很多人讨厌bash脚本。每当我要做最简单的事情时,我都必须查阅文档。如何将函数的参数转发给子命令?如何将字符串分配给变量,然后作为命令调用该字符串?如何检查两个字符串变量是否相等?如何分割字符串并获得后半部分?等等。不是我找不到这些答案,而是每次都必须查找它们。但是,我们不能否认将整个程序当作纯粹的功能发挥作用的能力,以及将一个程序的输出传递到另一个程序的自然程度。因此,我想知道,我们能否将bash的某些功能与Python结合起来?让我们从一个类开始。这是一个简单的方法,将其初始化参数保存到局部变量,然后使用subprocess.run
对其自身进行延迟求值并保存结果。import subprocess
class PipePy:
def __init__(self, *args):
self._args = args
self._result = None
def _evaluate(self):
if self._result is not None:
return
self._result = subprocess.run(self._args,
capture_output=True,
text=True)
@property
def returncode(self):
self._evaluate()
return self._result.returncode
@property
def stdout(self):
self._evaluate()
return self._result.stdout
def __str__(self):
return self.stdout
@property
def stderr(self):
self._evaluate()
return self._result.stderr
ls = PipePy('ls')
ls_l = PipePy('ls', '-l')
print(ls)
# <<
# ... main.py
# ... tags
print(ls_l)
# <<
# ... -rw-r--r-- 1 kbairak kbairak 125 Jan 22 08:53 files.txt
# ... -rw-r--r-- 1 kbairak kbairak 5425 Feb 1 21:54 main.py
# ... -rw-r--r-- 1 kbairak kbairak 1838 Feb 1 21:54 tags
ls_l = PipePy('ls', '-l')
print(ls_l)
ls = PipePy('ls')
print(ls('-l'))
PipePy('ls'
, '-l')
PipePy('ls')('-l')
值得庆幸的是,我们的类创建了惰性对象这一事实在很大程度上帮助了我们:class PipePy:
# __init__, etc
def __call__(self, *args):
args = self._args + args
return self.__class__(*args)
ls = PipePy('ls')
print(ls('-l'))
# <<
# ... -rw-r--r-- 1 kbairak kbairak 125 Jan 22 08:53 files.txt
# ... -rw-r--r-- 1 kbairak kbairak 5425 Feb 1 21:54 main.py
# ... -rw-r--r-- 1 kbairak kbairak 1838 Feb 1 21:54 tags
如果要向ls
传递更多参数,则可能会遇到--sort = size
。我们可以轻松地执行ls('-l','--sort = size')
。我们可以做得更好吗? class PipePy:
- def __init__(self, *args):
+ def __init__(self, *args, **kwargs):
self._args = args
+ self._kwargs = kwargs
self._result = None
def _evaluate(self):
if self._result is not None:
return
- self._result = subprocess.run(self._args,
+ self._result = subprocess.run(self._convert_args(),
capture_output=True,
text=True)
+ def _convert_args(self):
+ args = [str(arg) for arg in self._args]
+ for key, value in self._kwargs.items():
+ key = key.replace('_', '-')
+ args.append(f"--{key}={value}")
+ return args
- def __call__(self, *args):
+ def __call__(self, *args, **kwargs):
args = self._args + args
+ kwargs = {**self._kwargs, **kwargs}
- return self.__class__(*args)
+ return self.__class__(*args, **kwargs)
# returncode, etc
print(ls('-l'))
# <<
# ... -rw-r--r-- 1 kbairak kbairak 125 Jan 22 08:53 files.txt
# ... -rw-r--r-- 1 kbairak kbairak 5425 Feb 1 21:54 main.py
# ... -rw-r--r-- 1 kbairak kbairak 1838 Feb 1 21:54 tags
print(ls('-l', sort="size"))
# <<
# ... -rw-r--r-- 1 kbairak kbairak 5425 Feb 1 21:54 main.py
# ... -rw-r--r-- 1 kbairak kbairak 1838 Feb 1 21:54 tags
# ... -rw-r--r-- 1 kbairak kbairak 125 Jan 22 08:53 files.txt
ls = PipePy('ls')
grep = PipePy('grep')
print(ls | grep('tags'))
# <<
1、让__init__
和__call__
方法接受一个仅用于关键字的新_pipe_input
关键字参数,该参数将保存在self
上。2、在评估期间,如果设置了_pipe_input
,它将作为输入参数传递给subprocess.run
。3、重写__or__
方法以将左操作数的结果作为pipe
输入传递给右操作数。 class PipePy:
- def __init__(self, *args, **kwargs):
+ def __init__(self, *args, _pipe_input=None, **kwargs):
self._args = args
self._kwargs = kwargs
+ self._pipe_input = _pipe_input
self._result = None
- def __call__(self, *args, **kwargs):
+ def __call__(self, *args, _pipe_input=None, **kwargs):
args = self._args + args
kwargs = {**self._kwargs, **kwargs}
- return self.__class__(*args, **kwargs)
+ return self.__class__(*args, _pipe_input=_pipe_input, **kwargs)
def _evaluate(self):
if self._result is not None:
return
self._result = subprocess.run(self._convert_args(),
+ input=self._pipe_input,
capture_output=True,
text=True)
+ def __or__(left, right):
+ return right(_pipe_input=left.stdout)
让我们尝试一下(从之前稍微修改命令以证明它确实有效):ls = PipePy('ls')
grep = PipePy('grep')
print(ls('-l') | grep('tags'))
# <<
class PipePy:
# __init__, etc
def __bool__(self):
return self.returncode == 0
git = PipePy('git')
grep = PipePy('grep')
if git('branch') | grep('my_feature'):
print("Branch 'my_feature' found")
class PipePy:
# __init__, etc
def __gt__(self, filename):
with open(filename, 'w') as f:
f.write(self.stdout)
def __rshift__(self, filename):
with open(filename, 'a') as f:
f.write(self.stdout)
def __lt__(self, filename):
with open(filename) as f:
return self(_pipe_input=f.read())
ls = PipePy('ls')
grep = PipePy('grep')
cat = PipePy('cat')
ls > 'files.txt'
print(grep('main') 'files.txt')
# <<
ls >> 'files.txt'
print(cat('files.txt'))
# <<
# ... main.py
# ... tags
# ... files.txt
# ... main.py
# ... tags
class PipePy:
# __init__, etc
def __iter__(self):
return iter(self.stdout.split())
ls = PipePy('ls')
for name in ls:
print(name.upper())
# <<
# ... MAIN.PY
# ... TAGS
class PipePy:
# __init__, etc
def as_table(self):
lines = self.stdout.splitlines()
fields = lines[0].split()
result = []
for line in lines[1:]:
item = {}
for i, value in enumerate(line.split(maxsplit=len(fields) - 1)):
item[fields[i]] = value
result.append(item)
return result
ps = PipePy('ps')
print(ps)
# <<
# ... 4205 pts/4 00:00:00 zsh
# ... 13592 pts/4 00:00:22 ptipython
# ... 16253 pts/4 00:00:00 ps
ps.as_table()
# <<
# ... {'PID': '13592', 'TTY': 'pts/4', 'TIME': '00:00:22', 'CMD': 'ptipython'},
# ... {'PID': '16208', 'TTY': 'pts/4', 'TIME': '00:00:00', 'CMD': 'ps'}]
在子进程中更改工作目录不会影响当前的脚本或python shell。与更改环境变量相同,以下内容不是PipePy的补充,但很不错:import os
cd = os.chdir
export = os.environ.__setitem__
pwd = PipePy('pwd')
pwd
# <<
cd('..')
pwd
# <<
如果我在交互式shell中,则希望能够简单地键入ls
并完成它。class PipePy:
# __init__, etc
def __repr__(self):
return self.stdout + self.stderr
>>> ls = PipePy('ls')
>>> ls
files.txt
main.py
tags
我们的实例是惰性的,这意味着如果我们对它们的结果感兴趣,则将对它们进行评估,此后不再进行评估。如果我们只是想确保已执行该操作怎么办?例如,假设我们有以下脚本:from pipepy import PipePy
tar = PipePy('tar')
tar('-xf', 'some_archive.tar')
print("File extracted")
该脚本实际上不会执行任何操作,因为tar
调用实际上并未得到评估。我认为一个不错的惯例是,如果不带参数调用__call__
强制求值: class PipePy:
def __call__(self, *args, _pipe_input=None, **kwargs):
args = self._args + args
kwargs = {**self._kwargs, **kwargs}
- return self.__class__(*args, _pipe_input=_pipe_input, **kwargs)
+ result = self.__class__(*args, _pipe_input=_pipe_input, **kwargs)
+ if not args and not _pipe_input and not kwargs:
+ result._evaluate()
+ return result
因此在编写脚本时,如果要确保实际上已调用命令,则必须用一对括号来调用它:
from pipepy import PipePy
tar = PipePy('tar')
-tar('-xf', 'some_archive.tar')
+tar('-xf', 'some_archive.tar')()
print("File extracted")
date = PipePy('date')
date
# <<
# Wait 5 seconds
date
# <<
不好!date
没有改变。date
对象将其_result
保留在内存中。随后的评估实际上不会调用该命令,而只是返回存储的值。date = PipePy('date')
date()
# <<
# Wait 5 seconds
date()
# <<
另一个解决方案是:由PipePy
构造函数返回的实例不应该是惰性的,但由__call__
调用返回的实例将是惰性的。 class PipePy:
- def __init__(self, *args, _pipe_input=None, **kwargs):
+ def __init__(self, *args, _pipe_input=None, _lazy=False, **kwargs):
self._args = args
self._kwargs = kwargs
self._pipe_input = _pipe_input
+ self._lazy = _lazy
self._result = None
def __call__(self, *args, _pipe_input=None, **kwargs):
args = self._args + args
kwargs = {**self._kwargs, **kwargs}
- result = self.__class__(*args, _pipe_input=_pipe_input, **kwargs)
+ result = self.__class__(*args,
+ _pipe_input=_pipe_input,
+ _lazy=True,
+ **kwargs)
if not args and not _pipe_input and not kwargs:
result._evaluate()
return result
def _evaluate(self):
- if self._result is not None:
+ if self._result is not None and self._lazy:
return
self._result = subprocess.run(self._convert_args(),
input=self._pipe_input,
capture_output=True,
text=True)
date = PipePy('date')
date
# <<
# Wait 5 seconds
date
# <<
并且可以预见的是,使用空调用的返回值将具有之前的行为:date = PipePy('date')
d = date()
d
# <<
# Wait 5 seconds
d
# <<
好吧,ls('-l')
不错,但是如果我们像人类一样简单地做ls -l
,那就太好了。嗯,我有个主意:class PipePy:
# __init__, etc
def __sub__(left, right):
return left(f"-{right}")
ls = PipePy('ls')
ls - 'l'
# <<
# ... -rw-r--r-- 1 kbairak kbairak 46 Feb 1 23:04 files.txt
# ... -rw-r--r-- 1 kbairak kbairak 5425 Feb 1 21:54 main.py
# ... -rw-r--r-- 1 kbairak kbairak 1838 Feb 1 21:54 tags
l = 'l'
ls -l
import string
for char in string.ascii_letters:
if char in locals():
continue
locals()[char] = char
class PipePy:
# __init__, etc
用locals()
给了我一个灵感。为什么我们必须一直实例化PipePy
?我们无法在路径中找到所有可执行文件,并根据它们创建PipePy
实例吗?我们当然可以!import os
import stat
for path in os.get_exec_path():
try:
names = os.listdir(path)
except FileNotFoundError:
continue
for name in names:
if name in locals():
continue
if 'x'
in stat.filemode(os.lstat(os.path.join(path, name)).st_mode):
locals()[name] = PipePy(name)
因此,现在,将我们拥有的所有内容都放在一个python文件中,并删除脚本(这是实际bash脚本的转录):from pipepy import mysqladmin, sleep, drush, grep
for i in range(10):
if mysqladmin('ping',
host="mysql_drupal7",
user="user",
password="password"):
break
sleep(1)() # Remember to actually invoke
if not drush('status', 'bootstrap') | grep('-q', 'Successful'):
drush('-y', 'site-install', 'standard',
db_url="mysql://user:password@mysql_drupal7:3306/drupal",
acount_pass="kbairak")() # Remember to actually invoke
drush('en', 'tmgmt_ui', 'tmgmt_entity_ui', 'tmgmt_node_ui')()