SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems.

For global optimization, other choices of objective function, and other advanced features, consider using SciPy’s Global optimization tools or the LMFIT package.

Here is a step-by-step guide to setting up a project to use SciPy, with uv, a Python package manager. Install uv following, the instructions in the uv documentation.

scipy.signal. savgol_filter # savgol_filter(x, window_length, polyorder, deriv=0, delta=1.0, axis=-1, mode='interp', cval=0.0) [source] # Apply a Savitzky-Golay filter to an array. This is a 1-D filter. …

To demonstrate this function’s usage we use a signal x supplied with SciPy (see scipy.datasets.electrocardiogram). Let’s find all peaks (local maxima) in x whose amplitude …

Examples Try it in your browser! Let us consider the problem of minimizing the Rosenbrock function. This function is implemented in rosen in scipy.optimize.

scipy.interpolate. griddata # griddata(points, values, xi, method='linear', fill_value=nan, rescale=False) [source] # Convenience function for interpolating unstructured data in multiple …

It uses the iterative procedure scipy.sparse.linalg.lsmr for finding a solution of a linear least-squares problem and only requires matrix-vector product evaluations.

scipy.signal. butter # butter(N, Wn, btype='low', analog=False, output='ba', fs=None) [source] # Butterworth digital and analog filter design. Design an Nth-order digital or analog Butterworth …

It switches between two implementations depending on the problem definition. It is the most versatile constrained minimization algorithm implemented in SciPy and the most appropriate …