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Resample: use year-start as rule #351

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Mar 2, 2023
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Resample: use year-start as rule #351

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6 changes: 3 additions & 3 deletions pyleoclim/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4221,11 +4221,11 @@ def resample(self, rule, **kwargs):
This is a convenience method: doing
ser.resample('Y').mean()
ser.resample('AS').mean()
will do the same thing as
ser.pandas_method(lambda x: x.resample('Y').mean())
ser.pandas_method(lambda x: x.resample('AS').mean())
but will also accept some extra resampling rules, such as `'Ga'` (see below).
Expand Down Expand Up @@ -4281,7 +4281,7 @@ def resample(self, rule, **kwargs):
md['label'] = md['label'] + ' (' + rule + ' resampling)'

ser = self.to_pandas()
return SeriesResampler(f'{multiplier}Y', ser, md, kwargs)
return SeriesResampler(f'{multiplier}AS', ser, md, kwargs)


class SeriesResampler:
Expand Down
53 changes: 43 additions & 10 deletions pyleoclim/tests/test_core_Series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1107,7 +1107,7 @@ def test_resample_simple(self, rule, dataframe_dt, metadata):
result_ser = result.to_pandas()
expected_values = np.array([0., 1., 2., 3., 4.])
expected_idx = pd.DatetimeIndex(
['2018-12-31', '2019-12-31', '2020-12-31', '2021-12-31', '2022-12-31'],
['2018-01-01', '2019-01-01', '2020-01-01', '2021-01-01', '2022-01-01'],
name='datetime'
).as_unit('s')
expected_ser = pd.Series(expected_values, expected_idx, name='SOI')
Expand All @@ -1130,20 +1130,30 @@ def test_resample_simple(self, rule, dataframe_dt, metadata):
assert result.metadata == expected_metadata

@pytest.mark.parametrize(
('rule', 'expected_idx'),
('rule', 'expected_idx', 'expected_values'),
[
('1ga', [np.datetime64('2018-12-31'), np.datetime64('1000002018-12-31')]),
('1ma', [np.datetime64('2018-12-31'), np.datetime64('1002018-12-31')]),
('2ka', [np.datetime64('2018-12-31'), np.datetime64('4018-12-31')]),
(
'1ga',
pd.date_range(np.datetime64('0-01-01', 's'), np.datetime64('1000000000-01-01', 's'), freq='1000000000AS-JAN', unit='s'),
np.array([0., 1.]),
),
(
'1ma',
pd.date_range(np.datetime64('0-01-01', 's'), np.datetime64('1000000000-01-01', 's'), freq='1000000AS-JAN', unit='s'),
np.array([0.]+[np.nan]*999 + [1.]),
),
]
)
def test_resample_long_periods(self, rule, expected_idx, dataframe_dt, metadata):
ser = dataframe_dt.loc[:, 0]
def test_resample_long_periods(self, rule, expected_idx, expected_values, metadata):
ser_index = pd.DatetimeIndex([
np.datetime64('0000-01-01', 's'),
np.datetime64('1000000000-01-01', 's'),
])
ser = pd.Series(range(2), index=ser_index)
ts = pyleo.Series.from_pandas(ser, metadata)
result =ts.resample(rule).mean()
result_ser = result.to_pandas()
expected_values = np.array([0, 2.5])
expected_idx = pd.DatetimeIndex(expected_idx, name='datetime').as_unit('s')
expected_idx = pd.DatetimeIndex(expected_idx, freq=None, name='datetime')
expected_ser = pd.Series(expected_values, index=expected_idx, name='SOI')
expected_metadata = {
'time_unit': 'years CE',
Expand All @@ -1160,7 +1170,9 @@ def test_resample_long_periods(self, rule, expected_idx, dataframe_dt, metadata)
{2: 'sort_ts', 'direction': 'ascending'}
)
}
pd.testing.assert_series_equal(result_ser, expected_ser)
# check indexes match to within 10 seconds
assert np.abs(result_ser.index.to_numpy() - expected_ser.index.to_numpy()).max() <= 10
np.testing.assert_array_equal(result_ser.to_numpy(), expected_ser.to_numpy())
assert result.metadata == expected_metadata


Expand All @@ -1171,6 +1183,27 @@ def test_resample_invalid(self, dataframe_dt, metadata):
ts.resample('foo')
with pytest.raises(ValueError, match='Invalid rule provided, got: 412'):
ts.resample('412')


def test_resample_interpolate(self, metadata):
ser_index = pd.DatetimeIndex([
np.datetime64('0000-01-01', 's'),
np.datetime64('2000-01-01', 's'),
])
ser = pd.Series(range(2), index=ser_index)
ts = pyleo.Series.from_pandas(ser, metadata)
result_ser = ts.resample('ka').interpolate().to_pandas()
expected_idx = pd.DatetimeIndex(
[
np.datetime64('0-01-01', 's'),
np.datetime64('1000-01-01', 's'),
np.datetime64('2000-01-01', 's')
],
name='datetime'
)
expected_ser = pd.Series([0, 0.5, 1], name='SOI', index=expected_idx)
pd.testing.assert_series_equal(result_ser, expected_ser)


class TestUISeriesEquals():
''' Test for equals() method '''
Expand Down