Instance | Branch |
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;;;; regenerate.lisp - script for regenerating [redacted] static HTML from template files. | |
(ql:quickload "cl-emb") | |
(ql:quickload "cl-fad") | |
(ql:quickload "cl-ppcre") | |
(defparameter *configuration* '() "plist containing config parameters passed to EMB templates.") | |
(defparameter *essays* '() "plist containing essay descriptors generated by `defessay'.") | |
(defconstant +default-properties+ '(:title nil :url nil :orig-title nil :orig-url nil :date nil :orig-date nil :alt-translations nil :translators nil :editors nil :disabled nil :additional-html nil :part-of-hnp nil :description "")) |
F# | C# | Scala | Clojure | Python | Ruby | Haskell | SQL | OCaml | Common Lisp | Erlang | Smalltalk | Scheme | Ecmascript 5 | Perl 5 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
map | Select | map | map | map | collect | map | Select | map | mapcar | map | collect: | map | map | map |
filter | Where | filter | filter | filter | select | filter | Where | filter | remove-if-not | filter | select: | filter | filter | grep |
fold | Aggregate | foldLeft | reduce | reduce | inject | foldl |
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import socket | |
import threading | |
import os | |
import subprocess | |
Port = 123 #Shadowsocks 的端口 | |
ShadowsocksPath = "/home/ss" #Shadowsocks 的路径 | |
def CheckService(): | |
sk = socket.socket(socket.AF_INET, socket.SOCK_STREAM) |
See Also:
Level | Score | Regex | Credit |
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#include <stdio.h> | |
#include <stdlib.h> | |
int convert_file(char *szSourceFile, int bIsFastConv) | |
{ | |
char szNewFileName[256] = {0}; | |
int buffer[0x400]; | |
int i; | |
char flag; | |
char data; |
##ss-redir 的 iptables 配置(透明代理)
透明代理指对客户端透明,客户端不需要进行任何设置就使用了网管设置的代理规则
创建 /etc/ss-redir.json 本地监听 7777
运行ss-redir -v -c /etc/ss-redir.json
iptables -t nat -N SHADOWSOCKS
# 在 nat 表中创建新链
iptables -t nat -A SHADOWSOCKS -p tcp --dport 23596 -j RETURN
# 23596 是 ss 代理服务器的端口,即远程 shadowsocks 服务器提供服务的端口,如果你有多个 ip 可用,但端口一致,就设置这个
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try: | |
from urllib.parse import quote # Py 3 | |
except ImportError: | |
from urllib2 import quote # Py 2 | |
import os | |
import sys | |
BLOG_DIR = os.environ['BLOG_DIR'] | |
# BLOG_DIR = '/Users/cscorley/git/cscorley.github.io/' |
As of version 3.3, python includes the very promising concurrent.futures
module, with elegant context managers for running tasks concurrently. Thanks to the simple and consistent interface you can use both threads and processes with minimal effort.
For most CPU bound tasks - anything that is heavy number crunching - you want your program to use all the CPUs in your PC. The simplest way to get a CPU bound task to run in parallel is to use the ProcessPoolExecutor, which will create enough sub-processes to keep all your CPUs busy.
We use the context manager thusly:
with concurrent.futures.ProcessPoolExecutor() as executor: