updating jupyter and creedo links

www-root/userapi/demos/index.json

@@ -192,7 +192,7 @@
             "creedo"
           ]
         },
-        "logicalPath": "/Creedo/index.htm",
+        "logicalPath": "/Creedo/cM/start/",
         "title": "Discovering simple descriptors for crystal-structure classification",
         "top_of_list": false,
         "updated_at": "",
@@ -210,7 +210,7 @@
         ],
         "created_at": "",
         "description": "In this tutorial, we will use Gaussian process regression, GPR (or equivalently, Kernel Ridge Regression, KRR) to train and predict charges of atoms in small organic molecules.",
-        "editLink": "/jupyter/notebooks/shared/afekete/tutorial/learning_atomic_charges.ipynb",
+        "editLink": "/jupyter/cM/start/data/shared/afekete/tutorial/learning_atomic_charges.ipynb",
         "featured": true,
         "isPublic": true,
         "labels": {
@@ -308,7 +308,7 @@
         ],
         "created_at": "",
         "description": "In this tutorial we will be using a machine learning method (clustering) to analyse results of Grain Boundary (GB) calculations of alpha-iron. Along the way we will learn about different methods to describe local atomic environment in order to calculate properties of GBs. We will use these properties to separate the different regions of the GB using clustering methods. Finally we will determine how the energy of the GB is changing according to the angle difference of the regions.",
-        "editLink": "/jupyter/notebooks/shared/afekete/tutorial/GB_tutorial_Berlin_2017.ipynb",
+        "editLink": "/jupyter/cM/start/data/shared/afekete/tutorial/GB_tutorial_Berlin_2017.ipynb",
         "featured": true,
         "isPublic": true,
         "labels": {