from __future__ import annotations import contextlib import re import warnings from collections.abc import Callable, Hashable from datetime import datetime, timedelta from functools import partial from typing import TYPE_CHECKING, Union, cast import numpy as np import pandas as pd from pandas.errors import OutOfBoundsDatetime, OutOfBoundsTimedelta from xarray.coding.common import ( SerializationWarning, VariableCoder, lazy_elemwise_func, pop_to, safe_setitem, unpack_for_decoding, unpack_for_encoding, ) from xarray.compat.pdcompat import default_precision_timestamp, timestamp_as_unit from xarray.core import indexing from xarray.core.common import contains_cftime_datetimes, is_np_datetime_like from xarray.core.duck_array_ops import array_all, asarray, ravel, reshape from xarray.core.formatting import first_n_items, format_timestamp, last_item from xarray.core.utils import attempt_import, emit_user_level_warning from xarray.core.variable import Variable from xarray.namedarray.parallelcompat import T_ChunkedArray, get_chunked_array_type from xarray.namedarray.pycompat import is_chunked_array, to_numpy from xarray.namedarray.utils import is_duck_dask_array try: import cftime except ImportError: cftime = None from xarray.core.types import ( CFCalendar, CFTimeDatetime, NPDatetimeUnitOptions, PDDatetimeUnitOptions, T_DuckArray, ) T_Name = Union[Hashable, None] # standard calendars recognized by cftime _STANDARD_CALENDARS = {"standard", "gregorian", "proleptic_gregorian"} _NS_PER_TIME_DELTA = { "ns": 1, "us": int(1e3), "ms": int(1e6), "s": int(1e9), "m": int(1e9) * 60, "h": int(1e9) * 60 * 60, "D": int(1e9) * 60 * 60 * 24, } _US_PER_TIME_DELTA = { "microseconds": 1, "milliseconds": 1_000, "seconds": 1_000_000, "minutes": 60 * 1_000_000, "hours": 60 * 60 * 1_000_000, "days": 24 * 60 * 60 * 1_000_000, } _NETCDF_TIME_UNITS_CFTIME = [ "days", "hours", "minutes", "seconds", "milliseconds", "microseconds", ] _NETCDF_TIME_UNITS_NUMPY = _NETCDF_TIME_UNITS_CFTIME + ["nanoseconds"] TIME_UNITS = frozenset( [ "days", "hours", "minutes", "seconds", "milliseconds", "microseconds", "nanoseconds", ] ) _INVALID_LITERAL_TIMEDELTA64_ENCODING_KEYS = [ "add_offset", "scale_factor", ] def _is_standard_calendar(calendar: str) -> bool: return calendar.lower() in _STANDARD_CALENDARS def _is_numpy_compatible_time_range(times): if is_np_datetime_like(times.dtype): return True # times array contains cftime objects times = np.asarray(times) tmin = times.min() tmax = times.max() try: # before relaxing the nanosecond constrained # this raised OutOfBoundsDatetime for # times < 1678 and times > 2262 # this isn't the case anymore for other resolutions like "s" # now, we raise for dates before 1582-10-15 _check_date_is_after_shift(tmin, "standard") _check_date_is_after_shift(tmax, "standard") convert_time_or_go_back(tmin, pd.Timestamp) convert_time_or_go_back(tmax, pd.Timestamp) except pd.errors.OutOfBoundsDatetime: return False except ValueError as err: if err.args[0] == "year 0 is out of range": return False raise else: return True def _netcdf_to_numpy_timeunit(units: str) -> NPDatetimeUnitOptions: units = units.lower() if not units.endswith("s"): units = f"{units}s" return cast( NPDatetimeUnitOptions, { "nanoseconds": "ns", "microseconds": "us", "milliseconds": "ms", "seconds": "s", "minutes": "m", "hours": "h", "days": "D", }[units], ) def _numpy_to_netcdf_timeunit(units: NPDatetimeUnitOptions) -> str: return { "ns": "nanoseconds", "us": "microseconds", "ms": "milliseconds", "s": "seconds", "m": "minutes", "h": "hours", "D": "days", }[units] def _numpy_dtype_to_netcdf_timeunit(dtype: np.dtype) -> str: unit, _ = np.datetime_data(dtype) unit = cast(NPDatetimeUnitOptions, unit) return _numpy_to_netcdf_timeunit(unit) def _ensure_padded_year(ref_date: str) -> str: # Reference dates without a padded year (e.g. since 1-1-1 or since 2-3-4) # are ambiguous (is it YMD or DMY?). This can lead to some very odd # behaviour e.g. pandas (via dateutil) passes '1-1-1 00:00:0.0' as # '2001-01-01 00:00:00' (because it assumes a) DMY and b) that year 1 is # shorthand for 2001 (like 02 would be shorthand for year 2002)). # Here we ensure that there is always a four-digit year, with the # assumption being that year comes first if we get something ambiguous. matches_year = re.match(r".*\d{4}.*", ref_date) if matches_year: # all good, return return ref_date # No four-digit strings, assume the first digits are the year and pad # appropriately matches_start_digits = re.match(r"(\d+)(.*)", ref_date) if not matches_start_digits: raise ValueError(f"invalid reference date for time units: {ref_date}") ref_year, everything_else = (s for s in matches_start_digits.groups()) ref_date_padded = f"{int(ref_year):04d}{everything_else}" warning_msg = ( f"Ambiguous reference date string: {ref_date}. The first value is " "assumed to be the year hence will be padded with zeros to remove " f"the ambiguity (the padded reference date string is: {ref_date_padded}). " "To remove this message, remove the ambiguity by padding your reference " "date strings with zeros." ) warnings.warn(warning_msg, SerializationWarning, stacklevel=2) return ref_date_padded def _unpack_netcdf_time_units(units: str) -> tuple[str, str]: # CF datetime units follow the format: "UNIT since DATE" # this parses out the unit and date allowing for extraneous # whitespace. It also ensures that the year is padded with zeros # so it will be correctly understood by pandas (via dateutil). matches = re.match(r"(.+) since (.+)", units) if not matches: raise ValueError(f"invalid time units: {units}") delta_units, ref_date = (s.strip() for s in matches.groups()) ref_date = _ensure_padded_year(ref_date) return delta_units, ref_date def named(name: str, pattern: str) -> str: return "(?P<" + name + ">" + pattern + ")" def optional(x: str) -> str: return "(?:" + x + ")?" def trailing_optional(xs: list[str]) -> str: if not xs: return "" return xs[0] + optional(trailing_optional(xs[1:])) def build_pattern( date_sep: str = r"\-", datetime_sep: str = r"T", time_sep: str = r"\:", micro_sep: str = r".", ) -> str: pieces = [ (None, "year", r"[+-]?\d{4,5}"), (date_sep, "month", r"\d{2}"), (date_sep, "day", r"\d{2}"), (datetime_sep, "hour", r"\d{2}"), (time_sep, "minute", r"\d{2}"), (time_sep, "second", r"\d{2}"), (micro_sep, "microsecond", r"\d{1,6}"), ] pattern_list = [] for sep, name, sub_pattern in pieces: pattern_list.append((sep or "") + named(name, sub_pattern)) # TODO: allow timezone offsets? return "^" + trailing_optional(pattern_list) + "$" _BASIC_PATTERN = build_pattern(date_sep="", time_sep="") _EXTENDED_PATTERN = build_pattern() _CFTIME_PATTERN = build_pattern(datetime_sep=" ") _PATTERNS = [_BASIC_PATTERN, _EXTENDED_PATTERN, _CFTIME_PATTERN] def parse_iso8601_like(datetime_string: str) -> dict[str, str | None]: for pattern in _PATTERNS: match = re.match(pattern, datetime_string) if match: return match.groupdict() raise ValueError( f"no ISO-8601 or cftime-string-like match for string: {datetime_string}" ) def _parse_iso8601(date_type, timestr): default = date_type(1, 1, 1) result = parse_iso8601_like(timestr) replace = {} for attr in ["year", "month", "day", "hour", "minute", "second", "microsecond"]: value = result.get(attr, None) if value is not None: resolution = attr if attr == "microsecond": if len(value) <= 3: resolution = "millisecond" # convert match string into valid microsecond value value = 10 ** (6 - len(value)) * int(value) replace[attr] = int(value) return default.replace(**replace), resolution def _maybe_strip_tz_from_timestamp(date: pd.Timestamp) -> pd.Timestamp: # If the ref_date Timestamp is timezone-aware, convert to UTC and # make it timezone-naive (GH 2649). if date.tz is not None: return date.tz_convert("UTC").tz_convert(None) return date def _unpack_time_unit_and_ref_date( units: str, ) -> tuple[NPDatetimeUnitOptions, pd.Timestamp]: # same us _unpack_netcdf_time_units but finalizes ref_date for # processing in encode_cf_datetime time_unit, _ref_date = _unpack_netcdf_time_units(units) time_unit = _netcdf_to_numpy_timeunit(time_unit) ref_date = pd.Timestamp(_ref_date) ref_date = _maybe_strip_tz_from_timestamp(ref_date) return time_unit, ref_date def _unpack_time_units_and_ref_date_cftime(units: str, calendar: str): # same as _unpack_netcdf_time_units but finalizes ref_date for # processing in encode_cf_datetime time_units, ref_date = _unpack_netcdf_time_units(units) ref_date = cftime.num2date( 0, units=f"microseconds since {ref_date}", calendar=calendar, only_use_cftime_datetimes=True, ) return time_units, ref_date def _decode_cf_datetime_dtype( data, units: str, calendar: str | None, use_cftime: bool | None, time_unit: PDDatetimeUnitOptions = "ns", ) -> np.dtype: # Verify that at least the first and last date can be decoded # successfully. Otherwise, tracebacks end up swallowed by # Dataset.__repr__ when users try to view their lazily decoded array. values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data)) example_value = np.concatenate( [to_numpy(first_n_items(values, 1)), to_numpy(last_item(values))] ) try: result = decode_cf_datetime( example_value, units, calendar, use_cftime, time_unit ) except Exception as err: calendar_msg = ( "the default calendar" if calendar is None else f"calendar {calendar!r}" ) msg = ( f"unable to decode time units {units!r} with {calendar_msg!r}. Try " "opening your dataset with decode_times=False or installing cftime " "if it is not installed." ) raise ValueError(msg) from err else: dtype = getattr(result, "dtype", np.dtype("object")) return dtype def _decode_datetime_with_cftime( num_dates: np.ndarray, units: str, calendar: str ) -> np.ndarray: if TYPE_CHECKING: import cftime else: cftime = attempt_import("cftime") if num_dates.size > 0: return np.asarray( cftime.num2date(num_dates, units, calendar, only_use_cftime_datetimes=True) ) else: return np.array([], dtype=object) def _check_date_for_units_since_refdate( date, unit: NPDatetimeUnitOptions, ref_date: pd.Timestamp ) -> pd.Timestamp: # check for out-of-bounds floats and raise if date > np.iinfo("int64").max or date < np.iinfo("int64").min: raise OutOfBoundsTimedelta( f"Value {date} can't be represented as Datetime/Timedelta." ) delta = date * np.timedelta64(1, unit) if not np.isnan(delta): # this will raise on dtype overflow for integer dtypes if date.dtype.kind in "u" and not np.int64(delta) == date: raise OutOfBoundsTimedelta( "DType overflow in Datetime/Timedelta calculation." ) # this will raise on overflow if ref_date + delta # can't be represented in the current ref_date resolution return timestamp_as_unit(ref_date + delta, ref_date.unit) else: # if date is exactly NaT (np.iinfo("int64").min) return NaT # to make follow-up checks work return pd.Timestamp("NaT") def _check_timedelta_range(value, data_unit, time_unit): if value > np.iinfo("int64").max or value < np.iinfo("int64").min: OutOfBoundsTimedelta(f"Value {value} can't be represented as Timedelta.") # on windows multiplying nan leads to RuntimeWarning with warnings.catch_warnings(): warnings.filterwarnings( "ignore", "invalid value encountered in multiply", RuntimeWarning ) delta = value * np.timedelta64(1, data_unit) if not np.isnan(delta): # this will raise on dtype overflow for integer dtypes if value.dtype.kind in "u" and not np.int64(delta) == value: raise OutOfBoundsTimedelta( "DType overflow in Datetime/Timedelta calculation." ) # this will raise on overflow if delta cannot be represented with the # resolutions supported by pandas. pd.to_timedelta(delta) def _align_reference_date_and_unit( ref_date: pd.Timestamp, unit: NPDatetimeUnitOptions ) -> pd.Timestamp: # align to the highest needed resolution of ref_date or unit if np.timedelta64(1, ref_date.unit) > np.timedelta64(1, unit): # this will raise accordingly # if data can't be represented in the higher resolution return timestamp_as_unit(ref_date, cast(PDDatetimeUnitOptions, unit)) return ref_date def _check_date_is_after_shift( date: pd.Timestamp | datetime | CFTimeDatetime, calendar: str ) -> None: # if we have gregorian/standard we need to raise # if we are outside the well-defined date range # proleptic_gregorian and standard/gregorian are only equivalent # if reference date and date range is >= 1582-10-15 if calendar != "proleptic_gregorian" and date < type(date)(1582, 10, 15): raise OutOfBoundsDatetime( f"Dates before 1582-10-15 cannot be decoded " f"with pandas using {calendar!r} calendar: {date}" ) def _check_higher_resolution( flat_num_dates: np.ndarray, time_unit: PDDatetimeUnitOptions, ) -> tuple[np.ndarray, PDDatetimeUnitOptions]: """Iterate until fitting resolution found.""" res: list[PDDatetimeUnitOptions] = ["s", "ms", "us", "ns"] new_units = res[res.index(time_unit) :] for new_time_unit in new_units: if not ((np.unique(flat_num_dates % 1) > 0).any() and new_time_unit != "ns"): break flat_num_dates *= 1000 return flat_num_dates, new_time_unit def _decode_datetime_with_pandas( flat_num_dates: np.ndarray, units: str, calendar: str, time_resolution: PDDatetimeUnitOptions = "ns", ) -> np.ndarray: if not _is_standard_calendar(calendar): raise OutOfBoundsDatetime( f"Cannot decode times from a non-standard calendar, {calendar!r}, using " "pandas." ) # Work around pandas.to_timedelta issue with dtypes smaller than int64 and # NumPy 2.0 by casting all int and uint data to int64 and uint64, # respectively. See https://github.com/pandas-dev/pandas/issues/56996 for # more details. if flat_num_dates.dtype.kind == "i": flat_num_dates = flat_num_dates.astype(np.int64) elif flat_num_dates.dtype.kind == "u": flat_num_dates = flat_num_dates.astype(np.uint64) try: time_unit, ref_date = _unpack_time_unit_and_ref_date(units) ref_date = _align_reference_date_and_unit(ref_date, time_unit) # here the highest wanted resolution is set ref_date = _align_reference_date_and_unit(ref_date, time_resolution) except ValueError as err: # ValueError is raised by pd.Timestamp for non-ISO timestamp # strings, in which case we fall back to using cftime raise OutOfBoundsDatetime from err _check_date_is_after_shift(ref_date, calendar) with warnings.catch_warnings(): warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning) if flat_num_dates.size > 0: # avoid size 0 datetimes GH1329 _check_date_for_units_since_refdate( flat_num_dates.min(), time_unit, ref_date ) _check_date_for_units_since_refdate( flat_num_dates.max(), time_unit, 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) elif flat_num_dates.dtype.kind in "f": flat_num_dates = flat_num_dates.astype(np.float64) timedeltas = _numbers_to_timedelta( flat_num_dates, time_unit, ref_date.unit, "datetimes" ) # add timedeltas to ref_date return ref_date + timedeltas def decode_cf_datetime( num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None, time_unit: PDDatetimeUnitOptions = "ns", ) -> np.ndarray: """Given an array of numeric dates in netCDF format, convert it into a numpy array of date time objects. For standard (Gregorian) calendars, this function uses vectorized operations, which makes it much faster than cftime.num2date. In such a case, the returned array will be of type np.datetime64. Note that time unit in `units` must not be smaller than microseconds and not larger than days. See Also -------- cftime.num2date """ num_dates = to_numpy(num_dates) flat_num_dates = ravel(num_dates) if calendar is None: calendar = "standard" if use_cftime is None: try: dates = _decode_datetime_with_pandas( flat_num_dates, units, calendar, time_unit ) except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError): dates = _decode_datetime_with_cftime( flat_num_dates.astype(float), units, calendar ) # retrieve cftype dates_min = dates[np.nanargmin(num_dates)] dates_max = dates[np.nanargmax(num_dates)] cftype = type(dates_min) # create first day of gregorian calendar in current cf calendar type border = cftype(1582, 10, 15) # "ns" borders # between ['1677-09-21T00:12:43.145224193', '2262-04-11T23:47:16.854775807'] lower = cftype(1677, 9, 21, 0, 12, 43, 145224) upper = cftype(2262, 4, 11, 23, 47, 16, 854775) if dates_min < border: if _is_standard_calendar(calendar): emit_user_level_warning( "Unable to decode time axis into full " "numpy.datetime64 objects, continuing using " "cftime.datetime objects instead, reason: dates prior " "reform date (1582-10-15). To silence this warning specify " "'use_cftime=True'.", SerializationWarning, ) elif time_unit == "ns" and (dates_min < lower or dates_max > upper): emit_user_level_warning( "Unable to decode time axis into full " "numpy.datetime64[ns] objects, continuing using " "cftime.datetime objects instead, reason: dates out " "of range. To silence this warning use a coarser resolution " "'time_unit' or specify 'use_cftime=True'.", SerializationWarning, ) elif _is_standard_calendar(calendar): dates = cftime_to_nptime(dates, time_unit=time_unit) elif use_cftime: dates = _decode_datetime_with_cftime(flat_num_dates, units, calendar) else: dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar, time_unit) return reshape(dates, num_dates.shape) def to_datetime_unboxed(value, **kwargs): result = pd.to_datetime(value, **kwargs).to_numpy() assert np.issubdtype(result.dtype, "datetime64") return result def _numbers_to_timedelta( flat_num: np.ndarray, time_unit: NPDatetimeUnitOptions, ref_unit: PDDatetimeUnitOptions, datatype: str, target_unit: PDDatetimeUnitOptions | None = None, ) -> np.ndarray: """Transform numbers to np.timedelta64.""" # keep NaT/nan mask if flat_num.dtype.kind == "f": nan = np.asarray(np.isnan(flat_num)) elif flat_num.dtype.kind == "i": nan = np.asarray(flat_num == np.iinfo(np.int64).min) # in case we need to change the unit, we fix the numbers here # this should be safe, as errors would have been raised above ns_time_unit = _NS_PER_TIME_DELTA[time_unit] ns_ref_date_unit = _NS_PER_TIME_DELTA[ref_unit] if ns_time_unit > ns_ref_date_unit: flat_num = np.asarray(flat_num * np.int64(ns_time_unit / ns_ref_date_unit)) time_unit = ref_unit # estimate fitting resolution for floating point values # this iterates until all floats are fractionless or time_unit == "ns" if flat_num.dtype.kind == "f" and time_unit != "ns": flat_num, new_time_unit = _check_higher_resolution( flat_num, cast(PDDatetimeUnitOptions, time_unit) ) if time_unit != new_time_unit: if target_unit is None or np.timedelta64(1, target_unit) > np.timedelta64( 1, new_time_unit ): if datatype == "datetimes": kwarg = "decode_times" coder = "CFDatetimeCoder" else: kwarg = "decode_timedelta" coder = "CFTimedeltaCoder" formatted_kwarg = f"{kwarg}={coder}(time_unit={new_time_unit!r})" message = ( f"Can't decode floating point {datatype} to {time_unit!r} " f"without precision loss; decoding to {new_time_unit!r} " f"instead. To silence this warning pass {formatted_kwarg} " f"to your opening function." ) emit_user_level_warning(message, SerializationWarning) time_unit = new_time_unit # Cast input ordinals to integers and properly handle NaN/NaT # to prevent casting NaN to int with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) flat_num = flat_num.astype(np.int64) if nan.any(): flat_num[nan] = np.iinfo(np.int64).min # cast to wanted type return flat_num.astype(f"timedelta64[{time_unit}]") def decode_cf_timedelta( num_timedeltas, units: str, time_unit: PDDatetimeUnitOptions = "ns" ) -> np.ndarray: """Given an array of numeric timedeltas in netCDF format, convert it into a numpy timedelta64 ["s", "ms", "us", "ns"] array. """ num_timedeltas = to_numpy(num_timedeltas) unit = _netcdf_to_numpy_timeunit(units) # special case empty arrays is_empty_array = num_timedeltas.size == 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", "All-NaN slice encountered", RuntimeWarning) if not is_empty_array: _check_timedelta_range(np.nanmin(num_timedeltas), unit, time_unit) _check_timedelta_range(np.nanmax(num_timedeltas), unit, time_unit) timedeltas = _numbers_to_timedelta( num_timedeltas, unit, "s", "timedeltas", target_unit=time_unit ) pd_timedeltas = pd.to_timedelta(ravel(timedeltas)) if not is_empty_array and np.isnat(timedeltas).all(): empirical_unit = time_unit else: empirical_unit = pd_timedeltas.unit if is_empty_array or np.timedelta64(1, time_unit) > np.timedelta64( 1, empirical_unit ): time_unit = empirical_unit if time_unit not in {"s", "ms", "us", "ns"}: raise ValueError( f"time_unit must be one of 's', 'ms', 'us', or 'ns'. Got: {time_unit}" ) result = pd_timedeltas.as_unit(time_unit).to_numpy() return reshape(result, num_timedeltas.shape) def _unit_timedelta_cftime(units: str) -> timedelta: return timedelta(microseconds=_US_PER_TIME_DELTA[units]) def _unit_timedelta_numpy(units: str) -> np.timedelta64: numpy_units = _netcdf_to_numpy_timeunit(units) return np.timedelta64(1, numpy_units) def _infer_time_units_from_diff(unique_timedeltas) -> str: # todo: check, if this function works correctly wrt np.timedelta64 unit_timedelta: Callable[[str], timedelta] | Callable[[str], np.timedelta64] zero_timedelta: timedelta | np.timedelta64 unique_timedeltas = asarray(unique_timedeltas) if unique_timedeltas.dtype == np.dtype("O"): time_units = _NETCDF_TIME_UNITS_CFTIME unit_timedelta = _unit_timedelta_cftime zero_timedelta = timedelta(microseconds=0) else: time_units = _NETCDF_TIME_UNITS_NUMPY unit_timedelta = _unit_timedelta_numpy zero_timedelta = np.timedelta64(0, "ns") for time_unit in time_units: if array_all(unique_timedeltas % unit_timedelta(time_unit) == zero_timedelta): return time_unit return "seconds" def _time_units_to_timedelta(units: str) -> timedelta: return timedelta(microseconds=_US_PER_TIME_DELTA[units]) def infer_calendar_name(dates) -> CFCalendar: """Given an array of datetimes, infer the CF calendar name""" if is_np_datetime_like(dates.dtype): return "proleptic_gregorian" elif dates.dtype == np.dtype("O") and dates.size > 0: # Logic copied from core.common.contains_cftime_datetimes. if cftime is not None: sample = np.asarray(dates).flat[0] if is_duck_dask_array(sample): sample = sample.compute() if isinstance(sample, np.ndarray): sample = sample.item() if isinstance(sample, cftime.datetime): return sample.calendar # Error raise if dtype is neither datetime or "O", if cftime is not importable, and if element of 'O' dtype is not cftime. raise ValueError("Array does not contain datetime objects.") def infer_datetime_units(dates) -> str: """Given an array of datetimes, returns a CF compatible time-unit string of the form "{time_unit} since {date[0]}", where `time_unit` is 'days', 'hours', 'minutes' or 'seconds' (the first one that can evenly divide all unique time deltas in `dates`) """ dates = ravel(np.asarray(dates)) if np.issubdtype(np.asarray(dates).dtype, "datetime64"): dates = to_datetime_unboxed(dates) dates = dates[pd.notnull(dates)] reference_date = dates[0] if len(dates) > 0 else "1970-01-01" reference_date = pd.Timestamp(reference_date) else: reference_date = dates[0] if len(dates) > 0 else "1970-01-01" reference_date = format_cftime_datetime(reference_date) unique_timedeltas = np.unique(np.diff(dates)) units = _infer_time_units_from_diff(unique_timedeltas) return f"{units} since {reference_date}" def format_cftime_datetime(date) -> str: """Converts a cftime.datetime object to a string with the format: YYYY-MM-DD HH:MM:SS.UUUUUU """ return f"{date.year:04d}-{date.month:02d}-{date.day:02d} {date.hour:02d}:{date.minute:02d}:{date.second:02d}.{date.microsecond:06d}" def infer_timedelta_units(deltas) -> str: """Given an array of timedeltas, returns a CF compatible time-unit from {'days', 'hours', 'minutes' 'seconds'} (the first one that can evenly divide all unique time deltas in `deltas`) """ deltas = ravel(deltas) unique_timedeltas = np.unique(deltas[pd.notnull(deltas)]) return _infer_time_units_from_diff(unique_timedeltas) def cftime_to_nptime( times, raise_on_invalid: bool = True, time_unit: PDDatetimeUnitOptions = "ns" ) -> np.ndarray: """Given an array of cftime.datetime objects, return an array of numpy.datetime64 objects of the same size If raise_on_invalid is True (default), invalid dates trigger a ValueError. Otherwise, the invalid element is replaced by np.NaT.""" times = np.asarray(times) new = [] dt: np.datetime64 for _i, t in np.ndenumerate(times): try: # We expect either "us" resolution or "s" resolution depending on # whether 'microseconds' are defined for the input or not. dt = ( pd.Timestamp(np.datetime64(t.isoformat())).as_unit(time_unit).to_numpy() ) except ValueError as e: if raise_on_invalid: raise ValueError( f"Cannot convert date {t} to a date in the " f"standard calendar. Reason: {e}." ) from e else: dt = np.datetime64("NaT") new.append(dt) return np.asarray(new).reshape(times.shape) def convert_times(times, date_type, raise_on_invalid: bool = True) -> np.ndarray: """Given an array of datetimes, return the same dates in another cftime or numpy date type. Useful to convert between calendars in numpy and cftime or between cftime calendars. If raise_on_valid is True (default), invalid dates trigger a ValueError. Otherwise, the invalid element is replaced by np.nan for cftime types and np.NaT for np.datetime64. """ if date_type in (pd.Timestamp, np.datetime64) and not is_np_datetime_like( times.dtype ): return cftime_to_nptime(times, raise_on_invalid=raise_on_invalid) if is_np_datetime_like(times.dtype): # Convert datetime64 objects to Timestamps since those have year, month, day, etc. attributes times = pd.DatetimeIndex(times) new = np.empty(times.shape, dtype="O") for i, t in enumerate(times): try: dt = date_type( t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond ) except ValueError as e: if raise_on_invalid: raise ValueError( f"Cannot convert date {t} to a date in the " f"{date_type(2000, 1, 1).calendar} calendar. Reason: {e}." ) from e else: dt = np.nan new[i] = dt return new def convert_time_or_go_back(date, date_type): """Convert a single date to a new date_type (cftime.datetime or pd.Timestamp). If the new date is invalid, it goes back a day and tries again. If it is still invalid, goes back a second day. This is meant to convert end-of-month dates into a new calendar. """ if date_type == pd.Timestamp: date_type = default_precision_timestamp try: return date_type( date.year, date.month, date.day, date.hour, date.minute, date.second, date.microsecond, ) except OutOfBoundsDatetime: raise except ValueError: # Day is invalid, happens at the end of months, try again the day before try: return date_type( date.year, date.month, date.day - 1, date.hour, date.minute, date.second, date.microsecond, ) except ValueError: # Still invalid, happens for 360_day to non-leap february. Try again 2 days before date. return date_type( date.year, date.month, date.day - 2, date.hour, date.minute, date.second, date.microsecond, ) def _should_cftime_be_used( source, target_calendar: str, use_cftime: bool | None ) -> bool: """Return whether conversion of the source to the target calendar should result in a cftime-backed array. Source is a 1D datetime array, target_cal a string (calendar name) and use_cftime is a boolean or None. If use_cftime is None, this returns True if the source's range and target calendar are convertible to np.datetime64 objects. """ # Arguments Checks for target if use_cftime is not True: if _is_standard_calendar(target_calendar): if _is_numpy_compatible_time_range(source): # Conversion is possible with pandas, force False if it was None return False elif use_cftime is False: raise ValueError( "Source time range is not valid for numpy datetimes. Try using `use_cftime=True`." ) elif use_cftime is False: raise ValueError( f"Calendar '{target_calendar}' is only valid with cftime. Try using `use_cftime=True`." ) return True def _cleanup_netcdf_time_units(units: str) -> str: time_units, ref_date = _unpack_netcdf_time_units(units) time_units = time_units.lower() if not time_units.endswith("s"): time_units = f"{time_units}s" # don't worry about reifying the units if they're out of bounds or # formatted badly with contextlib.suppress(OutOfBoundsDatetime, ValueError): units = f"{time_units} since {format_timestamp(ref_date)}" return units def _encode_datetime_with_cftime(dates, units: str, calendar: str) -> np.ndarray: """Fallback method for encoding dates using cftime. This method is more flexible than xarray's parsing using datetime64[ns] arrays but also slower because it loops over each element. """ if TYPE_CHECKING: import cftime else: cftime = attempt_import("cftime") dates = np.asarray(dates) original_shape = dates.shape if np.issubdtype(dates.dtype, np.datetime64): # numpy's broken datetime conversion only works for us precision dates = dates.astype("M8[us]").astype(datetime) dates = np.atleast_1d(dates) # Find all the None position none_position = dates == None # noqa: E711 filtered_dates = dates[~none_position] # Since netCDF files do not support storing float128 values, we ensure # that float64 values are used by setting longdouble=False in num2date. # This try except logic can be removed when xarray's minimum version of # cftime is at least 1.6.2. try: encoded_nums = cftime.date2num( filtered_dates, units, calendar, longdouble=False ) except TypeError: encoded_nums = cftime.date2num(filtered_dates, units, calendar) if filtered_dates.size == none_position.size: return encoded_nums.reshape(original_shape) # Create a full matrix of NaN # And fill the num dates in the not NaN or None position result = np.full(dates.shape, np.nan) result[np.nonzero(~none_position)] = encoded_nums return result.reshape(original_shape) def cast_to_int_if_safe(num) -> np.ndarray: int_num = np.asarray(num, dtype=np.int64) if array_all(num == int_num): num = int_num return num def _division(deltas, delta, floor): if floor: # calculate int64 floor division # to preserve integer dtype if possible (GH 4045, GH7817). num = deltas // delta.astype(np.int64) num = num.astype(np.int64, copy=False) else: num = deltas / delta return num def encode_cf_datetime( dates: T_DuckArray, # type: ignore[misc] units: str | None = None, calendar: str | None = None, dtype: np.dtype | None = None, ) -> tuple[T_DuckArray, str, str]: """Given an array of datetime objects, returns the tuple `(num, units, calendar)` suitable for a CF compliant time variable. Unlike `date2num`, this function can handle datetime64 arrays. See Also -------- cftime.date2num """ dates = asarray(dates) if is_chunked_array(dates): return _lazily_encode_cf_datetime(dates, units, calendar, dtype) else: return _eagerly_encode_cf_datetime(dates, units, calendar, dtype) def _infer_needed_units_numpy(ref_date, data_units): needed_units, data_ref_date = _unpack_time_unit_and_ref_date(data_units) needed_units = _numpy_to_netcdf_timeunit(needed_units) ref_delta = abs(data_ref_date - ref_date).to_timedelta64() data_delta = _unit_timedelta_numpy(needed_units) if (ref_delta % data_delta) > np.timedelta64(0, "ns"): needed_units = _infer_time_units_from_diff(ref_delta) return needed_units def _infer_needed_units_cftime(ref_date, data_units, calendar): needed_units, data_ref_date = _unpack_time_units_and_ref_date_cftime( data_units, calendar ) ref_delta = abs(data_ref_date - ref_date) data_delta = _time_units_to_timedelta(needed_units) if (ref_delta % data_delta) > timedelta(seconds=0): needed_units = _infer_time_units_from_diff(ref_delta) return needed_units def _eagerly_encode_cf_datetime( dates: T_DuckArray, # type: ignore[misc] units: str | None = None, calendar: str | None = None, dtype: np.dtype | None = None, allow_units_modification: bool = True, ) -> tuple[T_DuckArray, str, str]: dates = asarray(dates) data_units = infer_datetime_units(dates) if units is None: units = data_units else: units = _cleanup_netcdf_time_units(units) if calendar is None: calendar = infer_calendar_name(dates) raise_incompatible_units_error = False raise_gregorian_proleptic_gregorian_mismatch_error = False try: if not _is_standard_calendar(calendar) or dates.dtype.kind == "O": # parse with cftime instead raise OutOfBoundsDatetime assert np.issubdtype(dates.dtype, "datetime64") if calendar in ["standard", "gregorian"] and np.nanmin(dates).astype( "=M8[us]" ).astype(datetime) < datetime(1582, 10, 15): raise_gregorian_proleptic_gregorian_mismatch_error = True time_unit, ref_date = _unpack_time_unit_and_ref_date(units) # calendar equivalence only for days after the reform _check_date_is_after_shift(ref_date, calendar) time_delta = np.timedelta64(1, time_unit) # Wrap the dates in a DatetimeIndex to do the subtraction to ensure # an OverflowError is raised if the ref_date is too far away from # dates to be encoded (GH 2272). # DatetimeIndex will convert to units of ["s", "ms", "us", "ns"] dates_as_index = pd.DatetimeIndex(ravel(dates)) time_deltas = dates_as_index - ref_date # retrieve needed units to faithfully encode to int64 needed_units = _infer_needed_units_numpy(ref_date, data_units) needed_time_delta = _unit_timedelta_numpy(needed_units) floor_division = np.issubdtype(dtype, np.integer) or dtype is None if time_delta > needed_time_delta: floor_division = False if dtype is None: emit_user_level_warning( f"Times can't be serialized faithfully to int64 with requested units {units!r}. " f"Resolution of {needed_units!r} needed. Serializing times to floating point instead. " f"Set encoding['dtype'] to integer dtype to serialize to int64. " f"Set encoding['dtype'] to floating point dtype to silence this warning." ) elif np.issubdtype(dtype, np.integer) and allow_units_modification: new_units = f"{needed_units} since {format_timestamp(ref_date)}" emit_user_level_warning( f"Times can't be serialized faithfully to int64 with requested units {units!r}. " f"Serializing with units {new_units!r} instead. " f"Set encoding['dtype'] to floating point dtype to serialize with units {units!r}. " f"Set encoding['units'] to {new_units!r} to silence this warning ." ) units = new_units time_delta = needed_time_delta floor_division = True elif np.issubdtype(dtype, np.integer) and not allow_units_modification: new_units = f"{needed_units} since {format_timestamp(ref_date)}" raise_incompatible_units_error = True # get resolution of TimedeltaIndex and align time_delta # todo: check, if this works in any case num = _division( time_deltas, time_delta.astype(f"=m8[{time_deltas.unit}]"), floor_division ) num = reshape(num.values, dates.shape) except (OutOfBoundsDatetime, OverflowError, ValueError): time_units, ref_date = _unpack_time_units_and_ref_date_cftime(units, calendar) time_delta_cftime = _time_units_to_timedelta(time_units) needed_units = _infer_needed_units_cftime(ref_date, data_units, calendar) needed_time_delta_cftime = _time_units_to_timedelta(needed_units) if ( np.issubdtype(dtype, np.integer) and time_delta_cftime > needed_time_delta_cftime ): new_units = f"{needed_units} since {format_cftime_datetime(ref_date)}" if allow_units_modification: emit_user_level_warning( f"Times can't be serialized faithfully to int64 with requested units {units!r}. " f"Serializing with units {new_units!r} instead. " f"Set encoding['dtype'] to floating point dtype to serialize with units {units!r}. " f"Set encoding['units'] to {new_units!r} to silence this warning ." ) units = new_units else: raise_incompatible_units_error = True num = _encode_datetime_with_cftime(dates, units, calendar) # do it now only for cftime-based flow # we already covered for this in pandas-based flow num = cast_to_int_if_safe(num) if raise_incompatible_units_error: raise ValueError( f"Times can't be serialized faithfully to int64 with requested units {units!r}. " f"Consider setting encoding['dtype'] to a floating point dtype to serialize with " f"units {units!r}. Consider setting encoding['units'] to {new_units!r} to " f"serialize with an integer dtype." ) if raise_gregorian_proleptic_gregorian_mismatch_error: raise ValueError( f"Unable to encode np.datetime64 values with {calendar} " f"calendar, because some or all values are prior to the reform " f"date of 1582-10-15. To encode these times, set " f"encoding['calendar'] to 'proleptic_gregorian' instead, which " f"is the true calendar that np.datetime64 values use. The " f"'standard' or 'gregorian' calendar is only equivalent to the " f"'proleptic_gregorian' calendar after the reform date." ) return num, units, calendar def _encode_cf_datetime_within_map_blocks( dates: T_DuckArray, # type: ignore[misc] units: str, calendar: str, dtype: np.dtype, ) -> T_DuckArray: num, *_ = _eagerly_encode_cf_datetime( dates, units, calendar, dtype, allow_units_modification=False ) return num def _lazily_encode_cf_datetime( dates: T_ChunkedArray, units: str | None = None, calendar: str | None = None, dtype: np.dtype | None = None, ) -> tuple[T_ChunkedArray, str, str]: if calendar is None: # This will only trigger minor compute if dates is an object dtype array. calendar = infer_calendar_name(dates) if units is None and dtype is None: if dates.dtype == "O": units = "microseconds since 1970-01-01" dtype = np.dtype("int64") else: netcdf_unit = _numpy_dtype_to_netcdf_timeunit(dates.dtype) units = f"{netcdf_unit} since 1970-01-01" dtype = np.dtype("int64") if units is None or dtype is None: raise ValueError( f"When encoding chunked arrays of datetime values, both the units " f"and dtype must be prescribed or both must be unprescribed. " f"Prescribing only one or the other is not currently supported. " f"Got a units encoding of {units} and a dtype encoding of {dtype}." ) chunkmanager = get_chunked_array_type(dates) num = chunkmanager.map_blocks( _encode_cf_datetime_within_map_blocks, dates, units, calendar, dtype, dtype=dtype, ) return num, units, calendar def encode_cf_timedelta( timedeltas: T_DuckArray, # type: ignore[misc] units: str | None = None, dtype: np.dtype | None = None, ) -> tuple[T_DuckArray, str]: timedeltas = asarray(timedeltas) if is_chunked_array(timedeltas): return _lazily_encode_cf_timedelta(timedeltas, units, dtype) else: return _eagerly_encode_cf_timedelta(timedeltas, units, dtype) def _eagerly_encode_cf_timedelta( timedeltas: T_DuckArray, # type: ignore[misc] units: str | None = None, dtype: np.dtype | None = None, allow_units_modification: bool = True, ) -> tuple[T_DuckArray, str]: data_units = infer_timedelta_units(timedeltas) if units is None: units = data_units # units take precedence in the case of zero-size array if timedeltas.size == 0: data_units = units time_delta = _unit_timedelta_numpy(units) time_deltas = pd.TimedeltaIndex(ravel(timedeltas)) # get resolution of TimedeltaIndex and align time_delta deltas_unit = time_deltas.unit time_delta = time_delta.astype(f"=m8[{deltas_unit}]") # retrieve needed units to faithfully encode to int64 needed_units = data_units if data_units != units: needed_units = _infer_time_units_from_diff(np.unique(time_deltas.dropna())) # needed time delta to encode faithfully to int64 needed_time_delta = _unit_timedelta_numpy(needed_units) floor_division = np.issubdtype(dtype, np.integer) or dtype is None if time_delta > needed_time_delta: floor_division = False if dtype is None: emit_user_level_warning( f"Timedeltas can't be serialized faithfully to int64 with requested units {units!r}. " f"Resolution of {needed_units!r} needed. Serializing timeseries to floating point instead. " f"Set encoding['dtype'] to integer dtype to serialize to int64. " f"Set encoding['dtype'] to floating point dtype to silence this warning." ) elif np.issubdtype(dtype, np.integer) and allow_units_modification: emit_user_level_warning( f"Timedeltas can't be serialized faithfully with requested units {units!r}. " f"Serializing with units {needed_units!r} instead. " f"Set encoding['dtype'] to floating point dtype to serialize with units {units!r}. " f"Set encoding['units'] to {needed_units!r} to silence this warning ." ) units = needed_units time_delta = needed_time_delta time_delta = time_delta.astype(f"=m8[{deltas_unit}]") floor_division = True elif np.issubdtype(dtype, np.integer) and not allow_units_modification: raise ValueError( f"Timedeltas can't be serialized faithfully to int64 with requested units {units!r}. " f"Consider setting encoding['dtype'] to a floating point dtype to serialize with " f"units {units!r}. Consider setting encoding['units'] to {needed_units!r} to " f"serialize with an integer dtype." ) num = _division(time_deltas, time_delta, floor_division) num = reshape(num.values, timedeltas.shape) return num, units def _encode_cf_timedelta_within_map_blocks( timedeltas: T_DuckArray, # type: ignore[misc] units: str, dtype: np.dtype, ) -> T_DuckArray: num, _ = _eagerly_encode_cf_timedelta( timedeltas, units, dtype, allow_units_modification=False ) return num def _lazily_encode_cf_timedelta( timedeltas: T_ChunkedArray, units: str | None = None, dtype: np.dtype | None = None ) -> tuple[T_ChunkedArray, str]: if units is None and dtype is None: units = _numpy_dtype_to_netcdf_timeunit(timedeltas.dtype) dtype = np.dtype("int64") if units is None or dtype is None: raise ValueError( f"When encoding chunked arrays of timedelta values, both the " f"units and dtype must be prescribed or both must be " f"unprescribed. Prescribing only one or the other is not " f"currently supported. Got a units encoding of {units} and a " f"dtype encoding of {dtype}." ) chunkmanager = get_chunked_array_type(timedeltas) num = chunkmanager.map_blocks( _encode_cf_timedelta_within_map_blocks, timedeltas, units, dtype, dtype=dtype, ) return num, units class CFDatetimeCoder(VariableCoder): """Coder for CF Datetime coding. Parameters ---------- use_cftime : bool, optional Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to ``np.datetime64`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64`` objects. If False, always decode times to ``np.datetime64`` objects; if this is not possible raise an error. May not be supported by all the backends. time_unit : PDDatetimeUnitOptions Target resolution when decoding dates. Defaults to "ns". """ def __init__( self, use_cftime: bool | None = None, time_unit: PDDatetimeUnitOptions = "ns", ) -> None: self.use_cftime = use_cftime self.time_unit = time_unit def encode(self, variable: Variable, name: T_Name = None) -> Variable: if np.issubdtype( variable.data.dtype, np.datetime64 ) or contains_cftime_datetimes(variable): dims, data, attrs, encoding = unpack_for_encoding(variable) units = encoding.pop("units", None) calendar = encoding.pop("calendar", None) dtype = encoding.get("dtype", None) # in the case of packed data we need to encode into # float first, the correct dtype will be established # via CFScaleOffsetCoder/CFMaskCoder if "add_offset" in encoding or "scale_factor" in encoding: dtype = data.dtype if data.dtype.kind == "f" else "float64" (data, units, calendar) = encode_cf_datetime(data, units, calendar, dtype) safe_setitem(attrs, "units", units, name=name) safe_setitem(attrs, "calendar", calendar, name=name) return Variable(dims, data, attrs, encoding, fastpath=True) else: return variable def decode(self, variable: Variable, name: T_Name = None) -> Variable: units = variable.attrs.get("units", None) if isinstance(units, str) and "since" in units: dims, data, attrs, encoding = unpack_for_decoding(variable) units = pop_to(attrs, encoding, "units") calendar = pop_to(attrs, encoding, "calendar") dtype = _decode_cf_datetime_dtype( data, units, calendar, self.use_cftime, self.time_unit ) transform = partial( decode_cf_datetime, units=units, calendar=calendar, use_cftime=self.use_cftime, time_unit=self.time_unit, ) data = lazy_elemwise_func(data, transform, dtype) return Variable(dims, data, attrs, encoding, fastpath=True) else: return variable def has_timedelta64_encoding_dtype(attrs_or_encoding: dict) -> bool: dtype = attrs_or_encoding.get("dtype") return isinstance(dtype, str) and dtype.startswith("timedelta64") def resolve_time_unit_from_attrs_dtype( attrs_dtype: str, name: T_Name ) -> PDDatetimeUnitOptions: dtype = np.dtype(attrs_dtype) resolution, _ = np.datetime_data(dtype) resolution = cast(NPDatetimeUnitOptions, resolution) if np.timedelta64(1, resolution) > np.timedelta64(1, "s"): time_unit = cast(PDDatetimeUnitOptions, "s") message = ( f"Following pandas, xarray only supports decoding to timedelta64 " f"values with a resolution of 's', 'ms', 'us', or 'ns'. Encoded " f"values for variable {name!r} have a resolution of " f"{resolution!r}. Attempting to decode to a resolution of 's'. " f"Note, depending on the encoded values, this may lead to an " f"OverflowError. Additionally, data will not be identically round " f"tripped; xarray will choose an encoding dtype of " f"'timedelta64[s]' when re-encoding." ) emit_user_level_warning(message) elif np.timedelta64(1, resolution) < np.timedelta64(1, "ns"): time_unit = cast(PDDatetimeUnitOptions, "ns") message = ( f"Following pandas, xarray only supports decoding to timedelta64 " f"values with a resolution of 's', 'ms', 'us', or 'ns'. Encoded " f"values for variable {name!r} have a resolution of " f"{resolution!r}. Attempting to decode to a resolution of 'ns'. " f"Note, depending on the encoded values, this may lead to loss of " f"precision. Additionally, data will not be identically round " f"tripped; xarray will choose an encoding dtype of " f"'timedelta64[ns]' when re-encoding." ) emit_user_level_warning(message) else: time_unit = cast(PDDatetimeUnitOptions, resolution) return time_unit class CFTimedeltaCoder(VariableCoder): """Coder for CF Timedelta coding. Parameters ---------- time_unit : PDDatetimeUnitOptions Target resolution when decoding timedeltas via units. Defaults to "ns". When decoding via dtype, the resolution is specified in the dtype attribute, so this parameter is ignored. decode_via_units : bool Whether to decode timedeltas based on the presence of a timedelta-like units attribute, e.g. "seconds". Defaults to True, but in the future will default to False. decode_via_dtype : bool Whether to decode timedeltas based on the presence of a np.timedelta64 dtype attribute, e.g. "timedelta64[s]". Defaults to True. """ def __init__( self, time_unit: PDDatetimeUnitOptions | None = None, decode_via_units: bool = True, decode_via_dtype: bool = True, ) -> None: self.time_unit = time_unit self.decode_via_units = decode_via_units self.decode_via_dtype = decode_via_dtype self._emit_decode_timedelta_future_warning = False def encode(self, variable: Variable, name: T_Name = None) -> Variable: if np.issubdtype(variable.data.dtype, np.timedelta64): dims, data, attrs, encoding = unpack_for_encoding(variable) dtype = encoding.get("dtype", None) units = encoding.pop("units", None) # in the case of packed data we need to encode into # float first, the correct dtype will be established # via CFScaleOffsetCoder/CFMaskCoder if "add_offset" in encoding or "scale_factor" in encoding: dtype = data.dtype if data.dtype.kind == "f" else "float64" resolution, _ = np.datetime_data(variable.dtype) attrs_dtype = f"timedelta64[{resolution}]" safe_setitem(attrs, "dtype", attrs_dtype, name=name) data, units = encode_cf_timedelta(data, units, dtype) safe_setitem(attrs, "units", units, name=name) return Variable(dims, data, attrs, encoding, fastpath=True) else: return variable def decode(self, variable: Variable, name: T_Name = None) -> Variable: units = variable.attrs.get("units", None) has_timedelta_units = isinstance(units, str) and units in TIME_UNITS has_timedelta_dtype = has_timedelta64_encoding_dtype(variable.attrs) is_dtype_decodable = has_timedelta_units and has_timedelta_dtype is_units_decodable = has_timedelta_units if (is_dtype_decodable and self.decode_via_dtype) or ( is_units_decodable and self.decode_via_units ): dims, data, attrs, encoding = unpack_for_decoding(variable) units = pop_to(attrs, encoding, "units") if is_dtype_decodable: attrs_dtype = attrs.pop("dtype") if self.time_unit is None: time_unit = resolve_time_unit_from_attrs_dtype(attrs_dtype, name) else: time_unit = self.time_unit else: if self._emit_decode_timedelta_future_warning: var_string = f"the variable {name!r}" if name else "" emit_user_level_warning( "In a future version, xarray will not decode " f"{var_string} into a timedelta64 dtype based on the " "presence of a timedelta-like 'units' attribute by " "default. Instead it will rely on the presence of a " "timedelta64 'dtype' attribute, which is now xarray's " "default way of encoding timedelta64 values.\n" "To continue decoding into a timedelta64 dtype, either " "set `decode_timedelta=True` when opening this " "dataset, or add the attribute " "`dtype='timedelta64[ns]'` to this variable on disk.\n" "To opt-in to future behavior, set " "`decode_timedelta=False`.", FutureWarning, ) if self.time_unit is None: time_unit = cast(PDDatetimeUnitOptions, "ns") else: time_unit = self.time_unit # Handle edge case that decode_via_dtype=False and # decode_via_units=True, and timedeltas were encoded with a # dtype attribute. We need to remove the dtype attribute # to prevent an error during round tripping. if has_timedelta_dtype: attrs.pop("dtype") dtype = np.dtype(f"timedelta64[{time_unit}]") transform = partial(decode_cf_timedelta, units=units, time_unit=time_unit) data = lazy_elemwise_func(data, transform, dtype=dtype) return Variable(dims, data, attrs, encoding, fastpath=True) else: return variable