diff --git a/Eval_Graphene_model.ipynb b/Eval_Graphene_model.ipynb
index 33f6430..20588ac 100644
--- a/Eval_Graphene_model.ipynb
+++ b/Eval_Graphene_model.ipynb
@@ -56,6 +56,8 @@
     "from skimage.filters import threshold_otsu\n",
     "from sklearn.mixture import GaussianMixture\n",
     "from sklearn.cluster import KMeans\n",
+    "from skimage.feature import blob_log\n",
+    "from scipy.ndimage import center_of_mass\n",
     "\n",
     "# for cropping function\n",
     "if drive:\n",
@@ -92,7 +94,7 @@
    "outputs": [],
    "source": [
     "# set mps, just for my computer\n",
-    "device = torch.device('mps')"
+    "# device = torch.device('mps')"
    ]
   },
   {
@@ -119,6 +121,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# should be someting like 'content/drive/My Drive/Gomb-Net files'\n",
     "if drive:\n",
     "    shared_folder = 'drive/My Drive/Gomb-Net files'\n",
     "else:\n",
@@ -226,7 +229,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loss_history = np.load(str(shared_folder + '/Pretrained_models/TwoLeggedGraphene256.pthloss_history.npz'))\n",
+    "loss_history = np.load(str(shared_folder + '/Pretrained_models/Graphene_model_loss_history.npz'))\n",
     "train_loss = loss_history['train_loss_history']\n",
     "val_loss = loss_history['val_loss_history']\n",
     "\n",
@@ -252,9 +255,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_path = str(shared_folder + '/Pretrained_models/TwoLeggedGraphene256_b.pth')\n",
+    "model_path = str(shared_folder + '/Pretrained_models/Graphene_model.pth')\n",
     "\n",
-    "checkpoint = torch.load(model_path, map_location=torch.device('mps'))\n",
+    "checkpoint = torch.load(model_path, map_location=device)\n",
     "model.load_state_dict(checkpoint['model_state_dict'])\n",
     "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
     "model.eval()"
@@ -335,14 +338,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#import blobdog\n",
-    "from skimage.feature import blob_log\n",
-    "from scipy.ndimage import center_of_mass"
+    "### Comparison to blob-finder"
    ]
   },
   {
@@ -364,6 +363,15 @@
     "plt.axis('off')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The following code is marked out, because it takes a few minutes to run.  Just measuring network performance metrics across the test dataset"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -439,7 +447,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "exp_data = np.load('./moire.npz')\n",
+    "exp_data = np.load(str(shared_folder + '/Experimental_datasets/moire.npz'))\n",
     "im_array = exp_data['im_array']\n",
     "pixel_size = exp_data['pixel_size']\n",
     "\n",
@@ -456,16 +464,6 @@
     "plt.imshow(im_array, cmap='gray')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sys.path.insert(0, '/Users/austin/Documents/GitHub/DataGenSTEM/DataGenSTEM')\n",
-    "import data_generator as dg"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -565,26 +563,9 @@
    "outputs": [],
    "source": [
     "# make dist_hist a 1D array\n",
-    "dist_hist = np.concatenate(dist_hist) * 1e10 / resize_factor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "median_dist = np.median(dist_hist)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "avg_dist = np.mean(dist_hist)\n",
+    "dist_hist = np.concatenate(dist_hist) * 1e10 / resize_factor\n",
     "\n",
+    "avg_dist = np.mean(dist_hist)\n",
     "\n",
     "plt.figure(figsize = (8,8), dpi=300)\n",
     "plt.hist(dist_hist, bins=100, color='gray')\n",
@@ -631,7 +612,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Now, let's try to get graphene order parameter vector\n"
+    "### Below is some old code not used in the paper\n"
    ]
   },
   {