Tools for working with MARC data in Catalogue Bridge.

Overview

catbridge_tools

Tools for working with MARC data in Catalogue Bridge.

Borrows heavily from PyMarc (https://pypi.org/project/pymarc/).

Requirements

Requires the regex module from https://bitbucket.org/mrabarnett/mrab-regex. The built-in re module is not sufficient.

Also requires py2exe.

Installation

From GitHub:

git clone https://github.com/victoriamorris/catbridge_tools
cd catbridge_tools

To install as a Python package:

python setup.py install

To create stand-alone executable (.exe) files for individual scripts:

python setup.py py2exe 

Executable files will be created in the folder \dist, and should be copied to an executable path.

Both of the above commands can be carried out by running the shell script:

compile_catbridge_tools.sh

Scripts

The scripts listed below can be run from anywhere, once the package is installed and the .exe files have been copied to an executable path.

Correspondence with original Catalogue Bridge tools

Original Catalogue Bridge tool New tool Original syntax Corresponding new syntax
cn-find cn-find CN-FIND cn_find -i -o -c
cn-tidy cn-find CN-FIND cn_find -i -o -c --tidy

Features common to all scripts

File formats

Unless otherwise specified, MARC files are in MARC 21 format, with .lex file extensions. Unless otherwise specified, text files are UTF-8-encoded, with .txt, .csv or .tsv file extensions. Config files are also text files, but may have the file extension .cfg for convenience.

Help

For any script, use the option --help to display help text.

Logs and debugging

Logs will be written to catbridge.log within the working directory. This is a UTF-8 encoded text field and can be read in any text editor. The default logging level is INFO; if option --debug is set, the logging level is changed to DEBUG. See https://docs.python.org/3/library/logging.html#levels for information about logging levels.

cn_find

cn_find is a utility which extracts extract control numbers from specified fields and subfields within a file of MARC records.

The fields and subfields to be extracted are specified in a config file.

Usage: cn_find -i 
   
     -o 
    
      -c 
     
       [options]

Options:
    --conv  Convert 10-digit ISBNs to 13-digit form where possible
    --rid   Include record ID as the first column of the output file
    --tidy  Sort and de-duplicate list

    --debug	Debug mode.
    --help	Show help message and exit.

     
    
   

Files

is the name of the input file, which must be a file of MARC 21 records.

is the name of the file to which the control numbers will be written. This should be a text file.

is the name of the file containing the configuration directives.

The config file

The format of the configuration file is as follows, with one entry per line

FIELD TAG $ subfield character [tab] control number specification

Each line must match the regular expression

^([0-9A-Z]{3})\s*\$?\s*([a-z0-9]?)\s*\t(.*?)\s*$

The field tag is specified using three numbers or UPPERCASE letters.

The subfield code are specified using a single number or lowercase letter. If '$' appears without any following subfield characters, all subfields will be searched for control numbers.

The control number specification tells the script what kind of control number to search for within the subfield. This can either take a value from a pre-defined list, or a regular expression can be used to search for control numbers with any other structure. Regular expressions are case-sensitive.

Control number specification Description Regular expression
ISBN Any structurally plausible ISBN* \b(?=(?:[0-9]+[- ]?){10})[0-9]{9}[0-9Xx]\b|\b(?=(?:[0-9]+[- ]?){13})[0-9]{1,5}[- ][0-9]+[- ][0-9]+[- ][0-9Xx]\b|\b97[89][0-9]{10}\b|\b(?=(?:[0-9]+[- ]){4})97[89][- 0-9]{13}[0-9]\b
ISBN10 Any structurally plausible 10-digit ISBN* \b(?=(?:[0-9]+[- ]?){10})[0-9]{9}[0-9Xx]\b|\b(?=(?:[0-9]+[- ]?){13})[0-9]{1,5}[- ][0-9]+[- ][0-9]+[- ][0-9Xx]\b
ISBN13 Any structurally plausible 13-digit ISBN* \b97[89][0-9]{10}\b|\b(?=(?:[0-9]+[- ]){4})97[89][- 0-9]{13}[0-9]\b
ISSN 8 digits with a hyphen in the middle, where the last digit may be an X \b[0-9]{4}[ -]?[0-9]{3}[0-9Xx]\b
BL001 9 digits \b[0-9]{9}\b
BNB See https://www.bl.uk/collection-metadata/metadata-services/structure-of-the-bnb-number \bGB([0-9]{7}|[A-Z][0-9][A-Z0-9][0-9]{4})\b
LCCN See https://www.loc.gov/marc/bibliographic/bd010.html \b[a-z][a-z ][a-z ]?[0-9]{2}[0-9]{6} ?\b
OCLC "(OCoLC)" followed by digits (OCoLC)[0-9]+\b
ISNI 16 digits separated into groups of 4 with spaces or hyphens \b[0]{4}[ -]?[0-9]{4}[ -]?[0-9]{4}[ -]?[0-9]{3}[0-9Xx]\b
FAST "fst" followed by digits \bfst[0-9]{8}\b

*Note: The ISBN check digit is not validated.

Multiple fields and subfields may be specified. Fields may be repeated with different subfields.

Example:

001 BL001
015$a	BNB
020	ISBN
020$z	ISBN10
500$a	\b[a-z]{7}\b
035$a	OCLC

In the example above, field 500 subfield $a is being searched for 7-character words.

Options

--conv

If option --conv is used, 10-digit ISBNs will be converted to 13-digit form whenever possible (i.e. whenever they are valid ISBNs).

--rid

By default, the output file consists of a single column of strings. If option --rid is used, the output file will consist of two columns: the first column will be the record control number from field 001 and the second column will be as per the default output.

--tidy

If option --tidy is used, the list of control numbers in the output file will be sorted and de-duplicated. Any duplicate control numbers will be written to an additional output file named with the prefix "dp-".

Note: option --tidy cannot be used at the same time as option --rid

Bamboolib - a GUI for pandas DataFrames

Community repository of bamboolib bamboolib is joining forces with Databricks. For more information, please read our announcement. Please note that th

Tobias Krabel 863 Jan 08, 2023
This module is used to create Convolutional AutoEncoders for Variational Data Assimilation

VarDACAE This module is used to create Convolutional AutoEncoders for Variational Data Assimilation. A user can define, create and train an AE for Dat

Julian Mack 23 Dec 16, 2022
Nobel Data Analysis

Nobel_Data_Analysis This project is for analyzing a set of data about people who have won the Nobel Prize in different fields and different countries

Mohammed Hassan El Sayed 1 Jan 24, 2022
Python beta calculator that retrieves stock and market data and provides linear regressions.

Stock and Index Beta Calculator Python script that calculates the beta (β) of a stock against the chosen index. The script retrieves the data and resa

sammuhrai 4 Jul 29, 2022
An easy-to-use feature store

A feature store is a data storage system for data science and machine-learning. It can store raw data and also transformed features, which can be fed straight into an ML model or training script.

ByteHub AI 48 Dec 09, 2022
Vaex library for Big Data Analytics of an Airline dataset

Vaex-Big-Data-Analytics-for-Airline-data A Python notebook (ipynb) created in Jupyter Notebook, which utilizes the Vaex library for Big Data Analytics

Nikolas Petrou 1 Feb 13, 2022
Flexible HDF5 saving/loading and other data science tools from the University of Chicago

deepdish Flexible HDF5 saving/loading and other data science tools from the University of Chicago. This repository also host a Deep Learning blog: htt

UChicago - Department of Computer Science 255 Dec 10, 2022
Pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.

weightedcalcs weightedcalcs is a pandas-based Python library for calculating weighted means, medians, standard deviations, and more. Features Plays we

Jeremy Singer-Vine 98 Dec 31, 2022
PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster (by @firmai)

PandaPy "I came across PandaPy last week and have already used it in my current project. It is a fascinating Python library with a lot of potential to

Derek Snow 527 Jan 02, 2023
Calculate multilateral price indices in Python (with Pandas and PySpark).

IndexNumCalc Calculate multilateral price indices using the GEKS-T (CCDI), Time Product Dummy (TPD), Time Dummy Hedonic (TDH), Geary-Khamis (GK) metho

Dr. Usman Kayani 3 Apr 27, 2022
Feature Detection Based Template Matching

Feature Detection Based Template Matching The classification of the photos was made using the OpenCv template Matching method. Installation Use the pa

Muhammet Erem 2 Nov 18, 2021
Leverage Twitter API v2 to analyze tweet metrics such as impressions and profile clicks over time.

Tweetmetric Tweetmetric allows you to track various metrics on your most recent tweets, such as impressions, retweets and clicks on your profile. The

Mathis HAMMEL 29 Oct 18, 2022
Transform-Invariant Non-Negative Matrix Factorization

Transform-Invariant Non-Negative Matrix Factorization A comprehensive Python package for Non-Negative Matrix Factorization (NMF) with a focus on learn

EMD Group 6 Jul 01, 2022
Picka: A Python module for data generation and randomization.

Picka: A Python module for data generation and randomization. Author: Anthony Long Version: 1.0.1 - Fixed the broken image stuff. Whoops What is Picka

Anthony 108 Nov 30, 2021
HyperSpy is an open source Python library for the interactive analysis of multidimensional datasets

HyperSpy is an open source Python library for the interactive analysis of multidimensional datasets that can be described as multidimensional arrays o

HyperSpy 411 Dec 27, 2022
Data Scientist in Simple Stock Analysis of PT Bukalapak.com Tbk for Long Term Investment

Data Scientist in Simple Stock Analysis of PT Bukalapak.com Tbk for Long Term Investment Brief explanation of PT Bukalapak.com Tbk Bukalapak was found

Najibulloh Asror 2 Feb 10, 2022
Ejercicios Panda usando Pandas

Readme Below we add configuration details to locally test your application To co

1 Jan 22, 2022
BErt-like Neurophysiological Data Representation

BENDR BErt-like Neurophysiological Data Representation This repository contains the source code for reproducing, or extending the BERT-like self-super

114 Dec 23, 2022
Improving your data science workflows with

Make Better Defaults Author: Kjell Wooding [email protected] This is the git re

Kjell Wooding 18 Dec 23, 2022