Vous êtes un professionnel et vous avez besoin d'une formation ? RAG (Retrieval-Augmented Generation)
et Fine Tuning d'un LLM Voir le programme détaillé

Module « numpy.matlib »

Fonction seterrcall - module numpy.matlib

Signature de la fonction seterrcall

def seterrcall(func) 

Description

help(numpy.matlib.seterrcall)

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
----------
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))
...

>>> 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'}

Vous êtes un professionnel et vous avez besoin d'une formation ? Machine Learning
avec Scikit-Learn Voir le programme détaillé