def main_random(args):
locations = sorted(glob(os.path.join(args.in_path, args.glob)))
locations = [os.path.abspath(loc) for loc in locations]
pool = multiprocessing.Pool(args.nproc)
bar = ProgressBar(maxval=args.rnd_num, widgets=[Timer(), " ",
SimpleProgress(), " ", Percentage(), " ", Bar(), " ", ETA()])
rewire_name = "grn_rewired_{0:.1f}.pkl".format(args.prob)
switch_name = "grn_switched_{0:d}.pkl".format(args.flip_num)
for path in locations:
filename = os.path.join(path, "trn.pkl")
if not os.path.exists(filename):
continue
ver = version_from_path(path)
base_path = os.path.join(args.out_path, ver)
if not os.path.isdir(base_path):
os.makedirs(base_path)
LOGGER.info(ver)
trn = pyorg.read_pickle(filename)
# we consider null-models on the projected level since that is the level
# on which we evaluate topological quantities
net = pyreg.to_simple(trn.to_grn())
if args.run_rewire:
LOGGER.info("Rewiring with probability %.1f", args.prob)
tasks = [(net, args.prob)] * args.rnd_num
res_it = pool.imap_unordered(rewire, tasks)
rands = list()
bar.start()
for rnd in res_it:
rands.append(rnd)
bar += 1
bar.finish()
pyorg.write_pickle(rands, os.path.join(base_path, rewire_name))
if args.run_switch:
LOGGER.info("Switch-randomizing each edge %d times", args.flip_num)
tasks = [(net, args.flip_num)] * args.rnd_num
res_it = pool.imap_unordered(switch, tasks)
success = list()
rands = list()
bar.start()
for (rnd, rate) in res_it:
rands.append(rnd)
success.append(rate)
bar += 1
bar.finish()
pyorg.write_pickle(rands, os.path.join(base_path, switch_name))
LOGGER.info("mean flip success rate: %.3G +/- %.3G", np.mean(success),
np.std(success))
pool.close()
def main_analysis(args):
locations = sorted(glob(os.path.join(args.in_path, args.glob)))
locations = [os.path.abspath(loc) for loc in locations]
tasks = list()
if args.run_rewire:
tasks.extend([(loc, "rewired", args.choose, args.prob) for loc in locations])
if args.run_switch:
tasks.extend([(loc, "switch", args.choose) for loc in locations])
out_name = os.path.join(args.out_path, args.file)
if not os.path.isdir(args.out_path):
os.makedirs(args.out_path)
if os.path.exists(out_name):
result = pd.read_csv(out_name, sep=str(";"), dtype={"version": str},
encoding=args.encoding)
else:
result = pd.DataFrame(columns=["version", "num_components",
"largest_component", "feed_forward", "feedback", "cycles",
"regulated_in_deg", "regulating_out_deg", "null_model"])
pool = multiprocessing.Pool(args.nproc)
rewire_name = "grn_rewired_{0:.1f}.pkl".format(args.prob)
rewire_descr = "rewired {0:.1f}".format(args.prob)
switch_name = "grn_switched_{0:d}.pkl".format(args.flip_num)
switch_descr = "switch {0:d}".format(args.flip_num)
for path in locations:
ver = version_from_path(path)
LOGGER.info(ver)
if args.run_rewire:
filename = os.path.join(path, rewire_name)
if os.path.exists(filename):
LOGGER.info("Analyzing rewired networks (probability %.1f)",
args.prob)
nets = pyorg.read_pickle(filename)
if args.choose is not None:
chosen_num = min(args.choose, len(nets))
LOGGER.info("Using %d/%d random networks", chosen_num,
len(nets))
nets = random.sample(nets, chosen_num)
tasks = [(net, ver, rewire_descr) for net in nets]
res_it = pool.imap_unordered(stats, tasks)
frames = list()
bar = ProgressBar(maxval=len(tasks), widgets=[Timer(), " ",
SimpleProgress(), " ", Percentage(), " ", Bar(), " ",
ETA()]).start()
for df in res_it:
frames.append(df)
bar += 1
bar.finish()
result = result.append(pd.concat(frames, ignore_index=True),
ignore_index=True)
result.to_csv(out_name, header=True, index=False,
sep=str(";"), encoding=args.encoding)
if args.run_switch:
filename = os.path.join(path, switch_name)
if os.path.exists(filename):
LOGGER.info("Analyzing switched networks (flip number %d)",
args.flip_num)
nets = pyorg.read_pickle(filename)
if args.choose is not None:
chosen_num = min(args.choose, len(nets))
LOGGER.info("Using %d/%d random networks", chosen_num, len(nets))
nets = random.sample(nets, chosen_num)
tasks = [(net, ver, switch_descr) for net in nets]
res_it = pool.imap_unordered(stats, tasks)
frames = list()
bar = ProgressBar(maxval=len(tasks), widgets=[Timer(), " ",
SimpleProgress(), " ", Percentage(), " ", Bar(), " ",
ETA()]).start()
for df in res_it:
frames.append(df)
bar += 1
bar.finish()
result = result.append(pd.concat(frames, ignore_index=True),
ignore_index=True)
result.to_csv(out_name, header=True, index=False,
sep=str(";"), encoding=args.encoding)
