{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a429a691",
   "metadata": {},
   "source": [
    "# Tutorial for R users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64173670",
   "metadata": {},
   "source": [
    "In this tutorial we will enrich *Acropora digitifera* observations with bathymetry information.\n",
    "Please make sure you already followed the [installation instructions](https://geoenrich.readthedocs.io/en/latest/r-install.html).\n",
    "\n",
    "Here is a summary of the main steps:\n",
    "- **Loading your dataset** and giving it a name (called reference here).\n",
    "- **Performing data enrichment** by specifying your dataset reference, the environmental variable you need, etc.\n",
    "- **Exporting the downloaded data** to the format you require.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66dad4fc",
   "metadata": {},
   "source": [
    "## 1. Load your occurrence data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7ff123",
   "metadata": {},
   "source": [
    "First import all functions from geoenrich"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c58342a",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "library(reticulate)\n",
    "\n",
    "os <- import(\"os\")\n",
    "dataloader <- import(\"geoenrich.dataloader\", convert=FALSE)\n",
    "enrichment <- import(\"geoenrich.enrichment\", convert=FALSE)\n",
    "exports <- import(\"geoenrich.exports\", convert=FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2715c829",
   "metadata": {},
   "source": [
    "**Three input formats are accepted. You can follow the instructions that match your format:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82746ad9",
   "metadata": {},
   "source": [
    "#### A. DarwinCore archive\n",
    "\n",
    "A DarwinCore archive is bundled into the package for user testing (GBIF Occurrence Download [10.15468/dl.megb8n](https://doi.org/10.15468/dl.megb8n)).\n",
    "If you don't have a dataset and you don't want to register to GBIF yet you can use this one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e91c6b72",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "example_path <- paste( os$path$split(dataloader$'__file__')[[1]],\n",
    "                      '/data/AcDigitifera.zip', sep = '')\n",
    "geodf <- dataloader$open_dwca(path = example_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd09f793",
   "metadata": {},
   "source": [
    "#### B. Georeferenced points (csv format)\n",
    "\n",
    "Fill in the path to your csv and the compulsory column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b785be9",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "geodf <- dataloader$import_occurrences_csv( path = '', id_col = '', date_col = '',\n",
    "                                            lat_col = '', lon_col = '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95f2a914",
   "metadata": {},
   "source": [
    "#### C. Area bounds (csv format)\n",
    "\n",
    "See [documentation](https://geoenrich.readthedocs.io/en/latest/dataloader.html#geoenrich.dataloader.load_areas_file) for information about input file format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aca1a7f",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "example_path <- paste( os$path$split(dataloader$'__file__')[[1]] ,\n",
    "                      '/data/areas.csv', sep = '')\n",
    "geodf <- dataloader$load_areas_file(example_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e06ea36",
   "metadata": {},
   "source": [
    "#### For all formats: Choose a dataset reference and associate it to your dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5a0d4b1",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "dataset_ref <- 'ac_digitifera'\n",
    "enrichment$create_enrichment_file(geodf, dataset_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c489fbfc",
   "metadata": {},
   "source": [
    "## 2. Enrich"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57131f0",
   "metadata": {},
   "source": [
    "#### Define enrichment scope"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94861222",
   "metadata": {},
   "source": [
    "- var_id: Pick a variable id from the catalog\n",
    "- geo_buff: the buffer around the occurences (in kilometers). Choose 0 to obtain nearest values.\n",
    "- time_buff: Choose a temporal buffer. In this case we download data from 7 days before the occurrence date, to the occurrence date. time_buff is only used for variables that have a time dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f058adfe",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "var_id <- 'bathymetry'\n",
    "dataset_ref <- 'ac_digitifera'\n",
    "geo_buff <- 115\n",
    "time_buff <- c(-7L, 0L)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a166d0b7",
   "metadata": {},
   "source": [
    "#### Start enrichment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29885eb",
   "metadata": {},
   "source": [
    "The slice argument allows you to only run the enrichment on a subset of the points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2359a32",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "enrichment$enrich(dataset_ref, var_id, geo_buff,\n",
    "                  time_buff, slice = c(0L,100L))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f992fd",
   "metadata": {},
   "source": [
    "Check the enrichment progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8db7498",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "enrichment$enrichment_status(dataset_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d698b3dc",
   "metadata": {},
   "source": [
    "## 3. Retrieve and export downloaded data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7422c630",
   "metadata": {},
   "source": [
    "Select the dataset reference and the variable that you want to export. Then you can choose one of four options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f626820",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "dataset_ref <- 'ac_digitifera'\n",
    "var_id <- 'bathymetry'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dde65ef",
   "metadata": {},
   "source": [
    "#### A. Export variable statistics for the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd23e2fe",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "exports$produce_stats(dataset_ref, var_id, out_path = './')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6850c5e2",
   "metadata": {},
   "source": [
    "#### B. Export data as a raster layer for a given occurrence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1327b0e6",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ids <- py_to_r(enrichment$read_ids(dataset_ref))\n",
    "occ_id <- ids[[1]] # first occurrence of the dataset\n",
    "\n",
    "exports$export_raster(dataset_ref, occ_id, var_id, path = './')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288623e8",
   "metadata": {},
   "source": [
    "#### C. Export data as a png file for a given occurrence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8efe9b8d",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ids <- py_to_r(enrichment$read_ids(dataset_ref))\n",
    "occ_id <- ids[[1]] # first occurrence of the dataset\n",
    "\n",
    "exports$export_png(dataset_ref, occ_id, var_id, path = './')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1daa34d",
   "metadata": {},
   "source": [
    "#### D. Retrieve the raw data (and plot it)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d229da7",
   "metadata": {},
   "source": [
    "Required libraries: ggplot2, reshape2, sf, pals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4757f325",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "library(reshape2)\n",
    "library(sf)\n",
    "library(pals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7320459",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Select an idea from the dataset\n",
    "\n",
    "ids <- enrichment$read_ids(dataset_ref)\n",
    "occ_id <- ids[[1]] # first occurrence of the dataset\n",
    "\n",
    "\n",
    "output <- exports$retrieve_data(dataset_ref, occ_id, var_id, shape = 'buffer')\n",
    "\n",
    "data <- py_to_r(output[[\"values\"]])\n",
    "unit <- output$unit\n",
    "coords <- py_to_r(output[[\"coords\"]])\n",
    "\n",
    "# Retrieve coordinates\n",
    "for (c in coords){\n",
    "    if (c[[1]] == 'latitude'){\n",
    "        lats = c[[2]]}\n",
    "    if (c[[1]] == 'longitude'){\n",
    "        longs = c[[2]]}}\n",
    "\n",
    "# Retrieve coordinates of the occurrence\n",
    "occurrences <- read.csv(file = paste(enrichment$biodiv_path, '/', dataset_ref,  '.csv', sep = ''), row.names = 1)\n",
    "occ_point <- st_coordinates(st_as_sf(occurrences[as.character(occ_id),], wkt = 'geometry'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "11450f72",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(data) <- longs\n",
    "rownames(data) <- lats\n",
    "data_df <- melt(data)\n",
    "colnames(data_df) <- c('latitude', 'longitude', var_id)\n",
    "title <- paste(var_id, ' (', unit, ')', sep = '')\n",
    "ggplot(data = data_df, aes(x=longitude, y=latitude, fill=eval(as.name(var_id)))) + \n",
    "       geom_tile()+\n",
    "       scale_fill_gradientn(title,colours=coolwarm(100), guide = \"colourbar\")+\n",
    "       geom_point(aes(x=occ_point[[1]], y=occ_point[[2]]), shape=4, size = 4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.2.1"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}