Module « numpy.matlib »

Fonction nan_to_num - module numpy.matlib

Signature de la fonction nan_to_num

def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None) 

Description

nan_to_num.__doc__

    Replace NaN with zero and infinity with large finite numbers (default
    behaviour) or with the numbers defined by the user using the `nan`, 
    `posinf` and/or `neginf` keywords.

    If `x` is inexact, NaN is replaced by zero or by the user defined value in
    `nan` keyword, infinity is replaced by the largest finite floating point 
    values representable by ``x.dtype`` or by the user defined value in 
    `posinf` keyword and -infinity is replaced by the most negative finite 
    floating point values representable by ``x.dtype`` or by the user defined 
    value in `neginf` keyword.

    For complex dtypes, the above is applied to each of the real and
    imaginary components of `x` separately.

    If `x` is not inexact, then no replacements are made.

    Parameters
    ----------
    x : scalar or array_like
        Input data.
    copy : bool, optional
        Whether to create a copy of `x` (True) or to replace values
        in-place (False). The in-place operation only occurs if
        casting to an array does not require a copy.
        Default is True.
        
        .. versionadded:: 1.13
    nan : int, float, optional
        Value to be used to fill NaN values. If no value is passed 
        then NaN values will be replaced with 0.0.
        
        .. versionadded:: 1.17
    posinf : int, float, optional
        Value to be used to fill positive infinity values. If no value is 
        passed then positive infinity values will be replaced with a very
        large number.
        
        .. versionadded:: 1.17
    neginf : int, float, optional
        Value to be used to fill negative infinity values. If no value is 
        passed then negative infinity values will be replaced with a very
        small (or negative) number.
        
        .. versionadded:: 1.17

        

    Returns
    -------
    out : ndarray
        `x`, with the non-finite values replaced. If `copy` is False, this may
        be `x` itself.

    See Also
    --------
    isinf : Shows which elements are positive or negative infinity.
    isneginf : Shows which elements are negative infinity.
    isposinf : Shows which elements are positive infinity.
    isnan : Shows which elements are Not a Number (NaN).
    isfinite : Shows which elements are finite (not NaN, not infinity)

    Notes
    -----
    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754). This means that Not a Number is not equivalent to infinity.

    Examples
    --------
    >>> np.nan_to_num(np.inf)
    1.7976931348623157e+308
    >>> np.nan_to_num(-np.inf)
    -1.7976931348623157e+308
    >>> np.nan_to_num(np.nan)
    0.0
    >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
    >>> np.nan_to_num(x)
    array([ 1.79769313e+308, -1.79769313e+308,  0.00000000e+000, # may vary
           -1.28000000e+002,  1.28000000e+002])
    >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
    array([ 3.3333333e+07,  3.3333333e+07, -9.9990000e+03, 
           -1.2800000e+02,  1.2800000e+02])
    >>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
    array([  1.79769313e+308,  -1.79769313e+308,   0.00000000e+000, # may vary
         -1.28000000e+002,   1.28000000e+002])
    >>> np.nan_to_num(y)
    array([  1.79769313e+308 +0.00000000e+000j, # may vary
             0.00000000e+000 +0.00000000e+000j,
             0.00000000e+000 +1.79769313e+308j])
    >>> np.nan_to_num(y, nan=111111, posinf=222222)
    array([222222.+111111.j, 111111.     +0.j, 111111.+222222.j])