from __future__ import annotations

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.variables import (

SerializationWarning,

VariableCoder,

lazy_elemwise_func,

pop_to,

safe_setitem,

unpack_for_decoding,

unpack_for_encoding,

)

from xarray.core import indexing

from xarray.core.common import contains_cftime_datetimes, is_np_datetime_like

from xarray.core.duck_array_ops import asarray, ravel, reshape

from xarray.core.formatting import first_n_items, format_timestamp, last_item

from xarray.core.pdcompat import default_precision_timestamp, timestamp_as_unit

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

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",

]

)

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 _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 if sep else "") + 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 _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(

[first_n_items(values, 1) or [0], last_item(values) or [0]]

)

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":

if 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, "datetime"

)

# 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 = np.asarray(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,

)

else:

if _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,

) -> 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:

msg = (

f"Can't decode floating point {datatype} to {time_unit!r} without "

f"precision loss, decoding to {new_time_unit!r} instead. "

f"To silence this warning use time_unit={new_time_unit!r} in call to "

f"decoding function."

)

emit_user_level_warning(msg, 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 = np.asarray(num_timedeltas)

unit = _netcdf_to_numpy_timeunit(units)

with warnings.catch_warnings():

warnings.filterwarnings("ignore", "All-NaN slice encountered", RuntimeWarning)

_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", "timedelta")

pd_timedeltas = pd.to_timedelta(ravel(timedeltas))

if np.isnat(timedeltas).all():

empirical_unit = time_unit

else:

empirical_unit = pd_timedeltas.unit

if 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

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 np.all(unique_timedeltas % unit_timedelta(time_unit) == zero_timedelta):

return time_unit

return "seconds"

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"

try:

units = f"{time_units} since {format_timestamp(ref_date)}"

except (OutOfBoundsDatetime, ValueError):

# don't worry about reifying the units if they're out of bounds or

# formatted badly

pass

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")

if np.issubdtype(dates.dtype, np.datetime64):

# numpy's broken datetime conversion only works for us precision

dates = dates.astype("M8[us]").astype(datetime)

def wrap_dt(dt):

# convert to cftime proleptic gregorian in case of datetime.datetime

# needed because of https://github.com/Unidata/cftime/issues/354

if isinstance(dt, datetime) and not isinstance(dt, cftime.datetime):

dt = cftime.datetime(

dt.year,

dt.month,

dt.day,

dt.hour,

dt.minute,

dt.second,

dt.microsecond,

calendar="proleptic_gregorian",

)

return dt

def encode_datetime(d):

# 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:

return (

np.nan

if d is None

else cftime.date2num(wrap_dt(d), units, calendar, longdouble=False)

)

except TypeError:

return np.nan if d is None else cftime.date2num(wrap_dt(d), units, calendar)

return reshape(np.array([encode_datetime(d) for d in ravel(dates)]), dates.shape)

def cast_to_int_if_safe(num) -> np.ndarray:

int_num = np.asarray(num, dtype=np.int64)

if (num == int_num).all():

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 _cast_to_dtype_if_safe(num: np.ndarray, dtype: np.dtype) -> np.ndarray:

with warnings.catch_warnings():

warnings.filterwarnings("ignore", message="overflow")

cast_num = np.asarray(num, dtype=dtype)

if np.issubdtype(dtype, np.integer):

if not (num == cast_num).all():

if np.issubdtype(num.dtype, np.floating):

raise ValueError(

f"Not possible to cast all encoded times from "

f"{num.dtype!r} to {dtype!r} without losing precision. "

f"Consider modifying the units such that integer values "

f"can be used, or removing the units and dtype encoding, "

f"at which point xarray will make an appropriate choice."

)

else:

raise OverflowError(

f"Not possible to cast encoded times from "

f"{num.dtype!r} to {dtype!r} without overflow. Consider "

f"removing the dtype encoding, at which point xarray will "

f"make an appropriate choice, or explicitly switching to "

"a larger integer dtype."

)

else:

if np.isinf(cast_num).any():

raise OverflowError(

f"Not possible to cast encoded times from {num.dtype!r} to "

f"{dtype!r} without overflow. Consider removing the dtype "

f"encoding, at which point xarray will make an appropriate "

f"choice, or explicitly switching to a larger floating point "

f"dtype."

)

return cast_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,