def request():
"""Called when plugin data are requested.
When this function is called, Location data are valid. In this function
should be set data for provider.
Data are set with Provider.serProperty(pluginName, key, value) method. This
method can by called multiple times, all keys are added to this plugin
datasource.
At the end, when all data are set, Provider.done(pluginName) method has to
be called indicating that all data are ready and set.
Location methods:
QString getCountryCode();
QString getCountry();
QString getCity();
QString getAddress(); // this method is usefull only when user has chosen
// manual configuration of location, otherwise its
// return is empty
QString getLongitude();
QString getLatitude();
double getRange(); // range in KM around location for which user wants
// information
Provider methods:
void setProperty(QString source, QString key, QString value);
void done(const QString source);
"""
Provider.setProperty(PLUGIN_NAME, "Hello!", "You are in %s." %
Location.getCity())
Provider.done(PLUGIN_NAME)
def push_image_to_providers(self, image_id, build_id, providers, credentials, job_cls = BuildJob, *args, **kwargs):
if not build_id:
build_id = self._latest_unpushed(image_id)
watcher = PushWatcher(image_id, build_id, len(providers), self.warehouse)
jobs = []
for provider in providers:
target = Provider.map_provider_to_target(provider)
target_image_id = self._target_image_for_build_and_target(build_id, target)
template = self._template_for_target_image_id(target_image_id)
job = job_cls(template, target, image_id, build_id, *args, **kwargs)
job.push_image(target_image_id, provider, credentials, watcher)
jobs.append(job)
self.job_registry.register(jobs)
return jobs
from os import listdir
import numpy as np
import Provider
import tensorflow as tf
# Directories
subsetDirBase = r'C:\Users\polat\Desktop\TEZ\LUNA\subset'
candidates = r'C:\Users\polat\Desktop\TEZ\LUNA\CSVFILES\candidates_V2.csv'
# Constants
NUM_SUBSET = 10
voxelWidth = 42
ZWidth = 2
candidatesList = Provider.readCSV(candidates)
# ***** TRAINING STARTS*****
inputVolume = tf.placeholder(tf.float32)
output = tf.placeholder(tf.float32)
W_conv1 = Provider.weight_variable([3, 3, 3, 1, 32])
B_conv1 = Provider.bias_variable([32])
h_conv1 = tf.nn.relu(
tf.nn.conv3d(inputVolume, W_conv1, [1, 1, 1, 1, 1], padding='SAME') +
B_conv1)
layer1 = tf.nn.max_pool3d(h_conv1,
ksize=[1, 2, 3, 3, 1],
strides=[1, 2, 2, 2, 1],
padding='SAME')
from os import listdir
import Provider
# Directories
ModelDirectory = r'ModelResults'
ModelList = []
outputFile = []
outputFile.append('seriesuid,coordX,coordY,coordZ,probability')
list = listdir(ModelDirectory)
for modelFile in list:
path = ModelDirectory+'/'+modelFile
ModelList.append(Provider.readCSV(path))
NumberOfCandidates = len(ModelList[0])
NumberOfModel = len(ModelList)
for i in range(NumberOfCandidates):
if (i == 0) :
continue
average = 0
for j in range(NumberOfModel):
average = average + float(ModelList[j][i][4])
average = average / NumberOfModel
outputFile.append(ModelList[0][i][0]+','+ModelList[0][i][1]+','+ModelList[0][i][2]+','+ModelList[0][i][3]+','+str(average))
print (str(i)+'\n')
import Provider
from datetime import datetime
import json
from aiohttp import web
import asyncio
import Orchestrator
import Authorization
import Consumer
consumer = Consumer.Consumer("emilsnyaste", "127.0.0.1", 8082)
provider = Provider.Provider("CurrentTimeSweden", "CurrentTime","/", 8081, "127.0.0.1", ["JSON"] ,"127.0.0.1:8442", {})
#Authorization.register_to_authorization(provider,"emilsnyaste", "127.0.0.1", 8082, "null")
print(Authorization.authorize(consumer, provider.name, provider.address, provider.port, provider.definition))
#provider.start()
#provider.registerToOrch()
#Orchestrator.register_to_orchestrator(provider, "emilsnya", "127.0.0.1", 8082, "null")
async def handle_request(request):
try:
time = str(datetime.now())
response = {'time':time}
print (response)
provider.stop()
return web.Response(text=json.dumps(response), status=200)
except Exception as e:
return web.Response(text=json.dumps({'message': 'Something went wrong'}), status=400)
0.65,
0,
0,
], [0, 0.8, 0.3, 0], [0, 0.6, 1, 0], [0, 0, 0, 1])
print(x)
prediction = tf.equal(tf.argmax(x, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))
sess = tf.Session()
print(sess.run(prediction))
anArray = np.array([1, 2, 3, 4]).reshape(1, 4)
dizi = Provider.numToArray(anArray)
a = np.array([1, 1, 1, 2, 2, 2])
print("dimension a: ")
print(a.shape)
b = np.array([[1, 1, 1], [1, 1, 1], [2, 2, 2], [1, 1, 1], [1, 1, 1], [1, 2,
3]])
print("dimension b: ")
print(b.shape)
c = np.matmul(a, b)
print(c)
print(Provider.numToArray(a[0]))
import numpy as np
from scipy import ndimage
import Provider
subsetDirb= 'subset'
resampledSubsetDir = 'resample'
cropSubsetDir = 'crop'
candidates = 'CSVFILES/candidates_V2.csv'
RESIZE_SPACING = [1, 1, 1]
voxelWidthXY = 36
voxelWidthZ = 24
if __name__ == '__main__':
candidatesList = Provider.readCSV(candidates)
for i in range(10):
subsetDirn=subsetDirb+str(i)
subsetDir=subsetDirb + '/' + subsetDirn
list = listdir(subsetDir)
subsetOuterList = []
for file in list:
if file.endswith(".mhd"):
file = file[:-4]
subsetOuterList.append(file)
count0 = 0
count1=0
label = []
for cand in candidatesList:
if (cand[0] in file):
fileName = cand[0] + '.mhd'
model.add(Dropout(0.20))
model.add(Flatten())
model.add(Dense(300, activation='relu'))
model.add(Dropout(0.20))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
# Save the model in order to restart it later
model.save_weights('model.h5')
writeToDebugFile('Keras Model Created')
candidatesList = Provider.readCSV(candidates)
writeToDebugFile('candidates Read')
for outerSet in range(NUM_SET):
# Restart the Model
model.load_weights('model.h5')
# Create test folder
subsetDir = originalSubsetDirectoryBase + str(outerSet)
list = listdir(subsetDir)
subsetOuterList = []
# Create Test Set
for file in list:
if file.endswith(".mhd"):
file = file[:-4]
key_size=2048,
backend=default_backend())
blockchain = blockchain.Blockchain() # the blockchain used in this test
# create a test record for a patient
patient_test_vc = [
"Administration Date: MAY-01-2021 10:00 AM", "Patient ID: 10132",
"Patient Name: John Doe", "Patient Address: 10 Example St. NE",
"Administered by: Dr. Jill Fakeington"
]
# additional info is non-personally identifying info stored with transaction
additional_data = ["Vaccine Type: Pfizer", "Vaccine ID: 1234"]
# create the provider
provider = Provider.Provider(provider_key)
# create the patient
patient = Patient.Patient(patient_key)
# create the third party verifier
verifier = Verifier.Verifier(verifier_key, blockchain)
# generate patient vaccine card
provider.generate_card(patient_test_vc)
# provider posts the transaction to the blockchain
provider.post_transaction(blockchain, patient_key.public_key(),
additional_data)
# a new block is created
blockchain.new_block()
# provider sends encrypted vaccine care to the patient
provider.send_patient_info(patient)
# Patient sends encrypted record to the verifier to prove his vaccination
patient.send_records(verifier, verifier.get_pub_key())
