{ "cells": [ { "cell_type": "markdown", "id": "fd68a188", "metadata": {}, "source": [ "# Open Ocean\n", "## Open Earth Fundation" ] }, { "cell_type": "markdown", "id": "6b29a0a6", "metadata": {}, "source": [ "## Step 3: \n", "Calculate a final number of `Marine Biodiversity Units (MBUs)` based on the modulating factors score" ] }, { "cell_type": "markdown", "id": "77b419cf", "metadata": {}, "source": [ "### Marine Biodiversity Units (MBUs)" ] }, { "cell_type": "markdown", "id": "18a88ff0", "metadata": {}, "source": [ "We think that to develop a scalable system of marine biodiversity credits, we must first define what the credit unit is. To achieve this, we aim to develop a metric that incorporates the ecological values of the ecosystems within each acreage—or 1 km$^2$—of ocean protected. \n", "\n", "The number of MBUs assigned to each km$^2$ is modulated by different factors, which we call \"Modularing Factors\".\n", "\n", "These Modulating Factors are:\n", "1. Normalize Marine Biodiversity Score\n", "2. Species richness\n", "3. Species distribution area\n", "4. Endemism\n", "5. Habitats' Survey\n", "6. Vulnerability of species\n", "\n", "**Note:** Some of these factors do not have a defined methodology yet.\n", "\n", "Each Modulating Factor has a weight factor that it's define by (...)(?)" ] }, { "cell_type": "markdown", "id": "68e853e6", "metadata": {}, "source": [ "**General Methodology**" ] }, { "cell_type": "markdown", "id": "dc0ca1b7", "metadata": {}, "source": [ "- Per km$^2$:\n", "$$\n", "\\begin{align}\n", "MBUs_{sqdkm} = \\sum_{i=1}^{6} C_i\\times MBUs_i\n", "\\end{align}\n", "$$\n", "\n", "where i represent each modulating factor and $C_i$ represent the weight factor of that modulating factor\n", "\n", "This means that each km$^2$ is assigned a number of MBUs per modulating factor\n", "\n", "- For the entire MPA:\n", "$$\n", "\\begin{align}\n", "\\text{Total MBUs} = \\sum_{j=1}^{N} MBUs_{sqdkm}\n", "\\end{align}\n", "$$\n", "\n", "where N represents the total size of the MPA in km$^2$" ] }, { "cell_type": "markdown", "id": "07ec4f99", "metadata": {}, "source": [ "## 3.1 Import Libraries" ] }, { "cell_type": "code", "execution_count": 14, "id": "41e35671-89cf-48ec-b94d-dca6b41bb94c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: boto3 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (1.40.71)\n", "Requirement already satisfied: numpy in d:\\srijan\\ocean\\.venv\\lib\\site-packages (2.3.4)\n", "Requirement already satisfied: pandas in d:\\srijan\\ocean\\.venv\\lib\\site-packages (2.3.3)\n", "Requirement already satisfied: shapely in d:\\srijan\\ocean\\.venv\\lib\\site-packages (2.1.2)\n", "Requirement already satisfied: matplotlib in d:\\srijan\\ocean\\.venv\\lib\\site-packages (3.10.7)\n", "Requirement already satisfied: geopandas in d:\\srijan\\ocean\\.venv\\lib\\site-packages (1.1.1)\n", "Requirement already satisfied: fiona in d:\\srijan\\ocean\\.venv\\lib\\site-packages (1.10.1)\n", "Requirement already satisfied: botocore<1.41.0,>=1.40.71 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from boto3) (1.40.71)\n", "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from boto3) (1.0.1)\n", "Requirement already satisfied: s3transfer<0.15.0,>=0.14.0 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from boto3) (0.14.0)\n", "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from botocore<1.41.0,>=1.40.71->boto3) (2.9.0.post0)\n", "Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from botocore<1.41.0,>=1.40.71->boto3) (2.5.0)\n", "Requirement already satisfied: six>=1.5 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.41.0,>=1.40.71->boto3) (1.17.0)\n", "Requirement already satisfied: pytz>=2020.1 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from pandas) (2025.2)\n", "Requirement already satisfied: tzdata>=2022.7 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from pandas) (2025.2)\n", "Requirement already satisfied: contourpy>=1.0.1 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from matplotlib) (1.3.3)\n", "Requirement already satisfied: cycler>=0.10 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from matplotlib) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from matplotlib) (4.60.1)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from matplotlib) (1.4.9)\n", "Requirement already satisfied: packaging>=20.0 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from matplotlib) (25.0)\n", "Requirement already satisfied: pillow>=8 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from matplotlib) (12.0.0)\n", "Requirement already satisfied: pyparsing>=3 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from matplotlib) (3.2.5)\n", "Requirement already satisfied: pyogrio>=0.7.2 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from geopandas) (0.11.1)\n", "Requirement already satisfied: pyproj>=3.5.0 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from geopandas) (3.7.2)\n", "Requirement already satisfied: attrs>=19.2.0 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from fiona) (25.4.0)\n", "Requirement already satisfied: certifi in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from fiona) (2025.11.12)\n", "Requirement already satisfied: click~=8.0 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from fiona) (8.3.0)\n", "Requirement already satisfied: click-plugins>=1.0 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from fiona) (1.1.1.2)\n", "Requirement already satisfied: cligj>=0.5 in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from fiona) (0.7.2)\n", "Requirement already satisfied: colorama in d:\\srijan\\ocean\\.venv\\lib\\site-packages (from click~=8.0->fiona) (0.4.6)\n" ] } ], "source": [ "!pip install boto3 numpy pandas shapely matplotlib geopandas fiona" ] }, { "cell_type": "code", "execution_count": 15, "id": "8612c3b9", "metadata": {}, "outputs": [], "source": [ "import os\n", "import glob\n", "import boto3\n", "\n", "import math\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "\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": 16, "id": "c0f85099", "metadata": {}, "outputs": [], "source": [ "import fiona; #help(fiona.open)" ] }, { "cell_type": "markdown", "id": "7c5d169a", "metadata": {}, "source": [ "**OEF Functions**" ] }, { "cell_type": "code", "execution_count": 17, "id": "314a68db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload" ] }, { "cell_type": "code", "execution_count": 18, "id": "949cb87e", "metadata": {}, "outputs": [], "source": [ "#Run this to reload the python file\n", "%autoreload 2\n", "from MBU_utils import *" ] }, { "cell_type": "markdown", "id": "e6b69257", "metadata": {}, "source": [ "## 3.2 General Data Needed" ] }, { "cell_type": "markdown", "id": "297c2ae8", "metadata": {}, "source": [ "**Import the entire marine protected area file**" ] }, { "cell_type": "code", "execution_count": 19, "id": "7898fbe9", "metadata": {}, "outputs": [], "source": [ "ACMC = gpd.read_file('https://ocean-program.s3.amazonaws.com/data/raw/MPAs/ACMC.geojson')" ] }, { "cell_type": "markdown", "id": "ad374205", "metadata": {}, "source": [ "Inspect the Coordinate Reference Systems (CRS)" ] }, { "cell_type": "code", "execution_count": 20, "id": "33c14143", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ACMC.crs" ] }, { "cell_type": "markdown", "id": "622facdd", "metadata": {}, "source": [ "**Grid gdf**" ] }, { "cell_type": "code", "execution_count": 21, "id": "9898f25f", "metadata": {}, "outputs": [], "source": [ "grid = create_grid(ACMC, grid_shape=\"hexagon\", grid_size_deg=1.)" ] }, { "cell_type": "markdown", "id": "ecf9794f", "metadata": {}, "source": [ "## 3.3 Calculations" ] }, { "cell_type": "markdown", "id": "6049fcf9", "metadata": {}, "source": [ "### Using geometries" ] }, { "cell_type": "markdown", "id": "9ee37265", "metadata": {}, "source": [ "**Import the species information from IUCN**" ] }, { "cell_type": "markdown", "id": "b2a24382", "metadata": {}, "source": [ "The IUCN Red List dataset does not have information on species abundance, to calculate some MFs that information is needed, so we cannot calculate them at this time." ] }, { "cell_type": "code", "execution_count": 22, "id": "dec4bae7", "metadata": {}, "outputs": [ { "ename": "DataSourceError", "evalue": "AWS_SECRET_ACCESS_KEY and AWS_NO_SIGN_REQUEST configuration options not defined, and C:\\Users\\Admin49\\.aws\\credentials not filled", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mDataSourceError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df1 = 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You can use the \u001b[39m\u001b[33m'\u001b[39m\u001b[33mcolumns\u001b[39m\u001b[33m'\u001b[39m\u001b[33m keyword \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 572\u001b[39m stacklevel=\u001b[32m3\u001b[39m,\n\u001b[32m 573\u001b[39m )\n\u001b[32m 574\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mcolumns\u001b[39m\u001b[33m\"\u001b[39m] = kwargs.pop(\u001b[33m\"\u001b[39m\u001b[33minclude_fields\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m576\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpyogrio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_bytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile 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\u001b[49m\u001b[43mskip_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskip_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 283\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 284\u001b[39m \u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 285\u001b[39m \u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 286\u001b[39m \u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 287\u001b[39m \u001b[43m \u001b[49m\u001b[43mfids\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 288\u001b[39m \u001b[43m \u001b[49m\u001b[43msql\u001b[49m\u001b[43m=\u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 289\u001b[39m \u001b[43m \u001b[49m\u001b[43msql_dialect\u001b[49m\u001b[43m=\u001b[49m\u001b[43msql_dialect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 290\u001b[39m \u001b[43m \u001b[49m\u001b[43mreturn_fids\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfid_as_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 291\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 292\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 294\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m use_arrow:\n\u001b[32m 295\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyarrow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpa\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32mD:\\Srijan\\Ocean\\.venv\\Lib\\site-packages\\pyogrio\\raw.py:198\u001b[39m, in \u001b[36mread\u001b[39m\u001b[34m(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, return_fids, datetime_as_string, **kwargs)\u001b[39m\n\u001b[32m 59\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Read OGR data source into numpy arrays.\u001b[39;00m\n\u001b[32m 60\u001b[39m \n\u001b[32m 61\u001b[39m \u001b[33;03mIMPORTANT: non-linear geometry types (e.g., MultiSurface) are converted\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 194\u001b[39m \n\u001b[32m 195\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 196\u001b[39m dataset_kwargs = _preprocess_options_key_value(kwargs) \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[32m--> \u001b[39m\u001b[32m198\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mogr_read\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 199\u001b[39m \u001b[43m \u001b[49m\u001b[43mget_vsi_path_or_buffer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_buffer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 200\u001b[39m \u001b[43m \u001b[49m\u001b[43mlayer\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlayer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 201\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 202\u001b[39m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 203\u001b[39m \u001b[43m \u001b[49m\u001b[43mread_geometry\u001b[49m\u001b[43m=\u001b[49m\u001b[43mread_geometry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 204\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_2d\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 205\u001b[39m \u001b[43m \u001b[49m\u001b[43mskip_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskip_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 206\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_features\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 207\u001b[39m \u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 208\u001b[39m \u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 209\u001b[39m \u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_mask_to_wkb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 210\u001b[39m \u001b[43m \u001b[49m\u001b[43mfids\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 211\u001b[39m \u001b[43m \u001b[49m\u001b[43msql\u001b[49m\u001b[43m=\u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 212\u001b[39m \u001b[43m \u001b[49m\u001b[43msql_dialect\u001b[49m\u001b[43m=\u001b[49m\u001b[43msql_dialect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 213\u001b[39m \u001b[43m \u001b[49m\u001b[43mreturn_fids\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreturn_fids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 214\u001b[39m \u001b[43m \u001b[49m\u001b[43mdataset_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataset_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 215\u001b[39m \u001b[43m \u001b[49m\u001b[43mdatetime_as_string\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdatetime_as_string\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 216\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mpyogrio/_io.pyx:1313\u001b[39m, in \u001b[36mpyogrio._io.ogr_read\u001b[39m\u001b[34m()\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32mpyogrio/_io.pyx:232\u001b[39m, in \u001b[36mpyogrio._io.ogr_open\u001b[39m\u001b[34m()\u001b[39m\n", "\u001b[31mDataSourceError\u001b[39m: AWS_SECRET_ACCESS_KEY and AWS_NO_SIGN_REQUEST configuration options not defined, and C:\\Users\\Admin49\\.aws\\credentials not filled" ] } ], "source": [ "df1 = gpd.read_file('s3://ocean-program/data/Test/gdf_range_status_filtered_shortV.shp')" ] }, { "cell_type": "code", "execution_count": null, "id": "6dc34f73", "metadata": {}, "outputs": [], "source": [ "#Locally\n", "#df1 = gpd.read_file('/Users/maureenfonseca/Desktop/oceanprogram/Marine_Ecosystem_Credits/Marine_Biodiversity/MBU_Methodology/gdf_range_status_filtered_shortV.shp')" ] }, { "cell_type": "markdown", "id": "5c625a96", "metadata": {}, "source": [ "**Import the habitats/ecosystems information**" ] }, { "cell_type": "code", "execution_count": null, "id": "3423569b", "metadata": {}, "outputs": [], "source": [ "#From AWS\n", "s3 = boto3.resource('s3')\n", "bucket = s3.Bucket('ocean-program')\n", "\n", "eco_names = []\n", "\n", "for obj in bucket.objects.filter(Prefix='data/raw/Ecosystems/'):\n", " name = obj.key[:]\n", " eco_names.append(f'https://ocean-program.s3.amazonaws.com/{name}')" ] }, { "cell_type": "code", "execution_count": null, "id": "acda4b59", "metadata": {}, "outputs": [], "source": [ "#Download and run it locally\n", "# 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/raw/Ecosystems/'\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(bucket_name, obj['Key'], file_name)\n", " print(f\"Downloaded file: {file_name}\")" ] }, { "cell_type": "markdown", "id": "6ac32e90", "metadata": {}, "source": [ "The downloaded files will be saved in the current working directory, so to list it:" ] }, { "cell_type": "code", "execution_count": null, "id": "619f83df", "metadata": {}, "outputs": [], "source": [ "source_dir = './'\n", "eco_names = np.sort(glob.glob(source_dir + \"/*.json\"))" ] }, { "cell_type": "code", "execution_count": null, "id": "1330a09f", "metadata": {}, "outputs": [], "source": [ "#Locally\n", "source_dir = '/Users/maureenfonseca/Desktop/Data-Oceans/Ecosystem_Typology_IUCN/'\n", "eco_names = np.sort(glob.glob(source_dir + \"/*.json\"))" ] }, { "cell_type": "markdown", "id": "01c5c215", "metadata": {}, "source": [ "**Weighted Factors**\n", "\n", "(To be define)" ] }, { "cell_type": "markdown", "id": "0765ae18", "metadata": {}, "source": [ "### 3.3.1 Indices and metrics" ] }, { "cell_type": "markdown", "id": "3eb8151b", "metadata": {}, "source": [ "**Shannon Index**" ] }, { "cell_type": "code", "execution_count": null, "id": "5a2a6cde", "metadata": {}, "outputs": [], "source": [ "%%time\n", "shannon = shannon(ACMC, df1, grid, 'abundance', 'IUCN')" ] }, { "cell_type": "code", "execution_count": null, "id": "c28de142", "metadata": {}, "outputs": [], "source": [ "ax = shannon.plot(column='shannon', figsize=(4, 4), cmap='viridis', edgecolor=\"grey\", legend = True)\n", "\n", "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n", "ax.axis('off')" ] }, { "cell_type": "markdown", "id": "9dfd4d08", "metadata": {}, "source": [ "**Simpson Index**" ] }, { "cell_type": "code", "execution_count": null, "id": "04a8125c", "metadata": {}, "outputs": [], "source": [ "%%time\n", "simpson = simpson(ACMC, df1, grid, 'abundance', 'IUCN')" ] }, { "cell_type": "code", "execution_count": null, "id": "29055af3", "metadata": {}, "outputs": [], "source": [ "ax = simpson.plot(column='simpson', figsize=(4, 4), cmap='viridis', edgecolor=\"grey\", legend = True)\n", "\n", "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n", "ax.axis('off')" ] }, { "cell_type": "markdown", "id": "a8a84582", "metadata": {}, "source": [ "**Species Richness**" ] }, { "cell_type": "code", "execution_count": null, "id": "ff5ab9bb", "metadata": {}, "outputs": [], "source": [ "%%time\n", "species_richness = species_richness(ACMC, df1, grid, 'IUCN')" ] }, { "cell_type": "code", "execution_count": null, "id": "caca3c0b", "metadata": {}, "outputs": [], "source": [ "ax = species_richness.plot(column='species_richness', figsize=(4, 4), cmap='viridis', edgecolor=\"grey\", legend = True)\n", "\n", "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n", "ax.axis('off')" ] }, { "cell_type": "markdown", "id": "e3816fbc", "metadata": {}, "source": [ "**Endemism**" ] }, { "cell_type": "code", "execution_count": null, "id": "99742766", "metadata": {}, "outputs": [], "source": [ "%%time\n", "endemism = endemism(ACMC, df1, grid)" ] }, { "cell_type": "code", "execution_count": null, "id": "7747c6fb", "metadata": {}, "outputs": [], "source": [ "ax = endemism.plot(column='endemism', figsize=(4, 4), cmap='viridis', edgecolor=\"grey\", legend = True)\n", "\n", "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n", "ax.axis('off')" ] }, { "cell_type": "markdown", "id": "a1e800a5", "metadata": {}, "source": [ "**WEGE**" ] }, { "cell_type": "code", "execution_count": null, "id": "56873db5", "metadata": {}, "outputs": [], "source": [ "%%time\n", "wege = wege(ACMC, df1, grid)" ] }, { "cell_type": "code", "execution_count": null, "id": "7d87f966", "metadata": {}, "outputs": [], "source": [ "ax = wege.plot(column='wege', figsize=(4, 4), cmap='viridis', edgecolor=\"grey\", legend = True)\n", "\n", "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n", "ax.axis('off')" ] }, { "cell_type": "markdown", "id": "0c6f6f5e", "metadata": {}, "source": [ "**Habitats Survey**" ] }, { "cell_type": "code", "execution_count": null, "id": "a605d469", "metadata": {}, "outputs": [], "source": [ "%%time\n", "#This calculation is independent of the species dataset\n", "habitats = habitats_survey(ACMC, grid, eco_names)" ] }, { "cell_type": "code", "execution_count": null, "id": "a8e6bd33", "metadata": {}, "outputs": [], "source": [ "ax = habitats.plot(column='habitats_survey', figsize=(4, 4), cmap='viridis', edgecolor=\"grey\", legend = True)\n", "\n", "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n", "ax.axis('off')" ] }, { "cell_type": "markdown", "id": "203f937b", "metadata": {}, "source": [ "### 3.3.2 Modulating Factors and MBU calculations" ] }, { "cell_type": "markdown", "id": "624290ad", "metadata": {}, "source": [ "crs for Central America in meters: 31970\n", "\n", "https://epsg.io/31970" ] }, { "cell_type": "markdown", "id": "0d391bdc", "metadata": {}, "source": [ "**Biodiversity Score**" ] }, { "cell_type": "code", "execution_count": null, "id": "167b65df", "metadata": {}, "outputs": [], "source": [ "biodiversity_score_MBUS = mbu_biodiversity_score(ACMC, df1, grid, 'IUCN', 31970)" ] }, { "cell_type": "code", "execution_count": null, "id": "e6e4b0fe", "metadata": {}, "outputs": [], "source": [ "biodiversity_score_MBUS" ] }, { "cell_type": "markdown", "id": "ae934f52", "metadata": {}, "source": [ "**Species Richness**" ] }, { "cell_type": "code", "execution_count": null, "id": "4db52ae5", "metadata": {}, "outputs": [], "source": [ "species_richness_MBUS = mbu_species_richness(ACMC, df1, grid, 'IUCN', 31970)" ] }, { "cell_type": "markdown", "id": "bfe1344f", "metadata": {}, "source": [ "**Endemism**" ] }, { "cell_type": "code", "execution_count": null, "id": "322f9f55", "metadata": {}, "outputs": [], "source": [ "endemism_MBUS = mbu_endemism(ACMC, df1, grid, 31970)" ] }, { "cell_type": "markdown", "id": "06bb4365", "metadata": {}, "source": [ "**Wege**" ] }, { "cell_type": "code", "execution_count": null, "id": "87683647", "metadata": {}, "outputs": [], "source": [ "wege_MBUS = mbu_wege(ACMC, df1, grid, 31970)" ] }, { "cell_type": "markdown", "id": "2f87d7e2", "metadata": {}, "source": [ "**Habitats Survey**" ] }, { "cell_type": "code", "execution_count": null, "id": "e1ea4cca", "metadata": {}, "outputs": [], "source": [ "habitats_survey_MBUS = mbu_habitats_survey(ACMC, grid, eco_names, 31970)" ] }, { "cell_type": "markdown", "id": "ae44c0bc", "metadata": {}, "source": [ "**Total MBUS**" ] }, { "cell_type": "code", "execution_count": null, "id": "f63ead7f", "metadata": {}, "outputs": [], "source": [ "TotalMBUS = biodiversity_score_MBUS['mbu_biodiversity_score'] + species_richness_MBUS['mbu_species_richness'] + endemism_MBUS['mbu_endemism'] + wege_MBUS['mbu_wege'] + habitats_survey_MBUS['mbu_habitats_survey']" ] }, { "cell_type": "code", "execution_count": null, "id": "85334f3c", "metadata": {}, "outputs": [], "source": [ "ax = mbu_habitats_survey.plot(column='TotalMBUS', figsize=(4, 4), cmap='viridis', edgecolor=\"grey\", legend = True)\n", "\n", "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n", "ax.axis('off')" ] }, { "cell_type": "markdown", "id": "7e07e520", "metadata": {}, "source": [ "### Using Observation Points" ] }, { "cell_type": "markdown", "id": "9b5a4c97", "metadata": {}, "source": [ "**Import species data from OBIS**" ] }, { "cell_type": "code", "execution_count": null, "id": "1dff1e6e", "metadata": {}, "outputs": [], "source": [ "from pyobis import occurrences" ] }, { "cell_type": "code", "execution_count": null, "id": "c6f86c2a", "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}))\"\n", "\n", "query = occurrences.search(geometry=geometry)\n", "query.execute()\n", "\n", "# Returns the data\n", "df2 = query.data " ] }, { "cell_type": "markdown", "id": "43e4a2c6", "metadata": {}, "source": [ "The OBIS dataset doesn't have information on species abundance, to calculate some MF, that information will be randomly assigned for this moment." ] }, { "cell_type": "code", "execution_count": null, "id": "2dce709f", "metadata": {}, "outputs": [], "source": [ "import random\n", "\n", "fake_abundance2 = [random.randint(1, 100) for _ in range(len(df2))]\n", "df2['abundance'] = fake_abundance2" ] }, { "cell_type": "markdown", "id": "e8fe7fcd", "metadata": {}, "source": [ "### 3.3.1 Indices and Metrics" ] }, { "cell_type": "markdown", "id": "49583ddc", "metadata": {}, "source": [ "**Shannon Index**" ] }, { "cell_type": "code", "execution_count": null, "id": "4b56236d", "metadata": {}, "outputs": [], "source": [ "%%time\n", "shannon = shannon(ACMC, df2, grid, 'abundance', 'obis')" ] }, { "cell_type": "markdown", "id": "039ef869", "metadata": {}, "source": [ "**Simpson Index**" ] }, { "cell_type": "code", "execution_count": null, "id": "f132a812", "metadata": {}, "outputs": [], "source": [ "%%time\n", "simpson = simpson(ACMC, df2, grid, 'abundance', 'obis')" ] }, { "cell_type": "markdown", "id": "f46f1ff3", "metadata": {}, "source": [ "**Species Richness**" ] }, { "cell_type": "code", "execution_count": null, "id": "ae765395", "metadata": {}, "outputs": [], "source": [ "%%time\n", "species_richness = species_richness(ACMC, df2, grid, 'obis')" ] }, { "cell_type": "markdown", "id": "022a035f", "metadata": {}, "source": [ "### Modulating Factors and MBU calculations" ] }, { "cell_type": "markdown", "id": "e9fb9344", "metadata": {}, "source": [ "crs for Central America in meters: 31970\n", "\n", "https://epsg.io/31970" ] }, { "cell_type": "markdown", "id": "f7b57bd2", "metadata": {}, "source": [ "**Biodiversity Score**" ] }, { "cell_type": "code", "execution_count": null, "id": "48fac02e", "metadata": {}, "outputs": [], "source": [ "mbu_biodiversity_score = mbu_biodiversity_score(ACMC, df2, grid, 'abundance', 'obis', 31970)" ] }, { "cell_type": "markdown", "id": "2b320335", "metadata": {}, "source": [ "**Species Richness**" ] }, { "cell_type": "code", "execution_count": null, "id": "ef20ebda", "metadata": {}, "outputs": [], "source": [ "mbu_species_richness = mbu_species_richness(ACMC, df2, grid, 'obis', 31970)" ] }, { "cell_type": "markdown", "id": "a85d9c34", "metadata": {}, "source": [ "## Total Score" ] }, { "cell_type": "code", "execution_count": null, "id": "e91c450c", "metadata": {}, "outputs": [], "source": [ "modulating_factor_names = ['species_richness', 'biodiversity_score', 'endemism', 'wege']" ] }, { "cell_type": "code", "execution_count": null, "id": "8bdb7ecd", "metadata": {}, "outputs": [], "source": [ "def give_mbu_score(modulating_factor_names, MPA, gdf, grid_size_deg, grid_shape, path_EFG, \n", " source, crs_transformation_kms):\n", " \n", " grid_gdf = create_grid(MPA, grid_shape, grid_size_deg)\n", " \n", " if not isinstance(modulating_factor_names, list):\n", " raise TypeError(\"......\")\n", " \n", " if source == 'OBIS':\n", "\n", " for x in modulating_factor_names:\n", "\n", " if x == 'species_richness':\n", " print('Calculating Species Richness')\n", " DF = mbu_species_richness(MPA, gdf, grid_gdf, source, crs_transformation_kms)\n", "\n", "\n", " if x == 'biodiversity_score':\n", " print('Calculating Biodiversity Score')\n", " DF = mbu_biodiversity_score(MPA, gdf, grid_gdf, source, crs_transformation_kms)\n", " \n", "\n", " if x == 'endemism':\n", " print('Endemism is not available for OBIS dataset')\n", " \n", "\n", " if x == 'wege':\n", " print('WEGE is not available for OBIS dataset')\n", " \n", "\n", " elif source == 'IUCN':\n", "\n", " for x in modulating_factor_names:\n", "\n", " if x == 'species_richness':\n", " print('Calculating Species Richness')\n", " DF = mbu_species_richness(MPA, gdf, grid_gdf, source, crs_transformation_kms)\n", "\n", " if x == 'biodiversity_score':\n", " print('Biodiversity Score is not available for IUCN dataset')\n", " \n", "\n", " if x == 'endemism':\n", " print('Calculating Endemism')\n", " DF = mbu_endemism(MPA, gdf, grid_gdf, source, crs_transformation_kms)\n", " \n", "\n", " if x == 'wege':\n", " print('Calculating WEGE')\n", " DF = mbu_wege(MPA, gdf, grid_gdf, source, crs_transformation_kms)\n", " \n", " else:\n", " raise ValueError(\"Unsupported source: {}\".format(source)) \n", " \n", " if isinstance(path_EFG, list):\n", " print('Calculating Habitats Survey')\n", " DF = mbu_habitats_survey(MPA, grid_gdf, path_EFG, crs_transformation_kms)\n", " \n", " if not isinstance(path_EFG, list):\n", " print('Habitats Survey cannot be calculated')\n", " \n", " return DF" ] }, { "cell_type": "code", "execution_count": null, "id": "e486bdaf", "metadata": {}, "outputs": [], "source": [ "give_mbu_score(modulating_factor_names, ACMC, df1, 0.1, 'hexagon', eco_names, 'IUCN', 31970)" ] } ], "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": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }