Comparison of Python Imports in Linux and Windows
While attempting to set various environment files for Linux and Windows, I discovered that the failure of the Python extension was caused by one specific setting. Combining multiple files into a single file can improve performance, particularly when using an HDD, as it allows for reading of large, contiguous chunks of data.
Python Import Linux vs Windows
For my new project, I need it to be compatible with both Windows and Linux operating systems. To illustrate my issue, I will refer to three files.
from .toto import Parser as TotoParser class Variable(object): def __str__(self): return "totoVariable" class Parser(object): @staticmethod def parse(data): return Variable() #!/usr/bin/env python3 from parser import TotoParser def main(): print(TotoParser.parse("")) if __name__ == '__main__': main() In my project, I organized various modules(files) into separate packages(directories). However, I faced a challenge when I needed to alter the name of the imported module. To overcome this difficulty, I utilized aliasing in the __init__ files. Although the project functioned seamlessly on Linux, I encountered an issue while attempting to run it on Windows.
The ‘TotoParser’ cannot be imported due to an ImportError.
My apologies for my English, I am currently in the process of learning it.
To ensure it works, kindly rename init.py to __init__.py. However, if it’s already named __init__.py, kindly disregard this response.
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What is the filepath difference between window and linux in python3?
Currently, I am generating a text document and using the following python 3 command to add text to it:
userFile = open("users\\"+userName+".txt","w") Upon execution of the program on a Linux system, a file named named users \userName.txt is created in the root folder instead of in the folder named users directory.
What distinguishes the path definition for Python 3 on Linux?
The variation lies in linux rather than python 3 in linux . In general, *nix dictates that file paths employ / as a separator for directories, while Windows implements \ as a separator (for unknown reasons).
By utilizing pathlib.Path in Python 3, you can isolate your code from the operating system, allowing you to perform tasks such as:
The symbol «~», denoted by ~ , represents the user’s home directory. Python can automatically handle the appropriate separators for the file system the code is running on, as indicated by map directory . Additionally, an alternative option is available.
When referring to a particular user directory, it’s important to note that the / represents the file system’s root. This should be functional on both Linux and Windows, although I have yet to verify this myself.
Follow the link to access the Python documentation for the «pathlib» module.
Drives in Windows, such as C:, D:, and X:, are accompanied by either a backslash or a double backslash.
The meaning of \ \ \ \ \ C:\Users\JohnSmith\ \ \ \ is equivalent to that of C:\Users\JohnSmith .
In Linux, forward slashes are used instead of drives (per se). For instance: /home/name .
To gain familiarity with paths, one can utilize the os module in Python. Simply enter the following code into your terminal: print(os.path.abspath(‘.’)) .
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VS Code PYTHONPATH for Windows and Linux
To configure the PYTHONPATH for a project located in Visual Studio Code , I have a file named .env that specifies the path. However, due to the presence of multiple directories in the path, I require the following method when working on Windows.
On Linux, employ a colon as a delimiter.
To maintain confidentiality, my .env is saved in the source repository. I attempted to assign distinct env files for Linux and Windows, but assigning python.envFile.windows resulted in the Python extension crashing.
What is the best method to configure the Visual Studio PYTHONPATH in a way that is compatible with developers working on both Linux and Windows operating systems?
At present, there is no provision in the operating system to support the specification of distinct paths for .env and PYTHONPATH files. If you wish to have this functionality, kindly submit a request for this feature to be included at https://github.com/microsoft/vscode-python.
If you are working on vscode, setting up a launch.json file for your project is recommended. Make sure to create two configurations at minimum — one for Windows and the other for Linux. The documentation on this can be found at https://code.visualstudio.com/docs/editor/debugging#_launch-configurations.
To ensure proper configuration, it is necessary to assign the correct value for PYTHONPATH to the environment field in each setting.
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Whats the performance difference between a python accessing multiple small .npy files vs one large .npy files
My ongoing project involves a repository containing numerous small files of .npy numpy or png image format, which are being read and written by the code.
Given the slow performance, I am contemplating whether consolidating all smaller npy files files into a larger one would speed up the code. If this is the case, what is the underlying reason? Could it be related to disk I/O?
To access each file, the operating system needs to retrieve data from the storage device through multiple requests related to the target file system. Retrieving a block from the device also requires a request, resulting in several requests per file. As files are typically stored in different locations that appear random, performing random fetches on storage devices can be slow. Additionally, storage devices have a limited number of IO operations per second (IOPS), with HDDs having a very low number of IOPS (around 75 for mainstream 7200 RPM HDDs). Although SSDs are faster, neither the hardware nor software stacks are currently optimized for performing many requests sequentially. As a result, multiple threads are often required to achieve a good IOPS.
Consolidating multiple files into one can significantly enhance performance, particularly when utilizing a hard disk drive (HDD) as it allows reading of larger, uninterrupted data segments.
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