Ejemplo n.º 1
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def test_random_beta():
    numpy.random.seed(0)
    x1 = activity.random('beta', alpha=2, beta=3, shape=(5, 2))

    numpy.random.seed(0)
    x2 = numpy.random.beta(2, 3, size=(5, 2))
    assert numpy.all((x1 - x2) < 0.0001)
Ejemplo n.º 2
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def test_random_normal():
    numpy.random.seed(0)
    x1 = activity.random('normal', mu=0, sigma=1, shape=(3, 4))

    numpy.random.seed(0)
    x2 = numpy.random.normal(0, 1, size=(3, 4))
    assert numpy.all((x1 - x2) < 0.0001)
Ejemplo n.º 3
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def test_random_normal():
    numpy.random.seed(0)
    x1 = activity.random('normal', mu=0, sigma=1, shape=(3, 4))

    numpy.random.seed(0)
    x2 = numpy.random.normal(0, 1, size=(3, 4))
    assert numpy.all((x1 - x2) < 0.0001)
Ejemplo n.º 4
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def test_random_beta():
    numpy.random.seed(0)
    x1 = activity.random('beta', alpha=2, beta=3, shape=(5, 2))

    numpy.random.seed(0)
    x2 = numpy.random.beta(2, 3, size=(5, 2))
    assert numpy.all((x1 - x2) < 0.0001)
Ejemplo n.º 5
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    def test_beta(self):
        numpy.random.seed(0)
        x1 = activity.random('beta', alpha=2, beta=3, shape=(5, 2))

        numpy.random.seed(0)
        x2 = numpy.random.beta(2, 3, size=(5, 2))
        nptest.assert_array_almost_equal(x1, x2)
Ejemplo n.º 6
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    def test_normal(self):
        numpy.random.seed(0)
        x1 = activity.random('normal', mu=0, sigma=1, shape=(3, 4))

        numpy.random.seed(0)
        x2 = numpy.random.normal(0, 1, size=(3, 4))
        nptest.assert_array_almost_equal(x1, x2)
Ejemplo n.º 7
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def test_plot_fit():
    # second
    fig2, ax2 = pyplot.subplots()
    norm_dist = dist.normal(μ=5.4, σ=2.5)
    data = activity.random('normal', μ=5.4, σ=2.5, shape=37)
    ax2 = activity.plot(norm_dist, ax=ax2, line_opts=dict(label='Theoretical PDF'))
    ax2 = activity.plot('normal', data=data, ax=ax2, line_opts=dict(label='Fit PDF'))
    ax2.legend()
Ejemplo n.º 8
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def test_distplot_lognormal():
    # fourth
    fig4, ax4 = pyplot.subplots()
    data = activity.random('lognormal', μ=0.75, σ=1.2, shape=125)
    logdata = numpy.log10(data)
    bins = numpy.logspace(logdata.min(), logdata.max(), num=30)
    distplot_opts = dict(rug=True, kde=False, norm_hist=True, bins=bins)
    line_opts = dict(color='firebrick', lw=3.5, label='Fit PDF')
    ax4 = activity.plot('lognormal', data=data, distplot=True,
                        xscale='log', pad=0.01, ax=ax4,
                        line_opts=line_opts,
                        distplot_opts=distplot_opts)
Ejemplo n.º 9
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def test_plot_pdf_fit():
    # second
    fig2, ax2 = pyplot.subplots()
    norm_dist = dist.normal(μ=5.4, σ=2.5)
    data = activity.random('normal', μ=5.4, σ=2.5, shape=37)
    ax2 = activity.plot(norm_dist,
                        ax=ax2,
                        line_opts=dict(label='Theoretical PDF'))
    ax2 = activity.plot('normal',
                        data=data,
                        ax=ax2,
                        line_opts=dict(label='Fit PDF'))
    ax2.legend()
    return fig2
Ejemplo n.º 10
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def test_plot_distplot():
    # third
    fig3, ax3 = pyplot.subplots()
    data = activity.random('normal', μ=5.4, σ=2.5, shape=37)
    ax3 = activity.plot('normal', data=data, distplot=True, ax=ax3)
    ax3.legend(loc='upper left')