diff --git a/Structure Tensor/StructureTensor2D_exercise.ipynb b/Structure Tensor/StructureTensor2D_exercise.ipynb
index 0047ea1068de536e31adfa1c23316a793b765044..ac55066aece212f584697e39aaca569ee0155682 100644
--- a/Structure Tensor/StructureTensor2D_exercise.ipynb	
+++ b/Structure Tensor/StructureTensor2D_exercise.ipynb	
@@ -7,10 +7,12 @@
     "# 2D Structure tensor -  workshop exercise\n",
     "The aim of this exercise is to get you familiarised with the structure tensor. You are going to learn how to:\n",
     "\n",
-    "- Understand the output of the structure tensor.\n",
-    "- Obtain the angle of the dominant orientation, and the degree of anisotropy of the material structures.\n",
+    "- Use the functionality of the structure tensor.\n",
+    "- Obtain the orientation angle and degree of anisotropy from the structure tensor.\n",
     "- Visualise the output of the structure tensor. \n",
-    "- Tune the input parameters for your data-set."
+    "- Tune the input parameters of the structure tensor.\n",
+    "\n",
+    "In some parts of code you will be asked to give user input, write in between the comments ###USER INPUT and ###END OF USER INPUT."
    ]
   },
   {
@@ -19,30 +21,66 @@
    "source": [
     "## Packages\n",
     "\n",
-    "First, let's run the cell below to import the packages we will use in this exercise. The most important ones for you to know are:\n",
+    "Let's import the packages we will use in this exercise. The most important ones for you to know are:\n",
     "\n",
     "- [numpy](www.numpy.org) is the basic package for scientific computing with Python.\n",
-    "- [skimage](https://scikit-image.org/) is one of the top image processing libraries in Python. \n",
+    "- [skimage](https://scikit-image.org/) is one of the top image processing libraries in Python. Today, we will use its intput/output module io to read in the images, and the transform module to...\n",
     "- [matplotlib](http://matplotlib.org) is the most commonly used package for plotting in Python.\n",
-    "- [st2d](https://lab.compute.dtu.dk/QIM/structure-tensor) is a repository with the structure tensor implementation by Vedrana A. Dahl (vand@dtu.dk). Clone or download the repository as a .zip and place the extracted folder in the parent directory of this exercise's directory.\n",
+    "- [st2d](https://lab.compute.dtu.dk/QIM/structure-tensor) contains the structure tensor implementation for 2D images by V.A. Dahl (vand@dtu.dk).\n",
     "- *utilsST* is a Python file containing helper functions for this exercise"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, run the cell right below to get the structure tensor repository. \n",
+    "\n",
+    "If this doesn't work, ignore the cell and instead do the following. \n",
+    "1. Download the [structure tensor repository](https://lab.compute.dtu.dk/QIM/structure-tensor) as a .zip.\n",
+    "2. Extract and rename to 'structure_tensor'\n",
+    "3. Place folder in the directory of this exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'structure-tensor'...\n",
+      "remote: Enumerating objects: 96, done.\u001b[K\n",
+      "remote: Counting objects: 100% (96/96), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (59/59), done.\u001b[K\n",
+      "remote: Total 96 (delta 33), reused 89 (delta 28), pack-reused 0\u001b[K\n",
+      "Unpacking objects: 100% (96/96), done.\n",
+      "Checking connectivity... done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!git clone \"https://lab.compute.dtu.dk/QIM/structure-tensor\"\n",
+    "import os\n",
+    "os.rename('structure-tensor','structure_tensor')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#from __future__ import print_function\n",
-    "from ipywidgets import interact, IntSlider, fixed # widgets to interact with the parameters\n",
     "import numpy as np\n",
     "import skimage.io\n",
-    "import scipy.ndimage\n",
-    "import skimage.transform\n",
+    "#import scipy.ndimage\n",
+    "#import skimage.transform\n",
     "import matplotlib.pyplot as plt\n",
-    "from structure_tensor_master import st2d\n",
-    "import utilsST # helper functions"
+    "from structure_tensor import st2d\n",
+    "import utilsST\n",
+    "from ipywidgets import interact, IntSlider, fixed "
    ]
   },
   {
@@ -53,17 +91,14 @@
     "The main functionality of the structure tensor is implemented in the two functions below: \n",
     "- Compute the Structure Tensor for 2D Data\n",
     "        S = st2d.structure_tensor(img, sigma, rho)\n",
-    "- Obtain the importance of the principal orientations of your structure (two per pixel in 2D), and the dominant orientation vector ($vec[0]\\hat{x}+vec[1]\\hat{y}$).\n",
+    "- Compute the eigen values for the principal orientations of your structure, and the dominant orientation vector ($vec[0]\\hat{x}+vec[1]\\hat{y}$).\n",
     "        val,vec = st2d.eig_special(S)\n",
-    " \n",
-    "![A test image](image.png) \n",
-    "**Help**\n",
     "\n",
-    "*To get HELP ON A FUNCTION, write funcion_name? in an empty cell.* \n",
+    "<img src=\"structuretensor.png\" width=\"100%\">\n",
     "\n",
-    "*To INSERT AN EMPTY CELL, press the plus symbol +, second icon on the left underneath File.* \n",
+    "*To get HELP ON A FUNCTION, write funcion_name? in an empty cell.* \n",
     "\n",
-    "**Vand** I am calling the eigen values weights, depending on what you say in the morning I may want to change this to eigen values. "
+    "*To INSERT AN EMPTY CELL, press the plus symbol +, second icon on the left underneath File.* "
    ]
   },
   {
@@ -85,9 +120,9 @@
     "1. Compute the structure tensor matrix and the orientation vectors.\n",
     "        Use the st2d functions described above\n",
     "2. Calculate the orientation angles and reshape back to an image.\n",
-    "        - Compute the angle[°] from the components of the dominant orientation (vec). \n",
-    "        Use np.pi and one of the trigonometric functions: np.arccos(), np.arcsin() or np.arctan2()\n",
-    "        - Reshape your output with output.reshape(image.shape)"
+    "        - Compute the angles[°] from the dominant orientations (vec). \n",
+    "        Use np.pi and one of the trigonometric functions: np.arccos(), np.arcsin() or np.arctan2(). Look at the figure above.\n",
+    "        - Reshape your output with output.reshape(img.shape)"
    ]
   },
   {
@@ -96,37 +131,43 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "No such file: '/Users/monj/Documents/PostDoc_3DImageAnalysis/gitlab/esrf-um-2021/Structure Tensor/StructureTensor/example_data_2D/drawn_fibres_B.png'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-63f1a69ecfd9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m## READ THE IMAGE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'./StructureTensor/example_data_2D/drawn_fibres_B.png'\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0;31m# path of your image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mskimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# read\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'The image has a shape {image.shape}, i.e. {image.size} pixels.'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# print image dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda3/lib/python3.7/site-packages/skimage/io/_io.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, as_gray, plugin, flatten, **plugin_args)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mfile_or_url_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_plugin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'imread'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplugin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplugin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplugin_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ndim'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda3/lib/python3.7/site-packages/skimage/io/manage_plugins.py\u001b[0m in \u001b[0;36mcall_plugin\u001b[0;34m(kind, *args, **kwargs)\u001b[0m\n\u001b[1;32m    208\u001b[0m                                (plugin, kind))\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda3/lib/python3.7/site-packages/imageio/core/functions.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(uri, format, **kwargs)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;31m# Get reader and read first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m     \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"i\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda3/lib/python3.7/site-packages/imageio/core/functions.py\u001b[0m in \u001b[0;36mget_reader\u001b[0;34m(uri, format, mode, **kwargs)\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;31m# Create request object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m     \u001b[0mrequest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m     \u001b[0;31m# Get format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda3/lib/python3.7/site-packages/imageio/core/request.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, uri, mode, **kwargs)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;31m# Parse what was given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_uri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0;31m# Set extension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda3/lib/python3.7/site-packages/imageio/core/request.py\u001b[0m in \u001b[0;36m_parse_uri\u001b[0;34m(self, uri)\u001b[0m\n\u001b[1;32m    276\u001b[0m                 \u001b[0;31m# Reading: check that the file exists (but is allowed a dir)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No such file: '%s'\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    279\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m                 \u001b[0;31m# Writing: check that the directory to write to does exist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: No such file: '/Users/monj/Documents/PostDoc_3DImageAnalysis/gitlab/esrf-um-2021/Structure Tensor/StructureTensor/example_data_2D/drawn_fibres_B.png'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image has a shape (50, 50), i.e. 2500 pixels.\n",
+      "Structure tensor information is carried in a (3, 2500) array.\n",
+      "Orientation information is carried in a (2, 2500) array.\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "## READ THE IMAGE\n",
-    "filename = './StructureTensor/example_data_2D/drawn_fibres_B.png'; # path of your image\n",
+    "## RUN THE STRUCTURE TENSOR\n",
+    "\n",
+    "# Read the image\n",
+    "filename = './structure_tensor/example_data_2D/drawn_fibres_B.png'; # path of your image\n",
     "image = skimage.io.imread(filename) # read\n",
     "print(f'The image has a shape {image.shape}, i.e. {image.size} pixels.') # print image dimensions\n",
     "\n",
-    "## RUN THE STRUCTURE TENSOR\n",
     "# Initialise parameters\n",
     "sigma = 0.5 \n",
     "rho = 2 \n",
     "\n",
+    "# Compute the structure tensor\n",
     "### USER INPUT HERE ### (≈ 2 lines of code)\n",
-    "S = st2d.structure_tensor(image, sigma, rho)# compute structure tensor matrix S\n",
-    "val,vec = st2d.eig_special(S) # compute dominant orientation vector (vec) and principal orientation weights (val)\n",
+    "S = # compute structure tensor matrix S\n",
+    "val, vec = # compute dominant orientation vector (vec) and principal orientation weights (val)\n",
     "### END USER INPUT ###  \n",
     "\n",
     "print(f'Structure tensor information is carried in a {S.shape} array.')\n",
@@ -134,11 +175,11 @@
     "\n",
     "# Compute orientation angles\n",
     "### USER INPUT HERE ### (≈ 1 line of code)\n",
-    "angles = np.arctan2(vec[1],vec[0])/np.pi # compute angles. \n",
-    "orientation_image = angles.reshape(image.shape) # shape as an image\n",
+    "angle = # compute angles\n",
+    "orientation_image = # shape as an image\n",
     "### END USER INPUT ###   \n",
     "\n",
-    "## VISUALISE THE RESULTS\n",
+    "# Visualise the results\n",
     "figsize = (10,5)\n",
     "fig, ax = plt.subplots(1, 2, figsize=figsize, sharex=True, sharey=True)\n",
     "\n",
@@ -155,18 +196,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, it may be more interesting to actually visualise the orientations overlaid on the image, so it is easier to correlate the angles with the structures. \n",
+    "With the structure tensor, we get an orientation value for every pixel, but actually, we are only interested in the orientations of the fibres.\n",
+    "\n",
+    "Because the fibres in this image are bright and the background is dark, the orientations can be overlaid on the image by multiplying the image pixels by the orientation values (see below). \n",
     "\n",
-    "We use the function st2d.plot_orientations() to the visualise the orientations as vectors.\n"
+    "We will also use the function `st2d.plot_orientations()` to the visualise the orientations as vectors."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "## VISUALISE THE ORIENTATIONS OVERLAID ON THE IMAGE\n",
+    "\n",
     "figsize = (10,5)\n",
     "fig, ax = plt.subplots(1, 2, figsize=figsize, sharex=True, sharey=True)\n",
     "\n",
@@ -184,34 +241,53 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When visualizing the dominant orientation, it may seem as orientation changes abruptly in some areas (e.g. where the structures merge). These areas of low anisotropy, where the orientation is smoothly changing from one dominant orientation to another dominant orientation, do not actually have a preferencial orientation. "
+    "When visualizing only the dominant orientation, it may seem as the orientation changes abruptly in some areas (e.g. where the fibres merge). These areas where the orientation is smoothly changing from one dominant orientation to another dominant orientation, are actually isotropic, meaning that they do not actually have a preferencial orientation. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Part 2. Linearity of the structures\n",
+    "## Part 2. Shape of the Structures\n",
     "\n",
-    "While some regions may have a preferencial orientation, other regions may be more isotropic, for example, the circular regions where fibres meet. The first output of the structure tensor (val) provides the weights for the two principal orientations of the material in the region around a pixel. In other words, val provides the degree of anisotropy (linearity in 2D) of the structures in the region, and tells us how reliable the dominant orientation given by vec actually is.\n",
+    "While some image regions may have a preferencial orientation, others don't. In this part, we are going to measure the isotropy of the structures to find out whether they have a preferencial direction. Are they more linear-like (isotropy of a line = 0) or circular-like (isotropy of a circle = 1)? We will use this information to give more importance to the angles in regions with preferential orientations.\n",
     "\n",
-    "The anisotropy is computed as the ratio between the weights for the two principal orientations. The closer to 1, the more isotropic the structure (circular shape in 2D). The larger the ratio, the more anisotropic (linear in 2D) the structure is. So, for large ratios, the dominant orientation is a preferential direction, but for ratios close to 1, the region does not really have a preferencial direction.\n",
+    "The first output of the structure tensor (`val`) indicates how fast the intensities change around a pixel in the two principal directions. Along the first principal direction of a line, the intensities will not change, and `val[0]` will be very small. Along the second principal direction, the intensities will change fast so `val[1]` will be large. For a circle, both values will be equal.\n",
     "\n",
-    "To obtain a more realistic idea of the orientations of our material, we weigh the dominant orientation by the anisotropy. In this way, the orientation angle will be shown in areas with preferencial directions, but not in highly isotropic areas. "
+    "The ratio between the components of `val` will give us the **degree of structural isotropy**. To obtain a more realistic idea of the orientations of our material, we will weigh the dominant orientation by the anisotropy. \n",
+    "\n",
+    "Can you find the formula for the anisotropy, given `val[0]` and `val[1]`?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Compute anisotropy\n",
-    "anisotropy = (1-val[0]/val[1]).reshape(image.shape)\n",
+    "## WEIGH THE ORIENTATIONS BY THE ANISOTROPY\n",
+    "\n",
+    "### USER INPUT\n",
+    "anisotropy = # compute anisotropy\n",
+    "### END OF USER INPUT\n",
+    "anisotropy = anisotropy.reshape(image.shape)\n",
     "\n",
+    "# Plot image, anisotropy and weighed orientations\n",
     "fig,ax = plt.subplots(2, 3, figsize=(15,10), sharex=True, sharey=True)\n",
     "\n",
-    "# Plot Image, anisotropy and weighed orientations\n",
     "ax[0][0].imshow(plt.cm.gray(image))\n",
     "ax[0][0].set_title('Image')\n",
     "\n",
@@ -239,18 +315,33 @@
    "metadata": {},
    "source": [
     "## Part 3. Understand the parameters sigma and rho\n",
-    "The aim of this part of the exercise is to get you familiarised with the parameters sigma and rho. In the structure tensor there is a first step to remove noise and a second step to compute the orientations, where the dominant direction is found as the direction in which the intensities change the least. \n",
+    "The aim of this part of the exercise is to get you familiarised with the parameters sigma and rho. \n",
     "\n",
-    "In the first step, noise removal, the image is smoothed with a kernel of size sigma. In the second step, the intensities are integrated around the pixel in a region of size defined by rho.\n",
+    "In the structure tensor, there is a first step to remove noise and a second step to compute the orientations, where the dominant direction is found as the direction in which the intensities change the least. In the first step, noise removal, the image is smoothed with a kernel of size sigma. In the second step, the intensities are integrated around the pixel in a region of size defined by rho.\n",
     "\n",
     "Now that you know that sigma is related to the scale of the image noise and rho to the scale of the structures of interest, play with the widgets to change the values of the parameters and see the effects on the results. Move the widgets and wait for the image to update."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45259d108df64bb7aff024a2667709d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=2.5, description='sigma', max=5.0, min=0.25, step=0.25), FloatSlider(v…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "interact(utilsST.st_and_plot, sigma = (0.25,5,0.25), rho = (0.1,15,0.5), image = fixed(image));"
    ]
@@ -260,7 +351,7 @@
    "metadata": {},
    "source": [
     "Set rho low (< 1) and play with the noise level (sigma in 0-1). See how the noise is reduced.\n",
-    "Increase rho and watch the orientations become more smooth as the size of the integration window covers the width of the structures. As rho is increased (try rho > 6) the smaller structures start to disapear in the anisotropy image and the orientations colours start to blend across structures."
+    "Increase rho and watch the orientations become more smooth as the size of the integration window covers the width of the structures. As rho is increased (try rho > 6), the smaller structures start to disapear in the anisotropy image and the orientations colours start to blend across structures."
    ]
   },
   {
@@ -271,7 +362,7 @@
     "\n",
     "The structure tensor is very useful for finding out whether your material has a preferencial orientation. This has numerous applications, from manufacturing of composite materials to understanding blood flow in your body.\n",
     "\n",
-    "We are going to see two examples, one about cardboard fibres and the other about retina vasculator. You will see how the orientation information can be accumulated into a histogram of angles and used to determine the preferencial orientations of your sample, if any!"
+    "We are going to see two examples, one about cardboard fibres and the other about retina vasculature. You will see how the orientation information can be accumulated into a histogram of angles and used to determine the preferencial orientations of your sample, if any!"
    ]
   },
   {
@@ -285,11 +376,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79f20b944a4b4e69af35b30465c329ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=11, description='rho', max=20, min=2, step=3), Output()), _dom_classes=(…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "filename = '../example_data_2D/10X.png';\n",
+    "filename = './structure_tensor/example_data_2D/10X.png';\n",
     "sigma = 0.5\n",
     "\n",
     "interact(utilsST.st_and_hists, sigma = fixed(sigma), rho = (2,20,3), filename = fixed(filename));"
@@ -310,31 +416,44 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "filename = '../example_data_2D/OCT_im_org.png';\n",
-    "sigma = 0.5\n",
-    "\n",
-    "interact(utilsST.st_and_hists, sigma = fixed(sigma), rho = (0,10,1), filename = fixed(filename));"
+    "Play with the widget and find the optimal rho value for capturing the orientation of the thicker vessels."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c857083d2b2644fb87753690d6e57337",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=5, description='rho', max=10), Output()), _dom_classes=('widget-interact…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "Play with the widget and find the optimal rho value for capturing the orientation of the thicker vessels."
+    "filename = './structure_tensor/example_data_2D/OCT_im_org.png';\n",
+    "sigma = 0.5\n",
+    "\n",
+    "interact(utilsST.st_and_hists, sigma = fixed(sigma), rho = (0,10,1), filename = fixed(filename));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When working with large amounts of data, the computational time becomes crucial. In [Github](https://github.com/Skielex/structure-tensor/tree/master/structure_tensor) you can find an optimised version of the structure tensor developed at the Technical University of Denmark (DTU )by Niels Jeppesen (niejep@dtu.dk). This implementation provides the option of using the package cupy, instead of numpy, to run the computations on a Graphical Processing Unit (GPU). \n",
-    "\n",
-    "This can result in a speed up of x ??, for a...**Billy**, can we test the timing?\n",
+    "When working with large amounts of data, the computational time becomes crucial. In [Github](https://github.com/Skielex/structure-tensor/tree/master/structure_tensor) you can find an optimised version of the structure tensor developed at the Technical University of Denmark (DTU) by Niels Jeppesen (niejep@dtu.dk). This implementation provides the option of using the package cupy, instead of numpy, to run the computations on a Graphical Processing Unit (GPU). \n",
     "\n",
     "*Please note that the visualisation functions used today would have to be adapted to work with Niels' code, as the output of his functions is in a different format.*"
    ]
@@ -343,7 +462,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This workshop exercise was made by Monica J. Emerson (monj@dtu.dk) based on the examples from Vedrana's repository (link)."
+    "This workshop exercise was made by Monica J. Emerson (monj@dtu.dk) based on the example scripts by Vedrana A. Dahl (vand@dtu.dk)"
    ]
   }
  ],
@@ -363,7 +482,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/Structure Tensor/StructureTensor2D_exercise_solutions.ipynb b/Structure Tensor/StructureTensor2D_exercise_solutions.ipynb
index 758a89200db7bd5e80aa4bcca4a816c73b3b1571..5846a2904e2dc7dd3ccbd3a5f64301a6a5715fd9 100644
--- a/Structure Tensor/StructureTensor2D_exercise_solutions.ipynb	
+++ b/Structure Tensor/StructureTensor2D_exercise_solutions.ipynb	
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -59,17 +59,6 @@
       "Unpacking objects: 100% (96/96), done.\n",
       "Checking connectivity... done.\n"
      ]
-    },
-    {
-     "ename": "OSError",
-     "evalue": "[Errno 66] Directory not empty: 'structure-tensor' -> 'structure_tensor'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-28c94fc19bd1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'git clone \"https://lab.compute.dtu.dk/QIM/structure-tensor\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'structure-tensor'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'structure_tensor'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mstructure_tensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mst2d\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 66] Directory not empty: 'structure-tensor' -> 'structure_tensor'"
-     ]
     }
    ],
    "source": [
@@ -80,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -216,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -272,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -296,7 +285,6 @@
     "### END OF USER INPUT\n",
     "anisotropy = anisotropy.reshape(image.shape)\n",
     "\n",
-    "\n",
     "# Plot image, anisotropy and weighed orientations\n",
     "fig,ax = plt.subplots(2, 3, figsize=(15,10), sharex=True, sharey=True)\n",
     "\n",
@@ -336,13 +324,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96b8254cac584834b5be6a8328d8dc64",
+       "model_id": "45259d108df64bb7aff024a2667709d9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -388,11 +376,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79f20b944a4b4e69af35b30465c329ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=11, description='rho', max=20, min=2, step=3), Output()), _dom_classes=(…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "filename = './structure_tensor_master/example_data_2D/10X.png';\n",
+    "filename = './structure_tensor/example_data_2D/10X.png';\n",
     "sigma = 0.5\n",
     "\n",
     "interact(utilsST.st_and_hists, sigma = fixed(sigma), rho = (2,20,3), filename = fixed(filename));"
@@ -413,29 +416,44 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "filename = './structure_tensor_master/example_data_2D/OCT_im_org.png';\n",
-    "sigma = 0.5\n",
-    "\n",
-    "interact(utilsST.st_and_hists, sigma = fixed(sigma), rho = (0,10,1), filename = fixed(filename));"
+    "Play with the widget and find the optimal rho value for capturing the orientation of the thicker vessels."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c857083d2b2644fb87753690d6e57337",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=5, description='rho', max=10), Output()), _dom_classes=('widget-interact…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "Play with the widget and find the optimal rho value for capturing the orientation of the thicker vessels."
+    "filename = './structure_tensor/example_data_2D/OCT_im_org.png';\n",
+    "sigma = 0.5\n",
+    "\n",
+    "interact(utilsST.st_and_hists, sigma = fixed(sigma), rho = (0,10,1), filename = fixed(filename));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When working with large amounts of data, the computational time becomes crucial. In [Github](https://github.com/Skielex/structure-tensor/tree/master/structure_tensor) you can find an optimised version of the structure tensor developed at the Technical University of Denmark (DTU )by Niels Jeppesen (niejep@dtu.dk). This implementation provides the option of using the package cupy, instead of numpy, to run the computations on a Graphical Processing Unit (GPU). \n",
+    "When working with large amounts of data, the computational time becomes crucial. In [Github](https://github.com/Skielex/structure-tensor/tree/master/structure_tensor) you can find an optimised version of the structure tensor developed at the Technical University of Denmark (DTU) by Niels Jeppesen (niejep@dtu.dk). This implementation provides the option of using the package cupy, instead of numpy, to run the computations on a Graphical Processing Unit (GPU). \n",
     "\n",
     "*Please note that the visualisation functions used today would have to be adapted to work with Niels' code, as the output of his functions is in a different format.*"
    ]