"""
``numpy.linalg``
================

The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient
low level implementations of standard linear algebra algorithms. Those
libraries may be provided by NumPy itself using C versions of a subset of their
reference implementations but, when possible, highly optimized libraries that
take advantage of specialized processor functionality are preferred. Examples
of such libraries are OpenBLAS, MKL (TM), and ATLAS. Because those libraries
are multithreaded and processor dependent, environmental variables and external
packages such as threadpoolctl may be needed to control the number of threads
or specify the processor architecture.

- OpenBLAS: https://www.openblas.net/
- threadpoolctl: https://github.com/joblib/threadpoolctl

Please note that the most-used linear algebra functions in NumPy are present in
the main ``numpy`` namespace rather than in ``numpy.linalg``.  There are:
``dot``, ``vdot``, ``inner``, ``outer``, ``matmul``, ``tensordot``, ``einsum``,
``einsum_path`` and ``kron``.

Functions present in numpy.linalg are listed below.


Matrix and vector products
--------------------------

   multi_dot
   matrix_power

Decompositions
--------------

   cholesky
   qr
   svd

Matrix eigenvalues
------------------

   eig
   eigh
   eigvals
   eigvalsh

Norms and other numbers
-----------------------

   norm
   cond
   det
   matrix_rank
   slogdet

Solving equations and inverting matrices
----------------------------------------

   solve
   tensorsolve
   lstsq
   inv
   pinv
   tensorinv

Exceptions
----------

   LinAlgError

"""
from __future__ import division, absolute_import, print_function

# To get sub-modules
from .linalg import *

from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
