItIhG3bFYtmwZFRUVdO7cmaqqKv744w+ys7Pr/BMRERH3Gnp49T5RQ7oPsb+o2MvhgKVLoaQEjjrKOvbbb/D447bGEpGW06g7FppfISIiYo9O0Z1cbY/YJG9fQUEwbRocc4y1/OyECTB0KBxyiN3J/FJcHFx66W7i4rQNgDS/Rt2xEBEREXsktkokOMAaWrS+cL3NaQ6gd2+46SarXVIC11+PZg/bIzgYEhOdGo0mLaJRdyzWr6/fN7LOnTs35vQiIiJyAAFGAJ1ad2LNjjWeW1gA3H8/vP8+bNwIn30GM2fChRfancrvFBbCF1+Ecs450KaN3WnE1zWqsDj44IMx9res3D6qqqoac3oRERH5B51bd2bNjjUUlBaws2wnUaFRdkeqKyoKnnkGhuyZA3LLLXD66frttoXt3g1LlwZz8sn61Evza1Rh8corr9QpLKqqqli3bh3Tp08nISGBG264wS0BRUREpLbOratHBGwo2kCP+B42pvkHgwdb/2bNgrw8uOsumDrV5lAi0lwaVVhcfvnlB3zszjvvpG/fvhQWFjY2k4iIiPyDmhO41xeu99zCAuDpp+GLL6C4GF54AUaOhOOOszuViDQDt0/ejoyM5IorruCJJ55w96lFRESE2ncsPHqeBUCnTvDAA9X9a66Bigr78ohIs2mWVaGcTie5ubnNcWoRERG/51WFBcCNN1orRYG1t8X//Z+9efxIZCT07l1OZKTdScQfuLWwKCoqYvbs2Tz66KMceeSR7jy1iIiI7OF1hUVgoLW3RcCeXzsmTIA//7Q3k5+IjoaTTy4nOtruJOIPGlVYBAQEEBgYWOdfmzZtOP/884mIiOC5555zd1YRERHBCzbJ25+jjrJWhgIoLdXeFi2kvBw2bgygvNzuJOIPGjV5e/z48XVWhTIMgzZt2tCtWzdOP/10goIadWoRERH5F1GhUcSExVBQWuAddyz2mjjR2ttiwwb4/HN45x246CK7U/m0bdvg7bcjGDMGOna0O434ukb99n/fffe5OYaIiIg0ROfWnSkoLWBD4QacppMAo1mmTbpXq1bW3haDBln9W2+FM8/UBgsiPsKt34XWrFnDypUr3XlKERER2Y+98ywqnBXkFefZnKYBzj+/etO8/Hy4805784iI2zSqsHj66acZPnx4rWNXXHEFhx12GEcccQR9+vQhPz/fLQFFRESkLq+cZ7HXlCnWztwAL74I335rbx4RcYtGFRYvvfQSiYmJrv5nn33G66+/zujRo5kyZQpr1qxhwoQJbgspIiIitXndylA1dewIkyZV96+5Bs0ubh4BARAebroW5BJpTo2aY/HXX3+RkpLi6s+cOZOuXbvy/PPPA5Cbm8sbb7zhnoQiIiJSh1cXFmCtCjV9Ovz0E6xYAY89BnffbXcqn5OYCDfcsIuEBG1kIc2vUfWruc/ycJ9//jlnnXWWq3/wwQdrgzwREZFmVLOwuGPeHaRNTSNrZZaNiRpo370txo2D0FBIS4MsL3ofIuLSqMIiKSmJDz74ALCGQW3atKlWYfH3338TExPjloAiIiJS1/L85a52lVlFdl426TPTvau4OPJIOOccq22a1nCo7GxIT1dx4Sb5+fDSSxFo6qu0hEYVFrfddhvz5s2jTZs2nHfeeaSkpHDGGWe4Hv/f//5Hr1693JVRRERE9vHsj8/W6puYGBhMXDDRpkSNtHZt7b5pgmFYe15Ik1VVQUFBAFVVdicRf9CoORbDhw8nNjaWOXPmEBMTw/XXX+/aEG/79u20bduWSy+91K1BRUREpNrqbavrHDMxydmWY0OaJvjjj7rHTBNyvOx9iEjjCguA0047jdNOO63O8bZt25Kl25ciIiLNKikuiWV5y2odMzBIjk22KVEjJSVZw5/2mb9Jspe9DxFx3wZ5u3fv5pVXXuH555/nr7/+ctdpRUREZD8yBmTU6hsYmJh1jnu8jIzq4U81XXWVPXlEpNEaVViMGjWKI444wtUvLy/n2GOP5aqrruKGG26gV69e/PLLL24LKSIiIrU5Uhxcnna5q39Q9EFkDctiSMoQ+0I1hsMBmZmQmmqtFLXXJ5/UvYshDda2LaSnl9C2rd1JxB80qrD48ssvcTgcrv5bb73Fb7/9xowZM/jtt99o166dNsgTERFpZhf0uMDVvjT1Uu8rKvZyOGDpUigqgk57dhSfOxdmz7Y1li8IDYWuXasIDbU7ifiDRhUWubm5HHzwwa7+rFmz6NOnDxdddBE9evTg6quvZtGiRe7KKCIiIvuRHFc9D2HV9lU2JnGTiAhro7y9br0VSktti+MLdu6E778PYedOu5OIP2hUYREZGUlBQQEAlZWVfPXVV7WWm42KiqKwsNAtAUVERGT/Do45mKAAax2WVdt8oLAAGDoUTjrJaq9ZA48/bmscb1dcbBUWxcV2JxF/0KjC4qijjuLFF1/kl19+YdKkSezcuZPzzjvP9fiff/5JYmKi20KKiIhIXUEBQXRr0w2wlp91mk6bE7mBYcDTT1fPt5g0CTZssDeTiNRLowqLSZMmkZ+fT58+fZgwYQLp6ekcc8wxrsc/+OADjj/+eLeFFBERkf3bOxyqpLKEv4v+tjmNm/TsCddfb7V374Y77rA3j4jUS6MKiz59+vD777+TlZXFl19+ycyZM12PFRQUcP3113Pbbbe5LaSIiIjsX1LbJFfbZ4ZDAUyYAHFxVvudd2DBAnvziMi/avQ+FvHx8QwaNIgBAwbUOh4TE8Mtt9xCr169mppNRERE/kVSrI8WFm3awIMPVvdvvhkqK+3L46XCwiAlpYKwMLuTiD9odGFRVVXFO++8wzXXXMOQIUPIzs4GoLCwkKysLPLy8twWUkRERPbPZwsLgCuvhKOOstrLlsELL9ibxwu1aQPnnFNGmzZ2JxF/0KjCoqCggOOPP56LL76Yt99+m48++ogtW7YA0KpVK26++WaeeuoptwYVERGRumouOZuzLcfGJM0gMBCmTKnu33svbN1qXx4vVFkJO3YYutkjLaJRhcV///tfli9fzmeffcaaNWswa+yMGRgYyAUXXMCcOXPcFlJERET2LzEykaiQKMAH71gAHHccXHqp1d6xA8aNszePl9myBV5+OZI9f/8VaVaNKixmzZrFTTfdxGmnnYZhGHUeT0pKYt26dU3NJiIiIv/CMAzXcKh1BesoqyyzOVEzePhhaNXKak+bBr/8Ym8eEdmvRhUWhYWFdO3a9YCPV1RUUKl7biIiIi1i73Aop+lkzY41NqdpBu3bw/jxVts04aabrP+KiEdpVGHRrVs3fv755wM+/vnnn9OjR49GhxIREZH6q7nkrM/Ns9jrllsgac/7/O47eOste/OISB2NKiyuuuoqXnnlFd59913X/ArDMCgrK+Oee+5h7ty5XHPNNW4NKiIiIvvn0ytD7RUSAjUXhrn9dti50748IlJHUGM+6JZbbmH58uVcdNFFxMTEAHDxxRezbds2Kisrueaaaxg1apQ7c4qIiMgB+EVhAXDmmXD++fDRR7B5M0yaBA89ZHcqj9a+Pdx2WzEJCRF2RxE/0KjCwjAMXnzxRS677DLef/99Vq9ejdPppFu3bgwbNowTTzzR3TlFRETkAPymsAB4/HH47DMoK7PaV15ZPURKRGzV4KFQu3fvxuFwMGPGDPr378+TTz7JJ598wqeffsozzzzT7EXFxo0bueSSS4iNjSU8PJyePXvy008/uR43TZPx48fTvn17wsPDGThwIKtXr651ju3btzNixAiio6OJiYlh1KhRFBcXN2tuERGR5hIVGkX7Vu0BH55jsVe3bnDbbVa7ogLGjLE3j4fbuhVmzAjX9h/SIhpcWERERPDFF1+we/fu5sjzj3bs2MHxxx9PcHAwn376KStWrOD//u//aFNjO8lHHnmEp59+mqlTp7Jo0SIiIyM544wzKC0tdT1nxIgRLF++nHnz5jF79my+/vprRo8e3eLvR0RExF323rXI35VPQWmBvWGa2113wUEHWe05c2D2bHvzeLCKCti8OZCKCruTiD9o1OTt/v37s3DhQndn+VcPP/wwnTp14tVXX+WYY46ha9eunH766XTr1g2w7lY8+eSTjBs3jkGDBpGamsr06dPZtGkTs2bNAmDlypXMnTuXl156ib59+9K/f3+mTJnCO++8w6ZNm1r8PYmIiLhDSECIq330i0eTtTLLxjTNLDISHnusup+eDmFhkJYGWT78vkU8XKMKi2eeeYZvvvmGcePG8ffff7s70wF99NFH9OnTh6FDh5KQkMCRRx7Jiy++6Hp87dq15ObmMnDgQNex1q1b07dvX1chtHDhQmJiYujTp4/rOQMHDiQgIIBFixa12HsRERFxl6yVWcxbO8/V/3P7n6TPTPft4mLYMNi7tH15uTXnIjvbKjJUXIjYolGTt9PS0qisrGTy5MlMnjyZoKAgQkNDaz3HMAwKCwvdEnKvNWvW8PzzzzN27FjuvvtufvzxR26++WZCQkK47LLLyM3NBSAxMbHWxyUmJroey83NJSEhodbjQUFBtG3b1vWcfZWVlVFWVr2TaVFREQBOpxOn0+m29+cOTqcT0zQ9Lpe0HF0DAroO/M2EryZgYGBiLQFvYmJgcP/X9zN38FyfvQ6MPeN7jL0HTBPTMGDCBMzBg+2K5VFqfi/w0ctA6qEpPxMa8jGNKizS09MxDOPfn+hmTqeTPn368OCDDwJw5JFH8ttvvzF16lQuu+yyZnvdyZMnM2HChDrHt2zZUmvuhidwOp0UFhZimiYBAY26ISVeTteAgK4Df5OzLcdVVOxlYpKzLYeCggKfvQ4S169n399GDNPEzMkhPz/flkyeprzcyQknlFJeHkZ+vu9dA1I/TfmZsLMB+8U0qrB47bXXGvNhTda+ffs6O3qnpKSQmZkJQLt27QDIy8ujffv2rufk5eXRq1cv13P2/WZTWVnJ9u3bXR+/r7vuuouxY8e6+kVFRXTq1In4+Hiio6Ob/L7cyel0YhgG8fHxPvlDRP6drgEBXQf+Jjk2mez87FrFhYFBcmwyMTExvnsdJCdjZmdjmNXv2zQM6N69zugEf+V0OomI2OK714DUS1N+JoSFhdX7uQ0qLEpLS/nwww9Zu3YtcXFxnHPOObV+gW9uxx9/PDk5tZfRW7VqFV26dAGga9eutGvXjvnz57sKiaKiIhYtWsR1110HQL9+/SgoKGDJkiX07t0bgP/97384nU769u2739cNDQ2tM9QLICAgwCP/JzUMw2OzScvQNSCg68CfZJyUQfrM9DrDocYPGO/b10FGhjWnogbDNCEjA8MX328j7NoFS5eG0L9/AFFR+pz4s8Z+L2jI8+tdWOTn53Pcccexdu1azD1/GYiIiGDWrFm1Jks3pzFjxnDcccfx4IMPMmzYMBYvXsy0adOYNm0aYH3Cbr31Vh544AEOO+wwunbtyr333kuHDh0YvGesZUpKCmeeeSZXX301U6dOpaKightvvJHhw4fToUOHFnkfIiIi7uRIcZA5LJOJCyayLG+Zq7jo0rqLzcmamcMBmZnwn//AunXWsSOPhCFDbI3lSYqKYP78UFJTISrK7jTi6+pdgtx///2sW7eOMWPGMHv2bJ588knCw8O55pprmjNfLUcffTQffPABb7/9NkcccQT3338/Tz75JCNGjHA954477uCmm25i9OjRHH300RQXFzN37txat3FmzJhB9+7dOfXUUzn77LPp37+/qzgRERHxRo4UB0uvXcrUc6e6jj3/0/M2JmohDgf88QccfLDV/+UXWOXju4+LeCjDNE3z358GycnJHH/88bzyyiuuY++++y4XX3wxK1asIDk5udlCepqioiJat25NYWGhR86xyM/PJyEhwTdve8u/0jUgoOvAn+0q30WHxztQVFZEeFA4P1/yM0mdknz/OnjsMbj9dqt9883w1FP25vEQGzc6eeKJ3YwZE0HHjj5+DcgBNeVnQkN+7633mdevX0///v1rHevfvz+maZKXl9eggCIiItI8IkMiuTztcgBKKkt4N+ddewO1lFGjICLCar/6KjRgJRsRcY96FxZlZWV1ZoXv7VdWVro3lYiIiDTadUdf52pPXzEdp+kHGxi0aQOXXGK1d+6E11+3N4+HCAmBLl0qCQn59+eKNFWDVoVat24dP//8s6u/dwO81atXExMTU+f5Rx11VNPSiYiISIN1j+vOqV1PZf7a+awpXMP8tfM549Az7I7V/G66CfbOmZwyBa6/Hnx9CNi/iI2FoUNLiY31rKHb4pvqPcciICBgv5vimaZZ5/jeY1VVVe5J6WE0x0I8ma4BAV0HAlkrs0ifaS3Fen7S+Xx40Yc2J2ohp5wCX35ptT/9FM480948NqusdLJxYz4dOyYQFKTvBf6qpeZY1PuOxauvvtqgECIiImKf85PPp2NURzbu3Mjs1bNZX7iezq072x2r+d18c3VhMWWK3xcWeXkwZUorxoyBjh3tTiO+rt6FxWWXXdacOURERMSNggKCGH3UaDIWZOA0nUxbMo0HTnnA7ljN77zzoEsX+OsvmDMHVq+Gww6zO5WIX9A9MRERER816shRBAVYf0N88ecXKassszlRCwgMhBtuqO4/+6x9WUT8jAoLERERH9U+qj1nH3w2APm78slamWVzohYyahSEh1vtV17R0rMiLUSFhYiIiA+7/PDLXe3nfnrOviAtqW1bLT0rYgMVFiIiIj7s2PbHcnj84QB8u/5bluUtszlRC7nppur2M8+A0w/28tiPhAS4/vpdJCTYnUT8gQoLERERH2YYBtf1qd4w77kf/eSuRc+ecNJJVjsnB+bNszWOXQIDISLCJDDQ7iTiD1RYiIiI+LgRPUfQKqQVAG8ue5PC0kKbE7WQm2+ubk+ZYl8OG23fDh98EMb27XYnEX+gwkJERMTHRYdGMzJ1JAC7KnYx/dfpNidqIeedB5337N3xySfW0rN+pqwM/vwziDI/WBBM7KfCQkRExA9cd3SN4VA/PYdpmjamaSFBQVp6VqQFqbAQERHxA0ckHMGJXU4E4Petv/PVuq/sDdRSRo2CsDCr/eqrWnpWpBmpsBAREfETNxxd/df7gW8MJG1qmu/vbREbW730bFGRtRRtWhpk+fj7FrGBCgsRERE/5DSdZOdlkz4z3feLi5SU6nZlJWRnQ3q6XxQXUVFw0kllREXZnUT8gQoLERERPzHpm0m1+iYmBgYTF0y0KVEL2XeDPNMEw4CJPv6+gVatoE+fClq1sjuJ+AMVFiIiIn5i1dZVdY6ZmORsy7EhTQtaVfd9Y5rW/hY+rqQEcnKCKCmxO4n4AxUWIiIifiIpLgkDo87xjlEdbUjTgpKSrDsUNRkGJCfbk6cFFRTAxx+HUVBgdxLxByosRERE/ETGgAzX8KeaNhdvJmerD//1PiPDukNRk2lax0XEbVRYiIiI+AlHioPMYZmkJqYSGhhKVIg1o3d3xW7Oeesctu3eZnPCZuJwQGYmdOhQfeyyy2DIEPsyifggFRYiIiJ+xJHiYOm1SykdV8rGsRtJTUwF4M8df+KY6aCs0ke3aHY4YP786v6GDfZlEfFRKixERET8VFRoFB9f9DGJkYkAfP3X11wz+xrf3ZU7ORm6drXa33zjF5vlBQVBQkIVQUF2JxF/oMJCRETEj3Vu3ZmPLvqIsCBrd+rXf32dh7972OZUzcQw4OyzrXZFRe07GD4qPh5GjiwhPt7uJOIPVFiIiIj4uWM6HsP0wdNd/bvm30XmikwbEzWjvYUFwJw59uUQ8UEqLERERIShhw/lgZMfcPUv/eBSftr0k42JmslJJ0GYdXeGOXPqrhblYzZvhscfj2TzZruTiD9QYSEiIiIA3H3C3YxMGwlASWUJ5719HhsKfWySc0SEVVwAbNwI2dm2xmkJTmfdvUtEmoMKCxEREQHAMAymnTuNEzqfAEBucS7nvX0exeXFNidzMw2HEmkWKixERETEJTQolKwLs+jWphsAv+b9ykWZF1HlrLI5mRuddVZ1W4WFiNuosBAREZFa4iLimH3xbGLCYgCYvWo2t8+73d5Q7nTooZCUZLW//x4KCmyNI+IrVFiIiIhIHd3juvP+0PcJCrA2QHjihyd44acXbE7lRnuHQ1VVwbx59mZpRnFxcPnlu4mLszuJ+AMVFiIiIrJfpx5yKs+d/Zyrf8OcG/hizRc2JnIjPxkOFRwMcXFOgoPtTiL+QIWFiIiIHNDVva9m7LFjAagyq7hg5gWs3LLS5lRucOKJ1gpRAJ9+Ck6nvXmaSUEBfPZZqEZ7SYtQYSEiIiL/6JHTHuH85PMBKCwr5Ny3z2Xr7q02p2qisDA49VSrnZcHv/xib55mUlIC2dnBlJTYnUT8gQoLERER+UeBAYHMcMygV7teAKzZsYYh7w6hrLLM3mBNpWVnRdxKhYWIiIj8q1Yhrfj4oo9p36o9AN+u/5arPr4K05t3rq45z+LTT+3LIeIjVFiIiIhIvRwUfRAfX/Qx4UHhALy57E0mfTPJ5lRN0KUL9OhhtX/4AbZ6+fAuEZupsBAREZF6692hN2863nT17/3yXmYun2ljoibaOxzKNOHzz+3N0gwiI+GYY8qJjLQ7ifgDFRYiIiLSII4UBw+d+pCrf9msy1j09yIbEzWBj8+ziI6GE08sJzra7iTiD1RYiIiISIPdcfwdXNHrCgBKK0vp/0p/wh4II21qGlkrs2xO1wDHH2+tEAUwYwakpkKWF+X/F2VlsGFDIGVePs9evIMKCxEREWkwwzCYeu5UesRbcxQqzUrKqsrIzssmfWa69xQXs2dDaWl1/7ffID3dZ4qL7dvh3XfD2b7d7iTiD1RYiIiISKOEBIbUOWZiYmAwccFEGxI1woQJYBjVfdO0+hO9JL+IB1FhISIiIo22ZvuaOsdMTHK25diQphFWrbKKiZpME3K8JL+IB1FhISIiIo2WFJeEgVHneGJkog1pGiEpqfYdC7D6ycn25BHxYiosREREpNEyBmS4hj/VlFucy7K8ZTalaoCMjOrhT3uZpnXcBwQEQKtWTgL0G5+0AF1mIiIi0miOFAeZwzJJTUwlLDCMNmFtACirKmPQO4PYutvDN51zOCAzE7p3rz7WtSsMGWJfJjdKTIRrr91NopfcQBLvpsJCREREmsSR4mDptUspGVfCxrEb6dOhDwDrCtYx7L1hVFRV2JzwXzgcsGIF9O5t9deutf6JSIOosBARERG3CQ8O54MLP3DNsfhy3Zf85/P/2JyqntLTq9s+stxsXh5MnRpBXp7dScQfqLAQERERtzoo+iA+uPAD13K0UxZP4eWfX7Y5VT04HNVtHyksnE4oLg7A6bQ7ifgDFRYiIiLidv069eP5c5539a/75Dq+3/C9jYnqITkZDj/can//PWzebG8eES+jwkJERESaxZVHXslNx9wEQIWzAse7Dv4u+tvmVP+i5l2LDz6wL4eIF1JhISIiIs3m/07/P07pegoAebvyGPzOYEoqSmxO9Q98cDiUSEtRYSEiIiLNJjgwmJkXzKRrTFcAlmxewtUfX425727XniItDQ45xGp/9RVs22ZrnKZq2xYuvLCEtm3tTiL+QIWFiIiINKvYiFg+HP4hkcGRAMzInsH/Lfw/m1MdgGFU37WoqoKPPrI3TxOFhkKnTlWEhtqdRPyBCgsRERFpdj0TezJ9yHRX/84v7mTuH3NtTPQPfGg4VFERfP11CEVFdicRf6DCQkRERFqEI8VBxoAMAJymk+HvD2fVtlU2p9qPvn2hQwer/fnnsHOnvXmaYNcuWLw4hF277E4i/kCFhYiIiLSY8QPGM6T7EAAKywoZ9M4gCksLbU61j4AAGGJlpLwcPvnE3jwiXkKFhYiIiLSYACOA1we/zhEJRwDw+9bfGZE1gipnlc3J9uFDw6FEWooKCxEREWlRUaFRfDj8Q9qGW0sVfbL6E+798l6bU+3jxBMhNtZqz5kDJR68RK6Ih1BhISIiIi3ukDaHMPOCmQQagQBM/nYy7/72rs2paggKgkGDrPauXdZcCy8UHg49e1YQHm53EvEHKixERETEFqceciqPn/G4q3/Fh1fw8+afbUy0j/T06raXDoeKiYEzzigjJsbuJOIPVFiIiIiIbW465iau6HUFACWVJQx+ZzD5u/JtTrXHqadCVJTV/ugjqKiwN08jVFTA1q0B3hhdvJAKCxEREbGNYRg8f87zHHvQsQBsKNrABTMvoLyq3OZkWLvLnXuu1S4ogC+/tDVOY2zdCq+9FsHWrXYnEX+gwkJERERsFRoUStawLDpEWXtHfLP+G9r/X3vCHwgnbWoaWSttHIZUczjU2WdDWprXDosSaW4qLERERMR27aPaM+vCWQQFBAGwvWQ7pVWlZOdlkz4z3b7ioqysul1VBdnZVrGh4kKkDhUWIiIi4hGO7ng07Vu1r3XMxMTAYOKCifaEevjh2n3TBMOAiTblEfFgKixERETEY2zZtaXOMROTnG05NqQBVq2qe8w0IcemPI0QEGDaHUH8hAoLERER8RhJcUkYGLWOGRgkxybbFCjJukNRK5AByTblaaD27WHs2F20b//vzxVpKhUWIiIi4jEyBmRgUvsv7CYmGQMybAqUUT38yRXItI6LSC0qLERERMRjOFIcZA7LpEd8D9ex0MBQTuxyok2BHJCZCT171i4uEhPtydNAW7bA9OnhbKk7wkzE7VRYiIiIiEdxpDhYfv1ybjz6RgDKqsp4atFTNgZywK+/wssvVx+7/3778jRAZSXk5wdSWWl3EvEHKixERETEI91+/O2u5WenLJ5CYWmhvYEuuQQOPthqz50LixfbGkfE06iwEBEREY/UuXVnRqaOBKCgtIDnfnzO3kDBwXD33dV9L7lrIdJSVFiIiIiIx/pv//8SYFi/rjzxwxPsrthtb6DLLoNOnaz27Nnwyy/25hHxICosRERExGMdFnsYFx5+IQBbdm/hxSUv2hsoJAT++9/qvofftYiJgfPOKyUmxu4k4g9UWIiIiIhHu/uE6uFHj3z/CGWVZTamAa68Ejp0sNoffADLltmb5x+Eh0NyciXh4XYnEX+gwkJEREQ82hEJRzAoeRAAm3Zu4vVfX7c3UFgY3Hlndf+BB+zL8i+Ki+Gnn4IpLrY7ifgDFRYiIiLi8e454R5X+6FvH6LSafP6qVdfXb2Xxfvvw4oV9uY5gJ074auvQtm50+4k4g9UWIiIiIjHO7rj0Zze7XQA1has5e3st+0NFB4Ot99utU0TJk2yN4+IB1BhISIiIl6h5l2Lyd9Oxmk6bUwDXHstxMVZ7XfegZwce/OI2EyFhYiIiHiFE7ucyAmdTwBg5daVfLDyA3sDRUbCbbdZbacTHnzQ3jwiNlNhISIiIl6j5l2LSd9MwjRNG9MA118Pbdta7Rkz4M8/7c2zj9BQ6NatktBQu5OIP1BhISIiIl7j9G6n07t9bwB+yf2FT//41N5AUVEwZozVrqqCyZPtzbOPtm1hyJBSV+0j0pxUWIiIiIjXMAyj1l2LB75+wP67FjfdBK1bW+3XX4d162yNU1NVFezebVBVZXcS8QcqLERERMSrDOo+iMPjDwdg4d8L+WrdV/YGat0abrnFaldWwkMP2Zunhvx8eO65SPLz7U4i/kCFhYiIiHiVACOg1m7ck77xgKVeb7nFGhYF8MorsGGDvXlEbOB1hcXBBx+MYRh1/t1www0AlJaWcsMNNxAbG0urVq1IT08nLy+v1jnWr1/POeecQ0REBAkJCdx+++1UVtq80Y6IiIjU27DDh9GtTTcA5q+dzw9//2BvoLZtrSFRABUV8PDD9uYRsYHXFRY//vgjmzdvdv2bN28eAEOHDgVgzJgxfPzxx7z33nssWLCATZs24XA4XB9fVVXFOeecQ3l5Od9//z2vv/46r732GuPHj7fl/YiIiEjDBQUEcVf/u1z94185nrSpaWStzLIv1Jgx1hK0AM89B2FhkJYGWTZmEmlBXldYxMfH065dO9e/2bNn061bNwYMGEBhYSEvv/wyjz/+OKeccgq9e/fm1Vdf5fvvv+eHH6y/ZHz++eesWLGCN998k169enHWWWdx//338+yzz1JeXm7zuxMREZH6igqNcrWdppPsvGzSZ6bbV1zExcHAgVbbNKGsDLKzIT1dxYX4hSC7AzRFeXk5b775JmPHjsUwDJYsWUJFRQUD9/5PDXTv3p3OnTuzcOFCjj32WBYuXEjPnj1JTEx0PeeMM87guuuuY/ny5Rx55JF1XqesrIyysjJXv6ioCACn04nTafOun/twOp2YpulxuaTl6BoQ0HUgFl+/DiZ9XXtuhYmJgcGEBRMYnDzYlkzGqlXWf12hTEzDgAkTMAe3fKb4eCc33riT+PgwfPQykHpoyveChnyMVxcWs2bNoqCggMsvvxyA3NxcQkJCiImJqfW8xMREcnNzXc+pWVTsfXzvY/szefJkJkyYUOf4li1bKC0tbeK7cC+n00lhYSGmaRIQ4HU3pMQNdA0I6DoQi69fBznbcuocMzHJ2ZpDvk3LICWuWVNdVOxhmCZmjj2ZnE4nJSWFbN3qm9eA1E9Tvhfs3Lmz3s/16sLi5Zdf5qyzzqJDhw7N+jp33XUXY8eOdfWLioro1KkT8fHxREdHN+trN5TT6cQwDOLj4/UNxE/pGhDQdSAWX78OkmOTyc7PxqR6HwsDg+5x3UlISLApVDJmdjZGjb01TMOA7vZk2rLFyRdfRDBsWCvi433vGpD6acr3grCwsHo/12sLi7/++osvvviCrBpjFtu1a0d5eTkFBQW17lrk5eXRrl0713MWL15c61x7V43a+5x9hYaGEhoaWud4QECAR36jNgzDY7NJy9A1IKDrQCy+fB1knJRB+sz0WsdMTDIGZNj3fjMyrDkVhmHNs8C6Y8H48Rg2ZKqshL/+CqKy0jevAam/xn4vaMjzvfYKe/XVV0lISOCcc85xHevduzfBwcHMnz/fdSwnJ4f169fTr18/APr160d2dnat25Hz5s0jOjqaHj16tNwbEBERkSZxpDjIHJZJl9ZdXMeGHz6cISlDbAzlgMxMOOIIq7jYy9h3gJSI7/HKwsLpdPLqq69y2WWXERRUfdOldevWjBo1irFjx/Lll1+yZMkSrrjiCvr168exxx4LwOmnn06PHj249NJL+fXXX/nss88YN24cN9xww37vSoiIiIjncqQ4mHvJXFe/wllhY5o9HA5Ytqz2SlC33WatEiXiw7yysPjiiy9Yv349V155ZZ3HnnjiCc4991zS09M58cQTadeuXa3hUoGBgcyePZvAwED69evHJZdcwsiRI5k4cWJLvgURERFxk8PaHkZ4UDgAv+b9anOaGgYNgpNPttpr1sCUKfbmEWlmhmnWmF0k9VJUVETr1q0pLCz0yMnb+fn5JCQkaCyln9I1IKDrQCz+dB0c8+Ix/LjpRwwMiu4qolVIK7sjWZYuhaOOsuZbREfD6tXQgpO4d+508u23O+jfvw1RUb59DciBNeV7QUN+79UVJiIiIl4vLTENsCZv/5b/m81paujVC0aNstpFRTB+fIu+fGQkHHlkhWtDcJHmpMJCREREvF5qYqqr/WuuBw2HArj/fmi15w7Kiy9au3G3kJISWLEiiJKSFntJ8WMqLERERMTrpbVLc7U9ap4FQLt2cM89VtvphLFjXUvRNreCApgzJ4yCghZ5OfFzKixERETE69W6Y+FphQXArbfCwQdb7S++gE8+sTONSLNQYSEiIiJeLyYsxrWfxbK8ZThNp82J9hEWBo88Ut3/z3+gvNy+PCLNQIWFiIiI+IS9dy2Ky4tZV7DO3jD7c8EF0L+/1V61Cp57zt48Im6mwkJERER8wt6VocADJ3CDtfv2E09U9ydMgG3bmvUlg4OhffsqgoOb9WVEABUWIiIi4iM8egL3Xn36wGWXWe2CArjvvmZ9ubg4GDGihLi4Zn0ZEUCFhYiIiPiIWncsPLWwAHjwQYiIsNrPPw8rV9qbR8RNVFiIiIiIT+jWthsRwdYv7B45FGqvDh3gv/+12lVV1kTuZrJ5Mzz2WCs2b262lxBxUWEhIiIiPiHACKBnQk8A1haspaisyOZE/+A//4FOnaz2p5/C3Ln25hFxAxUWIiIi4jNqDofKzmu5Ha4bLCICHn64uj92LFRW2pdHxA1UWIiIiIjP8IoJ3HsNHw7HHmu1V66EF16wN49IE6mwEBEREZ/h8UvO1rTv8rMZGbBjh315RJpIhYWIiIj4jJ6JPV1tj79jAdYdi4svttrbtsH997v19PHxMGrULuLj3Xpakf1SYSEiIiI+Izo0moSIBAAWbVxE2vNpZK3MsjnVv3joIQgPt9pPPAGhoZCWBllNzx0UBG3amAQFNflUIv9KhYWIiIj4jKyVWeTvznf1s/OzSZ+Z7tnFRadOcO651f3ycsjOhvT0JhcXO3bAJ5+EaoSVtAgVFiIiIuIzJiyYUKtvYmJgMHHBRJsS1dO+m+SZpjUHY2LTcpeWwsqVwZSWNuk0IvWiwkJERER8xqqtq+ocMzHJ2ZZjQ5oG+OOPusdME3I8PLdIDSosRERExGckxSVhYNQ6ZmCQHJtsU6J6Skqy7lDUZBiQ7OG5RWpQYSEiIiI+I2NABiZmrWMmJhkDMmxKVE8ZGdXDn/YyTbj2WvsyiTSQCgsRERHxGY4UB5nDMokKiXIde/6c5xmSMsTGVPXgcEBmJqSmQmBg9fE5c5p02lat4LjjymnVqon5ROpBhYWIiIj4FEeKg1v63uLqx0XE2ZimARwOWLoUtm+H9u2tYx9/bP1rpKgoq7CIivr354o0lQoLERER8Tm9O/R2tZdsWmJjkkaIjob/+7/q/i23QElJo05VVgZr1wZSVuambCL/QIWFiIiI+Jze7WsUFpu9rLAAGD4cTj7Zaq9da22i1wjbt0NmZjjbt7sxm8gBqLAQERERn3NQ9EHER8QDVmFhmua/fISHMQx45hlcW2Y//PD+l6QV8SAqLERERMTnGIbhGg61vWQ7fxX+ZXOiRujRA8aOtdplZXDzzdZKUSIeSoWFiIiI+KRaw6G8bZ7FXvfeCwcdZLU//RRmzbI1jsg/UWEhIiIiPsnr51mAtV7sE09U92+9FXbtqveHBwZCTIyz1gq2Is1FhYWIiIj4pForQ3lrYQGQng6nnWa116+HBx+s94cmJMBVV+0mIaGZsonUoMJCREREfFKn6E6uPSyWbPLCCdx7GQZMmQLBwVb/0UchJ8feTCL7ocJCREREfJJhGK7hUNtKtrG+cL3NiZogORluv91qV1TATTfVayJ3Xh48+2wkeXnNnE8EFRYiIiLiw3xinsVed98NnTtb7Xnz4P33//VDnE4oKTFwOps5mwgqLERERMSHefUO3PuKjISnnqrujxkDxcX25RHZhwoLERER8Vk+dccCYNAgOOssq71xI0ycaG8ekRpUWIiIiIjP6ty6M7HhsYCX7sC9r70TuUNDrf4TT8CKFfZmEtlDhYWIiIj4rJo7cG/dvZUNRRtsTuQG3brBnXda7cpKuOGGA07kjo2Fiy7aTWxsC+YTv6XCQkRERHyaT+zAva///he6drXaX30F77yz36eFhEDHjk5CQloumvgvFRYiIiLi03xungVAeDg8/XR1/z//gaKiOk8rKoIvvwzZ30MibqfCQkRERHxanw59XO3J304mbWoaWSuzbEzkJueeC+edZ7U3b4a2bSEtDbKq39uuXbBkSQi7dtmUUfyKCgsRERHxaT9t+snVdppOsvOySZ+Z7hvFxd4VogCqqiA7G9LTaxUXIi1FhYWIiIj4tIlf116S1cTEwGDiAh9YqnXq1Np907RWjtIytGIDFRYiIiLi01ZtXVXnmIlJzrYcG9K42aq67w3ThBwfeG/idVRYiIiIiE9LikvCwKh1zMAgOTbZpkRulJRk3aHYV7duAEREQK9eFUREtHAu8UsqLERERMSnZQzIwKT2Pg8mJhkDMmxK5EYZGdXDn2ras3FF69YwcGAZrVvbkE38jgoLERER8WmOFAfvD32fQCMQgKCAIDKHZTIkZYjNydzA4YDMTEhNtXbjDtjzq93XX8MXX1BRAXl5AVRU2BtT/IMKCxEREfF56T3SOaXrKQBUOis5puMxNidyI4cDli6F0tLak7mvu46tf5fyxhsRbN1qWzrxIyosRERExC/45A7c+xo1Co4/3mr/8QfGlCn25hG/osJCRERE/ELvDj64A/e+AgLghRcgKMjqP/ssxrZt9mYSv6HCQkRERPxCrTsWvlpYABx+ONx+OwBGZQVhX3xhTfAWaWYqLERERMQvHBxzMG3C2gDWUCjTl3/ZHjcOunbFwCTi7z8w3ptpdyLxAyosRERExC8YhuEaDpW3K49NOzfZnKgZRUTAc8/Rjjzu4iHaT7oJzeCW5qbCQkRERPyG3wyHAjjzTMwLLwSw5lncdpvNgcTXqbAQERERv+EXK0PVkH/PkzwTNpYtxMHrr8NXX9kdSXyYCgsRERHxG36xMlQNlW0T+Pv4IVSyZ5Woa66BsjJ7Q4nPUmEhIiIifqNrTNfqCdx+UFgAVKalYR61p6BatQoeftjeQOKzVFiIiIiI3zAMg6PaHwVAbnGub0/g3sswMB96CAIDrf6kSVaBIeJmKixERETEr/jbPAsAevSA//zHapeXw3XXaW8LcTsVFiIiIuJX/GmeRZs2MHhwCW3aAOPHQ5cu1gP/+x+8+aat2cT3qLAQERERv+JPS86GhcGhh1YRFgZERsJzz1U/OHYsbNtmWzbxPSosRERExK8c0uYQYsJiAN8fClVcDD/8EExx8Z4DZ58NQ4da7a1b4c47bcsmvkeFhYiIiPiVmhO4NxdvZvPOzTYnaj47d8K334ayc2eNg08+CdHRVvvll+Gbb+yIJj5IhYWIiIj4HX8aDlVHhw7w4IPV/WuusSZ0izSRCgsRERHxO3069HG1h7wzhLSpaWStzLIxUQu79lo45hirvXKlNak7PBzS0iDLjz4P4lYqLERERMTvbN211dWuNCvJzssmfWa6/xQXgYHwwgsQsOdXwdxcKC2F7GxIT1dxIY2iwkJERET8zgtLXqjVNzExMJi4YKJNiZpHWBgkJVVaq0Ltq1cvaNu29jHTBMOAib71eZCWocJCRERE/M6qbXV3njYxydmWY0Oa5tOmDZx/fqm1j8X+1JrVvYdpQo5vfR6kZaiwEBEREb+TFJdU55iBQXJssg1pmk9VFezcaVBVdYAnJCdbdyhqMgzruEgDqbAQERERv5MxIKPOMRNzv8e9WX4+vPBCJPn5B3hCRoZ1h6Im07SOizSQCgsRERHxO44UB68OetXVjwiOIGtYFkNShtiYygYOB2RmQo8etY9HRtqTR7yaCgsRERHxS5f3upykWGtIVKWzkrMPO9vmRDZxOGD5cnjppepj114Lu3bZl0m8kgoLERER8VvHdToOgPKqcn7e/LPNaWx25ZUwYIDVXrsW7rvP1jjifVRYiIiIiN/qd1A/V/v7Dd/bmMQDGAZMmwahoVb/8cfhZz8vtqRBVFiIiIiI36pZWCz8e6GNSZpHu3Zw663FtGtXzw9ISoJ777XaTidcfTVUVjZbPvEtKixERETEb/WI70F0aDRg3bEw910hycsZBgQF1V1R9h/dfjsccYTV/vlneOqpZskmvkeFhYiIiPitwIBA+nbsC8Dm4s2sL1xvcyL32rYN3n03nG3bGvBBISHWRO691cj48dacC5F/ocJCRERE/JovD4cqL4cNGwIpL2/gB/btCzfeaLV377ZWifKxuznifiosRERExK/tXRkKNIG7lkmT4KCDrPbnn8OMGfbmEY+nwkJERET8Wt+D+rravnbHokmiouC556r7Y8bA1q325RGPp8JCRERE/FpMWAw94q2dp5fmLmV3xW6bE3mQ886DoUOt9tat8J//2JtHPJoKCxEREfF7xx1kDYeqdFby06afbE7jPq1bw+mnl9K6dRNO8vTTEBNjtadPh3nz3BFNfJAKCxEREfF7/TrVmMC9wXeGQ0VEQGpqJRERTThJu3bw6KPV/WuvtSZ0i+xDhYWIiIj4PV9dGWr3bli2LKjpdcCoUTBggNVeswbuu6+p0cQHqbAQERERv5ccl0ybsDaAb22UV1gIn38eRmFhE09kGDBtGoSGWv3HH4dffmlyPvEtKixERETE7wUYARx70LEAbNm9hTU71ticyAMlJcG4cVa7qgquugoqK+3NJB5FhYWIiIgItYdDaT+LA7jjDjjiCKv988/w1FP25hGP4lWFRVVVFffeey9du3YlPDycbt26cf/999e6XWmaJuPHj6d9+/aEh4czcOBAVq9eXes827dvZ8SIEURHRxMTE8OoUaMoLi5u6bcjIiIiHqTmRnm+NM/CrUJC4MUXraFRAOPHw9q19mYSj+FVhcXDDz/M888/zzPPPMPKlSt5+OGHeeSRR5gyZYrrOY888ghPP/00U6dOZdGiRURGRnLGGWdQWlrqes6IESNYvnw58+bNY/bs2Xz99deMHj3ajrckIiIiHuKYjscQYFi/GvlKYRESAp06VRES4saTHnss3HCD1d69G667DnxkToo0jVcVFt9//z2DBg3inHPO4eCDD+aCCy7g9NNPZ/HixYB1t+LJJ59k3LhxDBo0iNTUVKZPn86mTZuYNWsWACtXrmTu3Lm89NJL9O3bl/79+zNlyhTeeecdNm3aZOO7ExERETtFhUbRKboTYG2U1/O5nmStzLI5VdPExsKFF5YQG+vmEz/4IBx0kNX+7DNrUndaGmR59+dLmsarCovjjjuO+fPns2rVKgB+/fVXvv32W8466ywA1q5dS25uLgMHDnR9TOvWrenbty8LF1p/eVi4cCExMTH06dPH9ZyBAwcSEBDAokWLWvDdiIiIiCfJWpnFX4V/ufrLtywnfWa6VxcXpmnNr3b7DYWoKLjkkup+RQVkZ0N6uooLPxZkd4CG+O9//0tRURHdu3cnMDCQqqoqJk2axIgRIwDIzc0FIDExsdbHJSYmuh7Lzc0lISGh1uNBQUG0bdvW9Zx9lZWVUVZW5uoXFRUB4HQ6cTqd7nlzbuJ0OjFN0+NyScvRNSCg60Asug4aZsJXEzAwMLF+CzcxMTCYsGACg5MH2xuukTZtcvLkk5HcequTjh3de25jzhzrv3sPmCamYcCECZiDB7v3xaRJmvK9oCEf41WFxcyZM5kxYwZvvfUWhx9+OEuXLuXWW2+lQ4cOXHbZZc32upMnT2bChAl1jm/ZsqXW3A1P4HQ6KSwsxDRNAgK86oaUuImuAQFdB2LRddAwOdtyXEXFXiYmOVtzyM/PtylV02zdCqWlgWzduovgYPeeOzEnp7qo2MMwTcwc7/18+aqmfC/YuXNnvZ/rVYXF7bffzn//+1+GDx8OQM+ePfnrr7+YPHkyl112Ge3atQMgLy+P9u3buz4uLy+PXr16AdCuXbs6F3tlZSXbt293ffy+7rrrLsaOHevqFxUV0alTJ+Lj44mOjnbnW2wyp9OJYRjEx8frh4if0jUgoOtALLoOGiY5Npns/OxaxYWBQfe47nVGO3iLigonYWElxMWFk5Dg5msgORkzOxuj5uqce4576+fLVzXle0FYWFi9n+tVhcXu3bvrfDICAwNdt2i6du1Ku3btmD9/vquQKCoqYtGiRVx33XUA9OvXj4KCApYsWULv3r0B+N///ofT6aRv3777fd3Q0FBC9+40WUNAQIBHfqM2DMNjs0nL0DUgoOtALLoO6i/jpAzSZ6bXOmZikjEgw2s/fwEBzXgNZGRYcyoMwzWJwwDo0wfDSz9fvqyx10FDnu9VX/XzzjuPSZMm8cknn7Bu3To++OADHn/8cYYMGQJYn7Bbb72VBx54gI8++ojs7GxGjhxJhw4dGLxnrF9KSgpnnnkmV199NYsXL+a7777jxhtvZPjw4XTo0MHGdyciIiJ2cqQ4yByWSZuwNq5jjwx8hCEpQ2xM5cEcDsjMhNRUaq1n+/77sHmzfbnENl5VWEyZMoULLriA66+/npSUFG677TauueYa7r//ftdz7rjjDm666SZGjx7N0UcfTXFxMXPnzq11G2fGjBl0796dU089lbPPPpv+/fszbdo0O96SiIiIeBBHioN7T7zX1Y+NcPc6rS0rIQGuuWYXzTYyyeGApUuhrAxGjbKOFRXB7bc30wuKJzNMUzuaNFRRURGtW7emsLDQI+dY5Ofnk5CQ4LW3baVpdA0I6DoQi66Dxpm/Zj4D37CWrr+l7y08eeaT9gZqgha9BrZuheRk2L7d6n/5JZx0UvO+ptRLU66Dhvzeq+8yIiIiIjWkJqa62svyltmYpOl27ICPPgpjx44WeLG4OJg8ubp/ww3W/hbiN1RYiIiIiNQQHxlP+1bW6pLL8pbhzYM7Skth1aogWmx1/KuugmOOsdorVsCTT7bQC4snUGEhIiIiso+9dy22lWxj085NNqfxIgEB8Nxz1kpRABMmwN9/25tJWowKCxEREZF9+NJwqBbXuzfsWeafXbtgzBh780iLUWEhIiIisg8VFk30wAMQH2+1338fPv/c3jzSIlRYiIiIiOyjVmGR772FRVQU9O9fRlRUC79wmzbw6KPV/RtvtJakFZ+mwkJERERkH93juhMcEAx49x2LVq3g2GMraNXKhhcfORL697faq1fXLjTEJ6mwEBEREdlHSGAIKfEpAPy+9XfKKr3zr+2lpfDHH4EttypUTYZhTeQODLT6kybB2rU2BJGWosJCREREZD/2DoeqdFaycutKm9M0zo4dMGtWeMvsY7E/PXvCzTdb7dJSuOUWm4JIS1BhISIiIrIfqQmawO0W990H7a19Qfj4Y+uf+CQVFiIiIiL7oZWh3CQ6Gh5/vLp/882we7d9eaTZqLAQERER2Q8VFm504YVwyilWe906mDzZ1jjSPFRYiIiIiOxHu1btiI+w9mLw1sIiKAhiY50EBdkcxDDg2Wch2Fppi0cesVaKEp+iwkJERERkPwzDcN21yNuVR15xns2JGi4+Hq64Yrdrrzpbde8O//mP1S4vt/a2ME17M4lbqbAQEREROQANh3KzceOgc2er/fnnkJlpbx5xKxUWIiIiIgdQs7A4+62zSZuaRtbKLBsTNUxuLjz9dCS5uXYn2SMyEp58srp/0UUQFgZpaZDlPZ9X2T8VFiIiIiIHsHX3Vle70llJdl426TPTvaa4ME0oLzc8a8TR4MFw5JFWu7ISysogOxvS01VceDkVFiIiIiIH8Pqvr9fqm5gYGExcMNGmRD7AMOouN2ua1vGJ+rx6MxUWIiIiIgfwx7Y/6hwzMcnZlmNDGh/y1191j5km5Ojz6s1UWIiIiIgcQFJcUp1jBgbJsck2pPEhSUnWHYqaDAOS9Xn1ZiosRERERA4gY0BGnWMm5n6Pe6K4OLj00t3ExdmdZB8ZGdXDn/YyTbjzTvsySZOpsBARERE5AEeKgxfPe9HVjwyOJGtYFkNShtiYqv6CgyEx0enal85jOBzWUrOpqRBQ49fR336zL5M0mQoLERERkX9w1VFXcWjbQwGocFZw5qFn2pyo/goL4YsvQikstDvJfjgcsHQp/P47hIRYxx57TDtyezEVFiIiIiL/YkCXAQCUV5WzaOMim9PU3+7dsHRpcJ1FmDzKYYfV3pH75pu1I7eXUmEhIiIi8i9O7HKiq/31X1/bmMRH3XMPHHSQ1Z47Fz7+2N480igqLERERET+hQqLZhYZCf/3f9X9W2+FkhLb4kjjqLAQERER+RcHxxxM59adAfh+w/eUV5XbnMgHDR0Kp5xitdeuhUcftTePNJgKCxEREZF62HvXoqSyhCWblticpn4iI6F373IiI+1OUg+GAVOmQFCQ1Z882SowxGuosBARERGphxM7e99wqOhoOPnkcqKj7U5STz16WJO3AUpLYexYe/NIg6iwEBEREamHmvMsFvy1wMYk9VdeDhs3BlDuTSO3MjIgMdFqz5plTeYWr6DCQkRERKQekmKTSIy0fuH9dv23VDmrbE7077Ztg7ffjmDbNruTNEB0dO35FTffDGVl9uWRelNhISIiIlIPhmG47lrsLN/Jr3m/2pzIh11yCfTvb7VXr4YnnrA3j9SLCgsRERGRetKysy1k70TugD2/qt5/P/z9t72Z5F+psBARERGpp707cIP3zLPwWr16wXXXWe3du+G222yNI/9OhYWIiIhIPR2ecDhtwtoA8M1f3+A0nTYn+mcBARAebrr+8O91Jk6EuDir/e678OWX9uaRf+Stl5mIiIhIiwswAjihywkAbCvZxsotK21O9M8SE+GGG3a5FlnyOm3bWvtZ7HXTTVBRYV8e+UcqLEREREQaQMOhWtiVV8LRR1vt5cvhmWfszSMHpMJCREREpAG8aQJ3fj689FIE+fl2J2mCgAB49llrQjdY+1zk5tqbSfZLhYWIiIhIA/Rq14tWIa0Aq7AwTdPmRAdWVQUFBQFUef6WG//s6KNh1CirvXMn3HmnvXlkv1RYiIiIiDRAUEAQh7Y5FIDNxZtJeTaFrJVZNqfyAw8+CDExVnv6dAgNhbQ0yNLn3lOosBARERFpgKyVWSzNW+rqr9q2ivSZ6Soumlt8PFxwQXW/vByysyE9XcWFh1BhISIiItIAExZMwMBw9U1MDAwmLphoYyo/sWhR7b5pWnMvJupz7wlUWIiIiIg0wKqtqzCpPa/CxCRnW45NiQ6sbVtITy+hbVu7k7jJ6tV1j5km5Hje594fqbAQERERaYCkuKRadywADAySY5NtSnRgoaHQtWsVoaF2J3GTpKTq1aH2MgxI9rzPvT9SYSEiIiLSABkDMlzDn/YyMckYkGFjqv3buRO+/z6EnTvtTuImGRnVw5/2Mk249177MomLCgsRERGRBnCkOMgclknPhJ6uY8EBwZzS9RQbU+1fcbFVWBQX253ETRwOyMyE1FRrf4u99r2LIbZQYSEiIiLSQI4UB79e9ys3Hn0jABXOCt5f8b7NqfyEwwFLl8KcOdXHHnrIunMhtlJhISIiItJIl6Zd6mq/sewNG5P4odNPh169rPaPP8JXX9mZRlBhISIiItJoR3c42jVpe8FfC1hXsM7eQP7EMOCOO6r7Dz1kXxYBVFiIiIiINJphGFyaWn3XYsayGTamqSssDFJSKggLsztJMxk6FLp2tdqffw6//GJvHj+nwkJERESkCS5JvcTVnr5sOqYHjfVv0wbOOaeMNm3sTtJMgoLgttuq+w8/bF8WUWEhIiIi0hRdYrowoMsAAFZtW8WPm360OVG1ykrYscOgstLuJM3oiisgPt5qv/ce/PmnvXn8mAoLERERkSYamTbS1Z7+63Qbk9S2ZQu8/HIkW7bYnaQZhYfDLbdYbacTHnvM3jx+TIWFiIiISBNd0OMCwoKsiQzv/PYO5VXlNifyM9dfD61aWe1XX4W8PHvz+CkVFiIiIiJNFB0azeDugwHYVrKNT1d/am8gf9OmDVxzjdUuK4Onn7Y3j59SYSEiIiLiBiNTq4dDaU8LG4wZA8HBVvvZZ6GoyN48fkiFhYiIiIgbnNbtNBIjEwH4eNXHbC/ZbnMiP9OxI1y6Z+nfwkKYNs3ePH5IhYWIiIiIGwQFBHFxz4sBKK8qZ+bymTYngvbt4bbbimnf3u4kLeT2262N8wAef9waFiUtRoWFiIiIiJvU3CxPw6Fs0L07DB5stTdvhjfftDWOv1FhISIiIuImvdr14oiEIwD4fsP3/LH9D1vzbN0KM2aEs3WrrTFa1p13VrcfeQSqquzL4mdUWIiIiIi4iWEYte5avLnM3r+YV1TA5s2BVFTYGqNl9e0LJ51ktVetgg8/tDWOP1FhISIiIuJGI3qOwMAa5//GsjcwTdPmRH6o5l2Lhx8GfQ1ahAoLERERETfqGN2RUw85FYA1O9bw/YbvbU7kh844A9LSrPbixfDVV7bG8RcqLERERETcrOaeFtN/nW5jEj9lGHXvWkizU2EhIiIi4mZDUoYQGhgKwLSfp9Hz+Z5krcxq8RwxMXD22aXExLT4S9tv6FDo2tVqf/YZhIZadzGyWv7r4C9UWIiIiIi42ed/fk5ZVfUeCsvzl5M+M73Fi4vwcOjRo5Lw8BZ9Wc8QFAQDB1b3y8shOxvS01VcNBMVFiIiIiJuNmHBBNcEbgATEwODiQsmtmiOXbvgl1+C2bWrRV/WcyxcWLtvmtYwqYkt+3XwFyosRERERNxs1dZVmNReicjEJGdbTovmKCqC+fNDKSpq0Zf1HH/sZx8R04Sclv06+AsVFiIiIiJulhSXVOuOBYCBQXJssk2J/FRSknWHoibDgGR9HZqDCgsRERERN8sYkOEa/rSXiUnGgAwbU/mhjIy6e1iYpnVc3E6FhYiIiIibOVIcZA7LJDUxtVZxkRKfYmMqP+RwQGYmHHJI9bHUVBgyxL5MPkyFhYiIiEgzcKQ4WHrtUv7v9P9zHXvj1zdaNENICHTpUklISIu+rGdxOGD1aujWzeovW2b1xe1UWIiIiIg0o4t6XkSgEQjAm9lv4jSdLfbasbEwdGgpsbEt9pKeKSAArrmmuj9tmn1ZfJgKCxEREZFm1K5VO8449AwA1heu5+u/vm6x13Y6oazM+q/fu/xyXLduXn3V+sSIW6mwEBEREWlml6Ze6mq35HCovDyYMqUVeXkt9pKeKz4eLrjAam/bZs29ELdSYSEiIiLSzAYlDyI6NBqA91a8x+6K3TYn8lM1h0NNnWpfDh+lwkJERESkmYUHh3NBivXX8p3lO/nw9w9tTuSnTjgBUvaszPXNN7B8ub15fIwKCxEREZEWMDJtpKv9xrKWXR1K9jAMuPba6v4LL9iXxQepsBARERFpASd0OYHOrTsD8Nmfn5FbnGtzIj916aUQHm61p0+HXbvszeNDVFiIiIiItIAAI8A1idtpOnk7++1mf82EBLj++l0kJDT7S3mPNm1g+HCrXVgI775rbx4fosJCREREpIXUXB1q+rLpzf56gYEQEWESGNjsL+VdNByqWaiwEBEREWkhyXHJHNPxGACW5i4lOy+7WV9v+3b44IMwtm9v1pfxPkcfDb16We3Fi+Hnn22N4ytUWIiIiIi0oFp7WjTzJO6yMvjzzyDtBbcvTeJuFiosRERERFrQ8COGExQQBMCM7BlUOatsTuSnLr4YWrWy2jNmQFGRvXl8gAoLERERkRYUFxHHOYedA8CmnZv439r/2ZzIT0VFwSWXWO1du6ziQppEhYWIiIhIC2vJ4VDyD2oOh5o6FUzTviw+QIWFiIiISAs7N+lcYsJiAMhcmUlxeXGzvE5UFJx0UhlRUc1yeu+XlgZ9+1rtZctg0SJ783g5ryssdu7cya233kqXLl0IDw/nuOOO48cff3Q9bpom48ePp3379oSHhzNw4EBWr15d6xzbt29nxIgRREdHExMTw6hRoygubp7/oUVERET2FRoUyoWHXwjA7ordfLDyg2Z5nVatoE+fCtdUAtmPfe9aSKN5XWFx1VVXMW/ePN544w2ys7M5/fTTGThwIBs3bgTgkUce4emnn2bq1KksWrSIyMhIzjjjDEpLS13nGDFiBMuXL2fevHnMnj2br7/+mtGjR9v1lkRERMQPjUwb6Wpf/uHlpE1NI2tllltfo6QEcnKCKClx62l9y7BhEBNjtadPh7Aw605Glnu/Fv7AqwqLkpISMjMzeeSRRzjxxBM59NBDue+++zj00EN5/vnnMU2TJ598knHjxjFo0CBSU1OZPn06mzZtYtasWQCsXLmSuXPn8tJLL9G3b1/69+/PlClTeOedd9i0aZO9b1BERET8xuadm11tp+kkOy+b9Jnpbi0uCgrg44/DKChw2yl9T0QEHH+81TZNa43e7GxIT1dx0UBBdgdoiMrKSqqqqggLC6t1PDw8nG+//Za1a9eSm5vLwIEDXY+1bt2avn37snDhQoYPH87ChQuJiYmhT58+rucMHDiQgIAAFi1axJAhQ+q8bllZGWU1FoAu2rMcmdPpxOl0uvttNonT6cQ0TY/LJS1H14CArgOx6DrwbBMXTKzVNzExMJiwYAKDkwe75TVqXgO6DA7MWL0ao+YB08Q0DJgwAXPwYJtSuU9Tvhc05GO8qrCIioqiX79+3H///aSkpJCYmMjbb7/NwoULOfTQQ8nNzQUgMTGx1sclJia6HsvNzSUhIaHW40FBQbRt29b1nH1NnjyZCRMm1Dm+ZcuWWkOsPIHT6aSwsBDTNAkI8KobUuImugYEdB2IRdeBZ8vZllPnmIlJztYc8vPz3fIaW7dCaWkgW7fuIjjYLaf0SYl//VXnmGGamDnu+1rYqSnfC3bu3Fnv53pVYQHwxhtvcOWVV9KxY0cCAwM56qijuOiii1iyZEmzveZdd93F2LFjXf2ioiI6depEfHw80dHRzfa6jeF0OjEMg/j4eP0Q8VO6BgR0HYhF14FnS45NJjs/G5PqJU4NDLrHda/zR9DGqqhwEhZWQlxcOAkJugYOKDkZMzsbo8Zys6ZhQHf3fS3s1JTvBfuOFPonXldYdOvWjQULFrBr1y6Kiopo3749F154IYcccgjt2rUDIC8vj/bt27s+Ji8vj169egHQrl27OpVnZWUl27dvd338vkJDQwkNDa1zPCAgwCO/URuG4bHZpGXoGhDQdSAWXQeeK+OkDNJnpmNguIoLE5OMARlu+3qFhEBiopOQEF0D/ygjw5pTUYNhmpCRgeEjn7fGfi9oyPO99jMVGRlJ+/bt2bFjB5999hmDBg2ia9eutGvXjvnz57ueV1RUxKJFi+jXrx8A/fr1o6CgoNYdjv/97384nU767l3HWERERKSZOVIcZA7LpHtcd9exQ9ocwpCUuvM9Gys+HkaOLCE+3m2n9E0OB2RmQlxc9bHRo2E/c2/lwLyusPjss8+YO3cua9euZd68eZx88sl0796dK664AsMwuPXWW3nggQf46KOPyM7OZuTIkXTo0IHBeybepKSkcOaZZ3L11VezePFivvvuO2688UaGDx9Ohw4d7H1zIiIi4lccKQ5W3LCC3u17A7BmxxrW7Fhjcyo/5XDAF19U93/6yb4sXsrrCovCwkJuuOEGunfvzsiRI+nfvz+fffYZwXtmJN1xxx3cdNNNjB49mqOPPpri4mLmzp1ba3zYjBkz6N69O6eeeipnn302/fv3Z9q0aXa9JREREfFzQ3sMdbXfW/6e2867eTM8/ngkmzf/+3MFa/+KvSuH/vyz9U/qzTDNGrNUpF6Kiopo3bo1hYWFHjl5Oz8/n4SEBI2l9FO6BgR0HYhF14H3WLNjDd2e7gZA7/a9+Wm0e/5avnGjkyee2M2YMRF07KhroF5eeKF6N+7rr4dnn7U3jxs05XtBQ37v1RUmIiIiYrND2hzCUe2PAmDJ5iUaDmWniy6yNs0DmDEDdu+2N48XUWEhIiIi4gFqDod6f8X7Nibxc9HRMGyY1S4stCZ1S72osBARERHxALXmWaxw3zwLaYSrrqpuv/iifTm8jAoLEREREQ/QrW03jmx3JAA/bfqJtTvWNvmccXFw+eW7a62iKvVw3HGQkmK1v/kGcuruki51qbAQERER8RDuHg4VHAxxcU72LJ4p9WUYte9avPyyfVm8iAoLEREREQ8x9HD3DocqKIDPPguloKDJp/I/l16KqyJ7/XUoL7c3jxdQYSEiIiLiIQ5teyi92vUC4MdNP7KuYF2TzldSAtnZwZSUND2b34mPhz0bLJOfD7Nn2xrHG6iwEBEREfEgWh3Kg9QcDvXSS/bl8BIqLEREREQ8iFaH8iADB0KXLlZ77lzYsMHePB5OhYWIiIiIBzks9jDSEtMAWLxxMX8V/GVzIj8WEABXXmm1TRNefdXePB5OhYWIiIiIhxl2+DBXuynDoSIj4ZhjyomMdEcqP3XFFdYqUWCtDlVVZW8eD6bCQkRERMTD1BwONXPFzEafJzoaTjyxnOhod6TyU506wZlnWu3162H+fHvzeDAVFiIiIiIexl3DocrKYMOGQMrK3JnOD119dXVbk7gPSIWFiIiIiAdyx+pQ27fDu++Gs327u1L5qXPPhYQEqz1rFmzZYmscT6XCQkRERMQDuXuzPGmC4GC4/HKrXVEB06fbGsdTqbAQERER8UBJsUl0aW0tdbpo4yJSnk0ha2WWzan82KhR1e3bb4e0NMjS16MmFRYiIiIiHihrZRZ/FVbPrfh96++kz0xXcWGX336rbpsmZGdDerqKixpUWIiIiIh4oAkLJmBg1DpmYDBxwcR6nyMgAFq1chKg3/iabsKE6mVnwSouDAMm1v/r4et0mYmIiIh4oFVbV2Fi1jpmYpKzLafe50hMhGuv3U1iorvT+aFVq6xioibThJz6fz18nQoLEREREQ+UFJe03zsWybHJNiXyc0lJte9YgNVP1tdjLxUWIiIiIh4oY0AGJmat4sLEJGNARr3PkZcHU6dGkJfXHAn9TEZG9fCnvUwTxo+3L5OHUWEhIiIi4oEcKQ4yh2WSmphaq7g4tO2h9T6H0wnFxQE4nc2R0M84HJCZCamptYuLzp3ty+RhVFiIiIiIeChHioOl1y5lyllTXMfeyn7LxkR+zuGApUth2rTqYy++aFscT6PCQkRERMTDDT18KIFGIADvLH8Hc99JxNKyhg+HVq2s9ltvQXGxvXk8hAoLEREREQ+XEJnAwEMGArCuYB0//P2DzYn8XKtWcNFFVru4GN591948HkKFhYiIiIgXuOiIi1zt+g6HatsWLrywhLZtmyuVH7v66up2zaFRfkyFhYiIiIgXGJIyhNDAUABmrphJpbPyXz8mNBQ6daoiNLS50/mhPn0gLc1qL14My5bZm8cDqLAQERER8QLRodGcm3QuAPm78vly7Zf/+jFFRfD11yEUFTV3Oj9kGLXvWmgStwoLEREREW9RazjUb/8+HGrXLli8OIRdu5ozlR8bMQLCw632m29CSYm9eWymwkJERETES5x92NlEhUQBkLUyi9LKUpsT+bmYGBg61GoXFMD779uZxnYqLERERES8RHhwOI4UBwBFZUV8uvpTmxMJo0dXt/18OJQKCxEREREv0tDhUNLMjjsOUlKs9jffwO+/25vHRiosRERERLzIqYecSnxEPACzV82mqOzAM7PDw6FnzwrXNABpBvtO4n7pJfuy2EyFhYiIiIgXCQoIYmgPa1x/aWUpH/7+4QGfGxMDZ5xRRkxMy2TzW5deCiEhVvv116GszN48NlFhISIiIuJlLu55sav9T8OhKipg69YAKipaIpUfi4sDhzX3ha1bYdYsW+PYRYWFiIiIiJfp16kfnVt3BmDen/PYsmvLfp+3dSu89loEW7e2ZDo/pT0tVFiIiIiIeJsAI4Dhhw8HoMqs4v0V/r3MqUc46STo1s1qz58Pf/5paxw7qLAQERER8UI1h0O9/dvbNiYRAAIC4Kqrqvsvv2xfFpuosBARERHxQqmJqaTEWcucfrP+G9YXrrc5kXD55RAUZLVffRV/m9yiwkJERETECxmGUWtPi25PdyNtahpZK7NqPS8gwGzpaP6rXTs4/3yrnZsLkZGQlgZZWf/8cT5ChYWIiIiIl2ob3tbVrnRWkp2XTfrMdFdx0b49jB27i/bt7Uroh3r0qG5XVEB2NqSn+0VxocJCRERExEtN+3larb6JiYHBxAUTbUokfLjPviKmaW2iN9H3vyYqLERERES81Kqtq+ocMzHJ2ZYDwJYtMH16OFv2vxqtNIfVq+seM03IyWn5LC1MhYWIiIiIl0qKS8LAqHXMwCA5NhmAykrIzw+kstKOdH4qKcm6Q1GTYUBysj15WpAKCxEREREvlTEgA5Pak7NNTDIGZNiUSMjIsO5Q1GSa1nEfp8JCRERExEs5UhxkDsukXat2rmMjU0cyJGWIjan8nMMBmZnQuXP1sQEDYIjvf01UWIiIiIh4MUeKg68v/9rV3zu/QmzkcFhzKmJirP7ixVBYaGuklqDCQkRERMTLHRZ7GKmJqQAs2riIDYUbAOv32vPOK3X9fistKCwMRoyw2iUl8O679uZpASosRERERHxAekq6q713H4vwcEhOriQ83K5Ufu7KK6vbr7xiX44WosJCRERExAfULCwyV2YCUFwMP/0UTHGxXan83JFHWjtvAyxaBMuX25unmamwEBEREfEBPeJ70D2uOwDfrv+W3OJcdu6Er74KZedOm8P5K8Oofdfi1Vfty9ICVFiIiIiI+ADDMFx3LUxMZv0+y95AYhkxAkJCrPb06VBRYW+eZpKeJlIAAB0MSURBVKTCQkRERMRH1BwO9f6K921MIi6xsTBokNXesgU++cTePM1IhYWIiIiIj+jVrhddY7oC8NW6r9hRssPmRAL4zSRuFRYiIiIiPsIwDC7ocQEAVWYV8zfMoVu3SkJDbQ7m7047DTp2tNpz5sDmzfbmaSYqLERERER8SM3hUHM3vcWQIaW0bWtjIIHAQLj8cqtdVQVvvGFrnOaiwkJERETEhxzd8WgOij4IgC/++JLNO4qoqrI5lFQXFmANhzJN26I0FxUWIiIiIj4kwAjA0d0BQOXONtzzSAH5+TaHEjj0UBgwwGrn5MDChfbmaQYqLERERER8zN55FgCrdqyyMYnU4uOTuFVYiIiIiPiY4zodR2JkIgDritaxq3yXzYkEgPR0iIqy2u++i69tia7CQkRERMTHBAYEMqT7EACqnJXMXzvf5kQCQGQkDB9utYuL4X3f2mtEhYWIiIiID0rvUb061Jw/5tiYRGrx4eFQKixEREREfNCALgNoG1cJJ0zmf/lvU1JRYnckAejbF1JSrPY338Aq35kDo8JCRERExAcFBwYzKOU8CCpnd1UxrR9qTdrUNLJWZtkdzb8ZRu27Fj16QFoaZHn/10WFhYiIiIiPiq7qCr9eArvbUuGsIDsvm/SZ6Sou7FZzx8KqKsjOtiZ2e3lxocJCRERExEfNXfU/2N4NqkIAMDExMJi4YKLNyfzcU0/V7pumdSdjond/XVRYiIiIiPiotTvW1jlmYpKzLceGNOKyv3kVpmltnOfFVFiIiIiI+KhubboBRq1jBgbJscn2BBJLUpJ1h6Imw4Bk7/66qLAQERER8VFjjh0DmLWOmZhkDMiwJ5BYMjKsOxQ1maZ13IupsBARERHxUcN7n83dlxxJdHT1X8fHnzieISlDbEwlOByQmWndudgrORmGePfXRYWFiIiIiI+KjISbzu3Ly+lPu45t2rnJxkTi4nDA77/DEUdY/Zwc+OMPezM1kQoLERERER9VUgIrVgQxoOOZRARHAPDB7x9Q6ay0OZkA1ryKkSOr+2+8YV8WN1BhISIiIuKjCgpgzpwwyndFcM5h5wCwrWQbC9YtsDeYVBsxAgL2/Eo+fTo4nfbmaQIVFiIiIiJ+YGiPoa72+yvetzGJ1NKhA5x2mtVetw6+/dbWOE2hwkJERETED5x12FmEB4UDkPV7FlXOKpsTiUvN4VDTp9uXo4lUWIiIiIj4gVYhrTjrsLMAyN+Vzzfrv7E5kbgMHgxRUVZ75kzYvdvWOI2lwkJERETERwUHQ/v2VQQHW/0LUi5wPabhUB4kIgKG7hmqtnMnfPihvXkaSYWFiIiIiI+Ki4MRI0qIi7P65yadS2hgKACZKzNxmt47Udjn+MBwKBUWIiIiIn4iKjSKMw89E4Dc4ly+W/+dzYnE5YQToEsXq/3557DJ+/YbUWEhIiIi4qM2b4bHHmvF5s3Vxy7ooeFQHikgoPquhdMJb71lb55GUGEhIiIi4kfOSzqPkMAQQMOhPM6ll1a3X38dTNO+LI2gwkJERETEj7QOa83p3U4HYOPOjSz6e5HNicTlsMOgXz+r/dtvsHSprXEaSoWFiIiIiJ+puTrUeyveszGJ1HHZZdVtL5vErcJCRERExM+cn3w+wQHWGrTvr3gf08uG3Pi0YcMgxBqqxltvQUWFvXkaQIWFiIiIiI+Kj4dRo3YRH1/7eJvwNgw8ZCAAG4o28OOmH21IJ/vVpg2cf77Vzs+Hzz6zN08DqLAQERER8VFBQdCmjUlQUN3HtDqUB/PS4VAeVVh8/fXXnHfeeXTo0AHDMJg1a1atx03TZPz48bRv357w8HAGDhzI6tWraz1n+/btjBgxgujoaGJiYhg1ahTFxcW1nrNs2TJOOOEEwsLC6NSpE4888khzvzURERGRFrdjB3zySSg7dtR9bFDyIAKNQMCaZ6HhUB7kjDNw3Wb66CP2+wX0QB5VWOzatYu0tDSeffbZ/T7+yCOP8PTTTzN16lQWLVpEZGQkZ5xxBqWlpa7njBgxguXLlzNv3jxmz57N119/zejRo12PFxUVcfrpp9OlSxeWLFnCo48+yn333ce0adOa/f2JiIiItKTSUli5Mpgavyq5xEbEckrXUwBYV7COnzf/3MLp5ICCg+Hii612WRnMnGlvnnryqMLirLPO4oEHHmDIkCF1HjNNkyeffJJx48YxaNAgUlNTmT59Ops2bXLd2Vi5ciVz587lpZdeom/fvvTv358pU6bwzjvvsGnP7oUzZsygvLycV155hcMPP5zhw4dz88038/jjj7fkWxURERGx3dAeQ13tvi/1JW1qGlkrs2xMJC41h0Nddx2kpUGWZ39tPKqw+Cdr164lNzeXgQMHuo61bt2avn37snDhQgAWLlxITEwMffr0cT1n4MCBBAQEsGjRItdzTjzxREL2zrYHzjjjDHJyctjhJbeZRERERNwhKKB68kWVWUV2XjbpM9NVXHiCNWuq26YJ2dmQnu7RxcV+pvJ4ptzcXAASExNrHU9MTHQ9lpubS0JCQq3Hg4KCaNu2ba3ndO3atc459j7Wpk2bOq9dVlZGWVmZq19YWAhAQUEBTqdn7VbpdDopKioiJCSEgACvqRvFjXQNCOg6EIuuAyksdFJaWkJhYTmRkXWvgf/78v+gxjApE2uexfi54zml/SktFVP2w8jIsP6794C556szfjzmKQ372jTle0FRUdGel//3OTheU1jYafLkyUyYMKHO8S5dutiQRkRERKRhDjB99YCWs5w2Y+v+sVU8wPLl1pK0LWznzp20bt36H5/jNYVFu3btAMjLy6N9+/au43l5efTq1cv1nPz8/FofV1lZyfbt210f365dO/Ly8mo9Z29/73P2dddddzF27FhX3+l0sn37dmJjYzEMY78fY5eioiI6derEhg0biI6OtjuO2EDXgICuA7HoOhBdAwJNuw5M02Tnzp106NDhX5/rNYVF165dadeuHfPnz3cVEkVFRSxatIjrrrsOgH79+lFQUMCSJUvo3bs3AP/73/9wOp307dvX9Zx77rmHiooKgoOtHSfnzZtHcnLyfodBAYSGhhIaGlrrWExMTDO8S/eJjo7WNxA/p2tAQNeBWHQdiK4BgcZfB/92p2IvjxpwWVxczNKlS1m6dClgTdheunQp69evxzAMbr31Vh544AE++ugjsrOzGTlyJB06dGDw4MEApKSkcOaZZ3L11VezePFivvvuO2688UaGDx/uqrIuvvhiQkJCGDVqFMuXL+fdd9/lqaeeqnVHQkREREREGsaj7lj89NNPnHzyya7+3l/2L7vsMl577TXuuOMOdu3axejRoykoKKB///7MnTuXsLAw18fMmDGDG2+8kVNPPZWAgADS09N5+umnXY+3bt2azz//nBtuuIHevXsTFxfH+PHja+11ISIiIiIiDeNRhcVJJ530jzPODcNg4sSJTJw48YDPadu2LW+99dY/vk5qairffPNNo3N6stDQUDIyMuoM3RL/oWtAQNeBWHQdiK4BgZa7DgxT+7eLiIiIiEgTedQcC5H/b+/eg6Iq/z+AvxdluaMkLJYJApakoNVahGmW4mIDZEnZlDniMEa5WJZpXpqwrKHM1PKKOYGXJhrs5i0cvGGj2IXQwnK9AFEKqQV4IV1gP78/+nG+HndVcBdW4P2a2RnPc56zz+e4n4X97DnPAxERERG1TSwsiIiIiIjIbiwsiIiIiIjIbiws2pHDhw9j1KhR8Pf3h6+vLwYPHoydO3eq+pSXlyMuLg6enp7Q6XSYNm0a6uvrnRQxtYTNmzcjKioKHh4e8PPzU5ZjbsQc6DguXryIO++8ExqNRlnGu9HPP/+MIUOGwN3dHT179sS8efOcEyQ5XFlZGZKTkxESEgIPDw+EhYUhLS0NZrNZ1Y850DEsXboUvXr1gru7O6KiovD99987OyRqIenp6bjnnnvg4+MDnU6HRx99FCaTSdXnwoULMBqN6NatG7y9vZGYmGj1h6PtwcKiHYmPj0d9fT127NiBwsJCDBgwAPHx8aisrAQANDQ0IC4uDmazGXv37sXq1auRlZWF119/3cmRk6N8/vnnGDduHCZMmIADBw5gz549ePrpp5X9zIGOZfr06Tb/UuqZM2dgMBgQHByMwsJCvPfee5gzZw5WrlzphCjJ0Q4dOgSLxYKMjAwcPHgQCxcuxIoVKzBr1iylD3OgY/jss8/w8ssvIy0tDT/99BMGDBiA2NhYnDx50tmhUQvIz8+H0WjEvn37kJeXh7q6OhgMBpw/f17p89JLL2Hjxo3IyclBfn4+Tpw4gdGjRzsuCKF24dSpUwJAdu/erbSdOXNGAEheXp6IiGzZskVcXFyksrJS6bN8+XLx9fWVixcvtnrM5Fh1dXXSo0cPWbVq1RX7MAc6ji1btkh4eLgcPHhQAEhRUZGyb9myZeLn56d6zV999VXp06ePEyKl1jBv3jwJCQlRtpkDHcO9994rRqNR2W5oaJBbbrlF0tPTnRgVtZaTJ08KAMnPzxcRkerqanF1dZWcnBylz2+//SYApKCgwCFj8opFO9GtWzf06dMHa9aswfnz51FfX4+MjAzodDro9XoAQEFBASIjIxEYGKgcFxsbizNnzuDgwYPOCp0c5KeffsLx48fh4uKCu+66CzfffDMefvhhFBcXK32YAx3DX3/9hYkTJ2Lt2rXw9PS02l9QUIAHHngAWq1WaYuNjYXJZEJVVVVrhkqtpKamBjfddJOyzRxo/8xmMwoLCxETE6O0ubi4ICYmBgUFBU6MjFpLTU0NACjv/cLCQtTV1alyIjw8HEFBQQ7LCRYW7YRGo8G2bdtQVFQEHx8fuLu7Y8GCBcjNzYWfnx8AoLKyUvWBEoCy3Xi7FLVdJSUlAIA5c+bgtddew6ZNm+Dn54cHH3wQ//zzDwDmQEcgIkhKSsJzzz2HgQMH2uzDPOhYjh49isWLFyMlJUVpYw60f6dPn0ZDQ4PN15mvcftnsVgwZcoU3H///YiIiADw33tbq9Wia9euqr6OzAkWFje4GTNmQKPRXPVx6NAhiAiMRiN0Oh2+/fZbfP/993j00UeRkJCAiooKZ58G2aGpOWCxWAAAs2fPRmJiIvR6PTIzM6HRaJCTk+PksyB7NTUPFi9ejLNnz2LmzJnODpkcrKk5cKnjx49j5MiReOKJJzBx4kQnRU5Erc1oNKK4uBjZ2dmtOm7nVh2Nmm3q1KlISkq6ap/Q0FDs2LEDmzZtQlVVFXx9fQEAy5YtQ15eHlavXo0ZM2age/fuVqtBNK4E0L179xaJn+zX1BxoLCD79u2rtLu5uSE0NBTl5eUAwBxow5rzs6CgoABubm6qfQMHDsTYsWOxevVqdO/e3WoVEObBja+pOdDoxIkTeOihhzBo0CCrSdnMgfbP398fnTp1svk68zVu31JTU7Fp0ybs3r0bt956q9LevXt3mM1mVFdXq65aODInWFjc4AICAhAQEHDNfrW1tQD+u3/yUi4uLso32dHR0Xj77bdx8uRJ6HQ6AEBeXh58fX1VH0bpxtLUHNDr9XBzc4PJZMLgwYMBAHV1dSgrK0NwcDAA5kBb1tQ8+PDDD/HWW28p2ydOnEBsbCw+++wzREVFAfgvD2bPno26ujq4uroC+C8P+vTpo9w6STeepuYA8N+Vioceeki5cnn57wbmQPun1Wqh1+uxfft2Zdlxi8WC7du3IzU11bnBUYsQEUyePBlffvkldu3ahZCQENV+vV4PV1dXbN++HYmJiQAAk8mE8vJyREdHOywIagdOnTol3bp1k9GjR8v+/fvFZDLJK6+8Iq6urrJ//34REamvr5eIiAgxGAyyf/9+yc3NlYCAAJk5c6aToydHefHFF6VHjx6ydetWOXTokCQnJ4tOp5N//vlHRJgDHVFpaanVqlDV1dUSGBgo48aNk+LiYsnOzhZPT0/JyMhwXqDkMH/++af07t1bhg8fLn/++adUVFQoj0bMgY4hOztb3NzcJCsrS3799Vd59tlnpWvXrqqVAan9eP7556VLly6ya9cu1fu+trZW6fPcc89JUFCQ7NixQ3788UeJjo6W6Ohoh8XAwqId+eGHH8RgMMhNN90kPj4+ct9998mWLVtUfcrKyuThhx8WDw8P8ff3l6lTp0pdXZ2TIiZHM5vNMnXqVNHpdOLj4yMxMTFSXFys6sMc6FhsFRYiIgcOHJDBgweLm5ub9OjRQ9555x3nBEgOl5mZKQBsPi7FHOgYFi9eLEFBQaLVauXee++Vffv2OTskaiFXet9nZmYqff7991+ZNGmS+Pn5iaenpzz22GOqLx3spfn/QIiIiIiIiK4bV4UiIiIiIiK7sbAgIiIiIiK7sbAgIiIiIiK7sbAgIiIiIiK7sbAgIiIiIiK7sbAgIiIiIiK7sbAgIiIiIiK7sbAgIiIiIiK7sbAgIiJyMIvFgoiICLz99ttW+0QEZrPZ7jFmzJiBqKgou5+HiMhRWFgQEd1gNBpNkx67du1ydqhOs2zZMmRlZTk7jCv69NNP8ccffyA1NVXVvm7dOvj7+8PHxwcTJkywWWBcuHABCxcuRFRUFLp06QJ3d3fcfvvtSE1NxeHDh5V+U6ZMwYEDB7Bhw4YWPx8ioqbQiIg4OwgiIvqfdevWqbbXrFmDvLw8rF27VtU+YsQIBAYGtmZoN4yIiAj4+/vfsMXVnXfeiaioKGRkZChtZWVliIyMxJw5cxAcHIw33ngD48aNw/Tp05U+p0+fxsiRI1FYWIj4+HjExMTA29sbJpMJ2dnZqKysVBUjTz75JCoqKrB79+5WPT8iIls6OzsAIiJSe+aZZ1Tb+/btQ15enlV7eyEiuHDhAjw8PNpFHEVFRThw4ADef/99VfuPP/6ImJgYTJ06FQDg6uqKVatWqQqLpKQkFBUVYf369UhMTFQdP3fuXMyePVvVNmbMGDzxxBMoKSlBaGioXXETEdmLt0IREbVBFosFixYtQr9+/eDu7o7AwECkpKSgqqpK1a9Xr16Ij4/Hrl27MHDgQHh4eCAyMlL5pv+LL75AZGQk3N3dodfrUVRUpDo+KSkJ3t7eKCkpQWxsLLy8vHDLLbfgzTffxOUXvJsb09atW5WYGr/Zz8zMxLBhw6DT6eDm5oa+ffti+fLlVscfPHgQ+fn5ym1hDz74IABgzpw50Gg0Vv9fWVlZ0Gg0KCsra1Ic1dXVmDJlCnr27Ak3Nzf07t0b7777LiwWyzVfm6+++gparRYPPPCAqj00NBS7d+9GXl4eTCYTVq5cidtuu03Z/91332Hz5s1ITk62KioAwM3NDfPnz1e1xcTEAAC+/vrra8ZFRNTSeMWCiKgNSklJQVZWFiZMmIAXXngBpaWlWLJkCYqKirBnzx64uroqfY8ePYqnn34aKSkpeOaZZzB//nwkJCRgxYoVmDVrFiZNmgQASE9Px5gxY2AymeDi8r/vnRoaGjBy5Ejcd999mDdvHnJzc5GWlob6+nq8+eab1xWTyWTCU089hZSUFEycOBF9+vQBACxfvhz9+vXDI488gs6dO2Pjxo2YNGkSLBYLjEYjAGDRokWYPHkyvL29lW/wr/eWMFtx1NbWYujQoTh+/DhSUlIQFBSEvXv3YubMmaioqMCiRYuu+px79+5FRESE6nwB4O6778bYsWNhMBgAAP3791fNE2mcKzFu3Lgmx9+lSxeEhYVhz549eOmll5p8HBFRixAiIrqhGY1GufTH9bfffisA5JNPPlH1y83NtWoPDg4WALJ3716lbevWrQJAPDw85Pfff1faMzIyBIDs3LlTaRs/frwAkMmTJyttFotF4uLiRKvVyqlTp647ptzcXKtzra2ttWqLjY2V0NBQVVu/fv1k6NChVn3T0tLE1q+2zMxMASClpaXXjGPu3Lni5eUlhw8fVrXPmDFDOnXqJOXl5VbPf6lbb71VEhMTr7j/2LFjUlhYKHV1dar2xx57TABIVVXVVZ//cgaDQe64445mHUNE1BJ4KxQRURuTk5ODLl26YMSIETh9+rTy0Ov18Pb2xs6dO1X9+/bti+joaGW7cYnSYcOGISgoyKq9pKTEasxLVzfSaDRITU2F2WzGtm3briumkJAQxMbGWo1z6fyGmpoanD59GkOHDkVJSQlqamqa/H/UVLbiyMnJwZAhQ+Dn56c6l5iYGDQ0NFxzovTff/8NPz+/K+4PDQ3F3Xffjc6d1TcNnDlzBgDg4+PTrHNojJOIyNl4KxQRURtz5MgR1NTUQKfT2dx/8uRJ1falxQPw3+0zANCzZ0+b7ZfPiXBxcbGaGHz77bcDgDJnobkxhYSE2Oy3Z88epKWloaCgALW1tap9NTU1SoyOYiuOI0eO4Oeff0ZAQIDNYy4/F1vkOhZc9PX1BQCcPXsWXbt2bfJxImJzXgkRUWtjYUFE1MZYLBbodDp88sknNvdf/oG4U6dONvtdqf16PhQ3NyZbKy8dO3YMw4cPR3h4OBYsWICePXtCq9Viy5YtWLhwYZMmTl/pA3ZDQ4PNdltxWCwWjBgxQrVa06Uai6or6datm1Vx1hTh4eEAgF9++QVDhgxp8nFVVVXw9/dv9nhERI7GwoKIqI0JCwvDtm3bcP/997fKEq0WiwUlJSWqD9SNf6itV69eDotp48aNuHjxIjZs2KC6ynL5bVTAlQuIxluQqqurVd/6//77702OIywsDOfOnVNWXGqu8PBwlJaWNvu4hIQEpKenY926dc0qLEpLSzFgwIBmj0dE5GicY0FE1MaMGTMGDQ0NmDt3rtW++vp6VFdXO3zMJUuWKP8WESxZsgSurq4YPny4w2JqvIJy6RWTmpoaZGZmWvX18vKy+ZxhYWEAoJoHcf78eaxevfqa4zcaM2YMCgoKsHXrVqt91dXVqK+vv+rx0dHRKC4uxsWLF5s8ZuNxI0eOxKpVq/DVV19Z7TebzXjllVdUbTU1NTh27BgGDRrUrLGIiFoCr1gQEbUxQ4cORUpKCtLT07F//34YDAa4urriyJEjyMnJwQcffIDHH3/cYeO5u7sjNzcX48ePR1RUFL755hts3rwZs2bNUm5xckRMBoMBWq0WCQkJSElJwblz5/DRRx9Bp9OhoqJC1Vev12P58uV466230Lt3b+h0OgwbNgwGgwFBQUFITk7GtGnT0KlTJ3z88ccICAhAeXl5k8532rRp2LBhA+Lj45GUlAS9Xo/z58/jl19+wfr161FWVnbVW49GjRqFuXPnIj8/X1latqnWrFkDg8GA0aNHIyEhAcOHD4eXlxeOHDmC7OxsVFRUqP6WxbZt2yAiGDVqVLPGISJqEc5ckoqIiK7t8uVmG61cuVL0er14eHiIj4+PREZGyvTp0+XEiRNKn+DgYImLi7M6FoAYjUZVW2lpqQCQ9957T2kbP368eHl5ybFjx8RgMIinp6cEBgZKWlqaNDQ0ODQmEZENGzZI//79xd3dXXr16iXvvvuufPzxx1ZLxVZWVkpcXJz4+PgIANXSs4WFhRIVFSVarVaCgoJkwYIFV1xu9kpxnD17VmbOnCm9e/cWrVYr/v7+MmjQIJk/f76YzWabx1yqf//+kpycfM1+ttTW1sr8+fPlnnvuEW9vb9FqtXLbbbfJ5MmT5ejRo6q+Tz75pAwePPi6xiEicjSNyHXM0iMiog4hKSkJ69evx7lz55wdSpuydu1aGI1GlJeXN2uFp+aorKxESEgIsrOzecWCiG4InGNBRETkYGPHjkVQUBCWLl3aYmMsWrQIkZGRLCqI6IbBORZEREQO5uLiguLi4hYd45133mnR5yciai5esSAiIiIiIrtxjgUREREREdmNVyyIiIiIiMhuLCyIiIiIiMhuLCyIiIiIiMhuLCyIiIiIiMhuLCyIiIiIiMhuLCyIiIiIiMhuLCyIiIiIiMhuLCyIiIiIiMhuLCyIiIiIiMhu/wcTVdgN/pA8YgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a proper sounding plot\n",
    "fig, ax = plt.subplots(figsize=(8, 10))\n",
    "\n",
    "# Plot temperature and dewpoint\n",
    "ax.plot(\n",
    "    slc.t - 273.15,\n",
    "    slc.isobaricInhPa,\n",
    "    color=\"red\",\n",
    "    marker=\"o\",\n",
    "    linewidth=2,\n",
    "    markersize=4,\n",
    "    label=\"Temperature\",\n",
    ")\n",
    "ax.plot(\n",
    "    slc.dpt - 273.15,\n",
    "    slc.isobaricInhPa,\n",
    "    color=\"green\",\n",
    "    marker=\"o\",\n",
    "    linewidth=2,\n",
    "    markersize=4,\n",
    "    label=\"Dew Point\",\n",
    ")\n",
    "\n",
    "# Format axes\n",
    "ax.invert_yaxis()\n",
    "ax.set_ylim(1000, 100)\n",
    "ax.set_ylabel(\"Pressure (hPa)\", fontsize=12)\n",
    "ax.set_xlabel(\"Temperature (°C)\", fontsize=12)\n",
    "ax.set_title(\n",
    "    f\"HRRR Model Sounding: {slc.point_stid.item()}\\n\"\n",
    "    f\"Valid: {slc.valid_time.dt.strftime('%Y-%m-%d %H:%M UTC').item()}\",\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.legend(loc=\"upper right\")\n",
    "\n",
    "# Add reference lines\n",
    "ax.axvline(0, color=\"blue\", linestyle=\"--\", alpha=0.5, linewidth=1)\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pick Points: Timeseries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n",
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 03:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n",
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 06:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n",
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 09:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n",
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 12:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n",
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 15:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n",
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 18:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n",
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 21:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n",
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-02 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7d6e21642490>]"
      ]
     },
     "execution_count": 17,
     "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": [
    "# TODO: Could demonstrate using FastHerbie here, if the kernel wouldn't crash (memory??)\n",
    "\n",
    "i = []\n",
    "for date in pd.date_range(\"2024-01-01\", periods=9, freq=\"3h\"):\n",
    "    ds = Herbie(date).xarray(\"(?:DPT|TMP):2 m\", remove_grib=False)\n",
    "    i.append(\n",
    "        ds.herbie.pick_points(\n",
    "            pd.DataFrame(\n",
    "                {\n",
    "                    \"latitude\": [40.77069],\n",
    "                    \"longitude\": [-111.96503],\n",
    "                    \"stid\": [\"KSLC\"],\n",
    "                }\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "slc_ts = xr.concat(i, dim=\"valid_time\")\n",
    "\n",
    "slc_ts.t2m.plot(x=\"valid_time\", marker=\".\", color=\"tab:red\")\n",
    "slc_ts.d2m.plot(x=\"valid_time\", marker=\".\", color=\"tab:green\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark\n",
    "\n",
    "Let's benchmark picking different numbers of points to understand performance scaling when harvesting _many_ points.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from herbie import Herbie\n",
    "from herbie.toolbox import EasyMap, ccrs, pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Mar-28 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "H = Herbie(\"2024-03-28 00:00\", model=\"hrrr\")\n",
    "ds = H.xarray(r\"TMP:\\d* mb\", remove_grib=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the model's own grid, I will generate 100 random samples to extract.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.718029</td>\n",
       "      <td>274.785946</td>\n",
       "      <td>SkNxqxCY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.961634</td>\n",
       "      <td>255.333244</td>\n",
       "      <td>DONsGClt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.380357</td>\n",
       "      <td>258.360720</td>\n",
       "      <td>zBYYWqvk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.499379</td>\n",
       "      <td>236.466228</td>\n",
       "      <td>PNKRmpQX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.763784</td>\n",
       "      <td>272.271894</td>\n",
       "      <td>kgiLfUwp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>34.706803</td>\n",
       "      <td>275.869335</td>\n",
       "      <td>RrdbpPvj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>25.423777</td>\n",
       "      <td>256.984473</td>\n",
       "      <td>ELGWpzoF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>21.841066</td>\n",
       "      <td>285.435707</td>\n",
       "      <td>iSCwEgju</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>35.878342</td>\n",
       "      <td>285.655062</td>\n",
       "      <td>UQBJvXzT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>45.136663</td>\n",
       "      <td>294.870079</td>\n",
       "      <td>KYvNRgCr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     latitude   longitude      stid\n",
       "0   29.718029  274.785946  SkNxqxCY\n",
       "1   26.961634  255.333244  DONsGClt\n",
       "2   47.380357  258.360720  zBYYWqvk\n",
       "3   48.499379  236.466228  PNKRmpQX\n",
       "4   37.763784  272.271894  kgiLfUwp\n",
       "..        ...         ...       ...\n",
       "95  34.706803  275.869335  RrdbpPvj\n",
       "96  25.423777  256.984473  ELGWpzoF\n",
       "97  21.841066  285.435707  iSCwEgju\n",
       "98  35.878342  285.655062  UQBJvXzT\n",
       "99  45.136663  294.870079  KYvNRgCr\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_test_points(ds, n):\n",
    "    \"\"\"Generate n random points from the model grid.\"\"\"\n",
    "    import random\n",
    "    import string\n",
    "\n",
    "    points = (\n",
    "        ds[[\"latitude\", \"longitude\"]]\n",
    "        .to_dataframe()[[\"latitude\", \"longitude\"]]\n",
    "        .sample(n)\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "    points[\"stid\"] = [\n",
    "        \"\".join(random.choices(string.ascii_letters, k=8)) for _ in range(n)\n",
    "    ]\n",
    "    return points\n",
    "\n",
    "\n",
    "points_self_100 = generate_test_points(ds, 100)\n",
    "points_self_100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "542 ms ± 144 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "y1 = ds.herbie.pick_points(points_self_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.84 s ± 234 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "y2 = ds.herbie.pick_points(points_self_100, method=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "541 ms ± 76.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "# Try the deprecated `nearest_points` method which used MetPy\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    y3 = ds.herbie.nearest_points(points_self_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/blaylock/GITHUB/Herbie/src/herbie/nearest_points.py:109: UserWarning: More than one time coordinate present for variable  \"t\".\n",
      "  ds = ds.metpy.assign_y_x()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do I get the same answer for the old and new method?\n",
    "y1 = ds.herbie.pick_points(points_self_100)\n",
    "y2 = ds.herbie.pick_points(points_self_100, method=\"weighted\")\n",
    "y3 = ds.herbie.nearest_points(points_self_100)\n",
    "\n",
    "all(y1.latitude == y3.latitude), all(y1.longitude == y3.longitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.48 s, sys: 257 ms, total: 1.73 s\n",
      "Wall time: 2.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<GeoAxes: label='inset_axes'>"
      ]
     },
     "execution_count": 25,
     "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": [
    "%%time\n",
    "ax = EasyMap(crs=ds.herbie.crs).ax\n",
    "\n",
    "ax.pcolormesh(\n",
    "    ds.longitude,\n",
    "    ds.latitude,\n",
    "    ds.isel(isobaricInhPa=0).t,\n",
    "    cmap=\"Spectral_r\",\n",
    "    transform=pc,\n",
    ")\n",
    "\n",
    "ax.scatter(y1.longitude, y1.latitude, color=\"k\", transform=pc)\n",
    "ax.scatter(\n",
    "    y1.point_longitude, y1.point_latitude, color=\"yellow\", marker=\".\", transform=pc\n",
    ")\n",
    "\n",
    "ax.EasyMap.INSET_GLOBE()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad. Less than half a second to extract 100 points from the HRRR grid.\n",
    "\n",
    "Now let's try _even more_...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Completed 1 points\n",
      "✓ Completed 3 points\n",
      "✓ Completed 5 points\n",
      "✓ Completed 7 points\n",
      "✓ Completed 9 points\n",
      "✓ Completed 10 points\n",
      "✓ Completed 30 points\n",
      "✓ Completed 50 points\n",
      "✓ Completed 70 points\n",
      "✓ Completed 90 points\n",
      "✓ Completed 100 points\n",
      "✓ Completed 300 points\n",
      "✓ Completed 500 points\n",
      "✓ Completed 700 points\n",
      "✓ Completed 900 points\n",
      "CPU times: user 1min, sys: 20.9 s, total: 1min 21s\n",
      "Wall time: 1min 23s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7d6e2157c2d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "n_samples = (\n",
    "    list(range(1, 10, 2)) + list(range(10, 100, 20)) + list(range(100, 1_000, 200))\n",
    "    # + list(range(1_000, 10_000, 2_000))\n",
    "    # + [100_000, 1_000_000]\n",
    ")\n",
    "\n",
    "times_pick_points_nearest = []\n",
    "times_pick_points_weighted = []\n",
    "times_nearest_points = []\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "\n",
    "    for n in n_samples:\n",
    "        points = generate_test_points(ds, n)\n",
    "\n",
    "        timer = pd.Timestamp(\"now\")\n",
    "        _ = ds.herbie.nearest_points(points)\n",
    "        times_nearest_points.append((pd.Timestamp(\"now\") - timer).total_seconds())\n",
    "\n",
    "        timer = pd.Timestamp(\"now\")\n",
    "        _ = ds.herbie.pick_points(points, method=\"nearest\")\n",
    "        times_pick_points_nearest.append((pd.Timestamp(\"now\") - timer).total_seconds())\n",
    "\n",
    "        timer = pd.Timestamp(\"now\")\n",
    "        _ = ds.herbie.pick_points(points, method=\"weighted\")\n",
    "        times_pick_points_weighted.append((pd.Timestamp(\"now\") - timer).total_seconds())\n",
    "\n",
    "        print(f\"✓ Completed {n:,} points\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.plot(\n",
    "    n_samples,\n",
    "    times_pick_points_nearest,\n",
    "    marker=\".\",\n",
    "    lw=2,\n",
    "    color=\"tab:blue\",\n",
    "    label=\"pick_points (nearest)\",\n",
    ")\n",
    "ax.plot(\n",
    "    n_samples,\n",
    "    times_pick_points_weighted,\n",
    "    marker=\".\",\n",
    "    lw=2,\n",
    "    color=\"tab:orange\",\n",
    "    label=\"pick_points (weighted)\",\n",
    ")\n",
    "ax.plot(\n",
    "    n_samples,\n",
    "    times_nearest_points,\n",
    "    marker=\".\",\n",
    "    lw=1,\n",
    "    color=\"k\",\n",
    "    ls=\"--\",\n",
    "    label=\"nearest_points (deprecated)\",\n",
    ")\n",
    "\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_title(\"Performance: Herbie methods to get data at a point\")\n",
    "ax.set_xlabel(\"Number of points picked\")\n",
    "ax.set_ylabel(\"Query Time (seconds)\")\n",
    "ax.set_ylim(ymin=0)\n",
    "ax.set_xlim(xmin=1)\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.legend(loc=\"upper center\", bbox_to_anchor=[0.5, -0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice! This BallTree method used by `pick_points()` scales well for harvesting _many_ points 😎\n",
    "\n",
    "Lets see how well this works for the GFS model...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=gfs ┊ \u001b[3mproduct=pgrb2.0p25\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "gfs = Herbie(\"2024-01-01\", model=\"gfs\").xarray(\":TMP:2 m \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'points_self' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m<timed eval>:1\u001b[39m\n",
      "\u001b[31mNameError\u001b[39m: name 'points_self' is not defined"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "gfs.herbie.pick_points(points_self)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Options\n",
    "\n",
    "The default behavior for `method='nearest'` is to return the first nearest neighbor. The default behavior for `method='weighted'` is to return the inverse-distance weighted mean of the nearest 4 grid points.\n",
    "\n",
    "You can get more or fewer neighbors by setting the `k` argument. This might be useful if you want to compute the standard deviation of the _k_ grid points surrounding a point of interest.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.13 s, sys: 819 ms, total: 2.95 s\n",
      "Wall time: 2.96 s\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 24kB\n",
       "Dimensions:              (k: 5, isobaricInhPa: 5, point: 100)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-28\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "  * isobaricInhPa        (isobaricInhPa) float64 40B 1e+03 925.0 ... 700.0 500.0\n",
       "    latitude             (k, point) float64 4kB 29.72 26.96 47.38 ... 35.9 45.11\n",
       "    longitude            (k, point) float64 4kB 274.8 255.3 ... 285.7 294.9\n",
       "    valid_time           datetime64[ns] 8B 2024-03-28\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (k, point) float64 4kB 0.0 0.0 0.0 ... 2.997 2.979\n",
       "    point_latitude       (point) float64 800B 29.72 26.96 47.38 ... 35.88 45.14\n",
       "    point_longitude      (point) float64 800B 274.8 255.3 258.4 ... 285.7 294.9\n",
       "    point_stid           (point) object 800B &#x27;SkNxqxCY&#x27; ... &#x27;KYvNRgCr&#x27;\n",
       "Dimensions without coordinates: k, point\n",
       "Data variables:\n",
       "    t                    (k, isobaricInhPa, point) float32 10kB 293.1 ... 259.9\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240328/subset_0befe27...\n",
       "    search:                  TMP:\\d* mb</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-84b3cd37-1535-4d0f-b869-4f91b44f6db4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-84b3cd37-1535-4d0f-b869-4f91b44f6db4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>k</span>: 5</li><li><span class='xr-has-index'>isobaricInhPa</span>: 5</li><li><span>point</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-af088490-c370-4722-a008-60b4735da53e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af088490-c370-4722-a008-60b4735da53e' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-28</div><input id='attrs-94d3dd85-4841-4b76-9c24-077a8fd7a815' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94d3dd85-4841-4b76-9c24-077a8fd7a815' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a93dc8f-d62e-4282-a02e-08cec1009f1c' class='xr-var-data-in' type='checkbox'><label for='data-9a93dc8f-d62e-4282-a02e-08cec1009f1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-28T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-155c3355-cc77-433d-8306-dde7fb52e593' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-155c3355-cc77-433d-8306-dde7fb52e593' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-373460fc-71fe-44ca-aa9f-7a9fce1d38dc' class='xr-var-data-in' type='checkbox'><label for='data-373460fc-71fe-44ca-aa9f-7a9fce1d38dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaricInhPa</span></div><div class='xr-var-dims'>(isobaricInhPa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 925.0 850.0 700.0 500.0</div><input id='attrs-fa42eb4c-1bd3-4a1d-8154-614ed5328dd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa42eb4c-1bd3-4a1d-8154-614ed5328dd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac04a820-50ca-440a-99d5-dc1387309012' class='xr-var-data-in' type='checkbox'><label for='data-ac04a820-50ca-440a-99d5-dc1387309012' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  925.,  850.,  700.,  500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.72 26.96 47.38 ... 35.9 45.11</div><input id='attrs-db8c9e76-5b6d-474d-888d-112259dd10c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db8c9e76-5b6d-474d-888d-112259dd10c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59cddc49-cb74-488d-afca-2ce5efb9a6ca' class='xr-var-data-in' type='checkbox'><label for='data-59cddc49-cb74-488d-afca-2ce5efb9a6ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[29.71802927, 26.96163362, 47.3803572 , 48.49937879, 37.76378392,\n",
       "        29.76079807, 38.78159159, 37.77155083, 25.34868883, 24.09091709,\n",
       "        48.21904103, 32.55089807, 23.88325503, 26.85301459, 43.83982832,\n",
       "        31.7839385 , 43.60484063, 24.86617392, 26.36376   , 23.94780624,\n",
       "        25.78375659, 29.47318637, 50.52346792, 46.75018289, 47.0888957 ,\n",
       "        50.04309976, 40.06193214, 41.89141069, 25.03795909, 25.07138841,\n",
       "        38.05425593, 34.19473626, 51.69005336, 27.71853763, 48.8591675 ,\n",
       "        40.66204354, 29.16708617, 43.70294892, 29.30684806, 23.87139192,\n",
       "        47.3055353 , 49.79955748, 27.38599534, 50.73968788, 30.06715876,\n",
       "        31.89893682, 29.47667758, 34.35955798, 35.96217838, 38.59837376,\n",
       "        46.40376306, 46.78336892, 39.76691907, 48.94305033, 25.88857297,\n",
       "        39.47190886, 45.79293708, 40.19032157, 51.16890414, 34.61957265,\n",
       "        38.21191723, 45.74384048, 25.85763001, 50.4309051 , 24.89130404,\n",
       "        28.75926867, 46.38161584, 34.61367302, 51.76801043, 47.30567028,\n",
       "        28.98303164, 24.74050889, 40.59730026, 27.95945205, 37.85700979,\n",
       "        37.83553258, 36.76282367, 32.39530722, 41.26031316, 27.01905295,\n",
       "        38.69504727, 40.87737387, 43.50727847, 28.25276676, 43.52362679,\n",
       "        49.75337739, 33.76542584, 21.30579586, 47.20877064, 35.35819077,\n",
       "        47.48099322, 42.26993646, 46.31867021, 29.65065009, 48.64508105,\n",
       "        34.70680336, 25.42377735, 21.84106626, 35.87834169, 45.13666342],\n",
       "...\n",
       "       [29.74446699, 26.98801417, 47.35374252, 48.47388462, 37.7906085 ,\n",
       "        29.78721718, 38.75483083, 37.79699744, 25.3746953 , 24.11688232,\n",
       "        48.19314519, 32.57751483, 23.90880869, 26.8791206 , 43.81345449,\n",
       "        31.81042227, 43.57808393, 24.89242878, 26.39010786, 23.97340264,\n",
       "        25.81010942, 29.49963314, 50.49756558, 46.72351673, 47.06350539,\n",
       "        50.01742932, 40.035531  , 41.86522159, 25.0640784 , 25.09754642,\n",
       "        38.07987342, 34.22143502, 51.66386443, 27.74431266, 48.83272923,\n",
       "        40.63675672, 29.19323419, 43.6761137 , 29.33277711, 23.89731479,\n",
       "        47.27918557, 49.77437947, 27.41169882, 50.7137484 , 30.09381617,\n",
       "        31.92548392, 29.50328544, 34.38646321, 35.98830204, 38.57158984,\n",
       "        46.37807242, 46.75687183, 39.74126197, 48.91693168, 25.9149272 ,\n",
       "        39.44529097, 45.76619298, 40.16392563, 51.14274804, 34.64629153,\n",
       "        38.23832599, 45.71779335, 25.88314904, 50.40460741, 24.91715611,\n",
       "        28.7858346 , 46.35544958, 34.64056188, 51.74180708, 47.28048054,\n",
       "        29.00877216, 24.76675265, 40.57050742, 27.98587749, 37.88363889,\n",
       "        37.86214368, 36.78886977, 32.42212282, 41.23484039, 27.04502118,\n",
       "        38.66809575, 40.85044713, 43.48136778, 28.27892797, 43.49720204,\n",
       "        49.72698154, 33.79173272, 21.3307209 , 47.18368107, 35.38467128,\n",
       "        47.45494753, 42.24467662, 46.29211029, 29.67627817, 48.61917951,\n",
       "        34.73344073, 25.45004802, 21.86618836, 35.90444404, 45.11151013]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>274.8 255.3 258.4 ... 285.7 294.9</div><input id='attrs-5ecc9178-df35-46c0-95df-8bf078c5c687' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ecc9178-df35-46c0-95df-8bf078c5c687' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a587d9f-2804-43f4-9a6b-edfa631094d7' class='xr-var-data-in' type='checkbox'><label for='data-8a587d9f-2804-43f4-9a6b-edfa631094d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([[274.78594576, 255.33324365, 258.36071956, 236.46622847,\n",
       "        272.2718938 , 249.66936307, 250.79958982, 231.35908964,\n",
       "        248.49624613, 250.67608543, 283.32868271, 250.68176715,\n",
       "        282.32953628, 277.38309524, 280.02636075, 276.64253907,\n",
       "        254.05866397, 264.66599132, 256.33079564, 281.72998763,\n",
       "        262.46566085, 274.09973702, 279.4986605 , 258.71403172,\n",
       "        291.06683257, 240.79302483, 243.57961311, 240.84136219,\n",
       "        272.66695646, 271.47494626, 233.15168081, 251.11346721,\n",
       "        266.80779457, 240.56509493, 255.43322777, 229.9018507 ,\n",
       "        244.84720767, 258.14412256, 241.09604678, 274.83106771,\n",
       "        248.72744008, 290.90276651, 239.91847263, 246.69100197,\n",
       "        257.40204158, 275.29117985, 256.82614976, 260.76658272,\n",
       "        239.61572463, 273.56703976, 236.94836074, 273.46990288,\n",
       "        233.63525829, 278.31021272, 260.15048902, 247.50649586,\n",
       "        264.91082617, 281.47899072, 271.39092741, 273.70860588,\n",
       "        281.44333719, 241.25716075, 239.19446212, 256.15680338,\n",
       "        246.16684424, 267.59603117, 243.81655097, 258.20818492,\n",
       "        260.71394571, 231.91072411, 238.73676205, 260.61631685,\n",
       "        272.77666522, 271.22266362, 247.70412196, 247.32776356,\n",
       "        238.38740055, 259.44354675, 293.09387183, 280.37171184,\n",
       "...\n",
       "        262.46564992, 274.10358713, 279.49105295, 258.71563277,\n",
       "        291.05487715, 240.802626  , 243.58680313, 240.84979379,\n",
       "        272.6701548 , 271.47777202, 233.14093656, 251.10945204,\n",
       "        266.80581688, 240.55801944, 255.43631777, 229.91417208,\n",
       "        244.84139113, 258.14587987, 241.08900237, 274.83488952,\n",
       "        248.73329591, 290.89033787, 239.91122233, 246.6981088 ,\n",
       "        257.40033321, 275.2955551 , 256.82426265, 260.76596862,\n",
       "        239.60752742, 273.56290053, 236.95897296, 273.46527103,\n",
       "        233.64607956, 278.30331719, 260.14974086, 247.51216114,\n",
       "        264.90982171, 281.47176559, 271.3868873 , 273.71258081,\n",
       "        281.45035682, 241.26592755, 239.18712193, 256.15965162,\n",
       "        246.16173165, 267.59771125, 243.82435458, 258.2066598 ,\n",
       "        260.71476705, 231.92354   , 238.72898901, 260.61572523,\n",
       "        272.77271044, 271.22550826, 247.6986509 , 247.32215625,\n",
       "        238.37867947, 259.4424914 , 293.0821788 , 280.37744583,\n",
       "        258.3807776 , 258.25163465, 237.79013954, 278.62740473,\n",
       "        279.3321386 , 257.78863424, 281.69053846, 237.71225404,\n",
       "        230.80865801, 279.50892061, 243.12783353, 294.9828915 ,\n",
       "        252.45804492, 236.67809964, 242.34106805, 275.87407707,\n",
       "        256.98272704, 285.44259583, 285.66334452, 294.8570001 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-28</div><input id='attrs-8358ef82-882a-42ca-a9a0-d271ced3af33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8358ef82-882a-42ca-a9a0-d271ced3af33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55c4cfcd-52e7-416b-9eb0-afaff9d8ed20' class='xr-var-data-in' type='checkbox'><label for='data-55c4cfcd-52e7-416b-9eb0-afaff9d8ed20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-28T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-be5f3c92-1bef-41c3-8b22-6c2a0353f266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be5f3c92-1bef-41c3-8b22-6c2a0353f266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-935c677b-055c-4543-a829-44e28c780e3e' class='xr-var-data-in' type='checkbox'><label for='data-935c677b-055c-4543-a829-44e28c780e3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 2.883 2.997 2.979</div><input id='attrs-344e7c7f-42c8-47d4-80b8-8feb0949159b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-344e7c7f-42c8-47d4-80b8-8feb0949159b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71350ebe-7ded-4cb5-b537-a57427952e13' class='xr-var-data-in' type='checkbox'><label for='data-71350ebe-7ded-4cb5-b537-a57427952e13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array([[0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "...\n",
       "       [2.9661365 , 2.94230155, 2.96241163, 2.95208098, 2.99965423,\n",
       "        2.96645844, 2.99985923, 2.99965886, 2.9255968 , 2.91119848,\n",
       "        2.9547934 , 2.98422469, 2.90870487, 2.9412384 , 2.98659895,\n",
       "        2.9799833 , 2.98776057, 2.92021549, 2.93634357, 2.90948293,\n",
       "        2.93030054, 2.96426462, 2.93001611, 2.96765114, 2.96488089,\n",
       "        2.9356529 , 2.9987883 , 2.99459441, 2.92215126, 2.92252573,\n",
       "        2.99980668, 2.99163963, 2.91526695, 2.94944232, 2.94848495,\n",
       "        2.99776003, 2.96185522, 2.98728279, 2.96296329, 2.90856382,\n",
       "        2.96305412, 2.93841258, 2.94635861, 2.92739601, 2.96872258,\n",
       "        2.98065074, 2.96429228, 2.99225595, 2.99701325, 2.99988884,\n",
       "        2.97035296, 2.9673848 , 2.99916935, 2.94762402, 2.93141172,\n",
       "        2.99947041, 2.97482824, 2.99859691, 2.92204301, 2.99317959,\n",
       "        2.99985767, 2.97516871, 2.93108015, 2.93112451, 2.92049789,\n",
       "        2.95853163, 2.97052356, 2.99315957, 2.9142273 , 2.963048  ,\n",
       "        2.96036927, 2.91878472, 2.9978886 , 2.95162474, 2.99971151,\n",
       "        2.99969899, 2.99854464, 2.98340473, 2.99639836, 2.94285715,\n",
       "        2.99987707, 2.99731062, 2.9882245 , 2.95421523, 2.98814829,\n",
       "        2.93893594, 2.98992477, 2.87514783, 2.96387254, 2.99548377,\n",
       "        2.96153428, 2.99332734, 2.97100201, 2.96562312, 2.95064137,\n",
       "        2.99347632, 2.92642003, 2.88251355, 2.99682004, 2.97920505]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.72 26.96 47.38 ... 35.88 45.14</div><input id='attrs-f95099cb-7569-4301-a995-d29124f13641' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f95099cb-7569-4301-a995-d29124f13641' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-491bb8eb-4070-455f-9a88-dad1e98b2250' class='xr-var-data-in' type='checkbox'><label for='data-491bb8eb-4070-455f-9a88-dad1e98b2250' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([29.71802927, 26.96163362, 47.3803572 , 48.49937879, 37.76378392,\n",
       "       29.76079807, 38.78159159, 37.77155083, 25.34868883, 24.09091709,\n",
       "       48.21904103, 32.55089807, 23.88325503, 26.85301459, 43.83982832,\n",
       "       31.7839385 , 43.60484063, 24.86617392, 26.36376   , 23.94780624,\n",
       "       25.78375659, 29.47318637, 50.52346792, 46.75018289, 47.0888957 ,\n",
       "       50.04309976, 40.06193214, 41.89141069, 25.03795909, 25.07138841,\n",
       "       38.05425593, 34.19473626, 51.69005336, 27.71853763, 48.8591675 ,\n",
       "       40.66204354, 29.16708617, 43.70294892, 29.30684806, 23.87139192,\n",
       "       47.3055353 , 49.79955748, 27.38599534, 50.73968788, 30.06715876,\n",
       "       31.89893682, 29.47667758, 34.35955798, 35.96217838, 38.59837376,\n",
       "       46.40376306, 46.78336892, 39.76691907, 48.94305033, 25.88857297,\n",
       "       39.47190886, 45.79293708, 40.19032157, 51.16890414, 34.61957265,\n",
       "       38.21191723, 45.74384048, 25.85763001, 50.4309051 , 24.89130404,\n",
       "       28.75926867, 46.38161584, 34.61367302, 51.76801043, 47.30567028,\n",
       "       28.98303164, 24.74050889, 40.59730026, 27.95945205, 37.85700979,\n",
       "       37.83553258, 36.76282367, 32.39530722, 41.26031316, 27.01905295,\n",
       "       38.69504727, 40.87737387, 43.50727847, 28.25276676, 43.52362679,\n",
       "       49.75337739, 33.76542584, 21.30579586, 47.20877064, 35.35819077,\n",
       "       47.48099322, 42.26993646, 46.31867021, 29.65065009, 48.64508105,\n",
       "       34.70680336, 25.42377735, 21.84106626, 35.87834169, 45.13666342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>274.8 255.3 258.4 ... 285.7 294.9</div><input id='attrs-ffd92e93-aaf9-4873-b148-685a26904a32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffd92e93-aaf9-4873-b148-685a26904a32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d40457c-5d75-4223-ac8d-afdde514aef2' class='xr-var-data-in' type='checkbox'><label for='data-6d40457c-5d75-4223-ac8d-afdde514aef2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([274.78594576, 255.33324365, 258.36071956, 236.46622847,\n",
       "       272.2718938 , 249.66936307, 250.79958982, 231.35908964,\n",
       "       248.49624613, 250.67608543, 283.32868271, 250.68176715,\n",
       "       282.32953628, 277.38309524, 280.02636075, 276.64253907,\n",
       "       254.05866397, 264.66599132, 256.33079564, 281.72998763,\n",
       "       262.46566085, 274.09973702, 279.4986605 , 258.71403172,\n",
       "       291.06683257, 240.79302483, 243.57961311, 240.84136219,\n",
       "       272.66695646, 271.47494626, 233.15168081, 251.11346721,\n",
       "       266.80779457, 240.56509493, 255.43322777, 229.9018507 ,\n",
       "       244.84720767, 258.14412256, 241.09604678, 274.83106771,\n",
       "       248.72744008, 290.90276651, 239.91847263, 246.69100197,\n",
       "       257.40204158, 275.29117985, 256.82614976, 260.76658272,\n",
       "       239.61572463, 273.56703976, 236.94836074, 273.46990288,\n",
       "       233.63525829, 278.31021272, 260.15048902, 247.50649586,\n",
       "       264.91082617, 281.47899072, 271.39092741, 273.70860588,\n",
       "       281.44333719, 241.25716075, 239.19446212, 256.15680338,\n",
       "       246.16684424, 267.59603117, 243.81655097, 258.20818492,\n",
       "       260.71394571, 231.91072411, 238.73676205, 260.61631685,\n",
       "       272.77666522, 271.22266362, 247.70412196, 247.32776356,\n",
       "       238.38740055, 259.44354675, 293.09387183, 280.37171184,\n",
       "       258.3792311 , 258.2499897 , 237.78031127, 278.62214702,\n",
       "       279.33887858, 257.78654118, 281.68384329, 237.7196381 ,\n",
       "       230.79541366, 279.50285108, 243.11960033, 294.99546536,\n",
       "       252.45383251, 236.68659674, 242.33234215, 275.86933537,\n",
       "       256.9844727 , 285.43570657, 285.65506168, 294.87007894])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_stid</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SkNxqxCY&#x27; ... &#x27;KYvNRgCr&#x27;</div><input id='attrs-744eee8c-dad6-42cd-a951-3e7e6814cd56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-744eee8c-dad6-42cd-a951-3e7e6814cd56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8e12971-9eb0-4495-965a-d0c82551312f' class='xr-var-data-in' type='checkbox'><label for='data-d8e12971-9eb0-4495-965a-d0c82551312f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point stid</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;SkNxqxCY&#x27;, &#x27;DONsGClt&#x27;, &#x27;zBYYWqvk&#x27;, &#x27;PNKRmpQX&#x27;, &#x27;kgiLfUwp&#x27;,\n",
       "       &#x27;ysJveLCx&#x27;, &#x27;qKKofUBX&#x27;, &#x27;GrmijnCo&#x27;, &#x27;gcyarlUK&#x27;, &#x27;FHafVKwO&#x27;,\n",
       "       &#x27;OWlrJRhK&#x27;, &#x27;zdGckwzg&#x27;, &#x27;SUESsMyV&#x27;, &#x27;QJvmoUlS&#x27;, &#x27;oRPUzpMV&#x27;,\n",
       "       &#x27;sgFVkdyt&#x27;, &#x27;naXYJbys&#x27;, &#x27;BfKhsYnA&#x27;, &#x27;iyhhijIL&#x27;, &#x27;KTtSbeqi&#x27;,\n",
       "       &#x27;XzqMxPdi&#x27;, &#x27;pCLmQNAW&#x27;, &#x27;nvHuqttb&#x27;, &#x27;FfJzGTHD&#x27;, &#x27;KopNihGt&#x27;,\n",
       "       &#x27;UmNUwEgr&#x27;, &#x27;EQcRxZnC&#x27;, &#x27;UyCOpjdy&#x27;, &#x27;kaZQQoYK&#x27;, &#x27;YWSQjyOM&#x27;,\n",
       "       &#x27;euDabKsK&#x27;, &#x27;fNUDJJUL&#x27;, &#x27;iENznCRg&#x27;, &#x27;MyqUdbwi&#x27;, &#x27;mYWnYGmV&#x27;,\n",
       "       &#x27;sVGRJWCX&#x27;, &#x27;ZLSOQRXI&#x27;, &#x27;ZhDzHdfB&#x27;, &#x27;acggKSRW&#x27;, &#x27;DkyGHdhH&#x27;,\n",
       "       &#x27;UOUEmFjV&#x27;, &#x27;xbqwHisD&#x27;, &#x27;COYaSChj&#x27;, &#x27;lsaLdfQT&#x27;, &#x27;ACDYwTkh&#x27;,\n",
       "       &#x27;oVYjIceO&#x27;, &#x27;POgGMDec&#x27;, &#x27;BlFoEsgt&#x27;, &#x27;SKiLECzT&#x27;, &#x27;FhTzwcSf&#x27;,\n",
       "       &#x27;VfbDZAau&#x27;, &#x27;VyKqeBwq&#x27;, &#x27;NKQWySSx&#x27;, &#x27;GibOqyXr&#x27;, &#x27;FaQqarhv&#x27;,\n",
       "       &#x27;ZfpzFGtw&#x27;, &#x27;EvvpmsJF&#x27;, &#x27;iDoeDoZO&#x27;, &#x27;XgPTLvir&#x27;, &#x27;ZZXVccKN&#x27;,\n",
       "       &#x27;cHQCByQC&#x27;, &#x27;qGlubThz&#x27;, &#x27;nDGTTccG&#x27;, &#x27;YckADDUA&#x27;, &#x27;iIVwgGPx&#x27;,\n",
       "       &#x27;HMvyYfjN&#x27;, &#x27;wCZbUYtR&#x27;, &#x27;HcHqwUiv&#x27;, &#x27;VtrBamHt&#x27;, &#x27;jLWFCSSJ&#x27;,\n",
       "       &#x27;XykPOeUI&#x27;, &#x27;bGlaEVWI&#x27;, &#x27;XhVkRDJa&#x27;, &#x27;DtmhPpVX&#x27;, &#x27;yYNXlAAQ&#x27;,\n",
       "       &#x27;uSMJRPrs&#x27;, &#x27;jXYfHeOe&#x27;, &#x27;IhnFibnQ&#x27;, &#x27;auDVceCU&#x27;, &#x27;WwiWpDbc&#x27;,\n",
       "       &#x27;PfwrWzuX&#x27;, &#x27;WICMDhwT&#x27;, &#x27;BvGvZchf&#x27;, &#x27;RmVmDBmr&#x27;, &#x27;PAZVzbUK&#x27;,\n",
       "       &#x27;hKITQzId&#x27;, &#x27;VPWMuckb&#x27;, &#x27;xptOwpfU&#x27;, &#x27;HIgUpTjt&#x27;, &#x27;IWcQOfST&#x27;,\n",
       "       &#x27;fSUENpjW&#x27;, &#x27;QeGnJBeN&#x27;, &#x27;KkjADoMz&#x27;, &#x27;GXyxNKkb&#x27;, &#x27;PTSkyVNd&#x27;,\n",
       "       &#x27;RrdbpPvj&#x27;, &#x27;ELGWpzoF&#x27;, &#x27;iSCwEgju&#x27;, &#x27;UQBJvXzT&#x27;, &#x27;KYvNRgCr&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8f8caff-6def-467e-a593-f908b03406dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a8f8caff-6def-467e-a593-f908b03406dd' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(k, isobaricInhPa, point)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>293.1 302.2 270.4 ... 262.3 259.9</div><input id='attrs-b1a6ed04-cd28-441b-bac4-8e1aae2d2fa4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1a6ed04-cd28-441b-bac4-8e1aae2d2fa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f774f22-010f-401c-adb6-45e84f6975ea' class='xr-var-data-in' type='checkbox'><label for='data-2f774f22-010f-401c-adb6-45e84f6975ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[[293.10034, 302.22534, 270.35034, ..., 296.66284, 286.03784,\n",
       "         285.85034],\n",
       "        [290.03687, 297.78687, 265.84937, ..., 290.84937, 287.09937,\n",
       "         287.22437],\n",
       "        [286.65894, 292.90894, 264.72144, ..., 292.53394, 287.90894,\n",
       "         282.90894],\n",
       "        [277.8683 , 278.7433 , 260.2433 , ..., 284.5558 , 278.5558 ,\n",
       "         273.6808 ],\n",
       "        [261.97247, 261.65997, 245.53497, ..., 268.15997, 262.40997,\n",
       "         259.84747]],\n",
       "\n",
       "       [[293.10034, 302.28784, 270.22534, ..., 296.66284, 286.03784,\n",
       "         285.85034],\n",
       "        [290.03687, 297.84937, 265.84937, ..., 290.84937, 287.16187,\n",
       "         287.16187],\n",
       "        [286.78394, 292.97144, 264.65894, ..., 292.59644, 287.90894,\n",
       "         282.90894],\n",
       "        [277.8683 , 278.7433 , 260.2433 , ..., 284.5558 , 278.6183 ,\n",
       "         273.6808 ],\n",
       "        [261.97247, 261.72247, 245.53497, ..., 268.15997, 262.40997,\n",
       "...\n",
       "         285.78784],\n",
       "        [290.16187, 297.72437, 265.91187, ..., 290.84937, 287.16187,\n",
       "         287.16187],\n",
       "        [286.59644, 292.90894, 264.78394, ..., 292.47144, 287.90894,\n",
       "         282.97144],\n",
       "        [277.8683 , 278.6808 , 260.3058 , ..., 284.5558 , 278.5558 ,\n",
       "         273.6808 ],\n",
       "        [261.97247, 261.65997, 245.53497, ..., 268.15997, 262.40997,\n",
       "         259.90997]],\n",
       "\n",
       "       [[293.10034, 302.10034, 270.35034, ..., 296.60034, 285.97534,\n",
       "         285.91284],\n",
       "        [290.03687, 297.66187, 265.84937, ..., 290.78687, 287.03687,\n",
       "         287.22437],\n",
       "        [286.53394, 292.78394, 264.78394, ..., 292.47144, 287.90894,\n",
       "         282.90894],\n",
       "        [277.8683 , 278.6808 , 260.3058 , ..., 284.4933 , 278.5558 ,\n",
       "         273.6808 ],\n",
       "        [261.97247, 261.65997, 245.53497, ..., 268.15997, 262.34747,\n",
       "         259.90997]]], shape=(5, 5, 100), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20966b25-0aa8-4b62-84f8-d74857337a83' class='xr-section-summary-in' type='checkbox'  ><label for='section-20966b25-0aa8-4b62-84f8-d74857337a83' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>isobaricInhPa</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2489a500-d7e1-403f-876d-05d5ac339f63' class='xr-index-data-in' type='checkbox'/><label for='index-2489a500-d7e1-403f-876d-05d5ac339f63' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0, 925.0, 850.0, 700.0, 500.0], dtype=&#x27;float64&#x27;, name=&#x27;isobaricInhPa&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b38ab5e-e0af-4df6-9b20-0088ea688689' class='xr-section-summary-in' type='checkbox'  ><label for='section-0b38ab5e-e0af-4df6-9b20-0088ea688689' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>hrrr</dd><dt><span>product :</span></dt><dd>sfc</dd><dt><span>description :</span></dt><dd>High-Resolution Rapid Refresh - CONUS</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20240328/conus/hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/hrrr/20240328/subset_0befe271__hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>search :</span></dt><dd>TMP:\\d* mb</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset> Size: 24kB\n",
       "Dimensions:              (k: 5, isobaricInhPa: 5, point: 100)\n",
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       "    latitude             (k, point) float64 4kB 29.72 26.96 47.38 ... 35.9 45.11\n",
       "    longitude            (k, point) float64 4kB 274.8 255.3 ... 285.7 294.9\n",
       "    valid_time           datetime64[ns] 8B 2024-03-28\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (k, point) float64 4kB 0.0 0.0 0.0 ... 2.997 2.979\n",
       "    point_latitude       (point) float64 800B 29.72 26.96 47.38 ... 35.88 45.14\n",
       "    point_longitude      (point) float64 800B 274.8 255.3 258.4 ... 285.7 294.9\n",
       "    point_stid           (point) object 800B 'SkNxqxCY' ... 'KYvNRgCr'\n",
       "Dimensions without coordinates: k, point\n",
       "Data variables:\n",
       "    t                    (k, isobaricInhPa, point) float32 10kB 293.1 ... 259.9\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240328/subset_0befe27...\n",
       "    search:                  TMP:\\d* mb"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Return the 5 nearest grid points to each request point\n",
    "ds.herbie.pick_points(points_self_100, method=\"nearest\", k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.12 s, sys: 341 ms, total: 3.46 s\n",
      "Wall time: 3.5 s\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 28kB\n",
       "Dimensions:              (isobaricInhPa: 5, point: 100, k: 9)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-03-28\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
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       "Attributes:\n",
       "    pick_point_method:  weighted\n",
       "    pick_point_k:       9</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d988e7f2-ce9f-4a85-b196-76a92a2e1e3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d988e7f2-ce9f-4a85-b196-76a92a2e1e3a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>isobaricInhPa</span>: 5</li><li><span>point</span>: 100</li><li><span>k</span>: 9</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c96a14d-bb2b-476c-84c2-0a9f30738a43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c96a14d-bb2b-476c-84c2-0a9f30738a43' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-28</div><input id='attrs-c4480e24-8f72-4ad9-b043-00b7d9121c00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4480e24-8f72-4ad9-b043-00b7d9121c00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54b18f3b-08e0-452b-ba16-3a50b39a315a' class='xr-var-data-in' type='checkbox'><label for='data-54b18f3b-08e0-452b-ba16-3a50b39a315a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-28T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-e8827c06-a02a-4a9d-8f99-5e94e262ffc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8827c06-a02a-4a9d-8f99-5e94e262ffc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b1fd992-bb34-45fd-9208-de63fcbbed46' class='xr-var-data-in' type='checkbox'><label for='data-2b1fd992-bb34-45fd-9208-de63fcbbed46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaricInhPa</span></div><div class='xr-var-dims'>(isobaricInhPa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 925.0 850.0 700.0 500.0</div><input id='attrs-a94ad159-5459-435c-a7bc-0b90d9beb007' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a94ad159-5459-435c-a7bc-0b90d9beb007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5336b8b-694c-4a73-9553-2369890f26ee' class='xr-var-data-in' type='checkbox'><label for='data-a5336b8b-694c-4a73-9553-2369890f26ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  925.,  850.,  700.,  500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-28</div><input id='attrs-a2858c19-ae24-49bd-bb4b-7113c6dfb777' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2858c19-ae24-49bd-bb4b-7113c6dfb777' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c41ba51-3705-484f-8729-3bcb957f9684' class='xr-var-data-in' type='checkbox'><label for='data-1c41ba51-3705-484f-8729-3bcb957f9684' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-03-28T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-a338855e-68ad-4a70-a408-0ba8ad10270a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a338855e-68ad-4a70-a408-0ba8ad10270a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-786887c8-58b8-4566-98d9-5694b247a7f7' class='xr-var-data-in' type='checkbox'><label for='data-786887c8-58b8-4566-98d9-5694b247a7f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.72 26.96 47.38 ... 35.88 45.14</div><input id='attrs-a8a0f6c3-4ad5-46f3-85ab-8f5a690d3194' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8a0f6c3-4ad5-46f3-85ab-8f5a690d3194' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe93e17-0a23-4a04-bc54-62bc35142f40' class='xr-var-data-in' type='checkbox'><label for='data-ebe93e17-0a23-4a04-bc54-62bc35142f40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([29.71802927, 26.96163362, 47.3803572 , 48.49937879, 37.76378392,\n",
       "       29.76079807, 38.78159159, 37.77155083, 25.34868883, 24.09091709,\n",
       "       48.21904103, 32.55089807, 23.88325503, 26.85301459, 43.83982832,\n",
       "       31.7839385 , 43.60484063, 24.86617392, 26.36376   , 23.94780624,\n",
       "       25.78375659, 29.47318637, 50.52346792, 46.75018289, 47.0888957 ,\n",
       "       50.04309976, 40.06193214, 41.89141069, 25.03795909, 25.07138841,\n",
       "       38.05425593, 34.19473626, 51.69005336, 27.71853763, 48.8591675 ,\n",
       "       40.66204354, 29.16708617, 43.70294892, 29.30684806, 23.87139192,\n",
       "       47.3055353 , 49.79955748, 27.38599534, 50.73968788, 30.06715876,\n",
       "       31.89893682, 29.47667758, 34.35955798, 35.96217838, 38.59837376,\n",
       "       46.40376306, 46.78336892, 39.76691907, 48.94305033, 25.88857297,\n",
       "       39.47190886, 45.79293708, 40.19032157, 51.16890414, 34.61957265,\n",
       "       38.21191723, 45.74384048, 25.85763001, 50.4309051 , 24.89130404,\n",
       "       28.75926867, 46.38161584, 34.61367302, 51.76801043, 47.30567028,\n",
       "       28.98303164, 24.74050889, 40.59730026, 27.95945205, 37.85700979,\n",
       "       37.83553258, 36.76282367, 32.39530722, 41.26031316, 27.01905295,\n",
       "       38.69504727, 40.87737387, 43.50727847, 28.25276676, 43.52362679,\n",
       "       49.75337739, 33.76542584, 21.30579586, 47.20877064, 35.35819077,\n",
       "       47.48099322, 42.26993646, 46.31867021, 29.65065009, 48.64508105,\n",
       "       34.70680336, 25.42377735, 21.84106626, 35.87834169, 45.13666342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>274.8 255.3 258.4 ... 285.7 294.9</div><input id='attrs-e1ace381-f7eb-46c2-91b3-091a21a03271' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1ace381-f7eb-46c2-91b3-091a21a03271' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c0f002e-996a-4d79-9c32-be860b297826' class='xr-var-data-in' type='checkbox'><label for='data-4c0f002e-996a-4d79-9c32-be860b297826' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([274.78594576, 255.33324365, 258.36071956, 236.46622847,\n",
       "       272.2718938 , 249.66936307, 250.79958982, 231.35908964,\n",
       "       248.49624613, 250.67608543, 283.32868271, 250.68176715,\n",
       "       282.32953628, 277.38309524, 280.02636075, 276.64253907,\n",
       "       254.05866397, 264.66599132, 256.33079564, 281.72998763,\n",
       "       262.46566085, 274.09973702, 279.4986605 , 258.71403172,\n",
       "       291.06683257, 240.79302483, 243.57961311, 240.84136219,\n",
       "       272.66695646, 271.47494626, 233.15168081, 251.11346721,\n",
       "       266.80779457, 240.56509493, 255.43322777, 229.9018507 ,\n",
       "       244.84720767, 258.14412256, 241.09604678, 274.83106771,\n",
       "       248.72744008, 290.90276651, 239.91847263, 246.69100197,\n",
       "       257.40204158, 275.29117985, 256.82614976, 260.76658272,\n",
       "       239.61572463, 273.56703976, 236.94836074, 273.46990288,\n",
       "       233.63525829, 278.31021272, 260.15048902, 247.50649586,\n",
       "       264.91082617, 281.47899072, 271.39092741, 273.70860588,\n",
       "       281.44333719, 241.25716075, 239.19446212, 256.15680338,\n",
       "       246.16684424, 267.59603117, 243.81655097, 258.20818492,\n",
       "       260.71394571, 231.91072411, 238.73676205, 260.61631685,\n",
       "       272.77666522, 271.22266362, 247.70412196, 247.32776356,\n",
       "       238.38740055, 259.44354675, 293.09387183, 280.37171184,\n",
       "       258.3792311 , 258.2499897 , 237.78031127, 278.62214702,\n",
       "       279.33887858, 257.78654118, 281.68384329, 237.7196381 ,\n",
       "       230.79541366, 279.50285108, 243.11960033, 294.99546536,\n",
       "       252.45383251, 236.68659674, 242.33234215, 275.86933537,\n",
       "       256.9844727 , 285.43570657, 285.65506168, 294.87007894])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_stid</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SkNxqxCY&#x27; ... &#x27;KYvNRgCr&#x27;</div><input id='attrs-2422b3ce-e49c-4a79-bcff-ecc488fa7266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2422b3ce-e49c-4a79-bcff-ecc488fa7266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60ea2658-9e19-4ea0-bc30-946e5f7db6d3' class='xr-var-data-in' type='checkbox'><label for='data-60ea2658-9e19-4ea0-bc30-946e5f7db6d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point stid</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;SkNxqxCY&#x27;, &#x27;DONsGClt&#x27;, &#x27;zBYYWqvk&#x27;, &#x27;PNKRmpQX&#x27;, &#x27;kgiLfUwp&#x27;,\n",
       "       &#x27;ysJveLCx&#x27;, &#x27;qKKofUBX&#x27;, &#x27;GrmijnCo&#x27;, &#x27;gcyarlUK&#x27;, &#x27;FHafVKwO&#x27;,\n",
       "       &#x27;OWlrJRhK&#x27;, &#x27;zdGckwzg&#x27;, &#x27;SUESsMyV&#x27;, &#x27;QJvmoUlS&#x27;, &#x27;oRPUzpMV&#x27;,\n",
       "       &#x27;sgFVkdyt&#x27;, &#x27;naXYJbys&#x27;, &#x27;BfKhsYnA&#x27;, &#x27;iyhhijIL&#x27;, &#x27;KTtSbeqi&#x27;,\n",
       "       &#x27;XzqMxPdi&#x27;, &#x27;pCLmQNAW&#x27;, &#x27;nvHuqttb&#x27;, &#x27;FfJzGTHD&#x27;, &#x27;KopNihGt&#x27;,\n",
       "       &#x27;UmNUwEgr&#x27;, &#x27;EQcRxZnC&#x27;, &#x27;UyCOpjdy&#x27;, &#x27;kaZQQoYK&#x27;, &#x27;YWSQjyOM&#x27;,\n",
       "       &#x27;euDabKsK&#x27;, &#x27;fNUDJJUL&#x27;, &#x27;iENznCRg&#x27;, &#x27;MyqUdbwi&#x27;, &#x27;mYWnYGmV&#x27;,\n",
       "       &#x27;sVGRJWCX&#x27;, &#x27;ZLSOQRXI&#x27;, &#x27;ZhDzHdfB&#x27;, &#x27;acggKSRW&#x27;, &#x27;DkyGHdhH&#x27;,\n",
       "       &#x27;UOUEmFjV&#x27;, &#x27;xbqwHisD&#x27;, &#x27;COYaSChj&#x27;, &#x27;lsaLdfQT&#x27;, &#x27;ACDYwTkh&#x27;,\n",
       "       &#x27;oVYjIceO&#x27;, &#x27;POgGMDec&#x27;, &#x27;BlFoEsgt&#x27;, &#x27;SKiLECzT&#x27;, &#x27;FhTzwcSf&#x27;,\n",
       "       &#x27;VfbDZAau&#x27;, &#x27;VyKqeBwq&#x27;, &#x27;NKQWySSx&#x27;, &#x27;GibOqyXr&#x27;, &#x27;FaQqarhv&#x27;,\n",
       "       &#x27;ZfpzFGtw&#x27;, &#x27;EvvpmsJF&#x27;, &#x27;iDoeDoZO&#x27;, &#x27;XgPTLvir&#x27;, &#x27;ZZXVccKN&#x27;,\n",
       "       &#x27;cHQCByQC&#x27;, &#x27;qGlubThz&#x27;, &#x27;nDGTTccG&#x27;, &#x27;YckADDUA&#x27;, &#x27;iIVwgGPx&#x27;,\n",
       "       &#x27;HMvyYfjN&#x27;, &#x27;wCZbUYtR&#x27;, &#x27;HcHqwUiv&#x27;, &#x27;VtrBamHt&#x27;, &#x27;jLWFCSSJ&#x27;,\n",
       "       &#x27;XykPOeUI&#x27;, &#x27;bGlaEVWI&#x27;, &#x27;XhVkRDJa&#x27;, &#x27;DtmhPpVX&#x27;, &#x27;yYNXlAAQ&#x27;,\n",
       "       &#x27;uSMJRPrs&#x27;, &#x27;jXYfHeOe&#x27;, &#x27;IhnFibnQ&#x27;, &#x27;auDVceCU&#x27;, &#x27;WwiWpDbc&#x27;,\n",
       "       &#x27;PfwrWzuX&#x27;, &#x27;WICMDhwT&#x27;, &#x27;BvGvZchf&#x27;, &#x27;RmVmDBmr&#x27;, &#x27;PAZVzbUK&#x27;,\n",
       "       &#x27;hKITQzId&#x27;, &#x27;VPWMuckb&#x27;, &#x27;xptOwpfU&#x27;, &#x27;HIgUpTjt&#x27;, &#x27;IWcQOfST&#x27;,\n",
       "       &#x27;fSUENpjW&#x27;, &#x27;QeGnJBeN&#x27;, &#x27;KkjADoMz&#x27;, &#x27;GXyxNKkb&#x27;, &#x27;PTSkyVNd&#x27;,\n",
       "       &#x27;RrdbpPvj&#x27;, &#x27;ELGWpzoF&#x27;, &#x27;iSCwEgju&#x27;, &#x27;UQBJvXzT&#x27;, &#x27;KYvNRgCr&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.72 26.96 47.38 ... 35.91 45.1</div><input id='attrs-f70708c7-32ab-4ff6-930d-b76ffe4f076d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f70708c7-32ab-4ff6-930d-b76ffe4f076d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59d024f9-ffbb-4b4a-b15f-93eacdc286f8' class='xr-var-data-in' type='checkbox'><label for='data-59d024f9-ffbb-4b4a-b15f-93eacdc286f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[29.71802927, 26.96163362, 47.3803572 , 48.49937879, 37.76378392,\n",
       "        29.76079807, 38.78159159, 37.77155083, 25.34868883, 24.09091709,\n",
       "        48.21904103, 32.55089807, 23.88325503, 26.85301459, 43.83982832,\n",
       "        31.7839385 , 43.60484063, 24.86617392, 26.36376   , 23.94780624,\n",
       "        25.78375659, 29.47318637, 50.52346792, 46.75018289, 47.0888957 ,\n",
       "        50.04309976, 40.06193214, 41.89141069, 25.03795909, 25.07138841,\n",
       "        38.05425593, 34.19473626, 51.69005336, 27.71853763, 48.8591675 ,\n",
       "        40.66204354, 29.16708617, 43.70294892, 29.30684806, 23.87139192,\n",
       "        47.3055353 , 49.79955748, 27.38599534, 50.73968788, 30.06715876,\n",
       "        31.89893682, 29.47667758, 34.35955798, 35.96217838, 38.59837376,\n",
       "        46.40376306, 46.78336892, 39.76691907, 48.94305033, 25.88857297,\n",
       "        39.47190886, 45.79293708, 40.19032157, 51.16890414, 34.61957265,\n",
       "        38.21191723, 45.74384048, 25.85763001, 50.4309051 , 24.89130404,\n",
       "        28.75926867, 46.38161584, 34.61367302, 51.76801043, 47.30567028,\n",
       "        28.98303164, 24.74050889, 40.59730026, 27.95945205, 37.85700979,\n",
       "        37.83553258, 36.76282367, 32.39530722, 41.26031316, 27.01905295,\n",
       "        38.69504727, 40.87737387, 43.50727847, 28.25276676, 43.52362679,\n",
       "        49.75337739, 33.76542584, 21.30579586, 47.20877064, 35.35819077,\n",
       "        47.48099322, 42.26993646, 46.31867021, 29.65065009, 48.64508105,\n",
       "        34.70680336, 25.42377735, 21.84106626, 35.87834169, 45.13666342],\n",
       "...\n",
       "       [29.74801411, 26.99006901, 47.35253955, 48.46647228, 37.79346337,\n",
       "        29.79092117, 38.75140674, 37.80595036, 25.37868042, 24.12023367,\n",
       "        48.18717958, 32.58094816, 23.91439915, 26.88337666, 43.80836741,\n",
       "        31.81452108, 43.57561862, 24.89304285, 26.39187299, 23.97882791,\n",
       "        25.81011506, 29.50298087, 50.4927227 , 46.72241401, 47.05535957,\n",
       "        50.01125708, 40.03002311, 41.8589398 , 25.0669724 , 25.1001021 ,\n",
       "        38.08832924, 34.22475154, 51.66263227, 27.75057266, 48.83069032,\n",
       "        40.62740538, 29.19830934, 43.67483782, 29.33891618, 23.90080607,\n",
       "        47.27520892, 49.76635101, 27.41813312, 50.70924458, 30.09529029,\n",
       "        31.92919412, 29.50492398, 34.38696541, 35.99493247, 38.56834989,\n",
       "        46.37074879, 46.75369432, 39.73293943, 48.91239655, 25.91559615,\n",
       "        39.44091271, 45.765487  , 40.15840128, 51.14020856, 34.64955806,\n",
       "        38.2438367 , 45.71166958, 25.88975108, 50.40278696, 24.92179028,\n",
       "        28.78730318, 46.35006045, 34.64181232, 51.74129271, 47.27178443,\n",
       "        29.01556815, 24.76728591, 40.56749936, 27.98838602, 37.88795367,\n",
       "        37.86656735, 36.7958519 , 32.42300935, 41.22604557, 27.05012569,\n",
       "        38.66688367, 40.84919813, 43.47423411, 28.28355548, 43.49230949,\n",
       "        49.72562326, 33.79729425, 21.33759761, 47.17467801, 35.38961674,\n",
       "        47.44937732, 42.23536716, 46.28919525, 29.68365909, 48.61340804,\n",
       "        34.73733419, 25.45162063, 21.87258027, 35.9111509 , 45.10227862]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>274.8 255.3 258.4 ... 285.6 294.9</div><input id='attrs-4104a8e9-15b3-45cc-bde4-0ebcfd6ccc91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4104a8e9-15b3-45cc-bde4-0ebcfd6ccc91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ea5ae16-5146-4616-85b7-f4751d061378' class='xr-var-data-in' type='checkbox'><label for='data-6ea5ae16-5146-4616-85b7-f4751d061378' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([[274.78594576, 255.33324365, 258.36071956, 236.46622847,\n",
       "        272.2718938 , 249.66936307, 250.79958982, 231.35908964,\n",
       "        248.49624613, 250.67608543, 283.32868271, 250.68176715,\n",
       "        282.32953628, 277.38309524, 280.02636075, 276.64253907,\n",
       "        254.05866397, 264.66599132, 256.33079564, 281.72998763,\n",
       "        262.46566085, 274.09973702, 279.4986605 , 258.71403172,\n",
       "        291.06683257, 240.79302483, 243.57961311, 240.84136219,\n",
       "        272.66695646, 271.47494626, 233.15168081, 251.11346721,\n",
       "        266.80779457, 240.56509493, 255.43322777, 229.9018507 ,\n",
       "        244.84720767, 258.14412256, 241.09604678, 274.83106771,\n",
       "        248.72744008, 290.90276651, 239.91847263, 246.69100197,\n",
       "        257.40204158, 275.29117985, 256.82614976, 260.76658272,\n",
       "        239.61572463, 273.56703976, 236.94836074, 273.46990288,\n",
       "        233.63525829, 278.31021272, 260.15048902, 247.50649586,\n",
       "        264.91082617, 281.47899072, 271.39092741, 273.70860588,\n",
       "        281.44333719, 241.25716075, 239.19446212, 256.15680338,\n",
       "        246.16684424, 267.59603117, 243.81655097, 258.20818492,\n",
       "        260.71394571, 231.91072411, 238.73676205, 260.61631685,\n",
       "        272.77666522, 271.22266362, 247.70412196, 247.32776356,\n",
       "        238.38740055, 259.44354675, 293.09387183, 280.37171184,\n",
       "...\n",
       "        262.49492437, 274.07319885, 279.53177189, 258.67673284,\n",
       "        291.09214803, 240.76267707, 243.55232313, 240.81462962,\n",
       "        272.64131788, 271.44888459, 233.17348433, 251.14174285,\n",
       "        266.84804006, 240.58714578, 255.39615332, 229.88085305,\n",
       "        244.8713465 , 258.10877636, 241.11874774, 274.80653337,\n",
       "        248.69445724, 290.92932268, 239.94017995, 246.65714346,\n",
       "        257.43114526, 275.26427454, 256.85483656, 260.79857201,\n",
       "        239.63981609, 273.59715709, 236.92173702, 273.50394707,\n",
       "        233.61271581, 278.34306147, 260.17904306, 247.47769417,\n",
       "        264.94816046, 281.50630393, 271.42857879, 273.68010104,\n",
       "        281.41673173, 241.22862306, 239.21548957, 256.11839045,\n",
       "        246.19023993, 267.56739791, 243.78644409, 258.23934315,\n",
       "        260.67244725, 231.88641236, 238.75842524, 260.64462906,\n",
       "        272.80798133, 271.19558144, 247.73239217, 247.35586492,\n",
       "        238.41120551, 259.47426001, 293.11604833, 280.34828644,\n",
       "        258.34625937, 258.21603729, 237.75443269, 278.59769538,\n",
       "        279.36856437, 257.74779987, 281.6588814 , 237.73901557,\n",
       "        230.77174564, 279.47643812, 243.08931566, 295.01701019,\n",
       "        252.41960735, 236.70759994, 242.30188786, 275.84166205,\n",
       "        257.01182294, 285.41552287, 285.63111639, 294.89263847]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 4.077 4.238 4.213</div><input id='attrs-ab67a8b0-587a-42c3-8d87-7500fd311073' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab67a8b0-587a-42c3-8d87-7500fd311073' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4490aac6-0c21-48ee-9bc1-85b135899387' class='xr-var-data-in' type='checkbox'><label for='data-4490aac6-0c21-48ee-9bc1-85b135899387' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array([[0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "...\n",
       "       [4.19476939, 4.1610569 , 4.18949004, 4.17492437, 4.24215301,\n",
       "        4.19522543, 4.2424422 , 4.24216232, 4.13744958, 4.11708461,\n",
       "        4.17874902, 4.22034367, 4.11357759, 4.15956878, 4.2237069 ,\n",
       "        4.21434974, 4.22533992, 4.12981328, 4.15262963, 4.11467634,\n",
       "        4.14407081, 4.19212156, 4.14370957, 4.1968987 , 4.19302275,\n",
       "        4.15169019, 4.24093263, 4.23501005, 4.13256887, 4.13309573,\n",
       "        4.24236961, 4.23082627, 4.12282162, 4.17118178, 4.16980209,\n",
       "        4.23948603, 4.18872448, 4.22466027, 4.19029705, 4.11336022,\n",
       "        4.19041537, 4.15560652, 4.16682307, 4.14000208, 4.19841531,\n",
       "        4.21529179, 4.19215132, 4.23169026, 4.23842739, 4.24248365,\n",
       "        4.20075292, 4.19653383, 4.24147273, 4.16860283, 4.14564722,\n",
       "        4.24189445, 4.20704593, 4.24066246, 4.13241566, 4.23300311,\n",
       "        4.24244035, 4.20755404, 4.14522269, 4.14525133, 4.13024481,\n",
       "        4.18400419, 4.20098367, 4.2329699 , 4.12134462, 4.19043528,\n",
       "        4.18663346, 4.12778926, 4.23965881, 4.17424361, 4.24223443,\n",
       "        4.24221683, 4.24059015, 4.21917478, 4.23756307, 4.16186345,\n",
       "        4.24246697, 4.23883926, 4.22601144, 4.17791977, 4.22589623,\n",
       "        4.15629376, 4.22840856, 4.06614087, 4.19160258, 4.23626354,\n",
       "        4.18827636, 4.23322728, 4.20164687, 4.19406408, 4.1728777 ,\n",
       "        4.23342407, 4.13859504, 4.07655105, 4.23815462, 4.21327356]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8a3773a-5a53-4b49-802f-514e3a891539' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8a3773a-5a53-4b49-802f-514e3a891539' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(isobaricInhPa, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>293.1 302.2 270.4 ... 262.4 259.8</div><input id='attrs-801ad734-6794-4388-b33c-22e315966ff7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-801ad734-6794-4388-b33c-22e315966ff7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a26518be-eb07-426b-9d7d-afc3832bbcca' class='xr-var-data-in' type='checkbox'><label for='data-a26518be-eb07-426b-9d7d-afc3832bbcca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[293.1003418 , 302.2253418 , 270.3503418 , 284.7253418 ,\n",
       "        283.9753418 , 301.1628418 , 292.2878418 , 285.5378418 ,\n",
       "        300.1003418 , 293.0378418 , 279.4128418 , 295.6628418 ,\n",
       "        295.9753418 , 296.4128418 , 277.0378418 , 289.8503418 ,\n",
       "        290.5378418 , 294.9128418 , 302.3503418 , 296.4753418 ,\n",
       "        292.8503418 , 294.1003418 , 275.7878418 , 270.7878418 ,\n",
       "        280.7878418 , 281.1003418 , 296.8503418 , 291.1003418 ,\n",
       "        297.5378418 , 296.5378418 , 284.9753418 , 293.7253418 ,\n",
       "        266.3503418 , 287.5378418 , 273.1003418 , 282.2878418 ,\n",
       "        287.7878418 , 284.1628418 , 286.6003418 , 299.9753418 ,\n",
       "        287.9128418 , 275.7878418 , 287.4753418 , 284.2253418 ,\n",
       "        298.6003418 , 296.4753418 , 298.0378418 , 287.5378418 ,\n",
       "        292.7253418 , 282.4753418 , 284.4128418 , 272.5378418 ,\n",
       "        284.1628418 , 273.6628418 , 297.2878418 , 293.0378418 ,\n",
       "        270.7878418 , 285.5378418 , 262.9753418 , 288.6003418 ,\n",
       "        283.4753418 , 282.6628418 , 287.9128418 , 270.1003418 ,\n",
       "        288.4128418 , 290.3503418 , 285.1628418 , 290.5378418 ,\n",
       "        269.3503418 , 281.9753418 , 286.9753418 , 298.2253418 ,\n",
       "        279.4753418 , 292.7253418 , 292.6003418 , 292.6628418 ,\n",
       "        285.2253418 , 294.9128418 , 289.8503418 , 297.1628418 ,\n",
       "...\n",
       "        263.65997314, 262.09747314, 249.53497314, 245.40997314,\n",
       "        257.22247314, 247.65997314, 252.22247314, 251.22247314,\n",
       "        265.28497314, 264.84747314, 250.40997314, 257.72247314,\n",
       "        246.28497314, 262.78497314, 246.28497314, 243.22247314,\n",
       "        260.90997314, 246.47247314, 261.65997314, 266.40997314,\n",
       "        250.15997314, 253.84747314, 263.03497314, 250.47247314,\n",
       "        258.84747314, 259.15997314, 259.90997314, 246.84747314,\n",
       "        255.78497314, 247.40997314, 243.90997314, 241.34747314,\n",
       "        248.28497314, 248.22247314, 263.53497314, 253.65997314,\n",
       "        243.72247314, 256.53497314, 247.65997314, 252.15997314,\n",
       "        258.22247314, 248.84747314, 264.28497314, 245.40997314,\n",
       "        265.22247314, 260.90997314, 249.84747314, 249.03497314,\n",
       "        245.22247314, 241.72247314, 261.78497314, 263.84747314,\n",
       "        247.03497314, 262.40997314, 255.22247314, 255.22247314,\n",
       "        255.03497314, 248.34747314, 262.28497314, 265.72247314,\n",
       "        246.90997314, 246.97247314, 246.97247314, 262.78497314,\n",
       "        247.22247314, 245.84747314, 261.65997314, 267.97247314,\n",
       "        241.65997314, 259.40997314, 249.53497314, 261.84747314,\n",
       "        249.84747314, 261.22247314, 248.78497314, 253.28497314,\n",
       "        262.59747314, 268.15997314, 262.40997314, 259.84747314]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d879a3c5-2de8-4b2d-9615-42822e011cde' class='xr-section-summary-in' type='checkbox'  ><label for='section-d879a3c5-2de8-4b2d-9615-42822e011cde' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>isobaricInhPa</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-548dae4e-cfb4-426d-b6e3-4f241e79f4d1' class='xr-index-data-in' type='checkbox'/><label for='index-548dae4e-cfb4-426d-b6e3-4f241e79f4d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0, 925.0, 850.0, 700.0, 500.0], dtype=&#x27;float64&#x27;, name=&#x27;isobaricInhPa&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9580fdc7-30fb-4a5b-9a8f-002585e02581' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9580fdc7-30fb-4a5b-9a8f-002585e02581' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>pick_point_method :</span></dt><dd>weighted</dd><dt><span>pick_point_k :</span></dt><dd>9</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 28kB\n",
       "Dimensions:              (isobaricInhPa: 5, point: 100, k: 9)\n",
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       "    time                 datetime64[ns] 8B 2024-03-28\n",
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       "  * isobaricInhPa        (isobaricInhPa) float64 40B 1e+03 925.0 ... 700.0 500.0\n",
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       "    point_latitude       (point) float64 800B 29.72 26.96 47.38 ... 35.88 45.14\n",
       "    point_longitude      (point) float64 800B 274.8 255.3 258.4 ... 285.7 294.9\n",
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       "    pick_point_method:  weighted\n",
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      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Compute the distance weighted mean for the 9 nearest grid points to\n",
    "# each request point\n",
    "ds.herbie.pick_points(points_self_100, method=\"weighted\", k=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose I wanted to calculate terrain roughness around each point using k-nearest neighbors by calculating the standard deviation of model surface height around each of my points.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=hrrr ┊ \u001b[3mproduct=sfc\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Jan-01 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ local\u001b[0m\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 282kB\n",
       "Dimensions:              (k: 100, point: 100)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-01-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    surface              float64 8B 0.0\n",
       "    latitude             (k, point) float64 80kB 29.72 26.96 ... 35.96 45.07\n",
       "    longitude            (k, point) float64 80kB 274.8 255.3 ... 285.8 294.7\n",
       "    valid_time           datetime64[ns] 8B 2024-01-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (k, point) float64 80kB 0.0 0.0 0.0 ... 16.95 16.85\n",
       "    point_latitude       (point) float64 800B 29.72 26.96 47.38 ... 35.88 45.14\n",
       "    point_longitude      (point) float64 800B 274.8 255.3 258.4 ... 285.7 294.9\n",
       "    point_stid           (point) object 800B &#x27;SkNxqxCY&#x27; ... &#x27;KYvNRgCr&#x27;\n",
       "Dimensions without coordinates: k, point\n",
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       "    orog                 (k, point) float32 40kB 6.263 1.444e+03 ... 0.01339\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240101/subset_6bef832...\n",
       "    search:                  HGT:surface</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7cc06797-7462-44ac-bda6-a441b9624a3c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7cc06797-7462-44ac-bda6-a441b9624a3c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>k</span>: 100</li><li><span>point</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-628d9607-12eb-42d1-ad76-34fe9b2053c3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-628d9607-12eb-42d1-ad76-34fe9b2053c3' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-01</div><input id='attrs-377fd29e-ea62-48cf-b38f-eb86f77ca83f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-377fd29e-ea62-48cf-b38f-eb86f77ca83f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43caf6a5-69fd-4062-a6a3-c44c5fa81d73' class='xr-var-data-in' type='checkbox'><label for='data-43caf6a5-69fd-4062-a6a3-c44c5fa81d73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-69aa9dac-7d0a-43c4-914a-f57b54d4ebe6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69aa9dac-7d0a-43c4-914a-f57b54d4ebe6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc3ee0b1-835f-4fe6-8ac0-cfba87964324' class='xr-var-data-in' type='checkbox'><label for='data-cc3ee0b1-835f-4fe6-8ac0-cfba87964324' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-b8ed3068-c607-4d74-a818-fbbaa84f7978' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8ed3068-c607-4d74-a818-fbbaa84f7978' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cec651ff-323c-4ceb-856b-cfce6cbe4b1e' class='xr-var-data-in' type='checkbox'><label for='data-cec651ff-323c-4ceb-856b-cfce6cbe4b1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.72 26.96 47.38 ... 35.96 45.07</div><input id='attrs-1c07723c-7b48-48b3-8d1e-c4158c762da7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c07723c-7b48-48b3-8d1e-c4158c762da7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18df4a60-c7d2-4915-ad8e-89088347b75a' class='xr-var-data-in' type='checkbox'><label for='data-18df4a60-c7d2-4915-ad8e-89088347b75a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[29.71802927, 26.96163362, 47.3803572 , ..., 21.84106626,\n",
       "        35.87834169, 45.13666342],\n",
       "       [29.69159316, 26.93525532, 47.40696979, ..., 21.81594666,\n",
       "        35.85223927, 45.16181404],\n",
       "       [29.71447484, 26.95957116, 47.38154922, ..., 21.8346698 ,\n",
       "        35.87162821, 45.14588808],\n",
       "       ...,\n",
       "       [29.59804742, 26.84784691, 47.49150136, ..., 21.71501611,\n",
       "        35.74706303, 45.27413811],\n",
       "       [29.62640272, 26.86428581, 47.48190636, ..., 21.76607467,\n",
       "        35.80065489, 45.2002026 ],\n",
       "       [29.80949718, 27.05885505, 47.27857927, ..., 21.9158983 ,\n",
       "        35.95580224, 45.07282426]], shape=(100, 100))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>274.8 255.3 258.4 ... 285.8 294.7</div><input id='attrs-79f3443c-569e-4c3e-b72f-ce947f287db5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79f3443c-569e-4c3e-b72f-ce947f287db5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d383a6b-60c4-41bd-8295-f6b711476e16' class='xr-var-data-in' type='checkbox'><label for='data-3d383a6b-60c4-41bd-8295-f6b711476e16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([[274.78594576, 255.33324365, 258.36071956, ..., 285.43570657,\n",
       "        285.65506168, 294.87007894],\n",
       "       [274.78185941, 255.33555212, 258.35895001, ..., 285.42882136,\n",
       "        285.64678464, 294.88316792],\n",
       "       [274.816385  , 255.30364816, 258.400024  , ..., 285.46276803,\n",
       "        285.68727348, 294.8344185 ],\n",
       "       ...,\n",
       "       [274.89119936, 255.22424679, 258.51112745, ..., 285.51629264,\n",
       "        285.75063249, 294.77960143],\n",
       "       [274.64799067, 255.46071419, 258.19616137, ..., 285.30002276,\n",
       "        285.49325162, 295.06522724],\n",
       "       [274.9242172 , 255.20547042, 258.52474175, ..., 285.57162854,\n",
       "        285.81720872, 294.67531313]], shape=(100, 100))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-01</div><input id='attrs-5f1c6406-c8f7-4f90-88a9-ca1204646c28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f1c6406-c8f7-4f90-88a9-ca1204646c28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0db4f448-9e53-493c-b4e5-df84bb6d520a' class='xr-var-data-in' type='checkbox'><label for='data-0db4f448-9e53-493c-b4e5-df84bb6d520a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-f18ca467-7a9f-4723-bdce-8f6990f39520' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f18ca467-7a9f-4723-bdce-8f6990f39520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dc49305-6634-469d-b8bc-37baf5b8a9e7' class='xr-var-data-in' type='checkbox'><label for='data-5dc49305-6634-469d-b8bc-37baf5b8a9e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_grid_distance</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 16.31 16.95 16.85</div><input id='attrs-f23d26a4-27ef-403f-98ed-687fbc3ee322' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f23d26a4-27ef-403f-98ed-687fbc3ee322' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77db4994-9620-4ebc-bb35-62c2f9671710' class='xr-var-data-in' type='checkbox'><label for='data-77db4994-9620-4ebc-bb35-62c2f9671710' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Distance between requested point and nearest grid point</dd><dt><span>units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 2.96593672,  2.94204452,  2.9621813 , ...,  2.88217253,\n",
       "         2.99675862,  2.9790451 ],\n",
       "       [ 2.96602327,  2.94216308,  2.96229142, ...,  2.88229969,\n",
       "         2.99678153,  2.97909582],\n",
       "       ...,\n",
       "       [16.77586196, 16.64029949, 16.75454624, ..., 16.30014404,\n",
       "        16.95151315, 16.84998591],\n",
       "       [16.77647292, 16.64075529, 16.75478301, ..., 16.3021124 ,\n",
       "        16.95187926, 16.85132924],\n",
       "       [16.78038653, 16.64611738, 16.75976034, ..., 16.30786513,\n",
       "        16.95291175, 16.85362037]], shape=(100, 100))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.72 26.96 47.38 ... 35.88 45.14</div><input id='attrs-d3c1d536-f900-4e94-862d-80e21126d210' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3c1d536-f900-4e94-862d-80e21126d210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf627321-7faa-4329-826e-922f2ad33168' class='xr-var-data-in' type='checkbox'><label for='data-bf627321-7faa-4329-826e-922f2ad33168' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([29.71802927, 26.96163362, 47.3803572 , 48.49937879, 37.76378392,\n",
       "       29.76079807, 38.78159159, 37.77155083, 25.34868883, 24.09091709,\n",
       "       48.21904103, 32.55089807, 23.88325503, 26.85301459, 43.83982832,\n",
       "       31.7839385 , 43.60484063, 24.86617392, 26.36376   , 23.94780624,\n",
       "       25.78375659, 29.47318637, 50.52346792, 46.75018289, 47.0888957 ,\n",
       "       50.04309976, 40.06193214, 41.89141069, 25.03795909, 25.07138841,\n",
       "       38.05425593, 34.19473626, 51.69005336, 27.71853763, 48.8591675 ,\n",
       "       40.66204354, 29.16708617, 43.70294892, 29.30684806, 23.87139192,\n",
       "       47.3055353 , 49.79955748, 27.38599534, 50.73968788, 30.06715876,\n",
       "       31.89893682, 29.47667758, 34.35955798, 35.96217838, 38.59837376,\n",
       "       46.40376306, 46.78336892, 39.76691907, 48.94305033, 25.88857297,\n",
       "       39.47190886, 45.79293708, 40.19032157, 51.16890414, 34.61957265,\n",
       "       38.21191723, 45.74384048, 25.85763001, 50.4309051 , 24.89130404,\n",
       "       28.75926867, 46.38161584, 34.61367302, 51.76801043, 47.30567028,\n",
       "       28.98303164, 24.74050889, 40.59730026, 27.95945205, 37.85700979,\n",
       "       37.83553258, 36.76282367, 32.39530722, 41.26031316, 27.01905295,\n",
       "       38.69504727, 40.87737387, 43.50727847, 28.25276676, 43.52362679,\n",
       "       49.75337739, 33.76542584, 21.30579586, 47.20877064, 35.35819077,\n",
       "       47.48099322, 42.26993646, 46.31867021, 29.65065009, 48.64508105,\n",
       "       34.70680336, 25.42377735, 21.84106626, 35.87834169, 45.13666342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>274.8 255.3 258.4 ... 285.7 294.9</div><input id='attrs-0276fc45-0247-4fd8-809a-56485c76bca0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0276fc45-0247-4fd8-809a-56485c76bca0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5123bf3-49c8-4d56-97d3-7905359ef357' class='xr-var-data-in' type='checkbox'><label for='data-d5123bf3-49c8-4d56-97d3-7905359ef357' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([274.78594576, 255.33324365, 258.36071956, 236.46622847,\n",
       "       272.2718938 , 249.66936307, 250.79958982, 231.35908964,\n",
       "       248.49624613, 250.67608543, 283.32868271, 250.68176715,\n",
       "       282.32953628, 277.38309524, 280.02636075, 276.64253907,\n",
       "       254.05866397, 264.66599132, 256.33079564, 281.72998763,\n",
       "       262.46566085, 274.09973702, 279.4986605 , 258.71403172,\n",
       "       291.06683257, 240.79302483, 243.57961311, 240.84136219,\n",
       "       272.66695646, 271.47494626, 233.15168081, 251.11346721,\n",
       "       266.80779457, 240.56509493, 255.43322777, 229.9018507 ,\n",
       "       244.84720767, 258.14412256, 241.09604678, 274.83106771,\n",
       "       248.72744008, 290.90276651, 239.91847263, 246.69100197,\n",
       "       257.40204158, 275.29117985, 256.82614976, 260.76658272,\n",
       "       239.61572463, 273.56703976, 236.94836074, 273.46990288,\n",
       "       233.63525829, 278.31021272, 260.15048902, 247.50649586,\n",
       "       264.91082617, 281.47899072, 271.39092741, 273.70860588,\n",
       "       281.44333719, 241.25716075, 239.19446212, 256.15680338,\n",
       "       246.16684424, 267.59603117, 243.81655097, 258.20818492,\n",
       "       260.71394571, 231.91072411, 238.73676205, 260.61631685,\n",
       "       272.77666522, 271.22266362, 247.70412196, 247.32776356,\n",
       "       238.38740055, 259.44354675, 293.09387183, 280.37171184,\n",
       "       258.3792311 , 258.2499897 , 237.78031127, 278.62214702,\n",
       "       279.33887858, 257.78654118, 281.68384329, 237.7196381 ,\n",
       "       230.79541366, 279.50285108, 243.11960033, 294.99546536,\n",
       "       252.45383251, 236.68659674, 242.33234215, 275.86933537,\n",
       "       256.9844727 , 285.43570657, 285.65506168, 294.87007894])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_stid</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SkNxqxCY&#x27; ... &#x27;KYvNRgCr&#x27;</div><input id='attrs-5dbe6e00-07d7-40f8-aaee-e41d617f2f3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dbe6e00-07d7-40f8-aaee-e41d617f2f3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea719165-170e-4452-a9f5-cb33bf0dbf55' class='xr-var-data-in' type='checkbox'><label for='data-ea719165-170e-4452-a9f5-cb33bf0dbf55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point stid</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;SkNxqxCY&#x27;, &#x27;DONsGClt&#x27;, &#x27;zBYYWqvk&#x27;, &#x27;PNKRmpQX&#x27;, &#x27;kgiLfUwp&#x27;,\n",
       "       &#x27;ysJveLCx&#x27;, &#x27;qKKofUBX&#x27;, &#x27;GrmijnCo&#x27;, &#x27;gcyarlUK&#x27;, &#x27;FHafVKwO&#x27;,\n",
       "       &#x27;OWlrJRhK&#x27;, &#x27;zdGckwzg&#x27;, &#x27;SUESsMyV&#x27;, &#x27;QJvmoUlS&#x27;, &#x27;oRPUzpMV&#x27;,\n",
       "       &#x27;sgFVkdyt&#x27;, &#x27;naXYJbys&#x27;, &#x27;BfKhsYnA&#x27;, &#x27;iyhhijIL&#x27;, &#x27;KTtSbeqi&#x27;,\n",
       "       &#x27;XzqMxPdi&#x27;, &#x27;pCLmQNAW&#x27;, &#x27;nvHuqttb&#x27;, &#x27;FfJzGTHD&#x27;, &#x27;KopNihGt&#x27;,\n",
       "       &#x27;UmNUwEgr&#x27;, &#x27;EQcRxZnC&#x27;, &#x27;UyCOpjdy&#x27;, &#x27;kaZQQoYK&#x27;, &#x27;YWSQjyOM&#x27;,\n",
       "       &#x27;euDabKsK&#x27;, &#x27;fNUDJJUL&#x27;, &#x27;iENznCRg&#x27;, &#x27;MyqUdbwi&#x27;, &#x27;mYWnYGmV&#x27;,\n",
       "       &#x27;sVGRJWCX&#x27;, &#x27;ZLSOQRXI&#x27;, &#x27;ZhDzHdfB&#x27;, &#x27;acggKSRW&#x27;, &#x27;DkyGHdhH&#x27;,\n",
       "       &#x27;UOUEmFjV&#x27;, &#x27;xbqwHisD&#x27;, &#x27;COYaSChj&#x27;, &#x27;lsaLdfQT&#x27;, &#x27;ACDYwTkh&#x27;,\n",
       "       &#x27;oVYjIceO&#x27;, &#x27;POgGMDec&#x27;, &#x27;BlFoEsgt&#x27;, &#x27;SKiLECzT&#x27;, &#x27;FhTzwcSf&#x27;,\n",
       "       &#x27;VfbDZAau&#x27;, &#x27;VyKqeBwq&#x27;, &#x27;NKQWySSx&#x27;, &#x27;GibOqyXr&#x27;, &#x27;FaQqarhv&#x27;,\n",
       "       &#x27;ZfpzFGtw&#x27;, &#x27;EvvpmsJF&#x27;, &#x27;iDoeDoZO&#x27;, &#x27;XgPTLvir&#x27;, &#x27;ZZXVccKN&#x27;,\n",
       "       &#x27;cHQCByQC&#x27;, &#x27;qGlubThz&#x27;, &#x27;nDGTTccG&#x27;, &#x27;YckADDUA&#x27;, &#x27;iIVwgGPx&#x27;,\n",
       "       &#x27;HMvyYfjN&#x27;, &#x27;wCZbUYtR&#x27;, &#x27;HcHqwUiv&#x27;, &#x27;VtrBamHt&#x27;, &#x27;jLWFCSSJ&#x27;,\n",
       "       &#x27;XykPOeUI&#x27;, &#x27;bGlaEVWI&#x27;, &#x27;XhVkRDJa&#x27;, &#x27;DtmhPpVX&#x27;, &#x27;yYNXlAAQ&#x27;,\n",
       "       &#x27;uSMJRPrs&#x27;, &#x27;jXYfHeOe&#x27;, &#x27;IhnFibnQ&#x27;, &#x27;auDVceCU&#x27;, &#x27;WwiWpDbc&#x27;,\n",
       "       &#x27;PfwrWzuX&#x27;, &#x27;WICMDhwT&#x27;, &#x27;BvGvZchf&#x27;, &#x27;RmVmDBmr&#x27;, &#x27;PAZVzbUK&#x27;,\n",
       "       &#x27;hKITQzId&#x27;, &#x27;VPWMuckb&#x27;, &#x27;xptOwpfU&#x27;, &#x27;HIgUpTjt&#x27;, &#x27;IWcQOfST&#x27;,\n",
       "       &#x27;fSUENpjW&#x27;, &#x27;QeGnJBeN&#x27;, &#x27;KkjADoMz&#x27;, &#x27;GXyxNKkb&#x27;, &#x27;PTSkyVNd&#x27;,\n",
       "       &#x27;RrdbpPvj&#x27;, &#x27;ELGWpzoF&#x27;, &#x27;iSCwEgju&#x27;, &#x27;UQBJvXzT&#x27;, &#x27;KYvNRgCr&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94f134e5-a580-4c55-958b-8026bc14faff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-94f134e5-a580-4c55-958b-8026bc14faff' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>orog</span></div><div class='xr-var-dims'>(k, point)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.263 1.444e+03 ... 0.01339 0.01339</div><input id='attrs-a6cc36a2-9b6f-42c6-800c-2a767953f28f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6cc36a2-9b6f-42c6-800c-2a767953f28f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48c7153b-c304-4f6c-b338-cd2dbdf2d3cc' class='xr-var-data-in' type='checkbox'><label for='data-48c7153b-c304-4f6c-b338-cd2dbdf2d3cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228002</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>1</dd><dt><span>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential_height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>orog</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Orography</dd><dt><span>GRIB_parameterName :</span></dt><dd>Geopotential height</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>gpm</dd><dt><span>GRIB_shapeOfTheEarth :</span></dt><dd>6</dd><dt><span>GRIB_shortName :</span></dt><dd>orog</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Orography</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>geopotential_height</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[ 6.2633896e+00,  1.4438884e+03,  6.0626337e+02, ...,\n",
       "        -1.7860413e-02,  1.3389587e-02,  1.3389587e-02],\n",
       "       [ 2.7633896e+00,  1.4580134e+03,  6.2879462e+02, ...,\n",
       "        -1.7860413e-02,  1.3389587e-02,  1.3389587e-02],\n",
       "       [ 8.1383896e+00,  1.4264197e+03,  6.0485712e+02, ...,\n",
       "        -1.7860413e-02,  1.3389587e-02,  1.3389587e-02],\n",
       "       ...,\n",
       "       [ 1.3389587e-02,  1.4333884e+03,  6.0301337e+02, ...,\n",
       "        -1.7860413e-02,  1.3389587e-02,  1.3389587e-02],\n",
       "       [ 1.3389587e-02,  1.3649197e+03,  5.6526337e+02, ...,\n",
       "        -1.7860413e-02,  1.3389587e-02,  1.3389587e-02],\n",
       "       [ 1.0232140e+01,  1.3766072e+03,  5.8810712e+02, ...,\n",
       "        -1.7860413e-02,  1.3389587e-02,  1.3389587e-02]],\n",
       "      shape=(100, 100), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3b8914a-5c46-4966-bf5d-39f3cfc978f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3b8914a-5c46-4966-bf5d-39f3cfc978f7' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-3a1925c6-fcd8-4999-a8ee-a54f3f2eb6fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a1925c6-fcd8-4999-a8ee-a54f3f2eb6fc' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>hrrr</dd><dt><span>product :</span></dt><dd>sfc</dd><dt><span>description :</span></dt><dd>High-Resolution Rapid Refresh - CONUS</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20240101/conus/hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/hrrr/20240101/subset_6bef8322__hrrr.t00z.wrfsfcf00.grib2</dd><dt><span>search :</span></dt><dd>HGT:surface</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 282kB\n",
       "Dimensions:              (k: 100, point: 100)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-01-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    surface              float64 8B 0.0\n",
       "    latitude             (k, point) float64 80kB 29.72 26.96 ... 35.96 45.07\n",
       "    longitude            (k, point) float64 80kB 274.8 255.3 ... 285.8 294.7\n",
       "    valid_time           datetime64[ns] 8B 2024-01-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_grid_distance  (k, point) float64 80kB 0.0 0.0 0.0 ... 16.95 16.85\n",
       "    point_latitude       (point) float64 800B 29.72 26.96 47.38 ... 35.88 45.14\n",
       "    point_longitude      (point) float64 800B 274.8 255.3 258.4 ... 285.7 294.9\n",
       "    point_stid           (point) object 800B 'SkNxqxCY' ... 'KYvNRgCr'\n",
       "Dimensions without coordinates: k, point\n",
       "Data variables:\n",
       "    orog                 (k, point) float32 40kB 6.263 1.444e+03 ... 0.01339\n",
       "Attributes:\n",
       "    GRIB_edition:            2\n",
       "    GRIB_centre:             kwbc\n",
       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US National Weather Service - NCEP\n",
       "    model:                   hrrr\n",
       "    product:                 sfc\n",
       "    description:             High-Resolution Rapid Refresh - CONUS\n",
       "    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....\n",
       "    local_grib:              /home/blaylock/data/hrrr/20240101/subset_6bef832...\n",
       "    search:                  HGT:surface"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = Herbie(\"2024-01-01\").xarray(\"HGT:surface\")\n",
    "dsp = ds.herbie.pick_points(points_self_100, k=100)\n",
    "dsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;orog&#x27; (point: 100)&gt; Size: 400B\n",
       "array([3.74864864e+00, 7.70284042e+01, 3.17707424e+01, 1.59296356e+02,\n",
       "       8.50554657e+00, 2.69303436e+02, 2.61035797e+02, 0.00000000e+00,\n",
       "       7.76310120e+01, 1.48034021e-02, 3.39158669e+01, 1.53166565e+02,\n",
       "       1.60940230e+00, 0.00000000e+00, 7.13127975e+01, 1.51575775e+01,\n",
       "       6.64364319e+01, 0.00000000e+00, 1.12974800e+02, 0.00000000e+00,\n",
       "       2.69054890e+00, 0.00000000e+00, 3.99752159e+01, 3.41971703e+01,\n",
       "       6.46535950e+01, 3.35994232e+02, 1.60176880e+02, 1.26123024e+02,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.03021851e+01,\n",
       "       1.89539986e+01, 0.00000000e+00, 4.04688110e+01, 0.00000000e+00,\n",
       "       0.00000000e+00, 4.26026802e+01, 0.00000000e+00, 6.81077968e-03,\n",
       "       1.19178711e+02, 2.45614243e+01, 0.00000000e+00, 6.05588112e+01,\n",
       "       1.23778839e+02, 1.43709764e+01, 1.18544037e+02, 1.87431240e+01,\n",
       "       2.04521179e+02, 1.97443123e+01, 1.72476761e+02, 9.15527344e-05,\n",
       "       0.00000000e+00, 1.38174944e+01, 2.12805542e+02, 2.74857578e+01,\n",
       "       1.04479446e+01, 1.16316414e+02, 1.93214893e+01, 6.84824142e+01,\n",
       "       1.96816833e+02, 5.76621933e+01, 0.00000000e+00, 2.23715057e+01,\n",
       "       0.00000000e+00, 0.00000000e+00, 1.37994690e+02, 2.15656357e+01,\n",
       "       5.08315516e+00, 0.00000000e+00, 0.00000000e+00, 3.51489334e+01,\n",
       "       1.04980412e+01, 0.00000000e+00, 2.73795258e+02, 3.37753448e+02,\n",
       "       1.87713989e+02, 2.93994026e+01, 0.00000000e+00, 0.00000000e+00,\n",
       "       2.48333950e+01, 1.72831936e+01, 3.04587769e+02, 4.41159105e+00,\n",
       "       2.12090187e+01, 4.74487686e+01, 1.65152624e-01, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.64283066e+01, 1.11062164e+02, 0.00000000e+00,\n",
       "       4.81408958e+01, 0.00000000e+00, 1.59145447e+02, 1.24982613e+02,\n",
       "       6.93545532e+01, 0.00000000e+00, 0.00000000e+00, 3.61904716e+01],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-01-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    surface              float64 8B 0.0\n",
       "    valid_time           datetime64[ns] 8B 2024-01-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_latitude       (point) float64 800B 29.72 26.96 47.38 ... 35.88 45.14\n",
       "    point_longitude      (point) float64 800B 274.8 255.3 258.4 ... 285.7 294.9\n",
       "    point_stid           (point) object 800B &#x27;SkNxqxCY&#x27; ... &#x27;KYvNRgCr&#x27;\n",
       "Dimensions without coordinates: point</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'orog'</div><ul class='xr-dim-list'><li><span>point</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c69ba93f-8f1e-4d15-909d-1c0eb00b4491' class='xr-array-in' type='checkbox' checked><label for='section-c69ba93f-8f1e-4d15-909d-1c0eb00b4491' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.749 77.03 31.77 159.3 8.506 269.3 ... 125.0 69.35 0.0 0.0 36.19</span></div><div class='xr-array-data'><pre>array([3.74864864e+00, 7.70284042e+01, 3.17707424e+01, 1.59296356e+02,\n",
       "       8.50554657e+00, 2.69303436e+02, 2.61035797e+02, 0.00000000e+00,\n",
       "       7.76310120e+01, 1.48034021e-02, 3.39158669e+01, 1.53166565e+02,\n",
       "       1.60940230e+00, 0.00000000e+00, 7.13127975e+01, 1.51575775e+01,\n",
       "       6.64364319e+01, 0.00000000e+00, 1.12974800e+02, 0.00000000e+00,\n",
       "       2.69054890e+00, 0.00000000e+00, 3.99752159e+01, 3.41971703e+01,\n",
       "       6.46535950e+01, 3.35994232e+02, 1.60176880e+02, 1.26123024e+02,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.03021851e+01,\n",
       "       1.89539986e+01, 0.00000000e+00, 4.04688110e+01, 0.00000000e+00,\n",
       "       0.00000000e+00, 4.26026802e+01, 0.00000000e+00, 6.81077968e-03,\n",
       "       1.19178711e+02, 2.45614243e+01, 0.00000000e+00, 6.05588112e+01,\n",
       "       1.23778839e+02, 1.43709764e+01, 1.18544037e+02, 1.87431240e+01,\n",
       "       2.04521179e+02, 1.97443123e+01, 1.72476761e+02, 9.15527344e-05,\n",
       "       0.00000000e+00, 1.38174944e+01, 2.12805542e+02, 2.74857578e+01,\n",
       "       1.04479446e+01, 1.16316414e+02, 1.93214893e+01, 6.84824142e+01,\n",
       "       1.96816833e+02, 5.76621933e+01, 0.00000000e+00, 2.23715057e+01,\n",
       "       0.00000000e+00, 0.00000000e+00, 1.37994690e+02, 2.15656357e+01,\n",
       "       5.08315516e+00, 0.00000000e+00, 0.00000000e+00, 3.51489334e+01,\n",
       "       1.04980412e+01, 0.00000000e+00, 2.73795258e+02, 3.37753448e+02,\n",
       "       1.87713989e+02, 2.93994026e+01, 0.00000000e+00, 0.00000000e+00,\n",
       "       2.48333950e+01, 1.72831936e+01, 3.04587769e+02, 4.41159105e+00,\n",
       "       2.12090187e+01, 4.74487686e+01, 1.65152624e-01, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.64283066e+01, 1.11062164e+02, 0.00000000e+00,\n",
       "       4.81408958e+01, 0.00000000e+00, 1.59145447e+02, 1.24982613e+02,\n",
       "       6.93545532e+01, 0.00000000e+00, 0.00000000e+00, 3.61904716e+01],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-62b3da07-9dab-4b78-b86a-4c0ff68407e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-62b3da07-9dab-4b78-b86a-4c0ff68407e0' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-01</div><input id='attrs-1a77e344-c37b-4c6d-bcbe-dde4b997ec21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a77e344-c37b-4c6d-bcbe-dde4b997ec21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-111d5e5b-9994-415b-bb39-a442f9b22029' class='xr-var-data-in' type='checkbox'><label for='data-111d5e5b-9994-415b-bb39-a442f9b22029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-2aeb0d97-cc11-459d-9457-0517645f8d8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2aeb0d97-cc11-459d-9457-0517645f8d8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1f55468-0906-4b5c-8aa5-b5feeff86d53' class='xr-var-data-in' type='checkbox'><label for='data-c1f55468-0906-4b5c-8aa5-b5feeff86d53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-1e25b1ef-5a0a-429f-a8a5-dcb83658ed62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e25b1ef-5a0a-429f-a8a5-dcb83658ed62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b19251bf-5ed0-40b6-9b59-6df2164dac6d' class='xr-var-data-in' type='checkbox'><label for='data-b19251bf-5ed0-40b6-9b59-6df2164dac6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-01</div><input id='attrs-fc9d555d-4086-4299-8664-cad8cbddcd7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc9d555d-4086-4299-8664-cad8cbddcd7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b123b05e-8748-4f5f-8bc9-c0a74f1f814b' class='xr-var-data-in' type='checkbox'><label for='data-b123b05e-8748-4f5f-8bc9-c0a74f1f814b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-79aa9b50-2e98-42d7-b9ec-c023241dd003' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79aa9b50-2e98-42d7-b9ec-c023241dd003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5833d48-2a30-4af9-a2d7-595164a0d0f1' class='xr-var-data-in' type='checkbox'><label for='data-b5833d48-2a30-4af9-a2d7-595164a0d0f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;unknown&quot;,BASEGEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6371229,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]]],CONVERSION[&quot;unknown&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,262.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,38.5,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>unknown</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>standard_parallel :</span></dt><dd>(38.5, 38.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>38.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>262.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>HRRR model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_latitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.72 26.96 47.38 ... 35.88 45.14</div><input id='attrs-1a66e755-1d20-4b7c-ba0c-48ee004d008d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a66e755-1d20-4b7c-ba0c-48ee004d008d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6ec3ee0-8be1-4374-96c3-654384b3f0b2' class='xr-var-data-in' type='checkbox'><label for='data-a6ec3ee0-8be1-4374-96c3-654384b3f0b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point latitude</dd></dl></div><div class='xr-var-data'><pre>array([29.71802927, 26.96163362, 47.3803572 , 48.49937879, 37.76378392,\n",
       "       29.76079807, 38.78159159, 37.77155083, 25.34868883, 24.09091709,\n",
       "       48.21904103, 32.55089807, 23.88325503, 26.85301459, 43.83982832,\n",
       "       31.7839385 , 43.60484063, 24.86617392, 26.36376   , 23.94780624,\n",
       "       25.78375659, 29.47318637, 50.52346792, 46.75018289, 47.0888957 ,\n",
       "       50.04309976, 40.06193214, 41.89141069, 25.03795909, 25.07138841,\n",
       "       38.05425593, 34.19473626, 51.69005336, 27.71853763, 48.8591675 ,\n",
       "       40.66204354, 29.16708617, 43.70294892, 29.30684806, 23.87139192,\n",
       "       47.3055353 , 49.79955748, 27.38599534, 50.73968788, 30.06715876,\n",
       "       31.89893682, 29.47667758, 34.35955798, 35.96217838, 38.59837376,\n",
       "       46.40376306, 46.78336892, 39.76691907, 48.94305033, 25.88857297,\n",
       "       39.47190886, 45.79293708, 40.19032157, 51.16890414, 34.61957265,\n",
       "       38.21191723, 45.74384048, 25.85763001, 50.4309051 , 24.89130404,\n",
       "       28.75926867, 46.38161584, 34.61367302, 51.76801043, 47.30567028,\n",
       "       28.98303164, 24.74050889, 40.59730026, 27.95945205, 37.85700979,\n",
       "       37.83553258, 36.76282367, 32.39530722, 41.26031316, 27.01905295,\n",
       "       38.69504727, 40.87737387, 43.50727847, 28.25276676, 43.52362679,\n",
       "       49.75337739, 33.76542584, 21.30579586, 47.20877064, 35.35819077,\n",
       "       47.48099322, 42.26993646, 46.31867021, 29.65065009, 48.64508105,\n",
       "       34.70680336, 25.42377735, 21.84106626, 35.87834169, 45.13666342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_longitude</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>274.8 255.3 258.4 ... 285.7 294.9</div><input id='attrs-e74baad4-a617-45c9-8b5c-19dec10c63f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e74baad4-a617-45c9-8b5c-19dec10c63f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5460698c-c30f-4499-9ff8-ed90a35162bd' class='xr-var-data-in' type='checkbox'><label for='data-5460698c-c30f-4499-9ff8-ed90a35162bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point longitude</dd></dl></div><div class='xr-var-data'><pre>array([274.78594576, 255.33324365, 258.36071956, 236.46622847,\n",
       "       272.2718938 , 249.66936307, 250.79958982, 231.35908964,\n",
       "       248.49624613, 250.67608543, 283.32868271, 250.68176715,\n",
       "       282.32953628, 277.38309524, 280.02636075, 276.64253907,\n",
       "       254.05866397, 264.66599132, 256.33079564, 281.72998763,\n",
       "       262.46566085, 274.09973702, 279.4986605 , 258.71403172,\n",
       "       291.06683257, 240.79302483, 243.57961311, 240.84136219,\n",
       "       272.66695646, 271.47494626, 233.15168081, 251.11346721,\n",
       "       266.80779457, 240.56509493, 255.43322777, 229.9018507 ,\n",
       "       244.84720767, 258.14412256, 241.09604678, 274.83106771,\n",
       "       248.72744008, 290.90276651, 239.91847263, 246.69100197,\n",
       "       257.40204158, 275.29117985, 256.82614976, 260.76658272,\n",
       "       239.61572463, 273.56703976, 236.94836074, 273.46990288,\n",
       "       233.63525829, 278.31021272, 260.15048902, 247.50649586,\n",
       "       264.91082617, 281.47899072, 271.39092741, 273.70860588,\n",
       "       281.44333719, 241.25716075, 239.19446212, 256.15680338,\n",
       "       246.16684424, 267.59603117, 243.81655097, 258.20818492,\n",
       "       260.71394571, 231.91072411, 238.73676205, 260.61631685,\n",
       "       272.77666522, 271.22266362, 247.70412196, 247.32776356,\n",
       "       238.38740055, 259.44354675, 293.09387183, 280.37171184,\n",
       "       258.3792311 , 258.2499897 , 237.78031127, 278.62214702,\n",
       "       279.33887858, 257.78654118, 281.68384329, 237.7196381 ,\n",
       "       230.79541366, 279.50285108, 243.11960033, 294.99546536,\n",
       "       252.45383251, 236.68659674, 242.33234215, 275.86933537,\n",
       "       256.9844727 , 285.43570657, 285.65506168, 294.87007894])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>point_stid</span></div><div class='xr-var-dims'>(point)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SkNxqxCY&#x27; ... &#x27;KYvNRgCr&#x27;</div><input id='attrs-08d121d0-d259-4025-a6d9-6639a92dcbc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08d121d0-d259-4025-a6d9-6639a92dcbc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-342da44b-14a6-4265-83ee-498e303bbb12' class='xr-var-data-in' type='checkbox'><label for='data-342da44b-14a6-4265-83ee-498e303bbb12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Requested point stid</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;SkNxqxCY&#x27;, &#x27;DONsGClt&#x27;, &#x27;zBYYWqvk&#x27;, &#x27;PNKRmpQX&#x27;, &#x27;kgiLfUwp&#x27;,\n",
       "       &#x27;ysJveLCx&#x27;, &#x27;qKKofUBX&#x27;, &#x27;GrmijnCo&#x27;, &#x27;gcyarlUK&#x27;, &#x27;FHafVKwO&#x27;,\n",
       "       &#x27;OWlrJRhK&#x27;, &#x27;zdGckwzg&#x27;, &#x27;SUESsMyV&#x27;, &#x27;QJvmoUlS&#x27;, &#x27;oRPUzpMV&#x27;,\n",
       "       &#x27;sgFVkdyt&#x27;, &#x27;naXYJbys&#x27;, &#x27;BfKhsYnA&#x27;, &#x27;iyhhijIL&#x27;, &#x27;KTtSbeqi&#x27;,\n",
       "       &#x27;XzqMxPdi&#x27;, &#x27;pCLmQNAW&#x27;, &#x27;nvHuqttb&#x27;, &#x27;FfJzGTHD&#x27;, &#x27;KopNihGt&#x27;,\n",
       "       &#x27;UmNUwEgr&#x27;, &#x27;EQcRxZnC&#x27;, &#x27;UyCOpjdy&#x27;, &#x27;kaZQQoYK&#x27;, &#x27;YWSQjyOM&#x27;,\n",
       "       &#x27;euDabKsK&#x27;, &#x27;fNUDJJUL&#x27;, &#x27;iENznCRg&#x27;, &#x27;MyqUdbwi&#x27;, &#x27;mYWnYGmV&#x27;,\n",
       "       &#x27;sVGRJWCX&#x27;, &#x27;ZLSOQRXI&#x27;, &#x27;ZhDzHdfB&#x27;, &#x27;acggKSRW&#x27;, &#x27;DkyGHdhH&#x27;,\n",
       "       &#x27;UOUEmFjV&#x27;, &#x27;xbqwHisD&#x27;, &#x27;COYaSChj&#x27;, &#x27;lsaLdfQT&#x27;, &#x27;ACDYwTkh&#x27;,\n",
       "       &#x27;oVYjIceO&#x27;, &#x27;POgGMDec&#x27;, &#x27;BlFoEsgt&#x27;, &#x27;SKiLECzT&#x27;, &#x27;FhTzwcSf&#x27;,\n",
       "       &#x27;VfbDZAau&#x27;, &#x27;VyKqeBwq&#x27;, &#x27;NKQWySSx&#x27;, &#x27;GibOqyXr&#x27;, &#x27;FaQqarhv&#x27;,\n",
       "       &#x27;ZfpzFGtw&#x27;, &#x27;EvvpmsJF&#x27;, &#x27;iDoeDoZO&#x27;, &#x27;XgPTLvir&#x27;, &#x27;ZZXVccKN&#x27;,\n",
       "       &#x27;cHQCByQC&#x27;, &#x27;qGlubThz&#x27;, &#x27;nDGTTccG&#x27;, &#x27;YckADDUA&#x27;, &#x27;iIVwgGPx&#x27;,\n",
       "       &#x27;HMvyYfjN&#x27;, &#x27;wCZbUYtR&#x27;, &#x27;HcHqwUiv&#x27;, &#x27;VtrBamHt&#x27;, &#x27;jLWFCSSJ&#x27;,\n",
       "       &#x27;XykPOeUI&#x27;, &#x27;bGlaEVWI&#x27;, &#x27;XhVkRDJa&#x27;, &#x27;DtmhPpVX&#x27;, &#x27;yYNXlAAQ&#x27;,\n",
       "       &#x27;uSMJRPrs&#x27;, &#x27;jXYfHeOe&#x27;, &#x27;IhnFibnQ&#x27;, &#x27;auDVceCU&#x27;, &#x27;WwiWpDbc&#x27;,\n",
       "       &#x27;PfwrWzuX&#x27;, &#x27;WICMDhwT&#x27;, &#x27;BvGvZchf&#x27;, &#x27;RmVmDBmr&#x27;, &#x27;PAZVzbUK&#x27;,\n",
       "       &#x27;hKITQzId&#x27;, &#x27;VPWMuckb&#x27;, &#x27;xptOwpfU&#x27;, &#x27;HIgUpTjt&#x27;, &#x27;IWcQOfST&#x27;,\n",
       "       &#x27;fSUENpjW&#x27;, &#x27;QeGnJBeN&#x27;, &#x27;KkjADoMz&#x27;, &#x27;GXyxNKkb&#x27;, &#x27;PTSkyVNd&#x27;,\n",
       "       &#x27;RrdbpPvj&#x27;, &#x27;ELGWpzoF&#x27;, &#x27;iSCwEgju&#x27;, &#x27;UQBJvXzT&#x27;, &#x27;KYvNRgCr&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da643dee-1d11-428b-be14-11130bbc07fe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-da643dee-1d11-428b-be14-11130bbc07fe' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8844fe05-6e0e-4f66-841f-cf73738be2b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8844fe05-6e0e-4f66-841f-cf73738be2b9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'orog' (point: 100)> Size: 400B\n",
       "array([3.74864864e+00, 7.70284042e+01, 3.17707424e+01, 1.59296356e+02,\n",
       "       8.50554657e+00, 2.69303436e+02, 2.61035797e+02, 0.00000000e+00,\n",
       "       7.76310120e+01, 1.48034021e-02, 3.39158669e+01, 1.53166565e+02,\n",
       "       1.60940230e+00, 0.00000000e+00, 7.13127975e+01, 1.51575775e+01,\n",
       "       6.64364319e+01, 0.00000000e+00, 1.12974800e+02, 0.00000000e+00,\n",
       "       2.69054890e+00, 0.00000000e+00, 3.99752159e+01, 3.41971703e+01,\n",
       "       6.46535950e+01, 3.35994232e+02, 1.60176880e+02, 1.26123024e+02,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.03021851e+01,\n",
       "       1.89539986e+01, 0.00000000e+00, 4.04688110e+01, 0.00000000e+00,\n",
       "       0.00000000e+00, 4.26026802e+01, 0.00000000e+00, 6.81077968e-03,\n",
       "       1.19178711e+02, 2.45614243e+01, 0.00000000e+00, 6.05588112e+01,\n",
       "       1.23778839e+02, 1.43709764e+01, 1.18544037e+02, 1.87431240e+01,\n",
       "       2.04521179e+02, 1.97443123e+01, 1.72476761e+02, 9.15527344e-05,\n",
       "       0.00000000e+00, 1.38174944e+01, 2.12805542e+02, 2.74857578e+01,\n",
       "       1.04479446e+01, 1.16316414e+02, 1.93214893e+01, 6.84824142e+01,\n",
       "       1.96816833e+02, 5.76621933e+01, 0.00000000e+00, 2.23715057e+01,\n",
       "       0.00000000e+00, 0.00000000e+00, 1.37994690e+02, 2.15656357e+01,\n",
       "       5.08315516e+00, 0.00000000e+00, 0.00000000e+00, 3.51489334e+01,\n",
       "       1.04980412e+01, 0.00000000e+00, 2.73795258e+02, 3.37753448e+02,\n",
       "       1.87713989e+02, 2.93994026e+01, 0.00000000e+00, 0.00000000e+00,\n",
       "       2.48333950e+01, 1.72831936e+01, 3.04587769e+02, 4.41159105e+00,\n",
       "       2.12090187e+01, 4.74487686e+01, 1.65152624e-01, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.64283066e+01, 1.11062164e+02, 0.00000000e+00,\n",
       "       4.81408958e+01, 0.00000000e+00, 1.59145447e+02, 1.24982613e+02,\n",
       "       6.93545532e+01, 0.00000000e+00, 0.00000000e+00, 3.61904716e+01],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2024-01-01\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    surface              float64 8B 0.0\n",
       "    valid_time           datetime64[ns] 8B 2024-01-01\n",
       "    gribfile_projection  object 8B None\n",
       "    point_latitude       (point) float64 800B 29.72 26.96 47.38 ... 35.88 45.14\n",
       "    point_longitude      (point) float64 800B 274.8 255.3 258.4 ... 285.7 294.9\n",
       "    point_stid           (point) object 800B 'SkNxqxCY' ... 'KYvNRgCr'\n",
       "Dimensions without coordinates: point"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terrain_roughness = dsp.orog.std(dim=\"k\")\n",
    "terrain_roughness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Terrain Variability from HRRR Model Elevation')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q1Mn0m7u7e5x9vHjxoihbtmyyx3b9+vWmdeIPkL6+vonKk9T+h4eHi379+pk1kYFRMYgh/jWyZs2aONuKPXgC4syZM4ke68xQGoQQ4tdffzW1KV26tBAi7tSEWq2Oc8MmR3LbjL18+PDhCZaHhYXFGdTGjRsXZ3loaGicPsaPH29aFvs89uvXz/R7VFRUnHVim1Lv378fZ9mxY8dStJ8ZrTSoVCrx448/JupnkVXrpvR4VK1aVQghxJAhQ0y/xTxcbGxsRGBgYJJKw61bt+L8vmDBgjjbuXnzZpzlCxcuFEIkHDvi88EHH5iWx37I16hRI8Xnb8KECYnub1YoDfGPU3Kf27dvm/r88MMPTb9PmjRJCCHEzp07Tb85OzuLyMhIU3tfX98kp1ljfz777LM4+5sWpcHNzc30e/ypUyGEyJMnj2l5165dEz0vderUibNO7OkrSZLMnqv4ZEhyJ39//xS3FULg5+eX6LJSpUolu37RokUTNcnu2LGDsWPHEhoaanb91MbPx49Ht7CwMH03GAwp7qddu3ZxzElr1qwhICCAv/76y/Rbr169TPsYERFB69atU2TujYqKSvR3V1dXXFxcUiwjwFdffcWqVauS3bektgnG6aLYxJ8SCQwMfCeZ0pt+/fphZ2cHGE2+p06dimMabtWqVYJ9SC8Su9YDAgLihNvGP142NjbY2trGaZ8Y+fLlM32PP90Qe1n8++hdruMYPD09EcaXkTifvn37priPSZMm8e2335oihXQ6HePHjzeZkrPjuskxYsQI0/eXL18C0LdvX9P0WGLEH0fjn//4/8ec/9j3UWLXa1JTke8ybvv4+KS4bUpYuXJlotdN/ARuifEuckNc2QcMGGD6HhMiHXtqomfPnnHG9oEDB5qmMc1hbhx8V2LvX2LnLvZvSY0B5p5XsceYlJAhPg3x4+pHjx4dZ3CKT1I3jo2NTbLbSqrNpk2bTN9tbW3Ztm0b9evXx9LSkoULFzJs2LBk+zaHWq2O83/8hDUpxcLCgu7du7No0SIAtm3bRsWKFeNcdLFzMxw/fpzXr1+b/h87diwTJ07E1dWV8PDwNB0zc8Q+nuXLl2fDhg2ULFkSlUpF165d48z7J0VMCFsMXl5ecf53dHR8Z7nSEzs7O/r168dvv/0GwLJly0y+KgD9+/fPsG0ndk5iEoDF3NTxj1dYWFgcpTixuUxIeK3GJjGFO6sZNGgQhQsXZty4cdSpU4crV64Axvn93r17mw3lzqp1k6N06dI0a9aMgwcPAsbxIrYikRjxx9H45z/+/zHnP/Z9FP+eS2y9xLZXtmxZszlhslMoZPzj1LdvX7PyxX6AdurUiWHDhhEcHMzjx485dOgQu3fvNi2Pfc+HhYXFeZn76KOP+P333/H09ESpVFKjRg2Tv1B64uzsbDqPiZ272L+ldAxI7fMKMkhpqFmzJkql0uTgp1arE42hf/LkCXfv3s2QePTY1osiRYrQtGlTwPj2tHXr1nTfXlro37+/SWkIDQ1lypQppmVVq1alfPnypv/jW2V69eplctiL/Vac3sTe7ocffkjZsmUBo9ae0syfa9eupXfv3qYH4bp160zLNBpNnP1Mb1QqFTqdDoDw8PAk2w0fPpz58+cjhGDDhg0m5c3d3d1s5s6MwNramooVK5oeXlu2bOHbb781ObeuWbMmTvvsnBAmNVhZWTFv3jw+/PBDwGgVnD59OitXrsy265pj5MiRJqWhadOmyVpSS5YsibOzs+lNc+3atQwePNjkgBjjyBpDzPmvVq0af/75J2B8oBw+fNjk6Hb69OkkHSHr1KnDuXPnAHj9+jU9evQgf/78cdrodDp2795NzZo1U7zfGU3JkiVxcXExjVERERGJPm+8vb05depUHMdKKysrunfvzu+//w7AZ599ZhofKlasSJUqVUxtg4KCTM80MFoeixQpAsDdu3e5du1akjLGfmibG38SI8bZHowOo97e3iYL0r59++JYTjJjDMgwS8OAAQNYunQpYPRsvXDhAnXq1MHS0pKXL19y5swZLl++TN++fWnevHm6y1CyZEn+/vtvwOg52qNHD0qXLs2+ffuyXRa26tWrU7ZsWdOUQ+yLKr62X7JkyTj/9+7dm27duvHkyZM4iaDSm5IlS3Ljxg0Ali5dikKhwNramj/++CPFpsqDBw/y0Ucf0aBBA06ePGmKngCjGTC1CblSQv78+U3JlubOnYufnx9WVlZUrlw5judwiRIlaNasGQcOHIhj7enTp0+WvJWPHTuWPn36AEYlu3r16nGiJ2LL3apVq0yXL6Np1KgRderU4fTp04DxwTlt2jQ8PT2z7bpJ0aJFC3bu3InBYEiRgqxQKBg9erTpJeLff/+lXr16NGvWjDt37sR5Sfjwww9N0yq9evVi2rRppuu3Q4cOfPrpp0iSZDZCacSIESxevJjIyEj8/f2pVKkSXbp0oWDBgoSGhnLr1i2OHj1KYGAgjx8/TvKtNjUkFT3h4OCQbNIjhULBmDFj+PrrrwHjy9OjR49o2rQpdnZ2vHnzhgsXLnD27Fnq1atHhw4d4qzfv39/k9IQW6GKb1l0c3PD0dHRNP0zffp0vL290el0rFixwuyURP78+Xnw4AEAq1atwsrKCjs7O4oWLZpAnviMHj2anTt3IoQgJCSE6tWr07NnT0JDQ+OcT2dn53eaBkw17+QBEYvk8jSEhYUlm6eBeI4z5hzgYmPOGS6G+/fvJxqXqlKpRK9eveL8FpuUOkLGly0lji7miO3hG/PRaDSJJu75+OOPkzyWsf+P7XwXP7lTUiS1Hxs2bEh0m3nz5hVNmzZN9HzEv0YaNWqUaB+FCxeO42CYEY6QsT3DY3+GDRuW4Bj89ddfCdrdvHkzyWOWFObkScnyGJLL05AvXz6zeRriX49J3bcpSZ6TGBmZp0GIhOdjyJAhWb5uSo9HjCOkOZJyhBTCmKehS5cuZs9/6dKlE+SbmT9/fpLXSvHixeOMGbHZvn27KdLD3Ce2jBnpCBn7ekprnobExowYSpcuHaedRqNJ1Fk8fm6dmE+5cuXiRFnFPw6//PJLouu1atXK1CYj8zTElyf+NfcuZFiVS2traw4cOMD69etp2bIl7u7uqFQqrKysKFq0KJ07d+b333+PE4OenhQrVozjx4/TrFkzrK2tsbW1pWHDhhw+fJgmTZpkyDbTQp8+fRLEPbdp0ybRugt//vkno0aNIm/evGg0GooVK8bMmTNZvnx5hsnXvXt3Nm/eTMWKFVGr1bi4uNCtWzfOnDlj1l8lNlOnTmX16tVUrlwZS0tLXFxc6Nu3L6dPn84wB8MYZsyYwciRIylQoECy8eUtW7akWLFipv9r1qxJmTJlMlQ+c8ydO5e///6bTp06kS9fPtRqNba2tlSqVIkpU6Zw7do103RRbqRVq1Zx0qevWLEijl9Pdlw3vVAqlWzevJktW7bQsmVL3NzcUKlUODg4ULNmTWbPns358+cT3IPDhg1j69atVK1aFQsLC1xdXenTpw9nz541e7+2b9+eGzduMGbMGMqXL4+trS1KpRIXFxdq167NuHHjOHXqVJKFybIKhULBmjVr2LNnD506daJAgQJoNBosLCzw9PSkTZs2/Pzzz3HqwcQmvlWhTZs2iTqLT5gwgQULFlCiRAnUajUeHh4MGjSIY8eOxXFKjs+wYcOYNm0aRYoUSZXFctSoUZw9e5Y+ffrg6emJRqPBysqK0qVLM3r0aK5fv54ip9H0QBLiHV0nZWTeAz7++GMOHDgAwOLFixk8eHAWSyQjIyOT9WQ/12kZmSzizp07Jn+bGIc1R0dHevXqlcWSycjIyGQPZKVBRuY/Zs2alcAjfcaMGWbNjjIyMjLvE7LSICMTDwsLC4oVK8bo0aPfqZy1jIyMTG5H9mmQkZGRkZGRSREZFj0hIyMjIyMjk7tI0/REZGRkqms3yMjIyMjIyGQNGo0GS0vLd14v1UpDZGQkH3zwAd7e3ri4uGBvb4+vry9BQUGmNlZWVri7u/PkyZPUbsaEjY0NTk5OvHjxIs19ycjIyOQWihQpwuvXr4mIiEjXfjUaDQUKFODRo0fp2q9M9sDDw4PHjx+/s+KQaqUhOjqaN2/e8Pz5c+zt7bl16xYzZ87AwkLFzJkTsbDQsGbNFiIiYMKEiandjAlfX186dGjP8ePbk03OIyMjI/O+8M8/p1iz5k82btyUqjdHc3Tp0pm5c6dSpUrG1YWRyXxCQkIpV64B0dHRmac0xGBvb4+9vT21atVi+/YdfP75YBYtWsOUKaPw9vbH2toROzu7NFXVitmOsdqXHyVLpr7anIyMjExuol275hw/fpZVq1YyfvyEdO27ZctWnDlzmUaNclcxNJnUk66OkEqlkrlzf+LGjXts2bKbkSMHcPPmNSZN+spUYTAtVKxYkcOHTxIdrU0HaWVkZGRyPpIkMWHCULZs2RKnlHt68PHHH3P06Gl0On3yjWXeC9I9esLe3p6FCxexdOkGbty4y8KFM3n06D5ffDHCbBWwlNC7dx/On79Oixa9mDZtLsePnyEqSnbElJGReb9xd8/DkCGfMGnSV+nqnF6iRAkcHR25cuVGuvUpk7PJkJDLYsWKMX/+fGbNWsiaNVuZO3cqkZGhDBr0KaGhoanut1q1amzfvoMdO3ZSpkxlVq7cSsuWvZk6dY6sQMjIyLzXtG//MTY2lixbtizd+pQkiaJFi3HnzoN061MmZ5Pq5E7BwcE4ODgQFBSEvb19om28vLwYNWokQmiZOnUsixev4flzL5YtW55o9cbU8Pz5c/bt28f+/ft4+vQp9erVYMiQT/DwyNiqiTIy2R29Hv7cZsvyFQ7cvavG2kbQsX0onw0KolChtE8XpjcPHqk4f0mDQgF1a0VRIJ9sEn9Xnj17Sf/+Y9i8eTNFi6bd9+vhw4d07dqFLVt+x9nZMe0CymQLgoND8PSsYvb5nRQZqjQAaLVafv55Htu2baNTp5Zs27YXe3sHNmzYiJOTU2o2nSTPnz+nT58+jBs3mLp1q6dr3zIyOQmdDvoNcGfPXlsUCoHBYHREVioFlpaC7X++onq1tE0Xphde3gqGjnXmn+NWpt8kSdC+VTjzfgjAwV5OWvsurF69mdOnr7B27TrUanWa+hoxYjj587vw+ed90kk6mexAWpSGDM8IqVarGTduPLNm/Y+wMEGzZh9Tp05dtNr0d2Z0dXXF19eXcuVKpXvfMjI5iYWLHNm7zwbApDAA6PUSkRESPXt7kEYXo3QhOESiZRc3jp2KG/YlhMSufdZ0/iQPGTBU5Gp69eqIhYWSjh07cO3atVT3c/36dc6dO0fv3h3TUTqZnE6mFaxq2LAhDRs2zNBtXL9+nUKF8uPgYJeh25GRyc4YDLD4dweSsiHqDRK+vip2/2VL506p9zFKD/7YaMPjpyqESBiSrddLXLhswV8HrOjQOn0TF+VmVCoVP//8LTt2HGDgwAG0b9+eUaNGY2Nj8079zJv3E717d8LW9t3Wk8nd5KraExcvXqR8+dJZLYaMTJbi5aXk9WsVkHRuFJVKcP6CReYJlQRrN9kkqdwAKBWC9Vsy/qF17qKGoWOdadLOjY69XVm7yYbwiLTllslKFAoFHTu2YP36BTx//ohWrVpy7NixFK9/9uxZ7t27R5curTNQSpmcSK4qjR0WFkZ4uPxGIvN+k9KEqapskFjV21eJOeVGb5B445VxggoBE6c58vsqO1RKgU4voZAER05YMne+Pbs2elMwf851yMyTx4VZsyZx9Ohpvv56EjVr1mTSpK9xcXFJch0hBHPnzmHAgG5YWma9YimTvchVloZBgwZx8eJ1zp27nNWiyMhkGXny6ClRIhpJSvoVXqeTaNgw6xXsfB56s3IqlYKCBTIu0mPFWht+X2WcztTpjcqLQUiAxPOXSnp+6mrWEpJTaNSoDhs2LECtFrRq1ZJt27YR3wfeYDBw8uRJBg/+jIAAf9q2bZZF0spkZ3KV0uDg4MBXX33F//63kMjIbODl9R5jMMDBf1X0/tqKuv1saD/amo371cinJeORJBj1RWCifgJgfBAXLxZNk4/CM1myhHzSI8zscr1eonc3821Si8EAvy62BxLXCvR6iRu3NJw+mzvetu3sbJk4cRg//PAVixcvpH//fjx//pyQkBBWr15NixYf8+23U6lSpRQrVsxFpcpVhmiZdCLDQy4zGyEEn346kGLFCjBkyCdZLc57SbQW+k+1Yu9JNUqFQG8wmnwNQqL0B3p2/RKOq2MueH3LxggB075z5tffnExmd0kSCAH58+v4a+crChfO+lwN4RESzTu4cfueGr0+rpKjUAga1Ili6xqfFE+5vAtPnympVD+f2TYqlWDk58FMHhec/gJkMi+9FNy4q0KjFlQqHcbmTZvZsmU3BoOBypXL07lzS2rWrIJCkaveJWUSIS0hl7lOlZQkiWnTvqVDh/Y0a9aAokULZ7VI7x0/rLBg3ynjpaU3xDb5wr1nCgZ9Z8X2n7L+LTcz0Gphz0ErDh6xJCpaokKZaHp1CcfVxZCh25Uk+HaqP+3bhrFilT23b2uwszPQrm0oXTqHYmOTPZQ2ayvB7k3ejJvixPbd1qbrRaMR9OkWyveTgzJEYYC312Zy6PQ51yESwMtXYux0O/Ye1ZisTzbW9gzu+RnLln2EWg358+fNYillcgq5ztIQw9KlSzlwYC9LlsySS2lnIuGRULKdHaHJeJ6fWRNKycIZ++DMap48U9KxjyuPn6lRKo1v+QhQqWDRT/50lMMI4+DlreDydQ0KCWpUjcLRIWMVG60WStfIh5+/+fFh3TIfWjaNzFBZMoqAIIkPezjx4o0igSVHkgRdW0WxaHoIaSxCLJPDyNbJnTIaPz8/JkyYwNKlSzlz5oyptkW/fv1QqTQ0a9aTkSOnsnz5Bs6fvyJHV2Qw1+4rk1UYJElw/FLuVuSio6FDnzw8e/mfxUUvYTBIGIREtBYGjXTm/GVNFkuZvXB3M/DxR5E0axyZ4QoDgFoNg/qGokjCEVOpFOTLq6N545ypMAAsXmfF89cJFQYwJtDa9Jcll27kOoOzTAaS46+Wbdu28ejRPYSIZNeu7Tx69JSCBQtQvnwFWrZsRd68eQkJCeH69Wv89ttqHj58RNGihalQobTpk9PqVGgxsFbzkKWauzxQhGCJkvbaQgyLKk1Jg0OWymZIgfFAglzhkW6Ovw5a8eRZUreX0b/gt99tWbPIP1PlkonL6KHBnL+k4fAxqwTptm1tBOuX+mbY9EhmsPpPyzgZQeOjUgrW7rCkavmsTfIlk3PI8UrDtWtXqVGjEoMG9QIgMjKSe/cecevWfS5cOM2tW/fw8fGjcuVKtGjRkrJlyxIVFcXt27fZt+8Es2bNx9bWhvLljQpEjRqV+OCDQlm8V0kTjZ4e1sc4onoNgJCMSsQG9SM2qx+zJexD6us9sky+csX0WGoEkdFJD1QGIVGzXM6NfU8J+w5ZoVSKRN/wwGh52HfICiGQTcNZiEYDG1f4smWHNcv/sOXBIzW2tgY6twtnUN9Q8ufNudepEODtZ96YrNNLvPLK8QbnDOeNt4Kbd9Ro1IJqlaOxskx+ndxKjlcaRoz4gp49e9K4cV2KFi2MpaUlFSqUoUKFMqY2Pj5+XL58g0uXbrBjxzaeP3+JhYUFHh7uVKhQAWtrG6KjtZw4cZFlyzbQpEk9Pv/8k2yZjvo3i9scUb0mfjSdThIYhOAT6xPcCumAVRadWnsb6N1Ky8qd6kQdzZRKQaUSeiqWzN3+DFFRyVtddDoJg+FtMiYff4klGy1Zu8sS/0AJD1cDfTtG8WmXSBzscrlpJgtRqaBH53B6dM5dzrmSBE4OAv9A85aGPM65+15MC96+CsZNdeCvg1Ymi42drYGhA0IZNzwkTVYovV5PaGg4ISGhhISEEhwcavqu1+uRJAlJUiBJ/Pc99sf4m6WlBS4uzri6OuPq6oSlZcZrM7nCEXL16tVs2bKR5cvnYmGR/DyxXq/H3z8QLy8fvLx88fb2wdvbj3/+OU2bNm3w8vLmxInjDBvWj1atPso2IUh6DJSx2463FGkuiR4Lw2vTQ1sk8wSLR1gEdBxrzbkbShRSTOSEQCFB3jyCvfPDKOSRux+Cs3+1Y9Yv9kmahiVJULyojrN/ewHw5IWCjz91wNdfiqVsCRQK+KCAgX3LgsjjnLuPmUz6M+UnGxattUrS4gWwe1kg9arLVcHiExgk0bi9G89eKhN1Iu3RMZwFPwa+c79Xrtzkm2/m4O3tg1KpxN7eHnt7Oxwc7LG3d8De3h6lUoUQBkD89/IhMBgMCCFMH4Dw8HB8fHzw9vYhMDAQGxvr/xQIF1xcnHBxcfzv/7cfFxdn9HodhQtXzZ6lsTMDg8HAp58OxNXVnk8+6Uy+fO6petC/euXFkCFf0atXbypXrsy3307DykrDuHGfU6JE2mvTp5XXUjhl7LebbaMSEv2iizM7MmtLg0dFw+aDalbvVvP0tQIXB0GPFlr6tonGMfsZcNKd114KKtTNi04PiWl4kiSY830gA3oZExc1H2DPxZuqRAd3pVLQqlE0q/8nzzvLvBtvfBQ06OqEf5CUaB6MxnWi2Tw/WJ4iS4RZv9gxe76dWZ+Qw9u9qVIh5QrX/v1HmDNnCd999x2NGjXCysoK6R0PfowVIv4zTqvV4ufnh7e3N97e3v8pEzF/vfDx8cbHxxc/P380GjXXrl1/f5UGAG9vbyZMGM/NmzeJjtZSpIgnRYt6UqRIIYoVK0zx4h/g6Ji8k+DLl68ZOvRrevToycCBA/njjz9YtGgRNWpUYuDAHhQpknX+Dn5SJMXs/zTbRiUkPosuyYzIqpkklUxSrNtizYgJTigUmAbsmJTJH38UyZpFfqhUcPOBknrdHc32pVAIbuwJIG8e2dog8248eqZg8CR7LlxXm35TKgTd20Yy+6vQ93p+3hyla3vwxjvp+QeVUtCnWxg/fR+UbF9CCJYv38D27ftZtGgxFSpUeGd5tm7dypo1a3jx4jkGg6BkyeKUKVOWMmXKUrp0aUqUKIFGkzJL+9OnTylatOj7ndzJzc2NlStXIYTAx8eHe/fucffuXe7fv8fBgyd5/PgJCxfOpEyZEmb7yZ8/L4sWzWTYsK+JjIxg5MhRdO7cmZUrV/LZZ+OpXbsqAwd2p3Dhgpm0Z29xEZaU1ztxUxFAUsqvThI00ZnPcieTOfTqEo5nQT2/LLbl8HFLhJD4wFPH4H6hDOgVRkyW3ss3k78NDQaJa3dV5M0jm5Fl3o0ihQz8vTaQ63eUXLmlRq0WfFg7GndXWQFNCiHAyyd5J9KXr5O/d6Ojtcyc+SsPHjxj8+Yt5M+fP1UyXbt2jbt37/Lhh3Xp378bvr7+3Lv3kH/+2c/ixQt588abIkU+oEyZMpQuXYZatWpRqlSpBP0olUpcXV1TJQPkIktDcqxfv54NG9aycuVPKcqp7uXly/DhX9O4cRMmTpyIJEkEBgayYsUK1q9fR716NRkwoBuFCqXuAkgt29VPGWB9MtFlSiFRwmDPydBWKMw5PchkOno96HRgkUgZg417NAyZmvyczZ/zg2lcS1YaZBIiBJy/qOHJMzVOjnoa1I1M9FqTSTlFqnoQEGje0tCtQzjz/xeYZJugoGAmTJiBtbU9v/zyK3Z2aZubff78OQsXLuDgwYO0b/8xffp0MlnQQ0PDuHfvEffuPeLu3UecPn0eT09PevToSYsWLeI4Sabl+f3eKA0Gg4F+/fry6tVL2rRpQuvWTXBxcTa7jp9fACNGTKZo0eJ89tlgypcvD4C/vz/Lly9j48aNNGpUhwEDumVqGta5FjeYbnkVpZDQSwKFAIMERfV27Aj7iALCJtNkkXlLQJDE+p2W7D2qITpaoko5LQO6RlKyiPmwvdc+EuVbOZlNa2xtJbh7wB9b6/SWWianc+pfC0aOd+Hho7fTD06OeiaNC+TTfrIfTGqZPNOexatszTqR7lrrQ/3a0XF+E0Jw48ZdDh48xqFDJ2jatClTpnyDWq1Oopd358mTJyxYsIB//vmHzp1b0blzK/LkiVvuPDpay5Ejp9i2bR+PHz+jQ4eO9OjRg8KFC8tKQ0oxGAycPXuWzZs3ceTIUWrWrEybNk2pVasqKlXiGmVQUAgbNuxg166D5M2blx49etK6dWssLS3x9fVl6dKlbNmymSZNGtCvX1fy5XPPlH25pwhiteYBtxWB2KGmjbYQbbQFsSDhfhiE4NJrLf4RgkIOSkq55ppZqWzDlVsqOgx2IChE+i9xlYRSafR8/mFcGIN7mc9E+vk3NmzZb5Go05UkCb74JJJpI3JXSKBM2jl73oI2XdzR60n02pkx1Z+hn4VkgWQ5n1dvFDRo7UZgcMKMmgqFoFHdKLau9DM5kT548MSkKOj1Blq1akXr1m0oU6ZMIr0nj16vT7YEwsOHD1mwYD6HD/+Dm5srlSqVpXLlslSqVI68ed1MTpYPHjxh+/b97N9/hHLlyvLZZ4OpV6+erDS8CwEBAezatYvNmzcRHBzMF18MoGnTBkm212q1HDt2hj//3MuDB0/o2PGt1nb5wXMWLf6ds4f+onnzhgwc0A139zyZuDdJs+NOJFOPhvAs6O3bbiUPFXOa2lM1n5zGOD0IDYeKLVwIDJaS9LTe8XsgDWsmPbUQFgG9xtpx7JzGlBQq5m/7JlH8Pj0UtazrycSjeTt3LlxKXNkEsLI0cOfKC+zlPB+p4v4jFYNGOXH1pgZjCXUJpULQpX04c78Lwt/vlUlR8PcP4uOPP6Z169ZUqZJ4tdCwsDBOnjyJr68vQUFBBAUFERwc+28wwcHBBAeHEB4eTo0a1Rk/foLJyp0U0dHR3Lp1i/Pnz3P+/DkuXbqEtbUVlSqVMykS7u55CAgIZNWqzTx/7s2mTZtlpSE1CCE4c+YMX3zxBbNmfUXVqsl7tT569JQ//9zH3n3/oC7zIRcKdkAAqogA8j3ch92z07Ru8SEVKpSiefNGWVYwa+ONCIbsSejZq5BApYC9PZ1lxSEdWLnFkjHTbUkqeYZSKWhUK5qtC82XVzYY4Og5NRv3WODtK1HAw0CvdlHUqqiTQ+JkEvD0mYpKtZPzqRIs+MmPnt3CMkWm3MqV62ouX1djoYEP60fi5BDBd9/N4+zZSzRp0oTWrdtQu3btJP3lbty4waZNG9m7dy/FixfBw8MNe3tb7O1tsbMzfuJ/t7S0ZPfuA6xcuZkGDRowevSYFDtR6vV67t+/z7lz5zh//jwXLlzA398fS0sLLC0tKVeuPCtWrJCVhrRw6NAhvv56EgsWzKRYscLJtn8VoqfxksdY//UtvuU6E1KwpmmZKtyPj7XnCb55nC+/HEz9+jXN9JQxRGgFpRZ4ExyV+OlVSFA9n5r9vV0SXS6TcvqOteevwxpT+e/EUKkE3hd85Ye/TLpx7oKG5u3M+1IplYJJ4wIZM8K8wpqeaLVw8ryGgCAFhfLpqVpBm6uu+9DQML788nscHV346ad5WFsn7mgUGhrK7t272bx5E97eXrRq1YS2bZtRoMC7+b8FBYWwatUmdu06SPfu3Rk8+PN3fubGPOZjpivS8vyWDZ7/0aRJE16/fs2gQeNwc3OlZs3K1KpVhSpVyiWamvOnf8PwNdiirjGY/Kd/IdKpMFpboz+DztqFPXzML32Ks3Xr3ixRGg48jEpSYQAwCDj7UsujAB1FnOTLIC3o9UbDpTlSUshLRuZd8HBPvi6GXg95U9AuvViz1Yrvf7bFN1a58RJFdPw0LYi61XJ+5I+fnz+jRk2jTJnyzJgxI4FlQQjBtWvX2LhxIwcO7KdChTL07duRevVqpChqLzEcHOwYOfJTOnduxcKFa2jatAnz5y+gevWUJ/B71wRS5pCfFrHo06cP3bp148qVK5w4cYKlSzfy6NEjKlQoS82alahVqypFi3qiM8D66+HoBehdiuFfogV5zy7ieaOvEUqjh6xCAi+Pmty48QvPn7+iYMHMzZ3wMlj/Xwpn8+1ehehlpSGNVKugZd9RTZKKg0IhqFJWnmKQSV8KFdRTs3ok5y+a82kQtG6ZOQ60S9dbM356wrfWB0+UdBjgzO7V/tSsnHMVh5cvX/PFF9/QtGlzxo8fH8dnITg4mJ07d7J58yYCAgJo06YJ69YtIG/e9KugbGlpSUREJC4uLri5ZV1lZnl6Ihn8/Pw4ffo0J04c5+RJY36EytUq84d/USIdC2Hl/xBrrxvYvzjPq5pDCc1vzMSoUkh8UsWNgtfXIUlRjBw5MFPlTsqfIT7/DnSVoynSiK+/RLnmLkRrQSQxRbH8f8F0/DgqkyWTye2cv6ihdScPdElET/zwrT+ff5rx0ROhYRIlG+QhPCLxhEgKhaBaBS0H1ufMUvD37z9m1Kip9OvXn0GDBgFGq8KlS5fYvHkTBw/+TZUq5Wnfvjm1a1dLMhovtZw6dZ4ZM37l448/Zvz4CWkuTCVPT2QgLi4utGnThqpVq3L48GFA4uH9R7h6XUcRFUyEc1HCPSrgV7q9aXrCiCCPjZqePXvSu3cvBg/ujaVl5mVbaVncAisVROgSXy4BZfKoKOmSNU6auQlXZ8HK2cH0HWuPQbwth61UCPQGic96hNOhuawwyKQ/1atGs32TF6MnOHPv/lunZhdnPZMnBNKvd+bkafjrsAXhEUmb0gwGiXNXNDx+puSDQjmr3PjlyzeYOHEm48aNp3PnzgDs3buX+fN/IywslDZtmrJx4yLc3VOfZTEpIiOj+O23lRw5cpoffphFw4YN030b74qsNKSQRYsW0rx5I3r27MDVqzeZv/sKbx7cwdr3Hgp9NMrIICJdixPhXAyDxhqdAXpXykMJDxuKFy/OP/+comXLxpkmr72Fgq/q2fHN0YRvGTG39oQ6tlx4JNCoBGULSKiUsv08tbRoFM3xzQEsWW/F3iPG5E6Vyur4rEcEHzeMlqcmZDKMOjWjOHPkNZevanjyVIWTk4F6tSNJx1xCyeLlo0SpNPpQmOONjyJHKQ0nTpzl22/n8cMPP9C0aVP0ej1z585h9+5djB8/lDp1qmVYdNy9e4+YOnUOBQt6snv3blxcsofTeq5RGoQwOptlVHRj5cpVWLJkEaNGDaJVqyYUq9mIxmv8iA4LxsL3PlZ+93G+vRuLoGdE2+Wj5sBvKOdhzMzYsGEjrl27lalKA8DwGtYoJPjhZChh2rezUHmsFZS1sOHTXxRE64ymCDd7GPmxkiFNFOnqNJMW9AZjOe3sIk9ylCqqZ96UUOZNSb8+X3kp2L7PEl9/Bfk99HRqGYmToxxzLxMXSYIqlaKpUik6+cYZgLurPlmFwdgu53gE79lziF9+Wc6CBQuoWbMmwcHBjBkzhoAAH1aunIerq/mMwmlBp9Pz2WfjGTVqFH379s1WY+C714/OAvR6OHYMNm2CEyfieqKfPw9duhhz+qtUUKoULFhgDPtJTzp06IC7e17WrNkCQElXFX/1cKawhyNh+argV74bzz/8mqdtf6VIqVLkvf22GmXZsmW5e/dR+gqUAiRJYlgNG+4Oz8Oqdo7MbWbPmnaOuPg5cPyimuhYUxfewfD1Zj1fbcrat4CIaMGvf2upOCUS1xGR5B0VydA1Udx+lXMGm/TAYIApP9pRvnEevpltx4KVNoyfbk+pBm4sWi3nkpbJXrRqEoWVZdLKrNGnIZoinjnDyrBu3TYWLFjDqlWrqVmzJo8fP6Zr1y7Y21uwcOEPGaowAKhUSvLl86BkyZLZSmGAHOAI+eefMGoUvHjx9jdPT/j1V2MBoK5djVr2fy/MJjNwixawYwfpaqJ78uQJnTp1YtWqeaZoCCEE/77QcsdXh7VaokkRCyz04XTpMphFixZTuXJl/P39adiwIUeObEl12E168dNePTN26M1GVRz/RkX5gpmvT4ZFCdr/GsWlpyKOfCoFKBWwZZiG+iXeDx+M6T/b8tMSG0QSCaN+mx5E707mU1PLyGQmi/+w5qsfEj4LFAqBQgG7VvpTu2r2jp7Q6/XMn7+SY8fOsmLFSgoXLsyJEycYO3YMDZt2Q5mnMwqFRN2KeupUMGTotOOCBSuRJBsmTZqU7n2n5fmdrS0NW7dC585xFQaAZ8+gfXvo3t34RqaL9cYshPGzbx/Mn5++8hQuXJgOHTqwbdte02+SJFGnoIYBla3pXs4KV2sFdna2fPppD2bOnIHBYMDZ2RkXF2cePXqWvgKlgpXHzCsMKgWsPZk1b/Wz9+kSKAwAOgNo9dBvWTRR2txvmg8KlvhtZdIKAwhm/mabInOwjExm8XmfcOZ+E4STQ9zxo3ABPduWBmR7hSEoKITRo6dx69YjNm7chKenJytWrGD06LEEO37NnH29mbvOgtl/aGg92pp6n1rx5FXGaQ116lTn2LGjGdZ/asm2SoNeDyNHJr4sRjHQaiEpO4kQRmtE6uwoSdOxY0cOHDiGTmd+xG7fvgUhIUHs2bMHiJmieJi+wrwjQgheJhPxpDPAE5/MfzBH6wQrT+qSVGgMAvzDYPfV3P+kPHDMguhoc4ORxGsvJRevZaKnm0ySvHypZONGO9auteP27fc7LfuA7hHcOebNliX+LPlfIPvX+XFhny/1a2aNr0VKuX//Mf37j8bTsyh//LEWBwcHJkyYwPr163lt+xsP/I0J+nR6Cd1/0VF3nyloNdqKoAwKUClfvjT+/v48efIkYzaQSrKt0nDkCLx6lbY+njyB0HQ+oaVLl8bZ2ZmzZy+ZbadSKfnii4HMmTOb0NBQgoKC8PcPSF9h3hFJkrCzMt9GqQAnm8yfQ3sZIAhOxtquVsLVZ7nf0hAcIiFJSeynhCn8Zdt+y3RXimVSTkiIxKefulOhQmGGDHFnxAh36tQpRMuW+Xn2LNf4mL8zGg00qR9N1zaR1Kyc/VNIHzp0giFDvmLQoMF8//10AgIC6N27Fz4+r6jd9ldehxRKtGy9Xi/x2ldi48GMUd5VKiW1alXl6NGjGdJ/asm2SsPr1+nTT3qHHUmSRPv2Hdi7959k29apU43ChQvSpk0bhNDSvXv79BUmFXSrpUBp5qzrDdC5ZuZfFhpV8iOLEGDxHrxcFyusTzxJVLyflvxhw7fzbDNHKJk46PXQvXs+duywTZBU6dw5S1q2zI+/f7YdXmUw+i8sWLCKefOWsmTJEnr06MG1a9fo3LkTFSqUYM6cKew84WBWMRcCNv+dcQpi3brVOHr0SIb1nxqy7VXt4ZG29ZVK+OgjSGPirERp27Ytp06dJyQkrhnDP0LPr5eCabPNm4+3ejHlZCBdPu1H2bLF+fHHyVhYZL3pcngzJTYWJKo4KCUomEew804kk/dEcOGZjlT6yb4z+RyhpIdk9q1EZ4Dm5dLXEdLHX+LmAyXeftnndahBrWgK5NWjUMQ69vHF++//X5bZcv3O+/tWm1UcPGjN6dNWpkResdHrJV6/VrF8uUMWSCaTEoKDQxgz5luuXLnD2rXrkCSJJUuWMGDAAIYO/YQRIwagVCoJCI1l2ksUiYCQjBs7ateuyqVLlwiNZTI3GAwsXLiATZs2Zth2zZFtlYYPP4S8ZoqBSZIxxDKpvAx6PUyYkDGyubm5UbVqFQ4fPmn67dzrKCqtec20U0GcfBnF2dfRLLoSQrtjKkr1HIqNTfYIkyvkKvHXOBWe/yUvUyqMdTIA9GodLwwRbLwczZLTUTRdFErnlWGEmCl8lV5IksTYj1VJavVKBdQsIlGtcPrcoDfuK+k6ypaSHztSr4cDpVo40mmELVfvZH10hkIBC38IMip2SU1T/IdSKVizNZk5J5l0Z+NGe5RKMwXhDBJ//JF70+vnZJ4+fUH//mM4c+Yi/v7+tG7dikmTJnLz5mV+/fU7WrR4m0+neEEDSkXS51mpEBQvmHGO4w4O9lSuXJ6dO3cCxsqZw4YNZf36dXzzzVSOHz+eYdtOimyrNKhU8PPPiS+LeRtduBCKFDF+VyqNvyuVxs/vv0PTphknX61atblzx+jYGBhpoMsuH8K0Ik7RIr0wOvB9cTiA82+yTxrh8gUVXJiuZscYFVM6KOleH3CMAPtohGR8o9f9dx8ce6Bj0MawTJGrS3UVU9qqkDAqCTF/Acrml1j7mUW6xCxfvqWkWX97/vlXbZoGEELi2Dk1zQfac+5a1r+5168ZzZ4//I3Xupld1uslHjzOennfN7y8lIlaGWLj55f1CqhMQp48eU7lyuX48sshTJs2mkOHNrNx40K+++5LypQpEadt/9baRP0ZYtAbJPq1ydiokC5dWvPHH2uIjo6me/duKJUGtm79nblzpzJx4kR8fX0zdPvxybZKAxhzMGzaBPniFYjMn9+Yv2HQILh5EzZvNoZfdugAkycbHSD/qymSYRQsWJCXL98AsP52GCHRCUMFY1BKsPByxheNeRckSaJhaQVfNFdw2S8SSZ248HoBB+7ouPUmc6IWxjRXc2GqBSOaqGhVUUHX6ko2DtHwz3gLXO3Sx8owcoYNUVoSDAZ6g4RWB19Mt84WDobVK2lxtDcviEIhsLfLBsK+ZxQsqDNraQBB3rxJFH6RyVIaNqzN5Mkj6dKlNeXLl8bKKuk57Fb19LSqq0vUMVmSBO0aamleK2PHxjp1qqHX6/j333/x8MiLvb0tlpaW1KtXg48+qsuYMaM5efJkpikP2f4VpWtX6NQJjh6FN2+MCkSDBm+nJdRqY0bILl0yV65ChQrx6pVRaTj0NCLJssgAOgGHn0ZmqDxR0fDguQKFBMUKGVCn8Mw+DxTc9jJvXlMqYO8tLWU8MufNqYibgqntMkafvX5XyfV7SR8cg0Hi7mMVF28qqVYu68M7O7eKYMUm6yTfag0GiY4tsleSp6go+GufDTduarCyFHzcLJwK5bN3yN270qtXMFu32iW5XKGAfv2CM1EimYxAoYCVUyOZt17Nkm0a/ION96Grg4HBnbSM6qFFkcGv3gqFgi5dWrFmzWpmz55Du3btKFZsH23bNmX48P7Mn7+Kn36azcOHj7GxsaFkyVIUL14MkIiOjiYqKoro6Ciio6OJjo5mxIgvKFSoUKrlyfZKA7x1asxOFCxYkNevvdDp9OhT8KKny6CXwaho+HGlBcu2awgONV69eZwMDOkazRc9o5KtxRGRgmRJCiA8lyRVevg8ZXf4w2fZQ2kY2jec9TusiIhMWPpYqRSUKKKjZePsM/V17IQlAwa74e+vRK0WGAzww2wnGtaPYNVSbxwdc0c68IYNI2jZMpR9+2wSRLoolYJixbT07Zt8aXqZ7I9aBeM/MSoID18Yx49iBVP+YpYetGrVhCVL1uHl5cWvv/7KlClTWLx4DQ0b1uajj+oycuQAJEni+fNX3Lv3iGfPXhpD7O0s0WjsMRj0rFy5mbx58+Lu7p78Bs2QI5SG7IidnR12drZ4eXlTM681p15GJak8KCWokTf9Iyd0OugxwZpjF1QYYg1cPgEKvl9iwZ3HChZPiTAbkVDAUYGVGiLMTMtpDVDKLXfMz9rbpkz5SWm7jKZwQT07VwTQ+wtH3ngrUakECGOSmSrltKz9LTBTqxma4/pNDV17ehCT90yrfXvhnTxtSfc+7uzb9Trbx+2nBEmClSvf8P33Lixf7kBEhPFholQK2rYNZc4cH+zkaaNchUYNpT/IGqXXxsaaTz/tSc+ePahSpQrDhw+ncOHCHD58mPnz1+Dt7U2jRnVo3LgujRvXjVOuwMvLl3HjvqdGjRr8+ONsrK2tCQ5OvRUs29eeyM506tSRwYN7kK90eSqvfm3WmrC+tSstPkhfL/dNB9R8/r35qIxt88L4sLr5udWxO8JZfS46UaVHAhysJG5/ZY+lOueP9lHRUKqFI4HBSVsc7GwM3N0fiJmpzkxHp4MDRy24eF2NRg1N6kdRtUL2Spwz8PM87NxtY9ZBcOfW1zSol7FTdZlNSIjE+fOW6HQSFStG4e6e9RYqmdxJaGgYBw8eZ/fuv3n16g3t2rWjU6fOqFQq9u3bx/79+/Dx8aFRo9o4ONjz8OFTrl27Rc+ePRk5chSK/+ZS0vL8lpWGNDB69CjKly9Kx44t+PNeOIMP+gGYHr5Kyfj9iyp2TKvjkO7Vyj4eYsP5m8oEZusYlEpB6wZaVn1vfs47INxA80WhPPIzxFEcYiIY1vWxoVmpbPI6mw4s2WjBxLk2SS7/9otwvuiTux5sGY1eDx6ehdHpkr7GVSpB754hzPvRLxMlk5HJnTx48ITdu/9m//4jFChQgM6du9C6dWu8vb3Zt28fgYGBeHh44OrqiqWlJX5+fvj6+uLn58ubN29YunRZqp7f8vREGihYsBCvXhlTV3YqYU1xJxWLr4Rw8EkkOoOgmocFn1W0pVnhjImjf/hckaTCAP+F4z1LflrByVrBwaG2zDsaxapzUQRHGs2vTUuo+LKxJVUL5q7L5LNuUYRFSMz63Qq93ugzo9cbnZ6+HBjBiN6ywvCuREZKZhUGAGGAkJBsHbAlI5OlPH6mZMEaa7b8ZUlImIRnAT0Du0UwoFs41vEeI8WKFWb06EEMG9aPEyfO8tdfe/jxxx+xtbXF398fEDg5OeHs7ISzswNOTo44Odnj5uaIp2c5li5NnYyypSENbN26lcOH9/HDD19lyfar97DlwXMFSQXyS5KgbiU9u39LeZ4FnV4QGCGw1khYa7KR7TsD8AuU2HZQwxtfBe4uBjo0jSaPszwPnRqEgFIVCuLtk7SCqVQIvhwdyMRxgZknmIxMDuHidRXtBjoRGSXFmuITSBJULqtj54oAbK3Nj08+Pn6EhYXj5OSIvb1tktbt4OAQPD2r5L7S2Nkda2trwsOz7q20a3Pz4T5CQOem7xbqplJKuNoqcr3CAODiKBjUNYopQyP4rFuUrDCkAUmCAX1D4qa+jodBQK8e2StfiYxMdkCngz4jHYmIlOL5BEkIIXH1loqZvyU9pRpDnjwuFC5cEAcHu3SfDo8hd9mdM5nnz5+TP3/awlfSQv920Sz9U4N/MAmcz5RKQSEPA52bZu8a9jK5h6GfB7F7rzV37mriXI+SJBBCYtrkAAoWkJ0EZd5fdDodvr7+eHv74u3th5eXD8HBIdx7BLpAJa4qHaBDQock6ZDQImH8bc+fOkp4lKNf3w5Zug+y0pAGjEpDGitrpQFXJ8GeBWH0+sqa+0+VqJTGNNZ6vUSF4nr+mBmOjVyWQCaTsLMV7N35mlmznVizzo6wMKMZrERxLV+ODqRzh8xJRy4jk13Q6fR8//08nj9/hZeXL35+/tjZ2eHu7oaHhwceHnlxdHQkKFSBkCR0BhVCqAEVAhUKwnFQb0ejeEaorj7u+aqkWSaDwcDDh09Tvb6sNKSB58+fUa1akyyVoXghA2fXhnLikpKz11VIEjSsqqNaWX22CseTeT+wtxPM/M6fKV8F8PyFCksLQcGCOvlalHkvUSgkHj58Sq1adenVqxfu7u5YWycMk5/xaxS7Tkej/88Qp5R8cFKvw0G1nTB9XV5FzSLaUIJixXyBd7fWeXn5cu7cZc6fv8r581eQpNR7JsiOkGmgceMPmTXrK0qUKJLVosjIyMjIZEOOHj3Nb7+tZv/+/aiTyMR26bqeqi3CUUsvcFKvxl51gBBdM/y1n6AVhZAkQcG8ei7v90tR2urQ0HAuX77OuXNXOHfuCt7evlSvXo26detRt25d8uTJY7RwyCGXmYdWq+XNG68snZ6QkZGRkcmeBAUFs3XrHrZu3UPRokXRarVJKg02mgdUzL+IsIDjBOna8iRiCzrx1l9OCIlRn4abVRiePXvJwYPHOHfuKrdv36N06VLUrVuPGTO6UaFCBTSat1mJ05IRUlYaUsnr16+xs7PFxsZ8RkYZGRmZ7IrBQIYXXHrfePHiNRs27GDfvn+oX78+S5b8ToUKFRJte/XqVRYtWsTFixdo16oVO05s4cFdN2MFVb3RoV2vlxg5MIx+XRIm6dPpdJw4cZZt2/Zz69ZdWrRoyaBBn1OzZs0MmwGQlYZU8vz5cwoUyJd8QxmZNBAWAQqJbJXSWiZn4xMIC7ep+GOfCr9gCSc7Qe+PdQzrqMPdOauly7ncuHGHdeu2c/bsJTp27MjOnbsoWLBgku3Pnz/P559/Tt++nZk8eSi2ttZ8MUJw4Hgg2/dbEByioEghHX06RVCmeFw/Bi8vH3buPMDOnQdxdXWlR4+eLF7cBhub5MMy04qsNKSSZ8+ekS9f1oVbymQuBgP8e0PB01cKHO0EH1bTY2WRcdv6Y4cFC9dace+x8RatVl7LyH4RtG6cu0pMy2Quz70lmo+2wNsf9P9lkw0IkVj4p4rNh1Qc+DkKTw85X0lKefLkOYcPn+Tw4VMEBgbTp08ffvhhLo6OjsmuGxYWRokSRfnkky6m35RKaPlhFC0/TLxy7bNnL5k/fxXnz1+hefNmLFq0mPLly2dYTobEkJWGVPIu4ZavRQRrdc+4bAhEg4ImSjc6KvNjLcmHPydw/LKCkXMtePL6rR3XzlowsW80Qzqlb2SAEDBimi3rd1vE6ffSTRV9xtrz9dAwvhxkvpaIjExsTp/TsGKdLTdvq3nlDyGKaIR9FCjfKgd6g4RvkGDoHDV75siKqTmePXvJ4cMnOHz4FF5evjRt2pRJkyZTs2bNJH0WEsPa2pqIiJTdyzqdjnXrtrFmzZ/069ePH3+clyLFJCOQn1qp5PnzZ9SoUTbZdn/qXvC59hIGBAaMKTh3Gl4xXXubbRZ1KKN4PyNPcgpnbijoNMESQ7yKuCHhEl8vsiBaKzGqR/ol0PrrHw3rdxvnImLHNcXUGJmx0IbmDaIpX1JOkiRjHiFg8gwHFi63M82NgwBU4G8FBYLB4u11pDdInL6u5O4ziZKFZGtDDHq9nlu37nPmzEVOnDjHq1deNG3ahPHjv6J27drvpCjEJqVKw507D5gx41dsbe3ZvHkzRYsWTdX20gtZaUglz58/p0OHj8y2uWQI4DPtRWI/b2K++xJNh6jTXLJsgo1scci2TF2iwSDAIBI3J8xaraZ/Gy0OtumzvaWbLFEqhMl0HB+VUrBiiyXzJsuJkmTMs36rNQuX2wGxM8b+91cPvLSDDwITlK659kBByULvt1Lq4+PHmTOXOHPmEufOXcbOzp769eszZsw46tSpEycSIbVYWVkREZF0GYLIyCiWLVvPjh0HGDVqFD179jSVts5K5KdVKtDpdDx79izZ6Yn52odIxGj3cdEj8CaKP/Uv+UTlmUGSyqSFZ28kzt0yXyU0Wgu7jqvo01KXLtu8fleVpMIAoNNLXL0j37Yy5hECfv3dzpTCOyES6JQQqgG7uNMRFur318qwZctudu48yPPnr6hZswb16tVn3Liv8fT0THe/ARsbG8LCwgkPj8A6XgnLixev8cMP8/nggyLs2rWLfPmyj9O9PPqkgn/++Yf8+fPh5uZqtt1+wxv0iSgMMSiAg/o3stKQTfEOSH6QUCrBKwXtUoqlhfkBW5IE1pbv76AukzL8AxTce5Cc2VxAuDqO0qBRCxpUMphZJ/eyZctu1q3bwYwZM6lWrRoWFhnk6fwfDg4OODo68tFHXbGwsMDV1RkXFycsLDTcu/eYSZMm0aZNm0x1ckwJstKQCtatW0vnzi2TPZk6zN98BiAqmTYyWYeHS/IPZ50e8qWgXUpp1ySaZZstExQgi03bj2RHNRnzpCbPr0IS9Gupx9Eu/eXJ7hw5corlyzeybt36TPMZsLKy4vDhf9DpdPj7++Pr64uPjw8BAQE0aNAAZ+fsGf+a9RMkOYyHDx9y8+ZNmjVrmGzb8pKD2QOsBCopHNNLNJl0poCboF5Fvdlyz1YW0KZB+kxNAHzWPQK1yjiAx0epELg6Cbq1TjwcS0YmBhdnA4UL6ZASuY7eIoFVNKr/oiha19Xz/aD3ryrulSs3mTHjNxYsWJglToYqlQo3NzfKlClDw4YNad++fbZVGEBWGt6ZDRs20LLlR1ilINvOYFWRZOwIEn2VhdNJMpmM4PvPo40P8QSKg/H/aZ9FY5eOSUGLFDKw+bdgbKwFIFAqhWlQd3MxsGNJEA528vSEjHkkCYYODEnS4qBQCKys9TT7KJLezfUcmBfFqslaNKkLBMixPHnynAkTZvDDDz9QtWrVrBYnRyAXrHoHwsPDadCgPsuXz8XTs0Cy7YUQDNdeZr3+OQreRk6okNAjmK+uTE9VoQyVWSbtnL+lYMzPGm48fOsU6eZsYMoALb1bpJ+VITah4bBlryXnrqpQKqFRzWjaNol+7wZ1mdRjMMDw8U5s+NMmVsil0WJlYyPYsdaHyhXeP8tCDBERkQwYMIauXXvQv3//rBYnAV5eEBEB+fJBOgRrxCEtz29ZaXgHNm3axO7d25k/f3qK1zEIwWb9C5boHnJNBKFCQVOFG8NVxaildMlAaWXSm+sPFDx5bUy7W6u8AZX5wAoZmSxHCNh3yJLlf9hw844GGxsDHVtHMKB3KHnd329/qmXL1vPgwUsWLVqcrZwNd+6E77+HixeN/zs4wOefw+TJYJtOod1peX7LjpDvwJ07dyhV6t3KYCskie6qgnRXFUQIka0uTpl3o3wxA+WLZbUUMjIpR5KgZdNIWjZNOh/A+4q/fyDVqlXPVmPy/PkwYkTcImJBQTBnDhw6BMeOQSaUlzCL7NPwDrRv3559+46i06XOJJ2eF6cQggtP9Mw5GM3/9kfzzx0dBoM81y0jIyOTEnQ6PSpV9nlvfvUKRo0yfo+fgVavh8uX4aefMl2sBGSfI5YDqFChAu7u7hw9+i9NmtTPMjleBxn4ZEUUF54aUCqMCd10BijiKvHHQEvK5JV1QRkZGRlz6HS6bKU0rFhhfrnBAAsXGqcpstI4Ij9d3gFJkujZsxd//rk3y2SI1AraLYjk8nOjKqo3GBUGgKf+gta/RfAm6P2eq5SRkZFJDldXZy5dupjVYpi4cyf5Nm/eQFgWZ5CXlYZ3pFWrVjx48ISHD59kyfa3X9Zx31ugT0Qv0BsgOAKWncwYj34ZGRmZ3EKfPp04e/YMp06dAsDPz4/OnTtx9erVLJHH1jZ5C4JSCRmcqDJZZKXhHbGysqJdu3bs3380S7a/9ZIOhZkLSy9g8wVZaZCRkZExh52dLaNGDWLatKlERkZib2/P48eP6dmzJ+vXr0+37RgM8OwZPH1q9E1Iii5dwJy7nEoF7dtDKotqphuy0pAKChUqREBAUJZsOyAckvN3DI6UHSJlZGRkkqNJk/rky+fGF198wcKFC3FycqJChdJs3LghzX0bDLBgARQtCp6eULiw8e+cOYkrD40bQ61aRmtCfGIsEBMnplmsNCMrDanAysqKyMisSeVb3E1CaeasKST4wFU+rTIyMjLJIUkSU6aMonTpQgQFvaFAAQ+ePXtJ7dp10tSvEMbcCsOHGy0MMbx8CePHQ69eCSMkJAn++gvq1jX+r1K9tSrY2sKOHVCtWprESheyj+toDsLS0jLLlIa+tdVsvpC0jcsgYEBd+bTmBqKiYO9Bax4+UeFgb6B18wjyepixb8rIyCSJEILXr724evUWERGRWFpaYGlpSXBwCE+fvuLq1ZsEBgZTuXIlunfvnqZtHTkCS5fGbDe+HLBpE3TtCh07xl3m4gJHj8K5c8YkTxERUKECdOsG1umYrj4tZMunixBZG1KSHFlpaahdRMEntZSsOZPw4aGQoG4xBd2qZcvTKvMO7N5nxYjxzgQFKVGpBHo9fDXVif59QvlhagDZKFJMRibbIYQgICCIN2+8uXnzHteu3ebKlZtERERQqVIlHB0diYiIICIiAmtra6pVq8Gnnw6lVKlS6RKGuXix0VKQlI+CUgmLFiVUGsD47KtZ0/jJjmSboefOHeNcz6ZNEB5unAcaNgwGDwbL5GtDZSpZqTRIksS8rhaUcNfx2xEtXsFGNdbeEgbUVTPhYzUaVTbWuGSS5egJC/p+7mr6X6cznk+9gOVrbBEC5kwPyCrx3mvu3FWze48NIaESxYtp6dAuDFsb2Ycoq3j8+BlHj57m9WsfAgKC/vsE4u3tiyRJeHi4U6ZMGWrVasCwYaMpUaIECkXGT9/evGneqVGvh1u3MlyMDCFbKA3Hj0Pz5saDHHOgHzyA0aNh61Y4eBCsrLJWxvjozbnBZjAKhcSwD9UMbqDioY9AZ4CieSQs1bKykBuYPtsRSQKDIeH5FEJixR+2jB4WTP688lRFZhEeLjHkC1d2/WWLUilQKECrhYmTXfjtJ186ts/i4Pn3iNevvfn772P8/fcJ7t17SKFChbCysiIiIoLw8DDCwyOoWbMmP/zwA3ny5MkSGR0cjBYDc5Wd7OwyT570JMuVhqgo6NQJoqPjOobEHOzTp2HGDJie8hpRGc7FixcpU6Z4VouBSilR0kNWFHITz18quXjFfCC2JMHOPdYM/TQkk6SSGTLClb/2GZP+6/WSyfs9IgI+HZIHFxc9DevL9R0ykgsXrrJkyVquXXv7im5vb4e1tQX58uUhb1438uZ1x93dlY0bd9GyZUt++ukn6tfP/Oy93brBmTNJL1cooEePzJMnPclypWH7dvD1TXp5TOrMb75J//KgqeX06VN0794qq8WQyYUEBiVvOlUqUtZOJn24dVvNrj2JlxcUQkKhEMya40TD+q8zWbKMRQjB+ceCdaf1PPMz4Gon0bWGko/KKFCYSxaTzkRGRuHvH4hGo6Zx4zr06tWBfPncyZfPAxsba7y9fblx4y43b97j8OGT3L59H0dHB2rVqoU6i5Ia9OsH//sfeHsnDK9UKo2WiMGDs0S0NJPlSsPFi8awEq2Zsu4BAfD8udHPIasJDQ3l5s1bVKmSDQJm04lHATpWXA7n8JNoDEJQt6CGTytbUyZPFmcReQ8pkE+PUinQ65MelLU6KFxITuCVWez6y8bsOTEYJM6ctcTHR0GePLkjhbveIBi+RsvGs8b6NnoDKBWCrecN1C0usXGoBlvLjFccgoJCaNbMGMkwatQgQOL1ay8ePXrGnTsPuHnzLuHhkZQrV5aKFSvx6aefU6FCBdzc3DJcNnM4OBgrUrZqBffvvw2d1GohXz5jaKWHR5aKmGqyXGnQaMzP+8SQ1akzY7hw4QLFixfB3j6HTkjF4697kfTfFYgQRkc7gEf+Eay6EsEvH9vTp0I2ifN5T3ByNNCmRTi791kn8ZASWFsL2rYMz3TZ3ldCQxUoFOaz+QGEhuUepWH2Xh2bzr6tbxP775kHgpFrtSz/NGNNv35+AQwf/jV2dnY0atSQ58/9kSSQJAUajYZmzVozbtzXFClSBGViGZGymOLFjQ7+Bw4YQzANBqhf36hI5OTopywXvWVLmDkz6eWSBKVLQ/786bM9X19jNbGdO43+FNWrw5AhxljYlHD69GmqVUth42zO00Ad/XcFojdAbL1N998/I/cHU8FNTUUP2eKQmUybGMjxU5YEBSviKA6SJBBCYu50f9ljPxMpWlRr1hMewMrSgLtb7nBMjYgWLDysJ6krTC9g+yUDU/0EhVwyztoQEBCEg4M93t5+2NnZM3Xq1AzbVkahUECLFsZPbiHLJ0br1IEaNZLWvISAr75Kn7wN588btb+vvjI6WF68CMuWQcWKKa9TrtVquXfvEdHRZuZTcgjLr0QgBEkODkoJllySvcIzG89Ceg7vekOLphEoFG/PTqkSWtYt86F7Z/mcZCadOoRiYSFI6k5RKgXdu4ZibZ07FLlLTwUhyfh0CgFHb2esklSsWGEWL55FpUplKVOmTIZuSyblZLnSIEmwa5fRmgBv827HKBHffgu9e6d9O6Gh8PHHEBISN0oj5g1i7FhjaGdyTJw4EUlS8/XX/0OX3OtHNufY0yjTlERi6AQcfRKdrtsMj5BYu8WKboOcadXThfHf2XPzbpYbvLIdhT31/PG7L3cuvOTQrjecO/KKUwff0LJZRFaL9t5hbyf4eY4vkkQcJQ6MCkP+fDq+Gpd78mZodckrPxIQnQmGlcjISC5cuErDhg0zfmMyKSLLlQYAd3e4dAm2bTOm1mzdGkaONM4HffNN+mxj7VqjQ2VS85JKJcyenXw/FhYWLFy4iKgoPd98MwedLueaJFPiS5Ke705Pnimp+XEeRkxy5NAxC06ft2DFehvqtXZjzoLEvdPfd/K4GqhaKZriRXXZOktqbqdb5zA2r/OiSqW3Sd0sLQ306RXCob2vco0vA0DZ/Aqz9W3AOC5U8cz4x8e5c1coVqxoljs2yrwl27ziqVTQoYPxkxEcPmw+2YZeb8z5nZIU1lZWVixevISBAwcyffovTJs2Jt3lzQwaFNJwy0eXpLVBpTC2SQ/0eug80IXXXkpAMlXqjJmzn/GzPcWK6GjfQo51l8meNGkcQZPGEbzxUhIaKpHXQ49NLvQtyWMv0bGqgm0XDSbnx9goFVAuv0SVwhmvNJw8eZ7GjT/K8O3IpJxsYWnIDPT65N+s41cdM4e1tTVLly7l0KHjBAUFp024LGJAZfOREXoDfF7VJl229fcxCx4+USUZtqZQCH75XbY2yGR/PNz1FCuqy5UKQwyzuqop6iYRPx2DUgFO1rBsYMY7R9+//5gjR07RtGnTDN+WTMp5b5SGOnXMWxAUCmOBkHcxAdva2vLBB4V59OhZ2gXMAoo4qfi9tQNKyej0GINKYZyznN3Unsp502dw+OekBSpV0oOswSBx5YaGwCDZBi8jk9U420r8PV7D5HYqCjobx4Q8djC8iZITky0o5p62R0dwcAirV28mKipxn6lnz14yatQ3jBs3nhIlSqRpWzLpS7aZnsho+veHqVONaV8TszgYDDBq1Lv3W7x4cR49ekrlyuXSLGNW0LG0FWXd1Cy7FM7hx0bHyPqF1HxWxYYK7un3NhFTdCll7VL3Bnc6LIgF/i84GhqAAahpbc8Q5/w0t3NJVX/ZAYMBTp/T8OK1EldnAw3rRJFFSe5k3jPsrSRGN1cxunn6PyYkSWL58g38889pZs6cQP78eU3L3rzxZvjwyXz66Wd07do13bctkzbeG6XBxcVY/Kp9e+NAHBP4oFQapy5GjoQuXd693+LFS/Dw4f10lTWzKemiYnZT+wzdRtUK0azcYG46RFAgnx4X59Q5lC3zf8W4Nw9QIhETYX4yLJBjYYGMdS3IZLcPUtVvVrL/H0vGTXXgxau3t6mLk55vJwbTq7Oc3Cm78PSZkjUbbbl9V42VtYHWzSNo1Swi26S9z2oCAoJwcnKI85udnS2NGtXlxo179O07murVK9K1axsKFcrPiBFT6NKlK/37988iiWXM8d5MT4Axwcb16/D558ZkUa6u0KwZ7N0L8+alLhdE8eLFefz4efoLm8vo0CoSB3uRIGQtBkmCz/uGpeoc3IkKY/ybBwDETkkTE9cy1/c5x0JzVkjcoWMW9PzMmZev42a68wtQMHyCE2s2yZk6swO/r7SlcoO8/LLYjn2HLNnxlzUDhrlSp5kHL15lvyyFmU1gYBDt2/fn2bOXCZa1bdsUhUJiy5YtFC9eljlzFvPFF1No3LgJw4cPzwJpZVLCe6U0AJQoAb/9Bi9egI+PUWFo0SL1yaNKlizJvXuP8PPzT19BcxnWVoK1C/3RqI2x7THEKBEtP4pk8CepS1q03P81CpI+gUpgiX/CQSu7IgRMmuHw3/f4+2WcvvlmlgNRUQlWlclEDhy2ZMI0J4SQ/nPwlUzlzJ88U9H5kzzv5FydXRFCoDWkbspw+/Z9REZG8fTpiwTLqlQpjxAG9u/fz2effcazZy+pVKnqf7lwZN+m7Mp7pzSkNwULFqRLly6MGjWNkJDQrBYnW1OvZjQndnvTr3s4zk56rK0MVCqnZdGPAayeH5DqfOznIoJIOumt0eJwPiLnlJG+fkvN/YfqRBSGGCSCghUcOmaZqXLJxGXeQvskLWd6vcTd+2oO5+BzdDcoiqH/vibvpvu4bbxHme0PmXvTjzBdyjSh6GgtW7fuoVSpkrx48SrBcoVCwU8/TWXfvr8YO3YMO3bs4LvvvpMVhmxOrlcahDD6MjRqBI6OxspiQ4fC3bvpt40JEyZQtmx5vvzyeyIj5TwD5ij2gZ4504J4eM6Ll9fecPhPX7p3iCAt9WY0UvKXsToHDURvvFNyWwre+Mjm76wiNEzi7AULk2UhMVQqwYHDOVNpOO0dTqP9T9nyJJio/6wMryN0zLzqS4u/nxGsTT6p3YsXr4mO1tGiRUueP0+8bLinZwEWL57F0aPH8PT0zJaFp2TikquVBiFg4ECjg+PJkxAUBF5esHSpsd5EStJGpwRJkpg+fQYuLm5MmpTz00tnJPcVwazRPGCB6g7fh99hkv89Zgc+5qE29Y59zW2dzV7IKiQ+zkERFHk9UpJlVCKve87NRprT0WpTpoRGR+ccZTUGrUHQ/+QrovWC+BmlDcCtwCh+uOaXbD8ffFAQa2srgoKCePHiTZLtrKwskSRJfuHKIeQ4peHFCxg3DtzcjGW1ixWDOXMgLJHp8JUrjR+Imz5apzPWNe/Y0ahIpAcqlYp5834mOtrA9Om/YMgNk5npiI8USSebf6hhv5uRVmeZbHORn/JdZLHrVWaF3qfay38Z7nsLrXj34/aJU16sFMpEL+aYIfsz53xpkj8zKVdKR+kSWhRSUlMuAmcnPU0ayINsRiIE7N5jTduOHnxQshClKxZk4tfOPHqswtHBQL68OsyFB+v1ULF8+tZuyQz2vQjFO1JPUneiXsCah4GEJzNNIUkSH31Uj5s3b/L06XP0SeTwf/z4OaqcXCv6PSNHKQ03bxotBPPmGZ0YtVp4+BAmTIC6dRMqAOYiIgwGCA+HP/5IP/ksLCxYvHgJT5++Zt68pYiUFHd4DwhHRxvbvzmm+u9tQ8L0NBd20RiK+YPCwPrQ10z0u/fO/bupNGwpVA4bhTKOO6QC47TEqgKlKWWRPpktMwNJgllTAo0FkhIoDgKQmPF1kBzSl4EIAV+MceWTAe6cPmNJYKCSN29ULFtpT91G+Tl5ypLB/UKTHF8kSWBtJejaIeeFxl4PiEKVjIEkXCd4Gpqw0u+9ew+pWbMVH3/ck717/6F8+ZLcv38Pd3cPhg+fjI9PXAtFUFAwX375HRMnTsTWVs4ImxPIMUqDEMZphqCghEWnDAa4ccNogYghKsr4m7nntkIB//6bvnLa2tqybNlyzp27yowZv8omN2Cz5jF3FcHoE3tzlgALPThFIoDVoS95o3v3sIDa1g5cLV6D792L0MTGiQ9tHJmYx5OrxWrQyt41zfuQ2TSoE82WlX58UDjuVFdedwNL5/nTvYNc7TIjWbvelrXr7QDipD7X6yWioyV69XOnV9dQPqwfiSQJpFjXtkopUCph+Xw/7Gxz3ouDhVJKUXo1S2VCzaJo0cJ07NiSgIAgvv12LhMnzsTfP4CpU6dRqVI1PvlkJGfOXAJAp9MxadIs6tWrT69evdJ5L2QyCkmk8nU4ODgYBwcHgoKCsLfP2MRAAMePQ3LVUS0s4M0bo8OjVkuyb2JKJfTqBatXp5uYJgIDA/nqq4k8ffqEmTMnULhwwfTfSA6hue0Bzit9STIYQADharjvggTMcS7JAPsCmShh9kUIuHhVzYtXSvK4GKhVLTpNTqMyKaNW/fzcu28ugkUw90c/+vQMYdV6W5autuXBIxUWGkGbFhGM+CyE8mUTvonnBK4HRNJg39Mkl0vAB3ZqLrT+IMlIByEEp06dZ9WqzVy/fpt169ZRrVo1jhw5woQJ45k0aQR37z7i8uXbrFnzBxrZbJappOX5nWMsDefPGy0D5oiKMk5hAKjVUL8+ZgdYvR4yqhaKo6MjCxcuokuXbnz66Zfcu/coYzaUA/CWIpNWGMA4CqmN5iMFECpkB78YJAmqVdLSvmUkdWvKCkNmEB4ucfeexozCYBxXzp23QK2GQX1DOffPG/wev+D1vZf8/ot/jlUYAMo7WdLQw5pEDAmAUcf/sqyL2dBISZKoV68Gy5bNoWPHlpw5cwaAWrVqIUkSvr7+bN26hzlz5soKQw4jxygNGk3yVSpj2sUwfnzCqYwYlErImzd1qaNTiiRJ9O/fn0KFChEYmE4elzmQQgYblOa0BgFEGZ+GeqC4Ws52KJN1JPdyAkY9N74Cl4OiepNlVb18VHExhouqpP/297/9+6q8Cz2KOCS9cjycnBwI+89Tfdu2beTP78H69TuYPHkyBQrIFsWcRo5xWW3WLHmlwdXV6CgZQ+vW8L//GR0lVSpj1ETMje3sbAy5tLDIOJljCA8PT7Ka2/vAJ9HFOK72SrqBBPhZowDyKDU0tco54ZEyuQ9LS0H1apFcvJR0HgadXqJRg9zrV+KoUbK/aSGOvgln+9MQgrV6PrDT8ElRB4rYvZtlwMrKCh+fMPR6PStXriAiIpzq1WvStm3bDJJeJiPJMUpDyZJGJWDfvqStB2PHJvRjGD8eWraExYvh4kWwtoYOHaBPH3BIubKcJoYOHcZvv/1KrVpVUKvVnHoTwfwbgRx5FYFBCKq6WjKkrANtPG1yZTa0ttpC1NXe51+VNwnGYAGEqVEEWqJAYpFrGVQpSNYkI5ORjBwWRO/+7okuUyoFefLoads6dWnPcwoKSaJxXhsa501b5JG1tSURET4cOnSI589f4Obmxrfffpsrx7r3gRzjCAkQGGisE3HmzNvqlDEWhEGDjIpBSkyLmY0Qgt69e1GzZnm0NZrx5b++KCVjvDNg+j60rAPTq5ufK8yphKPjG6tLrNU8JEr6L77bAPhbwSs7Gmtc+cqpCNUsMkmTk5FJhjnzHJgxyxmVUqDTG2t+SBK4OBvY+edrypTOuX4LmcnevYc5deoqbm5urF+/gdWrV1OrVq2sFuu9Ji3P7xylNIBRUdi7F9avN+ZqKFrUmPWxRo1MEyFV3Lt3j27du3Ozw3i0tk5JttvUxINmBXNOToF3JUiK5rLSGKtdTOuAVgeOCjVOSnUWSyYjk5DrNzSsWG3H1WsarK0EbVqF071rKA4OcvK2lHLkyCl27TrCsmXLefbsGR98kPPK1Oc23iulISfTbMiX3Hz6gldNE68Tr5SgYV4r/myec7IXysjIyJjj5s27DB48gTJlStOtW3c6deqU1SK997wXIZe5gaAaLbF8/RDrF4lXy9ILuOQr1zuWkZHJPZQtW5K9e/+gc+ePmTVrllybJ4cjKw2ZiKWNLT612pHn5FbQJ37jqBVGf4Zog2DPyxAW3/dn87OgFFWVk5GRkcmO2Nvb0axZQ5ycHLh8+XJWiyOTBmSlIRNpXSQPYcWrobeyxen6sQTLVRK0KGTN9ufBlPrrHr3/fcHXV70YfO4Vpf66z893fOV6FjIyMjmWmjUr86+Z3P2RkbBiBdSrB56exr8rVhh/l8keyEpDJjKgbH7sLdX41u+C86WDqEIDTMti4iXKuKoZePYlAdFGR6sYd6sIveDbGz78cjf5krQyMjIy2ZHChQvw7NmTRJcFBhoLDw4caKwJ9OyZ8e/AgcbfAwMzU1KZpJCVhkzExUrDgY5VscpbiOCSNchzehsIgUICtQJWNnJn2WN/s338eNuXEHmqQkZGJgdSoEA+nj59luiyzz+Hq1eN3w2GuH+vXoXBgzNBQJlkkZWGTKaGhyNPBjZkwphRuIT5Ue3mXiZWcuJaF088HVU8CI02W2EuQi/Y/zo00+SVkZHJHQQEKDh91oJzFzVEZZG/dcGCeXn2LKHS8OoVbNmSdOI+vR62boWXLzNYQJlkkZWGLMDeQsXYOmX458+NOL+4g/3Zvbhbq/CPSt6CoAD8UtBORkZGBiAoSGL4l86UrJqfVp3dad7eg1JV8zPnF/skH9IZhZtbHkJDwwgKiluL5/Tpt1aFpDAYjO1kshZZachC8uTJw+rVazh48DirVm2igHXyCY4MQCEbORGSjIxM8oSFS7Tq4s7GrTZotW8zzQYGKZk5x4EvxjmnqBBgeqFSKcmfP6G1IRcmwc21yEpDFuPh4cHq1WvYvv0A5/7aQw0XK7MnxUWjpImHbabJJyMjk3NZscaW23fU6PUJn8oCifVbbDl/KXNLUxcokFBpqFMnYdXQ+CgURodImaxFVhqyAQUKFGD16jWsW7eDj59fRq2QEpwY6b/P7MoeaBSyWi4jI5M8K9fZYjBjSVApBWs3Zt5LiJ+fPw8ePMHKyirO73nzQteuSSsOSqVxeT45WW6WIysN2YTChQuzatVqDm7YxuSoB1R3iXtTFbfTsK5OAToUlFN2v0+EhUt4+SiIfn8rq+cYIqPh6WsJn4Dk28bm+ks947ZG0ea3CHoujWTTeR2R2vSZM3j5SsXbgO6E6PQST55lTrHjyMhIvvxyOi1atKRx48YJli9aBJUrG7/HFB6M+Vu5srEgoUzWk2NKY78PFCtWjJUrV9KvX18mTBhKoebVeRGuxcVCSVkHi1xZ/VImca7dVPPjr3bsO2SFwSBhbWWgV5dwvhwejFseuVhSdiIoFGat0fDHPhVhEcZ7tFppPeP7aGlaI2lPQyEEM/ZqmXtQi0oBOgMoJNh3Q8/sAxI7h1uS3zFt73WODga8fZK2+yuVAleXjPeGNBgMTJv2E3nz5mf8+PGJtnFwgJMnYdMmWL4cXryAAgWMeRq6dQMLiwwXUyYFyAWrsiG7du1iy5b1/Pbb9KwWRSYLOHnGgk6fuKI3EGcuWqkUeLjpObTDGw+3lCsOAYEK/j2vQa+TqFg+mkIF5Oib9CIoFJqPtOTBcwV6w9tzpZAEBiHx29goerdIPGX8xnNahqxL3ISkVEBpDwXHx1um6WVh2g8OzF9in6hPQwzrV/jQomlEqreREn77bQWXL99m7dp1WFtbZ+i2ZJJHLliVy6hevTo3btxBn9nxULkIIeDBK7j+GILDs1qalKPTwaCRzuj0JBjo9XqJN95KJs9wTFFfkZEwboojparlo9enefjkc1cq1ctLz09d8PaRb/30YO56dQKFAcAgJEAw9hcN/kEJ1xNCMO9vbZITB3oD3Hhl4NSDtFmVBvcPxdHBgFKZ8N1QqRRUrxpFs8YZqzBs376PQ4dOsnjxEllhyAXII0c2JG/evNja2vHoUeKZ02TMs+UEVB8pUX2kggbjFRQfKPHFIgm/4KyWLHn+PmrJG28lBkPijxO9XmLHHiv8/M3fugYDfPK5KyvW2hIdK9ROCImD/1jRorMbQcHydFda0Opg9R51AoXhLRJaPWw6lHAW+HWQ4J63MJvITaWAQ7fT9uKQ10PP3j+9KFlcC4BCIZAk41abfxTBljXeyUYtvCtRUdGcOXORn376nS5dBrNkyVqWLPkdNze39N2QTJYg+zRkUypVqsT27fsYM+YzVCr5NKWU+bthyhoFUqzhOFonsf6o4NQtiUMzBU52WShgMty5r0apFGbNyXq9xKOnKlyck/aOPHrSgr+PWCW6TP+f89uKtbaMHhqSZpnfV/yDJYLDzCteKgXce55QwdOlwIAgSaDTp90hskQxHScPvuHcRQ2Xr1qgUgkaN4ikyAfpV6L61SsvTp8+z7//XuLChat4ehaiYcNG/PDD/6hYsSJqtZxbJrcgP42yKV9++SUTJ07gk09GUadOFfLkccXNzYU8eVzIk8cVFxcnVKp0fkXI4bz2h6l/GAdxEc/wqzdIPPUWzNsh8V2f7Fsp1MbKkGxmPABrK/ON1m+xMat8GAywZoONrDSkAWvL5K8jAdhaJWyXz0HC1RZ8zWSE1+qhsmf63OOSBDWrRVOzWurCcCIjJe7cMeZzKFUqGsv/9l0IwapVm1i7dht16tShRYu2zJw5G3d393SRWyb7ISsN2RRPT0/WrVvP/v37uXv3Lo8fv+Hff6/i5eWFt7c34eERVK5cnqlTx+Du7prV4mYL1h/FGF2WxFiuN0isPiSY2jP5RDJZxcdNIpn4nbkWAs8CekqXMP+W+PK1yqy1AiS8zHjVyySPnTU0qqLjxBVlklMUOr1E+4YJpxhUSonPGqiZtU+baB4FhQTONtCmQtaeo+homDXLmeXLHQgONspib69n0KAgxo714bfflnDp0k127txFgQIFslRWmcxBVhqyMQqFgpYtW9KyZcsEy0JDQ/n999/p23ckU6aMok6dau99SObjN8akWObewYPDJYLCBc7ZdIqiUAE93TqEs3mHdRJ+DRLjRwab4teTIq+7PplpDkEeFzl0M62M66Pl+GUlkiQQIu6xVioE9SvpqVIy8eM88iM1/z7Uc+SuIY6uq1SAhQrWfmqJRpV197ReD7175+Xw4bjXYnCwknnzrDh8eB7Fi/uzYcNGnJycskxOmcxFVhpyKLa2towZM4aqVasyceIEgoNDcHZ2wsnJgcKFCzJlysj3bh7RwRpzeWwAYyicdTaP9543M4CICImd+6xRKQVIxukECZgyLoienZMPB+nROYztfyXtqa5QQJ/ucrXUtFKnvIGVU6IY+qMFYZECtcoYuaPTSzSsomfllKgk6ypoVBKbBluy/qyOZSd0PPAxYK2BTlVUfN5QTZE8Weun/tdfNvz9t02C3xWKQPLn/xxvbycmTlyNk1PivjMyuRM5T0MuQAhBUFAQfn5++Pr6Mn369wwc2JVGjepktWiZyoX70HRS0gOtUiFoWR3WfJl9fRpic/2Wmj93WxEYqMCzkJ7uHcPI654y64DBAJ365OH4aYsEFgulUpA/r55je9/g6JAzjkV2JzQCth1RceeJAmtLQZv6eioWz9mWnA4d8nHihBWS9AInp9VoNE9RqV6gVr8kOLg9fn7f0LSpin37slpSmXclLc9vWWnIhaxdu5Z169bQsWMLqlatyAcfFHwvpi6EgG4/SPxzlQRzzApJoFLCwRmCikWySMBMJjxCYuJURzb8aYNO9/Z4NG4Qwfw5/ilWQGTeTypU8CQw8CBubt8SHNyeyMiKaLUF0GoLoNe7AFCkCDx8mMWCyrwzstIgE4fo6Gi2b9/Ov/+e5uzZc2g0ambOnEDZsiWzWrQMJywShsyX2H1WQqEQKCSjqdjVXrBslKBh+ayWMPPx8VVw8owFOp1ElYrRFE3HUDuZ3El4eARNmqwlMvIsr1/PITKyWoI2kgTVq8PZs1kgoEyakJUGmSQRQrB7926+//47pk4dQ716NbJapEzh/kvYdwEioqF0QWhRDdSyB4+MTIoYM+ZbXr604/DhH9DrHRJtI0nw668wfHgmCyeTZtLy/JaH0VyOJEm0bdsWd3d3RowYzpAhn9ChQ4usFivDKZ7f+JGRkXk3Hj9+xrVrt9m9+xi1atnw6pUxvXlsVCpjMam+fbNGRpmsQ04j/Z5Qs2ZN/vhjLStWbGbJkrWk0sCUbQmKNLDwbDjNV/tT93c/Bu8M4twLbVaLJSOTowgODmHBglV07NiRvHltOH4cKlY0LlMq3+Y3qVwZjh8Hu2wauiyTccjTE+8Zr1+/5tNPB9Kq1Yf07Nkhq8VJF2776Gi3NgDf8Le5/GNKDY+oZc23jW1ypSPoG28Ff2yx5swlNUolfFgnip4dI3Cwz10KoUzGYzAY2L37bxYtWkOdOnWYMuUbU+4FIeDcOaOSANCwIdR4P2Y5cy2yT4PMO3H8+HF+/PEH1q79LatFSTNavaDyQj/ehBhIKk3/4rb2dCtvmbmCZTB//W3BwNFO6PT8F1IpkCSwsxVsWepPjcqylUUmZdy8eZc5c5ag0wm++eYbqlevntUiyWQwcmlsmXeiVq1avHnjw7NnL7NalDSz734UL4OTVhgUwK9nwjJVpozm1j0V/Uc5odURKweDhBASoWESnT91xjeZKpgyMv7+gUyf/gujR0+jU6eubN++XVYYZJJFHlneQzQaDU2afMThwyeyWpQ0c+yxFpWZq9gA3PLWExSZe3ISLFljA4IEaYvBqESEhUms3Spn6cvOPH6i4u9/LPn3nEUCJ8OMRqfTs2nTLrp1+xxLSwf27z9A79695Wq6MilCvkreU9q2bcfkyV/zySddUGbX6k0pwJDC2bXEigLlVPYfsUBnphiVQcCBo5aM+ix3WVhyA/cfqBg7yZkTp94qdW559EwYE0j/PqFxUk5fuabhyHFL9AaoUTWa+nUik0xJnVJCQ8OYPPlHgoPDWbFiJeXLv4eJS2TShKw0vKfUrl0ba2sbjh07Q+PGdbNanFRTs6CaVZcjk1wuAR84KXG0zD2OkFptcvsiEZW6CsgyGcjjJyqatvUgNDSuaczbR8nYr1wIDFIwZkQwXt4K+g3Ow5nzliiVRm1Xr5coXlTLH8u8KVk8daaJV6/eMHbs95QtW54lS2ZgYZHNi7DIZEvk6Yn3FEmSGDBgABs27MhqUdJE+9KWuFhLKJN4jgpgaE2rXBU9Ua1StOlhkhhKpaCm7AiZ7Zg5x4HQUEWSlUdnznbk+QsF7bq5c/6S8YGu10um9o+eqGjVyQMv73cftq9du8XAgV/Spk1bZs+eLSsMMqlGVhreY1q3bs3r195cu3Yrq0VJNZYqiQ1dHbFSx1UcYr73KG9B/yq5a35/cJ8wMyWvjcWqBvSQpyayEyGhEjt225g9b0LAtz84cfe+JtF2er1EQKCCFWveLTnCpUvXGTPmO6ZM+YahQ4flKgVaJvORlYb3GI1GQ58+n7Bu3Y5Urf/w4ZNskSSqen41/w52ZkRtawo7KnCzkWhQWM26zg4saGOPIpcNkh/Vj2bkIGNZ69gWB6VSIEmCOVODKV5En1XiySSCr68yTtGwxFAq4dwFSxSKpO8pg0Fi45+2Kd6ul5cPX3/9P2bOnEnLli1TvJ6MTFLIeRrec4KCgvjww0asXv0LBQvmS/F6V6/e4rPPxlGiRFH69etCo0Z1crRDZU5k72ELFq+24dxlDQqF4MO60QwbEEqdavLURHYjMFBBkXIFEo14iUGpELi56XntZd7VzMFez5NbL5LdZlRUNIMHT6Bhww8ZPXrMO8ssk3uRkzvJpImZM2fg4/OSqVNTPrB8++08ChcuQdGiRVm8eBFabTRt2jQhNDQMb28/PvywDvXq1ZBNoTIy/9G9bx4OHbFKcopCkgQfNwvn78PWSUbHKBSCCuWiObL3jdltCSGYMeMXAgMjWbJkiazQy8RBTu4kkyaGDBnK6dMXuHHjTorah4aGcfToKbp27Urr1q3ZtWs348ZN4OlTX5RKO0qVqsjPPy9n6NBJ3Lp1L4Oll5HJGUwaF4hSSaLTD5IkGNQvhGGDQsyH0xqgf+/QZLd1+PBJrly5zdy5c2WFQSZdkZUGGZycnBg+fATz5i3FYEg+CVJ0tBaDwYDdf9VqFAoFTZo0Yfbs2YwbN47PPvuMffv207x5K0aPnsbkyT/y8uXrjN4NGZlsTYVyWnZu9sKzYEzIpFF50GgEI4cFM/PbAOrUiqJHl1AkKaFioVAI6tSKonvn5JWGU6cu0Lt3HxwcEi9rLSOTWmSlQQaAHj16EBmpZf/+o8m2dXZ2pFy50hw6dCjJNhqNhn79+vH334coXLg4vXuP4NUr8yZVGZncTq3qUVw89YrdW97w0yx/fp/vw93LL5j6ldEKIUkwf64fU78KJI/rW2dWWxsDQwcFs/UPbzSa5Ldz8+ZdKsaUp5SRSUdknwYZE//++y9ffjmWzZsXY2Njbbbtzp0HOHHiEsuWLU9R382aNeX777+kZMmi6SGqjEyuR6uFew/U6PVQvKgOK6uUDdVBQSG0atWHixcvyvkYZBJF9mmQSRdq165NxYoVWb16S7JtGzWqzb///kt0dMpSD0ZERGJllbsqTcrIZCRqNZQtraVCOW2KFQaAW7fuUqpUSVlhkMkQZKVBJg5ffTWJP//cw6tXXmbbOTjYY2try5s3KZtysLe34/nzV+khYq4jKEKw7ISW0ZuimbQtmpP39dki/4VMzuT+/ceUKlU6q8WQyaXISoNMHAoWLEjXrt1YsGBVsm3z5/fgxYvk48UBevfuw7p129MoXe5j1xUdpSdHMH6LlrX/6lh6XEeb36Jo+lMUPiGy4iDz7hQsmI/Hjx9ltRgyuRS5YJVMAoYOHUrTpk25evUWFSuWSbJdvnwpVxo6dOjAr7/+ys2bdylbtmSqZXuijWB/mC/hQk9pjS1NrZ1RSTlT9z37SM+AldEYhNGPXhcrcOXKcwNdF0dyeKwlCoWc6yKjEEIQHByKv38A/v6B+PkZ/yqVSvLmdSNfPnc8PNywtrZKsF5gYDBeXj54e/vi5eWLt7cvbm6uNG/eEHt7u3jt4fFjFRGRCjwLabG1zTiFsEqV8nzzzRwiIiKwsspdKdRlsh5ZaUgHtFrYuRO2bIHgYChVCgYNgjJJP2+zNXZ2dnzxxRf8/PMyli+fg0KR+EM5b143nj9/nqI+LS0t+eSTT1i7dhs//PDVO8sUbtDzhc9ttoV6IwESEnoEHkoNy9zLUtfK6Z37zGrm/a0FyfhAiY/eAFeeC47dM/BhKTnOPj0RQnDv3iP27DnMgQNHCQ0Nw9nZGVdXF1xdXXF1dUWn03P06DlevnyJl5c39vZ25M3rjpWVJd7eRgVBkiTc3d1wd3cnb958uLu7c/78TRYsWEWjRnVo164ZlSqVZctWO36c7cTDh8awB0tLAz17hDD5a3+cnJIPcX5XHBzsKVy4IJcvX6ZOnTrp3r/M+40cPZFGXr+Gpk3h5k1j7ni9HlQq0Olg6lSYNi2rJUwdOp2Odu3a0qtXO1q2/CjRNrt3/83u3f+wbt161Gp1sn0GBQXRuPGHrFw5j0KF8r+TPD1fX+NAuC/xh1gFoJYUHMpfjXIWKc/Jn5Vo9YK/rugZsFILCP7TghKgUkDv2irmdUtBjJ1Milm2bD0bNuykS5cudOjQgRIlSpjNXKrVannz5g0vX74kIiICDw8P3N3dcXJySnS927df8+OPf3LlylYMBkdu3lqCQeSJk0JaqRQUK6rlwP6XODgYMBgMnDt3BY1GTeXK5dKcSfXnn5dib+/OmDFy+miZhMhppLMIIaBmTbh82agkJMaaNdCnT8bK8fAhXLkCFhbQoAGk1+kwhmCOYdOmJdjaJgzBjI7WMmLEZMqUqcDUqVNT1OesWbN4/vwhM2ZMSLEclyKD+ejlhSSXK5FoY5OHlR7lUtxnVnHqnoEBy6PxDoGY5D5GjUGAUsRRHpQK6FRFyZJPZC/49OT+/ccMGTKRLVu28sEHH6Rbv0LAzJnw3XdG66NSqcPR8SdsbQ/x4uVKdPq4tV2USsHwYV5UrrSNDRt2YjAIdDodSqWCtm2b0b172xQp44lx6tR5fv99Azt37kqPXZPJZcghl1nEyZNw/nzSCoMkwQ8/JG5+Tg+ePTNaOYoVg86doU0b8PCA8eONg1ZaqV27NlWrVmP58g0A3LqlYfNmW3butCEwUIFGo2bWrEkcPfoP69evT1Gfw4YN4/btB+zb90+K5fgz1AtVYq/i/6FHsDvMmyiR/qbe9OTmSwMd50fja0roF8/EoJfe6hEYr5tSeXP/LWr0Kwjh/v3HnDlzET8//wzdXvHiH9C+fXOWL09ZjpGUMmcOTJ4M0dHGc6fTqfD1HUdISBsKFuiDWvl2Kk+hCMDBfiF7/urEgQOnGDduAvv3H+DQocN8++33nDp1iWnTfkKvT1210po1K+Pt7c2tWzm37L1M9kT2aUgD+/a9nYpIDCHg9m14+RIKFEi6n8hICAoCR0ejtSAleHtDnToQP+IxIsI4eL16BWvXpqwvc0yc+BWtWrVm9+7eXLxYzPS7RmNgwIBgvvsO5syZwpAhX2FtbU379u3N9mdnZ8ecOXMZPPgzKlYsQ758HsnKEGjQkpzepQfCDHoslNn3IfvzQR16AxgS3ZkY5eHtQoUEvWrmvls0NDSM8+evcObMZa5du8Xr195otVrc3d1wdnbm/v0HlChRlEaNatGoUW3y588LgMFgwMfHnxcvXvHixWtevXpDo0Z1KF26+DvL4OzsxOvXQem2T2FhRgtDQiT8/EZgMFhSoEBvAgIHotE8ws5uN2FhDXn+cjEXL5bH3v6t8linTh0qVqxIv359+fHHhUycOPydpytUKhUtWzZmy5YtKbYCysikhNw3ImUi0dFGa0JK2iXGw4cwfTqsX29so9FAz54wZQoUKWK+z59+MioMib2ICAHr1sHIkVC9evLymUOIfPj5DUShmIyl5TdERlb+b58ULFnigLe3kuXL4ZdfvmPMmG8JCgqib9++ZvusUqUKffp8wtSpc1m0aBYqlXlHv8IqK0hGbbCTlDgosu/lrNMLdlw0oDdrDBFgkFCoBAYBc7tqcLPPHZET9+494sSJs5w9e5nbt+9TtmwZ6tdvQNeufShYsCCurq4mh9uwsDBOnDjBwYMHWLFiJO7ubgC8fPkKhUKJp2chPD09cXXNw4gRU+jduwN9+nR+p8JMkZFR6RpZsH8/hJopCREQMIjo6A+ws9uHTu/K02e70Onyo1JBYmLY2NiwdOkyevfuxcKFqxk2rN87y9S2bTMGDvySCRMmYGkpJ1aTSR9kn4Y0sHEj9Ohhvo2zs/HhHn9q8uZNqFfPONDEtlSoVGBnB6dOQWkz+VlcXcHPL+nlKpUxgmPhwuT3wxyjRsH8+dHY26/GyWkV0dFF8Pf/jPDwesS8HR8+/JwqVaJ48uQ5X3zxDZ07d2HEiBFm3450Oh29evWkevVyfPppT7MyvNBFUuHp6STVBiXwuUNBpru++xtnZhEaKSg4JiqZVkafhjolJMY0U/NR6ZwfNSGEYMWKjWzevJtmzZpRv34DatWqleIx4/79aObPv0hoqJpSpQozYIALLi5vr6unT58yduwYVCqYOnUsefO6pajfAweOsmDBGlauXEnRomlPbb5smfF+Sxbprd+KSgUdO8CmjUnfJ15eXvTs2ZN27ZrwySdd3lmuzz+fSI8efWjXrt07ryuTe5F9GrKIjh0hTx5IIiIRhQKGDk2oMAD07w8hIQmnNnQ6Y9jmwIFJb1evN68wxPTzKo0JGIWAlStBr9cQEDCIx4//ISSkJW5u0yhYsAdKpS8qlWDDBmNMeuHCBfn99//x11+7mD79e7MVM1UqFXPmzGXTpt08ePDErBwFVJZ87Ww0vcQfXpX/LR/t5JmGPc14bCzAJZngDqUk8UUTFXtGWuYKhSEiIpJJk2Zx9Og5/vxzG99/P51mzZqlaJDS62H4cChZUsOCBbX5449qTJjgSr58Er/99radp6cnGzZspHbt+vTtO5L9+48k2l9QUDALF66mefOeTJgwA1tbG8qWLcbgwYPTZV+TswzGR6EwRlt9NdF8O3d3d1atWsXmzX+xffu+d5arTZsmbNmy+Z3Xk5FJCllpSAMaDfz5p9EPQRXLMi5Jxk+9evD11wnXu3bN6ECZlI+TXg///gs3biS+XKkEp2TSEqhURqfItKDVGhWYGISwICioB0+eHCAysiIFC/ZEkl7g7f125z083Fiy5H9cuHCO8ePHo0vK4QNj9sn69etx7drtZGUZ61SY3/KUoqDqrZlVhUQnW3cO5q+GizJ7hyVKksSA+kqUZmYbDMDABjlfWQB49cqLQYPGoVJZsXHjRgqYc+pJhPHjjVYyIYz3g1Zr/B4dDV98EddfR61WM3LkKBYuXMTvv6+ndeu+dO36OZ98MorBgycwcuQ3dOw4CF/fUJYuXUbt2g2ZP381d+48YsaMGemyv40aQeHCSb9AGKfXBOr/LlMXF9izGypVSn76qWDBgqxYsZIlS9Zy7Ni/7yTXRx/V4/79+1y8ePGd1pORSQp5eiIduHfP6GOwYQOEhxujGYYOhc8+S9yxccMGo+9CcmzaBF27Jr7syy/h55+TVjzAqHjUqpWiXUgUIYzTK4GBiS7F2XkJjo7rqV37Z+bPj3sNhIaG88UXU2jcuAnDh49Ichu//PIL3t7PGDduSIpkMgjBnegwIoSBImornJSpC0nLCoIiBB/Piea+l4jj2/BfwCWT26oY+3H29ctIKRcvXuPrr/9Hnz59GDp02Ds78fn4QL58STsYg/EB/fBhwod0ZGSkKZ9CREQE4eHhREREULx48TjTEDHDXlrzIcTm0CFo0QIMBuMnBqUSVCrBwE+NykL5ctCuHWg077btY8eOMXXqFLZsWZJsKGZUlPHFQamEXbsOsnHjbnbs2IkmJXW1ZXI98vREFlOiBCxebIyA0GqNERMjRiQdCWFjk7J+rc1Upx471ujXoErkGaNQQKdOxhwSaUGSjNMkifuXSfj7f46f3+fcvj2Emzfvxllqa2vN999/yapVq1i3bh3aJGJAW7VqxaFDJ/j77+MpkkkhSZSxsKWqpX2OUhgAHKwk9o3V0Leu8v/t3XdcU1cbB/DfTcKeskERFBG0Kgo44kIRBau4qnVVcVt363jFiVuqddZRt62TqsVVF2pxFRzUhQoqgigyXOwRkpz3j1uikQQSCCTA+X4+KOTe3HMOCbnPPfec50D3s6rXs2SwdbhWlQ8YCCE4evQ0AgNXYtmy5Zg0SflR/wBw8mTJAQMAJCSwuUm+pKurCycnJzRp0gQtW7aEl5cX/Pz8io1bYBhGpQEDAPj4AH//XTxQ79wZiIhgsHkTgyWLGQwYwCgdMABAx44dYWtrh1OnwmRuFwiArb+aoIVHXdjUdoK1XX0MHWYNG9ueMDExVPkUU6pmoj0NapCdDVhbs70S8hgasgMoSwowXrwAAgLYfBFFtLWB8ePZaZequKhISQHc3dmrvy8/yBkGGD4c6N//NBYvXoTly2ejVasWUvvcv/8Yq1dvhb9/H7n3jx88eIAxY0Zj3ryp8PLil7/SVUB2PkHCOwJdLcDJSvUnsMpUWFiIy5dvICTkFLKycrBly1Y4O5d9UOq6dWxPWglDYgCwJ+hOncpcTDGEKDYbShEJCey0aDu7kqdbKysyMhL/+99MHDmyHTo6n/7ABQJg4GBbXLnKTsUoyj7J5RIQAqwKvo1DByfi2LE/4eCg2eN/qIpHexqqGENDtqegJDNnlt4jUb8+cO0aOxNj/3527YvkZGDjRtUEDAA7LuKff4p/OOvrA4GB7Kjxnj17Ys2atQgMXIHU1LdS+7m5Nca0aaNx9OgRucs9N2vWDFu2bMWyZRsRESE/82N1YqjLoEkdDhpYc6pswPDxYwZ27z6MPn1G48iRsxg5cgz++utMuQIGgO25Ky1gYBhABZMe8O4dO+7I2prtoTM3B2bNYv+OysPREWjVSrUBAwC0adMGtWvXQVjYFanHf91miitX9UAII5WuWiRiQAgwb4Enevfug4ULF9Bl16lyqdr9oVXYokXAhw/A5s1s9z/DfBr0NWUKm6tBnsxMNlgoKACaN2cXxqrIxbEcHYGwMOD5c+D+fUBXl01XbfTZQn4dO3aEjo4uCguL9yt7eDSDUFgIb+/OGDFipMw8Dp6enti4cSOmTJmCOXMmo0uX9hXXIKpcnj59gT/+OIlLl26gUycvbN68Bc2bN1fZ8X19AVtbIDVVdvDA5QLdugH29uUrJymJTZCWlPRpbNCHD2xPx759bLCs7KyIypCXl4datUwlPxMCbN9pLDfzLCEMm1OGMwrR0b3w+vVr2Jf3l0fVWDRoUBMOB9i0iU3AtG8fe2Vja8t29zdoIPs5hYVsmtpffmEzPxbp2hXYtav8H6KladBAft3EYjEyMjJgampSbBuHw8GWLSsRGRmFo0ePyE3+xOfzsX37dkycOAEZGZno1+9rVVafKidCCPbsCcHhwycwaNBgnDu3ANbW1iovh8cDfvsN+PprNpj+fLAvlwuYmLC9aeU1fjw7LfnLwcRFU5qHD5e+9acJ3rx5g8TERHh6ukkey8pmkJRU8vgeDgd4/MQQ+vp6EMjLNkdRCqBBg5o5O8tLPyutoAAYNYqdefHlFcXffwN8PvDvv4CVYrltVO758+fg8bgwMJCdZc/W1go9e/pg06Y9ePXqldwrHXd3d+zffwCjRo3C+/cfMWbMkCrbfV+dCIVC/PTTFkRHP0No6HHUrq3cKqXK6toVuHoVWLiQnZUAsMFE//7A8uXl7wFISADOnJG/LoxQyCZYi44GmmjQOmiXLl1C69YeUuMZtBUYD8wwgI4OAZfLLTF/CkWVho5p0GDZ2ewHZO3a7C2Bgwdlf8gJheyAxXXrKr+OAJCQkIDRo0dj4sQRJZ7gtbS0wOd74tKlSyUer0GDBggJCcHly5GYP38VTp0Kw/37j5Gerrq1AijF5eTkYsaMJUhJ+YDDhw9XeMBQhM9nb4ulprIzkt69Y4NmVdwyuHdPsYXk/v23/GWp0qVLF9GxYyupx3R1CTp2yAWXK79BQiEDP99c1KplgoSEhAquJVWd0aBBQ2VmAh06sFdaimR2FInYQYmVITIyEps2bcLJkydx9epVDB8+HEOG9MKgQaWnqu3YsTXOnj1b6mAsW1tbHDx4EM7OX+HmzWisWvUrevYMwLp1O1TVDEoBQqEIkyfPh4WFDXbt2g0Tk+K3nyqalRXg6srellAVRQcKK7qAXGWJi4uDo2PxXroff0j/b/xH8b8rLpfAxUUAny656NHDG4cPH6r4ilLVFp1yqaF+/JEdu6DsyrgiUUlZ6cqPEILGjRvDxsYK9es7Ijk5Ff7+Phg8uI9Cz8/NzcO4cf9DgwYuCA4OVmohncTERPTt2xdnzuyT6p7VJAWFQGYOYGKgWLexpjt9Ogx//hmGo0ePShaUqg6ystiZQSVNe9bSYgN2C4vKq1dpli5dCrE4F9OmFc8zv/+AEX6cbilZRZXDYXsYXBoKcOzoG9S2EyE7Oxe9egXgxImTsLe3ByHs7c2bN9nbP926AW5uxQ5NVTN0ymU1k5fH9hooGzDUqlWxAQPAJsXZvXs3srJy0LevLw4e3KRwwAAA+vp62LbtJ2Rnf8SwYd+hoKC0RZw+qVu3Lpyc6uP69VtlqHnFep7EYPzPPNj310bDoTqoO0AbP2zk4VWaumtWdkKhELt3h2DatGnVKmAA2Jk/U6bIz8vA4bCJzTQpYACAr7/ujxMn/saxY9p4/lw6Kv1uaBYe3n+JOYEf0LtXNr4dkIUDvyfj+tVXqG3HfpgYGuqjWzcvhIQcxqNHbA9Oly7sbK05c9jZWN7ebF4WipKlen0SVBMJCSUvsysLl1vyIleqxOfzsXnzZixevE7pXPgAYGCgj59+mgsdHZ7SWer8/Xvh/PlwpcusSA/jGHSepoVjVzgoFLJnoYJCBvsvctB5mjZelHPhMHU5d+5vWFpaoWPHjuquSoVYtuxTOveizKpF//fsyaZp1xQiETtzqmPHRnj71hmzZp1Hy5YO8Pe3Q3z852u/iDBzejp270zD5l/e4uuvc4tlje3btzv++OMYOnYUIC7u0/GLLlKuXmWzW9JJFpQsNGjQQEr02ANgP+gsLIDp0yumPrK0bt0aW7eyCZnCw/9R+vlcLhczZ47H7t278OrVK4Wf16NHD9y8eRcZGVlKl1kRCAHGr+EhrwAQiaUvW0UiBunZwI+bquZ9ipcvk9CmDb/azl7h8djpzjdvAmPGAP7+7Oqz164Bx49r1niGCROAFSvY2ylpaQtgZrYNPN4bRETooVu3OkhKUnyhMxcXJzBMbYhEF2T2ZopE7KJ6x4+rrv5U9UGDBg3k6Ai4uCie0pbPZxPR2NpWaLWK8fT0xLZt27B8+S+YNWsprl+/BaFQ8Xsqjo72GDy4D0aMGIGnT58q9BwzMzN07twJCxeuRlaWkt0xFSAqlsGTl5xiAUMRkZjB1fucKtnbIBKJwJO1uEk1wjBs5satW9k1L7ZvZ1en1aQ46eFDYMeOT7M9BIKGyMgYAEvLFRCJGHz8yMXGjaUse/uF5OTBMDKSPyCSw2Fna5UkLy+P5nyogWjQoIEYhu2KlDdElctl547v28emkL56VX2Z69zd3XHhwgW0b++NX389gL59R+PXX/chIyOz9CcDGDVqEIYM6YUhQ4bg/PnzCj1n1arVsLW1x+jRM5GYmFSe6pdbTKICZxeOGIv+FKHRLAHsJgvADxJg+2UR8gs1O52vWCwGV/ZqZVQl+v334gvTvX8/Gbq6D2FgcAUiEYP9+41LTb0t/fyvoaPzDNraz2RuF4vZBFcAO/j57du3ePfuHXJyciR5Hpo3b46mTZvi/PnzNDV1DUKDBg313XdsjgaGYYMEDufTB0erVsCVK+w+FZk+WlG1atVCQEAATp06jU2bNuPNmw+YNGkePn7MwN9/30B+vvzBjgzD4JtvemD16vlYtCgIGzasLzX5jLa2NlauDMbgwUMxduwsREaqbzK9fmm3kngiwCoHfz0UISUDyBMAsclA4GERev4sRHa+5n7YikQ0aNAEb94UT6dNiAHevp0DS8ulYJh85OZykJOjePeIgwMXOTmdoK9/Q+Z2Hg/Q1d2GgQO/RcuWnvDx8YGPjw/c3d3RqFEjNG/eHM7ObHrYqVOnYtiw7/D48eMyt5GqOmjQoMHmzmXXe5g9m13qOiAAuHCBTW1rZqbu2hXHMAzc3NywYcNGNG/ugf79x2LFik0YNmwq7t6NLvG5LVo0wZ49a3H58kXMnj1bobJGjhyJ1at/xsKFq7F27XYkJ1f+VAXvFmJoa8k78RPALA/gQDIN7r9HQQDcSyBYelzJKTKVSCwWV9vxDFWJjY3sWVHZ2b4oLHSAmdmv0NMTw8BA8QB05MhMAGIQIjvqFYnykJi4Ft9+2x379/+C8PCjCA8/ioiIU/j776P46ad5KCgowKxZs2BkZAhjY10EBAzHnDlz8O7duzK2lKoKaJ4GDSIUAufOsYGCqSk7MMvcXN21KhuxWIxjx47Bz88Ply9fxsqVK+Dt3Q4TJ46AoaG+3OdlZ+fC3384/vrrDOzs7BQq6/Xr19i+fTtOnz6F9u1bY9iwb+DsXE9VTSnVgl1cbP6TC4IvTrDaQsAyT/aT/qOnDTz9WQuGuqo5Ob8vEOLAq494mJEPHQ4DX2sjdLcxBo+j/PF37DgAkUgHgYGBKqkbVTb377NTIWXR0noJB4d+aNlyN7ZsKWVZ3M/k5zPo0mU1kpK+RkZGr2Lbx4wBEhO7YeLEYTAw0Edi4hu8epWEV6+SkZiYhKSkZDg5OeHkyZM4deoUFi9ehLVrg7Bt2340atQM8+fPL2NrqcpA8zRUA2fPsgtO+fuzy2aPHMkObAwMVD5fgybgcDgYMGAAjIyM0Lt3b/z11xnk5IgwZMhE3LhxW+7zDA310bFjG5w+fVrhsurUqYMlS5YgLOwizM1tMXOmAot5qFDQCBGG+bL9x1wOAY9LwOUQQFv0ZRhRTJ4AeJqimlsUfyZloHFYLBY/TkVoUgZCXqdj+J1XaPP3MyTmlj5gjRDg3Hl99PvGFo71HbFhozNOnU7Fw4eaewulJnBzY3sZZXX6iMUOyMsbBYFgpVLjCnR1CdzcMtC9uz4MPos1rKyAVauAbdsAd3cPLF68Fps378PDh3EwNbWFv38/rF69Bjdu3MDJkycBAP7+/qhbty4EgkJoa2ujiSYt1kGpXPUeGl1FXL3KBgtF9y2L/i8sZP+ABQJg7Vr11U8VzM3NsWHDBly8eBGLFy+Ch0dT/PDDGJmrYvr6dsKWLfswbtw4pctwdXXFmzcvVVVthfC4wIapQkzqK0LIZQ7epjOwsyAo1BVh/YXS1zjgqSB0v/UhF2OjXkHq1vd/5SbkCtDnnwTc9HaGlpweB0KAufPM8es2U3C5BCIRA2GhLbicFLh7AiGHCPr1o7cq1GXnTvaE/ssvQH7+p8dbtQK2bx+LGTNO4Pz5cPj5dVb4mAUFeZg50wA7dwIxMew4hq+++jR2asWKFVixYoVCt6gEAgF4PB7u33+EZct+UrZ5VBVCexo0wLx57Ie2rJMLIewywIqsP1EV+Pj44K+/zkBfvxYGD56ES5euFdunVasWePfuHWJiYpQ+/q1bN+Hurp4rnYb2BAsCRNg4TYjAoSL0bc1IjWWQQgAUcGFQoI2bd7UQ/6Z8J+SNz9/KnSYoIkB8rgB/pcif0XLqlAF+3WbK7i9iD5SX7wYtrThwuU8weCiQoqIeEUp5PB57AZGSAhw7Bhw4wE7F/OcfoEkTbSxatBgbNuxCZqbi+UvS0zNgamoKAwPAw4Pt0fh8lgbDMAqPaSkoKEBiYhKMjY0rbUEzSj1o0KBmSUnswMaSJgwQAvzxR+XVqaIZGxtjxYoVWLt2HVau3Iz4+EQAQHQ8gykbePgqQA9vRJ0wYkYILt7OL+VonxBCcOvWbbRooRndo03qcODlyoD75V9ZIQd4Ywi8MULOGz3M3qQN9wA9fBekjYwc5cshhOB8ahZEJZzTuQxwJll+0LB1mwl7S+UzYnEtfPg4FhbmqyAUEuxULnknVQFMTIB+/dhMlp/fBeDz+WjXrh22bPldoeOIxWKkpr6FjY2NSuplZWWFXbsOoWXLVqXvTFVpNGhQM0UGGnO5n+ZMVyd8Ph+dOnVCREQUjl/joNNULRy+xEFaOoO3TBe8fRGGrtNSsWC7Yktif/z4Ee/evYONjWUF11xxu8by8FVt9mqNwwAQMUCyISAomsrISAZQnovkov8cHSiRHwsA25MgLKUTQEyAArndHkBUlK7MBFVZ2T2gr3cbhOQjIlK5elVVVTXlQGDgHPzzTxQCA1cgNbXkxSPevn0PLpcDU1NTlZS9ffsOdOvmi969S1/plqraaNCgZrVrl77IlFAIODhUTn0qW4cOHRB+9V+MXc2DSAwI/+saz+e5QsQYwaTgDJb9lolzkSXPQgDYfBE9evTAypWbSs31UFnMjRhcnMPDnnE8dHdjUJurA0bMADKGSIrEDO484eLCTeVyI/A4DBoYaJc46JIB0MRYflIJDkf2mdLY6E9kZfsC0INWNR4B9egRO/jY0JDtondxYW8LFq2nJhYD588DAweyGVi/+YbNIKlJg5QtLCxw5swZODo2xJAhk0tco+Xs2cvw9vZW2ZRaAwMDzJs3H+3bt1fJ8SjNRYMGNbOwAHr3ZnsT5NHVBb79tvLqVFZv3gCLF7Or5vn4AMHBpa+W1759e0Q/fASI8yF1ImUYpBjNhEXOTuggGetDSk8ZzTAMFi9ejLS0dAwZMhlnzlyCUCgsX6NUQIvHoI8nB/snaoFkaYMQ+R/UXA5ByEXlEyqNq1/y3FwOA3znID/VsLd3Lrhc6cCBYQpganIIH9NHAAC6di25DoQAFy8Cs2axS7sfPPjppKvJLl9m7+nv3w/k5LABwrNnwA8/AL6+QEYG0KsX4OcH/PknEBkJnDjB/t16eyu/uFxFMjQ0RGBgINavX4+tW3+XGTyLRCKEhp7HkCFD1VBDqqqjQYMGCA5mr3DkBQ5r1wKangrjxAmgXj1gyRL2Q/jSJXaAp6Mj+7M85ubmIIwuGOHHYtsKeA2RrtsXlumrceNB6T0NAHvFc+TIEcyc+T8cO3Ye/fuPw5Ejp0vMSlmZPmaVfGUnEjN4m6781d9IBzN0szaEVB9GPhdMZG1gf1M0Ot4KfxwwwfuPso89aWKGZABkESPD0xAUOkAobIpapsCw7+SX/+oVO5Cua1d2dcjNm4GhQ4E6ddgxO5oqPx/o35+dqfR5fFk0MPnaNaBbN3ZKNPBpn6Iehhs32MWkNE379u3B4XBx/37xLI0REVEwNDSEp6enGmpGVXU0aNAADRuyVy/e3tKP16/PXq19/7166qWomJhPH7yfX9iIxeyHcs+e7IBPeYioAGJGdtf5e/0h4JAcGOSeVbg+HA4HPj4+OHr0GFau/AnXrkWhb9/R2LfvKERq7k+ubUnAQP5Ncy6XwNFG+ZvqPA6DfS0dsLKJLRz1tYFEY2BVO5ATLmAeWyL6piGWrDNEky6WOH9Fu9jz27XNx5qf34JhyH89DgS1TH9DRkYAjIyAc2cBY2PZAUd+PvveffKE/VkoZN8LAPDhA3u1/vy50k2qFEeOAB8/yh+ILBYDt27J3y4SAYcOad7sJoZh0KtXb+zefRi3bt1FXh47oPjly9fYu/cPDBo0uEy3JqKjo7FixXIcOHAA76vjQCuqVDRo0BCurmyK6Jcv2SvzqCj2g3bwYHXXrHSbNrH/yxpAJhazeSa2b5f9XLFYDCLOB+HIud/OaOG98WSY5exGenq6UvViGAZ8Ph+//74PW7f+iosX/8GOHaUs3VfBRvQQyhrOICESMfiue9luqWhxGIyvb44L7i4w2ucBTgEPAANC2C8xYVAgAIZPM8Wz+OLdWqNGZuJW5CuMH5eBFm5Xoa+fi4ULuyDuGdCypfxKHznCvldl3Qkqev3Xry9TkypcVBSgVc6Vy0Ui4O+/VVMfVRo+fDhcXZtg8+Z96NZtMAYPnojRo2egeXNP9O/fX+njicViDBo0CM+fP8Hp08exfPmyCqg1pemq8dCmqqluXfarKjl9WvYJo4hIxO6zeHHxbXl5eWAYBvq6OsgtIBDLuN+fy20CD08PbNiwAUFBQWWqY/PmzfHrr9vQr18/fPVVQ3To0LpMxymvET2EOBTGQ0wCis1WYBiC/p1F4Dcp3yDOfcf0kJPLQCxjNgQhDERigu0H9LF6fvE5/Q0aFGL5svdYuPAwmjUbjDFjSv+IOHqUHcwr72pcKAQOH/4UXGoSbW3VzJbQgKEzxZiZmWHePDadc0ZGBp48eYKmTZvCwEDxdNOfi4qKgrW1JdasCcKHD+n45puxSEtLg5WVlSqrTWk42tNAlZsiH5hF3dVfys3Nhba2Fg4uFEBPB+AwRcs5sYMCGYZgw1QhguaOQGhoKN6WMLJSLGa7yP/9F8iUkZLA1tYWa9euxdKl6/HqlXr6kw30gL/W5GNgVxG0eJ/OVsYGBLOGCrHlfwK5SZoUdS5cp8S8HyIRg78u6cjdTgjB3bvRCo+Ez8wsOc8IwA4w1ERff13y+7e0mU1F2rRRTX0qiomJCdq0aVPmgAEATp8+DR+f9mAYBubmteDl1QZ/VKcEMpRCaNBAlVvbttKZ5L7E4wHyzj/GxsZo1KgR1i8Zj1Xf/oXAIQVo3YjAo6EY4/xFuLWtEMN8xbCwMIOdnTVev34t8zi7dwMNGrBLhXt4sCl3x44tnt+Cz+djzJixCAxcgfx8xRNHqZKJIbB5pgAxIXk49XM+zqzLR0xIHuYEFIKngpWoCwSyp3R+TlAof3tSUgry8vLRsGFDhcr7PPWwLBwOe/tNE3l5Ae7u8usvFgOtW8sfpMzjseM5XFwqro6aoLBQiDNnzqN2bR+8e8eeNgYM6InDhw+hUN4VAVUt0aCBKrfJk0u/PTFxouxtOjo6OHw4BAsXLsLlsIuIODoWs7pfxsV1hVgxToQGtT9djVtYmCMtrfjy10uXAqNHAwkJnx4rKAD27AHatQO+HAoxduxY1KvnhB9+WIT37z8o3lAVMzMG2ruJwW8ihp78C3+leTQtBI9b8mBL96byP+jv3o2Gp6cHOApeZo8fX/LrLxYDkyYpdKhKxzDsrbOi+KgoOOBy2W0rVrDbnZ2LLxjF4bB5Vn5XIAnjnTvs30m/fuxsi8jIqpNEKjQUcHMrwMePYvz4YzRcXR3g61sbQUGd8OGDNXr0DMO5c6X3NlHVAw0aqHLr2JE9cQPSV2RFV2+//CKd8vZLDMPAy8sLR44cRVDQYmze/Dvmzg3Gx4/SmSAtLc1w+/ZtPHjwAAkJCcjNzUV8PFA0zOHLD2GRiB2g9/PPxctbt249mjVrgeHDf8C9e4/K0GrNNWpQniRJliwiEYNxQ3Llbr9375FS6YCbNgUWLGC/l3Vi9fNjV2nUVLa2wL177NiMAQPY2T7TpwOxscCcOWwulVu3gNWr2R4FQ0N2ZtPSpcDdu2zgII9QyLa9ZUt25cjQUHbxKT6fLUtQ+uKjarVrFxvoxMQY4OXLYzA0vAhr64m4c0eAy5cNkJQ0DDFPDqL710Cv3lUjLwdVPgxRZj3Vz5RnPW6qegoLY0fJX73Knjy6dmWT/CibJC47OxurVq1CWNgFzJo1Ad7e7QAAly/fQEjISWRmZiMzMxNiMUHr1oewaVO9EjPzmZuzSaZkjRX466+/EBQUhNGjB2HQoN4qy5Cnblt/18fcn4wkK1YC7BgRkZjBxIAcLJuVLXfsxNChU7B48VKl5/Hv3w+sXAk8/i81gLU1MGUKm+xJu/gszxph7lw2D4usT1kOh+2B++WXyq+XIjIy2IAq77MUKQxTABubWRCLjZCauhxaWvGwtx+OF/HXwOEAkycBGzaor86UYspz/qZBA6Wx/vnnH8ydOwdNmrhg1qzvYWIi/T47dOg4tmw5i6dPQyAQWJR4rOxsQN4YsOfPn2PKlMlwcqqLuXOnQF9fT1VNUKvwCG1s2quPK5HaIGKgZfNCTBiWC/+uBSUOtuzdeyR2794LJycnpcskBEhNZQe+2tmVnOm0usvKAmxsgFz5nTrQ1mZzPJiXnNBTLbZvZ3PEfHmG4PGS4ej4NeLjL4LDyYG9/WC8iL8BgM1em5LMLqxFaa7ynL/p7QlKY7Vt2xanT/8FIyMzjB49s9iMh8GD+8DCoj1sbceBYeQPz9fSYj/M5GnQoAGOHDkKLlcXo0ZNR0LCK1U1Qa068QU4ui0daffS8PZBGs7u+4he3UoOGAAgOzsXhoaGZSqTYdgTpb19zQ4YADabZEkBA8Denrh0qXLqo6y4ONkDRIVCW2Rnd4aJyeH/UqJ/GsyQn88u101VXzRooDSaoaEhVqxYiUGDhmDs2Fl48EA6Le6MGaMhENSBre0MfP7hVYTHY9ftKO0EZmhoiPXrN2DgQLaciIg7KmyFejGM7FszshBCkJOTU66peRRL0fv7mjoOoFYt+YMb09NHwNT0IIyMzoJhpHfSxJwVlOrQoIHSeAzDYMyYMQgKWoTp05fg0qVrkm3t2gng7LwIWlqpMDffKPU8DocNGubMUbyckSNHYv36DViw4Gc8fvxUlc2oEorSDevr66u5JlWfm5ti+7VoUbH1KKtvv5UfNOTnN0NOTkfo6DzB23ezJI9zOOyUZ6r6okEDVWV0794d27Ztw+rV23DwYCgA9gp6//6PcHFZDROTIzA2PivpUrW2ZlNzf/WVcuW0a9cOCxcuxIwZS9SWBEpdcnJyYWCgr/B0S0q++vXZdTfk9XJxuWyOk5JmFqlT/frAqFHye6lSU1ciJWUdMjPZlNRcLvBNP3YsC1V90U8Gqkrx8PDA4cOHsWfPH3jxIhEAYGREEBICLF48F/b28xAYmIUTJ4DERKBDh7KV06tXL4waNRo//rio2NTP6owNGuitCVXZsYMd4/Fl4MDlAmZmiuV4UKetW9k8HBzOp547Kcyn218NGwJbtqilmlQlomtPUFWOo6Mj+vfvjz/+OIXAwE9Zg9q0sYRIlIf587Who4JkSWPGjEFy8hvMmLEEmzcvh55eCaMpq4mcnLIPgqSKs7dn05qvXcsGEB8+AKam7BX8jBnluyqPjY3FhQsXkJWVBXNzc5iZmaFWrVqS/3V0dMDlcqW+DAwMwFVihKqWFhs4zJvH5pjIzGTr/Po18NtvQGoaUKc2m3117FjAyKjs7aGqBjrlkqqSkpKS4O/fE6Ghu2FiYoSEhFeYPn0x/Py643//m62yckQiEaZOnYr8/Cz89NM88FSR51mD3bp1Fzt3huCPP46quyrVklBYcsptWV6+fIlnz54hKSkJSUmv8erVK7x69Qqpqanw8uLDxsYS6emZkq+PHzOQnp4BoVAIoVAEkUgEsVgEoVAEU1MT9OzZE71790Hjxo2rTV4SSjnlOX/TngaqSqpduzbatm2LI0dOoXnzrzBv3k+YOHESAlScepDL5WLNmjUYOXIEfv55K2bPnlStP2izsrJhaEgvFyuKogFDVlYWTp8+jWPHjuLly5do2NAJtrZWsLW1QocO7rC19YOLi5NSOUUIIXj27AXOnv0b48aNgYmJKXr16o1evXrBjg5EoBREexqoKuv+/fsYM2YMCCFYsWIFunXrVmFlffz4EYMHD4K/fxcMHdqvwspRt+3b90Mo1MLcufPUXZUaKSkpCdu2bcOpU6fg5tYY/v4+6NChDbS1tQAAhYWFSE19B319Pejr60FHR7tMQaxQKMKdO/dw9uzfuHo1Es2aNcPKlcE0eKghaE8DVSO5ubnh9u3blVJWrVq1sGXLVnz77QDw+Z6oX79upZRb2WJj49GrV/UNijRVWloaNmzYgLNnz+Drr7vg0KHNsLGxktrnxYtELFy4Gqmp7yAQCJCfn//fOAV96Ovrw8BADwsW/IBGjZxLLY/H46JNGw+0aeOBnJxc7Nt3FIMGDcLu3bvRoEGDimomVQ3QoIGiFFS/fn18//0ELFu2Adu3r6qW4xtiYp5hzhwl56hS5UIIwQ8/TIO1dS388cc2WFiYFdseGnoOW7b8hkmT2FtwHA4HQqEQubm5CA0Nxc8//4zWrVvAwaGO0uUbGOjj+++Hw9zcDN99NxTLl68AALx580YyjuLNmzdIS0uDr68vpk+fUSF5PPLz2UXDitau6dQJ+OabmrtuiaaiQQNFKWHkyJG4cOE8Dh78E8OHD1B3dVROS4uHnBz5Kbkp1Tt16hTev3+HDRsWQktLS2pbRkYWVq78BQkJSfj999/RuHFjyTYejweBQIDg4GC0beuJ6dPHQVe37NOGBgzoCRMTI6xe/RPMzExhY2MJGxtLtGzZGLa2nWBiYoQdOw7B398fwcHBaNmyZZnL+tLt20CPHuzCckXjPrZvZxfMOnMGaN5cZUVR5UTHNFCUkmJiYjBs2Hc4f/5gtUuCtHbtdhgbW2LmzFml70yVW3h4OAIDA7F06Uy0bNkcmZlZOHHiAvLy8lBQIEBY2FW0b98R8+fPl3t1//TpU/z0UzCePXsKd/dmkpM9+2UFa2tLlU0XJoTgwoUrWLt2O4YMGYJJkyaDp+x0kC8kJwONGrGLyn25Wi2Xyy5+FRvLLlFOqQZd5ZKiKhEhBO3atcXmzctRr171Gtvw778PERy8BefPX6jWs0TU7cWLF1ixYgWePXuKadNGS5Z/X7ZsA5KTP8DNzQ26urpo0qQJvL29FTpmVFQUYmJi8ObNG7x5k4SkpCQkJ6fg3bt3MDY2go2NFWxsrDBwoD/c3ZuWq/7JyWlYuHA1eDwdrFmztlwDKIOCgOXLiwcMRTgcdntgYJmLoL5AgwYNlp7OJnXZvRtIS2OTvYwdC4wcCdD0/lXXpEkT4enZCH37dld3VVRKJBKhR4/h2LVrN75SNv82pZDQ0FDMnz8fzs71sGTJLBQUCBAdHYOHD2Nw/fptnDt3DmZmZqUfSEECgQCpqal48+YNYmJisGXLZvj7d8WoUYPKtQy8UCjCzp0HEBp6DkuXLivz7KVGjYCYmJL3cXcHoqLKdHhKBho0aKikJDaN8cuXnxZ+Kbp4a9YMCA9ns8NRVc+lS5ewcOEC7Nu3EWZmpuqujkr9/vsRRETcx4ED1e/2iyaIi4vD8ePHcffuv3j4MBrGxkZo3rw5mjdvgU6dOsHJyalCy09JScHChQsQGRkJN7evwOd7gM/3gKOjfZl6l+7cuY+goDXo2rUbAgMDoVvSOvQy1K0LvCplNXoXl9IDC0pxNGjQUN7ewLVrspeK5XKBIUM0P/c8Jd/SpUvw7NkTrFu3SKnUvJpOKBRixIgfMXTocAwePFjd1dE4hBC8ffsW2dnZqF+/frmOJRaL1RaYffjwATdu3MCVK1dw/fo16Onpgs/3QOPGzsjLy0dGRhYyM7Px1VcN4evbqcRjffyYgaVL1+Pt249Yt269UtM2e/ViBzvKuz3B47GzKA4fVqJxVIlo0KCBHj8ufXVFHo/tjbCyKnk/SjMJBAIMGjQIHTq4Y+TIQequjko9ehSLH35YhNOnT8Pa2lrd1alQaWlpuH//Pu7du4u7d+8iJSUFurq60NXVhZ6enuR/kUj0Xwrn12AYBlwuB8OHB2Dq1KlVfvyHSCTCo0ePEB4ejpiYJzA2NoaxsQn09fXx+++/4eTJ32BoWPL9VLFYjJCQk9i16zBmzJiBQYMGKfR7OXcO6F7KXb7wcMDLS4kGUSWiQYMG2rULGDOm9P3OnCn9D4bSXJGRkViyZBEOHtyk7qqo3JIl6+Dk1BiTJk0qfecqJCYmBhEREbh37y7u37+Pjx/T0bhxQzRp0hBNmrjCzs7mv+RJBZKvgoICAEDt2raoU8cGZma1kJb2Dj/8sAi9evXGhAkT1dyqijNmzGi0bt0UAwb0VGj/2Ng4LFiwGs7OLli+fDlMS7kHSwgwYQKwbRt7+7bojMThsLd1p00D1q2Tv0Q3pTyaEVIDKdpbXY16tWskLS0tcLnV875/u3aeOH78UrUIGjIyMnDy5EkcPXoEaWlpaNPGA82aNcTQoT1Qv75jmRJ1WVtbonfvbkhMfF0BNdYc3303DMHBK9C/fw+Feg5cXJzw22/rsXbtdnh5eYHL5aCgQIAWLZqjR4+e8PX1lRroyTDsSpotWwJr1gBPnrCPN24MzJoFDBtGAwZNQoOGCtK5s3TULIuuLtC6deXViVK9+/fvw8Cgek6DcXdvhsWL1yE/P1/pwW2a4vXr11i3bi0uXboMD49mGDVqANq1a1nu3AJFMjOzSr2Sruo6dOiApUuFuHTpOnx8Oij0HD09XcybNxVjxgwBl8sBwzC4ffsewsLOIjh4JTw9PbF48RLUqcNmsGQYYPRodsnwjAz2ZxOTimwVVVY0aKggDg5Av37A8eOyB/hwOMD48fQPoyq7dOkSfv11K7ZsWanuqlSIWrVMYG9fG3fv3gWfz1d3dZRGCMHcuXNgY1MLR45sg6WlucrLSE/PhKNj9V7kicvlYtmy5Zg2bSrev/+Ib7/1V3gMh7X1p4xMfn6d4efXGZmZWRgwYBzev38vCRqKMAydUabpaNBQgXbuZKcS3brF3oYQiT797+sL/PSTumtIlcdvv+3F2LFD0KCBo7qrUmE8PJoiMjKySgYN58+fx5s3Sfj557mSVSJV6fHjpwgLu4odO4ar/Niahs/n49Chwxg/fjwiI/+Fk1NdWFpawMLCDNbWFmjUyFnhGUR5eQVIT8/E8ePHER4eDkNDQxgaGsLKygodO3asVjORqqPqeTNWQ5iaAtevA3/8wQYJzZuz04tOn2a/dMqeJp7SAB07euHevcfqrkaFatWqOf766zQyMzPVXRWlXbwYhgEDelZIwJCYmIQZM5YgKGgRmteQhRGcnJxw9OhRdOvWAwYGlkhISMPp0+FYsOBnjB8/G3FxCQodx9TUGLNnT4KxMRdZWal4/jwa169fxurVwejduxfCw8NRxvH5VCWgsycoSklxcXFYvXoVIiNvwsOjGdasWajuKlUYQsh/8+8zsGvXbmhXoSUHAwKGo29fH3Tq1Falx3379j3Gjp2FESNGYcSIESo9dlUkFArx22+/YevWLRg8uA9Gjy5bbg+xWIwLF65g27b9qF27DmbN+h/c3NxUXFsKKN/5m/Y0UJSS0tPT8c8//2D//l+qdcAAAAzDYO7cKeBwxJg1axbERalNq4C3b9/C3Fx16ZgBIDs7Bz/+uAg9e/rTgOE/PB4PAwcOROPGjRER8W+Zewk4HA78/DojJORXtGvXAuPGjcW0aVORl5en4hpT5UGDBopSkoeHB5o2bYqbN/9Vd1UqBY/Hw8qVc/DyZRyCg4PVXR2FvX37DhYWqg0aQkJOwt7eATNmzFTpcauyV69eYeDAb2Fioo9Nm5aVO9GVtrYWBg/ug+Dgubhx4wZE8lJFUmpBB0JSVBlMnjwFgYGz0atXN2hpqf6euabR19fDmjVBGDt2FmxsbDBq1Ch1V6lEBQUFyMzMhLl5LZUdUywW49SpMKxevabKZ4BUlTt37mDKlMn49tueGDFioOT3QghBTk4uMjOzkZ2dDX19fVhZWZQ4viQjIwtPn8YhNjYOT5/G4+7dhxg1ajQMDQ0rqzmUAsodNFTFAVIUVV6NGzeGra0tNm/+DaNGDVR3dSqFlhYPy5b9D9OnL0adOnXQpk0bdVdJrtevX8PY2Aj5+fnIz89XyTGjoh6Ax9NCw4YN6ecegKtXr2L27NkwMTFGVFQ0wsMjkZWVjezsHGRnZ4MQwMiInRmRk5ODjIxM1KplAisrS5iZmSI/vwCZmez6FpmZmRCJxKhXzxEuLq5o1swTAwYMRdOmTWX+ruPi4jBt2jQABMbGRjA2NoGpqSnq1nWAs7MznJ2dYW9vDy6Xi4KCAixZshjPnj2DQCCQSg/u6toIAwYMKNfS3lVRed6/ZR4ImZ+fj3r16iElJaXMhVMURVEUVflsbGwQHx+vdOK2MgcNABs4CASCsj6doiiKoig10NbWLlOm13IFDRRFURRF1Rx09gRFURRFUQqhQQNFURRFUQqhQQNFURRFUQqhQQNFURRFUQqp8cmd6AwQiqIoSlFlnXVQXdTooCE/Px96evUA0FwTFEVRVOnKmt+guqjRQQPbw5AC4BUAY4D5b/ZpUYZYhpTv+6Jjyfq+pH0qomypY1dA2cXKI4qVp6qyFW2fUmV/vq+MfaBkGeVt95f7K1xGCa9FieWV8b1d1nYXe7yk56mg7JLap9J2f9FGyfZKKPvL5ypTXrGy5W3/sjwZj6vjc1Rm2aX8TZdWdlYBUuzXSTJL1kQ1Omj4xBhgKjloqIiTpzrLLnasSg4aKuTErcAHTIWVrcp2lzFoUMvv/PPvKzhoqLR2yztGZZStynYrEDRozO9cXtnlDBqKttVgdCAkRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEKoUEDRVEURVEK4am7ApohEyDAf/98hlTc9wQA89/3zBff47+fy/P9l8eVOnYFlF2sPKJYeaoqW9H2KVX25/vK2AdKllHedn+5v8JllPBalFieEq+3Ktpd7PGSnqeCsktqn0rb/UUbJdsroewvn6tMecXKlrf9y/JkPK7Kz5aS2lfqe62Uv+nSys4qQE1Xo4MGQggMDQ2RnW3/3wOQ/p+iKIqiPmNjYwNtbW11V0NtanTQwDAMsrOz8erVKxgbG6u7OhUqMzMT9vb2tK3VTE1pa01pJ0Dbqum0tbWhq6ur7mqoTY0OGooYGxtXmTdsedG2Vk81pa01pZ0AbSulmehASIqiKIqiFEKDBoqiKIqiFFKjgwYdHR0EBQVBR0dH3VWpcLSt1VNNaWtNaSdA20ppNoYQQucKUBRFURRVqhrd00BRFEVRlOJo0EBRFEVRlEJo0EBRFEVRlEJo0EBRFEVRlEJqdNCwefNmODo6QldXF61bt8atW7fUXaVyWbRoERiGkfpydXWVbM/Pz8ekSZNgbm4OQ0NDfPPNN0hNTVVjjRV39epV+Pv7w87ODgzD4Pjx41LbCSFYuHAhbG1toaenBx8fHzx79kxqnw8fPmDo0KEwNjaGqakpRo8ejezs7EpshWJKa+uIESOKvc5+fn5S+1SFtq5cuRItW7aEkZERrKys0KdPH8TGxkrto8h7NjExET169IC+vj6srKwwa9YsCIXCymxKqRRpa6dOnYq9rt9//73UPlWhrVu3bkWzZs0kCZv4fD7Onj0r2V5dXtOaqsYGDSEhIZg+fTqCgoLw77//ws3NDb6+vkhLS1N31crlq6++QnJysuTr+vXrkm0//vgjTp06hSNHjuDKlSt48+YN+vXrp8baKi4nJwdubm7YvHmzzO2rVq3Cxo0b8euvv+LmzZswMDCAr68v8vPzJfsMHToUjx49QlhYGE6fPo2rV69i3LhxldUEhZXWVgDw8/OTep0PHToktb0qtPXKlSuYNGkSIiMjERYWhsLCQnTr1g05OTmSfUp7z4pEIvTo0QMCgQD//PMPfvvtN+zduxcLFy5UR5PkUqStADB27Fip13XVqlWSbVWlrXXq1EFwcDCioqJw584deHt7o3fv3nj06BGA6vOa1likhmrVqhWZNGmS5GeRSETs7OzIypUr1Vir8gkKCiJubm4yt6WnpxMtLS1y5MgRyWNPnjwhAEhEREQl1VA1AJDQ0FDJz2KxmNjY2JDVq1dLHktPTyc6Ojrk0KFDhBBCHj9+TACQ27dvS/Y5e/YsYRiGJCUlVVrdlfVlWwkhJCAggPTu3Vvuc6pqW9PS0ggAcuXKFUKIYu/ZM2fOEA6HQ1JSUiT7bN26lRgbG5OCgoLKbYASvmwrIYR4eXmRadOmyX1OVW0rIYTUqlWL7Ny5s1q/pjVFjexpEAgEiIqKgo+Pj+QxDocDHx8fREREqLFm5ffs2TPY2dmhfv36GDp0KBITEwEAUVFRKCwslGqzq6sr6tatW+XbHB8fj5SUFKm2mZiYoHXr1pK2RUREwNTUFJ6enpJ9fHx8wOFwcPPmzUqvc3mFh4fDysoKLi4umDBhAt6/fy/ZVlXbmpGRAQAwMzMDoNh7NiIiAk2bNoW1tbVkH19fX2RmZkqubDXRl20tcuDAAVhYWKBJkyaYM2cOcnNzJduqYltFIhEOHz6MnJwc8Pn8av2a1hQ1csGqd+/eQSQSSb0pAcDa2hoxMTFqqlX5tW7dGnv37oWLiwuSk5OxePFidOjQAdHR0UhJSYG2tjZMTU2lnmNtbY2UlBT1VFhFiuov6/Us2paSkgIrKyup7TweD2ZmZlWu/X5+fujXrx/q1auHuLg4zJ07F927d0dERAS4XG6VbKtYLMYPP/yAdu3aoUmTJgCg0Hs2JSVF5utetE0TyWorAAwZMgQODg6ws7PDgwcPMHv2bMTGxuLPP/8EULXa+vDhQ/D5fOTn58PQ0BChoaFo3Lgx7t27Vy1f05qkRgYN1VX37t0l3zdr1gytW7eGg4MD/vjjD+jp6amxZpQqDRo0SPJ906ZN0axZMzg5OSE8PBxdunRRY83KbtKkSYiOjpYag1NdyWvr52NOmjZtCltbW3Tp0gVxcXFwcnKq7GqWi4uLC+7du4eMjAwcPXoUAQEBuHLlirqrRalAjbw9YWFhAS6XW2zEbmpqKmxsbNRUK9UzNTVFw4YN8fz5c9jY2EAgECA9PV1qn+rQ5qL6l/R62tjYFBvkKhQK8eHDhyrf/vr168PCwgLPnz8HUPXaOnnyZJw+fRp///036tSpI3lckfesjY2NzNe9aJumkddWWVq3bg0AUq9rVWmrtrY2GjRoAA8PD6xcuRJubm7YsGFDtXxNa5oaGTRoa2vDw8MDly5dkjwmFotx6dIl8Pl8NdZMtbKzsxEXFwdbW1t4eHhAS0tLqs2xsbFITEys8m2uV68ebGxspNqWmZmJmzdvStrG5/ORnp6OqKgoyT6XL1+GWCyWfDhXVa9fv8b79+9ha2sLoOq0lRCCyZMnIzQ0FJcvX0a9evWktivynuXz+Xj48KFUkBQWFgZjY2M0bty4chqigNLaKsu9e/cAQOp1rQptlUUsFqOgoKBavaY1lrpHYqrL4cOHiY6ODtm7dy95/PgxGTduHDE1NZUasVvVzJgxg4SHh5P4+Hhy48YN4uPjQywsLEhaWhohhJDvv/+e1K1bl1y+fJncuXOH8Pl8wufz1VxrxWRlZZG7d++Su3fvEgBk7dq15O7du+Tly5eEEEKCg4OJqakpOXHiBHnw4AHp3bs3qVevHsnLy5Mcw8/Pj7Ro0YLcvHmTXL9+nTg7O5PBgwerq0lyldTWrKwsMnPmTBIREUHi4+PJxYsXibu7O3F2dib5+fmSY1SFtk6YMIGYmJiQ8PBwkpycLPnKzc2V7FPae1YoFJImTZqQbt26kXv37pFz584RS0tLMmfOHHU0Sa7S2vr8+XOyZMkScufOHRIfH09OnDhB6tevTzp27Cg5RlVpa2BgILly5QqJj48nDx48IIGBgYRhGHLhwgVCSPV5TWuqGhs0EELIL7/8QurWrUu0tbVJq1atSGRkpLqrVC4DBw4ktra2RFtbm9SuXZsMHDiQPH/+XLI9Ly+PTJw4kdSqVYvo6+uTvn37kuTkZDXWWHF///03AVDsKyAggBDCTrtcsGABsba2Jjo6OqRLly4kNjZW6hjv378ngwcPJoaGhsTY2JiMHDmSZGVlqaE1JSuprbm5uaRbt27E0tKSaGlpEQcHBzJ27NhiwW5VaKusNgIge/bskeyjyHs2ISGBdO/enejp6RELCwsyY8YMUlhYWMmtKVlpbU1MTCQdO3YkZmZmREdHhzRo0IDMmjWLZGRkSB2nKrR11KhRxMHBgWhraxNLS0vSpUsXScBASPV5TWsqujQ2RVEURVEKqZFjGiiKoiiKUh4NGiiKoiiKUggNGiiKoiiKUggNGiiKoiiKUggNGiiKoiiKUggNGiiKoiiKUggNGiiKoiiKUggNGiiKoiiKUggNGqgaJTw8HAzDFFswp6pKSEgAwzCSdQqqko4dO+LgwYPqroZMixYtQvPmzct9HEdHR6xfv77cx5FFIBDA0dERd+7cqZDjU5QsNGigKhzDMCV+LVq0qNLq0rZtWyQnJ8PExKTMxyg6URd9mZmZwcvLC9euXVNhTau3kydPIjU1VWqZ78rAMAyOHz9eqWVWFG1tbcycOROzZ89Wd1WoGoQGDVSFS05OlnytX78exsbGUo/NnDlTqeMVFhYWe0wgECj0XG1tbdjY2IBhGKXKlOXixYtITk7G1atXYWdnh549exZb0peSbePGjRg5ciQ4HPoRVB5Dhw7F9evX8ejRI3VXhaoh6F8sVeFsbGwkXyYmJmAYRuqxw4cPo1GjRtDV1YWrqyu2bNkieW7RVX1ISAi8vLygq6uLAwcOYMSIEejTpw+WL18OOzs7uLi4AAD27dsHT09PGBkZwcbGBkOGDJFaYvfL2xN79+6Fqakpzp8/j0aNGsHQ0BB+fn5ITk4utV3m5uawsbFBkyZNMHfuXMly3EWuXLmCVq1aQUdHB7a2tggMDIRQKJRsl9V13bx5c6mel5iYGLRv3x66urpo3LgxLl68KPNq+cWLF+jcuTP09fXh5uaGiIgIyTZF27hz5065r4NAIMDkyZNha2sLXV1dODg4YOXKlQDYZZ8XLVqEunXrQkdHB3Z2dpg6darc39vbt29x+fJl+Pv7Sz3OMAy2bduGnj17Ql9fH40aNUJERASeP3+OTp06wcDAAG3btkVcXJzcY5dUT0dHRwBA3759wTCM5GcACA4OhrW1NYyMjDB69Gjk5+fLLUOetLQ0+Pv7Q09PD/Xq1cOBAweK7ZOeno4xY8bA0tISxsbG8Pb2xv379wEAT58+BcMwiImJkXrOunXr4OTkJLPMWrVqoV27djh8+LDS9aWoMlHzgllUDbNnzx5iYmIi+Xn//v3E1taWHDt2jLx48YIcO3aMmJmZkb179xJCCImPjycAiKOjo2SfN2/ekICAAGJoaEiGDRtGoqOjSXR0NCGEkF27dpEzZ86QuLg4EhERQfh8PunevbukvKIVJD9+/Cipj5aWFvHx8SG3b98mUVFRpFGjRmTIkCFy21BUp7t37xJCCMnNzSUzZ84kAMjZs2cJIYS8fv2a6Ovrk4kTJ5InT56Q0NBQYmFhQYKCgiTHcXBwIOvWrZM6tpubm2QfoVBIXFxcSNeuXcm9e/fItWvXSKtWrQgAEhoaKlUXV1dXcvr0aRIbG0v69+9PHBwcJKsCKtLG0l6H1atXE3t7e3L16lWSkJBArl27Rg4ePEgIIeTIkSPE2NiYnDlzhrx8+ZLcvHmTbN++Xe7v788//yQGBgZEJBJJPQ6A1K5dm4SEhJDY2FjSp08f4ujoSLy9vcm5c+fI48ePSZs2bYifn5/cY5dUz7S0NMnKksnJyZIl40NCQoiOjg7ZuXMniYmJIfPmzSNGRkbEzc1NbjmydO/enbi5uZGIiAhy584d0rZtW6Knpyf1Gvv4+BB/f39y+/Zt8vTpUzJjxgxibm5O3r9/TwghxNPTk8yfP1/quB4eHsUe+9zs2bOJl5eXUnWlqLKiQQNVqb4MGpycnCQf6kWWLl1K+Hw+IeTTSXH9+vVS+wQEBBBra2tSUFBQYnm3b98mACTLQssKGgBILSG+efNmYm1tLfeYRXXS09MjBgYGhGEYAoB4eHgQgUBACCFk7ty5xMXFhYjFYqnjGhoaSk6WpQUNZ8+eJTweT2rZ4LCwMJlBw86dOyX7PHr0iAAgT548UbiNpb0OU6ZMId7e3lLtKbJmzRrSsGFDSdtLs27dOlK/fv1ijwOQOjlGREQQAGTXrl2Sxw4dOkR0dXXlHrukehaVUfS7K8Ln88nEiROlHmvdurVSQUNsbCwBQG7duiV57MmTJwSA5DW+du0aMTY2Jvn5+VLPdXJyItu2bSOEsL8bJyenYsctei1l2bBhA3F0dFS4rhRVHvT2BKU2OTk5iIuLw+jRo2FoaCj5WrZsWbEuaE9Pz2LPb9q0KbS1taUei4qKgr+/P+rWrQsjIyN4eXkBABITE+XWQ19fX6r719bWVuqWhjwhISG4e/cujh07hgYNGmDv3r3Q0tICADx58gR8Pl9q7ES7du2QnZ2N169fl3psAIiNjYW9vT1sbGwkj7Vq1Urmvs2aNZOqPwCpNpTURkVehxEjRuDevXtwcXHB1KlTceHCBcmxBgwYgLy8PNSvXx9jx45FaGio1G2YL+Xl5UFXV7fUdlhbWwNgX+fPH8vPz0dmZqbM55dUT3mePHmC1q1bSz3G5/NLfd6Xx+DxePDw8JA85urqClNTU8nP9+/fR3Z2NszNzaV+z/Hx8ZLf86BBg5CQkIDIyEgAwIEDB+Du7g5XV1e5Zevp6SE3N1ep+lJUWfHUXQGq5srOzgYA7Nixo9iHNpfLlfrZwMCg2PO/fCwnJwe+vr7w9fXFgQMHYGlpicTERPj6+pY4ULLoRF+EYRgQQkqtv729PZydneHs7AyhUIi+ffsiOjoaOjo6pT4XADgcTrFyZA3yVMTnbSgKVMRiscztRfsUla3I6+Du7o74+HicPXsWFy9exLfffgsfHx8cPXoU9vb2iI2NxcWLFxEWFoaJEydi9erVuHLlSrFyAcDCwgIfP35UuB2lte1zJdVT3bKzs2Fra4vw8PBi24qCCxsbG3h7e+PgwYNo06YNDh48iAkTJpR43A8fPsDS0rICakxRxdGeBkptrK2tYWdnhxcvXqBBgwZSX/Xq1VP6eDExMXj//j2Cg4PRoUMHuLq6KtRjoAr9+/cHj8eTDB4sGsT3eVBw48YNGBkZoU6dOgAAS0tLqcGImZmZiI+Pl/zs4uKCV69eSc3IuH37tsrrrujrYGxsjIEDB2LHjh0ICQnBsWPH8OHDBwDs1a6/vz82btyI8PBwRERE4OHDhzLLa9GiBVJSUuQGDuVVUj21tLQgEomk9m/UqJHUAFYAkit9Rbm6ukIoFCIqKkryWGxsrFQ+EHd3d6SkpIDH4xX7PVtYWEj2Gzp0KEJCQhAREYEXL16UOi01OjoaLVq0UKq+FFVWNGig1Grx4sVYuXIlNm7ciKdPn+Lhw4fYs2cP1q5dq/Sx6tatC21tbfzyyy948eIFTp48iaVLl1ZArYtjGAZTp05FcHAwcnNzMXHiRLx69QpTpkxBTEwMTpw4gaCgIEyfPl0yzdDb2xv79u3DtWvX8PDhQwQEBEj1sHTt2hVOTk4ICAjAgwcPcOPGDcyfP19SniqV9jqsXbsWhw4dQkxMDJ4+fYojR47AxsYGpqam2Lt3L3bt2oXo6Gi8ePEC+/fvh56eHhwcHGSW1aJFC1hYWODGjRsqbUNp9QTYGRSXLl2SClqmTZuG3bt3Y8+ePXj69CmCgoKKTWEMDQ0t8RaBi4sL/Pz8MH78eNy8eRNRUVEYM2YM9PT0JPv4+PiAz+ejT58+uHDhAhISEvDPP/9g3rx5Ugma+vXrh6ysLEyYMAGdO3eGnZ0dACApKQmurq64deuWVNnXrl1Dt27dyvV7oyhF0aCBUqsxY8Zg586d2LNnD5o2bQovLy/s3bu3TD0NlpaW2Lt3L44cOYLGjRsjODgYP//8cwXUWraAgAAUFhZi06ZNqF27Ns6cOYNbt27Bzc0N33//PUaPHi056QPAnDlz4OXlhZ49e6JHjx7o06eP1LgDLpeL48ePIzs7Gy1btsSYMWMwb948AJA7JqCsSnsdjIyMsGrVKnh6eqJly5ZISEjAmTNnwOFwYGpqih07dqBdu3Zo1qwZLl68iFOnTsHc3FxmWVwuFyNHjpQ5JbG8SqonAKxZswZhYWGwt7eXXJ0PHDgQCxYswP/+9z94eHjg5cuXxW4JZGRkIDY2tsSy9+zZAzs7O3h5eaFfv34YN24crKysJNsZhsGZM2fQsWNHjBw5Eg0bNsSgQYPw8uVLyfiNojb4+/vj/v37GDp0qOTxwsJCxMbGSo1fiIiIQEZGBvr371/2XxpFKYEhity8pShKI9y4cQPt27fH8+fP5c7drwpSUlLw1Vdf4d9//5XbI0GVbuDAgXBzc8PcuXPVXRWqhqADISlKg4WGhsLQ0BDOzs54/vw5pk2bhnbt2lXpgAFgB/zt2rULiYmJNGgoI4FAgKZNm+LHH39Ud1WoGoT2NFCUBvv999+xbNkyJCYmwsLCAj4+PlizZo3crn+KoqiKRIMGiqIoiqIUQgdCUhRFURSlEBo0UBRFURSlEBo0UBRFURSlEBo0UBRFURSlEBo0UBRFURSlEBo0UBRFURSlEBo0UBRFURSlEBo0UBRFURSlkP8Dr5ysi0K8lCMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = EasyMap().LAND().ax\n",
    "\n",
    "art = ax.scatter(\n",
    "    terrain_roughness.point_longitude,\n",
    "    terrain_roughness.point_latitude,\n",
    "    c=terrain_roughness,\n",
    "    cmap=\"winter\",\n",
    ")\n",
    "plt.colorbar(\n",
    "    art,\n",
    "    ax=ax,\n",
    "    label=\"Terrain Roughness (m std. dev.)\",\n",
    "    orientation=\"horizontal\",\n",
    "    pad=0.02,\n",
    "    shrink=0.8,\n",
    ")\n",
    "\n",
    "ax.set_title(\n",
    "    \"Terrain Variability from HRRR Model Elevation\", fontsize=14, fontweight=\"bold\"\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "herbie-data",
   "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": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}