{
 "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 3.1: Endemic Species  </h2>\n",
    "\n",
    "Endemic species are those that are exclusively found in a particular geographic region and nowhere else in the world. It is crucial to understand endemic species in order to maintain the planet's biodiversity, inform conservation efforts, advance our knowledge of evolutionary processes, and recognize the cultural and economic significance of these species. Endemic species in the ocean are difficult to find because they have more freedom of movement than species on land. Consequently, endemism is an important factor to consider when evaluating the importance of protecting a specific place in the ocean. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data needed for this project\n",
    "\n",
    "The data was output by `Step1_Curate_IUCN_RedList.ipynb` and are accessed through the Oceans Program s3 public bucket\n",
    "\n",
    "- IUCN Red List dataset for ACMC:\n",
    "1. s3://ocean-program/data/processed/ACMC_IUCN_RedList/gdf_ACMC_IUCN_range_status_filtered.shp\n",
    "2. https://ocean-program.s3.amazonaws.com/data/processed/ACMC_IUCN_RedList/gdf_ACMC_IUCN_range_status_filtered.shp\n",
    "\n",
    "\n",
    "- Our Marine Protected Area of interest -> ACMC polygon:\n",
    "\n",
    "https://ocean-program.s3.amazonaws.com/data/raw/MPAs/ACMC.geojson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import boto3\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from shapely.geometry import Polygon, Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fiona; #help(fiona.open)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OEF functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from MBU_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load data\n",
    "\n",
    "**Get the conservation area**\n",
    "\n",
    "Mostly for plotting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Coordinates for Cocos Island**\n",
    "\n",
    "Cocos Island is located at 05°31′41″N; 87°03′40″W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cocos_lat = 5+31/60+41/3600\n",
    "Cocos_lon = -(87+3/60+40/3600)\n",
    "Cocos = Point(Cocos_lon, Cocos_lat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import the entire marine protected area**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ACMC is the acronyms for the entire conservation area: Cocos Marine Conservation Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACMC = gpd.read_file('https://ocean-program.s3.amazonaws.com/data/raw/MPAs/ACMC.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ACMC.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get the species data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the outcome from [Step1_Curate_IUCN_RedList.ipynb](https://github.com/Open-Earth-Foundation/oceanprogram/blob/main/Marine%20Biodiversity%20Unit%20Methodology/Step1_Curate_IUCN_RedList.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The species information file is large (~2.1 GB), so trying to access it from the public AWS S3 bucket 'directly' can be time consuming.\n",
    "\n",
    "Two options:\n",
    "1. Have patience and run it anyway\n",
    "2. Download the file in your computer and read it locally"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Option 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df = gpd.read_file('s3://ocean-program/data/processed/ACMC_IUCN_RedList/gdf_ACMC_IUCN_range_status_filtered.shp') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Option 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize S3 client\n",
    "s3 = boto3.client('s3')\n",
    "\n",
    "# Set the name of the bucket and the path to the directory\n",
    "bucket_name = 'ocean-program'\n",
    "directory_path = 'data/processed/ACMC_IUCN_RedList/'\n",
    "\n",
    "# List all files in the directory\n",
    "response = s3.list_objects_v2(Bucket=bucket_name, Prefix=directory_path)\n",
    "\n",
    "# Download each file\n",
    "for obj in response['Contents']:\n",
    "    # Skip directories\n",
    "    if obj['Key'].endswith('/'):\n",
    "        continue\n",
    "    \n",
    "    # Download the file\n",
    "    file_name = obj['Key'].split('/')[-1]\n",
    "    s3.download_file(ocean-program, obj['Key'], file_name)\n",
    "    print(f\"Downloaded file: {file_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The downloaded files will be saved in the current working directory, so to read it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df = gpd.read_file('./gdf_ACMC_IUCN_range_status_filtered.shp') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Preliminary calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_of_conservation = ACMC.geometry.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_of_conservation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Maybe this modulating factor can have another name, because we can obtain more information from it, as local species or migratory species."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distribution Ratio #2**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's to define a distribution ratio based on the distribution area within total conservation area and the total distribution area for each species:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DistRatio 2 = $\\frac{\\text{distribution area within total conservation area}}{\\text{total distribution area}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis - Scenarios**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can have 3 scenarios:\n",
    "\n",
    "1. `Distribution Ratio 2 > 1`\n",
    "- This does not make sense because it implies that the range of the species within the total conservation area is greater than its total range.\n",
    "2. `Distribution Ratio 2 = 1` \n",
    "- This implies that both areas are the same, so that the species is endemic to the conservation area.\n",
    "3. `Distribution Ratio 2 < 1` \n",
    "\n",
    "    3.1 When Distribution `Ratio 2 is close to 1`: \n",
    "- This means that these species are not endemic to the conservation area, but could be considered local species (for example, the Eastern Central Pacific).\n",
    "\n",
    "    3.2 When Distribution `Ratio 2 << 1`: \n",
    "- These species are migratory\n",
    "- They might visit the conservation area seasonally and they can travel long distances\n",
    "- You can find them in other parts of the ocean around the world"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maps examples: https://towardsdatascience.com/plot-choropleth-maps-with-shapefiles-using-geopandas-a6bf6ade0a49\n",
    "https://stackoverflow.com/questions/65507374/plotting-a-geopandas-dataframe-using-plotly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing all the geometries outside ACMC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = gpd.clip(df.set_crs(epsg=4326, allow_override=True), ACMC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grid geo Dataframe\n",
    "grid = create_grid(ACMC, 'hexagon', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mbu_endemism(roi, gdf, grid_gdf, transform=\"log\"):\n",
    "    \"\"\"\n",
    "    inputs:\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 and the distribution polygons of each of them\n",
    "    grid_gdf <geopandas dataframe>: consists of polygons of grids typically generated by the gridding function\n",
    "                                  : containts atleast a geometry column and a unique grid_id\n",
    "    transform <str>: either \"none\" or \"square-root\" or \"log\"\n",
    "                   : if \"square-root\" then it is square-rooted\n",
    "                   : else 1/(-Log2(score))\n",
    "               \n",
    "    output(s):\n",
    "    gdf <geopandas dataframe>: with an additional column ('mbu_endemism') containing the number\n",
    "                             :of units for that grid or geometry\n",
    "    \"\"\"\n",
    "    \n",
    "    #Polygons of species distribution to be clipped to roi\n",
    "    df2 = gpd.clip(gdf.set_crs(epsg=4326, allow_override=True), roi)\n",
    "    \n",
    "    #Calculate the portion of the area covered by each species in roi with respect to its global distribution\n",
    "    dist_ratio2 = np.round(df2.area/gdf.area, decimals=4, out=None)\n",
    "    \n",
    "    if transform == \"none\":\n",
    "        \n",
    "        #Add these values into the new gdf\n",
    "        df2[\"dist_ratio2\"] = dist_ratio2\n",
    "        \n",
    "        #Function that calculates the sum of the individual log_dist2 values of all species of overlapping geometries\n",
    "        overlap_endemic_v = sum_values(df2,'dist_ratio2')\n",
    "        \n",
    "    elif transform == \"log\":\n",
    "        \n",
    "        #Calculate the log of that ratio\n",
    "        log_dist2 = 1/(-np.log2(dist_ratio2)+0.1)\n",
    "    \n",
    "        #Add these values into the new gdf\n",
    "        df2[\"log_dist2\"] = log_dist2\n",
    "    \n",
    "        #Function that calculates the sum of the individual log_dist2 values of all species of overlapping geometries\n",
    "        overlap_endemic_v = sum_values(df2,'log_dist2')\n",
    "        \n",
    "    elif transform == \"square-root\":\n",
    "            \n",
    "        #Calculate the log of that ratio\n",
    "        sqroot_dist2 = dist_ratio2**0.5\n",
    "    \n",
    "        #Add these values into the new gdf\n",
    "        df2[\"sqroot_dist2\"] = sqroot_dist2\n",
    "    \n",
    "        #Function that calculates the sum of the individual log_dist2 values of all species of overlapping geometries\n",
    "        overlap_endemic_v = sum_values(df2,'sqroot_dist2')       \n",
    "        \n",
    "    else:\n",
    "        raise Exception(\"Specify a none, log or square as the transformation.\")\n",
    "    \n",
    "    #Merged the log_dist2 values of overlapping geometries with the grid gdf\n",
    "    merged = gpd.sjoin(overlap_endemic_v, grid_gdf, how='left')\n",
    "    merged['n_value']= overlap_endemic_v['sum_overlaps']\n",
    "\n",
    "    # Compute stats per grid cell\n",
    "    #aggfunc: sum the values of all the geometries that dissolve\n",
    "    dissolve = merged.dissolve(by=\"index_right\", aggfunc={'n_value': 'sum'})\n",
    "\n",
    "    #Put this into cell\n",
    "    grid_gdf.loc[dissolve.index, 'endemism'] = dissolve.n_value.values\n",
    "    \n",
    "    return grid_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v8/b0fnjf9d4q31sfz_8q4h4zxm0000gn/T/ipykernel_35547/481667131.py:21: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  dist_ratio2 = np.round(df2.area/gdf.area, decimals=4, out=None)\n",
      "/Users/maureenfonseca/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:397: RuntimeWarning: divide by zero encountered in log2\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/Users/maureenfonseca/Desktop/oceanprogram/Marine_Ecosystem_Credits/Marine_Biodiversity/MBU_Methodology/MBU_utils.py:196: 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"
     ]
    },
    {
     "data": {
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       "      <th>Grid_ID</th>\n",
       "      <th>endemism</th>\n",
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       "      <th>12</th>\n",
       "      <td>POLYGON ((-85.32201 7.34679, -85.82201 8.21281...</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>POLYGON ((-83.82201 1.28461, -84.32201 2.15063...</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>14</th>\n",
       "      <td>POLYGON ((-83.82201 3.01666, -84.32201 3.88268...</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "                                             geometry  Grid_ID      endemism\n",
       "0   POLYGON ((-88.32201 2.15063, -88.82201 3.01666...        0           NaN\n",
       "1   POLYGON ((-88.32201 3.88268, -88.82201 4.74871...        1      0.000000\n",
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       "4   POLYGON ((-86.82201 1.28461, -87.32201 2.15063...        4           NaN\n",
       "5   POLYGON ((-86.82201 3.01666, -87.32201 3.88268...        5      0.000000\n",
       "6   POLYGON ((-86.82201 4.74871, -87.32201 5.61474...        6  11952.095261\n",
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       "8   POLYGON ((-86.82201 8.21281, -87.32201 9.07884...        8           NaN\n",
       "9   POLYGON ((-85.32201 2.15063, -85.82201 3.01666...        9      0.000000\n",
       "10  POLYGON ((-85.32201 3.88268, -85.82201 4.74871...       10   2501.337605\n",
       "11  POLYGON ((-85.32201 5.61474, -85.82201 6.48076...       11  20497.674516\n",
       "12  POLYGON ((-85.32201 7.34679, -85.82201 8.21281...       12      0.000000\n",
       "13  POLYGON ((-83.82201 1.28461, -84.32201 2.15063...       13           NaN\n",
       "14  POLYGON ((-83.82201 3.01666, -84.32201 3.88268...       14           NaN\n",
       "15  POLYGON ((-83.82201 4.74871, -84.32201 5.61474...       15           NaN\n",
       "16  POLYGON ((-83.82201 6.48076, -84.32201 7.34679...       16           NaN\n",
       "17  POLYGON ((-83.82201 8.21281, -84.32201 9.07884...       17           NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mbu_endemism(ACMC, df, grid, transform=\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v8/b0fnjf9d4q31sfz_8q4h4zxm0000gn/T/ipykernel_35547/3952723412.py:21: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  dist_ratio2 = np.round(df2.area/gdf.area, decimals=4, out=None)\n",
      "/Users/maureenfonseca/Desktop/oceanprogram/Marine_Ecosystem_Credits/Marine_Biodiversity/MBU_Methodology/MBU_utils.py:196: 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"
     ]
    },
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      "text/plain": [
       "                                             geometry  Grid_ID   endemism\n",
       "0   POLYGON ((-88.32201 2.15063, -88.82201 3.01666...        0        NaN\n",
       "1   POLYGON ((-88.32201 3.88268, -88.82201 4.74871...        1     0.0000\n",
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       "4   POLYGON ((-86.82201 1.28461, -87.32201 2.15063...        4        NaN\n",
       "5   POLYGON ((-86.82201 3.01666, -87.32201 3.88268...        5     0.0000\n",
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       "8   POLYGON ((-86.82201 8.21281, -87.32201 9.07884...        8        NaN\n",
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       "11  POLYGON ((-85.32201 5.61474, -85.82201 6.48076...       11  5854.0490\n",
       "12  POLYGON ((-85.32201 7.34679, -85.82201 8.21281...       12     0.0000\n",
       "13  POLYGON ((-83.82201 1.28461, -84.32201 2.15063...       13        NaN\n",
       "14  POLYGON ((-83.82201 3.01666, -84.32201 3.88268...       14        NaN\n",
       "15  POLYGON ((-83.82201 4.74871, -84.32201 5.61474...       15        NaN\n",
       "16  POLYGON ((-83.82201 6.48076, -84.32201 7.34679...       16        NaN\n",
       "17  POLYGON ((-83.82201 8.21281, -84.32201 9.07884...       17        NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mbu_endemism(ACMC, df, grid, transform=\"none\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v8/b0fnjf9d4q31sfz_8q4h4zxm0000gn/T/ipykernel_35547/3952723412.py:21: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  dist_ratio2 = np.round(df2.area/gdf.area, decimals=4, out=None)\n",
      "/Users/maureenfonseca/Desktop/oceanprogram/Marine_Ecosystem_Credits/Marine_Biodiversity/MBU_Methodology/MBU_utils.py:196: 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": [
    "test = mbu_endemism(ACMC, df, grid, transform=\"square-root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = test.plot(column='endemism', figsize=(6, 6), cmap='viridis', edgecolor=\"grey\", legend = True)\n",
    "#plt.autoscale(False)\n",
    "\n",
    "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n",
    "ax.axis('off')"
   ]
  },
  {
   "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grid geo Dataframe\n",
    "grid = create_grid(ACMC, 'hexagon', 1)"
   ]
  },
  {
   "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": 13,
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}