def update_nuclear_from_ceps(self):
mn.parse_arguments()
nuclear_only = [x for x in self.ceps_data if
x['type_secondary'] == 'JE' and x['start'] > datetime.now() - timedelta(days=mn.get_argv().days_back)]
for ceps in nuclear_only:
curr_item = {k: v for k, v in mn.saved_data.iteritems() if v['date'] == ceps['start'].strftime('%Y-%m-%d')}
if len(curr_item) != 0:
v = curr_item.values()[0]
v['content'][ceps['start'].hour] = ceps['quantity']
mn.saved_data[curr_item.keys()[0]] = v
else:
data = {}
data['date'] = ceps['start'].strftime('%Y-%m-%d')
data['country'] = self.ml_object.country
data['data_type_secondary'] = 'nuclear'
data['data_type'] = 'generation'
data['content'] = {ceps['start'].hour: ceps['quantity']}
data['approximation'] = []
mn.saved_data[len(mn.saved_data)] = data
# print data
params = {'country': self.ml_object.country, 'type': 'generation','type_secondary':'nuclear'}
mn.save_all_data(params)
def download_generation(self):
params = {}
mn.parse_arguments()
params['type'] = 'generation'
for country in dp.get_all_generation_countries():
print 'Begin country: ' + country
params['country'] = country
# ml = ML.ML(country)
# self.data = ml.data
# self.approx_data = ml.approximated_data
# self.all_sources = ml.all_sources
for gen_type in dp.get_all_generation_types():
print ' == Begin type: ' + dp.psr_types[gen_type]
params['type_secondary'] = dp.psr_types[gen_type]
params['start'] = datetime.now().replace(
microsecond=0, second=0, minute=0,
hour=0) - timedelta(days=mn.get_argv().days_back)
params['end'] = datetime.now().replace(
microsecond=0, second=0,
minute=0) # params['start'] + timedelta(days=2)
self.__get_all_dates_for_params(params)
# self.__append_aproximation_data(mn.saved_data)
# ml.close_database_connection()
mn.save_all_data(params)
def main():
start_time = time.time()
options = master.parse_arguments()
use_wavelet = options.wavelet
in_fasta_handle = open(options.in_fasta_filename, "rU")
# script not fully tested with multiple records
for record in SeqIO.parse(in_fasta_handle, "fasta"):
record_id = record.id
sequence = str(record.seq)
rotate_handle = open("rotate.txt", "w")
for i in range(0, len(sequence), 500):
wrap_sequence = sequence[i:] + sequence[:i]
# Two possible methods: wavelet transforms or multiscaling
if use_wavelet:
wavelet = master.WaveletTransform(options)
details_filename = wavelet.dwt(wrap_sequence)
wavelet_data = wavelet.get_data_from_R(details_filename)
origin_scaled = wavelet.get_origin(wavelet_data)
origin = int(origin_scaled * len(sequence))
else:
multiscale = master.Multiscaling(options)
origin = multiscale.multiscaling(wrap_sequence,
in_fasta_handle, record_id)
origin += i
while origin >= len(sequence):
origin -= len(sequence)
distance = min(origin, len(sequence) - origin)
print "i:", i, ", origin:", origin, ", distance:", distance
rotate_handle.write(str(i) + "\t" + str(distance) + "\n")
rotate_handle.close()
#rearrange_fasta(origin, record_id, sequence, options)
in_fasta_handle.close()
print "Runtime:", time.time() - start_time, "seconds"
def main():
start_time = time.time()
options = master.parse_arguments()
use_wavelet = options.wavelet
in_fasta_handle = open(options.in_fasta_filename, "rU")
# script not fully tested with multiple records
for record in SeqIO.parse(in_fasta_handle, "fasta"):
record_id = record.id
sequence = str(record.seq)
rotate_handle = open("rotate.txt", "w")
for i in range(0, len(sequence), 500):
wrap_sequence = sequence[i:] + sequence[:i]
# Two possible methods: wavelet transforms or multiscaling
if use_wavelet:
wavelet = master.WaveletTransform(options)
details_filename = wavelet.dwt(wrap_sequence)
wavelet_data = wavelet.get_data_from_R(details_filename)
origin_scaled = wavelet.get_origin(wavelet_data)
origin = int(origin_scaled * len(sequence))
else:
multiscale = master.Multiscaling(options)
origin = multiscale.multiscaling(wrap_sequence, in_fasta_handle, record_id)
origin += i
while origin >= len(sequence):
origin -= len(sequence)
distance = min(origin, len(sequence) - origin)
print "i:", i, ", origin:", origin, ", distance:", distance
rotate_handle.write(str(i) + "\t" + str(distance) + "\n")
rotate_handle.close()
#rearrange_fasta(origin, record_id, sequence, options)
in_fasta_handle.close()
print "Runtime:", time.time() - start_time, "seconds"
super(GatingMechanism, self).__init__()
self.params = params
with open(self.params.entity_tran, 'rb') as f:
transE_embedding = pkl.load(f)
self.enti_tran = nn.Embedding.from_pretrained(
torch.from_numpy(transE_embedding).float())
entity_num = transE_embedding.shape[0]
# gating 的参数
self.gate_theta = Parameter(
torch.empty(entity_num, self.params.hidden_dim))
nn.init.xavier_uniform_(self.gate_theta)
# self.dropout = nn.Dropout(self.params.dropout)
def forward(self, X: torch.FloatTensor, Y: torch.LongTensor):
'''
:param X: LSTM 的输出tensor |E| * H
:param Y: Entity 的索引 id |E|,
:return: Gating后的结果 |E| * H
'''
gate = torch.sigmoid(self.gate_theta[Y])
Y = self.enti_tran(Y)
output = torch.mul(gate, X) + torch.mul(-gate + 1, Y)
return output
if __name__ == '__main__':
from main import parse_arguments
GatingMechanism(parse_arguments())
raise ValueError("Unknown network")
ground_truth = os.path.join(WORKING_DIR, "datasets",
"network_%s.txt" % network)
measure = compute_scores(ground_truth, fname, parameters)
row = deepcopy(parameters)
row.update(measure)
pprint(row)
results.append(row)
else:
n_jobs_launched += 1
cmd_parameters = " ".join("--%s %s" % (key, parameters[key])
for key in sorted(parameters))
scripts_args = parse_arguments(shlex.split(cmd_parameters))
if make_hash(scripts_args) != job_hash:
pprint(scripts_args)
pprint(parameters)
raise ValueError("hash are not equal, all parameters are "
"not specified.")
cmd = submit(job_command="%s main.py %s" %
(sys.executable, cmd_parameters),
job_name=job_hash,
time="100:00:00",
memory=24000,
log_directory=LOG_DIRECTORY,
backend="slurm")
if not args["debug"]:
ground_truth = os.path.join(WORKING_DIR, "datasets",
"network_%s.txt" % network)
measure = compute_scores(ground_truth, fname, parameters)
row = deepcopy(parameters)
row.update(measure)
pprint(row)
results.append(row)
else:
n_jobs_launched += 1
cmd_parameters = " ".join("--%s %s" % (key, parameters[key])
for key in sorted(parameters))
scripts_args = parse_arguments(shlex.split(cmd_parameters))
if make_hash(scripts_args) != job_hash:
pprint(scripts_args)
pprint(parameters)
raise ValueError("hash are not equal, all parameters are "
"not specified.")
cmd = submit(job_command="%s main.py %s"
% (sys.executable, cmd_parameters),
job_name=job_hash,
time="100:00:00",
memory=24000,
log_directory=LOG_DIRECTORY,
backend="slurm")
if not args["debug"]:
fig_name = None
if not argv.plot_special_n3 and not argv.plot_special_n4:
fig, ax = plt.subplots()
if len(Ns) == 1:
Ns = list(range(1, len(Links) + 1))
xlabel = 'Number of Links with channel assigned'
ylabel = 'Frictional Network Interference'
fig_name = "base_fni_nlink.png"
else:
xlabel = 'Number of Nodes'
ylabel = 'Frictional Network Interference'
fig_name = "base_fni_n.png"
ax.plot(Ns, fni_list)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
# ax.set_yscale('log')
fig_path = os.path.join(argv.fig_root, fig_name)
print(f'Saving to {fig_path}')
plt.savefig(fig_path, format='png', bbox_inches='tight')
print()
return Ns, fni_list, xlabel, ylabel, fig_name
if __name__ == '__main__':
argv = main.parse_arguments("--plot sepcial n4 --use base".split())
test_base_method(argv)
from main import CONSOLE_ARGUMENTS
im_directory = CONSOLE_ARGUMENTS.im_directory
mask_directory = CONSOLE_ARGUMENTS.mask_directory
gt_directory = CONSOLE_ARGUMENTS.gt_directory
files_to_process = CONSOLE_ARGUMENTS.numFiles
tm = CONSOLE_ARGUMENTS.tm
signals_list = calculateImagesMetrics(im_directory,
mask_directory,
gt_directory,
files_to_process=files_to_process)
signal_type_dict = create_signal_type_dict(signals_list)
if (tm): print_results_signal_type_dict(signal_type_dict)
return signal_type_dict
def test_metrics():
from main import CONSOLE_ARGUMENTS
print(CONSOLE_ARGUMENTS)
files_to_process = CONSOLE_ARGUMENTS.numFiles
return get_dictionary()
if __name__ == '__main__':
# read arguments
from main import parse_arguments
parse_arguments()
test_metrics()
def test_gui():
sys.argv = ['main.py', 'gui']
main.parse_arguments()
return
if not argv.plot_special_n3 and not argv.plot_special_n4:
fig, ax = plt.subplots()
if len(Ns) == 1:
Ns = list(range(1, len(Links) + 1))
xlabel = 'Number of Links with channel assigned'
ylabel = 'Frictional Network Interference'
fig_name = "fni_nlink.png"
else:
xlabel = 'Number of Nodes'
ylabel = 'Frictional Network Interference'
fig_name = "fni_n.png"
ax.plot(Ns, fni_list)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
# ax.set_yscale('log')
fig_path = os.path.join(argv.fig_root, fig_name)
print(f'Saving to {fig_path}')
plt.savefig(fig_path, format='png', bbox_inches='tight')
print()
return Ns, fni_list, xlabel, ylabel, fig_name
if __name__ == '__main__':
argv = main.parse_arguments([])
test_our_method(argv)
