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Let .sel take tolerance for datetime or str time #349

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Mar 2, 2023
Merged

Let .sel take tolerance for datetime or str time #349

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82 changes: 74 additions & 8 deletions pyleoclim/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2396,7 +2396,8 @@ def sel(self, value=None, time=None, tolerance=0):
`self.time` is between slice.start and slice.stop.
If slice of `datetime` (or str containing datetime, such as `'2020-01-01'`),
then the Series will be sliced so that `self.datetime_index` is
between `time.start` and `time.stop`.
between `time.start` and `time.stop` (+/- `tolerance`, which needs to be
a `timedelta`).
tolerance : int, float, default 0.
Used by `value` and `time`, see above.

Expand Down Expand Up @@ -2483,6 +2484,30 @@ def sel(self, value=None, time=None, tolerance=0):
if time is not None:
if isinstance(time, (int, float)):
return self.slice([time-tolerance, time+tolerance])
if isinstance(time, dt.datetime):
if tolerance == 0:
tolerance = dt.timedelta(days=0)
if not isinstance(tolerance, dt.timedelta):
raise TypeError(
f"Invalid 'tolerance' passed. Expected timedelta, received: {type(tolerance)}"
)
return self.pandas_method(
lambda x: x[(x.index>=time-tolerance) & (x.index<=time+tolerance)]
)
if isinstance(time, str):
if tolerance == 0:
tolerance = dt.timedelta(days=0)
if not isinstance(tolerance, dt.timedelta):
raise TypeError(
f"Invalid 'tolerance' passed. Expected timedelta, received: {type(tolerance)}"
)
tolerance = np.timedelta64(tolerance, 's')
return self.pandas_method(
lambda x: x[
(x.index>=np.datetime64(time, 's')-tolerance)
& (x.index<=np.datetime64(time, 's')+tolerance)
]
)
if isinstance(time, slice):
if isinstance(time.start, (int, float)) and isinstance(time.stop, (int, float)):
return self.slice([time.start-tolerance, time.stop+tolerance])
Expand All @@ -2493,36 +2518,77 @@ def sel(self, value=None, time=None, tolerance=0):
new.value = new.value[mask]
return new
if isinstance(time.stop, (int, float)) and time.start is None:
mask = self.time <= time.stop-tolerance
mask = self.time <= time.stop+tolerance
new = self.copy()
new.time = new.time[mask]
new.value = new.value[mask]
return new
if isinstance(time.start, str) and isinstance(time.stop, str):
if tolerance == 0:
tolerance = dt.timedelta(days=0)
if not isinstance(tolerance, dt.timedelta):
raise TypeError(
f"Invalid 'tolerance' passed. Expected timedelta, received: {type(tolerance)}"
)
tolerance = np.timedelta64(tolerance, 's')
return self.pandas_method(
lambda x: x[(x.index>=(np.datetime64(time.start, 's'))) & (x.index<=np.datetime64(time.stop, 's'))]
lambda x: x[
(x.index>=np.datetime64(time.start, 's')-tolerance)
& (x.index<=np.datetime64(time.stop, 's')+tolerance)
]
)
if isinstance(time.start, str) and time.stop is None:
if tolerance == 0:
tolerance = dt.timedelta(days=0)
if not isinstance(tolerance, dt.timedelta):
raise TypeError(
f"Invalid 'tolerance' passed. Expected timedelta, received: {type(tolerance)}"
)
tolerance = np.timedelta64(tolerance, 's')
return self.pandas_method(
lambda x: x[x.index>=(np.datetime64(time.start, 's'))]
lambda x: x[x.index>=np.datetime64(time.start, 's')-tolerance]
)
if isinstance(time.stop, str) and time.start is None:
if tolerance == 0:
tolerance = dt.timedelta(days=0)
if not isinstance(tolerance, dt.timedelta):
raise TypeError(f"Invalid 'tolerance' passed. Expected timedelta, received: {type(tolerance)}")
tolerance = np.timedelta64(tolerance, 's')
return self.pandas_method(
lambda x: x[x.index<=(np.datetime64(time.stop, 's'))]
lambda x: x[x.index<=np.datetime64(time.stop, 's')+tolerance]
)
if isinstance(time.start, dt.datetime) and isinstance(time.stop, dt.datetime):
if tolerance == 0:
tolerance = dt.timedelta(days=0)
if not isinstance(tolerance, dt.timedelta):
raise TypeError(
f"Invalid 'tolerance' passed. Expected timedelta, received: {type(tolerance)}"
)
return self.pandas_method(
lambda x: x[(x.index>=time.start) & (x.index<=time.stop)]
lambda x: x[(x.index>=time.start-tolerance) & (x.index<=time.stop+tolerance)]
)
if isinstance(time.start, dt.datetime) and time.stop is None:
if tolerance == 0:
tolerance = dt.timedelta(days=0)
if not isinstance(tolerance, dt.timedelta):
raise TypeError(
f"Invalid 'tolerance' passed. Expected timedelta, received: {type(tolerance)}"
)
return self.pandas_method(
lambda x: x[x.index>=time.start]
lambda x: x[x.index>=time.start-tolerance]
)
if isinstance(time.stop, dt.datetime) and time.start is None:
if tolerance == 0:
tolerance = dt.timedelta(days=0)
if not isinstance(tolerance, dt.timedelta):
raise TypeError(
f"Invalid 'tolerance' passed. Expected timedelta, received: {type(tolerance)}"
)
return self.pandas_method(
lambda x: x[x.index<=time.stop]
lambda x: x[x.index<=time.stop+tolerance]
)
raise TypeError("Expected int or float, or slice of int/float/datetime/str.")
raise TypeError("Invalid combination of arguments received.")


def slice(self, timespan):
Expand Down
8 changes: 8 additions & 0 deletions pyleoclim/tests/test_core_Series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -468,6 +468,8 @@ def test_value(self, value, expected_time, expected_value, tolerance):
[
(1, np.array([1]), np.array([4]), 0),
(1, np.array([1, 2]), np.array([4, 6]), 1),
(dt.datetime(1948, 1, 1), np.array([2, 3]), np.array([6, 1]), dt.timedelta(days=365)),
('1948', np.array([2, 3]), np.array([6, 1]), dt.timedelta(days=365)),
(slice(1, 2), np.array([1, 2]), np.array([4, 6]), 0),
(slice(1, 2), np.array([1, 2, 3]), np.array([4, 6, 1]), 1),
(slice(1, None), np.array([1, 2, 3]), np.array([4, 6, 1]), 0),
Expand All @@ -478,6 +480,12 @@ def test_value(self, value, expected_time, expected_value, tolerance):
(slice(dt.datetime(1948, 1, 1), dt.datetime(1949, 1, 1)), np.array([1, 2]), np.array([4, 6]), 0),
(slice(dt.datetime(1947, 1, 1), None), np.array([1, 2, 3]), np.array([4, 6, 1]), 0),
(slice(None, dt.datetime(1948, 1, 1)), np.array([3]), np.array([1]), 0),
(slice(dt.datetime(1948, 1, 1), dt.datetime(1949, 1, 1)), np.array([1, 2, 3]), np.array([4, 6, 1]), dt.timedelta(days=365)),
(slice(dt.datetime(1947, 1, 1), None), np.array([1, 2, 3]), np.array([4, 6, 1]), dt.timedelta(days=365)),
(slice(None, dt.datetime(1948, 1, 1)), np.array([2, 3]), np.array([6, 1]), dt.timedelta(days=365)),
(slice('1948', '1949'), np.array([1, 2, 3]), np.array([4, 6, 1]), dt.timedelta(days=365)),
(slice('1947', None), np.array([1, 2, 3]), np.array([4, 6, 1]), dt.timedelta(days=365)),
(slice(None, '1948'), np.array([2, 3]), np.array([6, 1]), dt.timedelta(days=365)),
]
)
def test_time(self, time, expected_time, expected_value, tolerance):
Expand Down