A simple benchmark comparing numpy (vectorized) and numba (JIT-compiled parallel loop) for a sum-of-squares computation over a large array.
On a representative machine the JIT-compiled numba version is roughly 12–15x faster than the vectorized numpy version:
A small benchmark that compares the wall-clock performance of equivalent numeric
operations written with plain numpy and with numba @njit-compiled code.
On a large 1-D float array (10M elements) it compares five operations:
| Operation | Description |
| import numpy as np | |
| import healpy as hp | |
| import sys | |
| def test_synalm_cl_exactly_lmax_length(): | |
| cl = np.ones(33, dtype=np.float64) | |
| lmax = 32 | |
| np.random.seed(123) | |
| alm = hp.synalm(cl, lmax=lmax) | |
| assert alm.dtype == np.complex128 |
| """ | |
| Test script for Issue #268: Do not modify the input astropy table when converting to pandas dataframe. | |
| This script extracts all Python code blocks from episode 03-transform.md and executes them | |
| sequentially to verify the episode content runs correctly end-to-end. | |
| NOTE: The assert statements below (marked with "ASSERT: not part of the lesson") are added | |
| for automated testing purposes only. They are NOT part of the lesson content. | |
| """ |