{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "racial-marble",
   "metadata": {},
   "source": [
    "# Creating Plots of Temporal Changes using Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea790dc2-7180-4c0b-8004-b50457b8f1f4",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ai4er-cdt/gtc-biodiversity/main?filepath=notebooks%2F5-demo-nodediff.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d575656e-7a32-42c3-8fb6-9f45370abc8a",
   "metadata": {},
   "source": [
    "This tutorial shows how to create plots of dynamics and changes in GeoGraphs over time using Matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a34f2e3d-ea71-4095-8673-523c474d5b46",
   "metadata": {},
   "source": [
    "## 1. Setup and Loading package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "roman-security",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:37:54.611718Z",
     "start_time": "2021-03-23T12:37:54.554739Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T21:26:16.121669Z",
     "iopub.status.busy": "2021-03-25T21:26:16.121381Z",
     "iopub.status.idle": "2021-03-25T21:26:16.174473Z",
     "shell.execute_reply": "2021-03-25T21:26:16.173443Z",
     "shell.execute_reply.started": "2021-03-25T21:26:16.121600Z"
    },
    "nbsphinx": "hidden",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# %load ../jupyter_setup.txt\n",
    "# Convenient jupyter setup\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config IPCompleter.greedy=True\n",
    "%config IPCompleter.use_jedi=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "welsh-runner",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:37:59.535590Z",
     "start_time": "2021-03-23T12:37:54.791707Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T21:26:16.176343Z",
     "iopub.status.busy": "2021-03-25T21:26:16.176014Z",
     "iopub.status.idle": "2021-03-25T21:26:19.392164Z",
     "shell.execute_reply": "2021-03-25T21:26:19.390839Z",
     "shell.execute_reply.started": "2021-03-25T21:26:16.176301Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import rioxarray as rxr\n",
    "import geopandas as gpd\n",
    "import pylandstats as pls\n",
    "from geograph import GeoGraph\n",
    "from geograph.geotimeline import GeoGraphTimeline\n",
    "from geograph.constants import UTM35N\n",
    "from geograph.demo.binder_constants import DATA_DIR, ROIS\n",
    "from geograph.demo.plot_settings import ps_defaults, set_dim\n",
    "\n",
    "ps_defaults(use_tex=True)\n",
    "\n",
    "# Parse geotif landcover data\n",
    "chernobyl_path = (\n",
    "    lambda year: DATA_DIR / \"chernobyl\" / \"esa_cci\" / f\"esa_cci_{year}_chernobyl.tif\"\n",
    ")\n",
    "\n",
    "# Parse ROIS\n",
    "rois = gpd.read_file(ROIS)\n",
    "cez = rois[rois[\"name\"] == \"Chernobyl Exclusion Zone\"]\n",
    "ez = rois[rois.name.str.contains(\"Exclusion\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "primary-essay",
   "metadata": {},
   "source": [
    "## 2. Load Chernobyl Exclusion Zone data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "frozen-catch",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:37:59.687336Z",
     "start_time": "2021-03-23T12:37:59.654943Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:48:10.879983Z",
     "iopub.status.busy": "2021-03-25T11:48:10.879697Z",
     "iopub.status.idle": "2021-03-25T11:48:10.921824Z",
     "shell.execute_reply": "2021-03-25T11:48:10.921033Z",
     "shell.execute_reply.started": "2021-03-25T11:48:10.879954Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def clip_and_reproject(xrdata, clip_geometry=None, to_crs=UTM35N, x_res=300, y_res=300):\n",
    "\n",
    "    if clip_geometry is not None:\n",
    "        clipped_data = xrdata.rio.clip(clip_geometry)\n",
    "    else:\n",
    "        clipped_data = xrdata\n",
    "\n",
    "    if to_crs is not None:\n",
    "        reprojected_data = clipped_data.rio.reproject(to_crs, resolution=(x_res, y_res))\n",
    "    else:\n",
    "        reprojected_data = clipped_data\n",
    "\n",
    "    return reprojected_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "absent-vector",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:45:28.535177Z",
     "start_time": "2021-03-23T12:43:32.389115Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:48:51.489730Z",
     "iopub.status.busy": "2021-03-25T11:48:51.489355Z",
     "iopub.status.idle": "2021-03-25T11:48:57.636423Z",
     "shell.execute_reply": "2021-03-25T11:48:57.635446Z",
     "shell.execute_reply.started": "2021-03-25T11:48:51.489685Z"
    }
   },
   "outputs": [],
   "source": [
    "# Loading raster data\n",
    "years = list(range(2000, 2015))\n",
    "cez_rasters = {\n",
    "    year: clip_and_reproject(\n",
    "        rxr.open_rasterio(chernobyl_path(year)), clip_geometry=cez.geometry\n",
    "    )\n",
    "    for year in years\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ready-lincoln",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-25T11:48:58.356163Z",
     "iopub.status.busy": "2021-03-25T11:48:58.355854Z",
     "iopub.status.idle": "2021-03-25T11:50:20.827215Z",
     "shell.execute_reply": "2021-03-25T11:50:20.826169Z",
     "shell.execute_reply.started": "2021-03-25T11:48:58.356134Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysing year 2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1924/1924 [00:01<00:00, 1425.51it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1924/1924 [00:00<00:00, 67463.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1924 nodes and 4912 edges.\n",
      "Analysing year 2001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1931/1931 [00:01<00:00, 1439.89it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1931/1931 [00:00<00:00, 54821.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1931 nodes and 4918 edges.\n",
      "Analysing year 2002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1929/1929 [00:01<00:00, 1413.43it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1929/1929 [00:00<00:00, 61787.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1929 nodes and 4897 edges.\n",
      "Analysing year 2003\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1936/1936 [00:01<00:00, 1255.91it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1936/1936 [00:00<00:00, 64933.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1936 nodes and 4911 edges.\n",
      "Analysing year 2004\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1953/1953 [00:01<00:00, 1082.23it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1953/1953 [00:00<00:00, 50851.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1953 nodes and 4953 edges.\n",
      "Analysing year 2005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1960/1960 [00:01<00:00, 1202.18it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1960/1960 [00:00<00:00, 58944.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1960 nodes and 4973 edges.\n",
      "Analysing year 2006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 2004/2004 [00:01<00:00, 1136.57it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 2004/2004 [00:00<00:00, 61635.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 2004 nodes and 5113 edges.\n",
      "Analysing year 2007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1996/1996 [00:01<00:00, 1206.78it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1996/1996 [00:00<00:00, 59821.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1996 nodes and 5141 edges.\n",
      "Analysing year 2008\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1992/1992 [00:01<00:00, 1142.89it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1992/1992 [00:00<00:00, 39544.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1992 nodes and 5119 edges.\n",
      "Analysing year 2009\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1994/1994 [00:01<00:00, 1236.88it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1994/1994 [00:00<00:00, 66390.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1994 nodes and 5108 edges.\n",
      "Analysing year 2010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1988/1988 [00:01<00:00, 1088.69it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1988/1988 [00:00<00:00, 50291.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1988 nodes and 5080 edges.\n",
      "Analysing year 2011\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 2003/2003 [00:01<00:00, 1235.16it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 2003/2003 [00:00<00:00, 61921.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 2003 nodes and 5131 edges.\n",
      "Analysing year 2012\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1998/1998 [00:01<00:00, 1203.33it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1998/1998 [00:00<00:00, 65800.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1998 nodes and 5119 edges.\n",
      "Analysing year 2013\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 2003/2003 [00:01<00:00, 1127.01it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 2003/2003 [00:00<00:00, 64449.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 2003 nodes and 5140 edges.\n",
      "Analysing year 2014\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step 1 of 2: Creating nodes and finding neighbours: 100%|██████████| 1999/1999 [00:01<00:00, 1169.66it/s]\n",
      "Step 2 of 2: Adding edges: 100%|██████████| 1999/1999 [00:00<00:00, 65990.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph successfully loaded with 1999 nodes and 5117 edges.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "## NOTE: For faster loading you can load the graphs from memory.\n",
    "#        The demo path includes pre-loaded graphs for faster loading. Simply uncomment.\n",
    "# demo_path = DATA_DIR / \"chernobyl\" / \"graphs\"\n",
    "# years = list(range(2000,2015))\n",
    "# cez_graphs = {year: GeoGraph(chernobyl_path(yera))\n",
    "#              for year in years}\n",
    "\n",
    "# Polygonising raster and transforming into graph\n",
    "cez_graphs = {}\n",
    "for year, raster in cez_rasters.items():\n",
    "    print(f\"Analysing year {year}\")\n",
    "    # Load geograph from the raster data (construction takes ~5s)\n",
    "    cez_graphs[year] = GeoGraph(\n",
    "        data=raster.data.squeeze(),\n",
    "        transform=raster.rio.transform(),\n",
    "        mask=raster.data.squeeze() > 0,\n",
    "        crs=UTM35N,\n",
    "        connectivity=8,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "decreased-marriage",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:46:25.057049Z",
     "start_time": "2021-03-23T12:46:25.031898Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:50:20.830213Z",
     "iopub.status.busy": "2021-03-25T11:50:20.829988Z",
     "iopub.status.idle": "2021-03-25T11:50:21.488904Z",
     "shell.execute_reply": "2021-03-25T11:50:21.487905Z",
     "shell.execute_reply.started": "2021-03-25T11:50:20.830186Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cez_timeline = GeoGraphTimeline(cez_graphs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "promising-senegal",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:47:13.599945Z",
     "start_time": "2021-03-23T12:46:25.742726Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:50:21.490526Z",
     "iopub.status.busy": "2021-03-25T11:50:21.490309Z",
     "iopub.status.idle": "2021-03-25T11:51:04.880575Z",
     "shell.execute_reply": "2021-03-25T11:51:04.879962Z",
     "shell.execute_reply.started": "2021-03-25T11:50:21.490499Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<src.binary_graph_operations.NodeMap at 0x7f5264602df0>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f52245212e0>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f5264670dc0>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f51f9c24400>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f5264670fa0>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f51f9c24280>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f5264664e80>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f52244ba670>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f51fa26f6a0>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f51fa27bd90>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f51fa017130>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f51f9e4f130>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f51fa255250>,\n",
       " <src.binary_graph_operations.NodeMap at 0x7f51fa255f40>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform node identification between adjacent time slices (takes ~10s)\n",
    "cez_timeline.timestack()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "demographic-chase",
   "metadata": {},
   "source": [
    "## 3. Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cathedral-sandwich",
   "metadata": {},
   "source": [
    "Let us now visualise the ecosystem dynamics from 2013 to 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "becoming-bidding",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:47:34.842217Z",
     "start_time": "2021-03-23T12:47:25.957773Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:51:04.881741Z",
     "iopub.status.busy": "2021-03-25T11:51:04.881510Z",
     "iopub.status.idle": "2021-03-25T11:51:09.794607Z",
     "shell.execute_reply": "2021-03-25T11:51:09.793961Z",
     "shell.execute_reply.started": "2021-03-25T11:51:04.881715Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "node_index\n",
       "0       unchanged\n",
       "1       unchanged\n",
       "2       unchanged\n",
       "3       unchanged\n",
       "4       unchanged\n",
       "          ...    \n",
       "1994    unchanged\n",
       "1995    unchanged\n",
       "1996    unchanged\n",
       "1997    unchanged\n",
       "1998    unchanged\n",
       "Name: node_dynamic, Length: 1999, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify node dynamics for the year 2014\n",
    "cez_timeline.calculate_node_dynamics(2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "challenging-impossible",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:47:44.862467Z",
     "start_time": "2021-03-23T12:47:44.813519Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:51:09.795963Z",
     "iopub.status.busy": "2021-03-25T11:51:09.795762Z",
     "iopub.status.idle": "2021-03-25T11:51:09.834771Z",
     "shell.execute_reply": "2021-03-25T11:51:09.834144Z",
     "shell.execute_reply.started": "2021-03-25T11:51:09.795937Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unchanged', 'split', 'birth', 'grew', 'shrank', 'complex',\n",
       "       'merged'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cez_timeline[2014].df.node_dynamic.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "affecting-consequence",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:48:42.710428Z",
     "start_time": "2021-03-23T12:48:40.509829Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:51:09.836059Z",
     "iopub.status.busy": "2021-03-25T11:51:09.835858Z",
     "iopub.status.idle": "2021-03-25T11:51:10.951247Z",
     "shell.execute_reply": "2021-03-25T11:51:10.949848Z",
     "shell.execute_reply.started": "2021-03-25T11:51:09.836033Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 597.758x369.435 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = cez_timeline[2014]\n",
    "\n",
    "plot_scale_factor = 1\n",
    "dynamic_to_int = {\n",
    "    \"split\": 0,\n",
    "    \"shrank\": 1,\n",
    "    \"unchanged\": 2,\n",
    "    \"complex\": 3,\n",
    "    \"grew\": 4,\n",
    "    \"merged\": 5,\n",
    "    \"birth\": 6,\n",
    "}\n",
    "\n",
    "colors = sns.color_palette(\"Paired\").as_hex()\n",
    "dynamic = lambda x: dynamic_to_int[x]\n",
    "graph.df[\"dynamic_class\"] = graph.df.node_dynamic.map(dynamic)\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "plt.title(\n",
    "    \"Chernobyl Exclusion Zone 2013 to 2014\\n(ESA CCI 300m resolution)\",\n",
    "    fontsize=9 * plot_scale_factor,\n",
    ")\n",
    "set_dim(fig, fraction_of_line_width=plot_scale_factor)\n",
    "vmin, vmax = 0, 7\n",
    "cmap = mpl.colors.ListedColormap(\n",
    "    [colors[7], colors[6], \"lightgrey\", colors[0], colors[2], colors[3], colors[9]]\n",
    ")\n",
    "graph.df.plot(column=\"dynamic_class\", cmap=cmap, vmin=vmin, vmax=vmax, ax=ax)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "inset_text = (\n",
    "    \"Node dynamics (#):\\n\"\n",
    "    f\"  Splits: {np.sum(graph.df['node_dynamic'] == 'split')}\\n\"\n",
    "    f\"  Shrinking: {np.sum(graph.df['node_dynamic'] == 'shrank')}\\n\"\n",
    "    f\"  Unchanged: {np.sum(graph.df['node_dynamic'] == 'unchanged')}\\n\"\n",
    "    f\"  Complex: {np.sum(graph.df['node_dynamic'] == 'complex')}\\n\"\n",
    "    f\"  Merges: {np.sum(graph.df['node_dynamic'] == 'merged')}\\n\"\n",
    "    f\"  Growth: {np.sum(graph.df['node_dynamic'] == 'grew')}\\n\"\n",
    "    f\"  Births: {np.sum(graph.df['node_dynamic'] == 'birth')}\"\n",
    ")\n",
    "\n",
    "# these are matplotlib.patch.Patch properties\n",
    "props = dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8)\n",
    "\n",
    "# place a text box in upper left in axes coords\n",
    "ax.text(\n",
    "    0.03,\n",
    "    0.97,\n",
    "    inset_text,\n",
    "    transform=ax.transAxes,\n",
    "    fontsize=9 * plot_scale_factor,\n",
    "    verticalalignment=\"top\",\n",
    "    bbox=props,\n",
    ")\n",
    "\n",
    "from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "fontprops = fm.FontProperties(size=6 * plot_scale_factor)\n",
    "scalebar = AnchoredSizeBar(\n",
    "    ax.transData,\n",
    "    1e4,\n",
    "    \"10 km\",\n",
    "    \"lower left\",\n",
    "    pad=0,\n",
    "    borderpad=0.8,\n",
    "    color=\"black\",\n",
    "    frameon=False,\n",
    "    size_vertical=250,\n",
    "    fontproperties=fontprops,\n",
    ")\n",
    "\n",
    "ax.add_artist(scalebar)\n",
    "\n",
    "# add colorbar\n",
    "fig = ax.get_figure()\n",
    "cax = fig.add_axes(\n",
    "    [0.12, 0.08, 0.78, 0.05]\n",
    ")  # left-offset, # bottom offset # width, # height\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "sm._A = []\n",
    "cbar = fig.colorbar(sm, cax=cax, orientation=\"horizontal\")\n",
    "cbar.set_ticks(np.arange(vmin + 0.5, vmax + 1))\n",
    "cbar.set_ticklabels(\n",
    "    [\"Split\", \"Shrank\", \"Unchanged\", \"Complex\", \"Grew\", \"Merged\", \"Birth\"]\n",
    ")\n",
    "cbar.ax.tick_params(labelsize=9 * plot_scale_factor)\n",
    "ax.spines[\"bottom\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "\n",
    "# plt.savefig(\"CEZ_node_dynamics.svg\")\n",
    "# plt.savefig(\"CEZ_node_dynamics.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "egyptian-quantity",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:48:48.276675Z",
     "start_time": "2021-03-23T12:48:45.732343Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:51:10.952379Z",
     "iopub.status.busy": "2021-03-25T11:51:10.952164Z",
     "iopub.status.idle": "2021-03-25T11:51:13.642048Z",
     "shell.execute_reply": "2021-03-25T11:51:13.641276Z",
     "shell.execute_reply.started": "2021-03-25T11:51:10.952350Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 597.758x369.435 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scale_factor = 1\n",
    "fig, ax = plt.subplots(1)\n",
    "set_dim(fig, fraction_of_line_width=plot_scale_factor)\n",
    "\n",
    "cmap = sns.diverging_palette(6, 120, s=360, l=55, as_cmap=True)\n",
    "norm = mpl.colors.TwoSlopeNorm(vcenter=0, vmin=-10e5, vmax=10e5)\n",
    "\n",
    "graph.df.plot(\n",
    "    \"absolute_growth\", ax=ax, cmap=cmap, norm=norm, edgecolor=\"grey\", linewidth=0.1\n",
    ")\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "fontprops = fm.FontProperties(size=6 * plot_scale_factor)\n",
    "scalebar = AnchoredSizeBar(\n",
    "    ax.transData,\n",
    "    1e4,\n",
    "    \"10 km\",\n",
    "    \"lower left\",\n",
    "    pad=0,\n",
    "    borderpad=0.3,\n",
    "    color=\"black\",\n",
    "    frameon=False,\n",
    "    size_vertical=250,\n",
    "    fontproperties=fontprops,\n",
    ")\n",
    "\n",
    "ax.add_artist(scalebar)\n",
    "\n",
    "cbar = fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(norm=norm, cmap=cmap),\n",
    "    orientation=\"horizontal\",\n",
    "    label=\"Absolute growth rate [ha / yr]\",\n",
    "    # aspect=9,\n",
    "    shrink=0.88,\n",
    "    pad=0.04,\n",
    ")\n",
    "cbar.set_ticks(\n",
    "    [-10e5, -7.5 * 1e5, -5e5, -2.5 * 1e5, 0, 2.5 * 1e5, 5e5, 7.5 * 1e5, 10e5]\n",
    ")\n",
    "cbar.set_ticklabels([-100, -75, -50, -25, 0, 25, 50, 75, 100])\n",
    "cbar.ax.tick_params(labelsize=9 * plot_scale_factor)\n",
    "cbar.set_label(\"Absolute growth rate [ha/yr]\", fontsize=9 * plot_scale_factor)\n",
    "plt.title(\n",
    "    \"Chernobyl Exclusion Zone 2013 to 2014\\n(ESA CCI 300m resolution)\",\n",
    "    fontsize=9 * plot_scale_factor,\n",
    ")\n",
    "ax.spines[\"bottom\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "plt.savefig(\"CEZ_node_growth_rates.svg\")\n",
    "plt.savefig(\"CEZ_node_growth_rates.pdf\")\n",
    "plt.savefig(\"CEZ_node_growth_rates.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "institutional-steal",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:48:48.578630Z",
     "start_time": "2021-03-23T12:48:48.481853Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:51:13.646547Z",
     "iopub.status.busy": "2021-03-25T11:51:13.646225Z",
     "iopub.status.idle": "2021-03-25T11:51:13.753877Z",
     "shell.execute_reply": "2021-03-25T11:51:13.753156Z",
     "shell.execute_reply.started": "2021-03-25T11:51:13.646518Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "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>class_label</th>\n",
       "      <th>area</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>perimeter_area_ratio</th>\n",
       "      <th>shape_index</th>\n",
       "      <th>fractal_dimension</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>0.008889</td>\n",
       "      <td>1.154701</td>\n",
       "      <td>1.023003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>130</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.060660</td>\n",
       "      <td>1.009734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>30</td>\n",
       "      <td>810000.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>0.008148</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.089106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>60</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>1.057282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>100</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>1.057282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>160</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>1.057282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>30</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>1.443376</td>\n",
       "      <td>1.058689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1999 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            class_label      area  perimeter  perimeter_area_ratio  \\\n",
       "node_index                                                           \n",
       "0                   100  270000.0     2400.0              0.008889   \n",
       "1                    70   90000.0     1200.0              0.013333   \n",
       "2                   130  180000.0     1800.0              0.010000   \n",
       "3                    30   90000.0     1200.0              0.013333   \n",
       "4                    30   90000.0     1200.0              0.013333   \n",
       "...                 ...       ...        ...                   ...   \n",
       "1994                 30  810000.0     6600.0              0.008148   \n",
       "1995                 60  180000.0     2400.0              0.013333   \n",
       "1996                100  180000.0     2400.0              0.013333   \n",
       "1997                160  180000.0     2400.0              0.013333   \n",
       "1998                 30  270000.0     3000.0              0.011111   \n",
       "\n",
       "            shape_index  fractal_dimension  \n",
       "node_index                                  \n",
       "0              1.154701           1.023003  \n",
       "1              1.000000           1.000000  \n",
       "2              1.060660           1.009734  \n",
       "3              1.000000           1.000000  \n",
       "4              1.000000           1.000000  \n",
       "...                 ...                ...  \n",
       "1994           1.833333           1.089106  \n",
       "1995           1.414214           1.057282  \n",
       "1996           1.414214           1.057282  \n",
       "1997           1.414214           1.057282  \n",
       "1998           1.443376           1.058689  \n",
       "\n",
       "[1999 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cez_timeline[2014].get_patch_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "pending-monaco",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-23T12:48:50.769956Z",
     "start_time": "2021-03-23T12:48:50.137094Z"
    },
    "execution": {
     "iopub.execute_input": "2021-03-25T11:51:13.754969Z",
     "iopub.status.busy": "2021-03-25T11:51:13.754755Z",
     "iopub.status.idle": "2021-03-25T11:51:14.292820Z",
     "shell.execute_reply": "2021-03-25T11:51:14.292088Z",
     "shell.execute_reply.started": "2021-03-25T11:51:13.754944Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1195.52x738.87 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "plot_scale_factor = 2\n",
    "set_dim(fig, fraction_of_line_width=plot_scale_factor)\n",
    "\n",
    "cez_timeline[2013].df.loc[1629:1629].plot(ax=ax, color=\"red\", alpha=0.6)\n",
    "cez_timeline[2014].df.loc[1187:1187].plot(ax=ax, color=\"blue\", alpha=0.6)\n",
    "cez_timeline[2014].df.loc[1625:1625].plot(ax=ax, color=\"green\", alpha=0.8)\n",
    "\n",
    "inset_text = (\n",
    "    f\"Evergreen forest (70)\\n\\n\"\n",
    "    f\"Total area: {graph.df.area.loc[1625]/1e4:.0f} ha\\n\"\n",
    "    f\"Perimeter: {graph.df.perimeter.loc[1625]/1e3:.0f} km\\n\"\n",
    "    f\"Fractal dimension: {graph.df.fractal_dimension.loc[1625]:.2f}\\n\"\n",
    "    f\"Shape index: {graph.df.shape_index.loc[1625]:.2f}\"\n",
    ")\n",
    "\n",
    "# these are matplotlib.patch.Patch properties\n",
    "props = dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8)\n",
    "\n",
    "# place a text box in upper left in axes coords\n",
    "ax.text(\n",
    "    0.6,\n",
    "    0.95,\n",
    "    inset_text,\n",
    "    transform=ax.transAxes,\n",
    "    fontsize=8 * plot_scale_factor,\n",
    "    verticalalignment=\"top\",\n",
    "    bbox=props,\n",
    ")\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.spines[\"bottom\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "plt.savefig(\"CEZ-nodediff-example.svg\")\n",
    "plt.savefig(\"CEZ-nodediff-example.pdf\")"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3.9.1 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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