Python numpy.histogramdd() Examples

The following are 30 code examples of numpy.histogramdd(). 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_function_base.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def test_rightmost_binedge(self):
        # Test event very close to rightmost binedge. See Github issue #4266
        x = [0.9999999995]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0000000001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 0.0)
Example #2
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_rightmost_binedge(self):
        # Test event very close to rightmost binedge. See Github issue #4266
        x = [0.9999999995]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0000000001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 0.0)
        x = [1.0001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 0.0)
Example #3
Source File: test_function_base.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def test_rightmost_binedge(self):
        # Test event very close to rightmost binedge. See Github issue #4266
        x = [0.9999999995]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0000000001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 0.0)
Example #4
Source File: test_histograms.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def test_rightmost_binedge(self):
        # Test event very close to rightmost binedge. See Github issue #4266
        x = [0.9999999995]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0000000001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 0.0)
        x = [1.0001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 0.0)
Example #5
Source File: utils.py    From CausalGAN with MIT License 6 votes vote down vote up 
def calc_tvd(sess,Generator,Data,N=50000,nbins=10):
    Xd=sess.run(Data.X,{Data.N:N})
    step,Xg=sess.run([Generator.step,Generator.X],{Generator.N:N})

    p_gen,_ = np.histogramdd(Xg,bins=nbins,range=[[0,1],[0,1],[0,1]],normed=True)
    p_dat,_ = np.histogramdd(Xd,bins=nbins,range=[[0,1],[0,1],[0,1]],normed=True)
    p_gen/=nbins**3
    p_dat/=nbins**3
    tvd=0.5*np.sum(np.abs( p_gen-p_dat ))
    mvd=np.max(np.abs( p_gen-p_dat ))

    return step,tvd, mvd

    s_tvd=make_summary(Data.name+'_tvd',tvd)
    s_mvd=make_summary(Data.name+'_mvd',mvd)

    return step,s_tvd,s_mvd
    #return make_summary('tvd/'+Generator.name,tvd)
Example #6
Source File: test_function_base.py    From lambda-packs with MIT License 6 votes vote down vote up 
def test_rightmost_binedge(self):
        # Test event very close to rightmost binedge. See Github issue #4266
        x = [0.9999999995]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0000000001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 1.)
        x = [1.0001]
        bins = [[0., 0.5, 1.0]]
        hist, _ = histogramdd(x, bins=bins)
        assert_(hist[0] == 0.0)
        assert_(hist[1] == 0.0)
Example #7
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_shape_3d(self):
        # All possible permutations for bins of different lengths in 3D.
        bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
                (4, 5, 6))
        r = np.random.rand(10, 3)
        for b in bins:
            H, edges = histogramdd(r, b)
            assert_(H.shape == b)
Example #8
Source File: test_regression.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_histogramdd_too_many_bins(self):
        # Ticket 928.
        assert_raises(ValueError, np.histogramdd, np.ones((1, 10)), bins=2**10)
Example #9
Source File: test_function_base.py    From Computable with MIT License 5 votes vote down vote up 
def test_simple(self):
        x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], \
        [.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]])
        H, edges = histogramdd(x, (2, 3, 3), range=[[-1, 1], [0, 3], [0, 3]])
        answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]], [[0, 1, 0], [0, 0, 1],
            [0, 0, 1]]])
        assert_array_equal(H, answer)
        # Check normalization
        ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
        H, edges = histogramdd(x, bins=ed, normed=True)
        assert_(np.all(H == answer / 12.))
        # Check that H has the correct shape.
        H, edges = histogramdd(x, (2, 3, 4), range=[[-1, 1], [0, 3], [0, 4]],
            normed=True)
        answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]], [[0, 1, 0, 0],
            [0, 0, 1, 0], [0, 0, 1, 0]]])
        assert_array_almost_equal(H, answer / 6., 4)
        # Check that a sequence of arrays is accepted and H has the correct
        # shape.
        z = [np.squeeze(y) for y in split(x, 3, axis=1)]
        H, edges = histogramdd(z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]])
        answer = np.array([[[0, 0], [0, 0], [0, 0]],
                          [[0, 1], [0, 0], [1, 0]],
                          [[0, 1], [0, 0], [0, 0]],
                          [[0, 0], [0, 0], [0, 0]]])
        assert_array_equal(H, answer)

        Z = np.zeros((5, 5, 5))
        Z[list(range(5)), list(range(5)), list(range(5))] = 1.
        H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5)
        assert_array_equal(H, Z)
Example #10
Source File: test_function_base.py    From Computable with MIT License 5 votes vote down vote up 
def test_shape_3d(self):
        # All possible permutations for bins of different lengths in 3D.
        bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
            (4, 5, 6))
        r = rand(10, 3)
        for b in bins:
            H, edges = histogramdd(r, b)
            assert_(H.shape == b)
Example #11
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_empty(self):
        a, b = histogramdd([[], []], bins=([0, 1], [0, 1]))
        assert_array_max_ulp(a, np.array([[0.]]))
        a, b = np.histogramdd([[], [], []], bins=2)
        assert_array_max_ulp(a, np.zeros((2, 2, 2)))
Example #12
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_simple(self):
        x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5],
                      [.5,  .5, 1.5], [.5,  1.5, 2.5], [.5,  2.5, 2.5]])
        H, edges = histogramdd(x, (2, 3, 3),
                               range=[[-1, 1], [0, 3], [0, 3]])
        answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]],
                           [[0, 1, 0], [0, 0, 1], [0, 0, 1]]])
        assert_array_equal(H, answer)

        # Check normalization
        ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
        H, edges = histogramdd(x, bins=ed, density=True)
        assert_(np.all(H == answer / 12.))

        # Check that H has the correct shape.
        H, edges = histogramdd(x, (2, 3, 4),
                               range=[[-1, 1], [0, 3], [0, 4]],
                               density=True)
        answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]],
                           [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]])
        assert_array_almost_equal(H, answer / 6., 4)
        # Check that a sequence of arrays is accepted and H has the correct
        # shape.
        z = [np.squeeze(y) for y in np.split(x, 3, axis=1)]
        H, edges = histogramdd(
            z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]])
        answer = np.array([[[0, 0], [0, 0], [0, 0]],
                           [[0, 1], [0, 0], [1, 0]],
                           [[0, 1], [0, 0], [0, 0]],
                           [[0, 0], [0, 0], [0, 0]]])
        assert_array_equal(H, answer)

        Z = np.zeros((5, 5, 5))
        Z[list(range(5)), list(range(5)), list(range(5))] = 1.
        H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5)
        assert_array_equal(H, Z)
Example #13
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_finite_range(self):
        vals = np.random.random((100, 3))
        histogramdd(vals, range=[[0.0, 1.0], [0.25, 0.75], [0.25, 0.5]])
        assert_raises(ValueError, histogramdd, vals,
                      range=[[0.0, 1.0], [0.25, 0.75], [0.25, np.inf]])
        assert_raises(ValueError, histogramdd, vals,
                      range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]])
Example #14
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_shape_4d(self):
        # All possible permutations for bins of different lengths in 4D.
        bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4),
                (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6),
                (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7),
                (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5),
                (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
                (5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))

        r = np.random.rand(10, 4)
        for b in bins:
            H, edges = histogramdd(r, b)
            assert_(H.shape == b)
Example #15
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_weights(self):
        v = np.random.rand(100, 2)
        hist, edges = histogramdd(v)
        n_hist, edges = histogramdd(v, density=True)
        w_hist, edges = histogramdd(v, weights=np.ones(100))
        assert_array_equal(w_hist, hist)
        w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, density=True)
        assert_array_equal(w_hist, n_hist)
        w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2)
        assert_array_equal(w_hist, 2 * hist)
Example #16
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_inf_edges(self):
        # Test using +/-inf bin edges works. See #1788.
        with np.errstate(invalid='ignore'):
            x = np.arange(6).reshape(3, 2)
            expected = np.array([[1, 0], [0, 1], [0, 1]])
            h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]])
            assert_allclose(h, expected)
            h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])])
            assert_allclose(h, expected)
            h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]])
            assert_allclose(h, expected)
Example #17
Source File: test_histograms.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_edge_dtype(self):
        """ Test that if an edge array is input, its type is preserved """
        x = np.array([0, 10, 20])
        y = x / 10
        x_edges = np.array([0, 5, 15, 20])
        y_edges = x_edges / 10
        hist, edges = histogramdd((x, y), bins=(x_edges, y_edges))

        assert_equal(edges[0].dtype, x_edges.dtype)
        assert_equal(edges[1].dtype, y_edges.dtype)
Example #18
Source File: test_function_base.py    From Computable with MIT License 5 votes vote down vote up 
def test_weights(self):
        v = rand(100, 2)
        hist, edges = histogramdd(v)
        n_hist, edges = histogramdd(v, normed=True)
        w_hist, edges = histogramdd(v, weights=np.ones(100))
        assert_array_equal(w_hist, hist)
        w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, normed=True)
        assert_array_equal(w_hist, n_hist)
        w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2)
        assert_array_equal(w_hist, 2 * hist)
Example #19
Source File: test_function_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_finite_range(self):
        vals = np.random.random((100, 3))
        histogramdd(vals, range=[[0.0, 1.0], [0.25, 0.75], [0.25, 0.5]])
        assert_raises(ValueError, histogramdd, vals,
                      range=[[0.0, 1.0], [0.25, 0.75], [0.25, np.inf]])
        assert_raises(ValueError, histogramdd, vals,
                      range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]])
Example #20
Source File: test_function_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_inf_edges(self):
        # Test using +/-inf bin edges works. See #1788.
        with np.errstate(invalid='ignore'):
            x = np.arange(6).reshape(3, 2)
            expected = np.array([[1, 0], [0, 1], [0, 1]])
            h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]])
            assert_allclose(h, expected)
            h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])])
            assert_allclose(h, expected)
            h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]])
            assert_allclose(h, expected)
Example #21
Source File: test_function_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_bins_errors(self):
        # There are two ways to specify bins. Check for the right errors
        # when mixing those.
        x = np.arange(8).reshape(2, 4)
        assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5])
        assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1])
        assert_raises(
            ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 2, 3]])
        assert_raises(
            ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]])
        assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]]))
Example #22
Source File: test_function_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_identical_samples(self):
        x = np.zeros((10, 2), int)
        hist, edges = histogramdd(x, bins=2)
        assert_array_equal(edges[0], np.array([-0.5, 0., 0.5]))
Example #23
Source File: test_function_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_weights(self):
        v = rand(100, 2)
        hist, edges = histogramdd(v)
        n_hist, edges = histogramdd(v, normed=True)
        w_hist, edges = histogramdd(v, weights=np.ones(100))
        assert_array_equal(w_hist, hist)
        w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, normed=True)
        assert_array_equal(w_hist, n_hist)
        w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2)
        assert_array_equal(w_hist, 2 * hist)
Example #24
Source File: test_function_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_shape_4d(self):
        # All possible permutations for bins of different lengths in 4D.
        bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4),
                (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6),
                (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7),
                (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5),
                (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
                (5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))

        r = rand(10, 4)
        for b in bins:
            H, edges = histogramdd(r, b)
            assert_(H.shape == b)
Example #25
Source File: test_function_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_shape_3d(self):
        # All possible permutations for bins of different lengths in 3D.
        bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
                (4, 5, 6))
        r = rand(10, 3)
        for b in bins:
            H, edges = histogramdd(r, b)
            assert_(H.shape == b)
Example #26
Source File: test_function_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_simple(self):
        x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5],
                      [.5,  .5, 1.5], [.5,  1.5, 2.5], [.5,  2.5, 2.5]])
        H, edges = histogramdd(x, (2, 3, 3),
                               range=[[-1, 1], [0, 3], [0, 3]])
        answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]],
                           [[0, 1, 0], [0, 0, 1], [0, 0, 1]]])
        assert_array_equal(H, answer)

        # Check normalization
        ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
        H, edges = histogramdd(x, bins=ed, normed=True)
        assert_(np.all(H == answer / 12.))

        # Check that H has the correct shape.
        H, edges = histogramdd(x, (2, 3, 4),
                               range=[[-1, 1], [0, 3], [0, 4]],
                               normed=True)
        answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]],
                           [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]])
        assert_array_almost_equal(H, answer / 6., 4)
        # Check that a sequence of arrays is accepted and H has the correct
        # shape.
        z = [np.squeeze(y) for y in split(x, 3, axis=1)]
        H, edges = histogramdd(
            z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]])
        answer = np.array([[[0, 0], [0, 0], [0, 0]],
                           [[0, 1], [0, 0], [1, 0]],
                           [[0, 1], [0, 0], [0, 0]],
                           [[0, 0], [0, 0], [0, 0]]])
        assert_array_equal(H, answer)

        Z = np.zeros((5, 5, 5))
        Z[list(range(5)), list(range(5)), list(range(5))] = 1.
        H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5)
        assert_array_equal(H, Z)
Example #27
Source File: test_regression.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def test_histogramdd_too_many_bins(self):
        # Ticket 928.
        assert_raises(ValueError, np.histogramdd, np.ones((1, 10)), bins=2**10)
Example #28
Source File: test_function_base.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def test_finite_range(self):
        vals = np.random.random((100, 3))
        histogramdd(vals, range=[[0.0, 1.0], [0.25, 0.75], [0.25, 0.5]])
        assert_raises(ValueError, histogramdd, vals, 
                      range=[[0.0, 1.0], [0.25, 0.75], [0.25, np.inf]])
        assert_raises(ValueError, histogramdd, vals, 
                      range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]])
Example #29
Source File: test_function_base.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def test_inf_edges(self):
        # Test using +/-inf bin edges works. See #1788.
        with np.errstate(invalid='ignore'):
            x = np.arange(6).reshape(3, 2)
            expected = np.array([[1, 0], [0, 1], [0, 1]])
            h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]])
            assert_allclose(h, expected)
            h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])])
            assert_allclose(h, expected)
            h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]])
            assert_allclose(h, expected)
Example #30
Source File: test_function_base.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def test_bins_errors(self):
        # There are two ways to specify bins. Check for the right errors
        # when mixing those.
        x = np.arange(8).reshape(2, 4)
        assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5])
        assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1])
        assert_raises(
            ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 2, 3]])
        assert_raises(
            ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]])
        assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]]))