SnakeByte[16]: Enhancing Your Code Analysis with pyastgrep

Dalle 3’s idea of an Abstract Syntax Tree in R^3 space

If you would know strength and patience, welcome the company of trees.

~ Hal Borland

First, I hope everyone is safe. Second, I am changing my usual SnakeByte [] stance process. I am pulling this from a website I ran across. I saw the library mentioned, so I decided to pull from the LazyWebTM instead of the usual snake-based tomes I have in my library.

As a Python developer, understanding and navigating your codebase efficiently is crucial, especially as it grows in size and complexity. Trust me, it will, as does Entropy. Traditional search tools like grep or IDE-based search functionalities can be helpful, but they cannot often “‘understand” the structure of Python code – sans some of the Co-Pilot developments. (I’m using understand here *very* loosely Oh Dear Reader).

This is where pyastgrep it comes into play, offering a powerful way to search and analyze your Python codebase using Abstract Syntax Trees (ASTs). While going into the theory of ASTs is tl;dr for a SnakeByte[] , and there appears to be some ambiguity on the history and definition of Who actually invented ASTs, i have placed some references at the end of the blog for your reading pleasure, Oh Dear Reader. In parlance, if you have ever worked on compilers or core embedded systems, Abstract Syntax Trees are data structures widely used in compilers and the like to represent the structure of program code. An AST is usually the result of the syntax analysis phase of a compiler. It often serves as an intermediate representation of the program through several stages that the compiler requires and has a strong impact on the final output of the compiler.

So what is the Python Library that you speak of? i’m Glad you asked.

What is pyastgrep?

pyastgrep is a command-line tool designed to search Python codebases with an understanding of Python’s syntax and structure. Unlike traditional text-based search tools, pyastgrep it leverages the AST, allowing you to search for specific syntactic constructs rather than just raw text. This makes it an invaluable tool for code refactoring, auditing, and general code analysis.

Why Use pyastgrep?

Here are a few scenarios where pyastgrep excels:

  1. Refactoring: Identify all instances of a particular pattern, such as function definitions, class instantiations, or specific argument names.
  2. Code Auditing: Find usages of deprecated functions, unsafe code patterns, or adherence to coding standards.
  3. Learning: Explore and understand unfamiliar codebases by searching for specific constructs.

I have a mantra: Reduce, Refactor, and Reuse. Please raise your hand of y’all need to refactor your code? (C’mon now no one is watching… tell the truth…). See if it is possible to reduce the code footprint, refactor the code into more optimized transforms, and then let others reuse it across the enterprise.

Getting Started with pyastgrep

Let’s explore some practical examples of using pyastgrep to enhance your code analysis workflow.

Installing pyastgrep

Before we dive into how to use pyastgrep, let’s get it installed. You can install pyastgrep via pip:

(base)tcjr% pip install pyastgrep #dont actually type the tctjr part that is my virtualenv

Example 1: Finding Function Definitions

Suppose you want to find all function definitions in your codebase. With pyastgrep, this is straightforward:

pyastgrep 'FunctionDef'

This command searches for all function definitions (FunctionDef) in your codebase, providing a list of files and line numbers where these definitions occur. Ok pretty basic string search.

Example 2: Searching for Specific Argument Names

Imagine you need to find all functions that take an argument named config. This is how you can do it:

pyastgrep 'arg(arg=config)'

This query searches for function arguments named config, helping you quickly locate where configuration arguments are being used.

Example 3: Finding Class Instantiations

To find all instances where a particular class, say MyClass, is instantiated, you can use:

pyastgrep 'Call(func=Name(id=MyClass))'

This command searches for instantiations of MyClass, making it easier to track how and where specific classes are utilized in your project.

Advanced Usage of pyastgrep

For more complex queries, you can combine multiple AST nodes. For instance, to find all print statements in your code, you might use:

pyastgrep 'Call(func=Name(id=print))'

This command finds all calls to the print function. You can also use more detailed queries to find nested structures or specific code patterns.

Integrating pyastgrep into Your Workflow

Integrating pyastgrep into your development workflow can greatly enhance your ability to analyze and maintain your code. Here are a few tips:

  1. Pre-commit Hooks: Use pyastgrep in pre-commit hooks to enforce coding standards or check for deprecated patterns.
  2. Code Reviews: Employ pyastgrep during code reviews to quickly identify and discuss specific code constructs.
  3. Documentation: Generate documentation or code summaries by extracting specific patterns or structures from your codebase.

Example Script

To get you started, here’s a simple Python script using pyastgrep to search for all function definitions in a directory:

import os
from subprocess import run

def search_function_definitions(directory):
result = run(['pyastgrep', 'FunctionDef', directory], capture_output=True, text=True)

if __name__ == "__main__":
directory = "path/to/your/codebase" #yes this is not optimal folks just an example.

Replace "path/to/your/codebase" with the actual path to your Python codebase, and run the script to see pyastgrep in action.


pyastgrep is a powerful tool that brings the capabilities of AST-based searching to your fingertips. Understanding and leveraging the syntax and structure of your Python code, pyastgrep allows for more precise and meaningful code searches. Whether you’re refactoring, auditing, or simply exploring code, pyastgrep it can significantly enhance your productivity and code quality. This is a great direct addition to your arsenal. Hope it helps and i hope you found this interesting.

Until Then,

#iwishyouwater <- The best of the best at Day1 Tahiti Pro presented by Outerknown 2024

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

MUZAK to Blog By: SweetLeaf: A Stoner Rock Salute to Black Sabbath. While i do not really like bands that do covers, this is very well done. For other references to the Best Band In Existence ( Black Sabbath) i also refer you to Nativity in Black Volumes 1&2.


[1] Basics Of AST

[2] The person who made pyastgrep

[3] Wikipedia page on AST

SnakeByte [15]: Debugging With pdb In The Trenches

Dalle3 idea of debugging code from the view of the code itself.

If debugging is the process of removing software bugs, then programming must be the process of putting them in.

~ Edsger W. Dijkstra

Image Explanation: Above is the image showing the perspective of debugging code from the viewpoint of the code itself. The scene features lines of code on a large monitor, with sections highlighted to indicate errors. In the foreground, anthropomorphic code characters are working to fix these highlighted sections, set against a digital landscape of code lines forming a cityscape.

So meta and canonical.

In any event Dear Readers, it is Wednesday! That means as usual everyday is Wednesday in a startup, you actually work at a company where you enjoy the work or it is SnakeByte [ ] time!

i haven’t written a SnakeByte is quite some time. Also, recently, in a previous blog, I mentioned that I had a little oversite on my part, and my blog went down. i didn’t have alerting turned on ye ole AWS and those gosh darn binlogs where not deleting in ye ole WordPress as such we took the proverbial digger into downtime land. i re-provisioned with an additional sizing of the volume and changed the disc from magnetic to SSD, turned on alerts and we are back in business.

So per my usual routine i grab one of the python books from the book shelf randomly open then read about command or commands and attempt to write a blog as fast as possible.

Today’s random command from Python In A Nutshell is pdb, the Python Debugger. i’ll walk you through the basic of using pdb to debug your Python code, which, as it turns out is better than a bunch of print().

Getting Started with pdb

To leverage pdb, import it in your Python script. You can then set breakpoints manually with pdb.set_trace(). When the execution hits this line, the script pauses, allowing you to engage in an interactive debugging session.

Consider this straightforward example:

import pdb

def add(a, b):
result = a + b
return result

def main():
x = 10
y = 20
pdb.set_trace() # Breakpoint set here
total = add(x, y)
print(f'Total: {total}')

if __name__ == '__main__':

Here, we have a simple add function and a main function that invokes add. The pdb.set_trace() in the main function sets a breakpoint where the debugger will halt execution.

Running the Code with pdb

Execute the script from the command line to initiate the debugger:


When the execution reaches pdb.set_trace(), you will see the debugger prompt ((Pdb)):

> /path/to/
-> total = add(x, y)

Essential pdb Commands

Once at the debugger prompt, several commands can help you navigate and inspect your code. Key commands include:

  • l (list): Displays the surrounding source code.
  • n (next): Moves to the next line within the same function.
  • s (step): Steps into the next function call.
  • c (continue): Resumes execution until the next breakpoint.
  • p (print): Evaluates and prints an expression.
  • q (quit): Exits the debugger and terminates the program.

Let’s walk through using these commands in our example:

List the surrounding code:

Pdb) 1
  6         def main():
  7             x = 10
  8             y = 20
  9             pdb.set_trace()  # Breakpoint here
 10  ->         total = add(x, y)
 11             print(f'Total: {total}')
 13         if __name__ == '__main__':
 14             main()

Print variable values:

(Pdb) p x
(Pdb) p y

Step into the add function:

(Pdb) s
> /path/to/
-> result = a + b

Inspect parameters a and b:

(Pdb) p a
(Pdb) p b

Continue execution:

(Pdb) c
Total: 30

Advanced pdb Features

For more nuanced debugging needs, pdb offers advanced capabilities:

Conditional Breakpoints: Trigger breakpoints based on specific conditions:

if x == 10:

Post-Mortem Debugging: Analyze code after an exception occurs:

import pdb

def faulty_function():
    return 1 / 0

except ZeroDivisionError:
    pdb.post_mortem() #they need to add a pre-mortem what can go wrong will...

Command Line Invocation: Run a script under pdb control directly from the command line like the first simple example:

python -m pdb

Effective debugging is crucial for building robust and maintainable software. pdb provides a powerful, interactive way to understand and fix your Python code. By mastering pdb, you can streamline your debugging process, saving time and enhancing your productivity.

pdb, the Python Debugger, is an indispensable tool for any developer. It allows us to set breakpoints, step through code, inspect variables, and evaluate expressions on the fly. This level of control is crucial for diagnosing and resolving issues efficiently. While i used cli in the examples it also works with Jupyter Notebooks.

We’ve covered the basics and advanced features of pdb, equipping you with the knowledge to tackle even the most challenging bugs. As you integrate these techniques into your workflow, you’ll find that debugging becomes less of a chore and more of a strategic advantage unless you create a perfect design which is no code at all!

Until then,

#iwishyouwater <- La Vaca Gigante Wipeouts 2024

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr)

Muzak to Blog By: Apostrophe (‘) by Frank Zappa. i was part of something called the Zappa Ensemble in graduate school as one of the engineers. The musicians where amazing. Apostrohe (‘)is an amazing album. The solo on Uncle Remus is just astounding well as is most of his solos.