Reduce length of cftime resample tests (#2879)

* Reduce length of cftime resample tests

* Update per @jwenfai's suggestions; silence to_datetimeindex warnings

* Restore 91% coverage for resample_cftime.py

Author: spencerkclark

xarray/tests/test_cftimeindex_resample.py

@@ -6,97 +6,79 @@
 import xarray as xr
 from xarray.core.resample_cftime import CFTimeGrouper
 
+
 pytest.importorskip('cftime')
 pytest.importorskip('pandas', minversion='0.24')
 
 
-@pytest.fixture(
-    params=[
-        dict(start='2004-01-01T12:07:01', periods=91, freq='3D'),
-        dict(start='1892-01-03T12:07:01', periods=15, freq='41987T'),
-        dict(start='2004-01-01T12:07:01', periods=7, freq='3Q-AUG'),
-        dict(start='1892-01-03T12:07:01', periods=10, freq='3AS-JUN')
-    ],
-    ids=['3D', '41987T', '3Q_AUG', '3AS_JUN']
-)
-def time_range_kwargs(request):
-    return request.param
-
-
-@pytest.fixture()
-def datetime_index(time_range_kwargs):
-    return pd.date_range(**time_range_kwargs)
-
-
-@pytest.fixture()
-def cftime_index(time_range_kwargs):
-    return xr.cftime_range(**time_range_kwargs)
+# Create a list of pairs of similar-length initial and resample frequencies
+# that cover:
+# - Resampling from shorter to longer frequencies
+# - Resampling from longer to shorter frequencies
+# - Resampling from one initial frequency to another.
+# These are used to test the cftime version of resample against pandas
+# with a standard calendar.
+FREQS = [
+    ('8003D', '4001D'),
+    ('8003D', '16006D'),
+    ('8003D', '21AS'),
+    ('6H', '3H'),
+    ('6H', '12H'),
+    ('6H', '400T'),
+    ('3D', 'D'),
+    ('3D', '6D'),
+    ('11D', 'MS'),
+    ('3MS', 'MS'),
+    ('3MS', '6MS'),
+    ('3MS', '85D'),
+    ('7M', '3M'),
+    ('7M', '14M'),
+    ('7M', '2QS-APR'),
+    ('43QS-AUG', '21QS-AUG'),
+    ('43QS-AUG', '86QS-AUG'),
+    ('43QS-AUG', '11A-JUN'),
+    ('11Q-JUN', '5Q-JUN'),
+    ('11Q-JUN', '22Q-JUN'),
+    ('11Q-JUN', '51MS'),
+    ('3AS-MAR', 'AS-MAR'),
+    ('3AS-MAR', '6AS-MAR'),
+    ('3AS-MAR', '14Q-FEB'),
+    ('7A-MAY', '3A-MAY'),
+    ('7A-MAY', '14A-MAY'),
+    ('7A-MAY', '85M')
+]
 
 
 def da(index):
     return xr.DataArray(np.arange(100., 100. + index.size),
                         coords=[index], dims=['time'])
 
 
-@pytest.mark.parametrize('freq', [
-    '700T', '8001T',
-    '12H', '8001H',
-    '8D', '8001D',
-    '2MS', '3MS',
-    '2QS-AUG', '3QS-SEP',
-    '3AS-MAR', '4A-MAY'])
-@pytest.mark.parametrize('closed', [None, 'right'])
-@pytest.mark.parametrize('label', [None, 'right'])
+@pytest.mark.parametrize('freqs', FREQS, ids=lambda x: '{}->{}'.format(*x))
+@pytest.mark.parametrize('closed', [None, 'left', 'right'])
+@pytest.mark.parametrize('label', [None, 'left', 'right'])
 @pytest.mark.parametrize('base', [24, 31])
-def test_resampler(freq, closed, label, base,
-                   datetime_index, cftime_index):
-    # Fairly extensive testing for standard/proleptic Gregorian calendar
-    # For any frequencies which are not greater-than-day and anchored
-    # at the end, the default values for closed and label are 'left'.
-    loffset = '12H'
-    try:
-        da_datetime = da(datetime_index).resample(
-            time=freq, closed=closed, label=label, base=base,
-            loffset=loffset).mean()
-    except ValueError:
-        with pytest.raises(ValueError):
-            da(cftime_index).resample(
-                time=freq, closed=closed, label=label, base=base,
-                loffset=loffset).mean()
-    else:
-        da_cftime = da(cftime_index).resample(time=freq, closed=closed,
-                                              label=label, base=base,
-                                              loffset=loffset).mean()
-        da_cftime['time'] = da_cftime.indexes['time'].to_datetimeindex()
-        xr.testing.assert_identical(da_cftime, da_datetime)
-
+def test_resample(freqs, closed, label, base):
+    initial_freq, resample_freq = freqs
+    start = '2000-01-01T12:07:01'
+    index_kwargs = dict(start=start, periods=5, freq=initial_freq)
+    datetime_index = pd.date_range(**index_kwargs)
+    cftime_index = xr.cftime_range(**index_kwargs)
 
-@pytest.mark.parametrize('freq', [
-    '2M', '3M',
-    '2Q-JUN', '3Q-JUL',
-    '3A-FEB', '4A-APR'])
-@pytest.mark.parametrize('closed', ['left', None])
-@pytest.mark.parametrize('label', ['left', None])
-@pytest.mark.parametrize('base', [17, 24])
-def test_resampler_end_super_day(freq, closed, label, base,
-                                 datetime_index, cftime_index):
-    # Fairly extensive testing for standard/proleptic Gregorian calendar.
-    # For greater-than-day frequencies anchored at the end, the default values
-    # for closed and label are 'right'.
     loffset = '12H'
     try:
         da_datetime = da(datetime_index).resample(
-            time=freq, closed=closed, label=label, base=base,
+            time=resample_freq, closed=closed, label=label, base=base,
             loffset=loffset).mean()
     except ValueError:
         with pytest.raises(ValueError):
             da(cftime_index).resample(
-                time=freq, closed=closed, label=label, base=base,
+                time=resample_freq, closed=closed, label=label, base=base,
                 loffset=loffset).mean()
     else:
-        da_cftime = da(cftime_index).resample(time=freq, closed=closed,
-                                              label=label, base=base,
-                                              loffset=loffset).mean()
+        da_cftime = da(cftime_index).resample(
+            time=resample_freq, closed=closed,
+            label=label, base=base, loffset=loffset).mean()
         da_cftime['time'] = da_cftime.indexes['time'].to_datetimeindex()
         xr.testing.assert_identical(da_cftime, da_datetime)
 
@@ -111,6 +93,7 @@ def test_closed_label_defaults(freq, expected):
     assert CFTimeGrouper(freq=freq).label == expected
 
 
+@pytest.mark.filterwarnings('ignore:Converting a CFTimeIndex')
 @pytest.mark.parametrize('calendar', ['gregorian', 'noleap', 'all_leap',
                                       '360_day', 'julian'])
 def test_calendars(calendar):