def test_sel_time():
time_bounds = np.array([[0, 31], [31, 59], [59, 90]])
nv = np.array([0, 1])
time = np.array([15, 46, 74])
data = np.zeros((3))
var_name = 'a'
ds = xr.DataArray(data,
coords=[time],
dims=[TIME_STR],
name=var_name).to_dataset()
ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds,
coords=[time, nv],
dims=[TIME_STR, BOUNDS_STR],
name=TIME_BOUNDS_STR)
units_str = 'days since 2000-01-01 00:00:00'
ds[TIME_STR].attrs['units'] = units_str
ds = ensure_time_avg_has_cf_metadata(ds)
ds = set_grid_attrs_as_coords(ds)
ds = xr.decode_cf(ds)
da = ds[var_name]
start_date = np.datetime64('2000-02-01')
end_date = np.datetime64('2000-03-31')
result = sel_time(da, start_date, end_date)
assert result[SUBSET_START_DATE_STR].values == start_date
assert result[SUBSET_END_DATE_STR].values == end_date
def test_assert_has_data_for_time():
time_bounds = np.array([[0, 31], [31, 59], [59, 90]])
nv = np.array([0, 1])
time = np.array([15, 46, 74])
data = np.zeros((3))
var_name = 'a'
ds = xr.DataArray(data, coords=[time], dims=[TIME_STR],
name=var_name).to_dataset()
ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds,
coords=[time, nv],
dims=[TIME_STR, BOUNDS_STR],
name=TIME_BOUNDS_STR)
units_str = 'days since 2000-01-01 00:00:00'
ds[TIME_STR].attrs['units'] = units_str
ds = ensure_time_avg_has_cf_metadata(ds)
ds = set_grid_attrs_as_coords(ds)
ds = xr.decode_cf(ds)
da = ds[var_name]
start_date = np.datetime64('2000-01-01')
end_date = np.datetime64('2000-03-31')
_assert_has_data_for_time(da, start_date, end_date)
start_date_bad = np.datetime64('1999-12-31')
end_date_bad = np.datetime64('2000-04-01')
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date_bad, end_date)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date, end_date_bad)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date_bad, end_date_bad)
def test_assert_has_data_for_time_str_input():
time_bounds = np.array([[0, 31], [31, 59], [59, 90]])
nv = np.array([0, 1])
time = np.array([15, 46, 74])
data = np.zeros((3))
var_name = 'a'
ds = xr.DataArray(data, coords=[time], dims=[TIME_STR],
name=var_name).to_dataset()
ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds,
coords=[time, nv],
dims=[TIME_STR, BOUNDS_STR],
name=TIME_BOUNDS_STR)
units_str = 'days since 2000-01-01 00:00:00'
ds[TIME_STR].attrs['units'] = units_str
ds = ensure_time_avg_has_cf_metadata(ds)
ds = set_grid_attrs_as_coords(ds)
ds = xr.decode_cf(ds)
da = ds[var_name]
start_date = '2000-01-01'
end_date = '2000-03-31'
_assert_has_data_for_time(da, start_date, end_date)
start_date_bad = '1999-12-31'
end_date_bad = '2000-04-01'
# With strings these checks are disabled
_assert_has_data_for_time(da, start_date_bad, end_date)
_assert_has_data_for_time(da, start_date, end_date_bad)
_assert_has_data_for_time(da, start_date_bad, end_date_bad)
def test_assert_has_data_for_time_str_input():
time_bounds = np.array([[0, 31], [31, 59], [59, 90]])
nv = np.array([0, 1])
time = np.array([15, 46, 74])
data = np.zeros((3))
var_name = 'a'
ds = xr.DataArray(data,
coords=[time],
dims=[TIME_STR],
name=var_name).to_dataset()
ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds,
coords=[time, nv],
dims=[TIME_STR, BOUNDS_STR],
name=TIME_BOUNDS_STR)
units_str = 'days since 2000-01-01 00:00:00'
ds[TIME_STR].attrs['units'] = units_str
ds = ensure_time_avg_has_cf_metadata(ds)
ds = set_grid_attrs_as_coords(ds)
ds = xr.decode_cf(ds)
da = ds[var_name]
start_date = '2000-01-01'
end_date = '2000-03-31'
_assert_has_data_for_time(da, start_date, end_date)
start_date_bad = '1999-12-31'
end_date_bad = '2000-04-01'
# With strings these checks are disabled
_assert_has_data_for_time(da, start_date_bad, end_date)
_assert_has_data_for_time(da, start_date, end_date_bad)
_assert_has_data_for_time(da, start_date_bad, end_date_bad)
def test_assert_has_data_for_time_cftime_datetimes(calendar, date_type):
time_bounds = np.array([[0, 2], [2, 4], [4, 6]])
nv = np.array([0, 1])
time = np.array([1, 3, 5])
data = np.zeros((3))
var_name = 'a'
ds = xr.DataArray(data,
coords=[time],
dims=[TIME_STR],
name=var_name).to_dataset()
ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds,
coords=[time, nv],
dims=[TIME_STR, BOUNDS_STR],
name=TIME_BOUNDS_STR)
units_str = 'days since 0002-01-02 00:00:00'
ds[TIME_STR].attrs['units'] = units_str
ds[TIME_STR].attrs['calendar'] = calendar
ds = ensure_time_avg_has_cf_metadata(ds)
ds = set_grid_attrs_as_coords(ds)
with warnings.catch_warnings(record=True):
with xr.set_options(enable_cftimeindex=True):
ds = xr.decode_cf(ds)
da = ds[var_name]
start_date = date_type(2, 1, 2)
end_date = date_type(2, 1, 8)
_assert_has_data_for_time(da, start_date, end_date)
start_date_bad = date_type(2, 1, 1)
end_date_bad = date_type(2, 1, 9)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date_bad, end_date)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date, end_date_bad)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date_bad, end_date_bad)
def test_assert_has_data_for_time_cftime_datetimes(calendar, date_type):
time_bounds = np.array([[0, 2], [2, 4], [4, 6]])
nv = np.array([0, 1])
time = np.array([1, 3, 5])
data = np.zeros((3))
var_name = 'a'
ds = xr.DataArray(data, coords=[time], dims=[TIME_STR],
name=var_name).to_dataset()
ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds,
coords=[time, nv],
dims=[TIME_STR, BOUNDS_STR],
name=TIME_BOUNDS_STR)
units_str = 'days since 0002-01-02 00:00:00'
ds[TIME_STR].attrs['units'] = units_str
ds[TIME_STR].attrs['calendar'] = calendar
ds = ensure_time_avg_has_cf_metadata(ds)
ds = set_grid_attrs_as_coords(ds)
with warnings.catch_warnings(record=True):
with xr.set_options(enable_cftimeindex=True):
ds = xr.decode_cf(ds)
da = ds[var_name]
start_date = date_type(2, 1, 2)
end_date = date_type(2, 1, 8)
_assert_has_data_for_time(da, start_date, end_date)
start_date_bad = date_type(2, 1, 1)
end_date_bad = date_type(2, 1, 9)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date_bad, end_date)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date, end_date_bad)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date_bad, end_date_bad)
def test_sel_time():
time_bounds = np.array([[0, 31], [31, 59], [59, 90]])
nv = np.array([0, 1])
time = np.array([15, 46, 74])
data = np.zeros((3))
var_name = 'a'
ds = xr.DataArray(data, coords=[time], dims=[TIME_STR],
name=var_name).to_dataset()
ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds,
coords=[time, nv],
dims=[TIME_STR, BOUNDS_STR],
name=TIME_BOUNDS_STR)
units_str = 'days since 2000-01-01 00:00:00'
ds[TIME_STR].attrs['units'] = units_str
ds = ensure_time_avg_has_cf_metadata(ds)
ds = set_grid_attrs_as_coords(ds)
ds = xr.decode_cf(ds)
da = ds[var_name]
start_date = np.datetime64('2000-02-01')
end_date = np.datetime64('2000-03-31')
result = sel_time(da, start_date, end_date)
assert result[SUBSET_START_DATE_STR].values == start_date
assert result[SUBSET_END_DATE_STR].values == end_date
def test_ensure_time_avg_has_cf_metadata(ds_time_encoded_cf):
ds = ds_time_encoded_cf
time = ds[TIME_STR].values
time_bounds = ds[TIME_BOUNDS_STR].values
units_str = ds[TIME_STR].attrs['units']
cal_str = ds[TIME_STR].attrs['calendar']
with pytest.raises(KeyError):
ds[TIME_BOUNDS_STR].attrs['units']
with pytest.raises(KeyError):
ds[TIME_BOUNDS_STR].attrs['calendar']
ds = ensure_time_avg_has_cf_metadata(ds)
result = ds[TIME_BOUNDS_STR].attrs['units']
assert result == units_str
result = ds[TIME_BOUNDS_STR].attrs['calendar']
assert result == cal_str
avg_DT_data = np.diff(time_bounds, axis=1).squeeze()
average_DT_expected = xr.DataArray(avg_DT_data,
coords=[time],
dims=[TIME_STR],
name=TIME_WEIGHTS_STR)
average_DT_expected[TIME_STR].attrs['units'] = units_str
average_DT_expected.attrs['units'] = 'days'
average_DT_expected[TIME_STR].attrs['calendar'] = cal_str
assert ds[TIME_WEIGHTS_STR].identical(average_DT_expected)
assert ds[RAW_START_DATE_STR].values == [0]
assert ds[RAW_START_DATE_STR].attrs['units'] == units_str
assert ds[RAW_START_DATE_STR].attrs['calendar'] == cal_str
assert ds[RAW_END_DATE_STR].values == [90]
assert ds[RAW_END_DATE_STR].attrs['units'] == units_str
assert ds[RAW_END_DATE_STR].attrs['calendar'] == cal_str
def test_assert_has_data_for_time():
time_bounds = np.array([[0, 31], [31, 59], [59, 90]])
nv = np.array([0, 1])
time = np.array([15, 46, 74])
data = np.zeros((3))
var_name = 'a'
ds = xr.DataArray(data,
coords=[time],
dims=[TIME_STR],
name=var_name).to_dataset()
ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds,
coords=[time, nv],
dims=[TIME_STR, BOUNDS_STR],
name=TIME_BOUNDS_STR)
units_str = 'days since 2000-01-01 00:00:00'
ds[TIME_STR].attrs['units'] = units_str
ds = ensure_time_avg_has_cf_metadata(ds)
ds = set_grid_attrs_as_coords(ds)
ds = xr.decode_cf(ds)
da = ds[var_name]
start_date = np.datetime64('2000-01-01')
end_date = np.datetime64('2000-03-31')
_assert_has_data_for_time(da, start_date, end_date)
start_date_bad = np.datetime64('1999-12-31')
end_date_bad = np.datetime64('2000-04-01')
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date_bad, end_date)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date, end_date_bad)
with pytest.raises(AssertionError):
_assert_has_data_for_time(da, start_date_bad, end_date_bad)
def test_ensure_time_avg_has_cf_metadata(ds_time_encoded_cf):
ds = ds_time_encoded_cf
time = ds[TIME_STR].values
time_bounds = ds[TIME_BOUNDS_STR].values
units_str = ds[TIME_STR].attrs['units']
cal_str = ds[TIME_STR].attrs['calendar']
with pytest.raises(KeyError):
ds[TIME_BOUNDS_STR].attrs['units']
with pytest.raises(KeyError):
ds[TIME_BOUNDS_STR].attrs['calendar']
ds = ensure_time_avg_has_cf_metadata(ds)
result = ds[TIME_BOUNDS_STR].attrs['units']
assert result == units_str
result = ds[TIME_BOUNDS_STR].attrs['calendar']
assert result == cal_str
avg_DT_data = np.diff(time_bounds, axis=1).squeeze()
average_DT_expected = xr.DataArray(avg_DT_data,
coords=[time],
dims=[TIME_STR],
name=TIME_WEIGHTS_STR)
average_DT_expected[TIME_STR].attrs['units'] = units_str
average_DT_expected.attrs['units'] = 'days'
average_DT_expected[TIME_STR].attrs['calendar'] = cal_str
assert ds[TIME_WEIGHTS_STR].identical(average_DT_expected)
assert ds[RAW_START_DATE_STR].values == [0]
assert ds[RAW_START_DATE_STR].attrs['units'] == units_str
assert ds[RAW_START_DATE_STR].attrs['calendar'] == cal_str
assert ds[RAW_END_DATE_STR].values == [90]
assert ds[RAW_END_DATE_STR].attrs['units'] == units_str
assert ds[RAW_END_DATE_STR].attrs['calendar'] == cal_str
