#
# Created by: Pearu Peterson, March 2002
#
""" Test functions for linalg.basic module

"""
from __future__ import division, print_function, absolute_import

import warnings
import itertools
import numpy as np
from numpy import (arange, array, dot, zeros, identity, conjugate, transpose,
                   float32)
import numpy.linalg as linalg
from numpy.random import random

from numpy.testing import (TestCase, run_module_suite, assert_raises,
                           assert_equal, assert_almost_equal, assert_,
                           assert_array_almost_equal, assert_allclose,
                           assert_array_equal, dec)

from scipy.linalg import (solve, inv, det, lstsq, pinv, pinv2, pinvh, norm,
                          solve_banded, solveh_banded, solve_triangular,
                          solve_circulant, circulant, LinAlgError, block_diag,
                          matrix_balance)

from scipy.linalg.basic import LstsqLapackError
from scipy.linalg._testutils import assert_no_overwrite

from scipy._lib._version import NumpyVersion


"""
Bugs:
1) solve.check_random_sym_complex fails if a is complex
   and transpose(a) = conjugate(a) (a is Hermitian).
"""
__usage__ = """
Build linalg:
  python setup_linalg.py build
Run tests if scipy is installed:
  python -c 'import scipy;scipy.linalg.test()'
Run tests if linalg is not installed:
  python tests/test_basic.py
"""

REAL_DTYPES = [np.float32, np.float64, np.longdouble]
COMPLEX_DTYPES = [np.complex64, np.complex128, np.clongdouble]
DTYPES = REAL_DTYPES + COMPLEX_DTYPES


def _eps_cast(dtyp):
    """Get the epsilon for dtype, possibly downcast to BLAS types."""
    dt = dtyp
    if dt == np.longdouble:
        dt = np.float64
    elif dt == np.clongdouble:
        dt = np.complex128
    return np.finfo(dt).eps


class TestSolveBanded(TestCase):

    def test_real(self):
        a = array([[1.0, 20, 0, 0],
                   [-30, 4, 6, 0],
                   [2, 1, 20, 2],
                   [0, -1, 7, 14]])
        ab = array([[0.0, 20, 6, 2],
                    [1, 4, 20, 14],
                    [-30, 1, 7, 0],
                    [2, -1, 0, 0]])
        l, u = 2, 1
        b4 = array([10.0, 0.0, 2.0, 14.0])
        b4by1 = b4.reshape(-1, 1)
        b4by2 = array([[2, 1],
                       [-30, 4],
                       [2, 3],
                       [1, 3]])
        b4by4 = array([[1, 0, 0, 0],
                       [0, 0, 0, 1],
                       [0, 1, 0, 0],
                       [0, 1, 0, 0]])
        for b in [b4, b4by1, b4by2, b4by4]:
            x = solve_banded((l, u), ab, b)
            assert_array_almost_equal(dot(a, x), b)

    def test_complex(self):
        a = array([[1.0, 20, 0, 0],
                   [-30, 4, 6, 0],
                   [2j, 1, 20, 2j],
                   [0, -1, 7, 14]])
        ab = array([[0.0, 20, 6, 2j],
                    [1, 4, 20, 14],
                    [-30, 1, 7, 0],
                    [2j, -1, 0, 0]])
        l, u = 2, 1
        b4 = array([10.0, 0.0, 2.0, 14.0j])
        b4by1 = b4.reshape(-1, 1)
        b4by2 = array([[2, 1],
                       [-30, 4],
                       [2, 3],
                       [1, 3]])
        b4by4 = array([[1, 0, 0, 0],
                       [0, 0, 0, 1j],
                       [0, 1, 0, 0],
                       [0, 1, 0, 0]])
        for b in [b4, b4by1, b4by2, b4by4]:
            x = solve_banded((l, u), ab, b)
            assert_array_almost_equal(dot(a, x), b)

    def test_tridiag_real(self):
        ab = array([[0.0, 20, 6, 2],
                   [1, 4, 20, 14],
                   [-30, 1, 7, 0]])
        a = np.diag(ab[0, 1:], 1) + np.diag(ab[1, :], 0) + np.diag(
                                                                ab[2, :-1], -1)
        b4 = array([10.0, 0.0, 2.0, 14.0])
        b4by1 = b4.reshape(-1, 1)
        b4by2 = array([[2, 1],
                       [-30, 4],
                       [2, 3],
                       [1, 3]])
        b4by4 = array([[1, 0, 0, 0],
                       [0, 0, 0, 1],
                       [0, 1, 0, 0],
                       [0, 1, 0, 0]])
        for b in [b4, b4by1, b4by2, b4by4]:
            x = solve_banded((1, 1), ab, b)
            assert_array_almost_equal(dot(a, x), b)

    def test_tridiag_complex(self):
        ab = array([[0.0, 20, 6, 2j],
                   [1, 4, 20, 14],
                   [-30, 1, 7, 0]])
        a = np.diag(ab[0, 1:], 1) + np.diag(ab[1, :], 0) + np.diag(
                                                               ab[2, :-1], -1)
        b4 = array([10.0, 0.0, 2.0, 14.0j])
        b4by1 = b4.reshape(-1, 1)
        b4by2 = array([[2, 1],
                       [-30, 4],
                       [2, 3],
                       [1, 3]])
        b4by4 = array([[1, 0, 0, 0],
                       [0, 0, 0, 1],
                       [0, 1, 0, 0],
                       [0, 1, 0, 0]])
        for b in [b4, b4by1, b4by2, b4by4]:
            x = solve_banded((1, 1), ab, b)
            assert_array_almost_equal(dot(a, x), b)

    def test_check_finite(self):
        a = array([[1.0, 20, 0, 0],
                   [-30, 4, 6, 0],
                   [2, 1, 20, 2],
                   [0, -1, 7, 14]])
        ab = array([[0.0, 20, 6, 2],
                    [1, 4, 20, 14],
                    [-30, 1, 7, 0],
                    [2, -1, 0, 0]])
        l, u = 2, 1
        b4 = array([10.0, 0.0, 2.0, 14.0])
        x = solve_banded((l, u), ab, b4, check_finite=False)
        assert_array_almost_equal(dot(a, x), b4)

    def test_bad_shape(self):
        ab = array([[0.0, 20, 6, 2],
                    [1, 4, 20, 14],
                    [-30, 1, 7, 0],
                    [2, -1, 0, 0]])
        l, u = 2, 1
        bad = array([1.0, 2.0, 3.0, 4.0]).reshape(-1, 4)
        assert_raises(ValueError, solve_banded, (l, u), ab, bad)
        assert_raises(ValueError, solve_banded, (l, u), ab, [1.0, 2.0])

        # Values of (l,u) are not compatible with ab.
        assert_raises(ValueError, solve_banded, (1, 1), ab, [1.0, 2.0])

    def test_1x1(self):
        b = array([[1., 2., 3.]])
        x = solve_banded((1, 1), [[0], [2], [0]], b)
        assert_array_equal(x, [[0.5, 1.0, 1.5]])
        assert_equal(x.dtype, np.dtype('f8'))
        assert_array_equal(b, [[1.0, 2.0, 3.0]])

    def test_native_list_arguments(self):
        a = [[1.0, 20, 0, 0],
             [-30, 4, 6, 0],
             [2, 1, 20, 2],
             [0, -1, 7, 14]]
        ab = [[0.0, 20, 6, 2],
              [1, 4, 20, 14],
              [-30, 1, 7, 0],
              [2, -1, 0, 0]]
        l, u = 2, 1
        b = [10.0, 0.0, 2.0, 14.0]
        x = solve_banded((l, u), ab, b)
        assert_array_almost_equal(dot(a, x), b)


class TestSolveHBanded(TestCase):

    def test_01_upper(self):
        # Solve
        # [ 4 1 2 0]     [1]
        # [ 1 4 1 2] X = [4]
        # [ 2 1 4 1]     [1]
        # [ 0 2 1 4]     [2]
        # with the RHS as a 1D array.
        ab = array([[0.0, 0.0, 2.0, 2.0],
                    [-99, 1.0, 1.0, 1.0],
                    [4.0, 4.0, 4.0, 4.0]])
        b = array([1.0, 4.0, 1.0, 2.0])
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, [0.0, 1.0, 0.0, 0.0])

    def test_02_upper(self):
        # Solve
        # [ 4 1 2 0]     [1 6]
        # [ 1 4 1 2] X = [4 2]
        # [ 2 1 4 1]     [1 6]
        # [ 0 2 1 4]     [2 1]
        #
        ab = array([[0.0, 0.0, 2.0, 2.0],
                    [-99, 1.0, 1.0, 1.0],
                    [4.0, 4.0, 4.0, 4.0]])
        b = array([[1.0, 6.0],
                   [4.0, 2.0],
                   [1.0, 6.0],
                   [2.0, 1.0]])
        x = solveh_banded(ab, b)
        expected = array([[0.0, 1.0],
                          [1.0, 0.0],
                          [0.0, 1.0],
                          [0.0, 0.0]])
        assert_array_almost_equal(x, expected)

    def test_03_upper(self):
        # Solve
        # [ 4 1 2 0]     [1]
        # [ 1 4 1 2] X = [4]
        # [ 2 1 4 1]     [1]
        # [ 0 2 1 4]     [2]
        # with the RHS as a 2D array with shape (3,1).
        ab = array([[0.0, 0.0, 2.0, 2.0],
                    [-99, 1.0, 1.0, 1.0],
                    [4.0, 4.0, 4.0, 4.0]])
        b = array([1.0, 4.0, 1.0, 2.0]).reshape(-1, 1)
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, array([0., 1., 0., 0.]).reshape(-1, 1))

    def test_01_lower(self):
        # Solve
        # [ 4 1 2 0]     [1]
        # [ 1 4 1 2] X = [4]
        # [ 2 1 4 1]     [1]
        # [ 0 2 1 4]     [2]
        #
        ab = array([[4.0, 4.0, 4.0, 4.0],
                    [1.0, 1.0, 1.0, -99],
                    [2.0, 2.0, 0.0, 0.0]])
        b = array([1.0, 4.0, 1.0, 2.0])
        x = solveh_banded(ab, b, lower=True)
        assert_array_almost_equal(x, [0.0, 1.0, 0.0, 0.0])

    def test_02_lower(self):
        # Solve
        # [ 4 1 2 0]     [1 6]
        # [ 1 4 1 2] X = [4 2]
        # [ 2 1 4 1]     [1 6]
        # [ 0 2 1 4]     [2 1]
        #
        ab = array([[4.0, 4.0, 4.0, 4.0],
                    [1.0, 1.0, 1.0, -99],
                    [2.0, 2.0, 0.0, 0.0]])
        b = array([[1.0, 6.0],
                   [4.0, 2.0],
                   [1.0, 6.0],
                   [2.0, 1.0]])
        x = solveh_banded(ab, b, lower=True)
        expected = array([[0.0, 1.0],
                          [1.0, 0.0],
                          [0.0, 1.0],
                          [0.0, 0.0]])
        assert_array_almost_equal(x, expected)

    def test_01_float32(self):
        # Solve
        # [ 4 1 2 0]     [1]
        # [ 1 4 1 2] X = [4]
        # [ 2 1 4 1]     [1]
        # [ 0 2 1 4]     [2]
        #
        ab = array([[0.0, 0.0, 2.0, 2.0],
                    [-99, 1.0, 1.0, 1.0],
                    [4.0, 4.0, 4.0, 4.0]], dtype=float32)
        b = array([1.0, 4.0, 1.0, 2.0], dtype=float32)
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, [0.0, 1.0, 0.0, 0.0])

    def test_02_float32(self):
        # Solve
        # [ 4 1 2 0]     [1 6]
        # [ 1 4 1 2] X = [4 2]
        # [ 2 1 4 1]     [1 6]
        # [ 0 2 1 4]     [2 1]
        #
        ab = array([[0.0, 0.0, 2.0, 2.0],
                    [-99, 1.0, 1.0, 1.0],
                    [4.0, 4.0, 4.0, 4.0]], dtype=float32)
        b = array([[1.0, 6.0],
                   [4.0, 2.0],
                   [1.0, 6.0],
                   [2.0, 1.0]], dtype=float32)
        x = solveh_banded(ab, b)
        expected = array([[0.0, 1.0],
                          [1.0, 0.0],
                          [0.0, 1.0],
                          [0.0, 0.0]])
        assert_array_almost_equal(x, expected)

    def test_01_complex(self):
        # Solve
        # [ 4 -j  2  0]     [2-j]
        # [ j  4 -j  2] X = [4-j]
        # [ 2  j  4 -j]     [4+j]
        # [ 0  2  j  4]     [2+j]
        #
        ab = array([[0.0, 0.0, 2.0, 2.0],
                    [-99, -1.0j, -1.0j, -1.0j],
                    [4.0, 4.0, 4.0, 4.0]])
        b = array([2-1.0j, 4.0-1j, 4+1j, 2+1j])
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, [0.0, 1.0, 1.0, 0.0])

    def test_02_complex(self):
        # Solve
        # [ 4 -j  2  0]     [2-j 2+4j]
        # [ j  4 -j  2] X = [4-j -1-j]
        # [ 2  j  4 -j]     [4+j 4+2j]
        # [ 0  2  j  4]     [2+j j]
        #
        ab = array([[0.0, 0.0, 2.0, 2.0],
                    [-99, -1.0j, -1.0j, -1.0j],
                    [4.0, 4.0, 4.0, 4.0]])
        b = array([[2-1j, 2+4j],
                   [4.0-1j, -1-1j],
                   [4.0+1j, 4+2j],
                   [2+1j, 1j]])
        x = solveh_banded(ab, b)
        expected = array([[0.0, 1.0j],
                          [1.0, 0.0],
                          [1.0, 1.0],
                          [0.0, 0.0]])
        assert_array_almost_equal(x, expected)

    def test_tridiag_01_upper(self):
        # Solve
        # [ 4 1 0]     [1]
        # [ 1 4 1] X = [4]
        # [ 0 1 4]     [1]
        # with the RHS as a 1D array.
        ab = array([[-99, 1.0, 1.0], [4.0, 4.0, 4.0]])
        b = array([1.0, 4.0, 1.0])
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, [0.0, 1.0, 0.0])

    def test_tridiag_02_upper(self):
        # Solve
        # [ 4 1 0]     [1 4]
        # [ 1 4 1] X = [4 2]
        # [ 0 1 4]     [1 4]
        #
        ab = array([[-99, 1.0, 1.0],
                    [4.0, 4.0, 4.0]])
        b = array([[1.0, 4.0],
                   [4.0, 2.0],
                   [1.0, 4.0]])
        x = solveh_banded(ab, b)
        expected = array([[0.0, 1.0],
                          [1.0, 0.0],
                          [0.0, 1.0]])
        assert_array_almost_equal(x, expected)

    def test_tridiag_03_upper(self):
        # Solve
        # [ 4 1 0]     [1]
        # [ 1 4 1] X = [4]
        # [ 0 1 4]     [1]
        # with the RHS as a 2D array with shape (3,1).
        ab = array([[-99, 1.0, 1.0], [4.0, 4.0, 4.0]])
        b = array([1.0, 4.0, 1.0]).reshape(-1, 1)
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, array([0.0, 1.0, 0.0]).reshape(-1, 1))

    def test_tridiag_01_lower(self):
        # Solve
        # [ 4 1 0]     [1]
        # [ 1 4 1] X = [4]
        # [ 0 1 4]     [1]
        #
        ab = array([[4.0, 4.0, 4.0],
                    [1.0, 1.0, -99]])
        b = array([1.0, 4.0, 1.0])
        x = solveh_banded(ab, b, lower=True)
        assert_array_almost_equal(x, [0.0, 1.0, 0.0])

    def test_tridiag_02_lower(self):
        # Solve
        # [ 4 1 0]     [1 4]
        # [ 1 4 1] X = [4 2]
        # [ 0 1 4]     [1 4]
        #
        ab = array([[4.0, 4.0, 4.0],
                    [1.0, 1.0, -99]])
        b = array([[1.0, 4.0],
                   [4.0, 2.0],
                   [1.0, 4.0]])
        x = solveh_banded(ab, b, lower=True)
        expected = array([[0.0, 1.0],
                          [1.0, 0.0],
                          [0.0, 1.0]])
        assert_array_almost_equal(x, expected)

    def test_tridiag_01_float32(self):
        # Solve
        # [ 4 1 0]     [1]
        # [ 1 4 1] X = [4]
        # [ 0 1 4]     [1]
        #
        ab = array([[-99, 1.0, 1.0], [4.0, 4.0, 4.0]], dtype=float32)
        b = array([1.0, 4.0, 1.0], dtype=float32)
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, [0.0, 1.0, 0.0])

    def test_tridiag_02_float32(self):
        # Solve
        # [ 4 1 0]     [1 4]
        # [ 1 4 1] X = [4 2]
        # [ 0 1 4]     [1 4]
        #
        ab = array([[-99, 1.0, 1.0],
                    [4.0, 4.0, 4.0]], dtype=float32)
        b = array([[1.0, 4.0],
                   [4.0, 2.0],
                   [1.0, 4.0]], dtype=float32)
        x = solveh_banded(ab, b)
        expected = array([[0.0, 1.0],
                          [1.0, 0.0],
                          [0.0, 1.0]])
        assert_array_almost_equal(x, expected)

    def test_tridiag_01_complex(self):
        # Solve
        # [ 4 -j 0]     [ -j]
        # [ j 4 -j] X = [4-j]
        # [ 0 j  4]     [4+j]
        #
        ab = array([[-99, -1.0j, -1.0j], [4.0, 4.0, 4.0]])
        b = array([-1.0j, 4.0-1j, 4+1j])
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, [0.0, 1.0, 1.0])

    def test_tridiag_02_complex(self):
        # Solve
        # [ 4 -j 0]     [ -j    4j]
        # [ j 4 -j] X = [4-j  -1-j]
        # [ 0 j  4]     [4+j   4  ]
        #
        ab = array([[-99, -1.0j, -1.0j],
                    [4.0, 4.0, 4.0]])
        b = array([[-1j, 4.0j],
                   [4.0-1j, -1.0-1j],
                   [4.0+1j, 4.0]])
        x = solveh_banded(ab, b)
        expected = array([[0.0, 1.0j],
                          [1.0, 0.0],
                          [1.0, 1.0]])
        assert_array_almost_equal(x, expected)

    def test_check_finite(self):
        # Solve
        # [ 4 1 0]     [1]
        # [ 1 4 1] X = [4]
        # [ 0 1 4]     [1]
        # with the RHS as a 1D array.
        ab = array([[-99, 1.0, 1.0], [4.0, 4.0, 4.0]])
        b = array([1.0, 4.0, 1.0])
        x = solveh_banded(ab, b, check_finite=False)
        assert_array_almost_equal(x, [0.0, 1.0, 0.0])

    def test_bad_shapes(self):
        ab = array([[-99, 1.0, 1.0],
                    [4.0, 4.0, 4.0]])
        b = array([[1.0, 4.0],
                   [4.0, 2.0]])
        assert_raises(ValueError, solveh_banded, ab, b)
        assert_raises(ValueError, solveh_banded, ab, [1.0, 2.0])
        assert_raises(ValueError, solveh_banded, ab, [1.0])

    def test_1x1(self):
        x = solveh_banded([[1]], [[1, 2, 3]])
        assert_array_equal(x, [[1.0, 2.0, 3.0]])
        assert_equal(x.dtype, np.dtype('f8'))

    def test_native_list_arguments(self):
        # Same as test_01_upper, using python's native list.
        ab = [[0.0, 0.0, 2.0, 2.0],
              [-99, 1.0, 1.0, 1.0],
              [4.0, 4.0, 4.0, 4.0]]
        b = [1.0, 4.0, 1.0, 2.0]
        x = solveh_banded(ab, b)
        assert_array_almost_equal(x, [0.0, 1.0, 0.0, 0.0])


class TestSolve(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_20Feb04_bug(self):
        a = [[1, 1], [1.0, 0]]  # ok
        x0 = solve(a, [1, 0j])
        assert_array_almost_equal(dot(a, x0), [1, 0])

        # gives failure with clapack.zgesv(..,rowmajor=0)
        a = [[1, 1], [1.2, 0]]
        b = [1, 0j]
        x0 = solve(a, b)
        assert_array_almost_equal(dot(a, x0), [1, 0])

    def test_simple(self):
        a = [[1, 20], [-30, 4]]
        for b in ([[1, 0], [0, 1]], [1, 0],
                  [[2, 1], [-30, 4]]):
            x = solve(a, b)
            assert_array_almost_equal(dot(a, x), b)

    def test_simple_sym(self):
        a = [[2, 3], [3, 5]]
        for lower in [0, 1]:
            for b in ([[1, 0], [0, 1]], [1, 0]):
                x = solve(a, b, sym_pos=1, lower=lower)
                assert_array_almost_equal(dot(a, x), b)

    def test_simple_sym_complex(self):
        a = [[5, 2], [2, 4]]
        for b in [[1j, 0],
                  [[1j, 1j],
                   [0, 2]],
                  ]:
            x = solve(a, b, sym_pos=1)
            assert_array_almost_equal(dot(a, x), b)

    def test_simple_complex(self):
        a = array([[5, 2], [2j, 4]], 'D')
        for b in [[1j, 0],
                  [[1j, 1j],
                   [0, 2]],
                  [1, 0j],
                  array([1, 0], 'D'),
                  ]:
            x = solve(a, b)
            assert_array_almost_equal(dot(a, x), b)

    def test_nils_20Feb04(self):
        n = 2
        A = random([n, n])+random([n, n])*1j
        X = zeros((n, n), 'D')
        Ainv = inv(A)
        R = identity(n)+identity(n)*0j
        for i in arange(0, n):
            r = R[:, i]
            X[:, i] = solve(A, r)
        assert_array_almost_equal(X, Ainv)

    def test_random(self):

        n = 20
        a = random([n, n])
        for i in range(n):
            a[i, i] = 20*(.1+a[i, i])
        for i in range(4):
            b = random([n, 3])
            x = solve(a, b)
            assert_array_almost_equal(dot(a, x), b)

    def test_random_complex(self):
        n = 20
        a = random([n, n]) + 1j * random([n, n])
        for i in range(n):
            a[i, i] = 20*(.1+a[i, i])
        for i in range(2):
            b = random([n, 3])
            x = solve(a, b)
            assert_array_almost_equal(dot(a, x), b)

    def test_random_sym(self):
        n = 20
        a = random([n, n])
        for i in range(n):
            a[i, i] = abs(20*(.1+a[i, i]))
            for j in range(i):
                a[i, j] = a[j, i]
        for i in range(4):
            b = random([n])
            x = solve(a, b, sym_pos=1)
            assert_array_almost_equal(dot(a, x), b)

    def test_random_sym_complex(self):
        n = 20
        a = random([n, n])
        # XXX: with the following addition the accuracy will be very low
        a = a + 1j*random([n, n])
        for i in range(n):
            a[i, i] = abs(20*(.1+a[i, i]))
            for j in range(i):
                a[i, j] = conjugate(a[j, i])
        b = random([n])+2j*random([n])
        for i in range(2):
            x = solve(a, b, sym_pos=1)
            assert_array_almost_equal(dot(a, x), b)

    def test_check_finite(self):
        a = [[1, 20], [-30, 4]]
        for b in ([[1, 0], [0, 1]], [1, 0],
                  [[2, 1], [-30, 4]]):
            x = solve(a, b, check_finite=False)
            assert_array_almost_equal(dot(a, x), b)

    def test_scalar_a_and_1D_b(self):
        a = 1
        b = [1, 2, 3]
        x = solve(a, b)
        assert_array_almost_equal(x.ravel(), b)
        assert_(x.shape == (3,), 'Scalar_a_1D_b test returned wrong shape')

    def test_simple2(self):
        a = np.array([[1.80, 2.88, 2.05, -0.89],
                      [525.00, -295.00, -95.00, -380.00],
                      [1.58, -2.69, -2.90, -1.04],
                      [-1.11, -0.66, -0.59, 0.80]])

        b = np.array([[9.52, 18.47],
                      [2435.00, 225.00],
                      [0.77, -13.28],
                      [-6.22, -6.21]])

        x = solve(a, b)
        assert_array_almost_equal(x, np.array([[1., -1, 3, -5],
                                               [3, 2, 4, 1]]).T)

    def test_simple_complex2(self):
        a = np.array([[-1.34+2.55j, 0.28+3.17j, -6.39-2.20j, 0.72-0.92j],
                      [-1.70-14.10j, 33.10-1.50j, -1.50+13.40j, 12.90+13.80j],
                      [-3.29-2.39j, -1.91+4.42j, -0.14-1.35j, 1.72+1.35j],
                      [2.41+0.39j, -0.56+1.47j, -0.83-0.69j, -1.96+0.67j]])

        b = np.array([[26.26+51.78j, 31.32-6.70j],
                      [64.30-86.80j, 158.60-14.20j],
                      [-5.75+25.31j, -2.15+30.19j],
                      [1.16+2.57j, -2.56+7.55j]])

        x = solve(a, b)
        assert_array_almost_equal(x, np. array([[1+1.j, -1-2.j],
                                                [2-3.j, 5+1.j],
                                                [-4-5.j, -3+4.j],
                                                [6.j, 2-3.j]]))

    def test_hermitian(self):
        # An upper triangular matrix will be used for hermitian matrix a
        a = np.array([[-1.84, 0.11-0.11j, -1.78-1.18j, 3.91-1.50j],
                      [0, -4.63, -1.84+0.03j, 2.21+0.21j],
                      [0, 0, -8.87, 1.58-0.90j],
                      [0, 0, 0, -1.36]])
        b = np.array([[2.98-10.18j, 28.68-39.89j],
                      [-9.58+3.88j, -24.79-8.40j],
                      [-0.77-16.05j, 4.23-70.02j],
                      [7.79+5.48j, -35.39+18.01j]])
        res = np.array([[2.+1j, -8+6j],
                        [3.-2j, 7-2j],
                        [-1+2j, -1+5j],
                        [1.-1j, 3-4j]])
        x = solve(a, b, assume_a='her')
        assert_array_almost_equal(x, res)
        # Also conjugate a and test for lower triangular data
        x = solve(a.conj().T, b, assume_a='her', lower=True)
        assert_array_almost_equal(x, res)

    def test_pos_and_sym(self):
        A = np.arange(1, 10).reshape(3, 3)
        x = solve(np.tril(A)/9, np.ones(3), assume_a='pos')
        assert_array_almost_equal(x, [9., 1.8, 1.])
        x = solve(np.tril(A)/9, np.ones(3), assume_a='sym')
        assert_array_almost_equal(x, [9., 1.8, 1.])

    def test_singularity(self):
        a = np.array([[1, 0, 0, 0, 0, 0, 1, 0, 1],
                      [1, 1, 1, 0, 0, 0, 1, 0, 1],
                      [0, 1, 1, 0, 0, 0, 1, 0, 1],
                      [1, 0, 1, 1, 1, 1, 0, 0, 0],
                      [1, 0, 1, 1, 1, 1, 0, 0, 0],
                      [1, 0, 1, 1, 1, 1, 0, 0, 0],
                      [1, 0, 1, 1, 1, 1, 0, 0, 0],
                      [1, 1, 1, 1, 1, 1, 1, 1, 1],
                      [1, 1, 1, 1, 1, 1, 1, 1, 1]])
        b = np.arange(9)[:, None]
        assert_raises(LinAlgError, solve, a, b)

    def test_ill_condition_warning(self):
        a = np.array([[1, 1], [1+1e-16, 1-1e-16]])
        b = np.ones(2)
        with warnings.catch_warnings():
            warnings.simplefilter('error')
            assert_raises(RuntimeWarning, solve, a, b)

    def test_empty_rhs(self):
        a = np.eye(2)
        b = [[], []]
        x = solve(a, b)
        assert_(x.size == 0, 'Returned array is not empty')
        assert_(x.shape == (2, 0), 'Returned empty array shape is wrong')

    def test_multiple_rhs(self):
        a = np.eye(2)
        b = np.random.rand(2, 3, 4)
        x = solve(a, b)
        assert_array_almost_equal(x, b)

    def test_transposed_keyword(self):
        A = np.arange(9).reshape(3, 3) + 1
        x = solve(np.tril(A)/9, np.ones(3), transposed=1)
        assert_array_almost_equal(x, [1.2, 0.2, 1])
        x = solve(np.tril(A)/9, np.ones(3), transposed=0)
        assert_array_almost_equal(x, [9, -5.4, -1.2])

    def test_nonsquare_a(self):
        assert_raises(ValueError, solve, [1, 2], 1)

    def test_size_mismatch_with_1D_b(self):
        assert_array_almost_equal(solve(np.eye(3), np.ones(3)), np.ones(3))
        assert_raises(ValueError, solve, np.eye(3), np.ones(4))

    def test_assume_a_keyword(self):
        assert_raises(ValueError, solve, 1, 1, assume_a='zxcv')

    def test_all_type_size_routine_combinations(self):
        sizes = [10, 100, 1000]
        assume_as = ['gen', 'sym', 'pos', 'her']
        dtypes = [np.float32, np.float64, np.complex64, np.complex128]
        for size, assume_a, dtype in itertools.product(sizes,
                                                       assume_as,
                                                       dtypes):
            is_complex = dtype in (np.complex64, np.complex128)
            if assume_a == 'her' and not is_complex:
                continue

            err_msg = ("Failed for size: {}, assume_a: {},"
                       "dtype: {}".format(size, assume_a, dtype))

            a = np.random.randn(size, size).astype(dtype)
            b = np.random.randn(size).astype(dtype)
            if is_complex:
                a = a + (1j*np.random.randn(size, size)).astype(dtype)

            if assume_a == 'sym':  # Can still be complex but only symmetric
                a = a + a.T
            elif assume_a == 'her':  # Handle hermitian matrices here instead
                a = a + a.T.conj()
            elif assume_a == 'pos':
                a = a.conj().T.dot(a) + 0.1*np.eye(size)

            x = solve(a, b, assume_a=assume_a)
            tol = 1e-12 if dtype in (np.float64, np.complex128) else 1e-6
            assert_allclose(a.dot(x), b,
                            atol=tol * size,
                            rtol=tol * size,
                            err_msg=err_msg)


class TestSolveTriangular(TestCase):

    def test_simple(self):
        """
        solve_triangular on a simple 2x2 matrix.
        """
        A = array([[1, 0], [1, 2]])
        b = [1, 1]
        sol = solve_triangular(A, b, lower=True)
        assert_array_almost_equal(sol, [1, 0])

        # check that it works also for non-contiguous matrices
        sol = solve_triangular(A.T, b, lower=False)
        assert_array_almost_equal(sol, [.5, .5])

        # and that it gives the same result as trans=1
        sol = solve_triangular(A, b, lower=True, trans=1)
        assert_array_almost_equal(sol, [.5, .5])

        b = identity(2)
        sol = solve_triangular(A, b, lower=True, trans=1)
        assert_array_almost_equal(sol, [[1., -.5], [0, 0.5]])

    def test_simple_complex(self):
        """
        solve_triangular on a simple 2x2 complex matrix
        """
        A = array([[1+1j, 0], [1j, 2]])
        b = identity(2)
        sol = solve_triangular(A, b, lower=True, trans=1)
        assert_array_almost_equal(sol, [[.5-.5j, -.25-.25j], [0, 0.5]])

    def test_check_finite(self):
        """
        solve_triangular on a simple 2x2 matrix.
        """
        A = array([[1, 0], [1, 2]])
        b = [1, 1]
        sol = solve_triangular(A, b, lower=True, check_finite=False)
        assert_array_almost_equal(sol, [1, 0])


class TestInv(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_simple(self):
        a = [[1, 2], [3, 4]]
        a_inv = inv(a)
        assert_array_almost_equal(dot(a, a_inv), np.eye(2))
        a = [[1, 2, 3], [4, 5, 6], [7, 8, 10]]
        a_inv = inv(a)
        assert_array_almost_equal(dot(a, a_inv), np.eye(3))

    def test_random(self):
        n = 20
        for i in range(4):
            a = random([n, n])
            for i in range(n):
                a[i, i] = 20*(.1+a[i, i])
            a_inv = inv(a)
            assert_array_almost_equal(dot(a, a_inv),
                                      identity(n))

    def test_simple_complex(self):
        a = [[1, 2], [3, 4j]]
        a_inv = inv(a)
        assert_array_almost_equal(dot(a, a_inv), [[1, 0], [0, 1]])

    def test_random_complex(self):
        n = 20
        for i in range(4):
            a = random([n, n])+2j*random([n, n])
            for i in range(n):
                a[i, i] = 20*(.1+a[i, i])
            a_inv = inv(a)
            assert_array_almost_equal(dot(a, a_inv),
                                      identity(n))

    def test_check_finite(self):
        a = [[1, 2], [3, 4]]
        a_inv = inv(a, check_finite=False)
        assert_array_almost_equal(dot(a, a_inv), [[1, 0], [0, 1]])


class TestDet(TestCase):
    def setUp(self):
        np.random.seed(1234)

    def test_simple(self):
        a = [[1, 2], [3, 4]]
        a_det = det(a)
        assert_almost_equal(a_det, -2.0)

    def test_simple_complex(self):
        a = [[1, 2], [3, 4j]]
        a_det = det(a)
        assert_almost_equal(a_det, -6+4j)

    def test_random(self):
        basic_det = linalg.det
        n = 20
        for i in range(4):
            a = random([n, n])
            d1 = det(a)
            d2 = basic_det(a)
            assert_almost_equal(d1, d2)

    def test_random_complex(self):
        basic_det = linalg.det
        n = 20
        for i in range(4):
            a = random([n, n]) + 2j*random([n, n])
            d1 = det(a)
            d2 = basic_det(a)
            assert_allclose(d1, d2, rtol=1e-13)

    def test_check_finite(self):
        a = [[1, 2], [3, 4]]
        a_det = det(a, check_finite=False)
        assert_almost_equal(a_det, -2.0)


def direct_lstsq(a, b, cmplx=0):
    at = transpose(a)
    if cmplx:
        at = conjugate(at)
    a1 = dot(at, a)
    b1 = dot(at, b)
    return solve(a1, b1)


class TestLstsq(TestCase):

    lapack_drivers = ('gelsd', 'gelss', 'gelsy', None)

    def setUp(self):
        np.random.seed(1234)

    def test_simple_exact(self):
        for dtype in REAL_DTYPES:
            a = np.array([[1, 20], [-30, 4]], dtype=dtype)
            for lapack_driver in TestLstsq.lapack_drivers:
                    for overwrite in (True, False):
                        for bt in (((1, 0), (0, 1)), (1, 0),
                                   ((2, 1), (-30, 4))):
                            # Store values in case they are overwritten
                            # later
                            a1 = a.copy()
                            b = np.array(bt, dtype=dtype)
                            b1 = b.copy()
                            try:
                                out = lstsq(a1, b1,
                                            lapack_driver=lapack_driver,
                                            overwrite_a=overwrite,
                                            overwrite_b=overwrite)
                            except LstsqLapackError:
                                if lapack_driver is None:
                                    mesg = ('LstsqLapackError raised with '
                                            'lapack_driver being None.')
                                    raise AssertionError(mesg)
                                else:
                                    # can't proceed, skip to the next iteration
                                    continue

                            x = out[0]
                            r = out[2]
                            assert_(r == 2,
                                    'expected efficient rank 2, got %s' % r)
                            assert_allclose(
                                          dot(a, x), b,
                                          atol=25 * _eps_cast(a1.dtype),
                                          rtol=25 * _eps_cast(a1.dtype),
                                          err_msg="driver: %s" % lapack_driver)

    def test_simple_overdet(self):
        for dtype in REAL_DTYPES:
            a = np.array([[1, 2], [4, 5], [3, 4]], dtype=dtype)
            b = np.array([1, 2, 3], dtype=dtype)
            for lapack_driver in TestLstsq.lapack_drivers:
                for overwrite in (True, False):
                    # Store values in case they are overwritten later
                    a1 = a.copy()
                    b1 = b.copy()
                    try:
                        out = lstsq(a1, b1, lapack_driver=lapack_driver,
                                    overwrite_a=overwrite,
                                    overwrite_b=overwrite)
                    except LstsqLapackError:
                        if lapack_driver is None:
                            mesg = ('LstsqLapackError raised with '
                                    'lapack_driver being None.')
                            raise AssertionError(mesg)
                        else:
                            # can't proceed, skip to the next iteration
                            continue

                    x = out[0]
                    if lapack_driver == 'gelsy':
                        residuals = np.sum((b - a.dot(x))**2)
                    else:
                        residuals = out[1]
                    r = out[2]
                    assert_(r == 2, 'expected efficient rank 2, got %s' % r)
                    assert_allclose(abs((dot(a, x) - b)**2).sum(axis=0),
                                    residuals,
                                    rtol=25 * _eps_cast(a1.dtype),
                                    atol=25 * _eps_cast(a1.dtype),
                                    err_msg="driver: %s" % lapack_driver)
                    assert_allclose(x, (-0.428571428571429, 0.85714285714285),
                                    rtol=25 * _eps_cast(a1.dtype),
                                    atol=25 * _eps_cast(a1.dtype),
                                    err_msg="driver: %s" % lapack_driver)

    def test_simple_overdet_complex(self):
        for dtype in COMPLEX_DTYPES:
            a = np.array([[1+2j, 2], [4, 5], [3, 4]], dtype=dtype)
            b = np.array([1, 2+4j, 3], dtype=dtype)
            for lapack_driver in TestLstsq.lapack_drivers:
                for overwrite in (True, False):
                    # Store values in case they are overwritten later
                    a1 = a.copy()
                    b1 = b.copy()
                    try:
                        out = lstsq(a1, b1, lapack_driver=lapack_driver,
                                    overwrite_a=overwrite,
                                    overwrite_b=overwrite)
                    except LstsqLapackError:
                        if lapack_driver is None:
                            mesg = ('LstsqLapackError raised with '
                                    'lapack_driver being None.')
                            raise AssertionError(mesg)
                        else:
                            # can't proceed, skip to the next iteration
                            continue

                    x = out[0]
                    if lapack_driver == 'gelsy':
                        res = b - a.dot(x)
                        residuals = np.sum(res * res.conj())
                    else:
                        residuals = out[1]
                    r = out[2]
                    assert_(r == 2, 'expected efficient rank 2, got %s' % r)
                    assert_allclose(abs((dot(a, x) - b)**2).sum(axis=0),
                                    residuals,
                                    rtol=25 * _eps_cast(a1.dtype),
                                    atol=25 * _eps_cast(a1.dtype),
                                    err_msg="driver: %s" % lapack_driver)
                    assert_allclose(
                                x, (-0.4831460674157303 + 0.258426966292135j,
                                    0.921348314606741 + 0.292134831460674j),
                                rtol=25 * _eps_cast(a1.dtype),
                                atol=25 * _eps_cast(a1.dtype),
                                err_msg="driver: %s" % lapack_driver)

    def test_simple_underdet(self):
        for dtype in REAL_DTYPES:
            a = np.array([[1, 2, 3], [4, 5, 6]], dtype=dtype)
            b = np.array([1, 2], dtype=dtype)
            for lapack_driver in TestLstsq.lapack_drivers:
                for overwrite in (True, False):
                    # Store values in case they are overwritten later
                    a1 = a.copy()
                    b1 = b.copy()
                    try:
                        out = lstsq(a1, b1, lapack_driver=lapack_driver,
                                    overwrite_a=overwrite,
                                    overwrite_b=overwrite)
                    except LstsqLapackError:
                        if lapack_driver is None:
                            mesg = ('LstsqLapackError raised with '
                                    'lapack_driver being None.')
                            raise AssertionError(mesg)
                        else:
                            # can't proceed, skip to the next iteration
                            continue

                    x = out[0]
                    r = out[2]
                    assert_(r == 2, 'expected efficient rank 2, got %s' % r)
                    assert_allclose(x, (-0.055555555555555, 0.111111111111111,
                                        0.277777777777777),
                                    rtol=25 * _eps_cast(a1.dtype),
                                    atol=25 * _eps_cast(a1.dtype),
                                    err_msg="driver: %s" % lapack_driver)

    def test_random_exact(self):
        for dtype in REAL_DTYPES:
            for n in (20, 200):
                for lapack_driver in TestLstsq.lapack_drivers:
                    for overwrite in (True, False):
                        a = np.asarray(random([n, n]), dtype=dtype)
                        for i in range(n):
                            a[i, i] = 20 * (0.1 + a[i, i])
                        for i in range(4):
                            b = np.asarray(random([n, 3]), dtype=dtype)
                            # Store values in case they are overwritten later
                            a1 = a.copy()
                            b1 = b.copy()
                            try:
                                out = lstsq(a1, b1,
                                            lapack_driver=lapack_driver,
                                            overwrite_a=overwrite,
                                            overwrite_b=overwrite)
                            except LstsqLapackError:
                                if lapack_driver is None:
                                    mesg = ('LstsqLapackError raised with '
                                            'lapack_driver being None.')
                                    raise AssertionError(mesg)
                                else:
                                    # can't proceed, skip to the next iteration
                                    continue
                            x = out[0]
                            r = out[2]
                            assert_(r == n, 'expected efficient rank %s, '
                                    'got %s' % (n, r))
                            if dtype is np.float32:
                                assert_allclose(
                                          dot(a, x), b,
                                          rtol=500 * _eps_cast(a1.dtype),
                                          atol=500 * _eps_cast(a1.dtype),
                                          err_msg="driver: %s" % lapack_driver)
                            else:
                                assert_allclose(
                                          dot(a, x), b,
                                          rtol=1000 * _eps_cast(a1.dtype),
                                          atol=1000 * _eps_cast(a1.dtype),
                                          err_msg="driver: %s" % lapack_driver)

    def test_random_complex_exact(self):
        for dtype in COMPLEX_DTYPES:
            for n in (20, 200):
                for lapack_driver in TestLstsq.lapack_drivers:
                    for overwrite in (True, False):
                        a = np.asarray(random([n, n]) + 1j*random([n, n]),
                                       dtype=dtype)
                        for i in range(n):
                            a[i, i] = 20 * (0.1 + a[i, i])
                        for i in range(2):
                            b = np.asarray(random([n, 3]), dtype=dtype)
                            # Store values in case they are overwritten later
                            a1 = a.copy()
                            b1 = b.copy()
                            out = lstsq(a1, b1, lapack_driver=lapack_driver,
                                        overwrite_a=overwrite,
                                        overwrite_b=overwrite)
                            x = out[0]
                            r = out[2]
                            assert_(r == n, 'expected efficient rank %s, '
                                    'got %s' % (n, r))
                            if dtype is np.complex64:
                                assert_allclose(
                                          dot(a, x), b,
                                          rtol=400 * _eps_cast(a1.dtype),
                                          atol=400 * _eps_cast(a1.dtype),
                                          err_msg="driver: %s" % lapack_driver)
                            else:
                                assert_allclose(
                                          dot(a, x), b,
                                          rtol=1000 * _eps_cast(a1.dtype),
                                          atol=1000 * _eps_cast(a1.dtype),
                                          err_msg="driver: %s" % lapack_driver)

    def test_random_overdet(self):
        for dtype in REAL_DTYPES:
            for (n, m) in ((20, 15), (200, 2)):
                for lapack_driver in TestLstsq.lapack_drivers:
                    for overwrite in (True, False):
                        a = np.asarray(random([n, m]), dtype=dtype)
                        for i in range(m):
                            a[i, i] = 20 * (0.1 + a[i, i])
                        for i in range(4):
                            b = np.asarray(random([n, 3]), dtype=dtype)
                            # Store values in case they are overwritten later
                            a1 = a.copy()
                            b1 = b.copy()
                            try:
                                out = lstsq(a1, b1,
                                            lapack_driver=lapack_driver,
                                            overwrite_a=overwrite,
                                            overwrite_b=overwrite)
                            except LstsqLapackError:
                                if lapack_driver is None:
                                    mesg = ('LstsqLapackError raised with '
                                            'lapack_driver being None.')
                                    raise AssertionError(mesg)
                                else:
                                    # can't proceed, skip to the next iteration
                                    continue

                            x = out[0]
                            r = out[2]
                            assert_(r == m, 'expected efficient rank %s, '
                                    'got %s' % (m, r))
                            assert_allclose(
                                          x, direct_lstsq(a, b, cmplx=0),
                                          rtol=25 * _eps_cast(a1.dtype),
                                          atol=25 * _eps_cast(a1.dtype),
                                          err_msg="driver: %s" % lapack_driver)

    def test_random_complex_overdet(self):
        for dtype in COMPLEX_DTYPES:
            for (n, m) in ((20, 15), (200, 2)):
                    for lapack_driver in TestLstsq.lapack_drivers:
                        for overwrite in (True, False):
                            a = np.asarray(random([n, m]) + 1j*random([n, m]),
                                           dtype=dtype)
                            for i in range(m):
                                a[i, i] = 20 * (0.1 + a[i, i])
                            for i in range(2):
                                b = np.asarray(random([n, 3]), dtype=dtype)
                                # Store values in case they are overwritten
                                # later
                                a1 = a.copy()
                                b1 = b.copy()
                                out = lstsq(a1, b1,
                                            lapack_driver=lapack_driver,
                                            overwrite_a=overwrite,
                                            overwrite_b=overwrite)
                                x = out[0]
                                r = out[2]
                                assert_(r == m, 'expected efficient rank %s, '
                                        'got %s' % (m, r))
                                assert_allclose(
                                          x, direct_lstsq(a, b, cmplx=1),
                                          rtol=25 * _eps_cast(a1.dtype),
                                          atol=25 * _eps_cast(a1.dtype),
                                          err_msg="driver: %s" % lapack_driver)

    def test_check_finite(self):
        for dtype in REAL_DTYPES:
            a = np.array(((1, 20), (-30, 4)), dtype=dtype)
            for bt in (((1, 0), (0, 1)), (1, 0),
                       ((2, 1), (-30, 4))):
                for lapack_driver in TestLstsq.lapack_drivers:
                        for overwrite in (True, False):
                            for check_finite in (True, False):
                                b = np.array(bt, dtype=dtype)
                                # Store values in case they are overwritten
                                # later
                                a1 = a.copy()
                                b1 = b.copy()
                                try:
                                    out = lstsq(a1, b1,
                                                lapack_driver=lapack_driver,
                                                check_finite=check_finite,
                                                overwrite_a=overwrite,
                                                overwrite_b=overwrite)
                                except LstsqLapackError:
                                    if lapack_driver is None:
                                        mesg = ('LstsqLapackError raised with '
                                                'lapack_driver being None.')
                                        raise AssertionError(mesg)
                                    else:
                                        # can't proceed,
                                        # skip to the next iteration
                                        continue
                                x = out[0]
                                r = out[2]
                                assert_(r == 2, 'expected efficient rank 2, '
                                                'got %s' % r)
                                assert_allclose(
                                          dot(a, x), b,
                                          rtol=25 * _eps_cast(a.dtype),
                                          atol=25 * _eps_cast(a.dtype),
                                          err_msg="driver: %s" % lapack_driver)


class TestPinv(TestCase):

    def test_simple_real(self):
        a = array([[1, 2, 3], [4, 5, 6], [7, 8, 10]], dtype=float)
        a_pinv = pinv(a)
        assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
        a_pinv = pinv2(a)
        assert_array_almost_equal(dot(a, a_pinv), np.eye(3))

    def test_simple_complex(self):
        a = (array([[1, 2, 3], [4, 5, 6], [7, 8, 10]],
             dtype=float) + 1j * array([[10, 8, 7], [6, 5, 4], [3, 2, 1]],
                                       dtype=float))
        a_pinv = pinv(a)
        assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
        a_pinv = pinv2(a)
        assert_array_almost_equal(dot(a, a_pinv), np.eye(3))

    def test_simple_singular(self):
        a = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=float)
        a_pinv = pinv(a)
        a_pinv2 = pinv2(a)
        assert_array_almost_equal(a_pinv, a_pinv2)

    def test_simple_cols(self):
        a = array([[1, 2, 3], [4, 5, 6]], dtype=float)
        a_pinv = pinv(a)
        a_pinv2 = pinv2(a)
        assert_array_almost_equal(a_pinv, a_pinv2)

    def test_simple_rows(self):
        a = array([[1, 2], [3, 4], [5, 6]], dtype=float)
        a_pinv = pinv(a)
        a_pinv2 = pinv2(a)
        assert_array_almost_equal(a_pinv, a_pinv2)

    def test_check_finite(self):
        a = array([[1, 2, 3], [4, 5, 6.], [7, 8, 10]])
        a_pinv = pinv(a, check_finite=False)
        assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
        a_pinv = pinv2(a, check_finite=False)
        assert_array_almost_equal(dot(a, a_pinv), np.eye(3))

    def test_native_list_argument(self):
        a = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
        a_pinv = pinv(a)
        a_pinv2 = pinv2(a)
        assert_array_almost_equal(a_pinv, a_pinv2)


class TestPinvSymmetric(TestCase):

    def test_simple_real(self):
        a = array([[1, 2, 3], [4, 5, 6], [7, 8, 10]], dtype=float)
        a = np.dot(a, a.T)
        a_pinv = pinvh(a)
        assert_array_almost_equal(np.dot(a, a_pinv), np.eye(3))

    def test_nonpositive(self):
        a = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=float)
        a = np.dot(a, a.T)
        u, s, vt = np.linalg.svd(a)
        s[0] *= -1
        a = np.dot(u * s, vt)  # a is now symmetric non-positive and singular
        a_pinv = pinv2(a)
        a_pinvh = pinvh(a)
        assert_array_almost_equal(a_pinv, a_pinvh)

    def test_simple_complex(self):
        a = (array([[1, 2, 3], [4, 5, 6], [7, 8, 10]],
             dtype=float) + 1j * array([[10, 8, 7], [6, 5, 4], [3, 2, 1]],
                                       dtype=float))
        a = np.dot(a, a.conj().T)
        a_pinv = pinvh(a)
        assert_array_almost_equal(np.dot(a, a_pinv), np.eye(3))

    def test_native_list_argument(self):
        a = array([[1, 2, 3], [4, 5, 6], [7, 8, 10]], dtype=float)
        a = np.dot(a, a.T)
        a_pinv = pinvh(a.tolist())
        assert_array_almost_equal(np.dot(a, a_pinv), np.eye(3))


class TestVectorNorms(object):

    def test_types(self):
        for dtype in np.typecodes['AllFloat']:
            x = np.array([1, 2, 3], dtype=dtype)
            tol = max(1e-15, np.finfo(dtype).eps.real * 20)
            assert_allclose(norm(x), np.sqrt(14), rtol=tol)
            assert_allclose(norm(x, 2), np.sqrt(14), rtol=tol)

        for dtype in np.typecodes['Complex']:
            x = np.array([1j, 2j, 3j], dtype=dtype)
            tol = max(1e-15, np.finfo(dtype).eps.real * 20)
            assert_allclose(norm(x), np.sqrt(14), rtol=tol)
            assert_allclose(norm(x, 2), np.sqrt(14), rtol=tol)

    def test_overflow(self):
        # unlike numpy's norm, this one is
        # safer on overflow
        a = array([1e20], dtype=float32)
        assert_almost_equal(norm(a), a)

    def test_stable(self):
        # more stable than numpy's norm
        a = array([1e4] + [1]*10000, dtype=float32)
        try:
            # snrm in double precision; we obtain the same as for float64
            # -- large atol needed due to varying blas implementations
            assert_allclose(norm(a) - 1e4, 0.5, atol=1e-2)
        except AssertionError:
            # snrm implemented in single precision, == np.linalg.norm result
            msg = ": Result should equal either 0.0 or 0.5 (depending on " \
                  "implementation of snrm2)."
            assert_almost_equal(norm(a) - 1e4, 0.0, err_msg=msg)

    def test_zero_norm(self):
        assert_equal(norm([1, 0, 3], 0), 2)
        assert_equal(norm([1, 2, 3], 0), 3)

    def test_axis_kwd(self):
        a = np.array([[[2, 1], [3, 4]]] * 2, 'd')
        assert_allclose(norm(a, axis=1), [[3.60555128, 4.12310563]] * 2)
        assert_allclose(norm(a, 1, axis=1), [[5.] * 2] * 2)

    @dec.skipif(NumpyVersion(np.__version__) < '1.10.0')
    def test_keepdims_kwd(self):
        a = np.array([[[2, 1], [3, 4]]] * 2, 'd')
        b = norm(a, axis=1, keepdims=True)
        assert_allclose(b, [[[3.60555128, 4.12310563]]] * 2)
        assert_(b.shape == (2, 1, 2))
        assert_allclose(norm(a, 1, axis=2, keepdims=True), [[[3.], [7.]]] * 2)


class TestMatrixNorms(object):

    def test_matrix_norms(self):
        # Not all of these are matrix norms in the most technical sense.
        np.random.seed(1234)
        for n, m in (1, 1), (1, 3), (3, 1), (4, 4), (4, 5), (5, 4):
            for t in np.single, np.double, np.csingle, np.cdouble, np.int64:
                A = 10 * np.random.randn(n, m).astype(t)
                if np.issubdtype(A.dtype, np.complexfloating):
                    A = (A + 10j * np.random.randn(n, m)).astype(t)
                    t_high = np.cdouble
                else:
                    t_high = np.double
                for order in (None, 'fro', 1, -1, 2, -2, np.inf, -np.inf):
                    actual = norm(A, ord=order)
                    desired = np.linalg.norm(A, ord=order)
                    # SciPy may return higher precision matrix norms.
                    # This is a consequence of using LAPACK.
                    if not np.allclose(actual, desired):
                        desired = np.linalg.norm(A.astype(t_high), ord=order)
                        assert_allclose(actual, desired)

    def test_axis_kwd(self):
        a = np.array([[[2, 1], [3, 4]]] * 2, 'd')
        b = norm(a, ord=np.inf, axis=(1, 0))
        c = norm(np.swapaxes(a, 0, 1), ord=np.inf, axis=(0, 1))
        d = norm(a, ord=1, axis=(0, 1))
        assert_allclose(b, c)
        assert_allclose(c, d)
        assert_allclose(b, d)
        assert_(b.shape == c.shape == d.shape)
        b = norm(a, ord=1, axis=(1, 0))
        c = norm(np.swapaxes(a, 0, 1), ord=1, axis=(0, 1))
        d = norm(a, ord=np.inf, axis=(0, 1))
        assert_allclose(b, c)
        assert_allclose(c, d)
        assert_allclose(b, d)
        assert_(b.shape == c.shape == d.shape)

    @dec.skipif(NumpyVersion(np.__version__) < '1.10.0')
    def test_keepdims_kwd(self):
        a = np.arange(120, dtype='d').reshape(2, 3, 4, 5)
        b = norm(a, ord=np.inf, axis=(1, 0), keepdims=True)
        c = norm(a, ord=1, axis=(0, 1), keepdims=True)
        assert_allclose(b, c)
        assert_(b.shape == c.shape)


class TestOverwrite(object):
    def test_solve(self):
        assert_no_overwrite(solve, [(3, 3), (3,)])

    def test_solve_triangular(self):
        assert_no_overwrite(solve_triangular, [(3, 3), (3,)])

    def test_solve_banded(self):
        assert_no_overwrite(lambda ab, b: solve_banded((2, 1), ab, b),
                            [(4, 6), (6,)])

    def test_solveh_banded(self):
        assert_no_overwrite(solveh_banded, [(2, 6), (6,)])

    def test_inv(self):
        assert_no_overwrite(inv, [(3, 3)])

    def test_det(self):
        assert_no_overwrite(det, [(3, 3)])

    def test_lstsq(self):
        assert_no_overwrite(lstsq, [(3, 2), (3,)])

    def test_pinv(self):
        assert_no_overwrite(pinv, [(3, 3)])

    def test_pinv2(self):
        assert_no_overwrite(pinv2, [(3, 3)])

    def test_pinvh(self):
        assert_no_overwrite(pinvh, [(3, 3)])


class TestSolveCirculant(TestCase):

    def test_basic1(self):
        c = np.array([1, 2, 3, 5])
        b = np.array([1, -1, 1, 0])
        x = solve_circulant(c, b)
        y = solve(circulant(c), b)
        assert_allclose(x, y)

    def test_basic2(self):
        # b is a 2-d matrix.
        c = np.array([1, 2, -3, -5])
        b = np.arange(12).reshape(4, 3)
        x = solve_circulant(c, b)
        y = solve(circulant(c), b)
        assert_allclose(x, y)

    def test_basic3(self):
        # b is a 3-d matrix.
        c = np.array([1, 2, -3, -5])
        b = np.arange(24).reshape(4, 3, 2)
        x = solve_circulant(c, b)
        y = solve(circulant(c), b)
        assert_allclose(x, y)

    def test_complex(self):
        # Complex b and c
        c = np.array([1+2j, -3, 4j, 5])
        b = np.arange(8).reshape(4, 2) + 0.5j
        x = solve_circulant(c, b)
        y = solve(circulant(c), b)
        assert_allclose(x, y)

    def test_random_b_and_c(self):
        # Random b and c
        np.random.seed(54321)
        c = np.random.randn(50)
        b = np.random.randn(50)
        x = solve_circulant(c, b)
        y = solve(circulant(c), b)
        assert_allclose(x, y)

    def test_singular(self):
        # c gives a singular circulant matrix.
        c = np.array([1, 1, 0, 0])
        b = np.array([1, 2, 3, 4])
        x = solve_circulant(c, b, singular='lstsq')
        y, res, rnk, s = lstsq(circulant(c), b)
        assert_allclose(x, y)
        assert_raises(LinAlgError, solve_circulant, x, y)

    def test_axis_args(self):
        # Test use of caxis, baxis and outaxis.

        # c has shape (2, 1, 4)
        c = np.array([[[-1, 2.5, 3, 3.5]], [[1, 6, 6, 6.5]]])

        # b has shape (3, 4)
        b = np.array([[0, 0, 1, 1], [1, 1, 0, 0], [1, -1, 0, 0]])

        x = solve_circulant(c, b, baxis=1)
        assert_equal(x.shape, (4, 2, 3))
        expected = np.empty_like(x)
        expected[:, 0, :] = solve(circulant(c[0]), b.T)
        expected[:, 1, :] = solve(circulant(c[1]), b.T)
        assert_allclose(x, expected)

        x = solve_circulant(c, b, baxis=1, outaxis=-1)
        assert_equal(x.shape, (2, 3, 4))
        assert_allclose(np.rollaxis(x, -1), expected)

        # np.swapaxes(c, 1, 2) has shape (2, 4, 1); b.T has shape (4, 3).
        x = solve_circulant(np.swapaxes(c, 1, 2), b.T, caxis=1)
        assert_equal(x.shape, (4, 2, 3))
        assert_allclose(x, expected)

    def test_native_list_arguments(self):
        # Same as test_basic1 using python's native list.
        c = [1, 2, 3, 5]
        b = [1, -1, 1, 0]
        x = solve_circulant(c, b)
        y = solve(circulant(c), b)
        assert_allclose(x, y)


class TestMatrix_Balance(TestCase):

    def test_string_arg(self):
        assert_raises(ValueError, matrix_balance, 'Some string for fail')

    def test_infnan_arg(self):
        assert_raises(ValueError, matrix_balance,
                      np.array([[1, 2], [3, np.inf]]))
        assert_raises(ValueError, matrix_balance,
                      np.array([[1, 2], [3, np.nan]]))

    def test_scaling(self):
        _, y = matrix_balance(np.array([[1000, 1], [1000, 0]]))
        # Pre/post LAPACK 3.5.0 gives the same result up to an offset
        # since in each case col norm is x1000 greater and
        # 1000 / 32 ~= 1 * 32 hence balanced with 2 ** 5.
        assert_allclose(int(np.diff(np.log2(np.diag(y)))), 5)

    def test_scaling_order(self):
        A = np.array([[1, 0, 1e-4], [1, 1, 1e-2], [1e4, 1e2, 1]])
        x, y = matrix_balance(A)
        assert_allclose(solve(y, A).dot(y), x)

    def test_separate(self):
        _, (y, z) = matrix_balance(np.array([[1000, 1], [1000, 0]]),
                                   separate=1)
        assert_equal(int(np.diff(np.log2(y))), 5)
        assert_allclose(z, np.arange(2))

    def test_permutation(self):
        A = block_diag(np.ones((2, 2)), np.tril(np.ones((2, 2))),
                       np.ones((3, 3)))
        x, (y, z) = matrix_balance(A, separate=1)
        assert_allclose(y, np.ones_like(y))
        assert_allclose(z, np.array([0, 1, 6, 5, 4, 3, 2]))

    def test_perm_and_scaling(self):
        # Matrix with its diagonal removed
        cases = (  # Case 0
                 np.array([[0., 0., 0., 0., 0.000002],
                           [0., 0., 0., 0., 0.],
                           [2., 2., 0., 0., 0.],
                           [2., 2., 0., 0., 0.],
                           [0., 0., 0.000002, 0., 0.]]),
                 #  Case 1 user reported GH-7258
                 np.array([[-0.5, 0., 0., 0.],
                           [0., -1., 0., 0.],
                           [1., 0., -0.5, 0.],
                           [0., 1., 0., -1.]]),
                 #  Case 2 user reported GH-7258
                 np.array([[-3., 0., 1., 0.],
                           [-1., -1., -0., 1.],
                           [-3., -0., -0., 0.],
                           [-1., -0., 1., -1.]])
                 )

        for A in cases:
            x, y = matrix_balance(A)
            x, (s, p) = matrix_balance(A, separate=1)
            ip = np.empty_like(p)
            ip[p] = np.arange(A.shape[0])
            assert_allclose(y, np.diag(s)[ip, :])
            assert_allclose(solve(y, A).dot(y), x)


if __name__ == "__main__":
    run_module_suite()
