tslib.pyx

# cython: profile=False

cimport numpy as np
from numpy cimport (int32_t, int64_t, import_array, ndarray,
                    NPY_INT64, NPY_DATETIME, NPY_TIMEDELTA)
import numpy as np

from cpython cimport (
    PyTypeObject,
    PyFloat_Check,
    PyObject_RichCompareBool,
    PyString_Check
)

# Cython < 0.17 doesn't have this in cpython
cdef extern from "Python.h":
    cdef PyTypeObject *Py_TYPE(object)


from libc.stdlib cimport free

from util cimport is_integer_object, is_datetime64_object
cimport util

from datetime cimport *
from khash cimport *
cimport cython

from datetime import timedelta, datetime
from datetime import time as datetime_time
from pandas.compat import parse_date

cdef extern from "Python.h":
    int PySlice_Check(object)

# initialize numpy
import_array()
#import_ufunc()

# import datetime C API
PyDateTime_IMPORT

# in numpy 1.7, will prob need the following:
# numpy_pydatetime_import

cdef int64_t NPY_NAT = util.get_nat()


try:
    basestring
except NameError: # py3
    basestring = str

def ints_to_pydatetime(ndarray[int64_t] arr, tz=None):
    cdef:
        Py_ssize_t i, n = len(arr)
        pandas_datetimestruct dts
        ndarray[object] result = np.empty(n, dtype=object)

    if tz is not None:
        if _is_utc(tz):
            for i in range(n):
                if arr[i] == iNaT:
                    result[i] = np.nan
                else:
                    pandas_datetime_to_datetimestruct(arr[i], PANDAS_FR_ns, &dts)
                    result[i] = datetime(dts.year, dts.month, dts.day, dts.hour,
                                         dts.min, dts.sec, dts.us, tz)
        elif _is_tzlocal(tz) or _is_fixed_offset(tz):
            for i in range(n):
                if arr[i] == iNaT:
                    result[i] = np.nan
                else:
                    pandas_datetime_to_datetimestruct(arr[i], PANDAS_FR_ns, &dts)
                    dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                                  dts.min, dts.sec, dts.us, tz)
                    result[i] = dt + tz.utcoffset(dt)
        else:
            trans = _get_transitions(tz)
            deltas = _get_deltas(tz)
            for i in range(n):

                if arr[i] == iNaT:
                    result[i] = np.nan
                else:

                    # Adjust datetime64 timestamp, recompute datetimestruct
                    pos = trans.searchsorted(arr[i]) - 1
                    inf = tz._transition_info[pos]

                    pandas_datetime_to_datetimestruct(arr[i] + deltas[pos],
                                                      PANDAS_FR_ns, &dts)
                    result[i] = datetime(dts.year, dts.month, dts.day, dts.hour,
                                         dts.min, dts.sec, dts.us,
                                         tz._tzinfos[inf])
    else:
        for i in range(n):
            if arr[i] == iNaT:
                result[i] = np.nan
            else:
                pandas_datetime_to_datetimestruct(arr[i], PANDAS_FR_ns, &dts)
                result[i] = datetime(dts.year, dts.month, dts.day, dts.hour,
                                     dts.min, dts.sec, dts.us)

    return result

from dateutil.tz import tzlocal

def _is_tzlocal(tz):
    return isinstance(tz, tzlocal)

def _is_fixed_offset(tz):
    try:
        tz._transition_info
        return False
    except AttributeError:
        return True

# Python front end to C extension type _Timestamp
# This serves as the box for datetime64
class Timestamp(_Timestamp):
    """TimeStamp is the pandas equivalent of python's Datetime
    and is interchangable with it in most cases. It's the type used
    for the entries that make up a DatetimeIndex, and other timeseries
    oriented data structures in pandas.
    """

    @classmethod
    def fromordinal(cls, ordinal, offset=None, tz=None):
        """ passed an ordinal, translate and convert to a ts
            note: by definition there cannot be any tz info on the ordinal itself """
        return cls(datetime.fromordinal(ordinal),offset=offset,tz=tz)

    def __new__(cls, object ts_input, object offset=None, tz=None, unit=None):
        cdef _TSObject ts
        cdef _Timestamp ts_base

        if util.is_string_object(ts_input):
            try:
                ts_input = parse_date(ts_input)
            except Exception:
                pass

        ts = convert_to_tsobject(ts_input, tz, unit)

        if ts.value == NPY_NAT:
            return NaT

        # make datetime happy
        ts_base = _Timestamp.__new__(cls, ts.dts.year, ts.dts.month,
                                     ts.dts.day, ts.dts.hour, ts.dts.min,
                                     ts.dts.sec, ts.dts.us, ts.tzinfo)

        # fill out rest of data
        ts_base.value = ts.value
        ts_base.offset = offset
        ts_base.nanosecond = ts.dts.ps / 1000

        return ts_base

    def __repr__(self):
        result = self._repr_base
        zone = None

        try:
            result += self.strftime('%z')
            if self.tzinfo:
                zone = _get_zone(self.tzinfo)
        except ValueError:
            year2000 = self.replace(year=2000)
            result += year2000.strftime('%z')
            if self.tzinfo:
                zone = _get_zone(self.tzinfo)

        try:
            result += zone.strftime(' %%Z')
        except:
            pass
        zone = "'%s'" % zone if zone else 'None'

        return "Timestamp('%s', tz=%s)" % (result,zone)

    @property
    def _repr_base(self):
        result = '%d-%.2d-%.2d %.2d:%.2d:%.2d' % (self.year, self.month,
                                                  self.day, self.hour,
                                                  self.minute, self.second)

        if self.nanosecond != 0:
            nanos = self.nanosecond + 1000 * self.microsecond
            result += '.%.9d' % nanos
        elif self.microsecond != 0:
            result += '.%.6d' % self.microsecond

        return result

    @property
    def tz(self):
        """
        Alias for tzinfo
        """
        return self.tzinfo

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

    def __setstate__(self, state):
        self.value = state[0]
        self.offset = state[1]
        self.tzinfo = state[2]

    def __reduce__(self):
        object_state = self.value, self.offset, self.tzinfo
        return (Timestamp, object_state)

    def to_period(self, freq=None):
        """
        Return an period of which this timestamp is an observation.
        """
        from pandas.tseries.period import Period

        if freq is None:
            freq = self.freq

        return Period(self, freq=freq)

    @property
    def dayofweek(self):
        return self.weekday()

    @property
    def dayofyear(self):
        return self._get_field('doy')

    @property
    def week(self):
        return self._get_field('woy')

    weekofyear = week

    @property
    def quarter(self):
        return self._get_field('q')

    @property
    def freqstr(self):
        return getattr(self.offset, 'freqstr', self.offset)

    @property
    def asm8(self):
        return np.int64(self.value).view('M8[ns]')

    def tz_localize(self, tz):
        """
        Convert naive Timestamp to local time zone

        Parameters
        ----------
        tz : pytz.timezone

        Returns
        -------
        localized : Timestamp
        """
        if self.tzinfo is None:
            # tz naive, localize
            return Timestamp(self.to_pydatetime(), tz=tz)
        else:
            raise Exception('Cannot localize tz-aware Timestamp, use '
                            'tz_convert for conversions')

    def tz_convert(self, tz):
        """
        Convert Timestamp to another time zone or localize to requested time
        zone

        Parameters
        ----------
        tz : pytz.timezone

        Returns
        -------
        converted : Timestamp
        """
        if self.tzinfo is None:
            # tz naive, use tz_localize
            raise Exception('Cannot convert tz-naive Timestamp, use '
                            'tz_localize to localize')
        else:
            # Same UTC timestamp, different time zone
            return Timestamp(self.value, tz=tz)

    astimezone = tz_convert

    def replace(self, **kwds):
        return Timestamp(datetime.replace(self, **kwds),
                         offset=self.offset)

    def to_pydatetime(self, warn=True):
        """
        If warn=True, issue warning if nanoseconds is nonzero
        """
        cdef:
            pandas_datetimestruct dts
            _TSObject ts

        if self.nanosecond != 0 and warn:
            print 'Warning: discarding nonzero nanoseconds'
        ts = convert_to_tsobject(self, self.tzinfo, None)

        return datetime(ts.dts.year, ts.dts.month, ts.dts.day,
                        ts.dts.hour, ts.dts.min, ts.dts.sec,
                        ts.dts.us, ts.tzinfo)


_nat_strings = set(['NaT','nat','NAT','nan','NaN','NAN'])
_not_datelike_strings = set(['a','A','m','M','p','P','t','T'])
class NaTType(_NaT):
    """(N)ot-(A)-(T)ime, the time equivalent of NaN"""

    def __new__(cls):
        cdef _NaT base

        base = _NaT.__new__(cls, 1, 1, 1)
        mangle_nat(base)
        base.value = NPY_NAT

        return base

    def __repr__(self):
        return 'NaT'

    def weekday(self):
        return -1

    def toordinal(self):
        return -1

fields = ['year', 'quarter', 'month', 'day', 'hour',
          'minute', 'second', 'microsecond', 'nanosecond',
          'week', 'dayofyear']
for field in fields:
    prop = property(fget=lambda self: -1)
    setattr(NaTType, field, prop)


NaT = NaTType()

iNaT = util.get_nat()

cdef _tz_format(object obj, object zone):
    try:
        return obj.strftime(' %%Z, tz=%s' % zone)
    except:
        return ', tz=%s' % zone

def is_timestamp_array(ndarray[object] values):
    cdef int i, n = len(values)
    if n == 0:
        return False
    for i in range(n):
        if not is_timestamp(values[i]):
            return False
    return True


cpdef object get_value_box(ndarray arr, object loc):
    cdef:
        Py_ssize_t i, sz
        void* data_ptr
    if util.is_float_object(loc):
        casted = int(loc)
        if casted == loc:
            loc = casted
    i = <Py_ssize_t> loc
    sz = np.PyArray_SIZE(arr)

    if i < 0 and sz > 0:
        i += sz

    if i >= sz or sz == 0 or i < 0:
        raise IndexError('index out of bounds')

    if arr.descr.type_num == NPY_DATETIME:
        return Timestamp(util.get_value_1d(arr, i))
    else:
        return util.get_value_1d(arr, i)


# Add the min and max fields at the class level
# These are defined as magic numbers due to strange
# wraparound behavior when using the true int64 lower boundary
cdef int64_t _NS_LOWER_BOUND = -9223285636854775000LL
cdef int64_t _NS_UPPER_BOUND = 9223372036854775807LL
Timestamp.min = Timestamp(_NS_LOWER_BOUND)
Timestamp.max = Timestamp(_NS_UPPER_BOUND)


#----------------------------------------------------------------------
# Frequency inference

def unique_deltas(ndarray[int64_t] arr):
    cdef:
        Py_ssize_t i, n = len(arr)
        int64_t val
        khiter_t k
        kh_int64_t *table
        int ret = 0
        list uniques = []

    table = kh_init_int64()
    kh_resize_int64(table, 10)
    for i in range(n - 1):
        val = arr[i + 1] - arr[i]
        k = kh_get_int64(table, val)
        if k == table.n_buckets:
            kh_put_int64(table, val, &ret)
            uniques.append(val)
    kh_destroy_int64(table)

    result = np.array(uniques, dtype=np.int64)
    result.sort()
    return result


cdef inline bint _is_multiple(int64_t us, int64_t mult):
    return us % mult == 0


def apply_offset(ndarray[object] values, object offset):
    cdef:
        Py_ssize_t i, n = len(values)
        ndarray[int64_t] new_values
        object boxed

    result = np.empty(n, dtype='M8[ns]')
    new_values = result.view('i8')
    pass


# This is PITA. Because we inherit from datetime, which has very specific
# construction requirements, we need to do object instantiation in python
# (see Timestamp class above). This will serve as a C extension type that
# shadows the python class, where we do any heavy lifting.
cdef class _Timestamp(datetime):
    cdef readonly:
        int64_t value, nanosecond
        object offset       # frequency reference

    def __hash__(self):
        if self.nanosecond:
            return hash(self.value)
        else:
            return datetime.__hash__(self)

    def __richcmp__(_Timestamp self, object other, int op):
        cdef _Timestamp ots

        if isinstance(other, _Timestamp):
            ots = other
        elif type(other) is datetime:
            if self.nanosecond == 0:
                val = self.to_datetime()
                return PyObject_RichCompareBool(val, other, op)

            try:
                ots = Timestamp(other)
            except ValueError:
                return self._compare_outside_nanorange(other, op)
        else:
            if op == 2:
                return False
            elif op == 3:
                return True
            else:
                raise TypeError('Cannot compare Timestamp with '
                                '{0!r}'.format(other.__class__.__name__))

        self._assert_tzawareness_compat(other)

        if op == 2: # ==
            return self.value == ots.value
        elif op == 3: # !=
            return self.value != ots.value
        elif op == 0: # <
            return self.value < ots.value
        elif op == 1: # <=
            return self.value <= ots.value
        elif op == 4: # >
            return self.value > ots.value
        elif op == 5: # >=
            return self.value >= ots.value

    cdef _compare_outside_nanorange(self, object other, int op):
        dtval = self.to_datetime()

        self._assert_tzawareness_compat(other)

        if self.nanosecond == 0:
            if op == 2: # ==
                return dtval == other
            elif op == 3: # !=
                return dtval != other
            elif op == 0: # <
                return dtval < other
            elif op == 1: # <=
                return dtval <= other
            elif op == 4: # >
                return dtval > other
            elif op == 5: # >=
                return dtval >= other
        else:
            if op == 2: # ==
                return False
            elif op == 3: # !=
                return True
            elif op == 0: # <
                return dtval < other
            elif op == 1: # <=
                return dtval < other
            elif op == 4: # >
                return dtval >= other
            elif op == 5: # >=
                return dtval >= other

    cdef _assert_tzawareness_compat(self, object other):
        if self.tzinfo is None:
            if other.tzinfo is not None:
                raise Exception('Cannot compare tz-naive and '
                                'tz-aware timestamps')
        elif other.tzinfo is None:
            raise Exception('Cannot compare tz-naive and tz-aware timestamps')

    cpdef to_datetime(self):
        cdef:
            pandas_datetimestruct dts
            _TSObject ts
        ts = convert_to_tsobject(self, self.tzinfo, None)
        dts = ts.dts
        return datetime(dts.year, dts.month, dts.day,
                        dts.hour, dts.min, dts.sec,
                        dts.us, ts.tzinfo)

    def __add__(self, other):
        if is_integer_object(other):
            if self.offset is None:
                msg = ("Cannot add integral value to Timestamp "
                       "without offset.")
                raise ValueError(msg)
            else:
                return Timestamp((self.offset.__mul__(other)).apply(self))
        else:
            if isinstance(other, timedelta) or hasattr(other, 'delta'):
                nanos = _delta_to_nanoseconds(other)
                return Timestamp(self.value + nanos, tz=self.tzinfo)
            else:
                result = datetime.__add__(self, other)
                if isinstance(result, datetime):
                    result = Timestamp(result)
                    result.nanosecond = self.nanosecond
                return result

    def __sub__(self, other):
        if is_integer_object(other):
            return self.__add__(-other)
        else:
            return datetime.__sub__(self, other)

    cpdef _get_field(self, field):
        out = get_date_field(np.array([self.value], dtype=np.int64), field)
        return out[0]


cdef PyTypeObject* ts_type = <PyTypeObject*> Timestamp


cdef inline bint is_timestamp(object o):
    return Py_TYPE(o) == ts_type # isinstance(o, Timestamp)


cdef class _NaT(_Timestamp):

    def __hash__(_NaT self):
        # py3k needs this defined here
        return hash(self.value)

    def __richcmp__(_NaT self, object other, int op):
        # if not isinstance(other, (_NaT, _Timestamp)):
        #     raise TypeError('Cannot compare %s with NaT' % type(other))

        if op == 2: # ==
            return False
        elif op == 3: # !=
            return True
        elif op == 0: # <
            return False
        elif op == 1: # <=
            return False
        elif op == 4: # >
            return False
        elif op == 5: # >=
            return False




def _delta_to_nanoseconds(delta):
    try:
        delta = delta.delta
    except:
        pass
    return (delta.days * 24 * 60 * 60 * 1000000
            + delta.seconds * 1000000
            + delta.microseconds) * 1000


# lightweight C object to hold datetime & int64 pair
cdef class _TSObject:
    cdef:
        pandas_datetimestruct dts      # pandas_datetimestruct
        int64_t value               # numpy dt64
        object tzinfo

    property value:
        def __get__(self):
            return self.value

cpdef _get_utcoffset(tzinfo, obj):
    try:
        return tzinfo._utcoffset
    except AttributeError:
        return tzinfo.utcoffset(obj)

# helper to extract datetime and int64 from several different possibilities
cdef convert_to_tsobject(object ts, object tz, object unit):
    """
    Extract datetime and int64 from any of:
        - np.int64 (with unit providing a possible modifier)
        - np.datetime64
        - a float (with unit providing a possible modifier)
        - python int or long object (with unit providing a possible modifier)
        - iso8601 string object
        - python datetime object
        - another timestamp object
    """
    cdef:
        _TSObject obj
        bint utc_convert = 1

    if tz is not None:
        if isinstance(tz, basestring):
            tz = pytz.timezone(tz)

    obj = _TSObject()

    if ts is None or ts is NaT:
        obj.value = NPY_NAT
    elif is_datetime64_object(ts):
        obj.value = _get_datetime64_nanos(ts)
        pandas_datetime_to_datetimestruct(obj.value, PANDAS_FR_ns, &obj.dts)
    elif is_integer_object(ts):
        if ts == NPY_NAT:
            obj.value = NPY_NAT
        else:
            ts = ts * cast_from_unit(unit,None)
            obj.value = ts
            pandas_datetime_to_datetimestruct(ts, PANDAS_FR_ns, &obj.dts)
    elif util.is_float_object(ts):
        if ts != ts or ts == NPY_NAT:
            obj.value = NPY_NAT
        else:
            ts = cast_from_unit(unit,ts)
            obj.value = ts
            pandas_datetime_to_datetimestruct(ts, PANDAS_FR_ns, &obj.dts)
    elif util.is_string_object(ts):
        if ts in _nat_strings:
            obj.value = NPY_NAT
        else:
            _string_to_dts(ts, &obj.dts)
            obj.value = pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &obj.dts)
    elif PyDateTime_Check(ts):
        if tz is not None:
            # sort of a temporary hack
            if ts.tzinfo is not None:
                if (hasattr(tz, 'normalize') and
                    hasattr(ts.tzinfo, '_utcoffset')):
                    ts = tz.normalize(ts)
                    obj.value = _pydatetime_to_dts(ts, &obj.dts)
                    obj.tzinfo = ts.tzinfo
                else: #tzoffset
                    obj.value = _pydatetime_to_dts(ts, &obj.dts)
                    ts_offset = _get_utcoffset(ts.tzinfo, ts)
                    obj.value -= _delta_to_nanoseconds(ts_offset)
                    tz_offset = _get_utcoffset(tz, ts)
                    obj.value += _delta_to_nanoseconds(tz_offset)
                    pandas_datetime_to_datetimestruct(obj.value,
                                                      PANDAS_FR_ns, &obj.dts)
                    obj.tzinfo = tz
            elif not _is_utc(tz):
                try:
                    ts = tz.localize(ts)
                except AttributeError:
                    ts = ts.replace(tzinfo=tz)
                obj.value = _pydatetime_to_dts(ts, &obj.dts)
                obj.tzinfo = ts.tzinfo
            else:
                # UTC
                obj.value = _pydatetime_to_dts(ts, &obj.dts)
                obj.tzinfo = pytz.utc
        else:
            obj.value = _pydatetime_to_dts(ts, &obj.dts)
            obj.tzinfo = ts.tzinfo

        if obj.tzinfo is not None and not _is_utc(obj.tzinfo):
            offset = _get_utcoffset(obj.tzinfo, ts)
            obj.value -= _delta_to_nanoseconds(offset)

        if is_timestamp(ts):
            obj.value += ts.nanosecond
        _check_dts_bounds(obj.value, &obj.dts)
        return obj
    elif PyDate_Check(ts):
        # Keep the converter same as PyDateTime's
        ts = datetime.combine(ts, datetime_time())
        return convert_to_tsobject(ts, tz, None)
    else:
        raise ValueError("Could not construct Timestamp from argument %s" %
                         type(ts))

    if obj.value != NPY_NAT:
        _check_dts_bounds(obj.value, &obj.dts)

    if tz is not None:
        _localize_tso(obj, tz)

    return obj

cdef inline void _localize_tso(_TSObject obj, object tz):
    if _is_utc(tz):
        obj.tzinfo = tz
    elif _is_tzlocal(tz):
        pandas_datetime_to_datetimestruct(obj.value, PANDAS_FR_ns, &obj.dts)
        dt = datetime(obj.dts.year, obj.dts.month, obj.dts.day, obj.dts.hour,
                      obj.dts.min, obj.dts.sec, obj.dts.us, tz)
        delta = int(total_seconds(_get_utcoffset(tz, dt))) * 1000000000
        pandas_datetime_to_datetimestruct(obj.value + delta,
                                          PANDAS_FR_ns, &obj.dts)
        obj.tzinfo = tz
    else:
        # Adjust datetime64 timestamp, recompute datetimestruct
        trans = _get_transitions(tz)
        deltas = _get_deltas(tz)
        pos = trans.searchsorted(obj.value, side='right') - 1

        # statictzinfo
        if not hasattr(tz, '_transition_info'):
            pandas_datetime_to_datetimestruct(obj.value + deltas[0],
                                              PANDAS_FR_ns, &obj.dts)
            obj.tzinfo = tz
        else:
            inf = tz._transition_info[pos]
            pandas_datetime_to_datetimestruct(obj.value + deltas[pos],
                                              PANDAS_FR_ns, &obj.dts)
            obj.tzinfo = tz._tzinfos[inf]


def get_timezone(tz):
    return _get_zone(tz)

cdef inline bint _is_utc(object tz):
    return tz is UTC or isinstance(tz, _du_utc)

cdef inline object _get_zone(object tz):
    if _is_utc(tz):
        return 'UTC'
    else:
        try:
            zone = tz.zone
            if zone is None:
                return tz
            return zone
        except AttributeError:
            return tz


cdef inline _check_dts_bounds(int64_t value, pandas_datetimestruct *dts):
    cdef pandas_datetimestruct dts2
    if dts.year <= 1677 or dts.year >= 2262:
        pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts2)
        if dts2.year != dts.year:
            fmt = '%d-%.2d-%.2d %.2d:%.2d:%.2d' % (dts.year, dts.month,
                                                   dts.day, dts.hour,
                                                   dts.min, dts.sec)

            raise ValueError('Out of bounds nanosecond timestamp: %s' % fmt)

# elif isinstance(ts, _Timestamp):
#     tmp = ts
#     obj.value = (<_Timestamp> ts).value
#     obj.dtval =
# elif isinstance(ts, object):
#     # If all else fails
#     obj.value = _dtlike_to_datetime64(ts, &obj.dts)
#     obj.dtval = _dts_to_pydatetime(&obj.dts)

def datetime_to_datetime64(ndarray[object] values):
    cdef:
        Py_ssize_t i, n = len(values)
        object val, inferred_tz = None
        ndarray[int64_t] iresult
        pandas_datetimestruct dts
        _TSObject _ts

    result = np.empty(n, dtype='M8[ns]')
    iresult = result.view('i8')
    for i in range(n):
        val = values[i]
        if util._checknull(val):
            iresult[i] = iNaT
        elif PyDateTime_Check(val):
            if val.tzinfo is not None:
                if inferred_tz is not None:
                    if _get_zone(val.tzinfo) != inferred_tz:
                        raise ValueError('Array must be all same time zone')
                else:
                    inferred_tz = _get_zone(val.tzinfo)

                _ts = convert_to_tsobject(val, None, None)
                iresult[i] = _ts.value
                _check_dts_bounds(iresult[i], &_ts.dts)
            else:
                if inferred_tz is not None:
                    raise ValueError('Cannot mix tz-aware with tz-naive values')
                iresult[i] = _pydatetime_to_dts(val, &dts)
                _check_dts_bounds(iresult[i], &dts)
        else:
            raise TypeError('Unrecognized value type: %s' % type(val))

    return result, inferred_tz


def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False,
                      format=None, utc=None, coerce=False, unit=None):
    cdef:
        Py_ssize_t i, n = len(values)
        object val
        ndarray[int64_t] iresult
        ndarray[object] oresult
        pandas_datetimestruct dts
        bint utc_convert = bool(utc)
        _TSObject _ts
        int64_t m = cast_from_unit(unit,None)

    try:
        result = np.empty(n, dtype='M8[ns]')
        iresult = result.view('i8')
        for i in range(n):
            val = values[i]
            if util._checknull(val) or val is NaT:
                iresult[i] = iNaT
            elif PyDateTime_Check(val):
                if val.tzinfo is not None:
                    if utc_convert:
                        _ts = convert_to_tsobject(val, None, unit)
                        iresult[i] = _ts.value
                        _check_dts_bounds(iresult[i], &_ts.dts)
                    else:
                        raise ValueError('Tz-aware datetime.datetime cannot '
                                         'be converted to datetime64 unless '
                                         'utc=True')
                else:
                    iresult[i] = _pydatetime_to_dts(val, &dts)
                    if is_timestamp(val):
                        iresult[i] += (<_Timestamp>val).nanosecond
                    _check_dts_bounds(iresult[i], &dts)
            elif PyDate_Check(val):
                iresult[i] = _date_to_datetime64(val, &dts)
                _check_dts_bounds(iresult[i], &dts)
            elif util.is_datetime64_object(val):
                iresult[i] = _get_datetime64_nanos(val)

            # if we are coercing, dont' allow integers
            elif util.is_integer_object(val) and not coerce:
                if val == iNaT:
                    iresult[i] = iNaT
                else:
                    iresult[i] = val*m
            elif util.is_float_object(val) and not coerce:
                if val != val or val == iNaT:
                    iresult[i] = iNaT
                else:
                    iresult[i] = cast_from_unit(unit,val)
            else:
                try:
                    if len(val) == 0:
                       iresult[i] = iNaT
                       continue

                    elif val in _nat_strings:
                       iresult[i] = iNaT
                       continue

                    _string_to_dts(val, &dts)
                    iresult[i] = pandas_datetimestruct_to_datetime(PANDAS_FR_ns,
                                                                   &dts)
                    _check_dts_bounds(iresult[i], &dts)
                except ValueError:

                    # for some reason, dateutil parses some single letter len-1 strings into today's date
                    if len(val) == 1 and val in _not_datelike_strings:
                        if coerce:
                            iresult[i] = iNaT
                            continue
                        elif raise_:
                            raise
                    try:
                        result[i] = parse_date(val, dayfirst=dayfirst)
                    except Exception:
                        if coerce:
                           iresult[i] = iNaT
                           continue
                        raise TypeError
                    pandas_datetime_to_datetimestruct(iresult[i], PANDAS_FR_ns,
                                                      &dts)
                    _check_dts_bounds(iresult[i], &dts)
                except:
                    if coerce:
                        iresult[i] = iNaT
                        continue
                    raise

        return result
    except TypeError:
        oresult = np.empty(n, dtype=object)

        for i in range(n):
            val = values[i]
            if util._checknull(val):
                oresult[i] = val
            else:
                if len(val) == 0:
                    # TODO: ??
                    oresult[i] = 'NaT'
                    continue
                try:
                    oresult[i] = parse_date(val, dayfirst=dayfirst)
                except Exception:
                    if raise_:
                        raise
                    return values
                    # oresult[i] = val

        return oresult

def array_to_timedelta64(ndarray[object] values, coerce=True):
    """ convert an ndarray to an array of ints that are timedeltas
        force conversion if coerce = True,
        else return an object array """
    cdef:
        Py_ssize_t i, n
        object val
        ndarray[int64_t] result

    n = values.shape[0]
    result = np.empty(n, dtype='i8')
    for i in range(n):
        val = values[i]

        # in py3 this is already an int, don't convert
        if is_integer_object(val):
            result[i] = val

        elif isinstance(val,timedelta) or isinstance(val,np.timedelta64):

             if isinstance(val, np.timedelta64):
                 if val.dtype != 'm8[ns]':
                      val = val.astype('m8[ns]')
                 val = val.item()
             else:
                 val = _delta_to_nanoseconds(np.timedelta64(val).item())

             result[i] = val

        elif util._checknull(val) or val == iNaT or val is NaT:
             result[i] = iNaT

        else:

             # just return, don't convert
             if not coerce:
                 return values.copy()

             result[i] = iNaT

    return result

def repr_timedelta64(object value):
   """ provide repr for timedelta64 """

   ivalue = value.view('i8')

   # put frac in seconds
   frac   = float(ivalue)/1e9
   sign   = np.sign(frac)
   frac   = np.abs(frac)

   if frac >= 86400:
      days   = int(frac / 86400)
      frac  -= days * 86400
   else:
      days   = 0

   if frac >= 3600:
      hours  = int(frac / 3600)
      frac  -= hours * 3600
   else:
      hours  = 0

   if frac >= 60:
      minutes = int(frac / 60)
      frac   -= minutes * 60
   else:
      minutes  = 0

   if frac >= 1:
      seconds = int(frac)
      frac   -= seconds
   else:
      seconds = 0

   if frac == int(frac):
      seconds_pretty = "%02d" % seconds
   else:
      sp = abs(round(1e6*frac))
      seconds_pretty = "%02d.%06d" % (seconds,sp)

   if sign < 0:
       sign_pretty = "-"
   else:
       sign_pretty = ""

   if days:
       return "%s%d days, %02d:%02d:%s" % (sign_pretty, days, hours, minutes,
                                           seconds_pretty)

   return "%s%02d:%02d:%s" % (sign_pretty, hours, minutes, seconds_pretty)

def array_strptime(ndarray[object] values, object fmt):
    cdef:
        Py_ssize_t i, n = len(values)
        pandas_datetimestruct dts
        ndarray[int64_t] iresult
        int year, month, day, minute, hour, second, fraction, weekday, julian

    global _TimeRE_cache, _regex_cache
    with _cache_lock:
        if _getlang() != _TimeRE_cache.locale_time.lang:
            _TimeRE_cache = TimeRE()
            _regex_cache.clear()
        if len(_regex_cache) > _CACHE_MAX_SIZE:
            _regex_cache.clear()
        locale_time = _TimeRE_cache.locale_time
        format_regex = _regex_cache.get(fmt)
        if not format_regex:
            try:
                format_regex = _TimeRE_cache.compile(fmt)
            # KeyError raised when a bad format is found; can be specified as
            # \\, in which case it was a stray % but with a space after it
            except KeyError, err:
                bad_directive = err.args[0]
                if bad_directive == "\\":
                    bad_directive = "%"
                del err
                raise ValueError("'%s' is a bad directive in format '%s'" %
                                    (bad_directive, fmt))
            # IndexError only occurs when the format string is "%"
            except IndexError:
                raise ValueError("stray %% in format '%s'" % fmt)
            _regex_cache[fmt] = format_regex

    result = np.empty(n, dtype='M8[ns]')
    iresult = result.view('i8')

    dts.us = dts.ps = dts.as = 0

    cdef dict _parse_code_table = {
        'y': 0,
        'Y': 1,
        'm': 2,
        'B': 3,
        'b': 4,
        'd': 5,
        'H': 6,
        'I': 7,
        'M': 8,
        'S': 9,
        'f': 10,
        'A': 11,
        'a': 12,
        'w': 13,
        'j': 14,
        'U': 15,
        'W': 16,
        'Z': 17
    }
    cdef int parse_code

    for i in range(n):
        found = format_regex.match(values[i])
        if not found:
            raise ValueError("time data %r does not match format %r" %
                             (values[i], fmt))
        if len(values[i]) != found.end():
            raise ValueError("unconverted data remains: %s" %
                              values[i][found.end():])
        year = 1900
        month = day = 1
        hour = minute = second = fraction = 0
        tz = -1
        # Default to -1 to signify that values not known; not critical to have,
        # though
        week_of_year = -1
        week_of_year_start = -1
        # weekday and julian defaulted to -1 so as to signal need to calculate
        # values
        weekday = julian = -1
        found_dict = found.groupdict()
        for group_key in found_dict.iterkeys():
            # Directives not explicitly handled below:
            #   c, x, X
            #      handled by making out of other directives
            #   U, W
            #      worthless without day of the week
            parse_code = _parse_code_table[group_key]

            if parse_code == 0:
                year = int(found_dict['y'])
                # Open Group specification for strptime() states that a %y
                #value in the range of [00, 68] is in the century 2000, while
                #[69,99] is in the century 1900
                if year <= 68:
                    year += 2000
                else:
                    year += 1900
            elif parse_code == 1:
                year = int(found_dict['Y'])
            elif parse_code == 2:
                month = int(found_dict['m'])
            elif parse_code == 3:
            # elif group_key == 'B':
                month = locale_time.f_month.index(found_dict['B'].lower())
            elif parse_code == 4:
            # elif group_key == 'b':
                month = locale_time.a_month.index(found_dict['b'].lower())
            elif parse_code == 5:
            # elif group_key == 'd':
                day = int(found_dict['d'])
            elif parse_code == 6:
            # elif group_key == 'H':
                hour = int(found_dict['H'])
            elif parse_code == 7:
                hour = int(found_dict['I'])
                ampm = found_dict.get('p', '').lower()
                # If there was no AM/PM indicator, we'll treat this like AM
                if ampm in ('', locale_time.am_pm[0]):
                    # We're in AM so the hour is correct unless we're
                    # looking at 12 midnight.
                    # 12 midnight == 12 AM == hour 0
                    if hour == 12:
                        hour = 0
                elif ampm == locale_time.am_pm[1]:
                    # We're in PM so we need to add 12 to the hour unless
                    # we're looking at 12 noon.
                    # 12 noon == 12 PM == hour 12
                    if hour != 12:
                        hour += 12
            elif parse_code == 8:
                minute = int(found_dict['M'])
            elif parse_code == 9:
                second = int(found_dict['S'])
            elif parse_code == 10:
                s = found_dict['f']
                # Pad to always return microseconds.
                s += "0" * (6 - len(s))
                fraction = int(s)
            elif parse_code == 11:
                weekday = locale_time.f_weekday.index(found_dict['A'].lower())
            elif parse_code == 12:
                weekday = locale_time.a_weekday.index(found_dict['a'].lower())
            elif parse_code == 13:
                weekday = int(found_dict['w'])
                if weekday == 0:
                    weekday = 6
                else:
                    weekday -= 1
            elif parse_code == 14:
                julian = int(found_dict['j'])
            elif parse_code == 15 or parse_code == 16:
                week_of_year = int(found_dict[group_key])
                if group_key == 'U':
                    # U starts week on Sunday.
                    week_of_year_start = 6
                else:
                    # W starts week on Monday.
                    week_of_year_start = 0
            elif parse_code == 17:
                # Since -1 is default value only need to worry about setting tz
                # if it can be something other than -1.
                found_zone = found_dict['Z'].lower()
                for value, tz_values in enumerate(locale_time.timezone):
                    if found_zone in tz_values:
                        # Deal w/ bad locale setup where timezone names are the
                        # same and yet time.daylight is true; too ambiguous to
                        # be able to tell what timezone has daylight savings
                        if (time.tzname[0] == time.tzname[1] and
                           time.daylight and found_zone not in ("utc", "gmt")):
                            break
                        else:
                            tz = value
                            break
        # If we know the wk of the year and what day of that wk, we can figure
        # out the Julian day of the year.
        if julian == -1 and week_of_year != -1 and weekday != -1:
            week_starts_Mon = True if week_of_year_start == 0 else False
            julian = _calc_julian_from_U_or_W(year, week_of_year, weekday,
                                                week_starts_Mon)
        # Cannot pre-calculate datetime_date() since can change in Julian
        # calculation and thus could have different value for the day of the wk
        # calculation.
        if julian == -1:
            # Need to add 1 to result since first day of the year is 1, not 0.
            julian = datetime_date(year, month, day).toordinal() - \
                      datetime_date(year, 1, 1).toordinal() + 1
        else:  # Assume that if they bothered to include Julian day it will
               # be accurate.
            datetime_result = datetime_date.fromordinal(
                (julian - 1) + datetime_date(year, 1, 1).toordinal())
            year = datetime_result.year
            month = datetime_result.month
            day = datetime_result.day
        if weekday == -1:
            weekday = datetime_date(year, month, day).weekday()

        dts.year = year
        dts.month = month
        dts.day = day
        dts.hour = hour
        dts.min = minute
        dts.sec = second
        dts.us = fraction

        iresult[i] = pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts)
        _check_dts_bounds(iresult[i], &dts)

    return result


cdef inline _get_datetime64_nanos(object val):
    cdef:
        pandas_datetimestruct dts
        PANDAS_DATETIMEUNIT unit
        npy_datetime ival

    unit = get_datetime64_unit(val)
    if unit == 3:
        raise ValueError('NumPy 1.6.1 business freq not supported')

    ival = get_datetime64_value(val)

    if unit != PANDAS_FR_ns:
        pandas_datetime_to_datetimestruct(ival, unit, &dts)
        return pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts)
    else:
        return ival

cdef inline int64_t cast_from_unit(object unit, object ts):
    """ return a casting of the unit represented to nanoseconds
        round the fractional part of a float to our precision, p """
    if unit == 'D':
        m = 1000000000L * 86400
        p = 6
    elif unit == 's':
        m = 1000000000L
        p = 6
    elif unit == 'ms':
        m = 1000000L
        p = 3
    elif unit == 'us':
        m = 1000L
        p = 0
    else:
        m = 1L
        p = 0

    # just give me the unit back
    if ts is None:
        return m

    # cast the unit, multiply base/frace separately
    # to avoid precision issues from float -> int
    base = <int64_t> ts
    frac = ts-base
    return <int64_t> (base*m) + <int64_t> (round(frac,p)*m)

def cast_to_nanoseconds(ndarray arr):
    cdef:
        Py_ssize_t i, n = arr.size
        ndarray[int64_t] ivalues, iresult
        PANDAS_DATETIMEUNIT unit
        pandas_datetimestruct dts

    shape = (<object> arr).shape

    ivalues = arr.view(np.int64).ravel()

    result = np.empty(shape, dtype='M8[ns]')
    iresult = result.ravel().view(np.int64)

    if len(iresult) == 0:
        return result

    unit = get_datetime64_unit(arr.flat[0])
    if unit == 3:
        raise ValueError('NumPy 1.6.1 business freq not supported')

    for i in range(n):
        pandas_datetime_to_datetimestruct(ivalues[i], unit, &dts)
        iresult[i] = pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts)

    return result

#----------------------------------------------------------------------
# Conversion routines


def pydt_to_i8(object pydt):
    '''
    Convert to int64 representation compatible with numpy datetime64; converts
    to UTC
    '''
    cdef:
        _TSObject ts

    ts = convert_to_tsobject(pydt, None, None)

    return ts.value

def i8_to_pydt(int64_t i8, object tzinfo = None):
    '''
    Inverse of pydt_to_i8
    '''
    return Timestamp(i8)

#----------------------------------------------------------------------
# time zone conversion helpers

try:
    from dateutil.tz import tzutc as _du_utc
    import pytz
    UTC = pytz.utc
    have_pytz = True
except:
    have_pytz = False

def tz_convert(ndarray[int64_t] vals, object tz1, object tz2):
    cdef:
        ndarray[int64_t] utc_dates, result, trans, deltas
        Py_ssize_t i, pos, n = len(vals)
        int64_t v, offset
        pandas_datetimestruct dts

    if not have_pytz:
        import pytz

    # Convert to UTC

    if _get_zone(tz1) != 'UTC':
        utc_dates = np.empty(n, dtype=np.int64)
        if _is_tzlocal(tz1):
            for i in range(n):
                v = vals[i]
                pandas_datetime_to_datetimestruct(v, PANDAS_FR_ns, &dts)
                dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                              dts.min, dts.sec, dts.us, tz1)
                delta = (int(total_seconds(_get_utcoffset(tz1, dt)))
                         * 1000000000)
                utc_dates[i] = v - delta
        else:
            deltas = _get_deltas(tz1)
            trans = _get_transitions(tz1)
            pos = trans.searchsorted(vals[0]) - 1
            if pos < 0:
                raise ValueError('First time before start of DST info')

            offset = deltas[pos]
            for i in range(n):
                v = vals[i]
                if v >= [pos + 1]:
                    pos += 1
                    offset = deltas[pos]
                utc_dates[i] = v - offset
    else:
        utc_dates = vals

    if _get_zone(tz2) == 'UTC':
        return utc_dates

    result = np.empty(n, dtype=np.int64)
    if _is_tzlocal(tz2):
        for i in range(n):
            v = utc_dates[i]
            pandas_datetime_to_datetimestruct(v, PANDAS_FR_ns, &dts)
            dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                          dts.min, dts.sec, dts.us, tz2)
            delta = int(total_seconds(_get_utcoffset(tz2, dt))) * 1000000000
            result[i] = v + delta
            return result

    # Convert UTC to other timezone
    trans = _get_transitions(tz2)
    deltas = _get_deltas(tz2)
    pos = trans.searchsorted(utc_dates[0])
    if pos == 0:
        raise ValueError('First time before start of DST info')
    elif pos == len(trans):
        return utc_dates + deltas[-1]

    # TODO: this assumed sortedness :/
    pos -= 1

    offset = deltas[pos]
    for i in range(n):
        v = utc_dates[i]
        if v >= trans[pos + 1]:
            pos += 1
            offset = deltas[pos]
        result[i] = v + offset

    return result

def tz_convert_single(int64_t val, object tz1, object tz2):
    cdef:
        ndarray[int64_t] trans, deltas
        Py_ssize_t pos
        int64_t v, offset, utc_date
        pandas_datetimestruct dts

    if not have_pytz:
        import pytz

    # Convert to UTC
    if _is_tzlocal(tz1):
        pandas_datetime_to_datetimestruct(val, PANDAS_FR_ns, &dts)
        dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                      dts.min, dts.sec, dts.us, tz1)
        delta = int(total_seconds(_get_utcoffset(tz1, dt))) * 1000000000
        utc_date = val - delta
    elif _get_zone(tz1) != 'UTC':
        deltas = _get_deltas(tz1)
        trans = _get_transitions(tz1)
        pos = trans.searchsorted(val) - 1
        if pos < 0:
            raise ValueError('First time before start of DST info')
        offset = deltas[pos]
        utc_date = val - offset
    else:
        utc_date = val

    if _get_zone(tz2) == 'UTC':
        return utc_date
    if _is_tzlocal(tz2):
        pandas_datetime_to_datetimestruct(val, PANDAS_FR_ns, &dts)
        dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                      dts.min, dts.sec, dts.us, tz2)
        delta = int(total_seconds(_get_utcoffset(tz2, dt))) * 1000000000
        return utc_date + delta
    # Convert UTC to other timezone
    trans = _get_transitions(tz2)
    deltas = _get_deltas(tz2)
    pos = trans.searchsorted(utc_date) - 1
    if pos < 0:
        raise ValueError('First time before start of DST info')

    offset = deltas[pos]
    return utc_date + offset


trans_cache = {}
utc_offset_cache = {}

def _get_transitions(tz):
    """
    Get UTC times of DST transitions
    """
    try:
        # tzoffset not hashable in Python 3
        hash(tz)
    except TypeError:
        return np.array([NPY_NAT + 1], dtype=np.int64)

    if tz not in trans_cache:
        if hasattr(tz, '_utc_transition_times'):
            arr = np.array(tz._utc_transition_times, dtype='M8[ns]')
            arr = arr.view('i8')
            try:
                if tz._utc_transition_times[0].year == 1:
                    arr[0] = NPY_NAT + 1
            except Exception:
                pass
        else:
            arr = np.array([NPY_NAT + 1], dtype=np.int64)
        trans_cache[tz] = arr
    return trans_cache[tz]

def _get_deltas(tz):
    """
    Get UTC offsets in microseconds corresponding to DST transitions
    """
    try:
        # tzoffset not hashable in Python 3
        hash(tz)
    except TypeError:
        num = int(total_seconds(_get_utcoffset(tz, None))) * 1000000000
        return np.array([num], dtype=np.int64)

    if tz not in utc_offset_cache:
        if hasattr(tz, '_utc_transition_times'):
            utc_offset_cache[tz] = _unbox_utcoffsets(tz._transition_info)
        else:
            # static tzinfo
            num = int(total_seconds(_get_utcoffset(tz, None))) * 1000000000
            utc_offset_cache[tz] = np.array([num], dtype=np.int64)

    return utc_offset_cache[tz]

cdef double total_seconds(object td): # Python 2.6 compat
    return ((td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) //
            10**6)

def tot_seconds(td):
    return total_seconds(td)

cpdef ndarray _unbox_utcoffsets(object transinfo):
    cdef:
        Py_ssize_t i, sz
        ndarray[int64_t] arr

    sz = len(transinfo)
    arr = np.empty(sz, dtype='i8')

    for i in range(sz):
        arr[i] = int(total_seconds(transinfo[i][0])) * 1000000000

    return arr


@cython.boundscheck(False)
@cython.wraparound(False)
def tz_localize_to_utc(ndarray[int64_t] vals, object tz):
    """
    Localize tzinfo-naive DateRange to given time zone (using pytz). If
    there are ambiguities in the values, raise AmbiguousTimeError.

    Returns
    -------
    localized : DatetimeIndex
    """
    cdef:
        ndarray[int64_t] trans, deltas, idx_shifted
        Py_ssize_t i, idx, pos, ntrans, n = len(vals)
        int64_t *tdata
        int64_t v, left, right
        ndarray[int64_t] result, result_a, result_b
        pandas_datetimestruct dts

    # Vectorized version of DstTzInfo.localize

    if not have_pytz:
        raise Exception("Could not find pytz module")

    if tz == UTC or tz is None:
        return vals

    result = np.empty(n, dtype=np.int64)

    if _is_tzlocal(tz):
        for i in range(n):
            v = vals[i]
            pandas_datetime_to_datetimestruct(v, PANDAS_FR_ns, &dts)
            dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                          dts.min, dts.sec, dts.us, tz)
            delta = int(total_seconds(_get_utcoffset(tz, dt))) * 1000000000
            result[i] = v - delta
        return result

    trans = _get_transitions(tz)  # transition dates
    deltas = _get_deltas(tz)      # utc offsets

    tdata = <int64_t*> trans.data
    ntrans = len(trans)

    result_a = np.empty(n, dtype=np.int64)
    result_b = np.empty(n, dtype=np.int64)
    result_a.fill(NPY_NAT)
    result_b.fill(NPY_NAT)

    # left side
    idx_shifted = _ensure_int64(
        np.maximum(0, trans.searchsorted(vals - DAY_NS, side='right') - 1))

    for i in range(n):
        v = vals[i] - deltas[idx_shifted[i]]
        pos = bisect_right_i8(tdata, v, ntrans) - 1

        # timestamp falls to the left side of the DST transition
        if v + deltas[pos] == vals[i]:
            result_a[i] = v

    # right side
    idx_shifted = _ensure_int64(
        np.maximum(0, trans.searchsorted(vals + DAY_NS, side='right') - 1))

    for i in range(n):
        v = vals[i] - deltas[idx_shifted[i]]
        pos = bisect_right_i8(tdata, v, ntrans) - 1

        # timestamp falls to the right side of the DST transition
        if v + deltas[pos] == vals[i]:
            result_b[i] = v

    for i in range(n):
        left = result_a[i]
        right = result_b[i]
        if left != NPY_NAT and right != NPY_NAT:
            if left == right:
                result[i] = left
            else:
                stamp = Timestamp(vals[i])
                raise pytz.AmbiguousTimeError(stamp)
        elif left != NPY_NAT:
            result[i] = left
        elif right != NPY_NAT:
            result[i] = right
        else:
            stamp = Timestamp(vals[i])
            raise pytz.NonExistentTimeError(stamp)

    return result

cdef _ensure_int64(object arr):
    if util.is_array(arr):
        if (<ndarray> arr).descr.type_num == NPY_INT64:
            return arr
        else:
            return arr.astype(np.int64)
    else:
        return np.array(arr, dtype=np.int64)


cdef inline bisect_right_i8(int64_t *data, int64_t val, Py_ssize_t n):
    cdef Py_ssize_t pivot, left = 0, right = n

    # edge cases
    if val > data[n - 1]:
        return n

    if val < data[0]:
        return 0

    while left < right:
        pivot = left + (right - left) // 2

        if data[pivot] <= val:
            left = pivot + 1
        else:
            right = pivot

    return left


# Accessors
#----------------------------------------------------------------------

def build_field_sarray(ndarray[int64_t] dtindex):
    '''
    Datetime as int64 representation to a structured array of fields
    '''
    cdef:
        Py_ssize_t i, count = 0
        int isleap
        pandas_datetimestruct dts
        ndarray[int32_t] years, months, days, hours, minutes, seconds, mus

    count = len(dtindex)

    sa_dtype = [('Y', 'i4'), # year
                ('M', 'i4'), # month
                ('D', 'i4'), # day
                ('h', 'i4'), # hour
                ('m', 'i4'), # min
                ('s', 'i4'), # second
                ('u', 'i4')] # microsecond

    out = np.empty(count, dtype=sa_dtype)

    years = out['Y']
    months = out['M']
    days = out['D']
    hours = out['h']
    minutes = out['m']
    seconds = out['s']
    mus = out['u']

    for i in range(count):
        pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
        years[i] = dts.year
        months[i] = dts.month
        days[i] = dts.day
        hours[i] = dts.hour
        minutes[i] = dts.min
        seconds[i] = dts.sec
        mus[i] = dts.us

    return out

def get_time_micros(ndarray[int64_t] dtindex):
    '''
    Datetime as int64 representation to a structured array of fields
    '''
    cdef:
        Py_ssize_t i, n = len(dtindex)
        pandas_datetimestruct dts
        ndarray[int64_t] micros

    micros = np.empty(n, dtype=np.int64)

    for i in range(n):
        pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
        micros[i] = 1000000LL * (dts.hour * 60 * 60 +
                                 60 * dts.min + dts.sec) + dts.us

    return micros

@cython.wraparound(False)
def get_date_field(ndarray[int64_t] dtindex, object field):
    '''
    Given a int64-based datetime index, extract the year, month, etc.,
    field and return an array of these values.
    '''
    cdef:
        _TSObject ts
        Py_ssize_t i, count = 0
        ndarray[int32_t] out
        ndarray[int32_t, ndim=2] _month_offset
        int isleap, isleap_prev
        pandas_datetimestruct dts
        int mo_off, doy, dow, woy

    _month_offset = np.array(
        [[ 0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365 ],
         [ 0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366 ]],
         dtype=np.int32 )

    count = len(dtindex)
    out = np.empty(count, dtype='i4')

    if field == 'Y':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.year
        return out

    elif field == 'M':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.month
        return out

    elif field == 'D':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.day
        return out

    elif field == 'h':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.hour
        return out

    elif field == 'm':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.min
        return out

    elif field == 's':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.sec
        return out

    elif field == 'us':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.us
        return out
    elif field == 'ns':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.ps / 1000
        return out
    elif field == 'doy':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            isleap = is_leapyear(dts.year)
            out[i] = _month_offset[isleap, dts.month-1] + dts.day
        return out

    elif field == 'dow':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            ts = convert_to_tsobject(dtindex[i], None, None)
            out[i] = ts_dayofweek(ts)
        return out

    elif field == 'woy':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            ts = convert_to_tsobject(dtindex[i], None, None)
            isleap = is_leapyear(dts.year)
            isleap_prev = is_leapyear(dts.year - 1)
            mo_off = _month_offset[isleap, dts.month - 1]
            doy = mo_off + dts.day
            dow = ts_dayofweek(ts)

            #estimate
            woy = (doy - 1) - dow + 3
            if woy >= 0:
                woy = woy / 7 + 1

            # verify
            if woy < 0:
                if (woy > -2) or (woy == -2 and isleap_prev):
                    woy = 53
                else:
                    woy = 52
            elif woy == 53:
                if 31 - dts.day + dow < 3:
                    woy = 1

            out[i] = woy
        return out

    elif field == 'q':
        for i in range(count):
            if dtindex[i] == NPY_NAT: out[i] = -1; continue

            pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
            out[i] = dts.month
            out[i] = ((out[i] - 1) / 3) + 1
        return out

    raise ValueError("Field %s not supported" % field)


cdef inline int m8_weekday(int64_t val):
    ts = convert_to_tsobject(val, None, None)
    return ts_dayofweek(ts)

cdef int64_t DAY_NS = 86400000000000LL


def date_normalize(ndarray[int64_t] stamps, tz=None):
    cdef:
        Py_ssize_t i, n = len(stamps)
        pandas_datetimestruct dts
        _TSObject tso
        ndarray[int64_t] result = np.empty(n, dtype=np.int64)

    if tz is not None:
        tso = _TSObject()
        if isinstance(tz, basestring):
            tz = pytz.timezone(tz)
        result = _normalize_local(stamps, tz)
    else:
        for i in range(n):
            if stamps[i] == NPY_NAT:
                result[i] = NPY_NAT
                continue
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns, &dts)
            result[i] = _normalized_stamp(&dts)

    return result

cdef _normalize_local(ndarray[int64_t] stamps, object tz):
    cdef:
        Py_ssize_t n = len(stamps)
        ndarray[int64_t] result = np.empty(n, dtype=np.int64)
        ndarray[int64_t] trans, deltas, pos
        pandas_datetimestruct dts

    if _is_utc(tz):
        for i in range(n):
            if stamps[i] == NPY_NAT:
                result[i] = NPY_NAT
                continue
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns, &dts)
            result[i] = _normalized_stamp(&dts)
    elif _is_tzlocal(tz):
        for i in range(n):
            if stamps[i] == NPY_NAT:
                result[i] = NPY_NAT
                continue
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns,
                                              &dts)
            dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                          dts.min, dts.sec, dts.us, tz)
            delta = int(total_seconds(_get_utcoffset(tz, dt))) * 1000000000
            pandas_datetime_to_datetimestruct(stamps[i] + delta,
                                              PANDAS_FR_ns, &dts)
            result[i] = _normalized_stamp(&dts)
    else:
        # Adjust datetime64 timestamp, recompute datetimestruct
        trans = _get_transitions(tz)
        deltas = _get_deltas(tz)
        _pos = trans.searchsorted(stamps, side='right') - 1
        if _pos.dtype != np.int64:
            _pos = _pos.astype(np.int64)
        pos = _pos

        # statictzinfo
        if not hasattr(tz, '_transition_info'):
            for i in range(n):
                if stamps[i] == NPY_NAT:
                    result[i] = NPY_NAT
                    continue
                pandas_datetime_to_datetimestruct(stamps[i] + deltas[0],
                                                  PANDAS_FR_ns, &dts)
                result[i] = _normalized_stamp(&dts)
        else:
            for i in range(n):
                if stamps[i] == NPY_NAT:
                    result[i] = NPY_NAT
                    continue
                pandas_datetime_to_datetimestruct(stamps[i] + deltas[pos[i]],
                                                  PANDAS_FR_ns, &dts)
                result[i] = _normalized_stamp(&dts)

    return result

cdef inline int64_t _normalized_stamp(pandas_datetimestruct *dts):
    dts.hour = 0
    dts.min = 0
    dts.sec = 0
    dts.us = 0
    return pandas_datetimestruct_to_datetime(PANDAS_FR_ns, dts)


cdef inline void m8_populate_tsobject(int64_t stamp, _TSObject tso, object tz):
    tso.value = stamp
    pandas_datetime_to_datetimestruct(tso.value, PANDAS_FR_ns, &tso.dts)

    if tz is not None:
        _localize_tso(tso, tz)


def dates_normalized(ndarray[int64_t] stamps, tz=None):
    cdef:
        Py_ssize_t i, n = len(stamps)
        pandas_datetimestruct dts

    if tz is None or _is_utc(tz):
        for i in range(n):
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns, &dts)
            if (dts.hour + dts.min + dts.sec + dts.us) > 0:
                return False
    elif _is_tzlocal(tz):
        for i in range(n):
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns, &dts)
            if (dts.min + dts.sec + dts.us) > 0:
                return False
            dt = datetime(dts.year, dts.month, dts.day, dts.hour, dts.min,
                          dts.sec, dts.us, tz)
            dt = dt + tz.utcoffset(dt)
            if dt.hour > 0:
                return False
    else:
        trans = _get_transitions(tz)
        deltas = _get_deltas(tz)
        for i in range(n):
            # Adjust datetime64 timestamp, recompute datetimestruct
            pos = trans.searchsorted(stamps[i]) - 1
            inf = tz._transition_info[pos]

            pandas_datetime_to_datetimestruct(stamps[i] + deltas[pos],
                                              PANDAS_FR_ns, &dts)
            if (dts.hour + dts.min + dts.sec + dts.us) > 0:
                return False

    return True

# Some general helper functions
#----------------------------------------------------------------------

def isleapyear(int64_t year):
    return is_leapyear(year)

def monthrange(int64_t year, int64_t month):
    cdef:
        int64_t days
        int64_t day_of_week

    if month < 1 or month > 12:
        raise ValueError("bad month number 0; must be 1-12")

    days = days_per_month_table[is_leapyear(year)][month-1]

    return (dayofweek(year, month, 1), days)

cdef inline int64_t ts_dayofweek(_TSObject ts):
    return dayofweek(ts.dts.year, ts.dts.month, ts.dts.day)


cpdef normalize_date(object dt):
    '''
    Normalize datetime.datetime value to midnight. Returns datetime.date as a
    datetime.datetime at midnight

    Returns
    -------
    normalized : datetime.datetime or Timestamp
    '''
    if PyDateTime_Check(dt):
        return dt.replace(hour=0, minute=0, second=0, microsecond=0)
    elif PyDate_Check(dt):
        return datetime(dt.year, dt.month, dt.day)
    else:
        raise TypeError('Unrecognized type: %s' % type(dt))

cdef ndarray[int64_t] localize_dt64arr_to_period(ndarray[int64_t] stamps,
                                                 int freq, object tz):
    cdef:
        Py_ssize_t n = len(stamps)
        ndarray[int64_t] result = np.empty(n, dtype=np.int64)
        ndarray[int64_t] trans, deltas, pos
        pandas_datetimestruct dts

    if not have_pytz:
        raise Exception('Could not find pytz module')

    if _is_utc(tz):
        for i in range(n):
            if stamps[i] == NPY_NAT:
                result[i] = NPY_NAT
                continue
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns, &dts)
            result[i] = get_period_ordinal(dts.year, dts.month, dts.day,
                                           dts.hour, dts.min, dts.sec, freq)

    elif _is_tzlocal(tz):
        for i in range(n):
            if stamps[i] == NPY_NAT:
                result[i] = NPY_NAT
                continue
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns,
                                              &dts)
            dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                          dts.min, dts.sec, dts.us, tz)
            delta = int(total_seconds(_get_utcoffset(tz, dt))) * 1000000000
            pandas_datetime_to_datetimestruct(stamps[i] + delta,
                                              PANDAS_FR_ns, &dts)
            result[i] = get_period_ordinal(dts.year, dts.month, dts.day,
                                           dts.hour, dts.min, dts.sec, freq)
    else:
        # Adjust datetime64 timestamp, recompute datetimestruct
        trans = _get_transitions(tz)
        deltas = _get_deltas(tz)
        _pos = trans.searchsorted(stamps, side='right') - 1
        if _pos.dtype != np.int64:
            _pos = _pos.astype(np.int64)
        pos = _pos

        # statictzinfo
        if not hasattr(tz, '_transition_info'):
            for i in range(n):
                if stamps[i] == NPY_NAT:
                    result[i] = NPY_NAT
                    continue
                pandas_datetime_to_datetimestruct(stamps[i] + deltas[0],
                                                  PANDAS_FR_ns, &dts)
                result[i] = get_period_ordinal(dts.year, dts.month, dts.day,
                                               dts.hour, dts.min, dts.sec, freq)
        else:
            for i in range(n):
                if stamps[i] == NPY_NAT:
                    result[i] = NPY_NAT
                    continue
                pandas_datetime_to_datetimestruct(stamps[i] + deltas[pos[i]],
                                                  PANDAS_FR_ns, &dts)
                result[i] = get_period_ordinal(dts.year, dts.month, dts.day,
                                               dts.hour, dts.min, dts.sec, freq)

    return result


cdef extern from "period.h":
    ctypedef struct date_info:
        int64_t absdate
        double abstime
        double second
        int minute
        int hour
        int day
        int month
        int quarter
        int year
        int day_of_week
        int day_of_year
        int calendar

    ctypedef struct asfreq_info:
        int from_week_end
        int to_week_end

        int from_a_year_end
        int to_a_year_end

        int from_q_year_end
        int to_q_year_end

    ctypedef int64_t (*freq_conv_func)(int64_t, char, asfreq_info*)

    int64_t asfreq(int64_t dtordinal, int freq1, int freq2, char relation) except INT32_MIN
    freq_conv_func get_asfreq_func(int fromFreq, int toFreq)
    void get_asfreq_info(int fromFreq, int toFreq, asfreq_info *af_info)

    int64_t get_period_ordinal(int year, int month, int day,
                          int hour, int minute, int second,
                          int freq) except INT32_MIN

    int64_t get_python_ordinal(int64_t period_ordinal, int freq) except INT32_MIN

    int get_date_info(int64_t ordinal, int freq, date_info *dinfo) except INT32_MIN
    double getAbsTime(int, int64_t, int64_t)

    int pyear(int64_t ordinal, int freq) except INT32_MIN
    int pqyear(int64_t ordinal, int freq) except INT32_MIN
    int pquarter(int64_t ordinal, int freq) except INT32_MIN
    int pmonth(int64_t ordinal, int freq) except INT32_MIN
    int pday(int64_t ordinal, int freq) except INT32_MIN
    int pweekday(int64_t ordinal, int freq) except INT32_MIN
    int pday_of_week(int64_t ordinal, int freq) except INT32_MIN
    int pday_of_year(int64_t ordinal, int freq) except INT32_MIN
    int pweek(int64_t ordinal, int freq) except INT32_MIN
    int phour(int64_t ordinal, int freq) except INT32_MIN
    int pminute(int64_t ordinal, int freq) except INT32_MIN
    int psecond(int64_t ordinal, int freq) except INT32_MIN
    char *c_strftime(date_info *dinfo, char *fmt)
    int get_yq(int64_t ordinal, int freq, int *quarter, int *year)

# Period logic
#----------------------------------------------------------------------

cdef inline int64_t apply_mult(int64_t period_ord, int64_t mult):
    """
    Get freq+multiple ordinal value from corresponding freq-only ordinal value.
    For example, 5min ordinal will be 1/5th the 1min ordinal (rounding down to
    integer).
    """
    if mult == 1:
        return period_ord

    return (period_ord - 1) // mult

cdef inline int64_t remove_mult(int64_t period_ord_w_mult, int64_t mult):
    """
    Get freq-only ordinal value from corresponding freq+multiple ordinal.
    """
    if mult == 1:
        return period_ord_w_mult

    return period_ord_w_mult * mult + 1;

def dt64arr_to_periodarr(ndarray[int64_t] dtarr, int freq, tz=None):
    """
    Convert array of datetime64 values (passed in as 'i8' dtype) to a set of
    periods corresponding to desired frequency, per period convention.
    """
    cdef:
        ndarray[int64_t] out
        Py_ssize_t i, l
        pandas_datetimestruct dts

    l = len(dtarr)

    out = np.empty(l, dtype='i8')

    if tz is None:
        for i in range(l):
            pandas_datetime_to_datetimestruct(dtarr[i], PANDAS_FR_ns, &dts)
            out[i] = get_period_ordinal(dts.year, dts.month, dts.day,
                                        dts.hour, dts.min, dts.sec, freq)
    else:
        out = localize_dt64arr_to_period(dtarr, freq, tz)
    return out

def periodarr_to_dt64arr(ndarray[int64_t] periodarr, int freq):
    """
    Convert array to datetime64 values from a set of ordinals corresponding to
    periods per period convention.
    """
    cdef:
        ndarray[int64_t] out
        Py_ssize_t i, l

    l = len(periodarr)

    out = np.empty(l, dtype='i8')

    for i in range(l):
        out[i] = period_ordinal_to_dt64(periodarr[i], freq)

    return out

cdef char START = 'S'
cdef char END = 'E'

cpdef int64_t period_asfreq(int64_t period_ordinal, int freq1, int freq2,
                            bint end):
    """
    Convert period ordinal from one frequency to another, and if upsampling,
    choose to use start ('S') or end ('E') of period.
    """
    cdef:
        int64_t retval

    if end:
        retval = asfreq(period_ordinal, freq1, freq2, END)
    else:
        retval = asfreq(period_ordinal, freq1, freq2, START)

    if retval == INT32_MIN:
        raise ValueError('Frequency conversion failed')

    return retval

def period_asfreq_arr(ndarray[int64_t] arr, int freq1, int freq2, bint end):
    """
    Convert int64-array of period ordinals from one frequency to another, and
    if upsampling, choose to use start ('S') or end ('E') of period.
    """
    cdef:
        ndarray[int64_t] result
        Py_ssize_t i, n
        freq_conv_func func
        asfreq_info finfo
        int64_t val, ordinal
        char relation

    n = len(arr)
    result = np.empty(n, dtype=np.int64)

    func = get_asfreq_func(freq1, freq2)
    get_asfreq_info(freq1, freq2, &finfo)

    if end:
        relation = END
    else:
        relation = START

    for i in range(n):
        val = func(arr[i], relation, &finfo)
        if val == INT32_MIN:
            raise ValueError("Unable to convert to desired frequency.")
        result[i] = val

    return result

def period_ordinal(int y, int m, int d, int h, int min, int s, int freq):
    cdef:
        int64_t ordinal

    return get_period_ordinal(y, m, d, h, min, s, freq)


cpdef int64_t period_ordinal_to_dt64(int64_t ordinal, int freq):
    cdef:
        pandas_datetimestruct dts
        date_info dinfo

    get_date_info(ordinal, freq, &dinfo)

    dts.year = dinfo.year
    dts.month = dinfo.month
    dts.day = dinfo.day
    dts.hour = dinfo.hour
    dts.min = dinfo.minute
    dts.sec = int(dinfo.second)
    dts.us = dts.ps = 0

    return pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts)

def period_format(int64_t value, int freq, object fmt=None):
    cdef:
        int freq_group

    if fmt is None:
        freq_group = (freq // 1000) * 1000
        if freq_group == 1000: # FR_ANN
            fmt = b'%Y'
        elif freq_group == 2000: # FR_QTR
            fmt = b'%FQ%q'
        elif freq_group == 3000: # FR_MTH
            fmt = b'%Y-%m'
        elif freq_group == 4000: # WK
            left = period_asfreq(value, freq, 6000, 0)
            right = period_asfreq(value, freq, 6000, 1)
            return '%s/%s' % (period_format(left, 6000),
                              period_format(right, 6000))
        elif (freq_group == 5000 # BUS
              or freq_group == 6000): # DAY
            fmt = b'%Y-%m-%d'
        elif freq_group == 7000: # HR
            fmt = b'%Y-%m-%d %H:00'
        elif freq_group == 8000: # MIN
            fmt = b'%Y-%m-%d %H:%M'
        elif freq_group == 9000: # SEC
            fmt = b'%Y-%m-%d %H:%M:%S'
        else:
            raise ValueError('Unknown freq: %d' % freq)

    return _period_strftime(value, freq, fmt)


cdef list extra_fmts = [(b"%q", b"^`AB`^"),
                        (b"%f", b"^`CD`^"),
                        (b"%F", b"^`EF`^")]

cdef list str_extra_fmts = ["^`AB`^", "^`CD`^", "^`EF`^"]

cdef _period_strftime(int64_t value, int freq, object fmt):
    import sys
    cdef:
        Py_ssize_t i
        date_info dinfo
        char *formatted
        object pat, repl, result
        list found_pat = [False] * len(extra_fmts)
        int year, quarter

    if PyUnicode_Check(fmt):
        fmt = fmt.encode('utf-8')

    get_date_info(value, freq, &dinfo)
    for i in range(len(extra_fmts)):
        pat = extra_fmts[i][0]
        repl = extra_fmts[i][1]
        if pat in fmt:
            fmt = fmt.replace(pat, repl)
            found_pat[i] = True

    formatted = c_strftime(&dinfo, <char*> fmt)

    result = util.char_to_string(formatted)
    free(formatted)

    for i in range(len(extra_fmts)):
        if found_pat[i]:
            if get_yq(value, freq, &quarter, &year) < 0:
                raise ValueError('Unable to get quarter and year')

            if i == 0:
                repl = '%d' % quarter
            elif i == 1:  # %f, 2-digit year
                repl = '%.2d' % (year % 100)
            elif i == 2:
                repl = '%d' % year

            result = result.replace(str_extra_fmts[i], repl)

    # Py3?
    if not PyString_Check(result):
        result = str(result)

    # GH3363
    if sys.version_info[0] == 2:
       result = result.decode('utf-8','strict')

    return result

# period accessors

ctypedef int (*accessor)(int64_t ordinal, int freq) except INT32_MIN

def get_period_field(int code, int64_t value, int freq):
    cdef accessor f = _get_accessor_func(code)
    if <long>f == <long>NULL:
        raise ValueError('Unrecognized code: %s' % code)
    return f(value, freq)

def get_period_field_arr(int code, ndarray[int64_t] arr, int freq):
    cdef:
        Py_ssize_t i, sz
        ndarray[int64_t] out
        accessor f

    f = _get_accessor_func(code)
    if <long>f == <long>NULL:
        raise ValueError('Unrecognized code: %s' % code)

    sz = len(arr)
    out = np.empty(sz, dtype=np.int64)

    for i in range(sz):
        out[i] = f(arr[i], freq)

    return out



cdef accessor _get_accessor_func(int code):
    if code == 0:
        return &pyear
    elif code == 1:
        return &pqyear
    elif code == 2:
        return &pquarter
    elif code == 3:
        return &pmonth
    elif code == 4:
        return &pday
    elif code == 5:
        return &phour
    elif code == 6:
        return &pminute
    elif code == 7:
        return &psecond
    elif code == 8:
        return &pweek
    elif code == 9:
        return &pday_of_year
    elif code == 10:
        return &pweekday
    return NULL


def extract_ordinals(ndarray[object] values, freq):
    cdef:
        Py_ssize_t i, n = len(values)
        ndarray[int64_t] ordinals = np.empty(n, dtype=np.int64)
        object p

    for i in range(n):
        p = values[i]
        ordinals[i] = p.ordinal
        if p.freq != freq:
            raise ValueError("%s is wrong freq" % p)

    return ordinals

cpdef resolution(ndarray[int64_t] stamps, tz=None):
    cdef:
        Py_ssize_t i, n = len(stamps)
        pandas_datetimestruct dts
        int reso = D_RESO, curr_reso

    if tz is not None:
        if isinstance(tz, basestring):
            tz = pytz.timezone(tz)
        return _reso_local(stamps, tz)
    else:
        for i in range(n):
            if stamps[i] == NPY_NAT:
                continue
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns, &dts)
            curr_reso = _reso_stamp(&dts)
            if curr_reso < reso:
                reso = curr_reso
        return reso

US_RESO = 0
S_RESO = 1
T_RESO = 2
H_RESO = 3
D_RESO = 4

cdef inline int _reso_stamp(pandas_datetimestruct *dts):
    if dts.us != 0:
        return US_RESO
    elif dts.sec != 0:
        return S_RESO
    elif dts.min != 0:
        return T_RESO
    elif dts.hour != 0:
        return H_RESO
    return D_RESO

cdef _reso_local(ndarray[int64_t] stamps, object tz):
    cdef:
        Py_ssize_t n = len(stamps)
        int reso = D_RESO, curr_reso
        ndarray[int64_t] trans, deltas, pos
        pandas_datetimestruct dts

    if _is_utc(tz):
        for i in range(n):
            if stamps[i] == NPY_NAT:
                continue
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns, &dts)
            curr_reso = _reso_stamp(&dts)
            if curr_reso < reso:
                reso = curr_reso
    elif _is_tzlocal(tz):
        for i in range(n):
            if stamps[i] == NPY_NAT:
                continue
            pandas_datetime_to_datetimestruct(stamps[i], PANDAS_FR_ns,
                                              &dts)
            dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                          dts.min, dts.sec, dts.us, tz)
            delta = int(total_seconds(_get_utcoffset(tz, dt))) * 1000000000
            pandas_datetime_to_datetimestruct(stamps[i] + delta,
                                              PANDAS_FR_ns, &dts)
            curr_reso = _reso_stamp(&dts)
            if curr_reso < reso:
                reso = curr_reso
    else:
        # Adjust datetime64 timestamp, recompute datetimestruct
        trans = _get_transitions(tz)
        deltas = _get_deltas(tz)
        _pos = trans.searchsorted(stamps, side='right') - 1
        if _pos.dtype != np.int64:
            _pos = _pos.astype(np.int64)
        pos = _pos

        # statictzinfo
        if not hasattr(tz, '_transition_info'):
            for i in range(n):
                if stamps[i] == NPY_NAT:
                    continue
                pandas_datetime_to_datetimestruct(stamps[i] + deltas[0],
                                                  PANDAS_FR_ns, &dts)
                curr_reso = _reso_stamp(&dts)
                if curr_reso < reso:
                    reso = curr_reso
        else:
            for i in range(n):
                if stamps[i] == NPY_NAT:
                    continue
                pandas_datetime_to_datetimestruct(stamps[i] + deltas[pos[i]],
                                                  PANDAS_FR_ns, &dts)
                curr_reso = _reso_stamp(&dts)
                if curr_reso < reso:
                    reso = curr_reso

    return reso

#----------------------------------------------------------------------
# Don't even ask

"""Strptime-related classes and functions.

CLASSES:
    LocaleTime -- Discovers and stores locale-specific time information
    TimeRE -- Creates regexes for pattern matching a string of text containing
                time information

FUNCTIONS:
    _getlang -- Figure out what language is being used for the locale
    strptime -- Calculates the time struct represented by the passed-in string

"""
import time
import locale
import calendar
from re import compile as re_compile
from re import IGNORECASE
from re import escape as re_escape
from datetime import date as datetime_date

# Python 2 vs Python 3
try:
    from thread import allocate_lock as _thread_allocate_lock
except:
    try:
        from _thread import allocate_lock as _thread_allocate_lock
    except:
        try:
            from dummy_thread import allocate_lock as _thread_allocate_lock
        except:
            from _dummy_thread import allocate_lock as _thread_allocate_lock

__all__ = []

def _getlang():
    # Figure out what the current language is set to.
    return locale.getlocale(locale.LC_TIME)

class LocaleTime(object):
    """Stores and handles locale-specific information related to time.

    ATTRIBUTES:
        f_weekday -- full weekday names (7-item list)
        a_weekday -- abbreviated weekday names (7-item list)
        f_month -- full month names (13-item list; dummy value in [0], which
                    is added by code)
        a_month -- abbreviated month names (13-item list, dummy value in
                    [0], which is added by code)
        am_pm -- AM/PM representation (2-item list)
        LC_date_time -- format string for date/time representation (string)
        LC_date -- format string for date representation (string)
        LC_time -- format string for time representation (string)
        timezone -- daylight- and non-daylight-savings timezone representation
                    (2-item list of sets)
        lang -- Language used by instance (2-item tuple)
    """

    def __init__(self):
        """Set all attributes.

        Order of methods called matters for dependency reasons.

        The locale language is set at the offset and then checked again before
        exiting.  This is to make sure that the attributes were not set with a
        mix of information from more than one locale.  This would most likely
        happen when using threads where one thread calls a locale-dependent
        function while another thread changes the locale while the function in
        the other thread is still running.  Proper coding would call for
        locks to prevent changing the locale while locale-dependent code is
        running.  The check here is done in case someone does not think about
        doing this.

        Only other possible issue is if someone changed the timezone and did
        not call tz.tzset .  That is an issue for the programmer, though,
        since changing the timezone is worthless without that call.

        """
        self.lang = _getlang()
        self.__calc_weekday()
        self.__calc_month()
        self.__calc_am_pm()
        self.__calc_timezone()
        self.__calc_date_time()
        if _getlang() != self.lang:
            raise ValueError("locale changed during initialization")

    def __pad(self, seq, front):
        # Add '' to seq to either the front (is True), else the back.
        seq = list(seq)
        if front:
            seq.insert(0, '')
        else:
            seq.append('')
        return seq

    def __calc_weekday(self):
        # Set self.a_weekday and self.f_weekday using the calendar
        # module.
        a_weekday = [calendar.day_abbr[i].lower() for i in range(7)]
        f_weekday = [calendar.day_name[i].lower() for i in range(7)]
        self.a_weekday = a_weekday
        self.f_weekday = f_weekday

    def __calc_month(self):
        # Set self.f_month and self.a_month using the calendar module.
        a_month = [calendar.month_abbr[i].lower() for i in range(13)]
        f_month = [calendar.month_name[i].lower() for i in range(13)]
        self.a_month = a_month
        self.f_month = f_month

    def __calc_am_pm(self):
        # Set self.am_pm by using time.strftime().

        # The magic date (1999,3,17,hour,44,55,2,76,0) is not really that
        # magical; just happened to have used it everywhere else where a
        # static date was needed.
        am_pm = []
        for hour in (01,22):
            time_tuple = time.struct_time((1999,3,17,hour,44,55,2,76,0))
            am_pm.append(time.strftime("%p", time_tuple).lower())
        self.am_pm = am_pm

    def __calc_date_time(self):
        # Set self.date_time, self.date, & self.time by using
        # time.strftime().

        # Use (1999,3,17,22,44,55,2,76,0) for magic date because the amount of
        # overloaded numbers is minimized.  The order in which searches for
        # values within the format string is very important; it eliminates
        # possible ambiguity for what something represents.
        time_tuple = time.struct_time((1999,3,17,22,44,55,2,76,0))
        date_time = [None, None, None]
        date_time[0] = time.strftime("%c", time_tuple).lower()
        date_time[1] = time.strftime("%x", time_tuple).lower()
        date_time[2] = time.strftime("%X", time_tuple).lower()
        replacement_pairs = [('%', '%%'), (self.f_weekday[2], '%A'),
                    (self.f_month[3], '%B'), (self.a_weekday[2], '%a'),
                    (self.a_month[3], '%b'), (self.am_pm[1], '%p'),
                    ('1999', '%Y'), ('99', '%y'), ('22', '%H'),
                    ('44', '%M'), ('55', '%S'), ('76', '%j'),
                    ('17', '%d'), ('03', '%m'), ('3', '%m'),
                    # '3' needed for when no leading zero.
                    ('2', '%w'), ('10', '%I')]
        replacement_pairs.extend([(tz, "%Z") for tz_values in self.timezone
                                                for tz in tz_values])
        for offset,directive in ((0,'%c'), (1,'%x'), (2,'%X')):
            current_format = date_time[offset]
            for old, new in replacement_pairs:
                # Must deal with possible lack of locale info
                # manifesting itself as the empty string (e.g., Swedish's
                # lack of AM/PM info) or a platform returning a tuple of empty
                # strings (e.g., MacOS 9 having timezone as ('','')).
                if old:
                    current_format = current_format.replace(old, new)
            # If %W is used, then Sunday, 2005-01-03 will fall on week 0 since
            # 2005-01-03 occurs before the first Monday of the year.  Otherwise
            # %U is used.
            time_tuple = time.struct_time((1999,1,3,1,1,1,6,3,0))
            if '00' in time.strftime(directive, time_tuple):
                U_W = '%W'
            else:
                U_W = '%U'
            date_time[offset] = current_format.replace('11', U_W)
        self.LC_date_time = date_time[0]
        self.LC_date = date_time[1]
        self.LC_time = date_time[2]

    def __calc_timezone(self):
        # Set self.timezone by using time.tzname.
        # Do not worry about possibility of time.tzname[0] == timetzname[1]
        # and time.daylight; handle that in strptime .
        try:
            time.tzset()
        except AttributeError:
            pass
        no_saving = frozenset(["utc", "gmt", time.tzname[0].lower()])
        if time.daylight:
            has_saving = frozenset([time.tzname[1].lower()])
        else:
            has_saving = frozenset()
        self.timezone = (no_saving, has_saving)


class TimeRE(dict):
    """Handle conversion from format directives to regexes."""

    def __init__(self, locale_time=None):
        """Create keys/values.

        Order of execution is important for dependency reasons.

        """
        if locale_time:
            self.locale_time = locale_time
        else:
            self.locale_time = LocaleTime()
        base = super(TimeRE, self)
        base.__init__({
            # The " \d" part of the regex is to make %c from ANSI C work
            'd': r"(?P<d>3[0-1]|[1-2]\d|0[1-9]|[1-9]| [1-9])",
            'f': r"(?P<f>[0-9]{1,6})",
            'H': r"(?P<H>2[0-3]|[0-1]\d|\d)",
            'I': r"(?P<I>1[0-2]|0[1-9]|[1-9])",
            'j': r"(?P<j>36[0-6]|3[0-5]\d|[1-2]\d\d|0[1-9]\d|00[1-9]|[1-9]\d|0[1-9]|[1-9])",
            'm': r"(?P<m>1[0-2]|0[1-9]|[1-9])",
            'M': r"(?P<M>[0-5]\d|\d)",
            'S': r"(?P<S>6[0-1]|[0-5]\d|\d)",
            'U': r"(?P<U>5[0-3]|[0-4]\d|\d)",
            'w': r"(?P<w>[0-6])",
            # W is set below by using 'U'
            'y': r"(?P<y>\d\d)",
            #XXX: Does 'Y' need to worry about having less or more than
            #     4 digits?
            'Y': r"(?P<Y>\d\d\d\d)",
            'A': self.__seqToRE(self.locale_time.f_weekday, 'A'),
            'a': self.__seqToRE(self.locale_time.a_weekday, 'a'),
            'B': self.__seqToRE(self.locale_time.f_month[1:], 'B'),
            'b': self.__seqToRE(self.locale_time.a_month[1:], 'b'),
            'p': self.__seqToRE(self.locale_time.am_pm, 'p'),
            'Z': self.__seqToRE((tz for tz_names in self.locale_time.timezone
                                        for tz in tz_names),
                                'Z'),
            '%': '%'})
        base.__setitem__('W', base.__getitem__('U').replace('U', 'W'))
        base.__setitem__('c', self.pattern(self.locale_time.LC_date_time))
        base.__setitem__('x', self.pattern(self.locale_time.LC_date))
        base.__setitem__('X', self.pattern(self.locale_time.LC_time))

    def __seqToRE(self, to_convert, directive):
        """Convert a list to a regex string for matching a directive.

        Want possible matching values to be from longest to shortest.  This
        prevents the possibility of a match occuring for a value that also
        a substring of a larger value that should have matched (e.g., 'abc'
        matching when 'abcdef' should have been the match).

        """
        to_convert = sorted(to_convert, key=len, reverse=True)
        for value in to_convert:
            if value != '':
                break
        else:
            return ''
        regex = '|'.join(re_escape(stuff) for stuff in to_convert)
        regex = '(?P<%s>%s' % (directive, regex)
        return '%s)' % regex

    def pattern(self, format):
        """Return regex pattern for the format string.

        Need to make sure that any characters that might be interpreted as
        regex syntax are escaped.

        """
        processed_format = ''
        # The sub() call escapes all characters that might be misconstrued
        # as regex syntax.  Cannot use re.escape since we have to deal with
        # format directives (%m, etc.).
        regex_chars = re_compile(r"([\\.^$*+?\(\){}\[\]|])")
        format = regex_chars.sub(r"\\\1", format)
        whitespace_replacement = re_compile('\s+')
        format = whitespace_replacement.sub('\s+', format)
        while '%' in format:
            directive_index = format.index('%')+1
            processed_format = "%s%s%s" % (processed_format,
                                           format[:directive_index-1],
                                           self[format[directive_index]])
            format = format[directive_index+1:]
        return "%s%s" % (processed_format, format)

    def compile(self, format):
        """Return a compiled re object for the format string."""
        return re_compile(self.pattern(format), IGNORECASE)

_cache_lock = _thread_allocate_lock()
# DO NOT modify _TimeRE_cache or _regex_cache without acquiring the cache lock
# first!
_TimeRE_cache = TimeRE()
_CACHE_MAX_SIZE = 5 # Max number of regexes stored in _regex_cache
_regex_cache = {}

def _calc_julian_from_U_or_W(year, week_of_year, day_of_week, week_starts_Mon):
    """Calculate the Julian day based on the year, week of the year, and day of
    the week, with week_start_day representing whether the week of the year
    assumes the week starts on Sunday or Monday (6 or 0)."""
    first_weekday = datetime_date(year, 1, 1).weekday()
    # If we are dealing with the %U directive (week starts on Sunday), it's
    # easier to just shift the view to Sunday being the first day of the
    # week.
    if not week_starts_Mon:
        first_weekday = (first_weekday + 1) % 7
        day_of_week = (day_of_week + 1) % 7
    # Need to watch out for a week 0 (when the first day of the year is not
    # the same as that specified by %U or %W).
    week_0_length = (7 - first_weekday) % 7
    if week_of_year == 0:
        return 1 + day_of_week - first_weekday
    else:
        days_to_week = week_0_length + (7 * (week_of_year - 1))
        return 1 + days_to_week + day_of_week

# def _strptime_time(data_string, format="%a %b %d %H:%M:%S %Y"):
#     return _strptime(data_string, format)[0]