def queryBookings(typology):
books_cursor = queryToCollection(typology)
if (books_cursor == "err from cursor" or books_cursor.count() == 0):
return "err"
else:
# print books_cursor.count()
# bookings_df = pd.DataFrame(columns = pd.Series(books_cursor.next()).index)
bookings_df = pd.DataFrame(list(books_cursor))
bookings_df['duration_dr'] = bookings_df.driving.apply(
lambda x: float(x['duration'] / 60))
bookings_df['distance_dr'] = bookings_df.driving.apply(
lambda x: x['distance'])
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['start'] = bookings_df.coordinates.apply(lambda x: x[0])
bookings_df['end'] = 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[
'duration'] = bookings_df.final_date - bookings_df.init_date
bookings_df['duration'] = bookings_df['duration'].apply(
lambda x: x.days * 24 * 60 + x.seconds / 60)
bookings_df['duration_pt'] = bookings_df.public_transport.apply(
lambda x: x['duration'])
bookings_df['distance_pt'] = bookings_df.public_transport.apply(
lambda x: x['distance'])
bookings_df['arrival_date_pt'] = bookings_df.public_transport.apply(
lambda x: x['arrival_date'])
bookings_df['arrival_time_pt'] = bookings_df.public_transport.apply(
lambda x: x['arrival_time'])
bookings_df = bookings_df.drop('public_transport', 1)
bookings_df = bookings_df[bookings_df["start_lon"] <= 7.8]
return bookings_df
def EvalDistance(i, j):
Xi = i % gv.NColumns
Yi = int(i / gv.NColumns)
Xj = j % gv.NColumns
Yj = int(j / gv.NColumns)
CentalLoni = (Xi + 0.5) * gv.ShiftLon + gv.minLon
CentalLati = (Yi + 0.5) * gv.ShiftLat + gv.minLat
CentalLonj = (Xj + 0.5) * gv.ShiftLon + gv.minLon
CentalLatj = (Yj + 0.5) * gv.ShiftLat + gv.minLat
dh = sf.haversine(CentalLoni, CentalLati, CentalLonj, CentalLatj)
de = sqrt(pow((Xi - Xj), 2) + pow((Yi - Yj), 2))
return de, dh
def formatBookings(d):
bookings_df = ""
if (os.path.isfile('../input/bookings_' + d["city"]) == False):
collection = "enjoy_PermanentBookings"
if (d["provider"] == "car2go"):
collection = "PermanentBookings"
enjoy_bookings = dc.setup_mongodb(collection)
print("***********************")
print("city", d["city"])
print(
"initdate ",
datetime.fromtimestamp(int(
d["initdate"])).strftime('%Y-%m-%d %H:%M:%S'))
print(
"fianldate",
datetime.fromtimestamp(int(
d["finaldate"])).strftime('%Y-%m-%d %H:%M:%S'))
print("***********************")
bookings = enjoy_bookings.find({
"city": d["city"],
"init_time": {
"$gt": d["initdate"],
"$lt": d["finaldate"]
}
})
bookings_df = pd.DataFrame(list(bookings))
if ("Europe" not in d["timezone"]):
#convert from their time zone in our time zone
bookings_df['init_time'] = list(
map(AbroadStamptoLocal, bookings_df["timezone"],
bookings_df['init_time']))
bookings_df['final_time'] = list(
map(AbroadStamptoLocal, bookings_df["timezone"],
bookings_df['final_time']))
bookings_df.to_pickle('../input/bookings_' + d["city"])
else:
print("read")
bookings_df = pd.read_pickle('../input/bookings_' + d["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 d["city"] == "Torino":
bookings_df = bookings_df[bookings_df["start_lon"] <= 7.8]
return bookings_df
def main():
if (os.path.isfile("creating.txt") == False):
print("missing creating file")
exit(0)
city_config = open("creating.txt", "r")
city = city_config.readline().strip()
city_config.close()
gv.init()
dc.assingVariables(city)
collection = "enjoy_PermanentBookings"
if (gv.provider == "car2go"):
collection = "PermanentBookings"
enjoy_bookings = dc.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")
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