scipy-best-practices

SKILL.md

SciPy Best Practices

Expert guidelines for SciPy development, focusing on scientific computing, optimization, signal processing, and statistical analysis.

Code Style and Structure

Write concise, technical Python code with accurate SciPy examples

Prioritize numerical accuracy and computational efficiency

Use functional programming patterns for mathematical operations

Prefer vectorized operations over explicit loops

Use descriptive variable names reflecting scientific context

Follow PEP 8 style guidelines

scipy.optimize - Optimization

Use scipy.optimize.minimize() for general-purpose optimization

Choose appropriate method based on problem characteristics:

'BFGS' for smooth, unconstrained problems

'L-BFGS-B' for bounded problems

'SLSQP' for constrained optimization

'Nelder-Mead' for non-differentiable functions

Provide gradients when available for faster convergence

Use scipy.optimize.curve_fit() for nonlinear least squares fitting

Use scipy.optimize.root() for finding roots of equations

scipy.linalg - Linear Algebra

Prefer scipy.linalg over numpy.linalg for additional functionality

Use scipy.linalg.solve() instead of computing matrix inverse

Leverage specialized solvers for structured matrices (banded, triangular)

Use scipy.linalg.lu_factor() and lu_solve() for multiple right-hand sides

Use sparse matrix solvers from scipy.sparse.linalg for large sparse systems

scipy.stats - Statistics

Use distribution objects for probability calculations

Leverage scipy.stats.describe() for summary statistics

Use hypothesis testing functions: ttest_ind(), chi2_contingency(), mannwhitneyu()

Generate random samples with .rvs() method on distributions

Use .fit() for parameter estimation from data

scipy.interpolate - Interpolation

Use scipy.interpolate.interp1d() for 1D interpolation

Use scipy.interpolate.griddata() for scattered data interpolation

Choose appropriate interpolation method: 'linear', 'cubic', 'nearest'

Use spline functions for smooth interpolation: UnivariateSpline, BSpline

Consider RegularGridInterpolator for regular grid data

scipy.integrate - Integration

Use scipy.integrate.quad() for single integrals

Use scipy.integrate.dblquad(), tplquad() for multiple integrals

Use scipy.integrate.solve_ivp() for ordinary differential equations

Choose appropriate ODE method: 'RK45', 'BDF', 'LSODA'

Provide Jacobian for stiff systems to improve performance

scipy.signal - Signal Processing

Use scipy.signal.butter(), cheby1(), ellip() for filter design

Apply filters with scipy.signal.filtfilt() for zero-phase filtering

Use scipy.signal.welch() for power spectral density estimation

Use scipy.signal.find_peaks() for peak detection

Leverage scipy.signal.convolve() and correlate() for convolution

scipy.sparse - Sparse Matrices

Use appropriate sparse format for your use case:

csr_matrix for efficient row slicing and matrix-vector products

csc_matrix for efficient column slicing

coo_matrix for constructing sparse matrices

lil_matrix for incremental construction

Convert to optimal format before operations

Use scipy.sparse.linalg solvers for sparse linear systems

Performance Optimization

Use appropriate data types (float64 for precision, float32 for memory)

Leverage BLAS/LAPACK through SciPy for optimized linear algebra

Pre-allocate arrays when possible

Use in-place operations when available

Error Handling and Validation

Check convergence status of optimization routines

Validate numerical results for reasonableness

Handle ill-conditioned problems gracefully

Use appropriate tolerances for convergence criteria

Testing Scientific Code

Test against known analytical solutions

Use np.testing.assert_allclose() for numerical comparisons

Test edge cases and boundary conditions

Verify conservation laws and invariants

Key Conventions

Import specific submodules: from scipy import optimize, stats, linalg

Use snake_case for variables and functions

Document algorithm choices and parameters

Include convergence diagnostics in output