Exemplo n.º 1
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def test_scalar_quantity_distribution():
    # Regression test for gh-12336
    angles = Distribution([90., 30., 0.] * u.deg)
    sin_angles = np.sin(angles)  # This failed in 4.3.
    assert isinstance(sin_angles, Distribution)
    assert isinstance(sin_angles, u.Quantity)
    assert_array_equal(sin_angles, Distribution(np.sin([90., 30., 0.]*u.deg)))
Exemplo n.º 2
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def test_histogram():
    arr = np.random.randn(2, 3, 1000)
    distr = Distribution(arr)

    hist, bins = distr.pdf_histogram(bins=10)
    assert hist.shape == (2, 3, 10)
    assert bins.shape == (2, 3, 11)
Exemplo n.º 3
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def test_histogram():
    arr = np.random.randn(2, 3, 1000)
    distr = Distribution(arr)

    hist, bins = distr.pdf_histogram(bins=10)
    assert hist.shape == (2, 3, 10)
    assert bins.shape == (2, 3, 11)
Exemplo n.º 4
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    def setup_class(self):
        with NumpyRNGContext(12345):
            self.data = np.random.normal(np.array([1, 2, 3, 4])[:, np.newaxis],
                                         np.array([3, 2, 4, 5])[:, np.newaxis],
                                         (4, 10000))

        self.distr = Distribution(self.data * u.kpc)
Exemplo n.º 5
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def test_reprs():
    darr = np.arange(30).reshape(3, 10)
    distr = Distribution(darr * u.kpc)

    assert 'n_samples=10' in repr(distr)
    assert 'n_samples=10' in str(distr)

    assert r'n_{\rm samp}=10' in distr._repr_latex_()
Exemplo n.º 6
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def test_reprs():
    darr = np.arange(30).reshape(3, 10)
    distr = Distribution(darr * u.kpc)

    assert 'n_samples=10' in repr(distr)
    assert 'n_samples=10' in str(distr)

    assert r'n_{\rm samp}=10' in distr._repr_latex_()
Exemplo n.º 7
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def test_array_repr_latex():
    # as of this writing ndarray does not have a _repr_latex_, and this test
    # ensure distributions account for that. However, if in the future ndarray
    # gets a _repr_latex_, we can skip this.

    arr = np.random.randn(4, 1000)

    if hasattr(arr, '_repr_latex_'):
        pytest.skip('in this version of numpy, ndarray has a _repr_latex_')

    distr = Distribution(arr)
    assert distr._repr_latex_() is None
Exemplo n.º 8
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def test_array_repr_latex():
    # as of this writing ndarray does not have a _repr_latex_, and this test
    # ensure distributions account for that. However, if in the future ndarray
    # gets a _repr_latex_, we can skip this.


    arr = np.random.randn(4, 1000)

    if hasattr(arr, '_repr_latex_'):
        pytest.skip('in this version of numpy, ndarray has a _repr_latex_')

    distr = Distribution(arr)
    assert distr._repr_latex_() is None
Exemplo n.º 9
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def test_distr_cannot_view_new_dtype():
    # A Distribution has a very specific structured dtype with just one
    # element that holds the array of samples.  As it is not clear what
    # to do with a view as a new dtype, we just error on it.
    # TODO: with a lot of thought, this restriction can likely be relaxed.
    distr = Distribution([2., 3., 4.])
    with pytest.raises(ValueError, match='with a new dtype'):
        distr.view(np.dtype('f8'))

    # Check subclass just in case.
    ad = Angle(distr, 'deg')
    with pytest.raises(ValueError, match='with a new dtype'):
        ad.view(np.dtype('f8'))

    with pytest.raises(ValueError, match='with a new dtype'):
        ad.view(np.dtype('f8'), Distribution)
Exemplo n.º 10
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    def setup_class(self):
        with NumpyRNGContext(12345):
            self.data = np.random.normal(np.array([1, 2, 3, 4])[:, np.newaxis],
                                         np.array([3, 2, 4, 5])[:, np.newaxis],
                                         (4, 10000))

        self.distr = Distribution(self.data * u.kpc)
Exemplo n.º 11
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def test_init_scalar():
    parr = np.random.poisson(
        np.array([1, 5, 30, 400])[:, np.newaxis], (4, 1000))
    with pytest.raises(TypeError) as exc:
        Distribution(parr.ravel()[0])
    assert exc.value.args[
        0] == "Attempted to initialize a Distribution with a scalar"
Exemplo n.º 12
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 def test_quantity_init(self):
     # Test that we can initialize directly from a Quantity
     pq = self.parr << u.ct
     pqd = Distribution(pq)
     assert isinstance(pqd, u.Quantity)
     assert isinstance(pqd, Distribution)
     assert isinstance(pqd.value, Distribution)
     assert_array_equal(pqd.value.distribution, self.parr)
Exemplo n.º 13
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 def test_quantity_init_with_distribution(self):
     # Test that we can initialize a Quantity from a Distribution.
     pd = Distribution(self.parr)
     qpd = pd << u.ct
     assert isinstance(qpd, u.Quantity)
     assert isinstance(qpd, Distribution)
     assert qpd.unit == u.ct
     assert_array_equal(qpd.value.distribution, pd.distribution.astype(float))
Exemplo n.º 14
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def test_index_assignment_quantity():
    arr = np.random.randn(2, 1000)
    distr = Distribution(arr*u.kpc)
    d1q, d2q = distr
    assert isinstance(d1q, Distribution)
    assert isinstance(d2q, Distribution)

    ndistr = ds.normal(center=[1, 2]*u.kpc, std=[3, 4]*u.kpc, n_samples=1000)
    n1, n2 = ndistr
    assert isinstance(n1, ds.Distribution)
    assert isinstance(n2, ds.Distribution)
Exemplo n.º 15
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def test_index_assignment_array():
    arr = np.random.randn(2, 1000)
    distr = Distribution(arr)
    d1a, d2a = distr
    assert isinstance(d1a, Distribution)
    assert isinstance(d2a, Distribution)

    ndistr = ds.normal(center=[1, 2], std=[3, 4], n_samples=1000)
    n1, n2 = ndistr
    assert isinstance(n1, ds.Distribution)
    assert isinstance(n2, ds.Distribution)
Exemplo n.º 16
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    def test_add_distribution(self):
        another_data = (np.random.randn(4, 10000)
                        * np.array([1000, .01, 80, 10])[:, np.newaxis]
                        + np.array([2000, 0, 0, 500])[:, np.newaxis])
        # another_data is in pc, but main distr is in kpc
        another_distr = Distribution(another_data * u.pc)
        combined_distr = self.distr + another_distr

        expected = np.median(self.data + another_data/1000,
                             axis=-1) * self.distr.unit
        assert_quantity_allclose(combined_distr.pdf_median, expected)

        expected = np.var(self.data + another_data/1000, axis=-1) * self.distr.unit**2
        assert_quantity_allclose(combined_distr.pdf_var, expected)
Exemplo n.º 17
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def test_distr_angle_view_as_quantity():
    # Check that Quantity subclasses decay to Quantity appropriately.
    distr = Distribution([2., 3., 4.])
    ad = Angle(distr, 'deg')
    qd = ad.view(u.Quantity)
    assert not isinstance(qd, Angle)
    assert isinstance(qd, u.Quantity)
    assert isinstance(qd, Distribution)
    # View directly as DistributionQuantity class.
    qd2 = ad.view(qd.__class__)
    assert not isinstance(qd2, Angle)
    assert isinstance(qd2, u.Quantity)
    assert isinstance(qd2, Distribution)
    assert_array_equal(qd2.distribution, qd.distribution)
    qd3 = ad.view(qd.dtype, qd.__class__)
    assert not isinstance(qd3, Angle)
    assert isinstance(qd3, u.Quantity)
    assert isinstance(qd3, Distribution)
    assert_array_equal(qd3.distribution, qd.distribution)
Exemplo n.º 18
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def test_distr_angle():
    # Check that Quantity subclasses decay to Quantity appropriately.
    distr = Distribution([2., 3., 4.])
    ad = Angle(distr, 'deg')
    ad_plus_ad = ad + ad
    assert isinstance(ad_plus_ad, Angle)
    assert isinstance(ad_plus_ad, Distribution)

    ad_times_ad = ad * ad
    assert not isinstance(ad_times_ad, Angle)
    assert isinstance(ad_times_ad, u.Quantity)
    assert isinstance(ad_times_ad, Distribution)

    ad += ad
    assert isinstance(ad, Angle)
    assert isinstance(ad, Distribution)
    assert_array_equal(ad.distribution, ad_plus_ad.distribution)

    with pytest.raises(u.UnitTypeError):
        ad *= ad
Exemplo n.º 19
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 def test_numpy_init(self):
     # Test that we can initialize directly from a Numpy array
     Distribution(self.parr)
Exemplo n.º 20
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class TestDistributionStatistics():
    def setup_class(self):
        with NumpyRNGContext(12345):
            self.data = np.random.normal(np.array([1, 2, 3, 4])[:, np.newaxis],
                                         np.array([3, 2, 4, 5])[:, np.newaxis],
                                         (4, 10000))

        self.distr = Distribution(self.data * u.kpc)

    def test_shape(self):
        # Distribution shape
        assert self.distr.shape == (4, )
        assert self.distr.distribution.shape == (4, 10000)

    def test_size(self):
        # Total number of values
        assert self.distr.size == 4
        assert self.distr.distribution.size == 40000

    def test_n_samples(self):
        # Number of samples
        assert self.distr.n_samples == 10000

    def test_n_distr(self):
        assert self.distr.shape == (4,)

    def test_pdf_mean(self):
        # Mean of each PDF
        expected = np.mean(self.data, axis=-1) * self.distr.unit
        assert_quantity_allclose(self.distr.pdf_mean, expected)
        assert_quantity_allclose(self.distr.pdf_mean, [1, 2, 3, 4] * self.distr.unit, rtol=0.05)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_mean, Distribution)
        assert isinstance(self.distr.pdf_mean, u.Quantity)

    def test_pdf_std(self):
        # Standard deviation of each PDF
        expected = np.std(self.data, axis=-1) * self.distr.unit
        assert_quantity_allclose(self.distr.pdf_std, expected)
        assert_quantity_allclose(self.distr.pdf_std, [3, 2, 4, 5] * self.distr.unit, rtol=0.05)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_std, Distribution)
        assert isinstance(self.distr.pdf_std, u.Quantity)

    def test_pdf_var(self):
        # Variance of each PDF
        expected = np.var(self.data, axis=-1) * self.distr.unit**2
        assert_quantity_allclose(self.distr.pdf_var, expected)
        assert_quantity_allclose(self.distr.pdf_var, [9, 4, 16, 25] * self.distr.unit**2, rtol=0.1)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_var, Distribution)
        assert isinstance(self.distr.pdf_var, u.Quantity)

    def test_pdf_median(self):
        # Median of each PDF
        expected = np.median(self.data, axis=-1) * self.distr.unit
        assert_quantity_allclose(self.distr.pdf_median, expected)
        assert_quantity_allclose(self.distr.pdf_median, [1, 2, 3, 4] * self.distr.unit, rtol=0.1)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_median, Distribution)
        assert isinstance(self.distr.pdf_median, u.Quantity)

    @pytest.mark.skipif(not HAS_SCIPY, reason='no scipy')
    def test_pdf_mad_smad(self):
        # Median absolute deviation of each PDF
        median = np.median(self.data, axis=-1, keepdims=True)
        expected = np.median(np.abs(self.data - median), axis=-1) * self.distr.unit
        assert_quantity_allclose(self.distr.pdf_mad, expected)
        assert_quantity_allclose(self.distr.pdf_smad, self.distr.pdf_mad * SMAD_FACTOR, rtol=1e-5)
        assert_quantity_allclose(self.distr.pdf_smad, [3, 2, 4, 5] * self.distr.unit, rtol=0.05)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_mad, Distribution)
        assert isinstance(self.distr.pdf_mad, u.Quantity)
        assert not isinstance(self.distr.pdf_smad, Distribution)
        assert isinstance(self.distr.pdf_smad, u.Quantity)

    def test_percentile(self):
        expected = np.percentile(self.data, [10, 50, 90], axis=-1) * self.distr.unit
        percs = self.distr.pdf_percentiles([10, 50, 90])
        assert_quantity_allclose(percs, expected)
        assert percs.shape == (3, 4)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(percs, Distribution)
        assert isinstance(percs, u.Quantity)

    def test_add_quantity(self):
        distrplus = self.distr + [2000, 0, 0, 500] * u.pc
        expected = (np.median(self.data, axis=-1) + np.array([2, 0, 0, 0.5])) * self.distr.unit
        assert_quantity_allclose(distrplus.pdf_median, expected)
        expected = np.var(self.data, axis=-1) * self.distr.unit**2
        assert_quantity_allclose(distrplus.pdf_var, expected)

    def test_add_distribution(self):
        another_data = (np.random.randn(4, 10000)
                        * np.array([1000, .01, 80, 10])[:, np.newaxis]
                        + np.array([2000, 0, 0, 500])[:, np.newaxis])
        # another_data is in pc, but main distr is in kpc
        another_distr = Distribution(another_data * u.pc)
        combined_distr = self.distr + another_distr

        expected = np.median(self.data + another_data/1000,
                             axis=-1) * self.distr.unit
        assert_quantity_allclose(combined_distr.pdf_median, expected)

        expected = np.var(self.data + another_data/1000, axis=-1) * self.distr.unit**2
        assert_quantity_allclose(combined_distr.pdf_var, expected)
Exemplo n.º 21
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def test_quantity_init_T():
    # Test that we can initialize directly from a Quantity
    pq = np.random.poisson(np.array([1, 5, 30, 400]), (1000, 4)) * u.ct
    Distribution(pq.T)
Exemplo n.º 22
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def test_quantity_init():
    # Test that we can initialize directly from a Quantity
    pq = np.random.poisson(np.array([1, 5, 30, 400])[:, np.newaxis],
                           (4, 1000)) * u.ct
    Distribution(pq)
Exemplo n.º 23
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class TestDistributionStatistics():
    def setup_class(self):
        with NumpyRNGContext(12345):
            self.data = np.random.normal(np.array([1, 2, 3, 4])[:, np.newaxis],
                                         np.array([3, 2, 4, 5])[:, np.newaxis],
                                         (4, 10000))

        self.distr = Distribution(self.data * u.kpc)

    def test_shape(self):
        # Distribution shape
        assert self.distr.shape == (4, )
        assert self.distr.distribution.shape == (4, 10000)

    def test_size(self):
        # Total number of values
        assert self.distr.size == 4
        assert self.distr.distribution.size == 40000

    def test_n_samples(self):
        # Number of samples
        assert self.distr.n_samples == 10000

    def test_n_distr(self):
        assert self.distr.shape == (4,)

    def test_pdf_mean(self):
        # Mean of each PDF
        expected = np.mean(self.data, axis=-1) * self.distr.unit
        assert_quantity_allclose(self.distr.pdf_mean, expected)
        assert_quantity_allclose(self.distr.pdf_mean, [1, 2, 3, 4] * self.distr.unit, rtol=0.05)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_mean, Distribution)
        assert isinstance(self.distr.pdf_mean, u.Quantity)

    def test_pdf_std(self):
        # Standard deviation of each PDF
        expected = np.std(self.data, axis=-1) * self.distr.unit
        assert_quantity_allclose(self.distr.pdf_std, expected)
        assert_quantity_allclose(self.distr.pdf_std, [3, 2, 4, 5] * self.distr.unit, rtol=0.05)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_std, Distribution)
        assert isinstance(self.distr.pdf_std, u.Quantity)

    def test_pdf_var(self):
        # Variance of each PDF
        expected = np.var(self.data, axis=-1) * self.distr.unit**2
        assert_quantity_allclose(self.distr.pdf_var, expected)
        assert_quantity_allclose(self.distr.pdf_var, [9, 4, 16, 25] * self.distr.unit**2, rtol=0.1)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_var, Distribution)
        assert isinstance(self.distr.pdf_var, u.Quantity)

    def test_pdf_median(self):
        # Median of each PDF
        expected = np.median(self.data, axis=-1) * self.distr.unit
        assert_quantity_allclose(self.distr.pdf_median, expected)
        assert_quantity_allclose(self.distr.pdf_median, [1, 2, 3, 4] * self.distr.unit, rtol=0.1)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_median, Distribution)
        assert isinstance(self.distr.pdf_median, u.Quantity)

    @pytest.mark.skipif(not HAS_SCIPY, reason='no scipy')
    def test_pdf_mad_smad(self):
        # Median absolute deviation of each PDF
        median = np.median(self.data, axis=-1, keepdims=True)
        expected = np.median(np.abs(self.data - median), axis=-1) * self.distr.unit
        assert_quantity_allclose(self.distr.pdf_mad, expected)
        assert_quantity_allclose(self.distr.pdf_smad, self.distr.pdf_mad * SMAD_FACTOR, rtol=1e-5)
        assert_quantity_allclose(self.distr.pdf_smad, [3, 2, 4, 5] * self.distr.unit, rtol=0.05)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(self.distr.pdf_mad, Distribution)
        assert isinstance(self.distr.pdf_mad, u.Quantity)
        assert not isinstance(self.distr.pdf_smad, Distribution)
        assert isinstance(self.distr.pdf_smad, u.Quantity)

    def test_percentile(self):
        expected = np.percentile(self.data, [10, 50, 90], axis=-1) * self.distr.unit
        percs = self.distr.pdf_percentiles([10, 50, 90])
        assert_quantity_allclose(percs, expected)
        assert percs.shape == (3, 4)

        # make sure the right type comes out - should be a Quantity because it's
        # now a summary statistic
        assert not isinstance(percs, Distribution)
        assert isinstance(percs, u.Quantity)

    def test_add_quantity(self):
        distrplus = self.distr + [2000, 0, 0, 500] * u.pc
        expected = (np.median(self.data, axis=-1) + np.array([2, 0, 0, 0.5])) * self.distr.unit
        assert_quantity_allclose(distrplus.pdf_median, expected)
        expected = np.var(self.data, axis=-1) * self.distr.unit**2
        assert_quantity_allclose(distrplus.pdf_var, expected)

    def test_add_distribution(self):
        another_data = (np.random.randn(4, 10000)
                        * np.array([1000, .01, 80, 10])[:, np.newaxis]
                        + np.array([2000, 0, 0, 500])[:, np.newaxis])
        # another_data is in pc, but main distr is in kpc
        another_distr = Distribution(another_data * u.pc)
        combined_distr = self.distr + another_distr

        expected = np.median(self.data + another_data/1000,
                             axis=-1) * self.distr.unit
        assert_quantity_allclose(combined_distr.pdf_median, expected)

        expected = np.var(self.data + another_data/1000, axis=-1) * self.distr.unit**2
        assert_quantity_allclose(combined_distr.pdf_var, expected)
Exemplo n.º 24
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def test_numpy_init_T():
    rates = np.array([1, 5, 30, 400])
    parr = np.random.poisson(rates, (1000, 4))
    Distribution(parr.T)
Exemplo n.º 25
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 def test_quantity_init_T(self):
     # Test that we can initialize directly from a Quantity
     pq = self.parr_t << u.ct
     Distribution(pq.T)
Exemplo n.º 26
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def test_numpy_init():
    # Test that we can initialize directly from a Numpy array
    rates = np.array([1, 5, 30, 400])[:, np.newaxis]
    parr = np.random.poisson(rates, (4, 1000))
    Distribution(parr)
Exemplo n.º 27
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 def test_numpy_init_T(self):
     Distribution(self.parr_t.T)