def simulate(probabilities, n_mutations=1, n_sequences=50000):
'''
Simulates mutation of antibody sequences, given a list of mutations
and their probabilities.
Inputs
------
probabilities: a dictionary containing mutations as keys and probabilities
as values.
n_mutations: Number of mutations in each sequence. If n_mutations is
greater than 1, mutations are selected from the pool of supplied
mutations without replacement. Default is 1.
n_sequences: Number of mutated sequences to generate. Default is 50,000.
Returns
-------
Mean number of VRC01-like mutations (float) and 95% confidence interval
(tuple of floats).
'''
vrc01_muts = get_vrc01_class_mutations()
sim_muts = []
muts, probs = list(zip(*list(probabilities.items())))
start = datetime.now()
for i in range(n_sequences):
m = np.random.choice(muts, size=n_mutations, replace=False, p=probs)
sim_muts.append(m)
if (i + 1) % 100 == 0:
progress_bar(i + 1, n_sequences, start)
mut_counts = [len([x for x in sublist if x in vrc01_muts]) for sublist in sim_muts]
# calculate mean and 95% confidence interval
n, min_max, mean, var, skew, kurt = stats.describe(mut_counts)
std = np.sqrt(var)
R = stats.norm.interval(0.95, loc=mean, scale=std / np.sqrt(len(mut_counts)))
return mean, R
def monitor_update(results):
finished = 0
jobs = len(results)
while finished < jobs:
time.sleep(1)
finished = len([r for r in results if r.ready()])
progbar.progress_bar(finished, jobs)
progbar.progress_bar(finished, jobs)
def monitor_mp_jobs(results):
finished = 0
jobs = len(results)
while finished < jobs:
time.sleep(1)
ready = [ar for ar in results if ar.ready()]
finished = len(ready)
progbar.progress_bar(finished, jobs)
print('')
def monitor_mp_jobs(results):
finished = 0
jobs = len(results)
while finished < jobs:
time.sleep(1)
ready = [ar for ar in results if ar.ready()]
finished = len(ready)
progbar.progress_bar(finished, jobs)
print('')
def multiprocess_mongoimport(jsons, db, coll, args):
progbar.progress_bar(0, len(jsons))
async_results = []
p = mp.Pool()
for j in jsons:
async_results.append(p.apply_async(do_mongoimport, args=(j, args.ip, args.port, db, coll, args.user, args.password)))
monitor_results(async_results)
remove_temp_files(args)
print('')
def monitor_celery_jobs(results):
finished = 0
jobs = len(results)
while finished < jobs:
time.sleep(1)
succeeded = [ar for ar in results if ar.successful()]
failed = [ar for ar in results if ar.failed()]
finished = len(succeeded) + len(failed)
progbar.progress_bar(finished, jobs)
print('')
def monitor_celery_jobs(results):
finished = 0
jobs = len(results)
while finished < jobs:
time.sleep(1)
succeeded = [ar for ar in results if ar.successful()]
failed = [ar for ar in results if ar.failed()]
finished = len(succeeded) + len(failed)
progbar.progress_bar(finished, jobs)
print('')
def multiprocess_mongoimport(jsons, db, coll, args):
progbar.progress_bar(0, len(jsons))
async_results = []
p = mp.Pool()
for j in jsons:
async_results.append(
p.apply_async(do_mongoimport, args=(j, args.ip, args.port, db, coll, args.user, args.password))
)
monitor_results(async_results)
remove_temp_files(args)
print ("")
def update_db(db, standard, scores, collection, args):
db = mongodb.get_db(args.db, args.ip, args.port, args.user, args.password)
print_index_info()
mongodb.index(db, collection, ['seq_id'])
print_update_info()
start = time.time()
conn = mongodb.get_connection(args.ip, args.port,
args.user, args.password)
mongo_version = conn.server_info()['version']
standard = standard.replace('.', '_')
g = scores.groupby('identity')
groups = regroup(g.groups)
for g in range(0, len(groups), args.update_threads):
tlist = []
for group in groups[g:g + args.update_threads]:
t = Thread(target=update, args=(db, collection, group, standard, mongo_version, args))
t.start()
tlist.append(t)
for t in tlist:
t.join()
progbar.progress_bar(g + args.update_threads, len(groups))
# if platform.system().lower() == 'darwin' or args.debug or args.single_process_update:
# for i, group in enumerate(groups):
# update(db, collection, group, standard, mongo_version, args)
# progbar.progress_bar(i, len(groups))
# else:
# p = mp.Pool(processes=25)
# async_results = []
# for group in groups:
# async_results.append(p.apply_async(update, args=(db, collection, group, standard, mongo_version, args)))
# monitor_update(async_results)
# p.close()
# p.join()
print('')
run_time = time.time() - start
logger.info('Updating took {} seconds. ({} sequences per second)'.format(round(run_time, 2),
round(len(scores) / run_time, 1)))
