Example #1
0
def test_wrapped_func_with_kwargs():
    """
    Test wrapped functions with keyword args
    """
    def cos_plain(angle):
        return math.cos(angle)

    def cos_kwargs(angle, **kwargs):
        return math.cos(angle)

    def use_kwargs(angle, cos=True):
        if cos:
            return math.cos(angle)
        else:
            return math.sin(angle)

    # wrappings of these functions
    wrap_cos_plain  = wrap(cos_plain)
    wrap_cos_wderiv = wrap(cos_plain, [math.cos])
    wrap_cos_kwargs = wrap(cos_kwargs)
    wrap_use_kwargs = wrap(use_kwargs)
    umath_cos       = umath.cos
    umath_sin       = umath.sin

    # now test that the wrapped functions give the same results
    # as the umath versions for a variety of input values
    for a in (ufloat((0.2, 0.01)),  ufloat((0.7, 0.00001)),
              #ufloat((0.9, 0.3)),   ufloat((1.e-4, 0.3)),
              #ufloat((200.0, 0.3)), ufloat((1.e5, 0.3)),
              #0, 2, 1.25, 0.0, 1.e-5, 0.707, 1.5708
              ):
        ucos = umath_cos(a)
        usin = umath_sin(a)
        assert _numbers_close(ucos, wrap_cos_plain(a))
        assert _numbers_close(ucos, wrap_cos_wderiv(a))
        assert _numbers_close(ucos, wrap_cos_kwargs(a))
        assert _numbers_close(ucos, wrap_cos_kwargs(a, opt=None))
        assert _numbers_close(ucos, wrap_cos_kwargs(a, opt=None, opt2=True))
        assert _numbers_close(ucos, wrap_use_kwargs(a, cos=True))
        assert _numbers_close(usin, wrap_use_kwargs(a, cos=False))

    # affirm that calling a wrapped function with unsupported
    # keyword args raises a TypeError
    raised = False
    try:
        wrap_use_kwargs(a, other=False)
    except TypeError:
        raised = True
    assert raised
Example #2
0
    def wrapped_fsum():
        """
        Returns an uncertainty-aware version of math.fsum, which must
        be contained in _original_func.
        """

        # The fsum function is flattened, in order to use the
        # wrap() wrapper:

        flat_fsum = lambda *args: original_func(args)

        flat_fsum_wrap = wrap(flat_fsum, itertools.repeat(lambda *args: 1))

        return wraps(lambda arg_list: flat_fsum_wrap(*arg_list), original_func)
Example #3
0
    def wrapped_fsum():
        """
        Returns an uncertainty-aware version of math.fsum, which must
        be contained in _original_func.
        """

        # The fsum function is flattened, in order to use the
        # wrap() wrapper:

        flat_fsum = lambda *args: original_func(args)

        flat_fsum_wrap = wrap(
            flat_fsum, itertools.repeat(lambda *args: 1))

        return wraps(lambda arg_list: flat_fsum_wrap(*arg_list),
                     original_func)
Example #4
0
def test_wrapped_func():
    """
    Test uncertainty-aware functions obtained through wrapping.
    """

    # This function can be wrapped so that it works when 'angle' has
    # an uncertainty (math.cos does not handle numbers with
    # uncertainties):
    def f(angle, list_var):
        return math.cos(angle) + sum(list_var)

    f_wrapped = wrap(f)
    my_list = [1, 2, 3]

    # Test of a wrapped function that only calls the original function:
    assert f_wrapped(0, my_list) == 1 + sum(my_list)

    # As a precaution, the wrapped function does not venture into
    # calculating f with uncertainties when one of the argument is not
    # a simple number, because this argument might contain variables:
    angle = ufloat((0, 0.1))

    assert f_wrapped(angle, [angle, angle]) == NotImplemented
    assert f_wrapped(angle, my_list) == NotImplemented
Example #5
0
# for (name, attr) in vars(math).items():
for name in dir(math):

    if name in fixed_derivatives:  # Priority to functions in fixed_derivatives
        derivatives = fixed_derivatives[name]
    elif name in num_deriv_funcs:
        # Functions whose derivatives are calculated numerically by
        # this module fall here (isinf, fmod,...):
        derivatives = None  # Means: numerical calculation required
    else:
        continue  # 'name' not wrapped by this module (__doc__, e, etc.)

    func = getattr(math, name)

    setattr(this_module, name, wraps(wrap(func, derivatives), func))

    many_scalars_to_scalar_funcs.append(name)

###############################################################################

########################################
# Special cases: some of the functions from no_std_wrapping:

##########
# The math.factorial function is not converted to an uncertainty-aware
# function, because it does not handle non-integer arguments: it does
# not make sense to give it an argument with a numerical error
# (whereas this would be relevant for the gamma function).

##########
Example #6
0
# for (name, attr) in vars(math).items():
for name in dir(math):

    if name in fixed_derivatives:  # Priority to functions in fixed_derivatives
        derivatives = fixed_derivatives[name]
    elif name in num_deriv_funcs:
        # Functions whose derivatives are calculated numerically by
        # this module fall here (isinf, fmod,...):
        derivatives = None  # Means: numerical calculation required
    else:
        continue  # 'name' not wrapped by this module (__doc__, e, etc.)

    func = getattr(math, name)

    setattr(this_module, name,
            wraps(wrap(func, derivatives), func))

    many_scalars_to_scalar_funcs.append(name)

###############################################################################

########################################
# Special cases: some of the functions from no_std_wrapping:

##########
# The math.factorial function is not converted to an uncertainty-aware
# function, because it does not handle non-integer arguments: it does
# not make sense to give it an argument with a numerical error
# (whereas this would be relevant for the gamma function).

##########