def betameain():
studies, prefs = meta_studies(path="../config.json")
indices = [[2, 5, 6], [1, 3, 4]]
for study, idx in zip(studies, indices):
print(idx)
chnk_iterator = chunker.init(study, pickle=False)
corr_chnk = client.map(operation.check_n_correct, chnk_iterator)
def add_main():
# returns 'study' object with metadata as attributes
studies, prefs = meta_studies(path="../config.json")
for study in studies:
chnk_iterator = chunker.init(study, pickle=False)
for chnk in chnk_iterator:
corr_chnk = operation.check_n_correct(chnk)
lift_chnk = operation.liftover(corr_chnk)
ref_chnk = operation.reference(lift_chnk)
operation.write_db(ref_chnk)
db = redis.StrictRedis(db=8)
print(db.dbsize())
def run():
config_path = "/home/sevvy/PycharmProjects/CardioDiscover/test_config.json"
studies = reader.meta_studies(config_path)
for study in studies:
study = check_n_correct.init()
# # check and correct the GWAS file
# study = check_n_correct.init_check_correct(study)
# # make iterator in liftover and 1000ref
# study = liftover.iterator()
# stuy = reference_check.iterator()
# study = ''
# #call the stuff
def dif_main():
# returns 'study' object with metadata as attributes
studies, prefs = meta_studies(path="../config.json")
for study in studies:
chnk_iterator = chunker.init(study, pickle=False)
corr_chnk = delayed(operation.check_n_correct)(chnk_iterator)
lift_chnk = delayed(operation.liftover)(corr_chnk)
ref_chnk = delayed(operation.reference)(lift_chnk)
db_insert = delayed(operation.write_db)(ref_chnk)
db_insert.compute()
db = redis.StrictRedis(db=8)
print(db.dbsize())
def main():
# returns 'study' object with metadata as attributes
studies, prefs = meta_studies(path="../config.json")
def study_tasker(study):
chnk_iterator = client.submit(chunker.init, study, pickle=False)
corr_chnk = client.map(operation.check_n_correct, chnk_iterator)
lift_chnk = client.map(operation.liftover, corr_chnk)
ref_chnk = client.map(operation.reference, lift_chnk)
client.map(operation.write_db, ref_chnk)
start = client.map(study_tasker, studies)
db = redis.StrictRedis(db=8)
print(db.dbsize())
def main():
# original doc containing the metadata
meta_doc = read_meta(path="")
# returns 'study' object with metadata as attributes
files = meta_studies(path="")
for this_study in files:
# read the study file, find separator, create
GWASin = reader.init_reader(this_study)
# do something with headers, write to df or something
classifier.init_classifier(GWASin)
# additional meta data about study to perform update
add_meta = checker.init_check_correct(GWASin, this_study)
meta_doc = update_meta(meta_doc, add_meta)
write_meta(meta_doc)
def alt_main():
studies, prefs = meta_studies(path="../config.json")
study = studies[0]
chnk_iterator = chunker.init(study, pickle=False)
corr_chnk = client.map(operation.check_n_correct, chnk_iterator)
lift_chnk = client.map(operation.liftover, corr_chnk)
ref_chnk = client.map(operation.reference, lift_chnk)
for future in as_completed(ref_chnk):
print(future)
chnk = future.result()
db_submit = client.submit(operation.write_db, chnk)
db = redis.StrictRedis(db=8)
print(db.dbsize())
def an_main():
studies, prefs = meta_studies(path="../config.json")
indices = [[2, 5, 6], [1, 3, 4]]
for study, idx in zip(studies, indices):
print(idx)
chnk_iterator = chunker.init(study, pickle=False)
corr_chnk = client.map(operation.check_n_correct, chnk_iterator)
lift_chnk = client.map(operation.liftover, corr_chnk)
ref_chnk = client.map(operation.reference, lift_chnk)
for future in as_completed(ref_chnk):
print(future)
chnk = future.result()
client.submit(operation.write_db, chnk)
db = redis.StrictRedis(db=8)
print(db.dbsize())