Delete .venv directory

.venv/lib/python3.9/site-packages/numpy/linalg/__init__.py

@@ -1,80 +0,0 @@
-"""
-``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
-
-"""
-# To get sub-modules
-from . import linalg
-from .linalg import *
-
-__all__ = linalg.__all__.copy()
-
-from numpy._pytesttester import PytestTester
-test = PytestTester(__name__)
-del PytestTester

.venv/lib/python3.9/site-packages/numpy/linalg/__init__.pyi

@@ -1,26 +0,0 @@
-from typing import Any, List
-
-__all__: List[str]
-
-class LinAlgError(Exception): ...
-
-def tensorsolve(a, b, axes=...): ...
-def solve(a, b): ...
-def tensorinv(a, ind=...): ...
-def inv(a): ...
-def matrix_power(a, n): ...
-def cholesky(a): ...
-def qr(a, mode=...): ...
-def eigvals(a): ...
-def eigvalsh(a, UPLO=...): ...
-def eig(a): ...
-def eigh(a, UPLO=...): ...
-def svd(a, full_matrices=..., compute_uv=..., hermitian=...): ...
-def cond(x, p=...): ...
-def matrix_rank(M, tol=..., hermitian=...): ...
-def pinv(a, rcond=..., hermitian=...): ...
-def slogdet(a): ...
-def det(a): ...
-def lstsq(a, b, rcond=...): ...
-def norm(x, ord=..., axis=..., keepdims=...): ...
-def multi_dot(arrays, *, out=...): ...

.venv/lib/python3.9/site-packages/numpy/linalg/setup.py

@@ -1,83 +0,0 @@
-import os
-import sys
-
-def configuration(parent_package='', top_path=None):
-    from numpy.distutils.misc_util import Configuration
-    from numpy.distutils.system_info import (
-            get_info, system_info, lapack_opt_info, blas_opt_info)
-    config = Configuration('linalg', parent_package, top_path)
-
-    config.add_subpackage('tests')
-
-    # Configure lapack_lite
-
-    src_dir = 'lapack_lite'
-    lapack_lite_src = [
-        os.path.join(src_dir, 'python_xerbla.c'),
-        os.path.join(src_dir, 'f2c_z_lapack.c'),
-        os.path.join(src_dir, 'f2c_c_lapack.c'),
-        os.path.join(src_dir, 'f2c_d_lapack.c'),
-        os.path.join(src_dir, 'f2c_s_lapack.c'),
-        os.path.join(src_dir, 'f2c_lapack.c'),
-        os.path.join(src_dir, 'f2c_blas.c'),
-        os.path.join(src_dir, 'f2c_config.c'),
-        os.path.join(src_dir, 'f2c.c'),
-    ]
-    all_sources = config.paths(lapack_lite_src)
-
-    if os.environ.get('NPY_USE_BLAS_ILP64', "0") != "0":
-        lapack_info = get_info('lapack_ilp64_opt', 2)
-    else:
-        lapack_info = get_info('lapack_opt', 0)  # and {}
-
-    use_lapack_lite = not lapack_info
-
-    if use_lapack_lite:
-        # This makes numpy.distutils write the fact that lapack_lite
-        # is being used to numpy.__config__
-        class numpy_linalg_lapack_lite(system_info):
-            def calc_info(self):
-                info = {'language': 'c'}
-                if sys.maxsize > 2**32:
-                    # Build lapack-lite in 64-bit integer mode.
-                    # The suffix is arbitrary (lapack_lite symbols follow it),
-                    # but use the "64_" convention here.
-                    info['define_macros'] = [
-                        ('HAVE_BLAS_ILP64', None),
-                        ('BLAS_SYMBOL_SUFFIX', '64_')
-                    ]
-                self.set_info(**info)
-
-        lapack_info = numpy_linalg_lapack_lite().get_info(2)
-
-    def get_lapack_lite_sources(ext, build_dir):
-        if use_lapack_lite:
-            print("### Warning:  Using unoptimized lapack ###")
-            return all_sources
-        else:
-            if sys.platform == 'win32':
-                print("### Warning:  python_xerbla.c is disabled ###")
-                return []
-            return [all_sources[0]]
-
-    config.add_extension(
-        'lapack_lite',
-        sources=['lapack_litemodule.c', get_lapack_lite_sources],
-        depends=['lapack_lite/f2c.h'],
-        extra_info=lapack_info,
-    )
-
-    # umath_linalg module
-    config.add_extension(
-        '_umath_linalg',
-        sources=['umath_linalg.c.src', get_lapack_lite_sources],
-        depends=['lapack_lite/f2c.h'],
-        extra_info=lapack_info,
-        libraries=['npymath'],
-    )
-    config.add_data_files('*.pyi')
-    return config
-
-if __name__ == '__main__':
-    from numpy.distutils.core import setup
-    setup(configuration=configuration)

.venv/lib/python3.9/site-packages/numpy/linalg/tests/test_build.py

@@ -1,53 +0,0 @@
-from subprocess import PIPE, Popen
-import sys
-import re
-import pytest
-
-from numpy.linalg import lapack_lite
-from numpy.testing import assert_
-
-
-class FindDependenciesLdd:
-
-    def __init__(self):
-        self.cmd = ['ldd']
-
-        try:
-            p = Popen(self.cmd, stdout=PIPE, stderr=PIPE)
-            stdout, stderr = p.communicate()
-        except OSError as e:
-            raise RuntimeError(f'command {self.cmd} cannot be run') from e
-
-    def get_dependencies(self, lfile):
-        p = Popen(self.cmd + [lfile], stdout=PIPE, stderr=PIPE)
-        stdout, stderr = p.communicate()
-        if not (p.returncode == 0):
-            raise RuntimeError(f'failed dependencies check for {lfile}')
-
-        return stdout
-
-    def grep_dependencies(self, lfile, deps):
-        stdout = self.get_dependencies(lfile)
-
-        rdeps = dict([(dep, re.compile(dep)) for dep in deps])
-        founds = []
-        for l in stdout.splitlines():
-            for k, v in rdeps.items():
-                if v.search(l):
-                    founds.append(k)
-
-        return founds
-
-
-class TestF77Mismatch:
-
-    @pytest.mark.skipif(not(sys.platform[:5] == 'linux'),
-                        reason="no fortran compiler on non-Linux platform")
-    def test_lapack(self):
-        f = FindDependenciesLdd()
-        deps = f.grep_dependencies(lapack_lite.__file__,
-                                   [b'libg2c', b'libgfortran'])
-        assert_(len(deps) <= 1,
-                         """Both g77 and gfortran runtimes linked in lapack_lite ! This is likely to
-cause random crashes and wrong results. See numpy INSTALL.txt for more
-information.""")

.venv/lib/python3.9/site-packages/numpy/linalg/tests/test_deprecations.py

@@ -1,20 +0,0 @@
-"""Test deprecation and future warnings.
-
-"""
-import numpy as np
-from numpy.testing import assert_warns
-
-
-def test_qr_mode_full_future_warning():
-    """Check mode='full' FutureWarning.
-
-    In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
-    deprecated. The release date will probably be sometime in the summer
-    of 2013.
-
-    """
-    a = np.eye(2)
-    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
-    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
-    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
-    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')

.venv/lib/python3.9/site-packages/numpy/linalg/tests/test_regression.py

@@ -1,148 +0,0 @@
-""" Test functions for linalg module
-"""
-import warnings
-
-import numpy as np
-from numpy import linalg, arange, float64, array, dot, transpose
-from numpy.testing import (
-    assert_, assert_raises, assert_equal, assert_array_equal,
-    assert_array_almost_equal, assert_array_less
-)
-
-
-class TestRegression:
-
-    def test_eig_build(self):
-        # Ticket #652
-        rva = array([1.03221168e+02 + 0.j,
-                     -1.91843603e+01 + 0.j,
-                     -6.04004526e-01 + 15.84422474j,
-                     -6.04004526e-01 - 15.84422474j,
-                     -1.13692929e+01 + 0.j,
-                     -6.57612485e-01 + 10.41755503j,
-                     -6.57612485e-01 - 10.41755503j,
-                     1.82126812e+01 + 0.j,
-                     1.06011014e+01 + 0.j,
-                     7.80732773e+00 + 0.j,
-                     -7.65390898e-01 + 0.j,
-                     1.51971555e-15 + 0.j,
-                     -1.51308713e-15 + 0.j])
-        a = arange(13 * 13, dtype=float64)
-        a.shape = (13, 13)
-        a = a % 17
-        va, ve = linalg.eig(a)
-        va.sort()
-        rva.sort()
-        assert_array_almost_equal(va, rva)
-
-    def test_eigh_build(self):
-        # Ticket 662.
-        rvals = [68.60568999, 89.57756725, 106.67185574]
-
-        cov = array([[77.70273908,   3.51489954,  15.64602427],
-                     [3.51489954,  88.97013878,  -1.07431931],
-                     [15.64602427,  -1.07431931,  98.18223512]])
-
-        vals, vecs = linalg.eigh(cov)
-        assert_array_almost_equal(vals, rvals)
-
-    def test_svd_build(self):
-        # Ticket 627.
-        a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
-        m, n = a.shape
-        u, s, vh = linalg.svd(a)
-
-        b = dot(transpose(u[:, n:]), a)
-
-        assert_array_almost_equal(b, np.zeros((2, 2)))
-
-    def test_norm_vector_badarg(self):
-        # Regression for #786: Frobenius norm for vectors raises
-        # ValueError.
-        assert_raises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
-
-    def test_lapack_endian(self):
-        # For bug #1482
-        a = array([[5.7998084,  -2.1825367],
-                   [-2.1825367,   9.85910595]], dtype='>f8')
-        b = array(a, dtype='<f8')
-
-        ap = linalg.cholesky(a)
-        bp = linalg.cholesky(b)
-        assert_array_equal(ap, bp)
-
-    def test_large_svd_32bit(self):
-        # See gh-4442, 64bit would require very large/slow matrices.
-        x = np.eye(1000, 66)
-        np.linalg.svd(x)
-
-    def test_svd_no_uv(self):
-        # gh-4733
-        for shape in (3, 4), (4, 4), (4, 3):
-            for t in float, complex:
-                a = np.ones(shape, dtype=t)
-                w = linalg.svd(a, compute_uv=False)
-                c = np.count_nonzero(np.absolute(w) > 0.5)
-                assert_equal(c, 1)
-                assert_equal(np.linalg.matrix_rank(a), 1)
-                assert_array_less(1, np.linalg.norm(a, ord=2))
-
-    def test_norm_object_array(self):
-        # gh-7575
-        testvector = np.array([np.array([0, 1]), 0, 0], dtype=object)
-
-        norm = linalg.norm(testvector)
-        assert_array_equal(norm, [0, 1])
-        assert_(norm.dtype == np.dtype('float64'))
-
-        norm = linalg.norm(testvector, ord=1)
-        assert_array_equal(norm, [0, 1])
-        assert_(norm.dtype != np.dtype('float64'))
-
-        norm = linalg.norm(testvector, ord=2)
-        assert_array_equal(norm, [0, 1])
-        assert_(norm.dtype == np.dtype('float64'))
-
-        assert_raises(ValueError, linalg.norm, testvector, ord='fro')
-        assert_raises(ValueError, linalg.norm, testvector, ord='nuc')
-        assert_raises(ValueError, linalg.norm, testvector, ord=np.inf)
-        assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf)
-        with warnings.catch_warnings():
-            warnings.simplefilter("error", DeprecationWarning)
-            assert_raises((AttributeError, DeprecationWarning),
-                              linalg.norm, testvector, ord=0)
-        assert_raises(ValueError, linalg.norm, testvector, ord=-1)
-        assert_raises(ValueError, linalg.norm, testvector, ord=-2)
-
-        testmatrix = np.array([[np.array([0, 1]), 0, 0],
-                               [0,                0, 0]], dtype=object)
-
-        norm = linalg.norm(testmatrix)
-        assert_array_equal(norm, [0, 1])
-        assert_(norm.dtype == np.dtype('float64'))
-
-        norm = linalg.norm(testmatrix, ord='fro')
-        assert_array_equal(norm, [0, 1])
-        assert_(norm.dtype == np.dtype('float64'))
-
-        assert_raises(TypeError, linalg.norm, testmatrix, ord='nuc')
-        assert_raises(ValueError, linalg.norm, testmatrix, ord=np.inf)
-        assert_raises(ValueError, linalg.norm, testmatrix, ord=-np.inf)
-        assert_raises(ValueError, linalg.norm, testmatrix, ord=0)
-        assert_raises(ValueError, linalg.norm, testmatrix, ord=1)
-        assert_raises(ValueError, linalg.norm, testmatrix, ord=-1)
-        assert_raises(TypeError, linalg.norm, testmatrix, ord=2)
-        assert_raises(TypeError, linalg.norm, testmatrix, ord=-2)
-        assert_raises(ValueError, linalg.norm, testmatrix, ord=3)
-
-    def test_lstsq_complex_larger_rhs(self):
-        # gh-9891
-        size = 20
-        n_rhs = 70
-        G = np.random.randn(size, size) + 1j * np.random.randn(size, size)
-        u = np.random.randn(size, n_rhs) + 1j * np.random.randn(size, n_rhs)
-        b = G.dot(u)
-        # This should work without segmentation fault.
-        u_lstsq, res, rank, sv = linalg.lstsq(G, b, rcond=None)
-        # check results just in case
-        assert_array_almost_equal(u_lstsq, u)