def queryToCollection(typology):
city = "Torino"
provider = "car2go"
initdate = "2017-9-5T00:00:00"
finaldate = "2017-11-2T00:00:00"
collections = {}
d1 = {}
d1["enjoy"] = "enjoy_PermanentBookings"
d1["car2go"] = "PermanentBookings"
collections["bookings"] = d1
d2 = {}
d2["enjoy"] = "enjoy_PermanentParkings"
d2["car2go"] = "PermanentParkings"
collections["parkings"] = d2
collection = collections[typology][provider]
initdate = int(
time.mktime(
datetime.datetime.strptime(initdate,
"%Y-%m-%dT%H:%M:%S").timetuple()))
finaldate = int(
time.mktime(
datetime.datetime.strptime(finaldate,
"%Y-%m-%dT%H:%M:%S").timetuple()))
mongo_collection = sf.setup_mongodb(collection)
data = mongo_collection.find({
"city": city,
"init_time": {
"$gt": initdate,
"$lt": finaldate
}
})
return data
def main():
collection="enjoy_PermanentBookings"
if(gv.provider == "car2go"):
collection = "PermanentBookings"
enjoy_bookings = sf.setup_mongodb(collection)
bookings = enjoy_bookings.find({"city": gv.city,
"init_time" :{"$gt" : gv.initDate , "$lt" : gv.finalDate}});
# geolocator = Nominatim()
# location = geolocator.geocode("Torino")
#baselon = location.longitude
#baselat = location.latitude
i=0 #id del booking, numero progressivo
NumEvents=0
NumEventsFiltered=0
Discarted=0
for booking in bookings:
initt = booking['init_time']
finalt= booking['final_time']
duration = finalt - initt
coords = booking['origin_destination']['coordinates']
lon1 = coords[0][0]
lat1 = coords[0][1]
lon2 = coords[1][0]
lat2 = coords[1][1]
#d = haversine(baselon, baselat, lon2, lat2)
#d1 = haversine(baselon, baselat, lon1, lat1)
d2 = sf.haversine(lon1, lat1, lon2, lat2)
if(duration > 120 and duration<3600 and d2>500):
# if( sf.checkPerimeter(lat1, lon1) and sfcheckPerimeter(lat2, lon2) or
# (provider == "car2go" and ((checkPerimeter(lat1, lon1) and checkCasellePerimeter(lat2, lon2)) or (checkCasellePerimeter(lat1, lon1) and checkPerimeter(lat2, lon2))))):
if sf.checkPerimeter(lat1, lon1) and sf.checkPerimeter(lat2, lon2):
NumEvents+=1
id_events[i] = [booking['init_time'],booking['final_time'],EventBook(i,"s", booking["origin_destination"]['coordinates'][0]),EventBook(i ,"e", booking["origin_destination"]['coordinates'][1])]
if booking['init_time'] not in dict_bookings:
dict_bookings[booking['init_time']]=[]
dict_bookings[booking['init_time']].append([i,"s"])
if booking['final_time'] not in dict_bookings:
dict_bookings[booking['final_time']]=[]
dict_bookings[booking['final_time']].append([i,"e"])
i=i+1
if(i<1000):
if booking['init_time'] not in dict_bookings_short:
dict_bookings_short[booking['init_time']]=[]
dict_bookings_short[booking['init_time']].append(EventBook(i,"s", booking["origin_destination"]['coordinates'][0]))
if booking['final_time'] not in dict_bookings_short:
dict_bookings_short[booking['final_time']]=[]
dict_bookings_short[booking['final_time']].append(EventBook(i ,"e", booking["origin_destination"]['coordinates'][1]))
else:
Discarted+=1
with open("../events/"+ gv.city + "_" + gv.provider + "_dict_bookings.pkl", 'wb') as handle:
pickle.dump( dict_bookings, handle)
with open("../events/"+ gv.city + "_" + gv.provider + "_id_events.pkl", 'wb') as handle:
pickle.dump( id_events, handle)
print("End Pickles")
'''exit(0)
dict_bookings= pickle.load( open( "../events/"+operator+"_dict_bookings.pkl", "rb" ) )
id_events= pickle.load( open( "../events/"+operator+"_id_events.pkl", "rb" ) )
'''
print("Start")
to_delete = []
EventDeleted=0
for stamp in dict_bookings:
startbooking = 0
for event in dict_bookings[stamp]:
if(event[1]=="s"): startbooking+=1
if(startbooking>30):
EventDeleted+=startbooking
to_delete.append(stamp)
for stamp in to_delete:
events_to_delete = []
for event in dict_bookings[stamp]:
if(event[1] == "s"): events_to_delete.append(event[0])
for event in events_to_delete:
InitTime = id_events[event][0]
FinalTime = id_events[event][1]
InitInd = dict_bookings[InitTime].index([event,"s"])
FinalInd = dict_bookings[FinalTime].index([event,"e"])
del dict_bookings[InitTime][InitInd]
del dict_bookings[FinalTime][FinalInd]
if(len(dict_bookings[stamp])==0):
del dict_bookings[stamp]
for stamp in dict_bookings:
for i in range(0,len(dict_bookings[stamp])):
NumEventsFiltered+=1
EventT = dict_bookings[stamp][i]
if(EventT[1] == "s"): dict_bookings[stamp][i]=id_events[EventT[0]][2]
else: dict_bookings[stamp][i]=id_events[EventT[0]][3]
print("CPE, Num Events Filtered + Event deleted:",NumEventsFiltered+EventDeleted)
print("CPE, Num Events Filtered:", NumEventsFiltered)
print("CPE, Event Deleted:", EventDeleted)
print("CPE, Dicarded:", Discarted)
ordered_dict_booking = collections.OrderedDict(sorted(dict_bookings.items()))
ordered_dict_booking_short = collections.OrderedDict(sorted(dict_bookings_short.items()))
with open("../events/"+ gv.city + "_" + gv.provider + "_sorted_dict_events_obj.pkl", 'wb') as handle:
pickle.dump( ordered_dict_booking, handle)
with open("../events/"+ gv.city + "_" + gv.provider + "_sorted_dict_events_obj_short.pkl", 'wb') as handle:
pickle.dump( ordered_dict_booking_short, handle)
print ("CPE, end\n")
def formatBookings():
collection = "enjoy_PermanentBookings"
if gv.provider == "car2go":
collection = "PermanentBookings"
enjoy_bookings = sf.setup_mongodb(collection)
print("***********************")
print("city", gv.city)
print(
"initDate ",
datetime.datetime.fromtimestamp(int(
gv.initDate)).strftime('%Y-%m-%d %H:%M:%S'))
print(
"fianlDate",
datetime.datetime.fromtimestamp(int(
gv.finalDate)).strftime('%Y-%m-%d %H:%M:%S'))
print("***********************")
bookings = enjoy_bookings.find({
"city": gv.city,
"init_time": {
"$gt": gv.initDate,
"$lt": gv.finalDate
}
})
bookings_df = pd.DataFrame(list(bookings))
if gv.city == "Vancouver":
bookings_df["init_time"] = bookings_df["init_time"].sub(25200)
bookings_df["final_time"] = bookings_df["final_time"].sub(25200)
bookings_df.to_pickle('../input/bookings_' + gv.city)
bookings_df[
"duration"] = bookings_df["final_time"] - bookings_df["init_time"]
bookings_df["duration"] = bookings_df["duration"].astype(int)
bookings_df = bookings_df.drop('driving', 1)
bookings_df['type'] = bookings_df.origin_destination.apply(
lambda x: x['type'])
bookings_df['coordinates'] = bookings_df.origin_destination.apply(
lambda x: x['coordinates'])
bookings_df = bookings_df.drop('origin_destination', 1)
bookings_df['end'] = bookings_df.coordinates.apply(lambda x: x[0])
bookings_df['start'] = bookings_df.coordinates.apply(lambda x: x[1])
bookings_df = bookings_df.drop('coordinates', 1)
bookings_df['start_lon'] = bookings_df.start.apply(lambda x: float(x[0]))
bookings_df['start_lat'] = bookings_df.start.apply(lambda x: float(x[1]))
bookings_df = bookings_df.drop('start', 1)
bookings_df['end_lon'] = bookings_df.end.apply(lambda x: float(x[0]))
bookings_df['end_lat'] = bookings_df.end.apply(lambda x: float(x[1]))
bookings_df = bookings_df.drop('end', 1)
bookings_df['distance'] = bookings_df.apply(
lambda x: sf.haversine(float(x['start_lon']), float(x['start_lat']),
float(x['end_lon']), float(x['end_lat'])),
axis=1)
bookings_df = bookings_df[bookings_df["distance"] >= 700]
bookings_df = bookings_df[bookings_df["duration"] >= 120]
bookings_df = bookings_df[bookings_df["duration"] <= 3600]
if gv.city == "Torino":
bookings_df = bookings_df[bookings_df["start_lon"] <= 7.8]
return bookings_df
def main():
collection="enjoy_PermanentParkings"
if gv.provider == "car2go":
collection = "PermanentParkings"
collection_parkings = sf.setup_mongodb(collection)
parkings = collection_parkings.find({"city": gv.city,
"init_time": {"$gt": gv.initDate,
"$lt": gv.finalDate}
})
parkigns2 = parkings.clone()
if gv.fleetSize.isnumeric():
realFleetSize = int(gv.fleetSize)
else :
df = pd.DataFrame(list(parkigns2))
df["DailyDate"] = df["init_date"].apply(lambda x : x.strftime("%Y/%m/%d"))
carsPerDay = df.groupby('DailyDate')["plate"].nunique()
# carsPerDay = pd.Series(11)
if gv.fleetSize == "mean":
realFleetSize = int(round(carsPerDay.mean()))
elif gv.fleetSize == "max":
realFleetSize = int(carsPerDay.max())
elif gv.fleetSize == "min":
realFleetSize = int(carsPerDay.min())
else:
print("CCID, ERROR wrong fleetSize Value: "+str(gv.fleetSize))
return -1
parkigns2.close()
print ("CCID, realFleetSize:",str(realFleetSize), "gv.fleetSize:", str(gv.fleetSize))
currentFleetSize = 0
for val in parkings:
coords = val['loc']['coordinates']
lon1 = coords[0]
lat1 = coords[1]
#d = haversine(baselon, baselat, lon1, lat1)
# if( checkPerimeter(lat1, lon1) or
# (provider == "car2go" and checkCasellePerimeter(lat1, lon1)) and
# currentFleetSize <= FleetSize):
if currentFleetSize < realFleetSize:
if sf.checkPerimeter(lat1, lon1):
if val['plate'] not in dict_plates:
dict_plates[val['plate']] = PlatesData(val['init_time'], val["loc"]['coordinates'])
currentFleetSize += 1
else:
if dict_plates[val['plate']].timestamp >= val['init_time']: #se non erano in ordine nel dataset iniziale
dict_plates[val['plate']] = PlatesData(val['init_time'], val["loc"]['coordinates'])
else:
print("problem")
else :
print("CCID, len dict_plates:" + str(len(dict_plates)) + "FleetSize:" + str(realFleetSize))
print("CCID, cfs", currentFleetSize)
break
print("CCID", "Seen cars:", len(dict_plates))
print("cfs", currentFleetSize)
with open("../input/"+ gv.city + "_" + gv.provider + "_plates_appeareance_obj.pkl", 'wb') as handle:
pickle.dump(dict_plates, handle)
print ("CCID, col:", gv.NColumns, " row:", gv.NRows)
print ("CCID, End\n")
return