MATLAB Toolbox to Python Mapping

Filter design, spectral analysis, and signal transforms. The most commonly migrated MATLAB toolbox.

Statistical distributions, hypothesis testing, and regression. Maps to scipy.stats and pandas.

Image filtering, segmentation, and morphological operations. Maps to scikit-image and OpenCV.

Nonlinear optimization, root finding, and linear programming. Maps to scipy.optimize.

Transfer functions, state space models, and frequency domain analysis. Maps to python-control.

MATLAB's neural network stack. No 1:1 function mapping — deep learning frameworks differ in their whole mental model. Use PyTorch if you prefer defining layers in code; TensorFlow/Keras if you prefer a declarative builder. Both are mature, free, and dominant in research.

Fit curves to data. scipy.optimize.curve_fit covers 95% of MATLAB's fit() usage; polyfit/polyval match directly. For interactive fitting, the MATLAB `cftool` GUI has no Python equivalent — but a Jupyter notebook with matplotlib widgets comes close.

Discrete and continuous wavelet transforms, multilevel decomposition, and thresholding. Maps directly to PyWavelets.

MATLAB's parfor and spmd parallelize loops and distribute work across cores. Python's equivalent choices are joblib (simple, parallel map), multiprocessing (stdlib, explicit), or dask (for out-of-core data that doesn't fit in RAM). Pick joblib for drop-in parfor replacement.

Symbolic algebra — solve equations, simplify expressions, compute derivatives and integrals. sympy is a nearly-complete functional superset of MATLAB's Symbolic Math Toolbox, including LaTeX rendering in Jupyter. Most one-liners port directly.

Connect to SQL databases and run queries. Python's SQL ecosystem is richer than MATLAB's: SQLAlchemy for ORM and connection management, pandas.read_sql for tabular queries, DBAPI drivers for each engine. Migration is usually an improvement.