fixed engines notebook and binder badge

doc/source/conf.py

@@ -248,7 +248,7 @@ nbsphinx_prolog = """
 .. raw:: html
 
     <div class="admonition note">
-      Interactive online version:
+      Interactive version:
       <span style="white-space: nowrap;"><a href="https://mybinder.org/v2/gh/m-hannah/stormpy/master?filepath={{ docname }}"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>.</span>
     </div>
 """

doc/source/doc/building_models.ipynb

@@ -4,9 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Building Models\n",
-    "\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/m-hannah/stormpy/master?filepath=notebooks%2Fbuilding_models.ipynb)"
+    "# Building Models"
    ]
   },
   {

doc/source/doc/engines.ipynb

@@ -22,24 +22,51 @@
    "source": [
     "## Sparse engine\n",
     "\n",
-    "In all of the examples so far we used the default sparse engine:\n",
-    "\n",
+    "In all of the examples so far we used the default sparse engine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     ">>> import stormpy.examples\n",
     ">>> import stormpy.examples.files\n",
-    "    >>> prism_program = stormpy.parse_prism_program(stormpy.examples.files.prism_dtmc_die)\n",
+    ">>> prism_program = stormpy.parse_prism_program(stormpy.examples.files.prism_dtmc_die)\n",
     ">>> properties = stormpy.parse_properties('P=? [F \"one\"]', prism_program)\n",
     ">>> sparse_model = stormpy.build_sparse_model(prism_program, properties)\n",
-    ">>> print(type(sparse_model))\n",
-    "<class 'stormpy.storage.storage.SparseDtmc'>\n",
+    ">>> print(type(sparse_model))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     ">>> print(\"Number of states: {}\".format(sparse_model.nr_states))\n",
-    "Number of states: 13\n",
-    ">>> print(\"Number of transitions: {}\".format(sparse_model.nr_transitions))\n",
-    "Number of transitions: 20The model checking was also done in the sparse engine:\n",
-    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    ">>> print(\"Number of transitions: {}\".format(sparse_model.nr_transitions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     ">>> sparse_result = stormpy.check_model_sparse(sparse_model, properties[0])\n",
     ">>> initial_state = sparse_model.initial_states[0]\n",
-    ">>> print(sparse_result.at(initial_state))\n",
-    "0.16666666666666666"
+    ">>> print(sparse_result.at(initial_state))"
    ]
   },
   {
@@ -49,29 +76,89 @@
     "## Symbolic engine\n",
     "\n",
     "Instead of using the sparse engine, one can also use a symbolic representation in terms of binary decision diagrams (BDDs).\n",
-    "To use the symbolic (dd) engine, we use the symbolic versions for the building and model checking:\n",
-    "\n",
+    "To use the symbolic (dd) engine, we use the symbolic versions for the building and model checking:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     ">>> symbolic_model = stormpy.build_symbolic_model(prism_program, properties)\n",
-    ">>> print(type(symbolic_model))\n",
-    "<class 'stormpy.storage.storage.SymbolicSylvanDtmc'>\n",
-    ">>> print(\"Number of states: {}\".format(symbolic_model.nr_states))\n",
-    "Number of states: 13\n",
-    ">>> print(\"Number of transitions: {}\".format(symbolic_model.nr_transitions))\n",
-    "Number of transitions: 20\n",
+    ">>> print(type(symbolic_model))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    ">>> print(\"Number of states: {}\".format(symbolic_model.nr_states))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    ">>> print(\"Number of transitions: {}\".format(symbolic_model.nr_transitions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     ">>> symbolic_result = stormpy.check_model_dd(symbolic_model, properties[0])\n",
-    ">>> print(symbolic_result)\n",
-    "[0, 1] (range)We can also filter the computed results and only consider the initial states:\n",
-    "\n",
+    ">>> print(symbolic_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also filter the computed results and only consider the initial states:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     ">>> filter = stormpy.create_filter_initial_states_symbolic(symbolic_model)\n",
     ">>> symbolic_result.filter(filter)\n",
-    ">>> print(symbolic_result.min)\n",
-    "0.16666650772094727It is also possible to first build the model symbolically and then transform it into a sparse model:\n",
-    "\n",
-    ">>> print(type(symbolic_model))\n",
-    "<class 'stormpy.storage.storage.SymbolicSylvanDtmc'>\n",
+    ">>> print(symbolic_result.min)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to first build the model symbolically and then transform it into a sparse model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    ">>> print(type(symbolic_model))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     ">>> transformed_model = stormpy.transform_to_sparse_model(symbolic_model)\n",
-    ">>> print(type(transformed_model))\n",
-    "<class 'stormpy.storage.storage.SparseDtmc'>"
+    ">>> print(type(transformed_model))"
    ]
   },
   {
@@ -82,15 +169,28 @@
     "\n",
     "A third possibility is to use the hybrid engine, a combination of sparse and dd engines.\n",
     "It first builds the model symbolically.\n",
-    "The actual model checking is then performed with the engine which is deemed most suitable for the given task.\n",
-    "\n",
-    ">>> print(type(symbolic_model))\n",
-    "<class 'stormpy.storage.storage.SymbolicSylvanDtmc'>\n",
+    "The actual model checking is then performed with the engine which is deemed most suitable for the given task.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    ">>> print(type(symbolic_model))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     ">>> hybrid_result = stormpy.check_model_hybrid(symbolic_model, properties[0])\n",
     ">>> filter = stormpy.create_filter_initial_states_symbolic(symbolic_model)\n",
     ">>> hybrid_result.filter(filter)\n",
-    ">>> print(hybrid_result)\n",
-    "0.166667"
+    ">>> print(hybrid_result)"
    ]
   }
  ],
@@ -98,12 +198,24 @@
   "date": 1598178167.148,
   "filename": "engines.rst",
   "kernelspec": {
-   "display_name": "Python",
-   "language": "python3",
+   "display_name": "Python 3",
+   "language": "python",
    "name": "python3"
   },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  },
   "title": "Engines"
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}