Volcano/Volcano_data_multidomain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "amazing-gateway",
   "metadata": {},
   "source": [
    "# HOW TO RETRIEVE AND USE VOLCANO DATA\n",
    "\n",
    "# Multi-domain data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f633d89-f91b-4c03-9554-12b48bfa0c3b",
   "metadata": {},
   "source": [
    "For data available through [Tilde](https://tilde.geonet.org.nz/ui/data-discovery), there are cases where data from one location are provided through more than one domain.\n",
    "\n",
    "There are also cases where a user might want to work with data from [Tilde](https://tilde.geonet.org.nz/ui/data-discovery) together with data from a completely different source."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacab096-e283-478b-abf1-61935e8d931d",
   "metadata": {},
   "source": [
    "## &nbsp;Table of contents\n",
    "### 1. Basic requirements\n",
    "### 2. More than one Tilde domain - lake temperature data\n",
    "### 3. Tilde data and data from a different source - Volcanic Alert Level data and sulphur dioxide emissions\n",
    "### 4. Tilde data and data from a different source - Ruapehu crater lake temperature data and historic volcanic activity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a098d2a-e6a8-4744-92af-cefbfdb1fbd3",
   "metadata": {},
   "source": [
    "## &nbsp;1. Basic requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clear-possession",
   "metadata": {},
   "source": [
    "### Import required Python modules/libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "juvenile-vaccine",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for querying data\n",
    "import requests\n",
    "import json\n",
    "\n",
    "#pandas for data analysis\n",
    "import pandas as pd\n",
    "\n",
    "#matplotlib for graphing\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#for getting CSV format data\n",
    "from io import StringIO\n",
    "\n",
    "#for system functions\n",
    "import os\n",
    "\n",
    "#for VAL and volcanic activity datetime functionality\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065a5ddd-5d30-45bb-9a72-43f7142607bf",
   "metadata": {},
   "source": [
    "### Set the source URL\n",
    "\n",
    "Using [API data endpoint](https://tilde.geonet.org.nz/v4/api-docs/endpoint/data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7670945-55b0-4d8b-bb62-73ca39e14349",
   "metadata": {},
   "outputs": [],
   "source": [
    "#API version 4 data endpoint\n",
    "source = 'https://tilde.geonet.org.nz/v4/data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e44d60-e500-4d73-b530-0d165b43acdd",
   "metadata": {},
   "source": [
    "## &nbsp;2. More than one Tilde domain\n",
    "## Basic data retrieval and graphing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d47adb7-9b24-4016-a6e6-9dc6686fe2c5",
   "metadata": {},
   "source": [
    "We request lake temperature data from Inferno Crater using `enivirosensor` and `manualcollect` domains"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77df918e-97d2-440a-b734-70f065ecb9d3",
   "metadata": {},
   "source": [
    "**envirosensor**\n",
    "- domain = `envirosensor`\n",
    "\n",
    "The specific parameters are:\n",
    "  - station = `OT001`, the station code for Inferno Crater\n",
    "  - name (type of data) = `lake-temperature`\n",
    "  - sensorcode = `01`, code for the specific sensor number\n",
    "  - method (of data collection) = `snapshot`\n",
    "  - aspect = `-`, not set as not relevant for this query\n",
    "\n",
    "The resulting `key` specifies the data stream:\n",
    "  - `OT001/lake-temperature/01/snapshot/-`\n",
    "\n",
    "Date range:\n",
    "  - startdate = `2023-05-01`\n",
    "  - enddate = `2024-05-01`\n",
    "\n",
    "Complete request\n",
    "- `envirosensor/OT001/lake-temperature/01/snapshot/-/2023-05-01/2024-05-01`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97f37d5a-27b1-404f-9ba9-b68daea1c3d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = source+'envirosensor/OT001/lake-temperature/01/snapshot/-/2023-05-01/2024-05-01'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b685056-911e-4788-a07c-e55a8bf7b7a5",
   "metadata": {},
   "source": [
    "We will now query the URL and ask for a CSV format to be returned. A 200 response code indicates everything went okay, and the result has been returned (if any)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05455c11-2824-448a-a01a-8545f2058e95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(url, headers={'Accept':'text/csv'})\n",
    "print (r)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7569c3e6-f56d-4794-9f4b-337dc00e7131",
   "metadata": {},
   "source": [
    "To work with the observation data we will use python's [pandas module](https://pandas.pydata.org/). We will now store the response of our request in a pandas dataframe (`enviro`), using `pd.read_csv`. By using `parse_dates=['timestamp']` we can convert the 'timestamp' column to a datetime, which makes it easier to work with the data. We add `date_format='ISO8601'` to ensure that pandas uses the full `ISO 8601` datetime format we record our data in. Using `index_col`, we can set the date and time as the index of the dataframe. More information on `pd.read_csv` can be found here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html. We need to use the `StringIO` function with the text returned from our query. By printing the beginning of the result, with `enviro.head()`, we can see the first 5 lines of the dataframe from our query.\n",
    "\n",
    "Once we have the data in the dataframe, we can begin to our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17ee5721-f332-4f0b-b834-46271edb9131",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\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>station</th>\n",
       "      <th>name</th>\n",
       "      <th>sensorCode</th>\n",
       "      <th>method</th>\n",
       "      <th>aspect</th>\n",
       "      <th>value</th>\n",
       "      <th>valueUnit</th>\n",
       "      <th>error</th>\n",
       "      <th>errorUnit</th>\n",
       "      <th>qualityControl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-05-01 00:00:00+00:00</th>\n",
       "      <td>envirosensor</td>\n",
       "      <td>OT001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>1</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>65.11</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01 00:10:00+00:00</th>\n",
       "      <td>envirosensor</td>\n",
       "      <td>OT001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>1</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>65.01</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01 00:20:00+00:00</th>\n",
       "      <td>envirosensor</td>\n",
       "      <td>OT001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>1</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>64.97</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01 00:30:00+00:00</th>\n",
       "      <td>envirosensor</td>\n",
       "      <td>OT001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>1</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>64.91</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01 00:40:00+00:00</th>\n",
       "      <td>envirosensor</td>\n",
       "      <td>OT001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>1</td>\n",
       "      <td>snapshot</td>\n",
       "      <td>nil</td>\n",
       "      <td>64.81</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 domain station              name  sensorCode  \\\n",
       "timestamp                                                                       \n",
       "2023-05-01 00:00:00+00:00  envirosensor   OT001  lake-temperature           1   \n",
       "2023-05-01 00:10:00+00:00  envirosensor   OT001  lake-temperature           1   \n",
       "2023-05-01 00:20:00+00:00  envirosensor   OT001  lake-temperature           1   \n",
       "2023-05-01 00:30:00+00:00  envirosensor   OT001  lake-temperature           1   \n",
       "2023-05-01 00:40:00+00:00  envirosensor   OT001  lake-temperature           1   \n",
       "\n",
       "                             method aspect  value valueUnit  error errorUnit  \\\n",
       "timestamp                                                                      \n",
       "2023-05-01 00:00:00+00:00  snapshot    nil  65.11      degC    0.0      degC   \n",
       "2023-05-01 00:10:00+00:00  snapshot    nil  65.01      degC    0.0      degC   \n",
       "2023-05-01 00:20:00+00:00  snapshot    nil  64.97      degC    0.0      degC   \n",
       "2023-05-01 00:30:00+00:00  snapshot    nil  64.91      degC    0.0      degC   \n",
       "2023-05-01 00:40:00+00:00  snapshot    nil  64.81      degC    0.0      degC   \n",
       "\n",
       "                           qualityControl  \n",
       "timestamp                                  \n",
       "2023-05-01 00:00:00+00:00             NaN  \n",
       "2023-05-01 00:10:00+00:00             NaN  \n",
       "2023-05-01 00:20:00+00:00             NaN  \n",
       "2023-05-01 00:30:00+00:00             NaN  \n",
       "2023-05-01 00:40:00+00:00             NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enviro = pd.read_csv(StringIO(r.text),parse_dates=['timestamp'], date_format='ISO8601', index_col='timestamp')\n",
    "enviro.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e96bfe-5979-4e76-a62d-e14a203dbd97",
   "metadata": {},
   "source": [
    "##### **manualcollect**\n",
    "- domain = `manualcollect`\n",
    "\n",
    "The specific parameters are:\n",
    "  - station = `OT001`, the station code for Inferno Crater\n",
    "  - name (type of data) = `lake-temperature`\n",
    "  - sensorcode = `MC01`, code for the specific sensor number\n",
    "  - method (of data collection) = `thermocouple`\n",
    "  - aspect = `-`, not set as not relevant for this query\n",
    "\n",
    "The resulting `key` specifies the data stream:\n",
    "  - `OT001/lake-temperature/MC01/thermocouple/-`\n",
    "\n",
    "Date range:\n",
    "  - startdate = `2023-01-01`\n",
    "  - enddate = `2024-08-31`\n",
    "\n",
    "Complete request\n",
    "- `manualcollect/OT001/lake-temperature/MC01/thermocouple/-/2023-01-01/2024-08-31`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17f2ab35-dc66-48c1-96ba-8b07d74fdda7",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = source+'manualcollect/OT001/lake-temperature/MC01/thermocouple/-/2023-01-01/2024-08-31'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "75c38b07-cfb7-4773-b4ef-65a82ee3bc53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(url, headers={'Accept':'text/csv'})\n",
    "print (r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "30ed443b-f52a-40da-b9a7-2b2d840b33ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>station</th>\n",
       "      <th>name</th>\n",
       "      <th>sensorCode</th>\n",
       "      <th>method</th>\n",
       "      <th>aspect</th>\n",
       "      <th>value</th>\n",
       "      <th>valueUnit</th>\n",
       "      <th>error</th>\n",
       "      <th>errorUnit</th>\n",
       "      <th>qualityControl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-05-12 00:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>OT001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>MC01</td>\n",
       "      <td>thermocouple</td>\n",
       "      <td>nil</td>\n",
       "      <td>50.7</td>\n",
       "      <td>degC</td>\n",
       "      <td>2.2</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-20 00:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>OT001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>MC01</td>\n",
       "      <td>thermocouple</td>\n",
       "      <td>nil</td>\n",
       "      <td>60.3</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                  domain station              name sensorCode  \\\n",
       "timestamp                                                                       \n",
       "2023-05-12 00:00:00+00:00  manualcollect   OT001  lake-temperature       MC01   \n",
       "2024-03-20 00:00:00+00:00  manualcollect   OT001  lake-temperature       MC01   \n",
       "\n",
       "                                 method aspect  value valueUnit  error  \\\n",
       "timestamp                                                                \n",
       "2023-05-12 00:00:00+00:00  thermocouple    nil   50.7      degC    2.2   \n",
       "2024-03-20 00:00:00+00:00  thermocouple    nil   60.3      degC    0.0   \n",
       "\n",
       "                          errorUnit  qualityControl  \n",
       "timestamp                                            \n",
       "2023-05-12 00:00:00+00:00      degC             NaN  \n",
       "2024-03-20 00:00:00+00:00      degC             NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mancol = pd.read_csv(StringIO(r.text),parse_dates=['timestamp'], date_format='ISO8601', index_col='timestamp')\n",
    "mancol.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e57dbe6e-bb32-4237-99d0-01bc230cd07e",
   "metadata": {},
   "source": [
    "### Basic Data Plot\n",
    "By using the value column from our dataframe we are able to produce a basic graph of the lake temperature against time. The `envirosensor` data are plotted first, with the `manualcollect` data on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20bba64b-b46e-47e4-80ac-d81f0fa9cd77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'water temperature ($^o$C)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(15,5))\n",
    "ax.plot(enviro.index, enviro['value'], color='lightblue', label='envirosensor')\n",
    "ax.plot(mancol.index, mancol['value'], color='blue', marker='o', markersize=3, linestyle='none', label='manualcollect')\n",
    "ax.legend(loc='lower left')\n",
    "ax.set_ylabel('water temperature ($^o$C)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31737c1-1498-40d5-8b58-21664d9738ce",
   "metadata": {},
   "source": [
    "## &nbsp;3. Tilde data and data from a different source - Volcanic Alert Level data and sulphur dioxide emissions\n",
    "## Basic data retrieval and graphing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81607c2c-ba28-4a2e-82ad-c3610791a819",
   "metadata": {},
   "source": [
    "### Tilde\n",
    "- domain = `manualcollect`\n",
    "\n",
    "The specific parameters are:\n",
    "  - station = `WI000`, the station code for Whalaari/White Island (the whole island, not a specific feature)\n",
    "  - name (type of data) = `plume-SO2-gasflux`\n",
    "  - sensorcode = `MC01`, code for the specific sensor number\n",
    "  - method (of data collection) = `flyspec`\n",
    "  - aspect = `-`, not set as not relevant for this query\n",
    "\n",
    "The resulting `key` specifies the data stream:\n",
    "  - `WI000/plume-SO2-gasflux/MC01/flyspec/-`\n",
    "\n",
    "Date range:\n",
    "  - startdate = `2024-01-01`\n",
    "  - enddate = `2024-09-15`\n",
    "\n",
    "Complete request\n",
    "- `manualcollect/WI000/plume-SO2-gasflux/MC01/flyspec/-/2024-01-01/2024-09-15`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "165e86be-fd76-4b68-99d2-2a96327923fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = source+'manualcollect/WI000/plume-SO2-gasflux/MC01/flyspec/-/2024-01-01/2024-09-15'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "127a0aab-2adc-40a6-ad99-86e753e62715",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(url, headers={'Accept':'text/csv'})\n",
    "print (r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "012c2799-d009-418e-84e6-282cd97c5da2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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>domain</th>\n",
       "      <th>station</th>\n",
       "      <th>name</th>\n",
       "      <th>sensorCode</th>\n",
       "      <th>method</th>\n",
       "      <th>aspect</th>\n",
       "      <th>value</th>\n",
       "      <th>valueUnit</th>\n",
       "      <th>error</th>\n",
       "      <th>errorUnit</th>\n",
       "      <th>qualityControl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-23 23:30:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>WI000</td>\n",
       "      <td>plume-SO2-gasflux</td>\n",
       "      <td>MC01</td>\n",
       "      <td>flyspec</td>\n",
       "      <td>nil</td>\n",
       "      <td>4.0</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>0.04</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-26 00:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>WI000</td>\n",
       "      <td>plume-SO2-gasflux</td>\n",
       "      <td>MC01</td>\n",
       "      <td>flyspec</td>\n",
       "      <td>nil</td>\n",
       "      <td>3.2</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>0.23</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-26 12:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>WI000</td>\n",
       "      <td>plume-SO2-gasflux</td>\n",
       "      <td>MC01</td>\n",
       "      <td>flyspec</td>\n",
       "      <td>nil</td>\n",
       "      <td>3.2</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>0.23</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30 11:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>WI000</td>\n",
       "      <td>plume-SO2-gasflux</td>\n",
       "      <td>MC01</td>\n",
       "      <td>flyspec</td>\n",
       "      <td>nil</td>\n",
       "      <td>3.5</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>0.34</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26 23:30:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>WI000</td>\n",
       "      <td>plume-SO2-gasflux</td>\n",
       "      <td>MC01</td>\n",
       "      <td>flyspec</td>\n",
       "      <td>nil</td>\n",
       "      <td>8.6</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>1.83</td>\n",
       "      <td>kg/s</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  domain station               name  \\\n",
       "timestamp                                                             \n",
       "2024-01-23 23:30:00+00:00  manualcollect   WI000  plume-SO2-gasflux   \n",
       "2024-03-26 00:00:00+00:00  manualcollect   WI000  plume-SO2-gasflux   \n",
       "2024-03-26 12:00:00+00:00  manualcollect   WI000  plume-SO2-gasflux   \n",
       "2024-04-30 11:00:00+00:00  manualcollect   WI000  plume-SO2-gasflux   \n",
       "2024-05-26 23:30:00+00:00  manualcollect   WI000  plume-SO2-gasflux   \n",
       "\n",
       "                          sensorCode   method aspect  value valueUnit  error  \\\n",
       "timestamp                                                                      \n",
       "2024-01-23 23:30:00+00:00       MC01  flyspec    nil    4.0      kg/s   0.04   \n",
       "2024-03-26 00:00:00+00:00       MC01  flyspec    nil    3.2      kg/s   0.23   \n",
       "2024-03-26 12:00:00+00:00       MC01  flyspec    nil    3.2      kg/s   0.23   \n",
       "2024-04-30 11:00:00+00:00       MC01  flyspec    nil    3.5      kg/s   0.34   \n",
       "2024-05-26 23:30:00+00:00       MC01  flyspec    nil    8.6      kg/s   1.83   \n",
       "\n",
       "                          errorUnit  qualityControl  \n",
       "timestamp                                            \n",
       "2024-01-23 23:30:00+00:00      kg/s             NaN  \n",
       "2024-03-26 00:00:00+00:00      kg/s             NaN  \n",
       "2024-03-26 12:00:00+00:00      kg/s             NaN  \n",
       "2024-04-30 11:00:00+00:00      kg/s             NaN  \n",
       "2024-05-26 23:30:00+00:00      kg/s             NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(StringIO(r.text),parse_dates=['timestamp'], date_format='ISO8601', index_col='timestamp')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098f3c30-7afd-46e8-ab15-156f8bf7d77e",
   "metadata": {},
   "source": [
    "### Volcanic Alert Level (VAL)\n",
    "\n",
    "[VAL Data](https://doi.org/10.21420/we5s-1n52) are available from a [GitHub repository](https://github.com/GeoNet/data/tree/main/volcanic-alert-levels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e1d0fd2-b509-46e7-8d86-bd211adbd8bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/GeoNet/data/main/volcanic-alert-levels/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "486dbe82-269a-4679-bd50-ca2b8d6a9806",
   "metadata": {},
   "outputs": [],
   "source": [
    "valfile = url+'VAL_WhakaariWhiteIsland.csv'\n",
    "val = pd.read_csv(valfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa9ea5bc-ad42-4e90-95dc-311f7621115e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PeriodStart UTC</th>\n",
       "      <th>PeriodEnd UTC</th>\n",
       "      <th>VAL</th>\n",
       "      <th>VAL System Version</th>\n",
       "      <th>Description</th>\n",
       "      <th>PeriodStart LocalTime</th>\n",
       "      <th>PeriodEnd LocalTime</th>\n",
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       "      <th>37</th>\n",
       "      <td>2024-05-23T22:45:00Z</td>\n",
       "      <td>2024-05-24T04:59:59Z</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Minor volcanic eruption</td>\n",
       "      <td>2024-05-24 10:45:00 NZST</td>\n",
       "      <td>2024-05-24 16:59:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2024-05-24T05:00:00Z</td>\n",
       "      <td>2024-05-24T23:44:59Z</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Moderate to heightened volcanic unrest</td>\n",
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       "      <td>2024-05-25 11:44:59 NZST</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2024-05-24T23:45:00Z</td>\n",
       "      <td>2024-05-29T02:59:59Z</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Minor volcanic eruption</td>\n",
       "      <td>2024-05-25 11:45:00 NZST</td>\n",
       "      <td>2024-05-29 14:59:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2024-05-29T03:00:00Z</td>\n",
       "      <td>2024-08-09T04:59:59Z</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Moderate to heightened volcanic unrest</td>\n",
       "      <td>2024-05-29 15:00:00 NZST</td>\n",
       "      <td>2024-08-09 16:59:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2024-08-09T05:00:00Z</td>\n",
       "      <td>9999-01-01T00:00:00Z</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Minor volcanic eruption</td>\n",
       "      <td>2024-08-09T17:00:00 NZST</td>\n",
       "      <td>9999-01-01 00:00:00 NZST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         PeriodStart UTC         PeriodEnd UTC  VAL  VAL System Version  \\\n",
       "37  2024-05-23T22:45:00Z  2024-05-24T04:59:59Z    3                   3   \n",
       "38  2024-05-24T05:00:00Z  2024-05-24T23:44:59Z    2                   3   \n",
       "39  2024-05-24T23:45:00Z  2024-05-29T02:59:59Z    3                   3   \n",
       "40  2024-05-29T03:00:00Z  2024-08-09T04:59:59Z    2                   3   \n",
       "41  2024-08-09T05:00:00Z  9999-01-01T00:00:00Z    3                   3   \n",
       "\n",
       "                               Description     PeriodStart LocalTime  \\\n",
       "37                 Minor volcanic eruption  2024-05-24 10:45:00 NZST   \n",
       "38  Moderate to heightened volcanic unrest  2024-05-24 17:00:00 NZST   \n",
       "39                 Minor volcanic eruption  2024-05-25 11:45:00 NZST   \n",
       "40  Moderate to heightened volcanic unrest  2024-05-29 15:00:00 NZST   \n",
       "41                 Minor volcanic eruption  2024-08-09T17:00:00 NZST   \n",
       "\n",
       "         PeriodEnd LocalTime  \n",
       "37  2024-05-24 16:59:59 NZST  \n",
       "38  2024-05-25 11:44:59 NZST  \n",
       "39  2024-05-29 14:59:59 NZST  \n",
       "40  2024-08-09 16:59:59 NZST  \n",
       "41  9999-01-01 00:00:00 NZST  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe1fce3-c6b4-4aec-9bfa-f6621a17e6c8",
   "metadata": {},
   "source": [
    "#### Make VAL period end time suitable for datetime\n",
    "\n",
    "The VAL data use `9999-01-01T00:00:00Z` as the end time of the current VAL, as is standard GeoNet practice. But `9999-01-01T00:00:00Z` cannot be converted to a python datetime value, so we replace it with the current date/time. This applies to both UTC and LocalTime so users can use either time base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eccbab7b-e2b4-4ee4-8f12-b9c5acdc55dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "nowutc = dt.datetime.now(dt.timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ')\n",
    "nownzst = dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S NZST')\n",
    "val.replace(['9999-01-01T00:00:00Z', '9999-01-01 00:00:00 NZST'], [nowutc, nownzst], inplace=True)\n",
    "val[['PeriodStart UTC','PeriodEnd UTC']] = val[['PeriodStart UTC','PeriodEnd UTC']].apply(lambda x: pd.to_datetime(x,format='ISO8601'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f18047cf-8c22-4aea-8bbc-55c9a73c2702",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
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       "      <th>39</th>\n",
       "      <td>2024-05-24 23:45:00+00:00</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Minor volcanic eruption</td>\n",
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       "      <th>40</th>\n",
       "      <td>2024-05-29 03:00:00+00:00</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>2024-05-29 15:00:00 NZST</td>\n",
       "      <td>2024-08-09 16:59:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2024-08-09 05:00:00+00:00</td>\n",
       "      <td>2024-12-10 03:03:05+00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Minor volcanic eruption</td>\n",
       "      <td>2024-08-09T17:00:00 NZST</td>\n",
       "      <td>2024-12-10 16:03:05 NZST</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "             PeriodStart UTC             PeriodEnd UTC  VAL  \\\n",
       "37 2024-05-23 22:45:00+00:00 2024-05-24 04:59:59+00:00    3   \n",
       "38 2024-05-24 05:00:00+00:00 2024-05-24 23:44:59+00:00    2   \n",
       "39 2024-05-24 23:45:00+00:00 2024-05-29 02:59:59+00:00    3   \n",
       "40 2024-05-29 03:00:00+00:00 2024-08-09 04:59:59+00:00    2   \n",
       "41 2024-08-09 05:00:00+00:00 2024-12-10 03:03:05+00:00    3   \n",
       "\n",
       "    VAL System Version                             Description  \\\n",
       "37                   3                 Minor volcanic eruption   \n",
       "38                   3  Moderate to heightened volcanic unrest   \n",
       "39                   3                 Minor volcanic eruption   \n",
       "40                   3  Moderate to heightened volcanic unrest   \n",
       "41                   3                 Minor volcanic eruption   \n",
       "\n",
       "       PeriodStart LocalTime       PeriodEnd LocalTime  \n",
       "37  2024-05-24 10:45:00 NZST  2024-05-24 16:59:59 NZST  \n",
       "38  2024-05-24 17:00:00 NZST  2024-05-25 11:44:59 NZST  \n",
       "39  2024-05-25 11:45:00 NZST  2024-05-29 14:59:59 NZST  \n",
       "40  2024-05-29 15:00:00 NZST  2024-08-09 16:59:59 NZST  \n",
       "41  2024-08-09T17:00:00 NZST  2024-12-10 16:03:05 NZST  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae3638e-4a45-4c32-af2c-b88a3ac727f0",
   "metadata": {},
   "source": [
    "#### Select 2024 VAL data to match data from Tilde\n",
    "\n",
    "Select on VAL data where end data is after 2024-01-01."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f25508f1-a9c7-47a2-aa63-67197207b8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2024,1,1).replace(tzinfo=dt.timezone.utc)\n",
    "val24 = val[val['PeriodEnd UTC']>start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68b37006-c3e9-4805-9cab-cd8ddc4a8db1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Moderate to heightened volcanic unrest</td>\n",
       "      <td>2022-09-07 14:00:00 NZST</td>\n",
       "      <td>2024-05-24 10:44:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2024-05-23 22:45:00+00:00</td>\n",
       "      <td>2024-05-24 04:59:59+00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Minor volcanic eruption</td>\n",
       "      <td>2024-05-24 10:45:00 NZST</td>\n",
       "      <td>2024-05-24 16:59:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2024-05-24 05:00:00+00:00</td>\n",
       "      <td>2024-05-24 23:44:59+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Moderate to heightened volcanic unrest</td>\n",
       "      <td>2024-05-24 17:00:00 NZST</td>\n",
       "      <td>2024-05-25 11:44:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2024-05-24 23:45:00+00:00</td>\n",
       "      <td>2024-05-29 02:59:59+00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Minor volcanic eruption</td>\n",
       "      <td>2024-05-25 11:45:00 NZST</td>\n",
       "      <td>2024-05-29 14:59:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2024-05-29 03:00:00+00:00</td>\n",
       "      <td>2024-08-09 04:59:59+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Moderate to heightened volcanic unrest</td>\n",
       "      <td>2024-05-29 15:00:00 NZST</td>\n",
       "      <td>2024-08-09 16:59:59 NZST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2024-08-09 05:00:00+00:00</td>\n",
       "      <td>2024-12-10 03:03:05+00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Minor volcanic eruption</td>\n",
       "      <td>2024-08-09T17:00:00 NZST</td>\n",
       "      <td>2024-12-10 16:03:05 NZST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PeriodStart UTC             PeriodEnd UTC  VAL  \\\n",
       "36 2022-09-07 02:00:00+00:00 2024-05-23 22:44:59+00:00    2   \n",
       "37 2024-05-23 22:45:00+00:00 2024-05-24 04:59:59+00:00    3   \n",
       "38 2024-05-24 05:00:00+00:00 2024-05-24 23:44:59+00:00    2   \n",
       "39 2024-05-24 23:45:00+00:00 2024-05-29 02:59:59+00:00    3   \n",
       "40 2024-05-29 03:00:00+00:00 2024-08-09 04:59:59+00:00    2   \n",
       "41 2024-08-09 05:00:00+00:00 2024-12-10 03:03:05+00:00    3   \n",
       "\n",
       "    VAL System Version                             Description  \\\n",
       "36                   3  Moderate to heightened volcanic unrest   \n",
       "37                   3                 Minor volcanic eruption   \n",
       "38                   3  Moderate to heightened volcanic unrest   \n",
       "39                   3                 Minor volcanic eruption   \n",
       "40                   3  Moderate to heightened volcanic unrest   \n",
       "41                   3                 Minor volcanic eruption   \n",
       "\n",
       "       PeriodStart LocalTime       PeriodEnd LocalTime  \n",
       "36  2022-09-07 14:00:00 NZST  2024-05-24 10:44:59 NZST  \n",
       "37  2024-05-24 10:45:00 NZST  2024-05-24 16:59:59 NZST  \n",
       "38  2024-05-24 17:00:00 NZST  2024-05-25 11:44:59 NZST  \n",
       "39  2024-05-25 11:45:00 NZST  2024-05-29 14:59:59 NZST  \n",
       "40  2024-05-29 15:00:00 NZST  2024-08-09 16:59:59 NZST  \n",
       "41  2024-08-09T17:00:00 NZST  2024-12-10 16:03:05 NZST  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val24"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50c805e-2232-4602-a559-dd9d5c73a0bc",
   "metadata": {},
   "source": [
    "### Basic Data Plot\n",
    "The data values are completely different so plot on two separate axes. VAL is plotted as a \"steps\".\n",
    "\n",
    "As VAL is an interval, we have to plot `PeriodStart UTC` and `PeriodEnd UTC` to get the complete data, but with different steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "26f9b443-d508-4d6d-a711-3c6d06fa12a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Flyspec SO$_2$ flux (kg/s)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(axv,axf) = plt.subplots(2,1,figsize=(15,6), sharex=True)\n",
    "\n",
    "#mark transition between unrest and eruptive activity in VAL schema\n",
    "axv.axhline(2.5, linestyle='--', color='red', alpha=0.5)\n",
    "axv.text(0.02, 0.48, 'unrest', fontsize=12, color='grey', va='top', transform=axv.transAxes)\n",
    "axv.text(0.02, 0.52, 'eruptive activity', fontsize=12, color='grey', va='bottom', transform=axv.transAxes)\n",
    "\n",
    "axv.step(val24['PeriodStart UTC'],val24['VAL'], where='post', color='lightblue')\n",
    "axv.step(val24['PeriodEnd UTC'],val24['VAL'], where='pre', color='lightblue')\n",
    "axv.set_xlim(left=start)\n",
    "axv.set_ylim(0,5)\n",
    "axv.set_yticks(list(range(0,6)))\n",
    "axv.set_ylabel('VAL')\n",
    "\n",
    "\n",
    "axf.plot(df.index, df['value'], color='lightblue')\n",
    "axf.plot(df.index, df['value'], marker='o', markersize=3, color='blue', linestyle='none')\n",
    "axf.set_ylabel('Flyspec SO$_2$ flux (kg/s)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbcda58c-8932-4444-a780-969c3121ffa9",
   "metadata": {},
   "source": [
    "## &nbsp;4. Tilde data and data from a different source - Ruapehu crater lake temperature data and historic volcanic activity\n",
    "## Basic data retrieval and graphing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4fb8fa8-7eef-440d-a1b2-51540a579e97",
   "metadata": {},
   "source": [
    "### Tilde\n",
    "- domain = `manualcollect`\n",
    "\n",
    "The specific parameters are:\n",
    "  - station = `RU001`, the station code for Ruapehu Crater Lake\n",
    "  - name (type of data) = `lake-temperature`\n",
    "  - sensorcode = `MC01`, code for the specific sensor number\n",
    "  - method (of data collection) = `-`, all methods\n",
    "  - aspect = `-`, not set as not relevant for this query\n",
    "\n",
    "The resulting `key` specifies the data stream:\n",
    "  - `RU001/lake-temperature/MC01/-/-`\n",
    "\n",
    "Date range:\n",
    "- dates = `1950-01-01/2010-01-01`\n",
    "  - startdate = `1950-01-01`\n",
    "  - enddate = `2010-01-01`\n",
    "\n",
    "Complete request\n",
    "- `manualcollect/RU001/lake-temperature/MC01/-/-/1950-01-01/2010-01-01`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6073cfb0-788b-4ff6-a2b8-e67056991fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = source+'manualcollect/RU001/lake-temperature/MC01/-/-/1950-01-01/2010-01-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ac1a0ff-c6c6-4d8c-869c-763300a7e948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(url, headers={'Accept':'text/csv'})\n",
    "print (r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1065ea7b-e8de-4bc0-8baf-f005832fe612",
   "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>station</th>\n",
       "      <th>name</th>\n",
       "      <th>sensorCode</th>\n",
       "      <th>method</th>\n",
       "      <th>aspect</th>\n",
       "      <th>value</th>\n",
       "      <th>valueUnit</th>\n",
       "      <th>error</th>\n",
       "      <th>errorUnit</th>\n",
       "      <th>qualityControl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-03 00:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>RU001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>MC01</td>\n",
       "      <td>thermocouple</td>\n",
       "      <td>nil</td>\n",
       "      <td>22.1</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-11 00:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>RU001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>MC01</td>\n",
       "      <td>thermocouple</td>\n",
       "      <td>nil</td>\n",
       "      <td>39.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-24 00:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>RU001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>MC01</td>\n",
       "      <td>thermocouple</td>\n",
       "      <td>nil</td>\n",
       "      <td>31.5</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-06 00:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>RU001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>MC01</td>\n",
       "      <td>thermocouple</td>\n",
       "      <td>nil</td>\n",
       "      <td>34.5</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-07-17 00:00:00+00:00</th>\n",
       "      <td>manualcollect</td>\n",
       "      <td>RU001</td>\n",
       "      <td>lake-temperature</td>\n",
       "      <td>MC01</td>\n",
       "      <td>thermocouple</td>\n",
       "      <td>nil</td>\n",
       "      <td>17.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>degC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  domain station              name sensorCode  \\\n",
       "timestamp                                                                       \n",
       "1992-01-03 00:00:00+00:00  manualcollect   RU001  lake-temperature       MC01   \n",
       "1992-02-11 00:00:00+00:00  manualcollect   RU001  lake-temperature       MC01   \n",
       "1992-03-24 00:00:00+00:00  manualcollect   RU001  lake-temperature       MC01   \n",
       "1992-05-06 00:00:00+00:00  manualcollect   RU001  lake-temperature       MC01   \n",
       "1992-07-17 00:00:00+00:00  manualcollect   RU001  lake-temperature       MC01   \n",
       "\n",
       "                                 method aspect  value valueUnit  error  \\\n",
       "timestamp                                                                \n",
       "1992-01-03 00:00:00+00:00  thermocouple    nil   22.1      degC    0.0   \n",
       "1992-02-11 00:00:00+00:00  thermocouple    nil   39.0      degC    0.0   \n",
       "1992-03-24 00:00:00+00:00  thermocouple    nil   31.5      degC    0.0   \n",
       "1992-05-06 00:00:00+00:00  thermocouple    nil   34.5      degC    0.0   \n",
       "1992-07-17 00:00:00+00:00  thermocouple    nil   17.0      degC    0.0   \n",
       "\n",
       "                          errorUnit  qualityControl  \n",
       "timestamp                                            \n",
       "1992-01-03 00:00:00+00:00      degC             NaN  \n",
       "1992-02-11 00:00:00+00:00      degC             NaN  \n",
       "1992-03-24 00:00:00+00:00      degC             NaN  \n",
       "1992-05-06 00:00:00+00:00      degC             NaN  \n",
       "1992-07-17 00:00:00+00:00      degC             NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(StringIO(r.text),parse_dates=['timestamp'], date_format='ISO8601', index_col='timestamp')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23b8432-9af5-478d-b4f5-2fdbba857365",
   "metadata": {},
   "source": [
    "#### Split lake temperature data by collection method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44fe89f9-19fb-453c-8f1c-1bca24c40b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "thermometer = df[df['method']=='thermometer']\n",
    "thermocouple = df[df['method']=='thermocouple']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aff2377-6387-4146-8c26-e28046a5acf4",
   "metadata": {},
   "source": [
    "### Historic volcanic activity\n",
    "\n",
    "[Historic volcanic activity Data](https://doi.org/10.21420/bw31-2x60) are available from a [GitHub repository](https://github.com/GeoNet/data/tree/main/historic-volcanic-activity). We access the Ruapehu data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "908dc4e6-afff-4b8d-8a3c-20b29d3c999f",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/GeoNet/data/refs/heads/main/historic-volcanic-activity/historic_eruptive_activity_ruapehu.csv'\n",
    "hist = pd.read_csv(url, parse_dates=['Date LocalTime'], date_format='ISO8601')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb791a48-1dcc-463a-9e80-973fa27c7a7e",
   "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>Date LocalTime</th>\n",
       "      <th>Date Comment</th>\n",
       "      <th>Activity Description</th>\n",
       "      <th>Activity Scale</th>\n",
       "      <th>Additional Details</th>\n",
       "      <th>References</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>1997-11-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor phreatic eruptions</td>\n",
       "      <td>1</td>\n",
       "      <td>Confined to basin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>1998-11-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Small phreatic eruptions</td>\n",
       "      <td>1</td>\n",
       "      <td>Confined to basin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>2006-10-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor hydrothermal eruption</td>\n",
       "      <td>2</td>\n",
       "      <td>Confined to basin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>2007-09-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate hydrothermal eruptions with lahars do...</td>\n",
       "      <td>4</td>\n",
       "      <td>Confined to summit plateau and Whangaehu valle...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>2009-07-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Phreatic activity</td>\n",
       "      <td>2</td>\n",
       "      <td>Small snow slurry lahars in upper Whangaehu</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date LocalTime Date Comment  \\\n",
       "526     1997-11-01          NaN   \n",
       "527     1998-11-08          NaN   \n",
       "528     2006-10-04          NaN   \n",
       "529     2007-09-25          NaN   \n",
       "530     2009-07-13          NaN   \n",
       "\n",
       "                                  Activity Description  Activity Scale  \\\n",
       "526                           Minor phreatic eruptions               1   \n",
       "527                           Small phreatic eruptions               1   \n",
       "528                        Minor hydrothermal eruption               2   \n",
       "529  Moderate hydrothermal eruptions with lahars do...               4   \n",
       "530                                  Phreatic activity               2   \n",
       "\n",
       "                                    Additional Details References  \n",
       "526                                  Confined to basin        NaN  \n",
       "527                                  Confined to basin        NaN  \n",
       "528                                  Confined to basin        NaN  \n",
       "529  Confined to summit plateau and Whangaehu valle...        NaN  \n",
       "530        Small snow slurry lahars in upper Whangaehu        NaN  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbb7967-5939-4255-9073-5553b54a3882",
   "metadata": {},
   "source": [
    "### Basic Data Plot\n",
    "The data values are completely different so plot on two separate axes. Activity is plotted as a vertical lines. Set temperature x-axis limits to match temperature data, which has a shorter duration. As x-axes are shared, this forces both to have the same range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb38f5ff-5c23-40f1-9f17-2922b7795fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Activity scale value')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(axt,axe) = plt.subplots(2,1,figsize=(15,6), sharex=True)\n",
    "\n",
    "axt.set_xlim(left=dt.datetime(1950,1,1), right=dt.datetime(2010,1,1))\n",
    "\n",
    "axt.plot(thermometer.index ,thermometer['value'], color='lightblue', label='thermometer')\n",
    "axt.plot(thermocouple.index ,thermocouple['value'], color='steelblue', label='thermocouple')\n",
    "axt.legend(loc='upper left')\n",
    "axt.set_ylabel('Temperature ($^o$C)')\n",
    "\n",
    "axe.vlines(hist['Date LocalTime'], ymin=0, ymax=hist['Activity Scale'], color='lightcoral')\n",
    "axe.set_ylim(0,5.5)\n",
    "axe.set_ylabel('Activity scale value')"
   ]
  }
 ],
 "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
}