Source code for mars.dataframe.datasource.date_range

# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from datetime import datetime, date, time

import numpy as np
import pandas as pd
from pandas.tseries.frequencies import to_offset
from pandas.tseries.offsets import Tick
from pandas._libs.tslibs import timezones

from ... import opcodes as OperandDef
from ...config import options
from ...core import OutputType
from ...serialize import AnyField, Int64Field, BoolField, StringField
from ...tensor.utils import decide_chunk_sizes
from ..operands import DataFrameOperand, DataFrameOperandMixin
from ..utils import parse_index

try:
    from pandas._libs.tslib import normalize_date
except ImportError:  # pragma: no cover
    def normalize_date(dt):  # from pandas/_libs/tslibs/conversion.pyx
        if isinstance(dt, datetime):
            if isinstance(dt, pd.Timestamp):
                return dt.replace(hour=0, minute=0, second=0, microsecond=0,
                                  nanosecond=0)
            else:
                return dt.replace(hour=0, minute=0, second=0, microsecond=0)
        elif isinstance(dt, date):
            return datetime(dt.year, dt.month, dt.day)
        else:
            raise TypeError(f'Unrecognized type: {type(dt)}')


class DataFrameDateRange(DataFrameOperand, DataFrameOperandMixin):
    _op_type_ = OperandDef.DATE_RANGE

    _start = AnyField('start')
    _end = AnyField('end')
    _periods = Int64Field('periods')
    _freq = AnyField('freq')
    _tz = AnyField('tz')
    _normalize = BoolField('normalize')
    _name = StringField('name')
    _closed = StringField('closed')

    def __init__(self, start=None, end=None, periods=None,
                 freq=None, tz=None, normalize=None, name=None,
                 closed=None, output_types=None, **kw):
        super().__init__(_start=start, _end=end, _periods=periods,
                         _freq=freq, _tz=tz, _normalize=normalize,
                         _name=name, _closed=closed,
                         _output_types=output_types, **kw)
        if self.output_types is None:
            self.output_types = [OutputType.index]

    @property
    def start(self):
        return self._start

    @property
    def end(self):
        return self._end

    @property
    def periods(self):
        return self._periods

    @property
    def freq(self):
        return self._freq

    @property
    def tz(self):
        return self._tz

    @property
    def normalize(self):
        return self._normalize

    @property
    def name(self):
        return self._name

    @property
    def closed(self):
        return self._closed

    def __call__(self, shape, chunk_size=None):
        dtype = pd.Index([self._start]).dtype
        index_value = parse_index(pd.Index([], dtype=dtype),
                                  self._start, self._end, self._periods,
                                  self._tz)
        # gen index value info
        index_value.value._min_val = self._start
        index_value.value._min_val_close = True
        index_value.value._max_val = self._end
        index_value.value._max_val_close = True
        index_value.value._is_unique = True
        index_value.value._is_monotonic_increasing = True
        index_value.value._freq = self._freq
        return self.new_index(None, shape=shape, dtype=dtype,
                              index_value=index_value, name=self._name,
                              raw_chunk_size=chunk_size, freq=self._freq)

    @classmethod
    def tile(cls, op):
        out = op.outputs[0]
        start = op.start
        end = op.end
        freq = op.freq
        periods = op.periods
        closed = op.closed

        chunk_length = out.extra_params.raw_chunk_size or options.chunk_size
        chunk_length = decide_chunk_sizes(out.shape, chunk_length, out.dtype.itemsize)[0]

        if closed == 'right':
            # if left not close, add one more for the first chunk
            chunk_length = (chunk_length[0] + 1,) + chunk_length[1:]

        if freq is None:
            if periods > 1:
                freq = (end - op.start) / (periods - 1)
            else:
                freq = end - start

        out_chunks = []
        cum_nsplit = [0] + np.cumsum(chunk_length).tolist()
        for i, chunk_size in enumerate(chunk_length):
            chunk_op = op.copy().reset_key()
            chunk_op._periods = chunk_size
            if closed != 'right' or i > 0:
                chunk_op._closed = None
            chunk_i_start = cum_nsplit[i]
            if chunk_i_start > 0:
                chunk_start = chunk_op._start = start + freq * chunk_i_start
            else:
                chunk_start = chunk_op._start = start
            chunk_end = chunk_op._end = chunk_start + (chunk_size - 1) * freq

            # gen chunk index_value
            chunk_index_value = parse_index(out.index_value.to_pandas(), i, out)
            chunk_index_value.value._min_val = chunk_start
            chunk_index_value.value._min_val_close = True
            chunk_index_value.value._max_val = chunk_end
            chunk_index_value.value._max_val_close = True
            chunk_index_value.value._is_unique = True
            chunk_index_value.value._is_monotonic_increasing = True

            size = chunk_size - 1 if i == 0 and closed == 'right' else chunk_size
            out_chunk = chunk_op.new_chunk(None, shape=(size,),
                                           index=(i,), dtype=out.dtype,
                                           index_value=chunk_index_value,
                                           name=out.name)
            out_chunks.append(out_chunk)

        new_op = op.copy()
        params = out.params
        params['chunks'] = out_chunks
        params['nsplits'] = (tuple(c.shape[0] for c in out_chunks),)
        return new_op.new_indexes(None, kws=[params])

    @classmethod
    def execute(cls, ctx, op):
        start, end, periods = op.start, op.end, op.periods
        freq = op.freq
        if freq is not None:
            end = None
        ctx[op.outputs[0].key] = pd.date_range(
            start=start, end=end, periods=periods, freq=freq,
            tz=op.tz, normalize=op.normalize, name=op.name,
            closed=op.closed)


_midnight = time(0, 0)


def _maybe_normalize_endpoints(start, end, normalize):  # pragma: no cover
    _normalized = True

    if start is not None:
        if normalize:
            start = normalize_date(start)
            _normalized = True
        else:
            _normalized = _normalized and start.time() == _midnight

    if end is not None:
        if normalize:
            end = normalize_date(end)
            _normalized = True
        else:
            _normalized = _normalized and end.time() == _midnight

    return start, end, _normalized


def _infer_tz_from_endpoints(start, end, tz):  # pragma: no cover
    """
    If a timezone is not explicitly given via `tz`, see if one can
    be inferred from the `start` and `end` endpoints.  If more than one
    of these inputs provides a timezone, require that they all agree.

    Parameters
    ----------
    start : Timestamp
    end : Timestamp
    tz : tzinfo or None

    Returns
    -------
    tz : tzinfo or None

    Raises
    ------
    TypeError : if start and end timezones do not agree
    """
    try:
        inferred_tz = timezones.infer_tzinfo(start, end)
    except AssertionError:
        # infer_tzinfo raises AssertionError if passed mismatched timezones
        raise TypeError(
            "Start and end cannot both be tz-aware with different timezones"
        )

    inferred_tz = timezones.maybe_get_tz(inferred_tz)
    tz = timezones.maybe_get_tz(tz)

    if tz is not None and inferred_tz is not None:
        if not timezones.tz_compare(inferred_tz, tz):
            raise AssertionError("Inferred time zone not equal to passed time zone")

    elif inferred_tz is not None:
        tz = inferred_tz

    return tz


def _maybe_localize_point(ts, is_none, is_not_none, freq, tz, ambiguous, nonexistent):  # pragma: no cover
    """
    Localize a start or end Timestamp to the timezone of the corresponding
    start or end Timestamp

    Parameters
    ----------
    ts : start or end Timestamp to potentially localize
    is_none : argument that should be None
    is_not_none : argument that should not be None
    freq : Tick, DateOffset, or None
    tz : str, timezone object or None
    ambiguous: str, localization behavior for ambiguous times
    nonexistent: str, localization behavior for nonexistent times

    Returns
    -------
    ts : Timestamp
    """
    # Make sure start and end are timezone localized if:
    # 1) freq = a Timedelta-like frequency (Tick)
    # 2) freq = None i.e. generating a linspaced range
    if is_none is None and is_not_none is not None:
        # Note: We can't ambiguous='infer' a singular ambiguous time; however,
        # we have historically defaulted ambiguous=False
        ambiguous = ambiguous if ambiguous != "infer" else False
        localize_args = {"ambiguous": ambiguous, "nonexistent": nonexistent, "tz": None}
        if isinstance(freq, Tick) or freq is None:
            localize_args["tz"] = tz
        ts = ts.tz_localize(**localize_args)
    return ts


[docs]def date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, chunk_size=None, **kwargs): """ Return a fixed frequency DatetimeIndex. Parameters ---------- start : str or datetime-like, optional Left bound for generating dates. end : str or datetime-like, optional Right bound for generating dates. periods : int, optional Number of periods to generate. freq : str or DateOffset, default 'D' Frequency strings can have multiples, e.g. '5H'. See :ref:`here <timeseries.offset_aliases>` for a list of frequency aliases. tz : str or tzinfo, optional Time zone name for returning localized DatetimeIndex, for example 'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is timezone-naive. normalize : bool, default False Normalize start/end dates to midnight before generating date range. name : str, default None Name of the resulting DatetimeIndex. closed : {None, 'left', 'right'}, optional Make the interval closed with respect to the given frequency to the 'left', 'right', or both sides (None, the default). **kwargs For compatibility. Has no effect on the result. Returns ------- rng : DatetimeIndex See Also -------- DatetimeIndex : An immutable container for datetimes. timedelta_range : Return a fixed frequency TimedeltaIndex. period_range : Return a fixed frequency PeriodIndex. interval_range : Return a fixed frequency IntervalIndex. Notes ----- Of the four parameters ``start``, ``end``, ``periods``, and ``freq``, exactly three must be specified. If ``freq`` is omitted, the resulting ``DatetimeIndex`` will have ``periods`` linearly spaced elements between ``start`` and ``end`` (closed on both sides). To learn more about the frequency strings, please see `this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__. Examples -------- **Specifying the values** The next four examples generate the same `DatetimeIndex`, but vary the combination of `start`, `end` and `periods`. Specify `start` and `end`, with the default daily frequency. >>> import mars.dataframe as md >>> md.date_range(start='1/1/2018', end='1/08/2018').execute() DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'], dtype='datetime64[ns]', freq='D') Specify `start` and `periods`, the number of periods (days). >>> md.date_range(start='1/1/2018', periods=8).execute() DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'], dtype='datetime64[ns]', freq='D') Specify `end` and `periods`, the number of periods (days). >>> md.date_range(end='1/1/2018', periods=8).execute() DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28', '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D') Specify `start`, `end`, and `periods`; the frequency is generated automatically (linearly spaced). >>> md.date_range(start='2018-04-24', end='2018-04-27', periods=3).execute() DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00', '2018-04-27 00:00:00'], dtype='datetime64[ns]', freq=None) **Other Parameters** Changed the `freq` (frequency) to ``'M'`` (month end frequency). >>> md.date_range(start='1/1/2018', periods=5, freq='M').execute() DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30', '2018-05-31'], dtype='datetime64[ns]', freq='M') Multiples are allowed >>> md.date_range(start='1/1/2018', periods=5, freq='3M').execute() DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31', '2019-01-31'], dtype='datetime64[ns]', freq='3M') `freq` can also be specified as an Offset object. >>> md.date_range(start='1/1/2018', periods=5, freq=md.offsets.MonthEnd(3)).execute() DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31', '2019-01-31'], dtype='datetime64[ns]', freq='3M') Specify `tz` to set the timezone. >>> md.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo').execute() DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00', '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00', '2018-01-05 00:00:00+09:00'], dtype='datetime64[ns, Asia/Tokyo]', freq='D') `closed` controls whether to include `start` and `end` that are on the boundary. The default includes boundary points on either end. >>> md.date_range(start='2017-01-01', end='2017-01-04', closed=None).execute() DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D') Use ``closed='left'`` to exclude `end` if it falls on the boundary. >>> md.date_range(start='2017-01-01', end='2017-01-04', closed='left').execute() DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', freq='D') Use ``closed='right'`` to exclude `start` if it falls on the boundary. >>> md.date_range(start='2017-01-01', end='2017-01-04', closed='right').execute() DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D') """ # validate periods if isinstance(periods, (float, np.floating)): periods = int(periods) if periods is not None and not isinstance(periods, (int, np.integer)): raise TypeError(f'periods must be a number, got {periods}') if freq is None and any(arg is None for arg in [periods, start, end]): freq = 'D' if sum(arg is not None for arg in [start, end, periods, freq]) != 3: raise ValueError('Of the four parameters: start, end, periods, ' 'and freq, exactly three must be specified') freq = to_offset(freq) if start is not None: start = pd.Timestamp(start) if end is not None: end = pd.Timestamp(end) if start is pd.NaT or end is pd.NaT: raise ValueError('Neither `start` nor `end` can be NaT') start, end, _ = _maybe_normalize_endpoints(start, end, normalize) tz = _infer_tz_from_endpoints(start, end, tz) if start is None and end is not None: # start is None and end is not None # adjust end first end = pd.date_range(end=end, periods=1, freq=freq)[0] size = periods start = end - (periods - 1) * freq if closed == 'left': size -= 1 elif closed == 'right': # when start is None, closed == 'left' would not take effect # thus just ignore closed = None elif end is None: # end is None # adjust start first start = pd.date_range(start=start, periods=1, freq=freq)[0] size = periods end = start + (periods - 1) * freq if closed == 'right': size -= 1 elif closed == 'left': # when end is None, closed == 'left' would not take effect # thus just ignore closed = None else: if periods is None: periods = size = int((end - start) / freq + 1) else: size = periods if closed is not None: size -= 1 shape = (size,) op = DataFrameDateRange(start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize, closed=closed, name=name, **kwargs) return op(shape, chunk_size=chunk_size)