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attempted fix for #301
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@pochedls
pochedls committed Aug 4, 2022
1 parent 87790f8 commit 0d1352c
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Showing 2 changed files with 70 additions and 85 deletions.
127 changes: 60 additions & 67 deletions tests/test_temporal.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -419,19 +419,11 @@ def test_weighted_annual_averages(self):
"lon": expected.lon,
"time": xr.DataArray(
data=np.array(
[
"2000-01-01T00:00:00.000000000",
"2001-01-01T00:00:00.000000000",
],
dtype="datetime64[ns]",
[cftime.datetime(2000, 1, 1), cftime.datetime(2001, 1, 1)],
),
coords={
"time": np.array(
[
"2000-01-01T00:00:00.000000000",
"2001-01-01T00:00:00.000000000",
],
dtype="datetime64[ns]",
[cftime.datetime(2000, 1, 1), cftime.datetime(2001, 1, 1)],
)
},
dims=["time"],
Expand Down Expand Up @@ -470,19 +462,11 @@ def test_weighted_annual_averages_with_chunking(self):
"lon": expected.lon,
"time": xr.DataArray(
data=np.array(
[
"2000-01-01T00:00:00.000000000",
"2001-01-01T00:00:00.000000000",
],
dtype="datetime64[ns]",
[cftime.datetime(2000, 1, 1), cftime.datetime(2001, 1, 1)],
),
coords={
"time": np.array(
[
"2000-01-01T00:00:00.000000000",
"2001-01-01T00:00:00.000000000",
],
dtype="datetime64[ns]",
[cftime.datetime(2000, 1, 1), cftime.datetime(2001, 1, 1)],
)
},
dims=["time"],
Expand Down Expand Up @@ -527,12 +511,11 @@ def test_weighted_seasonal_averages_with_DJF_and_drop_incomplete_seasons(self):
"time": xr.DataArray(
data=np.array(
[
"2000-04-01T00:00:00.000000000",
"2000-07-01T00:00:00.000000000",
"2000-10-01T00:00:00.000000000",
"2001-01-01T00:00:00.000000000",
cftime.datetime(2000, 4, 1),
cftime.datetime(2000, 7, 1),
cftime.datetime(2000, 10, 1),
cftime.datetime(2001, 1, 1),
],
dtype="datetime64[ns]",
),
dims=["time"],
attrs={
Expand Down Expand Up @@ -577,13 +560,12 @@ def test_weighted_seasonal_averages_with_DJF_without_dropping_incomplete_seasons
"time": xr.DataArray(
data=np.array(
[
"2000-01-01T00:00:00.000000000",
"2000-04-01T00:00:00.000000000",
"2000-07-01T00:00:00.000000000",
"2000-10-01T00:00:00.000000000",
"2001-01-01T00:00:00.000000000",
cftime.datetime(2000, 1, 1),
cftime.datetime(2000, 4, 1),
cftime.datetime(2000, 7, 1),
cftime.datetime(2000, 10, 1),
cftime.datetime(2001, 1, 1),
],
dtype="datetime64[ns]",
),
dims=["time"],
attrs={
Expand Down Expand Up @@ -626,24 +608,22 @@ def test_weighted_seasonal_averages_with_JFD(self):
"time": xr.DataArray(
data=np.array(
[
"2000-01-01T00:00:00.000000000",
"2000-04-01T00:00:00.000000000",
"2000-07-01T00:00:00.000000000",
"2000-10-01T00:00:00.000000000",
"2001-01-01T00:00:00.000000000",
cftime.datetime(2000, 1, 1),
cftime.datetime(2000, 4, 1),
cftime.datetime(2000, 7, 1),
cftime.datetime(2000, 10, 1),
cftime.datetime(2001, 1, 1),
],
dtype="datetime64[ns]",
),
coords={
"time": np.array(
[
"2000-01-01T00:00:00.000000000",
"2000-04-01T00:00:00.000000000",
"2000-07-01T00:00:00.000000000",
"2000-10-01T00:00:00.000000000",
"2001-01-01T00:00:00.000000000",
cftime.datetime(2000, 1, 1),
cftime.datetime(2000, 4, 1),
cftime.datetime(2000, 7, 1),
cftime.datetime(2000, 10, 1),
cftime.datetime(2001, 1, 1),
],
dtype="datetime64[ns]",
)
},
dims=["time"],
Expand Down Expand Up @@ -692,12 +672,11 @@ def test_weighted_custom_seasonal_averages(self):
"time": xr.DataArray(
data=np.array(
[
"2000-02-01T00:00:00.000000000",
"2000-05-01T00:00:00.000000000",
"2000-08-01T00:00:00.000000000",
"2001-02-01T00:00:00.000000000",
cftime.datetime(2000, 2, 1),
cftime.datetime(2000, 5, 1),
cftime.datetime(2000, 8, 1),
cftime.datetime(2001, 2, 1),
],
dtype="datetime64[ns]",
),
dims=["time"],
attrs={
Expand Down Expand Up @@ -783,13 +762,12 @@ def test_weighted_monthly_averages(self):
"time": xr.DataArray(
data=np.array(
[
"2000-01-01T00:00:00.000000000",
"2000-03-01T00:00:00.000000000",
"2000-06-01T00:00:00.000000000",
"2000-09-01T00:00:00.000000000",
"2001-02-01T00:00:00.000000000",
cftime.datetime(2000, 1, 1),
cftime.datetime(2000, 3, 1),
cftime.datetime(2000, 6, 1),
cftime.datetime(2000, 9, 1),
cftime.datetime(2001, 2, 1),
],
dtype="datetime64[ns]",
),
dims=["time"],
attrs={
Expand Down Expand Up @@ -833,13 +811,12 @@ def test_weighted_monthly_averages_with_masked_data(self):
"time": xr.DataArray(
data=np.array(
[
"2000-01-01T00:00:00.000000000",
"2000-03-01T00:00:00.000000000",
"2000-06-01T00:00:00.000000000",
"2000-09-01T00:00:00.000000000",
"2001-02-01T00:00:00.000000000",
cftime.datetime(2000, 1, 1),
cftime.datetime(2000, 3, 1),
cftime.datetime(2000, 6, 1),
cftime.datetime(2000, 9, 1),
cftime.datetime(2001, 2, 1),
],
dtype="datetime64[ns]",
),
dims=["time"],
attrs={
Expand Down Expand Up @@ -876,13 +853,12 @@ def test_weighted_daily_averages(self):
"time": xr.DataArray(
data=np.array(
[
"2000-01-16T00:00:00.000000000",
"2000-03-16T00:00:00.000000000",
"2000-06-16T00:00:00.000000000",
"2000-09-16T00:00:00.000000000",
"2001-02-15T00:00:00.000000000",
cftime.datetime(2000, 1, 16),
cftime.datetime(2000, 3, 16),
cftime.datetime(2000, 6, 16),
cftime.datetime(2000, 9, 16),
cftime.datetime(2001, 2, 15),
],
dtype="datetime64[ns]",
),
dims=["time"],
attrs={
Expand Down Expand Up @@ -917,7 +893,24 @@ def test_weighted_hourly_averages(self):
coords={
"lat": expected.lat,
"lon": expected.lon,
"time": ds.time,
"time": xr.DataArray(
data=np.array(
[
cftime.datetime(2000, 1, 16, 12),
cftime.datetime(2000, 3, 16, 12),
cftime.datetime(2000, 6, 16, 0),
cftime.datetime(2000, 9, 16, 0),
cftime.datetime(2001, 2, 15, 12),
],
),
dims=["time"],
attrs={
"axis": "T",
"long_name": "time",
"standard_name": "time",
"bounds": "time_bnds",
},
),
},
dims=["time", "lat", "lon"],
attrs={
Expand Down
28 changes: 10 additions & 18 deletions xcdat/temporal.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1306,17 +1306,14 @@ def _drop_obsolete_columns(self, df_season: pd.DataFrame) -> pd.DataFrame:
return df_season

def _convert_df_to_dt(self, df: pd.DataFrame) -> np.ndarray:
"""Converts a DataFrame of datetime components to datetime objects.
"""Converts a DataFrame of datetime components to cftime datetime
objects.
datetime objects require at least a year, month, and day value. However,
some modes and time frequencies don't require year, month, and/or day
for grouping. For these cases, use default values of 1 in order to
meet this datetime requirement.
If the default value of 1 is used for the years, datetime objects
must be created using `cftime.datetime` because year 1 is outside the
Timestamp-valid range.
Parameters
----------
df : pd.DataFrame
Expand All @@ -1325,11 +1322,12 @@ def _convert_df_to_dt(self, df: pd.DataFrame) -> np.ndarray:
Returns
-------
np.ndarray
A numpy ndarray of datetime.datetime or cftime.datetime objects.
A numpy ndarray of cftime.datetime objects.
Notes
-----
Refer to [3]_ and [4]_ for more information on Timestamp-valid range.
We use cftime.datetime objects to avoid these time range issues.
References
----------
Expand All @@ -1344,18 +1342,12 @@ def _convert_df_to_dt(self, df: pd.DataFrame) -> np.ndarray:
if component not in df_new.columns:
df_new[component] = default_val

year_is_unused = self._mode in ["climatology", "departures"] or (
self._mode == "average" and self._freq != "year"
)
if year_is_unused:
dates = [
cftime.datetime(year, month, day, hour)
for year, month, day, hour in zip(
df_new.year, df_new.month, df_new.day, df_new.hour
)
]
else:
dates = pd.to_datetime(df_new)
dates = [
cftime.datetime(year, month, day, hour)
for year, month, day, hour in zip(
df_new.year, df_new.month, df_new.day, df_new.hour
)
]

return np.array(dates)

Expand Down

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