phette23

#!/usr/bin/env node
// a utility script for CCA to check for duplicate syllabi in VAULT
let defaults = {
    uuid: '9ec74523-e018-4e01-ab4e-be4dd06cdd68',
    // 50 is max length
    length: 50,
    term: 'Spring 2018',
}
// .equellarc file with credentials for API use
let options = require('rc')('equella', defaults)

phette23

<?php
// this proxies ByWater Solutions' "COCE" cover image service
// which does not work over HTTPS, so fill in our libraries.cca.edu
// server as our COCE server & it intercepts requests, sending along
// data from ByWater's COCE server

// we're sending JS
header( 'Content-Type:application/javascript; charset=utf-8' );

// requests look like

phette23

#!/usr/bin/env fish
# remove leading zeroes from JPG file names
# e.g. page001.jpg => page1.jpg

set start (pwd)

for dir in (ls)
    echo "About to rename files in $dir"
    # optional, makes me less afraid when I step through one folder at a time
    read

phette23

#!/usr/bin/env python
# usage:
# sum-majors.py "LI - Library students per term.csv" > "YEAR majors total.csv"
import csv
import fileinput
import sys

majors = csv.DictReader(fileinput.input(mode='rb'))
# mapping of degree codes to majors will change over time
# as will the "totals" dict below listing our majors

phette23

#!/usr/bin/env node
// usage:
// > randpw
// IKS1L2H1AMOx
// > randpw --length 22
// IKS1L2H1AMOxBs4d8qxDXY
// > randpw | pbcopy # pipe to Mac clipboard
var chance = new require('chance')()
var args = require('minimist')(process.argv.slice(2))
var pool = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890'

phette23

// see also: https://gist.github.com/phette23/5575987
// used to count edits at California College of the Arts
// during Art+Feminism edit-a-thon on March 7, 2015
var uns = [
  "Phette23",
  "Circa73",
  "Flyingpanther",
  "Tericlaude",
  "Berylbev",
  "Cd heaven",

phette23

// take http://digital2.library.unt.edu/vocabularies/agent-qualifiers/json/
// and insert into an EQUELLA taxonomy, preserving URL & definition info
// EQUELLA taxo format is CSV-like:
// term1/term/term2,key,value,key2,value2…
// can then upload with their included Python scripts or write your own
var fs = require('fs')
var data = JSON.parse(fs.readFileSync('agent-qualifiers.json'))
var terms = data.terms

var getDescription = function(term) {

phette23

Almost immediately after declaring a hiatus seems like a great time for a blog post.

Inspired by nina de jesus and Ruth Tillman's libtech level up project, here's something on the value of command-line text processing. Some of these common UNIX tools that have been around since practically the 1980s are great for the sort of data wrangling that many librarians find themselves doing, whether their responsibilities lie with systems, the web, metadata, or other areas. But the command prompt has a learning curve and if you already use text editor tools to accomplish some tasks, it might be tough to see why you should invest in learning. Here's one case I've found.

Scenario: our digital repository needs to maintain several vocabularies of faculty who teach in different departments. That information is, of course, within a siloed vendor product that has no viable APIs. I'm only able to export CSVs that looks like this:

"Namerer, Name","username"

"Othern

phette23

<#-- NOTE this style should be removed for home page portlet
also should really use an ID rather than hide all portlet headings -->
<style>
/* hide header */
.portlet_freemarker_content .box_title_wrapper h3 {
    display: none !important;
}
</style>
<#-- these role IDs will need to be researched & changed -->
<#if user.hasRole('490b1b93-10cd-b8fa-3291-93c357efe57b')>

phette23

for f in *.tif
  convert $f (basename -s .tif $f).jpg
end