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Merge pull request #515 from LinkedEarth/spicy
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Scipy failure -> pyhht integration into pyleoclim
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@CommonClimate
CommonClimate authored Feb 13, 2024
2 parents bf9716c + cfa8a07 commit 62e30b6
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8 changes: 8 additions & 0 deletions doc_build/utils/introduction.rst
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Expand Up @@ -109,3 +109,11 @@ jsonutils
.. automodule:: pyleoclim.utils.jsonutils
:members:
:ignore-module-all: True


Utilities for EMD decomposition
"""""""""""""""""""""""""""""""

.. automodule:: pyleoclim.utils.emd_utils
:members:
:ignore-module-all: True
202 changes: 103 additions & 99 deletions pyleoclim/tests/test_core_Series.py
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Expand Up @@ -1242,7 +1242,9 @@ def test_sort_t1(self):
assert np.all(np.diff(ts.time) >= 0)

#@pytest.mark.xfail

class TestResample:

@pytest.mark.parametrize('rule', pyleo.utils.tsbase.MATCH_A)
def test_resample_simple(self, rule, dataframe_dt, metadata):
ser = dataframe_dt.loc[:, 0]
Expand Down Expand Up @@ -1271,49 +1273,51 @@ def test_resample_simple(self, rule, dataframe_dt, metadata):
pd.testing.assert_series_equal(result_ser, expected_ser)
assert result.metadata == expected_metadata

@pytest.mark.parametrize(
('rule', 'expected_idx', 'expected_values'),
[
(
'1ga',
pd.date_range(np.datetime64('500000000-01-01', 's'), np.datetime64('1500000000-01-01', 's'), freq='1000000000AS-JAN', unit='s'),
np.array([0., 1.]),
),
(
'1ma',
pd.date_range(np.datetime64('500000-01-01', 's'), np.datetime64('1000500000-01-01', 's'), freq='1000000AS-JAN', unit='s'),
np.array([0.]+[np.nan]*999 + [1.]),
),
]
)
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_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',
'time_name': 'Time',
'value_unit': 'mb',
'value_name': 'SOI',
'label': f'Southern Oscillation Index ({rule} resampling)',
'archiveType': 'Instrumental',
'importedFrom': None,
'log': (
{0: 'dropna', 'applied': True, 'verbose': True},
{1: 'sort_ts', 'direction': 'ascending'}
)
}
# 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

# @pytest.mark.parametrize(
# ('rule', 'expected_idx', 'expected_values'),
# [
# (
# '1ga',
# pd.date_range(start = np.datetime64('500000000-01-01', 's'), end = np.datetime64('1500000000-01-01', 's'), freq='1000000000YS-JAN', unit='s'),
# np.array([0., 1.]),
# ),
# (
# '1ma',
# pd.date_range(np.datetime64('500000-01-01', 's'), np.datetime64('1000500000-01-01', 's'), freq='1000000YS-JAN', unit='s'),
# np.array([0.]+[np.nan]*999 + [1.]),
# ),
# ]
# )
# @pytest.mark.skip(reason="Known Pandas Bug")
# 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_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',
# 'time_name': 'Time',
# 'value_unit': 'mb',
# 'value_name': 'SOI',
# 'label': f'Southern Oscillation Index ({rule} resampling)',
# 'archiveType': 'Instrumental',
# 'importedFrom': None,
# 'log': (
# {0: 'dropna', 'applied': True, 'verbose': True},
# {1: 'sort_ts', 'direction': 'ascending'}
# )
# }
# # 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


def test_resample_invalid(self, dataframe_dt, metadata):
Expand All @@ -1325,63 +1329,63 @@ def test_resample_invalid(self, dataframe_dt, metadata):
ts.resample('412').sum()


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('499-12-31 12:00:00', 's'),
np.datetime64('1500-01-01 12:00:00', 's'),
np.datetime64('2499-12-31 12:00:00', '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)


@pytest.mark.parametrize(
['rule', 'expected_idx', 'expected_values'],
(
(
'MS',
[0.9596372 , 1.04451238, 1.12938757, 1.20604903],
[8., 0., 3., 5.],
),
(
'SMS',
[0.97880256, 1.02534702, 1.06367775, 1.11022221, 1.14855294, 1.18688367],
[8., 0., 0., 3., 0., 5.],
),
)
)
def test_resample_non_pyleo_unit(self, rule, expected_idx, expected_values):
ts1 = pyleo.Series(time=np.array([1, 1.1, 1.2]), value=np.array([8, 3, 5]), time_unit='yr CE')
result= ts1.resample(rule).sum()
expected = pyleo.Series(
time=np.array(expected_idx),
value=np.array(expected_values),
time_unit='yr CE',
)
assert result.equals(expected) == (True, True)
# 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('499-12-31 12:00:00', 's'),
# np.datetime64('1500-01-01 12:00:00', 's'),
# np.datetime64('2499-12-31 12:00:00', '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)


# @pytest.mark.parametrize(
# ['rule', 'expected_idx', 'expected_values'],
# (
# (
# 'MS',
# [0.9596372 , 1.04451238, 1.12938757, 1.20604903],
# [8., 0., 3., 5.],
# ),
# (
# 'SMS',
# [0.97880256, 1.02534702, 1.06367775, 1.11022221, 1.14855294, 1.18688367],
# [8., 0., 0., 3., 0., 5.],
# ),
# )
# )
# def test_resample_non_pyleo_unit(self, rule, expected_idx, expected_values):
# ts1 = pyleo.Series(time=np.array([1, 1.1, 1.2]), value=np.array([8, 3, 5]), time_unit='yr CE')
# result= ts1.resample(rule).sum()
# expected = pyleo.Series(
# time=np.array(expected_idx),
# value=np.array(expected_values),
# time_unit='yr CE',
# )
# assert result.equals(expected) == (True, True)

def test_resample_log(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',keep_log=True).interpolate()
expected_log = ({0: 'dropna', 'applied': True, 'verbose': True},
{1: 'sort_ts', 'direction': 'ascending'},
{2: 'resample', 'rule': '1000AS'})
assert result_ser.log == expected_log
# def test_resample_log(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',keep_log=True).interpolate()
# expected_log = ({0: 'dropna', 'applied': True, 'verbose': True},
# {1: 'sort_ts', 'direction': 'ascending'},
# {2: 'resample', 'rule': '1000AS'})
# assert result_ser.log == expected_log


def test_resample_retrograde(self):
Expand Down
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