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Framework for fitting functions to data with SciPy

Project description

Fitting SciKit

A framework for fitting functions to data with SciPy which unifies the various available interpolation methods and provides a common interface to them based on the following simple methods:

  • Fitter.__init__(p): set parameters of interpolation function, e.g. polynomial degree

  •, y): fit given input-output data

  • Fitter.__call__(x) or Fitter.eval(x): evaluate function on new input data

Each interpolation routine falls in one of two categories: scatter fitting or grid fitting. They share the same interface, only differing in the definition of input data x.

Scatter-fitters operate on unstructured scattered input data (i.e. not on a grid). The input data consists of a sequence of x coordinates and a sequence of corresponding y data, where the order of the x coordinates does not matter and their location can be arbitrary. The x coordinates can have an arbritrary dimension (although most classes are specialised for 1-D or 2-D data). If the dimension is bigger than 1, the coordinates are provided as an array of column vectors. These fitters have ScatterFit as base class.

Grid-fitters operate on input data that lie on a grid. The input data consists of a sequence of x-axis tick sequences and the corresponding array of y data. These fitters have GridFit as base class.

The module is organised as follows:

Scatter fitters

  • ScatterFit: Abstract base class for scatter fitters

  • LinearLeastSquaresFit: Fit linear regression model to data using SVD

  • Polynomial1DFit: Fit polynomial to 1-D data

  • Polynomial2DFit: Fit polynomial to 2-D data

  • PiecewisePolynomial1DFit: Fit piecewise polynomial to 1-D data

  • Independent1DFit: Interpolate N-dimensional matrix along given axis

  • Delaunay2DScatterFit: Interpolate scalar function of 2-D data, based on Delaunay triangulation and cubic / linear interpolation

  • NonLinearLeastSquaresFit: Fit a generic function to data, based on non-linear least squares optimisation

  • GaussianFit: Fit Gaussian curve to multi-dimensional data

  • Spline1DFit: Fit a B-spline to 1-D data

  • Spline2DScatterFit: Fit a B-spline to scattered 2-D data

  • RbfScatterFit: Do radial basis function (RBF) interpolation

Grid fitters

  • GridFit: Abstract base class for grid fitters

  • Spline2DGridFit: Fit a B-spline to 2-D data on a rectangular grid

Helper functions

  • squash: Flatten array, but not necessarily all the way to a 1-D array

  • unsquash: Restore an array that was reshaped by squash

  • sort_grid: Ensure that the coordinates of a rectangular grid are in ascending order

  • desort_grid: Undo the effect of sort_grid

  • vectorize_fit_func: Factory that creates vectorised version of function to be fitted to data

  • randomise: Randomise fitted function parameters by resampling residuals



Ludwig Schwardt <ludwig at>


0.7.2 (2023-10-10)

  • Remove distutils and move to pkgutil-style namespace package (#12)

  • Remove run_module_suite function (a nose leftover) (#11)

0.7.1 (2023-09-21)

  • Fix deprecated NumPy type alias (np.float) (#10)

0.7 (2018-09-20)

  • Python 3 support (#8)

  • Clean up tests and more flake8 (line lengths) (#9)

0.6 (2016-12-05)

  • Fix pip installation, clean up setup procedure, flake8 and add README (#3)

  • PiecewisePolynomial1DFit updated to work with scipy 0.18.0 (#4)

  • Delaunay2DScatterFit now based on scipy.interpolate.griddata, which is orders of magnitude faster, more robust and smoother. Its default interpolation changed from ‘nn’ (natural neighbours - no longer available) to ‘cubic’. (#5)

  • Delaunay2DGridFit removed as there is no equivalent anymore (#5)

0.5.1 (2012-10-29)

  • Use proper name for np.linalg.LinAlgError

0.5 (2011-09-26)

  • Initial release of scikits.fitting

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