ht-text-prep 1.2

Text processing and analysis for HathiTrust Research Center

ht_text_prep

Table of Contents

1. About ht_text_prep Library
2. Installation
3. Usage

About ht_text_prep Library

Python library to assist in processing HathiTrust full-text data received as part of HathiTrust's dataset request process. This library helps manage the data that is stored and arrives in pairtree format. This code is not intended for use on data within HathiTrust Research Center environments.

This library also has tools for finding and removing running headers and footers in a given volume, removing them, and concatenating page-level text files into a single text file per HathiTrust volume.

Installation

To install,

pip install ht_text_prep

That's it! This library is written for Python 3.6+. For Python beginners, you'll need pip.

Usage

Import the library for use:

import ht_text_prep as htp

Function: get_zips(data_dir: str, output_dir: str, delete_zips=False)

A function that will traverse the pairtree directory structure, find the zip files that contain full text data from HathiTrust, expand them, and move the files to an output directory.

Returns a new directory with one folder of page text files per volume

Inputs:

data_dir: path to folder holding the HathiTrust data to be cleaned/processed.

output_dir: path to new output folder that will hold the cleaned/processed data. Will return error if folder already exists.

delete_zips: default False, provide value True to delete original zipfiles after expansion, False to keep them.

Examples:

• Find and move zips for HathiTrust dataset, deleting zips after expanded:

import ht_text_prep as htp

data_dir = '/Users/rdubnic2/Desktop/data_download'
output_dir = '/Users/rdubnic2/Desktop/data'

htp.get_zips(data_dir, output_dir, delete_zips=True)

• Find and move zips for HathiTrust dataset, keeping original zips after expanded:

data_dir = '/Users/rdubnic2/Desktop/data_download'
output_dir = '/Users/rdubnic2/Desktop/data'

htp.get_zips(data_dir, output_dir, delete_zips=False)

• Using paths rather than variables, find and move zips for HathiTrust dataset, keeping original zips after expanded:

htp.get_zips('/Users/rdubnic2/Desktop/data_download', '/Users/rdubnic2/Desktop/data', delete_zips=False)

Function: clean_txt_file_names(file_name: str):

Given an input path to a single directory holding page text files, this function will normalize irregular page text file names in HathiTrust data, converting all page text files names to an 8-digit sequence number in format '00000001.txt' in ascending numerical order based on original file names. For example:

`0000000001.txt` becomes `00000001.txt`

`ark+=13960=t3mw3px6k_00000001.txt` becomes `00000001.txt`

This function will also normalize jumps in page numbers greater than +1 between files sorted in ascending numerical order. For example, given this file list, names would be normalized to:

`00000009.txt`  becomes  `00000009.txt`

`00000010.txt`  becomes  `00000010.txt`

`00000015.txt`  becomes  `00000011.txt`

`00000016.txt`  becomes  `00000012.txt`

The function returns nothing explicitly, but yields normalized file names within the input directory.

Inputs:

file_name: path to text file with a filename to be normalized

Examples:

• Normalize file names for one volume's text files in one directory:

import ht_text_prep as htp
test_directory = '/Users/username/Desktop/data_download/ark+=13960=t3mw3px6k'
htp.clean_txt_file_names(test_directory)

• To normalize page file names for multiple volumes held in one top directory, use iteratively:

top_dir = ['/Users/rdubnic2/Desktop/data_download/ark+=13960=t3mw3px6k',
	'/Users/rdubnic2/Desktop/data_download/ark+=23200=t5mw3px1j',
	'/Users/rdubnic2/Desktop/data_download/ark+=53960=t4mp1qr7x']
				
for folder in top_dir:
	htp.clean_txt_file_names(folder)

Function: load_vol(path: str, num_pages: int)

A function to load a HathiTrust volume, in the format of a folder of text files, and parse the page structure in advance of removing running headers and footers.

Returns a list of HtrcPage(*) objects (indexed lines of text), ready as input for clean_vol.

(*) See https://github.com/htrc/HTRC-Tools-RunningHeaders-Python/blob/develop/htrc/models.py

Inputs:

path: path to folder of text files for a given HathiTrust volume

num_pages: the number of page text files in the directory for the volume

Examples:

• Load a HathiTrust volume using explicit parameters:

import ht_text_prep as htp

htp.load_vol('/Users/rdubnic2/Desktop/data_download/ark+=13960=t3mw3px6k', 7)

• Load a HathiTrust volume using variables, generating a list of paths using glob:

import glob

vol_path = '/Users/rdubnic2/Desktop/data_download/ark+=13960=t3mw3px6k'
num_pages = len(glob.glob(str(vol_path)+'/**'))

htp.load_vol(vol_path, num_pages)

Function: check_vol(vol_dir_path: str | list, clean_dir_path: str)

Function to check an input directory to identify which volumes have already been processed by clean_vol. Intended as a helpful for very large worksets, where processing may be interrupted/stopped. This function will return a list of volume paths that can be used to incrementally resume volume processing.

Returns a list of volume directory paths that still require processing.

Inputs:

vol_dir_path: a string of a directory containing page text files or a list containing string universal paths to directories containing HathiTrust page text files.

out_dir: path to folder containing cleaned, concatenated text files.

Examples:

• Return a list of paths to volumes that have not yet been processed by clean_vol():

import ht_text_prep as htp

data_dir = ['/Users/rdubnic2/Desktop/data_download/ark+=13960=t3mw3px6k',
	'/Users/rdubnic2/Desktop/data_download/ark+=23200=t5mw3px6k',
	'/Users/rdubnic2/Desktop/data_download/ark+=53960=t4mp1qr7x']

out_dir = '/Users/rdubnic2/Desktop/clean_volumes/'
			
htp.check_vol(data_dir, out_dir)

> ['/Users/rdubnic2/Desktop/data_download/ark+=53960=t4mp1qr7x']

• Use check_vol() as part of removing running headers/footers workflow, with clean_vol():

data_dir = ['/Users/rdubnic2/Desktop/data_download/ark+=13960=t3mw3px6k',
	'/Users/rdubnic2/Desktop/data_download/ark+=23200=t5mw3px6k',
	'/Users/rdubnic2/Desktop/data_download/ark+=53960=t4mp1qr7x']

out_dir = '/Users/rdubnic2/Desktop/clean_volumes/'
			
to_be_cleaned = htp.check_vol(data_dir, out_dir)

htp.clean_vol(to_be_cleaned, out_dir)

Function: clean_vol(vol_dir_path: str | list, out_dir: str):

Function to parse the page structure of every HathiTrust volume in a supplied directory and write out only the page body text, removing running headers and footers.

Returns nothing explicitly, but yields a single concatenated text file made up of input pages with running headers and footers removed, located in out_dir.

NOTE: if the supplied vol_dir_path is a parent directory (a folder containing other files), this function checks to see if the enclosed data are “.txt” files. If so, it assumes the user has supplied a single HathiTrust volume. That volume is then processed. If subdirectories are not “.txt” files, clean_vol() transforms them into a list of paths and removes running headers/footers, concatenates the pages, and saves them to the outdir. If the supplied vol_dir_path is already a list of file paths, that list is sent to does_clean() to remove running headers/footers, concatenate the pages, and save them to the outdir.

Inputs:

vol_dir_path: a string path to a directory or a list containing strings of universal paths to directories containing HathiTrust page text files.

out_dir: string path to folder to contain cleaned, concatenated text files.

Examples:

• Remove running headers/footers for a directory containing sub-directories holding page text:

import ht_text_prep as htp

vol_dir = ['/Users/rdubnic2/Desktop/data_download/ark+=13960=t3mw3px6k',
	'/Users/rdubnic2/Desktop/data_download/ark+=23200=t5mw3px1jk',
	'/Users/rdubnic2/Desktop/data_download/ark+=53960=t4mp1qr7x']

out_dir = '/Users/rdubnic2/Desktop/final_vols/'
			
htp.clean_vol(vol_dir, out_dir)

> 'Cleaned 3 volume(s)'

Questions? Contact [e-mail]