Added predetermined headers feature

src/autobigs/engine/analysis/bigsdb.py

@@ -251,6 +251,16 @@ class BIGSdbIndex(AbstractAsyncContextManager):
 
     async def build_profiler_from_seqdefdb(self, local: bool, dbseqdef_name: str, scheme_id: int) -> BIGSdbMLSTProfiler:
         return get_BIGSdb_MLST_profiler(local, await self.get_bigsdb_api_from_seqdefdb(dbseqdef_name), dbseqdef_name, scheme_id)
+    
+    async def get_scheme_loci(self, dbseqdef_name: str, scheme_id: int) -> list[str]:
+        uri_path = f"{await self.get_bigsdb_api_from_seqdefdb(dbseqdef_name)}/db/{dbseqdef_name}/schemes/{scheme_id}"
+        async with self._http_client.get(uri_path) as response:
+            response_json = await response.json()
+            loci = response_json["loci"]
+            results = []
+            for locus in loci:
+                results.append(path.basename(locus))
+            return results
 
     async def close(self):
         await self._http_client.close()

src/autobigs/engine/writing.py

@@ -1,7 +1,7 @@
 from collections import defaultdict
 import csv
 from os import PathLike
-from typing import AsyncIterable, Collection, Mapping, Sequence, Union
+from typing import AsyncIterable, Collection, Iterable, Mapping, Sequence, Union
 
 from autobigs.engine.structures.mlst import Allele, MLSTProfile, NamedMLSTProfile
 
@@ -17,7 +17,7 @@ def alleles_to_text_map(alleles: Collection[Allele]) -> Mapping[str, Union[Seque
             result[locus] = tuple(result[locus]) # type: ignore
     return dict(result)
 
-async def write_mlst_profiles_as_csv(mlst_profiles_iterable: AsyncIterable[NamedMLSTProfile], handle: Union[str, bytes, PathLike[str], PathLike[bytes]]) -> Sequence[str]:
+async def write_mlst_profiles_as_csv(mlst_profiles_iterable: AsyncIterable[NamedMLSTProfile], handle: Union[str, bytes, PathLike[str], PathLike[bytes]], allele_names: Iterable[str]) -> Sequence[str]:
     failed = list()
     with open(handle, "w", newline='') as filehandle:
         header = None
@@ -30,7 +30,7 @@ async def write_mlst_profiles_as_csv(mlst_profiles_iterable: AsyncIterable[Named
                 continue
             allele_mapping = alleles_to_text_map(mlst_profile.alleles)
             if writer is None:
-                header = ["id", "st", "clonal-complex", *sorted(allele_mapping.keys())]
+                header = ["id", "st", "clonal-complex", *sorted(allele_names)]
                 writer = csv.DictWriter(filehandle, fieldnames=header)
                 writer.writeheader()
             row_dictionary = {