package scipy

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

Compute the matrix exponential using Pade approximation.

Parameters ---------- A : (M,M) array_like or sparse matrix 2D Array or Matrix (sparse or dense) to be exponentiated

Returns ------- expA : (M,M) ndarray Matrix exponential of `A`

Notes ----- This is algorithm (6.1) which is a simplification of algorithm (5.1).

.. versionadded:: 0.12.0

References ---------- .. 1 Awad H. Al-Mohy and Nicholas J. Higham (2009) 'A New Scaling and Squaring Algorithm for the Matrix Exponential.' SIAM Journal on Matrix Analysis and Applications. 31 (3). pp. 970-989. ISSN 1095-7162

Examples -------- >>> from scipy.sparse import csc_matrix >>> from scipy.sparse.linalg import expm >>> A = csc_matrix([1, 0, 0], [0, 2, 0], [0, 0, 3]) >>> A.todense() matrix([1, 0, 0], [0, 2, 0], [0, 0, 3], dtype=int64) >>> Aexp = expm(A) >>> Aexp <3x3 sparse matrix of type '<class 'numpy.float64'>' with 3 stored elements in Compressed Sparse Column format> >>> Aexp.todense() matrix([ 2.71828183, 0. , 0. ], [ 0. , 7.3890561 , 0. ], [ 0. , 0. , 20.08553692])

Compute the factorial and return as a float

Returns infinity when result is too large for a double

Compute the inverse of a sparse matrix

Parameters ---------- A : (M,M) ndarray or sparse matrix square matrix to be inverted

Returns ------- Ainv : (M,M) ndarray or sparse matrix inverse of `A`

Notes ----- This computes the sparse inverse of `A`. If the inverse of `A` is expected to be non-sparse, it will likely be faster to convert `A` to dense and use scipy.linalg.inv.

Examples -------- >>> from scipy.sparse import csc_matrix >>> from scipy.sparse.linalg import inv >>> A = csc_matrix([1., 0.], [1., 2.]) >>> Ainv = inv(A) >>> Ainv <2x2 sparse matrix of type '<class 'numpy.float64'>' with 3 stored elements in Compressed Sparse Column format> >>> A.dot(Ainv) <2x2 sparse matrix of type '<class 'numpy.float64'>' with 2 stored elements in Compressed Sparse Column format> >>> A.dot(Ainv).todense() matrix([ 1., 0.], [ 0., 1.])

.. versionadded:: 0.12.0

Check whether object is pydata/sparse matrix, avoiding importing the module.

Is x of a sparse matrix type?

Parameters ---------- x object to check for being a sparse matrix

Returns ------- bool True if x is a sparse matrix, False otherwise

Notes ----- issparse and isspmatrix are aliases for the same function.

Examples -------- >>> from scipy.sparse import csr_matrix, isspmatrix >>> isspmatrix(csr_matrix([5])) True

>>> from scipy.sparse import isspmatrix >>> isspmatrix(5) False

Solves the linear equation set ``a * x = b`` for the unknown ``x`` for square ``a`` matrix.

If the data matrix is known to be a particular type then supplying the corresponding string to ``assume_a`` key chooses the dedicated solver. The available options are

=================== ======== generic matrix 'gen' symmetric 'sym' hermitian 'her' positive definite 'pos' =================== ========

If omitted, ``'gen'`` is the default structure.

The datatype of the arrays define which solver is called regardless of the values. In other words, even when the complex array entries have precisely zero imaginary parts, the complex solver will be called based on the data type of the array.

Parameters ---------- a : (N, N) array_like Square input data b : (N, NRHS) array_like Input data for the right hand side. sym_pos : bool, optional Assume `a` is symmetric and positive definite. This key is deprecated and assume_a = 'pos' keyword is recommended instead. The functionality is the same. It will be removed in the future. lower : bool, optional If True, only the data contained in the lower triangle of `a`. Default is to use upper triangle. (ignored for ``'gen'``) overwrite_a : bool, optional Allow overwriting data in `a` (may enhance performance). Default is False. overwrite_b : bool, optional Allow overwriting data in `b` (may enhance performance). Default is False. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. assume_a : str, optional Valid entries are explained above. transposed: bool, optional If True, ``a^T x = b`` for real matrices, raises `NotImplementedError` for complex matrices (only for True).

Returns ------- x : (N, NRHS) ndarray The solution array.

Raises ------ ValueError If size mismatches detected or input a is not square. LinAlgError If the matrix is singular. LinAlgWarning If an ill-conditioned input a is detected. NotImplementedError If transposed is True and input a is a complex matrix.

Examples -------- Given `a` and `b`, solve for `x`:

>>> a = np.array([3, 2, 0], [1, -1, 0], [0, 5, 1]) >>> b = np.array(2, 4, -1) >>> from scipy import linalg >>> x = linalg.solve(a, b) >>> x array( 2., -2., 9.) >>> np.dot(a, x) == b array( True, True, True, dtype=bool)

Notes ----- If the input b matrix is a 1-D array with N elements, when supplied together with an NxN input a, it is assumed as a valid column vector despite the apparent size mismatch. This is compatible with the numpy.dot() behavior and the returned result is still 1-D array.

The generic, symmetric, hermitian and positive definite solutions are obtained via calling ?GESV, ?SYSV, ?HESV, and ?POSV routines of LAPACK respectively.

Solve the equation `a x = b` for `x`, assuming a is a triangular matrix.

Parameters ---------- a : (M, M) array_like A triangular matrix b : (M,) or (M, N) array_like Right-hand side matrix in `a x = b` lower : bool, optional Use only data contained in the lower triangle of `a`. Default is to use upper triangle. trans :

, 1, 2, 'N', 'T', 'C'

, optional Type of system to solve:

======== ========= trans system ======== ========= 0 or 'N' a x = b 1 or 'T' a^T x = b 2 or 'C' a^H x = b ======== ========= unit_diagonal : bool, optional If True, diagonal elements of `a` are assumed to be 1 and will not be referenced. overwrite_b : bool, optional Allow overwriting data in `b` (may enhance performance) check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- x : (M,) or (M, N) ndarray Solution to the system `a x = b`. Shape of return matches `b`.

Raises ------ LinAlgError If `a` is singular

Notes ----- .. versionadded:: 0.9.0

Examples -------- Solve the lower triangular system a x = b, where::

3 0 0 0 4 a = 2 1 0 0 b = 2 1 0 1 0 4 1 1 1 1 2

>>> from scipy.linalg import solve_triangular >>> a = np.array([3, 0, 0, 0], [2, 1, 0, 0], [1, 0, 1, 0], [1, 1, 1, 1]) >>> b = np.array(4, 2, 4, 2) >>> x = solve_triangular(a, b, lower=True) >>> x array( 1.33333333, -0.66666667, 2.66666667, -1.33333333) >>> a.dot(x) # Check the result array( 4., 2., 4., 2.)

Solve the sparse linear system Ax=b, where b may be a vector or a matrix.

Parameters ---------- A : ndarray or sparse matrix The square matrix A will be converted into CSC or CSR form b : ndarray or sparse matrix The matrix or vector representing the right hand side of the equation. If a vector, b.shape must be (n,) or (n, 1). permc_spec : str, optional How to permute the columns of the matrix for sparsity preservation. (default: 'COLAMD')

• ``NATURAL``: natural ordering.
• ``MMD_ATA``: minimum degree ordering on the structure of A^T A.
• ``MMD_AT_PLUS_A``: minimum degree ordering on the structure of A^T+A.
• ``COLAMD``: approximate minimum degree column ordering use_umfpack : bool, optional if True (default) then use umfpack for the solution. This is only referenced if b is a vector and ``scikit-umfpack`` is installed.

Returns ------- x : ndarray or sparse matrix the solution of the sparse linear equation. If b is a vector, then x is a vector of size A.shape1 If b is a matrix, then x is a matrix of size (A.shape1, b.shape1)

Notes ----- For solving the matrix expression AX = B, this solver assumes the resulting matrix X is sparse, as is often the case for very sparse inputs. If the resulting X is dense, the construction of this sparse result will be relatively expensive. In that case, consider converting A to a dense matrix and using scipy.linalg.solve or its variants.

Examples -------- >>> from scipy.sparse import csc_matrix >>> from scipy.sparse.linalg import spsolve >>> A = csc_matrix([3, 2, 0], [1, -1, 0], [0, 5, 1], dtype=float) >>> B = csc_matrix([2, 0], [-1, 0], [2, 0], dtype=float) >>> x = spsolve(A, B) >>> np.allclose(A.dot(x).todense(), B.todense()) True