I am stuck with middle of the development, Need some help to resolve this. I want to add the Combobox to my interface and selection should come from text file.Text file have all the list of items. I want to put text file list to combobox and after select and click ok, should run with shell script. My code as below.
__author__ = 'shanaka'
from tkinter import *
from tkinter import filedialog
from tkinter.filedialog import askopenfilename
import subprocess
import os
from tkinter import messagebox
import Pmw
from tkinter import tix
################Options
def sel():
# selection = "You selected the option " + str(var.get())
if str(var.get()) == '1':
label.config(text= 'Windows is not supporting yet')
if str(var.get())== '2':
label.config(text = 'Linux is supporting')
####
def helloCallBack():
messagebox.showinfo( "Hello Python", "Hello World")
#######
def handle_selection():
print("You've selected: " + var1.get())
#################
root = Tk()
root.title("Volatility")
root.geometry("600x300")
#########OS Selection GUI
var = IntVar()
R1 = Radiobutton(root, text="Windows", variable=var, value=1,command=sel)
R1.grid( row=0, column=0, sticky=W )
R2 = Radiobutton(root, text="Linux", variable=var, value=2,command=sel)
#R2.pack( anchor = W )
R2.grid( row=1, column=0, sticky=W )
label = Label(root)
label.grid(row=3, column=0, sticky=W)
#########
########Get Dump GUI
DumpButton = Button(root, text ="Create Memory Dump", command = helloCallBack)
DumpButton.grid(row=2, column=0, sticky=W)
###############
######Copy Dump
CopyButton = Button(root, text ="Copy Dump to analyis directory", command = helloCallBack)
CopyButton.grid(row=4, column=0, sticky=W)
#############################
########Cobobox
with open('Plugins') as f:
lines = f.readlines()
# options = OptionMenu(root,var1,*lines)
options = Pmw.Combobox(root,label_text='Plugins',scrolledlist_items=[*lines])
options.grid(row=5, column=0,sticky=W)
options.selectitem(lines[1])
# var1.set(lines[1])
b = Button(root,text="Select", command = handle_selection , width=10)
b.grid(row=5,column=1,sticky=W)
root.mainloop()
But giving below error.
line 73
options = Pmw.Combobox(root,label_text='Plugins',scrolledlist_items=[*lines])
^
SyntaxError: can use starred expression only as assignment target
Still no luck :(, error as below- Traceback (most recent call last): File "/home/shanaka/PycharmProjects/volatility/main2.py", line 73, in options = Pmw.ComboBox(root,label_text='Plugins',scrolledlist_items=lines) File "/usr/local/lib/python3.4/dist-packages/Pmw/Pmw_2_0_0/lib/PmwComboBox.py", line 147, in init self.initialiseoptions() File "/usr/local/lib/python3.4/dist-packages/Pmw/Pmw_2_0_0/lib/PmwBase.py", line 599, in initialiseoptions '" for ' + self.class.name) TypeError: descriptor 'join' requires a 'str' object but received a 'list' –
Thanks for the Support I found the solution , I used ttk.ComboBox, rather using Pmw. Thank you Jonrsharp option = ttk.Combobox(root,state="readonly",values=(lines))
Related
One of the goal of the project I'm working with is making Pepper robot patrolling hospital wards "autonomously". So I downloaded some basic application to start with navigation (https://github.com/aldebaran/naoqi_navigation_samples). The "explore" application is critical since it is needed by the other two (places and patrol). I tried to launch "explore" on Choregraphe, but the robot does not move (so it does not explore neither creates a map, obviously) and the application ends by saying the final sentence. In particular the block "Get map" gives an error. So, the application starts correctly but it does not work properly.
I saved "explore" as a robot application and tried in both autonomous life and not autonomous life.
I can not understand where I'm wrong: could you help me please?
Make sure the charging flap is not open when you run. Also try this code, it will create a map
#! /usr/bin/env python
# -*- encoding: UTF-8 -*-
"""Example: Use explore method."""
import qi
import argparse
import sys
import numpy
from PIL import Image
def main(session):
"""
This example uses the explore method.
"""
# Get the services ALNavigation and ALMotion.
navigation_service = session.service("ALNavigation")
motion_service = session.service("ALMotion")
# Wake up robot
motion_service.wakeUp()
# Explore the environement, in a radius of 2 m.
radius = 5.0
error_code = navigation_service.explore(radius)
if error_code != 0:
print ("Exploration failed.")
return
# Saves the exploration on disk
path = navigation_service.saveExploration()
print ("Exploration saved at path: \"" + path + "\"")
# Start localization to navigate in map
navigation_service.startLocalization()
# Come back to initial position
navigation_service.navigateToInMap([0., 0., 0.])
# Stop localization
navigation_service.stopLocalization()
# Retrieve and display the map built by the robot
result_map = navigation_service.getMetricalMap()
map_width = result_map[1]
map_height = result_map[2]
img = numpy.array(result_map[4]).reshape(map_width, map_height)
img = (100 - img) * 2.55 # from 0..100 to 255..0
img = numpy.array(img, numpy.uint8)
Image.frombuffer('L', (map_width, map_height), img, 'raw', 'L', 0, 1).show()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ip", type=str, default="127.0.0.1",
help="Robot IP address. On robot or Local Naoqi: use '127.0.0.1'.")
parser.add_argument("--port", type=int, default=9559,
help="Naoqi port number")
args = parser.parse_args()
session = qi.Session()
try:
session.connect("tcp://" + args.ip + ":" + str(args.port))
except RuntimeError:
print ("Can't connect to Naoqi at ip \"" + args.ip + "\" on port " + str(args.port) +".\n"
"Please check your script arguments. Run with -h option for help.")
sys.exit(1)
main(session)
I'm adapting a script.py to achieve transfer learning. I find many script to retrain a model by TFRecord files, but none of them worked for me bacause of something about TF2.0 and contrib, so I'm trying to convert a script to adapt to TF2 and to my model.
This is my script at the moment:
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
keras = tf.keras
EPOCHS = 1
# Data preprocessing
import pathlib
#data_dir = tf.keras.utils.get_file(origin="/home/pi/venv/raccoon_dataset/", fname="raccoons_dataset")
#data_dir = pathlib.Path(data_dir)
data_dir = "/home/pi/.keras/datasets/ssd_mobilenet_v1_coco_2018_01_28/saved_model/saved_model.pb"
######################
# Read the TFRecords #
######################
def imgs_input_fn(filenames, perform_shuffle=False, repeat_count=1, batch_size=1):
def _parse_function(serialized):
features = \
{
'image': tf.io.FixedLenFeature([], tf.string),
'label': tf.io.FixedLenFeature([], tf.int64)
}
# Parse the serialized data so we get a dict with our data.
parsed_example = tf.io.parse_single_example(serialized=serialized,
features=features)
print("\nParsed example:\n", parsed_example, "\nEnd of parsed example:\n")
# Get the image as raw bytes.
image_shape = tf.stack([300, 300, 3])
image_raw = parsed_example['image']
label = tf.cast(parsed_example['label'], tf.float32)
# Decode the raw bytes so it becomes a tensor with type.
image = tf.io.decode_raw(image_raw, tf.uint8)
image = tf.cast(image, tf.float32)
image = tf.reshape(image, image_shape)
#image = tf.subtract(image, 116.779) # Zero-center by mean pixel
#image = tf.reverse(image, axis=[2]) # 'RGB'->'BGR'
d = dict(zip(["image"], [image])), [label]
return d
dataset = tf.data.TFRecordDataset(filenames=filenames)
# Parse the serialized data in the TFRecords files.
# This returns TensorFlow tensors for the image and labels.
#print("\nDataset before parsing:\n",dataset,"\n")
dataset = dataset.map(_parse_function)
#print("\nDataset after parsing:\n",dataset,"\n")
if perform_shuffle:
# Randomizes input using a window of 256 elements (read into memory)
dataset = dataset.shuffle(buffer_size=256)
dataset = dataset.repeat(repeat_count) # Repeats dataset this # times
dataset = dataset.batch(batch_size) # Batch size to use
print("\nDataset batched:\n", dataset, "\nEnd dataset\n")
iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
print("\nIterator shape:\n", tf.compat.v1.data.get_output_shapes(iterator),"\nEnd\n")
#print("\nIterator:\n",iterator.get_next(),"\nEnd Iterator\n")
batch_features, batch_labels = iterator.get_next()
return batch_features, batch_labels
raw_train = tf.compat.v1.estimator.TrainSpec(input_fn=imgs_input_fn(
"/home/pi/venv/raccoon_dataset/data/train.record",
perform_shuffle=True,
repeat_count=5,
batch_size=20),
max_steps=1)
and this is the resulting screen:
Parsed example:
{'image': <tf.Tensor 'ParseSingleExample/ParseSingleExample:0' shape=() dtype=string>, 'label': <tf.Tensor 'ParseSingleExample/ParseSingleExample:1' shape=() dtype=int64>}
End of parsed example:
Dataset batched:
<BatchDataset shapes: ({image: (None, 300, 300, 3)}, (None, 1)), types: ({image: tf.float32}, tf.float32)>
End dataset
Iterator shape:
({'image': TensorShape([None, 300, 300, 3])}, TensorShape([None, 1]))
End
2019-11-20 14:01:14.493817: W tensorflow/core/framework/op_kernel.cc:1622] OP_REQUIRES failed at example_parsing_ops.cc:240 : Invalid argument: Feature: image (data type: string) is required but could not be found.
2019-11-20 14:01:14.495019: W tensorflow/core/framework/op_kernel.cc:1622] OP_REQUIRES failed at iterator_ops.cc:929 : Invalid argument: {{function_node __inference_Dataset_map__parse_function_27}} Feature: image (data type: string) is required but could not be found.
[[{{node ParseSingleExample/ParseSingleExample}}]]
Traceback (most recent call last):
File "transfer_learning.py", line 127, in <module>
batch_size=20),
File "transfer_learning.py", line 107, in imgs_input_fn
batch_features, batch_labels = iterator.get_next()
File "/home/pi/venv/lib/python3.7/site-packages/tensorflow_core/python/data/ops/iterator_ops.py", line 737, in get_next
return self._next_internal()
File "/home/pi/venv/lib/python3.7/site-packages/tensorflow_core/python/data/ops/iterator_ops.py", line 651, in _next_internal
output_shapes=self._flat_output_shapes)
File "/home/pi/venv/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_dataset_ops.py", line 2673, in iterator_get_next_sync
_six.raise_from(_core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __inference_Dataset_map__parse_function_27}} Feature: image (data type: string) is required but could not be found.
[[{{node ParseSingleExample/ParseSingleExample}}]] [Op:IteratorGetNextSync]
I don't know what I'm doing wrong.
I'm currently writing some code and am using pandas to export all of the data into csv files. My program runs multiple iterations until it has gone through all of the necessary files. Pandas is re-writing one file each iteration but when it moves onto the next file I need it to reset all of the data (I think).
Structure is roughly:
While loop>a few variables are named>program runs>dataframe=(pandas.DataFrame(averagepercentagelist,index=namelist,columns=header))
This part works with no problem for one file. When moving onto the next file, all of the arrays I use are reset and this I think is why pandas gives the error Shape of passed values is (1,1), indices imply (3,1).
Please let me know if I need to explain it better.
EDIT:
While True:
try:
averagepercentagelist=[]
namelist=[]
columns=[]
for row in database:
averagepercentagelist=["12","23"]
namelist=["Name0","Name1"]
columns=["Average percentage"]
dataframe=(pandas.DataFrame(averagepercentagelist,index=namelist,columns=header))
except Exception as e:
print e
break
SNIPPET:
dataframe= (pandas.DataFrame(averagepercentagelist,index=namelist,columns=header))
currentcalculatedatafrane = 'averages' + currentcalculate
dataframeexportpath = os.path.join(ROOT_PATH,'Averages',currentcalculatedatafrane)
dataframe.to_csv(dataframeexportpath)
FULL PROGRAM SO FAR:
import csv
import os
import re
import pandas
import tkinter as tk
from tkinter import messagebox
from os.path import isfile, join
from os import listdir
import time
ROOT_PATH = os.path.dirname(os.path.abspath(__file__))
indexforcalcu=0
line_count=0
testlist=[]
namelist=[]
header=['Average Percentage']
def clearvariables():
indexforcalcu=0
testlist=[]
def findaverageofstudent(findaveragenumber,numoftests):
total=0
findaveragenumber = findaveragenumber/numoftests
findaveragenumber = round(findaveragenumber, 1)
return findaveragenumber
def removecharacters(nameforfunc):
nameforfunc=str(nameforfunc)
elem=re.sub("[{'}]", "",nameforfunc)
return elem
def getallclasses():
onlyfiles = [f for f in listdir(ROOT_PATH) if isfile(join(ROOT_PATH, f))]
onlyfiles.remove("averagecalculatorv2.py")
return onlyfiles
def findaveragefunc():
indexforcalcu=-1
while True:
try:
totaltests=0
line_count=0
averagepercentagelist=[]
indexforcalcu=indexforcalcu+1
allclasses=getallclasses()
currentcalculate=allclasses[indexforcalcu]
classpath = os.path.join(ROOT_PATH, currentcalculate)
with open(classpath) as csv_file:
classscoredb = csv.reader(csv_file, delimiter=',')
for i, row in enumerate(classscoredb):
if line_count == 0:
while True:
try:
totaltests=totaltests+1
rowreader= {row[totaltests]}
except:
totaltests=totaltests-1
line_count = line_count + 1
break
else:
calculating_column_location=1
total=0
while True:
try:
total = total + int(row[calculating_column_location])
calculating_column_location = calculating_column_location + 1
except:
break
i=str(i)
name=row[0]
cleanname=removecharacters(nameforfunc=name)
namelist.append(cleanname)
findaveragenumbercal=findaverageofstudent(findaveragenumber=total,numoftests=totaltests)
averagepercentagelist.append(findaveragenumbercal)
line_count = line_count + 1
dataframe= (pandas.DataFrame(averagepercentagelist,index=namelist,columns=header))
currentcalculatedatafrane = 'averages' + i + currentcalculate
dataframeexportpath = os.path.join(ROOT_PATH,'Averages',currentcalculatedatafrane)
dataframe.to_csv(dataframeexportpath)
i=int(i)
except Exception as e:
print("ERROR!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n",e)
break
def makenewclass():
global newclassname
getclassname=str(newclassname.get())
if getclassname == "":
messagebox.showerror("Error","The class name you have entered is invalid.")
else:
classname = getclassname + ".csv"
with open(classname, mode='w') as employee_file:
classwriter = csv.writer(employee_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
classwriter.writerow(["Name","Test 1"])
root=tk.Tk()
root.title("Test result average finder")
findaveragebutton=tk.Button(root,text="Find Averages",command=findaveragefunc())
findaveragebutton.grid(row=2,column=2,padx=(10, 10),pady=(0,10))
classnamelabel=tk.Label(root, text="Class name:")
classnamelabel.grid(row=1, column=0,padx=(10,0),pady=(10,10))
newclassname = tk.Entry(root)
newclassname.grid(row=1,column=1,padx=(10, 10))
newclassbutton=tk.Button(root,text="Create new class",command=makenewclass)
newclassbutton.grid(row=1,column=2,padx=(0, 10),pady=(10,10))
root.mainloop()
Thanks in advance,
Sean
Use:
import glob, os
import pandas as pd
ROOT_PATH = os.path.dirname(os.path.abspath(__file__))
#extract all csv files to list
files = glob.glob(f'{ROOT_PATH}/*.csv')
print (files)
#create new folder if necessary
new = os.path.join(ROOT_PATH,'Averages')
if not os.path.exists(new):
os.makedirs(new)
#loop each file
for f in files:
#create DataFrame and convert first column to index
df = pd.read_csv(f, index_col=[0])
#count average in each row, rond and create one colum DataFrame
avg = df.mean(axis=1).round(1).to_frame('Average Percentage')
#remove index name if nncessary
avg.index.name = None
print (avg)
#create new path
head, tail = os.path.split(f)
path = os.path.join(head, 'Averages', tail)
print (path)
#write DataFrame to csv
avg.to_csv(path)
I use Anaconda and gdsCAD and get an error when all packages are installed correctly.
Like explained here: http://pythonhosted.org/gdsCAD/
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
My imports look like this (In the end I imported everything):
import numpy as np
from gdsCAD import *
import matplotlib.pyplot as plt
My example code looks like this:
something = core.Elements()
box=shapes.Box( (5,5),(1,5),0.5)
core.default_layer = 1
core.default_colors = 2
something.add(box)
something.show()
My error message looks like this:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-2f90b960c1c1> in <module>()
31 puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
32 bp.add(puffer_wafer)
---> 33 bp.show()
34 wafer = shapes.Circle((0.,0.), wafer_radius, wafer_line_thickness)
35 bp.add(wafer)
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _show(self)
80 ax.margins(0.1)
81
---> 82 artists=self.artist()
83 for a in artists:
84 a.set_transform(a.get_transform() + ax.transData)
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
952 art=[]
953 for p in self:
--> 954 art+=p.artist()
955 return art
956
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
475 poly = lines.buffer(self.width/2.)
476
--> 477 return [descartes.PolygonPatch(poly, lw=0, **self._layer_properties(self.layer))]
478
479
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _layer_properties(layer)
103 # Default colors from previous versions
104 colors = ['k', 'r', 'g', 'b', 'c', 'm', 'y']
--> 105 colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
106 color = colors[layer % len(colors)]
107 return {'color': color}
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The gdsCAD has been a pain from shapely install to this plotting issue.
This issue is because of wrong datatype being passed to colors function. It can be solved by editing the following line in core.py
colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
to
colors += list(matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15)))
If you dont know where the core.py is located. Just type in:
from gdsCAD import *
core
This will give you the path of core.py file. Good luck !
Well first, I'd ask that you please include actual code, as the 'example code' in the file is obviously different based on the traceback. When debugging, the details matter, and I need to be able to actually run the code.
You obviously have a data type problem. Chances are pretty good it's in the variables here:
puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
I had the same error thrown when I was running a call to Pandas. I changed the data to str(data) and the code worked.
I don't know if this helps I am fairly new to this myself, but I had a similar error and found that it is due to a type casting issue as suggested by previous answer. I can't see from the example in the question exactly what you are trying to do. Below is a small example of my issue and solution. My code is making a simple Random Forest model using scikit learn.
Here is an example that will give the error and it is caused by the third to last line, concatenating the results to write to file.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
This leads to an error of;
Traceback (most recent call last):
File "min_example.py", line 40, in <module>
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The solution is to make each variable a str() type on the third to last line then write to file. No other changes to then code have been made from the above.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(str(RFpreds[i])+",,"+str(yTest[i])+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
These examples are from a larger code so I hope the examples are clear enough.
I'm trying to create an application. The application gives the user 2 combo boxes. Combo Box 1 gives the first part of the file name the user wants, and Combo Box 2 gives the second part of the file name. E.g. Combo box 1 option 1 is 1 and Combo Box 2 option 1 is A; the selected file is 1_A.txt.
I have a load button which is to use the file name , and open a file with that name. If no file exists, the application opens a dialog saying "No Such File Exists"
from PySide import QtGui, QtCore
from PySide.QtCore import*
from PySide.QtGui import*
class MainWindow(QtGui.QMainWindow):
def __init__(self,):
QtGui.QMainWindow.__init__(self)
QtGui.QApplication.setStyle('cleanlooks')
#PushButtons
load_button = QPushButton('Load',self)
load_button.move(310,280)
run_Button = QPushButton("Run", self)
run_Button.move(10,340)
stop_Button = QPushButton("Stop", self)
stop_Button.move(245,340)
#ComboBoxes
#Option1
o1 = QComboBox(self)
l1 = QLabel(self)
l1.setText('Option 1:')
l1.setFixedSize(170, 20)
l1.move(10,230)
o1.move(200, 220)
o1.setFixedSize(100, 40)
o1.insertItem(0,'')
o1.insertItem(1,'A')
o1.insertItem(2,'B')
o1.insertItem(3,'test')
#Option2
o2 = QComboBox(self)
l2 = QLabel(self)
l2.setText('Option 2:')
l2.setFixedSize(200, 20)
l2.move(10,290)
o2.move(200,280)
o2.setFixedSize(100, 40)
o2.insertItem(0,'')
o2.insertItem(1,'1')
o2.insertItem(2,'2')
o2.insertItem(3,'100')
self.fileName = QLabel(self)
self.fileName.setText("Select Options")
o1.activated.connect(lambda: self.fileName.setText(o1.currentText() + '_' + o2.currentText() + '.txt'))
o2.activated.connect(lambda: self.fileName.setText(o1.currentText() + '_' + o2.currentText() + '.txt'))
load_button.clicked.connect(self.fileHandle)
def fileHandle(self):
file = QFile(str(self.fileName.text()))
open(file, 'r')
if __name__ == '__main__':
import sys
app = QtGui.QApplication(sys.argv)
window = MainWindow()
window.setWindowTitle("Test11")
window.resize(480, 640)
window.show()
sys.exit(app.exec_())
The error I'm getting is TypeError: invalid file: <PySide.QtCore.QFile object at 0x031382B0> and I suspect this is because the string described in the file handle isn't being inserted in the QFile properly. Can someone please help
The Python open() function doesn't have any knowledge of objects of type QFile. I doubt you actually need to construct a QFile object though.
Instead, just open the file directly via open(self.fileName.text(), 'r'). Preferably, you would do:
with open(self.fileName.text(), 'r') as myfile:
# do stuff with the file
unless you need to keep the file open for a long period of time
I came up with a solution also.
def fileHandle(self):
string = str(self.filename.text())
file = QFile()
file.setFileName(string)
file.open(QIODevice.ReadOnly)
print(file.exists())
line = file.readLine()
print(line)
What this does is that it takes the string of the filename field. Creates the file object. Names the file object the string, and then opens the file. I have exists to check if the file is there, and after reading the test document i have, ti seemed to work as I wanted.
Thanks anyway #three_pineapples, but I'm going to use my solution :P