extended getting started guide

doc/source/getting_started.rst

@@ -75,12 +75,12 @@ In order to label the states accordingly, we should notify storm upon building t
 Storm will then add the labels accordingly::
 
 	model = stormpy.build_model(prism_program, properties)
-	print("Labels: {}".format(model.labels)  # {"init", "deadlock", "s=2"})
+	print("Labels in the model: {}".format(model.labels)  # out: Labels in the model: {"init", "deadlock", "s=2"})
 
 Model building however now behaves slightly different: Only the properties passed are preserved, which means that model building might skip parts of the model.
 In particular, to check the probability of eventually reaching a state x where s=2, successor states of x are not relevant::
 
-    print("Number of states: {}".format(model.nr_states))  # out: 8
+    print("Number of states: {}".format(model.nr_states))  # out: Number of states: 8
 	
 If we consider another property, however, such as::
 
@@ -104,18 +104,39 @@ The result may contain information about all states. Merely printing does not gi
 
 	print(result) # out: [0,1] range
 	
-Instead, we can iterate over the results:
+Instead, we can iterate over the results::
 
-	
+    assert result.result_for_all_states
+    for x in result.get_values():
+        print(x)
 
+.. topic:: Results for all states
 
+    Some model checking algorithms do not provide results for all states. In those cases, the result is not valid for all states, and to iterate over them, a different method is required. We will explain this later.
 
 
 Instantiating parametric models
 ------------------------------------
 .. seealso:: ``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)
+
+In order to obtain a standard DTMC, MDP or other Markov model, we need to instantiate these models by means of a model instantiator::
+
+    instantiator = ModelInstantiator(model)
+
+Before we obtain an instantiated model, we need to map parameters to values: We build such a dictionary as follows::
 
+    point = dict()
+    parameters = model.collect_probability_parameters()
+    for x in parameters:
+        print(x.name)
+        point[x] = 0.4
+    instantiated_model = instantiator.instantiate(point)
+    result = stormpy.model_checking(instantiated_model, properties[0])
 
 
 Checking parametric models
@@ -129,6 +150,7 @@ Checking parametric models
 
 Investigating the model
 -------------------------------------
+.. seealso:: ``06-getting-started.py``
 
 
 

doc/source/index.rst

@@ -15,6 +15,8 @@ Stormpy Documentation
    
    installation
    getting_started
+   contributors
+
    
 Stormpy API Reference
 ====================================

lib/stormpy/__init__.py

@@ -3,6 +3,7 @@ from .core import *
 from . import storage
 from .storage import *
 from .version import __version__
+import stormpy.logic
 
 core._set_up("")
 

lib/stormpy/examples/files.py

@@ -9,5 +9,7 @@ def _path(folder, file):
 
 prism_dtmc_die = _path("dtmc", "die.pm")
 """Knuth Yao Die Example"""
+prism_pdtmc_die = _path("pdtmc", "parametric_die.pm")
+"""Knuth Yao Die -- 2 unfair coins Example"""
 prism_dtmc_brp = _path("dtmc", "brp-16-2.pm")
-"""Bounded Retransmission Protocol"""
+"""Bounded Retransmission Protocol"""

lib/stormpy/examples/files/pdtmc/parametric_die.pm

@@ -2,6 +2,7 @@
 dtmc
 
 const double p;
+const double q;
 
 module die
 
@@ -11,8 +12,8 @@ module die
 	d : [0..6] init 0;
 	
 	[] s=0 -> p : (s'=1) + (1-p) : (s'=2);
-	[] s=1 -> p : (s'=3) + (1-p) : (s'=4);
-	[] s=2 -> p : (s'=5) + (1-p) : (s'=6);
+	[] s=1 -> q : (s'=3) + (1-q) : (s'=4);
+	[] s=2 -> q : (s'=5) + (1-q) : (s'=6);
 	[] s=3 -> p : (s'=1) + (1-p) : (s'=7) & (d'=1);
 	[] s=4 -> p : (s'=7) & (d'=2) + (1-p) : (s'=7) & (d'=3);
 	[] s=5 -> p : (s'=7) & (d'=4) + (1-p) : (s'=7) & (d'=5);