def QuerySchema(self, descriptors):
(aggregatorScript, sourceIds) = self._AggregatorScriptFor(descriptors)
aggregator = Aggregator(cStringIO.StringIO(aggregatorScript))
result = Schema()
aggregator.run(
result,
[self.sources[sourceId].QuerySchema() for sourceId in sourceIds])
return result
def UpdateDescriptors(self, id, pool, descriptors=None):
if descriptors == None:
descriptors = pool.PresentAttributes()
scripts = self._DisgregatorScripts(descriptors)
for source, script in scripts.items():
if script == "": continue
disgregator = Aggregator(cStringIO.StringIO(script))
try:
result = self.sources[source].QueryDescriptors(id)
except:
result = Pool()
disgregator.run(result, [pool])
self.sources[source].UpdateDescriptors(id, result)
def QueryDescriptors(self, id, descriptors):
if self.verbose: print "++ Building aggregation script..."
(aggregatorScript, sourceIds) = self._AggregatorScriptFor(descriptors)
aggregator = Aggregator(cStringIO.StringIO(aggregatorScript))
result = Pool()
sourcesPools = []
for sourceId in sourceIds:
if self.verbose:
print "++ Querying descriptors from %s..." % sourceId
sourcePool = self.sources[sourceId].QueryDescriptors(id)
sourcesPools.append(sourcePool)
if self.verbose: print "++ Aggregating..."
aggregator.run(result, sourcesPools)
return result
if sanity_check_counter == int(sanity_interval / interval):
content = previous_content
sanity_check_counter = 0
if len(content) != 0:
socketio.emit('newdata', content, namespace='/api')
socketio.sleep(interval)
@app.route('/')
def hello_world():
return render_template('index.html')
@socketio.on('slider', namespace='/api')
def slider(data):
print('slider value updated: %s' % data.get('value'))
@socketio.on('connect', namespace='/api')
def connect():
socketio.emit('newdata', aggregator.get_content(), namespace='/api')
if __name__ == '__main__':
aggregator = Aggregator.Aggregator()
command = sys.argv[1] if len(sys.argv) > 1 else "python3 ./test.py"
aggregator.register_component(command)
aggregator.start_gathering()
socketio.start_background_task(target=update)
socketio.run(app, host='0.0.0.0', port=8080)
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
from Aggregator import *
from Pool import *
import sys
if sys.argv[1] == "-":
script = sys.stdin
else:
script = file(sys.argv[1])
sources = sys.argv[2:]
target = Pool()
aggregator = Aggregator(script)
aggregator.run(target, [Pool(file(source)) for source in sources])
target.Dump(sys.stdout)
def helperTestParser(self, input):
aggregator = Aggregator(cStringIO.StringIO(input))
sink = cStringIO.StringIO()
aggregator.dump(sink)
return sink.getvalue()
#Program Entry Point
from Generator import *
import tensorflow as tf
from Discriminator import *
from Aggregator import *
from DataPrep import *
#Add support for altering images. (ie flip image. etc..)
#All ops are for 3d tensors, so something like this has to be used..
#result = tf.map_fn(lambda img: tf.image.random_flip_left_right(img), images)
#Add leaky relu
with tf.Session() as sess:
batchSize = 64
numIters = 500
gen = Generator(batchSize)
discrim = Discriminator(batchSize, gen)
a = Aggregator(sess, discrim)
saver = tf.train.Saver()
try:
saver.restore(sess, "savedModel.ckpt")
print("Successfully Restored Model!!")
except:
sess.run(tf.global_variables_initializer())
print("No model available for restoration")
allData = loadAllData()
a.learn(allData, numIters, batchSize)
saver.save(sess, "savedModel.ckpt")