scipy-optimization-toolkit

# SciPy Optimization Toolkit ## Purpose Provides expert guidance on SciPy for scientific computing in physics, including optimization, integration, and signal processing. ## Capabilities - Nonlinear least squares fitting - Global optimization methods - Numerical integration (quadrature) - ODE/PDE solvers - Signal processing (FFT, filtering) - Sparse matrix operations ## Usage Guidelines 1. **Optimization**: Use appropriate optimizer for the problem type 2. **Fitting**: Apply nonlinear least squares for data fitting 3. **Integration**: Choose proper quadrature methods 4. **ODEs**: Solve differential equations with adaptive solvers 5. **Signal Processing**: Apply FFT and filtering techniques ## Tools/Libraries - SciPy - NumPy - lmfit