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)