implementing-code

# Implementing Code **Core philosophy**: Write it right the first time — clean, concise, conceptually dense code that doesn't need editing afterward. Zero linting violations (ruff or project-specified tools). Check CLAUDE.md for project-specific standards. ## Conceptual Density Fit as many related operations into one line as possible, where each line has an understandable big-picture purpose. Each line = one complete concept. 88 char max (ruff default). ```python # Inline calculations in dict construction — not scattered assignments results = { "chi2_E": hartlap_factor * (En @ np.linalg.solve(cov_E, En)), "pte_B": 1 - stats.chi2.cdf(chi2_B, nmodes), "cov": np.cov(np.array(samples).T), } # Eliminate repetition with comprehensions chi_E, chi_B, chi_EB = [ np.sum(signal**2 / noise**2) for signal, noise in zip((ee, bb, eb), (noise_ee, noise_bb, noise_eb)) ] # Consolidate repeated plotting blocks into loops for ax, (cl, err, fmt, label) in zip(axes, plot_data): ax.errorbar(ell_bins, cl, yerr=err, fmt=fmt) ax.set(xlabel=r"$\ell$", ylabel=label, title=f"{label} Power Spectrum") ``` ### Concise conditionals ```python # One-line conditionals over verbose if/else blocks n_eff = n_samples if cov_path_int is not None else npatch version_results = results_list[idx] or self.calculate_pure_eb() output_path = user_path or generate_default_path() # Short-circuit execution (var_method == "semianalytic") and self.calculate_semianalytic_cov() ``` ### Natural line breaking When code exceeds 88 chars, break at logical boundaries: - Comprehensions: after `[` and before `for` - Function calls: at commas between argument groups - Dicts: one key-value pair per line - Chained operations: at method calls or operators ## Anti-Patterns - **Unnecessary intermediates**: `temp = solve(A, b); result = factor * temp` -- combine into one conceptual line - **Scattered dict assignments**: `results["a"] = a; results["b"] = b` -- use dict construction with inline calculations - **Verbose conditionals**: 4-line if/else for simple assignment -- use ternary or `or` - **Defensive `.get()` with defaults**: `config.get("key", default)` hides missing config -- use `config["key"]` to fail fast - **Unnecessary try/except**: let errors propagate; missing files and bad inputs are real problems - **Session-specific comments**: "per your request", "switched to new method" -- comments should be timeless ## Comment Philosophy Comments explain *why* and *context*, never *what*. Good: `# Hartlap correction for finite sample bias`. Bad: `# Create numpy array`. Remove artifacts like "change to scipy implementation" from library code. ## Error Handling Trust scientific libraries to validate their domains. Skip defensive programming. Let errors propagate -- failed fast is better than hidden bugs. Use defensive patterns only for known edge cases with clear scientific justification. ## Validation - Spot-check that code runs without crashing - Test with toy data where feasible - Extended validation (literature comparison, edge cases) only when requested - Focus on "does it work" over exhaustive test suites ## Working Agreements - Before implementing numerical algorithms, think through stability and convergence - Deliver working code, basic validation, and integration notes (if connecting to existing code)