def save_to_mongo(_results, _saved, model_update_time, data_source):
done = True
for key in _results.keys():
result_pair = _results[key]
if result_pair[1].return_value is None:
done = False
logging.warn("Haven't finish yet for key %s, continue..." % key)
continue
else:
if _saved[key] == False:
_saved[key] = True
to_save = (result_pair[0], result_pair[1].return_value, result_pair[2])
region = to_save[0]
for single_hour_prediction in zip(to_save[1], to_save[2]):
p = Prediction()
p.setRegion(region)
p.setModelUpdateTime(model_update_time)
p.setPredictedValues(
float(single_hour_prediction[0][1]), math.sqrt(float(single_hour_prediction[0][2]))
)
p.setTime(str(single_hour_prediction[1]))
p_json = p.toDict()
if data_source == "twitter":
save_interface = PredictionInterface()
save_interface.setDB(TwitterConfig.prediction_db)
save_interface.setCollection(TwitterConfig.prediction_collection)
save_interface.saveDocument(p_json)
elif data_source == "instagram":
save_interface = PredictionInterface()
save_interface.setDB(InstagramConfig.prediction_db)
save_interface.setCollection(InstagramConfig.prediction_collection)
save_interface.saveDocument(p_json)
return done
def getNearestPrediction(self):
pi = PredictionInterface()
pi.setDB('citybeat')
pi.setCollection('online_prediction')
print 'set collection as ',self.prediction_collection
print 'search for '
self.region.display()
print str(self.cur_time)
return pi.getNearestPrediction(self.region, str(self.cur_time))
def getNearestPrediction(self):
pi = PredictionInterface()
pi.setDB('citybeat_production')
if self.data_source == 'instagram':
pi.setCollection('online_prediction_instagram')
else:
pi.setCreatedTime('online_prediction_twitter')
self.region.display()
return pi.getNearestPrediction(self.region, str(self.cur_time))
def getNearestPrediction(self):
pi = PredictionInterface()
pi.setDB('citybeat')
pi.setCollection('online_prediction')
print 'set collection as ', self.prediction_collection
print 'search for '
self.region.display()
print str(self.cur_time)
return pi.getNearestPrediction(self.region, str(self.cur_time))
from utility.prediction_interface import PredictionInterface
if __name__ == "__main__":
pi = PredictionInterface()
pi.setDB('citybeat')
pi.setCollection('online_prediction')
region = {
"min_lat": 40.79133132,
"max_lng": -73.93005052,
"min_lng": -73.9380568,
"max_lat": 40.7966366
}
condition = ({
'region.min_lat': region['min_lat'],
'region.min_lng': region['min_lng'],
'region.max_lat': region['max_lat'],
'region.max_lng': region['max_lng']
})
prediction = pi.getNearestPrediction(region, '1361801869') #1361772000
print prediction
def getNearestPrediction(self):
pi = PredictionInterface()
pi.setDB('citybeat')
pi.setCollection(self.prediction_collection)
return pi.getNearestPrediction(self.region, str(self.cur_time))
def getNearestPrediction(self):
pi = PredictionInterface()
pi.setDB("citybeat")
pi.setCollection(self.prediction_collection)
return pi.getNearestPrediction(self.region, str(self.cur_time))
from utility.prediction_interface import PredictionInterface
if __name__=="__main__":
pi = PredictionInterface()
pi.setDB('citybeat')
pi.setCollection('online_prediction')
region ={
"min_lat" : 40.79133132,
"max_lng" : -73.93005052,
"min_lng" : -73.9380568,
"max_lat" : 40.7966366
}
condition = ({'region.min_lat':region['min_lat'],
'region.min_lng':region['min_lng'],
'region.max_lat':region['max_lat'],
'region.max_lng':region['max_lng']})
prediction = pi.getNearestPrediction(region, '1361801869') #1361772000
print prediction