def getFilteredParkings(self, lat, lon, night_parking, free_parking,
car_park_type, type_of_parking_system):
nn = NN()
df = nn.getCurrentAvailability()
df = pd.merge(df,
self.parkings,
left_on=['carpark_number'],
right_on=['carpark_number'])
id = self.DistCalc(lat, lon)
# filtparkings=df[(df['night_parking']==night_parking) & (df['free_parking']==free_parking) &(df['car_park_type'].isin(car_park_type)) & (df['type_of_parking_system']==type_of_parking_system)]
# filteredparkings=filtparkings[filtparkings['carpark_number'].isin(id)]
# print(filteredparkings)
# res=filteredparkings[['carpark_number','lat','lng','lots_available']].set_index('carpark_number').T.to_json()
filtparkings = df
if night_parking != None:
filtparkings = filtparkings[(
filtparkings['night_parking'] == night_parking)]
if free_parking != None:
filtparkings = filtparkings[(
filtparkings['free_parking'] == free_parking)]
if car_park_type != None:
filtparkings = filtparkings[(
filtparkings['car_park_type'].isin(car_park_type))]
if type_of_parking_system != None:
filtparkings = filtparkings[(filtparkings['type_of_parking_system']
== type_of_parking_system)]
filteredparkings = filtparkings[filtparkings['carpark_number'].isin(
id)]
#filtparkings=df[(df['night_parking']==req.get("night_parking")) & (df['free_parking']==req.get("free_parking")) & (df['car_park_type'].isin(req.get("car_park_type"))) & (df['type_of_parking_system']==req.get("type_of_parking_system"))]
print(filteredparkings)
res = filteredparkings[[
'carpark_number', 'lat', 'lng', 'lots_available'
]].set_index('carpark_number').T.to_json()
return res
class Parking:
def __init__(self):
info_df = pd.read_csv('hdb-carpark-information-with-lat-lng.csv')
self.nn = NN()
df = self.nn.getCurrentAvailability()
self.info_df = pd.merge(df,
info_df,
left_on=['carpark_number'],
right_on=['carpark_number'])
self.parkings = self.info_df[[
'carpark_number', 'lat', 'lng', 'night_parking', 'free_parking',
'car_park_type', 'type_of_parking_system'
]]
self.db = firestore.client()
def initializeLocations(self):
#for every record in the df, store it in the parking_info collection using the parking ID as document ID
parking = self.info_df.set_index('carpark_number').T.to_json()
parking = json.loads(parking)
#sending the array to firestored
data = {u'parking': parking}
self.db.collection(u'parkingsinfo').document('parkings').set(data)
#return true after you're done
return (True, )
def updateCurrentAvailability(self):
next_call = time.time()
while True:
print("getting current availability")
parking = self.nn.getCurrentAvailability()
parking = self.info_df.set_index('carpark_number')[[
'lots_available'
]].T.to_json()
parking = json.loads(parking)
data = {u'current_availability': parking}
self.db.collection(u'parking_info').document('parkings').set(data)
print("pushed to db")
next_call += 60
print("sleeping")
time.sleep(next_call - time.time())
def DistCalc(self, latitude, longtitude):
R = 6373.0
location = []
lattemp = latitude
lontemp = longtitude
j = 0
for i in range(len(self.parkings.lat)):
lat1 = radians(self.parkings.lat[i])
lon1 = radians(self.parkings.lng[i])
lat2 = radians(lattemp)
lon2 = radians(lontemp)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance = R * c
if (distance < 1.0000):
location.append(self.parkings.carpark_number[i])
print(location[j])
j += 1
return location
def getFilteredParkings(self, lat, lon, night_parking, free_parking,
car_park_type, type_of_parking_system):
nn = NN()
df = nn.getCurrentAvailability()
df = pd.merge(df,
self.parkings,
left_on=['carpark_number'],
right_on=['carpark_number'])
id = self.DistCalc(lat, lon)
# filtparkings=df[(df['night_parking']==night_parking) & (df['free_parking']==free_parking) &(df['car_park_type'].isin(car_park_type)) & (df['type_of_parking_system']==type_of_parking_system)]
# filteredparkings=filtparkings[filtparkings['carpark_number'].isin(id)]
# print(filteredparkings)
# res=filteredparkings[['carpark_number','lat','lng','lots_available']].set_index('carpark_number').T.to_json()
filtparkings = df
if night_parking != None:
filtparkings = filtparkings[(
filtparkings['night_parking'] == night_parking)]
if free_parking != None:
filtparkings = filtparkings[(
filtparkings['free_parking'] == free_parking)]
if car_park_type != None:
filtparkings = filtparkings[(
filtparkings['car_park_type'].isin(car_park_type))]
if type_of_parking_system != None:
filtparkings = filtparkings[(filtparkings['type_of_parking_system']
== type_of_parking_system)]
filteredparkings = filtparkings[filtparkings['carpark_number'].isin(
id)]
#filtparkings=df[(df['night_parking']==req.get("night_parking")) & (df['free_parking']==req.get("free_parking")) & (df['car_park_type'].isin(req.get("car_park_type"))) & (df['type_of_parking_system']==req.get("type_of_parking_system"))]
print(filteredparkings)
res = filteredparkings[[
'carpark_number', 'lat', 'lng', 'lots_available'
]].set_index('carpark_number').T.to_json()
return res
def generateSequencePrediction(self):
next_call = time.time()
while True:
df_list = self.nn.getSequenceFromCurrentTime()
self.df_list_for_nn = self.nn.modifyDataframeListForNN(df_list)
predictions = {}
#generate prediction
for df in self.df_list_for_nn:
model = self.models[df.carpark_number.iloc[0]]
prediction = self.nn.generatePrediction(model, df)
prediction = prediction[0].tolist()
predictions[df.carpark_number.iloc[0]] = prediction
print("[", df.carpark_number.iloc[0], "] = ", prediction)
#push to firebase
data = {u'predictions': predictions}
self.db.collection(u'parking_predictions').document(
'predictions').set(data)
next_call += 60 * 15 # every 15 minutes
time.sleep(next_call - time.time())
def loadModels(self):
#read all the models from the folder
#use this:
self.models = {}
directory = r"./trained_models/"
for filename in os.listdir(directory):
#load model
model = load_model(directory + filename,
custom_objects=None,
compile=True,
options=None)
#get carpark id from filename
filename_split = filename.split('_')
carpark_id_split = filename_split[-1].split('.')
carpark_id = carpark_id_split[0]
#add model to dict with car_park id as key
self.models[carpark_id] = model
print("[" + carpark_id + "]=\t")
print(self.models[carpark_id])