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"""Endpoints for running correlations"""
import json
from functools import reduce
from flask import jsonify
from flask import Blueprint
from flask import request
from flask import make_response
from gn3.computations.correlations import compute_all_sample_correlation
from gn3.computations.correlations import compute_all_lit_correlation
from gn3.computations.correlations import compute_tissue_correlation
from gn3.computations.correlations import map_shared_keys_to_values
from gn3.db_utils import database_connector
from gn3.computations.partial_correlations import partial_correlations_entry
correlation = Blueprint("correlation", __name__)
@correlation.route("/sample_x/<string:corr_method>", methods=["POST"])
def compute_sample_integration(corr_method="pearson"):
"""temporary api to help integrate genenetwork2 to genenetwork3 """
correlation_input = request.get_json()
target_samplelist = correlation_input.get("target_samplelist")
target_data_values = correlation_input.get("target_dataset")
this_trait_data = correlation_input.get("trait_data")
results = map_shared_keys_to_values(target_samplelist, target_data_values)
correlation_results = compute_all_sample_correlation(corr_method=corr_method,
this_trait=this_trait_data,
target_dataset=results)
return jsonify(correlation_results)
@correlation.route("/sample_r/<string:corr_method>", methods=["POST"])
def compute_sample_r(corr_method="pearson"):
"""Correlation endpoint for computing sample r correlations\
api expects the trait data with has the trait and also the\
target_dataset data
"""
correlation_input = request.get_json()
# xtodo move code below to compute_all_sampl correlation
this_trait_data = correlation_input.get("this_trait")
target_dataset_data = correlation_input.get("target_dataset")
correlation_results = compute_all_sample_correlation(corr_method=corr_method,
this_trait=this_trait_data,
target_dataset=target_dataset_data)
return jsonify({
"corr_results": correlation_results
})
@correlation.route("/lit_corr/<string:species>/<int:gene_id>", methods=["POST"])
def compute_lit_corr(species=None, gene_id=None):
"""Api endpoint for doing lit correlation.results for lit correlation\
are fetched from the database this is the only case where the db\
might be needed for actual computing of the correlation results
"""
conn, _cursor_object = database_connector()
target_traits_gene_ids = request.get_json()
target_trait_gene_list = list(target_traits_gene_ids.items())
lit_corr_results = compute_all_lit_correlation(
conn=conn, trait_lists=target_trait_gene_list,
species=species, gene_id=gene_id)
conn.close()
return jsonify(lit_corr_results)
@correlation.route("/tissue_corr/<string:corr_method>", methods=["POST"])
def compute_tissue_corr(corr_method="pearson"):
"""Api endpoint fr doing tissue correlation"""
tissue_input_data = request.get_json()
primary_tissue_dict = tissue_input_data["primary_tissue"]
target_tissues_dict = tissue_input_data["target_tissues_dict"]
results = compute_tissue_correlation(primary_tissue_dict=primary_tissue_dict,
target_tissues_data=target_tissues_dict,
corr_method=corr_method)
return jsonify(results)
@correlation.route("/partial", methods=["POST"])
def partial_correlation():
"""API endpoint for partial correlations."""
def trait_fullname(trait):
return f"{trait['dataset']}::{trait['name']}"
def __field_errors__(args):
def __check__(acc, field):
if args.get(field) is None:
return acc + (f"Field '{field}' missing",)
return acc
return __check__
def __errors__(request_data, fields):
errors = tuple()
if request_data is None:
return ("No request data",)
return reduce(__field_errors__(args), fields, errors)
class OutputEncoder(json.JSONEncoder):
"""
Class to encode output into JSON, for objects which the default
json.JSONEncoder class does not have default encoding for.
"""
def default(self, o):
if isinstance(o, bytes):
return str(o, encoding="utf-8")
return json.JSONEncoder.default(self, o)
def __build_response__(data):
status_codes = {"error": 400, "not-found": 404, "success": 200}
response = make_response(
json.dumps(data, cls=OutputEncoder),
status_codes[data["status"]])
response.headers["Content-Type"] = "application/json"
return response
args = request.get_json()
request_errors = __errors__(
args, ("primary_trait", "control_traits", "target_db", "method"))
if request_errors:
return __build_response__({
"status": "error",
"messages": request_errors,
"error_type": "Client Error"})
conn, _cursor_object = database_connector()
corr_results = partial_correlations_entry(
conn, trait_fullname(args["primary_trait"]),
tuple(trait_fullname(trait) for trait in args["control_traits"]),
args["method"], int(args.get("criteria", 500)), args["target_db"])
return __build_response__(corr_results)
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