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SciPy: Scientic Toolkit SciPy SciPy is a collection of mathematical - PowerPoint PPT Presentation

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SciPy: Scientic Toolkit SciPy SciPy is a collection of mathematical algorithms and convience functions built on Numpy data structures Organized into subpackages covering dierent scientic computing areas A data-processing and

  1. SciPy: Scienti�c Toolkit

  2. SciPy SciPy is a collection of mathematical algorithms and convience functions built on Numpy data structures Organized into subpackages covering di�erent scienti�c computing areas A data-processing and prototyping environment rivaling MATLAB

  3. SciPy Submodules Special functions ( scipy.special ) Integration ( scipy.integrate ) Optimization ( scipy.optimize ) Interpolation ( scipy.interpolate ) Fourier Transforms ( scipy.fftpack ) Signal Processing ( scipy.signal ) Linear Algebra ( scipy.linalg ) Sparse Eigenvalue Problems with ARPACK Compressed Sparse Graph Routines ( scipy.sparse.csgraph ) Statistics ( scipy.stats ) Multi-dimensional image processing ( scipy.ndimage ) File IO ( scipy.io ) Weave ( scipy.weave ) And more. . .

  4. Common submodules: scipy.integrate Integrate the function: 4 x 2 f ( x ) = d x ∫ 0 import scipy.integrate ans, err = scipy.integrate.quad( lambda x: x ** 2, 0., 4) ans 21.333333333333336 See also: dblquad , tplquad , fixed_quad , trapz , simps

  5. Common submodules: scipy.linalg Matrix Inverse import numpy as np import scipy.linalg a = np.random.rand(3,3) scipy.linalg.inv(a) array([[ 2.09386567, 0.18794291, -2.33891785], [ 4.50278126, -2.39788758, -1.04738682], [-6.0432121 , 2.88320448, 3.46459537]]) Eigenvalues scipy.linalg.eigvals(a) array([ 2.08083995+0.j, 0.16847753+0.j, -0.30717139+0.j])

  6. Common submodules: scipy.interpolate Interpolate a function import numpy as np from scipy import interpolate, integrate x = np.arange(-1,11) y = np.exp(-x/3.0) f = interpolate.interp1d(x,y); f <scipy.interpolate.interpolate.interp1d at 0x1127a2728> Integrate the interpolated function ans, err = integrate.quad(f,0,10); ans 2.9197153790964223 Integrate the data integrate.simps(y[1:-1],x[1:-1]) 2.8550038226912573

  7. Why Python/Numpy/SciPy? A free alternative to MATLAB The power of the full Python language Object-oriented Procedural Functional (almost) More reasons come: MATLAB-like plotting Call C/C++/Fortran code directly MPI-style parallel programming (take my graduate course!)

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