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"""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/<unique_id>", 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/<token>", methods=["POST"])
def compute_k(token):
"""Given a genofile, traitfile, snpsfile, and the token, compute the k-valuen
and return <hash-of-inputs>.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/<chromosomes>/<token>", 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!")
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