Stormpy.core

class AtomicExpressionFormula

Formula with an atomic expression

class AtomicLabelFormula

Formula with an atomic label

class BinaryPathFormula

Path formula with two operands

property left_subformula
property right_subformula
class BinaryStateFormula

State formula with two operands

class BisimulationType

Types of bisimulation

STRONG = BisimulationType.STRONG
WEAK = BisimulationType.WEAK
class BitVector
get()
number_of_set_bits()
set()

Set

size()
class BooleanBinaryStateFormula

Boolean binary state formula

class BooleanLiteralFormula

Formula with a boolean literal

class BoundedUntilFormula

Until Formula with either a step or a time bound.

class BuilderOptions

Options for building process

property preserved_label_names

Labels preserved

set_add_out_of_bounds_state(self: stormpy.core.BuilderOptions, new_value: bool=True) → stormpy.core.BuilderOptions

Build with out of bounds state

set_add_overlapping_guards_label(self: stormpy.core.BuilderOptions, new_value: bool=True) → stormpy.core.BuilderOptions

Build with overlapping guards state labeled

set_build_with_choice_origins(self: stormpy.core.BuilderOptions, new_value: bool=True) → stormpy.core.BuilderOptions

Build choice origins

class CheckTask

Task for model checking

set_produce_schedulers(self: stormpy.core.CheckTask, produce_schedulers: bool=True) → None

Set whether schedulers should be produced (if possible)

class ChoiceLabeling

Labeling for choices

class ChoiceOrigins

This class represents the origin of choices of a model in terms of the input model spec.

get_choice_info()

human readable string

get_identifier_info()

human readable string

class ComparisonType
GEQ = ComparisonType.GEQ
GREATER = ComparisonType.GREATER
LEQ = ComparisonType.LEQ
LESS = ComparisonType.LESS
class ConditionalFormula

Formula with the right hand side being a condition.

class Constant

A constant in a Prism program

property defined

Is the constant defined?

property expression_variable

Expression variable

property name

Constant name

property type

The type of the constant

class ConstraintCollector

Collector for constraints on parametric Markov chains

property graph_preserving_constraints

Get the constraints ensuring the graph is preserved

property wellformed_constraints

Get the constraints ensuring a wellformed model

class CumulativeRewardFormula

Summed rewards over a the paths

class DistributionDouble

Finite Support Distribution

class Environment
property solver_environment

solver part of environment

class EquationSolverType

Solver type for equation systems

eigen = EquationSolverType.eigen
elimination = EquationSolverType.elimination
gmmxx = EquationSolverType.gmmxx
native = EquationSolverType.native
topological = EquationSolverType.topological
class EventuallyFormula

Formula for eventually

class ExplicitParametricQuantitativeCheckResult

Explicit parametric quantitative model checking result

at(self: stormpy.core.ExplicitParametricQuantitativeCheckResult, state: int) → carl::RationalFunction<carl::FactorizedPolynomial<carl::MultivariatePolynomial<cln::cl_RA, carl::MonomialComparator<&carl::Monomial::compareGradedLexical, true>, carl::StdMultivariatePolynomialPolicies<carl::NoReasons, carl::NoAllocator> > >, true>

Get result for given state

get_values(self: stormpy.core.ExplicitParametricQuantitativeCheckResult) → List[carl::RationalFunction<carl::FactorizedPolynomial<carl::MultivariatePolynomial<cln::cl_RA, carl::MonomialComparator<&carl::Monomial::compareGradedLexical, true>, carl::StdMultivariatePolynomialPolicies<carl::NoReasons, carl::NoAllocator> > >, true>]

Get model checking result values for all states

class ExplicitQualitativeCheckResult

Explicit qualitative model checking result

at(self: stormpy.core.ExplicitQualitativeCheckResult, state: int) → bool

Get result for given state

get_truth_values(self: stormpy.core.ExplicitQualitativeCheckResult) → storm::storage::BitVector

Get BitVector representing the truth values

class ExplicitQuantitativeCheckResult

Explicit quantitative model checking result

at(self: stormpy.core.ExplicitQuantitativeCheckResult, state: int) → float

Get result for given state

get_values(self: stormpy.core.ExplicitQuantitativeCheckResult) → List[float]

Get model checking result values for all states

property scheduler

get scheduler

class Expression

Holds an expression

And()
Eq()
Geq()
Greater()
Iff()
Implies()
Leq()
Less()
Minus()
Multiply()
Neq()
Or()
Plus()
contains_variable()

Check if the expression contains any of the given variables.

contains_variables()

Check if the expression contains variables.

get_variables()

Get the variables

has_boolean_type()

Check if the expression is a boolean

has_integer_type()

Check if the expression is an integer

has_rational_type()

Check if the expression is a rational

is_literal()

Check if the expression is a literal

property manager

Get the manager

substitute()
property type

Get the Type

class ExpressionManager

Manages variables for expressions

create_boolean()

Create expression from boolean

create_boolean_variable()

create Boolean variable

create_integer()

Create expression from integer number

create_integer_variable()

create Integer variable

create_rational()

Create expression from rational number

create_rational_variable()

create Rational variable

class ExpressionParser

Parser for storm-expressions

parse()
set_identifier_mapping()

sets identifiers

class ExpressionType

The type of an expression

property is_boolean
property is_integer
property is_rational
class FactorizedPolynomial

Represent a polynomial with its factorization

cache(self: pycarl.cln.cln.FactorizedPolynomial) → pycarl.cln.cln._FactorizationCache
property coefficient
constant_part(self: pycarl.cln.cln.FactorizedPolynomial) → pycarl.cln.cln.Rational
derive(self: pycarl.cln.cln.FactorizedPolynomial, variable: pycarl.core.Variable) → pycarl.cln.cln.FactorizedPolynomial

Compute the derivative

evaluate(self: pycarl.cln.cln.FactorizedPolynomial, arg0: Dict[pycarl.core.Variable, pycarl.cln.cln.Rational]) → pycarl.cln.cln.Rational
factorization(self: pycarl.cln.cln.FactorizedPolynomial) → pycarl.cln.cln.Factorization

Get factorization

gather_variables(self: pycarl.cln.cln.FactorizedPolynomial) → Set[pycarl.core.Variable]
is_constant(self: pycarl.cln.cln.FactorizedPolynomial) → bool
is_one(self: pycarl.cln.cln.FactorizedPolynomial) → bool
polynomial(self: pycarl.cln.cln.FactorizedPolynomial) → pycarl.cln.cln.Polynomial

Get underlying polynomial

to_smt2(self: pycarl.cln.cln.FactorizedPolynomial) → str
class FactorizedRationalFunction

Represent a rational function, that is the fraction of two factorized polynomials

constant_part(self: pycarl.cln.cln.FactorizedRationalFunction) → pycarl.cln.cln.Rational
property denominator
derive(self: pycarl.cln.cln.FactorizedRationalFunction, variable: pycarl.core.Variable) → pycarl.cln.cln.FactorizedRationalFunction

Compute the derivative

evaluate(self: pycarl.cln.cln.FactorizedRationalFunction, arg0: Dict[pycarl.core.Variable, pycarl.cln.cln.Rational]) → pycarl.cln.cln.Rational
gather_variables(self: pycarl.cln.cln.FactorizedRationalFunction) → Set[pycarl.core.Variable]
is_constant(self: pycarl.cln.cln.FactorizedRationalFunction) → bool
property numerator
rational_function(self: pycarl.cln.cln.FactorizedRationalFunction) → pycarl.cln.cln.RationalFunction
to_smt2(self: pycarl.cln.cln.FactorizedRationalFunction) → str
class FlatSet

Container to pass to program

insert(self: stormpy.core.FlatSet, arg0: int) → None
insert_set(self: stormpy.core.FlatSet, arg0: stormpy.core.FlatSet) → None
is_subset_of(self: stormpy.core.FlatSet, arg0: stormpy.core.FlatSet) → bool
class Formula

Generic Storm Formula

clone()
property is_probability_operator

is it a probability operator

property is_reward_operator

is it a reward operator

substitute()

Substitute variables

substitute_labels_by_labels()

substitute label occurences

class GloballyFormula

Formula for globally

class HybridParametricQuantitativeCheckResult

Symbolic parametric hybrid quantitative model checking result

get_values(self: stormpy.core.HybridParametricQuantitativeCheckResult) → List[carl::RationalFunction<carl::FactorizedPolynomial<carl::MultivariatePolynomial<cln::cl_RA, carl::MonomialComparator<&carl::Monomial::compareGradedLexical, true>, carl::StdMultivariatePolynomialPolicies<carl::NoReasons, carl::NoAllocator> > >, true>]

Get model checking result values for all states

class HybridQuantitativeCheckResult

Hybrid quantitative model checking result

get_values(self: stormpy.core.HybridQuantitativeCheckResult) → List[float]

Get model checking result values for all states

class InstantaneousRewardFormula

Instantaneous reward

class ItemLabeling

Labeling

add_label()

Add label

contains_label()

Check if the given label is contained in the labeling

get_labels()

Get all labels

class JaniAssignment

Jani Assignment

property expression
class JaniAutomaton

A Jani Automation

add_edge()
add_initial_location()
add_location()

adds a new location, returns the index

property edges

get edges

property initial_location_indices
property initial_states_restriction

initial state restriction

property location_variable
property locations
property name
property variables
class JaniBoundedIntegerVariable

A Bounded Integer

class JaniChoiceOrigins

This class represents for each choice the origin in the jani spec.

get_edge_index_set()

returns the set of edges that induced the choice

property model

retrieves the associated JANI model

class JaniConstant

A Constant in JANI

property defined

is constant defined by some expression

property expression_variable

expression variable for this constant

property name

name of constant

property type

type of constant

class JaniEdge

A Jani Edge

property action_index

action index

property color

color for the edge

property destinations

edge destinations

property guard

edge guard

has_silent_action()

Is the edge labelled with the silent action

property nr_destinations

nr edge destinations

property rate

edge rate

property source_location_index

index for source location

substitute()
property template_edge

template edge

class JaniEdgeDestination

Destination in Jani

property assignments
property probability
property target_location_index
class JaniLocation

A Location in JANI

property assignments

location assignments

property name

name of the location

class JaniModel

A Jani Model

add_automaton()

add an automaton (with a unique name)

property automata

get automata

check_valid()

Some basic checks to ensure validity

property constants

get constants

decode_automaton_and_edge_index()

get edge and automaton from edge/automaton index

define_constants()

define constants with a mapping from the corresponding expression variables to expressions

encode_automaton_and_edge_index()

get edge/automaton-index

property expression_manager

get expression manager

finalize()

finalizes the model. After this action, be careful changing the data structure.

get_automaton_index()

get index for automaton name

get_constant()

get constant by name

property global_variables
has_standard_composition()

is the composition the standard composition

property has_undefined_constants

Flag if program has undefined constants

property initial_states_restriction

initial states restriction

make_standard_compliant()

make standard JANI compliant

property model_type

Model type

property name

model name

remove_constant()

remove a constant. Make sure the constant does not appear in the model.

replace_automaton()

replace automaton at index

restrict_edges()

restrict model to edges given by set

set_model_type()

Sets (only) the model type

set_standard_system_composition()

sets the composition to the standard composition

substitute_constants()

substitute constants

substitute_functions()

substitute functions

to_dot()
property undefined_constants_are_graph_preserving

Flag if the undefined constants do not change the graph structure

class JaniModelType

Type of the Jani model

CTMC = JaniModelType.CTMC
CTMDP = JaniModelType.CTMDP
DTMC = JaniModelType.DTMC
HA = JaniModelType.HA
LTS = JaniModelType.LTS
MA = JaniModelType.MA
MDP = JaniModelType.MDP
PHA = JaniModelType.PHA
PTA = JaniModelType.PTA
SHA = JaniModelType.SHA
STA = JaniModelType.STA
TA = JaniModelType.TA
UNDEFINED = JaniModelType.UNDEFINED
class JaniOrderedAssignments

Set of assignments

add()
clone()

clone assignments (performs a deep copy)

substitute()

substitute in rhs according to given substitution map

class JaniTemplateEdge

Template edge, internal data structure for edges

add_destination()
property assignments
property destinations
property guard
class JaniTemplateEdgeDestination

Template edge destination, internal data structure for edge destinations

property assignments
class JaniVariable

A Variable in JANI

property expression_variable

expression variable for this variable

property name

name of constant

class JaniVariableSet

Jani Set of Variables

add_bounded_integer_variable()
add_variable()
empty()

is there a variable in the set?

get_variable_by_expr_variable()
get_variable_by_name()
class LongRunAvarageOperator

Long run average operator

class LongRunAverageRewardFormula

Long run average reward

class MinMaxMethod

Method for min-max equation systems

interval_iteration = MinMaxMethod.interval_iteration
linear_programming = MinMaxMethod.linear_programming
policy_iteration = MinMaxMethod.policy_iteration
sound_value_iteration = MinMaxMethod.sound_value_iteration
topological = MinMaxMethod.topological
topological_cuda = MinMaxMethod.topological_cuda
value_iteration = MinMaxMethod.value_iteration
class MinMaxSolverEnvironment

Environment for Min-Max-Solvers

property method
property precision
class ModelFormulasPair

Pair of model and formulas

property formulas

The formulas

property model

The model

class ModelType

Type of the model

CTMC = ModelType.CTMC
DTMC = ModelType.DTMC
MA = ModelType.MA
MDP = ModelType.MDP
POMDP = ModelType.POMDP
class OperatorFormula

Operator formula

property comparison_type

Comparison type of bound

property has_bound

Flag if formula is bounded

property has_optimality_type

Flag if an optimality type is present

property optimality_type

Flag for the optimality type

remove_bound()
remove_optimality_type()

remove the optimality type

set_bound()

Set bound

set_optimality_type()

set the optimality type (use remove optimiality type for clearing)

property threshold

Threshold of bound (currently only applicable to rational expressions)

property threshold_expr
class OptimizationDirection
Maximize = OptimizationDirection.Maximize
Minimize = OptimizationDirection.Minimize
class ParametricCheckTask

Task for parametric model checking

set_produce_schedulers(self: stormpy.core.ParametricCheckTask, produce_schedulers: bool=True) → None

Set whether schedulers should be produced (if possible)

class ParametricSparseMatrix

Parametric sparse matrix

get_row()

Get row

get_row_group_end()
get_row_group_start()
get_rows()

Get rows from start to end

property has_trivial_row_grouping

Trivial row grouping

property nr_columns

Number of columns

property nr_entries

Number of non-zero entries

property nr_rows

Number of rows

print_row()

Print row

row_iter()

Get iterator from start to end

submatrix()

Get submatrix

class ParametricSparseMatrixEntry

Entry of parametric sparse matrix

property column

Column

set_value()

Set value

value()

Value

class ParametricSparseMatrixRows

Set of rows in a parametric sparse matrix

class PathFormula

Formula about the probability of a set of paths in an automaton

class Polynomial

Represent a multivariate polynomial

constant_part(self: pycarl.cln.cln.Polynomial) → pycarl.cln.cln.Rational
degree(self: pycarl.cln.cln.Polynomial, arg0: pycarl.core.Variable) → int
derive(self: pycarl.cln.cln.Polynomial, variable: pycarl.core.Variable) → pycarl.cln.cln.Polynomial

Compute the derivative

evaluate(self: pycarl.cln.cln.Polynomial, arg0: Dict[pycarl.core.Variable, pycarl.cln.cln.Rational]) → pycarl.cln.cln.Rational
gather_variables(self: pycarl.cln.cln.Polynomial) → Set[pycarl.core.Variable]
is_constant(self: pycarl.cln.cln.Polynomial) → bool
property nr_terms
substitute(self: pycarl.cln.cln.Polynomial, arg0: Dict[pycarl.core.Variable, pycarl.cln.cln.Polynomial]) → pycarl.cln.cln.Polynomial
to_smt2(self: pycarl.cln.cln.Polynomial) → str
property total_degree
class PrismAssignment

An assignment in prism

property expression

Expression for the update

property variable

Variable that is updated

class PrismCommand

A command in a Prism program

property global_index

Get global index

property guard_expression

Get guard expression

property updates

Updates in the command

class PrismModelType

Type of the prism model

CTMC = PrismModelType.CTMC
CTMDP = PrismModelType.CTMDP
DTMC = PrismModelType.DTMC
MA = PrismModelType.MA
MDP = PrismModelType.MDP
UNDEFINED = PrismModelType.UNDEFINED
class PrismModule

A module in a Prism program

property commands

Commands in the module

property name

Name of the module

class PrismProgram

A Prism Program

property constants

Get Program Constants

define_constants()

Define constants

property expression_manager

Get the expression manager for expressions in this program

property has_undefined_constants

Flag if program has undefined constants

property model_type

Model type

property modules

Modules in the program

property nr_modules

Number of modules

restrict_commands()

Restrict commands

simplify()

Simplify

substitute_constants()

Substitute constants within program

to_jani()

Transform to Jani program

property undefined_constants_are_graph_preserving

Flag if the undefined constants do not change the graph structure

used_constants()

Compute Used Constants

class PrismUpdate

An update in a Prism command

property assignments

Assignments in the update

class ProbabilityOperator

Probability operator

class Property
property name

Obtain the name of the property

property raw_formula

Obtain the formula directly

class Rational

Class wrapping gmp-rational numbers

property denominator
property nominator
property numerator
class RationalFunction

Represent a rational function, that is the fraction of two multivariate polynomials

constant_part(self: pycarl.cln.cln.RationalFunction) → pycarl.cln.cln.Rational
property denominator
derive(self: pycarl.cln.cln.RationalFunction, variable: pycarl.core.Variable) → pycarl.cln.cln.RationalFunction

Compute the derivative

evaluate(self: pycarl.cln.cln.RationalFunction, arg0: Dict[pycarl.core.Variable, pycarl.cln.cln.Rational]) → pycarl.cln.cln.Rational
gather_variables(self: pycarl.cln.cln.RationalFunction) → Set[pycarl.core.Variable]
is_constant(self: pycarl.cln.cln.RationalFunction) → bool
property nominator
property numerator
to_smt2(self: pycarl.cln.cln.RationalFunction) → str
RationalRF

alias of pycarl.cln.cln.Rational

class RewardOperator

Reward operator

has_reward_name()
property reward_name
class SMTCounterExampleGenerator

Highlevel Counterexample Generator with SMT as backend

build(env: stormpy.core.Environment, stats: stormpy.core.SMTCounterExampleGeneratorStats, symbolic_model: storm::storage::SymbolicModelDescription, model: storm::models::sparse::Model<double, storm::models::sparse::StandardRewardModel<double> >, cex_input: storm::counterexamples::SMTMinimalLabelSetGenerator<double>::CexInput, dontcare: stormpy.core.FlatSet, options: stormpy.core.SMTCounterExampleGeneratorOptions) → List[stormpy.core.FlatSet]

Compute counterexample

precompute(env: stormpy.core.Environment, symbolic_model: storm::storage::SymbolicModelDescription, model: storm::models::sparse::Model<double, storm::models::sparse::StandardRewardModel<double> >, formula: storm::logic::Formula) → storm::counterexamples::SMTMinimalLabelSetGenerator<double>::CexInput

Precompute input for counterexample generation

class SMTCounterExampleGeneratorOptions

Options for highlevel counterexample generation

property add_backward_implication_cuts
property check_threshold_feasible
property continue_after_first_counterexample
property encode_reachability
property maximum_counterexamples
property maximum_iterations_after_counterexample
property silent
property use_dynamic_constraints
class SMTCounterExampleGeneratorStats

Stats for highlevel counterexample generation

property analysis_time
property cut_time
property iterations
property model_checking_time
property setup_time
property solver_time
class SMTCounterExampleInput

Precomputed input for counterexample generation

add_reward_and_threshold(self: stormpy.core.SMTCounterExampleInput, reward_name: str, threshold: float) → None

add another reward structure and threshold

class SchedulerChoiceDouble

A choice of a finite memory scheduler

property defined

Is the choice defined by the scheduler?

property deterministic

Is the choice deterministic (given by a Dirac distribution)?

get_choice()

Get the distribution over the actions

get_deterministic_choice()

Get the deterministic choice

class SchedulerDouble

A Finite Memory Scheduler

compute_action_support()
property deterministic

Is the scheduler deterministic?

get_choice()
property memory_size

How much memory does the scheduler take?

property memoryless

Is the scheduler memoryless?

class SolverEnvironment

Environment for solvers

property minmax_solver_environment
set_force_sound(self: stormpy.core.SolverEnvironment, new_value: bool=True) → None

force soundness

set_linear_equation_solver_type(self: stormpy.core.SolverEnvironment, new_value: stormpy.core.EquationSolverType, set_from_default: bool=False) → None

set solver type to use

class SparseCtmc

CTMC in sparse representation

class SparseDtmc

DTMC in sparse representation

class SparseMA

MA in sparse representation

class SparseMatrix

Sparse matrix

get_row()

Get row

get_row_group_end()
get_row_group_start()
get_rows()

Get rows from start to end

property has_trivial_row_grouping

Trivial row grouping

property nr_columns

Number of columns

property nr_entries

Number of non-zero entries

property nr_rows

Number of rows

print_row()

Print rows from start to end

row_iter()

Get iterator from start to end

submatrix()

Get submatrix

class SparseMatrixEntry

Entry of sparse matrix

property column

Column

set_value()

Set value

value()

Value

class SparseMatrixRows

Set of rows in a sparse matrix

class SparseMdp

MDP in sparse representation

apply_scheduler()

apply scheduler

property nondeterministic_choice_indices
class SparseModelAction

Action for state in sparse model

property id

Id

property transitions

Get transitions

class SparseModelActions

Actions for state in sparse model

class SparseModelState

State in sparse model

property actions

Get actions

property id

Id

property labels

Labels

class SparseModelStates

States in sparse model

class SparseParametricCtmc

pCTMC in sparse representation

class SparseParametricDtmc

pDTMC in sparse representation

class SparseParametricMA

pMA in sparse representation

class SparseParametricMdp

pMDP in sparse representation

apply_scheduler()

apply scheduler

property nondeterministic_choice_indices
class SparseParametricModelAction

Action for state in sparse parametric model

property id

Id

property transitions

Get transitions

class SparseParametricModelActions

Actions for state in sparse parametric model

class SparseParametricModelState

State in sparse parametric model

property actions

Get actions

property id

Id

property labels

Labels

class SparseParametricModelStates

States in sparse parametric model

class SparseParametricRewardModel

Reward structure for parametric sparse models

get_state_action_reward()
get_state_reward()
property has_state_action_rewards
property has_state_rewards
property has_transition_rewards
reduce_to_state_based_rewards()

Reduce to state-based rewards

property state_action_rewards
property state_rewards
property transition_rewards
class SparsePomdp

POMDP in sparse representation

property nr_observations
property observations
class SparseRewardModel

Reward structure for sparse models

get_state_action_reward()
get_state_reward()
get_zero_reward_states()

get states where all rewards are zero

property has_state_action_rewards
property has_state_rewards
property has_transition_rewards
reduce_to_state_based_rewards()

Reduce to state-based rewards

property state_action_rewards
property state_rewards
property transition_rewards
class StateFormula

Formula about a state of an automaton

class StateLabeling

Labeling for states

add_label_to_state()

Add label to state

get_labels_of_state()

Get labels of given state

get_states()

Get all states which have the given label

has_state_label()

Check if the given state has the given label

set_states()

Set all states which have the given label

exception StormError(message)

Base class for exceptions in Storm.

class SubsystemBuilderOptions

Options for constructing the subsystem

property build_action_mapping
property build_kept_actions
property build_state_mapping
property check_transitions_outside
class SubsystemBuilderReturnTypeDouble

Result of the construction of a subsystem

property kept_actions

Actions of the subsystem available in the original system

property model

the submodel

property new_to_old_action_mapping

for each action in result, the action index in the original model

property new_to_old_state_mapping

for each state in result, the state index in the original model

class SymbolicModelDescription

Symbolic description of model

as_jani_model(self: stormpy.core.SymbolicModelDescription) → storm::jani::Model

Return Jani model

as_prism_program(self: stormpy.core.SymbolicModelDescription) → storm::prism::Program

Return Prism program

instantiate_constants(self: stormpy.core.SymbolicModelDescription, constant_definitions: Dict[storm::expressions::Variable, storm::expressions::Expression]) → stormpy.core.SymbolicModelDescription

Instantiate constants in symbolic model description

property is_jani_model

Flag if program is in Jani format

property is_prism_program

Flag if program is in Prism format

parse_constant_definitions(self: stormpy.core.SymbolicModelDescription, String containing constants and their values: str) → Dict[storm::expressions::Variable, storm::expressions::Expression]

Parse given constant definitions

class SymbolicParametricQuantitativeCheckResult

Symbolic parametric quantitative model checking result

class SymbolicQualitativeCheckResult

Symbolic qualitative model checking result

class SymbolicQuantitativeCheckResult

Symbolic quantitative model checking result

class SymbolicSylvanCtmc

CTMC in symbolic representation

class SymbolicSylvanDtmc

DTMC in symbolic representation

class SymbolicSylvanMA

MA in symbolic representation

class SymbolicSylvanMdp

MDP in symbolic representation

class SymbolicSylvanParametricCtmc

pCTMC in symbolic representation

class SymbolicSylvanParametricDtmc

pDTMC in symbolic representation

class SymbolicSylvanParametricMA

pMA in symbolic representation

class SymbolicSylvanParametricMdp

pMDP in symbolic representation

class SymbolicSylvanParametricRewardModel

Reward structure for parametric symbolic models

property has_state_action_rewards
property has_state_rewards
property has_transition_rewards
class SymbolicSylvanRewardModel

Reward structure for symbolic models

property has_state_action_rewards
property has_state_rewards
property has_transition_rewards
class TimeOperator

The time operator

class UnaryBooleanStateFormula

Unary boolean state formula

class UnaryPathFormula

Path formula with one operand

property subformula

the subformula

class UnaryStateFormula

State formula with one operand

property subformula

the subformula

class UntilFormula

Path Formula for unbounded until

class Variable
property id
property is_no_variable
property name
property rank
property type
build_model(symbolic_description, properties=None)

Build a model in sparse representation from a symbolic description.

Parameters
  • symbolic_description – Symbolic model description to translate into a model.

  • properties (List[Property]) – List of properties that should be preserved during the translation. If None, then all properties are preserved.

Returns

Model in sparse representation.

build_model_from_drn(file)

Build a model in sparse representation from the explicit DRN representation.

Parameters

file (String) – DRN file containing the model.

Returns

Model in sparse representation.

build_parametric_model(symbolic_description, properties=None)

Build a parametric model in sparse representation from a symbolic description.

Parameters
  • symbolic_description – Symbolic model description to translate into a model.

  • properties (List[Property]) – List of properties that should be preserved during the translation. If None, then all properties are preserved.

Returns

Parametric model in sparse representation.

build_parametric_model_from_drn(file)

Build a parametric model in sparse representation from the explicit DRN representation.

Parameters

file (String) – DRN file containing the model.

Returns

Parametric model in sparse representation.

build_sparse_model(symbolic_description, properties=None)

Build a model in sparse representation from a symbolic description.

Parameters
  • symbolic_description – Symbolic model description to translate into a model.

  • properties (List[Property]) – List of properties that should be preserved during the translation. If None, then all properties are preserved.

Returns

Model in sparse representation.

build_sparse_model_from_explicit(transition_file: str, labeling_file: str, state_reward_file: Optional[str]='', transition_reward_file: Optional[str]='', choice_labeling_file: Optional[str]='') → storm::models::sparse::Model<double, storm::models::sparse::StandardRewardModel<double> >

Build the model model from explicit input

build_sparse_model_with_options(model_description: storm::storage::SymbolicModelDescription, options: storm::builder::BuilderOptions, use_jit: bool=False, doctor: bool=False) → storm::models::ModelBase

Build the model in sparse representation

build_sparse_parametric_model(symbolic_description, properties=None)

Build a parametric model in sparse representation from a symbolic description.

Parameters
  • symbolic_description – Symbolic model description to translate into a model.

  • properties (List[Property]) – List of properties that should be preserved during the translation. If None, then all properties are preserved.

Returns

Parametric model in sparse representation.

build_symbolic_model(symbolic_description, properties=None)

Build a model in symbolic representation from a symbolic description.

Parameters
  • symbolic_description – Symbolic model description to translate into a model.

  • properties (List[Property]) – List of properties that should be preserved during the translation. If None, then all properties are preserved.

Returns

Model in symbolic representation.

build_symbolic_parametric_model(symbolic_description, properties=None)

Build a parametric model in symbolic representation from a symbolic description.

Parameters
  • symbolic_description – Symbolic model description to translate into a model.

  • properties (List[Property]) – List of properties that should be preserved during the translation. If None, then all properties are preserved.

Returns

Parametric model in symbolic representation.

check_model_dd(model, property, only_initial_states=False, environment=<stormpy.core.Environment object>)

Perform model checking using dd engine. :param model: Model. :param property: Property to check for. :param only_initial_states: If True, only results for initial states are computed, otherwise for all states. :return: Model checking result. :rtype: CheckResult

check_model_hybrid(model, property, only_initial_states=False, environment=<stormpy.core.Environment object>)

Perform model checking using hybrid engine. :param model: Model. :param property: Property to check for. :param only_initial_states: If True, only results for initial states are computed, otherwise for all states. :return: Model checking result. :rtype: CheckResult

check_model_sparse(model, property, only_initial_states=False, extract_scheduler=False, environment=<stormpy.core.Environment object>)

Perform model checking on model for property. :param model: Model. :param property: Property to check for. :param only_initial_states: If True, only results for initial states are computed, otherwise for all states. :param extract_scheduler: If True, try to extract a scheduler :return: Model checking result. :rtype: CheckResult

compute_all_until_probabilities(arg0: stormpy.core.Environment, arg1: stormpy.core.CheckTask, arg2: storm::models::sparse::Ctmc<double, storm::models::sparse::StandardRewardModel<double> >, arg3: storm::storage::BitVector, arg4: storm::storage::BitVector) → List[float]

Compute forward until probabilities

compute_prob01_states(model, phi_states, psi_states)

Compute prob01 states for properties of the form phi_states until psi_states

Parameters
  • model (SparseDTMC) –

  • phi_states (BitVector) –

  • psi_states (BitVector) – Target states

compute_prob01max_states(model, phi_states, psi_states)
compute_prob01min_states(model, phi_states, psi_states)
compute_transient_probabilities(arg0: stormpy.core.Environment, arg1: storm::models::sparse::Ctmc<double, storm::models::sparse::StandardRewardModel<double> >, arg2: storm::storage::BitVector, arg3: storm::storage::BitVector, arg4: float) → List[float]

Compute transient probabilities

construct_submodel(model, states, actions, keep_unreachable_states=True, options=<stormpy.core.SubsystemBuilderOptions object>)
Parameters
  • model – The model

  • states – Which states should be preserved

  • actions – Which actions should be preserved

  • keep_unreachable_states – If False, run a reachability analysis.

Returns

A model with fewer states/actions

create_filter_initial_states_sparse(model: storm::models::sparse::Model<double, storm::models::sparse::StandardRewardModel<double> >) → stormpy.core._QualitativeCheckResult

Create a filter for the initial states on a sparse model

create_filter_initial_states_symbolic(model: storm::models::symbolic::Model<(storm::dd::DdType)1, double>) → stormpy.core._QualitativeCheckResult

Create a filter for the initial states on a symbolic model

eliminate_reward_accumulations()

Eliminate reward accumulations

export_parametric_to_drn(model: storm::models::sparse::Model<carl::RationalFunction<carl::FactorizedPolynomial<carl::MultivariatePolynomial<cln::cl_RA, carl::MonomialComparator<&carl::Monomial::compareGradedLexical, true>, carl::StdMultivariatePolynomialPolicies<carl::NoReasons, carl::NoAllocator> > >, true>, storm::models::sparse::StandardRewardModel<carl::RationalFunction<carl::FactorizedPolynomial<carl::MultivariatePolynomial<cln::cl_RA, carl::MonomialComparator<&carl::Monomial::compareGradedLexical, true>, carl::StdMultivariatePolynomialPolicies<carl::NoReasons, carl::NoAllocator> > >, true> > >, file: str) → None

Export parametric model in DRN format

export_to_drn(model: storm::models::sparse::Model<double, storm::models::sparse::StandardRewardModel<double> >, file: str) → None

Export model in DRN format

model_checking(model, property, only_initial_states=False, extract_scheduler=False, environment=<stormpy.core.Environment object>)

Perform model checking on model for property. :param model: Model. :param property: Property to check for. :param only_initial_states: If True, only results for initial states are computed, otherwise for all states. :param extract_scheduler: If True, try to extract a scheduler :return: Model checking result. :rtype: CheckResult

parse_jani_model(path: str) → Tuple[storm::jani::Model, Dict[str, stormpy.core.Property]]

Parse Jani model

parse_prism_program(path: str, prism_compat: bool=False, simplify: bool=True) → storm::prism::Program

Parse Prism program

parse_properties(formula_string: str, property_filter: Optional[Set[str]]=None) → List[stormpy.core.Property]

Parse properties given in the prism format.

Parameters
  • formula_str (str) – A string of formulas

  • property_filter (str) – A filter

Returns

A list of properties

parse_properties_for_jani_model(formula_string: str, jani_model: storm::jani::Model, property_filter: Optional[Set[str]]=None) → List[stormpy.core.Property]
parse_properties_for_prism_program(formula_string: str, prism_program: storm::prism::Program, property_filter: Optional[Set[str]]=None) → List[stormpy.core.Property]

Parses properties given in the prism format, allows references to variables in the prism program.

Parameters
  • formula_str (str) – A string of formulas

  • prism_program (PrismProgram) – A prism program

  • property_filter (str) – A filter

Returns

A list of properties

perform_bisimulation(model, properties, bisimulation_type)

Perform bisimulation on model. :param model: Model. :param properties: Properties to preserve during bisimulation. :param bisimulation_type: Type of bisimulation (weak or strong). :return: Model after bisimulation.

perform_sparse_bisimulation(model, properties, bisimulation_type)

Perform bisimulation on model in sparse representation. :param model: Model. :param properties: Properties to preserve during bisimulation. :param bisimulation_type: Type of bisimulation (weak or strong). :return: Model after bisimulation.

perform_symbolic_bisimulation(model, properties)

Perform bisimulation on model in symbolic representation. :param model: Model. :param properties: Properties to preserve during bisimulation. :return: Model after bisimulation.

prob01max_states(model, eventually_formula)
prob01min_states(model, eventually_formula)
topological_sort(model, forward=True, initial=[])
Parameters
  • model

  • forward

Returns

transform_to_discrete_time_model(model, properties)

Transform continuous-time model to discrete time model. :param model: Continuous-time model. :param properties: List of properties to transform as well. :return: Tuple (Discrete-time model, converted properties).

transform_to_sparse_model(model)

Transform model in symbolic representation into model in sparse representation. :param model: Symbolic model. :return: Sparse model.