Operator pitfalls and idioms for bioinformatics — floor division for codons, modulo for reading frames, GC content precedence, and membership testing
// vs / for codons: seq_length // 3 gives complete codons (int). / always returns float.% for reading frames: position % 3 gives the reading frame (0, 1, or 2). seq_length % 3 gives leftover nucleotides.g + c / total * 100 is wrong. Use (g + c) / total * 100.== vs is: use == for value comparison, is only for None/True/False. Never write if x == None.in checks dict keys, not values: "ATG" in codon_table checks keys. Use "Met" in codon_table.values() for values.in with sets is O(1), with lists is O(n). Convert to set for repeated membership tests.seq_length = 1000
complete_codons = seq_length // 3 # 333
leftover_nucleotides = seq_length % 3 # 1
reading_frame = position % 3 # 0, 1, or 2
# GC content in normal range?
0.40 <= gc_content <= 0.60 # more Pythonic than gc_content >= 0.40 and gc_content <= 0.60
in)is_stop = codon in {"TAA", "TAG", "TGA"} # set literal for O(1) lookup
passes_qc = (500 <= seq_length <= 3000
and 0.30 <= gc_content <= 0.70
and not has_ambiguous_bases)
# Rough estimate: avg AA mass ~110 Da, water lost per peptide bond = 18 Da
protein_mw = (num_amino_acids * 110) - ((num_amino_acids - 1) * 18)
PyTorch深度学习模式与最佳实践，用于构建稳健、高效且可复现的训练流程、模型架构和数据加载。