{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Latest\n",
    "\n",
    "Demonstrate `synoptic.services.Latest`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from synoptic import Latest\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚚💨 Speedy delivery from Synoptic latest service.\n",
      "📦 Received data from 12 stations.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (12, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_time</th><th>variable</th><th>sensor_index</th><th>is_derived</th><th>value</th><th>units</th><th>id</th><th>stid</th><th>name</th><th>elevation</th><th>latitude</th><th>longitude</th><th>mnet_id</th><th>state</th><th>timezone</th><th>elev_dem</th><th>period_of_record_start</th><th>period_of_record_end</th><th>is_restricted</th><th>is_active</th></tr><tr><td>datetime[μs, UTC]</td><td>str</td><td>u32</td><td>bool</td><td>f64</td><td>str</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>datetime[μs, UTC]</td><td>datetime[μs, UTC]</td><td>bool</td><td>bool</td></tr></thead><tbody><tr><td>2024-11-10 06:37:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>37.0</td><td>&quot;Fahrenheit&quot;</td><td>2169</td><td>&quot;BLUN2&quot;</td><td>&quot;BLUEWING MOUNTAIN&quot;</td><td>4570.0</td><td>40.50147</td><td>-119.12161</td><td>2</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>4655.5</td><td>1998-07-28 00:00:00 UTC</td><td>2024-11-10 05:37:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-11-10 06:59:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>40.0</td><td>&quot;Fahrenheit&quot;</td><td>2171</td><td>&quot;BUFN2&quot;</td><td>&quot;BUFFALO CREEK&quot;</td><td>3940.0</td><td>40.58058</td><td>-119.78881</td><td>2</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>3917.3</td><td>1998-07-28 00:00:00 UTC</td><td>2024-11-10 05:59:00 UTC</td><td>false</td><td>true</td></tr><tr><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>24903</td><td>&quot;D1623&quot;</td><td>&quot;DW1623 Gerlach&quot;</td><td>3947.0</td><td>40.65133</td><td>-119.35517</td><td>65</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>3950.1</td><td>2008-11-05 00:00:00 UTC</td><td>2012-11-22 12:44:00 UTC</td><td>false</td><td>false</td></tr><tr><td>2024-11-10 07:00:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>36.0</td><td>&quot;Fahrenheit&quot;</td><td>35321</td><td>&quot;UP973&quot;</td><td>&quot;PHIL&quot;</td><td>3998.0</td><td>40.62174</td><td>-119.41384</td><td>64</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>3930.4</td><td>2013-01-04 00:00:00 UTC</td><td>2024-11-10 05:22:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-11-10 06:36:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>39.0</td><td>&quot;Fahrenheit&quot;</td><td>35322</td><td>&quot;UP974&quot;</td><td>&quot;TREGO&quot;</td><td>4005.0</td><td>40.78996</td><td>-119.07088</td><td>64</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>3937.0</td><td>2013-01-04 00:00:00 UTC</td><td>2024-11-10 06:12:00 UTC</td><td>false</td><td>true</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2024-11-10 06:28:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>48.0</td><td>&quot;Fahrenheit&quot;</td><td>35347</td><td>&quot;UP999&quot;</td><td>&quot;REYNRD&quot;</td><td>3880.0</td><td>40.53186</td><td>-119.61354</td><td>64</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>3874.7</td><td>2013-01-04 00:00:00 UTC</td><td>2024-11-10 04:26:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2023-12-18 20:20:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>37.14</td><td>&quot;Fahrenheit&quot;</td><td>41973</td><td>&quot;NHUA&quot;</td><td>&quot;Hualapai Flat&quot;</td><td>4055.0</td><td>40.90111</td><td>-119.33306</td><td>62</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>4081.4</td><td>2013-12-19 00:00:00 UTC</td><td>2024-01-01 07:20:00 UTC</td><td>false</td><td>false</td></tr><tr><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>57082</td><td>&quot;COOPGERN2&quot;</td><td>&quot;GERLACH&quot;</td><td>3954.0</td><td>40.6517</td><td>-119.3579</td><td>73</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>3927.2</td><td>2016-07-18 18:18:00 UTC</td><td>2019-05-17 16:00:00 UTC</td><td>false</td><td>false</td></tr><tr><td>2024-11-10 07:00:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>41.0</td><td>&quot;Fahrenheit&quot;</td><td>61916</td><td>&quot;F0371&quot;</td><td>&quot;FW0371 Gerlach&quot;</td><td>3953.0</td><td>40.65176</td><td>-119.35799</td><td>65</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>3963.3</td><td>2017-02-12 08:19:00 UTC</td><td>2024-11-10 06:00:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-11-10 07:00:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>39.0</td><td>&quot;Fahrenheit&quot;</td><td>181031</td><td>&quot;G2749&quot;</td><td>&quot;GW2749 Gerlach&quot;</td><td>4028.0</td><td>40.85933</td><td>-119.34183</td><td>65</td><td>&quot;NV&quot;</td><td>&quot;America/Los_Angeles&quot;</td><td>4022.3</td><td>2023-01-03 22:09:00 UTC</td><td>2024-11-10 06:15:00 UTC</td><td>false</td><td>true</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (12, 20)\n",
       "┌───────────┬──────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ date_time ┆ variable ┆ sensor_in ┆ is_derive ┆ … ┆ period_of ┆ period_of ┆ is_restri ┆ is_active │\n",
       "│ ---       ┆ ---      ┆ dex       ┆ d         ┆   ┆ _record_s ┆ _record_e ┆ cted      ┆ ---       │\n",
       "│ datetime[ ┆ str      ┆ ---       ┆ ---       ┆   ┆ tart      ┆ nd        ┆ ---       ┆ bool      │\n",
       "│ μs, UTC]  ┆          ┆ u32       ┆ bool      ┆   ┆ ---       ┆ ---       ┆ bool      ┆           │\n",
       "│           ┆          ┆           ┆           ┆   ┆ datetime[ ┆ datetime[ ┆           ┆           │\n",
       "│           ┆          ┆           ┆           ┆   ┆ μs, UTC]  ┆ μs, UTC]  ┆           ┆           │\n",
       "╞═══════════╪══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 1998-07-2 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 0         ┆          ┆           ┆           ┆   ┆ 8         ┆ 0         ┆           ┆           │\n",
       "│ 06:37:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:37:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 1998-07-2 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 0         ┆          ┆           ┆           ┆   ┆ 8         ┆ 0         ┆           ┆           │\n",
       "│ 06:59:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:59:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ null      ┆ null     ┆ null      ┆ null      ┆ … ┆ 2008-11-0 ┆ 2012-11-2 ┆ false     ┆ false     │\n",
       "│           ┆          ┆           ┆           ┆   ┆ 5         ┆ 2         ┆           ┆           │\n",
       "│           ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 12:44:00  ┆           ┆           │\n",
       "│           ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2013-01-0 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 0         ┆          ┆           ┆           ┆   ┆ 4         ┆ 0         ┆           ┆           │\n",
       "│ 07:00:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:22:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2013-01-0 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 0         ┆          ┆           ┆           ┆   ┆ 4         ┆ 0         ┆           ┆           │\n",
       "│ 06:36:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 06:12:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ …         ┆ …        ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …         │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2013-01-0 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 0         ┆          ┆           ┆           ┆   ┆ 4         ┆ 0         ┆           ┆           │\n",
       "│ 06:28:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 04:26:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2023-12-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2013-12-1 ┆ 2024-01-0 ┆ false     ┆ false     │\n",
       "│ 8         ┆          ┆           ┆           ┆   ┆ 9         ┆ 1         ┆           ┆           │\n",
       "│ 20:20:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 07:20:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ null      ┆ null     ┆ null      ┆ null      ┆ … ┆ 2016-07-1 ┆ 2019-05-1 ┆ false     ┆ false     │\n",
       "│           ┆          ┆           ┆           ┆   ┆ 8         ┆ 7         ┆           ┆           │\n",
       "│           ┆          ┆           ┆           ┆   ┆ 18:18:00  ┆ 16:00:00  ┆           ┆           │\n",
       "│           ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2017-02-1 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 0         ┆          ┆           ┆           ┆   ┆ 2         ┆ 0         ┆           ┆           │\n",
       "│ 07:00:00  ┆          ┆           ┆           ┆   ┆ 08:19:00  ┆ 06:00:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2023-01-0 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 0         ┆          ┆           ┆           ┆   ┆ 3         ┆ 0         ┆           ┆           │\n",
       "│ 07:00:00  ┆          ┆           ┆           ┆   ┆ 22:09:00  ┆ 06:15:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "└───────────┴──────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = Latest(\n",
    "    bbox=[-120, 40, -119, 41],\n",
    "    showemptystations=True,\n",
    "    vars=\"air_temp\",\n",
    "    units=\"english\",\n",
    ").df()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f03762f5670>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "for i in df.iter_rows(named=True):\n",
    "    ax.text(\n",
    "        i[\"longitude\"],\n",
    "        i[\"latitude\"] + 0.01,\n",
    "        f'{i[\"stid\"]}\\n{i[\"value\"]} F',\n",
    "        va=\"bottom\",\n",
    "        ha=\"left\",\n",
    "    )\n",
    "    if i[\"value\"] is None:\n",
    "        art = ax.scatter(\n",
    "            i[\"longitude\"],\n",
    "            i[\"latitude\"],\n",
    "            color=None,\n",
    "            ec=\"k\",\n",
    "            s=100,\n",
    "        )\n",
    "    else:\n",
    "        art = ax.scatter(\n",
    "            i[\"longitude\"],\n",
    "            i[\"latitude\"],\n",
    "            c=i[\"value\"],\n",
    "            ec=\"k\",\n",
    "            cmap=\"Spectral_r\",\n",
    "            vmin=20,\n",
    "            vmax=80,\n",
    "            s=100,\n",
    "        )\n",
    "plt.colorbar(art, ax=ax, location=\"bottom\", pad=0.05, label=\"Air Temperature (F)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whoa, there is a station reporting 118 degree F and another reporting 18 F."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "synoptic2",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}