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DeepGaze/.gitignore

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+*.pyc
+__pycache__

DeepGaze/.ipynb_checkpoints/(inverse)_inverse_effect-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

DeepGaze/.ipynb_checkpoints/agent_test-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

DeepGaze/.ipynb_checkpoints/dg2E_act-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c809ceed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /home/pranjul/.cache/torch/hub/pytorch_vision_v0.6.0\n"
+     ]
+    }
+   ],
+   "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_dg1 = deepgaze_pytorch.DeepGazeI(pretrained=True).to(DEVICE)\n",
+    "\n",
+    "image = face()\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_dg1(image_tensor, centerbias_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])  # 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": "c0a46c9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "import helper_consol as helper\n",
+    "import pandas as pd\n",
+    "import pingouin as pg\n",
+    "import rsatoolbox\n",
+    "import torchvision\n",
+    "import rsatoolbox.data as rsd # abbreviation to deal with dataset\n",
+    "import rsatoolbox.rdm as rsr\n",
+    "from sklearn import preprocessing\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11c42ad4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pareidolia hypothesis - faces and pareidolia are same\n",
+    "# all distances are 0 between and within faces and pareidolia, but 1 between faces/pareidolia and objects\n",
+    "a = np.zeros((32,32))\n",
+    "b = np.zeros((32,32))\n",
+    "c = np.ones((32,32))\n",
+    "d = np.zeros((32,32))\n",
+    "e = np.zeros((32,32))\n",
+    "f = np.ones((32,32))\n",
+    "g = np.ones((32,32))\n",
+    "h = np.ones((32,32))\n",
+    "i = np.zeros((32,32))\n",
+    "hypo_1 = np.bmat([[a, b, c], [d, e, f], [g, h, i]])\n",
+    "plt.imshow(hypo_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0858982f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# face hypothesis - objects and pareidolia are same\n",
+    "# all distances are 0 between and within objects and pareidolia, but 1 between objects/pareidolia and faces\n",
+    "a = np.zeros((32,32))\n",
+    "b = np.ones((32,32))\n",
+    "c = np.ones((32,32))\n",
+    "d = np.ones((32,32))\n",
+    "e = np.zeros((32,32))\n",
+    "f = np.zeros((32,32))\n",
+    "g = np.ones((32,32))\n",
+    "h = np.zeros((32,32))\n",
+    "i = np.zeros((32,32))\n",
+    "hypo_2 = np.bmat([[a, b, c], [d, e, f], [g, h, i]])\n",
+    "plt.imshow(hypo_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "614acbaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_name = 'face_inanimate_400k_facesconsolidated'\n",
+    "config = helper.Config(config_file='./configs/vgg/' + network_name + '.yaml')\n",
+    "model, ckpt_data = config.get_model(pretrained=True, ngpus=1, dataParallel=False, epoch=110, consol=True)\n",
+    "# model, ckpt_data = config.get_model(pretrained=True, ngpus=1, dataParallel=True, epoch=-1)\n",
+    "model = model.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8173f9bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#network_name = 'face_inanimate_400k_facesconsolidated'\n",
+    "#config = helper.Config(config_file='./configs/vgg/' + network_name + '.yaml')\n",
+    "#model, ckpt_data = config.get_model(pretrained=True, ngpus=1, dataParallel=False, epoch=110, consol=True)\n",
+    "# model, ckpt_data = config.get_model(pretrained=True, ngpus=1, dataParallel=True, epoch=-1)\n",
+    "model_dg1 = model_dg1.cuda()\n",
+    "\n",
+    "'''\n",
+    "# Define the layer(s) you want to extract activations from\n",
+    "layers = [model.module.features[1], model.module.features[3], model.module.features[6],\n",
+    "          model.module.features[8], model.module.features[11], model.module.features[13],\n",
+    "          model.module.features[15], model.module.features[18], model.module.features[20],\n",
+    "          model.module.features[22], model.module.features[25], model.module.features[27],\n",
+    "          model.module.features[29], model.module.classifier[1], model.module.classifier[4]]\n",
+    "\n",
+    "\n",
+    "layers = [model.features[1], model.features[3], model.features[6],\n",
+    "          model.features[8], model.features[11], model.features[13],\n",
+    "          model.features[15], model.features[18], model.features[20],\n",
+    "          model.features[22], model.features[25], model.features[27],\n",
+    "          model.features[29], model.classifier[1], model.classifier[4]]\n",
+    "'''\n",
+    "\n",
+    "layers = [model_dg1.features.features[1].features[1], model_dg1.features.features[1].features[4],\n",
+    "         model_dg1.features.features[1].features[7], model_dg1.features.features[1].features[9],\n",
+    "         model_dg1.features.features[1].features[11], model_dg1.features.features[1].classifier[2],\n",
+    "         model_dg1.features.features[1].classifier[5], model_dg1.readout_network.conv0,\n",
+    "         model_dg1.finalizer.gauss]\n",
+    "\n",
+    "\n",
+    "# Register forward hooks to the selected layers\n",
+    "activation = {}\n",
+    "\n",
+    "def get_activation(name):\n",
+    "    def hook(model_dg1, input, output):\n",
+    "        activation[name] = output.detach()\n",
+    "    return hook\n",
+    "\n",
+    "for i, layer in enumerate(layers):\n",
+    "    layer.register_forward_hook(get_activation(f'layer_{i+1}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35566e1a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "bts_no = 1000\n",
+    "folder_dir = ['/home/pranjul/DeepGaze/all_images_net_input/']\n",
+    "\n",
+    "validator = helper.Validator(name='things_validator',\n",
+    "                             model=model,\n",
+    "                             batch_size=10,\n",
+    "                             data_dir=folder_dir,\n",
+    "                             ngpus=1,\n",
+    "                             workers=4,\n",
+    "                             task=None,\n",
+    "                             max_samples={'all_images_net_input': 32},\n",
+    "                             maxout=True,\n",
+    "                             read_seed=0,\n",
+    "                             shuffle=False,\n",
+    "                             data_subdir='test',\n",
+    "                             includePaths=True)\n",
+    "\n",
+    "num_imgs = len(validator.dataset)\n",
+    "centerbias_template = np.load('centerbias_mit1003.npy')\n",
+    "\n",
+    "img_act = []\n",
+    "spearmanr_values_hypo_1_bts, spearmanr_values_hypo_2_bts = [], []\n",
+    "\n",
+    "for i, image in enumerate(validator.dataset):\n",
+    "        \n",
+    "    filename = image[-1].split('/')[-1]\n",
+    "    image = image[0].unsqueeze(0).cuda()\n",
+    "    \n",
+    "    \n",
+    "    # rescale to match image size\n",
+    "    centerbias = zoom(centerbias_template, (image.shape[2]/centerbias_template.shape[0], image.shape[3]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
+    "    # renormalize log density\n",
+    "    centerbias -= logsumexp(centerbias)\n",
+    "\n",
+    "    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
+    "\n",
+    "    # Forward pass the image through the model\n",
+    "    output = model_dg1(image, centerbias_tensor)\n",
+    "\n",
+    "    # Retrieve the extracted activations\n",
+    "    activations = [activation[f'layer_{i+1}'] for i in range(len(layers))]\n",
+    "\n",
+    "    # Print out the extracted activations\n",
+    "    for j, act in enumerate(activations):\n",
+    "        print(f\"Layer {j+1} activation shape for {filename}: {act.shape}\")\n",
+    "        act = act.cpu().detach().numpy().squeeze()\n",
+    "        act = np.reshape(act, (np.prod(act.shape[0:])))\n",
+    "        img_act.append(np.array(act))\n",
+    "\n",
+    "\n",
+    "for boot_itr in range(bts_no):\n",
+    "    \n",
+    "    print(\"boot_itr: \", boot_itr + 1)\n",
+    "    \n",
+    "    np.random.seed(boot_itr)\n",
+    "\n",
+    "    randomRows_0 = []\n",
+    "    randomRows_1 = []\n",
+    "    randomRows_2 = []\n",
+    "\n",
+    "    randomRows_0 = np.random.choice(np.arange(0, 32), size=np.arange(32).shape, replace=True)\n",
+    "    randomRows_1 = np.random.choice(np.arange(32, 64), size=np.arange(32).shape, replace=True)\n",
+    "    randomRows_2 = np.random.choice(np.arange(64, 96), size=np.arange(32).shape, replace=True)\n",
+    "    #print(randomRows)\n",
+    "\n",
+    "    bootstrap_idx = [*randomRows_0, *randomRows_1, *randomRows_2]\n",
+    "    # print(bootstrap_idx)\n",
+    "\n",
+    "    \n",
+    "    img_act_layer = []\n",
+    "    dist_metric_layers_dual_task_fc = []\n",
+    "    \n",
+    "    for i in range(len(layers)):\n",
+    "        for j in bootstrap_idx:\n",
+    "            img_act_layer.append(img_act[i + (j*len(layers))])\n",
+    "\n",
+    "    for i in range(len(layers)):\n",
+    "        dist_metric = 1 - np.corrcoef(np.array(img_act_layer[i*96:(i+1)*96]))\n",
+    "        dist_metric_layers_dual_task_fc.append(dist_metric[np.triu_indices(96, k = 1)])\n",
+    "\n",
+    "\n",
+    "    data = np.vstack([dist_metric_layers_dual_task_fc, \n",
+    "                      np.array(hypo_1[np.triu_indices(96, k = 1)]),\n",
+    "                       np.array(hypo_2[np.triu_indices(96, k = 1)])\n",
+    "                   ])\n",
+    "\n",
+    "    # print(np.shape(data))\n",
+    "    df = pd.DataFrame(data=data.T)\n",
+    "\n",
+    "    col_names = []\n",
+    "\n",
+    "    for i in range(len(layers)):\n",
+    "        col_names.append('layer_' + str(i + 1))\n",
+    "\n",
+    "    col_names.extend(('hypo_1', 'hypo_2'))\n",
+    "    df.columns = col_names\n",
+    "\n",
+    "    spearmanr_values_hypo_1, spearmanr_values_hypo_2 = [], []\n",
+    "    layer_no = np.arange(1, len(layers)+1)\n",
+    "\n",
+    "    for i in range(len(layers)):\n",
+    "\n",
+    "        zero_indices = df.loc[df['layer_'+ str(i + 1)] == 0].index \n",
+    "        df_0_removed = df.drop(zero_indices)\n",
+    "        \n",
+    "        spearmanr_values_hypo_1.append(pg.partial_corr(data=df_0_removed, x='layer_' + str(i + 1), y='hypo_1', covar=['hypo_2'], method='spearman').round(3)['r'][0])\n",
+    "        spearmanr_values_hypo_2.append(pg.partial_corr(data=df_0_removed, x='layer_' + str(i + 1), y='hypo_2', covar=['hypo_1'], method='spearman').round(3)['r'][0])\n",
+    "        \n",
+    "    spearmanr_values_hypo_1_bts.append(np.array(spearmanr_values_hypo_1))\n",
+    "    spearmanr_values_hypo_2_bts.append(np.array(spearmanr_values_hypo_2))\n",
+    "    \n",
+    "#np.save('spearmanr_values_hypo_1_FC_bootstrap_new_2hypos.npy', np.array(spearmanr_values_hypo_1_bts))\n",
+    "#np.save('spearmanr_values_hypo_2_FC_bootstrap_new_2hypos.npy', np.array(spearmanr_values_hypo_2_bts))\n",
+    "\n",
+    "'''\n",
+    "# Plotting\n",
+    "fig = plt.figure(figsize=(14, 8))\n",
+    "ax = fig.add_subplot(111)\n",
+    "plt.plot(layer_no, spearmanr_values_hypo_1, '-o', label='Faces~Pareidolia', markersize=10)\n",
+    "plt.plot(layer_no, spearmanr_values_hypo_2, '-o', label='Pareidolia~Objects', markersize=10)\n",
+    "plt.plot(layer_no, spearmanr_values_hypo_3, '-o', label='Faces~Objects', markersize=10)\n",
+    "\n",
+    "plt.xticks(layer_no, fontsize=18)\n",
+    "plt.yticks(fontsize=18)\n",
+    "ax.set_xticklabels(layer_no)\n",
+    "plt.ylabel('Partial spearmanr (rho)', fontsize=18)\n",
+    "plt.yticks(np.arange(-0.15, 0.65, 0.05))\n",
+    "leg = plt.legend(loc = 2, prop={'size': 15})\n",
+    "leg.get_frame().set_edgecolor('k')\n",
+    "fig.set_size_inches(14.,8.)\n",
+    "#plt.savefig('3_hypos_partial_spearman.png', dpi=600)\n",
+    "\n",
+    "plt.show()\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4ab47a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_imgs = len(validator.dataset)\n",
+    "\n",
+    "img_act = []\n",
+    "\n",
+    "for i, image in enumerate(validator.dataset):\n",
+    "        \n",
+    "    filename = image[-1].split('/')[-1]\n",
+    "    image = image[0].unsqueeze(0).cuda()\n",
+    "\n",
+    "    # rescale to match image size\n",
+    "    centerbias = zoom(centerbias_template, (image.shape[2]/centerbias_template.shape[0], image.shape[3]/centerbias_template.shape[1]), order=0, mode='nearest')\n",
+    "    # renormalize log density\n",
+    "    centerbias -= logsumexp(centerbias)\n",
+    "\n",
+    "    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n",
+    "\n",
+    "    # Forward pass the image through the model\n",
+    "    output = model_dg1(image, centerbias_tensor)\n",
+    "\n",
+    "    # Retrieve the extracted activations\n",
+    "    activations = [activation[f'layer_{i+1}'] for i in range(len(layers))]\n",
+    "\n",
+    "    # Print out the extracted activations\n",
+    "    for j, act in enumerate(activations):\n",
+    "        # print(f\"Layer {j+1} activation shape for {filename}: {act.shape}\")\n",
+    "        act = act.cpu().detach().numpy().squeeze()\n",
+    "        act = np.reshape(act, (np.prod(act.shape[0:])))\n",
+    "        img_act.append(np.array(act))\n",
+    "\n",
+    "img_act_layer = []\n",
+    "dist_metric_layers_dual_task_fc = []\n",
+    "\n",
+    "\n",
+    "for i in range(len(layers)):\n",
+    "    for j in range(96):\n",
+    "        img_act_layer.append(img_act[i + (j*len(layers))])\n",
+    "\n",
+    "for i in range(len(layers)):\n",
+    "    dist_metric = 1 - np.corrcoef(np.array(img_act_layer[i*96:(i+1)*96]))\n",
+    "    dist_metric_layers_dual_task_fc.append(dist_metric[np.triu_indices(96, k = 1)])\n",
+    "\n",
+    "#np.save('dist_metric_layers_dual_task_fc_2_hypos.npy', np.array(dist_metric_layers_dual_task_fc))\n",
+    "\n",
+    "data = np.vstack([dist_metric_layers_dual_task_fc, \n",
+    "                np.array(hypo_1[np.triu_indices(96, k = 1)]),\n",
+    "               np.array(hypo_2[np.triu_indices(96, k = 1)]),\n",
+    "               ])\n",
+    "\n",
+    "# print(np.shape(data))\n",
+    "df = pd.DataFrame(data=data.T)\n",
+    "\n",
+    "col_names = []\n",
+    "\n",
+    "for i in range(len(layers)):\n",
+    "    col_names.append('layer_' + str(i + 1))\n",
+    "\n",
+    "col_names.extend(('hypo_1', 'hypo_2'))\n",
+    "df.columns = col_names\n",
+    "\n",
+    "spearmanr_values_hypo_1, spearmanr_values_hypo_2 = [], []\n",
+    "layer_no = np.arange(1, len(layers)+1)\n",
+    "\n",
+    "for i in range(len(layers)):\n",
+    "\n",
+    "    spearmanr_values_hypo_1.append(pg.partial_corr(data=df, x='layer_' + str(i + 1), y='hypo_1', covar=['hypo_2'], method='spearman').round(3)['r'][0])\n",
+    "    spearmanr_values_hypo_2.append(pg.partial_corr(data=df, x='layer_' + str(i + 1), y='hypo_2', covar=['hypo_1'], method='spearman').round(3)['r'][0])\n",
+    "\n",
+    "#np.save('inanimate_vgg_large_spearmanr_values_hypo_1_2hypos.npy', np.array(spearmanr_values_hypo_1))\n",
+    "#np.save('inanimate_vgg_large_spearmanr_values_hypo_2_2hypos.npy', np.array(spearmanr_values_hypo_2))\n",
+    "\n",
+    "#face_inanimate_400k_seed_spearmanr_values_hypo_1_bootstrap_new_2hypos\n",
+    "\n",
+    "# Plotting\n",
+    "fig = plt.figure(figsize=(14, 8))\n",
+    "ax = fig.add_subplot(111)\n",
+    "plt.plot(layer_no, spearmanr_values_hypo_1, '-o', label='Faces~Pareidolia', markersize=10)\n",
+    "plt.plot(layer_no, spearmanr_values_hypo_2, '-o', label='Pareidolia~Objects', markersize=10)\n",
+    "\n",
+    "plt.xticks(layer_no, fontsize=18)\n",
+    "plt.yticks(fontsize=18)\n",
+    "ax.set_xticklabels(layer_no)\n",
+    "plt.ylabel('Partial spearmanr (rho)', fontsize=18)\n",
+    "plt.yticks(np.arange(-0.15, 0.65, 0.05))\n",
+    "leg = plt.legend(loc = 2, prop={'size': 15})\n",
+    "leg.get_frame().set_edgecolor('k')\n",
+    "fig.set_size_inches(14.,8.)\n",
+    "#plt.savefig('3_hypos_partial_spearman.png', dpi=600)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78693e0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "face_vgg_large_spearmanr_values_hypo_1 = spearmanr_values_hypo_1_bts\n",
+    "face_vgg_large_spearmanr_values_hypo_1 = np.reshape(face_vgg_large_spearmanr_values_hypo_1, (bts_no, len(layers)))\n",
+    "face_vgg_large_spearmanr_values_hypo_2 = spearmanr_values_hypo_2_bts\n",
+    "face_vgg_large_spearmanr_values_hypo_2 = np.reshape(face_vgg_large_spearmanr_values_hypo_2, (bts_no, len(layers)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6aa9c33e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_1 = spearmanr_values_hypo_1\n",
+    "mean_2 = spearmanr_values_hypo_2\n",
+    "\n",
+    "std_1 = []\n",
+    "std_2 = []\n",
+    "\n",
+    "\n",
+    "for i in range(len(layers)):\n",
+    "    std_1.append(np.std(face_vgg_large_spearmanr_values_hypo_1, axis = 0)[i])\n",
+    "    std_2.append(np.std(face_vgg_large_spearmanr_values_hypo_2, axis = 0)[i])\n",
+    "\n",
+    "std_1 = np.array(std_1)\n",
+    "std_2 = np.array(std_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b813dc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plotting\n",
+    "fig = plt.figure(figsize=(14, 8))\n",
+    "ax = fig.add_subplot(111)\n",
+    "#plt.axis('on')\n",
+    "#ax = plt.gca()\n",
+    "#plt.plot(layer_no, mean_1, '-o', label='Faces~Pareidolia', markersize=10)\n",
+    "#plt.plot(layer_no, mean_2, '-o', label='Pareidolia~Objects', markersize=10)\n",
+    "#plt.errorbar(layer_no, mean_1, yerr=[confidence_interval_1_0, confidence_interval_1_1], fmt='-o', color = 'tab:blue', capsize=8, label='Faces~Pareidolia', markersize=2)\n",
+    "#plt.errorbar(layer_no, mean_2, yerr=[confidence_interval_2_0, confidence_interval_2_1], fmt='-o', color = 'tab:orange', capsize=8, label='Pareidolia~Objects', markersize=3)\n",
+    "#plt.errorbar(layer_no, mean_3, yerr=[confidence_interval_3_0, confidence_interval_3_1], fmt='-o', color = 'tab:green', capsize=8, label='Objects~Faces', markersize=3)\n",
+    "\n",
+    "plt.plot(layer_no, mean_1, color = 'tab:blue', label='Faces~Pareidolia')\n",
+    "plt.fill_between(layer_no, mean_1-std_1, mean_1+std_1, color = 'lightblue', alpha=.3)\n",
+    "\n",
+    "plt.plot(layer_no, mean_2, color = 'tab:orange', label='Pareidolia~Objects')\n",
+    "plt.fill_between(layer_no, mean_2-std_2, mean_2+std_2, color = 'peachpuff', alpha=.3)\n",
+    "\n",
+    "# plt.plot(layer_no, mean_3, color = 'tab:green', label='Objects~Faces')\n",
+    "# plt.fill_between(layer_no, mean_3-std_3, mean_3+std_3, color = 'lightgreen', alpha=.3)\n",
+    "\n",
+    "#plt.plot(layer_no, spearmanr_values_hypo_3, '-o', label='Faces~Objects', markersize=10)\n",
+    "#plt.axhline(y=0.5, color='r', linestyle='--', label='Random chance')\n",
+    "#plt.axvline(x=4, color='k', linestyle='--')\n",
+    "#plt.axvline(x=13,color='k', linestyle='--')\n",
+    "\n",
+    "plt.xticks(layer_no, fontsize=30)\n",
+    "plt.yticks(fontsize=30)\n",
+    "#ax.tick_params(axis='both', which='major')\n",
+    "ax.set_xticklabels(layer_no)\n",
+    "plt.axis([0, len(layers)+1, -0.2, 0.3])\n",
+    "plt.xlabel('CNN layer', fontsize=30)\n",
+    "plt.ylabel(\"CNN idealized model correlation \\n [Partial Spearman's r]\", fontsize=30)\n",
+    "plt.title('DG1_RSA', fontsize=30)\n",
+    "#ax.grid(which='both')\n",
+    "#ax.grid(which='minor', alpha=0.2)\n",
+    "#ax.grid(which='major', alpha=0.5)\n",
+    "#ax.grid(color='k', alpha=1, linestyle='--')\n",
+    "#plt.legend(loc = 4, prop={'size': 20})\n",
+    "leg = plt.legend(loc = 2, prop={'size': 20})\n",
+    "leg.get_frame().set_edgecolor('k')\n",
+    "#fig.set_size_inches(14.,8.)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('DG1_RSA.png', dpi=600)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0eac54e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f71f0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04f5607f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8da34189",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82e25b3b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7817cbe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1b73b99",
+   "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
+}

DeepGaze/.ipynb_checkpoints/dg2e_hg-checkpoint.ipynb

@@ -0,0 +1,1596 @@
+{
+ "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
+}

DeepGaze/.ipynb_checkpoints/dg2e_hg_mask-checkpoint.ipynb

@@ -0,0 +1,2106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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": 2,
+   "id": "a9c8a9e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.io"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 4,
+   "id": "c5ebf6a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgs, img_name = load_images_from_folder('/home/pranjul/Bachelorarbeit Pareidolie/stimuli/Bilder Original/Bilder mit Maske/')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "571c8db2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['95.png',\n",
+       " '85.png',\n",
+       " '91.jpg',\n",
+       " '75.png',\n",
+       " '2.jpg',\n",
+       " '70.png',\n",
+       " '22.jpg',\n",
+       " '122.png',\n",
+       " '128.jpg',\n",
+       " '117.jpg',\n",
+       " '147.jpg',\n",
+       " '11.jpg',\n",
+       " '151.jpg',\n",
+       " '36.jpg',\n",
+       " '17.jpg',\n",
+       " '97.png',\n",
+       " '139.jpg',\n",
+       " '74.png',\n",
+       " '82.png',\n",
+       " '152.jpg',\n",
+       " '60.png',\n",
+       " '27.jpg',\n",
+       " '49.png',\n",
+       " '103.png',\n",
+       " '44.jpg',\n",
+       " '111.jpg',\n",
+       " '76.png',\n",
+       " '78.png',\n",
+       " '146.jpg',\n",
+       " '98.png',\n",
+       " '48.jpg',\n",
+       " '81.png',\n",
+       " '38.jpg',\n",
+       " '57.png',\n",
+       " '141.jpg',\n",
+       " '106.png',\n",
+       " '66.png',\n",
+       " '67.png',\n",
+       " '25.jpg',\n",
+       " '149.jpg',\n",
+       " '121.png',\n",
+       " '150.jpg',\n",
+       " '148.jpg',\n",
+       " '42.jpg',\n",
+       " '127.jpg',\n",
+       " '132.jpg',\n",
+       " '157.jpg',\n",
+       " '112.jpg',\n",
+       " '102.png',\n",
+       " '154.jpg',\n",
+       " '84.png',\n",
+       " '80.png',\n",
+       " '119.png',\n",
+       " '159.jpg',\n",
+       " '125.png',\n",
+       " '89.png',\n",
+       " '123.png',\n",
+       " '21.jpg',\n",
+       " '58.png',\n",
+       " '50.png',\n",
+       " '3.jpg',\n",
+       " '30.jpg',\n",
+       " '108.jpg',\n",
+       " '131.jpg',\n",
+       " '113.jpg',\n",
+       " '55.png',\n",
+       " '64.png',\n",
+       " '156.jpg',\n",
+       " '72.png',\n",
+       " '39.jpg',\n",
+       " '79.png',\n",
+       " '133.jpg',\n",
+       " '144.jpg',\n",
+       " '110.jpg',\n",
+       " '145.jpg',\n",
+       " '7.jpg',\n",
+       " '35.jpg',\n",
+       " '56.png',\n",
+       " '116.jpg',\n",
+       " '126.jpg',\n",
+       " '109.jpg',\n",
+       " '137.jpg',\n",
+       " '69.png']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd911d2d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e99e7121",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "83"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(img_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6e65c80b",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "95"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(img_name[0].split('.')[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "303e82af",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(scipy.io.loadmat('par_dur.mat')['par_dur'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9cfe473e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(scipy.io.loadmat('par_first_onset.mat')['par_first_onset'][0])"
+   ]
+  },
+  {
+   "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",
+    "                \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.DeepGazeI(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": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded pretrained weights for efficientnet-b5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /home/pranjul/.cache/torch/hub/pytorch_vision_v0.6.0\n",
+      "Using cache found in /home/pranjul/.cache/torch/hub/pytorch_vision_v0.6.0\n"
+     ]
+    }
+   ],
+   "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",
+    "    # centerbias_template = np.zeros((1024, 1024))\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_mask', '{0}.jpg'.format(i)))\n",
+    "    plt.close()\n",
+    "    \n",
+    "    #break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5db452d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83bf9be6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "90d31035",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pareidolia['2']['a']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c416a753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(y_pareidolia['2']['a'])\n",
+    "plt.axis('off')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('HG_mars_face.png', dpi=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0a6bda6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2166932",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "dg_faces = []\n",
+    "eg_faces = []\n",
+    "ke = []\n",
+    "correlation_coef_faces = []\n",
+    "\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",
+    "        correlation_coef_faces.append(spearmanr(np.array(x[k]).flatten(),\n",
+    "                                                np.array(y_faces[k]['a']).flatten())[0])\n",
+    "        #correlation_coef = spearmanr(np.array(dg_faces).flatten(), np.array(eg_faces).flatten())\n",
+    "\n",
+    "    #break\n",
+    "\n",
+    "#spearmanr(scaler.fit_transform(cv2.resize(x['1397'], (800, 600))).flatten(), scaler.fit_transform(y_faces['1397']['a']).flatten())[0]\n",
+    "\n",
+    "    \n",
+    "# correlation_coef, p_value = spearmanr(np.array(dg_faces).flatten(), np.array(eg_faces).flatten())\n",
+    "# correlation_coef = np.corrcoef(np.array(dg_faces).flatten(), np.array(eg_faces).flatten())\n",
+    "# print(\"Correlation coefficient:\", correlation_coef)\n",
+    "# print(\"p-value:\", p_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0881d58",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "correlation_coef_faces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "723cc7fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(correlation_coef_faces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52e9c3ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.std(correlation_coef_faces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c187f3a1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(correlation_coef_faces, 'o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ae14f24",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e8ad2bb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70a4ecfb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "len(correlation_coef_faces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77266844",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "dg_objects = []\n",
+    "eg_objects = []\n",
+    "ke = []\n",
+    "correlation_coef_objects = []\n",
+    "\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(np.array(x[k]).flatten())\n",
+    "        #eg_objects.append(np.array(y_objects[k]['a']).flatten())\n",
+    "        correlation_coef_objects.append(spearmanr(np.array(x[k]).flatten(), np.array(y_objects[k]['a']).flatten())[0])\n",
+    "\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": "d5f30f29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df8da20e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(correlation_coef_objects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4c9834d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "np.std(correlation_coef_objects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9465aa0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(correlation_coef_objects, 'o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "beb428b2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "correlation_coef_objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "802ebc6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(correlation_coef_objects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0403de67",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "dg_pareidolia = []\n",
+    "eg_pareidolia = []\n",
+    "ke = []\n",
+    "correlation_coef_pareidolia = []\n",
+    "\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",
+    "        correlation_coef_pareidolia.append(spearmanr(np.array(x[k]).flatten(), \n",
+    "                                           np.array(y_pareidolia[k]['a']).flatten())[0])\n",
+    "        \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": "14e6d358",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(correlation_coef_pareidolia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30812b76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.std(correlation_coef_pareidolia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab74cd89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(correlation_coef_pareidolia, 'o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4980b11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(correlation_coef_pareidolia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10f01cdd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98aa156c",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Sample data with different lengths\n",
+    "#correlation_coef_faces = [0.5, 0.6, 0.7]\n",
+    "#correlation_coef_objects = [0.3, 0.4]\n",
+    "#correlation_coef_pareidolia = [0.2, 0.3, 0.1, 0.4]\n",
+    "\n",
+    "# Create a DataFrame with a common index\n",
+    "index = range(max(len(correlation_coef_faces), len(correlation_coef_objects), len(correlation_coef_pareidolia)))\n",
+    "\n",
+    "data = {\n",
+    "    'sr_f': correlation_coef_faces + [None] * (len(index) - len(correlation_coef_faces)),\n",
+    "    'sr_o': correlation_coef_objects + [None] * (len(index) - len(correlation_coef_objects)),\n",
+    "    'sr_p': correlation_coef_pareidolia + [None] * (len(index) - len(correlation_coef_pareidolia))\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data, index=index)\n",
+    "\n",
+    "# Specify the file name\n",
+    "csv_file = 'data.csv'\n",
+    "\n",
+    "# Save DataFrame to CSV file\n",
+    "df.to_csv(csv_file)\n",
+    "\n",
+    "print(f'Data saved to {csv_file}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37692ab8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "\n",
+    "# Sample data\n",
+    "data = [\n",
+    "    ['Name', 'Age', 'City'],\n",
+    "    ['Alice', 28, 'New York'],\n",
+    "    ['Bob', 35, 'Los Angeles'],\n",
+    "    ['Charlie', 22, 'Chicago']\n",
+    "]\n",
+    "\n",
+    "# Specify the file name\n",
+    "csv_file = 'data.csv'\n",
+    "\n",
+    "# Write data to CSV file\n",
+    "with open(csv_file, mode='w', newline='') as file:\n",
+    "    writer = csv.writer(file)\n",
+    "    writer.writerows(data)\n",
+    "\n",
+    "print(f'Data saved to {csv_file}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8816fcc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d73414c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e53bda0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fe39537",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a51040d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a93508",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "eca95def",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d69ce384",
+   "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.DeepGazeI(pretrained=True).to(DEVICE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8207585",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9d406ff",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "%%capture captured_output\n",
+    "# Your code here\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "984c0e9c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"DG1_arch.txt\", \"w\") as f:\n",
+    "    f.write(captured_output.stdout)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d170109",
+   "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
+}

DeepGaze/.ipynb_checkpoints/dg3_hg-checkpoint.ipynb

@@ -0,0 +1,2272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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\n",
+    "import pickle\n",
+    "import scipy.io\n",
+    "import cv2\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "32bd8589",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "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": 3,
+   "id": "c5ebf6a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgs, img_name = load_images_from_folder('stimuli')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "571c8db2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['67.jpg',\n",
+       " '1587.jpg',\n",
+       " '1458.jpg',\n",
+       " '91.jpg',\n",
+       " '1397.jpg',\n",
+       " '121.jpg',\n",
+       " '1324.jpg',\n",
+       " '1153.jpg',\n",
+       " '1597.jpg',\n",
+       " '1143.jpg',\n",
+       " '1039.jpg',\n",
+       " '1400.jpg',\n",
+       " '1209.jpg',\n",
+       " '75.jpg',\n",
+       " '1480.jpg',\n",
+       " '1093.jpg',\n",
+       " '1267.jpg',\n",
+       " '1227.jpg',\n",
+       " '2.jpg',\n",
+       " '1109.jpg',\n",
+       " '1558.jpg',\n",
+       " '1385.jpg',\n",
+       " '22.jpg',\n",
+       " '56.jpg',\n",
+       " '1118.jpg',\n",
+       " '1078.jpg',\n",
+       " '1532.jpg',\n",
+       " '1416.jpg',\n",
+       " '79.jpg',\n",
+       " '1485.jpg',\n",
+       " '1471.jpg',\n",
+       " '95.jpg',\n",
+       " '20.jpg',\n",
+       " '1042.jpg',\n",
+       " '1262.jpg',\n",
+       " '1288.jpg',\n",
+       " '15.jpg',\n",
+       " '128.jpg',\n",
+       " '72.jpg',\n",
+       " '117.jpg',\n",
+       " '1413.jpg',\n",
+       " '1266.jpg',\n",
+       " '1234.jpg',\n",
+       " '125.jpg',\n",
+       " '1293.jpg',\n",
+       " '147.jpg',\n",
+       " '1557.jpg',\n",
+       " '11.jpg',\n",
+       " '1645.jpg',\n",
+       " '1239.jpg',\n",
+       " '1394.jpg',\n",
+       " '151.jpg',\n",
+       " '010.jpg',\n",
+       " '1559.jpg',\n",
+       " '36.jpg',\n",
+       " '1065.jpg',\n",
+       " '1337.jpg',\n",
+       " '1294.jpg',\n",
+       " '17.jpg',\n",
+       " '143.jpg',\n",
+       " '1022.jpg',\n",
+       " '1016.jpg',\n",
+       " '60.jpg',\n",
+       " '98.jpg',\n",
+       " '1448.jpg',\n",
+       " '1224.jpg',\n",
+       " '139.jpg',\n",
+       " '1149.jpg',\n",
+       " '1455.jpg',\n",
+       " '76.jpg',\n",
+       " '1654.jpg',\n",
+       " '1438.jpg',\n",
+       " '152.jpg',\n",
+       " '1329.jpg',\n",
+       " '1249.jpg',\n",
+       " '1383.jpg',\n",
+       " '1642.jpg',\n",
+       " '102.jpg',\n",
+       " '27.jpg',\n",
+       " '1363.jpg',\n",
+       " '1108.jpg',\n",
+       " '28.jpg',\n",
+       " '1387.jpg',\n",
+       " '1538.jpg',\n",
+       " '1388.jpg',\n",
+       " '138.jpg',\n",
+       " '66.jpg',\n",
+       " '1079.jpg',\n",
+       " '1043.jpg',\n",
+       " '1317.jpg',\n",
+       " '44.jpg',\n",
+       " '1040.jpg',\n",
+       " '103.jpg',\n",
+       " '1644.jpg',\n",
+       " '1120.jpg',\n",
+       " '1125.jpg',\n",
+       " '1392.jpg',\n",
+       " '111.jpg',\n",
+       " '82.jpg',\n",
+       " '85.jpg',\n",
+       " '1627.jpg',\n",
+       " '106.jpg',\n",
+       " '57.jpg',\n",
+       " '1568.jpg',\n",
+       " '1029.jpg',\n",
+       " '1351.jpg',\n",
+       " '1087.jpg',\n",
+       " '83.jpg',\n",
+       " '146.jpg',\n",
+       " '1465.jpg',\n",
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+       " '80.jpg',\n",
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+       " '78.jpg',\n",
+       " '1443.jpg',\n",
+       " '1094.jpg',\n",
+       " '1131.jpg',\n",
+       " '1476.jpg',\n",
+       " '1483.jpg',\n",
+       " '013.jpg',\n",
+       " '1517.jpg',\n",
+       " '1586.jpg',\n",
+       " '1346.jpg',\n",
+       " '1535.jpg',\n",
+       " '1254.jpg',\n",
+       " '9.jpg',\n",
+       " '009.jpg',\n",
+       " '156.jpg',\n",
+       " '1190.jpg',\n",
+       " '1306.jpg',\n",
+       " '1067.jpg',\n",
+       " '49.jpg',\n",
+       " '115.jpg',\n",
+       " '1151.jpg',\n",
+       " '120.jpg',\n",
+       " '1192.jpg',\n",
+       " '39.jpg',\n",
+       " '84.jpg',\n",
+       " '1510.jpg',\n",
+       " '69.jpg',\n",
+       " '1360.jpg',\n",
+       " '1290.jpg',\n",
+       " '1433.jpg',\n",
+       " '1570.jpg',\n",
+       " '1489.jpg',\n",
+       " '1352.jpg',\n",
+       " '153.jpg',\n",
+       " '133.jpg',\n",
+       " '1469.jpg',\n",
+       " '144.jpg',\n",
+       " '110.jpg',\n",
+       " '145.jpg',\n",
+       " '7.jpg',\n",
+       " '1182.jpg',\n",
+       " '35.jpg',\n",
+       " '1369.jpg',\n",
+       " '1504.jpg',\n",
+       " '70.jpg',\n",
+       " '116.jpg',\n",
+       " '1488.jpg',\n",
+       " '126.jpg',\n",
+       " '1240.jpg',\n",
+       " '1651.jpg',\n",
+       " '012.jpg',\n",
+       " '1186.jpg',\n",
+       " '1393.jpg',\n",
+       " '109.jpg',\n",
+       " '137.jpg',\n",
+       " '1105.jpg',\n",
+       " '1297.jpg',\n",
+       " '1238.jpg',\n",
+       " '50.jpg',\n",
+       " '1589.jpg']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e99e7121",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "300"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(img_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0dc9ab34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_fix_from_folder(folder):\n",
+    "    fix_X = []\n",
+    "    fix_Y = []\n",
+    "    radius = []\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",
+    "        radius.append(scipy.io.loadmat(os.path.join(folder,filename))['currImData'][:,6])\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, radius, img_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 8,
+   "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": 9,
+   "id": "bb2809f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_circles(matrix, x_list, y_list, r_list):\n",
+    "    for x, y, r in zip(x_list, y_list, r_list):\n",
+    "        x, y, r = int(x), int(y), int(r)\n",
+    "        for i in range(max(0, y - r), min(matrix.shape[0], y + r + 1)):\n",
+    "            for j in range(max(0, x - r), min(matrix.shape[1], x + r + 1)):\n",
+    "                if (i - y) ** 2 + (j - x) ** 2 <= r ** 2:\n",
+    "                    matrix[i][j] += 1\n",
+    "    return matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5f1efd1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /home/pranjul/.cache/torch/hub/pytorch_vision_v0.6.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Folder 'DG3_HG_heatmaps/S10_fix' created successfully.\n",
+      "Folder 'DG3_HG_heatmaps/S11_fix' created successfully.\n",
+      "Folder 'DG3_HG_heatmaps/S12_fix' created successfully.\n",
+      "Folder 'DG3_HG_heatmaps/S13_fix' created successfully.\n",
+      "Folder 'DG3_HG_heatmaps/S14_fix' created successfully.\n",
+      "Folder 'DG3_HG_heatmaps/S15_fix' created successfully.\n",
+      "Folder 'DG3_HG_heatmaps/S16_fix' created successfully.\n"
+     ]
+    }
+   ],
+   "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",
+    "\n",
+    "for q in range(10, 56):\n",
+    "    \n",
+    "    x = []\n",
+    "    \n",
+    "    # Replace 'path/to/your/folder' with the folder path you want to check\n",
+    "    folder_path = 'S_fix/S'+ str(q) +'_fix'\n",
+    "    \n",
+    "    if folder_exists(folder_path):\n",
+    "    \n",
+    "        fix_X, fix_Y, radius, img_name = load_fix_from_folder('S_fix/S'+ str(q) +'_fix')\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "        # Replace 'path/to/your/folder' with the desired folder path\n",
+    "        folder_path = 'DG3_HG_heatmaps/S'+ 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/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",
+    "                radius_history = radius[i]/5\n",
+    "                \n",
+    "                #print(fixation_history_x, fixation_history_y, radius_history)\n",
+    "                \n",
+    "                # Create a 2D matrix filled with zeros of size (600, 800)\n",
+    "                matrix_size = (600, 800)\n",
+    "                matrix = np.zeros(matrix_size, dtype=int)\n",
+    "\n",
+    "                # Call the function to add circles to the matrix\n",
+    "                result_matrix = add_circles(matrix, fixation_history_x, fixation_history_y, radius_history)\n",
+    "                \n",
+    "                #plt.imshow(result_matrix)\n",
+    "                #plt.plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
+    "                #plt.axis('on')\n",
+    "                #plt.colorbar(fraction=0.046, pad=0.04)  # Adjust fraction and pad values as needed\n",
+    "                #plt.tight_layout()\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",
+    "                \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",
+    "                x.append((log_density_prediction.detach().cpu().numpy()[0, 0], str(img_name[i].split('.')[0]),\n",
+    "                          'S' + str(q), result_matrix))\n",
+    "                \n",
+    "                \n",
+    "                \n",
+    "                f, axs = plt.subplots(nrows=1, ncols=3, 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='white', 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='white', zorder=100)\n",
+    "                axs[1].set_axis_off()\n",
+    "                axs[2].matshow(result_matrix)\n",
+    "                axs[2].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
+    "                axs[2].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='white', zorder=100)\n",
+    "                axs[2].set_axis_off()\n",
+    "                #plt.show()\n",
+    "                plt.savefig(os.path.join('DG3_HG_heatmaps/S'+ str(q) +'_fix', img_name[i]))\n",
+    "                plt.close()\n",
+    "                #break\n",
+    "                \n",
+    "        # Open a file in binary write mode\n",
+    "        with open('DG3_HG_heatmaps/S'+ str(q) +'_fix/' + 'S'+ str(q) + '.pkl', 'wb') as file:\n",
+    "            pickle.dump(x, file)\n",
+    "                \n",
+    "        #break\n",
+    "    #break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7496b7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17e1a970",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "x_loaded = {}\n",
+    "\n",
+    "for q in range(1, 5):\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",
+    "    \n",
+    "    if folder_exists(folder_path):\n",
+    "        # Open a file in binary write mode\n",
+    "        with open('DG3_HG_heatmaps/S0'+ str(q) +'_fix/' + 'S0'+ str(q) + '.pkl', 'rb') as file:\n",
+    "            x_loaded[q] = pickle.load(file)\n",
+    "\n",
+    "#x_loaded = [x.tolist() for x in x_loaded]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8eba733e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_loaded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5c443d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(x_loaded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ea66668",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84c04b86",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e4f31b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_loaded_q = []\n",
+    "\n",
+    "for q in range(1, 5):\n",
+    "    if q in x_loaded:\n",
+    "        print(len(x_loaded[q]))\n",
+    "        x_loaded_q.append(x_loaded[q])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93264ea7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "np.shape(np.reshape(x_loaded_q, (849, 4)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7aaf41a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "283*3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18f03b5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.matshow(np.reshape(x_loaded_q, (849, 4))[100][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54701e93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.matshow(np.reshape(x_loaded_q, (849, 4))[100][3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8966156a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_loaded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e2a4d75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_loaded_q_reshaped = np.reshape(x_loaded_q, (849, 4))"
+   ]
+  },
+  {
+   "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.DeepGazeI(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": "d2f42e76",
+   "metadata": {
+    "scrolled": false
+   },
+   "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",
+    "    # centerbias_template = np.zeros((1024, 1024))\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[a] = str(img_name[i].split('.')[0])\n",
+    "    \n",
+    "    # Inside your loop\n",
+    "    x.append((log_density_prediction.detach().cpu().numpy()[0, 0], str(img_name[i].split('.')[0])))\n",
+    "\n",
+    "\n",
+    "\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": "fcb41708",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ad5624e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "90d31035",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pareidolia['2']['a']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcf0f6dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pareidolia['2']['a'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c416a753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(y_pareidolia['2']['a'])\n",
+    "plt.axis('off')\n",
+    "plt.colorbar(fraction=0.046, pad=0.04)  # Adjust fraction and pad values as needed\n",
+    "plt.tight_layout()\n",
+    "#plt.savefig('HG_mars_face.png', dpi=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0a6bda6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spearmanr([3,4,5,6,7], [1,2,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40524e56",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b89081d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ke = []\n",
+    "for k in range(len(x_loaded)):\n",
+    "    if x_loaded[k][1] in y_faces:\n",
+    "        #print(k)\n",
+    "        ke.append(k)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5f6584d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_loaded[2][3].flatten().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "726784f7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aadc4268",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c36eb153",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(ke)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2166932",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#dg_faces = []\n",
+    "#eg_faces = []\n",
+    "ke_faces = []\n",
+    "correlation_coef_faces = []\n",
+    "\n",
+    "for k in range(len(x_loaded_q_reshaped)):\n",
+    "    if x_loaded_q_reshaped[k][1] in y_faces:\n",
+    "        #print(k)\n",
+    "        ke_faces.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",
+    "        correlation_coef_faces.append(spearmanr(x_loaded_q_reshaped[k][0].flatten(),\n",
+    "                                                x_loaded_q_reshaped[k][3].flatten())[0])\n",
+    "        #correlation_coef = spearmanr(np.array(dg_faces).flatten(), np.array(eg_faces).flatten())\n",
+    "\n",
+    "    #break\n",
+    "\n",
+    "#spearmanr(scaler.fit_transform(cv2.resize(x['1397'], (800, 600))).flatten(), scaler.fit_transform(y_faces['1397']['a']).flatten())[0]\n",
+    "\n",
+    "    \n",
+    "# correlation_coef, p_value = spearmanr(np.array(dg_faces).flatten(), np.array(eg_faces).flatten())\n",
+    "# correlation_coef = np.corrcoef(np.array(dg_faces).flatten(), np.array(eg_faces).flatten())\n",
+    "# print(\"Correlation coefficient:\", correlation_coef)\n",
+    "# print(\"p-value:\", p_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0881d58",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "correlation_coef_faces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "723cc7fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(correlation_coef_faces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52e9c3ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.std(correlation_coef_faces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c187f3a1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(correlation_coef_faces, 'o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70a4ecfb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "len(correlation_coef_faces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77266844",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#dg_objects = []\n",
+    "#eg_objects = []\n",
+    "ke_objects = []\n",
+    "correlation_coef_objects = []\n",
+    "\n",
+    "for k in range(len(x_loaded_q_reshaped)):\n",
+    "    if x_loaded_q_reshaped[k][1] in y_objects:\n",
+    "        #print(k)\n",
+    "        ke_objects.append(k)\n",
+    "        #print(np.shape(x[k]))\n",
+    "        #print(y_faces[k])\n",
+    "        #dg_objects.append(np.array(x[k]).flatten())\n",
+    "        #eg_objects.append(np.array(y_objects[k]['a']).flatten())\n",
+    "        correlation_coef_objects.append(spearmanr(x_loaded_q_reshaped[k][0].flatten(), \n",
+    "                                                  x_loaded_q_reshaped[k][3].flatten())[0])\n",
+    "\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": "d5f30f29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df8da20e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(correlation_coef_objects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4c9834d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "np.std(correlation_coef_objects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9465aa0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(correlation_coef_objects, 'o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "beb428b2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "correlation_coef_objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "802ebc6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(correlation_coef_objects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0403de67",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#dg_pareidolia = []\n",
+    "#eg_pareidolia = []\n",
+    "ke_pareidolia = []\n",
+    "correlation_coef_pareidolia = []\n",
+    "\n",
+    "for k in range(len(x_loaded_q_reshaped)):\n",
+    "    if x_loaded_q_reshaped[k][1] in y_pareidolia:\n",
+    "        #print(k)\n",
+    "        ke_pareidolia.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",
+    "        correlation_coef_pareidolia.append(spearmanr(x_loaded_q_reshaped[k][0].flatten(), \n",
+    "                                           x_loaded_q_reshaped[k][3].flatten())[0])\n",
+    "        \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": "14e6d358",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(correlation_coef_pareidolia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30812b76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.std(correlation_coef_pareidolia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab74cd89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(correlation_coef_pareidolia, 'o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4980b11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(correlation_coef_pareidolia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10f01cdd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "303+252+297"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98aa156c",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Sample data with different lengths\n",
+    "#correlation_coef_faces = [0.5, 0.6, 0.7]\n",
+    "#correlation_coef_objects = [0.3, 0.4]\n",
+    "#correlation_coef_pareidolia = [0.2, 0.3, 0.1, 0.4]\n",
+    "\n",
+    "# Create a DataFrame with a common index\n",
+    "index = range(max(len(correlation_coef_faces), len(correlation_coef_objects), len(correlation_coef_pareidolia)))\n",
+    "\n",
+    "data = {\n",
+    "    'sr_f': correlation_coef_faces + [None] * (len(index) - len(correlation_coef_faces)),\n",
+    "    'sr_o': correlation_coef_objects + [None] * (len(index) - len(correlation_coef_objects)),\n",
+    "    'sr_p': correlation_coef_pareidolia + [None] * (len(index) - len(correlation_coef_pareidolia))\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data, index=index)\n",
+    "\n",
+    "# Specify the file name\n",
+    "csv_file = 'data.csv'\n",
+    "\n",
+    "# Save DataFrame to CSV file\n",
+    "df.to_csv(csv_file)\n",
+    "\n",
+    "print(f'Data saved to {csv_file}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37692ab8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "\n",
+    "# Sample data\n",
+    "data = [\n",
+    "    ['Name', 'Age', 'City'],\n",
+    "    ['Alice', 28, 'New York'],\n",
+    "    ['Bob', 35, 'Los Angeles'],\n",
+    "    ['Charlie', 22, 'Chicago']\n",
+    "]\n",
+    "\n",
+    "# Specify the file name\n",
+    "csv_file = 'data.csv'\n",
+    "\n",
+    "# Write data to CSV file\n",
+    "with open(csv_file, mode='w', newline='') as file:\n",
+    "    writer = csv.writer(file)\n",
+    "    writer.writerows(data)\n",
+    "\n",
+    "print(f'Data saved to {csv_file}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8816fcc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d73414c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e53bda0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fe39537",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a51040d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a93508",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "eca95def",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d69ce384",
+   "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.DeepGazeI(pretrained=True).to(DEVICE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8207585",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9d406ff",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "%%capture captured_output\n",
+    "# Your code here\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "984c0e9c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"DG1_arch.txt\", \"w\") as f:\n",
+    "    f.write(captured_output.stdout)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d170109",
+   "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
+}

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DeepGaze/.ipynb_checkpoints/helena_data_complete-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dab18dbb",
+   "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",
+    "import pickle\n",
+    "import scipy.io\n",
+    "import cv2\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "from scipy.stats import pearsonr, spearmanr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b48a6642",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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": "a79f3e8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgs, img_name = load_images_from_folder('stimuli')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2d3ff2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d00d5fcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(scipy.io.loadmat(fn)['Results']['Disp'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6957bb28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fn = '/home/pranjul/DeepGaze/Bachelorarbeit_Christine_Huschens/results/S04/FA_Block1.mat'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6af13d03",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "len(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][63][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67e9c2eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fn = '/home/pranjul/DeepGaze/Bachelorarbeit_Christine_Huschens/results/S04/FA_Block1.mat'\n",
+    "\n",
+    "for i in range(64):\n",
+    "    gavx, gavy, sttime, entime = [], [], [], []\n",
+    "    \n",
+    "    for j in range(len(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0])):\n",
+    "        gavx.append(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0][j][18][0][0])\n",
+    "        gavy.append(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0][j][19][0][0])\n",
+    "        sttime.append(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0][j][4][0][0])\n",
+    "        entime.append(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0][j][5][0][0])\n",
+    "\n",
+    "    fixendtimes       = scipy.io.loadmat(fn)['Results']['FixEndTimes'][0][0][0][i][0]\n",
+    "    stim_image_name   = scipy.io.loadmat(fn)['Results']['ImPath'][0][0][0][i][0].split('\\\\')[-1]\n",
+    "    stim_folder_name  = scipy.io.loadmat(fn)['Results']['ImPath'][0][0][0][i][0].split('\\\\')[-2]\n",
+    "\n",
+    "    break\n",
+    "\n",
+    "gavx   = np.array(gavx)\n",
+    "gavy   = np.array(gavy)\n",
+    "sttime = np.array(sttime)\n",
+    "entime = np.array(entime)\n",
+    "\n",
+    "res_width   = scipy.io.loadmat(fn)['Results']['Disp'][0][0][0][0][2][0][0][0][0][0]\n",
+    "res_height  = scipy.io.loadmat(fn)['Results']['Disp'][0][0][0][0][2][0][0][1][0][0]\n",
+    "ImWidth     = scipy.io.loadmat(fn)['Results']['ImWidth'][0][0][0][0]\n",
+    "ImHeight    = scipy.io.loadmat(fn)['Results']['ImHeight'][0][0][0][0]\n",
+    "\n",
+    "FixX = np.round(gavx) - (res_width/2) + (ImWidth/2)\n",
+    "FixY = np.round(gavy) - (res_height/2) + (ImHeight/2)\n",
+    "FixDur = entime - sttime\n",
+    "FixOnset = fixendtimes - FixDur + FixDur*.999\n",
+    "\n",
+    "exclude_ind = np.unique([np.where(FixDur < 100)[0][0], np.where(FixOnset < 0)[0][0]])\n",
+    "FixX = np.delete(FixX, exclude_ind)\n",
+    "FixY = np.delete(FixY, exclude_ind)\n",
+    "FixDur = np.delete(FixDur, exclude_ind)\n",
+    "FixOnset = np.delete(FixOnset, exclude_ind)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87e18819",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = cv2.imread('/home/pranjul/DeepGaze/Bachelorarbeit_Christine_Huschens/stimuli/' + stim_folder_name + '/' + stim_image_name)\n",
+    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "image = cv2.resize(image, (ImWidth, ImHeight))\n",
+    "plt.imshow(image)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e07be79d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17977642",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aad511e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scipy.io.loadmat(fn)['Results']['ImPath'][0][0][0][i][0].split('\\\\')[-2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6396d916",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#fixation_history_x = fix_X[i]/3\n",
+    "#print(fixation_history_x)\n",
+    "#fixation_history_y = fix_Y[i]/3\n",
+    "#radius_history = radius[i]/5\n",
+    "\n",
+    "#print(fixation_history_x, fixation_history_y, radius_history)\n",
+    "\n",
+    "# Create a 2D matrix filled with zeros of size (600, 800)\n",
+    "matrix_size = (1200, 1200)\n",
+    "matrix = np.zeros(matrix_size, dtype=int)\n",
+    "\n",
+    "# Call the function to add circles to the matrix\n",
+    "result_matrix = add_circles(matrix, FixY, FixX, FixDur/5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f396faf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(result_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c35bf1b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b9b0899",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e52c9c1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FixX(iFix) = round(FixData(iFix).gavx - Results.Disp.Resolution.width./2 + Results.ImWidth/2); \n",
+    "FixY(iFix) = round(FixData(iFix).gavy - Results.Disp.Resolution.height./2 + Results.ImHeight/2);\n",
+    "FixDur(iFix) = FixData(iFix).entime - FixData(iFix).sttime;\n",
+    "FixOnset(iFix) = Results.FixEndTimes{iIm}(iFix) - FixDur(iFix);\n",
+    "% Bug fix to correct error in fixation onsets\n",
+    "FixOnset(iFix) = FixOnset(iFix) + FixDur(iFix)*.999;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e8787a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "412324a5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2ebb782",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_fix_from_folder(folder):\n",
+    "    fix_X = []\n",
+    "    fix_Y = []\n",
+    "    radius = []\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",
+    "        radius.append(scipy.io.loadmat(os.path.join(folder,filename))['currImData'][:,6])\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, radius, img_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78ddfb7d",
+   "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": 4,
+   "id": "585be960",
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25fa0752",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_circles(matrix, x_list, y_list, r_list):\n",
+    "    for x, y, r in zip(x_list, y_list, r_list):\n",
+    "        x, y, r = int(x), int(y), int(r)\n",
+    "        for i in range(max(0, y - r), min(matrix.shape[0], y + r + 1)):\n",
+    "            for j in range(max(0, x - r), min(matrix.shape[1], x + r + 1)):\n",
+    "                if (i - y) ** 2 + (j - x) ** 2 <= r ** 2:\n",
+    "                    matrix[i][j] += 1\n",
+    "    return matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd2da5f7",
+   "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",
+    "n_f = '/home/pranjul/DeepGaze/Bachelorarbeit_Christine_Huschens_(1)/results/S'\n",
+    "\n",
+    "for q in range(30, 41):\n",
+    "    \n",
+    "    x = []\n",
+    "    \n",
+    "    # Replace 'path/to/your/folder' with the folder path you want to check\n",
+    "    folder_path = n_f + str(q)\n",
+    "    \n",
+    "    if folder_exists(folder_path):\n",
+    "    \n",
+    "        #fix_X, fix_Y, radius, img_name = load_fix_from_folder('S_fix/S'+ str(q) +'_fix')\n",
+    "\n",
+    "        # Replace 'path/to/your/folder' with the desired folder path\n",
+    "        folder_path = '/home/pranjul/DeepGaze/Bachelorarbeit_Christine_Huschens/DG3_HG_heatmaps_c/S'+ str(q) +'_fix_c'\n",
+    "        create_folder(folder_path)\n",
+    "\n",
+    "        for file in sorted(os.listdir(n_f + str(q))):\n",
+    "            if 'Block' in file:\n",
+    "                print(file)        \n",
+    "\n",
+    "                fn = n_f + str(q) + '/' + file\n",
+    "\n",
+    "                for i in range(64):\n",
+    "                    gavx, gavy, sttime, entime = [], [], [], []\n",
+    "\n",
+    "                    for j in range(len(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0])):\n",
+    "                        gavx.append(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0][j][18][0][0])\n",
+    "                        gavy.append(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0][j][19][0][0])\n",
+    "                        sttime.append(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0][j][4][0][0])\n",
+    "                        entime.append(scipy.io.loadmat(fn)['Results']['FixData'][0][0][0][i][0][j][5][0][0])\n",
+    "\n",
+    "                    fixendtimes       = scipy.io.loadmat(fn)['Results']['FixEndTimes'][0][0][0][i][0]\n",
+    "                    stim_image_name   = scipy.io.loadmat(fn)['Results']['ImPath'][0][0][0][i][0].split('\\\\')[-1]\n",
+    "                    stim_folder_name  = scipy.io.loadmat(fn)['Results']['ImPath'][0][0][0][i][0].split('\\\\')[-2]\n",
+    "\n",
+    "                    #break\n",
+    "\n",
+    "                    gavx   = np.array(gavx)\n",
+    "                    gavy   = np.array(gavy)\n",
+    "                    sttime = np.array(sttime)\n",
+    "                    entime = np.array(entime)\n",
+    "\n",
+    "                    res_width   = scipy.io.loadmat(fn)['Results']['Disp'][0][0][0][0][2][0][0][0][0][0]\n",
+    "                    res_height  = scipy.io.loadmat(fn)['Results']['Disp'][0][0][0][0][2][0][0][1][0][0]\n",
+    "                    ImWidth     = scipy.io.loadmat(fn)['Results']['ImWidth'][0][0][0][0]\n",
+    "                    ImHeight    = scipy.io.loadmat(fn)['Results']['ImHeight'][0][0][0][0]\n",
+    "\n",
+    "                    FixX = np.round(gavx) - (res_width/2) + (ImWidth/2)\n",
+    "                    FixY = np.round(gavy) - (res_height/2) + (ImHeight/2)\n",
+    "                    FixDur = entime - sttime\n",
+    "                    FixOnset = fixendtimes - FixDur + FixDur\n",
+    "                    \n",
+    "                    #print(FixDur)\n",
+    "                    #print(FixOnset)\n",
+    "                    dur_indices = np.where(FixDur < 100)[0]\n",
+    "                    onset_indices = np.where(FixOnset < 0)[0]\n",
+    "\n",
+    "                    exclude_ind = np.unique(np.concatenate([dur_indices, onset_indices]))\n",
+    "        \n",
+    "                    print(exclude_ind)\n",
+    "                    FixX = np.delete(FixX, exclude_ind)\n",
+    "                    FixY = np.delete(FixY, exclude_ind)\n",
+    "                    FixDur = np.delete(FixDur, exclude_ind)\n",
+    "                    FixOnset = np.delete(FixOnset, exclude_ind)        \n",
+    "            \n",
+    "                    image = cv2.imread('/home/pranjul/DeepGaze/Bachelorarbeit_Christine_Huschens/stimuli/' + stim_folder_name + '/' + stim_image_name)\n",
+    "                    create_folder(os.path.join(folder_path, stim_folder_name))\n",
+    "                    \n",
+    "                    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "                    image = cv2.resize(image, (ImWidth, ImHeight))             \n",
+    "                \n",
+    "                    if image is not None and len(FixX) > 3 and len(FixY > 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 = FixX\n",
+    "                        #print(fixation_history_x)\n",
+    "                        fixation_history_y = FixY\n",
+    "                        #radius_history = radius[i]/5\n",
+    "\n",
+    "                        #print(fixation_history_x, fixation_history_y, radius_history)\n",
+    "\n",
+    "                        # Create a 2D matrix filled with zeros of size (600, 800)\n",
+    "                        matrix_size = (ImWidth, ImHeight)\n",
+    "                        matrix = np.zeros(matrix_size, dtype=int)\n",
+    "\n",
+    "                        # Call the function to add circles to the matrix\n",
+    "                        result_matrix = add_circles(matrix, FixX, FixY, FixDur/5)\n",
+    "\n",
+    "                        #plt.imshow(result_matrix)\n",
+    "                        #plt.plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
+    "                        #plt.axis('on')\n",
+    "                        #plt.colorbar(fraction=0.046, pad=0.04)  # Adjust fraction and pad values as needed\n",
+    "                        #plt.tight_layout()\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",
+    "\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",
+    "                        x.append((log_density_prediction.detach().cpu().numpy()[0, 0], str(stim_folder_name), str(stim_image_name.split('.')[0]),\n",
+    "                                  'S' + str(q), result_matrix))\n",
+    "\n",
+    "                        f, axs = plt.subplots(nrows=1, ncols=3, 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='white', 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='white', zorder=100)\n",
+    "                        axs[1].set_axis_off()\n",
+    "                        axs[2].matshow(result_matrix)\n",
+    "                        axs[2].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
+    "                        axs[2].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='white', zorder=100)\n",
+    "                        axs[2].set_axis_off()\n",
+    "                        plt.tight_layout()\n",
+    "                        plt.savefig(os.path.join(folder_path, stim_folder_name, stim_image_name.split('.')[0] + '.png'))\n",
+    "                        #plt.show()\n",
+    "                        plt.close()\n",
+    "                    #break\n",
+    "\n",
+    "                # Open a file in binary write mode\n",
+    "                with open(folder_path + '/' + 'S' + str(q) + '.pkl', 'wb') as file:\n",
+    "                    pickle.dump(x, file)\n",
+    "\n",
+    "                #break\n",
+    "            #break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d083b6bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.shape(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b1a8ba5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "32*8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "970201dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_loaded = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "072a0b13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for q in range(10, 41):\n",
+    "\n",
+    "    # Replace 'path/to/your/folder' with the folder path you want to check\n",
+    "    folder_path = '/raid/pranjul/DG3_HG_heatmaps_c/S'+ str(q) +'_fix_c'\n",
+    "    \n",
+    "    if folder_exists(folder_path):\n",
+    "        # Open a file in binary write mode\n",
+    "        with open('/raid/pranjul/DG3_HG_heatmaps_c/S'+ str(q) +'_fix_c/' + 'S'+ str(q) + '.pkl', 'rb') as file:\n",
+    "            x_loaded[q] = pickle.load(file)\n",
+    "\n",
+    "#x_loaded = [x.tolist() for x in x_loaded]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eef8a427",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(x_loaded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "532e15cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_loaded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4e0f88c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "x_loaded[4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d0b7c18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(x_loaded[4][0][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7253f70f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(x_loaded[4][0][4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "48886c2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9472"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "256*37"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb4e8b68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(2):\n",
+    "    print(f\"Iter:{i+1}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9743923c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                    dg3     stim_folder  \\\n",
+      "0     [[-21.89091177095421, -21.89091177095421, -21....      pareidolia   \n",
+      "1     [[-23.752590900618024, -23.752590900618024, -2...  pareidolia_inv   \n",
+      "2     [[-22.717763718089778, -22.717763718089778, -2...           faces   \n",
+      "3     [[-23.13883206422637, -23.13883206422637, -23....         objects   \n",
+      "4     [[-20.465820021512595, -20.465820021512595, -2...  pareidolia_inv   \n",
+      "...                                                 ...             ...   \n",
+      "9204  [[-22.03320704181483, -22.03320704181483, -22....     objects_inv   \n",
+      "9205  [[-19.347162311089896, -19.347162311089896, -1...  pareidolia_inv   \n",
+      "9206  [[-21.903870228837253, -21.903870228837253, -2...         objects   \n",
+      "9207  [[-19.344542363086855, -19.344542363086855, -1...       faces_inv   \n",
+      "9208  [[-24.540417387594943, -24.540417387594943, -2...  pareidolia_inv   \n",
+      "\n",
+      "         stim_name  sub                                                 hg  \n",
+      "0               64  S04  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "1           56_inv  S04  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "2           face31  S04  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "3         16_match  S04  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "4           46_inv  S04  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "...            ...  ...                                                ...  \n",
+      "9204  74_match_inv  S40  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "9205        15_inv  S40  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "9206      39_match  S40  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "9207    face12_inv  S40  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "9208        06_inv  S40  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  \n",
+      "\n",
+      "[9209 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Assuming x_loaded is a dictionary\n",
+    "data_frames = []\n",
+    "\n",
+    "for key, value in x_loaded.items():\n",
+    "    df = pd.DataFrame(value, columns=['dg3', 'stim_folder', 'stim_name', 'sub', 'hg'])\n",
+    "    data_frames.append(df)\n",
+    "\n",
+    "# Concatenate DataFrames\n",
+    "df = pd.concat(data_frames, ignore_index=True)\n",
+    "\n",
+    "# Display the resulting DataFrame\n",
+    "print(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d51aabe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9fc92e53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_agg_hg = df.groupby(['stim_folder', 'stim_name'])['hg'].apply(lambda x: np.mean(x.tolist(), axis=0)).reset_index()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2e9d4054",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stim_folder</th>\n",
+       "      <th>stim_name</th>\n",
+       "      <th>hg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face01</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face02</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face03</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face04</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face05</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>75_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>78_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>80_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>81_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>83_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>256 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        stim_folder stim_name  \\\n",
+       "0             faces    face01   \n",
+       "1             faces    face02   \n",
+       "2             faces    face03   \n",
+       "3             faces    face04   \n",
+       "4             faces    face05   \n",
+       "..              ...       ...   \n",
+       "251  pareidolia_inv    75_inv   \n",
+       "252  pareidolia_inv    78_inv   \n",
+       "253  pareidolia_inv    80_inv   \n",
+       "254  pareidolia_inv    81_inv   \n",
+       "255  pareidolia_inv    83_inv   \n",
+       "\n",
+       "                                                    hg  \n",
+       "0    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "1    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "2    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "3    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "4    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "..                                                 ...  \n",
+       "251  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "252  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "253  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "254  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "255  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "\n",
+       "[256 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_agg_hg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1f15b7de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import base64\n",
+    "# Define a function to serialize the 2D arrays\n",
+    "def serialize_array(arr):\n",
+    "    return base64.b64encode(pickle.dumps(arr)).decode('utf-8')\n",
+    "\n",
+    "# Apply the serialization function to the column\n",
+    "df_agg_hg['hg'] = df_agg_hg['hg'].apply(serialize_array)\n",
+    "\n",
+    "# Save the DataFrame to a CSV file\n",
+    "df_agg_hg.to_csv('/raid/pranjul/agg_hg_37_subs_c.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "709b8028",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# Load the DataFrame from the CSV file\n",
+    "loaded_df_csv = pd.read_csv('/raid/pranjul/agg_hg_37_subs_c.csv')\n",
+    "\n",
+    "# Define a function to deserialize the 2D arrays\n",
+    "def deserialize_array(serialized_arr):\n",
+    "    return pickle.loads(base64.b64decode(serialized_arr.encode('utf-8')))\n",
+    "\n",
+    "# Apply the deserialization function to the column\n",
+    "loaded_df_csv['hg'] = loaded_df_csv['hg'].apply(deserialize_array)\n",
+    "\n",
+    "# Now, loaded_df contains the original DataFrame with 2D arrays in 'Array_Column'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "44d66ffb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stim_folder</th>\n",
+       "      <th>stim_name</th>\n",
+       "      <th>hg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face01</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face02</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face03</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face04</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>faces</td>\n",
+       "      <td>face05</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>75_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>78_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>80_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>81_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>pareidolia_inv</td>\n",
+       "      <td>83_inv</td>\n",
+       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>256 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        stim_folder stim_name  \\\n",
+       "0             faces    face01   \n",
+       "1             faces    face02   \n",
+       "2             faces    face03   \n",
+       "3             faces    face04   \n",
+       "4             faces    face05   \n",
+       "..              ...       ...   \n",
+       "251  pareidolia_inv    75_inv   \n",
+       "252  pareidolia_inv    78_inv   \n",
+       "253  pareidolia_inv    80_inv   \n",
+       "254  pareidolia_inv    81_inv   \n",
+       "255  pareidolia_inv    83_inv   \n",
+       "\n",
+       "                                                    hg  \n",
+       "0    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "1    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "2    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "3    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "4    [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "..                                                 ...  \n",
+       "251  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "252  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "253  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "254  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "255  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
+       "\n",
+       "[256 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded_df_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cad6488c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90dcd74f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3334d903",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8eea8a4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a5b6082",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp_corr = []\n",
+    "for i in range(len(df['dg3'])):\n",
+    "    sp_corr.append(spearmanr(df['dg3'][i].flatten(), df['hg'][i].flatten())[0])\n",
+    "    #break\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76a4cfab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# Save the list to a file\n",
+    "with open('/raid/pranjul/sp_corr_dg3_37_subs_c.pkl', 'wb') as file:\n",
+    "    pickle.dump(sp_corr, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19b45d8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the list from the file\n",
+    "with open('/raid/pranjul/sp_corr_dg3_37_subs_c.pkl', 'rb') as file:\n",
+    "    loaded_list = pickle.load(file)\n",
+    "\n",
+    "print(loaded_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9969e070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98174519",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b86e879c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5aaf588",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the Spearman correlation values to the DataFrame\n",
+    "df['sp_corr'] = sp_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3393ccd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop(columns=['dg3', 'hg'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74823bb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(df['sp_corr'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41901f26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f742b79b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f060d3ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the custom order\n",
+    "custom_order = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "\n",
+    "# Convert the 'stim_folder' column to a Categorical data type with the custom order\n",
+    "result_df['stim_folder'] = pd.Categorical(result_df['stim_folder'], categories=custom_order, ordered=True)\n",
+    "\n",
+    "# Sort the DataFrame based on the custom order\n",
+    "df_sorted = result_df.sort_values(by='stim_folder')\n",
+    "\n",
+    "# Print the sorted DataFrame\n",
+    "print(df_sorted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a8fb762",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6544090",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['stim_folder']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "588a1915",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['stim_folder'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "994c83f0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "result_df['stim_folder'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9ec7617",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7543ea0a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86af740e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ca5da07",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Assuming df is your original DataFrame\n",
+    "# If you don't have a DataFrame, you can create a sample one\n",
+    "# df = pd.DataFrame({'stim_folder': ['objects']*32 + ['faces']*32 + ['pareidolia']*31 + ['pareidolia_art_inv']*31 + ['pareidolia_inv']*31 + ['pareidolia_art']*31 + ['objects_inv']*31 + ['faces_inv']*30})\n",
+    "\n",
+    "# Get the unique values and their counts\n",
+    "value_counts = df['stim_folder'].value_counts()\n",
+    "\n",
+    "# Find the minimum count\n",
+    "min_count = value_counts.min()\n",
+    "\n",
+    "# Identify rows to be removed for each unique value\n",
+    "rows_to_remove = []\n",
+    "\n",
+    "for stim_folder, count in value_counts.items():\n",
+    "    if count > min_count:\n",
+    "        indices = df[df['stim_folder'] == stim_folder].sample(n=count - min_count).index\n",
+    "        rows_to_remove.extend(indices)\n",
+    "\n",
+    "# Remove the identified rows\n",
+    "result_df = df.drop(rows_to_remove)\n",
+    "\n",
+    "# Display the result DataFrame\n",
+    "print(result_df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76acbad9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95468f6f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "len(rows_to_remove)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1716d59",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4811d462",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import combinations\n",
+    "\n",
+    "# 'Object_and_face_identification'\n",
+    "# Given array\n",
+    "task_array = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "\n",
+    "# Generate all unique combinations of two tasks\n",
+    "combinations_list = list(combinations(task_array, 2))\n",
+    "\n",
+    "# Display the result\n",
+    "print(combinations_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd3bbdaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(df['sp_corr'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "345e9b3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65c5864f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"stim_folder\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2ffd235",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"sp_corr\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9163a8b5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1af78875",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combinations_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bddc8f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb842369",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_sorted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d16e1945",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1eabdc8b",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "from statannotations.Annotator import Annotator\n",
+    "\n",
+    "x = \"stim_folder\"\n",
+    "y = \"sp_corr\"\n",
+    "\n",
+    "# Filter the DataFrame based on the 'Time' condition\n",
+    "subset_df = df_sorted\n",
+    "\n",
+    "# Increase the figure size and font size\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.set(style=\"white\", rc={\"axes.edgecolor\": \"black\", \"grid.color\": \"black\", \"grid.linestyle\": \":\"}, font_scale=1.5)\n",
+    "\n",
+    "# Create a palette with distinct colors\n",
+    "palette = sns.color_palette(\"Set3\", n_colors=len(subset_df[x].unique()))  # You can choose any other colormap\n",
+    "\n",
+    "# Create the barplot with the distinct color palette\n",
+    "ax = sns.barplot(data=subset_df, x=x, y=y, palette=palette, capsize=0.1, errwidth=1.5)  # Added capsize and errwidth for error bars\n",
+    "#ax = sns.boxplot(data=subset_df, x=x, y=y, palette=palette)  # Added capsize and errwidth for error bars\n",
+    "\n",
+    "'''\n",
+    "# Add statistical annotations\n",
+    "annot = Annotator(ax, combinations_list, data=subset_df, x=x, y=y)\n",
+    "annot.new_plot(ax, combinations_list,\n",
+    "               data=subset_df, x=x, y=y)\n",
+    "annot.configure(test='Kruskal', text_format='star', loc='outside',\n",
+    "                comparisons_correction=\"fdr_bh\",\n",
+    "                hide_non_significant=True,\n",
+    "                pvalue_thresholds=[[1e-3, '∗∗∗'], [1e-2, \"∗∗\"], [0.05, \"∗\"]], verbose=2)\n",
+    "\n",
+    "#annot.apply_test()\n",
+    "\n",
+    "# Adjust the position of stars between lines\n",
+    "#annot.line_offset_to_group = 0.5\n",
+    "#annot.line_offset = 0.3  # Adjust this value to move the stars closer to the lines\n",
+    "#annot.text_offset = 0.05  # Adjust this value to move the stars closer to the lines\n",
+    "#ax, test_results = annot.annotate(line_offset = 0.01)\n",
+    "#ax, test_results = annot.annotate()\n",
+    "\n",
+    "ax, test_results = annot.apply_test().annotate(line_offset=0.001)\n",
+    "'''\n",
+    "# Remove top and right plot edges\n",
+    "sns.despine()\n",
+    "\n",
+    "# Define explicit legend handles\n",
+    "legend_labels = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "legend_handles = [plt.Rectangle((0, 0), 1, 1, color=color, label=label) for color, label in zip(palette, legend_labels)]\n",
+    "\n",
+    "# Create legend without edges and without error bars, and change the title to \"CNN\"\n",
+    "legend = ax.legend(handles=legend_handles, title=\"\", title_fontsize='16', loc='upper left', bbox_to_anchor=(1, 1), frameon=False, fontsize=16)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Remove x-axis ticks and labels\n",
+    "#ax.set_xticks([])\n",
+    "#ax.set_xticklabels([])\n",
+    "\n",
+    "# Set y-axis limits from 0 to 1\n",
+    "ax.set_ylim(0.25, 0.35)\n",
+    "ax.set_yticks(np.linspace(0.25, 0.35, num=6))\n",
+    "\n",
+    "# Show the tick marks on the left side of the y-axis\n",
+    "#ax.tick_params(axis='y', direction='out', length=5)  # Adjust 'length' as needed\n",
+    "ax.tick_params(tick1On=True)\n",
+    "\n",
+    "# Set font size for axis labels and title\n",
+    "ax.set_ylabel('DG3 & HG correlation [Spearman\\'s r]', fontsize=16)\n",
+    "#ax.set_ylabel('')\n",
+    "ax.set_xlabel('2 (orientation)  x 4 (stimuli condition)', fontsize=16)\n",
+    "\n",
+    "#ax.set_title('255ms:lay13-15', fontsize=18)\n",
+    "\n",
+    "# Set font size for tick labels\n",
+    "ax.tick_params(axis='both', labelsize=16)\n",
+    "\n",
+    "# Uncomment the following lines to show y-axis ticks and tick labels\n",
+    "# ax.set_yticks(np.linspace(0, 1.0, num=11))  # Uncomment if not already set\n",
+    "# ax.set_yticklabels(['0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0'])\n",
+    "\n",
+    "#plt.savefig('fig_5_3.png', dpi= 600, bbox_inches='tight')\n",
+    "#plt.savefig('fig_5_3.png', dpi= 600)\n",
+    "\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1999e662",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "from statannotations.Annotator import Annotator\n",
+    "\n",
+    "x = \"stim_folder\"\n",
+    "y = \"sp_corr\"\n",
+    "\n",
+    "# Filter the DataFrame based on the 'Time' condition\n",
+    "subset_df = result_df\n",
+    "\n",
+    "# Increase the figure size and font size\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.set(style=\"white\", rc={\"axes.edgecolor\": \"black\", \"grid.color\": \"black\", \"grid.linestyle\": \":\"}, font_scale=1.5)\n",
+    "\n",
+    "# Create a palette with distinct colors\n",
+    "palette = sns.color_palette(\"Set3\", n_colors=len(subset_df[x].unique()))  # You can choose any other colormap\n",
+    "\n",
+    "# Create the barplot with the distinct color palette\n",
+    "ax = sns.barplot(data=subset_df, x=x, y=y, palette=palette, capsize=0.1, errwidth=1.5)  # Added capsize and errwidth for error bars\n",
+    "# ax = sns.boxplot(data=subset_df, x=x, y=y, palette=palette)  # Added capsize and errwidth for error bars\n",
+    "\n",
+    "\n",
+    "# Add statistical annotations\n",
+    "annot = Annotator(ax, combinations_list, data=subset_df, x=x, y=y)\n",
+    "annot.new_plot(ax, combinations_list,\n",
+    "               data=subset_df, x=x, y=y)\n",
+    "annot.configure(test='Kruskal', text_format='star', loc='outside',\n",
+    "                comparisons_correction=\"fdr_bh\",\n",
+    "                hide_non_significant=True,\n",
+    "                pvalue_thresholds=[[1e-3, '∗∗∗'], [1e-2, \"∗∗\"], [0.05, \"∗\"]], verbose=2, line_height = .05)\n",
+    "\n",
+    "#annot.apply_test()\n",
+    "\n",
+    "# Adjust the position of stars between lines\n",
+    "#annot.line_offset_to_group = 0.5\n",
+    "#annot.line_offset = 0.3  # Adjust this value to move the stars closer to the lines\n",
+    "#annot.text_offset = 0.05  # Adjust this value to move the stars closer to the lines\n",
+    "#ax, test_results = annot.annotate(line_offset = 0.01)\n",
+    "#ax, test_results = annot.annotate()\n",
+    "\n",
+    "ax, test_results = annot.apply_test().annotate(line_offset=0.001)\n",
+    "\n",
+    "# Remove top and right plot edges\n",
+    "sns.despine()\n",
+    "\n",
+    "# Define explicit legend handles\n",
+    "legend_labels = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "legend_handles = [plt.Rectangle((0, 0), 1, 1, color=color, label=label) for color, label in zip(palette, legend_labels)]\n",
+    "\n",
+    "# Create legend without edges and without error bars, and change the title to \"CNN\"\n",
+    "legend = ax.legend(handles=legend_handles, title=\"\", title_fontsize='16', loc='upper left', bbox_to_anchor=(1, 1), frameon=False, fontsize=16)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Remove x-axis ticks and labels\n",
+    "#ax.set_xticks([])\n",
+    "#ax.set_xticklabels([])\n",
+    "\n",
+    "# Set y-axis limits from 0 to 1\n",
+    "#ax.set_ylim(0.25, 0.35)\n",
+    "#ax.set_yticks(np.linspace(0.25, 0.35, num=6))\n",
+    "\n",
+    "# Show the tick marks on the left side of the y-axis\n",
+    "#ax.tick_params(axis='y', direction='out', length=5)  # Adjust 'length' as needed\n",
+    "ax.tick_params(tick1On=True)\n",
+    "\n",
+    "# Set font size for axis labels and title\n",
+    "ax.set_ylabel('DG3 & HG correlation [Spearman\\'s r]', fontsize=16)\n",
+    "#ax.set_ylabel('')\n",
+    "#ax.set_xlabel('2 (orientation)  x 4 (stimuli condition)', fontsize=16)\n",
+    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
+    "\n",
+    "\n",
+    "#ax.set_title('255ms:lay13-15', fontsize=18)\n",
+    "\n",
+    "# Set font size for tick labels\n",
+    "ax.tick_params(axis='both', labelsize=16)\n",
+    "\n",
+    "# Uncomment the following lines to show y-axis ticks and tick labels\n",
+    "# ax.set_yticks(np.linspace(0, 1.0, num=11))  # Uncomment if not already set\n",
+    "#ax.set_yticklabels(['0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0'])\n",
+    "\n",
+    "#plt.savefig('fig_5_3.png', dpi= 600, bbox_inches='tight')\n",
+    "#plt.savefig('fig_5_3.png', dpi= 600)\n",
+    "\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2dc4e82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Length of x:\", len(subset_df[x]))\n",
+    "print(\"Length of y:\", len(subset_df[y]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68b2c5b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf6f386d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_df_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c57e732a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_agg_hg = loaded_df_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa645e29",
+   "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",
+    "\n",
+    "for i in range(len(df_agg_hg)):\n",
+    "    \n",
+    "    stim_folder_name = df_agg_hg['stim_folder'][i]\n",
+    "    stim_image_name = df_agg_hg['stim_name'][i]\n",
+    "        \n",
+    "    folder_path = '/home/pranjul/DeepGaze/Bachelorarbeit_Christine_Huschens/DG2E_HG_heatmaps_c/'\n",
+    "    \n",
+    "    image = cv2.imread('/home/pranjul/DeepGaze/Bachelorarbeit_Christine_Huschens/stimuli/' + stim_folder_name + '/' + stim_image_name + '.tif')\n",
+    "    #create_folder(os.path.join(folder_path, stim_folder_name))\n",
+    "\n",
+    "    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "    image = cv2.resize(image, (1200, 1200))  \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",
+    "    # centerbias_template = np.zeros((1024, 1024))\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.append(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",
+    "    f, axs = plt.subplots(nrows=1, ncols=3, 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='white', 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='white', zorder=100)\n",
+    "    axs[1].set_axis_off()\n",
+    "    axs[2].matshow(df_agg_hg['hg'][i])\n",
+    "    #axs[2].plot(fixation_history_x, fixation_history_y, 'o-', color='red')\n",
+    "    #axs[2].scatter(fixation_history_x[-1], fixation_history_y[-1], 100, color='white', zorder=100)\n",
+    "    axs[2].set_axis_off()\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig(os.path.join(folder_path, stim_folder_name, stim_image_name + '.png'))\n",
+    "    #plt.show()\n",
+    "    plt.close()\n",
+    "    '''\n",
+    "    \n",
+    "    #break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61eaa85b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91bef121",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bc2fa05",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24f5fd26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the Spearman correlation values to the DataFrame\n",
+    "df_agg_hg['dg2'] = x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d007260",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_agg_hg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fecd938",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fda8ef1e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c030811c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f707fe40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# Define a function to serialize the 2D arrays\n",
+    "def serialize_array(arr):\n",
+    "    return base64.b64encode(pickle.dumps(arr)).decode('utf-8')\n",
+    "\n",
+    "# Apply the serialization function to the column\n",
+    "df_agg_hg['hg'] = df_agg_hg['hg'].apply(serialize_array)\n",
+    "\n",
+    "# Apply the serialization function to the column\n",
+    "df_agg_hg['dg2'] = df_agg_hg['dg2'].apply(serialize_array)\n",
+    "\n",
+    "# Save the DataFrame to a CSV file\n",
+    "df_agg_hg.to_csv('/raid/pranjul/agg_hg_dg2_26_subs_c.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "412a8aa4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "\n",
+    "# Load the DataFrame from the CSV file\n",
+    "loaded_df_csv = pd.read_csv('/raid/pranjul/agg_hg_dg2_26_subs_c.csv')\n",
+    "\n",
+    "# Define a function to deserialize the 2D arrays\n",
+    "def deserialize_array(serialized_arr):\n",
+    "    return pickle.loads(base64.b64decode(serialized_arr.encode('utf-8')))\n",
+    "\n",
+    "# Apply the deserialization function to the column\n",
+    "loaded_df_csv['hg'] = loaded_df_csv['hg'].apply(deserialize_array)\n",
+    "\n",
+    "# Apply the deserialization function to the column\n",
+    "loaded_df_csv['dg2'] = loaded_df_csv['dg2'].apply(deserialize_array)\n",
+    "\n",
+    "# Now, loaded_df contains the original DataFrame with 2D arrays in 'Array_Column'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51936049",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_df_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9ff6f39",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42e4c616",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2db1eb6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Create two sample dataframes\n",
+    "data1 = {'SharedColumn': [1, 2, 3, 4, 5],\n",
+    "         'Data1_Column': ['A', 'B', 'C', 'D', 'E']}\n",
+    "df1 = pd.DataFrame(data1)\n",
+    "\n",
+    "data2 = {'SharedColumn': [1, 1, 2, 2, 3],\n",
+    "         'Data2_Column': [10, 20, 30, 40, 50]}\n",
+    "df2 = pd.DataFrame(data2)\n",
+    "\n",
+    "# Merge dataframes based on the shared column\n",
+    "merged_df = pd.merge(df1, df2, on='SharedColumn', how='outer')\n",
+    "\n",
+    "# Divide the dataframes based on unique values in the 'SharedColumn'\n",
+    "unique_values_df1 = merged_df[merged_df['Data2_Column'].isnull()].drop('Data2_Column', axis=1)\n",
+    "repeated_values_df2 = merged_df[merged_df['Data1_Column'].notnull()].drop('Data1_Column', axis=1)\n",
+    "\n",
+    "# Display the results\n",
+    "print(\"Unique Values in df1:\")\n",
+    "print(unique_values_df1)\n",
+    "\n",
+    "print(\"\\nRepeated Values in df2:\")\n",
+    "print(repeated_values_df2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fbe8c57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3964aa7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72ad2dbf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fba47112",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.merge(loaded_df_csv, df_sh, on='stim_folder', how='outer')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6873e2b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b568d804",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ec6401a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fbccdc1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e79d4b83",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05bf148f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3492dab1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp_corr_dg2 = []\n",
+    "for i in range(len(loaded_df_csv)):\n",
+    "    sp_corr_dg2.append(spearmanr(loaded_df_csv['dg2'][i].flatten(), loaded_df_csv['hg'][i].flatten())[0])\n",
+    "    #break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cb4d2a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the Spearman correlation values to the DataFrame\n",
+    "loaded_df_csv['sp_corr_dg2'] = sp_corr_dg2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9809da0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_df_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d679042",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_df_csv = loaded_df_csv.drop(columns=['dg2', 'hg'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db0c138a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_df_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "feb185b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_df_csv['stim_folder'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36efceb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the custom order\n",
+    "custom_order = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "\n",
+    "# Convert the 'stim_folder' column to a Categorical data type with the custom order\n",
+    "loaded_df_csv['stim_folder'] = pd.Categorical(loaded_df_csv['stim_folder'], categories=custom_order, ordered=True)\n",
+    "\n",
+    "# Sort the DataFrame based on the custom order\n",
+    "df_sorted = loaded_df_csv.sort_values(by='stim_folder')\n",
+    "\n",
+    "# Print the sorted DataFrame\n",
+    "print(df_sorted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d9bcec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_sorted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02efe074",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_sorted_sh = pd.merge(df_sorted, df_sh, on='stim_folder', how='outer')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "293790d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_sorted_sh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cea2334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Divide Column1 by Column2\n",
+    "df_sorted_sh['sh_corrected'] = df_sorted_sh['sp_corr_dg2'] / df_sorted_sh['sp_corr_splt_hlf']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af406de8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_sorted_sh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f2aa57c",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "from statannotations.Annotator import Annotator\n",
+    "\n",
+    "x = \"stim_folder\"\n",
+    "y = \"sh_corrected\"\n",
+    "\n",
+    "# Filter the DataFrame based on the 'Time' condition\n",
+    "subset_df = df_sorted_sh\n",
+    "\n",
+    "# Increase the figure size and font size\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.set(style=\"white\", rc={\"axes.edgecolor\": \"black\", \"grid.color\": \"black\", \"grid.linestyle\": \":\"}, font_scale=1.5)\n",
+    "\n",
+    "# Create a palette with distinct colors\n",
+    "palette = sns.color_palette(\"Set3\", n_colors=len(subset_df[x].unique()))  # You can choose any other colormap\n",
+    "\n",
+    "# Create the barplot with the distinct color palette\n",
+    "ax = sns.barplot(data=subset_df, x=x, y=y, palette=palette, capsize=0.1, errwidth=1.5)  # Added capsize and errwidth for error bars\n",
+    "# ax = sns.boxplot(data=subset_df, x=x, y=y, palette=palette)  # Added capsize and errwidth for error bars\n",
+    "\n",
+    "\n",
+    "# Add statistical annotations\n",
+    "annot = Annotator(ax, combinations_list, data=subset_df, x=x, y=y)\n",
+    "annot.new_plot(ax, combinations_list,\n",
+    "               data=subset_df, x=x, y=y)\n",
+    "annot.configure(test='Kruskal', text_format='star', loc='outside',\n",
+    "                comparisons_correction=\"fdr_bh\",\n",
+    "                hide_non_significant=True,\n",
+    "                pvalue_thresholds=[[1e-3, '∗∗∗'], [1e-2, \"∗∗\"], [0.05, \"∗\"]], verbose=2, line_height = .05)\n",
+    "\n",
+    "#annot.apply_test()\n",
+    "\n",
+    "# Adjust the position of stars between lines\n",
+    "#annot.line_offset_to_group = 0.5\n",
+    "#annot.line_offset = 0.3  # Adjust this value to move the stars closer to the lines\n",
+    "#annot.text_offset = 0.05  # Adjust this value to move the stars closer to the lines\n",
+    "#ax, test_results = annot.annotate(line_offset = 0.01)\n",
+    "#ax, test_results = annot.annotate()\n",
+    "\n",
+    "ax, test_results = annot.apply_test().annotate(line_offset=0.2)\n",
+    "\n",
+    "# Remove top and right plot edges\n",
+    "sns.despine()\n",
+    "\n",
+    "# Define explicit legend handles\n",
+    "legend_labels = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "legend_handles = [plt.Rectangle((0, 0), 1, 1, color=color, label=label) for color, label in zip(palette, legend_labels)]\n",
+    "\n",
+    "# Create legend without edges and without error bars, and change the title to \"CNN\"\n",
+    "legend = ax.legend(handles=legend_handles, title=\"\", title_fontsize='16', loc='upper left', bbox_to_anchor=(1, 1), frameon=False, fontsize=16)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Remove x-axis ticks and labels\n",
+    "#ax.set_xticks([])\n",
+    "#ax.set_xticklabels([])\n",
+    "\n",
+    "# Set y-axis limits from 0 to 1\n",
+    "ax.set_ylim(0.9, 1.1)\n",
+    "ax.set_yticks(np.linspace(0.9, 1.1, num=11))\n",
+    "\n",
+    "# Show the tick marks on the left side of the y-axis\n",
+    "#ax.tick_params(axis='y', direction='out', length=5)  # Adjust 'length' as needed\n",
+    "ax.tick_params(tick1On=True)\n",
+    "\n",
+    "# Set font size for axis labels and title\n",
+    "ax.set_ylabel('S-H corrected DG2E & HG correlation \\n [Spearman\\'s r]', fontsize=16)\n",
+    "#ax.set_ylabel('')\n",
+    "#ax.set_xlabel('2 (orientation)  x 4 (stimuli condition)', fontsize=16)\n",
+    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
+    "\n",
+    "\n",
+    "#ax.set_title('255ms:lay13-15', fontsize=18)\n",
+    "\n",
+    "# Set font size for tick labels\n",
+    "ax.tick_params(axis='both', labelsize=16)\n",
+    "\n",
+    "# Uncomment the following lines to show y-axis ticks and tick labels\n",
+    "# ax.set_yticks(np.linspace(0, 1.0, num=11))  # Uncomment if not already set\n",
+    "#ax.set_yticklabels(['0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0'])\n",
+    "\n",
+    "#plt.savefig('fig_5_3.png', dpi= 600, bbox_inches='tight')\n",
+    "#plt.savefig('fig_5_3.png', dpi= 600)\n",
+    "\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24057961",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5877b510",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9a4da8a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edfab771",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f454bd9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7559c80",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Get all unique values in the 'sub' column\n",
+    "unique_subs = df['sub'].unique()\n",
+    "unique_subs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc285653",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly shuffle the unique values\n",
+    "np.random.shuffle(unique_subs)\n",
+    "unique_subs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "486cf66c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate the index to split at (half of the unique values)\n",
+    "split_index = len(unique_subs) // 2\n",
+    "split_index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "162006ff",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Select the first half of unique values\n",
+    "selected_subs_df1 = unique_subs[:split_index]\n",
+    "selected_subs_df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9ad53ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the DataFrame into two based on the selected unique values\n",
+    "df1 = df[df['sub'].isin(selected_subs_df1)]\n",
+    "df2 = df[~df['sub'].isin(selected_subs_df1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "783a8dd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40436fc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d683c68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1_hg = df1.groupby(['stim_folder', 'stim_name'])['hg'].apply(lambda x: np.mean(x.tolist(), axis=0)).reset_index()\n",
+    "\n",
+    "# Rename the column 'old_col_name' to 'new_col_name'\n",
+    "df1_hg = df1_hg.rename(columns={'hg': 'hg_1'})\n",
+    "df1_hg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c8fa6bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2_hg = df2.groupby(['stim_folder', 'stim_name'])['hg'].apply(lambda x: np.mean(x.tolist(), axis=0)).reset_index()\n",
+    "\n",
+    "# Rename the column 'old_col_name' to 'new_col_name'\n",
+    "df2_hg = df2_hg.rename(columns={'hg': 'hg_2'})\n",
+    "df2_hg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b460ad5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the DataFrames based on 'stim_folder' and 'stim_name'\n",
+    "merged_df = pd.merge(df1_hg, df2_hg, on=['stim_folder', 'stim_name'], how='inner')\n",
+    "\n",
+    "# Display the resulting merged DataFrame\n",
+    "print(merged_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3be7535f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(merged_df['hg_1'][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48eb82a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(merged_df['hg_2'][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "778da74b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "955721a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp_corr_splt_hlf = []\n",
+    "for i in range(len(merged_df)):\n",
+    "    sp_corr_splt_hlf.append(spearmanr(merged_df['hg_1'][i].flatten(), merged_df['hg_2'][i].flatten())[0])\n",
+    "    #break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cf2d0f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the Spearman correlation values to the DataFrame\n",
+    "merged_df['sp_corr_splt_hlf'] = sp_corr_splt_hlf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e0ead15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_df = merged_df.drop(columns=['hg_1', 'hg_2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a2c8570",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d302dc86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_df = merged_df.groupby('stim_folder')['sp_corr_splt_hlf'].mean().reset_index()\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9dbab573",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the custom order\n",
+    "custom_order = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "\n",
+    "# Convert the 'stim_folder' column to a Categorical data type with the custom order\n",
+    "merged_df['stim_folder'] = pd.Categorical(merged_df['stim_folder'], categories=custom_order, ordered=True)\n",
+    "\n",
+    "# Sort the DataFrame based on the custom order\n",
+    "merged_df_sorted = merged_df.sort_values(by='stim_folder')\n",
+    "\n",
+    "# Print the sorted DataFrame\n",
+    "print(merged_df_sorted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "150d3228",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract the 'sp_corr_splt_hlf' column and save it to a NumPy array\n",
+    "sp_corr_splt_hlf_array = merged_df_sorted['sp_corr_splt_hlf'].to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01253db8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp_corr_splt_hlf_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30d27acc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2d9d05e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d751aabb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bcf36b3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ce5789",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Number of times to perform the bootstrap sampling\n",
+    "num_iterations = 50\n",
+    "sp_corr_splt_hlf_array = []\n",
+    "\n",
+    "for _ in range(num_iterations):\n",
+    "\n",
+    "    # Get all unique values in the 'sub' column\n",
+    "    unique_subs = df['sub'].unique()\n",
+    "    #print(unique_subs)\n",
+    "\n",
+    "    # Randomly shuffle the unique values\n",
+    "    np.random.shuffle(unique_subs)\n",
+    "    print(unique_subs)\n",
+    "\n",
+    "    # Calculate the index to split at (half of the unique values)\n",
+    "    split_index = len(unique_subs) // 2\n",
+    "    #print(split_index)\n",
+    "\n",
+    "    # Select the first half of unique values\n",
+    "    selected_subs_df1 = unique_subs[:split_index]\n",
+    "    #selected_subs_df1\n",
+    "\n",
+    "    # Split the DataFrame into two based on the selected unique values\n",
+    "    df1 = df[df['sub'].isin(selected_subs_df1)]\n",
+    "    df2 = df[~df['sub'].isin(selected_subs_df1)]\n",
+    "\n",
+    "    df1_hg = df1.groupby(['stim_folder', 'stim_name'])['hg'].apply(lambda x: np.mean(x.tolist(), axis=0)).reset_index()\n",
+    "\n",
+    "    # Rename the column 'old_col_name' to 'new_col_name'\n",
+    "    df1_hg = df1_hg.rename(columns={'hg': 'hg_1'})\n",
+    "    #df1_hg\n",
+    "\n",
+    "    df2_hg = df2.groupby(['stim_folder', 'stim_name'])['hg'].apply(lambda x: np.mean(x.tolist(), axis=0)).reset_index()\n",
+    "\n",
+    "    # Rename the column 'old_col_name' to 'new_col_name'\n",
+    "    df2_hg = df2_hg.rename(columns={'hg': 'hg_2'})\n",
+    "    #df2_hg\n",
+    "\n",
+    "    # Merge the DataFrames based on 'stim_folder' and 'stim_name'\n",
+    "    merged_df = pd.merge(df1_hg, df2_hg, on=['stim_folder', 'stim_name'], how='inner')\n",
+    "\n",
+    "    # Display the resulting merged DataFrame\n",
+    "    #print(merged_df)\n",
+    "\n",
+    "    sp_corr_splt_hlf = []\n",
+    "    for i in range(len(merged_df)):\n",
+    "        sp_corr_splt_hlf.append(spearmanr(merged_df['hg_1'][i].flatten(), merged_df['hg_2'][i].flatten())[0])\n",
+    "        #break\n",
+    "\n",
+    "    # Add the Spearman correlation values to the DataFrame\n",
+    "    merged_df['sp_corr_splt_hlf'] = sp_corr_splt_hlf\n",
+    "\n",
+    "    merged_df = merged_df.drop(columns=['hg_1', 'hg_2'])\n",
+    "\n",
+    "    merged_df = merged_df.groupby('stim_folder')['sp_corr_splt_hlf'].mean().reset_index()\n",
+    "\n",
+    "    # Define the custom order\n",
+    "    custom_order = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "\n",
+    "    # Convert the 'stim_folder' column to a Categorical data type with the custom order\n",
+    "    merged_df['stim_folder'] = pd.Categorical(merged_df['stim_folder'], categories=custom_order, ordered=True)\n",
+    "\n",
+    "    # Sort the DataFrame based on the custom order\n",
+    "    merged_df_sorted = merged_df.sort_values(by='stim_folder')\n",
+    "\n",
+    "    # Print the sorted DataFrame\n",
+    "    #print(merged_df_sorted)\n",
+    "\n",
+    "    # Extract the 'sp_corr_splt_hlf' column and save it to a NumPy array\n",
+    "    sp_corr_splt_hlf_array.append(merged_df_sorted['sp_corr_splt_hlf'].to_numpy())\n",
+    "\n",
+    "print(sp_corr_splt_hlf_array)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fa9422a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(sp_corr_splt_hlf_array, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10718a78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(sp_corr_splt_hlf_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ac85240",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tabulate import tabulate\n",
+    "\n",
+    "mapping = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "values = [0.64045339, 0.63962363, 0.65247639, 0.64685355, 0.69212828, 0.68226111, 0.63446804, 0.64820666]\n",
+    "\n",
+    "array_result = np.array(values)\n",
+    "\n",
+    "table = list(zip(mapping, array_result))\n",
+    "headers = [\"Conditions\", \"S-H rho\"]\n",
+    "\n",
+    "print(tabulate(table, headers=headers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b4c3fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Given data\n",
+    "mapping = ['faces', 'faces_inv', 'objects', 'objects_inv', 'pareidolia', 'pareidolia_inv', 'pareidolia_art', 'pareidolia_art_inv']\n",
+    "values = [0.64045339, 0.63962363, 0.65247639, 0.64685355, 0.69212828, 0.68226111, 0.63446804, 0.64820666]\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df_sh = pd.DataFrame({'stim_folder': mapping, 'sp_corr_splt_hlf': values})\n",
+    "\n",
+    "# Print the DataFrame\n",
+    "print(df_sh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04fd3c07",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10ac43f4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a60ccce0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "873461b1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbda9f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ae9f2d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "correlation_coef_objects_iter_img = []\n",
+    "\n",
+    "for keys in y_objects:\n",
+    "    mmm = []\n",
+    "    print(keys)\n",
+    "    for i in range(len(y_objects_subs)):\n",
+    "        if y_objects_subs[i][0][0] == keys:\n",
+    "            mmm.append(y_objects_subs[i][1])\n",
+    "            #print(y_faces_subs[i][1])\n",
+    "\n",
+    "    # Number of times to perform the bootstrap sampling\n",
+    "    num_iterations = 50\n",
+    "    correlation_coef_objects_iter = []\n",
+    "\n",
+    "    for _ in range(num_iterations):\n",
+    "\n",
+    "        # Define your dataset\n",
+    "        dataset = list(range(len(mmm)))\n",
+    "\n",
+    "        # Perform bootstrap sampling without replacement until no dataset is left\n",
+    "        bootstrap_samples = []\n",
+    "        correlation_coef_objects = []\n",
+    "\n",
+    "        while dataset:\n",
+    "            bootstrap_sample = random.sample(dataset, len(dataset))\n",
+    "            bootstrap_samples.append(bootstrap_sample)\n",
+    "            dataset = [x for x in dataset if x not in bootstrap_sample]\n",
+    "\n",
+    "        # Print the bootstrap samples\n",
+    "        #for i, sample in enumerate(bootstrap_samples):\n",
+    "        #    print(f\"Bootstrap Sample {k + 1}: {sample}\")\n",
+    "        \n",
+    "        temp1 = []\n",
+    "        temp2 = []\n",
+    "\n",
+    "        for i in bootstrap_samples[0][:int(len(bootstrap_samples[0])/2)]:\n",
+    "            temp1.append(mmm[i])\n",
+    "\n",
+    "        for i in bootstrap_samples[0][int(len(bootstrap_samples[0])/2):]:\n",
+    "            temp2.append(mmm[i])\n",
+    "\n",
+    "        #print(temp1)\n",
+    "        temp1 = np.mean(temp1, axis=0)\n",
+    "        temp2 = np.mean(temp2, axis=0)\n",
+    "            \n",
+    "            #plt.matshow(mmm[sample[i]])\n",
+    "            #plt.matshow(mmm[sample[i+1]])\n",
+    "        correlation_coef_objects.append(spearmanr(temp1.flatten(),\n",
+    "                                                temp2.flatten())[0])\n",
+    "\n",
+    "        correlation_coef_objects_iter.append(np.mean(correlation_coef_objects))\n",
+    "        \n",
+    "        #break\n",
+    "        \n",
+    "\n",
+    "    correlation_coef_objects_iter_img.append(np.mean(correlation_coef_objects_iter))\n",
+    "        \n",
+    "    #break\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f59ec104",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27c5282e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55fccdef",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "856344fb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
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+   "id": "68144351",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
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+   "id": "031db151",
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+  {
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+   "id": "6a111361",
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DeepGaze/.ipynb_checkpoints/helena_data_masks-checkpoint.ipynb

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+ "nbformat_minor": 5
+}

DeepGaze/.ipynb_checkpoints/mask corrs-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

DeepGaze/1448_face_mask.json

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",
+  "imageHeight": 600,
+  "imageWidth": 800
+}

DeepGaze/DG1_arch.txt

@@ -0,0 +1,40 @@
+DeepGazeI(
+  (features): FeatureExtractor(
+    (features): RGBalexnet(
+      (0): Normalizer()
+      (1): AlexNet(
+        (features): Sequential(
+          (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
+          (1): ReLU(inplace=True)
+          (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
+          (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+          (4): ReLU(inplace=True)
+          (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
+          (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (7): ReLU(inplace=True)
+          (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (9): ReLU(inplace=True)
+          (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (11): ReLU(inplace=True)
+          (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
+        )
+        (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
+        (classifier): Sequential(
+          (0): Dropout(p=0.5, inplace=False)
+          (1): Linear(in_features=9216, out_features=4096, bias=True)
+          (2): ReLU(inplace=True)
+          (3): Dropout(p=0.5, inplace=False)
+          (4): Linear(in_features=4096, out_features=4096, bias=True)
+          (5): ReLU(inplace=True)
+          (6): Linear(in_features=4096, out_features=1000, bias=True)
+        )
+      )
+    )
+  )
+  (readout_network): Sequential(
+    (conv0): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
+  )
+  (finalizer): Finalizer(
+    (gauss): GaussianFilterNd()
+  )
+)