NumPy参考 >通用功能(ufunc) > numpy.seterr
numpy.
seterr
(all=None, divide=None, over=None, under=None, invalid=None)[source]¶Set how floating-point errors are handled.
Note that operations on integer scalar types (such as int16
) are
handled like floating point, and are affected by these settings.
Set treatment for all types of floating-point errors at once:
ignore: Take no action when the exception occurs.
warn: Print a RuntimeWarning (via the Python warnings
module).
raise: Raise a FloatingPointError.
call: Call a function specified using the seterrcall
function.
print: Print a warning directly to stdout
.
log: Record error in a Log object specified by seterrcall
.
The default is not to change the current behavior.
Treatment for division by zero.
Treatment for floating-point overflow.
Treatment for floating-point underflow.
Treatment for invalid floating-point operation.
Dictionary containing the old settings.
Notes
The floating-point exceptions are defined in the IEEE 754 standard [1]:
Division by zero: infinite result obtained from finite numbers.
Overflow: result too large to be expressed.
Underflow: result so close to zero that some precision was lost.
Invalid operation: result is not an expressible number, typically indicates that a NaN was produced.
Examples
>>> old_settings = np.seterr(all='ignore') #seterr to known value
>>> np.seterr(over='raise')
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.seterr(**old_settings) # reset to default
{'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.int16(32000) * np.int16(3)
30464
>>> old_settings = np.seterr(all='warn', over='raise')
>>> np.int16(32000) * np.int16(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FloatingPointError: overflow encountered in short_scalars
>>> from collections import OrderedDict
>>> old_settings = np.seterr(all='print')
>>> OrderedDict(np.geterr())
OrderedDict([('divide', 'print'), ('over', 'print'), ('under', 'print'), ('invalid', 'print')])
>>> np.int16(32000) * np.int16(3)
30464