canvas.itemconfig not working with time.sleep

2021-04-08 03:01 SkiBum55 imported from Stackoverflow

def start_timer():

    for mins in range(WORK_MIN - 1, 0, -1):
        for secs in range(59, 0, -1):
            print(mins, secs)
            timer.itemconfig(timer_text, text=(f"{mins}:{secs}"))
            time.sleep(1)

I call this function with a button click which is supposed to start a countdown timer and update the Canvas (timer = Canvas()) text every second. If I comment out the time.sleep(1) command the displayed text seems to change as it ends at 1:1. However, with the time.sleep(1) command active, then the Canvas text never updates. I added The print(mins, secs) to verify that the loops were executing properly.

Question: anyone see anything that would prevent the timer.itemconfig from working properly with the time.sleep statement?

1 answer

answered 2021-04-08 04:14 acw1668

It is because time.sleep() will block tkinter mainloop from updating widgets. It is better to use after() instead.

Below is an example:

import tkinter as tk

WORK_MIN = 1

def start_timer(seconds=WORK_MIN*60):
    mins, secs = divmod(seconds, 60)
    timer.itemconfig(timer_text, text=f"{mins:02}:{secs:02}")
    if seconds > 0:
        timer.after(1000, start_timer, seconds-1)

root = tk.Tk()

timer = tk.Canvas(root)
timer.pack(fill="both", expand=1)

timer_text = timer.create_text(10, 10, font="Courier 48 bold", anchor="nw")

start_timer()  # start the countdown timer
root.mainloop()

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Sparse Matrix Creation : KeyError: 579 for text datasets
I am trying to use the make_sparse_matrix function to create a sparse matrix for my text dataset, and I face KeyError: 579. Does anyone has any leads on the root of the error.
```
def make_sparse_matrix(df, indexed_words, labels):
"""
Returns sparse matrix as dataframe.

df: A dataframe with words in the columns with a document id as an index (X_train or X_test)
indexed_words: index of words ordered by word id
labels: category as a series (y_train or y_test)
"""

nr_rows = df.shape[0]
nr_cols = df.shape[1]
word_set = set(indexed_words)
dict_list = []

for i in range(nr_rows):
    for j in range(nr_cols):
        
        word = df.iat[i, j]
        if word in word_set:
            doc_id = df.index[i]
            word_id = indexed_words.get_loc(word)
            category = labels.at[doc_id]
            
            item = {'LABEL': category, 'DOC_ID': doc_id,
                   'OCCURENCE': 1, 'WORD_ID': word_id}
            
            dict_list.append(item)

return pd.DataFrame(dict_list)



make_sparse_matrix( X_train, word_index,  y_test ) 
```
X_train is a DF that contains one single word in each cell, word_index contains all the index of words and y_test stores all labels.

The Key Error I am facing is:

--------------------------------------------------------------------------- KeyError Traceback (most recent call last) ~\New folder\envs\geo_env\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 3079 try: -> 3080 return self._engine.get_loc(casted_key) 3081 except KeyError as err:

pandas_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

pandas_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

KeyError: 579

The above exception was the direct cause of the following exception:

KeyError Traceback (most recent call last) in

in make_sparse_matrix(df, indexed_words, labels) 20 doc_id = df.index[i] 21 word_id = indexed_words.get_loc(word) ---> 22 category = labels.at[doc_id] 23 24 item = {'LABEL': category, 'DOC_ID': doc_id,

~\New folder\envs\geo_env\lib\site-packages\pandas\core\indexing.py in getitem(self, key) 2154 return self.obj.loc[key] 2155 -> 2156 return super().getitem(key) 2157 2158 def setitem(self, key, value):

~\New folder\envs\geo_env\lib\site-packages\pandas\core\indexing.py in getitem(self, key) 2101 2102 key = self._convert_key(key) -> 2103 return self.obj._get_value(*key, takeable=self._takeable) 2104 2105 def setitem(self, key, value):

~\New folder\envs\geo_env\lib\site-packages\pandas\core\series.py in _get_value(self, label, takeable) 959 960 # Similar to Index.get_value, but we do not fall back to positional --> 961 loc = self.index.get_loc(label) 962 return self.index._get_values_for_loc(self, loc, label) 963

~\New folder\envs\geo_env\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 3080 return self._engine.get_loc(casted_key) 3081 except KeyError as err: -> 3082 raise KeyError(key) from err 3083 3084 if tolerance is not None:

KeyError: 579
Finding part of string in list of strings
```
GCM = ([519,520,521,522,533],[534,525],[526,527,530,531], [4404])

slice = int(str(df["CGM"][row_count])[:3])
```
I am looking through a row in a csv file and taking out the number I want. i want the number that starts with the number I have in GCM. since they represent info I want in other columns. this has working fine with the slice function because all the number i wanted started with 3 digits. now that i need to look for any number that starts with 4404 and later on going to probably need to look for 57052 the slice function no longer work.

is there a way I can, instead of slicing and comparing to list, can take 5 digit number and see if part of it is in list. preferably look for it starting 3 or more same digits. the real point of that part of code is finding out which list in GCM list the number is. it need to be able to have the number 44042 and know that the part of it a care about is in GCM[3], but on the other side do not want it to say that 32519 is in DCM[0] since I only care about number that start with 519 not ends with it.

ps. I am norwegian and have been learning programming by myself. been some long nights. so something here can be lost in translation.
How to forecast a time series out-of-sample using an ARIMA model in Python?
I have seen similar questions at Stackoverflow. But, either the questions were different enough or if similar, they actually have not been answered. I gather it is something that modelers run into often, and have a challenge solving.

In my case I am using two variables, one Y and one X with 50 time series sequential observations. They are both random numbers representing % changes (they could be anything you want, their true value does not matter. This is just to set up an example of my coding problem). Here are my basic codes to build this ARIMAX(1,0,0) model.
```
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf

df = pd.read_excel('/Users/gaetanlion/Google Drive/Python/Arima/df.xlsx', sheet_name = 'final')

from statsmodels.tsa.arima_model import ARIMA
endo = df['y']
exo = df['x']
```
Next, I build the ARIMA model, using the first 41 observations
```
modelho = sm.tsa.arima.ARIMA(endo.loc[0:40], exo.loc[0:40], order =(1,0,0)).fit()
print(modelho.summary())
```
So far everything works just fine.

Next, I attempt to forecast or predict the next 9 observations out-of-sample. Here I want to use the X values over these 9 observations to predict Y. And, I just can't do it. I am showing below just the one code, that I think gets me the closest to where I need to go.
```
modelho.predict(exo.loc[41:49], start = 41, end = 49, dynamic = False)
TypeError: predict() got multiple values for argument 'start'
```
Hello, i need some help on python with tkinter

i am making a clicker game in python with tkinter. It doesnt give any error messages but the autoclicker on the game doesnt work. When i buy it for 1000 clicks, it turns to true. When boughtac = true and x_ = true, it is meant to call the function click() every 0.2 seconds. I have been at this for 8 hours today. If someone does manage to help me, can u try please keep it with the current imports i have. i dont really understand any other imports and im very new to python so it would be nice if you could keep it simple :)

https://paste.pythondiscord.com/nuhageciga.apache

Python tkinter - place function not working with multiple widgets

When making a program for a game database, I want to have the main menu to have multiple buttons leading to multiple topics. The place function isn't working for me. Here is my code:

windowFU = tk.Tk()
windowFU.title("MHFU Database")
windowFU.geometry("255x200+300+180")

frame = tk.Frame(master = windowFU, width = 255, height = 200)
frame.pack()

icon = tk.PhotoImage(file = "images/icon.png")
windowFU.iconphoto(False, icon)

welcome = tk.Label(

    master = frame,
    text = "What would you like to view?",
    width = 30,
    height = 2

)

searchEntry = tk.Entry(

    master = frame,
    width = 30

)

buttonMonstersFU = tk.Button(

    master = frame,
    text = "Monsters",
    width = 12,
    height = 2

)

# Here is the place function
buttonMonstersFU.place(x = 100, y = 100)

welcome.pack()
searchEntry.pack()
buttonMonstersFU.pack()

searchEntry.bind('<Return>', getEntry)

windowFU.mainloop()

Note: Currently I just have the place function set to x = 100, y = 100 to test, it is not my final location.

Here is an image of what I get:

Result

What should I do?

Pyinstaller imported code cannot load images

I have a game set up in several files like so: menu.py main.py

I use Tkinter module. All needed files are bundled with the .EXE correctly, and are always called with resource_path('folder/myImage.png')

menu.py has no problem, but when main.py is executed, files are not loaded correctly(error: no such file or directory)

menu.py runs main.py by importing it(issue here?)

Other than that, I am clueless regarding the issue..

Thanks in advance

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canvas.itemconfig not working with time.sleep

1 answer

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