Snakemake: Show Internal Commands In Terminal

Hey guys! Ever been in a situation where you're running a Snakemake workflow and desperately need to see the nitty-gritty details of what's happening under the hood? Specifically, those workflow-internal commands that normally pop up when you use the -p parameter? But then, BAM! They disappear when you structure them into certain try or cmd blocks. Frustrating, right? Well, let's dive into how to make those commands visible again, especially when you're using those blocks to ensure your commands are executing successfully and logging everything neatly.

Understanding the Issue

So, you're cruising along with Snakemake, building these awesome workflows, and you decide to use try or cmd blocks to handle command execution more robustly. Maybe you're checking for successful execution, creating log files, or just trying to manage errors more gracefully. All great stuff! But then you notice something weird: the commands that should be printed to the terminal, thanks to the -p parameter, are suddenly MIA.

Why does this happen? Well, when you encapsulate commands within these blocks, Snakemake's default behavior for displaying commands can sometimes get overridden. The -p flag is supposed to print the shell commands being executed, but the way try and cmd (or similar custom execution environments) handle the execution and output streams can interfere with this. It's like the commands are being run in a little black box, and you can't see what's going on inside.

Let's make one thing crystal clear: seeing those internal commands is super important. It's not just about satisfying curiosity; it's about debugging, understanding the flow of your workflow, and making sure everything is running as expected. Especially when things go south, those printed commands are your lifeline for figuring out what went wrong.

Solutions to Display Workflow-Internal Commands

Alright, enough with the problem. Let's get to the solutions! Here are a few ways you can ensure those workflow-internal commands show up in your terminal, even when they're hiding inside try or cmd blocks.

1. Ensuring -p Parameter Usage

First and foremost, double-check that you are actually using the -p parameter when you run Snakemake. I know it sounds obvious, but sometimes the simplest things are the easiest to overlook. The -p parameter is what tells Snakemake to print the commands to standard output before executing them. So, your command should look something like this:

snakemake -p --cores all  # Or whatever your other parameters are

Make sure that -p is included every time you run Snakemake, especially when you are modifying your workflow and expect to see the commands. It’s also worth noting that some IDEs or workflow management systems might override command-line arguments, so ensure that the -p flag is consistently being passed to the Snakemake execution.

2. Explicitly Printing Commands in try or cmd Blocks

Okay, so -p is there, but the commands are still hiding. No problem! Let's take matters into our own hands. Inside your try or cmd blocks, you can explicitly print the command to the terminal before executing it. This way, even if Snakemake's default printing is suppressed, you'll still see the command.

Here's how you can do it:

rule my_rule:
    output:
        "output.txt"
    shell:
        """
        cmd = "echo 'Hello, world!' > {output}"
        print(cmd)  # Explicitly print the command
        try:
            shell(cmd)
        except:  # Catching possible errors
            print("Command failed:", cmd)
            raise  # Re-raise the exception so Snakemake knows the rule failed
        """

In this example, before the shell(cmd) execution, we explicitly print the value of cmd to standard output. This ensures that the command is visible regardless of how Snakemake handles the execution environment.

3. Custom Logging with Command Printing

Building on the previous solution, let's create a more robust logging mechanism that includes command printing. Instead of just printing to the terminal, let's also log the command to a file. This way, you have a persistent record of what was executed, which can be incredibly useful for debugging and auditing.

import logging

logging.basicConfig(
    filename="workflow.log",
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

rule my_rule:
    output:
        "output.txt"
    shell:
        """
        cmd = "echo 'Hello, world!' > {output}"
        logging.info(f"Executing command: {cmd}")  # Log the command
        try:
            shell(cmd)
        except:  # Catching possible errors
            logging.error(f"Command failed: {cmd}")
            raise  # Re-raise the exception so Snakemake knows the rule failed
        """

Here, we're using Python's logging module to write the command to a log file (workflow.log). The logging.info function logs the command with an informative message. Similarly, if the command fails, logging.error logs the error along with the command. This approach provides a clear and persistent record of all executed commands.

4. Using run Blocks Instead of shell with Printing

Sometimes, the way Snakemake handles the shell directive can interfere with command printing. An alternative is to use a run block and execute the commands using Python's subprocess module. This gives you more control over the execution environment and output streams.

import subprocess

rule my_rule:
    output:
        "output.txt"
    run:
        cmd = f"echo 'Hello, world!' > {output[0]}"
        print(f"Executing command: {cmd}")  # Print the command
        try:
            subprocess.run(cmd, shell=True, check=True, capture_output=False)
        except subprocess.CalledProcessError as e:
            print(f"Command failed: {cmd}\n{e.stderr.decode()}")
            raise

In this example, we use subprocess.run to execute the command. We set shell=True to allow shell-like command execution, check=True to raise an exception if the command fails, and capture_output=False to ensure that the command's output is printed to the terminal. Before executing the command, we print it using Python's print function. Error handling is also included to catch any exceptions and print the error message.

5. Modify Snakemake's common.py (Not Recommended for Beginners)

Warning: This is an advanced technique and should be approached with caution. Modifying Snakemake's internal files can lead to unexpected behavior and compatibility issues. Only do this if you know what you're doing!

If you're feeling adventurous, you can modify Snakemake's common.py file to force command printing. Look for the function responsible for executing shell commands (usually within the shell directive's implementation) and add a print(command) statement before the command is executed. Again, this is not recommended for beginners and can break your Snakemake installation if not done carefully.

Best Practices for Workflow Command Visibility

Okay, we've covered the technical solutions. Now, let's talk about some best practices to ensure your workflow commands are always visible and easy to debug.

1. Consistent Logging

Always log your commands! Whether you're using a simple print statement or a more sophisticated logging module, make sure you're recording what commands are being executed and when. This is invaluable for debugging and auditing, especially in complex workflows.

2. Modularize Your Workflows

Break your workflows into smaller, more manageable modules. This makes it easier to isolate issues and understand the flow of execution. When each module is self-contained and well-documented, it's much easier to track down problems and ensure commands are being executed correctly.

3. Use Descriptive Rule Names

Give your rules descriptive names that clearly indicate what they're doing. This makes it easier to understand the overall workflow and identify which rules are responsible for specific commands. Clear rule names are a simple but effective way to improve workflow readability and debuggability.

4. Comment Your Code

This should be a no-brainer, but always comment your code! Explain what each rule is doing, why you're using certain commands, and any assumptions you're making. Comments are your best friend when you're trying to understand a complex workflow or debug an issue.

5. Test Your Workflows Regularly

Don't wait until something breaks to test your workflows. Regularly run your workflows with different inputs and configurations to ensure they're working as expected. Testing is a proactive way to catch issues early and prevent them from becoming major headaches.

Conclusion

So, there you have it! Several ways to ensure those elusive workflow-internal commands are visible in your terminal when using Snakemake, even when they're hiding inside try or cmd blocks. Remember to use the -p parameter, explicitly print commands within your blocks, implement custom logging, and consider using run blocks for more control. And don't forget those best practices: consistent logging, modular workflows, descriptive rule names, code comments, and regular testing.

By following these tips and tricks, you'll be able to debug your Snakemake workflows with confidence and ensure that everything is running smoothly. Happy Snakemake-ing!