scipy-best-practices

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6155 months ago

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Best practices for SciPy scientific computing, optimization, signal processing, and statistical analysis in Python

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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

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