diff --git a/Structure Tensor/StructureTensor2D_exercise.ipynb b/Structure Tensor/StructureTensor2D_exercise.ipynb
index 2173a0b4c504d48860e784b6e20a215d0288e573..7821ea3dd25629782a381740a603676f49ae981a 100644
--- a/Structure Tensor/StructureTensor2D_exercise.ipynb	
+++ b/Structure Tensor/StructureTensor2D_exercise.ipynb	
@@ -119,10 +119,9 @@
     "This first part will guide you through the implementation of your first analysis using structure tensor. You are going to write code to:\n",
     "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 angles back to an image.\n",
-    "> - Compute the angles[°] from the components of the dominant orientation (__vec__). <br>\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)`"
+    "2. Calculate the orientation angles\n",
+    "> - Compute the angles[rad] from the components of the dominant orientation (__vec__). <br>\n",
+    ">> Look at the figure above and use a trigonometric function: `np.arccos()`, `np.arcsin()` or `np.arctan2()`. \n"
    ]
   },
   {
@@ -167,7 +166,7 @@
     "# Compute the structure tensor\n",
     "### USER INPUT HERE ### (≈ 2 lines of code)\n",
     "S = # compute structure tensor matrix S\n",
-    "val,vec =  # compute dominant orientation vector (vec) and principal orientation weights (val)\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",
@@ -176,9 +175,12 @@
     "# Compute orientation angles\n",
     "### USER INPUT HERE ### (≈ 1 line of code)\n",
     "angle = # compute angles\n",
-    "orientation_image = # shape as an image\n",
     "### END USER INPUT ###   \n",
     "\n",
+    "# Prepare the angles to show as image \n",
+    "angle /= np.pi #scale from 0 to 1\n",
+    "orientation_image = angle.reshape(image.shape) # shape as an image\n",
+    "\n",
     "# Visualise the results\n",
     "figsize = (10,5)\n",
     "fig, ax = plt.subplots(1, 2, figsize=figsize, sharex=True, sharey=True)\n",
@@ -330,7 +332,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9348b6dffa2740ecb647efda73075c06",
+       "model_id": "ba20135d4767480c838913a395d806ce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -382,7 +384,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c63d444ec078465c9b9e0226c3149043",
+       "model_id": "dc0b06199211481ca9c3adc7700b37ea",
        "version_major": 2,
        "version_minor": 0
       },
@@ -430,7 +432,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60c02737b9554f67915bde19d5a431df",
+       "model_id": "9cce53c47d8f498fbcfe26212f4b317e",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/Structure Tensor/StructureTensor2D_exercise_solutions.ipynb b/Structure Tensor/StructureTensor2D_exercise_solutions.ipynb
index 425380e2554b6c7d16862bd57cfcada8132fcc2b..6c1271d8dc7d9c37650f6f55c24a19ed1be0acca 100644
--- a/Structure Tensor/StructureTensor2D_exercise_solutions.ipynb	
+++ b/Structure Tensor/StructureTensor2D_exercise_solutions.ipynb	
@@ -119,10 +119,9 @@
     "This first part will guide you through the implementation of your first analysis using structure tensor. You are going to write code to:\n",
     "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 angles back to an image.\n",
-    "> - Compute the angles[°] from the components of the dominant orientation (__vec__). <br>\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)`"
+    "2. Calculate the orientation angles\n",
+    "> - Compute the angles[rad] from the components of the dominant orientation (__vec__). <br>\n",
+    ">> Look at the figure above and use a trigonometric function: `np.arccos()`, `np.arcsin()` or `np.arctan2()`. \n"
    ]
   },
   {
@@ -175,10 +174,13 @@
     "\n",
     "# Compute orientation angles\n",
     "### USER INPUT HERE ### (≈ 1 line of code)\n",
-    "angle = np.arctan2(vec[1], vec[0])/np.pi # compute angles\n",
-    "orientation_image = angle.reshape(image.shape) # shape as an image\n",
+    "angle = np.arctan2(vec[1],vec[0]) # compute angles\n",
     "### END USER INPUT ###   \n",
     "\n",
+    "# Prepare the angles to show as image \n",
+    "angle /= np.pi #scale from 0 to 1\n",
+    "orientation_image = angle.reshape(image.shape) # shape as an image\n",
+    "\n",
     "# Visualise the results\n",
     "figsize = (10,5)\n",
     "fig, ax = plt.subplots(1, 2, figsize=figsize, sharex=True, sharey=True)\n",
@@ -330,7 +332,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9348b6dffa2740ecb647efda73075c06",
+       "model_id": "ba20135d4767480c838913a395d806ce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -382,7 +384,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c63d444ec078465c9b9e0226c3149043",
+       "model_id": "dc0b06199211481ca9c3adc7700b37ea",
        "version_major": 2,
        "version_minor": 0
       },
@@ -430,7 +432,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60c02737b9554f67915bde19d5a431df",
+       "model_id": "9cce53c47d8f498fbcfe26212f4b317e",
        "version_major": 2,
        "version_minor": 0
       },