Document Method Comparison

This skill provides a procedure for creating educational sections in Jupyter Notebooks that compare two or more methods (e.g., gwpy vs gwexpy).

Instructions

1. Technical Analysis:

   • Examine the source code of both methods to identify implementation differences.
     • Averaging/Integration: Does it downsample the data using a sliding window? (e.g., gwpy.heterodyne)
     • Filtering: Does it use a Low-Pass Filter (LPF) like Butterworth or FIR? (e.g., gwexpy.lock_in)
   • Compare numerical characteristics like time resolution and frequency response (aliasing).

2. Physical/Conceptual Context:

   • Check if there are differences between engineering definitions and package conventions.
     • Example: gwpy.heterodyne effectively performing "Homodyne" detection relative to the carrier frequency.
   • Identify the target use case for each (e.g., stationary signal vs transient analysis).

3. Construct Comparative Sample Code:

   • Create a synthetic signal that highlights the differences (e.g., a signal with rapid amplitude/phase changes).
   • Execute both methods on the same input signal with comparable parameters (e.g., matching the averaging stride with the filter bandwidth).
   • Visualize results in a single plot for direct comparison.
     • Use plt.step(..., where='post') for discrete averaged data.
     • Use plt.plot(...) for continuous filtered data.

4. Draft Markdown Explanation:

   • Use tables to summarize differences (Algorithm, Resolution, Post-Processing, Features).
   • Provide a clear summary statement on "which method to choose" for specific user goals (e.g., "Use .lock_in() for control system transient response").

5. Integration:

   • Use Python scripts to inject these cells (Markdown and Code) into the target .ipynb file, typically appending to the end or inserting into a relevant section.