http://arstechnica.com/articles/culture/conservapedias-evolutionary-foibles.ars
http://www.conservapedia.com/Conservapedia:Lenski_dialog

Reminds me of one of my favorite Steven Colbert quotes: “reality has a well known liberal bias” =P

Note that it doesn’t require access the trac python libs or database; it just uses HTTP. But it does require BeautifulSoup. Install it by adding it to your hooks post-commit script. (it takes the normal params of REPOS and REV).

#!/usr/bin/env python

import os, re, sys, urllib, urllib2

from BeautifulSoup import BeautifulSoup

TRAC_URL = 'http://my.trac.url'
TRAC_TICKET_REL_URL = '/xxx/ticket/'
TRAC_LOGIN_REL_URL = '/xxx/login/'
TRAC_USER = 'user'
TRAC_PASS = 'pass'
TRAC_REALM = 'myRealm'
#SVNLOOK = '/usr/bin/svnlook'
SVNLOOK = '/usr/local/bin/svnlook'

TRAC_TICKET_URL = TRAC_URL+TRAC_TICKET_REL_URL
TRAC_LOGIN_URL = TRAC_URL+TRAC_LOGIN_REL_URL

def get_commit_info(repos, rev):
    c = os.popen( SVNLOOK+' info "%s" -r "%s"' % (repos, rev) )
    user = c.readline().strip()
    date = c.readline().strip()
    ignore = c.readline().strip()
    msg = c.read()
    c.close()
    return user, date, msg

def get_trac_tickets(msg):
    p = re.compile(r'trac:d+')
    tickets = p.findall(msg)
    return [ t[5:] for t in tickets ]

def post_to_trac( ticket, msg ):
    auth = urllib2.HTTPDigestAuthHandler()
    auth.add_password(TRAC_REALM, TRAC_URL, TRAC_USER, TRAC_PASS)
    opener = urllib2.build_opener(auth, urllib2.HTTPCookieProcessor())
    req = urllib2.Request(TRAC_LOGIN_URL)
    req.add_header("Referer", TRAC_TICKET_URL+ticket)
    try:
        res = opener.open(req)
        html = res.read()
        soup = BeautifulSoup(html)
        form = soup.find('form', action=TRAC_TICKET_REL_URL+ticket+'#preview')
        data = {}
        for i in form.findAll('input'):
            try:
                if i['type']=='hidden' or i['type']=='text':
                    data[ i['name'] ] = i['value']
                if i['type']=='checkbox' or i['type']=='radio':
                    if i.get('checked'):
                        data[ i['name'] ] = i['value']
            except: pass
        for s in form.findAll('select'):
            for o in s.findAll('option'):
                if o.get('selected'):
                    data[ s['name'] ] = o.get('value',o.string)
        data['comment'] = msg
        #data['preview'] = 'Preview'
        #print 'n'.join([ str(x) for x in data.iteritems() ] )
        req = urllib2.Request(TRAC_TICKET_URL+ticket, urllib.urlencode(data))
        req.add_header("Referer", TRAC_TICKET_URL+ticket)
        res = opener.open(req)
        html = res.read()
        #print html

    except IOError, e:
        pass
#      if hasattr(e, 'code'):
#        if e.code != 401:
#            print 'We got another error'
#            print e.code
#        else:
#            print e.headers

def main(*args):

    if len(args)!=3:
        print 'usage: python commit-trac.py "repos" "rev"'
        return 1

    repos = args[1]
    rev = args[2]
    user, date, msg = get_commit_info(repos, rev)
    msg = 'svn commit to %s -r%s (%s):n%s' % (repos, rev, user, msg)
    print msg

    for ticket in get_trac_tickets(msg):
        post_to_trac( ticket, msg )

    return 0 # exit errorlessly

if __name__ == '__main__':
    sys.exit(main(*sys.argv))

http://www.nature.com/news/2008/080430/full/453012b.html (doi:10.1038/453012b)

Cool, eh?

(it’s from an old Simpson’s episode, if you don’t get it…)

Dear Students,

Congratulations. You have been selected for the NSF GK-12 fellowship. This fellowship as you recalled I mentioned pays XX,000 a year, starting with this Summer, plus an extra stipend for tuition.

If you recall, it involves setting up an after-school program for middle-schools to motivate students to choose computer science (or a STEM) for their future careers.

We will come up with the official letter (the one you have to sign) soon (probably next week), but I just wanted to inform you that you have been selected.

If you are still with us on this project, please reply to me acknowledging your acceptance. We had more candidates than positions, so I have to make sure you are all on board before I notify those who were not selected.

Best wishes, and I look forward to hearing from you.

–
Dr. Jorge A. Cobb

w00t! =P

Website: http://desigle.org/

Use if you wish.

Coffee may cut the risk of dementia by blocking the damage cholesterol can inflict on the body, research suggests.

The drink has already been linked to a lower risk of Alzheimer’s Disease, and a study by a US team for the Journal of Neuroinflammation may explain why.

W00t! =P

http://news.bbc.co.uk/2/hi/health/7326839.stm

If you would like to continue this dream, a charge of $19.95 will be applied to your credit card. Do you want to proceed?

[Yes] [No]

But my brain knew it was dreaming, and the absurdity of it made me laugh hard enough to wake me up again. So +1 for “weird dreams”.

P.S. Oddly enough, usually I can’t read in my dreams. The words are almost always confusingly jumbled, not just in word/letter transposition but in physical orientation and alignment. Sometimes if I focus I can pull them together (literally: they move about in what I can only describe as brownian motion) and make sense of them, but not often. It’s an interesting enough phenomenon that I often try to look for newspapers or books in my dreams when I realize I’m dreaming, and it’s a great litmus test for when I’m not sure.

I remember reading about it after it was announced (probably on /.), and promptly forgetting about it. I stumbled across it again listening to one of the Google lectures on MapReduce, and started poking around, thinking “maybe this would make a good test project for my new (I-just-want-to-play-sized) Hadoop cluster”. But then I needed an algorithm before I started parallelizing it, and, hey, what do you know, I’m getting really good numbers.

Of course, I’m getting my good numbers on a very small randomly selected subset of the entire project. So it could be just a fluke. And it’s slower than molasses in an Adirondack winter (current estimate: 10+ years to calculate the entire qualifying data set). But hey, it has my attention.