{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7324ca2d",
   "metadata": {},
   "source": [
    "# Dataset tutorial\n",
    "\n",
    "The GrIML package is used for the production of the Greenland ice-marginal lake inventory series, which is freely available through the [GEUS Dataverse](https://doi.org/10.22008/FK2/MBKW9N). This dataset is a series of annual inventories, mapping the extent and presence of lakes across Greenland that share a margin with the Greenland Ice Sheet and/or the surrounding ice caps and periphery glaciers. The full dataset readme description can be found on the GEUS Dataverse or here in our project documentation.\n",
    "\n",
    "Here, we will look at how to load and handle the dataset, and provide details on its contents. This tutorial is available as a [jupyter notebook](https://github.com/GEUS-Glaciology-and-Climate/GrIML/blob/main/tutorial/dataset_tutorial.ipynb). We also provide a [Binder environment]([https://mybinder.org/v2/gh/GEUS-Glaciology-and-Climate/GrIML/HEAD](https://mybinder.org/v2/gh/GEUS-Glaciology-and-Climate/GrIML/HEAD?urlpath=%2Fdoc%2Ftree%2Ftutorials%2Fdataset_tutorial.ipynb)) within which you can run the notebook (with no need to set up the requirements yourself locally).\n",
    "\n",
    "First let's load the packages that we will use in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c24abc23c315ee75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:01:14.269020Z",
     "start_time": "2025-05-14T12:01:14.266133Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import all relevant packages for this tutorial\n",
    "import wget\n",
    "import geopandas as gpd\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7ca518",
   "metadata": {},
   "source": [
    "## Getting started\n",
    "\n",
    "The GrIML ice-marginal lake inventory series dataset is available on the [GEUS Dataverse](https://doi.org/10.22008/FK2/MBKW9N), which can be downloaded using wget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:13.303344Z",
     "start_time": "2025-05-14T12:01:14.348647Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define GEUS Dataverse urls to download ice-marginal lake dataset\n",
    "urls = [\"https://dataverse.geus.dk/api/access/datafile/88448\",\n",
    "        \"https://dataverse.geus.dk/api/access/datafile/88440\",\n",
    "        \"https://dataverse.geus.dk/api/access/datafile/88442\",\n",
    "        \"https://dataverse.geus.dk/api/access/datafile/88447\",\n",
    "        \"https://dataverse.geus.dk/api/access/datafile/88446\",\n",
    "        \"https://dataverse.geus.dk/api/access/datafile/88443\",\n",
    "        \"https://dataverse.geus.dk/api/access/datafile/88445\",\n",
    "        \"https://dataverse.geus.dk/api/access/datafile/88449\",\n",
    "        ]\n",
    "\n",
    "# Download files\n",
    "for u in urls:\n",
    "    filename = wget.download(u)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c224e55d",
   "metadata": {},
   "source": [
    "One of the inventories in the dataset series can be opened and plotted in Python using geopandas. In this example, let's take the 2023 inventory (version 2). Make sure that the file path is correct in order to load the dataset correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e2158699f0959c51",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:14.016913Z",
     "start_time": "2025-05-14T12:06:13.346315Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Open the 2023 inventory\n",
    "iml = gpd.read_file(\"20230101-ESA-GRIML-IML-fv2.gpkg\")\n",
    "\n",
    "# Simple plot\n",
    "iml.plot(color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b29eeb5",
   "metadata": {},
   "source": [
    "Plotting all shapes can be difficult to see without zooming around and exploring the plot. We can dissolve all common lakes and then plot the centroid points of these to get a better overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "14168195b57c8dae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:15.122960Z",
     "start_time": "2025-05-14T12:06:14.054922Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dissolve inventory classifications by identification number\n",
    "iml_d = iml.dissolve(by=\"lake_id\")\n",
    "\n",
    "# Get centroid point for each lake\n",
    "iml_d[\"centroid\"] = iml_d.geometry.centroid\n",
    "\n",
    "# Plot lakes as points\n",
    "iml_d[\"centroid\"].plot(markersize=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542b5967",
   "metadata": {},
   "source": [
    "## Generating statistics\n",
    "\n",
    "We can extract basic statistics from an ice-marginal lake inventory in the dataset series using simple querying. Let's take the 2022 inventory (version 2) in this example and first determine the number of classified lakes, and then the number of unique lakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "655768d6a6d50b65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:16.056902Z",
     "start_time": "2025-05-14T12:06:15.161313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of detected lakes: 3561\n",
      "Total number of unique lakes: 2550\n"
     ]
    }
   ],
   "source": [
    "# Load the 2022 inventory\n",
    "iml = gpd.read_file(\"20220101-ESA-GRIML-IML-fv2.gpkg\")\n",
    "\n",
    "# Dissolve by lake id to get all unique lakes as dissolved polygons\n",
    "iml_d = iml.dissolve(by='lake_id')\n",
    "\n",
    "# Print counts\n",
    "print(\"Total number of detected lakes: \" + str(len(iml)))\n",
    "print(\"Total number of unique lakes: \" + str(len(iml_d)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2031e480",
   "metadata": {},
   "source": [
    "We can count the number of classifications from each method, where `SAR` denotes classifications from SAR backscatter classification, `VIS` denotes classifications from multi-spectral indices, and `DEM` denotes classifications using sink detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "32a8aa458a1ef9b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:16.096506Z",
     "start_time": "2025-05-14T12:06:16.093356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method\n",
      "DEM    2603\n",
      "SAR     841\n",
      "VIS     117\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count lakes by classifications\n",
    "print(iml['method'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b4f1cc",
   "metadata": {},
   "source": [
    "Let's say we would like to count the number of ice-marginal lakes that share a margin with the Greenland Ice Sheet and broken down by region. We can do this by first extracting all lakes classified by a common margin with the ice sheet (`ICE_SHEET`) and then count all lakes per region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5dd0bc51a9c0abb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:16.198979Z",
     "start_time": "2025-05-14T12:06:16.186765Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region\n",
      "SW    576\n",
      "NE    448\n",
      "NO    223\n",
      "NW    187\n",
      "CW    141\n",
      "CE    133\n",
      "SE    119\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Filter to lakes with an ice sheet margin\n",
    "iml_d_ice_sheet = iml_d[iml_d['margin'] == 'ICE_SHEET']\n",
    "\n",
    "# Count lakes by region\n",
    "print(iml_d_ice_sheet['region'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4688c06c",
   "metadata": {},
   "source": [
    "We can also determine the average, minimum and maximum lake size per region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c566f582c6ac72b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:16.281654Z",
     "start_time": "2025-05-14T12:06:16.272100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region\n",
      "CE    0.442842\n",
      "CW    0.718431\n",
      "NE    0.938450\n",
      "NO    0.434287\n",
      "NW    0.526338\n",
      "SE    0.500289\n",
      "SW    0.680217\n",
      "Name: area_sqkm, dtype: float64\n",
      "region\n",
      "CE    0.0502\n",
      "CW    0.0504\n",
      "NE    0.0505\n",
      "NO    0.0500\n",
      "NW    0.0501\n",
      "SE    0.0502\n",
      "SW    0.0502\n",
      "Name: area_sqkm, dtype: float64\n",
      "region\n",
      "CE      9.1090\n",
      "CW     35.8692\n",
      "NE    106.4783\n",
      "NO      6.3879\n",
      "NW     15.0309\n",
      "SE     10.9583\n",
      "SW     31.2105\n",
      "Name: area_sqkm, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate surface area of all unique lakes (dissolved)\n",
    "iml_d['area_sqkm'] = iml_d.geometry.area/10**6\n",
    "\n",
    "# Group lakes by region and determine average, min, max\n",
    "print(iml_d.groupby(['region'])['area_sqkm'].mean())\n",
    "print(iml_d.groupby(['region'])['area_sqkm'].min())\n",
    "print(iml_d.groupby(['region'])['area_sqkm'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "572671c5",
   "metadata": {},
   "source": [
    "## Cross inventory comparison\n",
    "\n",
    "All inventories in the ice-marginal lake inventory series can be treated as time-series to look at change in lake abundance and size over time. Let's take an example where we will generate a time-series of lake abundance change at the margins of Greenland's periphery ice caps and glaciers. First we load all inventories as a series of GeoDataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f8542638f295904d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:38.582308Z",
     "start_time": "2025-05-14T12:06:16.350992Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get list of all inventory series files\n",
    "in_dir = '*IML-fv2.gpkg'\n",
    "\n",
    "# Iterate through inventories\n",
    "gdfs=[]\n",
    "for f in list(sorted(glob.glob(in_dir))):\n",
    "\n",
    "    # Load inventory and dissolve by unique identifications\n",
    "    gdf = gpd.read_file(f)\n",
    "    gdf = gdf.loc[gdf.geometry.is_valid]\n",
    "    gdf = gdf.dissolve(by='lake_id')\n",
    "\n",
    "    # Update geometry area information\n",
    "    gdf['area_sqkm'] = gdf.geometry.area/10**6\n",
    "\n",
    "    # Append to list\n",
    "    gdfs.append(gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6b93fb",
   "metadata": {},
   "source": [
    "Then we count lakes with a shared ice cap/glacier margin from each inventory, splitting counts by region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e8ad755b55a1e22a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:38.654195Z",
     "start_time": "2025-05-14T12:06:38.618776Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create empty lists for region counts\n",
    "b=['NW', 'NO', 'NE', 'CE', 'SE', 'SW', 'CW']\n",
    "ic_nw=[]\n",
    "ic_no=[]\n",
    "ic_ne=[]\n",
    "ic_ce=[]\n",
    "ic_se=[]\n",
    "ic_sw=[]\n",
    "ic_cw=[]\n",
    "ice_cap_abun = [ic_nw, ic_no, ic_ne, ic_ce, ic_se, ic_sw, ic_cw]\n",
    "\n",
    "# Iterate through geodataframes\n",
    "for g in gdfs:\n",
    "\n",
    "    # Filter by margin type\n",
    "    icecap = g[g['margin'] == 'ICE_CAP']\n",
    "\n",
    "    # Append regional lake counts\n",
    "    for i in range(len(b)):\n",
    "        ice_cap_abun[i].append(icecap['region'].value_counts()[b[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ee4b48",
   "metadata": {},
   "source": [
    "We can then plot all of our lake counts as a stacked bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "70d842b3e2522b13",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:38.996540Z",
     "start_time": "2025-05-14T12:06:38.696850Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define plotting attributes\n",
    "years=list(range(2016,2024,1))\n",
    "col=['#045275', '#089099', '#7CCBA2', '#FCDE9C', '#F0746E', '#DC3977', '#7C1D6F']\n",
    "bottom=np.zeros(8)\n",
    "\n",
    "# Prime plotting area\n",
    "fig, ax = plt.subplots(1, figsize=(10,5))\n",
    "\n",
    "# Plot lake counts as stacked bar plots\n",
    "for i in range(len(ice_cap_abun)):\n",
    "    p = ax.bar(years, ice_cap_abun[i], 0.5, color=col[i], label=b[i], bottom=bottom)\n",
    "    bottom += ice_cap_abun[i]\n",
    "    ax.bar_label(p, label_type='center', fontsize=8)\n",
    "\n",
    "# Add legend\n",
    "ax.legend(bbox_to_anchor=(1.01,0.7))\n",
    "\n",
    "# Change plotting aesthetics\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(color='gray', linestyle='dashed', linewidth=0.5)\n",
    "ax.set_facecolor(\"#f2f2f2\")\n",
    "\n",
    "# Add title\n",
    "props = dict(boxstyle='round', facecolor='#6CB0D6', alpha=0.3)\n",
    "ax.text(0.01, 1.05, 'Periphery ice caps/glaciers lake abundance change',\n",
    "         fontsize=14, horizontalalignment='left', bbox=props, transform=ax.transAxes)\n",
    "\n",
    "# Add axis labels\n",
    "ax.set_xlabel('Year', fontsize=14)\n",
    "ax.set_ylabel('Lake abundance', fontsize=14)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e456038fd2323fef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-14T12:06:39.036984Z",
     "start_time": "2025-05-14T12:06:39.035264Z"
    }
   },
   "outputs": [],
   "source": []
  }
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