def __init__(self, name):
AssetContainer.__init__(self, name)
from Archive import Archive
self.archive = Archive(name)
from Headers import Headers
self._headers = Headers(name)
return
def run_expertise(wind, exp_id, emp_id):
win = QtGui.QWidget()
if exp_id == 11:
win = Calculator(emp_id)
elif exp_id == 12:
win = AlcoholExcretion(emp_id)
elif exp_id == 21:
win = BodyWeight(emp_id)
elif exp_id == 31:
win = BMI(emp_id)
elif exp_id == 41:
win = BioAgeKidneys(emp_id)
elif exp_id == 51:
win = DipPlane(emp_id)
elif exp_id == 61:
win = Archive()
win.show()
def __init__(self, fileName):
self.archive = Archive()
self.fileName = fileName
self.file = self.archive.openFile(fileName, 'r')
self.convFileToBin()
file = self.archive.openFile(self.fileName + '.binario', 'r')
self.str = file.readline()
self.archive.close(file)
self.getEthernet()
self.getIP()
if self.protocol == 'TCP':
self.getTCP()
else:
self.getUDP()
def main():
# Google static maps API key must be stored in api_key.txt
api_key = open('api_key.txt', 'r').read()
# Define start positions for the lat and log
lat_start = -27.458462
long_start = 153.035735
# Define the lat and long increments - this depends on the start position
lat_inc = -0.0007
long_inc = 0.0009
# Make a file list to add to an archive and upload to S3
file_list = []
# Iterate over a 10x10 grid and capture satellite images
for i, j in itertools.product(range(10), range(10)):
latitude = lat_start + (i * lat_inc)
longitude = long_start + (j * long_inc)
# Generate the Google maps URL
map_address = ('https://maps.googleapis.com/maps/api/staticmap?' +
'center={},{}'.format(latitude, longitude) +
'&zoom=20&size=640x640' +
'&maptype=satellite&key={}'.format(api_key))
# Specify the directory and filename to save the png to
directory = 'google_image_dump'
if not os.path.exists(directory):
os.makedirs(directory)
img_fname = os.path.join('google_image_dump', '{}{}.png'.format(i, j))
# Fetch the image at the URL and save it to the filename
with open(img_fname, 'wb') as f:
f.write(requests.get(map_address).content)
file_list.append(img_fname)
# Build an archive with the image files
archive = Archive(file_list)
buffer = archive.streamtargz()
# Upload the archive to the zepto-archive bucket in S3
s3bucket = S3Bucket('zepto-archive')
s3bucket.uploadstream(buffer, archive.name)
def archive(name):
from Archive import Archive
return Archive(name)
obs_space = env.get_obs_space()
n_train = 1000
n_test = 10
pop_size = 100
curr_gen = 0
print("Act space:", act_space)
print("Obs space:", obs_space)
# optimization stuff
nn = FFIndiv(obs_space, act_space, hidden_size=16)
optimizer = ES.CMAES(nn.get_params().shape[0], pop_size=pop_size)
# archives
sample_archive = Archive(max_size=n_train * pop_size)
thetas_archive = Archive(max_size=n_train)
# sampler
sampler = Samplers.BasicSampler(sample_archive, thetas_archive)
# training
for i in range(n_train):
batch = optimizer.ask(pop_size)
scores = np.zeros(pop_size)
# newly drawn samples
for j in range(pop_size):
nn.set_params(batch[j])
score = env.eval(nn, render=False)
