class MongoThread(QThread): # Thread for access MongoDB.
sig1 = app_signal(object, str) # Signal emit with data/error.
def __init__(self, parent=None): # Importing of the pymongo
super(MongoThread, self).__init__(parent)
def on_source(self, datas): # adapter for connection.
self.datas = datas # Receives the data as
def run(self): # dictionary and inserting to
try: # the mongo database. The
import pymongo # datas received contains
try: # insert or select string that
self.client = pymongo.MongoClient('localhost', 27017)
self.db = self.client['umongodb']
except pymongo.errors as err: # used for which operation
self.sig1.emit('', str(err)) # need to do. If insert - this
if self.datas[1] == "insert": # data will be inserted to the
posts = self.db.posts # database. If the value of
posts.insert_one(self.datas[0])
if self.datas[1] == "select": # datas is select - will find
dbdata = self.db.posts.find() # in the posts and emits this
self.sig1.emit(dbdata, '') # data or error if occured.
if self.isFinished(): # Checks if this thread is
self.quit() # finished and quits thread.
except Exception as err: # If some error will occur -
self.sig1.emit('', str(err)) # emits this error to app,
if self.isFinished(): # checks, if the thread is
self.quit() # finished, quits this thread.
class RPandas(QThread): # Thread for reading CSV file
sig1 = app_signal(object, str) # with Pandas. Emits an object
def __init__(self, parent=None): # (dataframe). Will read the
super(RPandas, self).__init__(parent) # file that specified with
def on_source(self, datas): # type of the file with an
self.datas = datas # representation to the pandas
def run(self): # dataframe that can be opened
try: # as csv file data, excel file
import pandas # data, html file data with
if self.datas[1] == "csv": # tags and can be used as html
df = pandas.read_csv(self.datas[0],
chunksize=self.datas[6],
engine=self.datas[4])
if self.datas[1] == "excel": # page, json representation.
df = pandas.read_excel(self.datas[0])
if self.datas[1] == "html": # Also, sets the options to
df = pandas.read_html(self.datas[0])
if self.datas[1] == "json": # display number of rows.
df = pandas.read_json(self.datas[0])
pandas.options.display.max_rows = self.datas[5]
for ch in df: # Returns data and reader that
self.sig1.emit(ch, '') # used as chunk sizes to split
time.sleep(0.1) # reading of the file data.
if self.isFinished(): # Checks if this thread is
self.quit() # finished and quits thread.
except Exception as err: # If some error will occur -
self.sig1.emit('', str(err)) # emits this error to app,
if self.isFinished(): # checks, if the thread is
self.quit() # finished, quits this thread.
class CouchThread(QThread): # Thread for access CouchDB.
sig1 = app_signal(object, str) # Signal will emit data/error.
def __init__(self, parent=None): # Importing of the couchdb
super(CouchThread, self).__init__(parent)
def on_source(self, datas): # adapter for connection. Then
self.datas = datas # receives the data as a
def run(self): # dictionary and inserting to
try: # the couch database. The
import couchdb # datas received contains
try: # insert or select string that
self.couch = couchdb.Server("http://127.0.0.1:5984/")
self.db = self.couch["u_couchdb"]
except Exception as err: # used for which operation
self.sig1.emit('', str(err)) # need to do. If insert - this
if self.datas[1] == "insert": # data will be inserted to the
self.db.save(self.datas[0]) # couch database If select
if self.datas[1] == "select": # value - emits the couchdb
self.sig1.emit(self.db, '') # data or error if occurs.
if self.isFinished(): # Checks if this thread is
self.quit() # finished and quits thread.
except Exception as err: # If some error will occur -
self.sig1.emit('', str(err)) # emits this error to app,
if self.isFinished(): # checks, if the thread is
self.quit() # finished, quits this thread.
class WPandas(QThread): # Thread for writing file with
sig1 = app_signal(object, str) # Pandas. Signal for emitting
def __init__(self, parent=None): # from this thread. Will write
super(WPandas, self).__init__(parent) # the pandas csv, excel,
def on_source(self, datas): # specified in the writer
self.datas = datas # parameter. The 'if'
def run(self): # statements checks the type
try: # of file to write with using
import pandas # pandas functions. List of
uindex = [i for i in range(self.datas[2])]
udata = {
"User_Name": range(0, self.datas[2]),
"User_email": range(0, self.datas[2]),
"User_password": range(0, self.datas[2]),
"User_data": range(0, self.datas[2])
}
df = pandas.DataFrame(udata,
columns=self.datas[3],
index=uindex,
dtype=self.datas[5])
df.index.name = "rows\columns" # indexes. The data is a dict
if self.datas[1] == "csv": # with specified keys and
df.to_csv(self.datas[0]) # columns, value pairs of
if self.datas[1] == "excel": # these keys, The index list
df.to_excel(self.datas[0]) # is the number of rows. Data
if self.datas[1] == "html": # can be written to the excel,
df.to_html(self.datas[0]) # html, json files also. The
if self.datas[1] == "json": # user data can be changed
df.to_json(self.datas[0]) # to the real world data. Then
if self.isFinished(): # checks if this thread is
self.quit() # finished and quits thread.
except Exception as err: # If some error will occur -
self.sig1.emit('', str(err)) # emits this error to app,
if self.isFinished(): # Checks, if the thread is
self.quit() # finished, quits this thread.
class UTools(object): # Class forsome functionality.
pandas_sig1 = app_signal(list) # Signal for pandas writing.
pandas_sig2 = app_signal(list) # Signal for pandas reading.
mongo_sig1 = app_signal(list) # Signals for communication
mongo_sig2 = app_signal(list) # with thread for MongoDB.
couch_sig1 = app_signal(list) # Signals for communication
couch_sig2 = app_signal(list) # with thread for CouchDB.
def __init__(self): # Constructor of the class.
self.us1 = "Application with Python." # String variable.
self.qtsql_db = QtSql.QSqlDatabase.addDatabase('QSQLITE')
self.qtsql_db.setDatabaseName("data/u_db.sqlite")
self.qtsql_db.open() # Path to DB. Open/Create DB.
def pickle_dump(self, obj=None, filename=None, proto=1, fimps=None):
fobj = open(filename, 'wb') # File object in binary mode.
if fimps is not None: # The fix_imports parameter.
pickle.dump(obj, fobj, proto, fimps)
else: # Maps the new Python3 names,
pickle.dump(obj, fobj) # Python2 old module names,
fobj.close() # not used with Python2.
def pickle_load(self,
filename=None,
fimps=None,
enc="ASCII",
err="strict"):
fobj = open(filename, 'rb') # Loads the file with data,
if fimps is not None: # fix_imports is not None,
pl = pickle.load(fobj, fimps, encoding=enc, errors=err)
else: # parameters for Python3,
pl = pickle.load(fobj) # defaults for Python3.
fobj.close() # Used for Python2.
return pl # Returns loaded data.
def shelve_open(self,
filename=None,
flag='c',
proto=None,
writeback=False):
return shelve.open(filename, flag, proto, writeback)
def csv_write(self,
csvfile=None,
fieldnames=None,
mode='a',
newline='',
rowdata=None,
dialect=csv.excel,
delimiter=' ',
quotechar='|',
lineterminator='\r\n'): # Function to write CSV.
csvpath = os.path.exists(r"%s/%s" % (pathf, csvfile))
csvf = open(csvfile, mode) # Opens file in append mode.
csvw = csv.DictWriter(csvf,
fieldnames=fieldnames,
dialect=dialect,
delimiter=delimiter,
lineterminator=lineterminator)
if csvpath is False: # If path of the file with
csvw.writeheader() # writer exist - add header.
csvdict = {k: v for (k, v) in zip(fieldnames, rowdata)}
csvw.writerow(csvdict) # Writing row with dictionary.
def csv_read(self, csvfile=None, mode='r', dialect=csv.excel):
csvf = open(csvfile, mode) # Opens file object (reading).
csvr = csv.DictReader(csvf, dialect=dialect, delimiter=' ')
return csvr # CSV dict reader object.
def pandas_write(self,
filename=None,
writer="csv",
data=None,
columns=None,
index=None,
dtype=object):
data = 1000000 # Function will write data
index = 1000000 # with Pandas. Integers are
datas = [filename, writer, data, columns, index, dtype]
self.pandas_thread1 = WPandas() # used to generate values for
self.pandas_sig1.connect(self.pandas_thread1.on_source)
self.pandas_sig1.emit(datas) # each cell of the CSV file,
self.pandas_thread1.start() # using thread, 1000000 rows.
def pandas_read(self,
filename=None,
reader="csv",
sep=',',
delimiter=None,
engine='python',
maxrows=9,
chunk=10000):
datas = [filename, reader, sep, delimiter, engine, maxrows, chunk]
self.pandas_thread2 = RPandas() # Function reads the data
self.pandas_sig2.connect(self.pandas_thread2.on_source)
self.pandas_sig2.emit(datas) # from the CSV file with
self.pandas_thread2.start() # Pandas using thread which
return self.pandas_thread2 # returned for operating.
def sqlite_insert(self, username=None, email=None, passw=None, data=None):
dbcon = sqlite3.connect("%s/data/u_db.sqlite" % pathf)
dbcursor = dbcon.cursor() # Connect to the database.
sql_query = """INSERT INTO u_dbtable (user_name, user_email,
user_passw, user_data) VALUES (?, ?, ?, ?);"""
udata = (str(username), str(email), str(passw), str(data))
dbcursor.execute(sql_query, udata) # Executes the SQL query that
dbcon.commit() # SQL text, retreived data,
dbcursor.close() # commit transaction then
dbcon.close() # closes cursor and database.
def sqlite_select(self): # Connect to the SQLite DB
dbcon = sqlite3.connect("%s/data/u_db.sqlite" % pathf)
dbcursor = dbcon.cursor() # and open db cursor.
sql_query = """SELECT info_date, user_name, user_email, user_passw,
user_data FROM u_dbtable;"""
dbcursor.execute(sql_query) # Executes the SQL query
dbdata = dbcursor.fetchall() # specified in the parameter,
dbcursor.close() # fetch all data selected,
dbcon.close() # closes cursor and database.
return dbdata # Returns fetched data.
def psql_insert(self, username=None, email=None, passw=None, data=None):
dbcon = psycopg2.connect(dbname="psqldb")
dbcursor = dbcon.cursor() # Connects to the PostgreSQL.
sql_query = """INSERT INTO u_dbtable (user_name, user_email,
user_passw, user_data) VALUES (%s, %s, %s, %s);"""
udata = (str(username), str(email), str(passw), str(data))
dbcursor.execute(sql_query, udata) # Executes the SQL query that
dbcon.commit() # specified in the parameter,
dbcursor.close() # commit transaction then
dbcon.close() # closes cursor and database.
def psql_select(self): # Connect to the PostgreSQL
dbcon = psycopg2.connect(dbname="psqldb")
dbcursor = dbcon.cursor() # database and open db cursor.
sql_query = """SELECT info_date, user_name, user_email, user_passw,
user_data FROM u_dbtable;"""
dbcursor.execute(sql_query) # Executes the SQL query that
dbdata = dbcursor.fetchall() # specified in the parameter
dbcursor.close() # fetch all data selected,
dbcon.close() # closes cursor and database.
return dbdata # Returns fetched data.
def mysql_insert(self, username=None, email=None, passw=None, data=None):
dbcon = pymysql.connect(host="localhost",
user="******",
password="******",
db="mysqldb")
dbcursor = dbcon.cursor() # Connects to MySQL database.
sql_query = """INSERT INTO u_dbtable (user_name, user_email,
user_passw, user_data) VALUES (%s, %s, %s, %s);"""
udata = (str(username), str(email), str(passw), str(data))
dbcursor.execute(sql_query, udata) # Executes the SQL query that
dbcon.commit() # specified in the parameter.
dbcursor.close() # commit transaction then
dbcon.close() # closes cursor and database.
def mysql_select(self): # Connects to MySQL database.
dbcon = pymysql.connect(host="localhost",
user="******",
password="******",
db="mysqldb")
dbcursor = dbcon.cursor() # Values with open db cursor.
sql_query = """SELECT info_date, user_name, user_email, user_passw,
user_data FROM u_dbtable;"""
dbcursor.execute(sql_query) # Executes the SQL query that
dbdata = dbcursor.fetchall() # specified in the parameter
dbcursor.close() # fetch all data selected,
dbcon.close() # closes cursor and database.
return dbdata # Returns fetched data.
def sql_qt_insert(self, username=None, email=None, passw=None, data=None):
query = QtSql.QSqlQuery(self.qtsql_db)
query.prepare("""INSERT INTO u_dbtable (user_name, user_email,
user_passw, user_data) VALUES (:user_name, :user_email,
:user_passw, :user_data);""")
query.bindValue(":user_name", username)
query.bindValue(":user_email", email)
query.bindValue(":user_passw", passw) # Inserts values to the sqlite
query.bindValue(":user_data", data) # database with QtSql module.
query.exec_() # Executes the query to DB.
def sql_qt_select(self): # Function for selection
query = QtSql.QSqlQuery(self.qtsql_db)
query.exec_("""SELECT * FROM u_dbtable;""")
return query # values, QtSql module query.
def mongo_insert(self, username=None, email=None, passw=None, data=None):
datas = [{
"User Name": username,
"Email": email,
"Password": passw,
"Data": data
}, "insert"] # Inserting values to the
self.mongo_thread1 = MongoThread() # Mongo database using thread
self.mongo_sig1.connect(self.mongo_thread1.on_source)
self.mongo_sig1.emit(datas) # and emitting of the data
self.mongo_thread1.start() # specified in the list.
def mongo_select(self): # Selection values from
datas = [{}, "select"] # MongoDB using thread
self.mongo_thread2 = MongoThread() # for reading. Sending to
self.mongo_sig2.connect(self.mongo_thread2.on_source)
self.mongo_sig2.emit(datas) # thread the empty dict and
self.mongo_thread2.start() # and second select string,
return self.mongo_thread2 # then returning thread.
def couch_insert(self, username=None, email=None, passw=None, data=None):
datas = [{
"User Name": username,
"User email": email,
"User password": passw,
"User Data": data
}, "insert"]
self.couch_thread1 = CouchThread() # Function for writing
self.couch_sig1.connect(self.couch_thread1.on_source)
self.couch_sig1.emit(datas) # to the Couch database with
self.couch_thread1.start() # data specified in the list.
def couch_select(self): # Selection values from
datas = [{}, "select"] # CouchDB with using
self.couch_thread2 = CouchThread() # thread. Returns the
self.couch_sig2.connect(self.couch_thread2.on_source)
self.couch_sig2.emit(datas) # database that will be
self.couch_thread2.start() # used to get the values
return self.couch_thread2 # and insert to text field.