Interpolation with SciPy and NumPy

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In many data-processing scenarios it is necessary to use a discrete set of available data-points to infer the value of a function at a new data-point. One approach to this problem is interpolation, which constructs a new model-function that goes through the original data-points. There are many forms of interpolation (polynomial, spline, kriging, radial basis function, etc.), and SciPy includes some of these interpolation forms. This webinar will review the interpolation modules available in SciPy and in the larger Python community and provide instruction on their use via example.

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Interpolation with SciPy and NumPy

  1. 1. Interpolation with Python November 20, 2009 Wednesday, December 2, 2009
  2. 2. Enthought Python Distribution (EPD) http://www.enthought.com/products/epd.php Wednesday, December 2, 2009
  3. 3. Enthought Training Courses Dec. 7 - 9 Dec. 10 - 11 Python Basics, NumPy, SciPy, Matplotlib, Traits, TraitsUI, Chaco… Wednesday, December 2, 2009
  4. 4. Enthought Consulting Wednesday, December 2, 2009
  5. 5. Interpolation Wednesday, December 2, 2009
  6. 6. Contrast with Curve-fitting Extrapolation Wednesday, December 2, 2009
  7. 7. 2-D Interpolation Wednesday, December 2, 2009
  8. 8. Curve interpolation and scattered samples Wednesday, December 2, 2009
  9. 9. Interpolation basic mathematics Given data points (xi , fi ) f (x) = fj φ(x − xj ) j φ(xi − xj ) = δij f (xi ) = fi Wednesday, December 2, 2009
  10. 10. Interpolation scipy.interpolate — General purpose Interpolation •1D Interpolating Class • Constructs callable function from data points and desired spline interpolation order. • Function takes vector of inputs and returns interpolated value using the spline. •1D and 2D spline interpolation (FITPACK) • Smoothing splines up to order 5 • Parametric splines 10 Wednesday, December 2, 2009
  11. 11. 1D Spline Interpolation >>> from scipy.interpolate import interp1d interp1d(x, y, kind='linear', axis=-1, copy=True, bounds_error=True, fill_value=numpy.nan) Returns a function that uses interpolation to find the value of new points. • x – 1d array of increasing real values which cannot contain duplicates • y – Nd array of real values whose length along the interpolation axis must be len(x) • kind – kind of interpolation (e.g. 'linear', 'nearest', 'quadratic', 'cubic'). Can also be an integer n>1 which returns interpolating spline (with minimum sum-of-squares discontinuity in nth derivative). • axis – axis of y along which to interpolate • copy – make internal copies of x and y • bounds_error – raise error for out-of-bounds • fill_value – if bounds_error is False, then use this value to fill in out-of-bounds. 11 Wednesday, December 2, 2009
  12. 12. 1D Spline Interpolation # demo/interpolate/spline.py from scipy.interpolate import interp1d from pylab import plot, axis, legend from numpy import linspace # sample values x = linspace(0,2*pi,6) y = sin(x) # Create a spline class for interpolation. # kind=5 sets to 5th degree spline. # kind='nearest' -> zeroth older hold. # kind='linear' -> linear interpolation # kind=n -> use an nth order spline spline_fit = interp1d(x,y,kind=5) xx = linspace(0,2*pi, 50) yy = spline_fit(xx) # display the results. plot(xx, sin(xx), 'r-', x, y, 'ro', xx, yy, 'b--', linewidth=2) axis('tight') legend(['actual sin', 'original samples', 'interpolated curve']) 12 Wednesday, December 2, 2009
  13. 13. 2D Spline Interpolation >>> from scipy.interpolate import interp2d interp2d(x, y, z, kind='linear') Returns a function, f, that uses interpolation to find the value of new points: z_new = f(x_new, y_new) x – 1d or 2d array y – 1d or 2d array z – 1d or 2d array representing function evaluated at x and y kind – kind of interpolation: 'linear', 'quadratic', or 'cubic' The shape of x, y, and z must be the same. Resulting function is evaluated at cross product of new inputs. 13 Wednesday, December 2, 2009
  14. 14. 2D Spline Interpolation EXAMPLE >>> from scipy.interpolate import ... interp2d >>> from numpy import hypot, mgrid, ... linspace >>> from scipy.special import j0 >>> x,y = mgrid[-5:6,-5:6] >>> z = j0(hypot(x,y)) >>> newfunc = interp2d(x, y, z, ... kind='cubic') >>> xx = linspace(-5,5,100) >>> yy = xx # xx and yy are 1-d # result is evaluated on the # cross product >>> zz = newfunc(xx,yy) >>> from enthought.mayavi import mlab >>> mlab.surf(x,y,z) >>> x2, y2 = mgrid[-5:5:100j, ... -5:5:100j] >>> mlab.surf(x2,y2,zz) 14 Wednesday, December 2, 2009
  15. 15. What else is in SciPy? • Interpolate: Fitpack interface • splrep : represent 1-d data as spline • splprep : represent N-d parametric curve as spline • splev : evaluate spline at new data points • splint : evaluate integral of spline • splalde : evaluate all derivatives of spline • bisplrep : reprsent 2-d surface as spline • bisplev : evaluate 2-d spline at new data points • Additional class-based interface: –UnivariateSpline –InterpolatedUnivariateSpline –LSQUnivariateSpline –BivariateSpline –SmoothBivariateSpline Wednesday, December 2, 2009
  16. 16. What else is in SciPy? • Interpolate: General spline interface (no fitpack) • splmake : create a general spline representation from data • spleval : evaluate spline at new data • spline : front end to splmake and spleval (all in one) • spltopp : take spline reprentation and return piece-wise polynomial representation of a spline • ppform : piecewise polynomial representation of spline • PiecewisePolynomial : class to generate arbitrary piecewise polynomial interpolator given data + derivatives • lagrange : Lagrange polynomial interpolator • BarycentricInterpolator : polynomial interpolation class • KroghInterpolator : polynomial interpolation class that allows setting derivatives as well Wednesday, December 2, 2009
  17. 17. What else is in SciPy? • Signal: Fast B-spline implementations (equally-spaced) • bspline : B-spline basis functions of order n • gauss_spline : Gaussian approximation to the B-spline • qspline1d : quadratic B-spline coefficients from 1-d data • cspline1d : cubic B-spline coefficients from 1-d data • qspline1d_eval : evaluate quadratic spline • cspline1d_eval : evaluat cubic spline • qspline2d : quadratic B-spline coefficients from 2-d data • cspline2d : cubic B-spline coefficients from 2-d data • spline_filter : spline filter using from coefficients • resample : sinc-interpolation using a Fourier method Wednesday, December 2, 2009
  18. 18. What else is in SciPy? • ndimage: N-d spline interpolation • map_coordinates : N-d interpolation • spline_filter : repeated interpolation from spline coefficients • interpolate : Radial Basis Functions • Rbf : N-d interpolation using radial basis functions Wednesday, December 2, 2009
  19. 19. Things I’d like to see • Monotonic Splines • PCHIP • Finishing out the splmake, spleval interface • Improving a simplified interface to all the tools • An interpnd Wednesday, December 2, 2009
  20. 20. Scientific Python Classes http://www.enthought.com/training Dec 7-11 Feb 22-26 Python for Scientists and Engineers (3 days) Advanced Modules (2 days) May 17-21 Take both together to receive a discount Aug 23-27 Wednesday, December 2, 2009

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