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DOCUMENTATION.md

File metadata and controls

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Module exportMetadata

Runs the rest of the scripts in the correct order and provides feedback in the form of a progress bar.

Please run pip install -r requirements.txt before running this script.

Functions

exportMetadata() : Main method which calls each submodule in turn. No options can be passed.

updateProgressBar(desc, t) :

Module iniToJson

Converts the metadata stored in an INI file into individual JSON files. The shortname, title, date and abstract are preserved as is, but the other attributes are transformed into more appropriate formats like arrays and objects.

Author emails are extracted from the entry data and are collated into an authors.json file.

Functions

deduplicate(name) : Many authors have spelt there name in many ways. This is a manual de-duplication of these.

iniToJson() : Iterates over each entry in the metadata/metadata file and extracts the information before outputting the data as a JSON file.

processName(val, authorsDictionary) : Extracts a string of authors into a python list. The website/email address is extracted here.

Note: Only one website/email address is kept per author, if there
are multiple values they are overwritten.

standardiseInitials(name) : Standardises the format of a name with initials.

Format is:
    * Initial always followed by a period
    * Initials are always seperated by a space

Module addOlderReleases

This script traverses the metadata/release-dates and metadata/releases files and adds all the releases (except the most recent) of each entry to its JSON file.

Functions

addOlderReleases() : First build a list of release dates, second traverse each release and add all but the most recent to its entry file.

Module addDependencies

The dependencies of an AFP entry are listed in the ROOT file, and as it is regular, this script uses a regular expression to extract the dependencies and adds them to the JSON file of the entry.

Functions

addDependencies() : For each entry in the thys/ directory, extract the dependencies and add them to the JSON file.

Module generateKeywords

Generates a list of keywords for the search autocomplete. Each entry’s abstract is sanitised and then the keywords are extracted with the RAKE algorithm.

Functions

generateKeywords() : RAKE is used to extract the keywords from every abstract.

The top 8 keywords are added to a list of all keywords and the keywords 
that appear in more than two abstracts are preserved. Finally, plurals 
are removed.

Module addRelatedEntries

This script generates related entries, using three metrics: * Sharing dependencies * Sharing keywords * Sharing keywords

These are weighted and used to find entries which are likely similar.

These are then added to the entries to improve site navigation.

Functions

addRelatedEntries() : First three dictionaries are created as follows:

dependencies = {"dependency": [list-of-entries, ...], ...}
keywords = {"keyword": [list-of-entries, ...], ...}
topics = {"topic": [list-of-entries, ...], ...}

Keywords that feature in more than 10 entries are dropped. Then
a dictionary is created with the relatedness scores between each 
entry. Finally, the top three related entries are chosen for each 
entry.

populateRelated(dataSet, relatedEntries, modifier=1) : This is a heavliy nested loop to create the relatedEntries dictionary.

For each of the categories, the list of entries associated with 
each key is iterated over twice and, if the entries are not the 
same, the modifier of that category is added to the relatedness 
score between the two entries in the dictionary. As the loop 
iterates twice over the value set, the resulting dictionary is 
bijective — i.e., the value for A->B will be equal to B->A.

topThree(dictionary) : Returns the highest three dictionary keys by value

Module exportJsonMetadata

This script creates a JSON release of the AFP's metadata

Functions

exportJsonMetadata() : Iterates over each entry and builds the output list

processEntry(entryPath, entry) : Removes the emails and related entries and returns the dictionary

Module addStatistics

Most the statistics for the site, are generated by Hugo. This script, generates other statistics like number of lines in the AFP using the scripts from the current AFP.

For this script to work, return data needs to be added at line 212 in templates.py

Functions

addStatistics() : Creates the necessary objects to generates the statistics, then outputs them to the data directory

Module getTheories

This script downloads and transforms the HTML documents for theory browsing.

By default this script only gets theories which do not have a theory file i.e., new theories. The --all flag can be passed to get all theories, but this should be run sparingly as it is intensive on the upstream server. A full run takes around 80 minutes.

Functions

defineArgParser() : Creates parser for command line arguments

dependancyLink(link) : Fixes dependency links to be internal

getTheories(all=False, entry='') : Entry point, either downloads one entry or all of them based on the passed flags

getTheory(url, name) : The theories are then downloaded, transformed, and concatenated together. The first transformation is to keep the and change it to be a

, as there can only be one body tag in a document. The next transformation is to select all lemmas in the document and add unique IDs to them. The resulting HTML and lemma names are returned to be added to the theory’s front matter.

processURL(entry, theoriesHtmlDir, theoriesJsonDir, entriesJsonDir) : Gets the theories for an entry and writes it to the requisite files

theoryLinks(entry) : Download the “Browse theories” page for an entry to get a list of theories.

updateProgressBar(desc, t) :