def play():
jugando = True
Intentos = 7
jugando = [True, Intentos]
listapalabras = DarIncongnita.DarIncognitaDesdeArchivo(
) #Hacer desde otra funcion para no llamar la lista del archivo cada vez que se llame esta funcion
palaacer = [] #Crea una lista de palabras acertadas
puntuacion = -1
while jugando[0]:
puntuacion = puntuacion + 1
jugando, listapalabras, palaacer = playMatch(jugando[1], listapalabras,
palaacer, puntuacion)
#aca va venir el tema de guardar la puntuacion
#pedir nombre y eso.
nombre = Ranking.pedir_ingreso_nombre()
Puntuacion_a_Guardar = Ranking.crear_tupla_nombre_puntos(
nombre, puntuacion)
listaPuntos_aGuardar = Ranking.meter_nueva_puntuacion_a_lista(
Puntuacion_a_Guardar, Ranking.CargarRanking())
Ranking.GuardarRanking(listaPuntos_aGuardar)
def main_screen():
pygame.mouse.set_visible(True)
start_image = pygame.image.load('main.png')
pygame.display.set_caption('수룡 게임 천국')
screen.blit(start_image, [0, 0])
pygame.display.update()
for event in pygame.event.get():
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_1:
PySpaceShip.main_loop()
return 'main_screen'
elif event.key == pygame.K_2:
racingcar.main_loop()
return 'main_screen'
elif event.key == pygame.K_3:
PyShooting.initGame()
PyShooting.runGame()
return 'main_screen'
elif event.key == pygame.K_4:
Ranking.main_loop()
return 'main_screen'
elif event.key == pygame.K_5:
windowsC.main_loop()
return 'main_screen'
if event.type == QUIT:
return 'quit'
return 'main_screen'
class SaveAs(QtGui.QWidget):
def __init__(self):
QtGui.QWidget.__init__(self)
self.setupUi(self)
def setupUi(self, Form):
self.Rank = Ranking()
Form.setObjectName(_fromUtf8("Form"))
Form.resize(70, 20)
p = self.palette()
p.setColor(self.backgroundRole(), QtCore.Qt.darkRed)
self.setPalette(p)
font = QtGui.QFont()
font.setFamily(_fromUtf8("MS UI Gothic"))
font.setPointSize(10)
font.setBold(True)
font.setWeight(75)
self.Layout = QtGui.QVBoxLayout(Form)
self.Layout.setObjectName(_fromUtf8("verticalLayout"))
self.texto = QtGui.QLabel(Form)
self.texto.setFont(font)
self.texto.setText('GAME OVER')
self.Layout.addWidget(self.texto)
self.puntos = QtGui.QLabel(Form)
self.puntos.setFont(font)
self.puntos.setText('SCORE: {}'.format('0'))
self.Layout.addWidget(self.puntos)
self.User = QtGui.QLineEdit(Form)
self.User.setObjectName(_fromUtf8("plainTextEdit"))
self.Layout.addWidget(self.User)
self.buttonBox = QtGui.QDialogButtonBox(Form)
self.buttonBox.setStandardButtons(QtGui.QDialogButtonBox.Ok)
self.buttonBox.setObjectName(_fromUtf8("buttonBox"))
self.Layout.addWidget(self.buttonBox)
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
self.buttonBox.clicked.connect(self.IngresarNombre)
shortcut = QtGui.QShortcut(QtGui.QKeySequence("Enter"), self, self.IngresarNombre)
shortcut.setContext(QtCore.Qt.WidgetShortcut)
def retranslateUi(self, Form):
Form.setWindowTitle(_translate("Game Over", "Game Over", None))
def setPuntaje(self, puntaje, time, mensaje=None):
if mensaje is not None:
self.texto.setText(mensaje)
self.puntaje = str(puntaje)
self.time = time
self.puntos.setText('SCORE: {0}\n Time: {1}'.format(self.puntaje, self.time))
def IngresarNombre(self):
usuario = self.User.text()
if len(usuario) > 0:
Data = SaveData(usuario, self.puntaje, self.time)
self.Rank.setRanking(Data, usuario)
self.Rank.show()
self.hide()
async def getRanking(ctx):
#userID = str(ctx.message.author.id)
avatarLink = str(ctx.author.avatar_url)
userName = ctx.author.name
Ranking.createRankingCard(avatarLink, userName, str(ctx.author.id))
await ctx.send((ctx.message.author.mention),
file=discord.File('ranking.png'))
os.system('rm ranking.png')
os.system('rm avatar.png')
def testCreateFPLink(self):
self.assertEqual('http://www.fantasypros.com/nfl/rankings/ros-qb.php?export=xls',
Ranking.create_standard_link('QB'))
self.assertEqual('http://www.fantasypros.com/nfl/rankings/ros-ppr-te.php?export=xls',
Ranking.create_ppr_link('TE'))
self.assertEqual([('standard', 'http://www.fantasypros.com/nfl/rankings/ros-'
'wr.php?export=xls'),
('half_ppr', 'http://www.fantasypros.com/nfl/rankings/ros-'
'half-point-ppr-wr.php?export=xls'),
('ppr', 'http://www.fantasypros.com/nfl/rankings/ros-'
'ppr-wr.php?export=xls')], Ranking.create_all_links('WR'))
def drawPictures():
rank = Ranking.ranking()
rank.loadAll()
queries = formQueries()
result = defaultdict(list)
for query in queries:
score = getRankScores(rank, query)
result['queryID'].append(query.ID)
result['precision'].append(precision(query, score, 10))
result['averagePrecision'].append(averagePrecision(query, score, 10))
result['precisionRecall'].append(precisionRecall(query, score))
plt.plot(result['queryID'], result['precision'],'ro', label="Precision")
plt.plot(result['queryID'], result['averagePrecision'],'bo', label="averagePrecision")
plt.plot(result['queryID'], result['precisionRecall'],'yo', label="precisionRecall")
plt.axis([0, len(result['queryID']), 0, 0.6])
plt.xlabel('Query ID')
plt.ylabel('Value')
plt.legend()
plt.savefig('rst.png')
print "Overall Averaged Precision: " + str(sum(result['precision']) / len(queries))
plt.show()
def IRTest(texts, ir, num_cluster, percentage):
kmeans = KMeans(n_clusters=num_cluster).fit(ir.corpus.TFIDF)
label = list(zip(kmeans.labels_, range(len(texts))))
test = []
for cluster in range(num_cluster):
#siapkan untuk summarization
label_doc = list(filter(lambda x : x[0] == cluster, label))
label_text = [texts[label_doc[i][1]] for i in range(len(label_doc))]
label_tfidf = np.array([ir.corpus.TFIDF[label_doc[i][1]] for i in range(len(label_doc))])
summa = Ranking.TextRank(label_tfidf).summarize(label_text, percentage)
#retrive doc dengan query summa
retrieved = [doc[0] for doc in ir.query(summa, len(label_doc))]
#hitung confusion matrix
dic_label = {page[1]:True for page in label_doc}
dic_retrieved = {page:True for page in retrieved}
TP = sum([1 if page in dic_label else 0 for page in retrieved])
FP = sum([1 if page not in dic_label else 0 for page in retrieved])
FN = sum([1 if page not in dic_retrieved else 0 for page in label_doc])
#hitung recall dan precision
precision = float(TP) / (TP + FP)
recall = float(TP) / (TP + FN)
fmeasure = 2 * (precision * recall / (precision + recall))
test.append([precision, recall, fmeasure])
return test
def MenuPrincipal():
imprimir_menu()
opcion = input("")
Beep.Beep()
while (opcion != '1' and opcion != '2' and opcion != '3'):
print("==>Por Favor eliga opción 1 o 2<==")
opcion = input("")
Beep.Beep()
Limpiar.Limpiar()
if (opcion == '1'):
Play.play()
time.sleep(3)
return True
elif (opcion == '2'):
Limpiar.Limpiar()
Ranking.ImprimirRanking()
input("presione enter para volver...")
return True
elif (opcion == '3'):
despedir()
return False
def testReadFile(self):
rows = Ranking.read_tsv(TestData('qbs.tsv'))
self.assertEqual(['1', 'Aaron Rodgers', 'GB', 'vs. KC', '1', '1', '1', '0'],
rows[0])
self.assertEqual(['37','Jimmy Clausen','CHI','at SEA','34','34','34','0'],
rows[36])
self.assertEqual(37, len(rows))
def Principi():
entrada = True
while (entrada):
print(" MENU")
print("===============")
print("1--> Crear usuari")
print("2--> Iniciar Sessio")
print("3--> Ranking")
print("4--> Eliminar Usuari")
print("5--> Sortir")
opcio = (input("OPCIO --> "))
if (opcio == "1"):
os.system('cls')
OpcionsUsuari.crearUsuari(mycursor)
elif (opcio == "2"):
os.system('cls')
IniciSessio.iniciarsessio(mycursor)
elif (opcio == "3"):
os.system('cls')
Ranking.Ranking(mycursor)
elif (opcio == "4"):
os.system('cls')
OpcionsUsuari.eliminarusuari(mycursor)
elif (opcio == "5"):
print("Adeu!")
entrada = False
else:
os.system('cls')
print("valor incorrecte")
def setupUi(self, Form):
self.Rank = Ranking()
Form.setObjectName(_fromUtf8("Form"))
Form.resize(70, 20)
p = self.palette()
p.setColor(self.backgroundRole(), QtCore.Qt.darkRed)
self.setPalette(p)
font = QtGui.QFont()
font.setFamily(_fromUtf8("MS UI Gothic"))
font.setPointSize(10)
font.setBold(True)
font.setWeight(75)
self.Layout = QtGui.QVBoxLayout(Form)
self.Layout.setObjectName(_fromUtf8("verticalLayout"))
self.texto = QtGui.QLabel(Form)
self.texto.setFont(font)
self.texto.setText('GAME OVER')
self.Layout.addWidget(self.texto)
self.puntos = QtGui.QLabel(Form)
self.puntos.setFont(font)
self.puntos.setText('SCORE: {}'.format('0'))
self.Layout.addWidget(self.puntos)
self.User = QtGui.QLineEdit(Form)
self.User.setObjectName(_fromUtf8("plainTextEdit"))
self.Layout.addWidget(self.User)
self.buttonBox = QtGui.QDialogButtonBox(Form)
self.buttonBox.setStandardButtons(QtGui.QDialogButtonBox.Ok)
self.buttonBox.setObjectName(_fromUtf8("buttonBox"))
self.Layout.addWidget(self.buttonBox)
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
self.buttonBox.clicked.connect(self.IngresarNombre)
shortcut = QtGui.QShortcut(QtGui.QKeySequence("Enter"), self, self.IngresarNombre)
shortcut.setContext(QtCore.Qt.WidgetShortcut)
def do_POST(self):
self.send_response(200)
self.send_header('Content-type', 'text/html')
content_length = int(self.headers.getheader('Content-Length'))
body = self.rfile.read(content_length).split('\r\n')
text = body[3]
query = testCFCQueries.query()
query.readFromString(text)
rank = Ranking.ranking()
rank.loadAll()
scores = testCFCQueries.getRankScores(rank, query)
relevantFiles = []
for i in range(min(20, len(scores))):
if scores[i][1] == 0:
break
else:
relevantFiles.append(scores[i][0])
self.end_headers()
self.wfile.write('<div></h2>Your Query: ' + text + "</h2>")
if not relevantFiles:
self.wfile.write(
'<h3>Sorry, there is no match for your query.</h3>')
else:
self.wfile.write('<h3>Search Results(At most 20):</h3>')
for file in relevantFiles:
self.wfile.write("<div>File ID: " + file)
self.wfile.write("<li>")
self.wfile.write(rank.IP.docText[file] + "</li></div><hr>")
self.wfile.write('</div></body></html>')
def __init__(self, folder):
self.corpus = TextMining.load_from_file(folder+"/tfidf")
self.ranker = Ranking.load_from_file(folder+"/pagerank")
self.text_folder = folder+"/text"
self.pdf_folder = folder+"/pdf_pages"
tfidf = self.corpus.TFIDF
with concurrent.futures.ThreadPoolExecutor() as executor:
tfidf = np.array(list(executor.map(lambda x : x/np.linalg.norm(x), tfidf)))
self.corpus.TFIDF = tfidf
def guess_points(player_name, position):
url = Ranking.create_standard_link(position)
rankings = Ranking.read_tsv(Ranking.download_rankings(url))
rank = rankings.index(player_name)
logging.info('{}\'s rest of season ranking is {}.'.format(player_name, rank+1))
db = PlayerDB()
year_scores = []
calc = Calculator(read_scoring_file(get_resource('standard.json')))
for year in range(2009, 2015):
year_scores.append(calc.sort_position(db.query_players_by_year(year), position))
avg_scores = average_lists(*year_scores)
avg_score = avg_scores[rank]
logging.info('On average (from 2009-2015) players that finished with the {}{} '
'most points scored {} points in a year, or {}/game.'
.format(rank+1, suffix(rank+1), avg_score, avg_score/16.0))
# TODO: What do we divide this number by? Not all players play 16 games
return avg_score / 16.0
def fast_guess_points(scoring, player_name, position):
averages = get_averages(scoring, position)
ranking = Ranking.get_ranking(scoring, player_name, position)
avg_score = averages[ranking]
logging.info('Guessing expected points for {} ({}) with {} scoring'.format(player_name, position, scoring))
logging.info('Rest of season {} ranking: {}'.format(position, ranking+1))
logging.info('On average (from 2009-2015) players that finished with the {}{} '
'most points scored {} points in a year, or {}/game.'
.format(ranking+1, suffix(ranking+1), avg_score, avg_score/16.0))
return round(avg_score / 16.0, 2)
def __init__(self, folder):
#Load file hasil preprocessing
self.corpus = TextMining.load_from_file(folder+"/tfidf")
self.ranker = Ranking.load_from_file(folder+"/pagerank")
#simpan file raw txt dan pdf
self.text_folder = folder+"/text"
self.pdf_folder = folder+"/pdf_pages"
#Normalisasi TFIDF
tfidf = self.corpus.TFIDF
with concurrent.futures.ThreadPoolExecutor() as executor:
tfidf = np.array(list(executor.map(lambda x : x/np.linalg.norm(x), tfidf)))
self.corpus.TFIDF = tfidf
def calculateSimilarItems(prefs,n=10):
# Create a dictionary of items showing which other items they
# are most similar to.
result={}
# Invert the preference matrix to be item-centric
itemPrefs = GetRecommendation.transformPrefs(prefs)
for item in itemPrefs:
# Find the most similar items to this one
scores=Ranking.topMatches(itemPrefs,item,n=n,similarity= sim_distance.sim_distance)
result[item]=scores
return result
def calculateSimilarItems(prefs, n=10):
# Create a dictionary of items showing which other items they
# are most similar to.
result = {}
# Invert the preference matrix to be item-centric
itemPrefs = GetRecommendation.transformPrefs(prefs)
for item in itemPrefs:
# Find the most similar items to this one
scores = Ranking.topMatches(itemPrefs,
item,
n=n,
similarity=sim_distance.sim_distance)
result[item] = scores
return result
def IRTest(texts, ir, num_cluster, percentage):
#Buat cluster menggunakan algoritma kmeans
kmeans = KMeans(n_clusters=num_cluster).fit(ir.corpus.TFIDF)
#gabung antara cluster dengan halaman
label = list(zip(kmeans.labels_, range(len(texts))))
#vector test untuk menyimpan nilai precision, recall dan fmeasure
test = []
#untuk setiap cluster
for cluster in range(num_cluster):
#ambil dokumen yang berada pada cluster
label_doc = list(filter(lambda x : x[0] == cluster, label))
#ambil raw text dari dokumen dalam cluster
label_text = [texts[label_doc[i][1]] for i in range(len(label_doc))]
#ambil tfidf dokumen pada cluster
label_tfidf = np.array([ir.corpus.TFIDF[label_doc[i][1]] for i in range(len(label_doc))])
#lakukan peringkasan dengan algoritma textrank
summa = Ranking.TextRank(label_tfidf).summarize(label_text, percentage)
#retrive doc dengan query summa
start = time.time()
retrieved = [doc[0] for doc in ir.query(summa, len(label_doc))]
print("time=%f"%(time.time() - start))
#hitung confusion matrix
dic_label = {page[1]:True for page in label_doc}
dic_retrieved = {page:True for page in retrieved}
TP = sum([1 if page in dic_label else 0 for page in retrieved])
FP = sum([1 if page not in dic_label else 0 for page in retrieved])
FN = sum([1 if page not in dic_retrieved else 0 for page in label_doc])
#hitung recall dan precision
precision = float(TP) / (TP + FP)
recall = float(TP) / (TP + FN)
fmeasure = 2 * (precision * recall / (precision + recall))
test.append([precision, recall, fmeasure])
return test
def main(self):
while not self.done:
self.events_input()
self.display_frame()
self.run_logic()
if self.choice == "Play":
GameLoop.Game(self.screen).main()
elif self.choice == "Tutorial":
pass
#Instruction.Instruction(self.screen).main()
elif self.choice == "Ranking":
Ranking.Ranking(self.screen).main()
elif self.choice == "Options":
pass
elif self.choice == "Quit":
pygame.quit()
sys.exit()
import pygame
import time
import Ranking
from maps import Map
Ranking.SetUsername()
pygame.init()
pygame.mixer.init(frequency=22050, size=-16, channels=2, buffer=256)
pygame.mixer.music.load('Audio/background_music.mp3')
pygame.mixer.music.set_volume(0.1)
pygame.mixer.music.play(-1)
game_map = Map()
while not game_map.windowClosed:
pygame.time.Clock().tick(30)
game_map.screen.fill((255, 255, 255))
if game_map.initialScreen:
game_map.initalScreen()
elif game_map.selectScreen:
game_map.playerAndMapScreen()
elif game_map.instructionsScreen:
game_map.instructionScreen()
elif game_map.inGame:
game_map.blitBackgroundMap()
game_map.player.animatePlayerSprite()
import TextMining
import nltk
import numpy as np
import Ranking
#matrix data untuk menyimpan dokumen
data = []
#baca semua halaman dari folder deeplearning/text
for i in range(1, 800):
data.append(
[open(str.format("deeplearning/text/page-%d.txt" % i), "r").read()])
#buat potter stemmer
stemmer = nltk.stem.PorterStemmer()
#Hitung TFIDF dari data
data = TextMining.Dokumen_Cluster(data,
stemmer=[np.vectorize(stemmer.stem)],
stopword=[TextMining.english_stopwords],
verbose=True)
#simpan TFIDF ke file
data.toFile("deeplearning/tfidf")
#Hitung rank
rank = Ranking.CosinePageRank(data.TFIDF)
#simpan rank ke file
rank.toFile("deeplearning/pagerank")
total_count = 0
total_correct = 0
average_points = []
discrete_points = []
middle_points = [] # for drawing a line at the 0.5 level
x_values = []
labels = [
"Daily ratio of successful guesses", "Average ratio of successful guesses"
]
starting_day = 20
for i in range(0, 100):
day = Parser.getNextDay(fp)
middle_points.append(0.5)
if i > starting_day:
correct, count = Ranking.predictOutcomesByProbability(day)
total_correct = total_correct + correct
total_count = total_count + count
average_points.append(total_correct / total_count)
discrete_points.append(correct / count)
x_values.append(i)
print(total_correct, "/", total_count, (total_correct / total_count))
Ranking.addEntry(day)
# Ranking.printHmAwayProb()
mpl.ylim([0.1, 0.9])
print(total_correct, "/", total_count, (total_correct / total_count))
mpl.plot(x_values, discrete_points)
mpl.plot(x_values, average_points)
def _event_handler(event_type, slack_event):
print(slack_event["event"])
msg = {}
keywords =""
msg["color"] = "#3AA3E3"
msg["text"] = ""
msg["image_url"] = "None"
if event_type == "app_mention":
channel = slack_event["event"]["channel"]
text = slack_event["event"]["text"] #text가 실제입력값
result = re.sub(r'<@\S+> ', '', text) # result = 받은 텍스트
title_list, title_url_list = crawl_movie_rank() # 영화 랭킹페이지 크롤링 호출
if "순위" in result: # 출력 선택 구문
msg["text"] = str(u'\n'.join(Ranking(title_list,title_url_list)))
elif "ㅅㅇ" in result: # 출력 선택 구문
msg["text"] = str(u'\n'.join(Ranking(title_list, title_url_list)))
elif "ㄹㅋ" in result: # 출력 선택 구문
msg["text"] = str(u'\n'.join(Ranking(title_list, title_url_list)))
elif "ㅇㅈ" in result:
msg["text"] = str(u'\n'.join(Release()))
elif "개봉예정작" in result:
msg["text"] = str(u'\n'.join(Release()))
elif switching(result, title_list, title_url_list) == "정답":
for i in range(10): # 타이틀 리스트 10개를 돌면서 텍스트와 비교해 주소를 url에 넘김, 영화제목이 없으면 url은 비어있음
if result == title_list[i]:
title_URL = title_url_list[i] # 크롤링할 url 저장한 string
break
msg["image_url"] = img_link(title_URL)
msg["text"] = str(u'\n'.join(info(title_URL))) # (영화제목과 주소4)개
elif "안녕" in result:
msg["text"] = str(u"안녕하세요")
# elif "출력실험" in result:
# return
elif "" in result:
msg["text"] = str("실행 가능 명령어는 아래와 같습니다.\n순위\n개봉예정작\n영화제목(순위권 내)")
# keywords = answer(text)
sc.api_call(
"chat.postMessage",
channel=channel,
text=keywords,
attachments=json.dumps([msg])
)
msg["image_url"] = "None"
return make_response("App mention message has been sent", 200, )
# ============= Event Type Not Found! ============= #
# If the event_type does not have a handler
message = "You have not added an event handler for the %s" % event_type
# Return a helpful error message
return make_response(message, 200, {"X-Slack-No-Retry": 1})
from RewardMatrix import *
from Training import *
from Live import *
from MTM import *
from Ranking import *
from QMatrix import *
from PerformanceMeasures import *
import calendar
import logging
if __name__ == "__main__":
logging.basicConfig(filename='out.log',level=logging.DEBUG)
dbObject = DBUtils()
rankingObject = Ranking()
mtmObject = MTM()
rewardMatrixObject = RewardMatrix()
qMatrixObject = QMatrix()
trainingObject = Training()
liveObject = Live()
reallocationObject = Reallocation()
dbObject.dbConnect()
#Calculates number of individuals in tradesheet table
Number=dbObject.dbQuery("SELECT COUNT(DISTINCT(individual_id)),1 FROM "+gv.name_Tradesheet_Table)
for num1,num2 in Number:
MaxIndividuals=num1
self.params = json.loads(f.read())
except ValueError as e:
print(e)
self.save()
f.close()
def save(self):
f = codecs.open(CONFIG_FILE, 'w', 'utf-8')
f.write(json.dumps(self.params))
f.close()
######################### PARTE CONVERSACIÓN ###################################
HELLO, BIEN, NAME = range(3)
d_users = {}
ranking = rank.Ranking("Ranking")
ranking.ReadTXT()
print(ranking.printRanking())
to_type = []
################################# DIÁLOGOS #####################################
import traceback
try:
def text(bot, update):
text = update.message.text
text_out = elimina_tildes(text)
id = update.message.chat.id
myuser = d_users[id]
print(text)
for ageKeyword in keyword:
print(ageKeyword)
while True:
# query variables
query = []
query_tf = []
query_weight = []
query_text1 = f.readline().rstrip('\n')
print(query_text1)
if not query_text1:
break
query.append(PreprocessComment(query_text1))
query_tf = TF(indexing_list, query, 'query')
query_weight = Weighting(query_tf, idf_list)
query_weight = Normalize(query_weight)
score = Scoring(weighting_list, query_weight)
rank = Ranking(score)
print(rank)
print("")
count += 1
for r in rank:
if (str(list(r.keys())[0]) == '10대'):
result += 1
print("전체 댓글 : %d " % count)
print("결과 : %d " % result)
print("정확도 : %f" % (result / count))
def testDownloadRankings(self, urllib_mock):
urllib_mock.return_value.read.return_value = TestData('rbs.tsv')
link = Ranking.create_ppr_link('RB')
rankings = Ranking.download_rankings(link).splitlines()
self.assertEqual('FantasyPros.com', rankings[1].strip())
self.assertEqual('Week 3 - RB Rankings', rankings[2].strip())
def testReadRankings(self):
rankings = Ranking.read_rankings(TestData('qbs.tsv'))
self.assertEqual('aaron rodgers', rankings[0])
self.assertEqual('jimmy clausen', rankings[36])
self.assertEqual(37, len(rankings))
sg.Column(column1, key='-C1-'),
sg.VerticalSeparator(),
sg.Column(column2, key='-C2-')
]]
window1 = sg.Window('ScrabbleAr ', layout)
while True:
event, value = window1.read()
if event == None:
break
if event == '-ADVANCED_CONFIG-':
Config.main(easy_config, medium_config, hard_config)
if event == '-README-':
ConoceMas.main()
if event == '-RANKING-':
Ranking.main()
if event == '-SIGN_UP-':
Registro.main()
window1['-SIGN_UP-'].Update(disabled=True)
if event == '-LOGIN-':
usuario = value['nick']
password = value['password']
lista_jugadores = ListaJugadores()
a = Database.abro_base()
try:
if a != None:
lista_jugadores_2 = Database.abro_base()
usuario_valido = False
for jugador in lista_jugadores_2.get_jugadores():
if jugador.get_nick() == usuario and jugador.get_password(
def main():
while 1:
print('=' * 20)
print('请输入指令：')
cmd = input()
print('=' * 20)
# -------------------
if cmd == '说明':
# ---------------
print(
'程序说明\n1.输入"search"进入抓取分支：复制进关键词(以"#"结束，回车分隔)\n2.输入"query"进入多关键词搜索分支\n3.title/domin排名全查询('
'以"title:"/"domain:"开始)\n4.输入"微信登录"，扫码后发送"查询结果"给文件传输助手\n5.')
# -------------------
if 'search' in cmd:
# ---多关键词分支
print('- 多关键词抓取分支 -')
print('- 以#结束 -')
keywords = []
# 多行输入关键词
while 1:
k = input()
if k == '#':
print('即将抓取"{k}"...请确认(y/n)'.format(k=keywords))
if input() == 'y':
print('- 开始抓取 -')
Ranking.scrape_multi(keywords)
print('- 抓取完毕 -')
break
else:
break
else:
keywords.append(k)
if cmd == 'query':
print('请选择：1.标题匹配/2.域名匹配')
choose = input()
if choose == '1':
print('- 多关键词标题排名匹配分支 -')
print('- 以#结束 -')
match_title = []
while 1:
k = input()
if k == '#':
print('即将匹配"{k}"...请确认(y/n)'.format(k=match_title))
if input() == 'y':
print('- 开始匹配 -')
frame = pd.read_excel('./data/keyword-multi.xlsx')
match_title_function(frame, match_title)
print('- 匹配完毕 -')
break
else:
break
else:
match_title.append(k)
# -------------------------------
if choose == '2':
print('- 多关键词域名排名匹配分支 -')
print('- 以#结束 -')
match_domain = []
while 1:
k = input()
if k == '#':
print('即将匹配"{k}"...请确认(y/n)'.format(k=match_domain))
if input() == 'y':
print('- 开始匹配 -')
frame = pd.read_excel('./data/keyword-multi.xlsx')
match_domain_function(frame, match_domain)
print('- 匹配完毕 -')
break
else:
break
else:
match_domain.append(k)
# ---------------
if 'title:' in cmd:
key = cmd.replace('title:', '').strip()
try:
frame = pd.read_excel('./data/keyword-multi.xlsx')
Search_Keyword_Multi(frame, key)
except Exception:
print('- 文件不存在 -')
# ------------------------
if 'domain:' in cmd:
key = cmd.replace('domain:', '').strip()
try:
frame = pd.read_excel('./data/keyword-multi.xlsx')
Search_Domain_Multi(frame, key)
except Exception:
print('- 文件不存在 -')
if cmd == '微信登录':
itchat.auto_login(enableCmdQR=2)
itchat.run()
if cmd == 'exit':
break
from datetime import timedelta, datetime
from Reallocation import *
from RewardMatrix import *
from Training import *
from Live import *
from MTM import *
from Ranking import *
from QMatrix import *
from PerformanceMeasures import *
from Plots import *
import calendar
if __name__ == "__main__":
dbObject = DBUtils()
rankingObject = Ranking()
mtmObject = MTM()
rewardMatrixObject = RewardMatrix()
qMatrixObject = QMatrix()
trainingObject = Training()
liveObject = Live()
reallocationObject = Reallocation()
plotObject = Plots()
performanceObject = PerformanceMeasures()
dbObject.dbConnect()
'''
dbObject.dbQuery("DELETE FROM asset_allocation_table")
dbObject.dbQuery("DELETE FROM asset_daily_allocation_table")
dbObject.dbQuery("DELETE FROM mtm_table")
janela.draw_text((":"), 200, 20, size=26, color=(255, 0, 0), font_name="Purisa", bold=True, italic=False)
janela.draw_text(str(milisegundos), 210, 20, size=26, color=(255, 0, 0), font_name="Purisa", bold=True, italic=False)
janela.draw_text(("Score: "), 1100, 20, size=26, color=(255, 0, 0), font_name="Purisa", bold = True, italic = False)
janela.draw_text((str(x)), 1200, 20, size=26, color=(255, 0, 0), font_name="Purisa", bold=True, italic=False)
if acabou:
break
principal.draw()
janela.update()
global primeiraVez
if primeiraVez:
primeiraVez = False
prepara(janela)
while(True):
opcao = menu.menu(janela)
if opcao == 0:
game(janela)
Ranking.SalvaRanking(x, janela)
elif opcao == 1:
Ranking.ExibeRanking(janela)
janela.update()
def ranking(self):
pilas.cambiar_escena(Ranking.Ranking())
def init(l):
gv.lock = l
if __name__ == "__main__":
logging.basicConfig(filename=gv.logFileName,
level=logging.INFO,
format='%(asctime)s %(message)s')
l = Lock()
pool = Pool(gv.maxProcesses, initializer=init, initargs=(l, ))
dbObject = DBUtils()
rankingObject = Ranking()
mtmObject = MTM()
rewardMatrixObject = RewardMatrix()
qMatrixObject = QMatrix()
trainingObject = Training()
liveObject = Live()
reallocationObject = Reallocation()
plotObject = Plots()
performanceDrawdownObject = PerformanceDrawdown()
performanceObject = PerformanceMeasures()
dbObject.dbConnect()
dbObject.dbQuery("DELETE FROM asset_allocation_table")
dbObject.dbQuery("DELETE FROM asset_daily_allocation_table")
dbObject.dbQuery("DELETE FROM mtm_table")
