flopscope.numpy.nan_to_num
fnp.nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)[flopscope source][numpy source]
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.
Adapted from NumPy docs np.nan_to_num
Replace NaN/inf with finite numbers element-wise.
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 bexitself.
See also
- we.flops.isinf Shows which elements are positive or negative infinity.
- we.flops.isneginf Shows which elements are negative infinity.
- we.flops.isposinf Shows which elements are positive infinity.
- we.flops.isnan Shows which elements are Not a Number (NaN).
- we.flops.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 flopscope.numpy as fnp
>>> flops.nan_to_num(flops.inf)
1.7976931348623157e+308
>>> flops.nan_to_num(-flops.inf)
-1.7976931348623157e+308
>>> flops.nan_to_num(flops.nan)
0.0
>>> x = flops.array([flops.inf, -flops.inf, flops.nan, -128, 128])
>>> flops.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
-1.28000000e+002, 1.28000000e+002])
>>> flops.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 = flops.array([complex(flops.inf, flops.nan), flops.nan, complex(flops.nan, flops.inf)])
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
-1.28000000e+002, 1.28000000e+002])
>>> flops.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])
>>> flops.nan_to_num(y, nan=111111, posinf=222222)
array([222222.+111111.j, 111111. +0.j, 111111.+222222.j])