def __init__(self):
self.flags = ArgumentParser().get_flags()
# Because -v and -p options are mutually exclusive, there's no need to read and compute the config and regexes.
if self.flags.version:
return
try:
self.repo_root = check_output(
['git', 'rev-parse', '--show-toplevel']).strip('\n')
except CalledProcessError:
print 'Try running codesearch from a valid git repository.'
exit(1)
self.config = self.__config()
self.include_folders_regex = self.__include_folders_regex()
self.file_extensions_regex = self.__file_extensions_regex()
self.exclude_folders_regex = self.__exclude_folders_regex()
import json
from argumentparser import ArgumentParser
from dbconnector import HostelDBConnector
from filehandler import FileHandler
def json2dict(filename):
raw_data = FileHandler.read(filename)
return json.loads(raw_data)
if __name__ == '__main__':
parser = ArgumentParser()
args = parser.parse_arguments()
students_data = json2dict(args['students_file'])
rooms_data = json2dict(args['rooms_file'])
connector = HostelDBConnector(args['config_file'])
connector.connect()
connector.create_tables()
connector.insert_rooms(rooms_data)
connector.insert_students(students_data)
# adding index on birthday
# - we have queries that use this column
# - no need to create index on enum column with only 2 elements ('Sex': 'M', 'F')
# - we do not use other column in select queries
index_creation = '''create index idx_student_birthday on hostel.student (birthday)
comment ''
name_list=output_name_list,
)
# Load galaxy plots
loadGalaxyPlots(
web,
config.output_directory,
num_galaxies_to_show,
output_name_list,
)
# Finish and output html file
html.render_web(web, config.output_directory)
# Compute how much time it took to run the script
time_end = time()
script_runtime = time_end - time_start
m, s = divmod(script_runtime, 60)
h, m = divmod(m, 60)
print(
f"The script was run in {h:.0f} hours, {m:.0f} minutes, and {s:.0f} seconds"
)
return
if __name__ == "__main__":
config_parameters = ArgumentParser()
main(config_parameters)
from model import Transformer, Generator, Discriminator
import torch
import torch.nn as nn
from torch import optim
from torch.autograd import grad
from argumentparser import ArgumentParser
from data_processing import train_loader
from utils import bit_entropy
arg = ArgumentParser()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
D1 = Discriminator().to(device)
G1 = Generator().to(device)
model = Transformer(arg.num_size).to(device)
loss_fn = nn.MSELoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=arg.lr)
optimizer_d = optim.Adam(D1.parameters(), lr=arg.lr)
optimizer_g = optim.Adam(G1.parameters(), lr=arg.lr)
def train(data_iter, model, D, G):
for i in range(arg.num_epoch):
model.train()
D1.train()
G1.train()
for it, data in enumerate(data_iter):
data = data.to(device)
out, input1, binary_code, out1 = model.forward(data)
out1 = torch.reshape(
out1, (arg.batch_size, -1, arg.window, arg.code_size))
from torch.utils.data import Dataset, DataLoader
import torch
import pandas as pd
from sklearn import preprocessing
from argumentparser import ArgumentParser
import numpy as np
arg = ArgumentParser()
class MTSdataset(Dataset):
def __init__(self, x_data):
self.len = int(x_data.shape[0] / arg.stride)
self.x_data = torch.from_numpy(x_data).float().view(
x_data.shape[0], x_data.shape[1])
def __getitem__(self, index):
data = self.x_data[arg.stride * index:arg.stride * index +
arg.window, :]
if data.shape[0] == arg.window:
data = data
else:
data = torch.zeros(arg.window, arg.num_size)
return data
def __len__(self):
return self.len
if arg.dataset == "air_quality":
num_data = pd.read_csv("./data/air_quality_data.csv")
import os, sys, logging
from argumentparser import ArgumentParser, ArgumentError
import statscollector
## Debugging for Visual Studio 2013 with Python Tools for Visual Studio
def dbg():
try:
import ptvsd
ptvsd.enable_attach(secret='s')
ptvsd.wait_for_attach()
except ImportError:
import pip
pip.main(['install','-Iv','ptvsd==2.2.0'])
dbg()
argParser = ArgumentParser(sys.argv)
try:
argParser.ValidateAllArgs()
except ArgumentError as err:
logging.error("\nERROR: " + err.message)
logging.error(argParser.GetUsage())
# Otherwise, store the pathname provided as an argument
else:
# Assume the Dicom To Nrrd Converter is in the same folder as this script
pathtoconverter = os.path.realpath(os.path.join(os.path.dirname(sys.argv[0]), "DicomToNrrdConverter.exe"))
# Get parsed arguments
pathtodicoms = argParser.GetArg("pathtodicoms")[0]
segmentationfile = argParser.GetArg("segmentationfile")[0]
foldersaveName = argParser.GetArg("foldersavename")[0]
keepnrrddir = argParser.GetArg("keepnrrddir")
snrsegment = argParser.GetArg("getsnr")[0]