ecological_data.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "e02ff9ce-029a-4a68-9b4a-1a4aeb0fe3a1",
   "metadata": {},
   "source": [
    "# Exploring some ecological datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "071ca92f-7f86-493c-97ee-d9fa9791fbbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from urllib.request import urlopen\n",
    "import json\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "from scipy.interpolate import griddata\n",
    "import pyepsg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd8c183-0a3c-4ac2-9039-d0313078fe8a",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "## IUCN API\n",
    "\n",
    "The IUCN API documentation is here: https://apiv3.iucnredlist.org/api/v3/docs\n",
    "\n",
    "All API calls will start with most of that as a stem. We will change the endpoints to access different types of information, sometimes requiring us to include additional parameters.\n",
    "\n",
    "For most endpoints we will also need an authorization token.\n",
    "\n",
    "Try one of the examples, and note how the URL is filled in with different parameters and token=XXXXX. We'll borrow that token for our work today, but if you do real work with this API, request your own token. You can then store the value of this token using the `os` package:\n",
    "\n",
    "`os.environ['IUCN_TOKEN'] = XXXXX`\n",
    "\n",
    " and retrieve it with the same package:\n",
    " \n",
    "`iucn_token = os.getenv('IUCN_TOKEN')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "577740a6-f00f-46a8-a3e5-3a44f4fec0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temporary token, derived from IUCN example website\n",
    "iucn_token=\"9bb4facb6d23f48efbf424bb05c0c1ef1cf6f468393bc745d42179ac4aca5fee\"\n",
    "\n",
    "# Root URL for IUCN\n",
    "iucn_url=\"https://apiv3.iucnredlist.org\"\n",
    "\n",
    "# URL for API endpoint\n",
    "spp_page_url = iucn_url+'/api/v3/species/page/2?token='+iucn_token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef85baf-c115-43de-83c9-f99f2581a97c",
   "metadata": {},
   "source": [
    "There are multiple ways to read in JSON data in Python. The `urlopen` functionality is quite easy to use, since it accepts a URL directly. This can then be combined with `json.loads` to convert this into a data array (similarly to `jsonlite::fromJSON` in R):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff50c823-8f3d-4f82-8d2e-a222b51fc8e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in JSON from URL \n",
    "spp_page = urlopen(spp_page_url)\n",
    "\n",
    "data_json = json.loads(spp_page.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715e9d69-9459-44ec-8715-0aed7c7f37ac",
   "metadata": {},
   "source": [
    "The above generates a long list of output; the data of interest are stored in a dictionary format, with a column name `result`. We can also use Pandas to convert this into a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e40f022-7030-41c7-8168-e8628410152c",
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "\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>taxonid</th>\n",
       "      <th>kingdom_name</th>\n",
       "      <th>phylum_name</th>\n",
       "      <th>class_name</th>\n",
       "      <th>order_name</th>\n",
       "      <th>family_name</th>\n",
       "      <th>genus_name</th>\n",
       "      <th>scientific_name</th>\n",
       "      <th>taxonomic_authority</th>\n",
       "      <th>infra_rank</th>\n",
       "      <th>infra_name</th>\n",
       "      <th>population</th>\n",
       "      <th>category</th>\n",
       "      <th>main_common_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43280</td>\n",
       "      <td>PLANTAE</td>\n",
       "      <td>TRACHEOPHYTA</td>\n",
       "      <td>LILIOPSIDA</td>\n",
       "      <td>POALES</td>\n",
       "      <td>BROMELIACEAE</td>\n",
       "      <td>Pitcairnia</td>\n",
       "      <td>Pitcairnia aequatorialis</td>\n",
       "      <td>L.B.Sm.</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>EN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43281</td>\n",
       "      <td>PLANTAE</td>\n",
       "      <td>TRACHEOPHYTA</td>\n",
       "      <td>LILIOPSIDA</td>\n",
       "      <td>POALES</td>\n",
       "      <td>BROMELIACEAE</td>\n",
       "      <td>Pitcairnia</td>\n",
       "      <td>Pitcairnia alata</td>\n",
       "      <td>L.B.Sm.</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>EN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43282</td>\n",
       "      <td>PLANTAE</td>\n",
       "      <td>TRACHEOPHYTA</td>\n",
       "      <td>LILIOPSIDA</td>\n",
       "      <td>POALES</td>\n",
       "      <td>BROMELIACEAE</td>\n",
       "      <td>Pitcairnia</td>\n",
       "      <td>Pitcairnia andreetae</td>\n",
       "      <td>H.Luther</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>VU</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43283</td>\n",
       "      <td>PLANTAE</td>\n",
       "      <td>TRACHEOPHYTA</td>\n",
       "      <td>LILIOPSIDA</td>\n",
       "      <td>POALES</td>\n",
       "      <td>BROMELIACEAE</td>\n",
       "      <td>Pitcairnia</td>\n",
       "      <td>Pitcairnia bergii</td>\n",
       "      <td>H.Luther</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>EN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43284</td>\n",
       "      <td>PLANTAE</td>\n",
       "      <td>TRACHEOPHYTA</td>\n",
       "      <td>LILIOPSIDA</td>\n",
       "      <td>POALES</td>\n",
       "      <td>BROMELIACEAE</td>\n",
       "      <td>Pitcairnia</td>\n",
       "      <td>Pitcairnia caduciflora</td>\n",
       "      <td>Rauh &amp;amp; E.Gross</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>EN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>61381</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>LEUCISCIDAE</td>\n",
       "      <td>Squalius</td>\n",
       "      <td>Squalius illyricus</td>\n",
       "      <td>Heckel &amp;amp; Kner, 1858</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NT</td>\n",
       "      <td>Illyrian chub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>61382</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Luciobarbus</td>\n",
       "      <td>Luciobarbus labiosa</td>\n",
       "      <td>(Pellegrin, 1920)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LC</td>\n",
       "      <td>Sebou Barbel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>61383</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>NEMACHEILIDAE</td>\n",
       "      <td>Nemacheilus</td>\n",
       "      <td>Nemacheilus sp. nov.</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>EN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>61384</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>LEUCISCIDAE</td>\n",
       "      <td>Ladigesocypris</td>\n",
       "      <td>Ladigesocypris mermere</td>\n",
       "      <td>(Ladiges, 1960)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>DD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>61385</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>LEUCISCIDAE</td>\n",
       "      <td>Rutilus</td>\n",
       "      <td>Rutilus karamani</td>\n",
       "      <td>Fowler, 1977</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LC</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      taxonid kingdom_name   phylum_name      class_name     order_name  \\\n",
       "0       43280      PLANTAE  TRACHEOPHYTA      LILIOPSIDA         POALES   \n",
       "1       43281      PLANTAE  TRACHEOPHYTA      LILIOPSIDA         POALES   \n",
       "2       43282      PLANTAE  TRACHEOPHYTA      LILIOPSIDA         POALES   \n",
       "3       43283      PLANTAE  TRACHEOPHYTA      LILIOPSIDA         POALES   \n",
       "4       43284      PLANTAE  TRACHEOPHYTA      LILIOPSIDA         POALES   \n",
       "...       ...          ...           ...             ...            ...   \n",
       "9995    61381     ANIMALIA      CHORDATA  ACTINOPTERYGII  CYPRINIFORMES   \n",
       "9996    61382     ANIMALIA      CHORDATA  ACTINOPTERYGII  CYPRINIFORMES   \n",
       "9997    61383     ANIMALIA      CHORDATA  ACTINOPTERYGII  CYPRINIFORMES   \n",
       "9998    61384     ANIMALIA      CHORDATA  ACTINOPTERYGII  CYPRINIFORMES   \n",
       "9999    61385     ANIMALIA      CHORDATA  ACTINOPTERYGII  CYPRINIFORMES   \n",
       "\n",
       "        family_name      genus_name           scientific_name  \\\n",
       "0      BROMELIACEAE      Pitcairnia  Pitcairnia aequatorialis   \n",
       "1      BROMELIACEAE      Pitcairnia          Pitcairnia alata   \n",
       "2      BROMELIACEAE      Pitcairnia      Pitcairnia andreetae   \n",
       "3      BROMELIACEAE      Pitcairnia         Pitcairnia bergii   \n",
       "4      BROMELIACEAE      Pitcairnia    Pitcairnia caduciflora   \n",
       "...             ...             ...                       ...   \n",
       "9995    LEUCISCIDAE        Squalius        Squalius illyricus   \n",
       "9996     CYPRINIDAE     Luciobarbus       Luciobarbus labiosa   \n",
       "9997  NEMACHEILIDAE     Nemacheilus      Nemacheilus sp. nov.   \n",
       "9998    LEUCISCIDAE  Ladigesocypris    Ladigesocypris mermere   \n",
       "9999    LEUCISCIDAE         Rutilus          Rutilus karamani   \n",
       "\n",
       "          taxonomic_authority infra_rank infra_name population category  \\\n",
       "0                     L.B.Sm.       None       None       None       EN   \n",
       "1                     L.B.Sm.       None       None       None       EN   \n",
       "2                    H.Luther       None       None       None       VU   \n",
       "3                    H.Luther       None       None       None       EN   \n",
       "4          Rauh &amp; E.Gross       None       None       None       EN   \n",
       "...                       ...        ...        ...        ...      ...   \n",
       "9995  Heckel &amp; Kner, 1858       None       None       None       NT   \n",
       "9996        (Pellegrin, 1920)       None       None       None       LC   \n",
       "9997                     None       None       None       None       EN   \n",
       "9998          (Ladiges, 1960)       None       None       None       DD   \n",
       "9999             Fowler, 1977       None       None       None       LC   \n",
       "\n",
       "     main_common_name  \n",
       "0                None  \n",
       "1                None  \n",
       "2                None  \n",
       "3                None  \n",
       "4                None  \n",
       "...               ...  \n",
       "9995    Illyrian chub  \n",
       "9996     Sebou Barbel  \n",
       "9997             None  \n",
       "9998             None  \n",
       "9999             None  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dictionary output of \"result\" field to a Pandas dataframe\n",
    "spp_page_df = pd.DataFrame.from_dict(data_json['result'])\n",
    "\n",
    "# Display dataframe\n",
    "spp_page_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0621b47d-9b16-465b-ba16-af2b3efcfca8",
   "metadata": {},
   "source": [
    "### Access specific info on a single species\n",
    "\n",
    "Humpback whale is ID \\# 13006. Let's find the countries where it can be found! (or try a different taxon ID from the previous step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a73c5ef-2e9a-47c4-a696-91d6d09b12d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL for API endpoint: humpback whales\n",
    "spp_country_url = iucn_url + '/api/v3/species/countries/id/' + '13006'+ '?token=' + iucn_token\n",
    "\n",
    "# Read in JSON from URL\n",
    "spp_ctry = urlopen(spp_country_url)\n",
    "data_json = json.loads(spp_ctry.read())\n",
    "\n",
    "# Convert dictionary output to Pandas dataframe\n",
    "spp_ctry_df = pd.DataFrame.from_dict(data_json['result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1aa8c02a-7208-4e1d-9544-34208f74f085",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>presence</th>\n",
       "      <th>origin</th>\n",
       "      <th>distribution_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AG</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AI</td>\n",
       "      <td>Anguilla</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AQ</td>\n",
       "      <td>Antarctica</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>WF</td>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>WS</td>\n",
       "      <td>Samoa</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>YE</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>YT</td>\n",
       "      <td>Mayotte</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>ZA</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Extant</td>\n",
       "      <td>Native</td>\n",
       "      <td>Native</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code               country presence  origin distribution_code\n",
       "0     AE  United Arab Emirates   Extant  Native            Native\n",
       "1     AG   Antigua and Barbuda   Extant  Native            Native\n",
       "2     AI              Anguilla   Extant  Native            Native\n",
       "3     AO                Angola   Extant  Native            Native\n",
       "4     AQ            Antarctica   Extant  Native            Native\n",
       "..   ...                   ...      ...     ...               ...\n",
       "160   WF     Wallis and Futuna   Extant  Native            Native\n",
       "161   WS                 Samoa   Extant  Native            Native\n",
       "162   YE                 Yemen   Extant  Native            Native\n",
       "163   YT               Mayotte   Extant  Native            Native\n",
       "164   ZA          South Africa   Extant  Native            Native\n",
       "\n",
       "[165 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spp_ctry_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc1fce0-daf7-4cf6-968d-529d5c44a46d",
   "metadata": {},
   "source": [
    "## WoRMS API\n",
    "\n",
    "Now let's try it with the World Register of Marine Species! https://www.marinespecies.org/rest\n",
    "\n",
    "### Access AphiaID for a genus name\n",
    "\n",
    "Let's find the ID for genus Balaenoptera, a group that includes the biggest whales in the oceans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f889e7dc-c51a-4e33-879d-950c2b322432",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Root URL for WoRMS \n",
    "worms_url = \"https://www.marinespecies.org/rest/\"\n",
    "\n",
    "# Endpoint URL\n",
    "worms_id_url = worms_url + 'AphiaIDByName/' + 'balaenoptera'\n",
    "\n",
    "# Read in JSON from URL\n",
    "worms_data = urlopen(worms_id_url)\n",
    "worms_id = json.loads(worms_data.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5697e6e-126b-4714-ad02-86aa0bd5e2e2",
   "metadata": {},
   "source": [
    "### Access children of a genus\n",
    "\n",
    "The ID for Balaenoptera is 137013, but it is stored in a variable already. Let's find the \"children\", i.e., the members of the next classification down (genus -\\> species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d172adc3-780a-48ff-85ed-3324265c51da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Endpoint URL for children\n",
    "worms_child_url = worms_url + 'AphiaChildrenByAphiaID/' + str(worms_id)\n",
    "\n",
    "# Read in JSON from URL\n",
    "worms_data = urlopen(worms_child_url)\n",
    "worms_child = json.loads(worms_data.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8475d0f5-f515-474a-882a-b777fc1d57d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to Pandas dataframe\n",
    "worms_datatable = pd.DataFrame(worms_child)\n",
    "\n",
    "# Filter out non-accepted data\n",
    "worms_accepted = worms_datatable[worms_datatable['status'] == 'accepted']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60142f93-c157-4b75-be7d-c31b2eeb1695",
   "metadata": {},
   "source": [
    "### Access classification of a species\n",
    "\n",
    "Not all returned data are equally easy to work with - here's a list of lists of lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e765b696-4edd-4db6-8da3-d49f0d828900",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Endpoint URL\n",
    "worms_class_url = worms_url + 'AphiaClassificationByAphiaID/' + str(worms_id)\n",
    "\n",
    "# Read in JSON\n",
    "worms_data = urlopen(worms_child_url)\n",
    "worms_class = json.loads(worms_data.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785e1015-c917-4f15-977b-9b093583940c",
   "metadata": {},
   "source": [
    "## Read in data\n",
    "\n",
    "### read in cleaned IUCN data\n",
    "\n",
    "This set of species includes all marine-associated mammals only.  For now, note IUCN species ID, scientific and common name, and risk category, among other information.  The risk category is what we want - up to date as of the end of 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "97cd15ce-e8e0-4598-9eba-c3aafd73b37b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in marine mammal data into Pandas dataframe\n",
    "iucn_spp_df = pd.read_csv('data/iucn_mar_mammal_info_v2021-3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37abeffe-4ae7-4f2d-937c-ca4fb7aa43df",
   "metadata": {},
   "source": [
    "### Read in cleaned AquaMaps species info\n",
    "\n",
    "This set of species includes all marine mammals only.  For now, note AquaMaps species ID, IUCN species ID, name, sciname, and risk category, among other information.  Here, the risk category is not up to date - this dataset was last updated in 2020.\n",
    "\n",
    "The critical piece here is the AquaMaps species ID, which we need to know for the spatial information in a moment.  We'll use IUCN id to join the up-to-date risk category to the AquaMaps species ID.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60ddbf56-53fe-476b-b2d9-cd59e8621462",
   "metadata": {},
   "outputs": [],
   "source": [
    "am_spp_df = pd.read_csv('data/am_mammal_info_v10_2019.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6c1dfd-aaba-4f4e-ba00-d327d88e761e",
   "metadata": {},
   "source": [
    "### Read in cleaned AquaMaps species-cell info\n",
    "\n",
    "Note the columns loiczid, AquaMaps species id, and probability of occurrence.  LOICZID is a cell code that we'll match to spatial information in a minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d818bbf-2c6b-4781-b299-1f43bd87ddc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "am_spp_cells_df = pd.read_csv('data/am_mammal_hcaf_cropped_v10_2019.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e1c5df-34fd-4692-9d3e-73bf6f10eeb5",
   "metadata": {},
   "source": [
    "### Read in cleaned HCAF data\n",
    "\n",
    "This is cropped to the Eastern Pacific Ocean - east of -150 longitude, north of the equator.  We will turn this into a raster for mapping our species!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "289ac434-5558-46fe-8051-d390e0cd0f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'CsquareCode', 'LOICZID', 'NLimit', 'Slimit', 'WLimit', 'ELimit',\n",
       "       'CenterLat', 'CenterLong', 'CellArea', 'OceanArea', 'PWater',\n",
       "       'ClimZoneCode', 'FAOAreaM', 'FAOAreaIn', 'CountryMain', 'CountrySecond',\n",
       "       'CountryThird', 'CountrySubMain', 'CountrySubSecond', 'CountrySubThird',\n",
       "       'EEZ', 'LME', 'LMEBorder', 'MEOW', 'OceanBasin', 'IslandsNo',\n",
       "       'Area0_20', 'Area20_40', 'Area40_60', 'Area60_80', 'Area80_100',\n",
       "       'AreaBelow100', 'ElevationMin', 'ElevationMax', 'ElevationMean',\n",
       "       'ElevationSD', 'DepthMin', 'DepthMax', 'DepthMean', 'DepthSD',\n",
       "       'SSTAnMean', 'SBTAnMean', 'SalinityMean', 'SalinityBMean',\n",
       "       'PrimProdMean', 'IceConAnn', 'OxyMean', 'OxyBMean', 'LandDist', 'Shelf',\n",
       "       'Slope', 'Abyssal', 'TidalRange', 'Coral', 'Estuary', 'Seamount',\n",
       "       'MPA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in full dataset\n",
    "am_hcaf_df = pd.read_csv('data/am_hcaf_cropped_v7.csv')\n",
    "\n",
    "# Display all column names\n",
    "am_hcaf_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "574069c5-94c6-4488-b942-bb5b35b340d6",
   "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>LOICZID</th>\n",
       "      <th>CenterLong</th>\n",
       "      <th>CenterLat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>129040</td>\n",
       "      <td>-100.25</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129039</td>\n",
       "      <td>-100.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128320</td>\n",
       "      <td>-100.25</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>128319</td>\n",
       "      <td>-100.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>129038</td>\n",
       "      <td>-101.25</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12932</th>\n",
       "      <td>41104</td>\n",
       "      <td>-148.25</td>\n",
       "      <td>61.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12936</th>\n",
       "      <td>41102</td>\n",
       "      <td>-149.25</td>\n",
       "      <td>61.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12937</th>\n",
       "      <td>41101</td>\n",
       "      <td>-149.75</td>\n",
       "      <td>61.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12938</th>\n",
       "      <td>40382</td>\n",
       "      <td>-149.25</td>\n",
       "      <td>61.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12939</th>\n",
       "      <td>40381</td>\n",
       "      <td>-149.75</td>\n",
       "      <td>61.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8780 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LOICZID  CenterLong  CenterLat\n",
       "0       129040     -100.25       0.25\n",
       "1       129039     -100.75       0.25\n",
       "2       128320     -100.25       0.75\n",
       "3       128319     -100.75       0.75\n",
       "4       129038     -101.25       0.25\n",
       "...        ...         ...        ...\n",
       "12932    41104     -148.25      61.25\n",
       "12936    41102     -149.25      61.25\n",
       "12937    41101     -149.75      61.25\n",
       "12938    40382     -149.25      61.75\n",
       "12939    40381     -149.75      61.75\n",
       "\n",
       "[8780 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select data where ocean_area is greater than zero, extract columns of interest\n",
    "longlat_id_df = am_hcaf_df[am_hcaf_df['OceanArea'] > 0][['LOICZID','CenterLong','CenterLat']]\n",
    "\n",
    "longlat_id_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40da05b7-1885-468a-9fbe-c8d88b4bc1cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc24bf48-c4cd-42d1-b165-d814b165ef4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = longlat_id_df['CenterLong']\n",
    "y = longlat_id_df['CenterLat']\n",
    "z = longlat_id_df.LOICZID\n",
    "\n",
    "\n",
    "xi = np.arange(np.min(x),np.max(x),0.5)\n",
    "yi = np.arange(np.min(y),np.max(y),0.5)\n",
    "xi,yi = np.meshgrid(xi,yi)\n",
    "zi = griddata((x,y),z,(xi,yi),method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a729e44-e030-426c-b3ea-497fe73e6848",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e895bbed-f42d-4f00-800f-ef0159bab32a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6f5f24cb-cf23-4e6c-9d74-2f880922bbcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-23-c3b126a716e6>:8: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  cf=ax.pcolor(xi,yi,zi)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = plt.axes()\n",
    "#ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "#ax.set_extent([-180,180,-90,90])   # change region of interest if desired\n",
    "#ax.coastlines()  # add coastlines\n",
    "\n",
    "# Add  data\n",
    "cf=ax.pcolor(xi,yi,zi)\n",
    "#fig.colorbar(cf,ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a404d58-4025-4f08-ad3b-7cf534ac6b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "zi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c22df701-50cf-4f5c-bae1-0bd210138ea7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f0de381-ff53-40e7-8292-63b7e61a18d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0686e92-a043-43f0-a538-248002575ecc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25e2dd03-783d-484a-8536-2ef3707b42cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "058a13ac-f754-402f-b556-fffbc11d6020",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}