Compute SHA1 hash of the given bytes.

Make a linear system Ax = b Args: A (cupy.ndarray or cupyx.scipy.sparse.spmatrix or cupyx.scipy.sparse.LinearOperator): spars

Determine order and strides as in NumPy's PyArray_NewLikeArray. (see: numpy/core/src/multiarray/ctors.c)

Given data x, construct the knot vector w/ not-a-knot BC. cf de Boor, XIII(12).

Return relative degree of transfer function from zeros and poles

Run a broadcasting binary op involving a 1-D array on the ``(1, N)`` backing, then finalize the result shape. ``run(backing, other)``

Apply an affine transformation. Given an output image pixel index vector ``o``, the pixel value is determined from the input image at positio

Cast the array elements to a specified type. Args: dtype: Target dtype. copy (bool): If ``True`` (default), the retur

Reverse the 1-D slices of `a` along axis `axis`. Returns axis_slice(a, step=-1, axis=axis).

Multidimensional binary dilation with the given structuring element. Args: input(cupy.ndarray): The input binary array_like to be dilated

Joins arrays along an axis. Args: tup (sequence of arrays): Arrays to be joined. All of these should have same dimensionaliti

Construct a new piecewise polynomial representing the derivative. Parameters ---------- nu : int, optional

Remove linear trend along axis from data. Parameters ---------- data : array_like The input data. axis : int, optional

Matrix-matrix or matrix-vector multiplication.

r""" Generic weighted sum of cosine terms window Parameters ---------- M : int Number of points in the output window a :

IIR digital and analog filter design given order and critical points. Design an Nth-order digital or analog filter and return the filter

Compute a definite integral over a piecewise polynomial. Parameters ---------- a : float Lower integrati

r""" Khatri-rao product A column-wise Kronecker product of two matrices Parameters ---------- a : (n, k) array_like Inpu

Load a cached kernel binary from disk. Args: name (str): The cache key (filename) for the compiled kernel. Returns:

Load a cached kernel binary. First checks local disk cache, then falls back to GCS if not found. Downloaded files are persis

Adjoint matrix-vector multiplication.

Solves a sparse linear system ``A x = b`` Args: A (cupyx.scipy.sparse.spmatrix): Sparse matrix with dimension ``(M, M)``.

Returns an array of samples drawn from the standard exp distribution. .. seealso:: - :func:`cupy.random.standard_exponential` fo

Convert system representation to `ZerosPolesGain`. Parameters ---------- kwargs : dict, optional Additio

Converts the matrix to Compressed Sparse Row format. Args: copy (bool): If ``False``, the method returns itself.

Calls ``cg::sync()``. Args: group: a valid cooperative group .. seealso:: `cg::sync`_ .. _cg::sync:

Return True if any argument is a sparse array (not matrix).

Convert the input into a C contiguous float array. NB: Upcasts half- and single-precision floats to double precision.

Convert scalars to a sequence, otherwise pass through ``x`` unchanged

Build compressed indptr from per-nnz major-axis assignments. Mirrors the histogram + prefix-sum recipe used by scipy.sparse, works with both

Compute the total polynomial divided by factors for each root.

Split into complex and real parts, combining conjugate pairs. The 1-D input vector `z` is split up into its complex (zc) and real (zr) e

Pure-CuPy CSR<->CSC transpose (int64 indices or non-float data). Uses ``_build_indptr`` + ``lexsort`` -- O(nnz log nnz) time, and is dtype-ag

Forward DCT/DST-II (or inverse DCT/DST-III) along a single axis Parameters ---------- x : cupy.ndarray The data to transform.

Forward DCT/DST-III (or inverse DCT/DST-II) along a single axis. Parameters ---------- x : cupy.ndarray The data to transform.

Encode a cubin binary to the on-disk format (hash prefix + cubin). Args: cubin (bytes): Raw compiled kernel binary data.

Perform spline prefiltering when needed. Spline orders > 1 need a prefiltering stage to preserve resolution. For boundary modes without anal

Handles shape and axes arguments for nd transforms.

True if x is any CSC sparse (array or matrix).

True if x is any CSR sparse (array or matrix).

Solve for the LSQ spline coeffs given x, y and knots. `y` is always 2D: for 1D data, the shape is ``(m, 1)``. `w` is always 1D: one weight val

3x3 diagonal CSC with values [1, 2, 3].

2-row CSR: row 0 → col 0 (val=1.0), row 1 → col _LARGE (val=2.0).