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 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 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 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 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 _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 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]
