agiliq

import random

class Markov(object):
	
	def __init__(self, open_file):
		self.cache = {}
		self.open_file = open_file
		self.words = self.file_to_words()
		self.word_size = len(self.words)
		self.database()

rduplain

"Customize Flask to select a template based on some criteria."

import os

from flask import Flask, request, render_template
from flask.helpers import locked_cached_property
from jinja2 import FileSystemLoader, TemplateNotFound

# Import a detection utility from your project, not defined here.
# Takes a request object and returns True if browser is mobile.

coldnebo

  { "keys": ["home"], "command": "move_to", "args": {"to": "bol"} },
  { "keys": ["end"], "command": "move_to", "args": {"to": "eol"} }

nherment

#!/bin/bash
# herein we backup our indexes! this script should run at like 6pm or something, after logstash
# rotates to a new ES index and theres no new data coming in to the old one. we grab metadatas,
# compress the data files, create a restore script, and push it all up to S3.
TODAY=`date +"%Y.%m.%d"`
INDEXNAME="logstash-$TODAY" # this had better match the index name in ES
INDEXDIR="/usr/local/elasticsearch/data/logstash/nodes/0/indices/"
BACKUPCMD="/usr/local/backupTools/s3cmd --config=/usr/local/backupTools/s3cfg put"
BACKUPDIR="/mnt/es-backups/"
YEARMONTH=`date +"%Y-%m"`

asmallteapot

# file: /etc/nginx/sites-available/example.com
# nginx configuration for example.com

server {
	listen 80;
	server_name example.com;
	access_log /srv/www/example.com/logs/access.log;
	error_log /srv/www/example.com/logs/error.log;
	
	# pass root to django

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

thbkrkr

[
  { "keys": ["f12"], "command": "htmlprettify"},
  { "keys": ["f1"], "command": "fold" },
  { "keys": ["f2"], "command": "unfold" },
  { "keys": ["ctrl+à"], "command": "show_overlay", "args": {"overlay": "goto", "text": "@"} },
  { "keys": ["ctrl+!"], "command": "show_overlay", "args": {"overlay": "goto", "text": ":"} },
  { "keys": ["ctrl+space"], "command": "auto_complete" },
  { "keys": ["ctrl+space"], "command": "replace_completion_with_auto_complete", "context":
    [
      { "key": "last_command", "operator": "equal", "operand": "insert_best_completion" },

heynemann

        upstream thumborbe {
            server thumbor.myhost.com:8090 ;
            server thumbor.myhost.com:8091 ;
            server thumbor.myhost.com:8092 ;
            server thumbor.myhost.com:8093 ;
            server thumbor.myhost.com:8094 ;
            server thumbor.myhost.com:8095 ;
        }

        location ~* "^/.{28}/.*(jpg|jpeg|gif|png)(#.*)?$" {

Flet

[
{ "keys": ["f12"], "command": "htmlprettify"},
{ "keys": ["f1"], "command": "fold" },
{ "keys": ["f2"], "command": "unfold" },
{ "keys": ["ctrl+l"], "command": "show_overlay", "args": {"overlay": "goto", "text": "@"} },

{ "keys": ["ctrl+space"], "command": "auto_complete" },
{ "keys": ["ctrl+space"], "command": "replace_completion_with_auto_complete", "context":
[
{ "key": "last_command", "operator": "equal", "operand": "insert_best_completion" },

glamp

import numpy as np
import pylab as pl

x = np.random.uniform(1, 100, 1000)
y = np.log(x) + np.random.normal(0, .3, 1000)

pl.scatter(x, y, s=1, label="log(x) with noise")
pl.plot(np.arange(1, 100), np.log(np.arange(1, 100)), c="b", label="log(x) true function")
pl.xlabel("x")
pl.ylabel("f(x) = log(x)")