Python numpy.geterr() Examples

The following are 30 code examples of numpy.geterr(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: test_core.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
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') 
Example #2
Source File: test_core.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up
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') 
Example #3
Source File: test_core.py    From coffeegrindsize with MIT License 6 votes vote down vote up
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') 
Example #4
Source File: test_core.py    From ImageFusion with MIT License 6 votes vote down vote up
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') 
Example #5
Source File: test_core.py    From mxnet-lambda with Apache License 2.0 6 votes vote down vote up
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') 
Example #6
Source File: test_core.py    From lambda-packs with MIT License 6 votes vote down vote up
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') 
Example #7
Source File: test_core.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up
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') 
Example #8
Source File: test_core.py    From pySINDy with MIT License 6 votes vote down vote up
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') 
Example #9
Source File: test_core.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up
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') 
Example #10
Source File: decorators.py    From ngraph-python with Apache License 2.0 6 votes vote down vote up
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 
Example #11
Source File: basic_stats_generator_test.py    From data-validation with Apache License 2.0 6 votes vote down vote up
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) 
Example #12
Source File: util.py    From Computable with MIT License 6 votes vote down vote up
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) 
Example #13
Source File: test_core.py    From vnpy_crypto with MIT License 6 votes vote down vote up
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') 
Example #14
Source File: test_core.py    From Computable with MIT License 6 votes vote down vote up
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') 
Example #15
Source File: test_core.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up
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') 
Example #16
Source File: test_core.py    From recruit with Apache License 2.0 6 votes vote down vote up
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') 
Example #17
Source File: test_core.py    From pySINDy with MIT License 5 votes vote down vote up
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') 
Example #18
Source File: hmm.py    From deepbgc with MIT License 5 votes vote down vote up
def predict(self, X):
        sample_vector = self.get_sample_vector(X)
        prev_level = np.geterr()['divide']
        np.seterr(divide='ignore')
        logprob, posteriors = self.model_.score_samples(sample_vector.reshape(-1, 1))
        np.seterr(divide=prev_level)
        # final prediction is maximum of the probability of the last two states
        prediction = posteriors[:,2:]
        return pd.Series(np.max(prediction, axis=1), X.index) 
Example #19
Source File: test_numeric.py    From pySINDy with MIT License 5 votes vote down vote up
def test_default(self):
        err = np.geterr()
        assert_equal(err,
                     dict(divide='warn',
                          invalid='warn',
                          over='warn',
                          under='ignore')
                     ) 
Example #20
Source File: sampling.py    From pynoddy with GNU General Public License v2.0 5 votes vote down vote up
def VMLookupTable():
    try:  # try loading ordered dict
        from collections import OrderedDict
    except ImportError:  # not installed, try on pythonpath
        try:
            from OrderedDict import OrderedDict
        except ImportError:
            "PyNoddy requires OrderedDict to run. Please download it and make it available on the pythonpath."

    kappa_lookup = OrderedDict()


    #disable numpy warnings
    err = np.geterr()
    np.seterr(all='ignore')
    
    
    # build lookup table
    for k in range(1000, 100, -20):
        ci = sc.stats.vonmises.interval(0.95, k)
        kappa_lookup[ci[1]] = k
    for k in range(100, 10, -1):
        ci = sc.stats.vonmises.interval(0.95, k)
        kappa_lookup[ci[1]] = k
    for k in np.arange(10, 0, -0.1):
        ci = sc.stats.vonmises.interval(0.95, k)
        kappa_lookup[ci[1]] = k

    #re-enable numpy warnings
    np.seterr(**err)
    
    # return lookup table
    return kappa_lookup


# build (static) lookup table 
Example #21
Source File: test_core.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up
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') 
Example #22
Source File: test_numeric.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up
def test_default(self):
        err = np.geterr()
        assert_equal(err,
                     dict(divide='warn',
                          invalid='warn',
                          over='warn',
                          under='ignore')
                     ) 
Example #23
Source File: test_numeric.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up
def test_default(self):
        err = np.geterr()
        self.assertEqual(err, dict(
            divide='warn',
            invalid='warn',
            over='warn',
            under='ignore',
        )) 
Example #24
Source File: test_util.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up
def test_numpy_errstate_is_default():
    # The defaults since numpy 1.6.0
    expected = {'over': 'warn', 'divide': 'warn', 'invalid': 'warn',
                'under': 'ignore'}
    import numpy as np
    from pandas.compat import numpy  # noqa
    # The errstate should be unchanged after that import.
    assert np.geterr() == expected 
Example #25
Source File: test_core.py    From coffeegrindsize with MIT License 5 votes vote down vote up
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') 
Example #26
Source File: electrode_placement.py    From simnibs with GNU General Public License v3.0 5 votes vote down vote up
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 
Example #27
Source File: test_numeric.py    From coffeegrindsize with MIT License 5 votes vote down vote up
def test_default(self):
        err = np.geterr()
        assert_equal(err,
                     dict(divide='warn',
                          invalid='warn',
                          over='warn',
                          under='ignore')
                     ) 
Example #28
Source File: test_core.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up
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') 
Example #29
Source File: test_numeric.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def test_default(self):
        err = np.geterr()
        assert_equal(err,
                     dict(divide='warn',
                          invalid='warn',
                          over='warn',
                          under='ignore')
                     ) 
Example #30
Source File: test_numeric.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_default(self):
        err = np.geterr()
        assert_equal(err,
                     dict(divide='warn',
                          invalid='warn',
                          over='warn',
                          under='ignore')
                     )