"""Endpoints for running the gemma cmd""" import os import redis from flask import Blueprint from flask import current_app from flask import jsonify from flask import request from gn3.commands import queue_cmd from gn3.commands import run_cmd from gn3.file_utils import get_hash_of_files from gn3.file_utils import jsonfile_to_dict from gn3.computations.gemma import generate_hash_of_string from gn3.computations.gemma import generate_pheno_txt_file from gn3.computations.gemma import generate_gemma_computation_cmd gemma = Blueprint("gemma", __name__) @gemma.route("/version") def get_version(): """Display the installed version of gemma-wrapper""" gemma_cmd = current_app.config["GEMMA_WRAPPER_CMD"] return jsonify( run_cmd(f"{gemma_cmd} -v | head -n 1")) # This is basically extracted from genenetwork2 # wqflask/wqflask/marker_regression/gemma_ampping.py @gemma.route("/k-gwa-computation", methods=["POST"]) def run_gemma(): """Generates a command for generating K-Values and then later, generate a GWA command that contains markers. These commands are queued; and the expected file output is returned. """ data = request.get_json() app_defaults = current_app.config __hash = generate_hash_of_string( f"{data.get('genofile_name')}_" ''.join(data.get("values", ""))) gemma_kwargs = { "geno_filename": os.path.join(app_defaults.get("GENODIR"), "bimbam", f"{data.get('geno_filename')}"), "trait_filename": generate_pheno_txt_file( tmpdir=app_defaults.get("TMPDIR"), values=data.get("values"), # Generate this file on the fly! trait_filename=(f"{data.get('dataset_groupname')}_" f"{data.get('trait_name')}_" f"{__hash}.txt"))} gemma_wrapper_kwargs = {} if data.get("loco"): gemma_wrapper_kwargs["loco"] = f"--input {data.get('loco')}" k_computation_cmd = generate_gemma_computation_cmd( gemma_cmd=app_defaults.get("GEMMA_WRAPPER_CMD"), gemma_wrapper_kwargs={"loco": f"--input {data.get('loco')}"}, gemma_kwargs=gemma_kwargs, output_file=(f"{app_defaults.get('TMPDIR')}/gn2/" f"{data.get('dataset_name')}_K_" f"{__hash}.json")) gemma_kwargs["lmm"] = data.get("lmm", 9) gemma_wrapper_kwargs["input"] = (f"{data.get('dataset_name')}_K_" f"{__hash}.json") gwa_cmd = generate_gemma_computation_cmd( gemma_wrapper_kwargs=gemma_wrapper_kwargs, gemma_cmd=app_defaults.get("GEMMA_WRAPPER_CMD"), gemma_kwargs=gemma_kwargs, output_file=(f"{data.get('dataset_name')}_GWA_" f"{__hash}.txt")) if not all([k_computation_cmd, gwa_cmd]): return jsonify(status=128, error="Unable to generate cmds for computation!"), 500 return jsonify( unique_id=queue_cmd(conn=redis.Redis(), email=data.get("email"), job_queue=app_defaults.get("REDIS_JOB_QUEUE"), cmd=f"{k_computation_cmd} && {gwa_cmd}"), status="queued", output_file=(f"{data.get('dataset_name')}_GWA_" f"{__hash}.txt")) @gemma.route("/status/", methods=["GET"]) def check_cmd_status(unique_id): """Given a (url-encoded) UNIQUE-ID which is returned when hitting any of the gemma endpoints, return the status of the command """ status = redis.Redis().hget(name=unique_id, key="status") if not status: return jsonify(status=128, error="The unique id you used does not exist!"), 500 return jsonify(status=status.decode("utf-8")) @gemma.route("/k-compute/", methods=["POST"]) def compute_k(token): """Given a genofile, traitfile, snpsfile, and the token, compute the k-valuen and return .json with a UNIQUE-ID of the job. The genofile, traitfile, and snpsfile are extracted from a metadata.json file. """ working_dir = os.path.join(current_app.config.get("TMPDIR"), token) _dict = jsonfile_to_dict(os.path.join(working_dir, "metadata.json")) try: genofile, phenofile, snpsfile = [os.path.join(working_dir, _dict.get(x)) for x in ["geno", "pheno", "snps"]] gemma_kwargs = {"g": genofile, "p": phenofile, "a": snpsfile} _hash = get_hash_of_files([genofile, phenofile, snpsfile]) k_output_filename = f"{_hash}-k-output.json" k_computation_cmd = generate_gemma_computation_cmd( gemma_cmd=current_app.config.get("GEMMA_WRAPPER_CMD"), gemma_wrapper_kwargs=None, gemma_kwargs=gemma_kwargs, output_file=(f"{current_app.config.get('TMPDIR')}/" f"{token}/{k_output_filename}")) return jsonify( unique_id=queue_cmd( conn=redis.Redis(), email=(request.get_json() or {}).get('email'), job_queue=current_app.config.get("REDIS_JOB_QUEUE"), cmd=f"{k_computation_cmd}"), status="queued", output_file=k_output_filename) # pylint: disable=W0703 except Exception: return jsonify(status=128, # use better message message="Metadata file non-existent!") @gemma.route("/k-compute/loco//", methods=["POST"]) def compute_k_loco(chromosomes, token): """Similar to 'compute_k' with the extra option of using loco given chromosome values. """ working_dir = os.path.join(current_app.config.get("TMPDIR"), token) _dict = jsonfile_to_dict(os.path.join(working_dir, "metadata.json")) try: genofile, phenofile, snpsfile = [os.path.join(working_dir, _dict.get(x)) for x in ["geno", "pheno", "snps"]] gemma_kwargs = {"g": genofile, "p": phenofile, "a": snpsfile} _hash = get_hash_of_files([genofile, phenofile, snpsfile]) k_output_filename = f"{_hash}-k-output.json" k_computation_cmd = generate_gemma_computation_cmd( gemma_cmd=current_app.config.get("GEMMA_WRAPPER_CMD"), gemma_wrapper_kwargs={"loco": f"--input {chromosomes}"}, gemma_kwargs=gemma_kwargs, output_file=(f"{current_app.config.get('TMPDIR')}/" f"{token}/{k_output_filename}")) return jsonify( unique_id=queue_cmd( conn=redis.Redis(), email=(request.get_json() or {}).get('email'), job_queue=current_app.config.get("REDIS_JOB_QUEUE"), cmd=f"{k_computation_cmd}"), status="queued", output_file=k_output_filename) # pylint: disable=W0703 except Exception: return jsonify(status=128, # use better message message="Metadata file non-existent!")