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Merge pull request #6 from newgene/prevalence-by-position
Add validation rules to PrevalenceByAAPositionHandler
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,145 @@ | ||
| import pandas as pd | ||
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|
||
| from web.handlers.genomics.base import BaseHandler | ||
| from web.handlers.genomics.util import compute_rolling_mean, parse_location_id_to_query | ||
|
|
||
|
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| class PrevalenceByAAPositionHandler(BaseHandler): | ||
| name = "prevalence-by-position" | ||
| kwargs = dict(BaseHandler.kwargs) | ||
| kwargs["GET"] = { | ||
| "pangolin_lineage": {"type": str, "default": None}, | ||
| "location_id": {"type": str, "default": None}, | ||
| "name": {"type": str, "required": True}, # should be deprecated | ||
| "gene": { | ||
| "type": str, | ||
| "required": False, | ||
| }, # replacement of name parameter for validator applying | ||
| "position": { | ||
| "type": int, | ||
| "required": False, | ||
| }, # replacement of name parameter for validator applying | ||
| } | ||
|
|
||
| async def _get(self): | ||
| query_str = self.args.name | ||
| query_location = self.args.location_id | ||
| query_lineage = self.args.pangolin_lineage | ||
| query_gene = self.args.gene | ||
| if not query_gene: | ||
| query_gene = query_str.split(":")[0] | ||
| query_aa_position = self.args.position | ||
| if query_aa_position is None: | ||
| query_aa_position = int(query_str.split(":")[1]) | ||
| # Get ref codon | ||
| query = { | ||
| "size": 0, | ||
| "aggs": { | ||
| "by_mutations": { | ||
| "nested": {"path": "mutations"}, | ||
| "aggs": { | ||
| "inner": { | ||
| "filter": { | ||
| "bool": { | ||
| "must": [ | ||
| {"match": {"mutations.codon_num": query_aa_position}}, | ||
| {"match": {"mutations.gene": query_gene}}, | ||
| ] | ||
| } | ||
| }, | ||
| "aggs": {"by_nested": {"top_hits": {"size": 1}}}, | ||
| } | ||
| }, | ||
| } | ||
| }, | ||
| } | ||
| resp = await self.asynchronous_fetch(query) | ||
| tmp_ref = resp["aggregations"]["by_mutations"]["inner"]["by_nested"]["hits"]["hits"] | ||
| dict_response = [] | ||
| if len(tmp_ref) > 0: | ||
| ref_aa = tmp_ref[0]["_source"]["ref_aa"] | ||
| query = { | ||
| "aggs": { | ||
| "by_date": { | ||
| "terms": {"field": "date_collected", "size": self.size}, | ||
| "aggs": { | ||
| "by_mutations": { | ||
| "nested": {"path": "mutations"}, | ||
| "aggs": { | ||
| "inner": { | ||
| "filter": { | ||
| "bool": { | ||
| "must": [ | ||
| { | ||
| "match": { | ||
| "mutations.codon_num": query_aa_position | ||
| } | ||
| }, | ||
| {"match": {"mutations.gene": query_gene}}, | ||
| ] | ||
| } | ||
| }, | ||
| "aggs": { | ||
| "by_name": {"terms": {"field": "mutations.alt_aa"}} | ||
| }, | ||
| } | ||
| }, | ||
| } | ||
| }, | ||
| } | ||
| } | ||
| } | ||
| if query_location is not None: | ||
| query["query"] = parse_location_id_to_query( | ||
| query_location, query["aggs"]["prevalence"]["filter"] | ||
| ) | ||
| if query_lineage is not None: | ||
| if "query" in query: | ||
| query["query"]["bool"]["must"].append( | ||
| {"term": {"pangolin_lineage": query_lineage}} | ||
| ) | ||
| else: | ||
| query["query"] = {"term": {"pangolin_lineage": query_lineage}} | ||
| resp = await self.asynchronous_fetch(query) | ||
| buckets = resp | ||
| path_to_results = ["aggregations", "by_date", "buckets"] | ||
| for i in path_to_results: | ||
| buckets = buckets[i] | ||
| flattened_response = [] | ||
| for d in buckets: | ||
| alt_count = 0 | ||
| for m in d["by_mutations"]["inner"]["by_name"]["buckets"]: | ||
| if m["key"] == "None": | ||
| continue | ||
| flattened_response.append( | ||
| { | ||
| "date": d["key"], | ||
| "total_count": d["doc_count"], | ||
| "aa": m["key"], | ||
| "aa_count": m["doc_count"], | ||
| } | ||
| ) | ||
| alt_count += m["doc_count"] | ||
| flattened_response.append( | ||
| { | ||
| "date": d["key"], | ||
| "total_count": d["doc_count"], | ||
| "aa": ref_aa, | ||
| "aa_count": d["doc_count"] - alt_count, | ||
| } | ||
| ) | ||
| df_response = ( | ||
| pd.DataFrame(flattened_response) | ||
| .assign( | ||
| date=lambda x: pd.to_datetime(x["date"], format="%Y-%m-%d"), | ||
| prevalence=lambda x: x["aa_count"] / x["total_count"], | ||
| ) | ||
| .sort_values("date") | ||
| ) | ||
| df_response = df_response.groupby("aa").apply( | ||
| compute_rolling_mean, "date", "prevalence", "prevalence_rolling" | ||
| ) | ||
| df_response.loc[:, "date"] = df_response["date"].apply(lambda x: x.strftime("%Y-%m-%d")) | ||
| dict_response = df_response.to_dict(orient="records") | ||
| resp = {"success": True, "results": dict_response} | ||
| return resp |