def test_cf_datetime(num_dates, units, calendar):
cftime = _import_cftime()
if cftime.__name__ == 'cftime':
expected = cftime.num2date(num_dates, units, calendar,
only_use_cftime_datetimes=True)
else:
expected = cftime.num2date(num_dates, units, calendar)
min_y = np.ravel(np.atleast_1d(expected))[np.nanargmin(num_dates)].year
max_y = np.ravel(np.atleast_1d(expected))[np.nanargmax(num_dates)].year
if min_y >= 1678 and max_y < 2262:
expected = cftime_to_nptime(expected)
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
'Unable to decode time axis')
actual = coding.times.decode_cf_datetime(num_dates, units,
calendar)
abs_diff = np.atleast_1d(abs(actual - expected)).astype(np.timedelta64)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff <= np.timedelta64(1, 's')).all()
encoded, _, _ = coding.times.encode_cf_datetime(actual, units,
calendar)
if '1-1-1' not in units:
# pandas parses this date very strangely, so the original
# units/encoding cannot be preserved in this case:
# (Pdb) pd.to_datetime('1-1-1 00:00:0.0')
# Timestamp('2001-01-01 00:00:00')
assert_array_equal(num_dates, np.around(encoded, 1))
if (hasattr(num_dates, 'ndim') and num_dates.ndim == 1 and
'1000' not in units):
# verify that wrapping with a pandas.Index works
# note that it *does not* currently work to even put
# non-datetime64 compatible dates into a pandas.Index
encoded, _, _ = coding.times.encode_cf_datetime(
pd.Index(actual), units, calendar)
assert_array_equal(num_dates, np.around(encoded, 1))
def test_cf_datetime(num_dates, units, calendar):
cftime = _import_cftime()
if cftime.__name__ == 'cftime':
expected = cftime.num2date(num_dates,
units,
calendar,
only_use_cftime_datetimes=True)
else:
expected = cftime.num2date(num_dates, units, calendar)
min_y = np.ravel(np.atleast_1d(expected))[np.nanargmin(num_dates)].year
max_y = np.ravel(np.atleast_1d(expected))[np.nanargmax(num_dates)].year
if min_y >= 1678 and max_y < 2262:
expected = cftime_to_nptime(expected)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', 'Unable to decode time axis')
actual = coding.times.decode_cf_datetime(num_dates, units, calendar)
abs_diff = np.atleast_1d(abs(actual - expected)).astype(np.timedelta64)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff <= np.timedelta64(1, 's')).all()
encoded, _, _ = coding.times.encode_cf_datetime(actual, units, calendar)
if '1-1-1' not in units:
# pandas parses this date very strangely, so the original
# units/encoding cannot be preserved in this case:
# (Pdb) pd.to_datetime('1-1-1 00:00:0.0')
# Timestamp('2001-01-01 00:00:00')
assert_array_equal(num_dates, np.around(encoded, 1))
if (hasattr(num_dates, 'ndim') and num_dates.ndim == 1
and '1000' not in units):
# verify that wrapping with a pandas.Index works
# note that it *does not* currently work to even put
# non-datetime64 compatible dates into a pandas.Index
encoded, _, _ = coding.times.encode_cf_datetime(
pd.Index(actual), units, calendar)
assert_array_equal(num_dates, np.around(encoded, 1))
def check_nptime(da):
'''
Check if dataArray time axis format is numpy datetime64 and apply conversion if not.
Parameters
----------
da : dataArray
Xarray data structure
Returns
-------
da: dataArray
Xarray data structure
'''
if not np.issubdtype(da['time'].dtype, np.datetime64):
try:
from xarray.coding.times import cftime_to_nptime
da['time'] = cftime_to_nptime(da['time'])
except ValueError as e:
print(e)
pass
return da
