Merge pull request #32 from score-p/origin/examples

tidy up examples and add monitoring example

examples/ExampleNotebook.ipynb → examples/ExampleBasic.ipynb

@@ -468,25 +468,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "### Presentation of Performance Data\n",
-    "\n",
-    "To inspect the collected performance data you can use tools such as [Vampir](https://vampir.eu/) (Trace) or [Cube](https://www.scalasca.org/software/cube-4.x/) (Profile).\n",
-    "\n",
-    "### Future Work\n",
-    "\n",
-    "The kernel is still under development. If you have any questions or wishes, please report to [email protected]\n",
-    " \n",
-    "PRs are welcome.\n",
-    "\n",
-    "### General Limitations \n",
-    "\n",
-    "For the execution of a cell, the kernel starts a new Python process either with Score-P or standalone. The kernel handles persistency between these processes on its own. Therefore it uses pickle/shelve and additional techniques. However this comes with the following drawbacks:\n",
-    "\n",
-    "- when dealing with big data structures, there might be a big runtime overhead at the beginning and the end of a cell. This is due to additional data saving and loading processes for persistency in the background. However this does not affect the actual user code and the Score-P measurements.\n",
-    "- Pickle/Shelve can not handle each kind ob Python object (e.g. file handles, network connections,...). Thus, they can not be shared between cells and your notebook might not work.\n",
-    "- Pickle/Shelve does not store class information but gives a reference to the class when storing a class instance. Thus, overwriting classes differs from the ordinary Python way. E.g. if you define a class and an object of this class in one cell and overwrite the class in a different cell, the defined object will also be changed. So please avoid class overwriting."
-   ]
+   "source": ""
   }
  ],
  "metadata": {