标签: cpython

我在大学里做过几个关于 C 的项目，但从未以专业身份使用过它。

最近我开始通读 cpython 的源代码，下面的语法让我很困惑：github

什么是PyAPI_FUNC(int) PyToken_OneChar(int);函数名前的部分是什么意思？它是动态构造返回类型的包装函数吗？

在这种情况下，我什至不确定 Google 搜索什么！

我正在运行以下 python 代码

import numpy as np
import pydensecrf.densecrf as dcrf
import matplotlib.pyplot as plt

这导致

    ModuleNotFoundError                       Traceback (most recent call last)
    <ipython-input-4-cbaa174fb5e7> in <module>
          1 import numpy as np
    ----> 2 import pydensecrf.densecrf as dcrf
          3 import matplotlib.pyplot as plt

ModuleNotFoundError: No module named 'pydensecrf'

我尝试安装： pip install pydensecrf 但每当我这样做时，我都会收到以下错误

C:\Users\Aparajita Das>pip install pydensecrf
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)")': /simple/pydensecrf/
Collecting pydensecrf
  Using cached pydensecrf-1.0rc3.tar.gz (1.0 …

我正在摆弄字符串并遇到以下行为：

>>> import dis
>>> dis.dis('"abcdefgh" * 513')
  1           0 LOAD_CONST               0 ('abcdefgh')
              2 LOAD_CONST               1 (513)
              4 BINARY_MULTIPLY
              6 RETURN_VALUE
>>> dis.dis('"abcdefgh" * 512')
  1           0 LOAD_CONST               0 ('abcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefghabcdefgh')
              2 RETURN_VALUE

正如你所看到的，当我乘以 时"abcdefgh"，512python 在编译阶段预先计算完整的字符串，但是当我将数字更改为513它时，它不再这样做，而是生成两个LOAD_CONST和 BINARY_MULTIPLY操作码（换句话说，python 不再预先计算编译阶段的结果）。有人可以解释这种行为的原因吗？

注意：我知道从代码的角度来看它工作得很好，但我很想知道这种行为的原因。

我在玩弄sum函数并观察到以下行为。

情况1：

source = """
class A:
    def __init__(self, a):
        self.a = a
    
    def __add__(self, other):
        return self.a + other;

sum([*range(10000)], start=A(10))
"""

import timeit
print(timeit.timeit(stmt=source))

正如您所看到的，我使用自定义类的实例作为函数start的参数sum。在我的系统中，对上面的代码进行基准测试大约需要几192.60747704200003秒钟的时间。

案例2：

source = """
class A:
    def __init__(self, a):
        self.a = a
    
    def __add__(self, other):
        return self.a + other;

sum([*range(10000)], start=10).  <- Here
"""

import timeit
print(timeit.timeit(stmt=source))

但如果我删除自定义类实例并int直接使用对象，则只需要111.48285191600007几秒钟。我很想知道这种速度差异的原因是什么？

我的系统信息：

>>> import platform
>>> platform.platform()
'macOS-12.5-arm64-arm-64bit'
>>> import sys
>>> sys.version
'3.11.0 …

例如,0 STORE_NAME 0 (sys)是指令的一部分import sys.该指令格式是否有任何文档？更重要的是,这种格式是Python的标准吗？还是具体实施？

sqlstring = 'INSERT INTO {}'
table = 'Product'
sqlstring.format(table)

不会导致'INSERT INTO产品'但仍然'INSERT INTO {}'为什么会这样？

您可能已经注意到,CPython有时会存储相同的不可变对象的单个副本.

例如

>>> a = "hello"
>>> b = "hello"
>>> a is b
True

>>> a, b = 7734, 7734
>>> a is b
True

我认为是堆的散列似乎是在类型推断之后执行的

>>> a, b = 7734, 07734
>>> a is b
False

>>> a, b = 7734, 017066
>>> a is b
True

有没有办法反省解释器并打印出这个假定的不可变对象堆？

我看到了建议(参见例如,在C中使用移位运算符进行乘法和除法实际上更快吗？),你不应该用移位运算符手动替换乘法,因为编译器必须自动执行,移位运算符会降低可读性.我写了一个简单的测试来检查这个:

import numpy as np
import time

array1 = np.random.randint(size=10 ** 6, low=0, high=10 ** 5)
array2 = np.zeros((10 ** 6,), dtype=np.int)

total = 0.0
for i in range(100):
    start = time.clock()
    for j in range(len(array2)):
        array2[j] = array1[j] * 2
    total += time.clock() - start
print("*2 time = " + str(round(total / 10, 5)) + " ms")


total = 0.0
for i in range(100):
    start = time.clock()
    for j in range(len(array2)):
        array2[j] = array1[j] << 1
    total …

我试图ThreadPoolExecutor在Windows 7上的Python 3.6中使用，似乎异常被忽略或停止了程序执行。示例代码：

#!/usr/bin/env python3

from time import sleep

from concurrent.futures import ThreadPoolExecutor

EXECUTOR = ThreadPoolExecutor(2)


def run_jobs():
    EXECUTOR.submit(some_long_task1)
    EXECUTOR.submit(some_long_task2, 'hello', 123)
    return 'Two jobs was launched in background!'


def some_long_task1():
    print("Task #1 started!")
    for i in range(10000000):
        j = i + 1
    1/0
    print("Task #1 is done!")


def some_long_task2(arg1, arg2):
    print("Task #2 started with args: %s %s!" % (arg1, arg2))
    for i in range(10000000):
        j = i + 1
    print("Task #2 is done!")


if __name__ == '__main__': …

在Cpython实现中,当我们删除dict中的键时,Cpython会将对应的条目设置为虚拟条目,为什么是虚拟条目？我可以让ertry的价值为零吗？

我不擅长C,所以我在python中嘲笑它,接下来是我的python的实现代码:

class DictEntry:
def __init__(self):
    self.key = None
    self.value = None
    self.hash = None
def __repr__(self):
    return ' %s %s %s' % (self.key, self.hash, self.value)


class Hashtable:
def __init__(self):
    self.size = 8
    self.used = 0
    self.mask = self.size - 1
    self.pow2 = 3
    self.entyies = [DictEntry() for _ in range(self.size)]

def insert(self, key, item):
    hash_value = _hash(key)
    _key = hash_value & (self.size - 1)
    if not self.is_slot_empty(_key):
        _key = self.next_slot(_key, hash_value)
    entry = self.entyies[_key]
    entry.key = _key
    entry.hash = …