def petition(handler):
"""
Redirecting petition to SoundClouds Service
"""
# client_object = Clients()
sound_cloud_client = Clients().sound_cloud_client()
handler.redirect(sound_cloud_client.authorize_url())
def sound_cloud_petition_resolved(handler):
"""
Getting back from redirection of SoundCloud and getting Access_Token
:param code: Code extracted from the URL that SoundCloud Redirect (Default)
"""
code = handler.get_argument('code')
sound_cloud_client = Clients().sound_cloud_client()
access_token = sound_cloud_client.exchange_token(code)
current_user = sound_cloud_client.get('/me').username
DANaix.initialize_client(current_user, access_token.access_token)
return "ok" # Improve messages. Change to Json
def read_clients(self, clientsFile):
"""
Read a file with clients into a collection.
Requires:
.clientsFile, str with the name of a text file with a list of clients.
Ensures:
.clientsList, a collection of clients
"""
openFile = open(clientsFile, 'r')
for i in range(7): # skips the header
openFile.readline()
clientsList = []
for line in openFile:
lineList = line.strip().split(
', '
) # splits the line (1 client) and inserts a comma between every field
clientsList.append(
Clients(lineList[0], lineList[1], lineList[2], lineList[3],
float(lineList[4]), lineList[5], lineList[6],
lineList[7])) #writes the experts field by field
return clientsList
def innskraning():
nyr = input("Ertu nýr viðskiptavinur?(ja/nei): ")
if nyr == "ja":
Clients.newClient()
rettnafn = input("Sláðu inn nafn til að skrá þig inn: ")
print("Velkominn aftur", rettnafn)
print()
elif nyr == "nei":
til = False
nafn = input("Hvað heitir þú? ")
for client in clientlist:
if client.nafn == nafn:
til = True
rettnafn = client.nafn
if til:
print("Velkominn aftur", rettnafn)
print()
else:
print("Enginn er skráður undir þessu nafni")
else:
print("Þú verður að velja ja eða nei")
return rettnafn
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans, AffinityPropagation
from sklearn.model_selection import train_test_split
import warnings
from Clients import Client, Clients
from Recommender import ColabRecommender, ContentRecommender
warnings.filterwarnings("ignore")
data = pd.read_csv('./data/german_credit_data.csv')
numeric_data = data.loc[:,["Age","Credit amount","Duration"]]
clients = Clients(numeric_data)
indices = np.arange(1000)
#An'alisis de datos...
X = clients.getX()
y = clients.getY()
X_train, X_test, Y_train, y_test,indices_train,indices_test = train_test_split(X,y,indices,test_size=0.2, random_state=1, stratify=y)
contentRecommender = ContentRecommender(X_train,Y_train)
# client = Client(age=45,credit_amount=2000,duration=2)
# print(indices_test)
def __str__(self):
"""
The string representation of the Clients and Experts
"""
return Clients.__str__(), Experts.__str__()
if bike.name == hjol and bike.status == "available":
til = True
rent = Rented(nafn, basis, hjol)
rentedlist.append(rent)
bike.status = "rented"
if til:
pass
else:
print(
"Þetta hjól er annaðhvort ekki til eða ekki laust. Endilega reynið aftur."
)
bikes = Bike.readBikes()
clientlist = Clients.readclient()
rentedlist = Rented.readRented()
flag = True
while flag:
val = ""
print("""
====== Bike Rental Shop ======
1. Display available bikes
2. Request a bike on hourly basis $5
3. Request a bike on daily basis $20
4. Request a bike on weekly basis $60
5. Return a bike
6. Save/Exit
""")