Performance bump

[rbx]

• Avoid np.array2string when render mode is not human
• Reduce number of numpy.rng calls in the hourly sampler
• vectorize part of the job age penalty calculation
• optimize hourly job data extraction

[coderabbitai]

📝 Walkthrough

Walkthrough

Three files modified for performance optimization and output refinement: environment.py conditionally guards debug prints based on render_mode, reward_calculation.py replaces loop-based max job age computation with vectorized NumPy operations, and sampler_hourly.py replaces datetime parsing and per-item loops with string slicing and batch sampling.

Changes

Cohort / File(s)	Summary
Debug Output Conditioning src/environment.py	Wrapped print statements with render_mode == 'human' guards to conditionally suppress verbose debug output (predicted prices, node/core allocation details).
Computation Optimizations src/reward_calculation.py, src/sampler_hourly.py	Replaced loop-based max job age computation with vectorized NumPy boolean masking in reward_calculation.py; replaced datetime parsing with direct string slicing and per-item job sampling with batch array operations in sampler_hourly.py.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title 'Performance bump' directly reflects the main objective of the pull request, which is to improve performance through multiple optimizations.
Description check	✅ Passed	The description clearly outlines four specific performance improvements that directly correspond to the actual changes made in the pull request.
Docstring Coverage	✅ Passed	Docstring coverage is 80.00% which is sufficient. The required threshold is 80.00%.

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✨ Finishing touches

• 📝 Generate docstrings

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