{"library":"scikit-fda","title":"scikit-fda","description":"scikit-fda is a Python package for functional data analysis (FDA). Version 0.10.1 requires Python >=3.10. It provides tools for representation, preprocessing, and statistical analysis of functional data, following scikit-learn like API. Releases are irregular, roughly 1-2 per year.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install scikit-fda","pip install scikit-fda[plot]"],"cli":null},"imports":["from skfda import FDataGrid","from skfda.preprocessing.dim_reduction import FPCA","from skfda.preprocessing.registration import least_squares_warping"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nfrom skfda import FDataGrid\nfrom skfda.preprocessing.dim_reduction import FPCA\n\n# Generate functional data: 10 curves, each with 50 points\nt = np.linspace(0, 1, 50)\ndata_matrix = np.random.randn(10, 50)  # 10 samples, 50 time points\nfd = FDataGrid(data_matrix, grid_points=t)\n\n# Perform FPCA\nfpca = FPCA(n_components=3)\nfpca.fit(fd)\nscores = fpca.transform(fd)\nprint(scores.shape)","lang":"python","description":"Creates a simple FDataGrid object and performs functional principal component analysis (FPCA).","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}