GitHub Repository containing files pertaining to a project to create a SQLite Database for working with loan rule data. Repository is a work in progress and subject to change.
I created a SQLite database just using DB Browser which has an easy import csv to table feature.
For the loan rule table in Sierra I simply exported it right from Sierra using the new export button and then imported it in db browser creating a table called loan_rule with a file I saved as loan_rules.db
For the determiner table
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I exported it the same way Opened it in Notepad++ and changed the encoding from UTF-8-BOM to UTF-8
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I also added a column name (id) to the first field that was blank by default
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Then I ran loan rule determiner parser.py which does two things it separate out any number ranges in the itype and ptype fields into the discrete numbers it uploads the resulting table into the database as a table called determiner
As a bonus I also imported the location_myuser and itype_property_myuser tables from SierraDNA into the database just to make translating those codes easier on myself within queries.
So that built most of the database, the one other thing I did after (which I could probably just build into the parser script in the future) was to run this query to take all the multi-value itype and ptype entries in the determiner table and break them out into their own rows, essentially making a linking table similar to something like bib_record_item_record_link in SierraDNA. I then saved the results as a view in the database for use with future queries.
Then with all that you can start playing around with queries. I’ve uploaded a folder with a few I’ve played with for auditing rules just using the database itself. And then for tying the information back into Sierra I have an example Python script that links the daily fine rates from the loan_rule database with recent checkins from the circ_trans table. This is my first attempt to try to compare activity at libraries that have gone fines free with those who have not.