def main():
if len(sys.argv) != 2:
parsing.error(0, "0")
inst = parsing.parsing(sys.argv[1])
print_rubik.print_rubik(rubik)
shuffle.shuffle(rubik, inst)
if utils.check_rubik(rubik) == False:
print("the cube is not solved")
else:
print("the cube is solved")
def lisp(filename):
tokens = lexing.lexing(filename)
# print tokens
ast = parsing.parsing(tokens)
print ast
interpret(ast)
print '*'*20
print "Succesfully ran %s." % filename
print env
print ""
def cfg_construction(file_data_model):
# recognize NSF-Facing Interface Data Model
fnfi = open(file_data_model, 'r')
line = fnfi.readline() # ignore first line
field = ''
requiredinfo = []
contentcfglist = []
order = 0
# declare stack for Data Model tree recognition
st = stack.Stack()
st.push('', False)
# read all line
while True:
line = fnfi.readline()
if not line:
break
# parsing line
lineparsing = parsing.parsing(line)
skip = lineparsing[0]
level = lineparsing[1]
field = lineparsing[2]
isContent = lineparsing[3]
# declare CFG node
if isContent:
cfgnode = TextfreeGrammar('content', level)
cfgnode.setinfo(len(requiredinfo))
requiredinfo.append(field)
contentcfglist.append(cfgnode)
else:
cfgnode = TextfreeGrammar('structure', level)
cfgnode.settag('<' + field + '>', '</' + field + '>')
cfgnode.setname(field)
cfgnode.setorder(order)
order += 1
# CFG node connection by investigating level
while st.level() != level:
st.pop()
cfgnode.setParent(st.topnode())
st.push(cfgnode, skip)
# print('now field: '+field+', level: '+str(st.level()))
fnfi.close()
print('Complete to construct CFG for ' + file_data_model + '\n')
#print(requiredinfo)
return [contentcfglist, requiredinfo]
def experimental_events():
# return events_TOY()
datalow = array(
'd',
map(
sub,
map(sub, map(sub, parsing("data ev/"), parsing("data e2pi/")),
parsing("data e4pi/")), parsing("data bkg/")))
ddatalow = array('d')
i = 0
for symb in parsing("data ev/"):
ddatalow.append(
float(
sqrt(symb + parsing("data de2pi/")[i]**2 +
parsing("data de4pi/")[i]**2 +
parsing("data dbkg/")[i]**2)))
i = i + 1
Ndata = datalow + parsing("data evh/")
dNdata = ddatalow + parsing("data devh/")
#print(Ndata)
i = 0
Ndata_true = array('d')
for el in Ndata:
Ndata_true.append(el)
Ndata[i] = Ndata[i] / dNdata[i]
i = i + 1
#print(Ndata)
print("number of points = ", len(dNdata))
with open('data/Ndata.txt', 'w') as file:
i = 0
for el in Ndata:
file.write(str(Ndata_true[i]))
file.write(' ')
file.write(str(dNdata[i]))
file.write("\n")
i = i + 1
return (Ndata, dNdata, Ndata_true)
def dfa_construction(file_data_model):
# recognize Consumer-Facing Interface Data Model
fcfi = open(file_data_model, 'r')
line = fcfi.readline() # ignore first line
field = ''
extractedinfo = []
index = 0
# declare stack for automatic construction
st = stack.Stack()
node_accepter = DFAnode('accepter')
st.push(node_accepter, False)
# read all line
while True:
line = fcfi.readline()
if not line:
break
# parsing line
lineparsing = parsing.parsing(line)
skip = lineparsing[0]
level = lineparsing[1]
field = lineparsing[2]
isExtractor = lineparsing[3]
# declare DFA node
if isExtractor == True:
dfanode = DFAnode('extractor')
dfanode.setinfo(index)
index += 1
extractedinfo.append(field)
else:
dfanode = DFAnode('middle')
# DFA node connection by investigating level
while st.level() != level:
st.pop()
st.topnode().connectNode(dfanode, field)
st.push(dfanode, skip)
# print('now field: '+field+', level: '+str(st.level()))
fcfi.close()
print('Complete to construct DFA for '+file_data_model)
return [node_accepter, extractedinfo]
def execute(cell_id=None):
"""Gets piece of code from cell_id and executes it"""
try:
cell_id = int(cell_id)
except ValueError as e:
logger.warning(e)
return redirect('/')
global current_execute_count
try:
current_execute_count += 1
execute_counters[cell_id] = current_execute_count
inputs[cell_id] = request.form['input{}'.format(cell_id)]
texts = parsing(inputs[cell_id].split())
result = antiplagiat.compare(texts)
except BaseException as e:
# anything could happen inside, even `exit()` call
result = [str(e)]
outputs[cell_id] = result
return redirect('/')
def handle_message(event):
hoge = event.message.text
command = parsing.parsing(hoge)
if command == None:
return
elif command['command'] == 'にゃーん':
line_bot_api.reply_message(event.reply_token, TextSendMessage("にゃ〜ん"))
elif command['command'] == 'せいせき':
room_id, _, room_name = my_database.current_tournament()
today = datetime.now(JST)
my_database.update_score("{:%Y%m%d}".format(today), room_id)
if today.time() < time(1, 0, 0):
my_database.update_score(
"{:%Y%m%d}".format(today - timedelta(days=1)), room_id)
score = my_database.get_score_sum(room_id)
text = f"{room_name}"
for r in score:
text += "\n"
text += r[0] + "：" + str(r[1])
print(text)
line_bot_api.reply_message(event.reply_token, TextSendMessage(text))
elif command['command'] == "こうしん":
if len(command['args']) != 2:
line_bot_api.reply_message(
event.reply_token,
TextSendMessage(text="usage : -こうしん [date(YYYYmmdd)] [大会名]"))
return
date, taikai = command['args']
sql = f"SELECT room_id FROM tournaments WHERE name='{taikai}'"
room = my_database.sql_requests(sql)[0][0]
res = my_database.update_score(date, room)
line_bot_api.reply_message(event.reply_token,
TextSendMessage(text=res))
# elif command["command"] == "たいかいとうろく":
# if len(command['args']) != 3:
# line_bot_api.reply_message(
# event.reply_token,
# TextSendMessage(text="usage : -たいかいとうろく [name] [roomid] [url]"))
# return
# name, room, url = command['args']
# text = my_database.set_tournament(name, room, url)
# line_bot_api.reply_message(
# event.reply_token,
# TextSendMessage(text=text))
elif command['command'] == "ゆーざー":
if len(command['args']) != 2:
line_bot_api.reply_message(
event.reply_token,
TextSendMessage(text="usage : -ゆーざー [name] [tenhouId]"))
return
nickname, tenhou_id = command['args']
my_database.set_user(nickname, tenhou_id)
line_bot_api.reply_message(
event.reply_token,
TextSendMessage(text=f"{tenhou_id}を{nickname}さんのアカウントとして記憶しました。"))
elif (result == 2):
value = 'undetermined'
color = '\033[95m'
print(color + key + ' is ' + value)
if __name__ == "__main__":
global rules
global initial
global facts
if (len(sys.argv) != 2 and len(sys.argv) != 3):
error('python main.py [file]')
filename = sys.argv[1]
content = ''
try:
f = open(filename, 'r')
content = f.read()
f.close()
except:
error('error opening file')
content = cleanContent(content)
checkLines(content)
initial = findLine(content, '=')
query = findLine(content, '?')
rules = getRules(content)
rules = parsing(rules)
params.initFacts()
params.setRules(rules)
params.setInitial(initial)
displayResult(query)
return (synonim)
except:
return False
def posi_nega(posi_list, nega_list):
try:
results = model.wv.most_similar(positive=posi_list, negative=nega_list)
synonim = []
for result in results:
synonim.append(result[0])
return (synonim)
except:
return False
while (True):
string = input('計算式:')
posi_list, nega_list = parsing(string)
posi_list = exist(posi_list)
nega_list = exist(nega_list)
if (len(posi_list) == 0):
print(nega_nega(nega_list))
elif (len(nega_list) == 0):
print(posi_posi(posi_list))
else:
print(posi_nega(posi_list, nega_list))
print("---")
from pylab import *
from plyfile import PlyData, PlyElement
import NearestNeighbors as nn
import parsing
import analysis
from sklearn import svm
from sklearn.svm import SVC
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
if __name__ == '__main__':
# load raw txt data
parser = parsing.parsing()
extracted_features_path = './Data/Cornell/features/office_data_nodefeats.txt'
raw_data = parser.parse_txt_data(extracted_features_path)
# load ply data, here just test for office scene 1
pic_path_set = ['./Data/Cornell/office_data_ply/scene5.ply']
for pic_path in pic_path_set:
office_data = parser.parse_ply_data(pic_path)
print(office_data)
# create analysis utils
analyser = analysis.analysis()
#=========================================================
# K-NN
#=========================================================
f'Запущен режим восстановления (допарсинга) из файла {FILE_RECOVERY}'
)
file_work = os.getcwd() + DIR_RECOVERY + FILE_RECOVERY
logger.info(f'Получаем из файла {file_work} данные для парсинга')
try:
article_numbers = get_article_from_file(file_work)
if article_numbers:
logger.success(
f'Успешно получены данные. Общее кол-во арктикулов {len(article_numbers)}'
)
else:
logger.warning(
f'Файл {file_work} пустой. Завершаем работу скрипта')
sys.exit(1)
except Exception as ex:
logger.error(f'Ошибка при получении данных из файла {ex}')
logger.warning(f'Завершаем работу скрипта с ошибкой')
sys.exit(1)
try:
# основной парсинг товаров
result_parsing = parsing.parsing(article_numbers=article_numbers)
# сохранение результатов парсинга в файл
save_result_to_csv_file(result=result_parsing)
logger.success(f'Скрипт успешно завершен')
except Exception as ex:
logger.critical(
f'Выполнение скрипта завершено с ошибкой. См. лог-файлы или обратитесь к разработчику {ex}'
)
def home():
news = parsing()
return render_template("home.html",
title="Home",
news=news,
day=str(date.today()))
def synthesis():
return parsing.parsing(request.get_json(force=True))
def test_list(self):
self.url = ["https://www.bbc.com/news/world-europe-52510545"]
self.assertIsInstance(parsing.parsing(self.url), list)
import os, os.path
import parsing
result = []
partys = ["AfD", "Die_Gruene", "Die_Linke", "Die_Partei", "MLPD", "Piratenpartei", "SPD", "Tierschutzpartei"]
for party in partys:
dict = {party:[]}
print(party)
plakats = os.listdir("dataset/{}".format(party))
if ".DS_Store" in plakats:
plakats.remove(".DS_Store")
for plakat in plakats:
print(plakat)
dict.get(party).append(parsing.parsing("{}/{}".format(party, plakat)))
result.append(dict)
print(result)
def main(filename):
rules = parsing(filename)
lexical_rules = transform(rules)
def test_size(self):
self.url = ["https://www.bbc.com/news/world-europe-52510545",
"https://www.bbc.com/news/world-europe-52510545"]
self.assertEqual(len(parsing.parsing(self.url)), 2)
