Tilde/Tilde_endpoint01-dataSummary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "amazing-gateway",
   "metadata": {},
   "source": [
    "# Exploring Data Summary from Tilde"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "central-current",
   "metadata": {},
   "source": [
    "## &nbsp;Table of contents\n",
    "### 1. Building the Basic Tilde Query\n",
    "### 2. Building a Tilde Query with a specific domain\n",
    "### 3. Find station locations and display on a map\n",
    "### 4. Station data and data range graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stupid-citation",
   "metadata": {},
   "source": [
    "## &nbsp;1. Building the Basic Tilde Query"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clear-possession",
   "metadata": {},
   "source": [
    "###### Import required modules and set the source URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "juvenile-vaccine",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.transforms import offset_copy\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "demonstrated-softball",
   "metadata": {},
   "outputs": [],
   "source": [
    "#API version 4\n",
    "source = 'https://tilde.geonet.org.nz/v4'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "controlling-seminar",
   "metadata": {},
   "source": [
    "#### A basic query with no parameters applied\n",
    "\n",
    "This query returns information on the different data domains held in Tilde using the 'dataSummary' endpoint `https://tilde.geonet.org.nz/v4/dataSummary/`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prime-threat",
   "metadata": {},
   "source": [
    "######  Set the URL of a basic data summary query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "possible-carnival",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = source+'/dataSummary/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "guided-revelation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(url)\n",
    "print (r)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daily-bankruptcy",
   "metadata": {},
   "source": [
    "###### Store the response as a nested dictionary and see the output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electronic-single",
   "metadata": {},
   "source": [
    "A nested dictionary in python is a dictionary within a dictionary. It's a collection of dictionaries into one single directory. <br>\n",
    "In TILDE, each data domain has different entries which sit within that specific domain name. For example, DART data sit within the _dart_ domain. By creating a nested dictionary we can specify the domain and then split it by domain name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "pretty-repair",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = r.json()\n",
    "type(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "infrared-necessity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'domains': {'coastal': {'domain': 'coastal',\n",
       "   'description': 'Coastal Tsunami Gauge Network',\n",
       "   'recordCount': 334122771,\n",
       "   'seriesCount': 73,\n",
       "   'stationCount': 19,\n",
       "   'earliestRecord': '2022-10-30T22:40:15Z',\n",
       "   'latestRecord': '2025-01-09T15:59:45Z'},\n",
       "  'dart': {'domain': 'dart',\n",
       "   'description': 'Deep-ocean Assessment and Reporting of Tsunami',\n",
       "   'recordCount': 318472911,\n",
       "   'seriesCount': 212,\n",
       "   'stationCount': 12,\n",
       "   'earliestRecord': '2019-12-13T00:00:00Z',\n",
       "   'latestRecord': '2025-01-09T12:00:00Z'},\n",
       "  'envirosensor': {'domain': 'envirosensor',\n",
       "   'description': 'Environmental Sensors',\n",
       "   'recordCount': 33569257,\n",
       "   'seriesCount': 292,\n",
       "   'stationCount': 19,\n",
       "   'earliestRecord': '1993-12-11T00:00:00Z',\n",
       "   'latestRecord': '2025-01-09T15:50:00Z'},\n",
       "  'geomag': {'domain': 'geomag',\n",
       "   'description': 'Geomagnetic Sensors',\n",
       "   'recordCount': 12463172,\n",
       "   'seriesCount': 32,\n",
       "   'stationCount': 5,\n",
       "   'earliestRecord': '2024-03-31T23:56:00Z',\n",
       "   'latestRecord': '2025-01-09T15:59:00Z'},\n",
       "  'manualcollect': {'domain': 'manualcollect',\n",
       "   'description': 'Manually Collected Samples',\n",
       "   'recordCount': 72114,\n",
       "   'seriesCount': 2079,\n",
       "   'stationCount': 236,\n",
       "   'earliestRecord': '1950-03-01T00:00:00Z',\n",
       "   'latestRecord': '2024-12-13T23:36:00Z'},\n",
       "  'scandoas': {'domain': 'scandoas',\n",
       "   'description': 'Continuous sulphur dioxide gas emission rates and supporting data',\n",
       "   'recordCount': 92132,\n",
       "   'seriesCount': 8,\n",
       "   'stationCount': 8,\n",
       "   'earliestRecord': '2009-08-01T22:41:25Z',\n",
       "   'latestRecord': '2025-01-06T23:58:42Z'}}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-favor",
   "metadata": {},
   "source": [
    "######  Specifying the domains from the summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "killing-charles",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'coastal': {'domain': 'coastal',\n",
       "  'description': 'Coastal Tsunami Gauge Network',\n",
       "  'recordCount': 334122771,\n",
       "  'seriesCount': 73,\n",
       "  'stationCount': 19,\n",
       "  'earliestRecord': '2022-10-30T22:40:15Z',\n",
       "  'latestRecord': '2025-01-09T15:59:45Z'},\n",
       " 'dart': {'domain': 'dart',\n",
       "  'description': 'Deep-ocean Assessment and Reporting of Tsunami',\n",
       "  'recordCount': 318472911,\n",
       "  'seriesCount': 212,\n",
       "  'stationCount': 12,\n",
       "  'earliestRecord': '2019-12-13T00:00:00Z',\n",
       "  'latestRecord': '2025-01-09T12:00:00Z'},\n",
       " 'envirosensor': {'domain': 'envirosensor',\n",
       "  'description': 'Environmental Sensors',\n",
       "  'recordCount': 33569257,\n",
       "  'seriesCount': 292,\n",
       "  'stationCount': 19,\n",
       "  'earliestRecord': '1993-12-11T00:00:00Z',\n",
       "  'latestRecord': '2025-01-09T15:50:00Z'},\n",
       " 'geomag': {'domain': 'geomag',\n",
       "  'description': 'Geomagnetic Sensors',\n",
       "  'recordCount': 12463172,\n",
       "  'seriesCount': 32,\n",
       "  'stationCount': 5,\n",
       "  'earliestRecord': '2024-03-31T23:56:00Z',\n",
       "  'latestRecord': '2025-01-09T15:59:00Z'},\n",
       " 'manualcollect': {'domain': 'manualcollect',\n",
       "  'description': 'Manually Collected Samples',\n",
       "  'recordCount': 72114,\n",
       "  'seriesCount': 2079,\n",
       "  'stationCount': 236,\n",
       "  'earliestRecord': '1950-03-01T00:00:00Z',\n",
       "  'latestRecord': '2024-12-13T23:36:00Z'},\n",
       " 'scandoas': {'domain': 'scandoas',\n",
       "  'description': 'Continuous sulphur dioxide gas emission rates and supporting data',\n",
       "  'recordCount': 92132,\n",
       "  'seriesCount': 8,\n",
       "  'stationCount': 8,\n",
       "  'earliestRecord': '2009-08-01T22:41:25Z',\n",
       "  'latestRecord': '2025-01-06T23:58:42Z'}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domains = summary['domains']\n",
    "domains"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graphic-finland",
   "metadata": {},
   "source": [
    "**Access Method 1 - Use the items method to access each entry**\n",
    "<br>\n",
    "To have actual information about the domain you can print out the key-value pairs as shown below. In this case we print out various item names that have been returned for our _dart_ domain in the response above. We are printing out each item line by line, with the domain name being at the top. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fiscal-reply",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Domain =  coastal\n",
      "\tDomain name = None\n",
      "\tDescription = Coastal Tsunami Gauge Network\n",
      "\tRecord count = 334122771\n",
      "\tSeries count = 73\n",
      "\tStation count = 19\n",
      "\tEarliest record = 2022-10-30T22:40:15Z\n",
      "\tLatest record = 2025-01-09T15:59:45Z\n",
      "For Domain =  dart\n",
      "\tDomain name = None\n",
      "\tDescription = Deep-ocean Assessment and Reporting of Tsunami\n",
      "\tRecord count = 318472911\n",
      "\tSeries count = 212\n",
      "\tStation count = 12\n",
      "\tEarliest record = 2019-12-13T00:00:00Z\n",
      "\tLatest record = 2025-01-09T12:00:00Z\n",
      "For Domain =  envirosensor\n",
      "\tDomain name = None\n",
      "\tDescription = Environmental Sensors\n",
      "\tRecord count = 33569257\n",
      "\tSeries count = 292\n",
      "\tStation count = 19\n",
      "\tEarliest record = 1993-12-11T00:00:00Z\n",
      "\tLatest record = 2025-01-09T15:50:00Z\n",
      "For Domain =  geomag\n",
      "\tDomain name = None\n",
      "\tDescription = Geomagnetic Sensors\n",
      "\tRecord count = 12463172\n",
      "\tSeries count = 32\n",
      "\tStation count = 5\n",
      "\tEarliest record = 2024-03-31T23:56:00Z\n",
      "\tLatest record = 2025-01-09T15:59:00Z\n",
      "For Domain =  manualcollect\n",
      "\tDomain name = None\n",
      "\tDescription = Manually Collected Samples\n",
      "\tRecord count = 72114\n",
      "\tSeries count = 2079\n",
      "\tStation count = 236\n",
      "\tEarliest record = 1950-03-01T00:00:00Z\n",
      "\tLatest record = 2024-12-13T23:36:00Z\n",
      "For Domain =  scandoas\n",
      "\tDomain name = None\n",
      "\tDescription = Continuous sulphur dioxide gas emission rates and supporting data\n",
      "\tRecord count = 92132\n",
      "\tSeries count = 8\n",
      "\tStation count = 8\n",
      "\tEarliest record = 2009-08-01T22:41:25Z\n",
      "\tLatest record = 2025-01-06T23:58:42Z\n"
     ]
    }
   ],
   "source": [
    "#iterate over key,value pairs and print the results\n",
    "for domain,detail in domains.items():\n",
    "    print ('For Domain = ', domain)     \n",
    "    print ('\\tDomain name =', detail.get('domain_name'))\n",
    "    print ('\\tDescription =', detail.get('description'))\n",
    "    print ('\\tRecord count =', detail.get('recordCount'))\n",
    "    print ('\\tSeries count =', detail.get('seriesCount'))\n",
    "    print ('\\tStation count =', detail.get('stationCount'))\n",
    "    print ('\\tEarliest record =', detail.get('earliestRecord'))\n",
    "    print ('\\tLatest record =', detail.get('latestRecord'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quick-mapping",
   "metadata": {},
   "source": [
    "**Access Method 2 - Summary info in table format**\n",
    "<br>\n",
    "Pandas dataframes can be quite useful because they enable the user to visualize information in a table instead of line-by-line as shown above. The dataframe calls for the values in the 'domains' variable that we set above and then the 'orient' (orientation) is specified to be the 'index' because we are using the highest level key as the index, and subsidiary keys as columns. Each domain has it's own row in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "grand-costume",
   "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>domain</th>\n",
       "      <th>description</th>\n",
       "      <th>recordCount</th>\n",
       "      <th>seriesCount</th>\n",
       "      <th>stationCount</th>\n",
       "      <th>earliestRecord</th>\n",
       "      <th>latestRecord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>coastal</th>\n",
       "      <td>coastal</td>\n",
       "      <td>Coastal Tsunami Gauge Network</td>\n",
       "      <td>334122771</td>\n",
       "      <td>73</td>\n",
       "      <td>19</td>\n",
       "      <td>2022-10-30T22:40:15Z</td>\n",
       "      <td>2025-01-09T15:59:45Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dart</th>\n",
       "      <td>dart</td>\n",
       "      <td>Deep-ocean Assessment and Reporting of Tsunami</td>\n",
       "      <td>318472911</td>\n",
       "      <td>212</td>\n",
       "      <td>12</td>\n",
       "      <td>2019-12-13T00:00:00Z</td>\n",
       "      <td>2025-01-09T12:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>envirosensor</th>\n",
       "      <td>envirosensor</td>\n",
       "      <td>Environmental Sensors</td>\n",
       "      <td>33569257</td>\n",
       "      <td>292</td>\n",
       "      <td>19</td>\n",
       "      <td>1993-12-11T00:00:00Z</td>\n",
       "      <td>2025-01-09T15:50:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geomag</th>\n",
       "      <td>geomag</td>\n",
       "      <td>Geomagnetic Sensors</td>\n",
       "      <td>12463172</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>2024-03-31T23:56:00Z</td>\n",
       "      <td>2025-01-09T15:59:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manualcollect</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>Manually Collected Samples</td>\n",
       "      <td>72114</td>\n",
       "      <td>2079</td>\n",
       "      <td>236</td>\n",
       "      <td>1950-03-01T00:00:00Z</td>\n",
       "      <td>2024-12-13T23:36:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scandoas</th>\n",
       "      <td>scandoas</td>\n",
       "      <td>Continuous sulphur dioxide gas emission rates ...</td>\n",
       "      <td>92132</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2009-08-01T22:41:25Z</td>\n",
       "      <td>2025-01-06T23:58:42Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      domain  \\\n",
       "coastal              coastal   \n",
       "dart                    dart   \n",
       "envirosensor    envirosensor   \n",
       "geomag                geomag   \n",
       "manualcollect  manualcollect   \n",
       "scandoas            scandoas   \n",
       "\n",
       "                                                     description  recordCount  \\\n",
       "coastal                            Coastal Tsunami Gauge Network    334122771   \n",
       "dart              Deep-ocean Assessment and Reporting of Tsunami    318472911   \n",
       "envirosensor                               Environmental Sensors     33569257   \n",
       "geomag                                       Geomagnetic Sensors     12463172   \n",
       "manualcollect                         Manually Collected Samples        72114   \n",
       "scandoas       Continuous sulphur dioxide gas emission rates ...        92132   \n",
       "\n",
       "               seriesCount  stationCount        earliestRecord  \\\n",
       "coastal                 73            19  2022-10-30T22:40:15Z   \n",
       "dart                   212            12  2019-12-13T00:00:00Z   \n",
       "envirosensor           292            19  1993-12-11T00:00:00Z   \n",
       "geomag                  32             5  2024-03-31T23:56:00Z   \n",
       "manualcollect         2079           236  1950-03-01T00:00:00Z   \n",
       "scandoas                 8             8  2009-08-01T22:41:25Z   \n",
       "\n",
       "                       latestRecord  \n",
       "coastal        2025-01-09T15:59:45Z  \n",
       "dart           2025-01-09T12:00:00Z  \n",
       "envirosensor   2025-01-09T15:50:00Z  \n",
       "geomag         2025-01-09T15:59:00Z  \n",
       "manualcollect  2024-12-13T23:36:00Z  \n",
       "scandoas       2025-01-06T23:58:42Z  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict(data=domains, orient='index')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prerequisite-rough",
   "metadata": {},
   "source": [
    "#### &nbsp;2. Building a Tilde Query with a specific domain\n",
    "\n",
    "This query returns information about the specific data domains provided. <br> \n",
    "The query is `https://tilde.geonet.org.nz/v4/dataSummary/[domain]`. For this example we will continue to use the dart domain."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solved-columbus",
   "metadata": {
    "tags": []
   },
   "source": [
    "######  Exploring summary for the dart domain\n",
    "<br>\n",
    "Following the same steps as above but specifying 'dart' domain after 'dataSummary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "loved-civilization",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydomain='dart'\n",
    "url = 'https://tilde.geonet.org.nz/v4/dataSummary/'+mydomain\n",
    "r = requests.get(url)\n",
    "summary = r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sealed-interface",
   "metadata": {},
   "source": [
    "#### Find available data methods, specifically for DART `water-height` data\n",
    "For the DART data there are two sampling rates for water-height, 15 minutes (15m) and 15 seconds (15s). There is also raw data which are collected from a DART station only when it is serviced."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cultural-consultation",
   "metadata": {},
   "source": [
    "**Dig down to the specific key for data methods**\n",
    "<br>\n",
    "This shows the different data methods that are within the summary file for water-height data. We can see the number of records, earliest and latest record, number of stations collecting data, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "junior-border",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'15m': {'method': '15m',\n",
       "  'aspects': {'nil': {'aspect': 'nil',\n",
       "    'recordCount': 1572808,\n",
       "    'stationCount': 12,\n",
       "    'earliestRecord': '2019-12-13T02:15:00Z',\n",
       "    'latestRecord': '2025-01-09T12:00:00Z'}}},\n",
       " '15s': {'method': '15s',\n",
       "  'aspects': {'nil': {'aspect': 'nil',\n",
       "    'recordCount': 358776,\n",
       "    'stationCount': 12,\n",
       "    'earliestRecord': '2020-03-14T10:00:00Z',\n",
       "    'latestRecord': '2024-12-17T05:59:45Z'}}},\n",
       " 'raw': {'method': 'raw',\n",
       "  'aspects': {'nil': {'aspect': 'nil',\n",
       "    'recordCount': 79219748,\n",
       "    'stationCount': 12,\n",
       "    'earliestRecord': '2019-12-13T00:00:00Z',\n",
       "    'latestRecord': '2024-07-02T22:00:00Z'}}}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatypes = summary['domain']['dart']['names']['water-height']['methods']\n",
    "datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bored-magnet",
   "metadata": {},
   "source": [
    "## &nbsp;3. Find station locations and display on a map\n",
    "<BR> For this we'll use the coastal tsunami gauge data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "trying-latin",
   "metadata": {},
   "source": [
    "**Access Method 1 - Using the specific key for station metadata**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8c93051-2074-40da-9b6e-487a93b115fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydomain='coastal'\n",
    "url = 'https://tilde.geonet.org.nz/v4/dataSummary/'+mydomain\n",
    "r = requests.get(url)\n",
    "summary = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fabulous-seattle",
   "metadata": {},
   "outputs": [],
   "source": [
    "stations = summary['domain']['coastal']['stations']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "german-click",
   "metadata": {},
   "source": [
    "Using stations.items we can specify what variables we are after, e.g. station, latitude, longitude and then print these. The key used is also the station. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "north-associate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUCT AUCT -36.8314 174.7865\n",
      "CHIT CHIT -44.024 -176.3675\n",
      "CHST CHST -41.903 171.4341\n",
      "CPIT CPIT -40.8993 176.2317\n",
      "GBIT GBIT -36.189 175.4889\n",
      "GIST GIST -38.6754 178.0229\n",
      "HRMT HRMT -38.8129 175.9963\n",
      "KAIT KAIT -42.4129 173.7028\n",
      "LOTT LOTT -37.5504 178.159\n",
      "MNKT MNKT -37.0466 174.5117\n",
      "NAPT NAPT -39.4757 176.9201\n",
      "NBRT NBRT -43.506 172.735\n",
      "NCPT NCPT -34.4148 173.0487\n",
      "OTAT OTAT -45.8143 170.6294\n",
      "PUYT PUYT -46.0848 166.5894\n",
      "RBCT RBCT -29.28 -177.8944\n",
      "RFRT RFRT -29.2511 -177.9038\n",
      "TAUT TAUT -37.6411 176.1812\n",
      "WLGT WLGT -41.2845 174.7799\n"
     ]
    }
   ],
   "source": [
    "for key,val in stations.items():\n",
    "    print (key, val['station'], val['latitude'], val['longitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "failing-timothy",
   "metadata": {},
   "source": [
    "**Access Method 2 - Direct to a Pandas dataframe**\n",
    "<br>\n",
    "For easier reading we can make this information into a pandas dataframe where we are only focusing on station, latitude, and longitude. Which we can specify using `columns=[]`. So the station becomes the dataframe index and the other keys the column names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aquatic-ambassador",
   "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>station</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUCT</th>\n",
       "      <td>AUCT</td>\n",
       "      <td>-36.8314</td>\n",
       "      <td>174.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHIT</th>\n",
       "      <td>CHIT</td>\n",
       "      <td>-44.0240</td>\n",
       "      <td>-176.3675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHST</th>\n",
       "      <td>CHST</td>\n",
       "      <td>-41.9030</td>\n",
       "      <td>171.4341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPIT</th>\n",
       "      <td>CPIT</td>\n",
       "      <td>-40.8993</td>\n",
       "      <td>176.2317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIT</th>\n",
       "      <td>GBIT</td>\n",
       "      <td>-36.1890</td>\n",
       "      <td>175.4889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIST</th>\n",
       "      <td>GIST</td>\n",
       "      <td>-38.6754</td>\n",
       "      <td>178.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HRMT</th>\n",
       "      <td>HRMT</td>\n",
       "      <td>-38.8129</td>\n",
       "      <td>175.9963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KAIT</th>\n",
       "      <td>KAIT</td>\n",
       "      <td>-42.4129</td>\n",
       "      <td>173.7028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOTT</th>\n",
       "      <td>LOTT</td>\n",
       "      <td>-37.5504</td>\n",
       "      <td>178.1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNKT</th>\n",
       "      <td>MNKT</td>\n",
       "      <td>-37.0466</td>\n",
       "      <td>174.5117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAPT</th>\n",
       "      <td>NAPT</td>\n",
       "      <td>-39.4757</td>\n",
       "      <td>176.9201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBRT</th>\n",
       "      <td>NBRT</td>\n",
       "      <td>-43.5060</td>\n",
       "      <td>172.7350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCPT</th>\n",
       "      <td>NCPT</td>\n",
       "      <td>-34.4148</td>\n",
       "      <td>173.0487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTAT</th>\n",
       "      <td>OTAT</td>\n",
       "      <td>-45.8143</td>\n",
       "      <td>170.6294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUYT</th>\n",
       "      <td>PUYT</td>\n",
       "      <td>-46.0848</td>\n",
       "      <td>166.5894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBCT</th>\n",
       "      <td>RBCT</td>\n",
       "      <td>-29.2800</td>\n",
       "      <td>-177.8944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFRT</th>\n",
       "      <td>RFRT</td>\n",
       "      <td>-29.2511</td>\n",
       "      <td>-177.9038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAUT</th>\n",
       "      <td>TAUT</td>\n",
       "      <td>-37.6411</td>\n",
       "      <td>176.1812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WLGT</th>\n",
       "      <td>WLGT</td>\n",
       "      <td>-41.2845</td>\n",
       "      <td>174.7799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     station  latitude  longitude\n",
       "AUCT    AUCT  -36.8314   174.7865\n",
       "CHIT    CHIT  -44.0240  -176.3675\n",
       "CHST    CHST  -41.9030   171.4341\n",
       "CPIT    CPIT  -40.8993   176.2317\n",
       "GBIT    GBIT  -36.1890   175.4889\n",
       "GIST    GIST  -38.6754   178.0229\n",
       "HRMT    HRMT  -38.8129   175.9963\n",
       "KAIT    KAIT  -42.4129   173.7028\n",
       "LOTT    LOTT  -37.5504   178.1590\n",
       "MNKT    MNKT  -37.0466   174.5117\n",
       "NAPT    NAPT  -39.4757   176.9201\n",
       "NBRT    NBRT  -43.5060   172.7350\n",
       "NCPT    NCPT  -34.4148   173.0487\n",
       "OTAT    OTAT  -45.8143   170.6294\n",
       "PUYT    PUYT  -46.0848   166.5894\n",
       "RBCT    RBCT  -29.2800  -177.8944\n",
       "RFRT    RFRT  -29.2511  -177.9038\n",
       "TAUT    TAUT  -37.6411   176.1812\n",
       "WLGT    WLGT  -41.2845   174.7799"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict(data=stations, orient='index', columns=['station', 'latitude', 'longitude'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exceptional-routine",
   "metadata": {},
   "source": [
    "**Map the positions, using the dataframe as the data source**\n",
    "<br>\n",
    "Using cartopy https://scitools.org.uk/cartopy/docs/latest/index.html and matplotlib we can then generate a map of the stations using the values (station, latitude and longitude) from the pandas dataframe `df` that we generated in the step above. Using the station variable we can also plot the names next to the stations using `ax.text`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "freelance-sculpture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree(central_longitude=180))\n",
    "ax.set_extent([165, 185, -50, -28], crs=ccrs.PlateCarree())\n",
    "\n",
    "# Put a background image on for nice sea rendering.\n",
    "ax.stock_img()\n",
    "\n",
    "ax.add_feature(cfeature.LAND)\n",
    "ax.add_feature(cfeature.COASTLINE)\n",
    "\n",
    "xx = df['longitude'].values\n",
    "yy = df['latitude'].values\n",
    "names = df['station'].values\n",
    "ax.plot(xx, yy, color='navy', marker='v', linestyle='None', transform=ccrs.Geodetic())\n",
    "\n",
    "geodetic_transform = ccrs.Geodetic()._as_mpl_transform(ax)\n",
    "text_transform = offset_copy(geodetic_transform, units='dots', y=-10)\n",
    "\n",
    "for x, y, n in zip(xx, yy, names):\n",
    "    ax.text(x, y, n, fontsize=8, verticalalignment='center', horizontalalignment='center', transform=text_transform)\n",
    "             "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4041791-b2c5-407a-9065-e892f4728774",
   "metadata": {},
   "source": [
    "## &nbsp;4. Station data and data plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46106dfa-3e4f-4bc2-866c-760ce416b768",
   "metadata": {},
   "source": [
    "We want to generate a dataframe that contains all the information summarising the data available at each station. For this example we'll use the `enivirosensor` domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a28379e9-bcd7-4ddb-87fe-c1b451d9e89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydomain = 'envirosensor'\n",
    "url = 'https://tilde.geonet.org.nz/v4/dataSummary/'+mydomain\n",
    "r = requests.get(url)\n",
    "summary = r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa49e35f-5656-4a9d-8489-1e10f8b07bc9",
   "metadata": {},
   "source": [
    "Each station in the envirosensor domain provides several data series, and virtually no two stations are alike. This makes for a long, complex data summary. And the JSON format doesn't make it easy to read. It will be very useful if we can list each of the stations and exactly what data streams are in each record in a table-like format. We'll make a pandas dataframe for this.\n",
    "\n",
    "We do this with a function and then call it with the output going to a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "13f11d5b-2532-400e-aafc-83cb6d49d730",
   "metadata": {},
   "outputs": [],
   "source": [
    "def station_data(summary, domain):\n",
    "    #dataframe for results\n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    #select just station part of dictionary\n",
    "    stations = summary['domain'][domain]['stations']\n",
    "\n",
    "    #get list of stations\n",
    "    # print ('##Station locations##')\n",
    "    stationlist = []\n",
    "    reccount = 0\n",
    "    for key,val in stations.items():\n",
    "        stationlist.append(key)\n",
    "        # print (key, val['station_name'], val['latitude'], val['longitude'])\n",
    "\n",
    "    #with each station, get the location information, and the sensor_codes part of the dictionary\n",
    "    # print('\\n')\n",
    "    # print ('##Station details##')\n",
    "    for sta in stationlist:\n",
    "        # print ('##'+sta+'##')\n",
    "        latitude = stations[sta].get('latitude')\n",
    "        longitude = stations[sta].get('longitude')\n",
    "        sensorcodes = stations[sta]['sensorCodes']\n",
    "        # print (sensorcodes)\n",
    "\n",
    "        codelist = []\n",
    "        for key,val in sensorcodes.items():\n",
    "            codelist.append(key)\n",
    "            # print ('sensor code = ', val['sensor_code'])\n",
    "            \n",
    "        #get the names part\n",
    "        for code in codelist:\n",
    "            # print ('code=',code)\n",
    "            names = sensorcodes[code]['names']\n",
    "\n",
    "            namelist = []\n",
    "            for key,val in names.items():\n",
    "                namelist.append(key)\n",
    "                # print (sta, code, val['measure_name'])\n",
    "\n",
    "            #get the methods part    \n",
    "            for name in namelist:\n",
    "                # print ('name='+name)\n",
    "                methods = names[name]['methods']\n",
    "\n",
    "                methodlist = []\n",
    "                for key,val in methods.items():\n",
    "                    methodlist.append(key)\n",
    "                    \n",
    "                #get the aspects part    \n",
    "                for method in methodlist:\n",
    "                    # print ('method=', method)\n",
    "                    aspects  = methods[method]['aspects']\n",
    "\n",
    "                    aspectlist = []\n",
    "                    for key,val in aspects.items():\n",
    "                        aspectlist.append(key)\n",
    "                        # print (sta, code, name, method, val['aspect'])\n",
    "                        \n",
    "                        #get the record data\n",
    "                        for aspect in aspectlist:\n",
    "                                # print ('aspect=', aspect)\n",
    "                                records = aspects[aspect]\n",
    "                                recordcount = records.get('recordCount')\n",
    "                                earliestrecord = records.get('earliestRecord')\n",
    "                                latestrecord = records.get('latestRecord')\n",
    "                                # print (recordcount, earliestrecord, latestrecord)\n",
    "                                            \n",
    "                                #dictionary containing the various data\n",
    "                                dic = {'station':sta, 'latitude':latitude, 'longitude':longitude, 'sensor_code':code, 'data_name':name, 'data_method':method, 'aspect':aspect, 'record_count':recordcount, 'earliestrecord':earliestrecord, 'latestrecord':latestrecord}\n",
    "                                #make a temprary dataframe from the dictionary\n",
    "                                tempdf = pd.DataFrame(dic, index=[reccount])\n",
    "                                #df = df.append(d, ignore_index=True)\n",
    "                                #concatenate with dataframe containing site information\n",
    "                                df = pd.concat([df, tempdf])\n",
    "                                reccount +=1\n",
    "\n",
    "    df['earliestrecord'] = pd.to_datetime(df['earliestrecord'])\n",
    "    df['latestrecord'] = pd.to_datetime(df['latestrecord'])\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d90b6544-8148-40dd-8062-e2015b300314",
   "metadata": {},
   "outputs": [],
   "source": [
    "#call function to get main station data, supply the domain name\n",
    "station_data = station_data(summary, mydomain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "643f6c87-0b90-445e-8e12-5a98e9fdaade",
   "metadata": {},
   "source": [
    "#### Use the dataframe of station record data and visualize this in different ways"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb53c1e7-8c6d-480f-864c-7a99dd159140",
   "metadata": {},
   "source": [
    "Use `station_data.head()` to see only the first few lines. If we used `station_data.tail()` we can see the last few lines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b4a25a56-49ad-4f8e-a4db-85c98d6d26e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\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>station</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>sensor_code</th>\n",
       "      <th>data_name</th>\n",
       "      <th>data_method</th>\n",
       "      <th>aspect</th>\n",
       "      <th>record_count</th>\n",
       "      <th>earliestrecord</th>\n",
       "      <th>latestrecord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALS1R</td>\n",
       "      <td>-36.9329</td>\n",
       "      <td>174.5764</td>\n",
       "      <td>01</td>\n",
       "      <td>total-rainfall</td>\n",
       "      <td>tot</td>\n",
       "      <td>nil</td>\n",
       "      <td>46683</td>\n",
       "      <td>2023-02-24 01:10:00+00:00</td>\n",
       "      <td>2024-02-25 13:50:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALS1R</td>\n",
       "      <td>-36.9329</td>\n",
       "      <td>174.5764</td>\n",
       "      <td>02</td>\n",
       "      <td>total-rainfall</td>\n",
       "      <td>tot</td>\n",
       "      <td>nil</td>\n",
       "      <td>46683</td>\n",
       "      <td>2023-02-24 01:10:00+00:00</td>\n",
       "      <td>2024-02-25 13:50:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALS2R</td>\n",
       "      <td>-36.9598</td>\n",
       "      <td>174.4718</td>\n",
       "      <td>01</td>\n",
       "      <td>total-rainfall</td>\n",
       "      <td>tot</td>\n",
       "      <td>nil</td>\n",
       "      <td>47831</td>\n",
       "      <td>2023-02-23 04:00:00+00:00</td>\n",
       "      <td>2024-03-13 20:50:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALS2R</td>\n",
       "      <td>-36.9598</td>\n",
       "      <td>174.4718</td>\n",
       "      <td>02</td>\n",
       "      <td>total-rainfall</td>\n",
       "      <td>tot</td>\n",
       "      <td>nil</td>\n",
       "      <td>47825</td>\n",
       "      <td>2023-02-23 04:00:00+00:00</td>\n",
       "      <td>2024-03-13 20:50:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOXAG</td>\n",
       "      <td>-43.5181</td>\n",
       "      <td>170.0490</td>\n",
       "      <td>01</td>\n",
       "      <td>total-rainfall</td>\n",
       "      <td>tot</td>\n",
       "      <td>nil</td>\n",
       "      <td>252079</td>\n",
       "      <td>2019-02-06 01:10:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  station  latitude  longitude sensor_code       data_name data_method aspect  \\\n",
       "0   ALS1R  -36.9329   174.5764          01  total-rainfall         tot    nil   \n",
       "1   ALS1R  -36.9329   174.5764          02  total-rainfall         tot    nil   \n",
       "2   ALS2R  -36.9598   174.4718          01  total-rainfall         tot    nil   \n",
       "3   ALS2R  -36.9598   174.4718          02  total-rainfall         tot    nil   \n",
       "4   FOXAG  -43.5181   170.0490          01  total-rainfall         tot    nil   \n",
       "\n",
       "   record_count            earliestrecord              latestrecord  \n",
       "0         46683 2023-02-24 01:10:00+00:00 2024-02-25 13:50:00+00:00  \n",
       "1         46683 2023-02-24 01:10:00+00:00 2024-02-25 13:50:00+00:00  \n",
       "2         47831 2023-02-23 04:00:00+00:00 2024-03-13 20:50:00+00:00  \n",
       "3         47825 2023-02-23 04:00:00+00:00 2024-03-13 20:50:00+00:00  \n",
       "4        252079 2019-02-06 01:10:00+00:00 2025-01-09 15:50:00+00:00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc83bd23-8c9d-4642-9685-6651d0c406fd",
   "metadata": {},
   "source": [
    "**Record count by station and sensor code**\n",
    "<br>\n",
    "By using `pd.pivot_table` we can transform the above table, so we can look at record_counts for each sensor_code at each station. The sensor_code is the number assigned to each physical sensor at a station. Not all stations have the same number of sensors, so at some stations the record_count will not be defined for some sensor_codes, and will be shown as `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6bee3040-268e-4557-9359-2551b6f83fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"15\" halign=\"left\">record_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_code</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>03</th>\n",
       "      <th>04</th>\n",
       "      <th>05</th>\n",
       "      <th>06</th>\n",
       "      <th>07</th>\n",
       "      <th>08</th>\n",
       "      <th>09</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALS1R</th>\n",
       "      <td>46683.0</td>\n",
       "      <td>46683.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALS2R</th>\n",
       "      <td>47831.0</td>\n",
       "      <td>47825.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOXAG</th>\n",
       "      <td>252079.0</td>\n",
       "      <td>252036.0</td>\n",
       "      <td>132165.527778</td>\n",
       "      <td>132187.851852</td>\n",
       "      <td>143786.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOXKP</th>\n",
       "      <td>60202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60129.500000</td>\n",
       "      <td>60393.500000</td>\n",
       "      <td>61133.000000</td>\n",
       "      <td>42477.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOXRB</th>\n",
       "      <td>93548.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90393.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "            record_count                                          \\\n",
       "sensor_code           01        02             03             04   \n",
       "station                                                            \n",
       "ALS1R            46683.0   46683.0            NaN            NaN   \n",
       "ALS2R            47831.0   47825.0            NaN            NaN   \n",
       "FOXAG           252079.0  252036.0  132165.527778  132187.851852   \n",
       "FOXKP            60202.0       NaN   60129.500000   60393.500000   \n",
       "FOXRB            93548.0       NaN   90393.666667            NaN   \n",
       "\n",
       "                                                                         \n",
       "sensor_code             05       06  07  08  09  10  11  12  13  14  15  \n",
       "station                                                                  \n",
       "ALS1R                  NaN      NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "ALS2R                  NaN      NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "FOXAG        143786.333333      NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "FOXKP         61133.000000  42477.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "FOXRB                  NaN      NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc = pd.pivot_table(station_data, index='station', values=['record_count'], columns='sensor_code')\n",
    "rc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d8c409-2ecb-44a2-8612-4112ed0eb011",
   "metadata": {},
   "source": [
    "We can then plot this as a bar plot using a horizontal bar chart `plot.barh`. This allows us to show the number of records for each station. We don't expect to see the same number of records for each station, but we should, largely, see the same number for each sensor_code at a given station, unless an extra sensor has been added after the station was installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf029f27-c1b4-49b1-aeb5-423e43e0216b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Record count by sensor_code')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rc.plot.barh(figsize=(10,5))\n",
    "ax.set_ylabel('record count')\n",
    "ax.set_title('Record count by sensor_code')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cdb2207-7e6d-4107-9c95-90d69f9d19aa",
   "metadata": {},
   "source": [
    "**Creating a data time-range plot, for one station, split by sensor_code**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da06986e-9558-48c0-833a-493ee714a758",
   "metadata": {},
   "source": [
    "Our intention is to show graphically the available data at one station, and to do that by sensor_code. There are too many stations to do that for all. Many stations collect data with more than one data_method, but these are all collected at the same time, so the number of `snapshot`, `min`, and `max` should always be the same. We'll therefore only display the data range for the `snapshot` data_method. So we first select only the records that have a`data_method` of `snapshot` and add then to a new dataframe called `snapshot`. <br>\n",
    "Creating a dataframe with a new column entry `stasen` which combines the station and sensor_code columns, this will produce a column with values such as NA002_01, OT013_02, TO006_01,...etc that we will subsequently use as y-axis labels. This step will give you a copy warning which you can usualy safely ignore. We can then sort by this column using `snapshot.sort_values` and specifying the 'stasen' column which we just generated. To view the output of this, add `snapsorted` or `snapsorted.head()` to a new line in the code cell below and run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0f0364b3-f1d9-4d6d-998e-e5bb4e4f6c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "TO003 = station_data[(station_data['data_method']=='snapshot') & (station_data['station']=='TO003')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5559b8d8-1ad7-4fab-a1a1-0dc0c415d4cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21969/3264754779.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TO003['stasen'] = TO003['station'] + '_' + TO003['sensor_code']\n"
     ]
    }
   ],
   "source": [
    "TO003['stasen'] = TO003['station'] + '_' + TO003['sensor_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a5ee5541-caee-442c-8edb-3ffda95199dd",
   "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>station</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>sensor_code</th>\n",
       "      <th>data_name</th>\n",
       "      <th>data_method</th>\n",
       "      <th>aspect</th>\n",
       "      <th>record_count</th>\n",
       "      <th>earliestrecord</th>\n",
       "      <th>latestrecord</th>\n",
       "      <th>stasen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>01</td>\n",
       "      <td>air-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>186374</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>02</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178552</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>03</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178537</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>04</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178576</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>05</td>\n",
       "      <td>ground-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>at-enclosure</td>\n",
       "      <td>131002</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>06</td>\n",
       "      <td>ground-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>20-m-north-of-enclosure</td>\n",
       "      <td>132446</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>07</td>\n",
       "      <td>ground-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>20-m-south-of-enclosure</td>\n",
       "      <td>131913</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>08</td>\n",
       "      <td>ground-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>40-m-south-of-enclosure</td>\n",
       "      <td>131919</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>09</td>\n",
       "      <td>ground-selfpotential</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>20-m-north-relative-to-enclosure</td>\n",
       "      <td>129542</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>10</td>\n",
       "      <td>ground-selfpotential</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>20-m-south-relative-to-enclosure</td>\n",
       "      <td>130626</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>11</td>\n",
       "      <td>ground-selfpotential</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>40-m-south-relative-to-enclosure</td>\n",
       "      <td>130577</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>12</td>\n",
       "      <td>air-CO2-conc</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>42033</td>\n",
       "      <td>2024-03-01 00:20:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>13</td>\n",
       "      <td>air-SO2-conc</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>44625</td>\n",
       "      <td>2024-03-01 00:20:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>14</td>\n",
       "      <td>air-H2S-conc</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>43945</td>\n",
       "      <td>2024-03-01 00:20:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>15</td>\n",
       "      <td>wind-direction</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>44062</td>\n",
       "      <td>2024-03-01 00:20:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>15</td>\n",
       "      <td>wind-speed</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>43621</td>\n",
       "      <td>2024-03-01 00:20:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station  latitude  longitude sensor_code             data_name  \\\n",
       "326   TO003  -39.1097   175.6721          01       air-temperature   \n",
       "329   TO003  -39.1097   175.6721          02  fumarole-temperature   \n",
       "332   TO003  -39.1097   175.6721          03  fumarole-temperature   \n",
       "335   TO003  -39.1097   175.6721          04  fumarole-temperature   \n",
       "338   TO003  -39.1097   175.6721          05    ground-temperature   \n",
       "341   TO003  -39.1097   175.6721          06    ground-temperature   \n",
       "344   TO003  -39.1097   175.6721          07    ground-temperature   \n",
       "347   TO003  -39.1097   175.6721          08    ground-temperature   \n",
       "350   TO003  -39.1097   175.6721          09  ground-selfpotential   \n",
       "353   TO003  -39.1097   175.6721          10  ground-selfpotential   \n",
       "356   TO003  -39.1097   175.6721          11  ground-selfpotential   \n",
       "359   TO003  -39.1097   175.6721          12          air-CO2-conc   \n",
       "362   TO003  -39.1097   175.6721          13          air-SO2-conc   \n",
       "365   TO003  -39.1097   175.6721          14          air-H2S-conc   \n",
       "368   TO003  -39.1097   175.6721          15        wind-direction   \n",
       "371   TO003  -39.1097   175.6721          15            wind-speed   \n",
       "\n",
       "    data_method                            aspect  record_count  \\\n",
       "326    snapshot                               nil        186374   \n",
       "329    snapshot                               nil        178552   \n",
       "332    snapshot                               nil        178537   \n",
       "335    snapshot                               nil        178576   \n",
       "338    snapshot                      at-enclosure        131002   \n",
       "341    snapshot           20-m-north-of-enclosure        132446   \n",
       "344    snapshot           20-m-south-of-enclosure        131913   \n",
       "347    snapshot           40-m-south-of-enclosure        131919   \n",
       "350    snapshot  20-m-north-relative-to-enclosure        129542   \n",
       "353    snapshot  20-m-south-relative-to-enclosure        130626   \n",
       "356    snapshot  40-m-south-relative-to-enclosure        130577   \n",
       "359    snapshot                               nil         42033   \n",
       "362    snapshot                               nil         44625   \n",
       "365    snapshot                               nil         43945   \n",
       "368    snapshot                               nil         44062   \n",
       "371    snapshot                               nil         43621   \n",
       "\n",
       "               earliestrecord              latestrecord    stasen  \n",
       "326 2021-05-28 04:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_01  \n",
       "329 2021-05-28 04:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_02  \n",
       "332 2021-05-28 04:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_03  \n",
       "335 2021-05-28 04:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_04  \n",
       "338 2022-06-28 01:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_05  \n",
       "341 2022-06-28 01:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_06  \n",
       "344 2022-06-28 01:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_07  \n",
       "347 2022-06-28 01:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_08  \n",
       "350 2022-06-28 01:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_09  \n",
       "353 2022-06-28 01:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_10  \n",
       "356 2022-06-28 01:00:00+00:00 2025-01-09 15:50:00+00:00  TO003_11  \n",
       "359 2024-03-01 00:20:00+00:00 2025-01-09 15:50:00+00:00  TO003_12  \n",
       "362 2024-03-01 00:20:00+00:00 2025-01-09 15:50:00+00:00  TO003_13  \n",
       "365 2024-03-01 00:20:00+00:00 2025-01-09 15:50:00+00:00  TO003_14  \n",
       "368 2024-03-01 00:20:00+00:00 2025-01-09 15:50:00+00:00  TO003_15  \n",
       "371 2024-03-01 00:20:00+00:00 2025-01-09 15:50:00+00:00  TO003_15  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TO003"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45759ade-ce8f-4697-88e0-d2610a6082f3",
   "metadata": {},
   "source": [
    "We now want to make a new column called 'pos' that will be the position on the y-axis to plot the data range line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c6ab528a-645e-4b80-bd9f-2c764f45071c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21969/622800220.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TO003['pos'] = len(TO003) - np.arange(len(TO003))\n"
     ]
    }
   ],
   "source": [
    "TO003['pos'] = len(TO003) - np.arange(len(TO003))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7a05371c-d4dd-48a6-88e4-ce3aa2262f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>station</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>sensor_code</th>\n",
       "      <th>data_name</th>\n",
       "      <th>data_method</th>\n",
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       "      <th>latestrecord</th>\n",
       "      <th>stasen</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>01</td>\n",
       "      <td>air-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>186374</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>02</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178552</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_02</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>03</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178537</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_03</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>04</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178576</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_04</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>05</td>\n",
       "      <td>ground-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>at-enclosure</td>\n",
       "      <td>131002</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_05</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station  latitude  longitude sensor_code             data_name  \\\n",
       "326   TO003  -39.1097   175.6721          01       air-temperature   \n",
       "329   TO003  -39.1097   175.6721          02  fumarole-temperature   \n",
       "332   TO003  -39.1097   175.6721          03  fumarole-temperature   \n",
       "335   TO003  -39.1097   175.6721          04  fumarole-temperature   \n",
       "338   TO003  -39.1097   175.6721          05    ground-temperature   \n",
       "\n",
       "    data_method        aspect  record_count            earliestrecord  \\\n",
       "326    snapshot           nil        186374 2021-05-28 04:00:00+00:00   \n",
       "329    snapshot           nil        178552 2021-05-28 04:00:00+00:00   \n",
       "332    snapshot           nil        178537 2021-05-28 04:00:00+00:00   \n",
       "335    snapshot           nil        178576 2021-05-28 04:00:00+00:00   \n",
       "338    snapshot  at-enclosure        131002 2022-06-28 01:00:00+00:00   \n",
       "\n",
       "                 latestrecord    stasen  pos  \n",
       "326 2025-01-09 15:50:00+00:00  TO003_01   16  \n",
       "329 2025-01-09 15:50:00+00:00  TO003_02   15  \n",
       "332 2025-01-09 15:50:00+00:00  TO003_03   14  \n",
       "335 2025-01-09 15:50:00+00:00  TO003_04   13  \n",
       "338 2025-01-09 15:50:00+00:00  TO003_05   12  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TO003.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "314366a8-566d-4a5b-b21a-ba5049654dd4",
   "metadata": {},
   "source": [
    "Now we can set up the labeling for station name vs yvalue. To set the variable `station_list` we use the `stasen` values from the dataframe, this way we will have entries for each station, sensor_code, data_method combination. The yvals are used to set the spacing between the yticks in the next plot. By using `range` which is `range([start], stop [,step])`. We can generate a list that starts at the number of station-sesnor_code pairs, stops at 0 and steps by -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "be10cfeb-e6cd-42ad-b7a9-51564ebca7f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "station_list = TO003['stasen'].values\n",
    "yvals = range((len(TO003)),0,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2609b324-37fb-49a2-b8e4-46db71cc0260",
   "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>station</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>sensor_code</th>\n",
       "      <th>data_name</th>\n",
       "      <th>data_method</th>\n",
       "      <th>aspect</th>\n",
       "      <th>record_count</th>\n",
       "      <th>earliestrecord</th>\n",
       "      <th>latestrecord</th>\n",
       "      <th>stasen</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>01</td>\n",
       "      <td>air-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>186374</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>02</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178552</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_02</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>03</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178537</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_03</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>04</td>\n",
       "      <td>fumarole-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>178576</td>\n",
       "      <td>2021-05-28 04:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_04</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>TO003</td>\n",
       "      <td>-39.1097</td>\n",
       "      <td>175.6721</td>\n",
       "      <td>05</td>\n",
       "      <td>ground-temperature</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>at-enclosure</td>\n",
       "      <td>131002</td>\n",
       "      <td>2022-06-28 01:00:00+00:00</td>\n",
       "      <td>2025-01-09 15:50:00+00:00</td>\n",
       "      <td>TO003_05</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station  latitude  longitude sensor_code             data_name  \\\n",
       "326   TO003  -39.1097   175.6721          01       air-temperature   \n",
       "329   TO003  -39.1097   175.6721          02  fumarole-temperature   \n",
       "332   TO003  -39.1097   175.6721          03  fumarole-temperature   \n",
       "335   TO003  -39.1097   175.6721          04  fumarole-temperature   \n",
       "338   TO003  -39.1097   175.6721          05    ground-temperature   \n",
       "\n",
       "    data_method        aspect  record_count            earliestrecord  \\\n",
       "326    snapshot           nil        186374 2021-05-28 04:00:00+00:00   \n",
       "329    snapshot           nil        178552 2021-05-28 04:00:00+00:00   \n",
       "332    snapshot           nil        178537 2021-05-28 04:00:00+00:00   \n",
       "335    snapshot           nil        178576 2021-05-28 04:00:00+00:00   \n",
       "338    snapshot  at-enclosure        131002 2022-06-28 01:00:00+00:00   \n",
       "\n",
       "                 latestrecord    stasen  pos  \n",
       "326 2025-01-09 15:50:00+00:00  TO003_01   16  \n",
       "329 2025-01-09 15:50:00+00:00  TO003_02   15  \n",
       "332 2025-01-09 15:50:00+00:00  TO003_03   14  \n",
       "335 2025-01-09 15:50:00+00:00  TO003_04   13  \n",
       "338 2025-01-09 15:50:00+00:00  TO003_05   12  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TO003.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1fcafa-c252-4d24-8cde-c508520d6682",
   "metadata": {},
   "source": [
    "Now we want to visualise this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2eb8fdb3-876e-4224-8393-a44393b14d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(15,15))\n",
    "\n",
    "#setting different variables, x1=earliest record, x2=latest record, y=position and c=colours of lines.\n",
    "#when using ax.plot, we are going from the earliest to the latest record on the x-axis, and the y-axis is set by\n",
    "#position.\n",
    "# for x1, x2, y in zip(snapsorted['earliestrecord'], snapsorted['latestrecord'], snapsorted['pos']):\n",
    "for x1, x2, y in zip(TO003['earliestrecord'], TO003['latestrecord'], TO003['pos']):\n",
    "    ax.plot([x1, x2], [y, y], color='cornflowerblue')\n",
    "\n",
    "#label y-axis with station_type name\n",
    "ax.set_yticks(yvals)\n",
    "ax.set_yticklabels(station_list)\n",
    "\n",
    "ax.grid(axis='x')\n",
    "\n",
    "ax.set_title('Data Range', fontsize=16)\n",
    "ax.set_ylabel('station_sensor');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "geonet-tutorial",
   "language": "python",
   "name": "geonet-tutorial"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}