Delete .venv directory
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Untriex Programming
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@@ -1,53 +0,0 @@
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from subprocess import PIPE, Popen
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import sys
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import re
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import pytest
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from numpy.linalg import lapack_lite
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from numpy.testing import assert_
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class FindDependenciesLdd:
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def __init__(self):
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self.cmd = ['ldd']
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try:
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p = Popen(self.cmd, stdout=PIPE, stderr=PIPE)
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stdout, stderr = p.communicate()
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except OSError as e:
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raise RuntimeError(f'command {self.cmd} cannot be run') from e
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def get_dependencies(self, lfile):
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p = Popen(self.cmd + [lfile], stdout=PIPE, stderr=PIPE)
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stdout, stderr = p.communicate()
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if not (p.returncode == 0):
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raise RuntimeError(f'failed dependencies check for {lfile}')
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return stdout
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def grep_dependencies(self, lfile, deps):
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stdout = self.get_dependencies(lfile)
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rdeps = dict([(dep, re.compile(dep)) for dep in deps])
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founds = []
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for l in stdout.splitlines():
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for k, v in rdeps.items():
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if v.search(l):
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founds.append(k)
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return founds
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class TestF77Mismatch:
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@pytest.mark.skipif(not(sys.platform[:5] == 'linux'),
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reason="no fortran compiler on non-Linux platform")
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def test_lapack(self):
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f = FindDependenciesLdd()
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deps = f.grep_dependencies(lapack_lite.__file__,
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[b'libg2c', b'libgfortran'])
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assert_(len(deps) <= 1,
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"""Both g77 and gfortran runtimes linked in lapack_lite ! This is likely to
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cause random crashes and wrong results. See numpy INSTALL.txt for more
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information.""")
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@@ -1,20 +0,0 @@
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"""Test deprecation and future warnings.
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"""
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import numpy as np
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from numpy.testing import assert_warns
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def test_qr_mode_full_future_warning():
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"""Check mode='full' FutureWarning.
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In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
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deprecated. The release date will probably be sometime in the summer
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of 2013.
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"""
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a = np.eye(2)
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assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
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assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
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assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
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assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')
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File diff suppressed because it is too large
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@@ -1,148 +0,0 @@
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""" Test functions for linalg module
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"""
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import warnings
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import numpy as np
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from numpy import linalg, arange, float64, array, dot, transpose
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from numpy.testing import (
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assert_, assert_raises, assert_equal, assert_array_equal,
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assert_array_almost_equal, assert_array_less
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)
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class TestRegression:
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def test_eig_build(self):
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# Ticket #652
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rva = array([1.03221168e+02 + 0.j,
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-1.91843603e+01 + 0.j,
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-6.04004526e-01 + 15.84422474j,
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-6.04004526e-01 - 15.84422474j,
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-1.13692929e+01 + 0.j,
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-6.57612485e-01 + 10.41755503j,
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-6.57612485e-01 - 10.41755503j,
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1.82126812e+01 + 0.j,
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1.06011014e+01 + 0.j,
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7.80732773e+00 + 0.j,
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-7.65390898e-01 + 0.j,
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1.51971555e-15 + 0.j,
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-1.51308713e-15 + 0.j])
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a = arange(13 * 13, dtype=float64)
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a.shape = (13, 13)
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a = a % 17
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va, ve = linalg.eig(a)
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va.sort()
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rva.sort()
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assert_array_almost_equal(va, rva)
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def test_eigh_build(self):
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# Ticket 662.
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rvals = [68.60568999, 89.57756725, 106.67185574]
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cov = array([[77.70273908, 3.51489954, 15.64602427],
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[3.51489954, 88.97013878, -1.07431931],
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[15.64602427, -1.07431931, 98.18223512]])
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vals, vecs = linalg.eigh(cov)
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assert_array_almost_equal(vals, rvals)
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def test_svd_build(self):
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# Ticket 627.
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a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
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m, n = a.shape
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u, s, vh = linalg.svd(a)
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b = dot(transpose(u[:, n:]), a)
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assert_array_almost_equal(b, np.zeros((2, 2)))
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def test_norm_vector_badarg(self):
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# Regression for #786: Frobenius norm for vectors raises
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# ValueError.
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assert_raises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
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def test_lapack_endian(self):
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# For bug #1482
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a = array([[5.7998084, -2.1825367],
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[-2.1825367, 9.85910595]], dtype='>f8')
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b = array(a, dtype='<f8')
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ap = linalg.cholesky(a)
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bp = linalg.cholesky(b)
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assert_array_equal(ap, bp)
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def test_large_svd_32bit(self):
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# See gh-4442, 64bit would require very large/slow matrices.
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x = np.eye(1000, 66)
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np.linalg.svd(x)
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def test_svd_no_uv(self):
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# gh-4733
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for shape in (3, 4), (4, 4), (4, 3):
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for t in float, complex:
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a = np.ones(shape, dtype=t)
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w = linalg.svd(a, compute_uv=False)
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c = np.count_nonzero(np.absolute(w) > 0.5)
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assert_equal(c, 1)
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assert_equal(np.linalg.matrix_rank(a), 1)
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assert_array_less(1, np.linalg.norm(a, ord=2))
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def test_norm_object_array(self):
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# gh-7575
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testvector = np.array([np.array([0, 1]), 0, 0], dtype=object)
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norm = linalg.norm(testvector)
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assert_array_equal(norm, [0, 1])
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assert_(norm.dtype == np.dtype('float64'))
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norm = linalg.norm(testvector, ord=1)
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assert_array_equal(norm, [0, 1])
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assert_(norm.dtype != np.dtype('float64'))
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norm = linalg.norm(testvector, ord=2)
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assert_array_equal(norm, [0, 1])
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assert_(norm.dtype == np.dtype('float64'))
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assert_raises(ValueError, linalg.norm, testvector, ord='fro')
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assert_raises(ValueError, linalg.norm, testvector, ord='nuc')
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assert_raises(ValueError, linalg.norm, testvector, ord=np.inf)
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assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf)
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with warnings.catch_warnings():
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warnings.simplefilter("error", DeprecationWarning)
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assert_raises((AttributeError, DeprecationWarning),
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linalg.norm, testvector, ord=0)
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assert_raises(ValueError, linalg.norm, testvector, ord=-1)
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assert_raises(ValueError, linalg.norm, testvector, ord=-2)
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testmatrix = np.array([[np.array([0, 1]), 0, 0],
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[0, 0, 0]], dtype=object)
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norm = linalg.norm(testmatrix)
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assert_array_equal(norm, [0, 1])
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assert_(norm.dtype == np.dtype('float64'))
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norm = linalg.norm(testmatrix, ord='fro')
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assert_array_equal(norm, [0, 1])
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assert_(norm.dtype == np.dtype('float64'))
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assert_raises(TypeError, linalg.norm, testmatrix, ord='nuc')
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assert_raises(ValueError, linalg.norm, testmatrix, ord=np.inf)
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assert_raises(ValueError, linalg.norm, testmatrix, ord=-np.inf)
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assert_raises(ValueError, linalg.norm, testmatrix, ord=0)
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assert_raises(ValueError, linalg.norm, testmatrix, ord=1)
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assert_raises(ValueError, linalg.norm, testmatrix, ord=-1)
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assert_raises(TypeError, linalg.norm, testmatrix, ord=2)
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assert_raises(TypeError, linalg.norm, testmatrix, ord=-2)
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assert_raises(ValueError, linalg.norm, testmatrix, ord=3)
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def test_lstsq_complex_larger_rhs(self):
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# gh-9891
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size = 20
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n_rhs = 70
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G = np.random.randn(size, size) + 1j * np.random.randn(size, size)
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u = np.random.randn(size, n_rhs) + 1j * np.random.randn(size, n_rhs)
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b = G.dot(u)
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# This should work without segmentation fault.
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u_lstsq, res, rank, sv = linalg.lstsq(G, b, rcond=None)
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# check results just in case
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assert_array_almost_equal(u_lstsq, u)
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