def ez_connect(corpus="AAC", es_config=None):
"""
Simplifies connecting to the Corpus
:param corpus:
:return: corpus instance
"""
# global MINERVA_ELASTICSEARCH_ENDPOINT
root_dir = ""
if corpus == "AAC":
root_dir = getRootDir("aac")
elif corpus == "PMC_CSC":
root_dir = getRootDir("pmc_coresc")
elif corpus is None:
root_dir = ""
else:
raise ValueError("Unknown corpus")
cp.useElasticCorpus()
if es_config:
celery_app.MINERVA_ELASTICSEARCH_ENDPOINT = celery_app.set_config(
es_config)
cp.Corpus.connectCorpus(root_dir,
endpoint=celery_app.MINERVA_ELASTICSEARCH_ENDPOINT)
if corpus:
cp.Corpus.setCorpusFilter(corpus)
return cp.Corpus
def import_sapienta_pmc_corpus():
"""
Do the importing of the Sapienta-annotated PMC corpus
"""
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
importer=CorpusImporter(reader=SapientaJATSXMLReader())
importer.collection_id="PMC_CSC"
importer.import_id="initial"
importer.generate_corpus_id=getPMC_CSC_corpus_id
## cp.useLocalCorpus()
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\pmc_coresc", endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
options={
## "reload_xml_if_doc_in_collection": True, # default: False
## "list_missing_references":True, # default: False
"convert_and_import_docs":False, # default: True
## "update_doc_references":False, # default: True
"force_generate_resolvable_citations":True, # default: False
"force_collection_id":"PMC_CSC", # this sets the collection_id again after updating references. Used for fixes
"force_import_id":"fixed",
}
## corpus_import.FILES_TO_PROCESS_FROM=4500
## corpus_import.FILES_TO_PROCESS_TO=10
## importer.restartCollectionImport(options)
importer.use_celery = True
importer.importCorpus("g:\\nlp\\phd\\pmc_coresc\\inputXML", file_mask="*.xml", import_options=options)
def connectToCorpus():
"""
"""
cp.useElasticCorpus()
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
cp.Corpus.connectCorpus(r"g:\nlp\phd\pmc_coresc",
endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
def connectToElastic():
"""
Does the basics of connecting to the ES endpoint
"""
cp.useElasticCorpus()
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
cp.Corpus.connectCorpus(r"g:\nlp\phd\pmc_coresc",
endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
def main():
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\aac", endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
cp.Corpus.setCorpusFilter("AAC")
exp=Experiment(experiment, options, True)
exp.run()
def connectToCorpus():
"""
"""
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
## cp.useLocalCorpus()
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\aac", endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
cp.Corpus.setCorpusFilter("AAC")
def main():
from multi.celery_app import MINERVA_ELASTICSEARCH_ENDPOINT
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\aac",
endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
cp.Corpus.setCorpusFilter("AAC")
## experiment["test_files"]=["456f8c80-9807-46a9-8455-cd4a7e346f9d"]
exp = Experiment(experiment, options, False)
exp.run()
def main():
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
cp.useElasticCorpus()
root_dir = getRootDir("aac")
cp.Corpus.connectCorpus(root_dir, endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
cp.Corpus.setCorpusFilter("AAC")
## experiment["test_files"]=["456f8c80-9807-46a9-8455-cd4a7e346f9d"]
exp = Experiment(experiment, options, False)
exp.run()
def checkCorpusConnection(
local_corpus_dir="",
corpus_endpoint=celery_app.MINERVA_ELASTICSEARCH_ENDPOINT):
"""
Connects this worker to the elasticsearch server. By default, uses
values from celery_app.py
"""
if not cp.Corpus.connectedToDB() and corpus_endpoint:
print("Celery tasks connecting to", corpus_endpoint)
cp.useElasticCorpus()
cp.Corpus.connectCorpus(local_corpus_dir, corpus_endpoint)
sleep(random() / float(100))
def main():
cp.useElasticCorpus()
cp.Corpus.connectCorpus(getRootDir("aac"),
endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
cp.Corpus.setCorpusFilter("AAC")
# fix_sentence_splitting_in_docs(cp.Corpus.listPapers())
fix_stranded_citations_in_docs(cp.Corpus.listPapers())
global num_removed_sent, num_papers_removed_sent
print("Removed {} sentences from {} papers".format(
num_removed_sent, num_papers_removed_sent))
def main():
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\pmc_coresc",
endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
## cp.Corpus.setCorpusFilter(collection_id="PMC_CSC")
## add_statistics_to_all_files(use_celery=True)
## add_statistics_to_all_files(use_celery=False, max_files=10)
aggregate_statistics()
## fix_collection_id()
pass
def test_read_doc(filename):
"""
Loads one Sapienta-annotated JATS file, prints out the resulting doc
"""
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\pmc_coresc", endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
reader=SapientaJATSXMLReader()
with open(filename,"r") as f:
xml=f.read()
doc=reader.read(xml, filename)
cp.Corpus.selectDocResolvableCitations(doc, max_year="2000b")
print (doc)
def fix_broken_scidocs():
"""
Iterates through the papers already in the collection. Tries to load
their scidoc. If KeyError occurs, it loads the XML again
"""
cp.useElasticCorpus()
from multi.config import MINERVA_ELASTICSEARCH_ENDPOINT
cp.Corpus.connectCorpus("g:\\nlp\\phd\\pmc_coresc", endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
importer=CorpusImporter("PMC_CSC","initial", use_celery=True)
importer.generate_corpus_id=getPMC_CSC_corpus_id
importer.reloadSciDocsOnly("metadata.collection_id:\"PMC_CSC\"",
"g:\\nlp\\phd\\pmc_coresc\\inputXML", "*.xml")
def find_new_citations_in_aac():
"""
Does another run through each AAC scidoc and tries to find citations that may have been missed
:return:
"""
from multi.celery_app import MINERVA_ELASTICSEARCH_ENDPOINT
from tqdm import tqdm
cp.useElasticCorpus()
cp.Corpus.connectCorpus(getRootDir("aac"),
endpoint=MINERVA_ELASTICSEARCH_ENDPOINT)
cp.Corpus.setCorpusFilter("AAC")
total_found = 0
total_could_match = 0
docs_with_new_ones = 0
existing_citations = 0
counter = tqdm(cp.Corpus.listPapers())
for guid in counter:
counter.set_description(
"{} docs_with_new_ones, {} total_found, {} total_could_match, {} existing_citations, "
.format(docs_with_new_ones, total_found, total_could_match,
existing_citations))
doc = cp.Corpus.loadSciDoc(guid)
for sent in doc.allsentences:
existing_citations += len(sent.get("citations", []))
new_citations, citations_found = annotatePlainTextCitationsInSentence(
sent, doc)
if len(citations_found) > 0:
# print(len(new_citations),":",new_citations)
total_found += len(citations_found)
total_could_match += len(new_citations)
docs_with_new_ones += 1
# print("\n NEW CITATION:", sent["text"])
# print(citations_found)
# print()
else:
if len(sent.get("citations", [])) > 0:
# print("ALREADY ANNOTATED:", sent["text"], "\n")
pass
cp.Corpus.saveSciDoc(doc)
print("Total citations found: ", total_found)
print("Total citations could match: ", total_could_match)
print("Docs with new citations: ", docs_with_new_ones)
print("Previously annotated citations: ", existing_citations)
def basicTest():
"""
"""
import db.corpora as cp
drive = "g"
cp.useElasticCorpus()
cp.Corpus.connectCorpus(drive + ":\\nlp\\phd\\aac")
## doc_list=cp.Corpus.selectRandomInputFiles(3,"*-paper.xml")
doc_list = [r"anthology\W\W10-0402-paper.xml"]
## doc_list=[r"anthology\W\W11-2166-paper.xml"]
generateSideBySide(doc_list)
def main():
drive = "g"
## cp.useLocalCorpus()
cp.useElasticCorpus()
cp.Corpus.setCorpusFilter(collection_id="AAC")
cp.Corpus.connectCorpus(drive + ":\\nlp\\phd\\aac")
## generator=AtharQueryGenerator(drive+r":\NLP\PhD\citation_context\doc_dict.json", reassign_guids=True)
## experiment["test_files"]=cp.Corpus.listPapers("year >= 2011")
experiment["test_files"] = cp.Corpus.listPapers("year:>=2011")
exp = Experiment(experiment, options)
exp.run()
pass
def fix_citation_parent_aac():
"""
"""
from proc.results_logging import ProgressIndicator
cp.useElasticCorpus()
cp.Corpus.connectCorpus(getRootDir("aac"))
guids = cp.Corpus.listPapers({"match": {"metadata.collection_id": "AAC"}})
progress = ProgressIndicator(True, len(guids), True)
for guid in guids:
doc = cp.Corpus.loadSciDoc(guid)
for cit in doc.citations:
if "parent" in cit:
cit["parent_s"] = cit.pop("parent")
cp.Corpus.saveSciDoc(doc)
progress.showProgressReport("Fixing badly imported PaperXML")
def main():
drive="g"
## cp.useLocalCorpus()
cp.useElasticCorpus()
cp.Corpus.setCorpusFilter(collection_id="AAC")
cp.Corpus.connectCorpus(drive+":\\nlp\\phd\\aac")
generator=AtharQueryGenerator(drive+r":\NLP\PhD\citation_context\doc_dict.json", reassign_guids=True)
experiment["test_files"]=list(generator.docs.keys())
## exp=Experiment(experiment, options)
exp=ContextGenerationPipeline(experiment,options)
exp.query_generator=generator
exp.run()
pass
def fix_authors_full_corpus():
"""
Fixes authors in each metadata entry having a "papers" key which they
shouldn't
"""
from proc.results_logging import ProgressIndicator
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\pmc_coresc")
guids=cp.Corpus.listPapers()
progress=ProgressIndicator(True, len(guids), True)
for guid in guids:
doc_meta=cp.Corpus.getMetadataByGUID(guid)
new_authors=[]
for old_author in doc_meta.authors:
del old_author["papers"]
cp.Corpus.updatePaper(doc_meta)
progress.showProgressReport("Removing redundant author information")
def main():
from multi.celery_app import set_config
endpoint = set_config("aws-server")
cp.useElasticCorpus()
cp.Corpus.connectCorpus(getRootDir("aac"), endpoint=endpoint)
cp.Corpus.createAndInitializeDatabase()
# import_aac_corpus(endpoint, use_celery=False)
# fix_citation_parent_aac()
## import corpora as cp
## cp.useElasticCorpus()
## cp.Corpus.connectCorpus("g:\\nlp\\phd\\aac")
## print(cp.Corpus.listPapers("year:>2010")[:100])
pass
def import_pmc_corpus():
"""
Do the importing of the PMC corpus
"""
def getPMC_corpus_id(filename):
"""
Returns the PMC id for a file
"""
## return os.path.split(filename)[1].replace("-paper.xml","").lower()
return ""
importer = CorpusImporter(reader=JATSXMLReader())
importer.collection_id = "PMC"
importer.import_id = "initial"
## importer.generate_corpus_id=getACL_corpus_id
## cp.useLocalCorpus()
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\pmc")
importer.importCorpus("g:\\nlp\\phd\\pmc\\inputXML", file_mask="*.nxml")
importer.updateInCollectionReferences(cp.Corpus.listPapers(), {})
from db.elastic_corpus import ElasticCorpus
import db.corpora as cp
from proc.general_utils import getRootDir
from multi.config import set_config
root_dir = getRootDir("aac")
cp.useElasticCorpus()
# cp.Corpus.connectCorpus(root_dir, endpoint=GCP_ENDPOINT)
cp.Corpus.connectCorpus(root_dir, endpoint=set_config("aws-server"))
print("")
# for index in ["scidocs", "papers", "venues", "cache", "authors", "links", "missing_references"]:
# for index in ["papers", "venues", "cache", "authors", "links", "missing_references"]:
# for index in ["papers", "venues", "cache", "authors", "links", "missing_references"]:
# if cp.Corpus.es.indices.exists(index):
# cp.Corpus.deleteIndex(index)
cp.Corpus.createAndInitializeDatabase()
# if cp.Corpus.es.indices.exists("scidocs"):
# cp.Corpus.deleteIndex("scidocs")
# settings = {
# "number_of_shards": 5,
# "number_of_replicas": 1
# }
# properties = {
# "scidoc": {"type": "string", "index": "no", "store": True, "doc_values": False},
# "guid": {"type": "string", "index": "not_analyzed", "store": True},
def main():
cp.useElasticCorpus()
cp.Corpus.connectCorpus("", endpoint={"host": ES_SERVER, "port": 9200})
annotate_one("c3eaadb3-0d3c-4d76-b485-83e8cb2af70f")
pass
def main():
cp.useElasticCorpus()
cp.Corpus.connectCorpus("g:\\nlp\\phd\\pmc_coresc",
endpoint={
"host": "129.215.90.202",
"port": 9200
})
## experiment["test_files"]=["456f8c80-9807-46a9-8455-cd4a7e346f9d"]
experiment["test_files"] = [
## "bdc9a118-cb76-4d26-9c4d-e886794428f5",
## "65a4319e-4324-4529-96fa-66c52e392da0",
## "0cc28fb0-b116-4990-b816-3dc066273c34",
## "ef3e4284-c527-4e83-9b59-f8996b09df76",
## "b3129460-d284-4f69-83a8-f87f588e7800",
## "d8548dab-ff28-4f93-b2ae-16887e59e8ad",
## "42efd8ec-4c06-4754-a527-3045eed87766",
## "f4374057-7ab2-4567-b73b-aa5b72328d3e",
## "cbf989c5-79f5-4317-8515-2192e2a3fe2a",
## "37d1cc24-68a5-4a36-b55d-94acdfad08c1",
## "2b5202ec-e71b-4d1a-8ef4-439c4f505342",
## "11ba9f31-13f8-4a40-8bfc-6c9c7725e7ba",
## "e047f55f-ff56-44a6-a07c-794887330752",
## "d39d353a-f9ca-4ce3-ab42-e9a16f5bd372",
## "a407716f-4516-4cba-9c52-d4e3b09bcda6",
## "680724b2-50e7-4809-a86f-e63326059f7e",
## "1ce857ab-7692-4a95-9ba0-f517179a940e",
## "e12b2e84-a91d-4170-88a6-6ba983ceab1b",
## "5a6c0a35-dbe0-486a-8edf-3c3d3638f06e",
## "c40d5876-208c-4eb4-b239-652ed14f8560",
## "9a764770-fd73-474e-8f38-cf0128371e2c",
## "54432fc8-c1c4-42f9-95b0-c5fad39f8317",
## "a7dab0f1-5891-4d83-92c2-d25069c49d27",
## "283ed90d-3ff9-4161-8c4d-4e55a555973e",
## "6478c6ca-e16c-473f-9f4c-060143b3cc8f",
## "666f2c58-3180-465b-877c-28d14cbcdf98",
## "f5dedb99-f2a1-4ae9-b4a0-3c23e33cbfc9",
## "e5ed924b-8b78-4c76-bb6c-54d9790c8a15",
## "b8ace4e7-8523-471f-847b-b45aee8ccfc1",
## "ff30447d-828e-4699-bbf7-ce586aae9764",
## "aec8d55c-43e0-42cb-b832-77f888c2325a",
## "067862a3-d8fd-4252-b831-f6f120af82a1",
## "64956609-5a4d-4e05-bad1-0445c3d1834d",
## "cd1cd1ec-ecc9-4e70-96b3-7f1447ec0df3",
## "d61b922b-622b-440c-b040-3db563fd6f0e",
## "51d71d97-5abb-4a4d-ba77-7d18a11343f0",
## "b4c2215a-0a38-4e44-a5ab-f0d0114d89fc",
## "d3265a02-86ba-47e9-879c-a15043ca5808",
## "3e53830f-33a5-4192-9159-bcd01a3e66d3",
## "be50acb5-e165-4afb-b259-eeb9f28d0f2e",
## "fb8e6675-46d9-41c8-8ba0-598842a63fe8",
## "34043f1e-3424-4c4a-b782-9489fc274db5",
## "e07f7715-d400-4958-a0ac-2e6dab3b1843",
## "44f52aea-cae3-4e1c-85ce-da0038cbcea1",
## "b99f5b2d-6edd-4787-a50d-fef7d030ff05",
## "55ef06b7-ffc6-4e43-9362-daf4b9f6735f",
## "89c63f73-988a-4ece-99fd-e6c91fc9f6fd",
## "83293e90-8f3e-45db-8dae-49a179568d3e",
## "7fbcd237-d40d-4d44-9c9f-7e2f462e547e",
## "5303a3a3-c1cd-458c-9bef-56df3080169d",
##
## "a3af153f-cf9d-40ea-b64d-c6e52e0a187b",
## "38a67959-7ad9-426d-8357-51ab376b7a4b",
## "5f71b848-2f22-49bf-b32a-c1cc441a6dbe",
## "e875b2dc-3757-4728-8e8b-47c6a1d8241c",
## "a1bd31a8-66bc-4be9-aa85-e2e821aa18f5",
## "ef030f01-cdcb-4aaf-8ec1-c1a8778095da",
## "5b4f7822-1127-4d45-84ca-6755e1debaab",
## "a11c9c92-e294-4dfc-8e73-f432ad460776",
## "6c647939-ef22-4d8b-b887-121272168829",
## "d4c97daa-790c-40fe-a17e-fffa8e7fbd36",
## "65ee7543-549d-4821-b51d-8fc27dbe85cb",
## "4fe8ae7f-47bf-41fd-95ef-14ee7831f37e",
## "45d1bde0-2bd5-413b-89e3-9151d5a73ffb",
## "7bedaa57-30ff-4569-8456-59236171a80f",
## "67a054e0-744e-477b-80a4-06a268064bc7",
## "51a51cbb-952b-450b-970e-f6a23ecf9ce6",
## "b14209b3-d868-41cd-b1d0-f1a1489220f3",
## "53230a94-3baf-4825-a039-e8125890e737",
## "bb576feb-658e-45ef-810a-617b586159e5",
## "7d5ad1b5-2f3d-4728-b583-ec1ebbc3dac6",
## "f27cf9db-0d2d-490b-9917-c076a5ebca2c",
## "3b6679e5-deae-43ee-a98b-cac7029e92f4",
## "0d44cbba-1989-4654-b250-1b41285359ea",
## "b0cceb78-5f66-4084-accb-171040521cda",
## "18bf7b21-2456-49da-882a-06032ec46bec",
## "588b99bd-c358-440b-b30c-e1f3dc10b96b",
## "c4c1d5c0-7f40-465a-bbdb-351b4c9948a8",
## "0efcb373-ecd3-4e10-9f2a-1bbd3a6cbf58",
## "9bb0db11-2821-4d55-8f34-9bfd5d58f444",
## "49ff3f83-b4d7-4979-800f-785460c95552",
## "58e0a5d1-6343-4e2e-b544-6f690bff023e",
## "5a84843d-d7b0-43b0-846c-d30d3196ee8a",
## "6f244f35-8f61-4eb9-9de0-dfbbca63532b",
## "9d3cf2ea-162b-4e78-a311-b7333ad65c3a",
## "75e80547-50f5-4a12-8db6-e799a2e5029b",
## "f6c70cbd-e6c3-4ea5-b99f-ac0c455d832a",
## "f72d2af0-1e8a-40f5-9acf-ddbe9ddc4a7b",
## "9a81337e-1280-4b11-9b00-7e516d298ea1",
## "c7a83006-6ed3-46ec-b476-c49770dc4979",
## "aa45f968-61a4-421d-a532-a036fb8336ef",
## "f38092bc-b1e2-4ba8-ad60-b964825e52ac",
## "a755f020-c04d-4640-8b43-fb63b560bd6e",
## "1f06edd3-09d6-4033-b65e-a96d6a78f748",
## "33273c27-bcb7-4a4d-b339-c8af16c97b91",
## "802d2b57-0425-410e-82b6-f0024bc6f0dd",
## "699e837a-a662-49a0-b4c5-b3ef113eff34",
## "5b019a09-e21f-4109-a757-2c8396c8f169",
## "d4a70f39-7c5c-4566-8b5c-72208f3929ea",
## "6cf4f22d-c77b-4f0a-9e3c-378d7803f62b",
## "5cc55656-d309-4906-9cbf-7e34e734c352",
## "4222902b-e5fc-4eef-a7a7-79ec85d8e7c0",
## "4e408d49-6e51-441a-9c1c-8720d0d7032a",
## "aba078e1-c385-45b2-9adf-1ab7901b373b",
## "772eaabf-8996-486f-9bba-355cbf0c15e1",
## "38048193-6565-45ea-9950-64c7e4c266a3",
## "4d36eeb9-9121-4510-847e-99b80c77473e",
## "7b9e39c9-18a3-4112-ba9f-36b70d60f60f",
## "e31fd474-e2a0-4b3c-9d36-b31003b3bbc6",
## "69b92870-e050-4277-bd00-08f79aa6d9e6",
## "a67be750-73dc-427b-ac6a-e46adcaf7430",
## "1f8a95c4-856a-4f39-9c45-52d309d8c075",
## "b3606482-e22f-4809-948a-385a4f1e47cb",
## "7a4a67fb-3f4c-4c26-a060-775e8a4b7480",
## "bc39625f-1bc1-49ce-8ea3-4f8debe90b01",
## "202ef49f-c3f9-4d3a-b971-2d8094c06242",
## "28967c8d-2584-4898-9b62-0bfc669e2490",
## "355d6857-06ee-4430-a511-aaf0e8eaf23d",
## "0ce7eebd-0815-4f5d-b1c9-fafb65584994",
## "870c4608-525d-44d1-960f-4eb73589618c",
## "2420a665-d848-459f-9f51-456275d42e8b",
## "deb5362b-af92-4973-970f-ebe3fec12ee9",
## "d244e4d9-808c-4abd-a627-02716e9609c9",
## "de3f08f8-12fa-41d1-82cc-30c2c43cf52e",
## "575e9d63-94d7-483d-a980-8c974afc0ad9",
## "31410622-7133-4472-8225-8cf6b1eb1683",
## "80f3bd59-c5d6-43a0-97e3-155c1af50275",
## "cf2e8b40-5fab-4b17-acb1-6b676d909aa6",
## "51375367-8a16-4070-bab7-5ebcca3427c4",
## "dc293831-b099-45e2-a9ef-87ec8ccb8722",
## "aad3943f-37f9-4774-aa77-f312650b699e",
## "cdf828fc-2fb5-4c6f-8b42-8ff7c8ff0ff0",
## "6569f681-77e3-4ceb-a956-04e7a751f2b3",
## "2edffd94-b1db-46b7-bc4b-e4da9dcf4f51",
## "45a922bc-814a-40bd-a76a-fcbeca77bc81",
## "be4f19f7-de07-4674-8f03-1fbec9c7dd04",
## "48d9f4cf-c081-4520-b350-6ca3142987a7",
## "f35243d1-a3e3-4402-99b3-e576a27cde0d",
## "e8f567f8-3179-4214-bcbc-79332c1cfd1d",
## "209e32f7-a3cd-4e86-afee-2935a1f25514",
## "1cd47a2c-58c1-4c89-a689-cbdc0dd1f6b7",
## "2c64d4d5-3883-4fee-8c2c-1c0afb3835cf",
## "1323e0b5-c986-4ca6-855a-0b147d938e50",
## "d293f62a-983f-4ddc-a227-84d82bb36af1",
## "5b6439c9-466d-4bc0-aff2-e85de8eb9337",
## "da2b0b43-26b1-458b-b57a-83279ceb314e",
## "c21e2afc-0f92-490d-aa1b-7f826a83221d",
## "c5e67372-cf98-45db-bb7a-e3f4e7662774",
## "c7f91884-cfc8-406b-919e-658008c21279",
## "753b9d9a-ce8d-4fba-ac74-106526416738",
## "799680bf-5150-4fb2-b9b6-91fd0edc2593",
]
exp = Experiment(experiment, options, True)
exp.run()
