3_interpolation_comparison.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "title: **Interpolation Comparison** \n",
    "\n",
    "**Methods**:\n",
    "\n",
    "- 1. [Shape-based interpolation](https://www.researchgate.net/publication/221400411_Efficient_Semiautomatic_Segmentation_of_3D_Objects_in_Medical_Images) inspired by Schenk *et al*.\n",
    "\n",
    "- 2. [*Pyspatiotemporalgeom*](https://dl.acm.org/doi/10.1145/2996913.2996973) library based interpolation \n",
    "\n",
    "- 3. Conditional CNN-VAE interpolation\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: \u001b[1mTiago F. R. Ribeiro\u001b[0m\n",
      "\n",
      "Github username: \u001b[1mTiago1Ribeiro\u001b[0m\n",
      "\n",
      "Last updated: 2023-06-22 02:27:29\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.9\n",
      "IPython version      : 8.11.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from watermark import watermark\n",
    "print(watermark(author=\"\\033[1m\" +\"Tiago F. R. Ribeiro\" + \"\\033[0m\", \n",
    "                github_username=\"\\033[1m\" +\"Tiago1Ribeiro\"+ \"\\033[0m\", \n",
    "                current_date=True, current_time=True, python=True, updated=True,\n",
    "                iversions=True,  globals_= globals()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['USE_PYGEOS'] = '0'      # to avoid a bug in geopandas\n",
    "import cv2                                          \n",
    "import numpy as np                                                  \n",
    "import math                                         \n",
    "from glob import glob                              \n",
    "from PIL import Image                              \n",
    "import matplotlib.pyplot as plt\n",
    "import scienceplots\n",
    "plt.style.use(['science','no-latex'])\n",
    "plt.style.use(['nature','no-latex'])\n",
    "from shapely import wkt              \n",
    "from shapely.geometry import Polygon, MultiPolygon, shape\n",
    "from shapely.affinity import scale\n",
    "from rasterio.features import shapes  \n",
    "from rasterio import Affine           \n",
    "from shapely.wkt import loads\n",
    "from shapely import hausdorff_distance\n",
    "# from shapely.geometry.base import geom_factory\n",
    "# from shapely.geos import lgeos\n",
    "from shapely.validation import make_valid\n",
    "import geopandas as gpd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIR = os.getcwd()\n",
    "# Reference U-Net Based Segmentations\n",
    "UNET_DIR = os.path.join(\"E:\\BurnedAreaUAV_files\\BurnedAreaUAV_dataset\\Results\")\n",
    "UNET_PNG_DIR = os.path.join(UNET_DIR, \"segs_base\")\n",
    "UNET_WKT_FILE = os.path.join(UNET_DIR, \"WKT_files\\segs_unet_base_new.wkt\")\n",
    "# Test Segmentation from BurnedAreaUAV dataset\n",
    "BA_UAV_DIR = os.path.normpath(\"E://BurnedAreaUAV_files//BurnedAreaUAV_dataset\")\n",
    "BA_UAV_WKT = os.path.join(BA_UAV_DIR, \"BurnedAreaUAV_dataset//WKT//test.wkt\")\n",
    "# Interpolation Directory\n",
    "INTERPOL_DIR = os.path.normpath(\"E://BurnedAreaUAV_files//Interpolation\")\n",
    "# Sampled Masks\n",
    "SAMPLED_MASKS = os.path.join(INTERPOL_DIR, \"reference_masks//sampled_masks.txt\")\n",
    "# Shape Based Interpolations\n",
    "SHP_DIR = os.path.join(INTERPOL_DIR, \"shape_interpol\")\n",
    "SHP_PNG_DIR = os.path.join(SHP_DIR, \"PNGs\")\n",
    "SHP_PNG_DIR_SAMPLED = os.path.join(SHP_DIR, \"PNGs_sampled\")\n",
    "SHP_WKT_FILE = os.path.join(SHP_DIR, \"shape_interpol.wkt\")\n",
    "SHP_WKT_FILE_SAMPLED = os.path.join(SHP_DIR, \"shape_interpol_sampled.wkt\")\n",
    "# Pyspatiotemporalgeom interpolations\n",
    "PSTG_DIR = os.path.join(INTERPOL_DIR, \"pyspatiotemporalgeom_interpol\")\n",
    "PSTG_PNG_DIR = os.path.join(PSTG_DIR, \"PNGs\")\n",
    "PSTG_PNG_DIR_SAMPLED = os.path.join(PSTG_DIR, \"PNGs_sampled\")\n",
    "PSTG_WKT_FILE = os.path.join(PSTG_DIR, \"pyspatial_interpol.wkt\")\n",
    "PSTG_WKT_FILE_SAMPLED = os.path.join(PSTG_DIR, \"pyspatial_interpol_sampled.wkt\")\n",
    "# Convolutional VAE interpolations (DESATUALIZADO)\n",
    "CVAE_DIR = os.path.join(INTERPOL_DIR, \"cvae_interpol\")\n",
    "CVAE_PNG_DIR = os.path.join(CVAE_DIR, \"PNGs\")\n",
    "CVAE_PNG_DIR_SAMPLED = os.path.join(CVAE_DIR, \"PNGs_sampled\")\n",
    "CVAE_WKT_FILE = os.path.join(CVAE_DIR, \"cvae_interpol.wkt\")\n",
    "CVAE_WKT_FILE_SAMPLED = os.path.join(CVAE_DIR, \"cvae_interpol_sampled.wkt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def msks_paths_to_polygon_list(msks_paths, out_dim = (512, 512)):\n",
    "    \"\"\"\n",
    "    Converts segmentation masks paths list to list of shapely multipolygons.\n",
    "\n",
    "    Parameters:\n",
    "        msks_paths {list} -- list of paths to the masks\n",
    "        out_dim {tuple} -- (width, height) desired dimensions of the masks \n",
    "    Returns:\n",
    "        pol_list {list} -- list of shapely multipolygons\n",
    "    \"\"\"\n",
    "    \n",
    "    pol_list = list()\n",
    "    for img_path in msks_paths:\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
    "        # image dimensions\n",
    "        h, w = img.shape\n",
    "        if (w, h) != out_dim:\n",
    "            img = cv2.resize(img, out_dim, interpolation=cv2.INTER_CUBIC)\n",
    "        polygon = mask_to_polygons(img)\n",
    "        pol_list.append(polygon)\n",
    "        # print progress of the conversion\n",
    "        print(f\"{msks_paths.index(img_path)+1}/{len(msks_paths)}\", end=\"\\r\") \n",
    "    return pol_list\n",
    "    \n",
    "def mask_to_polygons(mask_img):\n",
    "    \"\"\"\n",
    "    Converts segmentation mask to shapely multipolygon.\n",
    "    Adapted from: https://rocreguant.com/convert-a-mask-into-a-polygon-for-images-using-shapely-and-rasterio/1786/\n",
    "    \"\"\"\n",
    "    all_polygons = list()\n",
    "    \n",
    "    for shp, _ in shapes(source=mask_img.astype(np.uint8),\n",
    "                         mask=(mask_img>0), \n",
    "                         transform=Affine(1.0, 0, 0, 0, 1.0, 0)):\n",
    "        all_polygons.append(shape(shp))\n",
    "\n",
    "    all_polygons = MultiPolygon(all_polygons)\n",
    "\n",
    "    # Sometimes buffer() converts a simple Multipolygon to just a Polygon,\n",
    "    # need to keep it a Multipolygon throughout\n",
    "    if not all_polygons.is_valid:\n",
    "        all_polygons = all_polygons.buffer(0)\n",
    "        if all_polygons.geom_type == 'Polygon':\n",
    "            all_polygons = MultiPolygon([all_polygons])\n",
    "    \n",
    "    return all_polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# glob all segmentation masks\n",
    "msks_paths = glob(os.path.join(UNET_PNG_DIR, \"*.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampled indexes:  [0, 1900, 2800, 4800, 5600, 7400, 8200, 8700, 10600, 12800, 17300, 19500, 22500]\n"
     ]
    }
   ],
   "source": [
    "# extract indexes from SAMPLED_MASKS file\n",
    "idx_sampled = []\n",
    "with open(SAMPLED_MASKS, 'r') as f:\n",
    "    for line in f:\n",
    "        # for each line, extract number before the comma\n",
    "        idx_sampled.append(int(line.split(',')[0]))\n",
    "# multiply every element of idx_sampled by 100\n",
    "idx_sampled = [i*100 for i in idx_sampled]\n",
    "print(\"Sampled indexes: \", idx_sampled)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_ORIG_DIMS = (720, 1280)\n",
    "# default number of polygons\n",
    "NUM_POLYGONS = 22500"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auxiliary functions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iou_wkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iou_wkt(ground_truth_file, model_file, num_polygons=NUM_POLYGONS, discard_100=False):\n",
    "    \"\"\"\n",
    "    Calculates the Intersection over Union (IoU) between segmentation polygons\n",
    "    in WKT format.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ground_truth_file : str\n",
    "        Path to the ground truth WKT file.\n",
    "    model_file : str\n",
    "        Path to the model WKT file.\n",
    "    num_polygons : int, optional\n",
    "        Number of polygons to be used in the calculation.\n",
    "    \n",
    "    \"\"\"\n",
    "    # Read WKTS files\n",
    "    print(\"Reading WKTs files...\")\n",
    "    with open(ground_truth_file, \"r\") as f:\n",
    "        ground_truth_wkt = f.read().splitlines()\n",
    "        \n",
    "    with open(model_file, \"r\") as f:\n",
    "        model_wkt = f.read().splitlines()\n",
    "\n",
    "    # read WKTS file lines and convert to list of shapely polygons\n",
    "    ground_truth_polys = [loads(wkt) for wkt in ground_truth_wkt]\n",
    "    model_polys = [loads(wkt) for wkt in model_wkt]\n",
    "    g_t_polys = ground_truth_polys[:num_polygons]\n",
    "    model_polys = model_polys[:num_polygons]\n",
    "\n",
    "    if discard_100:\n",
    "        # discard polygons which index number is multiple of 100 (0, 100, 200, 300, etc)\n",
    "        g_t_polys = [g_t_polys[i] for i in range(len(g_t_polys)) if i % 100 != 0]\n",
    "        model_polys = [model_polys[i] for i in range(len(model_polys)) if i % 100 != 0]\n",
    "\n",
    "    iou_list = list()\n",
    "    print(\"Calculating IoU...\")\n",
    "    for i in range(len(g_t_polys)):\n",
    "        g_t_polys[i]=  make_valid(g_t_polys[i])\n",
    "        model_polys[i] = make_valid(model_polys[i])\n",
    "        intersection = g_t_polys[i].intersection(model_polys[i]).area\n",
    "        union = g_t_polys[i].union(model_polys[i]).area\n",
    "        # Calculate the IoU\n",
    "        iou = intersection / union\n",
    "        iou_list.append(iou)    \n",
    "\n",
    "    # Calculate the mean IoU\n",
    "    iou_mean = np.mean(iou_list)\n",
    "    # iou_mean = sum(iou_list) / len(iou_list)\n",
    "\n",
    "    return iou_mean, iou_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "temporal_consistency v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def temporal_consistency(wkt_file, num_polygons):\n",
    "    \"\"\"\n",
    "    Calculates the temporal consistency between segmentation polygons in \n",
    "    WKT format.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wkt_file : str\n",
    "        Path to the WKT file.\n",
    "    num_polygons : int\n",
    "        Number of polygons to be used in the calculation.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    t_c_mean : float\n",
    "        Mean temporal consistency.\n",
    "    t_c_list : list\n",
    "        List of temporal consistency values.    \n",
    "    \"\"\"\n",
    "\n",
    "    print(\"Reading WKTs file...\")\n",
    "    with open(wkt_file, \"r\") as f:\n",
    "        model_wkt = f.read().splitlines()\n",
    "\n",
    "    # read WKTS file lines and convert to list of shapely polygons\n",
    "    model_polys = [loads(wkt) for wkt in model_wkt]\n",
    "    model_polys = model_polys[:num_polygons]\n",
    "\n",
    "    t_c_list = list()\n",
    "    print(\"Calculating the temporal consistency...\")\n",
    "    for i in range(len(g_t_polys)-1):\n",
    "        model_polys[i] = make_valid(model_polys[i])\n",
    "        model_polys[i+1] = make_valid(model_polys[i+1])\n",
    "        # calculates the temporal consistency between two consecutive polygons\n",
    "        t_c = model_polys[i].difference(model_polys[i+1]).area / g_t_polys[i+1].area\n",
    "        t_c_list.append(t_c)\n",
    "    \n",
    "    # Calcula a média \n",
    "    t_c_mean = sum(t_c_list) / len(t_c_list)\n",
    "\n",
    "    return t_c_mean, t_c_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "strided_temporal_consistency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strided_temporal_consistency(polygons, num_polygons, stride=1, exp = True):\n",
    "    \"\"\"\n",
    "    Calculates the temporal consistency between polygons in a sequence with a \n",
    "    certain stride.\n",
    "\n",
    "    Args:\n",
    "        model_polys (list): A list of Shapely Polygon objects representing the \n",
    "                            sequence of polygons.\n",
    "        num_polygons (int): The total number of polygons in the sequence.\n",
    "        stride (int, optional): The number of polygons to skip between \n",
    "                                comparisons. Defaults to 1.\n",
    "\n",
    "    Returns:\n",
    "        dict: A dictionary containing the temporal consistency values and \n",
    "              related information. The keys are:\n",
    "            - i (list): A list of the starting indices of the compared polygon pairs.\n",
    "            - strd (int): The stride value used.\n",
    "            - tc (list): A list of the temporal consistency values calculated \n",
    "                         for each pair of polygons.\n",
    "            - tc_mean (float): The mean temporal consistency value.\n",
    "    \"\"\"\n",
    "\n",
    "    # Create empty dictionary to store the TC values and and indexes\n",
    "    t_c = {key: [] for key in [\"i\", \"strd\", \"tc\", \"tc_mean\"]}\n",
    "\n",
    "    # Make all polygons in the list valid before calculating temporal consistency\n",
    "    polygons = [make_valid(poly) for poly in polygons]\n",
    "\n",
    "    print(f\"Calculating the temporal consistency with stride {stride}...\")\n",
    "    for i in range(0, num_polygons - stride):\n",
    "        # Calculates the temporal consistency between two consecutive polygons\n",
    "        t_c[\"i\"].append(i)\n",
    "        tc_temp = 1 - (polygons[i].difference(polygons[i+stride]).area / polygons[i+stride].area)\n",
    "        if exp: \n",
    "            t_c[\"tc\"].append(np.power(tc_temp, 10))\n",
    "        else:\n",
    "            t_c[\"tc\"].append(tc_temp)\n",
    "    # print(t_c[\"tc\"])\n",
    "        \n",
    "    # Calculates the mean of the temporal consistency\n",
    "    t_c[\"tc_mean\"] =  np.mean(t_c[\"tc\"])\n",
    "    t_c[\"strd\"] = stride\n",
    "\n",
    "    return t_c"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hausdorff_dist_wkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hausdorff_dist_wkt(ground_truth_file, model_file, last_frame=22500, discard_100=False):\n",
    "    \"\"\"\n",
    "    Calculates the Hausdorff distance between the ground truth and the model.\n",
    "\n",
    "    Args:\n",
    "        ground_truth_file (str): The path to the ground truth WKTS file.\n",
    "        model_file (str): The path to the model WKTS file.\n",
    "        last_frame (int, optional): The last frame to consider. Defaults to 22500.\n",
    "        discard (bool, optional): Whether to discard polygons which index number \n",
    "        is multiple of 100 (0, 100, 200, 300, etc). Defaults to False.  \n",
    "\n",
    "    Returns:\n",
    "        dict: A dictionary containing the Hausdorff distance values and related \n",
    "        information. The keys are:\n",
    "            - i (list): A list of the starting indices of the compared polygon\n",
    "             pairs.\n",
    "            - hd (list): A list of the Hausdorff distance values calculated for \n",
    "            each pair of polygons.\n",
    "            - hd_mean (float): The mean Hausdorff distance value.\n",
    "\n",
    "    \"\"\"\n",
    "    # Read WKTS files\n",
    "    print(\"Reading WKTs files...\")\n",
    "    with open(ground_truth_file, \"r\") as f:\n",
    "        ground_truth_wkt = f.read().splitlines()\n",
    "        \n",
    "    with open(model_file, \"r\") as f:\n",
    "        model_wkt = f.read().splitlines()\n",
    "\n",
    "    # read WKTS file lines and convert to list of shapely polygons\n",
    "    ground_truth_polys = [loads(wkt) for wkt in ground_truth_wkt]\n",
    "    model_polys = [loads(wkt) for wkt in model_wkt]\n",
    "\n",
    "    g_t_polys = ground_truth_polys[:last_frame]\n",
    "    model_polys = model_polys[:last_frame]\n",
    "\n",
    "    if discard_100:\n",
    "        # discard polygons which index number is multiple of 100 (0, 100, 200, 300, etc)\n",
    "        g_t_polys = [g_t_polys[i] for i in range(len(g_t_polys)) if i % 100 != 0]\n",
    "        model_polys = [model_polys[i] for i in range(len(model_polys)) if i % 100 != 0]\n",
    "\n",
    "    hausdorff_dist_list = list()\n",
    "    print(\"Calculating Hausdorf Distance...\")\n",
    "    for i in range(len(g_t_polys)):\n",
    "        g_t_polys[i]=  make_valid(g_t_polys[i])\n",
    "        model_polys[i] = make_valid(model_polys[i])\n",
    "        # hausdorff_dist = loads(ground_truth_wkt[i]).hausdorff_distance(loads(model_polys[i]))\n",
    "        hausdorff_dist = g_t_polys[i].hausdorff_distance(model_polys[i])\n",
    "        hausdorff_dist_list.append(hausdorff_dist)    \n",
    "\n",
    "    # Calcula a média dos valores de IoU\n",
    "    hausdorff_dist_mean = sum(hausdorff_dist_list) / len(hausdorff_dist_list)\n",
    "\n",
    "    return hausdorff_dist_mean, hausdorff_dist_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.25 Hz Interpolations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with **UNET** generated segmentations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IoU metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs files...\n",
      "Calculating IoU...\n",
      "Reading WKTs files...\n",
      "Calculating IoU...\n",
      "Reading WKTs files...\n",
      "Calculating IoU...\n"
     ]
    }
   ],
   "source": [
    "iou_shp_pr, iou_list_shp_pr = iou_wkt(UNET_WKT_FILE, SHP_WKT_FILE, num_polygons=NUM_POLYGONS, discard_100=True)\n",
    "iou_cvae_pr, iou_list_cvae_pr = iou_wkt(UNET_WKT_FILE, CVAE_WKT_FILE, num_polygons=NUM_POLYGONS, discard_100=True)\n",
    "iou_pstg_pr, iou_list_pstg_pr = iou_wkt(UNET_WKT_FILE, PSTG_WKT_FILE, num_polygons=NUM_POLYGONS, discard_100=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape based interpolation: meanIoU 0.958 , std 0.011, min 0.870, max 0.982 \n",
      "\n",
      "Conditional VAE: meanIoU 0.951 , std 0.012, min 0.852, max 0.975 \n",
      "\n",
      "Pstg: meanIoU 0.892 , std 0.048, min 0.519, max 0.982 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print mean, std, min and max values\n",
    "print(f\"Shape based interpolation: meanIoU {iou_shp_pr:.3f} , std {np.std(iou_list_shp_pr):.3f}, min {np.min(iou_list_shp_pr):.3f}, max {np.max(iou_list_shp_pr):.3f} \\n\")\n",
    "print(f\"Conditional VAE: meanIoU {iou_cvae_pr:.3f} , std {np.std(iou_list_cvae_pr):.3f}, min {np.min(iou_list_cvae_pr):.3f}, max {np.max(iou_list_cvae_pr):.3f} \\n\")\n",
    "print(f\"Pstg: meanIoU {iou_pstg_pr:.3f} , std {np.std(iou_list_pstg_pr):.3f}, min {np.min(iou_list_pstg_pr):.3f}, max {np.max(iou_list_pstg_pr):.3f} \\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hausdorf Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs files...\n",
      "Calculating Hausdorf Distance...\n",
      "Reading WKTs files...\n",
      "Calculating Hausdorf Distance...\n",
      "Reading WKTs files...\n",
      "Calculating Hausdorf Distance...\n"
     ]
    }
   ],
   "source": [
    "hd_shp_mean, hd_list_shp_pr = hausdorff_dist_wkt(UNET_WKT_FILE, SHP_WKT_FILE, last_frame=NUM_POLYGONS, discard_100=True)\n",
    "hd_cvae_mean, hd_list_cvae_pr = hausdorff_dist_wkt(UNET_WKT_FILE, CVAE_WKT_FILE, last_frame=NUM_POLYGONS, discard_100=True)\n",
    "hd_pstg_mean, hd_list_pstg_pr = hausdorff_dist_wkt(UNET_WKT_FILE, PSTG_WKT_FILE, last_frame=NUM_POLYGONS, discard_100=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape based interpolation: mean Hausdorf Dist. 42.640 , std 37.503, min 9.849, max 243.994 \n",
      "\n",
      "Conditional VAE: mean Hausdorf Dist. 41.866 , std 26.045, min 9.849, max 242.745 \n",
      "\n",
      "Pstg: mean Hausdorf Dist. 72.195 , std 44.284, min 9.659, max 364.660 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print mean, std, min and max values\n",
    "\n",
    "print(f\"Shape based interpolation: mean Hausdorf Dist. {hd_shp_mean:.3f} , std {np.std(hd_list_shp_pr):.3f}, min {np.min(hd_list_shp_pr):.3f}, max {np.max(hd_list_shp_pr):.3f} \\n\")\n",
    "print(f\"Conditional VAE: mean Hausdorf Dist. {hd_cvae_mean:.3f} , std {np.std(hd_list_cvae_pr):.3f}, min {np.min(hd_list_cvae_pr):.3f}, max {np.max(hd_list_cvae_pr):.3f} \\n\")\n",
    "print(f\"Pstg: mean Hausdorf Dist. {hd_pstg_mean:.3f} , std {np.std(hd_list_pstg_pr):.3f}, min {np.min(hd_list_pstg_pr):.3f}, max {np.max(hd_list_pstg_pr):.3f} \\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with the BurnedAreaUAV **Test set**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs files...\n",
      "Filtering WKTs files...\n",
      "Shape based interpolation: 23 frames\n",
      "Conditional VAE interpolation: 23 frames\n",
      "Pyspatiotemporalgeom interpolation: 23 frames \n",
      "Test set: 23 frames\n"
     ]
    }
   ],
   "source": [
    "# test set frame indexes \n",
    "idx = np.linspace(20250, 22450, 23).astype(int)  \n",
    "\n",
    "# Read WKTS files\n",
    "print(\"Reading WKTs files...\")\n",
    "with open(BA_UAV_WKT, \"r\") as f:\n",
    "    test_wkt = f.read().splitlines()\n",
    "\n",
    "with open(SHP_WKT_FILE, \"r\") as f:\n",
    "    shp_wkt = f.read().splitlines()\n",
    "\n",
    "with open(CVAE_WKT_FILE, \"r\") as f:\n",
    "    cvae_wkt = f.read().splitlines()\n",
    "\n",
    "with open(PSTG_WKT_FILE, \"r\") as f:\n",
    "    pstg_wkt = f.read().splitlines()\n",
    "\n",
    "print(\"Filtering WKTs files...\")\n",
    "shp_wkt = [shp_wkt[i] for i in idx]\n",
    "cvae_wkt = [cvae_wkt[i] for i in idx]\n",
    "pstg_wkt = [pstg_wkt[i] for i in idx]\n",
    "# print the length of all wkt files\n",
    "print(f\"Shape based interpolation: {len(shp_wkt)} frames\")\n",
    "print(f\"Conditional VAE interpolation: {len(cvae_wkt)} frames\")\n",
    "print(f\"Pyspatiotemporalgeom interpolation: {len(pstg_wkt)} frames \")\n",
    "print(f\"Test set: {len(test_wkt)} frames\")\n",
    "\n",
    "# converts Test set ground truth polygon to shapely format\n",
    "g_t_polys = [loads(wkt) for wkt in test_wkt]\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hausdorf Distance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Hausdorf Distance...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Shape based interpolation\n",
    "model_polys = [loads(wkt) for wkt in shp_wkt]\n",
    "hd_list_shp_ts = list()\n",
    "print(\"Calculating Hausdorf Distance...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    g_t_polys[i] =  make_valid(g_t_polys[i])\n",
    "    model_polys[i] = make_valid(model_polys[i])\n",
    "    hausdorff_dist = g_t_polys[i].hausdorff_distance(model_polys[i])\n",
    "    hd_list_shp_ts.append(hausdorff_dist)    \n",
    "\n",
    "hd_mean_shp_ts = np.mean(hd_list_shp_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Hausdorf Distance...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# CVAE\n",
    "model_polys = [loads(wkt) for wkt in cvae_wkt]\n",
    "hd_list_cvae_ts = list()\n",
    "print(\"Calculating Hausdorf Distance...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    g_t_polys[i]=  make_valid(g_t_polys[i])\n",
    "    model_polys[i] = make_valid(model_polys[i])\n",
    "    hausdorff_dist = g_t_polys[i].hausdorff_distance(model_polys[i])\n",
    "    hd_list_cvae_ts.append(hausdorff_dist)    \n",
    "\n",
    "hd_mean_cvae_ts = np.mean(hd_list_cvae_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Hausdorf Distance...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# PSTG\n",
    "model_polys = [loads(wkt) for wkt in pstg_wkt]\n",
    "hd_list_pstg_ts = list()\n",
    "print(\"Calculating Hausdorf Distance...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    g_t_polys[i]=  make_valid(g_t_polys[i])\n",
    "    model_polys[i] = make_valid(model_polys[i])\n",
    "    hausdorff_dist = g_t_polys[i].hausdorff_distance(model_polys[i])\n",
    "    hd_list_pstg_ts.append(hausdorff_dist)    \n",
    "\n",
    "hd_mean_pstg_ts = np.mean(hd_list_pstg_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional VAE interpolation: mean Hausdorf Dist. 48.382, std 24.926, min 23.000, max 107.201 \n",
      "\n",
      "Shape based interpolation: mean Hausdorf Dist. 60.815, std 33.312, min 19.444, max 117.000 \n",
      "\n",
      "Pstg: mean Hausdorf Dist. 113.161 , std 33.832, min 86.279, max 266.303 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print mean, std, min, max\n",
    "print(f\"Conditional VAE interpolation: mean Hausdorf Dist. {hd_mean_cvae_ts:.3f}, std {np.std(hd_list_cvae_ts):.3f}, min {np.min(hd_list_cvae_ts):.3f}, max {np.max(hd_list_cvae_ts):.3f} \\n\")\n",
    "print(f\"Shape based interpolation: mean Hausdorf Dist. {hd_mean_shp_ts:.3f}, std {np.std(hd_list_shp_ts):.3f}, min {np.min(hd_list_shp_ts):.3f}, max {np.max(hd_list_shp_ts):.3f} \\n\")\n",
    "print(f\"Pstg: mean Hausdorf Dist. {hd_mean_pstg_ts:.3f} , std {np.std(hd_list_pstg_ts):.3f}, min {np.min(hd_list_pstg_ts):.3f}, max {np.max(hd_list_pstg_ts):.3f} \\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots: Hausdorf Distance Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Haussdorf Distance')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_ts = [hd_list_shp_ts, hd_list_cvae_ts, hd_list_pstg_ts]\n",
    "data_pr = [hd_list_shp_pr, hd_list_cvae_pr, hd_list_pstg_pr]\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "# left position of bars on x-axis\n",
    "left = [1, 1.5, 2]\n",
    "# right position of bars on x-axis\n",
    "right = [2.75, 3.25, 3.75]\n",
    "\n",
    "# labels for bars\n",
    "tick_label = ['Shape Based', 'C-VAE', 'Pyspatiotemporalgeom']\n",
    "\n",
    "# Create the boxplot with median annotation, showfliers=False, shape based green, cvae blue, pstg red\n",
    "boxplot_ts = ax.boxplot(data_ts,\n",
    "                        positions=left,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=2),\n",
    "                        patch_artist=True)\n",
    "\n",
    "# Create the boxplot with median annotation, showfliers=False, shape based green, cvae blue, pstg red\n",
    "boxplot_unet = ax.boxplot(data_pr,\n",
    "                        positions=right,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=2),\n",
    "                        patch_artist=True)\n",
    "\n",
    "colors = ['lightgreen', 'lightblue', 'lightcoral', 'lightgreen', 'lightblue', 'lightcoral']\n",
    "for patch, color in zip(boxplot_ts['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "for patch, color in zip(boxplot_unet['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "# Add dashed gridlines for y axis ONLY\n",
    "ax.grid(linestyle='--', linewidth=0.5, axis='y')\n",
    "# no x gridlines\n",
    "ax.grid(False, axis='x')\n",
    "\n",
    "# x labels\n",
    "ax.set_xticks([1.5, 3.25])\n",
    "ax.set_xticklabels(['Test Set', 'U-NET'])\n",
    "# increase4 font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "# add legend for each model, font size 14\n",
    "ax.legend([boxplot_ts[\"boxes\"][0], boxplot_ts[\"boxes\"][1], boxplot_ts[\"boxes\"][2]],\n",
    "            ['Shape Based', 'C-VAE', 'Mckenney'], fontsize=14)  \n",
    "# add y label\n",
    "ax.set_ylabel('Haussdorf Distance', fontsize=14)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IoU - Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating IoU...\n"
     ]
    }
   ],
   "source": [
    "# CVAE\n",
    "model_polys = [loads(wkt) for wkt in cvae_wkt]\n",
    "iou_list_cvae_pr_ts  = list()\n",
    "print(\"Calculating IoU...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    iou = g_t_polys[i].intersection(model_polys[i]).area / g_t_polys[i].union(model_polys[i]).area\n",
    "    iou_list_cvae_pr_ts .append(iou)\n",
    "    \n",
    "iou_mean_cvae_pr_ts = sum(iou_list_cvae_pr_ts) / len(iou_list_cvae_pr_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating IoU...\n"
     ]
    }
   ],
   "source": [
    "# Shape based\n",
    "model_polys = [loads(wkt) for wkt in shp_wkt]\n",
    "iou_list_shp_pr_ts  = list()\n",
    "print(\"Calculating IoU...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    iou = g_t_polys[i].intersection(model_polys[i]).area / g_t_polys[i].union(model_polys[i]).area\n",
    "    iou_list_shp_pr_ts .append(iou)\n",
    "\n",
    "iou_mean_shp_pr_ts = sum(iou_list_shp_pr_ts) / len(iou_list_shp_pr_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating IoU...\n"
     ]
    }
   ],
   "source": [
    "# Pyspatiotemporalgeom\n",
    "model_polys = [loads(wkt) for wkt in pstg_wkt]\n",
    "iou_list_pstg_pr_ts  = list()\n",
    "print(\"Calculating IoU...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    iou = g_t_polys[i].intersection(model_polys[i]).area / g_t_polys[i].union(model_polys[i]).area\n",
    "    iou_list_pstg_pr_ts .append(iou)\n",
    "\n",
    "iou_mean_pstg_pr_ts = sum(iou_list_pstg_pr_ts) / len(iou_list_pstg_pr_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional VAE interpolation: mean IoU 0.949, std 0.017, min 0.916, max 0.974 \n",
      "\n",
      "Shape based interpolation: mean IoU 0.959, std 0.016, min 0.925, max 0.977 \n",
      "\n",
      "Pyspatiotemporalgeom interpolation: mean IoU 0.822, std 0.073, min 0.493, max 0.864 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print mean, std, min and max values\n",
    "print(f\"Conditional VAE interpolation: mean IoU {iou_mean_cvae_pr_ts:.3f}, std {np.std(iou_list_cvae_pr_ts):.3f}, min {min(iou_list_cvae_pr_ts):.3f}, max {max(iou_list_cvae_pr_ts):.3f} \\n\")\n",
    "print(f\"Shape based interpolation: mean IoU {iou_mean_shp_pr_ts:.3f}, std {np.std(iou_list_shp_pr_ts):.3f}, min {min(iou_list_shp_pr_ts):.3f}, max {max(iou_list_shp_pr_ts):.3f} \\n\")\n",
    "print(f\"Pyspatiotemporalgeom interpolation: mean IoU {iou_mean_pstg_pr_ts:.3f}, std {np.std(iou_list_pstg_pr_ts):.3f}, min {min(iou_list_pstg_pr_ts):.3f}, max {max(iou_list_pstg_pr_ts):.3f} \\n\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots: IoU Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Jaccard Index')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot IoU for UNET and test set comparison\n",
    "# Combine the data into a list\n",
    "data_ts = [iou_list_shp_pr_ts, iou_list_cvae_pr_ts, iou_list_pstg_pr_ts]\n",
    "data_unet = [iou_list_shp_pr, iou_list_cvae_pr, iou_list_pstg_pr]\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "# y-axis from 0 to 1\n",
    "ax.set_ylim(0.6, 1)\n",
    "\n",
    "# left position of bars on x-axis\n",
    "left = [1, 1.5, 2]\n",
    "# right position of bars on x-axis\n",
    "right = [2.75, 3.25, 3.75]\n",
    " \n",
    "# labels for bars\n",
    "tick_label = ['Shape Based', 'C-VAE', 'Pyspatiotemporalgeom']\n",
    "\n",
    "# Create the boxplot with median annotation, showfliers=False, shape based green, cvae blue, pstg red\n",
    "boxplot_ts = ax.boxplot(data_ts,\n",
    "                        positions=left,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=2),\n",
    "                        patch_artist=True)\n",
    "\n",
    "# Create the boxplot with median annotation, showfliers=False, shape based green, cvae blue, pstg red\n",
    "boxplot_unet = ax.boxplot(data_unet,\n",
    "                        positions=right,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=2),\n",
    "                        patch_artist=True)\n",
    "                    \n",
    "\n",
    "colors = ['lightgreen', 'lightblue', 'lightcoral', 'lightgreen', 'lightblue', 'lightcoral']\n",
    "for patch, color in zip(boxplot_ts['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "for patch, color in zip(boxplot_unet['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "# Add dashed gridlines for y axis ONLY\n",
    "ax.grid(linestyle='--', linewidth=0.5, axis='y')\n",
    "# no x gridlines\n",
    "ax.grid(False, axis='x')\n",
    "\n",
    "# x labels\n",
    "ax.set_xticks([1.5, 3.25])\n",
    "ax.set_xticklabels(['Test Set', 'U-NET'])\n",
    "# increase4 font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "# add legend for each model, font size 14\n",
    "ax.legend([boxplot_ts[\"boxes\"][0], boxplot_ts[\"boxes\"][1], boxplot_ts[\"boxes\"][2]],\n",
    "            ['Shape Based', 'C-VAE', 'Pyspatiotemporalgeom'], fontsize=14)  \n",
    "# add y label\n",
    "ax.set_ylabel('Jaccard Index', fontsize=14)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Consistency (0,25 Hz Interpolations)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponetial Strided TC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "strides: [    1    10   100  1000 10000]\n"
     ]
    }
   ],
   "source": [
    "# Let a_n be some geometric progression a_n = a_1*r^(n−1)\n",
    "n = np.arange(1,6)\n",
    "a_1 = 1\n",
    "r = 10\n",
    "# calculate the geometric progression\n",
    "a_n = a_1*r**(n-1)\n",
    "strides = a_n\n",
    "print(f\"strides: {strides}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs file...\n",
      "Calculating the temporal consistency with stride 1...\n",
      "Conditional VAE interpolation: mean TC 9.9756e-01 with stride 1 \n",
      "\n",
      "Calculating the temporal consistency with stride 10...\n",
      "Conditional VAE interpolation: mean TC 9.9775e-01 with stride 10 \n",
      "\n",
      "Calculating the temporal consistency with stride 100...\n",
      "Conditional VAE interpolation: mean TC 9.9792e-01 with stride 100 \n",
      "\n",
      "Calculating the temporal consistency with stride 1000...\n",
      "Conditional VAE interpolation: mean TC 9.8743e-01 with stride 1000 \n",
      "\n",
      "Calculating the temporal consistency with stride 10000...\n",
      "Conditional VAE interpolation: mean TC 9.8201e-01 with stride 10000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# CVAE\n",
    "print(\"Reading WKTs file...\")\n",
    "with open(CVAE_WKT_FILE, \"r\") as f:\n",
    "    model_wkt = f.read().splitlines()\n",
    "\n",
    "# delete samples\n",
    "del model_wkt[::100]\n",
    "num_polygons = len(model_wkt)\n",
    "\n",
    "# read WKTS file lines and convert to list of shapely polygons\n",
    "polygons = [loads(wkt) for wkt in model_wkt]\n",
    "polygons = polygons[:num_polygons]\n",
    "tc_list = list()\n",
    "\n",
    "for str in strides:\n",
    "    t_c = strided_temporal_consistency(polygons, num_polygons, str, exp = False)\n",
    "    print(f\"Conditional VAE interpolation: mean TC {t_c['tc_mean']:.4e} with stride {t_c['strd']} \\n\")\n",
    "    tc_list.append(t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs file...\n",
      "Calculating the temporal consistency with stride 1...\n",
      "Shape based interpolation: mean TC 9.9974e-01 with stride 1 \n",
      "\n",
      "Calculating the temporal consistency with stride 10...\n",
      "Shape based interpolation: mean TC 9.9744e-01 with stride 10 \n",
      "\n",
      "Calculating the temporal consistency with stride 100...\n",
      "Shape based interpolation: mean TC 9.8064e-01 with stride 100 \n",
      "\n",
      "Calculating the temporal consistency with stride 1000...\n",
      "Shape based interpolation: mean TC 9.7121e-01 with stride 1000 \n",
      "\n",
      "Calculating the temporal consistency with stride 10000...\n",
      "Shape based interpolation: mean TC 9.8029e-01 with stride 10000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Shape based\n",
    "print(\"Reading WKTs file...\")\n",
    "with open(SHP_WKT_FILE, \"r\") as f:\n",
    "    model_wkt = f.read().splitlines()\n",
    "\n",
    "# delete samples\n",
    "del model_wkt[::100]\n",
    "num_polygons = len(model_wkt)\n",
    "\n",
    "# read WKTS file lines and convert to list of shapely polygons\n",
    "polygons = [loads(wkt) for wkt in model_wkt]\n",
    "polygons = polygons[:num_polygons]\n",
    "\n",
    "tc_shp_list = list()\n",
    "for str in strides:\n",
    "    t_c = strided_temporal_consistency(polygons, num_polygons, str, exp = False)\n",
    "    print(f\"Shape based interpolation: mean TC {t_c['tc_mean']:.4e} with stride {t_c['strd']} \\n\")\n",
    "    tc_shp_list.append(t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs file...\n",
      "Calculating the temporal consistency with stride 1...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\set_operations.py:77: RuntimeWarning: invalid value encountered in difference\n",
      "  return lib.difference(a, b, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pyspatiotemporalgeom interpolation: mean TC 9.9524e-01 with stride 1 \n",
      "\n",
      "Calculating the temporal consistency with stride 10...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.6766e-01 with stride 10 \n",
      "\n",
      "Calculating the temporal consistency with stride 100...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.5177e-01 with stride 100 \n",
      "\n",
      "Calculating the temporal consistency with stride 1000...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.5128e-01 with stride 1000 \n",
      "\n",
      "Calculating the temporal consistency with stride 10000...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.8606e-01 with stride 10000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Pyspatiotemporalgeom\n",
    "print(\"Reading WKTs file...\")\n",
    "with open( PSTG_WKT_FILE, \"r\") as f:\n",
    "    model_wkt = f.read().splitlines()\n",
    "\n",
    "# delete samples\n",
    "del model_wkt[::100]\n",
    "num_polygons = len(model_wkt)\n",
    "\n",
    "# read WKTS file lines and convert to list of shapely polygons\n",
    "polygons = [loads(wkt) for wkt in model_wkt]\n",
    "polygons = polygons[:num_polygons]\n",
    "\n",
    "tc_pstg_list = list()\n",
    "\n",
    "for str in strides:\n",
    "    t_c = strided_temporal_consistency(polygons, num_polygons, str, exp = False)\n",
    "    print(f\"Pyspatiotemporalgeom interpolation: mean TC {t_c['tc_mean']:.4e} with stride {t_c['strd']} \\n\")\n",
    "    tc_pstg_list.append(t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional VAE interpolation: mean TC 9.9253e-01 sd 6.6068e-03 min 9.8201e-01 max 9.9792e-01 \n",
      "\n",
      "Shape based interpolation: mean TC 9.8586e-01 sd 1.0950e-02 min 9.7121e-01 max 9.9974e-01 \n",
      "\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.7040e-01 sd 1.7788e-02 min 9.5128e-01 max 9.9524e-01 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print mean, std, min, max (compactly)\n",
    "print(f\"Conditional VAE interpolation: mean TC {np.mean([tc['tc_mean'] for tc in tc_list]):.4e} sd {np.std([tc['tc_mean'] for tc in tc_list]):.4e} min {np.min([tc['tc_mean'] for tc in tc_list]):.4e} max {np.max([tc['tc_mean'] for tc in tc_list]):.4e} \\n\")\n",
    "print(f\"Shape based interpolation: mean TC {np.mean([tc['tc_mean'] for tc in tc_shp_list]):.4e} sd {np.std([tc['tc_mean'] for tc in tc_shp_list]):.4e} min {np.min([tc['tc_mean'] for tc in tc_shp_list]):.4e} max {np.max([tc['tc_mean'] for tc in tc_shp_list]):.4e} \\n\")\n",
    "print(f\"Pyspatiotemporalgeom interpolation: mean TC {np.mean([tc['tc_mean'] for tc in tc_pstg_list]):.4e} sd {np.std([tc['tc_mean'] for tc in tc_pstg_list]):.4e} min {np.min([tc['tc_mean'] for tc in tc_pstg_list]):.4e} max {np.max([tc['tc_mean'] for tc in tc_pstg_list]):.4e} \\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance Sampled Interpolations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with **UNET** generated segmentations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IoU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs files...\n",
      "Calculating IoU...\n",
      "Reading WKTs files...\n",
      "Calculating IoU...\n",
      "Reading WKTs files...\n",
      "Calculating IoU...\n"
     ]
    }
   ],
   "source": [
    "iou_shp_mean_dst, iou_list_shp_dst = iou_wkt(UNET_WKT_FILE, SHP_WKT_FILE_SAMPLED, num_polygons=NUM_POLYGONS)\n",
    "iou_cvae_mean_dst, iou_list_cvae_dst = iou_wkt(UNET_WKT_FILE, CVAE_WKT_FILE_SAMPLED, num_polygons=NUM_POLYGONS)\n",
    "iou_pstg_mean_dst, iou_list_pstg_dst = iou_wkt(UNET_WKT_FILE, PSTG_WKT_FILE_SAMPLED, num_polygons=NUM_POLYGONS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape based interpolation: meanIou 0.928, std 0.020, min 0.845, max 0.982 \n",
      "\n",
      "Conditional VAE interpolation: meanIoU 0.905, std 0.026, min 0.825, max 0.987 \n",
      "\n",
      "Pyspatiotemporalgeom interpolation: meanIoU 0.876, std 0.040, min 0.763, max 0.978 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape based interpolation: meanIou {iou_shp_mean_dst:.3f}, std {np.std(iou_list_shp_dst):.3f}, min {np.min(iou_list_shp_dst):.3f}, max {np.max(iou_list_shp_dst):.3f} \\n\")\n",
    "print(f\"Conditional VAE interpolation: meanIoU {iou_cvae_mean_dst:.3f}, std {np.std(iou_list_cvae_dst):.3f}, min {np.min(iou_list_cvae_dst):.3f}, max {np.max(iou_list_cvae_dst):.3f} \\n\")\n",
    "print(f\"Pyspatiotemporalgeom interpolation: meanIoU {iou_pstg_mean_dst:.3f}, std {np.std(iou_list_pstg_dst):.3f}, min {np.min(iou_list_pstg_dst):.3f}, max {np.max(iou_list_pstg_dst):.3f} \\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hausdorf Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs files...\n",
      "Calculating Hausdorf Distance...\n",
      "Reading WKTs files...\n",
      "Calculating Hausdorf Distance...\n",
      "Reading WKTs files...\n",
      "Calculating Hausdorf Distance...\n"
     ]
    }
   ],
   "source": [
    "hd_shp_mean_pr_sp, hd_list_shp_pr_sp = hausdorff_dist_wkt(UNET_WKT_FILE, SHP_WKT_FILE_SAMPLED, last_frame=NUM_POLYGONS, discard_100=True)\n",
    "hd_cvae_mean_pr_sp, hd_list_cvae_pr_sp = hausdorff_dist_wkt(UNET_WKT_FILE, CVAE_WKT_FILE_SAMPLED, last_frame=NUM_POLYGONS, discard_100=True)\n",
    "hd_pstg_mean_pr_sp, hd_list_pstg_pr_sp = hausdorff_dist_wkt(UNET_WKT_FILE, PSTG_WKT_FILE_SAMPLED, last_frame=NUM_POLYGONS, discard_100=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape based interpolation: mean Hausdorf Dist. 68.315, std 38.443, min 10.296, max 306.026 \n",
      "\n",
      "Conditional VAE interpolation: mean Hausdorf Dist. 76.464, std 52.362, min 16.000, max 338.095 \n",
      "\n",
      "Pyspatiotemporalgeom interpolation: mean Hausdorf Dist. 85.426, std 38.922, min 10.471, max 269.194 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape based interpolation: mean Hausdorf Dist. {hd_shp_mean_pr_sp:.3f}, std {np.std(hd_list_shp_pr_sp):.3f}, min {np.min(hd_list_shp_pr_sp):.3f}, max {np.max(hd_list_shp_pr_sp):.3f} \\n\")\n",
    "print(f\"Conditional VAE interpolation: mean Hausdorf Dist. {hd_cvae_mean_pr_sp:.3f}, std {np.std(hd_list_cvae_pr_sp):.3f}, min {np.min(hd_list_cvae_pr_sp):.3f}, max {np.max(hd_list_cvae_pr_sp):.3f} \\n\")\n",
    "print(f\"Pyspatiotemporalgeom interpolation: mean Hausdorf Dist. {hd_pstg_mean_pr_sp:.3f}, std {np.std(hd_list_pstg_pr_sp):.3f}, min {np.min(hd_list_pstg_pr_sp):.3f}, max {np.max(hd_list_pstg_pr_sp):.3f} \\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with the BurnedAreaUAV **Test set**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs files...\n",
      "Filtering WKTs files...\n",
      "Shape based interpolation: 23 frames\n",
      "Conditional VAE interpolation: 23 frames\n",
      "Pyspatiotemporalgeom interpolation: 23 frames \n",
      "Test set: 23 frames\n"
     ]
    }
   ],
   "source": [
    "# test set frame indexes \n",
    "idx = np.linspace(20250, 22450, 23).astype(int)  \n",
    "\n",
    "# Read WKTS files\n",
    "print(\"Reading WKTs files...\")\n",
    "with open(BA_UAV_WKT, \"r\") as f:\n",
    "    test_wkt = f.read().splitlines()\n",
    "\n",
    "with open(SHP_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    shp_wkt = f.read().splitlines()\n",
    "\n",
    "with open(CVAE_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    cvae_wkt = f.read().splitlines()\n",
    "\n",
    "with open(PSTG_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    pstg_wkt = f.read().splitlines()\n",
    "\n",
    "print(\"Filtering WKTs files...\")\n",
    "shp_wkt = [shp_wkt[i] for i in idx]\n",
    "cvae_wkt = [cvae_wkt[i] for i in idx]\n",
    "pstg_wkt = [pstg_wkt[i] for i in idx]\n",
    "# print the length of all wkt files\n",
    "print(f\"Shape based interpolation: {len(shp_wkt)} frames\")\n",
    "print(f\"Conditional VAE interpolation: {len(cvae_wkt)} frames\")\n",
    "print(f\"Pyspatiotemporalgeom interpolation: {len(pstg_wkt)} frames \")\n",
    "print(f\"Test set: {len(test_wkt)} frames\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hausdorf Distance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Hausdorf Distance...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# converts Test set ground truth polygon to shapely format\n",
    "g_t_polys = [loads(wkt) for wkt in test_wkt]\n",
    "\n",
    "# Shape based interpolation\n",
    "model_polys = [loads(wkt) for wkt in shp_wkt]\n",
    "hd_list_shp_ts_sp  = list()\n",
    "print(\"Calculating Hausdorf Distance...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    g_t_polys[i] =  make_valid(g_t_polys[i])\n",
    "    model_polys[i] = make_valid(model_polys[i])\n",
    "    hausdorff_dist = g_t_polys[i].hausdorff_distance(model_polys[i])\n",
    "    hd_list_shp_ts_sp.append(hausdorff_dist)    \n",
    "\n",
    "hd_mean_shp_ts_sp = np.mean(hd_list_shp_ts_sp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Hausdorf Distance...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# CVAE\n",
    "model_polys = [loads(wkt) for wkt in cvae_wkt]\n",
    "hd_list_cvae_ts_sp = list()\n",
    "print(\"Calculating Hausdorf Distance...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    g_t_polys[i]=  make_valid(g_t_polys[i])\n",
    "    model_polys[i] = make_valid(model_polys[i])\n",
    "    hausdorff_dist = g_t_polys[i].hausdorff_distance(model_polys[i])\n",
    "    hd_list_cvae_ts_sp.append(hausdorff_dist)    \n",
    "\n",
    "hd_mean_cvae_ts_sp = np.mean(hd_list_cvae_ts_sp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Hausdorf Distance...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n",
      "c:\\Users\\tiago\\miniconda3\\envs\\interpol\\lib\\site-packages\\shapely\\measurement.py:224: RuntimeWarning: invalid value encountered in hausdorff_distance\n",
      "  return lib.hausdorff_distance(a, b, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# PSTG\n",
    "model_polys = [loads(wkt) for wkt in pstg_wkt]\n",
    "hd_list_pstg_ts_sp = list()\n",
    "print(\"Calculating Hausdorf Distance...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    g_t_polys[i]=  make_valid(g_t_polys[i])\n",
    "    model_polys[i] = make_valid(model_polys[i])\n",
    "    hausdorff_dist = g_t_polys[i].hausdorff_distance(model_polys[i])\n",
    "    hd_list_pstg_ts_sp.append(hausdorff_dist)    \n",
    "\n",
    "hd_mean_pstg_ts_sp = np.mean(hd_list_pstg_ts_sp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance Sampled Interpolation\n",
      "Shape based interpolation: mean Hausdorf Dist. 108.349, std 8.631, min 91.984, max 125.539 \n",
      "Conditional VAE interpolation: mean Hausdorf Dist. 85.220, std 14.827, min 52.773, max 108.853 \n",
      "Pyspatiotemporalgeom interpolation: mean Hausdorf Dist. 103.068, std 30.744, min 23.014, max 146.521 \n"
     ]
    }
   ],
   "source": [
    "print(\"Distance Sampled Interpolation\")\n",
    "print(f\"Shape based interpolation: mean Hausdorf Dist. {hd_mean_shp_ts_sp:.3f}, std {np.std(hd_list_shp_ts_sp):.3f}, min {np.min(hd_list_shp_ts_sp):.3f}, max {np.max(hd_list_shp_ts_sp):.3f} \")\n",
    "print(f\"Conditional VAE interpolation: mean Hausdorf Dist. {hd_mean_cvae_ts_sp:.3f}, std {np.std(hd_list_cvae_ts_sp):.3f}, min {np.min(hd_list_cvae_ts_sp):.3f}, max {np.max(hd_list_cvae_ts_sp):.3f} \")\n",
    "print(f\"Pyspatiotemporalgeom interpolation: mean Hausdorf Dist. {hd_mean_pstg_ts_sp:.3f}, std {np.std(hd_list_pstg_ts_sp):.3f}, min {np.min(hd_list_pstg_ts_sp):.3f}, max {np.max(hd_list_pstg_ts_sp):.3f} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Haussdorf Distance')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_ts = [hd_list_shp_ts_sp, hd_list_cvae_ts_sp, hd_list_pstg_ts_sp]\n",
    "data_pr = [hd_list_shp_pr_sp, hd_list_cvae_pr_sp, hd_list_pstg_pr_sp]\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "# left position of bars on x-axis\n",
    "left = [1, 1.5, 2]\n",
    "# right position of bars on x-axis\n",
    "right = [2.75, 3.25, 3.75]\n",
    "\n",
    "# labels for bars\n",
    "tick_label = ['Shape Based', 'C-VAE', 'Pyspatiotemporalgeom']\n",
    "\n",
    "# Create the boxplot with median annotation, showfliers=False, shape based green, cvae blue, pstg red\n",
    "boxplot_ts = ax.boxplot(data_ts,\n",
    "                        positions=left,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=2),\n",
    "                        patch_artist=True)\n",
    "\n",
    "# Create the boxplot with median annotation, showfliers=False, shape based green, cvae blue, pstg red\n",
    "boxplot_unet = ax.boxplot(data_pr,\n",
    "                        positions=right,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=2),\n",
    "                        patch_artist=True)\n",
    "\n",
    "colors = ['lightgreen', 'lightblue', 'lightcoral', 'lightgreen', 'lightblue', 'lightcoral']\n",
    "for patch, color in zip(boxplot_ts['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "for patch, color in zip(boxplot_unet['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "# Add dashed gridlines for y axis ONLY\n",
    "ax.grid(linestyle='--', linewidth=0.5, axis='y')\n",
    "# no x gridlines\n",
    "ax.grid(False, axis='x')\n",
    "\n",
    "# x labels\n",
    "ax.set_xticks([1.5, 3.25])\n",
    "ax.set_xticklabels(['Test Set', 'U-NET'])\n",
    "# increase4 font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "# add legend for each model, font size 14\n",
    "ax.legend([boxplot_ts[\"boxes\"][0], boxplot_ts[\"boxes\"][1], boxplot_ts[\"boxes\"][2]],\n",
    "            ['Shape Based', 'C-VAE', 'Pyspatiotemporalgeom'], fontsize=14)  \n",
    "# add y label\n",
    "ax.set_ylabel('Haussdorf Distance', fontsize=14)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IoU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating IoU...\n"
     ]
    }
   ],
   "source": [
    "# CVAE\n",
    "model_polys = [loads(wkt) for wkt in cvae_wkt]\n",
    "iou_list_cvae_dst_ts = list()\n",
    "print(\"Calculating IoU...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    iou = g_t_polys[i].intersection(model_polys[i]).area / g_t_polys[i].union(model_polys[i]).area\n",
    "    iou_list_cvae_dst_ts.append(iou)\n",
    "    \n",
    "iou_mean_cvae_dst_ts = sum(iou_list_cvae_dst_ts) / len(iou_list_cvae_dst_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating IoU...\n"
     ]
    }
   ],
   "source": [
    "# Shape based\n",
    "model_polys = [loads(wkt) for wkt in shp_wkt]\n",
    "iou_list_shp_dst_ts = list()\n",
    "print(\"Calculating IoU...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    iou = g_t_polys[i].intersection(model_polys[i]).area / g_t_polys[i].union(model_polys[i]).area\n",
    "    iou_list_shp_dst_ts.append(iou)\n",
    "\n",
    "iou_mean_shp_dst_ts = sum(iou_list_shp_dst_ts) / len(iou_list_shp_dst_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating IoU...\n"
     ]
    }
   ],
   "source": [
    "# Pyspatiotemporalgeom\n",
    "model_polys = [loads(wkt) for wkt in pstg_wkt]\n",
    "iou_list_pstg_dst_ts = list()\n",
    "print(\"Calculating IoU...\")\n",
    "for i in range(len(g_t_polys)):\n",
    "    iou = g_t_polys[i].intersection(model_polys[i]).area / g_t_polys[i].union(model_polys[i]).area\n",
    "    iou_list_pstg_dst_ts.append(iou)\n",
    "\n",
    "iou_mean_pstg_dst_ts = sum(iou_list_pstg_dst_ts) / len(iou_list_pstg_dst_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape based interpolation: mean IoU 0.910, std 0.021, min 0.887, max 0.964 \n",
      "\n",
      "Conditional VAE interpolation: mean IoU 0.930 , std 0.011 , min 0.889, max 0.928 \n",
      "\n",
      "Pyspatiotemporalgeom interpolation: mean IoU 0.850, std 0.038, min 0.799, max 0.960 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape based interpolation: mean IoU {iou_mean_shp_dst_ts:.3f}, std {np.std(iou_list_shp_dst_ts):.3f}, min {min(iou_list_shp_dst_ts):.3f}, max {max(iou_list_shp_dst_ts):.3f} \\n\")\n",
    "print(f\"Conditional VAE interpolation: mean IoU {iou_mean_cvae_dst_ts:.3f}, std {np.std(iou_list_cvae_dst_ts):.3f} , min {min(iou_list_cvae_dst_ts):.3f}, max {max(iou_list_cvae_dst_ts):.3f} \\n\")\n",
    "print(f\"Pyspatiotemporalgeom interpolation: mean IoU {iou_mean_pstg_dst_ts:.3f}, std {np.std(iou_list_pstg_dst_ts):.3f}, min {min(iou_list_pstg_dst_ts):.3f}, max {max(iou_list_pstg_dst_ts):.3f} \\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots: IoU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Jaccard Index')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot IoU for UNET and test set comparison\n",
    "# Combine the data into a list\n",
    "data_ts = [iou_list_cvae_dst_ts, iou_list_shp_dst_ts, iou_list_pstg_dst_ts]\n",
    "data_unet = [iou_list_cvae_dst, iou_list_shp_dst, iou_list_pstg_dst]\n",
    "\n",
    "# Set up the figure and axes~\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "\n",
    "# y-axis from 0 to 1\n",
    "ax.set_ylim(0.6, 1)\n",
    "\n",
    "# left position of bars on x-axis\n",
    "left = [1, 1.5, 2]\n",
    "# right position of bars on x-axis\n",
    "right = [2.75, 3.25, 3.75]\n",
    " \n",
    "# labels for bars\n",
    "tick_label = ['Shape Based', 'C-VAE', 'Pyspatiotemporalgeom']\n",
    "\n",
    "# Create the boxplot with median annotation, showfliers=False, shape based green, cvae blue, pstg red\n",
    "boxplot_ts = ax.boxplot(data_ts,\n",
    "                        positions=left,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=2),\n",
    "                        patch_artist=True)\n",
    "\n",
    "# Create the boxplot with median annotation, showfliers=False, shape based green, cvae blue, pstg red\n",
    "boxplot_unet = ax.boxplot(data_unet,\n",
    "                        positions=right,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=2),\n",
    "                        patch_artist=True)\n",
    "\n",
    "# Add dashed gridlines ONLY for y-axis\n",
    "ax.grid(linestyle='--', linewidth=0.5, axis='y')\n",
    "\n",
    "# x labels\n",
    "\n",
    "colors = ['lightgreen', 'lightblue', 'lightcoral', 'lightgreen', 'lightblue', 'lightcoral']\n",
    "for patch, color in zip(boxplot_ts['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "for patch, color in zip(boxplot_unet['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "# Add dashed gridlines for y axis ONLY\n",
    "ax.grid(linestyle='--', linewidth=0.5, axis='y')\n",
    "\n",
    "# x labels\n",
    "ax.set_xticks([1.5, 3.25])\n",
    "# right position of bars on x-axis\n",
    "ax.set_xticklabels(['Test Set', 'U-NET'])\n",
    "# increase4 font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "# add legend for each model, font size 14\n",
    "ax.legend([boxplot_ts[\"boxes\"][0], boxplot_ts[\"boxes\"][1], boxplot_ts[\"boxes\"][2]],\n",
    "            ['Shape Based', 'C-VAE', 'Pyspatiotemporalgeom'], fontsize=14)  \n",
    "# add y label\n",
    "ax.set_ylabel('Jaccard Index', fontsize=14)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Consistency (Distance Sampled Interpolations)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponetial Strided TC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs file...\n",
      "Calculating the temporal consistency with stride 1...\n",
      "Conditional VAE interpolation: mean TC 9.9998e-01 with stride 1 \n",
      "\n",
      "Calculating the temporal consistency with stride 10...\n",
      "Conditional VAE interpolation: mean TC 9.9989e-01 with stride 10 \n",
      "\n",
      "Calculating the temporal consistency with stride 100...\n",
      "Conditional VAE interpolation: mean TC 9.9980e-01 with stride 100 \n",
      "\n",
      "Calculating the temporal consistency with stride 1000...\n",
      "Conditional VAE interpolation: mean TC 9.9967e-01 with stride 1000 \n",
      "\n",
      "Calculating the temporal consistency with stride 10000...\n",
      "Conditional VAE interpolation: mean TC 9.9657e-01 with stride 10000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# CVAE\n",
    "print(\"Reading WKTs file...\")\n",
    "with open(CVAE_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    model_wkt = f.read().splitlines()\n",
    "\n",
    "# delete lines with number = idx_sampled (one liner)\n",
    "model_wkt = [item for i, item in enumerate(model_wkt) if i not in idx_sampled]\n",
    "num_polygons = len(model_wkt)\n",
    "\n",
    "# read WKTS file lines and convert to list of shapely polygons\n",
    "polygons = [loads(wkt) for wkt in model_wkt]\n",
    "polygons = polygons[:num_polygons]\n",
    "tc_cvae_list_sp = list()\n",
    "\n",
    "\n",
    "for str in strides:\n",
    "    t_c = strided_temporal_consistency(polygons, num_polygons, str, exp = False)\n",
    "    print(f\"Conditional VAE interpolation: mean TC {t_c['tc_mean']:.4e} with stride {t_c['strd']} \\n\")\n",
    "    tc_cvae_list_sp.append(t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs file...\n",
      "Calculating the temporal consistency with stride 1...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.9974e-01 with stride 1 \n",
      "\n",
      "Calculating the temporal consistency with stride 10...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.9751e-01 with stride 10 \n",
      "\n",
      "Calculating the temporal consistency with stride 100...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.8103e-01 with stride 100 \n",
      "\n",
      "Calculating the temporal consistency with stride 1000...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.4883e-01 with stride 1000 \n",
      "\n",
      "Calculating the temporal consistency with stride 10000...\n",
      "Pyspatiotemporalgeom interpolation: mean TC 9.8951e-01 with stride 10000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Pyspatiotemporalgeom\n",
    "print(\"Reading WKTs file...\")\n",
    "with open(PSTG_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    model_wkt = f.read().splitlines()\n",
    "\n",
    "# delete lines with number = idx_sampled (one liner)\n",
    "model_wkt = [item for i, item in enumerate(model_wkt) if i not in idx_sampled]\n",
    "num_polygons = len(model_wkt)\n",
    "\n",
    "# read WKTS file lines and convert to list of shapely polygons\n",
    "polygons = [loads(wkt) for wkt in model_wkt]\n",
    "polygons = polygons[:num_polygons]\n",
    "tc_pstG_list_sp = list()\n",
    "\n",
    "for str in strides:\n",
    "    t_c = strided_temporal_consistency(polygons, num_polygons, str, exp = False)\n",
    "    print(f\"Pyspatiotemporalgeom interpolation: mean TC {t_c['tc_mean']:.4e} with stride {t_c['strd']} \\n\")\n",
    "    tc_pstG_list_sp.append(t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs file...\n",
      "Calculating the temporal consistency with stride 1...\n",
      "Shape based interpolation: mean TC 9.9998e-01 with stride 1 \n",
      "\n",
      "Calculating the temporal consistency with stride 10...\n",
      "Shape based interpolation: mean TC 9.9980e-01 with stride 10 \n",
      "\n",
      "Calculating the temporal consistency with stride 100...\n",
      "Shape based interpolation: mean TC 9.9802e-01 with stride 100 \n",
      "\n",
      "Calculating the temporal consistency with stride 1000...\n",
      "Shape based interpolation: mean TC 9.8471e-01 with stride 1000 \n",
      "\n",
      "Calculating the temporal consistency with stride 10000...\n",
      "Shape based interpolation: mean TC 9.8897e-01 with stride 10000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Shape based\n",
    "print(\"Reading WKTs file...\")\n",
    "with open(SHP_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    model_wkt = f.read().splitlines()\n",
    "\n",
    "# delete lines with number = idx_sampled (one liner)\n",
    "model_wkt = [item for i, item in enumerate(model_wkt) if i not in idx_sampled]\n",
    "num_polygons = len(model_wkt)\n",
    "\n",
    "# read WKTS file lines and convert to list of shapely polygons\n",
    "polygons = [loads(wkt) for wkt in model_wkt]\n",
    "polygons = polygons[:num_polygons]\n",
    "tc_shp_list_sp = list()\n",
    "\n",
    "for str in strides:\n",
    "    t_c = strided_temporal_consistency(polygons, num_polygons, str, exp = False)\n",
    "    print(f\"Shape based interpolation: mean TC {t_c['tc_mean']:.4e} with stride {t_c['strd']} \\n\")\n",
    "    tc_shp_list_sp.append(t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CVAE sampled mean: 9.9918e-01, sd: 1.3102e-03, min: 9.9657e-01, max: 9.9998e-01\n",
      "PSTG sampled mean: 9.8332e-01, sd: 1.8461e-02, min: 9.4883e-01, max: 9.9974e-01\n",
      "SHP sampled mean: 9.9430e-01, sd: 6.2732e-03, min: 9.8471e-01, max: 9.9998e-01\n"
     ]
    }
   ],
   "source": [
    "# print mean, std, min, max (compactly)\n",
    "print(f\"CVAE sampled mean: {np.mean([tc_cvae_list_sp[i]['tc_mean'] for i in range(0, len(tc_cvae_list_sp))]):.4e}, sd: {np.std([tc_cvae_list_sp[i]['tc_mean'] for i in range(0, len(tc_cvae_list_sp))]):.4e}, min: {np.min([tc_cvae_list_sp[i]['tc_mean'] for i in range(0, len(tc_cvae_list_sp))]):.4e}, max: {np.max([tc_cvae_list_sp[i]['tc_mean'] for i in range(0, len(tc_cvae_list_sp))]):.4e}\")\n",
    "print(f\"PSTG sampled mean: {np.mean([tc_pstG_list_sp[i]['tc_mean'] for i in range(0, len(tc_pstG_list_sp))]):.4e}, sd: {np.std([tc_pstG_list_sp[i]['tc_mean'] for i in range(0, len(tc_pstG_list_sp))]):.4e}, min: {np.min([tc_pstG_list_sp[i]['tc_mean'] for i in range(0, len(tc_pstG_list_sp))]):.4e}, max: {np.max([tc_pstG_list_sp[i]['tc_mean'] for i in range(0, len(tc_pstG_list_sp))]):.4e}\")\n",
    "print(f\"SHP sampled mean: {np.mean([tc_shp_list_sp[i]['tc_mean'] for i in range(0, len(tc_shp_list_sp))]):.4e}, sd: {np.std([tc_shp_list_sp[i]['tc_mean'] for i in range(0, len(tc_shp_list_sp))]):.4e}, min: {np.min([tc_shp_list_sp[i]['tc_mean'] for i in range(0, len(tc_shp_list_sp))]):.4e}, max: {np.max([tc_shp_list_sp[i]['tc_mean'] for i in range(0, len(tc_shp_list_sp))]):.4e}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots: Temporal Consistency (Distance Sampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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jRw999dVXevXVV/Xmm29and+xY4ckad26dVY/fCdJJUuWzDJfy5z3ulnmDwMmJCRYnatdu7bVXFqJEiV09913W7TP65yXJD311FN69913NXv2bAUHB0uSdu3apd27d6tPnz5ZFqYBAMUDBVg4BG9vb4WFhSksLEySdPnyZUVGRmrWrFkaNGiQzpw5Y/EW681vpt6sRIkSFsVVSXr++ec1c+ZMVa5cWaGhoapQoYJ5qZU33nhD165dy7KvrPY4yzx285sWf/75p9LS0vTGG29k+3zJycnZnsvNfbO79+OPP66vv/5atWvXVs+ePVWuXDmVLFlSCQkJ+t///mfxbFWrVtWOHTs0fvx4rV692rx2ed26dTVhwgTzd//nn39K+nu5wcwlB3N6psx4ypUrl6fnAQAAjqF8+fI6cOCAzpw5ozp16tyy/Z49e6zyorZt25oLsA8//LDuvvtuLV++XH/99ZdKly6tTz75RGlpaYqIiLB4c+Hq1atavHixvLy89Nhjj1n02b9/f02aNEnz5s3LsgALAABQnJUtW1bdunXT0qVLFRYWpt9//10zZszIsm3m3E7FihXzdI8tW7bIzc1NXbp0yfJ85hzTW2+9lad+S5UqZXUsc+us9PT0XLXPvObm9nmd85L+njdr27atli9frkuXLqlMmTLmHxBk+WEAKN5YghgOycfHRzNnzlSVKlUUHx+vffv25aufCxcu6P3331ejRo104MABLViwQJMnT9b48eM1dOjQHK89f/58tsduLgCXKlVKZcqUkWEY2X6OHTuW65izum9W9965c6e+/vprhYSE6LffftPs2bP11ltvafz48dnuadagQQN9/vnn+vPPP/Xdd9/p9ddf17lz59SzZ09z4pmZtI4cOTLHZxo3bpxFPBcuXMjT8wAAAMeQuZ98Vm8tZCUiIsIqb7h5NYwSJUqof//+5uKqJM2fP18mk0lPPvmkRV9ffPGFEhISlJSUJE9PT5lMJvOnbt26kqQff/xRe/fuLYAnBQAAKFoGDRqkxMRERUREyM3NzWrrqEyZcztnzpzJU/8bNmyQh4eHOnXqpO3bt1udz5xjSkxMzHGO6U7J65xXpqFDh+ratWv65JNPlJKSoiVLlqhWrVoKCgq6Y7EDAOwPBVg4LJPJZLGnan4cPXpUhmGoQ4cO8vDwsDi3devWHK/N6nzmsfvuu898rGXLlrp06VKO+9Xmxe7du5WUlHTLex85ckSS1LVrV6t9YG/1bCVLllSrVq30xhtv6L333pNhGFq5cqUkqXnz5jKZTFZLOWencePGkqRt27ZZnUtKStKePXty1Q8AALBPERERcnZ21scff6yLFy/m2Da7lUX+LXMPr3nz5un777/Xb7/9pnbt2lltjTB37lxJUlhYmAYNGmT1yVyuOLMdAAAA/hESEqKKFSvqzJkz6t69u0qXLp1lOy8vL9WrV0/Hjh3L0/zWfffdp40bN8rFxUWdOnWyequ0ZcuWkv5ZitjW8jrnlemxxx5T2bJlNWfOHEVHR+vy5ctWe8gCAIofCrCwax999JF27tyZ5bnly5dr//798vX1VYMGDfLVf5UqVSRJ27dvt1ia+PTp0xozZkyO106cONFiud/Lly/rzTfflMlk0oABA8zHn3/+eUl/TyReunTJqp9z585p//79uY45ISHBammWzL0yGjRoYN4HI/PZ/l34/PXXXzV58mSrfnft2qXExESr45lvqLq5uUn6e5nB8PBwbd++Xe+8806WP4n4/fffKyUlRZIUEBCgwMBA7d271/wWS6ZJkyZluTcHAABwHDVr1tSoUaMUHx+vzp07Z7myx9WrVzVt2rQs99LKSt26ddW6dWvt3r1bI0eOlPT3Gxo3O3bsmDZt2qSqVatq2bJlmjNnjtVn2bJlcnd316effprr4i8AAEBx4ezsrOXLl+vLL7/Mcq7oZs8++6zS09P1zDPPKDU11eLc1atXzcv3/lvjxo21ceNGubq6qlOnThbzVM8884xKlCih5557TidPnrS6NiEhQbt3787Hk+VPXue8Mrm4uCgiIkK//fabIiMjVbJkSfP2GgCA4os9YGHX1qxZo6FDh6pmzZpq3bq1/P39lZycrN27d2vr1q1ycnLSrFmzzHu25lWFChXUo0cPxcTEqFmzZgoODtb58+e1cuVKBQcHm98izUrt2rXVoEED9ejRQ5IUExOj06dPa8SIEWrWrJm5XadOnTR27FhNnDhRNWvWVKdOnVSlShVdunRJhw8f1tatW/Xmm2/qnnvuyVXMDz74oD744AN9//33atWqlY4fP67o6Gi5u7ub95iQpBYtWqhFixaKiorS2bNn1apVK508eVIrVqxQ165d9fnnn1v0u2jRIn300UcKDAxUjRo1VKpUKf32229avXq17rrrLosl/2bNmqXff/9do0aN0qJFi3T//ffL19dXp06d0o8//qhDhw7p7Nmz5reK33//fbVu3Vr9+/fX8uXLVatWLf3www/auXOnHnzwwVu+kQsAAOzbm2++qatXr+rdd99VnTp11L59ezVo0EAlS5bUsWPH9O233+rSpUt68803c93noEGDzPtv+fr6Wu3xOm/ePBmGoQEDBshkMmXZh4+Pjx599FF99tlnWr58uXr27Gk+Fx8fn2NBeOjQoSpfvnyu4wUAFIyUlBQdOHAgT9dk/lBzXn64+WZ169a1WhULKC6aNWtmMY+VnWHDhmnz5s2KiopSrVq1FBoaqlKlSunkyZNat26d5s6dq+7du2d5baNGjbRx40YFBwerc+fOWr16tR588EE1aNBAs2bN0rBhw1SnTh116dJFNWrU0JUrV3T06FFt3rxZERER+vDDDwv4qbOX1zmvTE8//bSmTp2qP/74Qz169FC5cuXuWMwAAPtEAbaIOH/QvvbRLKh43n77bbVu3VrffPONtmzZorNnz0qSKlasqAEDBui5554zv/GZXwsWLFDVqlUVExOjGTNmKCAgQCNGjNDo0aOtipQ3i4qK0rhx47RkyRKdP39e1apV03vvvafhw4dbtZ0wYYICAwP13nvvacOGDUpISFCZMmVUrVo1jR8/Pts9NrJSvXp1ffDBBxo1apTef/99paenKygoSFOmTLH4LpydnbVy5Uq98sorWrt2rXbu3KlatWpp6tSp6ty5s9Wz9e7dW1evXlVcXJx++OEHXbt2TZUqVdKwYcP08ssvKyAgwNz2rrvu0vbt2zVz5kwtW7ZMixcvVkZGhsqXL6/GjRtr7Nix8vPzM7dv0KCB4uLiNHr0aK1du1br1q1TmzZtFBcXp6lTp1KABQAUC6ePHrZ1CBYKMh4nJydNmzZNffr00QcffKAtW7Zoy5YtysjIUIUKFRQSEqInn3xSHTp0yHWf4eHheuGFF3TlyhX16dPHvBqHJGVkZGjBggVWK49k5cknn9Rnn32muXPnWhRgL126pDfeeCPb67p3704BFgBs4MCBA/n+e37fvn3zdd2uXbvUpEmTfF2LouVgfLytQ7BiLzGZTCYtXbpUHTt21Jw5c/TJJ5/IMAxVrFhR4eHht/xz27BhQ6sibGBgoIYMGaJ7771X06ZN05YtW/T111/Lx8dHAQEBevHFF2+Z6xW0vM55ZapRo4Zat26tbdu2aciQIXc0ZgCAfTIZd3Inc0iSkpOT5eXlJenvPTBz2sf0p59+UtOmTbP9y8DJkydV9566Sk1JzeJq23L3cNeB/QcsCndFQVBQkDZv3pzlMiQoXLf68wAAyL285CPFUUHna/fcc4/VUmX2wMPDQ/v37y9y+dqdQm4CAAUvP2/Apqam6vjx46patarc3d3zfE97fQOWfC1nBZ6v1a2rlFT7m1+TJA93d+0/UPTm2IqSq1evqlKlSvLy8tLRo0fl5JT7nf/IKYG8488N7EVO+QhvwDq4gIAAHdh/QPF28tNwN/Pz8yMxBAAAxV5AQID2799PvgYAQC54eHjkayK1devWhRANiouAgADtP2Cf82sSOZsjmD9/vi5duqSRI0fmqfgKACi6KMDaWFhYmPr166fevXvnu4+AgACSMAAAkCdRUVG2DsFhkK8BAABbIF+7s8jXkB9TpkzRxYsX9dFHH6lcuXJ65plnbB0SAMBOUIC1sejoaJaQAQAAd1x4eLgGDhxo6zAcAvkaAACwBfI1wP6NGTNGJUuWVOPGjTVjxgz5+PjYOiQAgJ2gAAvkUWxsrK1DAAAAAAAAAGBjhmHYOgQAgJ1iQXoAAAAAAAAAAAAAKCAUYAEAAAAAAAA4rLCwMC1ZssTWYQAAgGImKioq23MUYAEAAAAAAAA4rOjoaPXu3dvWYQAAgGImPDw823MUYO2cyWSSJKWnp9s4EsD2Mv8cODnxny4AgP0gXyu+yE0AAHAM5GuwZ+SUAFA0lbB1AMhZ2bJlJUknTpxQ8+bNbRwNYFvHjx+X9M+fCwAA7AH5WvFFbgIAgGMgX4M9I6dEcZaSkqIDBw7k+br9+/db/JoXdevWlYeHR56vA/LKZBiGYesgipvk5GR5eXlJkpKSkuTp6Zlj+8aNG6tSpUr6+uuv+UkoFFsZGRnq1q2bzpw5oz179tg6HABweHnNR4ob8jXcCrkJAKCwka/ljHwNRQE5JYq7n376SU2bNr2j99y1a5eaNGlyR++JoiunfIQ3YB3Aq6++ql69eqlbt26KiIhQ1apV5ezsbOuwgDsiPT1dx48f14IFC7RmzRotXbrU1iEBAGCFfK34IDcBAMAxka/BnpBTAn+rW7eudu3alefrUlNTdfz4cVWtWlXu7u55vidwJ1CAdQCZm/hOmjQpxw19gaKscePGWrp0KX8GAAB2iXyt+CE3AQDAsZCvwR6RU6K48/DwyPfbqK1bty7gaICCxRLENnA7S8icOXNGFy9eVEZGRmGFB9gVJycnlS1bVhUrVrR1KABQpLCkXc7I15AdchMAwJ1CvpYz8jU4MnJKACgaWIK4CKlYsSL/YwYAALBj5GsAAAD2jXwNAAAUNrvdcX7nzp3q0qWLfH195enpqVatWikqKipPfezfv19PPPGEypcvL1dXV1WpUkUvvPCC/vzzzyzbm0ymbD8RERFZXpOYmKgRI0aoSpUqcnV1VdWqVfXyyy8rKSkpr48MAADgUGyRr129elUTJ05UvXr15ObmptKlS6tz586Ki4vLsn1ERESOOR4AAEBRVhD52h9//KEXXnhB9erVk6enp+6++261adNGixYtUnp6ulV75tcAAADs9A3YTZs2KSQkRG5uburVq5e8vb0VExOjnj176tSpUxo5cuQt+9ixY4c6dOig1NRUPfLII6pRo4b27Nmj9957T2vXrtX27dtVpkwZq+uqVKmSZTJ47733Wh1LTk5W27ZttWfPHnXs2FG9e/fW7t27NXXqVG3evFlbtmyRm5tbfr4CAAAAu2aLfO3q1asKDg7W9u3b1ahRIw0bNkwJCQmKiYlR27ZtFRMTo0ceeSTLe73wwgvy9fUtqMcHAACwewWRrx09elQtW7bUpUuXFBISom7duikxMVHLly9X//79tXHjRs2fP9/qOubXAABAsWfYmRs3bhg1atQwXF1djd27d5uPJyQkGLVr1zZcXFyM48eP37KfBg0aGJKMr776yuL4//3f/xmSjKefftrqGklG27Ztcx3r66+/bkgyRo8ebXF89OjRhiRj0qRJWV6XlJRkSDIkGUlJSbm+HwAAQEG5nXzEVvnaO++8Y0gywsLCjLS0NPPxw4cPG6VKlTLKli1rJCYmWlwzYMAAQ5Jx7NixPD0j+RoAALA1e8jXhg0bZkgypk+fbnH8r7/+MgICAgxJVv0wvwYAAIqLnPIRu1uCeOPGjTpy5Ij69Olj8VNxPj4+ioyM1PXr17Vw4cIc+zhy5Ih++eUXNW/eXKGhoRbnRo4cqTJlymjRokVKTk7Od5yGYWjOnDny8vLS2LFjLc6NHTtWXl5emjNnTr77d3RLliyxdQjII8bMMTFujolxczyMmSVb5WtfffWVJGn8+PFydnY2H69Ro4YGDhyoixcv6vPPPy+AJywe+Pfa8TBmjolxc0yMm+NhzCwVRL4m/f0GrCR16dLF4rivr6/atGkjSYqPj893nMyv5Yx/rx0T4+Z4GDPHxLg5nuI2ZnZXgI2NjZUkdezY0epcSEiIJGnz5s059nHu3DlJUrVq1azOOTk5KSAgQCkpKdqxY4fV+YSEBH388ceaNGmSPvzwQ+3bty/Lexw6dEh//PGHWrduLU9PT4tznp6eat26tY4ePapTp07lGGtRVdz+IBUFjJljYtwcE+PmeBgzS7bK13K6JvPYxo0bs7zfypUrNXnyZE2bNk1r1qzR9evXc4yvOODfa8fDmDkmxs0xMW6OhzGzVBD5miQ1aNBAkrR69WqL4wkJCYqLi1P58uVVr149q+uYXysY/HvtmBg3x8OYOSbGzfEUtzGzuz1gDx06JEmqVauW1bny5cvLy8vL3CY7fn5+kqRjx45ZncvIyNDJkyclSQcPHlRwcLDF+Z9//llPP/20xbFOnTpp4cKFKleuXK7izDy+bt06HTp0SJUrV7Y4ZxiG+Z9z+xZuVFSUwsPDc9U2Lwqr37S0tNt6wzg7jvY9OFK/jJlj9su4OWa/jjRujvbdMma57/fm57k5N8kNW+Vrfn5+Onz4sI4dO2Y10ZfZz8GDB7O833PPPWfx+woVKmj+/PnmCch/I1/LP0f7HhypX8bMMftl3ByzX0caN0f7bhmz3Pdr63xNkl5++WV9/fXXevHFF7V27Vo1atTIvAesh4eHvvzyS7m7u1tdx/xawSiK/14Xh34LY9wc7TtwtH75s+aY/TJujhWrVDTHLMd87Q4vh3xLDz30kCHJOHToUJbn/f39jVKlSuXYR0ZGhlG9enVDkrFy5UqLc//973/N6zH/ew+JkSNHGtu3bzfi4+ONxMREY/v27Ubnzp0NSUbz5s0t9hpbvHixIcl49dVXs4whMjLSkGR88cUXVufOnz9vjoEPHz58+PDhw8fWn/Pnz+eYW/2brfK1N954w5Bk9OzZ0yIvO3r0qOHj42NIMmrXrm3R19y5c42oqCjj5MmTRmpqqnHo0CFjwoQJhru7u+Hi4mLs3Lkzy/jI1/jw4cOHDx8+9vSxRb6W6dKlS+b5scyPu7u7MX78eCMlJcWqPfNrfPjw4cOHD5/i+Pl3vmZ3b8AWBJPJpFmzZqlbt24KDQ1V9+7dVaNGDf38889av369GjZsqH379snJyXIF5qlTp1r8/v7779fKlSvVvn17bd68WV999ZUee+yxO/koAAAARVJ+8rUXX3xRy5Yt07Jly3TgwAG1b99eCQkJiomJUdWqVbV3716r/G7gwIEWv69Zs6bGjh2rihUratCgQZowYYJWrFhxR54ZAADA0Rw+fFjdunWTl5eXtm7dqnvvvVcJCQn69NNP9dprr2ndunXaunWrnJ2dzdcwvwYAAGCHSxD7+PhIki5fvpzl+cTERJUuXfqW/YSEhGjr1q2aOHGiNm7cqFWrVqlBgwb68ssvtWHDBu3bt89iyZPsODk5aciQIdq8ebPi4uLMCWJu4ry53c38/Px0/vx5SZKHh4dMJtMt4wAAAChIhmEoJSVF0j/LAeeWrfI1b29vxcXFacKECfryyy81c+ZMlStXTkOHDtXDDz+swMDAXOV3kjRgwAA9++yziouLy/I8+RoAALA1e8jXIiIidOLECR09elTly5eXJHl5eemVV17R+fPnNX36dC1dulRPPPFEjv0wvwYAAIqinPI1uyvAZu75cOjQITVt2tTi3Llz55SUlKQWLVrkqq+WLVtq5cqVVsenT58uSWrWrFmu+sn80m5ey/nmOLOS0x4WTk5OuZ4cBAAAKCxeXl75us6W+Zqvr6+mTZumadOmWRxfsGBBlu2z4+zsLF9fX/31119ZnidfAwAA9sCW+dqVK1cUFxenJk2amIuvN2vXrp2mT5+u3bt337IAKzG/BgAAiqbs8jWnLI/aUNu2bSVJ69evtzq3bt06izb5ceLECW3btk316tVTw4YNc3XN999/L0mqWrWq+VitWrXk7++vuLg4q02Dk5OTFRcXp2rVqqly5cr5jhUAAMAe2WO+tnjxYklSr169ctX+5MmTOnfunEV+BwAAUFQURL52/fp1SVJ8fHyW5y9evChJcnV1zVVMzK8BAIDixO4KsMHBwapevbo+++wz7dmzx3z88uXLmjRpklxcXNS/f3/z8bNnz+rAgQNWS5UkJSXJMAyLY5cvX1a/fv2Unp6uyZMnW5zbt2+fbty4YRXP9u3b9fbbb6tkyZIKCwszHzeZTBo8eLCSkpI0ceJEi2smTpyopKQkDRkyJM/PDwAAYO9sla9J/yxDd7N3331X3377rR599FE1b97cfPzcuXM6c+aMVfuEhARFRERIkvr06ZOrZ3Z0v//+u2bMmKGIiAg1bNhQJUqUkMlk0ptvvmnr0Iq92xmbb7/9Vl26dJGfn5/c3d1Vt25dvfrqq0pKSroDkRdtthiXw4cPKyIiQpUqVZKrq6sqVaqkiIgIHT16tKAeq0hwpLG5cuWKIiMjVadOHbm7u8vPz09du3bVxo0b8/TMjqA4jEtGRoY++ugjtWzZUt7e3vL29lbLli318ccfW+Uz9qAg8rUyZcqoTp06OnnypObMmWPRf0JCgnmv13bt2pmPM78GAADw/xl2aOPGjUbJkiUNb29vY8iQIcaIESOMKlWqGJKMqVOnWrQdMGCAIcmYP3++xfFFixYZlStXNgYMGGCMGTPGGDRokFG2bFlDkjFx4kSrew4YMMDw8/Mzunfvbjz33HPGiBEjjJCQEMNkMhlOTk7GBx98YHVNUlKS0bhxY0OS0bFjR+OVV14xOnbsaEgymjdvbqSkpBTo92LPFi1aZDz11FNG06ZNDRcXlyzHBLaR17G5fPmy8eKLLxoBAQGGi4uLUaVKFeOll14yrly5cueCLkYKe3zS09ON9957z2jQoIHh5uZm+Pn5Gb169TKOHDlSSE9UdNjj2Kxdu9YIDAw0vLy8DG9vbyMoKMj49ttvb/dRHVJRGJ/ff//dCAsLM8qUKWO4ubkZjRo1MmbNmmVkZGTk+nuwJVvka4ZhGF5eXka3bt2MESNGGCNHjjSaNm1qSDKaNWtm/PnnnxZtN23aZJQoUcIIDAw0Bg0aZLzyyivGE088YZQpU8aQZLRv395ITU0t0O/FXr3wwguGJKtPdt8z7pz8js20adMMSYbJZDICAwONsLAwo3z58oYko06dOsbFixfv0BMUTXd6XLZt22Z4eHgYkoz69esbPXv2NOrXr29IMjw9PY3vvvuuMB7TITnK2Jw/f96oXbu2IcmoUKGCERYWZgQGBhomk8kwmUzGe++9d9vfhT0p6uOSlpZmPPbYY4Ykw8PDw+jWrZvRrVs3w93d3ZBkhIWFGenp6Xn70u6AgsjXVq9ebZQoUcKQZAQHBxsvvfSSRc7Wo0cPq36YX8s/5tfsF/Nr9s0e5wjwN3scG+bX/lEUxsee59fssgBrGIbx/fffG506dTJKlSpluLu7Gy1atDCWLl1q1S67BHHPnj3Gww8/bFSoUMEoWbKk4efnZzz88MPGxo0bs7zfF198YTzyyCNGtWrVDE9PT6NkyZJG5cqVjd69exvff/99tnEmJCQY//nPf4zKlSsbJUuWNAICAoyRI0caiYmJt/X8jiYzgffz8zP/MwmifcjL2CQlJRn33nuv+S89o0ePtvhLT3GZpL6TCnt8Bg8ebJ6UGDVqlNG3b1/DxcXFuOuuu4yDBw8W8tM5Nnsbm0WLFhmSjLJlyxrDhw83hg8fbpQtW9YwmUxGdHR0QT++3XP08fn1118NHx8fw8XFxejbt68xatQo86Th8OHDb/v7uVPudL5mGIYxdOhQo06dOoaHh4fh6elp3HfffcY777xjXL161artyZMnjcGDBxuNGzc2ypQpY5QoUcLw9fU1AgMDjQ8//NBIS0u77e/AUcyePdt46aW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      "text/plain": [
       "<Figure size 1900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# combine the data into a list and orders it according to the stride values\n",
    "data_shp = [tc_shp_list_sp[0]['tc'], tc_shp_list_sp[1]['tc'], tc_shp_list_sp[2]['tc'], tc_shp_list_sp[3]['tc'], tc_shp_list_sp[4]['tc']]\n",
    "data_cvae = [tc_cvae_list_sp[0]['tc'], tc_cvae_list_sp[1]['tc'], tc_cvae_list_sp[2]['tc'], tc_cvae_list_sp[3]['tc'], tc_cvae_list_sp[4]['tc']]\n",
    "data_pstG = [tc_pstG_list_sp[0]['tc'], tc_pstG_list_sp[1]['tc'], tc_pstG_list_sp[2]['tc'], tc_pstG_list_sp[3]['tc'], tc_pstG_list_sp[4]['tc']]\n",
    "\n",
    "plt.rcParams[\"axes.edgecolor\"] = \"black\"\n",
    "plt.rcParams[\"axes.linewidth\"] = 1.5\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, axs = plt.subplots(nrows=1, ncols=3, sharex=True, figsize=(19, 6))\n",
    "\n",
    "# Create the first boxplot for data_shp\n",
    "pos = [1, 2, 3, 4, 5]\n",
    "boxplot1 = axs[0].boxplot(data_shp,\n",
    "                        positions=pos,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=1),\n",
    "                        patch_artist=True)\n",
    "\n",
    "# # create anottation for median value for each boxplot\n",
    "for i in range(len(pos)):\n",
    "    axs[0].text(pos[i], 1.002, f\"x̃ = {np.median(data_shp[i]):.3f}\", horizontalalignment='center' ,fontsize=13)\n",
    "\n",
    "# # TypeError: 'numpy.int32' object is not callable\n",
    "\n",
    "# ax.annotate(\"?\", \n",
    "#              xy=(0, 1), \n",
    "#              xytext=(3, 1.5),\n",
    "#              arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "boxplot2 = axs[1].boxplot(data_cvae,\n",
    "                        positions=pos,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=1),\n",
    "                        patch_artist=True)\n",
    "\n",
    "for i in range(len(pos)):\n",
    "    axs[1].text(pos[i], 1.0002, f\"x̃ = {np.median(data_cvae[i]):.3f}\", horizontalalignment='center',fontsize=13)\n",
    "\n",
    "\n",
    "boxplot3 = axs[2].boxplot(data_pstG,\n",
    "                        positions=pos,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=1),\n",
    "                        patch_artist=True)\n",
    "\n",
    "for i in range(len(pos)):\n",
    "    axs[2].text(pos[i], 1.005, f\"x̃ = {np.median(data_pstG[i]):.3f}\", horizontalalignment='center', fontsize=13)\n",
    "\n",
    "\n",
    "ticks0 = np.arange(0.85, 1.01, 0.01)\n",
    "axs[0].set_yticks(ticks0)\n",
    "axs[0].grid(which='major', alpha=0.9, linewidth=1, axis='y')\n",
    "axs[0].tick_params(labelsize=14)\n",
    "axs[0].set_ylabel('Temporal Consistency', fontsize=18)\n",
    "axs[0].set_ylim(0.95, 1.005)\n",
    "\n",
    "# change the color of the boxplot\n",
    "colors = ['lightgreen'] *5\n",
    "for patch, color in zip(boxplot1['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "    \n",
    "\n",
    "# y axis decimal places for all subplots\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "axs[0].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "axs[1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "axs[2].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "\n",
    "ticks1 = np.arange(0.995, 1.001, 0.001)\n",
    "axs[1].set_yticks(ticks1)\n",
    "axs[1].grid(which='major', alpha=0.9, linewidth=1, axis='y')\n",
    "axs[1].tick_params(labelsize=14)\n",
    "axs[1].set_xticks(pos);\n",
    "axs[1].set_xticklabels([ '1', '10', '100', '1000', '10000'], fontsize=16);\n",
    "axs[1].set_xlabel('Stride', fontsize=16)\n",
    "axs[1].set_ylim(0.995, 1.0005)\n",
    "\n",
    "# change the color of the boxplot\n",
    "colors = ['lightblue'] *5\n",
    "for patch, color in zip(boxplot2['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "ticks2 = np.arange(0.850, 1.002, 0.025)\n",
    "axs[2].set_yticks(ticks2)\n",
    "\n",
    "axs[2].grid(which='major', alpha=0.9, linewidth=1, axis='y')\n",
    "axs[2].tick_params(labelsize=14)\n",
    "axs[2].set_ylim(0.85, 1.015)\n",
    "colors = ['lightcoral'] *5\n",
    "for patch, color in zip(boxplot3['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "# Add legend for each subplot. plot 1: shape based, plot 2: conditional VAE, plot 3: Pyspatiotemporalgeom\n",
    "axs[0].legend([boxplot1[\"boxes\"][0], boxplot1[\"boxes\"][1], boxplot1[\"boxes\"][2]],\n",
    "['Shape based'],  fontsize=14, framealpha=1, frameon=True, facecolor='white', edgecolor='black', fancybox=True, loc='lower left')\n",
    "\n",
    "axs[1].legend([boxplot2[\"boxes\"][0], boxplot2[\"boxes\"][1], boxplot2[\"boxes\"][2]],\n",
    "['C-VAE'],  fontsize=14, framealpha=1, frameon=True, facecolor='white', edgecolor='black', fancybox=True, loc='lower left')\n",
    "\n",
    "axs[2].legend([boxplot3[\"boxes\"][0], boxplot3[\"boxes\"][1], boxplot3[\"boxes\"][2]],\n",
    "['Mckenney'],  fontsize=14, framealpha=1, frameon=True, facecolor='white', edgecolor='black', fancybox=True, loc='lower left')\n",
    "\n",
    "    \n",
    "# Add some space between the subplots and the bottom of the figure\n",
    "fig.subplots_adjust(hspace=0.5, bottom=0.1)\n",
    "\n",
    "axs[0].grid(False, axis='x')\n",
    "axs[1].grid(False, axis='x')\n",
    "axs[2].grid(False, axis='x')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# combine the data into a list and orders it according to the stride values\n",
    "data_shp = [tc_shp_list[0]['tc'], tc_shp_list[1]['tc'], tc_shp_list[2]['tc'], tc_shp_list[3]['tc'], tc_shp_list[4]['tc']]\n",
    "data_cvae = [tc_list[0]['tc'], tc_list[1]['tc'], tc_list[2]['tc'], tc_list[3]['tc'], tc_list[4]['tc']]\n",
    "data_pstG = [tc_pstg_list[0]['tc'], tc_pstg_list[1]['tc'], tc_pstg_list[2]['tc'], tc_pstg_list[3]['tc'], tc_pstg_list[4]['tc']]\n",
    "\n",
    "plt.rcParams[\"axes.edgecolor\"] = \"black\"\n",
    "plt.rcParams[\"axes.linewidth\"] = 1.5\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, axs = plt.subplots(nrows=1, ncols=3, sharex=True, figsize=(19, 6))\n",
    "\n",
    "# Create the first boxplot for data_shp\n",
    "pos = [1, 2, 3, 4, 5]\n",
    "boxplot1 = axs[0].boxplot(data_shp,\n",
    "                        positions=pos,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=1),\n",
    "                        patch_artist=True)\n",
    "\n",
    "# # create anottation for median value for each boxplot\n",
    "for i in range(len(pos)):\n",
    "    axs[0].text(pos[i], 1.0016, f\"x̃ = {np.median(data_shp[i]):.3f}\", horizontalalignment='center' ,fontsize=13)\n",
    "\n",
    "# # TypeError: 'numpy.int32' object is not callable\n",
    "\n",
    "# ax.annotate(\"?\", \n",
    "#              xy=(0, 1), \n",
    "#              xytext=(3, 1.5),\n",
    "#              arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "boxplot2 = axs[1].boxplot(data_cvae,\n",
    "                        positions=pos,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=1),\n",
    "                        patch_artist=True)\n",
    "\n",
    "for i in range(len(pos)):\n",
    "    axs[1].text(pos[i], 1.0013, f\"x̃ = {np.median(data_cvae[i]):.3f}\", horizontalalignment='center',fontsize=13)\n",
    "\n",
    "\n",
    "boxplot3 = axs[2].boxplot(data_pstG,\n",
    "                        positions=pos,\n",
    "                        medianprops={'linewidth': 1, 'color': 'orange'},\n",
    "                        showfliers=False,\n",
    "                        flierprops=dict(markerfacecolor='r', markersize=1),\n",
    "                        patch_artist=True)\n",
    "\n",
    "for i in range(len(pos)):\n",
    "    axs[2].text(pos[i], 1.004, f\"x̃ = {np.median(data_pstG[i]):.3f}\", horizontalalignment='center', fontsize=13)\n",
    "\n",
    "\n",
    "ticks0 = np.arange(0.85, 1.01, 0.01)\n",
    "axs[0].set_yticks(ticks0)\n",
    "axs[0].grid(which='major', alpha=0.9, linewidth=1, axis='y')\n",
    "axs[0].tick_params(labelsize=14)\n",
    "axs[0].set_ylabel('Temporal Consistency', fontsize=18)\n",
    "axs[0].set_ylim(0.93, 1.006)\n",
    "\n",
    "# change the color of the boxplot\n",
    "colors = ['lightgreen'] *5\n",
    "for patch, color in zip(boxplot1['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "    \n",
    "\n",
    "# y axis decimal places for all subplots\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "axs[0].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "axs[1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "axs[2].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "\n",
    "ticks1 = np.arange(0.94, 1.001, 0.01)\n",
    "axs[1].set_yticks(ticks1)\n",
    "axs[1].grid(which='major', alpha=0.9, linewidth=1, axis='y')\n",
    "axs[1].tick_params(labelsize=14)\n",
    "axs[1].set_xticks(pos);\n",
    "axs[1].set_xticklabels([ '1', '10', '100', '1000', '10000'], fontsize=16);\n",
    "axs[1].set_xlabel('Stride', fontsize=16)\n",
    "axs[1].set_ylim(0.94, 1.005)\n",
    "\n",
    "# change the color of the boxplot\n",
    "colors = ['lightblue'] *5\n",
    "for patch, color in zip(boxplot2['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "ticks2 = np.arange(0.850, 1.002, 0.025)\n",
    "axs[2].set_yticks(ticks2)\n",
    "\n",
    "axs[2].grid(which='major', alpha=0.9, linewidth=1, axis='y')\n",
    "axs[2].tick_params(labelsize=14)\n",
    "axs[2].set_ylim(0.87, 1.012)\n",
    "colors = ['lightcoral'] *5\n",
    "for patch, color in zip(boxplot3['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "# Add legend for each subplot. plot 1: shape based, plot 2: conditional VAE, plot 3: Pyspatiotemporalgeom\n",
    "axs[0].legend([boxplot1[\"boxes\"][0], boxplot1[\"boxes\"][1], boxplot1[\"boxes\"][2]],\n",
    "['Shape based'],  fontsize=14, framealpha=1, frameon=True, facecolor='white', edgecolor='black', fancybox=True, loc='lower left')\n",
    "\n",
    "axs[1].legend([boxplot2[\"boxes\"][0], boxplot2[\"boxes\"][1], boxplot2[\"boxes\"][2]],\n",
    "['C-VAE'],  fontsize=14, framealpha=1, frameon=True, facecolor='white', edgecolor='black', fancybox=True, loc='lower left')\n",
    "\n",
    "axs[2].legend([boxplot3[\"boxes\"][0], boxplot3[\"boxes\"][1], boxplot3[\"boxes\"][2]],\n",
    "['Mckenney'],  fontsize=14, framealpha=1, frameon=True, facecolor='white', edgecolor='black', fancybox=True, loc='lower left')\n",
    "\n",
    "    \n",
    "# Add some space between the subplots and the bottom of the figure\n",
    "fig.subplots_adjust(hspace=0.5, bottom=0.1)\n",
    "\n",
    "axs[0].grid(False, axis='x')\n",
    "axs[1].grid(False, axis='x')\n",
    "axs[2].grid(False, axis='x')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading WKTs file...\n"
     ]
    }
   ],
   "source": [
    "# find minumum value in tc_pstG_list_sp and its index\n",
    "pstg_min = min(tc_pstG_list_sp[4][\"tc\"]), tc_pstG_list_sp[4][\"tc\"].index(min(tc_pstG_list_sp[4][\"tc\"]))\n",
    "\n",
    "# Pyspatiotemporalgeom\n",
    "print(\"Reading WKTs file...\")\n",
    "with open(PSTG_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    wkt_pstg = f.read().splitlines()\n",
    "\n",
    "# delete lines with number = idx_sampled (one liner)\n",
    "wkt_pstg = [item for i, item in enumerate(wkt_pstg) if i not in idx_sampled]\n",
    "num_polygons = len(wkt_pstg)\n",
    "\n",
    "# read WKTS file lines and convert to list of shapely polygons\n",
    "polygons_pstg_sp = [loads(wkt) for wkt in wkt_pstg]\n",
    "polygons_pstg_sp = polygons_pstg_sp[:num_polygons]\n",
    "\n",
    "\n",
    "with open(CVAE_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    wkt_cvae = f.read().splitlines()\n",
    "# delete lines with number = idx_sampled (one liner)\n",
    "wkt_cvae = [item for i, item in enumerate(wkt_cvae) if i not in idx_sampled]\n",
    "num_polygons = len(wkt_cvae)\n",
    "\n",
    "polygons_cvae_sp = [loads(wkt) for wkt in wkt_cvae]\n",
    "polygons_cvae_sp = polygons_cvae_sp[:num_polygons]\n",
    "\n",
    "with open(SHP_WKT_FILE_SAMPLED, \"r\") as f:\n",
    "    wkt_shp = f.read().splitlines()\n",
    "# delete lines with number = idx_sampled (one liner)\n",
    "wkt_shp = [item for i, item in enumerate(wkt_shp) if i not in idx_sampled]\n",
    "num_polygons = len(wkt_shp)\n",
    "\n",
    "polygons_shp_sp = [loads(wkt_s) for wkt_s in wkt_shp]\n",
    "polygons_shp_sp = polygons_shp_sp[:num_polygons]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(PSTG_WKT_FILE, \"r\") as f:\n",
    "    wkt_pstg = f.read().splitlines()\n",
    "num_polygons = len(wkt_pstg)\n",
    "\n",
    "polygons_pstg = [loads(wkt) for wkt in wkt_pstg]\n",
    "polygons_pstg = polygons_pstg[:num_polygons]\n",
    "\n",
    "with open(CVAE_WKT_FILE, \"r\") as f:\n",
    "    wkt_cvae = f.read().splitlines()\n",
    "num_polygons = len(wkt_cvae)\n",
    "\n",
    "polygons_cvae = [loads(wkt) for wkt in wkt_cvae]\n",
    "polygons_cvae = polygons_cvae[:num_polygons]\n",
    "\n",
    "with open(SHP_WKT_FILE, \"r\") as f:\n",
    "    wkt_shp = f.read().splitlines()\n",
    "num_polygons = len(wkt_shp)\n",
    "\n",
    "polygons_shp = [loads(wkt_s) for wkt_s in wkt_shp]\n",
    "polygons_shp = polygons_shp[:num_polygons]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate area of list of polygons\n",
    "area_pstg_sp = [polygons_pstg_sp[i].area for i in range(0, len(polygons_pstg_sp))]\n",
    "area_cvae_sp = [polygons_cvae_sp[i].area for i in range(0, len(polygons_cvae_sp))]\n",
    "area_shp_sp = [polygons_shp_sp[i].area for i in range(0, len(polygons_shp_sp))]\n",
    "area_cvae = [polygons_cvae[i].area for i in range(0, len(polygons_cvae))]\n",
    "area_pstg = [polygons_pstg[i].area for i in range(0, len(polygons_pstg))]\n",
    "area_shp = [polygons_shp[i].area for i in range(0, len(polygons_shp))]\n",
    "\n",
    "# 3 subplots\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 6), sharey=True)\n",
    "\n",
    "# increase legend font size for all subplots\n",
    "plt.rcParams['legend.fontsize'] = 12\n",
    "# increase label font size for all subplots\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "\n",
    "# plot area of list of polygons for each model\n",
    "ax3.plot(area_pstg, label=\"Mckenney - Periodic Sampling\", alpha=0.8)\n",
    "ax3.plot(area_pstg_sp, label=\"Mckenney - Distance Sampling\")\n",
    "\n",
    "ax3.legend()\n",
    "ax1.plot(area_cvae, label=\"C-VAE - Periodic Sampling\", alpha=0.8)\n",
    "ax1.plot(area_cvae_sp, label=\"C-VAE - Distance Sampling\")\n",
    "\n",
    "ax1.legend()\n",
    "ax2.plot(area_shp, label=\"Shape Based - Periodic Sampling\", alpha=0.8)\n",
    "ax2.plot(area_shp_sp, label=\"Shape Based - Distance Sampling\")\n",
    "ax2.legend()\n",
    "\n",
    "# y axis label\n",
    "ax1.set_ylabel(\"Region Area\")\n",
    "# x axis label\n",
    "ax2.set_xlabel(\"Region Number\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pyspatiotemporalgeom boxplots in a single figure\n",
    "fig, ax = plt.subplots(figsize=(22, 5))\n",
    "\n",
    "ax.set_title('Pyspatiotemporalgeom')\n",
    "ax.boxplot([tc_pstG_list[i][\"tc\"] for i in range(0, stride_rng)],\n",
    "              flierprops=dict(markerfacecolor='r', markersize=1))\n",
    "ax.set_xlabel(\"Stride\")\n",
    "ax.set_ylabel(\"Temporal Consistency\")\n",
    "\n",
    "# add annotations with the median value\n",
    "for i in range(0, stride_rng):\n",
    "    ax.annotate(f\"{np.median(tc_pstG_list[i]['tc']):.3e}\", (i+1, \n",
    "                np.median(tc_pstG_list[i]['tc'])), ha='left', va='center', \n",
    "                size=8, color='black', xytext=(32, 0), \n",
    "                textcoords='offset points')\n",
    "\n",
    "ax.grid( linestyle=\"--\", alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SHP boxplots in a single figure\n",
    "fig, ax = plt.subplots(figsize=(22, 5))\n",
    "\n",
    "ax.set_title('Shape based')\n",
    "ax.boxplot([tc_shp_list[i][\"tc\"] for i in range(0, stride_rng)],\n",
    "                flierprops=dict(markerfacecolor='r', markersize=1))\n",
    "ax.set_xlabel(\"Stride\")\n",
    "ax.set_ylabel(\"Temporal Consistency\")\n",
    "\n",
    "# add annotations with the median value\n",
    "for i in range(0, stride_rng):\n",
    "    ax.annotate(f\"{np.median(tc_shp_list[i]['tc']):.3e}\", (i+1, \n",
    "                np.median(tc_shp_list[i]['tc'])), ha='left', va='center', \n",
    "                size=8, color='black', xytext=(32, 0), \n",
    "                textcoords='offset points')\n",
    "\n",
    "ax.grid( linestyle=\"--\", alpha=0.5)\n",
    "plt.show()"
   ]
  }
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