Shawn Averkamp saverkamp

## lc_vocab_term_harvester.py
"""This script traverses all narrower terms of a http://id.loc.gov/ thesaurus
(or all terms of a term list) starting at a given term within the tree (replace
seedterm in the main code block with your URI of choice) and adds the URI and
label to a list. Outputs in CSV and JSON as well as JSONL as patterns for use in
rule-based NER with the NLP tool SpaCy.
(More info at: https://spacy.io/usage/rule-based-matching#entityruler)

NOTE the 5-second rate limit courtesy to the LC servers working hard for your
controlled vocabulary needs (see queryTerms() function). You might get away with
less, but don't be a jerk about it.

## CatcherUploadExample.csv
Alias,CDM_page_id,CDM_field,Value
byington,3160,transc,"Thursday October 25th 1900  Did two churnings this morning and finished putting the tomato pickle away.  I put in all the afternoon doing mending Will busy about the place and Mrs Evans helped Leonard with the corn the new man went back to town tonight.  Friday October 26th 1900  Will took the butter to town.  I was busy with the work till noon I did a lot of baking.  I cut out shirts and sewed all afternoon.  Saturday October 27th 1900  I sewed a little and got the dinner be a little after eleven.  Will and I went to town in the afternoon to have my teeth finished.  It has been a warm week, today was like summer.  I had a letter from Mother.  Sunday October 28th 1900  I was busy about the house most of the forenoon  Leonard and his wife were away all day.  It rained some in the forenoon.  We were up to Stevens in the afternoon.  I spent the evening reading.  Monday October 29th 1900  It rained in the morning so Leonard could not husk corn.  Will was bus

## catcher.py
from suds.client import Client

class Catcher(object):
    """A CONTENTdm Catcher session."""
    def __init__(self, url=url, user=user, password=password, license=license):
        self.transactions = []
        self.client = Client('https://worldcat.org/webservices/contentdm/catcher/6.0/CatcherService.wsdl')
        self.url = url
        self.user = user
        self.password = password

## gist:6957798
Erdrich, Louise	Louise Erdrich
Eugenides, Jeffrey	Jeffrey Eugenides
Farrakhan, Louis	Louis Farrakhan
Fatunde, Tunde	Tunde Fatunde
Ames, Jonathan	Jonathan Ames
Anshaw, Carol, 1946-	Carol Anshaw
Julavits, Heidi	Heidi Julavits
Mailer, Norman	Norman Mailer
Nissen, Thisbe, 1972-	Thisbe Nissen
Solnit, Rebecca	Rebecca Solnit

## sample-cdm2scripto.py
import codecs
import csv
import datetime
import pycdm
from HTMLParser import HTMLParser


#get input: alias + items to retrieve
alias = raw_input('collection alias: ')
items = raw_input('item identifiers (separate by commas): ')
	"""This script traverses all narrower terms of a http://id.loc.gov/ thesaurus
	(or all terms of a term list) starting at a given term within the tree (replace
	seedterm in the main code block with your URI of choice) and adds the URI and
	label to a list. Outputs in CSV and JSON as well as JSONL as patterns for use in
	rule-based NER with the NLP tool SpaCy.
	(More info at: https://spacy.io/usage/rule-based-matching#entityruler)

	NOTE the 5-second rate limit courtesy to the LC servers working hard for your
	controlled vocabulary needs (see queryTerms() function). You might get away with
	less, but don't be a jerk about it.
	from suds.client import Client

	class Catcher(object):
	"""A CONTENTdm Catcher session."""
	def __init__(self, url=url, user=user, password=password, license=license):
	self.transactions = []
	self.client = Client('https://worldcat.org/webservices/contentdm/catcher/6.0/CatcherService.wsdl')
	self.url = url
	self.user = user
	self.password = password
	Erdrich, Louise Louise Erdrich
	Eugenides, Jeffrey Jeffrey Eugenides
	Farrakhan, Louis Louis Farrakhan
	Fatunde, Tunde Tunde Fatunde
	Ames, Jonathan Jonathan Ames
	Anshaw, Carol, 1946- Carol Anshaw
	Julavits, Heidi Heidi Julavits
	Mailer, Norman Norman Mailer
	Nissen, Thisbe, 1972- Thisbe Nissen
	Solnit, Rebecca Rebecca Solnit
	import codecs
	import csv
	import datetime
	import pycdm
	from HTMLParser import HTMLParser


	#get input: alias + items to retrieve
	alias = raw_input('collection alias: ')
	items = raw_input('item identifiers (separate by commas): ')