package scipy

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

Build a block diagonal sparse matrix from provided matrices.

Parameters ---------- mats : sequence of matrices Input matrices. format : str, optional The sparse format of the result (e.g., 'csr'). If not given, the matrix is returned in 'coo' format. dtype : dtype specifier, optional The data-type of the output matrix. If not given, the dtype is determined from that of `blocks`.

Returns ------- res : sparse matrix

Notes -----

.. versionadded:: 0.11.0

See Also -------- bmat, diags

Examples -------- >>> from scipy.sparse import coo_matrix, block_diag >>> A = coo_matrix([1, 2], [3, 4]) >>> B = coo_matrix([5], [6]) >>> C = coo_matrix([7]) >>> block_diag((A, B, C)).toarray() array([1, 2, 0, 0], [3, 4, 0, 0], [0, 0, 5, 0], [0, 0, 6, 0], [0, 0, 0, 7])

Build a sparse matrix from sparse sub-blocks

Parameters ---------- blocks : array_like Grid of sparse matrices with compatible shapes. An entry of None implies an all-zero matrix. format : 'bsr', 'coo', 'csc', 'csr', 'dia', 'dok', 'lil', optional The sparse format of the result (e.g. 'csr'). By default an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional The data-type of the output matrix. If not given, the dtype is determined from that of `blocks`.

Returns ------- bmat : sparse matrix

See Also -------- block_diag, diags

Examples -------- >>> from scipy.sparse import coo_matrix, bmat >>> A = coo_matrix([1, 2], [3, 4]) >>> B = coo_matrix([5], [6]) >>> C = coo_matrix([7]) >>> bmat([A, B], [None, C]).toarray() array([1, 2, 5], [3, 4, 6], [0, 0, 7])

>>> bmat([A, None], [None, C]).toarray() array([1, 2, 0], [3, 4, 0], [0, 0, 7])

Turn seed into a np.random.RandomState instance

If seed is None (or np.random), return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. If seed is a new-style np.random.Generator, return it. Otherwise, raise ValueError.

Construct a sparse matrix from diagonals.

Parameters ---------- diagonals : sequence of array_like Sequence of arrays containing the matrix diagonals, corresponding to `offsets`. offsets : sequence of int or an int, optional Diagonals to set:

• k = 0 the main diagonal (default)
• k > 0 the kth upper diagonal
• k < 0 the kth lower diagonal shape : tuple of int, optional Shape of the result. If omitted, a square matrix large enough to contain the diagonals is returned. format : 'dia', 'csr', 'csc', 'lil', ..., optional Matrix format of the result. By default (format=None) an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional Data type of the matrix.

See Also -------- spdiags : construct matrix from diagonals

Notes ----- This function differs from `spdiags` in the way it handles off-diagonals.

The result from `diags` is the sparse equivalent of::

np.diag(diagonals0, offsets0)

1. ...
2. np.diag(diagonalsk, offsetsk)

Repeated diagonal offsets are disallowed.

.. versionadded:: 0.11

Examples -------- >>> from scipy.sparse import diags >>> diagonals = [1, 2, 3, 4], [1, 2, 3], [1, 2] >>> diags(diagonals, 0, -1, 2).toarray() array([1, 0, 1, 0], [1, 2, 0, 2], [0, 2, 3, 0], [0, 0, 3, 4])

Broadcasting of scalars is supported (but shape needs to be specified):

>>> diags(1, -2, 1, -1, 0, 1, shape=(4, 4)).toarray() array([-2., 1., 0., 0.], [ 1., -2., 1., 0.], [ 0., 1., -2., 1.], [ 0., 0., 1., -2.])

If only one diagonal is wanted (as in `numpy.diag`), the following works as well:

>>> diags(1, 2, 3, 1).toarray() array([ 0., 1., 0., 0.], [ 0., 0., 2., 0.], [ 0., 0., 0., 3.], [ 0., 0., 0., 0.])

Sparse matrix with ones on diagonal

Returns a sparse (m x n) matrix where the kth diagonal is all ones and everything else is zeros.

Parameters ---------- m : int Number of rows in the matrix. n : int, optional Number of columns. Default: `m`. k : int, optional Diagonal to place ones on. Default: 0 (main diagonal). dtype : dtype, optional Data type of the matrix. format : str, optional Sparse format of the result, e.g., format='csr', etc.

Examples -------- >>> from scipy import sparse >>> sparse.eye(3).toarray() array([ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]) >>> sparse.eye(3, dtype=np.int8) <3x3 sparse matrix of type '<class 'numpy.int8'>' with 3 stored elements (1 diagonals) in DIAgonal format>

Based on input (integer) arrays `a`, determine a suitable index data type that can hold the data in the arrays.

Parameters ---------- arrays : tuple of array_like Input arrays whose types/contents to check maxval : float, optional Maximum value needed check_contents : bool, optional Whether to check the values in the arrays and not just their types. Default: False (check only the types)

Returns ------- dtype : dtype Suitable index data type (int32 or int64)

Stack sparse matrices horizontally (column wise)

Parameters ---------- blocks sequence of sparse matrices with compatible shapes format : str sparse format of the result (e.g., 'csr') by default an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional The data-type of the output matrix. If not given, the dtype is determined from that of `blocks`.

See Also -------- vstack : stack sparse matrices vertically (row wise)

Examples -------- >>> from scipy.sparse import coo_matrix, hstack >>> A = coo_matrix([1, 2], [3, 4]) >>> B = coo_matrix([5], [6]) >>> hstack(A,B).toarray() array([1, 2, 5], [3, 4, 6])

Identity matrix in sparse format

Returns an identity matrix with shape (n,n) using a given sparse format and dtype.

Parameters ---------- n : int Shape of the identity matrix. dtype : dtype, optional Data type of the matrix format : str, optional Sparse format of the result, e.g., format='csr', etc.

Examples -------- >>> from scipy.sparse import identity >>> identity(3).toarray() array([ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]) >>> identity(3, dtype='int8', format='dia') <3x3 sparse matrix of type '<class 'numpy.int8'>' with 3 stored elements (1 diagonals) in DIAgonal format>

Is x either a scalar, an array scalar, or a 0-dim array?

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

kronecker product of sparse matrices A and B

Parameters ---------- A : sparse or dense matrix first matrix of the product B : sparse or dense matrix second matrix of the product format : str, optional format of the result (e.g. 'csr')

Returns ------- kronecker product in a sparse matrix format

Examples -------- >>> from scipy import sparse >>> A = sparse.csr_matrix(np.array([0, 2], [5, 0])) >>> B = sparse.csr_matrix(np.array([1, 2], [3, 4])) >>> sparse.kron(A, B).toarray() array([ 0, 0, 2, 4], [ 0, 0, 6, 8], [ 5, 10, 0, 0], [15, 20, 0, 0])

>>> sparse.kron(A, [1, 2], [3, 4]).toarray() array([ 0, 0, 2, 4], [ 0, 0, 6, 8], [ 5, 10, 0, 0], [15, 20, 0, 0])

kronecker sum of sparse matrices A and B

Kronecker sum of two sparse matrices is a sum of two Kronecker products kron(I_n,A) + kron(B,I_m) where A has shape (m,m) and B has shape (n,n) and I_m and I_n are identity matrices of shape (m,m) and (n,n), respectively.

Parameters ---------- A square matrix B square matrix format : str format of the result (e.g. 'csr')

Returns ------- kronecker sum in a sparse matrix format

Examples --------

Generate a sparse matrix of the given shape and density with uniformly distributed values.

Parameters ---------- m, n : int shape of the matrix density : real, optional density of the generated matrix: density equal to one means a full matrix, density of 0 means a matrix with no non-zero items. format : str, optional sparse matrix format. dtype : dtype, optional type of the returned matrix values. random_state : numpy.random.RandomState, int, np.random.Generator, optional Random number generator or random seed. If not given, the singleton numpy.random will be used.

Returns ------- res : sparse matrix

Notes ----- Only float types are supported for now.

See Also -------- scipy.sparse.random : Similar function that allows a user-specified random data source.

Examples -------- >>> from scipy.sparse import rand >>> matrix = rand(3, 4, density=0.25, format='csr', random_state=42) >>> matrix <3x4 sparse matrix of type '<class 'numpy.float64'>' with 3 stored elements in Compressed Sparse Row format> >>> matrix.todense() matrix([0.05641158, 0. , 0. , 0.65088847], [0. , 0. , 0. , 0.14286682], [0. , 0. , 0. , 0. ])

Generate a sparse matrix of the given shape and density with randomly distributed values.

Parameters ---------- m, n : int shape of the matrix density : real, optional density of the generated matrix: density equal to one means a full matrix, density of 0 means a matrix with no non-zero items. format : str, optional sparse matrix format. dtype : dtype, optional type of the returned matrix values. random_state : numpy.random.RandomState, int, optional Random number generator or random seed. If not given, the singleton numpy.random will be used. This random state will be used for sampling the sparsity structure, but not necessarily for sampling the values of the structurally nonzero entries of the matrix. data_rvs : callable, optional Samples a requested number of random values. This function should take a single argument specifying the length of the ndarray that it will return. The structurally nonzero entries of the sparse random matrix will be taken from the array sampled by this function. By default, uniform 0, 1) random values will be sampled using the same random state as is used for sampling the sparsity structure. Returns ------- res : sparse matrix Notes ----- Only float types are supported for now. Examples -------- >>> from scipy.sparse import random >>> from scipy import stats >>> class CustomRandomState(np.random.RandomState): ... def randint(self, k): ... i = np.random.randint(k) ... return i - i % 2 >>> np.random.seed(12345) >>> rs = CustomRandomState() >>> rvs = stats.poisson(25, loc=10).rvs >>> S = random(3, 4, density=0.25, random_state=rs, data_rvs=rvs) >>> S.A array([[ 36., 0., 33., 0.], # random [ 0., 0., 0., 0.], [ 0., 0., 36., 0.]]) >>> from scipy.sparse import random >>> from scipy.stats import rv_continuous >>> class CustomDistribution(rv_continuous): ... def _rvs(self, size=None, random_state=None): ... return random_state.randn( *size) >>> X = CustomDistribution(seed=2906) >>> Y = X() # get a frozen version of the distribution >>> S = random(3, 4, density=0.25, random_state=2906, data_rvs=Y.rvs) >>> S.A array([[ 0. , 0. , 0. , 0. ], [ 0.13569738, 1.9467163 , -0.81205367, 0. ], [ 0. , 0. , 0. , 0. ]])

Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive). Replaces `RandomState.randint` (with endpoint=False) and `RandomState.random_integers` (with endpoint=True).

Return random integers from the 'discrete uniform' distribution of the specified dtype. If high is None (the default), then results are from 0 to low.

Parameters ---------- gen: None, np.random.RandomState, np.random.Generator Random number generator. If None, then the np.random.RandomState singleton is used. low: int or array-like of ints Lowest (signed) integers to be drawn from the distribution (unless high=None, in which case this parameter is 0 and this value is used for high). high: int or array-like of ints If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None). If array-like, must contain integer values. size: None Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. dtype: str, dtype, optional Desired dtype of the result. All dtypes are determined by their name, i.e., 'int64', 'int', etc, so byteorder is not available and a specific precision may have different C types depending on the platform. The default value is np.int_. endpoint: bool, optional If True, sample from the interval low, high instead of the default low, high) Defaults to False. Returns ------- out: int or ndarray of ints size-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided.

Return a sparse matrix from diagonals.

Parameters ---------- data : array_like matrix diagonals stored row-wise diags : diagonals to set

• k = 0 the main diagonal
• k > 0 the k-th upper diagonal
• k < 0 the k-th lower diagonal m, n : int shape of the result format : str, optional Format of the result. By default (format=None) an appropriate sparse matrix format is returned. This choice is subject to change.

See Also -------- diags : more convenient form of this function dia_matrix : the sparse DIAgonal format.

Examples -------- >>> from scipy.sparse import spdiags >>> data = np.array([1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]) >>> diags = np.array(0, -1, 2) >>> spdiags(data, diags, 4, 4).toarray() array([1, 0, 3, 0], [1, 2, 0, 4], [0, 2, 3, 0], [0, 0, 3, 4])

Returns the nearest supported sparse dtype for the combination of one or more types.

upcast(t0, t1, ..., tn) -> T where T is a supported dtype

Examples --------

>>> upcast('int32') <type 'numpy.int32'> >>> upcast('bool') <type 'numpy.bool_'> >>> upcast('int32','float32') <type 'numpy.float64'> >>> upcast('bool',complex,float) <type 'numpy.complex128'>

Stack sparse matrices vertically (row wise)

Parameters ---------- blocks sequence of sparse matrices with compatible shapes format : str, optional sparse format of the result (e.g., 'csr') by default an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional The data-type of the output matrix. If not given, the dtype is determined from that of `blocks`.

See Also -------- hstack : stack sparse matrices horizontally (column wise)

Examples -------- >>> from scipy.sparse import coo_matrix, vstack >>> A = coo_matrix([1, 2], [3, 4]) >>> B = coo_matrix([5, 6]) >>> vstack(A, B).toarray() array([1, 2], [3, 4], [5, 6])