"""
Functions for changing global ufunc configuration
This provides helpers which wrap `umath.geterrobj` and `umath.seterrobj`
"""
import collections.abc
import contextlib
from .overrides import set_module
from .umath import (
UFUNC_BUFSIZE_DEFAULT,
ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,
SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,
)
from . import umath
__all__ = [
"seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
"errstate",
]
_errdict = {"ignore": ERR_IGNORE,
"warn": ERR_WARN,
"raise": ERR_RAISE,
"call": ERR_CALL,
"print": ERR_PRINT,
"log": ERR_LOG}
_errdict_rev = {value: key for key, value in _errdict.items()}
@set_module('numpy')
def seterr(all=None, divide=None, over=None, under=None, invalid=None):
"""
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.
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.
.. [1] https://en.wikipedia.org/wiki/IEEE_754
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
"""
pyvals = umath.geterrobj()
old = geterr()
if divide is None:
divide = all or old['divide']
if over is None:
over = all or old['over']
if under is None:
under = all or old['under']
if invalid is None:
invalid = all or old['invalid']
maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
(_errdict[over] << SHIFT_OVERFLOW) +
(_errdict[under] << SHIFT_UNDERFLOW) +
(_errdict[invalid] << SHIFT_INVALID))
pyvals[1] = maskvalue
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterr():
"""
Get the current way of handling floating-point errors.
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
--------
>>> from collections import OrderedDict
>>> sorted(np.geterr().items())
[('divide', 'warn'), ('invalid', 'warn'), ('over', 'warn'), ('under', 'ignore')]
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
>>> oldsettings = np.seterr(all='warn', over='raise')
>>> OrderedDict(sorted(np.geterr().items()))
OrderedDict([('divide', 'warn'), ('invalid', 'warn'), ('over', 'raise'), ('under', 'warn')])
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
"""
maskvalue = umath.geterrobj()[1]
mask = 7
res = {}
val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
res['divide'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_OVERFLOW) & mask
res['over'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_UNDERFLOW) & mask
res['under'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_INVALID) & mask
res['invalid'] = _errdict_rev[val]
return res
@set_module('numpy')
def setbufsize(size):
"""
Set the size of the buffer used in ufuncs.
Parameters
----------
size : int
Size of buffer.
"""
if size > 10e6:
raise ValueError("Buffer size, %s, is too big." % size)
if size < 5:
raise ValueError("Buffer size, %s, is too small." % size)
if size % 16 != 0:
raise ValueError("Buffer size, %s, is not a multiple of 16." % size)
pyvals = umath.geterrobj()
old = getbufsize()
pyvals[0] = size
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def getbufsize():
"""
Return the size of the buffer used in ufuncs.
Returns
-------
getbufsize : int
Size of ufunc buffer in bytes.
"""
return umath.geterrobj()[0]
@set_module('numpy')
def seterrcall(func):
"""
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))
...
>>> saved_handler = np.seterrcall(err_handler)
>>> save_err = np.seterr(all='call')
>>> from collections import OrderedDict
>>> 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...>
>>> OrderedDict(sorted(np.seterr(**save_err).items()))
OrderedDict([('divide', 'call'), ('invalid', 'call'), ('over', 'call'), ('under', '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...>
>>> OrderedDict(sorted(np.seterr(**save_err).items()))
OrderedDict([('divide', 'log'), ('invalid', 'log'), ('over', 'log'), ('under', 'log')])
"""
if func is not None and not isinstance(func, collections.abc.Callable):
if (not hasattr(func, 'write') or
not isinstance(func.write, collections.abc.Callable)):
raise ValueError("Only callable can be used as callback")
pyvals = umath.geterrobj()
old = geterrcall()
pyvals[2] = func
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterrcall():
"""
Return the current callback function used on floating-point errors.
When the error handling for a floating-point error (one of "divide",
"over", "under", or "invalid") is set to 'call' or 'log', the function
that is called or the log instance that is written to is returned by
`geterrcall`. This function or log instance has been set with
`seterrcall`.
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
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