def test_encode_expected_failures():
dates = pd.date_range("2000", periods=3)
with pytest.raises(ValueError, match="invalid time units"):
encode_cf_datetime(dates, units="days after 2000-01-01")
with pytest.raises(ValueError, match="invalid reference date"):
encode_cf_datetime(dates, units="days since NO_YEAR")
def test_time_units_with_timezone_roundtrip(calendar):
# Regression test for GH 2649
expected_units = "days since 2000-01-01T00:00:00-05:00"
expected_num_dates = np.array([1, 2, 3])
dates = decode_cf_datetime(expected_num_dates, expected_units, calendar)
# Check that dates were decoded to UTC; here the hours should all
# equal 5.
result_hours = DataArray(dates).dt.hour
expected_hours = DataArray([5, 5, 5])
assert_equal(result_hours, expected_hours)
# Check that the encoded values are accurately roundtripped.
result_num_dates, result_units, result_calendar = encode_cf_datetime(
dates, expected_units, calendar
)
if calendar in _STANDARD_CALENDARS:
np.testing.assert_array_equal(result_num_dates, expected_num_dates)
else:
# cftime datetime arithmetic is not quite exact.
np.testing.assert_allclose(result_num_dates, expected_num_dates)
assert result_units == expected_units
assert result_calendar == calendar
def _update_file(self, nc, row):
"""Update metadata of a single file."""
for add_mda in ADDITIONAL_METADATA:
add_mda = add_mda.copy()
var_name = add_mda.pop("name")
fill_value = add_mda.pop("fill_value")
data = row[var_name]
# Create nc variable
nc_var = self._create_nc_var(
nc=nc,
var_name=var_name,
fill_value=fill_value,
dtype=add_mda.pop("dtype"),
dims=data.dims,
shape=data.shape,
)
# Write data to nc variable. Since netCDF4 cannot handle NaN nor NaT, disable
# auto-masking, and set null-data to fill value manually.
nc_var.set_auto_mask(False)
if np.issubdtype(data.dtype, np.datetime64):
data, _, _ = encode_cf_datetime(data,
units=add_mda["units"],
calendar=add_mda["calendar"])
data = np.nan_to_num(data, nan=fill_value)
else:
data = data.fillna(fill_value).values
nc_var[:] = data
# Set attributes of nc variable
for key, val in add_mda.items():
nc_var.setncattr(key, val)
def test_encode_cf_datetime_overflow(shape):
# Test for fix to GH 2272
dates = pd.date_range("2100", periods=24).values.reshape(shape)
units = "days since 1800-01-01"
calendar = "standard"
num, _, _ = encode_cf_datetime(dates, units, calendar)
roundtrip = decode_cf_datetime(num, units, calendar)
np.testing.assert_array_equal(dates, roundtrip)
def test_encode_cf_datetime_overflow(shape):
# Test for fix to GH 2272
dates = pd.date_range('2100', periods=24).values.reshape(shape)
units = 'days since 1800-01-01'
calendar = 'standard'
num, _, _ = encode_cf_datetime(dates, units, calendar)
roundtrip = decode_cf_datetime(num, units, calendar)
np.testing.assert_array_equal(dates, roundtrip)
def test_encode_cf_datetime_pandas_min():
# GH 2623
dates = pd.date_range("2000", periods=3)
num, units, calendar = encode_cf_datetime(dates)
expected_num = np.array([0.0, 1.0, 2.0])
expected_units = "days since 2000-01-01 00:00:00"
expected_calendar = "proleptic_gregorian"
np.testing.assert_array_equal(num, expected_num)
assert units == expected_units
assert calendar == expected_calendar
def test_encode_cf_datetime_pandas_min():
# Test that encode_cf_datetime does not fail for versions
# of pandas < 0.21.1 (GH 2623).
dates = pd.date_range('2000', periods=3)
num, units, calendar = encode_cf_datetime(dates)
expected_num = np.array([0., 1., 2.])
expected_units = 'days since 2000-01-01 00:00:00'
expected_calendar = 'proleptic_gregorian'
np.testing.assert_array_equal(num, expected_num)
assert units == expected_units
assert calendar == expected_calendar
def test_encode_cf_datetime_pandas_min():
# Test that encode_cf_datetime does not fail for versions
# of pandas < 0.21.1 (GH 2623).
dates = pd.date_range('2000', periods=3)
num, units, calendar = encode_cf_datetime(dates)
expected_num = np.array([0., 1., 2.])
expected_units = 'days since 2000-01-01 00:00:00'
expected_calendar = 'proleptic_gregorian'
np.testing.assert_array_equal(num, expected_num)
assert units == expected_units
assert calendar == expected_calendar
def test_encode_cf_datetime_units_with_tz():
# Regression test for GH 2649
units = 'days since 2000-01-01T00:00:00-05:00'
calendar = 'proleptic_gregorian'
dates = pd.date_range('2000', periods=3, tz='US/Eastern').values
num, units, calendar = encode_cf_datetime(dates,
units=units,
calendar=calendar)
expected_num = np.array([0, 1, 2])
expected_units = 'days since 2000-01-01T00:00:00-05:00'
expected_calendar = 'proleptic_gregorian'
np.testing.assert_array_equal(num, expected_num)
assert units == expected_units
assert calendar == expected_calendar
def test_encode_cf_datetime_invalid_pandas_valid_cftime() -> None:
num, units, calendar = encode_cf_datetime(
pd.date_range("2000", periods=3),
# Pandas fails to parse this unit, but cftime is quite happy with it
"days since 1970-01-01 00:00:00 00",
"standard",
)
expected_num = [10957, 10958, 10959]
expected_units = "days since 1970-01-01 00:00:00 00"
expected_calendar = "standard"
assert_array_equal(num, expected_num)
assert units == expected_units
assert calendar == expected_calendar
def test_time_units_with_timezone_roundtrip(calendar):
# Regression test for GH 2649
expected_units = 'days since 2000-01-01T00:00:00-05:00'
expected_num_dates = np.array([1, 2, 3])
dates = decode_cf_datetime(expected_num_dates, expected_units, calendar)
# Check that dates were decoded to UTC; here the hours should all
# equal 5.
result_hours = DataArray(dates).dt.hour
expected_hours = DataArray([5, 5, 5])
assert_equal(result_hours, expected_hours)
# Check that the encoded values are accurately roundtripped.
result_num_dates, result_units, result_calendar = encode_cf_datetime(
dates, expected_units, calendar)
if calendar in _STANDARD_CALENDARS:
np.testing.assert_array_equal(result_num_dates, expected_num_dates)
else:
# cftime datetime arithmetic is not quite exact.
np.testing.assert_allclose(result_num_dates, expected_num_dates)
assert result_units == expected_units
assert result_calendar == calendar
