def test_pearsonr(self):
        # Tests some computations of Pearson's r
        x = ma.arange(10)
        with warnings.catch_warnings():
            # The tests in this context are edge cases, with perfect
            # correlation or anticorrelation, or totally masked data.
            # None of these should trigger a RuntimeWarning.
            warnings.simplefilter("error", RuntimeWarning)

            assert_almost_equal(mstats.pearsonr(x, x)[0], 1.0)
            assert_almost_equal(mstats.pearsonr(x, x[::-1])[0], -1.0)

            x = ma.array(x, mask=True)
            pr = mstats.pearsonr(x, x)
            assert_(pr[0] is masked)
            assert_(pr[1] is masked)

        x1 = ma.array([-1.0, 0.0, 1.0])
        y1 = ma.array([0, 0, 3])
        r, p = mstats.pearsonr(x1, y1)
        assert_almost_equal(r, np.sqrt(3)/2)
        assert_almost_equal(p, 1.0/3)

        # (x2, y2) have the same unmasked data as (x1, y1).
        mask = [False, False, False, True]
        x2 = ma.array([-1.0, 0.0, 1.0, 99.0], mask=mask)
        y2 = ma.array([0, 0, 3, -1], mask=mask)
        r, p = mstats.pearsonr(x2, y2)
        assert_almost_equal(r, np.sqrt(3)/2)
        assert_almost_equal(p, 1.0/3)
dataset/ETHPy150Open scipy/scipy/scipy/stats/tests/test_mstats_basic.py/TestCorr.test_pearsonr/original