solving

solving#

import qrules.solving

Functions to solve a particle reaction problem.

This module is responsible for solving a particle reaction problem stated by a QNProblemSet. The Solver classes (e.g. CSPSolver) generate new quantum numbers (for example belonging to an intermediate state) and validate the decay processes with the rules formulated by the conservation_rules module.

class EdgeSettings(conservation_rules: Set[GraphElementRule] = _Nothing.NOTHING, rule_priorities: Dict[GraphElementRule, int] = _Nothing.NOTHING, qn_domains: Dict[Any, list] = _Nothing.NOTHING)[source]#

Bases: object

Solver settings for a specific edge of a graph.

conservation_rules: Set[GraphElementRule][source]#

rule_priorities: Dict[GraphElementRule, int][source]#

qn_domains: Dict[Any, list][source]#

class NodeSettings(conservation_rules: Set[GraphElementRule | EdgeQNConservationRule | ConservationRule] = _Nothing.NOTHING, rule_priorities: Dict[GraphElementRule | EdgeQNConservationRule | ConservationRule, int] = _Nothing.NOTHING, qn_domains: Dict[Any, list] = _Nothing.NOTHING, interaction_strength: float = 1.0)[source]#

Bases: object

Container class for the interaction settings.

This class can be assigned to each node of a state transition graph. Hence, these settings contain the complete configuration information which is required for the solution finding, e.g:

set of conservation rules

mapping of rules to priorities (optional)

mapping of quantum numbers to their domains

strength scale parameter (higher value means stronger force)

conservation_rules: Set[GraphElementRule | EdgeQNConservationRule | ConservationRule][source]#

rule_priorities: Dict[GraphElementRule | EdgeQNConservationRule | ConservationRule, int][source]#

qn_domains: Dict[Any, list][source]#

interaction_strength: float[source]#

GraphSettings: TypeAlias = 'MutableTransition[EdgeSettings, NodeSettings]'#: (Mutable) mapping of settings on a Topology.

GraphElementProperties: TypeAlias = 'MutableTransition[GraphEdgePropertyMap, GraphNodePropertyMap]'#: (Mutable) mapping of edge and node properties on a Topology.

class QNProblemSet(initial_facts: GraphElementProperties, solving_settings: GraphSettings)[source]#

Bases: object

Particle reaction problem set, defined as a graph like data structure.

Parameters:

initial_facts – all of the known facts quantum numbers of the problem.
solving_settings – solving specific settings, such as the specific rules and variable domains for nodes and edges of the topology.

initial_facts: GraphElementProperties[source]#

solving_settings: GraphSettings[source]#

property topology: Topology[source]#

Bases: object

Defines a result to a problem set processed by the solving code.

solutions: List[MutableTransition[Dict[Type[pid | mass | width | spin_magnitude | spin_projection | charge | isospin_magnitude | isospin_projection | strangeness | charmness | bottomness | topness | baryon_number | electron_lepton_number | muon_lepton_number | tau_lepton_number | parity | c_parity | g_parity], int | float], Dict[Type[l_magnitude | l_projection | s_magnitude | s_projection | parity_prefactor], int | float]]][source]#

not_executed_node_rules: Dict[int, Set[str]][source]#

violated_node_rules: Dict[int, Set[str]][source]#

not_executed_edge_rules: Dict[int, Set[str]][source]#

violated_edge_rules: Dict[int, Set[str]][source]#

extend(other_result: QNResult) → None[source]#

class Solver[source]#

Bases: ABC

Interface of a Solver.

abstract find_solutions(problem_set: QNProblemSet) → QNResult[source]#

Find solutions for the given input.

It is expected that this function determines and returns all of the found solutions. In case no solutions are found a partial list of violated rules has to be given. This list of violated rules does not have to be complete.

Parameters:

problem_set (QNProblemSet) – states a problem set

Returns:

contains possible solutions, violated rules and not executed: rules due to requirement issues.

Return type:

QNResult

validate_full_solution(problem_set: QNProblemSet) → QNResult[source]#

Bases: Solver

Solver reducing the task to a Constraint Satisfaction Problem.

Solving this done with the python-constraint module.

The variables are the quantum numbers of particles/edges, but also some composite quantum numbers which are attributed to the interaction nodes (such as angular momentum \(L\)). The conservation rules serve as the constraints and a special wrapper class serves as an adapter.

find_solutions(problem_set: QNProblemSet) → QNResult[source]#

Find solutions for the given input.

It is expected that this function determines and returns all of the found solutions. In case no solutions are found a partial list of violated rules has to be given. This list of violated rules does not have to be complete.

Parameters:

problem_set (QNProblemSet) – states a problem set

Returns:

contains possible solutions, violated rules and not executed: rules due to requirement issues.

Return type:

QNResult

class Scoresheet[source]#

Bases: object

register_rule(graph_element_id: int, rule: GraphElementRule | EdgeQNConservationRule | ConservationRule) → Callable[[bool], None][source]#

property rule_calls: Dict[Tuple[int, GraphElementRule | EdgeQNConservationRule | ConservationRule], int][source]#

property rule_passes: Dict[Tuple[int, GraphElementRule | EdgeQNConservationRule | ConservationRule], int][source]#

solving

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solving#