python-3.x

问题 This question already has answers here : How to get the last N lines of a subprocess' stderr stream output? (2 answers) Closed 16 days ago . prova.sh contains: #!/bin/bash echo "Output that I don't want." echo "Output that I don't want." echo "Output that I don't want." echo -e "Output that I want.\nI want this too.\ \nI want this too." #This is the last command of the bash script, which is what I'm looking for. This solution: import subprocess output = subprocess.check_output('./prova.sh',

问题 We are in the transition at work from python 2.7 to python 3.5. It's a company wide change and most of our current scripts were written in 2.7 and no additional libraries. I've taken advantage of the Anaconda distro we are using and have already change most of our scripts over using the 2to3 module or completely rewriting them. I am stuck on one piece of code though, which I did not write and the original author is not here. He also did not supply comments so I can only guess at the whole of

问题 I have a simple test class @pytest.mark.incremental class TestXYZ: def test_x(self): print(self) def test_y(self): print(self) def test_z(self): print(self) When I run this I get the following output: test.TestXYZ object at 0x7f99b729c9b0 test.TestXYZ object at 0x7f99b7299b70 testTestXYZ object at 0x7f99b7287eb8 This indicates that the 3 methods are called on 3 different instances of TestXYZ object. Is there anyway to change this behavior and make pytest call all the 3 methods on the same

问题 I've noticed some strange behaviour that may or may not be specific to my system. (lenovo t430 running windows 8) With this script: import time now = time.time() while True: then = now now = time.time() dif = now - then print(dif) time.sleep(0.01) I get the following output (what I would consider nominal) with a browser open. However without a browser open I observe a severe per loop latency. Obviously this is counter-intuitive as I think anyone would expect better performance when you have

问题 I'd like Django to serve some media files (e.g. user-uploaded files) only for logged-in users. Since my site is quite low-traffic, I think I will keep things simple and do not use django-sendfile to tell Nginx when to serve a file. Instead I'll let Django/Gunicorn do the job. To me this seems a lot simpler and for a low traffic site this maybe more secure. But what is the best way to organize the file storage location? Media files are all stored below MEDIA_ROOT and this directory is served

问题 I am trying to open the file from folder and read it but it's not locating it. I am using Python3 Here is my code: import os import glob prefix_path = "C:/Users/mpotd/Documents/GitHub/Python-Sample- codes/Mayur_Python_code/Question/wx_data/" target_path = open('MissingPrcpData.txt', 'w') file_array = [os.path.abspath(f) for f in os.listdir(prefix_path) if f.endswith('.txt')] file_array.sort() # file is sorted list for f_obj in range(len(file_array)): file = os.path.abspath(file_array[f_obj])

问题 I am trying to open the file from folder and read it but it's not locating it. I am using Python3 Here is my code: import os import glob prefix_path = "C:/Users/mpotd/Documents/GitHub/Python-Sample- codes/Mayur_Python_code/Question/wx_data/" target_path = open('MissingPrcpData.txt', 'w') file_array = [os.path.abspath(f) for f in os.listdir(prefix_path) if f.endswith('.txt')] file_array.sort() # file is sorted list for f_obj in range(len(file_array)): file = os.path.abspath(file_array[f_obj])

问题 I have deployed a fastapi endpoint, from fastapi import FastAPI, UploadFile from typing import List app = FastAPI() @app.post('/work/test') async def testing(files: List(UploadFile)): for i in files: ....... # do a lot of operations on each file # after than I am just writing that processed data into mysql database # cur.execute(...) # cur.commit() ....... # just returning "OK" to confirm data is written into mysql return {"response" : "OK"} I can request output from the API endpoint and its

问题 I am new to deep learning and Keras and one of the improvement I try to make to my model training process is to make use of Keras's keras.callbacks.EarlyStopping callback function. Based on the output from training my model, does it seem reasonable to use the following parameters for EarlyStopping ? EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, verbose=0, mode='auto') Also, why does it appear to be stopped sooner than it should if it was to wait for 5 consecutive epochs

问题 I am new to deep learning and Keras and one of the improvement I try to make to my model training process is to make use of Keras's keras.callbacks.EarlyStopping callback function. Based on the output from training my model, does it seem reasonable to use the following parameters for EarlyStopping ? EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, verbose=0, mode='auto') Also, why does it appear to be stopped sooner than it should if it was to wait for 5 consecutive epochs