Python pandas.tseries.offsets.Tick() Examples

def process(string: str, freq: str) -> pd.Timestamp:
        """Create timestamp and align it according to frequency.
        """

        timestamp = pd.Timestamp(string, freq=freq)

        # operate on time information (days, hours, minute, second)
        if isinstance(timestamp.freq, Tick):
            return pd.Timestamp(
                timestamp.floor(timestamp.freq), timestamp.freq
            )

        # since we are only interested in the data piece, we normalize the
        # time information
        timestamp = timestamp.replace(
            hour=0, minute=0, second=0, microsecond=0, nanosecond=0
        )

        return timestamp.freq.rollforward(timestamp) 

def _add_offset(self, offset):
        assert not isinstance(offset, Tick)
        try:
            if self.tz is not None:
                values = self.tz_localize(None)
            else:
                values = self
            result = offset.apply_index(values)
            if self.tz is not None:
                result = result.tz_localize(self.tz)

        except NotImplementedError:
            warnings.warn("Non-vectorized DateOffset being applied to Series "
                          "or DatetimeIndex", PerformanceWarning)
            result = self.astype('O') + offset

        return type(self)._from_sequence(result, freq='infer') 

def _get_range_edges(axis, offset, closed='left', base=0):
    if isinstance(offset, compat.string_types):
        offset = to_offset(offset)

    if isinstance(offset, Tick):
        day_nanos = _delta_to_nanoseconds(timedelta(1))
        # #1165
        if (day_nanos % offset.nanos) == 0:
            return _adjust_dates_anchored(axis[0], axis[-1], offset,
                                          closed=closed, base=base)

    first, last = axis[0], axis[-1]
    if not isinstance(offset, Tick):  # and first.time() != last.time():
        # hack!
        first = tools.normalize_date(first)
        last = tools.normalize_date(last)

    if closed == 'left':
        first = Timestamp(offset.rollback(first))
    else:
        first = Timestamp(first - offset)

    last = Timestamp(last + offset)

    return first, last 

def _add_delta(self, delta):
        """
        Add a timedelta-like, Tick, or TimedeltaIndex-like object
        to self, yielding a new DatetimeArray

        Parameters
        ----------
        other : {timedelta, np.timedelta64, Tick,
                 TimedeltaIndex, ndarray[timedelta64]}

        Returns
        -------
        result : DatetimeArray
        """
        new_values = super(DatetimeArray, self)._add_delta(delta)
        return type(self)._from_sequence(new_values, tz=self.tz, freq='infer')

    # -----------------------------------------------------------------
    # Timezone Conversion and Localization Methods 

def test_tick_division(cls):
    off = cls(10)

    assert off / cls(5) == 2
    assert off / 2 == cls(5)
    assert off / 2.0 == cls(5)

    assert off / off.delta == 1
    assert off / off.delta.to_timedelta64() == 1

    assert off / Nano(1) == off.delta / Nano(1).delta

    if cls is not Nano:
        # A case where we end up with a smaller class
        result = off / 1000
        assert isinstance(result, offsets.Tick)
        assert not isinstance(result, cls)
        assert result.delta == off.delta / 1000

    if cls._inc < Timedelta(seconds=1):
        # Case where we end up with a bigger class
        result = off / .001
        assert isinstance(result, offsets.Tick)
        assert not isinstance(result, cls)
        assert result.delta == off.delta / .001 

def _from_datetime64(cls, data, freq, tz=None):
        """
        Construct a PeriodArray from a datetime64 array

        Parameters
        ----------
        data : ndarray[datetime64[ns], datetime64[ns, tz]]
        freq : str or Tick
        tz : tzinfo, optional

        Returns
        -------
        PeriodArray[freq]
        """
        data, freq = dt64arr_to_periodarr(data, freq, tz)
        return cls(data, freq=freq) 

def _add_timedeltalike_scalar(self, other):
        """
        Parameters
        ----------
        other : timedelta, Tick, np.timedelta64

        Returns
        -------
        result : ndarray[int64]
        """
        assert isinstance(self.freq, Tick)  # checked by calling function
        assert isinstance(other, (timedelta, np.timedelta64, Tick))

        if notna(other):
            # special handling for np.timedelta64("NaT"), avoid calling
            #  _check_timedeltalike_freq_compat as that would raise TypeError
            other = self._check_timedeltalike_freq_compat(other)

        # Note: when calling parent class's _add_timedeltalike_scalar,
        #  it will call delta_to_nanoseconds(delta).  Because delta here
        #  is an integer, delta_to_nanoseconds will return it unchanged.
        ordinals = super(PeriodArray, self)._add_timedeltalike_scalar(other)
        return ordinals 

def _add_delta(self, delta):
        if isinstance(delta, (Tick, timedelta)):
            inc = offsets._delta_to_nanoseconds(delta)
            new_values = (self.asi8 + inc).view(_NS_DTYPE)
            tz = 'UTC' if self.tz is not None else None
            result = DatetimeIndex(new_values, tz=tz, freq='infer')
            utc = _utc()
            if self.tz is not None and self.tz is not utc:
                result = result.tz_convert(self.tz)
        elif isinstance(delta, np.timedelta64):
            new_values = self.to_series() + delta
            result = DatetimeIndex(new_values, tz=self.tz, freq='infer')
        else:
            new_values = self.astype('O') + delta
            result = DatetimeIndex(new_values, tz=self.tz, freq='infer')
        return result 

def test_tick_division(cls):
    off = cls(10)

    assert off / cls(5) == 2
    assert off / 2 == cls(5)
    assert off / 2.0 == cls(5)

    assert off / off.delta == 1
    assert off / off.delta.to_timedelta64() == 1

    assert off / Nano(1) == off.delta / Nano(1).delta

    if cls is not Nano:
        # A case where we end up with a smaller class
        result = off / 1000
        assert isinstance(result, offsets.Tick)
        assert not isinstance(result, cls)
        assert result.delta == off.delta / 1000

    if cls._inc < Timedelta(seconds=1):
        # Case where we end up with a bigger class
        result = off / .001
        assert isinstance(result, offsets.Tick)
        assert not isinstance(result, cls)
        assert result.delta == off.delta / .001 

def _add_offset(self, offset):
        assert not isinstance(offset, Tick)
        try:
            if self.tz is not None:
                values = self.tz_localize(None)
            else:
                values = self
            result = offset.apply_index(values)
            if self.tz is not None:
                result = result.tz_localize(self.tz)

        except NotImplementedError:
            warnings.warn("Non-vectorized DateOffset being applied to Series "
                          "or DatetimeIndex", PerformanceWarning)
            result = self.astype('O') + offset

        return type(self)._from_sequence(result, freq='infer') 

def _add_delta(self, delta):
        """
        Add a timedelta-like, Tick, or TimedeltaIndex-like object
        to self, yielding a new DatetimeArray

        Parameters
        ----------
        other : {timedelta, np.timedelta64, Tick,
                 TimedeltaIndex, ndarray[timedelta64]}

        Returns
        -------
        result : DatetimeArray
        """
        new_values = super(DatetimeArray, self)._add_delta(delta)
        return type(self)._from_sequence(new_values, tz=self.tz, freq='infer')

    # -----------------------------------------------------------------
    # Timezone Conversion and Localization Methods 

def _from_datetime64(cls, data, freq, tz=None):
        """
        Construct a PeriodArray from a datetime64 array

        Parameters
        ----------
        data : ndarray[datetime64[ns], datetime64[ns, tz]]
        freq : str or Tick
        tz : tzinfo, optional

        Returns
        -------
        PeriodArray[freq]
        """
        data, freq = dt64arr_to_periodarr(data, freq, tz)
        return cls(data, freq=freq) 

def _add_timedeltalike_scalar(self, other):
        """
        Parameters
        ----------
        other : timedelta, Tick, np.timedelta64

        Returns
        -------
        result : ndarray[int64]
        """
        assert isinstance(self.freq, Tick)  # checked by calling function
        assert isinstance(other, (timedelta, np.timedelta64, Tick))

        if notna(other):
            # special handling for np.timedelta64("NaT"), avoid calling
            #  _check_timedeltalike_freq_compat as that would raise TypeError
            other = self._check_timedeltalike_freq_compat(other)

        # Note: when calling parent class's _add_timedeltalike_scalar,
        #  it will call delta_to_nanoseconds(delta).  Because delta here
        #  is an integer, delta_to_nanoseconds will return it unchanged.
        ordinals = super(PeriodArray, self)._add_timedeltalike_scalar(other)
        return ordinals 

def test_tick_division(cls):
    off = cls(10)

    assert off / cls(5) == 2
    assert off / 2 == cls(5)
    assert off / 2.0 == cls(5)

    assert off / off.delta == 1
    assert off / off.delta.to_timedelta64() == 1

    assert off / Nano(1) == off.delta / Nano(1).delta

    if cls is not Nano:
        # A case where we end up with a smaller class
        result = off / 1000
        assert isinstance(result, offsets.Tick)
        assert not isinstance(result, cls)
        assert result.delta == off.delta / 1000

    if cls._inc < Timedelta(seconds=1):
        # Case where we end up with a bigger class
        result = off / .001
        assert isinstance(result, offsets.Tick)
        assert not isinstance(result, cls)
        assert result.delta == off.delta / .001 

def _get_range_edges(first, last, offset, closed='left', base=0):
    if isinstance(offset, compat.string_types):
        offset = to_offset(offset)

    if isinstance(offset, Tick):
        is_day = isinstance(offset, Day)
        day_nanos = _delta_to_nanoseconds(timedelta(1))

        # #1165
        if (is_day and day_nanos % offset.nanos == 0) or not is_day:
            return _adjust_dates_anchored(first, last, offset,
                                          closed=closed, base=base)

    if not isinstance(offset, Tick):  # and first.time() != last.time():
        # hack!
        first = first.normalize()
        last = last.normalize()

    if closed == 'left':
        first = Timestamp(offset.rollback(first))
    else:
        first = Timestamp(first - offset)

    last = Timestamp(last + offset)

    return first, last 

def _maybe_convert_timedelta(self, other):
        if isinstance(
                other, (timedelta, np.timedelta64, offsets.Tick, np.ndarray)):
            offset = frequencies.to_offset(self.freq.rule_code)
            if isinstance(offset, offsets.Tick):
                if isinstance(other, np.ndarray):
                    nanos = np.vectorize(tslib._delta_to_nanoseconds)(other)
                else:
                    nanos = tslib._delta_to_nanoseconds(other)
                offset_nanos = tslib._delta_to_nanoseconds(offset)
                check = np.all(nanos % offset_nanos == 0)
                if check:
                    return nanos // offset_nanos
        elif isinstance(other, offsets.DateOffset):
            freqstr = other.rule_code
            base = frequencies.get_base_alias(freqstr)
            if base == self.freq.rule_code:
                return other.n
            msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
            raise IncompatibleFrequency(msg)
        elif isinstance(other, np.ndarray):
            if is_integer_dtype(other):
                return other
            elif is_timedelta64_dtype(other):
                offset = frequencies.to_offset(self.freq)
                if isinstance(offset, offsets.Tick):
                    nanos = tslib._delta_to_nanoseconds(other)
                    offset_nanos = tslib._delta_to_nanoseconds(offset)
                    if (nanos % offset_nanos).all() == 0:
                        return nanos // offset_nanos
        elif is_integer(other):
            # integer is passed to .shift via
            # _add_datetimelike_methods basically
            # but ufunc may pass integer to _add_delta
            return other
        # raise when input doesn't have freq
        msg = "Input has different freq from PeriodIndex(freq={0})"
        raise IncompatibleFrequency(msg.format(self.freqstr)) 

def _add_delta(self, delta):
        if isinstance(delta, (Tick, timedelta, np.timedelta64)):
            new_values = self._add_delta_td(delta)
            name = self.name
        elif isinstance(delta, TimedeltaIndex):
            new_values = self._add_delta_tdi(delta)
            # update name when delta is index
            name = com._maybe_match_name(self, delta)
        else:
            raise ValueError("cannot add the type {0} to a TimedeltaIndex"
                             .format(type(delta)))

        result = TimedeltaIndex(new_values, freq='infer', name=name)
        return result 

def _get_range_edges(first, last, offset, closed='left', base=0):
    if isinstance(offset, compat.string_types):
        offset = to_offset(offset)

    if isinstance(offset, Tick):
        is_day = isinstance(offset, Day)
        day_nanos = _delta_to_nanoseconds(timedelta(1))

        # #1165
        if (is_day and day_nanos % offset.nanos == 0) or not is_day:
            return _adjust_dates_anchored(first, last, offset,
                                          closed=closed, base=base)

    if not isinstance(offset, Tick):  # and first.time() != last.time():
        # hack!
        first = first.normalize()
        last = last.normalize()

    if closed == 'left':
        first = Timestamp(offset.rollback(first))
    else:
        first = Timestamp(first - offset)

    last = Timestamp(last + offset)

    return first, last 

def test_tick_add_sub(cls, n, m):
    # For all Tick subclasses and all integers n, m, we should have
    # tick(n) + tick(m) == tick(n+m)
    # tick(n) - tick(m) == tick(n-m)
    left = cls(n)
    right = cls(m)
    expected = cls(n + m)

    assert left + right == expected
    assert left.apply(right) == expected

    expected = cls(n - m)
    assert left - right == expected 

def _maybe_localize_point(ts, is_none, is_not_none, freq, tz):
    """
    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

    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 isinstance(freq, Tick) or freq is None:
        localize_args = {'tz': tz, 'ambiguous': False}
    else:
        localize_args = {'tz': None}
    if is_none is None and is_not_none is not None:
        ts = ts.tz_localize(**localize_args)
    return ts 

def __rdivmod__(self, other):
        # Note: This is a naive implementation, can likely be optimized
        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        other = lib.item_from_zerodim(other)
        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            other = Timedelta(other)

        res1 = other // self
        res2 = other - res1 * self
        return res1, res2

    # Note: TimedeltaIndex overrides this in call to cls._add_numeric_methods 

def __rmod__(self, other):
        # Note: This is a naive implementation, can likely be optimized
        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        other = lib.item_from_zerodim(other)
        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            other = Timedelta(other)
        return other - (other // self) * self 

def __mod__(self, other):
        # Note: This is a naive implementation, can likely be optimized
        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        other = lib.item_from_zerodim(other)
        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            other = Timedelta(other)
        return self - (self // other) * other 

def _add_offset(self, other):
        assert not isinstance(other, Tick)
        raise TypeError("cannot add the type {typ} to a {cls}"
                        .format(typ=type(other).__name__,
                                cls=type(self).__name__)) 

def _validate_fill_value(self, fill_value):
        if isna(fill_value):
            fill_value = iNaT
        elif isinstance(fill_value, (timedelta, np.timedelta64, Tick)):
            fill_value = Timedelta(fill_value).value
        else:
            raise ValueError("'fill_value' should be a Timedelta. "
                             "Got '{got}'.".format(got=fill_value))
        return fill_value 

def _is_convertible_to_td(key):
    return isinstance(key, (Tick, timedelta,
                            np.timedelta64, compat.string_types)) 

def _maybe_localize_point(ts, is_none, is_not_none, freq, tz):
    """
    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

    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 isinstance(freq, Tick) or freq is None:
        localize_args = {'tz': tz, 'ambiguous': False}
    else:
        localize_args = {'tz': None}
    if is_none is None and is_not_none is not None:
        ts = ts.tz_localize(**localize_args)
    return ts 

def _addsub_int_array(self, other, op):
        """
        Add or subtract array-like of integers equivalent to applying
        `_time_shift` pointwise.

        Parameters
        ----------
        other : Index, ExtensionArray, np.ndarray
            integer-dtype
        op : {operator.add, operator.sub}

        Returns
        -------
        result : same class as self
        """
        # _addsub_int_array is overriden by PeriodArray
        assert not is_period_dtype(self)
        assert op in [operator.add, operator.sub]

        if self.freq is None:
            # GH#19123
            raise NullFrequencyError("Cannot shift with no freq")

        elif isinstance(self.freq, Tick):
            # easy case where we can convert to timedelta64 operation
            td = Timedelta(self.freq)
            return op(self, td * other)

        # We should only get here with DatetimeIndex; dispatch
        # to _addsub_offset_array
        assert not is_timedelta64_dtype(self)
        return op(self, np.array(other) * self.freq) 

def _is_convertible_to_td(key):
    return isinstance(key, (Tick, timedelta,
                            np.timedelta64, compat.string_types)) 

def _add_offset(self, other):
        assert not isinstance(other, Tick)
        base = libfrequencies.get_base_alias(other.rule_code)
        if base != self.freq.rule_code:
            _raise_on_incompatible(self, other)

        # Note: when calling parent class's _add_timedeltalike_scalar,
        #  it will call delta_to_nanoseconds(delta).  Because delta here
        #  is an integer, delta_to_nanoseconds will return it unchanged.
        result = super(PeriodArray, self)._add_timedeltalike_scalar(other.n)
        return type(self)(result, freq=self.freq)