def _decode_datetime_with_pandas(flat_num_dates, units, calendar):
if calendar not in _STANDARD_CALENDARS:
raise OutOfBoundsDatetime(
"Cannot decode times from a non-standard calendar, {!r}, using "
"pandas.".format(calendar))
delta, ref_date = _unpack_netcdf_time_units(units)
delta = _netcdf_to_numpy_timeunit(delta)
try:
ref_date = pd.Timestamp(ref_date)
except ValueError:
# ValueError is raised by pd.Timestamp for non-ISO timestamp
# strings, in which case we fall back to using cftime
raise OutOfBoundsDatetime
# To avoid integer overflow when converting to nanosecond units for integer
# dtypes smaller than np.int64 cast all integer-dtype arrays to np.int64
# (GH 2002).
if flat_num_dates.dtype.kind == "i":
flat_num_dates = flat_num_dates.astype(np.int64)
# Cast input ordinals to integers of nanoseconds because pd.to_timedelta
# works much faster when dealing with integers (GH 1399).
flat_num_dates_ns_int = (flat_num_dates *
_NS_PER_TIME_DELTA[delta]).astype(np.int64)
# Use pd.to_timedelta to safely cast integer values to timedeltas,
# and add those to a Timestamp to safely produce a DatetimeIndex. This
# ensures that we do not encounter integer overflow at any point in the
# process without raising OutOfBoundsDatetime.
return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
def _decode_datetime_with_pandas(flat_num_dates, units, calendar):
if calendar not in _STANDARD_CALENDARS:
raise OutOfBoundsDatetime(
"Cannot decode times from a non-standard calendar, {!r}, using "
"pandas.".format(calendar)
)
delta, ref_date = _unpack_netcdf_time_units(units)
delta = _netcdf_to_numpy_timeunit(delta)
try:
ref_date = pd.Timestamp(ref_date)
except ValueError:
# ValueError is raised by pd.Timestamp for non-ISO timestamp
# strings, in which case we fall back to using cftime
raise OutOfBoundsDatetime
# fixes: https://github.com/pydata/pandas/issues/14068
# these lines check if the the lowest or the highest value in dates
# cause an OutOfBoundsDatetime (Overflow) error
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
# Cast input dates to integers of nanoseconds because `pd.to_datetime`
# works much faster when dealing with integers
# make _NS_PER_TIME_DELTA an array to ensure type upcasting
flat_num_dates_ns_int = (
flat_num_dates.astype(np.float64) * _NS_PER_TIME_DELTA[delta]
).astype(np.int64)
return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
def _decode_datetime_with_pandas(flat_num_dates, units, calendar):
if not _is_standard_calendar(calendar):
raise OutOfBoundsDatetime(
"Cannot decode times from a non-standard calendar, {!r}, using "
"pandas.".format(calendar)
)
delta, ref_date = _unpack_netcdf_time_units(units)
delta = _netcdf_to_numpy_timeunit(delta)
try:
ref_date = pd.Timestamp(ref_date)
except ValueError:
# ValueError is raised by pd.Timestamp for non-ISO timestamp
# strings, in which case we fall back to using cftime
raise OutOfBoundsDatetime
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
# To avoid integer overflow when converting to nanosecond units for integer
# dtypes smaller than np.int64 cast all integer and unsigned integer dtype
# arrays to np.int64 (GH 2002, GH 6589). Note this is safe even in the case
# of np.uint64 values, because any np.uint64 value that would lead to
# overflow when converting to np.int64 would not be representable with a
# timedelta64 value, and therefore would raise an error in the lines above.
if flat_num_dates.dtype.kind in "iu":
flat_num_dates = flat_num_dates.astype(np.int64)
# Cast input ordinals to integers of nanoseconds because pd.to_timedelta
# works much faster when dealing with integers (GH 1399).
flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
np.int64
)
# Use pd.to_timedelta to safely cast integer values to timedeltas,
# and add those to a Timestamp to safely produce a DatetimeIndex. This
# ensures that we do not encounter integer overflow at any point in the
# process without raising OutOfBoundsDatetime.
return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
