Python numpy 模块，geterr() 实例源码
我们从Python开源项目中，提取了以下36个代码示例，用于说明如何使用numpy.geterr()。
def setUp(self):
# Base data definition.
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
a10 = 10.
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
xm = masked_array(x, mask=m1)
ym = masked_array(y, mask=m2)
z = np.array([-.5, 0., .5, .8])
zm = masked_array(z, mask=[0, 1, 0, 0])
xf = np.where(m1, 1e+20, x)
xm.set_fill_value(1e+20)
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def setUp(self):
# Base data definition.
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
a10 = 10.
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
xm = masked_array(x, mask=m1)
ym = masked_array(y, mask=m2)
z = np.array([-.5, 0., .5, .8])
zm = masked_array(z, mask=[0, 1, 0, 0])
xf = np.where(m1, 1e+20, x)
xm.set_fill_value(1e+20)
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def with_error_settings(**new_settings):
"""
TODO.
Arguments:
**new_settings: TODO
Returns:
"""
@decorator.decorator
def dec(f, *args, **kwargs):
old_settings = np.geterr()
np.seterr(**new_settings)
ret = f(*args, **kwargs)
np.seterr(**old_settings)
return ret
return dec
def setUp(self):
# Base data definition.
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
a10 = 10.
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
xm = masked_array(x, mask=m1)
ym = masked_array(y, mask=m2)
z = np.array([-.5, 0., .5, .8])
zm = masked_array(z, mask=[0, 1, 0, 0])
xf = np.where(m1, 1e+20, x)
xm.set_fill_value(1e+20)
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def setUp(self):
# Base data definition.
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
a10 = 10.
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
xm = masked_array(x, mask=m1)
ym = masked_array(y, mask=m2)
z = np.array([-.5, 0., .5, .8])
zm = masked_array(z, mask=[0, 1, 0, 0])
xf = np.where(m1, 1e+20, x)
xm.set_fill_value(1e+20)
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def setUp(self):
# Base data definition.
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
a10 = 10.
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
xm = masked_array(x, mask=m1)
ym = masked_array(y, mask=m2)
z = np.array([-.5, 0., .5, .8])
zm = masked_array(z, mask=[0, 1, 0, 0])
xf = np.where(m1, 1e+20, x)
xm.set_fill_value(1e+20)
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def setUp(self):
# Base data definition.
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
a10 = 10.
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
xm = masked_array(x, mask=m1)
ym = masked_array(y, mask=m2)
z = np.array([-.5, 0., .5, .8])
zm = masked_array(z, mask=[0, 1, 0, 0])
xf = np.where(m1, 1e+20, x)
xm.set_fill_value(1e+20)
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def transform(value, left_scale, right_scale, scale=0):
if left_scale is None or right_scale is None:
raise Exception('Left or Right scales cannot be None.')
if scale not in [0, 1]:
raise Exception('Scale must be 0 or 1.')
invalid_err = np.geterr().get('invalid')
invalid_err = np.geterr().get('invalid')
np.seterr(invalid='ignore')
if scale == 0:
range_ = np.absolute(right_scale - left_scale)
translated_value = np.abs(value - left_scale)
ret_val = (translated_value / range_)
else:
if left_scale <= 0.0:
raise Exception()
ls = np.log10(left_scale)
rs = np.log10(right_scale)
range_ = rs - ls
translated_value = np.log10(value) - ls
ret_val = (translated_value / range_)
np.seterr(invalid=invalid_err)
return ret_val
def setUp(self):
# Base data definition.
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
a10 = 10.
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
xm = masked_array(x, mask=m1)
ym = masked_array(y, mask=m2)
z = np.array([-.5, 0., .5, .8])
zm = masked_array(z, mask=[0, 1, 0, 0])
xf = np.where(m1, 1e+20, x)
xm.set_fill_value(1e+20)
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def test_default(self):
err = np.geterr()
self.assertEqual(err, dict(
divide='warn',
invalid='warn',
over='warn',
under='ignore',
))
def test_set(self):
with np.errstate():
err = np.seterr()
old = np.seterr(divide='print')
self.assertTrue(err == old)
new = np.seterr()
self.assertTrue(new['divide'] == 'print')
np.seterr(over='raise')
self.assertTrue(np.geterr()['over'] == 'raise')
self.assertTrue(new['divide'] == 'print')
np.seterr(**old)
self.assertTrue(np.geterr() == old)
def setUp(self):
# Base data definition.
self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def __enter__(self):
self.err = numpy.geterr()
numpy.seterr(**self.kw)
def test_default(self):
err = np.geterr()
self.assertEqual(err, dict(
divide='warn',
invalid='warn',
over='warn',
under='ignore',
))
def test_set(self):
with np.errstate():
err = np.seterr()
old = np.seterr(divide='print')
self.assertTrue(err == old)
new = np.seterr()
self.assertTrue(new['divide'] == 'print')
np.seterr(over='raise')
self.assertTrue(np.geterr()['over'] == 'raise')
self.assertTrue(new['divide'] == 'print')
np.seterr(**old)
self.assertTrue(np.geterr() == old)
def setUp(self):
# Base data definition.
self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def index(self):
if self._instantiated_index is None:
if self._index_class is None:
raise RuntimeError("You should not instantiate Dataset.")
self._instantiated_index = self._index_class(
self, dataset_type=self.dataset_type)
# Now we do things that we need an instantiated index for
# ...first off, we create our field_info now.
oldsettings = np.geterr()
np.seterr(all='ignore')
self.create_field_info()
np.seterr(**oldsettings)
return self._instantiated_index
def __enter__(self):
self.err = numpy.geterr()
numpy.seterr(**self.kw)
def test_default(self):
err = np.geterr()
self.assertEqual(err, dict(
divide='warn',
invalid='warn',
over='warn',
under='ignore',
))
def test_set(self):
with np.errstate():
err = np.seterr()
old = np.seterr(divide='print')
self.assertTrue(err == old)
new = np.seterr()
self.assertTrue(new['divide'] == 'print')
np.seterr(over='raise')
self.assertTrue(np.geterr()['over'] == 'raise')
self.assertTrue(new['divide'] == 'print')
np.seterr(**old)
self.assertTrue(np.geterr() == old)
def setUp(self):
# Base data definition.
self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def test_default(self):
err = np.geterr()
self.assertEqual(err, dict(
divide='warn',
invalid='warn',
over='warn',
under='ignore',
))
def test_set(self):
with np.errstate():
err = np.seterr()
old = np.seterr(divide='print')
self.assertTrue(err == old)
new = np.seterr()
self.assertTrue(new['divide'] == 'print')
np.seterr(over='raise')
self.assertTrue(np.geterr()['over'] == 'raise')
self.assertTrue(new['divide'] == 'print')
np.seterr(**old)
self.assertTrue(np.geterr() == old)
def setUp(self):
# Base data definition.
self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def test_default(self):
err = np.geterr()
self.assertEqual(err, dict(
divide='warn',
invalid='warn',
over='warn',
under='ignore',
))
def test_set(self):
with np.errstate():
err = np.seterr()
old = np.seterr(divide='print')
self.assertTrue(err == old)
new = np.seterr()
self.assertTrue(new['divide'] == 'print')
np.seterr(over='raise')
self.assertTrue(np.geterr()['over'] == 'raise')
self.assertTrue(new['divide'] == 'print')
np.seterr(**old)
self.assertTrue(np.geterr() == old)
def setUp(self):
# Base data definition.
self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
def test_default(self):
err = np.geterr()
self.assertEqual(err, dict(
divide='warn',
invalid='warn',
over='warn',
under='ignore',
))
def test_set(self):
with np.errstate():
err = np.seterr()
old = np.seterr(divide='print')
self.assertTrue(err == old)
new = np.seterr()
self.assertTrue(new['divide'] == 'print')
np.seterr(over='raise')
self.assertTrue(np.geterr()['over'] == 'raise')
self.assertTrue(new['divide'] == 'print')
np.seterr(**old)
self.assertTrue(np.geterr() == old)
def _sample(self):
super()._sample()
self._fft_values[:] = numpy.abs(numpy.fft.rfft(self._window_function * self.buffer))
if self._output == 'fft':
pass
elif self._output == 'psd':
# Reminder for future-self:
# Our Input signal is clamped between -1.0 and +1.0 yet if we convert the values above ^ into dB
# we get values way beyond 0dB which makes no sense. Turns out we have to normalize the resulting
# vector.
# Thank you my hero: https://dsp.stackexchange.com/a/32080
# TODO: This can be pre-calculated!
self._fft_values[:] = numpy.power(self._fft_values * 2.0, 2) \
/ numpy.power(self._window_function_sum * self._reference_value, 2)
else:
raise PulsevizException('This should not happen.')
if self._scaling == 'lin':
pass
elif self._scaling == 'log':
numpy.seterr(divide='ignore')
self._fft_values[:] = 10.0 * numpy.log10(self._fft_values)
numpy.seterr(all='raise') # TODO: Use result of numpy.geterr instead?
else:
raise PulsevizException('This should not happen.')
if self._scaling == 'log':
self._fft_values[:] += self._fft_weights
else:
self._fft_values[:] *= numpy.power(10, self._fft_weights / 20) # TODO: Test this.
def inverse_transform(value, left_scale, right_scale, scale=0):
if left_scale is None or right_scale is None:
raise Exception('Left or Right scales cannot be None.')
if scale not in [0, 1]:
raise Exception('Scale must be 0 or 1.')
invalid_err = np.geterr().get('invalid')
np.seterr(invalid='ignore')
if scale == 0:
range_ = np.absolute(right_scale - left_scale)
translated_value = value * range_
if (left_scale > right_scale):
ret_val = left_scale - translated_value
else:
ret_val = left_scale + translated_value
else:
ls = np.log10(left_scale)
rs = np.log10(right_scale)
range_ = rs - ls
translated_value = value * range_
translated_value = np.round(translated_value, 3)
translated_value = translated_value + ls
ret_val = np.power(10, translated_value)
np.seterr(invalid=invalid_err)
return ret_val
###############################################################################
###############################################################################
def test_default(self):
err = np.geterr()
self.assertEqual(err, dict(
divide='warn',
invalid='warn',
over='warn',
under='ignore',
))
def test_set(self):
with np.errstate():
err = np.seterr()
old = np.seterr(divide='print')
self.assertTrue(err == old)
new = np.seterr()
self.assertTrue(new['divide'] == 'print')
np.seterr(over='raise')
self.assertTrue(np.geterr()['over'] == 'raise')
self.assertTrue(new['divide'] == 'print')
np.seterr(**old)
self.assertTrue(np.geterr() == old)
def setUp(self):
# Base data definition.
self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
self.err_status = np.geterr()
np.seterr(divide='ignore', invalid='ignore')
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
--------
>>> np.geterr()
{'over': 'warn', 'divide': 'warn', 'invalid': 'warn',
'under': 'ignore'}
>>> np.arange(3.) / np.arange(3.)
array([ NaN, 1., 1.])
>>> oldsettings = np.seterr(all='warn', over='raise')
>>> np.geterr()
{'over': 'raise', 'divide': 'warn', 'invalid': 'warn', 'under': 'warn'}
>>> np.arange(3.) / np.arange(3.)
__main__:1: RuntimeWarning: invalid value encountered in divide
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
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
>>> 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
"""
return umath.geterrobj()[2]