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CLN: Add missing types to tslibs/conversion.pyx #23984

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Nov 29, 2018
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27 changes: 16 additions & 11 deletions pandas/_libs/tslibs/conversion.pyx
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ from cython import Py_ssize_t

import numpy as np
cimport numpy as cnp
from numpy cimport int64_t, int32_t, ndarray
from numpy cimport uint8_t, int64_t, int32_t, ndarray
cnp.import_array()

import pytz
Expand Down Expand Up @@ -535,6 +535,7 @@ cdef inline void localize_tso(_TSObject obj, tzinfo tz):
int64_t[:] deltas
int64_t local_val
Py_ssize_t pos
str typ

assert obj.tzinfo is None

Expand Down Expand Up @@ -646,7 +647,7 @@ cdef inline int64_t[:] _tz_convert_dst(int64_t[:] values, tzinfo tz,
if not is_tzlocal(tz):
# get_dst_info cannot extract offsets from tzlocal because its
# dependent on a datetime
trans, deltas, typ = get_dst_info(tz)
trans, deltas, _ = get_dst_info(tz)
if not to_utc:
# We add `offset` below instead of subtracting it
deltas = -1 * np.array(deltas, dtype='i8')
Expand Down Expand Up @@ -690,7 +691,7 @@ cdef inline int64_t _tz_convert_tzlocal_utc(int64_t val, tzinfo tz,
"""
cdef:
npy_datetimestruct dts
int64_t result, delta
int64_t delta
datetime dt

dt64_to_dtstruct(val, &dts)
Expand Down Expand Up @@ -879,18 +880,20 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, object ambiguous=None,
localized : ndarray[int64_t]
"""
cdef:
ndarray[int64_t] trans
int64_t[:] deltas, idx_shifted, idx_shifted_left, idx_shifted_right
ndarray ambiguous_array
ndarray[uint8_t, cast=True] ambiguous_array, both_nat, both_eq
Py_ssize_t i, idx, pos, ntrans, n = len(vals)
Py_ssize_t delta_idx_offset, delta_idx, pos_left, pos_right
int64_t *tdata
int64_t v, left, right, val, v_left, v_right, new_local, remaining_mins
int64_t HOURS_NS = HOUR_SECONDS * 1000000000
ndarray[int64_t] result, result_a, result_b, dst_hours
ndarray[int64_t] trans, result, result_a, result_b, dst_hours
ndarray[int64_t] trans_idx, grp, delta, a_idx, b_idx, one_diff
npy_datetimestruct dts
bint infer_dst = False, is_dst = False, fill = False
bint shift = False, fill_nonexist = False
list trans_grp
str stamp

# Vectorized version of DstTzInfo.localize
if is_utc(tz) or tz is None:
Expand Down Expand Up @@ -923,7 +926,7 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, object ambiguous=None,
if len(ambiguous) != len(vals):
raise ValueError("Length of ambiguous bool-array must be "
"the same size as vals")
ambiguous_array = np.asarray(ambiguous)
ambiguous_array = np.asarray(ambiguous, dtype=bool)

if nonexistent == 'NaT':
fill_nonexist = True
Expand All @@ -933,7 +936,7 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, object ambiguous=None,
assert nonexistent in ('raise', None), ("nonexistent must be one of"
" {'NaT', 'raise', 'shift'}")

trans, deltas, typ = get_dst_info(tz)
trans, deltas, _ = get_dst_info(tz)

tdata = <int64_t*>cnp.PyArray_DATA(trans)
ntrans = len(trans)
Expand Down Expand Up @@ -984,7 +987,7 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, object ambiguous=None,
# years which is useful for checking that there is not an ambiguous
# transition in an individual year.
if trans_idx.size > 0:
one_diff = np.where(np.diff(trans_idx) != 1)[0] +1
one_diff = np.where(np.diff(trans_idx) != 1)[0] + 1
trans_grp = np.array_split(trans_idx, one_diff)

# Iterate through each day, if there are no hours where the
Expand Down Expand Up @@ -1172,13 +1175,14 @@ cdef int64_t[:] _normalize_local(int64_t[:] stamps, tzinfo tz):
result : int64 ndarray of converted of normalized nanosecond timestamps
"""
cdef:
Py_ssize_t n = len(stamps)
Py_ssize_t i, n = len(stamps)
int64_t[:] result = np.empty(n, dtype=np.int64)
ndarray[int64_t] trans
int64_t[:] deltas
str typ
Py_ssize_t[:] pos
npy_datetimestruct dts
int64_t delta
int64_t delta, local_val

if is_utc(tz):
with nogil:
Expand Down Expand Up @@ -1264,6 +1268,7 @@ def is_date_array_normalized(int64_t[:] stamps, object tz=None):
int64_t[:] deltas
npy_datetimestruct dts
int64_t local_val, delta
str typ

if tz is None or is_utc(tz):
for i in range(n):
Expand Down