fix: using altmetric for the ORCID integration

Author: andreishket

server/workers/common/common/utils.py

@@ -128,20 +128,21 @@ def push_metadata_to_queue(
     redis_store: redis.Redis,
     params: Dict[str, Union[str, List[str]]],
     metadata: pd.DataFrame,
-    source: Literal["crossref", "altmetric"]
+    source_list: List[str]
 ) -> str:
     """
     Sending metadata for processing into Redis queue and returning the request_id.
 
     :param redis_store: Object of the Redis store.
     :param params: Request params.
     :param metadata: DataFrame with default metadata.
-    :param source: define from which service additional metadata will be received.
+    :param source_list: define from which service additional metadata will be received (available values: "crossref", "altmetric").
     :return: request_id for the receiving of the request result.
     """
+    check_metadata_enrichment_source(source_list)
 
     request_id = str(uuid.uuid4())
-    params["metrics_sources"] = [source]
+    params["metrics_sources"] = source_list
     task_data = json.dumps({
         "id": request_id,
         "params": params,
@@ -152,6 +153,17 @@ def push_metadata_to_queue(
     return request_id
 
 
+def check_metadata_enrichment_source(source_list: List[str]) -> None:
+    """
+    Checks that source for metadata enrichment contains correct values.
+
+    :param source_list: List of sources from where metadata will be enriched.
+    :return: None.
+    """
+    if not all(source in ("crossref", "altmetric") for source in source_list):
+        raise ValueError("Source list must contain only 'crossref' or 'altmetric'")
+
+
 def fetch_enriched_metadata(redis_store: redis.Redis, request_id: str, timeout: int = 600) -> pd.DataFrame:
     """
     Getting enriched metadata from Redis.
@@ -216,7 +228,7 @@ def enrich_metadata(
     redis: redis.Redis,
     params: Dict[str, Union[str, List[str]]],
     metadata: pd.DataFrame,
-    source: Literal["crossref", "altmetric"],
+    source_list: List[str],
     integration: Literal["pubmed", "orcid"]
 ) -> pd.DataFrame:
     """
@@ -225,13 +237,15 @@ def enrich_metadata(
     :param redis: store object of Redis.
     :param params: params of the request.
     :param metadata: DataFrame with default metadata.
-    :param source: define from which service additional metadata will be received.
+    :param source: define from which service additional metadata will be received (available values: "crossref", "altmetric").
     :return: Enriched DataFrame with metadata.
     """
+    # Checks that source list contains valid values
+    check_metadata_enrichment_source(source_list)
 
     # Creates a request to metrics for metadata enrichment
     # and returns request_id for receiving the result later
-    request_id = push_metadata_to_queue(redis, params, metadata, source)
+    request_id = push_metadata_to_queue(redis, params, metadata, source_list)
 
     # Getting the result after metadata enrichment at metrics
     enriched_metadata = fetch_enriched_metadata(redis, request_id)

server/workers/orcid/src/orcid_service.py

@@ -388,9 +388,11 @@ def _retrieve_author_info_and_metadata(self, orcid: Orcid) -> Tuple[AuthorInfo,
 
     def _process_metadata(self, metadata: pd.DataFrame, author_info: AuthorInfo, params: Dict[str, str]) -> pd.DataFrame:
         metadata["authors"] = metadata["authors"].replace("", author_info.author_name)
-        source_for_metadata_enrichment = "crossref"
+
         integration = 'orcid'
+        source_for_metadata_enrichment = ["crossref", "altmetric"]
         metadata = enrich_metadata(self.redis_store, params, metadata, source_for_metadata_enrichment, integration)
+
         self.logger.debug(f'metadata shape after base enrichment: {metadata.shape}')
         author_info = self.enrich_author_info(author_info, metadata, params)
         self.logger.debug(f'metadata shape after enrichment: {metadata.shape}')

server/workers/pubmed/src/pubmed.py

@@ -41,9 +41,9 @@ def execute_search(self, params):
                 return raw_metadata
 
             metadata = pd.DataFrame(raw_metadata)
-            source_for_metadata_enrichment = "crossref"
-            integration = 'pubmed'
 
+            integration = 'pubmed'
+            source_for_metadata_enrichment = ["crossref"]
             metadata = enrich_metadata(self.redis_store, params, metadata, source_for_metadata_enrichment, integration)
 
             text = pd.DataFrame(raw_text)