What are some usage of python subprocess for workflow optimization

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Question: What are some usage examples of python subprocess (lets you interact with the operating system) for workflow optimization?

Rationale:
Before the python subprocess, I was quite comfortable using the os module in my research work. In fact, the os came in handy acting as a glue to connect modules, even with different programing languages as it is often the case with quantum chemistry programs. The subprocess module can be used in a similar way as the os module. I haven’t explore the subprocess module the way I had done with the os module. It appears one can launch many subprocess and even set communication between subprocesses.

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I have one simple example: sandbox_python/writeout.py at main · prehensilecode/sandbox_python · GitHub

Other examples including code I currently use for sysadmin work.

I used to use “os” but switched to using “subprocess” quite a few years ago, now. It’s pretty straightforward. Just try it out.

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I’ve been using subprocess instead of os.system for a while.

I know that this thread is a bit old, but an explanation of some of the benefits of using subprocess over os.system seems like it could be useful:

  1. Flexibility: The subprocess module offers more flexibility in running external commands, as it provides a range of functions like Popen, call, check_output, and run. These functions offer various options for controlling the execution of external commands, making it easier to customize the behavior of your code. Popen gives the greatest flexility and allows for cases where I might need multiple processes running. call replicates the blocking behavior of os.system(). check_output is good for scenarios where I want to run a command, capture the output, and check if it gave an exit code 0. run combines the functionality of all lower-level methods just described.

    import subprocess

# Using `subprocess.run`
subprocess.run(["ls", "-l"])

# Using `subprocess.Popen`
with subprocess.Popen(["ls", "-l"], stdout=subprocess.PIPE) as proc:
output = proc.stdout.read().decode('utf-8')
print(output)

  2. Better error handling: os.system only returns the exit code of the executed command, which can make error handling difficult. In contrast, subprocess allows you to catch exceptions like CalledProcessError and TimeoutExpired, enabling you to handle errors more effectively and make your code more robust. In the case of workflow optimization, this gives me to automate some possibilities, such as retrying a command that has an external dependency such as a call to API that could be available at another time or a sever that is might be rebooting or not ready.

    import subprocess

try:
    subprocess.run(["nonexistent_command"], check=True)
except subprocess.CalledProcessError as e:
    print(f"Error: {e}")

  3. Improved output control: With os.system, the output of the executed command is always sent to the standard output (usually the console). subprocess, on the other hand, allows you to capture the output of external commands as strings or bytes, which can then be processed within your Python script. This is particularly useful when you need to parse the output of a command or use it as input for another part of your code.

    import subprocess

# Capture output using `subprocess.run`
result = subprocess.run(["ls", "-l"], stdout=subprocess.PIPE, text=True)
print(result.stdout)

# Capture output using `subprocess.check_output`
output = subprocess.check_output(["ls", "-l"], text=True)
print(output)

  4. Security: os.system is less secure than subprocess, as it is susceptible to shell injection attacks when user-provided input is not sanitized. subprocess provides more control over the execution environment, including the ability to execute commands directly without invoking a shell, making it a safer choice for running external commands.

    import subprocess

# Unsafe
# user_input = "; rm -rf /"  # Malicious input
# os.system(f"ls {user_input}")

# Safe
user_input = "; rm -rf /"  # Malicious input
subprocess.run(["ls", user_input], shell=False)

The unsafe example could be disastrous!

  1. Better process control: subprocess provides better control over the execution of external processes, including the ability to set environment variables, redirect standard input/output/error, and interact with the process while it is running. This level of control can be crucial when developing highly adaptive workflows.

    import subprocess

# Redirecting stderr to stdout
result = subprocess.run(["ls", "-l", "/nonexistent_dir"], stderr=subprocess.STDOUT, stdout=subprocess.PIPE, text=True)
print(result.stdout)

# Setting environment variables
import os

my_env = os.environ.copy()
my_env["MY_VARIABLE"] = "my_value"
subprocess.run(["printenv", "MY_VARIABLE"], env=my_env, shell=False)

  2. Cross-platform compatibility: While both os.system and subprocess work on multiple platforms, the subprocess module offers better cross-platform compatibility. For instance, subprocess.run can automatically convert command arguments into the appropriate format for the target platform, making it easier to write platform-independent code. While this is not usually as important for us, it is still a reason to use subprocess instead of os.system.

    import subprocess

# Redirecting stderr to stdout
result = subprocess.run(["ls", "-l", "/nonexistent_dir"], stderr=subprocess.STDOUT, stdout=subprocess.PIPE, text=True)
print(result.stdout)

# Setting environment variables
import os

my_env = os.environ.copy()
my_env["MY_VARIABLE"] = "my_value"
subprocess.run(["printenv", "MY_VARIABLE"], env=my_env, shell=False)

I mainly use the output and exit codes to handle the various possible cases, such as time-outs or tracking progress.

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