def render_POST(self, request):
pre = get_arg(request, 'pre')
SEP = ":"
username = None
if pre:
username = random_adjspecies()
token = str(int(time.time())) + SEP + username
hmac_token = make_digest(token)
return json.dumps(
{'username': username,
'token': token + ':' + hmac_token})
token = get_arg(request, 'token')
if token:
print "TOKEN>>", token
try:
ts, claimed_username, received_hmac = token.split(SEP)
except ValueError:
return failure(self.action, request, 'bad request: expected token as ts:username:hmac', 400)
if ts < time.time() - 60 * 5:
return failure(self.action, request, 'bad request: expired token', 400)
expected = make_digest('%s:%s' % (ts, claimed_username))
if not hmac.compare_digest(received_hmac, expected):
return failure(self.action, request, 'bad request: corrupted hmac', 400)
username = claimed_username.lower()
if not check_args(request, 'idkey'):
return failure(self.action, request, 'bad request: empty idkey', 400)
if not check_args(request, 'linkkey'):
return failure(self.action, request, 'bad request: empty linkkey', 400)
idkey = get_arg(request, 'idkey')
linkkey = get_arg(request, 'linkkey')
if not username:
username = random_adjspecies()
try:
self.backend.new(username.lower(), idkey, linkkey)
except Exception as exc:
# XXX have retries here
request.setResponseCode(500)
return failure(self.action, request, 'error: %r' % exc, 500)
return success(self.action, username)
import inspect
from nose.tools import *
from nose.tools import assert_raises
from datetime import datetime
from boto3.dynamodb.conditions import Key, Attr
import botocore
# How to run test
# - nosetests test
# - nosetests test.test_sot
# - nosetests test.test_sot.test_sot_id_uniqness
LANGUAGE = 'hi'
USERID = 9090909090909090
RUNTAG = adjspecies.random_adjspecies('_', 7)
ACCESSTOKEN = 'test.sot.{}'.format(adjspecies.random_adjspecies('', 7))
TESTTS = "%.10f" % time.time()
def _getfname(fname):
return "{:<21}".format(fname[5:][:21])
class test_sot():
def __init__(self):
dynamodb = boto3.resource('dynamodb')
self.table = dynamodb.Table('sot')
def sot_insert(self, item):
return self.table.put_item(Item=item)
def pick_new_name(self): new_name = adjspecies.random_adjspecies(sep='_', maxlen=10, prevent_stutter=True) while new_name in self.dict: new_name = adjspecies.random_adjspecies(sep='_', maxlen=10, prevent_stutter=True) return new_name
RPpow = 0.5 print 'RPpow: ' + str(RPpow) # img_process # -1 = no absolute value # 0 = pow # 1 = hist EQ # 2 = AdaHist img_process = 0 RP_list = '' for k in RPs_in_level : RP_list = RP_list + str(k) rand_animal = adjspecies.random_adjspecies() # pickle_file = 'dt20_j' + str(jump_num)+'_' + RP_list + '_' + 'SR' + str(int(tar_freq/1000)) + 'kHz_' + rand_animal pickle_file = 'NNM_cla_'+ 'ss_'+ str(ss_mode)+ '_' + data_tag +'_j' + str(jump_num) +'_' + RP_list + '_' + 'SR' + str(int(tar_freq/1000)) + 'kHz_' + rand_animal # pickle_file = 'data_j' + str(jump_num)+'_01010101_' + 'SR12kHz' + '_NOA' +'_dm400' # pickle_file = 'test_mat' print 'pickle file name : ' + pickle_file speak = 0 ## Sample interval for each level # RPs_interval = [8*2048, 4*2048, 2*2048, 2048, 1024, 512, 256, 128, 64, 32] # RPs_interval = [8*2048, 4*2048, 2*2048, 24, 1024, 256, 256, 64, 64] # itv = 64 # RPs_interval = [512, 512, 512, 512, 512, 512, 512, 512] # RPs_interval = [itv, itv, itv, itv, itv, itv, itv, itv, itv, itv] # itv_stride = [[32],[64],[128],[256],[512],[1024],[2048],[4096]]
'meta/scd': self._float_feature([scd]),
'meta/proj_angle': self._float_feature([proj_angle]),
'meta/thres': self._float_feature([thres]),
'meta/filename': self._bytes_feature([filename.encode("utf8")
]),
'meta/patient_id': self._bytes_feature(
[patient_id.encode("utf8")]),
'meta/dir_hash': self._bytes_feature([dir_hash.encode("utf8")])
}))
del np_img
del np_vol
gc.collect()
yield example
session_nickname = adjspecies.random_adjspecies(
'-') + '-' + datetime.datetime.now().strftime("%Y-%m-%d--%Hh%Mm%Ss")
options = PipelineOptions(flags=sys.argv)
google_cloud_options = options.view_as(GoogleCloudOptions)
google_cloud_options.project = 'x-ray-reconstruction'
google_cloud_options.job_name = 'create-tfrecords-' + session_nickname
google_cloud_options.staging_location = 'gs://cxr-to-chest-ct2/binaries'
google_cloud_options.temp_location = 'gs://cxr-to-chest-ct2/temp'
# google_cloud_options.region = 'us-east4'
# google_cloud_options.machine_type = 'n1-highmem-2'
options.view_as(SetupOptions).save_main_session = True
with beam.Pipeline(options=options) as p:
train_dataset_prefix = os.path.join('gs://cxr-to-chest-ct2/tfrecords/',
session_nickname, 'train')
test_dataset_prefix = os.path.join('gs://cxr-to-chest-ct2/tfrecords/',
