from conf_analysis.meg import artifacts, preprocessing
from conf_analysis.behavior import empirical, metadata, keymap
import mne, locale
import numpy as np
import pickle
locale.setlocale(locale.LC_ALL, "en_US")
result = {}
raw = mne.io.read_raw_ctf(filename, system_clock='ignore')
trials = preprocessing.blocks(raw, full_file_cache=True)
trl, bl = trials['trial'], trials['block']
bcnt = 0
for b in np.unique(bl):
if len(trl[bl == b]) >= 75:
result[b] = bcnt
bcnt += 1
print((b, bcnt))
return result
block_map = {}
for snum in range(1, 16):
filenames = [metadata.get_raw_filename(snum, b) for b in range(4)]
block_map[snum] = {}
for session, filename in enumerate(filenames):
block_map[snum][session] = executor.submit(do_one, filename)
diagnostics.progress(block_map)
block_map = executor.gather(block_map)
pickle.dump(block_map, open('blockmap.pickle', 'w'))
SCHEDULER_PORT = 5678
SCHEDULER_HTTP_PORT = 9786
SCHEDULER_BOKEH_PORT = 12345
SCHEDULER_IP = '127.0.0.1'
HOME_PAGE = 'http://localhost:5050'
def test(one, two):
return 4
executor = Executor('{}:{}'.format(SCHEDULER_IP, SCHEDULER_PORT))
taskclient.add_user(HOME_PAGE)
for _ in range(5):
taskclient.add_job(HOME_PAGE)
result_list = []
num_iters = 50
#result = executor.map(taskclient.worker, [HOME_PAGE, HOME_PAGE], [0, 1])
result = executor.map(taskclient.worker,
itertools.repeat(HOME_PAGE, num_iters), range(num_iters))
result_list = result
distributed.diagnostics.progress(result_list)
print()
print(executor.who_has(result_list))
sim_results = executor.gather(result_list)
print('---------')
class DistributedContext(object):
io_loop = None
io_thread = None
def __init__(self,
ip="127.0.0.1",
port=8787,
spawn_workers=0,
write_partial_results=None,
track_progress=False,
time_limit=None,
job_observer=None):
"""
:type ip: string
:type port: int
:type spawn_workers: int
:type write_partial_results: int
:type track_progress: bool
:type time_limit: int
:type job_observer: JobObserver
"""
self.worker_count = spawn_workers
self.ip = ip
self.port = port
self.active = False
self.write_partial_results = write_partial_results
self.track_progress = track_progress
self.execution_count = 0
self.timeout = TimeoutManager(time_limit) if time_limit else None
self.job_observer = job_observer
if not DistributedContext.io_loop:
DistributedContext.io_loop = IOLoop()
DistributedContext.io_thread = Thread(
target=DistributedContext.io_loop.start)
DistributedContext.io_thread.daemon = True
DistributedContext.io_thread.start()
if spawn_workers > 0:
self.scheduler = self._create_scheduler()
self.workers = [self._create_worker()
for i in xrange(spawn_workers)]
time.sleep(0.5) # wait for workers to spawn
self.executor = Executor((ip, port))
def run(self, domain,
worker_reduce_fn, worker_reduce_init,
global_reduce_fn, global_reduce_init):
size = domain.steps
assert size is not None # TODO: Iterators without size
workers = 0
for name, value in self.executor.ncores().items():
workers += value
if workers == 0:
raise Exception("There are no workers")
batch_count = workers * 4
batch_size = max(int(round(size / float(batch_count))), 1)
batches = self._create_batches(batch_size, size, domain,
worker_reduce_fn, worker_reduce_init)
logging.info("Qit: starting {} batches with size {}".format(
batch_count, batch_size))
if self.job_observer:
self.job_observer.on_computation_start(batch_count, batch_size)
futures = self.executor.map(process_batch, batches)
if self.track_progress:
distributed.diagnostics.progress(futures)
if self.write_partial_results is not None:
result_saver = ResultSaver(self.execution_count,
self.write_partial_results)
else:
result_saver = None
timeouted = False
results = []
for future in as_completed(futures):
job = future.result()
if result_saver:
result_saver.handle_result(job.result)
if self.job_observer:
self.job_observer.on_job_completed(job)
results.append(job.result)
if self.timeout and self.timeout.is_finished():
logging.info("Qit: timeouted after {} seconds".format(
self.timeout.timeout))
timeouted = True
break
# order results
if not timeouted:
results = [j.result for j in self.executor.gather(futures)]
self.execution_count += 1
if worker_reduce_fn is None:
results = list(itertools.chain.from_iterable(results))
logging.info("Qit: finished run with size {} (taking {})".format(
len(results), domain.size))
results = results[:domain.size] # trim results to required size
if global_reduce_fn is None:
return results
else:
if global_reduce_init is None:
return reduce(global_reduce_fn, results)
else:
return reduce(global_reduce_fn, results, global_reduce_init)
def _create_scheduler(self):
scheduler = Scheduler(ip=self.ip)
scheduler.start(self.port)
return scheduler
def _create_worker(self):
worker = Worker(scheduler_ip=self.ip,
scheduler_port=self.port,
ncores=1)
worker.start(0)
return worker
def _create_batches(self, batch_size, size,
domain,
worker_reduce_fn,
worker_reduce_init):
batches = []
i = 0
while True:
new = i + batch_size
if i + batch_size <= size:
batches.append((domain, i, batch_size,
worker_reduce_fn, worker_reduce_init))
i = new
if new == size:
break
else:
batches.append((domain, i, size - i,
worker_reduce_fn, worker_reduce_init))
break
return batches
import argparse
def inject(url):
import config
from pymongo import MongoClient
import os
#connstring = config.db['host'] + ":" + config.db['port']
run = "sqlmap -u " + url + ' --batch '
command = os.popen(run).read()
data = {'url': url, 'output': command}
client = MongoClient('192.168.1.14:27017')
dbn = config.db['dbname']
db = client[dbn]
results = db.results
results.insert(data)
return command
executor = Executor()
parser = argparse.ArgumentParser()
parser.add_argument("-f")
args = parser.parse_args()
links = []
s = open(args.f, 'r')
for x in s:
links.append(x.rstrip().replace("\n", ""))
job = executor.map(inject, links)
print executor.gather(job)
if (not args.append) and (os.path.exists(out_path)):
shutil.rmtree(out_path)
if not os.path.exists(out_path):
os.makedirs(os.path.join(out_path, 'feat/df'))
os.makedirs(os.path.join(out_path, 'feat/desc'))
odDicts = [{
'flight_hdf': args.uvan,
'img_num': ii,
'kp_det_func': kp_type_dict[kp_type],
'kp_desc_func': kp_type_dict[kp_type],
'p_meta': tf_meta,
'o_path': out_path
} for ii in range(num_imgs)]
r = executor.map(extract_kp_from_frame, odDicts, pure=False)
kp_list = executor.gather(r)
kp_meta = pd.DataFrame(kp_list,
columns=[
'num_feat', 'center_lon', 'center_lat',
'df_path', 'desc_path', 'flight', 'img_num'
])
kp_meta.to_hdf(os.path.join(out_path, 'feat_meta.hdf'),
key='feat_meta')
print('what')
