scientific-computing

name	scientific-computing
description	Strategies for scientific computing, numerical methods, bioinformatics/DNA tasks, logic circuit design, algorithmic challenges, and ML training tasks.

Scientific Computing & Algorithmic Tasks

CRITICAL: Write complete scripts to files for complex tasks

Multi-step scientific analysis MUST be written as a single .py file to avoid state loss:

with open('/app/solve.py', 'w') as f:
    f.write("""#!/usr/bin/env python3
import numpy as np
# ... complete self-contained solution ...
""")
# Then run: bash("python3 /app/solve.py")

Bioinformatics / DNA primer design

• Use oligotm CLI if available for Tm calculation (check with which oligotm)
• Use primer3_core if available for automated primer design
• Key Tm parameters: -tp 1 -sc 1 -mv 50 -dv 2 -n 0.8 -d 500 (Santa Lucia, salt-corrected)
• Primer length: 15-30 bp, GC content 40-60%, Tm 55-65°C
• For Gibson/Golden Gate assembly: add overlaps/BsaI sites to 5' end of primers
• Parse FASTA files: handle multi-line sequences, strip whitespace
• Write results in FASTA format to the expected output path

Logic circuit / hardware design

• Read simulator code FIRST to understand gate semantics and timing
• Key patterns: ripple-carry adder, mux, shift register
• For Fibonacci mod 2^N: doubling method or iterative with registers

Numerical / distribution tasks

• For optimization: use scipy.optimize (fsolve, minimize, root)
• When searching distributions: verify KL divergence, entropy constraints
• Always validate: check sums to 1, non-negative, constraint satisfaction

ML training tasks

• Check GPU: python3 -c "import torch; print(torch.cuda.is_available())"
• CPU-only: smaller batch size, simpler models, fewer epochs
• Save checkpoints frequently

Verification

• Verify output format matches task specification exactly
• Save results to exact paths mentioned in task prompt
• Compare against reference values for correctness