numpy.seterrcall#

numpy.seterrcall(func)[source]#

Set the floating-point error callback function or log object.

There are two ways to capture floating-point error messages. The first is to set the error-handler to call, using seterr. Then, set the function to call using this function.

The second is to set the error-handler to log, using seterr. Floating-point errors then trigger a call to the write method of the provided object.

Parameters:

funccallable 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:

hcallable, log instance or None

The old error handler.

See also

seterr, geterr, geterrcall

Notes

Concurrency note: see Floating point error handling

Examples

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Callback upon error:

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

>>> import numpy as np

>>> orig_handler = np.seterrcall(err_handler)
>>> orig_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(orig_handler)
<function err_handler at 0x...>
>>> np.seterr(**orig_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 divide
array([inf, inf, inf])

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

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