2 SciPy User Guide Tutorials

SciPy User Guide Tutorials

Link: https://docs.scipy.org/doc/scipy/tutorial/index.html#user-guide

GitHub - https://github.com/scipy

Linear Algebra file

Documentation Link: https://docs.scipy.org/doc/scipy/tutorial/linalg.html

To import:

import scipy.linalg as slalg

• numpy.matrix vs 2-D numpy.ndarray web link (np.mat, np.array, .dot)
• Basic Routines web link
  • Finding the inverse (.inv, checking time)
  • Solving a linear system (.solve)
  • Finding the determinant (.det)
  • Computing norms (.norm)
  • Solving linear least-squares problems and pseudo-inverses
  • Generalized inverse
• Decompositions web link
  • Eigenvalues and eigenvectors (.eig, .eigvals)
  • Singular value decomposition
  • LU, Cholesky, QR
  • Schur
  • Interpolative
• Matrix functions web link
  • Arbitary function (.funm)
• Special matrices web link

Sparse file

Documentation Link: https://docs.scipy.org/doc/scipy/tutorial/sparse.html

To import:

from scipy import sparse

Different type of sparse arrays are discussed here. The following formats are available: scipy.sparse.bsr_array, scipy.sparse.coo_array, scipy.sparse.csr_array, scipy.sparse.csc_array, scipy.sparse.dia_array, scipy.sparse.dok_array, scipy.sparse.lil_array.

Sparse eigenvalue problems file

Documentation link: https://docs.scipy.org/doc/scipy/tutorial/arpack.html

All of the functionality provided in ARPACK is contained within the two high-level interfaces scipy.sparse.linalg.eigs and scipy.sparse.linalg.eigsh. eigs provides interfaces for finding the eigenvalues/vectors of real or complex nonsymmetric square matrices, while eigsh provides interfaces for real-symmetric or complex-hermitian matrices.

FFT file

Documentation link: https://docs.scipy.org/doc/scipy/reference/fft.html#module-scipy.fft