I’m working with a Python library that has incomplete documentation and examples. The GitHub repository shows basic usage but leaves out important details.
Current Issue
When I try to run the example code, I get errors because variables aren’t properly defined:
import matplotlib.pyplot as plt
import numpy as np
import NeuralSOM as ns
from sklearn.datasets import make_blobs
# Create network
network = ns.createNetwork(15, 15, input_data, boundaries=True)
This fails with NameError: name 'input_data' is not defined. Looking at the class definition, I can see the constructor expects specific parameters:
class createNetwork:
"""Self-organizing map implementation."""
def __init__(self, rows, cols, dataset, loadPath=None, wrap=False, periodic=True):
"""Initialize the network.
Parameters:
rows (int): Grid height
cols (int): Grid width
dataset (numpy.ndarray or list): Training data
...
"""
Questions
What’s the best approach to find complete API documentation when the official docs are lacking?
Is there a programmatic way to inspect function signatures and parameter types in Python to understand what data format is expected?
Can I automatically generate documentation from the docstrings and comments in the source code?
Check if the library has a __all__ attribute or module docstrings - they sometimes reveal hidden functions. Use pkgutil.walk_packages() to find submodules programmatically since the useful stuff is often buried in subpackages you won’t find otherwise. For your NeuralSOM error, you need to prep your dataset first. Try input_data = np.random.rand(100, 2) for testing, or just use the make_blobs output directly as your dataset parameter. I always search the library name on Stack Overflow or Reddit when docs suck - someone else has usually fought the same battle and posted working code. GitHub’s repo search is gold too for finding real implementations that actually work better than the official examples.
For poorly documented libraries, I dive straight into the test directory - tests show you real usage patterns. Also try inspect.getfullargspec() to dig deeper into parameters beyond basic signatures.
For your specific error, you’re missing the input_data creation step. Since you’ve got sklearn imported, throw this line before creating the network: input_data, _ = make_blobs(n_samples=300, centers=4, random_state=42).
Couple other tricks: hunt for Jupyter notebooks in the repo or find academic papers that reference the library. Research papers often have way better examples than the GitHub readme. You can also run pydoc on the module to pull local docs from whatever docstrings exist.
Been there way too many times. Dealing with incomplete docs is basically a daily thing in my work.
For your Python issues, use help() and the inspect module to dig into functions or classes. Try inspect.signature(ns.createNetwork) to see exactly what parameters it expects. Also dir() on any object shows all available methods.
But here’s what I actually do now - I automate the whole documentation discovery process. Instead of manually hunting through source code and testing different parameter combinations, I built a workflow that automatically extracts function signatures, scrapes docstrings, tests different input formats, and generates clean documentation.
I use this approach whenever I hit a new library with poor docs. The automation pulls everything together - source code analysis, parameter testing, even example generation. Saves me hours of manual detective work.
You could build something similar pretty easily. Parse the Python AST, extract all the class methods and their signatures, run some test inputs to see what works, then output everything in a readable format.
For your specific case with the input_data variable, looks like you just need to create some sample data first. Probably a numpy array based on the sklearn import.
The real game changer is having this documentation extraction process automated so you can run it on any new library. I handle all this kind of workflow automation through Latenode since it connects everything smoothly.