Python numpy.datetime64() Examples

def test_split(self):
        sampling = numpy.timedelta64(5, 's')
        points = 100000
        ts = carbonara.TimeSerie.from_data(
            timestamps=list(map(datetime.datetime.utcfromtimestamp,
                                six.moves.range(points))),
            values=list(six.moves.range(points)))
        agg = self._resample(ts, sampling, 'mean')

        grouped_points = list(agg.split())

        self.assertEqual(
            math.ceil((points / sampling.astype(float))
                      / carbonara.SplitKey.POINTS_PER_SPLIT),
            len(grouped_points))
        self.assertEqual("0.0",
                         str(carbonara.SplitKey(grouped_points[0][0], 0)))
        # 3600 × 5s = 5 hours
        self.assertEqual(datetime64(1970, 1, 1, 5),
                         grouped_points[1][0])
        self.assertEqual(carbonara.SplitKey.POINTS_PER_SPLIT,
                         len(grouped_points[0][1])) 

def test_get_measures_unknown_aggregation(self):
        metric2 = indexer.Metric(uuid.uuid4(),
                                 self.archive_policies['low'])
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
            incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 42),
            incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 12, 12, 45), 44),
        ])
        self.incoming.add_measures(metric2.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
            incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 42),
            incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 12, 12, 45), 44),
        ])
        self.assertRaises(storage.AggregationDoesNotExist,
                          processor.get_measures,
                          self.storage,
                          [processor.MetricReference(self.metric, 'last'),
                           processor.MetricReference(metric2, 'last')],
                          operations=["aggregate", "mean", [
                              "metric",
                              [str(self.metric.id), "last"],
                              [(metric2.id), "last"],
                          ]]) 

def test_aggregated_different_archive_no_overlap2(self):
        tsc1 = {'sampling': numpy.timedelta64(60, 's'),
                'size': 50, 'agg': 'mean'}
        tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
        tsc2 = carbonara.AggregatedTimeSerie(
            carbonara.Aggregation('mean', numpy.timedelta64(60, 's'), None))

        tsb1.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 3, 0), 4)],
                                    dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
                        before_truncate_callback=functools.partial(
                            self._resample_and_merge, agg_dict=tsc1))
        metric = mock.Mock(id=str(uuid.uuid4()))
        ref = processor.MetricReference(metric, "mean")
        self.assertRaises(exceptions.UnAggregableTimeseries,
                          processor.aggregated,
                          [tsc1['return'], (ref, tsc2)],
                          operations=["aggregate", "mean",
                                      ["metric", tsc1['return'][0].lookup_key,
                                       ref.lookup_key]]) 

def test_aggregated_different_archive_no_overlap(self):
        tsc1 = {'sampling': numpy.timedelta64(60, 's'),
                'size': 50, 'agg': 'mean', "name": "all"}
        tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
        tsc2 = {'sampling': numpy.timedelta64(60, 's'),
                'size': 50, 'agg': 'mean', "name": "all"}
        tsb2 = carbonara.BoundTimeSerie(block_size=tsc2['sampling'])

        tsb1.set_values(numpy.array([(datetime64(2014, 1, 1, 11, 46, 4), 4)],
                                    dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
                        before_truncate_callback=functools.partial(
                            self._resample_and_merge, agg_dict=tsc1))
        tsb2.set_values(numpy.array([(datetime64(2014, 1, 1, 9, 1, 4), 4)],
                                    dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
                        before_truncate_callback=functools.partial(
                            self._resample_and_merge, agg_dict=tsc2))

        dtfrom = datetime64(2014, 1, 1, 11, 0, 0)
        self.assertRaises(exceptions.UnAggregableTimeseries,
                          processor.aggregated,
                          [tsc1['return'], tsc2['return']],
                          from_timestamp=dtfrom,
                          operations=["aggregate", "mean", [
                              "metric", ["all", "mean"]]]) 

def __getitem__(self, key):
        if isinstance(key, numpy.datetime64):
            idx = numpy.searchsorted(self.timestamps, key)
            if self.timestamps[idx] == key:
                return self[idx]
            raise KeyError(key)
        if isinstance(key, slice):
            if isinstance(key.start, numpy.datetime64):
                start = numpy.searchsorted(self.timestamps, key.start)
            else:
                start = key.start
            if isinstance(key.stop, numpy.datetime64):
                stop = numpy.searchsorted(self.timestamps, key.stop)
            else:
                stop = key.stop
            key = slice(start, stop, key.step)
        return self.ts[key] 

def extract_months(time, months):
    """Extract times within specified months of the year.

    Parameters
    ----------
    time : xarray.DataArray
         Array of times that can be represented by numpy.datetime64 objects
         (i.e. the year is between 1678 and 2262).
    months : Desired months of the year to include

    Returns
    -------
    xarray.DataArray of the desired times
    """
    inds = _month_conditional(time, months)
    return time.sel(time=inds) 

def ensure_datetime(obj):
    """Return the object if it is a datetime-like object

    Parameters
    ----------
    obj : Object to be tested.

    Returns
    -------
    The original object if it is a datetime-like object

    Raises
    ------
    TypeError if `obj` is not datetime-like
    """
    _VALID_TYPES = (str, datetime.datetime, cftime.datetime,
                    np.datetime64)
    if isinstance(obj, _VALID_TYPES):
        return obj
    raise TypeError("datetime-like object required.  "
                    "Type given: {}".format(type(obj))) 

def truncate(self, oldest_point=None):
        """Truncate the time series up to oldest_point excluded.

        :param oldest_point: Oldest point to keep from, this excluded.
                             Default is the aggregation timespan.
        :type oldest_point: numpy.datetime64 or numpy.timedelta64
        :return: The oldest point that could have been kept.
        """
        last = self.last
        if last is None:
            return
        if oldest_point is None:
            oldest_point = self.aggregation.timespan
            if oldest_point is None:
                return
        if isinstance(oldest_point, numpy.timedelta64):
            oldest_point = last - oldest_point
        index = numpy.searchsorted(self.ts['timestamps'], oldest_point,
                                   side='right')
        self.ts = self.ts[index:]
        return oldest_point 

def test_corrupted_split(self):
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
        ])
        self.trigger_processing()

        aggregation = self.metric.archive_policy.get_aggregation(
            "mean", numpy.timedelta64(5, 'm'))

        with mock.patch('gnocchi.carbonara.AggregatedTimeSerie.unserialize',
                        side_effect=carbonara.InvalidData()):
            results = self.storage._get_splits_and_unserialize({
                self.metric: {
                    aggregation: [
                        carbonara.SplitKey(
                            numpy.datetime64(1387800000, 's'),
                            numpy.timedelta64(5, 'm'))
                    ],
                },
            })[self.metric][aggregation]
            self.assertEqual(1, len(results))
            self.assertIsInstance(results[0], carbonara.AggregatedTimeSerie)
            # Assert it's an empty one since corrupted
            self.assertEqual(0, len(results[0]))
            self.assertEqual(results[0].aggregation, aggregation) 

def test_get_splits_and_unserialize(self):
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
        ])
        self.trigger_processing()

        aggregation = self.metric.archive_policy.get_aggregation(
            "mean", numpy.timedelta64(5, 'm'))

        results = self.storage._get_splits_and_unserialize({
            self.metric: {
                aggregation: [
                    carbonara.SplitKey(
                        numpy.datetime64(1387800000, 's'),
                        numpy.timedelta64(5, 'm')),
                ],
            },
        })[self.metric][aggregation]
        self.assertEqual(1, len(results))
        self.assertIsInstance(results[0], carbonara.AggregatedTimeSerie)
        # Assert it's not empty one since corrupted
        self.assertGreater(len(results[0]), 0)
        self.assertEqual(results[0].aggregation, aggregation) 

def test_split_key(self):
        self.assertEqual(
            numpy.datetime64("2014-10-07"),
            carbonara.SplitKey.from_timestamp_and_sampling(
                numpy.datetime64("2015-01-01T15:03"),
                numpy.timedelta64(3600, 's')))
        self.assertEqual(
            numpy.datetime64("2014-12-31 18:00"),
            carbonara.SplitKey.from_timestamp_and_sampling(
                numpy.datetime64("2015-01-01 15:03:58"),
                numpy.timedelta64(58, 's')))

        key = carbonara.SplitKey.from_timestamp_and_sampling(
            numpy.datetime64("2015-01-01 15:03"),
            numpy.timedelta64(3600, 's'))

        self.assertGreater(key, numpy.datetime64("1970"))

        self.assertGreaterEqual(key, numpy.datetime64("1970")) 

def test_delete_nonempty_metric(self):
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
        ])
        self.trigger_processing()
        self.storage._delete_metric(self.metric)
        self.trigger_processing()

        aggregations = (
            self.metric.archive_policy.get_aggregations_for_method("mean")
        )

        self.assertRaises(storage.MetricDoesNotExist,
                          self.storage.get_aggregated_measures,
                          {self.metric: aggregations})
        self.assertEqual(
            {self.metric: None},
            self.storage._get_or_create_unaggregated_timeseries(
                [self.metric])) 

def test_get_aggregated_measures(self):
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, i, j), 100)
            for i in six.moves.range(0, 60) for j in six.moves.range(0, 60)])
        self.trigger_processing([self.metric])

        aggregations = self.metric.archive_policy.aggregations

        measures = self.storage.get_aggregated_measures(
            {self.metric: aggregations})
        self.assertEqual(1, len(measures))
        self.assertIn(self.metric, measures)
        measures = measures[self.metric]
        self.assertEqual(len(aggregations), len(measures))
        self.assertGreater(len(measures[aggregations[0]]), 0)
        for agg in aggregations:
            self.assertEqual(agg, measures[agg].aggregation) 

def test_no_truncation(self):
        ts = {'sampling': numpy.timedelta64(60, 's'), 'agg': 'mean'}
        tsb = carbonara.BoundTimeSerie()

        for i in six.moves.range(1, 11):
            tsb.set_values(numpy.array([
                (datetime64(2014, 1, 1, 12, i, i), float(i))],
                dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
                before_truncate_callback=functools.partial(
                    self._resample_and_merge, agg_dict=ts))
            tsb.set_values(numpy.array([
                (datetime64(2014, 1, 1, 12, i, i + 1), float(i + 1))],
                dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
                before_truncate_callback=functools.partial(
                    self._resample_and_merge, agg_dict=ts))
            self.assertEqual(i, len(list(ts['return'].fetch()))) 

def test_add_measures_update_subset(self):
        m, m_sql = self._create_metric('medium')
        measures = [
            incoming.Measure(datetime64(2014, 1, 6, i, j, 0), 100)
            for i in six.moves.range(2) for j in six.moves.range(0, 60, 2)]
        self.incoming.add_measures(m.id, measures)
        self.trigger_processing([m])

        # add measure to end, in same aggregate time as last point.
        new_point = datetime64(2014, 1, 6, 1, 58, 1)
        self.incoming.add_measures(m.id, [incoming.Measure(new_point, 100)])

        with mock.patch.object(self.incoming, 'add_measures') as c:
            self.trigger_processing([m])
        for __, args, __ in c.mock_calls:
            self.assertEqual(
                list(args[3])[0][0], carbonara.round_timestamp(
                    new_point, args[1].granularity * 10e8)) 

def test_get_measure_unknown_aggregation(self):
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
            incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 42),
            incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 12, 12, 45), 44),
        ])

        aggregations = (
            self.metric.archive_policy.get_aggregations_for_method("last")
        )

        self.assertRaises(
            storage.MetricDoesNotExist,
            self.storage.get_aggregated_measures,
            {self.metric: aggregations}) 

def test_aggregation_std_with_unique(self):
        ts = carbonara.TimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0)], [3])
        ts = self._resample(ts, numpy.timedelta64(60, 's'), 'std')
        self.assertEqual(0, len(ts), ts.values)

        ts = carbonara.TimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0),
             datetime64(2014, 1, 1, 12, 0, 4),
             datetime64(2014, 1, 1, 12, 0, 9),
             datetime64(2014, 1, 1, 12, 1, 6)],
            [3, 6, 5, 9])
        ts = self._resample(ts, numpy.timedelta64(60, 's'), "std")

        self.assertEqual(1, len(ts))
        self.assertEqual(1.5275252316519465,
                         ts[datetime64(2014, 1, 1, 12, 0, 0)][1]) 

def test_duplicate_timestamps(self):
        ts = carbonara.BoundTimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0),
             datetime64(2014, 1, 1, 12, 0, 9)],
            [10, 23])
        self.assertEqual(2, len(ts))
        self.assertEqual(10.0, ts[0][1])
        self.assertEqual(23.0, ts[1][1])

        ts.set_values(numpy.array([(datetime64(2014, 1, 1, 13, 0, 10), 3),
                                   (datetime64(2014, 1, 1, 13, 0, 11), 9),
                                   (datetime64(2014, 1, 1, 13, 0, 11), 8),
                                   (datetime64(2014, 1, 1, 13, 0, 11), 7),
                                   (datetime64(2014, 1, 1, 13, 0, 11), 4)],
                                  dtype=carbonara.TIMESERIES_ARRAY_DTYPE))
        self.assertEqual(4, len(ts))
        self.assertEqual(10.0, ts[0][1])
        self.assertEqual(23.0, ts[1][1])
        self.assertEqual(3.0, ts[2][1])
        self.assertEqual(9.0, ts[3][1]) 

def test_derived_hole(self):
        ts = carbonara.TimeSerie.from_data(
            [datetime.datetime(2014, 1, 1, 12, 0, 0),
             datetime.datetime(2014, 1, 1, 12, 0, 4),
             datetime.datetime(2014, 1, 1, 12, 1, 2),
             datetime.datetime(2014, 1, 1, 12, 1, 14),
             datetime.datetime(2014, 1, 1, 12, 1, 24),
             datetime.datetime(2014, 1, 1, 12, 3, 2),
             datetime.datetime(2014, 1, 1, 12, 3, 22),
             datetime.datetime(2014, 1, 1, 12, 3, 42),
             datetime.datetime(2014, 1, 1, 12, 4, 9)],
            [50, 55, 65, 66, 70, 105, 108, 200, 202])
        ts = self._resample(ts, numpy.timedelta64(60, 's'), 'last',
                            derived=True)

        self.assertEqual(4, len(ts))
        self.assertEqual(
            [(datetime64(2014, 1, 1, 12, 0, 0), 5),
             (datetime64(2014, 1, 1, 12, 1, 0), 4),
             (datetime64(2014, 1, 1, 12, 3, 0), 92),
             (datetime64(2014, 1, 1, 12, 4, 0), 2)],
            list(ts.fetch(
                from_timestamp=datetime64(2014, 1, 1, 12)))) 

def apply_time_offset(time, years=0, months=0, days=0, hours=0):
    """Apply a specified offset to the given time array.

    This is useful for GFDL model output of instantaneous values.  For example,
    3 hourly data postprocessed to netCDF files spanning 1 year each will
    actually have time values that are offset by 3 hours, such that the first
    value is for 1 Jan 03:00 and the last value is 1 Jan 00:00 of the
    subsequent year.  This causes problems in xarray, e.g. when trying to group
    by month.  It is resolved by manually subtracting off those three hours,
    such that the dates span from 1 Jan 00:00 to 31 Dec 21:00 as desired.

    Parameters
    ----------
    time : xarray.DataArray representing a timeseries
    years, months, days, hours : int, optional
        The number of years, months, days, and hours, respectively, to offset
        the time array by.  Positive values move the times later.

    Returns
    -------
    pandas.DatetimeIndex

    Examples
    --------
    Case of a length-1 input time array:

    >>> times = xr.DataArray(datetime.datetime(1899, 12, 31, 21))
    >>> apply_time_offset(times)
    Timestamp('1900-01-01 00:00:00')

    Case of input time array with length greater than one:

    >>> times = xr.DataArray([datetime.datetime(1899, 12, 31, 21),
    ...                       datetime.datetime(1899, 1, 31, 21)])
    >>> apply_time_offset(times) # doctest: +NORMALIZE_WHITESPACE
    DatetimeIndex(['1900-01-01', '1899-02-01'], dtype='datetime64[ns]',
                  freq=None)
    """
    return (pd.to_datetime(time.values) +
            pd.DateOffset(years=years, months=months, days=days, hours=hours)) 

def test_truncate(self):
        ts = carbonara.TimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0),
             datetime64(2014, 1, 1, 12, 0, 4),
             datetime64(2014, 1, 1, 12, 0, 9)],
            [3, 5, 6])
        ts = self._resample(ts, numpy.timedelta64(1, 's'), 'mean')

        ts.truncate(datetime64(2014, 1, 1, 12, 0, 0))

        self.assertEqual(2, len(ts))
        self.assertEqual(5, ts[0][1])
        self.assertEqual(6, ts[1][1]) 

def test_74_percentile_serialized(self):
        ts = carbonara.TimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0),
             datetime64(2014, 1, 1, 12, 0, 4),
             datetime64(2014, 1, 1, 12, 0, 9)],
            [3, 5, 6])
        ts = self._resample(ts, numpy.timedelta64(60, 's'), '74pct')

        self.assertEqual(1, len(ts))
        self.assertEqual(5.48, ts[datetime64(2014, 1, 1, 12, 0, 0)][1])

        # Serialize and unserialize
        key = ts.get_split_key()
        o, s = ts.serialize(key)
        saved_ts = carbonara.AggregatedTimeSerie.unserialize(
            s, key, ts.aggregation)

        self.assertEqual(ts.aggregation, saved_ts.aggregation)

        ts = carbonara.TimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0),
             datetime64(2014, 1, 1, 12, 0, 4),
             datetime64(2014, 1, 1, 12, 0, 9)],
            [3, 5, 6])
        ts = self._resample(ts, numpy.timedelta64(60, 's'), '74pct')
        saved_ts.merge(ts)

        self.assertEqual(1, len(ts))
        self.assertEqual(5.48, ts[datetime64(2014, 1, 1, 12, 0, 0)][1]) 

def test_binary_operator_with_two_references(self):
        metric2, __ = self._create_metric()
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
            incoming.Measure(datetime64(2014, 1, 1, 13, 1, 31), 42),
            incoming.Measure(datetime64(2014, 1, 1, 14, 2, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 15, 3, 45), 44),
        ])
        self.incoming.add_measures(metric2.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 5), 9),
            incoming.Measure(datetime64(2014, 1, 1, 13, 1, 41), 2),
            incoming.Measure(datetime64(2014, 1, 1, 14, 2, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 15, 3, 10), 4),
        ])
        self.trigger_processing([self.metric, metric2])

        values = processor.get_measures(
            self.storage,
            [processor.MetricReference(self.metric, "mean"),
             processor.MetricReference(metric2, "mean")],
            ["*", ["metric", str(self.metric.id), "mean"],
                  ["metric", str(metric2.id), "mean"]],
            granularities=[numpy.timedelta64(1, 'h')])["aggregated"]

        self.assertEqual([
            (datetime64(2014, 1, 1, 12, 0, 0),
             numpy.timedelta64(1, 'h'), 621),
            (datetime64(2014, 1, 1, 13, 0, 0),
             numpy.timedelta64(1, 'h'), 84),
            (datetime64(2014, 1, 1, 14, 0, 0),
             numpy.timedelta64(1, 'h'), 16),
            (datetime64(2014, 1, 1, 15, 0, 0),
             numpy.timedelta64(1, 'h'), 176),
        ], values) 

def test_95_percentile(self):
        ts = carbonara.TimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0),
             datetime64(2014, 1, 1, 12, 0, 4),
             datetime64(2014, 1, 1, 12, 0, 9)],
            [3, 5, 6])
        ts = self._resample(ts, numpy.timedelta64(60, 's'), '95pct')

        self.assertEqual(1, len(ts))
        self.assertEqual(5.9000000000000004,
                         ts[datetime64(2014, 1, 1, 12, 0, 0)][1]) 

def test_down_sampling(self):
        ts = carbonara.TimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0),
             datetime64(2014, 1, 1, 12, 0, 4),
             datetime64(2014, 1, 1, 12, 0, 9)],
            [3, 5, 7])
        ts = self._resample(ts, numpy.timedelta64(300, 's'), 'mean')

        self.assertEqual(1, len(ts))
        self.assertEqual(5, ts[datetime64(2014, 1, 1, 12, 0, 0)][1]) 

def test_resample_minus_2_on_left(self):
        metric2, __ = self._create_metric()
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
            incoming.Measure(datetime64(2014, 1, 1, 13, 1, 31), 42),
            incoming.Measure(datetime64(2014, 1, 1, 14, 2, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 15, 3, 45), 44),
        ])
        self.incoming.add_measures(metric2.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 5), 9),
            incoming.Measure(datetime64(2014, 1, 1, 13, 1, 41), 2),
            incoming.Measure(datetime64(2014, 1, 1, 14, 2, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 15, 3, 10), 4),
        ])
        self.trigger_processing([self.metric, metric2])

        values = processor.get_measures(
            self.storage,
            [processor.MetricReference(self.metric, "mean"),
             processor.MetricReference(metric2, "mean")],
            ["-",
             2,
             ["resample", "mean", numpy.timedelta64(1, 'D'),
              ["metric",
               [str(self.metric.id), "mean"],
               [str(metric2.id), "mean"]]]],
            granularities=[numpy.timedelta64(1, 'h')])

        self.assertEqual({
            str(self.metric.id): {
                "mean": [(datetime64(2014, 1, 1, 0, 0, 0),
                          numpy.timedelta64(1, 'D'), -37.75)]
            },
            str(metric2.id): {
                "mean": [(datetime64(2014, 1, 1, 0, 0, 0),
                          numpy.timedelta64(1, 'D'), -2.75)]
            }
        }, values) 

def test_down_sampling_and_truncate(self):
        ts = carbonara.TimeSerie.from_data(
            [datetime64(2014, 1, 1, 12, 0, 0),
             datetime64(2014, 1, 1, 12, 1, 4),
             datetime64(2014, 1, 1, 12, 1, 9),
             datetime64(2014, 1, 1, 12, 2, 12)],
            [3, 5, 7, 1])
        ts = self._resample(ts, numpy.timedelta64(60, 's'), 'mean')

        ts.truncate(datetime64(2014, 1, 1, 12, 0, 59))

        self.assertEqual(2, len(ts))
        self.assertEqual(6, ts[datetime64(2014, 1, 1, 12, 1, 0)][1])
        self.assertEqual(1, ts[datetime64(2014, 1, 1, 12, 2, 0)][1]) 

def test_fetch_nano(self):
        ts = {'sampling': numpy.timedelta64(200, 'ms'),
              'size': 10, 'agg': 'mean'}
        tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])

        tsb.set_values(numpy.array([
            (datetime64(2014, 1, 1, 11, 46, 0, 200123), 4),
            (datetime64(2014, 1, 1, 11, 46, 0, 340000), 8),
            (datetime64(2014, 1, 1, 11, 47, 0, 323154), 50),
            (datetime64(2014, 1, 1, 11, 48, 0, 590903), 4),
            (datetime64(2014, 1, 1, 11, 48, 0, 903291), 4)],
            dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
            before_truncate_callback=functools.partial(
                self._resample_and_merge, agg_dict=ts))

        tsb.set_values(numpy.array([
            (datetime64(2014, 1, 1, 11, 48, 0, 821312), 5)],
            dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
            before_truncate_callback=functools.partial(
                self._resample_and_merge, agg_dict=ts))

        self.assertEqual([
            (datetime64(2014, 1, 1, 11, 46, 0, 200000), 6.0),
            (datetime64(2014, 1, 1, 11, 47, 0, 200000), 50.0),
            (datetime64(2014, 1, 1, 11, 48, 0, 400000), 4.0),
            (datetime64(2014, 1, 1, 11, 48, 0, 800000), 4.5)
        ], list(ts['return'].fetch()))
        self.assertEqual(numpy.timedelta64(200000000, 'ns'),
                         ts['return'].aggregation.granularity) 

def test_add_and_get_measures_with_holes(self):
        metric2, __ = self._create_metric()
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
            incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 42),
            incoming.Measure(datetime64(2014, 1, 1, 12, 5, 31), 8),
            incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 12, 12, 45), 42),
        ])
        self.incoming.add_measures(metric2.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 5), 9),
            incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 2),
            incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 6),
            incoming.Measure(datetime64(2014, 1, 1, 12, 13, 10), 2),
        ])
        self.trigger_processing([self.metric, metric2])

        values = processor.get_measures(
            self.storage,
            [processor.MetricReference(self.metric, 'mean'),
             processor.MetricReference(metric2, 'mean')],
            operations=["aggregate", "mean", [
                "metric",
                [str(self.metric.id), "mean"],
                [str(metric2.id), "mean"],
            ]])["aggregated"]
        self.assertEqual([
            (datetime64(2014, 1, 1, 0, 0, 0),
             numpy.timedelta64(1, 'D'), 18.875),
            (datetime64(2014, 1, 1, 12, 0, 0),
             numpy.timedelta64(1, 'h'), 18.875),
            (datetime64(2014, 1, 1, 12, 0, 0),
             numpy.timedelta64(5, 'm'), 39.0),
            (datetime64(2014, 1, 1, 12, 5, 0),
             numpy.timedelta64(5, 'm'), 11.0),
            (datetime64(2014, 1, 1, 12, 10, 0),
             numpy.timedelta64(5, 'm'), 22.0)
        ], values) 

def test_resample(self):
        metric2, __ = self._create_metric()
        self.incoming.add_measures(self.metric.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69),
            incoming.Measure(datetime64(2014, 1, 1, 13, 1, 31), 42),
            incoming.Measure(datetime64(2014, 1, 1, 14, 2, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 15, 3, 45), 44),
        ])
        self.incoming.add_measures(metric2.id, [
            incoming.Measure(datetime64(2014, 1, 1, 12, 0, 5), 9),
            incoming.Measure(datetime64(2014, 1, 1, 13, 1, 41), 2),
            incoming.Measure(datetime64(2014, 1, 1, 14, 2, 31), 4),
            incoming.Measure(datetime64(2014, 1, 1, 15, 3, 10), 4),
        ])
        self.trigger_processing([self.metric, metric2])

        values = processor.get_measures(
            self.storage,
            [processor.MetricReference(self.metric, "mean"),
             processor.MetricReference(metric2, "mean")],
            ["resample", "mean", numpy.timedelta64(1, 'D'),
             ["metric",
              [str(self.metric.id), "mean"],
              [str(metric2.id), "mean"]]],
            granularities=[numpy.timedelta64(1, 'h')])

        self.assertEqual({
            str(self.metric.id): {
                "mean": [(datetime64(2014, 1, 1, 0, 0, 0),
                          numpy.timedelta64(1, 'D'), 39.75)]
            },
            str(metric2.id): {
                "mean": [(datetime64(2014, 1, 1, 0, 0, 0),
                          numpy.timedelta64(1, 'D'), 4.75)]
            }
        }, values)