Python numpy.core.numeric.floating() Examples

def masked_values (data, value, rtol=1.e-5, atol=1.e-8, copy=1):
    """
       masked_values(data, value, rtol=1.e-5, atol=1.e-8)
       Create a masked array; mask is nomask if possible.
       If copy==0, and otherwise possible, result
       may share data values with original array.
       Let d = filled(data, value). Returns d
       masked where abs(data-value)<= atol + rtol * abs(value)
       if d is of a floating point type. Otherwise returns
       masked_object(d, value, copy)
    """
    abs = umath.absolute
    d = filled(data, value)
    if issubclass(d.dtype.type, numeric.floating):
        m = umath.less_equal(abs(d-value), atol+rtol*abs(value))
        m = make_mask(m, flag=1)
        return array(d, mask = m, copy=copy,
                      fill_value=value)
    else:
        return masked_object(d, value, copy=copy) 

def masked_equal(x, value, copy=1):
    """masked_equal(x, value) = x masked where x == value
       For floating point consider masked_values(x, value) instead.
    """
    d = filled(x, 0)
    c = umath.equal(d, value)
    m = mask_or(c, getmask(x))
    return array(d, mask=m, copy=copy) 

def stdout_automatic_parser(result):
    """
    Try and automatically convert strings formatted as tables into a matrix.

    Under the hood, this function essentially applies the genfromtxt function
    to the stdout.

    Args:
      result (dict): the result to parse.
    """
    np.seterr(all='raise')
    parsed = {}

    # By default, if dtype is None, the order Numpy tries to convert a string
    # to a value is: bool, int, float. We don't like this, since it would give
    # us a mixture of integers and doubles in the output, if any integers
    # existed in the data. So, we modify the StringMapper's default mapper to
    # skip the int check and directly convert numbers to floats.
    oldmapper = np.lib._iotools.StringConverter._mapper
    np.lib._iotools.StringConverter._mapper = [(nx.bool_, np.lib._iotools.str2bool, False),
                                               (nx.floating, float, nx.nan),
                                               (nx.complexfloating, complex, nx.nan + 0j),
                                               (nx.longdouble, nx.longdouble, nx.nan)]

    file_contents = result['output']['stdout']

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        parsed = np.genfromtxt(io.StringIO(file_contents))

    # Here we restore the original mapper, so no side-effects remain.
    np.lib._iotools.StringConverter._mapper = oldmapper

    return parsed 

def automatic_parser(result, dtypes={}, converters={}):
    """
    Try and automatically convert strings formatted as tables into nested
    list structures.

    Under the hood, this function essentially applies the genfromtxt function
    to all files in the output, and passes it the additional kwargs.

    Args:
      result (dict): the result to parse.
      dtypes (dict): a dictionary containing the dtype specification to perform
        parsing for each available filename. See the numpy genfromtxt
        documentation for more details on how to format these.
    """
    np.seterr(all='raise')
    parsed = {}

    # By default, if dtype is None, the order Numpy tries to convert a string
    # to a value is: bool, int, float. We don't like this, since it would give
    # us a mixture of integers and doubles in the output, if any integers
    # existed in the data. So, we modify the StringMapper's default mapper to
    # skip the int check and directly convert numbers to floats.
    oldmapper = np.lib._iotools.StringConverter._mapper
    np.lib._iotools.StringConverter._mapper = [(nx.bool_, np.lib._iotools.str2bool, False),
                                               (nx.floating, float, nx.nan),
                                               (nx.complexfloating, complex, nx.nan + 0j),
                                               (nx.longdouble, nx.longdouble, nx.nan)]

    for filename, contents in result['output'].items():
        if dtypes.get(filename) is None:
            dtypes[filename] = None
        if converters.get(filename) is None:
            converters[filename] = None

        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            parsed[filename] = np.genfromtxt(io.StringIO(contents),
                                             dtype=dtypes[filename],
                                             converters=converters[filename]
                                             ).tolist()

    # Here we restore the original mapper, so no side-effects remain.
    np.lib._iotools.StringConverter._mapper = oldmapper

    return parsed