Skip some checks in doctests because the generated output is not deterministic

doc/source/doc/models/building_ctmcs.rst

@@ -30,7 +30,7 @@ In this example, we build the transition matrix using a numpy array::
 The following function call returns a sparse matrix with default row groups::
 
     >>> transition_matrix = stormpy.build_sparse_matrix(transitions)
-    >>> print(transition_matrix)
+    >>> print(transition_matrix) # doctest: +SKIP
             0	1	2	3
         ---- group 0/3 ----
     0	(	0	1.5	0	0	)	0
@@ -67,13 +67,13 @@ Building the Model
 Now, we can collect all components, including the choice labeling and the exit rates.
 To let the transition values be interpreted as rates we set `rate_transitions` to `True`::
 
-    components = stormpy.SparseModelComponents(transition_matrix=transition_matrix, state_labeling=state_labeling, rate_transitions=True)
-    components.exit_rates = exit_rates
+    >>> components = stormpy.SparseModelComponents(transition_matrix=transition_matrix, state_labeling=state_labeling, rate_transitions=True)
+    >>> components.exit_rates = exit_rates
 
 And finally, we can build the model::
 
     >>> ctmc = stormpy.storage.SparseCtmc(components)
-    >>> print(ctmc)
+    >>> print(ctmc) # doctest: +SKIP
     --------------------------------------------------------------
     Model type: 	CTMC (sparse)
     States: 	4

doc/source/doc/models/building_dtmcs.rst

@@ -96,7 +96,7 @@ Next, we set the associations between the remaining labels and states.::
 To set the same label for multiple states, we can use a BitVector representation for the set of states::
 
     >>> state_labeling.set_states('done', stormpy.BitVector(13, [7, 8, 9, 10, 11, 12]))
-    >>> print(state_labeling)
+    >>> print(state_labeling) # doctest: +SKIP
     9 labels
        * one -> 1 item(s)
        * four -> 1 item(s)
@@ -131,7 +131,7 @@ Next, we collect all components::
 And finally, we can build the DTMC::
 
     >>> dtmc = stormpy.storage.SparseDtmc(components)
-    >>> print(dtmc)
+    >>> print(dtmc) # doctest: +SKIP
     --------------------------------------------------------------
     Model type: 	DTMC (sparse)
     States: 	13

doc/source/doc/models/building_mas.rst

@@ -39,7 +39,7 @@ In the beginning, we create a numpy array that will be used to build the transit
 When building the matrix we define a custom row grouping by passing a list containing the starting row of each row group in ascending order::
 
     >>> transition_matrix = stormpy.build_sparse_matrix(transitions, [0, 2, 3, 4, 5])
-    >>> print(transition_matrix)
+    >>> print(transition_matrix) # doctest: +SKIP
 
             0	1	2	3	4
         ---- group 0/4 ----
@@ -65,17 +65,33 @@ we build a BitVector that contains the respective Markovian states::
 
 Building the Model
 ====================
+
+.. testsetup::
+    # Not displayed in documentation but needed for doctests
+
+    >>> state_labeling = stormpy.storage.StateLabeling(5)
+    >>> state_labels = {'init', 'deadlock'}
+    >>> for label in state_labels:
+    ...     state_labeling.add_label(label)
+    >>> state_labeling.add_label_to_state('init', 0)
+    >>> choice_labeling = stormpy.storage.ChoiceLabeling(6)
+    >>> choice_labels = {'alpha', 'beta'}
+    >>> for label in choice_labels:
+    ...    choice_labeling.add_label(label)
+    >>> choice_labeling.add_label_to_choice('alpha', 0)
+    >>> choice_labeling.add_label_to_choice('beta', 1)
+    >>> exit_rates = [0.0, 10.0, 12.0, 1.0, 1.0]
+
 Now, we can collect all components::
 
-    >>> components = stormpy.SparseModelComponents(transition_matrix=transition_matrix, state_labeling=state_labeling,
-                                               markovian_states=markovian_states)
+    >>> components = stormpy.SparseModelComponents(transition_matrix=transition_matrix, state_labeling=state_labeling, markovian_states=markovian_states)
     >>> components.choice_labeling = choice_labeling
     >>> components.exit_rates = exit_rates
 
 Finally, we can build the model::
 
     >>> ma = stormpy.storage.SparseMA(components)
-    >>> print(ma)
+    >>> print(ma) # doctest: +SKIP
     --------------------------------------------------------------
     Model type: 	Markov Automaton (sparse)
     States: 	5

doc/source/doc/models/building_mdps.rst

@@ -82,7 +82,7 @@ Recall that those actions where defined in row one and two of the transition mat
 
     >>> choice_labeling.add_label_to_choice('a', 0)
     >>> choice_labeling.add_label_to_choice('b', 1)
-    >>> print(choice_labeling)
+    >>> print(choice_labeling) # doctest: +SKIP
     Choice 2 labels
        * a -> 1 item(s)
        * b -> 1 item(s)
@@ -99,6 +99,23 @@ In this reward model the length of the action rewards coincides with the number
 
 Building the Model
 ====================
+
+.. testsetup::
+
+    # Not displayed in documentation but needed for doctests
+    >>> state_labeling = stormpy.storage.StateLabeling(13)
+    >>> labels = {'init', 'one', 'two', 'three', 'four', 'five', 'six', 'done', 'deadlock'}
+    >>> for label in labels:
+    ...     state_labeling.add_label(label)
+    >>> state_labeling.add_label_to_state('init', 0)
+    >>> state_labeling.add_label_to_state('one', 7)
+    >>> state_labeling.add_label_to_state('two', 8)
+    >>> state_labeling.add_label_to_state('three', 9)
+    >>> state_labeling.add_label_to_state('four', 10)
+    >>> state_labeling.add_label_to_state('five', 11)
+    >>> state_labeling.add_label_to_state('six', 12)
+    >>> state_labeling.set_states('done', stormpy.BitVector(13, [7, 8, 9, 10, 11, 12]))
+
 We collect the components::
 
     >>> components = stormpy.SparseModelComponents(transition_matrix=transition_matrix, state_labeling=state_labeling, reward_models=reward_models, rate_transitions=False)
@@ -107,7 +124,7 @@ We collect the components::
 We build the model::
 
     >>> mdp = stormpy.storage.SparseMdp(components)
-    >>> print(mdp)
+    >>> print(mdp) # doctest: +SKIP
     Model type: 	MDP (sparse)
     States: 	13
     Transitions: 	22