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How to use _is_executed method in localstack

Best Python code snippet using localstack_python

Interpreter.py

Source:Interpreter.py

1class InfiniteLoopException(Exception):2    pass3class Instruction:4    def __init__(self, operation_map, operation, argument):5        self._operation_map = operation_map6        self._operation = operation7        self._argument = argument8        self._is_executed = False9    def execute(self):10        # print("Executing", self._operation, self._argument)11        if self._is_executed:12            raise InfiniteLoopException13        self._operation_map[self._operation](self._argument)14        self._is_executed = True15    def get_operation(self):16        return self._operation17    def set_operation(self, new_operation):18        self._operation = new_operation19    def get_argument(self):20        return self._argument21    def set_argument(self, new_argument):22        self._argument = new_argument23class Interpreter:24    def __init__(self, instructions):25        self.operation_map = {26            "nop": self._nop,27            "jmp": self._jmp,28            "acc": self._acc29        }30        self._instructions = []31        for instruction in instructions:32            operation, argument = instruction.split(" ")33            self._instructions.append(Instruction(self.operation_map, operation, int(argument)))34        self._instructionPointer = 035        self._accumulator = 036    def reset(self):37        self._instructionPointer = 038        self._accumulator = 039        for instruction in self._instructions:40            instruction._is_executed = False41    def run(self):42        self.reset()43        while self._instructionPointer < len(self._instructions):44            self._instructions[self._instructionPointer].execute()45    def get_accumulator(self):46        return self._accumulator47    def get_instruction_set(self):48        return self._instructions49    def _nop(self, argument):50        self._instructionPointer += 151        pass52    def _jmp(self, argument):53        self._instructionPointer += argument54    def _acc(self, argument):55        self._accumulator += argument56        self._instructionPointer += 157    def print(self):58        print("----------- interpreter ----------")59        print("Accumulator:", self._accumulator)60        print("Instruction pointer:", self._instructionPointer)61        print("Instructions:")62        for instruction in self._instructions:...

node.py

Source:node.py

1from __future__ import annotations2from typing import Any, Callable, Dict, List, Optional3import threading4from tinydag.exceptions import NodeUnexecutedError5class Node:6    def __init__(7        self,8        name: str,9        func: Callable,10        depends: Optional[List[str]] = None,11    ) -> None:12        self._name = name13        self._depends = depends or []14        self._func = func15        self._next_nodes: List[Node] = []16        self._inputs: Dict[str, Any] = {}17        self._output: Optional[Any] = None18        self._is_executed = False19        self._lock = threading.RLock()20    @property21    def name(self) -> str:22        return self._name23    @property24    def depends(self) -> List[str]:25        return self._depends26    @property27    def next_nodes(self) -> List[Node]:28        return self._next_nodes29    @property30    def is_ready(self) -> bool:31        with self._lock:32            return len(self._depends) == len(self._inputs)33    @property34    def is_executed(self) -> bool:35        return self._is_executed36    @property37    def output(self) -> Any:38        if not self.is_executed:39            raise NodeUnexecutedError(f"Node <{self.name}> is not executed.")40        return self._output41    def add_next_node(self, next_node: Node) -> None:42        self._next_nodes.append(next_node)43    def set_input(self, depname: str, data: Any):44        with self._lock:45            self._inputs[depname] = data46    def run(self) -> Any:47        inputs: List[Any] = [48            self._inputs[depname] for depname in self._depends49            if depname in self._inputs50        ]51        if len(self._inputs) != len(self._depends):52            raise RuntimeError("Numbers of required and given "53                               "inputs is not matched.")54        self._output = self._func(*inputs)55        self._is_executed = True56        return self._output57    def __repr__(self) -> str:...

computation.py

Source:computation.py

1"""Contains code related to computations."""2from __future__ import annotations3import textwrap4import warnings5from dataclasses import dataclass6from typing import Callable, NewType7from .environment import Environment8from .record import Record9Identifier = NewType("Identifier", str)10class Computation:11    """Represents a computation."""12    def __init__(13        self,14        identifier: Identifier,15        environment: Environment,16        trigger: Callable[[], None],17    ) -> None:18        """Initialize the computation."""19        self.identifier = identifier20        self._environment = environment21        self._trigger = trigger22        self._is_executed = False23    def execute(self) -> ComputationRecord:24        """Execute the computation."""25        if self._is_executed:26            raise RuntimeError("Computation already executed!")27        with self._environment.consistency_check() as check:28            self._trigger()29        if not check.success:30            warnings.warn("Environment changed during execution!")31        self._is_executed = True32        return ComputationRecord(self.identifier, check.record)33    def __repr__(self) -> str:34        """Return a string representation of the computation."""35        return (36            f"{self.__class__.__name__}("37            f"identifier={repr(self.identifier)}, "38            f"environment={repr(self._environment)}, "39            f"trigger={repr(self._trigger)})"40        )41@dataclass(frozen=True)42class ComputationRecord:43    """Represents the association between an executed computation and its environmental record."""44    identifier: Identifier45    record: Record46    def __str__(self) -> str:47        """Return a human-readable representation of the computation record."""48        indent = 4 * " "49        string = "Computation Record:\n"50        string += textwrap.indent("Identifier: " + self.identifier + "\n" + str(self.record), indent)...

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