Python numpy.seterr() Examples

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 _entropy(self, *args):
        def integ(x):
            val = self._pdf(x, *args)
            return entr(val)

        # upper limit is often inf, so suppress warnings when integrating
        olderr = np.seterr(over='ignore')
        h = integrate.quad(integ, self.a, self.b)[0]
        np.seterr(**olderr)

        if not np.isnan(h):
            return h
        else:
            # try with different limits if integration problems
            low, upp = self.ppf([1e-10, 1. - 1e-10], *args)
            if np.isinf(self.b):
                upper = upp
            else:
                upper = self.b
            if np.isinf(self.a):
                lower = low
            else:
                lower = self.a
            return integrate.quad(integ, lower, upper)[0] 

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 evaluate(self,n, features, stack_float, stack_bool,labels=None):
        """evaluate node in program"""
        np.seterr(all='ignore')
        if len(stack_float) >= n.arity['f'] and len(stack_bool) >= n.arity['b']:
            if n.out_type == 'f':
                stack_float.append(
                    self.safe(self.eval_dict[n.name](n,features,stack_float,
                                                     stack_bool,labels)))
                if (np.isnan(stack_float[-1]).any() or
                    np.isinf(stack_float[-1]).any()):
                    print("problem operator:",n)
            else:
                stack_bool.append(self.safe(self.eval_dict[n.name](n,features,
                                                                   stack_float,
                                                                   stack_bool,
                                                                   labels)))
                if np.isnan(stack_bool[-1]).any() or np.isinf(stack_bool[-1]).any():
                    print("problem operator:",n) 

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_basic_stats_generator_no_runtime_warnings_close_to_max_int(self):
    # input has batches with values that are slightly smaller than the maximum
    # integer value.
    less_than_max_int_value = np.iinfo(np.int64).max - 1
    batches = ([
        pa.RecordBatch.from_arrays([pa.array([[less_than_max_int_value]])],
                                   ['a'])
    ] * 2)
    generator = basic_stats_generator.BasicStatsGenerator()
    old_nperr = np.geterr()
    np.seterr(over='raise')
    accumulators = [
        generator.add_input(generator.create_accumulator(), batch)
        for batch in batches
    ]
    generator.merge_accumulators(accumulators)
    np.seterr(**old_nperr) 

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 test_get_metrics(cls):
        np.seterr(divide='ignore', invalid='ignore')

        bins = 1000
        diff = 0.01
        metric = MultilabelAveragePrecision(bins=bins)
        size = [1000, 100]
        pred = Tensor(np.random.uniform(0, 1, size))
        gold = Tensor(np.random.randint(0, 2, size))
        metric.__call__(pred, gold)
        fast_ap = metric.get_metric()  # calls the fast get_metric
        ap = metric.get_metric(reset=True)  # calls the accurate get_metric
        assert (abs(ap - fast_ap)) < diff

        metric.__call__(pred, gold)
        metric.__call__(pred, gold)
        metric.__call__(pred, gold)
        fast_ap = metric.get_metric()
        ap = metric.get_metric(reset=True)
        assert (abs(ap - fast_ap)) < diff 

def tearDown(self):
        np.seterr(**self.olderr) 

def tearDown(self):
        np.seterr(**self.olderr) 

def setUp(self):
        self.olderr = np.seterr(invalid='ignore') 

def tearDown(self):
        np.seterr(**self.olderr) 

def perform(self, node, inp, out):
        rstate, size = inp
        o_rstate, o_sample = out
        n_elements = 1
        for s in size:
            n_elements *= s
        if n_elements > M1:
            # The limit is on the C and GPU code. This perform don't
            # have this limit.  But to have all of them behave the
            # same (and have DebugMode don't use too much memory for
            # some rng_mrg tests) I also add this limit here.
            raise ValueError("rng_mrg does not support more then (2**31 -1) samples")

        rstate = numpy.asarray(rstate)  # bring state from GPU if necessary
        if not self.inplace:
            rstate = rstate.copy()

        n_streams, _ = rstate.shape

        rval = numpy.zeros(n_elements, dtype=self.output_type.dtype)

        err_orig = numpy.seterr(over='ignore')
        try:
            for i in xrange(n_elements):
                sample = mrg_next_value(rstate[i % n_streams],
                                        rstate[i % n_streams])
                rval[i] = sample
        finally:
            numpy.seterr(**err_orig)

        # send to GPU if necessary
        o_rstate[0] = node.outputs[0].type.filter(rstate)
        o_sample[0] = node.outputs[1].type.filter(rval.reshape(size)) 

def beta_opt(self, method='mxie', apply=False, **kwargs):
        if method == 'mxie':
            beta_orig_x, beta_orig_y = self.betax, self.betay
            beta_orig = np.mean([beta_orig_x, beta_orig_y])
            
            fel_copy = deepcopy(self)
            def f(x, method=method):
                fel_copy.betax = fel_copy.betay = x
                fel_copy.eval(method=method)
                return fel_copy.lg3
            
            err_dict = np.geterr()
            np.seterr(all='ignore')
            beta_opt = fmin(f, beta_orig, disp=0, **kwargs)
            np.seterr(**err_dict)
            
        elif method == 'ssy_opt':
            beta_opt = self.beta_opt_calc
        
        else:
            _logger.error('method should be in ["mxie", "ssy_opt"]')
            raise ValueError('method should be in ["mxie", "ssy_opt"]')
            
        if apply:
            self.betax = beta_opt
            self.betay = beta_opt
            self.eval(method)
        else:
            return beta_opt[0] 

def setup(self):
        self.olderr = np.seterr(invalid='ignore') 

def tearDown(self):
        np.seterr(**self.olderr) 

def test_set(self):
        with np.errstate():
            err = np.seterr()
            old = np.seterr(divide='print')
            assert_(err == old)
            new = np.seterr()
            assert_(new['divide'] == 'print')
            np.seterr(over='raise')
            assert_(np.geterr()['over'] == 'raise')
            assert_(new['divide'] == 'print')
            np.seterr(**old)
            assert_(np.geterr() == old) 

def test_divide_err(self):
        with np.errstate(divide='raise'):
            try:
                np.array([1.]) / np.array([0.])
            except FloatingPointError:
                pass
            else:
                self.fail()
            np.seterr(divide='ignore')
            np.array([1.]) / np.array([0.]) 

def test_divide_err(self):
        with np.errstate(divide='raise'):
            try:
                np.array([1.]) / np.array([0.])
            except FloatingPointError:
                pass
            else:
                self.fail()
            np.seterr(divide='ignore')
            np.array([1.]) / np.array([0.]) 

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 tearDown(self):
        np.seterr(**self.olderr) 

def test_testUfuncRegression(self):
        f_invalid_ignore = ['sqrt', 'arctanh', 'arcsin', 'arccos',
                'arccosh', 'arctanh', 'log', 'log10', 'divide',
                'true_divide', 'floor_divide', 'remainder', 'fmod']
        for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
                'sin', 'cos', 'tan',
                'arcsin', 'arccos', 'arctan',
                'sinh', 'cosh', 'tanh',
                'arcsinh',
                'arccosh',
                'arctanh',
                'absolute', 'fabs', 'negative',
                # 'nonzero', 'around',
                'floor', 'ceil',
                # 'sometrue', 'alltrue',
                'logical_not',
                'add', 'subtract', 'multiply',
                'divide', 'true_divide', 'floor_divide',
                'remainder', 'fmod', 'hypot', 'arctan2',
                'equal', 'not_equal', 'less_equal', 'greater_equal',
                'less', 'greater',
                'logical_and', 'logical_or', 'logical_xor',
                ]:
            try:
                uf = getattr(umath, f)
            except AttributeError:
                uf = getattr(fromnumeric, f)
            mf = getattr(np.ma, f)
            args = self.d[:uf.nin]
            with np.errstate():
                if f in f_invalid_ignore:
                    np.seterr(invalid='ignore')
                if f in ['arctanh', 'log', 'log10']:
                    np.seterr(divide='ignore')
                ur = uf(*args)
                mr = mf(*args)
            self.assertTrue(eq(ur.filled(0), mr.filled(0), f))
            self.assertTrue(eqmask(ur.mask, mr.mask)) 

def setUp(self):
        self.olderr = np.seterr(invalid='ignore') 

def test_fix_invalid(self):
        "Checks fix_invalid."
        with np.errstate():
            np.seterr(invalid='ignore')
            data = masked_array([np.nan, 0., 1.], mask=[0, 0, 1])
            data_fixed = fix_invalid(data)
            assert_equal(data_fixed._data, [data.fill_value, 0., 1.])
            assert_equal(data_fixed._mask, [1., 0., 1.]) 

def tearDown(self):
        np.seterr(**self.olderr) 

def tearDown(self):
        np.seterr(**self.err_status) 

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 tearDown(self):
        np.seterr(**self.err_status)