Modify the function of get_score_set_variants_csv to allow downloading multiple data types together.

src/mavedb/lib/score_sets.py

@@ -4,7 +4,7 @@
 import logging
 from operator import attrgetter
 import re
-from typing import Any, BinaryIO, Iterable, Optional, TYPE_CHECKING, Sequence, Literal
+from typing import Any, BinaryIO, Iterable, List, Optional, TYPE_CHECKING, Sequence, Literal
 
 from mavedb.models.mapped_variant import MappedVariant
 import numpy as np
@@ -501,12 +501,13 @@ def find_publish_or_private_superseded_score_set_tail(
 def get_score_set_variants_as_csv(
     db: Session,
     score_set: ScoreSet,
-    data_type: Literal["scores", "counts"],
+    namespaces: List[Literal["scores", "counts"]],
+    namespaced: Optional[bool] = None,
     start: Optional[int] = None,
     limit: Optional[int] = None,
     drop_na_columns: Optional[bool] = None,
-    include_custom_columns: bool = True,
-    include_post_mapped_hgvs: bool = False,
+    include_custom_columns: Optional[bool] = True,
+    include_post_mapped_hgvs: Optional[bool] = False,
 ) -> str:
     """
     Get the variant data from a score set as a CSV string.
@@ -517,8 +518,10 @@ def get_score_set_variants_as_csv(
         The database session to use.
     score_set : ScoreSet
         The score set to get the variants from.
-    data_type : {'scores', 'counts'}
-        The type of data to get. Either 'scores' or 'counts'.
+    namespaces : List[Literal["scores", "counts"]]
+        The namespaces for data. Now there are only scores and counts. There will be ClinVar and gnomAD.
+    namespaced: Optional[bool] = None
+        Whether namespace the columns or not.
     start : int, optional
         The index to start from. If None, starts from the beginning.
     limit : int, optional
@@ -537,20 +540,26 @@ def get_score_set_variants_as_csv(
         The CSV string containing the variant data.
     """
     assert type(score_set.dataset_columns) is dict
-    custom_columns_set = "score_columns" if data_type == "scores" else "count_columns"
-    type_column = "score_data" if data_type == "scores" else "count_data"
-
-    columns = ["accession", "hgvs_nt", "hgvs_splice", "hgvs_pro"]
+    namespaced_score_set_columns: dict[str, list[str]] = {
+        "core": ["accession", "hgvs_nt", "hgvs_splice", "hgvs_pro"],
+        "mavedb": [],
+    }
     if include_post_mapped_hgvs:
-        columns.append("post_mapped_hgvs_g")
-        columns.append("post_mapped_hgvs_p")
-
+        namespaced_score_set_columns["mavedb"].append("post_mapped_hgvs_g")
+        namespaced_score_set_columns["mavedb"].append("post_mapped_hgvs_p")
+    for namespace in namespaces:
+        namespaced_score_set_columns[namespace] = []
     if include_custom_columns:
-        custom_columns = [str(x) for x in list(score_set.dataset_columns.get(custom_columns_set, []))]
-        columns += custom_columns
-    elif data_type == "scores":
-        columns.append(REQUIRED_SCORE_COLUMN)
-
+        if "scores" in namespaced_score_set_columns:
+            namespaced_score_set_columns["scores"] = [
+                col for col in [str(x) for x in list(score_set.dataset_columns.get("score_columns", []))]
+            ]
+        if "counts" in namespaced_score_set_columns:
+            namespaced_score_set_columns["counts"] = [
+                col for col in [str(x) for x in list(score_set.dataset_columns.get("count_columns", []))]
+            ]
+    elif "scores" in namespaced_score_set_columns:
+        namespaced_score_set_columns["scores"].append(REQUIRED_SCORE_COLUMN)
     variants: Sequence[Variant] = []
     mappings: Optional[list[Optional[MappedVariant]]] = None
 
@@ -587,13 +596,22 @@ def get_score_set_variants_as_csv(
         if limit:
             variants_query = variants_query.limit(limit)
         variants = db.scalars(variants_query).all()
+    rows_data = variants_to_csv_rows(variants, columns=namespaced_score_set_columns, namespaced=namespaced, mappings=mappings)  # type: ignore
+    rows_columns = [
+        (
+            f"{namespace}.{col}"
+            if (namespaced and namespace not in ["core", "mavedb"])
+            else (f"mavedb.{col}" if namespaced and namespace == "mavedb" else col)
+        )
+        for namespace, cols in namespaced_score_set_columns.items()
+        for col in cols
+    ]
 
-    rows_data = variants_to_csv_rows(variants, columns=columns, dtype=type_column, mappings=mappings)  # type: ignore
     if drop_na_columns:
-        rows_data, columns = drop_na_columns_from_csv_file_rows(rows_data, columns)
+        rows_data, rows_columns = drop_na_columns_from_csv_file_rows(rows_data, rows_columns)
 
     stream = io.StringIO()
-    writer = csv.DictWriter(stream, fieldnames=columns, quoting=csv.QUOTE_MINIMAL)
+    writer = csv.DictWriter(stream, fieldnames=rows_columns, quoting=csv.QUOTE_MINIMAL)
     writer.writeheader()
     writer.writerows(rows_data)
     return stream.getvalue()
@@ -631,9 +649,9 @@ def is_null(value):
 
 def variant_to_csv_row(
     variant: Variant,
-    columns: list[str],
-    dtype: str,
+    columns: dict[str, list[str]],
     mapping: Optional[MappedVariant] = None,
+    namespaced: Optional[bool] = None,
     na_rep="NA",
 ) -> dict[str, Any]:
     """
@@ -645,17 +663,18 @@ def variant_to_csv_row(
         List of variants.
     columns : list[str]
         Columns to serialize.
-    dtype : str, {'scores', 'counts'}
-        The type of data requested. Either the 'score_data' or 'count_data'.
+    namespaced: Optional[bool] = None
+        Namespace the columns or not.
     na_rep : str
         String to represent null values.
 
     Returns
     -------
     dict[str, Any]
     """
-    row = {}
-    for column_key in columns:
+    row: dict[str, Any] = {}
+    # Handle each column key explicitly as part of its namespace.
+    for column_key in columns.get("core", []):
         if column_key == "hgvs_nt":
             value = str(variant.hgvs_nt)
         elif column_key == "hgvs_pro":
@@ -664,7 +683,13 @@ def variant_to_csv_row(
             value = str(variant.hgvs_splice)
         elif column_key == "accession":
             value = str(variant.urn)
-        elif column_key == "post_mapped_hgvs_g":
+        if is_null(value):
+            value = na_rep
+
+        # export columns in the `core` namespace without a namespace
+        row[column_key] = value
+    for column_key in columns.get("mavedb", []):
+        if column_key == "post_mapped_hgvs_g":
             hgvs_str = get_hgvs_from_post_mapped(mapping.post_mapped) if mapping and mapping.post_mapped else None
             if hgvs_str is not None and is_hgvs_g(hgvs_str):
                 value = hgvs_str
@@ -676,21 +701,28 @@ def variant_to_csv_row(
                 value = hgvs_str
             else:
                 value = ""
-        else:
-            parent = variant.data.get(dtype) if variant.data else None
-            value = str(parent.get(column_key)) if parent else na_rep
         if is_null(value):
             value = na_rep
-        row[column_key] = value
-
+        key = f"mavedb.{column_key}" if namespaced else column_key
+        row[key] = value
+    for column_key in columns.get("scores", []):
+        parent = variant.data.get("score_data") if variant.data else None
+        value = str(parent.get(column_key)) if parent else na_rep
+        key = f"scores.{column_key}" if namespaced else column_key
+        row[key] = value
+    for column_key in columns.get("counts", []):
+        parent = variant.data.get("count_data") if variant.data else None
+        value = str(parent.get(column_key)) if parent else na_rep
+        key = f"counts.{column_key}" if namespaced else column_key
+        row[key] = value
     return row
 
 
 def variants_to_csv_rows(
     variants: Sequence[Variant],
-    columns: list[str],
-    dtype: str,
+    columns: dict[str, list[str]],
     mappings: Optional[Sequence[Optional[MappedVariant]]] = None,
+    namespaced: Optional[bool] = None,
     na_rep="NA",
 ) -> Iterable[dict[str, Any]]:
     """
@@ -702,8 +734,8 @@ def variants_to_csv_rows(
         List of variants.
     columns : list[str]
         Columns to serialize.
-    dtype : str, {'scores', 'counts'}
-        The type of data requested. Either the 'score_data' or 'count_data'.
+    namespaced: Optional[bool] = None
+        Namespace the columns or not.
     na_rep : str
         String to represent null values.
 
@@ -713,10 +745,10 @@ def variants_to_csv_rows(
     """
     if mappings is not None:
         return map(
-            lambda pair: variant_to_csv_row(pair[0], columns, dtype, mapping=pair[1], na_rep=na_rep),
+            lambda pair: variant_to_csv_row(pair[0], columns, mapping=pair[1], namespaced=namespaced, na_rep=na_rep),
             zip(variants, mappings),
         )
-    return map(lambda v: variant_to_csv_row(v, columns, dtype, na_rep=na_rep), variants)
+    return map(lambda v: variant_to_csv_row(v, columns, namespaced=namespaced, na_rep=na_rep), variants)
 
 
 def find_meta_analyses_for_score_sets(db: Session, urns: list[str]) -> list[ScoreSet]:

src/mavedb/routers/score_sets.py

@@ -1,7 +1,7 @@
 import json
 import logging
 from datetime import date
-from typing import Any, List, Optional, Sequence, TypedDict, Union
+from typing import Any, List, Literal, Optional, Sequence, TypedDict, Union
 
 import pandas as pd
 from arq import ArqRedis
@@ -110,20 +110,28 @@ async def enqueue_variant_creation(
             "hgvs_splice",
             "hgvs_pro",
         ] + item.dataset_columns.get("score_columns", [])
+        # score_columns = {
+        #         "core": ["hgvs_nt", "hgvs_splice", "hgvs_pro"],
+        #         "counts": item.dataset_columns["score_columns"],
+        #     }
         existing_scores_df = pd.DataFrame(
-            variants_to_csv_rows(item.variants, columns=score_columns, dtype="score_data")
+            variants_to_csv_rows(item.variants, columns=score_columns, namespaced=False)
         ).replace("NA", pd.NA)
 
     # create CSV from existing variants on the score set if no new dataframe provided
     existing_counts_df = None
     if new_counts_df is None and item.dataset_columns.get("count_columns"):
+        # count_columns = {
+        #     "core": ["hgvs_nt", "hgvs_splice", "hgvs_pro"],
+        #     "counts": item.dataset_columns["count_columns"],
+        # }
         count_columns = [
-            "hgvs_nt",
-            "hgvs_splice",
-            "hgvs_pro",
-        ] + item.dataset_columns["count_columns"]
+                            "hgvs_nt",
+                            "hgvs_splice",
+                            "hgvs_pro",
+                        ] + item.dataset_columns["count_columns"]
         existing_counts_df = pd.DataFrame(
-            variants_to_csv_rows(item.variants, columns=count_columns, dtype="count_data")
+            variants_to_csv_rows(item.variants, columns=count_columns, namespaced=False)
         ).replace("NA", pd.NA)
 
     # Await the insertion of this job into the worker queue, not the job itself.
@@ -638,7 +646,13 @@ def get_score_set_variants_csv(
     urn: str,
     start: int = Query(default=None, description="Start index for pagination"),
     limit: int = Query(default=None, description="Maximum number of variants to return"),
+    namespaces: List[Literal["scores", "counts"]] = Query(
+        default=["scores"],
+        description="One or more data types to include: scores, counts, clinVar, gnomAD"
+    ),
     drop_na_columns: Optional[bool] = None,
+    include_custom_columns: Optional[bool] = None,
+    include_post_mapped_hgvs: Optional[bool] = None,
     db: Session = Depends(deps.get_db),
     user_data: Optional[UserData] = Depends(get_current_user),
 ) -> Any:
@@ -648,12 +662,9 @@ def get_score_set_variants_csv(
     This differs from get_score_set_scores_csv() in that it returns only the HGVS columns, score column, and mapped HGVS
     string.
 
-    TODO (https://github.com/VariantEffect/mavedb-api/issues/446) We may want to turn this into a general-purpose CSV
+    TODO (https://github.com/VariantEffect/mavedb-api/issues/446) We may add another function for ClinVar and gnomAD.
     export endpoint, with options governing which columns to include.
 
-    Parameters
-    __________
-
     Parameters
     __________
     urn : str
@@ -662,6 +673,9 @@ def get_score_set_variants_csv(
         The index to start from. If None, starts from the beginning.
     limit : Optional[int]
         The maximum number of variants to return. If None, returns all variants.
+    namespaces: List[Literal["scores", "counts"]]
+        The namespaces of all columns except for accession, hgvs_nt, hgvs_pro, and hgvs_splice.
+        We may add ClinVar and gnomAD in the future.
     drop_na_columns : bool, optional
         Whether to drop columns that contain only NA values. Defaults to False.
     db : Session
@@ -701,12 +715,13 @@ def get_score_set_variants_csv(
     csv_str = get_score_set_variants_as_csv(
         db,
         score_set,
-        "scores",
+        namespaces,
+        True,
         start,
         limit,
         drop_na_columns,
-        include_custom_columns=False,
-        include_post_mapped_hgvs=True,
+        include_custom_columns,
+        include_post_mapped_hgvs,
     )
     return StreamingResponse(iter([csv_str]), media_type="text/csv")
 
@@ -762,7 +777,7 @@ def get_score_set_scores_csv(
 
     assert_permission(user_data, score_set, Action.READ)
 
-    csv_str = get_score_set_variants_as_csv(db, score_set, "scores", start, limit, drop_na_columns)
+    csv_str = get_score_set_variants_as_csv(db, score_set, ["scores"], False, start, limit, drop_na_columns)
     return StreamingResponse(iter([csv_str]), media_type="text/csv")
 
 
@@ -817,7 +832,7 @@ async def get_score_set_counts_csv(
 
     assert_permission(user_data, score_set, Action.READ)
 
-    csv_str = get_score_set_variants_as_csv(db, score_set, "counts", start, limit, drop_na_columns)
+    csv_str = get_score_set_variants_as_csv(db, score_set, ["counts"], False, start, limit, drop_na_columns)
     return StreamingResponse(iter([csv_str]), media_type="text/csv")
 
 

src/mavedb/scripts/export_public_data.py

@@ -147,12 +147,12 @@ def export_public_data(db: Session):
                 logger.info(f"{i + 1}/{num_score_sets} Exporting variants for score set {score_set.urn}")
                 csv_filename_base = score_set.urn.replace(":", "-")
 
-                csv_str = get_score_set_variants_as_csv(db, score_set, "scores")
+                csv_str = get_score_set_variants_as_csv(db, score_set, ["scores"])
                 zipfile.writestr(f"csv/{csv_filename_base}.scores.csv", csv_str)
 
                 count_columns = score_set.dataset_columns["count_columns"] if score_set.dataset_columns else None
                 if count_columns and len(count_columns) > 0:
-                    csv_str = get_score_set_variants_as_csv(db, score_set, "counts")
+                    csv_str = get_score_set_variants_as_csv(db, score_set, ["counts"])
                     zipfile.writestr(f"csv/{csv_filename_base}.counts.csv", csv_str)