Esempio n. 1
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    def test_rolling_kurt_edge_cases(self):

        all_nan = Series([np.NaN] * 5)

        # yields all NaN (0 variance)
        d = Series([1] * 5)
        x = mom.rolling_kurt(d, window=5)
        assert_series_equal(all_nan, x)

        # yields all NaN (window too small)
        d = Series(np.random.randn(5))
        x = mom.rolling_kurt(d, window=3)
        assert_series_equal(all_nan, x)

        # yields [NaN, NaN, NaN, 1.224307, 2.671499]
        d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401])
        expected = Series([np.NaN, np.NaN, np.NaN, 1.224307, 2.671499])
        x = mom.rolling_kurt(d, window=4)
        assert_series_equal(expected, x)
Esempio n. 2
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    def test_rolling_functions_window_non_shrinkage(self):
        # GH 7764
        s = Series(range(4))
        s_expected = Series(np.nan, index=s.index)
        df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
        df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
        df_expected_panel = Panel(items=df.index,
                                  major_axis=df.columns,
                                  minor_axis=df.columns)

        functions = [
            lambda x: mom.rolling_cov(
                x, x, pairwise=False, window=10, min_periods=5),
            lambda x: mom.rolling_corr(
                x, x, pairwise=False, window=10, min_periods=5),
            lambda x: mom.rolling_max(x, window=10, min_periods=5),
            lambda x: mom.rolling_min(x, window=10, min_periods=5),
            lambda x: mom.rolling_sum(x, window=10, min_periods=5),
            lambda x: mom.rolling_mean(x, window=10, min_periods=5),
            lambda x: mom.rolling_std(x, window=10, min_periods=5),
            lambda x: mom.rolling_var(x, window=10, min_periods=5),
            lambda x: mom.rolling_skew(x, window=10, min_periods=5),
            lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
            lambda x: mom.rolling_quantile(
                x, quantile=0.5, window=10, min_periods=5),
            lambda x: mom.rolling_median(x, window=10, min_periods=5),
            lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
            lambda x: mom.rolling_window(
                x, win_type='boxcar', window=10, min_periods=5),
        ]
        for f in functions:
            try:
                s_result = f(s)
                assert_series_equal(s_result, s_expected)

                df_result = f(df)
                assert_frame_equal(df_result, df_expected)
            except (ImportError):

                # scipy needed for rolling_window
                continue

        functions = [
            lambda x: mom.rolling_cov(
                x, x, pairwise=True, window=10, min_periods=5),
            lambda x: mom.rolling_corr(
                x, x, pairwise=True, window=10, min_periods=5),
            # rolling_corr_pairwise is depracated, so the following line should be deleted
            # when rolling_corr_pairwise is removed.
            lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5
                                                ),
        ]
        for f in functions:
            df_result_panel = f(df)
            assert_panel_equal(df_result_panel, df_expected_panel)
    def test_rolling_kurt_edge_cases(self):

        all_nan = Series([np.NaN] * 5)

        # yields all NaN (0 variance)
        d = Series([1] * 5)
        x = mom.rolling_kurt(d, window=5)
        assert_series_equal(all_nan, x)

        # yields all NaN (window too small)
        d = Series(np.random.randn(5))
        x = mom.rolling_kurt(d, window=3)
        assert_series_equal(all_nan, x)

        # yields [NaN, NaN, NaN, 1.224307, 2.671499]
        d = Series(
            [-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401])
        expected = Series([np.NaN, np.NaN, np.NaN, 1.224307, 2.671499])
        x = mom.rolling_kurt(d, window=4)
        assert_series_equal(expected, x)
Esempio n. 4
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    def test_rolling_functions_window_non_shrinkage(self):
        # GH 7764
        s = Series(range(4))
        s_expected = Series(np.nan, index=s.index)
        df = DataFrame([[1,5], [3, 2], [3,9], [-1,0]], columns=['A','B'])
        df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
        df_expected_panel = Panel(items=df.index, major_axis=df.columns, minor_axis=df.columns)

        functions = [lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
                     lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
                     lambda x: mom.rolling_max(x, window=10, min_periods=5),
                     lambda x: mom.rolling_min(x, window=10, min_periods=5),
                     lambda x: mom.rolling_sum(x, window=10, min_periods=5),
                     lambda x: mom.rolling_mean(x, window=10, min_periods=5),
                     lambda x: mom.rolling_std(x, window=10, min_periods=5),
                     lambda x: mom.rolling_var(x, window=10, min_periods=5),
                     lambda x: mom.rolling_skew(x, window=10, min_periods=5),
                     lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
                     lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
                     lambda x: mom.rolling_median(x, window=10, min_periods=5),
                     lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
                     lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
                    ]
        for f in functions:
            try:
                s_result = f(s)
                assert_series_equal(s_result, s_expected)

                df_result = f(df)
                assert_frame_equal(df_result, df_expected)
            except (ImportError):

                # scipy needed for rolling_window
                continue

        functions = [lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
                     lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
                     # rolling_corr_pairwise is depracated, so the following line should be deleted
                     # when rolling_corr_pairwise is removed.
                     lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
                    ]
        for f in functions:
            df_result_panel = f(df)
            assert_panel_equal(df_result_panel, df_expected_panel)