{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2683899d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.special import logsumexp\n",
    "import torch\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ab97d0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9c8a9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32bd8589",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "\n",
    "def load_images_from_folder(folder):\n",
    "    images = []\n",
    "    img_name = []\n",
    "    for filename in os.listdir(folder):\n",
    "        img = cv2.imread(os.path.join(folder,filename))\n",
    "        if img is not None:\n",
    "            images.append(img)\n",
    "            img_name.append(filename)\n",
    "    return images, img_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ebf6a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs, img_name = load_images_from_folder('fix_stimuli')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "571c8db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd911d2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e99e7121",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(img_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e65c80b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "int(img_name[0].split('.')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "303e82af",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "scipy.io.loadmat('S02_fix/S02_face_1.mat')['currImData'][:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5b41345",
   "metadata": {},
   "outputs": [],
   "source": [
    "scipy.io.loadmat('S02_fix/S02_face_1.mat')['currImData'][:,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e60e197d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "scipy.io.loadmat('S02_fix/S02_pareidolia_64.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a071e2e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for filename in os.listdir('S02_fix'):\n",
    "    print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dc9ab34",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_fix_from_folder(folder):\n",
    "    fix_X = []\n",
    "    fix_Y = []\n",
    "    img_name = []\n",
    "    for filename in os.listdir(folder):\n",
    "        fix_X.append(scipy.io.loadmat(os.path.join(folder,filename))['currImData'][:,4])\n",
    "        fix_Y.append(scipy.io.loadmat(os.path.join(folder,filename))['currImData'][:,5])\n",
    "        img_name.append(str(scipy.io.loadmat(os.path.join(folder,filename))['currImName'][0][0]) + '.jpg')\n",
    "        #print(filename)\n",
    "        #print(img_name)\n",
    "    return fix_X, fix_Y, img_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ad3e153",
   "metadata": {},
   "outputs": [],
   "source": [
    "fix_X, fix_Y, img_name = load_fix_from_folder('S_fix/S13_fix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43fc95a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "# Specify the directory containing the nested folder structure\n",
    "root_dir = '/home/pranjul/DeepGaze/fix_stimuli/'\n",
    "\n",
    "# Specify the image file extensions you want to load\n",
    "extensions = ['*.jpg', '*.jpeg', '*.png']\n",
    "\n",
    "# Create a list to store the image file paths\n",
    "image_paths = []\n",
    "\n",
    "# Traverse through all subdirectories and search for image files\n",
    "for extension in extensions:\n",
    "    search_pattern = os.path.join(root_dir, '**', extension)\n",
    "    image_paths.extend(glob.glob(search_pattern, recursive=True))\n",
    "\n",
    "# Print the paths of the loaded image files\n",
    "for image_path in image_paths:\n",
    "    print(image_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "864cb318",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def create_folder(folder_path):\n",
    "    try:\n",
    "        os.mkdir(folder_path)\n",
    "        print(f\"Folder '{folder_path}' created successfully.\")\n",
    "    except FileExistsError:\n",
    "        print(f\"Folder '{folder_path}' already exists.\")\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47b06581",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def folder_exists(folder_path):\n",
    "    return os.path.exists(folder_path) and os.path.isdir(folder_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d22fce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace 'path/to/your/folder' with the folder path you want to check\n",
    "folder_path = 'S_fix/S18_fix'\n",
    "if folder_exists(folder_path):\n",
    "    print(f\"Folder '{folder_path}' exists.\")\n",
    "else:\n",
    "    print(f\"Folder '{folder_path}' does not exist.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "031c09c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30f2ed42",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for f in range(13, 56):\n",
    "    print(f)\n",
    "    print('S_fix/S'+ str(f) +'_fix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5f1efd1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.special import logsumexp\n",
    "import torch\n",
    "\n",
    "import deepgaze_pytorch\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "# you can use DeepGazeI or DeepGazeIIE\n",
    "model = deepgaze_pytorch.DeepGazeIII(pretrained=True).to(DEVICE)\n",
    "\n",
    "#image = face()\n",
    "\n",
    "x = {}\n",
    "for q in range(1, 10):\n",
    "    \n",
    "    # Replace 'path/to/your/folder' with the folder path you want to check\n",
    "    folder_path = 'S_fix/S0'+ str(q) +'_fix'\n",
    "    if folder_exists(folder_path):\n",
    "    \n",
    "        fix_X, fix_Y, img_name = load_fix_from_folder('S_fix/S0'+ str(q) +'_fix')\n",
    "\n",
    "        # Replace 'path/to/your/folder' with the desired folder path\n",
    "        folder_path = 'DG3_heatmaps/S0'+ str(q) +'_fix'\n",
    "        create_folder(folder_path)\n",
    "\n",
    "\n",
    "        for i in range(len(img_name)):\n",
    "\n",
    "            image = cv2.imread('/home/pranjul/DeepGaze/fix_stimuli/' + img_name[i])\n",
    "\n",
    "            if image is not None and len(fix_X[i]) > 3 and len(fix_Y[i] > 3):\n",
    "\n",
    "                # location of previous scanpath fixations in x and y (pixel coordinates), starting with the initial fixation on the image.\n",
    "                #fixation_history_x = np.array([1024//2, 300, 500, 200, 200, 700])\n",
    "                #fixation_history_y = np.array([768//2, 300, 100, 300, 100, 500])\n",
    "\n",
    "                #print(img_name[i])\n",
    "\n",
    "                fixation_history_x = fix_X[i]/3\n",
    "                #print(fixation_history_x)\n",
    "                fixation_history_y = fix_Y[i]/3\n",
    "\n",
    "                # load precomputed centerbias log density (from MIT1003) over a 1024x1024 image\n",
    "                # you can download the centerbias from https://github.com/matthias-k/DeepGaze/releases/download/v1.0.0/centerbias_mit1003.npy\n",
    "                # alternatively, you can use a uniform centerbias via `centerbias_template = np.zeros((1024, 1024))`.\n",
    "                centerbias_template = np.load('centerbias_mit1003.npy')\n",
    "                # rescale to match image size\n",
    "                centerbias = zoom(centerbias_template, (image.shape[0]/centerbias_template.shape[0], image.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "                # renormalize log density\n",
    "                centerbias -= logsumexp(centerbias)\n",
    "\n",
    "                image_tensor = torch.tensor([image.transpose(2, 0, 1)]).to(DEVICE)\n",
    "                centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "                x_hist_tensor = torch.tensor([fixation_history_x[model.included_fixations]]).to(DEVICE)\n",
    "                y_hist_tensor = torch.tensor([fixation_history_y[model.included_fixations]]).to(DEVICE)\n",
    "\n",
    "                log_density_prediction = model(image_tensor, centerbias_tensor, x_hist_tensor, y_hist_tensor)\n",
    "\n",
    "                # Scale factor\n",
    "                #scale_factor = 3\n",
    "\n",
    "                # Calculate the new width and height\n",
    "                #new_width = image.shape[1] * scale_factor\n",
    "                #new_height = image.shape[0] * scale_factor\n",
    "\n",
    "                # Resize the image using cv2.resize()\n",
    "                #image = cv2.resize(image, (new_width, new_height))\n",
    "\n",
    "                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "\n",
    "                f, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 9))\n",
    "                axs[0].imshow(image)\n",
    "                #axs[0].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "                #axs[0].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "                axs[0].set_axis_off()\n",
    "                axs[1].matshow(log_density_prediction.detach().cpu().numpy()[0, 0])  # first image in batch, first (and only) channel\n",
    "                #axs[1].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "                #axs[1].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "                axs[1].set_axis_off()\n",
    "                plt.savefig(os.path.join('DG3_heatmaps/S0'+ str(q) +'_fix', img_name[i]))\n",
    "                plt.close()\n",
    "                #break\n",
    "        #break\n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb2809f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a041477",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0baeecb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70e54f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(fixation_history_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16ec0624",
   "metadata": {},
   "outputs": [],
   "source": [
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "964e517a",
   "metadata": {},
   "outputs": [],
   "source": [
    "S02_img_name[244]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67397109",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.where(np.array(S02_img_name) == '44.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02cd8a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = np.where(arr == 2)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a64314eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09b449d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2e3afae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8433595",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11bb0a30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "852d1d54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45392af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93a09086",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.special import logsumexp\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import deepgaze_pytorch\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "# you can use DeepGazeI or DeepGazeIIE\n",
    "model = deepgaze_pytorch.DeepGazeIIE(pretrained=True).to(DEVICE)\n",
    "\n",
    "# image = face()\n",
    "\n",
    "x = {}\n",
    "\n",
    "for i in range(len(image_paths)):\n",
    "    print(i)\n",
    "    \n",
    "    image = cv2.imread(image_paths[i])\n",
    "    \n",
    "    # load precomputed centerbias log density (from MIT1003) over a 1024x1024 image\n",
    "    # you can download the centerbias from https://github.com/matthias-k/DeepGaze/releases/download/v1.0.0/centerbias_mit1003.npy\n",
    "    # alternatively, you can use a uniform centerbias via `centerbias_template = np.zeros((1024, 1024))`.\n",
    "    centerbias_template = np.load('centerbias_mit1003.npy')\n",
    "    # rescale to match image size\n",
    "    centerbias = zoom(centerbias_template, (image.shape[0]/centerbias_template.shape[0], image.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "    # renormalize log density\n",
    "    centerbias -= logsumexp(centerbias)\n",
    "\n",
    "    image_tensor = torch.tensor([image.transpose(2, 0, 1)]).to(DEVICE)\n",
    "    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "\n",
    "    log_density_prediction = model(image_tensor, centerbias_tensor)\n",
    "    \n",
    "    #a = log_density_prediction.detach().cpu().numpy()[0, 0]\n",
    "    \n",
    "    #x[img_name[i].split('.')[0]] = a\n",
    "    \n",
    "    \n",
    "    f, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 9))\n",
    "    axs[0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "    # axs[0].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "    # axs[0].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "    axs[0].set_axis_off()\n",
    "    axs[1].matshow(log_density_prediction.detach().cpu().numpy()[0, 0])  # first image in batch, first (and only) channel\n",
    "    # axs[1].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "    # axs[1].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "    axs[1].set_axis_off()\n",
    "    plt.savefig(os.path.join('DG2_modified_imgs_heatmaps', '{0}.jpg'.format(i)))\n",
    "    \n",
    "    \n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e4e709a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bd1220a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60141a51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2f42e76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.special import logsumexp\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import deepgaze_pytorch\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "# you can use DeepGazeI or DeepGazeIIE\n",
    "model = deepgaze_pytorch.DeepGazeIIE(pretrained=True).to(DEVICE)\n",
    "\n",
    "# image = face()\n",
    "\n",
    "x = {}\n",
    "\n",
    "for i in range(len(imgs)):\n",
    "    \n",
    "    image = imgs[i]\n",
    "    \n",
    "    # load precomputed centerbias log density (from MIT1003) over a 1024x1024 image\n",
    "    # you can download the centerbias from https://github.com/matthias-k/DeepGaze/releases/download/v1.0.0/centerbias_mit1003.npy\n",
    "    # alternatively, you can use a uniform centerbias via `centerbias_template = np.zeros((1024, 1024))`.\n",
    "    centerbias_template = np.load('centerbias_mit1003.npy')\n",
    "    # rescale to match image size\n",
    "    centerbias = zoom(centerbias_template, (image.shape[0]/centerbias_template.shape[0], image.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "    # renormalize log density\n",
    "    centerbias -= logsumexp(centerbias)\n",
    "\n",
    "    image_tensor = torch.tensor([image.transpose(2, 0, 1)]).to(DEVICE)\n",
    "    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "\n",
    "    log_density_prediction = model(image_tensor, centerbias_tensor)\n",
    "    \n",
    "    a = log_density_prediction.detach().cpu().numpy()[0, 0]\n",
    "    \n",
    "    x[img_name[i].split('.')[0]] = a\n",
    "    \n",
    "    '''\n",
    "    f, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 9))\n",
    "    axs[0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "    # axs[0].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "    # axs[0].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "    axs[0].set_axis_off()\n",
    "    axs[1].matshow(log_density_prediction.detach().cpu().numpy()[0, 0])  # first image in batch, first (and only) channel\n",
    "    # axs[1].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "    # axs[1].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "    axs[1].set_axis_off()\n",
    "    # plt.savefig(os.path.join('DG2_heatmaps', '{0}.jpg'.format(i)))\n",
    "    '''\n",
    "    \n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca4ab04b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in x:\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26527272",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from scipy.io import loadmat\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3938f5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_faces = {}\n",
    "\n",
    "for filename in glob.glob('/home/pranjul/DeepGaze/heatmaps/faces/*.mat'): #assuming gif\n",
    "    \n",
    "    fn=loadmat(filename)\n",
    "    y_faces[filename.split('/')[-1].split('.')[0]] = fn\n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5902106",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_objects = {}\n",
    "\n",
    "for filename in glob.glob('/home/pranjul/DeepGaze/heatmaps/objects/*.mat'): #assuming gif\n",
    "    \n",
    "    fn=loadmat(filename)\n",
    "    y_objects[filename.split('/')[-1].split('.')[0]] = fn\n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6fa7c47",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pareidolia = {}\n",
    "\n",
    "for filename in glob.glob('/home/pranjul/DeepGaze/heatmaps/pareidolia/*.mat'): #assuming gif\n",
    "    \n",
    "    fn=loadmat(filename)\n",
    "    y_pareidolia[filename.split('/')[-1].split('.')[0]] = fn\n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2166932",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dg_faces = []\n",
    "eg_faces = []\n",
    "ke = []\n",
    "for k in x:\n",
    "    if k in y_faces:\n",
    "        print(k)\n",
    "        ke.append(k)\n",
    "        #print(np.shape(x[k]))\n",
    "        #print(y_faces[k])\n",
    "        dg_faces.append(scaler.fit_transform(np.array(x[k])).flatten())\n",
    "        eg_faces.append(scaler.fit_transform(np.array(y_faces[k]['a'])).flatten())\n",
    "    #break\n",
    "\n",
    "correlation_coef, p_value = spearmanr(np.array(dg_faces).flatten(), np.array(eg_faces).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77266844",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dg_objects = []\n",
    "eg_objects = []\n",
    "ke = []\n",
    "for k in x:\n",
    "    if k in y_objects:\n",
    "        print(k)\n",
    "        ke.append(k)\n",
    "        #print(np.shape(x[k]))\n",
    "        #print(y_faces[k])\n",
    "        dg_objects.append(scaler.fit_transform(np.array(x[k])).flatten())\n",
    "        eg_objects.append(scaler.fit_transform(np.array(y_objects[k]['a'])).flatten())\n",
    "    #break\n",
    "\n",
    "correlation_coef, p_value = spearmanr(np.array(dg_objects).flatten(), np.array(eg_objects).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0403de67",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dg_pareidolia = []\n",
    "eg_pareidolia = []\n",
    "ke = []\n",
    "for k in x:\n",
    "    if k in y_pareidolia:\n",
    "        print(k)\n",
    "        ke.append(k)\n",
    "        #print(np.shape(x[k]))\n",
    "        #print(y_faces[k])\n",
    "        dg_pareidolia.append(scaler.fit_transform(np.array(x[k])).flatten())\n",
    "        eg_pareidolia.append(scaler.fit_transform(np.array(y_pareidolia[k]['a'])).flatten())\n",
    "    #break\n",
    "\n",
    "correlation_coef, p_value = spearmanr(np.array(dg_pareidolia).flatten(), np.array(eg_pareidolia).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c297d11a",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(dg_faces).flatten(), np.array(eg_faces).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85e5edb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(dg_objects).flatten(), np.array(eg_objects).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d99c7309",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(dg_pareidolia).flatten(), np.array(eg_pareidolia).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9702314",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dg_pareidolia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c319f00b",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dg_faces[:83])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fa45e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dg_objects[:83])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e9fd2b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(dg_faces[:83]).flatten(), np.array(dg_objects[:83]).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9357f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(dg_faces[:83]).flatten(), np.array(dg_pareidolia[:83]).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f70021f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(dg_pareidolia[:83]).flatten(), np.array(dg_objects[:83]).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0df004a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(eg_pareidolia[:83]).flatten(), np.array(eg_objects[:83]).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1742ecb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(eg_pareidolia[:83]).flatten(), np.array(eg_faces[:83]).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a02bf88",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef, p_value = spearmanr(np.array(eg_faces[:83]).flatten(), np.array(eg_objects[:83]).flatten())\n",
    "# correlation_coef = np.corrcoef(a, b)\n",
    "print(\"Correlation coefficient:\", correlation_coef)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7ccd13e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdeb2fc5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7faa17c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9af3d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "# Generate two arrays with random data\n",
    "array1 = np.random.rand(100)\n",
    "array2 = np.random.rand(100)\n",
    "\n",
    "# Calculate Spearman's correlation coefficient and p-value\n",
    "correlation, p_value = spearmanr(array1, array2)\n",
    "\n",
    "print(\"Spearman's correlation coefficient:\", correlation)\n",
    "print(\"p-value:\", p_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7cd0d61",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3570f454",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a0a92be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "# define two eye gaze heatmaps\n",
    "heatmap1 = np.array([[0.2, 0.3, 0.1],\n",
    "                     [0.1, 0.4, 0.3],\n",
    "                     [0.3, 0.2, 0.1]])\n",
    "\n",
    "heatmap2 = np.array([[0.1, 0.2, 0.3],\n",
    "                     [0.2, 0.3, 0.2],\n",
    "                     [0.3, 0.1, 0.1]])\n",
    "\n",
    "# flatten the heatmaps into 1D arrays\n",
    "flat_heatmap1 = heatmap1.flatten()\n",
    "flat_heatmap2 = heatmap2.flatten()\n",
    "\n",
    "# calculate the Pearson correlation coefficient and p-value\n",
    "corr, p_value = pearsonr(flat_heatmap1, flat_heatmap2)\n",
    "\n",
    "print(\"Correlation coefficient:\", corr)\n",
    "print(\"p-value:\", p_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98a8e3c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.shape(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55b352bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.shape(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fe648aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.shape(correlation_coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd8e091b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(correlation_coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "884bf73a",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_coef[83:, :83]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a540fa9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(correlation_coef[83:, :83])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62cadea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(np.diagonal(correlation_coef[100:, :100]), 'o') #faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f05bd895",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(np.diagonal(correlation_coef[100:, :100]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e227007",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(np.diagonal(correlation_coef[100:, :100]), 'o') #obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12ed25e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(np.diagonal(correlation_coef[86:, :86]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccf0a569",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(np.diagonal(correlation_coef[83:, :83]), 'o') #pare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "923ed911",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(np.diagonal(correlation_coef[83:, :83]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27bac165",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f751bc29",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(y_objects['1153']['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c6a4bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40a93053",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a9b5849",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76eee42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.shape(imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cc44e0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d14b8ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feddeb52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.special import logsumexp\n",
    "import torch\n",
    "\n",
    "import deepgaze_pytorch\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "# you can use DeepGazeI or DeepGazeIIE\n",
    "model = deepgaze_pytorch.DeepGazeIII(pretrained=True).to(DEVICE)\n",
    "\n",
    "image = face()\n",
    "\n",
    "# location of previous scanpath fixations in x and y (pixel coordinates), starting with the initial fixation on the image.\n",
    "fixation_history_x = np.array([1024//2, 300, 500, 200, 200, 700])\n",
    "fixation_history_y = np.array([768//2, 300, 100, 300, 100, 500])\n",
    "\n",
    "# load precomputed centerbias log density (from MIT1003) over a 1024x1024 image\n",
    "# you can download the centerbias from https://github.com/matthias-k/DeepGaze/releases/download/v1.0.0/centerbias_mit1003.npy\n",
    "# alternatively, you can use a uniform centerbias via `centerbias_template = np.zeros((1024, 1024))`.\n",
    "centerbias_template = np.load('centerbias_mit1003.npy')\n",
    "# rescale to match image size\n",
    "centerbias = zoom(centerbias_template, (image.shape[0]/centerbias_template.shape[0], image.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "# renormalize log density\n",
    "centerbias -= logsumexp(centerbias)\n",
    "\n",
    "image_tensor = torch.tensor([image.transpose(2, 0, 1)]).to(DEVICE)\n",
    "centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "x_hist_tensor = torch.tensor([fixation_history_x[model.included_fixations]]).to(DEVICE)\n",
    "y_hist_tensor = torch.tensor([fixation_history_x[model.included_fixations]]).to(DEVICE)\n",
    "\n",
    "log_density_prediction = model(image_tensor, centerbias_tensor, x_hist_tensor, y_hist_tensor)\n",
    "\n",
    "f, axs = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
    "axs[0].imshow(image)\n",
    "axs[0].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "axs[0].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "axs[0].set_axis_off()\n",
    "axs[1].matshow(log_density_prediction.detach().cpu().numpy()[0, 0])  # first image in batch, first (and only) channel\n",
    "axs[1].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "axs[1].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "axs[1].set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b512963",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.included_fixations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33d6872d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixation_history_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bce1d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixation_history_x[model.included_fixations]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "751cb04e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3160caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.special import logsumexp\n",
    "import torch\n",
    "\n",
    "import deepgaze_pytorch\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "# you can use DeepGazeI or DeepGazeIIE\n",
    "model = deepgaze_pytorch.DeepGazeIII(pretrained=True).to(DEVICE)\n",
    "\n",
    "#image = face()\n",
    "\n",
    "x = {}\n",
    "\n",
    "for i in range(len(imgs)):\n",
    "    \n",
    "    image = imgs[i]\n",
    "    \n",
    "    # location of previous scanpath fixations in x and y (pixel coordinates), starting with the initial fixation on the image.\n",
    "    fixation_history_x = np.array([1024//2, 300, 500, 200, 200, 700])\n",
    "    fixation_history_y = np.array([768//2, 300, 100, 300, 100, 500])\n",
    "\n",
    "    # load precomputed centerbias log density (from MIT1003) over a 1024x1024 image\n",
    "    # you can download the centerbias from https://github.com/matthias-k/DeepGaze/releases/download/v1.0.0/centerbias_mit1003.npy\n",
    "    # alternatively, you can use a uniform centerbias via `centerbias_template = np.zeros((1024, 1024))`.\n",
    "    centerbias_template = np.load('centerbias_mit1003.npy')\n",
    "    # rescale to match image size\n",
    "    centerbias = zoom(centerbias_template, (image.shape[0]/centerbias_template.shape[0], image.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "    # renormalize log density\n",
    "    centerbias -= logsumexp(centerbias)\n",
    "\n",
    "    image_tensor = torch.tensor([image.transpose(2, 0, 1)]).to(DEVICE)\n",
    "    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "    x_hist_tensor = torch.tensor([fixation_history_x[model.included_fixations]]).to(DEVICE)\n",
    "    y_hist_tensor = torch.tensor([fixation_history_x[model.included_fixations]]).to(DEVICE)\n",
    "\n",
    "    log_density_prediction = model(image_tensor, centerbias_tensor, x_hist_tensor, y_hist_tensor)\n",
    "\n",
    "    f, axs = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
    "    axs[0].imshow(image)\n",
    "    axs[0].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "    axs[0].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "    axs[0].set_axis_off()\n",
    "    axs[1].matshow(log_density_prediction.detach().cpu().numpy()[0, 0])  # first image in batch, first (and only) channel\n",
    "    axs[1].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "    axs[1].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "    axs[1].set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa2d7d4e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "274b461a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f71d7915",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c4adce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.special import logsumexp\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import deepgaze_pytorch\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "# you can use DeepGazeI or DeepGazeIIE\n",
    "model = deepgaze_pytorch.DeepGazeIIE(pretrained=True).to(DEVICE)\n",
    "\n",
    "# image = face()\n",
    "\n",
    "x = {}\n",
    "\n",
    "for i in range(len(imgs)):\n",
    "    \n",
    "    image = imgs[i]\n",
    "    \n",
    "    # load precomputed centerbias log density (from MIT1003) over a 1024x1024 image\n",
    "    # you can download the centerbias from https://github.com/matthias-k/DeepGaze/releases/download/v1.0.0/centerbias_mit1003.npy\n",
    "    # alternatively, you can use a uniform centerbias via `centerbias_template = np.zeros((1024, 1024))`.\n",
    "    centerbias_template = np.load('centerbias_mit1003.npy')\n",
    "    # rescale to match image size\n",
    "    centerbias = zoom(centerbias_template, (image.shape[0]/centerbias_template.shape[0], image.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
    "    # renormalize log density\n",
    "    centerbias -= logsumexp(centerbias)\n",
    "\n",
    "    image_tensor = torch.tensor([image.transpose(2, 0, 1)]).to(DEVICE)\n",
    "    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
    "\n",
    "    log_density_prediction = model(image_tensor, centerbias_tensor)\n",
    "    \n",
    "    a = log_density_prediction.detach().cpu().numpy()[0, 0]\n",
    "    \n",
    "    x[img_name[i].split('.')[0]] = a\n",
    "    \n",
    "    '''\n",
    "    f, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 9))\n",
    "    axs[0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "    # axs[0].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "    # axs[0].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "    axs[0].set_axis_off()\n",
    "    axs[1].matshow(log_density_prediction.detach().cpu().numpy()[0, 0])  # first image in batch, first (and only) channel\n",
    "    # axs[1].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
    "    # axs[1].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='yellow', zorder=100)\n",
    "    axs[1].set_axis_off()\n",
    "    # plt.savefig(os.path.join('DG2_heatmaps', '{0}.jpg'.format(i)))\n",
    "    '''\n",
    "    \n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d69ce384",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d2b609",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80a068d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceddabde",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8207585",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "984c0e9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7196ebb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc722528",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20dd8746",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}