Automations with Python.
This code is designed to run Python programs on separate terminals asynchronously. I will explain step by step what each part of the code does.
1. Subprocess Module Import
import subprocess
The subprocess module allows you to create and manage operating system processes from a Python program. It is used here to launch Python programs in new terminal windows.
2. execute_program function
def executar_programa(caminho_programa): try: # Executa o programa em uma nova janela de terminal subprocess.Popen( ["python", caminho_programa], creationflags=subprocess.CREATE_NEW_CONSOLE ) print(f"Programa {caminho_programa} iniciado com sucesso.") except Exception as e: print(f"Erro ao iniciar o programa {caminho_programa}: {e}")
This function is responsible for running a Python program in a new terminal window:
Program_path argument: The absolute path of the Python script you want to run.
-
subprocess.Popen: Starts a new process in the operating system.
- The list ["python", program_path] is the command that will be executed in the terminal. The first "python" item is the Python interpreter and the second program_path item is the Python script to be executed.
- creationflags=subprocess.CREATE_NEW_CONSOLE: This flag creates a new terminal window for the process instead of running it in the current terminal window.
try and except: The try block attempts to execute the program. If an error occurs (such as an incorrect script path), the except block catches the exception and prints an error message.
3. main function
def main(): # Caminhos para os programas que você deseja executar programa1 = r"C:\Users\hbvbr\Documents\DEV\AlgotradingCopia\eaEquiti\eaEquiti108.py" programa2 = r"C:\Users\hbvbr\Documents\DEV\AlgotradingCopia\eaEquiti690\eaEquiti690.py" programa3 = r"C:\Users\hbvbr\Documents\DEV\AlgotradingCopia\eaFtmo\eaFtmo.py" programa4 = r"C:\Users\hbvbr\Documents\DEV\AlgotradingCopia\eaEquiti224\eaEquiti224.py" # Executa cada programa em um terminal separado executar_programa(programa1) executar_programa(programa2) executar_programa(programa3) executar_programa(programa4)
In the main function:
Defining paths for programs: Here, four variables are defined (program1, program2, program3, program4) with the absolute paths of the Python scripts you want to run. Paths are written as raw strings (prefixed with r) to avoid backslash issues.
Call to the execute_program function: For each program, the execute_program function is called. Each Python script runs in a new terminal window.
4. Conditional Execution if __name__ == "__main__":
import subprocess
- if __name__ == "__main__":: This condition checks whether the Python file is being executed directly (and not imported as a module in another script). If executed directly, the main() function will be called and programs will start.
Flow Summary:
- The script starts by executing the main function.
- The main function calls execute_programa for each of the four scripts, passing the program paths.
- The execute_program function runs each Python script in a new terminal window, using subprocess.Popen.
- If there is any error while trying to run any program, the error will be captured and a message will be displayed.
How it works in the operating system:
- The subprocess.Popen with CREATE_NEW_CONSOLE will open a new operating system terminal window (on Windows, usually cmd or PowerShell) and run the specified Python program in each of these windows.
This is the basic functioning of the code! If you need more details or adjustments, feel free to ask.
The above is the detailed content of Automations with Python.. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code
