{
 "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's \u001b[32mlatest\u001b[0m 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>2024-11-11 22:37:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>40.0</td><td>&quot;Fahrenheit&quot;</td><td>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>2024-11-11 21:37:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-11-11 21:59:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>42.0</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>2024-11-11 21:59:00 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>2024-11-11 22:22:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>39.0</td><td>&quot;Fahrenheit&quot;</td><td>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>2024-11-11 21:47:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-11-11 21:15:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>53.0</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>2024-11-11 21:15:00 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>2024-11-11 22:09:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>45.0</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>2024-11-11 22:09:00 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>2024-11-11 22:45:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>42.0</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>2024-11-11 22:15:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-11-11 22:45:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>43.0</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>2024-11-11 22:15:00 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",
       "│ 1         ┆          ┆           ┆           ┆   ┆ 8         ┆ 1         ┆           ┆           │\n",
       "│ 22:37:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 21:37:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 1998-07-2 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 1         ┆          ┆           ┆           ┆   ┆ 8         ┆ 1         ┆           ┆           │\n",
       "│ 21:59:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 21:59:00  ┆           ┆           │\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",
       "│ 1         ┆          ┆           ┆           ┆   ┆ 4         ┆ 1         ┆           ┆           │\n",
       "│ 22:22:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 21:47:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2013-01-0 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 1         ┆          ┆           ┆           ┆   ┆ 4         ┆ 1         ┆           ┆           │\n",
       "│ 21:15:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 21:15:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ …         ┆ …        ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …         │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2013-01-0 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 1         ┆          ┆           ┆           ┆   ┆ 4         ┆ 1         ┆           ┆           │\n",
       "│ 22:09:00  ┆          ┆           ┆           ┆   ┆ 00:00:00  ┆ 22:09:00  ┆           ┆           │\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",
       "│ 1         ┆          ┆           ┆           ┆   ┆ 2         ┆ 1         ┆           ┆           │\n",
       "│ 22:45:00  ┆          ┆           ┆           ┆   ┆ 08:19:00  ┆ 22:15:00  ┆           ┆           │\n",
       "│ UTC       ┆          ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆           │\n",
       "│ 2024-11-1 ┆ air_temp ┆ 1         ┆ false     ┆ … ┆ 2023-01-0 ┆ 2024-11-1 ┆ false     ┆ true      │\n",
       "│ 1         ┆          ┆           ┆           ┆   ┆ 3         ┆ 1         ┆           ┆           │\n",
       "│ 22:45:00  ┆          ┆           ┆           ┆   ┆ 22:09:00  ┆ 22:15:00  ┆           ┆           │\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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7ff10a726510>"
      ]
     },
     "execution_count": 3,
     "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 122 degree F and another reporting -44 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
}