affRo/strings.py

import json
from affro_cluster import *
import os
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, explode, first, collect_list
import sys

spark = SparkSession.builder.appName("JSONProcessing").getOrCreate()

folder_path = sys.argv[1]
hdfs_output_path = sys.argv[2]
working_dir_path = sys.argv[3]

#Version of affro application on a single raw_aff_string and returns just the Matchins set
def oalex_affro(aff_string):
    try:
        matchings = affro(aff_string)
        if not isinstance(matchings, list):
            matchings = [matchings]
        return matchings
    except Exception as e:
        print(f"Error processing affiliation string {aff_string}: {str(e)}")
        return []

explode = spark.read.json(folder_path) \
    .filter(col("doi").isNotNull()) \
    .select(
    col("doi").alias("DOI"),
    col("ror").alias("OAlex"),
    explode(col("raw_aff_string")).alias("affiliation") #this allows to split all the raw_aff_string and to parallelize better
    )

affs = explode \
    .select("affiliation") \
    .distinct() \
    .withColumn("Matchings", oalex_affro(col("aff_string")))


affs.join(explode, on = "affiliation") \
    .select(col("DOI"),
            col("OAlex"),
            explode("Matchins").alias("match")
            ) \
    .groupBy("DOI")  \
          .agg(first("OAlex").alias("OAlex"), #for each DOI it says what are the other columns Since OALEX is equal for each doi just select the first, while use the collect_list function to aggregate the Matchings
               collect_list("match").alias("Matchings") #each exploded match is collected again
               ) \
          .write \
          .mode("overwrite") \
          .option("compression","gzip") \
          .json(hdfs_output_path)