{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open Ocean\n",
    "# Open Earth Fundation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Step 2: calculate different metrics for each modulating factor </h1>\n",
    "\n",
    "This notebook is the second part of the `Step1_Curate_IUCN_RedList.ipynb`\n",
    "\n",
    "<h2> Modulating Factor 1: Normalize Biodiversity Score </h2>\n",
    "\n",
    "Species diversity refers to the variety of different species present in a given area, as well as their abundance and distribution. This includes the number of species, their relative abundances, and how evenly or unevenly distributed they are.\n",
    "Our proposal is: apply the Simpson and Shannon Index to obtain a local value of the MPA and normalize each sqd km value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data needed for this project\n",
    "\n",
    "- Species names\n",
    "- Species abundance\n",
    "- Species distribution\n",
    "\n",
    "Next Steps:\n",
    "\n",
    "1. Find a database or datasets with abundance and distribution information for the entire ACMC\n",
    "2. If it isn't reallistic, try to simulate that data\n",
    "\n",
    "Options:\n",
    "1. IUCN RED List and simulate abundance information\n",
    "2. GBIF species information and simulate abundance and distribution information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Importing libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load basic libraries\n",
    "import os\n",
    "import glob\n",
    "import boto3\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# to plot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# to manage shapefiles\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Polygon, Point, box\n",
    "from shapely.ops import linemerge, unary_union, polygonize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fiona; #help(fiona.open)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import OEF functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run this to reload the python file\n",
    "%autoreload 2\n",
    "from MBU_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACMC = gpd.read_file('https://ocean-program.s3.amazonaws.com/data/raw/MPAs/ACMC.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df = gpd.read_file('https://ocean-program.s3.amazonaws.com/data/processed/ACMC_IUCN_RedList/gdf_ACMC_IUCN_range_status_filtered.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.42 s, sys: 106 ms, total: 7.52 s\n",
      "Wall time: 7.59 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Locally\n",
    "df = gpd.read_file('/Users/maureenfonseca/Desktop/oceanprogram/Marine_Ecosystem_Credits/Marine_Biodiversity/MBU_Methodology/gdf_range_status_filtered_shortV.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = create_grid(ACMC, grid_shape=\"hexagon\", grid_size_deg=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Preliminary calculations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_abundance = np.random.randint(50, size = (len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['abundance'] = fake_abundance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Simpson Index**\n",
    "\n",
    "$\\text{D} = 1-(\\frac{\\sum{n(n-1)}}{N(N-1)})$\n",
    "\n",
    "n = the total number of organisms of a particular species\n",
    "\n",
    "N = the total number of organisms of all species\n",
    "\n",
    "The value of ***D*** ranges between 0 and 1. With this index, 1 represents infinite diversity and 0, no diversity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simpson(gdf_abundance_col):\n",
    "    \"\"\"\n",
    "    Calculates the value of D using the given values of abundance.\n",
    "    \n",
    "    Parameters:\n",
    "        - n (int): The sum of n(n-1)\n",
    "        - N (int): The total number of elements\n",
    "        \n",
    "    Returns:\n",
    "        - D (float): The calculated value of D\n",
    "    \"\"\"\n",
    "    \n",
    "    abundance = np.array(gdf_abundance_col)\n",
    "    N = np.sum(abundance)\n",
    "    \n",
    "    numerator = sum([n*(n-1) for n in abundance])\n",
    "    denominator = N * (N - 1)\n",
    "    \n",
    "    D = 1 - (numerator / denominator)\n",
    "    \n",
    "    return D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simpson(roi, gdf, grid_gdf, gdf_col_name):\n",
    "    \"\"\"\n",
    "    This function calculates the Simpson Index per grid cell and its corresponding MBU value\n",
    "    \n",
    "    input(s):\n",
    "    roi <shapely polygon in CRS WGS84:EPSG 4326>: region of interest or the total project area\n",
    "    gdf <geopandas dataframe>: contains at least the name of the species, the distribution polygons of each of them \n",
    "                             :and their abundance\n",
    "    grid_gdf <geopandas dataframe>: consists of polygons of grids typically generated by the gridding function\n",
    "                                  : containts at least a geometry column and a unique grid_id\n",
    "    gdf_col_name <string>: corresponds to the name of the abundance information column in the gdf\n",
    "    \n",
    "    output(s):\n",
    "    gdf <geopandas dataframe>: with an additional column ('mbu_habitat_survey') containing the number\n",
    "                             : of units for that grid or geometry\n",
    "    \"\"\"\n",
    "    \n",
    "    #Join in a gdf all the geometries within ROI\n",
    "    gdf = gpd.clip(gdf.set_crs(epsg=4326, allow_override=True), roi)\n",
    "\n",
    "    #This function calculates the sum of all abundances of overlapping species\n",
    "    overlap = map_algebra(gdf, gdf_col_name, 'sum')\n",
    "\n",
    "    #Merged the overlap values of overlapping geometries with the grid gdf\n",
    "    merged = gpd.sjoin(overlap, grid_gdf, how='left')\n",
    "    merged['n_value'] = overlap['algebra_overlaps']\n",
    "    \n",
    "    #Calculate the numerator and denominator needed per row\n",
    "    num = merged['n_value']*(merged['n_value']-1)\n",
    "    den = np.sum(merged['n_value'])*(np.sum(merged['n_value'])-1)\n",
    "    merged['num'] = num\n",
    "\n",
    "    #Dissolve the DataFrame by 'index_right' and aggregate using the calculated Shannon entropy\n",
    "    dissolve = merged.dissolve(by=\"index_right\", aggfunc={'num': 'sum'})\n",
    "    \n",
    "    #Calculate the Shannon index per grid\n",
    "    dissolve['simpson'] = 1-(dissolve['num']/den)\n",
    "\n",
    "    #Put this into cell\n",
    "    grid_gdf.loc[dissolve.index, 'Simpson'] = dissolve.simpson.values\n",
    "\n",
    "    #Normalization factor\n",
    "    Norm_factor = grid_gdf['Simpson']/grid_gdf['Simpson'].max()\n",
    "    \n",
    "    return grid_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/maureenfonseca/Desktop/oceanprogram/Marine_Ecosystem_Credits/Marine_Biodiversity/MBU_Methodology/MBU_utils.py:198: FutureWarning: Currently, index_parts defaults to True, but in the future, it will default to False to be consistent with Pandas. Use `index_parts=True` to keep the current behavior and True/False to silence the warning.\n",
      "  new_gdf = gdf.explode('geometry')\n"
     ]
    }
   ],
   "source": [
    "Simpson = simpson(ACMC, df, grid, 'abundance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "Simpson.plot(ax = ax, column = 'Simpson', cmap = 'viridis', edgecolor = 'grey', linewidth = 0.01)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**OBIS data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyobis import occurrences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a polygon to access the OBIS data\n",
    "min_x, min_y, max_x, max_y = ACMC.total_bounds\n",
    "geometry = f\"POLYGON(({max_x} {min_y}, {min_x} {min_y}, {min_x} {max_y}, {max_x} {max_y}, {max_x} {min_y}))\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching: [████████████████████████████████████████████████████████████████████████████████████████████████████] 4216/4216\n",
      "Fetched 4216 records.\n"
     ]
    },
    {
     "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>rightsHolder</th>\n",
       "      <th>infraphylum</th>\n",
       "      <th>country</th>\n",
       "      <th>date_year</th>\n",
       "      <th>scientificNameID</th>\n",
       "      <th>scientificName</th>\n",
       "      <th>individualCount</th>\n",
       "      <th>dropped</th>\n",
       "      <th>gigaclassid</th>\n",
       "      <th>aphiaID</th>\n",
       "      <th>...</th>\n",
       "      <th>sectionid</th>\n",
       "      <th>subsectionid</th>\n",
       "      <th>minimumElevationInMeters</th>\n",
       "      <th>maximumElevationInMeters</th>\n",
       "      <th>hab</th>\n",
       "      <th>varietyid</th>\n",
       "      <th>variety</th>\n",
       "      <th>parvorder</th>\n",
       "      <th>parvorderid</th>\n",
       "      <th>associatedMedia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CAS</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>urn:lsid:marinespecies.org:taxname:293586</td>\n",
       "      <td>Doryrhamphus excisus excisus</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>293586</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SIO</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>urn:lsid:marinespecies.org:taxname:306621</td>\n",
       "      <td>Scopelogadus bispinosus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>306621</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KU</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>urn:lsid:marinespecies.org:taxname:125636</td>\n",
       "      <td>Gymnothorax</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>125636</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urn:lsid:marinespecies.org:taxname:105857</td>\n",
       "      <td>Mobula birostris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1026118</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAS</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>Panama</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>urn:lsid:marinespecies.org:taxname:276587</td>\n",
       "      <td>Polydactylus approximans</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>276587</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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",
       "      <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>4211</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>urn:lsid:marinespecies.org:taxname:276587</td>\n",
       "      <td>Polydactylus approximans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>276587</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urn:lsid:marinespecies.org:taxname:127401</td>\n",
       "      <td>Diodon eydouxii</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>127401</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4213</th>\n",
       "      <td>LACM</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>urn:lsid:marinespecies.org:taxname:276400</td>\n",
       "      <td>Dactyloscopus lacteus</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>276400</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4214</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Idiacanthus antrostomus</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>275151</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4215</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gnathostomata</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oneirodes luetkeni</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>10194.0</td>\n",
       "      <td>272631</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4216 rows × 190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rightsHolder    infraphylum     country  date_year  \\\n",
       "0             CAS  Gnathostomata  Costa Rica     1998.0   \n",
       "1             SIO  Gnathostomata         NaN     1967.0   \n",
       "2              KU  Gnathostomata  Costa Rica     1954.0   \n",
       "3             NaN  Gnathostomata         NaN        NaN   \n",
       "4             CAS  Gnathostomata      Panama     1956.0   \n",
       "...           ...            ...         ...        ...   \n",
       "4211          NaN  Gnathostomata  Costa Rica     1975.0   \n",
       "4212          NaN  Gnathostomata         NaN        NaN   \n",
       "4213         LACM  Gnathostomata  Costa Rica     1964.0   \n",
       "4214          NaN  Gnathostomata         NaN     1925.0   \n",
       "4215          NaN  Gnathostomata         NaN     1925.0   \n",
       "\n",
       "                               scientificNameID                scientificName  \\\n",
       "0     urn:lsid:marinespecies.org:taxname:293586  Doryrhamphus excisus excisus   \n",
       "1     urn:lsid:marinespecies.org:taxname:306621       Scopelogadus bispinosus   \n",
       "2     urn:lsid:marinespecies.org:taxname:125636                   Gymnothorax   \n",
       "3     urn:lsid:marinespecies.org:taxname:105857              Mobula birostris   \n",
       "4     urn:lsid:marinespecies.org:taxname:276587      Polydactylus approximans   \n",
       "...                                         ...                           ...   \n",
       "4211  urn:lsid:marinespecies.org:taxname:276587      Polydactylus approximans   \n",
       "4212  urn:lsid:marinespecies.org:taxname:127401               Diodon eydouxii   \n",
       "4213  urn:lsid:marinespecies.org:taxname:276400         Dactyloscopus lacteus   \n",
       "4214                                        NaN       Idiacanthus antrostomus   \n",
       "4215                                        NaN            Oneirodes luetkeni   \n",
       "\n",
       "     individualCount dropped  gigaclassid  aphiaID  ...  sectionid  \\\n",
       "0                  1   False      10194.0   293586  ...        NaN   \n",
       "1                NaN   False      10194.0   306621  ...        NaN   \n",
       "2                NaN   False      10194.0   125636  ...        NaN   \n",
       "3                NaN   False          NaN  1026118  ...        NaN   \n",
       "4                  2   False      10194.0   276587  ...        NaN   \n",
       "...              ...     ...          ...      ...  ...        ...   \n",
       "4211             NaN   False      10194.0   276587  ...        NaN   \n",
       "4212             NaN   False      10194.0   127401  ...        NaN   \n",
       "4213               1   False      10194.0   276400  ...        NaN   \n",
       "4214               1   False      10194.0   275151  ...        NaN   \n",
       "4215               1   False      10194.0   272631  ...        NaN   \n",
       "\n",
       "      subsectionid minimumElevationInMeters maximumElevationInMeters  hab  \\\n",
       "0              NaN                      NaN                      NaN  NaN   \n",
       "1              NaN                      NaN                      NaN  NaN   \n",
       "2              NaN                      NaN                      NaN  NaN   \n",
       "3              NaN                      NaN                      NaN  NaN   \n",
       "4              NaN                      NaN                      NaN  NaN   \n",
       "...            ...                      ...                      ...  ...   \n",
       "4211           NaN                      NaN                      NaN  NaN   \n",
       "4212           NaN                      NaN                      NaN  NaN   \n",
       "4213           NaN                      NaN                      NaN  NaN   \n",
       "4214           NaN                      NaN                      NaN  NaN   \n",
       "4215           NaN                      NaN                      NaN  NaN   \n",
       "\n",
       "      varietyid  variety parvorder parvorderid associatedMedia  \n",
       "0           NaN      NaN       NaN         NaN             NaN  \n",
       "1           NaN      NaN       NaN         NaN             NaN  \n",
       "2           NaN      NaN       NaN         NaN             NaN  \n",
       "3           NaN      NaN       NaN         NaN             NaN  \n",
       "4           NaN      NaN       NaN         NaN             NaN  \n",
       "...         ...      ...       ...         ...             ...  \n",
       "4211        NaN      NaN       NaN         NaN             NaN  \n",
       "4212        NaN      NaN       NaN         NaN             NaN  \n",
       "4213        NaN      NaN       NaN         NaN             NaN  \n",
       "4214        NaN      NaN       NaN         NaN             NaN  \n",
       "4215        NaN      NaN       NaN         NaN             NaN  \n",
       "\n",
       "[4216 rows x 190 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = occurrences.search(geometry=geometry)\n",
    "query.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns the data\n",
    "obis = query.data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert OBIS dataframe to geodataframe\n",
    "obis = gpd.GeoDataFrame(obis, \n",
    "                       geometry=gpd.points_from_xy(obis.decimalLongitude, \n",
    "                                          obis.decimalLatitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "fake_abundance = [random.randint(1, 100) for _ in range(len(obis))]\n",
    "obis['abundance'] = fake_abundance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot protected area with marine species\n",
    "# select column name to plot\n",
    "column_name = 'scientificName'\n",
    "ax = ACMC.boundary.plot()\n",
    "obis.plot(ax=ax, markersize=5, column=column_name, \n",
    "        antialiased=False, legend_kwds={'bbox_to_anchor': (1, 1)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Join in a gdf all the geometries within ACMC\n",
    "df2 = gpd.clip(obis.set_crs(epsg=4326, allow_override=True), ACMC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spatial join of gdf and grid_gdf\n",
    "pointInPolys = sjoin(df2, grid, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#where num = n(n-1)\n",
    "pointInPolys['num'] = pointInPolys['abundance']*(pointInPolys['abundance']-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To count the total abundance number from all the species in a grid\n",
    "N = pd.DataFrame()\n",
    "N['N'] = pointInPolys.groupby('Grid_ID').apply(lambda x: x['abundance'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge the datasets based on the Grid_ID\n",
    "new = pd.merge(pointInPolys, N, on='Grid_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "new['simpson'] = 1-((new['num'])/(new['N']*(new['N']-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}