Python numpy.geterr方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
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') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
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') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
def handleError(errorStatus, sourcemsg):
    """Take error status and use error mode to handle it."""
    modes = np.geterr()
    if errorStatus & np.FPE_INVALID:
        if modes['invalid'] == "warn":
            print("Warning: Encountered invalid numeric result(s)", sourcemsg)
        if modes['invalid'] == "raise":
            raise MathDomainError(sourcemsg)
    if errorStatus & np.FPE_DIVIDEBYZERO:
        if modes['dividebyzero'] == "warn":
            print("Warning: Encountered divide by zero(s)", sourcemsg)
        if modes['dividebyzero'] == "raise":
            raise ZeroDivisionError(sourcemsg)
    if errorStatus & np.FPE_OVERFLOW:
        if modes['overflow'] == "warn":
            print("Warning: Encountered overflow(s)", sourcemsg)
        if modes['overflow'] == "raise":
            raise NumOverflowError(sourcemsg)
    if errorStatus & np.FPE_UNDERFLOW:
        if modes['underflow'] == "warn":
            print("Warning: Encountered underflow(s)", sourcemsg)
        if modes['underflow'] == "raise":
            raise UnderflowError(sourcemsg) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
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') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
def _calc_triangle_angles(p, eps=1e-5):
    p1 = p[:, 0]
    p2 = p[:, 1]
    p3 = p[:, 2]

    e1 = np.linalg.norm(p2 - p1, axis=1)
    e2 = np.linalg.norm(p3 - p1, axis=1)
    e3 = np.linalg.norm(p3 - p2, axis=1)
    # Law Of Cossines
    state = np.geterr()['invalid']
    np.seterr(invalid='ignore')
    a = np.zeros((p.shape[0], 3))
    v = (e1 > eps) * (e2 > eps)
    a[v, 0] = np.arccos((e2[v] ** 2 + e1[v] ** 2 - e3[v] ** 2) / (2 * e1[v] * e2[v]))
    a[~v, 0] = 0

    v = (e1 > eps) * (e3 > eps)
    a[v, 1] = np.arccos((e1[v] ** 2 + e3[v] ** 2 - e2[v] ** 2) / (2 * e1[v] * e3[v]))
    a[~v, 1] = 0

    v = (e2 > eps) * (e3 > eps)
    a[v, 2] = np.arccos((e2[v] ** 2 + e3[v] ** 2 - e1[v] ** 2) / (2 * e2[v] * e3[v]))
    a[~v, 2] = 0
    np.seterr(invalid=state)
    a[np.isnan(a)] = np.pi

    return a 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
def test_default(self):
        err = np.geterr()
        assert_equal(err,
                     dict(divide='warn',
                          invalid='warn',
                          over='warn',
                          under='ignore')
                     ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
def test_numpy_err_state_is_default():
    expected = {"over": "warn", "divide": "warn",
                "invalid": "warn", "under": "ignore"}
    import numpy as np

    # The error state should be unchanged after that import.
    assert np.geterr() == expected 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
def __init__(self, casting='same_kind', err=None, dtype=None, sparse=None, **kw):
        err = err if err is not None else np.geterr()
        super().__init__(_casting=casting, _err=err, _dtype=dtype, _sparse=sparse, **kw) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
def __init__(self, decimals=None, casting='same_kind', err=None, dtype=None, sparse=False, **kw):
        err = err if err is not None else np.geterr()
        super().__init__(_decimals=decimals, _casting=casting, _err=err, _dtype=dtype,
                         _sparse=sparse, **kw) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
def __init__(self, deg=None, casting='same_kind', err=None, dtype=None, sparse=False, **kw):
        err = err if err is not None else np.geterr()
        super().__init__(_deg=deg, _casting=casting, _err=err, _dtype=dtype, _sparse=sparse, **kw) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
def test_default(self):
        err = np.geterr()
        self.assertEqual(err, dict(
            divide='warn',
            invalid='warn',
            over='warn',
            under='ignore',
        )) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import geterr [as 別名]
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)