lbesnard/RLS_STAARS_example.ipynb

## RLS_STAARS_example.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RLS data download and STAARS climatology comparaison\n",
    "\n",
    "This is a demo example to retrieve ...\n",
    "\n",
    "## 1 RLS data download via Geoserver\n",
    "\n",
    "### 1.1 Geoserver (IMAS) overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "from owslib.etree import etree\n",
    "from owslib.fes import BBox, PropertyIsLike\n",
    "from owslib.wfs import WebFeatureService\n",
    "from owslib.wms import WebMapService\n",
    "import os\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "GEOSERVER_URL = 'http://geoserver.imas.utas.edu.au/geoserver/'\n",
    "GEOSERVER_WMS_LAYER = '{geoserver_url}/wms'.format(geoserver_url=GEOSERVER_URL)\n",
    "GEOSERVER_WFS_LAYER = '{geoserver_url}/wfs'.format(geoserver_url=GEOSERVER_URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'IMAS Web Mapping Service (WMS)'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wms = WebMapService(GEOSERVER_WFS_LAYER, version='1.1.1')\n",
    "wms.identification.title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rls:RLS_REEF_FISH_DATA', 'rls:RLS_REEF_FISH_MAP']\n"
     ]
    }
   ],
   "source": [
    "wms_wfs_ls = list(wms.contents)  # list containing ALL IMAS geoserver layers\n",
    "\n",
    "rls_collection_to_search_str = \"RLS_REEF_FISH\"\n",
    "matching = [s for s in wms_wfs_ls if rls_collection_to_search_str.lower() in s.lower()]\n",
    "print(matching)\n",
    "\n",
    "# we get a *_DATA (WFS) and a *_MAP (WMS layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Visualisation of the Reef Life Survey fish location around Tasmania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WMS and WFS layers definition\n",
    "layer1_wms_name = 'rls:RLS_REEF_FISH_MAP'\n",
    "layer1_wfs_name = 'rls:RLS_REEF_FISH_DATA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lbesnard/anaconda3/lib/python3.7/site-packages/cartopy/io/img_tiles.py:320: UserWarning: The StamenTerrain class was deprecated in v0.17. Please use Stamen('terrain-background') instead.\n",
      "  \"The StamenTerrain class was deprecated in v0.17. \"\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 5760x5040 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "from cartopy.io.img_tiles import StamenTerrain\n",
    "\n",
    "lon_min = 140\n",
    "lon_max = 154\n",
    "lat_min = -44\n",
    "lat_max = -39.5\n",
    "bounding_box_wms = [lon_min, lon_max, lat_min, lat_max]  # [lon_min, lon_max, lat_min, lat_max]\n",
    "\n",
    "tiler = StamenTerrain()\n",
    "fig = plt.figure(figsize=(80, 70))\n",
    "mercator = tiler.crs\n",
    "ax = fig.add_subplot(1, 1, 1, projection=mercator)\n",
    "ax.set_extent(bounding_box_wms, crs=ccrs.PlateCarree())\n",
    "ax.coastlines(resolution='10m', color='black', linewidth=1)\n",
    "\n",
    "ax.add_wms(wms=GEOSERVER_WMS_LAYER,\n",
    "           layers=layer1_wms_name)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Rerieve the associated data via the WFS layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "wfs = WebFeatureService(url=GEOSERVER_WFS_LAYER, version='1.1.0', timeout=30)\n",
    "rls_data = wfs[layer1_wfs_name]\n",
    "\n",
    "# retrieve WFS data with a bbox filter defined above\n",
    "response = wfs.getfeature(typename=layer1_wfs_name, \n",
    "                          bbox=(lon_min, lat_min, lon_max, lat_max), \n",
    "                          srsname='EPSG:4326', \n",
    "                          outputFormat='application/json')\n",
    "\n",
    "res = response.read()\n",
    "json_output = json.dumps(json.loads(res))\n",
    "data = json.loads(json_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Using Pandas dataframe\n",
    "\n",
    "We downloaded the data from Geoserver as a JSON file format. Loading this data into Pandas allows us to perform easily some queries on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>geometry.coordinates</th>\n",
       "      <th>geometry.type</th>\n",
       "      <th>geometry_name</th>\n",
       "      <th>id</th>\n",
       "      <th>properties.Block</th>\n",
       "      <th>properties.Country</th>\n",
       "      <th>properties.Depth</th>\n",
       "      <th>properties.Diver</th>\n",
       "      <th>properties.Ecoregion</th>\n",
       "      <th>properties.Family</th>\n",
       "      <th>properties.Realm</th>\n",
       "      <th>properties.Site</th>\n",
       "      <th>properties.SiteCode</th>\n",
       "      <th>properties.SiteLat</th>\n",
       "      <th>properties.SiteLong</th>\n",
       "      <th>properties.SurveyDate</th>\n",
       "      <th>properties.SurveyID</th>\n",
       "      <th>properties.Taxon</th>\n",
       "      <th>properties.Total</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[147.88, -40]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.129532</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11.0</td>\n",
       "      <td>GJE</td>\n",
       "      <td>Cape Howe</td>\n",
       "      <td>Kyphosidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Port Davies</td>\n",
       "      <td>TAS77</td>\n",
       "      <td>-40.00</td>\n",
       "      <td>147.88</td>\n",
       "      <td>2008-01-28T13:00:00Z</td>\n",
       "      <td>7000573</td>\n",
       "      <td>Scorpis aequipinnis</td>\n",
       "      <td>2</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[148.3, -41.87]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.126876</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>GJE</td>\n",
       "      <td>Bassian</td>\n",
       "      <td>Scorpaenidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Waubs Bay</td>\n",
       "      <td>TAS66</td>\n",
       "      <td>-41.87</td>\n",
       "      <td>148.30</td>\n",
       "      <td>2008-05-02T14:00:00Z</td>\n",
       "      <td>7000611</td>\n",
       "      <td>Neosebastes scorpaenoides</td>\n",
       "      <td>1</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[147.76, -40.09]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.126947</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RSS</td>\n",
       "      <td>Cape Howe</td>\n",
       "      <td>Labridae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>SE Prime Seal</td>\n",
       "      <td>TAS69</td>\n",
       "      <td>-40.09</td>\n",
       "      <td>147.76</td>\n",
       "      <td>2008-01-27T13:00:00Z</td>\n",
       "      <td>7000569</td>\n",
       "      <td>Pictilabrus laticlavius</td>\n",
       "      <td>12</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[147.84, -43.03]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.129200</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2.5</td>\n",
       "      <td>ESO</td>\n",
       "      <td>Bassian</td>\n",
       "      <td>Plesiopidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Sympathy Point</td>\n",
       "      <td>TAS49</td>\n",
       "      <td>-43.03</td>\n",
       "      <td>147.84</td>\n",
       "      <td>2009-01-03T13:00:00Z</td>\n",
       "      <td>7000637</td>\n",
       "      <td>Trachinops caudimaculatus</td>\n",
       "      <td>197</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[146.37, -43.74]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.86750</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>15.0</td>\n",
       "      <td>GJE</td>\n",
       "      <td>Bassian</td>\n",
       "      <td>Otariidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Mewstone Rock</td>\n",
       "      <td>TAS90</td>\n",
       "      <td>-43.74</td>\n",
       "      <td>146.37</td>\n",
       "      <td>2013-02-14T13:00:00Z</td>\n",
       "      <td>912342605</td>\n",
       "      <td>Arctocephalus pusillus</td>\n",
       "      <td>2</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  geometry.coordinates geometry.type geometry_name                         id  \\\n",
       "0        [147.88, -40]         Point          geom  RLS_REEF_FISH_DATA.129532   \n",
       "1      [148.3, -41.87]         Point          geom  RLS_REEF_FISH_DATA.126876   \n",
       "2     [147.76, -40.09]         Point          geom  RLS_REEF_FISH_DATA.126947   \n",
       "3     [147.84, -43.03]         Point          geom  RLS_REEF_FISH_DATA.129200   \n",
       "4     [146.37, -43.74]         Point          geom   RLS_REEF_FISH_DATA.86750   \n",
       "\n",
       "   properties.Block properties.Country  properties.Depth properties.Diver  \\\n",
       "0                 2          Australia              11.0              GJE   \n",
       "1                 1          Australia               8.0              GJE   \n",
       "2                 1          Australia               6.0              RSS   \n",
       "3                 2          Australia               2.5              ESO   \n",
       "4                 1          Australia              15.0              GJE   \n",
       "\n",
       "  properties.Ecoregion properties.Family       properties.Realm  \\\n",
       "0            Cape Howe        Kyphosidae  Temperate Australasia   \n",
       "1              Bassian      Scorpaenidae  Temperate Australasia   \n",
       "2            Cape Howe          Labridae  Temperate Australasia   \n",
       "3              Bassian       Plesiopidae  Temperate Australasia   \n",
       "4              Bassian         Otariidae  Temperate Australasia   \n",
       "\n",
       "  properties.Site properties.SiteCode  properties.SiteLat  \\\n",
       "0     Port Davies               TAS77              -40.00   \n",
       "1       Waubs Bay               TAS66              -41.87   \n",
       "2   SE Prime Seal               TAS69              -40.09   \n",
       "3  Sympathy Point               TAS49              -43.03   \n",
       "4   Mewstone Rock               TAS90              -43.74   \n",
       "\n",
       "   properties.SiteLong properties.SurveyDate  properties.SurveyID  \\\n",
       "0               147.88  2008-01-28T13:00:00Z              7000573   \n",
       "1               148.30  2008-05-02T14:00:00Z              7000611   \n",
       "2               147.76  2008-01-27T13:00:00Z              7000569   \n",
       "3               147.84  2009-01-03T13:00:00Z              7000637   \n",
       "4               146.37  2013-02-14T13:00:00Z            912342605   \n",
       "\n",
       "            properties.Taxon  properties.Total     type  \n",
       "0        Scorpis aequipinnis                 2  Feature  \n",
       "1  Neosebastes scorpaenoides                 1  Feature  \n",
       "2    Pictilabrus laticlavius                12  Feature  \n",
       "3  Trachinops caudimaculatus               197  Feature  \n",
       "4     Arctocephalus pusillus                 2  Feature  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.io.json import json_normalize\n",
    "df_rls_data = json_normalize(data['features'])\n",
    "df_rls_data.head() # only displaying the first few rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Example: Find unique fish families spoted in Port Davies (NE Tasmania)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kyphosidae', 'Cheilodactylidae', 'Plesiopidae', 'Pomacentridae',\n",
       "       'Diodontidae', 'Monacanthidae', 'Odacidae', 'Pentacerotidae',\n",
       "       'Serranidae', 'Labridae', 'Pempheridae', 'Enoplosidae', 'Mullidae'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_list = df_rls_data['properties.Site'].unique()\n",
    "df_rls_data[df_rls_data['properties.Site'] == 'Port Davies' ]['properties.Family'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>geometry.coordinates</th>\n",
       "      <th>properties.SurveyDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[147.88, -40]</td>\n",
       "      <td>2008-01-28T13:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>[147.88, -40]</td>\n",
       "      <td>2008-01-28T13:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>[147.88, -40]</td>\n",
       "      <td>2008-01-28T13:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>[147.88, -40]</td>\n",
       "      <td>2008-01-28T13:00:00Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     geometry.coordinates properties.SurveyDate\n",
       "0           [147.88, -40]  2008-01-28T13:00:00Z\n",
       "812         [147.88, -40]  2008-01-28T13:00:00Z\n",
       "2389        [147.88, -40]  2008-01-28T13:00:00Z\n",
       "3546        [147.88, -40]  2008-01-28T13:00:00Z"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rls_data[(df_rls_data['properties.Site'] == 'Port Davies') &  \n",
    "            (df_rls_data['properties.Family'] == 'Kyphosidae')][\n",
    "    ['geometry.coordinates', 'properties.SurveyDate']\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Export data to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rls_data.to_csv(path_or_buf='/tmp/data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.7 Alternative Example\n",
    "In this example, instead of looking for all species in a specific region, we're looking for a certain type of fish across the entire Reef Life Survey dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7.1 Retrieve the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    .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>geometry.coordinates</th>\n",
       "      <th>geometry.type</th>\n",
       "      <th>geometry_name</th>\n",
       "      <th>id</th>\n",
       "      <th>properties.Block</th>\n",
       "      <th>properties.Country</th>\n",
       "      <th>properties.Depth</th>\n",
       "      <th>properties.Diver</th>\n",
       "      <th>properties.Ecoregion</th>\n",
       "      <th>properties.Family</th>\n",
       "      <th>properties.Realm</th>\n",
       "      <th>properties.Site</th>\n",
       "      <th>properties.SiteCode</th>\n",
       "      <th>properties.SiteLat</th>\n",
       "      <th>properties.SiteLong</th>\n",
       "      <th>properties.SurveyDate</th>\n",
       "      <th>properties.SurveyID</th>\n",
       "      <th>properties.Taxon</th>\n",
       "      <th>properties.Total</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[148.79, -37.82]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.6898</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>14.0</td>\n",
       "      <td>AJW</td>\n",
       "      <td>Cape Howe</td>\n",
       "      <td>Otariidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Seal Gully</td>\n",
       "      <td>BR13</td>\n",
       "      <td>-37.82</td>\n",
       "      <td>148.79</td>\n",
       "      <td>2012-08-21T14:00:00Z</td>\n",
       "      <td>912341923</td>\n",
       "      <td>Arctocephalus pusillus</td>\n",
       "      <td>3</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[148.79, -37.82]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.6905</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>14.0</td>\n",
       "      <td>DTL</td>\n",
       "      <td>Cape Howe</td>\n",
       "      <td>Otariidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Seal Gully</td>\n",
       "      <td>BR13</td>\n",
       "      <td>-37.82</td>\n",
       "      <td>148.79</td>\n",
       "      <td>2012-08-21T14:00:00Z</td>\n",
       "      <td>912341923</td>\n",
       "      <td>Arctocephalus pusillus</td>\n",
       "      <td>4</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[149.98, -37.51]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.16108</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.3</td>\n",
       "      <td>DTL</td>\n",
       "      <td>Cape Howe</td>\n",
       "      <td>Otariidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Howe Central</td>\n",
       "      <td>CHMP-S5</td>\n",
       "      <td>-37.51</td>\n",
       "      <td>149.98</td>\n",
       "      <td>2011-02-03T13:00:00Z</td>\n",
       "      <td>3002553</td>\n",
       "      <td>Arctocephalus pusillus</td>\n",
       "      <td>1</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[150.81, -35.09]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.80385</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>13.0</td>\n",
       "      <td>JRA</td>\n",
       "      <td>Cape Howe</td>\n",
       "      <td>Otariidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Seal Colony</td>\n",
       "      <td>JBMP47</td>\n",
       "      <td>-35.09</td>\n",
       "      <td>150.81</td>\n",
       "      <td>2008-07-06T14:00:00Z</td>\n",
       "      <td>2000862</td>\n",
       "      <td>Arctocephalus pusillus</td>\n",
       "      <td>2</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[150.81, -35.09]</td>\n",
       "      <td>Point</td>\n",
       "      <td>geom</td>\n",
       "      <td>RLS_REEF_FISH_DATA.80403</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>13.0</td>\n",
       "      <td>STN</td>\n",
       "      <td>Cape Howe</td>\n",
       "      <td>Otariidae</td>\n",
       "      <td>Temperate Australasia</td>\n",
       "      <td>Seal Colony</td>\n",
       "      <td>JBMP47</td>\n",
       "      <td>-35.09</td>\n",
       "      <td>150.81</td>\n",
       "      <td>2008-07-06T14:00:00Z</td>\n",
       "      <td>2000862</td>\n",
       "      <td>Arctocephalus pusillus</td>\n",
       "      <td>1</td>\n",
       "      <td>Feature</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  geometry.coordinates geometry.type geometry_name                        id  \\\n",
       "0     [148.79, -37.82]         Point          geom   RLS_REEF_FISH_DATA.6898   \n",
       "1     [148.79, -37.82]         Point          geom   RLS_REEF_FISH_DATA.6905   \n",
       "2     [149.98, -37.51]         Point          geom  RLS_REEF_FISH_DATA.16108   \n",
       "3     [150.81, -35.09]         Point          geom  RLS_REEF_FISH_DATA.80385   \n",
       "4     [150.81, -35.09]         Point          geom  RLS_REEF_FISH_DATA.80403   \n",
       "\n",
       "   properties.Block properties.Country  properties.Depth properties.Diver  \\\n",
       "0                 2          Australia              14.0              AJW   \n",
       "1                 1          Australia              14.0              DTL   \n",
       "2                 1          Australia               8.3              DTL   \n",
       "3                 1          Australia              13.0              JRA   \n",
       "4                 2          Australia              13.0              STN   \n",
       "\n",
       "  properties.Ecoregion properties.Family       properties.Realm  \\\n",
       "0            Cape Howe         Otariidae  Temperate Australasia   \n",
       "1            Cape Howe         Otariidae  Temperate Australasia   \n",
       "2            Cape Howe         Otariidae  Temperate Australasia   \n",
       "3            Cape Howe         Otariidae  Temperate Australasia   \n",
       "4            Cape Howe         Otariidae  Temperate Australasia   \n",
       "\n",
       "  properties.Site properties.SiteCode  properties.SiteLat  \\\n",
       "0      Seal Gully                BR13              -37.82   \n",
       "1      Seal Gully                BR13              -37.82   \n",
       "2    Howe Central             CHMP-S5              -37.51   \n",
       "3     Seal Colony              JBMP47              -35.09   \n",
       "4     Seal Colony              JBMP47              -35.09   \n",
       "\n",
       "   properties.SiteLong properties.SurveyDate  properties.SurveyID  \\\n",
       "0               148.79  2012-08-21T14:00:00Z            912341923   \n",
       "1               148.79  2012-08-21T14:00:00Z            912341923   \n",
       "2               149.98  2011-02-03T13:00:00Z              3002553   \n",
       "3               150.81  2008-07-06T14:00:00Z              2000862   \n",
       "4               150.81  2008-07-06T14:00:00Z              2000862   \n",
       "\n",
       "         properties.Taxon  properties.Total     type  \n",
       "0  Arctocephalus pusillus                 3  Feature  \n",
       "1  Arctocephalus pusillus                 4  Feature  \n",
       "2  Arctocephalus pusillus                 1  Feature  \n",
       "3  Arctocephalus pusillus                 2  Feature  \n",
       "4  Arctocephalus pusillus                 1  Feature  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## string property\n",
    "filename_wfs_filter ='Arctocephalus pusillus'\n",
    "column = 'Taxon' \n",
    "filter_1 = PropertyIsLike(propertyname=column, literal=filename_wfs_filter, wildCard='%')\n",
    "\n",
    "filters_xml = etree.tostring(filter_1.toXML()).decode(\"utf-8\")\n",
    "\n",
    "response = wfs.getfeature(typename=layer1_wfs_name, filter=filters_xml, outputFormat='application/json')\n",
    "res = response.read()\n",
    "\n",
    "json_output = json.dumps(json.loads(res))\n",
    "data = json.loads(json_output)\n",
    "rls_filter_taxon_data = json_normalize(data['features'])\n",
    "\n",
    "rls_filter_taxon_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7.2 Plot the location of spotted fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x8640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "import numpy as np\n",
    "\n",
    "bounding_box_wms = [120, 170, -50, -20]  # [lon_min, lon_max, lat_min, lat_max]\n",
    "\n",
    "lats = np.stack(rls_filter_taxon_data['geometry.coordinates'].values)[:,1]\n",
    "lons = np.stack(rls_filter_taxon_data['geometry.coordinates'].values)[:,0]\n",
    "\n",
    "plt.figure(figsize=(80, 120))\n",
    "# Define the projection, scale, the corners of the map, and the resolution.\n",
    "m = Basemap(projection='merc',llcrnrlat=bounding_box_wms[2],urcrnrlat=bounding_box_wms[3],\\\n",
    "        llcrnrlon=bounding_box_wms[0],urcrnrlon=bounding_box_wms[1],resolution='l')\n",
    " \n",
    "# Draw coastlines and fill continents and water with color\n",
    "m.drawcoastlines()\n",
    "m.fillcontinents(color='grey')\n",
    "\n",
    "# build and plot coordinates onto map\n",
    "x,y = m(lons,lats)\n",
    "m.plot(x,y,'rx', markersize=40, marker='o')\n",
    "plt.title(\"My visitors\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Visualisation of SSTAARS climatology accessed through AODN S3 depository\n",
    "\n",
    "### Three ways to access AODN data:\n",
    "#### 1. AODN portal: https://portal.aodn.org.au/  (Michael notebook)\n",
    "#### 2. thredds server :  http://thredds.aodn.org.au/thredds/catalog.html\n",
    "####       SSTAARS example : http://thredds.aodn.org.au/thredds/catalog/CSIRO/Climatology/SSTAARS/2017/AODN-product/catalog.html\n",
    "#### OPENDAP: /thredds/dodsC/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_001.nc\n",
    "#### HTTPServer: /thredds/fileServer/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_001.nc\n",
    "\n",
    "#### 3. Amazon Web Services (AWS) s3 bucket - object oriented repository:  \n",
    "####               Explore s3 bucket : http://imos-data.s3-website-ap-southeast-2.amazonaws.com/\n",
    "####               Download from AWS s3 server https://help.aodn.org.au/downloading-data-from-servers/amazon-s3-servers/\n",
    "####               Accessing/ Downloading using python s3fs library (this notebook)\n",
    "\n",
    "\n",
    "### Accessing without downloading:\n",
    "#### In python, you can access the AODN files using OPENDAP or s3fs library.  The s3 bucket is the AODN original depository. To provide DAP access to the file, AODN developed some functions to retrieve (and cache) data files from S3. It is more direct to access the data through S3 directly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Accessing and opening the s3 file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['imos-data/CSIRO/Climatology/SSTAARS/2017/SSTAARS.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#libraries\n",
    "import xarray as xar\n",
    "import dask\n",
    "import s3fs\n",
    "\n",
    "#s3 FileSystem \n",
    "s3 = s3fs.S3FileSystem(anon = True)\n",
    "\n",
    "# imos bucket on AWS is named 'imos-data'\n",
    "# list directories using s3.ls\n",
    "s3.ls('imos-data')\n",
    "\n",
    "# list directories and files using \n",
    "s3.ls('imos-data/CSIRO/Climatology/SSTAARS/2017')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_001.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_002.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_003.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_004.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_005.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_006.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_007.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_008.nc',\n",
       " 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_009.nc']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original product SSTRAARS.nc (with harmonics) and AODN-product with daily fit already calculated\n",
    "# SSTAARS climatology : listing directories and daily files\n",
    "s3.ls('imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product')[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Opening a daily climatology from AODN-product:\n",
    "# # 28th of January is day 28\n",
    "# we can directly open a netcdf file using the s3 location\n",
    "file = 'imos-data/CSIRO/Climatology/SSTAARS/2017/AODN-product/SSTAARS_daily_fit_028.nc'\n",
    "\n",
    "# But unfortunatly the following does not work because of an attribute read error with NetCDF4 files, \n",
    "# so we will download the file locally\n",
    "#ds = xar.open_mfdataset(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading the netcdf from s3 bucket\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "temp_file = tempfile.mkstemp()\n",
    "temp_file_path = temp_file[1]\n",
    "\n",
    "# download command : s3.get (s3_file, local_file)\n",
    "s3.get(file, temp_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Opening the local file with xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (DAY_OF_YEAR: 1, LATITUDE: 4500, LONGITUDE: 6000)\n",
       "Coordinates:\n",
       "  * DAY_OF_YEAR         (DAY_OF_YEAR) int16 28\n",
       "  * LATITUDE            (LATITUDE) float32 -69.99 -69.97 -69.95 ... 19.97 19.99\n",
       "  * LONGITUDE           (LONGITUDE) float32 70.01 70.03 70.05 ... 189.97 189.99\n",
       "    DEPTH               float32 ...\n",
       "Data variables:\n",
       "    TEMP                (DAY_OF_YEAR, LATITUDE, LONGITUDE) float32 ...\n",
       "    TEMP_std_err        (DAY_OF_YEAR, LATITUDE, LONGITUDE) float32 ...\n",
       "    TEMP_mean           (LATITUDE, LONGITUDE) float32 ...\n",
       "    TEMP_mean_std_err   (LATITUDE, LONGITUDE) float32 ...\n",
       "    TEMP_trend          (LATITUDE, LONGITUDE) float32 ...\n",
       "    TEMP_trend_std_err  (LATITUDE, LONGITUDE) float32 ...\n",
       "Attributes:\n",
       "    abstract:                      25 years of Advanced Very High-Resolution ...\n",
       "    acknowledgement:               The User agrees that whenever the Product ...\n",
       "    author:                        Galibert, Guillaume\n",
       "    author_email:                  guillaume.galibert@utas.edu.au\n",
       "    citation:                      The citation in a list of references is: \"...\n",
       "    Conventions:                   CF-1.6,IMOS-1.4\n",
       "    data_centre:                   Australian Ocean Data Network (AODN)\n",
       "    data_centre_email:             info@aodn.org.au\n",
       "    date_created:                  2019-04-29T23:27:23Z\n",
       "    disclaimer:                    The User acknowledges that the Product was...\n",
       "    geospatial_lat_max:            19.99\n",
       "    geospatial_lat_min:            -69.99\n",
       "    geospatial_lon_max:            189.99\n",
       "    geospatial_lon_min:            70.01\n",
       "    geospatial_vertical_max:       0.2\n",
       "    geospatial_vertical_min:       0.2\n",
       "    institution:                   AODN\n",
       "    institution_references:        http://www.imos.org.au/aodn.html\n",
       "    keywords:                      SSTAARS, Climatology, sea surface temperat...\n",
       "    license:                       http://creativecommons.org/licenses/by/4.0/\n",
       "    lineage:                       The data have been processed following int...\n",
       "    naming_authority:              CSIRO\n",
       "    principal_investigator:        Wijffels, Susan\n",
       "    principal_investigator_email:  Susan.Wijffels@csiro.au\n",
       "    project:                       Integrated Marine Observing System (IMOS)\n",
       "    project_acknowledgement:       CSIRO Marine Laboratories\n",
       "    references:                    Wijffels, Susan E., Helen Beggs, Christoph...\n",
       "    source:                        One-day composites of night-only sea surfa...\n",
       "    standard_name_vocabulary:      NetCDF Climate and Forecast (CF) Metadata ...\n",
       "    time_coverage_step:            1.0\n",
       "    title:                         SST Atlas of Australian Regional Seas (SST...\n",
       "    time_coverage_end:             2017-01-28T00:00:00Z\n",
       "    time_coverage_start:           2017-01-28T00:00:00Z"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# opening the local file with xarray as a dataset\n",
    "# we need option decode_times=False because this is a climatology with no calendar date associated with it\n",
    "ds = xar.open_dataset(temp_file_path, decode_times=False)\n",
    "\n",
    "# exploring the xarray dataset\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Extracting arrays from the ds dataset for the region of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f74bd5355f8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Coordinates values found from WFS [147.88, -40] \n",
    "port_davies_lat_min = -42.0\n",
    "port_davies_lat_max = -39.0\n",
    "port_davies_lon_min = 146\n",
    "port_davies_lon_max = 149\n",
    "\n",
    "colormap='nipy_spectral'\n",
    "cmap = plt.get_cmap(colormap) \n",
    "\n",
    "ds.TEMP.sel(LATITUDE=slice(port_davies_lat_min, port_davies_lat_max), \n",
    "            LONGITUDE=slice(port_davies_lon_min, port_davies_lon_max)).plot(cmap=cmap)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}