feat: implement mlx.core.blackman

[Vlor999]

Description

This PR implements the Blackman window function (mlx.core.blackman), completing the standard trio of window functions (Hanning, Hamming, Blackman) and improving feature parity with NumPy.

Implementation Details

• C++: Implemented in mlx/ops.cpp.
  • Optimization: Instead of computing two separate cosine terms ($\cos(x)$ and $\cos(2x)$ ), I utilized the double-angle identity: $\cos(2x) = 2\cos^2(x) - 1$.
  • Performance: This reduces the computational cost to a single transcendental cos operation followed by cheap polynomial arithmetic ($0.34 - 0.5\cos(x) + 0.16\cos^2(x)$ ).
  • Edge Cases: Explicitly handles M < 1 (empty) and M = 1 (returns [1.0]), matching NumPy's behavior exactly.
• Python Bindings: Exposed via nanobind with nb::sig for proper type hinting and full LaTeX documentation.

Test Plan

Verified locally against NumPy for numerical accuracy and edge cases.

Verification script:

import mlx.core as mx
import numpy as np

# 1. Standard Case
M = 100
mx_blackman = mx.blackman(M)
np_blackman = np.blackman(M)

assert np.allclose(mx_blackman, np_blackman, atol=1e-6), "Mismatch in values"

# 2. Edge Case M=1
# Verified that both return [1.0]
assert mx.blackman(1).item() == 1.0
assert np.blackman(1).item() == 1.0

# 3. Edge Case M=0
assert mx.blackman(0).size == 0

print("✅ All verifications passed against numpy.blackman")

Unit Tests:

Added test_blackman in python/tests/test_ops.py.

Checklist

Put an x in the boxes that apply.

• I have read the CONTRIBUTING document
• I have run pre-commit run --all-files to format my code / installed pre-commit prior to committing changes
• I have added tests that prove my fix is effective or that my feature works
• I have updated the necessary documentation (if needed)