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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

help(numpy.matlib.nan_to_num)

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.
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.
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.
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.

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
--------
>>> import numpy as np
>>> 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])

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