diff --git a/doc/source/conf.py b/doc/source/conf.py
index 6f5193f..b33d6a3 100644
--- a/doc/source/conf.py
+++ b/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>
"""
\ No newline at end of file
diff --git a/doc/source/doc/building_models.ipynb b/doc/source/doc/building_models.ipynb
index 4e92d5f..8ce6c11 100644
--- a/doc/source/doc/building_models.ipynb
+++ b/doc/source/doc/building_models.ipynb
@@ -4,9 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Building Models\n",
- "\n",
- "[](https://mybinder.org/v2/gh/m-hannah/stormpy/master?filepath=notebooks%2Fbuilding_models.ipynb)"
+ "# Building Models"
]
},
{
diff --git a/doc/source/doc/engines.ipynb b/doc/source/doc/engines.ipynb
index c0d3569..61ca790 100644
--- a/doc/source/doc/engines.ipynb
+++ b/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
-}
\ No newline at end of file
+}