Exemplo n.º 1
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def test_solve_qps():
    for i0 in xrange(100):
        with numpy_seed(i0):
            qps = QPSolver(*get_random_problem())
        for i1 in xrange(10):
            if i1 == 0:
                free = np.ones(qps.nx, dtype=bool)
            else:
                with numpy_seed(i0*100 + i1):
                    free = get_random_free(qps)
            x = qps.solve(free)
            qps.check_feasible(x, free)
Exemplo n.º 2
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def test_esp_cost_solve():
    for irep in xrange(nrep):
        with numpy_seed(irep):
            A = np.random.uniform(-1, 1, (11, 11))
            B = np.random.uniform(-1, 1, 11)
            C = np.random.uniform(-1, 1, ())
        A = np.dot(A, A.T)
        cost = ESPCost(A, B, C, 10)

        # test without constraint
        x = cost.solve()
        assert abs(cost.gradient(x)).max() < 1e-8

        # test with constraint 0.0
        x = cost.solve(qtot=0.0)
        charges = x[:10]
        assert abs(charges.sum()) < 1e-8
        gradient = cost.gradient(x)
        assert abs(gradient[:10] - gradient[:10].mean()).max() < 1e-9
        assert abs(gradient[10:]).max() < 1e-9

        # test with constraint 1.0
        x = cost.solve(qtot=1.0)
        charges = x[:10]
        assert abs(charges.sum()-1) < 1e-9
        gradient = cost.gradient(x)
        assert abs(gradient[:10] - gradient[:10].mean()).max() < 1e-9
        assert abs(gradient[10:]).max() < 1e-9
Exemplo n.º 3
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def translate_random_rvecs(coordinates, rvecs, seed):
    """Translate nuclei by a random integer linear combination of cell vectors."""
    result = coordinates.copy()
    with numpy_seed(seed):
        for j in xrange(len(coordinates)):
            result[j] += np.dot(np.random.randint(-3, 4, 3), rvecs)
    return result
Exemplo n.º 4
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def test_esp_cost_solve_regularized():
    for irep in xrange(nrep):
        with numpy_seed(irep):
            A = np.random.uniform(-1, 1, (11, 11))
            B = np.random.uniform(-1, 1, 11)
            C = np.random.uniform(-1, 1, ())
        A = np.dot(A, A.T)
        cost = ESPCost(A, B, C, 10)

        # test without constraint
        ridge = 1e-6
        x = cost.solve(ridge=ridge)
        l = ridge*np.diag(cost._A)[:cost.natom].mean()
        ls = np.ones(cost._B.shape)*l
        ls[-1] = 0
        assert abs(cost.gradient(x) + 2*ls*x).max() < 1e-9

        # test with constraint 0.0
        x = cost.solve(qtot=0.0, ridge=ridge)
        charges = x[:10]
        assert abs(charges.sum()) < 1e-9
        gradient = cost.gradient(x) + 2*ls*x
        assert abs(gradient[:10] - gradient[:10].mean()).max() < 1e-10
        assert abs(gradient[10:]).max() < 1e-10

        # test with constraint 1.0
        x = cost.solve(qtot=1.0, ridge=ridge)
        charges = x[:10]
        assert abs(charges.sum()-1) < 1e-9
        gradient = cost.gradient(x) + 2*ls*x
        assert abs(gradient[:10] - gradient[:10].mean()).max() < 1e-10
        assert abs(gradient[10:]).max() < 1e-10
Exemplo n.º 5
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def test_value_charges2():
    for irep in xrange(nrep):
        cost = get_random_esp_cost_cube3d(irep)
        for ix in xrange(nrep):
            with numpy_seed(irep*10 + ix):
                x = np.random.normal(0, 1, len(cost._A))
            x -= x.sum()
            assert cost.value(x) >= cost.value_charges(x[:-1])
Exemplo n.º 6
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def test_orbitals_from_fock():
    with numpy_seed(1):
        a = np.random.normal(0, 1, (5, 5))
        fock = a+a.T
        a = np.random.normal(0, 1, (5, 5))
        olp = np.dot(a, a.T)
        orb = Orbitals(5)
        orb.from_fock(fock, olp)
        assert orb.error_eigen(fock, olp) < 1e-5
Exemplo n.º 7
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def get_random_esp_cost_cube0d_args(seed=1):
    """Generate random cube data for an aperiodic system."""
    shape = np.array([5, 5, 5])
    pbc = np.array([0, 0, 0])
    with numpy_seed(seed):
        coordinates = np.random.normal(0, 1, (5, 3))
        origin = np.random.uniform(-3, 3, 3)
        grid_rvecs = np.diag(np.random.uniform(2.0, 3.0, 3))
        grid_rvecs += np.random.uniform(-0.1, 0.1, (3, 3))
        vref = np.random.normal(0, 1, shape)
        weights = np.random.uniform(0, 1, shape)
    return coordinates, origin, grid_rvecs, shape, pbc, vref, weights
Exemplo n.º 8
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def test_orbitals_error_eigen():
    with numpy_seed(1):
        orb = Orbitals(5)
        a = np.random.normal(0, 1, (5, 5))
        fock = a+a.T
        evals, evecs = np.linalg.eigh(fock)
        orb.coeffs[:] = evecs
        orb.energies[:] = evals
        olp = np.identity(5)
        assert orb.error_eigen(fock, olp) < 1e-10
        orb.coeffs[:] += np.random.normal(0, 1e-3, (5, 5))
        assert orb.error_eigen(fock, olp) > 1e-10
Exemplo n.º 9
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def test_esp_cost_cube3d_gradient():
    for irep in xrange(nrep):
        # Some parameters
        coordinates, origin, grid_rvecs, shape, pbc, vref, weights = \
            get_random_esp_cost_cube3d_args(irep)
        grid = UniformGrid(origin, grid_rvecs, shape, pbc)
        cost = ESPCost.from_grid_data(coordinates, grid, vref, weights)

        with numpy_seed(irep):
            x0 = np.random.uniform(-0.5, 0.5, len(coordinates)+1)
            dxs = np.random.uniform(-1e-5, 1e-5, (100, len(coordinates)+1))
        check_delta(cost.value, cost.gradient, x0, dxs)
Exemplo n.º 10
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def test_esp_cost_cube3d_invariance_rcut():
    for irep in xrange(3):  # Repeating this only 3 times because it is so slow.
        # Some parameters
        coordinates, origin, grid_rvecs, shape, pbc, vref, weights = \
            get_random_esp_cost_cube3d_args(irep)
        grid = UniformGrid(origin, grid_rvecs, shape, pbc)
        # Generate costs with displaced origin
        costs = []
        for icut in xrange(nrep):
            with numpy_seed(irep*10 + icut):
                rcut = np.random.uniform(10, 30)
            alpha = 4.5/rcut
            gcut = 1.5*alpha
            cost = ESPCost.from_grid_data(coordinates, grid, vref, weights, rcut=rcut,
                                          alpha=alpha, gcut=gcut)
            costs.append(cost)
        # Compare the cost functions
        check_costs(costs, eps1=1e-8)
Exemplo n.º 11
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def test_tridiagsym_solve():
    N = 10
    A = np.zeros((N, N), float)
    for irep in xrange(100):
        with numpy_seed(irep):  # Make random numbers reproducible
            # randomize the diagonal
            A.ravel()[::N + 1] = np.random.uniform(1, 2, N)
            # randomize the upper diagonal
            A.ravel()[1::N + 1] = np.random.uniform(-1, 0, N - 1)
        # clone the lower diagonal
        A.ravel()[N::N + 1] = A.ravel()[1::N + 1]
        # test the inverse for all possible basis vectors
        Ainv = np.linalg.inv(A)
        for i in xrange(N):
            right = np.zeros(N, float)
            right[i] = 1.0
            solution = np.zeros(N, float)
            tridiagsym_solve(A.ravel()[::N + 1].copy(),
                             A.ravel()[N::N + 1].copy(), right, solution)
            error = abs(solution - Ainv[:, i]).max()
            assert (error < 1e-9)
Exemplo n.º 12
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def test_eval_spline_grid_add_random():
    npoint = 10
    cs = get_cosine_spline()

    for irep in xrange(10):
        with numpy_seed(irep):
            cell = get_random_cell(1.0, irep % 4)
            points = np.random.normal(-2, 3, (npoint, 3))
            g = IntGrid(points, np.random.normal(0, 1.0, npoint))
            center1 = np.random.uniform(-2, 2, 3)
            center2 = np.random.uniform(-2, 2, 3)

        output1 = np.zeros(npoint)
        g.eval_spline(cs, center1, output1, cell)

        output2 = np.zeros(npoint)
        g.eval_spline(cs, center2, output2, cell)

        output3 = np.zeros(npoint)
        g.eval_spline(cs, center1, output3, cell)
        g.eval_spline(cs, center2, output3, cell)

        assert abs(output1 + output2 - output3).max() < 1e-10
Exemplo n.º 13
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def test_eval_spline_grid_add_random():
    npoint = 10
    cs = get_cosine_spline()

    for irep in xrange(10):
        with numpy_seed(irep):
            cell = get_random_cell(1.0, irep % 4)
            points = np.random.normal(-2, 3, (npoint,3))
            g = IntGrid(points, np.random.normal(0, 1.0, npoint))
            center1 = np.random.uniform(-2, 2, 3)
            center2 = np.random.uniform(-2, 2, 3)

        output1 = np.zeros(npoint)
        g.eval_spline(cs, center1, output1, cell)

        output2 = np.zeros(npoint)
        g.eval_spline(cs, center2, output2, cell)

        output3 = np.zeros(npoint)
        g.eval_spline(cs, center1, output3, cell)
        g.eval_spline(cs, center2, output3, cell)

        assert abs(output1 + output2 - output3).max() < 1e-10
Exemplo n.º 14
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def test_consistent():
    for irep in xrange(nrep):
        with numpy_seed(irep):
            # random system
            natom = 5
            coordinates = np.random.uniform(0, 10, (natom, 3))
            charges = np.random.normal(0, 1, natom)
            charges -= charges.mean()

            # random grid
            origin = np.random.uniform(-3, 3, 3)
            size = 10
            grid_rvecs = np.diag(np.random.uniform(8.0, 10.0, 3)) / size
            grid_rvecs += np.random.uniform(-1.0, 1.0, (3, 3)) / size
            shape = np.array([size, size, size])
            pbc = np.array([1, 1, 1])
            ugrid = UniformGrid(origin, grid_rvecs, shape, pbc)

        # compute the ESP
        rcut = 20.0
        alpha = 3.0 / 10.0
        gcut = 1.1 * alpha
        esp = np.zeros(shape)
        compute_esp_grid_cube(ugrid, esp, coordinates, charges, rcut, alpha,
                              gcut)

        # Set up weights
        weights = np.ones(shape)
        for i in xrange(natom):
            multiply_near_mask(coordinates[i], ugrid, 1.0, 0.5, weights)

        # Fit the charges and test
        cost = ESPCost.from_grid_data(coordinates, ugrid, esp, weights)
        x = cost.solve()
        assert cost.value_charges(charges) < 1e-7
        assert cost.value(x) < 1e-7
        assert abs(charges - x[:-1]).max() < 1e-4
Exemplo n.º 15
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def test_consistent():
    for irep in xrange(nrep):
        with numpy_seed(irep):
            # random system
            natom = 5
            coordinates = np.random.uniform(0, 10, (natom, 3))
            charges = np.random.normal(0, 1, natom)
            charges -= charges.mean()

            # random grid
            origin = np.random.uniform(-3, 3, 3)
            size = 10
            grid_rvecs = np.diag(np.random.uniform(8.0, 10.0, 3))/size
            grid_rvecs += np.random.uniform(-1.0, 1.0, (3, 3))/size
            shape = np.array([size, size, size])
            pbc = np.array([1, 1, 1])
            ugrid = UniformGrid(origin, grid_rvecs, shape, pbc)

        # compute the ESP
        rcut = 20.0
        alpha = 3.0/10.0
        gcut = 1.1*alpha
        esp = np.zeros(shape)
        compute_esp_grid_cube(ugrid, esp, coordinates, charges, rcut, alpha, gcut)

        # Set up weights
        weights = np.ones(shape)
        for i in xrange(natom):
            multiply_near_mask(coordinates[i], ugrid, 1.0, 0.5, weights)

        # Fit the charges and test
        cost = ESPCost.from_grid_data(coordinates, ugrid, esp, weights)
        x = cost.solve()
        assert cost.value_charges(charges) < 1e-7
        assert cost.value(x) < 1e-7
        assert abs(charges - x[:-1]).max() < 1e-4
Exemplo n.º 16
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def test_tridiagsym_solve():
    N = 10
    A = np.zeros((N,N), float)
    for irep in xrange(100):
        with numpy_seed(irep):  # Make random numbers reproducible
            # randomize the diagonal
            A.ravel()[::N+1] = np.random.uniform(1,2,N)
            # randomize the upper diagonal
            A.ravel()[1::N+1] = np.random.uniform(-1,0,N-1)
        # clone the lower diagonal
        A.ravel()[N::N+1] = A.ravel()[1::N+1]
        # test the inverse for all possible basis vectors
        Ainv = np.linalg.inv(A)
        for i in xrange(N):
            right = np.zeros(N, float)
            right[i] = 1.0
            solution = np.zeros(N, float)
            tridiagsym_solve(
                A.ravel()[::N+1].copy(),
                A.ravel()[N::N+1].copy(),
                right, solution
            )
            error = abs(solution - Ainv[:,i]).max()
            assert(error < 1e-9)
Exemplo n.º 17
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def main():
    """Main program."""
    # Get command-line args
    example_paths, do_update, do_force, do_verbose = parse_args()

    # Silence the logger
    if not do_verbose:
        log.set_level(log.silent)

    # Exit status becomes 1 when some test results have changed beyond the thresholds, or
    # when previous or reference values are missing.
    exit_status = 0

    # Run all examples.
    for example_path in example_paths:
        print example_path

        # Sanity checks
        if not example_path.endswith(".py"):
            raise RegressionTestError('Example path should end with ".py": {}.'
                                      .format(example_path))
        with open(example_path) as f:
            example_lines = f.readlines()
            try:
                example_lines.index(comment_regtest)
            except ValueError:
                raise RegressionTestError('Missing regression-testing comment in example {}.'
                                          .format(example_path))

        # Run example and collect results.
        example_globals = {}
        with numpy_seed():
            print '   First random number:', np.random.rand()
        with open(example_path) as fh, numpy_seed():
            exec fh in example_globals  # pylint: disable=exec-used

        # Check that rt_results is defined in the example.
        if 'rt_results' not in example_globals:
            raise RegressionTestError('No rt_results defined in example {}.'
                                      .format(example_path))

        # Extract relevant variables from example.
        rt_results = example_globals['rt_results']
        rt_atols = example_globals.get('rt_atols', {})
        rt_rtols = example_globals.get('rt_rtols', {})
        rt_references = example_globals.get('rt_references', {})
        rt_previous = example_globals.get('rt_previous', {})
        known_keys = ['rt_results', 'rt_atols', 'rt_rtols', 'rt_references', 'rt_previous']
        for key in example_globals:
            if key.startswith('rt_') and key not in known_keys:
                print 'Unrecognized variable starting with rt_: {}.' % key

        # Compare results with expected or previous values, if previous values are present.
        rt_status = {}
        for key, result in rt_results.iteritems():
            target = rt_references.get(key, rt_previous.get(key))
            if target is None:
                rt_status[key] = 'PREVIOUS OR REFERENCE MISSING'
            else:
                atol = rt_atols.get(key, default_atol)
                rtol = rt_rtols.get(key, default_rtol)
                if not isinstance(result, target.__class__):
                    rt_status[key] = 'WRONG TYPE'
                elif isinstance(result, np.ndarray) and (result.shape != target.shape):
                    rt_status[key] = 'WRONG SHAPE'
                elif np.allclose(result, target, atol=atol, rtol=rtol):
                    rt_status[key] = 'OK'
                else:
                    rt_status[key] = 'BOUNDS EXCEEDED'

        if do_update:
            update_example(example_path, example_lines, rt_previous, rt_results,
                           rt_status, do_force)
        else:
            exit_status |= report_regressions(rt_results, rt_atols, rt_rtols, rt_references,
                                              rt_previous, rt_status)

    # Stop with proper exit code.
    sys.exit(exit_status)
Exemplo n.º 18
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def get_random_unitary(seed=1):
    """Return a random 3x3 unitary transformation."""
    with numpy_seed(seed):
        A = np.random.normal(0, 1, (3, 3))
        A = 0.5*(A+A.T)
        return np.linalg.eigh(A)[1]
Exemplo n.º 19
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def get_random_shift(seed=1):
    """Generate a random displacement in 3D Cartesian coordinates."""
    with numpy_seed(seed):
        return np.random.uniform(-3, 3, 3)
Exemplo n.º 20
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def main():
    """Main program."""
    # Get command-line args
    example_paths, do_update, do_force, do_verbose = parse_args()

    # Silence the logger
    if not do_verbose:
        log.set_level(log.silent)

    # Exit status becomes 1 when some test results have changed beyond the thresholds, or
    # when previous or reference values are missing.
    exit_status = 0

    # Run all examples.
    for example_path in example_paths:
        print example_path

        # Sanity checks
        if not example_path.endswith(".py"):
            raise RegressionTestError('Example path should end with ".py": {}.'
                                      .format(example_path))
        with open(example_path) as f:
            example_lines = f.readlines()
            try:
                example_lines.index(comment_regtest)
            except ValueError:
                raise RegressionTestError('Missing regression-testing comment in example {}.'
                                          .format(example_path))

        # Run example and collect results.
        example_globals = {}
        with numpy_seed():
            print '   First random number:', np.random.rand()
        with open(example_path) as fh, numpy_seed():
            exec fh in example_globals  # pylint: disable=exec-used

        # Check that rt_results is defined in the example.
        if 'rt_results' not in example_globals:
            raise RegressionTestError('No rt_results defined in example {}.'
                                      .format(example_path))

        # Extract relevant variables from example.
        rt_results = example_globals['rt_results']
        rt_atols = example_globals.get('rt_atols', {})
        rt_rtols = example_globals.get('rt_rtols', {})
        rt_references = example_globals.get('rt_references', {})
        rt_previous = example_globals.get('rt_previous', {})
        known_keys = ['rt_results', 'rt_atols', 'rt_rtols', 'rt_references', 'rt_previous']
        for key in example_globals:
            if key.startswith('rt_') and key not in known_keys:
                print 'Unrecognized variable starting with rt_: {}.' % key

        # Compare results with expected or previous values, if previous values are present.
        rt_status = {}
        for key, result in rt_results.iteritems():
            target = rt_references.get(key, rt_previous.get(key))
            if target is None:
                rt_status[key] = 'PREVIOUS OR REFERENCE MISSING'
            else:
                atol = rt_atols.get(key, default_atol)
                rtol = rt_rtols.get(key, default_rtol)
                if not isinstance(result, target.__class__):
                    rt_status[key] = 'WRONG TYPE'
                elif isinstance(result, np.ndarray) and (result.shape != target.shape):
                    rt_status[key] = 'WRONG SHAPE'
                elif np.allclose(result, target, atol=atol, rtol=rtol):
                    rt_status[key] = 'OK'
                else:
                    rt_status[key] = 'BOUNDS EXCEEDED'

        if do_update:
            update_example(example_path, example_lines, rt_previous, rt_results,
                           rt_status, do_force)
        else:
            exit_status |= report_regressions(rt_results, rt_atols, rt_rtols, rt_references,
                                              rt_previous, rt_status)

    # Stop with proper exit code.
    sys.exit(exit_status)