def __init__(self): conf = Configuration() self.ptext = TextProcess(conf) self.ds = DataSet(conf) self.mongo = MongoDB(self.ds.db, self.ds.collection) self.tweet = "" self.tokens = "" self.i = 0 self.enable_translation = self.ptext.translation self.translation_store = self.ptext.translation_store
def __init__(self, conf, q): self.ptext = TextProcess(conf) self.ds = DataSet(conf) self.cleaner = KeyCleaner() self.enable_translation = self.ptext.translation self.translation_store = self.ptext.translation_store self.tweets = q # Tweets queue self.tweet = "" self.tokens = "" self.i = 0 Thread.__init__(self)
def testText(self): tp = TextProcess('config.ini') tp.readfile('pubmed_result.txt')
from client import Client from motormanager import MotorManager from gallery import Gallery from PyQt5 import uic from PyQt5.QtCore import QFile, QRegExp from PyQt5.QtWidgets import QApplication, QFileDialog, QMainWindow, QMenu, QMessageBox, QTableWidgetItem #========================================================= # a class that handles the signal and callbacks of the GUI #========================================================= # UI config qtCreatorFile = "mainwindow.ui" Ui_MainWindow, QtBaseClass = uic.loadUiType(qtCreatorFile) client = Client() mm = MotorManager(client) tp = TextProcess(client, mm) #========================================================= # a class that handles the signal and callbacks of the GUI #========================================================= class GUI(QMainWindow, Ui_MainWindow): def __init__(self): QMainWindow.__init__(self) Ui_MainWindow.__init__(self) self.setupUi(self) self.setupFileMenu() self.setupHelpMenu() self.setupCallbacksLED() self.setupMotors()
"url": "http://pogledaj.to/art/zivot-je-cupav-i-dlakav/", "name": "Zivot je cupav i dlakav", "dictionary_path": "./Oznake vrsta rijeci/GRUPA1/6-oznake.txt", "content-selector": { "class": "main the-content" } }, ] for article in articles[0:1]: ### # Text processing ### # Create instance of TextProcess class that fetches text from url and filters it tp = TextProcess(url=article["url"], filename=article["name"], content_selector_dict=article["content-selector"]) # Get filtered senteces in list filtered_sentences = tp.get_filtered_sentences() ### # Dictionary extraction ### # Create instance of Dictionary class comparses filtered sentences with those in # dictionary and creates node and edge list wt = Dictionary(dictionary_path=article["dictionary_path"]) # Sets node nad edge list. First parameter is fitlered sentences, second is array # of wanted word types. Words that are not connected to other are set as node list # those that are connected are stored as edge list