numdifftools.tests package

Submodules

numdifftools.tests.conftest module

Dummy conftest.py for numdifftools.

If you don’t know what this is for, just leave it empty. Read more about conftest.py under: https://pytest.org/latest/plugins.html

numdifftools.tests.test_extrapolation module

class numdifftools.tests.test_extrapolation.TestExtrapolation(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

setUp()[source][source]
test_dea3_on_trapz_sin()[source][source]
test_dea_on_trapz_sin()[source][source]
test_epsal()[source][source]
test_richardson()[source][source]
class numdifftools.tests.test_extrapolation.TestRichardson(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

setUp()[source][source]
test_order_step_combinations()[source][source]

numdifftools.tests.test_hessian module

class numdifftools.tests.test_hessian.ClassicalHamiltonian

Bases: object

Hamiltonian

Parameters:

N : scalar

number of ions in the chain

w : scalar

angular trap frequency

C : scalar

Coulomb constant times the electronic charge in SI units.

m : scalar

the mass of a single trapped ion in the chain

initialposition()

Defines initial position as an estimate for the minimize process.

normal_modes(eigenvalues)

Return normal modes

the computed eigenvalues of the matrix Vx are of the form (normal_modes)2*m.

potential(positionvector)

Return potential

positionvector is an 1-d array (vector) of length N that contains the positions of the N ions

class numdifftools.tests.test_hessian.TestHessian(methodName='runTest')

Bases: unittest.case.TestCase

test_hessian()

numdifftools.tests.test_limits module

Created on 28. aug. 2015

@author: pab

class numdifftools.tests.test_limits.TestCStepGenerator(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_default_base_step()[source][source]
static test_default_generator()[source][source]
static test_fixed_base_step()[source][source]
class numdifftools.tests.test_limits.TestLimit(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

test_derivative_of_cos()[source][source]
test_difficult_limit()[source][source]
test_residue_1_div_1_minus_exp_x()[source][source]
test_sinx_div_x()[source][source]
class numdifftools.tests.test_limits.TestResidue(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

test_residue_1_div_1_minus_exp_x()[source][source]
test_residue_1_div_sin_x2()[source][source]

numdifftools.tests.test_multicomplex module

Created on 22. apr. 2015

@author: pab

class numdifftools.tests.test_multicomplex.BicomplexTester(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_add()[source][source]
static test_arccos()[source][source]
static test_arcsin()[source][source]
static test_arg_c()[source][source]
static test_assign()[source][source]
test_conjugate()[source][source]
static test_cos()[source][source]
static test_der_abs()[source][source]
static test_der_arccos()[source][source]
static test_der_arccosh()[source][source]
static test_der_arctan()[source][source]
static test_der_cos()[source][source]
static test_der_log()[source][source]
static test_division()[source][source]
static test_dot()[source][source]
static test_eq()[source][source]
test_flat()[source][source]
static test_ge()[source][source]
static test_gt()[source][source]
test_init()[source][source]
static test_le()[source][source]
static test_lt()[source][source]
static test_multiplication()[source][source]
test_neg()[source][source]
test_norm()[source][source]
static test_pow()[source][source]
test_repr()[source][source]
static test_rpow()[source][source]
static test_rsub()[source][source]
test_shape()[source][source]
static test_sub()[source][source]
static test_subsref()[source][source]
class numdifftools.tests.test_multicomplex.DerivativeTester(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_all_first_derivatives()[source][source]
static test_all_second_derivatives()[source][source]

numdifftools.tests.test_nd_algopy module

class numdifftools.tests.test_nd_algopy.TestDerivative(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_derivative_cube()[source][source]

Test for Issue 7

static test_derivative_exp()[source][source]
static test_derivative_on_log()[source][source]
test_derivative_on_sinh()[source][source]
static test_derivative_sin()[source][source]
static test_directional_diff()[source][source]
static test_fun_with_additional_parameters()[source][source]

Test for issue #9

static test_high_order_derivative_cos()[source][source]
class numdifftools.tests.test_nd_algopy.TestGradient(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_on_scalar_function()[source][source]
class numdifftools.tests.test_nd_algopy.TestHessdiag(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_forward()[source][source]
static test_reverse()[source][source]
class numdifftools.tests.test_nd_algopy.TestHessian(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_hessian_cos_x_y__at_0_0()[source][source]
test_run_hamiltonian()[source][source]
class numdifftools.tests.test_nd_algopy.TestJacobian(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_on_matrix_valued_function()[source][source]
static test_on_scalar_function()[source][source]
static test_on_vector_valued_function()[source][source]
static test_scalar_to_vector()[source][source]

numdifftools.tests.test_numdifftools module

Test functions for numdifftools module

class numdifftools.tests.test_numdifftools.TestDerivative(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

test_backward_derivative_on_sinh()[source][source]
test_central_and_forward_derivative_on_log()[source][source]
static test_default_scale()[source][source]
static test_derivative_cube()[source][source]

Test for Issue 7

static test_derivative_exp()[source][source]
static test_derivative_of_cos_x()[source][source]
static test_derivative_sin()[source][source]
static test_derivative_with_step_options()[source][source]
static test_directional_diff()[source][source]
static test_fun_with_additional_parameters()[source][source]

Test for issue #9

static test_high_order_derivative_cos()[source][source]
test_infinite_functions()[source][source]
class numdifftools.tests.test_numdifftools.TestGradient(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_directional_diff()[source][source]
static test_gradient()[source][source]
class numdifftools.tests.test_numdifftools.TestHessdiag(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

test_complex()[source][source]
test_default_step()[source][source]
test_fixed_step()[source][source]
class numdifftools.tests.test_numdifftools.TestHessian(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_hessian_cos_x_y_at_0_0()[source][source]
test_run_hamiltonian()[source][source]
class numdifftools.tests.test_numdifftools.TestJacobian(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_on_matrix_valued_function()[source][source]
static test_on_scalar_function()[source][source]
static test_on_vector_valued_function()[source][source]
static test_scalar_to_vector()[source][source]
class numdifftools.tests.test_numdifftools.TestRichardson(methodName='runTest')[source][source]

Bases: unittest.case.TestCase

static test_central_forward_backward()[source][source]
static test_complex()[source][source]
numdifftools.tests.test_numdifftools.approx_fprime(x, f, epsilon=None, args=(), kwargs=None, centered=True)[source][source]

Gradient of function, or Jacobian if function fun returns 1d array

Parameters:

x : array

parameters at which the derivative is evaluated

fun : function

fun(*((x,)+args), **kwargs) returning either one value or 1d array

epsilon : float, optional

Stepsize, if None, optimal stepsize is used. This is EPS**(1/2)*x for centered == False and EPS**(1/3)*x for centered == True.

args : tuple

Tuple of additional arguments for function fun.

kwargs : dict

Dictionary of additional keyword arguments for function fun.

centered : bool

Whether central difference should be returned. If not, does forward differencing.

Returns:

grad : array

gradient or Jacobian

Notes

If fun returns a 1d array, it returns a Jacobian. If a 2d array is returned by fun (e.g., with a value for each observation), it returns a 3d array with the Jacobian of each observation with shape xk x nobs x xk. I.e., the Jacobian of the first observation would be [:, 0, :]

numdifftools.tests.test_numdifftools_docstrings module

numdifftools.tests.test_numdifftools_docstrings.load_tests(loader=None, tests=None, ignore=None)
numdifftools.tests.test_numdifftools_docstrings.suite()

Module contents