get colun names from TermPairwiseSimilarity

src/oaklib/cli.py

@@ -44,6 +44,7 @@
 from linkml_runtime.utils.introspection import package_schemaview
 from prefixmaps.io.parser import load_multi_context
 from sssom.parsers import parse_sssom_table, to_mapping_set_document
+from oaklib.datamodels.similarity import TermPairwiseSimilarity
 
 import oaklib.datamodels.taxon_constraints as tcdm
 from oaklib import datamodels
@@ -2760,14 +2761,36 @@ def similarity(
                 set2it = query_terms_iterator(terms, impl)
         actual_predicates = _process_predicates_arg(predicates)
         if low_memory:
+            term_pairwise_similarity_attributes = [attr for attr in vars(TermPairwiseSimilarity) if not attr.startswith("class_")]
             impl.all_by_all_pairwise_similarity_quick(
                 set1it,
                 set2it,
                 predicates=actual_predicates,
                 min_jaccard_similarity=min_jaccard_similarity,
                 min_ancestor_information_content=min_ancestor_information_content,
+                outfile=output
             )
-            # TODO: Grab the outfile, add columns needed and run `fill-table`.
+
+            # Read the output file line by line and store the contents in a list
+            with open(output, "r") as f:
+                lines = f.readlines()
+
+            # Add the column names to the first line of the list
+            columns_already_present = lines[0].split("\t")
+            columns_missing = [col for col in term_pairwise_similarity_attributes if col not in columns_already_present]
+            columns_missing_as_str = "\t".join(columns_missing) + "\n"
+            header = lines[0].strip() + columns_missing_as_str
+            lines[0] = header
+
+            # Write the updated contents back to the output file
+            with open(output, "w") as file:
+                file.writelines(lines)
+
+            if autolabel:
+                pass
+                # TODO: Run `fill-table`.
+                # fill_table()
+
         else:
             for sim in impl.all_by_all_pairwise_similarity(
                 set1it,