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")