Moved documentation for parametric models into own file

doc/source/advanced_topics.rst

@@ -10,4 +10,5 @@ This guide is a collection of examples meant to bridge the gap between the getti
 
     doc/building_models
     doc/reward_models
-    doc/shortest_paths
+    doc/shortest_paths
+    doc/parametric_models

doc/source/doc/parametric_models.rst

@@ -0,0 +1,61 @@
+*****************
+Parametric Models
+*****************
+
+
+
+Instantiating parametric models
+===============================
+.. seealso:: `01-parametric-models.py <https://github.com/moves-rwth/stormpy/blob/master/examples//parametric_models/01-parametric-models.py>`_
+
+Input formats such as prism allow to specify programs with open constants. We refer to these open constants as parameters.
+If the constants only influence the probabilities or rates, but not the topology of the underlying model, we can build these models as parametric models::
+
+    >>> import stormpy
+    >>> import stormpy.examples
+    >>> import stormpy.examples.files
+    >>> path = stormpy.examples.files.prism_dtmc_die
+    >>> prism_program = stormpy.parse_prism_program(path)
+    >>> formula_str = "P=? [F s=7 & d=2]"
+    >>> properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
+
+    >>> model = stormpy.build_parametric_model(prism_program, properties)
+    >>> parameters = model.collect_probability_parameters()
+    >>> for x in parameters:
+    ...     print(x)
+
+In order to obtain a standard DTMC, MDP or other Markov model, we need to instantiate these models by means of a model instantiator::
+
+    >>> import stormpy.pars
+    >>> instantiator = stormpy.pars.PDtmcInstantiator(model)
+
+Before we obtain an instantiated model, we need to map parameters to values: We build such a dictionary as follows::
+
+    >>> point = dict()
+    >>> for x in parameters:
+    ...    print(x.name)
+    ...    point[x] = 0.4
+    >>> instantiated_model = instantiator.instantiate(point)
+    >>> result = stormpy.model_checking(instantiated_model, properties[0])
+
+Initial states and labels are set as for the parameter-free case.
+
+
+Checking parametric models
+==========================
+.. seealso:: `02-parametric-models.py <https://github.com/moves-rwth/stormpy/blob/master/examples//parametric_models/02-parametric-models.py>`_
+
+It is also possible to check the parametric model directly, similar as before in :ref:`getting-started-checking-properties`::
+
+    >>> result = stormpy.model_checking(model, properties[0])
+    >>> initial_state = model.initial_states[0]
+    >>> func = result.at(initial_state)
+
+We collect the constraints ensuring that underlying model is well-formed and the graph structure does not change.
+
+    >>> collector = stormpy.ConstraintCollector(model)
+    >>> for formula in collector.wellformed_constraints:
+    ...     print(formula)
+    >>> for formula in collector.graph_preserving_constraints:
+    ...     print(formula)
+

doc/source/getting_started.rst

@@ -126,58 +126,11 @@ A good way to get the result for the initial states is as follows::
     >>> print(result.at(initial_state))
     0.5
 
-Instantiating parametric models
-------------------------------------
-.. seealso:: `04-getting-started.py <https://github.com/moves-rwth/stormpy/blob/master/examples/04-getting-started.py>`_
-
-Input formats such as prism allow to specify programs with open constants. We refer to these open constants as parameters.
-If the constants only influence the probabilities or rates, but not the topology of the underlying model, we can build these models as parametric models::
-
-    >>> model = stormpy.build_parametric_model(prism_program, properties)
-    >>> parameters = model.collect_probability_parameters()
-    >>> for x in parameters:
-    ...     print(x)
-
-In order to obtain a standard DTMC, MDP or other Markov model, we need to instantiate these models by means of a model instantiator::
-
-    >>> import stormpy.pars
-    >>> instantiator = stormpy.pars.PDtmcInstantiator(model)
-
-Before we obtain an instantiated model, we need to map parameters to values: We build such a dictionary as follows::
-
-    >>> point = dict()
-    >>> for x in parameters:
-    ...    print(x.name)
-    ...    point[x] = 0.4
-    >>> instantiated_model = instantiator.instantiate(point)
-    >>> result = stormpy.model_checking(instantiated_model, properties[0])
-
-Initial states and labels are set as for the parameter-free case.
-
-
-Checking parametric models
-------------------------------------
-.. seealso:: `05-getting-started.py <https://github.com/moves-rwth/stormpy/blob/master/examples/05-getting-started.py>`_
-
-It is also possible to check the parametric model directly, similar as before in :ref:`getting-started-checking-properties`::
-
-    >>> result = stormpy.model_checking(model, properties[0])
-    >>> initial_state = model.initial_states[0]
-    >>> func = result.at(initial_state)
-
-We collect the constraints ensuring that underlying model is well-formed and the graph structure does not change.
-
-    >>> collector = stormpy.ConstraintCollector(model)
-    >>> for formula in collector.wellformed_constraints:
-    ...     print(formula)
-    >>> for formula in collector.graph_preserving_constraints:
-    ...     print(formula)
-
 .. _getting-started-investigating-the-model:
 
 Investigating the model
 -------------------------------------
-.. seealso:: `06-getting-started.py <https://github.com/moves-rwth/stormpy/blob/master/examples/06-getting-started.py>`_
+.. seealso:: `04-getting-started.py <https://github.com/moves-rwth/stormpy/blob/master/examples/04-getting-started.py>`_
 
 One powerful part of the Storm model checker is to quickly create the Markov chain from higher-order descriptions, as seen above::
 

examples/04-getting-started.py

@@ -1,40 +1,27 @@
 import stormpy
 import stormpy.core
 
-import pycarl
-import pycarl.core
-
 import stormpy.examples
 import stormpy.examples.files
 
-import stormpy._config as config
-
 
 def example_getting_started_04():
-    # Check support for parameters
-    if not config.storm_with_pars:
-        print("Support parameters is missing. Try building storm-pars.")
-        return
-
-    import stormpy.pars
-    path = stormpy.examples.files.prism_pdtmc_die
+    path = stormpy.examples.files.prism_dtmc_die
     prism_program = stormpy.parse_prism_program(path)
 
     formula_str = "P=? [F s=7 & d=2]"
     properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
-    model = stormpy.build_parametric_model(prism_program, properties)
-    print("Model supports parameters: {}".format(model.supports_parameters))
-    parameters = model.collect_probability_parameters()
-    assert len(parameters) == 2
-
-    instantiator = stormpy.pars.PDtmcInstantiator(model)
-    point = dict()
-    for x in parameters:
-        print(x.name)
-        point[x] = stormpy.RationalRF(0.4)
-    instantiated_model = instantiator.instantiate(point)
-    result = stormpy.model_checking(instantiated_model, properties[0])
-    print(result)
+    model = stormpy.build_model(prism_program, properties)
+
+    print(model.model_type)
+
+    for state in model.states:
+        if state.id in model.initial_states:
+            print(state)
+        for action in state.actions:
+            for transition in action.transitions:
+                print("From state {}, with probability {}, go to state {}".format(state, transition.value(),
+                                                                                  transition.column))
 
 
 if __name__ == '__main__':

examples/06-getting-started.py

@@ -1,26 +0,0 @@
-import stormpy
-import stormpy.core
-
-import stormpy.examples
-import stormpy.examples.files
-
-def example_getting_started_06():
-    path = stormpy.examples.files.prism_dtmc_die
-    prism_program = stormpy.parse_prism_program(path)
-
-    formula_str = "P=? [F s=7 & d=2]"
-    properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
-    model = stormpy.build_model(prism_program, properties)
-
-    print(model.model_type)
-
-    for state in model.states:
-        if state.id in model.initial_states:
-            print(state)
-        for action in state.actions:
-            for transition in action.transitions:
-                print("From state {}, with probability {}, go to state {}".format(state, transition.value(), transition.column))
-
-
-if __name__ == '__main__':
-    example_getting_started_06()

examples/parametric_models/01-parametric-models.py

@@ -0,0 +1,41 @@
+import stormpy
+import stormpy.core
+
+import pycarl
+import pycarl.core
+
+import stormpy.examples
+import stormpy.examples.files
+
+import stormpy._config as config
+
+
+def example_parametric_models_01():
+    # Check support for parameters
+    if not config.storm_with_pars:
+        print("Support parameters is missing. Try building storm-pars.")
+        return
+
+    import stormpy.pars
+    path = stormpy.examples.files.prism_pdtmc_die
+    prism_program = stormpy.parse_prism_program(path)
+
+    formula_str = "P=? [F s=7 & d=2]"
+    properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
+    model = stormpy.build_parametric_model(prism_program, properties)
+    print("Model supports parameters: {}".format(model.supports_parameters))
+    parameters = model.collect_probability_parameters()
+    assert len(parameters) == 2
+
+    instantiator = stormpy.pars.PDtmcInstantiator(model)
+    point = dict()
+    for x in parameters:
+        print(x.name)
+        point[x] = stormpy.RationalRF(0.4)
+    instantiated_model = instantiator.instantiate(point)
+    result = stormpy.model_checking(instantiated_model, properties[0])
+    print(result)
+
+
+if __name__ == '__main__':
+    example_parametric_models_01()

examples/05-getting-started.py → examples/parametric_models/02-parametric-models.py

@@ -11,7 +11,7 @@ import stormpy.examples.files
 import stormpy._config as config
 
 
-def example_getting_started_05():
+def example_parametric_models_02():
     # Check support for parameters
     if not config.storm_with_pars:
         print("Support parameters is missing. Try building storm-pars.")
@@ -45,4 +45,4 @@ def example_getting_started_05():
 
 
 if __name__ == '__main__':
-    example_getting_started_05()
+    example_parametric_models_02()