dchud

# dchud's 20 minute guide to installing DSpace 4.0 from the binary release
# on a clean ubuntu 12.04 server running on aws ec2 or where-have-you
# 
# this guide assumes you are already comfortable with *nix system administration
#
# this guide leaves out anything to do with the many details of configuring
# DSpace itself; it just gets you to the "it's up and running" step
#
# to start:  get your clean ubuntu-12.04 system up to date
$ sudo apt-get update && sudo apt-get upgrade

dchud

[["2014-09-22 22:00:00Z", 119], ["2014-09-22 09:00:00Z", 120], ["2014-09-22 03:00:00Z", 120], ["2014-09-22 08:00:00Z", 120], ["2014-09-22 21:00:00Z", 120], ["2014-09-22 14:00:00Z", 120], ["2014-09-22 06:00:00Z", 120], ["2014-09-22 18:00:00Z", 120], ["2014-09-22 01:00:00Z", 120], ["2014-09-22 00:00:00Z", 120], ["2014-09-22 02:00:00Z", 120], ["2014-09-22 10:00:00Z", 119], ["2014-09-22 11:00:00Z", 120], ["2014-09-22 12:00:00Z", 120], ["2014-09-22 16:00:00Z", 119], ["2014-09-22 20:00:00Z", 120], ["2014-09-22 07:00:00Z", 120], ["2014-09-22 17:00:00Z", 118], ["2014-09-22 15:00:00Z", 120], ["2014-09-22 04:00:00Z", 120], ["2014-09-22 05:00:00Z", 120], ["2014-09-22 23:00:00Z", 120], ["2014-09-22 13:00:00Z", 120], ["2014-09-22 19:00:00Z", 120], ["2014-09-09 22:00:00Z", 119], ["2014-09-09 09:00:00Z", 120], ["2014-09-09 03:00:00Z", 120], ["2014-09-09 08:00:00Z", 120], ["2014-09-09 21:00:00Z", 120], ["2014-09-09 14:00:00Z", 120], ["2014-09-09 06:00:00Z", 120], ["2014-09-09 18:00:00Z", 120], ["2014-09-09 01:00:00Z", 120], 

dchud

What makes the Anscombe's Quartet of datasets useful, as wikipedia explains, is their near-equivalent summary stats: the x and y sets share the same mean, sample variance, correlation and simple linear regression model. It's instructive as a clear example of what to watch out for when developing simple linear regressions, and the issues each dataset highlights come clear in the different diagnostic plots.

The animation allows visual tracking of each data point through the

dchud

class SolrPaginator:
    """
    Take a solr.py result and return a Django paginator-like object.
    """
    
    def __init__(self, q, result):
        self.q = q
        self.result = result
        
    @property

dchud

#!./bin/python

import os
import sqlite3

import web

import pymarc

print 'Clearing out old db'

dchud

#!/usr/bin/env python

import grp
import mimetypes
from optparse import OptionParser
import os
from pprint import pprint
import pwd
from stat import *
import sys

dchud

<?xml version="1.0" encoding="UTF-8" ?>
<!--
 Licensed to the Apache Software Foundation (ASF) under one or more
 contributor license agreements.  See the NOTICE file distributed with
 this work for additional information regarding copyright ownership.
 The ASF licenses this file to You under the Apache License, Version 2.0
 (the "License"); you may not use this file except in compliance with
 the License.  You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

dchud

NoMethodError in Resolve#index

Showing resolve/_service_errors.rhtml where line #25 raised:

You have a nil object when you didn't expect it!
The error occurred while evaluating nil.values
Extracted source (around line #25):

22:         <ul>
23:           <% failed_dispatches.each do | dispatch | %>

dchud

# debugging a problem ticketed at https://github.com/gwu-libraries/launchpad/issues/22
# note: reopened django ticket w/comment https://code.djangoproject.com/ticket/15313#comment:4

# t.py script:

import cx_Oracle
dsn_tns = cx_Oracle.makedsn('oracle.example.com', 1521, 'VGER')
conn = cx_Oracle.connect('user', 'pass', dsn_tns)
c = conn.cursor()
r = c.execute("""

dchud

In the middle of this script is a for loop that iterates over the arrays in the 3D data array. There must be a more d3ish way of doing this than using a javascript for loop, but I can't figure out how to retain each row's index position in the larger array to pass to the y() scaling function.