numpy.core._ufunc_config

Returns

getbufsize : int

Size of ufunc buffer in bytes.

Returns

res : dict

A dictionary with keys "divide", "over", "under", and "invalid", whose values are from the strings "ignore", "print", "log", "warn", "raise", and "call". The keys represent possible floating-point exceptions, and the values define how these exceptions are handled.

See Also

geterrcall, seterr, seterrcall

Notes

For complete documentation of the types of floating-point exceptions and treatment options, see seterr.

Examples

>>> np.geterr()
{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.)
array([nan,  1.,  1.])

>>> oldsettings = np.seterr(all='warn', over='raise')
>>> np.geterr()
{'divide': 'warn', 'over': 'raise', 'under': 'warn', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.)
array([nan,  1.,  1.])

Returns

errobj : callable, log instance or None

The current error handler. If no handler was set through seterrcall, None is returned.

See Also

seterrcall, seterr, geterr

Notes

For complete documentation of the types of floating-point exceptions and treatment options, see seterr.

Examples

>>> np.geterrcall()  # we did not yet set a handler, returns None

>>> oldsettings = np.seterr(all='call')
>>> def err_handler(type, flag):
...     print("Floating point error (%s), with flag %s" % (type, flag))
>>> oldhandler = np.seterrcall(err_handler)
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])

>>> cur_handler = np.geterrcall()
>>> cur_handler is err_handler
True

Parameters

size : int

Size of buffer.

Parameters

all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional

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.

divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional

Treatment for division by zero.

over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional

Treatment for floating-point overflow.

under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional

Treatment for floating-point underflow.

invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional

Treatment for invalid floating-point operation.

Returns

old_settings : dict

Dictionary containing the old settings.

See also

seterrcall : Set a callback function for the 'call' mode. geterr, geterrcall, errstate

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

>>> old_settings = np.seterr(all='print')
>>> np.geterr()
{'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
>>> np.int16(32000) * np.int16(3)
30464

Parameters

func : callable f(err, flag) or object with write method

Function to call upon floating-point errors ('call'-mode) or object whose 'write' method is used to log such message ('log'-mode).

The call function takes two arguments. The first is a string describing the type of error (such as "divide by zero", "overflow", "underflow", or "invalid value"), and the second is the status flag. The flag is a byte, whose four least-significant bits indicate the type of error, one of "divide", "over", "under", "invalid":

[0 0 0 0 divide over under invalid]

In other words, flags = divide + 2*over + 4*under + 8*invalid.

If an object is provided, its write method should take one argument, a string.

Returns

h : callable, log instance or None

The old error handler.

See Also

seterr, geterr, geterrcall

Examples

Callback upon error:

>>> def err_handler(type, flag):
...     print("Floating point error (%s), with flag %s" % (type, flag))
...

>>> saved_handler = np.seterrcall(err_handler)
>>> save_err = np.seterr(all='call')

>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])

>>> np.seterrcall(saved_handler)
<function err_handler at 0x...>
>>> np.seterr(**save_err)
{'divide': 'call', 'over': 'call', 'under': 'call', 'invalid': 'call'}

Log error message:

>>> class Log:
...     def write(self, msg):
...         print("LOG: %s" % msg)
...

>>> log = Log()
>>> saved_handler = np.seterrcall(log)
>>> save_err = np.seterr(all='log')

>>> np.array([1, 2, 3]) / 0.0
LOG: Warning: divide by zero encountered in true_divide
array([inf, inf, inf])

>>> np.seterrcall(saved_handler)
<numpy.core.numeric.Log object at 0x...>
>>> np.seterr(**save_err)
{'divide': 'log', 'over': 'log', 'under': 'log', 'invalid': 'log'}