sparkalow

/*
 Recusrsively add a list of files from a named foler to a sheet
 */

function onOpen() {
    var ss = SpreadsheetApp.getActiveSpreadsheet();
    var searchMenuEntries = [{
        name: "Create List from Folder",
        functionName: "start"
    }];

nz

# http://wiki.apache.org/solr/FAQ#How_can_I_delete_all_documents_from_my_index.3F
# http://wiki.apache.org/solr/UpdateXmlMessages#Updating_a_Data_Record_via_curl

curl "http://index.websolr.com/solr/a0b1c2d3/update?commit=true" -H "Content-Type: text/xml" --data-binary '<delete><query>*:*</query></delete>'

I'm amused at the traction this little gist is getting on Google! I would be remiss not to point out that six+ years later I'm still helping thousands of companies on a daily basis with their search index management, by providing managed Solr as a service over at Websolr, and hosted Elasticsearch at Bonsai. Check us out if you'd like an expert helping hand at Solr and Elasticsearch hosting, ops and support!

kwilczynski

import SimpleHTTPServer
import SocketServer

class FakeRedirect(SimpleHTTPServer.SimpleHTTPRequestHandler):
   def do_GET(self):
       print self.path
       self.send_response(301)
       new_path = '%s%s'%('http://localhost:8081', self.path)
       self.send_header('Location', new_path)
       self.end_headers()

nomatteus

##
# This is a quick script that will format/indent HTML
# HTML Tidy is often too destructive, especially with bad HTML, so we're using Beautiful Soup
##
# USAGE: Designed to be used on the command line, just pipe HTML to it, and it will output
#         cat file.html | python format_html.py
### 
# Download & Install Beautiful Soup, if you don't have it already:
# Go to the Beautiful Soup web site, http://www.crummy.com/software/BeautifulSoup/
# Download the package

mbostock

license: gpl-3.0

justinkamerman

#!/usr/bin/python -u
#
# Usage: ./trace.py <tweetId>
#

import sys
import tweepy
import Queue
import time
import json

sergeyf

import numpy as np

class Data_generator(object):
    
    def __init__(self,K,d,reward_type='binary'):
        
        self.d = d # dimension of the feature vector
        self.K = K # number of bandits
        self.reward_type = reward_type

sergeyf

import numpy as np
from matplotlib import pylab as plt
#from mpltools import style # uncomment for prettier plots
#style.use(['ggplot'])

# generate all bernoulli rewards ahead of time
def generate_bernoulli_bandit_data(num_samples,K):
    CTRs_that_generated_data = np.tile(np.random.rand(K),(num_samples,1))
    true_rewards = np.random.rand(num_samples,K) < CTRs_that_generated_data
    return true_rewards,CTRs_that_generated_data

tsertkov

<VirtualHost *:80>
  ServerName phpuc.dev.example.com

  # NB! It must be slow :-)
  ExtFilterDefine fixurls mode=output intype=text/html \
    cmd="/bin/sed \
      -e s/http:\\/\\/localhost:/http:\\/\\/phpuc.dev.example.com:/g \
      -e s/\\/phpundercontrol\\//\\//g \
      -e s/\\/cruisecontrol\\/artifacts\\//\\/artifacts\\//g \
      -e s/\\/cruisecontrol\\/logs\\//\\/logs\\//g \

rmminusrslash

import numpy as np
from matplotlib import pylab as plt
#from mpltools import style # uncomment for prettier plots
#style.use(['ggplot'])

# generate all bernoulli rewards ahead of time
def generate_bernoulli_bandit_data(num_samples,K):
    CTRs_that_generated_data = np.tile(np.random.rand(K),(num_samples,1))
    true_rewards = np.random.rand(num_samples,K) < CTRs_that_generated_data
    return true_rewards,CTRs_that_generated_data