period.py

# -*- coding: utf-8 -*-
from datetime import timedelta
import operator

import numpy as np

from pandas._libs.tslibs import (
    NaT, frequencies as libfrequencies, iNaT, period as libperiod)
from pandas._libs.tslibs.fields import isleapyear_arr
from pandas._libs.tslibs.period import (
    DIFFERENT_FREQ, IncompatibleFrequency, Period, get_period_field_arr,
    period_asfreq_arr)
from pandas._libs.tslibs.timedeltas import Timedelta, delta_to_nanoseconds
import pandas.compat as compat
from pandas.util._decorators import Appender, cache_readonly

from pandas.core.dtypes.common import (
    _TD_DTYPE, ensure_object, is_datetime64_dtype, is_float_dtype,
    is_list_like, is_period_dtype, pandas_dtype)
from pandas.core.dtypes.dtypes import PeriodDtype
from pandas.core.dtypes.generic import (
    ABCDataFrame, ABCIndexClass, ABCPeriodIndex, ABCSeries)
from pandas.core.dtypes.missing import isna, notna

import pandas.core.algorithms as algos
from pandas.core.arrays import datetimelike as dtl
import pandas.core.common as com

from pandas.tseries import frequencies
from pandas.tseries.offsets import DateOffset, Tick, _delta_to_tick


def _field_accessor(name, alias, docstring=None):
    def f(self):
        base, mult = libfrequencies.get_freq_code(self.freq)
        result = get_period_field_arr(alias, self.asi8, base)
        return result

    f.__name__ = name
    f.__doc__ = docstring
    return property(f)


def _period_array_cmp(cls, op):
    """
    Wrap comparison operations to convert Period-like to PeriodDtype
    """
    opname = '__{name}__'.format(name=op.__name__)
    nat_result = opname == '__ne__'

    def wrapper(self, other):
        op = getattr(self.asi8, opname)

        if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
            return NotImplemented

        if is_list_like(other) and len(other) != len(self):
            raise ValueError("Lengths must match")

        if isinstance(other, Period):
            self._check_compatible_with(other)

            result = op(other.ordinal)
        elif isinstance(other, cls):
            self._check_compatible_with(other)

            result = op(other.asi8)

            mask = self._isnan | other._isnan
            if mask.any():
                result[mask] = nat_result

            return result
        elif other is NaT:
            result = np.empty(len(self.asi8), dtype=bool)
            result.fill(nat_result)
        else:
            other = Period(other, freq=self.freq)
            result = op(other.ordinal)

        if self._hasnans:
            result[self._isnan] = nat_result

        return result

    return compat.set_function_name(wrapper, opname, cls)


class PeriodArray(dtl.DatetimeLikeArrayMixin, dtl.DatelikeOps):
    """
    Pandas ExtensionArray for storing Period data.

    Users should use :func:`period_array` to create new instances.

    Parameters
    ----------
    values : Union[PeriodArray, Series[period], ndarary[int], PeriodIndex]
        The data to store. These should be arrays that can be directly
        converted to ordinals without inference or copy (PeriodArray,
        ndarray[int64]), or a box around such an array (Series[period],
        PeriodIndex).
    freq : str or DateOffset
        The `freq` to use for the array. Mostly applicable when `values`
        is an ndarray of integers, when `freq` is required. When `values`
        is a PeriodArray (or box around), it's checked that ``values.freq``
        matches `freq`.
    copy : bool, default False
        Whether to copy the ordinals before storing.

    See Also
    --------
    period_array : Create a new PeriodArray.
    pandas.PeriodIndex : Immutable Index for period data.

    Notes
    -----
    There are two components to a PeriodArray

    - ordinals : integer ndarray
    - freq : pd.tseries.offsets.Offset

    The values are physically stored as a 1-D ndarray of integers. These are
    called "ordinals" and represent some kind of offset from a base.

    The `freq` indicates the span covered by each element of the array.
    All elements in the PeriodArray have the same `freq`.
    """
    # array priority higher than numpy scalars
    __array_priority__ = 1000
    _attributes = ["freq"]
    _typ = "periodarray"  # ABCPeriodArray
    _scalar_type = Period

    # Names others delegate to us
    _other_ops = []
    _bool_ops = ['is_leap_year']
    _object_ops = ['start_time', 'end_time', 'freq']
    _field_ops = ['year', 'month', 'day', 'hour', 'minute', 'second',
                  'weekofyear', 'weekday', 'week', 'dayofweek',
                  'day_of_year', 'quarter', 'qyear',
                  'days_in_month', 'daysinmonth']
    _datetimelike_ops = _field_ops + _object_ops + _bool_ops
    _datetimelike_methods = ['strftime', 'to_timestamp', 'asfreq']

    # --------------------------------------------------------------------
    # Constructors

    def __init__(self, values, freq=None, dtype=None, copy=False):
        freq = validate_dtype_freq(dtype, freq)

        if freq is not None:
            freq = Period._maybe_convert_freq(freq)

        if isinstance(values, ABCSeries):
            values = values._values
            if not isinstance(values, type(self)):
                raise TypeError("Incorrect dtype")

        elif isinstance(values, ABCPeriodIndex):
            values = values._values

        if isinstance(values, type(self)):
            if freq is not None and freq != values.freq:
                msg = DIFFERENT_FREQ.format(cls=type(self).__name__,
                                            own_freq=values.freq.freqstr,
                                            other_freq=freq.freqstr)
                raise IncompatibleFrequency(msg)
            values, freq = values._data, values.freq

        values = np.array(values, dtype='int64', copy=copy)
        self._data = values
        if freq is None:
            raise ValueError('freq is not specified and cannot be inferred')
        self._dtype = PeriodDtype(freq)

    @classmethod
    def _simple_new(cls, values, freq=None, **kwargs):
        # alias for PeriodArray.__init__
        return cls(values, freq=freq, **kwargs)

    @classmethod
    def _from_sequence(cls, scalars, dtype=None, copy=False):
        # type: (Sequence[Optional[Period]], PeriodDtype, bool) -> PeriodArray
        if dtype:
            freq = dtype.freq
        else:
            freq = None

        if isinstance(scalars, cls):
            validate_dtype_freq(scalars.dtype, freq)
            if copy:
                scalars = scalars.copy()
            return scalars

        periods = np.asarray(scalars, dtype=object)
        if copy:
            periods = periods.copy()

        freq = freq or libperiod.extract_freq(periods)
        ordinals = libperiod.extract_ordinals(periods, freq)
        return cls(ordinals, freq=freq)

    @classmethod
    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)

    @classmethod
    def _generate_range(cls, start, end, periods, freq, fields):
        periods = dtl.validate_periods(periods)

        if freq is not None:
            freq = Period._maybe_convert_freq(freq)

        field_count = len(fields)
        if start is not None or end is not None:
            if field_count > 0:
                raise ValueError('Can either instantiate from fields '
                                 'or endpoints, but not both')
            subarr, freq = _get_ordinal_range(start, end, periods, freq)
        elif field_count > 0:
            subarr, freq = _range_from_fields(freq=freq, **fields)
        else:
            raise ValueError('Not enough parameters to construct '
                             'Period range')

        return subarr, freq

    # -----------------------------------------------------------------
    # DatetimeLike Interface

    def _unbox_scalar(self, value):
        # type: (Union[Period, NaTType]) -> int
        if value is NaT:
            return value.value
        elif isinstance(value, self._scalar_type):
            if not isna(value):
                self._check_compatible_with(value)
            return value.ordinal
        else:
            raise ValueError("'value' should be a Period. Got '{val}' instead."
                             .format(val=value))

    def _scalar_from_string(self, value):
        # type: (str) -> Period
        return Period(value, freq=self.freq)

    def _check_compatible_with(self, other):
        if other is NaT:
            return
        if self.freqstr != other.freqstr:
            _raise_on_incompatible(self, other)

    # --------------------------------------------------------------------
    # Data / Attributes

    @cache_readonly
    def dtype(self):
        return self._dtype

    @property
    def freq(self):
        """
        Return the frequency object for this PeriodArray.
        """
        return self.dtype.freq

    def __array__(self, dtype=None):
        # overriding DatetimelikeArray
        return np.array(list(self), dtype=object)

    # --------------------------------------------------------------------
    # Vectorized analogues of Period properties

    year = _field_accessor('year', 0, "The year of the period")
    month = _field_accessor('month', 3, "The month as January=1, December=12")
    day = _field_accessor('day', 4, "The days of the period")
    hour = _field_accessor('hour', 5, "The hour of the period")
    minute = _field_accessor('minute', 6, "The minute of the period")
    second = _field_accessor('second', 7, "The second of the period")
    weekofyear = _field_accessor('week', 8, "The week ordinal of the year")
    week = weekofyear
    dayofweek = _field_accessor('dayofweek', 10,
                                "The day of the week with Monday=0, Sunday=6")
    weekday = dayofweek
    day_of_year = _field_accessor('day_of_year', 9,
                                  "The ordinal day of the year")
    quarter = _field_accessor('quarter', 2, "The quarter of the date")
    qyear = _field_accessor('qyear', 1)
    days_in_month = _field_accessor('days_in_month', 11,
                                    "The number of days in the month")
    daysinmonth = days_in_month

    @property
    def is_leap_year(self):
        """
        Logical indicating if the date belongs to a leap year
        """
        return isleapyear_arr(np.asarray(self.year))

    @property
    def start_time(self):
        return self.to_timestamp(how='start')

    @property
    def end_time(self):
        return self.to_timestamp(how='end')

    def to_timestamp(self, freq=None, how='start'):
        """
        Cast to DatetimeArray/Index.

        Parameters
        ----------
        freq : string or DateOffset, optional
            Target frequency. The default is 'D' for week or longer,
            'S' otherwise
        how : {'s', 'e', 'start', 'end'}

        Returns
        -------
        DatetimeArray/Index
        """
        from pandas.core.arrays import DatetimeArray

        how = libperiod._validate_end_alias(how)

        end = how == 'E'
        if end:
            if freq == 'B':
                # roll forward to ensure we land on B date
                adjust = Timedelta(1, 'D') - Timedelta(1, 'ns')
                return self.to_timestamp(how='start') + adjust
            else:
                adjust = Timedelta(1, 'ns')
                return (self + self.freq).to_timestamp(how='start') - adjust

        if freq is None:
            base, mult = libfrequencies.get_freq_code(self.freq)
            freq = libfrequencies.get_to_timestamp_base(base)
        else:
            freq = Period._maybe_convert_freq(freq)

        base, mult = libfrequencies.get_freq_code(freq)
        new_data = self.asfreq(freq, how=how)

        new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
        return DatetimeArray._from_sequence(new_data, freq='infer')

    # --------------------------------------------------------------------
    # Array-like / EA-Interface Methods

    def _formatter(self, boxed=False):
        if boxed:
            return str
        return "'{}'".format

    @Appender(dtl.DatetimeLikeArrayMixin._validate_fill_value.__doc__)
    def _validate_fill_value(self, fill_value):
        if isna(fill_value):
            fill_value = iNaT
        elif isinstance(fill_value, Period):
            self._check_compatible_with(fill_value)
            fill_value = fill_value.ordinal
        else:
            raise ValueError("'fill_value' should be a Period. "
                             "Got '{got}'.".format(got=fill_value))
        return fill_value

    # --------------------------------------------------------------------

    def _time_shift(self, periods, freq=None):
        """
        Shift each value by `periods`.

        Note this is different from ExtensionArray.shift, which
        shifts the *position* of each element, padding the end with
        missing values.

        Parameters
        ----------
        periods : int
            Number of periods to shift by.
        freq : pandas.DateOffset, pandas.Timedelta, or string
            Frequency increment to shift by.
        """
        if freq is not None:
            raise TypeError("`freq` argument is not supported for "
                            "{cls}._time_shift"
                            .format(cls=type(self).__name__))
        values = self.asi8 + periods * self.freq.n
        if self._hasnans:
            values[self._isnan] = iNaT
        return type(self)(values, freq=self.freq)

    @property
    def _box_func(self):
        return lambda x: Period._from_ordinal(ordinal=x, freq=self.freq)

    def asfreq(self, freq=None, how='E'):
        """
        Convert the Period Array/Index to the specified frequency `freq`.

        Parameters
        ----------
        freq : str
            a frequency
        how : str {'E', 'S'}
            'E', 'END', or 'FINISH' for end,
            'S', 'START', or 'BEGIN' for start.
            Whether the elements should be aligned to the end
            or start within pa period. January 31st ('END') vs.
            January 1st ('START') for example.

        Returns
        -------
        new : Period Array/Index with the new frequency

        Examples
        --------
        >>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
        >>> pidx
        <class 'pandas.core.indexes.period.PeriodIndex'>
        [2010, ..., 2015]
        Length: 6, Freq: A-DEC

        >>> pidx.asfreq('M')
        <class 'pandas.core.indexes.period.PeriodIndex'>
        [2010-12, ..., 2015-12]
        Length: 6, Freq: M

        >>> pidx.asfreq('M', how='S')
        <class 'pandas.core.indexes.period.PeriodIndex'>
        [2010-01, ..., 2015-01]
        Length: 6, Freq: M
        """
        how = libperiod._validate_end_alias(how)

        freq = Period._maybe_convert_freq(freq)

        base1, mult1 = libfrequencies.get_freq_code(self.freq)
        base2, mult2 = libfrequencies.get_freq_code(freq)

        asi8 = self.asi8
        # mult1 can't be negative or 0
        end = how == 'E'
        if end:
            ordinal = asi8 + mult1 - 1
        else:
            ordinal = asi8

        new_data = period_asfreq_arr(ordinal, base1, base2, end)

        if self._hasnans:
            new_data[self._isnan] = iNaT

        return type(self)(new_data, freq=freq)

    # ------------------------------------------------------------------
    # Rendering Methods

    def _format_native_types(self, na_rep=u'NaT', date_format=None, **kwargs):
        """
        actually format my specific types
        """
        values = self.astype(object)

        if date_format:
            formatter = lambda dt: dt.strftime(date_format)
        else:
            formatter = lambda dt: u'%s' % dt

        if self._hasnans:
            mask = self._isnan
            values[mask] = na_rep
            imask = ~mask
            values[imask] = np.array([formatter(dt) for dt
                                      in values[imask]])
        else:
            values = np.array([formatter(dt) for dt in values])
        return values

    # ------------------------------------------------------------------

    def astype(self, dtype, copy=True):
        # We handle Period[T] -> Period[U]
        # Our parent handles everything else.
        dtype = pandas_dtype(dtype)

        if is_period_dtype(dtype):
            return self.asfreq(dtype.freq)
        return super(PeriodArray, self).astype(dtype, copy=copy)

    @property
    def flags(self):
        # TODO: remove
        # We need this since reduction.SeriesBinGrouper uses values.flags
        # Ideally, we wouldn't be passing objects down there in the first
        # place.
        return self._data.flags

    # ------------------------------------------------------------------
    # Arithmetic Methods
    _create_comparison_method = classmethod(_period_array_cmp)

    def _sub_datelike(self, other):
        assert other is not NaT
        return NotImplemented

    def _sub_period(self, other):
        # If the operation is well-defined, we return an object-Index
        # of DateOffsets.  Null entries are filled with pd.NaT
        self._check_compatible_with(other)
        asi8 = self.asi8
        new_data = asi8 - other.ordinal
        new_data = np.array([self.freq * x for x in new_data])

        if self._hasnans:
            new_data[self._isnan] = NaT

        return new_data

    @Appender(dtl.DatetimeLikeArrayMixin._addsub_int_array.__doc__)
    def _addsub_int_array(
            self,
            other,   # type: Union[Index, ExtensionArray, np.ndarray[int]]
            op      # type: Callable[Any, Any]
    ):
        # type: (...) -> PeriodArray

        assert op in [operator.add, operator.sub]
        if op is operator.sub:
            other = -other
        res_values = algos.checked_add_with_arr(self.asi8, other,
                                                arr_mask=self._isnan)
        res_values = res_values.view('i8')
        res_values[self._isnan] = iNaT
        return type(self)(res_values, freq=self.freq)

    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)

    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_tdi(self, other):
        """
        Parameters
        ----------
        other : TimedeltaArray or ndarray[timedelta64]

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

        delta = self._check_timedeltalike_freq_compat(other)
        return self._addsub_int_array(delta, operator.add).asi8

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

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

        Returns
        -------
        result : PeriodArray
        """
        if not isinstance(self.freq, Tick):
            # We cannot add timedelta-like to non-tick PeriodArray
            _raise_on_incompatible(self, other)

        new_ordinals = super(PeriodArray, self)._add_delta(other)
        return type(self)(new_ordinals, freq=self.freq)

    def _check_timedeltalike_freq_compat(self, other):
        """
        Arithmetic operations with timedelta-like scalars or array `other`
        are only valid if `other` is an integer multiple of `self.freq`.
        If the operation is valid, find that integer multiple.  Otherwise,
        raise because the operation is invalid.

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

        Returns
        -------
        multiple : int or ndarray[int64]

        Raises
        ------
        IncompatibleFrequency
        """
        assert isinstance(self.freq, Tick)  # checked by calling function
        own_offset = frequencies.to_offset(self.freq.rule_code)
        base_nanos = delta_to_nanoseconds(own_offset)

        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            nanos = delta_to_nanoseconds(other)

        elif isinstance(other, np.ndarray):
            # numpy timedelta64 array; all entries must be compatible
            assert other.dtype.kind == 'm'
            if other.dtype != _TD_DTYPE:
                # i.e. non-nano unit
                # TODO: disallow unit-less timedelta64
                other = other.astype(_TD_DTYPE)
            nanos = other.view('i8')
        else:
            # TimedeltaArray/Index
            nanos = other.asi8

        if np.all(nanos % base_nanos == 0):
            # nanos being added is an integer multiple of the
            #  base-frequency to self.freq
            delta = nanos // base_nanos
            # delta is the integer (or integer-array) number of periods
            # by which will be added to self.
            return delta

        _raise_on_incompatible(self, other)

    def _values_for_argsort(self):
        return self._data


PeriodArray._add_comparison_ops()


def _raise_on_incompatible(left, right):
    """
    Helper function to render a consistent error message when raising
    IncompatibleFrequency.

    Parameters
    ----------
    left : PeriodArray
    right : DateOffset, Period, ndarray, or timedelta-like

    Raises
    ------
    IncompatibleFrequency
    """
    # GH#24283 error message format depends on whether right is scalar
    if isinstance(right, np.ndarray):
        other_freq = None
    elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period, DateOffset)):
        other_freq = right.freqstr
    else:
        other_freq = _delta_to_tick(Timedelta(right)).freqstr

    msg = DIFFERENT_FREQ.format(cls=type(left).__name__,
                                own_freq=left.freqstr,
                                other_freq=other_freq)
    raise IncompatibleFrequency(msg)


# -------------------------------------------------------------------
# Constructor Helpers

def period_array(data, freq=None, copy=False):
    # type: (Sequence[Optional[Period]], Optional[Tick]) -> PeriodArray
    """
    Construct a new PeriodArray from a sequence of Period scalars.

    Parameters
    ----------
    data : Sequence of Period objects
        A sequence of Period objects. These are required to all have
        the same ``freq.`` Missing values can be indicated by ``None``
        or ``pandas.NaT``.
    freq : str, Tick, or Offset
        The frequency of every element of the array. This can be specified
        to avoid inferring the `freq` from `data`.
    copy : bool, default False
        Whether to ensure a copy of the data is made.

    Returns
    -------
    PeriodArray

    See Also
    --------
    PeriodArray
    pandas.PeriodIndex

    Examples
    --------
    >>> period_array([pd.Period('2017', freq='A'),
    ...               pd.Period('2018', freq='A')])
    <PeriodArray>
    ['2017', '2018']
    Length: 2, dtype: period[A-DEC]

    >>> period_array([pd.Period('2017', freq='A'),
    ...               pd.Period('2018', freq='A'),
    ...               pd.NaT])
    <PeriodArray>
    ['2017', '2018', 'NaT']
    Length: 3, dtype: period[A-DEC]

    Integers that look like years are handled

    >>> period_array([2000, 2001, 2002], freq='D')
    ['2000-01-01', '2001-01-01', '2002-01-01']
    Length: 3, dtype: period[D]

    Datetime-like strings may also be passed

    >>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q')
    <PeriodArray>
    ['2000Q1', '2000Q2', '2000Q3', '2000Q4']
    Length: 4, dtype: period[Q-DEC]
    """
    if is_datetime64_dtype(data):
        return PeriodArray._from_datetime64(data, freq)
    if isinstance(data, (ABCPeriodIndex, ABCSeries, PeriodArray)):
        return PeriodArray(data, freq)

    # other iterable of some kind
    if not isinstance(data, (np.ndarray, list, tuple)):
        data = list(data)

    data = np.asarray(data)

    if freq:
        dtype = PeriodDtype(freq)
    else:
        dtype = None

    if is_float_dtype(data) and len(data) > 0:
        raise TypeError("PeriodIndex does not allow "
                        "floating point in construction")

    data = ensure_object(data)

    return PeriodArray._from_sequence(data, dtype=dtype)


def validate_dtype_freq(dtype, freq):
    """
    If both a dtype and a freq are available, ensure they match.  If only
    dtype is available, extract the implied freq.

    Parameters
    ----------
    dtype : dtype
    freq : DateOffset or None

    Returns
    -------
    freq : DateOffset

    Raises
    ------
    ValueError : non-period dtype
    IncompatibleFrequency : mismatch between dtype and freq
    """
    if freq is not None:
        freq = frequencies.to_offset(freq)

    if dtype is not None:
        dtype = pandas_dtype(dtype)
        if not is_period_dtype(dtype):
            raise ValueError('dtype must be PeriodDtype')
        if freq is None:
            freq = dtype.freq
        elif freq != dtype.freq:
            raise IncompatibleFrequency('specified freq and dtype '
                                        'are different')
    return freq


def dt64arr_to_periodarr(data, freq, tz=None):
    """
    Convert an datetime-like array to values Period ordinals.

    Parameters
    ----------
    data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
    freq : Optional[Union[str, Tick]]
        Must match the `freq` on the `data` if `data` is a DatetimeIndex
        or Series.
    tz : Optional[tzinfo]

    Returns
    -------
    ordinals : ndarray[int]
    freq : Tick
        The frequencey extracted from the Series or DatetimeIndex if that's
        used.

    """
    if data.dtype != np.dtype('M8[ns]'):
        raise ValueError('Wrong dtype: {dtype}'.format(dtype=data.dtype))

    if freq is None:
        if isinstance(data, ABCIndexClass):
            data, freq = data._values, data.freq
        elif isinstance(data, ABCSeries):
            data, freq = data._values, data.dt.freq

    freq = Period._maybe_convert_freq(freq)

    if isinstance(data, (ABCIndexClass, ABCSeries)):
        data = data._values

    base, mult = libfrequencies.get_freq_code(freq)
    return libperiod.dt64arr_to_periodarr(data.view('i8'), base, tz), freq


def _get_ordinal_range(start, end, periods, freq, mult=1):
    if com.count_not_none(start, end, periods) != 2:
        raise ValueError('Of the three parameters: start, end, and periods, '
                         'exactly two must be specified')

    if freq is not None:
        _, mult = libfrequencies.get_freq_code(freq)

    if start is not None:
        start = Period(start, freq)
    if end is not None:
        end = Period(end, freq)

    is_start_per = isinstance(start, Period)
    is_end_per = isinstance(end, Period)

    if is_start_per and is_end_per and start.freq != end.freq:
        raise ValueError('start and end must have same freq')
    if (start is NaT or end is NaT):
        raise ValueError('start and end must not be NaT')

    if freq is None:
        if is_start_per:
            freq = start.freq
        elif is_end_per:
            freq = end.freq
        else:  # pragma: no cover
            raise ValueError('Could not infer freq from start/end')

    if periods is not None:
        periods = periods * mult
        if start is None:
            data = np.arange(end.ordinal - periods + mult,
                             end.ordinal + 1, mult,
                             dtype=np.int64)
        else:
            data = np.arange(start.ordinal, start.ordinal + periods, mult,
                             dtype=np.int64)
    else:
        data = np.arange(start.ordinal, end.ordinal + 1, mult, dtype=np.int64)

    return data, freq


def _range_from_fields(year=None, month=None, quarter=None, day=None,
                       hour=None, minute=None, second=None, freq=None):
    if hour is None:
        hour = 0
    if minute is None:
        minute = 0
    if second is None:
        second = 0
    if day is None:
        day = 1

    ordinals = []

    if quarter is not None:
        if freq is None:
            freq = 'Q'
            base = libfrequencies.FreqGroup.FR_QTR
        else:
            base, mult = libfrequencies.get_freq_code(freq)
            if base != libfrequencies.FreqGroup.FR_QTR:
                raise AssertionError("base must equal FR_QTR")

        year, quarter = _make_field_arrays(year, quarter)
        for y, q in compat.zip(year, quarter):
            y, m = libperiod.quarter_to_myear(y, q, freq)
            val = libperiod.period_ordinal(y, m, 1, 1, 1, 1, 0, 0, base)
            ordinals.append(val)
    else:
        base, mult = libfrequencies.get_freq_code(freq)
        arrays = _make_field_arrays(year, month, day, hour, minute, second)
        for y, mth, d, h, mn, s in compat.zip(*arrays):
            ordinals.append(libperiod.period_ordinal(
                y, mth, d, h, mn, s, 0, 0, base))

    return np.array(ordinals, dtype=np.int64), freq


def _make_field_arrays(*fields):
    length = None
    for x in fields:
        if isinstance(x, (list, np.ndarray, ABCSeries)):
            if length is not None and len(x) != length:
                raise ValueError('Mismatched Period array lengths')
            elif length is None:
                length = len(x)

    arrays = [np.asarray(x) if isinstance(x, (np.ndarray, list, ABCSeries))
              else np.repeat(x, length) for x in fields]

    return arrays