"""Resampling for CFTimeIndex. Does not support non-integer freq.""" # The mechanisms for resampling CFTimeIndex was copied and adapted from # the source code defined in pandas.core.resample # # For reference, here is a copy of the pandas copyright notice: # # BSD 3-Clause License # # Copyright (c) 2008-2012, AQR Capital Management, LLC, Lambda Foundry, Inc. # and PyData Development Team # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. from __future__ import annotations import datetime import typing import numpy as np import pandas as pd from xarray.coding.cftime_offsets import ( BaseCFTimeOffset, Day, MonthEnd, QuarterEnd, Tick, YearEnd, cftime_range, normalize_date, to_offset, ) from xarray.coding.cftimeindex import CFTimeIndex from xarray.core.types import SideOptions if typing.TYPE_CHECKING: from xarray.core.types import CFTimeDatetime class CFTimeGrouper: """This is a simple container for the grouping parameters that implements a single method, the only one required for resampling in xarray. It cannot be used in a call to groupby like a pandas.Grouper object can.""" def __init__( self, freq: str | BaseCFTimeOffset, closed: SideOptions | None = None, label: SideOptions | None = None, base: int | None = None, loffset: str | datetime.timedelta | BaseCFTimeOffset | None = None, origin: str | CFTimeDatetime = "start_day", offset: str | datetime.timedelta | None = None, ): self.offset: datetime.timedelta | None self.closed: SideOptions self.label: SideOptions if base is not None and offset is not None: raise ValueError("base and offset cannot be provided at the same time") self.freq = to_offset(freq) self.loffset = loffset self.origin = origin if isinstance(self.freq, (MonthEnd, QuarterEnd, YearEnd)): if closed is None: self.closed = "right" else: self.closed = closed if label is None: self.label = "right" else: self.label = label else: # The backward resample sets ``closed`` to ``'right'`` by default # since the last value should be considered as the edge point for # the last bin. When origin in "end" or "end_day", the value for a # specific ``cftime.datetime`` index stands for the resample result # from the current ``cftime.datetime`` minus ``freq`` to the current # ``cftime.datetime`` with a right close. if self.origin in ["end", "end_day"]: if closed is None: self.closed = "right" else: self.closed = closed if label is None: self.label = "right" else: self.label = label else: if closed is None: self.closed = "left" else: self.closed = closed if label is None: self.label = "left" else: self.label = label if base is not None and isinstance(self.freq, Tick): offset = type(self.freq)(n=base % self.freq.n).as_timedelta() if offset is not None: try: self.offset = _convert_offset_to_timedelta(offset) except (ValueError, AttributeError) as error: raise ValueError( f"offset must be a datetime.timedelta object or an offset string " f"that can be converted to a timedelta. Got {offset} instead." ) from error else: self.offset = None def first_items(self, index: CFTimeIndex): """Meant to reproduce the results of the following grouper = pandas.Grouper(...) first_items = pd.Series(np.arange(len(index)), index).groupby(grouper).first() with index being a CFTimeIndex instead of a DatetimeIndex. """ datetime_bins, labels = _get_time_bins( index, self.freq, self.closed, self.label, self.origin, self.offset ) if self.loffset is not None: if isinstance(self.loffset, datetime.timedelta): labels = labels + self.loffset else: labels = labels + to_offset(self.loffset) # check binner fits data if index[0] < datetime_bins[0]: raise ValueError("Value falls before first bin") if index[-1] > datetime_bins[-1]: raise ValueError("Value falls after last bin") integer_bins = np.searchsorted(index, datetime_bins, side=self.closed)[:-1] first_items = pd.Series(integer_bins, labels) # Mask duplicate values with NaNs, preserving the last values non_duplicate = ~first_items.duplicated("last") return first_items.where(non_duplicate) def _get_time_bins( index: CFTimeIndex, freq: BaseCFTimeOffset, closed: SideOptions, label: SideOptions, origin: str | CFTimeDatetime, offset: datetime.timedelta | None, ): """Obtain the bins and their respective labels for resampling operations. Parameters ---------- index : CFTimeIndex Index object to be resampled (e.g., CFTimeIndex named 'time'). freq : xarray.coding.cftime_offsets.BaseCFTimeOffset The offset object representing target conversion a.k.a. resampling frequency (e.g., 'MS', '2D', 'H', or '3T' with coding.cftime_offsets.to_offset() applied to it). closed : 'left' or 'right' Which side of bin interval is closed. The default is 'left' for all frequency offsets except for 'M' and 'A', which have a default of 'right'. label : 'left' or 'right' Which bin edge label to label bucket with. The default is 'left' for all frequency offsets except for 'M' and 'A', which have a default of 'right'. origin : {'epoch', 'start', 'start_day', 'end', 'end_day'} or cftime.datetime, default 'start_day' The datetime on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a datetime is not used, these values are also supported: - 'epoch': `origin` is 1970-01-01 - 'start': `origin` is the first value of the timeseries - 'start_day': `origin` is the first day at midnight of the timeseries - 'end': `origin` is the last value of the timeseries - 'end_day': `origin` is the ceiling midnight of the last day offset : datetime.timedelta, default is None An offset timedelta added to the origin. Returns ------- datetime_bins : CFTimeIndex Defines the edge of resampling bins by which original index values will be grouped into. labels : CFTimeIndex Define what the user actually sees the bins labeled as. """ if not isinstance(index, CFTimeIndex): raise TypeError( "index must be a CFTimeIndex, but got " f"an instance of {type(index).__name__!r}" ) if len(index) == 0: datetime_bins = labels = CFTimeIndex(data=[], name=index.name) return datetime_bins, labels first, last = _get_range_edges( index.min(), index.max(), freq, closed=closed, origin=origin, offset=offset ) datetime_bins = labels = cftime_range( freq=freq, start=first, end=last, name=index.name ) datetime_bins, labels = _adjust_bin_edges( datetime_bins, freq, closed, index, labels ) labels = labels[1:] if label == "right" else labels[:-1] # TODO: when CFTimeIndex supports missing values, if the reference index # contains missing values, insert the appropriate NaN value at the # beginning of the datetime_bins and labels indexes. return datetime_bins, labels def _adjust_bin_edges( datetime_bins: np.ndarray, freq: BaseCFTimeOffset, closed: SideOptions, index: CFTimeIndex, labels: np.ndarray, ): """This is required for determining the bin edges resampling with daily frequencies greater than one day, month end, and year end frequencies. Consider the following example. Let's say you want to downsample the time series with the following coordinates to month end frequency: CFTimeIndex([2000-01-01 12:00:00, 2000-01-31 12:00:00, 2000-02-01 12:00:00], dtype='object') Without this adjustment, _get_time_bins with month-end frequency will return the following index for the bin edges (default closed='right' and label='right' in this case): CFTimeIndex([1999-12-31 00:00:00, 2000-01-31 00:00:00, 2000-02-29 00:00:00], dtype='object') If 2000-01-31 is used as a bound for a bin, the value on 2000-01-31T12:00:00 (at noon on January 31st), will not be included in the month of January. To account for this, pandas adds a day minus one worth of microseconds to the bin edges generated by cftime range, so that we do bin the value at noon on January 31st in the January bin. This results in an index with bin edges like the following: CFTimeIndex([1999-12-31 23:59:59, 2000-01-31 23:59:59, 2000-02-29 23:59:59], dtype='object') The labels are still: CFTimeIndex([2000-01-31 00:00:00, 2000-02-29 00:00:00], dtype='object') This is also required for daily frequencies longer than one day and year-end frequencies. """ is_super_daily = isinstance(freq, (MonthEnd, QuarterEnd, YearEnd)) or ( isinstance(freq, Day) and freq.n > 1 ) if is_super_daily: if closed == "right": datetime_bins = datetime_bins + datetime.timedelta(days=1, microseconds=-1) if datetime_bins[-2] > index.max(): datetime_bins = datetime_bins[:-1] labels = labels[:-1] return datetime_bins, labels def _get_range_edges( first: CFTimeDatetime, last: CFTimeDatetime, freq: BaseCFTimeOffset, closed: SideOptions = "left", origin: str | CFTimeDatetime = "start_day", offset: datetime.timedelta | None = None, ): """Get the correct starting and ending datetimes for the resampled CFTimeIndex range. Parameters ---------- first : cftime.datetime Uncorrected starting datetime object for resampled CFTimeIndex range. Usually the min of the original CFTimeIndex. last : cftime.datetime Uncorrected ending datetime object for resampled CFTimeIndex range. Usually the max of the original CFTimeIndex. freq : xarray.coding.cftime_offsets.BaseCFTimeOffset The offset object representing target conversion a.k.a. resampling frequency. Contains information on offset type (e.g. Day or 'D') and offset magnitude (e.g., n = 3). closed : 'left' or 'right' Which side of bin interval is closed. Defaults to 'left'. origin : {'epoch', 'start', 'start_day', 'end', 'end_day'} or cftime.datetime, default 'start_day' The datetime on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a datetime is not used, these values are also supported: - 'epoch': `origin` is 1970-01-01 - 'start': `origin` is the first value of the timeseries - 'start_day': `origin` is the first day at midnight of the timeseries - 'end': `origin` is the last value of the timeseries - 'end_day': `origin` is the ceiling midnight of the last day offset : datetime.timedelta, default is None An offset timedelta added to the origin. Returns ------- first : cftime.datetime Corrected starting datetime object for resampled CFTimeIndex range. last : cftime.datetime Corrected ending datetime object for resampled CFTimeIndex range. """ if isinstance(freq, Tick): first, last = _adjust_dates_anchored( first, last, freq, closed=closed, origin=origin, offset=offset ) return first, last else: first = normalize_date(first) last = normalize_date(last) first = freq.rollback(first) if closed == "left" else first - freq last = last + freq return first, last def _adjust_dates_anchored( first: CFTimeDatetime, last: CFTimeDatetime, freq: Tick, closed: SideOptions = "right", origin: str | CFTimeDatetime = "start_day", offset: datetime.timedelta | None = None, ): """First and last offsets should be calculated from the start day to fix an error cause by resampling across multiple days when a one day period is not a multiple of the frequency. See https://github.com/pandas-dev/pandas/issues/8683 Parameters ---------- first : cftime.datetime A datetime object representing the start of a CFTimeIndex range. last : cftime.datetime A datetime object representing the end of a CFTimeIndex range. freq : xarray.coding.cftime_offsets.BaseCFTimeOffset The offset object representing target conversion a.k.a. resampling frequency. Contains information on offset type (e.g. Day or 'D') and offset magnitude (e.g., n = 3). closed : 'left' or 'right' Which side of bin interval is closed. Defaults to 'right'. origin : {'epoch', 'start', 'start_day', 'end', 'end_day'} or cftime.datetime, default 'start_day' The datetime on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a datetime is not used, these values are also supported: - 'epoch': `origin` is 1970-01-01 - 'start': `origin` is the first value of the timeseries - 'start_day': `origin` is the first day at midnight of the timeseries - 'end': `origin` is the last value of the timeseries - 'end_day': `origin` is the ceiling midnight of the last day offset : datetime.timedelta, default is None An offset timedelta added to the origin. Returns ------- fresult : cftime.datetime A datetime object representing the start of a date range that has been adjusted to fix resampling errors. lresult : cftime.datetime A datetime object representing the end of a date range that has been adjusted to fix resampling errors. """ import cftime if origin == "start_day": origin_date = normalize_date(first) elif origin == "start": origin_date = first elif origin == "epoch": origin_date = type(first)(1970, 1, 1) elif origin in ["end", "end_day"]: origin_last = last if origin == "end" else _ceil_via_cftimeindex(last, "D") sub_freq_times = (origin_last - first) // freq.as_timedelta() if closed == "left": sub_freq_times += 1 first = origin_last - sub_freq_times * freq origin_date = first elif isinstance(origin, cftime.datetime): origin_date = origin else: raise ValueError( f"origin must be one of {{'epoch', 'start_day', 'start', 'end', 'end_day'}} " f"or a cftime.datetime object. Got {origin}." ) if offset is not None: origin_date = origin_date + offset foffset = (first - origin_date) % freq.as_timedelta() loffset = (last - origin_date) % freq.as_timedelta() if closed == "right": if foffset.total_seconds() > 0: fresult = first - foffset else: fresult = first - freq.as_timedelta() if loffset.total_seconds() > 0: lresult = last + (freq.as_timedelta() - loffset) else: lresult = last else: if foffset.total_seconds() > 0: fresult = first - foffset else: fresult = first if loffset.total_seconds() > 0: lresult = last + (freq.as_timedelta() - loffset) else: lresult = last + freq return fresult, lresult def exact_cftime_datetime_difference(a: CFTimeDatetime, b: CFTimeDatetime): """Exact computation of b - a Assumes: a = a_0 + a_m b = b_0 + b_m Here a_0, and b_0 represent the input dates rounded down to the nearest second, and a_m, and b_m represent the remaining microseconds associated with date a and date b. We can then express the value of b - a as: b - a = (b_0 + b_m) - (a_0 + a_m) = b_0 - a_0 + b_m - a_m By construction, we know that b_0 - a_0 must be a round number of seconds. Therefore we can take the result of b_0 - a_0 using ordinary cftime.datetime arithmetic and round to the nearest second. b_m - a_m is the remainder, in microseconds, and we can simply add this to the rounded timedelta. Parameters ---------- a : cftime.datetime Input datetime b : cftime.datetime Input datetime Returns ------- datetime.timedelta """ seconds = b.replace(microsecond=0) - a.replace(microsecond=0) seconds = int(round(seconds.total_seconds())) microseconds = b.microsecond - a.microsecond return datetime.timedelta(seconds=seconds, microseconds=microseconds) def _convert_offset_to_timedelta( offset: datetime.timedelta | str | BaseCFTimeOffset, ) -> datetime.timedelta: if isinstance(offset, datetime.timedelta): return offset elif isinstance(offset, (str, Tick)): return to_offset(offset).as_timedelta() else: raise ValueError def _ceil_via_cftimeindex(date: CFTimeDatetime, freq: str | BaseCFTimeOffset): index = CFTimeIndex([date]) return index.ceil(freq).item()