def station_output_auto(): """ Obtain predicted average daily rides at the best possible location. Add the new station to the station database. Regenerate the predicted average daily rides matrix. """ # get the user id number uid = userisactive_output() # google geo code API plugin to translate between address and lat/long googlegeo = makegeo() # get the user's directory growdir = getgrowdir(uid) # run the model on the best possible location prediction = gridpredict.autoinput(growdir) # get the list of stations, daily rides, and ranking of the new station stationslistdict, riderate, ranking = makeoutput(prediction, googlegeo) return render_template("output.html", riderate=riderate, ranking=ranking, stations=stationslistdict)
import gridpredict basedir = '../Data/Boston/' growdir = '../Data/Boston/growing/' gridpredict.resetiteration(basedir, growdir) test1 = gridpredict.autoinput(growdir) print(test1) import pdb; pdb.set_trace() test1 = gridpredict.autoinput(growdir) print(test1) import pdb; pdb.set_trace() test1 = gridpredict.autoinput(growdir) print(test1) test1 = gridpredict.autoinput(growdir) print(test1) test1 = gridpredict.autoinput(growdir) print(test1) test1 = gridpredict.autoinput(growdir) print(test1) test1 = gridpredict.autoinput(growdir) print(test1) # open street map for river outline # data thief
import gridpredict basedir = '../Data/Boston/' growdir = '../Data/Boston/growing/' gridpredict.resetiteration(basedir, growdir) test1 = gridpredict.autoinput(growdir) print(test1) import pdb pdb.set_trace() test1 = gridpredict.autoinput(growdir) print(test1) import pdb pdb.set_trace() test1 = gridpredict.autoinput(growdir) print(test1) test1 = gridpredict.autoinput(growdir) print(test1) test1 = gridpredict.autoinput(growdir) print(test1) test1 = gridpredict.autoinput(growdir) print(test1) test1 = gridpredict.autoinput(growdir) print(test1) # open street map for river outline # data thief