Here is the code.
import pyautogui as pg
from time import sleep
import random
import pyperclip
a = ['Василий', 'Петр', 'Иван', 'Сидор', 'Ануфрий'] #list
def rand_name():
name: str = random.choice(a) #choose a random name from the list
pg.doubleClick(724, 417) #click on the coordinates of the input field
sleep(0.5) #pause
pg.typewrite(name) #enter a randomly selected name
return name
n = rand_name() #function start
print(n) #examination
the selected word is printed to the console. But nothing appears in the input field.
why isn't my random word printed in the input field? Tell me please
The issue are the symbols, using plane English symbols works. I don't know how to enable other symbols but here is an easy workaround:
def _workaround_write(text):
pyperclip.copy(text)
pg.hotkey('ctrl', 'v')
pyperclip.copy('')
So instead of typing in the text you copy-paste it.
Your final code will look like this:
import pyautogui as pg
from time import sleep
import random
import pyperclip
a = ['Василий', 'Петр', 'Иван', 'Сидор', 'Ануфрий'] #list
def _workaround_write(text):
pyperclip.copy(text)
pg.hotkey('ctrl', 'v')
pyperclip.copy('')
def rand_name():
name: str = random.choice(a) #choose a random name from the list
pg.doubleClick(724, 417) #click on the coordinates of the input field
sleep(0.5) #pause
_workaround_write(name) #enter a randomly selected name
return name
n = rand_name() #function start
print(n)
Related
For a NLTK project I want to mass identify, if a new word is likely a "trash" word or a meaningful word. It fits to the architecture to do this in the early phase, so I can proceed with "true" words later - so I like this negative approach to identify this non meaningful words.
For this I have a training set with words/trash labeled.
I work with single characters for the labeling and have the error:
ValueError: empty vocabulary; perhaps the documents only contain stop words
The code I use is:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import svm
talk_data = pd.read_excel('nn_data_stack.xlsx', sheet_name='label2')
talk_data['word_char_list'] = [[*x] for x in talk_data['word'].astype(str)]
talk_data['word_char'] = [','.join(map(str, l)) for l in talk_data['word_char_list']]
talk_data['word_char'].replace(',',' ', regex=True, inplace=True)
z = talk_data['word_char']
y = talk_data['class']
z_train, z_test,y_train, y_test = train_test_split(z,y,test_size = 0.2)
cv = CountVectorizer()
features = cv.fit_transform(z_train)
Example of the data set I have for training
word
class
drawing
word
to be
word
龚莹author
trash
ï½°c
trash
Do I need to use an alternative to CountVectorizer?
I think I need to go to character embedding to ensure a proper input - but how?
Here I have a list which I am getting from the cursor of MYSQL connector. I have no problem in fetching the data. But I am interested in making a menu using the OptionMenu feature of tkinter with the data I received. Here, is what I am trying
from tkinter import *
r = Tk()
name_ = StringVar()
name = [('John',),('Jimmy',),('Smith',),('Rosel',)]
#Here I am directly giving the list, but this the output which I get from cursor of MYSQL
l=[]
for i in range(len(name)-1):
l.append(name[i][0])
print(l)
t = tuple(l)
name_.set('Select Student')
option = OptionMenu(r,name_,l)
option.pack()
But I don't think this helps as what I get is the whole list printed as a single option.
Please help me.....
You need to use python's * operator to expand the list when calling OptionMenu (eg: *l):
option = OptionMenu(r, name_, *l)
I hope it helps:
from tkinter import *
from tkinter import ttk
root = Tk()
root.title("mysql")
root.geometry(("400x400"))
class Mysql:
def __init__(self,parent):
self.parent = parent
self.options = [('John',),('Jimmy',),('Smith',),('Dave',)]
self.var = StringVar(self.parent)
self.var.set("Select Student")
self.option = ttk.OptionMenu(self.parent,self.var,*self.options)
self.option.pack()
Mysql(root)
root.mainloop()
Please let me know if this is not what you want:
python 3
from tkinter import *
from tkinter import ttk
root = Tk()
root.title("mysql")
root.geometry("400x400")
var = StringVar(root)
var.set("select student")
option = ttk.OptionMenu(root,var,'','John','Jimmy','Smith','Rosel')
option["width"] = 20
option.pack()
root.mainloop()
I need some help, I trying to update the etcprice label value after I push the button and after every 5 seconds, in terminal works, but in tk window not. I stucked here :( please, help me.
I tried to setup the "price" to "StringVar()" but in that case I got a lot of errors.
Many thanks
import urllib.request
from urllib.request import *
import json
import six
from tkinter import *
import tkinter as tk
import threading
price = '0'
def timer():
threading.Timer(5.0, timer).start()
currentPrice()
def currentPrice():
url = 'https://api.cryptowat.ch/markets/bitfinex/ethusd/price'
json_obj = urllib.request.urlopen(url)
data = json.load(json_obj)
for item, v in six.iteritems(data['result']):
# print("ETC: $", v)
price = str(v)
# print(type(etcar))
print(price)
return price
def windows():
root = Tk()
root.geometry("500x200")
kryptoname = Label(root, text="ETC price: ")
kryptoname.grid(column=0, row=0)
etcprice = Label(root, textvariable=price)
etcprice.grid(column=1, row=0)
updatebtn = Button(root, text="update", command=timer)
updatebtn.grid(column=0, row=1)
root.mainloop()
windows()
The solution was: I created a new String variable called “b” and I changed the etcprice Label variable to this.
After I added this b.set(price) in currentPrice() def: and is working.
The price variable is a global - if you're trying to change it, you need to do so explicitly:
def currentPrice():
global price
url = 'https://api.cryptowat.ch/markets/bitfinex/ethusd/price'
json_obj = urllib.request.urlopen(url)
data = json.load(json_obj)
for item, v in six.iteritems(data['result']):
# print("ETC: $", v)
price = str(v)
# print(type(etcar))
print(price)
return price
otherwise, Python will 'mirror' it as a local variable inside the function, and not modify the global.
It's not a good idea to keep on launching more and more threads each time you click the button - so:
updatebtn = Button(root, text="update", command=currentPrice)
probably makes more sense.
You don't need to use threads here, just to call functions 'in the background'. You can use tkinter's own .after function instead to delay caling functions. (it uses milliseconds, not float second values, btw)
def timer(delay_ms, root, func):
func()
root.after(delay_ms, timer, root, func)
might be a helpful kind of function.
Then before you launch your mainloop, or whenever you want the getting to start, call it once:
timer(5000, root, currentPrice)
If you want the currentPrice function to run in a separate thread, and so not block your main GUI thread if there is network lag, for instance, then you can use threads more like this:
Thread(target=currentPrice, daemon=True).start()
which will run it in a daemon-thread - which will automatically get killed if you close the program, or ctrl-c it, or whatever. So you could put that line in a getCurrentPriceBG or similar function.
I'm trying to create an autoencoder in keras with bucketing where the input and the output have different time steps.
model = Sequential()
#encoder
model.add(Embedding(vocab_size, embedding_size, mask_zero=True))
model.add(LSTM(units=hidden_size, return_sequences=False))
#decoder
model.add(RepeatVector(max_out_length))
model.add(LSTM(units=hidden_size, return_sequences=True))
model.add(TimeDistributed(Dense(num_class, activation='softmax')))
For the input there is no problem as the network can accept different length inputs as long as the whole batch has the same length. However the problem is with the output size as its determined by the RepeatVector length and there is not easy way to change it.
Is there a solution for such a problem?
If you mean "inputs with variable lengths" and "outputs with the same lengths as the inputs", you can do this:
Warning: this solution must work with batch size = 1
You will need to create an external loop and pass each sample as a numpy array with the exact length
You cannot use masking in this solution, and the right output depends on the correct length of the input
This is a working code using Keras + Tensorflow:
Imports:
from keras.layers import *
from keras.models import Model
import numpy as np
import keras.backend as K
from keras.utils.np_utils import to_categorical
Custom functions to use in Lambda layers:
#this function gets the length from the original input
#and stores it in the final output of the encoder
def storeLength(x):
inputTensor = x[0]
storeInto = x[1] #the final output
length = K.shape(inputTensor)[1]
length = K.cast(length,K.floatx())
length = K.reshape(length,(1,1))
#will put length as the first element in the final output
return K.concatenate([length,storeInto])
#this function expands the length of the input in the decoder
def expandLength(x):
#lenght is the first element in the encoded input
length = K.cast(x[0,0],'int32') #or int64 if necessary
#the remaining elements are the actual data to be decoded
data = x[:,1:]
#a tensor with shape (length,)
length = K.ones_like(K.arange(0,length))
#make both length tensor and data tensor 3D and with paired dimensions
length = K.cast(K.reshape(length,(1,-1,1)),K.floatx())
data = K.reshape(data,(1,1,-1))
#this automatically repeats the elements based on the paired shapes
return data*length
Creating the models:
I assumed the output is equal to the input, but since you're using an Embedding, I made "num_classes" equal to the number of words.
For this solution, we use a branching, thus I had to use the functional API Model. Which will be way better later, because you will want to train with autoencoder.train_on_batch and then just encode with encoder.predict() or just decode with decoder.predict().
vocab_size = 100
embedding_size = 7
num_class=vocab_size
hidden_size = 3
#encoder
inputs = Input(batch_shape = (1,None))
outputs = Embedding(vocab_size, embedding_size)(inputs)
outputs = LSTM(units=hidden_size, return_sequences=False)(outputs)
outputs = Lambda(storeLength)([inputs,outputs])
encoder = Model(inputs,outputs)
#decoder
inputs = Input(batch_shape=(1,hidden_size+1))
outputs = Lambda(expandLength)(inputs)
outputs = LSTM(units=hidden_size, return_sequences=True)(outputs)
outputs = TimeDistributed(Dense(num_class, activation='softmax'))(outputs)
decoder = Model(inputs,outputs)
#autoencoder
inputs = Input(batch_shape=(1,None))
outputs = encoder(inputs)
outputs = decoder(outputs)
autoencoder = Model(inputs,outputs)
#see each model's shapes
encoder.summary()
decoder.summary()
autoencoder.summary()
Just an example with fake data and the method that should be used for training:
inputData = []
outputData = []
for i in range(7,10):
inp = np.arange(i).reshape((1,i))
inputData.append(inp)
outputData.append(to_categorical(inp,num_class))
autoencoder.compile(loss='mse',optimizer='adam')
for epoch in range(1):
for inputSample,outputSample in zip(inputData,outputData):
print(inputSample.shape,outputSample.shape)
autoencoder.train_on_batch(inputSample,outputSample)
for inputSample in inputData:
print(autoencoder.predict(inputSample).shape)
I try to save my output as csv using the "import csv" and only get errors. Any reason why?
Since I can not make it run will it also notify if the file already exists?
Thanks a lot
from tkinter import *
from tkinter.filedialog import asksaveasfilename
from tkinter import ttk
import csv
def data():
...
output= <class 'list'> #just an example
...
def savefile():
name= asksaveasfilename()
create = csv.writer(open(name, "wb"))
create.writerow(output)
for x in output:
create.writerow(x)
root = Tk()
Mframe = ttk.Frame(root)
Mframe.grid(column=0, row=0, sticky=(N, W, E, S))
bSave=ttk.Button(Mframe, text='Save File', command=savefile)
bSave.grid(column=1, row=0)
root.mainloop()
You are opening the file, but not closing it. A good practise is to use a with statement make sure you close it. By the way, I don't know how is the output list, but if it isn't a list of lists, it makes more sense to me to call writerow once.
Besides, make sure this list is also a global variable, otherwise it won't be available within the scope of savefile. However, global variables are not a very good solution, so consider to pass it as an argument to savefile or use a class to hold all this data:
def savefile():
name = asksaveasfilename()
with open(name, 'w', newline='') as csvfile:
create = csv.writer(csvfile)
create.writerow(output)