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
