How to use the catalyst.pipeline.Pipeline function in catalyst

def initialize(context):
            pipeline = Pipeline()
            context.vwaps = []
            for length in vwaps:
                name = vwap_key(length)
                factor = VWAP(window_length=length)
                context.vwaps.append(factor)
                pipeline.add(factor, name=name)

            filter_ = (USEquityPricing.close.latest > 300)
            pipeline.add(filter_, 'filter')
            if set_screen:
                pipeline.set_screen(filter_)

            attach_pipeline(pipeline, 'test')

def test_single_factor(self):
        loader = self.loader
        assets = self.assets
        engine = SimplePipelineEngine(
            lambda column: loader, self.dates, self.asset_finder,
        )
        result_shape = (num_dates, num_assets) = (5, len(assets))
        dates = self.dates[10:10 + num_dates]

        factor = RollingSumDifference()
        expected_result = -factor.window_length

        # Since every asset will pass the screen, these should be equivalent.
        pipelines = [
            Pipeline(columns={'f': factor}),
            Pipeline(
                columns={'f': factor},
                screen=factor.eq(expected_result),
            ),
        ]

        for p in pipelines:
            result = engine.run_pipeline(p, dates[0], dates[-1])
            self.assertEqual(set(result.columns), {'f'})
            assert_multi_index_is_product(
                self, result.index, dates, assets
            )

            check_arrays(
                result['f'].unstack().values,
                full(result_shape, expected_result, dtype=float),

if mask is not NotSpecified:
                pipeline.add(mask, 'mask')

            results = run_pipeline(pipeline, start_date, end_date)
            pearson_results = results['pearson_factor'].unstack()
            spearman_results = results['spearman_factor'].unstack()
            if mask is not NotSpecified:
                mask_results = results['mask'].unstack()
                check_arrays(mask_results.values, expected_mask)

            # Run a separate pipeline that calculates returns starting
            # (correlation_length - 1) days prior to our start date. This is
            # because we need (correlation_length - 1) extra days of returns to
            # compute our expected correlations.
            results = run_pipeline(
                Pipeline(columns={'returns': returns}),
                dates[start_date_index - (correlation_length - 1)],
                dates[end_date_index],
            )
            returns_results = results['returns'].unstack()

            # On each day, calculate the expected correlation coefficients
            # between the asset we are interested in and each other asset. Each
            # correlation is calculated over `correlation_length` days.
            expected_pearson_results = full_like(pearson_results, nan)
            expected_spearman_results = full_like(spearman_results, nan)
            for day in range(num_days):
                todays_returns = returns_results.iloc[
                    day:day + correlation_length
                ]
                my_asset_returns = todays_returns.iloc[:, my_asset_column]
                for asset, other_asset_returns in todays_returns.iteritems():

returns_masked_1.linear_regression(
                target=returns_masked_2, regression_length=regression_length,
            )

        returns_5 = Returns(window_length=5, inputs=[self.col])
        returns_10 = Returns(window_length=10, inputs=[self.col])

        regression_factor = returns_5.linear_regression(
            target=returns_10, regression_length=regression_length,
        )

        columns = {
            output: getattr(regression_factor, output)
            for output in outputs
        }
        pipeline = Pipeline(columns=columns)

        results = run_pipeline(pipeline, start_date, end_date)

        output_results = {}
        expected_output_results = {}
        for output in outputs:
            output_results[output] = results[output].unstack()
            expected_output_results[output] = full_like(
                output_results[output], nan,
            )

        # Run a separate pipeline that calculates returns starting
        # (regression_length - 1) days prior to our start date. This is because
        # we need (regression_length - 1) extra days of returns to compute our
        # expected regressions.
        columns = {'returns_5': returns_5, 'returns_10': returns_10}

def test_estimates(self):
        dataset = QuartersEstimates(1)
        engine = SimplePipelineEngine(
            lambda x: self.loader,
            self.trading_days,
            self.asset_finder,
        )
        results = engine.run_pipeline(
            Pipeline({c.name: c.latest for c in dataset.columns}),
            start_date=self.trading_days[1],
            end_date=self.trading_days[-2],
        )
        for sid in self.ASSET_FINDER_EQUITY_SIDS:
            sid_estimates = results.xs(sid, level=1)
            # Separate assertion for all-null DataFrame to avoid setting
            # column dtypes on `all_expected`.
            if sid == max(self.ASSET_FINDER_EQUITY_SIDS):
                assert_true(sid_estimates.isnull().all().all())
            else:
                ts_sorted_estimates = self.events[
                    self.events[SID_FIELD_NAME] == sid
                ].sort(TS_FIELD_NAME)
                q1_knowledge = ts_sorted_estimates[
                    ts_sorted_estimates[FISCAL_QUARTER_FIELD_NAME] == 1
                ]

def _test_id_macro(self, df, dshape, expected, finder, add, dates=None):
        if dates is None:
            dates = self.dates
        expr = bz.data(df, name='expr', dshape=dshape)
        loader = BlazeLoader()
        ds = from_blaze(
            expr,
            loader=loader,
            no_deltas_rule='ignore',
            no_checkpoints_rule='ignore',
            missing_values=self.missing_values,
        )

        p = Pipeline()
        macro_inputs = []
        for column_name in add:
            column = getattr(ds, column_name)
            macro_inputs.append(column)
            with self.assertRaises(UnsupportedPipelineOutput):
                # Single column output terms cannot be added to a pipeline.
                p.add(column.latest, column_name)

        class UsesMacroInputs(CustomFactor):
            inputs = macro_inputs
            window_length = 1

            def compute(self, today, assets, out, *inputs):
                e = expected.loc[today]
                for i, input_ in enumerate(inputs):
                    # Each macro input should only have one column.

def test_overwrite(self):
        p = Pipeline()
        f = SomeFactor()
        other_f = SomeOtherFactor()

        p.add(f, 'f')
        self.assertEqual(p.columns, {'f': f})

        with self.assertRaises(KeyError) as e:
            p.add(other_f, 'f')
        [message] = e.exception.args
        self.assertEqual(message, "Column 'f' already exists.")

        p.add(other_f, 'f', overwrite=True)
        self.assertEqual(p.columns, {'f': other_f})

#       June 2014
        # Mo Tu We Th Fr Sa Su
        #                    1
        #  2  3  4  5  6  7  8
        #  9 10 11 12 13 14 15
        # 16 17 18 19 20 21 22
        # 23 24 25 26 27 28 29
        # 30
        all_sessions = self.nyse_sessions
        compute_dates = all_sessions[
            all_sessions.slice_indexer('2014-06-05', '2015-01-06')
        ]
        start_date, end_date = compute_dates[[0, -1]]

        pipe = Pipeline({
            'year': term.downsample(frequency='year_start'),
            'quarter': term.downsample(frequency='quarter_start'),
            'month': term.downsample(frequency='month_start'),
            'week': term.downsample(frequency='week_start'),
        })

        # Raw values for term, computed each day from 2014 to the end of the
        # target period.
        raw_term_results = self.run_pipeline(
            Pipeline({'term': term}),
            start_date=pd.Timestamp('2014-01-02', tz='UTC'),
            end_date=pd.Timestamp('2015-01-06', tz='UTC'),
        )['term'].unstack()

        expected_results = {
            'year': (raw_term_results

dates_to_test = self.dates[-30:]

        constants = {open_: 1, close: 2, volume: 3}
        loader = PrecomputedLoader(
            constants=constants,
            dates=self.dates,
            sids=self.asset_ids,
        )
        engine = SimplePipelineEngine(
            lambda column: loader, self.dates, self.asset_finder,
        )

        sumdiff = RollingSumDifference()

        result = engine.run_pipeline(
            Pipeline(
                columns={
                    'sumdiff': sumdiff,
                    'open': open_.latest,
                    'close': close.latest,
                    'volume': volume.latest,
                },
            ),
            dates_to_test[0],
            dates_to_test[-1]
        )
        self.assertIsNotNone(result)
        self.assertEqual(
            {'sumdiff', 'open', 'close', 'volume'},
            set(result.columns)
        )

self.expected_no_mask_result,
        )

        for mask, expected_mask in zip(masks, expected_mask_results):
            regression_factor = RollingLinearRegressionOfReturns(
                target=my_asset,
                returns_length=returns_length,
                regression_length=regression_length,
                mask=mask,
            )

            columns = {
                output: getattr(regression_factor, output)
                for output in outputs
            }
            pipeline = Pipeline(columns=columns)
            if mask is not NotSpecified:
                pipeline.add(mask, 'mask')

            results = run_pipeline(pipeline, start_date, end_date)
            if mask is not NotSpecified:
                mask_results = results['mask'].unstack()
                check_arrays(mask_results.values, expected_mask)

            output_results = {}
            expected_output_results = {}
            for output in outputs:
                output_results[output] = results[output].unstack()
                expected_output_results[output] = full_like(
                    output_results[output], nan,
                )