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fdatools

Functional data analysis for Python. fdatools is a clean-room Python rewrite of R's fda package (Ramsay, Hooker & Graves, version 6.3.0). It covers basis expansions, penalised smoothing, functional PCA and CCA, functional regression, curve registration and principal differential analysis.

Why fdatools

  • Same numbers as R. Every public function is tested against golden output from R fda 6.3.0 at rtol = 1e-8 (1e-5 for iterative fits such as smooth.monotone and register.fd). Where R is demonstrably less accurate, the difference is measured and documented, never hidden.
  • Small surface. About 40 public symbols replace 515 R functions. A curve is callable (fd(t)), arithmetic is plain Python (fd1 + fd2, fd1 @ fd2 for the inner product), and every method returns a new object.
  • Fits the Python stack. Estimators follow the scikit-learn API, so they go straight into a Pipeline or GridSearchCV. Arrays go through the Array API, so NumPy in gives NumPy out and PyTorch in gives PyTorch out, with gradients.

Install

uv add fdatools

With uv. For an extra, put it in brackets: uv add "fdatools[plot]".

Extra Adds
fdatools[plot] matplotlib, for .plot()
fdatools[pandas] pandas and xarray, for from_pandas / to_pandas / to_xarray
fdatools[io] rdata, for read_rds (load R fd objects)
fdatools[torch] PyTorch backend and fdatools.nn

fdatools supports Python 3.10 to 3.13.

A first look

import numpy as np
import fdatools as fdt

growth = fdt.datasets.load_growth()
basis = fdt.BSpline(domain=(1.0, 18.0), n_basis=12)
coefs, *_ = np.linalg.lstsq(basis(growth.age), growth.hgtf, rcond=None)
girls = fdt.FData(coefs, basis)
girls.derivative().mean()(np.array([5.0, 12.0]))   # mean growth speed at 5 and 12

Where to go next

  • Quickstart: the core objects and a full smoothing + FPCA workflow.
  • Migrating from R fda: every R function and its fdatools equivalent.
  • Tutorials: six end-to-end analyses (smoothing, FPCA, registration, regression, dynamics, machine learning).
  • API reference: every public class and function.

References

  • J. O. Ramsay and B. W. Silverman, Functional Data Analysis, 2nd ed., Springer, 2005.
  • J. O. Ramsay, G. Hooker and S. Graves, Functional Data Analysis with R and MATLAB, Springer, 2009.