# Path: src/numdifftools/tests/hamiltonian.py
# def run_hamiltonian(hessian, verbose=True):
#     c = ClassicalHamiltonian()
# 
#     xopt = optimize.fmin(c.potential, c.initialposition(), xtol=1e-10)
# 
#     hessian.fun = c.potential
#     hessian.full_output = True
# 
#     h, info = hessian(xopt)
#     true_h = np.array([[5.23748385e-12, -2.61873829e-12],
#                        [-2.61873829e-12, 5.23748385e-12]])
#     eigenvalues = linalg.eigvals(h)
#     normal_modes = c.normal_modes(eigenvalues)
# 
#     if verbose:
#         print(c.potential([-0.5, 0.5]))
#         print(c.potential([-0.5, 0.0]))
#         print(c.potential([0.0, 0.0]))
#         print(xopt)
#         print('h', h)
#         print('h-true_h', np.abs(h - true_h))
#         print('error_estimate', info.error_estimate)
# 
#         print('eigenvalues', eigenvalues)
#         print('normal_modes', normal_modes)
#     return h, info.error_estimate, true_h


from hypothesis import given, example, strategies as st
from numpy.testing import assert_allclose
from numdifftools.testing import rosen
from .hamiltonian import run_hamiltonian
import pytest
import numpy as np
    import algopy
    import numdifftools.nd_algopy as nd
# -*- coding:utf-8 -*-
""""""
from __future__ import absolute_import, division



try:
except ImportError:
    algopy = None
else:

pytestmark = pytest.mark.skipif(algopy is None, reason="algopy is not installed!")


class TestHessian(object):

    def test_run_hamiltonian(self):

# Path: src/numdifftools/tests/hamiltonian.py
# def run_hamiltonian(hessian, verbose=True):
#     c = ClassicalHamiltonian()
# 
#     xopt = optimize.fmin(c.potential, c.initialposition(), xtol=1e-10)
# 
#     hessian.fun = c.potential
#     hessian.full_output = True
# 
#     h, info = hessian(xopt)
#     true_h = np.array([[5.23748385e-12, -2.61873829e-12],
#                        [-2.61873829e-12, 5.23748385e-12]])
#     eigenvalues = linalg.eigvals(h)
#     normal_modes = c.normal_modes(eigenvalues)
# 
#     if verbose:
#         print(c.potential([-0.5, 0.5]))
#         print(c.potential([-0.5, 0.0]))
#         print(c.potential([0.0, 0.0]))
#         print(xopt)
#         print('h', h)
#         print('h-true_h', np.abs(h - true_h))
#         print('error_estimate', info.error_estimate)
# 
#         print('eigenvalues', eigenvalues)
#         print('normal_modes', normal_modes)
#     return h, info.error_estimate, true_h


# Path: src/numdifftools/tests/test_nd_algopy.py
from hypothesis import given, example, strategies as st
from numpy.testing import assert_allclose
from numdifftools.testing import rosen
from .hamiltonian import run_hamiltonian
import pytest
import numpy as np
    import algopy
    import numdifftools.nd_algopy as nd

# -*- coding:utf-8 -*-
""""""
from __future__ import absolute_import, division



try:
except ImportError:
    algopy = None
else:

pytestmark = pytest.mark.skipif(algopy is None, reason="algopy is not installed!")


class TestHessian(object):

    def test_run_hamiltonian(self):

        h, _error_estimate, true_h = run_hamiltonian(nd.Hessian(None),