Python内核精炼：500行实现的Python解释器

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"500 Lines or Less：一个用Python编写的Python解释器" 《500 Lines or Less：一个用Python编写的Python解释器》是一篇深入探讨小型、简洁编程实践的文章，由Allison Kaptur撰写，她是一位在Dropbox工作的工程师，负责维护世界上最大的Python客户端网络之一。在Recurse Center的经历使她在纽约成为程序员作家社区的一员，并在PyCon北美会议上分享过Python内部机制。Kaptur热衷于探索奇特的编程问题，并在她的博客（takaptur.com）上发表文章。该篇文章的主要焦点是ByteRun，一个完全用Python实现的Python解释器。作者惊讶地发现，Python解释器的核心结构可以轻松适应500行代码的限制。作者的目标并非详尽无遗地讲解解释器的所有细节，而是希望通过这个章节，读者能够理解解释器的基本构造，并为更深入的研究提供足够的背景知识。在文章中，你会看到一个简明的解析过程，涵盖了如何构建一个能够执行Python语句的环境，包括词法分析、语法解析、符号表管理和执行引擎等关键部分。通过这个小规模的实现，读者可以领略到编写高效且可扩展程序的优雅设计原则，以及如何利用Python的内建功能来简化复杂任务。此外，文章还可能涉及Python解释器的内存管理、错误处理、动态类型系统以及解释器与虚拟机之间的区别。尽管篇幅有限，但作者强调了这种小而美的项目如何作为学习和实践编程语言核心原理的有趣案例。这篇论文不仅是一份技术指南，也是对编程效率和简洁性的独特见解，适合那些对小型项目设计和Python底层工作原理感兴趣的开发者阅读。如果你想要挑战自己或深入了解Python解释器的工作方式，那么这篇只有500行代码的解释器无疑是个值得研究的宝藏。

2019/1/23 500 Lines or Less | A Python Interpreter Written in Python

http://aosabook.org/en/500L/a-python-interpreter-written-in-python.html 5/21

The arguments to an instruction can now mean two different things: They can either be an index into the "numbers"

list, or they can be an index into the "names" list. The interpreter knows which it should be by checking what

instruction it's executing. We'll break out this logic—and the mapping of instructions to what their arguments mean—

into a separate method.

class Interpreter:

def __init__(self):

self.stack = []

self.environment = {}

def STORE_NAME(self, name):

val = self.stack.pop()

self.environment[name] = val

def LOAD_NAME(self, name):

val = self.environment[name]

self.stack.append(val)

def parse_argument(self, instruction, argument, what_to_execute):

""" Understand what the argument to each instruction means."""

numbers = ["LOAD_VALUE"]

names = ["LOAD_NAME", "STORE_NAME"]

if instruction in numbers:

argument = what_to_execute["numbers"][argument]

elif instruction in names:

argument = what_to_execute["names"][argument]

return argument

def run_code(self, what_to_execute):

instructions = what_to_execute["instructions"]

for each_step in instructions:

instruction, argument = each_step

argument = self.parse_argument(instruction, argument, what_to_execute)

if instruction == "LOAD_VALUE":

self.LOAD_VALUE(argument)

elif instruction == "ADD_TWO_VALUES":

self.ADD_TWO_VALUES()

elif instruction == "PRINT_ANSWER":

self.PRINT_ANSWER()

elif instruction == "STORE_NAME":

self.STORE_NAME(argument)

elif instruction == "LOAD_NAME":

self.LOAD_NAME(argument)

Even with just five instructions, the run_code method is starting to get tedious. If we kept this structure, we'd need

one branch of the if statement for each instruction. Here, we can make use of Python's dynamic method lookup.

We'll always define a method called FOO to execute the instruction called FOO , so we can use Python's getattr

function to look up the method on the fly instead of using the big if statement. The run_code method then looks

like this:

def execute(self, what_to_execute):

instructions = what_to_execute["instructions"]

for each_step in instructions:

instruction, argument = each_step

t lf t(i t ti t h t t t )

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Python内核精炼：500行实现的Python解释器

(How to Write a (Lisp) Interpreter (in Python))中文版(包括上下篇)

500 line or less[Python学习宝典]

500lines代码学习资源集合

A Python Interpreter written in Rust

Python库 | vapl_interpreter-0.1.1.tar.gz

Python库 | django-python-interpreter-0.0.1.tar.gz

[WARNING]: Platform linux on host 10.1.18.146 is using the discovered Python interpreter at /usr/bin/python3, but future installation of another Python interpreter could change this. See https://docs.ansible.com/ansible/2.9/reference_appendices/interpreter_discovery.html for more information.

FAILED_PRECONDITION: Python interpreter state is not initialized. The process may be terminated. [[{{node PyFunc}}]]

W tensorflow/core/kernels/data/generator_dataset_op.cc:108] Error occurred when finalizing GeneratorDataset iterator: FAILED_PRECONDITION: Python interpreter state is not initialized. The process may be terminated. [[{{node PyFunc}}]]

You need to select a Python interpreter before you startdebugging. Tip: click on "Select Interpreter" in the status bar.

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