[oalex] register the UDF oalex_affro and the schema of the output to be used in the dataframe by pyspark

strings.py

@@ -1,15 +1,27 @@
 import json
+
+from pyspark.sql.types import StringType, ArrayType, StructType, StructField, DoubleType
+
 from affro_cluster import *
 import os
 from pyspark.sql import SparkSession
-from pyspark.sql.functions import col, explode, first, collect_list
+from pyspark.sql.functions import col, explode, first, collect_list, udf
 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]
+
+matchings_schema = ArrayType(
+    StructType([
+        StructField("Provenance", StringType(), nullable=True),
+        StructField("PID", StringType(), nullable=True),
+        StructField("Value", StringType(), nullable=True),
+        StructField("Confidence", DoubleType(), nullable=True),
+        StructField("Status", StringType(), nullable=True)
+    ])
+)
 
 #Version of affro application on a single raw_aff_string and returns just the Matchins set
 def oalex_affro(aff_string):
@@ -22,12 +34,14 @@ def oalex_affro(aff_string):
         print(f"Error processing affiliation string {aff_string}: {str(e)}")
         return []
 
+oalex_affro_udf = udf(oalex_affro, matchings_schema)
+
 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
+        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 \