import argparse import arvados import arvados.collection import time import subprocess import tempfile import json import logging import ruamel.yaml from bh20sequploader.qc_metadata import qc_metadata from bh20sequploader.qc_fasta import qc_fasta import pkg_resources from schema_salad.sourceline import add_lc_filename logging.basicConfig(format="[%(asctime)s] %(levelname)s %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO) logging.getLogger("googleapiclient.discovery").setLevel(logging.WARN) def validate_upload(api, collection, validated_project, fastq_project, fastq_workflow_uuid): col = arvados.collection.Collection(collection["uuid"]) if collection.get("status") in ("validated", "rejected"): return False # validate the collection here. Check metadata, etc. logging.info("Validating upload '%s' (%s)" % (collection["name"], collection["uuid"])) errors = [] dup = api.collections().list(filters=[["owner_uuid", "=", validated_project], ["portable_data_hash", "=", col.portable_data_hash()]]).execute() if dup["items"]: # This exact collection has been uploaded before. errors.append("Duplicate of %s" % ([d["uuid"] for d in dup["items"]])) if not errors: if "metadata.yaml" not in col: errors.append("Missing metadata.yaml", collection["name"]) else: try: metadata_content = ruamel.yaml.round_trip_load(col.open("metadata.yaml")) metadata_content["id"] = "http://arvados.org/keep:%s/metadata.yaml" % collection["portable_data_hash"] sample_id = metadata_content["sample"]["sample_id"] add_lc_filename(metadata_content, metadata_content["id"]) valid = qc_metadata(metadata_content) if not valid: errors.append("Failed metadata qc") except Exception as e: errors.append(str(e)) if not errors: try: tgt = None paired = {"reads_1.fastq": "reads.fastq", "reads_1.fastq.gz": "reads.fastq.gz"} for n in ("sequence.fasta", "reads.fastq", "reads.fastq.gz", "reads_1.fastq", "reads_1.fastq.gz"): if n not in col: continue with col.open(n, 'rb') as qf: tgt = qc_fasta(qf)[0] if tgt != n and tgt != paired.get(n): errors.append("Expected %s but magic says it should be %s", n, tgt) elif tgt in ("reads.fastq", "reads.fastq.gz", "reads_1.fastq", "reads_1.fastq.gz"): start_fastq_to_fasta(api, collection, fastq_project, fastq_workflow_uuid, n, sample_id) return False if tgt is None: errors.append("Upload '%s' does not contain sequence.fasta, reads.fastq or reads_1.fastq", collection["name"]) except Exception as v: errors.append(str(v)) if not errors: logging.info("Added '%s' to validated sequences" % collection["name"]) # Move it to the "validated" project to be included in the next analysis collection["properties"]["status"] = "validated" api.collections().update(uuid=collection["uuid"], body={ "owner_uuid": validated_project, "name": "%s (%s)" % (collection["name"], time.asctime(time.gmtime()))}).execute() return True else: # It is invalid logging.warn("'%s' (%s) has validation errors: %s" % ( collection["name"], collection["uuid"], "\n".join(errors))) collection["properties"]["status"] = "rejected" collection["properties"]["errors"] = errors api.collections().update(uuid=collection["uuid"], body={"properties": collection["properties"]}).execute() return False def run_workflow(api, parent_project, workflow_uuid, name, inputobj): project = api.groups().create(body={ "group_class": "project", "name": name, "owner_uuid": parent_project, }, ensure_unique_name=True).execute() with tempfile.NamedTemporaryFile() as tmp: tmp.write(json.dumps(inputobj, indent=2).encode('utf-8')) tmp.flush() cmd = ["arvados-cwl-runner", "--submit", "--no-wait", "--project-uuid=%s" % project["uuid"], "arvwf:%s" % workflow_uuid, tmp.name] logging.info("Running %s" % ' '.join(cmd)) comp = subprocess.run(cmd, capture_output=True) logging.info("Submitted %s", comp.stdout) if comp.returncode != 0: logging.error(comp.stderr.decode('utf-8')) return project def start_fastq_to_fasta(api, collection, analysis_project, fastq_workflow_uuid, tgt, sample_id): params = { "metadata": { "class": "File", "location": "keep:%s/metadata.yaml" % collection["portable_data_hash"] }, "ref_fasta": { "class": "File", "location": "keep:ffef6a3b77e5e04f8f62a7b6f67264d1+556/SARS-CoV2-NC_045512.2.fasta" }, "sample_id": sample_id } if tgt.startswith("reads.fastq"): params["fastq_forward"] = { "class": "File", "location": "keep:%s/%s" % (collection["portable_data_hash"], tgt) } elif tgt.startswith("reads_1.fastq"): params["fastq_forward"] = { "class": "File", "location": "keep:%s/reads_1.%s" % (collection["portable_data_hash"], tgt[8:]) } params["fastq_reverse"] = { "class": "File", "location": "keep:%s/reads_2.%s" % (collection["portable_data_hash"], tgt[8:]) } newproject = run_workflow(api, analysis_project, fastq_workflow_uuid, "FASTQ to FASTA", params) api.collections().update(uuid=collection["uuid"], body={"owner_uuid": newproject["uuid"]}).execute() def start_pangenome_analysis(api, analysis_project, pangenome_workflow_uuid, validated_project, schema_ref, exclude_list): validated = arvados.util.list_all(api.collections().list, filters=[["owner_uuid", "=", validated_project]]) inputobj = { "inputReads": [], "metadata": [], "subjects": [], "metadataSchema": { "class": "File", "location": schema_ref }, "exclude": { "class": "File", "location": exclude_list } } validated.sort(key=lambda v: v["portable_data_hash"]) for v in validated: inputobj["inputReads"].append({ "class": "File", "location": "keep:%s/sequence.fasta" % v["portable_data_hash"] }) inputobj["metadata"].append({ "class": "File", "location": "keep:%s/metadata.yaml" % v["portable_data_hash"] }) inputobj["subjects"].append("http://collections.lugli.arvadosapi.com/c=%s/sequence.fasta" % v["portable_data_hash"]) run_workflow(api, analysis_project, pangenome_workflow_uuid, "Pangenome analysis", inputobj) def get_workflow_output_from_project(api, uuid): cr = api.container_requests().list(filters=[['owner_uuid', '=', uuid], ["requesting_container_uuid", "=", None]]).execute() if cr["items"] and cr["items"][0]["output_uuid"]: container = api.containers().get(uuid=cr["items"][0]["container_uuid"]).execute() if container["state"] == "Complete" and container["exit_code"] == 0: return cr["items"][0] return None def copy_most_recent_result(api, analysis_project, latest_result_uuid): most_recent_analysis = api.groups().list(filters=[['owner_uuid', '=', analysis_project]], order="created_at desc").execute() for m in most_recent_analysis["items"]: wf = get_workflow_output_from_project(api, m["uuid"]) if wf: src = api.collections().get(uuid=wf["output_uuid"]).execute() dst = api.collections().get(uuid=latest_result_uuid).execute() if src["portable_data_hash"] != dst["portable_data_hash"]: logging.info("Copying latest result from '%s' to %s", m["name"], latest_result_uuid) api.collections().update(uuid=latest_result_uuid, body={"manifest_text": src["manifest_text"], "description": "Result from %s %s" % (m["name"], wf["uuid"])}).execute() break def move_fastq_to_fasta_results(api, analysis_project, uploader_project): projects = api.groups().list(filters=[['owner_uuid', '=', analysis_project], ["properties.moved_output", "!=", True]], order="created_at desc",).execute() for p in projects["items"]: wf = get_workflow_output_from_project(api, p["uuid"]) if wf: logging.info("Moving completed fastq2fasta result %s back to uploader project", wf["output_uuid"]) api.collections().update(uuid=wf["output_uuid"], body={"owner_uuid": uploader_project}).execute() p["properties"]["moved_output"] = True api.groups().update(uuid=p["uuid"], body={"properties": p["properties"]}).execute() break def upload_schema(api, workflow_def_project): schema_resource = pkg_resources.resource_stream('bh20sequploader.qc_metadata', "bh20seq-schema.yml") c = arvados.collection.Collection() with c.open("schema.yml", "wb") as f: f.write(schema_resource.read()) pdh = c.portable_data_hash() wd = api.collections().list(filters=[["owner_uuid", "=", workflow_def_project], ["portable_data_hash", "=", pdh]]).execute() if len(wd["items"]) == 0: c.save_new(owner_uuid=workflow_def_project, name="Metadata schema", ensure_unique_name=True) return "keep:%s/schema.yml" % pdh def print_status(api, uploader_project, fmt): pending = arvados.util.list_all(api.collections().list, filters=[["owner_uuid", "=", uploader_project]]) out = [] status = {} for p in pending: prop = p["properties"] out.append(prop) if "status" not in prop: prop["status"] = "pending" prop["created_at"] = p["created_at"] prop["uuid"] = p["uuid"] status[prop["status"]] = status.get(prop["status"], 0) + 1 if fmt == "html": print( """ """) print("

Total collections in upload project %s

" % len(out)) print("

Status %s

" % status) print( """ """) for r in out: print("") print("" % (r["uuid"], r["uuid"])) print("" % r["sequence_label"]) print("" % r["status"]) print("" % "\n".join(r.get("errors", []))) print("") print( """
Collection Sequence label Status Errors
%s%s%s
%s
""") else: print(json.dumps(out, indent=2)) def main(): parser = argparse.ArgumentParser(description='Analyze collections uploaded to a project') parser.add_argument('--uploader-project', type=str, default='lugli-j7d0g-n5clictpuvwk8aa', help='') parser.add_argument('--pangenome-analysis-project', type=str, default='lugli-j7d0g-y4k4uswcqi3ku56', help='') parser.add_argument('--fastq-project', type=str, default='lugli-j7d0g-xcjxp4oox2u1w8u', help='') parser.add_argument('--validated-project', type=str, default='lugli-j7d0g-5ct8p1i1wrgyjvp', help='') parser.add_argument('--workflow-def-project', type=str, default='lugli-j7d0g-5hswinmpyho8dju', help='') parser.add_argument('--pangenome-workflow-uuid', type=str, default='lugli-7fd4e-mqfu9y3ofnpnho1', help='') parser.add_argument('--fastq-workflow-uuid', type=str, default='lugli-7fd4e-2zp9q4jo5xpif9y', help='') parser.add_argument('--exclude-list', type=str, default='keep:lugli-4zz18-tzzhcm6hrf8ci8d/exclude.txt', help='') parser.add_argument('--latest-result-collection', type=str, default='lugli-4zz18-z513nlpqm03hpca', help='') parser.add_argument('--kickoff', action="store_true") parser.add_argument('--no-start-analysis', action="store_true") parser.add_argument('--once', action="store_true") parser.add_argument('--print-status', type=str, default=None) args = parser.parse_args() api = arvados.api() schema_ref = upload_schema(api, args.workflow_def_project) if args.kickoff: logging.info("Starting a single analysis run") start_pangenome_analysis(api, args.pangenome_analysis_project, args.pangenome_workflow_uuid, args.validated_project, schema_ref, args.exclude_list) return if args.print_status: print_status(api, args.uploader_project, args.print_status) exit(0) logging.info("Starting up, monitoring %s for uploads" % (args.uploader_project)) while True: move_fastq_to_fasta_results(api, args.fastq_project, args.uploader_project) new_collections = arvados.util.list_all(api.collections().list, filters=[["owner_uuid", "=", args.uploader_project]]) at_least_one_new_valid_seq = False for c in new_collections: at_least_one_new_valid_seq = validate_upload(api, c, args.validated_project, args.fastq_project, args.fastq_workflow_uuid) or at_least_one_new_valid_seq if at_least_one_new_valid_seq and not args.no_start_analysis: start_pangenome_analysis(api, args.pangenome_analysis_project, args.pangenome_workflow_uuid, args.validated_project, schema_ref, args.exclude_list) copy_most_recent_result(api, args.pangenome_analysis_project, args.latest_result_collection) if args.once: break time.sleep(15)