def temp_dir_verify(options_parser, options, master_logger):
"""
If a base temporary directory has been specified, make sure it exists or
can be created.
"""
if options.temp_dir is not None:
util.make_dir(options.temp_dir)
def temp_dir_verify(options_parser, options, master_logger):
"""
If a base temporary directory has been specified, make sure it exists or
can be created.
"""
if options.temp_dir is not None:
util.make_dir(options.temp_dir)
def create_state(self, ts, flow, isn):
new_state = flow_state()
new_state.flow = flow
new_state.isn = isn
new_state.ts = ts
new_state.last_access = self.current_time + 1
self.current_time += 1
index = self.fhash(new_state.flow)
# figure out which honeypot belongs to this flow
hps = self.hs.getOptions()["honeypots"]
ips = [flow.src, flow.dst]
new_state.honeypot = [i for i in hps if i in ips][0]
if new_state.honeypot == flow.src:
new_state.direction = "incoming"
else:
new_state.direction = "outgoing"
# path and name the file appropriately
new_state.outdir = self.outdir % new_state.honeypot
new_state.outdir += "/" + new_state.direction
make_dir(new_state.outdir)
new_state.fname = self._flow_filename(new_state)
if index in self.flow_hash:
tmp = self.flow_hash[index]
new_state.next = tmp
self.flow_hash[index] = new_state
else:
self.flow_hash[index] = new_state
return new_state
def setOutdir(self, dir):
self.outdir = dir
self.states.setOutdir(dir)
hps = self.hs.getOptions()["honeypots"]
for i in hps:
o = self.outdir % i
make_dir(o)
def setOutdir(self, dir):
self.outdir = dir
self.states.setOutdir(dir)
hps = self.hs.getOptions()["honeypots"]
for i in hps:
o = self.outdir % i
make_dir(o)
def parse_args():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('data_file', nargs='+', help='path(s) to data file(s) to be encoded and/or sampled',
metavar='DATA'),
parser.add_argument('-o', '--outdir', dest='out_dir', default='.',
help='directory where to save output files', metavar='DIR')
parser.add_argument('-v', '--val', dest='val', type=util.ratio, default=0, metavar='RATIO',
help='use this fraction of the data as a validation data-set')
parser.add_argument('-t', '--test', dest='test', type=util.ratio, default=0, metavar='RATIO',
help='use this fraction of the data as a test data-set')
parser.add_argument('-E', '--encoder', dest='encoder', metavar='FILE', default=None,
help='configuration file for numeric encoding. The encoding is performed over the training'
'data-set only. The test and validation data-sets are generated by mapping their raw'
'values to the corresponding codification in the training data-set')
parser.add_argument('--id', dest='id', metavar='ID', default=None,
help='identifier used to name the output files (e.g., ID_train.csv, ID_val.csv, ID_test.csv)')
parser.add_argument('--seed', dest='seed', metavar='S', type=util.uint, default=None,
help='specify an RNG integer seed')
options = parser.parse_args()
assert options.encoder is not None or options.val > 0 or options.test > 0
util.make_dir(options.out_dir)
if options.seed is not None:
random.seed(options.seed)
return options
def exact_solve(structure, system_size, fill, interaction_shape,
interaction_radius, path):
radius_dir_path = '{}/radius_{}'.format(path, interaction_radius)
util.make_dir(radius_dir_path)
start = time.time()
prob = maxcut.initialize_problem(structure, system_size, fill,
interaction_shape, interaction_radius)
min_energy, num_ground_states, sample_ground_state, num_total_states, all_energies = classical_algorithms.BruteForce(
).solve(prob)
if structure != 'free':
with open('{}/ground_state_partitions.csv'.format(radius_dir_path),
'w') as output_file:
header = sample_ground_state.keys()
dict_writer = csv.DictWriter(output_file, header)
dict_writer.writeheader()
dict_writer.writerow(sample_ground_state)
with open('{}/energies.csv'.format(radius_dir_path), 'w') as output_file:
writer = csv.writer(output_file)
writer.writerow(['energy'])
min_gap = float('inf')
prev_energy = 0.0
for energy in sorted(all_energies):
writer.writerow([energy])
if energy != prev_energy:
min_gap = min(abs(energy - prev_energy), min_gap)
prev_energy = energy
end = time.time()
return {
'radius': interaction_radius,
'min energy': min_energy,
'# ground states': num_ground_states,
'# states': num_total_states,
'min energy gap': min_gap,
'runtime': end - start
}
def setOutdir(self, dir):
"""
Set output directory for IRC log
If you just want output to stdout, don't call this function
"""
make_dir(dir)
self.dir = dir
def create_state(self, ts, flow, isn):
new_state = flow_state()
new_state.flow = flow
new_state.isn = isn
new_state.ts = ts
new_state.last_access = self.current_time+1
self.current_time +=1
index = self.fhash(new_state.flow)
# figure out which honeypot belongs to this flow
hps = self.hs.getOptions()["honeypots"]
ips = [flow.src, flow.dst]
new_state.honeypot = [i for i in hps if i in ips][0]
if new_state.honeypot == flow.src:
new_state.direction = "incoming"
else:
new_state.direction = "outgoing"
# path and name the file appropriately
new_state.outdir = self.outdir % new_state.honeypot
new_state.outdir += "/"+new_state.direction
make_dir(new_state.outdir)
new_state.fname = self._flow_filename(new_state)
if index in self.flow_hash:
tmp = self.flow_hash[index]
new_state.next = tmp
self.flow_hash[index] = new_state
else:
self.flow_hash[index] = new_state
return new_state
def mount_cloud(source: str, tmp: str = "~/tmp", mode='r') -> MountedFile:
if mode == 'r':
result = urllib.parse.urlparse(source)
bucket_name = result.netloc
key_name = result.path[1:]
# only useful to reads
# response = s3.head_object(Bucket=bucket_name, Key=key_name)
# size = response['ContentLength']
# print(size)
prefix = os.path.dirname(key_name)
local_dir = os.path.join(tmp, bucket_name, prefix)
local_dir = os.path.expanduser(local_dir)
local_dir = os.path.abspath(local_dir)
local_file = os.path.join(local_dir, os.path.basename(key_name))
if not os.path.exists(local_file):
util.make_dir(local_dir)
command = 'goofys --debug_s3 {0}:/{1} {2}'.format(
bucket_name, prefix, local_dir)
i = util.run(command)
return MountedFile(local_file, mode)
elif mode == 'w':
import tempfile
mktemp = tempfile.mktemp("temptocloud", dir=tmp)
return MountedFile(mktemp, mode)
def setOutdir(self, dir):
"""
Set output directory for IRC log
If you just want output to stdout, don't call this function
"""
make_dir(dir)
self.dir = dir
def parse_args():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('data_file', help='path to data file to be encoded and/or divided by windows', metavar='DATA'),
parser.add_argument('-o', '--outdir', dest='out_dir', default='.',
help='directory where to save output files', metavar='DIR')
parser.add_argument('-W', '--window', dest='width', metavar='W', type=util.ufloat, default=120,
help='Sliding window width (train + test) in hours')
parser.add_argument('-w', '--test-window', dest='test_width', metavar='W', type=util.ufloat, default=24,
help='Test sliding window width in hours')
parser.add_argument('-S', '--shift', dest='shift', metavar='S', type=util.ufloat, default=24,
help='Sliding window shift in hours')
parser.add_argument('-E', '--encoder', dest='encoder', metavar='FILE', default=None,
help='configuration file for numeric encoding. The encoding is performed over the training'
'data-set only. The test and validation data-sets are generated by mapping their raw'
'values to the corresponding codification in the training data-set')
parser.add_argument('--id', dest='id', metavar='ID', default='windows',
help='identifier used to name the output files '
'(e.g., ID_train(1).csv, ID_test(1).csv, where 1 is the window index)')
parser.add_argument('--seed', dest='seed', metavar='S', type=util.uint, default=None,
help='specify an RNG integer seed')
options = parser.parse_args()
util.make_dir(options.out_dir)
if options.seed is not None:
random.seed(options.seed)
return options
def param_search(structure, system_size, fill, interaction_shape,
interaction_radius, ensemble, path, exact_min_energy,
exact_min_gap):
radius_dir_path = '{}/radius_{}'.format(path, interaction_radius)
util.make_dir(radius_dir_path)
start = time.time()
algorithm = classical_algorithms.SimulatedAnnealing()
cooling_schedules = maxcut.get_cooling_schedules(
problem=maxcut.initialize_problem(structure, system_size, fill,
interaction_shape,
interaction_radius))
radius_sols = []
sample_best_probs = {}
for init_temp, cool_rate in cooling_schedules:
radius_sols.append(
run_trials(structure, system_size, fill, interaction_shape,
interaction_radius, algorithm, ensemble,
radius_dir_path, exact_min_energy, exact_min_gap,
init_temp, cool_rate, sample_best_probs))
with open('{}/param_results.csv'.format(radius_dir_path),
'w') as output_file:
header = radius_sols[0].keys()
dict_writer = csv.DictWriter(output_file, header)
dict_writer.writeheader()
dict_writer.writerows(radius_sols)
opt_sol = min(radius_sols, key=lambda sol: sol['step_from_exact'])
opt_init_temp = opt_sol['init_temp']
opt_cool_rate = opt_sol['cool_rate']
opt_step_from_exact = opt_sol['step_from_exact']
opt_step_from_entropy = opt_sol['step_from_entropy']
opt_prob_ground_state_per_run = opt_sol['prob_ground_state_per_run']
if structure != 'free':
sample_best_prob = sample_best_probs[(opt_init_temp, opt_cool_rate)]
sample_best_partition_hist = sample_best_prob.get_partition_history()
sample_best_energy_hist = sample_best_prob.get_energy_history()
with open('{}/ground_state_partitions.csv'.format(radius_dir_path),
'w') as output_file:
header = sample_best_partition_hist[0].keys()
dict_writer = csv.DictWriter(output_file, header)
dict_writer.writeheader()
dict_writer.writerows(sample_best_partition_hist)
with open('{}/ground_state_energies.csv'.format(radius_dir_path),
'w') as output_file:
writer = csv.writer(output_file)
writer.writerow(['energy'])
for energy in sample_best_energy_hist:
writer.writerow([energy])
end = time.time()
return dict(interaction_radius=interaction_radius,
init_temp=opt_init_temp,
cool_rate=opt_cool_rate,
step_from_exact=opt_step_from_exact,
step_from_entropy=opt_step_from_entropy,
prob_ground_state_per_run=opt_prob_ground_state_per_run,
search_runtime=(end - start),
exact_min_energy=exact_min_energy)
def __init__(self, keyword, save_dir):
options = Options()
options.add_argument("--headless")
self.browser = webdriver.Chrome(ChromeDriverManager().install(),
options=options)
self.keyword = keyword
self.save_dir = save_dir
make_dir(f'./{save_dir}')
def __init__(self, urls):
self.sizes = [480, 960, 1366, 1920]
self.browser_height = 1027
self.browser = webdriver.Chrome(ChromeDriverManager().install())
self.browser.maximize_window()
self.urls = urls
self.dir = 'responsive_test_result'
make_dir(f'./{self.dir}')
def save(self, file_path):
util.make_dir(file_path=file_path)
with open(file_path, 'w') as file:
writer = csv.writer(file, quoting=csv.QUOTE_NONNUMERIC)
header = ([self.timestamp_label] if self.has_timestamps else []) + [self.target_label] + self.input_labels
writer.writerow(header)
for inputs, target, timestamp in zip(self.inputs, self.targets, self.timestamps):
row = ([timestamp] if self.has_timestamps else []) + [target] + list(inputs)
writer.writerow(row)
def execute(in_dir, out_dir, record_id, algorithms, feature_selection, survival, oversampling, undersampling):
'''executes the learning task on the data in in_dir with the algorithms in algorithms.
The results are written to out_dir and subdirectories,
and the record_ and target_ids are used to differentiate attributes and non-attributes'''
print ('### executing learning algorithms on... ###')
# get the files
files = util.list_dir_csv(in_dir)
# stop if no files found
if not files:
print ('No appropriate csv files found. Select an input directory with appropriate files')
return
files_test = files
# create directory
util.make_dir(out_dir)
# execute each algorithm
for alg in algorithms:
print ('...{}'.format(alg))
util.make_dir(out_dir+'/'+alg+'/')
results_list = []
# list which will contain the results
# run algorithm alg for each file f
for f, f_test in zip(files,files_test):
fname = in_out.get_file_name(f, extension=False)
print (' ...{}'.format(fname))
# get data, split in features/target. If invalid stuff happened --> exit
X, y, headers, target_list = in_out.import_data(f, record_id, survival) # assumption: first column is patientnumber and is pruned, last is target
if type(X) == bool: return
print (' ...instances: {}, attributes: {}'.format(X.shape[0], X.shape[1]))
# train model and return model and best features
model, best_features, results = execute_with_algorithm(alg, X, y, fname, headers, out_dir+'/'+alg+'/', record_id, feature_selection, oversampling, survival, undersampling)
results_list.append(results)
try:
in_out.save_ROC(out_dir+'/'+alg+'/'+"roc.png", results_list, title='ROC curve')
except IndexError:
pass
try:
in_out.save_ROC(out_dir+'/'+alg+'_test/'+"roc.png", results_list2, title='ROC curve')
except NameError:
pass
# notify user
print ('## Learning Finished ##')
def dump_replay_memory_images_to_disk(self, directory):
print ">dump_replay_memory_images_to_disk", directory
util.make_dir(directory)
for idx in range(self.state_buffer_size):
if idx == 0:
pass # dummy zero state
elif idx in self.state_free_slots:
print "idx", idx, "in free slots; ignore"
else:
with open("%s/%05d.png" % (directory, idx), "wb") as f:
f.write(util.rgb_to_png_bytes(self.state[idx]))
def archive(self, data_type, file_names):
ds = datetime.now().strftime("%Y-%m-%d")
ts = datetime.now().strftime("%Y-%m-%dT%H%M%S")
ark_dir = "{}/archive/{}/{}".format(self.data_dir, data_type, ds)
make_dir(ark_dir)
files = [file_names] if type(file_names) is not list else file_names
for file in files:
_, naked_file_name, stem, ext = parse_file_name(file)
ark_file_name = "{}/{}--{}.{}".format(ark_dir, stem, ts, ext)
copyfile(file, ark_file_name)
self.log.write("INFO [{}] {} archived to {}".format(
data_type, naked_file_name, ark_file_name))
def write_data(base_dir, categories):
'''
Writes the text data in according to the
above defined category document structure.
'''
util.make_dir(base_dir)
for cat in categories:
util.make_dir(base_dir + '/' + cat)
for doc in categories[cat]:
new = '{}/{}/{}'.format(base_dir, cat, doc + '.txt')
util.write_file(new, categories[cat][doc].encode('utf-8'))
return True
def archive_v1(self,
data_type,
json_file_name,
csv_file_name,
copy_to_archive=True,
copy_to_current=True,
copy_to_master=True,
excelize=False,
xlsx_formats=None):
if copy_to_archive:
# make json/csv copies for archive
ds = datetime.now().strftime("%Y-%m-%d")
ts = datetime.now().strftime("%Y-%m-%dT%H%M%S")
ark_dir = "{}/archive/{}/{}".format(self.data_dir, data_type, ds)
make_dir(ark_dir)
ark_file_name = "{}/{}--{}.json".format(ark_dir, data_type, ts)
copyfile(json_file_name, ark_file_name)
self.log.write("INFO [{}] JSON archived to {}".format(
data_type, ark_file_name))
ark_file_name = "{}/{}--{}.csv".format(ark_dir, data_type, ts)
copyfile(csv_file_name, ark_file_name)
self.log.write("INFO [{}] CSV archived to {}".format(
data_type, ark_file_name))
if copy_to_current:
# copy csv to current
target_file_name = csv_file_name.replace("/processing/default/",
"/current/")
if os.path.exists(target_file_name):
os.remove(target_file_name)
copyfile(csv_file_name, target_file_name)
self.log.write("INFO [{}] CSV copied to {}".format(
data_type, target_file_name))
if excelize:
# make xlsx copy
formats = [] if xlsx_formats is None else xlsx_formats
xlsx_file_name = csv_2_xlsx(target_file_name, "", formats)
self.log.write("INFO [{}] CSV re-saved as XLSX {})".format(
data_type, xlsx_file_name))
if copy_to_master:
# copy csv to master
target_file_name = csv_file_name.replace("/processing/default/",
"/master/")
if os.path.exists(target_file_name):
os.remove(target_file_name)
copyfile(csv_file_name, target_file_name)
self.log.write("INFO [{}] CSV copied to {}".format(
data_type, target_file_name))
def set_dirs(PATHS):
MOVEEXEC = True
PATHS['BUILD'] = 'SRC'
PATHS['PROB'] = os.getcwd()
for n in range(len(sys.argv)):
if sys.argv[n] == '-dir' and n < len(sys.argv) - 1:
MOVEEXEC = False
PATHS['BUILD'] = util.sanitize_path(sys.argv[n+1])
PATHS['SRC'] = os.path.join(PATHS['BUILD'], 'source','')
for key in list(PATHS):
if PATHS[key][0] != '/':
PATHS[key] = os.path.join(PATHS['PROB'], PATHS[key], '')
util.make_dir(PATHS[key])
return MOVEEXEC
def main():
usage = 'usage: %prog [options] <motifs> <cell_line>'
parser = OptionParser(usage)
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide motifs and cell line.')
else:
motifs = args[0].split(',')
cell_line = int(args[1])
print('Processing')
print(motifs)
# load and get model layer
run_path = 'paper_runs/new_models/32_res/run-20211023_095131-w6okxt01'
layer = -3
model, bin_size = read_model(run_path, compile_model=False)
aux_model = tf.keras.Model(inputs=model.inputs,
outputs=model.layers[layer].output)
output_dir = util.make_dir('paper_GIA_csvs')
# load and threshold data
testset, targets = tfr_evaluate.collect_whole_testset(coords=True)
C, X, Y = util.convert_tfr_to_np(testset, 3)
for testset_type in [
'all threshold', 'cell line low coverage',
'cell line high coverage'
]:
print(testset_type)
selected_X = select_set(testset_type, C, X, Y, cell_line=cell_line)
gi = quant_GIA.GlobalImportance(model, targets)
gi.occlude_all_motif_instances(selected_X, motifs, func='mean')
df = gi.summary_remove_motifs[0]
file_prefix = '{}_in_{}_{}'.format(df['motif pattern'].values[0],
targets[cell_line], testset_type)
df.to_csv(os.path.join(output_dir, file_prefix + 'csv'), index=None)
def initUI(self):
self.ui.setWindowTitle('Visualization')
self.ui.show()
make_dir("./results")
logTextBox = QTextEditLogger(self.plainTextEdit)
# You can format what is printed to text box
logTextBox.setFormatter(
logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
logging.getLogger().addHandler(logTextBox)
# You can control the logging level
logging.getLogger().setLevel(logging.DEBUG)
self.input_btn.clicked.connect(self.get_input_image)
self.cls_btn.clicked.connect(self.selc_cls)
self.start_btn.clicked.connect(self.start)
def categorize_20newsgroup(base_dir):
corpus = \
fetch_20newsgroups(subset='train',
remove=('headers', 'footers', 'quotes'))
util.make_dir(base_dir)
for cat in range(20):
util.make_dir('{}/{}'.format(base_dir, 'c' + str(cat)))
for index in range(len(corpus.data)):
new = '{}/{}/{}'.format(base_dir,
'c' + str(list(corpus.target)[index]),
str(index) + '.txt')
util.write_file(new, corpus.data[index].encode('utf-8'))
return True
def setup_directories(self):
if self.local_dir and 'symlink' == self.link_type:
if not os.path.islink(self.package_dir) and os.path.isdir(self.package_dir):
shutil.rmtree(self.package_dir)
util.symlink(self.package_dir, self.local_dir)
else:
if os.path.islink(self.package_dir):
os.unlink(self.package_dir)
util.make_dir(self.package_dir)
if self.version_dir:
if os.path.islink(self.version_package_dir) or os.path.isfile(self.version_package_dir):
os.unlink(self.version_package_dir)
elif os.path.isdir(self.version_package_dir):
shutil.rmtree(self.version_package_dir)
if 'symlink' == self.link_type:
util.symlink(self.version_package_dir, os.path.relpath(self.package_dir, self.version_dir))
else: # hardlink
util.make_dir(self.version_package_dir)
def inference(cf, data_path, USE_CUDA):
checkpoints_dir = cf['data']['checkpoints_dir']
checkpoints_dir = make_dir(checkpoints_dir, cf)
files = os.listdir(data_path)
for f in files:
if f[-3:] == "mp4" and "no_ois" not in f and "no_shutter" not in f and "gimbal" not in f.lower(
) and "grid" not in f.lower() and "flo" not in f.lower():
video_name = f[:-4]
# Define the model
model = Model(cf)
load_model = cf["model"]["load_model"]
print("------Load Pretrined Model--------")
if load_model is not None:
checkpoint = torch.load(load_model)
print(load_model)
else:
load_last = os.path.join(checkpoints_dir,
cf['data']['exp'] + '_last.checkpoint')
checkpoint = torch.load(load_last)
print(load_last)
model.net.load_state_dict(checkpoint['state_dict'])
model.unet.load_state_dict(checkpoint['unet'])
if USE_CUDA:
model.net.cuda()
model.unet.cuda()
print("-----------Load Dataset----------")
test_loader = get_inference_data_loader(cf, data_path, no_flo=False)
data = test_loader.dataset.data[0]
start_time = time.time()
virtual_queue = run(model, test_loader, cf, USE_CUDA=USE_CUDA)
virtual_data = np.zeros((1, 5))
virtual_data[:, 1:] = virtual_queue[0, 1:]
virtual_data[:, 0] = data.frame[0, 0]
virtual_queue = np.concatenate((virtual_data, virtual_queue), axis=0)
print(virtual_queue.shape)
time_used = (time.time() - start_time) / 60
print("Time_used: %.4f minutes" % (time_used))
virtual_path = os.path.join("./test", cf['data']['exp'],
data_path.split("/")[-1] + '.txt')
np.savetxt(virtual_path, virtual_queue, delimiter=' ')
print("------Start Warping Video--------")
grid = get_grid(test_loader.dataset.static_options, \
data.frame[:data.length], data.gyro, data.ois, virtual_queue[:data.length,1:], no_shutter = False)
return data, virtual_queue, video_name, grid
def add_dir(item: UpdateDir, q: str = Query(None)):
safe_path = safe_path_join(path=q, root=os.environ["ROOT_DIR"])
dir_path = util.make_dir(safe_path, item.name)
items = util.get_dir(dir_path)
items_json_encoded = jsonable_encoder(items)
resp = {
"path": get_host_path(dir_path),
"count": len(items),
"items": items_json_encoded,
}
return JSONResponse(content=resp)
def main():
"""Set everything off and handle files/stdin etc"""
print_help, options, args = parseOptions()
if len(sys.argv)>1:
if options['honeypots'] is None:
print "No honeypots specified. Please use either -H or config file to specify honeypots.\n"
sys.exit(2)
hsingleton = HoneysnapSingleton.getInstance(options)
if not os.path.exists(options['output_data_directory']):
make_dir(options['output_data_directory'])
if os.path.exists(options['output_data_directory']):
for i in options["honeypots"]:
make_dir(options["output_data_directory"]+"/"+i)
# by default treat args as files to be processed
# handle multiple files being passed as args
if len(args):
for f in args:
if os.path.exists(f) and os.path.isfile(f):
processFile(f)
else:
print "File not found: %s" % f
sys.exit(2)
# no args indicating files, read from stdin
else:
# can't really do true stdin input, since we repeatedly parse
# the file, so create a tempfile that is read from stdin
# pass it to processFile
fh = sys.stdin
tmph, tmpf = tempfile.mkstemp()
tmph = open(tmpf, 'wb')
for l in fh:
tmph.write(l)
tmph.close()
processFile(tmpf)
# all done, delete the tmp file
os.unlink(tmpf)
cleanup(options)
else:
print_help()
def main():
"""Set everything off and handle files/stdin etc"""
print_help, options, args = parseOptions()
if len(sys.argv) > 1:
if options['honeypots'] is None:
print "No honeypots specified. Please use either -H or config file to specify honeypots.\n"
sys.exit(2)
hsingleton = HoneysnapSingleton.getInstance(options)
if not os.path.exists(options['output_data_directory']):
make_dir(options['output_data_directory'])
if os.path.exists(options['output_data_directory']):
for i in options["honeypots"]:
make_dir(options["output_data_directory"] + "/" + i)
# by default treat args as files to be processed
# handle multiple files being passed as args
if len(args):
for f in args:
if os.path.exists(f) and os.path.isfile(f):
processFile(f)
else:
print "File not found: %s" % f
sys.exit(2)
# no args indicating files, read from stdin
else:
# can't really do true stdin input, since we repeatedly parse
# the file, so create a tempfile that is read from stdin
# pass it to processFile
fh = sys.stdin
tmph, tmpf = tempfile.mkstemp()
tmph = open(tmpf, 'wb')
for l in fh:
tmph.write(l)
tmph.close()
processFile(tmpf)
# all done, delete the tmp file
os.unlink(tmpf)
cleanup(options)
else:
print_help()
def main(args=None):
config_file = args.config
dir_path = args.dir_path
cf = yaml.load(open(config_file, 'r'))
USE_CUDA = cf['data']["use_cuda"]
checkpoints_dir = cf['data']['checkpoints_dir']
checkpoints_dir = make_dir(checkpoints_dir, cf)
data_name = sorted(os.listdir(dir_path))
for i in range(len(data_name)):
print("Running: " + str(i + 1) + "/" + str(len(data_name)))
inference(cf, os.path.join(dir_path, data_name[i]), USE_CUDA)
return
def main():
rstDic = readResultCSV()
predictDic = readPredictResult()
ac_num = 0 #正确检测分屏数量
split_num = 0 #分屏的数量
lost_num = 0 #漏掉检测分屏的数量
error_num = 0 #错误检测为分屏的数量
total_num = len(predictDic.keys())
no_split_num = 0 #没分屏数量
no_split_right_num = 0 #没分屏预测正确
path_1_1 = os.path.join(TO_PATH, '1_1')
path_1_0 = os.path.join(TO_PATH, '1_0')
path_0_1 = os.path.join(TO_PATH, '0_1')
path_0_0 = os.path.join(TO_PATH, '0_0')
util.make_dir(path_1_1)
util.make_dir(path_1_0)
util.make_dir(path_0_1)
util.make_dir(path_0_0)
for key in predictDic.keys():
frompath = os.path.join(ORI_PATH, key)
if int(rstDic[key]) == 2:
split_num += 1
if int(predictDic[key]) == 1:
ac_num += 1
util.run_command('cp %s %s' % (frompath, path_1_1))
else:
lost_num += 1
util.run_command('cp %s %s' % (frompath, path_1_0))
else:
no_split_num += 1
if int(predictDic[key]) == 1:
error_num += 1
util.run_command('cp %s %s' % (frompath, path_0_1))
else:
no_split_right_num += 1
#util.run_command('cp %s %s' % (frompath, path_0_0))
util.log('tatal:%d accuracy: %d lost: %d error: %d right_nosplit:%d' %
(total_num, ac_num, lost_num, error_num, no_split_right_num))
sys.path.append('../')
import util
ratio_step = 10
rounds = 5
ratios = [r / 100 for r in range(0, 100 + ratio_step, ratio_step)]
# for the main program
iterations = list(itertools.product(*[ratios, [1.0], range(rounds)]))[:11]
model_name = 'nn'
dataframe = util.load_wildlab_df()
columns = [c for c in util.LABELS if c in dataframe.columns]
dataframe = dataframe[columns]
res_dir = '{}/exp-labDiffPackedBenign/{}'.format(exp_util.RES_ROOT, model_name)
util.make_dir(res_dir)
database = '{}/exp.db'.format(res_dir)
n_workers = 1
cores_per_worker = -1
sizes = dict(training_ratio=0.7,
testing_packed_benign_ratio=0.5,
testing_packed_malicious_ratio=1)
def process_dataset(df, seed):
'''
Process the entire dataset just one time to save memory
param df pandas dataframe
:rtype: Tuple(pandas.dataframe)
label.set_fontproperties(font)
fig.savefig(dir_name + "{}.png".format(file_name))
plt.close()
if __name__ == '__main__':
args = sys.argv
sns.set()
if len(args) == 2:
dir_png_name = "png/" + args[1] + "/"
dir_csv_name = "csv/" + args[1] + "/"
else:
dir_png_name = "png/"
dir_csv_name = "csv/"
make_dir("/" + dir_png_name)
make_dir("/" + dir_csv_name)
rewards = pd.read_csv(dir_csv_name + "rewards.csv")
regrets = pd.read_csv(dir_csv_name + "regrets.csv")
notGreedyCounts = pd.read_csv(dir_csv_name + "notGreedyCounts.csv")
agent_rewards = dfToNumpy(rewards)
agent_regrets = dfToNumpy(regrets)
agent_notGreedyCounts = dfToNumpy(notGreedyCounts)
# print(agent_rewards)
result_plot(agent_rewards["times"],
agent_rewards,
dir_png_name,
np.set_printoptions(precision=5, threshold=10000, suppress=True, linewidth=10000)
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--width', type=int, default=160, help="render width")
parser.add_argument('--height', type=int, default=120, help="render height")
parser.add_argument('--input-dir', type=str, default="runs/14/d/imgs/ep_00007")
parser.add_argument('--output-dir', type=str, default="/tmp")
agents.add_opts(parser)
models.add_opts(parser)
replay_memory.add_opts(parser)
util.add_opts(parser)
opts = parser.parse_args()
#opts.ckpt_dir = "runs/14/d/ckpts" # last known good
print >>sys.stderr, "OPTS", opts
util.make_dir(opts.output_dir)
# init our rl_agent
agent_cstr = eval("agents.NafAgent")
agent = agent_cstr(opts)
an = agent.network
# prepare three plots; one for each of block on left, in center, or on right
R = np.arange(-1, 1.25, 0.25)
X, Y = np.meshgrid(R, R)
for img_file in sorted(os.listdir(opts.input_dir)):
# prep background; img will be on left, surface on right
background = Image.new('RGB',
(10+320+10+320+10, 10+240+10),
(0, 0, 0))
def setOutdir(self, dir):
make_dir(dir)
self.outdir = dir
self.fp = open(dir + "/socks.txt", "w")
def setOutdir(self, dir):
make_dir(dir)
self.fp = open(dir + "/sebek.txt", "a")
def setOutdir(self, dir):
make_dir(dir)
if self.direction == "queried":
self.fp = open(dir + "/dns_queries.txt", "a")
else:
self.fp = open(dir + "/dns_served.txt", "a")
def setOutdir(self, dir):
self.outdir = dir
make_dir(dir)
def processFile(file):
"""
Process a pcap file.
file is the pcap file to parse
This function will run any enabled options for each pcap file
"""
hs = HoneysnapSingleton.getInstance()
options = hs.getOptions()
tmpf, deletetmp = check_pcap_file(file)
options["tmpf"] = tmpf
try:
if options["filename"] is not None:
out = rawPathOutput(options["filename"], mode="a+")
else:
out = outputSTDOUT()
except IOError:
# we have some error opening the file
# there is something at that path. Is it a directory?
if not os.path.isdir(options["output_data_directory"]):
print "Error: output_data_directory exists, but is not a directory."
else:
print "Unknown Error creating output file"
sys.exit(1)
out("\n\nAnalysing file: %s\n\n" % file)
if options["do_pcap"] == "YES":
out("Pcap file information:\n")
pi = pcapInfo(tmpf)
pi.setOutput(out)
pi.getStats()
myout = rawPathOutput(options["output_data_directory"] +"/pcapinfo.txt")
pi.setOutput(myout)
pi.getStats()
out("\n")
if options["do_packets"] == "YES":
out("\nIP packet summary for common ports:\n\n")
out("%-40s %10s\n" % ("Filter", "Packets"))
filters = setFilters(options)
for i in filters:
key, filt = i
out(key+"\n")
for hp in options['honeypots']:
p = pcap.pcap(tmpf)
c = Counter(p)
c.setOutput(out)
f = filt % hp
c.setFilter(f)
c.count()
del p
out("\n")
if options["do_incoming"] == "YES":
for hp in options["honeypots"]:
out("Counting incoming connections for %s\n" % hp)
outdir = options["output_data_directory"] + "/%s/conns" % hp
make_dir(outdir)
p = pcap.pcap(tmpf)
s = Summarize(p)
filt = 'dst host %s' % hp
s.setFilter(filt)
s.start()
fileout = rawPathOutput(outdir+"/incoming.txt", mode="a")
if options["print_verbose"] == "YES":
outputs = (fileout, out)
else:
outputs = (fileout,)
for output in outputs:
s.setOutput(output)
s.doOutput("\nIncoming connections for %s\n" % hp)
s.writeResults(limit=options["flow_count_limit"])
del p
if options["do_outgoing"] == "YES":
for hp in options["honeypots"]:
out("\nCounting outgoing connections for %s\n" % hp)
outdir = options["output_data_directory"] + "/%s/conns" % hp
make_dir(outdir)
p = pcap.pcap(tmpf)
s = Summarize(p)
filt = 'src host %s' % hp
s.setFilter(filt)
s.start()
fileout = rawPathOutput(outdir+"/outgoing.txt", mode="a")
if options["print_verbose"] == "YES":
outputs = (fileout, out)
else:
outputs = (fileout,)
for output in outputs:
s.setOutput(output)
s.doOutput("\nOutgoing connections for %s\n" % hp)
s.writeResults(limit=options["flow_count_limit"])
del p
if options["do_dns"] == "YES":
out("\nExtracting DNS data to file\n\n")
for hp in options["honeypots"]:
#out("\nHoneypot %s\n\n" % hp)
dns = dnsDecode(hp, direction="queried")
dns.setOutdir(options["output_data_directory"] + "/%s/dns" % hp)
dns.setOutput(out)
dns.run()
del dns
dns = dnsDecode(hp, direction="served")
dns.setOutdir(options["output_data_directory"] + "/%s/dns" % hp)
dns.setOutput(out)
dns.run()
del dns
if options["do_telnet"] == "YES":
out("\nExtracting telnet data to file\n")
for hp in options["honeypots"]:
#out("\nHoneypot %s\n\n" % hp)
tel = telnetDecode(hp)
tel.setOutdir(options["output_data_directory"] + "/%s/telnet" % hp)
tel.setOutput(out)
tel.run()
del tel
if options["do_irc"] == "YES":
"""
Here we will use PcapRE to find packets on irc_port with "PRIVMSG"
in the payload.
"""
for hp in options["honeypots"]:
out("\nLooking for packets containing PRIVMSG for %s\n\n" % hp)
p = pcap.pcap(tmpf)
r = pcapReCounter(p)
r.setFilter("host %s and tcp" % hp)
r.setRE('PRIVMSG')
r.setOutput(out)
r.start()
r.writeResults()
for port in r.server_ports(options['irc_ports'][hp]):
if port not in options['irc_ports'][hp]:
if port==80:
out("\nSaw PRIVMSG on port 80, but cowardly not adding it to IRC port list - check manually\n")
else:
out("\nAdding port %s to irc list for %s\n" % (port, hp))
options['irc_ports'][hp].add(port)
del p
del r
out("\nAnalysing IRC\n")
for hp in options["honeypots"]:
outdir = options["output_data_directory"] + "/%s/irc" % hp
for port in options["irc_ports"][hp]:
out("\nHoneypot %s, port %s\n\n" % (hp, port))
hirc = HoneySnapIRC()
hirc.connect(tmpf, "host %s and tcp and port %s" % (hp, port))
hd = ircDecode(hp)
hd.setOutput(out)
hd.setOutdir(outdir)
hd.setOutfile('irclog-%s.txt' % port)
hirc.addHandler("all_events", hd.decodeCB, -1)
hirc.ircobj.add_global_handler("all_events", hd.printLines, -1)
hirc.ircobj.process_once()
hd.printSummary()
del hd
del hirc
if options["all_flows"] == "YES":
out("\nExtracting all flows\n")
p = pcap.pcap(tmpf)
de = tcpflow.tcpFlow(p)
filt = "host "
filt += " or host ".join(options["honeypots"])
de.setFilter(filt)
de.setOutdir(options["output_data_directory"]+ "/%s/flows")
de.setOutput(out)
de.start()
de.dump_extract()
del de
del p
if options["do_http"] == "YES":
out("\nExtracting from HTTP\n\n")
p = pcap.pcap(tmpf)
de = tcpflow.tcpFlow(p)
de.setFilter("port 80")
de.setOutdir(options["output_data_directory"]+ "/%s/http")
de.setOutput(out)
decode = httpDecode.httpDecode()
decode.setOutput(out)
de.registerPlugin(decode.decode)
de.start()
de.dump_extract()
decode.print_summary()
del de
del p
if options["do_ftp"] == "YES":
out("\nExtracting from FTP\n\n")
p = pcap.pcap(tmpf)
de = tcpflow.tcpFlow(p)
de.setFilter("port 20 or port 21")
de.setOutdir(options["output_data_directory"] + "/%s/ftp")
de.setOutput(out)
decode = ftpDecode.ftpDecode()
decode.setOutput(out)
de.registerPlugin(decode.decode)
de.start()
de.dump_extract()
decode.print_summary()
del de
del p
if options["do_smtp"] == "YES":
out("\nExtracting from SMTP\n\n")
p = pcap.pcap(tmpf)
de = tcpflow.tcpFlow(p)
de.setFilter("port 25")
de.setOutdir(options["output_data_directory"] + "/%s/smtp")
de.setOutput(out)
decode = smtpDecode.smtpDecode()
decode.setOutput(out)
de.registerPlugin(decode.decode)
de.start()
de.dump_extract()
decode.print_summary()
del de
del p
if options["do_sebek"] == "YES":
out("\nExtracting Sebek data\n")
for hp in options["honeypots"]:
out("\nHoneypot %s\n\n" % hp)
sbd = sebekDecode(hp)
sbd.setOutdir(options["output_data_directory"] + "/%s/sebek" % hp)
sbd.setOutput(out)
sbd.run()
sbd.print_summary()
del sbd
if options["do_socks"] == "YES":
out("\nExtracting Socks proxy information:\n")
for hp in options["honeypots"]:
out("\nHoneypot %s\n\n" % hp)
p = pcap.pcap(tmpf)
socks = SocksDecode(p,hp)
socks.setOutdir(options["output_data_directory"] + "/%s/socks" % hp)
socks.setOutput(out)
socks.start()
# delete the tmp file we used to hold unzipped data
if deletetmp:
os.unlink(tmpf)
def insert(self, key, attrdict):
util.make_dir(self._key_dir(key))
self._write_attributes(key, attrdict)
self._hg(["commit", "-m", "Added entry %s." % key])
sys.path.insert(0, '../script/')
sys.path.insert(0, '../script/analysis/')
import util
import hdf5_to_dict as io
TMP_DIR = 'TMP'
TMP_BUILD = 'build_tmp.py'
util.safe_remove(TMP_DIR)
AUTO = False
for arg in sys.argv:
if arg == '-auto':
AUTO = True
RES = [16, 32, 64]#, 128]
util.make_dir(TMP_DIR)
os.chdir('../prob/mhdmodes2d/')
copyfile('build.py', TMP_BUILD)
# COMPILE CODE AT MULTIPLE RESOLUTIONS USING SEPARATE BUILD FILE
for n in xrange(len(RES)):
util.change_cparm('N1TOT', RES[n], TMP_BUILD)
util.change_cparm('N2TOT', RES[n], TMP_BUILD)
call(['python', TMP_BUILD, '-dir', TMP_DIR])
call(['cp', os.path.join(os.getcwd(), TMP_DIR, 'bhlight'),
'../../test/' + TMP_DIR + '/bhlight_' + str(RES[n])])
copyfile(os.path.join(os.getcwd(), TMP_DIR, 'param_template.dat'), '../../test/' +
TMP_DIR + '/param_template.dat')
util.safe_remove(TMP_BUILD)
util.safe_remove(TMP_DIR)
o = self.outdir % i
make_dir(o)
def setOutput(self, file):
self.outfile = file
def dump_extract(self):
for s in self.states.getFlowStates():
s.close()
for func in self.plugins:
func(s, self.states)
def writeResults(self):
"""TODO: I would like to implement some sort of summarization
of the data files that were written during the run...
"""
pass
if __name__ == "__main__":
# for testing. Edit suitably
import sys
options = { 'honeypots':['192.168.0.1', '192.168.0.2'] }
hsingleton = HoneysnapSingleton.getInstance(options)
f = sys.argv[1]
pcapObj = pcap.pcap(f)
tflow = tcpFlow(pcapObj)
make_dir ('output')
tflow.setOutdir("output/%s/")
tflow.setFilter("not port 445")
tflow.start()
def make_output_folders(self):
util.make_dir(self.output_path)
for pth in self._output_folders:
util.make_dir(pth)
def __init__(self):
make_dir(self.RES_DIR)
make_dir(self.WALLPAPER_DIR)
make_dir(self.LARGE_DIR )
pass
