test_blaze.py 文件源码

def test_novel_deltas_macro(self):
        asset_info = asset_infos[0][0]
        base_dates = pd.DatetimeIndex([
            pd.Timestamp('2014-01-01'),
            pd.Timestamp('2014-01-04')
        ])
        baseline = pd.DataFrame({
            'value': (0, 1),
            'asof_date': base_dates,
            'timestamp': base_dates,
        })
        expr = bz.data(baseline, name='expr', dshape=self.macro_dshape)
        deltas = bz.data(baseline, name='deltas', dshape=self.macro_dshape)
        deltas = bz.transform(
            deltas,
            value=deltas.value + 10,
            timestamp=deltas.timestamp + timedelta(days=1),
        )

        nassets = len(asset_info)
        expected_views = keymap(pd.Timestamp, {
            '2014-01-03': repeat_last_axis(
                np.array([10.0, 10.0, 10.0]),
                nassets,
            ),
            '2014-01-06': repeat_last_axis(
                np.array([10.0, 10.0, 11.0]),
                nassets,
            ),
        })

        cal = pd.DatetimeIndex([
            pd.Timestamp('2014-01-01'),
            pd.Timestamp('2014-01-02'),
            pd.Timestamp('2014-01-03'),
            # omitting the 4th and 5th to simulate a weekend
            pd.Timestamp('2014-01-06'),
        ])
        with tmp_asset_finder(equities=asset_info) as finder:
            expected_output = pd.DataFrame(
                list(concatv([10] * nassets, [11] * nassets)),
                index=pd.MultiIndex.from_product((
                    sorted(expected_views.keys()),
                    finder.retrieve_all(asset_info.index),
                )),
                columns=('value',),
            )
            self._run_pipeline(
                expr,
                deltas,
                expected_views,
                expected_output,
                finder,
                calendar=cal,
                start=cal[2],
                end=cal[-1],
                window_length=3,
                compute_fn=op.itemgetter(-1),
            )